PWS-ICdel mice also displayed a 48% reduction in proportionate interscapular brown adipose tissue (isBAT) weight with significant ‘beiging’ of abdominal WAT, and a 2°C increase in interscapular surface body temperature. Maintenance of PWS-ICdel mice under thermoneutral conditions (30°C) suppressed the thermogenic activity in PWS-ICdel males, but failed to elevate the abdominal WAT weight, possibly due to a normalisation of caloric intake. Interestingly, PWS-ICdel mice also showed exaggerated food hoarding behaviour with standard and high-fat diets, but despite becoming hyperphagic when switched to a high-fat diet, PWS-ICdel mice failed to gain weight. This evidence indicates that, unlike humans with PWS, loss of paternal gene expression from the PWS cluster in mice results in abdominal leanness. Although reduced subcutaneous insulation may lead to exaggerated heat loss and thermogenesis, abdominal leanness is likely to arise from a reduced lipid storage capacity rather than increased energy utilisation in BAT.Prader–Willi syndrome (PWS), a neurodevelopmental disorder caused by loss of paternal gene expression from 15q11–q13, is characterised by growth retardation, hyperphagia and obesity. However, as single gene mutation mouse models for this condition display an incomplete spectrum of the PWS phenotype, we have characterised the metabolic impairment in a mouse model for ‘full’ PWS, in which deletion of the imprinting centre (IC) abolishes paternal gene expression from the entire PWS cluster. We show that PWS-IC Prader–Willi syndrome (PWS) is caused by a lack of paternal gene expression from the 15q11–q13 imprinting cluster and results from large chromosomal deletions, chromosome 15 maternal uniparental disomy or imprinting-centre (IC) mutations. This disorder is associated with significant metabolic impairment. After severe neonatal hypotonia and a failure to thrive in infancy, subsequent development of hyperphagia and reduced satiety responses result in obesity, unless managed carefully. Indeed, as PWS is the most common syndromal cause of morbid obesity , elucidaMagel2 become obese  and wild-type (WT) mice used in this study were bred under the Authority of the Animals (scientific procedures) Act 1986 (UK), with subsequent procedures conforming with the institutional and national guidelines, including those for genetically modified animals, and specifically approved by local ethical review.The PWS-ICdel mice and WT littermates were generated by crossing ICdel-positive males with WT females. Given the nature of the epigenetic regulation of imprinted genes, paternally inherited IC deletion results in a lack of gene expression from the PWS interval. As PWS-ICdel animals on a pure C57BL/6J background suffer severe postnatal lethality, we crossed ICdel-positive males with CD1 females and selectively culled WT littermates (identified on the basis of their increased size 48 h after birth) leaving only 1 or 2 WT pups per litter, as previously described , unless stated otherwise.All animals were maintained on a 12-h light/darkness cycle (lights on 07:00 h), with ad libitum), 18-month-old male WT and PWS-ICdel littermate mice were weighed and killed by cervical dislocation. Inguinal, retroperitoneal and epididymal white adipose tissue (WAT) and interscapular brown adipose tissue (isBAT) fat depots and liver were excised, weighed, snap frozen and stored at −70°C for subsequent histological analysis (see below). Left tibiae were excised, and the length was determined with a hand-held micrometre.After an overnight fast (with water available ad libitum feeding (with water available to both groups ad libitum). After thoracotomy, blood samples were obtained by cardiac puncture using BD Microtainers , and aliquots of separated plasma were stored at −20°C for the quantification of circulating ghrelin and lipid profiling (see below).Two groups of 16- to 17-month-old female WT and PWS-IC littermate mice were weighed and killed by cervical dislocation after 24 h of total food removal or After analysis of BAT histology from the mice in study 1, surface body temperature was measured in 6- to 10-month-old male WT and PWS-IC mice as previously described  at either standard room temperature (20–22°C) or at a thermoneutral ambient temperature (30°C) for 9 weeks. 30°C represents the lower critical temperature in mice, below which non-shivering thermogenesis is induced  Fat, in which energy sources  were Crude Fat 10%; Crude Protein 20%; Carbohydrate 70% ) for 1 week, followed by a further 1-week period with a high-fat diet  were: Crude Fat 45% Crude Protein 20%; Carbohydrate 35%). Body weight and food intake were monitored daily, food consumption being normalised to body weight. After the first day of exposure to each diet, the proportion of ‘hoarded’ food was determined by expressing the mass of food in the inner (‘spill’) compartment of the food hopper  (with water provided ad libitum). Mice were placed individually in normal shoe-box cages under low lighting levels and presented with a pre-weighed pot of wet mash (1 part standard diet (SDS RM3):1 part water). Wet mash was used to overcome the propensity of PWS-ICdel mice to hoard diet pellets and spill powdered diet . Mice were allowed to feed freely for 60 min, the pots being weighed after 30 and 60 min to determine food consumption.Group housed 4- to 6-month-old male and female WT and PWS-ICAdipocyte size and hepatic lipid content were quantified as previously described  and b, d2O prior to quantification of Ucp-1 and β-actin cDNA by quantitative real-time PCR amplification using a Bio-Rad IQ5 thermal cycler and Precision 2× qPCR Mastermix reagents (Primerdesign). Oligonucleotide primers for mouse Ucp-1 were purchased from Primerdesign . PCR thermal cycling conditions included an initial enzyme activation for 10 min at 95°C (1 cycle), followed by 51 cycles of denaturation at 95°C for 15 s and real-time Fluorogenic Data Collection (FDC) at 60°C for 1 min. The final FDCs were acquired at 0.5°C temperature increments between 55°C and 95°C at 10-s intervals (81 cycles). The PCR products were quantified by incorporation of SYBR green into double stranded DNA. Single amplicon identity was verified by melt curve analysis. Individual samples were normalised to the expression of β-actin . The quantity of cDNA in each reaction was calculated by reference to a standard curve constructed from serial dilutions of cDNA from WT BAT (Uncoupling protein-1 (Ucp-1) mRNA expression in isBAT and inguinal WAT was quantified as previously described  concentrations were determined by RIA (Millipore/Linco (IAV: 3.28–8.05%)). Plasma lipids were extracted by the Folch method . Classess.e.m., and differences were established by 1-way and 2-way repeated measures ANOVA and Bonferroni post hoc test or unpaired Student’s t-test , as indicated in the figure and table legends, with P < 0.05 considered significantly different.Results are expressed as mean ± del foetuses weighed 96% of their WT counterparts (WT: 1.18 ± 0.044 g (n = 7); PWS-ICdel: 1.13 ± 0.044 g (n = 7); P = 0.45)) nor placental weight (WT: 100 ± 6.5 mg (n = 7); PWC-ICdel: 92 ± 4.1 mg (n = 7); P = 0.32) were significantly affected in PWS-ICdel pregnancies, PWS-ICdel mice failed to thrive after 24 h post-partum, leading to increased neonatal mortality. PWS-ICdel mice that survived to adulthood were significantly growth retarded, adult PWS-ICdel males showing a 40% reduction in body weight (P < 0.001), accompanied by a 7% reduction in tibial length . Comparable reductions in body weight were observed in similarly-aged PWS-ICdel females , 84% (P < 0.001) and 82% (P < 0.01), respectively, and reflected in a 69% reduction in adipocyte size (P < 0.001). Although proportionate liver weight was unaffected (del mice was disrupted. In contrast to the diffuse lipid staining in WT livers (del livers showed punctate staining (P < 0.01)), larger (increased by 66% (P < 0.001)) lipid droplets, with total lipid content reduced by 67% (P < 0.01).However, in contrast to humans with PWS, PWS-ICinguinal , epididyinguinal  and retringuinal  WAT weigaffected , hepaticT livers  inset, Pstaining  inset, wdel mice was only 79% of that in WT littermates , consistent with elevated thermogenesis. Similarly, retroperitoneal WAT (P < 0.05). Thermal imaging revealed that surface temperatures of the head and interscapular regions were increased in PWS-ICdel mice by 1.4°C and 2.0°C, respectively (P < 0.05), whereas dorsolumbar tail root and radiant temperatures were not significantly affected . After 9 weeks, proportionate isBAT weight in WT and PWS-ICdel males was elevated by 72% and 76%, respectively ; P = 0.183 ). Despite suppressed thermogenesis, proportionate WAT mass , whereas for male PWS-ICdel mice at thermoneutrality, retroperitoneal WAT mass was 97% of that in PWS-ICdel mice at room temperature. In addition, thermoneutrality had no significant effect on WAT Ucp-1 mRNA expression . This lack of weight gain may arise from the suppressive influence of thermoneutrality on daily food intake . When quantifying the amount of diet in the ‘spill’ compartment as a percentage of the remaining diet, PWS-ICdel males were found to ‘hoard’ more than 3-times as much diet as WT males . This behaviour was prominent with both standard and high-fat diets, PWS-ICdel males hoarding 86–91% of the remaining diet. Interestingly, this pattern of hoarding in PWS-ICdel males was broadly similar to that in both WT and PWS-ICdel females .Although constrained by the short period of monitoring, it is particularly striking that although PWS-ICtermates , althougdel mice show proportionate hyperphagia, we investigated individual meal consumption patterns after an overnight fast. When adjusted for body weight, PWS-ICdel mice consumed 48% more diet than WT littermates . This was due mainly to continued consumption after 30 min as evidenced by a significant interaction with the time of measurement .As PWS-ICdel mice.Loss of paternal gene expression from the imprinted gene cluster on human chromosome 15q11–q13 impairs neuroendocrine and metabolic function in PWS. However, investigating these impairments in mouse models for PWS has been hampered by high postnatal lethality. We have utilised a mixed genetic background to increase survival, enabling us to characterise metabolic status in a mouse model in which deletion of the homologous PWS-IC results in complete loss of paternal gene expression from the entire PWS locus . As similar growth results have been reported in another ‘full’ genetic mouse model for PWS  depots. Although reduced visceral adiposity has been reported in human females with PWS . When supplied with pelleted diet, individual PWS-ICdel mice from our Cardiff colony displayed increased diet distribution in both home and metabolic cages , whereas PWS-ICdel mice in a Florida colony packed bedding around high-fat diet pellets in the food hopper . This hoarding behaviour, previously described in birds, rodents and, in certain circumstances, humans , it is possible that leanness occurs in PWS-ICsorption , are unasorption . In addisorption , the abssorption  indicatedel mice is significantly reduced , maintael males . Howeverde novo lipogenesis and/or decreased lipolysis or lipid export, resulting in hypertrophy , whereas lipid accumulates in response to increased substrate supply/uptake, ertrophy .del mice was partly due to a 50% reduction in adipocyte number (data not shown). Evidence of reduced adipogenesis in subcutaneous WAT has recently been reported in human PWS, adipocyte progenitor number being halved ; the Prader–Willi Syndrome Association UK (to A R I and T W), Biotechnology and Biological Sciences Research Council  and Foundation for Prader–Willi Research (to T W); and Seed Corn Funding  (to T W and A R I).


The imaging of drugs inside tissues is pivotal in oncology to assess whether a drug reaches all cells in an adequate enough concentration to eradicate the tumor. Matrix-Assisted Laser Desorption Ionization Mass Spectrometry Imaging (MALDI-MSI) is one of the most promising imaging techniques that enables the simultaneous visualization of multiple compounds inside tissues. The choice of a suitable matrix constitutes a critical aspect during the development of a MALDI-MSI protocol since the matrix ionization efficiency changes depending on the analyte structure and its physico-chemical properties. The objective of this study is the improvement of the MALDI-MSI technique in the field of pharmacology; developing specifically designed nanostructured surfaces that allow the imaging of different drugs with high sensitivity and reproducibility. Among several nanomaterials, we tested the behavior of gold and titanium nanoparticles, and halloysites and carbon nanotubes as possible matrices. All nanomaterials were firstly screened by co-spotting them with drugs on a MALDI plate, evaluating the drug signal intensity and the signal-to-noise ratio. The best performing matrices were tested on control tumor slices, and were spotted with drugs to check the ion suppression effect of the biological matrix. Finally; the best nanomaterials were employed in a preliminary drug distribution study inside tumors from treated mice. In drug discovery and development it is important to understand the pharmacokinetics, investigating the absorption, distribution, metabolism, and excretion (ADME) of molecules. Several analytical methods, based on high-performance liquid chromatography (HPLC) and Liquid chromatography tandem-mass spectrometry (LC-MS/MS), have been developed and employed on plasma and tissue homogenates to establish drug concentration profiles of drugs . ClassicIn recent decades, several imaging techniques have been developed to investigate the distribution of compounds inside a tissue, such as magnetic resonance imaging (MRI), positron emission tomography (PET), or wall body autoradiography (WBA), and are routinely used in clinical diagnosis ,7,8,9,10W < 10,000 Da). Laser desorption/ionization based techniques have always been the most widely used in mass spectrometry for imaging [Mass spectrometry imaging (MSI) is one of the latest, rapidly growing surface analysis techniques for the detection, localization, and identification of molecules in tissues ,12,13. M imaging  and theyW < 1000 Da) like drugs and metabolites as well [MALDI is the prevailing MSI technique used in pre-clinical and clinical research ,18 to ob as well ,23,24,25The principle behind MALDI-MSI is to acquire a mass spectrum for each point of a tissue section by rastering a laser beam at defined geometrical coordinates. The UV laser energy is absorbed by a matrix present in a homogeneous layer on the tissue slice that facilitates analyte extraction in the MALDI ion source ,27. The A different approach based on the use of inorganic fine particles takes advantage of their physicochemical properties such as high photo-adsorption, low heat capacity, and large surface area. This ensures rapid heating and highly localized and uniform energy deposition, resulting in efficient sample desorption and ionization , allowinThe use of gold and platinum nanostructures in laser desorption/ionization (LDI) MS of low molecular weight compounds was reviewed by Bergman et al. . Gold naTitanium dioxide can be considered as a matrix for MALDI, since the laser wavelength falls in the range of its absorption band. In particular, the literature reports some examples of the use of this material for the determination of carbohydrates  and anti2Si2O5(OH)4·2H2O), is a two-layered aluminosilicate, with a predominantly hollow tubular structure. Chemically, the outer surface of the HNTs has properties similar to SiO2, while the inner cylinder core is related to Al2O3. In a recent paper, halloysite nanoclay showed good potential as a SALDI surface for the rapid analysis of low molecular mass polyesters and their degradation products [Halloysite (HNT), a naturally occurring aluminosilicate nanotube (Alproducts .Carbon nanotubes (CNTs) are allotropes of carbon with a cylindrical nanostructure. They have been reported to be an effective MALDI matrix for small molecules , eliminaWe compared the ability of these five nanostructured matrices to ionize six different anticancer drugs: taxans , tyrosine kinase inhibitors , an antineoplastic antibiotic , and a DNA binding protein  ,37. All The main objective of this study is the improvement of the MALDI-MSI technique in the field of pharmacology, developing specifically designed surfaces for the imaging of different anticancer drugs, with high spatial resolution, sensitivity, and reproducibility.All nanostructured materials were deeply characterized by the morphological, structural, and surface points of view, by ultraviolet-visible spectroscopy (UV), transmission electron microscope (TEM), scanning electron microscope (SEM), and dynamic light scattering (DLS) analyses. Relevant features are reported in All nanostructured matrices were firstly screened by co-spotting them with equimolar concentrations of six anticancer drugs on the MALDI plate and evaluating both the signal intensity and the S/N for each combination of drug and matrix. The detected MS fragments for each drug are reported in m/z 743 after losing a water molecule  and OTX (m/z 271) in negative ion mode, but they also allowed the ionization of smaller molecules like IMT (m/z 492) and LCT (m/z 373). AuNPs gave more intense signals, especially in positive ion mode for the tyrosine-kinase inhibitor IMT as a sodium adduct at m/z 515. Compared to TiO2 nanoparticles, AuNPs were more efficient for ionizing and visualizing smaller molecules such as IMT and LCT, giving a more intense signal in general. HNTs gave good visualization only for three of the six drugs: PTX, IMT, and LCT. CNTs efficiently ionized all drugs both in negative and positive ion modes, but could not be used for imaging.Almost all drugs were successfully ionized both in negative and positive ion modes using gold, halloysite, and titanium, while trabectedin (ET) was detectable only in positive ion mode at molecule . P25 andm/z 284 as a base peak. In this mass region the spectrum has a very high S/N, suggesting a good effectiveness of the technique with this kind of matrix. Unfortunately, CNTs tended to fly off from the target plate when subjected to the laser pulse, contaminating the ion source and interfering with the instrument functionality.The different performances of the nanomaterials towards the ionization of the drug molecules may be ascribed to many factors, starting with the physico-chemical properties , their am/z < 100).Both titanium samples, instead, showed a strong absorption (inflection point of the curves) particularly in the laser emission range. However, not all the absorbed energy could be transferred to the target molecules, as these materials are semiconductors. This yields a good compromise between the absorbed and released energy, with promising results in ionizing almost all the bigger drug molecules. Unfortunately, the released energy is still so high that the small fragments are not detected because of their complete mineralization  were tested to assess how the biological matrix influenced the intensity of the analyte ion signal and for interfering signals by spotting drug standards on control tumor tissue slices that were then sprayed with each matrix for imaging experiments  and OTX (m/z 260) in negative ion mode; TiO2 Hombikat efficiently ionized the two taxans PTX and OTX in negative ion mode but also IMT and OTX in positive ion mode. AuNPs efficiently visualized almost all drugs  spotted on tissue  for PTX, 400 mg/kg per os (p.o.) for IMT, 20 mg/kg p.o. for LCT). 2 based matrices appear to be suitable for visualizing the PTX distribution inside treated tissues in negative ion mode with high sensitivity, but did not give good visualization of IMT in negative or positive ion mode. In contrast, the nanogold-based matrix allowed a better visualization of the IMT distribution, highlighting its peripheral localization inside treated mesothelioma (even though there is high background noise). Both TiO2 and gold nanoparticles were not suitable for LCT distribution studies inside treated tumors.Based on the results obtained from the control tissues spotted with the drug standards, TiO2 nanoparticles have been previously reported to be suitable for MALDI mass spectrometry analysis of low molecular weight compounds, with almost complete absence of background noise [4 in water, which was introduced by Turkevitch and revisited by Kimling [The imaging of drugs inside biological tissues is pivotal in oncology to understand how a compound and its metabolites are localized and distributed inside a tissue, in order to check that they reach the intended target site. Therefore, there is a growing need for methods to assess whether a drug reaches all tumor cells in adequate concentrations and to develop new strategies to improve penetration and the outcome of chemotherapy . In thisnd noise , and recnd noise ,42. For  Kimling ,44.We tested common nanoparticles with different features with six types of drugs, currently used as anticancer drugs in clinical settings. While the carbon nanotubes based matrix has been reported to provide higher detection sensitivity than classical organic matrices , and it PTX efficiently ionizes with titanium based matrices which were however less suitable for the ionization of a smaller molecule such as IMT. AuNPs were convenient for the ionization and for the imaging of IMT, enlarging our ability to visualize different kind of molecules.These results confirm that there is not a single type of matrix that suits all drugs. Further studies are needed to clarify the interactions between matrix and analyte to define case-by-case how to choose the best matrix and the best fitting combination that gives high sensitivity and therefore a good detection of drug distribution in the imaging of molecules inside biological tissues.Further studies are also needed to accurately understand the mechanism of interaction between the matrix and analyte. Determining out why a particular matrix is more appropriate for the imaging of a certain drug could give clues for developing of new nanomaterials with designed texture for the imaging of a larger group of small molecules, to investigate drug distribution in primary tumors or metastases, while also offering new opportunities for the visualization of endogenous molecules.2O, all at a concentration of 100 pmol/µL.Paclitaxel , ortataxel , and imatinib  were dissolved in 50% ethanol, trabectedin  and lucitanib  were dissolved in 50% methanol, and doxorubicin  was dissolved in HFor mouse treatments, PTX was dissolved in 50% Cremophor EL  and 50% ethanol and further diluted in saline immediately before use. IMT and LCT were suspended in 0.5% Methocel.4  was heated to a boil while stirring. Then trisodium citrate  was added quickly to the boiling mixture. The solution was refluxed for 15 min, and then allowed to cool to room temperature. Subsequently, the gold colloid was centrifuged at 13.2 krpm for 20 min and resuspended in phosphate buffered saline (PBS), pH 7.4. The average diameter of the AuNPs was determined by UV, TEM, SEM, and DLS.Gold nanoparticles (AuNPs) of 20 nm diameter were synthesized as follows: an aqueous solution of HAuCl2 P25 and TiO2 Hombikat UV100, were purchased from Evonik  and Sachtleben Chemie GmbH , respectively. Multiwalled Carbon Nanotubes (CNTs) were purchased from NANOCYL® (NC7000™ series) and were produced by a Catalytic Chemical Vapor Deposition (CCVD) process. Halloysite nanoclay was purchased from Sigma Aldrich . P25 or Hombikat TiO2, AuNPs, and HNTs or CNTs matrices suspensions were prepared respectively at 1 mg/mL, 0.4 mM, and 0.15 mg/mL in 50% ethanol, and were vortexed and sonicated before use.Two different titanium nanoparticles, TiOUV-Visible measurements were recorded using a Hitachi UH 5300 Spectrophotometer , equipped with 1.0 cm path length quartz cells and recorded in a range of 400-700 nm. The morphology and shape of the samples have been evaluated by means of a Zeiss Evo50 Scanning Electron Microscope  with an accelerating voltage of 20 kV and a magnification of 24000 × TEM analyses were performed on a Jeol Jem 3010 UHR instrument .Diffuse reflectance spectra (DRS) of the powders were measured on a UV-Vis scanning spectrophotometer  equipped with a diffuse reflectance accessory. A ‘‘total white’’ Perkin Elmer reference material was used as a reference.Procedures involving animals and their care were conducted in conformity with the following laws, regulations, and policies governing the care and use of laboratory animals: Italian Governing Law ; Mario Negri Institutional Regulations and Policies providing internal authorization for persons conducting animal experiments ; the NIH Guide for the Care and Use of Laboratory Animals (2011 edition) and EU directives and guidelines (EEC Council Directive 2010/63/UE) and in line with Guidelines for the welfare and use of animals in cancer research . The staAnimal experiments have been reviewed and approved by the IRFMN Animal Care and Use Committee (IACUC) that includes members ad hoc for ethical issues. Animals were housed in the Institute’s Animal Care facilities which meet international standards. They were regularly checked by a certified veterinarian who is responsible for health monitoring, animal welfare supervision, experimental protocols, and procedures revision.7 A2780 cells or fragments of the rare MPM487 human malignant pleural mesothelioma . Tumor growth was measured with a digital caliper two/three times a week and the tumor volume (mm3) was calculated as (length (mm) × width2 (mm2))/2.Six to seven-week-old female NCr-nu/nu mice (from the Harlan Lab) were inoculated subcutaneously with 102 and all efforts were made to minimize suffering.When tumors reached approximately 500 mg, the animals were treated with different drugs or with the vehicle as a negative control. Mice bearing xenografts were treated with vehicle (CTRL) or a single dose of drug . Animals were sacrificed 1 hour after treatment (4 h for PTX) under COTumors and organs were explanted, then immediately snap-frozen in liquid nitrogen and stored at −80 °C until analysis.The different nanomaterials tested as matrices were firstly screened by spotting 0.5 µL of 100 pmol/µL drug standard solutions on the MALDI steel plate. After complete drying in air of the drug standard spots, 0.5 µL of matrix suspensions described above were spotted on top of the different drugs samples. Matrices were also tested on tissues to evaluate the influence of the biological matrix on the ion signal intensity, following the MSI protocol we recently published . Brieflywww.maldi-msi.org, M. Stoeckli, Novartis Pharma, Basel, Switzerland) with an imaging raster of 100 µm. Tissue View software 1.1  was used to process and display the ion distributions inside the tumor sections. The ions plotted for each drug are reported in A MALDI 4800 TOF-TOF  was used, equipped with a 355 nm Nd:YAG laser with a 200 Hz repetition rate, controlled by the 4000 Series Explorer TM software . MS spectra were acquired with 20 laser shots with an intensity of 6000 arbitrary units, with a bin size of 1.0 ns, acquiring spectra in reflectron, both in negative and positive-ion mode. Images of tissue sections were acquired using the 4800 Imaging Tool Software (Testing different types of materials has made it possible to ionize molecules with different molecular weights and chemical properties, overcoming the problem of high background noise in the low mass range that is typical in classic MALDI experiments. Moreover, with these nanomaterials we could visualize the mitotic inhibitor PTX and the tyrosine kinase inhibitor IMT inside tumors harvested from treated mice.


Parkinson’s disease (PD) is a disabling neurodegenerative disease that manifests with resting tremor, bradykinesia, rigidity and postural instability. Since the discovery of microRNAs (miRNAs) in 1993, miRNAs have been shown to be important biological molecules involved in diverse processes to maintain normal cellular functions. Over the past decade, many studies have reported dysregulation of miRNA expressions in PD. Here, we identified 15 miRNAs from 34 reported screening studies that demonstrated dysregulation in the brain and/or neuronal models, cerebrospinal fluid (CSF) and blood. Specific miRNAs-of-interest that have been implicated in PD pathogenesis include miR-30, miR-29, let-7, miR-485 and miR-26. However, there are several challenges and limitations in drawing definitive conclusions due to the small sample size in clinical studies, varied laboratory techniques and methodologies and their incomplete penetrance of the blood–brain barrier. Developing an optimal delivery system and unravelling druggable targets of miRNAs in both experimental and human models and clinical validation of the results may pave way for novel therapeutics in PD. Parkinson’s disease (PD) is the most common movement disorder in the aging population. An estimated prevalence of 1% of people above 60 years old suffer from PD . In addiCurrently, clinical treatments available for PD are mainly symptomatic which include medications such as Levodopa, dopamine agonists, catechol-O-methyl transferase (COMT) inhibitors and monoamine oxidase B inhibitors and non-pharmacological interventions such as deep brain stimulation ,8. WhileUsing the advanced search builder function in PubMed, a systemic search was performed with keywords ‘“MicroRNAs” (Majr) and Parkinson’s disease’ in September 2019. This resulted in 253 titles consisting of both reviews and original research articles. All relevant articles including screening studies and mechanistic studies were examined to identify the promising microRNAs in PD that are discussed in this review.MicroRNAs are small non-coding RNAs that are transcribed from miRNA genes and intronic sequences as primary miRNAs (pri-miRNAs) and stem-loop precursor miRNAs (pre-miRNAs) respectively. In the nucleus, pri-miRNAs are further processed to form pre-miRNAs by the Drosha/DGCR8 complex. Pre-miRNAs are then exported out of the nucleus by Exportin-5. In the cytosol, pre-miRNAs are cleaved by Dicer to produce double-stranded mature miRNAs. The mature guide strand, about 20–22 nucleotides long, is then loaded onto Argonaute proteins to form the RNA-induced silencing complex (RISC). The mature miRNA is responsible for associating RISC to its target messenger RNAs (mRNAs) by binding at complementary sequences located usually at the 3′-UTR of the mRNA ,15 and fSince the discovery of miRNAs in 1993, increasing evidence have revealed miRNAs to be important biological molecules involved in diverse processes to maintain normal cellular functions ,21. In aUsing techniques such as RNA sequencing, microarray and microRNA qPCR profiling, many screening studies have been conducted in attempt to characterise the miRNAs dysregulation in PD in both the central nervous system (CNS) and the periphery. Our search strategy revealed 34 screening studies that have been conducted to identify the clinically significant miRNAs in PD. We included studies on animal PD models and in vitro neuronal models as we believe that clinically significant miRNAs identified in PD patients will also be highlighted in these models. Moreover, due to the difficulty in obtaining human PD patients’ samples, the inclusion of these model studies can also reveal suitable PD models to study mechanisms of the identified miRNA(s). In The top 5 miRNAs listed in The miR-30 family consists of 5 members and 6 mature miRNA sequences, namely, miR-30a, miR-30b, miR-30c-1, miR-30c-2, miR-30d and miR-30e. The members share a common seed sequence near the 5′-end but has different compensatory sequences near the 3′-end, allowing the different members to target different genes and pathways . In the post-mortem human brain studies, miR-30b in the substantia nigra (SN)  and miR-Besides the CNS, differential expression levels of miR-30 family members have also been detected in the peripheral blood of PD patients as compared to healthy controls. Downregulated expressions of miR-30b and miR-30c in the PBMCs  and downUsing a PD mouse model, Dorval and colleagues (2014) revealed that miR-30a* was upregulated in the striatal tissues of LRRK2-KO mice  which inThe miR-29 family consists of 3 members and 4 mature miRNA sequences, namely, miR-29a, miR-29b-1, miR-29b-2 and miR-29c. Mature sequences have identical seed sequences at nucleotide positions 2-7, suggesting that the targets for miR-29 members heavily overlap . In the post-mortem human brain, miR-29a, miR-29b-1 and miR-29b-2 were observed to be upregulated in the anterior cingulate gyri of PD patients . The dysIn the periphery, miR-29a-3p was reported to be upregulated in the WBCs of L-dopa-treated PD patients but not in the untreated PD patients . On the miR-29 has been shown to regulate various processes that are important in PD development, such as apoptosis ,67,68 anThe let-7 family members consist of let-7a to let-7-k, miR-98 and miR-202. While the let-7 sequence is well-conserved from nematode to human, different species can express different members of let-7. For example, in humans, we do not express let-7h/j/k. Interestingly, all let-7 members share the same seed sequence (nucleotides 2–8) for target recognition . In post-mortem human brain studies, let-7b in the SN  and let-Caenorhabditis elegans (C. elegans) [In animal PD models, let-7f was observed to be upregulated in the striatal tissues of LRRK2-KO mice as compared to control mice  while leelegans) .C. elegans [Extensive research conducted on let-7 revealed that let-7 members regulate processes such as apoptosis ,80, immu elegans . TherefoLittle has been discovered about the functions of miR-485. However, this miRNA has been suggested to be dysregulated in PD. In the post-mortem human brain, miR-485-5p was reported to be downregulated in the SN  while thSome miR-485-mediated pathways suggested in the literature include apoptosis ,96, immuBeside apoptosis, miR-485 is also able to target the expression of peroxisome proliferator-activated receptor gamma (PPARγ) coactivator-1α (PGC-1α) . PGC-1α The miR-26 family consists of miR-26a-1, miR-26a-2 and miR-26b in humans . In the The role of miR-26 in neurodegenerative diseases has not been well-studied. However, in other disease models, miR-26 has been suggested to modulate processes such as DNA repair ,108, apoWe have highlighted several candidate miRNAs that may aggravate or mitigate PD progression through their actions in the CNS or periphery. miRNAs are capable of regulating several signaling pathways as each miRNA can target and bind to an average of 100–200 genes, making them potent modulators of gene expression . TherefoDespite the importance of miRNA research, the literature currently available on the actions of miRNA in PD is greatly lacking. Moreover, results of miRNA studies have not been consistent with each other. There are several potential limitations in published studies. It is difficult to identify clinically important miRNAs in diseases because clinical studies thus far are limited by their small sample size which may not have sufficient power to identify the effect size difference. In addition, the areas investigated also differ in the studies as noted in There are also several challenges in developing miRNA-based therapeutic therapies. One major problem is the delivery of candidate miRNA(s) to specific sites. The development of specific and effective miRNA delivery systems is vital as the delivery vehicle must allow miRNAs to cross the blood–brain barrier. As miRNAs are easily degradable, the delivery systems employed must also be able to stabilise and extend the life of the miRNAs. Furthermore, using miRNAs in treatment may be a double-edged sword. While it is advantageous that miRNAs as powerful modulators of gene expression have the ability to alter several signaling pathways at once to switch the cellular physiology from an apoptotic state to one that favors survival, this also means that side effects in unspecific sites could be problematic. Hence, specific and effective delivery systems in miRNA-based therapy are vital. The current experimental approaches used in miRNA research have many limitations. As miRNAs are small and easily degradable, the existing isolation methods can lead to a great loss in yield or a biased recovery of certain miRNAs. This can affect the potential of miRNAs as diagnostic markers for diseases. Moreover, the experimental approaches required for miRNA studies are also very costly. Hence, these challenges could slow down the progression of miRNAs research. We have highlighted several miRNAs that show promise in having therapeutic and/or diagnostic potential in PD as well as the pathophysiologic role of miRNAs in disease states. Developing an optimal delivery system and unravelling druggable targets of miRNAs in both experimental and human models and clinical validation of the results may pave the way for novel therapeutics in PD.


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Previous research suggests that family caregivers contemplate suicide at a higher rate than the general population. Much of this research has been disease specific and in relatively small samples. This study aimed to compare suicidal thoughts between non-caregivers and informal caregivers of people with a variety of conditions, in a large representative sample, and to identify significant risk factors.The general population study NEMESIS-2 (N at baseline = 6646) included 1582 adult caregivers at the second wave (2010–2012) who also participated at the third wave (2013–2015). Suicidal thoughts were assessed over 4 years, with the Suicidality Module of the Composite International Diagnostic Interview 3.0. The presence of suicidal thoughts was estimated and risk factors for suicidal thoughts were assessed with logistic regression analyses adjusted for age and gender.Thirty-six informal caregivers (2.9%) reported suicidal thoughts during the 4 year study period. The difference between caregivers and non-caregivers (3.0%) was not significant. Among caregivers, significant risk factors for suicidal thoughts included being unemployed, living without a partner, having lower levels of social support, having a chronic physical disorder, a mood disorder or an anxiety disorder, and having impaired social, physical and emotional functioning. These risk factors were also found in non-caregivers. No caregiving-related characteristics were associated with suicidal thoughts.There was no elevated rate of suicidal thoughts in caregivers and risk factors for suicidal thoughts in caregivers were consistent with risk factors in non-caregivers. No association between caregiving characteristics and suicidal thoughts was found. Caregivers with limited resources and in poorer health might still benefit from prevention and intervention efforts. Family caregivers provide the cornerstone of care for people living with long-term illnesses and disabilities, and in Europe alone there are more than 100 million of them . As wellMore than 50 years’ worth of research, however, has shown that caring takes a significant toll on the physical health, mental health, social engagement, career prospects, and financial security of family caregivers . More reThe vast majority of this research, however, has used purposive, and relatively small, study samples. And, of the few studies that have used large representative samples , 14, 16,The aim of this study was to compare the rate of suicidal thoughts in caregivers and non-caregivers in a large, representative sample, and to identify significant risk factors for suicidal thoughts in caregivers that might provide directions for prevention and intervention.NEMESIS-2  is a psychiatric epidemiological cohort study in the Dutch general population aged 18 to 64 years at baseline. It is based on a multi-stage, stratified random sampling procedure of households, with one respondent randomly selected in each household. The study was approved by the Medical Ethics Review Committee for Institutions on Mental Health Care. Participants provided written informed consent at each wave. The provision of informal care was assessed at the second wave (T1), and we therefore used the data of T1 and the subsequent wave (T2) for the current study to investigate suicidal thoughts.In the first wave of NEMESIS-2 , 6646 respondents were interviewed . This sample was nationally representative, although younger subjects were somewhat underrepresented . All T0 The face-to face interviews were laptop computer-assisted and mainly conducted at the respondent’s home. The interviews were conducted by trained professional interviewers who were selected for their experience with systematic face-to-face data collection, experience with sensitive topics, and ability to achieve a good response rate in other studies.Fieldwork was monitored over the entire data collection period by the NEMESIS-investigators and the fieldwork agency. For more information on quality checks of the data and a more detailed description of the design and fieldwork, see .n = 1759). Of these caregivers, 1582 (89.9%) also participated in the third wave (T2) and were included in the current study. This enabled us to investigate the presence of suicidal thoughts over a period of 4 years .At the second wave (T1), respondents were asked whether they provided unpaid care in the previous 12 months, to a family member, partner or friend who needed care because of physical problems, mental problems, or ageing  version 3.0, a fully structured lay-administered diagnostic interview for common mental disorders. This instrument was developed for use in the World Mental Health Survey Initiative .At T1, respondents were asked about thoughts of suicide in the previous 12 months (“Have you seriously thought about committing suicide?”) and at T2 they were asked about thoughts of suicide in the previous 3 years (i.e. since T1). At both waves, to encourage people to report their thoughts, plans or attempts of suicide, the experiences were not mentioned but listed in a booklet and referred to by number (“event A”), as was done in other studies as well (e.g. ). For thSociodemographics included: age; gender; educational level ; employment status , living with a partner ; and, parental status (cohabiting with child(ren) yes/no).Caregiving-related characteristics included: the type of relationship with the care recipient ; number of care recipients ; cohabiting with the care recipient ; reasons for care ; type of care ; time spent giving care ; and, duration of care .Other characteristics included:Perceived social support from three sources  was measured with two questions on instrumental and emotional support from each source. These referred to the extent respondents could rely on them for help if they had a problem and could open up to them if they needed to talk about worries. The four response categories ranged from one  to four (a lot). Perceived social support was calculated as the mean score from at least two sources—because not all respondents had a partner at the time of interview.◦ Chronic physical disorders were identified with a standard checklist of 17 chronic physical disorders, treated or monitored by a medical doctor in the 12 months prior to T1, including: asthma; chronic obstructive pulmonary disease; chronic bronchitis; emphysema; severe heart disease; heart attack; hypertension; stroke; stomach or intestinal ulcers; severe intestinal disorders; diabetes; thyroid disorder; chronic back pain; arthritis; migraine; impaired vision or hearing; or, any other chronic physical disorder [◦ disorder .Mood disorders  and Anxiety disorders  in the last 12 months were identified with the CIDI 3.0 [◦ CIDI 3.0 . ClinicaCIDI 3.0 .negative life events  in the 12 months prior to T1 were measured, based on the Brugha Life Events scale [◦ The presence of ten ts scale .Role functioning was assessed in the past 4 weeks for the physical, mental and social domains from the Medical Outcomes Study Short Form Health Survey (SF-36) [◦  (SF-36) . All thrA broad set of variables was used to identify potential risk factors for suicidal thoughts. These were all assessed at T1, except for educational level, which was assessed at T0. Obviously, the caregiving-related characteristics were not assessed in non-caregivers.p < 0.05. Analyses were conducted in SPSS 22 and STATA 12.1.First, we compared the baseline characteristics of caregivers who dropped out at T2 with caregivers who completed T2 by performing logistic regression analysis. Second, the rate of suicidal thoughts was calculated as the number of caregivers who reported these thoughts at any point during the 4 years, i.e. between the 12 months prior to T1 and T2. We checked whether the rate of suicidal thoughts differed between informal caregivers and non-caregivers, with a Chi-squared test using weighted data to correct for differences in the response rates in several sociodemographic groups at both waves and differences in the probability of selection of respondents within households at baseline . LogistiThe characteristics of the study sample are described in Table n = 177) were lost to follow-up. None of the baseline variables were significantly associated with loss to follow-up.Over the 3 year follow-up, 10% of the caregivers (p = 0.89)).Across the 4 years, 36 of the caregivers (2.9% weighted percentage) reported suicidal thoughts . The weighted prevalence of suicidal thoughts in informal caregivers did not differ significantly from people who did not provide informal care at T1 , p = 0.07). In non-caregivers, the same risk factors were found as in informal caregivers  and poor health. These risk factors were also found in non-caregivers. There was no significant association between suicidal thoughts and specific caregiving characteristics, such as type of caregiving.Our finding that the rate of suicidal thoughts between caregivers and non-caregivers did not differ is in line with a previous, relatively small study among a family caregivers of people with dementia in Japan , but conThe risk factors for suicidal thoughts in caregivers identified in the current study are broadly consistent with previous research. Poor physical health, poor mental health, and a lack of social support have all been previously identified as risk factors for suicidal thoughts in family caregivers , 14, 16 p = 0.07). In a study of caregivers in Taiwan, Huang and colleagues (2018) found that people caring for a family member with a mental disorder reported significantly higher rates of suicidal thoughts than those caring for a family member with a physical disorder [We did not find a significant association between reason for care  and suicidal thoughts, nor between type of caregiving  and suicidal thoughts. The association between suicidal thoughts and caring for someone with a mental disorder did, however, approach significance  and suicidal thoughts. Future research would benefit from samples that facilitate multivariate analysis, although this is complex when studying relatively rare events such as suicidal thoughts.Furthermore, because of the relatively small number of caregivers reporting suicidal thoughts, it was not considered appropriate to examine possible recurrence of suicidal thoughts or report the rate of suicide attempts in this population. Despite this, understanding how suicidal thoughts arises and persists across the caregiving trajectory, as well as how suicidal thoughts are related to suicide attempts in this population, remains important for the development of tailored prevention and intervention strategies. Previous research has shown that suicidal thoughts can occur while caring at home, following institutionalization, and after bereavement , 7 and tThe presence of suicidal thoughts was based on retrospective self-reports over the last 12 months (at T1) and the previous 3 years (at T2). Due to this relatively long time span, recall bias may have occurred and resulted in underreporting of suicidal thoughts. However, recall bias is not likely to differ between caregivers and non-caregivers in this study and therefore it is unlikely that this has influenced the comparison of the rate and risk factors of suicidal thoughts between caregivers and non-caregivers.Lastly, respondents were defined as informal caregivers if they provided unpaid care in the twelve months preceding the second wave (T1), but no data on their caring status 3 years later was available. It might be possible that some people were no longer providing informal care at the second wave and this may have influenced the presence or absence of suicidal thoughts.There is a small but rapidly growing body of research on suicidal thoughts in family caregivers and the current study makes a substantial contribution. The absence of any significant difference in the rate of suicidal thoughts between caregivers and non-caregivers stands in contrast to previous findings and highlights the need for more nuanced research to understand this complex phenomenon. Research that can identify which carers are at greater risk of suicidal thoughts, and when, will be key to developing effective intervention and prevention strategies. In the meantime, the current findings suggest that at the least, caregiving is not a protective factor, with caregivers no less likely to consider suicide than non-caregivers. Risk factors of suicidal thoughts in informal caregivers were largely similar to the risk factors in non-caregivers. The current findings suggest that caregivers with limited resources and poor health might benefit from prevention and intervention efforts. Finally, quantitative studies do not allow consideration of the broader social context in which both caring and living with a disability take place . Qualita


Cerebellar hemorrhage is a potentially life-threatening condition and neurologic deterioration during hospitalization could lead to severe disability and poor outcome. Finds out the factors influencing neurologic deterioration during hospitalization is essential for clinical decision-making.One hundred fifty-five consecutive patients who suffered a first spontaneous cerebellar hemorrhage (SCH) were evaluated in this 10-year retrospective study. This study aimed to identify potential clinical, radiological and clinical scales risk factors for neurologic deterioration during hospitalization and outcome at discharge.p = 0.002; 95% confidence interval (CI): 0.026 to 0.455) adjusted risk of neurologic deterioration compared with those without obliteration of basal cistern. An increase of 1 point in the ICH score on admission would increase the neurologic deterioration rate by 83.2% . The ROC curves showed that the AUC for ICH score on presentation was 0.719  and the cutoff value was 2.5 (sensitivity 80.5% and specificity 73.7%).Neurologic deterioration during hospitalization developed in 17.4% (27/155) of the patient cohort. Obliteration of basal cistern (p≦0.001) and hydrocephalus (p≦0.001) on initial brain computed tomography (CT), median Glasgow Coma Scale (GCS) score at presentation (p≦0.001) and median intracerebral hemorrhage (ICH) score (P≦0.001) on admission were significant factors associated with neurologic deterioration. Stepwise logistic regression analysis showed that patients with obliteration of basal cistern on initial brain CT scan had an odds ratio (OR) of 9.17 (Patients had obliteration of basal cistern on initial brain CT and ICH score greater or equal to 3 at admission implies a greater danger of neurologic deterioration during hospitalization. Cautious clinical assessments and repeated brain images study are mandatory for those high-risk patients to prevent neurologic deterioration during hospitalization. Spontaneous cerebellar hemorrhage (SCH) account for represent 5 to 13% of all cases of spontaneous intracerebral hemorrhage and about 15% of cerebellar strokes . It is oBecause of its unique neurological location near the brainstem, neurologic deterioration usually results from brain stem compression due to the direct mass effect of the haematoma and/ or the development of hydrocephalus . HoweverThis study aimed to identify potential clinical, radiological and clinical scales risk factors to predict neurologic deterioration during hospitalization and outcome at discharge in patients with SCH.This is a single-centre retrospective study. Medical records were retrospectively reviewed using pre-existing standardized evaluation forms as well as brain computed tomography (CT) findings for patients with SCH admitted to the Department of Neurology or Neurosurgery in our tertiary academic centre from January 2005 to April 2015. The study approved by the Institutional Review Board (IRB)/Ethics Committee .On admission, a detailed physical examination, the routine laboratory testing, and brain imaging were evaluated for all patients. The initial neurologic state was evaluated by the Glasgow Coma Scale (GCS). Then systolic blood pressure, diastolic blood pressure, heart rate, and body temperature were recorded immediately before brain computed tomography (CT) scanning. After CT scan, the ICH score was calculated by the all parameters . The ICHAcute SCH was diagnosed by the clinical history and brain CT. Patients were excluded if they had: 1) non- spontaneous cerebellar hemorrhage, such as traumatic cerebellar hemorrhage; 2) SCH caused by a primary or secondary brain tumor, cavernomas, arteriovenous malformations or aneurysms, or hemorrhagic transformation of a cerebellar infarct; and 3) preexisting neurological conditions with various neurological deficits . From January 2005 to April 2015, a total of 186 patients with acute SCH were admitted to our hospital. This study is to evaluate neurologic deterioration during hospitalization, therefore, GCS =3 and no brain stem reflex on presentation was excluded. Finally, a total of 155 patients were enrolled in the studies  only, midline suboccpital craniectomy for hematoma evacuation, and suboccpital craniotomy plus EVD.Patients with neurologic deterioration during hospitalization was defined that patients presented the identified episodes of one or more of the following: 1) a spontaneous decrease in GCS motor scores of 2 points or more from the previous neurologic examination; 2) development of loss of pupillary reactivity 3) pupillary asymmetry greater than 1 mm. .The patients were divided into two groups according to the discharge outcome: 1) The good outcome group with independent performance of daily activities  score = 4 or 5). 2) The poor outcome group with disability of daily living, vegetative states, or death  ..p < 0.05.The descriptive data were showed as both median and inter-quartile range (IQR). Categorical variables were accessed by the Chi-square test or Fisher’s exact test. The Mann-Whitney U test was used for the continuous variables analysis. The Spearman rank test was applied for correlation analysis for the relationship between age, GCS and laboratory data. Statistical significance was set at We used stepwise logistic regression analysis for evaluating the association between significant variables and patients with neurologic deterioration during hospitalization and outcome. ROC curve was generated to estimate an optimal cut-off value for ICH score on admission, and the area under ROC curve was measured. All statistical analyses were conducted using the SPSS software (IBM SPSS statistic version 22.0).From January 2005 to April 2015, a total of 186 patients with SCH were admitted to our hospital. Thirty-one patients whose GCS was 3 and without pupil reflex at presentation were excluded. A total of 155 patients finally were enrolled in this study  Hydrocephalus in initial image was presented in 69 patients (44.5%). The appearance of 4th ventricle compression was grade I in 30 (19.4%), grade II in 65 (41.9%), and grade III in 60 (38.7%) study sample patients. Eighty-four patients (54.2%) exhibited obliteration of the basal cistern.Neuroradiological characteristics and neuro-surgical treatment at presentation were listed in Table Forty-one patients (26.5%) received neurosurgical intervention, including 11 patients (7.1%) with suboccpital decompressive craniectomy, 4 patients (2.6%) with EVD only, and 26 patients (16.8%)with both suboccpital decompressive craniectomy and EVD. There were 10 patients underwent the re-operative craniectomy. The median time between symptoms and surgery was 2.46 h (IQR: 1.81–9.0 h). The median time of neurologic deterioration after admission was 12.5 h (IQR: 6–72 h).p = 0.038), liver cirrhosis (p = 0.005), the initial presentation with loss of consciousness (p = 0.029), and elevated heart rate (p = 0.010). Statistical analysis revealed significant associations at initial neuro-image findings are median CH volume (p≦0.001), the presentation of obliteration of basal cistern (p≦0.001) and hydrocephalus (p≦0.001). But there is no significant statistical difference for patients with presentation of intraventricular hemorrhage in initial CT scan(P = 0.108). GCS and ICH score at presentation showed significant statistical difference between the two groups (both p≦0.001).Twenty-six  patients died during hospitalization. Twenty-three died of the hemorrhage. Three patients died of unrelated causes, one was aspiration pneumonia, one was massive GI bleeding, and the other one was uncontrolled liver cirrhosis. Factors predict of neurologic deterioration during hospitalization and the outcome in patients with SCH were listed in Table The functional outcome at discharged, the neurologic deterioration group showed poor outcome compared to the no neurologic deterioration group. Only 1 of 27 patients in neurologic deterioration group had good outcome, compared to 113 of 128 patients in no neurologic deterioration group (p≦0.001). Of these 27 patients had neurologic deterioration during hospitalization, 22 died during hospitalization. Of the five patients survived, only one had good outcome (GOS =5) with minor neurologic deficit , two were vegetative state (GOS =2) and the other two were severe disability state (GOS =3) at discharged. Moreover, the SCH patients with neurologic deterioration during hospitalization revealed a specific high mortality rate during hospitalization. 22 of 27(81.4%) patients died during hospitalization in the neurologic deterioration group, compared to 4 of 128 (3.1%) patients died during hospitalization in no neurologic deterioration groups (P≦0.001).P = 0.002 and P = 0.010, respectively) The adjusted risk of SCH patients with neurologic deterioration during hospitalization with obliteran of basal cistern at initial brain CT scan had odds ratio (OR) of 9.17 : 0.026 to 0.455) compared with those without obliteran of basal cistern at initial brain CT scan. Furthermore, an increase of 1 point in ICH score on admission would increase the risks of neurologic deterioration during hospitalization rate by 83.2% .Stepwise logistic regression analysis identified obliteran of basal cistern at initial brain CT and the higher ICH score as independent risk factors for neurologic deterioration during hospitalization. . The cutoff value of ICH score on presentation was 2.5 (sensitivity 80.5% and specificity 73.7%).p = 0.002 and p = 0.010, respectively).In our study, 17.4% patients with SCH had neurologic deterioration during hospitalization and most of these patients  had poor outcome. Arterial hypertension, initial loss of consciousness, the presentation of obliteran of basal cistern, hydrocephalus and CH volume in the initial brain CT, lower GCS and higher ICH score are statistically significant in patients with risks of neurologic deterioration. Although those factors are statistically significant in patients with neurologic deterioration, only obliteran of basal cistern at initial brain CT and the higher ICH score as independent risk factors  had poor outcome. Obliteration of basal cistern and hydrocephalus in the initial brain CT, lower GCS scale and higher ICH score on presentation implies a greater danger of neurologic deterioration during hospitalization. The median time of neurologic deterioration after admission was 12.5 h. Cautious clinical assessment, repeated neuro-image study within the first 12 h after admission and possibility of early neurosurgical intervention needed to be recognized by clinical physicians for high-risk patients, especially in those with ICH score greater or equal to 3, in order to early management the presence of neurologic deterioration in SCH patients.


This strategy could provide standardization in CONUS tidal carbon accounting until such a time as modeling and mapping advancements can quantitatively improve accuracy and precision.Tidal wetlands produce long-term soil organic carbon (C) stocks. Thus for carbon accounting purposes, we need accurate and precise information on the magnitude and spatial distribution of those stocks. We assembled and analyzed an unprecedented soil core dataset, and tested three strategies for mapping carbon stocks: applying the average value from the synthesis to mapped tidal wetlands, applying models fit using empirical data and applied using soil, vegetation and salinity maps, and relying on independently generated soil carbon maps. Soil carbon stocks were far lower on average and varied less spatially and with depth than stocks calculated from available soils maps. Further, variation in carbon density was not well-predicted based on climate, salinity, vegetation, or soil classes. Instead, the assembled dataset showed that carbon density across the conterminous united states (CONUS) was normally distributed, with a predictable range of observations. We identified the simplest strategy, applying mean carbon density (27.0 kg C m Mapping tidal carbon stocks and fluxes is challenging, with substantial implications for ecology5, carbon markets7, resiliency9, and greenhouse gas (GHG) inventorying10.Tidal wetlands, herein including saltmarshes, tidal freshwater wetlands, and tidally influenced forests such as mangroves, are a substantial global sink of organic carbon (C)in-situ is deposited primarily by root addition into shallow anoxic soils11. As sea level rises, organic deposition, as well as inorganic sediment deposition, contribute new soil mass that, under the right conditions, allow the wetland surface to vertically accrete and gain elevation in equilibrium with relative sea-level rise13. Long-term storage properties are variable and depend on salinity, flooding, plant type, and microbial community activity14. Tidal wetlands can be a major source of carbon emissions when the soil is lost to erosion15, or other disturbances17. Processes such as drainage or diking can result in direct oxidation of soil carbon or the emission of methane depending on soil type, inundation, and salinity18. Erosion results in export of particulate and dissolved organic carbon to other aquatic systems, a portion of which is oxidized and returned to the atmosphere19.Tidal wetlands store carbon in their soil organic matter when they are stable and release carbon when they are degrading. Organic matter produced 20. We refer to precision throughout as the agreement among repeated comparisons of mapped and reference values. The lack of precision is referred to throughout as imprecision, also known as random error20.In order to both evaluate existing carbon stocks at sub-national to local scales, and estimate emissions from tidal wetlands that are lost during erosion and degradation events, we require accurate and precise soil carbon mapping strategies. We refer to accuracy throughout as the average difference between mapped and reference values. The lack of accuracy is referred to throughout as bias, also commonly referred to as systematic errorWetlands Supplement10 to the 2006 assessment report provides global default values based on a literature review. It also provides guidance for ‘higher tier’ analyses utilizing country-specific data, such as a more extensive and thorough review of country-specific soil core data22 or the use of soils maps23. The IPCC Wetland Supplement guidance recommends disaggregating wetland emissions based on soil type , as well as by vegetation community, salinity, and climate type. However, the relative importance of these factors and the efficacy of applying separate estimates have not been evaluated at the scale of the conterminous U.S. (CONUS).Herein we discuss three types of strategies for estimating carbon stocks: applying average carbon stock values from syntheses of soil core data, applying models fit using empirical data and applied spatially using soil, vegetation and salinity maps, and relying on independently generated soil carbon maps that intersect with mapped tidal wetlands. The International Panel on Climate Change (IPCC)’s 2013 et al.24 recently presented an ‘ideal mixing model’, which describes the physical and volume-limited nature of bulk density in tidal wetland soils. Bulk density and organic matter content are not independent variables; instead, bulk density is a predictable function of organic matter26, the product of the ‘self-packing densities’ of organic and mineral soil fractions24. Although Morris et al.24 fit this model to describe constraints on tidal wetlands’ resilience to relative sea-level rise, it also has important ramifications for carbon monitoring as organic matter self-packing density defines an effective upper-limit for likely ranges of observable organic matter density.Morris et al.23 utilized the United States Department of Agriculture (USDA)’s Soil Survey Geographic Database (SSURGO) for a CONUS-wide stock assessment, independent of the previously described soil core syntheses. SSURGO is a CONUS-wide series of high resolution soil maps27 that link soil classifications and descriptions to tables populated with associated bulk density and % organic matter28 depth series information. However, the underlying information used to populate SSURGO soils maps with organic matter content and bulk density values are not necessarily empirical. They can be based on laboratory measurements, or can be assembled from literature, interviews with experts, or interpreted using a soil scientist’s expert judgement27. Hinson et al.23 were not able to perform a full accuracy assessment of their maps because data were not readily available through the literature. One study provided a regional independent validation for SSURGO carbon data for tidal wetlands, among other land cover types, in Louisiana29. The study observed a weak positive correlation in organic matter content between SSURGO and independently collected soil cores but did not assess the accuracy of SSURGO-based carbon stock maps29.Hinson Wetlands Supplement guidance for reporting and applying soil carbon stock values based on soil type, climate type, and salinity and vegetation. Second we evaluated whether or not more complex, spatially-explicit approaches improved precision and accuracy over a simpler strategy, applying a single mean value based on an extensive empirical dataset.In the absence of, and with an interest in developing, a robust national-scale strategy for estimating tidal wetland carbon stocks, our goals are twofold. First, we evaluated the efficacy of IPCC We assembled a spatially explicit database totaling 1959 soil cores from 49 different studies across CONUS .We described carbon density using mean and standard deviation (s.d.) assuming a truncated normal distribution in which values could not be lower than 0. The fit of this distribution was an improvement over a log-normal distribution Fig. . For ourModel fitting occurred in three major steps: fitting the ideal mixing and organic matter density models, determining an appropriate threshold for categorizing organic- and mineral- dominated soils, and fitting two mixed effects models one with soil type as an independent variable (model 1) and one without (model 2), to describe the major categorical trends and effect sizes within the data.−3 ± s.e. 0.001 (p < 0.0001) and inorganic self-packing density (k2) was 1.67 g cm−3 ± s.e. 0.025 (p < 0.0001). When the mixing model was fit to separate 10 cm depth intervals, k1 ranged between 0.086 to 0.146 g cm−3 and k2 ranged between 1.34 to 2.32 g cm−3; neither k1 nor k2 exhibited a trend with depth to 1 m.For the ideal mixing model and organic matter density models Fig. , the sel2 = 0.30, p < 0.0001) in the carbon mass data than the prescribed definition of either >20%  or >35% .The organic matter density model is useful for describing variability along a spectrum of soils types. However soils are typically mapped using the binary categories of organic- and mineral-dominated. We detected a significant threshold at 13.2% organic matter  this equals 0.48 σc , with organic soils having higher carbon density than mineral soils . Pseudo R2 for model 2 decreased from 0.51 to 0.32 and AICc increased from 7278 to 8454. Climate, vegetation and salinity type, and depth interval as well as an interactive effect were present in the most parsimonious version of model 2  respectively , indicating that the model performed better than the application of the mean from the reference dataset , it did not incorporate any of the uncertainty in the underlying layers needed to create national-scale mapping products. These included SSURGO for soil type (model 1), and the Coastal Change Analysis Program (C-CAP) for salinity and vegetation types (models 1 and 2).r performance threshold .When model 1 was applied using a derivative SSURGO-based organic and mineral-dominated soils map and C-CAP maps for salinity and vegetation classes, precision decreased, and normalized total error increased above the 1 σr from 10 to 20 cm and −0.06 σr at 80 to 90 cm . Bias correcting SSURGO using the known relationship between organic matter content and bulk density24 substantially reduced bias, but did not improve precision or reduce RMSE* below the required threshold of 1 σr  values range from 0.24 to 0.30 g cm−3 average carbon mass assumption, we estimate 0.72 (Pg) of C for the top 1 meter of soil. SSURGO soils maps do not perfectly overlap mapped tidal wetlands because of missing or incomplete survey data; overlapping area covered 1.97 m ha (Table We mapped 2.67 million hectares (m ha) of coastal wetlands based on national scale maps. Given our most precise, and accurate applied strategy, a simple 27 kg C mComparing all approaches, the SSURGO-limited spatial mapping led to a tidal wetland C stock total of 0.53 Pg C, using the simple mean, and 0.54 using the bias-corrected SSURGO data, whereas models 1 and 2 resulted in slightly lower national-scale carbon stock values (0.43 and 0.37 Pg). In contrast, utilizing unadjusted SSURGO data and maps resulted in a CONUS stock estimate of 1.15 Pg C, thus 54% higher than the approach of applying a single average carbon density. We visualized the comparison between these maps for the Louisiana Delta in Fig. Since we detected negative bias in classifying organic soils using SSURGO organic matter content data and positive bias when calculating carbon density using both SSURGO organic matter content and bulk density data, we reviewed the empirical data that informs SSURGO at the pedon level. The National Cooperative Soil Survey (NCSS)’s pedon database archives quantitative data available to SSURGO soil scientists. One-hundred eleven pedons overlapped mapped tidal wetland area. These included 14 of the 22 tidal CONUS states and were both less numerous and less spatially representative than our empirical dataset. Further, a close inspection of the archived data shows very limited measurements from tidal wetland pedons; approximately one-third of the pedons lack any empirical bulk density or organic carbon data, and most of the remaining pedons are either missing empirical organic carbon or bulk density data, or missing some depth horizon data. Four of the 111 relevant pedons had both organic carbon and bulk density measurements complete and continuous down to 1 m depth  we found in our large empirical dataset is comparable to multiple previous syntheses in the U.S. and other locations. The mean value we observed using a Tier II (nation-specific) approach is within the confidence intervals for IPCC global default values for salt marshes (25.5 [25.4–29.7 95% CI] kg C m−3). Therefore, applying Tier I default using reference carbon stocks provided by the IPCC Wetland Supplement would have been reasonable. A recent independent CONUS-wide study supported this estimate reporting an adjusted 28.0 ± 7.8 s.d. kg C m−2 to 1 m in soils of saline wetlands21. For European tidal marshes, van Broek et al.32 report a mean of 26.1 kg C m−3. In Southeastern Australia mangroves and marshes have a mean carbon density of 25.3 kg C m−3 33, with no effect of vegetation type but geomorphic influences within drainages.The average carbon mass  across 323 samples from Australian salt marshes, but the distribution is strongly weighted toward mineral soils, less than 14% organic matter. The majority of the variability occurs within these mineral soils, and appears to be related to grain size associated with geomorphic conditions33. Similar to our study, a narrow distribution is observed in Australian salt marshes, around a relatively low mean of 16.5 ± 0.7 s.e. kg C m−2 34. This is consistent with the assumption of the organic matter density model, that carbon density should vary over a fairly narrow range, predictably increasing only along a spectrum of more to less mineral-dominated soils between 0 and 13.2% organic matter , and carbon mass changes less with the organic/inorganic composition ratio. This established an effective upper limit of 0.048 gC cm−3 (48.0 kg m−3) that highly organic soils should not theoretically exceed on average. We note that while the assumption of an additive relationship between organic and inorganic fractions was useful for describing soil properties in this synthesis, there is an argument and some evidence that this assumption may not apply to karstic mangrove soils of the Everglades and Gulf of Mexico31.The concepts introduced by the ideal mixing model and the organic matter density model provide some context for the low variability in observed carbon density. Macreadie Our analysis of competing mapping and modeling strategies indicated that many geographic categories previously emphasized in the literature  may be less important than previously assumed Fig. . Relyinget al.1, reported higher carbon densities for mangrove soil globally, and the IPCC Wetlands Supplement recommends a higher default estimate for mangroves compared to tidal marshes. However, after controlling for random submitter effect and soil type, we observed little effect of vegetation type on carbon density . These apparent biases may be a result of low data availability to SSURGO soil scientists . Although standardized precision, accuracy, and total error metrics indicated that model 1, dominated by soil type effects, had the potential to outperform the use of a single average carbon density value, in practice it did not. Our accuracy assessment indicated that the CONUS soil map misclassified as many as 42.8% of organic soil observations as mineral  than that estimated from either bias-corrected SSURGO  or from our simple empirical mean approach (0.0283 PgC). Barataria Basin has been documented to have had some of the highest wetland loss rates in Louisiana, (−12.10 ± 2.51 km2 yr−1 from 1985–2010)36. Assuming emissions commensurate with that loss rate using watershed averages as in Hinson et al.23 would have resulted in estimated emissions of 18.3 m tonnes CO2 emitted according to the null map, 10.2 for Bias-Corrected SSURGO and 37.1 for SSURGO from 1985 to 2010. Our previous analyses showed that on the national scale SSURGO and bias-corrected SSURGO lack precision compared to the null strategy  was not unsubstantial. Controlled comparative lab studies show that inter-lab variance in loss-on-ignition is higher than intra-lab differences, and lab-specific bias is associated with sample size, ignition time, and ignition temperature38. Future studies could attempt to further control for these three variables.For accuracy assessment purposes we made the assumption that reference values were an improvement over mapped values, and that they approximated ‘true values’10, future studies could improve total soil stock estimates by more explicitly measuring and reporting deposit depth. In our empirical dataset most cores were from shallow coring efforts (e.g. 24 cm for the Louisiana Coastwide Reference Monitoring System [CRMS]). Comparatively few studies reported reaching bedrock or non-marsh sediment interface  was the most precise approach we tested, and importantly, is unlikely to substantially bias coastal carbon monitoring efforts. Overall, our analysis supports the use of a simpler metric for both stock assessment and estimating emissions. However, we also present metrics by which future efforts could be assessed and intercompared for predicting carbon distributions at system-relevant spatial scales.Given a single average carbon stock estimate, an assumption of 1 m depth and an area of 2.67 m ha, we estimate CONUS tidal wetlands contain 0.72 Pg of soil organic carbon. Soil carbon stocks, based on a large empirical dataset, were far lower on average and varied far less spatially and with depth than stocks calculated from available national-scale soils maps. Soil type was the single most important driver of variability, with little evidence of climate, vegetation, or salinity type influencing C stocks enough to produce a predictive model. For tidal wetlands soils with >13.2% organic matter, organic matter density increases very little as organic fraction increases. We quantified upper limits on mean tidal soil carbon stock defined by organic matter’s self-packing density. Although tidal wetland carbon stocks are greater in organic compared to mineral soils69, reports72, public databases75, unpublished data in preparation for peer review submitted by co-authors or generous members of the scientific community Six hundred two cores reported measured organic carbon (%), 475 cores in addition to organic matter and 127 without. Carbonates were often removed physically by applying dripped dilute acid or fumigating with concentrated acid (n = 138). Four hundred twenty five cores reported organic carbon without specifying acid treatments or reporting other strategies for carbonate removal, which is typically most important in karst embayments. Thirty nine of the cores in the dataset measured % total carbon rather than organic carbon, but we assumed that carbonate was a minimal contribution to the total.et al.76. We used a subset of paired data points from six data sources that had published organic matter content by LOI and organic carbon by elemental analysis77. Studies spanned CONUS including the Pacific Northwest, San Francisco Bay, Louisiana, the Everglades, and Long Island Sound. We modeled organic carbon as a quadratic function of organic matter using multi-model inference, algorithm which selects optimal models based on Akaike’s Information Criteria for small datasets (AICc)79 in ‘R’80. A quadratic function outperformed a linear function in terms of parsimony relative to explanatory power .We independently verified a function for predicting organic carbon from organic matter published by Craft OC = fraction organic carbon.OM = fraction organic matter.We summarized empirical data bulk density, organic matter, and organic carbon content across 10 cm increments down to 1 m using a depth weighted average, normalizing sampling interval to 1 cm increments and summing across the 10 cm depth intervals. If the deepest sample depth covered >50% of the depth increment the carbon mass, bulk density and organic matter were extrapolated to the bottom of the interval. If not the increment was considered a ‘no data’ value. If a cores had LOI data we estimated organic carbon from Eq. −3, so we recast 0 values as 0.01 kg C m−3 for to generate the log-normal distribution. This was for the sake of the exercise in visually comparing the two datasets, and zero values were not recast for reporting the mean and s.d. assuming the normal distribution.We described central tendency and spread by fitting probability densities to histograms for both a normal distribution and a log-normal distribution. For the log-normal distribution we assumed all zero values were not truly zero but below a detection limit of 0.1 kg C mWe randomly sorted cores into two independent subsets, a calibration dataset used for generating averages uncertainties and fitting models, and a reference dataset used for performing accuracy assessments and calculating model performance statistics.et al.24, herein referred to as the organic matter density model, to describe how organic matter density varies along a spectrum of soil types from purely mineral to purely organic  and mineral (k2) self-packing densities, conceptually the average density of pure organic and mineral matter respectively .k1 and k2 refer to the self-packing density of pure organic matter and pure mineral matter respectively (g cmOrganic self-packing density (k1) has important practical and theoretical implications for tidal wetland carbon accounting because it defines a hypothetical upper limit of carbon mass in organic soils when OM = 1. OM density should approach, but on average, not exceed, organic self-packing density (k1) in organic soils. We refer to this equation throughout as the Organic Matter Density model Eq. .3\docume81. We refined model parameters k1 and k2 and generated uncertainty estimates using a bootstrapped approach including 1000 iterations81.For the calibration dataset, we fit the ideal mixing model to generate p-value82 for all cores and depth classes together.We defined organic and mineral soils using a bootstrapped piecewise linear regression83. We used four classes: mediterranean, subtropical, temperate cool, and temperate warm. Mediterranean was defined as within the state of California and south of 40° latitude. Subtropical was defined as the Gulf Coast and the Atlantic coast of Florida south of 30° latitude. Temperate warm included the Atlantic coast between 30 and 40° latitude. Temperate cool included the Pacific and Atlantic Coasts north of 40° latitude. We realize that these are do not perfectly match Köppen-Giel climate zones84, but we applied them to remain consistent with other community efforts83.We mapped climate zone using the same standards as the EPA’s Greenhouse Gas Inventory85, and that data was not available for our soil core database, we combined forested and scrub/shrub categories.We also tested combined salinity and vegetation type as potentially predictive . Vegetation categorization differs slightly from that recommended by the IPCC (marsh and mangrove). We did this to match classifications in the Coastal Change Analysis Program (C-CAP) a Landsat-based land cover and change product. Because C-CAP defines forested and scrub/shrub based on shrub or tree heights86, using the syntax in Eq. Because this effort synthesized data from multiple sources, and measurements such as LOI and dry bulk density can have laboratory specific biases, we integrated a random ‘submitter’ effect into our modeling structure. We considered a submitter to be the first author of an associated peer-reviewed publication, or the individual or organization responsible for actively managing the original dataset. We generated submitter codes as the last name of the submitter or commonly used acronym for an organization. Random effects were combined with fixed effects and all potential interactions using the R packages ‘lme4’lmer is a linear mixed effects model.climate is one of four mapped climate zonessalVeg is one of four mapped salinity and vegetation typesdepth is one of the 10 cm depth increments down between 0 and 1 msoil is either organic or inorganic dominatedThe intercept of the linear model is conditional on random variation associated with the data submitter79 in R to test model fit relative to parsimony as measured by AICc, for all possible permutations of factors. Specifically, we used the ‘dredge’ function in the ‘MuMIn’ package79. We selected the model with the highest ranking Akaike weight as ‘model 1’. We calculated the pseudo R2 value for model 1 also using the R package ‘MuMIn’79.We used multi-model inference2) using the ‘anova_stats’ function in the R package ‘sjstats’87. We also repeated the dredge process described previously on a version of equation We did two things to quantify and contextualize effect sizes for the various fixed effects. First we calculated an adjusted effect size  soil survey boundaries, there are 288 survey zones overlapping mapped tidal wetlands89. Of these 288 zones, there were sixteen zones that had incomplete or missing data.We calculated wetland area using both 2006–2010 C-CAP27.SSURGO ‘map units’ represent the spatial extents of soils using mapping techniques, soil surveys, and expert judgment taking into account landscape factors. SSURGO contains multiple linked data tables associated with those map units. Each map unit may have one or more components, soil descriptions that make up a percent of that map unit, indicated by the ‘component percent’. Each component can have one or more ‘horizons’, depth intervals, which contain organic matter content and bulk density data89. We extracted all SSURGO map units intersecting mapped tidal wetlands from NWI, and further extracted all components categorized as ‘hydric’. If bulk density data was present, but organic matter was not, organic matter was assumed to be 0%. If both values or bulk density were missing, the horizon was interpreted as a ‘no data’ value.We downloaded all SSURGO maps and tables corresponding to soil survey areas intersecting mapped tidal wetlands28. We did not perform rock fragment corrections, as it is not applicable to tidal wetland soils29. We estimated carbon density using the van Bemmelen factor (0.58 gOC gOM−1), as that is the recommended conversion factor for SSURGO28. Carbon density (gC cm−3) was converted to mass area−1 by multiplying by the depth interval (10 cm) and converting to kg C m−2 (1 kg 1000 g−1 and 103 cm2 m−2). We also assigned SSURGO map units a binary classification of ‘mineral-’ or ‘organic-dominated’ based on a detected empirical threshold of 13.2% organic matter.To summarize data at the map unit scale we first calculated depth weighted averages for organic matter content, bulk density, and organic matter density separately based on each component’s separate horizon data. We then summarize each of these variables for map units as the weighted average of all the components based on their reported component percentReference dataset members were additionally screened so that low-quality latitude-longitude coordinates were excluded (n = 960 cores). Location information was coded as coming from GPS measurements, map figures or site descriptions. If positional information was not able to be effectively matched to a SSURGO map unit they were excluded.We assessed ‘model skill’ at two phases, what we refer to throughout as a validation phase, and an application phase. For model validation we modeled carbon density based on ‘true values’. We used the ‘R’ predict function using only the fixed effects from the mixed effects models 1 and 2. This allowed us to determine whether or not the models were overfit or unduly influenced by outliers in the calibration dataset. For model application we modeled carbon density based on ‘mapped’ values following the same procedure for model validation, except using mapped values. Application compounded uncertainty in both the models and the underlying data products used to apply the model.90. We assumed no mapping errors in climate zone or depth intervals.To run models that took soil type, climate, vegetation and salinity, and/or depth interval as inputs, each reference dataset core was assigned ‘true values’ based on field descriptions and empirical data, and a ‘mapped values’ based on SSURGO for soils and C-CAP for salinity and vegetation. All spatial statistics were done in ArcGIS Pro30. We also calculated total normalized root mean square error  and unbiased root mean square error (RMSE*’) Eq. –830. We rror Eq. . Becausem and μr = means of the modeled and reference values respectively.μr = standard deviation of the reference values.σR = correlation coefficient.n = number of data points.i = ith modeled value.mi = ith reference value.rm = s.d. of modeled values.σIn addition to model 1 and 2 we performed these validation metrics of bias*, RMSE*’ and RMSE* on two different applications of SSURGO. First, we validated SSURGO as described above. However we detected a positive bias. Second, we attempted to ‘bias correct’ SSURGO bulk density using the ideal mixing model Eq. , 11 of ai = residual of data point i.ri = The i th bulk density.BDi = The i th organic matter fraction value.OMs and k2s = the organic and inorganic self-packing densities fit to SSURGO data.k1i* = standardized residual of data point i.rs = the standard deviation of residuals for SSURGO.σi is the ‘corrected’ bulk density value for point i.CBDc and k2c = the organic and inorganic self-packing densities fit to calibration data.k1c is the standard deviation of residuals for calibration dataset.σ37 .While assembling SSURGO data to test the efficacy of using it to generate improved carbon stock estimates, we made extensive observations and independently vetted the the empirical datasets available to soil scientists who populate SSURGO with values. The National Cooperative Soil Survey (NCSS) Pedon Database is a resource available to soil scientists; reports contain field and lab descriptions of pedons, three-dimensional soil structures that make up the most basic and disaggregated unit of soil abstraction in the USDA spatial data product hierarchyDetailed summary statistics and figures are available in the supplemental information. Soil carbon maps based on the Soil Survey Geographic Database, salinity and vegetation maps, and modeling efforts are available via the Oak Ridge National Laboratory’s Distributed Active Archive Center (DAAC) for biogeochemical dynamics (10.3334/ORNLDAAC/1612). Soil core data that is from previously published information will be made immediately available via the Coastal Carbon Research Coordination Network (10.25572/ccrcn/10088/35684). Soil core data from previously unpublished sources that are currently ‘in-preparation’ or ‘in-review’ will be made public via the Coastal Carbon Research Coordination network pending their status change to ‘in-press’. Until the time that the full dataset is made public, previously unpublished soil core data inquiries will be referred to the original data submitters as listed in Supplemental Table Supplemental MethodsSupplementary Dataset 1Supplementary Dataset 2Supplementary Dataset 3Supplementary Dataset 4Supplementary Dataset 5