bluuebunny/crossref_metadata_2025_split huggingface dataset:
Opioid trends in Finland: a register-based nationwide follow-up study
Authors: Keto Jaana , Heiskanen Tarja , Hamunen Katri , Kalliomäki Maija-Liisa , Linna Miika 
Published: May 2022

AbstractThe opioid epidemic in the U.S has gotten payers, prescribers, and policymakers alike interested in trends in opioid use. Despite no recognized opioid crisis in Europe, several countries have reported an increase in opioid-related deaths, which has further prompted discussion on the need of monitoring of opioid prescriptions. This study was conducted to offer information on opioid use during the escalation of the U.S. opioid epidemic in Finland, a Nordic country with universal tax-based health care. This is a nationwide retrospective register-based cohort study on all individuals in Finland who were dispensed opioids in 2009–2017 (n of unique patients = 1,761,584). By using the unique personal identification code assigned to every Finnish resident, we linked data from nationwide registers on dispensed drugs, medical history, and socio-demographic parameters. We report a wide set of patient demographics, dispensing trends for all opioid Anatomical Therapeutic Chemical (ATC) classes, and reasons for opioid initiation based on diagnostic coding for the most recent health care visit. For a cohort of incident opioid users with a four-year wash-out period (n = 1 370 057), we also present opioid use patterns in a three-year follow-up: the likelihood of becoming a persistent user or escalating from weak to strong opioids. A steady 7% of the Finnish population were dispensed opioids annually in 2009-2017. The mean annual quantity of dispensed opioids per opioid patient increased between 2009 and 2017 by 33%, reaching 2 583 oral morphine equivalent mg (OMEQ)/patient/year in 2017. The median quantity of dispensed opioids was lower: 315 OMEQ/year/patient. Depending on the opioid ATC class, there were either increasing or decreasing numbers of patients who had been dispensed said opioid class, and also in the mean quantity. The most common reason for opioid initiation was post-surgical pain (20%), followed by musculoskeletal pain (15%), injury (8.3%), and non-postsurgical dental pain (6.2%). 94% of new opioid initiators started with a weak opioid, i.e. codeine or tramadol. 85% of the patients who had been dispensed a weak opioid were not dispensed an opioid subsequently 3–6 months after the first one, and 95% of them had not escalated to a strong opioid in a 3-year follow-up. The number of patients dispensed opioids in Finland did not change during the escalation of the opioid epidemic in the U.S., but there were changes in the quantity of opioids dispensed per patient. Opioid therapy was typically initiated with weak opioid, the initial dispensed prescription was relatively small, and escalation to strong opioids was rare. A considerable share of patients had been prescribed opioids for chronic non-cancer pain - a type of pain where the risk-benefit ratio of opioids is controversial.
Metabolite fingerprinting of phytoconstituents from Fritillaria cirrhosa D. Don and molecular docking analysis of bioactive peonidin with microbial drug target proteins
Authors: Bhat Basharat Ahmad , Mir Wajahat Rashid , Sheikh Bashir Ahmad , Alkanani Mustafa , Mir Manzoor Ahmad 
Published: May 2022

AbstractFritillaria cirrhosa D. Don (Liliaceae), a valuable and critically endangered medicinal herb of northwest India, including Jammu and Kashmir, grows in temperate to alpine regions of the Himalaya. It is known as the traditional herb for cardiovascular diseases, respiratory diseases, and metabolic disorders. The plant bulbs are precious and are used to cure many other health complications. The current study analysed the phytoconstituents by liquid chromatography-mass spectrometry (LC–MS) of different crude extracts (methanolic, petroleum ether, and ethyl acetate) of F. cirrhosa. The LC–MS analysis from the bulbs of F. cirrhosa yielded 88 bioactive compounds, with the vast majority having therapeutic applications. Further, determination of minimum inhibitory concentrations (MICs) by broth microdilution method of F. cirrhosa against tested bacterial and fungal pathogens showed remarkable results with MICs ranging between 6.25–200 µg/mL and 50–400 µg/mL, respectively. Subsequently, these 88 identified phytocompounds were tested for their bioactivity through ADMET prediction by SwissADME and in silico molecular docking studies. Results revealed that Peonidin might have maximum antibacterial and antifungal activity against various microbial protein drug targets among the phytochemical compounds identified. Furthermore, the highest binding affinity complex was subjected to molecular dynamic simulation (MDS) analysis using Desmond Schrodinger v3.8. The root-mean-square deviation (RMSD) graphs obtained through the molecular dynamic simulations indicated the true bonding interactions, further validated using the root-mean-square fluctuation (RMSF) graphs which provided a better understanding of the amino acids present in the proteins responsible for the molecular motions and fluctuations. To our best knowledge, this is the first description of the phytochemical constituents of the bulbs of F.cirrhosa analyzed through LC–MS, which show pharmacological significance. The in silico molecular docking and molecular dynamics study of peonidin was also performed to confirm its broad-spectrum activities based on the binding interactions with the antibacterial and antifungal target proteins. The present study results will create a way for the invention of herbal medicines for several ailments by using F. cirrhosa plants, which may lead to the development of novel drugs.
Clinical features of acute focal bacterial nephritis in adults
Authors: Jiao Sumin , Yan Zhe , Zhang Congqin , Li Juan , Zhu Jiaomei 
Published: May 2022

AbstractAcute focal bacterial nephritis (AFBN) is a localized bacterial infection of the kidneys presenting as an inflammatory mass that can develop into renal abscess. The current reports on AFBN mostly are among children and rarely described in adults. This study was aimed to analyze the clinical features of AFBN in adults and make a review for the disease to give the clinicians some clues to suspect and recognize it in adults. From January 2014 to December 2019, AFBN was diagnosed by contrast-enhanced computed tomography (CT) in 238 adults at the Department of Nephrology, the Second Hospital of Hebei Medical University, Shijiazhuang, China. We reviewed the clinical records of these patients and asked them about their post-discharge status via telephone follow-up. Of all the patients, 195 were female and 43 were male, the median age were 46.87 years. 86.13% presented with fever, 55.89% presented with lower urinary tract symptoms and 97.9% presented with pyuria. In renal ultrasonography, abdominal findings were seen only 22.69% patients. E.coli accounted for 74.73% of the isolated pathogen. After 4 weeks of treatment, the patients had no recurrence of symptoms. We recommend that when a patient presents clinically with acute pyelonephritis, but the fever persist longer after antimicrobial treatment (≥ 4 days in our study), AFBN should be suspected. For the diagnosis, contrast-enhanced CT is the “gold standard”, magnetic resonance imaging (MRI) may be a good option, but the ultrasonography is probably not satisfied. 3–4 weeks of antibiotic therapy may be appropriate for AFBN in adults.
Internal validation and comparison of predictive models to determine success rate of infertility treatments: a retrospective study of 2485 cycles
Authors: Mehrjerd Ameneh , Rezaei Hassan , Eslami Saeid , Ratna Mariam Begum , Khadem Ghaebi Nayyere 
Published: May 2022

AbstractInfertility is a significant health problem and assisted reproductive technologies to treat infertility. Despite all efforts, the success rate of these methods is still low. Also, each of these methods has side effects and costs. Therefore, accurate prediction of treatment success rate is a clinical challenge. This retrospective study aimed to internally validate and compare various machine learning models for predicting the clinical pregnancy rate (CPR) of infertility treatment. For this purpose, data from 1931 patients consisting of in vitro fertilization (IVF) or intra cytoplasmic sperm injection (ICSI) (733) and intra uterine insemination (IUI) (1196) treatments were included. Also, no egg or sperm donation data were used. The performance of machine learning algorithms to predict clinical pregnancy were expressed in terms of accuracy, recall, F-score, positive predictive value (PPV), brier score (BS), Matthew correlation coefficient (MCC), and receiver operating characteristic. The significance of the features with CPR and AUCs was evaluated by Student's t test and DeLong’s algorithm. Random forest (RF) model had the highest accuracy in the IVF/ICSI treatment. The sensitivity, F1 score, PPV, and MCC of the RF model were 0.76, 0.73, 0.80, and 0.5, respectively. These values for IUI treatment were 0.84, 0.80, 0.82, and 0.34, respectively. The BS was 0.13 and 0.15 for IVF/ICS and IUI, respectively. In addition, the estimated AUCs of the RF model for IVF/ICS and IUI were 0.73 and 0.7, respectively. Some essential features were obtained based on RF ranking for the two datasets, including age, follicle stimulation hormone, endometrial thickness, and infertility duration. The results showed a strong relationship between clinical pregnancy and a woman's age. Also, endometrial thickness and the number of follicles decreased with increasing female age in both treatments.
Novel preparation of functional β-SiC fiber based In2O3 nanocomposite and controlling of influence factors for the chemical gas sensing
Authors: Otgonbayar Zambaga , Jun Joo Young , Youn Cho Kwang , Yul Park Sang , Youl Park Kwang , Oh Won-Chun 
Published: May 2022

AbstractThe gas sensing ability of a pure β-SiC fiber is limited due to its low-sensitivity and selectivity with poor recovery time during a gas sensing test. The combination of functional β-SiC fibers with metal-oxide (MO) can lead to excellent electronic conductivity, boosted chemical activity, and high reaction activity with the target gas and β-SiC–In2O3 sensor material. Influence factors such as amounts of MO, current collectors, and gas species (CO2, O2 and without gas) for the gas sensing ability of β-SiC–In2O3 nanocomposite were determined at standard room temperature (25 °C) and high temperature (350 °C) conditions. The gas sensing ability of the functional β-SiC fiber was significantly enhanced by the loading of In2O3 metal-oxide. In addition, the MO junction on the β-SiC fiber was mainly subjected to the Si–C–O–In bond sensor layer with an effective electron-transfer ability. The gas sensing mechanism was based on the transfer of charges, in which the sensing material acted as an absorber or a donor of charges. The sensor material could use different current- collectors to support the electron transfer and gas sensing ability of the material. A 1:0.5M SiC–In2O3 coated Ni-foil current collector sensor showed better sensing ability for CO2 and O2 gases than other gas sensors at room temperature and high temperature conditions. The sensing result of the electrode was obtained with different current density values without or with gas purging conditions because CO2 and O2 gases had electron acceptor properties. During the gas sensing test, the sensor material donated electrons to target gases. The current value on the CV graph then significantly changed. Our obtained sample analysis data and the gas sensing test adequately demonstrated that MO junctions on functional β-SiC fibers could improve the sensitivity of a sensor material and particularly upgrade the sensor material for gas sensing.
Uncertainty quantification techniques for data-driven space weather modeling: thermospheric density application
Authors: Licata Richard J. , Mehta Piyush M. 
Published: May 2022

AbstractMachine learning (ML) has been applied to space weather problems with increasing frequency in recent years, driven by an influx of in-situ measurements and a desire to improve modeling and forecasting capabilities throughout the field. Space weather originates from solar perturbations and is comprised of the resulting complex variations they cause within the numerous systems between the Sun and Earth. These systems are often tightly coupled and not well understood. This creates a need for skillful models with knowledge about the confidence of their predictions. One example of such a dynamical system highly impacted by space weather is the thermosphere, the neutral region of Earth’s upper atmosphere. Our inability to forecast it has severe repercussions in the context of satellite drag and computation of probability of collision between two space objects in low Earth orbit (LEO) for decision making in space operations. Even with (assumed) perfect forecast of model drivers, our incomplete knowledge of the system results in often inaccurate thermospheric neutral mass density predictions. Continuing efforts are being made to improve model accuracy, but density models rarely provide estimates of confidence in predictions. In this work, we propose two techniques to develop nonlinear ML regression models to predict thermospheric density while providing robust and reliable uncertainty estimates: Monte Carlo (MC) dropout and direct prediction of the probability distribution, both using the negative logarithm of predictive density (NLPD) loss function. We show the performance capabilities for models trained on both local and global datasets. We show that the NLPD loss provides similar results for both techniques but the direct probability distribution prediction method has a much lower computational cost. For the global model regressed on the Space Environment Technologies High Accuracy Satellite Drag Model (HASDM) density database, we achieve errors of approximately 11% on independent test data with well-calibrated uncertainty estimates. Using an in-situ CHAllenging Minisatellite Payload (CHAMP) density dataset, models developed using both techniques provide test error on the order of 13%. The CHAMP models—on validation and test data—are within 2% of perfect calibration for the twenty prediction intervals tested. We show that this model can also be used to obtain global density predictions with uncertainties at a given epoch.
Variability in frost occurrence under climate change and consequent risk of damage to trees of western Quebec, Canada
Authors: Marquis Benjamin , Bergeron Yves , Houle Daniel , Leduc Martin , Rossi Sergio 
Published: May 2022

AbstractClimate change affects timings, frequency, and intensity of frost events in northern ecosystems. However, our understanding of the impacts that frost will have on growth and survival of plants is still limited. When projecting the occurrence of frost, the internal variability and the different underlying physical formulations are two major sources of uncertainty of climate models. We use 50 climate simulations produced by a single-initial large climate ensemble and five climate simulations produced by different pairs of global and regional climate models based on the concentration pathway (RCP 8.5) over a latitudinal transect covering the temperate and boreal ecosystems of western Quebec, Canada, during 1955–2099 to provide a first-order estimate of the relative importance of these two sources of uncertainty on the occurrence of frost, i.e. when air temperature is < 0 °C, and their potential damage to trees. The variation in the date of the last spring frost was larger by 21 days (from 46 to 25 days) for the 50 climate simulations compared to the 5 different pairs of climate models. When considering these two sources of uncertainty in an eco-physiological model simulating the timings of budbreak for trees of northern environment, results show that 20% of climate simulations expect that trees will be exposed to frost even in 2090. Thus, frost damage to trees remains likely under global warming.
Polyhydroxyalkanoates production from short and medium chain carboxylic acids by Paracoccus homiensis
Authors: Szacherska Karolina , Moraczewski Krzysztof , Rytlewski Piotr , Czaplicki Sylwester , Ciesielski Sławomir , Oleskowicz-Popiel Piotr , Mozejko-Ciesielska Justyna 
Published: May 2022

AbstractThe aim of this study was to evaluate an effect of short and medium chain carboxylic acids (CAs) rich stream derived from acidogenic mixed culture fermentation of acid whey on polyhydroxyalkanoates (PHAs) synthesis by Paracoccus homiensis and compare it with the impact of individual synthetic CAs. The obtained results confirmed that the analyzed bacterium is able to metabolize synthetic CAs as the only carbon sources in the growth medium with maximum PHAs production yields of 26% of cell dry mass (CDM). The replacement of the individual CAs by a CAs-rich residual stream was found to be beneficial for the Paracoccus homiensis growth. The highest biomass concentration reached about 2.5 g/L with PHAs content of 17% of CDM. The purified PHAs were identified as poly(3-hydroxybutyrate-co-3-hydroxyvalerate) by applying gas chromatography coupled with mass spectrometry, Fourier transform infrared spectroscopic spectra and UV–Vis spectra. Furthermore, a differential scanning calorimetric, thermogravimetric and water contact angle analysis proved that the extracted copolymers have useful properties. The obtained data are promising in the perspective of developing a microbial PHAs production as a part of an integrated valorization process of high CAs content waste-derived streams.
Genomes from Verteba cave suggest diversity within the Trypillians in Ukraine
Authors: Gelabert Pere , Schmidt Ryan W. , Fernandes Daniel M. , Karsten Jordan K. , Harper Thomas K. , Madden Gwyn D. , Ledogar Sarah H. , Sokhatsky Mykhailo , Oota Hiroki , Kennett Douglas J. , Pinhasi Ron 
Published: May 2022

AbstractThe transition to agriculture occurred relatively late in Eastern Europe, leading researchers to debate whether it was a gradual, interactive process or a colonisation event. In the forest and forest-steppe regions of Ukraine, farming appeared during the fifth millennium BCE, associated with the Cucuteni-Trypillia cultural complex (CTCC, ~ 5000–3000 BCE). Across Europe, the Neolithisation process was highly variable across space and over time. Here, we investigate the population dynamics of early agriculturalists from the eastern forest-steppe region based on the analyses of 20 ancient genomes from the site of Verteba Cave (3935–825 cal BCE). Results reveal that the CTCC individuals’ ancestry is related to both western hunter-gatherers and Near Eastern farmers, has no local ancestry associated with Ukrainian Neolithic hunter-gatherers and has steppe ancestry. An Early Bronze Age individual has an ancestry profile related to the Yamnaya expansions but with 20% of ancestry related to the other Trypillian individuals, which suggests admixture between the Trypillians and the incoming populations carrying steppe-related ancestry. A Late Bronze Age individual dated to 980–825 cal BCE has a genetic profile indicating affinity to Beaker-related populations, detected close to 1000 years after the end of the Bell Beaker phenomenon during the third millennium BCE.
Multi-staged gene expression profiling reveals potential genes and the critical pathways in kidney cancer
Authors: Khouja Hamed Ishaq , Ashankyty Ibraheem Mohammed , Bajrai Leena Hussein , Kumar P. K. Praveen , Kamal Mohammad Amjad , Firoz Ahmad , Mobashir Mohammad 
Published: May 2022

AbstractCancer is among the highly complex disease and renal cell carcinoma is the sixth-leading cause of cancer death. In order to understand complex diseases such as cancer, diabetes and kidney diseases, high-throughput data are generated at large scale and it has helped in the research and diagnostic advancement. However, to unravel the meaningful information from such large datasets for comprehensive and minute understanding of cell phenotypes and disease pathophysiology remains a trivial challenge and also the molecular events leading to disease onset and progression are not well understood. With this goal, we have collected gene expression datasets from publicly available dataset which are for two different stages (I and II) for renal cell carcinoma and furthermore, the TCGA and cBioPortal database have been utilized for clinical relevance understanding. In this work, we have applied computational approach to unravel the differentially expressed genes, their networks for the enriched pathways. Based on our results, we conclude that among the most dominantly altered pathways for renal cell carcinoma, are PI3K-Akt, Foxo, endocytosis, MAPK, Tight junction, cytokine-cytokine receptor interaction pathways and the major source of alteration for these pathways are MAP3K13, CHAF1A, FDX1, ARHGAP26, ITGBL1, C10orf118, MTO1, LAMP2, STAMBP, DLC1, NSMAF, YY1, TPGS2, SCARB2, PRSS23, SYNJ1, CNPPD1, PPP2R5E. In terms of clinical significance, there are large number of differentially expressed genes which appears to be playing critical roles in survival.
Dynamic magnetic resonance imaging of muscle contraction in facioscapulohumeral muscular dystrophy
Authors: Deligianni Xeni , Santini Francesco , Paoletti Matteo , Solazzo Francesca , Bergsland Niels , Savini Giovanni , Faggioli Arianna , Germani Giancarlo , Monforte Mauro , Ricci Enzo , Tasca Giorgio , Pichiecchio Anna 
Published: May 2022

AbstractQuantitative muscle MRI (water-T2 and fat mapping) is being increasingly used to assess disease involvement in muscle disorders, while imaging techniques for assessment of the dynamic and elastic muscle properties have not yet been translated into clinics. In this exploratory study, we quantitatively characterized muscle deformation (strain) in patients affected by facioscapulohumeral muscular dystrophy (FSHD), a prevalent muscular dystrophy, by applying dynamic MRI synchronized with neuromuscular electrical stimulation (NMES). We evaluated the quadriceps muscles in 34 ambulatory patients and 13 healthy controls, at 6-to 12-month time intervals. While a subgroup of patients behaved similarly to controls, for another subgroup the median strain decreased over time (approximately 57% over 1.5 years). Dynamic MRI parameters did not correlate with quantitative MRI. Our results suggest that the evaluation of muscle contraction by NMES-MRI is feasible and could potentially be used to explore the elastic properties and monitor muscle involvement in FSHD and other neuromuscular disorders.
Altered functional connectivity in children born very preterm at school age
Authors: Cho Hye Jung , Jeong Hyejin , Park Chan-A , Son Dong Woo , Shim So-Yeon 
Published: May 2022

AbstractChildren born very preterm are at significant risk of neurodevelopmental impairment. This study sought to identify differences in cognitive function in children born very preterm compared to term-born controls and investigate alteration in white matter microstructure and functional connectivity (FC) based on tract-based spatial statistics (TBSS) and resting-state functional MRI, respectively. At 6 years of age, 36 children born very preterm (< 32 weeks' gestation) without major neurological disabilities and 26 term-born controls were tested using the Wechsler Intelligence Scale for Children, 4th edition, and Child Behavior Checklist. Whole-brain deterministic tractography and FC measurements were performed in both groups. The very preterm group had significantly lower intelligence scores than the term-born controls. The TBSS revealed no significant differences between the two groups, whereas FC was significantly increased between the frontoparietal network and the language network and was significantly decreased between the right salience network nodes in the very preterm group. The altered FC patterns between specific regions of the higher-order networks may reflect underlying deficits in the functional network architecture associated with cognitive function. Further studies are needed to demonstrate a direct connection between FC in these regions and cognitive function.