bluuebunny/crossref_metadata_2025_split huggingface dataset:
A unified model of the biology of peripartum depression
Authors: Levin Gal , Ein-Dor Tsachi 
Published: April 2023

AbstractPeripartum depression (PPD) is a prevalent and debilitating disorder that adversely affects the development of mothers and infants. Recently, there has been a plea for increased mental health screening during the peripartum period; however, currently, there is no accurate screening tool to identify women at risk of PPD. In addition, some women do not respond to current treatment schemes and develop treatment-resistant depression. The current perspective aims to propose a unified understanding of the biological underpinnings of PPD (UmPPD) that considers the heterogeneity in the onset, symptoms cluster, and severity of PPD. Such a model could promote basic and applied research on PPD and suggest new treatment avenues. The central hub of the model is the kynurenine pathway (KP) and the KP-serotonin ratio. The forces and specific processes at play that cause an imbalance within the KP and between KP and serotonin are inflammation, stress, reproductive hormones (especially estradiol and progesterone), and oxytocin. UmPPD predicts that the most severe PPD would comprise prolonged inflammation, ongoing or multiple stressors, excessive estrogen, progesterone resistance, and avoidance of breastfeeding, skin-to-skin contact, and social proximity. These factors would be associated with a higher likelihood of developing PPD, early onset, and more significant symptom severity. In addition, subtypes of PPD would consist of different compositions and expressions of these components, with one central common factor. UmPPD could aid in directing future research and possibly detecting critical processes that could help discover, develop, and utilize novel treatments for PPD.
An atlas of the bone marrow bone proteome in patients with dysproteinemias
Authors: Ho Matthew , Dasari Surendra , Visram Alissa , Drake Matthew T. , Charlesworth M. Cristine , Johnson Kenneth L. , Pujari Ganesh P. , Jevremovic Dragan , Kourelis Taxiarchis 
Published: April 2023

AbstractMultiple myeloma (MM) bone disease is a significant cause of morbidity but there is a paucity of data on the impact of malignant plasma cells on adjacent trabecular bone within the BM. Here, we characterize the proteome of trabecular bone tissue from BM biopsies of 56 patients with monoclonal gammopathy of undetermined significance (MGUS), smoldering (SMM), newly diagnosed (NDMM), relapsed MM (RMM), and normal controls. Proteins involved in extracellular matrix (ECM) formation and immunity pathways were decreased in SMM and active MM. Among the proteins most decreased were immunoglobulins, type IV collagen, and TIMP3, suggesting increased immunoparesis and decreased ECM remodelling within trabecular bone. Proteins most increased in SMM/MM were APP (enhances osteoclast activity), ENPP1 (enhances bone mineralization), and MZB1 (required for normal plasmablast differentiation). Pathway analyses showed that proteins involved in gamma -carboxylation, a pathway implicated in osteocalcin function, osteoblast differentiation, and normal hematopoiesis, were also overexpressed in SMM/MM. This study is the first comprehensive proteomic atlas of the BM bone proteome in dysproteinemias. We identify new key proteins and pathways for MM bone disease and potentially impaired hematopoiesis, and show for the first time that gamma -carboxylation pathways are increased in the bone tissue of SMM/MM.
Impact of donor age and relationship on outcomes of peripheral blood haploidentical hematopoietic cell transplantation
Authors: Pruitt Aaron , Gao Feng , De Togni Elisa , Cochran Hunter , Godbole Sonia , Slade Michael , Abboud Ramzi 
Published: April 2023

AbstractHere we describe a retrospective analysis of outcomes in 299 patients who underwent peripheral blood haplo-HCT with PTCy from July 2009 through May 2021 and their association with donor characteristics. Patients had mostly acute leukemias and high or very high DRI. Multivariate analyses were conducted examining OS, NRM, relapse, cytokine release syndrome, acute and chronic GVHD. Donor characteristics included age, sex, relationship, ABO status, CMV status, and HLA match grade. Our analysis revealed increasing donor age was associated with higher NRM (compared to age <30; age 30–44 HR, 1.65; P = 0.110, age >44 HR, 1.80; P = 0.056) but lower relapse risk (compared to age <30; age 30–44 HR, 0.61; P = 0.034, age > 44 HR, 0.71; P = 0.132). There were no differences in CRS, aGVHD or cGVHD. We found no difference in outcomes based on the donor-recipient relationship. No differences were found based on HLA match grade or DRB1 match status. Increasing donor age was associated with lower relapse risk but higher NRM, resulting in no difference in OS based on donor age. Other donor factors including relationship (parent/sibling/child/ maternal), CMV status, donor sex, HLA match grade, and DRB1 status were not associated with outcomes.
Single-step retrosynthesis prediction by leveraging commonly preserved substructures
Authors: Fang Lei , Li Junren , Zhao Ming , Tan Li , Lou Jian-Guang 
Published: April 2023

AbstractRetrosynthesis analysis is an important task in organic chemistry with numerous industrial applications. Previously, machine learning approaches employing natural language processing techniques achieved promising results in this task by first representing reactant molecules as strings and subsequently predicting reactant molecules using text generation or machine translation models. Chemists cannot readily derive useful insights from traditional approaches that rely largely on atom-level decoding in the string representations, because human experts tend to interpret reactions by analyzing substructures that comprise a molecule. It is well-established that some substructures are stable and remain unchanged in reactions. In this paper, we developed a substructure-level decoding model, where commonly preserved portions of product molecules were automatically extracted with a fully data-driven approach. Our model achieves improvement over previously reported models, and we demonstrate that its performance can be boosted further by enhancing the accuracy of these substructures. Analyzing substructures extracted from our machine learning model can provide human experts with additional insights to assist decision-making in retrosynthesis analysis.
Cargo-free particles divert neutrophil-platelet aggregates to reduce thromboinflammation
Authors: Banka Alison L. , Guevara M. Valentina , Brannon Emma R. , Nguyen Nhien Q. , Song Shuang , Cady Gillian , Pinsky David J. , Uhrich Kathryn E. , Adili Reheman , Holinstat Michael , Eniola-Adefeso Omolola 
Published: April 2023

AbstractThe combination of inflammation and thrombosis is a hallmark of many cardiovascular diseases. Under such conditions, platelets are recruited to an area of inflammation by forming platelet-leukocyte aggregates via interaction of PSGL-1 on leukocytes and P-selectin on activated platelets, which can bind to the endothelium. While particulate drug carriers have been utilized to passively redirect leukocytes from areas of inflammation, the downstream impact of these carriers on platelet accumulation in thromboinflammatory conditions has yet to be studied. Here, we explore the ability of polymeric particles to divert platelets away from inflamed blood vessels both in vitro and in vivo. We find that untargeted and targeted micron-sized polymeric particles can successfully reduce platelet adhesion to an inflamed endothelial monolayer in vitro in blood flow systems and in vivo in a lipopolysaccharide-induced, systemic inflammation murine model. Our data represent initial work in developing cargo-free, anti-platelet therapeutics specifically for conditions of thromboinflammation.
Robustness and innovation in synthetic genotype networks
Authors: Santos-Moreno Javier , Tasiudi Eve , Kusumawardhani Hadiastri , Stelling Joerg , Schaerli Yolanda 
Published: April 2023

AbstractGenotype networks are sets of genotypes connected by small mutational changes that share the same phenotype. They facilitate evolutionary innovation by enabling the exploration of different neighborhoods in genotype space. Genotype networks, first suggested by theoretical models, have been empirically confirmed for proteins and RNAs. Comparative studies also support their existence for gene regulatory networks (GRNs), but direct experimental evidence is lacking. Here, we report the construction of three interconnected genotype networks of synthetic GRNs producing three distinct phenotypes inEscherichia coli. Our synthetic GRNs contain three nodes regulating each other by CRISPR interference and governing the expression of fluorescent reporters. The genotype networks, composed of over twenty different synthetic GRNs, provide robustness in face of mutations while enabling transitions to innovative phenotypes. Through realistic mathematical modeling, we quantify robustness and evolvability for the complete genotype-phenotype map and link these features mechanistically to GRN motifs. Our work thereby exemplifies how GRN evolution along genotype networks might be driving evolutionary innovation.
Boosting the interfacial superionic conduction of halide solid electrolytes for all-solid-state batteries
Authors: Kwak Hiram , Kim Jae-Seung , Han Daseul , Kim Jong Seok , Park Juhyoun , Kwon Gihan , Bak Seong-Min , Heo Unseon , Park Changhyun , Lee Hyun-Wook , Nam Kyung-Wan , Seo Dong-Hwa , Jung Yoon Seok 
Published: April 2023

AbstractDesigning highly conductive and (electro)chemical stable inorganic solid electrolytes using cost-effective materials is crucial for developing all-solid-state batteries. Here, we report halide nanocomposite solid electrolytes (HNSEs) ZrO2(-ACl)-A2ZrCl6 (A = Li or Na) that demonstrate improved ionic conductivities at 30 °C, from 0.40 to 1.3 mS cm−1 and from 0.011 to 0.11 mS cm−1 for Li+ and Na+, respectively, compared to A2ZrCl6, and improved compatibility with sulfide solid electrolytes. The mechanochemical method employing Li2O for the HNSEs synthesis enables the formation of nanostructured networks that promote interfacial superionic conduction. Via density functional theory calculations combined with synchrotron X-ray and 6Li nuclear magnetic resonance measurements and analyses, we demonstrate that interfacial oxygen-substituted compounds are responsible for the boosted interfacial conduction mechanism. Compared to state-of-the-art Li2ZrCl6, the fluorinated ZrO2−2Li2ZrCl5F HNSE shows improved high-voltage stability and interfacial compatibility with Li6PS5Cl and layered lithium transition metal oxide-based positive electrodes without detrimentally affecting Li+ conductivity. We also report the assembly and testing of a Li-In||LiNi0.88Co0.11Mn0.01O2 all-solid-state lab-scale cell operating at 30 °C and 70 MPa and capable of delivering a specific discharge of 115 mAh g−1 after almost 2000 cycles at 400 mA g−1.
Deep learning forecast of rainfall-induced shallow landslides
Authors: Mondini Alessandro C. , Guzzetti Fausto , Melillo Massimo 
Published: April 2023

AbstractRainfall triggered landslides occur in all mountain ranges posing threats to people and the environment. Given the projected climate changes, the risk posed by landslides is expected to increase, and the ability to anticipate their occurrence is key for effective risk reduction. Empirical thresholds and physically-based models are used to anticipate the short-term occurrence of rainfall-induced shallow landslides. But, evidence suggests that they may not be effective for operational forecasting over large areas. We propose a deep-learning based strategy to link rainfall to landslide occurrence. We inform and test the system with rainfall and landslide data available for the last 20 years in Italy. Our results indicate that it is possible to anticipate effectively the occurrence of rainfall-induced landslides over large areas, and that their location and timing are controlled primarily by the precipitation, opening to the possibility of operational landslide forecasting based on rainfall measurements and quantitative meteorological forecasts.
Human TRPV1 structure and inhibition by the analgesic SB-366791
Authors: Neuberger Arthur , Oda Mai , Nikolaev Yury A. , Nadezhdin Kirill D. , Gracheva Elena O. , Bagriantsev Sviatoslav N. , Sobolevsky Alexander I. 
Published: April 2023

AbstractPain therapy has remained conceptually stagnant since the opioid crisis, which highlighted the dangers of treating pain with opioids. An alternative addiction-free strategy to conventional painkiller-based treatment is targeting receptors at the origin of the pain pathway, such as transient receptor potential (TRP) ion channels. Thus, a founding member of the vanilloid subfamily of TRP channels, TRPV1, represents one of the most sought-after pain therapy targets. The need for selective TRPV1 inhibitors extends beyond pain treatment, to other diseases associated with this channel, including psychiatric disorders. Here we report the cryo-electron microscopy structures of human TRPV1 in the apo state and in complex with the TRPV1-specific nanomolar-affinity analgesic antagonist SB-366791. SB-366791 binds to the vanilloid site and acts as an allosteric hTRPV1 inhibitor. SB-366791 binding site is supported by mutagenesis combined with electrophysiological recordings and can be further explored to design new drugs targeting TRPV1 in disease conditions.
Network Analysis of Academic Medical Center Websites in the United States
Authors: He Shuhan , Chen David , Black Kameron Collin , Chong Paul , Marzouk Sammer , Yoon Byung-Jun , Davis Kendrick , Lee Jarone 
Published: April 2023

AbstractHealthcare resources are published annually in repositories such as the AHA Annual Survey DatabaseTM. However, these data repositories are created via manual surveying techniques which are cumbersome in collection and not updated as frequently as website information of the respective hospital systems represented. Also, this resource is not widely available to patients in an easy-to-use format. Network analysis techniques have the potential to create topological maps which serve to aid in pathfinding for patients in their search for healthcare services. This study explores the topological structure of forty United States academic health center websites. Network analysis is utilized to analyze and visualize 48,686 webpages. Several elements of network structure are examined including basic network properties, and centrality measures distributions. The Louvain community detection algorithm is used to examine the extent to which these techniques allow identification of healthcare resources within networks. The results indicate that websites with related healthcare services tend to form observable clusters useful in mapping key resources within a hospital system.
OpCitance: Citation contexts identified from the PubMed Central open access articles
Authors: Hsiao Tzu-Kun , Torvik Vetle I. 
Published: April 2023

AbstractOpCitance contains all the sentences from 2 million PubMed Central open-access (PMCOA) articles, with 137 million inline citations annotated (i.e., the “citation contexts”). Parsing out the references and citation contexts from the PMCOA XML files was non-trivial due to the diversity of referencing style. Only 0.5% citation contexts remain unidentified due to technical or human issues, e.g., references unmentioned by the authors in the text or improper XML nesting, which is more common among older articles (pre-2000). PubMed IDs (PMIDs) linked to inline citations in the XML files compared to citations harvested using the NCBI E-Utilities differed for 70.96% of the articles. Using an in-house citation matcher, called Patci, 6.84% of the referenced PMIDs were supplemented and corrected. OpCitance includes fewer total number of articles than the Semantic Scholar Open Research Corpus, but OpCitance has 160 thousand unique articles, a higher inline citation identification rate, and a more accurate reference mapping to PMIDs. We hope that OpCitance will facilitate citation context studies in particular and benefit text-mining research more broadly.
Effects of wearing different face masks on cardiopulmonary performance at rest and exercise in a partially double-blinded randomized cross-over study
Authors: Marek Eike-Maximillian , van Kampen Vera , Jettkant Birger , Kendzia Benjamin , Strauß Bianca , Sucker Kirsten , Ulbrich Melanie , Deckert Anja , Berresheim Hans , Eisenhawer Christian , Hoffmeyer Frank , Weidhaas Simon , Behrens Thomas , Brüning Thomas , Bünger Jürgen 
Published: April 2023

AbstractThe use of face masks became mandatory during SARS-CoV-2 pandemic. Wearing masks may lead to complaints about laboured breathing and stress. The influence of different masks on cardiopulmonary performance was investigated in a partially double-blinded randomized cross-over design. Forty subjects (19–65 years) underwent body plethysmography, ergometry, cardiopulmonary exercise test and a 4-h wearing period without a mask, with a surgical mask (SM), a community mask (CM), and an FFP2 respirator (FFP2). Cardiopulmonary, physical, capnometric, and blood gas related parameters were recorded. Breathing resistance and work of breathing were significantly increased while wearing a mask. During exercise the increase in minute ventilation tended to be lower and breathing time was significantly longer with mask than without mask. Wearing a mask caused significant minimal decreases in blood oxygen pressure, oxygen saturation, an initial increase in blood and inspiratory carbon dioxide pressure, and a higher perceived physical exertion and temperature and humidity behind the mask under very heavy exercise. All effects were stronger when wearing an FFP2. Wearing face masks at rest and under exercise, changed breathing patterns in the sense of physiological compensation without representing a health risk. Wearing a mask for 4-h during light work had no effect on blood gases.
Expedited diagnosis of pediatric tuberculosis using Truenat MTB-Rif Dx and GeneXpert MTB/RIF
Authors: Singh Urvashi B. , Singh Manjula , Sharma Sangeeta , Mahajan Neeraj , Bala Kiran , Srivastav Abhinav , Singh Kh Jitenkumar , Rao M. V. V. , Lodha Rakesh , Kabra Sushil K. 
Published: April 2023

AbstractRapid, cost-effective, and sensitive diagnostic assays are essential for global tuberculosis (TB) control, especially in high TB burden, resource-limited settings. The current study was designed to evaluate diagnostic accuracy of Truenat MTB-Rif Dx (MolBio) in children less than 18 years of age, with symptoms suggestive of TB. Gastric aspirate, induced sputum, and broncho-alveolar lavage samples were subjected simultaneously to AFB-smear, GeneXpert MTB/RIF, liquid culture (MGIT-960) and Truenat MTB-Rif Dx. The index-test results were evaluated against microbiological reference standards (MRS). Truenat MTB-Rif Dx had a sensitivity of 57.1%, specificity of 92% against MRS. The sensitivity and specificity of the Truenat MTB-RIF Dx compared with liquid culture was 58.7% and 87.5% while GeneXpert MTB/RIF was 56% and 91.4%. The performance of both GeneXpert MTB/RIF and Truenat MTB-Rif Dx are comparable. Result of our study demonstrates that Truenat MTB-Rif can aid in early and efficient diagnosis of TB in children.
Optimization of bandgap reduction in 2-dimensional GO nanosheets and nanocomposites of GO/iron-oxide for electronic device applications
Authors: Zainab Sana , Azeem Muhammad , Awan Saif Ullah , Rizwan Syed , Iqbal Naseem , Rashid Jamshaid 
Published: April 2023

AbstractIn this report we have developed different fabrication parameters to tailor the optical bandgap of graphene oxide (GO) nanosheets to make it operational candidate in electronic industry. Here we performed two ways to reduce the bandgap of GO nanosheets. First, we have optimized the oxidation level of GO by reducing amount of oxidizing agent (i.e. KMnO4) to control the sp2/sp3hybridization ratio for a series of GO nanosheets samples. We noticed the reduction in primary band edge 3.93–3.2 eV while secondary band edge 2.98–2.2 eV of GO nanosheets as the amount of KMnO4is decreased from 100 to 30%. Second, we have fabricated a series of 2-dimensional nanocomposites sample containing GO/Iron-oxide by using a novel synthesis process wet impregnation method. XRD analysis of synthesized nanocomposites confirmed the presence of both phases,$$\alpha$$α-Fe2O3and Fe3O4of iron-oxide with prominent plane (001) of GO. Morphological investigation rules out all the possibilities of agglomerations of iron oxide nanoparticles and coagulation of GO nanosheets. Elemental mapping endorsed the homogeneous distribution of iron oxide nanoparticles throughout the GO nanosheets. Raman spectroscopy confirmed the fairly constant ID/IGratio and FWHM of D and G peaks, thus proving the fact that the synthesis process of nanocomposites has no effect on the degree of oxidation of GO flakes. Red shift in G peak position of all the nanocomposites samples showed the electronic interaction among the constituents of the nanocomposite. Linear decrease in the intensity of PL (Photoluminescence) spectra with the increasing of Iron oxide nanoparticles points towards the increased interaction among the iron oxide nanoparticles and GO flakes. Optical absorption spectroscopy reveals the linear decrease in primary edge of bandgap from 2.8 to 0.99 eV while secondary edge decrease 3.93–2.2 eV as the loading of$$\alpha$$α-Fe2O3nanoparticles is increased from 0 to 5% in GO nanosheets. Among these nanocomposites samples 5%-iron-oxide/95%-GO nanosheet sample may be a good contestant for electronic devices.
Clamping force prediction based on deep spatio-temporal network for machining process of deformable parts
Authors: Li Enming , Zhou Jingtao , Yang Changsen , Wang Mingwei , Zhang Shusheng 
Published: April 2023

AbstractAs an important component of the machining system, the influence of fixtures on the machining deformation of the workpiece cannot be ignored. By controlling the clamping force during the machining process is an effective means to suppress or improve the machining deformation. However, due to the dynamic coupling of part geometry, clamping method, manufacturing process and time-varying cutting forces, it is difficult to obtain accurate clamping forces, which hinders the realization of fixture-based deformation control. In this paper, the variation of clamping force is considered as the response of the joint action of cutting force and other working conditions in spatial and temporal terms, and a clamping force prediction method based on deep spatio-temporal network is proposed. The part geometry model is first parameterized based on voxels, after which the cutting forces are dynamically correlated with the clamping forces in spatial and temporal terms. Then, a convolutional network was designed to capture the spatial correlation between the working conditions such as cutting force and clamping force, and a gated recurrent cell network to capture the temporal correlation to predict the clamping force during machining. Finally, an experiment of milling a cylindrical thin-walled part illustrates the effectiveness of the proposed method.