bluuebunny/crossref_metadata_2025_split huggingface dataset:
Individualized prediction of three- and six-year outcomes of psychosis in a longitudinal multicenter study: a machine learning approach
Authors: de Nijs Jessica , Burger Thijs J. , Janssen Ronald J. , Kia Seyed Mostafa , van Opstal Daniël P. J. , de Koning Mariken B. , de Haan Lieuwe ,   GROUP investigators, Alizadeh Behrooz Z. , Bartels-Velthuis Agna A. , van Beveren Nico J. , Bruggeman Richard , de Haan Lieuwe , Delespaul Philippe , Luykx Jurjen J. , Myin-Germeys Inez , Kahn Rene S. , Schirmbeck Frederike , Simons Claudia J. P. , van Amelsvoort Therese , van Os Jim , van Winkel Ruud , Cahn Wiepke , Schnack Hugo G. 
Published: July 2021

AbstractSchizophrenia and related disorders have heterogeneous outcomes. Individualized prediction of long-term outcomes may be helpful in improving treatment decisions. Utilizing extensive baseline data of 523 patients with a psychotic disorder and variable illness duration, we predicted symptomatic and global outcomes at 3-year and 6-year follow-ups. We classified outcomes as (1) symptomatic: in remission or not in remission, and (2) global outcome, using the Global Assessment of Functioning (GAF) scale, divided into good (GAF ≥ 65) and poor (GAF < 65). Aiming for a robust and interpretable prediction model, we employed a linear support vector machine and recursive feature elimination within a nested cross-validation design to obtain a lean set of predictors. Generalization to out-of-study samples was estimated using leave-one-site-out cross-validation. Prediction accuracies were above chance and ranged from 62.2% to 64.7% (symptomatic outcome), and 63.5–67.6% (global outcome). Leave-one-site-out cross-validation demonstrated the robustness of our models, with a minor drop in predictive accuracies of 2.3% on average. Important predictors included GAF scores, psychotic symptoms, quality of life, antipsychotics use, psychosocial needs, and depressive symptoms. These robust, albeit modestly accurate, long-term prognostic predictions based on lean predictor sets indicate the potential of machine learning models complementing clinical judgment and decision-making. Future model development may benefit from studies scoping patient’s and clinicians' needs in prognostication.
Longitudinal associations of physical activity with plasma metabolites among colorectal cancer survivors up to 2 years after treatment
Authors: van Roekel Eline H. , Bours Martijn J. L. , van Delden Linda , Breukink Stéphanie O. , Aquarius Michèl , Keulen Eric T. P. , Gicquiau Audrey , Viallon Vivian , Rinaldi Sabina , Vineis Paolo , Arts Ilja C. W. , Gunter Marc J. , Leitzmann Michael F. , Scalbert Augustin , Weijenberg Matty P. 
Published: July 2021

AbstractWe investigated longitudinal associations of moderate-to-vigorous physical activity (MVPA) and light-intensity physical activity (LPA) with plasma concentrations of 138 metabolites after colorectal cancer (CRC) treatment. Self-reported physical activity data and blood samples were obtained at 6 weeks, and 6, 12 and 24 months post-treatment in stage I-III CRC survivors (n = 252). Metabolite concentrations were measured by tandem mass spectrometry (BIOCRATES AbsoluteIDQp180 kit). Linear mixed models were used to evaluate confounder-adjusted longitudinal associations. Inter-individual (between-participant differences) and intra-individual associations (within-participant changes over time) were assessed as percentage difference in metabolite concentration per 5 h/week of MVPA or LPA. At 6 weeks post-treatment, participants reported a median of 6.5 h/week of MVPA (interquartile range:2.3,13.5) and 7.5 h/week of LPA (2.0,15.8). Inter-individual associations were observed with more MVPA being related (FDR-adjusted q-value < 0.05) to higher concentrations of arginine, citrulline and histidine, eight lysophosphatidylcholines, nine diacylphosphatidylcholines, 13 acyl-alkylphosphatidylcholines, two sphingomyelins, and acylcarnitine C10:1. No intra-individual associations were found. LPA was not associated with any metabolite. More MVPA was associated with higher concentrations of several lipids and three amino acids, which have been linked to anti-inflammatory processes and improved metabolic health. Mechanistic studies are needed to investigate whether these metabolites may affect prognosis.
Forward and backward locomotion patterns in C. elegans generated by a connectome-based model simulation
Authors: Sakamoto Kazuma , Soh Zu , Suzuki Michiyo , Iino Yuichi , Tsuji Toshio 
Published: July 2021

AbstractCaenorhabditis elegans (C. elegans) can produce various motion patterns despite having only 69 motor neurons and 95 muscle cells. Previous studies successfully elucidate the connectome and role of the respective motor neuron classes related to movement. However, these models have not analyzed the distribution of the synaptic and gap connection weights. In this study, we examined whether a motor neuron and muscle network can generate oscillations for both forward and backward movement and analyzed the distribution of the trained synaptic and gap connection weights through a machine learning approach. This paper presents a connectome-based neural network model consisting of motor neurons of classes A, B, D, AS, and muscle, considering both synaptic and gap connections. A supervised learning method called backpropagation through time was adapted to train the connection parameters by feeding teacher data composed of the command neuron input and muscle cell activation. Simulation results confirmed that the motor neuron circuit could generate oscillations with different phase patterns corresponding to forward and backward movement, and could be switched at arbitrary times according to the binary inputs simulating the output of command neurons. Subsequently, we confirmed that the trained synaptic and gap connection weights followed a Boltzmann-type distribution. It should be noted that the proposed model can be trained to reproduce the activity patterns measured for an animal (HRB4 strain). Therefore, the supervised learning approach adopted in this study may allow further analysis of complex activity patterns associated with movements.
Prediction of skin disease using a new cytological taxonomy based on cytology and pathology with deep residual learning method
Authors: Bu Jin , Lin Yu , Qing Li-Qiong , Hu Gang , Jiang Pei , Hu Hai-Feng , Shen Er-Xia 
Published: July 2021

AbstractWith the development of artificial intelligence, technique improvement of the classification of skin disease is addressed. However, few study concerned on the current classification system of International Classification of Diseases, Tenth Revision (ICD)-10 on Diseases of the skin and subcutaneous tissue, which is now globally used for classification of skin disease. This study was aimed to develop a new taxonomy of skin disease based on cytology and pathology, and test its predictive effect on skin disease compared to ICD-10. A new taxonomy (Taxonomy 2) containing 6 levels (Project 2–4) was developed based on skin cytology and pathology, and represents individual diseases arranged in a tree structure with three root nodes representing: (1) Keratinogenic diseases, (2) Melanogenic diseases, and (3) Diseases related to non-keratinocytes and non-melanocytes. The predictive effects of the new taxonomy including accuracy, precision, recall, F1, and Kappa were compared with those of ICD-10 on Diseases of the skin and subcutaneous tissue (Taxonomy 1, Project 1) by Deep Residual Learning method. For each project, 2/3 of the images were included as training group, and the rest 1/3 of the images acted as test group according to the category (class) as the stratification variable. Both train and test groups in the Projects (2 and 3) from Taxonomy 2 had higher F1 and Kappa scores without statistical significance on the prediction of skin disease than the corresponding groups in the Project 1 from Taxonomy 1, however both train and test groups in Project 4 had a statistically significantly higher F1-score than the corresponding groups in Project 1 (P = 0.025 and 0.005, respectively). The results showed that the new taxonomy developed based on cytology and pathology has an overall better performance on predictive effect of skin disease than the ICD-10 on Diseases of the skin and subcutaneous tissue. The level 5 (Project 4) of Taxonomy 2 is better on extension to unknown data of diagnosis system assisted by AI compared to current used classification system from ICD-10, and may have the potential application value in clinic of dermatology.
Mucosa-associated gut microbiota reflects clinical course of ulcerative colitis
Authors: Nishihara Yuichiro , Ogino Haruei , Tanaka Masaru , Ihara Eikichi , Fukaura Keita , Nishioka Kei , Chinen Takatoshi , Tanaka Yoshimasa , Nakayama Jiro , Kang Dongchon , Ogawa Yoshihiro 
Published: July 2021

AbstractThis longitudinal study was designed to elucidate whether gut microbiota is associated with relapse and treatment response in ulcerative colitis (UC) patients. Fifty-one patients with UC were enrolled between 2012 and 2017, and followed up through 2020. Colon mucosal biopsy were obtained at enrollment, and 16S ribosomal RNA sequencing was performed using extracted RNA. Of the 51 patients, 24 were in remission and 27 had active UC at enrollment. Of the 24 patients in remission, 17 maintained remission and 7 developed relapse during follow-up. The 7 patients with relapse showed lower diversity, with a lower proportion of Clostridiales (p = 0.0043), and a higher proportion of Bacteroides (p = 0.047) at enrollment than those without relapse. The 27 patients with active UC were classified into response (n = 6), refractory (n = 13), and non-response (n = 8) groups according to their treatment response in 6 months. The refractory and non-response groups showed lower diversity with a lower proportion of Prevotella (p = 0.048 and 0.043) at enrollment than the response group. This study is the first demonstration that reduced diversity and particular microbes are associated with the later clinical course of relapse events and treatment response in UC.
Identification of new GLUT2-selective inhibitors through in silico ligand screening and validation in eukaryotic expression systems
Authors: Schmidl Sina , Ursu Oleg , Iancu Cristina V. , Oreb Mislav , Oprea Tudor I. , Choe Jun-yong 
Published: July 2021

AbstractGlucose is an essential energy source for cells. In humans, its passive diffusion through the cell membrane is facilitated by members of the glucose transporter family (GLUT, SLC2 gene family). GLUT2 transports both glucose and fructose with low affinity and plays a critical role in glucose sensing mechanisms. Alterations in the function or expression of GLUT2 are involved in the Fanconi–Bickel syndrome, diabetes, and cancer. Distinguishing GLUT2 transport in tissues where other GLUTs coexist is challenging due to the low affinity of GLUT2 for glucose and fructose and the scarcity of GLUT-specific modulators. By combining in silico ligand screening of an inward-facing conformation model of GLUT2 and glucose uptake assays in a hexose transporter-deficient yeast strain, in which the GLUT1-5 can be expressed individually, we identified eleven new GLUT2 inhibitors (IC50 ranging from 0.61 to 19.3 µM). Among them, nine were GLUT2-selective, one inhibited GLUT1-4 (pan-Class I GLUT inhibitor), and another inhibited GLUT5 only. All these inhibitors dock to the substrate cavity periphery, close to the large cytosolic loop connecting the two transporter halves, outside the substrate-binding site. The GLUT2 inhibitors described here have various applications; GLUT2-specific inhibitors can serve as tools to examine the pathophysiological role of GLUT2 relative to other GLUTs, the pan-Class I GLUT inhibitor can block glucose entry in cancer cells, and the GLUT2/GLUT5 inhibitor can reduce the intestinal absorption of fructose to combat the harmful effects of a high-fructose diet.
Frequency, risk factors, and outcomes of hospital readmissions of COVID-19 patients
Authors: Ramos-Martínez Antonio , Parra-Ramírez Lina Marcela , Morrás Ignacio , Carnevali María , Jiménez-Ibañez Lorena , Rubio-Rivas Manuel , Arnalich Francisco , Beato José Luis , Monge Daniel , Asín Uxua , Suárez Carmen , Freire Santiago Jesús , Méndez-Bailón Manuel , Perales Isabel , Loureiro-Amigo José , Gómez-Belda Ana Belén , Pesqueira Paula María , Gómez-Huelgas Ricardo , Mella Carmen , Díez-García Luis Felipe , Fernández-Sola Joaquim , González-Ferrer Ruth , Aroza Marina , Antón-Santos Juan Miguel , Bermejo Carlos Lumbreras 
Published: July 2021

AbstractTo determine the proportion of patients with COVID-19 who were readmitted to the hospital and the most common causes and the factors associated with readmission. Multicenter nationwide cohort study in Spain. Patients included in the study were admitted to 147 hospitals from March 1 to April 30, 2020. Readmission was defined as a new hospital admission during the 30 days after discharge. Emergency department visits after discharge were not considered readmission. During the study period 8392 patients were admitted to hospitals participating in the SEMI-COVID-19 network. 298 patients (4.2%) out of 7137 patients were readmitted after being discharged. 1541 (17.7%) died during the index admission and 35 died during hospital readmission (11.7%, p = 0.007). The median time from discharge to readmission was 7 days (IQR 3–15 days). The most frequent causes of hospital readmission were worsening of previous pneumonia (54%), bacterial infection (13%), venous thromboembolism (5%), and heart failure (5%). Age [odds ratio (OR): 1.02; 95% confident interval (95% CI): 1.01–1.03], age-adjusted Charlson comorbidity index score (OR: 1.13; 95% CI: 1.06–1.21), chronic obstructive pulmonary disease (OR: 1.84; 95% CI: 1.26–2.69), asthma (OR: 1.52; 95% CI: 1.04–2.22), hemoglobin level at admission (OR: 0.92; 95% CI: 0.86–0.99), ground-glass opacification at admission (OR: 0.86; 95% CI:0.76–0.98) and glucocorticoid treatment (OR: 1.29; 95% CI: 1.00–1.66) were independently associated with hospital readmission. The rate of readmission after hospital discharge for COVID-19 was low. Advanced age and comorbidity were associated with increased risk of readmission.
Spinal postural variability relates to biopsychosocial variables in patients with cervicogenic headache
Authors: Mingels Sarah , Dankaerts Wim , van Etten Ludo , Bruckers Liesbeth , Granitzer Marita 
Published: July 2021

AbstractPatients with cervicogenic headache (CeH) showed lower spinal postural variability (SPV). In a next step, the complex character of such SPV needs to be analysed. Therefore, variables influencing SPV need to be explored. A non-randomized repeated-measure design was applied to analyse relations between biopsychosocial variables and SPV within a CeH-group (n = 18), 29–51 years, and matched control-group (n = 18), 26–52 years. Spinal postural variability, expressed by standard deviations, was deducted from 3D-Vicon motion analysis of habitual spinal postures (degrees). Interactions between SPV and pain processing, lifestyle, psychosocial characteristics were analysed. Pain processing characteristics included symptoms of central sensitization (Central Sensitization Inventory), (extra)-cephalic pressure pain thresholds (kPa/cm2/s). Lifestyle characteristics included sleep quality (Pittsburgh Sleep Quality Index), physical activity, screen-time, sedentary-time (hours a week), position (cm) and inclination (degrees) of the laptop (= desk-setup). Psychosocial characteristics included degree of depression, anxiety and stress (Depression Anxiety Stress Scale-21), impact of headache on quality of life (Headache Impact Test-6). Spinal postural variability related significantly to intrinsic (stress, anxiety, extra-cephalic pressure pain thresholds, sleep-duration) and extrinsic (desk-setup, screen-time) variables in the CeH-group. In the control-group, SPV related significantly to extra-cephalic pressure pain thresholds. Spinal postural variability related to diverse variables in the CeH-group compared to the control-group. More research is needed into a possible causal relationship and its clinical implication.
Long-term vegetation restoration increases deep soil carbon storage in the Northern Loess Plateau
Authors: Lan Zhilong , Zhao Ying , Zhang Jianguo , Jiao Rui , Khan Muhammad Numan , Sial Tanveer Ali , Si Bingcheng 
Published: July 2021

AbstractAfforestation plays an important role in soil carbon storage and water balance. However, there is a lack of information on deep soil carbon and water storage. The study investigates the effect of returning farmland to the forest on soil carbon accumulation and soil water consumption in 20-m deep soil profile in the hilly and gully region of the Chinese Loess Plateau. Four sampling sites were selected: Platycladus orientalis (Linn.) Franco forest (PO: oriental arborvitae), Pinus tabulaeformis Carr. Forest (PT: southern Chinese pine), apple orchard (AO) and farmland (FL, as a control). Soil organic carbon (SOC) and soil inorganic carbon (SIC) content were measured in 50-cm sampling intervals of 20-m soil profiles, as well as the associated factors (e.g. soil water content). The mean SOC content of PT was the highest in the 1–5 m layer and that of FL was the lowest (p < 0.05). Compared with FL, the SOC storages of PO, PT and AO increased by 2.20, 6.33 and 0.90 kg m−2 (p > 0.05), respectively, in the whole profile. The SIC content was relatively uniform throughout the profile at all land-use types and SIC storage was 9–10 times higher than SOC storage. The soil water storage of PO, PT and AO was significantly different from that of FL with a decrease of 1169.32, 1161.60 and 1139.63 mm, respectively. After the 36-yrs implementation of the “Grain for Green” Project, SOC in 20 m soil profiles increased as a water depletion cost compared with FL. Further investigation is still needed to understand the deep soil water and carbon interactions regarding ecological restoration sustainability in the Northern Loess Plateau.
Genetic basis and network underlying synergistic roots and shoots biomass accumulation revealed by genome-wide association studies in rice
Authors: Zhao Yan , Yin Zhigang , Wang Xueqiang , Jiang Conghui , Aslam Muhammad Mahran , Gao Fenghua , Pan Yinghua , Xie Jianyin , Zhu Xiaoyang , Dong Luhao , Liu Yanhe , Zhang Hongliang , Li Jinjie , Li Zichao 
Published: July 2021

AbstractGenetic basis and network studies underlying synergistic biomass accumulation of roots and shoots (SBA) are conducive for rational design of high-biomass rice breeding. In this study, association signals for root weight, shoot weight, and the ratio of root-to-shoot mass (R/S) were identified using 666 rice accessions by genome-wide association study, together with their sub-traits, root length, root thickness and shoot length. Most association signals for root weight and shoot weight did not show association with their sub-traits. Based on the results, we proposed a top-to-bottom model for SBA, i.e. root weight, shoot weight and R/S were determined by their highest priority in contributing to biomass in the regulatory pathway, followed by a lower priority pathway for their sub-traits. Owing to 37 enriched clusters with more than two association signals identified, the relationship among the six traits could be also involved in linkage and pleiotropy. Furthermore, a discrimination of pleiotropy and LD at sequencing level using the known gene OsPTR9 for root weight, R/S and root length was provided. The results of given moderate correlation between traits and their corresponding sub-traits, and moderate additive effects between a trait and the accumulation of excellent alleles corresponding to its sub-traits supported a bottom-to-top regulation model for SBA. This model depicted each lowest-order trait (root length, root thickness and shoot length) was determined by its own regulation loci, and competition among different traits, as well as the pleiotropy and LD. All above ensure the coordinated development of each trait and the accumulation of the total biomass, although the predominant genetic basis of SBA is still indistinguishable. The presentation of the above two models and evidence of this study shed light on dissecting the genetic architecture of SBA.
A randomized double blinded placebo controlled study to evaluate motor unit abnormalities after experimentally induced sensitization using capsaicin
Authors: Evans Valerie , Koh Ryan G. L. , Duarte Felipe C. K. , Linde Lukas , Amiri Mohammadreza , Kumbhare Dinesh 
Published: July 2021

AbstractCentral sensitization is a condition that represents a cascade of neurological adaptations, resulting in an amplification of nociceptive responses from noxious and non-noxious stimuli. However, whether this abnormality translates into motor output and more specifically, ventral horn abnormalities, needs to be further explored. Twenty healthy participants aged 20–70 were randomly allocated to topical capsaicin or a placebo topical cream which was applied onto their left upper back to induce a transient state of sensitization. Visual analogue scale (VAS) ratings of pain intensity and brush allodynia score (BAS) were used to determine the presence of pain and secondary allodynia. Surface electromyography (sEMG) and intramuscular electromyography (iEMG) were used to record motor unit activity from the upper trapezius and infraspinatus muscles before and twenty minutes after application of capsaicin/placebo. Motor unit recruitment and variability were analyzed in the sEMG and iEMG, respectively. An independent t-test and Kruskal–Wallis H test were performed on the data. The sEMG results demonstrated a shift in the motor unit recruitment pattern in the upper trapezius muscle, while the iEMG showed a change in motor unit variability after application of capsaicin. These results suggest that capsaicin-induced central sensitization may cause changes in ventral horn excitability outside of the targeted spinal cord segment, affecting efferent pathway outputs. This preclinical evidence may provide some explanation for the influence of central sensitization on changes in movement patterns that occur in patients who have pain encouraging of further clinical investigation.Clinical Trials registration number: NCT04361149; date of registration: 24-Apr-2020.
Evaluation on soil fertility quality under biochar combined with nitrogen reduction
Authors: Tian Xiaoqin , Li Zhuo , Wang Yifan , Li Biao , Wang Longchang 
Published: July 2021

AbstractA two-year consecutive field experiment was conducted in purple soil in southwest China, to clarify the effects of biochar (0, 10, 20 and 40 t ha−1, namely, B0, B10, B20 and B40) combined with nitrogen reduction (100%, 80% and 60% of conventional nitrogen application rate, namely, N100, N80 and N60) on soil fertility. The performance of thirty-four indices related to soil chemical, physical and biological properties was evaluated by gray correlation analysis, principal component analysis and cluster analysis to determine the most appropriate mode for soil fertilization, and to identify the main soil environmental factors affecting rapeseed yield under the biochar combined with nitrogen reduction. The results indicated that available phosphorus, geometric mean diameter of water stability, fungi number, and the utilization of sugars, amino acids, polymers and carboxylic acids by microorganisms could be used as the main soil factors affecting rapeseed yield. The highest score of soil quality was observed in N100B10 treatment, followed by N80B10 and N100B20 treatments, which were almost in line with the results of rapeseed yields. Cluster analysis classified 12 treatments into 5 main groups on the basis of the measured parameters, which was mostly consistent with the result of soil quality scores. Considering both economic and environmental benefits, 10 t ha−1biochar combined with 144 kg ha−1nitrogen was the best combination to restore crop productivity and soil quality, and to achieve nitrogen decreasing and benefit increasing. This study provided scientific basis for the rational fertilization and scientific management of biochar combined with nitrogen fertilizer in purple soil area of southwest China.