bluuebunny/crossref_metadata_2025_split huggingface dataset:
The Effects of VR-assisted and Overground Gait Adaptation Training on Balance and Walking Ability in Stroke Patients
Authors: Wang Xinyuan , Qiu Jifang , Zhou Yuda , Liu Wenbing , Zhang Shuijing , Gong Yichen , Jiang Wei , Fang Linjie , Ji Changmeng , Yao Xin , Wang Weiqiang , Xu Sihui , Lu Zhouzhou , Ding Yong 
Published: December 2023

Abstract

Objective
This study compared the effects of virtual reality(VR)-assisted gait adaptation training with the overground gait adaptation training on balance and walking in patients with stroke.

Methods
Fifty-four eligible patients were enrolled. All patients were randomly divided into a VR and control group, with 27 patients in each group. The VR group received VR-assisted training on the treadmill, whereas the control group received overground training in a physical therapy room. After the intervention, patients were assessed using walking speed, obstacle avoidance ability, timed up and go (TUG) test, postural stability, and the Barthel Index (BI).

Results
Significant improvements in walking speed, obstacle avoidance ability, TUG test and eye-opening center of pressure (COP) speed were observed after the intervention (P < 0.05). No statistically significant differences were found in eye-closing COP speed, tandem COP speed, single-leg COP speed, and BI (P > 0.05).

Conclusions
Stroke patients may benefit from VR-assisted gait adaptation training in improving walking and static balance function and reducing the risk of falls.
Dystrophin deficiency impairs cell junction formation during embryonic myogenesis
Authors: Mozin Elise , Massouridès Emmanuelle , Mournetas Virginie , Lièvre Clémence , Bourdon Audrey , Jackson Dana L , Packer Jonathan S , Seong Juyoung , Trapnell Cole , Le Guiner Caroline , Adjali Oumeya , Pinset Christian , Mack David L , Dupont Jean-Baptiste 
Published: December 2023

SummaryMutations in theDMDgene lead to Duchenne muscular dystrophy, a severe X-linked neuromuscular disorder that manifests itself as young boys acquire motor functions. DMD is typically diagnosed at 2 to 4 years of age, but the absence of dystrophin negatively impacts muscle structure and function before overt symptoms appear in patients, which poses a serious challenge in the optimization of standards of care. In this report, we investigated the early consequences of dystrophin deficiency during skeletal muscle development. We used single-cell transcriptome profiling to characterize the myogenic trajectory of human pluripotent stem cells and showed that DMD cells bifurcate to an alternative branch when they reach the somite stage. Here, dystrophin deficiency was linked to marked dysregulations of cell junction protein families involved in the cell state transitions characteristic of embryonic somitogenesis. Altogether, this work demonstrates thatin vitro, dystrophin deficiency has deleterious effects on cell-cell communication during myogenic development, which should be considered in future therapeutic strategies for DMD.
A comprehensive overview and benchmarking analysis of fast algorithms for genome-wide association studies
Authors: Liu Fang , Zhang Jie , Zhao Yusheng , Schmidt Renate H. , Mascher Martin , Reif Jochen C. , Jiang Yong 
Published: December 2023

AbstractGenome-wide association studies (GWAS) are a ubiquitous tool for identifying genetic variants associated with complex traits in structured populations. During the past 15 years, many fast GWAS algorithms based on a state-of-the-art model, namely the linear mixed model, have been published to cope with the rapidly growing data size. In this study, we provide a comprehensive overview and benchmarking analysis of 33 commonly used GWAS algorithms. Key mathematical techniques implemented in different algorithms were summarized. Empirical data analysis with 12 selected algorithms showed differences regarding the identification of quantitative trait loci (QTL) in several plant species. The performance of these algorithms evaluated in 10,800 simulated data sets with distinct population size, heritability and genetic architecture revealed the impact of these parameters on the power of QTL identification and false positive rate. Based on these results, a general guide on the choice of algorithms for the research community is proposed.
Unlocking consumer coexistence: The role of self-organised spatial heterogeneity
Authors: Guill Christian , Nößler Felix , Klauschies Toni 
Published: December 2023

AbstractIn metacommunities, habitat heterogeneity facilitates species coexistence if superior competitors disperse maladaptively towards unfavourable habitats or if they hedge insufficiently against fluctuating environmental conditions. We show that similar mechanisms also operate in metacommunities with homogeneous habitat quality when heterogeneous biomass distributions emerge from self-organised pattern formation. Depending on whether the induced biomass patterns are static or fluctuating, either lower or higher dispersal rates can allow inferior competitors to coexist with their superior counterparts. Coexistence is further promoted when the inferiors can plastically reduce emigration from resource-rich patches. Furthermore, if the competitors differ in their abilities to induce pattern formation, a novel coexistence mechanism akin to relative non-linearity emerges, where the temporarily dominant competitor modifies the spatio-temporal variation in the biomass distributions such that it favours the recovery of the currently rare competitor. Self-organised pattern formation thus generically provides mechanisms for maintaining diversity in metacommunities without requiring a priori habitat heterogeneity.
HDAC10 blockade upregulates SPARC expression thereby repressing melanoma cell growth and BRAF inhibitor resistance
Authors: Ling Hongbo , Li Yixuan , Peng Changmin , Yang Shengyu , Seto Edward 
Published: December 2023

SUMMARYSecreted Protein Acidic and Rich in Cysteine (SPARC), a highly conserved secreted glycoprotein, is crucial for various bioprocesses. Here we demonstrate that histone deacetylase 10 (HDAC10) is a key regulator ofSPARCexpression. HDAC10 depletion or inhibition upregulates, while overexpression of HDAC10 downregulates, SPARC expression. Mechanistically, HDAC10 coordinates with histone acetyltransferase p300 to modulate the acetylation state of histone H3 lysine 27 (H3K27ac) atSPARCregulatory elements and the recruitment of bromodomain-containing protein 4 (BRD4) to these regions, thereby tuningSPARCtranscription. HDAC10 depletion and resultant SPARC upregulation repress melanoma cell growth, primarily by induction of autophagy via activation of AMPK signaling. Moreover, SPARC upregulation due to HDAC10 depletion partly accounts for the resensitivity of resistant cells to a BRAF inhibitor. Our work reveals the role of HDAC10 in gene regulation through epigenetic modification and suggests a potential therapeutic strategy for melanoma or other cancers by targeting HDAC10 and SPARC.HighlightsHDAC10 is the primary HDAC member that tightly controls SPARC expression.HDAC10 coordinates with p300 in modulating the H3K27ac state atSPARCregulatory elements and the recruitment of BRD4 to these regions.HDAC10 depletion and resultant SPARC upregulation inhibit melanoma cell growth by inducing autophagy via activation of AMPK signaling.SPARC upregulation as a result of HDAC10 depletion resensitizes resistant cells to BRAF inhibitors.
From Viral Infections to Alzheimer’s Disease: Unveiling the Mechanistic Links Through Systems Bioinformatics
Authors: Onisiforou Anna , Zanos Panos 
Published: December 2023

AbstractBackgroundEmerging evidence suggests that certain microorganisms, including viral infections, may contribute to the onset and/or progression of Alzheimer’s Disease (AD), a neurodegenerative condition characterized by memory impairment and cognitive decline. However, the precise extent of their involvement and the underlying mechanisms through which specific viruses increase AD susceptibility risk remain elusive.MethodsWe used an integrative systems bioinformatics approach to identity viral-mediated pathogenic mechanisms by which specific viral species, namely Herpes Simplex Virus 1 (HSV-1), Human Cytomegalovirus (HCMV), Epstein-Barr Virus (EBV), Kaposi Sarcoma-associated Herpesvirus (KSHV), Hepatitis B Virus (HBV), Hepatitis C Virus (HCV), Influenza A virus (IAV) and Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2), could facilitate the pathogenesis of AD via virus-host protein-protein interactions (PPIs). We also sought to uncover potential synergistic pathogenic effects resulting from the reactivation of specific herpesviruses (HSV-1, HCMV and EBV) during acute SARS-CoV-2 infection, potentially increasing AD susceptibility.ResultsOur findings show thatHerpesviridaeFamily members (HSV-1, EBV, KSHV, HCMV) impact AD-related processes like amyloid-beta formation, neuronal death, and autophagy. Hepatitis viruses (HBV, HCV) influence processes crucial for cellular homeostasis and dysfunction. Importantly, hepatitis viruses affect microglia activation via virus-host PPIs. Reactivation of HCMV during SARS-CoV-2 infection could potentially foster a lethal interplay of neurodegeneration, via synergistic pathogenic effects on AD-related processes like response to unfolded protein, regulation of autophagy, response to oxidative stress and amyloid-beta formation.ConclusionsCollectively, these findings underscore the complex link between viral infections and AD development. Perturbations in AD-related processes by viruses can arise from both shared and distinct mechanisms among viral species in different categories, potentially influencing variations in AD susceptibility.
Identification of candidate biomarkers and pathways associated with multiple sclerosis using bioinformatics and next generation sequencing data analysis
Authors: Vastrad Basavaraj , Vastrad Chanabasayya 
Published: December 2023

AbstractMultiple sclerosis (MS) is the main reason for disability caused by autoimmunity. However, effective biomarkers for MS have not been identified. This study explored the molecular mechanisms and potential therapeutic targets for MS occurrence and progression using bioinformatics and next generation sequencing (NGS) data analysis. NGS data GSE138614, including patients with MS and 25 normal control samples, were obtained from Gene Expression Omnibus (GEO) database Differentially expressed genes (DEGs) were filtered and subjected to gene ontology (GO) and pathway enrichment analyses. Protein–protein interaction (PPI) network and module analyses were performed based on the DEGs. MiRNA-hub gene regulatory network and TF-hub gene regulatory network analysis were built by Cytoscape to predict the underlying microRNAs (miRNAs) and transcription factors (TFs) associated with hub genes. We also validated the identified hub genes via receiver operating characteristic (ROC) curve analysis. A total of 959 DEGs (479 up regulated genes and 480 down regulated genes) were detected. The GO terms and pathways of DEGs involve immune system process, developmental process, immune system and regulation of cholesterol biosynthesis by SREBP (SREBF). The network analysis revealed that hub genes include LCK, PYHIN1, SLAMF1, DOK2, TAB2, CFTR, RHOB, LMNA, EGLN3 and ERBB3 might be involved in the development of MS. The present investigation illustrates a characteristic gene profile in MS, which might contribute to the interpretation of the progression of MS and provide novel biomarkers and therapeutic targets for MS.
Artificial Intelligence and Machine Learning in Cancer Related Pain: A Systematic Review
Authors: Salama Vivian , Godinich Brandon , Geng Yimin , Humbert-Vidan Laia , Maule Laura , Wahid Kareem A. , Naser Mohamed A. , He Renjie , Mohamed Abdallah S.R. , Fuller Clifton D. , Moreno Amy C. 
Published: December 2023

AbstractBackground/objectivePain is a challenging multifaceted symptom reported by most cancer patients, resulting in a substantial burden on both patients and healthcare systems. This systematic review aims to explore applications of artificial intelligence/machine learning (AI/ML) in predicting pain-related outcomes and supporting decision-making processes in pain management in cancer.MethodsA comprehensive search of Ovid MEDLINE, EMBASE and Web of Science databases was conducted using terms including “Cancer”, “Pain”, “Pain Management”, “Analgesics”, “Opioids”, “Artificial Intelligence”, “Machine Learning”, “Deep Learning”, and “Neural Networks” published up to September 7, 2023. The screening process was performed using the Covidence screening tool. Only original studies conducted in human cohorts were included. AI/ML models, their validation and performance and adherence to TRIPOD guidelines were summarized from the final included studies.ResultsThis systematic review included 44 studies from 2006-2023. Most studies were prospective and uni-institutional. There was an increase in the trend of AI/ML studies in cancer pain in the last 4 years. Nineteen studies used AI/ML for classifying cancer patients’ pain development after cancer therapy, with median AUC 0.80 (range 0.76-0.94). Eighteen studies focused on cancer pain research with median AUC 0.86 (range 0.50-0.99), and 7 focused on applying AI/ML for cancer pain management decisions with median AUC 0.71 (range 0.47-0.89). Multiple ML models were investigated with. median AUC across all models in all studies (0.77). Random forest models demonstrated the highest performance (median AUC 0.81), lasso models had the highest median sensitivity (1), while Support Vector Machine had the highest median specificity (0.74). Overall adherence of included studies to TRIPOD guidelines was 70.7%. Lack of external validation (14%) and clinical application (23%) of most included studies was detected. Reporting of model calibration was also missing in the majority of studies (5%).ConclusionImplementation of various novel AI/ML tools promises significant advances in the classification, risk stratification, and management decisions for cancer pain. These advanced tools will integrate big health-related data for personalized pain management in cancer patients. Further research focusing on model calibration and rigorous external clinical validation in real healthcare settings is imperative for ensuring its practical and reliable application in clinical practice.
NF1 modulates microtubule repair and sensitivity to antibody-drug conjugates
Authors: Achutti Duso Bruno , Messuti Eleonora , Tini Giulia , Bonetti Emanuele , Castiglioni Alessia , Frigè Gianmaria , Ciossani Giuseppe , Monzani Silvia , Soriani Chiara , Khuntsariya Daria , Roda Nicolò , Polazzi Andrea , Ippolito Marica R. , Doronzoro Elena G. , Mane Eltjona , Milani Martina , Bottosso Michele , Pagliuca Martina , Farfalla Alessia , Dondi Ambra , Nicolo’ Eleonora , Reduzzi Carolina , Jemos Costantino , Guerini-Rocco Elena , Rodighiero Simona , Tosoni Daniela , Medford Arielle , Davis Andrew , D’amico Paolo , Santaguida Stefano , Cristofanill Massimo , Braun Marcus , Lansky Zdenek , Scietti Luigi , Pelicci Pier Giuseppe , Mazzarella Luca 
Published: December 2023

AbstractAntibody-Drug Conjugates (ADC) have revolutionized the treatment of several tumors, and extensive research is being devoted to the identification of predictive biomarkers. These are particularly sought after in fields, like breast cancer, in which multiple ADCs with identical target but different payloads have been approved. NF1 is a tumor suppressor widely mutated across several cancers, best characterized as an inhibitor of RAS signaling. Additional functions have been proposed but not deeply investigated, due to its large size and complex domain structure. Whether somatic NF1 mutations can be used to guide clinical decisions is not known.Here, combining patient data,in vitro/in vivomodels and protein biochemistry, we show that NF1 loss sensitizes cancer cells to T-DM1, the first approved ADC in breast cancer, through a novel, RAS-independent function on microtubular dynamics and repair. NF1 exhibits all biochemical properties of a bona fide Microtubule-Associated Protein (MAP) and specifically enhances intratubular repair, a recently discovered phenomenon whose regulation remains poorly characterized. NF1 loss results in mitotic defects and low-grade aneuploidy in cell lines and patients. Increased sensitivity to T-DM1 upon NF1 loss is confirmed in breast cancer patients analysed across institutions in Europe and USA.Our results define NF1 as a key regulatory factor for microtubular repair and the first ADC payload-associated predictive biomarker identified to date.
Development of pre-clinical murine models for fibrolamellar hepatocellular carcinoma
Authors: He Andy , Coles Garry L. , Hartmann Griffin G. , Arand Julia , Le Vicky , Mauch Brandon , Xu Lei , Nguyen Thuyen , Hazard Florette K. , Sage Julien 
Published: December 2023

ABSTRACTFibrolamellar hepatocellular carcinoma (FLC) is a rare form of cancer that affects primarily adolescents and young adults. FLC tumors are typically associated with an intrachromosomal deletion resulting in expression of a fusion protein between the chaperone DNAJ1B and the protein kinase PKA. FLC is challenging to study because of its rarity and limited pre-clinical models. Here we developed a novel transgenic mouse model of FLC. In this model, DNAJ1B-PKA expression in the liver of mouse embryos results in perinatal lethality associated with liver developmental defects, while DNAJ1B-PKA expression in the liver of adult mice initiates tumors resembling FLC at low penetrance. We sought to developex vivocell models from these tumors but failed to establish long-term cell lines. New pre-clinical models of FLC will provide novel insights into the biology of this rare cancer and may help identify novel therapeutic strategies.
SOLUBILIZER OF BACTERIAL ORIGIN SURFACTIN INCREASES THE BIOLOGICAL ACTIVITY OF C60 FULLERENE
Authors: Emelyantsev Sergey , Prazdnova Evgeniya , Chistyakov Vladimir 
Published: December 2023

AbstractCurrently, there exists conflicting data regarding the biological activity of unmodified fullerene C60. Various sources report its toxicity, geroprotective activity, and potential interaction with DNA. Contradictory findings regarding the toxicity of C60may arise from the use of toxic solvents, as well as the influence of bioavailability and bioactivity on the preparation conditions of C60 suspensions. Furthermore, the microbiota of experimental animals can impact geroprotective activity results by releasing surfactants that facilitate substance penetration through the cell membrane.In this study, we selected conditions for solubilizing fullerene C60in a solution of surfactin, a surfactant of bacterial origin, as well as in a 2% aqueous solution of TWEEN 80, employing ultrasound. Through bioluminescent analysis using lux biosensors in E. coli MG1655, we observed that C60in surfactin reduced induced genotoxic and oxidative stress. Given that surfactin enhances membrane permeability to fullerene C60, suspensions of fullerene in designated concentrations of surfactin can be regarded as a DNA protector and antioxidant, warranting further investigation as a promising component of novel drugs.
Definition of the effector landscape across 13 Phytoplasma proteomes with LEAPH and EffectorComb
Authors: Calia Giulia , Cestaro Alessandro , Schuler Hannes , Janik Katrin , Donati Claudio , Moser Mirko , Bottini Silvia 
Published: December 2023

AbstractBackgroundCrop pathogens are a major threat to plants’ health, reducing the yield and quality of agricultural production. Among them, theCandidatusPhytoplasma genus, a group of fastidious phloem-restricted bacteria, can parasite a wide variety of both ornamental and agro-economically important plants. Several aspects of the interaction with the plant host are still unclear but it was discovered that phytoplasmas secrete certain proteins (effectors) responsible for the symptoms associated with the disease. Identifying and characterizing these proteins is of prime importance for globally improving plant health in an environmentally friendly context.ResultsWe challenged the identification of phytoplasma’s effectors by developing LEAPH, a novel machine-learning ensemble predictor for phytoplasmas pathogenicity proteins. The prediction core is composed of four models: Random Forest, XGBoost, Gaussian, and Multinomial Naive Bayes. The consensus prediction is achieved by a novel consensus prediction score. LEAPH was trained on 479 proteins from 53 phytoplasmas species, described by 30 features accounting for the biological complexity of these protein sequences. LEAPH achieved 97.49% accuracy, 95.26% precision, and 98.37% recall, ensuring a low false-positive rate and outperforming available state-of-the-art methods for putative effector prediction. The application of LEAPH to 13 phytoplasma proteomes yields a comprehensive landscape of 2089 putative pathogenicity proteins. We identified three classes of these proteins according to different secretion models: “classical”, presenting a signal peptide, “classically-like” and “non-classical”, lacking the canonical secretion signal. Importantly, LEAPH was able to identify 15 out of 17 known experimentally validated effectors belonging to the three classes. Furthermore, to help the selection of novel candidates for biological validation, we applied the Self-Organizing Maps algorithm and developed a shiny app called EffectorComb. Both tools would be a valuable resource to improve our understanding of effectors in plant–phytoplasmas interactions.ConclusionsLEAPH and EffectorComb app can be used to boost the characterization of putative effectors at both computational and experimental levels and can be employed in other phytopathological models. Both tools are available athttps://github.com/Plant-Net/LEAPH-EffectorComb.git.
Volumetric studies of phenylalanine in the pre micellar regions of Cetyl trimethyl ammonium bromide (CTAB) in aqueous solution
Authors: Ansari Nizamul Haque 
Published: December 2023

AbstractInteractions between amino acid (dl-phenylalanine) and surfactant (cetyltrimethyl ammonium bromide) was investigated using density and density data was utilized to calculate apparent molar volume, and partial molar volume also known as limiting molar volumeto find out solute-solvent interactions between amino acid and surfactant.
Machine-learning-based Structural Analysis of Interactions between Antibodies and Antigens
Authors: Zhang Grace , Su Zhaoqian , Zhang Tom , Wu Yinghao 
Published: December 2023

AbstractComputational analysis of paratope-epitope interactions between antibodies and their corresponding antigens can facilitate our understanding of the molecular mechanism underlying humoral immunity and boost the design of new therapeutics for many diseases. The recent breakthrough in artificial intelligence has made it possible to predict protein-protein interactions and model their structures. Unfortunately, detecting antigen-binding sites associated with a specific antibody is still a challenging problem. To tackle this challenge, we implemented a deep learning model to characterize interaction patterns between antibodies and their corresponding antigens. With high accuracy, our model can distinguish between antibody-antigen complexes and other types of protein-protein complexes. More intriguingly, we can identify antigens from other common protein binding regions with an accuracy of higher than 70% even if we only have the epitope information. This indicates that antigens have distinct features on their surface that antibodies can recognize. Additionally, our model was unable to predict the partnerships between antibodies and their particular antigens. This result suggests that one antigen may be targeted by more than one antibody and that antibodies may bind to previously unidentified proteins. Taken together, our results support the precision of antibody-antigen interactions while also suggesting positive future progress in the prediction of specific pairing.