bluuebunny/crossref_metadata_2025_split huggingface dataset:
Helicobacter pylori glycan biosynthesis modulates host immune cell recognition and response
Authors: Barrett Katharine A. , Kassama Francis Jacob , Surks William , Mulholland Andrew J. , Moulton Karen D. , Dube Danielle H. 
Published: March 2024

IntroductionThe pathogenic bacterium Helicobacter pylori has evolved glycan-mediated mechanisms to evade host immune defenses. This study tests the hypothesis that genetic disruption of H. pylori glycan biosynthesis alters immune recognition and response by human gastric epithelial cells and monocyte-derived dendritic cells.MethodsTo test this hypothesis, human cell lines were challenged with wildtype H. pylori alongside an array of H. pylori glycosylation mutants. The relative levels of immune response were measured via immature dendritic cell maturation and cytokine secretion.ResultsOur findings indicate that disruption of lipopolysaccharide biosynthesis diminishes gastric cytokine production, without disrupting dendritic cell recognition and activation. In contrast, variable immune responses were observed in protein glycosylation mutants which prompted us to test the hypothesis that phase variation plays a role in regulating bacterial cell surface glycosylation and subsequent immune recognition. Lewis antigen presentation does not correlate with extent of immune response, while the extent of lipopolysaccharide O-antigen elaboration does.DiscussionThe outcomes of this study demonstrate that H. pylori glycans modulate the host immune response. This work provides a foundation to pursue immune-based tailoring of bacterial glycans towards modulating immunogenicity of microbial pathogens.
Machine learning analysis of patients’ perceptions towards generic medication in Greece: a survey-based study
Authors: Kassandros Konstantinos , Saranti Evridiki , Misailidou Evropi , Tsiggou Theodora-Aiketerini , Sissiou Eleftheria , Kolios George , Constantinides Theodoros , Kontogiorgis Christos 
Published: March 2024

Introduction:This survey-based study investigates Greek patients’ perceptions and attitudes towards generic drugs, aiming to identify factors influencing the acceptance and market penetration of generics in Greece. Despite the acknowledged cost-saving potential of generic medication, skepticism among patients remains a barrier to their widespread adoption.Methods:Between February 2017 and June 2021, a mixed-methods approach was employed, combining descriptive statistics with advanced machine learning models (Logistic Regression, Support Vector Machine, Random Forest, Gradient Boosting, and XGBoost) to analyze responses from 2,617 adult participants. The study focused on optimizing these models through extensive hyperparameter tuning to predict patient willingness to switch to a generic medication.Results:The analysis revealed healthcare providers as the primary information source about generics for patients. Significant differences in perceptions were observed across demographic groups, with machine learning models successfully identifying key predictors for the acceptance of generic drugs, including patient knowledge and healthcare professional influence. The Random Forest model demonstrated the highest accuracy and was selected as the most suitable for this dataset.Discussion:The findings underscore the critical role of informed healthcare providers in influencing patient attitudes towards generics. Despite the study’s focus on Greece, the insights have broader implications for enhancing generic drug acceptance globally. Limitations include reliance on convenience sampling and self-reported data, suggesting caution in generalizing results.
Investigating the influence of yarn characteristics on microfibre release from knitted fabrics during laundering
Authors: Hazlehurst Alice , Sumner Mark , Taylor Mark 
Published: March 2024

Microfibres generated and released during the use and laundering of textiles have been identified as an important source of environmental pollution. The quantity of microfibres released from a fabric can be influenced by several external factors, such as laundering conditions and consumer use, as well as intrinsic factors such as the fibre composition, yarn characteristics, and fabric structure. This study investigates the influence of various yarn characteristics on microfibre release from knitted fabrics as well as exploring the generation of microfibres during the knitting process. Fibre composition and the yarn spinning system has the greatest influence on microfibre release. The greatest quantity of microfibre was released from ring-spun lyocell (1313 ± 140 mg/kg) and the lowest quantity was released from vortex-spun polyester (80 ± 26 mg/kg). The yarn spinning system was also found to have a significant influence, with vortex-spun yarns consistently demonstrating lower microfibre release than corresponding ring-spun yarns, likely due to the reduced hairiness of yarns spun by the vortex system. The importance of fibre damage during the knitting process was also investigated. Several yarns demonstrated significantly greater microfibre release in knitted form compared to loose, hank form, but the scale of difference was found to be dependent on fibre composition.
Discovery of an independent poor-prognosis subtype associated with tertiary lymphoid structures in breast cancer
Authors: Liu Ruiqi , Huang Xiaoqian , Yang Shiwei , Du Wenbo , Chen Xiaozhou , Li Huamei 
Published: March 2024

IntroductionTertiary lymphoid structures (TLSs) are ectopic lymphoid formations that arise in non-lymphoid tissues due to chronic inflammation. The pivotal function of TLSs in regulating tumor invasion and metastasis has been established across several cancers, such as lung cancer, liver cancer, and melanoma, with a positive correlation between increased TLS presence and improved prognosis. Nevertheless, the current research about the clinical significance of TLSs in breast cancer remains limited.MethodsIn our investigation, we discovered TLS-critical genes that may impact the prognosis of breast cancer patients, and categorized breast cancer into three distinct subtypes based on critical gene expression profiles, each exhibiting substantial differences in prognosis (p = 0.0046, log-rank test), with Cluster 1 having the best prognosis, followed by Cluster 2, and Cluster 3 having the worst prognosis. We explored the impact of the heterogeneity of these subtypes on patient prognosis, the differences in the molecular mechanism, and their responses to drug therapy and immunotherapy. In addition, we designed a machine learning-based classification model, unveiling highly consistent prognostic distinctions in several externally independent cohorts.ResultsA notable marker gene CXCL13 was identified in Cluster 3, potentially pivotal in enhancing patient prognosis. At the single-cell resolution, we delved into the adverse prognosis of Cluster 3, observing an enhanced interaction between fibroblasts, myeloid cells, and basal cells, influencing patient prognosis. Furthermore, we identified several significantly upregulated genes (CD46, JAG1, IL6, and IL6R) that may positively correlate with cancer cells' survival and invasive capabilities in this subtype.DiscussionOur study is a robust foundation for precision medicine and personalized therapy, presenting a novel perspective for the contemporary classification of breast cancer.
Identifying antinuclear antibody positive individuals at risk for developing systemic autoimmune disease: development and validation of a real-time risk model
Authors: Barnado April , Moore Ryan P. , Domenico Henry J. , Green Sarah , Camai Alex , Suh Ashley , Han Bryan , Walker Katherine , Anderson Audrey , Caruth Lannawill , Katta Anish , McCoy Allison B. , Byrne Daniel W. 
Published: March 2024

ObjectivePositive antinuclear antibodies (ANAs) cause diagnostic dilemmas for clinicians. Currently, no tools exist to help clinicians interpret the significance of a positive ANA in individuals without diagnosed autoimmune diseases. We developed and validated a risk model to predict risk of developing autoimmune disease in positive ANA individuals.MethodsUsing a de-identified electronic health record (EHR), we randomly chart reviewed 2,000 positive ANA individuals to determine if a systemic autoimmune disease was diagnosed by a rheumatologist. A priori, we considered demographics, billing codes for autoimmune disease-related symptoms, and laboratory values as variables for the risk model. We performed logistic regression and machine learning models using training and validation samples.ResultsWe assembled training (n = 1030) and validation (n = 449) sets. Positive ANA individuals who were younger, female, had a higher titer ANA, higher platelet count, disease-specific autoantibodies, and more billing codes related to symptoms of autoimmune diseases were all more likely to develop autoimmune diseases. The most important variables included having a disease-specific autoantibody, number of billing codes for autoimmune disease-related symptoms, and platelet count. In the logistic regression model, AUC was 0.83 (95% CI 0.79-0.86) in the training set and 0.75 (95% CI 0.68-0.81) in the validation set.ConclusionWe developed and validated a risk model that predicts risk for developing systemic autoimmune diseases and can be deployed easily within the EHR. The model can risk stratify positive ANA individuals to ensure high-risk individuals receive urgent rheumatology referrals while reassuring low-risk individuals and reducing unnecessary referrals.
Astrocyte expression of aging-associated markers positively correlates with neurodegeneration in the frontal lobe of the rhesus macaque brain
Authors: Horn Miranda D. , Forest Sophia C. , Saied Ahmad A. , MacLean Andrew G. 
Published: March 2024

IntroductionAs the population over the age of 65 increases, rates of neurodegenerative disorders and dementias will rise – necessitating further research into the cellular and molecular mechanisms that contribute to brain aging. With the critical importance of astrocytes to neuronal health and functioning, we hypothesized that alterations in astrocyte expression of aging-associated markers p16INK4a (p16) and sirtuin 1 (SIRT1) with age would correlate with increased rates of neurodegeneration, as measured by FluoroJade C (FJC) staining.MethodsTo test this hypothesis, 19 rhesus macaques at the Tulane National Primate Research Center were selected based on the following criteria: archival FFPE CNS tissue available to use, no noted neuropathology, and an age range of 5–30 years. Tissues were cut at 5 μm and stained for GFAP, p16, SIRT1, and FJC, followed by whole-slide imaging and HALO® image analysis for percentage of marker-positive cells and relative intensity of each stain.ResultsWe found the percentage of p16+ cells increases with age in total cells and astrocytes of the frontal (p = 0.0021, p = 0.0012 respectively) and temporal (p = 0.0226, p = 0.0203 respectively) lobes, as well as the relative intensity of p16 staining (frontal lobe: p = 0.0060; temporal lobe: p = 0.0269). For SIRT1, we found no correlation with age except for an increase in the relative intensity of SIRT1 in the temporal lobe (p = 0.0033). There was an increase in neurodegeneration, as measured by the percentage of FJC+ cells in the frontal lobe with age (p = 0.0057), as well as in the relative intensity of FJC staining in the frontal (p = 0.0030) and parietal (p = 0.0481) lobes. Importantly, increased p16 and SIRT1 expression in astrocytes correlated with increasing neurodegeneration in the frontal lobe (p = 0.0009, p = 0.0095 respectively).DiscussionTogether, these data suggest that age-associated alterations in astrocytes contribute to neurodegeneration and provide a target for mechanistic studies in the future.
Motor imagery for paediatric neurorehabilitation: how much do we know? Perspectives from a systematic review
Authors: Gentile Amalia Egle , Rinella Sergio , Desogus Eleonora , Verrelli Cristiano Maria , Iosa Marco , Perciavalle Vincenzo , Ruggieri Martino , Polizzi Agata 
Published: March 2024

BackgroundMotor Imagery (MI) is a cognitive process consisting in mental simulation of body movements without executing physical actions: its clinical use has been investigated prevalently in adults with neurological disorders.ObjectivesReview of the best-available evidence on the use and efficacy of MI interventions for neurorehabilitation purposes in common and rare childhood neurological disorders.Methodssystematic literature search conducted according to PRISMA by using the Scopus, PsycArticles, Cinahl, PUBMED, Web of Science (Clarivate), EMBASE, PsychINFO, and COCHRANE databases, with levels of evidence scored by OCEBM and PEDro Scales.ResultsTwenty-two original studies were retrieved and included for the analysis; MI was the unique or complementary rehabilitative treatment in 476 individuals (aged 5 to 18 years) with 10 different neurological conditions including, cerebral palsies, stroke, coordination disorders, intellectual disabilities, brain and/or spinal cord injuries, autism, pain syndromes, and hyperactivity. The sample size ranged from single case reports to cohorts and control groups. Treatment lasted 2 days to 6 months with 1 to 24 sessions. MI tasks were conventional, graded or ad-hoc. MI measurement tools included movement assessment batteries, mental chronometry tests, scales, and questionnaires, EEG, and EMG. Overall, the use of MI was stated as effective in 19/22, and uncertain in the remnant studies.ConclusionMI could be a reliable supportive/add-on (home-based) rehabilitative tool for pediatric neurorehabilitation; its clinical use, in children, is highly dependent on the complexity of MI mechanisms, which are related to the underlying neurodevelopmental disorder.
Diffusion of brain metabolites highlights altered brain microstructure in type C hepatic encephalopathy: a 9.4 T preliminary study
Authors: Mosso Jessie , Briand Guillaume , Pierzchala Katarzyna , Simicic Dunja , Sierra Alejandra , Abdollahzadeh Ali , Jelescu Ileana O. , Cudalbu Cristina 
Published: March 2024

IntroductionType C hepatic encephalopathy (HE) is a decompensating event of chronic liver disease leading to severe motor and cognitive impairment. The progression of type C HE is associated with changes in brain metabolite concentrations measured by 1H magnetic resonance spectroscopy (MRS), most noticeably a strong increase in glutamine to detoxify brain ammonia. In addition, alterations of brain cellular architecture have been measured ex vivo by histology in a rat model of type C HE. The aim of this study was to assess the potential of diffusion-weighted MRS (dMRS) for probing these cellular shape alterations in vivo by monitoring the diffusion properties of the major brain metabolites.MethodsThe bile duct-ligated (BDL) rat model of type C HE was used. Five animals were scanned before surgery and 6- to 7-week post-BDL surgery, with each animal being used as its own control. 1H-MRS was performed in the hippocampus (SPECIAL, TE = 2.8 ms) and dMRS in a voxel encompassing the entire brain (DW-STEAM, TE = 15 ms, diffusion time = 120 ms, maximum b-value = 25 ms/μm2) on a 9.4 T scanner. The in vivo MRS acquisitions were further validated with histological measures (immunohistochemistry, Golgi-Cox, electron microscopy).ResultsThe characteristic 1H-MRS pattern of type C HE, i.e., a gradual increase of brain glutamine and a decrease of the main organic osmolytes, was observed in the hippocampus of BDL rats. Overall increased metabolite diffusivities (apparent diffusion coefficient and intra-stick diffusivity—Callaghan’s model, significant for glutamine, myo-inositol, and taurine) and decreased kurtosis coefficients were observed in BDL rats compared to control, highlighting the presence of osmotic stress and possibly of astrocytic and neuronal alterations. These results were consistent with the microstructure depicted by histology and represented by a decline in dendritic spines density in neurons, a shortening and decreased number of astrocytic processes, and extracellular edema.DiscussiondMRS enables non-invasive and longitudinal monitoring of the diffusion behavior of brain metabolites, reflecting in the present study the globally altered brain microstructure in BDL rats, as confirmed ex vivo by histology. These findings give new insights into metabolic and microstructural abnormalities associated with high brain glutamine and its consequences in type C HE.
A natural sustained-intestinal release formulation of red chili pepper extracted capsaicinoids (Capsifen®) safely modulates energy balance and endurance performance: a randomized, double-blind, placebo-controlled study
Authors: Roopashree N. , Syam Das S. , Krishnakumar I. M. , Mala K. N. , Fleenor Bradley S. , Thomas Jestin 
Published: March 2024

IntroductionOverweight and obesity are major public health concerns, with a sharp increase in prevalence over the last few decades. The primary cause is an imbalance between calorie intake and expenditure due to a rise in calorie-rich processed food and reduced physical activity. Energy balance in humans involves complex processes including thermogenesis, a crucial factor in regulating energy expenditure.MethodsIn this randomized, double-blinded, placebo-controlled three-arm three-sequence study, we investigated the efficacy of Capsifen® (CapF), a pungency-masked sustained-intestinal release formulation of red chili extract, on energy expenditure, fat oxidation, and endurance using the Quark C-PET system in healthy overweight participants, with and without exercise. In the study, 105 healthy participants were randomized to receive either placebo, CapF 100 mg/day, or CapF 200 mg/day for 28 days.ResultsCapF demonstrated a dose-dependent response to increased energy expenditure and fatty acid oxidation with a concomitant reduction in body weight. Both CapF 100 and CapF 200 also increased the time to exhaustion.DiscussionThese results demonstrate the plausible efficacy of CapF in energy expenditure and physical performance in otherwise healthy adults who have a high body mass index.Clinical trial registrationhttps://ctri.nic.in/Clinicaltrials/pmaindet2.php?EncHid=MjQzNTg=&amp;Enc=&amp;userName=CTRI/2018/04/013157 dated 04 October 2018.
Tumor treating fields in glioblastoma: long-term treatment and high compliance as favorable prognostic factors
Authors: Wang Junjie , Du Quan , Chen Jiarui , Liu Jianjian , Gu Zhaowen , Wang Xiaoyu , Zhang Anke , Gao Shiqi , Shao Anwen , Zhang Jianmin , Wang Yongjie 
Published: March 2024

IntroductionTumor treating fields (TTFields) have earned substantial attention in recent years as a novel therapeutic approach with the potential to improve the prognosis of glioblastoma (GBM) patients. However, the impact of TTFields remains a subject of ongoing debate. This study aimed to offer real-world evidence on TTFields therapy for GBM, and to investigate the clinical determinants affecting its efficacy.MethodsWe have reported a retrospective analysis of 81 newly diagnosed Chinese GBM patients who received TTFields/Stupp treatment in the Second Affiliated Hospital of Zhejiang University. Overall survival (OS) and progression-free survival (PFS) were analyzed using Kaplan–Meier method. Cox regression models with time-dependent covariates were utilized to address non-proportional hazards and to assess the influence of clinical variables on PFS and OS.ResultsThe median PFS and OS following TTFields/STUPP treatment was 12.6 months (95% CI 11.0-14.1) and 21.3 months (95% CI 10.0–32.6) respectively. Long-term TTFields treatment (&gt;2 months) exhibits significant improvements in PFS and OS compared to the short-term treatment group (≤2 months). Time-dependent covariate COX analysis revealed that longer TTFields treatment was correlated with enhanced PFS and OS for up to 12 and 13 months, respectively. Higher compliance to TTFields (≥ 0.8) significantly reduced the death risk (HR=0.297, 95%CI 0.108-0.819). Complete surgical resection and MGMT promoter methylation were associated with significantly lower risk of progression (HR=0.337, 95% CI 0.176-0.643; HR=0.156, 95% CI 0.065-0.378) and death (HR=0.276, 95% CI 0.105-0.727; HR=0.249, 95% CI 0.087-0.710).ConclusionThe TTFields/Stupp treatment may prolong median OS and PFS in GBM patients, with long-term TTFields treatment, higher TTFields compliance, complete surgical resection, and MGMT promoter methylation significantly improving prognosis.
Prediction of pCR based on clinical-radiomic model in patients with locally advanced ESCC treated with neoadjuvant immunotherapy plus chemoradiotherapy
Authors: Wang Xiaohan , Gong Guanzhong , Sun Qifeng , Meng Xue 
Published: March 2024

BackgroundThe primary objective of this research is to devise a model to predict the pathologic complete response in esophageal squamous cell carcinoma (ESCC) patients undergoing neoadjuvant immunotherapy combined with chemoradiotherapy (nICRT).MethodsWe retrospectively analyzed data from 60 ESCC patients who received nICRT between 2019 and 2023. These patients were divided into two cohorts: pCR-group (N = 28) and non-pCR group (N = 32). Radiomic features, discerned from the primary tumor region across plain, arterial, and venous phases of CT, and pertinent laboratory data were documented at two intervals: pre-treatment and preoperation. Concurrently, related clinical data was amassed. Feature selection was facilitated using the Extreme Gradient Boosting (XGBoost) algorithm, with model validation conducted via fivefold cross-validation. The model’s discriminating capability was evaluated using the area under the receiver operating characteristic curve (AUC). Additionally, the clinical applicability of the clinical-radiomic model was appraised through decision curve analysis (DCA).ResultsThe clinical-radiomic model incorporated seven significant markers: postHALP, ΔHB, post-ALB, firstorder_Skewness, GLCM_DifferenceAverage, GLCM_JointEntropy, GLDM_DependenceEntropy, and NGTDM_Complexity, to predict pCR. The XGBoost algorithm rendered an accuracy of 0.87 and an AUC of 0.84. Notably, the joint omics approach superseded the performance of solely radiomic or clinical model. The DCA further cemented the robust clinical utility of our clinical-radiomic model.ConclusionThis study successfully formulated and validated a union omics methodology for anticipating the therapeutic outcomes of nICRT followed by radical surgical resection. Such insights are invaluable for clinicians in identifying potential nICRT responders among ESCC patients and tailoring optimal individualized treatment plans.