bluuebunny/crossref_metadata_2025_split huggingface dataset:
SPTnet: a deep learning framework for end-to-end single-particle tracking and motion dynamics analysis
Authors: Bi Cheng , Scrudders Kevin L. , Zheng Yue , Mahmoodi Maryam , Low-Nam Shalini T. , Huang Fang 
Published: February 2025

ABSTRACTSingle-particle tracking (SPT) provides high-resolution spatial-temporal information on biomolecule dynamics. However, localization inaccuracies, limited track lengths, heterogeneous fluorescence backgrounds, and potential molecular motion blur pose significant challenges that hinder the accurate extraction of movement trajectories and their underlying motion behavior. The conventional SPT pipeline struggles to comprehensively address detection, localization, linkage, and motion parameter inference simultaneously, resulting in information loss during sequential processing. To overcome these challenges, we propose SPTnet, an end-to-end deep learning framework that leverages a Transformer-based architecture to optimize trajectory and motion parameter estimations in parallel through a global loss. SPTnet bypasses traditional SPT processes, directly inferring molecular trajectories and motion parameters from fluorescence microscopy videos with a precision approaching the statistical information limit. Our results demonstrate that SPTnet outperforms conventional methods under commonly encountered but challenging conditions such as short trajectories, low signal-to-noise ratio (SNR), heterogeneous backgrounds, motion blur, and especially when molecules exhibit non-Brownian behaviors.
Persistent glycolysis defines the foreign body response to polymeric implants
Authors: Rempe Christian , Callaghan Neal , Fong-Hollohan Lauren , Nersesian Sarah , Froom Zachary , Medd Kyle , Ahmed Ibrahim , Karakach Tobias , Boudreau Jeanette E. , Bezuhly Michael , Huyer Locke Davenport 
Published: February 2025

AbstractNon-degradable polymeric implantable medical devices are a mainstay of modern healthcare but can frequently lead to severe complications. These complications are largely attributable to the foreign body response (FBR), which is characterized by excessive inflammation and fibrosis in response to implanted materials. The pathologic mechanisms underpinning the FBR remain elusive; however, metabolism is increasingly regarded as a critical regulator of innate immune function. We conducted comprehensive metabolic profiling of implant-associated macrophages and multinucleated giant cells in response to the subcutaneous implantation of clinically relevant implantable materials in a mouse model of implant fibrosis. Leveraging novel metabolic characterization methods for analysis of both metabolic dependence and enzyme expression in heterogeneous peri-implant tissues, we demonstrate that peri-implant macrophages are glycolytic at least up to six weeks post-implantation. Glycolytically dependent peri-implant macrophages’ expression of glucose transporter 1 (GLUT1) increased temporally and with proximity to the implant-tissue interface. Paired rate-limiting metabolic enzyme expression analysis showed notable increases in biosynthetic pathways (G6PD and ACC1), matched with increased mitochondrial staining intensity in GLUT1Hicells at chronic timepoints, which were not notable at early timepoints. Notably, we identified a glycolytic dependence of multinucleated macrophages associated with polymeric materials: these cells expressed higher levels of GLUT1 than mononuclear macrophages of comparable metabolic phenotype. Our findings highlight GLUT1-dependent glycolysis as the definitive metabolic system used by peri-implant macrophages and multinucleated cells in the FBR, highlighting this pathway as a potential target for the development of novel therapeutic approaches.
Estimating Protein Complex Model Accuracy Using Graph Transformers and Pairwise Similarity Graphs
Authors: Liu Jian , Neupane Pawan , Cheng Jianlin 
Published: February 2025

AbstractMotivationEstimation of protein complex structure accuracy is an essential step in protein complex structure prediction and is also important for users to select good structural models for various applications, such as protein function analysis and drug design. Despite the success of structure prediction methods such as AlphaFold2 and AlphaFold3, predicting the quality of predicted complex structures (structural models) and selecting top ones from large model pools remains challenging.ResultsWe present GATE, a novel method that uses graph transformers on pairwise model similarity graphs to predict the quality (accuracy) of complex structural models. By integrating single-model and multi-model quality features, GATE captures both the characteristics of individual models and the geometric similarity between them to make robust predictions. On the dataset of the 15th Critical Assessment of Protein Structure Prediction (CASP15), GATE achieved the highest Pearson’s correlation (0.748) and the lowest ranking loss (0.1191) compared to existing methods. In the blind CASP16 experiment, GATE was ranked 4th according to the overall sum of z-scores of multiple metrics based on both TM-score and Oligo-GDTTS scores. In terms of per-target average metrics based on TM-score, GATE achieved a Pearson’s correlation of 0.7076 (1st place among all methods), a Spearman’s correlation of 0.4514 (3rd place), a ranking loss of 0.1221 (3rd place), and an Area Under the Curve (AUC) score of 0.6680 (3rd place), highlighting its strong, balanced ability of estimating complex model accuracy and selecting good models.AvailabilityThe source code of GATE is freely available athttps://github.com/BioinfoMachineLearning/GATE.
Patients’ perception of anaesthesia and participation in their own perioperative safety- An observational study
Authors: Agarwal Jyotsna , Cherian Meena Nathan , Hussain Sana Yasmin , Panjiar Pratibha , Kumar Santosh , Cheng Davy 
Published: February 2025

AbstractPatients for Patients Safety (PFPS) is a World Health Organization (WHO) program which advocates meaningful engagement of patients and families to improve patient safety. Perioperative patient safety encompasses the surgical risks, as well as risks due to anesthesia. This study was conceptualized to assess patient perception of their involvement in enhancing their own perioperative safety and also assess the current state of patient knowledge regarding anesthesia.This observational cross-sectional study was conducted in a tertiary care teaching hospital of India. One hundred sixty-three consenting adults, aged 19 years to 75 years, scheduled for elective surgery, between May and July 2022, having American Society of Anesthesiologists grade 1 to 3 participated in the study. Patients having psychiatric disorder, cognitive impairment, speech and hearing problems were excluded.A pre-validated questionnaire of 21 questions was administered in pre-operative area. Data collection was done in online MS excel spreadsheet format.In our study it was encouraging to note that the general awareness on requirement of anesthesia for surgery was widely acknowledged. Nonetheless, the information about anesthesia as a separate specialty, and anesthesiologists as specialized doctors, was lacking (43/163, 28.8%). The inadequate awareness about anesthesia specialty as found in our study, might not be a true reflection of a wider population, which calls attention to invest in anesthesia awareness programs. Our study revealed a very promising initial step in patient empowerment, as majority of patients (100/163; 61.3%) were amenable to participate in their own perioperative safety. Regardless of education status, the population in general needs to be familiarized with the shared patient responsibility in enhancing patient safety, just like other public health programs. Our study aligns with the recently proposed concrete interventions for improving quality of perioperative care at a global stage which contributes to Sustainable Development Goals targets towards achieving Universal Health Coverage.
Impact of bariatric surgery on monthly earnings and employment: a national linked data study in England, 2014-2022
Authors: Bermingham Charlotte R. , Ayoubkhani Daniel , Zaccardi Francesco , Coulman Karen , Valabhji Jonathan , Khunti Kamlesh , Pournaras Dimitri J. , Santos Rita , Islam Nazrul , Razieh Cameron , Dolby Ted , Nafilyan Vahé 
Published: February 2025

AbstractObjectiveEvaluate the impact of bariatric surgery on monthly earnings and employee status among working-age adults, and examine variations across sociodemographic characteristics.DesignRetrospective longitudinal cohort study using national, linked administrative datasets.SettingHospital inpatient services in England between 1 April 2014 and 31 December 2022.Participants40,662 individuals who had a bariatric surgery procedure and obesity diagnosis during the study period, with no bariatric surgery history in the previous 5 years, and were 25 to 64 years old at the date of surgery. We also included 49,921 individuals sampled from the general population who had not had bariatric surgery matched by age and sex to those in the cohort who had bariatric surgery.Main outcome measuresMonthly employee pay – for all months and only months where the individual was in paid employment – expressed in 2023 prices; paid employee status.ResultsAmong people living with obesity who had bariatric surgery, there was a sustained increase in monthly employee pay from six months after surgery with a mean increase of £84 per month 5 years after surgery compared with the six months before surgery. Among those in paid employment, there was a sustained increase in the probability of being a paid employee from 4 months after bariatric surgery, with a mean increase of 4.3 percentage points 5 years after surgery. The increases in pay and probability of employment were greater for males. The increase in employee pay was not sustained over the 5-year follow up time for the youngest age groups.ConclusionsBariatric surgery is associated with an increased probability of being employed, resulting in increased earnings. These findings suggest that living with obesity negatively impacts labour market outcomes and that obesity management interventions are likely to generate economic benefits both to individuals and on a macroeconomic level by increasing the likelihood of employment of people living with obesity.
Senescence-related cytokine levels are associated with HIV-1 serostatus and persistence
Authors: Li Yijia , Soto-Ramirez Zoamy N. , Roscher Jenny , Medvec Tom , Alaoui-El-Azher Mounia , Piazza Paolo , Chen Yue , Sluis-Cremer Nicolas , Rinaldo Charles , Macatangay Bernard JC 
Published: February 2025

AbstractBackgroundHIV-1 is associated with accelerated aging. The senescence-associated secretory phenotype (SASP) includes biological and cytokine profiles that induce cellular senescence and inflammaging. In this study, we leveraged the Multicenter AIDS Cohort Study (MACS) to evaluate the role of SASP in aging, HIV-1 reservoir, and inflammation in people with HIV-1 (PWH) on long-term suppressive antiretroviral therapy (ART).MethodsIn this retrospective study we included plasma and serum samples from 27 virally- suppressed PWH and 10 people without HIV-1 (PWoH) collected in 2019 and 2023. SASP markers were quantified in the 2019 and 2023 samples. Plasma residual viremia, intact and defective proviral DNA were quantified in the 2019 samples. Correlations between SASP markers and HIV-1 reservoir were performed using the Spearman test, and the sparse partial least squares discrimination analysis was used to identify variables that distinguish HIV-1 serostatus.ResultsAll study participants were male with a median age of 59 years. SASP markers did not show significant changes longitudinally in either group. We identified a set of markers that had moderate performance in distinguishing PWH and PWoH, including CMV serum antibody titer, matrix metalloproteinase 9 (MMP-9), Growth/differentiation factor-15, Stanniocalcin-1 and SerpinE1. Among all the SASP markers, MMP-9 was significantly associated with intact HIV-1 proviral levels (rho=0.60, P=0.002).ConclusionIn this cohort study, we revealed the relationship between SASP markers and HIV-1 persistence. Future interventions targeting the senescence pathways may impact HIV-1 persistence.
Integrated Explainable Ensemble Machine Learning Prediction of Injury Severity in Agricultural Accidents
Authors: Mermer Omer , Zhang Eddie , Demir Ibrahim 
Published: February 2025

AbstractAgricultural injuries remain a significant occupational hazard, causing substantial human and economic losses worldwide. This study investigates the prediction of agricultural injury severity using both linear and ensemble machine learning (ML) models and applies explainable AI (XAI) techniques to understand the contribution of input features. Data from AgInjuryNews (2015–2024) was preprocessed to extract relevant attributes such as location, time, age, and safety measures. The dataset comprised 2,421 incidents categorized as fatal or non-fatal. Various ML models, including Naïve Bayes (NB), Decision Tree (DT), Support Vector Machine (SVM), Random Forest (RF), and Gradient Boosting (GB), were trained and evaluated using standard performance metrics. Ensemble models demonstrated superior accuracy and recall compared to linear models, with XGBoost achieving a recall of 100% for fatal injuries. However, all models faced challenges in predicting non-fatal injuries due to class imbalance. SHAP analysis provided insights into feature importance, with age, gender, location, and time emerging as the most influential predictors across models. This research highlights the effectiveness of ensemble ML models in injury prediction while emphasizing the need for balanced datasets and XAI techniques for actionable insights. The findings have practical implications for enhancing agricultural safety and guiding policy interventions.HighlightsThis study analyzed 2,421 agricultural injury incidents from AgInjuryNews (2015– 2024) and utilized machine learning models to predict injury severity, focusing on both fatal and non-fatal outcomes.Ensemble models, such as XGBoost and Random Forest, outperformed linear models in accuracy and recall, especially in predicting fatal injuries, although challenges in non-fatal predictions due to class imbalance were observed.Key predictors identified through SHAP analysis included age, gender, location, and time, providing interpretable insights into the factors influencing injury severity.The integration of explainable AI (XAI) enhanced the transparency of machine learning predictions, enabling stakeholders to prioritize targeted safety interventions effectively.This research highlights the potential of combining ensemble ML models with XAI techniques to improve agricultural safety practices and provides a foundation for addressing data challenges in future studies.Graphical Abstract
Comment on “Solving the time-dependent protein distributions for autoregulated bursty gene expression using spectral decomposition” [J. Chem. Phys. 160, 074105 (2024)]
Authors: Ramos Alexandre F. 
Published: February 2025

AbstractThe authors of the commented article claim that the exact time-dependent solutions of the stochastic model for a self-repressing gene previously obtained by us are both “incomplete and incorrect” because their calculations issued complex numbers as decaying rates of the system to steady state. We show that the imaginary decaying rates result from a methodological artifact. We use a linear algebraic approach to show that the decaying rates are those reported in [‘Exact time-dependent solutions for a self-regulating gene.’ Phys. Rev. E 83: 062902 (2011)]. Thus, our solution is both complete and correct. Additionally, the authors claim that they have discovered a new class of operator having complex eigenvalues and non-orthogonal eigenfunctions. We show that the operator can actually be written in the usual self-adjoint form, and, hence, it has real eigenvalues and orthogonal eigenfunctions.
Chemotherapeutic 6-thio-2’-deoxyguanosine selectively targets and inhibits telomerase by inducing a non-productive telomere-bound telomerase complex
Authors: Sanford Samantha L , Badstübner Mareike , Gerber Michael , Mannherz William , Lampl Noah , Dannenberg Rachel , Hinchie Angela , Schaich Matthew A , Myong Sua , Hedglin Mark , Agarwal Suneet , Alder Jonathan K , Stone Michael D. , Opresko Patricia L 
Published: February 2025

AbstractMost cancers upregulate the telomere lengthening enzyme telomerase to achieve unlimited cell division. How chemotherapeutic nucleoside 6-thio-2’-deoxyguanosine (6-thio-dG) targets telomerase to inhibit telomere maintenance in cancer cells and tumors was unclear. Here, we demonstrate that telomerase insertion of 6-thio-dGTP prevents synthesis of additional telomeric repeats but does not disrupt telomerase binding to telomeres. Specifically, 6-thio-dG inhibits telomere extension after telomerase translocates along its product DNA to reposition the template, inducing a non-productive complex rather than enzyme dissociation. Furthermore, we provide direct evidence that 6-thio-dG treatment inhibits telomere synthesis by telomerase in cancer cells. In agreement, telomerase-expressing cancer cells harboring critically short telomeres are more sensitive to 6-thio-dG and show a greater induction of telomere losses compared to cancer cells with long telomere reserves. Our studies reveal that telomere length and telomerase status determine 6-thio-dG sensitivity and uncover the molecular mechanism by which 6-thio-dG selectively inhibits telomerase synthesis of telomeric DNA.
Sex Differences in Cancer Functional Genomics: Gene Dependency and Drug Sensitivity
Authors: Zeltser Nicole , Zhu Chenghao , Oh Jieun , Li Constance H. , Boutros Paul C. 
Published: February 2025

AbstractPatient sex influences a wide range of cancer phenotypes, including prevalence, response to therapy and survival endpoints. Molecular sex differences across the central dogma have been identified that may drive these phenotypic differences. Despite a growing catalog of specific genomic, transcriptomic and proteomic sex differences in a range of cancer types, their functional consequences remain unclear. To assess how patient sex impacts cancer cell function, we evaluated 1,209 cell lines subjected to CRISPR knockout, RNAi knockdown or drug exposures. Despite limited statistical power, we identified pan- and per-cancer sex differences in gene essentiality in six sex-linked and fourteen autosomal genes, and in drug sensitivity for two compounds. These data expand our understanding of the propagation of sex-differing molecular features to functional outcomes, and their role in influencing patient phenotypes. They highlight the importance of considering sex-specific effects in mechanistic and functional studies.
Harnessing Adaptive Chaos: Exploring the role of oxygen-ozone therapy in modulating complexity and repair in spine disorders via a bio-informatic model
Authors: Chirumbolo Salvatore , Franzini Marianno , Tirelli Umberto , Ricevuti Giovanni , Loprete Fortunato , Richelmi Tommaso , Vaiano Francesco , Galoforo Antonio Carlo , Chierchia Marianna , Valdenassi Luigi 
Published: February 2025

AbstractAdaptive chaos represents a paradigm shift in understanding biological systems, where mild chaotic dynamics sustain homeostasis and resilience. This study delves into oxygen-ozone therapy’s ability to leverage adaptive chaos in treating spinal musculoskeletal disorders, particularly intervertebral disc degeneration (IVDD). Employing mathematical modelling and bioinformatic tools, the study evaluates chaos, complexity, and entropy across varying ozone doses to identify optimal therapeutic windows within a hormetic range. The results reveal that lower ozone doses (20–40 µg/ml O3) promote adaptive chaos, enhancing systemic complexity, reducing inflammatory cytokines, and fostering structural repair. Conversely, higher doses (60–80 µg/ml O3) induce excessive oxidative stress, exacerbating pathological chaos and impairing recovery. The integration of Shannon entropy, Lyapunov exponents, and fractal dimensions provides a novel framework for quantifying therapeutic outcomes. Findings highlight the role of controlled perturbations in stabilizing pathological systems, offering a dose-dependent roadmap for balancing flexibility and order. By correlating modelled insights with clinical evidence, the study underscores the significance of precision dosing, as the optimal range facilitates pain reduction and inflammatory control, correlating with improved complexity and reduced turbulence. This research pioneers the concept of adaptive chaos in medical interventions, emphasizing its translational potential for personalized and systemic medicine. Beyond IVDD, the findings establish a broader framework for adaptive chaos as a cornerstone of health restoration, advocating for therapies that optimize biological complexity. This work tries to bridge mathematical modelling and clinical evidence, offering a cutting-edge perspective on regenerative medicine and chaos-driven therapeutic strategies.
Pde5aDeficiency Prevents Diet-Induced Obesity via Adipose cAMP-PKA Activation Enhancing Fat Browning
Authors: Campolo F. , Giampaoli O. , Barbagallo F. , Palmisano B. , Di Maio A. , Sciarra F. , Rizzo F. , Monti S. , Cardarelli S. , Assenza M.R. , Poggiogalle E. , Patriarca A. , Sciubba F. , Filippini A. , Lenzi A. , Gianfrilli D. , Giorgi M. , Dolci S. , Naro F. , Sampaolesi M. , Riminucci M. , Mancini M. , Miccheli A. , Tessarollo L. , Venneri M.A , Isidori A.M 
Published: February 2025

ABSTRACTCyclic nucleotides are critical regulators of adaptive thermogenesis and adipogenesis, with their intracellular levels finely tuned by phosphodiesterases. Phosphodiesterase type 5 (PDE5A) modulates cyclic guanosine monophosphate levels in adipocytes. While PDE5A inhibition has shown promise in patients with diabetes, its role in metabolism remains unclear. UsingPde5aknockout mouse models, we demonstrated that mice lackingPde5aexhibit enhanced browning of white adipose tissue and reduced hepatic fat content. Following high-fat diet,Pde5a−/−mice are resistant to obesity, displaying improved glucose metabolism and enhanced thermogenesis. These protective effects stem from an early developmental knockdown ofPde5a, leading to a metabolic reprogramming driven by cAMP-PKA pathway activation. The convergence of cGMP and cAMP signaling orchestrates thermogenic and systemic metabolic adaptations. Our findings establish PDE5A as a novel regulator of energy homeostasis, suggesting its inhibition as a valuable adjuvant therapy for metabolic disorders.