bluuebunny/crossref_metadata_2025 huggingface dataset:
Sex differences in the association between metabolic disorder and inflammatory cytokines in Han Chinese patients with chronic schizophrenia
Authors: Tian Yinghan , Li Zhenkuo , Zhang Yun , Tang Pei , Zhuang Yu , Liu Lewei , Fan Haojie , Yao Xianhu , Li Wenzheng , Xia Lei , Liu Huanzhong
Published: January 2025

Background

Previous evidence suggests that immune-inflammatory dysfunction plays an important role in metabolic disorder (MD) of patients with schizophrenia, yet studies on sex differences in the association between both are limited. The current study aimed to investigate sex differences in the association between MD and inflammatory cytokines in Han Chinese patients with chronic schizophrenia (CS).

Methods

This multicentre cross-sectional study was conducted in one general hospital and two psychiatric hospitals in Anhui Province, China. General information, metabolic parameters and inflammatory cytokine indicators were collected from all participants, and the severity of their psychiatric symptoms was assessed using the Positive and Negative Syndrome Scale (PANSS).

Results

A total of 324 patients completed the assessment and were included in this study. The patients with MD had higher age and duration of illness, and lower chlorpromazine equivalents and negative symptom score than those without MD. Binary logistic regression showed that MD was significantly associated with a higher IL-1β level in male patients, while this association was not significant in females. Further multiple linear regression revealed that IL-1β level was negatively correlated with polypharmacy only in male patients.

Conclusion

Sex differences in the association between MD and IL-1β level are significant in patients with CS, and only in male patients is there a negative correlation between MD and IL-1β level. Sex-specific prevention and intervention strategies may contribute to reducing MD in patients with CS.
Green space is associated with new-onset stroke among Chinese middle-aged and older adults: data from China Health and Retirement Longitudinal Study (CHARLS)
Authors: Lu Qiong , Lian Cheng , Chen Xinglin
Published: January 2025

Background and aims

The relationship between green space and new-onset stroke is inconclusive. This study aimed to investigate the association between green space and stroke risk among Chinese middle-aged and older adults.

Methods

Data were taken from participants aged ≥45 years in the China Health and Retirement Longitudinal Study (CHARLS). Baseline data were collected in 2011 and new-onset stroke data were gathered during follow-up in 2013, 2015, 2018, and 2020. Multivariate Cox regression models were constructed to investigate the association between green space and stroke risk. Subgroup analysis was also performed.

Results

A total of 13,696 participants with a mean age of 59.3 ± 9.3 years were included. After a mean follow-up duration of 6.32 years, there were 728 stroke events during a total of 86,530 person-years of follow-up. The study found a L-shaped relationship between green space and the risk of new-onset stroke in participants. By using a two-piecewise linear regression model, we calculated that the inflection point for the per capita park green area was 10.61 square meters per person (log-likelihood ratio test p = 0.041). On the left of the inflection point, we observed a negative relationship between green space and the incidence of stroke (HR: 0.89, 95% CI: 0.84–0.94, p = 0.0001). On the right side of the inflection point, however, the relationship tended to be saturated (HR: 0.97, 95% CI: 0.94–1.01, p = 0.2111).

Conclusion

Our study found that the relationship between green space and the risk of new-onset stroke follows a L-shaped curve. A lower amount of green space is associated with an increased risk of new-onset stroke. These findings require confirmation in future studies.
ReIU: an efficient preliminary framework for Alzheimer patients based on multi-model data
Authors: Jiang Hao , Qian Yishan , Zhang Liqiang , Jiang Tao , Tai Yonghang
Published: January 2025

The rising incidence of Alzheimer’s disease (AD) poses significant challenges to traditional diagnostic methods, which primarily rely on neuropsychological assessments and brain MRIs. The advent of deep learning in medical diagnosis opens new possibilities for early AD detection. In this study, we introduce retinal vessel segmentation methods based on U-Net ad iterative registration Learning (ReIU), which extract retinal vessel maps from OCT angiography (OCT-A) facilities. Our method achieved segmentation accuracies of 79.1% on the DRIVE dataset, 68.3% on the HRF dataset. Utilizing a multimodal dataset comprising both healthy and AD subjects, ReIU extracted vascular density from fundus images, facilitating primary AD screening with a classification accuracy of 79%. These results demonstrate ReIU’s substantial accuracy and its potential as an economical, non-invasive screening tool for Alzheimer’s disease. This study underscores the importance of integrating multi-modal data and deep learning techniques in advancing the early detection and management of Alzheimer’s disease.
The reliability and quality of short videos as health information of guidance for lymphedema: a cross-sectional study
Authors: Zhou Xuchuan , Ma Gejia , Su Xuefeng , Li Xinyi , Wang Wenfei , Xia Linxi , Yang Chen , Liu Bin
Published: January 2025

Background

The prevalence of lymphedema is rising, necessitating accurate diagnostic and treatment information for affected patients. Short video-sharing platforms facilitate access to such information but require validation regarding the reliability and quality of the content presented. This study aimed to assess the reliability and quality of lymphedema-related information on Chinese short video-sharing platforms.

Methods

We collected 111 video samples addressing the diagnosis and treatment of lymphedema from four platforms: TikTok, Bilibili, WeChat, and Microblog. Two independent surgeons evaluated each video for content comprehensiveness, quality (using the Global Quality Score), and reliability (using the modified DISCERN tool). The videos from different sources were subsequently compared and analyzed.

Results

Out of 111 videos analyzed, 66 (59.46%) were uploaded by medical professionals, including breast surgeons, vascular surgeons, plastic surgeons, physical therapists, and gynecologists, while 45 (40.54%) were shared by non-medical professionals such as science bloggers, medical institutions, and lymphedema patients. Patient-uploaded videos received the highest engagement, with median likes of 2,257 (IQR: 246.25–10998.25) and favorites of 399 (IQR: 94.5–1794.75). 13 videos (11.71%) contained inaccuracies. Medical professionals’ videos generally showed higher content comprehensiveness, particularly those by plastic surgeons, compared to non-medical professionals. The GQS and modified DISCERN tool were used to assess video quality and reliability respectively, with medical professionals scoring higher on both metrics (z = 3.127, p = 0.002; z = 2.010, p = 0.044). The quality and reliability of recommendations provided by plastic surgeons surpassed that of other medical professionals (χ2 = 16.196, p = 0.003; χ2 = 9.700, p = 0.046). No significant differences in video quality and reliability were found among the three categories of non-medical professionals (χ2 = 3.491, p = 0.175; χ2 = 2.098, p = 0.350).

Conclusion

Our study shows that lymphedema-related videos on short video platforms vary widely in quality. Videos by medical professionals are generally more accurate and of higher quality than those by non-professionals. However, patient-uploaded videos often get more engagement due to their relatability. To ensure public access to reliable information, establishing basic standards for this content is essential.
Towards a unified regional surveillance system: the Gulf CDC’s first initiatives
Authors: Al Mudarra Sami , Alghamdi Lamis , AlHatlan Ahmed , Amato Gauci Andrew , Kramek Aleksandra Polkowska , Alqurashi Alaa , Alzuhayri Nouf , AlShiban Sadeem , Alghamdi Bushra , Penttinen Pasi
Published: January 2025

In January 2021, the Gulf Health Council (GHC), established the Gulf Centre for Disease Prevention and Control (Gulf CDC) in Riyadh, marking a pivotal step in harmonizing health strategies, enhancing knowledge generation, and promoting evidence-based approaches to both communicable (CD) and non-communicable diseases (NCD). The Gulf CDC’s mission includes consolidating the region’s health information systems, crucial for monitoring disease burden and shaping effective public health policies. An initial assessment of public health surveillance systems across the Gulf Cooperation Council (GCC) member states was conducted by the Gulf CDC. This revealed strong national surveillance coverage of CDs but identified areas for improvement, particularly in data quality and representativeness. These findings informed the development of the Gulf CDC’s health information strategy and confirmed the need for a regional surveillance system. Prior to the introduction of this system, senior experts in this field from all GCC member states were surveyed and a consensus process launched to agree on the first steps. This led to the strategic selection of a small number of priority communicable and non-communicable diseases for the pilot phase of the system. The final agreed list of diseases and conditions for the pilot are the CDs: acute respiratory infection (ARI), endemic dengue, brucellosis, measles and pulmonary tuberculosis together with the NCDs: cancer (registry) and road traffic injuries. The initiative will showcase the potential benefits of regional collaboration to improve health outcomes and will ultimately also contribute to global health security efforts.
How do neighborhood environments impact adolescent health: a comprehensive study from subjective and objective perspectives using machine learning method
Authors: Sheng Jie , Xiang Zhenhai , Ban Pengfei
Published: January 2025

Existing studies have established a linear relationship between urban environments and adolescent health, but the combined impacts of subjective and objective environments on multi-dimensional health status (including physical and mental health) have not been fully explored. Furthermore, while some studies have examined the non-linear relationship between urban environments and adult health, research specifically focusing on adolescents is sparse. Using Kunming, China, as a case study, we employ Random Forest model to examine the non-linear relationship between subjective/objective neighborhood environments and adolescent physical/mental health. The results indicate that the objective environment plays a more significant role in influencing physical and mental health in adolescents. There are generally non-linear correlations and threshold effects between neighborhood environment variables and adolescents’ health status. Specifically, the effects of distance to subway station, ratio of traffic safety facilities, and greening view index on adolescent physical and mental health differ. Additionally, subjective environments characterized by community management, community image, and community capital tend to positively influence adolescents’ health status. This study provides valuable insights for the planning of healthy communities, environmental interventions, and health promotion in specific dimensions among adolescents.
Effort-reward imbalance and health outcomes in emergency nurses: the mediating role of work–family conflict and intrinsic effort
Authors: Tong Le , Zhu Ling , Zhang Hao , Zhong Luying , Diao Dongmei , Chen Xiaoli , Zhang Jianna
Published: January 2025

Background

Emergency nurses experience high stress, but the mechanisms linking effort-reward imbalance to health outcomes are unclear. Work–family conflict might mediate this relationship, and intrinsic effort could moderate it. This study aimed to explore these interactions and their impact on nurse health.

Methods

A prospective cross-sectional survey was conducted from 30 tertiary hospitals across 19 provinces in mainland China. Participants completed validated scales to measure effort-reward imbalance, work–family conflict, somatic symptoms, and mental health symptoms. Statistical analyses, including mediation and moderation, were performed using the PROCESS macro to evaluate the relationships between the variables.

Results

In this study, 1,540 emergency nurses were finally included. The analysis revealed that 80.5% of participants reported experiencing effort-reward imbalance, which was significantly associated with increased somatic symptoms and mental symptoms. Work–family conflict was identified as a significant mediator in the relationship between effort-reward imbalance and health outcomes, with a direct effect of effort-reward imbalance on overall symptoms (β = 0.554, p < 0.001). Additionally, intrinsic effort moderated the impact of effort-reward imbalance on work–family conflict; moderate levels of emotional investment provided protection against burnout, while excessive intrinsic effort heightened the risk of mental health deterioration (β = −0.104, p < 0.001).

Conclusion

These findings underscored the urgent need for targeted interventions that address both effort-reward imbalance and work–family conflict in healthcare settings. Enhancing workplace conditions and supporting emotional engagement among emergency nurses are essential strategies for improving their well-being and reducing burnout risk. Future research should focus on longitudinal studies to assess the effectiveness of these interventions over time.
Building for speech: designing the next-generation of social robots for audio interaction
Authors: Addlesee Angus , Papaioannou Ioannis
Published: January 2025

There have been significant advances in robotics, conversational AI, and spoken dialogue systems (SDSs) over the past few years, but we still do not find social robots in public spaces such as train stations, shopping malls, or hospital waiting rooms. In this paper, we argue that early-stage collaboration between robot designers and SDS researchers is crucial for creating social robots that can legitimately be used in real-world environments. We draw from our experiences running experiments with social robots, and the surrounding literature, to highlight recurring issues. Robots need better speakers, a greater number of high-quality microphones, quieter motors, and quieter fans to enable human-robot spoken interaction in the wild. If a robot was designed to meet these requirements, researchers could create SDSs that are more accessible, and able to handle multi-party conversations in populated environments. Robust robot joints are also needed to limit potential harm to older adults and other more vulnerable groups. We suggest practical steps towards future real-world deployments of conversational AI systems for human-robot interaction.
EzSkiROS: enhancing robot skill composition with embedded DSL for early error detection
Authors: Rizwan Momina , Reichenbach Christoph , Caldas Ricardo , Mayr Matthias , Krueger Volker
Published: January 2025

When developing general-purpose robot software components, we often lack complete knowledge of the specific contexts in which they will be executed. This limits our ability to make predictions, including our ability to detect program bugs statically. Since running a robot is an expensive task, finding errors at runtime can prolong the debugging loop or even cause safety hazards. This paper proposes an approach to help developers catch these errors as soon as we have some context (typically at pre-launch time) with minimal additional efforts. We use embedded domain-specific language (DSL) techniques to enforce early checks. We describe design patterns suitable for robot programming and show how to use these design patterns for DSL embedding in Python, using two case studies on an open-source robot skill platform SkiROS2, designed for the composition of robot skills. These two case studies help us understand how to use DSL embedding on two abstraction levels: the high-level skill description that focuses on what the robot can do and under what circumstances and the lower-level decision-making and execution flow of tasks. Using our DSL EzSkiROS, we show how our design patterns enable robotics software platforms to detect bugs in the high-level contracts between the robot’s capabilities and the robot’s understanding of the world. We also apply the same techniques to detect bugs in the lower-level implementation code, such as writing behavior trees (BTs), to control the robot’s behavior based on its capabilities. We perform consistency checks during the code deployment phase, significantly earlier than the typical runtime checks. This enhances the overall safety by identifying potential issues with the skill execution before they can impact robot behavior. An initial study with SkiROS2 developers shows that our DSL-based approach is useful for finding bugs early and thus improving the maintainability of the code.
WearMoCap: multimodal pose tracking for ubiquitous robot control using a smartwatch
Authors: Weigend Fabian C. , Kumar Neelesh , Aran Oya , Ben Amor Heni
Published: January 2025

We present WearMoCap, an open-source library to track the human pose from smartwatch sensor data and leveraging pose predictions for ubiquitous robot control. WearMoCap operates in three modes: 1) a Watch Only mode, which uses a smartwatch only, 2) a novel Upper Arm mode, which utilizes the smartphone strapped onto the upper arm and 3) a Pocket mode, which determines body orientation from a smartphone in any pocket. We evaluate all modes on large-scale datasets consisting of recordings from up to 8 human subjects using a range of consumer-grade devices. Further, we discuss real-robot applications of underlying works and evaluate WearMoCap in handover and teleoperation tasks, resulting in performances that are within 2 cm of the accuracy of the gold-standard motion capture system. Our Upper Arm mode provides the most accurate wrist position estimates with a Root Mean Squared prediction error of 6.79 cm. To evaluate WearMoCap in more scenarios and investigate strategies to mitigate sensor drift, we publish the WearMoCap system with thorough documentation as open source. The system is designed to foster future research in smartwatch-based motion capture for robotics applications where ubiquity matters. www.github.com/wearable-motion-capture.
Predisposed vulnerabilities and survival among the Finnish soldiers of World War II: historical life course approach
Authors: Kivimäki Ville , Liski Virva , Taskinen Ilari
Published: January 2025

In this methodological paper we propose a historical life course approach to analyze soldiers’ predispositions to experience war-related violence and stress and to respond to it. We argue that a closer quantitative inspection of pre-war and wartime factors will help to understand the various causes leading to different exposures to stress and violence during the war, which have consequently had different outcomes for the war survivors’ later lives. Our methodology is designed for a rich data source, the Finnish Army in World War II Database (FA2W, N = 4,253), but is generally also applicable to other case studies. We will demonstrate in practice how we apply the historical life course approach to the study of soldiers’ pre-war background variables, wartime service paths, and measurable war stress exposures. In the final discussion, as one potential follow-up to our proposal, we will point to an advanced historical analysis of community-building and meaning-making linked to different war experience profiles combining the quantitative social historical methodology with a qualitative cultural history approach.
Physical activity and content in a variety of physically active learning: an observational case study of real-world practices
Authors: Johansen Jan-Michael , Mandelid Mathias Brekke , Reinboth Michael , Resaland Geir Kåre , Bratland-Sanda Solfrid
Published: January 2025

Background

Research on physically active learning (PAL) has mainly been investigated experimentally, where interventions have been introduced to study effects on, for example, physical activity (PA) levels. This might undermine real-world contexts and realistic PA levels when teachers have sustained PAL in their regular teaching practice for several years. The purpose of this study was to observe and describe the organization and content of a variety of teaching where PAL was enacted by experienced teachers and to describe the corresponding PA levels and PA intensity in real-world practices.

Methods

Fifty-eight pupils and four teachers from one primary school and one secondary school were enrolled across the first, sixth, and eighth grades. The pupils’ physical activity (PA) levels were assessed during 37 enacted physically active learning (PAL) segments within longer teaching lessons using waist-worn triaxial accelerometers. Evenson cut-off points were employed to define PA intensities. All enacted PAL were passively observed by the same observer regarding primary bodily movement, duration, subject, location, collaborative or individual work, and task orientation.

Results

On average, PAL consisted of 57.8% ± 15.7% sedentary time, 22.9% ± 7.5% light intensity PA, and 19.3% ± 10.8% moderate-to-vigorous intensity PA, displaying a large variety in different PAL segments. More intensive and higher volumes of PA were evident when PAL was facilitated outdoors, in gyms, or in large stairways, while lower PA volumes and intensity were assessed when PAL was enacted inside the classroom. The primary movement in PAL was mainly running and/or walking (78.3%), while the PAL activity was mainly organized as group work (83.7%) in non-competitive tasks (97.3%).

Conclusions

This study provides novel insights by being the first to investigate the organization and content of PAL enacted by experienced teachers in their teaching and the corresponding PA levels in their real-world practices. The results displayed a large diversity in PA levels and intensities and may serve as a starting point to further investigate the coherency of PA levels and PAL content in schools with sustained PAL teaching.
Improvement in the prediction power of an astrocyte genome-scale metabolic model using multi-omic data
Authors: Angarita-Rodríguez Andrea , Mendoza-Mejía Nicolás , González Janneth , Papin Jason , Aristizábal Andrés Felipe , Pinzón Andrés
Published: January 2025

Introduction

The availability of large-scale multi-omic data has revolution-ized the study of cellular machinery, enabling a systematic understanding of biological processes. However, the integration of these datasets into Genome-Scale Models of Metabolism (GEMs) re-mains underexplored. Existing methods often link transcriptome and proteome data independently to reaction boundaries, providing models with estimated maximum reaction rates based on individual datasets. This independent approach, however, introduces uncertainties and inaccuracies.

Methods

To address these challenges, we applied a principal component analysis (PCA)-based approach to integrate transcriptome and proteome data. This method facilitates the reconstruction of context-specific models grounded in multi-omics data, enhancing their biological relevance and predictive capacity.

Results

Using this approach, we successfully reconstructed an astrocyte GEM with improved prediction capabilities compared to state-of-the-art models available in the literature.

Discussion

These advancements underscore the potential of multi-omic inte-gration to refine metabolic modeling and its critical role in studying neurodegeneration and developing effective therapies.
Case report: FGF4L1 retrogene insertion is lacking in the tall dachshund phenotype
Authors: Sullivan Stacey , Szeremeta Katarzyna Julia , Kutzler Michelle
Published: January 2025

Two retrogene insertions, FGF4L1 (formerly 18-FGF4, colloquially CDPA) and FGF4L2 (formerly 12-FGF4, colloquially CDDY), have recently been discovered as determinants of short leg phenotype in dogs. This case study is comprised of a family of standard wirehaired dachshunds in which the dogs lacking the FGF4L1 gene exhibit a tall phenotype. The tall phenotype in the dachshunds of this report precludes the dog’s working function of den work. The data presented in this report provide information as to how FGF4L1 status could be used in making breeding decisions in dachshunds to maintain working ability without compromising animal health.
Spatial Accessibility Analysis of Snake Antivenom
Authors: Hao Wenjie , He Lanfen , Song Xingyue , Wang Juntao , Hu Yanlan , Chen Yu , Lv Chuanzhu , Yan Shijiao
Published: January 2025

Objectives

To analyze the spatial accessibility of antivenom immunizing agents equipped hospitals in Hainan Province.

Methods

This paper analyzes the spatial accessibility of medical institutions equipped with different types of snake antivenom using network analysis and two-step mobile search method, and evaluates the service level and spatial accessibility of medical institutions equipped with different types of antivenom immunizing agents in Hainan Province from the perspectives of both supply and demand.

Results

The number of people in Hainan Province who need to spend more than 1 h to reach an Agkistrodon Hyalys antivenom, Naja antivenom, Bungarus Multicnctus antivenom, Agkistrodon Acutus antivenom equipped hospital, and equipped with antivenom for all species of snakes in the country is approximately 856,000, 231,300, 3,071,000, 2,666,000 and 4,721,000 people, respectively. In the results of accessibility of hospital beds/health technicians equipped with antivenom in Hainan Province, Haikou and Sanya cities had the highest accessibility.

Conclusion

The accessibility of hospitals equipped with antivenom in Hainan Province is unevenly distributed, with areas of high accessibility in the southern and northern regions and accessibility in the rest of the country to be improved.
Liver Xenotransplantation: A Path to Clinical Reality
Authors: Sucu Serkan , Yankol Yucel , Fernandez Luis A. , Ekser Burcin
Published: January 2025

Liver xenotransplantation has emerged as a potential solution to the shortage of deceased human donor organs and is now becoming a reality due to recent developments in genetic engineering and immunosuppressive therapy. Early efforts using non-human primates and genetically modified pigs faced significant challenges such as thrombocytopenia and graft rejection. Understanding the mechanism behind those challenges and using novel genetically engineered pigs enabled researchers to overcome some of the hurdles, but more research is needed. However, new advances might allow pig liver xenotransplantation to potentially serve as a bridge to liver allotransplantation or allow native liver regeneration in the near future.
Antimicrobial Resistance and Biofilm-Forming Ability in ESBL-Producing and Non-ESBL-Producing Escherichia coli and Klebsiella pneumoniae Isolated from Canine Urinary Samples from Italy
Authors: Facchin Alessia , Filipe Joel , Mauri Irene , Tagliasacchi Filippo , Grilli Guido , Vitiello Tiziana , Ratti Gabriele , Musa Laura , Penati Martina , Scarpa Paola , Lauzi Stefania
Published: January 2025

Background: In dogs, bacterial urinary tract infections are a frequent cause of antimicrobial prescription, increasing the risk of selecting antibiotic-resistant bacteria. This study analyzed resistance patterns, the presence of extended-spectrum β-lactamases (ESBLs) and biofilm-forming capacity in E. coli and K. pneumoniae previously isolated from urine samples collected from 133 selected dogs admitted to the Veterinary Teaching Hospital of Milan, Italy, in 2021 and 2023. Methods: The E. coli and K. pneumoniae isolates were bacteriologically and genetically analyzed. Results: Overall, 53/133 (39.8%) samples had a positive microbiological culture. Thirty-four E. coli/K. pneumoniae isolates were detected, accounting for 26.5% of the examined samples. The 34 isolates included 28 E. coli and 6 K. pneumoniae. Four (11.8%) were ESBL-producing bacteria, all supported by blaCTX-M gene belonging to group 1. The K. pneumoniae isolates were significantly associated with ESBL production (p < 0.05). MIC analysis showed 11 (32.4%) multidrug-resistant isolates. Biofilm-forming capacity was observed in 23 (67.6%) isolates, regardless of bacterial species, including 20 weakly and 3 moderately adherent bacteria. All moderate biofilm producers were K. pneumoniae. Multidrug resistance (MDR) was significantly more present in strains with moderate biofilm-forming ability compared to strains with weak ability to form biofilm (p < 0.05). E. coli was confirmed as the most commonly identified urinary isolate in dogs. Conclusions: The high presence of ESBL producers and MDR in K. pneumoniae suggests mandatory in vitro susceptibility testing in the presence of this bacterium in dogs with UTI. The association of moderate biofilm production with MDR highlights the need for monitoring and surveillance of bacterial prevalence and resistance patterns of urinary isolates in dogs.
Advanced Data Framework for Sleep Medicine Applications: Machine Learning-Based Detection of Sleep Apnea Events
Authors: Zovko Kristina , Sadowski Yann , Perković Toni , Šolić Petar , Pavlinac Dodig Ivana , Pecotić Renata , Đogaš Zoran
Published: January 2025

Obstructive Sleep Apnea (OSA) is a prevalent condition that disrupts sleep quality and contributes to significant health risks, necessitating accurate and efficient diagnostic methods. This study introduces a machine learning-based framework aimed at detecting apnea events through analysis of polysomnographic (PSG) and oximetry data. The core component is a Long Short-Term Memory (LSTM) network, which is particularly suited to processing sequential time-series data, capturing complex temporal relationships within physiological signals such as oxygen saturation, heart rate, and airflow. Through extensive feature engineering and preprocessing, the framework optimizes data representation by normalizing, scaling, and encoding input features to enhance computational efficiency and model performance. Key results demonstrate the model’s effectiveness, achieving a balanced accuracy of 79%, precision of 68%, and recall of 76% on the test dataset, with validation set metrics similarly high, supporting the model’s ability to generalize effectively. Comprehensive hyperparameter tuning further contributed to a stable, robust architecture capable of accurately identifying apnea events, providing clinicians with a valuable tool for the early detection and tailored management of OSA. This data-driven framework offers an efficient, reliable solution for OSA diagnostics with the potential to improve clinical decision making and patient outcomes.
Class Activation Map Guided Backpropagation for Discriminative Explanations
Authors: Liu Yongjie , Guo Wei , Lu Xudong , Kong Lanju , Yan Zhongmin
Published: January 2025

The interpretability of neural networks has garnered significant attention. In the domain of computer vision, gradient-based feature attribution techniques like RectGrad have been proposed to utilize saliency maps to demonstrate feature contributions to predictions. Despite advancements, RectGrad falls short in category discrimination, producing similar saliency maps across categories. This paper pinpoints the ineffectiveness of threshold-based strategies in RectGrad for distinguishing feature gradients and introduces Class activation map Guided BackPropagation (CGBP) to tackle the issue. CGBP leverages class activation maps during backpropagation to enhance gradient selection, achieving consistent improvements across four models (VGG16, VGG19, ResNet50, and ResNet101) on ImageNet’s validation set. Notably, on VGG16, CGBP improves SIC, AIC, and IS scores by 10.3%, 11.5%, and 4.5%, respectively, compared to RectGrad while maintaining competitive DS performance. Moreover, CGBP demonstrates greater sensitivity to model parameter changes than RectGrad, as confirmed by a sanity check. The proposed method has broad applicability in scenarios like model debugging, where it identifies causes of misclassification, and medical image diagnosis, where it enhances user trust by aligning visual explanations with clinical insights.
Improvement of Dung Beetle Optimization Algorithm Application to Robot Path Planning
Authors: Liu Kezhen , Dai Yongqiang , Liu Huan
Published: January 2025

We propose the adaptive t-distribution spiral search Dung Beetle Optimization (TSDBO) Algorithm to address the limitations of the vanilla Dung Beetle Optimization Algorithm (DBO), such as vulnerability to local optima, weak convergence speed, and poor convergence accuracy. Specifically, we introduced an improved Tent chaotic mapping-based population initialization method to enhance the distribution quality of the initial population in the search space. Additionally, we employed a dynamic spiral search strategy during the reproduction phase and an adaptive t-distribution perturbation strategy during the foraging phase to enhance global search efficiency and the capability of escaping local optima. Experimental results demonstrate that TSDBO exhibits significant improvements in all aspects compared to other modified algorithms across 12 benchmark tests. Furthermore, we validated the practicality and reliability of TSDBO in robotic path planning applications, where it shortened the shortest path by 5.5–7.2% on a 10 × 10 grid and by 11.9–14.6% on a 20 × 20 grid.
Parameters Determination via Fuzzy Inference Systems for the Logistic Populations Growth Model
Authors: Gorrin-Ortega Yuney , Cardenas-Maciel Selene Lilette , Lopez-Renteria Jorge Antonio , Cazarez-Castro Nohe Ramon
Published: January 2025

This study addresses the fuzzy parameters (coefficient) determination for the logistic population growth model, proposing a novel methodology based on fuzzy logic concepts. Population dynamics are often modeled using differential equations whose parameters represent critical ecological information, where the parameters determination is a problem itself. Unlike those approaches, the proposed methodology leverages ecosystem variables as inputs to a fuzzy inference system, which then generates fuzzy coefficients that better capture the inherent uncertainties in population dynamics. The approach was tested on a case study involving marine fish populations, where the fuzzy coefficients for growth rate and carrying capacity were calculated and integrated into the logistic model. The results illustrate that the fuzzy model with the proposed coefficients provide a robust framework for modeling population growth, preserving the increasing trajectory of the population under different scenarios. This method allows for the incorporation of expert knowledge and linguistic variables into the model, offering a more flexible and accurate representation of real-world ecosystems. The study concludes that this methodology significantly enhances the model’s applicability and predictive power, particularly in situations where precise data are not available.
Psychosis of Epilepsy: An Update on Clinical Classification and Mechanism
Authors: Qiu Zhiruo , Guo Jiahui , Chen Bofei , Fang Jiajia
Published: January 2025

Epilepsy is a prevalent chronic neurological disorder that can significantly impact patients’ lives. The incidence and risk of psychosis in individuals with epilepsy are notably higher than in the general population, adversely affecting both the management and rehabilitation of epilepsy and further diminishing patients’ quality of life. This review provides an overview of the classification and clinical features of psychosis of epilepsy, with the aim of offering insights and references for the clinical diagnosis and treatment of various types of psychosis of epilepsy. Additionally, we examine the potential pathophysiological mechanisms underlying the psychosis of epilepsy from three perspectives: neuroimaging, neurobiology, and genetics. The alterations in brain structure and function, neurotransmitters, neuroinflammatory mediators, and genetic factors discussed in this review may offer insights into the onset and progression of psychotic symptoms in epilepsy patients and are anticipated to inform the identification of novel therapeutic targets in the future.
Immunological Regulation of Fibrosis During Heart Failure: It Takes Two to Tango
Authors: Kumar Vinay , Bansal Shyam S.
Published: January 2025

Immuno-fibrotic networks and their protein mediators, such as cytokines and chemokines, have increasingly been appreciated for their critical role in cardiac healing and fibrosis during cardiomyopathy. Immune activation, trafficking, and extravasation are tightly regulated to ensure a targeted and effective response against non-self antigens/pathogens while preserving tolerance towards self-antigens and coordinate fibrotic responses for efficient scar formation, a distinction that is severely compromised during chronic diseases. It is clear that immune cells are not only the critical regulators of post-infarct healing and scarring but are also the key players in regulating fibroblast activation during left-ventricular (LV) remodeling. Incomplete resolution coupled with sustained low-grade inflammation during dilated cardiomyopathy precipitates a “frustrated” immune cell response resulting in unconstrained pro-fibrotic and pro-hypertrophic signaling to induce maladaptive structural and functional changes in the myocardium. The aims of this review are to (i) briefly summarize the role of key immune cells that regulate wound healing during MI and fibrosis during LV remodeling; (ii) underscore phenotypic diversities in immune cells and their subsets to underscore their role in regulating fibrotic responses, and, last but not the least, (iii) highlight gaps in our understanding that restrict the translation of immuno-modulatory therapies from the preclinical models to heart failure patients.
A Pilot Study of Exosome Proteomic Profiling Reveals Dysregulated Metabolic Pathways in Endometrial Cancer
Authors: Kharrat Feras , Capaci Valeria , Conti Andrea , Golino Valentina , Campiglia Pietro , Balasan Nour , Aloisio Michelangelo , Licastro Danilo , Monasta Lorenzo , Caponneto Federica , Beltrami Antonio Paolo , Romano Federico , di Lorenzo Giovanni , Ricci Giuseppe , Ura Blendi
Published: January 2025

Background/Objectives: Endometrial cancer (EC) is the second most frequent gynecological malignant tumor in postmenopausal women. Pathogenic mechanisms related to the onset and development of the disease are still unknown. To identify dysregulated proteins associated with EC we exploited a combined in vitro/in silico approach analyzing the proteome of exosomes with advanced MS techniques and annotating their results by using Chymeris1 AI tools. Methods: To this aim in this pilot study, we performed a deep proteomics analysis with high resolution MS (HRMS), advanced computational tools and western blotting for proteomics data validation. Results: That allowed us to identify 3628 proteins in serum albumin-depleted exosomes from 10 patients with EC compared to 10 healthy controls. This is the largest number of proteins identified in EC serum EVs. After quantification and statistical analysis, we identified 373 significantly (p < 0.05) dysregulated proteins involved in neutrophil and platelet degranulation pathways. A more detailed bioinformatics analysis revealed 61 dysregulated enzymes related to metabolic and catabolic pathways linked to tumor invasion. Through this analysis, we identified 49 metabolic and catabolic pathways related to tumor growth. Conclusions: Altogether, data shed light on the metabolic pathways involved in tumors. This is very important for understanding the metabolism of EC and for the development of new therapies.
Gut Microbiota Secondary Metabolites: Key Roles in GI Tract Cancers and Infectious Diseases
Authors: Anwer Eman K. E. , Ajagbe Muhammad , Sherif Moustafa , Musaibah Abobaker S. , Mahmoud Shuaib , ElBanbi Ali , Abdelnaser Anwar
Published: January 2025

The gut microbiota, a dynamic ecosystem of trillions of microorganisms, produces secondary metabolites that profoundly influence host health. Recent research has highlighted the significant role of these metabolites, particularly short-chain fatty acids, indoles, and bile acids, in modulating immune responses, impacting epigenetic mechanisms, and contributing to disease processes. In gastrointestinal (GI) cancers such as colorectal, liver, and gastric cancer, microbial metabolites can drive tumorigenesis by promoting inflammation, DNA damage, and immune evasion. Conversely, these same metabolites hold therapeutic promise, potentially enhancing responses to chemotherapy and immunotherapy and even directly suppressing tumor growth. In addition, gut microbial metabolites play crucial roles in infectious disease susceptibility and resilience, mediating immune pathways that impact pathogen resistance. By consolidating recent insights into the gut microbiota’s role in shaping disease and health, this review underscores the therapeutic potential of targeting microbiome-derived metabolites for treating GI cancers and infectious diseases and calls for further research into microbiome-based interventions.
Theranostic Contact Lens for Ocular Cystinosis Utilizing Gold Nanoparticles
Authors: Ha Eunbe , Kang Hwajeong , Noh Hyeran
Published: January 2025

Ocular cystinosis is a disease in which accumulated cystine crystals cause damage to the eyes, necessitating timely treatment and ongoing monitoring of cystine levels. The current treatment involves frequent administration of cysteamine eye drops, which suffer from low bioavailability and can lead to drug toxicity, making it essential to prescribe an appropriate dosage based on the patient’s condition. Additionally, cystine crystal levels are typically assessed subjectively via slit-lamp examination, requiring frequent clinical visits and causing discomfort for the patient. In this study, we propose a theranostic contact lens that simultaneously performs therapy and diagnosis on a single platform utilizing gold nanoparticles (GNPs). The binding interactions between GNPs and cystine were confirmed in solution, and thermodynamic analysis further elucidated the bonding force between the two substances. With a comprehensive understanding of these interactions, we investigated the potential of the theranostic GNP-loaded contact lens (GNP-CL). Upon exposure to various concentrations of cystine, the GNP-CL demonstrated distinct color changes, transitioning from red to blue. This color shift enabled quantitative monitoring of cystine levels. The treatment efficacy was validated by confirming a reduction in cystine concentration following the reaction. This platform has the potential to improve disease management in ocular cystinosis by reducing the reliance on cysteamine and offering an objective self-monitoring tool that does not require specialized equipment.
Impact of the COVID-19 Pandemic on Histopathological Cancer Diagnostics in Lower Silesia: A Comparative Analysis of Prostate, Breast, and Colorectal Cancer from 2018 to 2022
Authors: Szkudlarek Danuta , Kalinowska Katarzyna , Wiatrak Benita
Published: January 2025

Background/Objective: The COVID-19 pandemic significantly disrupted healthcare systems worldwide including cancer diagnostics. This study aimed to assess the impact of the pandemic on histopathological cancer diagnostics in Lower Silesia, Poland, specifically focusing on prostate, breast, and colorectal cancer cases from 2018 to 2022. The objective was to evaluate diagnostic volumes and trends before, during, and after the pandemic and to understand the effect of healthcare disruptions on cancer detection. Methods: Histopathological and cytological data were collected from multiple laboratories across Lower Silesia. Samples were categorized into three periods: pre-pandemic (January 2018–February 2020), pandemic (March 2020–May 2022), and post-pandemic (June–December 2022). Statistical analyses included comparisons of diagnostic volumes and positive diagnoses across these periods. Results: A significant reduction in the number of histopathological examinations occurred during the pandemic, particularly during its early phase. This decline was accompanied by a higher frequency of positive cancer diagnoses, suggesting the prioritization of high-risk cases. Post-pandemic, diagnostic activity showed partial recovery, though it remained below the pre-pandemic levels, with notable differences across cancer types. Conclusions: The COVID-19 pandemic significantly disrupted cancer diagnostics in Lower Silesia, delaying detection and highlighting healthcare system vulnerabilities. These findings underscore the importance of resilient healthcare systems that can ensure the continuity of essential diagnostic services and address inequalities in access to care during crises.
A Novel In Vitro Model of the Bone Marrow Microenvironment in Acute Myeloid Leukemia Identifies CD44 and Focal Adhesion Kinase as Therapeutic Targets to Reverse Cell Adhesion-Mediated Drug Resistance
Authors: Ladikou Eleni E. , Sharp Kim , Simoes Fabio A. , Jones John R. , Burley Thomas , Stott Lauren , Vareli Aimilia , Kennedy Emma , Vause Sophie , Chevassut Timothy , Devi Amarpreet , Ashworth Iona , Ross David M. , Hartmann Tanja Nicole , Mitchell Simon A. , Pepper Chris J. , Best Giles , Pepper Andrea G. S.
Published: January 2025

Background/Objectives: Acute myeloid leukemia (AML) is an aggressive neoplasm. Although most patients respond to induction therapy, they commonly relapse due to recurrent disease in the bone marrow microenvironment (BMME). So, the disruption of the BMME, releasing tumor cells into the peripheral circulation, has therapeutic potential. Methods: Using both primary donor AML cells and cell lines, we developed an in vitro co-culture model of the AML BMME. We used this model to identify the most effective agent(s) to block AML cell adherence and reverse adhesion-mediated treatment resistance. Results: We identified that anti-CD44 treatment significantly increased the efficacy of cytarabine. However, some AML cells remained adhered, and transcriptional analysis identified focal adhesion kinase (FAK) signaling as a contributing factor; the adhered cells showed elevated FAK phosphorylation that was reduced by the FAK inhibitor, defactinib. Importantly, we demonstrated that anti-CD44 and defactinib were highly synergistic at diminishing the adhesion of the most primitive CD34high AML cells in primary autologous co-cultures. Conclusions: Taken together, we identified anti-CD44 and defactinib as a promising therapeutic combination to release AML cells from the chemoprotective AML BMME. As anti-CD44 is already available as a recombinant humanized monoclonal antibody, the combination of this agent with defactinib could be rapidly tested in AML clinical trials.