bluuebunny/crossref_metadata_2025_split huggingface dataset:
Red cell distribution width to albumin ratio is associated with asthma risk: a population-based study
Authors: Ding Jinzhen , Zhang Yixiang , Chen Xiaoyang 
Published: December 2024

BackgroundThe red cell distribution width to albumin ratio (RAR), a newly identified biomarker of inflammation, has been linked to a variety of inflammatory diseases. Asthma, a major burden on global health, is an inflammatory airway disease that is profoundly affected by inflammation. This study primarily sought to examine the influence of RAR on the risk of developing asthma.MethodsData from 1999 to 2020 was gathered from the NHANES database. The impact of RAR on asthma risk and their non-linear relationship were clarified by multivariate logit and restricted cubic spline (RCS) analyses. Subgroup and interaction analyses collectively formed the sensitivity analysis for this study.ResultsThis study was based on an analysis of 54,161 individuals. RAR has been identified as an independent risk factor for asthma, according to logit analysis. The moderate and high RAR groups had a 16% [95% confidence interval (CI): 1.06–1.27] and 43% (95% CI: 1.30–1.58) higher risk, respectively, compared to the lowest group. Every 0.5 unit increase in RAR almost doubled the risk of asthma [odds ratio (OR): 1.82, 95% CI: 1.55–2.12]. There was no non-linear relationship between RAR and asthma risk, based on RCS analysis. Combining subgroup and interaction analyses results, all subgroups in this study showed consistent trends with the overall population.ConclusionNotably, this article, the first to examine the relationship between RAR and asthma risk, unveiled a positive linear correlation between them. With an increase in RAR, whether analyzed as a categorical or continuous variable, asthma risk significantly increases. This finding was beneficial for clinicians to anticipate and assess the onset of asthma through stratified or dynamic management. Given RAR’s numerous advantages, its application in clinical settings held considerable promise.
Weakly supervised learning in thymoma histopathology classification: an interpretable approach
Authors: Wang Chunbao , Du Xianglong , Yan Xiaoyu , Teng Xiali , Wang Xiaolin , Yang Zhe , Chang Hongyun , Fan Yangyang , Ran Caihong , Lian Jie , Li Chen , Li Hansheng , Cui Lei , Jiang Yina 
Published: December 2024

IntroductionThymoma classification is challenging due to its diverse morphology. Accurate classification is crucial for diagnosis, but current methods often struggle with complex tumor subtypes. This study presents an AI-assisted diagnostic model that combines weakly supervised learning with a divide-and-conquer multi-instance learning (MIL) approach to improve classification accuracy and interpretability.MethodsWe applied the model to 222 thymoma slides, simplifying the five-class classification into binary and ternary steps. The model features an attention-based mechanism that generates heatmaps, enabling visual interpretation of decisions. These heatmaps align with clinically validated morphological differences between thymoma subtypes. Additionally, we embedded domain-specific pathological knowledge into the interpretability framework.ResultsThe model achieved a classification AUC of 0.9172. The generated heatmaps accurately reflected the morphological distinctions among thymoma subtypes, as confirmed by pathologists. The model's transparency allows pathologists to visually verify AI decisions, enhancing diagnostic reliability.DiscussionThis model offers a significant advancement in thymoma classification, combining high accuracy with interpretability. By integrating weakly supervised learning, MIL, and attention mechanisms, it provides an interpretable AI framework that is applicable in clinical settings. The model reduces the diagnostic burden on pathologists and has the potential to improve patient outcomes by making AI tools more transparent and clinically relevant.
Weizmannia coagulans BC99 alleviates hyperuricemia and oxidative stress via DAF-16/SKN-1 activation in Caenorhabditis elegan
Authors: Gao Yinyin , Li Cheng , Li Junfei , Duan Mengyao , Li Xuan , Zhao Lina , Wu Ying , Gu Shaobin 
Published: December 2024

IntroductionHyperuricemia (HUA) refers to the presence of excess uric acid (UA) in the blood, which increases the risk of chronic kidney disease and gout. Probiotics have the potential to alleviate HUA.MethodsThis study established a hyperuricemia model using Caenorhabditis elegans (C. elegans), and studied the anti-hyperuricemia activity and potential mechanisms of Weizmannella coagulans BC99 (W. coagulans) at different concentrations (107 CFU/mL BC99, 108 CFU/mL BC99). Subsequently, we utilized UPLC-Q-TOF/MS to investigate the impact of BC99 on endogenous metabolites in C. elegans and identified pathways and biomarkers through differential metabolomics analysis.ResultsThe results of this study showed that BC99 treatment significantly reduced the expression of P151.2 and T22F3.3 (p &lt; 0.05), reduced the levels of UA and xanthine oxidase (XOD) in nematodes (p &lt; 0.05), while extending their lifespan and movement ability (p &lt; 0.05). Mechanistically, BC99 activates the transcription factors DAF-16 and SKN-1, thereby inducing the expression of stress response genes, enhancing the activity of antioxidant enzymes and tolerance to heat stress in the body, and reducing the production of ROS (p &lt; 0.001). This effect was most significant in the H-BC99 group. Furthermore, non-targeted metabolomics indicated that BC99 predominantly regulated pathways associated with amino acid metabolism (Carnosine), glycerophospholipid metabolism, and purine metabolism.DiscussionThese results underscore BC99 as an effective and economical adjunct therapeutic agent for hyperuricemia, providing a scientific basis for further development and application.
Evidence of compensatory neural hyperactivity in a subgroup of breast cancer survivors treated with chemotherapy and its association with brain aging
Authors: Mulholland Michele M. , Stuifbergen Alexa , De La Torre Schutz Alexa , Franco Rocha Oscar Y. , Blayney Douglas W. , Kesler Shelli R. 
Published: December 2024

IntroductionChemotherapy-related cognitive impairment (CRCI) remains poorly understood in terms of the mechanisms of cognitive decline. Neural hyperactivity has been reported on average in cancer survivors, but it is unclear which patients demonstrate this neurophenotype, limiting precision medicine in this population.MethodsWe evaluated a retrospective sample of 80 breast cancer survivors and 80 non-cancer controls, aged 35–73, for which we had previously identified and validated three data-driven, biological subgroups (biotypes) of CRCI. We measured neural activity using the z-normalized percent amplitude of fluctuation from resting-state functional magnetic resonance imaging (MRI). We tested established, quantitative criteria to determine whether hyperactivity can accurately be considered compensatory. We also calculated the brain age gap by applying a previously validated algorithm to anatomic MRI.ResultsWe found that neural activity differed across the three CRCI biotypes and controls (F = 13.5, p &lt; 0.001), with Biotype 2 demonstrating significant hyperactivity compared to the other groups (p &lt; 0.004, corrected), primarily in prefrontal regions. Alternatively, Biotypes 1 and 3 demonstrated significant hypoactivity (p &lt; 0.02, corrected). Hyperactivity in Biotype 2 met several of the criteria to be considered compensatory. However, we also found a positive relationship between neural activity and the brain age gap in these patients (r = 0.45, p = 0.042).DiscussionOur results indicated that neural hyperactivity is specific to a subgroup of breast cancer survivors and, while it seems to support preserved cognitive function, it could also increase the risk of accelerated brain aging. These findings could inform future neuromodulatory interventions with respect to the risks and benefits of upregulation or downregulation of neural activity.
Sex differences in the reactivity of gastric myoelectrical activity and heart rate variability as putative psychophysiological markers in human pain research
Authors: Draganova Rossitza , Hartanto Genisius , Pawlik Robert Jan , Aulenkamp Jana Luisa , Elsenbruch Sigrid 
Published: December 2024

BackgroundThis study explored the potential of electrogastrography (EGG) and heart rate variability (HRV) as psychophysiological markers in experimental pain research related to the gut-brain axis. We investigated responses to the experience of pain from the visceral (rectal distension) and somatic (cutaneous heat) pain modalities, with a focus on elucidating sex differences in EGG and HRV responses.MethodsIn a sample of healthy volunteers (29 males, 43 females), EGG and ECG data were collected during a baseline and a pain phase. Data were analyzed for changes in gastric myoelectrical activity and cardiac autonomic regulation, with special attention to sex-specific patterns and correlations with perceptual responses to visceral and somatic pain stimuli, assessed by visual analogue scale ratings.ResultsAcute pain induced significant instability in EGG slow-wave frequency and amplitude, increased tachygastria, and decreased normogastric spectral power, without evidence of sex differences. HRV analyses revealed increases in SDNN, RMSSD, and pNN50 during pain, indicating sympathovagal regulation changes. While there were no significant sex differences in EGG responses, only female participants exhibited significant correlations between visceral pain unpleasantness and EGG alterations. HRV measures, particularly time-domain parameters, showed sex differences, independent of pain-induced autonomic reactivity.ConclusionThe experience of pain in the lower abdominal region may induce impaired gastric motility. EGG and HRV are sensitive to acute pain and offer insight into pain mechanisms along the gut-brain axis. While EGG responses were consistent across sexes, HRV revealed sex-specific differences, suggesting that autonomic regulation and gastric motility may be modulated differently by pain and psychosocial factors. Further research in patients with chronic visceral pain is warranted.
Broccoli (Brassica oleracea var. italica) leaves exhibit significant antidiabetic potential in alloxan-induced diabetic rats: the putative role of ABC vacuolar transporter for accumulation of Quercetin and Kaempferol
Authors: Latif Sara , Sameeullah Muhammad , Abbasi Hiffza Qadeer , Masood Zainab , Demiral Sert Tijen , Aslam Noreen , Pekdemir Turgay , Imren Mustafa , Çiftçi Vahdettin , Saba Kiran , Malik Muhammad Suleman , Ijaz Fatima , Batool Neelam , Mirza Bushra , Waheed Mohammad Tahir 
Published: December 2024

BackgroundThe global prevalence of diabetes among adults over 18 years of age is expected to increase from 10.5% to 12.2% (between 2021 and 2045). Plants can be a cost-effective source of flavonoids like quercetin and kaempferol with anti-diabetic properties.MethodologyWe aimed to assess the antidiabetic potential of leaves of Brassica oleracea cvs. Green Sprout and Marathon. Further, flavonoid contents were measured in broccoli leaves grown under light and dark conditions. The methanolic extracts of Green Sprout (GSL-M) and Marathon (ML-M) were first evaluated in vitro for their α-amylase and α-glucosidase inhibitory potential and then for antidiabetic activity in vivo in alloxan-induced diabetic rat models.ResultsTreatment with plant extracts promoted the reduced glutathione (GSH) content and CAT, POD, and SOD activities in the pancreas, liver, kidney, heart, and brain of diabetic rats, whereas lowered lipid peroxidation, H2O2, and nitrite concentrations. The histopathological studies revealed the protective effect of plant extracts at high dose (300 mg/kg), which could be due to broccoli’s rich content of chlorogenic acid, quercetin, and kaempferol. Strikingly, etiolated leaves of broccoli manifested higher levels of quercetin and kaempferol than green ones. The putative role of an ABC transporter in the accumulation of quercetin and kaempferol in etiolated leaves was observed as evaluated by qRT-PCR and in silico analyses.ConclusionIn conclusion, the present study shows a strong link between the antidiabetic potential of broccoli due to the presence of chlorogenic acid, quercetin, and kaempferol and the role of an ABC transporter in their accumulation within the vacuole.
Anti-diabetic therapies on dental implant success in diabetes mellitus: a comprehensive review
Authors: Ghorbani Hamzeh , Minasyan Arsen , Ansari Delaram , Ghorbani Parvin , Wood David A. , Yeremyan Rozi , Ghorbani Simin , Minasian Natali 
Published: December 2024

Background and ObjectiveDental implant therapy faces challenges in patients with Type 1 and Type 2 Diabetes Mellitus (T1DM and T2DM) due to adverse effects on bone metabolism and immune response. Despite advancements, diabetic patients face higher risks of peri-implantitis and compromised osseointegration. This review assesses the impact of anti-diabetic medications on implant outcomes, offering insights to bridge the gap between animal studies and clinical practice. By evaluating pharmacotherapeutic strategies in preclinical models, this review guides future research designs to improve implant success rates in diabetic individuals.MethodA comprehensive literature review identified 21 animal studies examining the impact of anti-diabetic medications on dental and bone implants. These studies explored diabetes models, medication regimens, and designs to assess outcomes related to bone metabolism, osseointegration, and peri-implant tissue responses. The findings are systematically summarized, highlighting the scope, design, and procedures of each study. An example includes placing a dental implant in the molar region of a mouse, providing insight into preclinical approaches.ResultsTwenty-one animal studies, primarily using rodents, investigate various anti-diabetic medications on dental and bone implants. Interventions include insulin, aminoguanidine, voglibose, sitagliptin, exenatide, and metformin, analyzing outcomes like bone-implant contact (BIC), bone volume (BV), and counter-torque values in T1DM and T2DM models. The impacts of these medications on implant osseointegration under diabetic conditions are detailed, with their benefits and shortcomings assessed.DiscussionThe findings and challenges of existing animal studies on diabetes mellitus (DM) and implant osseointegration are presented. Despite T2DM prevalence, research primarily focuses on T1DM models due to easier experimental practicalities, limiting applicability. Inconsistent protocols in studies compromise reliability regarding anti-diabetic treatments’ effectiveness on osseointegration. Standardized methodologies and long-term assessments of local drug delivery alongside systemic anti-DM treatments are crucial to manage DM-related complications in implant dentistry.ConclusionInsulin administration in short-term T1DM animal studies enhances implant osseointegration. However, the efficacy of non-insulin medications remains inconclusive. Rigorous experimental designs are needed to address inconsistencies and assess long-term impacts. Larger-sized (e.g., porcine) animal studies across various intraoral implant scenarios are required. Future research should focus on enhancing clinical applicability and improving implant stability in evolving conditions.
A new BWO-based RGB vegetation index and ensemble learning strategy for the pests and diseases monitoring of CCB trees using unmanned aerial vehicle
Authors: Hu Keliang , Liu Junchen , Xiao Hai , Zeng Qiangguo , Liu Jun , Zhang Lei , Li Man , Wang Zhihui 
Published: December 2024

IntroductionThe Cinnamomum Camphora var. Borneol (CCB) tree is a valuable timber species with significant medicinal importance, widely cultivated in mountainous areas but susceptible to pests and diseases, making manual surveillance costly.MethodsThis paper proposes a method for detecting CCB pests and diseases using Unmanned aerial vehicle (UAV) as an advanced data collection carrier, capable of gathering large-scale data. To tackle the high cost and challenging data processing issues associated with traditional hyper-spectral/multi-spectral sensors, this method only relies on UAV visible light RGB bands. The process first involves calculating and normalizing 24 visible light vegetation indices from the UAV RGB images of the monitoring area, along with the original RGB bands. To account for the collinearity relationship between indices, the random forest variable importance and correlation coefficient iterative analysis algorithm are employed to select indices, retaining the most important or lowest collinearity multiple vegetation indices. Subsequently, the Beluga Whale Optimization (BWO) algorithm is utilized to generate a new vegetation index, which is then combined with the multi-threshold segmentation method to propose a BWO-weighted ensemble strategy for obtaining the final pests and diseases detection results.Results and discussionThe experimental results suggest that the new BWO-based vegetation index has a higher feature expression ability than single indices, and the new BWO-based ensemble strategy can yield more accurate detection results. This approach provides an effective means for low-cost pests and diseases detection of CCB trees.
Automatic screening for posttraumatic stress disorder in early adolescents following the Ya’an earthquake using text mining techniques
Authors: Yuan Yuzhuo , Liu Zhiyuan , Miao Wei , Tian Xuetao 
Published: December 2024

BackgroundSelf-narratives about traumatic experiences and symptoms are informative for early identification of potential patients; however, their use in clinical screening is limited. This study aimed to develop an automated screening method that analyzes self-narratives of early adolescent earthquake survivors to screen for PTSD in a timely and effective manner.MethodsAn inquiry-based questionnaire consisting of a series of open-ended questions about trauma history and psychological symptoms, was designed to simulate the clinical structured interviews based on the DSM-5 diagnostic criteria, and was used to collect self-narratives from 430 survivors who experienced the Ya’an earthquake in Sichuan Province, China. Meanwhile, participants completed the PTSD Checklist for DSM-5 (PCL-5). Text classification models were constructed using three supervised learning algorithms (BERT, SVM, and KNN) to identify PTSD symptoms and their corresponding behavioral indicators in each sentence of the self-narratives.ResultsThe prediction accuracy for symptom-level classification reached 73.2%, and 67.2% for behavioral indicator classification, with the BERT performing the best.ConclusionsThese findings demonstrate that self-narratives combined with text mining techniques provide a promising approach for automated, rapid, and accurate PTSD screening. Moreover, by conducting screenings in community and school settings, this approach equips clinicians and psychiatrists with evidence of PTSD symptoms and associated behavioral indicators, improving the effectiveness of early detection and treatment planning.
The validity and reliability of the Slovenian version of the health literacy questionnaire short-form (HLS-EU-Q16) among adults and older adults
Authors: Lamot Monika , Kirbiš Andrej 
Published: December 2024

IntroductionHealth literacy is an important predictor of health behavior and self-rated health, playing a crucial role in shaping public health outcomes. Valid and reliable health literacy assessments are essential for effectively tailoring health interventions, particularly in different cultural contexts. Several questionnaires have been developed to measure health literacy, including the widely used 47-item Health Literacy Questionnaire and its shorter versions. However, the validity of these shorter and more time efficient HLS-EU versions, such as the 16-item HLS-EU-Q16, has been less extensively researched in different age subgroups. Given Slovenia’s aging population and the differences in health literacy between adults and older adults, it is important to assess whether the HLS-EU-Q16 performs reliably across these groups. Therefore, the present study aimed to examine the validity and reliability of the 16-item health literacy questionnaire (HLS-EU-Q16) in Slovenia across two age groups: adults and older adults.MethodsWe analyzed representative Slovenian sample data from the Health Literacy of Adults study. The sample included 2,327 adults aged 23–64 years (53.7% women) and 876 older adults aged 65–99 years (55.7% women).ResultsConstruct validity revealed a modified three-factor structure of the HLS-EU-Q16 among adults, reflecting a broader conceptualization of health literacy that encompasses overlapping domains of health promotion, disease prevention, and health care. In contrast, a two-factor structure emerged among older adults, indicating a more consolidated approach where these domains are perceived as part of an integrated health management strategy. This suggests that adults may conceptualize health literacy across multiple, yet interconnected dimensions, while older adults approach it more holistically. Convergent validity, reliability, concurrent-predictive, concurrent, and discriminant validity results were satisfactory for both age groups. Predictive validity, however, provided somewhat inadequate results, as the tool poorly predicted certain health behaviors, such as smoking, alcohol consumption, and physical activity, in both age groups.ConclusionOverall, the HLS-EU-Q16 demonstrated adequate validity and reliability among both adults and older adults, indicating that it is an appropriate instrument for assessing health literacy in Slovenia.
Relationship between functional independence and community integration of people with spinal cord injury in Bangladesh
Authors: Das Shazal Kumar , Bakhtiar Md , Sabrin Saiba Muhammad , Curtin Michael , Rahman Ehsanur , Nahid Zahid Bin Sultan , Rahman Zakia , Haque Md. Furatul , Patwary Md. Fazlul Karim , Alam Md. Jahangir , Hossain Md. Emran , Rahman Md. Atiar , Islam Shafiqul , Ashfaquzzaman Md. , Parvez Md. Anowar Khasru 
Published: December 2024

DesignProspective, cross-sectional study.ObjectivesTo determine the functional outcome and home and social integration of people who had spinal cord injury and completed their inpatient rehabilitation.SettingCentre for the Rehabilitation of the Paralysed (CRP), Bangladesh.MethodsSpinal Cord Independence Measure (SCIM) and Community Integration Questionnaire (CIQ) were used to analyse the relationship between the functional outcome and home and social integration at the end of rehabilitation. Descriptive and inferential statistics were performed to analyse the data.ResultsA total of two hundred participants (181 men and 19 women) were recruited for the study. Among the participants, 92.5% of them reported a history of trauma or accident, including road traffic accidents, falls and other injuries. Approximately 60% of participants presented with paraplegia and 62.5% of participants were categorized on the ASIA Impairment Scale (AIS) as Grade A, complete spinal cord injury. Participants with paraplegia and participants with a Grade B, incomplete injury, on the AIS were functionally more independent (p = 0.011)) compared with participants with tetraplegia and other AIS grades. Participants with paraplegia reported having a more active lifestyle (p = 0.040) in their home and social activities compared to those with tetraplegia. There was no significant association found between functional independence at pre-discharged and community integration one-month post-discharge of the people with SCI.ConclusionA month after discharge, there is no statistically significant relationship between community reintegration and functional independence. A measure of functional independence may not be a suitable indicator of community integration. It is proposed that to monitor a person's community integration the CIQ could be used with a measure of quality of life as this would indicate a person's contentment with their level of community integration.