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Senescence-associated LncRNAs TRMP and TRMP-S promote gastric cancer by activating IGFL4
Incidence and risk factors for post-stroke delirium in the elderly: A national inpatient sample (NIS) analysis
Background Post-stroke delirium (PSD) is a critical neuropsychiatric condition affecting up to 50% of elderly patients during hospitalization, often leading to poorer outcomes. Despite its prevalence, PSD remains underrecognized in clinical practice, and national-level studies exploring its risk factors are limited. Objective To examine the incidence and risk factors associated with PSD in elderly individuals (≥65 years) using a large, nationally representative dataset. Methods Data from the Healthcare Cost and Utilization Project National Inpatient Sample (2010–2019) were analyzed. Elderly patients with a primary diagnosis of stroke were selected, and PSD was defined using the International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM) and ICD-10-CM codes. Multivariate logistic regression, adjusted for demographic, clinical, and hospital variables, identified independent PSD risk factors. Results Among 1,644,773 elderly stroke patients, the incidence of PSD was 19.5%. PSD occurred in 18.9% of ischemic strokes and 24.7% of hemorrhagic strokes. Patients with PSD were significantly older, with a median age of 79 years, compared to 78 years in those without PSD ( p < 0.001). They also experienced prolonged hospital stays (5 days vs. 4 days, p < 0.001), incurred greater hospitalization costs ($44,863 vs. $35,787, p < 0.001), and exhibited a higher risk of in-hospital mortality (12.6% vs. 7.0%, p < 0.001). Major risk factors for PSD include: sepsis (OR = 2.364, 95%CI = 2.329–2.400), three or more comorbidities (OR = 2.049, 95%CI = 1.984–2.116) and fluid/electrolyte disorders (OR = 1.902, 95%CI = 1.886–1.918), psychoses (OR = 1.765, 95%CI = 1.725–1.806). Conclusions PSD is frequently observed in elderly stroke patients and is associated with adverse clinical outcomes. Advanced age, comorbidities, and stroke-related complications are significant risk factors. These results underscore the importance of developing focused prevention and intervention strategies to enhance outcomes for this high-risk population.
Reduced rehabilitation continuity after stroke in patients with substance use disorder based on a TriNetX retrospective cohort study
Cost-effectiveness of cast treatment vs. surgery in elderly patients with substantially displaced intra-articular distal radius fractures: A trial-based economic evaluation
Background The number of surgical procedures for distal radius fractures in the elderly has increased, even though most studies show little or no benefit over cast treatment. While medical costs of surgical treatment are higher than those of cast treatment, surgery may enable faster recovery and help patients maintain independence, potentially reducing their use of other healthcare resources and informal care. Methods We evaluated whether cast treatment is cost-effective compared to surgery for patients aged 65 years or older with substantially displaced intra-articular distal radius fractures. A multicentre randomized controlled non-inferiority trial with an economic evaluation in 19 hospitals in the Netherlands. Participants completed (cost) questionnaires at baseline, 3, 6, 9 and 12 months after trauma. A total of 138 patients were randomized between cast treatment and surgery. Health-related quality of life was measured with the EQ-5D-3L; wrist function was measured with the Patient Rated Wrist Evaluation (PRWE). Costs were assessed from a societal perspective, including intervention costs, healthcare use, informal care, unpaid productivity losses, and patients’ own expenses. Results The estimated difference in total societal costs was -€81 (95%CI -€3936 to €3773) in favour of cast treatment compared with surgical treatment. Compared with cast treatment, surgery resulted in improved wrist function (PRWE: −5.5; 95% CI: −10 to −0.7) and slightly higher QALYs (+0.039; 95%CI 0.012 to 0.066), equivalent to 14 days in perfect health. The incremental cost-effectiveness ratio was €15 per point improvement in PRWE and €2070 per QALY gained. Cast treatment’s probability of cost-effectiveness was low for all values of willingness to pay. Conclusion Although cast treatment had lower direct costs, these were offset by higher informal and secondary care costs. Surgical treatment offered clinically relevant short-term benefits. From a societal perspective, surgery appears to be the more favorable option for elderly patients with displaced distal radius fractures.
Investigating the effects of TMS-related somatosensory inputs on TMS-evoked potentials provides evidence against significant interaction
Abstract The combination of transcranial magnetic stimulation (TMS) and electroencephalography (EEG) has emerged as a non-invasive technique to probe cortical responsivity. However, interpreting TMS–EEG data is challenging due to sensory inputs generated by TMS, which cause peripherally evoked potentials (PEPs) that overlap with TMS-evoked potentials (TEPs). These sensory inputs may also modulate the cortical response, potentially distorting TEPs. To address this and evaluate methods for reducing PEP contamination, we compared two sham designs: a “PEP saturation” method, which delivers high-intensity somatosensory stimuli in both sham and real TMS to saturate PEPs in both conditions, and a “PEP individualized matching stimulus intensity calibration” method, which individually adjusts stimulus intensity to match the PEP amplitude of real TMS. In both conditions, the PEPs from sham and real TMS conditions should match, enabling the subtraction of this confounder. If the TEPs after this subtraction were not different between the two conditions this would indicate the absence of a relevant interaction between PEPs and TEPs, justifying the removal of PEPs from TEPs by subtraction. Our results showed no significant difference in TEPs within 110 ms post-stimulation after sham subtraction regardless of the sham protocol, and whether stimulating the primary motor cortex or the supplementary motor area. These findings provide evidence for the absence of a relevant interaction between TMS-related somatosensory input and TEPs, and indicate the appropriateness of the two sham protocols in removing PEPs from the TMS-EEG response.
A deep state-space analysis framework for cancer patient latent state estimation and classification from EHR time-series data
Advancements in deep learning technologies and an increase in medical data have enhanced the accuracy of disease diagnosis and treatment strategies. Notably, significant progress has been made in the use of deep learning-based time-series prediction models for short-term disease onset prediction and analysis of important features. However, research on explainable deep learning for long-term disease progression, such as cancer and chronic diseases, still faces challenges. The difficulty in estimating explainable gradual disease progression from observable patient test data is a key factor. To address this issue, we propose a new approach called the “deep state-space analysis framework.” This framework utilizes sequentially obtained electronic health records (EHRs) to estimate and visualize temporal changes in the latent states of patients related to disease progression. It enables the clustering of latent patient states according to the severity of disease progression and identifies key factors leading to a poor prognosis with medication. To validate our framework, a detailed analysis of data from 12,695 patients with cancer was conducted. The estimated transitions of the latent states capture the clinical status of the patients and their continuous temporal changes. Furthermore, anemia was identified as a poor prognostic factor during state transitions in patients with cancer. Significant features were also confirmed, such as immune cell abnormalities, which are poor prognostic factors in patients treated with Nivolumab, Osimertinib, and Afatinib. This technological innovation deepens our understanding of disease progression and supports early treatment adjustments, prognostic evaluations, and the formulation of optimal long-term strategies. With the advancements in deep learning, its application in healthcare has even greater potential.
Impact of authoritative and subjective cues on large language model reliability for clinical inquiries: an experimental study
Extended thrombotic prophylaxis in COVID-19 early discharge: A retrospective cohort study
Introduction/Background Due to limits in available staff and space during the COVID-19 pandemic, home monitoring programmes were introduced, reducing strain on resources, and preventing readmissions. Several hospitals included prophylaxis for venous thrombotic events (VTE), as COVID-19 appeared to be thrombogenic. Other hospitals did not, expecting patients to be more mobile while at home. Our aim was to determine whether the administration of nadroparin has led to a difference in VTE occurrence between two groups of previously included patients. Materials and methods Retrospective cohort study of two cohorts included in home monitoring with the same protocol, except for nadroparin prophylaxis. Results 663 patients were analysed in equal groups from two hospitals. No significant difference was found in occurrence of VTE after discharge, readmissions in general or readmissions due to VTE in otherwise comparable groups. Discussion As opposed to trials determining thrombotic prophylaxis was of benefit after discharge due to COVID-19, we found no difference between our groups. Our study was retrospective and comprised data compiled over almost two years, which provides a relatively large sample size and overview through different treatment regimes. Conclusion For the future, thrombotic prophylaxis for COVID-19 home monitoring might not be indicated and reconsidered for different home monitoring programmes.
Seismic response analysis of coal mine shaft tower structure considering PSSI effect under different sites
Exploring patient satisfaction with community pharmacy services in the United Arab Emirates: Implications for quality improvement
Introduction Patient satisfaction is a critical metric for enhancing service quality, meeting regulatory standards, and validating patient-reported outcomes in healthcare. Community pharmacies play a vital role in healthcare delivery, yet there is limited research on patient satisfaction with these services in the UAE. Aims This study aims to identify key factors influencing patient satisfaction with pharmaceutical care services provided by community pharmacies in the UAE. Methods A cross-sectional, questionnaire-based study was conducted from December 1st, 2023, to April 30th, 2024. A systematic intercept sampling method was used to ensure a representative sample of 505 patients from various regions of the UAE. Data were collected through structured questionnaires covering demographic details, pharmacy visit experiences, and satisfaction levels. Statistical analyses, including chi-square tests, t-tests, and binary logistic regression, were performed using SPSS 27. Results The study found that most participants most frequently used chain pharmacies (62.38%) and were highly satisfied with factors like lighting (91.29%) and pharmacist attentiveness (83.96%). Key drivers of satisfaction included convenient locations, accessible designs, and communication-related factors.. However, challenges such as the lack of private counseling areas (59.01%), limited access to medical files (77.62%), and inadequate prescription areas for private conversations (53.29%) were highlighted. Satisfaction was significantly lower in the Northern Emirates compared to Abu Dhabi, while differences involving Al Ain did not reach statistical significance. Providing sufficient time for medication advice (OR: 16.21, p < 0.001) and ease of waiting times (OR: 4.29, p = 0.016) improved satisfaction. Conclusion The findings underscore the importance of both environmental and interpersonal factors in shaping patient satisfaction with community pharmacies. Enhancing pharmacy accessibility, communication, and the quality of pharmacist-patient interactions can significantly improve patient experiences. Future research should further explore overall satisfaction and investigate targeted improvements in pharmacy practices across different regions and settings.
Improving the value of population health data for health policy and decision-making using machine learning algorithms in EQ-5D-5L index estimation
Abstract This study aimed to estimate patient-level EQ-5D-5L index scores using routinely collected sociodemographic and Minimum European Health Module (MEHM) data from seven extensive population surveys ( N = 9,324). Fourteen machine learning (ML) models were compared in five research scenarios using the recently developed G score. Based on the performance ranking shown across scenarios, AdaBoost emerged as the best model (mean rank 2.87), followed by Multilayer Perceptron (MLP) and XGBoost (mean ranks 2.94 and 3.60, respectively). AdaBoost achieved the best results when no imputation was done and both sociodemographic and MEHM data were included (G = 0.955), but its performance declined when the estimation was based solely on sociodemographics (G = 0.871). The results confirm that the EQ-5D-5L index can be well predicted from existing statistical data using ML methods and that the MEHM improves the estimation. Our findings also highlight the potentially undesirable effects of data imputation in ML-based estimations. The methods presented in this study enhance the usability of existing health data, giving analysts and decision-makers a practical way to populate health-economic evaluations when primary data collection is impractical or impossible. Nonetheless, even advanced ML algorithms have limitations, so direct EQ-5D-5L data collection should remain the preferred approach whenever feasible.
Tests of the Oddity Effect Hypothesis in mixed-species parid flocks
Although there are major benefits of group membership, there might be severe costs to being in a group for phenotypically rare individuals. Whereas the role of rarity in antipredator behavior is well-documented in fish species, there is little empirical evidence on how rarity affects this behavior within mixed-species avian groups. Understanding this is important for clarifying how various antipredator behaviors function in different social contexts. The Oddity Effect Hypothesis predicts that predators will choose phenotypically rare individuals within groups, and as a response to their oddity, these prey individuals should behave as inconspicuously as possible, often by delaying signaling. Here we examined the role of rarity in data taken from and analyzed separately in two different, published, field experiments. We measured the latency to call in mixed-species flocks with one versus two or more individuals of Carolina chickadees ( Poecile carolinensis ), tufted titmice ( Baeolophus bicolor ), or white-breasted nuthatches ( Sitta carolinensis ) after a predator model was presented. We also tested two alternative hypotheses, the ‘probability of calling’ and ‘recruitment’ hypotheses. In support of the Oddity Effect Hypothesis, we found evidence that single individuals took longer to call: chickadees in the first experiment, using a screech owl ( Megascops asio ) model, and titmice and nuthatches in the second experiment, using a Cooper’s hawk ( Accipiter cooperii ) model. For our alternative hypotheses, we found no evidence of shorter call latencies with more conspecifics in flocks due to simple probabilities of calling and no evidence of shorter calling latencies with fewer conspecifics due to increased motivation to recruit conspecifics. Our results lend support to the Oddity Effect Hypothesis, though we urge caution due to our small sample sizes.
Interaction of pulsed low frequency electromagnetic field (PEMF) with mitochondria
Abstract Pulsed electromagnetic field (PEMF) therapy is a non-invasive treatment that delivers electric and magnetic fields to tissues via inductive coils inducing salutary effects. Previously PEMF was reported to accelerate gas transport in the liquid phase. Drawing analogies from electrical elements, we hypothesized that PEMF can modulate transmembrane potential of biological membranes. Given that mitochondria interact with gaseous molecules such as oxygen and nitric oxide (NO) and contain voltage-sensitive channels, this study focused on the effects of a single PEMF device with a low input-energy and a 1ms, 30 kHz sine wave duty cycle per pulse on mitochondrial function. Experiments were conducted using cell cultures, tissue homogenates, and isolated mitochondria. We assessed mitochondrial membrane potential, NO levels, and mitochondrial respiration. We found no evidence that PEMF restores mitochondrial respiration inhibited by NO, but it can be restored by exposure of mitochondria to blue light. Our findings show that PEMF selectively stimulates respiration linked to ATP-synthesis affecting less uncoupled respiration. This suggests that changes in ATP-synthesis underlies the primary beneficial effects observed here. The underlying mechanisms may include interaction of PEMF with mitochondrial transport systems or activity of mitochondrial complexes. The exact mechanisms still should be investigated.
CatDive: A simple yet effective method for maximizing category diversity in sequential recommendation
How can we offer users items of diverse categories while considering their interests in sequential recommendation? Traditional sequential recommendation methods often prioritize accuracy over diversity, recommending only items similar to those with which users have previously interacted. Recommendation biased toward only the favorite category of each user fails to achieve novelty and serendipity, demoting users’ satisfaction. Thus, it is important to maximize category diversity in sequential recommendation. However, the state-of-the-art work on category diversified sequential recommendation tackles it only with a post-processing method which sacrifices accuracy to improve category diversity. In this paper, we propose CatDive, a simple yet effective method for maximizing category diversity without sacrificing accuracy in sequential recommendation. In the training phase, CatDive incorporates category information into the recommendation model by using multi-embedding, composed of item and category embeddings. Also, CatDive boosts category diversity by placing greater emphasis on users’ preferred categories when selecting negative samples, while still prioritizing high-confidence negatives based on item popularity to maintain accuracy. Finally, we enhance and control category diversity by prioritizing underrepresented categories in the reranking phase, using a tunable hyper-parameter to balance adjustments. CatDive achieves the state-of-the-art performance on real-world datasets, achieving up to 152.49% higher category diversity in the similar level of accuracy and up to 39.73% higher accuracy in the similar level of category diversity compared to the best competitor.
Field-ready DNA extraction from scat using magnetic nanoparticles for non-invasive wildlife monitoring
Abstract Scat is commonly used to non-invasively monitor the presence of wildlife. Samples are typically collected in the field and transported to a laboratory for downstream DNA analysis. Current gold-standard extraction methods rely on column-based kits that are expensive, labor-intensive, and require specialized instrumentation. Here we present a rapid and low-cost DNA extraction method using silica-coated magnetic beads, tested on our species of interest - Rangifer tarandus (a Species at Risk currently monitored in Canada using scat sampling) and extended to other Cervidae species. This portable approach requires only simple equipment, yields stable DNA, and is well suited for polymerase chain reaction (PCR) analysis both in the field and in centralized laboratories. We also designed and validated novel primers specific for R. tarandus mitochondrial DNA to enable reliable species identification from scat. In this study, we demonstrate the ease of use, DNA yield, stability and PCR success of our method compared with widely used kits. We further show its applicability across additional Cervidae scat, underscoring its transferability to other herbivorous mammals.
Facilitators to doctoral completion among international students in a Global South University: An appreciative inquiry perspective
In the context of the global knowledge economy, the Global South has emerged as an increasingly important destination for doctoral education, yet research on this phenomenon remains scarce. The present study addresses this research gap by investigating the factors that contribute to the successful completion of doctoral programs among international students from the Global South enrolled at a public research university in Malaysia. Employing the Appreciative Inquiry approach, this study conducted semi-structured interviews with 11 international doctoral graduates. Findings reveal that intrinsic factors, such as passion for research, perseverance, and self-discipline, are crucial for success. External factors, including support from supervisors, institutions, families, and friends, also play a significant role. Furthermore, the Global South participants in this study called for improving conditions for future cohorts and fostering cross-cultural understanding within South-South doctoral education, where resource constraints and uneven institutional support often necessitate mutual aid and solidarity, a dimension largely overlooked in studies focused on Global Northern higher education systems with more robust infrastructure. The study contributes to understanding doctoral experiences in the Global South, challenges the dominance of a northern-centric perspective in academic research, and offers practical implications for institutions seeking to enhance support for doctoral students.
Correction: The scaled sardine’s unique metabolic phenotype and its implications for the susceptibility of small tropical pelagic fishes to climate change
Graph neural networks and belief rule base collaborative modeling for automated and interpretable fault diagnosis in proton exchange membrane fuel cells
Proton exchange membrane fuel cells (PEMFC) are critical for clean energy conversion, but their reliability is severely compromised by complex faults, creating a pressing need for accurate and interpretable diagnostic methods. While the Belief Rule Base (BRB) provides a transparent reasoning framework, its practical deployment faces two fundamental challenges: the “combinatorial explosion” of rules with increasing system complexity, and a heavy reliance on domain experts to provide precise quantitative parameters. To address these issues, this paper proposes a novel GNN-BRB framework that synergistically integrates Graph Neural Networks (GNN) with BRB. Our solution introduces two key innovations: an exponential ordered weighting operator to systematically convert qualitative expert rankings into quantitative confidence parameters, and a GNN-based mechanism that models the BRB rule base as a graph to automatically generate initial parameters through information propagation among semantically related rules. Experimental results on a real-world PEMFC fault diagnosis case demonstrate that the proposed method significantly reduces dependency on manual expert input while achieving superior diagnostic performance. Ablation studies further validate the contribution of each model component. This work establishes a new paradigm for developing automated, highly accurate, and interpretable fault diagnosis systems for complex engineering applications.