Browse Articles

Process evaluation of a supportive intervention targeting social isolation among older people in Danish senior centres: Explanatory factors of implementation failure

This article explores the implementation of a supportive intervention in Danish municipal senior centres targeting social isolation among older people. The intervention, implemented between April 2022 and April 2023, comprised three key components: a start conversation for all new users; an assigned “buddy” among existing users; and monthly follow-up conversations. Skills development workshops for staff members were held prior to implementation of the intervention. The feasibility evaluation revealed concerns about the intervention implementation. This study describes the low level of implementation and explanatory factors contributing to the failure. We conducted a process evaluation as part of a feasibility evaluation of the intervention. The intervention was implemented in three municipal senior centres, ten senior centre staff members and 18 senior centre users participated. Data collection involved 23 semi-structured interviews with users and staff. Thematic analysis was conducted. Results are presented in two parts: 1) Overview of implemented components showing a low degree of fidelity in implementation, 2) Explanatory factors influencing implementation. The three factors identified were: A “too” systematic approach; Navigating frailty; and Lack of integration. These factors resulted in challenges recruiting participants and issues with performing some of the intervention elements. This evaluation provides insights into delivering interventions in municipal senior centres, emphasising explanatory factors to avoid implementation failures. The findings can support future development of contextually responsive interventions that can function as intended when delivered in real-world settings.

Intelligent decision-making for mine ventilation systems based on graph neural network and deep reinforcement learning fusion

Abstract Mine ventilation systems face significant challenges in dynamic control due to complex network topologies and uncertain underground environments. This paper proposes an intelligent decision-making framework that synergistically integrates graph neural networks (GNN) with deep reinforcement learning (DRL) for optimal ventilation control. A multi-level hierarchical graph representation method is developed to capture topological structures and spatial dependencies of ventilation networks, while an improved Actor-Critic algorithm with prioritized experience replay enables adaptive policy learning under safety constraints. The GNN encoder extracts graph-structured features that enhance the DRL agent’s state representation, facilitating efficient exploration and decision optimization. Experimental validation on simulation platforms and six-month field deployment in an operational coal mine demonstrate substantial improvements: 34.7% higher cumulative rewards compared to conventional methods, 23.7% reduction in energy consumption, and 98.4% safety compliance rate across diverse operational scenarios. The proposed framework advances intelligent mine ventilation management by simultaneously achieving enhanced safety assurance, improved energy efficiency, and robust adaptability to complex dynamic conditions.

Molecular characterization and phylogenetic analysis of major envelope protein gene (B2L) and ATPase protein gene (A32L) of orf virus isolates from goats in Southern, Thailand

Orf virus (ORFV), a member of the Poxviridae family, causes contagious ecthyma (CE), a viral skin disease in small ruminants. Due to its self-limiting nature, low mortality rate and economic consequences, CE is considered as a neglected disease, resulting in underreporting in Thailand. Despite its global presence, the genetic characterization of ORFV in Thailand, especially in the southern region where there is high density of goat farming, is largely unknown. To address the knowledge gap, we conducted genetic and evolutionary analysis including phylogenetic, BEAST, and discriminant analysis of principal components (DAPC), on ORFV isolated from Southern Thailand, utilizing conserved B2L gene (major envelope protein) and A32L gene (C-terminal ATPase protein). All suspected CE meat goats across 2 farms in Songkhla and Pattani provinces in 2024 tested ORFV positive via PCR using ORFV-specific primers. These isolates along with a positive control isolate (Pattani 2020) were used for molecular characterization and genetic analysis. The 2024 isolates clustered with Malaysian strains based on phylogenetic and population analysis. BEAST analysis of these isolates further indicated a shared evolutionary origin around 2007, suggesting transboundary spread. Although the 2024 isolates were highly related, some differentiation observed for both genes and they evolved separately from 2020 Pattani isolate suggesting ongoing local evolution and genetic variation in the regional population. Moreover, the Pattani 2020 isolate was phylogenetically distinct and revealed a distinct ancestral origin, suggesting a separate introduction event into the region. Notably, heterogeneity in C-terminal region of ATPase gene was observed, including 4 amino acid deletions in Songkhla 2024 and Pattani 2020 isolates, and unique substitutions (G258S and G260S) in Pattani 2024 which may cause a distinct cluster based on DAPC analysis. Collectively, our findings indicate diverse ORFV evolution in Southern Thailand, necessitating continued molecular surveillance and genetic characterization to improve national control strategies.

Plant-mediated synthesis of silver nanoparticles using Alcea rosea leaf aqueous extract and evaluation of the biological activities

An interpretable machine learning framework for adverse drug reaction prediction from drug-target interactions

Background Adverse drug reactions (ADRs) present challenges to patient safety and healthcare systems. Current pharmacovigilance methods, such as the Yellow Card Scheme (YCS), provide valuable post-marketing data, but the mechanistic causes of these ADRs are not fully understood. Leveraging drug-target interaction data with interpretable machine learning offers a promising approach to anticipate ADRs and understand their underlying mechanisms. Objective This study proposes an interpretable machine learning (ML) framework to predict significant ADRs using drug-target interaction data. The framework aims to identify key pharmacological relationships, helping to inform drug safety. Methods Drug-target interaction data from STITCH was combined with ADR reports from the YCS. Disproportionality analysis identified significant ADR signals which were used to train Random Forest classifiers across System Organ Class (SOC) categories. Class imbalance was addressed with SMOTE and Tomek, and Bayesian optimisation refined hyperparameters. Feature importance scores provided interpretability, and the top features were validated using known target-disease associations from DisGeNET. Results Prediction performance varied across SOC categories, with ROC AUC scores up to 0.94. Feature importance analysis identified pharmacologically relevant targets, validated using DisGeNET and comparisons with SIDER highlighted the added value of real-world data. Conclusions The interpretable ML framework links drug-target interactions to ADRs, offering a promising approach for predictive pharmacovigilance (PPV) and supporting safer drug development.

Assessment of land use transition, trend, shift & directional distribution in the Ganga Basin

The impact of expanded access to antiretroviral treatment on engagement in HIV care and viral suppression among pregnant women living with HIV in South Africa

Background Timing of engagement in HIV care in relation to pregnancy impacts maternal outcomes and the risk of vertical transmission of HIV. Option B+, a policy that mandates offering all pregnant women living with HIV (PWLH) lifelong antiretroviral therapy (ART) irrespective of their CD4 count, has been adopted across sub-Saharan Africa, including South Africa since 2015. This study aimed to assess the impact of expanded access to ART on engagement in HIV care and viral suppression among pregnant women in South Africa. Methods This observational study used data from pregnant women living with HIV who delivered at Rahima Moosa Mother and Child Hospital in Johannesburg, South Africa from 2013−2017. Linkage to a national HIV laboratory cohort (the NHLS National HIV cohort) was used to ascertain engagement in HIV care prior to antenatal care (ANC) entry and viral load outcomes. Analyses were stratified by the pre-Option B+ (2013−2014), Option B+ (2015−31 Aug 2016) and Universal Test and Treat (post-01 Sept 2016) eras. We compared engagement rates before and during the Option B+ era and assessed factors associated with HIV care engagement and viral suppression. Risk ratios were estimated using log-binomial regression. Results Among 4,865 PWLH, 65% had evidence of prior engagement in HIV care. Prior engagement in care was higher during the Option B+ (64%) and UTT (71%) eras compared to the pre-Option B+ era (55%). Younger women (18–24 years) were less likely to engage in HIV care than those aged 25–34 years (aRR 0.8, 95% CI: 0.6–0.9). Women with CD4 counts <200 cells/mm³ were less likely to have been engaged in care prior to pregnancy compared to those with CD4 ≥ 500 (aRR 0.6, 95% CI: 0.6–0.7). Primigravid women had a 30% lower likelihood of earlier HIV care engagement compared to those with 2–3 pregnancies (aRR 0.7, 95% CI: 0.5–0.8). Overall viral suppression was higher in women reporting ART use prior to pregnancy compared to those with no prior HIV care (33% vs. 19%, p < 0.001). During the four-year study period, the proportion of PWLH who had a viral load recorded but were not virally suppressed ranged from 22–36%. Conclusion We observed increased engagement in HIV care prior to pregnancy after implementation of policies that expanded access to ART. However, high prevalence of unsuppressed viral load across all policy eras highlights the need for continued monitoring and support to sustain the benefits of this policy. Pregnancy and antenatal care services remain an essential portal of entry to HIV care among PWLH in South Africa. Interventions to improve early ANC attendance and maternal engagement in HIV care prior to pregnancy are critical to eliminate vertical HIV transmission.

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.