Category: Nevin Manimala

Sci Rep. 2026 Jun 24;16(1):19582. doi: 10.1038/s41598-026-59770-7.

ABSTRACT

Spondyloarthropathies (SpA) are characterized by low back pain and limited mobility. Therefore, physical activity (PA) is an essential part of the treatment, yielding positive effects on clinical symptoms. Digital health applications (DHAs) present new opportunities to promote clinical outcomes, however, their long-term effectiveness is often limited by low adherence and high dropout rates.This study investigates whether integrating personalized or AI-driven coaching enhances the therapeutic benefits of DHA in patients with SpA. SpAs patients were randomized into one of 3 groups. They were instructed to exercise at least 2-3 times per week for 6 months with the DHA according to their group (intervention groups: ViViRA (with personal coaching) or Kaia Health (with AI-based coaching); control group: ViViRA (without coaching)). Personal coaching consisted of a one-time, 30-min online coaching session prior to using DHA, while the AI coaching consisted of video-based AI integrated into DHA to provide movement guidance during each session. At baseline, after 3 and 6 months sociodemographic, questionnaires and mobility were assessed. Data from 78 participants were analyzed (mean age 51 years; 68% female). All three digital interventions showed a significant improvement in mobility (Bath Ankylosing Spondylitis Metrology Index (BASM), range: 0-10, lower scores = better mobility; BL-3 month: mean BASMI change – 0.6 to – 0.7; all p < 0.001). Pain intensity decreased substantially in all arms (PainDETECT, neuropathic pain, range: 0-38, higher scores = more severe pain; BL-6 month: mean reduction – 4.6 to – 6.6 points; all p ≤ 0.006). PAHCO (Physical Activity-related Health Competence) control competence increased over time and reached statistical significance only in the ViViRA + coaching group (PAHCO: higher scores = better physical activity-related health competence; BL-6 month: + 1.02, p = 0.013) but did not exceed the other interventions in a direct comparison. Overall, none of the coaching strategies showed significant superiority over the stand-alone digital therapy. Adherence was the same in all groups after 3 months (2-3 weekly use of DHA). Digital movement therapy with the use of DHA improves mobility and pain independently of coaching in SpAs patients. In contrast, personal coaching has been shown to improve health-related skills which could indicate potential benefits for self-management and long-term treatment adherence.Trial registration The study is registered in the German clinical trial registry (DRKS) under the following ID: DRKS00035191, https://www.drks.de/search/de/trial/DRKS00035191/details, Registration date: 01.10.2024.

PMID:42342871 | DOI:10.1038/s41598-026-59770-7

Sci Rep. 2026 Jun 24. doi: 10.1038/s41598-026-58012-0. Online ahead of print.

ABSTRACT

Prematurity and term low birth weight (LBW) are important contributors to neonatal morbidity and mortality in Afghanistan. Evidence from hospital-based studies in Herat remains limited, particularly studies that distinguish prematurity from term LBW. This study aimed to identify maternal, socioeconomic, obstetric, and pregnancy-related factors associated with prematurity and term LBW among newborns delivered in Herat, western Afghanistan. An unmatched hospital-based case-control study was conducted at Herat Midwifery Hospital from June 15 to September 15, 2023. The study included 176 premature infants, 84 term LBW infants, and 290 full-term normal-birth-weight controls. Prematurity was defined as birth before 37 completed weeks of gestation, and term LBW was defined as birth at ≥ 37 completed weeks with birth weight < 2500 g. Data were collected from hospital records and maternal interviews. Separate adjusted binary logistic regression models were used to estimate odds ratios (ORs) and 95% confidence intervals (CIs) for prematurity and term LBW compared with full-term normal-birth-weight controls. Statistical significance was set at p < 0.05. Prematurity was associated with medium perceived economic status (OR = 2.21, 95% CI: 1.04-4.73), bad perceived economic status (OR = 5.16, 95% CI: 2.06-12.91), preeclampsia (OR = 5.98, 95% CI: 1.72-20.76), pregnancy-related health problems (OR = 13.76, 95% CI: 5.32-35.61), substance use during pregnancy (OR = 2.88, 95% CI: 1.11-7.45), and cesarean section (OR = 3.37, 95% CI: 1.99-5.73). Term LBW was associated with medium perceived economic status (OR = 3.98, 95% CI: 1.29-12.30), bad perceived economic status (OR = 19.62, 95% CI: 5.09-75.63), pregnancy-related health problems (OR = 8.88, 95% CI: 2.46-32.02), and cesarean section (OR = 9.76, 95% CI: 4.85-19.65). Poor perceived economic status and pregnancy-related health problems were associated with both prematurity and term LBW. Preeclampsia and substance use were associated mainly with prematurity. Cesarean section should be interpreted as a marker of high-risk obstetric conditions rather than as a direct causal factor. These findings support strengthening antenatal risk detection, management of pregnancy complications, and targeted maternal health interventions in Herat.

PMID:42342870 | DOI:10.1038/s41598-026-58012-0

Sci Rep. 2026 Jun 24. doi: 10.1038/s41598-026-55004-y. Online ahead of print.

ABSTRACT

To clarify the impact of lower limb and hindfoot alignment and its changes on foot and ankle-related quality of life (QOL) over a 4-year period in patients with rheumatoid arthritis (RA). A total of 258 RA patients (516 feet) who underwent plain X-ray examination with hip-to-calcaneal (HC) view at baseline and a 4-year follow-up, along with Self-Administered Foot Evaluation Questionnaire (SAFE-Q) data at the follow-up were analyzed after excluding patients with prior lower limb surgery or severe ankle destruction (Larsen classification ≥ III or Takakura-Tanaka classification ≥ IIIa). Radiographic parameters representing lower limb and hindfoot alignment were measured using HC view, including hip-knee-ankle angle (HKA), tibio-calcaneal angle (TCA), talar tilt angle (TTA), and the changes of these angles. Clinical and laboratory factors collected included age, sex, BMI, autoantibody titer and positivity, methotrexate (MTX) use, biologic and targeted synthetic disease-modifying antirheumatic drugs (b/tsDMARDs) use, cumulative glucocorticoid dose, and Clinical Disease Activity Index. The primary outcome was the association between clinical and radiographic factors and ankle-related QOL. A generalized linear mixed model was used for statistical analysis. The mean age was 62.4 years, 87.2% were female, and 89.9% were seropositive. Over 4 years, hindfoot valgus (TCA) progressed from 4.3° to 6.0°. GLMM showed that age and cumulative glucocorticoid dose negatively affected QOL, while male sex, methotrexate dose, and b/tsDMARDs use were positively associated. Among radiographic parameters, valgus progression of TCA was significantly associated with poorer SAFE-Q outcomes in the “Shoe-related” and “General Health Perception” domains. Baseline HKA predicted valgus progression of TCA, whereas higher BMI, male sex, and larger baseline TCA predicted varus progression. Progressive hindfoot valgus deformity over 4 years, rather than static alignment, negatively impacts foot- and ankle-related QOL in RA patients, particularly in shoe-related function and general health perception. Baseline knee varus deformity predicts longitudinal hindfoot valgus progression.

PMID:42342815 | DOI:10.1038/s41598-026-55004-y

Sci Rep. 2026 Jun 24. doi: 10.1038/s41598-026-59021-9. Online ahead of print.

ABSTRACT

Cognitive decline is a central challenge of aging, with subtle early changes laying the foundation for broader difficulties later in life. One domain that is particularly challenging to capture with standard assessments is processing efficiency. Previous research has shown age-related differences in processing efficiency using redundant-target detection tasks, but it remains unclear whether individual differences in cognitive ability within the older adults are associated with differences in processing efficiency. In the present study, 65 cognitively healthy older adults (aged 60-79) completed the Montreal Cognitive Assessment (MoCA) and a color-shape redundant-target detection task, from which we estimated resilience capacity (Rz), a processing efficiency metric that quantifies how well a system maintains its target processing speed in the presence of distractors, using Systems Factorial Technology (SFT). MoCA scores were significantly and positively correlated with the standardized resilience capacity summary, Rz (r = 0.35, 95%CI [0.12, 0.55], p = 0.004). This significant association persisted in a partial correlation analysis that controlled for age as a covariate (partial r = 0.35, p = 0.004). In a direct model comparison of four candidate processing-efficiency metrics – inverse efficiency scores (IES), redundancy gains (RG), mean RT of correct responses, and Rz – Rz was the strongest predictor of MoCA. Functional principal component analysis (fPCA) of R(t) identified a temporal component (PC2) on which individuals at the higher end of the MoCA distribution showed a later, more controlled rise in capacity, whereas those at the lower end showed earlier but less efficient processing. Together, these findings indicate that processing efficiency metric – and specifically resilience capacity under distractor interference – is continuously related to cognitive performance in older adults and may reflect aspects of cognitive reserve not captured by global screening scores or summary-statistic-based efficiency measures.

PMID:42342801 | DOI:10.1038/s41598-026-59021-9

Sci Rep. 2026 Jun 25. doi: 10.1038/s41598-026-59449-z. Online ahead of print.

ABSTRACT

Reliable prediction of drug solubility in supercritical carbon dioxide (SC-CO₂) is central to designing environmentally conscious pharmaceutical processes, yet experimental solubility measurements remain slow, resource-intensive, and often restricted to limited operating ranges. These constraints restrict the development of green technologies such as particle formation, controlled micronization, and solvent-free formulation strategies. This study introduces an automated computational framework that couples modern regression algorithms with bio-inspired optimization to deliver scalable solubility prediction across varying conditions. The approach employs Adaptive Boosting Regression and Light Gradient Boosting Regression as core learners, which are combined through hybrid ensemble schemes and tuned using two recent metaheuristic algorithms the Osprey Optimization Algorithm and the Artificial Protozoa Optimizer. Model behavior was assessed using repeated cross-validation, a suite of accuracy metrics (RMSE, R², MDAPE, SI, NSE), non-parametric statistical comparison, and ANOVA-based sensitivity evaluation. A multi-criteria ranking using the TOPSIS method identified the APO-driven ensemble (ALAP) as the most reliable configuration, achieving RMSE = 0.191, R² = 0.982, and MDAPE = 15.6% on the test set. Beyond its algorithmic design, the framework offers a practical computational alternative to extensive laboratory experimentation, providing a transferable, data-driven tool for efficient and eco-friendly pharmaceutical process design.

PMID:42342798 | DOI:10.1038/s41598-026-59449-z

Sci Rep. 2026 Jun 24. doi: 10.1038/s41598-026-58988-9. Online ahead of print.

ABSTRACT

Named Data Networking (NDN) represents a paradigm shift toward content-centric architectures but remains critically vulnerable to Interest Flooding Attacks (IFAs), where malicious actors overwhelm router Pending Interest Tables with spurious requests, causing service degradation and denial-of-service. To address the limitations of existing approaches, including high false positives in threshold-based methods and substantial overhead in centralized learning, we propose FL-IFAshield, a novel federated learning framework for adaptive IFA mitigation. Our solution integrates dynamic Poisson-EMA thresholding for accurate flood detection, entropy-aware federated aggregation to handle non-IID traffic distributions across edge routers, and Byzantine-robust mechanisms with differential privacy guarantees. Comprehensive evaluation on the FIT/IoT-LAB testbed with 100 routers demonstrates exceptional performance: 93.1% F1-score in attack detection, only 5% false positives, 28 ms average end-to-end latency ([Formula: see text]), and over 90% legitimate Interest Satisfaction Ratio under sophisticated collusive attacks, while maintaining minimal computational overhead (<9% CPU utilization on ARMv8 routers). FL-IFAshield significantly improves security performance, offering 35% higher accuracy than static thresholding and 60% lower communication overhead than centralized approaches. While simpler heuristic baselines naturally incur marginally lower computational footprints, our solution delivers the optimal overall operational balance among high precision, low end-to-end latency ([Formula: see text]), and resource efficiency in constrained edge computing environments.

PMID:42342794 | DOI:10.1038/s41598-026-58988-9

Sci Rep. 2026 Jun 24. doi: 10.1038/s41598-026-59129-y. Online ahead of print.

ABSTRACT

The importance of data clustering is importance in assisting the decision-making process in any given complex system especially in an environment where data is large-scale and high-dimensional and heterogeneous. This paper introduces an evolutionary adaptation of the Chimp Optimization Algorithm (EVCHOA) to enhance the quality and strength of clustering when applied to the real world. The suggested solution implements an adaptive evolutionary update mechanism into the chimp-inspired search process to increase the exploration-exploitation ratio and avoid the early convergence. The effectiveness of EVCHOA in practice can be illustrated with references to intelligent learning systems, where the use of clustering allows analyzing the information about student performance and assisting in the making of individual decisions. Tests are done on real-life datasets implemented in a distributed computing framework and the outcomes are resolved against known methods of clustering, such as K-means, DBSCAN, hierarchical clustering, mean shift, and Gaussian mixture models. The evaluation of performance is based on conventional validity indices including DaviesBouldin Index, Silhouette Score, Adjusted Rand Index (ARI) and CalinskiHarabasz Index. The findings indicate that EVCHOA always achieves better clustering performance resulting in more coherent group structures and better interpretability by decision-makers. Within the framework of intelligent learning environments, the offered approach allows identifying student profiles more accurately, interfering with the target intervention, distributing resources in a more adaptive manner, and may support data-informed instructional planning. It is evident in these results that evolutionary metaheuristics is a useful tool in the operational research field and provides data-driven and scalable solutions to decision support in a variety of application fields.

PMID:42342790 | DOI:10.1038/s41598-026-59129-y

Sci Rep. 2026 Jun 24. doi: 10.1038/s41598-026-58601-z. Online ahead of print.

ABSTRACT

Cloud computing is a key enabler of modern computing services, offering scalability and flexibility. However, efficient management of cloud resources remains challenging due to limited capacity and the increasing number of tasks requiring timely execution. An effective task scheduling strategy is therefore essential to improve resource allocation and utilization, reduce operational costs and energy consumption, and support high availability-especially for long-term jobs. In this paper, we propose a new scheduling approach that combines a Social-Based Algorithm (SBA) with Deep Reinforcement Learning (DRL), referred to as SBA-DRL. This method allocates tasks to resources by learning from workload patterns and adapting to workload characteristics in a batch scheduling context. We evaluate SBA-DRL using both a synthetic dataset and the real-world Google Cloud Jobs (GoCJ) under workloads ranging from 200 to 1,000 tasks. On the synthetic dataset, our method reduces cost by 20.21% and energy consumption by 25.31%, while improving resource utilization by 9.36%. On the GoCJ dataset, it achieves up to 28.94% lower cost, 8.16% less energy use, and a 14.04% increase in resource utilization. In both cases, SBA-DRL also demonstrates better performance in resource allocation and high-availability management compared to existing heuristics, meta-heuristics, hybrid, and machine learning-based schedulers. These results indicate that the proposed SBA-DRL approach effectively addresses key challenges in cloud task scheduling, offering a practical solution to enhance the efficiency and sustainability of cloud systems.

PMID:42342783 | DOI:10.1038/s41598-026-58601-z

Sci Rep. 2026 Jun 24. doi: 10.1038/s41598-026-58701-w. Online ahead of print.

ABSTRACT

Type D personality, defined by the co-occurrence of negative affectivity and social inhibition, has been associated with poorer health outcomes in chronic diseases, including asthma. However, the psychological mechanisms underlying the relationship between Type D personality and asthma control remain unclear. This cross-sectional study included 150 adult patients with asthma recruited from a tertiary outpatient clinic. Participants completed the Asthma Control Test (ACT), Type D Personality Scale (DS-14), Beck Depression Inventory (BDI), Beck Anxiety Inventory (BAI), and Pittsburgh Sleep Quality Index (PSQI). Type D personality traits were identified in 39.3% of the sample. Compared with non-Type D individuals, patients with Type D personality had higher depressive and anxiety symptom scores and poorer sleep quality (all p < 0.05). Mediation analyses using bootstrapping (5,000 resamples) revealed significant indirect effects of Type D personality on asthma control through depression (β = -0.088, 95% CI [-0.132, -0.047]), anxiety (β = -0.115, 95% CI [-0.158, -0.078]), and sleep quality (β = -0.057, 95% CI [-0.091, -0.029]). These indirect effects remained statistically significant after adjustment for age, sex, smoking status, and FEV1 (% predicted). These findings suggest that Type D personality is indirectly associated with poorer asthma control through increased psychological symptom burden and impaired sleep quality.

PMID:42342775 | DOI:10.1038/s41598-026-58701-w

Sci Rep. 2026 Jun 24. doi: 10.1038/s41598-026-58020-0. Online ahead of print.

ABSTRACT

Lung disease classification using chest X-ray (CXR) images has become essential for early diagnosis and improved clinical decision-making. However, challenges such as low image quality, feature similarity among diseases, and classification instability reduce diagnostic reliability. To address these issues, this study proposes a novel ELSTM-AZOA framework for multiclass lung disease classification using the NIH CXR dataset. Initially, the collected CXR images are preprocessed using the balance contrast enhancement technique to improve image quality. U-Net + + is then employed for accurate lung region segmentation, followed by feature extraction using statistical and gray level co-occurrence matrix features. The extracted features are classified using an enhanced long short-term memory (ELSTM) network, while the American zebra optimization algorithm (AZOA) optimizes the model parameters to maximize classification accuracy. The proposed framework classifies six categories: healthy lung, tuberculosis, pneumonia, lung cancer, COPD, and COVID-19. Experimental results demonstrate that the proposed ELSTM-AZOA model achieves superior performance compared with existing methods, obtaining 6.36% higher accuracy and 6.43% higher precision. The findings confirm that the proposed framework provides robust, reliable, and promising computer-aided lung disease classification.

PMID:42342773 | DOI:10.1038/s41598-026-58020-0