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The effect of comorbidity and frailty on dietary intake and protein losses in end-stage kidney disease patients undergoing peritoneal dialysis
Abstract End-stage kidney disease (ESKD) patients are advised to follow restricted diets, increasing their risk of malnutrition. We aimed to determine whether co-morbidity and frailty affected dietary intake or protein losses in ESKD patients treated with peritoneal dialysis (PD). We assessed 42 PD patients (mean age 62 ± 16 years) for malnutrition, using the Global Leadership Initiative in Malnutrition (GLIM) criteria, 24-hour dietary recall, bioimpedance, and hand grip strength (HGS), and measured effluent dialysate nitrogen losses. The malnutrition rate was 43%, significantly associated with heart failure (HF) odds ratio (OR) 22.6 (95% Confidence Interval (CI):2.0–259.5, p = 0.012) and low appendicular lean mass index (ALMI) OR 0.13 (95% CI:0.03–0.55, p = 0.006). Sarcopenia was associated with lower dietary protein intake (DPI) ( p < 0.05), and decreased protein losses ( p < 0.05), whereas Frailty was associated with older age ( p < 0.001), greater malnutrition risk ( p < 0.05) and reduced HGS ( p < 0.05). Diabetic patients also had higher malnutrition risk ( p < 0.01). HF was associated with male gender ( p < 0.05), and lower body fat ( p < 0.05). Increasing numbers of older more co-morbid ESKD patients are being treated with PD, and those with sarcopenia, frailty, diabetes, and HF are at an increased risk of malnutrition. These findings highlight the need for more individualized nutritional assessments.
A Korean native halophyte extract attenuates the virulence of methicillin-resistant Staphylococcus aureus by inhibiting biofilm formation
Compressive strength prediction of coconut fiber reinforced concrete using PSO optimized explainable machine learning
Abstract The use of natural fibers in concrete is a sustainable strategy to improve ductility and crack resistance; however, coconut fiber can introduce nonlinear interactions among mix constituents that complicate compressive strength prediction. This study proposes an optimized and explainable machine learning framework for compressive strength prediction of coconut fiber reinforced concrete (CFRC). Six regression algorithms, support vector machine (SVM), k-nearest neighbors (KNN), random forest (RF), light gradient boosting (LGB), extreme gradient boosting (XGB), and artificial neural network (ANN) were optimized using particle swarm optimization (PSO) on 586 experimental samples with eight inputs: cement, fine aggregate (FA), natural coarse aggregate (NCA), recycled coarse aggregate (RCA), water, fiber content, fiber length, and age. A nested grouped cross-validation scheme (outer 5-fold, inner 3-fold) was adopted for robust model selection, followed by evaluation on a locked 30% test set. The best model, XGB-PSO, achieved mean cross-validation performance of R 2 = 0.963 ± 0.013, RMSE = 3.018 ± 0.646 MPa, MAPE = 7.744 ± 1.008% and demonstrated strong generalization on the locked test set (R 2 = 0.953, RMSE = 3.71 MPa, MAPE = 8.875%). Model explainability using shapley additive explanations (SHAP), partial dependence plots (PDP), accumulated local effects (ALE), and individual conditional expectation (ICE) consistently identified age and RCA as dominant predictors, supporting data driven CFRC mix design and optimization.
Efficient multi-task CNN framework for joint identification of soybean leaf diseases and pesticide presence with explainable AI
Evaluation of the performance and gene expression of two strains of Japanese quail following supplementation with frankincense and Melissa officinalis
Abstract This study evaluated the effects of dietary supplementation with frankincense (FR) and Melissa officinalis (MO) on growth performance, meat quality, economic efficiency, and the expression of growth-, immunity-, and antioxidant-related genes in two strains of Japanese quail. A total of 300 fourteen-day-old female quails (150 brown, 150 white) were assigned to five treatments per strain: a control, two FR levels (10 and 12 g/L drinking water) and two MO levels (2.5 and 3 mL/L drinking water), in a completely randomized design with a 2 × 5 factorial arrangement for 28 days. Results revealed a significant treatment × strain interaction, indicating a strain-dependent response. In the brown strain, 10 and 12 g/L FR produced the most favorable responses, whereas in the white strain, 10 g/L FR and 3 mL/L MO were the most effective, resulting in improved growth performance and economic returns. At the treatment level, 10 g/L FR and 3 mL/L MO enhanced body weight, feed efficiency, dressing percentage, and economic indicators. These improvements were associated with upregulation of IGF-1 , GPX1 , and IL-6 , along with downregulation of MSTN . At the strain level, the white strain outperformed the brown strain in productive traits and showed higher IGF-1 expression with lower MSTN levels, whereas the brown strain exhibited relatively stronger GPX1 and IL-6 expressions. Therefore, these findings indicate that the effectiveness of phytogenic feed additives is strongly influenced by genetic background, highlighting the importance of optimizing both treatment level and strain selection to maximize productive performance, meat quality, and economic efficiency in Japanese quail. Specifically, FR at 10–12 g/L is recommended for brown quails, whereas 10 g/L FR or 3 mL/L MO is more suitable for white quails.
Meta-analysis of smartphone applications targeting eating behaviour for weight loss
Abstract Smartphone applications (“apps”) could be safe, cost-effective, and accessible tools for weight loss. Numerous weight-loss apps are available, but their longer-term efficacy is unclear. We conducted a meta-analysis of randomised controlled trials of weight loss apps that target eating behaviour in adults. We (a) compared the amount of weight lost in app-based interventions to other interventions and waitlists, and (b) estimated mean weight loss at six months and longer in app-based interventions. A database search identified 23 studies matching our criteria. We used three-level meta-analytic models and applied various bias-correction methods. More weight was lost in app-based interventions than on waitlists (mean difference 2.07 kg) and in non-app-based interventions (mean difference 1.64 kg) at six months. In app-based interventions, weight loss estimates ranged from 0.63 to 3.87 kg at six months; mean weight loss was 2.64 kg at longer follow-ups. We found no moderating effects of factors such as calorie counting, social support, involvement of medical workers, or automatic feedback reports. Smartphone apps could be cost-effective tools to facilitate weight loss and help maintain weight loss after surgery or drug treatments, but more studies on apps’ long-term effectiveness are needed.
Integrating transformer-based credibility signals into neural collaborative filtering for fake review-aware recommendation
Abstract Online recommender systems (RS) face growing trust challenges as deceptive reviews distort user feedback. Although RS optimisation and fake review detection have advanced separately, integrating credibility signals directly into recommendation training remains underexplored. This study proposes the Fake-Review-Aware Recommender System (FRARS), which embeds transformer-based deception probabilities into the training objective of a Neural Matrix Factorisation (NeuMF) model. Among several detectors, DeBERTa-v3-base performed best (ROC-AUC = 0.932 on YelpCHI, 0.921 on YelpNYC). FRARS applies these probabilities through two mechanisms: Hard Filtering removes interactions above a deception threshold, while Soft Weighting proportionally down-weights uncertain ones. We evaluate FRARS on two independent Yelp datasets—YelpCHI (67,395 reviews, ~ 49% deceptive) and YelpNYC (359,052 reviews, ~ 10% deceptive). FRARS-Soft improved NDCG@10 by 20.9% on YelpCHI and 19.5% on YelpNYC, with parallel gains in precision and recall; all transformer-based improvements were statistically significant ( p < 0.001). Detector quality and recommendation gains exhibited a significant monotonic relationship (Spearman ρ = 0.964 on YelpCHI, 0.929 on YelpNYC). These consistent results across different regions, scales, and deception levels indicate that FRARS offers a practical, modular pathway toward more trustworthy recommendation platforms.
Green credit risk assessment and prediction in China’s construction industry: based on the optuna-catboost model
Burden of incident cardiovascular events in giant cell arteritis using data from a nationwide French cohort
Briefing Chat: Spinosaurs with salt glands could have lived in marine environments
Body placement of inertial measurement units differentially affects physical activity assessment accuracy in drug-naïve Parkinson’s disease
Abstract Parkinson’s disease (PD) affects physical activity, and physical activity reduces the burden of PD. Although shown in studies using inertial measurement units (IMU), it remains unclear at which position physical activity change can best be detected in this population. Within the FAIRPARK-II trial, a subgroup of 25 newly diagnosed persons with PD (pwPD) not taking disease-specific medication yet documented their physical activity and, in parallel, wore IMUs on the most affected ankle, wrist and the lower back for two weeks. Participant-reported physical activity was transformed into Metabolic Equivalents of Tasks (METs) in 15-minute intervals using The Compendium of Physical Activities; Euclidean Norm Minus One (ENMO) values were calculated and averaged over the same intervals for the IMU data. Data of at least 3 days with at least four simultaneous 15-minute epochs of both valid IMU and diary data within the time window (9.00 to 18.00) per participant was included, resulting in a total of 8,494 15-minute epochs used for this analysis. Root mean square error (RMSE) values were calculated between scaled normalized IMU-derived ENMO and normalized MET values for each of the nine IMU-MET combinations (three IMU positions × three MET intensity levels). The wrist and lower back IMU showed comparable RMSE values across all MET intensity levels, with both IMU positions showing lower RMSE values than the ankle position. Tremor affected RMSE negatively, whereby the lower back position may be slightly favorable for the assessment of physical activity in those with tremor. This prospective longitudinal dataset from a very rare cohort provides novel insights into the assessment of physical activity during the earliest clinically evident phase of Parkinson’s disease without disease-specific medication, which may inform future clinical trials and observational studies.
Periodontal health in a longitudinal French cohort of people with Parkinson’s disease
Abstract Motor impairment associated with Parkinson’s disease (PD) can hinder daily oral hygiene. This study aimed to assess the microbiological and clinical evolution of periodontal health in people with PD. This prospective cohort study included participants with PD. Clinical oral health data and microbiological samples were collected at baseline and after a follow-up period (median 7 months [range: 3–21]). Forty people with PD were recruited (35% female; median [Q1–Q3] age: 65.3 [57.9–72.8] years). The participants had good hygiene habits; however, no significant changes were observed in clinical parameters, including an increase in the proportion with a ‘good’ plaque index (+ 21 points, p = 0.239) and the reduction in the frequency of periodontal pockets > 6 mm (from 20% to 3%, p = 0.131). In contrast, using mixed-effects models accounting for follow-up time and intra-individual variability, a significant reduction in Treponema denticola bacterial load was observed (93% reduction in CFU/mL; 95% CI: 69%–98%; adjusted p = 0.012). Clinical oral hygiene parameters remained stable, while a significant change was observed in one microbiological parameter. These findings require confirmation in larger studies to better define oral care support for PD patients. Trial registration : ClinicalTrials.gov Identifier: NCT03827551, registered on January 31, 2019, (https://clinicaltrials.gov/study/NCT03827551).
Europe is ditching US tech — what does this mean for researchers?
Contrast enhanced CT and MRI interchangeably reflect tumor characteristics in murine pancreatic cancer
Abstract Contrast-enhanced computed tomography (CE-CT) and dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) are commonly used for tissue characterization, with CE-CT typically preferred in human studies and DCE-MRI in murine models. This raises the question of comparability of intra-and cross-species study results, as techniques differ and signal intensities following contrast agent (CA) administration reflect a complex interplay of systemic physiology and local tissue characteristics. This pilot study compares in vivo mean values of regions of interest (ROIs) obtained from CE-µCT (NanoScan ® SPECT/CT) and 7 T DCE-MRI (Agilent Discovery MR901 magnet with Bruker AVANCE III HD electronics) and evaluates ex vivo CA distribution using laser ablation–inductively coupled plasma–mass spectrometry (LA-ICP-MSI). A genetically engineered mouse model of pancreatic ductal adenocarcinoma (PDAC) served as the experimental system. Image-derived regional mean Hounsfield unit (HU) and area under the curve at 60 s (AUC₆₀) values were correlated with mean local iodine (iomeprol) and gadolinium (gadopentetate dimeglumine) concentrations. Semi-quantitative analysis of HU and AUC₆₀ enabled excellent distinction of histologically defined tumor regions with low versus high tumor cellularity ( p < 0.0001 for both). A strong intermodal correlation was observed between regional HU and AUC₆₀ values ( r = 0.91, 95% CI = 0.78–0.97), as well as between iodine and gadolinium ion concentrations ( r = 0.86, 95% CI = 0.55–0.96). These findings demonstrate that CE‑µCT and DCE‑MRI show comparable trends as prognostic imaging biomarkers of tumor cellularity in murine PDAC, underscoring their complementary value for cross‑species translational imaging research.