Assessment of quality and microbial safety of chilli pepper powder

Prostate MRI quality assessment using PI-QUAL version 2: Comparative analysis between 1.5T and 3T scanners and its association with T-staging agreement.

Hormonal fluctuations throughout the menstrual cycle are associated with a higher prevalence of musculoskeletal injuries in women; however, their impact at the foot and ankle level remains underexplored. The aim of this systematic review and meta-analysis was to examine the potential biomechanical effect of the ovulatory phase on foot and ankle structure compared to other phases of the menstrual cycle, given its possible association with injury occurrence. A systematic search was conducted in PubMed, Scopus, Web Of Science, and Embase (last 10 years), following PRISMA guidelines. Cohort studies, non-randomized trials, and case-control studies were included. Quality assessment was performed using the JBI critical appraisal tool. A quantitative synthesis (meta-analysis) was performed for homogeneous variables (muscle stiffness). Fifteen studies were selected. The systematic review demonstrated increase in foot length, reduction in fascial thickness, and greater longitudinal arch collapse during the ovulatory phase. Tone and stiffness of the tibialis anterior and peroneus longus muscles were greater during the menstrual phase. During ovulation, lower stiffness was observed during active contraction and increased tibialis anterior activation. Greater postural sway and oscillation were recorded during ovulation in complex static tasks, and, along with the menstrual phase, in dynamic balance tests. The meta-analysis indicated a tendency towards lower tibialis anterior stiffness during the follicular phase compared to the ovulatory phase. The ovulatory phase appears to be associated with an interaction of structural alterations (ligament laxity and arch collapse) and neuromuscular changes (reduced muscle stiffness and inefficient motor control) that, together, could constitute a risk factor for local pathologies such as plantar fasciitis and chronic ankle instability.

Violence and aggression in healthcare is a growing concern. Whilst the experiences of healthcare workers are well understood, less is known about the impact on medical students. Often among the most junior members of clinical teams, they may be particularly vulnerable facing unique academic and career-related consequences. We explored the prevalence, impact of and interventions for violence towards medical students. A comprehensive literature search was conducted. Studies were eligible if they involved medical students exposed to violence or aggression from patients or their families and reported on prevalence, impact or interventions. Screening, data extraction and quality assessments were completed independently by two reviewers in line with PRISMA guidance. A narrative synthesis was used to describe results. Fifteen studies were included. Published between 2005 and 2024, studies spanned 10 countries and included sample sizes from 24 to 4505 students. Reported prevalence ranged from 12% to 68%. Impacts were psychological including suicidal ideation, depressive and anxiety symptoms alongside other health-related issues, namely, sleep disturbance and alcohol misuse. Academic and career-related implications were identified. Studies reported very low rates of incident reporting, citing fear of consequences and limited awareness of reporting processes. Evaluated interventions were exclusively educational and findings relied heavily on self-reported data. Implications Medical students experience violence at a concerning rate, with wide-ranging consequences. Underreporting is significant and existing interventions are limited in scope. There is a clear need for institutional policy development and further research to better protect medical students and support their professional development.

The performance and stability of pharmaceutical hydrogels depend on microstructural features such as particle size and agglomeration. Characterization of these features is commonly performed through manual microscopic assessment, which can be subjective when particle boundaries are indistinct or agglomerates lack clearly defined interfaces. This study applies computer vision to automate the analysis of hydrogel micrographs, including particle segmentation, agglomerate identification, and the extraction of size-related morphological parameters. Two deep instance segmentation networks, the Mask R-CNN and the Mask2Former, were applied for this purpose. This study introduced a particle dilation and a graph-centric approach for agglomerate identification, differing from prior methods where agglomerates were annotated individually, thus mitigating some annotation challenges. The networks, trained on a hydrogel database, exhibited an Average Precision of 92.47% for Mask R-CNN and 91.86% for Mask2Former. The Mask2Former demonstrated superior Average Recall (AR) at 76.6%, compared to Mask R-CNN's 72.32%. This study pioneers the application of Mask2Former, a Vision Transformer-based network, for particle segmentation which had superior AR performance. Considering the subjective nature of annotation for hydrogel micrographs, where false positives can be considered as valid particles, this study recommends the inclusion of AR as a metric for model selection. Furthermore, the extracted morphological features from segmented images showed close agreement with manual measurements. This workflow has potential to support formulation development and quality assessment in pharmaceutical settings.

Transforming Growth Factor-Beta 1 as a Predictive Biomarker for Radiation-Induced Lung Toxicity and Treatment Response in Lung Cancer: A Systematic Review and Meta-analysis.

Complications and Emergency Department Presentations Associated with Multidisciplinary Team Management of Patients on Home Enteral Nutrition: A Systematic Literature Review.

Shift work affects 15-20% of the global workforce and significantly increases risk for obesity, type 2 diabetes, and cardiovascular disease. While dietary disruption is a recognised mechanism, whether research adequately captures meal timing as a critical metabolic determinant remains limited. This scoping review systematically mapped dietary and chrononutrition assessment in shift workers following PRISMA-ScR guidelines, searching five databases through July 2025. Quality assessment was conducted using the Joanna Briggs Institute checklists. Twenty-four studies representing 27,537 participants from 13 countries were included. Shift workers demonstrated consistent dietary disruption with heterogeneous energy patterns: 55% reported excess intake (103-303 kcal/day), 14% showed substantial deficits (1521-1698 kcal/day), and 14% found no differences despite temporal redistribution. Despite energy variability, macronutrient patterns were consistent: increased saturated fat and simple carbohydrates (63%), reduced fibre (24% decrease), and lower fruits, vegetables, and micronutrients (vitamin D, calcium, iodine, iron, carotene). Diet quality indices deteriorated in 88% of studies. Critically, 54% of studies examining shift worker diet failed to assess chrononutrition variables. Among the 46% measuring meal timing, results were remarkably consistent: eating windows extended 2.3-2.6 h, caloric midpoint delayed ~2 h, and last meals occurred 2-3 h later during night shifts, with later timing and longer eating windows consistently associated with greater daily energy intake (β = 0.32(N)-0.47(D); p < 0.001). This evidence mapping reveals that shift work is associated with disruption of both dietary composition and meal timing, yet chrononutrition remains critically neglected. Future research should prioritise validated, time-stamped dietary assessments and test timing-based interventions addressing this modifiable risk factor.

Association between different biomarkers and mortality in moderate or asymptomatic severe aortic stenosis: A systematic review.

This study aimed at developing an integrated metabolomics and machine learning (ML) framework to identify robust chemical markers of Tuscan VOO authenticity while minimizing the confounding influence of milling. A multi-platform analytical workflow, combining 1H NMR, HS-SPME GC-MS, HPLC-DAD-FLD and sensory evaluation, was applied to 40 VOOs from seven cultivars (Frantoio, Leccino, Leccio del Corno, Moraiolo, Seggianese, Morcone della Valtiberina, Canino) and blends. Quantitative variables generated by the three analytical platforms were integrated into a single matrix to train ML models, including Random Forest (RF), TreeNet gradient boosting and Multivariate Adaptive Regression Splines. A two-stage RF procedure was implemented, first removing variables dominated by mill-related effects (13% of total chemical variance), then re-modelling the filtered dataset to identify mill-independent geographical markers. In summary, this approach enabled: (i) accurate geographical classification (≈80% cross-validated accuracy), with the most informative markers comprising secoiridoids, sterols, triterpenoids, aldehydes, terpenoids, and lipid-derived metabolites; (ii) the predictionof sensory attributes by linoleic and linolenic acids and their lipoxygenase-derived C6 aldehydes and alcohols, through ML models with high predictiveperformance(R² = 0.95 artichoke, 0.92 fruity, 0.90 tomato-like, 0.83 resinous, 0.93 heated, 0.95 rancid); iii) identificationof specific molecules, notably margaric acid, strongly associated with the olive cultivars examined. These results highlight links between cultivar traits, lipid precursors and aroma expression. This study introduces the first multi-platform ML minimizing mill-related confounding, improving the reliability of VOO geographical authentication and sensory prediction, and laying the groundwork for future applications in quality assessment and regional characterization.

Role of immunotherapy in the management of oral squamous cell carcinoma - A systematic review.

This study evaluated the validity and reliability of large language model (LLM) responses on dietary supplements (DS), a domain marked by scientific controversy and misinformation. The goal was to support informed consumer decisions and guide improvements in LLM performance. We collected responses from GPT-4 and GPT-4o on the effects of 30 DS on six diseases. Two medical professionals categorized each response as "Effective," "Uncertain," or "Not Effective." They also created a guideline to assess evidence-based effectiveness and compared it with LLM-generated responses to determine accuracy. Additionally, we conducted qualitative content analysis to identify response patterns and misleading content. GPT-4 and GPT-4o affirmed DS effectiveness in only 10% of cases, with 40% rated as "Uncertain" and 50% as "Not Effective." Accuracy was about 57%, considerably lower than that observed in nutrition-related studies (57% in DS vs. 80%∼in structured nutrition tasks"). Content analysis showed templated responses, frequent ambiguity, and occasional inclusion of irrelevant or incorrect information. Our findings suggest that ChatGPT's responses on dietary supplements are generally cautious but often ambiguous, with a moderate risk of misinformation. As generative AI becomes a common source for health advice, these limitations could mislead users. Enhancing LLMs' evidence-based accuracy and ensuring consistent professional guidance are essential. This is the first study to assess the validity and reliability of LLM-generated responses on dietary supplements using both quantitative and qualitative methods. We also developed a novel evidence-based framework to evaluate supplement effectiveness, providing a new tool for future research and supporting safer AI-assisted health communication.

A bio-based intelligent konjac glucomannan/carboxymethyl chitosan film with ZIF-67@carmine-enhanced freshness monitoring for seafood preservation.

Assessing the effectiveness of smart retainers for orthodontic retention: A systematic review and meta-analysis.

The aim of this study was to update the association between periodontitis and hypertension. A comprehensive literature search was conducted in the MEDLINE/PubMed, Web of Science, Scopus and Google Scholar databases to evaluate the association between periodontitis and hypertension up until May 2025. All observational studies, including cross-sectional, case-control, and cohort studies, were included. Two researchers independently conducted the screening of studies, data extraction, and quality assessment. Heterogeneity between studies was assessed using the Q-test (chi-square) and the I-squared statistic. A random-effects model/ fixed-effect model was applied to pool the effect sizes. The subgroup meta-analysis between periodontitis and hypertension was performed based on periodontitis indices, including probing depth (PD), clinical attachment loss (CAL), and their combination. Out of 2,145 studies identified in the initial search, 72 were included in the systematic review, and 60 were included in the meta-analysis. In random-effect meta-analysis, periodontitis increased significantly the odds of hypertension according to the PD index (OR: 1.33, 95% CI: 1.22-1.45)), the CAL index (OR: 1.62 (1.12-2.36)), and their combination (OR: 1.62 (1.29-2.03)). Moreover, the mean diastolic blood pressure was significantly higher in individuals with periodontitis than in those without periodontitis based on the PD-CAL index (MD: 5.40 (5.38-5.41)). This study provided evidence of a significant association between periodontitis and hypertension. Incorporating regular periodontal evaluations into patient care may contribute to better hypertension management and potentially reduce the risk of developing hypertension. The online version contains supplementary material available at 10.1007/s40200-026-01936-1.

To improve the quality of a fast multi-contrast MR protocol acquisition using deep learning. 350 patients (age: 64±17 yrs; 155 male) underwent both a fast brain MR multi-contrast sequence (NeuroMix), capturing five contrasts in a single 2.5-minute sequence, and conventional high-resolution imaging. This retrospective study was approved by the Stanford IRB (eProtocol 26147, IRB registration 6208). The paired images were used to enhance resolution and image quality using a Swin U-Net Transformer (SwinUNETR) approach, focusing on T1-weighted (T1w), T2-weighted (T2w), and T2 FLAIR images (NeuroMix-DL). Evaluation included standard image quality metrics, such as the root mean squared error (RMSE) and a clinical quality assessment using a five-point image quality scale (1=poor, 5=excellent). A pairwise t-test was calculated to evaluate the values of the qualitative and quantitative metrics across the various image processing approaches. We found significant improvement in image quality after applying the trained SwinUNETR, with RMSE reductions of 42±3%, 33±2%, and 33±9% for T1w, T2w, and FLAIR images, respectively (p<0.001 for all) compared to original NeuroMix images, using conventional sequences as reference. The clinical readers found higher image quality scores for NeuroMix-DL images compared to the original NeuroMix images (12±8%, 17±11%, and 15±6% for T1w, T2w, and FLAIR images, respectively). Visual quality assessment demonstrated improvements in prevalent artifacts, including motion, herringbone artifact, inhomogeneity artifact, and RF overflow. The SwinUNETR model offers a viable approach for improving the quality of fast multi-contrast MR images while effectively mitigating artifacts, improving the cost-benefit ratio of MRI.

Unraveling the thermal release dynamics and pyrolysis signatures of agarwood by gas-liquid microextraction and gas chromatography-mass spectrometry.

Learning personalization in block-based programming languages using clustering and static code analysis

Multifaceted interventions to enhance patient's adherence to self-monitoring blood glucose: A systematic review.

Exploring traditional Chinese medicine tongue diagnosis in potential cancer identification: A scoping review.