Nevin Manimala

Womens Health (Lond). 2025 Jan-Dec;21:17455057251399893. doi: 10.1177/17455057251399893. Epub 2025 Dec 4.

ABSTRACT

BACKGROUND: Menstrual cycles are a natural part of many people’s health, yet are subject to stigma and misinformation, which can affect the experience of menstrual-related symptoms and overall well-being.

OBJECTIVES: The study explored how perceptions of the menstrual cycle are associated with symptom severity and their impact on daily life, including work, social activities, and intimate relationships.

DESIGN: A cross-sectional observational study.

METHODS: An anonymised online survey, distributed through social media, email, and newsletters. Participants aged 18 and older who menstruated in the previous 12 months were included. Demographic data, menstrual cycle characteristics, and self-reported symptom severity were collected. Perceptions of menstruation were measured using a 5-point Likert scale. Nonparametric statistical tests, including Kruskal-Wallis, chi-square, and Spearman’s rank-order correlation, were used for data analysis.

RESULTS: In total, 4735 responses were included in the analysis. Positive perceptions of menstruation were associated with lower reported pain levels and reduced disruption to daily activities, including work and academic performance. Notably, 90.71% reported that menstrual symptoms disrupted their work, with 31.8% taking time off in the past 12 months. Intimate relationships were affected for 84.31% of participants, with significant correlations between comfort in seeking support from partners and the disruption of intimacy (r_s(8) = -0.117; p < 0.001). Participants who viewed menstruation positively experienced fewer mental health symptoms, such as depression and anxiety, compared to those with negative perceptions. The study found that positive perceptions of the menstrual cycle were associated with less severe symptoms and a reduced impact on daily activities.

CONCLUSIONS: The findings suggest an association between menstrual perceptions in shaping the experience of symptoms and their broader psychosocial impacts. Enhancing menstrual health literacy and promoting positive perceptions could improve individual health outcomes and societal attitudes. Future public health policies should integrate menstrual health education and supportive workplace policies to enhance the quality of life for those who menstruate.

REGISTRATION: Not applicable.

PMID:41342221 | DOI:10.1177/17455057251399893

Pharm Stat. 2026 Jan-Feb;25(1):e70057. doi: 10.1002/pst.70057.

ABSTRACT

Quantitative decision-making frameworks provide objective criteria for advancing a drug development program. Within this process, two types of decision error can occur: proceeding to the next phase and failing (incorrect go) or not proceeding when success would have been achieved (incorrect no-go). This paper discusses strategies to reduce these errors in the context of a phase 2/3 development programme by selecting an optimal go threshold, which minimises the combined risk of an incorrect go or incorrect no-go decision. If the importance of these two errors differs, we propose the use of a weighted decision error rate (WDER) to select the optimal go threshold. We explore the influence of the choice of weighting and the prior beliefs about the drug efficacy on the optimal decision rule and demonstrate how the WDER can guide phase 2 sample size determination, often resulting in a larger sample size paired with a more stringent go threshold for efficiency. We advocate for a new definition of the probability of success in phase 2, emphasizing that it should reflect the probability of making the correct decision about proceeding, rather than just the probability of advancing to phase 3. This approach ensures that decision-making in drug development is both robust and tailored, enhancing the risk-benefit assessment for each phase transition.

PMID:41342216 | DOI:10.1002/pst.70057

Restor Dent Endod. 2025 Nov;50(4):e41. doi: 10.5395/rde.2025.50.e41. Epub 2025 Nov 28.

ABSTRACT

OBJECTIVES: The study aimed to investigate how environmental conditions impact the setting time and microhardness of premixed calcium silicate-based sealers.

METHODS: The setting time and microhardness of three sealers (Endoseal MTA [MARUCHI], One-Fil [MEDICLUS], and Well-Root ST [VERICOM]) were evaluated under four environmental conditions: unsoaked, distilled water-soaked, phosphate-buffered saline-soaked, and pH 5-soaked gypsum molds (n = 12/group/condition). The setting time was measured with Gilmore needles, and microhardness was assessed using a Vickers tester after 3 days. Welch’s analysis of variance and Games-Howell post hoc tests were used for statistical analysis.

RESULTS: The sealer type and environmental conditions significantly influenced setting time and microhardness (p < 0.001). The initial and final setting times were the shortest in the unsoaked samples. For Endoseal MTA and One-Fil, the unsoaked condition exhibited significantly shorter setting times than the soaked conditions. Well-Root ST exhibited significantly longer setting times in acidic conditions. Surface microhardness was highest in the unsoaked group (p < 0.001). Among the soaked groups, the phosphate-buffered saline-soaked group had the lowest hardness for Endoseal MTA, whereas the pH 5-soaked group exhibited the lowest hardness for One-Fil and Well-Root ST. Endoseal MTA consistently demonstrated a lower microhardness than the other sealers (p < 0.001).

CONCLUSIONS: Moisture, pH, and solution chemistry influenced the setting time and microhardness of premixed calcium silicate sealers. Although acidic conditions generally prolong the setting time and reduce hardness, the effects vary based on the sealers used and the setting environment.

PMID:41342211 | DOI:10.5395/rde.2025.50.e41

JMIR Bioinform Biotechnol. 2025 Oct 8;6:e80735. doi: 10.2196/80735.

ABSTRACT

BACKGROUND: Approximately 90% of the 65,000 human diseases are infrequent, collectively affecting ~400 million people, substantially limiting cohort accrual. This low prevalence constrains the development of robust transcriptome-based machine learning (ML) classifiers. Standard data-driven classifiers typically require cohorts of more than 100 participants per group to achieve clinical accuracy while managing high-dimensional input (~25,000 transcripts). These requirements are infeasible for microcohorts of ~20 individuals, where overfitting becomes pervasive.

OBJECTIVE: To overcome these constraints, we developed a classification method that integrates three enabling strategies: (i) paired-sample transcriptome dynamics, (ii) N-of-1 pathway-based analytics, and (iii) reproducible machine learning operations (MLOps) for continuous model refinement.

METHODS: Unlike ML approaches relying on a single transcriptome per subject, within-subject paired-sample designs-such as pre- versus post-treatment or diseased versus adjacent-normal tissue-effectively control intraindividual variability under isogenic conditions and within-subject environmental exposures (eg, smoking history, other medications, etc), improve signal-to-noise ratios, and, when pre-processed as single- studies (N-of-1), can achieve statistical power comparable with that obtained in animal models. Pathway-level N-of-1 analytics further reduces each sample’s high-dimensional profile into ~4000 biologically interpretable features, annotated with effect sizes, dispersion, and significance. Complementary MLOp practices-automated versioning, continuous monitoring, and adaptive hyperparameter tuning-improve model reproducibility and generalization.

RESULTS: In two case studies of distinct diseases, human rhinovirus infection (HRV) versus matched healthy controls (n=16 training; n=3 test) and breast cancer tissues harboring TP53 or PIK3CA mutations versus adjacent normal tissue (n=27 training; n=9 test)-this approach achieved 90% precision and recall on an unseen breast cancer test set and 92% precision with 90% recall in rhinovirus fivefold cross-validation. Incorporating paired-sample dynamics boosted precision by up to 12% and recall by 13% in breast cancer and by 5% each in HRV. MLOps workflows yielded an additional ~14.5% accuracy improvement compared to traditional pipelines. Moreover, our method identified 42 critical gene sets (pathways) for rhinovirus response and 21 for breast cancer mutation status, selected as the most important features (mean decrease impurity) of the best-performing model, with retroactive ablation of top 20 features reducing accuracy by ~25%.

CONCLUSIONS: These proof-of-concept results support the utility of integrating intrasubject dynamics, “biological knowledge”-based feature reduction (pathway-level feature reduction grounded in prior biological knowledge; eg, N-of-1-pathway analytics), and reproducible MLOp workflows can overcome cohort size limitations in infrequent disease, offering a scalable, interpretable solution for high-dimensional transcriptomic classification. Future work will extend these advances across various therapeutic and small cohort designs.

PMID:41342203 | DOI:10.2196/80735

J Sci Food Agric. 2025 Dec 4. doi: 10.1002/jsfa.70365. Online ahead of print.

ABSTRACT

BACKGROUND: Sideroxylon spinosum L., an endemic Moroccan species, holds significant ecological and socioeconomic importance. Drought stress severely affects plant survival by disrupting metabolic and physiological processes. This study aimed to investigate drought-induced metabolic changes and identify potential biomarkers in two S. spinosum L. ecotypes that contrast primarily in their climatic origins – Aoulouz (Alz, inland) and Lakhssas (Lks, coastal) – using metabolomic analysis.

RESULTS: Gas chromatography-mass spectrometry analysis detected 700 and 600 peaks in Lks and Alz leaves, respectively, with 120 and 100 corresponding to known metabolites. Under drought stress conditions, statistical analysis (t-test, P < 0.05) identified 44 significant metabolites in Lks and 56 in Alz. According to the volcano plot (log₁₀(P) versus log₂ fold change), 34 metabolites were upregulated and 10 downregulated in Lks, whereas 25 were upregulated and 31 downregulated in Alz. The criteria for significance included a fold change ≥ 2.0 and false discovery rate < 0.05. Multivariate analyses showed clear separation between control and drought-stressed samples. Based on variable importance in projection scores and receiver operating characteristic curve analyses, ten potential drought-tolerance biomarkers were identified. In Alz, two upregulated metabolites (M65 (lupeol) and M102 (octadecane)) and three downregulated metabolites (M108 (octacosane), M123 (5-octadecene, E) and M200 (4-nitrobenzylidenenemalonic acid, diethyl ester)) were key. In contrast, Lks exhibited five upregulated biomarkers: M6 (hexadecanoic acid, methyl ester), M54 (1,3,6,10-cyclotetradecatetraene, 3,7,11-trimethyl-14-(1-methylethyl)), M88, M91 and M142. Metabolites M65 and M102 in Alz likely enhance cuticular integrity and reactive oxygen species scavenging, while M6 and M54 in Lks suggest a reliance on lipid signaling and energy metabolism for transient drought adaptation. However, the Lks ecotype could be less resilient under prolonged drought due to a greater ability to reallocation metabolism. These biomarkers offer valuable targets for breeding or biotechnological interventions.

CONCLUSION: This study offers valuable insights into the metabolic mechanisms involved in drought defense in S. spinosum L. and highlights specific biomarkers linked to drought tolerance. The Alz ecotype demonstrates enhanced resilience through cuticular reinforcement and oxidative stress mitigation, whereas the Lks ecotype relies on transient metabolic adjustments. These findings offer valuable insights and potential targets for improving drought tolerance in plants through future research. © 2025 Society of Chemical Industry.

PMID:41342197 | DOI:10.1002/jsfa.70365

Urologia. 2025 Dec 4:3915603251398256. doi: 10.1177/03915603251398256. Online ahead of print.

ABSTRACT

PURPOSE: To evaluate the safety and efficacy of single-session supine bilateral percutaneous nephrolithotomy (BPCNL) in patients with bilateral renal stones.

METHODS: We retrospectively identified patients from February 2019 to July 2023 with bilateral renal stones measuring >2 cm and <5 cm in their maximum dimension for each side mainly located in the renal pelvis that had been treated with single-session supine BPCNL. The stone-free rate was accepted when remaining fragments of ⩽ 2 mm were discovered by a computed tomography scan.

RESULTS: Fifty-two patients with bilateral renal stones measuring 2:5 cm in their maximum dimension for each side who had been treated with single-session supine BPCNL were included in the study; a statistically significant difference in serum creatinine level was detected on day 1 postoperatively (p < 0.0001) compared with the baseline values that became insignificant at 1 week and 1 month postoperatively (p = 0.403 and 0.471 respectively). Also, statistically significant difference in glomerular filtration rate was detected at day 1 postoperatively (p < 0.0001) compared with the baseline values that became insignificant at 1 week and 1 month postoperatively (p = 0.95 and 0.07 respectively implicating early renal affection that shortly returned to normal values. The mean operative time for both sides was 126.5 ± 22.9 min, and the mean hemoglobin drop after the procedure was 1.9 ± 0.99 g/dl. The primary stone-free rate was 75%, with 11.6% of the remaining patients had a residual insignificant stones >2 mm but still less than 6 mm. Finally, 13.4% of the patients needed ancillary procedures.

CONCLUSION: Single-session supine bilateral PCNL is both safe and effective for patients with bilateral renal stones. However, this is a complex procedure that should only be performed by expert surgeons in a tertiary centers.

TRIAL REGISTRATION NUMBER: (167) SPS/URS_008 retrospectively registered.

PMID:41342193 | DOI:10.1177/03915603251398256

JMIR Bioinform Biotechnol. 2025 Nov 27;6:e76736. doi: 10.2196/76736.

ABSTRACT

BACKGROUND: Bladder cancer is a disease characterized by complex perturbations in gene networks and is heterogeneous in terms of histology, mutations, and prognosis. Advances in high-throughput sequencing technologies, genome-wide association studies, and bioinformatics methods have revealed greater insights into the pathogenesis of complex diseases. Network biology-based approaches have been used to identify complex protein-protein interactions (PPIs) that can lead to potential drug targets. There is a need to better understand PPIs specific to urothelial carcinoma.

OBJECTIVE: This study aimed to elucidate PPIs specific to papillary and nonpapillary urothelial carcinoma and identify the most connected or “hub” proteins, as these are potential drug targets.

METHODS: A novel PPI analysis tool, Proteinarium, was used to analyze RNA sequencing data from 132 patients with papillary and 270 patients with nonpapillary urothelial carcinoma from the TCGA Cell 2017 dataset and 39 patients with papillary and 88 patients with nonpapillary urothelial carcinoma from the TCGA Nature 2014 dataset. Hub proteins were identified in distinct PPI networks specific to papillary and nonpapillary urothelial carcinoma. Statistical significance of clusters was assessed using the Fisher exact test (P<.001), and network separation was quantified using the interactome-based separation score.

RESULTS: RPS27A, UBA52, and VAMP8 were the most connected or “hub” proteins identified in the network specific to the papillary urothelial carcinoma. In the network specific to the nonpapillary carcinoma, GNB1, RHOA, UBC, and FPR2 were found to be the hub proteins. Notably, GNB1 and FPR2 were among the proteins that have existing drugs targeting them.

CONCLUSIONS: We identified distinct PPI networks and the hub proteins specific to papillary and nonpapillary urothelial carcinomas. However, these findings are limited by the use of transcriptomic data and require experimental validation to confirm the functional relevance of the identified targets.

PMID:41342186 | DOI:10.2196/76736

Diabetes Obes Metab. 2025 Dec 4. doi: 10.1111/dom.70343. Online ahead of print.

ABSTRACT

AIMS: Ramadan fasting poses challenges for patients with type 2 diabetes mellitus (T2DM) due to increased risks of hypoglycemia and metabolic fluctuations. Tirzepatide, a dual GIP/GLP-1 receptor agonist, has shown marked efficacy in glycemic control and weight reduction. This study aimed to evaluate the safety and effectiveness of tirzepatide among Bangladeshi patients with T2DM during Ramadan fasting.

METHODS: This prospective, multicentre, real-world evidence study included 109 adult patients with T2DM who intended to fast during Ramadan and were prescribed tirzepatide 2.5 mg weekly, either as monotherapy or in combination with other anti-hyperglycemic agents. Data on glycemic parameters, anthropometrics, blood pressure, lipid profile, renal and liver function were collected at 2-6 weeks before Ramadan and at 2-6 weeks after the end of Ramadan, along with incidences of adverse events. Statistical analysis was performed using SPSS 25.0.

RESULTS: The mean age of the study participants was 40.7 ± 12.8 (SD) years with female predominance (69.7%). About 86.7% of the participants were obese. The mean HbA1c significantly decreased from 7.6% (before Ramadan) to 6.5% (after Ramadan) (mean change: -1.1%; p <0.001). Fasting plasma glucose and 2-h postprandial glucose also showed significant reductions by -2 mmol/L and – 3.8 mmol/L, respectively (both p <0.001). Mean body weight reduction was 5.3 ± 3.9 kg (6.3% of baseline; p <0.001). Mild gastrointestinal events occurred in ~12% of participants, with no hypoglycemia reported.

CONCLUSION: Tirzepatide demonstrated significant improvements in glycaemic control and body weight, with good tolerability, among patients with type 2 diabetes in Bangladesh who fasted during Ramadan.

PMID:41342185 | DOI:10.1111/dom.70343

JMIR Bioinform Biotechnol. 2025 Sep 3;6:e68848. doi: 10.2196/68848.

ABSTRACT

BACKGROUND: Deep learning (DL) shows promise for automated lung cancer diagnosis, but limited clinical data can restrict performance. While data augmentation (DA) helps, existing methods struggle with chest computed tomography (CT) scans across diverse DL architectures.

OBJECTIVE: This study proposes Random Pixel Swap (RPS), a novel DA technique, to enhance diagnostic performance in both convolutional neural networks and transformers for lung cancer diagnosis from CT scan images.

METHODS: RPS generates augmented data by randomly swapping pixels within patient CT scan images. We evaluated it on ResNet, MobileNet, Vision Transformer, and Swin Transformer models, using 2 public CT datasets (Iraq-Oncology Teaching Hospital/National Center for Cancer Diseases [IQ-OTH/NCCD] dataset and chest CT scan images dataset), and measured accuracy and area under the receiver operating characteristic curve (AUROC). Statistical significance was assessed via paired t tests.

RESULTS: The RPS outperformed state-of-the-art DA methods (Cutout, Random Erasing, MixUp, and CutMix), achieving 97.56% accuracy and 98.61% AUROC on the IQ-OTH/NCCD dataset and 97.78% accuracy and 99.46% AUROC on the chest CT scan images dataset. While traditional augmentation approaches (flipping and rotation) remained effective, RPS complemented them, surpassing the performance findings in prior studies and demonstrating the potential of artificial intelligence for early lung cancer detection.

CONCLUSIONS: The RPS technique enhances convolutional neural network and transformer models, enabling more accurate automated lung cancer detection from CT scan images.

PMID:41342173 | DOI:10.2196/68848

JMIR Bioinform Biotechnol. 2025 Oct 20;6:e68476. doi: 10.2196/68476.

ABSTRACT

BACKGROUND: Sensitivity-expressed as percent positive agreement (PPA) with a reference assay-is a primary metric for evaluating lateral-flow antigen tests (ATs), typically benchmarked against a quantitative reverse transcription polymerase chain reaction (qRT-PCR). In SARS-CoV-2 diagnostics, ATs detect nucleocapsid protein, whereas qRT-PCR detects viral RNA copy numbers. Since observed PPA depends on the underlying viral load distribution (proxied by the number of cycle thresholds [Cts], which is inversely related to load), study-specific sampling can bias sensitivity estimates. Cohort differences-such as enrichment for high- or low-Ct specimens-therefore complicate cross-test comparisons, and real-world datasets often deviate from regulatory guidance to sample across the full concentration range. Although logistic models relating test positivity to Ct are well described, they are seldom used to reweight results to a standardized reference viral load distribution. As a result, reported sensitivities remain difficult to compare across studies, limiting both accuracy and generalizability.

OBJECTIVE: The aim of this study was to develop and validate a statistical methodology that estimates the sensitivity of ATs by recalibrating clinical performance data-originally obtained from uncontrolled viral load distributions-against a standardized reference distribution of target concentrations, thereby enabling more accurate and comparable assessments of diagnostic test performance.

METHODS: AT sensitivity is estimated by modeling the PPA as a function of qRT-PCR Ct values (PPA function) using logistic regression on paired test results. Raw sensitivity is the proportion of AT positives among PCR-positive samples. Adjusted sensitivity is calculated by applying the PPA function to a reference Ct distribution, correcting for viral load variability. This enables standardized comparisons across tests. The method was validated using clinical data from a community study in Chelsea, Massachusetts, demonstrating its effectiveness in reducing sampling bias.

RESULTS: Over a 2-year period, paired ATs and qRT-PCR-positive samples were collected from 4 suppliers: A (n=211), B (n=156), C (n=85), and D (n=43). Ct value distributions varied substantially, with suppliers A and D showing lower Ct (high viral load) values in the samples, and supplier C skewed toward higher Ct values (low viral load). These differences led to inconsistent raw sensitivity estimates. To correct for this, we used logistic regression to model the PPA as a function of Cts and applied these models to a standardized reference Ct distribution. This adjustment reduced bias and enabled more accurate comparisons of test performance across suppliers.

CONCLUSIONS: We present a distribution-aware framework that models PPA as a logistic function of Ct and reweights results to a standardized reference Ct distribution to produce bias-corrected sensitivity estimates. This yields fairer, more consistent comparisons across AT suppliers and studies, strengthens quality control, and supports regulatory review. Collectively, our results provide a robust basis for recalibrating reported sensitivities and underscore the importance of distribution-aware evaluation in diagnostic test assessment.

PMID:41342172 | DOI:10.2196/68476