Nevin Manimala

Med J Aust. 2026 May;224(5):e70206. doi: 10.5694/mja2.70206.

ABSTRACT

National linkage of the Australia and New Zealand Living Kidney Donor Registry and the Australia and New Zealand Dialysis and Transplant Registry provides the first Australian estimates of kidney failure treated with kidney replacement therapy (KRT) after living kidney donation (2004-2024). Out of 5291 donors (56,962 person-years; median follow-up, 10.96 years), three donors underwent KRT (0.53 per 10,000 person-years). No events occurred within 10 years of donation. Australian clinicians can now counsel and guide potential donors using local data: risk of kidney failure requiring KRT is very low, but late events warrant lifelong follow-up.

PMID:42177660 | DOI:10.5694/mja2.70206

Physiother Res Int. 2026 Jul;31(3):e70233. doi: 10.1002/pri.70233.

ABSTRACT

BACKGROUND: Diabetic polyneuropathy (DPN) contributes to impaired postural control and increases the risk of falls due to sensory and motor deficits. The Feldenkrais Method (FM), a sensorimotor approach that enhances body awareness and coordination, may offer therapeutic benefits in addressing these challenges.

OBJECTIVE: To evaluate the effectiveness of the Feldenkrais Method in improving dynamic balance, postural control, fear of falling, and quality of life in adults with DPN.

METHODS: Twenty-six participants aged 45-60 years with DPN were randomized into a study group receiving FM-based training or a control group receiving conventional balance training. Both interventions were administered twice weekly for 24 weeks. Primary outcomes included Timed Up and Go (TUG), limits of stability (LoS), Falls Efficacy Scale (FES), and Diabetes-39 Questionnaire for quality of life (QoL). Data were analyzed using repeated-measures ANOVA and post hoc Bonferroni tests.

RESULTS: Significant post-intervention improvements were observed in the FM group across all outcomes: TUG (p = 0.0001), LoS (p = 0.0001), FES (p = 0.0001), and QoL (p = 0.0001). Furthermore, the FM group demonstrated statistically significant superiority over the control group in all primary and secondary measures (p < 0.05). The control group showed no statistically significant changes.

CONCLUSION: The Feldenkrais Method appears effective in enhancing balance, postural control, and quality of life in individuals with DPN. These results support its integration into neurorehabilitation programs.

PMID:42177654 | DOI:10.1002/pri.70233

J Biopharm Stat. 2026 May 24:1-14. doi: 10.1080/10543406.2026.2670525. Online ahead of print.

ABSTRACT

Pharmaceutical polymorphism and crystallinity changes may have significant impact on drug’s quality, efficacy, and safety. The risk mitigation strategies include pharmaceutical development, monitoring and control strategy establishment during manufacturing, and stability monitoring program during storage. All of those are resources intensive. During data mining and analysis of risk mitigation strategies of new drug applications (NDAs) and abbreviated new drug applications (ANDAs) which may involve polymorphism and crystallinity change, one of the challenges is data heterogeneity encountered in submissions. This data heterogeneity may result in data not readily available for automated analysis which is called missing data. Modern statistical methodologies are available to handle this missing data and associated data analyses; however, very limited deployment of these methods to pharmaceutical chemistry, manufacturing, and controls (CMC) regulatory domain being reported. In big data era, consideration of statistical methodologies in this field will become continuously more important as the amount of available data in regulatory submissions increases. In this study, through data mining of approved NDAs and ANDAs by the FDA during the years 2017-2022 which had polymorphism and/or crystallinity keywords, we established a dataset which contained 148 approved NDAs and ANDAs and involved crystallinity monitoring and control strategy development of high-risk drug product manufacturing processes. Then, we applied several advanced machine learning techniques for exploratory pattern recognition and risk classification in the pharmaceutical manufacturing CMC domain. Furthermore, we conducted Monte Carlo simulations to demonstrate the feasibility of risk classification with generated synthetic outcomes using supervised machine learning techniques to the dataset established.

PMID:42177643 | DOI:10.1080/10543406.2026.2670525

J Biopharm Stat. 2026 May 24:1-7. doi: 10.1080/10543406.2026.2670518. Online ahead of print.

ABSTRACT

Parallelism is the prerequisite assumption that the test product behaves like a dilution or concentration of the reference product. It is the basis for defining the relative potency of a test product to the reference standard. Once parallelism between the test and reference curves is established, the relative potency will be constant at any effective response levels. This paper presents a review of most proposals for parallelism testing via significance, equivalence, and similarity testing approaches. The pros and cons of each proposal with equivalence margin determination are discussed. Recent approaches for testing parallelism are introduced in the paper.

PMID:42177641 | DOI:10.1080/10543406.2026.2670518

Cogn Emot. 2026 May 24:1-5. doi: 10.1080/02699931.2026.2678998. Online ahead of print.

ABSTRACT

Pek et al. (2026. What does a Z-curve analysis tell us? Cognition & Emotion, 1-16) comment on Soto and Schimmack (2025. Credibility of results in emotion science: A z-curve analysis of results in the journals Cognition & Emotion and Emotion. Cognition & Emotion) and raise concerns about the use of z-curve to evaluate the credibility of emotion research. Their central criticism is based on simulations showing that z-curve can overestimate the expected discovery rate when selection operates not only at the level of statistical significance but also within the set of significant results as a function of effect size. This point is correct: if researchers selectively publish larger significant effects while suppressing smaller significant ones, selection models that assume threshold-based filtering can be biased. However, this limitation is not unique to z-curve and applies equally to other selection models used in meta-analysis. More importantly, there is currently little empirical evidence for effect-size bias, while there is ample evidence of selection based on significance. Under these more realistic conditions, z-curve provides informative estimates of (a) selection bias, (b) the expected replication rate, and (c) the false positive risk. Our results also demonstrate substantial inflation of effect size estimates in traditional meta-analyses that ignore selection processes. For these reasons, we reject the recommendation to rely solely on standard meta-analytic approaches and advocate for the use of selection models to obtain more realistic estimates.

PMID:42177632 | DOI:10.1080/02699931.2026.2678998

Int J Rheum Dis. 2026 May;29(5):e70683. doi: 10.1111/1756-185x.70683.

ABSTRACT

OBJECTIVES: Tofacitinib, the first approved oral small-molecule Janus kinase inhibitor (JAKi), is widely used for treating rheumatoid arthritis (RA). This study aims to compare the drug retention rates of tofacitinib between elderly (≥ 65 years) and nonelderly (< 65 years) RA patients in a real-world clinical setting in Taiwan and to identify clinical factors associated with treatment discontinuation.

METHODS: We conducted a retrospective cohort study of RA patients receiving tofacitinib between 2015 and 2020, with follow-up until December 31, 2021. Patients were categorized into an elderly group (≥ 65 years, n = 82) and a nonelderly group (< 65 years, n = 224). Drug discontinuation was defined as the cessation of tofacitinib during follow-up. Primary outcomes included retention rates, reasons for discontinuation, and the incidence of adverse events. Kaplan-Meier analysis and multivariate Cox proportional hazards models were used to identify independent factors for discontinuation.

RESULTS: A total of 306 RA patients were included (mean age: 56.97 ± 12.44 years). The elderly group had a significantly higher prevalence of comorbidities, including hypertension, diabetes, hyperlipidemia, and osteoporosis. There was no significant difference in mean treatment duration (2.70 ± 1.73 vs. 2.61 ± 1.69 years, p = 0.7049). Kaplan-Meier analysis showed no statistically significant difference in retention rates (Log-rank test, p = 0.425). Multivariate analysis revealed that age was not an independent risk factor for discontinuation (adjusted HR = 1.086, 95% CI: 0.729-1.618, p = 0.685). Among 40 patients who discontinued due to adverse events, serious infection was the leading cause (35.0%), comprising 10 non-fatal serious infection events and 4 infection-related deaths. Major adverse cardiovascular events (MACE) occurred only in the elderly group (15.8%).

CONCLUSIONS: In real-world practice, the drug retention of tofacitinib in elderly RA patients is comparable to that in nonelderly patients. Despite a higher comorbidity burden, age itself is not an independent risk factor for treatment discontinuation. However, the risk of MACE is higher in elderly patients, necessitating careful cardiovascular assessment and monitoring before tofacitinib treatment. These findings suggest that tofacitinib is a viable option for elderly RA patients, provided individualized risk assessment is performed. Notably, discontinuation due to adverse events was substantially less frequent than discontinuation due to lack of efficacy, supporting the overall favorable tolerability of tofacitinib in this population.

PMID:42177625 | DOI:10.1111/1756-185x.70683

J Cardiothorac Surg. 2026 May 23. doi: 10.1186/s13019-026-04255-x. Online ahead of print.

ABSTRACT

BACKGROUND: Acute kidney injury (AKI) is a common complication following cardiac surgery. The RCR (ratio of RDW to total serum calcium (TSC)), which exhibits high predictive value for various perioperative diseases, may also be associated with kidney injury. Therefore, we tested the hypothesis that the postoperative RCR index is associated with kidney injury following major cardiac surgery.

METHODS: This was a retrospective observational cohort study. We included 7,872 patients who underwent cardiac surgery from the Medical Information Mart for Intensive Care IV (MIMIC-IV) database. The exposure was the first postoperative measurements of red blood cell distribution width (RDW) and Total serum calcium (TSC). The primary outcome was severe AKI, defined as stage 3 AKI or new-onset dialysis. Secondary outcomes included AKI of any stage, length of hospital stay, and length of intensive care unit (ICU) stay.

RESULTS: A total of 5,992 patients (76.1%) developed postoperative AKI, among whom 3,348 (42.5%) had stage 2 AKI, 654 (8.3%) had stage 3 AKI, and 160 (2.0%) required new-onset dialysis. In the multivariate regression model, the RCR index (categorical variable) was significantly associated with severe AKI (adjusted odds ratio [OR], 2.02 [95% CI, 1.54-2.68]) and AKI of any stage (OR, 1.26 [95% CI, 1.05-1.50]). When the RCR index was treated as a continuous variable, this association remained statistically significant.

CONCLUSIONS: Elevated postoperative RCR index is associated with postoperative AKI in patients who undergo cardiac surgery.

PMID:42177596 | DOI:10.1186/s13019-026-04255-x

J Transl Med. 2026 May 23. doi: 10.1186/s12967-026-08266-z. Online ahead of print.

ABSTRACT

BACKGROUND: Gliomas constitute 80% of malignant brain tumors, with glioblastoma (GBM) being the most aggressive subtype. The single-cell-level mechanisms underlying gliomagenesis are poorly understood, hindering therapeutic development. We combine genome-wide association studies (GWAS) with bulk tissue and single-cell multi-omics to prioritize genetically supported candidate genes and to explore potential cell-type-specific mechanisms relevant to gliomagenesis.

METHODS: We integrated the largest glioma GWAS with brain-specific multi-omics to prioritize genetically supported candidate genes using two broad categories of prioritized methods. Biological enrichment, differential gene expression, and CRISPR/miRNA were used to assess target enrichment and druggability. By integrating single-cell multi-omics data (genomics, transcriptomics, epigenomics), we investigated GBM-relevant cells, tumor microenvironment (TME) interactions, and cell-type-specific mechanisms in glioblastomagenesis. Additionally, phenome-wide association studies (PheWAS) and drug repurposing analyses were conducted to annotate genetic pleiotropy and enhance drug repositioning.

RESULTS: We prioritized 11 high-confidence and 47 putatively causal genes, most of which are druggable. Astrocytes and oligodendrocyte precursor cells (OPCs) were implicated as GBM-relevant cell populations, with significantly increased TME cell communication between these populations and neurons. We further identified 14 putative cell-type-specific effects related to glioblastomagenesis, including three high-confidence genes (EGFR in astrocytes, CDKN2A in OPCs, and JAK1 in excitatory neurons). Most effects (85.7%, 12/14) were associated with non-GBM-relevant cell cells, encompassing both glial and neural cells.

CONCLUSIONS: This study systematically identifies genetically supported candidate genes in gliomagenesis and their cell-type-specific effects. These findings provide a resource for future mechanistic investigation and may help inform the development of more precise therapeutic hypotheses.

PMID:42177594 | DOI:10.1186/s12967-026-08266-z

J Health Popul Nutr. 2026 May 23. doi: 10.1186/s41043-026-01347-1. Online ahead of print.

ABSTRACT

BACKGROUND: Child malnutrition remains a major global public health concern, with a disproportionately higher burden in developing countries such as Bangladesh. It is a leading contributor to childhood morbidity and mortality and has long-term consequences for physical growth, cognitive development, educational attainment, and adult productivity. Multiple socioeconomic and demographic factors are responsible for this condition. Therefore, this study aims to identify the factors associated with child malnutrition in Bangladesh using multivariate analytical approaches.

METHODS: In this study, malnutrition-related data from the Bangladesh Demographic and Health Survey (BDHS) 2022 were used. Multivariate analysis of variance (MANOVA) and multivariate linear regression models were performed to identify significant factors of child malnutrition in Bangladesh (p[Formula: see text]).

RESULTS: Multivariate linear regression analysis showed that the Z-scores for height-for-age (HAZ), weight-for-age (WAZ), and weight-for-height (WAH) among children aged 12-23 months were significantly lower compared to those aged < 12 months, with reductions of 32.63, 24.01, and 16.40 units, respectively (p < 0.001). Maternal underweight was negatively associated with all three anthropometric outcomes, whereas higher maternal education and better household wealth status were positively associated with improved nutritional outcomes.

CONCLUSION: The findings of this study suggest that both governmental and non-governmental organizations should prioritize improving maternal education and nutritional status, enhancing household income-generating activities among disadvantaged populations, and promoting adequate birth spacing.

PMID:42177590 | DOI:10.1186/s41043-026-01347-1

Arch Public Health. 2026 May 23. doi: 10.1186/s13690-026-01959-3. Online ahead of print.

ABSTRACT

BACKGROUND: The COVID-19 pandemic underscored the importance of integrating human behaviour in infectious disease modelling approaches, yet an in-depth assessment of how behavioural components are incorporated remains limited. We conducted a scoping review of COVID-19 models applied to Belgian data to examine how behavioural dynamics, both voluntary and policy-driven, were represented within model structures. Our aim was to identify current practices, highlight methodological gaps, and provide recommendations for the development of behaviourally integrated epidemiological models.

METHODS: Using Scopus and PubMed, we identified 98 studies published between March 2020 and October 2024, describing 105 models in total. Models were classified by model class (mathematical, statistical, or ensemble), objectives, approaches used to incorporate behavioural factors, and types of behaviour data employed.

RESULTS: Behavioural integration was confined to specific modelling contexts, with only half of the 105 models incorporating behavioural components. Mechanistic models, particularly compartmental models, were the most likely to include behavioural features, especially in studies assessing non-pharmaceutical interventions or conducting long-term forecasts and scenario analyses. Behavioural change was most commonly represented through modifications to transmission parameters or contact matrices. These adjustments were frequently informed by social contact surveys or mobility data derived from various sources.

CONCLUSIONS: In contrast to previous reviews that focused exclusively on behavioural models, this study evaluates the full landscape of Belgian COVID-19 models, offering a comprehensive perspective on how behavioural representation varies across modelling approaches. Our findings recommend that effective behavioural integration relies on timely, routine, and disaggregated surveillance and behaviour data, alongside the use of flexible mechanistic models.

PMID:42177562 | DOI:10.1186/s13690-026-01959-3