Category: Nevin Manimala

Sci Rep. 2026 Jul 14. doi: 10.1038/s41598-026-62137-7. Online ahead of print.

ABSTRACT

Cardiometabolic multimorbidity (CMM), defined as the coexistence of two or more cardiometabolic diseases, poses a growing global health burden. However, few studies have jointly evaluated the risk of CMM using indicators of both insulin resistance and frailty. Using data from the China Health and Retirement Longitudinal Study (CHARLS), we constructed a composite index integrating the metabolic score for insulin resistance (METS-IR) and the frailty index (FI) and investigated its association with incident CMM among 2,968 middle-aged and older Chinese adults. During the follow-up period, 230 participants (7.75%) developed CMM. In fully adjusted Cox regression models, each 1-SD increase in baseline and cumulative METSIR-FI was associated with a 35% (HR = 1.35, 95%CI: 1.22-1.49) and 45% (HR = 1.45, 95%CI: 1.31-1.61) higher risk of CMM, respectively. Participants in the highest tertile of baseline METSIR-FI had a significantly higher risk of CMM (HR = 2.78, 95%CI: 1.79-4.30), with even stronger associations observed for cumulative METSIR-FI (HR = 4.29, 95%CI: 2.63-6.98) and the highest-risk cluster (HR = 3.69, 95%CI: 2.46-5.55). Restricted cubic spline analyses revealed significant nonlinear dose-response relationships with threshold effects. Incorporating METSIR-FI into conventional risk models improved the C-statistic from 71.32% to 77.47%, with significant net reclassification improvement and integrated discrimination improvement. These findings suggest that a joint metabolic-frailty burden indicator provides incremental prognostic value beyond conventional risk factors and may serve as a practical composite indicator for CMM risk stratification among middle-aged and older adults.

PMID:42449137 | DOI:10.1038/s41598-026-62137-7

Sci Rep. 2026 Jul 14. doi: 10.1038/s41598-026-61429-2. Online ahead of print.

ABSTRACT

Fluvial terraces preserve critical records of Quaternary tectonic uplift and river incision, yet their regional-scale identification remains hampered by the subjectivity and spatial discontinuity of conventional methods. This study presents a convolutional neural network (CNN) framework, built on a modified U-Net encoder-decoder architecture, for automated, multi-level terrace sequence recognition along the approximately 600-km Yichang-to-Wuhan corridor of the middle Yangtze River. The model ingests six-channel DEM-derived morphometric inputs-elevation, slope, profile curvature, planform curvature, topographic wetness index, and topographic position index-computed from the ALOS AW3D30 DEM, with training labels derived from 46 RTK-GNSS-surveyed terrace cross-sections. Spatial block cross-validation was employed to partition the dataset, ensuring geographic isolation between training and test tiles. Evaluated against independent test data, the CNN achieves an overall accuracy of 92.3%, a Kappa coefficient of 0.896, and a macro-averaged F1 score of 89.0%, outperforming slope-threshold, object-based image analysis, and Random Forest benchmarks-all of which underwent equivalent hyperparameter optimization-particularly for higher, more fragmented terrace levels. Four terrace levels (T1-T4) were mapped across the study corridor, revealing a systematic west-to-east decline in terrace completeness that mirrors the regional tectonic gradient from the Huangling anticline to the subsiding Jianghan Basin. Quantitative analysis of terrace deformation yields tectonic uplift rates ranging from 0.28 mm/year near Yichang to 0.06 mm/year in the basin interior, with localized elevation offsets of 8-15 m across major fault zones. A tectonic activity intensity index (TAI), supported by spatial buffer statistics, highlights the Yichang and Wuhan sub-reaches as the most actively deforming segments. We acknowledge that climatic oscillations modulate terrace formation and that the current framework cannot fully decouple tectonic from climatic signals; nonetheless, the spatially coherent deformation patterns localized along fault traces favor a predominantly tectonic interpretation. These findings demonstrate that CNN-assisted terrace mapping can provide spatially continuous neotectonic insights unattainable by field methods alone.

PMID:42449136 | DOI:10.1038/s41598-026-61429-2

Sci Rep. 2026 Jul 14. doi: 10.1038/s41598-026-61282-3. Online ahead of print.

ABSTRACT

Osteoarthritis is the most common degenerative joint condition, yet its pathogenesis remains incompletely understood. This study aims to identify key differentially expressed genes (DEGs) in osteoarthritis-affected synovial tissues and investigate potential underlying immune mechanisms using bioinformatics approaches. Gene expression datasets GSE12021, GSE55235, and GSE55457 from the Gene Expression Omnibus (GEO) were analyzed, comprising 30 osteoarthritis and 28 normal synovial tissue samples. Differentially expressed genes (DEGs) were identified using the limma R package. Functional enrichment analysis was performed using Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG). Hub genes were screened via protein-protein interaction (PPI) networks and multiple topological network algorithms. Diagnostic performance was evaluated using receiver operating characteristic (ROC) curves. Immune cell infiltration was assessed with CIBERSORT. In situ hybridization validated hub gene expression in an independent cohort of 9 osteoarthritis and 8 control synovial tissue samples. A total of 388 DEGs were identified, including 167 upregulated and 221 downregulated genes. KEGG pathway analysis associated these genes with immune-related pathways such as TNF, IL-17, and NF-κB signaling. Network-based topological analysis using ten cytoHubba algorithms identified CXCL2, JUNB, CX3CR1, and CCL19 as crucial diagnostic genes for osteoarthritis, which were further validated using an expanded testing set (GSE1919, GSE55457, GSE206848; 22 OA vs. 22 controls). The combined 4-gene signature demonstrated high diagnostic accuracy (AUC = 0.988) in the training set and an AUC of 0.938 in the independent testing set. In situ hybridization demonstrated elevated CX3CR1 mRNA levels and reduced CXCL2 and JUNB levels in osteoarthritis synovial tissues; the directional change of CCL19 mRNA was consistent with bioinformatic predictions, but did not reach statistical significance in the clinical validation cohort. Immune cell infiltration analysis revealed significant differences in eight immune cell types between patients and healthy individuals. An associative negative correlation was observed between JUNB and resting mast cells (R = – 0.54, p = 0.0021). Furthermore, computational analyses, presented as a hypothesis-generating resource, predicted potential regulatory networks involving small-molecule drugs, competing endogenous RNAs (ceRNAs), and transcription factors (TFs) targeting the hub genes. CXCL2, JUNB, and CX3CR1 represent potential diagnostic biomarkers for osteoarthritis, with CCL19 identified bioinformatically as a contributing component of the multi-gene signature, though its clinical validation requires further study. Memory B cells, resting memory CD4⁺ T cells, plasma cells, activated NK cells, and especially resting mast cells, may be associated with the disease’s onset and progression.

PMID:42449128 | DOI:10.1038/s41598-026-61282-3

Sci Rep. 2026 Jul 15. doi: 10.1038/s41598-026-52612-6. Online ahead of print.

ABSTRACT

Glioblastoma (GBM) is a highly aggressive brain tumor with limited prognostic markers to predict patient outcomes reliably. This study investigates the perioperative levels of tryptophan, kynurenine, and neopterin in patients with GBM to assess their potential as biomarkers for prognosis. Twenty-four patients with supratentorial GBM and 24 age- and gender-matched healthy controls were included. Blood and urine samples were collected at four time points: preoperatively, three days postoperatively, prior to adjuvant therapy, and post-adjuvant therapy. Analytical measurements of tryptophan, kynurenine, and neopterin levels were conducted using liquid chromatography-tandem mass spectrometry, and statistical analyses were performed to determine their correlation with survival outcomes. The analysis revealed significant perioperative fluctuations in neopterin, kynurenine and tryptophan levels in GBM patients. Moreover, a trend (p = 0.08) of an association between preoperatively obtained blood level of kynurenine and survival probability was observed supporting further validation of kynurenine-pathway biomarkers in larger, molecularly annotated cohorts.

PMID:42449127 | DOI:10.1038/s41598-026-52612-6

Nutr Diabetes. 2026 Jul 14. doi: 10.1038/s41387-026-00454-6. Online ahead of print.

ABSTRACT

BACKGROUND: The ketogenic diet (KD) is a widely used nutritional intervention for weight loss. The beneficial effects of the KD are intrinsically linked to the state of physiological ketosis, where ketone bodies (KBs) raise, even though minimal effective threshold of blood ketone concentration that correlates with significant weight loss remains unclear. Therefore, the main purpose of this study was to identify the optimal β-hydroxybutyrate (βHB) threshold associated with weight loss in individuals with overweight or obesity undergoing a KD.

METHODS: This secondary analysis included 217 participants (111 males and 106 females) with overweight or obesity, who followed a KD for 14 days. Time to Ketosis (TtK)-defined as the number of days needed to reach and maintain a given ketone concentration-was calculated for each threshold.

RESULTS: Regression analysis showed that a βHB concentration of ≥0.5 mmol/L was the most associated with significant weight loss. Moreover, body weight and gender significantly influenced TtK, suggesting interindividual variability in achieving effective ketosis.

CONCLUSIONS: Achieving and maintaining a ketonemia of at least 0.5 mmol/L may represent a clinically meaningful threshold to optimize weight loss in individuals undergoing a KD. Monitoring βHB levels and reducing TtK may improve individual responsiveness to KD-based interventions.

PMID:42449121 | DOI:10.1038/s41387-026-00454-6

Clin Drug Investig. 2026 Jul 14. doi: 10.1007/s40261-026-01583-7. Online ahead of print.

ABSTRACT

BACKGROUND: Janus kinase (JAK) inhibitors are a new class of medications that are changing how we treat inflammatory skin diseases. Even though these drugs are increasingly being approved for various skin conditions, there is still limited detailed comparison of their effectiveness, safety, and long-term effects across different diseases.

OBJECTIVE: In this study, we systematically evaluated the use of JAK inhibitors in atopic dermatitis, psoriasis, vitiligo, alopecia areata, and hidradenitis suppurativa across both trial and real-world settings.

METHODS: We conducted a systematic review following PRISMA 2020 guidelines and searched major databases for randomized controlled trials (RCTs), open-label extensions, and real-world cohort studies published through May 2025. The main effectiveness outcomes included disease-specific measures: Eczema Area and Severity Index (EASI) for atopic dermatitis, Psoriasis Area and Severity Index (PASI) for psoriasis, Severity of Alopecia Tool (SALT) for alopecia areata, Vitiligo Area Scoring Index (VASI) for vitiligo, and Hidradenitis Suppurativa Clinical Response (HiSCR) for hidradenitis suppurativa. Safety outcomes included rates of adverse events, infections, thromboembolism, and cancer. Patient-centered outcomes included quality of life (DLQI) and pruritus reduction. Risk of bias was assessed using the Cochrane Risk of Bias tool 2.0 (RoB 2).

RESULTS: We included 68 studies (42 RCTs, 14 open-label extensions, and 12 real-world cohorts) with 15,427 patients across five inflammatory skin diseases. Oral JAK inhibitors demonstrated efficacy across multiple conditions compared with placebo, with EASI-75 response rates ranging from 38 to 73% in atopic dermatitis, PASI-75 rates ranging from 33 to 67% in psoriasis, and SALT-50 rates from 30 to 62% in alopecia areata. Topical JAK inhibitors have proven effective for atopic dermatitis and vitiligo but have shown limited benefit in alopecia areata. Safety profiles were generally similar across drugs, with higher rates of upper respiratory infections (5-14%) and herpes zoster reactivation (1-3%) compared with placebo. Serious adverse events, such as major cardiovascular events, venous thromboembolism, and cancers, were rare but did occur.

CONCLUSIONS: JAK inhibitors demonstrate significant efficacy across several inflammatory skin diseases, with a safety profile that warrants ongoing monitoring, particularly in patients with cardiovascular risk factors. Comparative data suggest that selective JAK1 inhibitors appear to offer the most favorable balance between efficacy and safety for atopic dermatitis, whereas JAK1/2 inhibitors showed numerically higher efficacy for alopecia areata, though this difference did not reach statistical significance. However, the interpretation of long-term safety and comparative effectiveness is constrained by the limited duration of controlled trials, under-representation of diverse populations, and reliance on indirect comparisons. PROSPERO registration: CRD420251045477.

PMID:42449091 | DOI:10.1007/s40261-026-01583-7

Ther Innov Regul Sci. 2026 Jul 14. doi: 10.1007/s43441-026-01014-x. Online ahead of print.

ABSTRACT

Bayesian methods are increasingly used in clinical development and pharmacovigilance, especially when evidence is sparse, accrues sequentially, or must incorporate external information. Translation of high-level regulatory principles into an auditable, submission-ready Bayesian package remains inconsistent. Publicly available FDA, EMA, and ICH documents relevant to Bayesian implementation were reviewed, including Bayesian-specific guidance and recommendations that materially shape Bayesian analyses (for example, adaptive designs, externally controlled trials, estimands/sensitivity analyses, and pharmacovigilance signal management). Findings are synthesized into a practical framework organized around four deliverables that repeatedly emerge as regulatory decision drivers: (1) governed external information and prior specification, including quantification of prior influence and safeguards for prior-data conflict; (2) decision criteria paired with simulation-based characterization of operating characteristics under realistic scenarios; (3) structured sensitivity analyses that probe priors, borrowing, model forms, estimand-related assumptions, and key pharmacovigilance definitions; and (4) transparency and reproducibility, including pre-specification, traceability of data sources and transformations, and clear interpretation of Bayesian outputs for the intended decision (triage vs. confirmation). These deliverables are mapped to post-authorization pharmacovigilance workflows, where Bayesian shrinkage and hierarchical modeling are frequently used despite method-neutral guidance. The resulting framework provides a concise “regulatory-ready” evidence blueprint applicable across development programs and pharmacovigilance systems. This synthesis incorporates FDA’s January 2026 draft guidance on Bayesian methodology in clinical trials of drug and biological products [9], which increases cross-agency expectations for prior justification, prospective evaluation of operating characteristics, and computational transparency.

PMID:42449087 | DOI:10.1007/s43441-026-01014-x

J Thromb Thrombolysis. 2026 Jul 14. doi: 10.1007/s11239-026-03364-7. Online ahead of print.

ABSTRACT

Distal radial access (DRA) has been proposed as a physiologically superior alternative to conventional proximal transradial access (TRA) for coronary angiography and percutaneous coronary intervention, with the potential to preserve radial artery patency. This systematic review and meta-analysis evaluated the comparative safety and efficacy of DRA versus TRA. PubMed/MEDLINE, Embase, Cochrane CENTRAL, and Web of Science were searched from inception to January 2026. Randomized controlled trials comparing DRA (anatomical snuffbox or dorsal distal radial puncture) with proximal TRA in adults undergoing diagnostic or interventional cardiac catheterization were included. Two reviewers independently performed study selection, data extraction, and risk-of-bias assessment using the Cochrane RoB 2 tool. Random-effects meta-analysis using the restricted maximum likelihood (REML) estimator with Knapp-Hartung adjustment was performed to pool odds ratios (ORs) and mean differences (MDs) with 95% confidence intervals (CIs) and 95% prediction intervals. Pre-specified subgroup analyses, meta-regression, trial sequential analysis (TSA), and GRADE certainty assessment were conducted. Twenty randomized trials enrolling 8,718 patients (3,966 randomized to DRA and 4,752 to TRA) were included. DRA significantly reduced radial artery occlusion (RAO) compared with TRA (OR 0.26, 95% CI 0.18-0.36; prediction interval 0.12-0.56; I² = 21.3%; NNT = 27). DRA also significantly reduced bleeding (OR 0.53, 95% CI 0.35-0.80; prediction interval 0.12-2.26; I² = 67.9%) and hematoma formation (OR 0.42, 95% CI 0.30-0.58). Access-site crossover was significantly higher with DRA (OR 2.28, 95% CI 1.27-4.08), and procedural success was lower (OR 0.53). There were no statistically significant differences between DRA and TRA in time to hemostasis, access time, total procedural time, fluoroscopy time, radial artery spasm, or number of puncture attempts. Subgroup analyses demonstrated consistent benefit of DRA on RAO across clinical settings, imaging guidance, sheath sizes, trial sizes, and follow-up durations. Trial sequential analysis confirmed that the evidence for RAO reduction is conclusive, while the evidence for bleeding has approached the required information size. In this updated meta-analysis of 20 randomized trials, distal radial access significantly reduced radial artery occlusion and bleeding compared with conventional proximal transradial access. These benefits were achieved at the cost of greater technical demand, as evidenced by higher crossover rates and lower procedural success with DRA. Distal radial access should be preferred when preservation of the radial artery is a clinical priority.

PMID:42449086 | DOI:10.1007/s11239-026-03364-7

Adv Ther. 2026 Jul 14. doi: 10.1007/s12325-026-03713-1. Online ahead of print.

ABSTRACT

INTRODUCTION: Lutein is a dietary xanthophyll carotenoid with established roles in ocular health, yet its systemic bioavailability is highly variable and dependent on the nature of the formulation. This study was designed to compare the bioavailability and pharmacokinetic profile of a newly formulated oil suspension of lutein (LUT) with a commercially available reference lutein formulation (REF), following a single oral dose under fed conditions in healthy adults.

METHODS: This was a randomized, double-blind, single-dose, parallel-group comparative bioavailability study conducted in healthy adult male subjects (n = 80). Subjects received a single oral dose of 25 mg lutein as either LUT or REF under standardized high-fat fed conditions. Serial blood samples were collected – 2.00 and 0.00 h (pre-dose) and 2.00, 4.00, 6.00, 8.00, 10.00, 12.00, 24.00, 48.00 and 72.00 h (post-dose). Serum lutein concentrations were quantified using a validated HPLC method. Pharmacokinetic parameters, including Cmax, Tmax, AUC_0-24, AUC_0-48, AUC_0-72, AUC_0-t, and AUC_0-∞, were calculated using Phoenix WinNonlin 6.4 software by non-compartmental analysis. Statistical comparisons were performed on log-transformed parameters using ANOVA by SAS^® version 9.4.

RESULTS: Peak serum lutein concentrations were observed between 12 and 24 h post-dose for both formulations. LUT demonstrated significantly higher baseline-corrected serum lutein concentrations than REF from 8 h through 72 h (p < 0.05). Maximum serum concentration (C_max) for LUT was approximately 32% higher than REF (p < 0.05). Total systemic bioavailability was significantly greater for LUT, with AUC_0-48, AUC_0-72, and AUC_0-t increased by approximately 35%, 39%, and 38%, respectively, compared with REF (p < 0.05). AUC_0-∞ for LUT was 78% higher compared to REF. Further, LUT showed consistently > 2 folds higher serum lutein levels from baseline across all post-dose time points compared to REF. Both formulations were well tolerated, with no product-related adverse events reported.

CONCLUSION: The LUT formulation provides significantly greater systemic lutein bioavailability than the reference formulation following single-dose administration under fed conditions, highlighting the critical influence of formulation design on lutein bioavailability. Future clinical efficacy studies are warranted to evaluate the impact of this enhanced bioavailability.

TRIAL REGISTRATION: Clinical Trials Registry of India: CTRI/2018/01/011167.

PMID:42449082 | DOI:10.1007/s12325-026-03713-1

J Imaging Inform Med. 2026 Jul 14. doi: 10.1007/s10278-026-02113-9. Online ahead of print.

ABSTRACT

The use of artificial intelligence (AI) models to assist clinical decisions for diagnosis and prediction has been shown to improve patient outcomes and clinical decision-making. However, the performance of an AI model deployed in a clinical setting can vary over time or between initial evaluations and clinical use because of data drift. Monitoring the performance of a deployed AI model may help to (i) ensure the AI model performs as expected in the clinical environment and (ii) detect performance deviations and alert stakeholders to these deviations. In this study, we investigate how a change in the performance of an AI model caused by an abrupt data drift can be detected using a cumulative sum (CUSUM) control chart. We demonstrate the use of CUSUM for computer-aided breast cancer detection using data from the publicly available Emory Breast Imaging Dataset (EMBED). Our results indicate that, when the magnitude of the drift is 1.5 times the standard deviation of the performance metric, CUSUM is able to detect changes in the test negativity rate of an AI model within an average of 5 days following the onset of performance drift, with a long duration ( $\sim$ 293 days) between false alarms. Our results also indicate that when the average number of samples is 40/day, leading to a reasonable standard deviation for the performance metric, almost no false alarms are expected in a period of 60 days, with the choice of CUSUM parameters that lead to a small true alarm detection delay. We also demonstrate that CUSUM can be applied for monitoring the performance of AI models even when ground-truth labels are not available. We evaluate the robustness of the CUSUM chart to non-normal distributions and show that when tuned to detect relatively small parameter shifts, the CUSUM chart can be robust to non-normal distributions. We demonstrate that the sensitivity of CUSUM can be controlled to balance the mean time between false alarms and the delay in detecting a true change in performance. We conclude that CUSUM, which is a well-studied statistical process control method, can easily be adapted for monitoring the performance of AI models targeted for assisting in medical diagnosis.

PMID:42449079 | DOI:10.1007/s10278-026-02113-9