Tag: nevin manimala

Brain Topogr. 2025 Dec 22;39(1):14. doi: 10.1007/s10548-025-01169-0.

ABSTRACT

Infrared neural stimulation (INS) represents an invasive technique for modulating brain activity in animals, particularly primates, which serve as effective models for human brain research. Noninvasive approaches, such as transcranial laser stimulation, are safer but have lower spatial and temporal resolution, primarily altering metabolic processes rather than directly stimulating specific neurons. Invasive techniques provide better resolution by targeting neurons with focused laser beams but require intricate surgeries that damage the meninges, limiting studies to short-term experiments conducted mostly on anesthetized animals. We present a minimally invasive approach for long-term, high-resolution laser INS that does not disrupt brain tissue integrity and minimizes the risk of inflammation. Laser radiation is delivered through contact between a flexible optical fiber and the outer surface of the dura mater, allowing for chronic experiments on non-anesthetized primates who maintain their cognitive functions and physical activities. This method has enabled us to conduct a multi-day INS experiment and collect statistically reliable data on neurophysiological responses in a cognitively intact primate subjected to targeted high-resolution INS. We analyzed electrocorticogram and evoked potentials in various cortical areas while applying infrared laser stimulation directed at a selected point on the primary visual cortex of a rhesus macaque. Results indicated that even low-intensity laser stimulation (below conscious perception thresholds) caused synchronous biopotential changes not only at the stimulation site but also in certain distant cortical regions, suggesting a more complex brain response mechanism to INS than merely the activation of stimulated neurons. We believe the presented method will significantly facilitate chronic INS studies, further contributing to fundamental and clinical outcomes.

PMID:41428267 | DOI:10.1007/s10548-025-01169-0

Eur J Pediatr. 2025 Dec 22;185(1):33. doi: 10.1007/s00431-025-06693-6.

ABSTRACT

PURPOSE: Differentiating among exanthematous diseases is frequently challenging due to their overlapping symptomatology. We, therefore, aimed to evaluate the diagnostic accuracy of a consultant physician, a resident physician, and various AI models (ChatGPT-5, Gemini, Copilot) in this context.

METHODS: We prospectively enrolled 291 patients treated for exanthematous diseases at our clinic between January 2024 and July 2025. The AI models were first tasked with making a diagnosis based solely on cutaneous images and subsequently with both images and accompanying clinical findings. The diagnoses rendered by the consultant, the resident, and the AI models were then compared against the definitive diagnosis.

RESULTS: When benchmarked against the definitive diagnosis, the consultant achieved the highest diagnostic accuracy (96.6%), followed by ChatGPT (with clinical data, 86.9%), Copilot (with clinical data, 81.4%), Gemini (with clinical data, 78.7%), and the resident physician (72.5%). In contrast, models without clinical data performed poorly, with the lowest accuracy recorded at 30.6% by Copilot. In ROC analysis against the consultant, the resident (AUC: .875) and AI models with clinical data-ChatGPT (AUC: .898), Gemini (AUC: .856), and Copilot (AUC: .818)-all demonstrated good diagnostic power (p < .001). The ChatGPT model without clinical data showed moderate diagnostic power, whereas the Copilot and Gemini models without data were not statistically significant. Performance metrics (sensitivity, specificity) were: ChatGPT (with data) (89.7%, 90.0%); Copilot (with data) (83.6%, 80.0%); Gemini (with data) (81.1%, 90.0%); the resident (75.1%, 100.0%); ChatGPT (no data) (51.6%, 90.0%); Gemini (no data) (33.5%, 100.0%); and Copilot (no data) (31.7%, 100.0%). The consultant’s diagnostic performance was significantly superior to all other interpreters and models (p < .001 for all comparisons).

CONCLUSION: This study establishes the diagnostic utility of AI models in pediatric exanthematous diseases, with ChatGPT-5 demonstrating the greatest accuracy when augmented with clinical data. The findings position these models as powerful assistive tools for clinicians but affirm that they do not yet supplant the indispensable expertise of a consultant physician, who remains the gold standard for diagnosis.

WHAT IS KNOWN: • Overlapping clinical features of exanthematous diseases often lead to diagnostic uncertainty. • Rash-focused artificial intelligence models frequently perform better when supplemented with clinical context rather than image data alone.

WHAT IS NEW: • This study provides the first large-scale, multimodal comparison of three next-generation artificial intelligence models (ChatGPT-5, Gemini, Copilot) specifically in pediatric exanthematous diseases. • The study uniquely demonstrates the diagnostic performance gap between image-only and image-plus-clinical-data modes across multiple artificial intelligence models, quantifying the exact improvement provided by clinical context. • By benchmarking artificial intelligence performance simultaneously against both a consultant and a resident physician, this work introduces a novel dual-reference standard, offering more nuanced insight into real-world clinical use cases.

PMID:41428260 | DOI:10.1007/s00431-025-06693-6

J Robot Surg. 2025 Dec 22;20(1):112. doi: 10.1007/s11701-025-03029-2.

ABSTRACT

To evaluate the effect of a multidisciplinary team (MDT) continuing care model led by diabetes specialist nurses on patients with benign adrenal tumors and type 2 diabetes mellitus undergoing robotic daytime surgery. This retrospective cohort study enrolled 60 type 2 diabetes patients undergoing robot-assisted adrenal tumor resection at the Day Surgery Department of Shanxi Bethune Hospital between October 2024 and May 2025. Patients were divided into two groups based on recorded nursing patterns: the observation group (n = 30) received structured multidisciplinary team (MDT) extended care in addition to standard nursing, while the control group (n = 30) received routine follow-up management. The study compared blood glucose control parameters (fasting glucose, 2-hour postprandial glucose, and glycated hemoglobin), 8-day postoperative wound healing outcomes, diabetes self-management behavior scores, patient satisfaction, and 48-hour delayed discharge rates under different surgical management models. Before the intervention, there was no statistically significant difference in various indicators between the two groups (P > 0.05). After the intervention, the fasting blood glucose, 2-hour postprandial blood glucose, and glycated hemoglobin indicators in the observation group were better than those in the control group. The postoperative wound healing and patient self-management behavior results were superior to the control group. Patient satisfaction was higher in the observation group, and the 48-hour delayed discharge rate under the daytime surgery model was lower than that in the control group. These differences were statistically significant (P < 0.05). This retrospective analysis indicates that, in clinical practice, implementing a diabetes-specialist-nurse-led MDT transitional care model for diabetic patients with adrenal tumors undergoing robotic ambulatory surgery is significantly associated with better glycemic control, enhanced postoperative wound healing, improved patient self-management ability and satisfaction, and reduced delayed discharge rates. This model can serve as a beneficial strategy to optimize the management of ambulatory surgical patients.

PMID:41428247 | DOI:10.1007/s11701-025-03029-2

Biometrics. 2025 Oct 8;81(4):ujaf165. doi: 10.1093/biomtc/ujaf165.

ABSTRACT

In many genomic studies, gene co-expression graphs are influenced by subject-level covariates like single nucleotide polymorphisms. Traditional Gaussian graphical models ignore these covariates and estimate only population-level networks, potentially masking important heterogeneity. Covariate-dependent Gaussian graphical regressions address this limitation by regressing the precision matrix on covariates, thereby modeling how graph structures vary with high-dimensional subject-specific covariates. To fit the model, we adopt a multi-task learning approach that achieves lower error rates than node-wise regressions. Yet, the important problem of statistical inference in this setting remains largely unexplored. We propose a class of debiased estimators based on multi-task learners, which can be computed quickly and separately. In a key step, we introduce a novel projection technique for estimating the inverse covariance matrix, reducing optimization costs to scale with the sample size n. Our debiased estimators achieve fast convergence and asymptotic normality, enabling valid inference. Simulations demonstrate the utility of the method, and an application to a brain cancer gene-expression dataset reveals meaningful biological relationships.

PMID:41428236 | DOI:10.1093/biomtc/ujaf165

Biometrics. 2025 Oct 8;81(4):ujaf164. doi: 10.1093/biomtc/ujaf164.

ABSTRACT

Multi-state models are widely used to study complex interrelated life events. In resource-limited settings, nested case-control (NCC) sampling may be employed to extract subsamples from a cohort for an event of interest, followed by a conditional likelihood analysis. However, conditioning restricts the reuse of NCC data for studying additional events. An alternative approach constructs pseudolikelihoods using inverse probability weighting (IPW) for inference with NCC data. Existing IPW-based pseudolikelihood methods focus primarily on estimating relative risks for multiple outcomes or secondary endpoints. In this work, we extend these methods to predict transition probabilities under general multi-state models and evaluate their efficiency. As the standard IPW methods for the prediction of transition probabilities may suffer from inefficiency, we propose two novel approaches for more efficient prediction and derive explicit variance estimates for these methods. The first approach calibrates the design weights using cohort-level information, while the second jointly models transitions originating from the same state. A simulation study demonstrates that either approach substantially improves efficiency and that their combined application yields further gains. We illustrate these methods with real data from the Prostate, Lung, Colorectal, and Ovarian Cancer Screening Trial.

PMID:41428235 | DOI:10.1093/biomtc/ujaf164

Planta. 2025 Dec 22;263(1):33. doi: 10.1007/s00425-025-04898-5.

ABSTRACT

To elucidate the phytobiochemical mechanisms underlying differentiation among wild populations of Tithonia diversifolia (Helms) A. Gray and establish how soil parameters regulate metabolic pathways.

METHODS: 90 individuals from three populations (Ixtaczoquitlán, Orizaba, Rafael Delgado; Veracruz, Mexico) were analyzed. A multi-analytical approach included lipid profiling by gas chromatography-mass spectrometry (GC-MS), identification of secondary metabolites via HPTLC, quantitative bromatological analyses, photosynthetic pigment quantification, and comprehensive edaphoclimatic characterization. Statistical modeling integrated soil chemistry, climatic dynamics, and phytobiochemical responses.

RESULTS: Populations exhibited distinct metabolic phenotypes shaped by edaphic stress. Plants from Rafael Delgado expressed a classical hormetic response under moderate stress (neutral pH, high EC and CEC, low organic matter, clayey soil), with upregulation of biosynthetic pathways resulting in higher protein content (27.25 ± 1.12% DW) and a diverse fatty acid profile (seven compounds). In contrast, Ixtaczoquitlán and Orizaba populations, under more favorable soils, maintained homeostatic regulation prioritizing primary metabolism, with higher chlorophyll accumulation (1.96 ± 0.10 mg g^-1) but reduced synthesis of defensive compounds. Foliar pH remained stable (6.7 ± 0.3) across sites, suggesting a robust self-regulation capacity despite edaphoclimatic variability.

CONCLUSIONS: Stress-induced metabolic switching emerges as a key adaptive mechanism in this non-model species, highlighting how environmental gradients reprogram biosynthetic pathways. Hormesis-driven enhancement of bioactive compounds positions T. diversifolia as a promising system for biotechnology aimed at stress-induced biocompound production. These findings advance the state of the art in plant metabolic plasticity and support the sustainable exploitation of renewable ethnobotanical resources.

PMID:41428232 | DOI:10.1007/s00425-025-04898-5

Langenbecks Arch Surg. 2025 Dec 22. doi: 10.1007/s00423-025-03950-y. Online ahead of print.

ABSTRACT

PURPOSE: The indication for surgical treatment of rectus diastasis (RD) without a coexisting hernia remains controversial. Although guidelines exist, the lack of robust data allows only weak recommendations. This study aimed to provide comprehensive nationwide data on the surgical management of RD without hernia.

METHODS: This retrospective observational multicenter study analyzed anonymous data from the German nationwide hospital discharge dataset (2010-2023). Patients with coexisting hernia or under 18 years were excluded. The primary endpoint was the annual number of RD surgeries without hernia. Secondary endpoints included trends over 13 years, patient demographics, mesh use, and early postoperative complications.

RESULTS: A total of 2,768 cases were identified (mean age 46.2 ± 13.2 years; 76.2% female). The annual case number ranged from 120 to 253, with no consistent trend. A mesh was used in 28.0% (n = 775), while 72.0% underwent reconstruction without documented mesh. Data on surgical approach (open vs. minimally invasive) were not available. The overall early complication rate was 6.9%, with bleeding and wound infections most common. Male patients had significantly higher complication rates. Major limitations include potential coding bias, underreporting, and missing data on surgical technique.

CONCLUSION: This is the first real-world big data analysis of RD repair without hernia in Germany. On average, 198 procedures are performed annually with a low complication rate. The findings support surgical treatment in selected symptomatic cases and emphasize the need for standardized coding and prospective registry data.

PMID:41428229 | DOI:10.1007/s00423-025-03950-y

Eur J Clin Pharmacol. 2025 Dec 22;82(1):14. doi: 10.1007/s00228-025-03959-8.

ABSTRACT

PURPOSE: This systematic review aimed at consolidating evidence regarding vancomycin dosing regimens, therapeutic drug monitoring (TDM) practices, clinical efficacy, and toxicity profiles in neonates admitted to neonatal intensive care units (NICUs). The primary research question focused on identifying the most effective and safe vancomycin protocols for this vulnerable population.

METHODS: Following PRISMA guidelines, a comprehensive search was conducted in six databases: PubMed, Scopus, Embase, Web of Science, Cochrane Library, and Lilacs. Eligible studies included observational cohorts and randomized clinical trials involving neonates receiving intravenous vancomycin with reported dosing and TDM data. Data extraction encompassed study design, dosing regimens, serum concentration targets, efficacy outcomes, and toxicity reports.

RESULTS: Thirty-two studies met inclusion criteria, revealing substantial heterogeneity in vancomycin dosing strategies, target trough concentrations, and evaluation methods for efficacy and toxicity. Target serum concentrations varied widely (5-30 mg/L), and dosing regimens ranged from 10 to 61 mg/kg/day. Only three studies assessed clinical efficacy, and eleven evaluated nephrotoxicity risk, with reported nephrotoxicity rates between 1.1% and 14%. Variability in methods limited the comparability across studies.

CONCLUSIONS: Vancomycin use in neonates lacks standardized dosing and monitoring practices, making it challenging to define an optimal therapeutic protocol. Greater consistency in clinical approaches and further high-quality studies are urgently needed to establish safe and effective vancomycin management strategies for neonatal patients.

PMID:41428193 | DOI:10.1007/s00228-025-03959-8

Adv Ther. 2025 Dec 22. doi: 10.1007/s12325-025-03441-y. Online ahead of print.

ABSTRACT

INTRODUCTION: PTEN hamartoma tumor syndrome (PHTS) is a rare genetic disorder caused by germline pathogenic variants in the PTEN tumor suppressor gene. Everolimus, an oral mTORC1 inhibitor, is approved for the treatment of tuberous sclerosis complex-related tumors; however, evidence for its efficacy in PHTS remains limited. A recent randomized controlled trial (RCT) reported safety and efficacy findings, but the composite primary efficacy endpoint did not reach statistical significance.

METHODS: We conducted a sensitivity analysis of this RCT to further evaluate the efficacy of everolimus in PHTS. Five statistical approaches were applied: analysis of covariance and four linear mixed-effects models. Outcomes included the composite neurocognitive score as a primary endpoint and multiple secondary neurocognitive and behavioral measures.

RESULTS: Across all analysis approaches, everolimus did not significantly improve the composite neurocognitive score compared with placebo. However, several secondary outcomes showed consistent benefits. Fine motor function assessed by the Purdue Pegboard Test (left hand) demonstrated sustained improvement over placebo across models. Social functioning, assessed by the total score (higher values indicating better functioning) of the reverse-coded Social Responsiveness Scale, second edition, improved over time, with significant differences observed at 6 months in the everolimus group. Several additional secondary endpoints showed consistent trends favoring everolimus.

CONCLUSION: Although the composite primary endpoint did not demonstrate significant improvement, sensitivity analyses identified potential benefits of everolimus in motor and social domains in individuals with PHTS. These results are consistent with the original trial findings and provide further support for investigating everolimus as a therapeutic option in this population.

PMID:41428178 | DOI:10.1007/s12325-025-03441-y

Curr Med Sci. 2025 Dec 22. doi: 10.1007/s11596-025-00146-9. Online ahead of print.

ABSTRACT

OBJECTIVE: This study aimed to investigate the associations of the triglyceride-glucose (TyG) index with kidney function decline, cardiovascular disease (CVD) events, and all-cause mortality across different glucose tolerance statuses.

METHODS: We analyzed 8,434 participants from the China Cardiometabolic Disease and Cancer Cohort (4C) Study. The primary outcomes were kidney function decline, CVD events, and all-cause mortality. Associations between the TyG index and outcomes were evaluated using binary logistic regression models.

RESULTS: During a 5-year follow-up, 150 participants (1.80%) developed kidney function decline, 357 (4.30%) experienced CVD events, and 335 (4.00%) died from all causes. An elevated TyG index was associated with increased risks of kidney function decline, nonfatal CVD events, and all-cause mortality in the overall population and among participants with diabetes (quartile 4 [Q4] vs. quartile 1 [Q1]: hazard ratio [HR] [95% confidence interval, P-value] = 4.97 [1.41-31.71, P = 0.034], 4.63 [1.25-30.19, P = 0.047], and 4.54 [1.70-15.88, P = 0.007], respectively). These associations were not statistically significant in participants with normal glucose tolerance or prediabetes. Notably, an elevated TyG index was significantly associated with increased risk of fatal CVD events in the overall population and across all glucose tolerance subgroups, with the strongest association observed in participants with prediabetes rather than diabetes.

CONCLUSIONS: The TyG index is significantly associated with the risks of kidney function decline, CVD events, and all-cause mortality, and these associations differ by glucose tolerance status.

PMID:41428156 | DOI:10.1007/s11596-025-00146-9