Explore the vast range of titles available.

Introduction Type 2 diabetes mellitus (T2DM) poses a major public health challenge, particularly in regions with limited representation in predictive modeling studies. This research aimed to develop and interpret robust machine learning (ML) models for early T2DM risk prediction using data from the Dena Cohort in Iran. Methods Data from 3,203 adults aged 35–70 years were preprocessed through outlier removal, median/mode imputation, feature selection via LightGBM, and class balancing with the Synthetic Minority Over‑sampling Technique (SMOTE). Two gradient‑boosting algorithms, XGBoost and CatBoost, underwent hyperparameter tuning and 10‑fold cross‑validation. Model performance was assessed using accuracy, F1‑score, and the area under the receiver operating characteristic curve (AUC). Shapley Additive Explanations (SHAP) provided global and case‑specific interpretability of predictive features. Results XGBoost achieved the highest performance (accuracy = 96.07%, AUC = 99.29%), outperforming CatBoost and demonstrating substantial improvement with SMOTE balancing. Key predictors included fasting blood sugar, fatty liver, urolithiasis, red blood cell indices, and lifestyle factors such as energy drink consumption and prolonged television viewing. SHAP visualizations enhanced model transparency and facilitated individualized risk interpretation. Conclusion This study demonstrates that combining advanced gradient‑boosting models with SHAP explainability yields highly accurate, interpretable T2DM risk prediction in an underrepresented population. These findings support integrating interpretable ML into clinical workflows for personalized prevention and early intervention strategies. Graphical abstract

Over the past 30 years, no consistent survival benefits have been recorded for anticancer agents of advanced hepatocellular carcinoma (HCC), except for the multikinase inhibitor sorafenib (Nexavar ), which clinically achieves only ~3 months overall survival benefit. This modest benefit is attributed to limited aqueous solubility, slow dissolution rate and, consequently, limited absorption from the gastrointestinal tract. Thus, novel formulation modalities are in demand to improve the bioavailability of the drug to attack HCC in a more efficient manner. In the current study, we aimed to design a novel sorafenib-loaded carbon nanotubes (CNTs) formula that is able to improve the therapeutic efficacy of carried cargo against HCC and subsequently investigate the antitumour activity of this formula. Sorafenib was loaded on functionalized CNTs through physical adsorption, and an alginate-based method was subsequently applied to microcapsulate the drug-loaded CNTs (CNTs-SFN). The therapeutic efficacy of the new formula was estimated and compared to that of conventional sorafenib, both in vitro (against HepG2 cells) and in vivo (in a DENA-induced HCC rat model). The in vitro MTT anti-proliferative assay revealed that the drug-loaded CNTs formula was at least two-fold more cytotoxic towards HepG2 cells than was sorafenib itself. Moreover, the in vivo animal experiments proved that our innovative formula was superior to conventional sorafenib at all assessed end points. Circulating AFP-L3% was significantly decreased in the CNTs-SFN-MCs-treated group (14.0%) in comparison to that of the DENA (40.3%) and sorafenib (38.8%) groups. This superiority was further confirmed by Western blot analysis and immunofluorescence assessment of some HCC-relevant biomarkers. Our results firmly suggest the distinctive cancer-suppressive nature of CNTs-SFN-MCs, both against HepG2 cells in vitro and in a DENA-induced HCC rat model in vivo, with a preferential superiority over conventional sorafenib.

Nanoscale biosensors have gained attention in recent years due to their unique characteristics and size. Manufacturing steps, cost, and other shortcomings limit the widespread use and commercialization of nanoscale electrodes. In this work, a nano-size electrode fabricated by directed electrochemical nanowire assembly and parylene-C insulation is introduced. Results show that the diameter of the platinum nanowire and electrode tip length can be tuned down to 120 nm and 1.2 µm, respectively, where the exposed nanowires on the electrode tips are chemically active and their surfaces can be modified for specific biosensing applications. The biosensing testing with glucose and dopamine demonstrate limits of detection of 30 nM and 0.01 mM, respectively. The R -squared values for peak current versus concentration are 0.985 and 0.994, indicating strong linear correlations. These nanoscale electrodes hold great promise for single-cell biosensing applications due to their compact size, biocompatibility, and rapid fabrication.

Background Type 2 diabetes mellitus (T2DM) develops gradually and often remains undiagnosed until complications emerge. Early detection through transparent machine-learning models can improve prevention and targeted screening. This study developed and evaluated an interpretable Extra Trees Classifier (ETC) for early detection of T2DM within the PERSIAN Dena Cohort, emphasizing probability calibration, fairness, and clinical interpretability. Methods Data from 3,203 adults aged 35–70 years were analyzed. Seventy-nine demographic, lifestyle, anthropometric, comorbidity, and biochemical variables were considered; fifteen informative predictors were retained after preprocessing and feature elimination. The ETC was optimized by randomized hyperparameter search and evaluated through ten-fold cross-validation with an additional 80 / 20 internal–external split. Isotonic regression was used to calibrate probability estimates. Model transparency and feature influence were examined using SHapley Additive exPlanations (SHAP) and Morris sensitivity analysis. Results Cross-validated performance showed mean accuracy 0.69 ± 0.03 and AUC 0.69 ± 0.04, indicating moderate discrimination and stable internal consistency. On the 20% hold-out set, the uncalibrated model achieved AUC 0.67 and F1 0.66. After isotonic calibration, AUC declined to 0.64 and the Brier score increased to 0.48 (slope 0.09; intercept − 1.50), revealing under-confident probability estimates. Excluding fasting blood sugar (FBS) improved performance (AUC 0.77), whereas categorizing FBS into deciles reduced AUC to 0.57. Across sex and age subgroups, AUCs ranged 0.63–0.70 without systematic bias. SHAP and Morris analyses identified FBS, fatty-liver status, age, kidney-stone history, and triglycerides as dominant predictors, with lifestyle factors such as beverage and vegetable intake exerting secondary, modifiable influence. Conclusions Although overall predictive power was limited, the calibrated ETC provided transparent insight into feature interactions, calibration behavior, and data limitations. The framework highlights that interpretability and fairness are as essential as accuracy for trustworthy clinical AI. Future research should expand predictor diversity, address class imbalance, and validate across other PERSIAN cohorts to develop a more generalizable, interpretable model for early T2DM risk prediction. Graphical abstract

The article examines the contribution of contemporary devout Catholic fiction to postsecular conversations around the mediation of religious experience by secular history with specific attention to a novel not yet discussed within literary studies: Dena Hunt's Treason, a work of historical fiction about Catholic suppression in Elizabethan England. The article argues that (1) Treason analyzes early formations of secular institutions and narratives within Elizabethan England as co-opting Christian expressions of agapeic love, and (2) responds to that co-optation by engaging in a historical method of constructing narratives rooted in instances of self-denying affection and devotion found within religious communities.

Questions : The highest mountain peak of Zagros is located in the Dena mountain system (4409 m a.s.l.), which is identified as the second richest center of plant endemism of Zagros. In this study we (1) investigate floristic affinities of Dena Mts to adjacent mountain ranges based on the endemic species of the Iranian Plateau, (2) identify the species reaching the subnival zone, and (3) characterize the plant communities of the subnival zone of Dena Mts. Study area : Dena Mts is a calcareous mountain system in southern Zagros, Iran. Methods : The list of taxa endemic to the Iranian Plateau present also in Dena Mts was taken from our previously published data. Novel vegetation data were collected using the methodology of Braun-Blanquet. Classification was carried out in JUICE using the Modified TWINSPAN method. Results : Of the 242 taxa endemic to the Iranian Plateau present in Dena Mts, 22 taxa are endemic to the latter. Dena Mts have the strongest floristic affinity with the Yazd-Kerman massif, with which they share 84 taxa compared to 51 taxa shared with Alborz, 37 taxa shared with the Azerbaijan Plateau, and 15 taxa shared with Kopet Dagh-Khorassan. In Dena Mts, 38 taxa reach the subnival zone, most of them being endemic to the Iranian Plateau (68%). From scree habitats in the subnival zone, two new plant associations are described, Aethionemetum umbellati and Zerdanetum anchonioidei . These constitute a newly described alliance Galion pseudokurdici , classified within the class Didymophyso aucheri-Dracocephaletea aucheri . Conclusions : Although Dena Mts lie within a protected area, this will not prevent shrinking of alpine and subnival habitats due to global warming. Consequently, strong attention to the conservation of all range-restricted species of this mountain system, especially of alpine and subnival species, is highly recommended. Taxonomic reference : Flora of Iran (Assadi et al. 1989–2021) and, for families not yet covered in the previous source, Flora Iranica (Rechinger 1963–2015).

Dena B. Dubal is a physician–scientist and the endowed chair in aging and neurodegenerative disease in the neurology department at the University of California, San Francisco. She has received awards from the National Institutes of Health and American Federation for Aging Research.

Large animal models are still used in many studies because of their likeness to humans. It has not been documented that regular-sized conventional farm-breed pigs, generally bred for meat production, can be used to generate hepatocellular carcinoma (HCC) animal models. The goal of this study was to investigate how N-diethylnitrosamine (DENA) and phenobarbital (PB) together can generate HCC in ordinary farmed pigs. Conventional domestic swine ( ) were used. DENA 15 mg/kg was intraperitoneally injected weekly for 12 weeks, while PB tablets (4 mg/kg) were also administered through food for 16 weeks. Blood testing and ultrasonography evaluation were performed to monitor the progress. Subsequently, computed tomography was conducted in cases with suspected nodules, followed by histopathological examination to confirm the diagnosis. Ten swine (seven males, three females; age: 2 months; weight: 9-15 kg) were included in the study and followed up for 25 months; nine were experimental, and one was control for ethical considerations. The maximum weight of animals during this study reached 162-228 kg. The weight gain seen in the intervention swine was predominantly lower than that documented in the control. The laboratory analysis revealed no notable abnormalities in liver function markers but did demonstrate statistically significant changes in urea (p = 0.028) and creatinine (p = 0.003) levels. Ultrasonography and computed tomography showed multiple liver nodules with characteristics resembling HCC. Serial imaging screening and more extended observations revealed that all animals eventually developed tumors. Histopathological confirmation at 15-22 weeks post-induction revealed that all intervened swine developed multiple nodules of well-differentiated HCC and some with hepatic angiosarcoma. This study successfully generated HCC in conventional domestic swine with a DENA and PB combination. This investigation required at least 15 months to develop tumors. This model will be beneficial for future investigations of HCC in large animals.

The present paper examines the reasons that caused the fall of Dena Bank, which led to its merger with Bank of Baroda and Vijaya Bank (the merged bank is now known as Bank of Baroda). This paper measures the extent of deviations in financial performance and profitability of Dena Bank from 2007–08 to 2017–18. The findings of the study indicate that increasing non-performing assets and costs adversely impact the financial position of the fallen bank (Adhana, D., and Raghuvanshi, R. R. 2020. Big Bank Theory: A Study of Amalgamation Plan of 10 Public Sector Banks into 4 Entities. Available at SSRN 3559291 ). Further post-merger analysis from 2018–19 to 2021–22 shows that Bank of Baroda eventually incorporated the changes well. The finding would be helpful to investors, practitioners, academics, and policymakers to check the financial position of the bank to gain insights into the financial health of the bank and causes that led to its merger (Patel, R. 2018. Pre & post-merger financial performance: an Indian perspective. Journal of Central Banking Theory and Practice 7 (3): 181–200.).