Nevin Manimala

Chin Med J (Engl). 2026 May 26. doi: 10.1097/CM9.0000000000004113. Online ahead of print.

ABSTRACT

BACKGROUND: Diabetic wounds (DBW) are characterized by high levels of reactive oxygen species (ROS) and pro-inflammatory factors; reducing inflammation is therefore a key strategy in the treatment of chronic diabetic wounds. X-box binding protein-1 (XBP1), a crucial transcription factor activated during endoplasmic reticulum stress, has been found to mediate mitochondrial damage, thereby activating the mitochondrial deoxyribonucleic acid/cyclic GMP-AMP synthase/stimulator of interferon genes (mtDNA/cGAS/STING) pathway and inducing Kupffer cell M1 polarization, which leads to excessive secretion of inflammatory cytokine. However, its role in regulating DBW healing remains unclear. Therefore, this study aims to explore the underlying mechanism of XBP1 in diabetic wound healing.

METHODS: Human skin samples were collected from diabetic and non-diabetic patients at the First Affiliated Hospital with Nanjing Medical University between January 2021 and December 2023 to evaluate XBP1 expression. A wound model was constructed using macrophage-specific knockout and wild-type diabetic mice. Wound healing rate, inflammatory cytokine levels, macrophage polarization, mitochondrial integrity, and activation of the mtDNA/cGAS/STING/NOD-, LRR- and pyrin domain-containing protein 3 (NLRP3) pathway were assessed. Statistical analyses were performed using GraphPad Prism 10.0 software (GraphPad, La Jolla, USA), and a P value <0.05 was considered statistically significant difference.

RESULTS: We found that XBP1 was highly expressed in macrophages within DBW(P <0.0001). Specific deletion of XBP1 in macrophages significantly reduced inflammatory cytokine secretion, increased M2 macrophage polarization, and accelerated wound healing. Further investigation revealed that knocking out Xbp1 in macrophages restored mitochondrial integrity, promoted ROS and mtDNA clearance, and inhibited the cGAS/STING/NLRP3 inflammatory pathway. Additionally, treatment with the XBP1 inhibitor toyocamycin markedly accelerated DBW healing.

CONCLUSIONS: In summary, under hyperglycemic stress, XBP1-induced mitochondrial damage and activation of the mtDNA/cGAS/STING/NLRP3 pathway are a key mechanism underlying DBW healing impairment. Thus, XBP1 may serve as a promising therapeutic target for DBW treatment.

PMID:42192237 | DOI:10.1097/CM9.0000000000004113

J Appl Clin Med Phys. 2026 May;27(5):e70639. doi: 10.1002/acm2.70639.

ABSTRACT

BACKGROUND: Parametric mapping is a pivotal technique in diagnostic and prognostic cardiovascular magnetic resonance (CMR). However, data pertinent to mapping parameters in healthy Asian populations, particularly with GE scanners, are limited.

PURPOSE: To establish normal reference values for T1, T2, and ECV using 1.5-T GE CMR in a healthy Thai population.

METHODS: Healthy volunteers undergoing orthopedic MRI with gadolinium injection were recruited. Health status was confirmed through medical history, ECG, laboratory investigations, and echocardiography. CMR scans included MOLLI for T1 mapping, MEFSE for T2 mapping, and ECV calculation. Inter- and intra-observer variation were assessed.

RESULTS: Fifty-one participants were included, 18 (35.29%) were males with a mean age of 41.49 ± 15.96 years. Mean global native T1, post-contrast T1, T2, and ECV were 1037.19 ± 51.84 ms, 456.76 ± 43.98 ms, 49.93 ± 4.67 ms, and 27.06 ± 4.50%, respectively. No significant differences were observed between sexes or age group (p > 0.05). Reproducibility was classed as good to excellent (ICC ≥ 0.75) CONCLUSIONS: Reference values for T1, T2, post-contrast T1, and ECV mapping in a healthy Thai population using a 1.5-T GE scanner, were established. These findings provide critical reference data for cardiac MR examinations.

PMID:42192234 | DOI:10.1002/acm2.70639

J Appl Clin Med Phys. 2026 May;27(5):e70633. doi: 10.1002/acm2.70633.

ABSTRACT

BACKGROUND: A super-resolution deep learning (DL) image reconstruction algorithm (Precise Image Quality Engine (PIQE)) was originally designed for cardiac CT, but is now available for abdominal CT.

PURPOSE: To examine objective and subjective image quality (IQ) improvements PIQE compared to Advanced Intelligent Clear-IQ Engine (AiCE) in abdominal CT.

METHODS: A retrospective analysis was conducted on 69 adult patient routine contrast enhanced abdominopelvic CT exams on a single Aquilion ONE/INSIGHT CT system (Canon Medical Systems, Otawara Japan). Images were reconstructed using PIQE (strength level L1 and L2) and AiCE (L1- the institutional standard). Four blinded radiologists assessed image noise, image contrast, small structure visibility, image sharpness, artifacts, and overall image preference with Likert scales. Reader agreement was assessed with Krippendorff’s alpha. Circular regions of interest were placed on five slices on the left and right liver, portal vein, aorta, subcutaneous fat, and bilateral psoas muscles. CT number, noise, signal-to-noise ratios (SNRs), and contrast-to-noise ratios (CNRs) were determined. All significant differences between reconstructions were assessed via the Friedman test with post-hoc Dunn-Sidak corrections.

RESULTS: Reader agreement was fair ( $\overline{\alpha }=0.20$ ). PIQE L2 was preferred for image contrast and image noise and PIQE L1 was preferred for image sharpness (p < 0.05). CT numbers were significantly different between AiCE L1 and PIQE (p < 0.05) and noise was statistically lowest in PIQE L2 compared to AiCE L1 (p < 0.05). SNR and CNR differences were statistically significant (p < 0.003), with PIQE L2 demonstrating the highest SNR and CNR.

CONCLUSION: The best subjective IQ metrics for image contrast, image noise, and image sharpness were obtained with PIQE. The best objective IQ metrics (SNR and CNR) were obtained with PIQE L2. This work supports improved image contrast and decreased noise when using PIQE as compared to AiCE.

PMID:42192227 | DOI:10.1002/acm2.70633

Cancer Med. 2026 Jun;15(6):e71949. doi: 10.1002/cam4.71949.

ABSTRACT

Cervical cancer remains an important cause of cancer morbidity and mortality among women in Nigeria despite the availability of preventive screening such as the Pap smear. This study assessed knowledge, perceptions and uptake of cervical cancer screening among women attending antenatal clinics in Ogun State, Nigeria. A descriptive cross-sectional study was conducted using a multistage sampling approach. Selected antenatal care facilities were identified using predefined eligibility criteria, the sample was allocated proportionately by clinic attendance, and eligible women were recruited by systematic random sampling. Data were collected using a semi-structured questionnaire and analysed with descriptive and regression methods. Awareness of cervical cancer was reported by 61.3% of respondents, and 74.7% had heard of the Pap smear, yet only 18.2% had previously undergone screening. Frequently reported barriers included cost of screening (70.3%), embarrassment (69.4%), anticipated pain (47.5%), misconceptions such as perceived loss of virginity (83.0%) and partner disapproval (51.9%). Reduced uptake of screening was associated with higher service costs, longer waiting times and greater distance to the health facility. Although awareness of cervical cancer and Pap smear testing was relatively high, screening utilisation remained low. Improving affordability, reducing service-related barriers and strengthening education within routine antenatal care may help increase uptake.

PMID:42192225 | DOI:10.1002/cam4.71949

Med Phys. 2026 May;53(5):e70497. doi: 10.1002/mp.70497.

ABSTRACT

BACKGROUND: In boron neutron capture therapy (BNCT), Monte Carlo (MC) dose calculations are commonly employed because of the complicated neutron reactions. However, MC dose calculations are generally time-consuming. Recently, deep learning (DL)-based dose prediction/calculation has attracted increasing attention; however, the applications of DL models in BNCT are limited and have not been investigated extensively. In addition, there are no practical DL models that can be employed in BNCT clinical practice.

PURPOSE: We propose a practical DL model for head and neck cancers using a commercial treatment planning system (TPS) for BNCT. To increase the speed of the MC dose calculations, the proposed DL model converts the BNCT dose components calculated by the coarse dose calculation grid size and low statistical uncertainty in the MC calculation into the dose components calculated under the fine setting.

METHODS: In this study, we considered 114 head and neck cancer patients who underwent accelerator-based BNCT at our center. Here, we randomly divided 102 patients for training/validation and 12 patients for testing. The BNCT dose components (i.e., boron, nitrogen, hydrogen, and gamma doses) were calculated for all patients using a commercial TPS for BNCT. We employed the hierarchically dense U-net and converted the BNCT dose components calculated by the coarse setting (grid size/uncertainty = 5 mm/10%) into doses calculated by the fine setting (2 mm/5%). In addition, a physical density map was added to the DL input to improve the conversion accuracy. Taking the fine dose as the ground truth, we evaluated the γ-passing rates with various criteria for each dose component of the coarse and DL doses. The calculation time was also measured in the fine, coarse, and DL doses.

RESULTS: In the boron dose, the DL dose exhibited significantly higher γ-passing rates of ≥ 95% with a criterion of 1%/2 mm (dose difference/distance to agreement) than the coarse dose. In the nitrogen and hydrogen doses, the DL dose also demonstrated high γ-passing rates of 95.3% and 94.7% with a criterion of 5%/2 mm. The density map was effective for the hydrogen and nitrogen doses. In addition, the average γ-passing rate with the criterion of 3%/2 mm in the gamma dose achieved 96.2% for the DL dose. The average calculation times for the fine and coarse settings were 984.2 ± 470.2 min and 11.0 ± 2.9 min, respectively, and the average conversion time in the DL model was 0.091 ± 0.020 min.

CONCLUSIONS: In this study, the proposed DL model was developed to convert each dose component calculated in the coarse setting to the fine dose to increase the speed of commercial MC dose calculations in BNCT for head and neck cancers. The conversion speed from the coarse dose to the fine dose was considerably rapid, and its performance was highly accurate. The proposed DL model can provide accurate BNCT dose distributions at high speed, thereby contributing to improving the quality of BNCT treatment planning.

PMID:42192222 | DOI:10.1002/mp.70497

Kaohsiung J Med Sci. 2026 May 26:e70240. doi: 10.1002/kjm2.70240. Online ahead of print.

ABSTRACT

Metabolic dysfunction-associated steatotic liver disease (MASLD) is increasingly prevalent among lean Asian populations, yet effective strategies for identifying high-risk individuals remain limited. We investigated the associations of body fat percentage (BF%) and the triglyceride-glucose (TyG) index with lean MASLD and evaluated their incremental diagnostic value in two independent studies (the NAGALA cohort and a Chinese health check-up study). Lean MASLD was defined as imaging-confirmed hepatic steatosis in individuals with BMI < 23 kg/m². In both studies, participants with MASLD were older, more often male, and exhibited less favorable metabolic profiles. Multivariable analyses showed that the TyG index was consistently associated with increased odds of lean MASLD (adjusted OR per unit increase: 3.41 in NAGALA and 6.37 in the Chinese study), whereas associations of BF% varied by cohort and sex, with significant associations observed in NAGALA men and Chinese women (adjusted OR per unit increase: 1.20 and 1.24, respectively). In ROC analyses, the TyG index showed good discrimination (C-statistics 0.778-0.875), and the addition of BF% further improved performance (0.805-0.901), corresponding to an absolute increase of approximately 0.02-0.05, with consistent improvements in net reclassification and discrimination (all p < 0.05). Mendelian randomization analyses supported a potential causal association between the TyG index and NAFLD, while no significant causal association was observed for BF%. Overall, BF% and the TyG index provide complementary information, and their combined use improves the identification of lean MASLD.

PMID:42192212 | DOI:10.1002/kjm2.70240

Sci Rep. 2026 May 26. doi: 10.1038/s41598-026-54492-2. Online ahead of print.

ABSTRACT

The timing of the initial spring water-level rise represents a key indicator of seasonal hydrological transition in snowmelt-dominated river systems of high-latitude regions. This study evaluates the capability of ensemble machine learning (ML) models to estimate the onset date of the spring water-level rise in Arctic-subarctic rivers of the Anadyr-Kolyma basin district in northeastern Russia using a station-year dataset for the period 2008-2022, combining hydrological observations with meteorological and basin-related predictors. Five regression algorithms were tested using grouped cross-validation by year. CatBoost achieved the highest predictive accuracy with an out-of-fold mean absolute error of 4.54 days, RMSE of 9.79 days, and [Formula: see text], slightly outperforming ExtraTrees (MAE 4.66 days) and RandomForest (MAE 4.70 days). Spatial analysis shows that most gauging stations exhibit prediction errors within 0.5-3 days, whereas errors exceeding 10 days occur mainly in small or topographically complex basins with limited observational coverage. Model interpretation using SHapley Additive exPlanations (SHAP) and partial dependence (PDP) analysis indicates that predictors describing thermal forcing during late winter and early spring dominate the model response, with positive degree days during March-April, the first thaw day, and indicators of rapid water-level rise providing the largest contributions. The onset of spring water-level rise in the studied Arctic-subarctic river systems is primarily associated with the interaction between temperature-driven snowmelt processes and the early hydrological response of the river network, whereas precipitation and spatial descriptors exhibit comparatively smaller contributions. These statistical relationships are conditioned on the 2008-2022 period and may vary under different climatic conditions or longer observational records, which should be considered when applying the model for prediction.

PMID:42192198 | DOI:10.1038/s41598-026-54492-2

Sci Rep. 2026 May 26. doi: 10.1038/s41598-026-50251-5. Online ahead of print.

ABSTRACT

The growing demand for sustainable construction materials has accelerated research into eco-friendly alternatives to traditional Portland cement. This research explores the potential of geopolymer concrete formulated from agricultural-waste ashes as a sustainable replacement for conventional Portland cement. Banana peel ash (BPA) and sugarcane bagasse ash (SCBA) were employed as aluminosilicate precursors, and their combined effects were systematically examined through controlled variations in blend proportion, alkaline activator molarity, sodium silicate-to-sodium hydroxide (SS/SH) ratio, and aggregate-to-binder ratio. The influence of these parameters on fresh and hardened properties-including workability, compressive strength, and flexural strength-was rigorously evaluated. Within the defined experimental domain, an optimal formulation comprising 52.5% SCBA and 47.5% BPA activated with 10 M NaOH achieved compressive and flexural strengths of 33.17 MPa and 9.95 MPa, respectively, demonstrating structural-grade performance suitable for practical applications. Detailed microstructural investigations employing SEM-EDS, XRD, FTIR and TGA techniques confirmed that both ashes exhibit high silica content, significant pozzolanic behaviour, and that increased activator concentration enhanced the dissolution of aluminosilicate phases leading to a denser geopolymeric matrix with improved durability. To further strengthen the analytical framework and enable predictive mix optimization, artificial intelligence-based models-Gene Expression Programming (GEP) and Artificial Neural Networks (ANN)-were developed. Both models achieved excellent predictive performance (R² > 0.98) with respect to slump Flexural and compressive strength; however, the GEP model consistently exhibited superior accuracy, lower error indices and better alignment with measured results than the ANN. Performance was validated through statistical metrics including Mean Square Error (MSE), Root Mean Square Error (RMSE), Mean Absolute Error (MAE) and coefficient of determination (R²), confirming the robustness of the machine-learning framework in capturing the complex, non-linear interactions among mix variables. The novelty of this study lies in demonstrating that low-value agricultural waste ashes can be engineered into reliable, structural-grade geopolymer binders, producing a high-performance BPA-SCBA concrete that repurposes agricultural residues and reduces the carbon footprint of cement production. Additionally, the integration of AI-based optimization provides a robust decision-support tool for mix design, enabling data-driven, sustainable construction practices.

PMID:42192170 | DOI:10.1038/s41598-026-50251-5

Sci Rep. 2026 May 26. doi: 10.1038/s41598-026-55540-7. Online ahead of print.

ABSTRACT

Single-shade composite restorations can adapt to the tooth structure, improve esthetics, and reduce reliance on shade selection. The objective of this study was to assess the effect of four different children’s beverages on the color changes, gloss, and surface microhardness of single-shade resin composites compared to universal composite. Forty-eight specimens were prepared from each Shade A2 universal (Filtek Z250) and single-shade resin composite material (Vittra APS Unique). Then, the specimens were divided into four subgroups: Distilled Water (control group), Pepsi Cola Drink, Orange Juice, and Chocolate Milk, all at 24 °C. These specimens were immersed in their respective beverages for 30 min daily. Color changes, gloss, and microhardness values were evaluated at baseline before immersion and at the 1st, 7th, and 30th days of immersion. Data were collected and statistically analyzed using two-way variance analysis (ANOVA) and Tukey’s post-hoc test (p < 0.05). The results showed color change was significantly greater in Vittra APS than in Filtek Z250 across all time intervals, especially in Pepsi Cola after 30th days. For gloss, Vittra APS and Filtek Z250 at all time periods for four storage media showed statistically significant differences. Microhardness of Vittra APS was significantly affected by all time periods and all media except water. Filtek Z250 showed significant differences across all storage media at baseline and after 1st day. Overall, the single-shade composite showed more color change, gloss loss, and hardness reduction-especially in storage media like Pepsi Cola and Orange Juice-compared to the universal composite.

PMID:42192168 | DOI:10.1038/s41598-026-55540-7

J Perinatol. 2026 May 26. doi: 10.1038/s41372-026-02731-9. Online ahead of print.

ABSTRACT

OBJECTIVE: Evaluate Neonatal Intensive Care Unit (NICU) interpreter access and utilization, unit-based interpreter policies and initiatives, staff awareness and confidence in understanding language-access laws, and perceptions of language-based inequities.

STUDY DESIGN: An exploratory national survey of NICU staff was distributed via the National Association of Neonatal Nurses and the American Academy of Pediatrics (AAP) (10/2024-4/2025). Descriptive statistics and qualitative analysis were used for survey results.

RESULT: The 189 respondents represented all ten AAP districts. Most were aware of NICU-based interpreter policies (76%). 81% were not aware of additional state laws/provisions and many lacked confidence understanding federal (43%) or state (63%) language-access laws. Many respondents disagreed that language-discordance resulted in worse quality of care (40%) and outcomes (59%) in their NICU.

CONCLUSION: Results highlight the need for additional education on federal and state laws and provisions as well as the broad and systemic nature of language-based healthcare inequities across institutions.

PMID:42192163 | DOI:10.1038/s41372-026-02731-9