Nevin Manimala

Front Allergy. 2026 May 11;7:1800197. doi: 10.3389/falgy.2026.1800197. eCollection 2026.

ABSTRACT

BACKGROUND: Pollen-induced allergic rhinitis (PIAR) is a major public health burden in high-pollen regions of northern China (e.g., Ordos, southern Inner Mongolia Plateau). However, regional variations in PIAR across ecological zones (urban, agropastoral, desert, and mining zones), dominant allergens, and key risk factors remain understudied due to prior small-sample or narrow-scope research.

OBJECTIVE: This study aimed to investigate the prevalence, major risk factors, and current treatment patterns for PIAR in Ordos.

METHODS: From March to July 2023, a multicenter, randomized, stratified cross-sectional survey was conducted across nine areas in Ordos. Participants were recruited to complete in-person questionnaires and undergo skin prick tests (SPTs) for 16 common allergens. Pollen was collected and counted to monitor exposure levels.

RESULTS: Among the 4,303 participants, the prevalence rates of self-reported allergic rhinitis (SRAR), physician-diagnosed allergic rhinitis (PDAR), and PIAR were 52.89% (2,276/4,303), 34.70% (1,493/4,303), and 31.51% (1,356/4,303), respectively. The prevalence rates of PIAR in urban, agropastoral, desert, and mining areas were 30.46%, 39.55%, 29.09%, and 19.72%, respectively. Among patients with PIAR, the incidence of symptom onset was highest among urban residents and lowest among mining area residents. Poplar pollen allergen dominated in spring, whereas in autumn, Artemisia pollen was predominant. Clinical symptoms were greatest in July, preceding the autumn pollen peak in September.

CONCLUSION: PIAR is highly prevalent in northern China’s grasslands, with marked zone-specific variations. Artemisia pollen exposure is the main sensitization driver, supporting targeted PIAR prevention/control.

PMID:42200171 | PMC:PMC13199316 | DOI:10.3389/falgy.2026.1800197

Patient Prefer Adherence. 2026 May 19;20:602433. doi: 10.2147/PPA.S602433. eCollection 2026.

ABSTRACT

INTRODUCTION: Existing instruments for assessing medication nonadherence lack sufficient insights into the specific barriers to Low-Middle-Income-Countries (LMICs), thus limiting their utility in developing tailored intervention approaches. This cross-sectional scale development and validation study therefore aimed to develop and validate a patient-centered, self-reported scale to identify patients’ medication adherence and barriers relevant to LMICs contexts among people with chronic diseases.

METHODS: This study had three-phases: (i) item development, (ii) content and face validity, and (iii) psychometric analysis. After item development, the items were refined through Item Content Validity Index (I-CVI) assessment and patient pre-testing. The third phase evaluated psychometric properties along with an assessment of agreement with objective clinical indicators (blood pressure and fasting blood glucose) and comparison with the validated scales Medication Adherence Report Scale-5 (MARS-5) and VAS Adherence using 419 patients. Statistical analyses included confirmatory factor analysis (CFA) to determine model fit. We also determined reliability and assessed test-retest reliability.

RESULTS: A final set of 15 items were selected, which were grouped into 4 dimensions: patient, medication, healthcare system, and socioeconomic related-barriers. The CFA showed that the data was fit for the model (χ²⁼179.477, P<0.001, RMSEA=0.052, CFI=0.961, GFI=0.947). The reliability test showed a good internal consistency (Cronbach’s-α=0.727 and McDonalds- Ω=0.812) and test-retest reliability (correlation coefficient 0.802). The new scale exhibited strong negative correlations with MARS-5 (r=-0.707) and VAS Adherence (r=-0.725), and a moderate negative correlation with clinical measurements (r=-0.632).

CONCLUSION: A psychometrically validated 15-item scale to assess medication adherence and its barriers in patients with chronic disease was developed, offering a reliable, contextually relevant tool that addresses information gaps and supports tailored interventions for improved patient outcomes in LMIC settings.

PMID:42200170 | PMC:PMC13199714 | DOI:10.2147/PPA.S602433

Patient Prefer Adherence. 2026 May 19;20:611334. doi: 10.2147/PPA.S611334. eCollection 2026.

ABSTRACT

BACKGROUND: Elderly chronic disease management is often complicated by multimorbidity and the need for lifelong polypharmacy, yet poor adherence severely impedes progress. Existing studies mainly focus on exploring influencing factors of medication adherence, while most predictive models lack model interpretability and rarely integrate psychological and social support factors for community elderly populations. Predictive models identifying risk of adherence could enable proactive intervention.

OBJECTIVE: To develop an interpretable machine learning prediction model that fills the above research gap to predict the medication adherence of elderly patients with chronic diseases in China.

METHODS: From January to December 2024, data were collected from chronic disease patients aged 60 years and older receiving home-based medication therapy through face-to-face interviews conducted by pharmacists. Variables included demographic information, comorbidities, chronic diseases and medications information, medication adherence, self-efficacy in rational drug use, medication beliefs, social support, and medication literacy. The dataset was randomly divided into a training set and a test set at a 7:3 ratio. Multivariate logistic regression analysis was performed on all data, and predictors were selected from the training set via the Least Absolute Shrinkage and Selection Operator (LASSO). Six machine learning algorithms were applied in R software to develop predictive models using the training set, and their performance was compared on the test set. The Shapley Additive Explanations (SHAP) approach was used to interpret the optimal model.

RESULTS: A total of 1722 patients were included in the statistical analysis. The gradient boosting machine (GBM) exhibited the best predictive performance among the six models (AUC = 0.811, 95% CI 0.774-0.840), with its core predictors being self-efficacy in rational drug use, medication practice, concern beliefs, and availability of social support. Through SHAP analysis, the interpretability of the model was significantly enhanced, providing a clear decision-making basis for clinicians.

CONCLUSION: We constructed a prediction model for home medication adherence in elderly patients with chronic diseases, which incorporates important social and psychological factors affecting patients’ adherence and provides robust evidence for developing targeted interventions.

PMID:42200167 | PMC:PMC13199717 | DOI:10.2147/PPA.S611334

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