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Nevin Manimala Statistics

Rapid Assessment of Honey Physicochemical Properties by Mid-Infrared Spectroscopy and Chemometric Modeling

ACS Omega. 2026 Jul 22;11(30):44682-44691. doi: 10.1021/acsomega.5c13091. eCollection 2026 Aug 4.

ABSTRACT

This study aims to develop multivariate calibration models for the physicochemical parameters of Brazilian honey samples using mid-infrared (MIR) spectroscopy. A total of 97 samples of honey from Apis melliferabees from different botanical and geographical origins in Brazil were subjected to physicochemical analyses of pH, moisture, total soluble solids, free acidity, lactonic acidity, total acidity, proline, diastatic activity, hydroxymethylfurfural, and MIR. Principal component analysis (PCA) was performed as an exploratory technique to assess the dispersion of the samples across physicochemical parameters. To predict physicochemical parameters of honey from MIR spectral data, spectral interval selection via partial least-squares regression (iPLS) was used. The PCA was performed based on physicochemical parameters, with the samples dispersed according to the quality standards set by Brazilian legislation. MIR spectroscopy, combined with iPLS analysis, allowed the construction of models with high predictive capacity for all parameters evaluated in honey samples. The models presented statistical validation parameters, including correlation coefficients ranging from 0.81 to 0.97, performance ratios for deviation from 1.7 to 4.4, and interval error ratios between 5.7 and 16.3. The results demonstrate the potential of using MIR spectroscopy in conjunction with iPLS for the simultaneous, fast, and nondestructive quantification of physicochemical parameters of honey.

PMID:42569096 | PMC:PMC13449194 | DOI:10.1021/acsomega.5c13091

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Nevin Manimala Statistics

High-dose olanzapine versus standard-dose olanzapine: Effect on all-cause readmission rates in adult psychiatric patients

Ment Health Clin. 2026 Aug 3;16(4):196-201. doi: 10.9740/mhc.2026.08.196. eCollection 2026 Aug.

ABSTRACT

INTRODUCTION: Olanzapine doses in adults with treatment-resistant schizophrenia or treatment-resistant schizoaffective disorder have been compared in a few open-label trials, with higher doses associated with increased symptom improvement. The most significant adverse effects observed at off-label doses above 30 mg/day have been weight gain, sedation, or drowsiness.

METHODS: This single-center, retrospective cohort study aimed to evaluate patient factors and outcomes in adult patients admitted to a county psychiatric hospital that received high-dose olanzapine therapy (HD-olanzapine, ≥40 mg/day) compared with standard-dose olanzapine (SD-olanzapine, 5-30 mg/day). Patients 18 years or older were included if they received at least 5 days of scheduled olanzapine treatment for psychotic symptoms or psychosis while admitted.

RESULTS: Subjects on HD-olanzapine (n = 139) and SD-olanzapine regimens (n = 70) were on average 35 years of age, and most were White. There was no statistical difference regarding 30-day all-cause readmissions, 30-day all-cause emergency-treatment service visits, or average duration of olanzapine therapy. The average length of stay was longer in the HD-olanzapine group (31.3 days vs 14.5, P = 0.009). Constipation was more common in the HD-olanzapine group (8.6% vs 1.4%, P = 0.0418).

DISCUSSION: Although 30-day all-cause readmissions and emergency-treatment service visits were similar between groups, patients who received olanzapine doses of 40 mg/day or greater for at least 5 days of treatment experienced significantly longer average length of stay and more constipation during an admission.

PMID:42569083 | PMC:PMC13449231 | DOI:10.9740/mhc.2026.08.196

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Nevin Manimala Statistics

Mapping the applications and methodological characteristics of the TreeScan method in pharmacovigilance and beyond: a scoping review of 44 studies

Front Pharmacol. 2026 Jul 24;17:1905426. doi: 10.3389/fphar.2026.1905426. eCollection 2026.

ABSTRACT

BACKGROUND: The TreeScan method is an emerging tool for active safety signal surveillance and has been increasingly applied in post-marketing monitoring of pharmaceuticals and vaccines.

OBJECTIVE: To evaluate methodological developments and application patterns of the TreeScan method.

METHODS: A scoping review was conducted by searching Embase, Medline, Cochrane Library, China National Knowledge Infrastructure, Wanfang, VIP, and SinoMed from inception to 16 May 2025. Two researchers independently screened studies and extracted data. Descriptive analyses were performed on study characteristics, methodologies, and application domains. Included studies were categorized as methodological or applied research.

RESULTS: Forty-four articles were included, comprising 13 methodological studies (29.5%) and 31 applied studies (70.5%). Applied studies included drug safety surveillance (n = 10), vaccine safety surveillance (n = 16), and other areas such as drug repurposing and epidemiology (n = 5). Most studies originated from the United States (n = 28) and South Korea (n = 8). The Bernoulli model (43.2%), Poisson model (18.2%), and tree-temporal scan statistic (29.5%) were the most frequently used approaches. Positive controls we1re used in 63.6% of studies, while 36.4% employed within-group controls. Vaccine safety surveillance represented the most common application area, whereas methodological innovations focused on improving statistical performance, controlling confounding, and extending TreeScan to new data structures.

CONCLUSION: TreeScan research is increasingly application-oriented, particularly in vaccine safety surveillance Recent methodological advances have improved its performance in handling confounding, hierarchical outcomes, and complex data structures. Future research should should prioritize validating newer TreeScan variants across diverse real-world databases and expanding applications beyond safety surveillance.

PMID:42569077 | PMC:PMC13449071 | DOI:10.3389/fphar.2026.1905426

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Nevin Manimala Statistics

Assessment of the knowledge, attitudes and practices level on toxoplasmosis and its seroprevalence in selected high-risk groups in Cebu, Philippines

Front Vet Sci. 2026 Jul 24;13:1758610. doi: 10.3389/fvets.2026.1758610. eCollection 2026.

ABSTRACT

Toxoplasmosis, a zoonotic disease caused by Toxoplasma gondii, poses significant health risks, especially to high-risk individuals who are exposed to cats. This study aimed to assess the knowledge, attitudes, and practices (KAP) related to toxoplasmosis and determine its seroprevalence among selected high-risk groups in Cebu, Philippines-specifically veterinary students, veterinary personnel, and cat owners. A total of 416 participants completed a structured questionnaire evaluating their demographics, exposure history, and KAP levels, while 250 underwent further rapid serological testing for T. gondii. Results of the relative proportions revealed that there were more veterinary students who were having high level of knowledge (61%), compared to veterinary personnel (46%) and cat owners (24%). Relative proportions revealed that there were more veterinary students who were having high level of knowledge (61%), compared to veterinary personnel (46%) and cat owners (24%). Similar pattern was observed in the attitude level for veterinary students (54%), veterinary personnel (36%) and cat owners (32%). However, practice level showed that more veterinary personnel (54%) had high level than the veterinary students (35%) and cat owners (35%). The over-all seroprevalence of T. gondii was 29.2%, with the highest relative detection rates among veterinary personnel (43.8%) and cat owners (39%). While statistical analyses revealed no significant differences between the KAP levels of the three groups, significant associations between seropositivity and profile variables, including age, educational attainment, previous medical history, and frequency of cat contact (P ≤ 0.05) were found. These results highlight the need for sustained public education, improved veterinary and public health training, responsible pet ownership, and better access to diagnostic services.

PMID:42569037 | PMC:PMC13449146 | DOI:10.3389/fvets.2026.1758610

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Nevin Manimala Statistics

Sonographic Grading of Non-alcoholic Fatty Liver Disease and Its Association With Serum Biomarkers, Echocardiography, and Electrocardiogram

Cureus. 2026 Jul 8;18(7):e112256. doi: 10.7759/cureus.112256. eCollection 2026 Jul.

ABSTRACT

INTRODUCTION: Non-alcoholic fatty liver disease (NAFLD) is the hepatic manifestation of metabolic syndrome and is increasingly recognized as a systemic disorder associated with cardiovascular morbidity. Sonographic grading of NAFLD may reflect progressive metabolic and cardiovascular involvement; however, evidence evaluating the association between ultrasonographic severity and serum biomarkers, echocardiographic parameters, and electrocardiographic findings remains limited. This study aimed to evaluate the association between sonographic grading of NAFLD and serum biomarkers, echocardiographic parameters, and electrocardiographic findings.

MATERIALS AND METHODS: This prospective observational study included 96 adults with sonographically diagnosed NAFLD attending a tertiary care center between March 2024 and October 2025. Participants were categorized into Grade 1, Grade 2, and Grade 3 NAFLD based on ultrasonographic findings. Serum lipid profile and liver enzymes were evaluated, followed by transthoracic echocardiography and 12-lead electrocardiography. Continuous variables were compared using one-way analysis of variance with Tukey’s post-hoc test, while categorical variables were analyzed using the chi-squared test or Fisher’s exact test, as appropriate. A p-value of <0.05 was considered statistically significant.

RESULTS: Increasing sonographic grade of NAFLD was associated with progressive elevations in total cholesterol, triglyceride, low-density lipoprotein (LDL), very-low-density lipoprotein (VLDL), aspartate aminotransferase (AST), and alanine aminotransferase (ALT) levels, together with a significant reduction in high-density lipoprotein (HDL) levels (all p<0.001). Echocardiographic evaluation demonstrated significant increases in interventricular septal thickness, left ventricular mass, left ventricular mass index, left ventricular dimensions, and progressive reductions in ejection fraction and early-to-late ventricular filling velocity ratio (E/A ratio) (all p<0.001). The prevalence of left ventricular diastolic dysfunction increased from 10 (22.7%) in Grade 1 to 18 (52.9%) in Grade 2 and 14 (77.8%) in Grade 3 (p<0.001). QTc prolongation similarly increased from 6 (13.6%) to 11 (32.4%) and 12 (66.7%) across Grades 1-3 (p<0.001). Although P-wave and T-wave abnormalities were more frequent in advanced disease, these associations were not statistically significant.

CONCLUSIONS: Increasing sonographic severity of NAFLD is associated with worsening biochemical abnormalities, adverse cardiac remodelling, left ventricular diastolic dysfunction, and QTc prolongation. Ultrasonographic grading, combined with routine biochemical, echocardiographic, and electrocardiographic evaluation, may facilitate the early cardiovascular risk stratification and comprehensive management of patients with NAFLD.

PMID:42569034 | PMC:PMC13449055 | DOI:10.7759/cureus.112256

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Nevin Manimala Statistics

Perceived barriers and facilitators to the feasibility and acceptability of a peer-to-peer (P2P) HIV self-testing distribution model among adolescents and young people aged 15-24 years in Uganda: a qualitative study

BMJ Public Health. 2026 Aug 5;4(3):e005074. doi: 10.1136/bmjph-2026-005074. eCollection 2026.

ABSTRACT

INTRODUCTION: Peer-to-peer HIV self-testing (HIVST) has shown promise in addressing barriers to HIV testing challenges by leveraging social networks and peer influence to promote health-seeking behaviours.

OBJECTIVE: To explore the perceived feasibility and acceptability of the peer-to-peer HIVST distribution model among adolescents and young people aged 15-24 years in Uganda.

METHODS: We conducted a descriptive exploratory qualitative study in Wakiso District, Uganda. Data were collected through six focus group discussions (n=68) and 14 in-depth interviews, including interviews with peer leaders. Participants were purposively recruited through community meetings with support from Village Health Teams. Discussions explored perceptions of peer-to-peer HIVST distribution, including potential barriers and facilitators. Interviews were audio-recorded, transcribed verbatim, translated into English and analysed using thematic analysis.

RESULTS: Participants perceived the peer-to-peer HIVST distribution model as both feasible and acceptable. Key facilitators of feasibility included peer trust, structured training, community-based delivery and integration with existing health structures. Perceived barriers included logistical constraints, inadequate supervision, kit storage challenges and concerns about equitable reach. Acceptability was driven by trust, confidentiality, emotional support, autonomy and the relatability of peer distributors. However, concerns about gossip, stigma, confidentiality breaches and the emotional burden placed on peer distributors were identified as potential barriers to sustained acceptability.

CONCLUSION: Peer-to-peer HIVST distribution was perceived as a feasible and acceptable approach for increasing HIV testing among adolescents and young people. Future implementation efforts should strengthen peer training and supervision, address confidentiality and stigma concerns, provide psychosocial support for peer distributors, and ensure equitable access for socially marginalised adolescents.

PMID:42569013 | PMC:PMC13448566 | DOI:10.1136/bmjph-2026-005074

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Nevin Manimala Statistics

Comparative Evaluation of Machine Learning-Assisted Statistical Modeling of Biopolymer Hydrogel Swelling for Material Optimization

ACS Omega. 2026 Jul 22;11(30):44614-44633. doi: 10.1021/acsomega.5c10279. eCollection 2026 Aug 4.

ABSTRACT

This study presents an in-depth examination of the swelling characteristics of biopolymeric hydrogels developed with sodium alginate, employing both conventional and modern modeling techniques to predict and optimize the process. Hydrogel swelling properties have found critical applications in controlled drug delivery, wound dressing, and food technology applications, and thus have developed a high demand for predictive and tuning strategies. In this case, different concentrations of polymers, cross-linkers, microalgal cells, and media volume are set with different compositional parameters. The measurements of experimental swelling degrees were conducted under controlled conditions to obtain both equilibrium and dynamic measurements. Initial data analysis was performed by response surface methodology (RSM) with a central composite design (CCD), which explained the effects of independent and interactive formulation factors. Then, an artificial neural network (ANN) model was implemented and trained on the same data set to elicit possible complex and nonlinear associations that are not considered in traditional approaches. The results of the two models were evaluated intensively based on the statistical parameters such as R2, RMSE, and the analysis of the residuals. The ANN model appeared more predictive and produced finer nonlinear trends in the swelling response when compared to RSM, which generated understandable equations and response surfaces required to develop mechanistic insight. It is found that the use of hybrid modeling is one of the possible future directions because the complementary strengths were evaluated in terms of comparative analysis. The results show that machine learning and statistical design are synergistic in the study and optimization of hydrogel systems. The article develops the methodological toolkit that can be used to study hydrogels as well as establishes a basis for intelligent material design with data-centric design, which is more likely to be practical with better functionality and performance.

PMID:42569012 | PMC:PMC13448944 | DOI:10.1021/acsomega.5c10279

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Nevin Manimala Statistics

Spatial Heterogeneity in Heat-Related Mortality in the Valencian Region: Implications for Climate Adaptation Beyond Administrative Boundaries

Geohealth. 2026 Aug 7;10(8):e2025GH001699. doi: 10.1029/2025GH001699. eCollection 2026 Aug.

ABSTRACT

The intensification of extreme heat events in the Mediterranean basin poses growing threats to public health, particularly in climate-vulnerable regions like the Valencian Region (eastern Spain). Despite the implementation of provincial and municipal heat prevention plans, their efficacy remains unevaluated, and most studies have focused on provincial capitals, potentially overlooking subregional variability. This study addresses these gaps by quantifying moderate and extreme heat-attributable mortality across 28 thermoclimatic areas (TAs)-a climatic subdivision of the Valencian Region- from 1975 to 2019, using a two-stage time-series analysis of high-resolution temperature and mortality data. Results reveal a distinct coastal-inland gradient, with significantly higher mortality impacts in inland TAs compared to coastal provincial capitals. Areas most impacted by extreme heat are concentrated in interior regions, while coastal capitals exhibit lower risks. These findings underscore the limitations of relying on administrative boundaries for public health surveillance and adaptation planning. The study highlights the value of climatically coherent zones, such as TAs, for improving the spatial resolution of early warning systems and tailoring heat prevention strategies. This scalable methodological framework provides critical insights for enhancing the design of heat adaptation plans not only in Spain but also in other Mediterranean and climate-sensitive regions.

PMID:42569010 | PMC:PMC13449177 | DOI:10.1029/2025GH001699

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Association Between Glycemic Parameters and Severity of Periodontitis in Nepal: A Hospital-Based Cross-Sectional Study

Int J Dent. 2026 Aug 7;2026:3153277. doi: 10.1155/ijod/3153277. eCollection 2026.

ABSTRACT

BACKGROUND: Periodontitis is a prevalent inflammatory disease affecting the supporting structures of the teeth and has been strongly associated with systemic conditions such as diabetes mellitus. Poor glycemic control has been reported to exacerbate periodontal tissue destruction; however, evidence from many developing countries remains limited. This study aimed to evaluate the association between glycemic parameters and periodontitis severity in a hospital-based population in Nepal.

METHODS: A hospital-based cross-sectional study was conducted among 396 adult patients attending the dental outpatient department of a tertiary care hospital in Nepal. Participants were categorized according to the severity into mild (n = 132), moderate (n = 132), and severe (n = 132) groups based on periodontal clinical examination. The terminology was aligned with the 2017 World Workshop classification of periodontal and peri-implant diseases and conditions. Full periodontal grading was not assigned because longitudinal data on disease progression were unavailable. Patients presenting with a molar-incisor pattern suggestive of the previously described aggressive periodontitis phenotype were excluded. Demographic variables, smoking status, periodontal clinical parameters, and glycemic indicators, including fasting blood glucose (FBG), postprandial blood sugar (PPBS), and glycated hemoglobin (HbA1c), were recorded. Descriptive statistics were used to summarize the study variables. Differences in glycemic parameters across periodontitis severity groups were assessed using the Kruskal-Wallis test. Spearman correlation analysis was applied to assess the association between glycemic markers and periodontal clinical parameters. Ordinal logistic regression was conducted to determine the independent predictors of increasing severity of periodontitis.

RESULTS: Glycemic parameters worsened with increasing severity of periodontitis. Patients with severe periodontitis had markedly increased levels of FBG, PPBS, and HbA1c in comparison to those with mild and moderate periodontitis (p < 0.001). Periodontal clinical parameters, such as probing depth and clinical attachment loss (CAL), were most strongly correlated with HbA1c (p < 0.001). Ordinal logistic regression analysis revealed that HbA1c was the most significant independent predictor of increasing severity of periodontitis, followed by age and current smoking status.

CONCLUSIONS: Severe periodontitis was significantly associated with worsening glycemic status. Of all the assessed glycemic markers, HbA1c exhibited the most robust relationship with periodontal destruction. These findings highlight the importance of glycemic control in periodontal health and support the need for integrated medical-dental management strategies for patients with metabolic disorders.

PMID:42569004 | PMC:PMC13449026 | DOI:10.1155/ijod/3153277

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An interpretable machine learning model for predicting 1-year major adverse cardiovascular events in patients with type 2 diabetes and hypertension

Front Med (Lausanne). 2026 Jul 24;13:1871693. doi: 10.3389/fmed.2026.1871693. eCollection 2026.

ABSTRACT

BACKGROUND: Patients with coexisting type 2 diabetes mellitus (T2DM) and hypertension (HTN) face a synergistically elevated risk of major adverse cardiovascular events (MACE). Evidence for prediction models developed specifically in established T2DM-HTN comorbidity population remains limited.

OBJECTIVE: To methodologically explore and preliminarily evaluate an interpretable machine learning framework for 1-year MACE prediction in hospitalized patients with coexisting T2DM and HTN using routine clinical data.

METHODS: This retrospective study included 1,054 hospitalized patients with T2DM and HTN, of whom 249 (23.6%) experienced MACE during 1-year follow-up. The dataset was randomly divided into training (60%), validation (20%), and independent test (20%) cohorts using stratified sampling. LASSO regression was applied for feature selection from 69 clinical variables. Four algorithms, including logistic regression, random forest, support vector machine, and XGBoost, were developed and compared. Model performance was assessed using discrimination, calibration, and clinical utility metrics. SHapley Additive exPlanations (SHAP) were used to interpret the final model.

RESULTS: LASSO identified six stable predictors: HbA1c, age, hypertension duration, cystatin C (CysC), T2DM duration, and carotid intima-media thickness (CIMT). Sex was additionally incorporated based on clinical relevance. Multivariable logistic regression showed that HbA1c, age, hypertension duration, T2DM duration, CysC, and CIMT were associated with 1-year MACE risk, whereas sex was not statistically significant. Logistic regression showed the best relative balance between discrimination, calibration, and simplicity on the validation set, although learning curves indicated limited incremental improvement with increasing training sample size. After isotonic regression recalibration, the final logistic regression model achieved an ROC-AUC of 0.828, a PR-AUC of 0.656, and a Brier score of 0.116 on the independent test set. Decision curve analysis indicated potential clinical net benefit. SHAP linked model predictions to glycemic burden, aging, cumulative disease exposure, renal-related risk, and subclinical atherosclerosis.

CONCLUSION: An interpretable logistic regression model based on seven routine clinical variables showed relatively good internal performance for predicting 1-year composite MACE risk in hospitalized patients with coexisting T2DM and HTN. CysC provided additional prognostic information beyond its conventional role as a renal filtration marker, although this association should be interpreted as prognostic rather than causal. External validation is required before the model can be considered for clinical decision support.

PMID:42568987 | PMC:PMC13448788 | DOI:10.3389/fmed.2026.1871693