Tag: pubmed

BMC Chem. 2026 Jun 25. doi: 10.1186/s13065-026-01853-7. Online ahead of print.

ABSTRACT

The development of environmentally sustainable analytical methodologies capable of resolving highly overlapped multicomponent systems remains an important challenge in modern analytical chemistry. UV spectrophotometry offers several practical advantages, including minimal solvent-consumption, rapid analysis, low operational cost, and instrumental simplicity; however, its quantitative application is often constrained by severe spectral-overlap among analytes. In the present study, a dual-matrix chemometric framework was developed for the simultaneous determination of five spectrally overlapping analytes-hydrochlorothiazide (HCD), losartan potassium (LOS), ramipril (RAP), N-nitrosodiethylamine (NDA), and toluene (TLN)-in pharmaceutical formulations and fortified human plasma samples. The proposed methodology integrates a five-factor, five-level multilevel experimental design for calibration, Sobol quasi-random sampling for external validation, and Firefly Algorithm-optimized Partial Least Squares (FA-PLS) modeling for adaptive wavelength selection and multivariate calibration. The developed models demonstrated excellent analytical performance. For pharmaceutical formulations, mean recoveries ranged from 99.68 to 100.57%, with RMSEP values between 0.037 and 0.094 µg/mL. For fortified human plasma samples, mean recoveries ranged from 98.18 to 98.97%, with RMSEP values between 0.059 and 0.146 µg/mL, demonstrating the feasibility of the proposed approach under biologically relevant matrix conditions. The environmental profile of the proposed methodology was comprehensively evaluated using multiple complementary tools, including NEMI, ComplexGAPI, AGREE, the Multicolor Assessment Tool, carbon footprint estimation, and the Need-Quality-Sustainability index. Collectively, these assessments confirmed the favorable greenness and overall sustainability characteristics of the developed spectrophotometric approach. To the best of our knowledge, no previous UV spectrophotometric chemometric method has been reported for the simultaneous determination of LOS, RAP, and HCD in the presence of both NDA and TLN within a single analytical workflow without a prior separation step. The proposed dual-matrix FA-PLS framework demonstrates that UV spectrophotometry, when coupled with advanced chemometric optimization and statistically designed calibration strategies, can provide an accurate, sensitive, and environmentally sustainable platform for multicomponent pharmaceutical analysis and proof-of-concept application to biological matrices.

PMID:42351205 | DOI:10.1186/s13065-026-01853-7

AIDS Res Ther. 2026 Jun 26. doi: 10.1186/s12981-026-00912-2. Online ahead of print.

ABSTRACT

BACKGROUND: Anemia is a significant concern for people with HIV worldwide, as it is associated with reduced life expectancy, fatigue, weakness, reduced physical functioning, psychological distress such as depression and anxiety, and poorer quality of life. This study aimed to assess the prevalence of anemia and identify factors associated with anemia among people with HIV receiving antiretroviral therapy at public health institutions in Jigjiga, Ethiopia.

METHODS AND MATERIALS: An institution-based cross-sectional study was conducted from July 11 to August 12, 2023, involving 392 people with HIV receiving antiretroviral therapy in Jigjiga, Ethiopia. Data were collected using a structured questionnaire and hemoglobin measurements, then coded and entered in EpiData version 3.1. Statistical analysis was performed using SPSS version 20, with multivariate logistic regression used to identify factors associated with anemia. Statistical significance was set at p < 0.05.

RESULTS: The overall prevalence of anemia in the study population was 39% (95% CI: 34.3-44.2). Among those with anemia, 0.6% had severe anemia, 33.5% had moderate anemia, and 4.9% had mild anemia. Female sex, presence of opportunistic infections, and low dietary diversity scores were significantly linked to anemia in multivariate analysis (p < 0.05).

CONCLUSION: Anemia was identified as a moderate public health problem among people with HIV receiving ART at the study sites. The findings highlight the importance of providing targeted support to women and ensuring timely diagnosis and treatment of opportunistic infections to reduce the severity and impact of anemia. Furthermore, patients with poor dietary diversity should be offered nutritional counselling to enhance their health outcomes.

SIGNIFICANCE FOR PUBLIC HEALTH: Anemia continues to pose a substantial public health burden among people living with HIV/AIDS, particularly in low-resource settings. This study provides context-specific evidence from Jigjiga, a region with limited existing data, helping to fill a critical knowledge gap. The results offer valuable insights for healthcare providers and policymakers to design targeted interventions, improve anemia screening and management, and enhance the quality of life for People with HIV on ART.

PMID:42351202 | DOI:10.1186/s12981-026-00912-2

BioData Min. 2026 Jun 25. doi: 10.1186/s13040-026-00579-5. Online ahead of print.

ABSTRACT

BACKGROUND: Amyotrophic lateral sclerosis (ALS) is a progressive neurodegenerative disease with median survival of 3-5 years. Patient responses to treatments vary widely, highlighting the need for personalized care. Clustering patients based on disease progression could improve prognosis, guide clinical decision-making, and optimize clinical trial design. This study aimed to identify robust ALS patient clusters using ALS Functional Rating Scale-Revised (ALSFRS-R) scores and to determine diagnostic parameters predictive of cluster membership, enabling earlier stratification and targeted management.

METHODS: Data from the Tours ALS center registry (April 1997-October 2023) were analyzed; after preprocessing, 353 patients monitored every three months between January 2004 and July 2023 with ALSFRS-R, clinical, biological, and demographic data were retained. After preprocessing to handle missing or aberrant data, a weakly supervised approach labeled patient pairs based on their ALSFRS-R sequences. These labels were used to train a classifier to learn a distance for off-the-shelf clustering algorithms. Multiple configurations were tested, varying clustering algorithms, dimensionality reduction method, and number of clusters. Random Forest (RF) model predicted cluster membership from diagnostic parameters. Optimal clustering was selected using silhouette score, validated with Kaplan-Meier survival analysis. Stability and robustness were assessed with the Adjusted Rand Index (ARI) and silhouette score respectively. Predictive performance was evaluated using specificity, sensitivity, positive predictive value (PPV), and negative predictive value (NPV). Diagnostic parameters associated with clusters were identified using Kruskal-Wallis and chi-squared tests for continuous and categorical variables.

RESULTS: Three clusters (n = 139, 121, 93) were identified, demonstrating strong separation (silhouette ≈ 0.6) and high stability of results (ARI ≈ 0.7). Survival differed significantly among clusters: over 50% of patients in the third cluster survived beyond 50 months, compared to less than 25% in the other clusters. Thirteen diagnostic parameters-including ALSFRS-R subscores, IgG levels, albumin quotient, and time to diagnosis-were key predictors of cluster membership. Cluster prediction achieved specificity and NPV ≈ 0.75, with close sensitivity and PPV compared to state-of-the-art methods.

CONCLUSION: This framework successfully stratifies ALS patients into clinically meaningful clusters, revealing underlying disease heterogeneity and providing strong prognostic insight. Such classification can facilitate personalized care, guide therapeutic decisions, and inform the design of targeted interventions to improve outcomes.

CLINICAL TRIAL NUMBER: Not applicable.

PMID:42351201 | DOI:10.1186/s13040-026-00579-5

J Cardiothorac Surg. 2026 Jun 25. doi: 10.1186/s13019-026-04482-2. Online ahead of print.

ABSTRACT

OBJECTIVES: This study compared clinical outcomes of cardiac surgeries performed by a single surgeon (operating alone [OA]) versus those performed by multiple surgeons (operating together [OT]) to evaluate the feasibility and safety of different surgical staffing models.

METHODS: We retrospectively reviewed adult patients who underwent cardiac surgery at Chung-Ang University Hospital from September 2016 to August 2021. Between September 2016 and August 2018, two or three surgeons assisted each other (OT), while from September 2018 to August 2021, a single surgeon operated alone (OA). After propensity score matching, 79 patients were selected in each group for analysis.

RESULTS: Major postoperative adverse events, including mortality, stroke, new-onset atrial fibrillation, and low cardiac output, did not statistically significant differ between groups. Other postoperative outcomes such as, acute kidney injury, ICU stay length, hospital days, CPB time, ACC time was not statistically different, whereas operation time was shorter in the OT group than in the OA group (median 300 vs. 379 min, P < 0.001).

CONCLUSIONS: Except for differences in operation time, there were not significantly different short-term postoperative outcomes in OT and OA groups. Given the various limitations of the present study, the result should be interpreted cautiously.

PMID:42351185 | DOI:10.1186/s13019-026-04482-2

Diabetol Metab Syndr. 2026 Jun 25. doi: 10.1186/s13098-026-02223-y. Online ahead of print.

ABSTRACT

BACKGROUND AND AIM: Olanzapine is a widely used second-generation antipsychotic with well-established efficacy in the treatment of schizophrenia, bipolar disorder, and related psychiatric conditions. However, its use has been consistently linked to metabolic adverse effects, particularly disturbances in glucose regulation. This systematic review and meta-analysis aimed to quantitatively evaluate the effects of olanzapine on key diabetes-related biomarkers, fasting blood sugar (FBS), fasting insulin, and glycated hemoglobin (HbA1c), in human populations, using evidence derived exclusively from randomized controlled trials (RCTs).

METHODS: A comprehensive literature search was conducted in PubMed/MEDLINE, Scopus, Web of Science, and Embase from inception to July 1, 2025, without language or date restrictions. Eligible studies were RCTs involving human participants that compared olanzapine with placebo or no treatment and reported pre- and post-intervention data for FBS, fasting insulin, or HbA1c. Study quality was assessed using the Cochrane Risk of Bias 2 tool, and the certainty of evidence was evaluated using the GRADE framework. Pooled effect estimates were calculated as weighted mean differences (WMDs) with 95% confidence intervals (CIs) using random-effects models. Subgroup, sensitivity, and publication bias analyses were performed to explore heterogeneity and assess result robustness.

RESULTS: Fifteen RCTs comprising 18 comparison arms were included in the meta-analysis. Olanzapine treatment was associated with a statistically significant increase in FBS (WMD: 2.618 mg/dL; 95% CI: 0.283 to 4.954) and fasting insulin levels (WMD: 3.636 µIU/mL; 95% CI: 1.964 to 5.307). In contrast, no significant change was observed in HbA1c levels (WMD: -0.029%; 95% CI: -0.284 to 0.226). Subgroup analyses suggested larger glycemic changes with higher doses (≥ 10 mg/day), longer treatment durations (≥ 12 weeks), and lower baseline body mass index, although these trends were not consistently statistically significant. Substantial heterogeneity was observed for FBS outcomes, whereas fasting insulin results were highly consistent across studies.

CONCLUSION: Olanzapine use is associated with modest but significant elevations in FBS and insulin levels, indicating early disturbances in glucose homeostasis and a potential risk of insulin resistance, while effects on HbA1c remain inconclusive. Clinically, this highlights the need for routine monitoring of glucose and insulin levels, early lifestyle interventions, and consideration of pharmacologic strategies in high-risk patients to prevent progression to diabetes. These findings underscore the importance of proactive metabolic monitoring and individualized risk assessment in patients receiving olanzapine.

PMID:42351181 | DOI:10.1186/s13098-026-02223-y

Int J Behav Nutr Phys Act. 2026 Jun 25. doi: 10.1186/s12966-026-01941-w. Online ahead of print.

ABSTRACT

BACKGROUND: An unhealthy diet, excessive screen time, low levels of physical activity and suboptimal sleep are known risk factors for childhood overweight and obesity (OW/OB). These energy balance-related behaviors tend to co-occur and combine into “lifestyle patterns” (LPs) that may have a synergistic effect on OW/OB. However, few studies have examined these LPs before preschool age, and none investigated the possible involvement of parental feeding practices (e.g., breastfeeding and timing of complementary feeding), non-screen leisure activities and sleep quality. We aimed to identify such LPs during the first 2 years of life and assess their associations with OW/OB at age 5 years and age at adiposity rebound (AR).

METHODS: Participants were children from the French nationwide ELFE (Etude Longitudinale Française depuis l’Enfance) birth cohort. We used principal component analysis of 18 items characterizing parental feeding practices and children’s energy balance-related behaviors (n = 13,121) to derive LPs. We analyzed the associations of LPs with OW/OB at age 5 years (International Obesity Task Force definition; n = 8,388) and age at AR (in days; n = 7,845) using multivariable logistic and linear regression models, respectively.

RESULTS: We identified three LPs: “Early complementary feeding, discretionary consumption, high screen time”, “Balanced diet, non-screen leisure activities”, and “Healthy feeding practices, low dairy consumption, suboptimal sleep” patterns. The first pattern was positively associated with OW/OB at age 5 years (OR [95% CI] = 1.09 [1.03, 1.16] per 1-SD increase) and inversely associated with age at AR (β [95% CI] = – 12.1 days [- 19.1; -5.0] per 1-SD increase). No association was observed for the second LP. For the third pattern, no clear evidence of associations was found, although the effect sizes were comparable to those of the first LP (OR = 1.07 [0.99; 1.15]; β = -7.6 days [- 15.5; 0.3] per 1-SD increase).

CONCLUSION: These results emphasize the perinatal period as a critical window for the emergence of interrelated lifestyle behaviors associated with OW/OB. They highlight the need for interventional studies to evaluate the effectiveness of early, integrated, multi-behavioral strategies to prevent childhood obesity.

PMID:42351177 | DOI:10.1186/s12966-026-01941-w

BMC Med Educ. 2026 Jun 26. doi: 10.1186/s12909-026-09736-0. Online ahead of print.

ABSTRACT

BACKGROUND: Postgraduate dental education is a demanding process characterized by increased clinical responsibility and academic expectations. While psychological distress among undergraduate dental students has been widely studied, evidence regarding postgraduate populations remains limited. This study aimed to assess levels of stress, anxiety, and depression among postgraduate dental students and to examine their associations with sociodemographic factors.

METHODS: A cross-sectional observational study was conducted among 192 postgraduate dental students. Data were collected using a demographic questionnaire and the Depression Anxiety Stress Scale-21 (DASS-21). Subscale scores were multiplied by two for comparability with standard severity classifications. Statistical analyses included Cronbach’s alpha, Pearson correlation, and linear regression. Multiple regression models were used to assess associations between psychological variables and sociodemographic factors. Group comparisons were performed using t-tests and one-way ANOVA with Scheffé post hoc tests.

RESULTS: Significant positive associations were observed among stress, anxiety, and depression (p < .001). In multiple regression analysis, stress (β = 0.543, p < .001) and anxiety (β = 0.317, p < .001) were significantly associated with depression, accounting for a substantial proportion of the observed variance (R² = 0.696). Participants with “normal” perceived financial status tended to show higher distress levels than those with “good” status, although this association did not reach statistical significance in multivariable analysis. No significant differences were found for training year, specialty, residence, or smoking status. The majority of participants were classified within the normal range across all DASS-21 subscales.

CONCLUSIONS: Stress, anxiety, and depression are closely interrelated among postgraduate dental trainees. Psychological distress appears to be associated with individual and perceived contextual factors; however, these associations were not consistently supported in the multivariable model and should therefore be interpreted with caution.

PMID:42351166 | DOI:10.1186/s12909-026-09736-0

Plant Methods. 2026 Jun 25. doi: 10.1186/s13007-026-01554-1. Online ahead of print.

ABSTRACT

Wheat is one of the most important cereals worldwide, yet significant gaps remain in our understanding of genetic variability in root traits, especially those associated with deeper rooting that support resource acquisition in challenging environments. Root traits are typically controlled by many genes with small effects and often display low heritability. Our aim was to develop a statistical approach to analyse root variation across soil depth and to determine where genetic differences in root intensity are most detectable. An experiment was conducted at the RadiMax semi-field facility, which is designed to measure deep root systems. Five years of phenotypic data recorded each June produced observations from 1500 rows. Each row captured root intensity across the soil profile from 0.6 m to 2.6 m, enabling detailed analysis of vertical root distribution. Across the five years, 513 winter wheat cultivars were grown in the facility, and among those 409 were genotyped with SNP chips. Depth-resolved regression models with random coefficients were used to quantify genetic and non-genetic variation in root intensity across soil depths, while accounting for spatial variation between rows. Random variation within rows was found to be constant across depths. The models showed that genetic variance for cumulative root intensity increased substantially below 1.1 m, with the deepest layers exhibiting the largest differences between wheat lines. Narrow-sense heritability of point measurements peaked at approximately 1.5 m ([Formula: see text]).

PMID:42351160 | DOI:10.1186/s13007-026-01554-1

BMC Med Educ. 2026 Jun 25. doi: 10.1186/s12909-026-09810-7. Online ahead of print.

ABSTRACT

BACKGROUND: Antimicrobial resistance is a major global public health threat, necessitating improved antibiotic stewardship. India’s Competency-Based Medical Education (CBME) framework requires robust methods to assess and cultivate clinical reasoning. This study evaluated a rule-based Digital Case-Based Learning (DCBL) module with algorithm-triggered formative feedback for antibiotic-stewardship decision-making among undergraduate medical learners.

METHODS: A quasi-experimental, single-group pre-post study evaluated three rule-based DCBL micro-cases (upper respiratory tract infection [URTI], urinary tract infection [UTI], and acute watery diarrhoea) in 271 third- and fourth-phase MBBS learners at a tertiary-care teaching hospital in South India. Script Concordance Test (SCT) performance was assessed using parallel pre- and post-test forms. Usability and workload were measured using the System Usability Scale (SUS) and two NASA Task Load Index (NASA-TLX) items.

RESULTS: Overall SCT scores changed from 5.65 +/- 4.73 to 6.05 +/- 4.48 (mean change = 0.40; 95% CI: -0.41 to 1.21; p = 0.33; Cohen’s d = 0.059). The UTI case showed a statistically significant domain-specific improvement (mean change = 0.58; p = 0.003; Cohen’s d = 0.18), which falls below the conventional small-effect threshold. URTI and diarrhoea cases showed no significant change. The mean SUS score was 62.4 +/- 18.7; 76.0% of learners rated platform usability as average or good, with moderate cognitive workload.

CONCLUSION: A single-session rule-based DCBL intervention was feasible and acceptable in routine CBME teaching and showed a domain-specific SCT signal in the UTI case. The overall SCT change was not statistically significant and should not be interpreted as evidence of effectiveness or generalised across antibiotic-stewardship scenarios. Controlled, multisite, longitudinal studies are needed to confirm these preliminary findings.

PMID:42351153 | DOI:10.1186/s12909-026-09810-7

BMC Health Serv Res. 2026 Jun 25. doi: 10.1186/s12913-026-15002-2. Online ahead of print.

ABSTRACT

BACKGROUND: With the advancement of hierarchical diagnosis and treatment systems in China, primary healthcare institutions have become pivotal in delivering basic medical services. Accurate prediction and optimal allocation of healthcare resources are indispensable for improving service quality and ensuring the effective operation of the healthcare system.

METHODS: This research utilizes the Sparrow Search Algorithm (SSA) to optimize the hyperparameters of Backpropagation Neural Network (BPNN) and Long Short-Term Memory (LSTM) models, aiming to enhance their predictive performance for primary healthcare resource planning.

RESULTS: The findings demonstrate that the SSA-LSTM model significantly outperforms the SSA-BPNN model. Specifically, in predicting the number of primary healthcare institutions, the SSA-LSTM model reduces the root mean squared error (RMSE) by 45% (from 1.429 to 0.78765) and the mean absolute error (MAE) by 36.4% (from 0.99575 to 0.63306) on the test set. Across all prediction tasks, including personnel quantity and total health costs, the SSA-LSTM model achieves an average RMSE reduction of 23.5% and MAE reduction of 17.1% on the test set compared with SSA-BPNN. Similar improvements are evident in the training set, with RMSE and MAE decreasing by 19.2% and 15.4%, respectively.

CONCLUSION: The SSA-LSTM model offers robust data-driven decision support for healthcare policymakers. Its superior predictive accuracy enables dynamic adjustments to resource allocation, which is essential for bridging regional disparities in China’s primary healthcare system. By accurately forecasting key healthcare indicators, the model facilitates optimized staffing, institutional planning, and budget distribution, thereby laying a solid foundation for evidence-based resource optimization and enhancing the overall efficiency and equity of primary care services.

PMID:42351145 | DOI:10.1186/s12913-026-15002-2