Nevin Manimala

BMC Health Serv Res. 2025 Aug 13;25(1):1070. doi: 10.1186/s12913-025-13266-8.

ABSTRACT

BACKGROUND: The Automated Dispensing Cabinets (ADCs) represent one of the most widely deployed forms of technology integrated with today’s medication-use systems. Despite the rise of ADC use and subsequent benefits, research exploring the impacts of ADCs on staff acceptance and satisfaction is still relatively limited and not thoroughly investigated. The present study aims to address this by assessing the impact of ADC implementation on healthcare staff satisfaction.

METHODS: This cross-sectional study was conducted in Almoosa Specialist hospital, Al-Ahsa, KSA, involving 203 healthcare staff participants selected through a convenience sampling approach considering the busy and tough schedule of staff. The questionnaire, named ADC User Acceptance Survey (ADC-UAS), was developed using a 10-item scale designed to measure Perceived Ease of Use (PEOU), Perceived Usefulness (PU), and Behavioral Intention to Use ADCs. This instrument employed a 7-point Likert scale and was based on the Modified Technology Acceptance Model (TAM). Pearson’s correlation was computed to investigate the correlation between demographic and TAM factors. The Artificial Neural Network (ANN) model was applied to assess the influential factors, and results were declared statistically significant if p < 0.05.

RESULTS: Out of 203 healthcare professionals, the majority were nurses (82.8%) and females (86.7%), with a mean age of 31.94 ± 5.96 years. The findings demonstrated high ADC acceptance and satisfaction, with 87.2% of participants reporting improved efficiency and 92.1% acknowledging enhanced patient safety. The strong positive relationship between current unit experience and acceptance (r = 0.304, p = 0.000) showed that individuals with more experience in their current unit are more likely to accept the system. Acceptance of ADC was significantly correlated with its usefulness (r = 0.820, p = 0.000). Positive correlation was also observed between professional experience and the perceived usefulness of the system (r = 0.144, p = 0.040). The result of the ANN model identified professional experience (100%), current unit experience (99.9%), and automation experience (97.8%) as the strongest predictors of ADC acceptance.

CONCLUSION: The study revealed high acceptance and satisfaction with ADCs among Almoosa healthcare staff, emphasizing that these systems make work more manageable and efficient. Given the high levels of acceptance and satisfaction among healthcare professionals regarding ADCs, it is recommended that healthcare facilities continue to invest in and expand the use of ADC systems.

PMID:40796839 | DOI:10.1186/s12913-025-13266-8

BMC Psychiatry. 2025 Aug 12;25(1):785. doi: 10.1186/s12888-025-07213-4.

ABSTRACT

BACKGROUND: Major depressive disorder (MDD) is a prevalent mental health condition, wherein the JAK/STAT signaling pathway serves as a potent cellular mechanism implicated in its pathophysiology. Increased expression of JAK2, STAT3, and subsequently IDO1 genes appears to be linked to depressive symptoms. With their antioxidant capabilities and improved absorption due to the nano formula, selenium nanoparticles could potentially modulate this molecular pathway. This study aimed to assess the impact of nano-selenium supplementation on the expression of JAK2, STAT3, and IDO1 genes in patients with MDD.

METHODS: A triple-blind, randomized, placebo-controlled trial was conducted at the Psychosomatic Clinic of Imam Khomeini Hospital Complex. A total of 50 participants, newly diagnosed with MDD were randomized to either a nano-selenium (55 µg/day) or placebo group for 12 weeks. All participants were receiving their standard treatment (sertraline 50 mg/day). Blood samples were collected at baseline and post-intervention to measure the gene expression using RT-qPCR.

RESULTS: At the end of the study, both groups showed reductions in JAK2 and STAT3 relative gene expression after 12 weeks (P < 0.05). Although the reduction was more in the nano-selenium group, the between-group differences were not statistically significant.

CONCLUSIONS: This study is the first to examine nano-selenium as a novel potential adjunct treatment for MDD. Though the degree of reduction in JAK2 and STAT3 levels was greater within the nano-selenium group, it appears that additional investigations are needed to elucidate its effects.

TRIAL REGISTRATION: The research received approval from the Research Ethics Committees of Iran University of Medical Sciences (Approval ID: IR IUMS.REC.1402.206, dated 2023-06-13) and was duly registered with the Iranian Registry of Clinical Trials (IRCT; registration number: IRCT20091114002709N62, dated 2023-07-29).

PMID:40796838 | DOI:10.1186/s12888-025-07213-4

BMC Psychiatry. 2025 Aug 12;25(1):786. doi: 10.1186/s12888-025-07245-w.

ABSTRACT

BACKGROUND: Depression and asthma share several pathophysiologic risk factors, and their precise connection remains unclear. Our research seeks to assess the relationship between depression and asthma.

METHODS: The association between depression and asthma was assessed through a multivariable logistic regression analysis, with data sourced from The National Health and Nutrition Examination Survey (NHANES) 2007-2018 and the English Longitudinal Study of Ageing (ELSA) 2004-2019. Subsequently, a linkage disequilibrium score regression (LDSC) analysis was conducted to evaluate the genetic correlation between depression and asthma. Moreover, a two-sample Mendelian randomization (MR) analysis was conducted by employing genome-wide association study (GWAS) summary statistics by means of both univariable MR (UVMR) and multivariable MR (MVMR).

RESULTS: This study included 31,434 participants from NHANES and 17,021 participants from ELSA for observational research. In the unadjusted model, participants with depression had a significantly increased risk of asthma in comparison to participants without depression, both in NHANES (OR = 2.002, 95%CI: 1.827-2.193, P < 0.001) and in ELSA (OR = 1.753, 95%CI: 1.581-1.943, P < 0.001). After adjusting potential confounders, the results remain significant. The LDSC result revealed a significant positive genetic correlation between depression and asthma (rg = 0.352, P < 0.001).The UVMR results further substantiated a genetically predicted causality of depression on asthma (OR = 1.291, 95%CI: 1.157-1.442, P < 0.001), while the reverse causality does not stand. Similar findings from MVMR were obtained for the causality investigation after adjusting smoking (OR = 1.326, 95%CI: 1.156-1.520, P < 0.001), drinking (OR = 1.375, 95%CI: 1.186-1.593, P < 0.001), and education (OR = 1.425, 95%CI: 1.253-1.621, P < 0.001).

CONCLUSION: Our findings indicate that depression may play a contributory role in the development of asthma, underscoring the potential benefit of implementing prevention strategies aimed at managing depression to mitigate asthma risk.

PMID:40796827 | DOI:10.1186/s12888-025-07245-w

BMC Public Health. 2025 Aug 12;25(1):2741. doi: 10.1186/s12889-025-24129-6.

ABSTRACT

INTRODUCTION: After losing a spouse, a woman experiences diverse changes in her life, and varying cultural settings add to them. Research has shown the impact of widowhood on one’s quality of life. This study evaluated the quality of life and its predictors of widows of Jodhpur enrolled in Ekal Nari Samman Pension Yojna, a state-sponsored pension scheme for widowed, divorced, or abandoned women from an economically weaker section of Rajasthan.

METHODOLOGY: A cross-sectional study was conducted among 260 widows aged 18-45 from Jodhpur City. A semi-structured questionnaire was used to collect data after obtaining informed written consent. The CDC HRQOL-14 questionnaire was used to collect data on quality of life. Data was analysed using descriptive statistics and logistic regression analysis.

RESULTS: The study findings revealed that the number of children, chronic illness, depression, and anxiety were consistently significant predictors of quality of life among widows in Jodhpur.

CONCLUSION: The findings highlight the association of physical and psychological health with the quality of life of widows. There is a critical need to integrate mental health services and chronic illness management into widows’ welfare programs to ensure comprehensive support.

PMID:40796822 | DOI:10.1186/s12889-025-24129-6

BMC Public Health. 2025 Aug 12;25(1):2732. doi: 10.1186/s12889-025-23968-7.

ABSTRACT

BACKGROUND: Effective early childhood education (ECE) programs, including elementary schools, kindergartens, and daycare facilities, are instrumental in fostering cognitive, social, emotional, and motor development. Access to water, sanitation, and hygiene (WASH) facilities, as mandated by Sustainable Development Goals (SDGs) 6, is integral in bolstering health and enhancing educational engagement globally. This study examines the impact of WASH facilities and sociodemographic factors on ECE enrollment in Bangladesh.

METHODS: Data were extracted from the 2022 Bangladesh Demographic and Health Survey (BDHS), on which 2494 children’s information and socioeconomic characteristics were analyzed. The outcome variable was ECE enrollment, and the exposure variable was WASH facilities-defined as having an improved water source, an improved nonshared toilet, and a basic handwashing facility-at the household level. Crude and adjusted logistic regression models were applied to determine significant associations between ECE enrollment and WASH facilities, including other covariates.

RESULTS: Children from households with basic handwashing facilities presented greater odds of ECE enrollment (AOR = 1.33, 95% CI 1.00-1.76, p value = 0.043), whereas improved toilet facilities were correlated with a greater chance of participation (AOR = 1.24, 95% CI 0.97-1.59, p value = 0.085). Treating drinking water significantly increased the probability of enrollment (AOR = 1.55, 95% CI 1.15-2.90, p value = 0.004). Regional disparities were mentionable; children in Rangpur (AOR = 1.94, 95% CI 1.33-2.81, p value = 0.001), Rajshahi (AOR = 1.47, 95% CI 1.01-2.13, p value = 0.043), Mymensingh (AOR = 1.39, 95% CI 0.97-1.99, p value = 0.096), and Khulna (AOR = 1.34, 95% CI 0.93-1.94, p value = 0.116) had higher odds of enrollment than Barishal did. Mothers’ education is also a key determinant; children of mothers with secondary education were 40% more likely to enroll (AOR = 1.40, 95% CI 1.00-1.96, p value = 0.049), and those with higher-educated mothers exhibited a similar trend (AOR = 1.43, 95% CI 0.95-2.16, p value = 0.088) compared with children of mothers with no education.

CONCLUSIONS: Our findings highlight the significance of household access to water, sanitation, and hygiene (WASH) facilities in early childhood education enrollment in Bangladesh. To advance progress toward SDGs 4 (quality education) and 6 (clean water and sanitation), policymakers should prioritize well-established WASH facilities, maternal education programs, and region-specific strategies.

PMID:40796821 | DOI:10.1186/s12889-025-23968-7

Acta Psychol (Amst). 2025 Aug 11;259:105374. doi: 10.1016/j.actpsy.2025.105374. Online ahead of print.

ABSTRACT

This meta-analysis investigates the impact of smartphone usage on university students’ academic performance, with a focus on identifying moderating factors. A total of 45 studies were analyzed, revealing a small but statistically significant negative effect of smartphone usage frequency on academic performance (r = -0.12). Moderation analyses were conducted on variables such as the source of data, region, usage purpose, and terminological differences in measuring smartphone use. Results show that smartphone addiction and problematic use yield more pronounced negative impacts on academic outcomes compared to general usage measures. Furthermore, multitasking during class demonstrated the highest negative effect among smartphone-related behaviors. The study emphasizes the potential benefits of using objective data collection methods, such as app-based tracking, while acknowledging that self-reported measures can still offer valuable insights, though they may be influenced by recall bias. These findings call for targeted educational interventions, promoting information literacy and self-regulation in smartphone use, in order to mitigate the detrimental effects on academic performance. Future research should explore longitudinal designs and standardized measurement frameworks to provide a more comprehensive understanding of the relationship between smartphone use and academic success.

PMID:40795445 | DOI:10.1016/j.actpsy.2025.105374

Ecotoxicol Environ Saf. 2025 Aug 11;303:118843. doi: 10.1016/j.ecoenv.2025.118843. Online ahead of print.

ABSTRACT

The effects of perfluoroalkyl and polyfluoroalkyl substances (PFAS) on kidney function across physiological conditions remain inconclusive, and previous research has not assessed the potential mediating effect of iron status. We aimed to examine the relationships between PFAS exposure and kidney function in various demographic groups, as well as to evaluate the potential mediating role of iron status. This study included 7369 Chinese adults aged 18 years or older from the China Health and Nutrition Survey (CHNS). Estimated glomerular filtration rate (eGFR) levels were used to reflect the efficiency of kidney function. Generalized linear models and weighted quantile sum regression models indicated negative associations between PFAS and eGFR levels, with PFNA and PFHxS emerging as the dominant contributors. Subgroup analysis revealed that the adverse effects of PFAS on eGFR levels were more pronounced in the males, young and middle age, non-hypertension, and non-diabetes subgroups. Further mediation analyses demonstrated that iron status (ferritin, transferrin, and hemoglobin) partially mediated these associations, with mediation proportions ranging from 8.89 % to 60.84 %. Our study established PFNA and PFHxS as critical nephrotoxic PFAS in China while pioneering the identification of iron status as a novel mechanistic mediator between PFAS exposure and kidney dysfunction, advancing mechanistic understanding of environmental nephrotoxicity.

PMID:40795427 | DOI:10.1016/j.ecoenv.2025.118843

Ecotoxicol Environ Saf. 2025 Aug 11;303:118842. doi: 10.1016/j.ecoenv.2025.118842. Online ahead of print.

ABSTRACT

Exposure to fine particulate matter (PM_2.5) components and ozone (O₃) is associated with reduced lung function. This study aimed to examine the interaction effects of PM_2.5 components and O₃ on lung function in young adults. A cohort study involving 1697 participants was conducted in Shandong Province, China from September 2019 to November 2020. Pollutant data were obtained from the China High Air Pollutants (CHAP) dataset and the Tracking Air Pollution in China (TAP) dataset. Forced Vital Capacity (FVC), first-second forceful expiratory volume (FEV_1.0), peak expiratory flow rate (PEF) and 50 % forceful expiratory flow rate (FEF_{50 %}) were used as lung function indices. A linear mixed-effects model was employed to evaluate the impact of PM_2.5 components and its interaction effects with O₃ on lung function. Each 1 μg/m³ increase in black carbon (BC) concentration was significantly associated with 0.4027 L/s decrease in PEF (95 % confidence interval (CI): 0.2420 L/s, 0.5634 L/s). Increases in other PM_2.5 components were also associated with various reduced lung function indices. Notably, the interaction term for BC and O₃ was significantly associated with reduced PEF (-0.0243, 95 % CI: -0.0472, -0.0014). Synergistic effects between PM_2.5 components [organic matter (OM), nitrate (NO₃^–)] and O₃ adversely impacted lung function. A more proactive policy should be adopted to address the synergistic effects of air pollution.

PMID:40795423 | DOI:10.1016/j.ecoenv.2025.118842

Oral Oncol. 2025 Aug 11;168:107594. doi: 10.1016/j.oraloncology.2025.107594. Online ahead of print.

NO ABSTRACT

PMID:40795411 | DOI:10.1016/j.oraloncology.2025.107594

Syst Biol. 2025 Aug 8:syaf054. doi: 10.1093/sysbio/syaf054. Online ahead of print.

ABSTRACT

Branch length estimation is a fundamental problem in Statistical Phylogenetics and a core component of tree reconstruction algorithms. Traditionally, general time-reversible mutation models are employed, and many software tools exist for this scenario. With the advent of CRISPR/Cas9 lineage tracing technologies, there has been significant interest in the study of branch length estimation under irreversible mutation models. Under the CRISPR/Cas9 mutation model, irreversible mutations – in the form of DNA insertions or deletions – are accrued during the experiment, which are then read out at the single-cell level to reconstruct the cell lineage tree. However, most of the analyses of CRISPR/Cas9 lineage tracing data have so far been limited to the reconstruction of single-cell tree topologies, which depict lineage relationships between cells, but not the amount of time that has passed between ancestral cell states and the present. Time-resolved trees, known as chronograms, would allow one to study the evolutionary dynamics of cell populations at an unprecedented level of resolution. Indeed, time-resolved trees would reveal the timing of events on the tree, the relative fitness of subclones, and the dynamics underlying phenotypic changes in the cell population – among other important applications. In this work, we introduce the first scalable and accurate method to refine any given single-cell tree topology into a single-cell chronogram by estimating its branch lengths. To do this, we perform regularized maximum likelihood estimation under a general irreversible mutation model, paired with a conservative version of maximum parsimony that reconstructs only the ancestral states that we are confident about. To deal with the particularities of CRISPR/Cas9 lineage tracing data – such as double-resection events which affect runs of consecutive sites – we avoid making our model more complex and instead opt for using a simple but effective data encoding scheme. Similarly, we avoid explicitly modeling the missing data mechanisms – such as heritable missing data – by instead assuming that they are missing completely at random. We stabilize estimates in low-information regimes by using a simple penalized version of maximum likelihood estimation (MLE) using a minimum branch length constraint and pseudocounts. All this leads to a convex MLE problem that can be readily solved in seconds with off-the-shelf convex optimization solvers. We benchmark our method using both simulations and real lineage tracing data, and show that it performs well on several tasks, matching or outperforming competing methods such as TiDeTree and LAML in terms of accuracy, while being 10 ∼ 100 × faster. Notably, our statistical model is simpler and more general, as we do not explicitly model the intricacies of CRISPR/Cas9 lineage tracing data. In this sense, our contribution is twofold: (1) a fast and robust method for branch length estimation under a general irreversible mutation model, and (2) a data encoding scheme specific to CRISPR/Cas9-lineage tracing data which makes it amenable to the general model. Our branch length estimation method, which we call ‘ConvexML’, should be broadly applicable to any evolutionary model with irreversible mutations (ideally, with high diversity) and an approximately ignorable missing data mechanism. ‘ConvexML’ is available through the convexml open source Python package.

PMID:40795361 | DOI:10.1093/sysbio/syaf054