Tag: statistics

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

Sleep. 2025 Aug 8:zsaf226. doi: 10.1093/sleep/zsaf226. Online ahead of print.

ABSTRACT

STUDY OBJECTIVES: Atrial fibrillation (AF) and obstructive sleep apnea (OSA) are interrelated conditions that substantially increase the risk of cardiovascular complications. However, concurrent detection of these conditions remains a critical unmet need in clinical practice. Current home sleep apnea test (HSAT) devices often fail to detect arrhythmias essential for diagnosing OSA-associated AF due to limited ECG monitoring capabilities, and their integration with continuous positive airway pressure (CPAP) data for treatment optimization remains underutilized.

METHODS: This study introduces SHHDeepNet, an advanced deep learning-based framework designed for the detection of OSA in patients with AF, leveraging enhanced features extracted from single-lead electrocardiogram (ECG) signals. The ECG signals were preprocessed and refined using reconstruction independent component analysis (RICA), which isolates statistically independent features for improved data representation. These features were subsequently classified using the customized SHHDeepNet architecture. SHHDeepNet utilizes advanced signal processing and deep learning techniques to enhance ECG-based detection of AF-associated OSA.

RESULTS: The framework was validated using overnight ECG recordings from 101 subjects derived from the Sleep Heart Health Study Visit 1 (SHHS1) database, encompassing 36 prevalent AF (PAF) cases, 25 incident AF (IAF) cases, and 40 OSA cases. Detection performance was evaluated through binary classification (AF AH vs. AF non-AH) and multi-class classification (AF AH, AF non-AH, non-AF AH, and non-AF non-AH). During 5-fold cross-validation (5fold-CV), the framework achieved a binary classification accuracy of 98.22%, sensitivity of 96.8%, specificity of 99%, and an area under the curve (AUC) of 0.9981. For multi-class classification, 5fold-CV yielded 98.36% accuracy, 97.14% sensitivity, 98.77% specificity, and an AUC of 0.9975. Validation using leave-one-subject-out cross-validation (LOSO-CV) achieved a binary classification accuracy of 86.42%, sensitivity of 79.4%, specificity of 90.2%, and an AUC of 0.9372. For multi-class classification under LOSO-CV, the average accuracy, sensitivity, and F1-score were 86.7%, 72.6%, and 0.7224, respectively. External validation was performed on a cohort of 123 subjects from the Osteoporotic Fractures in Men (MrOS) database, comprising 68 cases of PAF and 55 cases of OSA. The proposed method achieved a multi-class classification accuracy of 88.51%, sensitivity of 73.50%, specificity of 91.34%, and an AUC of 0.9363.

CONCLUSIONS: These findings underscore the significance of simultaneous detection of AF and OSA, providing a more comprehensive evaluation of cardiovascular health. The proposed SHHDeepNet framework offers a promising tool to support clinical decision-making, enhance management strategies, and improve patient outcomes by mitigating the risks associated with these conditions.

PMID:40795334 | DOI:10.1093/sleep/zsaf226

Int J Neuropsychopharmacol. 2025 Aug 8:pyaf059. doi: 10.1093/ijnp/pyaf059. Online ahead of print.

ABSTRACT

BACKGROUND: Ulotaront is an experimental antipsychotic for schizophrenia, but its optimal dose is unclear. This study aimed to evaluate dose-response relationships for efficacy and safety in people with schizophrenia.

METHOD: A systematic review of four databases (until January 22, 2025; INPLASY202510091) identified randomized clinical trials assessing ulotaront. Outcomes included efficacy, measured by changes in the Positive and Negative Syndrome Scale (PANSS) total score (primary outcome), positive and negative subdomains, and the Clinical Global Impression Scale-Severity (CGI-S), and safety, assessed by all-cause dropout (co-primary outcome, dropout due to adverse event, serious, non-serious, and specific adverse events. We employed one-stage dose-response meta-analysis (random-effects model) calculating standardized mean differences (SMDs) and risk ratios (RRs) with 95% confidence intervals (CIs).

RESULTS: Analysis of three randomized clinical trials (n=1,144) indicated that the 100 mg dose of ulotaront provided the greatest improvement in PANSS total score (SMD = -0.23 [95% CI: -0.43, -0.02]), PANSS positive symptom score (-0.30 [-0.70, 0.10]), and PANSS negative symptom score (-0.28 [-0.48, -0.08]). However, CGI-S scores did not exhibit a clear dose-response relationship. Regarding safety, all-cause dropout (RR at 100mg =1.10 [95% CI: 0.57, 2.12]), adverse event-related dropout, serious, non-serious, and most specific adverse events showed no significant dose-response relationship. The risk of anxiety-related adverse events was significantly higher than placebo at 50 mg and 75 mg doses (RR at 75mg =2.06 [95% CI: 1.11, 3.80]).

CONCLUSION: Ulotaront 100 mg appears greatest efficacy with favorable safety for acute schizophrenia. However, effect sizes were small, and higher ulotaront doses should be tested.

PMID:40795331 | DOI:10.1093/ijnp/pyaf059

J Med Internet Res. 2025 Aug 12;27:e70414. doi: 10.2196/70414.

ABSTRACT

BACKGROUND: Virtual care interventions have the potential to improve access to care and serial medication intensification for patients with chronic heart failure with reduced ejection fraction (HFrEF). However, concerns remain that these interventions might unintentionally create or widen existing disparities in care delivery and patient outcomes.

OBJECTIVE: This study aimed to characterize the health care use patterns of patients who have HFrEF, including specialty type and frequency of in-person and virtual visits.

METHODS: We conducted a retrospective cohort study of patients with HFrEF within a large health system. Inclusion criteria were patients alive with an ejection fraction ≤40% as of September 1, 2021, and at least one virtual or in-person outpatient visit to a primary care or cardiology clinician in the prior year. Descriptive statistics were used to evaluate baseline patient demographics and clinical use data and outcomes. Univariate analyses were performed both with virtual visits as a variable (received or did not receive) using the chi-square test for association and as a discrete outcome using the Wilcoxon rank-sum test to capture potentially important predictor variables that could influence use or frequency of using virtual visits. The primary outcome of interest was the odds of at least one virtual visit during the 1-year evaluation period from 2021 to 2022. Descriptive statistics were used to evaluate baseline patient demographics and care use. A logistic regression model was used to model at least one primary care or cardiology virtual visit.

RESULTS: A total of 8481 patients were included in the analysis. The mean age was 65.9 years (SD 15.1), 5672 (66.9%) patients were male and 6608 (77.9%) patients were non-Hispanic White. The majority of patients had no cardiology (7938/8481, 93.6%) or primary care (7955/8481, 93.8%) virtual visits during the evaluation period. Multivariable logistic regression showed significantly higher odds of having at least one virtual visit for patients with certain digital access-for example, email on file (odds ratio [OR] 9.3, P≤.001), cell phone on file (OR 2.9, P≤.001), and active electronic health record patient portal (OR 2.8, P≤.001)-than those without. Age, race, ethnicity, rurality, and Social Vulnerability Index were not associated with virtual visits.

CONCLUSIONS: Only a minority of patients with HFrEF were seen via virtual visits. Patients who regularly used digital technology were more likely to have virtual visits. Patients were more likely to be seen in a cardiology clinic than by a primary care provider. Although there was no evidence of an association between social determinants of health factors like race, ethnicity, or rurality with digital divide indicators, these findings should be interpreted with caution given the limitations of these data. Future studies should aim to replicate the findings of this study and explore ways to enhance the effective and equitable use of virtual visits.

PMID:40795329 | DOI:10.2196/70414

J Bone Miner Res. 2025 Aug 8:zjaf091. doi: 10.1093/jbmr/zjaf091. Online ahead of print.

ABSTRACT

The aim of this international meta-analysis was to quantify the predictive value of body mass index (BMI) for incident fracture and relationship of this risk with age, sex, follow-up time and bone mineral density (BMD). 1 667 922 men and women from 32 countries (63 cohorts), followed for a total of 16.0 million person-years were studied. 293 325 had femoral neck BMD measured (2.2 million person-years follow-up). An extended Poisson model in each cohort was used to investigate relationships between WHO-defined BMI categories (Underweight:<18.5 kg/m2; Normal:18.5-24.9 kg/m2; Overweight:25.0-29.9 kg/m2; Obese I:30.0-34.9 kg/m2; Obese II:≥35.0 kg/m2) and risk of incident osteoporotic, major osteoporotic and hip fracture (HF). Inverse-variance weighted β-coefficients were used to merge the cohort-specific results. For the subset with BMD available, in models adjusted for age and follow-up time, the hazard ratio (95%CI) for HF comparing underweight with normal weight was 2.35 (2.10-2.60) in women and for men was 2.45 (1.90-3.17). HF risk was lower in overweight and obese categories compared to normal weight [obese II vs normal: women 0.66 (0.55-0.80); men 0.91 (0.66-1.26). Further adjustment for femoral neck BMD T-score attenuated the increased risk associated with underweight [underweight vs normal: women 1.69 (1.47-1.96); men 1.46 (1.00-2.13)]. In these models, the protective effects of overweight and obesity were attenuated, and in both sexes the direction of association reversed to higher fracture risk in Obese II category [Obese II vs Normal: women 1.24 (0.97-1.58); men 1.70 (1.06-2.75)]. Results were similar for other fracture outcomes. Underweight is a risk factor for fracture in both men and women regardless of adjustment for BMD. However, whilst overweight/obesity appeared protective base models, they became risk factors after additional adjustment for femoral neck BMD, particularly in the Obese II category. This effect in the highest BMI categories was of greater magnitude in men than women. These results will inform the second iteration of FRAX.

PMID:40795319 | DOI:10.1093/jbmr/zjaf091

J Immunol. 2025 Aug 7:vkaf155. doi: 10.1093/jimmun/vkaf155. Online ahead of print.

ABSTRACT

Tumor clearance by T cells is impaired by insufficient tumor antigen recognition, insufficient tumor infiltration, and the immunosuppressive tumor microenvironment. Although targeted T cell therapy circumvents failures in tumor antigen recognition, suppression by the tumor microenvironment and failure to infiltrate the tumor can hinder tumor clearance. Checkpoint inhibitors (CPIs) promise to reverse T cell suppression and can be combined with bispecific antibody armed T cell (BAT) therapy to improve clinical outcomes. We hypothesize that adoptively transferred T cell function may be improved by the addition of CPIs if the inhibitory pathway is functionally active. This study develops a kinetic-dynamic model of killing of hormone receptor-positive breast cancer cells mediated by BATs using single-cell transcriptomic and temporal protein data to identify T cell phenotypes and quantify inhibitory receptor expression. LAG3, PD-1, and TIGIT were identified as inhibitory receptors expressed by cytotoxic effector CD8 BATs upon exposure to hormone receptor-positive breast cancer cell lines. These data were combined with real-time tumor cytotoxicity data in a multivariate statistical analysis framework to predict the relevant contributions of T cells expressing each receptor to tumor reduction. A mechanistic kinetic-dynamic mathematical model was developed and parametrized using protein expression and cytotoxicity data for in silico validation of the findings of the multivariate statistical analysis. The model corroborated the predictions of the multivariate statistical analysis which identified LAG3+ BATs as the primary effectors, while TIGIT expression dampened cytotoxic function. These results inform CPI selection for BATs combination therapy and provide a framework to maximize BATs antitumor function.

PMID:40795300 | DOI:10.1093/jimmun/vkaf155