Tag: statistics

Sci Rep. 2025 Aug 5;15(1):28599. doi: 10.1038/s41598-025-13419-z.

ABSTRACT

From cellular mechanotransduction to the formation of embryonic tissues and organs, mechanics has been shown to play an important role in the control of cell behavior and embryonic development. Most of our existing knowledge of how mechanics affects cell behavior comes from in vitro studies, mainly because measuring cell and tissue mechanics in 3D multicellular systems, and especially in vivo, remains challenging. Oil microdroplet sensors, and more recently gel microbeads, use surface deformations to directly quantify mechanical stresses within developing tissues, in vivo and in situ, as well as in 3D in vitro systems like organoids or multicellular spheroids. However, an automated analysis software able to quantify the spatiotemporal evolution of stresses and their characteristics from particle deformations is lacking. Here we develop STRESS (Surface Topography Reconstruction for Evaluation of Spatiotemporal Stresses), an analysis software to quantify the geometry of deformable particles of spherical topology, such as microdroplets or gel microbeads, that enables the automatic quantification of the temporal evolution of stresses in the system and the spatiotemporal features of stress inhomogeneities in the tissue. As a test case, we apply these new code to measure the temporal evolution of mechanical stresses using oil microdroplets in developing zebrafish tissues. Starting from a 3D timelapse of a droplet, the software automatically calculates the statistics of local anisotropic stresses, decouples the deformation modes associated with tissue- and cell-scale stresses, obtains their spatial features on the droplet surface and analyzes their spatiotemporal variations using spatial and temporal stress autocorrelations. We provide fully automated software in Matlab/Python and also in Napari (napari-STRESS), which allows the visualization of mechanical stresses on the droplet surface together with the microscopy images of the biological systems. The automated nature of the analysis will help users obtain quantitative information about mechanical stresses in a wide range of 3D multicellular systems, from developing embryos or tissue explants to organoids.

PMID:40764636 | DOI:10.1038/s41598-025-13419-z

Sci Rep. 2025 Aug 5;15(1):28617. doi: 10.1038/s41598-025-11357-4.

ABSTRACT

Optimization techniques are particularly useful when designing different free-form surfaces and manufacturing products in the engineering and CAD/CAM fields. Recently, many real-world problems utilize optimization techniques with objective functions to get their desired solution. In this paper, the shape optimization of GHT-Bézier developable surfaces by using a meta-heuristic technique called Improved-Grey Wolf Optimization (I-GWO, in short) technique is presented. This Grey Wolf optimization algorithm impersonates the hunting tactics of grey wolves in nature. Three optimization models arc length (AL), minimum energy (En), and curvature variation energy (CVEn) of dual and interpolation curves, are used to formulate this technique. The shape control parameters are considered as optimization variables. So, our aim is to find the optimal shape control parameters by applying the I-GWO algorithm to the optimization models through an iterative process. By using the duality principle between the points and planes, the construction of GHT-Bézier developable surfaces is formulated by the optimal choice of shape parameters obtained from I-GWO technique. Furthermore, the developability degree of GHT-Bézier developable surfaces is also determined. Some applications of the proposed method are given and the effectiveness of this method is illustrated.

PMID:40764629 | DOI:10.1038/s41598-025-11357-4

Parasit Vectors. 2025 Aug 5;18(1):335. doi: 10.1186/s13071-025-06988-5.

ABSTRACT

BACKGROUND: Onchocerca lupi is a zoonotic, vector-borne filarioid nematode that mainly infects wild and domestic canids in the Southwestern USA, Europe, Asia, and Africa. Clinical canine infections are associated with ocular disease, characterized by the presence of nodules and conjunctivitis. Subclinical cases can be challenging to diagnose, even with evaluation of cutaneous tissues for microfilariae. Current diagnostic tests include conventional polymerase chain reaction (cPCR) to detect O. lupi DNA, and, alternatively, real-time PCR (qPCR), which provides more rapid results and higher throughput. The objectives of this study were to: I) optimize a novel qPCR assay that detects O. lupi and II) to assess the prevalence of O. lupi in shelter dogs from Albuquerque, NM, USA.

METHODS: This probe-based qPCR was optimized with a detection threshold of 0.33 pg for DNA of an adult female O. lupi. We further optimized the assay by performing a dynamic range test to determine the ideal dilution factor and inclusion of an internal positive control. We collected skin snips from the interscapular region of 404 dogs between January and September 2023. Demographics were recorded, including age, sex, American Kennel Club breed groups, and coat color. Dogs were separated into age groups, including juveniles ≤ 1 year old (n = 120; 29.7%), adults > 1-7 years old (n = 260; 64.3%), and seniors > 7 years old (n = 24; 5.9%). Of those, 194 were female, and 210 were male. We also had nine different American Kennel Club breed groups represented, as well as two coat colors: single (33.0%) and mixed (67.0%). Genomic DNA was subjected to cPCR followed by Sanger sequencing and our probe-based qPCR. Both PCRs targeted a fragment of the cytochrome oxidase c subunit 1 (cox1) of the mitochondrial DNA. We performed statistical analysis to assess any association between exposure factors, such as age, sex, breed, and coat color and the outcome, whether O. lupi was present.

RESULTS: Overall, eight (1.9%; 95% confidence interval (CI) 0.8-3.8%) dogs tested O. lupi-positive via qPCR and five (1.2%; 95% CI 0.4-2.8%) via cPCR. Of the qPCR-positive dogs, six were adults and two were juveniles. Age (P = 0.704), sex (P = 0.910), breed groups (P = 0.217), and coat color (P = 0.781) were not statistically associated with a qPCR-positive result with a cutoff of P < 0.2. In addition, 20 dogs tested positive for Cercopithifilaria bainae via cPCR and sequencing, but these did not cross-react with our qPCR.

CONCLUSIONS: This is the first epidemiological study on O. lupi in a canine population from an urban center within an endemic area in North America. Active surveillance using reliable diagnostic tools can better elucidate the epidemiology of this zoonotic parasite and enable the implementation of strategies for control and prevention.

PMID:40764591 | DOI:10.1186/s13071-025-06988-5

BMC Med. 2025 Aug 5;23(1):458. doi: 10.1186/s12916-025-04282-w.

ABSTRACT

BACKGROUND: Parental obesity has been identified as one of the most important early risk factors for childhood obesity, but molecular mechanisms driving this greater predisposition remain to be elucidated.

METHODS: In this study, we recruited a cohort comprising children with obesity (body mass index over two z-scores above the age/sex-adjusted mean of the Spanish reference population, age range: 6-12 years), born to parents with obesity (N = 18) or without obesity (N = 41), as well as matched healthy controls (N = 26). Plasma and erythrocyte samples were collected for comprehensive biochemical and metabolomics analyses, this latter by applying high-throughput liquid chromatography-mass spectrometry. Then, a combination of multivariate and univariate statistical tools was applied to unravel the molecular pathogenic impairments that parental obesity may imprint in the offspring.

RESULTS: Interestingly, we found parental obesity to be associated with exacerbated unhealthy metabolic outcomes in the offspring with obesity, as mirrored in higher fasting insulin levels (p = 2.8 × 10^-8) and HOMA-IR scores (p = 1.3 × 10^-8). This was in turn accompanied by altered concentrations in 87 plasma and 51 erythroid metabolites (p < 0.05) involved in a variety of obesity-related pathways that are known to be tightly regulated by insulin action, namely energy-related metabolism, branched-chain amino acids, nitrogen homeostasis, redox systems, and steroid synthesis (i.e., steroid hormones, bile acids). Additional analyses demonstrated that most metabolomics associations were largely attenuated after adjusting for the HOMA-IR scores.

CONCLUSIONS: Therefore, we hypothesize that insulin resistance could be a major driving force in mediating deleterious programming mechanisms induced by parental obesity in the offspring.

PMID:40764579 | DOI:10.1186/s12916-025-04282-w

BMC Vet Res. 2025 Aug 6;21(1):505. doi: 10.1186/s12917-025-04946-y.

ABSTRACT

BACKGROUND: Canine obesity is a common disorder accompanied by a low-grade chronic inflammation and is considered a risk factor for liver and heart diseases. The present study aimed to investigate whether an olive oil-derivative enriched in N-acylethanolamines (Olaliamid^®, OLA) may protect dogs against obesity-induced comorbidities.

RESULTS: Twenty-seven dogs of mixed breed and size with a body condition score ≥ 7/9 were included in the trial, once provided they were otherwise healthy. Dogs were fed a commercial maintenance diet for two weeks before enrolment, and randomized in two groups, i.e., OLA (n = 14) and placebo (OLA vehicle; n = 13). Both treatments were administered orally by the owners in a liquid form at 0.7 ml/5kg body weight, once a day for three months. At baseline and three months later dogs underwent physical examination, blood draw, and echocardiography. At the same timepoints, owners were given a questionnaire about their dog’s general condition. OLA prevented the increase in leptin observed in the placebo group (P = 0.011), decreased IL-6 (P = 0.043) and derivatives-reactive oxygen metabolites (d-ROMs, P = 0.008), and increased biological antioxidant potential (BAP) compared to the placebo group (P = 0.032). Moreover, OLA protected the liver, with ALT levels being decreased in the OLA group compared to the placebo one (P = 0.005) and bilirubin levels being decreased in the OLA group (P = 0.030) but not in the placebo one. OLA showed a cardioprotective effect, with a significant decrease of IVSdN (P = 0.028), LVPWdN (P = 0.047), IVSd/LVIDd (P = 0.015) and LVPWd/LVIDd (P = 0.034) compared to the placebo group. According to dog owners, the difficulty rising from lying down significantly increased in the placebo group (P = 0.039) but not in the OLA one.

CONCLUSION: Overall, OLA improved obesity-induced meta-inflammation and oxidative status and helped to ameliorate liver and heart health as well.

PMID:40764576 | DOI:10.1186/s12917-025-04946-y

BMC Health Serv Res. 2025 Aug 6;25(1):1031. doi: 10.1186/s12913-025-13207-5.

ABSTRACT

BACKGROUND: Artificial Intelligence (AI) can empower clinicians to make data-driven decisions, treatments and streamline administrative tasks. However, it is vital to understand their perception towards AI for seamless implementation in practice. Therefore, the study aimed to assess the attitude, receptivity and readiness of medical and dental practitioners towards the use of AI in clinical practice.

METHODS: A cross-sectional study employing non-probability convenience sampling was conducted from April to August 2024. A questionnaire was distributed among practitioners working in public and private sector hospitals. The questionnaire included a validated tool, the General Attitude towards Artificial Intelligence Scale (GAAIS), comprising of total 20 items with two subscales; positive and negative attitudes. They were rated on a 5-point Likert scale, ranging from strongly disagree (1) to strongly agree (5). The items of negative attitudes were reverse coded. Self-formulated questions to assess the readiness of medical and dental practitioners to incorporate AI in practice were also included. Data was analyzed using IBM SPSS v.25. Results were reported as frequencies and percentages. Statistical analysis was performed using Mann-Whitney U, and Chi-squared test for continuous and categorical variables. The association between two continuous variables was assessed through Spearman’s correlation.

RESULTS: A total of 451 responses were analyzed. The mean score for positive attitudes toward AI was 3.6 ± 0.54, whereas for negative attitudes it was estimated to be 2.8 ± 0.71. Approximately 187 (41.5%) respondents believed AI was superior to humans in routine jobs. About 190 (42.1%) respondents agreed that AI can make errors. Most respondents 334 (74.1%) were aware of AI applications in healthcare, and 329 (72.9%) reported familiarity with AI technologies. However, only 153 (33.9%) felt confident in operating AI systems. While 282 (62.5%) expressed eagerness to incorporate AI into diagnosis and treatment planning, a significant difference was observed between groups, with dental practitioners showing greater willingness (p = 0.004). Additionally, dentists exhibited higher confidence in using AI compared to medical practitioners (p = 0.047).

CONCLUSIONS: The findings suggest that most practitioners had a positive attitude towards AI and were receptive towards incorporating the technology as a beneficial tool in their practice. Ethical guidelines and extensive training can mitigate negative perceptions associated with the use of AI technology. It is also imperative that resources should be provided, specially to public-sector healthcare systems as they serve underprivileged communities. This will help practitioners gain familiarity with AI technology and will enable them to develop proficiency in AI use.

PMID:40764570 | DOI:10.1186/s12913-025-13207-5

BMC Psychiatry. 2025 Aug 5;25(1):761. doi: 10.1186/s12888-025-07237-w.

ABSTRACT

BACKGROUND: This study addresses the challenge of accurately classifying the severity of schizophrenia in patients through a clever approach. By leveraging electroencephalography (EEG) signals, we aim to establish a method for evaluating patient conditions, thereby contributing to the psychiatric diagnosis and treatment field.

METHODS: Our research methodology encompasses a comprehensive system framework designed to analyze EEG signals with the Positive and Negative Syndrome Scale (PANSS) for correlation analysis. The process involves: (1) administering the PANSS test to create a database of schizophrenia patients; (2) developing a visual concentration test system that measures EEG signals in real-time; (3) processing these signals to construct an EEG feature database; (4) employing support vector machine and decision tree methods for illness severity classification; (5) conducting statistical analysis to correlate PANSS scores with EEG features, assessing the effectiveness of these correlations in clinical applications.

RESULTS: The study successfully demonstrated the potential of a concentration detection system, integrating EEG signal analysis with PANSS scores, to classify schizophrenia severity accurately. Applying SVM and decision tree methods established significant correlations between EEG features and clinical scales, indicating the system’s efficacy in supporting psychiatric diagnosis.

CONCLUSIONS: Our findings suggest that the proposed analytical methods, focusing on EEG signals and employing a novel system framework, can effectively assist in classifying the severity of schizophrenia. This approach offers promising implications for enhancing diagnostic accuracy and tailoring treatment strategies for patients with schizophrenia.

PMID:40764561 | DOI:10.1186/s12888-025-07237-w

BMC Public Health. 2025 Aug 5;25(1):2658. doi: 10.1186/s12889-025-23654-8.

ABSTRACT

BACKGROUND: Diabetes mellitus (DM) is a significant public health concern, particularly among older adults who face an elevated risk of complications and increased mortality. This study assessed overtime changes in the prevalence, awareness, treatment coverage, and glycemic control of DM in Iranian adults aged 60 and older.

METHODS: This is a nationwide repeated cross-sectional study based on data from the 2016 and 2021 STEPwise Approach to Non-communicable Disease Risk Factor Surveillance (STEPS), the most recent surveys conducted across all provinces of Iran. We reported prevalence estimates as weighted percentages with 95% confidence intervals (CIs). Logistic regression was used to evaluate the association between selected demographic and health characteristics and diabetes mellitus (DM). All analyses were performed using R statistical software version 4.0.5.

RESULTS: From 2016 to 2021, the prevalence of prediabetes increased from 23.86 to 28.96% in males and from 22.86 to 30.86% in females, while DM prevalence rose from 19.37 to 25.43% in males and from 26.22 to 30.26% in females. In 2016, DM awareness was 74.47% in males and 82.19% in females, and in 2021, it was 71.20% in males and 78.73% in females. Treatment coverage was 60.18% in males and 67.63% in females in 2016, and 63.71% in males and 71.14% in females in 2021. Glycemic control was 59.77% in males and 57.98% in females in 2016, and 55.96% in males and 53.66% in females in 2021. Obesity significantly increased the odds of DM (odds ratio (OR) 1.97, p < 0.001), while being underweight (OR 0.21, p < 0.001) and having sufficient physical activity (OR 0.77, p = 0.02) were protective factors.

CONCLUSION: The findings from the 2016 and 2021 STEPS surveys indicate a rising burden of prediabetes and DM among older adults in Iran, particularly among females. Although awareness and treatment coverage were high, optimal glycemic control remains a challenge, emphasizing the need to improve access to healthcare resources and develop tailored community-based interventions.

PMID:40764559 | DOI:10.1186/s12889-025-23654-8

Virol J. 2025 Aug 5;22(1):267. doi: 10.1186/s12985-025-02900-w.

ABSTRACT

Antiretroviral therapy (ART) has transformed HIV from a rapidly progressive and fatal disease to a chronic disease with limited impact on life expectancy. However, people living with HIV(PLWHs) faced high critical illness risk due to the increased prevalence of various comorbidities and are admitted to the Intensive Care Unit(ICU). This study aimed to use machine learning to predict ICU admission risk in PLWHs. 1530 HIV patients (199 admitted to ICU) from Beijing Ditan Hospital, Capital Medical University were enrolled in the study. Classification models were built based on logistic regression(LOG), random forest (RF), k-nearest neighbor (KNN), support vector machine (SVM), artificial neural network(ANN), and extreme gradient boosting(XGB). The risk of ICU admission was predicted using the Brier score, area under the receiver operating characteristic curve (ROC-AUC), and area under the precision-recall curve(PR-ROC) for internal validation and ranked by Shapley plot. The ANN model performed best in internal validation (Brier score = 0.034, ROC-AUC = 0.961, PR-AUC = 0.895) to predict the risk of ICU admission for PLWHs. 11 important features were identified to predict predict ICU admission risk by the Shapley plot: respiratory failure, multiple opportunistic infections in the respiratory system, AIDS defining cancers, baseline viral load, PCP, baseline CD4 cell count, and unexplained infections. An intelligent healthcare prediction system could be developed based on the medical records of PLWHs, and the ANN model performed best in effectively predicting the risk of ICU admission, which helped physicians make timely clinical interventions, alleviate patients suffering, and reduce healthcare cost.

PMID:40764558 | DOI:10.1186/s12985-025-02900-w

BMC Health Serv Res. 2025 Aug 5;25(1):1027. doi: 10.1186/s12913-025-13048-2.

ABSTRACT

BACKGROUND: Monitoring a patient’s functional status in the rehabilitation process is essential for the ongoing improvement of the quality of the rehabilitation services offered. This possibility is provided by the use of the International Classification of Functioning, Disability and Health (ICF) and the ICF Core Sets developed on its basis. The purpose of this study was to validate and evaluate the use of the ICF Rehabilitation Set in rehabilitation patients in clinical practice in southeastern Poland.

METHODS: This study included patients who required comprehensive rehabilitation in a tertiary care rehabilitation centre. The study used the Polish version of a rating reference guide for the ICF Rehabilitation Set and the Barthel Index. The assessment was carried out by an interdisciplinary team, which established the ICF qualifier values by consensus. Interrater reliability was assessed using intraclass correlation coefficients (ICCs). Convergent validity was assessed using Spearman correlation coefficients between the Disability Index (DI) and the Barthel Index (BI). To assess the effectiveness of rehabilitation in the study group, a paired Wilcoxon test was used to compare the measurements of the two ICF codes.

RESULTS: This study demonstrated the consistency and reliability of the reference guide for the ICF Rehabilitation Set. The ICC values were very high (for B620; D450; D640, the ICC was 1.000) and high (for E155, the ICC was 0.793). The individual entries from the ICF Rehabilitation Set were found to have very high concordance for assessing individual qualifiers between researchers (the ICC for the DI was 0.995). The ICF Rehabilitation Set scores and the calculated DI were correlated with the BI scores. The ICF Rehabilitation Set is sensitive to changes in the functional status of patients undergoing rehabilitation and is consistent with the results obtained by the BI measure. There was also a statistically significant improvement in patients’ functional status after rehabilitation.

CONCLUSIONS: The ICF Rehabilitation Set and the developed rating reference guide are reproducible, consistent and relevant and can be used in clinical settings to collect health information from people undergoing rehabilitation. Further research is needed to strengthen the evidence to validate the ICF in Poland.

TRIAL REGISTRATION: The study was registered in the clinical trial registry at ClinicalTrials.gov (NCT06718036, date of registration 2024-11-30, retrospectively registered).

PMID:40764554 | DOI:10.1186/s12913-025-13048-2