Tag: statistics

Sci Total Environ. 2025 May 6;981:179593. doi: 10.1016/j.scitotenv.2025.179593. Online ahead of print.

ABSTRACT

Wastewater Based Epidemiology (WBE) has been identified as a tool for monitoring and predicting patterns of SARS-CoV-2 in communities. Several factors may lead to a day-to-day variation in the measurement of viral genetic material. Wastewater samples are systematically collected from the two major wastewater treatment plants in Crete, Greece. Physico-chemical factors were tested, viral concentration was determined by RT-real time PCR and the results were normalized. The influence of restriction measures, rain and physico-chemical agents was addressed. Statistics together with machine learning (ML) were applied to predict human cases. 781 samples were analyzed. RNA concentration was reduced during lockdown and was impacted by rain. Fluctuations in pH and total solids’ concentrations were associated with changes in viral load. Conductivity was mainly related to chloride ions. In Heraklion, wastewater viral load preceded human cases by three days on average. Cross- correlation estimates did not perform likewise in Chania. According to ML, the ratio of sewage RNA measurements to reported cases decreased in comparison to the first wave, due to different variants, climatological parameters, testing rate and behaviors related to seeking healthcare. The model developed showed a close approximation between recorded and predicted cases. Parameters such as total solids, pH, conductivity, rain and inhibitors can significantly impact the recovery of viral RNA. The correlation between viral load in wastewater and human cases is not straightforward. The application of ML may fill some but not every gap. Existing models cannot be directly applied to different Wastewater Treatment Plants or countries.

PMID:40334465 | DOI:10.1016/j.scitotenv.2025.179593

Sci Total Environ. 2025 May 6;981:179578. doi: 10.1016/j.scitotenv.2025.179578. Online ahead of print.

ABSTRACT

Clothing can act as a barrier and a source of skin exposure to chemicals due to reversible accumulation on and within textile fibers. The partition coefficient quantifies the equilibrium relationship between textile and air for a specific chemical and is a key parameter in models estimating dermal exposure. Here, textiles composed of natural and/or synthetic fibers were exposed for 26 days in environmental test chambers under different climatic conditions to semi-volatile organic compounds (SVOCs) that have similar, but not identical, physical and chemical properties. The nine textiles tested included a single fiber type or blends of cotton, polyester, nylon, linen and/or elastane. The seven SVOCs are found commonly indoors and in consumer products: 4-t-octylphenol (4t-OP),4-nonylphenol (4-NP); di-n-butyl adipate (DnBA); galaxolide (HHCB); di-n-butyl phthalate (DnBP); benzophenone-3 (BP-3) and 3-(4′-methylbenzylidene)camphor (4-MBC). The chamber air concentrations and masses accumulated on textiles were measured and the mass based (K_m, m³/g), area based (K_a, m) and volume based (K_v, dimensionless) partition coefficients were calculated. Partition coefficients among all chemicals were generally lower for polyester and higher for cotton and blends. A hierarchical cluster analysis combined with fiber specific matrix analysis showed that, across the SVOCs tested, the partition coefficients for nylon/elastane were ~ 7 to 70 times higher than for jeans cotton, while the partition coefficient for jeans cotton were ~ 2 to 7 time higher than for polyester. K_m, K_a and K_v were lowest for HHCB and highest for 4-NP, DnBP and 4-MBC. However, chemical-textile partition coefficients were not statistically correlated with respect to physical and environmental properties of the SVOCs. The values were statistically the same for different chamber air concentrations of the chemicals tested, and from 24 °C to 33 °C there was only a weak reduction in the partition coefficients.

PMID:40334462 | DOI:10.1016/j.scitotenv.2025.179578

Schizophr Res. 2025 May 6;281:132-137. doi: 10.1016/j.schres.2025.04.036. Online ahead of print.

ABSTRACT

BACKGROUND: Racially and ethnically minoritized emerging adults in the United States have a higher prevalence of psychotic experiences when compared to their White peers. Racism gives rise to social stressors, including ethno-racial discrimination, which can increase the risk for psychotic experiences. Racism is ever-shapeshifting into new forms of racism, including online racism, yet little research has examined its associations with psychotic experiences.

METHODS: We used Qualtrics panels to recruit emerging adults, including Black (N = 1200) and Asian American/Pacific Islander/Native Hawaiian (N = 1600). Using multivariable logistic regression, we examined the association between online racism (Online Racism Scale – Very Brief) and psychotic experiences (reporting at least one psychotic experience using the WHO CIDI psychosis screen), controlling for sociodemographic characteristics, everyday discrimination, internet usage, and mental health.

RESULTS: Approximately 42 % of the sample reported a lifetime psychotic experience. In multivariable logistic regression models, a one-unit increase on the online racism scale was associated with a 13 % increase in odds of having a lifetime psychotic experience. This association attenuated slightly after adjusting for sociodemographic characteristics and further attenuated after adjusting for everyday discrimination, total internet usage, and depression and anxiety. After accounting for all covariates, a one-unit increase in online racism was associated with a 5 % increase in odds of psychotic experiences.

CONCLUSION: Online racism is more common than in-person discrimination, and people of color use social media platforms at high rates, where they inevitably face various types of online racism. Online racism is pervasive, often anonymous, and unmoderated, and these exposures are associated with greater odds of psychotic experiences.

PMID:40334439 | DOI:10.1016/j.schres.2025.04.036

J Environ Manage. 2025 May 6;385:125651. doi: 10.1016/j.jenvman.2025.125651. Online ahead of print.

ABSTRACT

Coastal areas are one of the most concentrated and fastest urbanizing areas for human activities. Land use carbon emissions (LUCE) related to human activities are recognized as an essential contributor of climate change. Nevertheless, carbon emissions linked to changes in land use in coastal areas remain unclear. While nighttime light images can effectively indicate the human activity intensity in different geographic spaces and monitor the spatio-temporal dynamics of human social activities. Here, we investigated the spatio-temporal changes in LUCE using nighttime light images during 1991-2020 in Shandong Province. The influential drivers of LUCE were detected by employing GeoDetector. The results demonstrated that (1) Carbon emissions from construction land at the city scale can be modeled with nighttime lighting data. (2) Cities with highest carbon emissions were Weifang (27.9 MtCO₂e) and Qingdao (31.63 MtCO₂e) in the study area. Average annual growth rate for LUCE was the highest during 2000-2010 (315.42%), and reached an inflection point in 2013 during the study period. (3) The mean center of LUCE has been in Weifang for most of the last 30 years. (4) GDP had the largest q statistic of 0.781, and was the main factor affecting LUCE. (5) Low-carbon development in coastal areas needs to increase carbon sinks in addition to reducing carbon sources. The results provide a theoretical basis for improving the ecological environment in Shandong Province and a scientific reference for the development of low-carbon in coastal areas.

PMID:40334405 | DOI:10.1016/j.jenvman.2025.125651

Cytokine. 2025 May 6;191:156945. doi: 10.1016/j.cyto.2025.156945. Online ahead of print.

ABSTRACT

BACKGROUND: Frailty is a common symptom in Multiple Sclerosis (MS), yet its precise mechanism remains elusive, and the clinical implications of frailty in MS are uncertain. Moreover, inflammation is closely linked to frailty. This study aims to assess serum cytokine levels in individuals with MS and explore their correlation with frailty.

METHODS: A case-control study included 83 primary MS patients and 100 healthy individuals undergoing health check-ups. Serum cytokine levels were measured, and MS severity was determined using the Expanded Disability Status Scale (EDSS) score. Additionally, a comprehensive frailty index (FI) was calculated based on health deficits from various domains following standardized procedures.

RESULTS: Serum IL-6 and TNF-α levels were significantly higher in the frail group than in the non-frail group, with a statistically significant difference (P < 0.05). After adjusting for disease duration, sex, age, BMI, SBP, and DBP, serum IL-6 independently correlated with frailty in MS patients (OR = 1.46; 95 % CI = 1.02-1.93; P = 0.003). Moreover, increased serum IL-6 levels were associated positively with the frailty index (β = 0.123, P = 0.008).

CONCLUSION: Our initial findings suggest elevated levels of pro-inflammatory cytokines in MS patients with frailty, with IL-6 showing a positive correlation with frail indices. These results underscore the potential impact of inflammatory responses on frailty development in MS.

PMID:40334398 | DOI:10.1016/j.cyto.2025.156945

Poult Sci. 2025 May 1;104(7):105242. doi: 10.1016/j.psj.2025.105242. Online ahead of print.

ABSTRACT

Bacteria are the major component of poultry gastrointestinal tract (GIT) microbiota and play an important role in host health, nutrition, physiology regulation, intestinal development, and growth. Bacterial community profiling based on the 16S ribosomal RNA (rRNA) gene amplicon sequencing approach has become the most popular method to determine the taxonomic composition and diversity of the poultry microbiota. The 16S rRNA gene profiling involves numerous steps, including sample collection and storage, DNA isolation, 16S rRNA gene primer selection, Polymerase Chain Reaction (PCR), library preparation, sequencing, raw sequencing reads processing, taxonomic classification, α- and β-diversity calculations, and statistical analysis. However, there is currently no standardized protocol for 16S rRNA gene analysis profiling and data deposition for poultry microbiota studies. Variations in DNA storage and isolation, primer design, and library preparation are known to introduce biases, affecting community structure and microbial population analysis leading to over- or under-representation of individual bacteria within communities. Additionally, different sequencing platforms, bioinformatics pipeline, and taxonomic database selection can affect classification and determination of the microbial taxa. Moreover, detailed experimental design and DNA processing and sequencing methods are often inadequately reported in poultry 16S rRNA gene sequencing studies. Consequently, poultry microbiota results are often difficult to reproduce and compare across studies. This manuscript reviews current practices in profiling poultry microbiota using 16S rRNA gene amplicon sequencing and proposes the development of guidelines for protocol for 16S rRNA gene sequencing that spans from sample collection through data deposition to achieve more reliable data comparisons across studies and allow for comparisons and/or interpretations of poultry studies conducted worldwide.

PMID:40334389 | DOI:10.1016/j.psj.2025.105242

J Optom. 2025 May 6;18(3):100555. doi: 10.1016/j.optom.2025.100555. Online ahead of print.

ABSTRACT

OBJECTIVE: To evaluate the features of retinal and choroidal microcirculation and structure in patients with amblyopia compared to healthy adolescents of the same age (>10 years old). To classify and diagnose amblyopia using machine learning techniques on optical coherence tomographic angiography (OCTA) images.

METHODS: Nineteen adolescents aged 11-17 with hyperopic refractive amblyopia and 22 age-matched healthy controls underwent 12 × 12 mm macular OCTA scans. The eyes were classified into three groups: amblyopic, contralateral non-amblyopic, and control. Retinal thickness (RT), choroidal thickness (ChT), and perfusion densities in the superficial capillary plexus (SCP) and deep capillary plexus (DCP) were measured across nine regions. A combination of statistical analysis and machine learning, including cross-validation and Random Forest classification, was used to enhance the diagnostic accuracy and classify amblyopic and normal eyes.

RESULTS: Retinal thickness was significantly higher in the amblyopic eyes compared to the control group in multiple regions, including the central (p < 0.001), nasal (p < 0.01), and temporal zones(p < 0.01). Choroidal thickness was also greater in the amblyopic eyes, particularly in the central and nasal regions (p < 0.05). However, no significant differences were observed in the perfusion densities of SCP and DCP. The machine learning classification model incorporating cross-validation achieved an accuracy of 92%, with Random Forest demonstrating improved classification and feature importance analysis.

CONCLUSION: The results indicate that eyes with refractive amblyopia have notably thicker retinal and choroidal layers, particularly in the central and nasal regions. Combining OCTA data with machine learning creates a strong diagnostic framework for detecting changes in the retina and choroid associated with refractive amblyopia. Utilizing sophisticated classification methods, like Random Forest and cross-validation, improves diagnostic precision and presents new possibilities for automated clinical evaluation.

PMID:40334350 | DOI:10.1016/j.optom.2025.100555

J Mech Behav Biomed Mater. 2025 May 2;168:107043. doi: 10.1016/j.jmbbm.2025.107043. Online ahead of print.

ABSTRACT

Osteosynthesis screws are critical in orthopaedic surgery for stabilizing and aligning bone fracture fragments. Despite their importance, screw failure remains a significant complication, often due to excessive movement at the implant-bone interface resulting from both physiological loading and external mechanical forces. This study aims to enhance understanding of screw failure mechanisms by investigating the relationship between peri-implant CT-based trabecular bone morphology and screw failure under axial-, shear-, and mixed loading conditions, including the effect of plate elevation. Using high-resolution micro-computed tomography (micro-CT) and mechanical testing, 100 porcine epiphyseal bone samples were extracted and analysed to measure key CT-based trabecular morphometric indices and correlate them with mechanical failure. The study tested screws under ten different loading configurations. Statistical analyses revealed that bone volume (BV) and bone volume over total volume (BV/TV) are strong predictors of screw failure force, explaining 70-90 % of the variance in failure forces across different loading scenarios. The findings suggest that BV and BV/TV can be used to determine optimal screw implantation sites based on local bone morphology, potentially improving surgical outcomes and reducing postoperative complications. This research contributes to a more comprehensive understanding of orthopaedic screw behaviour and offers a predictive model for clinical use.

PMID:40334349 | DOI:10.1016/j.jmbbm.2025.107043

Clin Imaging. 2025 May 3;123:110489. doi: 10.1016/j.clinimag.2025.110489. Online ahead of print.

NO ABSTRACT

PMID:40334343 | DOI:10.1016/j.clinimag.2025.110489

Oral Oncol. 2025 May 6;165:107341. doi: 10.1016/j.oraloncology.2025.107341. Online ahead of print.

ABSTRACT

BACKGROUND: Recently, a molecular characterization of adenoid cystic carcinoma (ACC) has been proposed, based on upregulation of c-Myc, a proto-oncogene involved in carcinogenesis, and p63, a transcription factor of the p53 gene family. The aim of this study was to evaluate c-Myc and p63 expression and classify patients with ACC into the proposed molecular groups.

METHODS: We included in the analysis 50 patients with ACC of the head and neck region diagnosed treated between 2000 and 2021 in four tertiary referral centers in Greece. Patient demographics and disease characteristics were retrieved from patients’ medical records. C-Myc and p63 expression were evaluated by immunohistochemistry.

RESULTS: Forty-seven patients were eligible for classification. The majority of patients had primary non-metastatic tumors. P63 protein was found to be overexpressed in 39 patients (78 %), who were classified as ACC type II (ACC-II). Eight patients with no p63 overexpression were classified as ACC type I (ACC-I). Among ACC-II patients, 31 had negative expression of c-Myc and 8 patients had low c-Myc expression. We found a statistically significant difference in the primary tumor location between the two groups (p = 0.0470). Notably, patients that belonged to the ACC-II subgroup had more favorable prognosis (50 months for ACC-I vs. 135 months for ACC-II, p = 0.0066).

CONCLUSIONS: We confirmed the recently published data regarding the c-myc/p63 prognostic classification for ACC. Evaluation of c-Myc and p63 should be routinely performed in ACC tumors to allow for accurate stratification and implementation of ACC subtyping for clinical trials.

PMID:40334310 | DOI:10.1016/j.oraloncology.2025.107341