Category: Statistics

Cell Tissue Bank. 2025 Aug 11;26(3):34. doi: 10.1007/s10561-025-10182-3.

ABSTRACT

The study explores the effects of the COVID-19 pandemic on the Musculoskeletal Tissue Bank (MSTB) in Milan, with a particular focus on tissue harvesting and its subsequent use in surgical procedures. A retrospective descriptive epidemiological analysis compared data from the pre-pandemic period (2018-2019) with that of the pandemic period (2020-2022), revealing a 24.8% reduction in tissue retrievals during the pandemic. Although there was a decrease in the number of eligible donors not collected (from 93 to 67, from 36.05 to 34.54%), this reduction was not statistically significant. The decline in tissue retrievals was due to decreased non-COVID-related pathologies, a lower number of potential donors from reduced accidents and increase in COVID-positive deaths. However, the MSTB successfully met tissue demands throughout this period. Notably, the reduction in retrievals at the MSTB was lower than national averages (- 24.8 vs. – 47.5%). Logistic regression analysis showed no significant organizational issues in donor collection. Despite the challenges, the MSTB remained resilient and adaptable, continuing its essential services. This underscores the broader impact of the pandemic on healthcare systems and emphasizes the importance of a flexible healthcare infrastructure during public health emergencies.

PMID:40784995 | DOI:10.1007/s10561-025-10182-3

Sci Rep. 2025 Aug 10;15(1):29244. doi: 10.1038/s41598-025-12267-1.

ABSTRACT

The integration of AI simulation models within smart electrical prosthetic systems represents a significant advancement in disability disease diagnosis. However, the selection and evaluation of these AI models interpret some multi-criteria decision-making dilemmas because of the presence of uncertainty and bipolarity (positive and negative aspects) of the selection criteria. Current literature lacks the selection and evaluation of AI simulation models that consider both bipolarity and uncertainty of the criteria, while prevailing Choquet integral aggregation operators despite their strong capabilities for handling information relationships, fail to efficiently process bipolar fuzzy information. The existence of this limitation makes it challenging to identify element interactions and non-linear relationships in uncertain environments containing both positive and negative aspects. To overcome these gaps, first, we develop two operators that are the bipolar fuzzy Choquet integral averaging and bipolar fuzzy Choquet integral geometric operators that uniquely integrate dual aspects (bipolarity) with criterion interaction modeling capabilities, fundamentally differing from traditional fuzzy approaches that cannot simultaneously process dual aspects of criterion. Secondly, we design a new multi-criteria decision-making approach using these operators to assess AI simulation models for prosthetic systems, since the criteria involved such as diagnostic accuracy, computational efficiency, and system reliability, have both positive and negative aspects that need to be considered together. Our method was applied in detail to select AI simulation models for smart electrical prosthetic systems and compared with some prevailing methods and standard Choquet integral approaches. This showed that our method is more precise and produces better evaluation results. It introduces a new theoretical basis for bipolar fuzzy Choquet integral aggregation and offers medical professionals a reliable way to pick the best AI simulation models for important prosthetic applications that influence patient outcomes and the functioning of prosthetics.

PMID:40784993 | DOI:10.1038/s41598-025-12267-1

Neurosurg Rev. 2025 Aug 11;48(1):597. doi: 10.1007/s10143-025-03749-x.

ABSTRACT

Intracranial hemorrhage (ICH) is a severe condition associated with high morbidity and mortality. Coagulation disorders, such as thrombophilia, thrombocytopenia, hemophilia, and vitamin K deficiency, significantly influence the pathophysiology of bleeding, and therefore the outcomes of ICH patients. This study aims to examine the effects of these disorders on outcomes related to ICH. This study retrospectively examined the impact of these coagulation disorders on ICH outcomes using the Nationwide Inpatient Sample (NIS) database from 2011 to 2020. A total of 260,049 hospitalizations for ICH were included, and patients were grouped based on the presence of specific coagulation disorders. The outcomes assessed were in-hospital mortality and length of stay (LOS), with case-controlled matching applied to account for confounding variables such as age, sex, race, and comorbidities. Overall, the mortality rate across all 269,044 patients was 21.9%. ICH patients with vitamin K deficiency had the highest mortality rate (40.5%), followed by thrombocytopenia (28.2%) and primary thrombophilia (30.8%) (p < 0.001). Hemophilia and von Willebrand disease were associated with mortality rates of 21.6% and 32.1%, respectively. Additionally, vitamin K deficiency and hemophilia were linked to the longest LOS among the conditions studied (17.6 ± 23.9 days and 14.1 ± 18.1 days respectively) p < 0.001). Case-controlled matching confirmed significant differences in mortality and LOS based on the type of coagulation disorder after controlling for confounding variables. This study demonstrates the significant role of coagulation disorders in determining ICH outcomes. Vitamin K deficiency and thrombocytopenia were associated with particularly severe outcomes, including increased mortality and extended hospital stays. Early identification and targeted interventions for these coagulation disorders are crucial for improving ICH management and patient prognosis. Further research is needed to develop comprehensive guidelines for managing ICH patients with coagulation disorders.

PMID:40784989 | DOI:10.1007/s10143-025-03749-x

Clin Child Fam Psychol Rev. 2025 Aug 11. doi: 10.1007/s10567-025-00532-8. Online ahead of print.

ABSTRACT

Past studies have consistently found that different informants disagree on ratings of youth’s experiences. For instance, parents and youth report different prevalence and frequency ratings of youth’s exposure to community violence (ECV), with past studies demonstrating that parents typically underreport youth’s ECV compared to the youth. However, recent studies with advanced statistical analyses revealed more nuanced patterns of reports, with some parents overreporting their youth’s ECV, some underreporting it, and other parent-youth dyads agreeing that the youth either did or did not experience ECV. These report patterns are theorized to provide valuable insight into parent-child relationships and family functioning and have implications for youth emotional and behavioral development. The current systematic review synthesized 14 existing studies (N = 12,824 parent-youth dyads) on parent-youth discrepancies in youth ECV to elucidate patterns of informant discrepancies and their correlates to parent-youth relationship quality, family functioning, and youth outcomes. Studies that used advanced analytic approaches (k = 2), such as latent class analysis and polynomial regression, identified multiple patterns of parent-youth reports (e.g., parent-youth agreement on either low or high levels of youth ECV, parental underreporting, parental overreporting compared to youth). Poor parent-youth relationship and family functioning (e.g., lower parental warmth, higher parental hostility) were associated with higher parent-youth discrepancies in youth ECV. There were mixed findings with patterns of informant discrepancies in youth ECV and youth functioning. Suggestions for future directions for research on parent-youth discrepancies in youth ECV were made.

PMID:40784981 | DOI:10.1007/s10567-025-00532-8

Sci Rep. 2025 Aug 10;15(1):29275. doi: 10.1038/s41598-025-15169-4.

ABSTRACT

Data on liver issues including liver cirrhosis, hepatocellular carcinoma, and hepatic failure in renal transplant patients with HCV infection are scarce. In the present study, we conducted a large-scale population-based analysis to investigate the long-term outcomes of renal recipients with HCV infection. Propensity score matching with a ratio of 1:1 was applied. A total of 6,473renal recipients with HCV infection in case group were enrolled after PSM. Our findings showed that subjects with HCV infection in kidney transplant had significantly higher risk of hepatoma, cirrhosis, hepatic failure, and overall hepatic disease than those without HCV infection. (hepatoma: HR: 8.957; 95% CI: 5.324-15.069; cirrhosis: HR: 5.378; 95% CI: 4.363-6.631; hepatic failure: HR: 3.258; 95% CI: 2.527-4.200; overall hepatic disease: HR: 4.128; 95% CI: 3.428-4.971). In the present study, our findings show that renal recipients with HCV infection is significantly associated with a remarkably high risk of hepatic complications post-kidney transplantation.

PMID:40784973 | DOI:10.1038/s41598-025-15169-4

Sci Rep. 2025 Aug 10;15(1):29260. doi: 10.1038/s41598-025-14476-0.

ABSTRACT

Alzheimer’s disease is a progressive neurodegenerative disorder marked by cognitive decline, memory loss, and behavioral changes. Early diagnosis, particularly identifying Early Mild Cognitive Impairment (EMCI), is vital for managing the disease and improving patient outcomes. Detecting EMCI is challenging due to the subtle structural changes in the brain, making precise slice selection from MRI scans essential for accurate diagnosis. In this context, the careful selection of specific MRI slices that provide distinct anatomical details significantly enhances the ability to identify these early changes. The chief novelty of the study is that instead of selecting all slices, an approach for identifying the important slices is developed. The ADNI-3 dataset was used as the dataset when running the models for early detection of Alzheimer’s disease. Satisfactory results have been obtained by classifying with deep learning models, vision transformers (ViT) and by adding new structures to them, together with the model proposal. In the results obtained, while an accuracy of 99.45% was achieved with EfficientNetB2 + FPN in AD vs. LMCI classification from the slices selected with SSIM, an accuracy of 99.19% was achieved in AD vs. EMCI classification, in fact, the study significantly advances early detection by demonstrating improved diagnostic accuracy of the disease at the EMCI stage. The results obtained with these methods emphasize the importance of developing deep learning models with slice selection integrated with the Vision Transformers architecture. Focusing on accurate slice selection enables early detection of Alzheimer’s at the EMCI stage, allowing for timely interventions and preventive measures before the disease progresses to more advanced stages. This approach not only facilitates early and accurate diagnosis, but also lays the groundwork for timely intervention and treatment, offering hope for better patient outcomes in Alzheimer’s disease. The study is finally evaluated by a statistical significance test.

PMID:40784967 | DOI:10.1038/s41598-025-14476-0

Environ Monit Assess. 2025 Aug 11;197(9):1004. doi: 10.1007/s10661-025-14460-1.

ABSTRACT

Wet deposition plays a critical role in the material cycling of terrestrial ecosystems. However, studies on inorganic elements wet deposition in northern China pandemic-induced emission reduction background conditions remain limited. To address this gap, we investigated the composition, potential sources, influencing factors, and driving mechanisms of inorganic elements in wet deposition during a cold surge in November 2022, at the late stage of the COVID-19 pandemic. Precipitation samples were collected from 15 cities across northern China. The results showed that the last rainfall amount (LRA), the output value (IBTG1) and structural composition (IBTG2) of the industrial, construction, transportation, and catering sectors, resident population (POP), and GDP were the major factors affecting inorganic element deposition. Among the three subregions, Northwest China (NWC) exhibited relatively high concentrations of inorganic elements such as Pb and Cd, likely due to rapid industrialization and unsustainable development. Potential sources analysis indicated that urban areas and nearby industrial zones were the dominant potential sources, even during the cold surge. The major contributors were dust, coal combustion, and traffic-related emissions. Mechanistic analysis of anthropogenic-sourced elements (AS6) revealed that POP, GDP, and LRA had the most significant impacts. POP and IBTG1 showed positive effects, while GDP and LRA showed negative effects. Wind speed (WDSP) had a U-shaped influence-initially positive, then negative at higher speeds. Based on these findings, we recommend timely industrial restructuring and the promotion of a “small population, high-efficiency economy” model to support sustainable urban development.

PMID:40784964 | DOI:10.1007/s10661-025-14460-1

Sci Rep. 2025 Aug 10;15(1):29278. doi: 10.1038/s41598-025-15123-4.

ABSTRACT

To explore the value of infrared thermal imaging technology in the diagnosis of emergency pulmonary infections. 200 patients who received emergency treatment at our hospital from October 2020 to December 2021 were selected as the study subjects. General information was collected from all patients, including 108 patients with acute pulmonary infection and 92 patients without pulmonary infection. All patients were tested using infrared thermal imaging technology and infrared thermal imaging equipment, with X-ray examination as the gold standard, The chest X-ray scan was performed using an X-ray camera produced by Siemens, and the temperature difference between the affected areas detected by infrared thermal imaging technology in two groups of patients was compared. The detection rate of infrared thermal imaging in the lung infection group was analyzed, and the diagnostic efficacy was evaluated by plotting the ROC curve to calculate the area under the curve. Compared with the uninfected group, the infrared radiation temperature of the infected group was significantly increased in the body surface, chest, abdomen, bilateral lungs, and midpoint of the breasts, with a statistically significant difference (P < 0.05). The positive rate of infrared thermal imaging detection in the lung infection group was 93 cases (86.11%), and the positive rate of gold standard X-ray detection was 102 cases (94.44%), the difference is statistically significant (P < 0.05). The AUC value of infrared thermal imaging technology for detecting emergency pulmonary infection patients is 0.933, the sensitivity is 90.36%, the specificity is 92.28%, and the Jordan index is 0.92. Infrared thermography has important applications in the diagnosis of lung infections in emergency medicine.It can reflect the distribution range of abnormal hot spots in patients’ lung infections by evaluating temperature changes, and has good diagnostic value in clinical practice.

PMID:40784954 | DOI:10.1038/s41598-025-15123-4

BMC Pediatr. 2025 Aug 10;25(1):617. doi: 10.1186/s12887-025-05983-0.

ABSTRACT

OBJECTIVE: Our study aimed to investigate the link between serum carotenoids and obesity in a large, representative children and adolescents.

METHODS: We conducted a cross-sectional analysis using data from the National Health and Nutrition Examination Survey 2017-2018. The impact of individual exposure to six serum carotenoids (α-carotene, β-carotene, α-cryptoxanthin, β-cryptoxanthin, combined lutein/zeaxanthin, and total lycopene) on adiposity measures, including BMI and obesity, was assessed through survey-weighted logistic and linear regression models. Weighted quantile sum (WQS) regression was adopted to estimate the effect of exposure to a combination of six serum carotenoids on adiposity measures.

RESULTS: Our study included 1,329 child and adolescent participants (mean age 12.84 years, 50.11% male). The overall mean BMI was 22.03 kg/m² (SE = 0.16), with 324 participants (24.39%) classified as obese. After adjusting for potential confounders, higher levels of all serum carotenoids (α-carotene, β-carotene, α-cryptoxanthin, β-cryptoxanthin, and combined lutein/zeaxanthin) except lycopene were associated with lower BMI and prevalence of obesity in children and adolescents. In addition, there was a negative correlation between the combination of all six carotenoids and BMI (β=-1.56, 95% CI: -1.95, -1.16, P < 0.01) and obesity (OR = 0.48, 95% CI: 0.39, 0.60, P < 0.01), with β-carotene having the greatest weighting in body mass index and prevalence of obesity, 0.708 and 0.709.

CONCLUSIONS: Our study provides evidence that serum carotenoids, in particular β-carotene, may be associated with a lower prevalence of obesity in children and adolescents.

PMID:40784950 | DOI:10.1186/s12887-025-05983-0

BMC Med Inform Decis Mak. 2025 Aug 11;25(1):274. doi: 10.1186/s12911-025-03118-0.

ABSTRACT

BACKGROUND: Large language models (LLMs) are being used to reduce the administrative burden in long-term care by automatically generating and summarising case notes. However, LLMs can reproduce bias in their training data. This study evaluates gender bias in summaries of long-term care records generated with two state-of-the-art, open-source LLMs released in 2024: Meta’s Llama 3 and Google Gemma.

METHODS: Gender-swapped versions were created of long-term care records for 617 older people from a London local authority. Summaries of male and female versions were generated with Llama 3 and Gemma, as well as benchmark models from Meta and Google released in 2019: T5 and BART. Counterfactual bias was quantified through sentiment analysis alongside an evaluation of word frequency and thematic patterns.

RESULTS: The benchmark models exhibited some variation in output on the basis of gender. Llama 3 showed no gender-based differences across any metrics. Gemma displayed the most significant gender-based differences. Male summaries focus more on physical and mental health issues. Language used for men was more direct, with women’s needs downplayed more often than men’s.

CONCLUSION: Care services are allocated on the basis of need. If women’s health issues are underemphasised, this may lead to gender-based disparities in service receipt. LLMs may offer substantial benefits in easing administrative burden. However, the findings highlight the variation in state-of-the-art LLMs, and the need for evaluation of bias. The methods in this paper provide a practical framework for quantitative evaluation of gender bias in LLMs. The code is available on GitHub.

PMID:40784946 | DOI:10.1186/s12911-025-03118-0