Tag: pubmed

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

Ther Innov Regul Sci. 2025 Aug 10. doi: 10.1007/s43441-025-00858-z. Online ahead of print.

NO ABSTRACT

PMID:40784934 | DOI:10.1007/s43441-025-00858-z

Sci Rep. 2025 Aug 10;15(1):29272. doi: 10.1038/s41598-025-14253-z.

ABSTRACT

SARS-CoV-2 population-based seroprevalence surveys are useful for estimating the extent of SARS-CoV-2 infections, which may be underestimated by COVID-19 case counts. Surveys conducted in October 2020 in four Nigerian states showed that SARS-CoV-2 seroprevalence ranged from 9.3% in Gombe (northeast) to 25.2% in Enugu (southeast) after the first COVID-19 wave, more than 100 and 700 times higher than the official number of COVID-19 cases in these two states, respectively. We conducted a serosurvey after the second COVID-19 wave to evaluate the extent of SARS-CoV-2 infections, attitudes to COVID-19 vaccines, and COVID-19 vaccination coverage in two regions of Nigeria. Using the World Health Organization (WHO) Unity protocol, 34 enumeration areas (EAs) each in the Federal Capital Territory (FCT) (Northcentral Zone) and Kano State (Northwest Zone) were sampled in June 2021, using probability proportional to estimated size; 20 households in one EA were randomly selected. All consenting and assenting members of a household were asked about risk behaviors; adults who were 18 years and above (the eligible population for COVID-19 vaccination in Nigeria) responded to questions on COVID-19 vaccine attitudes and receipt. Blood and nasal/oropharyngeal samples were taken from all consenting and assenting household members. Blood samples collected were tested with the Luminex xMAP® SARS-CoV-2 Multi-Antigen IgG Assay and swabs by reverse-transcriptase-PCR (RT-PCR). Overall response rates were 76.8% in the FCT (n = 1,505 blood draws) and 80.4% in Kano State (n = 2,178 blood draws). Following the second COVID-19 wave in Nigeria, more than 40% of residents in the FCT (40.3%, 95% CI: 34.7-45.9) and Kano State (42.6%, 95% CI: 39.4-45.8) had evidence of prior SARS-CoV-2 infection. There were no active SARS-CoV-2 infections detected by RT-PCR in either the FCT or Kano State. In the FCT and Kano State, 3.4% and 1.6% of people surveyed reported receipt of any COVID-19 vaccine, three months after vaccines were available in country. In the FCT, 77.5% of adults were aware of COVID-19 vaccines, of whom 46.9% reported willingness to receive them. In Kano State, 48.7% of adults were aware of COVID-19 vaccines, of whom 61.1% were willing to receive them. In both regions, about 84% of those reporting unwillingness to accept COVID-19 vaccines cited concerns over vaccine safety. “Serosurvey findings revealed that SARS-CoV-2 infection was far more widespread in both the Federal Capital Territory and Kano State than indicated by reported case numbers. Despite high awareness, COVID-19 vaccine uptake remained low, primarily due to concerns about vaccine safety. These results highlight the urgent need for targeted risk communication to address vaccine hesitancy and improve coverage. Serosurveys provide valuable insights that can guide public health interventions and future pandemic preparedness in Nigeria.”

PMID:40784905 | DOI:10.1038/s41598-025-14253-z

Cardiovasc Diabetol. 2025 Aug 10;24(1):326. doi: 10.1186/s12933-025-02867-6.

ABSTRACT

BACKGROUND: Early detection of vulnerable carotid plaques is critical for stroke prevention. This study aimed to develop a machine learning model based on routine blood tests and derived indices to predict plaque vulnerability and assess sex-specific risk patterns across biomarker value ranges.

METHODS: We retrospectively included 1701 hospitalized patients from Suzhou Municipal Hospital (2019-2020), selected from an initial cohort of 10,028 individuals. All patients underwent carotid ultrasound, with vulnerable plaques identified using predefined imaging criteria. A total of 30 laboratory variables-including blood count, coagulation, and biochemistry-were extracted, alongside derived indices such as triglyceride-glucose index (TyG), atherogenic index of plasma (AIP), neutrophil-to-lymphocyte ratio (NLR) and others. Features were standardized and selected based on statistical and clinical relevance. Five machine learning models were trained using a 7:3 train-test split and evaluated by cross-validation. Model performance was assessed using AUC, sensitivity, and specificity. The best model was interpreted using SHapley Additive exPlanations (SHAP) analysis. Sex differences were explored using Mann-Whitney U tests and restricted cubic spline (RCS) modeling across value intervals.

RESULTS: The Random Forest model showed the highest predictive performance (AUC = 0.847; 95% CI 0.791-0.895; specificity = 89.4%; sensitivity = 64.2%). SHAP analysis identified gender, age, fibrinogen, NLR, creatinine, fasting blood glucose, uric acid to high-density lipoprotein ratio (UHR), TyG, systemic inflammation response index (SIRI), and lymphocyte count as top predictors. Significant sex-specific differences in SHAP values were observed for key biomarkers, including age, UHR, TyG, SIRI, and others. RCS modeling further revealed distinct sex-related patterns in plaque vulnerability across biomarker value ranges.

CONCLUSION: A Random Forest model integrating routine blood markers and derived indices accurately predicted vulnerable carotid plaques. The results underscore the importance of sex-specific risk assessment, highlighting differential effects of key biomarkers across genders and value intervals.

PMID:40784899 | DOI:10.1186/s12933-025-02867-6

Diagn Progn Res. 2025 Aug 11;9(1):17. doi: 10.1186/s41512-025-00202-x.

ABSTRACT

BACKGROUND: Helicobacter pylori (H. pylori) is a bacterium that colonizes the stomach and is a major risk factor for gastric cancer, with an estimated 89% of non-cardia gastric cancer cases worldwide attributable to H. pylori. Prospective studies provide reliable evidence for quantifying the association between gastric cancer and H. pylori, as they circumvent the risk of a false negative due to possible reduction in antibody levels before cancer development.

METHODS: In a large-scale prospective study within the China Kadoorie Biobank, H. pylori infection is being analysed as a risk factor for gastric cancer. The presence of infection is typically determined by serological tests. The immunoblot test, although well established, is more labour intensive and uses a larger amount of plasma than the alternative high-throughput multiplex serology test. Immunoblot outputs a binary positive/negative serostatus classification, while multiplex outputs a vector of continuous antigen measurements. When mapping such multidimensional continuous measurements onto a binary classification, statistical challenges arise in defining classification cut-offs and accounting for the differences in infection evidence provided by different antigens. We discuss these challenges and propose a novel solution to optimize the translation of the continuous measurements from multiplex serology into probabilities of H. pylori infection, using classification algorithms (Bayesian additive regressive trees (BART), multidimensional monotone BART, logistic regression, random forest and elastic net). We (i) calibrate and apply classification models to predict probabilities of H. pylori infection given multiplex measurements, (ii) compare the predictive performance of the models using immunoblot as reference, (iii) discuss reasons for the differences in predictive performance and (iv) apply the calibrated models to gain insights on the relative strengths of infection evidence provided by the various antigens.

RESULTS: All models showed high discriminative ability with at least 95% area under the curve (AUC) estimates on the training and test data. There was no substantial difference between the performance of models on the training and test data.

CONCLUSIONS: Classification algorithms can be used to calibrate the H. pylori multiplex serology test to the immunoblot test in the China Kadoorie Biobank. This study furthers our understanding of the applicability of classification algorithms to the context of serologic tests.

PMID:40784889 | DOI:10.1186/s41512-025-00202-x

Jt Dis Relat Surg. 2025 Jul 8;36(3):702-710. doi: 10.52312/jdrs.2025.2264. Epub 2025 Jul 8.

ABSTRACT

OBJECTIVES: This study aims to compare the functional scores and gait analysis data of patients undergoing conservative treatment after calcaneal fractures with healthy individuals and to evaluate both success of conservative treatment and the applicability and effectiveness of a novel smartphone-based gait analysis method in assessing post-fracture mobility.

PATIENTS AND METHODS: Between January 2017 and December 2022, a total of 30 patients (10 females, 20 males; mean age: 48.6±12.6 years; range, 19 to 65 years) who underwent conservative treatment due to calcaneal fractures and 30 healthy controls (12 females, 18 males; mean age: 45.3±12.7 years; range, 21 to 63 years) were retrospectively analyzed. Patients with completed fracture union and mobilized by full weight bearing on the fractured extremity were evaluated with ankle joint range of motion (ROM), American Orthopaedic Foot and Ankle Society (AOFAS), Short Form-36 (SF-36), Visual Analog Scale (VAS) functional scoring and gait analysis using the smartphone-based Gait Analyzer application, and the results were compared with the control group.

RESULTS: After conservative treatment, there was no statistically significant difference in the ankle ROM values (dorsiflexion p=0.359, plantarflexion p=0.240), AOFAS (p=0.211), and SF-36 scores (physical function p=0.188, pain p=0.483, health change p=0.894) of the patient and control groups. The mean VAS score of the patient group was 2.83±1.80, indicating higher scores than those of the control group (p=0.035). There was a statistically significant change between the groups in terms of all gait parameters (gait velocity p=0.010, step time p<0.001, step length p<0.001, cadence p<0.001, step time symmetry p<0.001, step length symmetry p<0.001, vert-COM p<0.001).

CONCLUSION: Although the functionality and gait patterns of the patients may be affected after conservative treatment of calcaneal fractures, the fact that there was no significant difference between the patient and control groups indicates that this treatment method can be preferred in this group of patients, particularly in extraarticular and Sanders type 1 intra-articular fractures, with appropriate rehabilitation.

PMID:40784003 | DOI:10.52312/jdrs.2025.2264