Tag: pubmed

Eye (Lond). 2025 Sep 26. doi: 10.1038/s41433-025-04036-1. Online ahead of print.

ABSTRACT

BACKGROUND: This study evaluated the long-term structural, functional, and microvascular outcomes in school-aged children with a history of laser photocoagulation (LP) therapy for the treatment of type 1 retinopathy of prematurity (ROP). The macular integrity, retinal sensitivity, fixation stability, and microvascular parameters were assessed using microperimetry and optical coherence tomography angiography (OCT-A).

METHODS: This retrospective cross-sectional study included children aged 10-15 who underwent detailed ophthalmologic examinations. Participants were divided into three groups: (1) children who had received LP for the treatment of ROP in infancy, (2) prematurely born infants without any ROP diagnosis, and (3) full-term controls. Statistical analysis was conducted to compare fixation stability, foveal avascular zone (FAZ) area, and vascular density (VD).

RESULTS: A significant difference was found in fixation stability among the groups, demonstrating more stable fixation in term-born children compared to preterm groups and significantly eccentric fixation in laser-treated infants (p = 0.019 and p < 0.001, respectively). Furthermore, FAZ areas were substantially smaller, and VD values were higher in laser-treated infants (p < 0.001 for both). A negative correlation was identified between fixation instability and FAZ size (p = 0.016 and p = 0.015), suggesting a relationship between fixation behaviour and foveal microvascular integrity.

CONCLUSION: The diagnosis and treatment of ROP can significantly affect long-term visual functions, fixation behaviours, and macular vascular development. These findings emphasise the importance of long-term follow-up for preterm infants and the need for further studies to investigate the relationship between fixation stability and retinal microvasculature.

PMID:41006681 | DOI:10.1038/s41433-025-04036-1

Sci Rep. 2025 Sep 26;15(1):33010. doi: 10.1038/s41598-025-18390-3.

ABSTRACT

Knowing the predictors of pregnancy outcomes in patients with recurrent spontaneous abortion (RSA) is extremely critical. Accordingly, we aimed to determine the effects of the difference between mean gestational sac diameter and crown-rump length (mGSD-CRL) on the pregnancy outcomes in patients with RSA at 6-10 gestational weeks, as well as to explore its significance in predicting the pregnancy outcomes of patients with RSA. This retrospective cohort study included 256 pregnant women at 6-10 weeks of gestation and with RSA who had visited our hospital from January 2020 to March 2023. The patients were allocated to three groups based on the mGSD-CRL difference: Group A: mGSD-CRL ≤ 10 mm, Group B: 10 mm < mGSD-CRL ≤ 15 mm, and Group C: mGSD-CRL > 15 mm. The pregnancy failure rate in Group A was 22%, which was higher than those that in Group B (5.5%) and Group C (9.4%), with statistically significant differences (P < 0.05). Binary logistic regression analysis revealed that the mGSD (odd ratio [OR] = 1.14, 95% confidence interval [CI] = 1.06-1.23, P = 0.001), the CRL (OR = 1.16, 95% CI = 1.05-1.28, P = 0.004), and mGSD-CRL (OR = 1.12, 95% CI = 1.01-1.24, P = 0.026) were independent risk factors affecting the pregnancy outcome of patients with RSA. However, the uterine artery peak systolic value to end-diastolic value (UtA-S/D), D-dimer (DD), adenosine diphosphate (ADP), and arachidonic acid (AA) were not related (P > 0.05). The area under the receiver operator characteristic (ROC) curve of mGSD-CRL at 6-10 weeks of pregnancy was 0.566, with a cutoff value of 9.50 mm. The sensitivity and specificity were 90% and 36%, respectively. Compared with their prediction value, the combined prediction of mGSD-CRL, mGSD, and CRL exhibited a higher value (AUC = 0.718) in predicting pregnancy outcomes. A weak negative correlation was detected between ADP and mGSD-CRL difference (r = – 0.165, P = 0.025). In patients with RSA, mGSD-CRL acts as an independent risk factor affecting pregnancy outcomes, thereby effectively predicting the early pregnancy outcomes of patients with RSA. Thus, a low mGSD-CRL difference signifies the heightened probability of miscarriage, thereby urgently requiring clinicians to pay timely attention. Trial registration: The study is registered at ClinincalTrails.gov (Trial registration number: NCT06081556, October 13, 2023).

PMID:41006676 | DOI:10.1038/s41598-025-18390-3

Nat Neurosci. 2025 Sep 26. doi: 10.1038/s41593-025-02066-2. Online ahead of print.

ABSTRACT

Gonadal hormone receptors are widely distributed across the brain, yet their influence on brain structure remains understudied. Here, using precision imaging, we examined four females, including one with endometriosis and one using oral contraceptives (OC), across a monthly period. Whole-brain analyses revealed spatiotemporal patterns of brain volume changes, with substantial variations across the monthly period. In typical cycles, spatiotemporal patterns were associated with serum progesterone levels, while in cycles with endometriosis and during OC intake, patterns were associated with serum estradiol levels. The volume changes were widely distributed rather than region-specific, suggesting a widespread but coordinated influence of hormonal fluctuations. These findings underscore the importance of considering diverse hormonal milieus beyond typical menstrual cycles in understanding structural brain dynamics and suggest that hormonal rhythms may drive widespread structural brain changes.

PMID:41006668 | DOI:10.1038/s41593-025-02066-2

Sci Rep. 2025 Sep 26;15(1):33214. doi: 10.1038/s41598-025-18306-1.

ABSTRACT

This single-center retrospective study evaluated the performance of four multimodal large language models (MLLMs) (ChatGPT-4o, Claude 3.5 Sonnet, Google Gemini 1.5 Pro, Perplexity Sonar Large) in detecting and grading the severity of age-related macular degeneration (AMD) from ultrawide field fundus images. Images from 76 patients (136 eyes; mean age 81.1 years; 69.7% female) seen at the University of California San Diego were graded independently for AMD severity by two junior retinal specialists (and an adjudicating senior retina specialist for disagreements) using the Age-Related Eye Disease Study (AREDS) classification. The cohort included 17 (12.5%) eyes with ‘No AMD’, 18 (13.2%) with ‘Early AMD’, 50 (36.8%) with ‘Intermediate AMD’, and 51 (37.5%) with ‘Advanced AMD’. Between December 2024 and February 2025, each MLLM was prompted with single images and standardized queries to assess the primary outcomes of accuracy, sensitivity, and specificity in binary disease classification, disease severity grading, open-ended diagnosis, and multiple-choice diagnosis (with distractor diseases). Secondary outcomes included precision, F1 scores, Cohen’s kappa, model performance comparisons, and error analysis. ChatGPT-4o demonstrated the highest accuracy for binary disease classification [mean 0.824 (95% confidence interval (CI)): 0.743, 0.875)], followed by Perplexity Sonar Large [mean 0.815 (95% CI: 0.744, 0.879)], both of which were significantly more accurate (P < 0.00033) Than Gemini 1.5 Pro [mean 0.669 (95% CI: 0.581, 0.743)] and Claude 3.5 Sonnet [mean 0.301 (95% CI: 0.221, 0.375)]. For severity grading, Perplexity Sonar Large was most accurate [mean 0.463 (95% CI: 0.368, 0.537)], though differences among models were not statistically significant. ChatGPT-4o led in open-ended and multiple-choice diagnostic tasks. In summary, while MLLMs show promise for automated AMD detection and grading from fundus images, their current reliability is insufficient for clinical application, highlighting the need for further model development and validation.

PMID:41006661 | DOI:10.1038/s41598-025-18306-1

Commun Med (Lond). 2025 Sep 26;5(1):400. doi: 10.1038/s43856-025-01104-1.

ABSTRACT

BACKGROUND: Knee osteoarthritis (KOA) is common in older adults and may relate to cognitive decline. We explore whether changes in specific brain areas and body inflammation levels help explain this connection, focusing on the hippocampus-a memory-critical brain region.

METHODS: We studied 36 older adults with KOA over time. Using brain scans, we measured volumes of hippocampal subregions (especially the fimbria). Blood tests tracked six inflammation markers, including brain-derived neurotrophic factor (BDNF), interferon-gamma (IFN-γ), programmed death 1(PD-1), recombinant cannabinoid receptor 1 (CNR1), recombinant cannabinoid receptor 2 (CNR2), and T cell immunoglobulin domain and mucin domain 3 (TIM3). Memory was tested using the Wechsler Memory Scale – Chinese Revision (WMS-CR), while global cognition used the Montreal Cognitive Assessment (MoCA). Relationships between knee pain, brain structure, inflammation, and cognition were analyzed statistically.

RESULTS: Here, we show that shrinking fimbria volume predicts cognitive decline in those developing dementia. We identify a robust correlation between fimbria volume and cognitive performance. Higher IFN-γ levels are protective against cognitive decline. Critically, fimbria volume serves as a mediator in the relationship between pain, TIM3/IFN-γ levels, and cognitive scores.

CONCLUSIONS: Fimbria serves as a key pathway through which KOA may drive cognitive impairment, while IFN-γ could help protect memory. Monitoring these hippocampal changes and inflammation levels might help identify at-risk patients early.

PMID:41006612 | DOI:10.1038/s43856-025-01104-1

Pediatr Cardiol. 2025 Sep 26. doi: 10.1007/s00246-025-04044-8. Online ahead of print.

ABSTRACT

Neonatal portal vein thrombosis (PVT) is an uncommon but clinically important vascular complication, often associated with umbilical venous catheter (UVC) use. The optimal management strategy, including the role of anticoagulation, remains uncertain. This retrospective case series included 13 neonates with PVT diagnosed in a level III NICU between January 2021 and December 2024. Clinical and imaging data were compared between infants with spontaneous thrombus resolution and those with persistent thrombosis. The median gestational age was 30.1 weeks, and the median birth weight was 875 g. All infants had UVC placement; every thrombus involved the left portal vein with intrahepatic extension, and all extended into the IVC. Two neonates (15.4%) received anticoagulation; the remainder were managed conservatively. Spontaneous resolution occurred in 6 of 13 cases (46.2%). Earlier diagnosis and higher birth weight were more frequent in the resolution group, although not statistically significant. No thrombus-related acute complications occurred during a median follow-up of 6 months. In this case series, nearly half of neonates with non-occlusive PVT showed spontaneous resolution without anticoagulation. These findings suggest that conservative management can be considered in clinically stable infants, but the short follow-up precludes firm conclusions regarding long-term safety. Ongoing surveillance is essential to detect late complications such as portal hypertension or portosystemic shunting.

PMID:41006583 | DOI:10.1007/s00246-025-04044-8

Sci Rep. 2025 Sep 26;15(1):33035. doi: 10.1038/s41598-025-18079-7.

ABSTRACT

When predicting adverse complications due to Type 2 Diabetes, often two different approaches are taken: predictions based on clinical data or those using administrative health data. No studies have assessed whether these two approaches reach comparable predictions. This study compares the predictive performance of these two data sources and examines the algorithmic fairness of the developed models. We developed XGBoost models to predict the two-year risk of nephropathy, tissue infection, and cardiovascular events in Type 2 Diabetes patients. The models using only clinical data achieved an average AUC of 0.78, while the models using administrative health data alone achieved 0.77. A hybrid model combining both data types resulted in an average AUC of 0.80, across complications. The models showed that laboratory data were key for predicting nephropathy, whereas comorbidity and diabetes age were most important for tissue infection. For cardiovascular events, age and a history of congestive heart failure was the most important predictors. Our analysis identified bias on the feature sex in all three outcomes: models tended to underestimate risk for females and overestimate it for males, indicating a need to address fairness in these applications. This study demonstrates the effectiveness of ML models using both data types for predicting diabetes complications. However, the presence of sex bias highlights the importance of improving model fairness for reliable clinical use.

PMID:41006578 | DOI:10.1038/s41598-025-18079-7

Sci Rep. 2025 Sep 26;15(1):33002. doi: 10.1038/s41598-025-18230-4.

ABSTRACT

Accurately predicting the discharge coefficient (C_d) in weir structures is crucial for improving hydraulic designs and ensuring their safe operation. This study focuses on developing and testing advanced Machine Learning (ML) models to estimate C_d in Semicircular Labyrinth Weirs (SCLWs). The models explored include a Tabular Neural Network (TabNet) optimized with the Moth Flame Optimization algorithm (TabNet-MFO), an Extreme Learning Machine (ELM) enhanced with the Jaya and Firefly Algorithms (ELM-JFO), a Decision Tree (DT), and a Light Gradient Boosting Machine (LightGBM). One of the key innovations in this study is the introduction of the TabNet-MFO framework. Through sensitivity analysis, using tools like the Explainable Boosting Machine (EBM) and SHapley Additive exPlanations (SHAP), the study found that the ratio of upstream flow depth to weir height (h/P) is the most significant factor affecting C_d predictions. Other important factors include the number of weir cycles (N) and the ratio of crest length to weir height (l_C/P). The dataset was split into 75% for train and 25% for validation. The performance of each model was gauged using a number of statistical indicators. They were the coefficient of determination (R²), the Root Mean Square Error (RMSE), the symmetric Mean Absolute Percentage Error (sMAPE), the Scatter Index (SI), and the Weighted Mean Absolute Percentage Error, or WMAPE and along with Taylor diagrams and the Performance Index (PI) for comparison. In the training phase, the ELM-JFO model delivered the best results in predicting C_d, with a PI of 166 and a normalized centered RMSE (E’) of 0.0052. The TabNet-MFO model also performed well, with a PI of 142 and an E’ of 0.0068. The LightGBM and DT models produced good results as well, with PIs of 89.45 and 89.36, respectively. In the testing phase, the TabNet-MFO model remained the top performer (PI = 81.92, E’ = 0.0118), followed by ELM-JFO (PI = 69.71, E’ = 0.0139). LightGBM and DT showed lower accuracy, with PIs of 60.62 and 47.55 and E’ values of 0.0159 and 0.0199, respectively. The novelty of this research lies in combining interpretable and hybrid ML techniques for C_d estimation, offering a reliable alternative to traditional empirical and regression-based methods. These results show the potential of ML in improving flow prediction accuracy and supporting better hydraulic structure design.

PMID:41006552 | DOI:10.1038/s41598-025-18230-4

Sci Rep. 2025 Sep 26;15(1):33092. doi: 10.1038/s41598-025-16712-z.

ABSTRACT

With the promotion of AI applications, people have undergone various changes, especially high school students who have a strong sense of engagement in AI-related activities. In order to explore the interactive mechanism and influencing factors between AI literacy and computing thinking level of high school students in H city, this study focuses on the relationship between student background, AI literacy, and computational thinking. By conducting a questionnaire survey of hundreds of high school students and using SPSS for descriptive statistics, analysis of variance, and correlation analysis, the focus is on exploring the impact and interrelationships between the three dimensions of AI literacy and the five dimensions of computational thinking. A structural equation model (SEM) was constructed by using AMOS software to further explore its internal complex correlation relationship. The results show that parental education and daily use of AI tools significantly affect students’ AI knowledge and skills, while factors such as gender and family location have different degrees of positive or negative effects on creativity, algorithmic thinking, and critical thinking. In addition, artificial intelligence literacy is moderately positively correlated with some dimensions of computational thinking. This study provides empirical support for the rational planning of AI courses in the basic education stage, strengthening the cultivation of students’ computational thinking and optimizing teaching practice.

PMID:41006537 | DOI:10.1038/s41598-025-16712-z

Sci Rep. 2025 Sep 26;15(1):33277. doi: 10.1038/s41598-025-18925-8.

ABSTRACT

Protein glycation is crucial in the pathogenesis of diabetes and its cardiovascular complications. Little is known about the antiglycation properties of amlodipine, a long-acting calcium channel blocker used to treat high blood pressure. In our study, amlodipine’s antiglycoxidant activity was assayed in sugars (glucose, fructose, and ribose), aldehydes (glyoxal), and chloramine T-modified bovine serum albumin (BSA). Aminoguanidine and N-acetylcysteine were used as standard glycation/oxidation inhibitors. The content of oxidation, glycoxidation, and glycation protein products was measured colorimetrically and fluorimetrically. A one-way analysis of variance (ANOVA) followed by Tukey’s post hoc test was used for statistical analysis. The mechanism of amlodipine’s antiglycoxidant activity was also evaluated using in-silico molecular docking. Amlodipine protects against BSA oxidation, as evidenced by enhanced total thiol content and mitigated protein carbonyls/advanced oxidation protein products. Amlodipine also increased the fluorescence of tryptophan and decreased the contents of kynurenine, N-formylkynurenine, and dityrosine. In addition, amlodipine effectively prevents protein glycation, as evidenced by a reduction in amyloid-beta structure, Amadori products, and advanced glycation end products (AGEs). In in silico analysis, amlodipine’s antiglycation properties were indicated during its interaction with BSA, glycosidases, and AGEs/receptor for AGEs (RAGE) pathway proteins. Among all proteins, amlodipine docked best with c-Jun N-terminal kinases. Summarizing, we have demonstrated the anti-glycation and antioxidant activity of amlodipine in vitro. This effect may be particularly important in patients with diabetes and atherosclerosis, where excessive glycation accelerates the development of vascular complications. Further studies are needed to confirm the antidiabetic activity of amlodipine in vivo.

PMID:41006534 | DOI:10.1038/s41598-025-18925-8