AIM: To investigate variation patterns of corneal biomechanical parameters and binocular symmetry among children of different genders aged 8-16 years.METHODS:A retrospective study was conducted, and children who underwent optometric examinations at the ophthalmology department of our hospital were enrolled between January 2022 and December 2024. Measurements included the flat keratometry(K1), steep keratometry(K2), and mean curvature(Km)of the anterior corneal surface, horizontal visible iris diameter(HVID), central corneal thickness(CCT), corneal endothelial cell density(CECD), average cell size(ACS), coefficient of variation(CV), and hexagonality(HEX). Corneal parameters and binocular differences were compared between genders and across age groups.RESULTS:A total of 621 children(1 242 eyes)were enrolled in this study, including 284 males(568 eyes), 337 females(674 eyes), 528 children aged 8-12 years(1 056 eyes), and 93 children aged 13-16 years(186 eyes). In children aged 8-16 years, the K1, K2, Km and CV of both eyes, as well as the interocular CCT differences in boys were significantly lower than those in girls(all P<0.05), while the HVID and HEX of both eyes, as well as the CCT of the left eye in boys were significantly higher than those in girls(all P<0.05). Children aged 8-12 years had significantly higher K1, Km, CECD and HEX in both eyes, and significantly lower ACS in both eyes than those aged 13-16 years(all P<0.05). K1, K2, Km, CECD and HEX in both eyes were negatively correlated with age(P<0.05); ACS in both eyes was positively correlated with age(P<0.001); K1 and Km of the right eye were positively correlated with the CECD of the right eye(P<0.05), and K1 and CCT of the left eye were positively correlated with the CECD of the left eye(P<0.05).CONCLUSION:Significant gender differences exist in corneal parameters among children aged 8 to 16 years, while binocular symmetry remained stable.

Objective Based on the radiomics features extracted from the unenhanced CT images of the lower abdomen, a variety of machine learning models were constructed to explore their application value in the assessment of split renal function. Methods A retrospective analysis was conducted on the unenhanced CT images from 240 single kidneys in patients with clinically suspected renal dysfunction. Based on the results of single-photon emission computed tomography renal dynamic imaging, the cases were classified into the normal glomerular filtration rate group (n=118) and the decreased glomerular filtration rate group (n=122). The region of interest was outlined on the unenhanced CT images and the radiomics features were extracted. The features were selected by correlation analysis and least absolute shrinkage and selection operator, and the machine learning models were constructed based on the algorithms of decision tree, support vector machine, random forest, logistic regression, and extreme gradient boosting. Area under the receiver operating characteristic curve, accuracy, sensitivity, and specificity were calculated to compare the performance of different models. Results Sixteen radiomics features were selected for constructing the machine learning models. The support vector machine model showed relatively high performance for the assessment of split renal function on the test set, with an area under the receiver operating characteristic curve value of 0.883 (95% confidence interval: 0.804-0.961), an accuracy of 0.778, a sensitivity of 0.811, and a specificity of 0.743. Conclusion The machine learning models constructed based on unenhanced CT radiomics can be used to preliminarily assess split renal function, which provides an innovative, convenient, and safe method for clinical diagnosis and has positive significance for treatment.

Objective To investigate the relationship between primary pulmonary mucinous adenocarcinoma (PPMA) mass type and pneumonia type and their difference in malignant degree, and to analyze the role of clinical manifestations and CT features in the diagnosis of this disease. Methods The clinical data of PPMA patients admitted in the First Affiliated Hospital of Xiamen University from May 2011 to March 2022 were retrospectively analyzed. According to CT features, they were divided into a mass type group and a pneumonia type group. The clinical manifestations, CT features and the degree of malignancy between the two groups were analyzed and compared. Results A total of 57 PPMA patients were enrolled. There were 17 males and 40 females, with an average age of (53.82±10.65) years, and 28 (49%) patients had reversed hato-like sign. There were 42 patients in the mass type group and 15 patients in the pneumonia type group. PPMA often occurs in both lower lungs, with clinical manifestations mainly of coughing and expectorating white mucoid sputum. There were statistical differences between the two groups in the maximum diameter of tumor (P<0.001), boundary condition (P<0.001) and pleural indentation sign (P=0.019). There was no statistical difference between the two groups in Ki-67 index (P>0.05). Conclusion There is no statistical difference in the degree of malignancy between the two types of PPMA. Considering their clinical manifestations and differences in imaging features, it is supported that the pneumonia type is just a progression of the mass type. CT can present various manifestations, among which the reversed hato-like sign is expected to become an important imaging feature. Combined with a high proportion of solid components, pleural indentation sign, and vacuole sign, reversed hato-like sign can play a significant role in the diagnosis of PPMA.

OBJECTIVE To evaluate the cost-utility of benmelstobart combined with anlotinib and chemotherapy as first-line treatment for extensive-stage small cell lung cancer （ES-SCLC） from the perspective of China’s healthcare system. METHODS Based on the data from the ETER 701 study， a partitioned survival model was constructed with a cycle of 3 weeks to simulate the total cost， quality-adjusted life years （QALY）， and incremental cost-effectiveness ratio （ICER） over 10 years for patients with ES- SCLC treated with benmelstobart plus anlotinib and chemotherapy， or chemotherapy alone. One-way sensitivity analysis and probability sensitivity analysis were performed to verify the robustness of the simulation results. The willingness-to-pay （WTP） threshold was set at 3 times the per capita gross domestic product （GDP） of China in 2023， which amounted to 268 074 yuan/QALY. RESULTS Compared with chemotherapy alone， benmelstobart combined with anlotinib and chemotherapy gained 0.438 QALY more at the cost of 403 505.55 yuan more， with an ICER of 922 031.37 yuan/QALY， which was higher than the WTP threshold set in this study. One-way sensitivity analysis showed that benmelstobart’s cost and utility value of the progression-free survival state had a greater impact on the ICER value； probabilistic sensitivity analysis confirmed the robustness of the model； only when the price of benmelstobart was reduced by 75.4%， the combined regimen would be cost-effective. CONCLUSIONS The first-line treatment of ES-SCLC with benmelstobart combined with anlotinib and chemotherapy is not cost-effective from the perspective of China’s healthcare system at present.

ObjectiveThis study aimed to explore the effects of aerobic exercise on cognitive function in aging mice and to elucidate the underlying molecular mechanisms by which aerobic exercise ameliorates cognitive decline through the regulation of gut microbiota-metabolite network. By providing novel insights into the interplay between exercise, gut microbiota, and cognitive health, this research seeks to offer a robust theoretical foundation for developing anti-aging strategies and personalized exercise interventions targeting aging-related cognitive dysfunction. MethodsUsing naturally aged C57BL/6 mice as the experimental model, this study employed a multi-omics approach combining 16S rRNA sequencing and wide-targeted metabolomics analysis. A total of 18 mice were divided into 3 groups: young control (YC, 4-month-old), old control (OC, 21-month-old), and old+exercise (OE, 21-month-old with 12 weeks of moderate-intensity treadmill training) groups. Behavioral assessments, including the Morris water maze (MWM) test, were conducted to evaluate cognitive function. Histopathological examinations of brain tissue sections provided morphological evidence of neuronal changes. Fecal samples were collected for gut microbiota and metabolite profiling via 16S rRNA sequencing and ultra-performance liquid chromatography coupled with quadrupole-time-of-flight mass spectrometry (UPLC-QTOF-MS). Data were analyzed using a combination of statistical and bioinformatics tools to identify differentially abundant microbial taxa and metabolites and to construct interaction networks between them. ResultsBehavioral tests revealed that 12 weeks of aerobic exercise significantly improved spatial learning and memory capacity of aged mice, as evidenced by reduced escape latency and increased target area exploration and platform crossings in the MWM. Histopathological analysis demonstrated that exercise mitigated aging-related neuronal damage in the hippocampus, enhancing neuronal density and morphology. 16S rRNA sequencing indicated that exercise increased gut microbiota α‑diversity and enriched beneficial bacterial genera, including Bifidobacterium, Parabacteroides, and Rikenella. Metabolomics analysis identified 32 differentially regulated metabolites between OC and OE groups, with 94 up-regulated and 30 down-regulated in the OE group when compared with OC group. These metabolites were primarily involved in energy metabolism reprogramming (e.g., L-homocitrulline), antioxidant defense (e.g., L-carnosine), neuroprotection (e.g., lithocholic acid), and DNA repair (e.g., ADP-ribose). Network analysis further revealed strong positive correlations between specific bacteria and metabolites, such as Parabacteroides with ADP-ribose and Bifidobacterium with lithocholic acid, suggesting potential neuroprotective pathways mediated by the gut microbiota-metabolite axis. ConclusionThis study provides comprehensive evidence that aerobic exercise elicits cognitive benefits in aging mice by modulating the gut microbiota-metabolite network. These findings highlight three key mechanisms: (1) the proliferation of beneficial gut bacteria enhances metabolic reprogramming to boost DNA repair pathways; (2) elevated neuroinflammation-inhibiting factors reduce neurodegenerative changes; and (3) enhanced antioxidant defenses maintain neuronal homeostasis. These results underscore the critical role of the “microbiota-metabolite-brain” axis in mediating the cognitive benefits of aerobic exercise. This study not only advances our understanding of the gut-brain axis in aging but also offers a scientific basis for developing personalized exercise and probiotic-based interventions targeting aging-related cognitive decline. Future research should further validate these mechanisms in non-human primates and human clinical trials to establish the translational potential of exercise-induced gut microbiota-metabolite modulation for combating neurodegenerative diseases.

OBJECTIVE To evaluate the cost-effectiveness of sacituzumab tirumotecan （ST） versus chemotherapy treatment physician’s choice （TPC） as second-line and later-line treatment for metastatic triple-negative breast cancer （mTNBC） from the perspective of China’s healthcare system. METHODS A partitioned survival model was constructed based on the OptiTROP-Breast 01 trial， with a cycle length of 4 weeks and a time horizon of 10 years， applying a 5% discount rate. Quality adjusted life year （QALY） and costs were used as outcome measures， and the incremental cost-effectiveness ratio （ICER） of ST versus TPC for second-line and later-line treatment of mTNBC was calculated. Sensitivity analyses were conducted to validate the robustness of the base-case results. RESULTS At a willingness-to-pay threshold （WTP） of 3 times China’s 2024 per capita gross domestic product （GDP） （287 247 yuan/QALY）， patients receiving ST gained incremental utility （0.42 QALY） at a higher cost， yielding an ICER of 205 562.07 yuan/QALY， which was lower than WTP， indicating that ST was more cost-effective compared to TPC. One-way sensitivity analysis revealed that key factors influencing the ICER included the utility value of progression-free survival and the price of ST. Probabilistic sensitivity analysis and scenario analysis showed that the base-case results were robust. CONCLUSIONS From the perspective of China’s healthcare system， at a WTP of 3 times China’s per capita GDP， ST is more cost-effective than TPC as second-line and later-line treatment for mTNBC.

Body weight abnormalities, including overweight, obesity, and underweight, have become a dual public health challenge in Chinese adults: overweight and obesity lead to a variety of chronic complications, while underweight increases the risks of malnutrition, sarcopenia, and organ dysfunction. To systematically address these issues, multidisciplinary experts in endocrinology, sports science, nutrition, and psychiatry from various regions have held multiple weight management seminars. Based on the latest epidemiological data and clinical evidence, they expanded the guideline to include assessment and intervention strategies for underweight, in addition to the core content of obesity management. This guideline outlines the etiological mechanisms, evaluation methods, and multidimensional management strategies for overweight and obesity, covering key areas such as diagnosis and assessment, medical nutrition therapy, exercise prescription, pharmacological intervention, and psychological support. It is intended to provide a scientific and standardized approach to weight management across the adult population, aiming to curb the rising prevalence of obesity, mitigate complications associated with abnormal body weight, and improve nutritional status and overall quality of life.

Extremely unbalanced data refers to datasets with independent or dependent variables showing severe imbalances in proportions, which might lead to deviation of classical test statistics from theoretical distribution and difficulties in controlling type Ⅰ error. The increased availability of genome-wide resources from large population cohorts has highlighted the growing demand for efficient and accurate statistical methods for the process of extremely unbalanced data to improve the development of genetic statistical methods. This paper introduces two widely used correction methods in current genome-wide association study for extremely unbalanced data, i.e. Firth correction and saddle point approximation, describes their effectiveness in controlling type Ⅰ errors confirmed by simulation experiments, finally, and summarizes the commonly used software for extremely unbalanced genomic data to provide theoretical reference and suggestion for its application for the statistical analysis on extremely unbalanced data in future.

Objective:Middle ear cholesteatoma is a common otolaryngological disease, and traditional diagnostic methods have certain limitations. This study aims to construct a computer-aided diagnosis model for middle ear cholesteatoma based on integrated convolutional neural networks (CNNs) to improve diagnostic accuracy and efficiency.Methods:Firstly, Data were collected from patients who visited the Department of Otorhinolaryngology Head and Neck Surgery at the First People′s Hospital of Yinchuan between January 2020 and December 2021. 8 000 temporal bone CT images were collected, including 5 000 images diagnosed pathologically as middle ear cholesteatoma and 3 000 normal images. A five-fold cross-validation method was used to divide the dataset into training and testing sets. Next, a transfer learning approach was used to initialize model parameters, and the AlexNet, GoogleNet, and ResNet networks were pre-trained to extract deep features from the images. Then, the Softmax classification algorithm was applied to classify the features, resulting in three independent classifiers. These classifiers were combined using an ensemble learning method with a weighted voting approach to obtain the final diagnostic results. Finally, the model was evaluated by comparing the ensemble classifier with individual classifiers to assess its accuracy, precision, sensitivity, specificity, and diagnostic time, and a comparison with low-mid-and high-experience physician groups was conducted to comprehensively evaluate the model′s diagnostic performance.Results:The experimental results showed that the model achieved an accuracy of 88.8%(178/200), precision of 92.9%,(112/120) sensitivity of 89.8%(108/120), and specificity of 88.1%(70/80). The average diagnostic time for individual patient temporal bone CT images was reduced to 2-3 seconds. Compared to the diagnostic results from low-mid-and high-experience physician groups, the model demonstrated significant advantages and effectively assisted clinicians in making rapid and accurate middle ear cholesteatoma diagnoses.Conclusion:The proposed middle ear cholesteatoma diagnostic model based on integrated convolutional neural networks exhibits high recognition accuracy and rapid diagnostic speed, significantly improving clinical diagnostic efficiency, especially in early screening and auxiliary diagnosis, making it of considerable value in clinical practice.

Objective:To retrieve and integrate the best evidence on foot offloading management for diabetic high-risk foot patients, and to provide evidence-based basis for effectively preventing the occurrence and development of diabetic foot ulcers.Methods:Clinical practice guidelines, evidence summaries, systematic reviews and expert consensus on the management of foot offloading at high risk of diabetes were searched in Chinese and English databases, domestic and foreign diabetes association websites, clinical decision websites and guideline websites, retrieval time for libraries to March 31, 2024, respectively, by the 2 researchers quality evaluation, evidence extraction, the final summary.Results:A total of 16 articles were included, including 6 guidesline, 5 expert consensuses, 1 evidence summary and 4 systematic reviews. According to the existing evidence, the best evidence of foot offloading management in patients with diabetic high-risk foot was summarized from 6 aspects: evaluation, monitoring, referral, offloading brace, exercise advice and risk factor intervention, 14 subcategories, 32 best evidences for foot offloading management in patients with high-risk diabetic foot.Conclusions:This study summarized the best evidence for foot offloading management in patients with high-risk of diabetic foot, which is convenient for the further development of clinical practice of foot offloading management in patients with high-risk of diabetes, and provides evidence-based basis for clinical medical staff to expand related research.