OBJECTIVE: To explore the application value of artificial intelligence (AI)-assisted chromosomal karyotype analysis in the diagnosis of prenatal chromosomal mosaicism. METHODS: A retrospective analysis was conducted on 172 pregnant women who underwent amniocentesis at the Department of Medical Genetics and Prenatal Diagnosis, the Third Affiliated Hospital of Zhengzhou University between January 2019 and December 2024. All cases whose fetuses were diagnosed with chromosomal mosaicism via karyotype analysis and stratified into two groups based on the analytical software employed: the conventional analysis group (n = 70), which utilized Leica analysis software for karyotype image recognition and cell counting; and the AI-assisted analysis group (n = 102), which utilized AI-assisted software for the same procedures. The clinical performance of AI-assisted karyotype analysis in diagnosing chromosomal mosaicism was comprehensively evaluated by comparing the types of mosaic karyotypes, distribution of mosaic ratios, and verification outcomes of different detection modalities between the two groups. This study was approved by the Medical Ethics Committee of the Third Affiliated Hospital of Zhengzhou University (Ethics No.: 2024-406-01). RESULTS: No statistically significant difference was observed in baseline characteristics (maternal age, gestational week, and indications for prenatal diagnosis) between the two groups. Regarding the detection efficacy for numerical and structural mosaicisms, no significant difference was found in the detection of numerical mosaicism. However, the conventional analysis group exhibited a significantly higher detection rate of autosomal structural mosaicism compared to the AI-assisted group (11.43% vs. 0.98%, P < 0.05). Numerical mosaicism cases were further verified using copy number variation sequencing (CNV-seq) and/or fluorescence in situ hybridization (FISH). The AI-assisted group demonstrated a significantly lower inconsistency rate (5.56% vs. 20.41%, P < 0.05) compared to the conventional group. For low-proportion (< 10%) chromosomal mosaicism, the AI-assisted group had a significantly lower detection rate (13.25% vs. 29.69%, P < 0.05). Subsequent validation of low-proportion mosaicism by CNV-seq and/or FISH showed a higher consistency rate in the AI-assisted group (81.82% vs. 54.55%), though the difference did not reach statistical significance (P = 0.360). CONCLUSION For the karyotyping analysis of prenatal chromosomal mosaicism, AI-assisted karyotype analysis shows high accuracy and consistency in identifying numerical chromosomal mosaicism, particularly in reducing the detection of low-proportion (< 10%) mosaicism while improving verification accuracy. AI-assisted analysis can significantly improve the detection accuracy of numerical mosaicism and mitigate the risk of misclassification for low-proportion (< 10%) mosaicism, thereby providing more precise clinical evidence for the prenatal diagnosis of chromosomal mosaicisms.
Humans ; Female ; Mosaicism ; Pregnancy ; Karyotyping/methods* ; Artificial Intelligence ; Prenatal Diagnosis/methods* ; Adult ; Retrospective Studies ; Chromosome Disorders/genetics* ; Amniocentesis

Heart and spleen deficiency syndrome is the most common syndrome type in patients with insomnia. Based on the theory of disease syndrome-combined animal model， this paper used multiple databases to search for the keywords "heart and spleen deficiency"， "insomnia"， "sleepless"， "disease syndrome-combined animal model"， "model evaluation"， etc. It selected the literature related to the animal model of insomnia with heart and spleen deficiency in the past 20 years to evaluate from the aspects of model establishment， modeling factors， syndrome model， disease model， macro characterization & macro characterization evaluation scale， micro indicators， etc. It is found that the existing animal model of insomnia with heart and spleen deficiency is not completely constructed by the method of disease syndrome combination of disease modeling factors and syndrome modeling factors. In the model using this method， the single establishment factor of heart and spleen deficiency does not conform to the clinical reality of disease， and the selection of the factors for the insomnia model is not closely related to or even separated from the syndrome performance. There is a problem of insufficient quantification of macro representation when the macro representation of the model replaces the symptoms related to heart and spleen deficiency syndrome and insomnia in an equivalent manner for macro representation evaluation， which can be improved according to the quantitative ideas and examples of the existing macro representation and macro representation evaluation scale. There are few specific indicators of heart and spleen deficiency syndrome in micro indicators. The micro research of heart and spleen deficiency syndrome and the essence of other traditional Chinese medicine （TCM） syndromes can be carried out by metabonomics and other technologies combined with the theory of corresponding prescription and syndrome， along the specific related ideas of "prescription and syndrome， treatment principle and selection of prescription， treatment principle and selection of acupoints， as well as therapeutic mechanism and syndrome essence". The future users and researchers of animal models of insomnia with heart and spleen deficiency can get improved methods and ideas through the shortcomings of animal models of heart and spleen deficiency listed in this paper and construct animal models of insomnia with heart and spleen deficiency that are more suitable for clinical practice， so as to establish a more perfect modeling method and evaluation system of disease syndrome-combined animal model.

Heart and spleen deficiency syndrome is the most common syndrome type in patients with insomnia. Based on the theory of disease syndrome-combined animal model， this paper used multiple databases to search for the keywords "heart and spleen deficiency"， "insomnia"， "sleepless"， "disease syndrome-combined animal model"， "model evaluation"， etc. It selected the literature related to the animal model of insomnia with heart and spleen deficiency in the past 20 years to evaluate from the aspects of model establishment， modeling factors， syndrome model， disease model， macro characterization & macro characterization evaluation scale， micro indicators， etc. It is found that the existing animal model of insomnia with heart and spleen deficiency is not completely constructed by the method of disease syndrome combination of disease modeling factors and syndrome modeling factors. In the model using this method， the single establishment factor of heart and spleen deficiency does not conform to the clinical reality of disease， and the selection of the factors for the insomnia model is not closely related to or even separated from the syndrome performance. There is a problem of insufficient quantification of macro representation when the macro representation of the model replaces the symptoms related to heart and spleen deficiency syndrome and insomnia in an equivalent manner for macro representation evaluation， which can be improved according to the quantitative ideas and examples of the existing macro representation and macro representation evaluation scale. There are few specific indicators of heart and spleen deficiency syndrome in micro indicators. The micro research of heart and spleen deficiency syndrome and the essence of other traditional Chinese medicine （TCM） syndromes can be carried out by metabonomics and other technologies combined with the theory of corresponding prescription and syndrome， along the specific related ideas of "prescription and syndrome， treatment principle and selection of prescription， treatment principle and selection of acupoints， as well as therapeutic mechanism and syndrome essence". The future users and researchers of animal models of insomnia with heart and spleen deficiency can get improved methods and ideas through the shortcomings of animal models of heart and spleen deficiency listed in this paper and construct animal models of insomnia with heart and spleen deficiency that are more suitable for clinical practice， so as to establish a more perfect modeling method and evaluation system of disease syndrome-combined animal model.

Stroke is a critical health issue in China, and carotid artery stenosis and plaque play key roles in its prevalence. Despite the acknowledged significance of this condition, detailed information regarding the prevalence of carotid artery stenosis and plaque across the Chinese population has been scarce. This study analyzed data from the China Stroke High-risk Population Screening and Intervention Program for 2020-2021, focusing on 194 878 Chinese adults aged 40 years and above. It assessed the prevalence of carotid artery stenosis and plaque and identified their associated risk factors. Results revealed a standardized prevalence of 0.40% for carotid artery stenosis and 36.27% for carotid plaque. Notably, the highest rates of stenosis were observed in north and south China at 0.61%, while southwestern China exhibited the highest plaque prevalence at 43.17%. Key risk factors included older age, male gender, hypertension, diabetes, stroke, smoking, and atrial fibrillation. This study highlights significant geographical and demographic disparities in the prevalence of these conditions, underlining the urgent need for targeted interventions and policy reforms. These measures are essential for reducing the incidence of stroke and improving patient outcomes, addressing this significant health challenge in China.
Humans ; China/epidemiology* ; Male ; Female ; Prevalence ; Middle Aged ; Carotid Stenosis/epidemiology* ; Risk Factors ; Aged ; Adult ; Plaque, Atherosclerotic/epidemiology* ; Stroke/epidemiology* ; Aged, 80 and over

OBJECTIVES: This study aimed to construct a risk prediction model for the occurrence of the demora-lization syndrome in patients with oral cancer and provide a scientific basis for the prevention of this syndrome in patients with oral cancer and the development of personalized care programs. METHODS: A total of 486 patients with oral cancer in West China Hospital of Stomatology of Sichuan University and Sun Yat-sen Memorial Hospital of Sun Yat-sen University from 2024 March to July were selected by convenience sampling. We integrated clinical data and evidence from previous studies to identify the key variables affecting the demoralization syndrome in patients with oral cancer. The 486 patients were divided into a training set and a validation set in an 8∶2 ratio. A clinical risk prediction model was established based on the individual data of 365 patients in the development cohort. Through least absolute shrinkage and selection operator (LASSO) regression, a moderate to severe risk prediction model of demoralization syndrome in oral cancer was constructed, and a clinical machine-learning nomogram was constructed. Bootstrap resampling was used for internal validation. The data of 121 patients in the validation cohort were externally validated. RESULTS: The incidence of the demoralization syndrome in patients with oral cancer was 405 cases (83.3%), of which 279 cases (57.4%) were mild, 176 cases (36.2%) were moderate, and 31 cases (6.4%) were severe. The core model, including patient education level, disease understanding, and MDASI-HN score, was used to predict the risk of outcome. Internal validation of the model yielded C statistic of 0.783 6 (95% CI: 0.78-0.87), beta of 0.843 4, and calibration intercept of -0.040 6. Through external validation, the validation set C statistic was 0.80 (95%CI: 0.71-0.87), beta was 0.80, and calibration intercept was -0.08. CONCLUSIONS Our risk prediction mo-del of the demoralization syndrome in patients with oral cancer performed robustly in validation cohorts of different nur-sing environments. The model has good correction and good discrimination and can be used as an evaluation and prediction item at admission.
Humans ; Mouth Neoplasms/complications* ; Male ; Female ; Nomograms ; Middle Aged ; Syndrome ; Aged ; Adult ; Risk Factors ; Risk Assessment ; Machine Learning

OBJECTIVE: To explore the prenatal features and genetic etiology of a fetus with Short-rib cage dysplasia (SRTD) due to variants of DYNC2H1 gene. METHODS: A pregnant women presented at Xinxiang Central Hospital in June 2020 for abnormal prenatal ultrasound findings was selected as the study subject. With informed consent obtained, amniotic fluid sample was extracted from the woman, and clinical data of the fetus were collected. Whole exome sequencing (WES) was carried out, and candidate variants were verified by Sanger sequencing. This study was approved by the Medical Ethics Committee of Xinxiang Central Hospital [Ethics No.: 2025-214-01(K)]. RESULTS: At 25⁺⁶ weeks gestation, genetic testing revealed that the fetus has harbored compound heterozygous variants of the DYNC2H1 gene, namely c.10585C>T (p.Arg3529Ter) and c.8954T>G (p.Val2985Gly), which were derived from its father and mother, respectively. Based on the guidelines from the American College of Medical Genetics and Genomics (ACMG), the c.10585C>T (p.Arg3529Ter) and c.8954T>G (p.Val2985Gly) variants were classified as pathogenic (PVS1+PM2_supporting+PM3+PP5) and likely pathogenic (PM1+PM2_supporting+PM3+PP3), respectively. Bioinformatics analysis suggested that both variants may affect the 3D structure of the DYNC2H1 protein. CONCLUSION The compound heterozygous variants of c.10585C>T (p.Arg3529Ter) and c.8954T>G (p.Val2985Gly) of the DYNC2H1 gene probably underlay the pathogenesis of SRTD in the fetus. Above findings had facilitated prenatal diagnosis and genetic counseling for the couple.
Humans ; Female ; Pregnancy ; Cytoplasmic Dyneins/chemistry* ; Prenatal Diagnosis ; Adult ; Short Rib-Polydactyly Syndrome/diagnostic imaging* ; Mutation ; Exome Sequencing ; Fetus/abnormalities* ; Ultrasonography, Prenatal

Purpose/Significance To explore the construction pathway of an intelligent automation model tailored for primary health-care institutions,aiming to address the issue of repetitive reporting.Method/Process Through methods such as on-site investigations and expert consultations,a field study is conducted in primary healthcare institutions in Dongcheng District,Beijing.Utilizing information resource planning methods and data integration and mapping technologies,the business interactions and information flow within these in-stitutions are analyzed to investigate the construction pathway of an automated reporting model.Result/Conclusion The business flow and data flow are mapped out by the modeling process,a repository of relevant reporting indicators is organized,and a multi-source data au-tomatic mapping model and rules are developed.The study provides a feasible reference pathway for realizing intelligent reporting in pri-mary healthcare institutions.

Purpose/Significance To strengthen the construction of intelligent primary health information systems,and to provide ref-erences for improving the level of primary health services.Method/Process The paper systematically summarizes the current construction of primary health information systems at home and abroad,focuses on analyzing the current technical architectures and intelligent applica-tions of the existing systems,and puts forward improvement suggestions for the shortcomings in the construction mode,data connectivity and intelligent application.Result/Conclusion Countermeasures such as strengthening top-level design,promoting multi-source heter-ogeneous data fusion and strengthening intelligent applications are proposed to provide references for the construction of intelligent primary health information systems.

Objective:To explore the value of baseline PET/CT parameters for predicting prognosis in patients with double-expression lymphoma (DEL).Methods:The clinical and 18F-FDG PET/CT data of 118 patients (66 males, 52 females; age: 28-85 years) with diffuse large B-cell lymphoma (DLBCL) diagnosed in Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University and the First Affiliated Hospital of Nanjing Medical University from June 2015 to September 2022 were retrospectively analyzed. The optimal thresholds for SUV max, total metabolic tumor volume (TMTV) and total lesion glycolysis (TLG) in predicting overall survival (OS) rate were determined using ROC curve analysis. Univariate and multivariate analyses, along with Kaplan-Meier survival analysis were performed to construct a survival prediction model. The effect of the model was evaluated by the calibration curve for the model, the time-dependent ROC curve analysis and decision curve analysis. Results:As of the last follow-up, 25 patients died, and the OS rate was 78.8%(93/118). The AUC of the ROC curve for TMTV was 0.705, with a corresponding optimal threshold of 230.9 cm 3. In multivariate analysis, Eastern Cooperative Oncology Group performance status (ECOG PS) score (hazard ratio ( HR)=3.886, 95% CI: 1.455-10.375; P=0.007) and TMTV ( HR=4.649, 95% CI: 1.665-12.979; P=0.003) were identified as independent predictors of OS. The combined model of ECOG PS score and TMTV was superior to ECOG PS score model and TMTV model alone in predicting OS. Conclusion:TMTV, a metabolic indicator, and ECOG PS score, a clinical risk factor, are independent predictors of OS in patients with DEL, and their combination can provide more accurate prognostic predictions.

Objective:To investigate the relationship between non-alcoholic fatty liver disease(NAFLD) and hepatic fibrosis and skeletal muscle mass in patients with type 2 diabetes mellitus(T2DM).Methods:A total of 685 T2DM patients diagnosed at the Endocrinology department of Lanzhou University First Hospital, from April 2022 to May 2023, were divided into NAFLD and Non-NAFLD groups, and the NAFLD group was further categorized into fibrosis and non-fibrosis based on aspartate aminotransferase(AST) /alanine aminotransferase(ALT) level. The differences in appendicular skeletal muscle mass(ASM), appendicular skeletal muscle mass index(ASMI), and the prevalence of muscle mass loss were compared across groups. The correlations between ASMI and NAFLD, as well as liver fibrosis were analyzed by binary logistic regression.Results:Among male T2DM patients, those with NAFLD had lower ASMI levels and a higher prevalence of muscle mass reduction compared to non-NAFLD group. Among female T2DM patients, those with NAFLD had lower levels of ASM and ASMI, and a higher prevalence of muscle mass reduction compared to non-NAFLD group. ASMI levels in both male and female T2DM patients were independently negatively correlated with NAFLD risk( OR=-0.696, 95% CI 0.579-0.837; OR=-0.757, 95% CI 0.629-0.911). In NAFLD patients, ASM and ASMI levels were lower in those with liver fibrosis compared to those without fibrosis; however, the prevalence of muscle mass reduction did not differ significantly. Among male NAFLD patients, ASMI levels were independently negatively correlated with the risk of liver fibrosis( OR=-0.726, 95% CI 0.537-0.983), while no correlation was found in female patients. Conclusion:Reduced muscle mass is independently associated with the risk of NAFLD in both male and female T2DM patients. In males, reduced muscle mass is also independently related to the risk of liver fibrosis.