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

Objectives:This study aims to evaluate the causal relationship between plasma proteins and myocardial infarction(MI)using two-sample bidirectional Mendelian randomization(MR)analysis,identify key biomarkers,and validate their expression.Methods:The study utilized publicly available genome-wide association study(GWAS)data of 4 907 plasma proteins as the exposure factor,with single nucleotide polymorphisms(SNPs)as instrumental variables,and four MI datasets as outcomes.Two-sample MR analysis was performed using the inverse variance weighted(IVW)method,complemented by simple model,weighted model,weighted median estimator(WME),and MR-Egger regression methods to assess the causal relationship between exposure factors and outcomes.Venn diagrams and word clouds were used to screen proteins associated with MI as candidate biomarkers.Reverse MR analysis was conducted to evaluate reverse causality.Sensitivity analysis was performed to assess the robustness of the results.Immunohistochemistry(IHC)was used to validate the expression of proteasome activator subunit 1(PSME1)and vacuolar protein sorting 29(VPS29)in the aorta of mice,and enzyme-linked immunosorbent assay(ELISA)was used to verify the expression of PSME1 and VPS29 in plasma from patients with acute myocardial infarction(AMI).Results:The two-sample MR analysis indicated that PSME1 was significantly negatively associated with myocardial infarction in all four datasets,with OR(95%CI)of 0.684(0.557-0.839),0.990(0.987-0.993),0.579(0.448-0.748),and 0.993(0.990-0.996),respectively,with all P<0.001.Similarly,VPS29 also showed a significant negative association with MI in all four datasets,with OR(95%CI)of 0.902(0.862-0.945),0.998(0.997-0.999),0.866(0.808-0.929),and 0.998(0.997-0.999),respectively,with all P<0.001.Reverse MR analysis did not detect reverse causality,and sensitivity analysis confirmed the robustness of the results.IHC results showed significantly reduced expression of PSME1 and VPS29 in the aortas of AMI mice with an atherosclerotic background compared to control mice(both P<0.05).ELISA results indicated significantly lower plasma levels of PSME1 and VPS29 in AMI patients compared to healthy controls(both P<0.05).Conclusions:Higher levels of PSME1 and VPS29 are negatively associated with the risk of MI,suggesting that PSME1 and VPS29 may serve as protective biomarkers for cardiovascular diseases.

Objective To explore the research status and development direction by analyzing the registered clinical trials of TCM prevention and treatment of osteoporosis.Methods The clinical trials of TCM in the prevention and treatment of osteoporosis were retrieved from Chinese Clinical Trial Registry(ChiCTR)and the ClinicalTrials.gov from the establishment of databases to September 2024.Excel 2024 was used to input the screening data,and the registration number,research topic,registration time,region,institution,funding source,research disease,research type,stage and design scheme,randomized method and blind method,intervention measures,participation center,sample size and outcome index of the included trials were analyzed.Results A total of 101 clinical trials of TCM in the prevention and treatment of osteoporosis were included in the study,involving 4 countries.China's registered projects involved 58 research institutions in 21 provincial administrative regions,of which Beijing,Shanghai and Guangzhou had a large number of registered projects;the studies were dominated by single-center,interventional,and randomized controlled trials,with the most application of simple randomization methods;blinding was applied to 20 trials;postmenopausal osteoporosis was the dominant disease under study,and Chinese patent medicines were the main interventions;the main outcome indicators were bone mineral density,biochemical indicators of bone metabolism and visual analog score.Conclusion The number of clinical trials of TCM in the prevention and treatment of osteoporosis has been increasing rapidly,and researchers have been paying more attention to the studies.However,there are some problems,such as unbalanced research area,less preventive and observational research,insufficient use of blind method,unclear outcome indicators,and low attention paid to some treatment methods.

The rabies virus(RABV)that causes rabies mainly attacks the peripheral and central nervous systems.In the later stages of infection,it is scattered in the salivary glands and transmit-ted to other susceptible animals through infectious saliva.To study dispersion of the RABV in the three pairs of salivary gland tissues,the street strain PB4 of the RABV was inoculated into 21-day-old female mice through the hind limb muscles.During the moribund stage of the mice,the sublin-gual gland,submandibular gland and parotid gland were collected,respectively.The TCID50 titer of RABV in the three kinds of glands of the mice and the copy number of the RABV N gene were de-tected,and RABV in different salivary glands was observed by immunofluorescence.The results showed that PB4 was dispersed in all three kinds of salivary glands of the mice,with the largest a-mounts in the parotid gland,followed by the submandibular gland,and the lowest amount in the sublingual gland.Three-month-old dogs were inoculated with PB4 through the cranial cavity,and saliva were collected every 12 h after inoculation.The saliva samples were detected by TCID50 and RT-qPCR.And during the moribund stage of the dogs when the disease occurred,the three pairs of salivary glands were collected.Through the determination of the TCID50 titer,RT-qPCR and immu-nofluorescence detection,it was demonstrated that among the three different salivary glands of the dogs,the largest amount of PB4 was found in the parotid gland and the lowest in the sublingual gland.Our results in mice and dogs clearly proved that the parotid gland was consistently found to exhibit the highest content of street RABV among the three major salivary glands,which could en-rich experimental data for analyzing the dispersion of RABV in the salivary glands and interpreta-tion of the intermittent secretion of saliva in clinically rabid dogs.

Cancer cachexia is a complex syndrome caused by multiple factors,which seriously affects the quality of life and prognosis of patients.Its overall pathogenesis is related to the deficiency of spleen qi,insufficiency of kidney essence,internal generation of turbid toxins,and the obstruction of the production of qi,blood and essential qi,which cannot nourish the muscles and bones.Under the guidance of the dynamic diagnosis and treatment system of"spleen and kidney exhaustion as the root cause and internal accumulation of turbid toxins as the manifestation",the overall regulation is carried out from four dimensions:opening and closing the spleen and stomach,nourishing the kidney and promoting transportation,transforming turbid toxins and detoxification,and tonifying qi and nourishing yin.It has shown unique value in the intervention of cancer cachexia and can provide ideas and references for the clinical practice of TCM in treating cancer cachexia.

Intravascular large B cell lymphoma (IVLBCL) is a rare, aggressive subtype of diffuse large B cell lymphoma, with nasal turbinate involvement being uncommon. We report a 51-year-old woman with a 1-month history of fever of unknown origin. Laboratory findings showed cytopenia, hypertriglyceridemia, elevated ferritin, increased soluble CD25, and bone marrow hemophagocytosis. No infectious cause was identified. PET-CT revealed abnormal 18F-fluorodeoxyglucose (FDG) uptake in the nasal turbinates. Turbinate biopsy revealed tumor cells localized predominantly within vascular lumens, positive for CD20, BCL6, PAX5, and MUM1, with a Ki-67 index >60%, confirming a diagnosis of IVLBCL with hemophagocytic lymphohistiocytosis (HLH). The patient received one cycle of the DEP regimen (liposomal doxorubicin, etoposide, and methylprednisolone) for HLH, followed by five cycles of R-CHOP (rituximab, cyclophosphamide, doxorubicin, vincristine, and prednisone) and consolidation with auto-HSCT, achieving sustained complete remission. IVLBCL outcomes are heterogeneous; early diagnosis and prompt treatment improve survival, and R-CHOP plus auto-HSCT may be an effective strategy.

ObjectiveTo investigate the potential mechanisms of Zhimu （Anemarrhena asphodeloides）-Huangbai （Phellodendron amurense） medicinal pair in alleviating postmenopausal osteoporosis （PMOP）. MethodsSixty unpregnant female SD rats were randomly divided into five groups， blank group， model group， low-dose Zhimu-Huangbai group， high-dose Zhimu-Huangbai group， and estradiol group， with 12 rats in each group. Except for the blank group， all other groups had their ovaries removed to create PMOP rat models， while the blank group only had the fat tissue around the ovaries removed. One week after the ovarian removal， the low-dose and high-dose Zhimu-Huangbai groups received concentrated solution of Zhimu and Huangbai with 1.8， 7.2 g/（kg·d） via gavage， the estradiol group received estradiol solution 0.09 mg/（kg·d） via gavage， and the blank group and the model group received 10 ml/（kg·d） of normal saline via gavage， once daily for 12 weeks. Before sampling， the body mass of the rats was recorded， and uterine tissue was taken to calculate the uterine index. The levels of interleukin-1β （IL-1β）， interleukin-6 （IL-6） and tumor necrosis factor-α （TNF-α） in serum were detected by ELISA； micro CT was used to examine the parameters of femoral microstructure， including bone volume/tissue volume （BV/TV）， trabecular number （Tb.N）， trabecular thickness （Tb.Th）， bone mineral density （BMD）， trabecular separation （Tb.Sp）， and cortical bone area （Ct.Ar）. HE staining was used to observe pathological changes in the femur； RT-qPCR was used to detect the mRNA expression of osteogenic-adipogenic differentiation-related factors in femoral tissue， including Runt-related transcription factor 2 （Runx2）， bone morphogenetic protein 2 （BMP-2）， peroxisome proliferator-activated receptor γ （PPARγ）， chemerin and chemokine-like receptor 1 （CMKLR1）. ResultsCompared with the blank group， the model group showed a significant increase in body mass， a significant decrease in the uterine index， BV/TV， Tb.N， Tb.Th and BMD， a significant increase in Tb.Sp， and serum IL-1β， IL-6， and TNF-α levels， a significant reduction of mRNA expression of Runx2 and BMP-2 in bone tissue， and a significant increased mRNA expression of PPARγ， chemerin， and CMKLR1 （P＜0.01）. HE staining revealed that the femoral tissue showed a reduction and sparsity of trabeculae， a significant enlargement of the medullary cavity， and a large number of fat cells. Compared to the model group， the low-dose， high-dose Zhimu-Huangbai groups， and estradiol group showed significant improvements in all the above-mentioned indicators （P＜0.05 or P＜0.01）. HE staining revealed a significant increase in trabeculae， more organized arrangement， and a marked reduction in fat cells. Compared to low-dose Zhimu-Huangbai group， the high-dose Zhimu-Huangbai group exhibited a significant increase in the uterine index and BMD， and a significant reduction in body mass and PPARγ and Chemerin mRNA expression （P＜0.05 or P＜0.01）. Compared to high-dose Zhimu-Huangbai group， the estradiol group showed a decrease in uterine index， BV/TV， Tb.N， Tb.Th， BMD， and BMP-2 mRNA expression， while the levels of IL-1β， TNF-α， and IL-6， as well as Tb.Sp and the mRNA expressions of PPARγ， chemerin， and CMKLR1 increased （P＜0.05 or P＜0.01）. ConclusionThe Zhimu-Huangbai medicinal pair can alleviate PMOP bone loss， and its mechanism of action is related to reducing the levels of inflammatory factors， correcting the disorder of osteogenic-adipogenic differentiation of bone marrow mesenchymal stem cells （BMSCs）， and promoting the differentiation of BMSCs into osteoblasts.

To address the problem of large reconstruction errors in 3D pulse signals caused by excessively small out-of-plane displacement of the contact membrane in the existing traditional Chinese medicine fingertip tactile binocular vision detection technology, this study proposes a 3D pulse image detection method based on subtle motion magnification technology and explores its application in pulse pattern recognition. Firstly, a 3D pulse image detection system based on binocular vision to obtain pulse image signals is developed as experimental data. Then, the phase motion video magnification algorithm is used to amplify the original signals, and the amplified signals are reconstructed in three dimensions to obtain 3D pulse signals. On this basis, nine features are extracted from the 3D pulse signals and features selection is performed using a two-sample Kolmogorov-Smirnov test. Finally, machine learning algorithms such as decision trees and random forests are used to identify the five types of pulse conditions: deep pulse, intermittent pulse, flooding pulse, slippery pulse, and rapid pulse. The experimental results show that compared to the methods without subtle motion magnification technology, the proposed method significantly improves waveform clarity, amplitude stability, and periodic regularity. Meanwhile, the average accuracy in pulse pattern recognition reaches 96.29%±0.26%.
Algorithms ; Imaging, Three-Dimensional/methods* ; Pattern Recognition, Automated ; Medicine, Chinese Traditional ; Motion ; Humans ; Pulse ; Signal Processing, Computer-Assisted ; Machine Learning

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