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

Lung transplantation is a definitive treatment for end-stage lung disease and can significantly improve patient prognosis. However, postoperative complications such as infection, rejection, ischemia-reperfusion injury and chronic lung allograft dysfunction intertwine to form a complex pathological network, posing persistent challenges to long-term patient survival. In 2025, research teams worldwide have made systematic progress in the field of basic lung transplantation research. By integrating cutting-edge technologies including single-cell multi-omics, spatial transcriptomics and novel animal models, significant breakthroughs have been achieved in the evolutionary dynamics of drug-resistant infections, molecular mechanisms of immune regulation, programmed cell death, optimization of donor lung protection strategies and early warning of chronic lung allograft dysfunction. This article systematically reviews the representative advances in basic lung transplantation research worldwide in 2025, and deeply analyzes the implications of mechanistic breakthroughs for optimizing diagnosis and treatment strategies, aiming to anchor the direction for innovative breakthroughs and clinical translation in basic lung transplantation research.

Objective:To explore the interrelationship between brain functional networks and features in functional magnetic resonance imaging(fMRI)of patients with Alzheimer's disease(AD),and to construct mixed-function networks(MFN),and apply them in machine learning classification models to improve the accuracy of AD classification.Methods:102 AD patients and 227 healthy subjects in the Alzheimer's Neuroimaging Initiative(ADNI)dataset were retrospectively analyzed.The partial correlation brain network of the blood oxygen level dependent(BOLD)signal was calculated and fused with low-frequency wave amplitude(ALFF),fractional low-frequency wave amplitude(fALFF)and local consistency(ReHo)features to construct MFN.Network topology parameters were extracted,and a variety of machine learning classification models were constructed based on MFN topological parameters,accuracy,precision,recall and area under the curve(AUC)were used to evaluate the predictive efficiency of the models.Results:By constructed MFN and calculated intra group to inter group ratio(IIGR),35 features could be obtained from ALFF,fALFF and ReHo feature topological parameter analysis,after rank sum test and FDR correction,there were statistical differences among 28 features(P＜0.05).The classification results show that,all the five classifiers have high classification performance on the test data set.The accuracy,precision and recall rates of random forest(RF),adaptive lifting algorithm(AdaBoost),guided aggregation algorithm(Bagging)and support vector machine(SVM)were all 99.7％,and the AUC values were up to 100％,99.5％,99.1％and 99.5％,respectively.The accuracy(98.5％),precision(98.5％),recall(98.5％),and AUC(99.1％)of the multi-layer perceptron(MLP)were slightly lower than other models,but remained excellent.It was worth noting that RF has the highest AUC value of all models at 100.0％,while Bagging has the lowest AUC value(99.1％)in the integrated approach.The results of performance comparison show that,MFN classification model can significantly improve the recognition and classification of AD disease,and greatly improve the performance of various indicators of the classifier.The results showed that,MFN classification model was superior to intelligent classification based fusion,DBN-based multitask learning,PVT-TSVM,unsupervised learning and clustering,SVM and SVM of degree 3 polynomial kernel function in key indicators such as accuracy(99.13％),AUC(99.42％),recall rate(99.46％)and specificity(99.42％)with plasma proteins,machine learning algorithms.It was further proved that MFN classification model has good generalization ability and robustness in AD disease classification.Conclusion:The AD classification model constructed based on brain mixed function network topology parameters and machine learning can improve the accuracy of AD classification.

Objective·To investigate the role of interleukin-1β(IL-1β)in predicting the severity of oral and maxillofacial space infection(OMSI),and to explore the key mechanisms regulating IL-1β release,the critical immune cell subpopulations involved,and the intercellular communication networks among immune cells in OMSI patients.Methods·A total of 62 OMSI patients admitted to the Department of Oral Surgery,Shanghai Ninth People's Hospital,Shanghai Jiao Tong University School of Medicine,from January to November 2023 were enrolled,including 20 patients with moderate infection,21 with severe infection,and 21 with extremely severe infection.Logistic regression analysis was performed to identify risk factors for extremely severe infection,and receiver operating characteristic(ROC)curves were constructed to evaluate the ability of the above indicators to predict extremely severe infection.Peripheral blood mononuclear cells(PBMCs)from 2 patients in each group(moderate,severe and extremely severe)and 2 healthy controls(GSE224198)were analyzed using single-cell RNA sequencing(scRNA-seq)to identify key pro-inflammatory cell subtypes and genes,and to examine their changing trends with increasing infection severity.Cell-cell communication was assessed using CellChat.Quantitative real-time polymerase chain reaction(qPCR)and Western blotting were used to validate inflammasome activation levels in PBMCs.Results·Compared with patients with moderate and severe infections,levels of procalcitonin(PCT)(P<0.05)and IL-1β(P<0.05)were significantly elevated in patients with extremely severe infection.Logistic regression identified IL-1β as an independent risk factor for extremely severe infection(OR=1.814,95%CI 1.256?2.621,P=0.002).The area under the ROC curve(AUC)for the combined prediction of extremely severe infection using IL-1β and PCT was 0.943.scRNA-seq revealed continuous upregulation of NLRP3(NOD-like receptor family pyrin domain-containing 3)and IL1B gene expression in monocytes as infection severity increased,with intermediate monocytes being the main IL1B-expressing cell subtype.IL-1Β-IL-1R signaling,C-C motif chemokine ligand(CCL)and intercellular adhesion molecule(ICAM)signaling were significantly enhanced in monocytes.Macrophage migration inhibitory factor(MIF)signaling between T cells and monocytes also increased notably.With infection progression,the mRNA levels of NLRP3 and IL1B in peripheral blood rose steadily,and the protein levels of NLRP3,caspase-1 p20,apoptosis-associated speck-like protein containing a CARD(ASC)and IL-1β were persistently elevated.Conclusion·The combined levels of IL-1β and PCT at admission can effectively predict extremely severe OMSI.NLRP3 inflammasome activation is observed in PBMCs of OMSI patients.The elevation of IL-1β is closely associated with intermediate monocytes.Monocyte-mediated IL-1Β-IL-1R,CCL and ICAM signaling pathways,along with T cell-mediated MIF signaling pathways,collectively promote the inflammatory response.

Objective To analyze the implementation of MPOWER tobacco control policies in different regions and populations in China,as well as the impact of perceptions of tobacco control policies on individual smoking behavior and quit intentions,to promote the fairness of policy implementation and protection for vulnerable groups.Methods A multivariable regression model was constructed utilizing raw data from the China Adult Tobacco Survey to analyze disparities in perceived MPOWER policy implementation among various social demographics and its impact on smoking behavior and quitting intentions.Results Regarding protection from tobacco smoke(P),local economic level,urban-rural divide were significantly correlated with awareness of comprehensive smoking bans.For offering help to quit smoking(O),local tobacco industry revenue and individual age were associated with the doctor's advice for quitting.As to the warning about the harm of tobacco(W),economic level,geography and urban-rural disparity were correlated with the visibility of health warnings.About the tobacco advertising,promotion and sponsorship(E),geography was related to the exposure to tobacco advertisements,local tobacco industry revenue was associated with the tobacco promotion.For tobacco taxes(R),education level and age were significantly correlated with tobacco affordability.People who perceived comprehensive smoking bans(OR=0.69,95%CI:0.59-0.81)was associated with less smoking behavior,while people perceiving tobacco promotional activities(OR=2.51,95%CI:2.00-3.17)were more likely to smoke.Additionally,people who perceived comprehensive smoking bans(OR=1.70,95%CI:1.25-2.31)and health warning(OR=2.09,95%CI:1.48-3.01)had higher intention to quit smoking.Conclusion In economically disadvantaged regions and among specific socially vulnerable groups(such as low-income individuals,rural residents,and the elderly)in China,the perception of tobacco control policy implementation is relatively low,the perception of tobacco control policies can influence smoking behavior and quitting intentions.Legislative and enforcement efforts should be increased targeting these groups with lower perceptions of the policies to enhance the fairness of tobacco control measures.

Objective To investigate the value of the absolute number of lymphocyte subpopulations as an early warning indicator for children with SMPP(severe mycoplasma pneumonia,SMPP)and to analyze the character-istics observed via bronchoscopy,thereby providing a valuable reference for the early diagnosis of SMPP.Methods This study included 102 children with Mycoplasma pneumoniae pneumonia(MPP),comprising 54 cases of common MPP and 48 cases of SMPP.The lymphocyte subpopulations,clinical characteristics,and laboratory indicators were analyzed.Results There were statistically significant differences between the two groups in the absolute number levels of lymphocyte subpopulations CD3+CD19-T,CD4+T,CD3-CD19+B,CD3-/CD16+CD56+NK cells(P<0.05).The absolute numbers of CD3+CD19-T,CD4+T,CD3-CD19+B,and CD3-/CD16+CD56+NK cells showed negative correlations with serum ferritin,LDH,CRP,and D-D,respectively(P<0.05).Multifactorial logistic regression analysis identified the absolute numbers of CD3+CD19-T,CD4+T,CD3-CD19+B,and CD3-/CD16+CD56+NK cells as independent risk factors for severe Mycoplasma pneumoniae pneumonia(SMPP).ROC analysis demonstrated that the areas under the curve for diagnosing SMPP based on the absolute numbers of CD3+CD19-T,CD4+T,CD3-CD19+B,and CD3-/CD16+CD56+NK cells were 0.711,0.887,0.856,and 0.860,respectively,with sensitivities of 47.4%,80.8%,82.1%,and 92.3%,and specificities of 89.7%,87.2%,75%,and 70.5%,respectively.The combined ROC curve of the four lymphocyte subsets had an area of 0.983,with a sensitivity of 97.4%and specificity of 92.3%.The proportions of bronchoscopy findings and microscopic examination of mucus plugs in the SMPP group were significantly higher than those in the ordinary MPP group(P<0.05).In the mucoid plug subgroup,the absolute numbers of CD3?CD19?T cells and CD4?T cells were significantly lower compared to the non-mucoid plug subgroup,while the percentage of CD8?T cells increased and the CD4?/CD8? ratio decreased(all P<0.05).Conclusions The absolute number of CD3+CD19-T,CD4+T,CD3-CD19+B,and CD3-/CD16+CD56+NK cells in peripheral blood serves as a highly sensitive and specific predictor of small airway mucus plugging phenomenon(SMPP)and can thus be utilized as a potential biomarker for SMPP.Microscopic analysis under SMPP conditions reveals a high prevalence of mucus plugs,necessitating proactive bronchoscopic intervention.Furthermore,the significant imbalance in T cell subpopulations is strongly correlated with the formation of mucus plugs observed during bronchoscopy.

Objective To investigate the coagulation effect of a cold atmospheric plasma(CAP)jet device with helium as the working gas and to study its coagulation mechanism preliminarily.Methods A CAP jet device treatment group,a helium airflow treatment group,a hot air treatment group(60℃)and a natural coagulation group were formed according to the treatment modes of the blood samples,with 10 μL of blood samples involved in each group,in order to validate the coagulation effect of the CAP jet device in vitro;the coagulation mechanism of the CAP jet device was explored by its application to the treatment of anticoagulated whole blood,platelet-rich plasma and platelet-depleted plasma;the coagulation effect of the CAP jet device in vivo was verified with a mouse liver punctate hemorrhage model and a rabbit mesenteric hemorrhage model.Results The CAP jet device can significantly accelerate the coagulation of anticoagulated blood droplets,and the coagulation time of anticoagulated blood droplets in the CAP jet device-treated group was shortened from 28 min in the natural coagulation group to(23±1.56)s,with the difference statistically significant(P<0.05),and the CAP jet device treatment group gained advantages significantly over the helium airflow treatment group(P<0.05)and the hot air(60℃)treatment group(P<0.05)in coagulation-promoting effect;the procoagulant effect of the CAP jet device rose with the increase of platelet content in blood droplets,and the coagulation effect of platelet-rich blood droplets was significantly better than that of whole blood(P<0.05),while no coagulation was observed in platelet-poor droplets.The CAP jet device could rapidly stop hemostasis of punctate hemorrhage in mouse liver and mesenteric hemorrhage in rabbits without delayed hemorrhage occurring within 10 min,and no obvious structural abnormality of the liver and thermal damage of the tissue were found microscopically.Conclusion The CAP jet device plays procoagulant and hemostatic effects in vivo and in vitro,and its effect is not dependent on temperature and airflow evaporation effects and is considered to be related to platelet activation,with low thermal damage to living tissue.[Chinese Medical Equipment Journal,2025,46(6):20-27]

Objective To investigate the distribution and antimicrobial resistance profiles of common pathogens isolated from cerebrospinal fluid(CSF)in CHINET program from 2015 to 2021.Methods The bacterial strains isolated from CSF were identified in accordance with clinical microbiology practice standards.Antimicrobial susceptibility test was conducted using Kirby-Bauer method and automated systems per the unified CHINET protocol.Results A total of 14 014 bacterial strains were isolated from CSF samples from 2015 to 2021,including the strains isolated from inpatients(95.3％)and from outpatient and emergency care patients(4.7％).Overall,19.6％of the isolates were from children and 80.4％were from adults.Gram-positive and Gram-negative bacteria accounted for 68.0％and 32.0％,respectively.Coagulase negative Staphylococcus accounted for 73.0％of the total Gram-positive bacterial isolates.The prevalence of MRSA was 38.2％in children and 45.6％in adults.The prevalence of MRCNS was 67.6％in adults and 69.5％in children.A small number of vancomycin-resistant Enterococcus faecium(2.2％)and linezolid-resistant Enterococcus faecalis(3.1％)were isolated from adult patients.The resistance rates of Escherichia coli and Klebsiella pneumoniae to ceftriaxone were 52.2％and 76.4％in children,70.5％and 63.5％in adults.The prevalence of carbapenem-resistant E.coli and K.pneumoniae(CRKP)was 1.3％and 47.7％in children,6.4％and 47.9％in adults.The prevalence of carbapenem-resistant Acinetobacter baumannii(CRAB)and Pseudomonas aeruginosa(CRPA)was 74.0％and 37.1％in children,81.7％and 39.9％in adults.Conclusions The data derived from antimicrobial resistance surveillance are crucial for clinicians to make evidence-based decisions regarding antibiotic therapy.Attention should be paid to the Gram-negative bacteria,especially CRKP and CRAB in central nervous system(CNS)infections.Ongoing antimicrobial resistance surveillance is helpful for optimizing antibiotic use in CNS infections.

Objective To investigate the antimicrobial resistance of clinical isolates from elderly patients(≥65 years)in major medical institutions across China.Methods Bacterial strains were isolated from elderly patients in 52 hospitals participating in the CHINET Antimicrobial Resistance Surveillance Program during the period from 2015 to 2021.Antimicrobial susceptibility test was carried out by disk diffusion method and automated systems according to the same CHINET protocol.The data were interpreted in accordance with the breakpoints recommended by the Clinical and Laboratory Standards Institute(CLSI)in 2021.Results A total of 514 715 nonduplicate clinical isolates were collected from elderly patients in 52 hospitals from January 1,2015 to December 31,2021.The number of isolates accounted for 34.3％of the total number of clinical isolates from all patients.Overall,21.8％of the 514 715 strains were gram-positive bacteria,and 78.2％were gram-negative bacteria.Majority(90.9％)of the strains were isolated from inpatients.About 42.9％of the strains were isolated from respiratory specimens,and 22.9％were isolated from urine.More than half(60.7％)of the strains were isolated from male patients,and 39.3％isolated from females.About 51.1％of the strains were isolated from patients aged 65-＜75 years.The prevalence of methicillin-resistant strains(MRSA)was 38.8％in 32 190 strains of Staphylococcus aureus.No vancomycin-or linezolid-resistant strains were found.The resistance rate of E.faecalis to most antibiotics was significantly lower than that of Enterococcus faecium,but a few vancomycin-resistant strains(0.2％,1.5％)and linezolid-resistant strains(3.4％,0.3％)were found in E.faecalis and E.faecium.The prevalence of penicillin-susceptible S.pneumoniae(PSSP),penicillin-intermediate S.pneumoniae(PISP),and penicillin-resistant S.pneumoniae(PRSP)was 94.3％,4.0％,and 1.7％in nonmeningitis S.pneumoniae isolates.The resistance rates of Klebsiella spp.(Klebsiella pneumoniae 93.2％)to imipenem and meropenem were 20.9％and 22.3％,respectively.Other Enterobacterales species were highly sensitive to carbapenem antibiotics.Only 1.7％-7.8％of other Enterobacterales strains were resistant to carbapenems.The resistance rates of Acinetobacter spp.(Acinetobacter baumannii 90.6％)to imipenem and meropenem were 68.4％and 70.6％respectively,while 28.5％and 24.3％of P.aeruginosa strains were resistant to imipenem and meropenem,respectively.Conclusions The number of clinical isolates from elderly patients is increasing year by year,especially in the 65-＜75 age group.Respiratory tract isolates were more prevalent in male elderly patients,and urinary tract isolates were more prevalent in female elderly patients.Klebsiella isolates were increasingly resistant to multiple antimicrobial agents,especially carbapenems.Antimicrobial resistance surveillance is helpful for accurate empirical antimicrobial therapy in elderly patients.

Objective:To explore the interrelationship between brain functional networks and features in functional magnetic resonance imaging(fMRI)of patients with Alzheimer's disease(AD),and to construct mixed-function networks(MFN),and apply them in machine learning classification models to improve the accuracy of AD classification.Methods:102 AD patients and 227 healthy subjects in the Alzheimer's Neuroimaging Initiative(ADNI)dataset were retrospectively analyzed.The partial correlation brain network of the blood oxygen level dependent(BOLD)signal was calculated and fused with low-frequency wave amplitude(ALFF),fractional low-frequency wave amplitude(fALFF)and local consistency(ReHo)features to construct MFN.Network topology parameters were extracted,and a variety of machine learning classification models were constructed based on MFN topological parameters,accuracy,precision,recall and area under the curve(AUC)were used to evaluate the predictive efficiency of the models.Results:By constructed MFN and calculated intra group to inter group ratio(IIGR),35 features could be obtained from ALFF,fALFF and ReHo feature topological parameter analysis,after rank sum test and FDR correction,there were statistical differences among 28 features(P＜0.05).The classification results show that,all the five classifiers have high classification performance on the test data set.The accuracy,precision and recall rates of random forest(RF),adaptive lifting algorithm(AdaBoost),guided aggregation algorithm(Bagging)and support vector machine(SVM)were all 99.7％,and the AUC values were up to 100％,99.5％,99.1％and 99.5％,respectively.The accuracy(98.5％),precision(98.5％),recall(98.5％),and AUC(99.1％)of the multi-layer perceptron(MLP)were slightly lower than other models,but remained excellent.It was worth noting that RF has the highest AUC value of all models at 100.0％,while Bagging has the lowest AUC value(99.1％)in the integrated approach.The results of performance comparison show that,MFN classification model can significantly improve the recognition and classification of AD disease,and greatly improve the performance of various indicators of the classifier.The results showed that,MFN classification model was superior to intelligent classification based fusion,DBN-based multitask learning,PVT-TSVM,unsupervised learning and clustering,SVM and SVM of degree 3 polynomial kernel function in key indicators such as accuracy(99.13％),AUC(99.42％),recall rate(99.46％)and specificity(99.42％)with plasma proteins,machine learning algorithms.It was further proved that MFN classification model has good generalization ability and robustness in AD disease classification.Conclusion:The AD classification model constructed based on brain mixed function network topology parameters and machine learning can improve the accuracy of AD classification.