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.

Three evaluation methods were recommended for the key properties of the intravascular catheter lubricating coating such as stability,lubricity and integrity,including insoluble particle test method,friction test procedure and appearance detection method.Fifteen batches of microcatheters produced by different manufacturers were selected for testing to clarify the three methods in test principle,step,result,characteristic.References were provided for the design,production,evaluation and regulation of intravascular catheters with lubricant coatings.[Chinese Medical Equipment Journal,2025,46(1):66-72]

Three evaluation methods were recommended for the key properties of the intravascular catheter lubricating coating such as stability,lubricity and integrity,including insoluble particle test method,friction test procedure and appearance detection method.Fifteen batches of microcatheters produced by different manufacturers were selected for testing to clarify the three methods in test principle,step,result,characteristic.References were provided for the design,production,evaluation and regulation of intravascular catheters with lubricant coatings.[Chinese Medical Equipment Journal,2025,46(1):66-72]

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.

Purpose To explore the value of contrast-enhanced ultrasound(CEUS)in the differential diagnosis of gallbladder polypoid lesions(GPLs)(diameter≥10 mm).Materials and Methods A prospective enrollment of 229 patients with GPLs who underwent cholecystectomy in 17 hospitals from December 1 2021 to June 30 2024 was conducted to analyze the relationship between general data,conventional ultrasound,CEUS characteristics and the nature of GPLs.Multivariate Logistic regression was employed to identify independent risk factors for neoplastic polyps,the differential diagnostic value of different indicators was compared.Results Among 229 patients with GPLs,there were 108 cases of cholesterol polyps,102 cases of adenoma and 19 cases of gallbladder cancer.Age(Z=-4.476,P<0.001),polyp number(χ2=15.561,P<0.001),diameter(Z=-8.149,P<0.001),echogenicity(χ2=9.241,P=0.010),vascularity(χ2=23.107,P<0.001),enhancement intensity(χ2=47.610,P<0.001),enhancement pattern(χ2=6.468,P=0.011),vascular type(χ2=84.470,P<0.001),integrity of gallbladder wall(χ2=7.662,P=0.006)and stalk width(Z=-9.831,P<0.001)between cholesterol polyps and neoplastic polyps were statistically significant.Age,location,diameter,echogenicity,enhancement pattern,vascular type and stalk width between adenoma and gallbladder cancer were statistically significant(Z=-4.333,-3.902,-5.042,all P<0.05).Multivariate Logistic regression analysis showed that hyper-enhancement,branched vascular type and stalk width were independent risk factors for neoplastic polyps(OR=4.563,5.770,3.075,all P<0.001).The combination of independent risk factors was better than single factor and diameter in the differential diagnosis of cholesterol polyps and neoplastic polyps(all P<0.01).Conclusion CEUS can effectively identify the nature of GPLs and provide a valuable imaging reference for the selection of treatment methods.

Patients with severe trauma require an extremely timely treatment and transfusion plays an irreplaceable role in the emergency treatment of such patients. An increasing number of evidence-based medicinal evidences and clinical practices suggest that patients with severe traumatic bleeding benefit from early transfusion of low-titer group O whole blood or hemostatic resuscitation with red blood cells, plasma and platelet of a balanced ratio. However, the current domestic mode of blood supply cannot fully meet the requirements of timely and effective blood transfusion for emergency treatment of patients with severe trauma in clinical practice. In order to solve the key problems in blood supply and blood transfusion strategies for emergency treatment of severe trauma, Branch of Clinical Transfusion Medicine of Chinese Medical Association, Group for Trauma Emergency Care and Multiple Injuries of Trauma Branch of Chinese Medical Association, Young Scholar Group of Disaster Medicine Branch of Chinese Medical Association organized domestic experts of blood transfusion medicine and trauma treatment to jointly formulate Chinese expert consensus on blood support mode and blood transfusion strategies for emergency treatment of severe trauma patients ( version 2024). Based on the evidence-based medical evidence and Delphi method of expert consultation and voting, 10 recommendations were put forward from two aspects of blood support mode and transfusion strategies, aiming to provide a reference for transfusion resuscitation in the emergency treatment of severe trauma and further improve the success rate of treatment of patients with severe trauma.

Objective Viral encephalitis is an infectious disease severely affecting human health.It is caused by a wide variety of viral pathogens,including herpes viruses,flaviviruses,enteroviruses,and other viruses.The laboratory diagnosis of viral encephalitis is a worldwide challenge.Recently,high-throughput sequencing technology has provided new tools for diagnosing central nervous system infections.Thus,In this study,we established a multipathogen detection platform for viral encephalitis based on amplicon sequencing. Methods We designed nine pairs of specific polymerase chain reaction(PCR)primers for the 12 viruses by reviewing the relevant literature.The detection ability of the primers was verified by software simulation and the detection of known positive samples.Amplicon sequencing was used to validate the samples,and consistency was compared with Sanger sequencing. Results The results showed that the target sequences of various pathogens were obtained at a coverage depth level greater than 20×,and the sequence lengths were consistent with the sizes of the predicted amplicons.The sequences were verified using the National Center for Biotechnology Information BLAST,and all results were consistent with the results of Sanger sequencing. Conclusion Amplicon-based high-throughput sequencing technology is feasible as a supplementary method for the pathogenic detection of viral encephalitis.It is also a useful tool for the high-volume screening of clinical samples.

Objective:To explore the difference of lymphocyte subsets in peripheral blood(PB)between aplastic anemia(AA)and hypoplastic myelodysplastic syndrome(hypo-MDS)patients,meanwhile to compare the clinical parameters obtained from PB and bone marrow(BM).Methods:The lymphocyte subsets in hypo-MDS(n=25)and AA(n=33)patients were investigated by flow cytometry.Meanwhile,the differences in PB cell counts,biochemical indicators,BM cell counts and abnormal chromosomes between the two groups were analyzed.Results:The percentage of CD8+T cells in A A group was significantly higher than that in hypo-MDS group(P=0.001),while the percentage of CD4+T cells and the CD4+/CD8+ratio in AA group were obviously lower than those in hypo-MDS group(P=0.015 and0.001,respectively).Furthermore,the proportion of CD4+andCD8+activated T(TA)cells,and memory Tregs in AA group was distinctly lower than those in hypo-MDS group(P=0.043,0.015 and 0.024,respectively).Nevertheless,the percentage of CD8+naive T(TN)cells in AA patients was remarkably higher(P=0.044).And hypo-MDS patients had declined lymphocyte counts(P=0.025),increased levels of total bilirubin(TBil),lactate dehydrogenase(LDH),vitamin B12 and proportion of BM blasts than AA patients(P=0.019,0.023,0.027 and 0.045,respectively).Conclusion:In this study it was confirmed that the percentages of CD4+and CD8+TA cells,memory Tregs and CD8+TN cells were significantly different between AA and hypo-MDS patients,which provide an essential basis for the identification of these two diseases.

OBJECTIVE: To evaluate the expression level of melatonin and its effects on immune function in aplastic anemia (AA) patients. METHODS: The enzyme-linked immunosorbent assay (ELISA) was used to detect the plasma levels of melatonin in AA patients, and the correlation between melatonin levels and laboratory indexs was analyzed. The activation, proliferation, and apoptosis of T cells from AA patients were analyzed by flow cytometry with or without melatonin in vitro. RESULTS: The plasma levels of melatonin in AA patients were significantly lower compared with healthy controls (HC) (12.23 pg/ml vs 20.04 pg/ml, P < 0.01), while the plasma melatonin levels of AA patients in remission group after immunosuppressive therapy (IST) were significantly higher than those in non-remission group (29.16 pg/ml vs 11.73 pg/ml, P =0.04). Moreover, the melatonin levels were positively correlated with platelets (r =0.49), the absolute reticulocyte count (r =0.45), and the percentage of neutrophils (r =0.43). Meanwhile, there was a negative correlation between melatonin levels and the percentages of lymphocytes (r =-0.45). The expressions of CD25 and CD69 in both CD4⁺ and CD8⁺ T cells from AA patients were remarkably inhibited by melatonin in vitro (all P < 0.05). When cultured with melatonin, the proliferation rates of both CD4⁺ and CD8⁺ T cells from AA patients were markedly suppressed (P =0.01 andP < 0.01). CONCLUSION The plasma levels of melatonin were decreased in AA patients, which might play an important role in the mechanism of immunological abnormalities. The hyperimmune status of AA patients could be partially ameliorated by melatonin in vitro.
Humans ; Anemia, Aplastic ; CD8-Positive T-Lymphocytes ; Melatonin ; Blood Cell Count

Animals ; Biological Assay ; Nucleic Acid Amplification Techniques ; Phlebovirus/isolation & purification* ; Psychodidae/virology* ; Recombinases/metabolism* ; Reverse Transcription