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: This study explored the job choice preferences of Center for Disease Prevention and Control (CDC) workers to provide CDC management information and recommendations for optimizing employee retention and motivation policies. METHODS: A discrete choice experiment was conducted in nine provinces across China. Seven key attributes were identified to analyze the job preferences of CDC workers. Mixed logit models, latent class models, and policy simulation tools were used. RESULTS: A valid sample of 5,944 cases was included in the analysis. All seven attributes significantly influenced the job choices of CDC workers. Heterogeneity analyses identified two main groups based on different levels of preference for attribute utility. Income-prioritizers were concerned with income and opportunities for career development, whereas bianzhi-prioritizers were concerned with bianzhi and welfare benefits. The policy simulation analysis revealed that income-prioritizers had a relatively higher sensitivity to multiple job preference incentives. CONCLUSION Income and bianzhi were the two key attributes influencing the job choices and retention preferences of CDC workers. Heterogeneity in job preferences was also identified. Based on the preference characteristics of different subgroups, policy content should be skewed to differentiate the importance of incentives.
China ; Humans ; Male ; Female ; Adult ; Centers for Disease Control and Prevention, U.S. ; Middle Aged ; Choice Behavior ; Career Choice ; Motivation

Purpose To investigate the clinicopathological features,differential diagnosis and prognosis of atypical fibroxanthoma(AFX).Methods Pathological features of 15 cases of AFX and 3 cases of pleomorphic dermal sarcoma(PDS)misdiagnosed as AFX were retrospectively analyzed by hematoxylin and eosin staining and immunohistochemical EnVision staining technology.Clinical information was collected and analyzed,and the relevant literatures were re-viewed.Results The age of the 15 patients with AFX ranged from 18 to 78 years,with an average age of 57 years.4 cases occurred in the head and neck,and 11 cases occurred in the trunk and limbs.3 patients with PDS misdiagnosed as AFX were aged from 56 to 60 years,with an average age of 58 years.The tumors were located in the trunk and limbs.Microscopically,15 cases of AFX and 3 cases of PDS misdiagnosed as AFX were composed of proliferative pleo-morphic and atypical spindle cells interspersed with a varying number of multinucleated cells.15 cases of AFX tumors were superficial and located in the dermis.In 3 cases of PDS misdiagnosed as AFX,1 case was located in subcutane-ous adipose tissue,1 case had superficial subcutaneous extension,and the third case had positive basal margin.Immu-nohistochemically,the immunophenotypes of the two groups were consistent.CD10 was expressed in all cases,CD68 was positive in most cases,SMA was expressed in a few cases,desmin was focal expressed in a very few cases,and S-100,SOX10,CD34,HMB-45,Melan A,STAT6 and CK(AE1/AE3)were not expressed in all cases.Ki67 prolifera-tion index ranged from 2％to 30％.15 patients with AFX were followed up from 12 to 108 months.One patient had tumor recurrence 1 year and 3 years after operation due to positive basal margin.Most of the other patients underwent extended resection after diagnosis and were in good condition without tumor recurrence and metastasis.3 cases of PDS misdiagnosed as AFX were followed up for 31 to 78 months.One patient had lung metastasis after 2 years,one patient recurred 4 times after operation,and the other patient died after 4 times of recurrence.Conclusion AFX is a rare dis-ease with similar pathological characteristics and immunophenotype to PDS.AFX can be diagnosed only when the tumor is small and completely confined to the dermis.When the maximum diameter of the tumor is more than 3 cm,or the presence of any form of subcutaneous extension requires a high level of vigilance for PDS.Careful differentiation and correct classification of AFX and PDS are very important for the treatment and prognosis of the disease.

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 investigate the clinicopathological features,differential diagnosis and prognosis of atypical fibroxanthoma(AFX).Methods Pathological features of 15 cases of AFX and 3 cases of pleomorphic dermal sarcoma(PDS)misdiagnosed as AFX were retrospectively analyzed by hematoxylin and eosin staining and immunohistochemical EnVision staining technology.Clinical information was collected and analyzed,and the relevant literatures were re-viewed.Results The age of the 15 patients with AFX ranged from 18 to 78 years,with an average age of 57 years.4 cases occurred in the head and neck,and 11 cases occurred in the trunk and limbs.3 patients with PDS misdiagnosed as AFX were aged from 56 to 60 years,with an average age of 58 years.The tumors were located in the trunk and limbs.Microscopically,15 cases of AFX and 3 cases of PDS misdiagnosed as AFX were composed of proliferative pleo-morphic and atypical spindle cells interspersed with a varying number of multinucleated cells.15 cases of AFX tumors were superficial and located in the dermis.In 3 cases of PDS misdiagnosed as AFX,1 case was located in subcutane-ous adipose tissue,1 case had superficial subcutaneous extension,and the third case had positive basal margin.Immu-nohistochemically,the immunophenotypes of the two groups were consistent.CD10 was expressed in all cases,CD68 was positive in most cases,SMA was expressed in a few cases,desmin was focal expressed in a very few cases,and S-100,SOX10,CD34,HMB-45,Melan A,STAT6 and CK(AE1/AE3)were not expressed in all cases.Ki67 prolifera-tion index ranged from 2％to 30％.15 patients with AFX were followed up from 12 to 108 months.One patient had tumor recurrence 1 year and 3 years after operation due to positive basal margin.Most of the other patients underwent extended resection after diagnosis and were in good condition without tumor recurrence and metastasis.3 cases of PDS misdiagnosed as AFX were followed up for 31 to 78 months.One patient had lung metastasis after 2 years,one patient recurred 4 times after operation,and the other patient died after 4 times of recurrence.Conclusion AFX is a rare dis-ease with similar pathological characteristics and immunophenotype to PDS.AFX can be diagnosed only when the tumor is small and completely confined to the dermis.When the maximum diameter of the tumor is more than 3 cm,or the presence of any form of subcutaneous extension requires a high level of vigilance for PDS.Careful differentiation and correct classification of AFX and PDS are very important for the treatment and prognosis of the disease.

Humans ; Proto-Oncogene Proteins p21(ras)/genetics* ; Prognosis ; Biomarkers, Tumor/genetics* ; Mutation/genetics* ; Colorectal Neoplasms/genetics* ; Proto-Oncogene Proteins B-raf/metabolism*

Lipopolysaccharides (LPS) are major outer membrane components of Gram-negative bacteria. Unlike most Gram-negative bacteria, Acinetobacter baumannii can still survive and acquire polymyxin resistance after complete loss of LPS. Previous studies of LPS-deficient Acinetobacter baumannii mostly focused on LPS-deficient strains induced by polymyxin, whose background was complex and unstable. To investigate LPS loss-mediated polymyxin resistant-Acinetobacter baumannii, this study constructed a stable and clear background LPS-deficient strain by knocking out lpxC gene in Acinetobacter baumannii ATCC 19606 with CIRSPR/Cas9, and then studied the phenotypic changes of the lpxC-deficient strain including morphology, growth rate, antibiotic susceptibility, virulence, membrane permeability, and membrane potential. Animal experiments were approved by the Animal Care and Welfare Committee Institute of Medicinal Biotechnology, CAMS and PUMC (approval number: IMB-20240119D₉). The results indicated that the lpxC gene of Acinetobacter baumannii ATCC 19606 was successfully knocked out. After losing the lpxC, the strain underwent the morphological change from rod-shaped to spherical. Furthermore, it leads to reduced growth rate, enhanced membrane permeability, decreased membrane potential, lower virulence, and increased antibiotic susceptibility to β-lactams, quinolones, aminoglycosides, macrolides, glycopeptides. The lpxC deletion results in significant changes in membrane homeostasis and adaptability of Acinetobacter baumannii. Understanding the phenotypic changes of colistin-resistant Acinetobacter baumannii mediated by LPS loss is useful for exploring the resistance mechanism of Acinetobacter baumannii and developing new therapeutic strategies.

Purpose To investigate the clinicopathologic,immunophenotypic features,genetic alterations and prognosis of melanotic Xp11 neoplasms/melanotic TFE3-rearrangement neo-plasms.Methods Five cases were selected from the Depart-ment of Pathology,Union Hospital,Huazhong University of Sci-ence and Technology from November 2018 to July 2023.The clinicopathologic,immunohistochemical,FISH assays,next-generation sequencing(NGS)and follow-up details were collect-ed.Results There were 1 male and 4 females,with their ages ranging from 16 to 59 years(mean 28.2 years).The maximum diameters of the masses were 3-6 cm(average 4.7 cm).The tumors located in right kidneys(3 cases),tubal interstitium(1 case)and pelvis(1 case).Microscopically,most tumors shared similar morphology such as nested,acinar structures sep-arated by a delicate vascular network.Epithelioid tumor cells presented with clear to granular eosinophilic cytoplasm.Lym-phocytic infiltration was seen in the background;melanin depo-sition was noted in the cases;neoplastic necrosis was detected in 4 cases.Mitotic activity was low with 5 cases showing＜3/10 HPF.Intravascular tumor thrombus was detected in 2 cases,no lymphovascular and nerve invasions were detected in other 3 ca-ses.Immunohistochemically,all 5 cases expressed TFE3 dif-fusely,and expressed HMB45,Melan A to varying degrees,CK(AE1/AE3),CK7,EMA,PAX8,TFEB,S-100,SOX10,SMA,desmin were all non-reactive in the 5 cases.The Ki67-la-beling index was＜20％.TFE3 separation signal in 4 cases were detected by FISH,1 case was interpreted as negative due to atypical signal which was confirmed by next-generation se-quencing(NGS)assay as RBM10-TFE3.Clinical follow-up was available for five patients for 2-60 months,in which four pa-tients were alive with no evidence of disease after initial resec-tion,and one patient with thoracic spine metastasis was currently in stable condition.Conclusion Melanotic Xp1 1 neoplasms/melanotic TFE3-rearrangement neoplasms has unique morpholog-ic,immunophenotypic and genetic characteristics.It might be reclassified into a distinctive malignant mesenchymal tumor enti-ty.

Humans ; Mesothelioma/pathology* ; Mesothelioma, Malignant ; Pleural Neoplasms/diagnosis* ; Pleural Effusion, Malignant ; Pleural Effusion/pathology*

The modernization and development of traditional Chinese medicine has led to higher standards for the quality of traditional Chinese medicine products. The extraction process is a crucial component of traditional Chinese medicine production, and it directly impacts the final quality of the product. However, the currently relied upon methods for quality assurance of the extraction process, such as simple wet chemical analysis, have several limitations, including time consumption and labor intensity, and do not offer precise control of the extraction process. As a result, there is significant value in incorporating near-infrared spectroscopy (NIRS) in the production process of traditional Chinese medicine to improve the quality control of the final products. In this study, we focused on the extraction process of Xiao'er Xiaoji Zhike oral liquid (XXZOL), using near-infrared spectra collected by both a Fourier transform near-infrared spectrometer and a portable near-infrared spectrometer. We used the concentration of synephrine, a quality control index component specified by the pharmacopoeia, to achieve rapid and accurate detection in the extraction process. Moreover, we developed a model transfer method to facilitate the transfer of models between the two types of near-infrared spectrometers (analytical grade and portable), thus resolving the low resolution, poor performance, and insufficient prediction accuracy issues of portable instruments. Our findings enable the rapid screening and quality analysis of XXZOL onsite, which is significant for quality monitoring during the traditional Chinese medicine production process.