Objective:To evaluate the performance of machine learning (ML) based on automated breast volume scanner (ABVS) radiomics in distinguishing benign and malignant BI-RADS 4 lesions.Methods:Between May to December 2020, patients with BI-RADS 4 lesions from the Affiliated Hospital of Southwest Medical University (Center 1) and Guangdong Provincial Hospital of Traditional Chinese Medicine (Center 2) were prospectively collected and divided into training cohort (Center 1) and external validation cohort (Center 2). The radiomics features of BI-RADS 4 lesions were extracted from the axial, sagittal and coronal ABVS images by MaZda software. In the training cohort, 7 feature selection methods and thirteen ML algorithms were combined in pairs to construct different ML models, and the 6 models with the best performance were verified in the external validation cohort to determine the final ML model. Finally, the diagnostic performance and confidence (5-point scale) of radiologists (R1, R2 and R3, with 3, 6 and 10 years of experience, respectively) with or without the model were evaluated.Results:①A total of 251 BI-RADS 4 lesions were enrolled, including 178 lesions (91 benign, 87 malignant) in the training cohort and 73 lesions (44 benign, 29 malignant) in the external validation cases. ②In the training cohort, the 6 ML models (DNN-RFE, AdaBoost-RFE, LR-RFE, LDA-RFE, Bagging-RFE and SVM-RFE) had the best diagnostic performance, and their AUCs were 0.972, 0.969, 0.968, 0.968, 0.967 and 0.962, respectively. ③In the external validation cohort, the DNN-RFE still had the best performance with the AUC, accuracy, sensitivity, specificity, PPV and NPV were 0.886, 0.836, 0.934, 0.776, 86.8% and 82.5%, respectively. ④Before model assistance, R1 had the worst diagnostic performance with the accuracy, sensitivity, specificity, PPV and NPV were 0.680, 0.750, 0.640, 57% and 81%, respectively. After model assistance, the diagnostic performance of R1 was significantly improved ( P=0.039), and its diagnostic sensitivity, specificity, accuracy, PPV and NPV improved to 0.730, 0.810, 0.930, 68% and 94%; while the improvement of R2 and R3 were not significantly ( P=0.811, 0.752). Meanwhile, the overall diagnostic confidence of the 3 radiologists increased from 2.8 to 3.3 ( P<0.001). Conclusions:The performance of ML based on ABVS radiomics in distinguishing between benign and malignant BI-RADS 4 lesions is good, which may improve the diagnostic performance of inexperienced radiologists and enhance diagnostic confidence.

Objective:To investigate the value of number of negative lymph nodes (NLNs) in predicting the prognosis of patients with esophageal cancer after neoadjuvant therapy and the construction of nomogram prodiction model.Methods:The retrospective cohort study was conducted. The clinicopathological data of 1 924 patients with esophageal cancer after neoadjuvant therapy uploaded to the Surveillance, Epidemiology, and End Results Database of the National Cancer Institute from 2004 to 2015 were collected. There were 1 624 males and 300 females, aged 63 (range, 23?85)years. All 1 924 patients were randomly divided into the training dataset of 1 348 cases and the validation dataset of 576 cases with a ratio of 7:3 based on random number method in the R software (3.6.2 version). The training dataset was used to constructed the nomogram predic-tion model, and the validation dataset was used to validate the performance of the nomogrram prediction model. The optimal cutoff values of number of NLNs and number of examined lymph nodes (ELNs) were 8, 14 and 10, 14, respectively, determined by the X-tile software (3.6.1 version), and then data of NLNs and ELNs were converted into classification variables. Observation indicators: (1) clinicopathological characteristics of patients in the training dataset and the validation dataset; (2) survival of patients in the training dataset and the validation dataset; (3) prognostic factors analysis of patients in the training dataset; (4) survival of patients in subgroup of the training dataset; (5) prognostic factors analysis in subgroup of the training dataset; (6) construction of nomogram prediction model and calibration curve. Measurement data with normal distribution were represented as Mean± SD, and comparison between groups was conducted using the t test. Measurement data with skewed distribution were represented as M(range), and comparison between groups was conducted using the Mann-Whitney U test. Count data were described as absolute numbers, and comparison between groups was conducted using the chi-square test. The Kaplan-Meier method was used to draw survival curve and Log-Rank test was used for survival analysis. The COX proportional hazard model was used for univariate and multivariate analyses. Based on the results of multivariate analysis, the nomogram prediction model was constructed. The prediction efficacy of nomogram prediction model was evaluated using the area under curve (AUC) of the receiver operating characteristic curve and the Harrell′s c index. Errors of the nomogram prediction model in predicting survival of patients for the training dataset and the validation dataset were evaluated using the calibration curve. Results:(1) Clinicopathological characteristics of patients in the training dataset and the validation dataset. There was no significant difference in clinicopatholo-gical characteristics between the 1 348 patients of the training dataset and the 576 patients of the validation dataset ( P>0.05). (2) Survival of patients in the training dataset and the validation dataset. All 1 924 patients were followed up for 50(range, 3?140)months, with 3-year and 5-year cumulative survival rate as 59.4% and 49.5%, respectively. The 3-year cumulative survival rate of patients with number of NLNs as <8, 8?14 and >14 in the training dataset was 46.7%, 62.0% and 66.0%, respectively, and the 5-year cumulative survival rate was 38.1%, 52.1% and 59.7%, respectively. There was a significant difference in the survival of these patients in the training dataset ( χ2=33.70, P<0.05). The 3-year cumulative survival rate of patients with number of NLNs as <8, 8?14 and >14 in the validation dataset was 51.1%, 54.9% and 71.2%, respectively, and the 5-year cumulative survival rate was 39.3%, 42.5% and 55.7%, respectively. There was a significant difference in the survival of these patients in the validation dataset ( χ2=14.49, P<0.05). The 3-year cumulative survival rate of patients with number of ELNs as <10, 10?14 and >14 in the training dataset was 53.9%, 60.0% and 62.7%, respectively, and the 5-year cumulative survival rate was 44.7%, 49.1% and 56.9%, respectively. There was a significant difference in the survival of these patients in the training dataset ( χ2=9.88, P<0.05). The 3-year cumulative survival rate of patients with number of ELNs as <10, 10?14 and >14 in the validation dataset was 56.2%, 47.9% and 69.3%, respectively, and the 5-year cumula-tive survival rate was 44.9%, 38.4% and 51.9%, respectively. There was a significant difference in the survival of these patients in the validation dataset ( χ2=9.30, P<0.05). (3) Prognostic factors analysis of patients in the training dataset. Results of multivariate analysis showed that gender, neoadjuvant pathological (yp) T staging, ypN staging (stage N1, stage N2, stage N3) and number of NLNs (8?14, >14) were independent influencing factors for the prognosis of patients with esophageal cancer after neoadjuvant therapy ( hazard ratio=0.65, 1.44, 1.96, 2.41, 4.12, 0.69, 0.56, 95% confidence interval as 0.49?0.87, 1.17?1.78, 1.59?2.42, 1.84?3.14, 2.89?5.88, 0.56?0.86, 0.45?0.70, P<0.05). (4) Survival of patients in subgroup of the training dataset. Of the patients with NLNs in the training dataset, the 3-year cumulative survival rate of patients with number of NLNs as <8, 8?14 and >14 was 61.1%, 71.6% and 76.8%, respectively, and the 5-year cumulative survival rate was 50.7%, 59.9% and 70.1%, respectively. There was a significant difference in the survival of these patients in the training dataset ( χ2=12.66, P<0.05). Of the patients with positive lymph nodes in the training dataset, the 3-year cumulative survival rate of patients with number of NLNs as <8, 8?14 and >14 was 26.1%, 42.9% and 44.7%, respectively, and the 5-year cumulative survival rate was 20.0%, 36.5% and 39.3%, respectively. There was a significant difference in the survival of these patients in the training dataset ( χ2=20.39, P<0.05). (5) Prognostic factors analysis in subgroup of the training dataset. Results of multivariate analysis in patients with NLNs in the training dataset showed that gender, ypT staging and number of NLNs (>14) were independent influencing factors for the prognosis of patients with esophageal cancer after neoadju-vant therapy ( hazard ratio=0.67, 1.44, 0.56, 95% confidence interval as 0.47?0.96, 1.09?1.90, 0.41?0.77, P<0.05). Results of multi-variate analysis in patients with positive lymph nodes in the training dataset showed that race as others, histological grade as G2, ypN staging as stage N3 and number of NLNs (8?14, >14) were independent influencing factors for the prognosis of patients with esophageal cancer after neoadjuvant therapy ( hazard ratio=2.73, 0.70, 2.08, 0.63, 0.59, 95% confidence interval as 1.43?5.21, 0.54?0.91, 1.44?3.02, 0.46?0.87, 0.44?0.78, P<0.05). (6) Construction of nomogram prediction model and calibration curve. Based on the multivariate analysis of prognosis in patients of the training dataset ,the nomogram prediction model for the prognosis of patients with esophageal cancer after neoadju-vant treatment was constructed based on the indicators of gender, ypT staging, ypN staging and number of NLNs. The AUC of nomogram prediction model in predicting the 3-, 5-year cumulative survival rate of patients in the training dataset and the validation dataset was 0.70, 0. 70 and 0.71, 0.71, respectively. The Harrell′s c index of nomogram prediction model of patients in the training dataset and the validation dataset was 0.66 and 0.63, respectively. Results of calibration curve showed that the predicted value of the nomogram prediction model of patients in the training dataset and the validation dataset was in good agreement with the actual observed value. Conclusion:The number of NLNs is an independent influencing factor for the prognosis of esophageal cancer patients after neoadjuvant therapy, and the nomogram prediction model based on number of NLNs can predict the prognosis of esophageal cancer patients after neoadjuvant therapy.

The Omicron family of SARS-CoV-2 variants are currently driving the COVID-19 pandemic. Here we analyzed the clinical laboratory test results of 9911 Omicron BA.2.2 sublineages-infected symptomatic patients without earlier infection histories during a SARS-CoV-2 outbreak in Shanghai in spring 2022. Compared to an earlier patient cohort infected by SARS-CoV-2 prototype strains in 2020, BA.2.2 infection led to distinct fluctuations of pathophysiological markers in the peripheral blood. In particular, severe/critical cases of COVID-19 post BA.2.2 infection were associated with less pro-inflammatory macrophage activation and stronger interferon alpha response in the bronchoalveolar microenvironment. Importantly, the abnormal biomarkers were significantly subdued in individuals who had been immunized by 2 or 3 doses of SARS-CoV-2 prototype-inactivated vaccines, supporting the estimation of an overall 96.02% of protection rate against severe/critical disease in the 4854 cases in our BA.2.2 patient cohort with traceable vaccination records. Furthermore, even though age was a critical risk factor of the severity of COVID-19 post BA.2.2 infection, vaccination-elicited protection against severe/critical COVID-19 reached 90.15% in patients aged ≽ 60 years old. Together, our study delineates the pathophysiological features of Omicron BA.2.2 sublineages and demonstrates significant protection conferred by prior prototype-based inactivated vaccines.
Humans ; Aged ; Middle Aged ; COVID-19/prevention & control* ; SARS-CoV-2 ; Pandemics/prevention & control* ; China/epidemiology* ; Disease Outbreaks/prevention & control* ; Vaccination

Emerging evidence indicates that the gut microbiome contributes to the host immune response to infectious diseases. Here, to explore the role of the gut microbiome in the host immune responses in COVID-19, we conducted shotgun metagenomic sequencing and immune profiling of 14 severe/critical and 24 mild/moderate COVID-19 cases as well as 31 healthy control samples. We found that the diversity of the gut microbiome was reduced in severe/critical COVID-19 cases compared to mild/moderate ones. We identified the abundance of some gut microbes altered post-SARS-CoV-2 infection and related to disease severity, such as Enterococcus faecium, Coprococcus comes, Roseburia intestinalis, Akkermansia muciniphila, Bacteroides cellulosilyticus and Blautia obeum. We further analyzed the correlation between the abundance of gut microbes and host responses, and obtained a correlation map between clinical features of COVID-19 and 16 severity-related gut microbe, including Coprococcus comes that was positively correlated with CD3⁺/CD4⁺/CD8⁺ lymphocyte counts. In addition, an integrative analysis of gut microbiome and the transcriptome of peripheral blood mononuclear cells (PBMCs) showed that genes related to viral transcription and apoptosis were up-regulated in Coprococcus comes low samples. Moreover, a number of metabolic pathways in gut microbes were also found to be differentially enriched in severe/critical or mild/moderate COVID-19 cases, including the superpathways of polyamine biosynthesis II and sulfur oxidation that were suppressed in severe/critical COVID-19. Together, our study highlighted a potential regulatory role of severity related gut microbes in the immune response of host.
COVID-19 ; Clostridiales ; Gastrointestinal Microbiome ; Humans ; Immunity ; Leukocytes, Mononuclear ; SARS-CoV-2

Objective To investigate the effect of different concentrations of Echinococcus multilocularis secretion antigen (Em-sAg) on the phenotype and function of mouse bone marrow-derived dendritic cells (BMDCs) induced by lipopolysaccharide (LPS). Methods The bone marrow precursor cells isolated from the mouse bone marrow cavity were stimulated by mouse recombinant granulocyte-macrophage colony-stimulating factor (GM-CSF) to form BMDCs, and then cell morphology was observed under an inverted microscope. After the purity of BMDCs was identified by flow cytometry, BMDCs were divided into control group, positive control group (LPS 1 μg/ml), LPS+3 mg/ml Em-sAg group, LPS+1.5 mg/ml Em-sAg group, LPS+0.75 mg/ml Em-sAg group, and LPS+0.375 mg/ml Em-sAg group. Flow cytometry was used to measure the expression of BMDC surface molecules (CD80, CD86, and MHC-Ⅱ molecules) in each group, and ELISA was used to measure the expression level of the cytokine IL-12p70. A one-way analysis of variance was used for comparison of normally distributed continuous data between multiple groups, and the least significant difference t -test was used for further comparison between two groups. Results Observation under an inverted microscope showed that after 8-10 days of culture, the cells had burr-like protrusions and were in a state of complete suspension. Flow cytometry showed that the positive rate of CD11c was above 70% and most of the cultured cells were identified as BMDCs based on this. Flow cytometry further showed that compared with the control group, the LPS group had significant increases in the cell molecules CD80, CD86, and MHC-Ⅱ on surface (all P < 0.05); compared with the LPS group, the LPS+3 mg/ml Em-sAg group, the LPS+1.5 mg/ml Em-sAg group, the LPS+0.75 mg/ml Em-sAg group, and the LPS+0.375 mg/ml Em-sAg group had a significant reduction in CD80 ( F =34.870, P < 0.001), while there were no significant reductions in CD86 and MHC-Ⅱ( P > 0.05). ELISA showed that there was a significant difference in the level of IL-12 p70 between groups ( F =73.140, P < 0.05); compared with the control group, the LPS group had a significant increase in the expression level of IL-12p70 after stimulation ( P < 0.05); compared with the positive control group, the LPS+3 mg/ml Em-sAg group, the LPS+1.5 mg/ml Em-sAg group, the LPS+0.75 mg/ml Em-sAg group, and the LPS+0.375 mg/ml Em-sAg group had a significant reduction in the expression level of IL-12p70 ( P < 0.05), and the degree of reduction in the pro-inflammatory factor IL-12p70 increased with the increase in the concentration of Em-sAg. Conclusion Different concentrations of Em-sAg can inhibit LPS-induced maturity of BMDCs and the expression of the pro-inflammatory cytokine IL-12p70.

Objective: To investigate the risk factors affecting the patency time of the125I seeds stent in malignant obstructive jaundice. Methods: A retrospective analysis of 113 patients with malignant obstructive jaundice underwent biliary tract125I seeds stent implantation. The gender, age, obstruction site, type of125I seeds stent, primary tumor type, and postoperative response to treatment of tumor were enrolled for analysis to evaluate the related risk factors affecting the patency time of the stent. Results: Univariate analysis showed that the location of biliary obstruction, the type of125I seeds stent, the type of primary tumor, and the type of primary tumor were the main factors affecting the patency time of the stent(P<0.001); Cox multivariate regression analysis showed biliary obstruction location, the type of125I seeds stent, and whether the primary tumor treated were independent factors that affected the patency time of the stent(P<0.001). Conclusion Multi-factor analysis shows that the location of biliary obstruction, the type of125I seeds stent, and the primary tumor are independent risk factors that affect the patency of the stent for malignant obstructive jaundice, which shows important markers for evaluating the prognosis of patients treated with this method.

The gene is frequently mutated and abnormally activated in many cancers，and plays an important role in cancer development. Metabolic reprogramming occurs in malignant tumors，which can be one of the key targets for anti-tumor therapy. gene can regulate lipid metabolism through AKT-mTORC1 single axis or multiple pathways，such as lipid synthesis pathways and degradation pathways. Similarly，lipid metabolism can also modify and activate RAS protein and its downstream signaling pathways. This article overviews the current research progress on the interaction between lipid metabolism and ，to provide insight in therapeutic strategies of lipid metabolism for -driven tumors.
Genes, ras ; Humans ; Lipid Metabolism/genetics* ; Neoplasms/genetics* ; Signal Transduction ; ras Proteins/metabolism*

Objective To investigate the association between serum macrophage polarization-related factors and liver fibrosis in patients with alveolar echinococcosis (AE). Methods A total of 120 patients with AE who attended Department of Hepatobiliary and Pancreatic Surgery in The Affiliated Hospital of Qinghai University from September 2018 to October 2020 were enrolled as AE group, and 33 healthy controls were enrolled as normal control group. The two groups and the patients with varying degrees of liver fibrosis were compared in terms of the levels of interleukin-6 (IL-6), interleukin-10 (IL-10), tumor necrosis factor-α (TNF-α), and transforming growth factor-β1 (TGF-β1). Comparison of normally distributed continuous data between two groups was made by the independent samples t -test, while comparison of non-normally distributed continuous data was made by the Mann-Whitney U test or Kruskal-Wallis H test. Comparison of categorical data between groups was made by the chi-square test. Univariate and multivariate logistic regression analyses were used to investigate the association between serum macrophage polarization-related factors and liver fibrosis in patients with AE, and the receiver operating characteristic (ROC) curve was used to analyze the value of serological examination in the diagnosis of liver fibrosis in patients with AE. A Spearman correlation analysis was used to analyze the correlation of each index with HAI score and Metavir score. Results Compared with the normal control group, the AE group had significant increases in the serum levels of IL-6 [13.97 (9.64-23.62) pg/mL vs 1.30 (0.35-2.71) pg/mL, Z =-5.980, P < 0.001], TNF-α [2.26 (1.65-4.13) pg/mL vs 1.40 (1.04-2.10) pg/mL, Z =-3.114, P < 0.01], and TGF-β1 [3.64(2.71-5.72) pg/mL vs 2.91(2.20-3.35) pg/mL, Z =-2.594, P < 0.05], and increases in the serum levels of IL-6 (hazard ratio [ HR ]=2.721, 95% confidence interval [ CI ]: 1.730-4.280, P < 0.05) and TNF-α( HR =3.527, 95% CI : 1.158-10.747, P < 0.05) were independent risk factors for the onset of liver fibrosis in AE patients. The ROC curve analysis showed that hydatid IgG combined with the serum levels of IL-6 and TNF-α had a sensitivity of 88.4%, a specificity of 95.8%, and an area under the ROC curve of 0.951(95% CI : 0.937-0.964) in the diagnosis of liver fibrosis, which were significantly higher than those of IL-6, TNF-α, or hydatid IgG alone ( Z =-3.458, -4.011, and 2.379, all P < 0.05). The Spearman analysis showed that the serum levels of IL-6, TNF-α, and TGF-β1 were positively correlated with HAI score ( r =0.560, 0.644, and 0.465, all P < 0.001) and Metavir fibrosis score ( r =0.530, 0.758, and 0.567, all P < 0.001), and the serum level of IL-10 was negatively correlated with HAI score ( r =-0.232, P =0.011) and Metavir fibrosis score ( r =-0.288, P =0.001). Conclusion Macrophage polarization is often observed in patients with hepatic AE, and the levels of the macrophage polarization-related factors IL-6, TNF-α, and TGF-β1 are associated with the development and progression of liver fibrosis, which can provide certain reference information for predicting the onset of liver fibrosis.

Objective:To optimize the extraction process of Huoxue-Sanyu effervescent tablets. Methods:The hydroxysafflor yellow A content, paeoniflorin and dry extract yield were used as the evaluation indexes, by using analytic hierarchy process (AHP) method, integrative graded indicators method combined with orthogonal test and back propagation (BP) artificial neural network. To optimize the technics parameters, such as the amount of water, extraction time and frequency.Results:Paeoniflorin and hydroxysafflor yellow A showed good linear relation in the range of 0.079 5-1.590 4 μg and 0.038 5-1.539 2 μg respectively, with the average recovery rates of 98.18% and 96.22%. RSD was 0.77% and 1.31% respectively. The optimum extraction technic was 9 times of water, 3 times of heating reflux extraction, and each extraction last for 1 hour. Conclusions:The optimized combined method of orthogonal experiment and BP artificial neural network is practical with high efficiency. The optimal extraction process is stable and reasonable.

The ongoing pandemic of Coronavirus disease 19 (COVID-19) is caused by a newly discovered β Coronavirus named severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). How long the adaptive immunity triggered by SARS-CoV-2 can last is of critical clinical relevance in assessing the probability of second infection and efficacy of vaccination. Here we examined, using ELISA, the IgG antibodies in serum specimens collected from 17 COVID-19 patients at 6-7 months after diagnosis and the results were compared to those from cases investigated 2 weeks to 2 months post-infection. All samples were positive for IgGs against the S- and N-proteins of SARS-CoV-2. Notably, 14 samples available at 6-7 months post-infection all showed significant neutralizing activities in a pseudovirus assay, with no difference in blocking the cell-entry of the 614D and 614G variants of SARS-CoV-2. Furthermore, in 10 blood samples from cases at 6-7 months post-infection used for memory T-cell tests, we found that interferon γ-producing CD4
Adaptive Immunity/physiology* ; Adult ; Aged ; Antibodies, Neutralizing/blood* ; COVID-19/immunology* ; Cohort Studies ; Female ; Humans ; Immunoglobulin G/blood* ; Male ; Middle Aged ; SARS-CoV-2/immunology* ; T-Lymphocytes/physiology* ; Time Factors ; Viral Proteins/immunology*