A 20-year-old male patient with T-lymphoblastic lymphoma/leukemia received 9/10 human leukocyte antigen-compatible unrelated peripheral blood stem cell transplantation. He was transplanted with 5.91×10 8 mononuclear cells/kg and 2.88×10 6 CD34 + cells/kg, and neutrophil engraftment was obtained at +11 days and platelet engraftment at +9 days. After transplantation, he presented with repeatedly increased serum creatinine levels, BK virus (BKV) -associated hemorrhagic cystitis, and BKV viremia. BK virus nephropathy was diagnosed based on renal biopsy and metagenomic next-generation sequencing. After adjusting the immunosuppressant, intravenous immunoglobulin, and donor lymphocyte infusion treatment, the patient’s renal function deteriorated progressively, and he eventually died of multiple organ failure at +289 days.

Objective:To investigate the value of machine learning models based on CT radiomics for predicting the outcome of neoadjuvant chemotherapy (NAC) in patients with locally advanced gastric cancer (LAGC).Methods:A retrospective case series study was conducted. A total of 279 LAGC patients receiving NAC before surgery in Shanxi Province Cancer Hospital from January 2018 to November 2020 were included. According to a ratio of 7∶3, all patients were randomly divided into the training set (196 cases) and the validation set (83 cases). According to the tumor regression grade (TRG), the pathological grade was divided into the good response of NAC (GR) group (TRG 0-1, 55 cases) and the poor response of NAC (PR) group (TRG 2-3, 224 cases). The clinicopathological data of patients were collected, such as age, gender, differentiation degree, clinical T and N staging, carcinoembryonic antigen (CEA), and carbohydrate antigen 199 (CA199) level. Radiomics features were extracted from the enhanced CT images in the vein phase, and the features were screened by 3-step dimensionality reduction. And then 5 machine learning algorithms including logistic regression (LR), naive bayes (NB), random forest (RF), support vector machine (SVM) and extreme gradient boosting (XGB) were applied to build prediction models based on the CT radiomics. The receiver operating characteristic (ROC) curve and the decision analysis (DCA) curve were drawn to evaluate the predictive performance and clinical benefit of each model on the outcome of NAC in patients with LAGC.Results:Among 196 patients in the training set, there were 39 cases in GR group and 157 cases in PR group; among 83 patients in the validation set, there were 16 cases in GR group and 67 cases in PR group. There were no statistically significant differences in clinicopathological data of patients between the training and validation sets, or between GR and PR groups in the training and validation sets (all P > 0.05). A total of 102 radiomics features were extracted from region of interest of CT images in the vein phase, and 6 key features were finally selected including original_firstorder_10Percentile, original_firstorder_RoubustMeanAbsoluteDeviation, original_glcm_Idmn, original_glcm_MCC, original_ngtdm_Busyness, original_ngtdm_Contrast; and there were statistically significant differences in 6 features between the GR and PR groups (all P < 0.05). LR, NB, RF, SVM and XGB machine learning algorithms were used to construct 5 prediction models based on the CT radiomics. The area under ROC curve for NAC prediction in the training set was 0.553, 0.709, 0.668, 0.772 and 0.790, respectively; in the validation set was 0.662, 0.622, 0.683, 0.752 and 0.784, respectively. The model constructed by XGB showed the best comprehensive performance, and its accuracy, sensitivity and specificity was 0.771, 0.562 and 0.821, respectively. In the DCA of 5 machine learning models in the training set, XGB-based model provided a higher net benefit. Conclusions:Machine learning models based on enhanced CT radiomics in the vein phase have a high predictive efficacy in the outcome of NAC in LAGC patients before surgery and it helps make clinical personalized treatment decisions.

Objective:To investigate the value of MRI radiomics model in evaluating the pathological complete response (pCR) status of human epidermal growth factor receptor 2(HER-2) positive breast cancer after neoadjuvant therapy.Methods:The study was a cross-sectional study. The clinical, pathological, and MRI data of 243 HER-2 positive breast cancer patients who received neoadjuvant therapy in Shanxi Province Cancer Hospital from January 2021 to June 2023 were retrospectively analyzed. All patients were female, aged 26?75 years. All patients were randomly divided into training set (146 cases) and validation set (97 cases) at a ratio of 6∶4 according to the simple random sampling method. Univariate and multivariate logistic regression were used to screen independent predictors of pCR. Radiomics features were extracted from the early-phase (the 2nd phase) images of breast dynamic contrast-enhanced-MRI after neoadjuvant therapy.The four-step procedure was adopted for feature screening. The radiomics model was constructed by logistic regression. A combined model was constructed by integrating radiomics features and independent predictors. Two radiologists (Reader 1 with 10 years experience and Reader 2 with 13 years experience) who major in breast MRI visually evaluated the pCR status of breast cancer after neoadjuvant therapy. The receiver operating characteristic curve and area under the curve (AUC) were used to evaluate the efficacy of Reader 1, Reader 2, the radiomics model, and the combined model in predicting pCR status. The Hosmer-Lemeshow goodness-of-fit test was used to evaluate the calibration of the model.Results:Among 243 HER-2 positive breast cancer patients, totally 118 achieved pCR. In clinical and pathological features, HER-2 3+ was an independent predictor of pCR ( OR=2.71, 95% CI 1.03?7.12, P=0.043). In the training set and validation set, the AUCs of the radiomics model in predicting pCR status were 0.899 and 0.853, respectively.The AUCs of the combined model were 0.917 and 0.890, respectively. In the validation set, the AUC value of the radiomics model in predicting pCR status was higher than that of Reader 1 and Reader 2. Hosmer-Lemeshow goodness-of-fit test showed that there was no significant difference between the prediction of pCR status by the combined model and radiomics model and the actual results in the training set and validation set, and the fitting was good ( P>0.05). Conclusion:The MRI-based radiomics model can be used to predict pCR status in HER-2 positive breast cancer and outperforms the visual qualitative assessments of radiologists.

Objective:To investigate the value of MRI radiomics model in evaluating the pathological complete response (pCR) status of human epidermal growth factor receptor 2(HER-2) positive breast cancer after neoadjuvant therapy.Methods:The study was a cross-sectional study. The clinical, pathological, and MRI data of 243 HER-2 positive breast cancer patients who received neoadjuvant therapy in Shanxi Province Cancer Hospital from January 2021 to June 2023 were retrospectively analyzed. All patients were female, aged 26?75 years. All patients were randomly divided into training set (146 cases) and validation set (97 cases) at a ratio of 6∶4 according to the simple random sampling method. Univariate and multivariate logistic regression were used to screen independent predictors of pCR. Radiomics features were extracted from the early-phase (the 2nd phase) images of breast dynamic contrast-enhanced-MRI after neoadjuvant therapy.The four-step procedure was adopted for feature screening. The radiomics model was constructed by logistic regression. A combined model was constructed by integrating radiomics features and independent predictors. Two radiologists (Reader 1 with 10 years experience and Reader 2 with 13 years experience) who major in breast MRI visually evaluated the pCR status of breast cancer after neoadjuvant therapy. The receiver operating characteristic curve and area under the curve (AUC) were used to evaluate the efficacy of Reader 1, Reader 2, the radiomics model, and the combined model in predicting pCR status. The Hosmer-Lemeshow goodness-of-fit test was used to evaluate the calibration of the model.Results:Among 243 HER-2 positive breast cancer patients, totally 118 achieved pCR. In clinical and pathological features, HER-2 3+ was an independent predictor of pCR ( OR=2.71, 95% CI 1.03?7.12, P=0.043). In the training set and validation set, the AUCs of the radiomics model in predicting pCR status were 0.899 and 0.853, respectively.The AUCs of the combined model were 0.917 and 0.890, respectively. In the validation set, the AUC value of the radiomics model in predicting pCR status was higher than that of Reader 1 and Reader 2. Hosmer-Lemeshow goodness-of-fit test showed that there was no significant difference between the prediction of pCR status by the combined model and radiomics model and the actual results in the training set and validation set, and the fitting was good ( P>0.05). Conclusion:The MRI-based radiomics model can be used to predict pCR status in HER-2 positive breast cancer and outperforms the visual qualitative assessments of radiologists.

A 20-year-old male patient with T-lymphoblastic lymphoma/leukemia received 9/10 human leukocyte antigen-compatible unrelated peripheral blood stem cell transplantation. He was transplanted with 5.91×10 8 mononuclear cells/kg and 2.88×10 6 CD34 + cells/kg, and neutrophil engraftment was obtained at +11 days and platelet engraftment at +9 days. After transplantation, he presented with repeatedly increased serum creatinine levels, BK virus (BKV) -associated hemorrhagic cystitis, and BKV viremia. BK virus nephropathy was diagnosed based on renal biopsy and metagenomic next-generation sequencing. After adjusting the immunosuppressant, intravenous immunoglobulin, and donor lymphocyte infusion treatment, the patient’s renal function deteriorated progressively, and he eventually died of multiple organ failure at +289 days.

Objective:To investigate the value of machine learning models based on CT radiomics for predicting the outcome of neoadjuvant chemotherapy (NAC) in patients with locally advanced gastric cancer (LAGC).Methods:A retrospective case series study was conducted. A total of 279 LAGC patients receiving NAC before surgery in Shanxi Province Cancer Hospital from January 2018 to November 2020 were included. According to a ratio of 7∶3, all patients were randomly divided into the training set (196 cases) and the validation set (83 cases). According to the tumor regression grade (TRG), the pathological grade was divided into the good response of NAC (GR) group (TRG 0-1, 55 cases) and the poor response of NAC (PR) group (TRG 2-3, 224 cases). The clinicopathological data of patients were collected, such as age, gender, differentiation degree, clinical T and N staging, carcinoembryonic antigen (CEA), and carbohydrate antigen 199 (CA199) level. Radiomics features were extracted from the enhanced CT images in the vein phase, and the features were screened by 3-step dimensionality reduction. And then 5 machine learning algorithms including logistic regression (LR), naive bayes (NB), random forest (RF), support vector machine (SVM) and extreme gradient boosting (XGB) were applied to build prediction models based on the CT radiomics. The receiver operating characteristic (ROC) curve and the decision analysis (DCA) curve were drawn to evaluate the predictive performance and clinical benefit of each model on the outcome of NAC in patients with LAGC.Results:Among 196 patients in the training set, there were 39 cases in GR group and 157 cases in PR group; among 83 patients in the validation set, there were 16 cases in GR group and 67 cases in PR group. There were no statistically significant differences in clinicopathological data of patients between the training and validation sets, or between GR and PR groups in the training and validation sets (all P > 0.05). A total of 102 radiomics features were extracted from region of interest of CT images in the vein phase, and 6 key features were finally selected including original_firstorder_10Percentile, original_firstorder_RoubustMeanAbsoluteDeviation, original_glcm_Idmn, original_glcm_MCC, original_ngtdm_Busyness, original_ngtdm_Contrast; and there were statistically significant differences in 6 features between the GR and PR groups (all P < 0.05). LR, NB, RF, SVM and XGB machine learning algorithms were used to construct 5 prediction models based on the CT radiomics. The area under ROC curve for NAC prediction in the training set was 0.553, 0.709, 0.668, 0.772 and 0.790, respectively; in the validation set was 0.662, 0.622, 0.683, 0.752 and 0.784, respectively. The model constructed by XGB showed the best comprehensive performance, and its accuracy, sensitivity and specificity was 0.771, 0.562 and 0.821, respectively. In the DCA of 5 machine learning models in the training set, XGB-based model provided a higher net benefit. Conclusions:Machine learning models based on enhanced CT radiomics in the vein phase have a high predictive efficacy in the outcome of NAC in LAGC patients before surgery and it helps make clinical personalized treatment decisions.

Objective: To investigate the potential association among preoperative breast MRI features, axillary nodal burden (ANB), and disease-free survival (DFS) in patients with early-stage breast cancer. Materials and Methods: We retrospectively reviewed 297 patients with early-stage breast cancer (cT1-2N0M0) who underwent preoperative MRI between December 2016 and December 2018. Based on the number of positive axillary lymph nodes (LNs) determined by postoperative pathology, the patients were divided into high nodal burden (HNB; ≥3 positive LNs) and non-HNB (<3 positive LNs) groups. Univariable and multivariable logistic regression analyses were performed to identify independent risk factors associated with ANB. Predictive efficacy was evaluated using the receiver operating characteristic (ROC) curve and area under the curve (AUC). Univariable and multivariable Cox proportional hazards regression analyses were performed to determine preoperative features associated with DFS. Results: We included 47 and 250 patients in the HNB and non-HNB groups, respectively. Multivariable logistic regression analysis revealed that multifocality/multicentricity (adjusted odds ratio [OR] = 3.905, 95% confidence interval [CI]: 1.685– 9.051, P= 0.001) and peritumoral edema (adjusted OR = 3.734, 95% CI: 1.644–8.479, P = 0.002) were independent risk factors for HNB. Combined peritumoral edema and multifocality/multicentricity achieved an AUC of 0.760 (95% CI: 0.707– 0.807) for predicting HNB, with a sensitivity and specificity of 83.0% and 63.2%, respectively. During the median follow-up period of 45 months (range, 5–61 months), 26 cases (8.75%) of breast cancer recurrence were observed. Multivariable Cox proportional hazards regression analysis indicated that younger age (adjusted hazard ratio [HR] = 3.166, 95% CI: 1.200–8.352, P= 0.021), larger tumor size (adjusted HR = 4.370, 95% CI: 1.671–11.428, P= 0.002), and multifocality/multicentricity (adjusted HR = 5.059, 95% CI: 2.166–11.818, P< 0.001) were independently associated with DFS. Conclusion Preoperative breast MRI features may be associated with ANB and DFS in patients with early-stage breast cancer.

Objective:To explore the clinical characteristics of pregnancy complicated by primary Sj?gren's syndrome (pSS) and the related factors of adverse outcomes in pregnant women with pSS.Methods:A retrospective analysis was conducted on the clinical data of 32 pregnancies complicated by pSS treated in the Department of Obstetrics at the Third Affiliated Hospital of Guangzhou Medical University from February 2017 to August 2022. The patients were divided into two groups according to whether they had perinatal adverse outcomes: an adverse outcome group ( n=20) and a favorable outcome group ( n=12). The clinical characteristics of the two groups were compared with two independant sample t-test, Mann-Whitney U test, and Fisher's exact test, and multivariate logistic regression analysis was used to analyze the related factors of adverse outcomes in pregnant women with pSS. Results:(1) The average maternal age of the 32 pSS pregnancies was (32.9±4.6) years, the pre-pregnancy body mass index was (21.1±3.8) kg/m 2, and the median gestational age at delivery was 37.8 (35.4-38.5) weeks. There were 18 women (56.3%, 18/32) were diagnosed before pregnancy and 14 women (43.7%, 14/32) during pregnancy. Out of the 32 pregnancies, 25 (79.1%, 25/32) received therapy with glucocorticoids and/or hydroxychloroquine during pregnancy, whereas seven (21.9%, 7/32) had no medication during pregnancy. (2) The main adverse maternal outcomes included oligohydramnios (25%, 8/32), hypertensive disorder of pregnancy (18.8%, 6/32), preterm birth (18.8%, 6/32), fetal growth restriction (15.6%, 5/32), miscarriage (12.5%, 4/32), gestational diabetes mellitus (9.4%, 3/32), and postpartum hemorrhage (3.1%, 1/32). (3) Adverse neonatal outcomes included low birth weight infants in seven cases (25.0%, 7/28), neonatal asphyxia in seven cases (25.0%, 7/28), and two cases of congenital heart block (7.1%, 2/28). (4) The rate of diagnosis before pregnancy in the favorable outcome group was higher than the adverse outcome group [10/12 vs. 40.0%(8/20), Fisher's exact test, P=0.028]. There were no significant differences between the two groups concerning maternal age, pre-pregnancy BMI, weight gain during pregnancy, parity, rates of positivity for autoantibodies (antinuclear antibody, Sj?gren-specific antibody A, Sj?gren-specific antibody B, anti-Ro-52), and proportion of drug treatment (glucocorticoids, hydroxychloroquine) (all P>0.05). (5) Multivariate logistic regression analysis showed that diagnosis before pregnancy ( OR=0.02, 95% CI: 0.00-0.62, P=0.024) and positive Sj?gren-specific antibody B ( OR=0.01,95% CI: 0.00-0.75, P=0.038) were the protective factors. Conclusions:The clinical manifestations of pSS among pregnant women are varied and atypical,often with insidious onset. For pregnant women with pSS, being diagnosed before pregnancy, positive Sj?gren-specific antibody B may reduce adverse outcomes. It is important to address pre-pregnancy examination, early diagnosis and timely intervention to reduce the occurrence of adverse outcomes in pregnant women with pSS.

The angiogenic microenvironment is a new blood vessel with different molecular and functional characteristics that sprouts on the original blood vessels through different mechanisms,which directly affects the process of tumor cell growth,proliferation,and migration and has an important impact on the occurrence and development of precancerous lesions of gastric cancer.Correa mode has shown that precancerous lesions of gastric cancer is the key pathological stage before the occurrence of gastric cancer,and it is of great significance to advance the prevention and treatment strategy to this stage.TCM believes that qi deficiency and blood stasis is the key pathogenesis of precancerous lesions of gastric cancer,and its basic treatment is to replenish qi and remove blood stasis,and based on the syndrome differentiation,drugs with the efficacy of nourishing yin and tonifying stomach,soothing the liver and regulating qi,resolving phlegm and dispersing lumps,and clearing heat and dampness for treatment.This article discussed the correlation between precancerous lesions of gastric cancer and angiogenic microenvironment and its regulatory pathways,and summarized the methods and mechanisms of TCM in the treatment of precancerous lesions of gastric cancer from the perspective of regulating angiogenic microenvironment-related pathways,in order to provide a reference for the treatment of precancerous lesions of gastric cancer with TCM.

Objective:This study aims to explore the prevalence of hepatitis E virus (HEV) infection in patients and the screening value of serological indicators for HEV infection patients.Methods:Retrospective analysis was conducted on 97 440 cases of anti-HEV IgM and IgG simultaneously tested in two Beijing hospitals from January 1, 2018 to August 31, 2023. Among them, there were 61 005 males and 36 435 females, with an average age of 51.65±13.05 years old. According to the positivity of anti HEV specific antibodies, they were divided into anti-HEV IgM positive group (3 588 cases), anti-HEV IgG positive group (18 083 cases), and anti-HEV antibody negative group (78 892 cases). Results of HEV RNA, liver function, AFP, PIVKA-Ⅱ and PT were collected, and their basic clinical information were recorded. The prevalence of HEV infection in patients, as well as the relationship between the positivity of anti-HEV specific antibodies and the patient′s age group, HEV RNA, and clinical characteristics were analyzed.Results:Among 97 440 patients who tested anti-HEV IgM and IgG simultaneously, the positivity rate of anti-HEV IgM was 3.68% (3 588/97 440), and was 18.56% for anti-HEV IgG (18 083/97 440). The overall positivity rates of anti-HEV IgM in two Beijing hospitals from 2018 to 2023 were 2.51%, 2.53%, 3.02%, 4.59%, 5.72%, and 4.26% ( χ2=1 401.73, P<0.001), while the positivity rates of anti-HEV IgG were 12.56%, 12.32%, 12.85%, 22.65%, 27.42%, and 26.66% ( χ2=1 058.29, P<0.001). These rates showed a gradual increase until 2023 when a decline was observed. The positivity rates of anti-HEV IgM (2.28%, 3.60%, 4.47%) ( χ2=89.62, P<0.001) and IgG (4.71%, 17.86%, 25.94%) ( χ2=2 017.32, P<0.001) increased with age in patients who aged 1-30, >30-60, and over 60 years old. The age and ALB values of patients in the anti-HEV IgM positive group were lower than the IgG-positive group, while the proportion of males, TBIL, ALT, AFP and PT values were higher than the IgG-positive group, and the differences were statistically significance ( P<0.05). Furthermore, patients in both the anti-HEV IgM and IgG positive groups had higher age, male proportion, TBIL, ALT, AFP, PIVKA-Ⅱ, and PT values than the anti-HEV negative group. Additionally, both groups had lower ALB values than the anti-HEV negative group, all of which were statistically significant ( P<0.05). 2 162 HEV infected patients were grouped based on HEV RNA positivity. The proportion of anti-HEV IgM single positive, IgG single positive, IgM+IgG double positive, and antibody negative patients in the HEV RNA positive group were 5.42% (18/332), 3.62% (12/332), 90.36% (300/332), and 0.60% (2/332), respectively. Among them, the proportion of anti-HEV IgM+IgG double positive patients in the HEV RNA positive group was higher than that in the HEV RNA negative group ( χ2=302.87, P<0.001), while the proportion of anti-HEV IgG single positive ( χ2=174.36, P<0.001) and anti-HEV antibody negative patients ( χ2=59.28, P<0.001) were lower than that in the HEV RNA negative group, both of which were statistically significant ( P<0.001). In addition, the positive rates of HEV RNA in anti-HEV IgM positive, IgG positive, and antibody negative patients were 29.23% (318/1 088), 17.59% (312/1 774), and 0.65% (2/306), respectively. Conclusion:The HEV infection rate among patients declined in 2023. HEV infection is age-related, with older individuals being more susceptible. Abnormal liver function and jaundice were commonly observed during HEV infection. It is crucial to note that the absence of anti-HEV specific antibodies cannot rule out HEV infection; therefore, additional testing for HEV RNA and/or HEV Ag is necessary for accurate diagnosis.