Background Per- and poly-fluoroalkyl substances (PFAS) can influence gestational diabetes mellitus (GDM); however, current studies on their association are limited and have yielded inconsistent findings. Objective To investigate the association between maternal exposure to PFAS, as measured by urinary concentrations in early pregnancy, and the risk of developing GDM. Methods Based on the Wuhu Birth Cohort in Anhui Province conducted between 2020 and 2023, this study included 1910 pregnant women. Urine samples were collected during the first trimester, and a 75 g oral glucose tolerance test (OGTT) was completed at 24–28 gestational weeks. The concentrations of six PFAS in urine were measured using ultra-performance liquid chromatography-tandem mass spectrometry (UPLC-MS/MS). Participants were categorized into low, medium, and high exposure groups according to tertiles of individual PFAS concentrations. Logistic regression analysis was employed to assess the potential impact of early-pregnancy PFAS exposure levels on the risk of GDM, with stratified analyses by maternal age and fetal sex. A generalized weighted quantile sum regression (gWQS) model was applied to evaluate the relative weight distribution of the six PFAS in their mixture effect. Results The logistic regression results revealed that both medium exposure to perfluorohexane sulfonate (PFHxS) (OR=1.475, 95%CI: 1.040, 2.093) and high exposure to perfluorononanoic acid (PFNA) (OR=1.430, 95%CI: 1.013, 2.018) were positively associated with diagnosed GDM. This association was more pronounced among women older than 28 years. Among pregnant women carrying male fetuses, high-level exposures to per fluorohexanoic acid (PFHxA) (OR=1.708, 95%CI: 1.006, 2.899) and PFHxS (OR=2.116, 95%CI: 1.247, 3.584) showed positive associations with diagnosed GDM. In contrast, among those carrying female fetuses, moderate perfluorooctanesulfonic acid (PFOS) exposure was associated with a lower risk of GDM (OR=0.542, 95%CI: 0.311, 0.946). The gWQS results demonstrated that PFOS (0.48) and perfluorobutane sulfonate (PFBS) (0.34) contributed the most to the mixture effect. Conclusion Early-pregnancy exposure to PFAS (particularly PFHxS and PFNA) may be an etiological factor for GDM, with maternal age and fetal sex potentially acting as effect modifiers in this association.

To develop and validate a model for predicting intensive care unit (ICU) mortality risk in patients with malignant tumors complicated by acute respiratory failure (ARF) based on an explainable machine learning framework.

Objective:To explore the application value of the artificial intelligence (AI) morphological assisted analysis system in the pre-classification of blood cells in patients with acute myeloid leukemia, myelodysplasia-related (AML-MR).Methods:A retrospective analysis was conducted on the bone marrow and peripheral blood cell morphology of patients initially diagnosed with AML-MR at Taizhou Hospital in Zhejiang Province from September 1, 2022, to December 31, 2023. A total of 44 patients, including 25 males and 19 females, with a median age of 71 (63.5, 75.3) years. Bone marrow and peripheral blood morphology were examined using the Morphogo cell morphology assisted analysis system, with the artificial classification results serving as the gold standard. A confusion matrix was constructed to evaluate the precision, sensitivity, and specificity of the AI system in identifying various cell types in bone marrow and peripheral blood for AML-MR diagnosis. The impact of dysplastic hematopoiesis on AI pre-classification was analyzed by comparing AI and manual classification results.Results:The AI system completed the pre-classification of 44 bone marrow smears and 42 corresponding peripheral blood smears from AML-MR patients. For bone marrow smears, the precision, sensitivity, and specificity of AI in pre-classifying blast cells were 85.78%, 91.01%, and 94.58%, respectively. For peripheral blood smears, these values were 87.11%, 87.05%, and 98.29%, respectively. The precision and sensitivity of AI in pre-classifying promyelocytes were 54.26% and 46.93%, respectively, while for monocytes, they were 58.16% and 68.34%, both lower than those for blast cells. The precision and sensitivity of AI in identifying myelocytes and metamyelocytes also decreased (77.47%, 66.25% and 81.91%, 63.29%, respectively). The precision and sensitivity of AI in pre-classifying erythroblasts/proerythroblasts (67.71%, 69.89%) were lower than those for polychromatic and orthochromatic normoblasts (83.43%, 85.53% and 92.97%, 86.96%, respectively). The confusion matrix and comparative analysis of AI and manual classification indicated that the decline in AI pre-classification precision and sensitivity was due to frequent misclassification between promonocytes and monocytes, as well as between monocytes and promyelocytes. Additionally, this decline is associated with dysplasia. However, the impact of dysplasia on the AI pre-classification of mature-stage granulocytes was minimal.Conclusion:The AI system demonstrated high precision, sensitivity, and specificity in pre-classifying blast cells in bone marrow and peripheral blood smears from AML-MR patients. The AI-assisted morphological analysis system can be effectively utilized for the pre-classification of blood cells in AML-MR patients.

Objective To observe the value of transabdominal bowel ultrasound for monitoring therapeutic efficacy of vedolizumab in patients with moderate-to-severe ulcerative colitis.Methods Totally 47 ulcerative colitis patients with Mayo endoscopic score(MES)≥2 and treated with vedolizumab were retrospectively included.Transabdominal bowel ultrasound examinations were performed at baseline,at the end of induction therapy,also 3 and 6 months after maintenance therapy,while colonoscopy was performed at baseline and 6 months after maintenance therapy.According to colonoscopy results 6 months after maintenance therapy,the patients were divided into improved group(MES≤1 or MES reduction≥1,n=25)and non-improved group(n=22),and ultrasonic findings of sigmoid colon were compared between and within groups.Results At baseline,no significant difference of ultrasonic findings of sigmoid colon was observed between groups(all P＞0.05),whereas differences of bowel wall thickness at the end of induction therapy,of Limberg grade and abnormal perienteric lymph nodes 3 months after maintenance therapy,as well as bowel wall stratification 6 months after maintenance therapy were noticed between groups(all P＜0.05).Within improved group,compared with those in baseline,bowel wall thickness improved at all time points after the beginning of treatment,Limberg grade improved 3 and 6 months after maintenance therapy,while bowel wall stratification,abnormal perienteric lymph node and perienteric fat edema improved 6 months after maintenance therapy(all P＜0.05).Meanwhile,no significant change of ultrasonic findings of sigmoid colon was observed in non-improved group during the above periods(all P＞0.05).Conclusion Transabdominal bowel ultrasound could be used to accurately monitor the therapeutic efficacy of vedolizumab in patients with moderate-to-severe ulcerative colitis.

Objective: To analyze the heterogeneity of mobile phone dependence development trajectory in middle school students and its association with loneliness and selfcontrol ability, so as to provide reference for the prevention of mobile phone dependence in middle school students. Methods: A total of 941 grade 1 students from 4 public middle schools in Xiangxi Autonomous Prefecture, Hunan Province were selected for the followup survey by random cluster sampling from October 2023 to April 2024 and October 2024. Mobile Phone Addiction Index (MPAI), University of California, Los Angeles Loneliness Scale-20 (UCLA-20) and Selfcontrol Scales (SCS) were used for questionnaire survey. The heterogeneity of the developmental trajectory of middle school students mobile phone dependence was analyzed by the latent growth curve model (LGMM), and the influencing factors of the developmental trajectory of middle school students mobile phone dependence were explored by multiple Logistic regression analysis. Results: The development trajectory of middle school students mobile phone dependence could be divided into four categories: C1 "low risk slow decline group (n=438,44.6%)", C2 "medium risk slow rise group (n=272,29.7%)", C3 "high risk rapid decline group (n=73,8.6%)" and C4 "high risk rapid rise group (n=158,17.1%)". There were significant differences in the distribution of mobile phone dependence development track heterogeneity subgroups among sex, only child, lodging, and leftbehind students (χ2=117.79, 44.88, 37.09, 130.50, P <0.01). The results of the multinomial Logistic regression model analysis showed that, with C1 group as the reference, C2, C3, and C4 were positively correlated with students loneliness [OR(95%CI)=1.04 (1.02-1.06), 1.11(1.08-1.14), 1.12(1.09-1.14)]; C2 and C4 groups were negatively correlated with students selfcontrol [OR(95%CI)=0.97(0.96-0.99), 0.95(0.93-0.97)] (P<0.01). Conclusions The development trajectory of mobile phone dependence among middle school students is heterogeneous. Reducing the loneliness of individuals and cultivating good selfcontrol ability are helpful to alleviate mobile phone dependence behavior among middle school students.

Objective:This study retrospectively analyzed the clinical characteristics of patients newly diagnosed with acute myeloid leukemia (AML) who were admitted to the hematology intensive care unit (HCU) with critical illness. It also examined factors associated with critical illness and early mortality in these patients.Methods:Clinical data were collected from 91 newly diagnosed AML patients admitted to the HCU of the Department of Hematology, First Affiliated Hospital of Soochow University, from October 2020 to 2024. Reasons for HCU admission, major therapeutic interventions, and risk factors for critical illness and early mortality were analyzed.Results:The median time from diagnosis to HCU admission was 3 days ( IQR: 3–9 days), and the median HCU stay was 10 days ( IQR: 3–23 days). Of the 91 patients, 71 were admitted to the HCU before induction chemotherapy, while 20 were transferred to the HCU after its initiation. The leading causes of HCU admission were pulmonary infection (78.0% ), respiratory failure (44.0% ), hepatic insufficiency (28.6% ), renal insufficiency (27.5% ), disseminated intravascular coagulation (DIC; 25.3% ), and sepsis (23.1% ). Median Acute Physiology and Chronic Health Evaluation Ⅱ (APACHE Ⅱ) and SOFA scores at HCU admission were 14 ( IQR: 11–18) and the median Sepsis Related Organ Failure Assessment (SOFA) score was 7 ( IQR: 4, 10). Major HCU interventions included vasoactive drugs, noninvasive and invasive mechanical ventilation, continuous renal replacement therapy, therapeutic leukocyte clearance, and cardiopulmonary resuscitation. Among patients receiving induction chemotherapy, the composite complete remission rate was 65.4%, and the overall remission rate was 88.5%. Thirty-five (38.5% ) patients died within 28 days of HCU admission. Independent risk factors for 28-day mortality were DIC ( OR=9.350, 95% CI 1.999–43.745, P=0.005), sepsis ( OR=6.817, 95% CI 1.571–29.582, P=0.010), and cardiac insufficiency ( OR=12.281, 95% CI 2.385–63.254, P=0.003) . Conclusion:The main reason for HCU admission in newly diagnosed critically ill AML patients was pulmonary infection. Nearly 40% of patients experisenced early death, and DIC, sepsis, and heart failure were factors influencing early mortatlity.

This report presents the management of a critically ill 36-year-old woman diagnosed with Philadelphia chromosome-positive acute lymphoblastic leukemia (Ph +ALL) at 28 weeks of gestation. The patient rapidly deteriorated, developing disseminated intravascular coagulation (DIC) , diffuse alveolar hemorrhage (DAH) , septic shock, and multi-organ dysfunction, necessitating admission to the hematological intensive care unit. Given her critical condition and advanced pregnancy, a chemotherapy-free induction regimen comprising imatinib and dexamethasone was initiated, alongside comprehensive supportive measures, including mechanical ventilation, continuous renal replacement therapy (CRRT) , broad-spectrum antibiotics, and high-dose corticosteroids. During treatment, intrauterine fetal demise occurred, and a stillborn was delivered following obstetric intervention. With aggressive treatment, the patient's respiratory failure, DIC, and DAH gradually resolved, and she achieved complete remission. She subsequently received consolidation chemotherapy, CAR-T cell therapy, and allogeneic hematopoietic stem cell transplantation, achieving sustained complete molecular remission on long-term follow-up. This case demonstrates that for critically ill pregnant patients with Ph + ALL, a chemotherapy-free regimen of targeted therapy and corticosteroids, when combined with intensive supportive care, is a safe and effective approach that may offer a therapeutic option for similar cases.

CD46 is a type Ⅰ transmembrane glycoprotein that participates in the classical pathway and alternative complement activation cascade in the innate immune system. CD46 is highly expressed on the surface of various tumor cells，playing a significant role in promoting tumor progression and immune evasion. Studies have found that the expression level of CD46 is closely related to the prognosis and immunotherapy outcomes of multiple tumors. Inhibition of CD46 expression or function can enhance the sensitivity of tumor cells to oncolytic adenovirus therapy，among others. However，the clinical application of CD46 in tumor immunotherapy still faces some challenges. This review summarizes the research progress in CD46 in tumor immunotherapy，aiming to further explore the mechanisms of CD46 and the clinical potential of potential therapeutic targets.

Objective:To explore the application value of the artificial intelligence (AI) morphological assisted analysis system in the pre-classification of blood cells in patients with acute myeloid leukemia, myelodysplasia-related (AML-MR).Methods:A retrospective analysis was conducted on the bone marrow and peripheral blood cell morphology of patients initially diagnosed with AML-MR at Taizhou Hospital in Zhejiang Province from September 1, 2022, to December 31, 2023. A total of 44 patients, including 25 males and 19 females, with a median age of 71 (63.5, 75.3) years. Bone marrow and peripheral blood morphology were examined using the Morphogo cell morphology assisted analysis system, with the artificial classification results serving as the gold standard. A confusion matrix was constructed to evaluate the precision, sensitivity, and specificity of the AI system in identifying various cell types in bone marrow and peripheral blood for AML-MR diagnosis. The impact of dysplastic hematopoiesis on AI pre-classification was analyzed by comparing AI and manual classification results.Results:The AI system completed the pre-classification of 44 bone marrow smears and 42 corresponding peripheral blood smears from AML-MR patients. For bone marrow smears, the precision, sensitivity, and specificity of AI in pre-classifying blast cells were 85.78%, 91.01%, and 94.58%, respectively. For peripheral blood smears, these values were 87.11%, 87.05%, and 98.29%, respectively. The precision and sensitivity of AI in pre-classifying promyelocytes were 54.26% and 46.93%, respectively, while for monocytes, they were 58.16% and 68.34%, both lower than those for blast cells. The precision and sensitivity of AI in identifying myelocytes and metamyelocytes also decreased (77.47%, 66.25% and 81.91%, 63.29%, respectively). The precision and sensitivity of AI in pre-classifying erythroblasts/proerythroblasts (67.71%, 69.89%) were lower than those for polychromatic and orthochromatic normoblasts (83.43%, 85.53% and 92.97%, 86.96%, respectively). The confusion matrix and comparative analysis of AI and manual classification indicated that the decline in AI pre-classification precision and sensitivity was due to frequent misclassification between promonocytes and monocytes, as well as between monocytes and promyelocytes. Additionally, this decline is associated with dysplasia. However, the impact of dysplasia on the AI pre-classification of mature-stage granulocytes was minimal.Conclusion:The AI system demonstrated high precision, sensitivity, and specificity in pre-classifying blast cells in bone marrow and peripheral blood smears from AML-MR patients. The AI-assisted morphological analysis system can be effectively utilized for the pre-classification of blood cells in AML-MR patients.

Objective:To establish a model based on the characteristics of breast cancer ultrasound images through deep learning methods to predict the risk of axillary lymph node metastasis（ALNM）in patients with breast cancer（maximum diameter ≤2.5 cm）before surgery.Methods:A total of 419 patients（3 433 breast tumor ultrasound images）with breast cancer（maximum diameter ≤2.5 cm）who underwent axillary lymph node dissection at Beijing Tiantan Hospital，Capital Medical University from January 2019 to December 2024 were retrospectively included. According to the pathological results of axillary lymph nodes，they were divided into 220 cases in the ALNM occurrence group（positive group）and 199 cases in the non-ALNM occurrence group（negative group）. The breast cancer ultrasound images of the two groups of cases were randomly classified into the training set（2 404 images），the validation set（687 images）and the test set（342 images）according to a ratio of 7∶2∶1. YOLOv8 was used as the basic model of You Only Look Once（YOLO）and optimized. The optimized model was applied to locate and capture the potential ultrasound features of breast cancer cases in the training set. A prediction model was constructed based on the captured ultrasound features. The model was adjusted and optimized through the validation set，and then matched with the case images in the test set. The confusion classification matrix graph and the curve graph for measuring the model performance were used to evaluate the model prediction performance and interpret the model，and the efficacy of this model in identifying breast cancer patients at risk of ALNM was analyzed.Results:There were statistically significant differences between the positive and negative groups in terms of the pathological maximum diameter of breast tumors，pathological T staging，the differentiation degree，the presence of distant metastasis，the maximum diameter measured by ultrasound，the quadrant of breast tumor occurrence，the Breast Imaging - Reporting and Data System（BI-RADS）classification of breast tumors，and the presence of abnormal ultrasound features of lymph node（all P<0.05）. The established deep learning model could automatically perform bounding box localization for the breast cancer of patients.The breast tumors in the positive group had potential ultrasound features that could be captured by the model compared with those in the negative group. The mean average precision（mAP）50 was 0.883，mAP 50-95 was 0.636，PR-AUC was 0.884 5，strict PR-AUC was 0.636 4，the sensitivity was 90.5%，and the specificity was 91.2%，and it had a good predictive efficacy. Conclusions:This prediction model based on the ultrasound characteristics of breast cancer through deep learning can effectively predict breast cancer（maximum diameter ≤ 2.5 cm）with the risk of ALNM，providing an effective basis for the clinical management of axillary lymph nodes in breast cancer patients.