Objective To investigate the predictive value of artificial intelligence-based Alberta Stroke Program Early CT Score(ASPECTS)combined with diffusion-weighted imaging(DWI)cere-bral infarct volume for poor prognosis in wake-up stroke(WUS)patients.Methods A total of 100 patients with acute ischemic stroke after waking up with unknown time window admitted to Kaifeng Cen-tral Hospital from September 2022 to June 2023 were selected as the research objects.All patients un-derwent emergency non-contrast-enhanced cranial CT and magnetic resonance imaging(MRI)scan,followed by reperfusion therapy.The patients were followed up for 3 months after treatment,and were divided into good prognosis[modified Rankin Scale(mRS)≤2]and poor prognosis groups mRS＞2]according to the mRS score.The baseline data,artificial intelligence ASPECTS,and DWI cerebral in-farct volumes were compared between the two groups.Multivariate logistic regression analysis was used to identify prognostic factors,and receiver operating characteristic(ROC)curves were employed to e-valuate the diagnostic efficacy of artificial intelligence ASPECTS combined with DWI cerebral infarct vol-ume.Results After 3 months of follow-up,the poor prognosis rate of patients was 32.00％(32/100).The artificial intelligence ASPECTS at admission in the poor prognosis group was lower than that in the good prognosis group,and the DWI cerebral infarction volume at admission was larger than that in the good prognosis group,with statistically significant differences(P＜0.05).The results of mul-tivariate logistics analysis showed that age(OR=2.190;95％CI,1.412 to 3.398),blood pressure variability(OR=1.726;95％CI,1.192 to 2.500),homocysteine(OR=1.902;95％CI,1.268 to 2.854),D-dimer(OR=2.275;95％CI,1.274 to 4.064),white blood cell count(OR=2.614;95％CI,1.484 to 4.606),neutrophil-to-lymphocyte ratio(OR=2.921;95％CI,1.350 to 6.323),National Institutes of Health Stroke Scale score(OR=3.171;95％CI,1.754 to 5.731),and DWI infarct volume(OR=3.586;95％CI,1.634 to 7.869)were identified as factors affecting poor prognosis(P＜0.05),while high artificial intelligence ASPECTS was identified as a protective factor(OR=0.534;95％CI,0.352 to 0.810;P＜0.05).The sensitivity,specificity and area under the curve of the combined prediction model were 96.88％,85.29％and 0.947,respectively.The sensitivity and AUC of the combined prediction model were higher than that of the single prediction(P＜0.05),and the specificity was similar to that of the single prediction.Conclusion The com-bined application of artificial intelligence ASPECTS and DWI infarct volume significantly enhances predictive efficacy for poor prognosis in WUS patients,providing a more accurate prognostic evalua-tion tool for clinical decision-making,and it has the value of guiding personalized treatment.

Objective To investigate the effectiveness of optimized flow sensitive black blood(FSBB)imaging in detecting the number of branches and measuring the depth of lenticulostriate arteries(LSAs)in patients with acute ischemic stroke.Methods The ima-ging and clinical data of 39 patients with acute ischemic stroke who underwent vascular recanalization under digital subtraction angi-ography(DSA)were prospectively collected.All patients received 3.0T MR FSBB imaging within 48-96 hours postoperatively.The number and depth of LSAs branches on the affected side were observed,measured and recorded by the post-processing workstations for both FSBB and DSA images.The difference and correlation of the number and average depth of LSAs branches detected by FSBB and DSA were analyzed.The consistency of the average depth of LSAs branches detected by FSBB and DSA was evaluated.Results There was no statistically significant difference in the number and depth of bilateral LSAs branches detected by FSBB and DSA(P＞0.05).FSBB and DSA showed strong positive correlations in detecting the number and depth of LSAs branches(r=0.786,0.704;P＜0.05).The number of average depth difference of bilateral LSAs branches detected by FSBB and DSA exceeded the limit of agreement(LoA)was 3,accounting for 4.92％(＜5％).Conclusion Optimized FSBB imaging can be used to assess the detection of the number and depth measurement of LSAs branches in patients with acute ischemic stroke,showing good consistency with DSA.It provides valua-ble imaging evidence for the morphological assessment of LSAs in clinical practice.

Objective:To explore the clinical application value of convolutional neural network（CNN）based on deep learning in the automatic measurement of dynamic pelvic floor ultrasound video parameters and the diagnosis and classification of cystocele.Methods:A retrospective analysis was conducted on dynamic pelvic floor ultrasound videos from 398 postpartum women who underwent examinations at the First People's Hospital of Foshan between June 2020 and June 2022. The lowest point of the posterior bladder wall（PWB），urethral rotation angle（URA），and retrovesical angle（RVA）were manually measured by a senior radiologist（R1）and a junior radiologist（R2），and cystocele was classified according to the Green standard. The CNN model was employed to automatically extract the above parameters and to diagnose and classify cystocele. Using R1 measurements as a reference，intraclass correlation coefficient（ICC）was used to evaluate the consistency between the CNN model and R1，as well as between R2 and R1. The Kappa value was used to assess the agreement between the CNN model，R2，and R1 in the diagnosis and classification of cystocele. Additionally，the time consumption of the three measurement methods was compared.Results:The CNN model showed good consistency with R1 in measuring PWB and URA（ICC = 0.983，0.894），while its consistency in measuring RVA was moderate（ICC = 0.614）. The ICC between R2 and R1 in measuring PWB，URA，and RVA was 0.979，0.815，and 0.627，respectively. In the measurement of PWB and URA，the consistency between the CNN model and R1 was superior to that between R2 and R1. For cystocele diagnosis，the Kappa value between the CNN model and R1 was 0.924，which was higher than that between R2 and R1（0.904）. In cystocele classification，the Kappa value between the CNN model and R1 was 0.503，also higher than that between R2 and R1（0.426）. The CNN model processed a single video in 2.5（0.6）s，significantly faster than R1［59.9（16.9）s］and R2［56.8（11.2）s］（all P < 0.001）. Conclusions:The CNN model demonstrates high accuracy and efficiency in the measurement，diagnosis，and classification of cystocele，outperforming a junior radiologist and showing potential for clinical application.

Objective To investigate the effectiveness of optimized flow sensitive black blood(FSBB)imaging in detecting the number of branches and measuring the depth of lenticulostriate arteries(LSAs)in patients with acute ischemic stroke.Methods The ima-ging and clinical data of 39 patients with acute ischemic stroke who underwent vascular recanalization under digital subtraction angi-ography(DSA)were prospectively collected.All patients received 3.0T MR FSBB imaging within 48-96 hours postoperatively.The number and depth of LSAs branches on the affected side were observed,measured and recorded by the post-processing workstations for both FSBB and DSA images.The difference and correlation of the number and average depth of LSAs branches detected by FSBB and DSA were analyzed.The consistency of the average depth of LSAs branches detected by FSBB and DSA was evaluated.Results There was no statistically significant difference in the number and depth of bilateral LSAs branches detected by FSBB and DSA(P＞0.05).FSBB and DSA showed strong positive correlations in detecting the number and depth of LSAs branches(r=0.786,0.704;P＜0.05).The number of average depth difference of bilateral LSAs branches detected by FSBB and DSA exceeded the limit of agreement(LoA)was 3,accounting for 4.92％(＜5％).Conclusion Optimized FSBB imaging can be used to assess the detection of the number and depth measurement of LSAs branches in patients with acute ischemic stroke,showing good consistency with DSA.It provides valua-ble imaging evidence for the morphological assessment of LSAs in clinical practice.

Objective:To explore the clinical application value of convolutional neural network（CNN）based on deep learning in the automatic measurement of dynamic pelvic floor ultrasound video parameters and the diagnosis and classification of cystocele.Methods:A retrospective analysis was conducted on dynamic pelvic floor ultrasound videos from 398 postpartum women who underwent examinations at the First People's Hospital of Foshan between June 2020 and June 2022. The lowest point of the posterior bladder wall（PWB），urethral rotation angle（URA），and retrovesical angle（RVA）were manually measured by a senior radiologist（R1）and a junior radiologist（R2），and cystocele was classified according to the Green standard. The CNN model was employed to automatically extract the above parameters and to diagnose and classify cystocele. Using R1 measurements as a reference，intraclass correlation coefficient（ICC）was used to evaluate the consistency between the CNN model and R1，as well as between R2 and R1. The Kappa value was used to assess the agreement between the CNN model，R2，and R1 in the diagnosis and classification of cystocele. Additionally，the time consumption of the three measurement methods was compared.Results:The CNN model showed good consistency with R1 in measuring PWB and URA（ICC = 0.983，0.894），while its consistency in measuring RVA was moderate（ICC = 0.614）. The ICC between R2 and R1 in measuring PWB，URA，and RVA was 0.979，0.815，and 0.627，respectively. In the measurement of PWB and URA，the consistency between the CNN model and R1 was superior to that between R2 and R1. For cystocele diagnosis，the Kappa value between the CNN model and R1 was 0.924，which was higher than that between R2 and R1（0.904）. In cystocele classification，the Kappa value between the CNN model and R1 was 0.503，also higher than that between R2 and R1（0.426）. The CNN model processed a single video in 2.5（0.6）s，significantly faster than R1［59.9（16.9）s］and R2［56.8（11.2）s］（all P < 0.001）. Conclusions:The CNN model demonstrates high accuracy and efficiency in the measurement，diagnosis，and classification of cystocele，outperforming a junior radiologist and showing potential for clinical application.

Objective:To verify the predictive value of the Second Revision of the International Staging System (R2-ISS) in newly diagnosed patients with multiple myeloma (MM) who underwent first-line autologous hematopoietic stem cell transplantation (ASCT) in a new drug era in China.Methods:This multicenter retrospective cohort study enrolled patients with newly diagnosed MM from three centers in China (Beijing Chao-Yang Hospital, Capital Medical University; the First Affiliated Hospital, Sun Yat-Sen University, and the Second Affiliated Hospital of Naval Medical University) from June 2008 to June 2018. A total of 401 newly diagnosed patients with MM who were candidates for ASCT were enrolled in this cohort, all received proteasome inhibitor and/or immunomodulator-based induction chemotherapy followed by ASCT. Baseline and follow-up data were collected. The patients were regrouped using R2-ISS. Progression-free survival (PFS) and overall survival (OS) were analyzed. The Kaplan-Meier method was used to analyze the survival curve and two survival curves were compared using the log-rank test. Cox regression analysis were performed to analyze the relationship between risk factors and survival.Results:The median age of the patients was 53 years (range 25-69 years) and 59.5% (240 cases) were men. Newly diagnosed patients with renal impairment accounted for 11.5% (46 cases). According to Revised-International Staging System (R-ISS), 74 patients (18.5 %) were diagnosed with stage Ⅰ, 259 patients (64.6%) with stage Ⅱ, and 68 patients (17.0%) with stage Ⅲ. According to the R2-ISS, the distribution of patients in each group was as follows: 50 patients (12.5%) in stage Ⅰ, 95 patients (23.7%) in stage Ⅱ, 206 patients (51.4%) in stage Ⅲ, and 50 patients (12.5%) in stage Ⅳ. The median follow-up time was 35.9 months (range, 6-119 months). According to the R2-ISS stage, the median PFS in each group was: 75.3 months for stage Ⅰ; 62.0 months for stage Ⅱ, 39.2 months for stage Ⅲ, and 30.3 months for stage Ⅳ; and the median OS was not reached, 86.6 months, 71.6 months, and 38.5 months, respectively. There were statistically significant differences in PFS and OS between different groups (both P<0.001). Multivariate Cox regression analysis showed that stages Ⅲ and Ⅳ of the R2-ISS were independent prognostic factors for PFS ( HR=2.37, 95% CI 1.30-4.30; HR=4.50, 95% CI 2.35-9.01) and OS ( HR=4.20, 95% CI 1.50-11.80; HR=9.53, 95% CI 3.21-28.29). Conclusions:The R2-ISS has significant predictive value for PFS and OS for transplant-eligible patients with MM in the new drug era. However, the universality of the R2-ISS still needs to be further verified in different populations.

【Objective】 To determine the risk factors of urinary sepsis secondary to minimally invasive percutaneous nephrolithotomy (mPCNL) in patients with negative preoperative urine culture (UC). 【Methods】 A total of 274 patients with negative preoperative UC treated with mPCNL during Jan.2016 and Jun. 2021 were retrospectively analyzed. The incidence of urinary sepsis was observed, and the general data of patients with or without urinary sepsis after mPCNL were compared. logistic regression model was used to analyze the risk factors of urinary sepsis after mPCNL. 【Results】 Urinary sepsis occurred in 11 cases (4.01%). Univariate analysis showed that urinary sepsis was associated with gender, body mass index, stone load, diabetes, urine WBC ≥2+, urinary nitrite, procalcitonin, and operation time. Multivariate logistic regression analysis showed that the independent risk factors of urinary sepsis after mPCNL included diabetes (OR=2.34, 95%CI=1.051-5.43, P=0.037), stone load (OR=7.51, 95%CI=3.17-7.38, P=0.045), urine WBC≥2+ (OR=4.57, 95%CI=6.75-11.38, P=0.032), urinary nitrite positive (OR=6.45, 95%CI=0.93-26.87, P=0.028) and operation time≥120 min (OR=3.53, 95%CI=1.41-8.85, P=0.042). 【Conclusion】 Diabetes, stone load, urinary WBC ≥2+, positive urinary nitrite and operation time ≥120 minutes are the risk factors of urinary sepsis after mPCNL in patients with negative urine culture.

Chimeric antigen receptor T cell (CAR-T) is a kind of adoptive cell immunotherapy, in which T cells are genetically modified to exert targeted killing effect on tumors. CAR-T cell therapy has shown remarkable antitumor efficacy for the treatment of tumors, especially for hematological malignancies, but is less effective in solid tumors. Single-target CAR-T is prone to off-target effect during application, and there is a risk of relapse or more refractory treatment. The development of double-target or multi-target CAR-T is expected to extend the antigen coverage of target cells, effectively avoids antigen escape and prevents tumor recurrence, and prolongs the survival time of patients. This article reviews the advances of multi-target chimeric antigen receptor T cell, and discusses the prospect of its development.

Objective:To investigate the significance of clinical factors combined with transvaginal ultrasound and contrast-enhanced ultrasound(CEUS) in guiding the choice of treatment plan for cesarean scar pregnancy(CSP).Methods:The clinical and transvaginal ultrasound and CEUS data of 120 patients with CSP from January 2016 to June 2021 in the First People′s Hospital of Foshan were retrospectively analyzed, and they were divided into ultrasound-guided curettage/ hysteroscopic group (Group A, 91 cases) and laparoscopic group (Group B, 29 cases) according to treatment option, and the differences in clinical and ultrasound factors between the two groups were compared, and to determine the relevant clinical and ultrasound indicators for the choice of treatment option.Results:There were statistical differences between the 2 groups in comparison of whether the gestational sac/mass protruded toward the plasma membrane, gestational sac/mass diameter, the main blood supply site of the gestational sac/mass, the site of the chorion/early placenta and scar thickness (all P<0.05). Logistic regression analysis indicated that CEUS showing major blood supply site of the gestational sac/mass ( OR=6.029, P=0.003) and uterine scar thickness ( OR=12.998, P=0.002) were independent risk factors for minimally invasive surgery for CSP. Conclusions:Ultrasound combined with clinical factors have a certain value in the selection of treatment options for CPS, and the thickness of the uterine scar and the main blood supply site of the gestational sac/mass showed in CEUS may be key factors affecting the minimally invasive surgical treatment of CSP.

Backgrounds::Inadequate sleep duration is associated with a higher risk of type 2 diabetes and the relationship is nonlinear. We aim to assess the curve relationship between night sleep duration and the incidence of type 2 diabetes in China.Methods::A cohort of 11,539 participants from the REACTION study without diabetes at baseline (2011) were followed until 2014 for the development of type 2 diabetes. The average number of hours of sleep per night was grouped. Incidence rates and odds ratios (ORs) were calculated for the development of diabetes in each sleep duration category.Results::Compared to people who sleep for 7 to 8 h/night, people with longer sleep duration (≥9 h/night) had a greater risk of type 2 diabetes (OR: 1.27; 95% CI: 1.01-1.61), while shorter sleep (<6 h/night) had no significant difference in risk of type 2 diabetes. When the dataset was stratified based on selected covariates, the association between type 2 diabetes and long sleep duration became more evident among individuals <65 years of age, male, body mass index <24 kg/m 2 or with hypertension or hyperlipidemia, no interaction effects were observed. Furthermore, compared to people persistently sleeping 7 to 9 h/night, those who persistently slept ≥9 h/night had a higher risk of type 2 diabetes. The optimal sleep duration was 6.3 to 7.5 h/night. Conclusions::Short or long sleep duration was associated with a higher risk of type 2 diabetes. Persistently long sleep duration increased the risk.