Objective: Accurate evaluation of inflammation severity in ulcerative colitis (UC) can guide treatment strategy selection. The potential value of the pericolic fat attenuation index (FAI) on CT as an indicator of disease severity remains unknown.This study aimed to assess the diagnostic accuracy of pericolic FAI in predicting UC severity. Materials and Methods: This retrospective study enrolled 148 patients (mean age 48 years; 87 males). The fat attenuation on CT was measured in four different locations: the mesocolic vascular side (MS) and opposite side of MS (OMS) around the most severe bowel lesion, the retroperitoneal space (RS), and the subcutaneous area. The fat attenuation indices (FAI MS, FAI OMS, and FAI RS) were calculated as the fat attenuation measured in MS, OMS, and RS, respectively, minus that of the subcutaneous area, and were obtained in the non-enhanced, arterial, and delayed phases. Correlations between the FAI and UC Endoscopic Index of Severity (UCEIS) were assessed using Spearman’s correlation. Predictors of severe UC (UCEIS ≥7) were selected by univariable analysis. The performance of FAI in predicting severe UC was evaluated using the area under the receiver operating characteristic curve (AUC). Results: The FAIMS and FAI OMS scores were significantly higher than FAI RS in three phases (all P < 0.001). The FAIMS and FAI OMS scores moderately correlated with the UCEIS score (r = 0.474–0.649 among the three phases). Additionally, FAI MS and FAI OMS identified severe UC, with AUC varying from 0.77 to 0.85. Conclusion Increased CT attenuation of pericolic adipose tissue could serve as a noninvasive marker for evaluating UC severity. FAI MS and FAI OMS of three phases showed similar prediction accuracies for severe UC identification.

Objective: Accurate evaluation of inflammation severity in ulcerative colitis (UC) can guide treatment strategy selection. The potential value of the pericolic fat attenuation index (FAI) on CT as an indicator of disease severity remains unknown.This study aimed to assess the diagnostic accuracy of pericolic FAI in predicting UC severity. Materials and Methods: This retrospective study enrolled 148 patients (mean age 48 years; 87 males). The fat attenuation on CT was measured in four different locations: the mesocolic vascular side (MS) and opposite side of MS (OMS) around the most severe bowel lesion, the retroperitoneal space (RS), and the subcutaneous area. The fat attenuation indices (FAI MS, FAI OMS, and FAI RS) were calculated as the fat attenuation measured in MS, OMS, and RS, respectively, minus that of the subcutaneous area, and were obtained in the non-enhanced, arterial, and delayed phases. Correlations between the FAI and UC Endoscopic Index of Severity (UCEIS) were assessed using Spearman’s correlation. Predictors of severe UC (UCEIS ≥7) were selected by univariable analysis. The performance of FAI in predicting severe UC was evaluated using the area under the receiver operating characteristic curve (AUC). Results: The FAIMS and FAI OMS scores were significantly higher than FAI RS in three phases (all P < 0.001). The FAIMS and FAI OMS scores moderately correlated with the UCEIS score (r = 0.474–0.649 among the three phases). Additionally, FAI MS and FAI OMS identified severe UC, with AUC varying from 0.77 to 0.85. Conclusion Increased CT attenuation of pericolic adipose tissue could serve as a noninvasive marker for evaluating UC severity. FAI MS and FAI OMS of three phases showed similar prediction accuracies for severe UC identification.

Objective: Accurate evaluation of inflammation severity in ulcerative colitis (UC) can guide treatment strategy selection. The potential value of the pericolic fat attenuation index (FAI) on CT as an indicator of disease severity remains unknown.This study aimed to assess the diagnostic accuracy of pericolic FAI in predicting UC severity. Materials and Methods: This retrospective study enrolled 148 patients (mean age 48 years; 87 males). The fat attenuation on CT was measured in four different locations: the mesocolic vascular side (MS) and opposite side of MS (OMS) around the most severe bowel lesion, the retroperitoneal space (RS), and the subcutaneous area. The fat attenuation indices (FAI MS, FAI OMS, and FAI RS) were calculated as the fat attenuation measured in MS, OMS, and RS, respectively, minus that of the subcutaneous area, and were obtained in the non-enhanced, arterial, and delayed phases. Correlations between the FAI and UC Endoscopic Index of Severity (UCEIS) were assessed using Spearman’s correlation. Predictors of severe UC (UCEIS ≥7) were selected by univariable analysis. The performance of FAI in predicting severe UC was evaluated using the area under the receiver operating characteristic curve (AUC). Results: The FAIMS and FAI OMS scores were significantly higher than FAI RS in three phases (all P < 0.001). The FAIMS and FAI OMS scores moderately correlated with the UCEIS score (r = 0.474–0.649 among the three phases). Additionally, FAI MS and FAI OMS identified severe UC, with AUC varying from 0.77 to 0.85. Conclusion Increased CT attenuation of pericolic adipose tissue could serve as a noninvasive marker for evaluating UC severity. FAI MS and FAI OMS of three phases showed similar prediction accuracies for severe UC identification.

Objective: Accurate evaluation of inflammation severity in ulcerative colitis (UC) can guide treatment strategy selection. The potential value of the pericolic fat attenuation index (FAI) on CT as an indicator of disease severity remains unknown.This study aimed to assess the diagnostic accuracy of pericolic FAI in predicting UC severity. Materials and Methods: This retrospective study enrolled 148 patients (mean age 48 years; 87 males). The fat attenuation on CT was measured in four different locations: the mesocolic vascular side (MS) and opposite side of MS (OMS) around the most severe bowel lesion, the retroperitoneal space (RS), and the subcutaneous area. The fat attenuation indices (FAI MS, FAI OMS, and FAI RS) were calculated as the fat attenuation measured in MS, OMS, and RS, respectively, minus that of the subcutaneous area, and were obtained in the non-enhanced, arterial, and delayed phases. Correlations between the FAI and UC Endoscopic Index of Severity (UCEIS) were assessed using Spearman’s correlation. Predictors of severe UC (UCEIS ≥7) were selected by univariable analysis. The performance of FAI in predicting severe UC was evaluated using the area under the receiver operating characteristic curve (AUC). Results: The FAIMS and FAI OMS scores were significantly higher than FAI RS in three phases (all P < 0.001). The FAIMS and FAI OMS scores moderately correlated with the UCEIS score (r = 0.474–0.649 among the three phases). Additionally, FAI MS and FAI OMS identified severe UC, with AUC varying from 0.77 to 0.85. Conclusion Increased CT attenuation of pericolic adipose tissue could serve as a noninvasive marker for evaluating UC severity. FAI MS and FAI OMS of three phases showed similar prediction accuracies for severe UC identification.

Objective: Accurate evaluation of inflammation severity in ulcerative colitis (UC) can guide treatment strategy selection. The potential value of the pericolic fat attenuation index (FAI) on CT as an indicator of disease severity remains unknown.This study aimed to assess the diagnostic accuracy of pericolic FAI in predicting UC severity. Materials and Methods: This retrospective study enrolled 148 patients (mean age 48 years; 87 males). The fat attenuation on CT was measured in four different locations: the mesocolic vascular side (MS) and opposite side of MS (OMS) around the most severe bowel lesion, the retroperitoneal space (RS), and the subcutaneous area. The fat attenuation indices (FAI MS, FAI OMS, and FAI RS) were calculated as the fat attenuation measured in MS, OMS, and RS, respectively, minus that of the subcutaneous area, and were obtained in the non-enhanced, arterial, and delayed phases. Correlations between the FAI and UC Endoscopic Index of Severity (UCEIS) were assessed using Spearman’s correlation. Predictors of severe UC (UCEIS ≥7) were selected by univariable analysis. The performance of FAI in predicting severe UC was evaluated using the area under the receiver operating characteristic curve (AUC). Results: The FAIMS and FAI OMS scores were significantly higher than FAI RS in three phases (all P < 0.001). The FAIMS and FAI OMS scores moderately correlated with the UCEIS score (r = 0.474–0.649 among the three phases). Additionally, FAI MS and FAI OMS identified severe UC, with AUC varying from 0.77 to 0.85. Conclusion Increased CT attenuation of pericolic adipose tissue could serve as a noninvasive marker for evaluating UC severity. FAI MS and FAI OMS of three phases showed similar prediction accuracies for severe UC identification.

Objective: To investigate the correlations among clinicopathological features, histopathological growth patterns and prognosis of extrapulmonary multiple metastatic(stage ⅣB) pulmonary adenocarcinoma with epidermal growth factor receptor(EGFR) mutations. Methods : A total of 488 eligible patients with adenocarcinoma of stage ⅣB. Clinicopathological data,EGFRgene mutation subtypes, metastatic sites, histopathological growth patterns and survival information were collected. The chi-square test(χ2test) and Fisher's exact probability method were used to detect the correlation between the metastasis status and various clinical characteristics; the Kaplan-Meier method was used to conduct survival analysis on the median Progression-Free Survival(PFS) under different clinical characteristics. Cox univariate and multivariate regression analyses were conducted to evaluate the impact of various clinical characteristics on prognosis. Results : The metastatic patterns of stage ⅣB pulmonary adenocarcinoma withEGFRmutations was correlated with histopathological growth patterns(P<0.05). In the group with multiple metastases in a single organ, the proportion of micropapillary type in the group with multiple metastases in a single organ was higher than that in the group with multiple-organ metastases(51.1%vs41.1%), while the proportion of solid type in the group with multiple-organ metastases was higher than that in the group with multiple metastases in a single organ(23.8%vs14.2%). Multiple brain or multiple bone metastases were correlated with histopathological growth patterns and tumor differentiation degree. Compared with the multiple bone metastases group, the proportion of acinar type decreases in the multiple brain metastasis group, while the proportion of micropapillary type increased. Moreover, the proportion of poorly differentiated tumors increased significantly(P<0.05). Compared with multiple bone metastases, the proportion of poorly differentiated tumors significantly increases in the group with multiple brain metastases. The median progression-free survival(PFS) of patients with a predominant solid growth pattern was shorter than that of patients with other growth patterns(12.7 monthsvs17.8 months,P<0.05). The PFS of patients in the poorly differentiated group was worse than that in the moderately differentiated group(15.6 monthsvs17.8 months,P<0.05). There were significant differences in PFS among patients with common sensitive mutations and rare mutationsEGFR(17.3 monthsvs10.2 months,P<0.01). Cox proportional hazards regression model suggested that solid growth pattern, poor differentiation and rare single gene mutation were adverse prognostic factors. Conclusion In stage ⅣB pulmonary adenocarcinoma patients withEGFRmutations, both the metastatic patterns and metastatic sites are significantly correlated with the histopathological growth patterns of tumors. Moreover, theEGFRmutation subtypes as well as the histopathological growth patterns and differentiation degree of tumors significantly affect the prognosis of patients.

The REDISCOVER international guidelines on the perioperative care of surgical patients with borderline-resectable and locally advanced pancreatic cancer were released in July 2024,and based on the existing clinical challenges,the guidelines provide important recommendations for the diagnosis,staging,and surgical treatment of borderline-resectable and locally advanced pancreatic cancer from an evidence-based perspective.This article gives an interpretation of the guidelines,in order to better guide clinical practice.

Heart development protein with EGF-like domains 1 (HEG1) is a novel mucin-like membrane protein with a long O-glycosylation region and EGF domain. HEG1 plays critical roles in embryo development and cardiogenesis, and is closely related to the occurrence and progression of malignant tumors. Here this article demonstrates the research progress on HEG1 in cardiovascular formation and tumor development in recent years, to inspire new ideas for the pathogenesis, diagnosis and treatment of related diseases.

Objective: To provide high-quality clinical evidence of the efficacy of Tibetan medicine Honghua Ruyi (HHRY) pills for endometriosis-associated dysmenorrhea. Methods: This study constitutes a multicenter, randomized, double-blind, placebo-controlled trial encompassing a three-menstrual cycle intervention followed by a three-menstrual cycle follow-up period. A total of 164 eligible females with endometriosis-associated dysmenorrhea were randomly divided into HHRY pills and placebo groups in a 1:1 ratio. The primary outcome included dysmenorrhea symptoms assessed using Visual Analog Scale (VAS) scores and quality of life, whereas the secondary outcome measures included the maximum VAS for non-menstrual pelvic pain, duration of pain episodes (in days), frequency and quantity of the consumption of ibuprofen sustained-release capsules (or other non-steroidal anti-inflammatory drugs), and days off work/study for staff/student due to dysmenorrhea, ovarian cyst, and/or pelvic nodule size. The safety was monitored throughout the treatment period. All the analyses were based on the intention-to-treat principle. For continuous outcomes, simple or multiple linear regressions were used to estimate the differences between the HHRY pills and placebo groups, with categorical data expressed as the number and percentage of occurrences. Differences were compared using the chi-square test or Fisher's exact test. The predefined analysis was adjusted for concomitant treatment, a variable considered to be associated with outcomes but unaffected by treatment allocation. Estimates of treatment effects were reported with 95% confidence intervals. Two-tailed P values ≤ .05 were considered statistically significant. Conclusion Positive results from this trial, upon completion would provide robust evidence for the efficacy and safety of HHRY pills in treating dysmenorrhea in patients with endometriosis.

Background::In vitro fertilization (IVF) has emerged as a transformative solution for infertility. However, achieving favorable live-birth outcomes remains challenging. Current clinical IVF practices in IVF involve the collection of heterogeneous embryo data through diverse methods, including static images and temporal videos. However, traditional embryo selection methods, primarily reliant on visual inspection of morphology, exhibit variability and are contingent on the experience of practitioners. Therefore, an automated system that can evaluate heterogeneous embryo data to predict the final outcomes of live births is highly desirable. Methods::We employed artificial intelligence (AI) for embryo morphological grading, blastocyst embryo selection, aneuploidy prediction, and final live-birth outcome prediction. We developed and validated the AI models using multitask learning for embryo morphological assessment, including pronucleus type on day 1 and the number of blastomeres, asymmetry, and fragmentation of blastomeres on day 3, using 19,201 embryo photographs from 8271 patients. A neural network was trained on embryo and clinical metadata to identify good-quality embryos for implantation on day 3 or day 5, and predict live-birth outcomes. Additionally, a 3D convolutional neural network was trained on 418 time-lapse videos of preimplantation genetic testing (PGT)-based ploidy outcomes for the prediction of aneuploidy and consequent live-birth outcomes.Results::These two approaches enabled us to automatically assess the implantation potential. By combining embryo and maternal metrics in an ensemble AI model, we evaluated live-birth outcomes in a prospective cohort that achieved higher accuracy than experienced embryologists (46.1% vs. 30.7% on day 3, 55.0% vs. 40.7% on day 5). Our results demonstrate the potential for AI-based selection of embryos based on characteristics beyond the observational abilities of human clinicians (area under the curve: 0.769, 95% confidence interval: 0.709–0.820). These findings could potentially provide a noninvasive, high-throughput, and low-cost screening tool to facilitate embryo selection and achieve better outcomes. Conclusions::Our study underscores the AI model’s ability to provide interpretable evidence for clinicians in assisted reproduction, highlighting its potential as a noninvasive, efficient, and cost-effective tool for improved embryo selection and enhanced IVF outcomes. The convergence of cutting-edge technology and reproductive medicine has opened new avenues for addressing infertility challenges and optimizing IVF success rates.