Deep learning with biopsy whole slide images for pretreatment prediction of pathological complete response to neoadjuvant chemotherapy in breast cancer：A multicenter study

IntroductionPredicting pathological complete response (pCR) for patients receiving neoadjuvant chemotherapy (NAC) is crucial in establishing individualized treatment. Whole-slide images (WSIs) of tumor tissues reflect the histopathologic information of the tumor, which is important for therapeutic response effectiveness. In this study, we aimed to investigate whether predictive information for pCR could be detected from WSIs.Materials and methodsWe retrospectively collected data from four cohorts of 874 patients diagnosed with biopsy-proven breast cancer. A deep learning pathological model (DLPM) was constructed to predict pCR using biopsy WSIs in the primary cohort, and it was then validated in three external cohorts. The DLPM could generate a deep learning pathological score (DLPs) for each patient; stromal tumor-infiltrating lymphocytes (TILs) were selected for comparison with DLPs.ResultsThe WSI feature-based DLPM showed good predictive performance with the highest area under the curve (AUC) of 0.72 among the cohorts. Alternatively, the combination of the DLPM and clinical characteristics offered a better prediction performance (AUC >0.70) in all cohorts. We also evaluated the performance of DLPM in three different breast subtypes with the best prediction for the triple-negative breast cancer (TNBC) subtype (AUC: 0.73). Moreover, DLPM combined with clinical characteristics and stromal TILs achieved the highest AUC in the primary cohort (AUC: 0.82) and validation cohort 1 (AUC: 0.80).ConclusionOur study suggested that WSIs integrated with deep learning could potentially predict pCR to NAC in breast cancer. The predictive performance will be improved by combining clinical characteristics. DLPs from DLPM can provide more information compared to stromal TILs for pCR prediction.     

Longistylin A,a natural stilbene isolated from the leaves of Cajanus cajan,exhibits significant anti-MRSA activity

Longistylin A (LLA) is an abundant stilbene isolated from the leaves of Cajanus cajan (L.) Millsp. However, the antibacterial effect of LLA is not yet understood. Therefore, in this study, a detailed investigation of the antibacterial effect of LLA, particularly against methicillin-resistant Staphylococcus aureus (MRSA), was conducted. In vitro, LLA exhibited strong antibacterial activity against MRSA with a minimum inhibitory concentration (MIC) of 1.56 µg/mL and displayed much more rapid bactericidal activity (3-log decrease in MRSA survival within 8 h) than vancomycin. A membrane-targeting experiment suggested that the antibacterial activity of LLA is associated with perturbation of the bacterial membrane potential and increased membrane permeability. Notably, LLA had relatively weak cytotoxicity to murine macrophages [50% cytotoxic concentration (CC₅₀) = 8.61 ± 0.57 µg/mL]. In vivo, topical treatment of a skin injury with LLA improved wound healing and closure in an MRSA-infected wound healing mouse model. After 3 days treatment, LLA decreased MRSA bacterial counts in the wounded region, reduced the accumulation of immune cells at the injury site, and alleviated induction of the inflammatory cytokines tumour necrosis factor-alpha (180.74 ± 10.78 pg/mL vs. 606.57 ± 68.99 pg/mL) and interleukin-6 (87.25 ± 10.19 pg/mL vs. 280.58 ± 42.27 pg/mL) in serum.     

中医药文献语义标注系统研究与开发

目的研究和开发支持中医和现代生物医学本体和术语集的语义标注系统。方法以MedPortal本体库和中医临床术语集等为术语资源库,设计语义标注系统工作流程和功能框架,并开发Web应用系统。结果构建了一个基于Web的中医药文献语义标注系统,支持语料库管理与维护、术语词典管理、语义标注和语义检索等功能,既可以为基于机器学习的信息抽取算法研究提供训练集,又能实现语义层面的多来源数据集成与知识融合。结论该中医药文献语义标注系统设计方案已经过实际项目验证,可为其他同类系统研发提供参考。     

精索静脉曲张手术后患者的管理策略

手术治疗精索静脉曲张在改善精液质量、提高配偶自然怀孕率和缓解局部疼痛症状中的作用已为多数医生所认同。随着手术方式的改进,越来越多的精索静脉曲张患者接受手术治疗,然而,临床对手术后患者的管理还重视不够。本文系统介绍了对精索静脉曲张手术后患者的疗效判断和后续治疗选择,包括观察随诊、非手术治疗方法和再手术。