Information autour du dépistage organisé du cancer du sein. L’INCa et Cancer Rose répondent-ils aux critères des outils d’aide à la décision ?

BackgroundControversies around organized breast cancer screening emphasize the need for information for women. In France, the institute of cancer (INCa) is in charge of conveying this information. Cancer Rose's website (CR) provides complementary information considering INCa's incomplete. The objective of this study was to identify if these informations meet criteria for decision aid.MethodsInformation documents were selected and analyzed using International Patient Decision Aid Standards (IPDAS). Each item was noted A (absent), P (present) or I (present but incomplete).ResultsInformation booklet and press kit for INCa and studies section for CR were the document meeting most criteria. The document meeting fewer criteria were the INCa's video and information leaflet for CR. Videos are more accessible tools for people with lower levels of health literacy. INCa's video did not present the risks of screening and CR's emphasized the risks.ConclusionThese documents have not been evaluated for intelligibility, clarity and readability. Addressing these criteria limits social inequities and improves health literacy.     

The role of National Immunization Technical Advisory Groups (NITAG) in strengthening health system governance: Lessons from three middle-income countries—Argentina,Jordan, and South Africa (2017–2018)

IntroductionToward the Global Vaccine Action Plan 2020 goal, almost 90% of countries have established a National Immunization Technical Advisory Group (NITAG). However, little is known about NITAG's contributions to governance.MethodsIn 2017–2018, a two-step, qualitative retrospective study was conducted. Jordan (JO), Argentina (AR), and South Africa (SA) were selected owing to government-financed NITAGs from middle-income countries (MICs), geographic diversity, and a vaccine introduction with NITAG support. Country case studies were developed, collecting data through desk review and face-to-face key informant interviews (KIIs) from Ministry of Health (MoH) and NITAG. Case studies were analyzed together, to assess governance applying the European Observatory on Health Systems and Policies framework focusing on transparency, accountability, participation, integrity, and policy capacity (TAPIC).ResultsDocument review and 53 KII (22 AR, 20 SA, 11 JO) showed NITAGs played a pivotal role as advisors promoting a culture of evidence-informed policies. NITAGs strengthened governance, although practices varied among countries. Meetings were conducted behind-closed-doors, participation restricted to members, only in one country agendas, and recommendations were public (AR). To increase participation, policy capacity, and transparency, countries considered adding experts in communications, advocacy, and economics. AR and SA contemplated including community members. NITAGs functioned autonomously from the government, with no established internal or external monitoring or supervision. NITAG meeting minutes allowed the review of integrity, adherence to terms of reference, standard operating procedures, and conflict of interest (CoI). For the most part, NITAGs abided by their mandates. Significant issues were related to the level of MoH support and oversight of CoI declaration and documentation.ConclusionsSystematically implementing governance approaches could improve processes, better tailor policies, and implementation. The long-term survival and resilience of NITAGs in these countries showed they play a significant role in strengthening governance. Lessons learned could be useful to those promoting country-driven evidence-informed decision-making.     

动物瘢痕模型的相关研究进展

皮肤损伤后的异常愈合会导致病理性瘢痕的产生。病理性瘢痕的出现不仅影响美观,严重时还会造成心理和生理功能障碍。病理性瘢痕的机制研究对于瘢痕治疗有极为重要的意义。其中,动物瘢痕模型是目前研究病理性瘢痕的重要模型手段之一。理想的动物瘢痕模型应该在组织学、细胞学等层面尽可能接近于人类的病理性瘢痕。该文分别从传统技术动物瘢痕中的啮齿类动物模型、兔耳模型和猪模型,以及新技术动物瘢痕模型这两个方面,结合近年来在瘢痕领域应用较多的研究,进行了相应系统的阐述。     

生物信息学在肝细胞癌风险预测中的应用

生物信息学是一门交叉科学,通过综合运用数学、计算机科学和生物学的各种工具,来阐明和理解大量生物数据所包含的生物学意义。随着基因组学测序技术的不断发展,产生大量的生物学数据资源,从大数据中挖掘所蕴藏的生物学意义,已成为了当前亟待解决的主要任务之一。本文主要归纳总结基于特征基因的肝癌风险预测模型,为肝癌的早期检测、预后和治疗方案的优化提供新观点。     

¿Es el intervalo libre de docetaxel un factor predictivo para el cáncer de próstata resistente a la castración?

ObjectiveProstate cancer (PCa) is the second most common solid tumor in men and the fifth leading cause of cancer-related death. In advanced stage, palliative treatments are used instead of curative therapies. Therefore, finding predictive indicators seems crucial. Patients with castration-resistant prostate cancer (CRPC) that received Dx chemotherapy have been retrospectively reviewed. The aim of this study was to investigate whether docetaxel (Dx)-free interval could have a predictive value for PCa and influence other sequential therapies.Material and methodsThis clinical trial study was performed on 104 patients at Medeniyet University Oncology Clinic in 2018-2020. All CRPC patients had metastases, received Dx as first-line treatment and underwent androgen receptor axis targeted (ARAT) therapy after disease progression. We analyzed patients’ progression time after Dx therapy and the effects on sequential treatment.ResultsAfter Dx therapy, all patients received ARAT (abiraterone (ABI) n: 49 (47.1%) and enzalutamide (ENZ) n: 54 (51.9%)) as a second-line treatment, except for one patient who received cabazitaxel. There was a statistically significant relationship between the Dx-free interval and duration of response to ARAT (P<.001). The response time of ARAT treatment was <10.5 months in all patients whose Dx-free interval period was <9 months.ConclusionsOur findings support the theory that Dx-free interval can be a predictive factor for CRPC. CRPC disease can be classified as Dx-sensitive disease or Dx-resistance disease, based on the Dx-free interval. Decision on subsequent treatments could be made considering this information.     

高龄产妇产后抑郁影响因素的质性研究

目的 探究高龄产妇产后抑郁的影响因素，为护理人员今后在临床工作中提供个性化的护理服务提供参考依据。方法 采用质性研究中的现象学研究法对2021年12月至2022年4月在内蒙古医科大学附属医院妇产科建档分娩的16例高龄产妇进行半结构式访谈，并以Colaizzi现象学研究方法进行资料分析。结果 提炼出影响高龄产妇产后抑郁的4个主题：躯体性压力源；负性心理体验；社会支持缺乏，矛盾出现；角色转变对高龄产妇的影响。结论 需重视高龄产妇产后的主观感受，加强产前产后健康宣教，构建以家庭为中心的高龄产妇产后护理模型，减少产后抑郁的发生。     

肿瘤性复杂性胸壁缺损的修复策略及对肿瘤治疗的积极影响

复杂性胸壁缺损的修复一直是一项极具挑战性的工作。肿瘤性复杂性胸壁缺损的修复决策及其执行困难是限制胸壁肿瘤治疗方法选择及影响预后的重要因素之一。皮瓣解剖学研究的深入、胸壁支持结构重建技术的进步、显微外科技术的发展、麻醉护理的发展、对综合治疗的重视和治疗手段的进步等,使传统认为不可切除的胸壁肿瘤得以彻底地切除和安全有效地修复,从而使与缺损修复相关的肿瘤切除及辅助治疗的禁忌证缩减到最小程度,有效地提高了胸壁肿瘤患者的生存质量,并很大程度上延长了生存率。作者以湖南省肿瘤医院整形外科15年565例胸壁肿瘤切除后修复重建的临床资料为依据,充实了胸壁肿瘤切除及修复的策略:(1)可靠的胸壁骨性支架重建;(2)有效的软组织修复;(3)麻醉及护理与手术团队的合作;(4)系统有序的综合治疗。并进一步明确了复杂胸壁肿瘤切除及重建的细节理念,包括胸部肿瘤治疗中加强多学科合作的密切性和科学性,整形外科医生参与肿瘤治疗整体规划的主动性和时机前移等。     

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.