Improved recovery of urinary small extracellular vesicles by differential ultracentrifugation.

Extracellular vesicles (EVs) are lipid-membrane enclosed structures that are associated with several diseases, including those of genitourinary tract. Urine contains EVs derived from urinary tract cells. Owing to its non-invasive collection, urine represents a promising source of biomarkers for genitourinary disorders, including cancer. The most used method for urinary EVs separation is differential ultracentrifugation (UC), but current protocols lead to a significant loss of EVs hampering its efficiency. Moreover, UC protocols are labor-intensive, further limiting clinical application. Herein, we sought to optimize an UC protocol, reducing the time spent and improving small EVs (SEVs) yield. By testing different ultracentrifugation times at 200,000g to pellet SEVs, we found that 48 min and 60 min enabled increased SEVs recovery compared to 25 min. A step for pelleting large EVs (LEVs) was also evaluated and compared with filtering of the urine supernatant. We found that urine supernatant filtering resulted in a 1.7-fold increase on SEVs recovery, whereas washing steps resulted in a 0.5 fold-decrease on SEVs yield. Globally, the optimized UC protocol was shown to be more time efficient, recovering higher numbers of SEVs than Exoquick-TC (EXO). Furthermore, the optimized UC protocol preserved RNA quality and quantity, while reducing SEVs separation time.

Cervical cytology and HPV distribution in Cape Verde: A snapshot of a country taken during its first HPV nation-wide vaccination campaign.

Cervical cancer ranks as the third most common female cancer in Cape Verde and is the leading cause of cancer-related deaths among women in the country. While Human Papillomavirus (HPV) vaccination, which started in 2021, is anticipated to significantly reduce disease incidence, cervical screening remains crucial for non-vaccinated women. We retrospectively reviewed gynecologic cytology exams and HPV tests performed in Cape Verde between 2017 and April 2023 and processed at IMP Diagnostics. For this study, we considered 13035 women with cytology examinations performed and, 2013 of these, also with an HPV molecular test. Cytology diagnostics comprised 83 % NILM cases; 12 % ASC-US; 2.7 % LSIL; 1.2 % ASC-H; 0.5 % HSIL and 0.1 % SCC. In 505 (25.1 %) high-risk HPV infection was detected. Prevalence of HPV infection varied with age, peaking at young ages - ≤24 years old (55.5 %) and 25-35-year-old women (31.5 %) - and the lowest after 66 years old (9.7 %). Herein we present a comprehensive study regarding Cape Verde's cervical cytology and HPV distribution, aiming to provide a snapshot of the country's cervical cytology results and HPV distribution in recent years. Moreover, these data may contribute to establish a baseline to assess, in the future, the vaccination impact in the country.

An interpretable machine learning system for colorectal cancer diagnosis from pathology slides.

Considering the profound transformation affecting pathology practice, we aimed to develop a scalable artificial intelligence (AI) system to diagnose colorectal cancer from whole-slide images (WSI). For this, we propose a deep learning (DL) system that learns from weak labels, a sampling strategy that reduces the number of training samples by a factor of six without compromising performance, an approach to leverage a small subset of fully annotated samples, and a prototype with explainable predictions, active learning features and parallelisation. Noting some problems in the literature, this study is conducted with one of the largest WSI colorectal samples dataset with approximately 10,500 WSIs. Of these samples, 900 are testing samples. Furthermore, the robustness of the proposed method is assessed with two additional external datasets (TCGA and PAIP) and a dataset of samples collected directly from the proposed prototype. Our proposed method predicts, for the patch-based tiles, a class based on the severity of the dysplasia and uses that information to classify the whole slide. It is trained with an interpretable mixed-supervision scheme to leverage the domain knowledge introduced by pathologists through spatial annotations. The mixed-supervision scheme allowed for an intelligent sampling strategy effectively evaluated in several different scenarios without compromising the performance. On the internal dataset, the method shows an accuracy of 93.44% and a sensitivity between positive (low-grade and high-grade dysplasia) and non-neoplastic samples of 0.996. On the external test samples varied with TCGA being the most challenging dataset with an overall accuracy of 84.91% and a sensitivity of 0.996.