HCV elimination in a Swiss opioid agonist therapy programme - a cohort study.

BACKGROUND: In opioid agonist therapy (OAT) programmes, chronic hepatitis C is highly prevalent and directly observed therapy guarantees optimal adherence. Since 2017, all patients with chronic hepatitis C in Switzerland can be treated with pangenotypic direct-acting antivirals irrespective of liver fibrosis stage. Until the end of 2021, however, prescription was restricted to infectious disease specialists, gastroenterologists and certain addiction specialists. Difficult venous access after long-term intravenous drug use and, in the case of referral to a specialist, difficulties keeping appointments are barriers to HCV diagnosis and treatment. AIMS: To assess whether minimally invasive point-of-care tests and a "test-and-treat / vaccinate on-site" approach can improve human immunodeficiency virus (HIV) / hepatitis C virus (HCV) screening, HCV treatment uptake and immunity against hepatitis A/B. METHODS: Since September 2018, an infectious disease specialist and a study nurse performed 4-weekly visits in the OAT programme "HAG" (heroin dispensation of the canton Aargau), offering HIV/HCV antibody rapid testing (20 min) and HCV RNA quantification (GeneXpert®, 60 min) from capillary blood, noninvasive liver fibrosis assessment (Fibroscan®, 5-10 min) and HCV treatment prescription on-site. Recommended venous blood draws for HAV/HBV serology and HAV/HBV vaccinations were performed by the staff of the "HAG". Project performance was assessed by annual cross-sectional chart review. RESULTS: Of the 128 patients registered in April 2018, 79 (62%) were still present in May 2021. With 72 newly registered, a total of 200 patients could be assessed, of whom 129 (65%) were still present in May 2021. Between April 2018 and May 2021, the proportion ever tested for HIV antibodies increased from 79% (101/128) to 91% (117/129), the proportion ever tested for HCV antibodies from 83% (106/128) to 93% (120/129) and the proportion of those HCV antibody positive ever tested for HCV RNA tested from 89% (47/53) to 98% (56/57). The proportion with adequate HCV management (last HCV antibody test ≤1 year ago, if HCV antibody negative or last HCV RNA test ≤1 year ago, if HCV antibody-positive and RNA-negative) improved from 23% ([15 + 15]/128) to 80% ([55 + 48]/129). Overall, HCV treatment uptake increased from 60% (21/35) to 92% (55/60) and HCV RNA prevalence among the HCV antibody positives decreased from 38% (18/47) to 7% (6/84). Between 2018 and 2021, 19 non-cirrhotic chronic hepatitis C patients were successfully treated on-site (18 sustained virological responses [SVR] 12, 1 SVR4), with excellent adherence (≥93%) and, so far, no reinfection. The proportion with known HAV/HBV serostatus increased from 38%/51% to 64%/76%. Immunity against HAV/HBV improved from 19%/23% to 50%/57%. CONCLUSION: Capillary blood point-of-care tests and a "test-and-treat / vaccinate on-site" approach remove crucial barriers to diagnosis and treatment, making hepatitis elimination in OAT programmes achievable. A high fluctuation rate requires HIV/HCV/HAV/HBV testing at admission, but also allows more patients to be screened.

Automated scoring of pre-REM sleep in mice with deep learning.

Reliable automation of the labor-intensive manual task of scoring animal sleep can facilitate the analysis of long-term sleep studies. In recent years, deep-learning-based systems, which learn optimal features from the data, increased scoring accuracies for the classical sleep stages of Wake, REM, and Non-REM. Meanwhile, it has been recognized that the statistics of transitional stages such as pre-REM, found between Non-REM and REM, may hold additional insight into the physiology of sleep and are now under vivid investigation. We propose a classification system based on a simple neural network architecture that scores the classical stages as well as pre-REM sleep in mice. When restricted to the classical stages, the optimized network showed state-of-the-art classification performance with an out-of-sample F1 score of 0.95 in male C57BL/6J mice. When unrestricted, the network showed lower F1 scores on pre-REM (0.5) compared to the classical stages. The result is comparable to previous attempts to score transitional stages in other species such as transition sleep in rats or N1 sleep in humans. Nevertheless, we observed that the sequence of predictions including pre-REM typically transitioned from Non-REM to REM reflecting sleep dynamics observed by human scorers. Our findings provide further evidence for the difficulty of scoring transitional sleep stages, likely because such stages of sleep are under-represented in typical data sets or show large inter-scorer variability. We further provide our source code and an online platform to run predictions with our trained network.