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Cohort profile: PRESTIGIO, an Italian prospective registry-based cohort of people with HIV-1 resistant to reverse transcriptase, protease and integrase inhibitors

Por: Clemente · T. · Galli · L. · Lolatto · R. · Gagliardini · R. · Lagi · F. · Ferrara · M. · Cattelan · A. M. · Foca · E. · Di Biagio · A. · Cervo · A. · Calza · L. · Maggiolo · F. · Marchetti · G. · Cenderello · G. · Rusconi · S. · Zazzi · M. · Santoro · M. M. · Spagnuolo · V. · Castagna · A.
Purpose

The PRESTIGIO Registry was established in 2017 to collect clinical, virological and immunological monitoring data from people living with HIV (PLWH) with documented four-class drug resistance (4DR). Key research purposes include the evaluation of residual susceptibility to specific antiretrovirals and the validation of treatment and monitoring strategies in this population.

Participants

The PRESTIGIO Registry collects annual plasma and peripheral blood mononuclear cell samples and demographic, clinical, virological, treatment and laboratory data from PLWH followed at 39 Italian clinical centres and characterised by intermediate-to-high genotypic resistance to ≥1 nucleoside reverse transcriptase inhibitors, ≥1 non-nucleoside reverse transcriptase inhibitors, ≥1 protease inhibitors, plus either intermediate-to-high genotypic resistance to ≥1 integrase strand transfer inhibitors (INSTIs) or history of virological failure to an INSTI-containing regimen. To date, 229 people have been recorded in the cohort. Most of the data are collected from the date of the first evidence of 4DR (baseline), with some prebaseline information obtained retrospectively. Samples are collected from the date of enrollment in the registry.

Findings to date

The open-ended cohort has been used to assess (1) prognosis in terms of survival or development of AIDS-related or non-AIDS-related clinical events; (2) long-term efficacy and safety of different antiretroviral regimens and (3) virological and immunological factors predictive of clinical outcome and treatment efficacy, especially through analysis of plasma and cell samples.

Future plans

The registry can provide new knowledge on how to implement an integrated approach to study PLWH with documented resistance to the four main antiretroviral classes, a population with a limited number of individuals characterised by a high degree of frailty and complexity in therapeutic management. Given the scheduled annual updates of PLWH data, the researchers who collaborate in the registry can send study proposals at any time to the steering committee of the registry, which evaluates every 3 months whether the research studies can be conducted on data and biosamples from the registry and whether they are aimed at a better understanding of a specific health condition, the emergence of comorbidities or the effect of potential treatments or experimental drugs that may have an impact on disease progression and quality of life. Finally, the research studies should aim to be inclusive, innovative and in touch with the communities and society as a whole.

Trial registration number

NCT04098315.

Evaluating deep learning-based melanoma classification using immunohistochemistry and routine histology: A three center study

by Christoph Wies, Lucas Schneider, Sarah Haggenmüller, Tabea-Clara Bucher, Sarah Hobelsberger, Markus V. Heppt, Gerardo Ferrara, Eva I. Krieghoff-Henning, Titus J. Brinker

Pathologists routinely use immunohistochemical (IHC)-stained tissue slides against MelanA in addition to hematoxylin and eosin (H&E)-stained slides to improve their accuracy in diagnosing melanomas. The use of diagnostic Deep Learning (DL)-based support systems for automated examination of tissue morphology and cellular composition has been well studied in standard H&E-stained tissue slides. In contrast, there are few studies that analyze IHC slides using DL. Therefore, we investigated the separate and joint performance of ResNets trained on MelanA and corresponding H&E-stained slides. The MelanA classifier achieved an area under receiver operating characteristics curve (AUROC) of 0.82 and 0.74 on out of distribution (OOD)-datasets, similar to the H&E-based benchmark classification of 0.81 and 0.75, respectively. A combined classifier using MelanA and H&E achieved AUROCs of 0.85 and 0.81 on the OOD datasets. DL MelanA-based assistance systems show the same performance as the benchmark H&E classification and may be improved by multi stain classification to assist pathologists in their clinical routine.
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