Same-day initiation of oral pre-exposure prophylaxis among gay, bisexual, and other cisgender men who have sex with men and transgender women in Brazil, Mexico, and Peru (ImPrEP): a prospective, single-arm, open-label, multicentre implementation study

BACKGROUND

Although gay, bisexual, and other cisgender men who have sex with men (MSM) and transgender women have the highest HIV burden in Latin America, pre-exposure prophylaxis (PrEP) implementation is poor. We aimed to assess the feasibility of same-day oral PrEP delivery in Brazil, Mexico, and Peru.

METHODS

Implementation PrEP (ImPrEP) was a prospective, single-arm, open-label, multicentre PrEP implementation study conducted in Brazil (14 sites), Mexico (four sites), and Peru (ten sites). MSM and transgender women were eligible to participate if they were aged 18 years or older, HIV-negative, and reported one or more prespecified criteria. Enrolled participants received same-day initiation of daily oral PrEP (tenofovir disoproxil fumarate [300 mg] coformulated with emtricitabine [200 mg]). Follow-up visits were scheduled at week 4 and quarterly thereafter. We used logistic regression models to identify factors associated with early loss to follow-up (not returning after enrolment), PrEP adherence (medication possession ratio ≥0·6), and long-term PrEP engagement (attending three or more visits within 52 weeks). This study is registered at the Brazilian Registry of Clinical Trials, U1111-1217-6021.

FINDINGS

From Feb 6, 2018, to June 30, 2021, 9979 participants were screened and 9509 were enrolled (Brazil n=3928, Mexico n=3288, and Peru n=2293). 543 (5·7%) participants were transgender women, 8966 (94·3%) were cisgender men, and 2481 (26·1%) were aged 18-24 years. There were 12 185·25 person-years of follow-up. 795 (8·4%) of 9509 participants had early loss to follow-up, 6477 (68·1%) of 9509 were adherent to PrEP, and 5783 (70·3%) of 8225 had long-term PrEP engagement. Transgender women (adjusted odds ratio 1·60, 95% CI 1·20-2·14), participants aged 18-24 years (1·80, 1·49-2·18), and participants with primary education (2·18, 1·29-3·68) had increased odds of early loss to follow-up. Transgender women (0·56, 0·46-0·70), participants aged 18-24 years (0·52, 0·46-0·58), and those with primary education (0·60, 0·40-0·91) had lower odds of PrEP adherence. Transgender women (0·56, 0·45-0·71), participants aged 18-24 years (0·56, 0·49-0·64), and those with secondary education (0·74, 0·68-0·86) had lower odds of long-term PrEP engagement. HIV incidence was 0·85 per 100 person-years (95% CI 0·70-1·03) and was higher for transgender women, participants from Peru, those aged 18-24 years, Black and mixed-race participants, and participants who were non-adherent to PrEP.

INTERPRETATION

Same-day oral PrEP is feasible for MSM and transgender women in Latin America. Social and structural determinants of HIV vulnerability need to be addressed to fully achieve the benefits of PrEP.

FUNDING

Unitaid, WHO, and Ministries of Health in Brazil, Mexico, and Peru.

TRANSLATIONS

For the Portuguese and Spanish translations of the abstract see Supplementary Materials section.

Supervised Machine Learning Methods and Hyperspectral Imaging Techniques Jointly Applied for Brain Cancer Classification

Hyperspectral imaging techniques (HSI) do not require contact with patients and are non-ionizing as well as non-invasive. As a consequence, they have been extensively applied in the medical field. HSI is being combined with machine learning (ML) processes to obtain models to assist in diagnosis. In particular, the combination of these techniques has proven to be a reliable aid in the differentiation of healthy and tumor tissue during brain tumor surgery. ML algorithms such as support vector machine (SVM), random forest (RF) and convolutional neural networks (CNN) are used to make predictions and provide in-vivo visualizations that may assist neurosurgeons in being more precise, hence reducing damages to healthy tissue. In this work, thirteen in-vivo hyperspectral images from twelve different patients with high-grade gliomas (grade III and IV) have been selected to train SVM, RF and CNN classifiers. Five different classes have been defined during the experiments: healthy tissue, tumor, venous blood vessel, arterial blood vessel and dura mater. Overall accuracy (OACC) results vary from 60% to 95% depending on the training conditions. Finally, as far as the contribution of each band to the OACC is concerned, the results obtained in this work are 3.81 times greater than those reported in the literature.

Loperamide exerts a direct bactericidal effect against M. tuberculosis, M. bovis, M. terrae and M. smegmatis

Tuberculosis (TB) is caused by Mycobacterium tuberculosis. TB is highly prevalent, characterized by the constant occurrence of drug-resistant cases, and confounded by the incidence of respiratory disease caused by non-tuberculous mycobacteria (NTB). Expanding the spectrum of drugs for the treatment of TB is indispensable. Loperamide, an antidiarrhoeal drug, enhances immune-driven antimycobacterial activity, and we aimed to evaluate its bactericidal activity against M. tuberculosis, Mycobacterium bovis BCG, Mycobacterium terrae and Mycobacterium smegmatis. Loperamide exhibited an inhibitory effect against all mycobacterial species tested, with MICs of 100 and 150 μg ml^-1 . Thus, loperamide is a mycobactericidal drug with potential as adjunctive therapy for TB and NTB infections.

Accelerating the K-Nearest Neighbors Filtering Algorithm to Optimize the Real-Time Classification of Human Brain Tumor in Hyperspectral Images

The use of hyperspectral imaging (HSI) in the medical field is an emerging approach to assist physicians in diagnostic or surgical guidance tasks. However, HSI data processing involves very high computational requirements due to the huge amount of information captured by the sensors. One of the stages with higher computational load is the K-Nearest Neighbors (KNN) filtering algorithm. The main goal of this study is to optimize and parallelize the KNN algorithm by exploiting the GPU technology to obtain real-time processing during brain cancer surgical procedures. This parallel version of the KNN performs the neighbor filtering of a classification map (obtained from a supervised classifier), evaluating the different classes simultaneously. The undertaken optimizations and the computational capabilities of the GPU device throw a speedup up to 66.18× when compared to a sequential implementation.

Spatio-spectral classification of hyperspectral images for brain cancer detection during surgical operations

Surgery for brain cancer is a major problem in neurosurgery. The diffuse infiltration into the surrounding normal brain by these tumors makes their accurate identification by the naked eye difficult. Since surgery is the common treatment for brain cancer, an accurate radical resection of the tumor leads to improved survival rates for patients. However, the identification of the tumor boundaries during surgery is challenging. Hyperspectral imaging is a non-contact, non-ionizing and non-invasive technique suitable for medical diagnosis. This study presents the development of a novel classification method taking into account the spatial and spectral characteristics of the hyperspectral images to help neurosurgeons to accurately determine the tumor boundaries in surgical-time during the resection, avoiding excessive excision of normal tissue or unintentionally leaving residual tumor. The algorithm proposed in this study to approach an efficient solution consists of a hybrid framework that combines both supervised and unsupervised machine learning methods. Firstly, a supervised pixel-wise classification using a Support Vector Machine classifier is performed. The generated classification map is spatially homogenized using a one-band representation of the HS cube, employing the Fixed Reference t-Stochastic Neighbors Embedding dimensional reduction algorithm, and performing a K-Nearest Neighbors filtering. The information generated by the supervised stage is combined with a segmentation map obtained via unsupervised clustering employing a Hierarchical K-Means algorithm. The fusion is performed using a majority voting approach that associates each cluster with a certain class. To evaluate the proposed approach, five hyperspectral images of surface of the brain affected by glioblastoma tumor in vivo from five different patients have been used. The final classification maps obtained have been analyzed and validated by specialists. These preliminary results are promising, obtaining an accurate delineation of the tumor area.

Evaluation of a colour test for rapid detection of Salmonella

The performance of a colour test (Wellcolex) for rapid detection of the most frequently isolated O serogroups of salmonella (A, B, C, D, E/G) and the Vi antigen was evaluated using colonies grown on enteric differential agar media and in selenite broth. The test had excellent sensitivity (100%) and specificity (100%) with colonies taken directly from primary culture plates. Initial tests in selenite broth showed limited sensitivity (62.1%) but by using high quality media and modifying the inoculation procedure, sensitivity was greatly improved (99%).

[Combined chemotherapy (mebendazole plus praziquantel) in patients with hydatidosis]

In February 1989 we began a combined treatment with mebendazole (3 g/day for 30 days) and praziquantel (1.8 g/day for 15 days) with variable rest periods, in a patients with hydatid disease who had been previously unsuccessful treated only with mebendazole. All the patients were monitored with serological techniques and radiography. Generally the treatment was without side-effects. According to radiography results, there was a reduction in the size of the cyst in 5 patients, in 2 there was no change and 1 patient, the cyst continued to increase in size. Results suggest that the combination of mebendazole more praziquantel could be a pharmacological alternative in the medical treatment of hydatid disease.