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Protein structure determination has made progress with the aid of deep learning models, enabling the prediction of protein folding from protein sequences. However, obtaining accurate predictions becomes essential in certain cases where the protein structure remains undescribed. This is particularly challenging when dealing with rare, diverse structures and complex sample preparation. Different metrics assess prediction reliability and offer insights into result strength, providing a comprehensive understanding of protein structure by combining different models. In a previous study, two proteins named ARM58 and ARM56 were investigated. These proteins contain four domains of unknown function and are present in Leishmania spp. ARM refers to an antimony resistance marker. The study’s main objective is to assess the accuracy of the model’s predictions, thereby providing insights into the complexities and supporting metrics underlying these findings. The analysis also extends to the comparison of predictions obtained from other species and organisms. Notably, one of these proteins shares an ortholog with Trypanosoma cruzi and Trypanosoma brucei, leading further significance to our analysis. This attempt underscored the importance of evaluating the diverse outputs from deep learning models, facilitating comparisons across different organisms and proteins. This becomes particularly pertinent in cases where no previous structural information is available.

Background: Retention in treatment is crucial for the success of interventions targeting alcohol use disorder (AUD), which affects over 100 million people globally. Most previous studies have used classical statistical techniques to predict treatment dropout, and their results remain inconclusive. This study aimed to use novel machine learning tools to identify models that predict dropout with greater precision, enabling the development of better retention strategies for those at higher risk. Methods: A retrospective observational study of 39,030 (17.3% female) participants enrolled in outpatient-based treatment for alcohol use disorder in a state-wide public treatment network has been used. Participants were recruited between 1 January 2015 and 31 December 2019. We applied different machine learning algorithms to create models that allow one to predict the premature cessation of treatment (dropout). With the objective of increasing the explainability of those models with the best precision, considered as black-box models, explainability technique analyses were also applied. Results: Considering as the best models those obtained with one of the so-called black-box models (support vector classifier (SVC)), the results from the best model, from the explainability perspective, showed that the variables that showed greater explanatory capacity for treatment dropout are previous drug use as well as psychiatric comorbidity. Among these variables, those of having undergone previous opioid substitution treatment and receiving coordinated psychiatric care in mental health services showed the greatest capacity for predicting dropout. Conclusions: By using novel machine learning techniques on a large representative sample of patients enrolled in alcohol use disorder treatment, we have identified several machine learning models that help in predicting a higher risk of treatment dropout. Previous treatment for other substance use disorders (SUDs) and concurrent psychiatric comorbidity were the best predictors of dropout, and patients showing these characteristics may need more intensive or complementary interventions to benefit from treatment. [ABSTRACT FROM AUTHOR]

Protozoa of the Leishmania genus cause a variety of disease forms that rank at the top of the list of neglected tropical diseases. Anti-leishmanial drugs based on pentavalent antimony have been the mainstay of therapy for over 60 years and resistance against them is increasingly encountered in the field. The biochemical basis for this is poorly understood and likely diverse. No stringent correlation between genetic markers and antimony resistance has so far been shown, prompting us to use a functional cloning approach to identify markers of resistance. Using gene libraries derived from drug-resistant and drug-sensitive Leishmania braziliensis clinical isolates in a functional cloning strategy, we repeatedly selected one gene locus located on chromosome 20 whose amplification confers increased antimony (III) resistance in vitro to an otherwise sensitive L. braziliensis clone. The gene responsible for the effect encodes a previously hypothetical protein that we dubbed LbrARM58. It comprises four repeats of a domain of unknown function, DUF1935, one of them harbouring a potential trans-membrane domain. The gene is so far unique to the Leishmania genus, while a structurally related gene without antimony resistance functionality is also found in Trypanosoma spp. Overexpression of LbrARM58 also confers antimony resistance to promastigotes and intracellular amastigotes of the related species Leishmania infantum, indicating a conserved function in Old World and New World Leishmania species. Our results also show that in spite of their RNAi system, L. braziliensis promastigotes can serve as acceptor cells for episomally propagated cosmid libraries, at least for the initial stages of functional cloning efforts.