Applicability of machine learning algorithm to predict the therapeutic intervention success in Brazilian smokers.

Smoking cessation is an important public health policy worldwide. However, as far as we know, there is a lack of screening of variables related to the success of therapeutic intervention (STI) in Brazilian smokers by machine learning (ML) algorithms. To address this gap in the literature, we evaluated the ability of eight ML algorithms to correctly predict the STI in Brazilian smokers who were treated at a smoking cessation program in Brazil between 2006 and 2017. The dataset was composed of 12 variables and the efficacies of the algorithms were measured by accuracy, sensitivity, specificity, positive predictive value (PPV) and area under the receiver operating characteristic curve. We plotted a decision tree flowchart and also measured the odds ratio (OR) between each independent variable and the outcome, and the importance of the variable for the best model based on PPV. The mean global values for the metrics described above were, respectively, 0.675±0.028, 0.803±0.078, 0.485±0.146, 0.705±0.035 and 0.680±0.033. Supporting vector machines performed the best algorithm with a PPV of 0.726±0.031. Smoking cessation drug use was the roof of decision tree with OR of 4.42 and importance of variable of 100.00. Increase in the number of relapses also promoted a positive outcome, while higher consumption of cigarettes resulted in the opposite. In summary, the best model predicted 72.6% of positive outcomes correctly. Smoking cessation drug use and higher number of relapses contributed to quit smoking, while higher consumption of cigarettes showed the opposite effect. There are important strategies to reduce the number of smokers and increase STI by increasing services and drug treatment for smokers.

Correction: Applicability of machine learning technique in the screening of patients with mild traumatic brain injury.

[This corrects the article DOI: 10.1371/journal.pone.0290721.].

Applicability of machine learning technique in the screening of patients with mild traumatic brain injury.

Even though the demand of head computed tomography (CT) in patients with mild traumatic brain injury (TBI) has progressively increased worldwide, only a small number of individuals have intracranial lesions that require neurosurgical intervention. As such, this study aims to evaluate the applicability of a machine learning (ML) technique in the screening of patients with mild TBI in the Regional University Hospital of Maringá, Paraná state, Brazil. This is an observational, descriptive, cross-sectional, and retrospective study using ML technique to develop a protocol that predicts which patients with an initial diagnosis of mild TBI should be recommended for a head CT. Among the tested models, he linear extreme gradient boosting was the best algorithm, with the highest sensitivity (0.70 ± 0.06). Our predictive model can assist in the screening of mild TBI patients, assisting health professionals to manage the resource utilization, and improve the quality and safety of patient care.

Systematic review of disulfiram as an antibacterial agent: what is the evidence?

The objective of this systematic review was to retrieve and examine published studies related to in vitro and in vivo evaluation of disulfiram for the treatment of bacterial infections. Five scientific databases (PubMed, Embase, Scopus, Web of Science, and Latin American and Caribbean Health Sciences Literature) were searched to retrieve the maximum literature regarding the study's aim. The search strategy retrieved a total of 870 studies, of which 31 were included and 19 approached disulfiram as the primary aim and 12 included it as a secondary finding from other investigational objectives. The evidence pointed out five main aspects of pre-clinical testing regarding disulfiram antibacterial activity, namely spectrum of antimicrobial action, drug combinations, intracellular studies, animal studies and bacterial targets. Findings to emerge from this study are the observed potential of disulfiram as a non-antibiotic drug being proposed as a potential drug to contribute to the treatment of bacterial diseases usually with few treatment alternatives in the context of drug resistance. We evaluated the potency and selectivity of disulfiram, which indeed until now shows potential to be explored for use as an adjunctive chemical to antimicrobial ones. Even with the level of evidence being reserved, the potential of combining disulfiram with other drugs, already used or new to be used for the treatment of mycobacterial diseases, as well as its likely immunomodulatory effect, deserve to be further investigated. Furthermore, the copper-dependent mode of action in Gram-positive bacteria is an alternative to be explored in drug design or repurposing of chemicals.

The role of metalloproteases in Leishmania species infection in the New World: a systematic review.

This is a systematic review on the role of metalloproteases in the pathogenicity of the American tegumentary leishmaniasis (ATL) caused by New World Leishmania species. The review followed the PRISMA method, searching for articles in PubMed, EMBASE, LILACS and ISI Web of Science, by employing the following terms: 'leishmaniasis', 'cutaneous leishmaniasis', 'mucocutaneous leishmaniasis', 'diffuse cutaneous leishmaniasis', 'Leishmania' and 'metalloproteases'. GP63 of New World Leishmania species is a parasite metalloproteases involved in the degradation and cleavage of many biological molecules as kappa-B nuclear factor, fibronectin, tyrosine phosphatases. GP63 is capable of inhibiting the activity of the complement system and reduces the host's immune functions, allowing the survival of the parasite and its dissemination. High serological/tissue levels of host matrix metalloproteases (MMP)-9 have been associated with tissue damage during the infection, while high transcriptional levels of MMP-2 related with a satisfactory response to treatment. Host MMPs serological and tissue levels have been investigated using Western Blot, zymography, and Real Time polymerase chain reaction. GP63 detection characterizes species and virulence in promastigotes isolated from lesions samples using techniques mentioned previously. The monitoring of host MMPs levels and GP63 in Leishmania isolated from host samples could be used on the laboratory routine to predict the prognostic and treatment efficacy of ATL.