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.

AI-based approach for transcribing and classifying unstructured emergency call data: A methodological proposal.

Emergency care-sensitive conditions (ECSCs) require rapid identification and treatment and are responsible for over half of all deaths worldwide. Prehospital emergency care (PEC) can provide rapid treatment and access to definitive care for many ECSCs and can reduce mortality in several different settings. The objective of this study is to propose a method for using artificial intelligence (AI) and machine learning (ML) to transcribe audio, extract, and classify unstructured emergency call data in the Serviço de Atendimento Móvel de Urgência (SAMU) system in southern Brazil. The study used all "1-9-2" calls received in 2019 by the SAMU Novo Norte Emergency Regulation Center (ERC) call center in Maringá, in the Brazilian state of Paraná. The calls were processed through a pipeline using machine learning algorithms, including Automatic Speech Recognition (ASR) models for transcription of audio calls in Portuguese, and a Natural Language Understanding (NLU) classification model. The pipeline was trained and validated using a dataset of labeled calls, which were manually classified by medical students using LabelStudio. The results showed that the AI model was able to accurately transcribe the audio with a Word Error Rate of 42.12% using Wav2Vec 2.0 for ASR transcription of audio calls in Portuguese. Additionally, the NLU classification model had an accuracy of 73.9% in classifying the calls into different categories in a validation subset. The study found that using AI to categorize emergency calls in low- and middle-income countries is largely unexplored, and the applicability of conventional open-source ML models trained on English language datasets is unclear for non-English speaking countries. The study concludes that AI can be used to transcribe audio and extract and classify unstructured emergency call data in an emergency system in southern Brazil as an initial step towards developing a decision-making support tool.

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.

The Impact of Time, Region, and Income Level on Stillbirth and Neonatal Mortality in Brazil, 2000-2019.

OBJECTIVE: To describe trends in perinatal loss across Brazil, a country that transitioned in 2006 from a lower-middle income to an upper-middle income country, from 2000 to 2019 and analyze the effect of municipal wealth status on perinatal outcomes. STUDY DESIGN: We conducted an ecological cohort study, based on publicly available data from the Brazilian Ministry of Health's data repository on live births and deaths. The Atlas of Human Development in Brazil was used to associate each region with a World Bank income classification. RESULTS: The national neonatal mortality rate (NMR) for infants born at ≥22 weeks of gestation decreased from 21.2 in 2000 to 12.4 in 2019. The stillbirth rate (SBR) decreased from 12.0 to 10.2 during this period. For infants born between 22 and 27 weeks of gestation, worsening perinatal outcomes were seen after 2012. In 2019, the median rates of neonatal mortality and stillbirth were both 4 points higher in lower- to middle-income municipalities compared with high-income municipalities (P < .01). CONCLUSION: Brazil has made significant progress in neonatal mortality and stillbirth from 2000 to 2019, yet inequity in perinatal outcomes remains and is correlated with municipal economic status. Nationally, ongoing improvement is needed for infants <28 weeks of gestation, and closer exploration is needed into why there are increasing rates of negative perinatal outcomes among infants born at 22-27 weeks of gestation after 2012.

Identification of new putative inhibitors of Mycobacterium tuberculosis 3-dehydroshikimate dehydratase from a combination of ligand- and structure-based and deep learning in silico approaches.

The development of new drugs against Mycobacterium tuberculosis is an essential strategy for fighting drug resistance. Although 3-dehydroquinate dehydratase (MtDHQ) is known to be a highly relevant target for M. tuberculosis, current research shows new putative inhibitors of MtDHQ selected by a large-scale ensemble-docking strategy combining ligand- and target-based chemoinformatic methods to deep learning. Initial chemical library was reduced from 216 million to approximately 460 thousand after pharmacophore, toxicity and molecular weight filters. Final library was subjected to an ensemble-docking protocol in GOLD which selected the top 300 molecules (GHITS). GHITS displayed different structures and characteristics when compared to known inhibitors (KINH). GHITS were further screened by post-docking analysis in AMMOS2 and deep learning virtual screening in DeepPurpose. DeepPurpose predicted that a number of GHITS had comparable or better affinity for the target than KINH. The best molecule was selected by consensus ranking using GOLD, AMMOS2 and DeepPurpose scores. Molecular dynamics revealed that the top hit displayed consistent and stable binding to MtDHQ, making strong interactions with active-site loop residues. Results forward new putative inhibitors of MtDHQ and reinforce the potential application of artificial intelligence methods for drug design. This work represents the first step in the validation of these molecules as inhibitors of MtDHQ.

Binding of piperine to mycobacterial RNA polymerase improves the efficacy of rifampicin activity against Mycobacterium leprae and nontuberculous mycobacteria.

Piperine (PPN) is a known inhibitor of efflux pumps in Mycobacterium tuberculosis and in vitro synergism with rifampicin (RIF) has been proven. The current study evaluates the activity of PPN and synergism with RIF in rapidly and slowly growing nontuberculous mycobacteria (NTM). Also, to propose a possible mechanism of interaction of PPN with M. leprae (Mlp) RNA polymerase (RNAp). Minimal inhibitory concentration and drug combination assay was determined by resazurin microtiter assay and resazurin drug combination assay, respectively. In silico evaluation of PPN binding was performed by molecular docking and molecular dynamics (MD). PPN showed higher antimicrobial activity against rapidly growing NTM (32-128 mg/L) rather than for slowly growing NTM (≥ 256 mg/L). Further, 77.8% of NTM tested exhibited FICI ≤ 0.5 when exposed to PPN and RIF combination, regardless of growth speed. Docking and MD simulations showed a possible PPN binding site at the interface between ß and ß' subunits of RNAp, in close proximity to the trigger-helix and bridge-helix elements. MD results indicated that PPN binding hindered the mobility of these elements, which are essential for RNA transcription. We hypothesize that PPN binding might affect mycobacterial RNAp activity, and, possibly, RIF activity and that this mechanism is partially responsible for synergic behaviors with RIF reported in vitro. Communicated by Ramaswamy H. Sarma.

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 anti-Leishmania potential of bioactive compounds derived from naphthoquinones and their possible applications. A systematic review of animal studies.

Leishmaniasis affects millions of people worldwide, and available treatments have severe limitations. Natural and derivative products are significant sources of innovative therapeutic agents. Naphthoquinones are natural or synthetic chemical compounds with broad biological activity. This systematic review aimed to evaluate the potential anti-Leishmania activity of bioactive compounds derived from naphthoquinones in animal models. Conducted in accordance with PRISMA guidelines, two blocks of MeSH terms were assembled: group I, Leishmania OR Leishmaniasis; group II, Atovaquone OR Lapachol OR Beta lapachone OR Naphthoquinones. The search was performed on PubMed, Web of Science, SCOPUS, EMBASE, and Lilacs databases. Twenty-four articles were retrieved and submitted for quality assessment using the SYRCLE critical appraisal tool. The in vivo anti-Leishmania potential of naphthoquinones was evaluated in visceral and cutaneous leishmaniasis using several measurement parameters. Analyzed compounds varied in structure, association with reference drugs, and encapsulation using a drug delivery system. The study design, including treatment protocol, differed between studies. The findings of the studies in this systematic review indicate the anti-Leishmania potential of naphthoquinones in vivo, with different treatment regimens directed against different Leishmania species. The employed drug delivery systems improve the results concerning selectivity, distribution, and required therapeutic dose. The immunomodulatory action was shown to be beneficial to the host, favoring an adequate immune response against infection by Leishmania parasites since it favored Th1 responses. All studies presented a moderate to high risk of bias. These findings suggest that more studies are needed to assess the overall effectiveness and safety of these treatments.

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.