Impact of the COVID-19 pandemic on perinatal care and outcomes: A retrospective study in a tertiary hospital in Northern Ghana.

BACKGROUND: Perinatal mortality remains a global challenge. This challenge may be worsened by the negative effects of the COVID-19 pandemic on maternal and child health. OBJECTIVES: Examine the impact of the COVID-19 pandemic on perinatal care and outcomes in the Tamale Teaching Hospital in northern Ghana. METHODS: A hospital-based retrospective study was conducted in the Tamale Teaching Hospital. We compared antenatal care attendance, total deliveries, cesarean sections, and perinatal mortality before the COVID-19 pandemic (March 1, 2019 to February 28, 2020) and during the COVID-19 pandemic (March 1, 2020 to February 28, 2021). Interrupted time series analyses was performed to evaluate the impact of the COVID-19 pandemic on perinatal care and outcomes at TTH. RESULTS: A total number of 35,350 antenatal visits and 16,786 deliveries were registered at TTH from March 2019 to February 2021. Antenatal care, early neonatal death, and emergency cesarean section showed a rapid decline after the onset of the pandemic, with a progressive recovery over the following months. The total number of deliveries and fresh stillbirths showed a step change with a marked decrease during the pandemic, while the macerated stillbirths showed a pulse change, a temporary marked decrease with a quick recovery over time. CONCLUSION: The COVID-19 pandemic had a negative impact on perinatal care and outcomes in our facility. Pregnancy monitoring through antenatal care should be encouraged and continued even as countries tackle the pandemic.

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.].

Antibacterial and immunological properties of piperine evidenced by preclinical studies: a systematic review.

Aim: To review in vitro, in vivo, and in silico studies examining the antibacterial and immunomodulatory properties of piperine (PPN). Methods: This systematic review followed PRISMA guidelines, and five databases were searched. Results: A total of 40 articles were included in this study. Six aspects of PPN activity were identified, including antibacterial spectrum, association with antibiotics, efflux pump inhibition, biofilm effects, protein target binding, and modulation of immune functions/virulence factors. Most studies focused on Mycobacterium spp. and Staphylococcus aureus. Cell lineages and in vivo models were employed to study PPN antibacterial effects. Conclusion: We highlight PPN as a potential adjuvant in the treatment of bacterial infections. PPN possesses several antibacterial properties that need further exploration to determine the mechanisms behind its pharmacological activity.

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.

Validation of the Pediatric Resuscitation and Trauma Outcome (PRESTO) model in injury patients in Tanzania.

INTRODUCTION: Sub-Saharan Africa has the highest rate of unintentional paediatric injury deaths. The Pediatric Resuscitation and Trauma Outcome (PRESTO) model predicts mortality using patient variables available in low-resource settings: age, systolic blood pressure (SBP), heart rate (HR), oxygen saturation, need for supplemental oxygen (SO) and neurologic status (Alert Verbal Painful Unresponsive (AVPU)). We sought to validate and assess the prognostic performance of PRESTO for paediatric injury patients at a tertiary referral hospital in Northern Tanzania. METHODS: This is a cross-sectional study from a prospective trauma registry from November 2020 to April 2022. We performed exploratory analysis of sociodemographic variables and developed a logistic regression model to predict mortality using R (V.4.1). The logistic regression model was evaluated using area under the receiver operating curve (AUC). RESULTS: 499 patients were enrolled with a median age of 7 years (IQR 3.41-11.18). 65% were boys, and in-hospital mortality was 7.1%. Most were classified as alert on AVPU Scale (n=326, 86%) and had normal SBP (n=351, 98%). Median HR was 107 (IQR 88.5-124). The logistic regression model based on the original PRESTO model revealed that AVPU, HR and SO were statistically significant to predict in-hospital mortality. The model fit to our population revealed AUC=0.81, sensitivity=0.71 and specificity=0.79. CONCLUSION: This is the first validation of a model to predict mortality for paediatric injury patients in Tanzania. Despite the low number of participants, our results show good predictive potential. Further research with a larger injury population should be done to improve the model for our population, such as through calibration.

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.

Three decades of clinical trials on immunotherapy for human leishmaniases: a systematic review and meta-analysis.

Aim: Current treatments for leishmaniases are not satisfactory, thus alternatives are needed. We searched for clinical trials with immunotherapeutic approaches for patients with leishmaniasis. Materials & methods: Out of 205 articles, 24 clinical trials were selected, and eight submitted to meta-analysis. Results: A reduction in healing time was observed in patients with tegumentary leishmaniasis treated with pentavalent antimony plus granulocyte-macrophage colony-stimulating factor, and therapeutic vaccines. Overall meta-analysis indicated that immunotherapy associated with the standard chemotherapy generated a significantly reduced risk of treatment failure than the pentavalent antimony alone (p = 0.03). Conclusion: Our review confirmed the efficacy of immunotherapies for the treatment of cutaneous and visceral leishmaniasis and highlighted the importance of clinical trials using immunotherapies for leishmaniases.

Cost analysis of hemophilia treatment in a Brazilian public blood center / Custo-análise do tratamento da hemofilia em um hemocentro público brasileiro

Abstract Background hemophilia is a rare coagulopathy, treated by replacing the missing blood clotting factor. Objective to assess the direct costs of hemophilia treatment from the perspective of the Unified Health System, highlighting the impact costs of new therapeutic modalities. Method partial economic assessment of the direct costs of hemophilia, in which were collected data from patient records from 2011 to 2015, at the Blood Center in the city of Juiz de Fora. Costs were assigned to consultations, exams, hospitalizations, and medications according to the price list of the National Health Surveillance Agency (in Portuguese ANVISA) and the Table of Procedures, and Medications. Results among 98 patients evaluated, 76 had hemophilia A, and 43.3% presented severe hemophilia. The number of consultations and the consumption of Clotting Factor Concentrates (CFCs) were higher in severe hemophilia. Hospitalizations were rare. Direct costs increased 286.8% from 2011 to 2015. The mean annual cost per patient was R$57,416.43, with no significant difference between hemophilia A and B. The expenditures for factor concentrates amounted to 99.46% of total costs. The actual impact cost was more than R$6,000,000.00. Conclusion the direct costs of hemophilia were high, mainly due to factor concentrates. There was an increase in costs with the incorporation of technologies, although there are some areas with potential inefficiencies.

Resumo Introdução hemofilia é uma coagulopatia rara, tratada com reposição do fator de coagulação deficiente. Objetivo avaliar custos diretos do tratamento da hemofilia pela perspectiva do Sistema Único de Saúde brasileiro, destacando o impacto nos custos das novas modalidades terapêuticas. Método análise econômica parcial dos custos diretos da hemofilia. Foram coletados dados de prontuários de pacientes do Hemocentro de Juiz de Fora de 2011-2015. Aos atendimentos, exames, hospitalizações e medicamentos, foram atribuídos custos conforme as Tabelas de Preços da Agência de Vigilância Sanitária e de Procedimentos e Medicamentos do SUS. Resultados entre 98 pacientes avaliados, 76 tinham hemofilia A e 43,3%, hemofilia grave. O número de consultas e o consumo de concentrados de fatores da coagulação foram mais altos na hemofilia grave. Hospitalizações foram raras. Os custos diretos aumentaram 286,8% entre 2011-2015. O custo anual médio por paciente foi R$57.416,43 sem diferença significativa entre hemofilia A e B. Os concentrados de fator corresponderam a 99,46% dos custos totais. O impacto nos custos foi de mais de R$6.000.000,00. Conclusão custos diretos da hemofilia são altos, principalmente devido aos concentrados de fator. O aumento nos custos com as novas tecnologias foi muito elevado embora ainda haja áreas com ineficiências no tratamento da hemofilia.

Possible Binding of Piperine in Mycobacterium tuberculosis RNA Polymerase and Rifampin Synergism.

The activity of rifampin (RIF) and piperine was evaluated at the relative transcript levels of 12 efflux pumps (EPs), and an additional mechanism was proposed to be behind the synergic interactions of piperine plus RIF in Mycobacterium tuberculosis AutoDock v4.2.3 and Molegro v6 programs were used to evaluate PIP binding in M. tuberculosis RNA polymerase (RNAP). A hypothesis has been raised that piperine interferes in M. tuberculosis growth through RNAP inhibition, differently from what was previously endorsed for EP inhibition only.

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.