scholarly journals Colombian Contributions Fighting Leishmaniasis: A Systematic Review on Antileishmanials Combined with Chemoinformatics Analysis

Molecules ◽  
2020 ◽  
Vol 25 (23) ◽  
pp. 5704
Author(s):  
Jeysson Sánchez-Suárez ◽  
Freddy A. Bernal ◽  
Ericsson Coy-Barrera
Keyword(s):  
Systematic Review ◽  
Public Health Problem ◽  
Structural Features ◽  
Machine Learning Algorithms ◽  
Clustering Methods ◽  
Holistic View ◽  
Molecular Features ◽  
Leishmania Panamensis ◽  
Tropical Areas ◽  
Conventional Machine

Leishmaniasis is a parasitic morbid/fatal disease caused by Leishmania protozoa. Twelve million people worldwide are appraised to be currently infected, including ca. two million infections each year, and 350 million people in 88 countries are at risk of becoming infected. In Colombia, cutaneous leishmaniasis (CL) is a public health problem in some tropical areas. Therapeutics is based on traditional antileishmanial drugs, but this practice has several drawbacks for patients. Thus, the search for new antileishmanial agents is a serious need, but the lack of adequately funded research programs on drug discovery has hampered its progress. Some Colombian researchers have conducted different research projects focused on the assessment of the antileishmanial activity of naturally occurring and synthetic compounds against promastigotes and/or amastigotes. Results of such studies have separately demonstrated important hits and reasonable potential, but a holistic view of them is lacking. Hence, we present the outcome from a systematic review of the literature (under PRISMA guidelines) on those Colombian studies investigating antileishmanials during the last thirty-two years. In order to combine the general efforts aiming at finding a lead against Leishmania panamensis (one of the most studied and incident parasites in Colombia causing CL) and to recognize structural features of representative compounds, fingerprint-based analyses using conventional machine learning algorithms and clustering methods are shown. Abstraction from such a meta-description led to describe some function-determining molecular features and simplify the clustering of plausible isofunctional hits. This systematic review indicated that the Colombian efforts for the antileishmanials discovery are increasingly intensified, though improvements in the followed pathways must be definitively pursued. In this context, a brief discussion about scope, strengths and limitations of such advances and relationships is addressed.

Attitudes Toward Intimate Partner Violence Against Women in the European Union: A Systematic Review

2020 ◽  
Vol 25 (2) ◽  
pp. 104-121 ◽  
Author(s):  
Enrique Gracia ◽  
Marisol Lila ◽  
Faraj A. Santirso
Keyword(s):  
Systematic Review ◽  
Intimate Partner Violence ◽  
Violence Against Women ◽  
Partner Violence ◽  
Public Health Problem ◽  
Intimate Partner ◽  
Cochrane Library ◽  
Effective Prevention ◽  
Partner Violence Against Women ◽  
The Eu

Abstract. Attitudes toward intimate partner violence against women (IPVAW) are increasingly recognized as central to understanding of this major social and public health problem, and guide the development of more effective prevention efforts. However, to date this area of research is underdeveloped in western societies, and in particular in the EU. The present study aims to provide a systematic review of quantitative studies addressing attitudes toward IPVAW conducted in the EU. The review was conducted through Web of Science, PsychINFO, Medline, EMBASE, PUBMED, and the Cochrane Library, in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) recommendations. This review aimed to identify empirical studies conducted in the EU, published in English in peer-reviewed journals from 2000 to 2018, and analyzing attitudes toward IPVAW. A total of 62 of 176 eligible articles were selected according to inclusion criteria. Four sets of attitudes toward IPVAW were identified as the main focus of the studies: legitimation, acceptability, attitudes toward intervention, and perceived severity. Four main research themes regarding attitudes toward IPVAW emerged: correlates of attitudes, attitudes as predictors, validation of scales, and attitude change interventions. Although interest in this research area has been growing in recent years, the systematic review revealed important gaps in current knowledge on attitudes toward IPVAW in the EU that limits its potential to inform public policy. The review outlines directions for future study and suggests that to better inform policy making, these future research efforts would benefit from an EU-level perspective.

A Systematic Review on Popularity, Application and Characteristics of Protein Secondary Structure Prediction Tools

2019 ◽  
Vol 16 (2) ◽  
pp. 159-172 ◽  
Author(s):  
Elaheh Kashani-Amin ◽  
Ozra Tabatabaei-Malazy ◽  
Amirhossein Sakhteman ◽  
Bagher Larijani ◽  
Azadeh Ebrahim-Habibi
Keyword(s):  
Systematic Review ◽  
Secondary Structure ◽  
Structure Prediction ◽  
Web Of Science ◽  
Secondary Structure Prediction ◽  
Structural Information ◽  
Protein Secondary Structure ◽  
Structural Features ◽  
Prediction Tools ◽  
Insight Into

Background: Prediction of proteins’ secondary structure is one of the major steps in the generation of homology models. These models provide structural information which is used to design suitable ligands for potential medicinal targets. However, selecting a proper tool between multiple Secondary Structure Prediction (SSP) options is challenging. The current study is an insight into currently favored methods and tools, within various contexts. Objective: A systematic review was performed for a comprehensive access to recent (2013-2016) studies which used or recommended protein SSP tools. Methods: Three databases, Web of Science, PubMed and Scopus were systematically searched and 99 out of the 209 studies were finally found eligible to extract data. Results: Four categories of applications for 59 retrieved SSP tools were: (I) prediction of structural features of a given sequence, (II) evaluation of a method, (III) providing input for a new SSP method and (IV) integrating an SSP tool as a component for a program. PSIPRED was found to be the most popular tool in all four categories. JPred and tools utilizing PHD (Profile network from HeiDelberg) method occupied second and third places of popularity in categories I and II. JPred was only found in the two first categories, while PHD was present in three fields. Conclusion: This study provides a comprehensive insight into the recent usage of SSP tools which could be helpful for selecting a proper tool.

Diagnostic test accuracy for use of machine learning in diagnosis of autism spectrum disorder: A Systematic Review and Meta-Analysis (Preprint)

2019 ◽  
Author(s):  
Sun Jae Moon ◽  
Jin Seub Hwang ◽  
Rajesh Kana ◽  
John Torous ◽  
Jung Won Kim
Keyword(s):  
Machine Learning ◽  
Systematic Review ◽  
Autism Spectrum Disorder ◽  
Meta Analysis ◽  
Learning Algorithms ◽  
Structural Mri ◽  
Autism Spectrum ◽  
Machine Learning Algorithms ◽  
Spectrum Disorder ◽  
Test Accuracy

BACKGROUND Over the recent years, machine learning algorithms have been more widely and increasingly applied in biomedical fields. In particular, its application has been drawing more attention in the field of psychiatry, for instance, as diagnostic tests/tools for autism spectrum disorder. However, given its complexity and potential clinical implications, there is ongoing need for further research on its accuracy. OBJECTIVE The current study aims to summarize the evidence for the accuracy of use of machine learning algorithms in diagnosing autism spectrum disorder (ASD) through systematic review and meta-analysis. METHODS MEDLINE, Embase, CINAHL Complete (with OpenDissertations), PsyINFO and IEEE Xplore Digital Library databases were searched on November 28th, 2018. Studies, which used a machine learning algorithm partially or fully in classifying ASD from controls and provided accuracy measures, were included in our analysis. Bivariate random effects model was applied to the pooled data in meta-analysis. Subgroup analysis was used to investigate and resolve the source of heterogeneity between studies. True-positive, false-positive, false negative and true-negative values from individual studies were used to calculate the pooled sensitivity and specificity values, draw SROC curves, and obtain area under the curve (AUC) and partial AUC. RESULTS A total of 43 studies were included for the final analysis, of which meta-analysis was performed on 40 studies (53 samples with 12,128 participants). A structural MRI subgroup meta-analysis (12 samples with 1,776 participants) showed the sensitivity at 0.83 (95% CI-0.76 to 0.89), specificity at 0.84 (95% CI -0.74 to 0.91), and AUC/pAUC at 0.90/0.83. An fMRI/deep neural network (DNN) subgroup meta-analysis (five samples with 1,345 participants) showed the sensitivity at 0.69 (95% CI- 0.62 to 0.75), the specificity at 0.66 (95% CI -0.61 to 0.70), and AUC/pAUC at 0.71/0.67. CONCLUSIONS Machine learning algorithms that used structural MRI features in diagnosis of ASD were shown to have accuracy that is similar to currently used diagnostic tools.

scholarly journals A Systematic Review on the Application of Machine Learning in Exploiting Mineralogical Data in Mining and Mineral Industry

Minerals ◽  
2021 ◽  
Vol 11 (8) ◽  
pp. 816
Author(s):  
Mohammad Jooshaki ◽  
Alona Nad ◽  
Simon Michaux
Keyword(s):  
Machine Learning ◽  
Systematic Review ◽  
Raw Materials ◽  
Relevant Literature ◽  
Geological Structure ◽  
Subject Area ◽  
Ore Deposits ◽  
Machine Learning Algorithms ◽  
Global Demand ◽  
Ore Grade

Machine learning is a subcategory of artificial intelligence, which aims to make computers capable of solving complex problems without being explicitly programmed. Availability of large datasets, development of effective algorithms, and access to the powerful computers have resulted in the unprecedented success of machine learning in recent years. This powerful tool has been employed in a plethora of science and engineering domains including mining and minerals industry. Considering the ever-increasing global demand for raw materials, complexities of the geological structure of ore deposits, and decreasing ore grade, high-quality and extensive mineralogical information is required. Comprehensive analyses of such invaluable information call for advanced and powerful techniques including machine learning. This paper presents a systematic review of the efforts that have been dedicated to the development of machine learning-based solutions for better utilizing mineralogical data in mining and mineral studies. To that end, we investigate the main reasons behind the superiority of machine learning in the relevant literature, machine learning algorithms that have been deployed, input data, concerned outputs, as well as the general trends in the subject area.

scholarly journals A Systematic Review and Meta-Analysis of Pharmacogenetic Studies in Patients with Chronic Kidney Disease

2021 ◽  
Vol 22 (9) ◽  
pp. 4480
Author(s):  
Maria Tziastoudi ◽  
Georgios Pissas ◽  
Georgios Raptis ◽  
Christos Cholevas ◽  
Theodoros Eleftheriadis ◽  
...  
Keyword(s):  
Systematic Review ◽  
Chronic Kidney Disease ◽  
Kidney Disease ◽  
Meta Analysis ◽  
Public Health Problem ◽  
Response To Treatment ◽  
Genotype Distribution ◽  
Global Public Health ◽  
High Prevalence ◽  
Additive Inheritance

Chronic kidney disease (CKD) is an important global public health problem due to its high prevalence and morbidity. Although the treatment of nephrology patients has changed considerably, ineffectiveness and side effects of medications represent a major issue. In an effort to elucidate the contribution of genetic variants located in several genes in the response to treatment of patients with CKD, we performed a systematic review and meta-analysis of all available pharmacogenetics studies. The association between genotype distribution and response to medication was examined using the dominant, recessive, and additive inheritance models. Subgroup analysis based on ethnicity was also performed. In total, 29 studies were included in the meta-analysis, which examined the association of 11 genes (16 polymorphisms) with the response to treatment regarding CKD. Among the 29 studies, 18 studies included patients with renal transplantation, 8 involved patients with nephrotic syndrome, and 3 studies included patients with lupus nephritis. The present meta-analysis provides strong evidence for the contribution of variants harbored in the ABCB1, IL-10, ITPA, MIF, and TNF genes that creates some genetic predisposition that reduces effectiveness or is associated with adverse events of medications used in CKD.

Betalogger: Smartphone Sensor-based Side-channel Attack Detection and Text Inference Using Language Modeling and Dense MultiLayer Neural Network

2021 ◽  
Vol 20 (5) ◽  
pp. 1-17
Author(s):  
Abdul Rehman Javed ◽  
Saif Ur Rehman ◽  
Mohib Ullah Khan ◽  
Mamoun Alazab ◽  
Habib Ullah Khan
Keyword(s):  
Neural Network ◽  
Language Modeling ◽  
Attack Detection ◽  
Machine Learning Algorithms ◽  
Second Phase ◽  
Sensitive Data ◽  
Phase Sequence ◽  
Smartphone Technology ◽  
Conventional Machine ◽  
Sequence Generator

With the recent advancement of smartphone technology in the past few years, smartphone usage has increased on a tremendous scale due to its portability and ability to perform many daily life tasks. As a result, smartphones have become one of the most valuable targets for hackers to perform cyberattacks, since the smartphone can contain individuals’ sensitive data. Smartphones are embedded with highly accurate sensors. This article proposes BetaLogger , an Android-based application that highlights the issue of leaking smartphone users’ privacy using smartphone hardware sensors (accelerometer, magnetometer, and gyroscope). BetaLogger efficiently infers the typed text (long or short) on a smartphone keyboard using Language Modeling and a Dense Multi-layer Neural Network (DMNN). BetaLogger is composed of two major phases: In the first phase, Text Inference Vector is given as input to the DMNN model to predict the target labels comprising the alphabet, and in the second phase, sequence generator module generate the output sequence in the shape of a continuous sentence. The outcomes demonstrate that BetaLogger generates highly accurate short and long sentences, and it effectively enhances the inference rate in comparison with conventional machine learning algorithms and state-of-the-art studies.

scholarly journals Anemia as a risk factor for tuberculosis: a systematic review and meta-analysis

2021 ◽  
Vol 26 (1) ◽  
Author(s):  
Yemataw Gelaw ◽  
Zegeye Getaneh ◽  
Mulugeta Melku
Keyword(s):  
Systematic Review ◽  
Risk Factor ◽  
Infectious Diseases ◽  
Low Socioeconomic Status ◽  
Meta Analysis ◽  
Public Health Problem ◽  
Cochrane Library ◽  
Major Public Health Problem ◽  
Double Burden ◽  
Mesh Terms

Abstract Background Tuberculosis is a major public health problem caused by Mycobacterium tuberculosis, occurring predominantly in population with low socioeconomic status. It is the second most common cause of death from infectious diseases. Tuberculosis becomes a double burden among anemic patients. Anemia increases an individual’s susceptibility to infectious diseases including tuberculosis by reducing the immunity level. Therefore, the purpose of this study was to determine whether anemia is a risk factor for tuberculosis. Method Relevant published articles were searched in electronic databases like PubMed, Google Scholar, EMBASE, and Cochrane Library using the following MeSH terms: risk factor, predictors, tuberculosis, TB, Anaemia, Anemia, hemoglobin, Hgb, and Hb. Articles written in the English, observational studies conducted on the incidence/prevalence of tuberculosis among anemic patients, or papers examined anemia as risk factors for tuberculosis were included. From those studies meeting eligibility criteria, the first author’s name, publication year, study area, sample size and age of participants, study design, and effect measure of anemia for tuberculosis were extracted. The data were entered using Microsoft Excel and exported to Stata version 11 for analysis. The random-effects model was applied to estimate the pooled OR and HR, and 95% CI. The sources of heterogeneity were tested by Cochrane I-squared statistics. The publication bias was assessed using Egger’s test statistics. Results A total of 17 articles with a 215,294 study participants were included in the analysis. The odd of tuberculosis among anemic patients was 3.56 (95% CI 2.53–5.01) times higher than non-anemic patients. The cohort studies showed that the HR of tuberculosis was 2.01 (95% CI 1.70–2.37) times higher among anemic patients than non-anemic patients. The hazard of tuberculosis also increased with anemia severity (HR 1.37 (95% CI 0.92–2.05), 2.08 (95% CI 1.14–3.79), and 2.66 (95% CI 1.71–4.13) for mild, moderate, and severe anemia, respectively). Conclusion According to the current systematic review and meta-analysis, we can conclude that anemia was a risk factor for tuberculosis. Therefore, anemia screening, early diagnose, and treatment should be provoked in the community to reduce the burden of tuberculosis.

On purpose of multiple cases: Quaternary prevention on mental health – “Primum non-nocere”

2017 ◽  
Vol 41 (S1) ◽  
pp. S581-S581
Author(s):  
K.L. Lazo Chavez
Keyword(s):  
Mental Health ◽  
Systematic Review ◽  
Public Health Problem ◽  
Treatment Intervention ◽  
Sick Role ◽  
Professional Activities ◽  
Do No Harm ◽  
Competing Interest ◽  
Quaternary Prevention ◽  
Different Levels

IntroductionQuaternary prevention, concept coined by the Belgian Marc Jamoulle, are the actions taken to avoid or mitigate the consequences of unnecessary or excessive intervention of the health system. The concept alludes to actions to avoid the over-diagnoses and over-treatment, trying to reduce the incidence of iatrogeny in patients, which is a serious public health problem and even more in mental health.MethodsSystematic review of bibliography.ObjectivesDo a systematic review of bibliography and through the results invite to the analytic and critic reflection of our professional activities and the current situation of mental health.ResultsThere is not enough studies about quaternary prevention in mental health.–Some studies found that about one-third of diseases of a hospital are iatrogenic, most of them for pharmacological causes.–There is iatrogeny at different levels of the attention of mental health: primary prevention, diagnosis and treatment.–Non-treatment indication avoids in multiple cases iatrogenesis and contributes to the correct distribution of the economic and care resources.ConclusionsSince one of the fundaments of medicine is “primun non nocere” that means “first do no harm” and one of principles of bioethics is “non-maleficence”, quaternary prevention should prevail over any other preventive or curative option.–We should define in a more realistic way the limits, benefits and damages of our interventions in order to not promote a passive and sick role.–Must be recognized the non-treatment intervention as a therapeutic and useful intervention, and one of the best tools of quaternary prevention.Disclosure of interestThe author has not supplied his/her declaration of competing interest.

Productivity monitoring in building construction projects: a systematic review

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Wesam Salah Alaloul ◽  
Khalid M. Alzubi ◽  
Ahmad B. Malkawi ◽  
Marsail Al Salaheen ◽  
Muhammad Ali Musarat
Keyword(s):  
Systematic Review ◽  
Construction Projects ◽  
Small Sample ◽  
Machine Learning Algorithms ◽  
Building Construction ◽  
Content Type ◽  
Construction Productivity ◽  
Automated Monitoring ◽  
Monitoring Tools ◽  
Meta Analyses

PurposeThe unique nature of the construction sector makes it fall behind other sectors in terms of productivity. Monitoring construction productivity is crucial for the construction project's success. Current practices for construction productivity monitoring are time-consuming, manned and error prone. Although previous studies have been implemented toward reducing these limitations, a gap still exists in the automated monitoring of construction productivity.Design/methodology/approachThis study aims to investigate and assess the different techniques used for monitoring productivity in building construction projects. Therefore, a mixed review methodology (bibliometric analysis and systematic review) was adopted. All the related publications were collected from different databases, which were further screened to get the most relevant based on the Preferred Reporting Items for Systematic Review and Meta-Analyses (PRISMA) criteria.FindingsA detailed review was performed, and it was found that traditional methods, computer vision-based and photogrammetry are the most adopted data acquisition for productivity monitoring of building projects, respectively. Machine learning algorithms (ANN, SVM) and BIM were integrated with monitoring tools and technologies to enhance the automated monitoring performance in construction productivity. Also, it was observed that current studies did not cover all the complex construction job sites and they were applied based on a small sample of construction workers and machines separately.Originality/valueThis review paper contributes to the literature on construction management by providing insight into different productivity monitoring techniques.

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