Free Access Databases Research Articles

Biomarker panels for fruit intake assessment: a metabolomics analysis in the ELSA-Brasil study.

Food intake biomarkers are used to estimate dietary exposure; however, selecting a single biomarker to evaluate a specific dietary component is difficult due to the overlap of diverse compounds from different foods. Therefore, combining two or more biomarkers can increase the sensitivity and specificity of food intake estimates. This study aimed to evaluate the ability of metabolite panels to distinguish between self-reported fruit consumers and non-consumers among participants in the Longitudinal Study of Adult Health. A total of 93 healthy adults of both sexes were selected from the Longitudinal Study of Adult Health. A 24-h dietary recall was obtained using the computer-assisted 24-h food recall GloboDiet software, and 24-h urine samples were collected from each participant. Metabolites were identified in urine using liquid chromatography coupled with high-resolution mass spectrometry by comparing their exact mass and fragmentation patterns using free-access databases. Multivariate receiver operating characteristic curve (ROC) analysis and partial least squares discriminant analysis were used to verify the ability of the metabolite combination to classify daily and non-daily fruit consumers. Fruit intake was identified using a 24h dietary recall (24h-DR). Bananas, grapes, and oranges are included in the summary. The panel of biomarkers exhibited an area under the curve (AUC) > 0.6 (Orange AUC = 0.665; Grape AUC = 0.622; Bananas AUC = 0.602; All fruits AUC = 0.679; Citrus AUC = 0.693) and variable importance projection score > 1.0, and these were useful for assessing the sensitivity and predictability of food intake in our population. A panel of metabolites was able to classify self-reported fruit consumers with strong predictive power and high specificity and sensitivity values except for banana and total fruit intake.

Artificial intelligence based glaucoma and diabetic retinopathy detection using MATLAB - retrained AlexNet convolutional neural network.

Glaucoma and diabetic retinopathy (DR) are the leading causes of irreversible retinal damage leading to blindness. Early detection of these diseases through regular screening is especially important to prevent progression. Retinal fundus imaging serves as the principal method for diagnosing glaucoma and DR. Consequently, automated detection of eye diseases represents a significant application of retinal image analysis. Compared with classical diagnostic techniques, image classification by convolutional neural networks (CNN) exhibits potential for effective eye disease detection. This paper proposes the use of MATLAB - retrained AlexNet CNN for computerized eye diseases identification, particularly glaucoma and diabetic retinopathy, by employing retinal fundus images. The acquisition of the database was carried out through free access databases and access upon request. A transfer learning technique was employed to retrain the AlexNet CNN for non-disease (Non_D), glaucoma (Sus_G) and diabetic retinopathy (Sus_R) classification. Moreover, model benchmarking was conducted using ResNet50 and GoogLeNet architectures. A Grad-CAM analysis is also incorporated for each eye condition examined. Metrics for validation accuracy, false positives, false negatives, precision, and recall were reported. Validation accuracies for the NetTransfer (I-V) and netAlexNet ranged from 89.7% to 94.3%, demonstrating varied effectiveness in identifying Non_D, Sus_G, and Sus_R categories, with netAlexNet achieving a 93.2% accuracy in the benchmarking of models against netResNet50 at 93.8% and netGoogLeNet at 90.4%. This study demonstrates the efficacy of using a MATLAB-retrained AlexNet CNN for detecting glaucoma and diabetic retinopathy. It emphasizes the need for automated early detection tools, proposing CNNs as accessible solutions without replacing existing technologies.

Prevalence of Diabetes Mellitus among individuals with chronic kidney disease: systematic review and meta-analysis

BACKGROUND: Diabetes Mellitus (DM) is currently considered a global epidemic, with alarming estimates for the coming years on all continents, with Chronic Kidney Disease (CKD) as one of its main consequences when a timely diagnosis is not made. OBJECTIVE: The objective of this study is to estimate the prevalence of DM among individuals diagnosed with CKD by means of a systematic review and meta-analysis. METHODS: A systematic review was carried out in the main free-access databases such as Pubmed (Medlaine), Lilacs, Scopus and Scielo. Two researchers selected the articles, extracted the data and evaluated the quality. The collected data were evaluated using a random effects model. RESULTS: Of 994 articles, 17 studies were included that looked at three continents. The group prevalence of DM among individuals with CKD (95% CI) was 29% (23-35%), with heterogeneity I2 = 99,86% and p = 0.00, which was not explained by meta-regression and subgroups. CONCLUSIONS: The present study confirmed the high prevalence of DM among individuals with CKD, especially among those with end-stage renal disease, demonstrating the need for early diagnosis and timely treatment of DM and new studies in this area, considering the social and economic impact of these diseases worldwide.

Artificial intelligence based glaucoma and diabetic retinopathy detection using MATLAB — retrained AlexNet convolutional neural network

Background: Glaucoma and diabetic retinopathy are the leading causes of blindness due to an irreversible damage to the retina which results in vision loss. Early detection of these diseases through regular screening is especially important to prevent progression. The image of retinal fundus is the main evaluating strategy for the glaucoma and diabetic retinopathy detection. Then, automated eye disease detection is an important application of retinal image analysis. Compared with classical diagnostic techniques, image classification by convolutional neural networks (CNN) have the potential for better cost-effective performance. Methods: In this paper, we propose the use of MATLAB – retrained AlexNet CNN for computerized eye diseases identification, particularly glaucoma and diabetic retinopathy, by employing retinal fundus images. The acquisition of the database was carried out through free access databases and access upon request. A transfer learning technique is used for retraining the AlexNet CNN. Specifically, the model divides the fundus image dataset into training and testing data. Results: As datasets were added by network training, different values were reported for validation accuracy, false positives and false negatives, precision, and recall. Thus, having NetTransfer I with a validation accuracy value of 94.3% for two classes. NetTransfer II with a validation accuracy value of 91.8% for two classes. NetTransfer III with a validation accuracy value of 89.7% for three classes. Net transfer IV with a validation accuracy value of 93.1% for three classes. Finally, NetTransfer V with a validation accuracy value of 92.1% for three classes. Conclusions: Re-training of the AlexNet network proved to be a powerful tool to create disease detection systems having high accuracy values and being able to discern between more than two diseases.

Distribution of the bat Family Mormoopidae in Honduras

Honduras is the second country in central America along with Panamá with the greatest bat diversity, and the insectivorous bats are the most diverse in the Chiroptera order. The family Mormoopidae has the largest number of records in Honduras and these bats are important as pest control in crops where they are present. To understand the importance of the Mormoopidae family in Honduras, we need to know where it is distributed. A species distribution model is a tool that provides information on the distribution of the species, based on presences records and climate variables. We collected information from free access databases like GBIF, reports, and paper publication to obtain presence data in Honduras. To run the models, we used Maxent in dismo package. Our results show a similar distribution for all species of Mormoopidae family, the principal causes that limited the distribution of the species are ecosystem type and altitude; some species tolerated evergreen forest and others prefer dry forest, in terms of elevation, in some species is limited from lowland to 1500 m.a.s.l., like Mormoops megalophylla, Pteronotus psilotis, and Pteronotus gymnonotus, and others can inhabit above 2800 m.a.s.l., like Pteronotus fulvus, and Pteronotus mesoamericanus. We need to obtain more records of species like M. megalophylla, P. psilotis, and P. gymnonotus. More surveys in the eastern part of Honduras are necessary where there are many information gaps, this would help us to have more robust models and a better understanding of the distribution of these species.

Photovoltaic Power Generation Forecasting for Regional Assessment Using Machine Learning

Solar energy currently plays a significant role in supplying clean and renewable electric energy worldwide. Harnessing solar energy through PV plants requires problems such as site selection to be solved, for which long-term solar resource assessment and photovoltaic energy forecasting are fundamental issues. This paper proposes a fast-track methodology to address these two critical requirements when exploring a vast area to locate, in a first approximation, potential sites to build PV plants. This methodology retrieves solar radiation and temperature data from free access databases for the arbitrary division of the region of interest into land cells. Data clustering and probability techniques were then used to obtain the mean daily solar radiation per month per cell, and cells are clustered by radiation level into regions with similar solar resources, mapped monthly. Simultaneously, temperature probabilities are determined per cell and mapped. Then, PV energy is calculated, including heat losses. Finally, PV energy forecasting is accomplished by constructing the P50 and P95 estimations of the mean yearly PV energy. A case study in Mexico fully demonstrates the methodology using hourly data from 2000 to 2020 from NSRDB. The proposed methodology is validated by comparison with actual PV plant generation throughout the country.

Long-term missing wind data recovery using free access databases and deep learning for bridge health monitoring

Wind environment monitoring at the bridge site is essential in structural health monitoring (SHM). However, as the performance of the electronic equipment for bridge SHM system deteriorates, wind monitoring data often suffer from long-term continuous data missing. The duration of data missing may be as long as several months, creating barriers to safety monitoring and monitoring data mining of the bridge structures. The conventional interpolation techniques hardly achieve the desired data recovery. Therefore, a framework was proposed for long-term missing wind data recovery based on a deep neural network (DNN) utilizing a free access database (European Center for Medium-Range Weather Forecasts, ECMWF). This framework consisted of one regression task (Task 1) and one temporal super-resolution task (Task 2). In Task 1, the hourly wind data provided by ECMWF were first learned to the hourly ones of the bridge SHM system. In Task 2, the low-resolution wind data (hourly averages) were upsampled to high-resolution ones (10-min averages). The U-net architecture provided the basis for the DNNs in both tasks. Unlike the conventional time-domain loss function used in Task 1, Task 2 adopted a time-frequency cross-domain loss function for training, which innovatively employed a spectrum magnitude balance strategy to enhance the reconstruction of the high-frequency components of wind speed signals. The proposed framework's feasibility and effectiveness were verified through a case study of recovering long-term continuous missing wind data in the SHM system of Sutong Bridge, China. The proposed methodology provides a new perspective for recovering long-term continuous missing SHM data.

Salmonella enterica subsp. enterica Welikade: guideline for phylogenetic analysis of serovars rarely involved in foodborne outbreaks

BackgroundSalmonella spp. is a major foodborne pathogen with a wide variety of serovars associated with human cases and food sources. Nevertheless, in Europe a panel of ten serovars is responsible for up to 80% of confirmed human cases. Clustering studies by single nucleotide polymorphism (SNP) core-genome phylogenetic analysis of outbreaks due to these major serovars are simplified by the availability of many complete genomes in the free access databases. This is not the case for outbreaks due to less common serovars, such as Welikade, for which no reference genomes are available. In this study, we propose a method to solve this problem. We propose to perform a core genome MLST (cgMLST) analysis based on hierarchical clustering using the free-access EnteroBase to select the most suitable genome to use as a reference for SNP phylogenetic analysis. In this study, we applied this protocol to a retrospective analysis of a Salmonella enterica serovar Welikade (S. Welikade) foodborne outbreak that occurred in France in 2016. Finally, we compared the cgMLST and SNP analyses. SNP phylogenetic reconstruction was carried out considering the effect of recombination events identified by the ClonalFrameML tool. The accessory genome was also explored by phage content and virulome analyses.ResultsOur findings revealed high clustering concordance using cgMLST and SNP analyses. Nevertheless, SNP analysis allowed for better assessment of the genetic distance among strains. The results revealed epidemic clones of S. Welikade circulating within the poultry and dairy sectors in France, responsible for sporadic and non-sporadic human cases between 2012 and 2019.ConclusionsThis study increases knowledge on this poorly described serovar and enriches public genome databases with 42 genomes from human and non-human S. Welikade strains, including the isolate collected in 1956 in Sri Lanka, which gave the name to this serovar. This is the first genomic analysis of an outbreak due to S. Welikade described to date.

The methodology of food design. Part 2. Digital nutritiology in personal food

Atherosclerosis (the main cause of a wide range of cardiovascular diseases) and other multifactorial diseases depend on several nutrition factors, defined in general by lifestyle that directly and constantly affects the human body. The modern level of science and technology development is able to form a diet, taking into account all personal characteristics in such a way that makes nutrition an effective preventive measure against diseases in order to keep a person healthy. The purpose of this article is to define and study all the limitations (the scope of its coverage in the scientific literature) that arose in the process of research aimed to formation of an integrated personal approach to designing of nutrition to prevent atherosclerosis. The object of the study was scientific literature, which is available in open source and free access databases: PubMed, ScienceDirect, eLIBRARY.RU, www.fips.ru, Patentscope. The language of search is Russian and English, search depth is 12 years. In the course of the research two food design concepts were found that affect process of digitalization in the food sector: the concept “FoodTech” (food technology) and digital nutritiology. It was established that in Russia only one company — LLC “City Supermarket” (Moscow), that works with the brand “Azbuka Vkusa” — acts in the sphere of “FoodTech” on the Russian market. This company selects personalized food, taking into account the results of personal nutrigenetic tests, in cooperation with LLC “Genotech” (Moscow). There is a need to use biological information, statistical information processing (nutrigenetic studies, nutrigenomic research) and machine-aided data processing (machine learning) for further generation of automatic algorithm that compiles personal recommendations. The relevance of generation of a national domestic database on chemical composition of food products (presented in the market) to simplify the preparation of individual personal diets is observed. We underline the necessity to use the test-organisms, i. e. dorio fish / zebrafish (Danio rerio) and nematodes (Caenorhabditis elegans)), which were used to determine the activity of candidate substances — the biologically active substances that feature antiatherosclerotic properties. In the future the authors plan to conduct a nutrigenomic and nutrigenetic study, using digital achievements. To collect information about consumers, it is necessary to apply digital devices, and use biological informatics to process the results; after that it is necessary to generate the algorithm for automatic selection of personalized dietary recommendations.

Imperial Russian Newspapers

Imperial Russian Newspapers is a free access database containing a rich collection of digitized newspapers published in the Russian Empire. The project is still in progress and it is actively supported by the National Library of Russia. With nearly 40,000 issues, this database is an excellent resource for scholars and researchers as it provides a unique perspective on the historical events in Russia from 1767 to the Bolshevik Revolution in 1917. Currently, the collection covers 25 titles and it will continue to grow in the future.

Trends in Free-access Genomic Data Accelerate Advances in Cyanobacteria Taxonomy.

Free access databases of DNA sequences containing microbial genetic information have changed the way scientists look at the microbial world. Currently, the NCBI database includes about 516 distinct search results for Cyanobacterial genomes distributed in a taxonomy based on a polyphasic approach. While their classification and taxonomic relationships are widely used as is, recent proposals to alter their grouping include further exploring the relationship between Cyanobacteria and Melainabacteria. Nowadays, most cyanobacteria still are named under the Botanical Code; however, there is a proposal made by the Genome Taxonomy Database (GTDB) to harmonize cyanobacteria nomenclature with the other bacteria, an initiative to standardize microbial taxonomy based on genome phylogeny, in order to contribute to an overall better phylogenetic resolution of microbiota. Furthermore, the assembly level of the genomes and their geographical origin demonstrates some trends of cyanobacteria genomics on the scientific community, such as low availability of complete genomes and underexplored sampling locations. By describing how available cyanobacterial genomes from free-access databases fit within different taxonomic classifications, this mini-review provides a holistic view of the current knowledge of cyanobacteria and indicates some steps towards improving our efforts to create a more cohesive and inclusive classifying system, which can be greatly improved by using large-scale sequencing and metagenomic techniques.

Air quality and health information systems in Latin America and Caribbean: ¿are they a proxy of social disparities between countries?

BACKGROUND AND AIM: Poor air quality is one of the main public health problems around world and it’s the fourth risk factor for death. For 2019, 6.7 million deaths were attributed to it. Among countries in the same region, local air pollution and its impacts show unequal management due to socioeconomic disparities. Assuming that the presence of “Air Quality and Health Information Systems” (AQHIS) can be a proxy for environmental public health management at the national level, this research compared some socioeconomic characteristics between countries with and without AQHIS in the Latin American and Caribbean region. METHODS: A descriptive study was carried out between 20118 and 2019, in which socioeconomic, environmental and health characteristics were analyzed. The information was obtained from free access databases, that were found in online repositories of multilateral institutions (UNESCO, UNDP, Inter-American Development Bank, World Bank and PAHO). A univariate analysis for each characteristic studied were made and they were compared between countries as they had or not AQHIS. RESULTS:According to official pages environmental or health authority, 26% of countries in the region had HAQIS, only three analyzed health indicators in relation to air quality phenomenon. Differences between the countries with and without HAQIS regarding the number of inhabitants were found, reporting a greater number of people in those with an information system (p= 0.000), and higher proportion of cities with more than one million inhabitants (92.3% vs. 10.8%, p = 0.000). A lower average of years of schooling was reported in countries with AQHIS (p = 0.22). Similar findings were found for other socioeconomic indicators. CONCLUSIONS:Differences were identified for some socioeconomic indicators in relation to the existence of AQHIS in the region. An analysis of the regional context is necessary to understand how this indicator could reflect some social disparities in environmental health. KEYWORDS: Information System, Air Pollution, Health, Latin America and Caribbean region

Impact of the COVID-19 Outbreak on Orthopedic Surgery: A Nationwide Analysis of the First Pandemic Year.

PurposeTo analyze the impact of the coronavirus disease 2019 (COVID-19) outbreak during the first pandemic year in a single country.MethodsA cross-sectional study was designed. The free access database of the Chilean Department of Statistics and Health Information (DEIS) was used to compare the number of orthopedic procedures between 2019 and 2020. Country mobility was exported from the Institute of Complex Engineering Systems (ISCI) free-access database; this corresponds to a direct measurement of the degree of confinement of the country. Spearman correlation (rho) was used to analyze the total monthly COVID infection trend and mobility to orthopedics procedures.ResultsThe number of orthopedic surgeries fell by 22.8% during the first year of the pandemic. All surgical procedures were adversely affected, with the fracture/trauma surgeries being the least affected. The maximum adverse impact was seen in knee arthroplasty (-64%), followed by hip arthroplasty (-41%) and knee ligament reconstruction (-44%). The number of orthopedic procedures had a mild correlation to the monthly number of COVID-19 cases (rho=-0.53, p=0.08) and a strong correlation with the country's mobility (rho=0.94, p=0.0001).ConclusionsThe COVID-19 outbreak diminished the number of orthopedic procedures during 2020, and the impact was directly correlated to the country's mobility. The public health network did have a more significant adverse impact in elective surgeries due to a slower recovery than private institutions. An increase in the waiting list should be expected, which will widen the difference in access to orthopedic surgery in Chile.

Chemical Space, Scaffolds, and Halogenated Compounds of CMNPD: A Comprehensive Chemoinformatic Analysis.

The comprehensive marine natural products database (CMNPD) is a new free access and comprehensive database developed originally by Lyu's team of our research group, including more than 30 000 marine natural products (MNPs) reported from the 1960s. In this article, we aimed to present CMNPD's value in drug discovery and to present several characteristics of MNPs based on our new comprehensive data. We used chemoinformatic analysis methods to report the molecular properties, chemical space, and several scaffold assessments of CMNPD compared with several databases. Then, we reported the characteristics of MNPs from the aspect of halogens, comparing MNPs with terrestrial natural products (TNPs) and drugs. We found that CMNPD had a low proportion (2.91%) of scaffolds utilized by drugs, and high similarities between CMNPD and NPAtlas (a microbial natural products database), which are worth further investigation. The proportion of bromides in MNPs is outstandingly higher (11.0%) in contrast to other halogens. Furthermore, the results showed great differences in halogenated structures between MNPs and drugs, especially brominated substructures. Finally, we found that many marine species (2.52%) reported only halogenated compounds. It can be concluded from these results that CMNPD is a promising source for drug discovery and has many scientific issues relative to MNPs that need to be further investigated.

Emerging outbreak of Elizabethkingia anopheles: a systematic review of the literature

The recent outbreak of Elizabethkingia anopheles in Midwest countries has caused a number of deaths. Notably Elizabethkingia anopheles causes neonatal meningitis, bacteraemia, sepsis, blood stream infections and respiratory infections. This infection may pose serious threats to public health because of lack of sufficient research and its endemic potential unknown.
 This systematic review was meant to develop a deeper insight into the current status of E. anophelis related evidence and to highlight areas that need further research. Reviewing existing literature will help other researchers in identifying and addressing the knowledge gaps.
 Various free access databases such as Google Scholar, Scopus, PubMed, and Science Direct were employed for literature survey. All articles that have been published since 2011, when the outbreak was reported for the first time have been included in this systematic review. The research related to this subject is in earlier stages and little information is currently available. Future studies must focus on the molecular basis, control, prevention, and therapeutics of E. anophelis infection to mitigate its increasing risk. This review is meant to provide baseline data for future research. Scientific community must carry out research on infections caused by E. anophelis mosquito else it will result in a disastrous outbreak.

A Systematic Review of the Outbreak of Elizabethkingia Anophelis

The recent outbreak of Elizabethkingia anophelis in the Midwestern states of USA caused a number of deaths. Notably, these deaths occurred due to E. anophelis causing neonatal meningitis, bacteraemia, sepsis, blood stream infections and respiratory infections. These infections may pose serious threats to public health. This systematic review is meant to develop a deeper insight into the current status of E. anophelis related evidence and to highlight areas that need further research. Reviewing the existing literature will help other researchers to identify and address the knowledge gaps. Various free access databases such as Google Scholar, Scopus, PubMed, and Science Direct were employed for literature survey. All articles published since 2011, when the outbreak was reported for the first time, were consulted for this systematic review. Research related to this subject is in its earlier stages and little information is currently available. Future studies must focus on the molecular basis, control, prevention, and therapeutics of E. anophelis to mitigate its increasing risk. This review is meant to provide baseline data for future research. Scientific community must carry out research on the infections caused by E. anophelis mosquito, else it may result in a disastrous outbreak. Copyright (c) 2021 NoshabaRani, BrairaWahid

Trends in Orthopedic Surgery in Chile: Analysis Between 2004 and 2020.

PurposeTo describe the trends of orthopedic surgery in Chile since 2004 in terms of the number and gender of surgeons, the incidence of procedures per 100,000 inhabitants (IR), and access by health insurance and type of health center.MethodsA cross-sectional study was designed. Three databases were analyzed: the free access database of the Chilean Department of Statistics and Health Information (DEIS), which had information on all procedures performed in health institutions in Chile from 2004 to 2020. Then, the orthopedic surgeon registry was requested from the National Superintendence of Health (NSH). Finally, the database of the Chilean Society of Orthopaedic Surgeons (SCHOT) was analyzed. Spearman's correlation was used to determine significant trends during the analyzed period.ResultsThe NSH reported 1770 orthopedic surgeons in 2020; 56% were affiliated with SCHOT. An upward trend in the proportion of female orthopedic surgeons was found, from 4.8% in 2004 to 7.6% in 2020. Since 2004, the IR of orthopaedic surgeries has been increasing significantly in both health insurances; the growth in public insurance follows a linear model (R2 = 0.970) of parameters β0 = - 55982.6 (p <0.000) and β1 = 28.02 (p <0.000) while in private insurance, the growth is also linear (R2 = 0.890) but with a greater slope: β0 = - 104136 (p <0.000) and β1 = 52.15 (p <0.000). A significant downward trend was found in the proportion of surgeries performed in the public health network (rho = -0.797, p = 0.0002).ConclusionsThere is a significant increase in the number of orthopedic surgeons and the number of procedures per 100,000 inhabitants. Nevertheless, there is evident inequity in access to orthopedic surgery in Chile and low gender diversity.

Wiping out the covid-19 pandemic through bioinformatics: a review on database and web tools applications

The COVID-19 is an illness caused by SARS-CoV-2 that has ended up a widespread since March 2020 as declared by WHO. This condition gives a huge impact on miscellaneous sectors enforcing advance use of technology and information system, especially in scientific and medical community. Bioinformatics as a multidisciplinary method plays important role to overcome the COVID-19 outbreak in the early-stage through data exchange in virtual databases. There are a number of free access databases containing basic to complex information of SARS-CoV-2, such as genetic data sequence, epidemiology, evolutionary analysis, pharmacology, and so on. Bioinformatics allows us to analyze the data further to reveal new information applied in biomedical technology activities. Thus, bioinformatics helps the scientists, clinicians, and government learn the genomic characteristics of SARS-CoV-2, to trace the SARS-CoV-2 spread, and select and develop biomarker for reliable diagnostic tools, and design the drug and vaccine for SARS-CoV-2. This review aims to view insights on uses of bioinformatics methods and the databases related to SARS-CoV-2

Agrochemicals Residues in Human Milk, Scientific Evidence or Overestimated Risk?

Background: Exposures to agrochemicals can be directly through the handling of chemicals as well as indirectly when consuming food containing residues of them. Such exposure may affect the health of the population if accumulated in the human body, including in human milk. Objective: This literature review aims to assess the potential impact of agrochemical residues in human milk as a barrier to breastfeeding. Methods: The search followed PRISMA guidelines (Preferred Reporting Items for Systematic Reviews and Meta Analyses), by selecting original articles published between 1995 and 2019, extracted from the free access databases PubMed, Scielo, Web of Sciences and Scopus using as keywords: agrochemicals, pesticides, breastfeeding, human milk, contamination and infant. Medical Subject Headings (MeSH) were used as descriptors. Results: We selected 11 studies. Taking as inclusion criteria those most aligned with the objective of our study. Conclusion: The studies presented in this review ratified the benefits of breastfeeding compensating for the potential risks of agrochemical residues in human milk.

Trends in Knee and Hip Arthroplasty in Chile Between 2004 and 2019.

IntroductionThe purpose of this study is to describe the incidence rate (IR) per 100,000 inhabitants of arthroplasty in Chile between 2004 and 2019, emphasizing knee and hip arthroplasty.MethodsThis is a cross-sectional study. Patients who underwent arthroplasty between 2004 and 2019 were identified in the free access database of the Chilean Department of Statistics and Health Information (DEIS), which depends directly on the Ministry of Health. This register stores all hospital discharges of the country from private or public health centers. The trend during the period of study was analyzed using Spearman's correlation.ResultsFrom a total of 111,303 patients, 133,518 arthroplasties were performed. Hip arthroplasty (HA) accounted for 73.35%, followed by knee arthroplasty (KA) (23,92%). A significant upward trend was found in HA (rho=0.95, p<0.000) and KA (rho=0.98, p<0.000). Most of the surgeries were done within the Public Health Network (61,6%), but 20% of patients affiliated with public insurance underwent arthroplasty in a private center. Patients above 60 years of age affiliated with private insurance underwent 1.8 HA and 2.5 KA for every one HA and KA undergone by patients of the same age group who were affiliated with public insurance.ConclusionHA was more frequent than KA. A significant gap was found in the incidence of arthroplasty as compared to countries belonging to the Organization for Cooperation and Economic Development, given by a less aged population and by inequity in health access. Wider coverage and a national registry for arthroplasty must be considered in Chilean health policies.