Menopause is an influential aspect of a woman's life, especially if she is a sportswoman. It is important to consider the role that women play in athletics and have also reached menopause, since the loss of muscle mass and gain of fat tissue influences their sports performance. In this line, nutrition is a fundamental component, so it is relevant to know how it influences the development of a particular somatotype. To improve the performance of menopausal women athletes, a specific somatotype is required, which should be provided not only by adequate training, but also by optimal nutrition. In the present investigation, a cross-sectional and quantitative observational study was conducted. The population sample consists of 5 women between 49-60 years old who are members of an athletic club in the province of Valencia (Spain) federated in the Royal Spanish Athletic Federation. The average caloric intake of the athletes was 2073 Kcal/day which meet 96% of the recommended intake. However, the distribution of the macronutrients in the diet did not meet the established recommendations: 55% of carbohydrates, 30% of fats and 15% of proteins. Instead, the athletes ingested 43% of carbohydrates, 41% of fats and 17% of proteins. These showed a significant excess in the intake of fats (37% extra) and carbohydrates (22% extra) which would lead to an abnormal distribution and accumulation of body fat that would be enhanced by the menopausal state of the athletes. All together will influence body composition that will be associated with an inadequate somatotype which would clearly impair the sports performance of the athletes.

Objective: Circulating microRNAs (miRNAs) have been proposed as emerging biomarkers for obesity and metabolic comorbidities. Our aim was to characterize miRNA signatures and assess their utility to discriminate between insulin resistance (IR) phenotypes in paediatric obesity and evaluate their role in diverse metabolic pathways.

Methods: Observational, case-control study, including prepubertal children between 6 and 10 years, divided in three study groups: a) healthy control (n=3), b) metabolically healthy obese children (n=3) and c) IR obese children (n=3). Obese patients were defined by a body mass index > 2 SD for age and sex. IR patients fulfilled at least one of the ADA’s insulin resistance criteria (Basal Insulin>15 U/mL; Insulin along the OGTT>150 U/mL; Insulin>75U/mL at 120’ on the OGTT; and/or, iHOMA>3,5). We first screened 179 miRNAs to identify differentially expressed miRNAs between groups. Total RNA was extracted from plasma using the miRNeasy Serum/Plasma Advanced Kit (Qiagen). Correlations between miRNA levels and clinical parameters were investigated.

Results: The established criteria for miRNA candidate’s selection were high expression levels (Max. Cq<39 and detected in at least 95% of all samples) and statistical significance (p<0.05). We found 19 miRNAs highly expressed and differentially detected between a) metabolically healthy obese vs. IR obese children and b) healthy subjects vs. obese children, including miRNAs with previously reported roles in iron and glucose metabolism, oxidative stress, inflammation and erythrocyte integrity.

Conclusion: The miRNA profile identified new candidates related to pediatric obesity, and enables to differentiate between IR phenotype and metabolically healthy obese children.

Traditional agriculture research programs have used classical breeding and molecular biology approaches for crop improvement. Besides, they are proved inadequate to deal collectively with a major number of problems. High throughput sequencing has shown a way towards overcoming those barriers along with storing and evaluating various big scale datasets on experimental basis. Artificial intelligence with Machine and deep learning techniques uses a training dataset as a calibrator for performing identification, classification, quantification and prediction. Different algorithms can interpret the same data to different desirable outputs; the output includes a simpler solution for the complex problems in link with a given dataset. Its application has moved research towards less biased and high precision results which are extensively accepted on a global level ^[1-3].

The sophisticated application of AI and machine learning is prevalent in genomics, transcriptomics, proteomics, metabolomics and systems biology^[4]. The approach of Interpreting a given dataset with deep learning algorithms mentioned in figure 1 has been used for predicting translational initiation site recognition^[5],signal peptide prediction^[6], subcellular localisation^[7], plant effectors^[8], fungal effectors^[9], promoter recognition^[10], mRNA based alternative splicing^[11], m5cap^[12], poly A site^[13], RNA editing^[14], epistatic state^[15], gene^[16] and protein function and interaction^[17],mutational analysis^[18], epigenetic interaction^[19], gene expression analysis^[20], transcription factor binding^[21], Chromatin signature^[21], gene–environment interactions^[22], SNP detection for QTL and interactome analysis^[23-25].

Single nucleotide polymorphism is one of the major molecular markers for the indication of genetic diversity for crop improvement programs. It is majorly used for the assessment of genomic breeding values. Approaches like NGS are used to locate SNP in economic improvement traits, for the easy and early domestication of beneficial crops. However, the error-prone fashion of the available NGS analysis tools is still a big concern which can lead to false-positive results. Machine learning methods have paved a way towards more precise SNP screening from the sequenced data available in large natural population ^[23-25]. Fig.1 depicts the available machine learning algorithm used in SNP detection. In addition to it, “Integrated SNP Mining and Utilization” (ISMU) Pipeline ^[26] and “SNP machine learning” (SNP‐ML)^[27] are two of the ML based models presently in use for SNP based QTL analysis. Use of molecular marker datasets with machine learning algorithm holds promising results in genetic analysis and hybrid breeding ^[28].

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Biomedical research often involves studying patient data that contain personal information. Inappropriate use of these data might lead to leakage of sensitive information, which can put patient privacy at risk. The problem of preserving patient privacy has received increasing attentions in the era of big data. Therefore, for a biomedical research with this type of technology to be carried out correctly, it is essential to take care of the personal rights of the population and it is a necessary point that should not be overlooked.

Ascariasis is a worldwide disease that causes a great impact in the pig sector and, also, in Public Health because it affects humans. This is the reason which makes that this disease must be controlled in slaughterhouses. The aim of the study was to determine the prevalence of ascariasis in pigs in a slaughterhouse in Valencia in 2018, which was 10.19 %. For this purpose, 464,659 animals from 525 farms in different Autonomous Communities were studied. In addition, we wanted to determine the influence of factors such as the origin of the farm and the month in which the animals are slaughtered in the onset of the disease.

Coffee (Coffea arabica L.) has a gelatinous layer, called mucilage, that covers the seed and that in its transformation process is eliminated as a waste. In the province of Pastaza two varieties of the species have been cultivated, respectively the "catuai" and the "castle", therefore the present research aims to determine the amount of caffeine, the total content of polyphenols and the antioxidant activity of the concentrate mucilage of the two varieties. The caffeine content was evaluated using the UV-Vis spectrophotometer method, the total polyphenols through the Folin-Ciocalteu method and the antioxidant activity with the FRAP method. The coffee mucilage concentrate presented respectively a caffeine content of 4.87mg / 10g for the Catuai variety and 2.37mg / 10g for the Castillo variety. As for the polyphenols, values ​​of 22.87 mg / g were obtained for the Catuai variety and 21.1 mg / g for the Castillo variety, while the antioxidants of the Catuai variety presented 17.78 mg / g and the Castillo variety 17.46 mg / g. The Catuai variety has a higher content of caffeine and polyphenols and both varieties show a promising antioxidant activity. The results of the study allow recommending new studies of the C. arabica mucilage concentrates as potential ingredients of functional or cosmetic foods.

NASADEM (NASA Digital Elevation Model) is a merged digital elevation product prepared by the National Aeronautics and Space Administration (NASA) from SRTM (Shuttle Radar Topography Mission) DEM as primary data along with other secondary datasets generated from remote sensing-based techniques like satellite photogrammetry and spaceborne LiDAR. These DEM products of NASADEM are reanalysis datasets produced from SRTM and datasets such as ASTER (Advanced Spaceborne Thermal Emission and Reflection Radiometer) DEM; Ice, Cloud, and land Elevation Satellite (ICESat) - Geoscience Laser Altimeter System (GLAS) elevation datasets; and Advanced Land Observing Satellite (ALOS) - Panchromatic Remote-sensing Instrument for Stereo Mapping (PRISM) DEM datasets, available at other locations globally. Three sites were chosen, namely Kendrapara (Odisha), Jaipur (Rajasthan), and Dehradun (Uttarakhand) with plain, moderate, and highly undulating terrain conditions for the assessment of NASADEM. The RMSE results were compared with other merged DEM products namely EarthEnv-DEM90 and MERIT (Multi-Error-Removed Improved Terrain) DEM. The ground control points (GCPs) collected through differential GNSS (DGNSS) surveys were used for the assessment of vertical accuracy and the statistical parameters, such as mean error (ME), mean absolute error (MAE), and root mean square error (RMSE). The RMSE of 4.71m at the Dehradun site depicts that in undulating regions NASADEM is performing better than both EarthEnv-DEM90 and MERIT DEM. However, in the case of urban and plain regions, the performance of MERIT DEM and EarthEnv DEM is superior to that of NASADEM.

Objective To conduct data-mining of montelukast-related adverse events after marketing to provide a reference for safe clinical medication. Methods We use reporting odds ratio (ROR) and proportional reporting ratio (PRR) methods to mine the adverse reaction signals of montelukast on the adverse reaction report data of 22 quarters from 2015Q1 to 2020Q2, extracted from the FAERS database. Results Totally 467 signals were detected with ROR and PRR, and the most relevant 50 preferred terms are conducted based on the signal strength and signal frequency, 55.32% of signals were not reported in the proved label. Adverse reaction signals of montelukast involve 27 systems and organs, in addition to psychiatric diseases, majority of adverse events included respiratory, thoracic, and mediastinal disorders and examination. Conclusion Clinical use of montelukast should pay attention to the patient's neuropsychiatric symptoms, especially those not reported in the proved label, such as Separation anxiety disorder, Sleep terror and PANDAS. For patients with mental history, phenylketonuria and autoimmune diseases who use the montelukast，relevant workers should pay attention to monitoring to ensure safe and rational drug use.

Recurrent pregnancy loss (RPL) usually means two or more pregnancy failures occured within 20 weeks of conception, with an incidence as great as 3% to 5% of pregnancies [1]. RPL is a complex disease with diverse causes, including heredity, age, antiphospholipid syndrome, uterine anomalies, thrombosis, hormone or metabolic disorders, infection, autoimmunity, sperm quality, lifestyle, and mental, psychological, and environmental factors [2, 3], which brings a substantial adverse impact on society and female health care. Therefore, understanding the gene regulation network of RPL is beneficial to clarify the etiology of recurrent abortion and possible to prevent another miscarriage through some intervention, which will have great clinical significance and social benefits.

Genetic abnormalities are the main factor of RPL[4]. The correlation between chromosomal abnormalities and abortion has been clear and the relative genetic risk is easy to predict. For example, an abnormal karyotype in either partner, especially featuring a translocation and/or an inversion, is considered to be a cause of RPL, due to unbalanced chromosomal segregation in meiosis, which cause significant chromosomal imbalances (i.e., disomies and nullisomies) in their gametes with subsequent partial aneuploidies in the conceptuses [5, 6]. In theory, the incidence of normal, carrier and imbalances in the offspring of reciprocal translocation, Roche translocation and inversion were 1/18-1/18-16/18, 1/6-1/6-4/6 and 1/4-1/4-2/4, respectively, which are calculated according to the principle of chromosome separation and recombination. With the development of preimplantation genetic test (PGT) technology that derived from in-vitro fertilization (IVF), it is found that the actual genetic risk is quite different from theoretical calculation, which is not only related to the sex of the carrier, but also the location of the breakpoint and the chromosome involved[7-9]. A study by Xie etc. showed that the ratio of euploid and translocation balance embryos in reciprocal translocation and Robert translocation were 27.8% and 44%, the rest of all were aneuploid or translocation imbalance embryos [10]. Wang reported that 104 embryos from 11 Roche translocation carriers were detected, including normal, translocation carriers and unbalanced chromosome embryos were 22%, 19%, 59% [11].

Apart from chromosomal factors, abortion is also related to endocrine, immune, thrombotic, male sperm and other factors. With the development and clinical application of gene detection technology, current studies showed that these so-called clinical factors may eventually be attributed to genetic abnormalities. Examples of SNPs and copy number variants (CNVs) that may contribute to a genetic susceptibility to miscarriage include variances in the following genes: AR, DNMT3, FOXP3, CGB5, NLRP7, TIMP2 and CTNNA3[12-18]. A recent systematic review of 428 case-control studies from 1990 to 2015 evaluated 472 variants in 187 genes [19]. Meta-analysis could only be performed for 36 variants in 16 genes, because the other studies had never been replicated. The investigators reported modest associations between RPL and 21 variants in genes (odds ratio [OR] 0.51–2.37) involved in the immune response (IFNG, IL10, KIR2DS2, KIR2DS3, KIR2DS4, MBL, TNF), coagulation (F2, F5, PAI-1, PROZ), metabolism (GSTT1, MTHFR), and angiogenesis (NOS3, VEGFA). In addition, mutations in the thrombophilia genes, including MTHFR, F2, and F5, and deficiencies in protein C, protein S, and antithrombin III, may also increase the risk of second- or third-trimester loss[20-23].

Epigenetic modifications, including DNA methylation, noncoding RNA, genomic imprinting, and histone modification, refer to the heritable changes in gene functions without changing the genetic genes. Abnormal DNA methylation was found in the decidual chorionic villi of RPL with normal karyotype, especially at the loci of the imprinting genes [24, 25]. Aberrant microRNAs (miRNAs), which are endogenous small noncoding RNAs and ~22 nucleotides, were found in unexplained RPL [26]. Over the past few years, some studies have verified a clear correlation between lncRNAs and placental development, such as the lncRNAs HOTAIR, HOXA11-AS, and MEG3 and MALAT1, and these lncRNAs appear to be involved in some pregnancy pathologies[27-29].

Above all, genetic abnormalities or variants and related gene expression abnormalities play an important role in RPL. Understanding the correlation between the expression differences of these changes in different populations, different tissues (peripheral blood, amniotic fluid, villi, embryo, etc.) and abortion, is of great significance for clinical abortion counseling and genetic counseling, as well as for the exploration of potential factors of unexplained RPL and the development of beneficial interventions, so as to reduce or avoid abortion risks, improve women's survival quality.

Los modelos PTMLIF (Perturbation Theory, Machine Learning and Information Fusion) son la combinación de la teoría de la perturbación con el aprendizaje automático y la fusión de la información. Estos modelos se usaron en esta investigación para predecir la probabilidad de que los complejos nanopartícula decoradas con fármacos (Drugs-decorated Nanoparticles ó DDNP) tengan actividad antipalúdica en este caso de estudio contra el Plasmodium. Esta enfermedad es la causa de la malaria en los seres humanos que se transmite a través de la picadura del mosquito hembra del género Anopheles. Se fusionó 107 características de entrada y 249.990 ejemplos aproximadamente desde la base de datos ChEMBL. El mejor modelo de clasificación fue proporcionado por método Random Forest, con solo 27 características seleccionadas de fármacos / compuestos y nanopartículas en todas las condiciones experimentales consideradas. El alto rendimiento del modelo se demostró mediante el área media bajo las características operativas del receptor (AUC) en un subconjunto de prueba con un valor de 0,9921 ± 0,000244 (validación cruzada de 10 veces). En este trabajo también se demostró el poder de la fusión de información de las características experimentales de fármacos / compuestos y nanopartículas para la predicción de la actividad antipalúdica de nanopartículas-compuestos.