The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is responsible for the coronavirus disease (COVID-19), a highly contagious infectious disease that has caused many deaths worldwide. Despite global efforts, it continues to cause great losses, and leaving multiple unknowns that we must resolve in order to face the pandemic more effectively. One of the questions that has arisen recently is what happens, after recovering from COVID-19. For this reason, the objective of this study is to identify the risk of presenting persistent symptoms in recovered from COVID-19. This case-control study was conducted in one state of Mexico. Initially the data were obtained from the participants, through a questionnaire about symptoms that they had at the moment of the interview. Initially were captured the collected data, to make a dataset. After the pre-processed using the R project tool to eliminate outliers or missing data. Obtained finally a total of 219 participants, 141 recovered and 78 controls. It was used confidence level of 90% and a margin of error of 7%. From results it was obtained that all symptoms have an associated risk in those recovered. The relative risk of the selected symptoms in the recovered patients goes from 3 to 22 times, being infinite for the case of dyspnea, due to the fact that there is no control that presents this symptom at the moment of the interview, followed by the nausea and the anosmia with a RR of 8.5. Therefore, public health strategies must be rethought, to treat or rehabilitate, avoiding chronic problems in patients recovered from COVID-19.

Background: Worldwide preeclampsia (PE) is the leading cause of maternal death and affects 5 to 8% of pregnant women. PE is characterized by elevated blood pressure and proteinuria. Doppler Ultrasound (US) evaluation has been considered a useful method for prediction of PE; however, there is no complete data about the most frequently altered US parameters in the pathology. The aim of this study was to evaluate the uterine, umbilical, and the middle cerebral arteries using Doppler US parameters [resistance index (RI), pulsatility index (PI), notch (N), systolic peak (SP) and their combinations] in pregnant women, in order to make a global evaluation of hemodynamic repercussion caused by the established PE. Results: A total of 102 pregnant Mexican women (65 PE women and 37 normotensive women) were recruited in a cases and controls study. Blood velocity waveforms from uterine, umbilical, and middle cerebral arteries, in pregnancies from 24 to 37 weeks of gestation were recorded by trans-abdominal examination with a Toshiba Ultrasound Power Vision 6000 SSA-370A, with a 3.5 MHz convex transducer. Abnormal general Doppler US profile showed a positive association with PE [odds ratio (OR) = 2.93, 95% confidence interval (CI) = 1.2 - 7.3, P = 0.021)], and a specificity and predictive positive value of 89.2% and 88.6%, respectively. Other parameters like N presence, RI and PI of umbilical artery, as well as the PI of middle cerebral artery, showed differences between groups (P values < 0.05). Conclusion: General Doppler US result, as well as N from uterine vessel, RI from umbilical artery, and PI from umbilical and middle cerebral arteries in their individual form, may be considered as tools to determine hemodynamic repercussion caused by PE.

In recent times the statistical computation techniques are gaining a lot of interest for analyzing the behavior of various mathematical distributions. This paper derives the likelihood distribution of image priors which has been further used to denoise the image. This paper aims to maximize the likelihood estimation of the parameters of interest i.e. prior and posterior estimation. We have proposed a prior-based distribution model which has been applied to additive, multiplicative and mixed noise cases. The various estimation parameters such as statistical variance and mean parameters have been used to evaluate the maximum likelihood of image priors for these noise models. Later, we have used an optimization technique based on the likelihood to reconstruct noise-free images efficiently. This paper uses conditional likelihood and wavelet transformation-based minimization techniques to minimize the noise in the pixels and a final denoised image is recovered. The conditional likelihood of the image has been optimized using pixel-based minimization w.r.t. the wavelet transformation coefficients. The simulation and analytical results have also been presented for the different noise cases.

The prevalence of diabetes mellitus is increasing worldwide, causing health and economic implications. One of the principal microvascular complications of type 2 diabetes is Distal Symmetric Polyneuropathy (DSPN), affecting 42.6% of the population in Mexico. Therefore, the purpose of this study was to find out the predictors of this complication. The dataset contained a total number of 140 subjects, including clinical and paraclinical features. A multivariate analysis was constructed using Boruta as a feature selection method and Random Forest as a classification algorithm applying the strategy of K-Folds Cross Validation and Leave One Out Cross Validation. Then, the models were evaluated through a statistical analysis based on sensitivity, specificity, area under the curve (AUC) and receiving operating characteristic (ROC) curve. The results present significant values obtained by the model with this approach, presenting 67% of AUC with only three features as predictors. It is possible to conclude that this proposed methodology can classify patients with DSPN, obtaining a preliminary computer-aided diagnosis tool for the clinical area in helping to identify the diagnosis of DSPN.

One of the main microvascular complications presented in the Mexican population is diabetic retinopathy which affects 27.50% of individuals with type 2 diabetes. Therefore, the purpose of this study is to construct a predictive model to find out the risk factors of this complication. The dataset contained a total of 298 subjects, including clinical and paraclinical features. An analysis was constructed using machine learning techniques including Boruta as a feature selection method, and random forest as classification algorithm. The model was evaluated through a statistical test based on sensitivity, specificity, area under the curve (AUC), and receiving operating characteristic (ROC) curve. The results present significant values obtained by the model obtaining 69% of AUC. Moreover, a risk evaluation was incorporated to evaluate the impact of the predictors. The proposed method identifies creatinine, lipid treatment, glomerular filtration rate, waist hip ratio, total cholesterol, and high density lipoprotein as risk factors in Mexican subjects. The odds ratio increases by 3.5916 times for control patients which have high levels of cholesterol. It is possible to conclude that this proposed methodology is a preliminary computer-aided diagnosis tool for clinical decision-helping to identify the diagnosis of DR.