Background: Vaccination is our main strategy to control SARS-CoV-2 infection. Given the decrease in quantitative SARS-CoV-2 spike 1–2 IgG antibody titers three months after the second BNT162b2 dose, healthcare workers received a third booster six months after completing the original protocol. This study aimed to analyze the quantitative SARS-CoV-2 spike 1–2 IgG antibody titers and the safety of the third dose. Material and methods: A prospective longitudinal cohort study included healthcare workers who received a third booster six months after completing the BNT162b2 regimen. We assessed the quantitative SARS-CoV-2 spike 1–2 IgG antibody titers 21–28 days after the first and second dose, three months after the completed protocol, 1–7 days following the third dose, and 21–28 days after booster administration. Results: The cohort comprised 168 participants aged 41(10) years old, 67% of whom were female. The third dose was associated with an increase in quantitative antibody titers, regardless of previous SARS-CoV-2 history. In cases with a negative SARS-CoV-2 history, the median (IQR) antibody titer values increased from 379 (645.4) to 2960 (2010) AU/ml, whereas in cases with a positive SARS-CoV-2 history, from 590 (1262) to 3090 (2080) AU/ml (p<0.001). The third dose caused a lower number of total (local and systemic) adverse events following immunization (AEFI) compared with the first two vaccines. However, in terms of specific symptoms such as fatigue, myalgia, arthralgia, fever, and adenopathy, the proportion was higher in comparison with the first and second doses (p<0.05). The most common AEFI after the third BNT162b2 vaccine was pain at the injection site (n = 82, 84.5%), followed by fatigue (n = 45, 46.4%) of mild severity (n = 36, 37.1%). Conclusion: The third dose applied six months after the original BNT162b2 regimen increased the quantitative SARS-CoV-2 spike 1–2 IgG antibody titers. The booster dose was well tolerated and caused no severe AEFI.

The electrocardiogram (ECG) is the most common tool used to predict left ventricular hypertrophy (LVH). However, it is limited by its low accuracy (<60%) and sensitivity (30%). We set forth the hypothesis that the Machine Learning (ML) C5.0 algorithm could optimize the ECG in the prediction of LVH by echocardiography (Echo) while also establishing ECG-LVH phenotypes. We used Echo as the standard diagnostic tool to detect LVH and measured the ECG abnormalities found in Echo-LVH. We included 432 patients (power = 99%). Of these, 202 patients (46.7%) had Echo-LVH and 240 (55.6%) were males. We included a wide range of ventricular masses and Echo-LVH severities which were classified as mild (n = 77, 38.1%), moderate (n = 50, 24.7%) and severe (n = 75, 37.1%). Data was divided into a training/testing set (80%/20%) and we applied logistic regression analysis on the ECG measurements. The logistic regression model with the best ability to identify Echo-LVH was introduced into the C5.0 ML algorithm. We created multiple decision trees and selected the tree with the highest performance. The resultant five-level binary decision tree used only six predictive variables and had an accuracy of 71.4% (95%CI, 65.5–80.2), a sensitivity of 79.6%, specificity of 53%, positive predictive value of 66.6% and a negative predictive value of 69.3%. Internal validation reached a mean accuracy of 71.4% (64.4–78.5). Our results were reproduced in a second validation group and a similar diagnostic accuracy was obtained, 73.3% (95%CI, 65.5–80.2), sensitivity (81.6%), specificity (69.3%), positive predictive value (56.3%) and negative predictive value (88.6%). We calculated the Romhilt-Estes multilevel score and compared it to our model. The accuracy of the Romhilt-Estes system had an accuracy of 61.3% (CI95%, 56.5–65.9), a sensitivity of 23.2% and a specificity of 94.8% with similar results in the external validation group. In conclusion, the C5.0 ML algorithm surpassed the accuracy of current ECG criteria in the detection of Echo-LVH. Our new criteria hinge on ECG abnormalities that identify high-risk patients and provide some insight on electrogenesis in Echo-LVH.

Purpose. Autonomic dysfunction is a common nonmotor feature and early manifestation of Parkinsons disease (PD). Autonomic dysfunction in PD is associated with a worse prognosis. We sought to characterize autonomic dysfunction and identify associated factors in patients with early PD. Methods. An observational, cross-sectional, descriptive, and analytical study was conducted to evaluate patients with early PD from the Parkinsons Progression Markers Initiative. We utilized the Scales for Outcomes in Parkinsons Disease-Autonomic dysfunction questionnaire to determine the prevalence and frequencies of autonomic symptomatology. The cohort was grouped into high and low dysautonomic scores. A regression model identified variables that independently explained dysautonomic scores in our early PD cohort. Results. 414 PD patients had a mean age of 61.1 (SD 9.7) years at diagnosis and mean disease duration of 6.7 (SD 6.6) months. Among all patients, 43.7% (181/414) had high dysautonomic scores. Urinary and gastrointestinal symptoms were the most prevalent and frequently reported dysautonomic symptoms. Patients with fatigue (beta = 4.28, ), probable rapid eye movement sleep behavior disorder (beta = 2.71, ), excessive daytime sleepiness (beta = 1.88, ), impulsivity and compulsivity (beta = 2.42, ), and increasing age (beta = 1.05, ) were more likely to have high dysautonomic scores. Conclusion. Lower urinary tract and gastrointestinal symptoms are prevalent and frequent in early PD patients. Fatigue, sleep disorders, impulsivity and compulsivity, and age are predictors of autonomic dysfunction. Autonomic symptoms predominated in this group of early PD patients in the disease course and were associated with more severe disease.

Background: People from Latin America (LA) and the Hispanic/Latino community living in the United States (LUS) exhibit a high prevalence of diabetes (DM) and obesity (OB). The Gut Microbiome (GM) is capable of altering energy regulation and glucose metabolism, but for the expression of these diseases a combination of multiple factors such as ethnicity, genetic and nutritional factors are required. A systematic research was conducted to understand if the prevalence of OB and/or DM has an interaction with the GM in LA and LUS. Methods: Research was conducted in PubMed, Web of Science, Scielo, Embase and Google Scholar for articles between 1990 and 2017. It was restricted to human studies published in English, Spanish, or Portuguese that applied genetic techniques to study the GM in LA or LUS and discussed the association with OB and/or DM. Results: Different gut Firmicutes/Bacteroidetes relationships in several populations from LA influenced by geography, diet and lifestyles interacted with OB. Healthy people from the Mexico and US border had the same imbalance between Firmicutes and Bacteroidetes found in OB or Type 2 DM. High levels of Bacteroides and a reduced proportion of Prevotella, Megamonas, and Acidaminococcus were found in newly diagnosed type 1 DM. Once the patient was treated with insulin, an increase of Prevotella levels was seen. Inverse Firmicutes/Bacteroidetes relationship was reported before the development of Type 1 DM. Conclusion: An important relation between GM and OB and/or DM exists in LA and LUS. Further elucidation of pathophysiologic mechanisms is required.

Depression and anxiety are hightly prevalent among patients with Type 2 Diabetes (T2D), however not commonly related to outcomes, treatment and comorbidities. Eating behaviors could also have an implication. To evaluate the relation between mood and eating behaviors with demographic, physical, treatment, biochemical profiles and chronic comorbidities in T2D we conducted this exploratory cross sectional study in a population from Mexico. Hospital Anxiety and Depression Scale and the Three Factor Eating Questionnaire Revised 21 (TFEQ-R21) were correlated with age, gender, blood pressure, treatment and comorbidities (nephropathy, neuropathy, retinopathy and cardiopathy). Multi-linear regression models and 2^k factorial analyses were conducted. Sixty-one patients (31 male) 55 years old (SD 13) with at least 5 years of T2D were included. Anxiety correlated with depression (r = 0.25, p < 0.05). Gender (women) (std B 0.026, p < 0.001), insulin therapy (std B 0.3, p = 0.11), systolic blood pressure (std B 0.263, p = 0.02) and cardiovascular disease (std B 0.232, p = 0.035) predicted depression. Insulin therapy had a positive effect in cognitive restraint. (std B 0.32, p = 0.001). Age (std B 0.37, p = 0.003) and systolic blood pressure (std B 0.237, p = 0.048) predicted positively emotional eating (not previously published). 2^k factorial analyses proved additive interaction between complications and insulin therapy with depression. This study supports the relation between mood disorders and eating behaviors with demographic, physical, treatment, biochemical profiles and chronic comorbidities in T2D and the additive interaction between factors and mood disorders.