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

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 intellectual disability syndrome characterized by seizures and dysmorphic features was initially described in 2017 and was associated with genetic variants in the OTUD6B gene, identified by exome sequencing (ES) in a large cohort. This multisystem disorder primarily affects the central nervous system, the gastrointestinal, and the skeletal systems. In this article, we describe the first Mexican patient diagnosed by ES. The homozygous c.433C>T (p.Arg145*) variant of the OTUD6B gene confirmed this intellectual disability syndrome. In addition to seizures and other more frequently reported manifestations of this condition, this is the third patient with associated hypothyroidism and hypogammaglobulinemia, underscoring the value of screening for these conditions in other patients. The current challenge with this patient is to ensure medical management of his seizures and provide him with a better quality of life. The possibilities of additional therapeutic approaches may increase by understanding the physiopathology of the involved pathways.

BackgroundStructural equation modeling (SEM) can help understanding complex functional relationships among obesity, non-alcoholic fatty liver disease (NAFLD), family history of obesity, targeted metabolomics and pro-inflammatory markers. We tested two hypotheses: 1) If obesity precedes an excess of free fatty acids that increase oxidative stress and mitochondrial dysfunction, there would be an increase of serum acylcarnitines, amino acids and cytokines in obese subjects. Acylcarnitines would be related to non-alcoholic fatty disease that will induce insulin resistance. 2) If a positive family history of obesity and type 2 diabetes are the major determinants of the metabolomic profile, there would be higher concentration of amino acids and acylcarnitines in patients with this background that will induce obesity and NAFLD which in turn will induce insulin resistance.Methods/Results137 normoglycemic subjects, mean age (SD) of 30.61 (8.6) years divided in three groups: BMI<25 with absence of NAFLD (G1), n = 82; BMI>30 with absence of NAFLD (G2), n = 24; and BMI>30 with NAFLD (G3), n = 31. Family history of obesity (any) was present in 53%. Both models were adjusted in SEM. Family history of obesity predicted obesity but could not predict acylcarnitines and amino acid concentrations (effect size <0.2), but did predict obesity phenotype.ConclusionFamily history of obesity is the major predictor of obesity, and the metabolic abnormalities on amino acids, acylcarnitines, inflammation, insulin resistance, and NAFLD.

Background Left ventricular hypertrophy detected by echocardiography (Echo-LVH) is an independent predictor of mortality. Integration of the Philips DXL-16 algorithm into the electrocardiogram (ECG) extensively analyses the electricity of the heart. Machine learning techniques such as the C5.0 could lead to a new decision tree criterion to detect Echo-LVH. Objectives To search for a new combination of ECG parameters predictive of Echo-LVH. The final model is called the Cardiac Hypertrophy Computer-based model (CHCM). Methods We extracted the 458 ECG parameters provided by the Philips DXL-16 algorithm in patients with Echo-LVH and controls. We used the C5.0 ML algorithm to train, test, and validate the CHCM. We compared its diagnostic performance to validate state-of-the-art criteria in our patient cohort. Results We included 439 patients and considered an alpha value of 0.05 and a power of 99%. The CHCM includes T voltage in I (≤0.055 mV), peak-to-peak QRS distance in aVL (>1.235 mV), and peak-to-peak QRS distance in aVF (>0.178 mV). The CHCM had an accuracy of 70.5% (CI95%, 65.2–75.5), a sensitivity of 74.3%, and a specificity of 68.7%. In the external validation cohort (n = 156), the CHCM had an accuracy of 63.5% (CI95%, 55.4–71), a sensitivity of 42%, and a specificity of 82.9%. The accuracies of the most relevant state-of-the-art criteria were: Romhilt-Estes (57.4%, CI95% 49–65.5), VDP Cornell (55.7%, CI95%47.6–63.7), Cornell (59%, CI95%50.8–66.8), Dalfó (62.9%, CI95%54.7–70.6), Sokolow Lyon (53.9%, CI95%45.7–61.9), and Philips DXL-16 algorithm (54.5%, CI95%46.3–62.5). Conclusion ECG computer-based data and the C5.0 determined a new set of ECG parameters to predict Echo-LVH. The CHCM classifies patients as Echo-LVH with repolarization abnormalities or LVH with increased voltage. The CHCM has a similar accuracy, and is slightly more sensitive than the state-of-the-art criteria.