The excessive intake of alcohol is a serious public health problem, especially given the severe damage provoked by chronic or prenatal exposure to alcohol that affects many physiological processes, such as memory, motor function, and cognitive abilities. This damage is related to the ethanol oxidation in the brain. The metabolism of ethanol to acetaldehyde and then to acetate is associated with the production of reactive oxygen species that accentuate the oxidative state of cells. This metabolism of ethanol can induce the oxidation of the fatty acids in phospholipids, and the bioactive aldehydes produced are known to be associated with neurotoxicity and neurodegeneration. As such, here we will review the role of lipids in the neuronal damage induced by ethanol‐related oxidative stress and the role that lipids play in the related compensatory or defense mechanisms.

Caffeine is recognized as the first-line therapeutic agent for apnea of prematurity. The dosage regimen is 10 mg/kg loading dose and 2.5 mg/kg maintenance dose. However, the plasma concentration achieved, not always, is therapeutically useful. It makes necessary to increase the doses to reach plasma concentration up to 30 or 35 μg/mL or even higher to attain therapeutic effect. To study why neonates have these differences, and whether these effects are linked to prenatal caffeine exposure, we had to develop an analytical method for an accurate measurement of caffeine and metabolites concentration. The analysis was carried out using fetal bovine serum (FBS) as biological matrix in a high-performance liquid chromatography with an ultraviolet detector method. This method allows acceptable chromatographic resolution between analytes in 15 minutes. It was validated and proved to be linear in the 0.1–40 µg/mL range for caffeine, paraxanthine, theobromine, and theophylline in the same chromatographic analysis. Accuracy for quality control samples for intra- and interday assays was ranged from 96.5 to 105.2% and 97.1 to 106.2%. Precision had CV no more than 10% in all concentration levels for all analytes. No differences were observed between quantification in human and FBS. This method was applied to quantify plasma drug concentration in mothers and their newborns in a Mexican northeast population. In our study, we confirmed self-reported caffeine maternal intake in 85.2% (n=23); meanwhile, in their newborn’s plasma, it was detected only in 78% (n=21). Caffeine plasma concentrations in mother and newborn had a linear relationship, and no differences were observed between groups (mothers versus children). These results suggest that our analytical method and substitution of biological matrix was linear, precise, and accurate for caffeine quantification and could be used for measuring prenatal exposure and let us to study, in the future, concentration differences observed during apnea clinical treatment.

This study examines the spatial structure of children with cleft lip and palate (CLP) and its association with polluted areas in the Monterrey Metropolitan Area (MMA). The Nearest Neighbor Index (NNI) and the Spatial Statistical Scan (SaTScan) determined that the CLP cases are agglomerated in spatial clusters distributed in different areas of the city, some of them grouping up to 12 cases of CLP in a radius of 1.2 km. The application of the interpolation by empirical Bayesian kriging (EBK) and the inverse distance weighted (IDW) method showed that 95% of the cases have a spatial interaction with values of particulate matter (PM₁₀) of more than 50 points. The study also shows that 83% of the cases interacted with around 2000 annual tons of greenhouse gases. This study may contribute to other investigations applying techniques for the identification of environmental and genetic factors possibly associated with congenital malformations and for determining the influence of contaminating substances in the incidence of these diseases, particularly CLP.

We have shown increased cyclooxygenase-2 (COX-2) expression in rats with kidney failure. Increased angiotensin II concentration, hypertension, and renal mass reduction have been described during development of kidney failure. Thus we explored each of these mechanisms, because any one of them could be responsible for COX-2 induction. Kidney failure increased systolic blood pressure from 104 ± 5 to 138 ± 2 mmHg, urinary PGE₂from 74 ± 17 to 185 ± 25 ng/24 h, and COX-2 expression from 0.06 ± 0.04 to 0.17 ± 0.03 arbitraty units (AU). Treatment of the rats with ramipril or losartan prevented the increase in blood pressure, urinary PGE₂, and COX-2 expression in the rats with kidney failure. Infusion of angiotensin II increased blood pressure from 101 ± 6 to 132 ± 6 mmHg, urinary PGE₂excretion from 62 ± 15 to 155 ± 17 ng/24 h, and COX-2 expression from 0.23 ± 0.01 to 1.6 ± 0.3 AU. When the angiotensin II-infused rats were treated with nitrendipine, blood pressure decreased from 132 ± 6 to 115 ± 2 mmHg, and urinary PGE₂excretion decreased from 152 ± 18 to 97 ± 12 ng/24 h, whereas COX-2 expression was 1.6 ± 0.7 and 1.7 ± 0.5 AU for rats with and without nitrendipine. Blood pressure of the rats with renal pole resection was similar to that in sham rats (97 ± 7 and 91 ± 4 mmHg, respectively), whereas COX-2 expression was increased in rats with renal pole resection, from 0.06 ± 0.04 to 0.12 ± 0.03 AU. We suggest that in kidney failure, the increase in angiotensin II concentration regulates COX-2 expression, thereby increasing prostaglandin synthesis, which contributes to the development of kidney failure.

Diffuse large B-cell lymphoma (DLBCL) is classified into germinal center-like (GCB) and non-germinal center-like (non-GCB) cell-of-origin groups, entities driven by different oncogenic pathways with different clinical outcomes. DLBCL classification by immunohistochemistry (IHC)-based decision tree algorithms is a simpler reported technique than gene expression profiling (GEP). There is a significant discrepancy between IHC-decision tree algorithms when they are compared to GEP. To address these inconsistencies, we applied the machine learning approach considering the same combinations of antibodies as in IHC-decision tree algorithms. Immunohistochemistry data from a public DLBCL database was used to perform comparisons among IHC-decision tree algorithms, and the machine learning structures based on Bayesian, Bayesian simple, Naïve Bayesian, artificial neural networks, and support vector machine to show the best diagnostic model. We implemented the linear discriminant analysis over the complete database, detecting a higher influence of BCL6 antibody for GCB classification and MUM1 for non-GCB classification. The classifier with the highest metrics was the four antibody-based Perfecto–Villela (PV) algorithm with 0.94 accuracy, 0.93 specificity, and 0.95 sensitivity, with a perfect agreement with GEP (κ = 0.88, P < 0.001). After training, a sample of 49 Mexican-mestizo DLBCL patient data was classified by COO for the first time in a testing trial. Harnessing all the available immunohistochemical data without reliance on the order of examination or cut-off value, we conclude that our PV machine learning algorithm outperforms Hans and other IHC-decision tree algorithms currently in use and represents an affordable and time-saving alternative for DLBCL cell-of-origin identification.