Analysis of alcohol-metabolizing enzymes genetic variants and RAR/RXR expression in patients diagnosed with fetal alcohol syndrome: a case-control study.

Understanding the mechanisms underlying alcohol metabolism and its regulation, including the effect of polymorphisms in alcohol-metabolizing enzymes, is crucial for research on Fetal Alcohol Spectrum Disorders. The aim of this study was to identify specific single nucleotide polymorphisms in key alcohol-metabolizing enzymes in a cohort of 71 children, including children with fetal alcohol syndrome, children prenatally exposed to ethanol but without fetal alcohol spectrum disorder, and controls. We hypothesized that certain genetic variants related to alcohol metabolism may be fixed in these populations, giving them a particular alcohol metabolism profile. In addition, the difference in certain isoforms of these enzymes determines their affinity for alcohol, which also affects the metabolism of retinoic acid, which is key to the proper development of the central nervous system. Our results showed that children prenatally exposed to ethanol without fetal alcohol spectrum disorder traits had a higher frequency of the ADH1B*3 and ADH1C*1 alleles, which are associated with increased alcohol metabolism and therefore a protective factor against circulating alcohol in the fetus after maternal drinking, compared to FAS children who had an allele with a lower affinity for alcohol. This study also revealed the presence of an ADH4 variant in the FAS population that binds weakly to the teratogen, allowing increased circulation of the toxic agent and direct induction of developmental abnormalities in the fetus. However, both groups showed dysregulation in the expression of genes related to the retinoic acid pathway, such as retinoic acid receptor and retinoid X receptor, which are involved in the development, regeneration, and maintenance of the nervous system. These findings highlight the importance of understanding the interplay between alcohol metabolism, the retinoic acid pathway and genetic factors in the development of fetal alcohol syndrome.

Prevalence of Psychoactive Substance Use During Pregnancy in Argentine Women: A Pilot Study Testing Maternal Hair.

BACKGROUND: The use of psychoactive substances (PSs) during pregnancy is a major public health concern because of their increasing prevalence worldwide. This study examined the understudied issue of gestational PS consumption in a cohort of Argentine delivering mothers. METHODS: A cross-sectional pilot study involving 51 women receiving delivery care was conducted at the Santa Rosa Hospital in La Pampa, Argentina. Information on maternal sociodemographic characteristics, pregnancy history, and drug use was obtained through standardized interviews. Maternal hair samples were analyzed for alcohol, tobacco, licit, illicit, and prescription substance biomarkers using ultra-high-performance liquid chromatography high-resolution mass spectrometry and gas chromatography mass spectrometry. RESULTS: During pregnancy, 49.0% of participants reported alcohol consumption, 25.5% reported tobacco use, and 23.5% reported cannabis use. Hair samples from 56.9% of the women were positive for illicit PSs, with the most frequent being cocaine (41.2%) and cannabis (15.7%). Among the women, 47.1% consumed alcohol during pregnancy. Of the 24 women with hair ethyl glucuronide ≥5 pg/mg, 33.3% drank until the end of gestation and 58.3% started a social drinking habit in the second half. The analysis also detected prescription substances (anticonvulsants, antidepressants, methadone, opioids, antihistamines, antiemetics, and analgesics), caffeine (70.6%), lidocaine, and levamisole, some of which were cocaine or opioid adulterants. CONCLUSIONS: This is the first study to objectively assess the consumption of licit and illicit PSs during pregnancy in Argentina. In contrast to most nearby countries, cocaine was the most detected illicit PS in this cohort of Argentine delivering women. This finding highlights the importance of regular monitoring of local trends in PS use during pregnancy.

Lysine Methyltransferase 9 (KMT9) Is an Actionable Target in Muscle-Invasive Bladder Cancer.

Novel treatment modalities are imperative for the challenging management of muscle-invasive and metastatic BC to improve patient survival rates. The recently identified KMT9, an obligate heterodimer composed of KMT9α and KMT9ß, regulates the growth of various types of tumors such as prostate, lung, and colon cancer. While the overexpression of KMT9α was previously observed to be associated with aggressive basal-like MIBC in an analysis of patients' tissue samples, a potential functional role of KMT9 in this type of cancer has not been investigated to date. In this study, we show that KMT9 regulates proliferation, migration, and invasion of various MIBC cell lines with different genetic mutations. KMT9α depletion results in the differential expression of genes regulating the cell cycle, cell adhesion, and migration. Differentially expressed genes include oncogenes such as EGFR and AKT1 as well as mediators of cell adhesion or migration such as DAG1 and ITGA6. Reduced cell proliferation upon KMT9α depletion is also observed in Pten/Trp53 knockout bladder tumor organoids, which cannot be rescued with an enzymatically inactive KMT9α mutant. In accordance with the idea that the catalytic activity of KMT9 is required for the control of cellular processes in MIBC, a recently developed small-molecule inhibitor of KMT9 (KMI169) also impairs cancer cell proliferation. Since KMT9α depletion also restricts the growth of xenografts in mice, our data suggest that KMT9 is an actionable novel therapeutic target for the treatment of MIBC.

Machine learning algorithms to the early diagnosis of fetal alcohol spectrum disorders.

Introduction: Fetal alcohol spectrum disorders include a variety of physical and neurocognitive disorders caused by prenatal alcohol exposure. Although their overall prevalence is around 0.77%, FASD remains underdiagnosed and little known, partly due to the complexity of their diagnosis, which shares some symptoms with other pathologies such as autism spectrum, depression or hyperactivity disorders. Methods: This study included 73 control and 158 patients diagnosed with FASD. Variables selected were based on IOM classification from 2016, including sociodemographic, clinical, and psychological characteristics. Statistical analysis included Kruskal-Wallis test for quantitative factors, Chi-square test for qualitative variables, and Machine Learning (ML) algorithms for predictions. Results: This study explores the application ML in diagnosing FASD and its subtypes: Fetal Alcohol Syndrome (FAS), partial FAS (pFAS), and Alcohol-Related Neurodevelopmental Disorder (ARND). ML constructed a profile for FASD based on socio-demographic, clinical, and psychological data from children with FASD compared to a control group. Random Forest (RF) model was the most efficient for predicting FASD, achieving the highest metrics in accuracy (0.92), precision (0.96), sensitivity (0.92), F1 Score (0.94), specificity (0.92), and AUC (0.92). For FAS, XGBoost model obtained the highest accuracy (0.94), precision (0.91), sensitivity (0.91), F1 Score (0.91), specificity (0.96), and AUC (0.93). In the case of pFAS, RF model showed its effectiveness, with high levels of accuracy (0.90), precision (0.86), sensitivity (0.96), F1 Score (0.91), specificity (0.83), and AUC (0.90). For ARND, RF model obtained the best levels of accuracy (0.87), precision (0.76), sensitivity (0.93), F1 Score (0.84), specificity (0.83), and AUC (0.88). Our study identified key variables for efficient FASD screening, including traditional clinical characteristics like maternal alcohol consumption, lip-philtrum, microcephaly, height and weight impairment, as well as neuropsychological variables such as the Working Memory Index (WMI), aggressive behavior, IQ, somatic complaints, and depressive problems. Discussion: Our findings emphasize the importance of ML analyses for early diagnoses of FASD, allowing a better understanding of FASD subtypes to potentially improve clinical practice and avoid misdiagnosis.