Mexico shows a high birth prevalence of congenital hypothyroidism (CH) due to thyroid dysgenesis (TD). PAX8 defects underlie only 1% of these cases and NKX2-1 does not seem to be involved. Here, we analyzed other TD-related genes in 128 non-related Mexican patients (females 77.3%; 6 months to 16.6 years) with non-syndromic CH-TD diagnosis established by clinical evaluation, thyroid hormone serum profiling, and scintigraphy (74%) or ultrasonography (26%). We performed Sanger sequencing of FOXE1, NKX2-5, and TSHR and evaluated copy number variations (CNVs) in TSHR, FOXE1, PAX8, and NKX2-1 by multiplex ligation-dependent probe amplification. Odds ratios for TD risk were explored for FOXE1 polyalanine stretches [polyAla-rs71369530] in cases and controls (N=116). Five rare missense changes cataloged as benign (NKX2-5:p.(Ala119Ser)-rs137852684), of unknown significance (FOXE1:p.(Ala335Gly)-rs543372757; TSHR:p.(Asp118Asn)-rs1414102266), and likely pathogenic (FOXE1:p.(Gly124Arg)-rs774035532; TSHR:p.(Trp422Arg)-rs746029360) accounted for 1.5% (N=2/128) of clinically relevant genotypes (supported in part by protein modeling) in CH-TD. No CNVs were identified, nor did polyAla >14 alanines in FOXE1 significantly protect against TD. The present and previously published data collectively show that small clinically relevant germline variants in PAX8, FOXE1, and TSHR are found in only a very small proportion (2.5%) of isolated CH-TD Mexican patients.

Background/Objectives: Thyroid cancer, particularly well-differentiated thyroid cancer, is one of the most prevalent endocrine malignancies, with a rising incidence. Although it generally has a favorable prognosis, recurrence is common. Accurate prediction of recurrence is crucial for optimizing treatment plans and improving patient outcomes. This study aimed to advance the state-of-the-art in thyroid cancer recurrence prediction by refining feature engineering techniques and exploring a diverse ensemble of machine learning algorithms and an artificial neural network, using the differentiated thyroid cancer dataset from the UCI Machine Learning Repository; Methods: Various unsupervised data engineering methods, such as dimensionality reduction and clustering, were employed to enhance feature quality and mitigate noise, using stratified 10-fold cross-validation. The best-performing dimensionality reduction techniques were used to build classification model pipelines employing each of several machine learning models and an artificial neural network. The performance of these classification pipelines were assessed using metrics sensitive to class imbalance; Results: Principal Component Analysis and Truncated Singular Value Decomposition achieved superior clustering performance and moderate variances in their first principal components. Logistic Regression, Random Forest, Support Vector Machine, K-Nearest Neighbors, and Feedforward Neural Network models all achieved high performance, with Logistic Regression pipelines demonstrating balanced accuracy, F1 score, AUC, sensitivity, specificity, and precision—all greater than 0.93 on the test set and slightly lower in 10-fold cross-validation. Gradient Boosting classification pipelines performed the lowest, though still with respectable metrics; Conclusions: This study shows that employing feature engineering techniques like Principal Component Analysis or Truncated Singular Value Decomposition in Logistic Regression, Random Forest, Feedforward Neural Networks, Support Vector Machine, and K-Nearest Neighbors classification pipelines can improve thyroid cancer recurrence prediction accuracy and reliability, supporting more personalized treatment strategies in post-treatment patients.

A separate group consists of compounds that are able to simultaneously interact with both orthosteric and allosteric sites, which are classified as bitopic GPCR ligands [74, 76-81]. They have two pharmacophores, one of which binds with high affinity to the orthosteric site, while the other, with lower affinity, binds to the allosteric site. If these sites are spatially separated in the receptor, then the pharmacophores in the bitopic ligand must be connected with a flexible linker, the length of which exactly corresponds to the distance between the orthosteric and allosteric sites. At the same time, it is important that the linker does not significantly affect the conformational rearrangements in the receptor caused by its activation by orthosteric and (or) allosteric agonists [74, 79].

Neuroendocrine neoplasms (NENs) comprise a heterogeneous group of rare malignancies mainly originated from hormones secreting cells, which are widespread in human tissues. The identification of mutations in ATRX/DAXX genes in sporadic NENs, as well as the high burden of mutations scattered throughout MEN-1 gene in both sporadic and inherited syndromes, provided new insights into the molecular biology of tumour development. Other molecular mechanisms, such as the NOTCH signaling pathway, have shown to play an important role in the pathogenesis of NENs. NOTCH receptors are expressed on neuroendocrine cells and generally, act as tumour suppressor proteins, but in some contexts can function as oncogenes. The biological heterogeneity of NENs suggests that to fully understand the roles and the potential therapeutic implications of gene mutations and NOTCH signaling in NENs, a comprehensive analysis of genetic alterations, NOTCH expression patterns and their potential roles across all NEN subtypes is required.

The literature on the causation of multiple sclerosis (MS), both genetic and environmental, extends over hundreds of years, with no firm conclusions on the exact role of autoimmunity and lifestyle. The epidemiology of MS was the basis for this review, but with a new, extensive examination of genes pertaining to each disorder, and disease of first, and second, degree relatives of those with MS. The author’s motivation was to discover some relationship between MS, and notable familial conditions, as the heredity of MS is concluded to be 30%, and the disorders had a chronic and/or idiopathic nature. This investigation hoped to further understand the randomness of MS- who acquires it, and what symptoms develop- after the author’s decades of observing several incidences of multiple members developing MS in a single family. Online databases for the human genome were used to link genes to MS, and symptoms, including excessive depression, fatigue and suicide rates, in coordination with linking genes for specific familial conditions including seizures, stroke, mental illness, bowel disorders, and thyroid conditions. Interesting associations were found, notably a cluster of Th2 cytokines, known to cure the animal model of MS, important receptors for neurotransmitters and hormones, a gene specific to Epstein Barr Virus, and potential genes for mitochondrial dysfunction. The results surprised the author, showing polygenic regions of chromosome 2 and 5, especially a cluster at loci 5q31-q33, may be dysregulated. The conclusion agrees with past hypotheses MS results not from a single gene, but from various genes, including those expressed in glial cells. The individual theories to the causation of MS, starting with Charcot may be explained by multiple pathways converging into a single disease outcome. In coordination with a sunlight factor, chromosome 2 appears to mediate the immune system, and inflammation, through ultraviolet radiation producing vitamin D3 in the skin, but additionally through peptides formed in the melanocyte stimulating and concentrating hormone class. The impact of stress in MS could be primary, given the loci of several stress-related and stress-modulated genes on these chromosomes, and calls for more appreciation of, and greater care for, the MS patients’ state of mind.