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As a consequence of insufficient removal during treatment of wastewater released from industry and households, different classes of organic micropollutants are nowadays detected in surface and drinking water. Among these micropollutants, bioactive substances, e.g., endocrine disrupting compounds and pharmaceuticals, have been incriminated in negative effects on living organisms in aquatic biotope. Much research was done in the last years on the fate and removal of those compounds from wastewater. An important point it is to understand the role of applied treatment conditions (sludge retention time (SRT), biomass concentration, temperature, pH value, dominant class of micropollutants, etc.) for the efficiency of conventional treatment plants (CTP) and membrane bioreactors (MBR) concerning the removal of micropollutants such as pharmaceuticals, steroid- and xeno-estrogens. Nevertheless, the removal rates differ even from one compound to the other and are related to the physico-chemical characteristics of the xenobiotics.

This study evaluates the potential of bio-augmentation to improve the degradation of recalcitrant nonylphenol during the wastewater treatment in membrane bioreactors (MBR). One MBR containing activated sludge was bio-augmented using multistep inoculation with freeze dried Sphingomonas sp. strain TTNP3, whereas a second control reactor contained activated sludge solely. The ¹⁴C-labeled-nonylphenol isomer (4-[1-ethyl-1,3-dimethylpentyl]phenol) was applied as a single pulse. Bio-augmentation resulted in an immediate increase of dissolved radioactivity in the effluent in comparison to the control reactor (13% and 2% of initially applied radioactivity after 1 day, respectively). After 5 days of operation, the retentate of the bio-augmented reactor contained only 7% of the initial radioactivity in contrast to 50% in the control reactor. The radioactivity associated to the mixed liquor suspended solids, i.e., the suspension of biomass and other solids on the retentate side of the membrane, was mainly found as non-extractable residues that were increasingly formed during prolonged reactor operation, especially for the control MBR. HPLC-LSC and GC-MSn analyses revealed that the bio-augmented reactor produced more polar hydroquinone as main degradation intermediate, whereas the control reactor effluent contained a complex mixture of apolar compounds with shortened oxidized alkyl chains. Thus, the apparent differences in the behavior of nonylphenol between the reactors were due to the catabolism of nonylphenol conferred by bio-augmentation with Sphingomonas sp. strain TTNP3.

This case report describes a novel intronic mutation, CYP21A2:c.738+75C>T (rs1463196531), identified in a 4-year-old male with congenital adrenal insufficiency, and expands the known mutation spectrum associated with this condition. The patient, born full-term to unrelated parents, presented with adrenal failure within the first month of life, characterized by acute adrenal crisis symptoms such as vomiting, dehydration, weight loss, hypotension, and electrolyte imbalances. Hormonal evaluations confirmed primary adrenocortical insufficiency, necessitating ongoing hydrocortisone and fludrocortisone therapy. Using family trio-based amplicon sequencing of the CYP21A2 gene, we identified compound heterozygosity consisting of a full gene deletion and a novel pathogenic intronic mutation. Additionally, analysis of WGS data was performed to rule out pathogenic variants in genes that might lead to a similar phenotype, thereby eliminating the possibility of other genes contributing to the proband’s disease. This case demonstrates the potential of using amplicon sequencing in molecular genetic diagnostic testing to detect rare intronic variants in the CYP21A2 gene in cases of early-onset adrenal failure. It also contributes to a better understanding of the genetic basis of congenital adrenal hyperplasia (CAH), which remains a significant autosomal recessive disorder affecting cortisol and aldosterone production, with an incidence of 1 in 10,000 to 1 in 15,000 globally.

Orthostatic hypotension is a sharp decrease in blood pressure when an individual transitions from a supine to an upright position. OH affects at least 30% of older adults. It is attributed to the dysfunction of the autonomic innervation and decreased vascular bed capacity. Genomic (n = 2526), methylomic (n = 910), and transcriptomic (n = 391) data from centenarians aged 90 years and older were used to examine molecular and genetic factors for OH. No statistically significant genetic predictors of OH were identified. However, the study revealed numerous epigenetic markers of OH indicative of general aging, such as DNA hypomethylation. The predictive DNA methylation-based model for orthostatic hypotension demonstrated an average accuracy of 79%. The transcriptome analyses highlighted associations between OH and inflammation pathways, as well as other age-related biological processes. Integrated omics and clinical data have identified six key mechanisms associated with orthostatic hypotension: metabolic dysregulation, impaired muscle tone, altered cell proliferation, inflammation, humoral regulation, and neural regulation.

Minimally invasive diagnostics based on liquid biopsy makes it possible early detection of lung cancer (LC). The blood plasma circulating cell-free DNA (cfDNA) fragments reflect the genome and chromatin status and are considered as integral cancer biomarkers and the biological entities for 'cancer-of-origin' prediction. The aim of this work is to create a method for processing next-generation sequencing (NGS) data and an interpretable binary classification model (CM), which analyzed cfDNA fragmentation features for distinguishing healthy subjects and subjects with LC. 148 healthy subjects and 138 subjects with LC were included in the study. cfDNA fractions, isolated from blood plasma biospecimens, were used for DNA libraries preparations and NGS on the NovaSeq 6,000 Illumina system with a coverage of 100 million reads/sample. Twelve variables, describing the abundance and length distribution of cfDNA fragments within each genomic interval, and 40 variables based on the values of position-weight matrices, describing combinations of 5-bp-long terminal motifs of cfDNA fragments, were used to characterize genomic fragmentation. Classification models of the first phase of machine learning were based either on logistic regression with L1- and L2-regularization or were probabilistic CMs based on Gaussian processes. The second phase CM was based on kernel logistic regression. The final CM can distinguish healthy subjects and subjects with LC with AUC values of 0.872-0.875. The performance of developed CM was evaluated using datum and testing sets for each LC stage category. Sensitivity values ranged from 66.7 to 85.7%, from 77.8 to 100%, and from 70 to 80% for LC stages I, II, and III, respectively. Specificity values ranged from 79.3 to 90.0%. Thus, the CM has a good diagnostic value and does not require clinical or other data on tumor-associated biomarkers. The current method for LC detection has some advantages for future clinical implementation as a decision-making support system due to the performance of the CM requires data exclusively from NGS-analysis of blood plasma cfDNA fragmentation; the accuracy of the CM does not depend on any additional clinical data; the CM is highly interpretable and traceable; CM has appropriate modular architecture.