The hybridization of conventional thermal power plants by incorporation of renewable energy systems has witnessed widespread adoption in recent years. This trend aims not only to mitigate carbon emissions but also to enhance the overall efficiency and performance of these power generation facilities. However, calculating the performance of such intricate systems using fundamental thermodynamic equations proves to be both laborious and time-intensive. Nevertheless, possessing accurate and real-time insights into their performance is of utmost significance to ensure optimal plant operation, facilitate decision-making, and streamline power production planning. In this paper, we delve into the application of machine learning techniques, particularly hybrid methodologies, to predict the performance of power plants. Specifically, we employ three approaches: the Adaptive Neuro-Fuzzy Inference System (ANFIS), the ANFIS optimized via Particle Swarm Optimization (ANFIS-PSO), and the ANFIS optimized through Genetic Algorithm (ANFIS-GA). These techniques are alternatively applied to a complex hybrid thermal power plant, namely an Integrated Solar Combined Cycle Power Plant (ISCCPP). This ISCCPP comprises a steam turbine section, a gas turbine section, and a concentrated solar power system utilizing troughs as collectors. The energy transfer fluid, in conjunction with exhaust from the gas turbine section, heats water within a vessel or steam generator, producing steam to propel the steam turbine's rotor. Electricity generation is facilitated by both gas and steam turbines, which are coupled to a generator. The findings of this study underscore the accuracy and appropriateness of ANFIS-based models in evaluating and predicting the performance of intricate hybrid power plants. The ANFIS model exhibited an impressive overall coefficient of correlation of 0.9991. Notably, the integration of evolutionary algorithms (PSO and GA) into the ANFIS framework elevated model performance, yielding correlation coefficients of 0.9994 for ANFIS-PSO and 0.9997 for ANFIS-GA. It is noteworthy that ANFIS-GA demonstrated superior predictive capability. The modeling framework developed in this research offers valuable support to designers, energy managers, and decision-makers. It provides a robust and dependable tool to assess the performance of hybrid thermal power plants, which are poised for a global surge in numbers amid the ongoing energy transition.

Endometriosis is an oestrogen-dependant reproductive disease, with genetic, vascular, neural, inflammatory and auto-immune characteristics. There are many theories about the etiology of endometriosis, however, all of these theories have limitations and do not explain all the locations that endometriosis is found or types of patients with endometriosis. The objective of this paper is to postulate the hypothesis that endometriosis is caused by Maternal Microchimerism, the presence of maternal cells in the fetus. A literature review was conducted, analysing the characteristics, current etiological theories of endometriosis, theory limitations and relationship of maternal microchimerism and endometriosis. At time of writing, there was no literature on maternal microchimerism and endometriosis. These results suggest that Maternal Microchimerism could be a cause of endometriosis. This could account for the genetic and auto-immune characteristics seen in people with endometriosis, inducing a micro-environment for vascular, neural and epigenetic changes. This could also account for account for endometriosis seen in non-menstruating patients, such as men, fetuses and post-menopausal women and endometriosis found in non-peritoneal locations. If the hypothesis of Maternal Microchimerism is correct, endometriosis could be considered a pregnancy-related disease that could affect all humans, changing the accepted demographics of patients and potentially new diagnostic techniques and treatment options for patients with endometriosis. Further studies are needed to test this hypothesis.

The Maastricht VI/Florence consensus recommends, as one of the measures to enhance the efficacy of Helicobacter pylori (H. pylori) infection eradication, a personalized treatment approach involving the selection of an antimicrobial agent based on the pre-determined resistance of H. pylori. To meet the need to develop test systems for personalized drug selection, we conducted a study of H. pylori molecular resistance in Moscow using a self-developed Sanger sequencing test platform. We validated our test system on 25 pure culture samples of H. pylori with known resistance. Sensitivity and specificity for detecting resistance to clarithromycin was 100%, and to levofloxacin - 93% and 92%, respectively. Then we tested our platform on 112 H. pylori -positive patients who had not previously received proton pump inhibitors (PPI) or antibacterial drugs. Mutations indicating resistance to clarithromycin were found in 27 (24%) samples, and to levofloxacin in 26 (23%) samples. Double resistance was observed in 16 (14%) samples. The most common mutations leading to clarithromycin resistance were 2143G and 2142G, to levofloxacin resistance – 261A and 271A in the gyrA gene, which account for 69% of all identified genetic determinants in levofloxacin-resistant bacteria. Thus, a personalized approach to the selection of H. pylori eradication therapy based on the detection of bacterial resistance before prescribing first-line therapy could help avoid the prescription of ineffective H. pylori eradication therapies and, overall, contribute to the control of antibiotic resistance of H. pylori.

One in 12 babies is born with a rare genetic disease. Sadly, most cases are undetected until later age, missing time for early treatment and opportunity to prevent complications. Humanity has entered a new era where Big Data collected by governments, including 2D and 3D facial scans, are available. Many rare genetic diseases can be identified by artificial intelligence (AI) analysis of the facial photo. Phenotyping AI utilizations facilitate comprehensive and accurate genetic evaluations. AI processing of this Big Data to identify rare genetic diseases could bring unimaginable benefits to healthcare, although this would be a questionable step in terms of citizen privacy and could lead to future "Orwellian" ramifications with government abuse. Going forward, a balance must be found between protecting the privacy of citizens and the enticing use of AI for their health risks and cost savings through prevention. The unimaginable potential of AI early diagnostics from facial photos also raises various ethical and legal concerns. This paper presents concept, protentional methods and legal and other limitations within EU legal framework in contrast with potential benefits. This paper is focused on AI utilization to early diagnostic of rare genetic diseases. Shift of paradigm in the screening for rare genetic diseases in population with AI face analysis is expected to have a significant impact. The potential of AI algorithms utilizations similar to face2gene app in general population or systematically on Big governmental datasets recording facial traits changes in time can have significant impact on public health but at the same time give raise to profound concern as violation of one’s privacy.

Objectives: Multicentric Osteolysis, Nodulosis, and Arthropathy (MONA) syndrome is a rare genetic skeletal dysplasia. Its diagnosis can be deceptively similar to childhood-onset genetic skeletal dysplasias and juvenile idiopathic arthritis. We aimed to report the syndrome’s clinical and radiologic features with emphasis on skeletal manifestations. And establish relevant phenotype-genotype correlations. Methods: We evaluated two boys, 4-and-7-years-old with MONA syndrome. Both patients had consanguineous parents. We verified the diagnosis by correlating the outcomes of clinical, radiologic and molecular analysis. We specifically evaluated the craniofacial morphology and clinical and radiographic skeletal abnormalities. We contextualized the resultant phenotype-genotype correlations to publications on MONA and its differential diagnosis. Results: Skeletal manifestations were the presenting symptoms and mostly restricted to hands and feet in terms of fixed extension deformity of the metacarpophalangeal and flexion deformity of the interphalangeal joints with extension deformity of big toes. There were arthritic symptoms in the older patient especially of the wrists and minute pathologic fractures. The skeletal radiographs showed osteopenia/dysplastic changes of hands and feet. Both patients had variants in the matrix metalloproteinase2 gene which conformed to phenotype of previously reported literature in one patient while the other had a novel variant which conformed to MONA phenotype. Craniofacial abnormalities were present. However, minimal extra-skeletal manifestations. Conclusion: Overall, there is an emerging distinctive skeletal pattern of involvement in terms of both clinical and radiographic features. This includes age of onset and location of presenting skeletal manifestations, chronological order of joint affection, longitudinal disease progression, specifics of skeletal radiographic pathology and craniofacial features. Nevertheless, physicians are cautioned against differential diagnosis of similar genetic skeletal dysplasias and juvenile idiopathic arthritis.

Gilbert's syndrome (GS) is a common hereditary condition characterized by mild increases in serum bilirubin levels due to inherited defects in bilirubin metabolism. This review, based on data from peer-reviewed articles and journals spanning from 1977 to January 2024, provides a comprehensive overview of over four decades of research on GS. Early studies primarily focused on defining the clinical and genetic characteristics of the syndrome, laying the foundation for subsequent investigations. More recent research has delved into the genetic mechanisms underlying the reduced expression of bilirubin UDP-glucuronosyltransferase, which has significantly enhanced our understanding of the pathogenesis of GS. Furthermore, recent studies have shed light on the clinical implications of GS, including its association with non-alcoholic fatty liver disease and mortality risk, highlighting the complex interplay between genetic factors, bilirubin metabolism, and clinical outcomes. Despite its generally benign nature, thorough research is essential to fully understand the impact of GS on patients' lives. This review emphasizes the importance of ongoing research to deepen our understanding of GS and its broader implications.

Both SARS-CoV-2 (COVID-19) and SARS coronaviruses (CoVs) are members of the subgenus Sarbecovirus. To understand the origin of SARS-CoV-2, protein sequences from sarbecoviruses were analyzed to identify highly-specific molecular markers consisting of conserved inserts or deletions (termed CSIs) in the spike (S) and nucleocapsid (N) proteins that are specific for either particular clusters/lineages of these viruses or are commonly shared by specific lineages. Three novel CSIs in the N-terminal domain of the spike protein S1-subunit (S1-NTD) are uniquely shared by the SARS-CoV-2, BatCoV-RaTG13 and most pangolin CoVs, distinguishing this cluster of viruses (SARS-CoV-2r) from all others. In the same positions, where these CSIs are found, related CSIs are also present in two other sarbecoviruses (viz. CoVZXC21 and CoVZC45 forming CoVZC cluster), which form an out group of the SARS-CoV-2r cluster. These three CSIs are not found in the SARS-CoVs. However, both SARS and SARS-CoV-2r CoVs contain two large CSIs in the C-terminal domain of S1 (S1-CTD), which binds the human ACE-2 receptor, that are absent in the CoVZC cluster of CoVs. These results indicate that while the S1-NTD of the SARS-CoV-2r viruses possesses the sequence characteristics of the CoVZC cluster of CoVs, their S1-CTD resembles the SARS viruses. Thus, the spike protein of SARS-CoV-2r viruses has likely originated from a recombination event between the S1-NTD of the CoVZC viruses and the S1-CTD of SARS viruses. This inference is also supported by the amino acid sequence similarity of the S1-NTD and S1-CTD from SARS-CoV-2 compared to the CoVZC and SARS CoVs. We also present evidence that one of the pangolin-CoV_MP789, whose receptor-binding domain is most similar to the SARS-CoV-2, is also derived by a recent recombination between the S1-NTD of the CoVZC CoVs and the S1-CTD of a SARS-CoV-2 related virus. Several other identified CSIs are specific for others clusters of sarbecoviruses including a clade consisting of bat SARS-CoVs (BM48-31/BGR/2008 and SARS_BtKY72). Structural mappings studies show that the identified CSIs are located within surface-exposed loops and form distinct patches on the surface of the spike protein. These surface loops/patches are predicted to interact with other host components and play important role in the biology/pathology of SARS-CoV-2 virus. Lastly, the CSIs specific for the SARS-CoV-2r clade provide novel means for development of new diagnostic and therapeutic targets for these viruses.