Bioinformatics and artificial intelligence (AI) have emerged as transformative tools in modern medicine, revolutionizing the landscape of medical diagnosis and treatment. Herein, we provide an overview of the synergistic relationship between bioinformatics and AI, elucidating their pivotal roles in deciphering complex biological data and advancing precision medicine and in particular endocrinology. We explore various applications of bioinformatics and AI in medical research, including genomic analysis, drug discovery, disease diagnosis, and personalized treatment strategies. Additionally, we discuss challenges and future directions in leveraging these technologies to enhance healthcare outcomes.

Extracellular vesicles (EVs) are a heterogeneous group of lipid-bound vesicles released by cells into the extracellular space. EVs are an important mediator of intercellular communications and carry a wide variety of molecules that exert a biological function, such as lipids, nucleic acids, proteins, ions, and adenosine triphosphate (ATP). Extracellular vesicles are classified into microvesicles, exosomes, and apoptotic bodies depending on their biogenesis and size. Exosomes are spherical lipid-bilayer vesicles with a diameter of about 40 to 100 nm. Exosomes originate from intracellular endosomal compartments, while microvesicles originated directly from a cell’s plasma membrane and apoptotic bodies originate from cells undergoing apoptosis and are released via outward blebbing and fragmentation of the plasma membrane. Specifically, exosomes have garnered great attention since they display great potential as both biomarkers and carriers of therapeutic molecules.

The evolutionary conserved Notch signaling pathway functions as a mediator of direct cell–cell communication between neighboring cells during development. Notch plays a crucial role in various fundamental biological processes in a wide range of tissues. Accordingly, the aberrant signaling of this pathway underlies multiple genetic pathologies such as developmental syndromes, congenital disorders, neurodegenerative diseases, and cancer. Over the last two decades, significant data have shown that the Notch signaling pathway displays a significant function in the mature brains of vertebrates and invertebrates beyond neuronal development and specification during embryonic development. Neuronal connection, synaptic plasticity, learning, and memory appear to be regulated by this pathway. Specific mutations in human Notch family proteins have been linked to several neurodegenerative diseases including Alzheimer’s disease, CADASIL, and ischemic injury. Neurodegenerative diseases are incurable disorders of the central nervous system that cause the progressive degeneration and/or death of brain nerve cells, affecting both mental function and movement (ataxia). There is currently a lot of study being conducted to better understand the molecular mechanisms by which Notch plays an essential role in the mature brain. In this study, an in silico analysis of polymorphisms and mutations in human Notch family members that lead to neurodegenerative diseases was performed in order to investigate the correlations among Notch family proteins and neurodegenerative diseases. Particular emphasis was placed on the study of mutations in the Notch3 protein and the structure analysis of the mutant Notch3 protein that leads to the manifestation of the CADASIL syndrome in order to spot possible conserved mutations and interpret the effect of these mutations in the Notch3 protein structure. Conserved mutations of cysteine residues may be candidate pharmacological targets for the potential therapy of CADASIL syndrome.

Breast milk, often referred to as "liquid gold," is a complex biofluid that provides essential nutrients, immune factors, and developmental cues for newborns. Recent advancements in the field of exosome research have shed light on the critical role of exosomes in breast milk. Exosomes are nanosized vesicles that carry bioactive molecules, including proteins, lipids, nucleic acids, and miRNAs. These tiny messengers play a vital role in intercellular communication and are now being recognized as key players in infant health and development. This paper explores the emerging field of milk exosomics, emphasizing the potential of exosome fingerprinting to uncover valuable insights into the composition and function of breast milk. By deciphering the exosomal cargo, we can gain a deeper understanding of how breast milk influences neonatal health and may even pave the way for personalized nutrition strategies.

Biomedical datasets constitute a rich source of information, containing multivariate data collected during medical practice. In spite of inherent challenges, such as missing or imbalanced data, these types of datasets are increasingly utilized as a basis for the construction of predictive machine-learning models. The prediction of disease outcomes and complications could inform the process of decision-making in the hospital setting and ensure the best possible patient management according to the patient's features. Multi-label classification algorithms, which are trained to assign a set of labels to input samples, can efficiently tackle outcome prediction tasks. Myocardial infarction (MI) represents a widespread health risk, accounting for a significant portion of heart disease-related mortality. Moreover, the danger of potential complications occurring in patients with MI during their period of hospitalization underlines the need for systems to efficiently assess the risks of patients with MI. In order to demonstrate the critical role of applying machine-learning methods in medical challenges, in the present study, a set of multi-label classifiers was evaluated on a public dataset of MI-related complications to predict the outcomes of hospitalized patients with MI, based on a set of input patient features. Such methods can be scaled through the use of larger datasets of patient records, along with fine-tuning for specific patient sub-groups or patient populations in specific regions, to increase the performance of these approaches. Overall, a prediction system based on classifiers trained on patient records may assist healthcare professionals in providing personalized care and efficient monitoring of high-risk patient subgroups.

Epigenetics is the study of heritable changes in gene expression that occur without changes to the underlying DNA sequence. Epigenetic modifications can include DNA methylation, histone modifications, and non-coding RNAs, among others. These modifications can influence the expression of genes by altering the way DNA is packaged and accessed by transcriptional machinery, thereby affecting cellular function and behavior. Epigenetic modifications can be influenced by a variety of factors, including environmental exposures, lifestyle factors, and aging, whilst abnormal epigenetic modifications have been implicated in a range of diseases, including cancer, neurodegenerative disorders, and cardiovascular disease. The study of epigenetics has the potential to provide new insights into the mechanisms of disease and could lead to the development of new diagnostic and therapeutic strategies. Exosomes can transfer epigenetic information to recipient cells, thereby influencing various physiological and pathological processes, and the identification of specific epigenetic modifications that are associated with a particular disease could lead to the development of targeted therapies that restore normal gene expression patterns. In recent years, the emerging role of exosomal epigenetics in human breast milk, highlighting its significance in infant nutrition and immune development. Milk exosomes are shown to carry epigenetic regulators, including miRNAs and long non-coding RNAs, which can modulate gene expression in recipient cells. These epigenetic modifications mediated by milk exosomal RNAs have implications for the development of the gastrointestinal tract, immune system, and metabolic processes in infants.

Matchmaking has a great position in the rational allocation of resources in several fields, ranging from market operation to people's daily lives. Matchmakers have evolved through artificial intelligence technologies and are being introduced in numerous aspects of industry, research, and academia in solving decision issues, research innovation design, and building robust and efficient networks. The goal of this report is to describe the collaborative platforms and matchmaking algorithms for research and education, as well as the establishment and optimization of consortia.

Exploring the origin of plastidsPlastids is an interesting theme for study because it enhances our knowledge of the basis of photosynthesis in floraPhotosynthesis in flora. PlastidsPlastids, which are organelles, are actually the major sites of photosynthesis in eukaryotic cells. PlastidsPlastids are also every chloroplast which contains cytoplasmic organelles, enabling the harvesting and conversion of light and carbon dioxide into food and energy. PlastidsPlastids can be found in eukaryotic cells, and according to their structure in their membrane, they can be separated in primary (which can be found in most algae and plants) and secondary plastidsPlastids (which can be found in plankton).

There have been numerous attempts to establish a correlation between obesity and stress, inflammatory, and dysmetabolism biomarkers in children and adolescents. Here, we performed a meta-analysis of existing studies to shed light on the elusive correlations of childhood and adolescent obesity with physiological indicators of stress, inflammation, and metabolism before and after lifestyle interventions. Observational studies, meta-analyses, narrative and systematic reviews were excluded. From a total of 53 articles, 11 were selected according to specific criteria. The biomarkers examined were circulating glucose, insulin, HDL, LDL, triglycerides, adiponectin, leptin, CRP, TNF-alpha, interleukin (IL)-6, systolic and diastolic blood pressure, and HOMA-IR. All analyses were performed using IBM SPSS Statistics Version 28.0.1.0 (142). The current meta-analysis provides evidence of a beneficial effect of a lifestyle intervention and/or drugs in children and adolescents living with obesity or overweight, consistent with a significant reduction in body fat-but not in BMI or waist circumference-an increase in circulating adiponectin and/or a reduction in serum insulin levels and diastolic blood pressure, and a trend towards a reduction of circulating leptin and glucose levels, as well as of the HOMA-IR. This meta-analysis indicates that lifestyle interventions could reduce overweight-/obesity-associated systemic inflammation and dysmetabolism even without an apparent decrease in BMI.

All living organisms have been programmed to maintain a complex inner equilibrium called homeostasis, despite numerous adversities during their lifespan. Any threatening or perceived as such stimuli for homeostasis is termed a stressor, and a highly conserved response system called the stress response system has been developed to cope with these stimuli and maintain or reinstate homeostasis. The glucocorticoid receptor, a transcription factor belonging to the nuclear receptors protein superfamily, has a major role in the stress response system, and research on its interactome may provide novel information regarding the mechanisms underlying homeostasis maintenance. A list of 149 autosomal genes that have an essential role in GR function or are prime examples of GRE-containing genes was composed in order to gain a comprehensive view of the GR interactome. A search for SNPs on those particular genes was conducted on a dataset of 3554 Japanese individuals, with mentioned polymorphisms being annotated with relevant information from the ClinVar, LitVar, and dbSNP databases. Forty-two SNPs of interest and their genomic locations were identified. These SNPs have been associated with drug metabolism and neuropsychiatric, metabolic, and immune system disorders, while most of them were located in intronic regions. The frequencies of those SNPs were later compared with a dataset consisting of 1465 Korean individuals in order to find population-specific characteristics based on some of the identified SNPs of interest. The results highlighted.that rs1043618 frequencies were different in the two populations, with mentioned polymorphism having a potential role in chronic obstructive pulmonary disease in response to environmental stressors. This SNP is located in the HSPA1A gene, which codes for an essential GR co-chaperone, and such information showcases that similar gene may be novel genomic targets for managing or combatting stress-related pathologies.

Over the past few decades, research at the molecular level has focused on the part of the genome that does not encode protein sequences. Since the discovery of transcriptional evidence from the hitherto considered 'junk' DNA, this region of the genome, which is currently termed dark DNA, is constantly gaining interest. The term borrows an analogy from the corresponding eminent fields of dark matter and dark energy in physics and cosmology. In fact, an increasing number of attempts are being made to enhance the current understanding of the non-coding RNA (ncRNA) transcripts produced by such regions. Although the base-pair length and gene number appear to be very diverse between species, it appears that the amount of the non-coding regions of the genome of an organism is a sign of evolutional superiority. ncRNA molecules are able to orchestrate the expression of genetic information in the most complex, rapid and reversible manner, participating in almost every major biological process. A prime example of such a process is the maintenance of homeostasis, the internal physiological balance, despite internal and external stressful stimuli. These molecules have been shown to be excellent regulators of gene expression, with marked spatiotemporal specificity, rendering them ideal tools for regulating stress responses. Herein, an attempt is made to extract and fuse information from a repertoire of studies, which have demonstrated that the expression of a number of these molecules was modified following exposure to acute and chronic stress, as well as in patients with anxiety disorders and their respective animal models. All in all, ncRNAs have the potential to be used either as biomarkers or as therapeutic targets for disorders resulting from the loss of equilibrium, the disruption of homeostasis and the destabilization of the hypothalamic-pituitary-adrenal axis.

Mental disorders are strongly connected with several psychiatric conditions including depression, bipolar disorder, schizophrenia, eating disorder, and suicides. There are many biological conditions and pathways that define these complicated illnesses. For example, eating disorders are complex mental health conditions that require the intervention of geneticists, psychiatrists, and medical experts in order to alleviate their symptoms. A patient with suicidal ideation should first be identified and consequently monitored by a similar team of specialists. Both genetics and epigenetics can shed light on eating disorders and suicides as they are found in the main core of such investigations. In the present study, an analysis has been performed on two specific members of the GPCR family toward drawing conclusions regarding their functionality and implementation in mental disorders. Specifically, evolutionary and structural studies on the adrenoceptor alpha 2b (ADRA2B) and the 5-hydroxytryptamine receptor 1A (HTR1A) have been carried out. Both receptors are classified in the biogenic amine receptors sub-cluster of the GPCRs and have been connected in many studies with mental diseases and malnutrition conditions. The major goal of this study is the investigation of conserved motifs among biogenic amine receptors that play an important role in this family signaling pathway, through an updated evolutionary analysis and the correlation of this information with the structural features of the HTR1A and ADRA2B. Furthermore, the structural comparison of ADRA2B, HTR1A, and other members of GPCRs related to mental disorders is performed.

SARS-CoV-2 is a coronavirus responsible for one of the most serious, modern worldwide pandemics, with lasting and multifaceted effects. By late 2021, SARS-CoV-2 has infected more than 180 million people and has killed more than 3 million. The virus gains entrance to human cells through binding to ACE2 via its surface spike protein and causes a complex disease of the respiratory system, termed COVID-19. Vaccination efforts are being made to hinder the viral spread, and therapeutics are currently under development. Toward this goal, scientific attention is shifting toward variants and SNPs that affect factors of the disease such as susceptibility and severity. This genomic grammar, tightly related to the dark part of our genome, can be explored through the use of modern methods such as natural language processing. We present a semantic analysis of SARS-CoV-2-related publications, which yielded a repertoire of SNPs, genes, and disease ontologies. Population data from the 1000 Genomes Project were subsequently integrated into the pipeline. Data mining approaches of this scale have the potential to elucidate the complex interaction between COVID-19 pathogenesis and host genetic variation; the resulting knowledge can facilitate the management of high-risk groups and aid the efforts toward precision medicine.

TANK binding kinase 1 protein (TBK1) is a kinase that belongs to the IκB (IKK) family. TBK1, also known as T2K, FTDALS4, NAK, IIAE8 and NF-κB, is responsible for the phosphorylation of the amino acid residues Serine and Threonine. This enzyme is involved in various key biological processes, including interferon activation and production, homeostasis, cell growth, autophagy, insulin production and the regulation of TNF-α, IFN-β and IL-6. Mutations in the TBK1 gene alter the protein’s normal function and may lead to an array of pathological conditions, including disorders of the Central Nervous System. The present study sought to elucidate the role of the TBK1 protein in Amyotrophic Lateral Sclerosis (ALS), a human neurodegenerative disorder. A broad evolutionary and phylogenetic analysis of TBK1 was performed across numerous organisms to distinguish conserved regions important for the protein’s function. Subsequently, mutations and SNPs were explored and their potential effect on the enzyme’s function was investigated. These analytical steps, in combination with the study of the secondary, tertiary, and quaternary structure of TBK1, enabled the identification of conserved motifs, which can function as novel pharmacological targets and inform therapeutic strategies for Amyotrophic Lateral Sclerosis.

Cognitive and behavioral disorders are subgroups of mental health disorders. Both cognitive and behavioral disorders can occur in people of different ages, genders, and social backgrounds, and they can cause serious physical, mental, or social problems. The risk factors for these diseases are numerous, with a range from genetic and epigenetic factors to physical factors. In most cases, the appearance of such a disorder in an individual is a combination of his genetic profile and environmental stimuli. To date, researchers have not been able to identify the specific causes of these disorders, and as such, there is urgent need for innovative study approaches. The aim of the present study was to identify the genetic factors which seem to be more directly responsible for the occurrence of a cognitive and/or behavioral disorder. More specifically, through bioinformatics tools and software as well as analytical methods such as systemic data and text mining, semantic analysis, and scoring functions, we extracted the most relevant single nucleotide polymorphisms (SNPs) and genes connected to these disorders. All the extracted SNPs were filtered, annotated, classified, and evaluated in order to create the "genomic grammar" of these diseases. The identified SNPs guided the search for top suspected genetic factors, dopamine receptors D and neurotrophic factor BDNF, for which regulatory networks were built. The identification of the "genomic grammar" and underlying factors connected to cognitive and behavioral disorders can aid in the successful disease profiling and the establishment of novel pharmacological targets and provide the basis for personalized medicine, which takes into account the patient's genetic background as well as epigenetic factors.

Huntington's disease (HD) is a neurodegenerative disorder characterized by severe motor, cognitive and psychiatric symptoms. Patients of all ages can present with a dysfunction of the nervous system, which leads to the progressive loss of movement control and disabilities in speech, swallowing, communications, etc. The molecular basis of the disease is well-known, as HD is related to a mutated gene, a trinucleotide expansion, which encodes to the huntingtin protein. This protein is linked to neurogenesis and the loss of its function leads to neurodegenerative disorders. Although the genetic cause of the disorder has been known for decades, no effective treatment is yet available to prevent onset or to eliminate the progression of symptoms. Thus, the present review focused on the development of novel methods for the timely and accurate diagnosis of HD in an aim to aid the development of therapies which may reduce the severity of the symptoms and control their progression. The majority of the therapies include gene-silencing mechanisms of the mutated huntingtin gene aiming to suppress its expression, and the use of various substances as drugs with highly promising results. In the present review, the latest approaches on the diagnosis of HD are discussed along with the need for genetic counseling and an up-to-date presentation of the applied treatments.

The synthesis and release of glucocorticoids in living organisms are related to their response to unfavorable stressful conditions in order to maintain homeostatic functions and survive. One such hormone in humans is cortisol, which is produced by the hypothalamic‑pituitary‑adrenal cortex axis and binds with the glucocorticoid receptor (GR) following its secretion. GR controls a number of distinct gene networks. Non‑coding RNAs (ncRNAs), such as microRNAs (miRNAs) and long non‑coding RNAs (lncRNAs), regulate the expression and function of GR, having a considerable impact on various biological processes and treatment approaches for numerous disorders. In the present review, the GR pathways and signaling as part of the stress response system are discussed. A detailed report on the role of miRNAs and lncRNAs in glucocorticoid signaling is also presented.

Multiple sclerosis (MS) is an autoimmune neurodegenerative disease whose prevalence has increased worldwide. The resultant symptoms may be debilitating and can substantially reduce the of patients. Computational biology, which involves the use of computational tools to answer biomedical questions, may provide the basis for novel healthcare approaches in the context of MS. The rapid accumulation of health data, and the ever-increasing computational power and evolving technology have helped to modernize and refine MS research. From the discovery of novel biomarkers to the optimization of treatment and a number of quality-of-life enhancements for patients, computational biology methods and tools are shaping the field of MS diagnosis, management and treatment. The final goal in such a complex disease would be personalized medicine, i.e., providing healthcare services that are tailored to the individual patient, in accordance to the particular biology of their disease and the environmental factors to which they are subjected. The present review article summarizes the current knowledge on MS, modern computational biology and the impact of modern computational approaches of MS.

The availability of single nucleotide polymorphisms (SNPs) that have been identified in a given gene and have been related to several diseases provides the opportunity to study complex biological pathways and can assist researchers in better associating genes and disease‑linked terms in the areas of genetics, genomics and epigenetics. The study of the glucocorticoid receptor (GR) interactome through SNP observations in ‘key‑player’ genes will provide researcher with the opportunity to draw the ‘genomic grammar’ of the complex biological mechanisms associated with GR function and will open new horizons in biology, medicine, pharmacology and even extend to personalized medicine. The GR interactome is extensive, and is involved in several physiological and pathological processes of the organism. Glucocorticoids are the final product of the hypothalamic‑pituitary‑adrenal axis and an inextricable part of the stress system. These hormones are implicated in various critical systems and processes for the human organism, such as the immune system, development, metabolism and several others. GR is the protein that mediates their actions and is involved in several interactions with specific genes and proteins. In the present study, in order to unravel new beneficial knowledge on genetic targets regarding the GR interactome, a data mining and semantic pipeline was performed using the available literature. More specifically, through bioinformatics tools and methods, the most relevant SNPs and genes connected to the GR interactome were extracted. Subsequently, the outcome SNPs were filtered, annotated, classified and evaluated in order to create the ‘genomic grammar’ and identify the related disease with the interactome of GR. Genomic background and heredity play a significant role in the GR interactome. A more in‑depth understanding of the biological pathways and complex actions of the GR may lead to the design and development of more effective treatments for inflammatory and autoimmune diseases, as well as cancers.