Complex Disease Research Articles

Exploring the role of endoplasmic reticulum stress in recurrent spontaneous abortion: Identification of diagnostic biomarkers and immune cell interactions

Dysregulated endoplasmic reticulum stress (ERS) is associated with recurrent spontaneous abortion (RSA) and is involved in the mechanisms that govern immune balance and vascular regulation at the maternal-fetal interface. The molecular intricacies of these mechanisms remain elusive. This study employed microarray and bioinformatics techniques to examine genetic abnormalities in endometrial tissues from RSA patients, with the objective of identifying potential ERS-related biomarkers. By integrating two publicly available microarray datasets, consisting of 88 RSA and 42 control samples, we conducted an extensive analysis, including differential expression, functional annotation, molecular interactions, and immune cell infiltration. Analysis of immune cell characteristics suggests an inflammatory immune imbalance as a potential contributor to RSA progression. Both innate and adaptive immunity were found to play roles in RSA development, with M1 macrophages constituting a significant proportion of immune infiltration. We identified five key ERS-associated genes (TMEM33, QRICH1, MBTPS2, ERN1, and BAK1) linked to immune-related mechanisms, with RT-qPCR results aligning with bioinformatics findings. Our research findings offer a fresh and comprehensive perspective on the ERS-related genes' pathways and interaction networks, offering significant insights for the advancement of innovative therapy techniques for RSA.

G6PC3 is involved in spermatogenesis by maintaining meiotic sex chromosome inactivation.

Meiosis, a process unique to germ cells, involves formation and repair of double-stranded nicks in DNA, pairing and segregation of homologous chromosomes, which ultimately achieves recombination of homologous chromosomes. Genetic abnormalities resulted from defects in meiosis are leading causes of infertility in humans. Meiotic sex chromosome inactivation (MSCI) plays a crucial role in the development of male germ cells in mammals, yet its underlying mechanisms remain poorly understood. In this study, we illustrate the predominant presence of a protein known as glucose 6 phosphatase catalyzed 3 (G6PC3) in pachytene spermatocytes, with a high concentration in the sex body (XY body), suggesting its significant involvement in male germ cell development. By employing CRISPR-Cas9 technology, we generate mice deficient in the G6pc3 gene, resulting in complete meiotic arrest at the pachytene stage in spermatocytes and are completely sterile. Additionally, we observe abnormal XY body formation and impaired MSCI in G6pc3-knockout spermatocytes. These findings underscore G6pc3 as a new essential regulator that is essential for meiotic progression. G6PC3 is involved in spermatocyte during male spermatogenesis development by the maintenance of meiosis chromosome silencing.

Improving precision base editing of the zebrafish genome by Rad51DBD-incorporated single-base editors

Single-base editors, including cytosine base editors (CBEs) and adenine base editors (ABEs), facilitate accurate C⋅G to T⋅A and A⋅T to G⋅C, respectively, holding promise for the precise modeling and treatment of human hereditary disorders. Efficient base editing and expanded base conversion range have been achieved in human cells through base editors fusing with Rad51 DNA binding domain (Rad51DBD) such as hyA3A-BE4max. Here, we show that hyA3A-BE4max catalyzes C-to-T substitution in the zebrafish genome and extends editing positions (C12–C16) proximal to the protospacer adjacent motif. We develop the codon-optimized counterpart zhyA3A-CBE5, which exhibits substantially high C-to-T conversion with 1.59- to 3.50-fold improvement compared to the original hyA3A-BE4max. With these tools, disease-relevant hereditary mutations can be more efficaciously generated in zebrafish. We introduce human genetic mutation rpl11Q42∗ and abcc6aR1463C by zhyA3A-CBE5 in zebrafish, mirroring Diamond-Blackfan anemia and Pseudoxanthoma Elasticum, respectively. Our study expands the base editing platform targeting the zebrafish genomic landscape and the application of single-base editors for disease modeling and gene function study.

Recent Insight into Herbal Bioactives-based Novel Approaches for Chronic Intestinal Inflammatory Disorders Therapy.

Inflammatory bowel disease (IBD) is a life-threatening complex disease. It causes chronic intestinal inflammation in GIT. IBD significantly affects people's lifestyles and carries a high risk of colon cancer. IBD involves the rectum, ileum, and colon, with clinical manifestations of bloody stools, weight loss, diarrhea, and abdominal pain. The prevalence of inflammatory disease is increasing dramatically worldwide. Over 16 million people are affected annually in India, with an economic burden of $6.8- $8.8 billion for treatment. Modern medicine can manage IBD as immunosuppressive agents, corticosteroids, tumor necrosis factor antagonists, integrin blockers, and amino-salicylates. However, these approaches are allied with limitations such as limited efficacy, drug resistance, undesired side effects, and overall cost, which cannot be ignored. Hence, the herbal bioactives derived from various plant resources can be employed in managing IBD. Science Direct, PubMed, Google, and Scopus databases have been searched for conclusively relevant herbal plant-based anti-inflammatory agent compositions. Studies were screened through analysis of previously published review articles. Eminent herbal bioactives, namely curcumin, resveratrol, ellagic acid, silybin, catechin, kaempferol, icariin, glycyrrhizin acid, berberine, quercetin, rutin, and thymol are reported to be effective against IBD. Herbal leads are promising treatment options for IBD; they have been shown to display antiinflammatory and antioxidant properties by targeting enzymes and regulating the expressions of various inflammatory mediators. Natural products have been reported to have anti-inflammatory properties in various clinical and preclinical studies, and some are available as herbal preparations. Herbal medicine would be promising in association with the implication of a novel drug delivery system for managing IBD.

Learning personalized drug features and differentiated drug-pair interaction information for drug–drug interaction prediction

Multi-drug combination therapies are increasingly used for complex diseases but carry risks of harmful drug interactions. Effective drug–drug interaction prediction (DDIP) is essential for assessing risks among numerous drug pairs. Most DDIP methods involve two main steps: drug representation and drug pair interaction extraction, respectively challenged by the loss of personalized drug information and the need for differentiated interaction data, and are rarely studied. Specifically, personalized drug information refers to the distinct features of each drug. These properties can be easily confused by neighboring information during graph propagation. This issue is especially prominent in drug interaction graphs with long-tail distributions, which poses challenges for personalized drug information learning. Furthermore, it is crucial to learn interactions with differentiation in order to identify diverse drug relationships. Some methods simply concatenate drug features, often ignoring the differences of different drug relationships, while other methods based on substructures rely on professional pharmacological knowledge and are computationally complex. To address these issues, we propose a novel method, learning personalized Drug Features and differentiated Drug-Pair interaction information for drug–drug interaction prediction (DFPDDI). This approach employs a contrastive learning network with edge-aware augmentations and mutual information estimators to capture personalized drug features across various graph distributions. Furthermore, it applies a mutual information constraint to drug-pair representations, enhancing the accuracy of interaction predictions by better distinguishing between different types of drug relationships. The results evaluated on three public datasets demonstrate competitive performance compared to baselines. It also shows potential for accurate predictions, particularly in imbalanced-distribution graphs.

Single-Batch Expression of an Experimental Recombinant Snakebite Antivenom Based on an Oligoclonal Mixture of Human Monoclonal Antibodies.

Oligoclonal antibodies, which are carefully defined mixtures of monoclonal antibodies, are valuable for the treatment of complex diseases, such as infectionss and cancer. In addition to these areas of medicine, they could be utilized for the treatment of snakebite envenoming, where recombinantly produced monoclonal human antibodies could overcome many of the drawbacks accompanying traditional antivenoms. However, producing multiple individual batches of monoclonal antibodies in an industrial setting is associated with significant costs. Therefore, it is attractive to produce oligoclonal antibodies by mixing multiple antibody-producing cell lines in a single batch to have only one upstream and downstream process. In this study, we selected four antibodies that target different toxins found in the venoms of various elapid snake species, such as mambas and cobras, and generated stable antibody-producing cell lines. Upon co-cultivation, we found the cell line ratios to be stable over 7 days. The purified oligoclonal antibody cocktail contained the anticipated antibody concentrations and bound to the target toxins as expected. These results thus provide a proof of concept for the strategy of mixing multiple cell lines in a single batch to manufacture tailored antivenoms recombinantly, which could be utilized for the treatment of snakebite envenoming and in other fields where oligoclonal antibody mixtures could find utility.

Perceptual and semantic deficits in face recognition in semantic dementia

State of the artSemantic dementia (SD) patients including semantic variant primary progressive aphasia (svPPA) and semantic behavioral variant frontotemporal dementia (sbvFTD) patients show semantic difficulties identifying faces and known people related to right anterior temporal lobe (ATL) atrophy. However, it remains unclear whether they also have perceptual deficits in face recognition. MethodologyWe selected 74 SD patients (54 with svPPA and predominant left ATL atrophy and 20 with sbvFTD and predominant right ATL atrophy) and 36 cognitively healthy controls (HC) from UCSF Memory and Aging Center. They underwent a perceptual face processing test (Benton facial recognition test-short version; BFRT-S), and semantic face processing tests (UCSF Famous people battery – Recognition, Naming, Semantic associations – pictures and words subtests), as well as structural magnetic resonance imaging (MRI). Neural correlates with the task’s performance were conducted with a Voxel-based morphometry approach using CAT12. ResultssvPPA and sbvFTD patients were impaired on all semantic face processing tests, with sbvFTD patients performing significantly lower on the famous faces’ recognition task in comparison to svPPA, and svPPA performing significantly lower on the naming task in comparison to sbvFTD. These tasks predominantly correlated with gray matter (GM) volumes in the right and left ATL, respectively. Compared to HC, both svPPA and sbvFTD patients showed preserved performance on the perceptual face processing test (BFRT-S), and performance on the BFRT-S negatively correlated with GM volume in the right posterior superior temporal sulcus (pSTS). ConclusionOur results suggest that early in the disease, with the atrophy mostly restricted to the anterior temporal regions, SD patients do not present with perceptual deficits. However, more severe SD cases with atrophy in right posterior temporal regions might show lower performance on face perception tests, in addition to the semantic face processing deficits. Early sparing of face perceptual deficits in SD patients, regardless of hemispheric lateralization, furthers our understanding of clinical phenomenology and therapeutical approaches of this complex disease.

The interplay of immunity and growth: A case of combined variable immunodeficiency and growth hormone deficiency

Introduction: Common Variable Immunodeficiency (CVID) is one of the more common immunodeficiencies seen in clinical practice with a complex disease pathology; while Growth Hormone Deficiency (GHD) is a disorder characterized by complete or relative absence of the human Growth Hormone. Case Presentation: This case report presents a 13-year-old female patient with a long history of recurrent respiratory tract and ear infections along with a notable failure to hit her developmental milestones early in the second decade of her life. The diagnosis was based on a thorough investigation of serum immunoglobulins for CVID and a GH stimulation test for GHD. For these, the patient was placed on a tailored regimen of IVIGs, somatropin therapy and antibiotics for the recurrent infections. Case Discussion: CVID patients characteristically present with recurrent respiratory and ear infections, showing a marked decrease in immunity. Often diagnosed in childhood, GHD typically presents as growth failure along with developmental delays in dentition. There has been a notable rise in the coexistence of immunodeficiency syndromes and endocrinopathies studied in the past few decades. The case highlights and discusses the complex underlying pathology at play that links the two conditions to each other, while also excluding the various differentials. Conclusion: The report highlights the various challenges faced by both clinicians and patients when dealing with dual health conditions that may have a relatively non-specific presentation. Some of which include the diagnostic difficulties, financial strains on the patient leading to poor follow-up, and in the long-term, the development of various complications. This emphasizes the importance of early disease diagnosis and strict management protocols for the said disease, for the overall betterment of the patient’s quality of life.

Biomarkers for systemic lupus erythematosus: A scoping review.

In recent years, newly discovered potential biomarkers have great research potential in the diagnosis, disease activity prediction, and treatment of systemic lupus erythematosus (SLE). In this study, a scoping review of potential biomarkers for SLE over several years has identified the extent to which studies on biomarkers for SLE have been conducted, the specificity, sensitivity, and diagnostic value of potential biomarkers of SLE, the research potential of these biomarkers in disease diagnosis, and activity detection is discussed. In PubMed and Google Scholar databases, "SLE," "biomarkers," "predictor," "autoimmune diseases," "lupus nephritis," "neuropsychiatric SLE," "diagnosis," "monitoring," and "disease activity" were used as keywords to systematically search for SLE molecular biomarkers published from 2020 to 2024. Analyze and summarize the literature that can guide the article. Recent findings suggest that some potential biomarkers may have clinical application prospects. However, to date, many of these biomarkers have not been subjected to repeated clinical validation. And no single biomarker has sufficient sensitivity and specificity for SLE. It is not scientific to choose only one or several biomarkers to judge the complex disease of SLE. It may be a good direction to carry out a meta-analysis of various biomarkers to find SLE biomarkers suitable for clinical use, or to evaluate SLE by combining multiple biomarkers through mathematical models. At the same time, advanced computational methods are needed to analyze large data sets and discover new biomarkers, and strive to find biomarkers that are sensitive and specific enough to SLE and can be used in clinical practice, rather than only staying in experimental research and data analysis.

Interpretable machine learning uncovers epithelial transcriptional rewiring and a role for Gelsolin in COPD.

Transcriptomic analyses have advanced the understanding of complex disease pathophysiology including chronic obstructive pulmonary disease (COPD). However, identifying relevant biologic causative factors has been limited by the integration of high dimensionality data. COPD is characterized by lung destruction and inflammation with smoke exposure being a major risk factor. To define novel biological mechanisms in COPD, we utilized unsupervised and supervised interpretable machine learning analyses of single cell-RNA sequencing data from the gold standard mouse smoke exposure model to identify significant latent factors (context-specific co-expression modules) impacting pathophysiology. The machine learning transcriptomic signatures coupled to protein networks uncovered a reduction in network complexity and novel biological alterations in actin-associated gelsolin (GSN), which was transcriptionally linked to disease state. GSN was altered in airway epithelial cells in the mouse model and in human COPD. GSN was increased in plasma from COPD patients, and smoke exposure resulted in enhanced GSN release from airway cells from COPD patients. This method provides insights into rewiring of transcriptional networks that are associated with COPD pathogenesis and provide a novel analytical platform for other diseases.

Additive manufacturing of personalized scaffolds for vascular cell studies in large arteries: A case study on carotid arteries in sickle cell disease patients

Patient-specific models have increasingly gained significance in medical and research domains. In the context of hemodynamic studies, computational fluid dynamics emerges as a highly innovative and promising approach. We propose to augment these computational studies with cell-based experiments in individualized artery geometries using personalized scaffolds and vascular cell experiments. Previous research has demonstrated that the development of Sickle Cell Disease (SCD)-Related Vasculopathy is dependent on personal geometries and flow characteristics of the carotid artery. This fact leaves conventional animal experiments unsuitable for gaining patient-specific insights into cellular signaling, as they cannot replicate the personalized geometry. These personalized dynamics of cellular signaling may further impact disease progression, yet remains unclear. This paper presents a six-step methodology for creating personalized large artery scaffolds, focusing on high-precision models that yield biologically interpretable patient-specific results. The methodology outlines the creation of personalized large artery models via Additive Manufacturing suitably for supporting cell culture and other cellular experiments. Additionally, it discusses how different Computer-Aided-Design (CAD) construction modes can be used to obtain high-precision personalized models, while simplifying model reconfigurations and facilitating adjustments to general designs such as system connections to bioreactors, fluidic systems and visualization tools. A proposal for quality control measures to ensure geometric congruence for biological relevance of the results is added. This innovative, interdisciplinary approach appears promising for gaining patient-specific insights into pathophysiology, highlighting the importance of personalized medicine for understanding complex diseases.

Unveiling the Role of Artificial Intelligence (AI) in Polycystic Ovary Syndrome (PCOS) Diagnosis: A Comprehensive Review.

Polycystic Ovary Syndrome (PCOS) is one of the most widespread endocrine and metabolic disorders affecting women of reproductive age. Major symptoms include hyperandrogenism, polycystic ovary, irregular menstruation cycle, excessive hair growth, etc., which sometimes may lead to more severe complications like infertility, pregnancy complications and other co-morbidities such as diabetes, hypertension, sleep apnea, etc. Early detection and effective management of PCOS are essential to enhance patients' quality of life and reduce the chances of associated health complications. Artificial intelligence (AI) techniques have recently emerged as a popular methodology in the healthcare industry for diagnosing and managing complex diseases such as PCOS. AI utilizes machine learning algorithms to analyze ultrasound images and anthropometric and biochemical test result data to diagnose PCOS quickly and accurately. AI can assist in integrating different data sources, such as patient histories, lab findings, and medical records, to present a clear and complete picture of an individual's health. This information can help the physician make more informed and efficient diagnostic decisions. This review article provides a comprehensive analysis of the evolving role of AI in various aspects of the management of PCOS, with a major focus on AI-based diagnosis tools.

Key questions and gaps in understanding adipose tissue macrophages and early-life metabolic programming.

The global obesity epidemic, with its associated comorbidities and increased risk of early mortality, underscores the urgent need for enhancing our understanding of the origins of this complex disease. It is increasingly clear that metabolism is programmed early in life and that metabolic programming can have life-long health consequences. As a critical metabolic organ sensitive to early-life stimuli, proper development of adipose tissue (AT) is crucial for life-long energy homeostasis. Early-life nutrients, especially fatty acids (FAs), significantly influence the programming of AT and shape its function and metabolism. Of growing interest are the dynamic responses during pre- and postnatal development to proinflammatory omega-6 (n6) and anti-inflammatory omega-3 (n3) FA exposures in AT. In the US maternal diet, the ratio of "pro-inflammatory" n6- to "anti-inflammatory" n3-FAs has grown dramatically due to the greater prevalence of n6-FAs. Notably, AT macrophages (ATMs) form a significant population within adipose stromal cells, playing not only an instrumental role in AT formation and maintenance but also acting as key mediators of cell-to-cell lipid and cytokine signaling. Despite rapid advances in ATM and immunometabolism fields, research has focused on responses to obesogenic diets and during adulthood. Consequently, there is a significant gap in identifying the mechanisms contributing metabolic health, especially regarding lipid exposures during the establishment of ATM physiology. Our review highlights the current understanding of ATM diversity, their critical role in AT, their potential role in early-life metabolic programming, and the broader implications for metabolism and health.

CDSS Adoption and the Role of Artificial Intelligence in Saudi Arabian Primary Healthcare

The integration of advanced Clinical Decision Support Systems (CDSS) is crucial in healthcare, especially in developing countries. However, the challenges to CDSS implementation in these settings are often overlooked, leading to implementation gaps. This study investigates the adoption of CDSS in primary healthcare facilities in Saudi Arabia through in-depth interviews with general practitioners. The advancement of primary healthcare systems in developing nations necessitates appropriate technological solutions to address issues such as equitable access, service quality, and patient satisfaction. The adoption of CDSS can potentially improve patient satisfaction and enhance medical consultations. Artificial intelligence -driven technologies like the Velys robot are transforming the medical field by assisting doctors in surgical procedures. In healthcare, Artificial intelligence (AI) can address issues related to complex diseases, aging populations, and workforce shortages. AI solutions can streamline patient flow, improve care coordination, and support personalized healthcare management. By assessing the impact of AI on patient care, healthcare efficiency, and effectiveness can be enhanced. This research highlights the challenges and opportunities in CDSS adoption in developing countries, with implications for healthcare improvement in Saudi Arabia.

Myelodysplastic syndrome (MDS)/Myeloproliferative neoplasm (MPN) with loss of long arm of chromosome 5 (del 5q): Clinical case and treatment perspectives.

this clinical case report presents a patient diagnosed with a myelodysplastic syndrome (MDS)/myeloproliferative neoplasm (MPN), specifically myelofibrosis with a deletion of the long arm of chromosome 5 (del 5q), an exceedingly rare anomaly in chronic myeloproliferative disorders. We performed a thorough analysis of the patient’s clinical, laboratory, and molecular-genetic characteristics to evaluate their impact on prognosis, treatment decisions, and therapeutic outcomes. After excluding other genetic abnormalities, the patient was treated with lenalidomide and prednisolone, resulting in improved clinical and hematological parameters. These findings are consistent with previous studies and suggest the potential efficacy of lenalidomide in treating patients with MDS/MPN characterized by del 5q, especially when no additional genetic abnormalities are present.

MicroRNA expression signature in gastrointestinal stromal tumour & their molecular & histological features.

Background & objectives In gastrointestinal stromal tumour (GIST), not only genetic abnormalities are responsible for adverse clinical events, but epigenetic modifications also play a crucial role. MicroRNA (miRNA) dysregulation plays a significant role in carcinogenesis as miRNAs serve as natural silencer for their targets. Our study aimed to explore the miRNAs expression and its association with molecular and histopathological characteristics of GIST. Methods Fifty GIST samples, including 45 formalin fixed paraffin embedded (FFPE) and fresh tissues were included. Peripheral non-tumour tissues were used as controls. All the cases were confirmed using immunohistochemistry. RNA was extracted using miRNA-specific kit, and the expression was performed using RT-qPCR. The data were evaluated using AriaMx software version 1.5 (Agilent, US). MiRNAs expression was analyzed by using the relative quantification method (ΔΔCT). Results miR-221, miR-222, miR-494 and miR-34a showed significant down-regulation in tumours relative to non-tumour tissues. The expression levels of these miRNAs were significantly down-regulated in c-KIT (proto-oncogene encoding the tyrosine kinase transmembrane receptor)-positive tumours compared to c-KIT-negative. Further analysis revealed that reduced expression was associated with spindle subtypes and gastric localization. However, there was no significant correlation with other histological features. Additionally, miR-221/222, and miR-494 were down-regulated in most of the KIT exon 11 mutant subtypes, while miRNA-34a was associated with platelet derived growth factor receptor alpha (PDGFRA) mutations. Interpretation & conclusions The present study showed that the down-regulation of these miRNAs may help better molecular classification and characterization of GISTs. Our results offer new insight into the association between miRNAs and histological features, enabling a more thorough understanding of GISTs at the molecular level.

Assessment of B2-Microglobulin Analysis for Renal Insufficiency in Iraqi β-Thalassemia Major Patients

Background: Beta-thalassemia is the most prevalent genetic hemoglobin apathy in the world. It is caused by a reduction or absence of beta-globin chain production, which is typically a portion of adult hemoglobin (HbA, which is α2β2). This genetic anomaly will lead to a rapid erythrocyte turnover, severe anemia, and compensatory ineffective erythropoiesis. The purpose of the current research is to reveal diagnostic, and predictive biomarkers that can be performed to detect the decline in renal function in β-thalassemia major patients with early stage renal impairment with high sensitivity and specificity, ascertain changes in B2-microglobulin as a biomarker in β-thalassemia patient. Objective: The samples were collected from Karama Hospital-Genetic Hematology Center/Baghdad during the period from the 1st of September 2022 until the 1st of January 2023. Methods: The patient group consisted of 45 patients with β-thalassemia major with repeated blood transfusion and the control group consisted of 45 individuals who seemed to be healthy. Age ranged between (18-35) years (for patients and control). The serum samples were used to measure biochemical parameters, S.Creatinine, blood urea, and Urine B2-microglobulin were measured from urine samples. B2-microglobulin level in Urine was estimated by Sandwich enzyme-linked immunosorbent assay (ELISA) technique, while serum Blood Urea and Serum Creatinine concentration were calculated by Colorimetric Kit by Spectrophotometer method, and Ferritin level in blood was estimated by BioMrieux Mini Vidas. Results: The statistical examination was carried out using SPSS software. B2-microglobulin biomarkers in the thalassemia Patient group were significantly higher than those in the control group (p<0.001). While serum ferritin, B.Urea and S.Cr increased in patient groups compared to the control group (p<0. 005). Conclusions: All parameters included in this study are significantly higher in β-thalassemia patients than in healthy subjects. Renal hemosiderosis and asymptomatic renal dysfunction are prevalent among β-thalassemia major patients with repeated blood transfusions, which are not found in routine renal investigations.

Prognostic prediction of breast cancer patients using machine learning models: a retrospective analysis.

Breast cancer is a common and complex disease, with various clinical features affecting prognosis. Accurate prediction of prognosis is essential for guiding personalized treatment strategies. This study aimed to develop machine learning models for predicting prognosis in breast cancer patients using retrospective data. A total of 6,477 patients from Affiliated Sir Run Run Shaw Hospital were included, and their electronic medical records (EMRs) were thoroughly examined to identify 15 clinical features significantly associated with breast cancer survival. We employed eight different machine learning algorithms, including Logistic Regression (LR), Support Vector Machine (SVM), Random Forest (RF), and Extreme Gradient Boosting (XGBoost), to develop and evaluate the predictive performance of the models. In addition, to investigate the sensitivity of different training/testing set radio to model performance, we examined five sets of ratios: 50:50, 60:40, 70:30, 80:20, 90:10. Among these models, XGBoost demonstrated the highest performance with receiver operating characteristic (ROC) area under the curve (AUC) of 0.813, accuracy of 0.739, sensitivity of 0.815, and specificity of 0.735. Further statistical analysis identified several significant predictors of prognosis, including age, tumor size, lymph node status, and hormone receptor status. The XGBoost model was found to exhibit superior predictive power compared to established prognostic models such as the Nottingham Prognostic Index (NPI) and Predict Breast. Based on the successful performance of the XGBoost model, we developed a prognosis prediction tool specifically designed for breast cancer, providing valuable insights to clinicians, and aiding them in making informed treatment decisions tailored to individual patients. Our study highlights the potential of machine learning models in accurately predicting prognosis for breast cancer patients, ultimately facilitating personalized treatment strategies. Further research and validation are warranted to fully integrate these models into clinical practice.

A review of gut microbial metabolites and therapeutic approaches in hypertension

Abstract Background Hypertension is a prevalent and complex disease that is increasingly recognized to be influenced by the gut microbiome and its metabolites. Understanding the relationship between gut microbial metabolites and blood pressure regulation could provide new therapeutic avenues. Main body This review examines the role of key microbial metabolites—short-chain fatty acids, trimethylamine N-oxide, tryptophan derivatives, polyamines, bile acids, and phenylacetylglutamine—in blood pressure regulation. Short-chain fatty acids, produced through dietary fiber fermentation, can lower blood pressure by modulating immune responses and reducing inflammation. Elevated trimethylamine N-oxide levels are associated with increased cardiovascular risk and hypertension, influencing cholesterol metabolism and promoting atherosclerosis. Tryptophan derivatives interact with vascular and renal functions to modulate blood pressure. Polyamines affect blood pressure regulation through their impact on nitric oxide synthesis and vascular tone. Bile acids influence blood pressure via gut microbiota modulation and activation of metabolic receptors. Phenylacetylglutamine has been linked to hypertension through its effects on platelet hyperactivity and thrombosis. Therapeutic approaches targeting these metabolites, including probiotics, prebiotics, fecal microbiota transplantation, dietary interventions, and polyphenols, have shown varying degrees of success. Probiotics and prebiotics promote the growth of beneficial gut bacteria and may lower blood pressure. Dietary interventions, such as the Mediterranean diet, positively affect blood pressure and cardiovascular health by modulating the gut microbiota. Polyphenols, known for their antioxidant properties, are associated with blood pressure reductions and improved vascular function. Fecal microbiota transplantation shows promise in restoring gut microbial balance and improving metabolic health, potentially influencing blood pressure regulation. Conclusion The review highlights the significant role of gut microbial metabolites in regulating blood pressure, offering new avenues for hypertension management. Key metabolites, including short-chain fatty acids, trimethylamine N-oxide, and bile acids, play critical roles in blood pressure modulation. Therapeutic strategies targeting these metabolites, such as probiotics, prebiotics, and dietary interventions, hold promise, though further research is needed to fully understand their mechanisms and optimize their use. Advancing microbiota-based interventions through large-scale studies and exploring personalized therapies will be essential for developing effective treatments in hypertension management.

Ulcerative colitis and concomitant arterial hypertension (literature review)

ulcerative colitis is a complex disease that results from a complex interaction of genetic predisposition, environmental factors, which leads to dysregulation of the immune system, chronic intestinal inflammation with damage to the mucous membrane. Systemic chronic inflammation can cause the development of arterial hypertension, thereby complicating the course of the primary disease. However, the cause-and-effect relationship between these diseases is not fully understood. Early detection of high blood pressure in patients with ulcerative colitis, a multidisciplinary approach to diagnosis makes it possible to achieve optimal treatment results in a timely manner and prevent the occurrence of intestinal complications. The aim of this study was to provide a comprehensive review of diagnostic procedures such as colonoscopy, inflammatory biomarkers (faecal calprotectin, faecal lactoferrin, serum zonulin), and to study the features of the colon structure in ultrasound diagnostics in patients with ulcerative colitis and concomitant hypertension. The data from PubMed and Ukrainian scientific sources on the comorbidity of these diseases, the possibility of using non-invasive diagnostic methods to assess the condition of the intestine in patients with ulcerative colitis and arterial hypertension were analysed. The literature review includes 50 scientific sources. The main focus is on the pathophysiological relationships of diseases, peculiarities of non-invasive diagnosis of ulcerative colitis during follow-up and early detection of arterial hypertension, which makes it possible to prevent complications in ulcerative colitis. Given the literature review, it can be concluded that the problem of timely diagnosis of hypertension in patients with ulcerative colitis is relevant today. For a rational approach to the management of patients with ulcerative colitis and concomitant hypertension, it is necessary to carry out a set of diagnostic procedures, including outpatient blood pressure measurement, ultrasound examination of the intestinal wall, and the use of non-invasive inflammatory biomarkers.