The study of association between reduced folate carrier 1 (RFC1) polymorphism and non-syndromic cleft lip/palate in Iranian population

Introduction: Cleft lip/palate is one of the most common congenital defects and is supposed to have multifactorial etiology, including a complex interaction between genetics and environment. Reduced folate carrier 1 (RFC1) gene takes part in folate transportation within the cells. In this study, the association of A80G polymorphism in the RFC1 gene with the non-syndromic cleft lip/palate (nsCL/P) was investigated in Iranian infants for the first time. Methods: In this case-control survey, 122 Iranian infants with nsCL/P and 164 healthy infants were investigated for RFC1 polymorphism by PCR and RFLP methods. The results were statistically compared with control group, odds ratios with 95% CI were estimated by univariate and multivariate logistic regression model and a P <0.05 was considered statistically significant. Results: The RFC1 G allele was significantly higher (P=0.001; OR=7, 95% CI: 4.7-10.2) in the cases (60.3%) compared with the controls (17.9%). Not only the RFC1 AG genotype was significantly higher (P<0.001; OR=44, 95% CI: 14.6-133) in cases (67.8%) than the controls (27.4%), but also GG genotype (P<0.001; OR=85, 95% CI: 20.5-352) was much higher in cases (26.4%) than the controls (4.3%). Conclusion: Our study indicated that the RFC1 (A80G) polymorphism was associated with the nsCL/P in Iranian population. Moreover, 80GG homozygosity was significant in the cases. The presence of G allele can be considered as a risk factor for the nsCL/P. Infants with the GG and AG genotypes were more prone to cleft lip/palate as compared to the AA ones. This finding emphasizes the role of RFC1 gene and the intracellular levels of folate

The emerging role of regulatory cell-based therapy in autoimmune disease

Autoimmune disease, caused by unwanted immune responses to self-antigens, affects millions of people each year and poses a great social and economic burden to individuals and communities. In the course of autoimmune disorders, including rheumatoid arthritis, systemic lupus erythematosus, type 1 diabetes mellitus, and multiple sclerosis, disturbances in the balance between the immune response against harmful agents and tolerance towards self-antigens lead to an immune response against self-tissues. In recent years, various regulatory immune cells have been identified. Disruptions in the quality, quantity, and function of these cells have been implicated in autoimmune disease development. Therefore, targeting or engineering these cells is a promising therapeutic for different autoimmune diseases. Regulatory T cells, regulatory B cells, regulatory dendritic cells, myeloid suppressor cells, and some subsets of innate lymphoid cells are arising as important players among this class of cells. Here, we review the roles of each suppressive cell type in the immune system during homeostasis and in the development of autoimmunity. Moreover, we discuss the current and future therapeutic potential of each one of these cell types for autoimmune diseases

Cancer stem cells in colorectal cancer: Signaling pathways involved in stemness and therapy resistance

Colorectal cancer (CRC) is the third cause of cancer death worldwide. Although, in some cases, treatment can increase patient survival and reduce cancer recurrence, in many cases, tumors can develop resistance to therapy leading to recurrence. One of the main reasons for recurrence and therapy resistance is the presence of cancer stem cells (CSCs). CSCs possess a self-renewal ability, and their stemness properties lead to the avoidance of apoptosis, and allow a new clone of cancer cells to emerge. Numerous investigations inidicated the involvment of cellular signaling pathways in embryonic development, and growth, repair, and maintenance of tissue homeostasis, also participate in the generation and maintenance of stemness in colorectal CSCs. This review discusses the role of Wnt, NF-κB, PI3K/AKT/mTOR, Sonic hedgehog, and Notch signaling pathways in colorectal CSCs, and the possible modulating drugs that could be used in treatment for resistant CR

Diagnostic Accuracy of Ultrasonography for Identification of Elbow Fractures in Children; a Systematic Review and Meta-analysis

Introduction: In spite of the results of previous studies regarding the benefits of ultrasonography for diagnosis of elbow fractures in children, the exact accuracy of this imaging modality is still under debate. Therefore, in this diagnostic systematic review and meta-analysis, we aimed to investigate the accuracy of ultrasonography in this regard. Methods: Two independent reviewers performed systematic search in Web of Science, Embase, PubMed, Cochrane, and Scopus for studies published from inception of these databases to May 2023. Quality assessment of the included studies was performed using Quality Assessment Tool for Diagnostic Accuracy Studies (QUADAS-2). Meta-Disc software version 1.4 and Stata statistical software package version 17.0 were used for statistical analysis. Results: A total of 648 studies with 1000 patients were included in the meta-analysis. The pooled sensitivity and specificity were 0.95 (95% CI: 0.93-0.97) and 0.87 (95% CI: 0.84-0.90), respectively. Pooled positive likelihood ratio (PLR) was 6.71 (95% CI: 3.86-11.67), negative likelihood ratio (NLR) was 0.09 (95% CI: 0.03-0.22), and pooled diagnostic odds ratio (DOR) of ultrasonography in detection of elbow fracture in children was 89.85 (95% CI: 31.56-255.8). The area under the summary receiver operating characteristic (ROC) curve for accuracy of ultrasonography in this regard was 0.93. Egger's and Begg's analyses showed that there is no significant publication bias (P=0.11 and P=0.29, respectively). Conclusion: Our meta-analysis revealed that ultrasonography is a relatively promising diagnostic imaging modality for identification of elbow fractures in children. However, clinicians employing ultrasonography for diagnosis of elbow fractures should be aware that studies included in this meta-analysis had limitations regarding methodological quality and are subject to risk of bias. Future high-quality studies with standardization of ultrasonography examination protocol are required to thoroughly validate ultrasonography for elbow fractures

Artificial Intelligence in Cancer Care: From Diagnosis to Prevention and Beyond

<p>Artificial Intelligence (AI) has made significant strides in revolutionizing cancer care, encompassing various aspects from diagnosis to prevention and beyond. With its ability to analyze vast amounts of data, recognize patterns, and make accurate predictions, AI has emerged as a powerful tool in the fight against cancer. This article explores the applications of AI in cancer care, highlighting its role in diagnosis, treatment decision-making, prevention, and ongoing management. In the realm of cancer diagnosis, AI has demonstrated remarkable potential. By processing patient data, including medical imaging, pathology reports, and genetic profiles, AI algorithms can assist in early detection and accurate diagnosis. Image recognition algorithms can analyze radiological images, such as mammograms or CT scans, to detect subtle abnormalities and assist radiologists in identifying potential tumors. AI can also aid pathologists in analyzing tissue samples, leading to more precise and efficient cancer diagnoses. AI's impact extends beyond diagnosis into treatment decision-making. The integration of AI algorithms with clinical data allows for personalized treatment approaches. By analyzing patient characteristics, disease stage, genetic markers, and treatment outcomes, AI can provide valuable insights to oncologists, aiding in treatment planning and predicting response to specific therapies. This can lead to more targeted and effective treatment strategies, improving patient outcomes and reducing unnecessary treatments and side effects. Furthermore, AI plays a crucial role in cancer prevention. By analyzing genetic and environmental risk factors, AI algorithms can identify individuals at higher risk of developing certain cancers. This enables targeted screening programs and early interventions, allowing for timely detection and prevention of cancer. Additionally, AI can analyze population-level data to identify trends and patterns, contributing to the development of public health strategies for cancer prevention and control. AI's involvement in cancer care goes beyond diagnosis and treatment, encompassing ongoing management and survivorship. AI-powered systems can monitor treatment response, track disease progression, and detect recurrence at an early stage. By continuously analyzing patient data, including imaging, laboratory results, and clinical assessments, AI algorithms can provide real-time insights, facilitating timely interventions and adjustments to treatment plans. This proactive approach to disease management improves patient outcomes and enhances quality of life.</p&gt