Safa Mohammed Salim

Inflammatory joint diseases, including osteoarthritis, are multifactorial disorders in which dysregulated innate immune signaling and non-coding RNA (ncRNA)-mediated regulation of gene expression play essential roles. MicroRNA-155 (miR-155), its host gene MIR155HG, and Toll-like receptor 4 (TLR4) form a tightly linked inflammatory signaling axis, yet their combined genetic variability in chronic joint inflammation remains insufficiently characterized. The aim of this study was to investigate genetic variants in MIR155HG exon 3, mature miR-155, and TLR4 exon 3 and assess their potential synergistic role in chronic inflammatory joint disease. A case–control study was conducted with 100 cases (50 osteoarthritis patients and 50 matched healthy controls). Genomic DNA was analysed using polymerase chain reaction (PCR) and Sanger sequencing. Variant alleles and genotypes were identified, and their allele frequencies and genotypes were calculated using Mutation Surveyor. Detected variants were compared with public databases, and in silico tools were used to estimate the structural impact of TLR4 missense mutations. Sixteen heterozygous variants were identified in MIR155HG exon 3, most of them novel and population-specific. Interestingly, the highest variant frequencies for MIR155HG exon 3 were observed at positions 12448G>GC and 12481T>TA (both 64.3%), followed by 12442T>TC (57.1%). Additionally, two novel variants were detected in the miR-155 gene (chr21:29,694,314 G>A and chr21:29,646,351 T>C), each present at an allele frequency of 7.1% and absent from current external variant databases. Moreover, two rare TLR4 exon-3 variants were identified; a synonymous variant, c.147C>A (Pro49Pro; rs375037549), and a missense mutation, c.148G>A (Asp50Asn; rs776561489). Notably, in silico analyses and molecular dynamic simulations indicated that the Asp50Asn (D50N) substitution destabilizes the TLR4 protein. Conclusion: Concurrent variants in MIR155HG, miR-155, and TLR4 suggest a convergent regulatory molecular axis that may contribute to disease susceptibility and inflammatory progression.

To examine key areas and emerging trends in Machine learning (ML) research within the field of lung cancer. Methods Research literature on ML in the field of lung cancer was gathered from the Science Citation Index Expanded within the Scopus database. This data was then analyzed for insights on publication years, countries/regions, affiliation, funding sponsor, citations, and keywords. Co-occurrence network graphs were created using the VOSviewer library and CiteSpace tools on the online analysis platform. Result A total of 3,341 pertinent research articles from various nations were selected. From 2023 to 2024, there was a notable rise in the quantity of indexed documents, reaching a zenith of roughly 2000 in 2024. Nevertheless, this expansion was not maintained, and in 2025, the total declined precipitously to around 500. Geographically, India and China dominate research output, with India marginally in the lead. The United States occupies the third position but trails considerably behind the top two, with the United Kingdom in fourth place. Other nations, such as Italy, Canada, South Korea, Japan, Saudi Arabia, and Germany, provide relatively fewer documentation. The findings demonstrate significant research effort in Asia, especially in India and China, while North America and Europe also contribute considerably. Disparities in research output can be ascribed to elements such as funding, institutional backing, and national policies that affect publication trends, underscoring the necessity for additional exploration into the political and economic aspects of academic publishing. This research provides a comprehensive analysis of the challenges and prospective applications of machine learning in digital clinical diagnostic systems (DCDS) for the detection of LC. Conclusions The study identified key focus areas and emerging trends in ML for lung cancer diagnosis and indicating that this technology could greatly improve early detection.

The current study aims to identify the main areas and emerging trends in the field of artificial intelligence research in field of combating drug addiction. It relies on an accurate and organized scientific methodology to achieve its objectives, as research literature related to artificial intelligence in the field of combating drug addiction was collected from the Science Citation Index Expanded database within the Web of Science Core Collection. The period from 2015 to 2024 witnessed a significant increase in the number of research articles published on the use of artificial intelligence (AI) in combating addiction, reflecting a growing global interest in this field. 31 research studies from several countries were analyzed, with the results showing that the United States of America leads research production with 4.34% (14 research papers), followed by Italy (5 research papers), indicating the significant challenges these countries face in the field of addiction. Countries such as Poland, Canada, India (two research papers each), Iran, Spain, Indonesia, China and Brazil have also begun to increase their focus on developing innovative solutions using AI. The current study identified key focus areas and emerging trends in the use of AI to diagnose and treat drug addiction. The findings suggest that AI will play an increasingly important role in efforts to combat this problem.

Study of autism via a methodical search strategy predominantly through the Scopus database. The search employed terms including "autism," "autism spectrum disorder," "ASD," alongside "cytogenetics," "chromosome," "chromosomal abnormality," "copy number variation," "CNV," and "aneuploidy." The inquiry was confined to English, peer-reviewed publications without temporal limitations. The inclusion criteria emphasized original research, reviews, and case reports that elucidate cytogenetic or chromosomal investigations in persons with autism, encompassing classic karyotyping, aCGH, and SNP arrays, accompanied by explicit descriptions of findings and diagnoses. The exclusion criteria eliminated research focused on single-gene mutations lacking a cytogenetic component, non-English publications, editorials, and studies in which chromosomal mosaicism was a secondary observation. The findings indicated a vigorous scientific production in autism research, with annual publications continuously over 300 from 2021 to 2023; however, a decrease was observed in 2024 and 2025. The United States prominently led in publications, with over 600 documents, followed by Italy, the United Kingdom, China, and Canada. Prominent authors such as J.D. Buxbaum and A. Kolezvon significantly influenced research productivity. The review methodologically emphasized the growing integration of advanced genetic testing, such as Chromosomal Microarray Analysis (CMA), Whole Exome Sequencing (WES), and Whole Genome Sequencing (WGS), in conjunction with behavioral evaluations. Chromosomal Microarray Analysis (CMA) has become the recommended initial genetic assessment, detecting harmful copy number variations (CNVs) in 10-20% of autism spectrum disorder (ASD) cases, with elevated rates among individuals with intellectual disabilities. WES functioned as an ancillary instrument, providing diagnoses in 15-30% of ASD cohorts by identifying de novo pathogenic single-nucleotide variants (SNVs) and copy number variations (CNVs). Whole Genome Sequencing (WGS), albeit costly, provided the most thorough genomic perspective, detecting complex structural variants and copy number variations (CNVs) overlooked by alternative methods.

Abnormalities in chromosomes were assessed using cytogenetic and molecular cytogenetic analyses utilizing the FISH technique on blood samples from patients diagnosed with chronic lymphocytic leukemia. The study included the selection of 50 patients (32 males and 18 females) for both the early diagnosis phase (before therapy) and the treatment phase, examining various factors such as sex, age, and occupation. The results revealed that most patients in the over-70 age demographic are male. The majority lacked a familial history of this condition. Patients with chronic lymphocytic leukemia exhibited a higher prevalence of the chromosomal defect deletion (13) (q14) at 43.8%, followed by deletion (11) (q23) at 18.8%. A trisomy 12 alteration was observed at 12.5%, along with a loss on chromosome 17, also present at 12.5%. Tetraploidy occurred seldom (6.2%), notwithstanding the existence of chromosomal defects, specifically deletions (6) at q25-q27. The current study indicates that structural chromosomal modifications were more prevalent than numerical changes regarding chromosomal aberrations, with both types associated with chronic lymphocytic leukemia.