Dalia Mohammed Osman Khalfallah

Background: Combined with nanotechnology, AI, and IoT, the field of healthcare is experiencingsignificant changes in the approaches that are used to diagnose diseases and determine furthertreatment plans. Therefore, the purpose of this research will be to establish the effects of thesetechnologies in the delivery of healthcare.Objective:To evaluate how healthcare professionals are aware and using nanotechnology, AI, and IoT indiagnostics and treatment processes and the key difficulties and perceived advantages of thosetechnologies; and to figure out the present state of implementation of nanotechnology, AI, and IoT inhealthcare.Methods:From this study, a quantitative, cross-sectional survey design was used. A quantitative surveymethod that adopts a set of standard questions was employed for healthcare practitioners, scholars,technologists, and clients. Qualitative and both quantitative research methods were employed wheredescriptive statistics and inferential statistics were used in analyzing the data. The normality of thedata was tested using the Shapiro-Wilk test, survey reliability was tested with a Cronbach alpha testwhile, and the model accuracy was tested using the Random Forest classifier.

The genotyping of long non-coding RNA (lncRNA)-related single-nucleotide polymorphisms (SNPs) could be associated with cancer risk and/or progression. This study aimed to analyze the angiogenesis-related lncRNAs MALAT1 (rs3200401) and MIAT (rs1061540) variants in patients with ovarian cancer (OC) using “Real-Time allelic discrimination polymerase chain reaction” in 182 formalin-fixed paraffin-embedded (FFPE) samples of benign, borderline, and primary malignant ovarian tissues. Differences in the genotype frequencies between low-grade ovarian epithelial tumors (benign/borderline) and malignant tumors and between high-grade malignant epithelial tumors and malignant epithelial tumors other than high-grade serous carcinomas were compared. Odds ratios (ORs)/95% confidence intervals were calculated as measures of the association strength. Additionally, associations of the genotypes with the available pathological data were analyzed. The heterozygosity of MALAT1 rs3200401 was the most common genotype (47.8%), followed by C/C (36.3%). Comparing the study groups, no significant differences were observed regarding this variant. In contrast, the malignant epithelial tumors had a higher frequency of the MIAT rs1061540 C/C genotype compared to the low-grade epithelial tumor cohorts (56.7% vs. 37.6, p = 0.031). The same genotype was significantly higher in high-grade serous carcinoma than its counterparts (69.4% vs. 43.8%, p = 0.038). Multivariate Cox regression analysis showed that the age at diagnosis was significantly associated with the risk of OC development. In contrast, the MIAT T/T genotype was associated with a low risk of malignant epithelial tumors under the homozygote comparison model (OR = 0.37 (0.16–0.83), p = 0.017). Also, MIAT T allele carriers were less likely to develop high-grade serous carcinoma under heterozygote (CT vs. CC; OR = 0.33 (0.12–0.88), p = 0.027) and homozygote (TT vs. CC; OR = 0.26 (0.07–0.90), p = 0.034) comparison models. In conclusion, our data provide novel evidence for a potential association between the lncRNA MIAT rs1061540 and the malignant condition of ovarian cancer, suggesting the involvement of such lncRNAs in OC development.