Pancreatic cancer (PC) is a malignancy with little/no warning signs before the disease reaches its ultimate stages. Currently early detection of PC is very difficult because most patients have non-specific symptoms leading to postponing the correct diagnosis. In this study, using multiple bioinformatics tools, we integrated various serum expression profiles of miRNAs to find the most significant miRNA signatures helpful in diagnosis of PC and constructed novel miRNA diagnosis models for PC. Altogether, 27 differentially expressed miRNAs (DEMs) showed area under curve (AUC) score >80%. The most promising miRNAs, miR-1469 and miR-4530, were individually able to distinguish two groups with the highest specificity and sensitivity. By using multivariate cox regression analyses, 5 diagnostic models consisting of different combinations of miRNAs, based on their significant expression algorithms and functional properties were introduced. The correlation model consisting of miR-125a-3p, miR-5100 and miR-642b-3p was the most promising model in the diagnosis of PC patients from healthy controls with an AUC of 0.95, Sensitivity 0.98 and Specificity 0.97. Validation analysis was conducted for considered miRNAs on a final cohort consist of the microarray data from two other datasets (GSE112264 & GSE124158) . These results provide some potential biomarkers for PC diagnosis after testing in large case-control and cohort studies.

Male infertility contributes to 20–30% of infertility cases, yet traditional diagnostic and treatment methods face limitations in accuracy and consistency. Artificial intelligence (AI) promises to transform male infertility management within in vitro fertilization (IVF) by enhancing precision and efficiency. This study aims to map current AI applications in male infertility, evaluate their performance in IVF contexts, identify gaps in research, and propose strategies for clinical adoption. We conducted a mapping review of 14 studies, sourced from PubMed, Scopus, IEEE, and Web of Science up to 2024. Using PRISMA guidelines, we systematically searched titles and abstracts with keywords like "IVF," "AI," and "sperm analysis." Two authors independently screened records, extracted data on AI techniques, sample sizes, and outcomes, and categorized applications through content analysis, resolving discrepancies via consensus. AI employs tools like support vector machines (SVM), multi-layer perceptrons (MLP), and deep neural networks across six key areas. These include sperm morphology (e.g., SVM with AUC 88.59% on 1400 sperm), motility (e.g., SVM with 89.9% accuracy on 2817 sperm), and non-obstructive azoospermia (NOA) sperm retrieval (e.g., gradient boosting trees [GBT] with AUC 0.807 and 91% sensitivity on 119 patients). AI also predicts IVF success (e.g., random forests with AUC 84.23% on 486 patients) and assesses sperm DNA fragmentation. Research surged since 2021, with 8 of 14 studies (57%) published between 2021 and 2023, reflecting growing interest.. AI enhances diagnostic accuracy and treatment outcomes in male infertility. Future steps include multicenter validation trials, AI-driven sperm selection for IVF/ICSI, and standardized methods to ensure clinical reliability. Addressing ethical concerns like data privacy will further enable AI to improve IVF success globally.

Expression Analysis of Two Cancer-testis Genes, FBXO39 and TDRD4, in Breast Cancer Tissues and Cell Lines Breast cancer;cell lines;cancer-testis genes;FBXO39;TDRD4; Breast cancer accounts for one third of new cancer cases among women. The need for biomarkers for early detection is the stimulus to researchers to evaluate altered expression of genes in tumours. Cancer-testis (CT) genes are a group with limited expression in normal tissues except testis but up-regulation in a wide variety of cancers. We here evaluated expression of two CT genes named FBXO39 and TDRD4 in 32 invasive ductal carcinoma samples, 10 fibroadenomas and 6 normal breast tissue samples, in addition to two breast cancer cell lines, MCF-7 and MDA-MB-231, by the means of quantitative real time RT-PCR. FBXO39 showed significant up-regulation in invasive ductal carcinoma samples in comparison with normal samples. It also was expressed in both cell lines and after RHOXF1 gene knock down it was down-regulated in MCF-7 but up-regulated in the MDA-MB-231 cell line. TDRD4 was not expressed in the MCF-7 cell line and any of the tissue samples except testis. However, it was expressed in MDA-MB-231 and was up-regulated after RHOXF1 gene knock down. Our results show that FBXO39 but not TDRD4 can be used for cancer detection and if proved to be immunogenic, might be a putative candidate for breast cancer immunotherapy.

MicroRNA (miRNA) expression dysregulations in pancreatic ductal adenocarcinoma (PDAC) have been studied widely for their diagnostic and prognostic utility. By the use of bioinformatics-based methods, in our previous study, we identified some potential miRNA panels for diagnosis of pancreatic cancer patients from noncancerous controls (the screening stage). In this report, we used 142 plasma samples from people with and without pancreatic cancer (PC) to conduct RT-qPCR differential expression analysis to assess the strength of the first previously proposed diagnostic panel (consisting of miR-125a-3p, miR-4530, and miR-92a-2-5p). As the result, we identified significant upregulation for all the three considered miRNAs in the serum of PC patients. After that, a three-miRNA panel in serum was developed. The area under the receiver operating characteristic curves (AUC) for the panel were 0.850, 0.910, and 0.86, respectively, indicating that it had a higher diagnostic value than individual miRNAs. Therefore, we detected a promising three-miRNA panel in the plasma for noninvasive PC diagnosis (miR-125a-3p, miR-4530, and miR-92a-2-5p).

Background: Pancreatic ductal adenocarcinoma (PDAC) is one of the deadliest malignancies and a major health problem worldwide. There were no major advances in conventional treatments in inhibiting tumor progression and increasing patient survival time. In order to suppress mechanisms responsible for tumor cell development such as those with oncogenic roles, more advanced therapeutic strategies should be sought. One of the most important oncogenes of pancreatic cancer is the MYC gene. The overexpression of MYC can activate many tumorigenic processes such as cell proliferation and pancreatic cancer cell invasion. MiRNAs are important molecules that are confirmed by targeting mRNA transcripts to regulate the expression of the MYC gene. Therefore, restoring MYC-repressing miRNAs expression tends to be an effective method of treating MYC-driven cancers. Objective: The purpose of this study was to identify all validated microRNAs targeting C-MYC expression to inhibit PDAC progression by conducting a systematic review. Methods: In this systematic review study, the papers published between 2000 and 2020 in major online scientific databases including PubMed, Scopus, and Web of Science were screened, following inclusion and exclusion criteria. We extracted all the experimental studies that showed miRNAs could target the expression of the MYC gene in PDAC. Results: Eight papers were selected from a total of 89 papers. We found that six miRNAs (Let-7a, miR-145, miR-34a, miR-375, miR-494, and miR-148a) among the selected studies were validated for targeting MYC gene and three of them confirmed Let-7a as a direct MYC expression regulator in PC cells. Finally, we summarized the latest shreds of evidence of experimentally validated miRNAs targeting the MYC gene with respect to PDAC’s therapeutic potential. Conclusion: Restoring the expression of MYC-repressing miRNAs tends to be an effective way to treat MYC-driven cancers such as PDAC. Several miRNAs have been proposed to target this oncogene via bioinformatics tools, but only a few have been experimentally validated for pancreatic cancer cells and models. Further studies should be conducted to find the interaction network of miRNA-MYC to develop more successful therapeutic strategies for PC, using the synergistic effects of these miRNAs.