Colorectal cancer (CRC) biomarker discovery is frequently limited by signature instability, methodological heterogeneity, and insufficient external validation. This study aimed to develop a reproducible bioinformatics and machine learning framework for identifying a compact mature-microRNA (miRNA) signature capable of discriminating CRC tissues from matched normal controls. Small RNA-seq data from a paired discovery cohort (PRJNA387172; 104 tumor–normal pairs) and three independent external high-throughput sequencing cohorts (GSE125961, GSE183437, and GSE236991) were processed through uniform pre-processing, mature-miRNA quantification, counts per-million filtering, and TMM/logCPM normalization. Within the discovery cohort, pair-preserving 80:20 partitioning was combined with repeated stratified five-fold cross-validation with 10 repeats, train-only differential-expression analysis, and consensus feature selection using Elastic Net, Boruta, and random forest recursive feature elimination. This framework identified a stable locked three-miRNA signature comprising hsa-miR-135b-5p, hsa-miR-378a-5p, and hsa-miR-549a-5p. A random forest classifier trained on this panel achieved perfect discrimination in the discovery partitions, with an area under the curve (AUC) of 1.000 in both the training and independent test subsets, and showed preliminary external performance, with panel AUCs of 0.917, 1.000, and 0.840 across three small validation cohorts and 0.961 in pooled external analysis. Expression direction was preserved across datasets, with miR-135b-5p and miR-549a-5p upregulated and miR-378a-5p downregulated in tumors. Downstream target integration analysis recognized 401 overlap-supported miRNA-target interactions spanning 393 unique genes, with enrichment in Wnt, Hippo, transforming growth factor beta, focal adhesion, and CRC-related pathways. These findings support a biologically coherent three-miRNA random forest model for CRC biomarker discovery and prioritize candidates for future independent and experimental validation.
The aim of this review is to establish baseline data of anthropogenic and environmental radioactivity levels in soils of the Kurdistan region before potential radiological releases from regional nuclear incidents in 2025. This review collects and analyses previous research and measurements of man-made radionuclides, such as 137Cs and naturally occurring radionuclides, such as 226Ra, 232Th, and 40K from a variety of locations in the study area. The data analysis indicates that the average activity concentrations of the detected radionuclides are typically lower than the global average levels as reported by the United Nations Scientific Committee on the Effects of Atomic Radiation. The findings provide an accurate framework for assessing the safety of the environment and identifying possible future changes in soil radioactivity.
Morphing attacks on facial images pose a serious threat to face recognition systems during identity verification. In this paper, we introduce a parameter-efficient family of convolutional neural networks, named EfficientNet-Lite (EN_Lite), for morph attack detection (MAD). The experimental framework uses eight datasets, four morph generation methods, and ten homogeneous and heterogeneous evaluation scenarios under a leave-one-datasetout protocol. Performance was assessed using dataset-size-weighted average equal error rate (EERWA ), computed across the held-out datasets within each scenario. EN_Lite was evaluated using ImageNet1K and VGGFace2-HQ pre-training, with the latter comprising 1,107,817 successfully processed facial images. The ImageNetpretrained IEN_Lite-1 achieved a mean EERWA of 2.99%, compared with 2.65% for EfficientNet-B3 while using approximately 76% fewer parameters. It also achieved a lower mean EERWA than GhostNet-1.0x (4.01%), MobileNetV3-Large (5.03%), MobileNetV3-Small (6.03%), and ShuffleNet V2 (5.31%). The VGGFace2-HQ-pretrained variants produced higher error rates than their corresponding ImageNet-pretrained variants under the evaluated protocol. Performance remained strong in homogeneous scenarios but degraded under heterogeneous cross-morph evaluation. Overall, EN_Lite provides a favorable efficiency–performance trade-off for MAD, while high-security deployment should be assessed at application-specific operating points.
A part of this study investigates the factors associated with infant death in Iraq based on the multiple indicator cluster survey round sex survey dataset. Furthermore, the other part of this study is to investigate the potential association between the COVID-19 pandemic and tuberculosis (TB) cases, consisting of 1381 TB patients recorded over 4 years (2019–2022) at Iraq TB hospitals. The methodology adapted for analyzing is known as binary logistic regression for predicting the probability of determinants that are related to infant death and its component periods in the region. Furthermore, we used TB cases in 2019 as a control group and baseline for comparison. To determine statistically significant differences in average TB cases between groups, we employed one-way analysis of variance with post hoc multiple comparisons. We analyzed TB case frequency by quarter. Our findings reveal a statistically significant increase in TB cases during the second and third quarters of 2022, coinciding with the decline of COVID-19 cases in the region. This suggests a stronger overall TB incidence after the pandemic’s peak compared to pre pandemic levels in 2020. These findings are highly beneficial for the fields of public health, epidemiology, and health policy-making. In practice, this analysis applies to strategic resource allocation and the design of targeted maternal-child health interventions. It helps policymakers identify high-risk demographic groups to reduce infant mortality and enables health systems to address the resurgence of neglected infectious diseases like TB.
—Breast cancer (BC) remains a health challenge in Iraq, with patients presenting at younger ages and advanced stages. While gene fusions, particularly ETV6-NTRK3, are established oncogenic drivers in subtypes like secretory breast carcinoma (SBC), their prevalence within the Iraqi population remains unexplored. This study aimed to detect the ETV6-NTRK3 gene fusion in Iraqi patients with BC to define its diagnostic specificity and clinicopathological correlates. Formalin-fixed paraffin-embedded tissue blocks from 60 women (aged 20–50 years) diagnosed with BC were collected. Samples were classified into three equal groups: SBC, Invasive Carcinoma of no special type, and invasive lobular carcinoma (n = 20 for each). The ETV6-NTRK3 fusion was detected using fluorescence in situ hybridization with a dual-color break-apart probe. The ETV6-NTRK3 fusion was detected exclusively in the SBC, with a prevalence of 100% (19/19 successfully scored cases), while it was absent in all scored cases of other groups, yielding a positive predictive value of 100% and a negative predictive value of 100%. Fusion-positive SBC tumors were associated with clinicopathological features: they were exclusively low to intermediate histological grade (Grades I-II) and predominantly presented at early pathological stages (Stage I-II: 84.2%). The fusion-positive cases were 93 times more likely to present with an early stage of disease compared with fusion-negative cases (odds ratio: 93.33; 95% confidence interval: 16.15–539.19). In conclusion, the ETV6-NTRK3 fusion may be a definitive molecular hallmark of SBC in Iraqi patients, and could demonstrate perfect diagnostic specificity and association with a less aggressive phenotype.
