A segmentation model to detect cevical lesions based on machine learning of colposcopic images.

Background: Semantic segmentation is crucial in medical image diagnosis. Traditional deep convolutional neural networks excel in image classification and object detection but fall short in segmentation tasks. Enhancing the accuracy and efficiency of detecting high-level cervical lesions and invasive cancer poses a primary challenge in segmentation model development.

Ki-67/MKI67 as a Predictive Biomarker for Clinical Outcome in Gastric Cancer Patients: an Updated Meta-analysis and Systematic Review involving 53 Studies and 7078 Patients.

Gastric cancer (GC) threatens human health worldwide and we performed this meta-analysis to evaluate the clinical value of Ki-67/MKI67 in patients with GC. The combined hazard ratio (HR), odds ratio (OR) and 95% confidence interval (95% CI) were calculated to assess the relationships of Ki-67/MKI67 expression with prognoses and clinicopathological characteristics. Genes co-expressed with MKI67 were collected for Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway and protein-protein interaction (PPI) network analyses.

Downregulation of miR‑136‑5p in hepatocellular carcinoma and its clinicopathological significance.

The clinical significance of microRNA (miR)‑136‑5p in hepatocellular carcinoma (HCC) has not been verified. Therefore, in the current study, the authors aimed to explore miR‑136‑5p expression and its clinical significance in HCC, as well as to investigate its potential target genes function. The authors detected the levels of miR‑136‑5p in 101 pairs of HCC and para‑cancer tissues via reverse transcription‑quantitative polymerase chain reaction.