Prostate cancer (PCa) ranks as the second most prevalent malignancy among men worldwide. Early diagnosis, personalized treatment, and prognosis prediction of PCa play a crucial role in improving patients' survival rates. The advancement of artificial intelligence (AI), particularly the utilization of deep learning (DL) algorithms, has brought about substantial progress in assisting the diagnosis, treatment, and prognosis prediction of PCa. The introduction of the foundation model has revolutionized the application of AI in medical treatment and facilitated its integration into clinical practice. This review emphasizes the clinical application of AI in PCa by discussing recent advancements from both pathological and imaging perspectives. Furthermore, it explores the current challenges faced by AI in clinical applications while also considering future developments, aiming to provide a valuable point of reference for the integration of AI and clinical applications.
Humans ; Prostatic Neoplasms/diagnosis* ; Male ; Artificial Intelligence ; Deep Learning ; Prognosis

The small-molecule alkaloid halofuginone (HF) is obtained from febrifugine. Recent studies on HF have aroused widespread attention owing to its universal range of noteworthy biological activities and therapeutic functions, which range from parasite infections and fibrosis to autoimmune diseases. In particular, HF is believed to play an excellent anticancer role by suppressing the proliferation, adhesion, metastasis, and invasion of cancers. This review supports the goal of demonstrating various anticancer effects and molecular mechanisms of HF. In the studies covered in this review, the anticancer molecular mechanisms of HF mainly included transforming growth factor-β (TGF-β)/Smad-3/nuclear factor erythroid 2-related factor 2 (Nrf2), serine/threonine kinase proteins (Akt)/mechanistic target of rapamycin complex 1(mTORC1)/wingless/integrated (Wnt)/β-catenin, the exosomal microRNA-31 (miR-31)/histone deacetylase 2 (HDAC2) signaling pathway, and the interaction of the extracellular matrix (ECM) and immune cells. Notably, HF, as a novel type of adenosine triphosphate (ATP)-dependent inhibitor that is often combined with prolyl transfer RNA synthetase (ProRS) and amino acid starvation therapy (AAS) to suppress the formation of ribosome, further exerts a significant effect on the tumor microenvironment (TME). Additionally, the combination of HF with other drugs or therapies obtained universal attention. Our results showed that HF has significant potential for clinical cancer treatment.

Objective: To establish a real-time quantitative PCR detection system for Torque teno virus (TTV) and verify the sensitivity and specificity of the detection system. Methods: Primers and FAM-Eclipse probes were designed based on the TTV6 gene sequence registered in GenBank, and were to establish a real-time fluorescent quantitative PCR detecting way based on the FAM-Eclipse probe, the standard curve was constructed and sensitivity and specificity were analyzed. Results: A quantitative PCR method for the specific detection of TTV6 were established that the standard curve equation was y=-3.0921x + 28.36, and the amplification efficiency and R2 were 99.6% and 1.000, respectively. The sensitivity of TTV6 was 1.0×10 copies/μl, and there was no cross-reactivity with other viruses. There was 1 case positive for TTV6 out of 56 throat swab samples from the patients with clinical respiratory infection. Conclusions The real-time fluorescent quantitative PCR for detecting TTV6 established by FAM-Eclipse probe had the advantages of high sensitivity and specificity. It provides an effective way for detection and quantification of viral content of TTV6 in clinical specimens.