Impact of gut microbiota and its metabolites on immunometabolism in colorectal cancer.

Colorectal cancer (CRC) is highly prevalent, accounting for approximately one-tenth of cancer cases and deaths globally. It stands as the second most deadly and third most common cancer type. Although the gut microbiota has been implicated in CRC carcinogenesis for the last several decades, it remains one of the least understood risk factors for CRC development, as the gut microbiota is highly diverse and variable.

Identification of a Novel Subset of Human Airway Epithelial Basal Stem Cells.

The basal cell maintains the airway's respiratory epithelium as the putative resident stem cell. Basal cells are known to self-renew and differentiate into airway ciliated and secretory cells. However, it is not clear if every basal cell functions as a stem cell.

A Scaled Proteomic Discovery Study for Prostate Cancer Diagnostic Markers Using Proteograph and Trapped Ion Mobility Mass Spectrometry.

There is a significant unmet need for clinical reflex tests that increase the specificity of prostate-specific antigen blood testing, the longstanding but imperfect tool for prostate cancer diagnosis. Towards this endpoint, we present the results from a discovery study that identifies new prostate-specific antigen reflex markers in a large-scale patient serum cohort using differentiating technologies for deep proteomic interrogation. We detect known prostate cancer blood markers as well as novel candidates.

Congestive Hepatopathy: Pathophysiology, Workup, and Imaging Findings with Pathologic Correlation.

Liver congestion is increasingly encountered in clinical practice and presents diagnostic pitfalls of which radiologists must be aware. The complex altered hemodynamics associated with liver congestion leads to diffuse parenchymal changes and the development of benign and malignant nodules. Distinguishing commonly encountered benign hypervascular lesions, such as focal nodular hyperplasia (FNH)-like nodules, from hepatocellular carcinoma (HCC) can be challenging due to overlapping imaging features.

Artificial Intelligence in Radiology: Opportunities and Challenges.

Artificial intelligence's (AI) emergence in radiology elicits both excitement and uncertainty. AI holds promise for improving radiology with regards to clinical practice, education, and research opportunities. Yet, AI systems are trained on select datasets that can contain bias and inaccuracies.