Antimicrobial activity and immunomodulation of four novel cathelicidin genes isolated from the tiger frog Hoplobatrachus rugulosus.

Cathelicidin is a family of antimicrobial peptides in vertebrates that plays an important role in resistance and immunization against pathogenic microorganisms. In the present study, the full-length cDNA sequences of four novel cathelicidins (cathelicidin-1 to cathelicidin-4) in the tiger frog Hoplobatrachus rugulosus, encoding 153, 188, 132, and 160 amino acids, respectively, were firstly cloned by rapid amplification of the cDNA ends (RACE) technique. Sequence comparison and phylogenetic tree analysis indicated that the structures of the four cathelicidins are highly diverse.

Novel steroidal β-carboline derivatives as promising antibacterial candidates against methicillin-resistant Staphylococcus aureus.

A novel series of steroidal β-carboline quaternary ammonium derivatives (SCQADs) derived from natural cholic acid and its derivatives was designed, synthesized and biologically evaluated against four Gram-positive bacteria for the first time. Most of these derivatives exhibited promising antibacterial activity against the tested strains, particularly, compound 21g displayed strong antibacterial activity against MRSA (MIC = 0.5-1 μg/mL) with low cytotoxicity.

A multi-view contrastive learning and semi-supervised self-distillation framework for early recurrence prediction in ovarian cancer.

Objective: This study presents a novel framework that integrates contrastive learning and knowledge distillation to improve early ovarian cancer (OC) recurrence prediction, addressing the challenges posed by limited labeled data and tumor heterogeneity.

Methods: The research utilized CT imaging data from 585 OC patients, including 142 cases with complete follow-up information and 125 cases with unknown recurrence status. To pre-train the teacher network, 318 unlabeled images were sourced from public datasets (TCGA-OV and PLAGH-202-OC).

CXCR4-targeted sensitive magnetic particle imaging for abdominal aortic aneurysm early detection and prognosis evaluation by recognizing total inflammatory cells.

Background: The maximum aortic diameter remains the diagnostic criteria and the indicator for prognosis prediction of abdominal aortic aneurysms (AAA). An additional imaging modality is currently needed to help evaluate the prognosis of AAA as well as early detection of AAA formation. This study evaluated the most effective inflammatory markers for AAA using single-cell sequencing and, from these, developed probes to facilitate in vivo multimodal imaging of AAA inflammation.

Machine learning-based radiomics prognostic model for patients with proximal esophageal cancer after definitive chemoradiotherapy.

Objectives: To explore the role of radiomics in predicting the prognosis of proximal esophageal cancer and to investigate the biological underpinning of radiomics in identifying different prognoses.

Methods: A total of 170 patients with pathologically and endoscopically confirmed proximal esophageal cancer from two centers were enrolled. Radiomics models were established by five machine learning approaches.