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Background: The preservation of parathyroid glands is crucial in endoscopic thyroid surgery to prevent hypocalcemia and related complications. However, current methods for identifying and protecting these glands have limitations. We propose a novel technique that has the potential to improve the safety and efficacy of endoscopic thyroid surgery. Purpose: Our study aims to develop a deep learning model called PTAIR 2.0 (Parathyroid gland Artificial Intelligence Recognition) to enhance parathyroid gland recognition during endoscopic thyroidectomy. We compare its performance against traditional surgeon‐based identification methods. Materials and methods: Parathyroid tissues were annotated in 32 428 images extracted from 838 endoscopic thyroidectomy videos, forming the internal training cohort. An external validation cohort comprised 54 full‐length videos. Six candidate algorithms were evaluated to select the optimal one. We assessed the model's performance in terms of initial recognition time, identification duration, and recognition rate and compared it with the performance of surgeons. Results: Utilizing the YOLOX algorithm, we developed PTAIR 2.0, which demonstrated superior performance with an AP50 score of 92.1%. The YOLOX algorithm achieved a frame rate of 25.14 Hz, meeting real‐time requirements. In the internal training cohort, PTAIR 2.0 achieved AP50 values of 94.1%, 98.9%, and 92.1% for parathyroid gland early prediction, identification, and ischemia alert, respectively. Additionally, in the external validation cohort, PTAIR outperformed both junior and senior surgeons in identifying and tracking parathyroid glands (p < 0.001). Conclusion: The AI‐driven PTAIR 2.0 model significantly outperforms both senior and junior surgeons in parathyroid gland identification and ischemia alert during endoscopic thyroid surgery, offering potential for enhanced surgical precision and patient outcomes. [ABSTRACT FROM AUTHOR]

The article presents a case study of a 4-year-old girl diagnosed with persistent truncus arteriosus (PTA), highlighting her imaging manifestations as observed through echocardiography (ECHO) and cardiac computed tomography angiography (CTA). Topics discussed include the clinical presentation of PTA, the embryonic development failure leading to this condition, and the importance of early diagnosis and surgical intervention to improve patient outcomes.

We describe purification and characterization of an oligopeptide permease protein (Hiy-OppA) from Vibrio furnissii that has multifaceted functions in solute binding, in in vitro hemolysis, in antibiotic resistance, and as a virulence factor in bacterial pathogenesis. The solute-binding function was revealed by N-terminal and internal peptide sequences of the purified protein and was confirmed by discernible effects on oligopeptide binding, by accumulation of fluorescent substrates, and by fluorescent substrate-antibiotic competition assay experiments. The purified protein exhibited host-specific in vitro hemolytic activity against various mammalian erythrocytes and apparent cytotoxicity in CHO-K1 cells. Recombinant Hly-OppA protein and an anti-Hly-OppA monoclonal antibody exhibited and neutralized the in vitro hemolytic activity, respectively, which further confirmed the hemolytic activity of the gene product. In addition, a V. furnissii hly-oppA knockout mutant caused less mortality than the wild-type strain when it was inoculated into BALB/c mice, indicating the virulence function of this protein. Finally, the in vitro hemolytic activity was also confirmed with homologous ATP-binding cassette-type transporter proteins from other Vibrio species.