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BACKGROUND: This study is to propose a clinically applicable 2-echelon (2e) diagnostic criteria for the analysis of thyroid nodules such that low-risk nodules are screened off while only suspicious or indeterminate ones are further examined by histopathology, and to explore whether artificial intelligence (AI) can provide precise assistance for clinical decision-making in the real-world prospective scenario. METHODS: In this prospective study, we enrolled 1036 patients with a total of 2296 thyroid nodules from three medical centers. The diagnostic performance of the AI system, radiologists with different levels of experience, and AI-assisted radiologists with different levels of experience in diagnosing thyroid nodules were evaluated against our proposed 2e diagnostic criteria, with the first being an arbitration committee consisting of 3 senior specialists and the second being cyto- or histopathology. RESULTS: According to the 2e diagnostic criteria, 1543 nodules were classified by the arbitration committee, and the benign and malignant nature of 753 nodules was determined by pathological examinations. Taking pathological results as the evaluation standard, the sensitivity, specificity, accuracy, and area under the receiver operating characteristic curve (AUC) of the AI systems were 0.826, 0.815, 0.821, and 0.821. For those cases where diagnosis by the Arbitration Committee were taken as the evaluation standard, the sensitivity, specificity, accuracy, and AUC of the AI system were 0.946, 0.966, 0.964, and 0.956. Taking the global 2e diagnostic criteria as the gold standard, the sensitivity, specificity, accuracy, and AUC of the AI system were 0.868, 0.934, 0.917, and 0.901, respectively. Under different criteria, AI was comparable to the diagnostic performance of senior radiologists and outperformed junior radiologists (all P < 0.05). Furthermore, AI assistance significantly improved the performance of junior radiologists in the diagnosis of thyroid nodules, and their diagnostic performance was comparable to that of senior radiologists when pathological results were taken as the gold standard (all p > 0.05). CONCLUSIONS: The proposed 2e diagnostic criteria are consistent with real-world clinical evaluations and affirm the applicability of the AI system. Under the 2e criteria, the diagnostic performance of the AI system is comparable to that of senior radiologists and significantly improves the diagnostic capabilities of junior radiologists. This has the potential to reduce unnecessary invasive diagnostic procedures in real-world clinical practice.

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A novel multitope protein-peptide vaccine against Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) infection and disease is described in this report. The initial development and characterization experiments are presented along with proof-of-concept studies for the vaccine candidate UB-612. UB-612 consists of eight components rationally designed for induction of potently neutralizing antibodies and broad T cell responses against SARS-CoV-2: the S1-RBD-sFc fusion protein, six synthetic peptides (one universal peptide and five SARS-CoV-2-derived peptides), a proprietary CpG TLR-9 agonist at low concentration as an excipient, and aluminum phosphate adjuvant. Through immunogenicity studies in Guinea pigs and rats, we optimized the design of protein/peptide immunogens and selected an adjuvant system, yielding a vaccine that provides excellent S1-RBD binding and high neutralizing antibody responses, robust cellular responses, and a Th1-oriented response at low doses. In challenge studies, UB- 612 vaccination reduced viral load and prevented development of disease in mouse and non-human primate challenge models. With a Phase 1 trial completed, a Phase 2 trial ongoing in Taiwan, and additional trials planned to support global authorizations, UB-612 is a highly promising and differentiated vaccine candidate for prevention of SARS-CoV-2 infection and COVID-19 disease. Author SummarySARS-CoV-2 virus, the causative agent of Coronavirus Disease 2019 (COVID-19), has spread globally since its origin in 2019, causing an unprecedented public health crisis that has resulted in greater than 4.7 million deaths worldwide. Many vaccines are under development to limit disease spread and reduce the number of cases, but additional candidates that promote a robust immune response are needed. Here, we describe a multitope protein-peptide vaccine platform that is unique among COVID-19 vaccines. The advantages of our approach are induction of both high levels of neutralizing antibodies as well as a Th/CTL response in the vaccinated host, which mimics the immune response that occurs after natural infection with SARS-CoV-2. We demonstrate that our vaccine is immunogenic and effective in preventing disease in several animal models, including AAV- hACE-2 transduced mice, and both rhesus and cynomolgus macaques. Importantly, no immunopathology was observed in the lungs of immunized animals, therefore showing that antibody-dependent enhancement (ADE) does not occur. Our study provides an additional, novel vaccine candidate for advancement in clinical trials to treat and prevent SARS-CoV-2 infection and COVID-19 disease.