Synthesis of N-Tosyl Allylic Amines from Substituted Alkenes via Vanadoxaziridine Catalysis.

Herein, we report the catalytic allylic amination of α-methylalkenes with V2O3Dipic2(HMPA)2 and chloramine T as the quantitative source of N. The reaction works with high yields and stereoselectivities for α-methylalkenes. A proposed tosylnitrene-free catalytic cycle involving the formation of vanadoxaziridine complex 1 as the active catalyst and aminovanadation across the substrate as the rate-determining step has been proposed. Initial kinetic and competition experiments provide evidence for the proposed mechanism.

Synthesis of aminoalcohols from substituted alkenes via tungstenooxaziridine catalysis.

Herein we report the WO2Dipic(H2O) promoted oxyamination of alkenes using sulfonamides as the quantitative source of N. The reaction works for activated and unactivated alkenes in high yields, diastereoselectivities, and stereospecificity. A catalytic cycle involving the formation of tungstenooxaziridine complex 1 as the active catalyst and hydrolysis of tungstenooxazolidine intermediate A as the rate-determining-step has been proposed. Initial kinetic and competition experiments provide evidence for the proposed mechanism.

MRA of the Supraaortic Vasculature: Comparison of Gadobutrol and Gadoterate Meglumine at 1.5 T.

BACKGROUND: Gadobutrol (GB) and gadoterate meglumine (GM) are contrast agents used for contrast-enhanced magnetic resonance angiography (CEMRA). Supraaortic vasculature (SAV) CEMRAs are used to evaluate stroke risk and neurologic symptoms. There is a need to compare the SAV CEMRA image quality obtained with GB and GM. PURPOSE: To intra-individually compare MRA images obtained with equimolar GB and GM at 1.5 T in the SAV. STUDY TYPE: Prospective, crossover. POPULATION: Twenty-eight subjects (54 ± 13 years; 17 female). FIELD STRENGTH/SEQUENCE: 1.5 T; three-dimensional (3D) gradient recalled echo. ASSESSMENT: Quantitative image quality was measured by normalized signal intensity (SIn ) [SIn  = SI blood/SD blood] and contrast ratio (CR) [CR = SI blood/SI muscle], determined by an observer (JWC) with 1 year of vascular imaging experience. Three radiologists (AS, PA, and MU) with (5, 5, and 6 years of) vascular imaging experience evaluated image quality by Likert-scale ratings (of image impression, wall conspicuity, and artifact absence). STATISTICAL TESTS: SIn and CR were compared with paired t-tests or Wilcoxon signed-rank tests and Bland-Altman plots. Qualitative ratings were compared with Wilcoxon signed-rank test. RESULTS: No significant difference in SIn was found between GB and GM. CRs with GB were significantly higher than GM at the right common carotid (6.9 ± 2.5 vs. 4.8 ± 1), left internal carotid (7.3 ± 2 vs. 4.4 ± 1.2), right internal carotid (7.7 ± 2.2 vs. 5 ± 1.1), and left vertebral (6.6 ± 2.2 vs. 4.5 ± 1.1) arteries. Bland-Altman plots showed relatively greater differences between GB and GM at higher CRs and SIn s. GM showed significantly higher artifact than GB (3.56 ± 0.52 vs. 3.36 ± 0.46) and significantly lower overall image quality (10.73 ± 1.45 vs. 11.26 ± 1.58) at the left vertebral artery. DATA CONCLUSION: At 1.5 T and equimolar demonstration, GB (0.1 mL/kg, i.e., 0.1 mmol/kg) showed higher CRs in the SAV compared to GM (0.2 mL/kg, i.e., 0.1 mmol/kg) at most vessels. Subjective image quality was not significantly different between the two agents for most vessels. LEVEL OF EVIDENCE: 2 TECHNICAL EFFICACY: Stage 2.

Assessing the clinical reasoning of ChatGPT for mechanical thrombectomy in patients with stroke.

BACKGROUND: Artificial intelligence (AI) has become a promising tool in medicine. ChatGPT, a large language model AI Chatbot, shows promise in supporting clinical practice. We assess the potential of ChatGPT as a clinical reasoning tool for mechanical thrombectomy in patients with stroke. METHODS: An internal validation of the abilities of ChatGPT was first performed using artificially created patient scenarios before assessment of real patient scenarios from the medical center's stroke database. All patients with large vessel occlusions who underwent mechanical thrombectomy at Tulane Medical Center between January 1, 2022 and December 31, 2022 were included in the study. The performance of ChatGPT in evaluating which patients should undergo mechanical thrombectomy was compared with the decisions made by board-certified stroke neurologists and neurointerventionalists. The interpretation skills, clinical reasoning, and accuracy of ChatGPT were analyzed. RESULTS: 102 patients with large vessel occlusions underwent mechanical thrombectomy. ChatGPT agreed with the physician's decision whether or not to pursue thrombectomy in 54.3% of the cases. ChatGPT had mistakes in 8.8% of the cases, consisting of mathematics, logic, and misinterpretation errors. In the internal validation phase, ChatGPT was able to provide nuanced clinical reasoning and was able to perform multi-step thinking, although with an increased rate of making mistakes. CONCLUSION: ChatGPT shows promise in clinical reasoning, including the ability to factor a patient's underlying comorbidities when considering mechanical thrombectomy. However, ChatGPT is prone to errors as well and should not be relied on as a sole decision-making tool in its present form, but it has potential to assist clinicians with more efficient work flow.

Chat GPT as a Neuro-Score Calculator: Analysis of a Large Language Model's Performance on Various Neurological Exam Grading Scales.

BACKGROUND: ChatGPT is a large language model artificial intelligence chatbot that has been applied to different aspects of the medical field. Our study aims to assess the quality of chatGPT to evaluate patients based on their exams for different scores including Glasgow Coma Scale (GCS), intracranial hemorrhage score (ICH), and Hunt & Hess (H&H) classification. METHODS: We created batches of patient test cases with detailed neurological exams, totaling 20 cases and created variants of increasing complex phrasing of the test cases. Using ChatGPT, we assessed repeatability and quantified the errors, including the average error rate (AER) and magnitude of errors (AME). We repeated this process for the H&H and the ICH score using base cases. Specific prompts were created for each calculator. RESULTS: The GCS calculator on 10 base test cases had an AER/AME of 10%/0.150. The accuracy of ChatGPT decreased with increasing complexity; for example, in a variation where crucial information was missing, the AER was 45% for 20 cases. For H&H, AER/AME was 13%/0.13 and for ICH, AER/AME was 27.5%/0.325. Using a simple prompt resulted in a significantly higher error rate of 70%. CONCLUSIONS: ChatGPT demonstrates ability in this proof-of-concept experiment in evaluating neuroexams using established assessment scales including GCS, ICH, and H&H. However, it has limitations in accuracy and may "hallucinate" with complex or vague descriptions. Nonetheless, ChatGPT, has promising potential in medicine.