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Feasibility and Functional Reactivity of Expanded Tumor-infiltrating Lymphocytes from Non-clear-cell Renal Cell Carcinoma.
No full textEvaluation of Artificial Intelligence Chatbots for Facial Injection Planning: Comparative Performance and Safety Limitations.
No full textBACKGROUND: To evaluate the performance of artificial intelligence (AI)-powered chatbots in generating treatment plans for facial aesthetic injections, focusing on their accuracy, safety, and clinical applicability.
METHODS: A comparative observational study was conducted in an otolaryngology tertiary care department according to STROBE guidelines. Patients seeking facial injections were recruited from July to October 2024. Forty patients (85% female; mean age: 45.8 years) underwent photographic documentation and received AI-generated treatment plans for botulinum toxin and hyaluronic acid injections. Six AI chatbots and three generative vision models were evaluated based on five criteria: product selection, injection strategy, facial analysis, alignment with patient preferences, and safety. Likert scale ratings, each ranging from - 2 to + 2, were analyzed using Friedman and Durbin-Conover pairwise tests to identify significant differences (p \u3c 0.05). The sum of the five Likert scales provided an overall score ranging from - 10 to + 10.
RESULTS: ChatGPTo1 and ChatGPT4o achieved higher scores than other chatbots across most evaluation criteria, with mean total scores of 7.87 ± 0.29 and 7.85 ± 0.44, respectively (p = 0.295). Both chatbots were statistically superior (p \u3c 0.05) to Claude, CopilotPro, and Llama in product selection (ChatGPT4o = 1.92 ± 0.05), injection strategy precision (ChatGPTo1 = 1.67 ± 0.08), alignment with patient preferences (ChatGPTo1 = 1.95 ± 0.03) and safety (ChatGPTo1 = 1.30 ± 0.17). Claude provided relevant facial analysis (1.50 ± 0.16) without significant difference compared to ChatGPT models (all p \u3e 0.05). Generative vision models failed to produce relevant visual annotations.
CONCLUSION: Among the AI systems tested, ChatGPT-based chatbots demonstrated relatively superior performance in generating treatment plans for facial injections. However, safety limitations remain and preclude unsupervised clinical use.
LEVEL OF EVIDENCE IV: This journal requires that authors assign a level of evidence to each article. For a full description of these Evidence-Based Medicine ratings, please refer to the Table of Contents or the online Instructions to Authors www.springer.com/00266
Impact of Maternal Diabetes on the Incidence of Critical Congenital Heart Disease in the United States.
No full textBACKGROUND: Critical congenital heart disease (CCHD) represents a significant subset of congenital heart disease (CHD). While the association between maternal diabetes mellitus and offspring CHD is well established, the specific relationship between maternal diabetes and CCHD remains underexplored.
OBJECTIVES: This study aims to investigate the association between maternal diabetes and the incidence of offspring CCHD.
METHODS: We analyzed natality data from the Centers for Disease Control and Prevention Wide-Ranging Online Data for Epidemiologic Research (CDC WONDER) from 2016 to 2021. The data set included information on maternal and paternal attributes, pregnancy history, prenatal care, and congenital anomalies among newborns. We included all live births in the United States, focusing on single births at a gestational age of ≥20 weeks. Multivariable logistic regression was used to explore the relationship between gestational diabetes, pregestational diabetes, and CCHD.
RESULTS: Among 22,646,079 live births, 13,533 cases of CCHD were identified, with an incidence of 6 per 10,000 live births. Pregestational diabetes was associated with a 4.33-fold higher risk of CCHD (aOR: 4.33; 95% CI: 3.93-4.76), and gestational diabetes with a 1.47-fold higher risk (aOR: 1.47; 95% CI: 1.38-1.57). Additional risk factors included pregestational hypertension, gestational hypertension, and late initiation of antenatal care. A longer gestational age was associated with a lower risk of CCHD.
CONCLUSIONS: Maternal diabetes, both pregestational and gestational, significantly increases the risk of CCHD. These findings highlight the need for targeted interventions and monitoring of diabetic mothers to mitigate the risk of CCHD in their offspring
Quantitative EEG Biomarkers in the Genetic Epilepsies and Associations With Neurologic Outcomes.
No full textBACKGROUND AND OBJECTIVES: EEG plays an integral part in the diagnosis and management of children with genetic epilepsies. Nevertheless, how quantitative EEG features differ between genetic epilepsies and neurologic outcomes remains largely unknown. In this study, we aimed to identify quantitative EEG biomarkers in
METHODS: We retrospectively collected clinical scalp EEGs from the Children\u27s Hospital of Philadelphia. After removing artifacts and epochs with excess noise or altered state from EEGs, we extracted spectral features. We validated our preprocessing pipeline by comparing automatically detected posterior dominant rhythm (PDR) with annotations from clinical EEG reports. Next, as a coarse measure of pathologic slowing, we compared the alpha-delta bandpower ratio between controls and patients with different genetic epilepsies. We then trained random forest models with localized spectral features to predict diagnoses of
RESULTS: We evaluated EEGs from individuals with pathogenic variants in
DISCUSSION: These results suggest that some genetic epilepsies and functional outcome measures have distinct quantitative EEG signatures. Furthermore, EEG spectral features are predictive of some functional outcome measures. Large-scale retrospective quantitative analysis of clinical EEGs has the potential to discover novel biomarkers and to quantify and track individuals\u27 disease progression across development
Predictors of In-Stent Stenosis After Flow Diversion of Intracranial Aneurysms Using the FRED-X Device: A Multicenter Analysis of 6-month and 12-Month Outcomes.
No full textBACKGROUND AND OBJECTIVES: In-stent stenosis (ISS) occurs in approximately 0% to 55.3% of cases after flow diversion. The Flow Redirection Endoluminal Device-X (FRED-X) is a newer generation flow diverter with surface modifications. Our study identifies predictors of ISS after flow diversion using the FRED-X.
METHODS: This was a multicenter retrospective study of patients who underwent flow diversion of an intracranial aneurysm using the FRED-X device between February 2022 and February 2024. Multivariate logistic regression was used to analyze for predictors of ISS at 6-month or 12-month follow-up.
RESULTS: One fifty-four patients with 161 aneurysms underwent flow diversion with 164 FRED-X devices. At 6 months, 15.1% of cases (n = 21) developed ISS. Overall, 61.9% of patients (n = 13) developed mild ISS, 33.3% (n = 7) patients had moderate ISS, and 4.7% (n = 1) patients had severe ISS. On multivariate regression, cardiovascular disease and FRED-X stent length were associated with 6.25-fold (95% CI: 1.19-32.86) and 1.09-fold (95% CI: 1.00-1.20) higher odds of developing ISS at 6 months, whereas aneurysm width was associated with decreased odds of developing ISS (odds ratio: 0.61, 95% CI: 0.38-0.88). At 12 months, 12.0% of cases (n = 10) developed ISS. Overall 80% of this cohort (n = 8) developed mild ISS, whereas 1 patient each developed moderate and severe ISS.
CONCLUSION: Our study identified cardiovascular disease, smaller aneurysm width, and stent length as independent predictors of ISS after flow diversion using the FRED-X. Patients with ISS were asymptomatic and did not require retreatment. Further prospective studies are necessary to validate these findings
