Current medical research and opinion
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Multicenter Study Observational Study
Use of advanced systemic therapy in patients with moderate-to-severe atopic dermatitis in the TARGET-DERM AD Registry.
Moderate-to-severe atopic dermatitis (AD) significantly impacts quality of life. Advanced systemic therapeutics (AST) represent a new generation of medications targeting AD pathogenesis, but many who may benefit from these medications are AST-naïve. We compared patients in the United States who had started AST with those who had not started AST to evaluate associated characteristics. ⋯ Disease severity and patient access to AST are major factors driving AST initiation. However, some patients are undertreated. This analysis supports AD patient advocacy for those inadequately managed with conventional therapies. Further investigations are necessary to delineate AST initiation barriers and relevant outcomes.
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Polyethylene glycol recombinant human granulocyte colony-stimulating factors (PEG-rhG-CSFs) are used to prevent or treat chemotherapy-induced neutropenia (CIN) and febrile neutropenia (FN). This study aimed to compare the efficacy and safety of same-day versus next-day PEG-rhG-CSF administration following chemotherapy and the effects of 3 mg versus 6 mg dosages. ⋯ These findings suggest that same-day administration of PEG-rhG-CSF is as effective and safe as next-day administration in preventing FN and CIN during chemotherapy.
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The purpose of this study was to conduct a systematic investigation of the potential of artificial intelligence (AI) models in the prediction, detection of diagnostic biomarkers, and progression of diabetic kidney disease (DKD). In addition, we compared the performance of non-logistic regression (LR) machine learning (ML) models to conventional LR prediction models. ⋯ ML models showed solid DKD prediction effectiveness, with pooled AUROC values over 0.8, suggesting good performance. These data demonstrated that non-LR and LR models perform similarly in overall CKD management, but the RF model outperforms the LR model, particularly in predicting the occurrence of DKD. These findings highlight the promise of AI technologies for better DKD management. To improve model reliability, future study should include extended follow-up periods as well as external validation.