Daily Endocrinology Research Analysis

BACKGROUND: Pancreatic β-cell regeneration represents a promising therapeutic strategy for diabetes, yet safe and effective treatments remain elusive. PURPOSE: We aimed to investigate whether and how mulberry twig (Sangzhi) alkaloids (SZ-A), a newly approved anti-diabetic Chinese medicine, promoted β-cell regeneration. METHODS: Diabetic db/db mice and pancreatic α-cell lineage-tracing mice were treated with SZ-A, acarbose, or vehicle daily via intragastric gavage. Blood glucose and plasma insulin levels were measured. The areas of islets, α-cells and β-cells were quantified. Cell transdifferentiation was assessed by double-immunostaining of glucagon or α-cell lineage-tracing marker with β-cell-specific markers. Mouse α-cells were incubated with SZ-A or its three main components, and the mRNA levels of cell transdifferentiation-related genes were detected. RNA-sequencing was performed to screen potential targets. The activities of five mitochondrial complexes were detected following treatment, and specific inhibitor was utilized to validate the involvement. RESULTS: Both SZ-A and acarbose improved glycemic control, but only SZ-A enlarged islet and β-cell areas in the diabetic mice. SZ-A induced α-to-β-cell conversion, as indicated by glucagon and insulin double-immunostaining and confirmed by α-cell lineage-tracing. In cultured α-cells, SZ-A and its main component 1-deoxynojirimycin (DNJ) downregulated the expressions of α-cell-specific markers, while upregulated the expressions of β-cell-specific markers. DNJ-induced differentially expressed genes were enriched in the mitochondrial protein complex term. DNJ inhibited mitochondrial complex I activity, and the complex inhibitor induced α-to-β-cell conversion. CONCLUSION: SZ-A, especially its main component DNJ, induces α-to-β-cell transdifferentiation via inhibiting mitochondrial complex I. Our finding provides a potential strategy for β-cell regeneration and diabetes treatment.

OBJECTIVE: This study aims to assess diagnostic performance of high-resolution dynamic contrast-enhanced (DCE) MRI with deep learning-based compressed sensing and super-resolution (DLCS-SR) reconstruction for identifying microadenomas. MATERIALS AND METHODS: This prospective study included 126 participants with suspected pituitary microadenomas who underwent DCE MRI between June 2023 and January 2024. Four image groups were derived from single-scan DCE MRI, which included 1.5-mm slice thickness images using DLCS-SR (1.5-mm DLCS-SR images), 1.5-mm slice thickness images with deep learning-based compressed sensing reconstruction (1.5-mm DLCS images), 1.5-mm routine images, and 3-mm slice thickness images using DLCS-SR (3-mm DLCS-SR images). Diagnostic criteria were established by incorporating laboratory findings, clinical symptoms, medical histories, previous imaging, and certain pathologic reports. Two readers assessed the diagnostic performance in identifying pituitary abnormalities and microadenomas. Diagnostic agreements were assessed using κ statistics, and intergroup comparisons for microadenoma detection were performed using the DeLong and McNemar tests. RESULTS: The 1.5-mm DLCS-SR images (κ = 0.746-0.848) exhibited superior diagnostic agreement, outperforming 1.5-mm DLCS (κ = 0.585-0.687), 1.5-mm routine (κ = 0.449-0.487), and 3-mm DLCS-SR images (κ = 0.347-0.369) (p < 0.001 for all). Additionally, the performance of 1.5-mm DLCS-SR images in identifying microadenomas [area under the receiver operating characteristic curve (AUC), 0.89-0.94] surpassed that of 1.5-mm DLCS (AUC, 0.83-0.87; p = 0.042 and 0.011, respectively), 1.5-mm routine (AUC, 0.76-0.78; p < 0.001), and 3-mm DLCS-SR images (AUC, 0.72-0.74; p < 0.001). CONCLUSION: The findings revealed superior diagnostic performance of 1.5-mm DLCS-SR images in identifying pituitary abnormalities and microadenomas, indicating the clinical-potential of high-resolution DCE MRI. KEY POINTS: Question What strategies can overcome the resolution limitations of conventional dynamic contrast-enhanced (DCE) MRI, and which contribute to a high false-negative rate in diagnosing pituitary microadenomas? Findings Deep learning-based compressed sensing and super-resolution reconstruction applied to DCE MRI achieved high resolution while improving image quality and diagnostic efficacy. Clinical relevance DCE MRI with a 1.5-mm slice thickness and high in-plane resolution, utilizing deep learning-based compressed sensing and super-resolution reconstruction, significantly enhances diagnostic accuracy for pituitary abnormalities and microadenomas, enabling timely and effective patient management.

OBJECTIVE: Advanced cardiac autonomic neuropathy (CAN) is associated with increased mortality in people with diabetes. Early identification and reduction of risk factors can limit the progression of CAN. However, the diagnosis of early CAN relies on cardiac autonomic reflex testing (CART's) which is not widely available. We have compared the diagnostic utility of corneal confocal microscopy (CCM) to CART's in diagnosing CAN. RESEARCH DESIGN AND METHODS: Two-hundred and thirty eight individuals with type 1 and type 2 diabetes and thirty seven healthy controls were assessed using CARTs and CCM. RESULTS: There was a progressive and significant reduction in DB-HRV, E:I ratio, 30:15 ratio, corneal nerve fibre density (CNFD), corneal nerve branch density (CNBD) and corneal nerve fibre length (CNFL) with increasing severity of CAN. The receiver operating characteristic (ROC) area under the curve (AUC) and sensitivity/specificity of CCM were comparable to those of CARTs for identifying early and definite CAN. CONCLUSION: CCM is a rapid, non-invasive ophthalmic test which could be used to detect early and established CAN.