People with type 1 diabetes have an increased risk of developing microvascular complications, which have a negative impact on the quality of life and reduce life expectancy. Numerous studies in animals with experimental diabetes show that c-peptide supplementation exerts beneficial effects on diabetes-induced damage in peripheral nerves and kidneys. There is substantial evidence that c-peptide counteracts the detrimental changes caused by hyperglycemia at the cellular level, such as decreased activation of endothelial nitric oxide synthase and sodium potassium ATPase, and increase in formation of pro-inflammatory molecules mediated by nuclear factor kappa-light-chain-enhancer of activated B cells: cytokines, chemokines, cell adhesion molecules, vascular endothelial growth factor, and transforming growth factor beta. However, despite positive results from cell and animal studies, no successful c-peptide replacement therapies have been developed so far. Therefore, it is important to improve our understanding of the impact of c-peptide on the pathophysiology of microvascular complications to develop novel c-peptide-based treatments. This article aims to review current knowledge on the impact of c-peptide on diabetic neuro- and nephropathy and to evaluate its potential therapeutic role.

Background: Accurate estimation of glycated hemoglobin (HbA1c) from continuous glucose monitoring (CGM) remains challenging in clinic. We propose two statistical models and validate them in real-life conditions against the current standard, glucose management indicator (GMI). Materials and Methods: Modeling utilized routinely collected data from patients with type 1 diabetes from central Poland (eligibility criteria: age >1 year, diabetes duration >3 months, and CGM use between 01/01/2015 and 12/31/2019). CGM records were extracted from dedicated Medtronic/Abbott databases and cross-referenced with HbA1c values; 28-day periods preceding HbA1c measurement with >75% of the sensor-active time were analyzed. We developed a mixed linear regression, including glycemic variability indices and patient's ID (glucose variability-based patient specific model, GV-PS) intended for closed-group use and linear regression using patient-specific error of GMI (proportional error-based patient agnostic model, PE-PA) for general use. Models were validated with either new HbA1cs from closed-group patients or separate patient-HbA1c pool. External validation was performed with data from clinical trials. Performance metrics included bias, its 95% confidence interval (95% CI), coefficient of determination ( R ² ), and root mean square error (RMSE). Results: We included 723 HbA1c-CGM pairs from 174 patients (mean age 9.9 ± 4.4 years and diabetes duration 3.7 ± 3.6 years). GMI yielded R ² = 0.58, with different bias between Medtronic and Abbott devices [0.120% vs. −0.152%, P < 0.0001], and overall 95% CI = −0.9% to +1%, RMSE = 0.47%. GV-PS successfully captured patient-specific variance (closed-group validation: R ² = 0.83, bias = 0.026%, 95% CI = −0.562% to 0.591%, RMSE = 0.31%). PE-PA performed similarly on new patients ( R ² = 0.76, bias = −0.069%, 95% CI = −0.790% to 0.653%, RMSE = 0.37%). In external validation GMI, GV-PS, and PE-PA produced 73.8%, 87.5%, and 91.0% predictions within 0.5% (5.5 mmol/mol) from the true value. Conclusion: Constructed models performed better than GMI. PE-PA provided an accurate estimate of HbA1c with fast and straightforward implementation.

Background: Accurate evaluation of bone marrow involvement is important for staging and treatment monitoring in pediatric neuroblastoma. This study evaluated flow cytometry for detecting neuroblastoma cells in bone marrow aspirates. Methods: In this prospective exploratory correlative analysis, samples from 28 children were assessed through flow cytometry and cytomorphology; available histopathological and iodine-123 metaiodobenzylguanidine (MIBG) scintigraphy results were compared. Neuroblastoma cells were identified as nucleated CD56⁺CD45⁻CD36⁻CD71⁻ events. Results: Flow cytometry detected neuroblastoma cell populations across a broad quantitative range, including low-level populations. Agreement between flow cytometry and cytomorphology was 90.0% (Cohen’s κ = 0.56). In 16 paired aspirate-level records with at least one positive result, flow cytometric and cytomorphological percentages showed a positive Pearson correlation (r = 0.878, p < 0.0001), although this association was sensitive to one high-value observation. Agreement with histopathology was 92.1–94.8% (κ = 0.59–0.64), whereas agreement with MIBG scintigraphy was slight to fair (κ = 0.12–0.23). Conclusions: Flow cytometry provided rapid, quantitative detection of neuroblastoma cell populations in bone marrow aspirates. It should be considered complementary to cytomorphology and histopathology, as repeated examinations, the limited panel, and the lack of an independent reference standard preclude definitive estimates of diagnostic performance.