Polyaminoglycoside-mediated cell reprogramming system for the treatment of diabetes mellitus

Serum SARM1 Levels and Diabetic Peripheral Neuropathy in Type 2 Diabetes: Correlation with Clinical Neuropathy Scales and Nerve Conduction Studies and Impact of COVID-19 vaccination

Patients with peripheral neuropathy with type 2 diabetes mellitus (T2DM) are more likely to have functional impairments. Recently, the gene for serum sterile alpha and toll/interleukin receptor motif-containing protein 1 (SARM1), which may contribute to the pathogenesis of Wallerian degeneration, was discovered in mice models of peripheral neuropathy. We set out to assess serum SARM1's activity as a potential biomarker for the early identification of diabetic peripheral neuropathy in T2DM patients while also examining the impact of the COVID-19 vaccine on SARM1 levels. We assessed the cross-sectional relationships between the SARM1 biomarker, clinical neuropathy scales, and nerve conduction parameters in 80 participants aged between 30 years and 60 years. The analysis was carried out after the patients were split into two groups since we discovered a significant increase in SARM1 levels following the second dose of the COVID-19 vaccination, where group A received one dose of the COVID-19 vaccine inoculation, and group B received two doses of the COVID-19 vaccine. SARM1 was correlated significantly (p < 0.05) with MNSIe and NSS in group A and showed a consistent positive correlation with the other neuropathy clinical scales in group A and group B without reaching statistical significance. Additionally, SARM1 was negatively correlated significantly (p < 0.05) with the median sensory amplitude in group A and showed a consistent negative correlation with the six other sensory and motor nerves' potential amplitude in group A and group B without reaching statistical significance. In conclusion, SARM1 showed a consistent correlation with clinical neuropathy scales and nerve conduction parameters after accounting for the influence of COVID-19 vaccination doses.

Hydroxyl-rich branched polycations for nucleic acid delivery

Recently, nucleic acid delivery has become an amazing route for the treatment of various malignant diseases, and polycationic vectors are attracting more and more attention among gene vectors. However, conventional polycationic vectors still face many obstacles in nucleic acid delivery, such as significant cytotoxicity, high protein absorption behavior, and unsatisfactory blood compatibility caused by a high positive charge density. To solve these problems, the fabrication of hydroxyl-rich branched polycationic vectors has been proposed. For the synthesis of hydroxyl-rich branched polycations, a one-pot method is considered as the preferred method due to its simple preparation process. In this review, typical one-pot methods for fabricating hydroxyl-rich polycations are presented. In particular, amine-epoxide ring-opening polymerization as a novel approach is mainly introduced. In addition, various therapeutic scenarios of hydroxyl-rich branched polycations via one-pot fabrication are also generalized. We believe that this review will motivate the optimized design of hydroxyl-rich branched polycations for potential nucleic acid delivery and their bio-applications.

Detecting High-Risk Factors and Early Diagnosis of Diabetes Using Machine Learning Methods

Diabetes is a chronic disease that can cause several forms of chronic damage to the human body, including heart problems, kidney failure, depression, eye damage, and nerve damage. There are several risk factors involved in causing this disease, with some of the most common being obesity, age, insulin resistance, and hypertension. Therefore, early detection of these risk factors is vital in helping patients reverse diabetes from the early stage to live healthy lives. Machine learning (ML) is a useful tool that can easily detect diabetes from several risk factors and, based on the findings, provide a decision-based model that can help in diagnosing the disease. This study aims to detect the risk factors of diabetes using ML methods and to provide a decision support system for medical practitioners that can help them in diagnosing diabetes. Moreover, besides various other preprocessing steps, this study has used the synthetic minority over-sampling technique integrated with the edited nearest neighbor (SMOTE-ENN) method for balancing the BRFSS dataset. The SMOTE-ENN is a more powerful method than the individual SMOTE method. Several ML methods were applied to the processed BRFSS dataset and built prediction models for detecting the risk factors that can help in diagnosing diabetes patients in the early stage. The prediction models were evaluated using various measures that show the high performance of the models. The experimental results show the reliability of the proposed models, demonstrating that k-nearest neighbor (KNN) outperformed other methods with an accuracy of 98.38%, sensitivity, specificity, and ROC/AUC score of 98%. Moreover, compared with the existing state-of-the-art methods, the results confirm the efficacy of the proposed models in terms of accuracy and other evaluation measures. The use of SMOTE-ENN is more beneficial for balancing the dataset to build more accurate prediction models. This was the main reason it was possible to achieve models more accurate than the existing ones.