Insulin-like growth factor-1 (IGF-1) levels in multiple sclerosis patients: A systematic review and meta-analysis.

BACKGROUND AND OBJECTIVE: Multiple sclerosis (MS) is a chronic progressive autoimmune disorder of the central nervous system (CNS) that can cause inflammation, demyelination, and axon degeneration. Insulin-like growth factor-1 (IGF-1) is a single-chain polypeptide mainly synthesized in the liver and brain. IGF-1 causes neuronal and non-neuronal cell proliferation, survival, and differentiation. Therefore, it can be used in treating neuro-demyelinating diseases such as MS. The current systematic review and meta-analysis aims to compare the levels of IGF-1 in MS patients and healthy controls and also investigates IGF binding proteins (IGF-BP) and growth hormone (GH) levels between MS patients and healthy controls. METHODS: In this study, we systematically searched electronic databases of PubMed, Scopus, Web of Science (WOS), and Google Scholar, up to December 2022. Studies that measured IGF-1, GH, IGFBP-1, IGFBP-2, or IGFBP-3 in MS patients and healthy controls in either blood or cerebral spinal fluid (CSF) were identified. We calculated Standardized mean differences (SMD) to compare levels of IGF-1, GH, IGFBP-1, IGFBP-2, or IGFBP-3 in MS patients and controls. RESULTS: Finally, we included 11 eligible studies from 1998 to 2018. The sample size of included studies varied from 20 to 200 resulting in a total sample size of 1067 individuals, 531 MS patients, and 536 healthy controls. The mean age of the patient and control groups were 38.96 and 39.38, respectively. The average EDSS among patients was 4.56. We found that blood levels of IGF-1 (SMD = 0.20, 95% CI = -0.20 to 0.59, I2 = 82.4%, K = 8, n = 692), CSF level of IGF-1 (SMD = 0.25, 95% CI = -0.06 to 0.56, I2 = 0.0%, K = 3 n = 164) and blood levels of GH were not significantly higher in MS patients than controls (SMD = 0.08, 95% CI = -0.33 to 0.49, I2 = 77.0% K = 3, n = 421). Moreover, the blood levels of IGFBP-1 (SMD = 0.70, 95% CI = 0.01 to 1.40, I2 = 77%, K = 4, n = 255) were significantly higher in MS cases than in controls. However, the blood levels of IGFBP-2 (SMD = 0.43, 95% CI = -0.34 to 1.21, I2 = 64.2%, K = 3, n = 78) and blood levels of IGFBP-3 (SMD = 1.04, 95% CI = -0.09 to 2.17, I2 = 95.6%, K = 6, n = 443) were not significantly higher in patients than controls. CONCLUSION: Our meta-analysis revealed no significant difference in serum levels of IGF-1, GH, IGFBP-2, and IGFBP-3 between the MS group and healthy controls, except for IGFBP1. However, our systematic review showed that the studies were controversial for IGFBP-3 serum levels. Some studies found an increase in serum level of IGFBP-3 in MS patients compared to the healthy group, while others showed a decrease.

Lipid variability and risk of microvascular complications in patients with diabetes: a systematic review and meta-analysis.

BACKGROUND AND AIMS: The current systematic review aimed to elucidate the effects of lipid variability on microvascular complication risk in diabetic patients. The lipid components studied were as follows: High-density lipoprotein (HDL), High-density lipoprotein (LDL), Triglyceride (TG), Total Cholesterol (TC), and Remnant Cholesterol (RC). METHOD: We carried out a systematic search in multiple databases, including PubMed, Web of Science, and SCOPUS, up to October 2nd, 2023. After omitting the duplicates, we screened the title and abstract of the studies. Next, we retrieved and reviewed the full text of the remaining articles and included the ones that met our inclusion criteria in the study. RESULT: In this research, we examined seven studies, comprising six cohort studies and one cross-sectional study. This research was conducted in Hong Kong, China, Japan, Taiwan, Finland, and Italy. The publication years of these articles ranged from 2012 to 2022, and the duration of each study ranged from 5 to 14.3 years. The study group consisted of patients with type 2 diabetes aged between 45 and 84 years, with a diabetes history of 7 to 12 years. These studies have demonstrated that higher levels of LDL, HDL, and TG variability can have adverse effects on microvascular complications, especially nephropathy and neuropathic complications. TG and LDL variability were associated with the development of albuminuria and GFR decline. Additionally, reducing HDL levels showed a protective effect against microalbuminuria. However, other studies did not reveal an apparent relationship between lipid variations and microvascular complications, such as retinopathy. Current research lacks geographic and demographic diversity. Increased HDL, TG, and RC variability have been associated with several microvascular difficulties. Still, the pathogenic mechanism is not entirely known, and understanding how lipid variability affects microvascular disorders may lead to novel treatments. Furthermore, the current body of this research is restricted in its coverage. This field's lack of thorough investigations required a more extensive study and comprehensive effort. CONCLUSION: The relationship between lipid variation (LDL, HDL, and TG) (adverse effects) on microvascular complications, especially nephropathy and neuropathic (and maybe not retinopathy), is proven. Physicians and health policymakers should be highly vigilant to lipid variation in a general population.

DeepCompoundNet: enhancing compound-protein interaction prediction with multimodal convolutional neural networks.

Virtual screening has emerged as a valuable computational tool for predicting compound-protein interactions, offering a cost-effective and rapid approach to identifying potential candidate drug molecules. Current machine learning-based methods rely on molecular structures and their relationship in the network. The former utilizes information such as amino acid sequences and chemical structures, while the latter leverages interaction network data, such as protein-protein interactions, drug-disease interactions, and protein-disease interactions. However, there has been limited exploration of integrating molecular information with interaction networks. This study presents DeepCompoundNet, a deep learning-based model that integrates protein features, drug properties, and diverse interaction data to predict chemical-protein interactions. DeepCompoundNet outperforms state-of-the-art methods for compound-protein interaction prediction, as demonstrated through performance evaluations. Our findings highlight the complementary nature of multiple interaction data, extending beyond amino acid sequence homology and chemical structure similarity. Moreover, our model's analysis confirms that DeepCompoundNet gets higher performance in predicting interactions between proteins and chemicals not observed in the training samples.Communicated by Ramaswamy H. Sarma.

Practical fractional-order nonsingular terminal sliding mode control of spacecraft.

In this study, a new adaptive fractional-order nonsingular terminal sliding mode (AFONTSM) controller is presented. A novel multi-purpose sliding surface is constructed, with the aim of bringing the reaction wheels in to rest after every attitude stabilization maneuver, utilizing the fractional-order difference of the quaternion error and the reaction wheels angular momentum error. The closed-loop system's practical fixed-time stability is investigated using the Lyapunov theorem under uncertainty and external disturbance. The AFONTSM controller's performance is compared with the existing nonsingular terminal sliding mode (NTSM), full-order NTSM, and fractional-order sliding mode controllers. Finally, the proposed AFONTSM controller's effectiveness is studied in close-to-reality situations through practical experiments on the spacecraft attitude control subsystem simulator under internal/external disturbance and uncertainty; then, the results are compared with previous studies.