Search for: All records

Diabetes is a major risk factor for Alzheimer’s disease (AD). Amino acid compound 2 (AAC2) improves glycemic and cognitive functions in diabetic mouse models through mechanisms distinct from insulin. Our goal was to compare the effects of AAC2, insulin, and their nanofiber-forming combination on early asymptomatic AD pathogenesis in APP/PS1 mice. Insulin, but not AAC2 or the combination treatment (administered intraperitoneally every 48 h for 120 days), increased seizure-related mortality, altered the brain fat-to-lean mass ratio, and improved specific cognitive functions in APP/PS1 mice. NanoString and pathway analysis of cerebral gene expression revealed dysregulated synaptic mechanisms, with upregulation of Bdnf and downregulation of Slc1a6 in insulin-treated mice, correlating with insulin-induced seizures. In contrast, AAC2 promoted the expression of Syn2 and Syp synaptic genes, preserved brain composition, and improved survival. The combination of AAC2 and insulin counteracted free insulin’s effects. None of the treatments influenced canonical amyloidogenic pathways. This study highlights AAC2’s potential in regulating synaptic gene expression in AD and insulin-induced contexts related to seizure activity. 

Fibromyalgia (FM) is a chronic central sensitivity syndrome characterized by augmented pain processing at diffuse body sites and presents as a multimorbid clinical condition. Long COVID (LC) is a heterogenous clinical syndrome that affects 10–20% of individuals following COVID-19 infection. FM and LC share similarities with regard to the pain and other clinical symptoms experienced, thereby posing a challenge for accurate diagnosis. This research explores the feasibility of using surface-enhanced Raman spectroscopy (SERS) combined with soft independent modelling of class analogies (SIMCAs) to develop classification models differentiating LC and FM. Venous blood samples were collected using two supports, dried bloodspot cards (DBS, n = 48 FM and n = 46 LC) and volumetric absorptive micro-sampling tips (VAMS, n = 39 FM and n = 39 LC). A semi-permeable membrane (10 kDa) was used to extract low molecular fraction (LMF) from the blood samples, and Raman spectra were acquired using SERS with gold nanoparticles (AuNPs). Soft independent modelling of class analogy (SIMCA) models developed with spectral data of blood samples collected in VAMS tips showed superior performance with a validation performance of 100% accuracy, sensitivity, and specificity, achieving an excellent classification accuracy of 0.86 area under the curve (AUC). Amide groups, aromatic and acidic amino acids were responsible for the discrimination patterns among FM and LC syndromes, emphasizing the findings from our previous studies. Overall, our results demonstrate the ability of AuNP SERS to identify unique metabolites that can be potentially used as spectral biomarkers to differentiate FM and LC. 

Fibromyalgia (FM) is a chronic muscle pain disorder that shares several clinical features with other related rheumatologic disorders. This study investigates the feasibility of using surface-enhanced Raman spectroscopy (SERS) with gold nanoparticles (AuNPs) as a fingerprinting approach to diagnose FM and other rheumatic diseases such as rheumatoid arthritis (RA), systemic lupus erythematosus (SLE), osteoarthritis (OA), and chronic low back pain (CLBP). Blood samples were obtained on protein saver cards from FM (n = 83), non-FM (n = 54), and healthy (NC, n = 9) subjects. A semi-permeable membrane filtration method was used to obtain low-molecular-weight fraction (LMF) serum of the blood samples. SERS measurement conditions were standardized to enhance the LMF signal. An OPLS-DA algorithm created using the spectral region 750 to 1720 cm−1 enabled the classification of the spectra into their corresponding FM and non-FM classes (Rcv > 0.99) with 100% accuracy, sensitivity, and specificity. The OPLS-DA regression plot indicated that spectral regions associated with amino acids were responsible for discrimination patterns and can be potentially used as spectral biomarkers to differentiate FM and other rheumatic diseases. This exploratory work suggests that the AuNP SERS method in combination with OPLS-DA analysis has great potential for the label-free diagnosis of FM. 

Diabetes poses a high risk for debilitating complications in neural tissues, regulating glucose uptake through insulin-dependent and predominantly insulin-independent pathways. Supramolecular nanostructures provide a flexible strategy for combinatorial regulation of glycemia. Here, we compare the effects of free insulin to insulin bound to positively charged nanofibers comprised of self-assembling amino acid compounds (AACs) with an antioxidant-modified side chain moiety (AAC2) in both in vitro and in vivo models of type 1 diabetes. Free AAC2, free human insulin (hINS) and AAC2-bound-human insulin (AAC2-hINS) were tested in streptozotocin (STZ)-induced mouse model of type 1 diabetes. AAC2-hINS acted as a complex and exhibited different properties compared to free AAC2 or hINS. Mice treated with the AAC2-hINS complex were devoid of hypoglycemic episodes, had improved levels of insulin in circulation and in the brain, and increased expression of neurotransmitter taurine transporter, Slc6a6. Consequently, treatment with AAC2-hINS markedly advanced both physical and cognitive performance in mice with STZ-induced and genetic type 1 diabetes compared to treatments with free AAC2 or hINS. This study demonstrates that the flexible nanofiber AAC2 can serve as a therapeutic platform for the combinatorial treatment of diabetes and its complications.