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Bacterial vaginosis (BV) affects reproductive-age women and can lead to pelvic inflammatory disease, postpartum endometritis, and preterm labor/delivery and predisposes the infection of sexually transmitted diseases. Typically, BV diagnosis involves the analysis of vaginal swab samples via microscopy operated by highly skilled personnel. Hence, novel approaches for BV diagnosis are an existing need. In response, the first immunosensing platform targeting sialidase, a BV biomarker, is reported. The nanophotonic operational principle of this biosensing platform allows for a cheaper, faster, and simpler analysis when compared with an indirect enzyme-linked immunosorbent assay (ELISA). The clinical evaluation of such a nanotechnology is highlighted, where 162 vaginal swab samples were analyzed with high sensitivity and specificity (96.29%, respectively). The resulting nanoimmunosensing platform offers a resourceful approach to perform a timely BV diagnosis.

Objective. We evaluated the association between four polymorphisms in the CRP gene with circulating levels of C-reactive protein (CRP), type 2 diabetes (T2D), obesity, and risk score of coronary heart disease. Methods. We studied 402 individuals and classified them into four groups: healthy, obese, T2D obese, and T2D without obesity, from Guerrero, Southwestern Mexico. Blood levels of CRP, glucose, cholesterol, triglycerides, and leukocytes were measured. Genotyping was performed by PCR/RFLP, and the risk score for coronary heart disease was determined by the Framingham's methodology. Results. The TT genotype of SNP rs1130864 was associated with increased body mass index and T2D patients with obesity. We found that the haplotype 2 (TGAG) was associated with increased levels of CRP (β = 0.3; 95%CI: 0.1, 0.5; P = 0.005) and haplotype 7 (TGGG) with higher body mass index (BMI) (β = 0.2; 95%CI: 0.1, 0.3; P < 0.001). The risk score for coronary heart disease was associated with increased levels of CRP, but not with any polymorphism or haplotype. Conclusions. The association between the TT genotype of SNP rs1130864 with obesity and the haplotype 7 with BMI may explain how obesity and genetic predisposition increase the risk of diseases such as T2D in the population of Southwestern Mexico.

Purpose:We aim to identify Th1 and Th2 cell clusters in young subjects, including their clinical and metabolic characteristics and the Th1/Th2 balance. Patients and Methods: A total of 100 participants were included. The frequencies of Th1 and Th2 cells in peripheral blood were determined by flow cytometry. Serum C-reactive protein was measured using a turbidimetric assay, and insulin levels were quantified with an enzyme-linked immunosorbent assay. Circulating cytokine levels were analyzed using a multiplex system. Results: A cluster analysis was performed to determine the Th1/Th2 balance in a group of young people, and 3 clusters were formed with the following characteristics: 1) subjects with a higher prevalence of hyperglycemia (38%), dyslipidemia (38-75%), and insulin resistance (50%), as well as a higher percentage of Th1 cells and Th1/Th2 ratio, including elevated IFN-ɣ levels; 2) subjects with a lower prevalence of hyperglycemia (23%) and insulin resistance (15.4%), but a higher prevalence of dyslipidemia (8-85%) with a predominance of Th2 cells, and lower Th1/Th2 ratio; 3) subjects with a lower prevalence of hyperglycemia (6%), insulin resistance (41%), and dyslipidemia (10-63%), as well as a balance of Th1 and Th2 cells and lower Th1/Th2 ratio, including low IFN-ɣ levels. Positive correlations between Th1 cells with IFN-γ, IL-12, and IL-1β and between Th2 cells with IFN-γ, IL-2, and IL-4 were found (p < 0.05). A significant increase in Th1 cells was observed in the presence of hyperglycemia and high LDL-C levels, as well as increased Th2 cells in the absence of abdominal obesity and high blood pressure, including low HDL-C levels. The Th1/Th2 ratio was higher in the group with high cardiometabolic risk (p = 0.03). Conclusion: Th1/Th2 balance is related to metabolic abnormalities that may occur in young population, and thus the timely identification of different phenotypes may help predict an increased cardiometabolic risk.