Quantification of glycated hemoglobin and glucose in vivo using Raman spectroscopy and artificial neural networks

Undiagnosed type 2 diabetes (T2D) remains a major public health concern. The global estimation of undiagnosed diabetes is about 46%, being this situation more critical in developing countries. Therefore, we proposed a non-invasive method to quantify glycated hemoglobin (HbA1c) and glucose in vivo. We developed a technique based on Raman spectroscopy, RReliefF as a feature selection method, and regression based on feed-forward artificial neural networks (FFNN). The spectra were obtained from the forearm, wrist, and index finger of 46 individuals. The use of FFNN allowed us to achieve an error in the predictive model of 0.69% for HbA1c and 30.12 mg/dL for glucose. Patients were classified according to HbA1c values into three categories: healthy, prediabetes, and T2D. The proposed method obtained a specificity and sensitivity of 87.50% and 80.77%, respectively. This work demonstrates the benefit of using artificial neural networks and feature selection techniques to enhance Raman spectra processing to determine glycated hemoglobin and glucose in patients with undiagnosed T2D

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ABSTRACT. The tumor necrosis factor-alpha (TNF-α) gene plays an important role in cell proliferation, differentiation, apoptosis, lipid metabolism, coagulation, insulin resistance, and endothelial function. Polymorphisms of TNF-α have been associated with cancer. We examined the role of the -308G>A polymorphism in this gene by comparing the genotypes of 294 healthy Mexican women with those of 465 Mexican women with breast cancer. The observed genotype frequencies for controls and breast cancer patients were 1 and 14% for AA, 13 and 21% for GA, and 86 and 65% for GG, respectively. We found that the odds ratio (OR) for AA genotype was 2.4, with a 95% confidence interval (95%CI) of 5.9-101.1 (P = 0.0001). The association was also evident when comparing the distribution of the AA-GA genotype in patients in the following categories: 1) premenopause and obesity I (OR = 3.5, 95%CI = 1.3-9.3, P = 0.008), 2) Her-2 neu and tumor stage I-II (OR = 2.5, 95%CI = 1.31-4.8, P = 0.004), 3) premenopause and tumor stage III-IV (OR = 1.7, 95%CI = 1.0-2.9, P = 0.034), 4) chemotherapy non-response and abnormal hematocrit (OR = 2.4, 95%CI = 1.2-4.8, P = 0.015), 5) body mass index and Her-2 neu and III-IV tumor stage (OR = 2.8, 95%CI = 1.2-6.6, P = 0.016), and 6) nodule metastasis and K-I67 (OR = 4.0, 95%CI = 1.01-15.7, P = 0.038). We concluded that the genotypes AA-GA of the -308G>A polymorphism in TNF-α significantly contribute to breast cancer susceptibility in the analyzed sample from the Mexican population

Constraints on the χ_(c1) versus χ_(c2) polarizations in proton-proton collisions at √s = 8 TeV

The polarizations of promptly produced χ_(c1) and χ_(c2) mesons are studied using data collected by the CMS experiment at the LHC, in proton-proton collisions at √s=8  TeV. The χ_c states are reconstructed via their radiative decays χ_c → J/ψγ, with the photons being measured through conversions to e⁺e⁻, which allows the two states to be well resolved. The polarizations are measured in the helicity frame, through the analysis of the χ_(c2) to χ_(c1) yield ratio as a function of the polar or azimuthal angle of the positive muon emitted in the J/ψ → μ⁺μ⁻ decay, in three bins of J/ψ transverse momentum. While no differences are seen between the two states in terms of azimuthal decay angle distributions, they are observed to have significantly different polar anisotropies. The measurement favors a scenario where at least one of the two states is strongly polarized along the helicity quantization axis, in agreement with nonrelativistic quantum chromodynamics predictions. This is the first measurement of significantly polarized quarkonia produced at high transverse momentum