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The wide range of symptoms of the coronavirus disease 2019 (COVID-19) makes it challenging to predict the disease evolution using a single parameter. Therefore, to describe the pathophysiological response to SARS-CoV-2 infection in hospitalized patients with severe COVID-19, we compared according to survival or death, the sociodemographic and clinical characteristics, the biochemical and immunological attenuated total reflection-Fourier transform infrared (ATR-FTIR) spectra from saliva samples and their correlation with chemometric findings. Herein, we demonstrate that ATR-FTIR spectroscopy allows the description of the events related to cell damage, such as lipids biogenesis and the secondary structure of proteins associated with lactate dehydrogenase and albumin levels. Moreover, humoral (IgM) and cellular (IFN-γ, TNF-α, IL-10, and IL-6) responses were also increased in patients who died from COVID-19.

Abstract The coronavirus disease 2019 (COVID-19) is the latest biological hazard for the novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Even though numerous diagnostic tests for SARS-CoV-2 have been proposed, new diagnosis strategies are being developed, looking for less expensive methods to be used as screening. This study aimed to establish salivary vibrational modes analyzed by attenuated total reflection-Fourier transform infrared (ATR-FTIR) spectroscopy to detect COVID-19 biological fingerprints that allow the discrimination between COVID-19 and healthy patients. Clinical dates, laboratories, and saliva samples of COVID-19 patients (N = 255) and healthy persons (N = 1209) were obtained and analyzed through ATR-FTIR spectroscopy. Then, a multivariate linear regression model (MLRM) was developed. The COVID-19 patients showed low SaO2, cough, dyspnea, headache, and fever principally. C-reactive protein, lactate dehydrogenase, fibrinogen, d-dimer, and ferritin were the most important altered laboratory blood tests, which were increased. In addition, changes in amide I and immunoglobulin regions were evidenced in the FTIR spectra analysis, and the MLRM showed clear discrimination between both groups. Specific salivary vibrational modes employing ATR-FTIR spectroscopy were established; moreover, the COVID-19 biological fingerprint in saliva was characterized, allowing the COVID-19 detection using an MLRM, which could be helpful for the development of new diagnostic devices.

An optical crystalline silicon biosensor was developed for the detection of heavy metals in surface water, deep water, mollusks and sediment in the lagoon of La Paz, Baja California Sur, Mexico, to monitor the presence of heavy metals, the biosensor was built using self-assembled monolayers, the silicon supports were cut with a diameter of 0.5 cm × 0.5 cm, chemical modifications were made on the surface by adding KOH to obtain Si-OH groups, for the functionalization this was carried out by 3-aminopropyltrimethoxysilane to add NH2 groups on the surface of the biosensor, the activation was with EDC/NHS as a crosslinking agent, Finally, the urease enzyme was immobilized on the surface of the biosensor in an orbital shaker at 100 rpm in PBS and proceeded to detect each of the concentrations of standard heavy metals methylmercury chloride, cadmium, lead, chromium oxide VI, arsenic oxide III and silver iodide at different concentrations. Subsequently, detection was performed on biological samples by taking the internal part of tissues placed in PBS under refrigeration and water samples were measured without treatment. The self-assembly and detection was characterized by FTIR in the region from 370 to 4000 cm−1 taking the region from 1000 to 1200 cm−1, 1500 to 1700 cm−1 and from 2800 to 3100 cm−1 as the most important regions for the principal component analysis, showing that in these regions the characteristic bonds of silicon are present, The functionalization showed the region of the primary and secondary amide and finally the detection was taken as the inhibition of the enzymatic activity, the principal component analysis showed the region where the detection of each heavy metal is performed and corroborates the results obtained in FTIR.

Heavy metals are among the toxic substances longest recognized by man. Today, due to the myriad sources of exposure, such as contaminated water, food, or air, they have become a major public health problem. This work presents the effects manifested in the infrared spectrum behavior caused by the presence of Cd2+, Cr6+, and Pb2+ at different concentrations in three different matrices: water, casein, and milk; observing that the spectral modifications in the regions of different vibrational modes of nucleophilic groups such as -OH, COO- and NH2 depending on the nature of the metal and its concentration. These findings were correlated in-silico using optimized models in Gabedit software and structural optimization was performed with MOPAC 2016 showing stable structures between the metals and Gln, Hys, Glu, and Phe of casein. By applying chemometrics (Principal Component Analysis), it was possible to observe a good correlation between the experimental data and to discriminate between the type of metal, the matrix that contains it, and the concentration could be represented through linear models that showed adjustments with a value of r2 ≥ 0.95.

Aiming at producing a reduced fat cheese (RFC) as an alternative to full-fat Panela cheese, a highly consumed fresh Mexican dairy product, thermosonication (TS) processes (24 kHz, 400 W nominal power, 2, 4 and 6 min; 50, 55 and 60 °C) were evaluated to treat WPC (80% protein) blended with reduced-fat milk (1 and 2% fat), which were later LTLT pasteurized. TS blends were compared in terms of their technological properties (water holding capacity-WPC, gel firmness- GF, color, pH and titratable acidity) with those of a regular full fat (3%) LTLT pasteurized milk used as a control. Afterwards, a regression analysis was carried out with the obtained data in order to select the most appropriate conditions for cheesemaking purposes (similar GF, higher WHC with respect to the control), minimize both fat content and TS treatment duration to minimize energy expenses. According to these restrictions, the selected conditions were 1.5% fat milk-WPC blend, TS treated at 60 °C for 120 s; 1% fat milk-WPC blend, TS treated at 50 °C for 120 s and 1% fat milk-WPC blend, 50 °C for 144 s, which allowed preparing low fat cheeses (LFCs). These TS treatments were applied in a larger scale to elaborate Panela-type LFCs comparing different technological properties (cheese yield, syneresis, water content, texture profile analysis, color and titratable acidity) with those of a full fat variety, at day 1 and during 14 days of refrigerated storage. Results showed similar texture profiles of LFC cheeses and full fat milk cheeses throughout their storage period with significant changes in composition parameters (higher moisture, protein and salt contents, with low fat percentages), syneresis, selected color parameters (hue, b*), with no observed changes in cheese yield, TA and pH during cheese storage. These promising results are encouraging to develop LFCs with no physicochemical or technological defects using novel processing techniques that may help reducing calorie consumption without compromising sensory acceptability.

The bacterial community of the artisanal Adobera cheese from Los Altos de Jalisco was described through high-throughput sequencing of 16S rRNA gene libraries. Samples were collected in two different seasons (dry and rainy) during four key steps of the manufacturing process (raw milk, fresh curd, matured curd, and cheese). Bacterial diversity was higher in early steps in comparison with the final elaboration stages. Firmicutes and Proteobacteria were the most abundant phyla, strongly represented by the Streptococcaceae, Enterobacteriaceae and Lactobacillaceae families, and core bacteria genera such as Streptococcus spp., Lactococcus spp., and Lactobacillus spp. Undesirable bacteria, including Pseudomonas spp. and Acinetobacter spp., were also detected in raw milk but almost undetectable at the end of the cheese manufacturing process, and seemed to be displaced by lactic-acid bacteria-related genera. Seasonal effects were observed on the community structure but did not define the core microbiota composition. Predictive metabolism was related to membrane transport, and amino-acid, lipid, and carbohydrate metabolism pathways. Our results contribute to deduce the role of bacteria involved in Adobera cheese manufacturing in terms of the metabolism involved, cheese microbial safety, and how undesirable bacterial populations could be regulated by process standardization as a potential tool to improve safety.