The inclusion of online, in situ biosensors in microfluidic cell cultures is important to monitor and characterize a physiologically mimicking environment. This work presents the performance of second-generation electrochemical enzymatic biosensors to detect glucose in cell culture media. Glutaraldehyde and ethylene glycol diglycidyl ether (EGDGE) were tested as cross-linkers to immobilize glucose oxidase and an osmium-modified redox polymer on the surface of carbon electrodes. Tests employing screen printed electrodes showed adequate performance in a Roswell Park Memorial Institute (RPMI-1640) media spiked with fetal bovine serum (FBS). Comparable first-generation sensors were shown to be heavily affected by complex biological media. This difference is explained in terms of the respective charge transfer mechanisms. Under the tested conditions, electron hopping between Os redox centers was less vulnerable than H₂O₂ diffusion to biofouling by the substances present in the cell culture matrix. By employing pencil leads as electrodes, the incorporation of these electrodes in a polydimethylsiloxane (PDMS) microfluidic channel was achieved simply and at a low cost. Under flow conditions, electrodes fabricated using EGDGE presented the best performance with a limit of detection of 0.5 mM, a linear range up to 10 mM, and a sensitivity of 4.69 μA mM⁻¹ cm⁻².

The combination of microscopy, spectroscopy, and electrochemical techniques reveals crosslinker effects in enzymatic hydrogels and allows probing for a possible degradation mechanism of these gels.

Alzheimer's disease (AD) is the most prevalent form of dementia, and current indications show that twenty-nine million people live with AD worldwide, a figure expected rise exponentially over the coming decades. AD is characterize with several pathologies this disease, amyloid plaques, composed of the β -amyloid peptide and γ -amyloid peptide are hallmark neuropathological lesions in Alzheimer's disease brain. Indeed, a wealth of evidence suggests that β -amyloid is central to the pathophysiology of AD and is likely to play an early role in this intractable neurodegenerative disorder. For this reason, we developed a new QSAR (QSAR) model to discover new drugs. A public databases ChEMBL contain Big Data sets of inhibitors of β-secretase. We revised QSAR studies using method of Artificial Neural Network (ANN) in order to understand the essential structural requirement for binding with receptor for β-secretase inhibitors.

Hypertension is a multifactorial disease in which blood vessels are extensively exposed to a higher voltage than usual, this tension endures more strain on the heart leading to greater cardiac output to pump blood to the body. Hypertension is classified by the World Health Organization (WHO) as one of the main risk factors for disability and premature death in the world population. WHO has strengthened various health services around the world, listing the groups of basic medicines for high blood pressure such as: angiotensin-converting enzyme inhibitors, thiazide diuretics, beta blockers, long-acting calcium channel blockers, among other groups for drug treatment to the population with this condition. The discovery of new drugs with better activity and less toxicity for the treatment of Hypertension is a goal of the major importance. In this sense, theoretical models as QSAR can be useful to discover new drugs for hypertension treatment. For this reason, we developed a new multi-target-QSAR (mt-QSAR) model to discover new drugs. A public databases ChEMBL contain Big Data sets of multi-target assays of inhibitors of a group of receptors with special relevance in Hypertension was used. However, almost all the computational models known focus in only one target or receptor. In this work, Beta-2 adrenergic receptor, Adrenergic receptor beta, Type-1 angiotensin II receptor, Angiotensin-converting enzyme, Beta-adrenergic receptor, Cytochrome P450 11B2 and Renin were used as receptor inputs in the model. A Artificial Neural Network (ANN) is our statistical analysis. In that way, we used as input Topological Indices, in specific Wiener, Barabasi and Harary indices calculated by Dragon software. These operators quantify the deviations of the structure of one drug from the expected values for all drugs assayed in different boundary conditions such as type of receptor, type of assay, type of target, target mapping. Overall training performance was 90%. Overall Validation predictability performance was 90%.