An update on pathogenesis and clinical scenario for Parkinson's disease: diagnosis and treatment.

In severe cases, Parkinson's disease causes uncontrolled movements known as motor symptoms such as dystonia, rigidity, bradykinesia, and tremors. Parkinson's disease also causes non-motor symptoms such as insomnia, constipation, depression and hysteria. Disruption of dopaminergic and non-dopaminergic neural networks in the substantia nigra pars compacta is a major cause of motor symptoms in Parkinson's disease. Furthermore, due to the difficulty of clinical diagnosis of Parkinson's disease, it is often misdiagnosed, highlighting the need for better methods of detection. Treatment of Parkinson's disease is also complicated due to the difficulties of medications passing across the blood-brain barrier. Moreover, the conventional methods fail to solve the aforementioned issues. As a result, new methods are needed to detect and treat Parkinson's disease. Improved diagnosis and treatment of Parkinson's disease can help avoid some of its devastating symptoms. This review explores how nanotechnology platforms, such as nanobiosensors and nanomedicine, have improved Parkinson's disease detection and treatment. Nanobiosensors integrate science and engineering principles to detect Parkinson's disease. The main advantages are their low cost, portability, and quick and precise analysis. Moreover, nanotechnology can transport medications in the form of nanoparticles across the blood-brain barrier. However, because nanobiosensors are a novel technology, their use in biological systems is limited. Nanobiosensors have the potential to disrupt cell metabolism and homeostasis, changing cellular molecular profiles and making it difficult to distinguish sensor-induced artifacts from fundamental biological phenomena. In the treatment of Parkinson's disease, nanoparticles, on the other hand, produce neurotoxicity, which is a challenge in the treatment of Parkinson's disease. Techniques must be developed to distinguish sensor-induced artifacts from fundamental biological phenomena and to reduce the neurotoxicity caused by nanoparticles.

Sensitivity and Specificity of Non-Invasive Blood Glucose Level Measurement Optical Device to Detect Hypoglycaemia.

Hypoglycemia is related to lethargy, psychiatric disorders, and impaired brain metabolism. Hypoglycemia is one of the leading factors of death in blood glucose level (BGL) metabolism disorders. Optical methods have been heavily researched due to its potential to eliminate drawbacks of conventional hypoglycemia detection; however, clinical data are still scarce. This study objective was to measure the sensitivity and specificity of non-invasive BGL Measurement Optical Device (NI-BGL-MOD) to detect hypoglycemia. The reference standard is venipuncture spectrophotometry. Researcher has developed NI-BGL-MOD, which we have used in a clinical trial in December 2015. The researchers have used spectral data collected from the device to measure the BGL of randomly selected 110 participants who were older than 17 y old. Each participant was measured five times. There are a total of 550 data sets that were then compared to BGL measurement using the reference standard. The spectral data were optimized using Discrete Fourier Transform and inferred to BGL prediction using the Fast Artificial Neural Network. Researchers have defined hypoglycemia case with BGL level at 75 mg/dL or lower. The researchers have calculated sensitivity and specificity using epiR in Rstudio. Respondents' BGL values were between 67 to 96 mg/dL. Researchers have classified eighty-nine cases as hypoglycemia. There are 461 cases classified as not hypoglycemia. The sensitivity was 54%, and the specificity was 97%. Diagnostic accuracy was 86%, and the number to diagnose was 1.96. The newly developed method NI-BGL-MOD could be used to detect hypoglycemia.

A Coupled Phase-Temperature Model for Dynamics of Transient Neuronal Signal in Mammals Cold Receptor.

We propose a theoretical model consisting of coupled differential equation of membrane potential phase and temperature for describing the neuronal signal in mammals cold receptor. Based on the results from previous work by Roper et al., we modified a nonstochastic phase model for cold receptor neuronal signaling dynamics in mammals. We introduce a new set of temperature adjusted functional parameters which allow saturation characteristic at high and low steady temperatures. The modified model also accommodates the transient neuronal signaling process from high to low temperature by introducing a nonlinear differential equation for the "effective temperature" changes which is coupled to the phase differential equation. This simple model can be considered as a candidate for describing qualitatively the physical mechanism of the corresponding transient process.