The Impact of Metabolic and Bariatric Surgery on Apo B100 Levels in Individuals with high BMI: A Multi-Centric Prospective Cohort Study.

BACKGROUND: Metabolic and Bariatric surgery (MBS) leads to significant weight loss and improvements in obesity-related comorbidities. However, the impact of MBS on Apolipoprotein B100 (Apo-B100) regulation is unclear. Apo-B100 is essential for the assembly and secretion of serum lipoprotein particles. Elevated levels of these factors can accelerate the development of atherosclerotic plaques in blood vessels. This study aimed to evaluate changes in Apo-B100 levels following MBS. METHODS: 121 participants from the Iranian National Obesity and Metabolic Surgery Database (INOSD) underwent Laparoscopic Sleeve Gastrectomy (LSG) (n = 43), One-Anastomosis Gastric Bypass (OAGB) (n = 70) or Roux-en-Y Gastric Bypass (RYGB) (n = 8). Serum Apo-B100, lipid profiles, liver enzymes, and fasting glucose were measured preoperatively and six months postoperatively. RESULTS: Apo-B100 levels significantly decreased from 94.63 ± 14.35 mg/dL preoperatively to 62.97 ± 19.97 mg/dL after six months (p < 0.01), alongside reductions in total cholesterol, triglycerides, LDL, VLDL, AST, and ALT (p < 0.05). Greater Apo-B100 reductions occurred in non-diabetics versus people with diabetes (p = 0.012) and strongly correlated with baseline Apo-B100 (r = 0.455, p < 0.01) and LDL levels (r = 0.413, p < 0.01). However, surgery type did not impact Apo-B100 changes in multivariate analysis (p > 0.05). CONCLUSION: Bariatric surgery leads to a significant reduction in Apo-B100 levels and improvements in lipid profiles and liver enzymes, indicating a positive impact on dyslipidemia and cardiovascular risk in individuals with high BMI.

Application of p and n-Type Silicon Nanowires as Human Respiratory Sensing Device.

Accurate and fast breath monitoring is of great importance for various healthcare applications, for example, medical diagnoses, studying sleep apnea, and early detection of physiological disorders. Devices meant for such applications tend to be uncomfortable for the subject (patient) and pricey. Therefore, there is a need for a cost-effective, lightweight, small-dimensional, and non-invasive device whose presence does not interfere with the observed signals. This paper reports on the fabrication of a highly sensitive human respiratory sensor based on silicon nanowires (SiNWs) fabricated by a top-down method of metal-assisted chemical-etching (MACE). Besides other important factors, reducing the final cost of the sensor is of paramount importance. One of the factors that increases the final price of the sensors is using gold (Au) electrodes. Herein, we investigate the sensor's response using aluminum (Al) electrodes as a cost-effective alternative, considering the fact that the electrode's work function is crucial in electronic device design, impacting device electronic properties and electron transport efficiency at the electrode-semiconductor interface. Therefore a comparison is made between SiNWs breath sensors made from both p-type and n-type silicon to investigate the effect of the dopant and electrode type on the SiNWs respiratory sensing functionality. A distinct directional variation was observed in the sample's response with Au and Al electrodes. Finally, performing a qualitative study revealed that the electrical resistance across the SiNWs renders greater sensitivity to breath than to dry air pressure. No definitive research demonstrating the mechanism behind these effects exists, thus prompting our study to investigate the underlying process.

An Approach toward Artificial Intelligence Alzheimer's Disease Diagnosis Using Brain Signals.

BACKGROUND: Electroencephalography (EEG) signal analysis is a rapid, low-cost, and practical method for diagnosing the early stages of dementia, including mild cognitive impairment (MCI) and Alzheimer's disease (AD). The extraction of appropriate biomarkers to assess a subject's cognitive impairment has attracted a lot of attention in recent years. The aberrant progression of AD leads to cortical detachment. Due to the interaction of several brain areas, these disconnections may show up as abnormalities in functional connectivity and complicated behaviors. METHODS: This work suggests a novel method for differentiating between AD, MCI, and HC in two-class and three-class classifications based on EEG signals. To solve the class imbalance, we employ EEG data augmentation techniques, such as repeating minority classes using variational autoencoders (VAEs), as well as traditional noise-addition methods and hybrid approaches. The power spectrum density (PSD) and temporal data employed in this study's feature extraction from EEG signals were combined, and a support vector machine (SVM) classifier was used to distinguish between three categories of problems. RESULTS: Insufficient data and unbalanced datasets are two common problems in AD datasets. This study has shown that it is possible to generate comparable data using noise addition and VAE, train the model using these data, and, to some extent, overcome the aforementioned issues with an increase in classification accuracy of 2 to 7%. CONCLUSION: In this work, using EEG data, we were able to successfully detect three classes: AD, MCI, and HC. In comparison to the pre-augmentation stage, the accuracy gained in the classification of the three classes increased by 3% when the VAE model added additional data. As a result, it is clear how useful EEG data augmentation methods are for classes with smaller sample numbers.

Piezoresistance Characterization of Silicon Nanowires in Uniaxial and Isostatic Pressure Variation.

Silicon nanowires (SiNWs) are known to exhibit a large piezoresistance (PZR) effect, making them suitable for various sensing applications. Here, we report the results of a PZR investigation on randomly distributed and interconnected vertical silicon nanowire arrays as a pressure sensor. The samples were produced from p-type (100) Si wafers using a silver catalyzed top-down etching process. The piezoresistance response of these SiNW arrays was analyzed by measuring their I-V characteristics under applied uniaxial as well as isostatic pressure. The interconnected SiNWs exhibit increased mechanical stability in comparison with separated or periodic nanowires. The repeatability of the fabrication process and statistical distribution of measurements were also tested on several samples from different batches. A sensing resolution down to roughly 1m pressure was observed with uniaxial force application, and more than two orders of magnitude resistance variation were determined for isostatic pressure below atmospheric pressure.