Aiding gastrointestinal diagnostic laboratory by designing a device for the non invasive detection of peptic ulcer.

Peptic ulcer (PU) has been recognized as an utmost gastrointestinal problem that affects the lining of the stomach and duodenum, specifically triggering soreness. It is a life-threatening condition, while roots of the infection are not identified yet. There are various risk factors for the cause of peptic ulcer disease, but the most significant is "Helicobacter pylori" (H. pylori). The detection of this disease involves different invasive procedures which are painful and not feasible for everyone. The aim of this device is to identify the peptic ulcer non-invasively by unmasking the presence of H. Pylori bacterium by monitoring crucial parameters of the disease which include respiration rate, heart rate, ECG, pH of Saliva, and temperature. Multiple investigations related to PU authenticate the alteration in these physicochemical aspects of the body. The increase in the level of stomach acid in PU is responsible for belching and bloating. Heart rate, temperature, and respiratory rate are also elevated during peptic ulcers while the pH of Saliva is decreased toward the acidic side. The disturbance in the QRS complex of the ECG wave is also observed. These biosignals are examined as analog input from the body, sent into MCP3008, and converted into digital input signals. Then these digital inputs are directed toward Raspberry pi 3 which processes, received inputs, and shows output on the LCD. The values of parameters obtained are then compared with standard values and a conclusion is made that whether a patient has a peptic ulcer or not.

Predictors of post-stroke cognitive impairment using acute structural MRI neuroimaging: A systematic review and meta-analysis.

BACKGROUND: Stroke survivors are at an increased risk of developing post-stroke cognitive impairment and post-stroke dementia; those at risk could be identified by brain imaging routinely performed at stroke onset. AIM: This systematic review aimed to identify features which are associated with post-stroke cognitive impairment (including dementia) on magnetic resonance imaging (MRI) performed at stroke diagnosis. SUMMARY OF REVIEW: We searched the literature from inception to January 2022 and identified 10,284 records. We included studies that performed MRI at the time of stroke (0-30 days after a stroke) and assessed cognitive outcome at least 3 months after stroke. We synthesized findings from 26 papers, comprising 27 stroke-populations (N = 13,114, average age range = 40-80 years, 19-62% female). When data were available, we pooled unadjusted (ORu) and adjusted (ORa) odds ratios.We found associations between cognitive outcomes and presence of cerebral atrophy (three studies, N = 453, ORu = 2.48, 95% CI = 1.15-4.62), presence of microbleeds (two studies, N = 9151, ORa = 1.36, 95% CI = 1.08-1.70), and increasing severity of white matter hyperintensities (three studies, N = 704, ORa = 1.26, 95% CI = 1.06-1.49). Increasing cerebral small vessel disease score was associated with cognitive outcome following unadjusted analysis only (two studies, N = 499, ORu = 1.34, 95%CI = 1.12-1.61; three studies, N = 950, ORa = 1.23, 95% CI = 0.96-1.57). Associations remained after controlling for pre-stroke cognitive impairment. We did not find associations between other stroke features and cognitive outcome, or there were insufficient data. CONCLUSION: Acute stroke MRI features may enable healthcare professionals to identify patients at risk of post-stroke cognitive problems. However, there is still substantial uncertainty about the prognostic utility of acute MRI for this.