Background of the Study: The prevailing cause of Diabetes is a decline in insulin production, the outcome of which is an elevated glucose level in the blood. The superabundance of glucose in the blood can cause severe complications, damaging other body organs, including kidneys, nerves, heart, and upper and lower limbs. However, the common complication in diabetic patients is foot ulcer, which is directly associated with Diabetic peripheral neuropathy (DPN), which is the extensive cause of this. DPN is the inability of nerves to sense any external change, due to which the foot plantar pressure is altered and evolves because of the high glucose level. Methodology: This paper provides a solution in the form of a portable and cost-effective device based on force sensors for diabetic patients to monitor the change in foot plantar pressure at home and overcome the risk of foot ulceration. The device is implemented on 30 participants to characterize the plantar pressure values with flat foot and normal foot types for the Control group and diabetic group. Results: An evident difference in the value of Mid-foot pressure is observed for both the groups, Control group (Normal foot = 144+2.63 kPa, Flat foot = 150+2.72 kPa) and Diabetic Group (Normal foot = 213+2 kPa, Flat foot = 216+1 kPa). Deviation in these values discriminates the mid-foot pressure for the two groups, thus providing us a range for the individuals of the control group for the alarming situation. Conclusion: Noticing the plantar pressure through the proposed device helps diabetes patients reduce their risk. Keywords: Blood glucose, type 1 diabetes mellitus, flat foot, metatarsal, foot orthosis, diabetic neuropathy, foot ulcers.

Fragility fractures, caused by low-energy trauma, are a significant global health concern, with 158 million people aged 50 and over at risk. Hip fractures, a common issue in elderly patients, are often linked to underlying conditions such as osteoporosis. This study proposed a cost-effective solution using a non-wheeled smart walker with load sensors to measure gait parameters, addressing the high cost of traditional gait analysis equipment, the prototype used PASCO load cells PS2200 for force measurement, eliminating the need for Arduino UNO or microcontroller-based hardware. A lightweight amplifier PS2198 amplified the signal, which was transmitted via USB to a personal computer. PASCO capstone software was used for data recording and visualization. The smart walker was tested on forty volunteers divided into two equal groups: those with osteoporosis and those without, by performing a 10 m walk test three times. ANOVA comparing spatiotemporal parameters (TSPs) of the two participant groups (α = 0.05) showed that significant differences lay in terms of time taken to complete the walk test (p < 0.01), left step length (p = 0.03), walking speed (p = 0.02), and stride length (p < 0.02). The results indicate that this smart walker is a reliable tool for assessing gait patterns in individuals with osteoporosis. The proposed system can be an alternative for time consuming and costly methods such as motion capture, and for socially stigmatizing devices such as exoskeletons. It can also be used further to identify risk factors of osteoporosis.

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.

Dyspnea is a disorder that occurs because of shortness of breath and may lead to multiple complications. Intoxicated dyspnea patients are those who are addicted to alcohol, as well as have breathing problems. Mostly, problems faced by dyspnea patients are inhalation, when they take alcohol with severe concentration. For intoxicated dyspnea patients, there is a continuous need for alcohol breath testing along with breath indicators, since, an individual is prone to alcohol addiction, and faces problems of breathing such as asthma, hypertension, etc. The severe outbreak of dyspnea in these individuals may be deadly. To handle this condition, an alcoholic meter along with an incentive spirometer is designed that helps in the assessment of breathing problems by measuring the alcohol concentration and inhaled air. Several parameters have opted for both inhalation detection and alcohol concentration which include the severity of health problems encompassing asthma, hypertension for the detection of inhaled air, and normal, moderate, severe alcohol concentration. The working of the proposed device is validated through the analysis of these parameters. The results are obtained on the graph as well as on display, which shows a variation in the range among every individual with respect to selected parameters either in incentive spirometer (inhalation detection) or alcoholic meter, and these ranges are also displayed in a numeric form on LCD. The idea is to design an economical and portable device for intoxicated dyspnea patients for emergency conditions which is a combination of an alcoholic meter with an incentive spirometer (IS) for the measurement of alcohol concentration along with breathing problems by making use of sensors. This unique idea of combining an alcoholic meter with an incentive spirometer will aid intoxicated dyspnea patients in case of emergency.

Membrane proteins constitute essential biomolecules attached to or integrated into cellular and organelle membranes, playing diverse roles in cellular processes. Their precise localization is crucial for understanding their functions. Existing protein subcellular localization predictors are predominantly trained on globular proteins; their performance diminishes for membrane proteins, explicitly via deep learning models. To address this challenge, the proposed study segregates membrane proteins into three distinct locations, including the plasma membrane, internal membrane, and membrane of the organelle, using deep learning algorithms including recurrent neural networks (RNN) and Long Short-Term Memory (LSTM). A redundancy-curtailed dataset of 3000 proteins from the MemLoci approach is selected for the investigation, along with incorporating pseudo amino acid composition (PseAAC). PseAAC is an exemplary technique for extracting protein information hidden in the amino acid sequences. After extensive testing, the results show that the accuracy for LSTM and RNN is 83.4% and 80.5%, respectively. The results show that the LSTM model outperforms the RNN and is most commonly employed in proteomics.

Membrane proteins are those biomolecules that are attached to or incorporated into the membranes of cells and their organelles. Depending on their functions, they are located in various regions of a cell and are essential to several cellular processes. The locale revelation of these biomolecules is critical as it portrays their activities. Most protein subcellular localization predictors have been trained particularly on globular type and perform poorly on those residing on membranes, specifically through Deep Learning. To overcome this issue, membrane proteins are forecasted in three distinct locations, (a) plasma membrane, (b) internal membrane, and (c) organelle membrane. Features are extracted through Pseudo Amino Acid Composition and some other features from a redundancy curtailed MemLoci dataset. Pseudo Amino Acid Composition is an illustrious approach that excerpts factual protein information through amino acid sequences. Another key feature is that the Pseudo Amino Acid Composition’s impact is unrelated to the Deep Learning Execution of these membrane proteins. This novel study employs four deep learning models, including (a) Artificial Neural Networks (ANN), (b) Recurrent neural networks (RNN), (c) Convolutional neural networks (CNN), and (c) Long Short Term Memory (LSTM). After extensive experimentation, the accuracy of yields is 83.2%, 83.4%, 82.4%., and 80.5% respectively. The outcomes indicate that the simple RNN and ANN models, which are less used in the research, are more suitable compared to the other two models CNN and LSTM which are frequently implemented models in proteomics. The results of the first two models is approximately similar, with a difference of 0.2% among each other, however, they surpass the other two models with better of outcomes in the range of 2 - 3%.