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In general, deficient birth weight neonates suffer from hypoglycemia, and this can be quite disadvantageous. Like oxygen, glucose is a building block of life and constitutes the significant share of energy produced by the fetus and the neonate during gestation. The fetus receives glucose from the placenta continuously during gestation, but this substrate delivery changes abruptly, and the fetus’s metabolism changes significantly at birth. Hypoglycemia is one of the most frequent pathologies affecting the change of newborns in neonatal critical care units. This work is now introducing a system, HAPI-BELT, empowered by dual intelligent sensors and Deep Learning (DL) algorithms for tracking and continuously detecting hypoglycemia in preterm newborns. This article comprises a smart belt with an intelligent camera and photoplethysmography (PPG) attached. This device tracks changes in the infant’s motion, skin colour, and breathing patterns; this is done through a PPG sensor strapped either on the belly or chest of an infant, logging information on heart functioning. The digital data gathered by this PPG sensor and image data captured from the smart camera are then processed by a Raspberry Pi Zero 2 W. It does most of the data analysis and decision-making. Feature Extraction (FE) is done through CAT-Swarm Optimization. Based on features, the sorted-out data gets evaluated through a GRU-LSTM (Gated Recurrent Unit - Long Short-Term Memory) network to identify the state of the infant as usual and suggestive of hypoglycemia—blood glucose below 70 mg/dL , pale complexion, profuse perspiration. When hypoglycemia is identified, an alert is sent to the medical professionals to take necessary action with utmost urgency. Therefore, an integrated approach ensuring timely medical interventions and real-time monitoring can help better outcomes for preterm newborns.

The correct sitting posture in a wheelchair is crucial for paralyzed people. This helps prevent problems such as pressure ulcers, muscle contractures, and respiratory problems. A paralyzed person with poor sitting posture is highly likely to slip out of their wheelchair. To prevent this from happening and consistently maintain paralyzed individuals under observation, a new model, the Emperor Penguin Optimized Sensor-Infused Wheelchair (EPIC), has been designed to monitor the position and health of the individual in the wheelchair in real-time. A Force Sensitive Resistor (FSR) sensor and an ultrasonic sensor continuously transmit information to the Arduino UNO R4 Wi-Fi board. The Emperor Penguin Optimizer Algorithm (EPOA) was used to select the features sent from the Arduino board to the ESP8266-Wi-Fi module. A Deep Maxout Network (DMN) was used to predict the posture of a wheelchair-using patient following the feature selection phase. A mobile application for Android collects data from the ESP32 module and estimates posture to inform the caretaker about the user’s current posture and health status. Evaluation metrics such as precision, accuracy, sensitivity, and specificity have been used to determine the efficiency in the EPIC framework, which improves overall accuracy by 10.1%, 7.73%, and 2.84% for better posture recognition.

Summary The G allele of the FOXO3 single nucleotide polymorphism (SNP) rs2802292 exhibits a consistently replicated genetic association with longevity in multiple populations worldwide. The aims of this study were to quantify the mortality risk for the longevity-associated genotype and to discover the particular cause(s) of death associated with this allele in older Americans of diverse ancestry. It involved a 17-year prospective cohort study of 3584 older American men of Japanese ancestry from the Honolulu Heart Program cohort, followed by a 17-year prospective replication study of 1595 white and 1056 black elderly individuals from the Health Aging and Body Composition cohort. The relation between FOXO3 genotype and cause-specific mortality was ascertained for major causes of death including coronary heart disease (CHD), cancer, and stroke. Age-adjusted and multivariable Cox proportional hazards models were used to compute hazard ratios (HRs) for all-cause and cause-specific mortality. We found G allele carriers had a combined (Japanese, white, and black populations) risk reduction of 10% for total (all-cause) mortality (HR = 0.90; 95% CI, 0.84-0.95; P = 0.001). This effect size was consistent across populations and mostly contributed by 26% lower risk for CHD death (HR = 0.74; 95% CI, 0.64-0.86; P = 0.00004). No other causes of death made a significant contribution to the survival advantage for G allele carriers. In conclusion, at older age, there is a large risk reduction in mortality for G allele carriers, mostly due to lower CHD mortality. The findings support further research on FOXO3 and FoxO3 protein as potential targets for therapeutic intervention in aging-related diseases, particularly cardiovascular disease.

AbstractObjectiveThe Cognitive Abilities Screening Instrument (CASI) is a screening test of global cognitive function used in research and clinical settings. However, the CASI was developed using face validity and has not been investigated via empirical tests such as factor analyses. Thus, we aimed to develop and test a parsimonious conceptualization of the CASI rooted in cognitive aging literature reflective of crystallized and fluid abilities. DesignSecondary data analysis implementing confirmatory factor analyses where we tested the proposed two-factor solution, an alternate one-factor solution, and conducted a χ 2 difference test to determine which model had a significantly better fit. SettingN/A. ParticipantsData came from 3,491 men from the Kuakini Honolulu-Asia Aging Study. MeasurementsThe Cognitive Abilities Screening Instrument. ResultsFindings demonstrated that both models fit the data; however, the two-factor model had a significantly better fit than the one-factor model. Criterion validity tests indicated that participant age was negatively associated with both factors and that education was positively associated with both factors. Further tests demonstrated that fluid abilities were significantly and negatively associated with a later-life dementia diagnosis. ConclusionsWe encourage investigators to use the two-factor model of the CASI as it could shed light on underlying cognitive processes, which may be more informative than using a global measure of cognition.