ABSTRACT
Resilience increases the ability of an individual to overcome adversity. It has not yet been determined how resilience is linked to quality of life among individuals experiencing knee osteoarthritis symptoms. To explore the inter-relationships of psychological distress, resilience and quality of life among older individuals with knee osteoarthritis. The study examined older adults in Kuala Lumpur and Selangor, identifying osteoarthritis through verified physician diagnosis. Various factors, including resilience, psychological status, and quality of life, were measured. In the study with 338 older adults, 50.9% had knee osteoarthritis. Higher resilience was linked to lower depression, anxiety, and stress, and better quality of life in both groups with and without knee osteoarthritis. Psychological factors consistently mediated the link between resilience and quality of life even after controlling potential confounders. Analysis showed that depression, anxiety, and stress mediate the relationship between resilience and quality of life, indicating a significant influence even when considering various factors. Resilience appears to influence psychological well-being and quality of life among older adult with knee osteoarthritis.
ABSTRACT
BACKGROUND: Diabetic peripheral neuropathy (DSPN), a major form of diabetic neuropathy, is a complication that arises in long-term diabetic patients. Even though the application of machine learning (ML) in disease diagnosis is a very common and well-established field of research, its application in diabetic peripheral neuropathy (DSPN) diagnosis using composite scoring techniques like Michigan Neuropathy Screening Instrumentation (MNSI), is very limited in the existing literature. METHOD: In this study, the MNSI data were collected from the Epidemiology of Diabetes Interventions and Complications (EDIC) clinical trials. Two different datasets with different MNSI variable combinations based on the results from the eXtreme Gradient Boosting feature ranking technique were used to analyze the performance of eight different conventional ML algorithms. RESULTS: The random forest (RF) classifier outperformed other ML models for both datasets. However, all ML models showed almost perfect reliability based on Kappa statistics and a high correlation between the predicted output and actual class of the EDIC patients when all six MNSI variables were considered as inputs. CONCLUSIONS: This study suggests that the RF algorithm-based classifier using all MNSI variables can help to predict the DSPN severity which will help to enhance the medical facilities for diabetic patients.
ABSTRACT
This work reports synthesis, thin film characterizations, and study of an organic semiconductor 2-aminoanthraquinone (AAq) for humidity and temperature sensing applications. The morphological and phase studies of AAq thin films are carried out by scanning electron microscope (SEM), atomic force microscope (AFM), and X-ray diffraction (XRD) analysis. To study the sensing properties of AAq, a surface type Au/AAq/Au sensor is fabricated by thermally depositing a 60 nm layer of AAq at a pressure of ~10-5 mbar on a pre-patterned gold (Au) electrodes with inter-electrode gap of 45 µm. To measure sensing capability of the Au/AAq/Au device, the variations in its capacitance and resistance are studied as a function of humidity and temperature. The Au/AAq/Au device measures and exhibits a linear change in capacitance and resistance when relative humidity (%RH) and temperature are varied. The AAq is a hydrophobic material which makes it one of the best candidates to be used as an active material in humidity sensors; on the other hand, its high melting point (575 K) is another appealing property that enables it for its potential applications in temperature sensors.
