METHOD: To overcome the limitation, the use of artificial intelligence along with technical tools has been extensively investigated for AD diagnosis. For developing a promising artificial intelligence strategy that can diagnose AD early, it is critical to supervise neuropsychological outcomes and imaging-based readouts with a proper clinical review.
CONCLUSION: Profound knowledge, a large data pool, and detailed investigations are required for the successful implementation of this tool. This review will enlighten various aspects of early diagnosis of AD using artificial intelligence.
AIMS: This study aimed to determine common combinations of medications used among women aged 77-96 years and to describe characteristics associated with these combinations.
METHODS: A cohort study of older women enroled in the Australian Longitudinal Study on Women's Health over a 15-year period was used to determine combinations of medications using latent class analysis. Multinomial logistic regression was used to determine characteristics associated with these combinations.
RESULTS: The highest medication users during the study were for the cardiovascular (2003: 80.28%; 2017: 85.63%) and nervous (2003: 66.03%; 2017: 75.41%) systems. A 3-class latent model described medication use combinations: class 1: 'Cardiovascular & neurology anatomical group' (27.25%) included participants using medications of the cardiovascular and nervous systems in their later years; class 2: 'Multiple anatomical group' (16.49%) and class 3: 'Antiinfectives & multiple anatomical group' (56.27%). When compared to the reference class (class 1), the risk of participants being in class 3 was slightly higher than being in class 2 if they had > 4 general practitioner visits (RRR 2.37; 95% CI 2.08, 2.71), Department of Veterans Affairs' coverage (RRR 1.59; 95% CI 1.36, 1.86), ≥ 4 chronic diseases (RRR 3.16; 95% CI 2.56, 3.90) and were frail (RRR 1.47; 95% CI 1.27, 1.69).
CONCLUSION: Identification of combinations of medication use may provide opportunities to develop multimorbidity guidelines and target medication reviews, and may help reduce medication load for older individuals.
Results: Candidal growth was found in 21.42% (n = 24) of COPD cases and 1.1% (n = 11) of control cases (p < 0.05) (95% CI 0.45, 0.59). The DMFT score was 8.26 in COPD subjects and 4.6 in controls, the SiC score was 16.42 in COPD subjects and 10.25 in controls, and the CPITN score for both COPD and control cases was score 2.
Conclusion: In conclusion, there was a higher candidal load among subjects suffering from COPD. Theophylline medication can be a risk factor for increased candidal load in COPD patients.