Methods: A community-based participatory research method was utilized. Two focus group discussions (FGDs) were conducted in Malaysian sign language (BIM) with a total of 10 DHH individuals. Respondents were recruited using purposive sampling. Video-recordings were transcribed and analyzed using a thematic approach.
Results: Two themes emerged: (I) challenges and scepticism of the healthcare system; and (II) features of the mHealth app. Respondents expressed fears and concerns about accessing healthcare services, and stressed on the need for sign language interpreters. There were also concerns about data privacy and security. With regard to app features, the majority preferred videos instead of text to convey information about their disease and medication, due to their lower literacy levels.
Conclusions: For an mHealth app to be effective, app designers must ensure the app is individualised according to the cultural and linguistic diversity of the target audience. Pharmacists should also educate patients on the potential benefits of the app in terms of assisting patients with their medicine-taking.
Materials and Methods: We have developed and validated 2D and 3D QSAR models by using multiple linear regression, partial least square regression, and k-nearest neighbor-molecular field analysis methods.
Results: 2D QSAR models had q2: 0.950 and pred_r2: 0.877 and 3D QSAR models had q2: 0.899 and pred_r2: 0.957. These results showed that the models werere predictive.
Conclusion: Parameters such as hydrogen count and hydrophilicity were involved in 2D QSAR models. The 3D QSAR study revealed that steric and hydrophobic descriptors were negatively contributed to neuraminidase inhibitory activity. The results of this study could be used as platform for design of better anti-influenza drugs.
METHODS: Prospective data from patients hospitalized in ICUs were collected through INICC Surveillance Online System. CDC-NHSN definitions for device-associated healthcare-associated infection (DA-HAI) were applied.
RESULTS: We collected data from 428,847 patients, for an aggregate of 2,815,402 bed-days, 1,468,216 central line (CL)-days, 1,053,330 mechanical ventilator (MV)-days, 1,740,776 urinary catheter (UC)-days. We found 7,785 CL-associated bloodstream infections (CLAB), 12,085 ventilator-associated events (VAE), and 5,509 UC-associated urinary tract infections (CAUTI). Pooled DA-HAI rates were 5.91% and 9.01 DA-HAIs/1,000 bed-days. Pooled CLAB rate was 5.30/1,000 CL-days; VAE rate was 11.47/1,000 MV-days, and CAUTI rate was 3.16/1,000 UC-days. P aeruginosa was non-susceptible (NS) to imipenem in 52.72% of cases; to colistin in 10.38%; to ceftazidime in 50%; to ciprofloxacin in 40.28%; and to amikacin in 34.05%. Klebsiella spp was NS to imipenem in 49.16%; to ceftazidime in 78.01%; to ciprofloxacin in 66.26%; and to amikacin in 42.45%. coagulase-negative Staphylococci and S aureus were NS to oxacillin in 91.44% and 56.03%, respectively. Enterococcus spp was NS to vancomycin in 42.31% of the cases.
CONCLUSIONS: DA-HAI rates and bacterial resistance are high and continuous efforts are needed to reduce them.