Artificial stem cells mediated inflammation-tropic delivery of antiviral drugs for pneumonia treatment.

BACKGROUND: Cytomegalovirus (CMV) pneumonia is a major cause of morbidity and mortality in immunodeficiency individuals, including transplant recipients and Acquired Immune Deficiency Syndrome patients. Antiviral drugs ganciclovir (GCV) and phosphonoformate (PFA) are first-line agents for pneumonia caused by herpesvirus infection. However, the therapy suffers from various limitations such as low efficiency, drug resistance, toxicity, and lack of specificity. METHODS: The antiviral drugs GCV and PFA were loaded into the pH-responsive nanoparticles fabricated by poly(lactic-co-glycolic acid) (PLGA) and 1,2-dioleoyl-3-trimethylammonium-propane (DOTAP), and further coated with cell membranes derived from bone marrow mesenchymal stem cells to form artificial stem cells, namely MPDGP. We evaluated the viral suppression effects of MPDGP in vitro and in vivo. RESULTS: MPDGP showed significant inflammation tropism and efficient suppression of viral replication and virus infection-associated inflammation in the CMV-induced pneumonia model. The synergistic effects of the combination of viral DNA elongation inhibitor GCV and viral DNA polymerase inhibitor PFA on suppressing the inflammation efficiently. CONCLUSION: The present study develops a novel therapeutic intervention using artificial stem cells to deliver antiviral drugs at inflammatory sites, which shows great potential for the targeted treatment of pneumonia. To our best knowledge, we are the first to fabricate this kind of artificial stem cell to deliver antiviral drugs for pneumonia treatment.

Problem-Based mHealth Literacy Scale (PB-mHLS): Development and Validation.

BACKGROUND: Mobile devices have greatly facilitated the use of digital health resources, particularly during the COVID-19 pandemic. Mobile health (mHealth) has become a common and important way to monitor and improve health conditions for people from different social classes. The ability to utilize mHealth affects its effectiveness; therefore, the widespread application of mHealth technologies calls for an instrument that can accurately measure health literacy in the era of mobile media. OBJECTIVE: We aimed to (1) identify the components of mHealth literacy for ordinary users and (2) develop a systematic scale for appropriately measuring individuals' self-perceived mHealth literacy through a problem-based framework. METHODS: We conducted an exploratory study involving in-depth interviews and observations (15 participants) in January 2020 and used exploratory factor analysis and confirmatory factor analysis to identify the components of mHealth literacy and develop an item pool. In February 2020, we conducted a pilot survey with 148 participants to explore the factor structures of items identified during the exploratory study. Subsequently, 2 surveys were administrated using quota sampling. The first survey (conducted in Guangdong, China) collected 552 responses during March 2020; we assessed composite reliability, convergent validity, and discriminant validity. The second survey (conducted in China nationwide) collected 433 responses during October 2021; we assessed criterion-related validity using structural equation modeling. RESULTS: We identified 78 items during the exploratory study. The final scale-the Problem-Based mHealth Literacy Scale-consists of 33 items that reflect 8 domains of mHealth literacy. The first web-based survey suggested that mHealth literacy consists of 8 factors (ie, subscales), namely, mHealth desire, mobile phone operational skills, acquiring mHealth information, acquiring mHealth services, understanding of medical terms, mobile-based patient-doctor communication, evaluating mHealth information, and mHealth decision-making. These factors were found to be reliable (composite reliability >0.7), with good convergent validity (average variance extracted >0.5) and discriminant validity (square root of average variance extracted are greater than the correlation coefficients between factors). The findings also revealed that these 8 factors should be grouped under a second-order factor model (χ2/df=2.701; comparative fit index 0.921; root mean square error of approximation 0.056; target coefficient 0.831). The second survey revealed that mHealth use had a significant impact (ß=0.43, P<.001) on mHealth literacy and that mHealth literacy had a significant impact (ß=0.23, P<.001) on health prevention behavior. CONCLUSIONS: This study revealed the distinctiveness of mHealth literacy by placing mHealth needs, the ability to understand medical terms, and the skills in patient-doctor interactions in the foreground. The Problem-Based mHealth Literacy Scale is a useful instrument for comprehensively measuring individuals' mHealth literacy and extends the concept of health literacy to the context of mobile communication.