Mexican Coccoloba uvifera L. Leaf and Fruit Extracts: Identification of Pentacyclic Triterpenes and Volatile Profile by GC-MS.

Mexican Coccoloba uvifera fruit contains polyphenols, flavonoids, and anthocyanins, while in the leaves, lupeol, α- and ß-amyrin have been previously identified by HPLC. However, the low resolution by HPLC of pentacyclic triterpenes (PTs) is a limitation. Moreover, the volatile profile of C. uvifera fruit is still unknown. Therefore, this study aimed to identify PTs in C. uvifera leaf and fruit extracts by CG-MS analysis and to determine the volatile profile of C. uvifera pulp by headspace solid-phase microextraction. The results showed trimethylsilylated compounds of standards lupeol, α- and ß-amyrin, indicating that the silylation reaction was suitable. These trimethylsilylated compounds were identified in leaf and fruit extracts. The fruit volatile profile revealed the presence of 278 esters, 20 terpenes, 9 aldehydes, 5 alcohols, and 4 ketones. The fruit showed a high content of esters and terpenes. Due to their flavour properties, esters are essential for the food, cosmetics, and pharmaceutics industries. Moreover, terpenes in the fruit, such as menthone, ß-elemene, junipene, and ß-caryophyllene have the potential as anticancer and phytopathogen agents. The results indicated that GC-MS is an alternative to HPLC approaches for identifying PTs. Besides, identifying volatile compounds in the fruit will increase the value of this plant and expand its application. Identifying PTs and volatile compounds in Mexican C. uvifera leads to a better understanding of the potential benefits of this plant. This would increase the consumption of Mexican C. uvifera fresh or as functional ingredients in nutraceutical or pharmaceutical products.

Impact of Digital Health Technology on Quality of Life in Patients With Heart Failure.

BACKGROUND: Digital health tools may improve quality of life (QoL) in patients with heart failure (HF) by promoting self-care, knowledge, and engagement. OBJECTIVES: This study evaluates the effect of 3 digital technologies on QoL in patients with HF. METHODS: A total of 182 patients were randomized to usual care or one of the technologies promoting self-care: Bodyport (cardiac scale), Conversa (conversational platform), or Noom (smartphone application). The primary outcome was 90-day change in QoL, as assessed by the Kansas City Cardiomyopathy Questionnaire (KCCQ) Overall Summary Score (OSS). RESULTS: A total of 151 participants (83%) completed their 90-day surveys. The median age of enrolled participants was 61 years (IQR: 53-69 years), and 37.9% were women. No group had any significant change in KCCQ OSS or improvement relative to usual care. However, symptoms and physical function at 90 days, as assessed by the Total Symptom Score (TSS) and Clinical Summary Score (CSS), were significantly improved in the Noom group relative to usual care: TSS median change of +4.2 points (IQR -1 to +16.7) vs -1 points (IQR: -13.5 to +7.8; P = 0.006); CSS median change of +2.8 points (IQR: -1 to +14.6) vs -3.1 points (IQR: -10.2 to +3; P = 0.002). CONCLUSIONS: Three digital interventions showed no independent effect on QoL as assessed by the KCCQ OSS. However, participants randomized to the Noom technology demonstrated improved KCCQ TSS and CSS relative to usual care. Although digital tools may be an important component of longitudinal care for patients with HF, larger studies are needed to better understand their effectiveness and optimal deployment. (Evaluating Efficacy of Digital Health Technology in the Treatment of Congestive Heart Failure; NCT04394754).

Pragmatic randomized trial assessing the impact of digital health technology on quality of life in patients with heart failure: Design, rationale and implementation.

BACKGROUND: Self-care and patient engagement are important elements of heart failure (HF) care, endorsed in the guidelines. Digital health tools may improve quality of life (QOL) in HF patients by promoting care, knowledge, and engagement. This manuscript describes the rationale and challenges of the design and implementation of a pragmatic randomized controlled trial to evaluate the efficacy of three digital health technologies in improving QOL for patients with HF. HYPOTHESIS: We hypothesize that digital health interventions will improve QOL of HF patients through the early detection of warning signs of disease exacerbation, the opportunity of self-tracking symptoms, and the education provided, which enhances patient empowerment. METHODS: Using a fully electronic enrollment and consent platform, the trial will randomize 200 patients across HF clinics in the Yale New Haven Health system to receive either usual care or one of three digital technologies designed to promote self-management and provide critical data to clinicians. The primary outcome is the change in QOL as assessed by the Kansas City Cardiomyopathy Questionnaire at 3 months. RESULTS: First enrollment occurred in September 2021. Recruitment was anticipated to last 6-8 months and participants were followed for 6 months after randomization. Our recruitment efforts have highlighted the large digital divide in our population of interest. CONCLUSION: Assessing clinical outcomes, patient usability, and ease of clinical integration of digital technologies will be beneficial in determining the feasibility of the integration of such technologies into the healthcare system.

Development and external validation of a diagnostic model for biopsy-proven acute interstitial nephritis using electronic health record data.

BACKGROUND: Patients with acute interstitial nephritis (AIN) can present without typical clinical features, leading to a delay in diagnosis and treatment. We therefore developed and validated a diagnostic model to identify patients at risk of AIN using variables from the electronic health record. METHODS: In patients who underwent a kidney biopsy at Yale University between 2013 and 2018, we tested the association of >150 variables with AIN, including demographics, comorbidities, vital signs and laboratory tests (training set 70%). We used least absolute shrinkage and selection operator methodology to select prebiopsy features associated with AIN. We performed area under the receiver operating characteristics curve (AUC) analysis with internal (held-out test set 30%) and external validation (Biopsy Biobank Cohort of Indiana). We tested the change in model performance after the addition of urine biomarkers in the Yale AIN study. RESULTS: We included 393 patients (AIN 22%) in the training set, 158 patients (AIN 27%) in the test set, 1118 patients (AIN 11%) in the validation set and 265 patients (AIN 11%) in the Yale AIN study. Variables in the selected model included serum creatinine {adjusted odds ratio [aOR] 2.31 [95% confidence interval (CI) 1.42-3.76]}, blood urea nitrogen:creatinine ratio [aOR 0.40 (95% CI 0.20-0.78)] and urine dipstick specific gravity [aOR 0.95 (95% CI 0.91-0.99)] and protein [aOR 0.39 (95% CI 0.23-0.68)]. This model showed an AUC of 0.73 (95% CI 0.64-0.81) in the test set, which was similar to the AUC in the external validation cohort [0.74 (95% CI 0.69-0.79)]. The AUC improved to 0.84 (95% CI 0.76-0.91) upon the addition of urine interleukin-9 and tumor necrosis factor-α. CONCLUSIONS: We developed and validated a statistical model that showed a modest AUC for AIN diagnosis, which improved upon the addition of urine biomarkers. Future studies could evaluate this model and biomarkers to identify unrecognized cases of AIN.