Q.D. Research

This study determines if there is a correlation between rising carbon dioxide levels and global warming. Historical data were reviewed from three different time periods spanning 500 million years. It showed that the curves and trends were too dissimilar to establish a connection. Observations from CO2/temp ratios showed that the CO2 and the temperature moved in opposite directions 42% of the time. Many ratios displayed zero or near zero values, reflecting a lack of response. As much as 87% of the ratios revealed negative or near zero values, which strongly negate a correlation. The infrared spectra showed the Greenhouse Gases had an exceptionally low absorption band between 11.67 μm to 9.1 μm, which is a zone called the infrared atmospheric window. Most of the Greenhouse Gases absorb little infrared inside that zone. And that zone is where the Earth's surface emits almost all infrared radiation. Even with minimal absorbance, water vapor captures the most infrared radiation. It absorbs 84 times more than CO2, 407 thousand times more than methane, 452 thousand times more than ozone and 2.3 million times more than nitrous oxide. The Intergovernmental Panel on Climate Change (IPCC) and the United States EPA excluded water vapor because it was not associated with man-made activities. They reported that water vapor and clouds were simply feedback mechanisms from CO2. Clouds reflect radiation from the sun. The Northern Hemisphere is 2.7°F warmer than the Southern Hemisphere because of clouds. The world cloud cover has gone down 4.1% from 1982 to 2018. Calculations show that this could be responsible for 2.4°F of the 2.7°F. The research shows that most of the recent increase in temperature (89.9%) is because of fewer clouds.

This study seeks to identify and characterize key barriers associated with PrEP therapy as self-reported by users on social media platforms. We used data mining and unsupervised machine learning approaches to collect and analyze COVID-19 and PrEP-related posts from three social media platforms including Twitter, Reddit, and Instagram. Predominant themes detected by unsupervised machine learning and manual annotation included users expressing uncertainty about PrEP treatment adherence due to COVID-19, challenges related to accessibility of clinics, concerns about PrEP costs and insurance coverage, perceived lower HIV risk leading to lack of adherence, and misinformation about PrEP use for COVID-19 prevention.

Background: Heart failure (HF) caregivers (CGs) may experience stress due to unpredictable patient disease trajectory. Interleukin-6 (IL-6) and high-sensitivity C-reactive protein (hs-CRP) are reported to be elevated in dementia CGs, but less is known about HF CGs response to stress or relationships with social determinants of health such as education and race. Objective: To determine if social factors (race, education) modify the relationship between patient disease severity trajectory (NYHA class) and CG stress as measured by inflammatory biomarkers of IL-6 and hs-CRP among HF CGs. Methods: A secondary analysis was conducted using baseline data from Caregiver Opportunities for Optimizing Lifestyle (COOL) trial. Education was dichotomized into technical/high school or less versus college or post-graduate degree. Race was dichotomized as African American (AA) or White/Asian due to the low number of Asian CGs. Two-way analysis of variance tests were conducted for IL-6 and hs-CRP to test interaction effects between CG perceived NYHA class and education or race. Results: Participants (n=127) were predominantly female (n=117, 92.1%), AA (n=74, 58.3 %), and the majority (70.1%) reported a college or post-graduate education. Perceived NYHA class were reported as: I (n=32, 25.6%), II (n=31, 24.8%), III (n= 47, 37.6%), and IV (n=15, 12.0%). For hs-CRP, there was a significant interaction between race and NYHA class (p=.013). Higher hs-CRP levels were noted in class II, compared to class I or class III for AA CGs (p=.004). White/Asian CGs did not have significant hs-CRP differences between NYHA classes (p=.634). While similar trends were observed for IL-6 between race and NYHA class, it did not reach statistical significance. In the model for education and perceived disease severity, NYHA class was significant (p=.038) for hs-CRP. In the post hoc test for NYHA class, hs-CRP was highest among NYHA class II (p<.02) for lower education levels. For IL-6, NYHA class was significant (p=.047); but the interaction effect with education was not significant (p=.687). Conclusions: Our findings suggest that CGs of AA race and/or lower education may experience higher stress earlier in the HF disease trajectory and may therefore benefit from targeted CG support interventions.

ObjectiveThe objective of this study was to develop an inductive coding approach specific to characterizing user-generated social media conversations about transition of use of different tobacco and alternative and emerging tobacco products (ATPs).ResultsA total of 40,206 tweets were collected from the Twitter public API stream that were geocoded from 2018 to 2019. Using data mining approaches, these tweets were then filtered for keywords associated with tobacco and ATP use behavior. This resulted in a subset of 5718 tweets, with 657 manually annotated and identified as associated with user-generated conversations about tobacco and ATP use behavior. The 657 tweets were coded into 9 parent codes: inquiry, interaction, observation, opinion, promote, reply, share knowledge, use characteristics, and transition of use behavior. The highest number of observations occurred under transition of use (43.38%, n = 285), followed by current use (39.27%, n = 258), opinions about use (0.07%, n = 46), and product promotion (0.06%, n = 37). Other codes had less than ten tweets that discussed these themes. Results provide early insights into how social media users discuss topics related to transition of use and their experiences with different and emerging tobacco product use behavior.

With over 127 million cases globally, the COVID-19 pandemic marks a sentinel event in global health. However, true case estimations have been elusive due to lack of testing and diagnostic capacity, asymptomatic cases, and individuals who do not get tested or seek care. Concomitantly, new digital surveillance tools to detect, characterize, and report COVID-19 cases are emerging, including using structured and unstructured data from users self-reporting COVID-19-related experiences on the Internet and social media platforms. In this study, we develop and evaluate a hybrid unsupervised and supervised machine learning approach to detect self-reported COVID-19-related symptoms on Twitter during the early stages of the pandemic. Tweets were collected from the public API stream from March 3 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">rd</sup> -31 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">st</sup> 2020, filtered for COVID-19-related terms. We used the biterm topic model to cluster tweets into theme-associated groups for the first 18 days of tweets, which were then extracted and manually annotated to identify users self-reporting suspected COVID-19 symptoms or status. Using this manually annotated data as a training set, we used an XLNet deep learning model for classifying symptom-related tweets from a larger corpus and also evaluated model performance. From 4,492,954 tweets collected, the unsupervised learning process yielded 3,465 (<; 1%) symptom tweets used to form our ground-truth COVID-19 symptoms dataset (n = 11,550). The XLNet text classifier achieved the highest accuracy (.91) and f1 (.62) compared to baseline models evaluated for classification. After re-training with adjusted loss function, we boosted the classifier's precision to 0.81 while maintaining a high f1 (0.66), resulting in identification of an additional 2,622 symptom-related tweets when applied to an additional 11 days of tweets collected. Our study used a hybrid machine learning approach to enable high precision identification of Twitter user-generated COVID-19 symptom discussions. The model is a digital epidemiology tool that can identify social media users who self-report symptoms during the early periods of an outbreak.

58 Background: It is estimated that more than 40% of patients with mCRPC have functional loss of phosphatase and tensin homolog (PTEN) tumor suppressor gene, which is associated with unfavorable prognosis and reduced response to androgen receptor-targeting therapy. We describe patient characteristics and survival outcomes by PTEN LOF status among patients with mCRPC in real-world clinical practice. Methods: We conducted a retrospective cohort study using data from the nationwide Flatiron Health-Foundation Medicine mCRPC Clinico-Genomic database (FH-FMI CGDB), a de-identified database linking data derived from electronic health records with genomic data derived from FMI comprehensive genomic profiling (CGP) tests. The study included patients ≥18 years old, with a primary diagnosis of mCRPC between 1/1/2013 and 6/30/2019 who underwent FMI CGP testing and who had a valid PTEN LOF status. Patients were included if their PTEN report date and mCRPC diagnosis date occurred before death or censoring. PTEN LOF status was identified via FMI’s CGP testing. Kaplan-Meier (KM) methods assessed overall survival (OS) by PTEN LOF status from the date of mCRPC diagnosis (later of metastasis and castration resistance) until death or end of study follow-up. A stratified Cox regression model was used to estimate the hazard of death. The Cox model was adjusted for age, race and sequence of metastasis/CRPC diagnoses, and was stratified by the year of mCRPC diagnosis. Adjustments to account for left-truncation and survivorship bias were made in the KM analysis and the Cox regression model. Results: Among the 458 patients who met the eligibility criteria, 174 (38%) had PTEN LOF. The majority of the study sample (76%) was diagnosed with castration-resistance after metastasis. The PTEN LOF group had a higher percentage of white patients (80% vs. 68%; p= 0.01) compared to the PTEN non-LOF group. The mean age of the study sample was 68 years, and there was no difference in mean age at diagnosis by PTEN LOF status ( p= 0.17). Based on the KM estimates adjusted for left-truncation, the median OS was 14.3 months (95% confidence interval [CI]: 11.1-19.7) in the PTEN LOF group compared to 18.3 months (95%CI: 15.5-21.5) in the PTEN non-LOF group (log-rank p= 0.049). In the multivariable Cox model, the PTEN LOF group had numerically 30% higher risk of death compared to the PTEN non-LOF group (hazard ratio = 1.30; 95% CI: 0.99-1.71; p= 0.057). Conclusions: Among real-world patients with mCRPC in the CGDB, PTEN LOF could be associated with poorer survival outcomes, potentially highlighting the unmet need among these patients. Additional studies with larger cohorts are needed to better evaluate the survival outcomes of patients with PTEN LOF. Therapeutic agents acting on the PTEN/PI3K/AKT/mTOR pathway are being tested in clinical trials, and could potentially improve outcomes in this subgroup of patients with mCRPC.

A byproduct of computations performed by semiconductor devices is heat. Many compute intensive functions are currently performed in the cloud by hardware placed in data centers, where heat must be removed at additional energy cost. Granite Mountain Technologies is exploring an energy conserving model and technology for decentralizing computation from data centers to areas where heat is required. This compute model can provide vast energy savings benefits, while also redistributing monetary gains from large organizations capable of running data centers to individuals. We report on a decentralized computing appliance implemented with relative timed integrated circuits in the 12nm technology node. The appliance can accept calculations at a variable rate which allows heat created from computations to achieve the desired temperature of a room. The benefits of applying this approach to residential heating is evaluated.

Background: Hormone receptor positive (HR+), human epidermal growth factor receptor 2 negative (HER2-) is the most common biologic subtype of breast cancer. Although adjuvant therapy has demonstrated a survival benefit in clinical trials, its use is poorly understood in the real-world among elderly breast cancer patients since age is a barrier to receiving adjuvant therapy. An examination of treatment patterns and outcomes associated with receipt of adjuvant/neoadjuvant therapy among elderly HR + HER2-breast cancer patients was undertaken. Methods: There were 18,470 HR + HER2-breast cancer patients from the linked SEER-Medicare database. Patients were diagnosed with stage I-III disease between 1/1/2007-12/31/2011, ≥66 years, enrolled in Medicare Parts A, B and D, and underwent breast cancer surgery after diagnosis. Time-varying Cox proportional hazards regression assessed overall survival. Results: There were 13,670 (74%) patients treated with adjuvant/neoadjuvant therapy and 4800 (26%) untreated. Compared to treated patients, untreated patients were older, had earlier stage, lower grade, smaller tumors, poorer performance, higher comorbidity score, and less use of a 21-gene recurrence score (RS) assay (p n RS assay was associated with lower risks. The Cox model showed a 48% higher risk of death in untreated compared to treated patients. In a subset of 8967 patients with stage I disease, tumor size Conclusions: Older patients with favorable clinical characteristics (earlier stage, smaller tumor, lower grade) are less likely to be treated and have a higher risk of death compared to adjuvant/neoadjuvant treated patients. An unmet need among older breast cancer patients persists.

Effective leadership starts with communication. The ability to communicate can help healthcare leaders gain the trust and confidence of their entire team, leading to better employee engagement and patient outcomes. As the scope of roles, adoption of technology, and needs of the workforce evolve, it is important for leaders to have a tool kit to help them communicate strategically consistent messages to key stakeholders. This article presents an array of evidence-based tools to strengthen leadership skills and drive better, sustainable results for healthcare organizations.

The purpose of this study is to review the literature for Diabetes Self-Management Education and Support (DSMES) to ensure the National Standards for DSMES (Standards) align with current evidence-based practices and utilization trends. The 10 Standards were divided among 20 interdisciplinary workgroup members. Members searched the current research for diabetes education and support, behavioral health, clinical, health care environment, technical, reimbursement, and business practice for the strongest evidence that guided the Standards revision. Diabetes Self-Management Education and Support facilitates the knowledge, skills, and ability necessary for diabetes self-care as well as activities that assist a person in implementing and sustaining the behaviors needed to manage their condition on an ongoing basis. The evidence indicates that health care providers and people affected by diabetes are embracing technology, and this is having a positive impact of DSMES access, utilization, and outcomes. Quality DSMES continues to be a critical element of care for all people with diabetes. The DSMES services must be individualized and guided by the concerns, preferences, and needs of the person affected by diabetes. Even with the abundance of evidence supporting the benefits of DSMES, it continues to be underutilized, but as with other health care services, technology is changing the way DSMES is delivered and utilized with positive outcomes.