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Background Clinical trials initiated during emerging infectious disease outbreaks must quickly enroll participants to identify treatments to reduce morbidity and mortality. This may be at odds with enrolling a representative study population especially when the population affected is undefined. Methods We evaluated the utility of CDC COVID-19-Associated Hospitalization Surveillance Network (COVID-NET), COVID-19 Case Surveillance System (CCSS), and 2020 U.S. Census data to determine demographic representation in the four stages of the Adaptive COVID-19 Treatment Trial (ACTT). We compared the cumulative proportion of participants by sex, race, ethnicity, and age enrolled at U.S. ACTT sites, with the respective 95% confidence intervals, to the reference data in forest plots. Results U.S. ACTT sites enrolled 3,509 adults hospitalized with COVID-19. When compared with COVID-NET, ACTT enrolled a similar or higher proportion of Hispanic or Latino and White participants depending on the stage, and a similar proportion of African American participants in all stages. In contrast, ACTT enrolled a higher proportion of these groups when compared with U.S. Census and CCSS. The proportion of participants ages 65 years was either similar or lower than COVID-NET and higher than CCSS and U.S. Census. The proportion of females enrolled in ACTT was lower than the proportion of females in the reference datasets. Conclusions While surveillance data of hospitalized cases may not be available early in an outbreak, it is a better comparator than U.S. Census data and surveillance of all cases, which may not reflect the population affected and at higher risk of severe disease.

Background Texas has one of the highest rates of uninsured populations in the US (Fig1). We hypothesized that those without insurance were more likely to be hospitalized for COVID-19 but were at higher risk for severe disease and mortality due to uncontrolled medical illness prior to hospitalization. Methods We conducted a retrospective analysis of patients hospitalized in 81 hospitals in Dallas- Ft. Worth (DFW) area. All inpatients and outpatients with COVID-19 from 3/1/2020 to 4/1/2021 were included to examine risk for hospitalization. Subset analysis included those hospitalized with COVID-19. Data presented is comparison between Medicaid and uninsured population as they were similar in age distribution (Fig 2). Results 198, 174 COVID-19 unique individuals were identified; 7.5% had Medicaid and 25.1% were uninsured. Among Medicaid, 29.6% were hospitalized vs. 25.8% of Medicaid vs. uninsured (adjusted odds ratio (aOR) 1.26 (1.20, 1.33). Among the 71,778 (∼36%) hospitalized for COVID-19, comparing Medicaid vs. uninsured: 44.9 vs. 56.3% had ICU care; 51.4% vs. 80.2% had pneumonia, 41.1% vs. 67.5% had respiratory failure with higher odds of developing these outcomes in uninsured (see Table 1). Median duration of hospital stay was longer in uninsured than Medicaid ( see Table 2a) and mortality in the hospital was 7.4% vs. 7.9% among Medicaid vs. uninsured (p< 0.0001). Thirty-day readmission rates were lower for Medicaid vs. uninsured (Table 3). In the year prior to the COVID-19 hospitalizations, use of health care among the uninsured was lower compared to Medicaid patients for outpatient, inpatient, and ER visits (p< 0.0001 for all comparisons; see Table 2b). Conclusion Uninsured in North Texas had lower odds of hospitalization vs. Medicaid patients, likely due to younger age, but once hospitalized had higher risk for COVID pneumonia, ICU care, and respiratory failure. Median number of days was higher among uninsured but in-hospital mortality was higher among Medicaid population. Prior contact with healthcare system was lower among uninsured and 30-day readmissions were also lower, suggesting barriers accessing health care. Poorer outcomes among uninsured once hospitalized may be due to untreated co-morbidities (Fig 3). Expansion of Medicaid has the potential of ameliorating these disparities. Figure 3Conclusion graphic Disclosures Mamta K. Jain, MD, MPH, Gilead Sciences: Grant/Research Support|GSK/ViiV: Grant/Research Support|Janssen: Grant/Research Support|Merck: Grant/Research Support|Regeneron: Grant/Research Support Mae Thamer, PhD, Gilead: Grant/Research Support Kavita Bhavan, MD, MPH, Gilead: Grant/Research Support.

Background Direct-acting antivirals can cure hepatitis C virus (HCV). Persons with HCV/HIV and living with substance use are disadvantaged in benefiting from advances in HCV treatment. Methods In this randomized controlled trial, participants with HCV/HIV were randomized between February 2016 and January 2017 to either care facilitation or control. Twelve-month follow-up assessments were completed in January 2018. Care facilitation group participants received motivation and strengths-based case management addressing retrieval of HCV viral load results, engagement in HCV/HIV care, and medication adherence. Control group participants received referral to HCV evaluation and an offer of assistance in making care appointments. Primary outcome was number of steps achieved along a series of 8 clinical steps (eg, receiving HCV results, initiating treatment, sustained virologic response [SVR]) of the HCV/HIV care continuum over 12 months postrandomization. Results Three hundred eighty-one individuals were screened and 113 randomized. Median age was 51 years; 58.4% of participants were male and 72.6% were Black/African American. Median HIV-1 viral load was 27 209 copies/mL, with 69% having a detectable viral load. Mean number of steps completed was statistically significantly higher in the intervention group vs controls (2.44 vs 1.68 steps; χ 2 [1] = 7.36, P = .0067). Men in the intervention group completed a statistically significantly higher number of steps than controls. Eleven participants achieved SVR with no difference by treatment group. Conclusions The care facilitation intervention increased progress along the HCV/HIV care continuum, as observed for men and not women. Study findings also highlight continued challenges to achieve individual-patient SVR and population-level HCV elimination. Clinical Trials Registration NCT02641158., In this era of direct-acting antivirals that can cure hepatitis C virus (HCV) infection in 1–3 months, a short-term care facilitation intervention for persons living with HIV and HCV who use drugs is efficacious in making meaningful advances toward HCV cure.

Background New therapies to achieve hepatitis B surface antigen (HBsAg) clearance are under development. However, gaps in knowledge exist in understanding the incidence and predictors of HBsAg clearance in a racially diverse HIV population. Methods We examined the incidence and risk of HBsAg clearance in a retrospective cohort of people with HIV/hepatitis B virus (HBV). Included patients had sufficient data to establish chronic infection based on Centers for Disease Control and Prevention guidelines. We examined the incident rate for HBsAg loss and hazard rate ratios to evaluate predictors for HBsAg clearance in a multivariable model. Results Among 571 HIV/HBV patients, 87% were male, 61% were Black, 45% had AIDS, 48% were HBeAg positive, and the median follow-up was 88 months. Incident HBsAg clearance was 1.5 per 100 person-years. In the multivariate model, those with AIDS at baseline (adjusted hazard ratio [aHR], 2.43; 95% CI, 1.37–4.32), Hispanics (aHR, 3.57; 95% CI, 1.33–9.58), and those with injection drug use as an HIV risk factor (aHR, 3.35; 95% CI, 1.26–8.89) were more likely to lose HBsAg, whereas those who were HBeAg positive (aHR, 0.34; 95% CI, 0.19–0.63) were less likely to lose HBsAg. The median change in CD4 cell count during the observation period was 231 cells/mm3 in those with HBsAg loss vs 112 cells/mm3 in those with HBsAg persistence (P = .004). Conclusions HBsAg loss occurs in about 10% of those with chronic HBV infection. Being Hispanic, having AIDS at baseline, having an injection drug use history, and having HBeAg-negative status at baseline predicted the likelihood of HBsAg loss. Immune restoration may be a mechanism through which HBsAg loss occurs in HIV patients.

Background Remdesivir (RDV) shortens time to recovery time in patients with severe COVID-19. Its effect in patients with moderate COVID-19 remains unclear. Methods We conducted an open-label, phase 3 trial (NCT04252664) involving hospitalized patients with confirmed SARS-CoV-2 infection, evidence of pulmonary infiltrates, and oxygen saturation >94% on room air. Patients were randomly assigned 1:1:1 to receive up to 5d or 10d of RDV with standard of care (SoC), or SoC alone; patients could be discharged prior to completing per-protocol assigned treatment duration. RDV was dosed intravenously at 200 mg on d1, 100 mg daily thereafter. Patients were evaluated daily while hospitalized, and via telephone if discharged. The primary endpoint was clinical status on d11 assessed on a 7-point ordinal scale. Results regarding the primary endpoint are expected to be published before IDWeek 2020; we plan to present d28 results at the meeting. Results In total, 584 patients underwent randomization and started their assigned treatment (191, 5d RDV; 193, 10d RDV; 200, SoC). By d11, ³ 2 point improvement on the ordinal scale occurred in 70% of patients in the 5d arm, 65% in the 10d arm, and 61% in the SoC arm. Patients in the 5d RDV arm were significantly more likely to have an improvement in clinical status than those receiving SoC (odds ratio [OR], 1.65; 95% confidence interval [CI], 1.09–2.48; P=0.017); OR of improvement for the 10d RDV arm compared to SoC was 1.31 (95% CI, 0.88–1.95]; p=0.183). This improvement in the 5-day arm over the SOC arm was noted from d6 through d11. We observed a peak of discharges corresponding with the assigned treatment duration of RDV, with increased discharges at d6 in the 5-day arm and at d11 in the 10-day arm. A worsening of clinical status of ≥ 1 point in the ordinal scale was observed more commonly in the SoC am (n=19, 10%) versus the 5d RDV (n=7, 4%) and 10d RDV (n=9, 5%). Conclusion RDV for up to 5 days was superior to SoC in improving the clinical status of patients with moderate COVID-19 by d11. We will report d28 outcomes at the meeting. Disclosures Francisco M. Marty, MD, Allovir (Consultant)Amplyx (Consultant)Ansun (Scientific Research Study Investigator)Avir (Consultant)Cidara (Scientific Research Study Investigator)F2G (Consultant, Scientific Research Study Investigator)Kyorin (Consultant)Merck (Consultant, Grant/Research Support, Scientific Research Study Investigator)New England Journal of Medicine (Other Financial or Material Support, Honorarium for Video)Regeneron (Consultant, Scientific Research Study Investigator)ReViral (Consultant)Scynexis (Scientific Research Study Investigator)Symbio (Consultant)Takeda (Scientific Research Study Investigator)United Medical (Consultant)WHISCON (Scientific Research Study Investigator) Prashant Malhotra, MD, Gilead Sciences Inc. (Scientific Research Study Investigator) Robert L. Gottlieb, MD, Gilead Sciences Inc. (Scientific Research Study Investigator) Karen T. Tashima, MD, Bristol-Myers Squibb (Research Grant or Support)Gilead Sciences Inc. (Grant/Research Support, Scientific Research Study Investigator)GlaxoSmithKline (Research Grant or Support)Merck (Research Grant or Support)Tibotec (Research Grant or Support)Viiv Healthcare (Research Grant or Support) Massimo Galli, MD, Gilead Sciences Inc. (Grant/Research Support, Scientific Research Study Investigator, Advisor or Review Panel member, Other Financial or Material Support, Personal fees) Louis Yi Ann Chai, MD, Gilead Sciences Inc. (Scientific Research Study Investigator) Devi SenGupta, MD, Gilead Sciences Inc. (Employee, Shareholder) Robert H. Hyland, MD, Gilead Sciences Inc. (Employee, Shareholder) Hongyuan Wang, PhD, Gilead Sciences Inc. (Employee, Shareholder) Lijie Zhong, PhD, Gilead Sciences Inc. (Employee, Shareholder) Huyen Cao, MD, Gilead Sciences Inc. (Employee, Shareholder) Anand Chokkalingam, PhD, Gilead Sciences (Employee) Anu Osinusi, MD, Gilead Sciences (Employee) Diana M. Brainard, MD, Gilead Sciences (Employee) Michael Brown, MD, Gilead Sciences Inc. (Scientific Research Study Investigator) Ane Josune Goikoetxea, MD, Gilead Sciences Inc. (Scientific Research Study Investigator) Mamta Jain, MD, Gilead Sciences Inc. (Scientific Research Study Investigator, Research Grant or Support)GlaxoSmithKline (Advisor or Review Panel member)Janssen (Research Grant or Support)Merck (Research Grant or Support) David Shu Cheong Hui, MD, Gilead Sciences Inc. (Scientific Research Study Investigator) Enos Bernasconi, MD, Gilead Sciences Inc. (Scientific Research Study Investigator) Christoph Spinner, MD, AbbVie (Advisor or Review Panel member, Other Financial or Material Support, Travel)Bristol-Myers Squibb (Grant/Research Support, Advisor or Review Panel member, Other Financial or Material Support, Travel)Gilead Sciences Inc. (Grant/Research Support, Scientific Research Study Investigator, Advisor or Review Panel member, Other Financial or Material Support, Travel)Janssen (Grant/Research Support, Advisor or Review Panel member, Other Financial or Material Support, Travel)MSD (Grant/Research Support, Advisor or Review Panel member, Other Financial or Material Support, Travel)Viiv Healthcare (Grant/Research Support, Advisor or Review Panel member, Other Financial or Material Support, Travel)

Background Hepatitis B virus (HBV) coinfection is common in people with HIV. Compared with HBV mono-infected individuals, those that are HIV/HBV coinfected show evidence of more rapid progression to advanced liver disease (ALD) and increased mortality rate. In this study, we identified characteristics in an HIV/HBV cohort associated with ALD. Methods We retrospectively examined an HIV/HBV coinfected cohort to determine the prevalence of ALD and its correlation with selected variables. Data were drawn from HIV and HBsAg+ patients at three HIV clinics in Houston, Dallas, and San Antonio, Texas. Those without chronic HBV were excluded. ALD was defined as cirrhosis, decompensation, and/or hepatocellular carcinoma, as determined by imaging. Variables included demographics, HIV risk factors, comorbidities, HBsAg loss, HepBeAg, CD4+ count, HBV DNA, and HIV RNA viral load. Bivariate analysis was performed using chi-square and student t-test as appropriate; a logistic regression model was used to identify independent associations among significant variables (STATA). Results Within those with HIV/HBV coinfection (n = 501), 89 (18%) met the criteria for ALD (92% male, 47% Black, 33% White, 16% Hispanic, 73% >40 years old). Amongst these (n = 89), significant differences were observed with race (P = 0.039), age (P = 0.001), patients identified as MSM/Bisexuals (P = 0.047), diabetes mellitus (DM) (P = 0.01) and hepatitis C virus (HCV) coinfection (P ≤ 0.001). Compared with Whites, Blacks are less likely to have ALD (95% CI 0.27, 0.79, P = 0.004), and those age 40–49 (95% CI 1.28, 10.92, P = 0.016) and >50 (95% CI 1.63, 15.54, P = 0.005) were more likely. The multivariate logistic regression analysis showed patients that are White race, age >50, have DM, and those with HCV coinfection had increased risk for ALD (Table 1). No differences were seen with gender, insurance, alcohol use, HBsAg loss, HepBeAg status or baseline CD4+ count, HBV DNA, HIV RNA, and AIDS. Conclusion Increased monitoring for the presence of ADL should be conducted in HIV/HBV coinfection. Particular attention and surveillance should be paid to those with the following risk factors: Whites, elder age (>50), and comorbidities of DM and HCV. These should be taken into consideration when approaching the development and treatment of ADL in HIV/HBV patients. Disclosures All authors: No reported disclosures.

Background Few HIV clinics train HIV providers on initiation of HCV treatment for their coinfected patients. We sought to evaluate the changes in comfort levels, attitude and knowledge of HCV evaluation and treatment among providers over time and track the uptake of treatment initiation. Methods Our program was implemented in an urban, Ryan-White outpatient clinic in 2018. Providers were given initial didactics and completed a survey that assessed their level of comfort and knowledge with treating HCV. We developed pocket cards and flow charts to help providers navigate the HCV cascade to cure. Additional training one year later through presentations and case-based discussions was given and the same survey was conducted again. We evaluated the number of HCV evaluation visits in 2018 and prescriptions written for HCV treatment. Results The first survey was completed by 21 and the second by 20 out of 28 providers; 70% attended at least one of the trainings. After the initial training, 38% of providers felt “confident” about discussing liver disease progression which increased to 50% after the second training. Similarly, 48% of providers felt “somewhat comfortable” talking about HIV/HCV drug interactions which increased to 80%. 33% of providers noted they were “not comfortable” discussing drug interactions which decreased to 15%. Approximately 1/3 providers felt “confident” talking about HCV treatment in both surveys. About 20% of providers disagreed with treating HIV/HCV patients with active substance use and this did not change. The median knowledge score was 7 (IQR) (6–9) after the first survey and changed to 8(5–9) after the second. Between 2018–2019, 81 HCV evaluation visits were scheduled and 64 (80% Male, 53% Black, 39% uninsured, 23% Medicaid, 73% with history of substance use) were completed and 69% of patients were prescribed HCV treatment. Among all HIV providers, 89% completed a median of 1 (1–3) HCV evaluation visits, and 71% prescribed treatment a median of 1 (1–2). Conclusion Implementation of HCV training to all HIV providers requires continued education and resulted in the initiation of HCV evaluation and treatment amongst the large majority of HIV providers who had previously never treated HCV. Disclosures All authors: No reported disclosures.

Background In 2016, we implemented a hepatitis C (HCV) screening program for baby boomers (BB) born between 1945 and 1965) using a best practice alert (BPA) in the electronic medical record and patient navigation (PN) in our safety-net hospital system. We now examine barriers to HCV treatment among those who received PN for linkage to care (LTC). Methods The BPA prompts providers to order a HCV antibody (Ab) for any unscreened BB who has an outpatient appointment. Those with HCV Ab+ with a confirmatory RNA receive telephone navigation, using a pre-defined script, if LTC did not occur within 2 months of RNA testing. After LTC, a person was considered as untreated if HCV treatment had not occurred within 1 year of initial visit. Insured patients received treatment through prior authorizations and uninsured through pharmaceutical patient assistance programs. We examined demographics, homelessness, insurance, fibrosis score, substance use, and psychiatric illness, as potential predictors to treatment initiation using univariate and multivariate logistic regression analysis. Results Among the 16,363 BBs screened from March 1, 2016 to December 31, 2017, 1,445 (8.8%) were HCV Ab+ and 1,038 (72%) had HCV RNA completed. Among the 724 (5%) with confirmed HCV infection, 139 (19%) received LTC without navigation, 299 (41%) received navigation, and 286 (40%) could not be contacted after three attempts. Among those who received navigation, 225 (75%) completed a follow-up visit of which 81 (36%) did not start treatment, 34 (15%) are awaiting treatment initiation, and 110 (49%) started treatment. Gender, race/ethnicity, psychiatric illness, and homelessness were not predictive of starting HCV treatment. In univariate analysis, current substance use vs. none/past use (OR 0.52 (0.29, 0.93)) was associated lower likelihood of starting treatment and advanced fibrosis (OR 2.25 (1.20, 4.21)) was associated with higher likelihood of starting treatment). Compared with uninsured patients, Medicaid patients were less likely to start treatment (AOR 0.15 (0.67, 0.34)) in a multivariate analysis. Conclusion Insurance status was independent predictor of starting treatment among patients at our safety-net hospital. Medicaid remains a barrier to HCV treatment access in safety-net systems. Disclosures A. Singal, Gilead Sciences: Grant Investigator, Research support. M. K. Jain, Gilead Sciences: Grant Investigator, Grant recipient and Research support. Janssen: Investigator, Research support. GSK/ViiV: Investigator, Consulting fee and Research support. Merck: Investigator, Research support

Background HCV treatment has increased since direct-acting antivirals became available. HIV clinics are scaling-up treatment to eradicate HCV. Little is known about HIV patients and access to HCV treatment. Methods We retrospectively analyzed all HIV/HCV co-infected patients within our safety-net hospital system who received outpatient care in our HIV clinics from November 1, 2015 to October 31, 2017. Data were abstracted on demographics, insurance, HIV RNA, drug use, homelessness, and number of visits. No visits for >1 year was lost to care and missed visits was missing >1 primary care visit. We examined the association of these variables to risk of having a detectable HCV RNA (surrogate marker for HCV treatment) through multivariate logistic regression. Results We identified 914 with HIV/HCV (72% male, 55% Black, 35% Medicaid, 29% Medicare, 16% Ryan White, 13% homeless) of which 47% were heterosexual, 36% MSM, and 14% IDU. HIV was undetectable in 74%, 69% were between age 46 and 65, 17% had active alcohol use and 33% had drug use. HCV RNA was available for 868 and was detected in (57%). Whites and Hispanics compared with Blacks were less likely to have detectable HCV RNA. Detectable HCV RNA was more likely in those >50 years of age compared with 1 missed visit, and lost to care. Conclusion We found that those at risk for not being treated for HCV were Blacks, older patients and those not engaged in HIV care or not suppressed on HIV treatment. To achieve HCV eradication will require efforts to engage older patients, Blacks, those noncompliant with ART, and not engaged in HIV care. HCV Virus Detected (n = 497) HCV Virus Undetected (n = 371) AOR (95% CI) Race/ethnicity Black 311 (63) 171 (46) Reference White 126 (25) 116 (31) 0.54 (0.38, 0.77)*** Hispanic 56 (11) 70 (19) 0.55 (0.35, 0.86** Other 4 (0.8) 14 (4) 0.25 (0.08, 0.81)** Age group < 40 56 (11) 63 (17) Reference 40–49 84 (17) 78 (21) 1.35 (0.80, 2.25) 50–59 229 (46) 146 (39) 1.83 (1.14, 2.91)* > = 60 128 (60) 84 (23) 2.29 (1.35, 3.89)* HIV detected no 341 (69) 310 (84) Reference yes 156 (31) 61 (28) 1.92 (1.33, 2.76) *** Lost to care no 395 (79) 336 (91) Reference yes 102 (21) 35 (9) 2.17(1.39, 3.39)*** Missed >1 visit No 214 (43.1) 229(61.7) reference Yes 283 (56.9) 142 (38.3) 1.77 (1.31, 2.39)*** *** P < 0.001 P < 0.01 P < 0.05. Disclosures M. K. Jain, Gilead Sciences: Grant Investigator, Grant recipient and Research support. Janssen: Investigator, Research support. GSK/ViiV: Investigator, Consulting fee and Research support. Merck: Investigator, Research support.

Background Those in safety-net hospitals are especially vulnerable to lack of access to specialty care and curative treatment (Tx) for hepatitis C (HCV). We examined predictors of receiving HCV Tx in safety-net hospitals. Methods We retrospectively examined all adults who received care 1/1/11–2/28/17 in our two safety-net hospitals in California and Texas and had a diagnosis of HCV. We examined age, race/ethnicity, gender, insurance status, body mass index, liver-related complications (heptocelluar cancer (HCC), cirrhosis, ascites, non-alcoholic fatty liver disease, (NAFLD) hepatic encephalopathy, variceal bleeding) non-liver related co-morbidities [HIV, non-HCC cancers, mental health, cardiovascular disease (CVD), hypertension (HTN), diabetes (DM)] alcohol use, and drug use. We evaluated the predictors of receiving HCV Tx using multivariate logistic regression models. Results Among 14,776 HCV patients in the study, most of the HCV patients (61% male, 43% Black, and 16% Hispanic) had Indigent care (42%), Medicare (14%), and Medicaid (27%). Co-morbidities in this population included mental health (71%), CVD (60%), HTN (51%), DM (21%), and HIV (9%). Indigent care, mental health and CVD co-morbidities were associated with lower rates of HCV Tx but Other race and NAFLD were associated with higher rates of Tx (see Table 1). Table 1: Logistic regression results predicting HCV TX in Safety Net Hospitals in Texas and California Total (n = 14,776) N (%) HCV TX (n = 646) N (%) OR 95% Confidence Interval White (ref) Black 6339 (42.9) 245 (37.9) 0.98 0.80–1.19 Other 152 (1.0) 34 (5.3) 1.52 1.10–2.09 Hispanic 2330 (15.8) 120 (18.6) 1.07 0.84-1.35 Commercial (ref) Medicare 2032 (13.8) 87 (13.5) 0.70 0.47-1.04 Medicaid 3927 (26.6) 162 (25.1) 0.78 0.55-1.10 Indigent 6217 (42.1) 244 (37.8) 0.63 0.44-0.88 HIV 1380 (9.3) 71 (11) 1.29 0.98-1.68 Mental Health 10,507 (71.1) 403 (62.4) 0.82 0.67-0.99 NAFLD 443 (3) 26 (4) 1.65 1.04-2.61 Cirrhosis 2631 (17.8) 122 (18.9) 0.96 0.76-1.22 CAD 8820 (59.7) 297 (46) 0.56 0.47-0.68 Conclusion In our safety-net hospitals only 4.4% received HCV Tx. Those with indigent funding, mental illness or cardiac disease were significantly less likely to receive HCV Tx. These data indicate that certain populations are less likely to have access to HCV care. The reasons for this remains unknown. If we are to move towards HCV elimination, we must find strategies to increase access. Disclosures M. K. Jain, Gilead: Grant Investigator, Research grant and Research support. Merck: Grant Investigator, Research grant. R. Wong, Gilead Sciences: Grant Investigator, Research grant