# contributed equally to the work Running title: FAM35A is a novel DNA repair factor.

Objective: To quantify preferences for attributes of potential analgesic treatments for moderate-to-severe pain associated with osteoarthritis (OA) and/or chronic low back pain (CLBP) as relevant to injectable nerve growth factor (NGF)einhibitors, nonsteroidal anti-inflammatory drugs (NSAIDs), and opioids. Methods: We used a discrete-choice experiment (DCE) to elicit preferences for attributes of OA and CLBP pharmaceutical treatments, and a best-worst scaling (BWS) exercise to further characterize the relative importance of treatment-related side-effect risks. The survey was completed online by 602 US residents with self-reported chronic, moderate-to-severe OA pain and/or CLBP who had tried, had contraindications for, or were unwilling to take currently available pharmaceutical therapies. In the DCE, respondents repeatedly chose between two hypothetical treatments defined by six attributes (symptom control; treatment-related risks of (1) severe joint problems, (2) heart attack, and (3) physical dependence; mode/frequency of administration; and cost). In the BWS exercise, respondents evaluated ten side-effect risks. Random-parameters logit models were estimated; conditional relative attribute importance, maximum acceptable risks, and willingness to pay were calculated. Results: The most important DCE attributes were improving symptom control (scaled conditional relative importance, 10.00) and reducing risk of physical dependence (6.99). The three most important BWS attributes were, in rank order, risks of stroke, physical dependence, and heart attack. Respondents were willing to accept a > 4% treatment-related risk of severe joint problems for even modest symptom improvement. Conclusion: A pharmaceutical treatment with a risk of severe joint problems was viewed as an acceptable alternative to other treatments with comparable efficacy but risks associated with NSAIDs or opioids.

DNA double‐strand breaks (DSBs) can be repaired by two major pathways: non‐homologous end‐joining (NHEJ) and homologous recombination (HR). DNA repair pathway choice is governed by the opposing activities of 53BP1, in complex with its effectors RIF1 and REV7, and BRCA1. However, it remains unknown how the 53BP1/RIF1/REV7 complex stimulates NHEJ and restricts HR to the S/G2 phases of the cell cycle. Using a mass spectrometry (MS)‐based approach, we identify 11 high‐confidence REV7 interactors and elucidate the role of SHLD2 (previously annotated as FAM35A and RINN2) as an effector of REV7 in the NHEJ pathway. FAM35A depletion impairs NHEJ‐mediated DNA repair and compromises antibody diversification by class switch recombination (CSR) in B cells. FAM35A accumulates at DSBs in a 53BP1‐, RIF1‐, and REV7‐dependent manner and antagonizes HR by limiting DNA end resection. In fact, FAM35A is part of a larger complex composed of REV7 and SHLD1 (previously annotated as C20orf196 and RINN3), which promotes NHEJ and limits HR. Together, these results establish SHLD2 as a novel effector of REV7 in controlling the decision‐making process during DSB repair.

Background The risk that COVID-19 patients develop critical illness that can be fatal depends on their age and immune status and may also be affected by comorbidities like hypertension. The goal of this study was to develop models that predict outcome using parameters collected at admission to the hospital. Methods and Results This is a retrospective single-center cohort study of COVID-19 patients at the Seventh Hospital of Wuhan City, China. Forty-three demographic, clinical and laboratory parameters collected at admission plus discharge/death status, days from COVID-19 symptoms onset and days of hospitalization were analyzed. From 157 patients, 120 were discharged and 37 died. Pearson correlations showed that hypertension and systolic blood pressure (SBP) were associated with death and respiratory distress parameters. A penalized logistic regression model efficiently predicts the probability of death with 13 of 43 variables. A regularized Cox regression model predicts the probability of survival with 7 of above 13 variables. SBP but not hypertension was a covariate in both mortality and survival prediction models. SBP was elevated in deceased compared to discharged COVID-19 patients. Conclusions Using an unbiased approach, we developed models predicting outcome of COVID-19 patients based on data available at hospital admission. This can contribute to evidence-based risk prediction and appropriate decision-making at hospital triage to provide the most appropriate care and ensure the best patient outcome. High SBP, a cause of end-organ damage and an important comorbid factor, was identified as a covariate in both mortality and survival prediction models.