Person:
Jin, Xin

Profile Picture

Email Address

AA Acceptance Date

Birth Date

Research Projects

Organizational Units

Job Title

Last Name

Jin

First Name

Xin

Name

Jin, Xin

Filters

2020 - 20214

Settings

Author's Publications: search.filters.isAuthorOfPublication.d6f956c0-8aec-4a95-bf29-fabb0a3547a5

Search Results

Now showing 1 - 4 of 4
  • No Thumbnail Available
    Publication
    Population-scale longitudinal mapping of COVID-19 symptoms, behaviour and testing
    (Springer Science and Business Media LLC, 2020-08-26) Allen, William; Altae-Tran, Han; Briggs, James; Jin, Xin; McGee, Glen; Shi, Andy; Raghavan, Rumya; Kamariza, Mireille; Nova, Nicole; Pereta, Albert; Danford, Chris; Kamel, Amine; Gothe, Patrik; Milam, Evrhet; Aurambault, Jean; Primke, Thorben; Li, Weijie; Inkenbrandt, Josh; Huynh, Tuan; Chen, Evan; Lee, Christina; Croatto, Michael; Bentley, Helen; Lu, Wendy; Murray, Robert; Travassos, Mark; Coull, Brent; Openshaw, John; Greene, Casey S.; Shalem, Ophir; King, Gary; Probasco, Ryan; Cheng, David R.; Silbermann, Ben; Zhang, Feng; Lin, Xihong
    Despite the widespread implementation of public health measures, COVID-19 continues to spread in the United States. To facilitate an agile response to the pandemic, we developed How We Feel, a web and mobile application that collects longitudinal self-reported survey responses on health, behavior, and demographics. Here we report results from over 500,000 users in the United States from April 2, 2020 to May 12, 2020. We show that self-reported surveys can be used to build predictive models to identify likely COVID-19 positive individuals. We find evidence among our users for asymptomatic or presymptomatic presentation, show a variety of exposure, occupation, and demographic risk factors for COVID-19 beyond symptoms, reveal factors for which users have been SARS-CoV-2 PCR tested, and highlight the temporal dynamics of symptoms and self-isolation behavior. These results highlight the utility of collecting a diverse set of symptomatic, demographic, exposure, and behavioral self-reported data to fight the COVID-19 pandemic.
  • No Thumbnail Available
    Publication
    Corticosterone inhibits GAS6 to govern hair follicle stem-cell quiescence
    (Springer Science and Business Media LLC, 2021-03-31) Choi, Sekyu; Zhang, Bing; Ma, Sai; Gonzalez Celeiro, Meryem; Stein, Daniel; Jin, Xin; Kim, Seung Tea; Kang, Yuan-Lin; Besnard, Antoine; Rezza, Amelie; Grisanti, Laura; Buenrostro, Jason; Rendl, Michael; Nahrendorf, Matthias; Sahay, Amar; Hsu, Ya-chieh
    Chronic, sustained exposure to stressors can profoundly impact tissue homeostasis, although the mechanisms by which these changes occur are largely unknown. Here, we report the adrenal gland-derived stress hormone corticosterone (the rodent equivalent of cortisol) regulates hair follicle stem cell (HFSC) quiescence and hair growth in mice. Without systemic corticosterone, HFSCs enter substantially more rounds of the regeneration cycle throughout life. Conversely, under chronic stress, elevated corticosterone levels prolong HFSC quiescence and keep hair follicles in an extended resting phase. Mechanistically, corticosterone acts on dermal papilla (DP) to suppress the expression of a secreted factor, Growth Arrest Specific 6 (Gas6). Restoring Gas6 expression overcomes stress-induced inhibition of HFSC activation and hair growth. Our work identifies corticosterone as a systemic inhibitor of HFSC activity via its impact on the niche, and demonstrates that removal of such inhibition drives HFSCs into frequent regeneration cycles with no observable defects long-term.
  • No Thumbnail Available
    Publication
    Clinical Validation of a Cas13-Based Assay for the Detection of SARS-CoV-2 RNA
    (2020-08-26) Petchsung, Maturada; Jantarug, Krittapas; Pattama, Archiraya; Aphicho, Kanokpol; Suraritdechachai, Surased; Meesawat, Piyachat; Sappakhaw, Khomkrit; Leelahakorn, Nattawat; Ruenkam, Theerawat; Wongsatit, Thanakrit; Athipanyasilp, Niracha; Eiamthong, Bhumrapee; Lakkanasirorat, Benya; Phoodokmai, Thitima; Niljianskul, Nootaree; Pakotiprapha, Danaya; Chanarat, Sittinan; Homchan, Aimorn; Tinikul, Ruchanok; Kamutira, Philaiwarong; Phiwkaow, Kochakorn; Soithongcharoen, Sahachat; Kantiwiriyawanitch, Chadaporn; Pongsupasa, Vinutsada; Trisrivirat, Duangthip; Jaroensuk, Juthamas; Wongnate, Thanyaporn; Maenpuen, Somchart; Chaiyen, Pimchai; Kamnerdnakta, Sirichai; Swangsri, Jirawat; Chuthapisith, Suebwong; Sirivatanauksorn, Yongyut; Chaimayo, Chutikarn; Sutthent, Ruengpung; Kantakamalakul, Wannee; Joung, Julia; Ladha, Alim; Jin, Xin; Gootenberg, Jonathan; Abudayyeh, Omar; Zhang, Feng; Horthongkham, Navin; Uttamapinant, Chayasith; Uttamapinant
  • No Thumbnail Available
    Publication
    Population-scale Longitudinal Mapping of COVID-19 Symptoms, Behaviour, and Testing
    (Nature Human Behaviour, 2020-08-26) Allen, William E.; Altae-Tran, Han; Briggs, James; Jin, Xin; McGee, Glen; Shi, Andy; Raghavan, Rumya; Kamariza, Mireille; Nova, Nicole; Pereta, Albert; Danford, Chris; Kamel, Amine; Gothe, Patrik; Milam, Evrhet; Aurambault, Jean; Primke, Thorben; Li, Weijie; Inkenbrandt, Josh; Huynh, Tuan; Chen, Evan; Lee, Christina; Croatto, Michael; Bentley, Helen; Lu, Wendy; Murray, Robert; Travassos, Mark; Coull, Brent; Openshaw, John; Greene, Casey S.; Shalem, Ophir; King, Gary; Probasco, Ryan; Cheng, David R.; Silbermann, Ben; Zhang, Feng; Lin, Xihong
    Despite the widespread implementation of public health measures, COVID-19 continues to spread in the United States. To facilitate an agile response to the pandemic, we developed How We Feel, a web and mobile application that collects longitudinal self-reported survey responses on health, behavior, and demographics. Here we report results from over 500,000 users in the United States from April 2, 2020 to May 12, 2020. We show that self-reported surveys can be used to build predictive models to identify likely COVID-19 positive individuals. We find evidence among our users for asymptomatic or presymptomatic presentation, show a variety of exposure, occupation, and demographic risk factors for COVID-19 beyond symptoms, reveal factors for which users have been SARS-CoV-2 PCR tested, and highlight the temporal dynamics of symptoms and self-isolation behavior. These results highlight the utility of collecting a diverse set of symptomatic, demographic, exposure, and behavioral self-reported data to fight the COVID-19 pandemic.