Since December 2019, coronavirus disease 2019 (COVID‐19) has emerged as an international pandemic. COVID‐19 patients with myocardial injury might need special attention. However, an understanding on this aspect remains unclear. This study aimed to illustrate clinical characteristics and the prognostic value of myocardial injury to COVID‐19 patients.This retrospective, single‐center study finally included 304 hospitalized COVID‐19 cases confirmed by real‐time reverse‐transcriptase polymerase chain reaction from January 11 to March 25, 2020. Myocardial injury was determined by serum high‐sensitivity troponin I (Hs‐TnI). The primary endpoint was COVID‐19‐associated mortality.p < .001), procalcitonin levels , inflammatory and immune response markers; more frequent need for noninvasive ventilation, invasive mechanical ventilation; and was associated with higher mortality incidence  than those without myocardial injury. Myocardial injury , senior age, CRP levels, and novel coronavirus pneumonia types on admission were independent predictors to mortality in COVID‐19 patients.Of 304 COVID‐19 patients , 88 patients (27.3%) died . COVID‐19 patients with myocardial injury had more comorbidities ; lower lymphocyte counts, higher C‐reactive protein  and higher HsTNI are both strongest independent predictors to COVID‐related mortality after adjusting confounding factors.COVID patients with myocardial injury on admission is associated with more severe clinical presentation and biomarkers. Myocardial injury (hazard ratio = 4.55;  Sixteen cases without significant biomarkers, including Hs‐TnI and creatinine kinase–myocardial band (CK‐MB) levels, were excluded. Thus, a total of 304 patients were finally included in the study. The confirmed diagnosis of COVID‐19 was defined as a positive result by using real‐time reverse‐transcriptase polymerase‐chain‐reaction (RT‐PCR) detection for routine nasal and pharyngeal swab specimens or anti‐SARS‐CoV‐2 antibody assay.2.2The demographic characteristics, clinical data, and results of cardiac biomarkers were obtained from the hospital's electronic medical records according to previously designed standardized data collection forms. The date of symptom onset, initial diagnosis of COVID‐19, and death were recorded. The clinical features of symptoms and signs and comorbidities were collected on admission. Laboratory analyses included complete blood count, hepatic function, kidney function, coagulation function, C‐reactive protein (CRP), lactate dehydrogenase (LDH), myocardial enzymes, procalcitonin (PCT), electrolytes test, and status of other viral infection. Cardiac biomarkers including N‐terminal pro‐B‐type natriuretic peptide (NT‐proBNP), Hs‐TnI, CK‐MB, and myoglobin were measured on admission and during hospitalization. Routine bacterial and fungal examinations were also performed. Radiologic assessments included digital radiography (DR) and/or computed tomography (CT). Two researchers collected and checked the final database.2.3Fever was defined as an axillary temperature of 37.5°C or higher.2.4 SD or median (interquartile range [IQR]) as appropriate. Student's t test, Mann–Whitney test, χ2 test or Fisher's exact test was used where appropriate to assess the difference between different cohorts. The Pearson correlation coefficient and Spearman rank correlation coefficient were used for correlation analysis. Survival curves were plotted using the Kaplan–Meier method between patients with and without myocardial injury. Univariate and multivariate Cox regression models were used to determine the independent risk factors for COVID‐19‐associated death in the hospital. The NCP types was listed as rank variable. Statistical analysis was performed with SPSS, version 21.0 (IBM). A two‐sided p < .05 was considered statistically significant.Categorical variables are shown as frequency rates or percentages. Continuous variables as mean ±3p < .001).A total of 304 hospitalized patients with COVID‐19 were consecutively enrolled in our study, including 96 patients (31.6%) with myocardial injury and 208 patients (68.4%) without myocardial injury. The demographic and clinical characteristics of the patients are listed in Table p < .001). Comparing with patients without myocardial injury, patients with myocardial injury had even lower CD3, CD4, CD8 counts, higher IL‐6, CRP, and PCT levels , and CD3, CD4, CD8 counts showed strong correlations (R > 0.77) with lymphocytes counts and moderate correlations (R: −0.39 to −0.45) with CRP levels  and serum urea nitrogen levels  , IgM, IgA, and complement 3 (C3) levels were all within the normal range in both groups Table . Howeverp < .001) compared with patients without myocardial injury. Figure Regarding the cardiac, hepatic, renal, and coagulation function Table , patientThe imaging manifestations of the 304 patients with COVID‐19 were listed in Table Oxygen treatment was provided to 206/304 patients (67.8%). Sixty‐two (20.4%) patients received noninvasive ventilation, and 10.2% of the patients (31 patients) were placed in mechanical ventilation. The proportion treated with antiviral therapy was the highest , followed by high‐dose glucocorticoid therapy , intravenous immunoglobulin therapy , antibiotic therapy  and hemoperfusion . Only 8 patients (2.6%) among all participants were given plasmapheresis therapy. Overall, 100 patients (27.3%) had kidney injury during hospitalization, and 65 patients (21.4%) had hepatic injury.p < .001) (Table p < .001) was lower in patients with myocardial injury. The comparison of typical managements, comorbidities, complications, and time duration from symptom onset to death were made in 20 random patients with myocardial injury or not  Table . Howevert Figure , indicatp < .001; Table p < .001; Figure p < .001) to primary endpoint, after adjusting for age, sex, pre‐existing comorbidities, CRP levels, D‐dimer levels, NCP types (Table p = .04), CRP levels , and NCP types (p = .007) were other independent risk factors for mortality with COVID‐19. In addition, Cox regression model with Hs‐TnI as continuous variable showed that higher Hs‐TnI was also associated with mortality  (Table During the median durations for about 45.4 days from onset of symptoms to follow‐up range: 3–84 days), a total of 88/304 patients (27.3%) died, among which 61 patients had myocardial injury, while 27 patients did not have myocardial injury  Table .4This study summarizes the clinical characteristics, laboratory, cardiac, and radiographic findings in a large cohort of 304 hospitalized COVID‐19 patients, and provides novel information of the prognostic value of pre‐existing comorbidities and myocardial injury. Myocardial injury, senior age, NCP types, and CRP levels were independently associated with higher risk of mortality during hospitalization. The myocardial injury was probably associated with inflammation response. The prognostic value of elevated Hs‐TnI in patients with COVID‐19 should be of great interest to a broad readership, as a simple blood biomarker test was such a strong predictor of mortality and that sophisticated, expensive, and time‐consuming cardiac testing with CT and magnetic resonance imaging are not needed.With the high infectivity, COVID‐19 has managed to supersede severe acute respiratory syndrome (SARS) in 2003 and Middle East respiratory syndrome (MERS) in 2012 in terms of death toll.In our study, COVID‐19 patients with myocardial injury had obviously elevated myoglobin, and NT‐proBNP levels, providing independent corroborating evidence of myocardial injury.Two prior studies indicated that there was a strong correlation between myocardial injury and prognosis of COVID‐19 patients during hospitalization.To date, the exact mechanism of cardiac involvement in COVID‐19 remains under investigation. Current studies suggested immune change in patients with MERS,We acknowledge some limitations in our study. First, this was a retrospective, single‐center study of patients admitted to hospital; multi‐center investigations for a larger cohort would be better to assess the clinical characteristics and confirm the outcomes of myocardial injury after infection with COVID‐19. Second, because of the logistical limitations at the onset of these emerging infections in Wuhan, some data, such as inflammation biomarker and imaging data were lacking on admission, which limits further confirmation of potential mechanisms of myocardial injury. Third, the data in this study permit a preliminary assessment of the clinical course and outcomes of patients with COVID‐19. The causes of death may involve multiple organ dysfunction in most cases, and it is difficult to differentiate the myocardial injury as the main and direct cause in an individual case. Long‐term observation and prospective study design on the effectiveness of treatments specific for the myocardial injury are needed.5In conclusion, myocardial injury is common in patients hospitalized with COVID‐19. Patients with myocardial injury had more severe presentation and complex comorbidities. Furthermore, myocardial injury is independently associated with increased in‐hospital mortality in patients with COVID‐19.This study obtained approval from the Research Ethics Committee of the Renmin Hospital of Wuhan University, Wuhan, China . Written consent was obtained from all participants.Jian He, Bicheng Zhang, Quan Zhou, Wenjing Yang, Jing Xu, Tingting Liu, Haijun Zhang, Zhiyong Wu, Dong Li, Qing Zhou, Jie Yan, Cuizhen Zhang: Data curation; formal analysis. Bicheng Zhang, Quan Zhou, JZ: Data curation; resources; investigation; Jian He, Wenjing Yang, Jing Xu: Writing; methodology. Haiyan Qian, Minjie Lu, Xiaoyang Zhou: Methodology, project administration, conceptualization. Minjie Lu and Xiaoyang Zhou: Conceptualization; validation; supervision; funding acquisition. All authors have read and approved the manuscript.Supplementary information.Click here for additional data file.


To identify risk factors of disease severity and between mild and severe colon ischaemia (CI) patients and to improve clinical outcomes, this study aimed to explore a novel scoring model.Retrospective analyses of hospital records between January 2009 and December 2019 were included. Clinical manifestations, mortality, Oakland score, laboratory tests, colonoscopy, and histopathology were collected. Risk factors of severe CI were determined by univariate and multivariate logistic regression and used for the predicting model.p < .001). The Oakland score was much higher in the severe CI group . The histopathological finding of fibrin thrombi was an independent risk factor that predicted poor outcomes . Patients present with CAR ≥3.33, Oakland score ≥12, and histopathological fibrin thrombi were independent risk factors. In addition, the final scoring model was 0.042 × Oakland score + 1.040 × CAR + 3.412 × fibrin thrombi, the area under the curve (AUC) was 0.960 , and the sensitivity and specificity of the novel scoring model were 95% and 92%, respectively.A total of 203 patients with CI were included. Serum C-reactive protein (CRP) and albumin ratio (CAR) were much higher in the severe CI group compared with that of the mild CI group , Oakland score, and histopathological findings (fibrin thrombi) can be assessed efficiently for colon ischaemia outcomes.The novel prognostic model was established to predict CI severity and clinical outcomes efficiently. CI is a vascular disease of mesenteric ischaemia, loss of blood supply, and ischaemic injuries, manifested as acute or chronic onset. CI is a quite common sign of gastrointestinal injuries; moreover, it is the second-most common cause of acute lower gastrointestinal bleeding (LGIB) . CI is aThere is no consensus on whether biopsies should be routinely performed. The American College of Gastroenterology (ACG) guidelines state that biopsies are not helpful and could entail a high risk of perforation. However, colonoscopy is strongly recommended by most gastroenterologists, and it is safe when performed by experienced endoscopists . In a reRegrettably, there are limited clinical studies of a prognostic tool for CI patients. CRP is a positive reactant synthesized by the liver during the acute phase and increases in level in response to inflammation and infection . In our Serum albumin can maintain colloid pressure and transport free fatty acids, bilirubin, and drug metabolites . Serum aThe Oakland score is a risk assessment score of acute LGIB and can be widely used to evaluate bleeding whether massive or minor . PatientLittle attention has been devoted to a predictive tool to assess disease severity and clinical outcome, so we hypothesized that certain demographic and clinical factors may predispose the patients to a negative prognosis. In this paper, a new prognostic tool will be developed efficiently for CI patients.This study was reviewed and approved by the Institutional Review Board (IRB) of Nanfang Hospital, Southern Medical University (Ethics Review Code: NFEC-201609-K6). Hospital Information Software (HIS) was used to collect clinical documentations. Personal information was made anonymous before analysis.Two hundred and three patients with CI were enrolled in our retrospective study from January 2009 to December 2019 at Nanfang Hospital, Southern Medical University, Zhuhai People’s Hospital , and Zhejiang Provincial Hospital. CI diagnosis was based on the presence of clinical manifestations and endoscopic and pathological findings .Inclusion criteria included (1) a colonoscopic or surgical evaluation of the bowel; (2) a biopsy or surgical pathology finding that consistent with the diagnosis of CI and (3) a CT abdomen of bowel evaluation for CI. All 203 patients submitted a stool culture, patients with bacterial colitis and inflammatory bowel disease were excluded . WrittenAll patients were divided into mild and severe CI groups. The mild group included patients who did not require surgery and could achieve positive outcomes. The severe group included patients who required abdominal surgeries or died.Gender, age, underlying diseases, clinical manifestations (temperature >38.0 °C), CAR, Oakland score, colonoscopy and histopathological findings were all collected into our medical documentations.Medical treatment included empirical antibiotics, nutritional supply, fluid therapy, and restrictive blood transfusion if necessary.Surgery was indicated for patients with severe peritonitis, intestinal perforation, massive bleeding, or clinical deterioration.We defined urgent colonoscopy as colonoscopy required within 24 h and standard colonoscopy as not required within 24 h. The category of colonoscopy biopsy findings was divided into mucosal erosion, ulceration, haemorrhage, necrosis, inflammatory cells infiltration, dilated vessel, thickening vessels, and fibrin thrombi.Colon ischaemia was divided into two types, according to endoscopic findings: (1) a “non-ulcer” type for findings of pale, friable, or oedematous mucosa with petechial haemorrhages, scattered erosion, or segmental erythema; (2) an “ulcer” type for findings of blue-black mucosal nodules, or mucosal granularity with deep ulcerations .All tissue samples obtained from these 203 patients with forceps or surgical specimens were fixed in 4% buffered paraformaldehyde, processed routinely, embedded in paraffin, and stained with hematoxylin–eosin (HE) staining. Two senior pathologists were blinded to examine the slides under light microscopy and recorded the histopathological report .The diagnosis of CI was made based on clinical manifestations and negative stool studies for infections, CT scanning, colonoscopy with histopathological findings. Patients were divided in two groups. The mild group included patients who could achieve positive outcomes with medical treatment. The severe group included patients who required abdominal surgeries or died.T test. Categorical variables were expressed as numbers and proportion. The χ2 statistics were used to compare most categorical numbers, and Fisher’s exact test was used for small numbers. Multiple logistic regression analysis was used to determine risk factors of CI. The area under the receiver operating characteristics (ROC) curve (AUC) represented discrimination of the model. Confidence intervals (CIs) were assessed for a 95% level. Data analysis used a statistical software package . Two-tailed p < .05 was considered statistically significant.Descriptive variables were expressed as means with standard deviation and were compared using Student’s p < .001)  .n = 183) and severe CI group (n = 20). The median age was 64.11 ± 10.31 (mean ± standard deviation [SD]) and 67.20 ± 12.04 (mean ± SD) years old, respectively (p = .724). There was no substantial statistical difference in hypertension (p = .056), diabetes (p = .132), coronary heart disease (p = .066), stroke (p = .262), or atrial fibrillation (p = .099) between the mild and severe CI groups . In addition, clinical manifestations of fever (body temperature >38.0 °C), abdominal pain, and diarrhoea were documented completely. We found that they did not show a significant difference between the groups   .Two patients died in the severe CI group (10%), one patient from heart failure, the other from acute respiratory distress syndrome (ARDS); both patients were older than 70 years.p = .094)  . Colonos = .094) .p < .001) (p = .040) , thickening vessel in seven patients (3.83%), fibrin thrombi in two patients (1.09%), mucosal erosion in 22 patients (12.02%), mucosal ulceration in 23 patients (12.57%), necrosis in 22 patients (11.48%), massive inflammatory cell infiltration in 70 patients (38.25%), and haemorrhage in 23 patients (12.57%) . Biopsie < .001) . The odd < .001) . However = .040) . Biopsy  = .040) .CAR and CRP were much higher in the severe CI group  . HoweverWe calculated a novel scoring model with three variables based on the estimated logistics model. The preceding variables were Oakland score, CAR, and fibrin thrombi. The final model was 0.042 × Oakland score + 1.040 × CAR + 3.412 × fibrin thrombi; the AUC was 0.960, 95% CIs were 0.930–0.990, the sensitivity was 95%, and the specificity was 92% . AdditioCI is the most common manifestation of ischaemic colon disease, and that rate is rising rapidly to approximately three per 1000 hospital admissions in American medical centers . Elderlyp = .213). Our data were very consistent with previous studies.In this study, we show that mortality in this series of patients with severe CI was only 10.00%. We suggest two possible explanations for the lower mortality in China. First, severe CI cases are not more numerous than those among Western patients because our state is a developing country. Second, the timing of surgical intervention remains controversial; CI is a relatively common surgical entity in the West, it has been reported that intestinal resection for CI was associated high with in-hospital mortality . In addiIt remains controversial whether to perform urgent or standard colonoscopy for acute LGIB patients; European experts recommend that early colonoscopy reduces hospital stay . In JapaAlthough diagnosis of CI was difficult due to unspecific symptoms and pathological examination, colonoscopy biopsies remained valuable for differentiated diagnosis . Americap < .001), and neutrophilic inflammation showed a comparatively positive outcome in the severe CI group (p = .040). We believe that minimal thrombi combining with local inflammation might cause peritonitis, perforation, sepsis, and shock. In addition, fibrin thrombi might affect potential target-organ failure or even cause death. The need for further large-scale study in the near future is indisputable.However, retrospective studies in the United States demonstrated that patients with CI who had endoscopic biopsies showing necrosis and fibrin thrombi were more likely to have poor outcomes . UnfortuCRP has been widely used to evaluate the severity of inflammation and infection. It was not only a serum marker of clinical deterioration and a factor in decision making for diagnosis or management strategies but also the means for early detection of inflammation and infection in postoperative patients . The CRPSerum albumin was a negative acute-phase protein that was rapidly regulated by inflammatory signals . Low serp < .001). Therefore, our data were identical to previous study. It is possible to believe that patients with lower haemoglobin count might need blood transfusion or surgery, or even to have life-threatening complications. There was significant difference of haemoglobin between the mild and severe CI group, however, haemoglobin was one part of Oakland score. Therefore, we chose Oakland score for logistic regression analysis.Acute LGIB was associated with recurrent bleeding and a requirement for blood transfusion . HaemoglA high Oakland score was a novel sign of acute LGIB and useful for assessment of whether massive bleeding was present. It was the first score that could be used easily for LGIB. Patients with a score of ≤8 points indicated safe discharge from the emergency department and achievement of a positive outcome. In contrast, patients with a score of >8 points were classified as experiencing massive bleeding, and those patients were more likely to be hospitalized or to have a much higher risk of severe complications or even death ,13. A hiOur scoring model was based on three independent factors: as Oakland score (points), CAR (serum test), and fibrin thrombi (microscopic finding). The model was 0.042 × Oakland score + 1.040 × CAR + 3.412 × fibrin thrombi. Furthermore, the sensitivity and specificity of this novel scoring model were 95% and 92%, respectively. Our findings were consistent with previous studies, and the aforementioned variables were risk factors for early recognition of patients with severe CI ,34. TherOur study had some limitations. First, this was a retrospective study with a small sample; we aim to make a prospective study soon. Second, randomized colonoscopy biopsies might contain bias in our study; a nationwide consensus of endoscopic biopsies is necessary, with large-scale research. Finally, interventional technique was not added to the present study. We intend to conduct another clinical trial soon.We identified laboratory tests , Oakland score (points ≥12), and histopathological findings (fibrin thrombi) in patients with LGIB caused by colon ischaemia. We did find that combining these novel prediction tools for CI severity and clinical outcomes was valuable. Overall, simple and valuable prognostic tools might remind gastroenterologists to intervene early to improve clinical outcomes and decrease mortality.


N2020 = 230), we found that having a greater number of virtual interaction partners was associated with better mental health. This relationship was statistically mediated by decreased loneliness and increased perceptions of social support. We replicated these findings during the pandemic 1 year later (N2021 = 256) and found that these effects held even after controlling for the amount of time people spent interacting online. Convergent with previous literature on social interactions, these findings suggest that virtual interactions may benefit overall mental health, particularly during physical distancing and other circumstances where opportunities to interact in-person with different people are limited.Social interactions play an extremely important role in maintaining health and well-being. The COVID-19 pandemic and associated physical distancing measures, however, restricted the number of people one could physically interact with on a regular basis. A large percentage of social interactions moved online, resulting in reports of “Zoom fatigue,” or exhaustion from virtual interactions. These reports focused on how online communication differs from in-person communication, but it is possible that when in-person interactions are restricted, virtual interactions may benefit mental health overall. In a survey conducted near the beginning of the COVID-19 pandemic (https://osf.io/6jsr2/.Open Science Framework repository:  Social interactions can mitigate the harmful effects of stress5, and provide protective benefits against a range of negative health outcomes. For example, social interactions can buffer against cognitive decline (associated with aging)6, dementia6, upper respiratory infections7, cancer recurrence8, and mortality11.A robust body of research demonstrates that social interactions are crucial in maintaining health and well-being15. Relatedly, social interactions can also increase perceptions of social support, which can enable better emotion regulation and facilitate positive feelings of belonging and purpose17. Thus, social interactions can improve well-being by decreasing the harmful impact of negative emotional experiences, such as loneliness, while also increasing perceptions of social support and associated positive emotions that benefit overall mental health.While social interactions likely shape health and well-being in a number of ways, one important outcome of social connection is reduced feelings of loneliness , which have consistently been linked to worse physical and mental health outcomes16. In-person interactions that used to take place in passing, such as casual chats with neighbors or coworkers, became limited by governmental recommendations to work from home and maintain physical distance from others, reducing the number of in-person interaction partners that people could have on a regular basis.The COVID-19 pandemic and consequent physical distancing measures, however, restricted the number of people that individuals could interact with in-person on a regular basis. In other words, people have been more physically isolated than before the pandemic. Outside of the COVID-19 context, people often engage in a variety of in-person interactions in their everyday lives, including, those with a romantic partner, close family members, friends, and other members of one’s community21. Given the recency of this drastic shift, limited research has examined how virtual interactions affect well-being. Empirical research comparing in-person and virtual interactions has begun to emerge suggesting that Zoom fatigue may arise from several nonverbal mechanisms that are unique to video communication 23. These reports suggest that virtual interactions can be more taxing than in-person interactions. However, it remains unclear whether virtual interactions can benefit mental health in general, particularly when in-person social interactions, which have been repeatedly demonstrated to be important for health and well-being, are forcibly limited.Many social interactions moved online, including professional and recreational gatherings. This shift in how individuals interact with others on a regular basis has resulted in reports of “Zoom fatigue”, or exhaustion from virtual interactions. Countless news articles have reported on this phenomenon, describing the ways in which online interactions can increase mental load, including, but not limited to, less privacy when video-calling from living spaces, added stress from worrying about lighting, sound quality, and internet speed during important conversations, and minimal separation of different aspects of one’s life, such as work, friends, and familyN2020 = 230), including recommendations to work from home, wear a face covering in any public space, and avoid contact with individuals outside of one’s immediate household , we tested two competing hypotheses: (1) based on prior literature on the benefits of social interactions, the number of one’s virtual interaction partners will be associated with better mental health; or (2) based on reports of increased fatigue associated with virtual interactions, the number of one’s virtual interaction partners will be associated with worse mental health. In line with prior literature, we followed-up on these analyses to explore potential mediators of the relationship between the quantity of one’s virtual interaction partners and mental health, including reduced loneliness and increased perceived social support.Given this unique historical moment and its potential to shape future work environments and social communication, the present research examined the relationship between virtual interactions and overall mental health. We focused our investigation on how the quantity of one’s virtual interaction partners  affects mental health. In a survey following the instantiation of COVID-19-related physical distancing measures in early-mid 2020 (N2021 = 256) as the COVID-19 pandemic continued to unfold globally, but vaccinations became available in the United States (where this research was conducted) and governmental recommendations began shifting to allow physical contact between vaccinated individuals. During this time, much of education and work continued to take place remotely, and our undergraduate sample had just begun to get access to vaccinations, such that many everyday social interactions remained online rather than in-person. We were also interested in assessing the effects of the amount of time spent interacting with others online because of the emergence of preliminary research demonstrating that frequency of virtual interaction was not associated with well-being24 and that virtual interactions were more fatiguing and less enjoyable than in-person interactions25. In this second survey, we aimed to assess: (1) whether the positive association between the number of one’s virtual interaction partners and mental health was consistent between 2020 and 2021, even as the number of one’s in-person interactions were likely to have grown; and (2) the association between amount of time spent interacting with others online and mental health.We then replicated this study approximately 1 year later in early-mid 2021  and overall mental health at two time points during the COVID-19 pandemic.Our procedure was approved by the UCLA Institutional Review Board committee. All experiments were performed in accordance with the relevant guidelines and regulations. Informed consent was obtained from all participants.The rationale for our target sample size of 300 participants for each study is based on the maximum number of participants that can be recruited through our institution’s subject pool in 1 year. For the first study, we began data collection soon after physical distancing measures went into place around the nation , and we aimed to enroll as many participants as possible in a small timeframe to capture the unique historical moment. One year later, we collected data in a new sample  as the pandemic continued to unfold globally, but when physical distancing measures were starting to ease due to vaccination rates in the United States, where this research was conducted.Participants for both studies were recruited from the UCLA Psychology Department’s undergraduate population using an online human participant management platform (SONA systems). Participants were granted university credit upon completion.N = 230) was 20.54 years (SD = 2.43). Approximately 88% of participants resided in California during participation and 3% lived outside of the U.S. Approximately 89% of participants reported living in cities with mandatory orders to work from home and maintain physical distance from others. Approximately 72% of participants were female, 26% male, < 1% non-binary, 34% Asian, 30% White, 13% Latinx, and 3% Black. The remaining participants selected another identity or chose not to answer.In the 2020 sample, the mean age of the final sample (N = 256) was 20.56 years (SD = 3.11). Approximately 91% of participants resided in California during participation and 5% lived outside of the U.S. Approximately 42% of participants reported living in cities with mandatory orders to work from home and maintain physical distance from others. Approximately 76% of participants were female, 22% male, 1% non-binary, 38% Asian, 24% White, 12% Latinx, and 3% Black. The remaining participants selected another identity or chose not to answer.In the 2021 sample, the mean age of the final sample  with a total of 14 items such as: “During the past month, how often did you feel happy/good at managing the responsibilities of your daily life/that your life has a sense of direction or meaning to it?”; and (ii) self-reported average number of daily and weekly virtual interaction partners . For our measure of virtual interaction, we asked participants to focus on meetings over Zoom, phone calls, texts, virtual gaming, or other similar means of communication that did not involve in-person interaction. While our primary research questions centered on the effects of how many different virtual interaction partners participants had, we simultaneously measured participants’ self-reported average number of daily and weekly in-person interaction partners. For these items, we asked participants to focus on people they socialized with face-to-face. In 2021, we additionally included a measure of time spent interacting with others, separately for virtual and in-person interactions, at both the daily and weekly level .To assess the extent to which social interactions impacted mental health during physical distancing, we used the following measures: (i) the short form of the Mental Health Continuum (MHC-SF)27, which includes a total of 20 items such as: “How often do you feel alone/that no one really knows you well/that there are people you can turn to?”; and (ii) the Social Provisions Scale28, which includes a total of 24 items such as: “There is someone I could talk to about important decisions in my life,” “There are people who enjoy the same social activities I do,” and “I have relationships where my competence and skill are recognized.” Detailed information for our measures, including access to the full list of items for each scale, are provided in our Open Science Framework (OSF) repository.To assess whether loneliness and perceived social support mediated the relationship between number of virtual interaction partners and mental health, we used the following measures: (i) UCLA Loneliness ScaleThis data was collected as part of a larger multi-investigator project examining individuals’ social interactions and experiences during the COVID-19 pandemic. A full list of measures included in the parent project is available upon request. Participants signed up for the study through our institution’s subject pool where they could access a link to our survey hosted on Qualtrics.com. The 2020 survey was estimated to take approximately 50 min to complete, and the 2021 survey was estimated to take approximately 30 min to complete.N2020 = 292; N2021 = 300), we used several criteria to clean our dataset. Specifically, people were excluded for completing the survey in less than half the anticipated study length , for failing attention checks , and for being outliers (more than three standard deviations away from the mean) on at least one of the primary variables of interest N2020 = 11; N2021 = 12). Additionally, in the 2021 sample, participants were excluded from the final sample if they reported having completed the same survey in 2020 (N2021 = 4). Please see Table Participants who reported being younger than 18 or not being proficient in English were not enrolled in the study. Since these studies were completed online  in both studies were skewed right. In 2021, our measures of time spent interacting with others were also skewed right. As such, these variables were each log-transformed before further analysis.29. Our data and analysis materials are hosted on OSF. First, we tested the effect of the number of participants’ virtual interaction partners on their mental health during the COVID-19 pandemic by running simple regression models including either number of daily virtual interaction partners or number of weekly virtual interaction partners as the predictor of mental health. We followed up on these analyses with simple mediation analyses to examine whether loneliness  and perceived social support  statistically mediated the relationship between the number of one’s daily/weekly virtual interaction partners and mental health. We examined these mediators separately  since prior literature has separately examined the mediating effects of these two variables on the relationship between social interactions and mental health, and our measures of loneliness and perceived support were highly correlated (r = − 0.78), which can create issues of multicollinearity. Using the “mediation” package in R31, we estimated average causal mediation effects (ACMEs) and average direct effects (ADEs), and tested the significance of these effects using bootstrapping procedures. Unstandardized indirect effects  were computed for each of 10,000 bootstrapped samples, and the 95% confidence interval was computed by determining the indirect effects at the 2.5th and 97.5th percentiles.All analyses were conducted using the statistical software R (Version 3.6.1)underlie associations between virtual interaction and overall mental health. Although we recognize that mediational analyses have the connotation of being time-lagged, our proposed model fits with the standard definition of statistical mediation, which occurs when one variable  helps to explain the association between a predictor of interest  and a particular outcome . Thus, while we will use the same formal tests of mediation, these analyses should be conceptualized as a ‘levels of analysis’ mediation  rather than a ‘time-lagged’ mediation .We would like to note that although our model is described as a statistical mediational model in which each examined variable is separated in time, we are not suggesting a time-lagged mediational model here . Rather, we assessed whether decreased loneliness and increased perceptions of social support M = 37.56, SD = 14.73) to 2021 , t(459.04) = 1.57, p = 0.117. Since the 2021 study included a measure of time spent interacting with others, we additionally tested whether time spent interacting with others online was associated with mental health.We performed the same analytic procedure in both the 2020 and 2021 datasets. Overall mental health did not significantly change from 2020  the relationship between the number of one’s in-person interaction partners and mental health; (ii) whether this relationship was statistically mediated by decreased loneliness and increased perceived social support; and (iii) the relationship between time spent interacting with others in-person and mental health. These analyses and results are described in our Supplemental Materials .t(224) = 2.86, p = 0.005, and the weekly level, β = 4.83, 95% CI , t(224) = 4.14, p < 0.001  = 3.11, p = 0.002, and the weekly level, β = 2.61, 95% CI , t(251) = 2.78, p = 0.006  = 1.83, p = 0.069, and a significant increase in number of weekly virtual interaction partners from 2020 to 2021, t(397.51) = 2.32, p = 0.021.There was a marginally significant increase in average number of daily virtual interaction partners between 2020 and 2021 Table , t(418.6t(224) = − 2.59, p = 0.010. When controlling for the effect of quantity of daily virtual interaction partners on mental health, β = 2.56, 95% CI , t(223) = 1.78, p = 0.077, loneliness was negatively associated with mental health, β = -0.69, 95% CI , t(223) = − 8.65, p < 0.001. There was a significant indirect effect  of daily virtual interaction partners on mental health, βACME = 2.12, 95% CI , p = 0.006, and a marginally significant direct effect of daily virtual interaction partners on mental health , βADE = 2.56, 95% CI , p = 0.085. Thus, the effect of one’s number of daily virtual interaction partners on mental health was fully mediated by decreased loneliness in 2020  = − 3.49, p < 0.001, and when controlling for the effect of quantity of weekly virtual interaction partners on mental health, β = 2.85, 95% CI , t(223) = 2.72, p = 0.007, loneliness was negatively associated with mental health, β = − 0.66, 95% CI , t(223) = − 8.32, p < 0.001. There was a significant indirect effect, βACME = 1.98, 95% CI , p = 0.001, and significant direct effect, βADE = 2.85, 95% CI , p = 0.007, of weekly virtual interaction partners on mental health, indicating that the effect of number of weekly virtual interaction partners on mental health was partially mediated by decreased loneliness in 2020.Similar to the effects of daily virtual interaction partners described above, quantity of weekly virtual interaction partners was negatively associated with loneliness, β = − 2.98, 95% CI , t(251) = − 2.30, p = 0.022. When controlling for quantity of daily virtual interaction partners, β = 2.06, 95% CI , t(250) = 2.25, p = 0.025, loneliness was negatively associated with mental health, β = − 0.65, 95% CI , t(250) = − 8.85, p < 0.001. There was a significant indirect effect of daily virtual interaction partners on mental health, βACME = 1.17, 95% CI , p = 0.033, and a significant direct effect of daily virtual interaction partners on mental health, βADE = 2.06, 95% CI , p = 0.018. Thus, the effect of one’s number of daily virtual interaction partners on mental health was partially mediated by decreased loneliness in 2021.Consistent with our findings from 2020, quantity of daily virtual interaction partners was negatively associated with loneliness, β = − 1.80, 95% CI , t(251) = − 1.59, p = 0.113. Thus, no further mediation analyses were performed.In 2021, there was no significant association between number of weekly virtual interaction partners and loneliness, β = − 1.13, 95% CI , t(224) = 2.53, p = 0.012. When controlling for quantity of daily virtual interaction partners, β = 3.25, 95% CI , t(223) = 2.08, p = 0.038, perceived support was positively associated with mental health, β = 0.51, 95% CI , t(223) = 5.51, p < 0.001. Thus, the effect of one’s number of daily virtual interaction partners on mental health was partially mediated by increased perceived support in 2020, βACME = 1.43, 95% CI , p = 0.009, βADE = 3.25, 95% CI , p = 0.039  = 4.35, p < 0.001, and when controlling for quantity of weekly virtual interaction partners, β = 3.23, 95% CI , t(223) = 2.80, p = 0.006, perceived support was positively associated with mental health, β = 0.47, 95% CI , t(223) = 4.96, p < 0.001. The effect of number of weekly virtual interaction partners on mental health was partially mediated by increased perceived support in 2020, βACME = 1.60, 95% CI , p < 0.001, βADE = 3.23, 95% CI , p = 0.009.Quantity of weekly virtual interaction partners was positively associated with perceived social support, β = 3.44, 95% CI , t(251) = 2.13, p = 0.034. When controlling for quantity of daily virtual interaction partners, β = 2.40, 95% CI , t(250) = 2.47, p = 0.014, perceived support was positively associated with mental health, β = 0.48, 95% CI , t(250) = 6.41, p < 0.001. The effect of one’s number of daily virtual interaction partners on mental health was partially mediated by increased perceived support in 2021, βACME = 0.83, 95% CI , p = 0.045, βADE = 2.40, 95% CI , p = 0.010  = 1.41, p = 0.161. As such, no further mediation analyses were performed.In 2021, there was no significant association between number of weekly virtual interaction partners and perceived social support, β = 1.03, 95% CI , t(251) = 1.30, p = 0.195, but we did find a significant positive relationship between weekly virtual interaction hours and mental health, β = 1.86, 95% CI , t(251) = 2.10, p = 0.037.Thus far we have discussed the effects of interacting with different people on mental health. It may be, however, that time spent interacting with others online contributes to Zoom fatigue, which could worsen overall mental health. While we did not have a measure of time spent interacting with others in the 2020 survey, in the 2021 survey we asked participants how many hours they spent virtually interacting with others on a daily and weekly basis. We found no relationship between daily virtual interaction hours and mental health, β = 1.86, 95% CI , t(250) = − 0.14, p = 0.890, we found that number of virtual interaction partners was positively associated with mental health at the daily level, β = 3.30, 95% CI , t(250) = 2.82, p = 0.005. Similarly, when controlling for time spent interacting online, β = 0.93, 95% CI , t(250) = 0.94, p = 0.349, number of virtual interaction partners was positively associated with mental health at the weekly level, β = 2.16, 95% CI , t(250) = 2.04, p = 0.043.To further understand the relationship between virtual interaction and mental health, we tested whether the number of one’s virtual interaction partners predicted mental health over and above time spent interacting online by including both number of virtual interaction partners and time spent interacting online in linear regression models, separately for daily and weekly interactions. When controlling for time spent interacting online, β = − 0.22, 95% CI , not significantly associated with mental health at the daily level, in line with recent work suggesting that frequency of virtual interactions was not associated with well-being24. However, time spent interacting with others online was significantly positively associated with mental health at the weekly level, suggesting that time spent online may also benefit mental health.The present study examined the relationship between the number of one’s virtual interaction partners during COVID-19 physical distancing and overall mental health. In a survey conducted in early-mid 2020, we found that the more daily and weekly virtual interaction partners one had, the better their mental health. This relationship was mediated by decreased loneliness and increased perceptions of social support. We replicated these effects in a second sample collected in early-mid 2021, and additionally found that the amount of time spent interacting with others online was 23, but our research suggests that virtual interactions—and in particular, interacting with many different people online—may nonetheless confer mental health benefits during physical distancing when in-person interactions are limited. These findings are consistent with recent work suggesting that people consistently undervalue the overall benefit of voice-based interactions such as video chat and phone calls32.These findings extend a robust body of work demonstrating that social interactions are important for health and well-being. Virtual interactions and their associated practical and social challenges  have been a reported source of stress for many people throughout the pandemic35, it is crucial to assess the impact of virtual interactions on our well-being. Interestingly, virtual interactions may benefit mental health through similar mechanisms as in-person interactions typically do insofar as virtual interactions afford the opportunity to expand the number of social interaction partners one can have, particularly while maintaining physical distance from others. Even as physical distancing measures change over time and we have more in-person access to the people around us, this work has implications for individuals with limited mobility, such as older individuals, for whom virtual interactions have been found to reduce feelings of loneliness and increase connections with friends and family36.Given that the COVID-19 pandemic has the potential to shape future work norms, with companies and universities increasingly experimenting with and adopting remote work models25. It is possible that this discrepancy between in-person and virtual interactions will shrink as we become more accustomed to interacting virtually, and virtual interfaces become increasingly user-friendly and interactive. Interestingly, our data suggest that even as the average number of in-person interactions has expanded at the daily and weekly level from 2020 to 2021 in our sample, so have the weekly number of virtual interaction partners. Thus, future work can experimentally parse how specific features of virtual interactions  and different methods of communicating virtually  shape well-being, mental health, and feelings of social connection in order to improve the quality of virtual interactions32. Importantly, some features of in-person interaction cannot be approximated in a virtual environment. For example, in-person interactions can facilitate affectionate touch, which has been robustly demonstrated to promote relational, psychological, and physical well-being across age groups and relationships38. Thus, virtual interactions may not adequately replace in-person interactions in the long-run, particularly if experiences like affectionate touch and shared sensory experiences are mechanistically important in buffering against the inverse effects of loneliness.Although this research elucidates the possible protective benefits of virtual interactions, particularly as it relates to expanding the number of people one interacts with on a regular basis, preliminary research also suggests that virtual interactions are less enjoyable, more fatiguing, and more taxing in general than in-person interactionsImportantly, our work is not meant to discount experiences of burnout associated with virtual interactions. A primary source of “Zoom fatigue” may be the cognitive effort expended to adjust to a new interactive context, as well as the physical toll of staying in one’s chair and looking at a bright screen for extended periods of time. Our research indicated that the number of one’s virtual interaction partners at the daily and weekly level was positively associated with overall mental health, and that time spent interacting with others online at the weekly level was also positively associated with overall mental health. Thus, it will be important for future work to continue examining sources of Zoom fatigue, and how to mitigate these effects without losing the potential benefits of virtual interaction.the number of different people one interacts with on a regular basis and mental health. Of course, future research using longitudinal or experimental approaches are imperative to further understand this association. Large-scale survey data can additionally be used to explore how factors such as occupational or social network status relate to social interaction during COVID-19. However, our research is among the first to test both number of interaction partners and time spent interacting with others virtually during COVID-19, and indicates that it is worth considering the potential mental health benefits of virtual interactions during COVID-19 and other contexts that restrict the number of people we can regularly interact with. Thus, this work lays the groundwork for future research to continue to examine the ways in which in-person versus virtual interactions shape well-being as our society adjusts to new ways of communicating in the workplace and everyday life.A crucial limitation of our results is that they are derived from cross-sectional data, so we are not able to determine the directionality of the relationship between the number of one’s virtual interaction partners and mental health. In other words, it is possible that individuals with better overall mental health are more likely to virtually interact with a large number of people, as opposed to the other way around. Importantly, time spent interacting with others online was not associated with mental health at the daily level, and although it was positively associated with mental health at the weekly level, number of weekly interaction partners was positively associated with mental health when controlling for weekly time spent interacting online. These findings suggest that those who are more social in general  are not necessarily those with stronger mental health, but rather that there is something specific about the association between In sum, this work answers an important question about the relationship between virtual interactions and mental health through these challenging times. While we may be tempted to limit our virtual interactions as much as possible given the fatigue associated with such forms of communication relative to in-person communication, this research suggests that it is worth maintaining a variety of interaction partners as we continue to navigate an unprecedented environment.Supplementary Information.


N = 277) and T2 (N = 249), from Romanian entrepreneurs. We analyzed autoregressive, causal, reversed, and reciprocal models to test the mediation model. In the linkage between predictor and outcome variable, the reversed model is the best-fitting model, showing that proactive vitality management is only a distal precursor of performance. However, the best-fitting models for the relationship between predictor and mediator and between mediator and outcome were the reciprocal models. Thus, proactive vitality management and work–home enrichment have reciprocal effects on each other over time, as was the case between work–home enrichment and entrepreneurial performance. These results are in line with the resource gain cycle perspective of the Conservation of Resources theory. Employing proactive behaviors to optimize functioning at work enables the transfer of resources to the home role. Potentiating one role through aspects of another will thus generate additional resources reflecting on entrepreneurial performance. Hence, this study provides insights into precursors and mechanisms that can shape entrepreneurial performance.This study provides a cross-lagged examination of the relationships between proactive vitality management, work–home enrichment, and entrepreneurial performance. Specifically, based on the Job Demands-Resources and Conservation of Resources theories, we postulate a mediation model where proactive vitality management leads to entrepreneurs transferring resources developed in their work role to thrive in their home role , resulting in augmented entrepreneurial performance. The hypotheses were tested with data collected at two time points, 1 onth apart—T1 ( Research on entrepreneurs as individuals uncovered various indicators that entrepreneurs consider when evaluating their business success. For entrepreneurs, personal factors, such as entrepreneurial wellbeing and work–life balance, are vital elements in determining entrepreneurial success, being equal to or even outranking business profitability and income indicators . Indeed,In this respect, scholars have already identified various fixed  shift the perspective from factors contributing to entrepreneurs experiencing work–home conflict, to factors that can help entrepreneurs thrive in both roles , (2) estThe JD-R theory is well known for explaining the interplay between contextual resources  and work performance, employing a motivational component  as an explanatory mechanism . When inProactive vitality management encompasses “individual, goal-oriented behaviors aimed at managing physical and mental energy to promote optimal functioning at work”  p. 493. To replenish their resource–reservoir, entrepreneurs could detach from work through recovery activities  that will be transferred back to their home role, thus being able to handle the work–home mélange better. Based on these arguments, we posit that:Hypothesis 3a: Work–home enrichment at T1 has a positive impact on entrepreneurial performance at T2.Hypothesis 3b: Entrepreneurial performance at T1 has a positive impact on work–home enrichment at T2.Hypothesis 3c: Work–home enrichment and entrepreneurial performance have positive and reciprocal effects on each other over time.via email, inviting them to participate in the study, and asked them to recommend other entrepreneurs who could be invited to participate. Before starting the survey, the entrepreneurs were informed about the aim of the study and assured about their data confidentiality. No incentives were offered for participation.Data were collected 1 month apart in January  and February 2021 , a time lag that permits the observation of changes in this studies’ variables . ParticiM = 41.35, SD = 11.06), with the majority being male (60.3%). In terms of education, most respondents have at least a bachelor’s degree (65.7%), and the majority are either married (66.8%) or in a committed relationship (14.8%). The samples’ mean entrepreneurial experience is 12.12 years (SD = 12.05), and the mean firm tenure is 10.02 years (SD = 8.25). At T2, the same entrepreneurs were invited to fill out the survey again, resulting in 249 complete responses . The final sample has an age range between 20–80 years old , again with most of the respondents being male (60.6%). The mean entrepreneurial experience is 12.13 years (SD = 9.11) and a mean business tenure of 9.85 years (SD = 8.29). The sample is heterogeneous in industries, spanning from consultancy services to construction, food industry, software development, or the hospitality industry.At T1, 277 Romanian entrepreneurs filled out the survey regarding the study variables. In the initial sample, respondents’ age ranged between 18–79 years old  to verify the psychometric properties of our six-factor model . We conducted a longitudinal CFA to test for measurement invariance across the two measurement waves . First, χ2), the comparative fit index (CFI), the Tucker–Lewis index (TLI), the root mean square error of approximation (RMSEA), and the standardized root mean square residual (SRMR). Following All procedures were carried out in R software , using tvia structural equation modeling (SEM) in R software  and cutoff values  as described in the model measurement section have been used to assess model fit. Model comparison of the structural models was carried out through a Attrition analyses were conducted to test whether entrepreneurs who dropped out at T2 differ from entrepreneurs who completed both surveys regarding several demographic and this study’s variables . No statr = 0.64, p < 0.001), work–home enrichment , and entrepreneurial performance . The internal consistency of the scales is good to excellent  = 1230.97, p < 0.001, CFI = 0.90; TLI = 0.89; RMSEA = 0.06, 90% CI [0.06–0.07], SRMR = 0.06. Thus, we proceeded to test measurement invariance and structural models using the improved six-factor model.Results of the conducted CFA are reported in All measurement invariance models—Mconfigural, Mmetric, Mscalar, and Mstrict yield satisfactory fit indices regarding the measurement invariance across the two waves. The best fit was shown by the configural model, with the chi-square difference tests indicating a decrease in model fit for the other models, hinting at the possibility that the data were not invariant. However, χ2(2) = 10.82, p < 0.01). The reciprocal model (M1reciproc) is also superior to the stability model  = 12.77, p < 0.01) and the causal model  = 10.26, p < 0.01), but not the reciprocal model  = 1.95, p > 0.05). Both in the causal and the reciprocal model the relationship between proactive vitality management at T1 and entrepreneurial performance at T2 (H1a) was non-significant . Thus, the reversed model (M1reversed) is the best model where all relationships are statistically significant, leading us to consider the reversed model as the single acceptable model  = 197.12, p < 0.001, CFI = 0.96; TLI = 0.95; RMSEA = 0.08, 90% CI [0.07–0.10], SRMR = 0.05). As depicted in β = 0.63, p < 0.001), while entrepreneurial performance at T1 predicts itself at T2  as well as proactive vitality management at T2 .χ2(48) = 83.59, p < 0.001, CFI = 0.98; TLI = 0.98; RMSEA = 0.06, 90% CI [0.04–0.07], SRMR = 0.03), which is statistically significantly better than the stability model  = 16.36, p < 0.001), the causal model  = 6.95, p < 0.01), and the reversed model  = 7.89, p < 0.01). This suggests that there is both a potential causal and a reversed relationship between the predictor and mediator. As depicted in β = 0.61, p < 0.001) and work–home enrichment at T2 . Conversely, work–home enrichment at T1 predicts itself at T2 , and proactive vitality management at T2 .Regarding the potential causal relationship between proactive vitality management (predictor) and work–home enrichment (mediator), all structural models yield excellent fit indices see . The besχ2(71) = 146.61, p < 0.001, CFI = 0.97; TLI = 0.96; RMSEA = 0.07, 90% CI [0.05–0.08], SRMR = 0.04), which is statistically significantly better than the stability model  = 22.63, p < 0.001), the causal model  = 9.38, p < 0.01), and the reversed model  = 12.58, p < 0.001). This suggests that there is both a potential causal and a reversed relationship between the mediator and outcome. As shown in β = 0.66, p < 0.001) and entrepreneurial performance at T2 . Conversely, entrepreneurial performance at T1 predicts itself at T2 , and work–home enrichment at T2 .Regarding the potential causal relationship between work–home enrichment (mediator) and entrepreneurial performance (outcome), all structural models also yield excellent fit indices see . The besSummarizing, the hypothesized mediation model was partly confirmed. Proactive vitality at T1 did not predict entrepreneurial performance at T2 thus rejecting Hypothesis 1a, while entrepreneurial performance at T1 was a predictor of proactive vitality management at T2 conferring support to Hypothesis 1b. Furthermore, proactive vitality management at T1 did predict work–home enrichment at T2, and work–home enrichment at T1 was an antecedent of proactive vitality management at T2, conferring support to Hypotheses 2a and 2b. Hypotheses 3a and 3b also found support, with work–home enrichment at T1 being a precursor of entrepreneurial performance at T2 and entrepreneurial performance at T1 predicting work–home enrichment at T2. Regarding the proposed mutual effects between the variables, Hypothesis 1c was partly supported, because only entrepreneurial performance had a cross-lagged effect on proactive vitality management (M1reversed). Additionally, we established that proactive vitality management and work–home enrichment have a positive and reciprocal effect on each other over time , favoring Hypothesis 2c. That was also the case in the relationship between work–home enrichment and entrepreneurial performance, which show a positive and reciprocal effect on each other over time , providing support to Hypothesis 3c. Thus, work–home enrichment appears to be a mediator in the linkage between entrepreneurs’ proactive vitality management and entrepreneurial performance.Rooted in the JD-R  and COR Contrary to our expectations, in the relationship between predictor and outcome variable, the reversed model is the best-fitting model. Entrepreneurial performance has a direct effect on entrepreneurs engaging in proactive behaviors directed at optimizing their energy levels for work, but not the other way around. Based on our results, self-regulatory actions, such as exercising in the morning or meditating about the meaning of their job, do not have a direct effect on entrepreneurs’ satisfaction with aspects of their business. While previous studies establish a correlational link between proactive vitality management and performance , predictCOR theory can also best explain the mutual effects between predictor and mediator, and between mediator and outcome captured in this study. First, engaging in proactive vitality management appears to trigger a resource gain cycle, where entrepreneurs transfer resources between their work role and their home role. For instance, entrepreneurs can meditate about the meaning of their work and how their business helps their community . This can generate a positive state  which caSimilarly, when entrepreneurs experience work–home enrichment, thus being satisfied with how aspects of their work enhance their home functioning, they also appear to be more satisfied with their entrepreneurial performance. Our results complement and extend the findings of Summarizing, our results highlight that the work and home role of entrepreneurs can, in fact, act as potential allies, yet entrepreneurs must develop and employ relevant behavioral tools  to handle both roles efficiently. Furthermore, the model underlines the importance of integrating the role of an individual with a private life into models investigating factors related to entrepreneurs’ success indicators. Examining both sides of the coin  in a dynamic, longitudinal manner will enable more accurate modeling of factors that shape entrepreneurial performance.Some limitations of the current study are to be noted. First, we relied on self-report questionnaires to obtain the data and assess this studies’ variables. While this is a common practice in psychological studies, especially in ones that evaluate participants’ perceptions , it alsoSecond, while it has been argued that cross-lagged studies enable researchers to capture causal relationships among variables , other sThird, our sample consists of entrepreneurs from Eastern Europe, which may inhibit the generalization of our findings. While some authors argue that entrepreneurs are similar in characteristics irrespective of their cultural background , contextThis study contributes to entrepreneurship literature in several important ways. First, heeding the call of Second, by identifying proactive vitality management as a precursor of entrepreneurial success, our study also demonstrates that the recent JD-R expansion—the inclusion of individual strategies into the theoretical framework , appliesThird, we managed to establish a longitudinal causal relationship between entrepreneurs’ work and home roles from a positive perspective. This is a relatively new and unexplored area in entrepreneurship research that requires the focus of scholars to better understand the interplay between the two roles entrepreneurs assume. Although scholars tend to agree that the work and home roles of entrepreneurs are closely intertwined , they acFrom a practical perspective, we provide entrepreneurs and practitioners with a set of moldable tools that can be cultivated to allow entrepreneurs to manage their work and home role efficiently and concomitantly. Regarding the development of vitality management, scholars indicate that individuals can engage in various activities, such as practicing sports in the morning , keeping a balanced diet, or ensuring they get enough sleep at night . ImportaAdditionally, Entrepreneurs’ work and private life are closely intertwined, yet entrepreneurs can find it challenging to juggle between the two competing roles and satisfy both. This study uncovered proactive vitality management as a malleable behavioral component that helps entrepreneurs handle their work and home role efficiently, by transferring resources from their work role to enhance aspects of their home role. As such, entrepreneurs will ensure that work-related activities can help satisfy both their social (work–home enrichment) and financial  aspirations, reflecting on their entrepreneurial performance. Results also reveal that the two roles entrepreneurs assume have mutually beneficial effects on each other over time. When their work and home role act as allies, this enables entrepreneurs to enter a positive resource gain cycle. One concrete strategy toward attaining this objective is to engage in behaviors directed at managing mental and physical energies to promote optimal functioning at work.The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.Ethical review and approval was not required for the study on human participants in accordance with the local legislation and institutional requirements. The patients/participants provided their written informed consent to participate in this study.LT and DV conceived the presented idea. LT developed the theory and performed the computations. DV verified the analytical methods and guided the theoretical argumentation process. All authors contributed to the article and approved the submitted version.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


Genes have two alleles or copies, with one inherited from each parent. The loss of one of these gene copies, termed as loss of heterozygosity (LOH), is one of the most common genetic alterations in cancer . LOH hasSmall cell lung cancer (SCLC) is featured by rapid growth and tendency to metastasize, with grim prognosis and high relapse rate . HoweverP  =  0.0001; Fig. P  =  0.0003; Fig. P  =  0.5796; Fig. P  =  0.0305; Fig. P  =  0.035; Additional file P  =  0.2862; Fig. A total of 178 histologically confirmed SCLC patients were collected from Shandong Cancer Hospital and Institute. This study was approved by the Ethics Committee of Shandong Cancer Hospital and Institute. All included patients in this study offered written informed consent. Whole-exome-sequencing (WES) analyses were performed to detect the of the tumor mutational burden (TMB) and tumor neoantigen burden (TNB) for SCLC patients. We have demonstrated that SCLC patients with higher LOH were associated with lower tumor mutational burden (TMB) (R square  =  0.0825, To the best of our knowledge, we are the first to analyze the association between immune-related markers including TMB, TNB, CD8  +  TIL, PD-L1 and LOH for patients with SCLC. We not only demonstrated the negative association between LOH and TMB, TNB in SCLC, but also revealed that low LOH is associated with prolonged PFS. We concluded that LOH may predict PFS by negatively affecting TMB and TNB in SCLC. Our finding suggests that LOH is a very valuable benchmark that predicts PFS in SCLC. Undeniably, more clinical and translational researches are warranted for confirmation of LOH’s role in SCLC.Additional file 1: Table S1. Multivariate Cox regression analyses to evaluate the prognostic factors for PFS.


Background. Wound healing is a complex process of replacing devitalized cellular structures and tissues with healthy cells and tissue. Nanotechnology has been increasingly proposed as a novel platform to treat wounds and skin regeneration. The aim of this study was to evaluate the antibacterial, antioxidant, cytotoxic, and cutaneous wound healing activities of phytosynthesized Au NPs using Abelmoschus esculentus (okra) and synthesized Au NPs by using the citrate synthesis method. The Ok Au NPs were characterized using various techniques like UV-Vis absorption spectroscopy, FTIR, X-ray diffraction (XRD), and transmission electron microscopy (TEM). Cutaneous wounds were created on 30 rats and randomized into three groups: untreated and two groups treated with Ch Au NPs and Ok Au NPs. The treatment was carried out daily for 12 days. A peak characterized the Ok Au NPs at 538 nm in the UV-Vis spectrum. Based on the results of FTIR spectroscopy, various functional oxygenated groups such as hydroxyl, carboxyl, and nitrogenous groups were observed. XRD confirmed the crystalline nature of the nanoparticles. TEM images of Ok Au NPs showed a spherical shape and size range of 75 nm. DPPH test showed similar antioxidant potentials for Au NPs. The Au NPs showed cell viability in a dose-dependent manner, and this technique was found to be nontoxic. Agar well diffusion, which is the method to determine antibacterial characteristics of Au NPs, showed a significant beneficial effect against a variety of bacterial species. In addition, histopathological results showed that Au NPs could accelerate wound closure. Therefore, Au NPs could be suitable for wound healing applications. The skin is the largest organ in our body and acts as the first effective defense against the outside environment. The skin protects the body from exogenous organisms and substances. When a wound occurs to the skin, a series of active events begin rapidly, resulting in significant changes in the immune system –3. ThereIn recent years, a lot of attention has been paid to nanoscience and nanotechnology. Nanotechnology is concerned with nanoscale visualization, handling, and manufacture of substances . NanoparIn most of the physical techniques used for synthesizing nanoparticles, empirical factors are controlled in the presence of a reducing agent to regulate the nanoparticle composition and characteristics without contamination. The chemical reduction of metal ions in solutions is the easiest and commonest method used for their synthesis . The appOwing to its simplicity, nontoxicity, and cost-effectiveness, the biological method is considered the best technique for synthesizing nanoparticles . The comGold nanoparticles (Au NPs) have been investigated for medical applications such as wound healing because of their chemical properties, optical stability, and ease of surface modifications. For gold nanoparticles, fusion or surface modification with other biomolecules is needed before wound healing applications. For example, adding polysaccharide peptides to Au NPs enhances their potency to improve healing .β1 levels [Gold nanoparticles act as antioxidants by inhibiting free radicals such as hydroxyl, nitric oxide, and hydrogen peroxide . Gold na1 levels . Gold na1 levels . Besides1 levels .Nanoparticles can be applied for the treatment of wound infections due to their unique properties. Therefore, researchers have tried to discover an alternative therapeutic method using safer green options such as plants. The synthesis of plant-based nanoparticles is stable, fast, and economical. Furthermore, these nanoparticles can be synthesized with diverse shapes and sizes .Abelmoschus esculentus (L.) (okra) belongs to the Malvaceae family, a flowering plant. It is a herbal medicine used to treat wound healing with various advantageous properties, including antioxidant, antibacterial, antidiabetic, antiplasmodial, analgesic, anticancer, antidiarrheal, and anti-inflammation properties [operties . The okroperties . Saponinoperties . Flavonooperties . In thisA. esculentus fruit was obtained from the mountain of Kermanshah province (west of Iran).Phosphate buffer solution (PBS), Dulbecco's modified Eagle's medium (DMEM), penicillin-streptomycin solution, fetal bovine serum, dimethyl sulfoxide (DMSO), and 2, 2 diphenyl-1-picrylhydrazyl (DPPH) were obtained from Sigma-Aldrich  in analytical grade with maximum purity. Fresh Fresh okra was washed 3 times with deionized water, dried at 30°C in the oven, and powdered by a mortar, and then 1 g of obtained powder was boiled in 200 mL of water for 20 min until the solution color changed to light yellow. The extract was filtered through Whatman filter no. 1.4 × H2O (95 mL) at room temperature. The light violet color appeared for 5 minutes, indicating the formation of Au NPs. The solution was stirred for 1 h, centrifuged at 12000 rpm for 10 min, and removed from the upper phase to complete the reduction process. The obtained Ok Au NPs were washed three times with deionized water. They were then dried in an oven at 50 °C.For a typical experiment, freshly okra extract (5 mL) was mixed with an aqueous solution of (1 mM) HAuCl3) was freshly prepared in a beaker under a hood. Magnetic stir bar, 200 ml two-neck flask, stopper, and condenser were soaked in aqua for 15 min. Then, the glassware was washed with deionized water 3 times. (2) HAuCl4 solution, 2 ml of 50 mM, was added to 98 ml of Millipore water into the two-neck flask. (3) The condenser was connected to one neck of the flask, the stopper was placed in the other neck, and the flask was put on the hot plate to reflux while stirring. (4) As the refluxing was initiated, the stopper was removed, immediately 10 ml of 38.8 mM sodium citrate was added, and the stopper was replaced. The color changed from yellow to dark red in 1 min. The system was refluxed for 20 min and again for 5 min [Ch Au NPs were synthesized through 5 steps. (1) Aqua , different concentrations of Au NPs were prepared using an aqueous extract of okra and sodium citrate was mixed with 3 mL 0.004 DPPH solution. The control solution contained 1 mL DPPH and 3 mL methanol. The obtained solutions were shaken and then kept in the dark at room temperature for 30 min. Finally, the absorption rate of each sample was measured at 517 nm. All tests were done in triplicate. Ascorbic acid was used as the standard antioxidant. In addition, the percentage of DPPH scavenging activity was evaluated [HDF cell lines were obtained from the Pasture Institute of Tehran (Iran). Cell lines were cultured in DMEM and supplemented with 10% FBS and 1% penicillin-streptomycin .μg/mL) for 24 h. Then, 20 μL/well MTT (5 mg/mL) was added to each well and was incubated at 37 °C for 4 h. Finally, 100 μL of DMSO was added to the wells. The absorbance at 570 nm was measured with an ELISA reader.The effect of Au NPs on cell viability was measured using the MTT -2, 5-diphenyltetrazolium bromide) assay . At 80% Escherichia coli ATCC 11775, Klebsiella pneumoniae ATCC 13883 (Gram-negative bacterium), Staphylococcus aureus ATCC 33591, and Bacillus subtilis ATCC 6051 were purchased from the Persian Type Culture Collection. The culture medium alone and medium containing bacteria without tested compounds were used as controls. Bacterial culture was performed under aerobic conditions at 37 °C in a brain-heart infusion medium supplemented with fetal bovine serum (FBS 2 mL/litre) purchased from Sigma, UK. For each experiment, bacteria were grown in aerobic conditions as previously described [μl of MTT solution was added to every single well, and the 96-well microtiter plates were incubated for 30 min at 28°C. To avoid loss of formed formazan granules, 80% of the MTT solution was carefully removed. The insoluble purple formazan granules were solubilized with MTT lysis buffer , and the absorbance was measured at 540 nm. To optimize the incubation time of the bacterial suspension with MTT solution, several incubation times (15 and 30 min) were tested. The ultimate absorbance for each well was calculated as follows: Ab (540 nm) of the sample − Ab (540 nm) of the control.The MIC test was performed on a sterile 96-cell plate using the CLSI standard microdilution method and European Committee on Antimicrobial Susceptibility Testing (EUCAST). For evaluating the antibacterial activity, the four bacterial species escribed . Twenty p < 0.05), and therefore, the studied nanoparticles had a lethal effect on microorganisms. The following equation was used to measure growth inhibition values (the results were expressed as mean ± SD) [In another method, by creating a well on the culture medium by sterile pipette (with pipette number 5 and depth 4 mm), the bacterial suspension with a concentration equivalent to half McFarland was spread on the surface of the culture medium with a swap. All MIC and MBC analyses were performed in 2 replications. SPSS software (version 20) was used to analyze the data, and one-way analysis of variance (ANOVA) was used to analyze the differences between different treatments. The statistical analysis results showed a significant level for the studied nanoparticles (an ± SD) : growth Sprague Dawley rats  were used to assess the position of wound healing. All experiments were performed according to the Animal Care and Use Protocol of Kermanshah University of Medical Sciences (Iran) and approved by the Ethical Committee of the Kermanshah University of Medical Sciences .Adult male μm were prepared with Masson's trichrome (MT) and H&E method and histological changes surveyed by optical microscopy (Olympus).Based on the Helsinki Protocol . The rats were kept in separate cages at 20 ± 2 °C with a 12 h/12 h light/dark cycle and free access to food and water. The animals were anesthetized by intramuscular administration of 50 mg/kg i.p. body weight of ketamine. After anaesthesia induction, the animals' back hair was shaven using an electronic shaver, and the exposed skin was washed with 70% ethanol. A full-thickness skin excision wound was made (2 cm in diameter). After creating the cutaneous wound, the animals were randomized into three groups ; the untreated group, the group treated with 0.1 ml of Ok Au NPs colloidal solution, and the group treated with Ch Au NPs colloidal solution. The treatment was performed for the wound on 12 consequent days. A digital calliper was used to measure the changes in wound area diameter (mm). For histological examination, all animals were euthanised on the 12th day of the surgery. The wound and the surrounding skin were thoroughly removed for histopathological staining. The samples were fixed with 10% formalin, dehydrated with different graded alcohol concentrations, cleared with xylene, and embedded in paraffin. The sections of 5 P < 0.01 was a limit of significance.The data were analyzed by one-way ANOVA using the SPSS v22 software package. 3+ may also be involved in this. Generally, there is a positive correlation between the reduction of gold ions to Au NPs and the intensity of the SPR peak. The complete reduction of gold ions to Au NPs is shown by developing a stable dark purple color in the reaction mixture over 5 minutes [At first, Au NPs synthesis in the solution and their surface plasmon resonance (SPR peaks) and size were confirmed using a UV-Vis spectrophotometer  in the range of 200 to 700 nm. Curve (a) in  minutes .−1 (O–H stretch), 2926 cm−1 (C–H stretch), 1612 cm−1 (C = O stretch), 1444 cm−1 (C–C stretch), and 1045 cm−1 (C–N stretch). −1 (O–H stretch), 2924 and 2854 cm−1 (C–H stretch), 1618 cm−1 (C = O stretch), 1458 cm−1, 1379 cm−1 (C–C stretch), and 1163 cm−1 (C–N stretch). −1 (O–H stretch), 2954 and 2924 cm−1 (C–H stretch), 1618 cm−1 (C = O stretch), 1458 cm−1, 1379 cm−1 (C–C stretch), and 1165 cm−1 (C–N stretch). As a result, one band at 540 cm−1 disappeared and another new band was developed at 476 cm−1 , (200), (220), and (311) facets of a face-centred cubic crystal structure (JCPDS no. 004–0784). Therefore, the crystalline structure of prepared Au NPs with okra extract was confirmed . We haveThe size and shape of Ok Au NPs were determined by transmission electron microscopy (TEM). TEM images obtained from the Zeiss-EM10C-100 KV instrument are shown in Recently, the application of medicinal plants with antioxidant and anti-inflammatory activity has increased. Antioxidant compounds reduce the free radicals in the wound area and enhance wound healing . Metal nμg/mL) against HDF cell line using MTT assay for 24 h . This reaction is carried out by light energy in air or Hbacteria . It shoubacteria . Therefobacteria .This study showed an enhanced percentage of wound closure and diameter of the wound area Figures  and 10. epidermis, dermis (connective tissue with skin appendages), and hypodermis are observed  and Masson's trichrome (MT) staining were performed in health and wound tissue samples. In healthy skin, the stratum of the observed . A wide observed . In grouobserved . In agres spectroscopy, FTIR, XRD, and TEM techniques were used to characterize the Ok Au NPs synthesized. Furthermore, the favorable antibacterial and antioxidant activities of the green synthesis of Au NPs could be a safe and effective adjuvant for wound healing. Therefore, Ok Au NPs may be ideal candidates for future studies discovering their use in therapeutic and pharmaceutical applications.This study indicated that synthesizing Au NPs using aqueous okra extract is an ecofriendly, simple, and useful method. UV/Vi