Background Privacy restrictions limit access to protected patient-derived health information for research purposes. Consequently, data anonymization is required to allow researchers data access for initial analysis before granting institutional review board approval. A system installed and activated at our institution enables synthetic data generation that mimics data from real electronic medical records, wherein only fictitious patients are listed. Objective This paper aimed to validate the results obtained when analyzing synthetic structured data for medical research. A comprehensive validation process concerning meaningful clinical questions and various types of data was conducted to assess the accuracy and precision of statistical estimates derived from synthetic patient data. Methods A cross-hospital project was conducted to validate results obtained from synthetic data produced for five contemporary studies on various topics. For each study, results derived from synthetic data were compared with those based on real data. In addition, repeatedly generated synthetic datasets were used to estimate the bias and stability of results obtained from synthetic data. Results This study demonstrated that results derived from synthetic data were predictive of results from real data. When the number of patients was large relative to the number of variables used, highly accurate and strongly consistent results were observed between synthetic and real data. For studies based on smaller populations that accounted for confounders and modifiers by multivariate models, predictions were of moderate accuracy, yet clear trends were correctly observed. Conclusions The use of synthetic structured data provides a close estimate to real data results and is thus a powerful tool in shaping research hypotheses and accessing estimated analyses, without risking patient privacy. Synthetic data enable broad access to data (eg, for out-of-organization researchers), and rapid, safe, and repeatable analysis of data in hospitals or other health organizations where patient privacy is a primary value.

Background The coronavirus disease 2019 (COVID‐19) crisis and consequent changes in medical practice have engendered feelings of distress in diverse populations, potentially adversely affecting the psychological well‐being of cancer patients. Aim The purpose of this observational longitudinal study was to evaluate psychosocial perspectives among patients with cancer on intravenous treatment during the COVID‐19 pandemic. Methods and results The study recruited 164 cancer patients undergoing intravenous anti‐neoplastic therapy in a tertiary cancer center. Psychosocial indices were assessed at two points in time, corresponding with the beginning of the first wave of COVID‐19 pandemic in Israel (March 2020) and the time of easing of restrictions implemented to curtail spread of infection (May 2020). At Time 1 (T1), elevated COVID‐19 distress levels (score 1 and 2 on 5‐point scale) were observed in 44% of patients, and associated with pre‐existing hypertension and lung disease in multivariate analyses but no demographic or cancer related factors. At Time 2 (T2), 10% had elevated anxiety and 24% depression as indicated by Hospital Anxiety and Depression Scale (HADS‐A/D). COVID‐19 distress at T1 was related to higher levels of HADS‐A at T2 (Spearman 0.33 p < .01), but not HADS‐D. Patients with breast cancer expressed greater COVID‐19 distress compared with other cancer types (p < .01), while both HADS‐A and HADS‐D were highest for patients with GI cancer. Patient report of loneliness and decreased support from relatives were factors associated with HADS‐A (p = .03 and p < .01, respectively), while HADS‐D was not similarly related to the factors evaluated. Conclusion Patients with cancer undergoing intravenous treatment may be vulnerable to acute adverse psychological ramifications of COVID‐19, specifically exhibiting high levels of anxiety. These appear unrelated to patient age or disease stage. Those with underlying comorbidities, breast cancer or reduced social support may be at higher risk.

Background: COVID-19 is a newly recognized illness with a predominantly respiratory presentation. It is important to characterize the differences in disease presentation and trajectory between COVID-19 patients and other patients with common respiratory illnesses. These differences can enhance knowledge of pathogenesis and help in guiding treatment.Methods: Data from electronic medical records were obtained from individuals admitted with respiratory illnesses to Rambam Health Care Campus, Haifa, Israel, between October 1st, 2014 and October 1st, 2020. Four groups of patients were defined: COVID-19 (693), influenza (1,612), severe acute respiratory infection (SARI) (2,292), and Others (4,054). The variable analyzed include demographics (7), vital signs (8), lab tests (38), and comorbidities (15) from a total of 8,651 hospitalized adult patients. Statistical analysis was performed on biomarkers measured at admission and for their disease trajectory in the first 48 h of hospitalization, and on comorobidity prevalence.Results: COVID-19 patients were overall younger in age and had higher body mass index, compared to influenza and SARI. Comorbidity burden was lower in the COVID-19 group compared to influenza and SARI. Severely- and moderately-ill COVID-19 patients older than 65 years of age suffered higher rate of in-hospital mortality compared to hospitalized influenza patients. At admission, white blood cells and neutrophils were lower among COVID-19 patients compared to influenza and SARI patients, while pulse rate and lymphoctye percentage were higher. Trajectories of variables during the first 2 days of hospitalization revealed that white blood count, neutrophils percentage and glucose in blood increased among COVID-19 patients, while decreasing among other patients.Conclusions: The intrinsic virulence of COVID-19 appeared higher than influenza. In addition, several critical functions, such as immune response, coagulation, heart and respiratory function, and metabolism were uniquely affected by COVID-19.

Growth extent and direction determine cell and whole-organ architecture. How they are spatiotemporally modulated to control size and shape? Here we tackled this question by studying the effect of brassinosteroid (BR) signaling on the structure of the root meristem. Quantification of the 3D geometry of thousands of individual meristematic cells across different tissue types showed that modulation of BR signaling yields distinct changes in growth rate and anisotropy, which affects the time cells spend in the meristem and has a strong impact on final root form. By contrast, the hormone effect on cell volume was minor, establishing cell volume as invariant to the effect of BR. Thus, BR has highest effect on cell shape and growth anisotropy, regulating overall radial growth of the meristem, while maintaining a coherent distribution of cell sizes. Moving from single-cell quantification to the whole organ, we developed a computational model of radial growth that demonstrates how differential growth regulation by BR between the inner and outer tissues shapes the meristem. The model explains the unintuitive outcomes of tissue-specific perturbation of BR signaling and suggests that the inner and outer tissues have independent but coordinated roles in growth regulation.

Background: Non-invasive oxygen saturation (SpO2) measurement is a central vital sign that supports the management of COVID-19 patients. However, reports on SpO2 characteristics (patterns and dynamics) are scarce and none, to our knowledge, has analysed high resolution continuous SpO2 in COVID-19. Methods: SpO2 signal sampled at 1Hz and clinical data were collected from COVID-19 departments at the Rambam Health Care Campus (Haifa, Israel) between May 1st, 2020 and February 1st, 2021. Data from a total of 367 COVID-19 patients, totalling 27K hours of continuous SpO2 recording, could be retrieved, including 205 non-critical and 162 critical cases. Desaturations based on different SpO2 threshold definitions and oximetry derived digital biomarkers (OBMs) were extracted and compared across severity and support levels. Findings: An absolute SpO2 threshold at 93% was the most efficient in discriminating between critical and non-critical patients without support or under oxygen support. Under no support, the non-critical group depicted a fold change (FC) of 1,8 times more frequent desaturations compared to the critical group. However, the hypoxic burden was 1,6 times more important in critical versus non-critical patients. Other OBMs depicted significant differences, notably the percentage of time below 93% SpO2 (CT93) was the most discriminating OBM. Mechanical ventilation depicted a strong effect on SpO2 by significantly reducing the frequency (1,85 FC) and depth (1,21 FC) of desaturations. OBMs related to periodicity and hypoxic burden were markedly affected up to several hours before the initiation of the mechanical ventilation. Interpretation: This is the first report investigating continuous SpO2 measurements in hospitalized patients affected with COVID-19. SpO2 characteristics differ between critical and non-critical patients and are impacted by the level of support. OBMs from high resolution SpO2 signal may enable to anticipate clinically relevant events, monitoring of treatment response and may be indicative of future deterioration.