The correct interpretation of a gradient of the morphogen Hedgehog (Hh) during development requires phosphorylation of the Hh signaling activator Smoothened (Smo); however, the molecular mechanism by which Smo transduces graded Hh signaling is not well understood. We show that regulation of the phosphorylation status of Smo by distinct phosphatases at specific phosphorylated residues creates differential thresholds of Hh signaling. Phosphorylation of Smo was initiated by adenosine 3′,5′-monophosphate (cAMP)–dependent protein kinase (PKA) and further enhanced by casein kinase I (CKI). We found that protein phosphatase 1 (PP1) directly dephosphorylated PKA-phosphorylated Smo to reduce signaling mediated by intermediate concentrations of Hh, whereas PP2A specifically dephosphorylated PKA-primed, CKI-phosphorylated Smo to restrict signaling by high concentrations of Hh. We also established a functional link between sequentially phosphorylated Smo species and graded Hh activity. Thus, we propose a sequential phosphorylation model in which precise interpretation of morphogen concentration can be achieved upon versatile phosphatase-mediated regulation of the phosphorylation status of an essential activator in developmental signaling.

Severe or life threatening infections are common among patients in the intensive care unit (ICU). Most infections in the ICU are bacterial or fungal in origin and require antimicrobial therapy for clinical resolution. Antibiotics are the cornerstone of therapy for infected critically ill patients. However, antibiotics are often not optimally administered resulting in less favorable patient outcomes including greater mortality. The timing of antibiotics in patients with life threatening infections including sepsis and septic shock is now recognized as one of the most important determinants of survival for this population. Individuals who have a delay in the administration of antibiotic therapy for serious infections can have a doubling or more in their mortality. Additionally, the timing of an appropriate antibiotic regimen, one that is active against the offending pathogens based on in vitro susceptibility, also influences survival. Thus not only is early empiric antibiotic administration important but the selection of those agents is crucial as well. The duration of antibiotic infusions, especially for β-lactams, can also influence antibiotic efficacy by increasing antimicrobial drug exposure for the offending pathogen. However, due to mounting antibiotic resistance, aggressive antimicrobial de-escalation based on microbiology results is necessary to counterbalance the pressures of early broad-spectrum antibiotic therapy. In this review, we examine time related variables impacting antibiotic optimization as it relates to the treatment of life threatening infections in the ICU. In addition to highlighting the importance of antibiotic timing in the ICU we hope to provide an approach to antimicrobials that also minimizes the unnecessary use of these agents. Such approaches will increasingly be linked to advances in molecular microbiology testing and artificial intelligence/machine learning. Such advances should help identify patients needing empiric antibiotic therapy at an earlier time point as well as the specific antibiotics required in order to avoid unnecessary administration of broad-spectrum antibiotics.

Background Synthetic data may provide a solution to researchers who wish to generate and share data in support of precision healthcare. Recent advances in data synthesis enable the creation and analysis of synthetic derivatives as if they were the original data; this process has significant advantages over data deidentification. Objectives To assess a big-data platform with data-synthesizing capabilities (MDClone Ltd., Beer Sheva, Israel) for its ability to produce data that can be used for research purposes while obviating privacy and confidentiality concerns. Methods We explored three use cases and tested the robustness of synthetic data by comparing the results of analyses using synthetic derivatives to analyses using the original data using traditional statistics, machine learning approaches, and spatial representations of the data. We designed these use cases with the purpose of conducting analyses at the observation level (Use Case 1), patient cohorts (Use Case 2), and population-level data (Use Case 3). Results For each use case, the results of the analyses were sufficiently statistically similar (P > 0.05) between the synthetic derivative and the real data to draw the same conclusions. Discussion and conclusion This article presents the results of each use case and outlines key considerations for the use of synthetic data, examining their role in clinical research for faster insights and improved data sharing in support of precision healthcare.

Obliterative bronchiolitis (OB) is a clinical syndrome marked by progressive dyspnea and cough with the absence of parenchymal lung disease on radiographic studies. Pulmonary function testing reveals an obstructive ventilatory defect that is typically not reversed by inhaled bronchodilator. Transbronchial biopsies are insufficiently sensitive to achieve diagnosis, and in most cases, clinical, physiological, and radiological data obviate the need for the increased risk associated with open lung biopsy. This diagnosis has been documented in a variety of exposures, including fumes from flavoring plants, smoke from burn pits, and environmental sulfur gas. Among lung transplant recipients, "bronchiolitis obliterans syndrome," a disorder with clinical and histopathological similarity to OB, represents the leading cause of long-term allograft dysfunction and mortality. After hematopoietic stem cell transplantation, chronic graft versus host disease of the lung manifests most frequently with similar clinical and pathological features. In all circumstances, immunologic and nonimmunologic mechanisms are thought to lead to airway epithelial dysfunction, which results in progressive airflow obstruction and debility. Augmentation of immunosuppression is occasionally effective in slowing or reversing the progression of disease though a significant number of patients will be nonresponders. Other immunomodulatory methods have been attempted in each circumstance where this pathology has been identified. Unfortunately, OB is poorly understood and often results in sufficient progression of disease to warrant evaluation for lung transplantation (or retransplantation). Here, we review what is known regarding pathophysiology and discuss clinical, pathological, radiological, and therapeutic factors associated with the spectrum of OB-related disease with a particular focus on lung transplantation.

There is limited data on longitudinal outcomes for COVID-19 hospitalizations that account for transitions between clinical states over time. Using electronic health record data from a St. Louis-region hospital network, we performed multi-state analyses to examine longitudinal transitions and outcomes among hospitalized adults with laboratory-confirmed COVID-19 with respect to fifteen mutually-exclusive clinical states. Between March 15 and July 25, 2020, 1,577 patients were hospitalized with COVID-19 (49.9% male, median age 63 years [IQR 50, 75], 58.8% Black). Overall, 34.1% (95% confidence interval [CI] 26.4%, 41.8%) had an ICU admission and 12.3% (CI 8.5%, 16.1%) received invasive mechanical ventilation (IMV). The risk of decompensation peaked immediately after admission, discharges peaked around day 3 to 5, and deaths plateaued between days 7 and 16. At 28 days, 12.6% (CI 9.6%, 15.6%) of patients had died (4.2% [CI 3.2%, 5.2%] received IMV) and 80.8% (CI 75.4%, 86.1%) were discharged. Among those receiving IMV, 39.1% (CI 32.0%, 46.2%) remained intubated after 14 days; after 28 days, 37.6% (CI 30.4%, 44.7%) had died and only 37.7% (CI 30.6%, 44.7%) were discharged. Multi-state methods offer granular characterizations of the clinical course of COVID-19 and provide essential information for guiding both clinical decision-making and public health planning.

Although vaccines have been evaluated and approved for SARS-CoV-2 infection prevention, there remains a lack of effective treatments to reduce the mortality of COVID-19 patients already infected with SARS-CoV-2. The global data on COVID-19 showed that men have a higher mortality rate than women. We further observed that the proportion of mortality of females increases starting from around the age of 55 significantly. Thus, sex is an essential factor associated with COVID-19 mortality, and sex related genetic factors could be interesting mechanisms and targets for COVID-19 treatment. However, the associated sex factors and signaling pathways remain unclear. Here, we propose to uncover the potential sex associated factors using systematic and integrative network analysis. The unique results indicated that estrogens, e.g., estrone and estriol, (1) interacting with ESR1/2 receptors, (2) can inhibit SARS-CoV-2 caused inflammation and immune response signaling in host cells; and (3) estrogens are associated with the distinct fatality rates between male and female COVID-19 patients. Specifically, a high level of estradiol protects young female COVID-19 patients, and estrogens drop to an extremely low level in females after about 55 years of age causing the increased fatality rate of women. In conclusion, estrogen, interacting with ESR1/2 receptors, is an essential sex factor that protects COVID-19 patients from death by inhibiting inflammation and immune response caused by SARS-CoV-2 infection. Moreover, medications boosting the down-stream signaling of ESR1/ESR2, or inhibiting the inflammation and immune-associated targets on the signaling network can be potentially effective or synergistic combined with other existing drugs for COVID-19 treatment.

OBJECTIVES: Assess the impact of heterogeneity among established sepsis criteria (Sepsis-1, Sepsis-3, Centers for Disease Control and Prevention Adult Sepsis Event, and Centers for Medicare and Medicaid severe sepsis core measure 1) through the comparison of corresponding sepsis cohorts. DESIGN: Retrospective analysis of data extracted from electronic health record. SETTING: Single, tertiary-care center in St. Louis, MO. PATIENTS: Adult, nonsurgical inpatients admitted between January 1, 2012, and January 6, 2018. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: In the electronic health record data, 286,759 encounters met inclusion criteria across the study period. Application of established sepsis criteria yielded cohorts varying in prevalence: Centers for Disease Control and Prevention Adult Sepsis Event (4.4%), Centers for Medicare and Medicaid severe sepsis core measure 1 (4.8%), International Classification of Disease code (7.2%), Sepsis-3 (7.5%), and Sepsis-1 (11.3%). Between the two modern established criteria, Sepsis-3 (n = 21,550) and Centers for Disease Control and Prevention Adult Sepsis Event (n = 12,494), the size of the overlap was 7,763. The sepsis cohorts also varied in time from admission to sepsis onset (hr): Sepsis-1 (2.9), Sepsis-3 (4.1), Centers for Disease Control and Prevention Adult Sepsis Event (4.6), and Centers for Medicare and Medicaid severe sepsis core measure 1 (7.6); sepsis discharge International Classification of Disease code rate: Sepsis-1 (37.4%), Sepsis-3 (40.1%), Centers for Medicare and Medicaid severe sepsis core measure 1 (48.5%), and Centers for Disease Control and Prevention Adult Sepsis Event (54.5%); and inhospital mortality rate: Sepsis-1 (13.6%), Sepsis-3 (18.8%), International Classification of Disease code (20.4%), Centers for Medicare and Medicaid severe sepsis core measure 1 (22.5%), and Centers for Disease Control and Prevention Adult Sepsis Event (24.1%). CONCLUSIONS: The application of commonly used sepsis definitions on a single population produced sepsis cohorts with low agreement, significantly different baseline demographics, and clinical outcomes.

Background The Coronavirus Disease 2019 (COVID-19) pandemic has infected over 10 million people globally with a relatively high mortality rate. There are many therapeutics undergoing clinical trials, but there is no effective vaccine or therapy for treatment thus far. After affected by the Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2), molecular signaling pathways of host cells play critical roles during the life cycle of SARS-CoV-2. Thus, it is significant to identify the involved molecular signaling pathways within the host cells. Drugs targeting these molecular signaling pathways could be potentially effective for COVID-19 treatment. Methods In this study, we developed a novel integrative analysis approach to identify the related molecular signaling pathways within host cells, and repurposed drugs as potentially effective treatments for COVID-19, based on the transcriptional response of host cells. Results We identified activated signaling pathways associated with the infection caused SARS-CoV-2 in human lung epithelial cells through integrative analysis. Then, the activated gene ontologies (GOs) and super GOs were identified. Signaling pathways and GOs such as MAPK, JNK, STAT, ERK, JAK-STAT, IRF7-NFkB signaling, and MYD88/CXCR6 immune signaling were particularly activated. Based on the identified signaling pathways and GOs, a set of potentially effective drugs were repurposed by integrating the drug-target and reverse gene expression data resources. In addition to many drugs being evaluated in clinical trials, the dexamethasone was top-ranked in the prediction, which was the first reported drug to be able to significantly reduce the death rate of COVID-19 patients receiving respiratory support. Conclusions The integrative genomics data analysis and results can be helpful to understand the associated molecular signaling pathways within host cells, and facilitate the discovery of effective drugs for COVID-19 treatment.