A Review of the Pathophysiology and Novel Treatments for Erectile Dysfunction

Erectile dysfunction (ED) affects up to 50% of men between the ages of 40 and 70. Treatment with PDE-5 inhibitors is effective in the majority of men with ED. However, PDE-5 inhibitors are not effective when levels of nitric oxide (NO), the principle mediator of erection, are low. The pharmacologic actions of three new potential treatments for ED are discussed in this paper: (1) sGC stimulators/activators, (2) Rho-kinase inhibitors, and (3) sodium nitrite

Corporate Governance for Sustainability

The current model of corporate governance needs reform. There is mounting evidence that the practices of shareholder primacy drive company directors and executives to adopt the same short time horizon as financial markets. Pressure to meet the demands of the financial markets drives stock buybacks, excessive dividends and a failure to invest in productive capabilities. The result is a ‘tragedy of the horizon’, with corporations and their shareholders failing to consider environmental, social or even their own, long-term, economic sustainability. With less than a decade left to address the threat of climate change, and with consensus emerging that businesses need to be held accountable for their contribution, it is time to act and reform corporate governance in the EU. The statement puts forward specific recommendations to clarify the obligations of company boards and directors and make corporate governance practice significantly more sustainable and focused on the long term

Development of a Definition of Postacute Sequelae of SARS-CoV-2 Infection

IMPORTANCE: SARS-CoV-2 infection is associated with persistent, relapsing, or new symptoms or other health effects occurring after acute infection, termed postacute sequelae of SARS-CoV-2 infection (PASC), also known as long COVID. Characterizing PASC requires analysis of prospectively and uniformly collected data from diverse uninfected and infected individuals. OBJECTIVE: To develop a definition of PASC using self-reported symptoms and describe PASC frequencies across cohorts, vaccination status, and number of infections. DESIGN, SETTING, AND PARTICIPANTS: Prospective observational cohort study of adults with and without SARS-CoV-2 infection at 85 enrolling sites (hospitals, health centers, community organizations) located in 33 states plus Washington, DC, and Puerto Rico. Participants who were enrolled in the RECOVER adult cohort before April 10, 2023, completed a symptom survey 6 months or more after acute symptom onset or test date. Selection included population-based, volunteer, and convenience sampling. EXPOSURE: SARS-CoV-2 infection. MAIN OUTCOMES AND MEASURES: PASC and 44 participant-reported symptoms (with severity thresholds). RESULTS: A total of 9764 participants (89% SARS-CoV-2 infected; 71% female; 16% Hispanic/Latino; 15% non-Hispanic Black; median age, 47 years [IQR, 35-60]) met selection criteria. Adjusted odds ratios were 1.5 or greater (infected vs uninfected participants) for 37 symptoms. Symptoms contributing to PASC score included postexertional malaise, fatigue, brain fog, dizziness, gastrointestinal symptoms, palpitations, changes in sexual desire or capacity, loss of or change in smell or taste, thirst, chronic cough, chest pain, and abnormal movements. Among 2231 participants first infected on or after December 1, 2021, and enrolled within 30 days of infection, 224 (10% [95% CI, 8.8%-11%]) were PASC positive at 6 months. CONCLUSIONS AND RELEVANCE: A definition of PASC was developed based on symptoms in a prospective cohort study. As a first step to providing a framework for other investigations, iterative refinement that further incorporates other clinical features is needed to support actionable definitions of PASC

Transmission of Yellow Fever Vaccine Virus Through Blood Transfusion and Organ Transplantation in the USA in 2021: Report of an Investigation

BACKGROUND: In 2021, four patients who had received solid organ transplants in the USA developed encephalitis beginning 2-6 weeks after transplantation from a common organ donor. We describe an investigation into the cause of encephalitis in these patients. METHODS: From Nov 7, 2021, to Feb 24, 2022, we conducted a public health investigation involving 15 agencies and medical centres in the USA. We tested various specimens (blood, cerebrospinal fluid, intraocular fluid, serum, and tissues) from the organ donor and recipients by serology, RT-PCR, immunohistochemistry, metagenomic next-generation sequencing, and host gene expression, and conducted a traceback of blood transfusions received by the organ donor. FINDINGS: We identified one read from yellow fever virus in cerebrospinal fluid from the recipient of a kidney using metagenomic next-generation sequencing. Recent infection with yellow fever virus was confirmed in all four organ recipients by identification of yellow fever virus RNA consistent with the 17D vaccine strain in brain tissue from one recipient and seroconversion after transplantation in three recipients. Two patients recovered and two patients had no neurological recovery and died. 3 days before organ procurement, the organ donor received a blood transfusion from a donor who had received a yellow fever vaccine 6 days before blood donation. INTERPRETATION: This investigation substantiates the use of metagenomic next-generation sequencing for the broad-based detection of rare or unexpected pathogens. Health-care workers providing vaccinations should inform patients of the need to defer blood donation for at least 2 weeks after receiving a yellow fever vaccine. Despite mitigation strategies and safety interventions, a low risk of transfusion-transmitted infections remains. FUNDING: US Centers for Disease Control and Prevention (CDC), the Biomedical Advanced Research and Development Authority, and the CDC Epidemiology and Laboratory Capacity Cooperative Agreement for Infectious Diseases

Real-world characterization of blood glucose control and insulin use in the intensive care unit

The heterogeneity of critical illness complicates both clinical trial design and real-world management. This complexity has resulted in conflicting evidence and opinion regarding the optimal management in many intensive care scenarios. Understanding this heterogeneity is essential to tailoring management to individual patients. Hyperglycaemia is one such complication in the intensive care unit (ICU), accompanied by decades of conflicting evidence around management strategies. We hypothesized that analysis of highly-detailed electronic medical record (EMR) data would demonstrate that patients vary widely in their glycaemic response to critical illness and response to insulin therapy. Due to this variability, we believed that hyper- and hypoglycaemia would remain common in ICU care despite standardised approaches to management. We utilized the Medical Information Mart for Intensive Care III v1.4 (MIMIC) database. We identified 19,694 admissions between 2008 and 2012 with available glucose results and insulin administration data. We demonstrate that hyper- and hypoglycaemia are common at the time of admission and remain so 1 week into an ICU admission. Insulin treatment strategies vary significantly, irrespective of blood glucose level or diabetic status. We reveal a tremendous opportunity for EMR data to guide tailored management. Through this work, we have made available a highly-detailed data source for future investigation

Predicting hypoglycemia in critically Ill patients using machine learning and electronic health records

Abstract Hypoglycemia is a common occurrence in critically ill patients and is associated with significant mortality and morbidity. We developed a machine learning model to predict hypoglycemia by using a multicenter intensive care unit (ICU) electronic health record dataset. Machine learning algorithms were trained and tested on patient data from the publicly available eICU Collaborative Research Database. Forty-four features including patient demographics, laboratory test results, medications, and vitals sign recordings were considered. The outcome of interest was the occurrence of a hypoglycemic event (blood glucose < 72 mg/dL) during a patient’s ICU stay. Machine learning models used data prior to the second hour of the ICU stay to predict hypoglycemic outcome. Data from 61,575 patients who underwent 82,479 admissions at 199 hospitals were considered in the study. The best-performing predictive model was the eXtreme gradient boosting model (XGBoost), which achieved an area under the received operating curve (AUROC) of 0.85, a sensitivity of 0.76, and a specificity of 0.76. The machine learning model developed has strong discrimination and calibration for the prediction of hypoglycemia in ICU patients. Prospective trials of these models are required to evaluate their clinical utility in averting hypoglycemia within critically ill patient populations