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Quality of Life Research - During the COVID-19 pandemic, widespread public health measures were implemented to control community transmission. The association between these measures and...  相似文献   
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Journal of Neuro-Oncology - The optimal treatment strategy of asymptomatic, convexity meningiomas, remains unclear. The purpose of this study was to define the safety and efficacy of stereotactic...  相似文献   
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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.

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BACKGROUNDTo understand the features of a replicating vaccine that might drive potent and durable immune responses to transgene-encoded antigens, we tested a replication-competent adenovirus type 4 encoding influenza virus H5 HA (Ad4-H5-Vtn) administered as an oral capsule or via tonsillar swab or nasal spray.METHODSViral shedding from the nose, mouth, and rectum was measured by PCR and culturing. H5-specific IgG and IgA antibodies were measured by bead array binding assays. Serum antibodies were measured by a pseudovirus entry inhibition, microneutralization, and HA inhibition assays.RESULTSAd4-H5-Vtn DNA was shed from most upper respiratory tract–immunized (URT-immunized) volunteers for 2 to 4 weeks, but cultured from only 60% of participants, with a median duration of 1 day. Ad4-H5-Vtn vaccination induced increases in H5-specific CD4+ and CD8+ T cells in the peripheral blood as well as increases in IgG and IgA in nasal, cervical, and rectal secretions. URT immunizations induced high levels of serum neutralizing antibodies (NAbs) against H5 that remained stable out to week 26. The duration of viral shedding correlated with the magnitude of the NAb response at week 26. Adverse events (AEs) were mild, and peak NAb titers were associated with overall AE frequency and duration. Serum NAb titers could be boosted to very high levels 2 to 5 years after Ad4-H5-Vtn vaccination with recombinant H5 or inactivated split H5N1 vaccine.CONCLUSIONReplicating Ad4 delivered to the URT caused prolonged exposure to antigen, drove durable systemic and mucosal immunity, and proved to be a promising platform for the induction of immunity against viral surface glycoprotein targets.TRIAL REGISTRATIONClinicalTrials.gov NCT01443936 and NCT01806909.FUNDINGIntramural and Extramural Research Programs of the NIAID, NIH (U19 AI109946) and the Centers of Excellence for Influenza Research and Surveillance (CEIRS), NIAID, NIH (contract HHSN272201400008C).  相似文献   
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