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Although prostate-specific antigen (PSA) serum level is currently the standard of care for prostate cancer screening in the United States, it lacks ideal specificity and additional biomarkers are needed to supplement or potentially replace serum PSA testing. Emerging evidence suggests that monitoring the noncoding RNA transcript PCA3 in urine may be useful in detecting prostate cancer in patients with elevated PSA levels. Here, we show that a multiplex panel of urine transcripts outperforms PCA3 transcript alone for the detection of prostate cancer. We measured the expression of seven putative prostate cancer biomarkers, including PCA3, in sedimented urine using quantitative PCR on a cohort of 234 patients presenting for biopsy or radical prostatectomy. By univariate analysis, we found that increased GOLPH2, SPINK1, and PCA3 transcript expression and TMPRSS2:ERG fusion status were significant predictors of prostate cancer. Multivariate regression analysis showed that a multiplexed model, including these biomarkers, outperformed serum PSA or PCA3 alone in detecting prostate cancer. The area under the receiver-operating characteristic curve was 0.758 for the multiplexed model versus 0.662 for PCA3 alone (P = 0.003). The sensitivity and specificity for the multiplexed model were 65.9% and 76.0%, respectively, and the positive and negative predictive values were 79.8% and 60.8%, respectively. Taken together, these results provide the framework for the development of highly optimized, multiplex urine biomarker tests for more accurate detection of prostate cancer. [Cancer Res 2008;68(3):645-9]

K. pneumoniae commonly infects hospitalized patients, and these infections are increasingly resistant to carbapenems, the antibiotics of last resort for life-threatening bacterial infections. To prevent and treat these infections, we must better understand how K. pneumoniae causes disease and discover new ways to predict and detect infections. This study demonstrates that colonization with K. pneumoniae in the intestinal tract is strongly linked to subsequent infection. This finding helps to identify a potential time frame and possible approach for intervention: the colonizing strain from a patient could be isolated as part of a risk assessment, and antibiotic susceptibility testing could guide empirical therapy if the patient becomes acutely ill.

Background: The underlying changes of peripheral blood inflammatory cells (PBICs) in COVID-19 patients are little known. Moreover, the risk factors for the underlying changes of PBICs and their predicting role in severe COVID-19 patients remain uncertain. Material and methods: This retrospective study including two cohorts: the main cohort enrolling 45 patients of severe type serving as study group, and the secondary cohort enrolling 12 patients of no-severe type serving as control group. The PBICs analysis was based on blood routine and lymphocyte subsets. The inflammatory cell levels were compared among patients according to clinical classifications, disease-associated phases, as well as one-month outcomes. Results: Compared with patients of non-severe type, the patients of severe type suffered from significantly decreased counts of lymphocytes, eosinophils, basophils, but increased counts of neutrophils. These PBICs alterations got improved in recovery phase, but persisted or got worse in aggravated phase. Compared with patients in discharged group, the patients in un-discharged/died group suffered from decreased counts of total T lymphocytes, CD4 + T lymphocytes, CD8 + T lymphocytes, as well as NK cells at 2 weeks after treatment. Clinical classification-critically severe was the independently risk factor for lymphopenia (OR = 7.701, 95%CI:1.265-46.893, P = 0.027), eosinopenia (OR = 5.595, 95%CI:1.008-31.054, P = 0.049), and worse onemonth outcome (OR = 8.984; 95%CI:1.021-79.061, P = 0.048). Conclusion: Lymphopenia and eosinopenia may serve as predictors of disease severity and disease progression in COVID-19 patients, and enhancing the cellular immunity may contribute to COVID-19 treatment. Thus, PBICs might become a sentinel of COVID-19, and it deserves attention during COVID-19 treatment. IntroductionSince December 2019, an increasing number of pneumonia cases emerged in Wuhan, and rapidly spread throughout China [1]. The causative virus was officially named as 2019-novel coronavirus (2019-nCoV), and its relevant infected disease was also officially designated

Rationale: The Epic Deterioration Index (EDI) is a proprietary prediction model implemented in over 100 U.S. hospitals that was widely used to support medical decision-making during the coronavirus disease (COVID-19) pandemic. The EDI has not been independently evaluated, and other proprietary models have been shown to be biased against vulnerable populations. Objectives: To independently evaluate the EDI in hospitalized patients with COVID-19 overall and in disproportionately affected subgroups. Methods: We studied adult patients admitted with COVID-19 to units other than the intensive care unit at a large academic medical center from March 9 through May 20, 2020. We used the EDI, calculated at 15-minute intervals, to predict a composite outcome of intensive care unit–level care, mechanical ventilation, or in-hospital death. In a subset of patients hospitalized for at least 48 hours, we also evaluated the ability of the EDI to identify patients at low risk of experiencing this composite outcome during their remaining hospitalization. Results: Among 392 COVID-19 hospitalizations meeting inclusion criteria, 103 (26%) met the composite outcome. The median age of the cohort was 64 (interquartile range, 53–75) with 168 (43%) Black patients and 169 (43%) women. The area under the receiver-operating characteristic curve of the EDI was 0.79 (95% confidence interval, 0.74–0.84). EDI predictions did not differ by race or sex. When exploring clinically relevant thresholds of the EDI, we found patients who met or exceeded an EDI of 68.8 made up 14% of the study cohort and had a 74% probability of experiencing the composite outcome during their hospitalization with a sensitivity of 39% and a median lead time of 24 hours from when this threshold was first exceeded. Among the 286 patients hospitalized for at least 48 hours who had not experienced the composite outcome, 14 (13%) never exceeded an EDI of 37.9, with a negative predictive value of 90% and a sensitivity above this threshold of 91%. Conclusions: We found the EDI identifies small subsets of high-risk and low-risk patients with COVID-19 with good discrimination, although its clinical use as an early warning system is limited by low sensitivity. These findings highlight the importance of independent evaluation of proprietary models before widespread operational use among patients with COVID-19.

Klebsiella pneumoniae is a common cause of infections in the health care setting. This work supports a paradigm for K. pneumoniae pathogenesis where the accessory genome, composed of genes present in some but not all isolates, influences whether a strain causes infection or asymptomatic colonization, after accounting for patient-level factors. Identification of patients at high risk of infection could allow interventions to prevent or rapidly treat K. pneumoniae infections.

microRNA-134 (miR-134) has been reported to be a brain-specific miRNA and is differently expressed in brain tissues subjected to ischemic injury. However, the underlying mechanism of miR-134 in regulating cerebral ischemic injury remains poorly understood. The current study was designed to delineate the molecular basis of miR-134 in regulating cerebral ischemic injury. Using the oxygen-glucose deprivation (OGD) model of hippocampal neuron ischemia in vitro, we found that the overexpression of miR-134 mediated by recombinant adeno-associated virus (AAV) vector infection significantly promoted neuron death induced by OGD/reoxygenation, whereas the inhibition of miR-134 provided protective effects against OGD/reoxygenation-induced cell death. Moreover, cyclic AMP (cAMP) response element-binding protein (CREB) as a putative target of miR-134 was downregulated and upregulated by miR-134 overexpression or inhibition, respectively. The direct interaction between miR-134 and the 3'-untranslated region (UTR) of CREB mRNA was further confirmed by dual-luciferase reporter assay. Overexpression of miR-134 also inhibited the expression of the downstream gene of CREB, including brain-derived neurotrophic factor (BDNF) and the anti-apoptotic gene Bcl-2, whereas the inhibition of miR-134 upregulated the expression of BDNF and Bcl-2 in neurons after OGD/reoxygenation. Notably, the knockdown of CREB by CREB siRNA apparently abrogated the protective effect of anti-miR-134 on OGD/reoxygenation-induced cell death. Taken together, our study suggests that downregulation of miR-134 alleviates ischemic injury through enhancing CREB expression and downstream genes, providing a promising and potential therapeutic target for cerebral ischemic injury.

Introduction:The coronavirus disease 2019 pandemic is straining the capacity of U.S. healthcare systems. Accurately identifying subgroups of hospitalized COVID-19 patients at high-and low-risk for complications would assist in directing resources.Objective: To validate the Epic Deterioration Index (EDI), a predictive model implemented in over 100 U.S. hospitals that has been recently promoted for use in COVID-19 patients. Methods:We studied adult patients admitted with COVID-19 to non-ICU level care at a large academic medical center from March 9 through April 7, 2020. We used the EDI, calculated at 15-minute intervals, to predict a composite adverse outcome of ICU-level care, mechanical ventilation, or death during the hospitalization. In a subset of patients hospitalized for at least 48 hours, we also evaluated the ability of the EDI (range 0-100) to identify patients at low risk of experiencing this composite outcome during their remaining hospitalization. We evaluated model discrimination and calibration using both raw EDI scores and their slopes.Results: Among 174 COVID-19 patients meeting inclusion criteria, 61 (35%) experienced the composite outcome. Area under the receiver-operating-characteristic curve (AUC) of the EDI was 0.76 (95% CI 0.68-0.84). Patients who met or exceeded an EDI of 64.8 made up 17% of the study cohort and had an 80% probability of experiencing the outcome during their hospitalization with a median lead time of 28 hours from when the threshold was first exceeded to the outcome. Employing the EDI slope lowered the AUCs to 0.68 (95% CI 0.60-0.77) and 0.67 (95% CI 0.59-0.75) for slopes calculated over 4 and 8 hours, respectively. In a subset of 109 patients hospitalized for at least 48 hours and who had not experienced the composite outcome, 14 (13%) patients who never exceeded an EDI of 37.9 had a 93% probability of not experiencing the outcome throughout the rest of their hospitalization, suggesting low risk.