Low-dose metformin treatment in the subacute phase improves the locomotor function of a mouse model of spinal cord injury

Metformin,a first-line drug for type-2 diabetes,has been shown to improve locomotor recovery after spinal cord injury.However,there are studies reporting no beneficial effect.Recently,we found that high dose of metformin(200 mg/kg,intraperitoneal)and acute phase administration(immediately after injury)led to increased mortality and limited locomotor function recovery.Consequently,we used a lower dose(100 mg/kg,i.p.)metformin in mice,and compared the effect of immediate administration after spinal cord injury(acute phase)with that of administration at 3 days post-injury(subacute phase).Our data showed that metformin treatment starting at the subacute phase significantly improved mouse locomotor function evaluated by Basso Mouse Scale(BMS)scoring.Immunohistochemical studies also revealed significant inhibitions of microglia/macrophage activation and astrogliosis at the lesion site.Furthermore,metformin treatment at the subacute phase reduced neutrophil infiltration.These changes were in parallel with the increased survival rate of spinal neurons in animals treated with metformin.These findings suggest that low-dose metformin treatment for subacute spinal cord injury can effectively improve the functional recovery possibly through anti-inflammation and neuroprotection.This study was approved by the Institute Animal Care and Use Committee at the University of Texas Medical Branch(approval No.1008041C)in 2010.

Virus load and virus shedding of SARS-CoV-2 and their impact onpatient outcomes

BACKGROUND Understanding a virus shedding patterns in body fluids/secretions is importantto determine the samples to be used for diagnosis and to formulate infectioncontrol measures.AIM To investigate the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)shedding patterns and its risk factors.METHODS All laboratory-confirmed coronavirus disease 2019 patients with completemedical records admitted to the Shenzhen Third People’s Hospital from January28, 2020 to March 8, 2020 were included. Among 145 patients (54.5% males;median age, 46.1 years), three (2.1%) died. The bronco-alveolar lavage fluid(BALF) had the highest virus load compared with the other samples. The viralload peaked at admission (3.3 × 108 copies) and sharply decreased 10 d afteradmission.RESULTS The viral load was associated with prolonged intensive care unit (ICU) duration.Patients in the ICU had significantly longer shedding time compared to those inthe wards (P < 0.0001). Age > 60 years [hazard ratio (HR) = 0.6;95% confidenceinterval (CI): 0.4-0.9] was an independent risk factor for SARS-CoV-2 shedding,while chloroquine (HR = 22.8;95%CI: 2.3-224.6) was a protective factor.CONCLUSION BALF had the highest SARS-CoV-2 load. Elderly patients had higher virus loads,which was associated with a prolonged ICU stay. Chloroquine was associatedwith shorter shedding duration and increased the chance of viral negativity.

Acute kidney injury in traumatic brain injury intensive care unit patients

BACKGROUND The exact definition of Acute kidney injury(AKI)for patients with traumatic brain injury(TBI)is unknown.AIM To compare the power of the“Risk,Injury,Failure,Loss of kidney function,and End-stage kidney disease”(RIFLE),Acute Kidney Injury Network(AKIN),Creatinine kinetics(CK),and Kidney Disease Improving Global Outcomes(KDIGO)to determine AKI incidence/stage and their association with the inhospital mortality rate of patients with TBI.METHODS This retrospective study collected the data of patients admitted to the intensive care unit for neurotrauma from 2001 to 2012,and 1648 patients were included.The subjects in this study were assessed for the presence and stage of AKI using RIFLE,AKIN,CK,and KDIGO.In addition,the propensity score matching method was used.RESULTS Among the 1648 patients,291(17.7%)had AKI,according to KDIGO.The highest incidence of AKI was found by KDIGO(17.7%),followed by AKIN(17.1%),RIFLE(12.7%),and CK(11.5%)(P=0.97).Concordance between KDIGO and RIFLE/AKIN/CK was 99.3%/99.1%/99.3%for stage 0,36.0%/91.5%/44.5%for stage 1,35.9%/90.6%/11.3%for stage 2,and 47.4%/89.5%/36.8%for stage 3.The in-hospital mortality rates increased with the AKI stage in all four definitions.The severity of AKI by all definitions and stages was not associated with inhospital mortality in the multivariable analyses(all P>0.05).CONCLUSION Differences are seen in AKI diagnosis and in-hospital mortality among the four AKI definitions or stages.This study revealed that KDIGO is the best method to define AKI in patients with TBI.

Clinical diagnosis of severe COVID-19:A derivation and validation of a prediction rule

BACKGROUND The widespread coronavirus disease 2019(COVID-19)has led to high morbidity and mortality.Therefore,early risk identification of critically ill patients remains crucial.AIM To develop predictive rules at the time of admission to identify COVID-19 patients who might require intensive care unit(ICU)care.METHODS This retrospective study included a total of 361 patients with confirmed COVID-19 by reverse transcription-polymerase chain reaction between January 19,2020,and March 14,2020 in Shenzhen Third People’s Hospital.Multivariate logistic regression was applied to develop the predictive model.The performance of the predictive model was externally validated and evaluated based on a dataset involving 126 patients from the Wuhan Asia General Hospital between December 2019 and March 2020,by area under the receiver operating curve(AUROC),goodness-of-fit and the performance matrix including the sensitivity,specificity,and precision.A nomogram was also used to visualize the model.RESULTS Among the patients in the derivation and validation datasets,38 and 9 participants(10.5%and 2.54%,respectively)developed severe COVID-19,respectively.In univariate analysis,21 parameters such as age,sex(male),smoker,body mass index(BMI),time from onset to admission(>5 d),asthenia,dry cough,expectoration,shortness of breath,asthenia,and Rox index<18(pulse oxygen saturation,SpO2)/(FiO2×respiratory rate,RR)showed positive correlations with severe COVID-19.In multivariate logistic regression analysis,only six parameters including BMI[odds ratio(OR)3.939;95%confidence interval(CI):1.409-11.015;P=0.009],time from onset to admission(≥5 d)(OR 7.107;95%CI:1.449-34.849;P=0.016),fever(OR 6.794;95%CI:1.401-32.951;P=0.017),Charlson index(OR 2.917;95%CI:1.279-6.654;P=0.011),PaO2/FiO2 ratio(OR 17.570;95%CI:1.117-276.383;P=0.041),and neutrophil/lymphocyte ratio(OR 3.574;95%CI:1.048-12.191;P=0.042)were found to be independent predictors of COVID-19.These factors were found to be significant risk factors for severe patients confirmed with COVID-19.The AUROC was 0.941(95%CI:0.901-0.981)and 0.936(95%CI:0.886-0.987)in both datasets.The calibration properties were good.CONCLUSION The proposed predictive model had great potential in severity prediction of COVID-19 in the ICU.It assisted the ICU clinicians in making timely decisions for the target population.