RESUMO
Stress is a primary risk factor in the onset of neuropsychiatric disorders, including major depressive disorder (MDD). We have previously used the chronic mild stress (CMS) model of depression in male rats to show that CMS induces morphological, functional, and molecular changes in the hippocampus of vulnerable animals, the majority of which were recovered using acute subanesthetic ketamine in just 24 h. Here, we focused our attention on the medial prefrontal cortex (mPFC), a brain area regulating emotional and cognitive functions, and asked whether vulnerability/resilience to CMS and ketamine antidepressant effects were associated with molecular and functional changes in the mPFC of rats. We found that most alterations induced by CMS in the mPFC were selectively observed in stress-vulnerable animals and were rescued by acute subanesthetic ketamine, while others were found only in resilient animals or were induced by ketamine treatment. Importantly, only a few of these modifications were also previously demonstrated in the hippocampus, while most are specific to mPFC. Overall, our results suggest that acute antidepressant ketamine rescues brain-area-specific glutamatergic changes induced by chronic stress.
Assuntos
Transtorno Depressivo Maior , Ketamina , Ratos , Masculino , Animais , Ketamina/farmacologia , Ketamina/uso terapêutico , Depressão/tratamento farmacológico , Depressão/etiologia , Transtorno Depressivo Maior/tratamento farmacológico , Estresse Psicológico , Antidepressivos/farmacologia , Antidepressivos/uso terapêutico , Córtex Pré-FrontalRESUMO
BACKGROUND: Haemoglobin variation (ΔHb) induced by fluid transfer through the intestitium has been proposed as a useful tool for detecting hydrostatic pulmonary oedema (HPO). However, its use in the emergency department (ED) setting still needs to be determined. METHODS: In this observational retrospective monocentric study, ED patients admitted for acute dyspnoea were enrolled. Hb values were recorded both at ED presentation (T0) and after 4 to 8 h (T1). ΔHb between T1 and T0 (ΔHbT1-T0) was calculated as absolute and relative value. Two investigators, unaware of Hb values, defined the cause of dyspnoea as HPO and non-HPO. ΔHbT1-T0 ability to detect HPO was evaluated. A machine learning approach was used to develop a predictive tool for HPO, by considering the ability of ΔHb as covariate, together with baseline patient characteristics. RESULTS: Seven-hundred-and-six dyspnoeic patients (203 HPO and 503 non-HPO) were enrolled over 19 months. Hb levels were significantly different between HPO and non-HPO patients both at T0 and T1 (p < 0.001). ΔHbT1-T0 were more pronounced in HPO than non-HPO patients, both as relative (-8.2 [-11.2 to -5.6] vs. 0.6 [-2.1 to 3.3] %) and absolute (-1.0 [-1.4 to -0.8] vs. 0.1 [-0.3 to 0.4] g/dL) values (p < 0.001). A relative ΔHbT1-T0 of -5% detected HPO with an area under the receiver operating characteristic curve (AUROC) of 0.901 [0.896-0.906]. Among the considered models, Gradient Boosting Machine showed excellent predictive ability in identifying HPO patients and was used to create a web-based application. ΔHbT1-T0 was confirmed as the most important covariate for HPO prediction. CONCLUSIONS: ΔHbT1-T0 in patients admitted for acute dyspnoea reliably identifies HPO in the ED setting. The machine learning predictive tool may represent a performing and clinically handy tool for confirming HPO.
RESUMO
Background: Due to the high prevalence of hepatic steatosis (HS), the aim of the study is to verify the frequency of HS incidentally detected in chest computed tomography (CT) imaging in our population affected by SARS-CoV-2 and to investigate its association with the severity of the infection and outcome in terms of hospitalization. Design and methods: We retrospectively analyzed 500 patients with flu syndrome and clinically suspected of having Sars-CoV-2 infection who underwent unenhanced chest CT and have positive RT-PCR tests for Sars-CoV-2 RNA. Two radiologists both with >5 years of thoracic imaging experience, evaluated the images in consensus, without knowing the RT-PCR results. Liver density was measured by a region of interest (ROI), using a liver attenuation value ≤40 Hounsfield units (HU). Results: On 480 patients, 23.1% (111/480) had an incidental findings of HS on chest CT. The steatosis group, included 83 (74.7%) males and 28 (25.3%) females. Patients with HS were more likely to be hospitalized in the intensive care unit (ICU). On univariate analysis, there is a correlation between probability to be intubate (access in the ICU) and HS: patients with HS are twice as likely to be intubated (OR 2.04, CI 95% 1.11-3.73). Conclusion: Chest CT is an important diagnostic tool for COVID-19 and can provide information about the prognosis of the disease. HS can easily be detected on chest CT taken for the diagnosis of the COVID-19 disease, is an important sign for a poor prognosis and possible predictor of admission in ICU.
RESUMO
INTRODUCTION: The clinical course of Coronavirus Disease 2019 (COVID-19) is highly heterogenous, ranging from asymptomatic to fatal forms. The identification of clinical and laboratory predictors of poor prognosis may assist clinicians in monitoring strategies and therapeutic decisions. MATERIALS AND METHODS: In this study, we retrospectively assessed the prognostic value of a simple tool, the complete blood count, on a cohort of 664 patients (F 260; 39%, median age 70 (56-81) years) hospitalized for COVID-19 in Northern Italy. We collected demographic data along with complete blood cell count; moreover, the outcome of the hospital in-stay was recorded. RESULTS: At data cut-off, 221/664 patients (33.3%) had died and 453/664 (66.7%) had been discharged. Red cell distribution width (RDW) (χ 2 10.4; p < 0.001), neutrophil-to-lymphocyte (NL) ratio (χ 2 7.6; p = 0.006), and platelet count (χ 2 5.39; p = 0.02), along with age (χ 2 87.6; p < 0.001) and gender (χ 2 17.3; p < 0.001), accurately predicted in-hospital mortality. Hemoglobin levels were not associated with mortality. We also identified the best cut-off for mortality prediction: a NL ratio > 4.68 was characterized by an odds ratio for in-hospital mortality (OR) = 3.40 (2.40-4.82), while the OR for a RDW > 13.7% was 4.09 (2.87-5.83); a platelet count > 166,000/µL was, conversely, protective (OR: 0.45 (0.32-0.63)). CONCLUSION: Our findings arise the opportunity of stratifying COVID-19 severity according to simple lab parameters, which may drive clinical decisions about monitoring and treatment.
Assuntos
Contagem de Células Sanguíneas , COVID-19/sangue , COVID-19/mortalidade , Regras de Decisão Clínica , Mortalidade Hospitalar , Índice de Gravidade de Doença , Adulto , Idoso , Idoso de 80 Anos ou mais , COVID-19/diagnóstico , Feminino , Humanos , Itália/epidemiologia , Masculino , Pessoa de Meia-Idade , Análise Multivariada , Prognóstico , Estudos RetrospectivosRESUMO
Clinical features and natural history of coronavirus disease 2019 (COVID-19) differ widely among different countries and during different phases of the pandemia. Here, we aimed to evaluate the case fatality rate (CFR) and to identify predictors of mortality in a cohort of COVID-19 patients admitted to three hospitals of Northern Italy between March 1 and April 28, 2020. All these patients had a confirmed diagnosis of SARS-CoV-2 infection by molecular methods. During the study period 504/1697 patients died; thus, overall CFR was 29.7%. We looked for predictors of mortality in a subgroup of 486 patients (239 males, 59%; median age 71 years) for whom sufficient clinical data were available at data cut-off. Among the demographic and clinical variables considered, age, a diagnosis of cancer, obesity and current smoking independently predicted mortality. When laboratory data were added to the model in a further subgroup of patients, age, the diagnosis of cancer, and the baseline PaO2/FiO2 ratio were identified as independent predictors of mortality. In conclusion, the CFR of hospitalized patients in Northern Italy during the ascending phase of the COVID-19 pandemic approached 30%. The identification of mortality predictors might contribute to better stratification of individual patient risk.