Influence of excess fat on cardiac morphology and function: study in uncomplicated obesity

To evaluate whether or not "uncomplicated" obesity (without associated comorbidities) is really associated with cardiac abnormalities. We evaluated cardiac parameters in obese subjects with long-term obesity, normal glucose tolerance, normal blood pressure, and regular plasma lipids. We selected 75 obese patients [body mass index (BMI) >30 kg/m(2)], who included 58 women and 17 men (mean age, 33.7 +/- 11.9 years; BMI, 37.8 +/- 5.5 kg/m(2)) with a > or =10-year history of excess fat, and 60 age-matched normal-weight controls, who included 47 women and 13 men (mean age, 32.7 +/- 10.4 years; BMI, 23.1 +/- 1.4 kg/m(2)). Each subject underwent an oral glucose tolerance test to exclude impaired glucose tolerance or diabetes mellitus, bioelectrical impedance analysis to calculate fat mass and fat-free mass, and echocardiography. Obese patients presented diastolic function impairment, hyperkinetic systole, and greater aortic root and left atrium compared with normal subjects. No statistically significant differences between obese subjects and normal subjects were found in indexed left ventricular mass (LVM/body surface area, LVM/height(2.7), and LVM/fat-free mass(kg)), and no changes in left ventricular geometry were observed. No statistically significant differences in cardiac parameters between extreme (BMI > 40 kg/m(2)) and mild obesity (BMI < 35 kg/m(2)) were observed. In conclusion, our data showed that obesity, in the absence of glucose intolerance, hypertension, and dyslipidemia, seems to be associated only with an impairment of diastolic function and hyperkinetic systole, and not with left ventricular hypertrophy

Predicting Outcome of Traumatic Brain Injury: Is Machine Learning the Best Way?

One of the main challenges in traumatic brain injury (TBI) patients is to achieve an early and definite prognosis. Despite the recent development of algorithms based on artificial intelligence for the identification of these prognostic factors relevant for clinical practice, the literature lacks a rigorous comparison among classical regression and machine learning (ML) models. This study aims at providing this comparison on a sample of TBI patients evaluated at baseline (T0), after 3 months from the event (T1), and at discharge (T2). A Classical Linear Regression Model (LM) was compared with independent performances of Support Vector Machine (SVM), k-Nearest Neighbors (k-NN), Na&iuml;ve Bayes (NB) and Decision Tree (DT) algorithms, together with an ensemble ML approach. The accuracy was similar among LM and ML algorithms on the analyzed sample when two classes of outcome (Positive vs. Negative) approach was used, whereas the NB algorithm showed the worst performance. This study highlights the utility of comparing traditional regression modeling to ML, particularly when using a small number of reliable predictor variables after TBI. The dataset of clinical data used to train ML algorithms will be publicly available to other researchers for future comparisons

External Validation and Calibration of the DecaPreT Prediction Model for Decannulation in Patients with Acquired Brain Injury

We propose a new set of clinical variables for a more accurate early prediction of safe decannulation in patients with severe acquired brain injury (ABI), during a post-acute rehabilitation course. Starting from the already validated DecaPreT scale, we tested the accuracy of new logistic regression models where the coefficients of the original predictors were reestimated. Patients with tracheostomy were retrospectively selected from the database of the neurorehabilitation unit at the S. Anna Institute of Crotone, Italy. New potential predictors of decannulation were screened from variables collected on admission during clinical examination, including (a) age at injury, (b) coma recovery scale-revised (CRS-r) scores, and c) length of ICU period. Of 273 patients with ABI (mean age 53.01 years; 34% female; median DecaPreT = 0.61), 61.5% were safely decannulated before discharge. In the validation phase, the linear logistic prediction model, created with the new multivariable predictors, obtained an area under the receiver operating characteristics curve of 0.901. Our model improves the reliability of simple clinical variables detected at the admission of the post-acute phase in predicting decannulation of ABI patients, thus helping clinicians to plan better rehabilitation

Outcome prediction in disorders of consciousness: the role of coma recovery scale revised

Abstract Background To evaluate the utility of the revised coma remission scale (CRS-r), together with other clinical variables, in predicting emergence from disorders of consciousness (DoC) during intensive rehabilitation care. Methods Data were retrospectively extracted from the medical records of patients enrolled in a specialized intensive rehabilitation unit. 123 patients in a vegetative state (VS) and 57 in a minimally conscious state (MCS) were included and followed for a period of 8 weeks. Demographical and clinical factors were used as outcome measures. Univariate and multivariate Cox regression models were employed for examining potential predictors for clinical outcome along the time. Results VS and MCS groups were matched for demographical and clinical variables (i.e., age, aetiology, tracheostomy and route of feeding). Within 2 months after admission in intensive neurorehabilitation unit, 3.9% were dead, 35.5% had a full recovery of consciousness and 66.7% remained in VS or MCS. Multivariate analysis demonstrated that the best predictor of functional improvement was the CRS-r scores. In particular, patients with values greater than 12 at admission were those with a favourable likelihood of emergence from DoC. Conclusions Our study highlights the role of the CRS-r scores for predicting a short-term favorable outcome

Historia Augusta

Los fragmentos del Dión Casio traducidos por Giorgio MerulaEditor y segunda fecha constan en colofón, al cual sigue: Adolpho Rincho, Alberto Keio CossSign.: [alfa]-[gamma]\p6\s, [1-4]\p6\s, a-z\p6\s, aa-zz\p6\s, aaa-bbb\p6\s, ccc\p4\s, ddd-lll\p6\s, mmm\p8\sEntre los numerosos errores de pag., numera 673 en lugar de 695Front. grab. xil. historiado con los doce trabajos de Hércules, y firmado con las iniciales "AW" en el capitel de la columna central arriba, i.e.: Anton WoensamOratio Heliogabali Ro. Imperatoris habita in concione ad meretrices / quam a Leonardo Aretino compositam plerique credunt, p. 330-33

The tomato genome sequence provides insights into fleshy fruit evolution

Tomato (Solanum lycopersicum) is a major crop plant and a model system for fruit development. Solanum is one of the largest angiosperm genera1 and includes annual and perennial plants from diverse habitats. Here we present a high-quality genome sequence of domesticated tomato, a draft sequence of its closest wild relative, Solanum pimpinellifolium2, and compare them to each other and to the potato genome (Solanum tuberosum). The two tomato genomes show only 0.6% nucleotide divergence and signs of recent admixture, but show more than 8% divergence from potato, with nine large and several smaller inversions. In contrast to Arabidopsis, but similar to soybean, tomato and potato small RNAs map predominantly to gene-rich chromosomal regions, including gene promoters. The Solanum lineage has experienced two consecutive genome triplications: one that is ancient and shared with rosids, and a more recent one. These triplications set the stage for the neofunctionalization of genes controlling fruit characteristics, such as colour and fleshiness

Tocilizumab for patients with COVID-19 pneumonia. The single-arm TOCIVID-19 prospective trial

BackgroundTocilizumab blocks pro-inflammatory activity of interleukin-6 (IL-6), involved in pathogenesis of pneumonia the most frequent cause of death in COVID-19 patients.MethodsA multicenter, single-arm, hypothesis-driven trial was planned, according to a phase 2 design, to study the effect of tocilizumab on lethality rates at 14 and 30 days (co-primary endpoints, a priori expected rates being 20 and 35%, respectively). A further prospective cohort of patients, consecutively enrolled after the first cohort was accomplished, was used as a secondary validation dataset. The two cohorts were evaluated jointly in an exploratory multivariable logistic regression model to assess prognostic variables on survival.ResultsIn the primary intention-to-treat (ITT) phase 2 population, 180/301 (59.8%) subjects received tocilizumab, and 67 deaths were observed overall. Lethality rates were equal to 18.4% (97.5% CI: 13.6-24.0, P=0.52) and 22.4% (97.5% CI: 17.2-28.3, P&lt;0.001) at 14 and 30 days, respectively. Lethality rates were lower in the validation dataset, that included 920 patients. No signal of specific drug toxicity was reported. In the exploratory multivariable logistic regression analysis, older age and lower PaO2/FiO2 ratio negatively affected survival, while the concurrent use of steroids was associated with greater survival. A statistically significant interaction was found between tocilizumab and respiratory support, suggesting that tocilizumab might be more effective in patients not requiring mechanical respiratory support at baseline.ConclusionsTocilizumab reduced lethality rate at 30 days compared with null hypothesis, without significant toxicity. Possibly, this effect could be limited to patients not requiring mechanical respiratory support at baseline.Registration EudraCT (2020-001110-38); clinicaltrials.gov (NCT04317092)