The Healthy Microbiome—What Is the Definition of a Healthy Gut Microbiome?

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非传统脂质指标与缺血性卒中

缺血性卒中预防和治疗的个性化管理依然是神经病学领域的重中之重。文章旨在阐述非传统脂质谱与传统脂质在急性缺血性卒中发病以及复发中的作用,以期为卒中预防、风险分级和高危人群筛查提供全新指标,并试图探讨非传统脂质指标的潜在预测价值。     

How to diagnose heart failure with preserved ejection fraction: the HFA–PEFF diagnostic algorithm: a consensus recommendation from the Heart Failure Association (HFA) of the European Society of Cardiology (ESC)

Making a firm diagnosis of chronic heart failure with preserved ejection fraction (HFpEF) remains a challenge. We recommend a new stepwise diagnostic process, the ‘HFA–PEFF diagnostic algorithm’. Step 1 (P=Pre‐test assessment) is typically performed in the ambulatory setting and includes assessment for heart failure symptoms and signs, typical clinical demographics (obesity, hypertension, diabetes mellitus, elderly, atrial fibrillation), and diagnostic laboratory tests, electrocardiogram, and echocardiography. In the absence of overt non‐cardiac causes of breathlessness, HFpEF can be suspected if there is a normal left ventricular (LV) ejection fraction, no significant heart valve disease or cardiac ischaemia, and at least one typical risk factor. Elevated natriuretic peptides support, but normal levels do not exclude a diagnosis of HFpEF. The second step (E: Echocardiography and Natriuretic Peptide Score) requires comprehensive echocardiography and is typically performed by a cardiologist. Measures include mitral annular early diastolic velocity (e′), LV filling pressure estimated using E/e′, left atrial volume index, LV mass index, LV relative wall thickness, tricuspid regurgitation velocity, LV global longitudinal systolic strain, and serum natriuretic peptide levels. Major (2 points) and Minor (1 point) criteria were defined from these measures. A score ≥5 points implies definite HFpEF; ≤1 point makes HFpEF unlikely. An intermediate score (2–4 points) implies diagnostic uncertainty, in which case Step 3 (F₁: Functional testing) is recommended with echocardiographic or invasive haemodynamic exercise stress tests. Step 4 (F₂: Final aetiology) is recommended to establish a possible specific cause of HFpEF or alternative explanations. Further research is needed for a better classification of HFpEF.     

Sympathetic nervous system activation and heart failure: Current state of evidence and the pathophysiology in the light of novel biomarkers

Heart failure (HF) is a complex clinical syndrome characterized by the activation of at least several neurohumoral pathways that have a common role in maintaining cardiac output and adequate perfusion pressure of target organs and tissues. The sympathetic nervous system (SNS) is upregulated in HF as evident in dysfunctional baroreceptor and chemoreceptor reflexes, circulating and neuronal catecholamine spillover, attenuated parasympathetic response, and augmented sympathetic outflow to the heart, kidneys and skeletal muscles. When these sympathoexcitatory effects on the cardiovascular system are sustained chronically they initiate the vicious circle of HF progression and become associated with cardiomyocyte apoptosis, maladaptive ventricular and vascular remodeling, arrhythmogenesis, and poor prognosis in patients with HF. These detrimental effects of SNS activity on outcomes in HF warrant adequate diagnostic and treatment modalities. Therefore, this review summarizes basic physiological concepts about the interaction of SNS with the cardiovascular system and highlights key pathophysiological mechanisms of SNS derangement in HF. Finally, special emphasis in this review is placed on the integrative and up-to-date overview of diagnostic modalities such as SNS imaging methods and novel laboratory biomarkers that could aid in the assessment of the degree of SNS activation and provide reliable prognostic information among patients with HF.     

Advances in radiotherapy and targeted therapies for rectal cancer

The last decade witnessed a significant progress in understanding the biology and immunology of colorectal cancer alongside with the technical innovations in radiotherapy.The stepwise implementation of intensitymodulated and image-guided radiation therapy by means of megavolt computed tomography and helical tomotherapy enabled us to anatomically sculpt dose delivery,reducing treatment related toxicity.In addition,the administration of a simultaneous integrated boost offers excellent local control rates.The novel challenge is the development of treatment strategies for medically inoperable patient and organ preserving approaches.However,distant control remains unsatisfactory and indicates an urgent need for biomarkers that predict the risk of tumor spread.The expected benefit of target?ed therapies that exploit the tumor genome alone is so far hindered by high cost techniques and pharmaceuticals,hence hardly justifying rather modest improvements in patient outcomes.On the other hand,the immune landscape of colorectal cancer is now better clarified with regard to the immunosuppressive network that promotes immune escape.Both N2 neutrophils and myeloid-derived suppressor cells(MDSC)emerge as useful clinical biomarkers of poor prognosis,while the growing list of anti-MDSC agents shows promising ability to boost antitumor T-cell immunity in preclinical settings.Therefore,integration of genetic and immune biomarkers is the next logical step towards effective targeted therapies in the context of personalized cancer treatment.     

A machine learning approach using EEG data to predict response to SSRI treatment for major depressive disorder

Objective

The problem of identifying, in advance, the most effective treatment agent for various psychiatric conditions remains an elusive goal. To address this challenge, we investigate the performance of the proposed machine learning (ML) methodology (based on the pre-treatment electroencephalogram (EEG)) for prediction of response to treatment with a selective serotonin reuptake inhibitor (SSRI) medication in subjects suffering from major depressive disorder (MDD).

Methods

A relatively small number of most discriminating features are selected from a large group of candidate features extracted from the subject’s pre-treatment EEG, using a machine learning procedure for feature selection. The selected features are fed into a classifier, which was realized as a mixture of factor analysis (MFA) model, whose output is the predicted response in the form of a likelihood value. This likelihood indicates the extent to which the subject belongs to the responder vs. non-responder classes. The overall method was evaluated using a “leave-n-out” randomized permutation cross-validation procedure.

Results

A list of discriminating EEG biomarkers (features) was found. The specificity of the proposed method is 80.9% while sensitivity is 94.9%, for an overall prediction accuracy of 87.9%. There is a 98.76% confidence that the estimated prediction rate is within the interval [75%, 100%].

Conclusions

These results indicate that the proposed ML method holds considerable promise in predicting the efficacy of SSRI antidepressant therapy for MDD, based on a simple and cost-effective pre-treatment EEG.

Significance

The proposed approach offers the potential to improve the treatment of major depression and to reduce health care costs.