Machine learning and blood pressure

Machine learning (ML) is a type of artificial intelligence (AI) based on pattern recognition. There are different forms of supervised and unsupervised learning algorithms that are being used to identify and predict blood pressure (BP) and other measures of cardiovascular risk. Since 1999, starting with neural network methods, ML has been used to gauge the relationship between BP and pulse wave forms. Since then, the scope of the research has expanded to using different cardiometabolic risk factors like BMI, waist circumference, waist‐to‐hip ratio in concert with BP and its various pharmaceutical agents to estimate biochemical measures (like HDL cholesterol, LDL and total cholesterol, fibrinogen, and uric acid) as well as effectiveness of anti‐hypertensive regimens. Data from large clinical trials like the SPRINT are being re‐analyzed by ML methods to unearth new findings and identify unique relationships between predictors and outcomes. In summary, AI and ML methods are gaining immense attention in the management of chronic disease. Elevated BP is a very important early metric for the risk of development of cardiovascular and renal injury; therefore, advances in AI and ML will aid in early disease prediction and intervention.     

外泌体作为靶向治疗载体的研究进展

外泌体是来源于细胞内膜的纳米级的囊泡，包含有核酸、蛋白和脂质等生命物质，作为一种细胞间交流的机制，在生命活动中发挥着重要作用。相对于脂质体载体，外泌体更稳定，生物相容性好，免疫原性低，而且可以渗透生物屏障，例如血脑屏障、胎盘屏障等。重点介绍外泌体载体靶向性的基础、外泌体载体装载负荷的方法及外泌体载体目前的应用进展和前景。     

Transient-evoked otoacoustic emission signals predicting outcomes of acute sensorineural hearing loss in patients with Ménière’s disease

Abstract

Background: Fluctuating hearing loss is characteristic of Ménière’s disease (MD) during acute episodes. However, no reliable audiometric hallmarks are available for counselling the hearing recovery possibility.

Aims/objectives: To find parameters for predicting MD hearing outcomes.

Material and methods: We applied machine learning techniques to analyse transient-evoked otoacoustic emission (TEOAE) signals recorded from patients with MD. Thirty unilateral MD patients were recruited prospectively after onset of acute cochleo-vestibular symptoms. Serial TEOAE and pure-tone audiogram (PTA) data were recorded longitudinally. Denoised TEOAE signals were projected onto the three most prominent principal directions through a linear transformation. Binary classification was performed using a support vector machine (SVM). TEOAE signal parameters, including signal energy and group delay, were compared between improved (PTA improvement: ≥15?dB) and nonimproved groups using Welch’s t-test.

Results: Signal energy did not differ (p?=?.64) but a significant difference in 1-kHz (p?=?.045) group delay was recorded between improved and nonimproved groups. The SVM achieved a cross-validated accuracy of >80% in predicting hearing outcomes.

Conclusions and significance: This study revealed that baseline TEOAE parameters obtained during acute MD episodes, when processed through machine learning technology, may provide information on outer hair cell function to predict hearing recovery.     

Les directives anticipées chez des patients en hémodialyse chronique : étude de faisabilité

Respect of patient's autonomy is essential. So that patients are able to write their advance directives in case of a situation where they are unable to make decisions for themselves. Currently, few people have written advance directives. We studied the feasibility of a systematic implementation of advance directives in haemodialysis patients. This prospective, single-center study was conducted in an ambulatory hemodialysis center. There were 4 steps: caregivers survey about advance directives; selection of patients and information about advance directives; writing advance directives with the interested patients; and finally, non-participation causes assessment of the other informed patients. Caregivers are not comfortable with advance directives, and have reluctances: the patient's lack of medical knowledge; the anxiety generated by end-of-life talk. Fifty-six patients (51.6%) were included and received the information. Nine of them wanted to write their advance directives on a suitable form. Eight finalised them (7.4% of the initial population). The majority wanted a therapeutic limitation. Twenty-nine patients, who have received the information about advance directives, didn’t want to write them, the main reason was that they felt healthy or that they thought that their relatives would take the right decisions. Eighteen patients left the centre during the study. The development of advance directives requires information and training of caregivers and patient support. Few patients went to the end of the process. The limit of the patient's ability to decide for himself is difficult to define. The role of the doctor is central to accompany the patient during this process.     

支持向量机法及其在中药研究中的应用

在中医药现代化进程中，有效地结合现代科学技术手段对传统中医药进行更为合理的分析是一个重要研究方向。计算机科学技术与人工智能算法在中医药研究领域已取得长足发展，尤其是分类算法所具有的快速鉴别和分析能力，使其在复杂的中医药研究体系中发挥举足轻重的作用。对支持向量机分类算法及其结合其他算法在解决中医药相关领域问题方面及成果进行综述，以相关智能算法为基础探讨中医药研究过程中的诸多难题，为推动中医药现代化进程提供支撑。