Hemispheric asymmetry is a fundamental organizing principle of the human brain. Answering the genetic effects of the asymmetry is a prerequisite for elucidating developmental mechanisms of brain asymmetries. Multi-modal magnetic resonance imaging (MRI) has provided an important tool for comprehensively interpreting human brain asymmetry and its genetic mechanism. By combining MRI data, individual differences in brain structural asymmetry have been investigated with quantitative genetic brain mapping using gene-heritability. Twins provide a useful natural model for studying the effects of genetics and environment on the brain. Studies based on MRI have found that the asymmetry of human brain structure has a genetic basis. From the perspective of quantitative genetic analysis, this article reviews recent findings on the genetic effects of asymmetry and genetic covariance between hemispheres from three aspects: the asymmetry of heritability, the heritability of asymmetry and the genetic correlation. At last, the article shows the limitations and future research directions in this field. The purpose of this systematic review is to quickly guide researchers to understand the origins and genetic mechanism of interhemispheric differences, and provide a genetic basis for further understanding and exploring individual differences in laterized cognitive behavior.
Clinically, non-contrastive computed tomography (NCCT) is used to quickly diagnose the type and area of stroke, and the Alberta stroke program early computer tomography score (ASPECTS) is used to guide the next treatment. However, in the early stage of acute ischemic stroke (AIS), it’s difficult to distinguish the mild cerebral infarction on NCCT with the naked eye, and there is no obvious boundary between brain regions, which makes clinical ASPECTS difficult to conduct. The method based on machine learning and deep learning can help physicians quickly and accurately identify cerebral infarction areas, segment brain areas, and operate ASPECTS quantitative scoring, which is of great significance for improving the inconsistency in clinical ASPECTS. This article describes current challenges in the field of AIS ASPECTS, and then summarizes the application of computer-aided technology in ASPECTS from two aspects including machine learning and deep learning. Finally, this article summarizes and prospects the research direction of AIS-assisted assessment, and proposes that the computer-aided system based on multi-modal images is of great value to improve the comprehensiveness and accuracy of AIS assessment, which has the potential to open up a new research field for AIS-assisted assessment.
ObjectiveTo investigate the risk of myocarditis caused by immune checkpoint inhibitors (ICI). MethodsThe adverse reaction (ADR) reports on myocarditis caused by atelizumab, duvalizumab, pabolizumab, and navulizumab were downloaded from the FDA Adverse Event Reporting System (FAERS) from January 1, 2014 to September 30, 2022. The relevant analysis was conducted on the gender, age, medication dosage, and occurrence time of ICI related myocarditis patients. ResultsA total of 1 892 reports of myocarditis induced by ICI were included. The proportion of myocarditis caused by ICI was higher in males than in females (1.9∶1). The incidence of myocarditis in patients with basic diseases such as diabetes and heart disease, and in the age group 65-75 was relatively high. The incidence of myocarditis caused by navulizumab was high within 30 days with the use of conventional doses, and that of the other three drugs were high within 31 to 90 days. And the incidence of myocarditis is higher when used in combination than when used alone. ConclusionDifferent varieties of ICI can lead to the occurrence of myocarditis, and male, elderly, underlying diseases, and combination therapy may be risk factors for myocarditis caused by ICI.
Recently, sponsored by the Science Popularization Department of the China Anti Cancer Association, jointly organized by the Rehabilitation Branch of the China Anti Cancer Association and the Mijian Digital Cancer Patient Course Management Platform, and co-organized by the Science Popularization Special Committee of the China Anti Cancer Association, The "2022 White Paper on the Quality of Life of Chinese Lung Cancer Patients" has been officially released (herein after referred to as the "White Paper"), which mainly elaborates on the basic situation of Chinese lung cancer patients and the medical, social, and economic impacts caused by the disease. This article interprets the White Paper in order to help the public understand the real situation of lung cancer patients and provide important empirical evidence and valuable insights for the diagnosis, treatment, and rehabilitation of lung cancer in China.
The effective classification of multi-task motor imagery electroencephalogram (EEG) is helpful to achieve accurate multi-dimensional human-computer interaction, and the high frequency domain specificity between subjects can improve the classification accuracy and robustness. Therefore, this paper proposed a multi-task EEG signal classification method based on adaptive time-frequency common spatial pattern (CSP) combined with convolutional neural network (CNN). The characteristics of subjects' personalized rhythm were extracted by adaptive spectrum awareness, and the spatial characteristics were calculated by using the one-versus-rest CSP, and then the composite time-domain characteristics were characterized to construct the spatial-temporal frequency multi-level fusion features. Finally, the CNN was used to perform high-precision and high-robust four-task classification. The algorithm in this paper was verified by the self-test dataset containing 10 subjects (33 ± 3 years old, inexperienced) and the dataset of the 4th 2018 Brain-Computer Interface Competition (BCI competition Ⅳ-2a). The average accuracy of the proposed algorithm for the four-task classification reached 93.96% and 84.04%, respectively. Compared with other advanced algorithms, the average classification accuracy of the proposed algorithm was significantly improved, and the accuracy range error between subjects was significantly reduced in the public dataset. The results show that the proposed algorithm has good performance in multi-task classification, and can effectively improve the classification accuracy and robustness.