Real-world studies (RWSs) data are based on real medical scenes and reflect clinical facts. Besides, RWSs adapts to the characteristics of therapeutic principles of traditional Chinese medicine and the medical reality of the combination of Western and traditional Chinese medicine, which makes the safety assessment of herb-drug interaction more efficient and economical. During RWSs, more attention should be paid on the validity and reliability of data, especially the standardization of the data collection process and its contents. The safety assessment of herb-drug interaction will combine the methods of active surveillance study, big data analysis, and be based on precision medicine in the future
In order to promote the openness, transparency and standardization of clinical trials, improve the scientific and reliability of results, and reserve the manpower, material, and financial resources in the process of clinical trials, this study constructed an integrated intelligent management platform for clinical trials, which could carry out various types of clinical trials such as randomized controlled trials, non-randomized controlled trials, cohort studies, case-control studies, and cross-sectional studies simultaneously. The platform covers the whole process of scheme design, recruitment, follow-up, data analysis, and quality control. This paper mainly introduced the practical needs, design concept, basic framework and technical highlights to provide auxiliary tools for promoting the standardization and intelligence of clinical trials with energy saving and optimal efficiency.
Protein structure determines function, and structural information is critical for predicting protein thermostability. This study proposes a novel method for protein thermostability prediction by integrating graph embedding features and network topological features. By constructing residue interaction networks (RINs) to characterize protein structures, we calculated network topological features and utilize deep neural networks (DNN) to mine inherent characteristics. Using DeepWalk and Node2vec algorithms, we obtained node embeddings and extracted graph embedding features through a TopN strategy combined with bidirectional long short-term memory (BiLSTM) networks. Additionally, we introduced the Doc2vec algorithm to replace the Word2vec module in graph embedding algorithms, generating graph embedding feature vector encodings. By employing an attention mechanism to fuse graph embedding features with network topological features, we constructed a high-precision prediction model, achieving 87.85% prediction accuracy on a bacterial protein dataset. Furthermore, we analyzed the differences in the contributions of network topological features in the model and the differences among various graph embedding methods, and found that the combination of DeepWalk features with Doc2vec and all topological features was crucial for the identification of thermostable proteins. This study provides a practical and effective new method for protein thermostability prediction, and at the same time offers theoretical guidance for exploring protein diversity, discovering new thermostable proteins, and the intelligent modification of mesophilic proteins.
Brain-computer interface (BCI) can establish a direct communications pathway between the human brain and the external devices, which is independent of peripheral nerves and muscles. Compared with invasive BCI, non-invasive BCI has the advantages of low cost, low risk, and ease of operation. In recent years, using non-invasive BCI technology to control devices has gradually evolved into a new type of human-computer interaction manner. Moreover, the control strategy for BCI is an essential component of this manner. First, this study introduced how the brain control techniques were developed and classified. Second, the basic characteristics of direct and shared control strategies were thoroughly explained. And then the benefits and drawbacks of these two strategies were compared and further analyzed. Finally, the development direction and application prospects for non-invasive brain control strategies were suggested.
ObjectTo observe the clinical efficacy and safety of the combination therapy of atorvastatin and JiangZhi Decoction (ZJD) for primary hyperlipidemia (Tan Zhuo Zu E Zheng) and to analyze the interactions of drugs in hypolipidemic effect. MethodsA 2*2 factorial design, single-blind, stratified randomized controlled trial according to the level of lipid was conducted. Primary hyperlipidemia (Tan Zhuo Zu E Zheng) patients met the inclusion criteria were divided into 5 groups:ATV 10 mg group (group A), ATV 20 mg group (group B), ATV 10 mg+JZD group (group C), ATV 20 mg+JZD group (group D), JZD group (group E). After two weeks treatment, the efficacy and safety among the 5 groups were compared. ResultsA total of 92 patients were included, of which, 20 were in group A, 25 in group B, 21 in group C, 17 in group D, and 9 in group E. The results showed that:(1) There was no significant difference between group C and group B in the reduction of serum total cholesterol (TC) and low-density lipoprotein cholesterol (LDL-C) (PTC=0.226, PLDL-C=0.818). (2) The results of 2*2 factorial analysis showed that, there was no significant interaction between TCM factor and western medicine factor (PTC=0.605, PLDL-C=0.843). (3) There were no significant differences in safety outcomes among 5 groups (all P values >0.05). ConclusionATV 10 mg+JZD and ATV 20 mg have a similar efficacy in reducing TC and LDL-C. There is no obvious interaction between JZD and ATV in hypolipidemic effect, and the combination therapy of ATV and JZD is safe.
Medical whole-body positron emission tomography (PET), one of the most successful molecular imaging technologies, has been widely used in the fields of cancer diagnosis, cardiovascular disease diagnosis and cranial nerve study. But, on the other hand, the sensitivity, spatial resolution and signal-noise-ratio of the commercial medical whole-body PET systems still have some shortcomings and a great room for improvement. The sensitivity, spatial resolution and signal-noise-ratio of PET system are largely affected by the performances of the scintillators and the photo detectors. The design of a PET system is usually a trade-off in cost and performance. A better image quality can be achieved by optimizing and balancing the key components which affect the system performance the most without dramatically increases in cost. With the development of the scintillator, photo-detector and high speed electronic system, the performance of medical whole-body PET system would be dramatically improved. In this paper, we report current progresses and discuss future directions of the developments of technologies in medical whole-body PET system.
The eye-computer interaction technology based on electro-oculogram provides the users with a convenient way to control the device, which has great social significance. However, the eye-computer interaction is often disturbed by the involuntary eye movements, resulting in misjudgment, affecting the users’ experience, and even causing danger in severe cases. Therefore, this paper starts from the basic concepts and principles of eye-computer interaction, sorts out the current mainstream classification methods of voluntary/involuntary eye movement, and analyzes the characteristics of each technology. The performance analysis is carried out in combination with specific application scenarios, and the problems to be solved are further summarized, which are expected to provide research references for researchers in related fields.
Atherosclerosis is a complex disease characterized by lipid accumulation in the vascular wall and influenced by multiple genetic and environmental factors. To understand the mechanisms of molecular regulation related to atherosclerosis better, a protein interaction network was constructed in the present study. Genes were collected in nucleotide database and interactions were downloaded from Biomolecular Object Network Database (BOND). The interactional data were imported into the software Cytoscape to construct the interaction network, and then the degree characteristics of the network were analyzed for Hub proteins. Statistical significance pathways and diseases were figured out by inputting Hub proteins to KOBAS2.0. The complete pathway network related to atherosclerosis was constructed. The results identified a series of key genes related to atherosclerosis, which would be the potential promising drug targets for effective prevention.
Electronic skin has shown great application potential in many fields such as healthcare monitoring and human-machine interaction due to their excellent sensing performance, mechanical properties and biocompatibility. This paper starts from the materials selection and structures design of electronic skin, and summarizes their different applications in the field of healthcare equipment, especially current development status of wearable sensors with different functions, as well as the application of electronic skin in virtual reality. The challenges of electronic skin in the field of wearable devices and healthcare, as well as our corresponding strategies, are discussed to provide a reference for further advancing the research of electronic skin.
ObjectiveTo investigate the correlation between expression of stromal interaction molecule 1 (STIM1) and tumor malignant degree or lymph node metastasis in patients with gastric cancer. MethodsA total of 83 patients with gastric cancer treated in the Affiliated Hospital of Southwest Medical University and Sichuan Mianyang 404 Hospital from October 2018 to April 2021 were collected. The expression of STIM1 protein in the gastric cancer tissues and the corresponding adjacent normal gastric tissues was detected by immunohistochemistry method. Meanwhile the correlation between the expression of STIM1 protein and clinicopathologic features or postoperative lymph node status of the patients with gastric cancer was analyzed. ResultsThe positive rate of STIM1 protein expression in the gastric cancer tissues was 95.2% (79/83), including 62 (74.7%) patients with high expression (STIM1 scoring 5–7) and 21 (25.3%) patients with low expression (STIM1 scoring 2–4), which in the corresponding adjacent normal gastric tissues was 41.0% (34/83), the difference was statistically significant (χ2=58.078, P<0.001). The expression of STIM1 protein was not related to gender, age, and tumor size of the patients with gastric cancer (P>0.05), while the proportions of the patients with high expression of STIM1 protein in the gastric cancer patients with low/undifferentiated tumor, T3+T4 of infiltration depth, TNM stage Ⅲ, and lymph node metastasis were higher than those with high/medium differentiation (χ2=11.052, P=0.001), T1+T2 of infiltration depth (χ2=24.720, P<0.001), TNM stage Ⅰ+Ⅱ (χ2=9.980, P=0.002), and non-lymph node metastasis (χ2=6.097, P=0.014). The expression intensity of STIM1 protein was positively correlated with the number of lymph node metastasis (r=0.552, Z=–3.098, P=0.002) and the rate of lymph node metastasis (r=0.561, Z=–6.387, P<0.001). ConclusionsPositive rate of STIM1 protein expression in gastric cancer tissues is relatively high. STIM1 protein expression in gastric cancer tissue is closely related to tumor malignancy and lymph node metastasis, so it might play an important role in progression of gastric cancer.