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.
A new type of testing system used for antithrombotic pressure circulatory equipment has been developed, which realized a new method for the calibration of pressure sensor. Multi-path control and acquisition functions are achieved by this method based on human-computer interaction testing system. The precision of pressure sensor is obtained by polynomial fitting for each test point using linear interpolation method. The result showed that the precision test of pressure sensor could be realized easily and efficiently, using the developed testing system, and the parameters of pressure sensor could be calibrated effectively, so that it could be accurately used in the antithrombotic pressure circulatory equipment. The developed testing system has a prosperous future in the aspects of promotion and application.
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.
In an anti-thrombotic pressure circulatory device, relays and solenoid valves serve as core execution units. Thus the therapeutic efficacy and patient safety of the device will directly depend on their performance. A new type of testing system for relays and solenoid valves used in the anti-thrombotic device has been developed, which can test action response time and fatigue performance of relay and solenoid valve. PC, data acquisition card and test platform are used in this testing system based on human-computer interaction testing modules. The testing objectives are realized by using the virtual instrument technology, the high-speed data acquisition technology and reasonable software design. The two sets of the system made by relay and solenoid valve are tested. The results proved the universality and reliability of the testing system so that these relays and solenoid valves could be accurately used in the anti-thrombotic pressure circulatory equipment. The newly-developed testing system has a bright future in the aspects of promotion and application prospect.
Objective To evaluate the risk factors for cognitive impairment and their interactions in acute ischemic stroke (IS) patients. Methods IS patients admitted to the Department of Neurology, the People’s Hospital of Mianyang between January 2019 and January 2022 were selected. Patients were divided into a cognitive impairment group and a cognitive normal group. The demographic characteristics and clinical data of the subjects were collected, and the traditional risk factors for cognitive impairment were determined by univariate and multivariate logistic regression analysis. The multifactor dimensionality reduction test was used to detect the possible interactions between risk factors. Results A total of 255 patients were included. Among them, 88 cases (34.5%) in the cognitive impairment group and 167 cases (65.5%) in the cognitive normal group. The results of factor logistic regression analysis showed that after adjusting for covariates, big and medium infarction volume, severe IS, moderate to severe carotid artery stenosis as well as high hypersensitive C-reactive protein (hs-CRP) were associated with post-IS cognitive impairment (P<0.05). The cognitive impairment increased by 22.632 times [odds ratio=22.632, 95% confidence interval (5.980, 85.652), P<0.001] in patients with big and medium infarction volume, severe IS and high hs-CRP. Conclusions The cognitive impairment is common in acute IS. Patients with big and medium infarction volume, non-mild stroke, carotid artery stenosis, high hs-CRP, and non-right sided infarction are prone to cognitive impairment, and there are complex interactions among these risk factors.
Objective To explore the influencing factors of internet game addiction among middle school students. Methods Students from a certain district in Sichuan between September 2022 and March 2023 were included as participants. Basic information such as gender, age, whether the subjects were only children, place of residence, parental education, and subjective economic status were investigated. The nine-item Internet Gaming Disorder Scale-short form was used to investigate whether participants had internet game addiction, and the Berkman-Syme Social Network Index was used to evaluate the participants’ social level. Multiple linear regression analysis was used to conduct multivariate analysis to explore the influencing factors of internet game addiction. Results A total of 594 questionnaires were distributed, and 592 valid questionnaires were ultimately obtained. The detection rate of internet game addiction was 12.0%. Multiple linear regression analysis showed that gender (t=?8.281, P<0.001), age (t=3.211, P=0.001), subjective economic status in the region (t=2.025, P=0.043), and social level (t=?4.239, P<0.001) were the influencing factors of online game addiction. Due to the P value was close to the set test level (0.05), subjective economic status in the region was not considered an influencing factor of internet game addiction. Conclusion Teenagers with male gender, older age, and lower social skills are more likely to develop addiction to internet games.
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
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.
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.
Features and interaction between features of liver disease is of great significance for the classification of liver disease. Based on least absolute shrinkage and selection operator (LASSO) and interaction LASSO, the generalized interaction LASSO model is proposed in this paper for liver disease classification and compared with other methods. Firstly, the generalized interaction logistic classification model was constructed and the LASSO penalty constraints were added to the interactive model parameters. Then the model parameters were solved by an efficient alternating directions method of multipliers (ADMM) algorithm. The solutions of model parameters were sparse. Finally, the test samples were fed to the model and the classification results were obtained by the largest statistical probability. The experimental results of liver disorder dataset and India liver dataset obtained by the proposed methods showed that the coefficients of interaction features of the model were not zero, indicating that interaction features were contributive to classification. The accuracy of the generalized interaction LASSO method is better than that of the interaction LASSO method, and it is also better than that of traditional pattern recognition methods. The generalized interaction LASSO method can also be popularized to other disease classification areas.
