Application of artificial intelligence in ophthalmic plastic surgery / 国际眼科杂志(Guoji Yanke Zazhi)

The advancement of computers and data explosion have ushered in the third wave of artificial intelligence(AI). AI is an interdisciplinary field that encompasses new ideas, new theories, and new technologies, etc. AI has brought convenience to ophthalmology application and promoted its intelligent, precise, and minimally invasive development. At present, AI has been widely applied in various fields of ophthalmology, especially in oculoplastic surgery. AI has made rapid progress in image detection, facial recognition, etc., and its performance and accuracy have even surpassed humans in some aspects. This article reviews the relevant research and applications of AI in oculoplastic surgery, including ptosis, single eyelid, pouch, eyelid mass, and exophthalmos, and discusses the challenges and opportunities faced by AI in oculoplastic surgery, and provides prospects for its future development, aiming to provide new ideas for the development of AI in oculoplastic surgery.

Transfer learning enhanced graph neural network for aldehyde oxidase metabolism prediction and its experimental application

Aldehyde oxidase (AOX) is a molybdoenzyme that is primarily expressed in the liver and is involved in the metabolism of drugs and other xenobiotics. AOX-mediated metabolism can result in unexpected outcomes, such as the production of toxic metabolites and high metabolic clearance, which can lead to the clinical failure of novel therapeutic agents. Computational models can assist medicinal chemists in rapidly evaluating the AOX metabolic risk of compounds during the early phases of drug discovery and provide valuable clues for manipulating AOX-mediated metabolism liability. In this study, we developed a novel graph neural network called AOMP for predicting AOX-mediated metabolism. AOMP integrated the tasks of metabolic substrate/non-substrate classification and metabolic site prediction, while utilizing transfer learning from 13C nuclear magnetic resonance data to enhance its performance on both tasks. AOMP significantly outperformed the benchmark methods in both cross-validation and external testing. Using AOMP, we systematically assessed the AOX-mediated metabolism of common fragments in kinase inhibitors and successfully identified four new scaffolds with AOX metabolism liability, which were validated through in vitro experiments. Furthermore, for the convenience of the community, we established the first online service for AOX metabolism prediction based on AOMP, which is freely available at https://aomp.alphama.com.cn.

Optimization of ethanol extraction process for Yihuang powder by orthogonal experiment combined with AHP and GA-BP neural network / 中国药房

OBJECTIVE To optimize ethanol extraction process of Yihuang powder. METHODS An orthogonal experiment was designed by reflux extraction with ethanol volume fraction， liquid-to-material ratio， and extraction time as investigation factors. The parameters used were the contents of hesperidin， nobiletin， tangeretin， gallic acid， chebulagic acid， chebulinic acid， liquiritin， glycyrrhizin， eugenol， and the paste-forming rate. The analytic hierarchy process （AHP） was used to calculate the comprehensive score. The optimal ethanol extraction process parameters of Yihuang powder were determined by verifying the results predicted by orthogonal experiment and genetic algorithm （GA）-back propagation neural network （BP neural network）. RESULTS The optimal ethanol extraction process parameters， as optimized by orthogonal experiment， were as follows: ethanol volume fraction of 60%， liquid-solid ratio of 14∶1 （mL/g)， extraction time of 90 min， and extraction for 2 times. The comprehensive score obtained by verification was 79.19. Meanwhile， the optimal ethanol extraction process parameters， optimized by GA-BP neural network， were ethanol volume fraction of 65%， liquid-solid ratio of 14∶1 （mL/g )， extraction time of 60 min， and extraction for 2 times. The comprehensive score obtained by verification was 85.30， higher than the results obtained from orthogonal experiment. CONCLUSIONS The optimization method of orthogonal experiment combined with GA-BP neural network is superior to the traditional orthogonal experiment optimization method. The optimized ethanol extraction process of Yihuang powder is stable and reliable.

Optimization of water extraction technology of Xiangqin jiere granules by orthogonal design based on G1-entropy weight compared with BP neural network / 中国药房

OBJECTIVE Optimizing the water extraction technology of Xiangqin jiere granules. METHODS The orthogonal test of 3 factors and 3 levels was designed， and comprehensive scoring was conducted for the above indexes by using G1-entropy weight to obtain the optimized water extraction technology of Xiangqin jiere granules with water addition ratio， extraction time and extraction times as factors， using the contents of forsythoside A， baicalin， phillyrin， oroxylin A-7-O-β-D-glycoside， wogonoside， baicalein and wogonin， and extraction rate as evaluation indexes. BP neural network modeling was used to optimize the network model and water extraction process using the results of 9 groups of orthogonal tests as test and training data， the water addition multiple， decocting time and extraction times as input nodes， and the comprehensive score as output nodes. Then the two analysis methods were compared by verification test to find the best water extraction process parameters. RESULTS The water extraction technology optimized by the orthogonal test was 8-fold water， extracting 3 times， extracting for 1 h each time. Comprehensive score was 96.84 （RSD＝0.90%）. The optimal water extraction technology obtained by BP neural network modeling included 12-fold water， extracting 4 times， extracting for 0.5 h each time. The comprehensive score was 92.72 （RSD＝0.77%）， which was slightly lower than that of the orthogonal test. CONCLUSIONS The water extraction technology of Xiangqin jiere granules is optimized successfully in the study， which includes adding 8-fold water， extracting 3 times， and extracting for 1 hour each time.

The Effectiveness of Machine Learning Systems' Accuracy in Predicting Heart Stroke Using Socio-Demographic and Risk Factors - A Comparative Analysis of Various Models

Background: Cardiologists can more appropriately classify patients' cardiovascular diseases by executing ac-curate diagnoses and prognoses, enabling them to administer the most appropriate care. Due to machine learning's ability to identify patterns in data, its applications in the medical sector have grown. Diagnosticians can avoid making mistakes by classifying the incidence of cardiovascular illness using machine learning. To lower the fatality rate brought on by cardiovascular disorders, our research developed a model that can cor-rectly forecast these conditions.Methods: This study emphasized a model that can correctly forecast cardiovascular illnesses to lower the death rate brought on by these conditions. We deployed four well-known classification machine learning al-gorithms like K nearest Neighbour, Logistic Regression, Artificial Neural network, and Decision tree. Results: The proposed models were evaluated by their performance matrices. However logistic regression performed high accuracy concerning AUC (0.955) 95% CI (0.872-0.965) followed by the artificial neural net-work. AUC (0.864) 95% CI (0.826-0.912). Conclusion: Individuals' risk of having a cardiac event may be predicted using machine learning, and those who are most at risk can be identified. Predictive models may be developed via machine learning to pinpoint those who have a high chance of suffering a heart attack

Prediction of CW chord after cataract surgery from IOL Master 700 measurement data based on SVR algorithm and BP neural network / 国际眼科杂志(Guoji Yanke Zazhi)

AIM: To observe the changes in the Chang-Warning chord(CW chord)before and after cataract surgery using the IOL Master 700 and predict the CW chord using an artificial intelligence prediction model and preoperative measurement data.METHODS: The analysis was conducted on the preoperative and postoperative IOL Master 700 measurements of 304 cataract patients. This included astigmatism vector value, average keratometry, axial length, anterior chamber depth, lens thickness, corneal central thickness, white-to-white, the position of the Purkinje reflex I image relative to the corneal center and pupil center, and the CW chord. A prediction model based on the SVR algorithm and the BP neural network algorithm was established to predict the postoperative CW chord using the preoperative CW chord and ocular biological parameters.RESULTS: The X component of the CW chord showed a slight shift in the temporal direction in both the left and right eyes after cataract surgery, while the Y component changed little. The SVR model, using the preoperative CW chord and other preoperative biometric parameters as input data, was able to predict the X and Y components of the CW chord more accurately than the BP neural network.CONCLUSION: The CW chord can be directly measured with a coaxial fixation light using various biometers, corneal topographers, or tomographers. The use of the SVR algorithm can accurately predict the postoperative CW chord before cataract surgery.

Optimization of Escherichia coli shake flask culture medium by artificial neural network / 中国生物制品学杂志

@#Objective To optimize a shake flask culture medium for Escherichia coli(E.coli)with high biomass and viability using artificial neural networks(ANN). Methods Using the proportion of glucose(Glu),yeast extract(YE),yeast peptone(YP),soy peptone(SP)and yeast nitrogen base(YNB)as the mixture component,and the A_(600)(A1)value of cell suspension,wet bacterial weight(G,g/L)of culture and cell viability(A2,A_(460))as the response values,the mixture design was used to screen the mixture components that had a significant effect on the response value. The ANN model was constructed with the test results of mixture design as training and verification data samples. The input variables were mixture components and restricted the upper and lower limits of the mixture components,and the output variables were mixture design response values. The optimized medium formula and reference values were obtained by the constructed ANN. The medium formula was further adjusted by Monte Carlo simulation to obtain the shake flask medium formula of E.coli,which was then verified for 10 times. Results The shake flask culture medium of E.coli was composed of Glu 26 g/L,SP 26 g/L,YNB13 g/L with the total concentration of 65 g/L. The verification results showed that the probability of A1 ≥ 14 was 60%,the probability of G ≥ 77 g/L was 50% and the probability of A2 ≥ 11 was 40%. The mean values of the incubation result data were equivalent to the reference values. Conclusion The shake flask culture medium of E.coli optimized in this study can obtain E.coli with high biomass and bacterial activity.

Evaluation method of assistance effect of extravehicular activity glove exoskeleton under weightlessness / 中国康复理论与实践

ObjectiveTo establish a multi index fusion hand grip fatigue prediction model to evaluate the power-assisted effect of the glove exoskeleton prototype for extravehicular clothing. MethodsBP neural network algorithm was used to establish a hand fatigue prediction model. The related factors of hand fatigue were determined with isometric grasping fatigue experiment, and the input variables of BP neural network were determined as cylinder diameter, grasping force, grasping duration and root mean square of electromyography. The fatigue data corresponding to variables of each group were obtained through experiments and subjective fatigue measurement scales, and a fatigue evaluation model based on multi-source fusion of BP neural network algorithm was established. The relationship model between fatigue and assistance effect was established, and the assistance effect of the exoskeleton prototype was evaluated through the degree of fatigue relief. ResultsThe correlation coefficient was 0.974 between the predicted results of the model and the target value. Moreover, it effectively predicted the assistance effect of different prototypes. ConclusionThe BP neural network model established by combining the grasping strength, grasping object parameters and human electromyography can predict hand fatigue, which can be used to evaluate the assistance effect of glove exoskeleton and other hand aids.

Effectiveness of early warning management of nosocomial infection based on neural network model and decision tree model / 公共卫生与预防医学

Objective To predict the effectiveness of nosocomial infection management and effectively control the risk of nosocomial infection. Methods In this study, with the population of ICU patients in a Grade A hospital , 345 ICU patients seen from June 2020 to June 2021 were included in the analysis to collect the infection data in the hospital. Based on the use of the decision tree model to analyze the influencing factors of nosocomial infection, the neural network model was also used to predict the risk of developing nosocomial infection. Results The decision tree model showed that advanced age (age> 80 years) influenced the root node. Type 2 diabetes, gender by male, and BMI level were child nodes, which had different synergistic effects on the occurrence of nosocomial infection. At the same time, random forest (RF), support vector machine (SVM), logical regression (LR) and K nearest neighbor (KNN) algorithms were used to construct a neural network prediction model of nosocomial infection risk, suggesting that the condition, sex and body size of basic diseases are related to the occurrence of nosocomial infection. The combined use of the above model in parallel can effectively increase the specificity and reduce the missed diagnosis. Conclusion The neural network model joint decision tree model in parallel and joint early warning of nosocomial infection risk have excellent effect, and can effectively provide information support for the prevention, management and disposal of nosocomial infection.

Application of artificial intelligence based on data enhancement and hybrid neural network to site identification during esophagogastroduodenoscopy / 中华消化内镜杂志

Objective:To evaluate artificial intelligence constructed by deep convolutional neural network (DCNN) for the site identification in upper gastrointestinal endoscopy.Methods:A total of 21 310 images of esophagogastroduodenoscopy from the Cancer Hospital of Chinese Academy of Medical Sciences from January 2019 to June 2021 were collected. A total of 19 191 images of them were used to construct site identification model, and the remaining 2 119 images were used for verification. The performance differences of two models constructed by DCCN in the identification of 30 sites of the upper digestive tract were compared. One model was the traditional ResNetV2 model constructed by Inception-ResNetV2 (ResNetV2), the other was a hybrid neural network RESENet model constructed by Inception-ResNetV2 and Squeeze-Excitation Networks (RESENet). The main indices were the accuracy, the sensitivity, the specificity, positive predictive value (PPV) and negative predictive value (NPV).Results:The accuracy, the sensitivity, the specificity, PPV and NPV of ResNetV2 model in the identification of 30 sites of the upper digestive tract were 94.62%-99.10%, 30.61%-100.00%, 96.07%-99.56%, 42.26%-86.44% and 97.13%-99.75%, respectively. The corresponding values of RESENet model were 98.08%-99.95%, 92.86%-100.00%, 98.51%-100.00%, 74.51%-100.00% and 98.85%-100.00%, respectively. The mean accuracy, mean sensitivity, mean specificity, mean PPV and mean NPV of ResNetV2 model were 97.60%, 75.58%, 98.75%, 63.44% and 98.76%, respectively. The corresponding values of RESENet model were 99.34% ( P<0.001), 99.57% ( P<0.001), 99.66% ( P<0.001), 90.20% ( P<0.001) and 99.66% ( P<0.001). Conclusion:Compared with the traditional ResNetV2 model, the artificial intelligence-assisted site identification model constructed by RESENNet, a hybrid neural network, shows significantly improved performance. This model can be used to monitor the integrity of the esophagogastroduodenoscopic procedures and is expected to become an important assistant for standardizing and improving quality of the procedures, as well as an significant tool for quality control of esophagogastroduodenoscopy.

Dose volume histogram prediction method for organ at risk in VMAT planning of nasopharyngeal carcinoma based on equivalent uniform dose / 中华放射肿瘤学杂志

Objective:To evaluate the practicability of dose volume histogram (DVH) prediction model for organ at risk (OAR) of radiotherapy plan by minimizing the cost function based on equivalent uniform dose (EUD).Methods:A total of 66 nasopharyngeal carcinoma (NPC) patients received volume rotational intensity modulated arc therapy (VMAT) at Cancer Hospital & Shenzhen Hospital, Chinese Academy of Medical Sciences from 2020 to 2021 were retrospectively selected for this study. Among them, 50 patients were used to train the recurrent neutral network (RNN) model and the remaining 16 cases were used to test the model. DVH prediction model was constructed based on RNN. A three-dimensional equal-weighted 9-field conformal plan was designed for each patient. For each OAR, the DVHs of individual fields were acquired as the model input, and the DVH of VMAT plan was regarded as the expected output. The prediction error obtained by minimizing EUD-based cost function was employed to train the model. The prediction accuracy was characterized by the mean and standard deviation between predicted and true values. The plan was re-optimized for the test cases based on the DVH prediction results, and the consistency and variability of the EUD and DVH parameters of interest (e.g., maximum dose for serial organs such as the spinal cord) were compared between the re-optimized plan and the original plan of OAR by the Wilcoxon paired test and box line plots.Results:The neural network obtained by training the cost function based on EUD was able to obtain better DVH prediction results. The new plan guided by the predicted DVH was in good agreement with the original plan: in most cases, the D 98% in the planning target volume (PTV) was greater than 95% of the prescribed dose for both plans, and there was no significant difference in the maximum dose and EUD in the brainstem, spinal cord and lens (all P>0.05). Compared with the original plan, the average reduction of optic chiasm, optic nerves and eyes in the new plans reached more than 1.56 Gy for the maximum doses and more than 1.22 Gy for EUD, and the average increment of temporal lobes reached 0.60 Gy for the maximum dose and 0.30 Gy for EUD. Conclusion:The EUD-based loss function improves the accuracy of DVH prediction, ensuring appropriate dose targets for treatment plan optimization and better consistency in the plan quality.

Short-term prognostic predictive value of deep-learning assisted quantitative myocardial contrast echocardiography in ST-elevated myocardial infarction after primary percutaneous coronary intervention / 中华超声影像学杂志

Objective:To explore the prognostic predictive value of deep neural network (DNN) assisted myocardial contrast echocardiography (MCE) quantitative analysis of ST-elevated myocardial infarction (STEMI) patients after successful percutaneous coronary intervention(PCI).Methods:A retrospective analysis was performed in 97 STEMI patients with thrombolysis in myocardial infarction-3 flow in infarct vessel after primary PCI in Renmin Hospital of Wuhan University from June to November 2021. MCE was performed within 48 h after PCI. Patients were followed up to 120 days. The adverse events were defined as cardiac death, hospitalization for congestive heart failure, reinfarction, stroke and recurrent angina. The framework consisted of the U-net and hierarchical convolutional LSTMs. The plateau myocardial contrast intensity (A), micro-bubble rate constant (β), and microvascular blood flow (MBF) for all myocardial segments were obtained by the framework, and then underwent variability analysis. Patients were divided into low MBF group and high MBF group based on MBF values, the baseline characteristics and adverse events were compared between the two groups. Other variables included biomarkers, ventricular wall motion analysis, MCE qualitative analysis, and left ventricular ejection fraction. The relationship between various variables and prognosis was investigated using Cox regression analysis. The ROC curve was plotted to evaluate the diagnostic efficacy of the models, and the diagnostic efficacy of the models was compared using the integrated discrimination improvement index (IDI).Results:The time-cost for processing all 3 810 frames from 97 patients was 377 s. 92.89% and 7.11% of the frames were evaluated by an experienced echocardiographer as "good segmentation" and "correction needed". The correlation coefficients of A, β, and MBF ranged from 0.97 to 0.99 for intra-observer and inter-observer variability. During follow-up, 20 patients met the adverse events. Multivariate Cox regression analysis showed that for each increase of 1 IU/s in MBF of the infarct-related artery territory, the risk of adverse events decreased by 6% ( HR 0.94, 95% CI =0.91-0.98). There was a 4.5-fold increased risk of adverse events in the low MBF group ( HR 5.50, 95% CI=1.55-19.49). After incorporating DNN-assisted MCE quantitative analysis into qualitative analysis, the IDI for prognostic prediction was 15% (AUC 0.86, sensitivity 0.78, specificity 0.73). Conclusions:MBF of the area supplied by infarct-related artery after STEMI-PCI is an independent protective factor for short-term prognosis. The DNN-assisted MCE quantitative analysis is an objective, efficient, and reproducible method to evaluate microvascular perfusion. Assessment of culprit-MBF after PCI in STEMI patients adds independent short-term prognostic information over qualitative analysis.It has the potential to be a valuable tool for risk stratification and clinical follow-up.

Application of artificial intelligence based on neural network radiation field in repair of soft tissue defects at lower limbs / 中华创伤骨科杂志

Objective:To investigate the application of artificial intelligence based on the neural network radiation field in repair of soft tissue defects at lower limbs.Methods:A retrospective analysis was performed of the 23 patients who had been admitted to Department of Orthopedic Surgery, Renmin Hospital of Wuhan University from June 2020 to May 2022 for soft tissue defects at lower limbs. There were 14 males and 9 females, aged (38.6±6.7) years. Causes for soft tissue defects: traffic injury in 9 cases, benign or malignant primary soft tissue tumor in 6 cases, mechanical injury in 4 cases, crush injury in 2 cases, and chronic ulcer in 2 cases. Defect locations: the thigh in 3 cases, the lower leg in 7 cases, and the ankle and distal foot in 13 cases. The areas of soft tissue defect ranged from 6.0 cm×3.8 cm to 14.7 cm×12.8 cm. The defects were repaired and reconstructed by transplantation of an anterolateral femoral free flap in 7 cases and a pedicled flap in 16 cases with the assistance of artificial intelligence based on the neural network radiation field, a cutting-edge artificial intelligence algorithm that can quickly construct and process three-dimensional model images through volume rendering under the radiation field. The flap survival rate, aesthetic satisfaction before and after treatment, time for skin flap harvesting and transplantation, functional recovery of lower limbs and incidence of complications were recorded.Results:All the 23 patients were followed up for 32(28, 36) weeks. All the flaps were harvested smoothly and survived. The time for flap harvesting and transplantation was 65.8(50.0, 76.0) min. The aesthetic satisfaction scored (2.3±0.7) points before treatment and (8.4±1.6) points 4 weeks after treatment, showing a statistically significant difference ( P<0.05). The skin flaps healed well with no complications such as hematoma or infection in all but one patient who suffered from superficial necrosis at the distal skin flap due to venous crisis but healed with a scar. On average, the functional recovery of lower limbs scored 23.7(22.0, 25.0) points at 12 weeks after operation according to the Enneking evaluation system, and the functional recovery of lower limbs was 79% (23.7/30.0). Conclusion:Application of artificial intelligence based on the neural network radiation field can achieve ideal results in repair of soft tissue defects at lower limbs, due to its advantages of rapid and accurate surgical procedures, limited damage to the donor site, and a short learning curve.

Prediction of concentration immediately dangerous to life or health of benzene and its derivatives based on quantitative structure-activity relationship / 环境与职业医学

Background With the increasing exposure to hazardous chemicals in the workplace and frequency of occupational injuries and occupational safety accidents, the acquisition of occupational exposure limits of hazardous chemicals is imminent. Objective To obtain more unknown immediately dangerous to life or health (IDLH) concentrations of hazardous chemicals in the workplace by exploring the application of quantitative structure-activity relationship (QSAR) prediction method to IDLH concentrations, and to provide a theoretical basis and technical support for the assessment and prevention of occupational injuries. Methods QSAR was used to correlate the IDLH values of 50 benzene and its derivatives with the molecular structures of target compounds. Firstly, affinity propagation algorithm was applied to cluster sample sets. Secondly, Dragon 2.1 software was used to calculate and pre-screen 537 molecular descriptors. Thirdly, the genetic algorithm was used to select six characteristic molecular descriptors as dependent variables and to construct a multiple linear regression model (MLR) and two nonlinear models using support vector machine (SVM) and artificial neural network (ANN) respectively. Finally, model performance was evaluated by internal and external validation and Williams diagram was drawn to determine the scopes of selected models. Results The ANN model results showed that \begin{document}$ {R}_{\mathrm{t}\mathrm{r}\mathrm{a}\mathrm{i}\mathrm{n}}

Construction of iPSC-derived Inhibitory Neural Network Tissue with Synaptic Transmission Potentials / 中山大学学报(医学科学版)

ObjectiveDirected differentiation of human induced pluripotent stem cells （hiPSCs） into spinal cord γ-aminobutyric acid （GABA）-ergic progenitor cells were implanted into an decellularized optical nerve （DON） bioscaffold to construct a hiPSC-derived inhibitory neural network tissue with synaptic activities. This study aimed to provide a novel stem cell-based tissue engineering product for the study and the repair of central nervous system injury. MethodsThe combination of stepwise directional induction and tissue engineering technology was applied in this study. After hiPSCs were directionally induced into human neural progenitor cells （hNPCs） in vitro， they were seeded into a DON for three-dimensional culture， allowing further differentiation into inhibitory GABAergic neurons under the specific neuronal induction environment. Transmission electron microscopy and whole cell patch clamp technique were used to detect whether the hiPSCs differentiated neurons could form synapse-like structures and whether these neurons had spontaneous inhibitory postsynaptic currents， respectively， in order to validate that the hiPSC-derived neurons would form neural networks with synaptic transmission potentials from a structural and functional perspective. ResultsThe inhibitory neurons of GABAergic phenotype were successfully induced from hiPSCs in vitro， and maintained good viability after 28 days of culture. With the transmission electron microscopy， it was observed that many cell junctions were formed between hiPSC-derived neural cells in the three-dimensional materials， some of which presented a synapse- like structure， manifested as the slight thickness of cell membrane and a small number of vesicles within one side of the cell junctions， the typical structure of a presynatic component， and focal thickness of the membrane of the other side of the cell junctions， a typical structure of a postsynaptic component. According to whole-cell patch-clamp recording， the hiPSC-derived neurons had the capability to generate action potentials and spontaneous inhibitory postsynaptic currents were recorded in this biotissue. ConclusionsThe results of this study indicated that hiPSCs can be induced to differentiate into GABAergic progenitor cells in vitro and can successfully construct iPSC-derived inhibitory neural network tissue with synaptic transmission after implanted into a DON for three-dimensional culture. This study would provide a novel neural network tissue for future research and treatment of central nervous system injury by stem cell tissue engineering technology.

Recurrent neural network prediction on clinical usage of red blood cells / 中国输血杂志

【Objective】 To explore the prediction of clinical red blood cells (RBCs) consumption under major public health emergencies, so as to provide scientific basis for blood collection and blood inventory management. 【Methods】 The clinical consumption of different types of RBCs in Yichang from 2001 to 2017 was analyzed and modeled using the recurrent neural network (RNN) model, and the clinical RBCs consumption between January 2019 and December 2021(36 months) were scientifically predicted. 【Results】 The RNN model showed good prediction performance. The root mean square errors (RMSE) of RNN prediction values of A, B, O, AB type of RBCs were 156.7, 133.4, 204.2 and 51.3, respectively, with the average relative errors (MRE) at 6.4%, 6.6%, 8.5% and 7.1%, respectively. The model predicted the changing trend of RBCs consumption during the first round of COVID-19 outbreak (January to June, 2020) and forecasted the lowest level of consumption in February 2020 and a subsequent recovery in growth. The prediction of RBCs consumption during the second round of COVID-19 pandemic (January to June, 2021) was of high accuracy. For example, the relative errors of RNN models for A type RBCs consumption were 5.2% in Feb 2021 (the lowest level, 1 621.5 U) and 2.5% in May 2021 (the highest level, 2 397.0 U). 【Conclusion】 The artificial intelligence RNN model can predict clinical RBCs consumption well under major public health emergencies.

Postmortem Interval Estimation Using Protein Chip Technology Combined with Multivariate Analysis Methods / 法医学杂志

OBJECTIVES@#To estimate postmortem interval (PMI) by analyzing the protein changes in skeletal muscle tissues with the protein chip technology combined with multivariate analysis methods.@*METHODS@#Rats were sacrificed for cervical dislocation and placed at 16 ℃. Water-soluble proteins in skeletal muscles were extracted at 10 time points (0 d, 1 d, 2 d, 3 d, 4 d, 5 d, 6 d, 7 d, 8 d and 9 d) after death. Protein expression profile data with relative molecular mass of 14 000-230 000 were obtained. Principal component analysis (PCA) and orthogonal partial least squares (OPLS) were used for data analysis. Fisher discriminant model and back propagation (BP) neural network model were constructed to classify and preliminarily estimate the PMI. In addition, the protein expression profiles data of human skeletal muscles at different time points after death were collected, and the relationship between them and PMI was analyzed by heat map and cluster analysis.@*RESULTS@#The protein peak of rat skeletal muscle changed with PMI. The result of PCA combined with OPLS discriminant analysis showed statistical significance in groups with different time points (P<0.05) except 6 d, 7 d and 8 d after death. By Fisher discriminant analysis, the accuracy of internal cross-validation was 71.4% and the accuracy of external validation was 66.7%. The BP neural network model classification and preliminary estimation results showed the accuracy of internal cross-validation was 98.2%, and the accuracy of external validation was 95.8%. There was a significant difference in protein expression between 4 d and 25 h after death by the cluster analysis of the human skeletal muscle samples.@*CONCLUSIONS@#The protein chip technology can quickly, accurately and repeatedly obtain water-soluble protein expression profiles in rats' and human skeletal muscles with the relative molecular mass of 14 000-230 000 at different time points postmortem. The establishment of multiple PMI estimation models based on multivariate analysis can provide a new idea and method for PMI estimation.

Automatic sleep staging model based on single channel electroencephalogram signal / 生物医学工程学杂志

Sleep staging is the basis for solving sleep problems. There's an upper limit for the classification accuracy of sleep staging models based on single-channel electroencephalogram (EEG) data and features. To address this problem, this paper proposed an automatic sleep staging model that mixes deep convolutional neural network (DCNN) and bi-directional long short-term memory network (BiLSTM). The model used DCNN to automatically learn the time-frequency domain features of EEG signals, and used BiLSTM to extract the temporal features between the data, fully exploiting the feature information contained in the data to improve the accuracy of automatic sleep staging. At the same time, noise reduction techniques and adaptive synthetic sampling were used to reduce the impact of signal noise and unbalanced data sets on model performance. In this paper, experiments were conducted using the Sleep-European Data Format Database Expanded and the Shanghai Mental Health Center Sleep Database, and achieved an overall accuracy rate of 86.9% and 88.9% respectively. When compared with the basic network model, all the experimental results outperformed the basic network, further demonstrating the validity of this paper's model, which can provide a reference for the construction of a home sleep monitoring system based on single-channel EEG signals.

Modulation of motor responses and neural activities with transcranial ultrasound stimulation based on closed-loop control / 生物医学工程学杂志

Closed-loop transcranial ultrasound stimulation technology is based on real-time feedback signals, and has the potential for precise regulation of neural activity. In this paper, firstly the local field potential (LFP) and electromyogram (EMG) signals of mice under different intensities of ultrasound stimulation were recorded, then the mathematical model of ultrasound intensity and mouse LFP peak/EMG mean was established offline based on the data, and the closed-loop control system of LFP peak and EMG mean based on PID neural network control algorithm was simulated and built to realize closed-loop control of LFP peak and EMG mean of mice. In addition, using the generalized minimum variance control algorithm, the closed-loop control of theta oscillation power was realized. There was no significant difference between the LFP peak, EMG mean and theta power under closed-loop ultrasound control and the given value, indicating a significant control effect on the LFP peak, EMG mean and theta power of mice. Transcranial ultrasound stimulation based on closed-loop control algorithms provides a direct tool for precise modulation of electrophysiological signals in mice.

A multi-behavior recognition method for macaques based on improved SlowFast network / 生物医学工程学杂志

Macaque is a common animal model in drug safety assessment. Its behavior reflects its health condition before and after drug administration, which can effectively reveal the side effects of drugs. At present, researchers usually rely on artificial methods to observe the behavior of macaque, which cannot achieve uninterrupted 24-hour monitoring. Therefore, it is urgent to develop a system to realize 24-hour observation and recognition of macaque behavior. In order to solve this problem, this paper constructs a video dataset containing nine kinds of macaque behaviors (MBVD-9), and proposes a network called Transformer-augmented SlowFast for macaque behavior recognition (TAS-MBR) based on this dataset. Specifically, the TAS-MBR network converts the red, green and blue (RGB) color mode frame input by its fast branches into residual frames on the basis of SlowFast network and introduces the Transformer module after the convolution operation to obtain sports information more effectively. The results show that the average classification accuracy of TAS-MBR network for macaque behavior is 94.53%, which is significantly improved compared with the original SlowFast network, proving the effectiveness and superiority of the proposed method in macaque behavior recognition. This work provides a new idea for the continuous observation and recognition of the behavior of macaque, and lays the technical foundation for the calculation of monkey behaviors before and after medication in drug safety evaluation.