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}}

Heart sound classification algorithm based on time-frequency combination feature and adaptive fuzzy neural network / 生物医学工程学杂志

Feature extraction methods and classifier selection are two critical steps in heart sound classification. To capture the pathological features of heart sound signals, this paper introduces a feature extraction method that combines mel-frequency cepstral coefficients (MFCC) and power spectral density (PSD). Unlike conventional classifiers, the adaptive neuro-fuzzy inference system (ANFIS) was chosen as the classifier for this study. In terms of experimental design, we compared different PSDs across various time intervals and frequency ranges, selecting the characteristics with the most effective classification outcomes. We compared four statistical properties, including mean PSD, standard deviation PSD, variance PSD, and median PSD. Through experimental comparisons, we found that combining the features of median PSD and MFCC with heart sound systolic period of 100-300 Hz yielded the best results. The accuracy, precision, sensitivity, specificity, and F1 score were determined to be 96.50%, 99.27%, 93.35%, 99.60%, and 96.35%, respectively. These results demonstrate the algorithm's significant potential for aiding in the diagnosis of congenital heart disease.

The inverse stochastic resonance in a small-world neuronal network under electromagnetic stimulation / 生物医学工程学杂志

Electromagnetic stimulation is an important neuromodulation technique that modulates the electrical activity of neurons and affects cortical excitability for the purpose of modulating the nervous system. The phenomenon of inverse stochastic resonance is a response mechanism of the biological nervous system to external signals and plays an important role in the signal processing of the nervous system. In this paper, a small-world neural network with electrical synaptic connections was constructed, and the inverse stochastic resonance of the small-world neural network under electromagnetic stimulation was investigated by analyzing the dynamics of the neural network. The results showed that: the Levy channel noise under electromagnetic stimulation could cause the occurrence of inverse stochastic resonance in small-world neural networks; the characteristic index and location parameter of the noise had significant effects on the intensity and duration of the inverse stochastic resonance in neural networks; the larger the probability of randomly adding edges and the number of nearest neighbor nodes in small-world networks, the more favorable the anti-stochastic resonance was; by adjusting the electromagnetic stimulation parameters, a dual regulation of the inverse stochastic resonance of the neural network can be achieved. The results of this study provide some theoretical support for exploring the regulation mechanism of electromagnetic nerve stimulation technology and the signal processing mechanism of nervous system.

Alzheimer's disease classification based on nonlinear high-order features and hypergraph convolutional neural network / 生物医学工程学杂志

Alzheimer's disease (AD) is an irreversible neurodegenerative disorder that damages patients' memory and cognitive abilities. Therefore, the diagnosis of AD holds significant importance. The interactions between regions of interest (ROIs) in the brain often involve multiple areas collaborating in a nonlinear manner. Leveraging these nonlinear higher-order interaction features to their fullest potential contributes to enhancing the accuracy of AD diagnosis. To address this, a framework combining nonlinear higher-order feature extraction and three-dimensional (3D) hypergraph neural networks is proposed for computer-assisted diagnosis of AD. First, a support vector machine regression model based on the radial basis function kernel was trained on ROI data to obtain a base estimator. Then, a recursive feature elimination algorithm based on the base estimator was applied to extract nonlinear higher-order features from functional magnetic resonance imaging (fMRI) data. These features were subsequently constructed into a hypergraph, leveraging the complex interactions captured in the data. Finally, a four-dimensional (4D) spatiotemporal hypergraph convolutional neural network model was constructed based on the fMRI data for classification. Experimental results on the Alzheimer's Disease Neuroimaging Initiative (ADNI) database demonstrated that the proposed framework outperformed the Hyper Graph Convolutional Network (HyperGCN) framework by 8% and traditional two-dimensional (2D) linear feature extraction methods by 12% in the AD/normal control (NC) classification task. In conclusion, this framework demonstrates an improvement in AD classification compared to mainstream deep learning methods, providing valuable evidence for computer-assisted diagnosis of AD.

A bio-inspired hierarchical spiking neural network with biological synaptic plasticity for event camera object recognition / 生物医学工程学杂志

With inherent sparse spike-based coding and asynchronous event-driven computation, spiking neural network (SNN) is naturally suitable for processing event stream data of event cameras. In order to improve the feature extraction and classification performance of bio-inspired hierarchical SNNs, in this paper an event camera object recognition system based on biological synaptic plasticity is proposed. In our system input event streams were firstly segmented adaptively using spiking neuron potential to improve computational efficiency of the system. Multi-layer feature learning and classification are implemented by our bio-inspired hierarchical SNN with synaptic plasticity. After Gabor filter-based event-driven convolution layer which extracted primary visual features of event streams, we used a feature learning layer with unsupervised spiking timing dependent plasticity (STDP) rule to help the network extract frequent salient features, and a feature learning layer with reward-modulated STDP rule to help the network learn diagnostic features. The classification accuracies of the network proposed in this paper on the four benchmark event stream datasets were better than the existing bio-inspired hierarchical SNNs. Moreover, our method showed good classification ability for short event stream input data, and was robust to input event stream noise. The results show that our method can improve the feature extraction and classification performance of this kind of SNNs for event camera object recognition.

MF-SuP-pKa: Multi-fidelity modeling with subgraph pooling mechanism for pKa prediction

Acid-base dissociation constant (pKa) is a key physicochemical parameter in chemical science, especially in organic synthesis and drug discovery. Current methodologies for pKa prediction still suffer from limited applicability domain and lack of chemical insight. Here we present MF-SuP-pKa (multi-fidelity modeling with subgraph pooling for pKa prediction), a novel pKa prediction model that utilizes subgraph pooling, multi-fidelity learning and data augmentation. In our model, a knowledge-aware subgraph pooling strategy was designed to capture the local and global environments around the ionization sites for micro-pKa prediction. To overcome the scarcity of accurate pKa data, low-fidelity data (computational pKa) was used to fit the high-fidelity data (experimental pKa) through transfer learning. The final MF-SuP-pKa model was constructed by pre-training on the augmented ChEMBL data set and fine-tuning on the DataWarrior data set. Extensive evaluation on the DataWarrior data set and three benchmark data sets shows that MF-SuP-pKa achieves superior performances to the state-of-the-art pKa prediction models while requires much less high-fidelity training data. Compared with Attentive FP, MF-SuP-pKa achieves 23.83% and 20.12% improvement in terms of mean absolute error (MAE) on the acidic and basic sets, respectively.

Progress in neural network mechanism of tinnitus using functional magnetic resonance imaging / 临床耳鼻咽喉头颈外科杂志

Tinnitus refers to the perception of abnormal sound in the absence of external sound stimulation. It can have an impact on a person's mood, memory, attention, and mental state, although the mechanism of tinnitus is still unclear. In recent years, the research on the central neural mechanism of tinnitus has attracted the attention of scholars.Functional magnetic resonance imaging （fMRI）,as an effective imaging technology, has been actively employed in this field. This paper provides a systematic summary of studies on the central neural mechanism of tinnitus by fMRI in recent years,revealed the changes of functional connections among tinnitus-related neural networks,such as auditory network,limbic system,default mode network and salience network. The central neural mechanism of tinnitus involves multiple networks that interact with each other. By understanding this mechanism, we hope to develop more targeted prevention and treatment strategies to help patients alleviate long-term tinnitus.