基于ANN-PSO算法的pH依赖-时滞型地锦草结肠靶向给药微丸制备工艺优化

目的应用人工神经网络结合粒子群优化法(ANN-PSO)筛选地锦草结肠靶向给药微丸制备工艺参数。方法以pH依赖-时滞型地锦草结肠靶向给药微丸的制备为研究模型,薄膜包衣增重、增塑剂与成膜材料用量比为变量,微丸体外释放总评归一值为因变量,应用BP(back-propagation,反向传播)人工神经网络建模,并结合粒子群优化法筛选微丸的处方工艺参数。结果按优化的处方工艺参数制备的地锦草结肠靶向给药微丸,在体外实验中可满足结肠定位释放的要求。结论人工神经网络建模与粒子群法寻优相结合,为解决药物制剂工艺涉及的多维复杂非线性系统的优化问题提供了有效的途径。     

基于人工神经网络-粒子群优化法的灯盏花缓释微丸处方工艺优化研究

目的:优化灯盏花缓释微丸处方工艺。方法:应用人工神经网络映射缓释微丸处方工艺过程变量与因变量之间的关系,并结合粒子群优化法筛选处方工艺参数。结果:依据优化处方工艺参数制备的微丸缓释效果明显,微丸中药物释放属扩散与骨架溶蚀协同作用机制。结论:人工神经网络建模结合粒子群优化法为解决制剂处方工艺涉及的多维复杂非线性系统的优化问题提供了有效的途径。     

Neural networks to identify multiple sclerosis with optical coherence tomography

Purpose:  To compare axonal loss in ganglion cells detected with spectral‐domain optical coherence tomography (OCT) in eyes of patients with multiple sclerosis (MS) versus healthy control subjects using an artificial neural network (ANN). To analyse the capability of the ANN technique to improve the detection of retinal nerve fibre layer (RNFL) damage in patients with multiple sclerosis. Methods:  Patients with multiple sclerosis (n = 106) and age‐matched healthy subjects (n = 115) were enrolled. The Spectralis OCT system was used to obtain the circumpapillary RNFL thickness in both eyes. The 768 RNFL thickness measurements provided by the Spectralis OCT were performed to obtain thickness measurements from 24 uniformly divided locations around the peripapillary RNFL. The performance of the ANN technique for identifying RNFL loss in patients with multiple sclerosis was evaluated. Receiver‐operating characteristic (ROC) curves were used to display the ability of the test to discriminate between MS and healthy eyes in our population. ROC curves obtained using ANN and parameters provided by OCT (mean and 6 sector thicknesses) were compared. Results:  The capability of the ANN technique to detect RNFL loss in patients with multiple sclerosis compared with healthy subjects was good. The area under the ROC curve was 0.945. Compared with the OCT‐provided parameters, the ANN had the largest area under the ROC curve. Conclusions:  Measurements of RNFL thickness obtained with Spectralis OCT have a good ability to differentiate between healthy and individuals with multiple sclerosis. Based on the area under the ROC curve, the ANN performed better than any single OCT parameter.     

基于无尺度网络法的4 116 张胃癌中医处方研究

本研究借助临床科研信息共享系统,使用无尺度网络挖掘方法,分析了江苏省中医院门诊994 例胃癌患者中医治疗的4 116 张处方,以图示化直观展示了核心药物及配伍,揭示出该院胃癌的中医治疗处方用药特点。该方法具有较高的可信度,对于中医处方的研究具有一定的意义和价值。     

Weekly Nowcasting of New COVID-19 Cases Using Past Viral Load Measurements

The rapid spread of the coronavirus disease COVID-19 has imposed clinical and financial burdens on hospitals and governments attempting to provide patients with medical care and implement disease-controlling policies. The transmissibility of the disease was shown to be correlated with the patient’s viral load, which can be measured during testing using the cycle threshold (Ct). Previous models have utilized Ct to forecast the trajectory of the spread, which can provide valuable information to better allocate resources and change policies. However, these models combined other variables specific to medical institutions or came in the form of compartmental models that rely on epidemiological assumptions, all of which could impose prediction uncertainties. In this study, we overcome these limitations using data-driven modeling that utilizes Ct and previous number of cases, two institution-independent variables. We collected three groups of patients (n = 6296, n = 3228, and n = 12,096) from different time periods to train, validate, and independently validate the models. We used three machine learning algorithms and three deep learning algorithms that can model the temporal dynamic behavior of the number of cases. The endpoint was 7-week forward number of cases, and the prediction was evaluated using mean square error (MSE). The sequence-to-sequence model showed the best prediction during validation (MSE = 0.025), while polynomial regression (OLS) and support vector machine regression (SVR) had better performance during independent validation (MSE = 0.1596, and MSE = 0.16754, respectively), which exhibited better generalizability of the latter. The OLS and SVR models were used on a dataset from an external institution and showed promise in predicting COVID-19 incidences across institutions. These models may support clinical and logistic decision-making after prospective validation.     

完全随机型神经网络在1,6-二磷酸果糖制备条件优化上的应用

采用改进模拟退火法作为人工神经网络的学习算法,提出了适用于连续型输入变量、整体优化的完全随机型神经网络,并在1,6-二磷酸果糖制备条件优化中得到了成功应用,单位体积产率显著提高,为工业化生产提供了有利条件。     

Spindle–slow oscillation coupling correlates with memory performance and connectivity changes in a hippocampal network after sleep

After experiences are encoded, post‐encoding reactivations during sleep have been proposed to mediate long‐term memory consolidation. Spindle–slow oscillation coupling during NREM sleep is a candidate mechanism through which a hippocampal‐cortical dialogue may strengthen a newly formed memory engram. Here, we investigated the role of fast spindle‐ and slow spindle–slow oscillation coupling in the consolidation of spatial memory in humans with a virtual watermaze task involving allocentric and egocentric learning strategies. Furthermore, we analyzed how resting‐state functional connectivity evolved across learning, consolidation, and retrieval of this task using a data‐driven approach. Our results show task‐related connectivity changes in the executive control network, the default mode network, and the hippocampal network at post‐task rest. The hippocampal network could further be divided into two subnetworks of which only one showed modulation by sleep. Decreased functional connectivity in this subnetwork was associated with higher spindle–slow oscillation coupling power, which was also related to better memory performance at test. Overall, this study contributes to a more holistic understanding of the functional resting‐state networks and the mechanisms during sleep associated to spatial memory consolidation.     

模式识别在中药质量评价中的应用进展

目的 :对模式识别在中药质量评价中研究进展进行综述。方法 :总结有关化学模式识别、显微图像模式识别及人工神经网络在中药质量评价中应用的研究文献 ,综述不同方法在该领域中的应用。结果 :到目前为止 ,红外、紫外、裂解 高分辨气相色谱、GC-Mass总叠加质谱等被应用于中药的化学总特征的表现 ;基于体视学和计算机图像测试技术的图像定量分析 ,以及图形生成理论和计算机图形学的三维重建和显示技术被应用于中药组织的体视学参数的确定 ;误差反传等类型人工神经网络在中药质量评价中也有重要的应用。结论 :化学模式识别、显微图像模式识别、人工神经网络在中药质量评价中起到了重要的作用 ,具有广阔的发展前景。     

Neural network-based computer-aided diagnosis in distinguishing malignant from benign solitary pulmonary nodules by computed tomography

Background Computer-aided diagnosis (CAD) of lung cancer is the subject of many current researches. Statistical methods and artificial neural networks have been applied to more quantitatively characterize solitary pulmonary nodules (SPNs). In this study, we developed a CAD scheme based on an artificial neural network to distinguish malignant from benign SPNs on thin-section computed tomography (CT) images, and investigated how the CAD scheme can help radiologists with different levels of experience make diagnostic decisions.Methods Two hundred thin-section CT images of SPNs with proven diagnoses (135 small peripheral lung cancers and 65 benign nodules) were analyzed. Three clinical features and nine CT signs of each case were studied by radiologists, and the indices of qualitative diagnosis were quantified. One hundred and forty nodules were selected randomly to form training samples, on which the neural network model was built. The remaining 60 nodules, forming test samples, were presented to 9 radiologists with 3–20 years of clinical experience, accompanied by standard reference images. The radiologists were asked to determine whether a nodule was malignant or benign first without and then with CAD output. Diagnostic performance was evaluated by receiver operating characteristic (ROC) analysis.Results CAD outputs on test samples had higher agreement with pathological diagnoses (Kappa=0.841, P<0.001). Compared with diagnostic results without CAD output, the average area under the ROC curve with CAD output was 0.96 (P<0.001) for junior radiologists, 0.94 (P=0.014) for secondary radiologists and 0.96 (P=0.221) for senior radiologists, respectively. The differences in diagnostic performance with CAD output among the three levels of radiologists were not statistically significant (P=0.584, 0.920 and 0.707, respectively). Conclusions This CAD scheme based on an artificial neural network could improve diagnostic performance and assist radiologists in distinguishing malignant from benign SPNs on thin-section CT images.     

动物细胞培养过程中pH和溶氧的关联控制

本文通过对动物细胞培养过程中pH值和溶氧(DO)水平控制要求的分析,设计了用空气、氧气、氮气和二氮化碳四组气体关联控制pH值和DO水平的控制系统。该系统根据对瞬时pH值和DO值参数的检测,通过微处理机进行一系列的逻辑判断和具有PI调节规律的DDC增量词节计算,再经四气体关联互补计算和多重时序控制环节,综合调节四组气体电磁阀的相互开闭时间,实现对培养过程中pH值和DO水平的控制,满足动物细胞生长的需要。