An integrated approach of network pharmacology, molecular docking, and experimental verification uncovers kaempferol as the effective modulator of HSD17B1 for treatment of endometrial cancer.

BACKGROUND: Endometrial cancer (EC) is one of the most common gynecological malignancies globally, and the development of innovative, effective drugs against EC remains a key issue. Phytoestrogen kaempferol exhibits anti-cancer effects, but the action mechanisms are still unclear. METHOD: MTT assays, colony-forming assays, flow cytometry, scratch healing, and transwell assays were used to evaluate the proliferation, apoptosis, cell cycle, migration, and invasion of both ER-subtype EC cells. Xenograft experiments were used to assess the effects of kaempferol inhibition on tumor growth. Next-generation RNA sequencing was used to compare the gene expression levels in vehicle-treated versus kaempferol-treated Ishikawa and HEC-1-A cells. A network pharmacology and molecular docking technique were applied to identify the anti-cancer mechanism of kaempferol, including the building of target-pathway network. GO analysis and KEGG pathway enrichment analysis were used to identify cancer-related targets. Finally, the study validated the mRNA and protein expression using real-time quantitative PCR, western blotting, and immunohistochemical analysis. RESULTS: Kaempferol was found to suppress the proliferation, promote apoptosis, and limit the tumor-forming, scratch healing, invasion, and migration capacities of EC cells. Kaempferol inhibited tumor growth and promotes apoptosis in a human endometrial cancer xenograft mouse model. No significant toxicity of kaempferol was found in human monocytes and normal cell lines at non-cytotoxic concentrations. No adverse effects or significant changes in body weight or organ coefficients were observed in 3-7 weeks' kaempferol-treated animals. The RNA sequencing, network pharmacology, and molecular docking approaches identified the overall survival-related differentially expressed gene HSD17B1. Interestingly, kaempferol upregulated HSD17B1 expression and sensitivity in ER-negative EC cells. Kaempferol differentially regulated PPARG expression in EC cells of different ER subtypes, independent of its effect on ESR1. HSD17B1 and HSD17B1-associated genes, such as ESR1, ESRRA, PPARG, AKT1, and AKR1C1\2\3, were involved in several estrogen metabolism pathways, such as steroid binding, 17-beta-hydroxysteroid dehydrogenase (NADP+) activity, steroid hormone biosynthesis, and regulation of hormone levels. The molecular basis of the effects of kaempferol treatment was evaluated. CONCLUSIONS: Kaempferol is a novel therapeutic candidate for EC via HSD17B1-related estrogen metabolism pathways. These results provide new insights into the efficiency of the medical translation of phytoestrogens.

Efficacy and Safety of Ferric Citrate on Hyperphosphatemia among Chinese Patients with Chronic Kidney Disease Undergoing Hemodialysis: A Phase III Multicenter Randomized Open-Label Active-Drug-Controlled Study.

INTRODUCTION: Hyperphosphatemia in chronic kidney disease (CKD) patients is positively associated with mortality. Ferric citrate is a potent phosphorus binder that lowers serum phosphorus level and improves iron metabolism. We compared its efficacy and safety with active drugs in Chinese CKD patients with hemodialysis. METHODS: Chinese patients undergoing hemodialysis were randomized into two treatment groups in a 1:1 ratio, receiving either ferric citrate or sevelamer carbonate, respectively, for 12 weeks. Serum phosphorus levels, calcium concentration, and iron metabolism parameters were evaluated every 2 weeks. Frequency and severity of adverse events were recorded. RESULTS: 217 (90.4%) patients completed the study with balanced demographic and baseline characteristics between two groups. Ferric citrate decreased the serum phosphorus level to 0.59 ± 0.54 mmol/L, comparable to 0.56 ± 0.62 mmol/L by sevelamer carbonate. There was no significant difference between two groups (p > 0.05) in the proportion of patients with serum phosphorus levels reaching the target range, the response rate to the study drug, and the changes of corrected serum calcium concentrations, and intact-PTH levels at the end of treatment. The change of iron metabolism indicators in the ferric citrate group was significantly higher than those in the sevelamer carbonate group. There are 47 (40.5%) patients in the ferric citrate group, and 26 (21.3%) patients in the sevelamer carbonate group experienced drug-related treatment emergent adverse events (TEAEs); most were mild and tolerable. Common drug-related TEAEs were gastrointestinal disorders, including diarrhea (12.9 vs. 2.5%), fecal discoloration (14.7 vs. 0%), and constipation (1.7 vs. 7.4%) in ferric citrate and sevelamer carbonate group. CONCLUSION: Ferric citrate capsules have good efficacy and safety in the control of hyperphosphatemia in adult patients with CKD undergoing hemodialysis. Efficacy is not inferior to sevelamer carbonate. The TEAEs were mostly mild and tolerated by the patients.

Citriquinolinones A and B: Rare Isoquinolinone-Embedded Citrinin Analogues and Related Metabolites from the Deep-Sea-Derived Aspergillus versicolor 170217.

A systematic chemical investigation of the deep-sea-derived fungus Aspergillus versicolor 170217 resulted in the isolation of six new (1-6) and 45 known (7-51) compounds. The structures of the new compounds were established on the basis of exhaustive analysis of their spectroscopic data and theoretical-statistical approaches including GIAO-NMR, TDDFT-ECD/ORD calculations, DP4+ probability analysis, and biogenetic consideration. Citriquinolinones A (1) and B (2) feature a unique isoquinolinone-embedded citrinin scaffold, representing the first exemplars of a citrinin-isoquinolinone hybrid. Dicitrinones K-L (3-4) are two new dimeric citrinin analogues with a rare CH-CH3 bridge. Biologically, frangula-emodin (32) and diorcinol (17) displayed remarkable anti-food allergic activity with IC50 values of 7.9 ± 3.0 µM and 13.4 ± 1.2 µM, respectively, while diorcinol (17) and penicitrinol A (20) exhibited weak inhibitory activity against Vibrio parahemolyticus, with MIC values ranging from 128 to 256 µM.

PSTCNN: Explainable COVID-19 diagnosis using PSO-guided self-tuning CNN.

Since 2019, the coronavirus disease-19 (COVID-19) has been spreading rapidly worldwide, posing an unignorable threat to the global economy and human health. It is a disease caused by severe acute respiratory syndrome coronavirus 2, a single-stranded RNA virus of the genus Betacoronavirus. This virus is highly infectious and relies on its angiotensin-converting enzyme 2-receptor to enter cells. With the increase in the number of confirmed COVID-19 diagnoses, the difficulty of diagnosis due to the lack of global healthcare resources becomes increasingly apparent. Deep learning-based computer-aided diagnosis models with high generalisability can effectively alleviate this pressure. Hyperparameter tuning is essential in training such models and significantly impacts their final performance and training speed. However, traditional hyperparameter tuning methods are usually time-consuming and unstable. To solve this issue, we introduce Particle Swarm Optimisation to build a PSO-guided Self-Tuning Convolution Neural Network (PSTCNN), allowing the model to tune hyperparameters automatically. Therefore, the proposed approach can reduce human involvement. Also, the optimisation algorithm can select the combination of hyperparameters in a targeted manner, thus stably achieving a solution closer to the global optimum. Experimentally, the PSTCNN can obtain quite excellent results, with a sensitivity of 93.65%±1.86%, a specificity of 94.32%±2.07%, a precision of 94.30%±2.04%, an accuracy of 93.99%±1.78%, an F1-score of 93.97%±1.78%, Matthews Correlation Coefficient of 87.99%±3.56%, and Fowlkes-Mallows Index of 93.97%±1.78%. Our experiments demonstrate that compared to traditional methods, hyperparameter tuning of the model using an optimisation algorithm is faster and more effective.

Vitamin D receptor gene polymorphism predicts left ventricular hypertrophy in maintenance hemodialysis.

BACKGROUND: To verify that the single nucleotide polymorphisms (SNP) of vitamin D receptor (VDR) may lead to genetic susceptibility to left ventricular hypertrophy (LVH), the present study was designed to study four SNPs of VDR associated with LVH in maintenance hemodialysis (MHD) patients of Han nationality. METHODS: 120 MHD patients were recruited at Department of Nephrology, Zhongnan Hospital of Wuhan University to analyze the expression of genotype, allele and haplotype of Fok I, Bsm I, Apa I and Taq I in blood samples, and to explore their correlation with blood biochemical indexes and ventricular remodeling. RESULTS: The results showed that the risks of CVD included gender, dialysis time, heart rate, SBP, glycated hemoglobin, calcium, iPTH and CRP concentration. Moreover, LAD, LVDd, LVDs, IVST and LVMI in B allele of Bsm I increased significantly. Fok I, Apa I and Taq I polymorphisms have no significant difference between MHD with LVH and without LVH. Further study showed that VDR expression level decreased significantly in MHD patients with LVH, and the B allele was positively correlated with VDR Expression. CONCLUSION: VDR Bsm I gene polymorphism may predict cardiovascular disease risk of MDH patients, and provided theoretical basis for early detection and prevention of cardiovascular complications.

AVNC: Attention-Based VGG-Style Network for COVID-19 Diagnosis by CBAM.

(Aim) To detect COVID-19 patients more accurately and more precisely, we proposed a novel artificial intelligence model. (Methods) We used previously proposed chest CT dataset containing four categories: COVID-19, community-acquired pneumonia, secondary pulmonary tuberculosis, and healthy subjects. First, we proposed a novel VGG-style base network (VSBN) as backbone network. Second, convolutional block attention module (CBAM) was introduced as attention module into our VSBN. Third, an improved multiple-way data augmentation method was used to resist overfitting of our AI model. In all, our model was dubbed as a 12-layer attention-based VGG-style network for COVID-19 (AVNC) (Results) This proposed AVNC achieved the sensitivity/precision/F1 per class all above 95%. Particularly, AVNC yielded a micro-averaged F1 score of 96.87%, which is higher than 11 state-of-the-art approaches. (Conclusion) This proposed AVNC is effective in recognizing COVID-19 diseases.

Prenatal diagnosis of type A fetal interrupted aortic arch by four-dimensional echocardiography with HD-flow STIC: A case report.

Interrupted aortic arch (IAA) is a rare complex congenital heart disease characterized by interrupted continuity between ascending aorta and descending aorta. Prenatal diagnosis of IAA by echocardiography is not uncommonly reported despite its rarity. However, employing four-dimensional ultrasound HD-flow imaging and spatiotemporal image correlation (STIC) in diagnosis of this condition has seldom been reported. We report a case of fetal IAA prenatally diagnosed by two-dimensional echocardiography and HD-flow STIC.

VISPNN: VGG-inspired Stochastic Pooling Neural Network.

Aim: Alcoholism is a disease that a patient becomes dependent or addicted to alcohol. This paper aims to design a novel artificial intelligence model that can recognize alcoholism more accurately. Methods: We propose the VGG-Inspired stochastic pooling neural network (VISPNN) model based on three components: (i) a VGG-inspired mainstay network, (ii) the stochastic pooling technique, which aims to outperform traditional max pooling and average pooling, and (iii) an improved 20-way data augmentation (Gaussian noise, salt-and-pepper noise, speckle noise, Poisson noise, horizontal shear, vertical shear, rotation, Gamma correction, random translation, and scaling on both raw image and its horizontally mirrored image). In addition, two networks (Net-I and Net-II) are proposed in ablation studies. Net-I is based on VISPNN by replacing stochastic pooling with ordinary max pooling. Net-II removes the 20-way data augmentation. Results: The results by ten runs of 10-fold cross-validation show that our VISPNN model gains a sensitivity of 97.98±1.32, a specificity of 97.80±1.35, a precision of 97.78±1.35, an accuracy of 97.89±1.11, an F1 score of 97.87±1.12, an MCC of 95.79±2.22, an FMI of 97.88±1.12, and an AUC of 0.9849, respectively. Conclusion: The performance of our VISPNN model is better than two internal networks (Net-I and Net-II) and ten state-of-the-art alcoholism recognition methods.

NAGNN: Classification of COVID-19 based on neighboring aware representation from deep graph neural network.

COVID-19 pneumonia started in December 2019 and caused large casualties and huge economic losses. In this study, we intended to develop a computer-aided diagnosis system based on artificial intelligence to automatically identify the COVID-19 in chest computed tomography images. We utilized transfer learning to obtain the image-level representation (ILR) based on the backbone deep convolutional neural network. Then, a novel neighboring aware representation (NAR) was proposed to exploit the neighboring relationships between the ILR vectors. To obtain the neighboring information in the feature space of the ILRs, an ILR graph was generated based on the k-nearest neighbors algorithm, in which the ILRs were linked with their k-nearest neighboring ILRs. Afterward, the NARs were computed by the fusion of the ILRs and the graph. On the basis of this representation, a novel end-to-end COVID-19 classification architecture called neighboring aware graph neural network (NAGNN) was proposed. The private and public data sets were used for evaluation in the experiments. Results revealed that our NAGNN outperformed all the 10 state-of-the-art methods in terms of generalization ability. Therefore, the proposed NAGNN is effective in detecting COVID-19, which can be used in clinical diagnosis.

Diagnosis of COVID-19 Pneumonia via a Novel Deep Learning Architecture.

COVID-19 is a contagious infection that has severe effects on the global economy and our daily life. Accurate diagnosis of COVID-19 is of importance for consultants, patients, and radiologists. In this study, we use the deep learning network AlexNet as the backbone, and enhance it with the following two aspects: 1) adding batch normalization to help accelerate the training, reducing the internal covariance shift; 2) replacing the fully connected layer in AlexNet with three classifiers: SNN, ELM, and RVFL. Therefore, we have three novel models from the deep COVID network (DC-Net) framework, which are named DC-Net-S, DC-Net-E, and DC-Net-R, respectively. After comparison, we find the proposed DC-Net-R achieves an average accuracy of 90.91% on a private dataset (available upon email request) comprising of 296 images while the specificity reaches 96.13%, and has the best performance among all three proposed classifiers. In addition, we show that our DC-Net-R also performs much better than other existing algorithms in the literature. Supplementary Information: The online version contains supplementary material available at 10.1007/s11390-020-0679-8.

Color painting predicts clinical symptoms in chronic schizophrenia patients via deep learning.

BACKGROUND: Individuals with psychiatric disorders perceive the world differently. Previous studies indicated impaired color vision and weakened color discrimination ability in psychotic patients. Examining the paintings from psychotic patients can measure the visual-motor function. However, few studies examined the potential changes in the color painting behavior in these individuals. The current study aims to discriminate schizophrenia patients from healthy controls (HCs) and predict PANSS scores of schizophrenia patients according to their paintings. METHODS: In the present study, we retrospectively analyzed the paintings colored by 281 chronic schizophrenia patients and 35 HCs. The images were scanned and processed using series of computational analyses. RESULTS: The results showed that schizophrenia patients tend to use less color and exhibit different strokes compared to HCs. Using a deep learning residual neural network (ResNet), we were able to discriminate patients from HCs with over 90% accuracy. Further, we developed a novel convolutional neural network to predict PANSS positive, negative, general psychopathology, and total scores. The Root Mean Square Error (RMSE) of the prediction was low, which indicates higher accuracy of prediction. CONCLUSION: In conclusion, the deep learning paradigm showed the large potential to discriminate schizophrenia patients from HCs based on color paintings. Besides, this color painting-based paradigm can effectively predict clinical symptom severity for chronic schizophrenia patients. The color paintings by schizophrenia patients show potential as a tool for clinical diagnosis and prognosis. These findings show potential as a tool for clinical diagnosis and prognosis among schizophrenia patients.

A Niemann-pick C1 disease child with BCG-itis: a case report and analysis.

BACKGROUND: Niemann-Pick C disease is a rare autosomal recessive lysosomal lipid storage disorder. Some primary immunodeficiency diseases patients developed regional disease or disseminated disease after vaccinating BCG. It is unclear whether NPC gene deficiency is associated with Mycobacteria infection. CASE PRESENTATION: We report and discuss a case of a child who presented at the age of 6 months with NPC1 and BCG-itis. The patient was treated with Miglustat and the symptom of lymphadenopathy was improved. CONCLUSIONS: We reasonably speculate that NPC1 is a susceptibility gene of Mtb infection and mainly affects innate immunity. Once diagnosed, the infant should not be vaccinated with BCG and early treated.

A Heuristic Neural Network Structure Relying on Fuzzy Logic for Images Scoring.

Traditional deep learning methods are sub-optimal in classifying ambiguity features, which often arise in noisy and hard to predict categories, especially, to distinguish semantic scoring. Semantic scoring, depending on semantic logic to implement evaluation, inevitably contains fuzzy description and misses some concepts, for example, the ambiguous relationship between normal and probably normal always presents unclear boundaries (normal - more likely normal - probably normal). Thus, human error is common when annotating images. Differing from existing methods that focus on modifying kernel structure of neural networks, this study proposes a dominant fuzzy fully connected layer (FFCL) for Breast Imaging Reporting and Data System (BI-RADS) scoring and validates the universality of this proposed structure. This proposed model aims to develop complementary properties of scoring for semantic paradigms, while constructing fuzzy rules based on analyzing human thought patterns, and to particularly reduce the influence of semantic conglutination. Specifically, this semantic-sensitive defuzzier layer projects features occupied by relative categories into semantic space, and a fuzzy decoder modifies probabilities of the last output layer referring to the global trend. Moreover, the ambiguous semantic space between two relative categories shrinks during the learning phases, as the positive and negative growth trends of one category appearing among its relatives were considered. We first used the Euclidean Distance (ED) to zoom in the distance between the real scores and the predicted scores, and then employed two sample t test method to evidence the advantage of the FFCL architecture. Extensive experimental results performed on the CBIS-DDSM dataset show that our FFCL structure can achieve superior performances for both triple and multiclass classification in BI-RADS scoring, outperforming the state-of-the-art methods.

ResGNet-C: A graph convolutional neural network for detection of COVID-19.

The widely spreading COVID-19 has caused thousands of hundreds of mortalities over the world in the past few months. Early diagnosis of the virus is of great significance for both of infected patients and doctors providing treatments. Chest Computerized tomography (CT) screening is one of the most straightforward techniques to detect pneumonia which was caused by the virus and thus to make the diagnosis. To facilitate the process of diagnosing COVID-19, we therefore developed a graph convolutional neural network ResGNet-C under ResGNet framework to automatically classify lung CT images into normal and confirmed pneumonia caused by COVID-19. In ResGNet-C, two by-products named NNet-C, ResNet101-C that showed high performance on detection of COVID-19 are simultaneously generated as well. Our best model ResGNet-C achieved an averaged accuracy at 0.9662 with an averaged sensitivity at 0.9733 and an averaged specificity at 0.9591 using five cross-validations on the dataset, which is comprised of 296 CT images. To our best knowledge, this is the first attempt at integrating graph knowledge into the COVID-19 classification task. Graphs are constructed according to the Euclidean distance between features extracted by our proposed ResNet101-C and then are encoded with the features to give the prediction results of CT images. Besides the high-performance system, which surpassed all state-of-the-art methods, our proposed graph construction method is simple, transferrable yet quite helpful for improving the performance of classifiers, as can be justified by the experimental results.

MIDCAN: A multiple input deep convolutional attention network for Covid-19 diagnosis based on chest CT and chest X-ray.

BACKGROUND: COVID-19 has caused 3.34m deaths till 13/May/2021. It is now still causing confirmed cases and ongoing deaths every day. METHOD: This study investigated whether fusing chest CT with chest X-ray can help improve the AI's diagnosis performance. Data harmonization is employed to make a homogeneous dataset. We create an end-to-end multiple-input deep convolutional attention network (MIDCAN) by using the convolutional block attention module (CBAM). One input of our model receives 3D chest CT image, and other input receives 2D X-ray image. Besides, multiple-way data augmentation is used to generate fake data on training set. Grad-CAM is used to give explainable heatmap. RESULTS: The proposed MIDCAN achieves a sensitivity of 98.10±1.88%, a specificity of 97.95±2.26%, and an accuracy of 98.02±1.35%. CONCLUSION: Our MIDCAN method provides better results than 8 state-of-the-art approaches. We demonstrate the using multiple modalities can achieve better results than individual modality. Also, we demonstrate that CBAM can help improve the diagnosis performance.

Covid-19 classification by FGCNet with deep feature fusion from graph convolutional network and convolutional neural network.

(Aim) COVID-19 is an infectious disease spreading to the world this year. In this study, we plan to develop an artificial intelligence based tool to diagnose on chest CT images. (Method) On one hand, we extract features from a self-created convolutional neural network (CNN) to learn individual image-level representations. The proposed CNN employed several new techniques such as rank-based average pooling and multiple-way data augmentation. On the other hand, relation-aware representations were learnt from graph convolutional network (GCN). Deep feature fusion (DFF) was developed in this work to fuse individual image-level features and relation-aware features from both GCN and CNN, respectively. The best model was named as FGCNet. (Results) The experiment first chose the best model from eight proposed network models, and then compared it with 15 state-of-the-art approaches. (Conclusion) The proposed FGCNet model is effective and gives better performance than all 15 state-of-the-art methods. Thus, our proposed FGCNet model can assist radiologists to rapidly detect COVID-19 from chest CT images.

COVID-19 classification by CCSHNet with deep fusion using transfer learning and discriminant correlation analysis.

AIM: : COVID-19 is a disease caused by a new strain of coronavirus. Up to 18th October 2020, worldwide there have been 39.6 million confirmed cases resulting in more than 1.1 million deaths. To improve diagnosis, we aimed to design and develop a novel advanced AI system for COVID-19 classification based on chest CT (CCT) images. METHODS: : Our dataset from local hospitals consisted of 284 COVID-19 images, 281 community-acquired pneumonia images, 293 secondary pulmonary tuberculosis images; and 306 healthy control images. We first used pretrained models (PTMs) to learn features, and proposed a novel (L, 2) transfer feature learning algorithm to extract features, with a hyperparameter of number of layers to be removed (NLR, symbolized as L). Second, we proposed a selection algorithm of pretrained network for fusion to determine the best two models characterized by PTM and NLR. Third, deep CCT fusion by discriminant correlation analysis was proposed to help fuse the two features from the two models. Micro-averaged (MA) F1 score was used as the measuring indicator. The final determined model was named CCSHNet. RESULTS: : On the test set, CCSHNet achieved sensitivities of four classes of 95.61%, 96.25%, 98.30%, and 97.86%, respectively. The precision values of four classes were 97.32%, 96.42%, 96.99%, and 97.38%, respectively. The F1 scores of four classes were 96.46%, 96.33%, 97.64%, and 97.62%, respectively. The MA F1 score was 97.04%. In addition, CCSHNet outperformed 12 state-of-the-art COVID-19 detection methods. CONCLUSIONS: : CCSHNet is effective in detecting COVID-19 and other lung infectious diseases using first-line clinical imaging and can therefore assist radiologists in making accurate diagnoses based on CCTs.

CGNet: A graph-knowledge embedded convolutional neural network for detection of pneumonia.

Pneumonia is a global disease that causes high children mortality. The situation has even been worsening by the outbreak of the new coronavirus named COVID-19, which has killed more than 983,907 so far. People infected by the virus would show symptoms like fever and coughing as well as pneumonia as the infection progresses. Timely detection is a public consensus achieved that would benefit possible treatments and therefore contain the spread of COVID-19. X-ray, an expedient imaging technique, has been widely used for the detection of pneumonia caused by COVID-19 and some other virus. To facilitate the process of diagnosis of pneumonia, we developed a deep learning framework for a binary classification task that classifies chest X-ray images into normal and pneumonia based on our proposed CGNet. In our CGNet, there are three components including feature extraction, graph-based feature reconstruction and classification. We first use the transfer learning technique to train the state-of-the-art convolutional neural networks (CNNs) for binary classification while the trained CNNs are used to produce features for the following two components. Then, by deploying graph-based feature reconstruction, we, therefore, combine features through the graph to reconstruct features. Finally, a shallow neural network named GNet, a one layer graph neural network, which takes the combined features as the input, classifies chest X-ray images into normal and pneumonia. Our model achieved the best accuracy at 0.9872, sensitivity at 1 and specificity at 0.9795 on a public pneumonia dataset that includes 5,856 chest X-ray images. To evaluate the performance of our proposed method on detection of pneumonia caused by COVID-19, we also tested the proposed method on a public COVID-19 CT dataset, where we achieved the highest performance at the accuracy of 0.99, specificity at 1 and sensitivity at 0.98, respectively.

MFBCNNC: Momentum factor biogeography convolutional neural network for COVID-19 detection via chest X-ray images.

AIM: By October 6, 2020, Coronavirus disease 2019 (COVID-19) was diagnosed worldwide, reaching 3,355,7427 people and 1,037,862 deaths. Detection of COVID-19 and pneumonia by the chest X-ray images is of great significance to control the development of the epidemic situation. The current COVID-19 and pneumonia detection system may suffer from two shortcomings: the selection of hyperparameters in the models is not appropriate, and the generalization ability of the model is poor. METHOD: To solve the above problems, our team proposed an improved intelligent global optimization algorithm, which is based on the biogeography-based optimization to automatically optimize the hyperparameters value of the models according to different detection objectives. In the optimization progress, after selecting the immigration of suitable index vector and the emigration of suitable index vector, we proposed adding a comparison operation to compare the value of them. According to the different numerical relationships between them, the corresponding operations are performed to improve the migration operation of biogeography-based optimization. The improved algorithm (momentum factor biogeography-based optimization) can better perform the automatic optimization operation. In addition, our team also proposed two frameworks: biogeography convolutional neural network and momentum factor biogeography convolutional neural network. And two methods for detection COVID-19 based on the proposed frameworks. RESULTS: Our method used three convolutional neural networks (LeNet-5, VGG-16, and ResNet-18) as the basic classification models for chest X-ray images detection of COVID-19, Normal, and Pneumonia. The accuracy of LeNet-5, VGG-16, and ResNet-18 is improved by 1.56%, 1.48%, and 0.73% after using biogeography-based optimization to optimize the hyperparameters of the models. The accuracy of LeNet-5, VGG-16, and ResNet-18 is improved by 2.87%, 6.31%, and 1.46% after using the momentum factor biogeography-based optimization to optimize the hyperparameters of the models. CONCLUSION: Under the same experimental conditions, the performance of the momentum factor biogeography-based optimization is superior to the biogeography-based optimization in optimizing the hyperparameters of the convolutional neural networks. Experimental results show that the momentum factor biogeography-based optimization can improve the detection performance of the state-of-the-art approaches in terms of overall accuracy. In future research, we will continue to use and improve other global optimization algorithms to enhance the application ability of deep learning in medical pathological image detection.

COVID-19 in Hemodialysis Patients: A Report of 5 Cases.

In December 2019, an outbreak of coronavirus disease 2019 (COVID-19) due to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) began in China and spread rapidly worldwide. It is unknown whether hemodialysis patients represent a distinct group of patients with certain characteristics that may make them susceptible to infection or severe disease. In this case report, we describe the clinical and epidemiologic features of COVID-19 infection in 201 maintenance hemodialysis patients in Zhongnan Hospital of Wuhan University, including 5 maintenance hemodialysis patients who contracted COVID-19 infection. Of the 5 patients with COVID-19 infection, one had a definite history of contact with an infected person. The age range of the patients was 47 to 67 years. Diarrhea (80%), fever (60%), and fatigue (60%) were the most common symptoms. Lymphopenia occurred in all patients. Computed tomography of the chest showed ground glass opacity in the lungs of all patients. Up to February 13, 2020, none of the patients had developed severe complications (acute respiratory distress syndrome, shock, or multiple organ dysfunction) or died.