Pathomorphological characteristics of tuberculous placenta and its clinical implication.

BACKGROUND: The study of pathologic diagnosis of placental TB is rare. The aim of this study is analyzing the pathomorphological characteristics of tuberculosis (TB) placenta during pregnancy and its clinical significance. METHODS: Nineteen cases of placental tissue specimens during pregnancy were collected from June 2015 to February 2022 at Shanghai Public Health Clinical Center, the only inpatient center for pregnant women with TB in Shanghai, China. Hematoxylin-eosin staining, acid-fast staining, and molecular testing were applied to analyze them comprehensively in combination with clinical information. RESULTS: Among the 19 cases, 7 cases caused intrauterine stillbirth, 3 cases received artificial abortion required by the pregnant woman, the other 9 cases received standard delivery and the infants survived, however, 3 of them were low-weight preterm infants, and another 1 case suffered mild intrauterine asphyxia. The 9 surviving infants were followed-up, of which 3 cases got congenital TB. For pathological characteristics of placental tissues under light microscopy, there were 3 cases of epithelioid granuloma formation, 13 cases of acute fetal membranitis, 4 cases of caseous necrosis, 7 cases of inflammatory necrosis, 10 cases of coagulative necrosis, and 6 cases with small focal calcifications. All placental tissues were positive for acid-fast staining and polymerase chain reaction (PCR). Molecular pathological diagnosis showed that 18 cases were positive for Mycobacterium tuberculosis, with 1 case not having received examination. CONCLUSIONS: Combining acid-fast staining and molecular pathological testing is helpful for accurately diagnosing placental TB.

Contrastive Multi-View Kernel Learning.

Kernel method is a proven technique in multi-view learning. It implicitly defines a Hilbert space where samples can be linearly separated. Most kernel-based multi-view learning algorithms compute a kernel function aggregating and compressing the views into a single kernel. However, existing approaches compute the kernels independently for each view. This ignores complementary information across views and thus may result in a bad kernel choice. In contrast, we propose the Contrastive Multi-view Kernel - a novel kernel function based on the emerging contrastive learning framework. The Contrastive Multi-view Kernel implicitly embeds the views into a joint semantic space where all of them resemble each other while promoting to learn diverse views. We validate the method's effectiveness in a large empirical study. It is worth noting that the proposed kernel functions share the types and parameters with traditional ones, making them fully compatible with existing kernel theory and application. On this basis, we also propose a contrastive multi-view clustering framework and instantiate it with multiple kernel k-means, achieving a promising performance. To the best of our knowledge, this is the first attempt to explore kernel generation in multi-view setting and the first approach to use contrastive learning for a multi-view kernel learning.

circANKRD28 inhibits cisplatin resistance in non-small-cell lung cancer through the miR-221-3p/SOCS3 axis.

BACKGROUND: Non-small-cell lung cancer (NSCLC) is a common cancer. Chemotherapeutic drug resistance limits the therapeutic effect of NSCLC and leads to a poor prognosis. As a result, new specific targets may be better identified by studying the mechanism of drug resistance to cisplatin in NSCLC. METHODS: In the present study, we performed a quantitative real-time polymerase chain reaction and western blotting to detect mRNA and protein levels. The proliferation of cells was analyzed by a Cell Counting Kit-8 and colony formation assays. Cell invasion was measured via the Transwell assay. A scratch assay was performed to measure cell migration in cisplatin (DDP)-resistant NSCLC cells. Apoptosis of cells was examined using flow cytometry. RESULTS: We found that circANKRD28 was notably decreased in NSCLC. The results showed that circANKRD28 expression was not affected, and its half-life was more than 12 h. Functional experiments revealed that circANKRD28 overexpression inhibited DDP resistance in NSCLC cells in vitro. Mechanistic findings demonstrated that circANKRD28 regulated tumor cell progression and DDP sensitivity through the miR-221-3p/SOCS3 axis. CONCLUSIONS: The present study revealed the regulatory effects and molecular mechanism of circANKRD28 on the development and cisplatin resistance in NSCLC, which may provide experimental basis and theoretical support to identify new targets for therapy of DDP resistance in NSCLC.

Population Density and Host Preference of the Japanese Pine Sawyer (Monochamus alternatus) in the Qinling-Daba Mountains of China.

Monochamus alternatus is a serious trunk-boring pest and is the most important and effective vector of the pine wood nematode Bursaphelenchus xylophilus, which causes pine wilt disease. The pine wilt disease poses a serious threat to forest vegetation and ecological security in the Qinling-Daba Mountains and their surrounding areas. In order to clarify whether the population density of M. alternatus larvae is related to the host preference of M. alternatus adults, we investigated the population density of M. alternatus overwintering larvae and explored the host preference of M. alternatus adults on Pinus tabuliformis, P. armandii, and P. massoniana. The results show that the population density of M. alternatus larvae was significantly higher on P. armandii than those on P. massoniana and P. tabuliformis. The development of M. alternatus larvae was continuous according to the measurements of the head capsule width and the pronotum width. Adults of M. alternatus preferred to oviposit on P. armandii rather than on P. massoniana and P. tabuliformis. Our results indicate that the difference in the population density of M. alternatus larvae between different host plants was due to the oviposition preference of M. alternatus adults. In addition, the instars of M. alternatus larvae could not be accurately determined, because Dyar's law is not suitable for continuously developing individuals. This study could provide theoretical basis for the comprehensive prevention and control of the pine wilt disease in this region and adjacent areas.

High expression of HIV-1 matrix protein p17 in both lymphoma and lymph node tissues of AIDS patients.

BACKGROUND: HIV-1 matrix protein p17 was found to be associated with lymphoma development in vitro. This study aimed to elucidate the pathogenetic roles of HIV-1 p17 in AIDS-related lymphoma. METHODS: Expression of HIV-1 proteins p17, p24, nef and tat were evaluated in tumor tissue samples from 60 lymphoma patients and lymph node samples from 23 non-lymphoma patients with HIV-1 infection by immunohistochemistry. Microvascular density (MVD) determined by CD34 were also assessed in tumor tissues. Clinicopathological data of AIDS patients with lymphoma were collected retrospectively. RESULTS: The subtypes of lymphoma among sixty AIDS patients were diffuse large B-cell lymphoma (32 cases), Burkitt lymphoma (23 cases), Hodgkin's lymphoma (4 cases), and plasmablastic lymphoma (1 case). The expression rate of HIV-1 p17 in lymphoma and non-lymphoma group was 63 % (38/60) and 61 % (14/29) respectively, with no significant difference (p = 0.835). The positive expression rate of p17 in both groups was significantly higher than that of p24, nef and tat (p < 0.05). The expression of p17 was associated with a higher MVD in the lymphoma group (p < 0.05). There were no significant differences in the 2-years overall survival between p17 positive and negative group (61 % vs. 50 %, p = 0.525). CONCLUSION: The common expression of HIV-1 matrix protein p17 in both lymphoma and lymph node tissues of AIDS patients and the association between p17 expression and the higher MVD suggest that the accumulation and persistence of p17 in tissues may play a role in lymphoma development.

Multiview Deep Anomaly Detection: A Systematic Exploration.

Anomaly detection (AD), which models a given normal class and distinguishes it from the rest of abnormal classes, has been a long-standing topic with ubiquitous applications. As modern scenarios often deal with massive high-dimensional complex data spawned by multiple sources, it is natural to consider AD from the perspective of multiview deep learning. However, it has not been formally discussed by the literature and remains underexplored. Motivated by this blank, this article makes fourfold contributions: First, to the best of our knowledge, this is the first work that formally identifies and formulates the multiview deep AD problem. Second, we take recent advances in relevant areas into account and systematically devise various baseline solutions, which lays the foundation for multiview deep AD research. Third, to remedy the problem that limited benchmark datasets are available for multiview deep AD, we extensively collect the existing public data and process them into more than 30 multiview benchmark datasets via multiple means, so as to provide a better evaluation platform for multiview deep AD. Finally, by comprehensively evaluating the devised solutions on different types of multiview deep AD benchmark datasets, we conduct a thorough analysis on the effectiveness of the designed baselines and hopefully provide other researchers with beneficial guidance and insight into the new multiview deep AD topic.

Multiview Subspace Clustering via Co-Training Robust Data Representation.

Taking the assumption that data samples are able to be reconstructed with the dictionary formed by themselves, recent multiview subspace clustering (MSC) algorithms aim to find a consensus reconstruction matrix via exploring complementary information across multiple views. Most of them directly operate on the original data observations without preprocessing, while others operate on the corresponding kernel matrices. However, they both ignore that the collected features may be designed arbitrarily and hard guaranteed to be independent and nonoverlapping. As a result, original data observations and kernel matrices would contain a large number of redundant details. To address this issue, we propose an MSC algorithm that groups samples and removes data redundancy concurrently. In specific, eigendecomposition is employed to obtain the robust data representation of low redundancy for later clustering. By utilizing the two processes into a unified model, clustering results will guide eigendecomposition to generate more discriminative data representation, which, as feedback, helps obtain better clustering results. In addition, an alternate and convergent algorithm is designed to solve the optimization problem. Extensive experiments are conducted on eight benchmarks, and the proposed algorithm outperforms comparative ones in recent literature by a large margin, verifying its superiority. At the same time, its effectiveness, computational efficiency, and robustness to noise are validated experimentally.

Measurement and spatiotemporal analysis of high-quality development of China's industry.

BACKGROUND: China's economy has been transitioning from a phase of rapid growth to high-quality development. The high-quality development of industry is the foundation of a sustainable and healthy growth of national economy, and is of great significance to improve people's living standards, and to meet people's needs for a better life. METHODS: We develop an evaluation index system of high-quality development of industry from the perspectives of industrial benefit, innovation ability, coordination ability, green ability, opening ability and sharing ability. Based on a panel data of 30 provinces in China during 1999-2018, we evaluate the level of high-quality development of industry using the entropy-weight method and Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) method. Meanwhile we select six specific years and adopt the Natural Breaks method to classify the provinces according to their levels. At last, Moran's I index is used to analyze the spatial correlation among the provinces. RESULTS: Opening ability and innovation ability are found to have greater impacts on industrial high-quality development than other indices, and their influence has been increasing in recent years. There are large spatial and temporal differences among different provinces. Municipalities and coastal provinces are found to be at constantly high levels. The levels in the central region dropped first and then increased, however it was the opposite in the western region. In the northeast region, the levels fluctuated greatly. Overall, the high-quality development of industry among China's provinces shows positive spatial correlation. Most provinces in China are in High-High and Low-Low clustering States. The High-High clustering type is mainly distributed in the eastern region and the Low-Low clustering type is mainly distributed in the western and central regions. CONCLUSION: (1) Innovation ability and open ability are the most important factors. (2) Green ability has not sufficiently contributed to China's industrial development. (3) Regional and time evolution differences are significant. (4) There is a significant and stable spatial clustering effect in the high-quality development of industry among China's provinces.

Case report: histopathology and molecular pathology analysis on enteric tissue of a COVID-19 patient.

AIMS: Patients with COVID-19 can also have enteric symptoms. Here we analyzed the histopathology of intestinal detachment tissue from a patient with COVID-19. METHODS: The enteric tissue was examined by hematoxylin & eosin stain, PAS (Periodic acid-Schiff) staining, Gram staining, Ziehl-Neelsen stain and Grocott's Methenamine Silver (GMS) Stain. The distribution of CD3, CD4, CK20 and CD68, cytomegalovirus (CMV) and Herpes Simplex Virus (HSV) antigen were determined by immunohistochemistry. In situ hybridization (ISH) of SARS-CoV-2 and Epstein-Barr virus-encoded small RNA (EBER) were also performed. RESULTS: We observed mucosal epithelium shedding, intestinal mucosal erosion, focal inflammatory necrosis with hemorrhage, massive neutrophil infiltration, macrophage proliferation accompanied by minor lymphocyte infiltration. Fungal spores and gram positive cocci but not mycobacteria tuberculosis were identified. Immunohistochemistry staining showed abundant CD68+ macrophages but few lymphocytes infiltration. HSV, CMV and EBV were negative. ISH of SARS-CoV-2 RNA showed positive signal which mostly overlapped with CD68 positivity. CONCLUSIONS: The in situ detection of SARS-CoV-2 RNA in intestinal macrophages implicates a possible route for gastrointestinal infection. Further study is needed to further characterize the susceptibility of enteric cells to SARS-CoV-2 infection.

Dual-branch combination network (DCN): Towards accurate diagnosis and lesion segmentation of COVID-19 using CT images.

The recent global outbreak and spread of coronavirus disease (COVID-19) makes it an imperative to develop accurate and efficient diagnostic tools for the disease as medical resources are getting increasingly constrained. Artificial intelligence (AI)-aided tools have exhibited desirable potential; for example, chest computed tomography (CT) has been demonstrated to play a major role in the diagnosis and evaluation of COVID-19. However, developing a CT-based AI diagnostic system for the disease detection has faced considerable challenges, which is mainly due to the lack of adequate manually-delineated samples for training, as well as the requirement of sufficient sensitivity to subtle lesions in the early infection stages. In this study, we developed a dual-branch combination network (DCN) for COVID-19 diagnosis that can simultaneously achieve individual-level classification and lesion segmentation. To focus the classification branch more intensively on the lesion areas, a novel lesion attention module was developed to integrate the intermediate segmentation results. Furthermore, to manage the potential influence of different imaging parameters from individual facilities, a slice probability mapping method was proposed to learn the transformation from slice-level to individual-level classification. We conducted experiments on a large dataset of 1202 subjects from ten institutes in China. The results demonstrated that 1) the proposed DCN attained a classification accuracy of 96.74% on the internal dataset and 92.87% on the external validation dataset, thereby outperforming other models; 2) DCN obtained comparable performance with fewer samples and exhibited higher sensitivity, especially in subtle lesion detection; and 3) DCN provided good interpretability on the loci of infection compared to other deep models due to its classification guided by high-level semantic information. An online CT-based diagnostic platform for COVID-19 derived from our proposed framework is now available.

Prediction of COVID-19 Patients at High Risk of Progression to Severe Disease.

In order to develop a novel scoring model for the prediction of coronavirus disease-19 (COVID-19) patients at high risk of severe disease, we retrospectively studied 419 patients from five hospitals in Shanghai, Hubei, and Jiangsu Provinces from January 22 to March 30, 2020. Multivariate Cox regression and orthogonal projections to latent structures discriminant analysis (OPLS-DA) were both used to identify high-risk factors for disease severity in COVID-19 patients. The prediction model was developed based on four high-risk factors. Multivariate analysis showed that comorbidity [hazard ratio (HR) 3.17, 95% confidence interval (CI) 1.96-5.11], albumin (ALB) level (HR 3.67, 95% CI 1.91-7.02), C-reactive protein (CRP) level (HR 3.16, 95% CI 1.68-5.96), and age ≥60 years (HR 2.31, 95% CI 1.43-3.73) were independent risk factors for disease severity in COVID-19 patients. OPLS-DA identified that the top five influencing parameters for COVID-19 severity were CRP, ALB, age ≥60 years, comorbidity, and lactate dehydrogenase (LDH) level. When incorporating the above four factors, the nomogram had a good concordance index of 0.86 (95% CI 0.83-0.89) and had an optimal agreement between the predictive nomogram and the actual observation with a slope of 0.95 (R2 = 0.89) in the 7-day prediction and 0.96 (R2 = 0.92) in the 14-day prediction after 1,000 bootstrap sampling. The area under the receiver operating characteristic curve of the COVID-19-American Association for Clinical Chemistry (AACC) model was 0.85 (95% CI 0.81-0.90). According to the probability of severity, the model divided the patients into three groups: low risk, intermediate risk, and high risk. The COVID-19-AACC model is an effective method for clinicians to screen patients at high risk of severe disease.

Pricing Interval European Option with the Principle of Maximum Entropy.

This paper develops the interval maximum entropy model for the interval European option valuation by estimating an underlying asset distribution. The refined solution for the model is obtained by the Lagrange multiplier. The particle swarm optimization algorithm is applied to calculate the density function of the underlying asset, which can be utilized to price the Shanghai Stock Exchange (SSE) 50 Exchange Trades Funds (ETF) option of China and the Boeing stock option of the United States. Results show that maximum entropy distribution provides precise estimations for the underlying asset of interval number situations. In this way, we can get the distribution of the underlying assets and apply it to the interval European option pricing in the financial market.

Clinicopathologic characteristics of HIV/AIDS-related plasmablastic lymphoma.

Plasmablastic lymphoma is a rare and aggressive B cell lymphoma that is considered to be strongly associated with HIV infection. This article explores the histological morphology and immunohistochemical characteristics of HIV/AIDS-related plasmablastic lymphoma with the goal of improving the diagnosis and treatment of this rare tumor. According to criteria of the World Health Organization Classification of Tumors of Hematopoietic and Lymphoid Tissues (2008), six plasmablastic lymphoma cases admitted to the Shanghai Public Health Clinical Center were comprehensively analyzed with conventional hematoxylin-eosin staining, immunohistochemical staining and in situ hybridization. The morphological features of six tumors were consistent with PBL. Immunohistochemical staining showed that all six cases were negative for CD19, CD20, and CD79a, and positive for OCT-2, BOB-1, VS38c, and melanoma ubiquitous mutated 1. The Ki67 proliferation index was higher than 90% in all six cases. In situ hybridization indicated that four cases were EBER-positive. In addition, three cases had C-MYC translocation rearrangement. Our results showed that the immunophenotypes of PBL vary, which makes PBL diagnosis difficult. Therefore, morphological characteristics, immunophenotypic markers, and clinical data should be used in combination to enable an accurate diagnosis, especially in the presence of immunophenotypic variation, as this approach will facilitate timely treatment.

Hardware assisted hypervisor introspection.

In this paper, we introduce hypervisor introspection, an out-of-box way to monitor the execution of hypervisors. Similar to virtual machine introspection which has been proposed to protect virtual machines in an out-of-box way over the past decade, hypervisor introspection can be used to protect hypervisors which are the basis of cloud security. Virtual machine introspection tools are usually deployed either in hypervisor or in privileged virtual machines, which might also be compromised. By utilizing hardware support including nested virtualization, EPT protection and #BP, we are able to monitor all hypercalls belongs to the virtual machines of one hypervisor, include that of privileged virtual machine and even when the hypervisor is compromised. What's more, hypercall injection method is used to simulate hypercall-based attacks and evaluate the performance of our method. Experiment results show that our method can effectively detect hypercall-based attacks with some performance cost. Lastly, we discuss our furture approaches of reducing the performance cost and preventing the compromised hypervisor from detecting the existence of our introspector, in addition with some new scenarios to apply our hypervisor introspection system.

Accurate mobile malware detection and classification in the cloud.

As the dominator of the Smartphone operating system market, consequently android has attracted the attention of s malware authors and researcher alike. The number of types of android malware is increasing rapidly regardless of the considerable number of proposed malware analysis systems. In this paper, by taking advantages of low false-positive rate of misuse detection and the ability of anomaly detection to detect zero-day malware, we propose a novel hybrid detection system based on a new open-source framework CuckooDroid, which enables the use of Cuckoo Sandbox's features to analyze Android malware through dynamic and static analysis. Our proposed system mainly consists of two parts: anomaly detection engine performing abnormal apps detection through dynamic analysis; signature detection engine performing known malware detection and classification with the combination of static and dynamic analysis. We evaluate our system using 5560 malware samples and 6000 benign samples. Experiments show that our anomaly detection engine with dynamic analysis is capable of detecting zero-day malware with a low false negative rate (1.16 %) and acceptable false positive rate (1.30 %); it is worth noting that our signature detection engine with hybrid analysis can accurately classify malware samples with an average positive rate 98.94 %. Considering the intensive computing resources required by the static and dynamic analysis, our proposed detection system should be deployed off-device, such as in the Cloud. The app store markets and the ordinary users can access our detection system for malware detection through cloud service.