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Abstract The role of the compromised immune microenvironment, including immune checkpoints, in myelodysplastic syndromes (MDS) has been identified as critical This study aimed to investigate the expression patterns of immune checkpoints, particularly soluble PD-1/PD-L1 (sPD-1/sPD-L1) as well as PD-1 on effector T cell subsets, and assess their prognostic value and potential regulatory roles in MDS. 161 MDS patients were enrolled, including 129 patients were primarily diagnosed with de novo MDS, together with 59 MDS patients who underwent hypomethylating agents (HMAs) therapy. Plasma sPD-L1 level was elevated in newly diagnosed MDS patients, which was also found to be associated with MDS disease progression that further increase in higher IPSS-R score group. Patients with increased sPD-L1 expression at diagnosis exhibited notably poorer overall survival, and multivariate Cox analysis indicated that elevated sPD-L1 was an independent risk factor. Furthermore, the levels of multiple cytokines and membrane-bound PD-1 on T cells were found to correlate with sPD-1/sPD-L1 levels in plasma. Importantly, we also found sPD-L1 levels significantly increased in MDS patients who showed progression of disease following HMAs therapy. In conclusion, we found elevated plasma sPD-L1 levels in MDS patients are associated with disease progression and poorer overall survival. This study showed that sPD-L1 is a potential biomarker for prognosis and a target for immunotherapy in MDS.

IntroductionAcne vulgaris, one of the most common skin conditions, affects up to 85% of late adolescents, currently no universally accepted assessment system. The biomechanical properties of skin provide valuable information for the assessment and management of skin conditions. Wave-based optical coherence elastography (OCE) quantitatively assesses these properties of tissues by analyzing induced elastic wave velocities. However, velocity estimation methods require significant expertise and lengthy image processing times, limiting the clinical translation of OCE technology. Recent advances in machine learning offer promising solutions to simplify velocity estimation process.MethodsIn this study, we proposed a novel end-to-end deep-learning model, named velocity prediction network (VP-Net), aiming to accurately predict elastic wave velocity from raw OCE data of in vivo healthy and abnormal human skin. A total of 16,424 raw phase slices from 1% to 5% agar-based tissue-mimicking phantoms, 28,270 slices from in vivo human skin sites including the palm, forearm, back of the hand from 16 participants, and 580 slices of facial closed comedones were acquired to train, validate, and test VP-Net.ResultsVP-Net demonstrated highly accurate velocity prediction performance compared to other deep-learning-based methods, as evidenced by small evaluation metrics. Furthermore, VP-Net exhibited low model complexity and parameter requirements, enabling end-to-end velocity prediction from a single raw phase slice in 1.32 ms, enhancing processing speed by a factor of ∼100 compared to a conventional wave velocity estimation method. Additionally, we employed gradient-weighted class activation maps to showcase VP-Net’s proficiency in discerning wave propagation patterns from raw phase slices. VP-Net predicted wave velocities that were consistent with the ground truth velocities in agar phantom, two age groups (20s and 30s) of multiple human skin sites and closed comedones datasets.DiscussionThis study indicates that VP-Net could rapidly and accurately predict elastic wave velocities related to biomechanical properties of in vivo healthy and abnormal skin, offering potential clinical applications in characterizing skin aging, as well as assessing and managing the treatment of acne vulgaris.

Background: Chimeric antigen receptor (CAR) T-cell therapy carries the risk of inducing severe and life-threatening toxicities such as cytokine release syndrome (CRS), neurotoxicity, and infection. Although CRS and infections have similar symptoms, their treatment strategies differ, and early diagnosis is very important. For CRS and infections, the fastest detection time currently takes more than 24 h, so a quick and simple method to identify a fever after CAR T-cell infusion is urgently needed. Methods: We enrolled 27 patients with recurrent fever treated with different types of CAR T-cells, including cluster of differentiation (CD) 7, CD19, CD22, and CD19-CD22 bicistronic CAR T-cells, and evaluated the infection events occurring in these patients. We detailed the morphology of CAR T-cells in peripheral blood smears (PBS) and reported the infection events, CAR transgene copy number, and inflammatory indicators within the first month after treatment. Results: Similar morphological characteristics were observed in the PBS of different CAR T-cells, namely, enlarged cell bodies, deep outside and shallow inside basophilic blue cytoplasm, and natural killer (NK) cell-like purplish red granules. There were ten infections in nine of the twenty-seven patients (33%). The percentage of atypical lymphocytes in PBS was significantly associated with CAR transgene copy number and absolute lymphocyte count in all patients. The atypical lymphocyte percentage was significantly higher in the non-infection group. Conclusions: In conclusion, the unique morphology of CAR T-cells in PBS can be used to evaluate CAR T-cell kinetics and provide reliable evidence for the rapid early identification of fever after CAR T-cell infusion. Clinical Trial Registrations: ChiCTR-OPN-16008526; ChiCTR-OPN-16009847; ChiCTR2000038641; NCT05618041; NCT05388695.

Abstract Moreau score has been used to differentiate chronic lymphocytic leukemia (CLL) from other mature B‐cell neoplasms. However, it showed limitations in Asian patients. Therefore, we conducted a new score system replacing CD5 and CD23 with CD43 and CD180 to evaluate its diagnostic value of CLL. 237 untreated samples diagnosed with mature B‐cell neoplasms were collected and were randomly divided into an exploratory and a validation cohort by a 2:1 ratio. The expression of CD5, CD19, CD20, CD23, CD43, CD79b, CD180, CD200, FMC7, and surface immunoglobulin (SmIg) were analyzed among all the samples. A proposed score was developed based on the logistic regression model. The sensitivity and specificity of the proposed score were calculated by ROC curves. CD43/CD180, CD200, FMC7, and CD79b were included in our new CLL score, which showed a sensitivity of 91.8% and a specificity of 83.1%. These results were confirmed in a validation cohort with a sensitivity of 90.5% (p = 0.808) and a specificity of 79.5% (p = 0.639). In CD5 negative or CD23 negative CLL group, the new CLL score displayed improved sensitivity of 79.4% compared to Moreau score and CLLflow score (41.2% and 47.1%, respectively). In atypical CLL group, the new CLL score showed improved sensitivity of 84.2% compared to Moreau score and CLLflow score (61.4% and 64.9%, respectively). This proposed atypical CLL score helped to offer an accurate differentiation of CLL from non‐CLL together with morphological and molecular methods, particularly in Chinese patients with atypical immunophenotype.

Alzheimer’s disease (AD) is a progressive, irreversible neurodegenerative disorder that requires early diagnosis for timely treatment. Functional magnetic resonance imaging (fMRI) is a non-invasive neuroimaging technique for detecting brain activity. To improve the accuracy of Alzheimer’s disease diagnosis, we propose a new network architecture called Dynamic Multi-Task Graph Isomorphism Network (DMT-GIN). This approach uses fMRI images transformed into brain network structures to classify Alzheimer’s disease more effectively. In the DMT-GIN architecture, we integrate an attention mechanism with the Graph Isomorphism Network (GIN) to capture node features and topological structure information. To further enhance AD classification performance, we incorporate auxiliary tasks of gender and age classification prediction alongside the primary AD classification task in the network. This is achieved through sharing network parameters and adaptive weight adjustments for simultaneous task optimization. Additionally, we introduce a method called GradNorm for dynamically balancing gradient updates between tasks. Evaluation results demonstrate that the DMT-GIN model outperforms existing baseline methods on the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database, leading in various metrics with a prediction accuracy of 90.44%. This indicates that our DMT-GIN model effectively captures brain network features, providing a powerful auxiliary means for the early diagnosis of Alzheimer’s disease.

COVID-19 virus is raging across the planet. In countries where the epidemic is under control, the main mode of virus transmission is through the transport of imported refrigerated food from epidemic areas. Blockchain is a great way for the government to trace every piece of food. However, the high-performance requirements of the blockchain system for nodes limit its wide application. Several sharding-based blockchain systems have been proposed to solve this limitation. Which blocks should be saved by nodes in the sharding-based blockchain system is a new problem. To solve this problem, the optimized data storage method is proposed in this paper. Five features of block popularity are presented, including the objective feature of a block, the objective feature of the block associated with the node, the historical popularity, the hidden popularity and the storage requirements. Then the ELM classifier is used in the optimized model due to its high performance of training and classification. Finally, the experimental results on synthetic data demonstrate the accuracy and efficiency of the optimized data storage model.

Gene regulatory network can intuitively reflect the interaction between genes, and an in-depth study of these relationships plays a significant role in the treatment and prevention of clinical diseases. Therefore, correct reconstruction of gene regulatory network has become the first critical step in the study of disease treatment and prevention at the genetic level. Among the methods for gene regulatory network reconstruction, the Bayesian network model has been widely concerned because of its advantages of expressing both the regulatory relationship and the degree of strength between genes. Nevertheless, the complexity of the Bayesian network model in structure learning is extremely high, making the efficiency of the reconstruction network is low and the scale is limited. Therefore, this paper proposed a dynamic Bayesian network modeling based on structure prediction (DBN-SP). The method combines the correlation model with the dynamic Bayesian network model. On the premise of making full use of the search strategy of dynamic Bayesian network model structure learning, the candidate parent node set is selected based on the structure prediction firstly. Based on this, some redundant information can be removed and the search space can be reduced in the DBN structure learning to improves the efficiency of the network reconstruction. After the network is reconstructed, structure optimization by using the conditional mutual information method can further remove redundant edges and make the network more accurate. The experimental results show that DBN-SP greatly improves the efficiency and scale of the gene regulatory network reconstruction, and the accuracy and other indexes are also improved. DBN-SP is freely accessible at https://github.com/quluxuan/DBN-SP.git.

Blockchain has become increasingly popular for data management in recent years. However, the existing blockchain systems lack efficient semantic queries, particularly keyword queries. To address this issue, we propose a learned-index-based semantic keyword query architecture on blockchain. First, our architecture records data semantics information to support semantic keyword queries. Second, we establish the lookup table index for semantic information among blocks and the block-level recursive model index for blocks to improve the query efficiency. We store the lookup table in the extended block headers to maintain the result’s completeness, and we store recursive model indexes off chain to optimize the maintenance efficiency. Third, we propose a verifiable query algorithm based on our proposed architecture to maintain the result’s correctness. Finally, the experimental results show that combining the lookup table and the learned index effectively improves the query efficiency on blockchain.

BackgroundThe prognosis of relapsed/refractory multiple myeloma (RRMM) patients with the extramedullary disease was significantly poor. Extramedullary multiple myeloma (EMM) patients gained limited benefits from traditional drugs. Anti-B cell maturation antigen (BCMA) chimeric antigen receptor (CAR) T-cell therapy seems to be a promising approach to treat RRMM patients. However, very few clinical studies are designed for EMM. Our study aimed to compare and assess the safety, efficacy, and pharmacokinetics of anti-BCMA CAR-T cell therapy in EMM and non-EMM.MethodsThe results from published anti-BCMA CAR-T clinical trials, in which raw data of EMM patients were available, were reviewed and summarized. Two trials conducted in our clinical centers were analyzed and presented with detailed data.ResultsAccording to published anti-BCMA CAR-T clinical trials, the ORR of EMM ranged from 57% to 100%, with the complete remission (CR) rate of 29% to 60%. Between February 22, 2017, and September 26, 2019, a total of 61 subjects (EMM 25; non-EMM 36) received anti-BCMA CAR-T cell infusion. The data-cutoff date was April 1, 2021. There were no statistical differences between EMM and non-EMM groups in adverse events (AEs), including cytokine release syndrome (CRS). The most common AEs of grade ≥ 3 in both groups were hematologic toxicities. There was no significant difference in the objective response rate (ORR) and ≥ complete remission (CR) rate between both groups. However, the ≥ CR rate of the EMM group was lower than the non-EMM group receiving the fully human anti-BCMA CAR-T cell therapy (p = 0.026). The median progression-free survival (PFS) for EMM and the non-EMM group was 121 days and 361 days, respectively (p = 0.001). The median overall survival (OS) for EMM and the non-EMM group was 248 days and 1024 days, respectively (p = 0.005). The Cmax and AUC0-28d for EMM group were lower than non-EMM group (Cmax, p = 0.016; AUC0-28d, p = 0.016). Extramedullary disease was an independent prognostic risk factor for PFS (hazard ratio, 2.576; 95% CI, 1.343 to 4.941; p = 0.004) and OS (hazard ratio, 2.312; 95% CI, 1.165 to 4.592; p = 0.017) in RRMM patients receiving anti-BCMA CAR-T cell therapy.ConclusionsBased on our results, EMM patients could benefit from the two anti-BCMA CAR products, although they had a shorter PFS and OS compared with non-EMM patients.Clinical Trial Registrationhttp://www.chictr.org.cn, identifier ChiCTR-OPC-16009113 and ChiCTR1800018137.

Extreme learning machine (ELM) is characterized by good generalization performance, fast training speed and less human intervention. With the explosion of large amount of data generated on the Internet, the learning algorithm in the single-machine environment cannot meet the huge memory consumption of matrix computing, so the implement of distributed ELM algorithm has gradually become one of the research focuses. In view of the research significance and implementation value of distributed ELM, this paper first introduced the research background of ELM and improved ELM. Secondly, this paper elaborated the implementation method of distributed ELM from the two directions: ensemble and matrix operation. Finally, we summarized the development status of distributed ELM and discussed the future research direction.

Extended Field of View Ultrasound Sonography (EFOV-US) uses the existing ultrasound images for image stitching, so as to display the shape and scope of organ occupation and the relationship with surrounding tissues comprehensively. However, there are still some problems in Extended Field of View Ultrasound Sonography, such as matching error and unstable quality of image stitching. In view of these problems, we propose Dual-enhanced EFOV-US method that overcomes the limitation and produces higher quality results. Firstly, the gray enhancement method is used to improve the image contrast and reduce the noise interference. Then the super-resolution method based on the generative adversarial network is used to improve the resolution of the ultrasonic image further and increase the number of feature point matching between stitching images. The high quality ultrasound wide-range image is gotten by stitching and fusing the double enhanced image. The experimental results show that the proposed method is effective and practical.

Computer-Aided Diagnosis (CAD) can improve the accuracy of diagnosis effectively, reduce the rate of misdiagnosis, and provide the support for the valid decision. In clinical applications, high requirements are often imposed on the execution speed and accuracy of CAD systems. The classifier is regarded as the core of the CAD system, that is, the performance of the classifier will have a decisive influence on the operating affection of the CAD system. Extreme Learning Machine (ELM) is a fast learning algorithm using Single Hidden Layer Feedforward Neural Network (SLFN) structure. With its advantages in training speed, generalization performance and accuracy, ELM has draw attention in many research fields, including the development of CAD system. The applications of ELM in CAD are reviewed in this research. First, the mathematical model of ELM and framework of CAD system are briefly introduced. Then, the application of ELM in CAD is reviewed in detail, including the feature modeling method combined with ELM in CAD and the specific application of ELM. Finally, we summarized the current research status of CAD systems based on ELM, and the future work is prospected.

Optical coherence tomography-based angiography (OCTA) has attracted attention in clinical applications as a non-invasive and high-resolution imaging modality. Motion artifacts are the most seen artifact in OCTA. Eigen-decomposition (ED) algorithms are popular choices for OCTA reconstruction, but have limitations in the reduction of motion artifacts. The OCTA data do not meet one of the requirements of ED, which is that the data should be normally distributed. To overcome this drawback, we propose an easy-to-deploy development of ED, windowed-ED (wED). wED applies a moving window to the input data, which can contrast the blood-flow signals with significantly reduced motion artifacts. To evaluate our wED algorithm, pre-acquired dorsal wound healing data in a murine model were used. The ideal window size was optimized by fitting the data distribution with the normal distribution. Lastly, the cross-sectional and en face results were compared among several OCTA reconstruction algorithms, Speckle Variance, A-scan ED (aED), B-scan ED, and wED. wED could reduce the background noise intensity by 18% and improve PSNR by 4.6%, compared to the second best-performed algorithm, aED. This study can serve as a guide for utilizing wED to reconstruct OCTA images with an optimized window size.

Background: The occurrence and development of breast cancer has a strong correlation with a person’s genetics. Therefore, it is important to analyze the genetic factors of breast cancer for future development of potential targeted therapies from the genetic level. Methods: In this study, we complete an analysis of the relevant protein–protein interaction network relating to breast cancer. This includes three steps, which are breast cancer-relevant genes selection using mutual information method, protein–protein interaction network reconstruction based on the STRING database, and vital genes calculating by nodes centrality analysis. Results: The 230 breast cancer-relevant genes were chosen in gene selection to reconstruct the protein–protein interaction network and some vital genes were calculated by node centrality analyses. Node centrality analyses conducted with the top 10 and top 20 values of each metric found 19 and 39 statistically vital genes, respectively. In order to prove the biological significance of these vital genes, we carried out the survival analysis and DNA methylation analysis, inquired about the prognosis in other cancer tissues and the RNA expression level in breast cancer. The results all proved the validity of the selected genes. Conclusions: These genes could provide a valuable reference in clinical treatment among breast cancer patients.

In the diagnosis of Alzheimer’s Disease (AD), the brain network analysis method is often used. The traditional network can only reflect the pairwise association between two brain regions, but ignore the higher-order relationship between them. Therefore, a brain network construction method based on hypergraph, called hyperbrain network, is adopted. The brain network constructed by the conventional static hyperbrain network cannot reflect the dynamic changes in brain activity. Based on this, the construction of a dynamic hyperbrain network is proposed. In addition, graph convolutional networks also play a huge role in AD diagnosis. Therefore, an evolving hypergraph convolutional network for the dynamic hyperbrain network is proposed, and the attention mechanism is added to further enhance the ability of representation learning, and then it is used for the aided diagnosis of AD. The experimental results show that the proposed method can effectively improve the accuracy of AD diagnosis up to 99.09%, which is a 0.3 percent improvement over the best existing methods.

As the brain standard template for medical image registration has only been constructed with an MRI template, there is no three-dimensional fMRI standard template for use, and when the subject’s brain structure is quite different from the standard brain structure, the registration to the standard space will lead to large errors. Registration to an individual space can avoid this problem. However, in the current fMRI registration algorithm based on individual space, the reference image is often selected by researchers or randomly selected fMRI images at a certain time point. This makes the quality of the reference image very dependent on the experience and ability of the researchers and has great contingency. Whether the reference image is appropriate and reasonable affects the rationality and accuracy of the registration results to a great extent. Therefore, a method for constructing a 3D custom fMRI template is proposed. First, the data are preprocessed; second, by taking a group of two-dimensional slices corresponding to the same layer of the brain in three-dimensional fMRI images at multiple time points as image sequences, each group of slice sequences are registered and fused; and finally, a group of fused slices corresponding to different layers of the brain are obtained. In the process of registration, in order to make full use of the correlation information between the sequence data, the feature points of each two slices of adjacent time points in the sequence are matched, and then according to the transformation relationship between the adjacent images, they are recursively forwarded and mapped to the same space. Then, the fused slices are stacked in order to form a three-dimensional customized fMRI template with individual pertinence. Finally, in the classic registration algorithm, the difference in the registration accuracy between using a custom fMRI template and different standard spaces is compared, which proves that using a custom template can improve the registration effect to a certain extent.

A computer-aided diagnosis (CAD) system based on mammograms enables early breast cancer detection, diagnosis, and treatment. However, the accuracy of the existing CAD systems remains unsatisfactory. This paper explores a breast CAD method based on feature fusion with convolutional neural network (CNN) deep features. First, we propose a mass detection method based on CNN deep features and unsupervised extreme learning machine (ELM) clustering. Second, we build a feature set fusing deep features, morphological features, texture features, and density features. Third, an ELM classifier is developed using the fused feature set to classify benign and malignant breast masses. Extensive experiments demonstrate the accuracy and efficiency of our proposed mass detection and breast cancer classification method.

Outsourced data, as significant service offered by the cloud service provider (CSP), can effectively facilitate the data owner (DO) overcoming the storage limitations on massive data. To ensure the availability of data, DO usually outsources the data replications to multiple CSPs (multi-CSPs) and utilizes a third party metadata management (TPMM) to dominate the metadata of the corresponding replications. However, during the outsourced procedures, DO can hardly confirm the confidence of the TPMM who may take some malicious behaviors to affect the reliability of data. Thus, DO inevitably faces data security issues caused by the over-reliance on the semi-trusted TPMM to manage the metadata of replications. In this paper, we focus on the problem of reliable outsourced data service among multi-CSPs in untrusted environment, that is, how to reliably store and verify the metadata of the data replications in untrusted multi-CSPs environment. To address the problem, we use the novel blockchain technology as a medium to build a trusted outsourced service platform. Moreover, we fully consider the innovative characteristics of blockchain including decentralized architecture, redundancy storage, collective maintenance, and tamper resistant to ensure the data cannot be changed maliciously. We first design a blockchain-based outsourced service framework for storing data replications in untrusted environment, which contains three key layers, that is, storage layer, verification layer, and blockchain layer. Then, we devise a novel concept of verification peer (VP) for maintaining metadata stored by a form of blockchain, and each of which holds the entire blockchain locally to prevent metadata from being maliciously tampered with. Finally, based on the proposed model, we introduce a collaborative algorithm invoked by VPs to store and verify the metadata of replications. We present a completed analysis and conduct extensive experiments on multi-CSPs scenario. The evaluation results demonstrate that our proposed approach achieves superior performance.

As an extension of the static network, the dynamic functional brain network can show continuous changes in the brain’s connections. Then, limited by the length of the fMRI signal, it is difficult to show every instantaneous moment in the construction of a dynamic network and there is a lack of effective prediction of the dynamic changes of the network after the signal ends. In this paper, an extensible dynamic brain function network model is proposed. The model utilizes the ability of extracting and predicting the instantaneous state of the dynamic network of neural dynamics on complex networks (NDCN) and constructs a dynamic network model structure that can provide more than the original signal range. Experimental results show that every snapshot in the network obtained by the proposed method has a usable network structure and that it also has a good classification result in the diagnosis of cognitive impairment diseases.

Abstract Background POEMS (polyneuropathy, organomegaly, endocrinopathy, monoclonal gammopathy, and skin changes) syndrome still has no standard treatment. On the basis that both POEMS syndrome and myeloma have an underlying plasma cell dyscrasia, anti-myeloma therapy can be expected to be useful for POEMS syndrome. Chimeric antigen receptor T (CAR-T) cells targeting B cell maturation antigen (BCMA) has been used in the treatment of relapsed and refractory multiple myeloma (RRMM). No POEMS syndrome cases treated with anti-BCMA CAR-T cells have been reported. Case presentation Here, we, for the first time, report a POEMS syndrome case treated with anti-BCMA CAR-T cells. A 49-year-old female with incapacitating POEMS syndrome that progressed on lenalidomide treatment was enrolled in a phase I study involving anti-BCMA CAR-T cells (ChiCTR-OPC-16009113). Another patient with RRMM who had undergone six prior lines treatments was also enrolled in the study. They received infusions of anti-BCMA CAR-T cells. Both patients achieved a stringent complete response. Complete remission persisted in the patient with POEMS syndrome and lasted for 7.6 months before a relapse in RRMM patient. Both patients had toxicity consistent with the grade 1 cytokine release syndrome. Conclusions This is the first report of treatment by anti-BCMA CAR-T cells in POEMS syndrome. Our findings demonstrate the anti-BCMA CAR-T cell treatment may be a feasible therapeutic option for patients with POEMS syndrome and RRMM who do not respond well to traditional therapies. Trial registration ChiCTR-OPC, ChiCTR-OPC-16009113. Registered 29 August 2016.

Abstract Background The Union for International Cancer Control (UICC) tumor-node-metastasis (TNM) classification is a key gastric cancer prognosis system. This study aimed to create a new TNM system to provide a reference for the clinical diagnosis and treatment of gastric cancer. Methods A review of gastric cancer patients’ records was conducted in The First Hospital of China Medical University and the Liaoning Cancer Hospital and Institute. Based on patients’ prognoses data, computer-aided unsupervised clustering was performed for all possible TNM staging situations to create a new staging division system. Results The primary outcome measure was 5-year survival, analyzed according to TNM classifications. Computer-aided unsupervised clustering for all TNM staging situations was used to create TNM division criteria that were more consistent with clinical situations. Furthermore, unsupervised clustering for the number of lymph node metastasis in the N stage led to the formulation of a classification method that differs from the existing N stage criteria, and unsupervised clustering for tumor size provided an additional reference for prognosis estimates. Conclusions Finally, we developed a TNM staging system based on the computer-aided unsupervised clustering method; this system was more in line with clinical prognosis data when compared with the 7th edition of UICC gastric cancer TNM classification.

Skyline query is widely used in multi-standard decision making process. As the extension to skyline query, p-dominant skyline query controls the base of the result set by adjusting data proportion, which is suitable for the application on fast deciding data stream. However, skyline query on data stream cannot satisfy the need for skyline query of corresponding value ratio, and the traditional p-dominant skyline query cannot provide skyline query with frequent data update. As a result, a data stream-based algorithm that can achieve the goal of p-dominant skyline query is proposed. First, the definitions of full-dominant relationship and p-dominance are given, after which the definitions of p-dominant skyline and p-dominant skyline based on data stream are proposed. Second, by analyzing the characters of data stream-based p-dominant skyline, a new data filtering method is established. Based on the method, p-Dominant Skyline Query on data stream over Sliding Window (DSSW) is proposed in order to improve the efficiency of p-dominant skyline query. After that, we expend the p-dominant skyline query of data stream into n-of-N p-dominant skyline query and the (n1, n2)-of-Np-dominant skyline query in data stream. Finally, we prove that DSSW has obvious advantages in responding time and storage skyline space by comparing with the traditional p-dominant skyline query algorithms through abundant experiments.

Growing evidence now suggests that circulating tumour DNA (ctDNA) has great potential as a non-invasive biomarker for disease monitoring, since ctDNA carries tumour-specific modifications. In particular, monitoring ctDNA has important implications for identifying patients with haematological malignancies at clinical risk of disease progression. We hereby describe three patients with B-cell non-Hodgkin lymphoma and investigate the clinical value of sequential ctDNA profiling for the early detection of tumour relapse. Somatic mutations in diagnostic tumour biopsy samples of these three patients were identified by applying high-throughput next-generation sequencing. Droplet digital PCR probes and primers were designed and tested for each hotspot mutation. Serial ctDNA analysis was subsequently conducted among these three patients. We found that the longitudinal monitoring of plasma ctDNA could predict for at least one month in advance compared with flow cytometry, cytology and conventional imaging modalities. Therefore, our results support liquid biopsy based on ctDNA as a non-invasive complementary modality to other detection methods for detecting early relapse and contribute to more precise management for non-Hodgkin lymphoma patients.

Diabetes is one of the most common comorbidities in adult patients with coronavirus disease 2019 (COVID-19). This study aimed to analyze the mortality risk factors of diabetic patients with COVID-19. A total of 167 patients with severe COVID-19, including 55 diabetic patients and 112 nondiabetic patients at Tongji Hospital, Wuhan, China from January 28, 2020, to March 10, 2020, were collected. The laboratory, radiological, management information, and medical history was retrospectively reviewed. Potential mortality risk factors in diabetic patients with COVID-19 were evaluated by the proportional hazard Cox model. The clinical information of 167 patients with severe COVID-19 was analyzed. The median age was 65.0 years. Approximately 32.9% of patients had diabetes. In total patients, older age, diabetes, and lymphocyte count were associated with increased risk of death. In diabetic patients, increased mortality was associated with decreased lymphocyte count (≤0.45×10⁹/L, HR 0.196, 95% CI 0.049-0.781, P = 0.021), lactate dehydrogenase >600 U/L (HR 8.010, 95% CI 1.540-41.670, P = 0.013), hsCRP >90 mg/L (HR 4.551, 95% CI 1.472-14.070, P = 0.009) and interleukin-10 >10 U/mL (HR 5.362, 95% CI 1.239-23.199, P = 0.025). COVID-19 patients with diabetes had a poor prognosis, especially when they had two or more of the following abnormalities (χ2 = 58.62, P600 U/L, hsCRP was >90 mg/L and IL-10 was >10 U/mL. For diabetic patients with COVID-19, more attention should be paid to the dynamic monitoring of cytokine levels, and the control of hyperglycemia.