Up-front autologous hematopoietic stem cell transplantation after first complete remission improved prognosis of advanced extra-nodal NKT cell lymphoma: A multicenter real-world study in China.

OBJECTIVES: The advanced extra-nodal NK/T-cell lymphoma (ENKTL) is highly aggressive and lacks effective treatment with a poor prognosis. This study aimed to investigate the effectiveness and safety of autologous hematopoietic stem cell transplantation (ASCT) in CR1. METHODS: Forty of 121 patients with advanced ENKTL from four Chinese hospitals between January 2006 to December 2021 who achieved first complete remission (CR1) and received at least 4 cycles chemotherapy, were enrolled for analysis. Twenty patients received ASCT as up-front consolidation therapy (Group A), and 20 patients only received chemotherapy (Group B). Clinical features, treatment and follow-up information were collected. RESULTS: With a median follow-up of 27 months (range, 4-188 months), the 2-year overall survival (OS) in Group A, 61% (95% CI 37%-85%), was better than that in Group B, 26% (95% CI 2%-50%), p = .018. The 2-year progression-free survival (PFS) was 56% (95% CI 32%-80%) in Group A, 26% (95% CI 2%-50%) in Group B, p = .026. III-IV grade hematological toxicity was the most common adverse event. No treatment-related deaths were observed in both groups. CONCLUSION: Up-front ASCT could improve survival of advanced ENKTL patients in first complete remission, but need be confirmed by a prospective clinical trial.

Prognostic Significance of Mixed-Lineage Leukemia (MLL) Gene Detected by Real-Time Fluorescence Quantitative PCR Assay in Acute Myeloid Leukemia.

BACKGROUND The overall prognosis of acute myeloid leukemia (AML) patients with mixed-lineage leukemia (MLL) gene-positivity is unfavorable. In this study, we evaluated the expression levels of the MLL gene in AML patients. MATERIAL AND METHODS We enrolled 68 MLL gene-positive patients out of 433 newly diagnosed AML patients, and 216 bone marrow samples were collected. Real-time fluorescence quantitative PCR (RQ-PCR) was used to precisely detect the expression levels of the MLL gene. RESULTS We divided 41 patients into 2 groups according to the variation of MRD (minimal residual disease) level of the MLL gene. Group 1 (n=22) had a rapid reduction of MRD level to ≤10^-4 in all samples collected in the first 3 chemotherapy cycles, while group 2 (n=19) had MRD levels constantly >10^-4 in all samples collected in the first 3 chemotherapy cycles. Group 1 had a significantly better overall survival (p=0.001) and event-free survival (p=0.001) compared to group 2. Moreover, the patients with >10^-4 MRD level before the start of HSCT (hematopoietic stem cell transplantation) had worse prognosis and higher risk of relapse compared to patients with ≤10^-4 before the start of HSCT. CONCLUSIONS We found that a rapid reduction of MRD level to ≤10^-4 appears to be a prerequisite for better overall survival and event-free survival during the treatment of AML. The MRD levels detected by RQ-PCR were basically in line with the clinical outcome and may be of great importance in guiding early allogeneic HSCT (allo-HSCT) treatment.

Revolutionising healthcare with artificial intelligence: A bibliometric analysis of 40 years of progress in health systems.

The development of artificial intelligence (AI) has revolutionised the medical system, empowering healthcare professionals to analyse complex nonlinear big data and identify hidden patterns, facilitating well-informed decisions. Over the last decade, there has been a notable trend of research in AI, machine learning (ML), and their associated algorithms in health and medical systems. These approaches have transformed the healthcare system, enhancing efficiency, accuracy, personalised treatment, and decision-making. Recognising the importance and growing trend of research in the topic area, this paper presents a bibliometric analysis of AI in health and medical systems. The paper utilises the Web of Science (WoS) Core Collection database, considering documents published in the topic area for the last four decades. A total of 64,063 papers were identified from 1983 to 2022. The paper evaluates the bibliometric data from various perspectives, such as annual papers published, annual citations, highly cited papers, and most productive institutions, and countries. The paper visualises the relationship among various scientific actors by presenting bibliographic coupling and co-occurrences of the author's keywords. The analysis indicates that the field began its significant growth in the late 1970s and early 1980s, with significant growth since 2019. The most influential institutions are in the USA and China. The study also reveals that the scientific community's top keywords include 'ML', 'Deep Learning', and 'Artificial Intelligence'.

The biology of VSIG4: Implications for the treatment of immune-mediated inflammatory diseases and cancer.

V-set and immunoglobulin domain containing 4 (VSIG4), a type I transmembrane receptor exclusively expressed in a subset of tissue-resident macrophages, plays a pivotal role in clearing C3-opsonized pathogens and their byproducts from the circulation. VSIG4 maintains immune homeostasis by suppressing the activation of complement pathways or T cells and inducing regulatory T-cell differentiation, thereby inhibiting the development of immune-mediated inflammatory diseases but enhancing cancer progression. Consequently, VSIG4 exhibits a potential therapeutic effect for immune-mediated inflammatory diseases, but also is regarded as a novel target of immune checkpoint inhibition in cancer therapy. Recently, soluble VSIG4, the extracellular domain of VSIG4, shed from the surface of macrophages, has been found to be a biomarker to define macrophage activation-related diseases. This review mainly summarizes recent new findings of VSIG4 in macrophage phagocytosis and immune homeostasis, and discusses its potential diagnostic and therapeutic usage in infection, inflammation, and cancer.

Polygonal Approximation Learning for Convex Object Segmentation in Biomedical Images with Bounding Box Supervision.

As a common and critical medical image analysis task, deep learning based biomedical image segmentation is hindered by the dependence on costly fine-grained annotations. To alleviate this data dependence, in this paper, a novel approach, called Polygonal Approximation Learning (PAL), is proposed for convex object instance segmentation with only bounding-box supervision. The key idea behind PAL is that the detection model for convex objects already contains the necessary information for segmenting them since their convex hulls, which can be generated approximately by the intersection of bounding boxes, are equivalent to the masks representing the objects. To extract the essential information from the detection model, a repeated detection approach is employed on biomedical images where various rotation angles are applied and a dice loss with the projection of the rotated detection results is utilized as a supervised signal in training our segmentation model. In biomedical imaging tasks involving convex objects, such as nuclei instance segmentation, PAL outperforms the known models (e.g., BoxInst) that rely solely on box supervision. Furthermore, PAL achieves comparable performance with mask-supervised models including Mask R-CNN and Cascade Mask R-CNN. Interestingly, PAL also demonstrates remarkable performance on non-convex object instance segmentation tasks, for example, surgical instrument and organ instance segmentation. Our code is available at https://github.com/shenmishajing/PAL.

A Mutually Supervised Graph Attention Network for Few-Shot Segmentation: The Perspective of Fully Utilizing Limited Samples.

Fully supervised semantic segmentation has performed well in many computer vision tasks. However, it is time-consuming because training a model requires a large number of pixel-level annotated samples. Few-shot segmentation has recently become a popular approach to addressing this problem, as it requires only a handful of annotated samples to generalize to new categories. However, the full utilization of limited samples remains an open problem. Thus, in this article, a mutually supervised few-shot segmentation network is proposed. First, the feature maps from intermediate convolution layers are fused to enrich the capacity of feature representation. Second, the support image and query image are combined into a bipartite graph, and the graph attention network is adopted to avoid losing spatial information and increase the number of pixels in the support image to guide the query image segmentation. Third, the attention map of the query image is used as prior information to enhance the support image segmentation, which forms a mutually supervised regime. Finally, the attention maps of the intermediate layers are fused and sent into the graph reasoning layer to infer the pixel categories. Experiments are conducted on the PASCAL VOC- 5i dataset and FSS-1000 dataset, and the results demonstrate the effectiveness and superior performance of our method compared with other baseline methods.

Assessing cloud QoS predictions using OWA in neural network methods.

Quality of Service (QoS) is the key parameter to measure the overall performance of service-oriented applications. In a myriad of web services, the QoS data has multiple highly sparse and enormous dimensions. It is a great challenge to reduce computational complexity by reducing data dimensions without losing information to predict QoS for future intervals. This paper uses an Induced Ordered Weighted Average (IOWA) layer in the prediction layer to lessen the size of a dataset and analyse the prediction accuracy of cloud QoS data. The approach enables stakeholders to manage extensive QoS data better and handle complex nonlinear predictions. The paper evaluates the cloud QoS prediction using an IOWA operator with nine neural network methods-Cascade-forward backpropagation, Elman backpropagation, Feedforward backpropagation, Generalised regression, NARX, Layer recurrent, LSTM, GRU and LSTM-GRU. The paper compares results using RMSE, MAE, and MAPE to measure prediction accuracy as a benchmark. A total of 2016 QoS data are extracted from Amazon EC2 US-West instance to predict future 96 intervals. The analysis results show that the approach significantly decreases the data size by 66%, from 2016 to 672 records with improved or equal accuracy. The case study demonstrates the approach's effectiveness while handling complexity, reducing data dimension with better prediction accuracy.

CariesNet: a deep learning approach for segmentation of multi-stage caries lesion from oral panoramic X-ray image.

Dental caries has been a common health issue throughout the world, which can even lead to dental pulp and root apical inflammation eventually. Timely and effective treatment of dental caries is vital for patients to reduce pain. Traditional caries disease diagnosis methods like naked-eye detection and panoramic radiograph examinations rely on experienced doctors, which may cause misdiagnosis and high time-consuming. To this end, we propose a novel deep learning architecture called CariesNet to delineate different caries degrees from panoramic radiographs. We firstly collect a high-quality panoramic radiograph dataset with 3127 well-delineated caries lesions, including shallow caries, moderate caries, and deep caries. Then we construct CariesNet as a U-shape network with the additional full-scale axial attention module to segment these three caries types from the oral panoramic images. Moreover, we test the segmentation performance between CariesNet and other baseline methods. Experiments show that our method can achieve a mean 93.64% Dice coefficient and 93.61% accuracy in the segmentation of three different levels of caries.

A Corresponding Region Fusion Framework for Multi-modal Cervical Lesion Detection.

Cervical lesion detection (CLD) using colposcopic images of multi-modality (acetic and iodine) is critical to computer-aided diagnosis (CAD) systems for accurate, objective, and comprehensive cervical cancer screening. To robustly capture lesion features and conform with clinical diagnosis practice, we propose a novel corresponding region fusion network (CRFNet) for multi-modal CLD. CRFNet first extracts feature maps and generates proposals for each modality, then performs proposal shifting to obtain corresponding regions under large position shifts between modalities, and finally fuses those region features with a new corresponding channel attention to detect lesion regions on both modalities. To evaluate CRFNet, we build a large multi-modal colposcopic image dataset collected from our collaborative hospital. We show that our proposed CRFNet surpasses known single-modal and multi-modal CLD methods and achieves state-of-the-art performance, especially in terms of Average Precision.

[Transcriptional Modification and Potential Intracellular Signaling Mechanisms in Human Macrophages Primed by Interferon-γ].

OBJECTIVE: To explore the transcriptional gene expression profile up-regulated in human macrophages stimulated by interferon-γ (IFN-γ) and the underlying intracellular signaling mechanisms. METHODS: RNA-seq was used to sequence and compare the differential gene expression profiles of human macrophage cell line U937 before and after IFN-γ stimulation, and the significantly up-regulated genes were screened out, which were verified by fluorescence-based real-time quantitative polymerase chain reaction (qPCR) in U937 and THP1 cell lines, respectively. JAK/STAT, MAPK/ERK and PI3K/AKT pathway inhibitors were added to simultaneously to the cultured U937 cells upon IFN-γ priming to detect their effects on the expressions of the up-regulated genes to explore the key regulatory mechanisms. RESULTS: RNA-seq and qPCR results showed that, the well-recognized chemokines CXCL9, CXCL10 and CXCL11, the APOL family including APOL1, APOL2, APOL3, APOL4, APOL6 and GBP family GBP1, GBP2, GBP3, GBP4 and GBP5 as well were significantly up-regulated in IFN-γ-stimulated U937 cells. JAK/STAT3 pathway inhibitor inhibited the upregulation of APOL1, APOL4, GBP1, GBP4 and GBP5 genes induced by IFN-γ, while MAPK/ERK pathway inhibitor inhibited the upregulation of CXCL10 gene. PI3K/AKT pathway inhibitor inhibited the upregulation of APOL1,APOL4, APOL6, GBP1 and GBP5 genes induced by IFN-γ, all three signal pathway inhibitors could inhibit the upregulation of CXCL9 gene, and none of them could inhibit the upregulation of APOL3 gene. CONCLUSION: Upon IFN-γ stimulation, some family molecules of APOL and GBP in macrophages are significantly up-regulated, and PI3K/AKT, JAK/STAT3 and MAPK/ERK pathways have positive regulation on their expressions, respectively.

Discriminative Cervical Lesion Detection in Colposcopic Images With Global Class Activation and Local Bin Excitation.

Accurate cervical lesion detection (CLD) methods using colposcopic images are highly demanded in computer-aided diagnosis (CAD) for automatic diagnosis of High-grade Squamous Intraepithelial Lesions (HSIL). However, compared to natural scene images, the specific characteristics of colposcopic images, such as low contrast, visual similarity, and ambiguous lesion boundaries, pose difficulties to accurately locating HSIL regions and also significantly impede the performance improvement of existing CLD approaches. To tackle these difficulties and better capture cervical lesions, we develop novel feature enhancing mechanisms from both global and local perspectives, and propose a new discriminative CLD framework, called CervixNet, with a Global Class Activation (GCA) module and a Local Bin Excitation (LBE) module. Specifically, the GCA module learns discriminative features by introducing an auxiliary classifier, and guides our model to focus on HSIL regions while ignoring noisy regions. It globally facilitates the feature extraction process and helps boost feature discriminability. Further, our LBE module excites lesion features in a local manner, and allows the lesion regions to be more fine-grained enhanced by explicitly modelling the inter-dependencies among bins of proposal feature. Extensive experiments on a number of 9888 clinical colposcopic images verify the superiority of our method (AP .75 = 20.45) over state-of-the-art models on four widely used metrics.

A Multi-Scale Activity Transition Network for Data Translation in EEG Signals Decoding.

Electroencephalogram (EEG) is a non-invasive collection method for brain signals. It has broad prospects in brain-computer interface (BCI) applications. Recent advances have shown the effectiveness of the widely used convolutional neural network (CNN) in EEG decoding. However, some studies reveal that a slight disturbance to the inputs, e.g., data translation, can change CNN's outputs. Such instability is dangerous for EEG-based BCI applications because signals in practice are different from training data. In this study, we propose a multi-scale activity transition network (MSATNet) to alleviate the influence of the translation problem in convolution-based models. MSATNet provides an activity state pyramid consisting of multi-scale recurrent neural networks to capture the relationship between brain activities, which is a translation-invariant feature. In the experiment, Kullback-Leibler divergence is applied to measure the degree of translation. The comprehensive results demonstrate that our method surpasses the AUC of 0.0080, 0.0254, 0.0393 in 1, 5, and 10 KL divergence compared to competitors with various convolution structures.

A Deep Learning Approach for Colonoscopy Pathology WSI Analysis: Accurate Segmentation and Classification.

Colorectal cancer (CRC) is one of the most life-threatening malignancies. Colonoscopy pathology examination can identify cells of early-stage colon tumors in small tissue image slices. But, such examination is time-consuming and exhausting on high resolution images. In this paper, we present a new framework for colonoscopy pathology whole slide image (WSI) analysis, including lesion segmentation and tissue diagnosis. Our framework contains an improved U-shape network with a VGG net as backbone, and two schemes for training and inference, respectively (the training scheme and inference scheme). Based on the characteristics of colonoscopy pathology WSI, we introduce a specific sampling strategy for sample selection and a transfer learning strategy for model training in our training scheme. Besides, we propose a specific loss function, class-wise DSC loss, to train the segmentation network. In our inference scheme, we apply a sliding-window based sampling strategy for patch generation and diploid ensemble (data ensemble and model ensemble) for the final prediction. We use the predicted segmentation mask to generate the classification probability for the likelihood of WSI being malignant. To our best knowledge, DigestPath 2019 is the first challenge and the first public dataset available on colonoscopy tissue screening and segmentation, and our proposed framework yields good performance on this dataset. Our new framework achieved a DSC of 0.7789 and AUC of 1 on the online test dataset, and we won the [Formula: see text] place in the DigestPath 2019 Challenge (task 2). Our code is available at https://github.com/bhfs9999/colonoscopy_tissue_screen_and_segmentation.

A Transfer Learning Based Super-Resolution Microscopy for Biopsy Slice Images: The Joint Methods Perspective.

Higher-resolution biopsy slice images reveal many details, which are widely used in medical practice. However, taking high-resolution slice images is more costly than taking low-resolution ones. In this paper, we propose a joint framework containing a novel transfer learning strategy and a deep super-resolution framework to generate high-resolution slice images from low-resolution ones. The super-resolution framework called SRFBN+ is proposed by modifying a state-of-the-art framework SRFBN. Specifically, the structure of the feedback block of SRFBN was modified to be more flexible. Besides, it is challenging to use typical transfer learning strategies directly for the tasks on slice images, as the patterns on different types of biopsy slice images are varying. To this end, we propose a novel transfer learning strategy, called Channel Fusion Transfer Learning (CF-Trans). CF-Trans builds a middle domain by fusing the data manifolds of the source domain and the target domain, serving as a springboard for knowledge transfer. Thus, in the transfer learning setting, SRFBN+ can be trained on the source domain and then the middle domain and finally the target domain. Experiments on biopsy slice images validate SRFBN+ works well in generating super-resolution slice images, and CF-Trans is an efficient transfer learning strategy.

[Safety and Effectiveness of High Dose Tigecycline for Treating Patients with Acute Leukemia after Ineffctiveness of Carbapenems Chemotherapy Combinating with Febrile Neutropenia: Retrospective study].

OBJECTIVE: To investigate the safety and efficacy of high dose tigecycline for treatment of fibric neutrope-nia in acute leukemia patients after ineffectiveness of carbapenems chemotherapy of acute leukemia. METHODS: The clinical data of 41 acute leukemia patients with febrile ncutropenia received high dose tigecycline (100 mg q12h), who showed ineffectiveness of treatment with carbapenems, from 20151.30-2017.1. 29 in our hospital were collected and analyzed retrospectively. The temperature, inflammatory indicators as well as hepatic and renal function before and after treatment with tigecycline were compared. RESULTS: Among 41 patients treated with tigecycline due to ineffectiveness of treatment with carbapenems, the infection had been controled in 34 cases, 7 patients died due to ineffectiveness of anti-infective treatment, these patients all were patients with relapse/refractory leukemia. 41 patients were examined etialogically, as a result, 22 patients showed possitive, among them the gram-negative bacill was found in 11(11/22) cases. The average deferves counce time of tigecycline was 28.2±12.0 hours. The temperature of patients treated with tigecycline for 48 hours decreased significantly (P<0.05). There were no significant differences in calcitonin and C-reactive protein levels after treatment with tigecycline (P>0.05), but cacitonin level displayed decrease tread. There was no hepatic and renal impairment after treatment with tigecycline, but levels of as partate aminotransferase, total bilirubin and blood area nitrogen in blood significantly increased as compared with levels before treatment with tigecycline (P<0.05). CONCLUSION: The application of high dose tigecycline for treatment of febrile neutropenia is safety and effective. The high dose tigecycline can decrease the temperature, calcitonin and C-reactive protein levels, and can control infection without the hepatic and renal impairment, but it needs to be confimed by more prospective studies.

[Retrospective Study of Ph+/BCR-ABL+ Acute Myelogenous Leukemia].

OBJECTIVE: To investigate the clinical manifestation, features of laboratorial examination results and prognosis of patients with Ph+/BCR-ABL+ acute myelogenous leukemia(AML). METHODS: The clinical data of 5 AML patients with Ph+/BCR-ABL+ admitted in Department of Hematology of Chinese PLA general hospital from July 2007 to May 2015 were collected and their clinical characteristics, laboatorial examination results and long-term survival were analyzed. RESULTS: The median age of 5 cases was 39 years old, and 2 cases with splenomegaly. All the cases were assayed for BCR-ABL fusion gene, and 2 of them were accompanied with other molecular abnormalities. In 4 cases, Ph chromosome was not found in one case, and one was with complex karyotype. 3 cases still are live till now and are treated by traditional chemotherapy combined with TKI, and consolidated by allo-HSCT. One case treated by traditional chemotherapy survived for 6 months. And one case treated by traditional chemotherapy combined with TKI survives till to now. CONCLUSION: The survival time of Ph+/BCR-ABL+ acute myelogenous leukemia is improved by the traditional chemotherapy combined with TKI and the consolidation with allo-HSCT.

[Efficacy and Safety of Low-dose Amphotericin B in Different Antifungal Strategies for Treatment of Invasive Fungal Disease in Patients with Hematological Malignancies].

OBJECTIVE: To evaluate the efficacy and safety of low-dose amphotericin B (AmB) in different antifungal strategies for treatment of invasive fungal disease(IFD) in patients with hematologic malignancies. Metheds: The clinical dada of the patients were collected and analyzed retrospectively and the levels of creatinine (Cr), urea nitrogen (BUN) and potassium (K+) before and after using low-dose AmB were compared and statistically analyzed. RESULTS: Among 97 cases, 2 cases were diagnosed as invasive fungal disease (IFD), 11 cases were diagnosed as clinical probable IFD, 15 cases were diagnosed as possible IFD, 69 cases were undefined IFD. The response rate of all patients treated with low-dose AmB was 69.4%, the response rate for targed therapy was 72.7%, the response rate for diagnosis-driven therapy was 63.6%, the response rate of empirical therapy was 75%, the efficacy of the combination with other antibiotics was 50%, 66.7% and 75%. According to all the patients received AmB, only 7 cases was detected with higher level of Cr (7.2) than normal and this level come back to normal with in 7 days after drug withdrew. Although the Cr level in serum after 1 day of drug withdrew was higher than that before administration of drug(64.86±3.00 vs 58.76±1.67 µmol/L) and was with statistical difference(P<0.05), but did not show significant difference in comparison with the level after drug withdrew 7 days (58.43±1.68 µmol/L,P>0.05). CONCLUSION: AmB injection is an effective and safe method in empirical therapy and diagnosis-driven antifungal therapy for neutropenic, febrile patients with hematological malignancies.

MicroRNAs and regulated interaction networks reveal differences between adult and pediatric acute myeloid leukemia.

OBJECTIVES: The purpose of this study was to identify featured microRNAs and their regulated network between adult and pediatric acute myeloid leukemia (AML) and find potential utility as biomarkers for diagnosis and treatment of pediatric AML. METHODS: We downloaded the microRNA expression dataset GSE35320 from Gene Expression Omnibus database and selected expression chips from bone marrow of 71 pediatric AML samples and 6 adulthood AML samples. Differentially expressed microRNAs were identified by Wilcox test. The target genes of these microRNAs were predicted using an integrative method and their functional enrichment analysis was performed using DAVID. Finally, STRING database and Cytoscape software was used to construct and analyze the interaction network. RESULTS: A total of 7 differentially expressed microRNAs were identified and the remarkably up-regulated and down-regulated microRNAs were miR-16 and miR-142-5p which included 323 and 22 predicted target genes, respectively. The target genes of 7 microRNAs were most associated with regulation of cell cycle, p53 signaling pathway, Wnt signaling pathway and neurotrophin signaling pathway. The interaction network of miR-16 target genes was constructed among 354 high confidence interaction pairs. The core genes of the network, such as TP53, BCL2, VEGFA, had a role in prognosis of children with AML. CONCLUSIONS: The featured microRNAs and their target genes are significant in the occurrence and development of pediatric AML, which is likely to be important for the identification of therapeutic targets and biomarkers for these patients.