Signet-ring cell lymphoma: clinicopathologic, immunohistochemical, and fluorescence in situ hybridization studies of 7 cases

Context Signet-ring cell lymphoma (SRCL) is a rare morphologic variant of non–Hodgkin lymphoma. Although it was initially reported as a rare morphologic variant of follicular lymphoma (FL), SRCL has to date been described in most types of non–Hodgkin lymphoma, mostly as single-case reports. Objective To study SRCL systematically by immunohistochemical stains and fluorescent in situ hybridization analyses. Design Seven SRCL cases were stained for CD3, CD5, CD20, PAX-5, CD10, CD21, CD23, cyclin D1, BCL2, BCL6, Ki-67, and MUM-1, and were analyzed by fluorescent in situ hybridization for BCL2, BCL6, MYC, and MALT1 rearrangements. Clinical information and patient outcome were reviewed in all patients. Results The patients were 3 women and 3 men, ranging in age from 31 to 75 years (average 60.3 years). The lesions involved lymph nodes, tonsil, parotid gland, soft tissue, and breast. There were 4 FLs, 1 diffuse large B-cell lymphoma (DLBCL), 1 DLBCL with FL, and 1 DLBCL with marginal zone lymphoma. All cases had typical signet-ring cell morphology. They were positive for CD20 and BCL-2, and had low-to-intermediate Ki-67 proliferation index (10%-40%) except in the parotid DLBCL with FL (70%). BCL-6 was detected in all but 1 FL (6/7). Fluorescent in situ hybridization detected IGH/BCL2 translocation in 1 FL, increased BCL6 copy number in another FL, BCL6 rearrangement, and increased copy number of MYC and MALT1 in the DLBCL with marginal zone lymphoma. Conclusions The FL with signet-ring cell morphology (1/5) tends to lack IGH/BCL2 translocation, and an extended immunohistochemical study is recommended for correct diagnosis and classification of SRCL

Loss of Asxl1 leads to myelodysplastic syndrome-like disease in mice

ASXL1 is mutated/deleted with high frequencies in multiple forms of myeloid malignancies, and its alterations are associated with poor prognosis. De novo ASXL1 mutations cause Bohring-Opitz syndrome characterized by multiple congenital malformations. We show that Asxl1 deletion in mice led to developmental abnormalities including dwarfism, anophthalmia, and 80% embryonic lethality. Surviving Asxl1(-/-) mice lived for up to 42 days and developed features of myelodysplastic syndrome (MDS), including dysplastic neutrophils and multiple lineage cytopenia. Asxl1(-/-) mice had a reduced hematopoietic stem cell (HSC) pool, and Asxl1(-/-) HSCs exhibited decreased hematopoietic repopulating capacity, with skewed cell differentiation favoring granulocytic lineage. Asxl1(+/-) mice also developed mild MDS-like disease, which could progress to MDS/myeloproliferative neoplasm, demonstrating a haploinsufficient effect of Asxl1 in the pathogenesis of myeloid malignancies. Asxl1 loss led to an increased apoptosis and mitosis in Lineage(-)c-Kit(+) (Lin(-)c-Kit(+)) cells, consistent with human MDS. Furthermore, Asxl1(-/-) Lin(-)c-Kit(+) cells exhibited decreased global levels of H3K27me3 and H3K4me3 and altered expression of genes regulating apoptosis (Bcl2, Bcl2l12, Bcl2l13). Collectively, we report a novel ASXL1 murine model that recapitulates human myeloid malignancies, implying that Asxl1 functions as a tumor suppressor to maintain hematopoietic cell homeostasis. Future work is necessary to clarify the contribution of microenvironment to the hematopoietic phenotypes observed in the constitutional Asxl1(-/-) mice

Ephedra sinica polysaccharide regulate the anti-inflammatory immunity of intestinal microecology and bacterial metabolites in rheumatoid arthritis

IntroductionEphedra sinica polysaccharide (ESP) exerts substantial therapeutic effects on rheumatoid arthritis (RA). However, the mechanism through which ESP intervenes in RA remains unclear. A close correlation has been observed between enzymes and derivatives in the gut microbiota and the inflammatory immune response in RA.MethodsA type II collagen-induced arthritis (CIA) mice model was treated with Ephedra sinica polysaccharide. The therapeutic effect of ESP on collagen-induced arthritis mice was evaluated. The anti-inflammatory and cartilage-protective effects of ESP were also evaluated. Additionally, metagenomic sequencing was performed to identify changes in carbohydrate-active enzymes and resistance genes in the gut microbiota of the ESP-treated CIA mice. Liquid chromatography-mass spectrometry and gas chromatography-mass spectrometry were performed to observe the levels of serum metabolites and short-chain fatty acids in the gut. Spearman’s correlational analysis revealed a correlation among the gut microbiota, antibiotic-resistance genes, and microbiota-derived metabolites.ResultsESP treatment significantly reduced inflammation levels and cartilage damage in the CIA mice. It also decreased the levels of pro-inflammatory cytokines interleukin (IL)-6, and IL-1-β and protected the intestinal mucosal epithelial barrier, inhibiting inflammatory cell infiltration and mucosal damage. Here, ESP reduced the TLR4, MyD88, and TRAF6 levels in the synovium, inhibited the p65 expression and pp65 phosphorylation in the NF-κB signaling pathway, and blocked histone deacetylase (HDAC1 and HDAC2) signals. ESP influenced the gut microbiota structure, microbial carbohydrate-active enzymes, and microbial resistance related to resistance genes. ESP increased the serum levels of L-tyrosine, sn-glycero-3-phosphocholine, octadecanoic acid, N-oleoyl taurine, and decreased N-palmitoyl taurine in the CIA mice.ConclusionESP exhibited an inhibitory effect on RA. Its action mechanism may be related to the ability of ESP to effectively reduce pro-inflammatory cytokines levels, protect the intestinal barrier, and regulate the interaction between mucosal immune systems and abnormal local microbiota. Accordingly, immune homeostasis was maintained and the inhibition of fibroblast-like synoviocyte (FLS) proliferation through the HDAC/TLR4/NF-κB pathway was mediated, thereby contributing to its anti-inflammatory and immune-modulating effects

Characteristics of SARS-CoV-2 Omicron BA.5 variants in Shanghai after ending the zero-COVID policy in December 2022: a clinical and genomic analysis

IntroductionAn unprecedented surge of Omicron infections appeared nationwide in China in December 2022 after the adjustment of the COVID-19 response policy. Here, we report the clinical and genomic characteristics of SARS-CoV-2 infections among children in Shanghai during this outbreak.MethodsA total of 64 children with symptomatic COVID-19 were enrolled. SARS-CoV-2 whole genome sequences were obtained using next-generation sequencing (NGS) technology. Patient demographics and clinical characteristics were compared between variants. Phylogenetic tree, mutation spectrum, and the impact of unique mutations on SARS-CoV-2 proteins were analysed in silico.ResultsThe genomic monitoring revealed that the emerging BA.5.2.48 and BF.7.14 were the dominant variants. The BA.5.2.48 infections were more frequently observed to experience vomiting/diarrhea and less frequently present cough compared to the BF.7.14 infections among patients without comorbidities in the study. The high-frequency unique non-synonymous mutations were present in BA.5.2.48 (N:Q241K) and BF.7.14 (nsp2:V94L, nsp12:L247F, S:C1243F, ORF7a:H47Y) with respect to their parental lineages. Of these mutations, S:C1243F, nsp12:L247F, and ORF7a:H47Y protein were predicted to have a deleterious effect on the protein function. Besides, nsp2:V94L and nsp12:L247F were predicted to destabilize the proteins.DiscussionFurther in vitro to in vivo studies are needed to verify the role of these specific mutations in viral fitness. In addition, continuous genomic monitoring and clinical manifestation assessments of the emerging variants will still be crucial for the effective responses to the ongoing COVID-19 pandemic

High Edge-Quality Light-Field Salient Object Detection Using Convolutional Neural Network

The detection result of current light-field salient object detection methods suffers from loss of edge details, which significantly limits the performance of subsequent computer vision tasks. To solve this problem, we propose a novel convolutional neural network to accurately detect salient objects, by digging effective edge information from light-field data. In particular, our method is divided into four steps. Firstly, the network extracts multi-level saliency features from light-field data. Secondly, edge features are extracted from low-level saliency features and optimized by ground-truth guidance. Then, to sufficiently leverage high-level saliency features and edge features, the network hierarchically fuses them in a complementary manner. Finally, spatial correlations between different levels of fused features are considered to detect salient objects. Our method can accurately locate salient objects with exquisite edge details, by extracting clear edge information and accurate saliency information and fully fusing them. We conduct extensive evaluations on three widely used benchmark datasets. The experimental results demonstrate the effectiveness of our method, and it is superior to eight state-of-the-art methods

Treatments for patients with advanced neuroendocrine tumors: a network meta-analysis

Background: It remains unknown which is the most effective regimen among the available therapies for advanced well-differentiated neuroendocrine tumors (NETs). We performed a network meta-analysis to address this important issue. Methods: PubMed, Embase, Web of Science, Cochrane Library, and major international scientific meetings were searched for relevant randomized controlled trials (RCTs). Progression-free survival (PFS) data was the primary outcome of interest, and overall survival (OS) and serious adverse events (SAEs) were the secondary outcomes of interests, reported as hazard ratio (HR), or odds ratio (OR) and 95% confidence intervals (CIs). Results: Included in the meta-analysis were 21 eligible articles reporting 15 RCTs with a total of 2922 patients randomized to receive 11 treatments. Peptide receptor radionuclide therapy (PRRT) showed significant PFS advantage over somatostatin analogs (SSA) (HR = 0.21, 95% CI: 0.11–0.41), everolimus (HR = 0.25, 95% CI: 0.11–0.53), sunitinib (HR = 0.29, 95% CI: 0.10–0.82), everolimus+SSA (HR = 0.26, 95% CI: 0.12–0.54), and everolimus+bevacizumab (HR = 0.31, 95% CI: 0.11–0.82). OS findings were not significantly different between treatments. In terms of treatment rankings of PFS, PRRT had the highest probability (96%) of being the most effective treatment, followed by SSA+bevacizumab (86%) and SSA+interferon-α (IFN-α) (78%). As for toxicity, risk of SAEs was similar between the three treatments. Based on the benefit–risk ratio, PRRT, SSA+bevacizumab, and SSA+IFN-α seemed to be the best, second-, and third-best treatment, respectively. Conclusions: PRRT is likely to be the most preferable treatment for patients with advanced well-differentiated NETs. SSA+bevacizumab and SSA+IFN-α also seem to be more effective regimens with limited risk of SAEs

Daily Natural Gas Load Forecasting Based on a Hybrid Deep Learning Model

Forecasting daily natural gas load accurately is difficult because it is affected by various factors. A large number of redundant factors existing in the original dataset will increase computational complexity and decrease the accuracy of forecasting models. This study aims to provide accurate forecasting of natural gas load using a deep learning (DL)-based hybrid model, which combines principal component correlation analysis (PCCA) and (LSTM) network. PCCA is an improved principal component analysis (PCA) and is first proposed here in this paper. Considering the correlation between components in the eigenspace, PCCA can not only extract the components that affect natural gas load but also remove the redundant components. LSTM is a famous DL network, and it was used to predict daily natural gas load in our work. The proposed model was validated by using recent natural gas load data from Xi’an (China) and Athens (Greece). Additionally, 14 weather factors were introduced into the input dataset of the forecasting model. The results showed that PCCA–LSTM demonstrated better performance compared with LSTM, PCA–LSTM, back propagation neural network (BPNN), and support vector regression (SVR). The lowest mean absolute percentage errors of PCCA–LSTM were 3.22% and 7.29% for Xi’an and Athens, respectively. On these bases, the proposed model can be regarded as an accurate and robust model for daily natural gas load forecasting

Numerical Simulations of Fracture Propagation in Jointed Shale Reservoirs under CO2 Fracturing

Water-based hydraulic fracturing for the exploitation of shale gas reservoirs may be limited by two main factors: (1) water pollution and chemical pollution after the injection process and (2) permeability decrease due to clay mineral swelling upon contact with the injection water. Besides, shale rock nearly always contains fractures and fissures due to geological processes such as deposition and folding. Based on the above, a damage-based coupled model of rock deformation and gas flow is used to simulate the fracturing process in jointed shale wells with CO2 fracturing. We validate our model by comparing the simulation results with theoretical solutions. The research results show that the continuous main fractures are formed along the direction of the maximum principal stress, whilst hydraulic fractures tend to propagate along the preexisting joints due to the lower strength of the joints. The main failure type is tensile damage destruction among these specimens. The preexisting joints can aggravate the damage of the numerical specimens; the seepage areas of the layered jointed sample, vertical jointed sample, and orthogonal jointed sample are increased by 32.5%, 29.16%, and 35.05%, respectively, at time t=39 s compared with the intact sample. The preexisting horizontal joints or vertical joints promote the propagation of hydraulic fractures in the horizontal direction or vertical direction but restrain the expansion of hydraulic fractures in the vertical or horizontal direction

Fracture Propagation and Morphology Due to Non-Aqueous Fracturing: Competing Roles between Fluid Characteristics and In Situ Stress State

Non-aqueous or gaseous stimulants are alternative working fluids to water for hydraulic fracturing in shale reservoirs, which offer advantages including conserving water, avoiding clay swelling and decreasing formation damage. Hence, it is crucial to understand fluid-driven fracture propagation and morphology in shale formations. In this research, we conduct fracturing experiments on shale samples with water, liquid carbon dioxide, and supercritical carbon dioxide to explore the effect of fluid characteristics and in situ stress on fracture propagation and morphology. Moreover, a numerical model that couples rock property heterogeneity, micro-scale damage and fluid flow was built to compare with experimental observations. Our results indicate that the competing roles between fluid viscosity and in situ stress determine fluid-driven fracture propagation and morphology during the fracturing process. From the macroscopic aspect, fluid-driven fractures propagate to the direction of maximum horizontal stress direction. From the microscopic aspect, low viscosity fluid easily penetrates into pore throats and creates branches and secondary fractures, which may deflect the main fracture and eventually form the fracture networks. Our results provide a new understanding of fluid-driven fracture propagation, which is beneficial to fracturing fluid selection and fracturing strategy optimization for shale gas hydraulic fracturing operations

The Identification of Influential Nodes Based on Neighborhood Information in Asymmetric Networks

Identifying influential nodes, with pivotal roles in practical domains like epidemic management, social information dissemination optimization, and transportation network security enhancement, is a critical research focus in complex network analysis. Researchers have long strived for rapid and precise identification approaches for these influential nodes that are significantly shaping network structures and functions. The recently developed SPON (sum of proportion of neighbors) method integrates information from the three-hop neighborhood of each node, proving more efficient and accurate in identifying influential nodes than traditional methods. However, SPON overlooks the heterogeneity of neighbor information, derived from the asymmetry properties of natural networks, leading to its lower accuracy in identifying essential nodes. To sustain the efficiency of the SPON method pertaining to the local method, as opposed to global approaches, we propose an improved local approach, called the SSPN (sum of the structural proportion of neighbors), adapted from the SPON method. The SSPN method classifies neighbors based on the h-index values of nodes, emphasizing the diversity of asymmetric neighbor structure information by considering the local clustering coefficient and addressing the accuracy limitations of the SPON method. To test the performance of the SSPN, we conducted simulation experiments on six real networks using the Susceptible–Infected–Removed (SIR) model. Our method demonstrates superior monotonicity, ranking accuracy, and robustness compared to seven benchmarks. These findings are valuable for developing effective methods to discover and safeguard influential nodes within complex networked systems