Kinetic Promoters for Sulfur Cathodes in Lithium-Sulfur Batteries.

ConspectusLithium-sulfur (Li-S) batteries have attracted worldwide attention as promising next-generation rechargeable batteries due to their high theoretical energy density of 2600 Wh kg-1. The actual energy density of Li-S batteries at the pouch cell level has significantly exceeded that of state-of-the-art Li-ion batteries. However, the overall performances of Li-S batteries under practical working conditions are limited by the sluggish conversion kinetics of the sulfur cathodes. To overcome the above challenge, various kinetic promotion strategies have been proposed to accelerate the multiphase and multi-electron cathodic redox reactions between sulfur, lithium polysulfides (LiPSs), and lithium sulfide. Nowadays, kinetic promoters have been massively employed in sulfur cathodes to achieve Li-S batteries with high energy densities, high rates, and long lifespans. A comprehensive and timely summary of cutting-edge kinetic promoters for sulfur cathodes is of great essence to afford an in-depth understanding of the unique Li-S electrochemistry.In this Account, we outline the recent efforts on the design of sulfur cathode kinetic promoters for advanced Li-S batteries. The latest progress is discussed in detail regarding heterogeneous, homogeneous, and semi-immobilized kinetic promoters. Heterogeneous promoters, representatively known as electrocatalysts, function mainly by reducing the energy barriers of the interfacial electrochemical reactions. The working mechanism, activity regulation strategies, and reconstitution/deactivation processes of the heterogeneous promoters are reviewed to provide guiding principles for rational design. In comparison, homogeneous promoters are able to fully contact with the reaction interfaces and regulate the electron/ion-inaccessible reactants in working Li-S batteries. Redox mediators and redox comediators are typical homogeneous promoters. The former establishes extra chemical reaction pathways to circumvent the originally sluggish steps and boost the overall kinetics, while the latter fundamentally modifies the LiPS molecules to enhance their redox kinetics. For semi-immobilized promoters, the active units are generally anchored on the cathode substrate through flexible chains with mobile characteristics. Such a design endows the promoter with both heterogeneous and homogeneous characteristics to comprehensively regulate the multiphase sulfur redox reactions involving both mobile and immobile reactants.Overall, this Account summarizes the fundamental electrochemistry, design principles, and practical promotion effects of the various kinetic promoters used for the sulfur cathodes in Li-S batteries. We believe that this Account will provide an in-depth and cutting-edge understanding of the unique sulfur electrochemistry, thereby providing guidance for further development of high-performance Li-S batteries and analogous rechargeable battery systems.

Outer Sphere Electron Transfer Enabling High-Voltage Aqueous Electrolytes.

Aqueous electrolytes with a low voltage window (1.23 V) and prone side reactions, such as hydrogen evolution reaction and cathode dissolution, compromise the advantages of high safety and low cost of aqueous metal-ion batteries. Herein, introducing catechol (CAT) into the aqueous electrolyte, an outer sphere electron transfer mechanism is initiated to inhibit the water reactivity, achieving an electrochemical window of 3.24 V. In a typical Zn-ion battery, the outer sphere electrons jump from CAT to Zn2+-H2O at a geometrically favorable situation and between the solvation molecules without breaking or forming chemical bonds as that of the inner sphere electron transfers. The excited state π-π stacking further leads to the outer sphere electron transfer occurring at the electrolyte/electrode interface. This high-voltage electrolyte allows achieving an operating voltage two times higher than that of the usual aqueous electrolytes and provides almost the highest energy density and power density for the V2O5-based aqueous Zn-ion full batteries. The Zn//Zn symmetric battery delivers a 4000 h lifespan, and the Zn//V2O5 full battery achieves a ∼380 W h kg-1 energy density and a 92% capacity retention after 3000 cycles at 1 A g-1 and a 2.4 V output voltage. This outer sphere electron transfer strategy paves the way for designing high-voltage aqueous electrolytes.

FASN-mediated fatty acid biosynthesis remodels immune environment in Clonorchis sinensis infection-related intrahepatic cholangiocarcinoma.

BACKGROUND & AIMS: Intrahepatic cholangiocarcinoma (iCCA) is the second most common primary liver cancer and is highly lethal. Clonorchis sinensis (C. sinensis) infection is an important risk factor for iCCA. Here we investigated the clinical impact and underlying molecular characteristics of C. sinensis infection-related iCCA. METHODS: We performed single-cell RNA sequencing, whole-exome sequencing, RNA sequencing, metabolomics and spatial transcriptomics in 251 patients with iCCA from three medical centers. Alterations in metabolism and the immune microenvironment of C. sinensis-related iCCAs were validated through an in vitro co-culture system and in a mouse model of iCCA. RESULTS: We revealed that C. sinensis infection was significantly associated with iCCA patients' overall survival and response to immunotherapy. Fatty acid biosynthesis and the expression of fatty acid synthase (FASN), a key enzyme catalyzing long-chain fatty acid synthesis, were significantly enriched in C. sinensis-related iCCAs. iCCA cell lines treated with excretory/secretory products of C. sinensis displayed elevated FASN and free fatty acids. The metabolic alteration of tumor cells was closely correlated with the enrichment of tumor-associated macrophage (TAM)-like macrophages and the impaired function of T cells, which led to formation of an immunosuppressive microenvironment and tumor progression. Spatial transcriptomics analysis revealed that malignant cells were in closer juxtaposition with TAM-like macrophages in C. sinensis-related iCCAs than non-C. sinensis-related iCCAs. Importantly, treatment with a FASN inhibitor significantly reversed the immunosuppressive microenvironment and enhanced anti-PD-1 efficacy in iCCA mouse models treated with excretory/secretory products from C. sinensis. CONCLUSIONS: We provide novel insights into metabolic alterations and the immune microenvironment in C. sinensis infection-related iCCAs. We also demonstrate that the combination of a FASN inhibitor with immunotherapy could be a promising strategy for the treatment of C. sinensis-related iCCAs. IMPACT AND IMPLICATIONS: Clonorchis sinensis (C. sinensis)-infected patients with intrahepatic cholangiocarcinoma (iCCA) have a worse prognosis and response to immunotherapy than non-C. sinensis-infected patients with iCCA. The underlying molecular characteristics of C. sinensis infection-related iCCAs remain unclear. Herein, we demonstrate that upregulation of FASN (fatty acid synthase) and free fatty acids in C. sinensis-related iCCAs leads to formation of an immunosuppressive microenvironment and tumor progression. Thus, administration of FASN inhibitors could significantly reverse the immunosuppressive microenvironment and further enhance the efficacy of anti-PD-1 against C. sinensis-related iCCAs.

In Silico Design of Heterogeneous Microvascular Trees Using Generative Adversarial Networks and Constrained Constructive Optimization.

OBJECTIVE: Designing physiologically adequate microvascular trees is of crucial relevance for bioengineering functional tissues and organs. Yet, currently available methods are poorly suited to replicate the morphological and topological heterogeneity of real microvascular trees because the parameters used to control tree generation are too simplistic to mimic results of the complex angiogenetic and structural adaptation processes in vivo. METHODS: We propose a method to overcome this limitation by integrating a conditional deep convolutional generative adversarial network (cDCGAN) with a local fractal dimension-oriented constrained constructive optimization (LFDO-CCO) strategy. The cDCGAN learns the patterns of real microvascular bifurcations allowing for their artificial replication. The LFDO-CCO strategy connects the generated bifurcations hierarchically to form microvascular trees with a vessel density corresponding to that observed in healthy tissues. RESULTS: The generated artificial microvascular trees are consistent with real microvascular trees regarding characteristics such as fractal dimension, vascular density, and coefficient of variation of diameter, length, and tortuosity. CONCLUSIONS: These results support the adoption of the proposed strategy for the generation of artificial microvascular trees in tissue engineering as well as for computational modeling and simulations of microcirculatory physiology.

Multiple Treatment of Triple-Negative Breast Cancer Through Gambogic Acid-Loaded Mesoporous Polydopamine.

Triple-negative breast cancer (TNBC) is a highly heterogeneous subtype of breast cancer, characterized by aggressiveness and high recurrence rate. As monotherapy provides limited benefit to TNBC patients, combination therapy emerges as a promising treatment approach. Gambogic acid (GA) is an exceedingly promising anticancer agent. Nonetheless, its application potential is hampered by low drug loading efficiency and associated toxic side effects. To overcome these limitations, using mesoporous polydopamine (MPDA) endowed with photothermal conversion capabilities is considered as a delivery vehicle for GA. Meanwhile, GA can inhibit the activity of heat shock protein 90 (HSP90) to enhance the photothermal effect. Herein, GA-loaded MPDA nanoparticles (GA@MPDA NPs) are developed with a high drug loading rate of 75.96% and remarkable photothermal conversion performance. GA@MPDA NPs combined with photothermal treatment (PTT) significantly inhibit the tumor growth, and effectively trigger the immunogenic cell death (ICD), which thereby increase the number of activated effector T cells (CD8+ T cells and CD4+ T cells) in the tumor, and hoist the level of immune-inflammatory cytokines (IFN-γ, IL-6, and TNF-α). The above results suggest that the combination of GA@MPDA NPs with PTT expected to activate the antitumor immune response, thus potentially enhancing the clinical therapeutic effect on TNBC.

Loss of ZNF451 mediates fibroblast activation and promotes lung fibrosis.

BACKGROUND: No effective therapies for pulmonary fibrosis (PF) exist because of the unclear molecular pathogenesis and the lack of effective therapeutic targets. Zinc finger protein 451 (ZNF451), a transcriptional regulator, plays crucial roles in the pathogenesis of several diseases. However, its expression pattern and function in PF remain unknown. This study was designed to investigate the role of ZNF451 in the pathogenesis of lung fibrosis. METHODS: GEO dataset analysis, RT‒PCR, and immunoblot assays were used to examine the expression of ZNF451 in PF; ZNF451 knockout mice and ZNF451-overexpressing lentivirus were used to determine the importance of ZNF451 in PF progression; and migration assays, immunofluorescence staining, and RNA-seq analysis were used for mechanistic studies. RESULTS: ZNF451 is downregulated and negatively associated with disease severity in PF. Compared with wild-type (WT) mice, ZNF451 knockout mice exhibited much more serious PF changes. However, ZNF451 overexpression protects mice from BLM-induced pulmonary fibrosis. Mechanistically, ZNF451 downregulation triggers fibroblast activation by increasing the expression of PDGFB and subsequently activating PI3K/Akt signaling. CONCLUSION: These findings uncover a critical role of ZNF451 in PF progression and introduce a novel regulatory mechanism of ZNF451 in fibroblast activation. Our study suggests that ZNF451 serves as a potential therapeutic target for PF and that strategies aimed at increasing ZNF451 expression may be promising therapeutic approaches for PF.

Relative validity of an intelligent ordering system to estimate dietary intake among university students from a medical school in Shanghai, China.

BACKGROUND: Dietary assessment methods have limitations in capturing real-time eating behaviour accurately. Equipped with automated dietary-data-collection capabilities, the "intelligent ordering system" (IOS) has potential applicability in obtaining long-term consecutive, relatively detailed on-campus dietary records among university students with little resource consumption. We investigated (1) the relative validity of IOS-derived nutrient/food intakes compared to those from the 7-day food diary (7DFD); (2) whether including a supplemental food frequency questionnaire (SFFQ) improves IOS accuracy; and (3) sex differences in IOS dietary intake estimation. METHODS: Medical students (n = 221; age = 22.2 ± 2.4 years; 38.5% male and 61.5% female) completed the 7DFD and SFFQ. During the consecutive 7-day survey period, students weighed and photographed each meal before and after consumption. Then, students reviewed their 3-month diet and completed the SFFQ, which includes eight underprovided school-canteen food items (e.g., dairy, fruits, nuts). Meanwhile, 9385 IOS dietary data entries were collected. We used Spearman coefficients and linear regression models to estimate the associations among the different dietary intake assessment methods. Individual- and group-level agreement was assessed using the Wilcoxon signed-rank test, cross-classification, and Bland‒Altman analysis. RESULTS: IOS mean daily energy, protein, fat, and carbohydrate intake estimations were significantly lower (-15-20%) than those of the 7DFD. The correlation coefficients varied from 0.52 (for added sugar) to 0.88 (for soybeans and nuts), with fruits (0.37) and dairy products (0.29) showing weaker correlations. Sixty-two (milk and dairy products) to 97% (soybeans and nuts) of participants were classified into the same or adjacent dietary intake distribution quartile using both methods. The energy and macronutrient intake differences between the IOS + SFFQ and 7DFD groups decreased substantially. The separate fruit intake measurements from each assessment method did not significantly differ from each other (p > 0.05). IOS and IOS + SFFQ regression models generally yielded higher R2 values for males than for females. CONCLUSION: Despite estimation differences, the IOS can be reliable for medical student dietary habit assessment. The SFFQ is useful for measuring consumption of foods that are typically unavailable in school cafeterias, improving the overall dietary evaluation accuracy. The IOS assessment was more accurate for males than for females.

Eight-month intensive meditation-based intervention improves refractory hallucinations and delusions and quality of life in male inpatients with schizophrenia: a randomized controlled trial.

AIM: This study investigated the impact of an 8-month daily-guided intensive meditation-based intervention (iMI) on persistent hallucinations/delusions and health-related quality of life (QoL) in male inpatients with schizophrenia with treatment-refractory hallucinations and delusions (TRHDs). METHODS: A randomized controlled trial assigned 64 male inpatients with schizophrenia and TRHD equally to an 8-month iMI plus general rehabilitation program (GRP) or GRP alone. Assessments were conducted at baseline and the third and eighth months using the Positive and Negative Syndrome Scale (PANSS), 36-Item Short Form-36 (SF-36), and Five Facet Mindfulness Questionnaire (FFMQ). Primary outcomes measured PANSS reduction rates for total score, positive symptoms, and hallucinations/delusions items. Secondary outcomes assessed PANSS, SF-36, and FFMQ scores for psychotic symptoms, health-related QoL, and mindfulness skills, respectively. RESULTS: In the primary outcome, iMI significantly improved the reduction rates of PANSS total score, positive symptoms, and hallucination/delusion items compared with GRP at both the third and eighth months. Treatment response rates (≥25% reduction) for these measures significantly increased in the iMI group at the eighth month. Concerning secondary outcomes, iMI significantly reduced PANSS total score and hallucination/delusion items, while increasing scores in physical activity and mindfulness skills at both the third and eighth months compared with GRP. These effects were more pronounced with an 8-month intervention compared with a 3-month intervention. CONCLUSIONS: An iMI benefits patients with TRHDs by reducing persistent hallucinations/delusions and enhancing health-related QoL. Longer iMI duration yields superior treatment outcomes.

A low-cost process for complete utilization of bauxite residue.

Cost-effective treatment or even valorization of the bauxite residue (red mud) from the alumina industry is in demand to improve their environmental and economic liabilities. This study proposes a strategy that provides a near-complete conversion of bauxite residue to valuable products. The first step involves dilute acid leaching, which allowed the fractionation of raw residues into (1) an aqueous fraction rich in silica and aluminium and (2) a solid residue rich in iron, titanium and rare earth elements. For the proposed process, 91% of the original silicon, 67% of the aluminium, 78% of the scandium and 69% of the cerium were recovered. The initial cost evaluation suggested that this approach is profitable with a gross margin of 167 $US per tonne. This "Residue2Product" approach should be considered for large-scale practices as one of the most economical and sustainable solutions to this environmental and economic liability for the alumina industry.

Identify Regioselective Residues of Ginsenoside Hydrolases by Graph-Based Active Learning from Molecular Dynamics.

Identifying the catalytic regioselectivity of enzymes remains a challenge. Compared to experimental trial-and-error approaches, computational methods like molecular dynamics simulations provide valuable insights into enzyme characteristics. However, the massive data generated by these simulations hinder the extraction of knowledge about enzyme catalytic mechanisms without adequate modeling techniques. Here, we propose a computational framework utilizing graph-based active learning from molecular dynamics to identify the regioselectivity of ginsenoside hydrolases (GHs), which selectively catalyze C6 or C20 positions to obtain rare deglycosylated bioactive compounds from Panax plants. Experimental results reveal that the dynamic-aware graph model can excellently distinguish GH regioselectivity with accuracy as high as 96-98% even when different enzyme-substrate systems exhibit similar dynamic behaviors. The active learning strategy equips our model to work robustly while reducing the reliance on dynamic data, indicating its capacity to mine sufficient knowledge from short multi-replica simulations. Moreover, the model's interpretability identified crucial residues and features associated with regioselectivity. Our findings contribute to the understanding of GH catalytic mechanisms and provide direct assistance for rational design to improve regioselectivity. We presented a general computational framework for modeling enzyme catalytic specificity from simulation data, paving the way for further integration of experimental and computational approaches in enzyme optimization and design.

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Spinal muscular atrophy (SMA) is an autosomal recessive neuromuscular disorder. With the emergence of disease-modifying therapies, the prognosis of SMA has significantly improved, drawing increased attention to the importance of home rehabilitation and nursing management. Long-term, standardized home rehabilitation and nursing can delay the progression of SMA, enhance the psychological well-being, and improve the quality of life of both patients and caregivers. This article provides an overview of the goals of home rehabilitation, basic functional training methods, respiratory management, and nutritional management for SMA patients, as well as psychological health issues, emphasizing the significance of obtaining appropriate home rehabilitation and support during the care process.

Unique ghost surface phonon polaritons in biaxially hyperbolic materials.

We predicted peculiar ghost surface phonon polaritons in biaxially hyperbolic materials, where the two hyperbolic principal axes lie in the plane of propagation. We took the biaxially-hyperbolic α-MoO3 as one example of the materials to numerically simulate the ghost surface phonon polaritons. We found three unique ghost surface polaritons to appear in three enclosed wavenumber-frequency regions, respectively. These ghost surface phonon polaritons have different features from the surface phonon polaritons found previously, i.e., they are some hybrid-polarization surface waves composed of two coherent evanescent branch-waves in the α-MoO3 crystal. The interference of branch-waves leads to that their Poynting vector and electromagnetic fields both exhibit the oscillation-attenuation behavior along the surface normal, or a series of rapidly attenuated fringes. We found that the in-plane hyperbolic anisotropy and low-symmetric geometry of surface are the two necessary conditions for the existence of these ghost surface polaritons.

An enhanced consensus algorithm for blockchain.

Consensus plays a crucial role in blockchain technology, with the deleted proof of stake (DPoS) consensus mechanism commonly utilized in both public and hybrid chains. However, the current DPoS mechanism faces challenges such as low node engagement in voting and potential security risks posed by malicious nodes. In response, we propose the DL-DPoS (deep link-delegated proof of stake) mechanism, which builds upon the DPoS framework. The DL-DPoS incorporates the LINK incentive mechanism to encourage inactive nodes to participate in voting and leader selection. Furthermore, a comprehensive credit scoring system based on wealth, performance, and stability is introduced to enhance the security of elected nodes. The verification process is optimized to involve all nodes except the leader node, and mechanisms are in place to handle malicious attacks by degrading or removing offending nodes and redistributing their responsibilities to the LINK group. Performance testing of the DL-DPoS mechanism, conducted through blockchain simulation tests using the GO language, shows a 23% increase in throughput compared to DPoS, with over 95% node participation and improved distribution of rights and equity. These results indicate the enhanced performance, security, and stability of the DL-DPoS consensus mechanism.

The underlying mechanism and targeted therapy strategy of miRNAs cross-regulating EMT process through multiple signaling pathways in hepatocellular carcinoma.

The consistent notion holds that hepatocellular carcinoma (HCC) initiation, progression, and clinical treatment failure treatment failure are affected by the accumulation of various genetic and epigenetic alterations. MicroRNAs (miRNAs) play an irreplaceable role in a variety of physiological and pathological states. meanwhile, epithelial-mesenchymal transition (EMT) is a crucial biological process that controls the development of HCC. miRNAs regulate the intermediation state of EMTor mesenchymal-epithelial transition (MTE)thereby regulating HCC progression. Notably, miRNAs regulate key HCC-related molecular pathways, including the Wnt/ß-catenin pathway, PTEN/PI3K/AKT pathway, TGF-ß pathway, and RAS/MAPK pathway. Therefore, we comprehensively reviewed how miRNAs produce EMT effects by multiple signaling pathways and their potential significance in the pathogenesis and treatment response of HCC. emphasizing their molecular pathways and progression in HCC initiation. Additionally, we also pay attention to regulatory mechanisms that are partially independent of signaling pathways. Finally, we summarize and propose miRNA-targeted therapy and diagnosis and defense strategies forHCC. The identification of the mechanism leading to the activation of EMT programs during HCC disease processes also provides a new protocol for the plasticity of distinct cellular phenotypes and possible therapeutic interventions. Consequently, we summarize the latest progress in this direction, with a promising path for further insight into this fast-moving field.

Seizure Detection of EEG Signals Based on Multi-Channel Long- and Short-Term Memory-Like Spiking Neural Model.

Seizure is a common neurological disorder that usually manifests itself in recurring seizure, and these seizures can have a serious impact on a person's life and health. Therefore, early detection and diagnosis of seizure is crucial. In order to improve the efficiency of early detection and diagnosis of seizure, this paper proposes a new seizure detection method, which is based on discrete wavelet transform (DWT) and multi-channel long- and short-term memory-like spiking neural P (LSTM-SNP) model. First, the signal is decomposed into 5 levels by using DWT transform to obtain the features of the components at different frequencies, and a series of time-frequency features in wavelet coefficients are extracted. Then, these different features are used to train a multi-channel LSTM-SNP model and perform seizure detection. The proposed method achieves a high seizure detection accuracy on the CHB-MIT dataset: 98.25% accuracy, 98.22% specificity and 97.59% sensitivity. This indicates that the proposed epilepsy detection method can show competitive detection performance.

Ensemble learning for retinal disease recognition under limited resources.

Retinal optical coherence tomography (OCT) images provide crucial insights into the health of the posterior ocular segment. Therefore, the advancement of automated image analysis methods is imperative to equip clinicians and researchers with quantitative data, thereby facilitating informed decision-making. The application of deep learning (DL)-based approaches has gained extensive traction for executing these analysis tasks, demonstrating remarkable performance compared to labor-intensive manual analyses. However, the acquisition of retinal OCT images often presents challenges stemming from privacy concerns and the resource-intensive labeling procedures, which contradicts the prevailing notion that DL models necessitate substantial data volumes for achieving superior performance. Moreover, limitations in available computational resources constrain the progress of high-performance medical artificial intelligence, particularly in less developed regions and countries. This paper introduces a novel ensemble learning mechanism designed for recognizing retinal diseases under limited resources (e.g., data, computation). The mechanism leverages insights from multiple pre-trained models, facilitating the transfer and adaptation of their knowledge to retinal OCT images. This approach establishes a robust model even when confronted with limited labeled data, eliminating the need for an extensive array of parameters, as required in learning from scratch. Comprehensive experimentation on real-world datasets demonstrates that the ensemble models constructed by the proposed ensemble method show superior performance over the baseline models under sparse labeled data, especially the triple ensemble model, which achieves the accuracy of 92.06%, which is 8.27%, 7.99%, and 11.14% better than the three baseline models, respectively. In addition, compared with the three baseline models learned from scratch, the triple ensemble model has fewer trainable parameters, only 3.677M, which is lower than the three baseline models of 8.013M, 4.302M, and 20.158M, respectively.

A Robust Causal Brain Network Measure and Its Application on Ictal Electrocorticogram Analysis of Drug-resistant Epilepsy.

Measuring causal brain network is a significant topic for exploring complex brain functions. While various data-driven algorithms have been proposed, they still have some drawbacks such as ignoring time non-separability, cumbersome parameter settings, and poor robustness. To solve these deficiencies, we developed a novel framework: "time-shift permutation cross-mapping, TPCM," integrating steps of (1) delayed improved phase-space reconstruction (DIPSR), (2) rank transformation of embedding vectors' distances, (3) cross-mapping with a fitting estimation, and (4) causality quantification using multi-delays. Based on synthetic models and comparison with baseline methods, numerical validation results demonstrate that TPCM significantly improves the robustness for data length with or without noise interference, and achieves the best quantification accuracy in detecting time delay and coupling strength, with the highest determination coefficient ( R2 = 0. 96 ) of fitting verse coupling parameters. The developed TPCM was finally applied to ictal electrocorticogram (ECoG) analysis of patients with drug-resistant epilepsy (DRE). A total of 17 patients with DRE were included into the retrospective study. For 8 patients undergoing successful surgeries, the causal coupling strength (0.58 ± 0.20) within epileptogenic zone network is significantly higher than those suffering failed surgeries (0.38 ± 0.16) with P < 0. 001 through Mann-Whitney-U-test. Therefore, the epileptic brain network measured by TPCM is a credible biomarker for predicting surgical outcomes. These findings additionally confirm TPCM's superior performance and promising potential to advance precision medicine for neurological disorders.

Plasmablastic lymphoma: current knowledge and future directions.

Plasmablastic lymphoma (PBL) is an aggressive non-Hodgkin lymphoma associated with HIV infection and immunodeficiency. However, PBL can also be seen immunocompetent individuals in recent studies. PBL was characterized by distinct clinical and pathological features, such as plasmablastic morphology and universal expression of plasma cell markers. The clinicopathologic features were different between HIV-negative and HIV-positive patients. Gene expression analysis identified the unique molecular feature in PBL, including frequent c-MYC rearrangement and downregulation of BCR signaling pathway. Despite the recent advances in the treatment of PBL, the prognosis of PBL patients remains dismal. The objectives of this review are to summarize the current knowledge on the epidemiology, molecular profiles, clinical and pathological features, differential diagnosis, treatment strategies, prognostic factors, and potential novel therapeutic approaches in PBL patients.

Multiple-in-Single-Out Object Detector Leveraging Spiking Neural Membrane Systems and Multiple Transformers.

Most existing multi-scale object detectors depend on multi-level feature maps. The Feature Pyramid Networks (FPN) is a significant architecture for object detection that utilizes these multi-level feature maps. However, the use of FPN also increases the detector's complexity. For object detection methods that only use a single-level feature map, the detection performance is limited to some extent because the single-level feature map cannot balance deep semantic information and shallow detail information. We introduce a novel detector - the Spiking Neural P Multiple-in-Single-out (SNPMiSo) detector to address these challenges. The SNPMiSo detector is constructed based on SNP-like neurons. In SNPMiSo, we employ two kinds of Transformers to boost the important features across different-level feature maps separately. After enhancing the features, we use an incremental upsampling module to upsample and merge the two feature maps. This combined feature map is input into the NAF dilated residual module and the NAF dual-branch detection head. This process allows us to extract multi-scale features and carry out detection tasks. Our tests show promising results: On the COCO dataset, SNPMiSo attains an Average Precision (AP) of 38.7, an improvement of 1.0 AP over YOLOF. In addition, SNPMiSo demonstrates a quicker detection speed, outperforming some advanced multi-level and single-level object detectors.

Multi-level feature interaction image super-resolution network based on convolutional nonlinear spiking neural model.

Image super-resolution (ISR) is designed to recover lost detail information from low-resolution images, resulting in high-quality and high-definition high-resolution images. In the existing single ISR (SISR) methods based on convolutional neural networks (CNN), however, most of the models cannot effectively combine global and local information and are also easy to ignore the correlation between different hierarchical feature information. To address these problems, this study proposes a multi-level feature interactive image super-resolution network, which is constructed by the convolutional units inspired by nonlinear spiking mechanism in nonlinear spiking neural P systems, including shallow feature processing, deep feature extraction and fusion, and reconstruction modules. The different omni domain self-attention blocks are introduced to extract global information in the deep feature extraction and fusion stage and formed a feature enhancement module having a Transformer structure using a novel convolutional unit for extracting local information. Furthermore, to adaptively fuse features between different hierarchies, we design a multi-level feature fusion module, which not only can adaptively fuse features between different hierarchies, but also can better interact with contextual information. The proposed model is compared with 16 state-of-the-art or baseline models on five benchmark datasets. The experimental results show that the proposed model not only achieves good reconstruction performance, but also strikes a good balance between model parameters and performance.