Role of NLRP3 inflammasome in central nervous system diseases.

The central nervous system (CNS) is the most delicate system in human body, with the most complex structure and function. It is vulnerable to trauma, infection, neurodegeneration and autoimmune diseases, and activates the immune system. An appropriate inflammatory response contributes to defence against invading microbes, whereas an excessive inflammatory response can aggravate tissue damage. The NLRP3 inflammasome was the first one studied in the brain. Once primed and activated, it completes the assembly of inflammasome (sensor NLRP3, adaptor ASC, and effector caspase-1), leading to caspase-1 activation and increased release of downstream inflammatory cytokines, as well as to pyroptosis. Cumulative studies have confirmed that NLRP3 plays an important role in regulating innate immunity and autoimmune diseases, and its inhibitors have shown good efficacy in animal models of various inflammatory diseases. In this review, we will briefly discuss the biological characteristics of NLRP3 inflammasome, summarize the recent advances and clinical impact of the NLRP3 inflammasome in infectious, inflammatory, immune, degenerative, genetic, and vascular diseases of CNS, and discuss the potential and challenges of NLRP3 as a therapeutic target for CNS diseases.

Voice Changes of Patients With Nasopharyngeal Carcinoma in Eleven Years After Radiotherapy: A Cross-Sectional Study.

OBJECTIVES: The objective of this study was to assess voice changes in patients with nasopharyngeal carcinoma (NPC) using subjective and objective assessment tools and to make inferences regarding the underlying pathological causes for different phases of radiotherapy (RT). METHODS: A total of 187 (123 males and 64 females) patients with post-RT NPC with no recurrence of malignancy or other voice diseases and 17 (11 males and 6 females) healthy individuals were included in this study. The patients were equally divided into 11 groups according to the number of years after RT. The acoustic analyses, GRBAS (grade, roughness, breathiness, asthenia, and strain) scales, and Voice Handicap Index (VHI)-10 scores were collected and analyzed. RESULTS: The fundamental frequency (F0) parameters in years 1 and 2 and year 11 were significantly lower in patients with NPC than in healthy individuals. The maximum phonation times in years 1 and 11 were significantly shorter than those in healthy individuals. The jitter parameters were significantly different between year 1 and from years 8 to 11 and the healthy individuals. The shimmer parameters were significantly different between years 1, from years 9 to 11, and healthy individuals. Hoarseness was the most prominent problem compared to other items of the GRBAS. The VHI-10 scores were significantly different between years 1 and 2 and year 11 after RT in patients with NPC. CONCLUSIONS: Voice quality was worse in the first 2 years and from years 8 to 11 but remained relatively normal from years 3 to 7 after RT. Patient-reported voice handicaps began during year 3 after RT. The most prominent problem was perceived hoarseness, which was evident in the first 2 years and from years 9 to 11 after RT. The radiation-induced mucous edema, laryngeal intrinsic muscle fibrosis, nerve injuries, upper respiratory tract changes, and decreased lung capacity might be the pathological reasons for voice changes in post-RT patients with NPC.

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.

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.

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.

Upgrading CO2 to sustainable aromatics via perovskite-mediated tandem catalysis.

The directional transformation of carbon dioxide (CO2) with renewable hydrogen into specific carbon-heavy products (C6+) of high value presents a sustainable route for net-zero chemical manufacture. However, it is still challenging to simultaneously achieve high activity and selectivity due to the unbalanced CO2 hydrogenation and C-C coupling rates on complementary active sites in a bifunctional catalyst, thus causing unexpected secondary reaction. Here we report LaFeO3 perovskite-mediated directional tandem conversion of CO2 towards heavy aromatics with high CO2 conversion (> 60%), exceptional aromatics selectivity among hydrocarbons (> 85%), and no obvious deactivation for 1000 hours. This is enabled by disentangling the CO2 hydrogenation domain from the C-C coupling domain in the tandem system for Iron-based catalyst. Unlike other active Fe oxides showing wide hydrocarbon product distribution due to carbide formation, LaFeO3 by design is endowed with superior resistance to carburization, therefore inhibiting uncontrolled C-C coupling on oxide and isolating aromatics formation in the zeolite. In-situ spectroscopic evidence and theoretical calculations reveal an oxygenate-rich surface chemistry of LaFeO3, that easily escape from the oxide surface for further precise C-C coupling inside zeolites, thus steering CO2-HCOOH/H2CO-Aromatics reaction pathway to enable a high yield of aromatics.

Silent information regulator sirtuin 1 ameliorates acute liver failure via the p53/glutathione peroxidase 4/gasdermin D axis.

BACKGROUND: Acute liver failure (ALF) has a high mortality with widespread hepatocyte death involving ferroptosis and pyroptosis. The silent information regulator sirtuin 1 (SIRT1)-mediated deacetylation affects multiple biological processes, including cellular senescence, apoptosis, sugar and lipid metabolism, oxidative stress, and inflammation. AIM: To investigate the association between ferroptosis and pyroptosis and the upstream regulatory mechanisms. METHODS: This study included 30 patients with ALF and 30 healthy individuals who underwent serum alanine aminotransferase (ALT) and aspartate aminotransferase (AST) testing. C57BL/6 mice were also intraperitoneally pretreated with SIRT1, p53, or glutathione peroxidase 4 (GPX4) inducers and inhibitors and injected with lipopolysaccharide (LPS)/D-galactosamine (D-GalN) to induce ALF. Gasdermin D (GSDMD)-/- mice were used as an experimental group. Histological changes in liver tissue were monitored by hematoxylin and eosin staining. ALT, AST, glutathione, reactive oxygen species, and iron levels were measured using commercial kits. Ferroptosis- and pyroptosis-related protein and mRNA expression was detected by western blot and quantitative real-time polymerase chain reaction. SIRT1, p53, and GSDMD were assessed by immunofluorescence analysis. RESULTS: Serum AST and ALT levels were elevated in patients with ALF. SIRT1, solute carrier family 7a member 11 (SLC7A11), and GPX4 protein expression was decreased and acetylated p5, p53, GSDMD, and acyl-CoA synthetase long-chain family member 4 (ACSL4) protein levels were elevated in human ALF liver tissue. In the p53 and ferroptosis inhibitor-treated and GSDMD-/- groups, serum interleukin (IL)-1ß, tumour necrosis factor alpha, IL-6, IL-2 and C-C motif ligand 2 levels were decreased and hepatic impairment was mitigated. In mice with GSDMD knockout, p53 was reduced, GPX4 was increased, and ferroptotic events (depletion of SLC7A11, elevation of ACSL4, and iron accumulation) were detected. In vitro, knockdown of p53 and overexpression of GPX4 reduced AST and ALT levels, the cytostatic rate, and GSDMD expression, restoring SLC7A11 depletion. Moreover, SIRT1 agonist and overexpression of SIRT1 alleviated acute liver injury and decreased iron deposition compared with results in the model group, accompanied by reduced p53, GSDMD, and ACSL4, and increased SLC7A11 and GPX4. Inactivation of SIRT1 exacerbated ferroptotic and pyroptotic cell death and aggravated liver injury in LPS/D-GalN-induced in vitro and in vivo models. CONCLUSION: SIRT1 activation attenuates LPS/D-GalN-induced ferroptosis and pyroptosis by inhibiting the p53/GPX4/GSDMD signaling pathway in ALF.

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.

Assessing recent recurrence after hepatectomy for hepatitis B-related hepatocellular carcinoma by a predictive model based on sarcopenia.

BACKGROUND: Sarcopenia may be associated with hepatocellular carcinoma (HCC) following hepatectomy. But traditional single clinical variables are still insufficient to predict recurrence. We still lack effective prediction models for recent recurrence (time to recurrence < 2 years) after hepatectomy for HCC. AIM: To establish an interventable prediction model to estimate recurrence-free survival (RFS) after hepatectomy for HCC based on sarcopenia. METHODS: We retrospectively analyzed 283 hepatitis B-related HCC patients who underwent curative hepatectomy for the first time, and the skeletal muscle index at the third lumbar spine was measured by preoperative computed tomography. 94 of these patients were enrolled for external validation. Cox multivariate analysis was per-formed to identify the risk factors of postoperative recurrence in training cohort. A nomogram model was developed to predict the RFS of HCC patients, and its predictive performance was validated. The predictive efficacy of this model was evaluated using the receiver operating characteristic curve. RESULTS: Multivariate analysis showed that sarcopenia [Hazard ratio(HR) = 1.767, 95%CI: 1.166-2.678, P < 0.05], alpha-fetoprotein ≥ 40 ng/mL (HR = 1.984, 95%CI: 1.307-3.011, P < 0.05), the maximum diameter of tumor > 5 cm (HR = 2.222, 95%CI: 1.285-3.842, P < 0.05), and hepatitis B virus DNA level ≥ 2000 IU/mL (HR = 2.1, 95%CI: 1.407-3.135, P < 0.05) were independent risk factors associated with postoperative recurrence of HCC. Based on the sarcopenia to assess the RFS model of hepatectomy with hepatitis B-related liver cancer disease (SAMD) was established combined with other the above risk factors. The area under the curve of the SAMD model was 0.782 (95%CI: 0.705-0.858) in the training cohort (sensitivity 81%, specificity 63%) and 0.773 (95%CI: 0.707-0.838) in the validation cohort. Besides, a SAMD score ≥ 110 was better to distinguish the high-risk group of postoperative recurrence of HCC. CONCLUSION: Sarcopenia is associated with recent recurrence after hepatectomy for hepatitis B-related HCC. A nutritional status-based prediction model is first established for postoperative recurrence of hepatitis B-related HCC, which is superior to other models and contributes to prognosis prediction.

Electrophysiological Changes on Laryngeal Motor Neuropathways Cause Voice Disorders for Postradiotherapy Patients with Nasopharyngeal Carcinoma.

OBJECTIVE: This study explored electrophysiological changes in the laryngeal motor neuropathway and determined whether lesions in the laryngeal motor cortex (LMC) and its descending tract contribute to voice deterioration and peripheral nerve palsy in patients with nasopharyngeal carcinoma (NPC) postradiotherapy (RT). STUDY DESIGNS: Prospective cohort study. METHODS: Twenty-two patients with NPC at 2 to 4years post-RT (8 female and 14 male), 22 patients with NPC at 8 to 10years post-RT (8 female and 14 male), and 22 healthy individuals (9 female and 13 male) were selected to test their magnetic evoked potentials (MEP), motor nerve conduction, and voice quality using transcranial magnetic stimulation, laryngeal electromyography, and the XION DiVAS acoustic analysis software. Three groups were matched according to approximate age. Multiple comparisons were performed among the three groups. RESULTS: The voice quality of post-RT patients with NPC deteriorated compared to that of healthy individuals. Bilateral LMC and their corticonuclear tracts to the bilateral ambiguous nuclei of post-RT patients with NPC were impaired according to multigroup comparisons of MEP amplitudes, latencies, and resting motor thresholds. The vagus and recurrent laryngeal nerves (RLN) of post-RT patients with NPC were impaired according to multigroup comparisons of the amplitude and latencies of the compound muscle action potential and latencies of f-waves. CONCLUSIONS: The voice quality of patients with NPC deteriorated after RT. The pathogenesis of post-RT voice deterioration may involve radiation-induced injuries to the vagus, RLN, and bilateral LMC. Furthermore, radiation-induced injuries to the bilateral LMC may contribute to vagus and RLN palsies. These findings support the use of transcranial approaches to treating voice disorders and peripheral nerve palsies in post-RT patients with NPC. TRIAL REGISTRATION: ChiCTR2100054425; Electrophysiological Study of Vocal-Fold Mobility Disorders After Radiotherapy for NPC Patients via Magnetic Evoked Potential and Their Correlation with Voice Quality Assessment; https://www.chictr.org.cn/bin/project/edit?pid=144429.

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.

Multitask Adversarial Networks Based on Extensive Nonlinear Spiking Neuron Models.

Deep learning technology has been successfully used in Chest X-ray (CXR) images of COVID-19 patients. However, due to the characteristics of COVID-19 pneumonia and X-ray imaging, the deep learning methods still face many challenges, such as lower imaging quality, fewer training samples, complex radiological features and irregular shapes. To address these challenges, this study first introduces an extensive NSNP-like neuron model, and then proposes a multitask adversarial network architecture based on ENSNP-like neurons for chest X-ray images of COVID-19, called MAE-Net. The MAE-Net serves two tasks: (i) converting low-quality CXR images to high-quality images; (ii) classifying CXR images of COVID-19. The adversarial architecture of MAE-Net uses two generators and two discriminators, and two new loss functions have been introduced to guide the optimization of the network. The MAE-Net is tested on four benchmark COVID-19 CXR image datasets and compared them with eight deep learning models. The experimental results show that the proposed MAE-Net can enhance the conversion quality and the accuracy of image classification results.

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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.

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.

The Epidemiology, Management and Therapeutic Outcomes of Subdural Empyema in Neonates with Acute Bacterial Meningitis.

Background: Subdural empyema is one of the more serious complications of bacterial meningitis and therapeutic challenges to clinicians. We aimed to evaluate the clinical characteristics, treatment, and outcome of subdural empyema in neonates with bacterial meningitis. Methods: A retrospective cohort study was conducted in two medical centers in Taiwan that enrolled all cases of neonates with subdural empyema after bacterial meningitis between 2003 and 2020. Results: Subdural empyema was diagnosed in 27 of 153 (17.6%) neonates with acute bacterial meningitis compared with cases of meningitis without subdural empyema. The demographics and pathogen distributions were comparable between the study group and the controls, but neonates with subdural empyema were significantly more likely to have clinical manifestations of fever (85.2%) and seizure (81.5%) (both p values < 0.05). The cerebrospinal fluid results of neonates with subdural empyema showed significantly higher white blood cell counts, lower glucose levels and higher protein levels (p = 0.011, 0.003 and 0.006, respectively). Neonates with subdural empyema had a significantly higher rate of neurological complications, especially subdural effusions and periventricular leukomalacia. Although the final mortality rate was not increased in neonates with subdural empyema when compared with the controls, they were often treated much longer and had a high rate of long-term neurological sequelae. Conclusions: Subdural empyema is not uncommon in neonates with acute bacterial meningitis and was associated with a high risk of neurological complications, although it does not significantly increase the final mortality rate. Close monitoring of the occurrence of subdural empyema is required, and appropriate long-term antibiotic treatment after surgical intervention may lead to optimized outcomes.

Inhalation of 2, 4-di-tert-butylphenol-Loaded micelles suppresses respiratory syncytial virus infection in mice.

Human respiratory syncytial virus (RSV) is a common cause of respiratory infections in infants, young children, and elderly people. However, there are no effective treatments or vaccines available in most countries. In this study, we explored the anti-RSV potential of 2, 4-Di-tert-butylphenol (2, 4-DTBP), a compound derived from Houttuynia cordata Thunb. To overcome the poor solubility of 2, 4-DTBP, we encapsulated it in polymeric micelles and delivered it by inhalation. We found that 2, 4-DTBP-loaded micelles inhibited RSV infection in vitro and improved survival, lung pathology, and viral clearance in RSV-infected mice. Our results suggested that 2, 4-DTBP-loaded micelle is a promising novel therapeutic agent for RSV infection.

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.

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.

A Parallel Convolutional Network Based on Spiking Neural Systems.

Deep convolutional neural networks have shown advanced performance in accurately segmenting images. In this paper, an SNP-like convolutional neuron structure is introduced, abstracted from the nonlinear mechanism in nonlinear spiking neural P (NSNP) systems. Then, a U-shaped convolutional neural network named SNP-like parallel-convolutional network, or SPC-Net, is constructed for segmentation tasks. The dual-convolution concatenate (DCC) and dual-convolution addition (DCA) network blocks are designed, respectively, in the encoder and decoder stages. The two blocks employ parallel convolution with different kernel sizes to improve feature representation ability and make full use of spatial detail information. Meanwhile, different feature fusion strategies are used to fuse their features to achieve feature complementarity and augmentation. Furthermore, a dual-scale pooling (DSP) module in the bottleneck is designed to improve the feature extraction capability, which can extract multi-scale contextual information and reduce information loss while extracting salient features. The SPC-Net is applied in medical image segmentation tasks and is compared with several recent segmentation methods on the GlaS and CRAG datasets. The proposed SPC-Net achieves 90.77% DICE coefficient, 83.76% IoU score and 83.93% F1 score, 86.33% ObjDice coefficient, 135.60 Obj-Hausdorff distance, respectively. The experimental results show that the proposed model can achieve good segmentation performance.