CCL4 as a potential serum factor in differential diagnosis of central nervous system inflammatory diseases and gliomas.

Computed tomography (CT) scans and magnetic resonance imaging (MRI) are commonly utilized to detect brain gliomas and central nervous system inflammation diseases. However, there are instances where depending solely on medical imaging for a precise diagnosis may result in unsuitable medications or treatments. Pathological analysis is regarded as the definitive method for diagnosing brain gliomas or central nervous system inflammation diseases. To achieve this, a craniotomy or stereotaxic biopsy is necessary to collect brain tissue, which can lead to complications such as cerebral hemorrhage, neurological deficits, cerebrospinal fluid leaks, and cerebral edema. Consequently, the advancement of non-invasive or minimally invasive diagnostic techniques is currently a high priority. This study included samples from four glioma patients and five patients with central nervous system inflammatory diseases, comprising both serum and paired cerebrospinal fluid (CSF). A total of 40 human cytokines were identified in these samples. We utilized a receiver operating characteristic (ROC) analysis to assess the sensitivity and specificity for distinguishing central nervous system inflammation diseases and gliomas. Additionally, we examined the correlation of these factors between serum and CSF in the patients. Ultimately, the identified factors were validated using serum from patients with clinically confirmed gliomas and central nervous system inflammation diseases followed by detection and statistical analysis through ELISA. The levels of serum factors IL-4, IFN-α, IFN-γ, IL-6, TNF-α, CCL4, CCL11, and VEGF were found to be significantly higher in gliomas compared with inflammatory diseases of the central nervous system (p < 0.05). Furthermore, a strong correlation was observed between the levels of CCL4 in serum and CSF, with a correlation coefficient of r = 0.92 (95% CI = 0.20-0.99, p = 0.027). We gathered more clinical samples to provide further validation of the abundance of CCL4 expression. A clinical study analyzing serum samples from 19 glioma patients and 22 patients with central nervous system inflammation diseases revealed that CCL4 levels were notably elevated in the inflammatory group compared with the glioma group (p < 0.001). These results suggest that assessing serum CCL4 levels may be useful in distinguishing those patients for clinical diagnostic purposes.

Predicting cerebellar mutism syndrome in children using lesion map combined with clinical features.

OBJECTIVE: This study aimed to develop a predictive model for cerebellar mutism syndrome (CMS) in pediatric patients with posterior fossa tumors, integrating lesion-symptom mapping (LSM) data with clinical factors, and to assess the model's performance. METHODS: A cohort of pediatric patients diagnosed with posterior fossa tumors and undergoing surgery at Beijing Children's Hospital from July 2013 to December 2023 was analyzed. Clinical variables gender, age at surgery, tumor characteristics, hydrocephalus, surgical route and pathology were collected. LSM was used to link tumor locations with CMS outcomes. Lasso regression and logistic regression were employed for feature selection and model construction, respectively. Model performance was assessed using area under the curve (AUC) and accuracy metrics. RESULTS: The study included 197 patients in total, with CMS rates consistent across training, validation, and prospective groups. Significant associations were found between CMS and gender, tumor type, hydrocephalus, paraventricular edema, surgical route, and pathology. A predictive model combining voxel location data from LSM with clinical factors achieved high predictive performance (C-index: training 0.956, validation 0.933, prospective 0.892). Gender, pathology, and voxel location were identified as key predictors for CMS. CONCLUSION: The study established an effective predictive model for CMS in pediatric posterior fossa tumor patients, leveraging LSM data and clinical factors. The model's accuracy and robustness suggest its potential utility in clinical practice for early CMS risk assessment and intervention planning.

Carbon Dots-Mediated Photodynamic Treatment Reduces Postharvest Senescence and Decay of Grapes by Regulating the Antioxidant System.

Grapes are susceptible to mold and decay during postharvest storage, and developing new technologies to extend their storage period has important application value. Photodynamic technology (PDT) in concurrence with carbon dots (CDs) proposes an innovative and eco-friendly preservation strategy. We examined the effects of carbon dots combined with photodynamic treatment on postharvest senescence and antioxidant system of table grape. The compounding of photodynamic technology with a 0.06 g L-1 CDs solution could possibly extend the postharvest storage period of grape berries. Through this strategy, we achieved a decreased rate of fruit rotting and weight loss alongside the delayed deterioration of fruit firmness, soluble solids, and titratable acid. As paired with photodynamic technology, CDs considerably decreased the postharvest storage loss of phenols, flavonoids, and reducing sugars as compared to the control group. Concurrently, it remarkably postponed the build-up of hydrogen peroxide (H2O2), superoxide anion (O2∙-), and malondialdehyde (MDA); elevated the levels of reduced ascorbic acid (AsA) and reduced glutathione (GSH); lowered the levels of dehydroascorbic acid (DHA) and oxidized glutathione (GSSG); raised the ratios of AsA/DHA and GSSH/GSSG; encouraged the activities of superoxide dismutase (SOD) and phenylalanine ammonia-lyase (PAL); and inhibited the activities of polyphenol oxidase (PPO) and lipoxygenase (LOX). Furthermore, it enhanced the iron reduction antioxidant capacity (FRAP) and DPPH radical scavenging capacity of grape berries. CDs combined with photodynamic treatment could efficiently lessen postharvest senescence and decay of grape berry while extending the storage time.

Causal Inference Meets Deep Learning: A Comprehensive Survey.

Deep learning relies on learning from extensive data to generate prediction results. This approach may inadvertently capture spurious correlations within the data, leading to models that lack interpretability and robustness. Researchers have developed more profound and stable causal inference methods based on cognitive neuroscience. By replacing the correlation model with a stable and interpretable causal model, it is possible to mitigate the misleading nature of spurious correlations and overcome the limitations of model calculations. In this survey, we provide a comprehensive and structured review of causal inference methods in deep learning. Brain-like inference ideas are discussed from a brain-inspired perspective, and the basic concepts of causal learning are introduced. The article describes the integration of causal inference with traditional deep learning algorithms and illustrates its application to large model tasks as well as specific modalities in deep learning. The current limitations of causal inference and future research directions are discussed. Moreover, the commonly used benchmark datasets and the corresponding download links are summarized.

Nature-Inspired Intelligent Computing: A Comprehensive Survey.

Nature, with its numerous surprising rules, serves as a rich source of creativity for the development of artificial intelligence, inspiring researchers to create several nature-inspired intelligent computing paradigms based on natural mechanisms. Over the past decades, these paradigms have revealed effective and flexible solutions to practical and complex problems. This paper summarizes the natural mechanisms of diverse advanced nature-inspired intelligent computing paradigms, which provide valuable lessons for building general-purpose machines capable of adapting to the environment autonomously. According to the natural mechanisms, we classify nature-inspired intelligent computing paradigms into 4 types: evolutionary-based, biological-based, social-cultural-based, and science-based. Moreover, this paper also illustrates the interrelationship between these paradigms and natural mechanisms, as well as their real-world applications, offering a comprehensive algorithmic foundation for mitigating unreasonable metaphors. Finally, based on the detailed analysis of natural mechanisms, the challenges of current nature-inspired paradigms and promising future research directions are presented.

Brain-Inspired Learning, Perception, and Cognition: A Comprehensive Review.

The progress of brain cognition and learning mechanisms has provided new inspiration for the next generation of artificial intelligence (AI) and provided the biological basis for the establishment of new models and methods. Brain science can effectively improve the intelligence of existing models and systems. Compared with other reviews, this article provides a comprehensive review of brain-inspired deep learning algorithms for learning, perception, and cognition from microscopic, mesoscopic, macroscopic, and super-macroscopic perspectives. First, this article introduces the brain cognition mechanism. Then, it summarizes the existing studies on brain-inspired learning and modeling from the perspectives of neural structure, cognitive module, learning mechanism, and behavioral characteristics. Next, this article introduces the potential learning directions of brain-inspired learning from four aspects: perception, cognition, understanding, and decision-making. Finally, the top-ten open problems that brain-inspired learning, perception, and cognition currently face are summarized, and the next generation of AI technology has been prospected. This work intends to provide a quick overview of the research on brain-inspired AI algorithms and to motivate future research by illuminating the latest developments in brain science.

Improving the classification of multiple sclerosis and cerebral small vessel disease with interpretable transfer attention neural network.

As an autoimmune-mediated inflammatory demyelinating disease of the central nervous system, multiple sclerosis (MS) is often confused with cerebral small vessel disease (cSVD), which is a regional pathological change in brain tissue with unknown pathogenesis. This is due to their similar clinical presentations and imaging manifestations. That misdiagnosis can significantly increase the occurrence of adverse events. Delayed or incorrect treatment is one of the most important causes of MS progression. Therefore, the development of a practical diagnostic imaging aid could significantly reduce the risk of misdiagnosis and improve patient prognosis. We propose an interpretable deep learning (DL) model that differentiates MS and cSVD using T2-weighted fluid-attenuated inversion recovery (FLAIR) images. Transfer learning (TL) was utilized to extract features from the ImageNet dataset. This pioneering model marks the first of its kind in neuroimaging, showing great potential in enhancing differential diagnostic capabilities within the field of neurological disorders. Our model extracts the texture features of the images and achieves more robust feature learning through two attention modules. The attention maps provided by the attention modules provide model interpretation to validate model learning and reveal more information to physicians. Finally, the proposed model is trained end-to-end using focal loss to reduce the influence of class imbalance. The model was validated using clinically diagnosed MS (n=112) and cSVD (n=321) patients from the Beijing Tiantan Hospital. The performance of the proposed model was better than that of two commonly used DL approaches, with a mean balanced accuracy of 86.06 % and a mean area under the receiver operating characteristic curve of 98.78 %. Moreover, the generated attention heat maps showed that the proposed model could focus on the lesion signatures in the image. The proposed model provides a practical diagnostic imaging aid for the use of routinely available imaging techniques such as magnetic resonance imaging to classify MS and cSVD by linking DL to human brain disease. We anticipate a substantial improvement in accurately distinguishing between various neurological conditions through this novel model.

A bioactive calcium silicate nanowire-containing hydrogel for organoid formation and functionalization.

Organoids, which are 3D multicellular constructs, have garnered significant attention in recent years. Existing organoid culture methods predominantly utilize natural and synthetic polymeric hydrogels. This study explored the potential of a composite hydrogel mainly consisting of calcium silicate (CS) nanowires and methacrylated gelatin (GelMA) as a substrate for organoid formation and functionalization, specifically for intestinal and liver organoids. Furthermore, the research delved into the mechanisms by which CS nanowires promote the structure formation and development of organoids. It was discovered that CS nanowires can influence the stiffness of the hydrogel, thereby regulating the expression of the mechanosensory factor yes-associated protein (YAP). Additionally, the bioactive ions released by CS nanowires in the culture medium could accelerate Wnt/ß-catenin signaling, further stimulating organoid development. Moreover, bioactive ions were found to enhance the nutrient absorption and ATP metabolic activity of intestinal organoids. Overall, the CS/GelMA composite hydrogel proves to be a promising substrate for organoid formation and development. This research suggested that inorganic biomaterials hold significant potential in organoid research, offering bioactivities, biosafety, and cost-effectiveness.

Lycium barbarum polysaccharide reverses drug resistance in oxaliplatin-resistant colon cancer cells by inhibiting PI3K/AKT-dependent phosphomannose isomerase.

Objective: Here, we aimed to explore the effect of LBP in combination with Oxaliplatin (OXA) on reversing drug resistance in colon cancer cells through in vitro and in vivo experiments. We also aimed to explore the possible mechanism underlying this effect. Finally, we aimed to determine potential targets of Lycium barbarum polysaccharide (LBP) in colon cancer (CC) through network pharmacology and molecular docking. Methods: The invasion ability of colon cancer cells was assessed using the invasion assay. The migration ability of these cells was assessed using the migration assay and wound healing assay. Cell cycle analysis was carried out using flow cytometry. The expression levels of phosphomannose isomerase (PMI) and ATP-binding cassette transport protein of G2 (ABCG2) proteins were determined using immunofluorescence and western blotting. The expression levels of phosphatidylinositol3-kinase (PI3K), protein kinase B (AKT), B-cell lymphoma 2 (Bcl-2), and BCL2-Associated X (Bax) were determined using western blotting. Forty BALB/c nude mice purchased from Weitong Lihua, Beijing, for the in vivo analyses. The mice were randomly divided into eight groups. They were administered HCT116 and HCT116-OXR cells to prepare colon cancer xenograft models and then treated with PBS, LBP (50 mg/kg), OXA (10 mg/kg), or LBP + OXA (50 mg/kg + 10 mg/kg). The tumor weight and volume of treated model mice were measured, and organ toxicity was evaluated using hematoxylin and eosin staining. The expression levels of PMI, ABCG2, PI3K, and AKT proteins were then assessed using immunohistochemistry. Moreover, PMI and ABCG2 expression levels were analyzed using immunofluorescence and western blotting. The active components and possible targets of LBP in colon cancer were explored using in silico analysis. GeneCards was used to identify CC targets, and an online Venn analysis tool was used to determine intersection targets between these and LBP active components. The PPI network for intersection target protein interactions and the PPI network for interactions between the intersection target proteins and PMI was built using STRING and Cytoscape. To obtain putative targets of LBP in CC, we performed GO function enrichment and KEGG pathway enrichment analyses. Results: Compared with the HCT116-OXR blank treatment group, both invasion and migration abilities of HCT116-OXR cells were inhibited in the LBP + OXA (2.5 mg/mL LBP, 10 µΜ OXA) group (p < 0.05). Cells in the LBP + OXA (2.5 mg/mL LBP, 10 µΜ OXA) group were found to arrest in the G1 phase of the cell cycle. Knockdown of PMI was found to downregulate PI3K, AKT, and Bcl-2 (p < 0.05), while it was found to upregulate Bax (p < 0.05). After treatment with L. barbarum polysaccharide, 40 colon cancer subcutaneous tumor models showed a decrease in tumor size. There was no difference in the liver index after LBP treatment (p > 0.05). However, the spleen index decreased in the OXA and LBP + OXA groups (p < 0.05), possibly as a side effect of oxaliplatin. Immunohistochemistry, immunofluorescence, and western blotting showed that LBP + OXA treatment decreased PMI and ABCG2 expression levels (p < 0.05). Moreover, immunohistochemistry showed that LBP + OXA treatment decreased the expression levels of PI3K and AKT (p < 0.05). Network pharmacology analysis revealed 45 active LBP components, including carotenoids, phenylpropanoids, quercetin, xanthophylls, and other polyphenols. It also revealed 146 therapeutic targets of LBP, including AKT, SRC, EGFR, HRAS, STAT3, and MAPK3. KEGG pathway enrichment analysis showed that the LBP target proteins were enriched in pathways, including cancer-related signaling pathways, PI3K/AKT signaling pathway, and IL-17 signaling pathways. Finally, molecular docking experiments revealed that the active LBP components bind well with ABCG2 and PMI. conclusion: Our in vitro experiments showed that PMI knockdown downregulated PI3K, AKT, and Bcl-2 and upregulated Bax. This finding confirms that PMI plays a role in drug resistance by regulating the PI3K/AKT pathway and lays a foundation to study the mechanism underlying the reversal of colon cancer cell drug resistance by the combination of LBP and OXA. Our in vivo experiments showed that LBP combined with oxaliplatin could inhibit tumor growth. LBP showed no hepatic or splenic toxicity. LBP combined with oxaliplatin could downregulate the expression levels of PMI, ABCG2, PI3K, and AKT; it may thus have positive significance for the treatment of advanced metastatic colon cancer. Our network pharmacology analysis revealed the core targets of LBP in the treatment of CC as well as the pathways they are enriched in. It further verified the results of our in vitro and in vivo experiments, showing the involvement of multi-component, multi-target, and multi-pathway synergism in the drug-reversing effect of LBP in CC. Overall, the findings of the present study provide new avenues for the future clinical treatment of CC.

Carbon Dot-Mediated Photodynamic Treatment Improves the Quality Attributes of Post-Harvest Goji Berries (Lycium barbarum L.) via Regulating the Antioxidant System.

Carbon dots (CDs) have been proposed as photosensitizers in photodynamic treatment (PDT), owing to their excellent biological attributes and budding fruit preservation applications. In the present study, CDs (4.66 nm) were synthesized for photodynamic treatment to improve the quality attributes in post-harvest goji berries. The prepared CDs extended the storage time of the post-harvest goji berries by 9 d. The CD-mediated PDT postponed the hardness and decay index loss, reduced the formation of malondialdehyde (MDA), hydrogen peroxide (H2O2), and superoxide anion (O2•-) significantly, and delayed the loss of vital nutrients like the total protein, phenols, and flavonoids. The CD-mediated PDT improved the catalase (CAT), ascorbate peroxidase (APX), peroxidase (POD), phenylalanine ammonia-lyase (PAL), glutathione reductase (GR), and superoxide dismutase (SOD) activities, but did not improve polyphenol oxidase (PPO) activity. In addition, The CD-mediated PDT induced the accumulation of ascorbic acid (ASA) and glutathione (GSH). Overall, a CD-mediated PDT could extend the storage time and augment the quality attributes in post-harvest fresh goji berries by regulating the antioxidant system.