Diagnostic Value of Artificial Intelligence in Minimal Breast Lesions Based on Real-Time Dynamic Ultrasound Imaging.

Purpose: : To explore the diagnostic value of artificial intelligence (AI)-based on real-time dynamic ultrasound imaging system for minimal breast lesions. Patients and Methods: Minimal breast lesions with a maximum diameter of ≤10mm were selected in this prospective study. The ultrasound equipment and AI system were activated Simultaneously. The ultrasound imaging video is connected to the server of AI system to achieve simultaneous output of AI and ultrasound scanning. Dynamic observation of breast lesions was conducted via ultrasound. And these lesions were evaluated and graded according to the Breast Imaging Reporting and Data System (BI-RADS) classification system through deep learning (DL) algorithms in AI. Surgical pathology was taken as the gold standard, and ROC curves were drawn to determine the area under the curve (AUC) and the optimal threshold values of BI-RADS. The diagnostic efficacy was compared with the use of a BI-RADS category >3 as the threshold for clinically intervening in diagnosing minimal breast cancers. Results: 291 minimal breast lesions were enrolled in the study, of which 228 were benign (78.35%) and 63 were malignant (21.65%). The AUC of the ROC curve was 0.833, with the best threshold value >4A. When using >BI-RADS 3 and >BI-RADS 4A as threshold values, the sensitivity and negative predictive value for minimal breast cancers were higher for >BI-RADS 3 than >BI-RADS 4A (100% vs 65.08%, 100% vs 89.91%, P values <0.001). However, the corresponding specificity, positive predictive value, and accuracy were lower than those for >BI-RADS 4A (42.11% vs 85.96%, 32.31% vs 56.16%, and 54.64% vs 81.44%, P values <0.001). Conclusion: The AI-based real-time dynamic ultrasound imaging system shows good capacity in diagnosing minimal breast lesions, which is helpful for early diagnosis and treatment of breast cancer, and improves the prognosis of patients. However, it still results in some missed diagnoses and misdiagnoses of minimal breast cancers.

The siEGFR nanoplexes for the enhanced brain glioma treatment: Endoplasmic reticulum biomimetic strategy to induce homing effect and non-degradable intracellular transport.

The epidermal growth factor receptor (EGFR) plays a pivotal role in tumor progression and is an essential therapeutic target for treating malignant gliomas. Small interfering RNA (siRNA) has the potential to selectively degrade EGFR mRNA, yet its clinical utilization is impeded by various challenges, such as inefficient targeting and limited escape from lysosomes. Our research introduces polyethylene glycol (PEG) and endoplasmic reticulum membrane-coated siEGFR nanoplexes (PEhCv/siEGFR NPs) as an innovative approach to brain glioma therapy by overcoming several obstacles: 1) Tumor-derived endoplasmic reticulum membrane modifications provide a homing effect, facilitating targeted accumulation and cellular uptake; 2) Endoplasmic reticulum membrane proteins mediate a non-degradable "endosome-Golgi-endoplasmic reticulum" transport pathway, circumventing lysosomal degradation. These nanoplexes demonstrated significantly enhanced siEGFR gene silencing in both in vitro and in vivo U87 glioma models. The findings of this study pave the way for the advanced design and effective application of nucleic acid-based therapeutic nanocarriers.

Dihydroartemisinin Regulated the MMP-Mediated Cellular Microenvironment to Alleviate Rheumatoid Arthritis.

Rheumatoid arthritis (RA) is an autoimmune disease with features of synovial inflammation, cartilage erosion, bone destruction, and pain and is currently lacking a satisfactory treatment strategy. Dihydroartemisinin (DHA), the active metabolite of artemisinin, has exhibited outstanding suppressive effects on RA without obvious side effects. However, the underlying mechanisms remain unclear, which limits its further clinical application. The purpose of this study is to reveal the pharmacodynamic mechanism of DHA against RA by means of a combination of single-cell RNA sequencing (RNA-seq), proteomics, as well as transcriptomics both in vivo and in vitro. In our results, DHA effectively reduced the degree of redness, swelling, and pain in RA rats and dramatically changed the synovial tissue microenvironment under the pathological state. Within this microenvironment, fibroblasts, macrophages, B cells, and endothelial cells were the major affected cell types, primarily through DHA targeting the extracellular matrix (ECM) structural constituent signaling pathway. In addition, we confirmed that DHA regulated the ECM by modulating matrix metalloproteinase 2 (MMP2) and MMP3 in the synovial tissue of RA rats. Moreover, DHA induced apoptosis in MH7A cells, further validating the bioinformatics data. In conclusion, DHA effectively reduced the inflammatory response and improved the immune microenvironment in synovial tissue by inhibiting MMP2 and MMP3. Our findings provide a basis for the application of DHA in the treatment of RA.

CACER: Clinical concept Annotations for Cancer Events and Relations.

OBJECTIVE: Clinical notes contain unstructured representations of patient histories, including the relationships between medical problems and prescription drugs. To investigate the relationship between cancer drugs and their associated symptom burden, we extract structured, semantic representations of medical problem and drug information from the clinical narratives of oncology notes. MATERIALS AND METHODS: We present Clinical concept Annotations for Cancer Events and Relations (CACER), a novel corpus with fine-grained annotations for over 48 000 medical problems and drug events and 10 000 drug-problem and problem-problem relations. Leveraging CACER, we develop and evaluate transformer-based information extraction models such as Bidirectional Encoder Representations from Transformers (BERT), Fine-tuned Language Net Text-To-Text Transfer Transformer (Flan-T5), Large Language Model Meta AI (Llama3), and Generative Pre-trained Transformers-4 (GPT-4) using fine-tuning and in-context learning (ICL). RESULTS: In event extraction, the fine-tuned BERT and Llama3 models achieved the highest performance at 88.2-88.0 F1, which is comparable to the inter-annotator agreement (IAA) of 88.4 F1. In relation extraction, the fine-tuned BERT, Flan-T5, and Llama3 achieved the highest performance at 61.8-65.3 F1. GPT-4 with ICL achieved the worst performance across both tasks. DISCUSSION: The fine-tuned models significantly outperformed GPT-4 in ICL, highlighting the importance of annotated training data and model optimization. Furthermore, the BERT models performed similarly to Llama3. For our task, large language models offer no performance advantage over the smaller BERT models. CONCLUSIONS: We introduce CACER, a novel corpus with fine-grained annotations for medical problems, drugs, and their relationships in clinical narratives of oncology notes. State-of-the-art transformer models achieved performance comparable to IAA for several extraction tasks.

Ecological environmental flow estimation for rivers with complicated hydraulic conditions.

Estimating ecological environmental flow in tidal rivers is one of the major challenges for sustainable water resource management in estuaries and river basins. This paper presents an ecological environmental flow framework that was developed to accommodate highly dynamic medium tidal estuaries found along the Yellow Sea coast of China. The framework not only proposes a method of water quality-based ecological flow for tidal gate-controlled rivers but also proposes a method of water demand for scouring and silting to protect ports in coastal viscous sediment environments. The framework integrates the instream water requirements of water quality, sediment and basic ecological flow, and considers the temporal and spatial variation differences for the environmental flow requirements of tidal rivers. This study emphasizes the significance and necessity of continuous monitoring of ecological data in determining the environmental flow of tidal rivers. The output of this study could provide vital references for decision-making and management of the water resource allocation and ecological protection in tidal rivers.

X-linked severe combined immunodeficiency complicated by disseminated bacillus Calmette-Guérin disease caused by a novel pathogenic mutation in exon 3 of the IL2RG gene: a case report and literature review.

X-linked severe combined immunodeficiency (X-SCID), caused by mutations in the gamma-chain gene of the interleukin-2 receptor (IL2RG), is a prevalent form of SCID characterized by recurrent and fatal opportunistic infections that occur early in life. The incidence of disseminated bacillus Calmette-Guérin (BCG) disease among children with SCID is much higher than in the general population. Here, we report the case of a 4-month-old male infant who presented with subcutaneous induration, fever, an unhealed BCG vaccination site, and hepatosplenomegaly. Metagenomic next-generation sequencing in blood, and the detection of gastric juice and skin nodule pus all confirmed the infection of Mycobacterium tuberculosis. Lymphocyte subset analysis confirmed the presence of T-B+NK immunodeficiency. Whole-exome and Sanger sequencing revealed a novel microdeletion insertion mutation (c.316_318delinsGTGAT p.Leu106ValfsTer42) in the IL2RG gene, resulting in a rare shift in the amino acid sequence of the coding protein. Consequently, the child was diagnosed with X-SCID caused by a novel mutation in IL2RG, complicated by systemic disseminated BCG disease. Despite receiving systemic anti-infection treatment and four days of hospitalization, the patient died three days after discharge. To the best of our knowledge, this specific IL2RG mutation has not been previously reported. In our systemic review, we outline the efficacy of systemic anti-tuberculosis therapy, hematopoietic stem cell transplantation, and gene therapy in children with SCID and BCG diseases caused by IL2RG gene mutation.

Effects of Glycosylation Combined with Phosphate Treatment on the Allergenicity and Structure of Tropomyosin in Litopenaeus vannamei.

Tropomyosin (TM) is the main allergen in shrimp (Litopenaeus vannamei). In this study, the effects of allergenicity and structure of TM by glycosylation (GOS-TM), phosphate treatment (SP-TM), and glycosylation combined with phosphate treatment (GOS-SP-TM) were investigated. Compared to GOS-TM and SP-TM, the IgG/IgE binding capacity of GOS-SP-TM was significantly decreased with 63.9 ± 2.0 and 49.7 ± 2.7%, respectively. Meanwhile, the α-helix content reduced, surface hydrophobicity increased, and 10 specific amino acids (K30, K38, S39, K48, K66, K74, K128, K161, S210, and K251) were modified by glycosylation on six IgE linear epitopes of GOS-SP-TM. In the BALB/c mice allergy model, GOS-SP-TM could significantly reduce the levels of specific IgE, IgG1, and CD4+IL-4+, while the levels of IgG2a, CD4+CD25+Foxp3+, and CD4+IFN-γ+ were increased, which equilibrated Th1 and Th2 cells, thus alleviating allergic symptoms. These results indicated that glycosylation combined with phosphate treatment can provide a new insight into developing hypoallergenic shrimp food.

Association between caffeine intake from foods and beverages in the diet and hearing loss in United States adults.

Background: Hearing loss (HL) is the third most prevalent condition, significantly affecting individuals and society. Recent research has explored the potential impact of nutrition, particularly caffeine intake, on HL. While some studies focus on coffee, caffeine intake should be assessed across all dietary sources. This study examines the association between dietary caffeine intake and HL. Methods: Our cross-sectional study included 6,082 participants from the National Health and Nutrition Examination Survey (NHANES). Participants were divided into two groups based on their median caffeine intake: low and high. The study investigated two types of HL: speech-frequency hearing loss (SFHL) and high-frequency hearing loss (HFHL). Binary logistic regression analyzed the correlation between caffeine intake and HL, and a restricted cubic spline (RCS) model assessed potential non-linear associations. Subgroup analyses were also conducted. Results: High caffeine intake was associated with significantly higher rates of SFHL and HFHL compared to low intake (SFHL: 15.4% vs. 10%, HFHL: 30.5% vs. 20.6%, both p < 0.001). Unadjusted logistic regression showed a higher likelihood of SFHL (OR[95%CI] = 1.65[1.41-1.92]) and HFHL (OR[95%CI] = 1.69[1.50-1.90]) in high caffeine consumers. After adjusting for confounders, high caffeine intake remained significantly associated with SFHL (OR[95%CI] = 1.35[1.09-1.66]) but not HFHL (OR[95%CI] = 1.14[0.96-1.35]). The RCS model indicated a linear increase in the risk of SFHL and HFHL with higher caffeine intake (non-linear p = 0.229 for SFHL, p = 0.894 for HFHL). Subgroup analysis revealed that increased caffeine intake was linked to higher SFHL and HFHL risks in participants under 65 years but not in those 65 years and older (SFHL: p for interaction = 0.002; HFHL: p for interaction <0.001). Conclusion: Our study indicates a strong correlation between dietary caffeine intake and the risk of HL in American adults, particularly those under 65. High caffeine intake was linked to an increased risk of SFHL, but not HFHL, after adjusting for relevant variables.

Machine learning model for non-alcoholic steatohepatitis diagnosis based on ultrasound radiomics.

BACKGROUND: Non-Alcoholic Steatohepatitis (NASH) is a crucial stage in the progression of Non-Alcoholic Fatty Liver Disease(NAFLD). The purpose of this study is to explore the clinical value of ultrasound features and radiological analysis in predicting the diagnosis of Non-Alcoholic Steatohepatitis. METHOD: An SD rat model of hepatic steatosis was established through a high-fat diet and subcutaneous injection of CCl4. Liver ultrasound images and elastography were acquired, along with serum data and histopathological results of rat livers.The Pyradiomics software was used to extract radiomic features from 2D ultrasound images of rat livers. The rats were then randomly divided into a training set and a validation set, and feature selection was performed through dimensionality reduction. Various machine learning (ML) algorithms were employed to build clinical diagnostic models, radiomic models, and combined diagnostic models. The efficiency of each diagnostic model for diagnosing NASH was evaluated using Receiver Operating Characteristic (ROC) curves, Clinical Decision Curve Analysis (DCA), and calibration curves. RESULTS: In the machine learning radiomic model for predicting the diagnosis of NASH, the Area Under the Curve (AUC) of ROC curve for the clinical radiomic model in the training set and validation set were 0.989 and 0.885, respectively. The Decision Curve Analysis revealed that the clinical radiomic model had the highest net benefit within the probability threshold range of > 65%. The calibration curve in the validation set demonstrated that the clinical combined radiomic model is the optimal method for diagnosing Non-Alcoholic Steatohepatitis. CONCLUSION: The combined diagnostic model constructed using machine learning algorithms based on ultrasound image radiomics has a high clinical predictive performance in diagnosing Non-Alcoholic Steatohepatitis.

Deep learning-assisted diagnosis of benign and malignant parotid gland tumors based on automatic segmentation of ultrasound images: a multicenter retrospective study.

Objectives: To construct deep learning-assisted diagnosis models based on automatic segmentation of ultrasound images to facilitate radiologists in differentiating benign and malignant parotid tumors. Methods: A total of 582 patients histopathologically diagnosed with PGTs were retrospectively recruited from 4 centers, and their data were collected for analysis. The radiomics features of six deep learning models (ResNet18, Inception_v3 etc) were analyzed based on the ultrasound images that were obtained under the best automatic segmentation model (Deeplabv3, UNet++, and UNet). The performance of three physicians was compared when the optimal model was used and not. The Net Reclassification Index (NRI) and Integrated Discrimination Improvement (IDI) were utilized to evaluate the clinical benefit of the optimal model. Results: The Deeplabv3 model performed optimally in terms of automatic segmentation. The ResNet18 deep learning model had the best prediction performance, with an area under the receiver-operating characteristic curve of 0.808 (0.694-0.923), 0.809 (0.712-0.906), and 0.812 (0.680-0.944) in the internal test set and external test sets 1 and 2, respectively. Meanwhile, the optimal model-assisted clinical and overall benefits were markedly enhanced for two out of three radiologists (in internal validation set, NRI: 0.259 and 0.213 [p = 0.002 and 0.017], IDI: 0.284 and 0.201 [p = 0.005 and 0.043], respectively; in external test set 1, NRI: 0.183 and 0.161 [p = 0.019 and 0.008], IDI: 0.205 and 0.184 [p = 0.031 and 0.045], respectively; in external test set 2, NRI: 0.297 and 0.297 [p = 0.038 and 0.047], IDI: 0.332 and 0.294 [p = 0.031 and 0.041], respectively). Conclusions: The deep learning model constructed for automatic segmentation of ultrasound images can improve the diagnostic performance of radiologists for PGTs.

Geochemical characteristics and detrital zircon U-Pb ages of the Yimin Formation, Kelulun Depression, Hailar Basin and constraints on uranium mineralization.

The sandstone uranium deposits in the Kelulun Depression are the first commercially viable uranium deposits discovered in the Hailar Basin and the ore-bearing strata corresponding to the Lower Cretaceous Yimin Formation. However, the source of sedimentary matter, uranium source conditions, and uranium mineralization processes in the region have not been characterized. Accordingly, we analyzed the lithology, whole-rock geochemistry, zircon U-Pb ages, and trace elements of the Yimin Formation sandstones. The Yimin Formation sandstones were primarily composed of detrital grains with low compositional maturity. A geochemical analysis indicated that the parent rocks are felsic igneous rocks formed at an active continental margin with a moderately high degree of weathering. The detrital zircon U-Pb ages of the Yimin Formation 215-287 Ma with a peak at 230-260 Ma. Based on chronological, geochemical, and lithological data, we conclude that the Yimin Formation matter is derived from the Adunchulu Uplift on the western side of the Kelulun Depression and its parent rocks are Triassic granites. The Adunchulu uplift since the late Early Cretaceous and weathering and denudation of its uranium-rich granites provided ample matter and uranium. Therefore, the Kelulun Depression is a promising area for the exploration of sandstone uranium deposits.

Adiposity in Chinese people with type 1 diabetes.

Adiposity, synonymous with obesity, is prevalent among both children and adults with type 1 diabetes in China. Recent literature underscored the patho-physiological and socioeconomic factors associated with adiposity, and consistently highlighted its impact on cardiovascular, kidney, and metabolic diseases among Chinese individuals with type 1 diabetes. Addressing and managing adiposity in individuals with type 1 diabetes are complicated and entail comprehensive approaches including lifestyle modifications, cognitive-behavioral therapy, insulin dose titration, and other diabetes treatment medications. The condition calls for coordination among policymakers, researchers, clinicians, and patients.

Cube is a good form: Hyperspectral band selection via multi-dimensional and high-order structure preserved clustering.

As an effective strategy for reducing the noisy and redundant information for hyperspectral imagery (HSI), hyperspectral band selection intends to select a subset of original hyperspectral bands, which boosts the subsequent different tasks. In this paper, we introduce a multi-dimensional high-order structure preserved clustering method for hyperspectral band selection, referred to as MHSPC briefly. By regarding original hyperspectral images as a tensor cube, we apply the tensor CP (CANDECOMP/PARAFAC) decomposition on it to exploit the multi-dimensional structural information as well as generate a low-dimensional latent feature representation. In order to capture the local geometrical structure along the spectral dimension, a graph regularizer is imposed on the new feature representation in the lower dimensional space. In addition, since the low rankness of HSIs is an important global property, we utilize a nuclear norm constraint on the latent feature representation matrix to capture the global data structure information. Different to most of previous clustering based hyperspectral band selection methods which vectorize each band as a vector without considering the 2-D spatial information, the proposed MHSPC can effectively capture the spatial structure as well as the spectral correlation of original hyperspectral cube in both local and global perspectives. An efficient alternatively updating algorithm with theoretical convergence guarantee is designed to solve the resultant optimization problem, and extensive experimental results on four benchmark datasets validate the effectiveness of the proposed MHSPC over other state-of-the-arts.

Large-scale photonic computing with nonlinear disordered media.

Neural networks find widespread use in scientific and technological applications, yet their implementations in conventional computers have encountered bottlenecks due to ever-expanding computational needs. Photonic computing is a promising neuromorphic platform with potential advantages of massive parallelism, ultralow latency and reduced energy consumption but mostly for computing linear operations. Here we demonstrate a large-scale, high-performance nonlinear photonic neural system based on a disordered polycrystalline slab composed of lithium niobate nanocrystals. Mediated by random quasi-phase-matching and multiple scattering, linear and nonlinear optical speckle features are generated as the interplay between the simultaneous linear random scattering and the second-harmonic generation, defining a complex neural network in which the second-order nonlinearity acts as internal nonlinear activation functions. Benchmarked against linear random projection, such nonlinear mapping embedded with rich physical computational operations shows improved performance across a large collection of machine learning tasks in image classification, regression and graph classification. Demonstrating up to 27,648 input and 3,500 nonlinear output nodes, the combination of optical nonlinearity and random scattering serves as a scalable computing engine for diverse applications.

FBXL18 is required for ovarian cancer cell proliferation and migration through activating AKT signaling.

BACKGROUND: F-box and leucine-rich repeat protein 18 (FBXL18) is an F-box protein that functions as an E3-ubiquitin ligase, and it plays pivotal roles in multiple disease processes. However, its role and underlying mechanism in ovarian cancer (OC) are still unknown. We investigated the impact and mechanism of FBXL18 in OC cell growth and tumorigenesis. METHODS: Silent interfering RNAs and overexpression plasmids were employed to knock down and overexpress FBXL18 in OC cells (A2780 and OVCAR3). CCK-8, colony formation, cell migration, and nude mouse xenograft assays were used to assess the effect of FBXL18 on OC cell proliferation and migration. Western blotting and co-immunoprecipitation followed by ubiquitination assays were performed to detect the mechanism of the FBXL18/AKT axis in OC. RESULTS: FBXL18 knockdown inhibited OC cell proliferation and migration, whereas FBXL18 overexpression showed the opposite results. Phosphorylated-AKT (S473) protein expression was increased by FBXL18 overexpression and markedly decreased after phosphorylated-AKT inhibitor (MK-2206) treatment. Co-immunoprecipitation assays demonstrated that FBXL18 strongly interacted with AKT in OC cells. Ubiquitination assays revealed that FBXL18 promoted K63-linked AKT ubiquitination to activate AKT. MK-2206 treatment reversed the increase in proliferation and migration of OC cells induced by FBXL18 overexpression. CONCLUSIONS: FBXL18 promoted OC cell proliferation and migration and facilitated OC tumorigenesis. Mechanically, FBXL18 interacted with AKT and promoted K63-linked ubiquitination of AKT to activate AKT in OC cells. Our study revealed that the FBXL18/AKT axis plays a crucial role in the OC process, indicating that FBXL18 may be a valuable target for OC diagnosis and treatment.

Preparation of chitosan photodynamic antibacterial film loaded with VK3 complex in the preservation of chilled mutton.

To effectively utilize the photodynamic antibacterial ability of vitamin K3 (VK3), by solving the photothermal instability of VK3, it was combined with natural polymers to apply the preservation of chilled mutton. We encapsulated VK3 in the (2-Hydroxypropyl)-ß-cyclodextrin (HP-ß-CD) to construct VK3-HP-ß-CD complex and then introduced the complex to chitosan (CS) and polyvinyl alcohol (PVA) to fabricate an antibacterial film (CS/PVA-VK3-HP-ß-CD film). Through the packaging performance test of the film, the content of VK3-HP-ß-CD was an important factor determining the properties of film including tensile strength, elongation at break, water vapor permeability, water content and water contact angle. Meanwhile, CS/PVA-VK3-HP-ß-CD films could continuously release ROS under light and suspended in dark, thus realizing >99 % antibacterial rate for Escherichia coli and Staphylococcus aureus. In the application experiment of chilled mutton, CS/PVA-VK3-1-HP-ß-CD film could significantly inhibit the increase of total viable count (TVC), pH value (pH) and total volatile base nitrogen (TVB-N) of chilled mutton, and extended its shelf life for at least 12 days. These results indicated that the CS/PVA film with the VK3-HP-ß-CD complex might have promising potential as an antibacterial material for packaging and preserving food.

Rosemary leaf powder improves egg quality, antioxidant status, gut barrier function, and cecal microbiota and metabolites of late-phase laying hens.

This study sought to determine the effects of rosemary leaf powder (RP) on laying performance, egg quality, serum indices, gut barrier function, and cecal microbiota and metabolites of late-phase laying hens. A total of 84 "Jing Tint 6" laying hens at 65-week old were randomly divided into 2 groups and fed either a basal diet (CON) or a basal diet supplemented with 0.3% RP. Our study revealed that RP improved the Haugh unit and decreased yolk n-6/n-3 polyunsaturated fatty acid (PUFA) ratio of laying hens, increased serum superoxide dismutase (SOD), jejunal activities of SOD and catalase (CAT), and jejunal zonula occludens-1 (ZO-1) expression, as well as decreased serum tumor necrosis factor-α (TNF-α) level and jejunal TNF-α mRNA expression. Rosemary leaf powder markedly enhanced (P < 0.05) cecal abundances of Rikenellaceae, Rikenellaceae_RC9_gut_group, and Turicibacter, tended to promote (P = 0.076) butyrate concentration, and reduced (P < 0.05) cecal abundances of Erysipelatoclostridiaceae, Sutterellaceae, Fusobacteriaceae, Campylobacteraceae, Sutterella, Campylobacter, and Fusobacterium, which were closely linked with Haugh unit, yolk n-6/n-3 PUFA ratio, serum SOD and TNF-α. In addition, RP altered the metabolic functions of cecal microbiota and enhanced the abundances of butyrate-synthesizing enzymes, including lysine 2,3-aminomutase, ß-lysine 5,6-aminomutase, and 3-oxoacid CoA-transferase. Together, 0.3% RP has the potential to enhance egg quality by partially modulating serum antioxidant status, jejunal barrier function, and cecal microbiota structure and metabolites, indicating that RP could be considered a promising feed additive to promote the production performance of late-phase laying hens.

Allergenicity and Linear Epitope Analysis of Scy p 8, an Allergen from Mud Crab.

Scy p 8 (triosephosphate isomerase) as a crab allergen in inducing distinct T-helper (Th) cell differentiation and a linear epitope associated with allergenicity remain elusive. In this study, mice sensitized with Scy p 8 exhibited significantly upregulated levels of IgE, IgG1, and IL-4 release, inducing a Th2 immune response. Moreover, the release of IFN-γ (Th1) and the levels of Treg cells were downregulated, while IL-17A (Th17) was upregulated, indicating that Scy p 8 disrupted the Th1/Th2 balance and Th17/Treg balance in mice. Furthermore, bioinformatics prediction and serum samples from crab-allergic patients and mice enabled the discovery of 8 linear epitopes of Scy p 8. Meanwhile, the analysis of peptide similarity and tertiary superposition revealed that 8 epitopes of Scy p 8 exhibited conservation across various species, potentially resulting in cross-reactivity. These findings possess the potential to enhance the comprehension of crab allergens, thereby establishing a foundation for investigating cross-reactivity.

The biomedical application of inorganic metal nanoparticles in aging and aging-associated diseases.

Aging and aging-associated diseases (AAD), including neurodegenerative disease, cancer, cardiovascular diseases, and diabetes, are inevitable process. With the gradual improvement of life style, life expectancy is gradually extended. However, the extended lifespan has not reduced the incidence of disease, and most elderly people are in ill-health state in their later years. Hence, understanding aging and AAD are significant for reducing the burden of the elderly. Inorganic metal nanoparticles (IMNPs) predominantly include gold, silver, iron, zinc, titanium, thallium, platinum, cerium, copper NPs, which has been widely used to prevent and treat aging and AAD due to their superior properties (essential metal ions for human body, easily synthesis and modification, magnetism). Therefore, a systematic review of common morphological alternations of senescent cells, altered genes and signal pathways in aging and AAD, and biomedical applications of IMNPs in aging and AAD is crucial for the further research and development of IMNPs in aging and AAD. This review focus on the existing research on cellular senescence, aging and AAD, as well as the applications of IMNPs in aging and AAD in the past decade. This review aims to provide cutting-edge knowledge involved with aging and AAD, the application of IMNPs in aging and AAD to promote the biomedical application of IMNPs in aging and AAD.

Gambogic acid exhibits promising anticancer activity by inhibiting the pentose phosphate pathway in lung cancer mouse model.

BACKGROUND: The pentose phosphate pathway (PPP) plays a crucial role in the material and energy metabolism in cancer cells. Targeting 6-phosphogluconate dehydrogenase (6PGD), the rate-limiting enzyme in the PPP metabolic process, to inhibit cellular metabolism is an effective anticancer strategy. In our previous study, we have preliminarily demonstrated that gambogic acid (GA) induced cancer cell death by inhibiting 6PGD and suppressing PPP at the cellular level. However, it is unclear whether GA could suppress cancer cell growth by inhibiting PPP pathway in mouse model. PURPOSE: This study aimed to confirm that GA as a covalent inhibitor of 6PGD protein and to validate that GA suppresses cancer cell growth by inhibiting the PPP pathway in a mouse model. METHODS: Cell viability was detected by CCK-8 assays as well as flow cytometry. The protein targets of GA were identified using a chemical probe and activity-based protein profiling (ABPP) technology. The target validation was performed by in-gel fluorescence assay, the Cellular Thermal Shift Assay (CETSA). A lung cancer mouse model was constructed to test the anticancer activity of GA. RNA sequencing was performed to analyze the global effect of GA on gene expression. RESULTS: The chemical probe of GA exhibited high biological activity in vitro. 6PGD was identified as one of the binding proteins of GA by ABPP. Our findings revealed a direct interaction between GA and 6PGD. We also found that the anti-cancer activity of GA depended on reactive oxygen species (ROS), as evidenced by experiments on cells with 6PGD knocked down. More importantly, GA could effectively reduce the production of the two major metabolites of the PPP in lung tissue and inhibit cancer cell growth in the mouse model. Finally, RNA sequencing data suggested that GA treatment significantly regulated apoptosis and hypoxia-related physiological processes. CONCLUSION: These results demonstrated that GA was a covalent inhibitor of 6PGD protein. GA effectively suppressed cancer cell growth by inhibiting the PPP pathway without causing significant side effects in the mouse model. Our study provides in vivo evidence that elucidates the anticancer mechanism of GA, which involves the inhibition of 6PGD and modulation of cellular metabolic processes.