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This study aimed to examine the unique longitudinal role of theory of mind (ToM) on reading comprehension among primary school children, while controlling for other influencing factors. It also examined how this impact varies by grade, text genre, and processing level. A sample of 430 Chinese children in Grades 2, 4, and 6 was observed over a period of 6 months. For each grade, longitudinal hierarchical regression analyses were conducted to examine the unique contribution of ToM to reading comprehension across different text genres and processing levels. By controlling for variables such as reading frequency, decoding, vocabulary, nonverbal intelligence, listening comprehension, and executive function, ToM cannot predict the overall reading comprehension of children in Grade 2, but can predict it of children in Grades 4 and 6. For specific text genres, ToM predicted narrative comprehension in both Grades 4 and 6 and expository comprehension only in Grade 6. For specific processing levels, ToM predicted advanced comprehension in Grades 4 and 6 and basic comprehension only in Grade 6. Furthermore, ToM predicted advanced narrative and expository comprehension in Grades 4 and 6, and basic expository comprehension in Grade 6. These findings highlight the important role of ToM in reading comprehension development, particularly in the middle and upper stages of primary school. These results lay the foundation for further research on exploring the underlying mechanisms of the impact of ToM on reading comprehension and provide a social-cognitive perspective to enhance the development of reading comprehension.

In order to address the needs of the continually growing number of Chinese language learners, the present study developed and presented initial validation of a 100-item Chinese vocabulary proficiency test (CVPT) for learners of Chinese as a second/foreign language (CS/FL) using Item Response Theory among 170 CS/FL learners from Indonesia and 354 CS/FL learners from Thailand. Participants were required to translate or explain the meanings of the Chinese words using Indonesian or Thai. The results provided preliminary evidence for the construct validity of the CVPT for measuring CS/FL learners' receptive Chinese vocabulary knowledge in terms of content, substantive, structural, generalizability, and external aspects. The translation-based CVPT was an attempt to measure CS/FL learners' vocabulary proficiency by exploring their performance in a vocabulary translation task, potentially revealing test-takers' high-degree vocabulary knowledge. Such a CVPT could be useful for Chinese vocabulary instruction and designing future Chinese vocabulary measurement tools.

Abstract Hepatocellular carcinoma (HCC) still presents poor prognosis with low overall survival rates and limited therapeutic options available. Recently, attention has been drawn to peptidomic analysis, an emerging field of proteomics for the exploration of new potential peptide drugs for the treatment of various diseases. However, research on the potential function of HCC peptides is lacking. Here, we analyzed the peptide spectrum in HCC tissues using peptidomic techniques and explored the potentially beneficial peptides involved in HCC. Changes in peptide profiles in HCC were examined using liquid chromatography-mass spectrometry (LC–MS/MS). Analyze the physicochemical properties and function of differently expressed peptides using bioinformatics. The effect of candidate functional peptides on HCC cell growth and migration was evaluated using the CCK-8, colony formation, and transwell assays. Transcriptome sequencing analysis and western blot were employed to delve into the mode of action of potential peptide on HCC. Peptidomic analysis of HCC tissue yielded a total of 8683 peptides, of which 452 exhibited up-regulation and 362 showed down-regulation. The peptides that were differentially expressed, according to bioinformatic analysis, were closely linked to carbon metabolism and the mitochondrial inner membrane. The peptide functional validation identified a novel peptide, PDLC (peptide derived from liver cancer), which was found to dramatically boost HCC cell proliferation through the Ras/Raf/MEK/ERK signaling cascade. Our research defined the peptide’s properties and pattern of expression in HCC and identified a novel peptide, PDLC, with a function in encouraging HCC progression, offering an entirely new potential therapeutic target the disease.

Abstract Chiral assemblies have become one of the most active research areas due to their versatility, playing an increasingly important role in bio-detection, imaging and therapy. In this work, chiral UCNPs/CuxOS@ZIF nanoprobes are prepared by encapsulating upconversion nanoparticles (UCNPs) and CuxOS nanoparticles (NPs) into zeolitic imidazolate framework-8 (ZIF-8). The novel excited-state energy distribution-modulated upconversion nanostructure (NaYbF4@NaYF4: Yb, Er) is selected as the fluorescence source and energy donor for highly efficient fluorescence resonance energy transfer (FRET). CuxOS NP is employed as chiral source and energy acceptor to quench upconversion luminescence (UCL) and provide circular dichroism (CD) signal. Utilizing the natural adsorption and sorting advantages of ZIF-8, the designed nanoprobe can isolate the influence of other common disruptors, thus achieve ultra-sensitive and highly selective UCL/CD dual-mode quantification of H2S in aqueous solution and in living cells. Notably, the nanoprobe is also capable of in vivo intra-tumoral H2S tracking. Our work highlights the multifunctional properties of chiral nanocomposites in sensing and opens a new vision and idea for the preparation and application of chiral nanomaterials in biomedical and biological analysis.

Abstract Lenvatinib, a multitarget kinase inhibitor, has been proven to be effective in the treatment of advanced hepatocellular carcinoma. It has been previously demonstrated that tumour associated macrophages (TAMs) in tumour tissues can promote HCC growth, invasion and metastasis. Furthermore, lenvatinib has certain immunomodulatory effects on the treatment of HCC. However, the role of lenvatinib in macrophage polarization during HCC treatment has not been fully explored. In this study, we used a variety of experimental methods both in vitro and in vivo to investigate the effect of lenvatinib on TAMs during HCC progression. This study is the first to show that lenvatinib can alter macrophage polarization in both humans and mice. Moreover, macrophages treated with lenvatinib in vitro displayed enhanced classically activated macrophages (M1) activity and suppressed liver cancer cell proliferation, invasion, and migration. Furthermore, during the progression of M1 macrophage polarization induced by lenvatinib, STAT-1 was the main target transcription factor, and inhibiting STAT-1 activity reversed the effect of lenvatinib. Overall, the present study provides a theoretical basis for the immunomodulatory function of lenvatinib in the treatment of HCC.

Abstract Immune checkpoint blockade therapy targeting the programmed death-1(PD-1) pathway has shown remarkable efficacy and durable response in patients with various cancer types. Early prediction of therapeutic efficacy is important for optimizing treatment plans and avoiding potential side effects. In this work, we developed an efficient machine learning prediction method using routine hematologic and biochemical parameters to predict the efficacy of PD-1 combination treatment in Pan-Cancer patients. A total of 431 patients with nasopharyngeal carcinoma, esophageal cancer and lung cancer who underwent PD-1 checkpoint inhibitor combination therapy were included in this study. Patients were divided into two groups: progressive disease (PD) and disease control (DC) groups. Hematologic and biochemical parameters were collected before and at the third week of PD-1 therapy. Six machine learning models were developed and trained to predict the efficacy of PD-1 combination therapy at 8–12 weeks. Analysis of 57 blood biomarkers before and after three weeks of PD-1 combination therapy through statistical analysis, heatmaps, and principal component analysis did not accurately predict treatment outcome. However, with machine learning models, both the AdaBoost classifier and GBDT demonstrated high levels of prediction efficiency, with clinically acceptable AUC values exceeding 0.7. The AdaBoost classifier exhibited the highest performance among the 6 machine learning models, with a sensitivity of 0.85 and a specificity of 0.79. Our study demonstrated the potential of machine learning to predict the efficacy of PD-1 combination therapy based on changes in hematologic and biochemical parameters.

AIM: To describe the characteristics of peripapillary hyperreflective ovoid mass-like structure (PHOMS) in myopic children and to investigate factors associated with PHOMS. METHODS: This retrospective observational study included 101 eyes of 101 children (age ≤17y) with myopia. All included patients underwent comprehensive clinical examination. Optic nerve canal parameters, including disc diameter, optic nerve head (ONH) tilt angle, and border tissue angle were measured using serial enhanced-depth imaging spectral-domain optical coherence tomography (EDI-OCT). Based on the optic disc drusen consortium's definition of PHOMS, eyes were classified as PHOMS group and non-PHOMS group. PHOMS was categorized according to height. RESULTS: Sixty-seven (66.3%) eyes were found with PHOMS. Small PHOMS could only be detected by optical coherence tomography (OCT). Medium PHOMS could be seen with blurred optic disc borders corresponding to OCT. The most frequent location of PHOMS was at the nasosuperior (91%, 61 of 67 eyes) to ONH disc. The axial length and spherical equivalent were more myopic in the PHOMS group than in the non-PHOMS group (both P

Abstract Mitochondria play a crucial role in the progression of nasopharyngeal carcinoma (NPC). YME1L, a member of the AAA ATPase family, is a key regulator of mitochondrial function and has been implicated in various cellular processes and diseases. This study investigates the expression and functional significance of YME1L in NPC. YME1L exhibits significant upregulation in NPC tissues from patients and across various primary human NPC cells, while its expression remains relatively low in adjacent normal tissues and primary nasal epithelial cells. Employing genetic silencing through the shRNA strategy or knockout (KO) via the CRISPR-sgRNA method, we demonstrated that YME1L depletion disrupted mitochondrial function, leading to mitochondrial depolarization, reactive oxygen species (ROS) generation, lipid peroxidation, and ATP reduction within primary NPC cells. Additionally, YME1L silencing or KO substantially impeded cell viability, proliferation, cell cycle progression, and migratory capabilities, concomitant with an augmentation of Caspase-apoptosis activation in primary NPC cells. Conversely, ectopic YME1L expression conferred pro-tumorigenic attributes, enhancing ATP production and bolstering NPC cell proliferation and migration. Moreover, our findings illuminate the pivotal role of YME1L in Akt-mTOR activation within NPC cells, with Akt-S6K phosphorylation exhibiting a significant decline upon YME1L depletion but enhancement upon YME1L overexpression. In YME1L-silenced primary NPC cells, the introduction of a constitutively-active Akt1 mutant (caAkt1, at S473D) restored Akt-S6K phosphorylation, effectively ameliorating the inhibitory effects imposed by YME1L shRNA. In vivo studies revealed that intratumoral administration of YME1L-shRNA-expressing adeno-associated virus (AAV) curtailed subcutaneous NPC xenograft growth in nude mice. Furthermore, YME1L downregulation, concurrent with mitochondrial dysfunction and ATP reduction, oxidative injury, Akt-mTOR inactivation, and apoptosis induction were evident within YME1L-silenced NPC xenograft tissues. Collectively, these findings shed light on the notable pro-tumorigenic role by overexpressed YME1L in NPC, with a plausible mechanism involving the promotion of Akt-mTOR activation.

Abstract Background Deuterium metabolic imaging (DMI) has emerged as a promising non-invasive technique for studying metabolism in vivo. This review aims to summarize the current developments and discuss the futures in DMI technique in vivo. Methods A systematic literature review was conducted based on the PRISMA 2020 statement by two authors. Specific technical details and potential applications of DMI in vivo were summarized, including strategies of deuterated metabolites detection, deuterium-labeled tracers and corresponding metabolic pathways in vivo, potential clinical applications, routes of tracer administration, quantitative evaluations of metabolisms, and spatial resolution. Results Of the 2,248 articles initially retrieved, 34 were finally included, highlighting 2 strategies for detecting deuterated metabolites: direct and indirect DMI. Various deuterated tracers (e.g., [6,6′-2H2]glucose, [2,2,2′-2H3]acetate) were utilized in DMI to detect and quantify different metabolic pathways such as glycolysis, tricarboxylic acid cycle, and fatty acid oxidation. The quantifications (e.g., lactate level, lactate/glutamine and glutamate ratio) hold promise for diagnosing malignancies and assessing early anti-tumor treatment responses. Tracers can be administered orally, intravenously, or intraperitoneally, either through bolus administration or continuous infusion. For metabolic quantification, both serial time point methods (including kinetic analysis and calculation of area under the curves) and single time point quantifications are viable. However, insufficient spatial resolution remains a major challenge in DMI (e.g., 3.3-mL spatial resolution with 10-min acquisition at 3 T). Conclusions Enhancing spatial resolution can facilitate the clinical translation of DMI. Furthermore, optimizing tracer synthesis, administration protocols, and quantification methodologies will further enhance their clinical applicability. Relevance statement Deuterium metabolic imaging, a promising non-invasive technique, is systematically discussed in this review for its current progression, limitations, and future directions in studying in vivo energetic metabolism, displaying a relevant clinical potential. Key points • Deuterium metabolic imaging (DMI) shows promise for studying in vivo energetic metabolism. • This review explores DMI’s current state, limits, and future research directions comprehensively. • The clinical translation of DMI is mainly impeded by limitations in spatial resolution. Graphical Abstract

Abstract A risk factor for thyroid cancer (TC) may be a history of former cancer and cancer therapy. The precise risk of a second primary thyroid carcinoma has not yet been revealed. In this study, we evaluated standardized incidence ratios (SIRs) of second primary thyroid cancer (SPTC) with consideration of different conditions and further analyzed the clinicopathological characteristics and survival of these patients. The cohort was selected from the US Surveillance, Epidemiology, and End Results (SEER) Program between 1975 and 2019. The standardized incidence ratios, morbidity risk, clinicopathological features, and survival of second primary thyroid carcinoma were analyzed. Propensity score matching (PSM) was used to balance covariates. Kaplan–Meier method was performed to assess the survival outcomes. Overall, 7066 patients with SPTC and 83,113 patients with primary TC were identified. The SIR of TC in tumor patients was 1.51/10,000, statistically higher than the natural population (0.94/10,000, P

Abstract Purpose This study aimed to investigate the risk factors for liver disease comorbidity among older adults in eastern, central, and western China, and explored binary, ternary and quaternary co-morbid co-causal patterns of liver disease within a health ecological model. Method Basic information from 9,763 older adults was analyzed using data from the China Health and Retirement Longitudinal Study (CHARLS). LASSO regression was employed to identify significant predictors in eastern, central, and western China. Patterns of liver disease comorbidity were studied using association rules, and spatial distribution was analyzed using a geographic information system. Furthermore, binary, ternary, and quaternary network diagrams were constructed to illustrate the relationships between liver disease comorbidity and co-causes. Results Among the 9,763 elderly adults studied, 536 were found to have liver disease comorbidity, with binary or ternary comorbidity being the most prevalent. Provinces with a high prevalence of liver disease comorbidity were primarily concentrated in Inner Mongolia, Sichuan, and Henan. The most common comorbidity patterns identified were "liver-heart-metabolic", "liver-kidney", "liver-lung", and "liver-stomach-arthritic". In the eastern region, important combination patterns included "liver disease-metabolic disease", "liver disease-stomach disease", and "liver disease-arthritis", with the main influencing factors being sleep duration of less than 6 h, frequent drinking, female, and daily activity capability. In the central region, common combination patterns included "liver disease-heart disease", "liver disease-metabolic disease", and "liver disease-kidney disease", with the main influencing factors being an education level of primary school or below, marriage, having medical insurance, exercise, and no disabilities. In the western region, the main comorbidity patterns were "liver disease-chronic lung disease", "liver disease-stomach disease", "liver disease-heart disease", and "liver disease-arthritis", with the main influencing factors being general or poor health satisfaction, general or poor health condition, severe pain, and no disabilities. Conclusion The comorbidities associated with liver disease exhibit specific clustering patterns at both the overall and local levels. By analyzing the comorbidity patterns of liver diseases in different regions and establishing co-morbid co-causal patterns, this study offers a new perspective and scientific basis for the prevention and treatment of liver diseases.

Abstract Background The treatment of hepatocellular carcinoma (HCC) patients exhibiting high-risk characteristics (Vp4, and/or bile duct invasion, and/or tumor occupancy ≥ 50%) lacks standardized approaches and yields unfavorable results. This study endeavors to evaluate the safety, efficacy, and prognostic impacts of employing hepatic arterial infusion chemotherapy (HAIC), lenvatinib, and humanized programmed death receptor-1 (PD-1) in the treatment of high-risk HCC patients. Methods In this retrospective analysis, HCC patients with high-risk features were treated with either lenvatinib combined with PD-1 (LEN-PD1) or a combination of HAIC, lenvatinib, and PD-1 (HAIC-LEN-PD1). The study assessed the antitumor efficacy by calculating overall survival (OS), progression-free survival (PFS), objective response rate (ORR), and disease control rate (DCR). Treatment-related adverse events (TRAEs) were analyzed to assess the safety profiles. Results Between June 2019 and September 2022, a total of 61 patients were included in the LEN-PD1 group, while 103 patients were enrolled in the HAIC-LEN-PD1 group. The OS was 9.8 months in the LEN-PD1 group, whereas the HAIC-LEN-PD1 group exhibited a significantly longer median OS of 19.3 months (HR = 0.43, p

Food colloids refer to the common multicomponent heterogeneous dispersion systems found in foods, which can be divided into protein-based and polysaccharide-based colloids according to their components. Those colloids play an important role in food processing, such as thickening, emulsification, suspension, gelling, adhesion, water retention, stabilization and film formation, and are widely used in many fields such as the food processing industry. This article reviews the basic concept, composition, and classification of food colloids, with an emphasis on the functional properties of common polysaccharide-based colloids (carrageenan, agar, sodium alginate, Arabic gum, guar gum, konjac glucomannan, xanthan gum, gellan gum and curdlan) including rheological properties, gel properties, stability and combined use. Furthermore, this article also summarizes the current status and future perspectives of colloids based on polysaccharides from marine algae, plants and microbes in the food processing industry. We believe that this review will provide a theoretical basis for further development and utilization of polysaccharide-based food colloids.

Abstract Background Pathogenic missense variants in the dystrophin (DMD) gene are rarely reported in dystrophinopathies. Most DMD missense variants are of uncertain significance and their pathogenicity interpretation remains complicated. We aimed to investigate whether DMD missense variants would cause aberrant splicing and re-interpret their pathogenicity based on mRNA and protein studies. Methods Nine unrelated patients who had an elevated serum creatine kinase level with or without muscle weakness were enrolled. They underwent a detailed clinical, imaging, and pathological assessment. Routine genetic testing and muscle-derived mRNA and protein studies of dystrophin and sarcoglycan genes were performed in them. Results Three of the 9 patients presented with a Duchenne muscular dystrophy (DMD) phenotype and the remaining 6 patients had a suspected diagnosis of Becker muscular dystrophy (BMD) or sarcoglycanopathy based on their clinical and pathological characteristics. Routine genetic testing detected only 9 predicted DMD missense variants in them, of which 6 were novel and interpreted as uncertain significance. Muscle-derived mRNA studies of sarcoglycan genes didn’t reveal any aberrant transcripts in them. Dystrophin mRNA studies confirmed that 3 predicted DMD missense variants (c.2380G > C, c.4977C > G, and c.5444A > G) were in fact splicing and frameshift variants due to aberrant splicing. The 9 DMD variants were re-interpreted as pathogenic or likely pathogenic based on mRNA and protein studies. Therefore, 3 patients with DMD splicing variants and 6 patients with confirmed DMD missense variants were diagnosed with DMD and BMD, respectively. Conclusion Our study highlights the importance of muscle biopsy and aberrant splicing for clinical and genetic interpretation of uncertain DMD missense variants.

Abstract This article focuses on the cascading failure problem and node importance evaluation method in complex networks. To address the issue of identifying important nodes in dynamic networks, the method used in static networks is introduced and the necessity of re-evaluating node status during node removal is proposed. Studies have found that the methods for identifying dynamic and static network nodes are two different directions, and most literature only uses dynamic methods to verify static methods. Therefore, it is necessary to find suitable node evaluation methods for dynamic networks. Based on this, this article proposes a method that integrates local and global correlation properties. In terms of global features, we introduce an improved k-shell method with fusion degree to improve the resolution of node ranking. In terms of local features, we introduce Solton factor and structure hole factor improved by INCC (improved network constraint coefficient), which effectively improves the algorithm’s ability to identify the relationship between adjacent nodes. Through comparison with existing methods, it is found that the KPDN–INCC method proposed in this paper complements the KPDN method and can accurately identify important nodes, thereby helping to quickly disintegrate the network. Finally, the effectiveness of the proposed method in identifying important nodes in a small-world network with a random parameter less than 0.4 was verified through artificial network experiments.

Abstract Artificial Intelligence (AI) models for medical diagnosis often face challenges of generalizability and fairness. We highlighted the algorithmic unfairness in a large thyroid ultrasound dataset with significant diagnostic performance disparities across subgroups linked causally to sample size imbalances. To address this, we introduced the Quasi-Pareto Improvement (QPI) approach and a deep learning implementation (QP-Net) combining multi-task learning and domain adaptation to improve model performance among disadvantaged subgroups without compromising overall population performance. On the thyroid ultrasound dataset, our method significantly mitigated the area under curve (AUC) disparity for three less-prevalent subgroups by 0.213, 0.112, and 0.173 while maintaining the AUC for dominant subgroups; we also further confirmed the generalizability of our approach on two public datasets: the ISIC2019 skin disease dataset and the CheXpert chest radiograph dataset. Here we show the QPI approach to be widely applicable in promoting AI for equitable healthcare outcomes.

High-quality surface reconstruction and texture reconstruction of large-scale scene play a pivotal role in the domains of ancient architecture, cultural heritage preservation, and 3D urban modeling. Given that point cloud data has emerged as a crucial medium for representing three-dimensional spatial information, its utilization for surface and texture reconstruction becomes indispensable. In this research, we propose a novel framework for reconstructing surface and texture from unstructured colorful point clouds without normal information to restore large-scale real-world scenes. Specifically, we first introduce an automatic virtual viewpoint selection method to generate virtual views by rendering the point cloud from multiple viewpoints. Subsequently, we construct a two-step network to facilitate accurate visibility prediction and texture inpainting for each virtual view. Then, the visibility information from multiple perspectives is integrated to solve an optimization problem incorporating visibility constraints, resulting in the generation of a 3D mesh. Subsequently, texture information from various perspectives is integrated, filtering techniques are applied to determine the optimal perspective for texturing, and a texture atlas is generated. Precise texture mapping is then performed, ultimately leading to the production of a comprehensive textured model. In contrast to alternative learning-based methodologies, our framework exclusively learns from two-dimensional images, encompassing the prediction of both visible and invisible points as well as the execution of image inpainting tasks. This approach exhibits exceptional versatility in managing large-scale point clouds while effectively leveraging the color and intensity attributes of the data for precise texture reconstruction. The experimental results demonstrate that our approach achieves a significant improvement of 2.06% in F-scores for outdoor surface reconstruction compared to the current state-of-the-art learning-based methods, while also outperforming them in texture reconstruction.

Purpose: Pure mucinous breast cancer (PMC) is a rare histological type with a favourable prognosis. However, cases with recurrence have been reported and diagnosed in clinical practice. The mechanisms underlying PMC recurrence remain unknown. In this study, we aimed to identify the prognostic factors associated with PMC. Materials and methods: A total of 166 patients diagnosed with PMC were included. We compared the clinicopathological characteristics between patients with and without recurrence. The 21-gene assay was performed in 10 patients with recurrence and 20 TNM stage-matched patients without recurrence. Whole-exon sequencing was performed in 12 PMC primary tumours and four positive lymph nodes (LNs). Results: Tumour size, lymph node status and TNM staging differed significantly between recurrent group and non-recurrent group. And the 21-gene recurrence scores did not differ significantly between recurrent group and its TNM stage-matched non-recurrent group. The most frequently mutated genes in the primary tumours of regional LN-positive PMCs were ADCY10 (3/6) and SHANK3 (3/6), and they more recurrently harboured gains of 15q23, 17q23.2 and 20p11.21, and loss of 21p11.2. And these alterations were not detected in primary tumours of regional LN-negative PMCs. Conclusion: TNM stage is an important prognostic factor in PMC. Although we revealed that regional LN-positive PMCs show increased occurrence of duplication variants at 15q23, 17q23.2 and 20p11.21, and deletion variants at 21p11.2. Further investigation, including multi-omics studies, are needed and may provide additional insights into the nature of PMC.

Glycerol monolaurate (GML) is a common food additive known for its antimicrobial and anti-inflammatory properties. This study aims to assess the impact of GML on experimental colitis, both in vivo and in vitro. The results indicated that GML significantly inhibited the expression of MIP-3α, TNF-α, IL-1β, and IL-6, while upregulating the expression of claudin-3 and claudin-4 in DSS-treated Caco-2 cell monolayers. Oral administration of GML effectively alleviated colitis in mice, as demonstrated by mitigating body weight loss, lowering the DAI score, and improving colon shrinkage. GML significantly suppressed the expression of NF-κB p65, IL-1β, IL-6, TNF-α, and IL-18 in DSS-treated mice. Moreover, GML increased the cecal microbial alpha-diversity index and restored the dysbiosis. GML significantly increased the relative abundance of Lactobacillus, Alistipes, and Bacteroides, which were found to be negatively correlated with the DAI score and pro-inflammatory cytokine expression. In conclusion, the results highlight the potential therapeutic benefits of GML in preventing colitis and emphasize the role of gut microbiota in this process.

ABSTRACT: Macrophages play a crucial role in both innate and adaptive immunity. However, their abnormal activation can lead to undesirable inflammatory reactions. This study aimed to investigate the effects of glycerol monolaurate (GML), a natural monoester known for its anti-inflammatory and immunoregulatory properties, on avian macrophages using the HD11 cell line. The results indicated that a concentration of 10 μg/mL of GML enhanced the phagocytic activity of HD11 cells (P < 0.05) without affecting cell viability (P > 0.05). GML decreased the expression of M1 macrophage polarization markers, such as CD86 and TNF-α genes (P < 0.05), while increasing the expression of M2 macrophage polarization markers, such as TGF-β1 and IL-10 genes (P < 0.05). GML suppressed ROS production, apoptosis, and the expression of proinflammatory genes (IL-1β and IL-6) induced by LPS (P < 0.05). GML also promoted the expression of TGF-β1 and IL-10 (P < 0.05), both in the presence and absence of LPS exposure. Moreover, GML suppressed the gene expression of TLR4 and NF-κB p65 induced by LPS (P < 0.05), as well as the phosphorylation of NF-κB p65 (P < 0.05). In conclusion, GML exhibited regulatory effects on the polarized state of avian macrophages and demonstrated significant anti-apoptotic and anti-inflammatory properties by suppressing intracellular ROS and the NF-κB signaling pathway.

Liver cancer, which most commonly manifests as hepatocellular carcinoma (HCC), is the sixth most common cancer in the world. In HCC, the immune system plays a crucial role in the growth and proliferation of tumor cells. HCC achieve immune escape through the tumor microenvironment, which significantly promotes the development of this cancer. Here, this article introduces and summarizes the functions and effects of regulatory T cells (Tregs) in the tumor microenvironment, highlighting how Tregs inhibit and regulate the functions of immune and tumor cells, cytokines, ligands and receptors, etc, thereby promoting tumor immune escape. In addition, it discusses the mechanism of CAR-T therapy for HCC and elaborate on the relationship between CAR-T and Tregs.

This study proposed the novel idea of anaolian sand-red mud-fly ash cemented paste backfill (ARFCPB) to realize low-carbon backfill mining. This idea realizes large-scale disposal and resource utilization of red mud (RM) and fly ash (FA) solid wastes. The influence of the FA/RM mass ratio and mass concentration on the mechanism of action of ARFCPB was explored through comprehensive rheological performance, mechanical property, microscopic, and leaching toxicity tests. The results showed that the fresh ARFCPB slurry could be described by the Herschel–Bulkley model (R2 ≥ 0.964). As the FA/RM mass ratio increased, the ARFCPB slurry yield stress decreased, the thixotropy decreased, and the slump increased. This is because RM has a large specific surface area and an unsmooth surface, resulting in greater water absorption by RM than by FA. In addition, the uniaxial compressive strength (UCS) of ARFCPB can meet the needs of backfill mining with higher strength requirements. UCS first increases and then decreases with increasing FA/RM mass ratio, which is optimal when the mass ratio of FA to RM is 4. When the FA/RM mass ratio was 4, the UCSs of specimens A4 and B4 with mass concentrations of 78% and 80%, respectively, were 5.6 MPa and 6.4 MPa after 28 days of curing. The leaching results of heavy metal ions in group B4 meet the Class III groundwater standards and can be used for centralized drinking water sources and industrial and agricultural water. Considering the fluidity characteristics, mechanical properties, and leaching results, B4 was identified as the optimal formulation. Finally, the microstructure of the ARFCPB was studied using ultrasonic pulse velocity (UPV) and SEM. The results can contribute to clean production in the aluminum production industry, coal-fired power plants, and coal mines.

Cryptocaryon irritans is a highly detrimental parasite in mariculture, causing significant economic losses to the aquaculture industry of Larimichthys crocea. In recent years, copper and copper alloy materials have been used to kill parasites. In this study, the effect of copper plates on the tomont period of C. irritans was explored. The findings indicated that copper plates effectively eradicated tomonts, resulting in a hatching rate of 0. The metabolomic analysis revealed that a total of 2,663 differentially expressed metabolites (1,032 up-regulated and 1,631 down-regulated) were screened in the positive ion mode, and 2,199 differentially expressed metabolites (840 up-regulated and 1,359 down-regulated) were screened in the negative ion mode. L-arginine and L-aspartic acid could be used as potential biomarkers. Copper plate treatment affected 25 metabolic pathways in the tomont, most notably influencing histidine metabolism, retinol metabolism, the biosynthesis of phenylalanine, tyrosine, and tryptophan, as well as arginine and proline metabolism. It was shown that high concentrations of copper ions caused a certain degree of disruption to the metabolome of tomonts in C. irritans, thereby impacting their metabolic processes. Consequently, this disturbance ultimately leads to the rapid demise of tomonts upon exposure to copper plates. The metabolomic changes observed in this study elucidate the lethal impact of copper on C. irritans tomonts, providing valuable reference data for the prevention and control of C. irritans in aquaculture.

BackgroundAlthough numerous studies have reported the association between tertiary lymphoid structures (TLSs) and clinical outcomes in cancer patients treated with immune checkpoint inhibitors (ICIs), there remains a lack of a newer and more comprehensive meta-analysis. The main objective of this study is to explore prognostic biomarkers in immunotherapy-related patients, through analyzing the associations between tertiary lymphoid structures (TLSs) and clinical outcomes in cancer patients treated with ICIs, so as to investigate their prognostic value in cancer patients treated with ICIs.MethodsA comprehensive search was conducted until February 2024 across PubMed, Embase, Web of Science, and the Cochrane Library databases to identify relevant studies evaluating the association between tertiary lymphoid structures and clinical outcomes in cancer patients treated with ICIs. The clinical outcomes were overall survival (OS), progression‐free survival (PFS), and objective response rate (ORR).ResultsThirteen studies were incorporated in this meta-analysis, among which nine evaluated the prognostic value of TLSs. The results showed the high levels of TLSs predicted a significantly prolonged OS (pooled HR = 0.35, 95% CI: 0.24–0.53, p

Abstract Global human‐induced warming has intensified water circulation in the atmospheric environment and altered the streamflow generation regime. The VIC hydrological model approach for impact assessment of climate change and human activities mainly focuses on variations in streamflow, but ignores other critical flooding characteristics induced by extreme streamflow, especially bivariate flooding characteristics. In this work, the copula functions are employed to structure the flooding risk under the shared socioeconomic pathway (SSP) across the Yellow River basin (YRB). This is based on the multi‐model ensemble (MME) and Delta downscaling outputs (Delta‐MME) of the CMIP6 global climate models (GCMs), as well as the flooding characteristics simulated by VIC hydrological model. Compared to the reference period (1995–2014), Delta‐MME reveals a significant warming and humidifying trend under three SSPs over the YRB. Despite uncertainties originating from climate variables and hydrological model, multiple findings underscore the substantial influence of climate change on the flooding generation regime in YRB. This includes: (a) an increase in the streamflow under all SSPs; (b) a larger flooding peak (Q) and volume (W) under SSP585, with Q and W at the Huayuankou hydrologic station (HYK) increasing by 52.7% and 44.8%, respectively; (c) an advancement in the bivariate flooding risk, particularly in SSP585 where flooding co‐occurrence return period at HYK may be more than 50 times earlier. This study underscores that the urgent need to enhance social resilience to climate change in the YRB.

Abstract The fast booming of wearable electronics provides great opportunities for intelligent gas detection with improved healthcare of mining workers, and a variety of gas sensors have been simultaneously developed. However, these sensing systems are always limited to single gas detection and are highly susceptible to the inference of ubiquitous moisture, resulting in less accuracy in the analysis of gas compositions in real mining conditions. To address these challenges, we propose a synergistic strategy based on sensor integration and machine learning algorithms to realize precise NH3 and NO2 gas detections under real mining conditions. A wearable sensing array based on the graphene and polyaniline composite is developed to largely enhance the sensitivity and selectivity under mixed gas conditions. Further introduction of backpropagation neural network (BP‐NN) and partial least squares (PLS) algorithms could improve the accuracy of gas identification and concentration prediction and settle the inference of moisture, realizing over 99% theoretical prediction level on NH3 and NO2 concentrations within a wide relative humidity range, showing great promise in real mining detection. As proof of concept, a wireless wearable bracelet, integrated with sensing arrays and machine‐learning algorithms, is developed for wireless real‐time warning of hazardous gases in mines under different humidity conditions.

Abstract G‐protein‐coupled receptors (GPCRs) transmit downstream signals predominantly via G‐protein pathways. However, the conformational basis of selective coupling of primary G‐protein remains elusive. Histamine receptors H2R and H3R couple with Gs‐ or Gi‐proteins respectively. Here, three cryo‐EM structures of H2R‐Gs and H3R‐Gi complexes are presented at a global resolution of 2.6‐2.7 Å. These structures reveal the unique binding pose for endogenous histamine in H3R, wherein the amino group interacts with E2065.46 of H3R instead of the conserved D1143.32 of other aminergic receptors. Furthermore, comparative analysis of the H2R‐Gs and H3R‐Gi complexes reveals that the structural geometry of TM5/TM6 determines the primary G‐protein selectivity in histamine receptors. Machine learning (ML)‐based structuromic profiling and functional analysis of class A GPCR–G‐protein complexes illustrate that TM5 length, TM5 tilt, and TM6 outward movement are key determinants of the Gs and Gi/o selectivity among the whole Class A family. Collectively, the findings uncover the common structural geometry within class A GPCRs that determines the primary Gs‐ and Gi/o‐coupling selectivity.

Addressing the challenges in detecting surface defects on ceramic disks, such as difficulty in detecting small defects, variations in defect sizes, and inaccurate defect localization, we propose an enhanced YOLOv5s algorithm. Firstly, we improve the anchor frame structure of the YOLOv5s model to enhance its generalization ability, enabling robust defect detection for objects of varying sizes. Secondly, we introduce the ECA attention mechanism to improve the model's accuracy in detecting small targets. Under identical experimental conditions, our enhanced YOLOv5s algorithm demonstrates significant improvements, with precision, F1 scores, and mAP values increasing by 3.1 %, 3 %, and 4.5 % respectively. Moreover, the accuracy in detecting crack, damage, slag, and spot defects increases by 0.2 %, 4.7 %, 5.4 %, and 1.9 % respectively. Notably, the detection speed improves from 232 frames/s to 256 frames/s. Comparative analysis with other algorithms reveals superior performance over YOLOv3 and YOLOv4 models, showcasing enhanced capability in identifying small target defects and achieving real-time detection.

Introduction: Premenstrual dysphoric disorder (PMDD), a severe form of premenstrual syndrome (PMS), is a serious health disorder that affects patient moods. It is caused by cyclic psychological symptoms and its pathogenesis is still unclear. Abnormalities in the basolateral amygdala (BLA) orexin system, which are important causes of the development of depressive mood, have not been reported in PMDD, so exploring its intrinsic mechanisms is meaningful for enriching the pathomechanisms of PMDD.Methods: High performance liquid chromatography was used for the determination of the active ingredients of Jingqianshu granules. Developing a rat model of premenstrual depression using the forced swimming test (FST). The experiment consisted of two parts. In Part 1, the rats were divided into the control group, the model group, the model + Jingqianshu group, and the model + fluoxetine group. The FST, open field test, and elevated plus maze test, were used to assess the behavior of the rats as well as to evaluate the effect of drug intervention. Immunofluorescence and RT-qPCR were used to detect the expression of orexin and its receptors OX1R and OX2R genes and proteins. The expression of Toll-like receptor 4, nuclear factor kappa-B, tumor necrosis factor-α, interleukin 6, and interleukin-1β in the BLA brain region was detected by Western-Blot. In part 2, the rats were injected intracerebrally with orexin-A. Observe the behavioral activities of rats in the control group, model group, and model+orexin-A group. Immunofluorescence was used to detect microglia in the BLA area of rats, and the expression levels of the above inflammatory factors were detected by Western-Blot.Results: The five components of Jingqianshu granules are: paeoniflorin, erulic acid, liquiritin, hesperidin, and paeonol. During the estrous cycle, rats exhibited depressive-like behavior during the non-receptive phase of the behavioral test, which disappeared during the receptive phase. Immunofluorescence and RT-qPCR showed reduced gene and protein expression of orexin, OX1R, and OX2R in the BLA region of rats in the model group.WB showed elevated levels of inflammatory factors. All returned to control levels after drug treatment. In part 2, injection of orexin-A into the BLA brain region of model rats resulted in reduced immunoreactivity of microglia and decreased expression levels of inflammatory factors.Discussion: Jianqianshu granules can achieve the purpose of treating premenstrual depression by regulating orexin-mediated inflammatory factors, which provides a new idea for further research on the pathogenesis of PMDD. However, the current study is still preliminary and the pathogenesis of PMDD is complex. Therefore, more in-depth exploration is needed.

The study aimed to determine the potential anti-diabetic activities of the hydrolysates of camel milk proteins (CMPH). Camel milk proteins were hydrolyzed independently by using 11 proteases, and their anti-diabetic activities were analyzed by in vitro, and in vivo. The results showed that CMPH-Flavourzyme exhibited the best α-glucosidase and dipeptidyl peptidase-IV inhibitory activity, while CMPH-Papain exhibited the highest α-amylase inhibitory activity, and best proliferative effect on streptozotocin-induced NIT-1 cells in vitro. Furthermore, CMPH-Papain exhibited the most effect in decreasing the serum levels of fasting blood glucose, oral glucose tolerance test, pro-inflammatory factors (IL-1β, IL-6) and triglyceride, as well as increasing insulin levels on in vivo streptozotocin-induced diabetic mice. Peptides identification followed with molecular docking and network pharmacology analysis also found the potential anti-diabetic peptides from CMPH-Flavourzyme and CMPH-Papain. Our results indicated the type of protease used for the treatment significantly influences the anti-diabetic activities of the resulting CMPH.

This study investigated the regulation effect of magnetic field combined with low temperature storage on postharvest quality and cell wall pectic-polysaccharide degradation of wampee stored for 15 d at 4 °C and 15 °C. Results showed that magnetic field combined with low temperature storage reduced browning rate of fruit after 15 d storage, but its effect on weight loss rate and total soluble solids (TSS) did not surpass that of storage temperature. Interestingly, contents of flavonoid, total phenols and malondialdehyde (MDA) were also lowered at varying degrees by combined treatment. Furthermore, molecular weight distribution and monosaccharide compositions of cell wall pectic-polysaccharides were also affected, which resulted from the coordinated action of cell wall pectin-degrading enzymes. The activities of these enzymes during storage, including polygalacturonase (PG), pectin methylesterase (PME) and β-galactosidase (β-Gal) in treated wampee decreased. These findings suggested that magnetic field combined with low temperature storage was an effective technology and had great potential in preservation of postharvest wampee in future.