Dual-specificity phosphatase 26 protects against kidney injury caused by ischaemia-reperfusion through restraint of TAK1-JNK/p38-mediated apoptosis and inflammation of renal tubular epithelial cells.

Dual-specificity phosphatase 26 (DUSP26) acts as a pivotal player in the transduction of signalling cascades with its dephosphorylating activity. Currently, DUSP26 attracts extensive attention due to its particular function in several pathological conditions. However, whether DUSP26 plays a role in kidney ischaemia-reperfusion (IR) injury is unknown. Aims of the current work were to explore the relevance of DUSP26 in kidney IR damage. DUSP26 levels were found to be decreased in renal tubular epithelial cells following hypoxia-reoxygenation (HR) and kidney samples subjected to IR treatments. DUSP26-overexpressed renal tubular epithelial cells exhibited protection against HR-caused apoptosis and inflammation, while DUSP26-depleted renal tubular epithelial cells were more sensitive to HR damage. Upregulation of DUSP26 in rat kidneys by infecting adenovirus expressing DUSP26 markedly ameliorated kidney injury caused by IR, while also effectively reducing apoptosis and inflammation. The mechanistic studies showed that the activation of transforming growth factor-ß-activated kinase 1 (TAK1)-JNK/p38 MAPK, contributing to kidney injury under HR or IR conditions, was restrained by increasing DUSP26 expression. Pharmacological restraint of TAK1 markedly diminished DUSP26-depletion-exacebated effects on JNK/p38 activation and HR injury of renal tubular cells. The work reported a renal-protective function of DUSP26, which protects against IR-related kidney damage via the intervention effects on the TAK1-JNK/p38 axis. The findings laid a foundation for understanding the molecular pathogenesis of kidney IR injury and provide a prospective target for treating this condition.

Serum CXCL13 level is related to treatment response and predicts disease prognosis in Waldenström macroglobulinemia.

Waldenström macroglobulinemia (WM) is a type of B-cell lymphoma that produces IgM. Our study aimed to investigate the role of CXCL13, a chemokine essential for B lymphocytes, in the evaluation of treatment response and prognosis in WM. We collected serum samples and clinical data from 72 WM patients, with 69 patients receiving systemic therapy and 3 patients opting not to receive treatment. Serum CXCL13 levels at baseline and after six months of treatments were measured by enzyme-linked immunosorbent assay. The median serum level of CXCL13 was 1 539.2 pg/ml (range 10.0-21 389.9) at baseline and significantly decreased to 123.1 pg/ml (range 0.0-6 741.5) after 6 months of treatments. At baseline, higher CXCL13 levels were associated with lower hemoglobin levels (p = 0.001), higher ß2-microglobulin levels (p = 0.001), lower albumin levels (p = 0.046), and higher IPSS-WM scores (p = 0.013). After 6 months of treatment, patients who achieved PR/VGPR had significantly lower CXCL13 levels compared to those with SD (70.2 pg/ml vs 798.6 pg/ml, p = 0.002). The median follow-up period was 40 months (range 4.2-188). Eight patients died during the follow-up period. Overall survival differed based on CXCL13 levels. When grouped by baseline CXCL13 levels, the median OS was 60.0 months in patients with serum CXCL13 > 2 000 pg/ml, while it was not reached in patients with low CXCL13 levels (p < 0.001). Based on CXCL13 levels after the treatments, the median OS was 74.0 months in patients with serum CXCL13 > 200 pg/ml, while it was not reached in patients with CXCL13 ≤ 200 pg/ml. In a subgroup of 28 patients with a series of serum samples, the increase of serum CXCL13 level was associated with disease progression or the start of next-line therapy (p < 0.001). Our study concludes that serum CXCL13 levels decrease in WM patients treated with various regimens and correlate with treatment response. Detecting serum CXCL13 at baseline or after treatment help in predicting prognosis.

Effect of AR gene-specific knockout on the process of radiation-induced pulmonary fibrosis and its mechanism.

Numerous studies have proved that epithelial-mesenchymal transition (EMT) of lung epithelial cells is one of the important causes of radiation-induced pulmonary fibrosis (RIPF). Aldose reductase (AR) is a monomer enzyme in the polyglycolic metabolic pathway and belongs to the aldo-keno reductase protein superfamily. Our previous studies have found that AR as one of the most significantly up-regulated genes was associated with the development of bleomycin-induced PF in rats. It is not clear whether aldose reductase is related to the regulation of radiation-induced EMT and mediates RIPF. AR-knockout mice, wild-type mice and lung epithelial cells were induced by radiation to establish a RIPF animal model and EMT system, to explore whether AR is mediation to RIPF through the EMT pathway. In vivo, AR deficiency significantly alleviated radiation-induced histopathological changes, reduced collagen deposition and inhibited collagen I, matrix metalloproteinase 2 (MMP2) and Twist1 expression. In addition, AR knockout up-regulated E-cadherin expression and up-regulated α-SMA and Vimentin expression. In vitro, AR, collagen I and MMP2 expression were increased in lung epithelial cells after radiation, which was accompanied by Twist1 expression up-regulation and EMT changes evidenced by decreased E-cadherin expression and increased α-SMA and Vimentin expression. Knockdown or inhibition of AR inhibited the expressions of Twist1, MMP2 and collagen I, and reduced cell migration and reversed radiation-induced EMT. These results indicated that aldose reductase may be related to radiation-induced lung epithelial cells EMT, and that inhibition of aldose reductase might be a promising treatment for RIPF.

Public Attitudes Toward Anxiety Disorder on Sina Weibo: Content Analysis.

BACKGROUND: Anxiety disorder has become a major clinical and public health problem, causing a significant economic burden worldwide. Public attitudes toward anxiety can impact the psychological state, help-seeking behavior, and social activities of people with anxiety disorder. OBJECTIVE: The purpose of this study was to explore public attitudes toward anxiety disorders and the changing trends of these attitudes by analyzing the posts related to anxiety disorders on Sina Weibo, a Chinese social media platform that has about 582 million users, as well as the psycholinguistic and topical features in the text content of the posts. METHODS: From April 2018 to March 2022, 325,807 Sina Weibo posts with the keyword "anxiety disorder" were collected and analyzed. First, we analyzed the changing trends in the number and total length of posts every month. Second, a Chinese Linguistic Psychological Text Analysis System (TextMind) was used to analyze the changing trends in the language features of the posts, in which 20 linguistic features were selected and presented. Third, a topic model (biterm topic model) was used for semantic content analysis to identify specific themes in Weibo users' attitudes toward anxiety. RESULTS: The changing trends in the number and the total length of posts indicated that anxiety-related posts significantly increased from April 2018 to March 2022 (R2=0.6512; P<.001 to R2=0.8133; P<.001, respectively) and were greatly impacted by the beginning of a new semester (spring/fall). The analysis of linguistic features showed that the frequency of the cognitive process (R2=0.1782; P=.003), perceptual process (R2=0.1435; P=.008), biological process (R2=0.3225; P<.001), and assent words (R2=0.4412; P<.001) increased significantly over time, while the frequency of the social process words (R2=0.2889; P<.001) decreased significantly, and public anxiety was greatly impacted by the COVID-19 pandemic. Feature correlation analysis showed that the frequencies of words related to work and family are almost negatively correlated with those of other psychological words. Semantic content analysis identified 5 common topical areas: discrimination and stigma, symptoms and physical health, treatment and support, work and social, and family and life. Our results showed that the occurrence probability of the topical area "discrimination and stigma" reached the highest value and averagely accounted for 26.66% in the 4-year period. The occurrence probability of the topical area "family and life" (R2=0.1888; P=.09) decreased over time, while that of the other 4 topical areas increased. CONCLUSIONS: The findings of our study indicate that public discrimination and stigma against anxiety disorder remain high, particularly in the aspects of self-denial and negative emotions. People with anxiety disorders should receive more social support to reduce the impact of discrimination and stigma.

"Green" Extraction and On-Site Rapid Detection of Aflatoxin B1, Zearalenone and Deoxynivalenol in Corn, Rice and Peanut.

The common mycotoxins in polluted grains are aflatoxin B1(AFB1), zearalenone (ZEN) and deoxynivalenol (DON). Because of the potential threat to humans and animals, it is necessary to detect mycotoxin contaminants rapidly. At present, later flow immunoassay (LFIA) is one of the most frequently used methods for rapid analysis. However, multistep sample pretreatment processes and organic solvents are also required to extract mycotoxins from grains. In this study, we developed a one-step and "green" sample pretreatment method without using organic solvents. By combining with LFIA test strips and a handheld detection device, an on-site method for the rapid detection of AFB1, ZEN and DON was developed. The LODs for AFB1, ZEN and DON in corn are 0.90 µg/kg, 7.11 µg/kg and 10.6 µg/kg, respectively, and the working ranges are from 1.25 µg/kg to 40 µg/kg, 20 µg/kg to 2000 µg/kg and 35 µg/kg to 1500 µg/kg, respectively. This method has been successfully applied to the detection of AFB1, ZEN and DON in corn, rice and peanut, with recoveries of 89 ± 3%-106 ± 3%, 86 ± 2%-108 ± 7% and 90 ± 2%-106 ± 10%, respectively. The detection results for the AFB1, ZEN and DON residues in certified reference materials by this method were in good agreement with their certificate values.

A unified serum IgG4 cut-off level for the diagnosis of IgG4-related disease using a wide array of kits.

OBJECTIVES: An immunoglobulin G4 (IgG4) level above 1350 mg/L is one of the comprehensive criteria for the diagnosis of IgG4-related disease (IgG4-RD). The purpose of this study was to evaluate the differences in IgG4 levels determined using reagents from two main manufacturers and their concordance with clinical diagnosis. METHODS: IgG4 levels were measured in 309 patients, including 146, 40, 42, 41, and 40 patients with untreated IgG4-RD, pancreatic cancer, primary Sjogren syndrome, systemic lupus erythematosus, and idiopathic retroperitoneal fibrosis, respectively, and 141 healthy controls. The results obtained using the Binding Site and Siemens reagents were compared in patients with IgG4-RD. RESULTS: The serum IgG4 level measured using the Siemens reagent was almost two times that measured using the Binding Site reagent. The rate of IgG4-negative patients, which was 19.9% based on measurement using the Binding Site reagent, was only 4.8% based on measurement using the Siemens reagent (p < .001). CONCLUSIONS: There were significant differences in serum IgG4 levels based on commonly used reagents from different manufacturers. The IgG4 cut-off level of 1350 mg/L was not suitable for all detection reagents. Clinicians and patients should be cognizant of these differences associated with the specific detection reagents when evaluating the test results.

IL-17 in intervertebral disc degeneration: Mechanistic insights and therapeutic implications.

Intervertebral disc degeneration (IDD) serves as an independent risk factor for lower back pain and is closely associated with spinal musculoskeletal disorders, including lumbar disc herniation, radiculopathy, and myelopathy. Interleukin-17 (IL-17), also named IL-17A, is a critical signature cytokine of T-helper 17 cells. Upon binding to the IL-17 receptor A/C heterodimeric complex, IL-17 can trigger multiple signal transduction pathways to stimulate gene transcription and increase messenger RNA stability. IL-17 expression is significantly increased in degenerative disc tissue and shows a positive correlation with disease severity. IL-17 has been shown to accelerate the development of IDD by promoting extracellular matrix degradation, enhancing inflammatory response, inducing neoangiogenesis, and inhibiting nucleus pulposus cell autophagy and proliferation. Targeting IL-17 represents a novel and promising approach for the therapeutic intervention of IDD. In this review, we summarized the recent progression about the role of IL-17 in IDD and highlighted its therapeutic implications.

Deep Hierarchical Ensemble Model for Suicide Detection on Imbalanced Social Media Data.

As a serious worldwide problem, suicide often causes huge and irreversible losses to families and society. Therefore, it is necessary to detect and help individuals with suicidal ideation in time. In recent years, the prosperous development of social media has provided new perspectives on suicide detection, but related research still faces some difficulties, such as data imbalance and expression implicitness. In this paper, we propose a Deep Hierarchical Ensemble model for Suicide Detection (DHE-SD) based on a hierarchical ensemble strategy, and construct a dataset based on Sina Weibo, which contains more than 550 thousand posts from 4521 users. To verify the effectiveness of the model, we also conduct experiments on a public Weibo dataset containing 7329 users' posts. The proposed model achieves the best performance on both the constructed dataset and the public dataset. In addition, in order to make the model applicable to a wider population, we use the proposed sentence-level mask mechanism to delete user posts with strong suicidal ideation. Experiments show that the proposed model can still effectively identify social media users with suicidal ideation even when the performance of the baseline models decrease significantly.

Asymmetric Incorporation of Silver Nanoparticles in Polymeric Assemblies by Coassembly of Tadpole-Like Nanoparticles and Amphiphilic Block Copolymers.

A general approach to asymmetrically localize nanoparticles (NPs) in larger polymeric nanostructures is demonstrated by coassembly of tadpole-like silver NPs (AgNPs) and amphiphilic block copolymers (BCPs). The tadpole-like AgNPs are prepared by template synthesis using a tailor-made A(BC)20 star polymer, namely poly(ethylene glycol)[poly(acrylic acid)-block-polystyrene]20 [PEG(PAA-b-PS)20 ], as template resulting in AgNPs decorated with twenty short PS chains and one long PEG chain, named Ag@PEG(PS)20 . The asymmetric distribution of these AgNPs in various polymeric nanostructures, e.g., spherical micelles, cylindrical micelles, vesicles, and sponge phase, is achieved via coassembly of the as-prepared Ag@PEG(PS)20 and PEG-b-PS in solution driven by the anisotropic nature of the Ag@PEG(PS)20 . This report not only provides a new strategy for the fabrication of tadpole-like NPs but also offers opportunity for off-center distributing NPs in hybrid assemblies, which may find applications in, e.g., sensing, catalysis, and diagnostics.

MRG1/2 histone methylation readers and HD2C histone deacetylase associate in repression of the florigen gene FT to set a proper flowering time in response to day-length changes.

Day-length changes represent an important cue for modulating flowering time. In Arabidopsis, the expression of the florigen gene FLOWERING LOCUS T (FT) exhibits a 24-h circadian rhythm under long-day (LD) conditions. Here we focus on the chromatin-based mechanism regarding the control of FT expression. We conducted co-immunoprecipitation assays along with LC-MS/MS analysis and identified HD2C histone deacetylase as the binding protein of the H3K4/H3K36 methylation reader MRG2. HD2C and MRG1/2 regulate flowering time under LD conditions, but not under short-day conditions. Moreover, HD2C functions as an effective deacetylase in planta, mainly targeting H3K9ac, H3K23ac and H3K27ac. At dusk, HD2C is recruited to FT to deacetylate histones and repress transcription in an MRG1/2-dependent manner. More importantly, HD2C competes with CO for the binding of MRG2, and the accumulation of HD2C at the FT locus occurs at the end of the day. Our findings not only reveal a histone deacetylation mechanism contributing to prevent FT overexpression and precocious flowering, but also support the model in which the histone methylation readers MRG1/2 provide a platform on chromatin for connecting regulatory factors involved in activating FT expression in response to daylight and decreasing FT expression around dusk under long days.

Linking PHYTOCHROME-INTERACTING FACTOR to Histone Modification in Plant Shade Avoidance.

Shade avoidance syndrome (SAS) allows a plant grown in a densely populated environment to maximize opportunities to access to sunlight. Although it is well established that SAS is accompanied by gene expression changes, the underlying molecular mechanism needs to be elucidated. Here, we identify the H3K4me3/H3K36me3-binding proteins, Morf Related Gene (MRG) group proteins MRG1 and MRG2, as positive regulators of shade-induced hypocotyl elongation in Arabidopsis (Arabidopsis thaliana). MRG2 binds PHYTOCHROME-INTERACTING FACTOR7 (PIF7) and regulates the expression of several common downstream target genes, including YUCCA8 and IAA19 involved in the auxin biosynthesis or response pathway and PRE1 involved in brassinosteroid regulation of cell elongation. In response to shade, PIF7 and MRG2 are enriched at the promoter and gene-body regions and are necessary for increase of histone H4 and H3 acetylation to promote target gene expression. Our study uncovers a mechanism in which the shade-responsive factor PIF7 recruits MRG1/MRG2 that binds H3K4me3/H3K36me3 and brings histone-acetylases to induce histone acetylations to promote expression of shade responsive genes, providing thus a molecular mechanistic link coupling the environmental light to epigenetic modification in regulation of hypocotyl elongation in plant SAS.

Theoretical Study of PAH Growth by Phenylacetylene Addition.

Although there was significant advancement on polycyclic aromatic hydrocarbon (PAH) formation, current mechanisms are still limited in providing an integrated and accurate scheme of PAH yield in combustion conditions; thus, a more detailed and comprehensive understanding is necessary. This work provides a systematic investigation of PAH growth by phenylacetylene addition. A combination of the density functional theory (DFT/B3LYP/6-311+G(d,p)) and the complete basis set method (CBS-QB3) is utilized to calculate the potential energy surfaces. The reaction system is initiated by the H elimination reaction of phenylacetylene + H → o-ethynylphenyl + H2, and then, the addition reaction of phenylacetylene and o-ethynylphenyl can produce PAHs with one, two, three, and four rings. The temperature- and pressure-dependent reaction rate coefficients are calculated via a combination of conventional transition state theory (TST) and Rice-Ramsperger-Kassel-Marcus (RRKM) theory with solving the master equation in the temperature range of 500-2500 K and at the pressure range of 0.01-10 atm. There are 263 species and 65 reactions in this reaction system. It shows that the rate constants of this reaction system are highly temperature-dependent and slightly sensitive to the pressure at temperatures lower than 1300 K. To evaluate the yield distributions of various PAH products in the whole reaction network, a closed 0-D batch reactor model in Chemkin is used to calculate the C6H5C2H-C2H2-H-Ar reaction system. The results showed that the prevailing products of this system are three-ring PAHs with side chain structures. Compared with the traditional HACA pathways, the investigated reaction system presents higher efficiency in large PAH formations, which could subsequently promote the formation of soot particles. The phenylacetylene and o-ethynylphenyl reaction network emphasizes the importance of species with side chains, and it enriches current PAH growth pathways aside from the addition of small species such as C2H2.

Regulation of arabidopsis flowering by the histone mark readers MRG1/2 via interaction with CONSTANS to modulate FT expression.

Day-length is important for regulating the transition to reproductive development (flowering) in plants. In the model plant Arabidopsis thaliana, the transcription factor CONSTANS (CO) promotes expression of the florigen FLOWERING LOCUS T (FT), constituting a key flowering pathway under long-day photoperiods. Recent studies have revealed that FT expression is regulated by changes of histone modification marks of the FT chromatin, but the epigenetic regulators that directly interact with the CO protein have not been identified. Here, we show that the Arabidopsis Morf Related Gene (MRG) group proteins MRG1 and MRG2 act as H3K4me3/H3K36me3 readers and physically interact with CO to activate FT expression. In vitro binding analyses indicated that the chromodomains of MRG1 and MRG2 preferentially bind H3K4me3/H3K36me3 peptides. The mrg1 mrg2 double mutant exhibits reduced mRNA levels of FT, but not of CO, and shows a late-flowering phenotype under the long-day but not short-day photoperiod growth conditions. MRG2 associates with the chromatin of FT promoter in a way dependent of both CO and H3K4me3/H3K36me3. Vice versa, loss of MRG1 and MRG2 also impairs CO binding at the FT promoter. Crystal structure analyses of MRG2 bound with H3K4me3/H3K36me3 peptides together with mutagenesis analysis in planta further demonstrated that MRG2 function relies on its H3K4me3/H3K36me3-binding activity. Collectively, our results unravel a novel chromatin regulatory mechanism, linking functions of MRG1 and MRG2 proteins, H3K4/H3K36 methylations, and CO in FT activation in the photoperiodic regulation of flowering time in plants.

Response of Escherichia coli to Acid Stress: Mechanisms and Applications-A Narrative Review.

Change in pH in growth conditions is the primary stress for most neutralophilic bacteria, including model microorganism Escherichia coli. However, different survival capacities under acid stress in different bacteria are ubiquitous. Research on different acid-tolerance mechanisms in microorganisms is important for the field of combating harmful gut bacteria and promoting fermentation performance of industrial strains. Therefore, this study aimed to carry out a narrative review of acid-stress response mechanism of E. coli discovered so far, including six AR systems, cell membrane protection, and macromolecular repair. In addition, the application of acid-tolerant E. coli in industry was illustrated, such as production of industrial organic acid and developing bioprocessing for industrial wastes. Identifying these aspects will open the opportunity for discussing development aspects for subsequent research of acid-tolerant mechanisms and application in E. coli.

Natural Language Processing for Depression Prediction on Sina Weibo: Method Study and Analysis.

Background: Depression represents a pressing global public health concern, impacting the physical and mental well-being of hundreds of millions worldwide. Notwithstanding advances in clinical practice, an alarming number of individuals at risk for depression continue to face significant barriers to timely diagnosis and effective treatment, thereby exacerbating a burgeoning social health crisis. Objective: This study seeks to develop a novel online depression risk detection method using natural language processing technology to identify individuals at risk of depression on the Chinese social media platform Sina Weibo. Methods: First, we collected approximately 527,333 posts publicly shared over 1 year from 1600 individuals with depression and 1600 individuals without depression on the Sina Weibo platform. We then developed a hierarchical transformer network for learning user-level semantic representations, which consists of 3 primary components: a word-level encoder, a post-level encoder, and a semantic aggregation encoder. The word-level encoder learns semantic embeddings from individual posts, while the post-level encoder explores features in user post sequences. The semantic aggregation encoder aggregates post sequence semantics to generate a user-level semantic representation that can be classified as depressed or nondepressed. Next, a classifier is employed to predict the risk of depression. Finally, we conducted statistical and linguistic analyses of the post content from individuals with and without depression using the Chinese Linguistic Inquiry and Word Count. Results: We divided the original data set into training, validation, and test sets. The training set consisted of 1000 individuals with depression and 1000 individuals without depression. Similarly, each validation and test set comprised 600 users, with 300 individuals from both cohorts (depression and nondepression). Our method achieved an accuracy of 84.62%, precision of 84.43%, recall of 84.50%, and F1-score of 84.32% on the test set without employing sampling techniques. However, by applying our proposed retrieval-based sampling strategy, we observed significant improvements in performance: an accuracy of 95.46%, precision of 95.30%, recall of 95.70%, and F1-score of 95.43%. These outstanding results clearly demonstrate the effectiveness and superiority of our proposed depression risk detection model and retrieval-based sampling technique. This breakthrough provides new insights for large-scale depression detection through social media. Through language behavior analysis, we discovered that individuals with depression are more likely to use negation words (the value of "swear" is 0.001253). This may indicate the presence of negative emotions, rejection, doubt, disagreement, or aversion in individuals with depression. Additionally, our analysis revealed that individuals with depression tend to use negative emotional vocabulary in their expressions ("NegEmo": 0.022306; "Anx": 0.003829; "Anger": 0.004327; "Sad": 0.005740), which may reflect their internal negative emotions and psychological state. This frequent use of negative vocabulary could be a way for individuals with depression to express negative feelings toward life, themselves, or their surrounding environment. Conclusions: The research results indicate the feasibility and effectiveness of using deep learning methods to detect the risk of depression. These findings provide insights into the potential for large-scale, automated, and noninvasive prediction of depression among online social media users.

Quercetin Promotes the M1-to-M2 Macrophage Phenotypic Switch During Liver Fibrosis Treatment by Modulating the JAK2/STAT3 Signaling Pathway.

OBJECTIVE: To investigate the underlying mechanism by which quercetin (Que) regulates macrophage polarization and its subsequent therapeutic effect on liver fibrosis, an important pathological precondition for hepatocellular carcinoma (HCC). METHODS: In vitro experiments were performed on the RAW264.7 mouse macrophage line. After the induction of M1-type macrophages with LPS, the effects of Que on cell morphology, M1/M2 surface marker expression, cytokine expression, and JAK2/STAT3 expression were analyzed. In vivo, male SD rats were used as a model of CCL4-induced hepatic fibrosis, and the effects of Que on serum aminotransferase levels, the histopathological structure of liver tissues, and macrophage-associated protein expression in liver tissues were analyzed. RESULTS: In vitro experiments revealed that Que can suppress the activation of the JAK2/STAT3 signaling pathway, leading to decreases in the expression of M1 macrophage surface markers and cytokines. Additionally, Que was found to increase the expression of M2 macrophage surface markers and cytokines. In vivo, assays demonstrated that Que significantly ameliorated the development of inflammation and fibrosis in a rat liver fibrosis model. CONCLUSION: Que can inhibit hepatic fibrosis by promoting M1 to M2 macrophage polarization, which could be associated with its ability to suppress the JAK2/STAT3 signaling pathway in macrophages.

Artificial Intelligence Application for Anti-tumor Drug Synergy Prediction.

Currently, the main therapeutic methods for cancer include surgery, radiation therapy, and chemotherapy. However, chemotherapy still plays an important role in tumor therapy. Due to the variety of pathogenic factors, the development process of tumors is complex and regulated by many factors, and the treatment of a single drug is easy to cause the human body to produce a drug-resistant phenotype to specific drugs and eventually leads to treatment failure. In the process of clinical tumor treatment, the combination of multiple drugs can produce stronger anti-tumor effects by regulating multiple mechanisms and can reduce the problem of tumor drug resistance while reducing the toxic side effects of drugs. Therefore, it is still a great challenge to construct an efficient and accurate screening method that can systematically consider the synergistic anti- tumor effects of multiple drugs. However, anti-tumor drug synergy prediction is of importance in improving cancer treatment outcomes. However, identifying effective drug combinations remains a complex and challenging task. This review provides a comprehensive overview of cancer drug synergy therapy and the application of artificial intelligence (AI) techniques in cancer drug synergy prediction. In addition, we discuss the challenges and perspectives associated with deep learning approaches. In conclusion, the review of the AI techniques' application in cancer drug synergy prediction can further advance our understanding of cancer drug synergy and provide more effective treatment plans and reasonable drug use strategies for clinical guidance.

Exploring serum N-glycome patterns as candidate non-invasive biomarkers in inguinal hernia.

Introduction: Although inguinal hernia (IH) is prevalent in elderly males, research on its specific diagnostic biomarkers is limited. Protein N-glycosylation is one of the most important and ubiquitous post-translational modifications and often results in a remarkable heterogeneity of protein glycoforms. Protein N-glycosylation often changes in a disease and holds great potential for discovering non-invasive biomarkers. This study aimed to gain insights into total serum protein N-glycosylation of IH to identify candidate non-invasive biomarkers for diagnosis and subtype classification of IH. Methods: Linkage-specific sialylation derivatization combined with matrix-assisted laser desorption/ionization time-of-flight mass spectrometry detection was used to analyze serum protein N-glycosylation patterns in IH patients and healthy controls. Results: IH patients had abnormal glycan fucosylation and sialylation compared to healthy controls (HC), of which two glycan traits representing linkage-specific sialylation within monoantennary glycans showed high potential as diagnostic biomarkers for IH with an area under the curve (AUC) of 0.75. Additionally, serum N-glycans were different between indirect IH and direct IH in glycosylation features, namely complexity, fucosylation, galactosylation, sialylation, and α2,6-linked sialylation. Four distinctive glycans between the two subtypes showed good performance with AUC >0.8, suggesting that these glycan traits have potential as biomarkers for subtype classification. Conclusions: We first reported the serum N-glycomic features of IH patients. Furthermore, we identified several potential biomarkers for the diagnosis and subtype classification of IH. These findings can deepen the understanding of IH.

Mesoporous zinc-polyphenol nanozyme for attenuating renal ischemia-reperfusion injury.

Aim: To target the reactive oxygen species (ROS) accumulation and renal tubular epithelial cell (rTEC) death in renal ischemia-reperfusion injury (IRI), we constructed a nanoparticle that offers ROS scavenging and rTEC-death inhibition: mesoporous zinc-tannic acid nanozyme (ZnTA).Materials & methods: After successfully constructing ZnTA, we proceeded to examine its effect on ROS accumulation, cellular ferroptosis and apoptosis, as well as injury severity.Results: Malondialdehyde, Fe2+ amounts and 4-HNE staining demonstrated that ZnTA effectively attenuated rTEC ferroptosis. TUNEL staining confirmed that Zn2+ carried by ZnTA could effectively inhibit caspase 3 and caspase 9, mitigating apoptosis. Finally, it reduced renal IRI through the synergistic effect of ROS scavenging and cell-death inhibition.Conclusion: This study is expected to provide a paradigm for a combined therapeutic strategy for renal IRI.

[Box: see text].

Reactive Oxygen Species-Scavenging Mesoporous Poly(tannic acid) Nanospheres Alleviate Acute Kidney Injury by Inhibiting Ferroptosis.

Acute kidney injury (AKI), predominantly associated with the excess production of endogenous ROS, is a serious renal dysfunction syndrome. Ferroptosis characterized by iron-dependent regulated cell death has significant involvement in AKI pathogenesis. As symptomatic treatment of AKI remains clinically limited, a new class of effective therapies has emerged, which is referred to as nanozyme. In our research, a natural mesoporous poly(tannic acid) nanosphere (referred to as PTA) was developed that can successfully mimic the activity of superoxide dismutase (SOD) by Mussel-inspired interface deposition strategy, for effective ROS scavenging and thus inhibition of ferroptosis to attenuate AKI. As anticipated, PTA mitigated oxidative stress and inhibited ferroptosis, as opposed to other modes of cell death such as pyroptosis or necrosis. Furthermore, PTA exhibited favorable biocompatibility and safeguarded the kidney against ferroptosis by enhancing the expression of SLC7a11/glutathione peroxidase 4(GPX4) and Nrf2/HO-1, while reducing the levels of ACSL4 protein in the ischemia and reperfusion injury (IRI)-induced AKI model. Moreover, PTA effectively suppressed aberrant expression of inflammatory factors. Overall, this study introduced antioxidative nanozymes in the form of mesoporous polyphenol nanospheres, showcasing exceptional therapeutic efficacy in addressing ROS-related diseases. This novel approach holds promise for clinical AKI treatment and broadens the scope of biomedical applications for nanozymes.