Tumor-associated macrophage subtypes on cancer immunity along with prognostic analysis and SPP1-mediated interactions between tumor cells and macrophages.

Tumor-associated macrophages (TAM) subtypes have been shown to impact cancer prognosis and resistance to immunotherapy. However, there is still a lack of systematic investigation into their molecular characteristics and clinical relevance in different cancer types. Single-cell RNA sequencing data from three different tumor types were used to cluster and type macrophages. Functional analysis and communication of TAM subpopulations were performed by Gene Ontology-Biological Process and CellChat respectively. Differential expression of characteristic genes in subpopulations was calculated using zscore as well as edgeR and Wilcoxon rank sum tests, and subsequently gene enrichment analysis of characteristic genes and anti-PD-1 resistance was performed by the REACTOME database. We revealed the heterogeneity of TAM, and identified eleven subtypes and their impact on prognosis. These subtypes expressed different molecular functions respectively, such as being involved in T cell activation, apoptosis and differentiation, or regulating viral bioprocesses or responses to viruses. The SPP1 pathway was identified as a critical mediator of communication between TAM subpopulations, as well as between TAM and epithelial cells. Macrophages with high expression of SPP1 resulted in poorer survival. By in vitro study, we showed SPP1 mediated the interactions between TAM clusters and between TAM and tumor cells. SPP1 promoted the tumor-promoting ability of TAM, and increased PDL1 expression and stemness of tumor cells. Inhibition of SPP1 attenuated N-cadherin and ß-catenin expression and the activation of AKT and STAT3 pathway in tumor cells. Additionally, we found that several subpopulations could decrease the sensitivity of anti-PD-1 therapy in melanoma. SPP1 signal was a critical pathway of communication between macrophage subtypes. Some specific macrophage subtypes were associated with immunotherapy resistance and prognosis in some cancer types.

Targeting PAX8 sensitizes ovarian cancer cells to ferroptosis by inhibiting glutathione synthesis.

Ovarian cancer is a malignant tumor originating from the ovary, characterized by its high mortality rate and propensity for recurrence. In some patients, especially those with recurrent cancer, conventional treatments such as surgical resection or standard chemotherapy yield suboptimal results. Consequently, there is an urgent need for novel anti-cancer therapeutic strategies. Ferroptosis is a distinct form of cell death separate from apoptosis. Ferroptosis inducers have demonstrated promising potential in the treatment of ovarian cancer, with evidence indicating their ability to enhance ovarian cancer cell sensitivity to cisplatin. However, resistance of cancer cells to ferroptosis still remains an inevitable challenge. Here, we analyzed genome-scale CRISPR-Cas9 loss-of function screens and identified PAX8 as a ferroptosis resistance protein in ovarian cancer. We identified PAX8 as a susceptibility gene in GPX4-dependent ovarian cancer. Depletion of PAX8 rendered GPX4-dependent ovarian cancer cells significantly more sensitive to GPX4 inhibitors. Additionally, we found that PAX8 inhibited ferroptosis in ovarian cancer cells. Combined treatment with a PAX8 inhibitor and RSL3 suppressed ovarian cancer cell growth, induced ferroptosis, and was validated in a xenograft mouse model. Further exploration of the molecular mechanisms underlying PAX8 inhibition of ferroptosis mutations revealed upregulation of glutamate-cysteine ligase catalytic subunit (GCLC) expression. GCLC mediated the ferroptosis resistance induced by PAX8 in ovarian cancer. In conclusion, our study underscores the pivotal role of PAX8 as a therapeutic target in GPX4-dependent ovarian cancer. The combination of PAX8 inhibitors such as losartan and captopril with ferroptosis inducers represents a promising new approach for ovarian cancer therapy.

Photobiomodulation therapy moderates cancer cachexia-associated muscle wasting through activating PI3K/AKT/FoxO3a pathway.

Cancer cachexia-associated muscle wasting as a multifactorial wasting syndrome, is an important factor affecting the long-term survival rate of tumor patients. Photobiomodulation therapy (PBMT) has emerged as a promising tool to cure and prevent many diseases. However, the effect of PBMT on skeletal muscle atrophy during cancer progression has not been fully demonstrated yet. Here, we found PBMT alleviated the atrophy of myotube diameter induced by cancer cells in vitro, and prevented cancer-associated muscle atrophy in mice bearing tumor. Mechanistically, the alleviation of muscle wasting by PBMT was found to be involved in inhibiting E3 ubiquitin ligases MAFbx and MuRF-1. In addition, transcriptomic analysis using RNA-seq and GSEA revealed that PI3K/AKT pathway might be involved in PBMT-prevented muscle cachexia. Next, we showed the protective effect of PBMT against muscle cachexia was totally blocked by AKT inhibitor in vitro and in vivo. Moreover, PBMT-activated AKT promoted FoxO3a phosphorylation and thus inhibiting the nucleus entry of FoxO3a. Lastly, in cisplatin-treated muscle cachexia model, PBMT had also been shown to ameliorate muscle atrophy through enhancing PI3K/AKT pathway to suppress MAFbx and MuRF-1 expression. These novel findings revealed that PBMT could be a promising therapeutic approach in treating muscle cachexia induced by cancer.

A novel real-time model for predicting acute kidney injury in critically ill patients within 12 hours.

BACKGROUND: A major challenge in prevention and early treatment of acute kidney injury (AKI) is the lack of high-performance predictors in critically ill patients. Therefore, we innovatively constructed U-AKIpredTM for predicting AKI in critically ill patients within 12 h of panel measurement. METHODS: The prospective cohort study included 680 patients in the training set and 249 patients in the validation set. After performing inclusion and exclusion criteria, 417 patients were enrolled in the training set and 164 patients were enrolled in the validation set finally. AKI was diagnosed by Kidney Disease Improving Global Outcomes (KDIGO) criteria. RESULTS: Twelve urinary kidney injury biomarkers (mALB, IgG, TRF, α1MG, NAG, NGAL, KIM-1, L-FABP, TIMP2, IGFBP7, CAF22 and IL-18) exhibited good predictive performance for AKI within 12 h in critically ill patients. U-AKIpredTM, combined with three crucial biomarkers (α1MG, L-FABP and IGFBP7) by multivariate logistic regression analysis, exhibited better predictive performance for AKI in critically ill patients within 12 h than the other twelve kidney injury biomarkers. The area under the curve (AUC) of the U-AKIpredTM, as a predictor of AKI within 12 h, was 0.802 (95% CI: 0.771-0.833, P < 0.001) in the training set and 0.844 (95% CI: 0.792-0.896, P < 0.001) in validation cohort. A nomogram based on the results of the training and validation sets of U-AKIpredTM was developed which showed optimal predictive performance for AKI. The fitting effect and prediction accuracy of U-AKIpredTM was evaluated by multiple statistical indicators. To provide a more flexible predictive tool, the dynamic nomogram (https://www.xsmartanalysis.com/model/U-AKIpredTM) was constructed using a web-calculator. Decision curve analysis (DCA) and a clinical impact curve were used to reveal that U-AKIpredTM with the three crucial biomarkers had a higher net benefit than these twelve kidney injury biomarkers respectively. The net reclassification index (NRI) and integrated discrimination index (IDI) were used to improve the significant risk reclassification of AKI compared with the 12 kidney injury biomarkers. The predictive efficiency of U-AKIpredTM was better than the NephroCheck® when testing for AKI and severe AKI. CONCLUSION: U-AKIpredTM is an excellent predictive model of AKI in critically ill patients within 12 h and would assist clinicians in identifying those at high risk of AKI.

MFS-DBF: A trustworthy multichannel feature sieve and decision boundary formulation system for Obstructive Sleep Apnea detection.

The fine identification of sleep apnea events is instrumental in Obstructive Sleep Apnea (OSA) diagnosis. The development of sleep apnea event detection algorithms based on polysomnography is becoming a research hotspot in medical signal processing. In this paper, we propose an Inverse-Projection based Visualization System (IPVS) for sleep apnea event detection algorithms. The IPVS consists of a feature dimensionality reduction module and a feature reconstruction module. First, features of blood oxygen saturation and nasal airflow are extracted and used as input data for event analysis. Then, visual analysis is conducted on the feature distribution for apnea events. Next, dimensionality reduction and reconstruction methods are combined to achieve the dynamic visualization of sleep apnea event feature sets and the visual analysis of classifier decision boundaries. Moreover, the decision-making consistency is explored for various sleep apnea event detection classifiers, which provides researchers and users with an intuitive understanding of the detection algorithm. We applied the IPVS to an OSA detection algorithm with an accuracy of 84% and a diagnostic accuracy of 92% on a publicly available dataset. The experimental results show that the consistency between our visualization results and prior medical knowledge provides strong evidence for the practicality of the proposed system. For clinical practice, the IPVS can guide users to focus on samples with higher uncertainty presented by the OSA detection algorithm, reducing the workload and improving the efficiency of clinical diagnosis, which in turn increases the value of trust.

Biomimetic nanomedicine cocktail enables selective cell targeting to enhance ovarian Cancer chemo- and immunotherapy.

Ovarian cancer is one of the deadliest cancers, and combined chemo- and immunotherapies are potential strategies to combat it. However, the anti-cancer efficacy of the combined therapies may be limited by the non-selective co-delivery of chemotherapy and immunotherapy. Herein, a combined chemo- and immunotherapy is designed to selectively target ovarian tumor (ID8) cells and dendritic cells (DCs) using ID8 cell membrane (IM) and bacterial outer membrane vesicles (OMVs), respectively. Doxorubicin (DOX) and Ovalbumin (OVA) peptide (OVA257-264) are chosen as model chemotherapy and immunotherapy agents, respectively. A DNA nanocube capable of easily loading DOX or OVA257-264 is chosen as the carrier. Firstly, the DNA nanocube is used to load DOX or OVA257-264 to prepare cube-DOX or cube-OVA. This nanocube was then encapsulated with IM to form IM@Cube-DOX and with OMV to form OMV@Cube-OVA. IM@Cube-DOX can be selectively taken up by ID8 cells, leading to effective cell killing, while OMV@Cube-OVA targets and activates DC2.4 cells in vitro. Both IM@Cube-DOX and OMV@Cube-OVA show increased accumulation at ID8 tumors in C57BL/6 mice. Combined IM@Cube-DOX + OMV@Cube-OVA therapy demonstrates better anti-tumor efficacy than non-selective delivery methods such as OMV@(Cube-DOX + Cube-OVA) or IM@(Cube-DOX + Cube-OVA) in ID8-OVA tumor-bearing mice. In conclusion, this study demonstrates a biomimetic delivery strategy that enables selective drug delivery to tumor cells and DCs, thereby enhancing the anti-tumor efficacy of combined chemo- and immunotherapy through the selective delivery strategy.

Biomedical potentials of alginate via physical, chemical, and biological modifications.

Alginate is a linear polysaccharide with a modifiable structure and abundant functional groups, offers immense potential for tailoring diverse alginate-based materials to meet the demands of biomedical applications. Given the advancements in modification techniques, it is significant to analyze and summarize the modification of alginate by physical, chemical and biological methods. These approaches provide plentiful information on the preparation, characterization and application of alginate-based materials. Physical modification generally involves blending and physical crosslinking, while chemical modification relies on chemical reactions, mainly including acylation, sulfation, phosphorylation, carbodiimide coupling, nucleophilic substitution, graft copolymerization, terminal modification, and degradation. Chemical modified alginate contains chemically crosslinked alginate, grafted alginate and oligo-alginate. Biological modification associated with various enzymes to realize the hydrolysis or grafting. These diverse modifications hold great promise in fully harnessing the potential of alginate for its burgeoning biomedical applications in the future. In summary, this review provides a comprehensive discussion and summary of different modification methods applied to improve the properties of alginate while expanding its biomedical potentials.

Simvastatin alleviates glymphatic system damage via the VEGF-C/VEGFR3/PI3K-Akt pathway after experimental intracerebral hemorrhage.

Current clinical practice primarily relies on surgical intervention to remove hematomas in patients with intracerebral hemorrhage (ICH), given the lack of effective drug therapies. Previous research indicates that simvastatin (SIM) may enhance hematoma absorption and resolution in the acute phase of ICH, though the precise mechanisms remain unclear. Recent findings have highlighted the glymphatic system (GS) as a crucial component in intracranial cerebrospinal fluid circulation, playing a significant role in hematoma clearance post-ICH. This study investigates the link between SIM efficacy in hematoma resolution and the GS. Our experimental results show that SIM alleviates GS damage in ICH-induced rats, resulting in improved outcomes such as reduced brain edema, neuronal apoptosis, and degeneration. Further analysis reveals that SIM's effects are mediated through the VEGF-C/VEGFR3/PI3K-Akt pathway. This study advances our understanding of SIM's mechanism in promoting intracranial hematoma clearance and underscores the potential of targeting the GS for ICH treatment.

Trimethylamine N-oxide impairs ß-cell function and glucose tolerance.

ß-Cell dysfunction and ß-cell loss are hallmarks of type 2 diabetes (T2D). Here, we found that trimethylamine N-oxide (TMAO) at a similar concentration to that found in diabetes could directly decrease glucose-stimulated insulin secretion (GSIS) in MIN6 cells and primary islets from mice or humans. Elevation of TMAO levels impairs GSIS, ß-cell proportion, and glucose tolerance in male C57BL/6 J mice. TMAO inhibits calcium transients through NLRP3 inflammasome-related cytokines and induced Serca2 loss, and a Serca2 agonist reversed the effect of TMAO on ß-cell function in vitro and in vivo. Additionally, long-term TMAO exposure promotes ß-cell ER stress, dedifferentiation, and apoptosis and inhibits ß-cell transcriptional identity. Inhibition of TMAO production improves ß-cell GSIS, ß-cell proportion, and glucose tolerance in both male db/db and choline diet-fed mice. These observations identify a role for TMAO in ß-cell dysfunction and maintenance, and inhibition of TMAO could be an approach for the treatment of T2D.

Galectin-3 impairs calcium transients and ß-cell function.

In diabetes, macrophages and inflammation are increased in the islets, along with ß-cell dysfunction. Here, we demonstrate that galectin-3 (Gal3), mainly produced and secreted by macrophages, is elevated in islets from both high-fat diet (HFD)-fed and diabetic db/db mice. Gal3 acutely reduces glucose-stimulated insulin secretion (GSIS) in ß-cell lines and primary islets in mice and humans. Importantly, Gal3 binds to calcium voltage-gated channel auxiliary subunit gamma 1 (CACNG1) and inhibits calcium influx via the cytomembrane and subsequent GSIS. ß-Cell CACNG1 deficiency phenocopies Gal3 treatment. Inhibition of Gal3 through either genetic or pharmacologic loss of function improves GSIS and glucose homeostasis in both HFD-fed and db/db mice. All animal findings are applicable to male mice. Here we show a role of Gal3 in pancreatic ß-cell dysfunction, and Gal3 could be a therapeutic target for the treatment of type 2 diabetes.

The effects of Shaoma Zhijing granules and its main components on Tourette syndrome.

BACKGROUND: Tourette syndrome (TS) represents a neurodevelopmental disorder characterized by an uncertain etiology and influencing factors. Frequently, it co-occurs with conditions such as attention deficit hyperactivity disorder, obsessive-compulsive disorder, and sleep disturbances, which have garnered substantial attention from the research community in recent years. Clinical trials have demonstrated that Shaoma Zhijing Granules (SMZJG, 5-ling granule, also known as TSupport or T92 under U.S. development), a traditional Chinese medicine compound, is an effective treatment for TS. PURPOSE: To conduct scientometric analysis on developing trends, research countries and institutions, current status, hot spots of TS and discuss the underlying mechanisms of SMZJG and its main components on TS. The aim is to provide valuable reference for ongoing clinical and basic research on TS and SMZJG. STUDY DESIGN & METHODS: Using Tourette syndrome, SMZJG and its main components along with their synonyms as keywords, we conducted a comprehensive search across major scientific databases including the Web of Science Core Collection, PubMed and China National Knowledge Infrastructure (CNKI) databases. A total of 5952 references and 99 patents were obtained. Among these, 5039 articles and reviews, as well as 54 patents were analyzed by Citespace and VOSviewer software. RESULTS: The available evidence indicates that the SMZJG's components likely exert their mechanisms in treating TS by regulating the dopaminergic pathway system, neurotransmitter imbalances, reducing neuroinflammation, promoting the repair of nerve damage and improving sleep disorders. CONCLUSION: This comprehensive analysis lays the foundation for an extensive exploration of the feasibility and clinical applications of SMZJG in TS treatment.

Changes in meat quality, metabolites and microorganisms of mutton during cold chain storage.

During the cold chain storage process, changes in metabolites and microorganisms are highly likely to lead to changes in meat quality. To elucidate the changes in the composition of metabolites and microbiota during cold chain storage of mutton, this study utilized untargeted metabolome and 5R 16S rRNA sequencing analyses to investigate the changes in the longissimus dorsi under different cold chain temperatures (4 °C and -20 °C). With the extension of cold chain storage time, the meat color darkened and the content of C18:2n-6, C20:3n-6, and C23:0 were significantly increased in mutton. In this study, nine metabolites, including 1,2-Dioleoyl-sn-glycero-3-phosphoethanolamine, alanylphenylala-nine, indole-3-acrylic acid and the others, were significantly altered during cold chain storage. The abundance of the dominant microorganisms, including Brachymonas, Aeromonas, Corynebacterium and Steroidobacter, was significantly altered. Furthermore, a high correlation was observed between the different metabolites and microorganisms. These findings provide an in-depth understanding of the effects of different cold chain storage temperatures and times on the quality of mutton.

Ocular image-based deep learning for predicting refractive error: A systematic review.

Background: Uncorrected refractive error is a major cause of vision impairment worldwide and its increasing prevalent necessitates effective screening and management strategies. Meanwhile, deep learning, a subset of Artificial Intelligence, has significantly advanced ophthalmological diagnostics by automating tasks that required extensive clinical expertise. Although recent studies have investigated the use of deep learning models for refractive power detection through various imaging techniques, a comprehensive systematic review on this topic is has yet be done. This review aims to summarise and evaluate the performance of ocular image-based deep learning models in predicting refractive errors. Main text: We search on three databases (PubMed, Scopus, Web of Science) up till June 2023, focusing on deep learning applications in detecting refractive error from ocular images. We included studies that had reported refractive error outcomes, regardless of publication years. We systematically extracted and evaluated the continuous outcomes (sphere, SE, cylinder) and categorical outcomes (myopia), ground truth measurements, ocular imaging modalities, deep learning models, and performance metrics, adhering to PRISMA guidelines. Nine studies were identified and categorised into three groups: retinal photo-based (n â€‹= â€‹5), OCT-based (n â€‹= â€‹1), and external ocular photo-based (n â€‹= â€‹3).For high myopia prediction, retinal photo-based models achieved AUC between 0.91 and 0.98, sensitivity levels between 85.10% and 97.80%, and specificity levels between 76.40% and 94.50%. For continuous prediction, retinal photo-based models reported MAE ranging from 0.31D to 2.19D, and R 2 between 0.05 and 0.96. The OCT-based model achieved an AUC of 0.79-0.81, sensitivity of 82.30% and 87.20% and specificity of 61.70%-68.90%. For external ocular photo-based models, the AUC ranged from 0.91 to 0.99, sensitivity of 81.13%-84.00% and specificity of 74.00%-86.42%, MAE ranges from 0.07D to 0.18D and accuracy ranges from 81.60% to 96.70%. The reported papers collectively showed promising performances, in particular the retinal photo-based and external eye photo -based DL models. Conclusions: The integration of deep learning model and ocular imaging for refractive error detection appear promising. However, their real-world clinical utility in current screening workflow have yet been evaluated and would require thoughtful consideration in design and implementation.