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Background Differentiation of fibroblasts into myofibroblasts is necessary for wound healing, but excessive myofibroblast presence and persistence can result in scarring. Treatment for scarring is limited largely due to a lack of comprehensive understanding of how fibroblasts and myofibroblasts differ at the transcript level. The purpose of this study was to characterize transcriptional profiles of injured fibroblasts relative to normal fibroblasts, utilizing fibroblasts from the vocal fold as a model. Results Utilizing bulk RNA sequencing technology, we identified differentially expressed genes between four cell lines of normal fibroblasts (cVFF), one line of scarred fibroblasts (sVFF), and four lines of fibroblasts treated with transforming growth factor-beta 1 (TGF-β1), representing an induced-scar phenotype (tVFF). Principal component analysis revealed clustering of normal fibroblasts separate from the clustering of fibroblasts treated with TGF-β1; scarred fibroblasts were more similar to normal fibroblasts than fibroblasts treated with TGF-β1. Enrichment analyses revealed pathways related to cell signaling, receptor-ligand activity, and regulation of cell functions in scarred fibroblasts, pathways related to cell adhesion in normal fibroblasts, and pathways related to ECM binding in fibroblasts treated with TGF-β1. Although transcriptomic profiles between scarred fibroblasts and fibroblasts treated with TGF-β1 were relatively dissimilar, the most highly co-expressed genes were enriched in pathways related to actin cytoskeleton binding, which supports the use of fibroblasts treated with TGF-β1 to represent a scarred cell phenotype. Conclusions Transcriptomics of normal fibroblasts differ from myofibroblasts, including from those retrieved from scar and those treated with TGF-β1. Despite large differences in transcriptomics between tVFF and sVFF, tVFF serve as a useful in vitro model of myofibroblasts and highlight key similarities to myofibroblasts extracted from scar pathology, as well as expected differences related to normal fibroblasts from healthy vocal folds.

Cell type annotation is an essential step in single-cell RNA-sequencing analysis, and numerous annotation methods are available. Most require a combination of computational and domain-specific expertise, and they frequently yield inconsistent results that can be challenging to interpret. Large language models have the potential to expand accessibility while reducing manual input and improving accuracy, but existing approaches suffer from hyperconfidence, hallucinations, and lack of reasoning. To address these limitations, we developed CASSIA for automated, accurate, and interpretable cell annotation of single-cell RNA-sequencing data. As demonstrated in analyses of 970 cell types, CASSIA improves annotation accuracy in benchmark datasets as well as complex and rare cell populations, and also provides users with reasoning and quality assessment to ensure interpretability, guard against hallucinations, and calibrate confidence. Assigning cell types in single-cell RNA-seq is essential yet challenging, as it requires expertise, time, and is often subjective. Here, the authors present CASSIA, a multi-agent AI system that provides automated, interpretable, and quality-controlled annotations with high accuracy.

Predicting which patients will progress to metastatic disease after surgery for non-metastatic clear cell renal cell carcinoma (ccRCC) is difficult; however, recent data suggest that tumor immune cell infiltration could be used as a biomarker. We evaluated the quantity and type of immune cells infiltrating ccRCC tumors for associations with metastatic progression following attempted curative surgery. We quantified immune cell densities in the tumor microenvironment and validated our findings in two independent patient cohorts with multi-region sampling to investigate the impact of heterogeneity on prognostic accuracy. For non-metastatic ccRCC, increased CD8+ T cell infiltration was associated with a reduced likelihood of progression to metastatic disease. Interestingly, patients who progressed to metastatic disease also had increased percentages of exhausted CD8+ T cells. Finally, we evaluated the spatial heterogeneity of the immune infiltration and demonstrated that patients without metastatic progression had CD8+ T cells in closer proximity to ccRCC cells. These data strengthen the evidence for CD8+ T cell infiltration as a prognostic biomarker in non-metastatic ccRCC and demonstrate that multi-region sampling may be necessary to fully characterize immune infiltration within heterogeneous tumors. Tumor CD8+ T cell infiltration should be investigated as a biomarker in adjuvant systemic therapy clinical trials for high-risk non-metastatic RCC.

Cell type annotation is an essential step in single-cell RNA-sequencing analysis, and numerous annotation methods are available. Most require a combination of computational and domain-specific expertise, and they frequently yield inconsistent results that can be challenging to interpret. Large language models have the potential to expand accessibility while reducing manual input and improving accuracy, but existing approaches suffer from hyperconfidence, hallucinations, and lack of reasoning. To address these limitations, we developed CASSIA for automated, accurate, and interpretable cell annotation of single-cell RNA-sequencing data. As demonstrated in analyses of over 970 cell types, CASSIA improves annotation accuracy in benchmark datasets as well as complex and rare cell populations, and also provides users with reasoning and quality assessment to ensure interpretability, guard against hallucinations, and calibrate confidence.

Spatial transcriptomics (ST) experiments provide spatially localized measurements of genome-wide gene expression allowing for an unprecedented opportunity to investigate cellular heterogeneity and organization within a tissue. Statistical and computational frameworks exist that implement robust methods for pre-processing and analyzing data in ST experiments. However, the lack of an interactive suite of tools for visualizing ST data and results currently limits the full potential of ST experiments. To fill this gap, we developed SpatialView, an open-source web browser-based interactive application for visualizing data and results from multiple 10x Genomics ST experiments. We anticipate SpatialView will be useful to a broad array of clinical and basic science investigators utilizing ST to study disease. SpatialView is available at https://github.com/kendziorski-lab/SpatialView ; a demo application is available at https://www.biostat.wisc.edu/~kendzior/spatialviewdemo/ cmohanty2@wisc.edu, ckendziorski@wisc.edu Supplementary data are available at Bioinformatics online.

Background Cardiac fibrosis is significant global health problem, which is associated with numerous cardiovascular diseases, and ultimately leads to the progression to heart failure. β-adrenergic receptor (β-AR) overactivation play a role in the development of cardiac fibrosis. Miglustat (Mig) has shown anti-fibrosis effects in multiple fibrotic diseases. However, it is unclear whether and how Mig can ameliorate cardiac fibrosis induced by β-AR overactivation. Methods In vivo, mice were injected with isoproterenol (ISO) to induce cardiac fibrosis and treated with Mig. In vitro, primary cardiac fibroblasts were stimulated by ISO and treated with Mig. Levels of cardiac fibrosis, cardiac dysfunction, activation of cardiac fibroblasts were evaluated by real-time polymerase chain reaction, western blots, sirius red staining, immunohistochemistry staining and echocardiography. Through GEO data and knockdown UDP-glucose ceramide glycosyltransferase (UGCG) in primary cardiac fibroblasts, whether Mig alleviates cardiac fibrosis by targeting UGCG was explored. Results The results indicated that Mig alleviated ISO-induced cardiac dysfunction. Consistently, Mig also suppressed ISO-induced cardiac fibrosis. Moreover, Mig attenuated ISO-induced cardiac fibroblasts (CFs) activation. To identify the protective mechanism of Mig on cardiac fibrosis, several classical β-AR downstream signaling pathways, including ERK, STAT3, Akt and GSK3β, were further analyzed. As expected, ISO activated the ERK, STAT3, Akt and GSK3β in both CFs and mouse hearts, but this effect was reversed pretreated with Mig. Besides, Mig ameliorates ISO-induced cardiac fibrosis by targeting UDP-glucose ceramide glycosyltransferase (UGCG) in CFs. Conclusions Mig ameliorates β-AR overactivation-induced cardiac fibrosis by inhibiting ERK, STAT3, Akt and GSK3β signaling and UGCG may be a potential target for the treatment of cardiac fibrosis.

(1) Background: Heart failure (HF) is the final stage of multiple cardiac diseases, which have now become a severe public health problem worldwide. β-Adrenergic receptor (β-AR) overactivation is a major pathological factor associated with multiple cardiac diseases and mediates cardiac fibrosis and inflammation. Previous research has demonstrated that Bruton’s tyrosine kinase (BTK) mediated cardiac fibrosis by TGF-β related signal pathways, indicating that BTK was a potential drug target for cardiac fibrosis. Zanubrutinib, a second-generation BTK inhibitor, has shown anti-fibrosis effects in previous research. However, it is unclear whether Zanubrutinib can alleviate cardiac fibrosis induced by β-AR overactivation; (2) Methods: In vivo: Male C57BL/6J mice were treated with or without the β-AR agonist isoproterenol (ISO) to establish a cardiac fibrosis animal model; (3) Results: In vivo: Results showed that the BTK inhibitor Zanubrutinib (ZB) had a great effect on cardiac fibrosis and inflammation induced by β-AR. In vitro: Results showed that ZB alleviated β-AR-induced cardiac fibroblast activation and macrophage pro-inflammatory cytokine production. Further mechanism studies demonstrated that ZB inhibited β-AR-induced cardiac fibrosis and inflammation by the BTK, STAT3, NF-κB, and PI3K/Akt signal pathways both in vivo and in vitro; (4) Conclusions: our research provides evidence that ZB ameliorates β-AR-induced cardiac fibrosis and inflammation.

PurposeTo investigate the role of magnetic resonance imaging (MRI) based on radiomics using T2-weighted imaging fat suppression (T2WI-FS) and contrast enhanced T1-weighted imaging (CE-T1WI) sequences in differentiating T1-category nasopharyngeal carcinoma (NPC) from nasopharyngeal lymphoid hyperplasia (NPH).Materials and methodsThis study enrolled 614 patients (training dataset: n = 390, internal validation dataset: n = 98, and external validation dataset: n = 126) of T1-category NPC and NPH. Three feature selection methods were used, including analysis of variance, recursive feature elimination, and relief. The logistic regression classifier was performed to construct the radiomics signatures of T2WI-FS, CE-T1WI, and T2WI-FS + CE-T1WI to differentiate T1-category NPC from NPH. The performance of the optimal radiomics signature (T2WI-FS + CE-T1WI) was compared with those of three radiologists in the internal and external validation datasets.ResultsTwelve, 15, and 15 radiomics features were selected from T2WI-FS, CE-T1WI, and T2WI-FS + CE-T1WI to develop the three radiomics signatures, respectively. The area under the curve (AUC) values for radiomics signatures of T2WI-FS + CE-T1WI and CE-T1WI were significantly higher than that of T2WI-FS (AUCs = 0.940, 0.935, and 0.905, respectively) for distinguishing T1-category NPC and NPH in the training dataset (Ps all < 0.05). In the internal and external validation datasets, the radiomics signatures based on T2WI-FS + CE-T1WI and CE-T1WI outperformed T2WI-FS with no significant difference (AUCs = 0.938, 0.925, and 0.874 for internal validation dataset and 0.932, 0.918, and 0.882 for external validation dataset; Ps > 0.05). The radiomics signature of T2WI-FS + CE-T1WI significantly performed better than three radiologists in the internal and external validation datasets.ConclusionThe MRI-based radiomics signature is meaningful in differentiating T1-category NPC from NPH and potentially helps clinicians select suitable therapy strategies.

Inflammation is an important biological process which contributes to risk for depression, in part as a result of the production of proinflammatory cytokines and of alterations in glutamatergic neurotransmission. Ketamine has anti-inflammatory properties which might contribute to its antidepressant effects. This study was designed to clarify mechanisms of action for ketamine and its active metabolites, (2 )-hydroxynorketamine (HNK), which also appear to play a major role in ketamine's rapid antidepressant effects. An HMC3 human microglial cell line was used as a model system to test a possible role for ketamine in immune response regulation that might contribute to its antidepressant effects. Our results highlight the fact that ketamine and its two active metabolites can regulate the type I interferon pathway mediated, at least partially, through signal transducer and activation of transcription 3 (STAT3) which plays a major role in the immune response. Specifically, STAT3 downstream genes that were modulated by either ketamine or its active metabolites were enriched in the \"response to type I interferon\" pathway. Our data also suggest that STAT3 might play a role in ketamine's antidepressant effects, mediated, at least in part, through eukaryotic elongation factor 2 (EEF2), resulting in the augmentation of brain-derived neurotropic factor (BDNF) expression and promoting the synthesis of synaptic proteins postsynaptic density protein 95 (PSD95) and synapsin I (SYN1).