The Glabridin from Huangqin Decoction Prevents the Development of Ulcerative Colitis into Colitis-Associated Colorectal Cancer by Modulating MMP1/MMP3 Activity.

BACKGROUND AND AIM: Huangqin decoction (HQD) is a Chinese medicine used to treat colitis and colorectal cancer (CRC). However, the specific compounds and mechanisms of HQD remain unclear despite its good curative clinical results. Through bioinformatics, network pharmacology, and experiments, this study aims to explore the progressive mechanisms of colitis-associated colorectal cancer (CAC) from ulcerative colitis (UC) while examining the protective effects of HQD and its compounds against this. METHODS: Bioinformatics was utilized to identify the hub genes between UC and CRC, and their clinical predictive significance, function, and expression were validated. Employing network pharmacology in combination with hub genes, key targets of HQD for preventing the development of UC into CAC were identified. Molecular docking and molecular dynamics (MD) were utilized to procure compounds that effectively bind to these targets and their transcription factors (TFs). Finally, the expression and mechanism of key targets were demonstrated in mice with UC or CAC. RESULTS: (1) Joint analysis of UC and CRC gene sets resulted in 14 hub genes, mainly related to extracellular matrix receptor binding, biological processes in the extracellular matrix, focal adhesion and neutrophil migration; (2) Network pharmacology results show HQD has 133 core targets for treating UC and CRC, acting on extracellular matrix, inflammatory bowel disease, chemical carcinogen receptor activation and other pathways; (3) The intersection of hub genes and core targets yielded two key targets, MMP1 and MMP3; (4) STAT3 is a shared TF of MMP1 and MMP3. (5) Molecular docking and MD verified that the dockings between Glabridin and STAT3/MMP1/MMP3 are stable and reliable; (6) In murine vivo experiments verified that Glabridin reduces inflammation, extracellular matrix degradation, and the occurrence of epithelial-mesenchymal transition to prevent UC transforming into CAC by inhibiting the phosphorylation of STAT3 and regulating the activity of MMP1/3.

Histone demethylase KDM7A regulates bone homeostasis through balancing osteoblast and osteoclast differentiation.

Histone methylation plays a crucial role in various cellular processes. We previously reported the in vitro function of histone lysine demethylase 7 A (KDM7A) in osteoblast and adipocyte differentiation. The current study was undertaken to investigate the physiological role of KDM7A in bone homeostasis and elucidate the underlying mechanisms. A conditional strategy was employed to delete the Kdm7a gene specifically in osterix-expressing osteoprogenitor cells in mice. The resulting mutant mice exhibited a significant increase in cancellous bone mass, accompanied by an increase in osteoblasts and bone formation, as well as a reduction in osteoclasts, marrow adipocytes and bone resorption. The bone marrow stromal cells (BMSCs) and calvarial pre-osteoblastic cells derived from the mutant mice exhibited enhanced osteogenic differentiation and suppressed adipogenic differentiation. Additionally, osteoclastic precursor cells from the mutant mice exhibited impaired osteoclast differentiation. Co-culturing BMSCs from the mutant mice with wild-type osteoclast precursor cells resulted in the inhibition of osteoclast differentiation. Mechanistic investigation revealed that KDM7A was able to upregulate the expression of fibroblast activation protein α (FAP) and receptor activator of nuclear factor κB ligand (RANKL) in BMSCs through removing repressive di-methylation marks of H3K9 and H3K27 from Fap and Rankl promoters. Moreover, recombinant FAP attenuated the dysregulation of osteoblast and adipocyte differentiation in BMSCs from Kdm7a deficient mice. Finally, Kdm7a deficiency prevented ovariectomy-induced bone loss in mice. This study establish the role of KDM7A in bone homeostasis through its epigenetic regulation of osteoblast and osteoclast differentiation. Consequently, inhibiting KDM7A may prove beneficial in ameliorating osteoporosis. KDM7A suppresses osteoblast differentiation and bone formation through. upregulating FAP expression and inactivating canonical Wnt signaling, and conversely promotes osteoclast differentiation and bone resorption through upregulating RANKL expression. These are based on its epigenetic removal of the repressive H3K9me2 and H3K27me2 marks from Fap and Rankl promoters. As a result, the expression of KDM7A in osteoprogenitor cells tends to negatively modulate bone mass.

Recent progress and clinical applications of advanced biomaterials in cosmetic surgery.

Materials of different allogeneic or xenogeneic or autologous origins are widely used as soft-tissue fillers or structural scaffolds in the field of cosmetic surgery, while complications including prosthesis infection, donor site deformity and filler embolization have always been difficult problems for plastic surgeons. The application of novel biomaterials may bring in hopeful solutions for these problems. Recently, some advanced biomaterials, such as regenerative biomaterials can effectively promote the repair of defective tissues, which have been proven to have good therapeutic as well as cosmetic effects in cosmetic surgery. Therefore, biomaterials with active compounds have drawn significant attention for the tissue regeneration of reconstructive and esthetic treatment. Some of these applications have achieved better clinical outcomes than traditional biological materials. This review summarized recent progress and clinical applications of advanced biomaterials in cosmetic surgery.

Intratumor graph neural network recovers hidden prognostic value of multi-biomarker spatial heterogeneity.

Biomarkers are indispensable for precision medicine. However, focused single-biomarker development using human tissue has been complicated by sample spatial heterogeneity. To address this challenge, we tested a representation of primary tumor that synergistically integrated multiple in situ biomarkers of extracellular matrix from multiple sampling regions into an intratumor graph neural network. Surprisingly, the differential prognostic value of this computational model over its conventional non-graph counterpart approximated that of combined routine prognostic biomarkers (tumor size, nodal status, histologic grade, molecular subtype, etc.) for 995 breast cancer patients under a retrospective study. This large prognostic value, originated from implicit but interpretable regional interactions among the graphically integrated in situ biomarkers, would otherwise be lost if they were separately developed into single conventional (spatially homogenized) biomarkers. Our study demonstrates an alternative route to cancer prognosis by taping the regional interactions among existing biomarkers rather than developing novel biomarkers.

Chondrocyte-laden GelMA hydrogel combined with 3D printed PLA scaffolds for auricle regeneration.

The current gold standard for auricular reconstruction after microtia or ear trauma is the autologous cartilage graft with an autologous skin flap overlay. Harvesting autologous cartilage requires an additional surgery that may result in donor area complications. In addition, autologous cartilage is limited and the auricular reconstruction requires complex sculpting, which requires excellent clinical skill and is very time consuming. This work explores the use of 3D printing technology to fabricate bioactive artificial auricular cartilage using chondrocyte-laden gelatin methacrylate (GelMA) and polylactic acid (PLA) for auricle reconstruction. In this study, chondrocytes were loaded within GelMA hydrogel and combined with the 3D-printed PLA scaffolds to biomimetic the biological mechanical properties and personalized shape. The printing accuracy personalized scaffolds, biomechanics and chondrocyte viability and biofunction of artificial auricle have been studied. It was found that chondrocytes were fixed in the PLA auricle scaffolds via GelMA hydrogels and exhibited good proliferative properties and cellular activity. In addition, new chondrocytes and chondrogenic matrix, as well as type II collagen were observed after 8 weeks of implantation. At the same time, the transplanted auricle complex kept full and delicate auricle shape. This study demonstrates the potential of using 3D printing technology to construct in vitro living auricle tissue. It shows a great prospect in the clinical application of auricle regeneration.

Large-scale tumor-associated collagen signatures identify high-risk breast cancer patients.

The notion of personalized medicine demands proper prognostic biomarkers to guide the optimal therapy for an invasive breast cancer patient. However, various risk prediction models based on conventional clinicopathological factors and emergent molecular assays have been frequently limited by either a low strength of prognosis or restricted applicability to specific types of patients. Therefore, there is a critical need to develop a strong and general prognosticator. Methods: We observed five large-scale tumor-associated collagen signatures (TACS4-8) obtained by multiphoton microscopy at the invasion front of the breast primary tumor, which contrasted with the three tumor-associated collagen signatures (TACS1-3) discovered by Keely and coworkers at a smaller scale. Highly concordant TACS1-8 classifications were obtained by three independent observers. Using the ridge regression analysis, we obtained a TACS-score for each patient based on the combined TACS1-8 and established a risk prediction model based on the TACS-score. In a blind fashion, consistent retrospective prognosis was obtained from 995 breast cancer patients in both a training cohort (n= 431) and an internal validation cohort (n = 300) collected from one clinical center, and in an external validation cohort (n = 264) collected from a different clinical center. Results: TACS1-8 model alone competed favorably with all reported models in predicting disease-free survival (AUC: 0.838, [0.800-0.872]; 0.827, [0.779-0.868]; 0.807, [0.754-0.853] in the three cohorts) and stratifying low- and high-risk patients (HR 7.032, [4.869-10.158]; 6.846, [4.370-10.726], 4.423, [2.917-6.708]). The combination of these factors with the TACS-score into a nomogram model further improved the prognosis (AUC: 0.865, [0.829-0.896]; 0.861, [0.816-0.898]; 0.854, [0.805-0.894]; HR 7.882, [5.487-11.323]; 9.176, [5.683-14.816], and 5.548, [3.705-8.307]). The nomogram identified 72 of 357 (~20%) patients with unsuccessful 5-year disease-free survival that might have been undertreated postoperatively. Conclusions: The risk prediction model based on TACS1-8 considerably outperforms the contextual clinical model and may thus convince pathologists to pursue a TACS-based breast cancer prognosis. Our methodology identifies a significant portion of patients susceptible to undertreatment (high-risk patients), in contrast to the multigene assays that often strive to mitigate overtreatment. The compatibility of our methodology with standard histology using traditional (non-tissue-microarray) formalin-fixed paraffin-embedded (FFPE) tissue sections could simplify subsequent clinical translation.

Knowledge-guided analysis of "omics" data using the KnowEnG cloud platform.

We present Knowledge Engine for Genomics (KnowEnG), a free-to-use computational system for analysis of genomics data sets, designed to accelerate biomedical discovery. It includes tools for popular bioinformatics tasks such as gene prioritization, sample clustering, gene set analysis, and expression signature analysis. The system specializes in "knowledge-guided" data mining and machine learning algorithms, in which user-provided data are analyzed in light of prior information about genes, aggregated from numerous knowledge bases and encoded in a massive "Knowledge Network." KnowEnG adheres to "FAIR" principles (findable, accessible, interoperable, and reuseable): its tools are easily portable to diverse computing environments, run on the cloud for scalable and cost-effective execution, and are interoperable with other computing platforms. The analysis tools are made available through multiple access modes, including a web portal with specialized visualization modules. We demonstrate the KnowEnG system's potential value in democratization of advanced tools for the modern genomics era through several case studies that use its tools to recreate and expand upon the published analysis of cancer data sets.

Preparation, characterization and cytotoxicity of carbon nanotube-chitosan-phycocyanin complex.

Photodynamic therapy (PDT) or photothermal therapy (PTT) using nanomaterials has shown great prospect for cancer treatment. Phycocyanin (PC) is a photoharvesting pigment and is also an attractive candidate for PDT. The multiwalled carbon nanotube (MWNT) is a potent candidate for PTT due to its extraordinary photo-to-thermal energy conversion efficiency upon excitation with near-infrared (NIR) light. To date, although MWNT-CS complexes have been well studied, no report about the reconjugation of MWNT-CS with phycocyanin is available in the literature. Here, by using water-soluble chitosan (CS), we prepared and characterized a novel biomaterial, MWNT-CS-PC, with the potential for PDT and PTT. The cytotoxicity experiments found that MWNT-CS-PC exhibited cell growth inhibition activity. Moreover, with irradiation of NIR light (808 nm) or visible light (532 nm), the photoinduced cytotoxicity was indeed enhanced. These results suggest that MWNT-CS-PC may potentially serve as a future photodynamic and photothermal therapy for cancer.