Discovery of Raf Family Is a Milestone in Deciphering the Ras-Mediated Intracellular Signaling Pathway.

The Ras-Raf-MEK-ERK signaling pathway, the first well-established MAPK pathway, plays essential roles in cell proliferation, survival, differentiation and development. It is activated in over 40% of human cancers owing to mutations of Ras, membrane receptor tyrosine kinases and other oncogenes. The Raf family consists of three isoforms, A-Raf, B-Raf and C-Raf. Since the first discovery of a truncated mutant of C-Raf as a transforming oncogene carried by a murine retrovirus, forty years of extensive studies have provided a wealth of information on the mechanisms underlying the activation, regulation and biological functions of the Raf family. However, the mechanisms by which activation of A-Raf and C-Raf is accomplished are still not completely understood. In contrast, B-Raf can be easily activated by binding of Ras-GTP, followed by cis-autophosphorylation of the activation loop, which accounts for the fact that this isoform is frequently mutated in many cancers, especially melanoma. The identification of oncogenic B-Raf mutations has led to accelerated drug development that targets Raf signaling in cancer. However, the effort has not proved as effective as anticipated, inasmuch as the mechanism of Raf activation involves multiple steps, factors and phosphorylation of different sites, as well as complex interactions between Raf isoforms. In this review, we will focus on the physiological complexity of the regulation of Raf kinases and their connection to the ERK phosphorylation cascade and then discuss the role of Raf in tumorigenesis and the clinical application of Raf inhibitors in the treatment of cancer.

(20R)-Panaxadiol as a Natural Active Component with Anti-Obesity Effects on ob/ob Mice via Modulating the Gut Microbiota.

Obesity is an important cause of diseases such as type 2 diabetes, non-alcoholic fatty liver and atherosclerosis. The use of ingredients extracted from traditional Chinese medicine for weight loss is now receiving more and more attention. Ginseng has been recorded since ancient times for the treatment of diabetes. The (20R)-Panaxadiol (PD) belongs to the ginseng diol type compounds, which are moderately bioavailable and may remain in the intestinal tract for a longer period of time. This study investigated the potential positive effect of PD in ob/ob mice and evaluated its effect against obesity. The ob/ob mice were administered PD for ten weeks. Our study showed that PD could improve obesity, glucose tolerance disorder, as well as gut dysbiosis. Panaxadiol decreased ob/ob mice's Firmicutes/Bacteroidetes (F/B). Furthermore, 16S rRNA gene sequencing of the fecal microbiota suggested that PD changed the composition of the gut microbiota in ob/ob mice and modulated specific bacteria such as lactobacillus, prevotellace and so on. Moreover, PD improved the intestinal wall integrity. In conclusion, our results suggest that (20R)-Panaxadiol, as an active ingredient of the traditional Chinese medicinal herb ginseng, may improve obesity to some extent via improving gut microbiota.

Analysis of Serum Metabolomics in Rats with Osteoarthritis by Mass Spectrometry.

Osteoarthritis is a common multifactorial chronic disease that occurs in articular cartilage, subchondral bone, and periarticular tissue. The pathogenesis of OA is still unclear. To investigate the differences in serum metabolites between OA and the control group, liquid chromatography/mass spectrometry (LC/MS)-based metabolomics was used. To reveal the pathogenesis of OA, 12 SD male rats were randomly divided into control and OA groups using collagenase to induce OA for modeling, and serum was collected 7 days after modeling for testing. The OA group was distinguished from the control group by principal component analysis and orthogonal partial least squares-discriminant analysis, and six biomarkers were finally identified. These biomarkers were metabolized through tryptophan metabolism, glutamate metabolism, nitrogen metabolism, spermidine metabolism, and fatty acid metabolism pathways. The study identified metabolites that may be altered in OA, suggesting a role in OA through relevant metabolic pathways. Metabolomics, as an important tool for studying disease mechanisms, provides useful information for studying the metabolic mechanisms of OA.

Roflumilast prevents lymphotoxin α (TNF-ß)-induced inflammation activation and degradation of type 2 collagen in chondrocytes.

BACKGROUND AND PURPOSE: Osteoarthritis (OA) is a chronic disease accompanied by severe inflammation. The inflammation activation in the chondrocytes and the degradation of the extracellular matrix were reported to be involved in the progress of OA. Roflumilast is a selective PDE4 inhibitor used for treating chronic obstructive pulmonary disease (COPD) and exerts significant anti-inflammation effects. The present study aims to investigate the effects of Roflumilast on tumor necrosis factor-ß (TNF-ß)-induced inflammation activation and degradation of type 2 collagen in chondrocytes. METHODS: TNF-ß was used to establish the in-vitro inflammation model on ATDC5 chondrocytes. Quantitative real-time polymerase chain reaction (QRT-PCR) and western blot were used to determine the expression level of tumor necrosis factor receptor 2 (TNFR2), cyclooxygenase-2 (COX-2), inducible nitric oxide synthase (iNOS), matrix metalloproteinase 3 (MMP-3), matrix metalloproteinase 13 (MMP-13), type 2 collagen and nuclear factor kappa B (NF-κB) p65. The release of prostaglandin E2 (PGE2), MMP-3, and MMP-13 were evaluated by ELISA. The production of NO was determined by DAF-FM DA staining and the function of the NF-κB promoter was evaluated by Luciferase activity assay. RESULTS: TNFR2 and COX-2 were upregulated and the release of PGE2 was promoted by TNF-ß stimulation, which were all inhibited by Roflumilast. Roflumilast suppressed the promoted iNOS expression and NO production induced by TNF-ß. MMP-3 and MMP-13 were up-regulated, and type 2 collagen was down-regulated by TNF-ß stimulation, which were all reversed by Roflumilast. Roflumilast inhibited the promoted releasing of Interleukin-8 (IL-8) and Interleukin-12 (IL-12), expression of up-regulated NF-κB, and activation of NF-κB transcriptional activity induced by TNF-ß. CONCLUSION: Roflumilast may prevent TNF-ß-induced inflammation activation and degradation of type 2 collagen in chondrocytes.

Engineering next-generation oxygen-generating scaffolds to enhance bone regeneration.

In bone, an adequate oxygen (O2) supply is crucial during development, homeostasis, and healing. Oxygen-generating scaffolds (OGS) have demonstrated significant potential to enhance bone regeneration. However, the complexity of O2 delivery and signaling in vivo makes it challenging to tailor the design of OGS to precisely meet this biological requirement. We review recent advances in OGS and analyze persisting engineering and translational hurdles. We also discuss the potential of computational and machine learning (ML) models to facilitate the integration of novel imaging data with biological readouts and advanced biomanufacturing technologies. By elucidating how to tackle current challenges using cutting-edge technologies, we provide insights for transitioning from traditional to next-generation OGS to improve bone regeneration in patients.

3D printed O2-generating scaffolds enhance osteoprogenitor- and type H vessel recruitment during bone healing.

Oxygen (O2)-delivering tissue substitutes have shown tremendous potential for enhancing tissue regeneration, maturation, and healing. As O2 is both a metabolite and powerful signaling molecule, providing controlled delivery is crucial for optimizing its beneficial effects in the treatment of critical-sized injuries. Here, we report the design and fabrication of 3D-printed, biodegradable, O2-generating bone scaffold comprising calcium peroxide (CPO) that once hydrolytically activated, provides long-term generation of oxygen at a controlled, concentration-dependent manner, and polycaprolactone (PCL), a hydrophobic polymer that regulate the interaction of CPO with water, preventing burst release of O2 at early time points. When anoxic conditions were simulated in vitro, CPO-PCL scaffolds maintained the survival and proliferation of human adipose-derived stem/stromal cells (hASCs) relative to PCL-only controls. We assessed the in vivo osteogenic efficacy of hASC-seeded CPO-PCL scaffolds implanted in a non-healing critical-sized 4-mm calvarial defects in nude mice for 8 weeks. Even without exogenous osteoinductive factors, CPO-PCL scaffolds demonstrated increased new bone volume compared to PCL-only scaffolds as verified by both microcomputed tomography analysis and histological assessments. Lastly, we employed a quantitative 3D lightsheet microscopy platform to determine that O2-generating scaffolds had similar vascular volumes with slightly higher presence of CD31hiEmcnhi pro-osteogenic, type H vessels and increased number of Osterix+ skeletal progenitor cells relative to PCL-only scaffolds. In summary, 3D-printed O2 generating CPO-PCL scaffolds with tunable O2 release rates provide a facile, customizable strategy for effectively treating, craniofacial bone defects. STATEMENT OF SIGNIFICANCE: Oxygen(O2)-delivering bone substitutes show promise in defect repair applications by supplying O2 to the cells within or around the graft, improving cell survivability and enhancing bone matrix mineralization. A novel O2-generating bone scaffold has been 3D printed for the first-time which ensures patient and defect specificity. 3D printed calcium peroxide-polycaprolactone (CPO-PCL) bone scaffold provides uninterrupted O2 supply for 22 days allowing cell survival in deprived O2 and nutrient conditions. For the first time, O2-driven bone regenerative environment in mice calvaria has been captured by light-sheet imaging which illuminates abundance of Osterix+ cells, angiogenesis at a single cell resolution indicating active site of bone remodeling and growth in the presence of O2.

Association between serum potassium and Parkinson's disease in the US (NHANES 2005-2020).

Background: Evaluating the correlation between serum potassium and Parkinson's disease (PD) in US adults. Methods: A cross-sectional study was conducted on 20,495 adults aged 40 years or older using NHANES data from 2005 to 2020. The study utilized one-way logistic regression and multifactorial logistic regression to examine the correlation between serum potassium levels and PD. Additionally, a smoothed curve fitting approach was employed to assess the concentration-response relationship between serum potassium and PD. Stratified analyses were carried out to investigate potential interactions between serum potassium levels and PD with variables such as age, sex, race, marital status, education, BMI, smoking and medical conditions like coronary, stroke, diabetes, hypertension, and hypercholesterolemia. Results: In this study, a total of 20,495 participants, comprising 403 PD and 20,092 non-PD individuals, were included. After adjusted for covariates, multivariable logistic regression revealed that high serum potassium level was an independent risk factor for PD (OR:1.86, 95% CI:1.45 ~ 2.39, p < 0.01).The linear association between serum potassium and PD was described using fitted smoothing curves. Age, sex, race, education, marital, BMI, coronary, stroke, diabetes, hypertension and hypercholesterolemia were not significantly correlated with this positive connection, according to subgroup analysis and interaction testing (P for interaction >0.05). Conclusion: Serum potassium levels are elevated in patients with Parkinson's disease compared to non-PD patients. Additional prospective studies are required to explore the significance of serum potassium levels in individuals with Parkinson's disease.

Dose-Incorporated Deep Ensemble Learning for Improving Brain Metastasis Stereotactic Radiosurgery Outcome Prediction.

PURPOSE: To develop a novel deep ensemble learning model for accurate prediction of brain metastasis (BM) local control outcomes after stereotactic radiosurgery (SRS). METHODS AND MATERIALS: A total of 114 brain metastases (BMs) from 82 patients were evaluated, including 26 BMs that developed biopsy-confirmed local failure post-SRS. The SRS spatial dose distribution (Dmap) of each BM was registered to the planning contrast-enhanced T1 (T1-CE) magnetic resonance imaging (MRI). Axial slices of the Dmap, T1-CE, and planning target volume (PTV) segmentation (PTVseg) intersecting the BM center were extracted within a fixed field of view determined by the 60% isodose volume in Dmap. A spherical projection was implemented to transform planar image content onto a spherical surface using multiple projection centers, and the resultant T1-CE/Dmap/PTVseg projections were stacked as a 3-channel variable. Four Visual Geometry Group (VGG-19) deep encoders were used in an ensemble design, with each submodel using a different spherical projection formula as input for BM outcome prediction. In each submodel, clinical features after positional encoding were fused with VGG-19 deep features to generate logit results. The ensemble's outcome was synthesized from the 4 submodel results via logistic regression. In total, 10 model versions with random validation sample assignments were trained to study model robustness. Performance was compared with (1) a single VGG-19 encoder, (2) an ensemble with a T1-CE MRI as the sole image input after projections, and (3) an ensemble with the same image input design without clinical feature inclusion. RESULTS: The ensemble model achieved an excellent area under the receiver operating characteristic curve (AUCROC: 0.89 ± 0.02) with high sensitivity (0.82 ± 0.05), specificity (0.84 ± 0.11), and accuracy (0.84 ± 0.08) results. This outperformed the MRI-only VGG-19 encoder (sensitivity: 0.35 ± 0.01, AUCROC: 0.64 ± 0.08), the MRI-only deep ensemble (sensitivity: 0.60 ± 0.09, AUCROC: 0.68 ± 0.06), and the 3-channel ensemble without clinical feature fusion (sensitivity: 0.78 ± 0.08, AUCROC: 0.84 ± 0.03). CONCLUSIONS: Facilitated by the spherical image projection method, a deep ensemble model incorporating Dmap and clinical variables demonstrated excellent performance in predicting BM post-SRS local failure. Our novel approach could improve other radiation therapy outcome models and warrants further evaluation.

Synergistic treatment of osteosarcoma with biomimetic nanoparticles transporting doxorubicin and siRNA.

Introduction: Osteosarcoma tumors are the most common malignant bone tumors in children and adolescents. Their treatment usually requires surgical removal of all detectable cancerous tissue and multidrug chemotherapy; however, the prognosis for patients with unresectable or recurrent osteosarcoma is unfavorable. To make chemotherapy safer and more effective for osteosarcoma patients, biomimetic nanoparticles (NPs) camouflaged by mesenchymal stem cell membranes (MSCMs) were synthesized to induce osteosarcoma cell apoptosis by co-delivering the anticancer drug doxorubicin hydrochloride(DOX) and a small interfering RNA (siRNA). Importantly, these NPs have high biocompatibility and tumor-homing ability. This study aimed to improve the efficacy of osteosarcoma therapy by using the synergistic combination of DOX and an siRNA targeting the apoptosis suppressor gene survivin. Methods: Biomimetic NPs (DOX/siSUR-PLGA@MSCM NPs) were synthesized by coloading DOX and survivin siRNA (siSUR) into poly (lactide-co-glycolide acid) (PLGA) via a double-emulsion solvent evaporation method. The NPs were camouflaged by MSCMs to deliver both DOX and survivin-targeting siRNA and characterized and evaluated in terms of cellular uptake, in vitro release, in vitro and in vivo antitumor effects, and biosafety. Results: DOX/siSUR-PLGA@MSCM NPs had good tumor-homing ability due to the MSCMs modification. The drug-laden biomimetic NPs had good antitumor effects in homozygous MG63 tumor-bearing mice due to the synergistic effect of the drug combination. Conclusion: DOX/siSUR-PLGA@MSCM NPs can show improved therapeutic effects in osteosarcoma patients due to the combination of a chemotherapeutic drug and gene therapy based on their good tumor targeting and biosafety.

Predicting busulfan exposure in patients undergoing hematopoietic stem cell transplantation using machine learning techniques.

PURPOSE: This study aimed to establish an optimal model to predict the busulfan (BU) area under the curve at steady state (AUCss) by using machine learning (ML). PATIENTS AND METHODS: Seventy-nine adult patients (age ≥18 years) who received BU intravenously and underwent therapeutic drug monitoring from 2013 to 2021 at Fujian Medical University Union Hospital were enrolled in this retrospective study. The whole dataset was divided into a training group and test group at the ratio of 8:2. BU AUCss were considered as the target variable. Nine different ML algorithms and one population pharmacokinetic (pop PK) model were developed and validated, and their predictive performance was compared. RESULTS: All ML models were superior to the pop PK model (R2 = 0.751, MSE = 0.722, 14 and RMSE = 0.830) in model fitting and had better predictive accuracy. The ML model of BU AUCss established through support vector regression (SVR) and gradient boosted regression trees (GBRT) had the best predictive ability (R2 = 0.953 and 0.953, MSE = 0.323 and 0.326, and RMSE = 0.423 and 0.425). CONCLUSION: All the ML models can potentially be used to estimate BU AUCss with the aim of facilitating rational use of BU on the individualized level, especially models built by SVR and GBRT algorithms.