Gene expression and immune infiltration analysis comparing lesioned and preserved subchondral bone in osteoarthritis.

Background: Osteoarthritis (OA) is a degenerative disease requiring additional research. This study compared gene expression and immune infiltration between lesioned and preserved subchondral bone. The results were validated using multiple tissue datasets and experiments. Methods: Differentially expressed genes (DEGs) between the lesioned and preserved tibial plateaus of OA patients were identified in the GSE51588 dataset. Moreover, functional annotation and protein-protein interaction (PPI) network analyses were performed on the lesioned and preserved sides to explore potential therapeutic targets in OA subchondral bones. In addition, multiple tissues were used to screen coexpressed genes, and the expression levels of identified candidate DEGs in OA were measured by quantitative real-time polymerase chain reaction. Finally, an immune infiltration analysis was conducted. Results: A total of 1,010 DEGs were identified, 423 upregulated and 587 downregulated. The biological process (BP) terms enriched in the upregulated genes included "skeletal system development", "sister chromatid cohesion", and "ossification". Pathways were enriched in "Wnt signaling pathway" and "proteoglycans in cancer". The BP terms enriched in the downregulated genes included "inflammatory response", "xenobiotic metabolic process", and "positive regulation of inflammatory response". The enriched pathways included "neuroactive ligand-receptor interaction" and "AMP-activated protein kinase signaling". JUN, tumor necrosis factor α, and interleukin-1ß were the hub genes in the PPI network. Collagen XI A1 and leucine-rich repeat-containing 15 were screened from multiple datasets and experimentally validated. Immune infiltration analyses showed fewer infiltrating adipocytes and endothelial cells in the lesioned versus preserved samples. Conclusion: Our findings provide valuable information for future studies on the pathogenic mechanism of OA and potential therapeutic and diagnostic targets.

The value of synthetic MRI for quantitative analysis in the diagnosis of cervical lymph node metastasis in thyroid cancer.

BACKGROUND: Preoperative effective assessment of cervical lymph node metastasis in thyroid cancer plays an important role in formulating the surgical plan. PURPOSE: To investigate the significance of synthetic magnetic resonance imaging (MRI) for quantitatively analyzing cervical lymph node metastasis in thyroid cancer. MATERIAL AND METHODS: A retrospective analysis was conducted on 30 patients with thyroid cancer, consisting of 19 thyroid cancer nodules, 45 metastatic lymph nodes, and 47 non-metastatic lymph nodes. Regions of interest (ROIs) for each type of nodule were manually delineated using a workstation. Quantitative parameters, such as T1, T2, and proton density (PD) values, were automatically extracted from synthetic MRI scans. Statistical tests and regression analysis were performed to assess differences and correlations among the quantitative parameters. RESULTS: There were no significant differences in the quantitative parameter values between the primary tumor and metastatic lymph node tissues (P > 0.05). However, significant differences were observed in the quantitative parameters between the primary tumor and non-metastatic lymph node tissues and between the metastatic and non-metastatic lymph node tissues (P < 0.05). The diagnostic accuracy for cervical lymph node metastasis in thyroid cancer was 94.4% for the T1 and T2 combined index, 91.9% for T2, 86.8% for T1, and 71.7% for PD values. CONCLUSION: The application of quantitative parameters from synthetic MRI can assist clinicians in accurately planning surgical interventions for thyroid cancer patients before surgery.

Chondroprotective effects of Apolipoprotein D in knee osteoarthritis mice through the PI3K/AKT/mTOR signaling pathway.

BACKGROUND: Because the pathophysiology of osteoarthritis (OA) has not been fully elucidated, targeted treatments are lacking. In this study, we assessed the role and underlying mechanism apolipoprotein D (APOD) on the development of OA. METHODS: To establish an in vitro OA model, we extracted primary chondrocytes from the cartilage of C57BL/6 mice and stimulated the chondrocytes with IL-1ß. After APOD intervention or incubation with an overexpressing plasmid, we detected inflammatory-related markers using RT-qPCR, Western blotting, and ELISA. To detect apoptosis and autophagy-related markers, we used flow cytometry, immunofluorescence, and transmission electron microscopy (TEM). Finally, we measured the level of oxidative stress. We also used RNA-seq to identify the APOD-regulated downstream signaling pathways. We used an in vivo mice OA model of the anterior cruciate ligament transection (ACLT) and administered intra-articular adenovirus overexpressing APOD. To examine cartilage damage severity, we used immunohistochemical analysis (IHC), micro-CT, scanning electron microscopy (SEM), and Safranin O-fast green staining. RESULTS: Our results showed that APOD inhibited chondrocyte inflammation, degeneration, and apoptosis induced by IL-1ß. Additionally, APOD reversed autophagy inhibition and oxidative stress and also blocked activation of the PI3K/AKT/mTOR signaling pathway induced by IL-1ß. Finally, overexpression of the APOD gene through adenovirus was sufficient to mitigate OA progression. CONCLUSIONS: Our findings revealed that APOD had a chondroprotective role in OA progression by the PI3K/AKT/mTOR signaling pathway.

Uric acid-driven NLRP3 inflammasome activation triggers lens epithelial cell senescence and cataract formation.

Excessive uric acid (UA) is associated with age-related cataract. A previous study showed that a high UA level in the aqueous humor stimulated the senescence of lens epithelial cells (LECs), leading to cataract progression. To better understand the underlying mechanisms, we investigated UA-driven senescence in human lens tissue samples obtained during surgery, rat lens organ cultures, and in vivo experiments, using senescence-associated ß-galactosidase (SA-ß-gal) staining, electronic microscopy, Western blotting, and histological analyses. Initially, we identified markedly higher expressions of NLRP3 and caspase-1 in the lens capsules of hyper-uricemic patients compared to normo-uricemic patients. This increase was accompanied by a significant rise in the SA-ß-gal positive rate. We next built a cataract model in which rat lenses in an organ culture system were treated with an increasing dosage of UA. Notably, opacification was apparent in the lenses treated with 800 µM of UA starting on the fifth day. Mechanistically, UA treatment not only significantly induced the expression of NLRP3, caspase-1, and IL-1ß, but also upregulated the levels of SA-ß-gal and the senescence regulators p53 and p21. These effects were fully reversed, and lens opacification was ameliorated by the addition of MCC950, a selective NLRP3 antagonist. Moreover, an in vivo model showed that intravitreal UA injection rapidly induced cataract phenotypes within 21 days, an effect significantly mitigated by co-injection with MCC950. Together, our findings suggest that targeting the UA-induced NLRP3 inflammasome with MCC950 could be a promising strategy for preventing cataract formation associated with inflammageing.

Synergistic Interaction between Metal Single-Atoms and Defective WO3- x Nanosheets for Enhanced Sonodynamic Cancer Therapy.

Although metal single-atom (SA)-based nanomaterials are explored as sonosensitizers for sonodynamic therapy (SDT), they normally exhibit poor activities and need to combine with other therapeutic strategies. Herein, the deposition of metal SAs on oxygen vacancy (OV)-rich WO3- x nanosheets to generate a synergistic effect for efficient SDT is reported. Crystalline WO3 and OV-rich WO3- x nanosheets are first prepared by simple calcination of the WO3·H2O nanosheets under an air and N2 atmosphere, respectively. Pt, Cu, Fe, Co, and Ni metal SAs are then deposited on WO3- x nanosheets to obtain metal SA-decorated WO3- x nanocomposites (M-WO3- x). Importantly, the Cu-WO3- x sonosensitizer exhibits a much higher activity for ultrasound (US)-induced production of reactive oxygen species than that of the WO3- x and Cu SA-decorated WO3, which is also higher than other M-WO3- x nanosheets. Both the experimental and theoretical results suggest that the excellent SDT performance of the Cu-WO3- x nanosheets should be attributed to the synergistic effect between Cu SAs and WO3- x OVs. Therefore, after polyethylene glycol modification, the Cu-WO3- x can quickly kill cancer cells in vitro and effectively eradicate tumors in vivo under US irradiation. Transcriptome sequencing analysis and further molecular validation suggest that the Cu-WO3- x-mediated SDT-activated apoptosis and TNF signaling pathways are potential drivers of tumor apoptosis induction.

Release characteristics of volatile organic compounds at residential garbage collection points: a case study of Hangzhou, China.

With the implementation of garbage classification, perishable waste has become increasingly concentrated. This has led to a significant change in the VOC release characteristics at residential garbage collection points, posing a potential risk with unknown characteristics. This study investigated the release characteristics, odor pollution, and health risks of VOCs at garbage collection points under different classification effectiveness, seasons, garbage drop-off periods, and garbage collection point types. The results showed that the average concentration of VOCs released from the garbage sorting collection points (SPs) was 341.43 ± 261.16 µg/m3, and oxygenated compounds (e.g., ethyl acetate and acetone) were the main VOC components. The VOC concentration increased as the community classification effectiveness improved, and it was higher in summer (followed by spring, autumn, and winter). Moreover, the VOC concentrations were higher in the evenings than in the mornings and at centralized garbage collection points (CPs) than at SPs. Further, odor activity value (OAV) assessments indicated that acrolein, styrene, and ethyl acetate were the critical odorous components, with an average OAV of 0.87 ± 0.85, implying marginal odor pollution in some communities. Health risk assessments further revealed that trichloroethylene, benzene, and chlorotoluene were the critical health risk substances, with an average carcinogenic risk (CR) value of 10-6-10-4, and a non-carcinogenic risk (HI) value < 1. These results indicated that HIs were acceptable, but potential CRs existed in the communities. Therefore, VOC pollution prevention and control measures should be urgently strengthened at the garbage collection points in high pollution risk scenarios.

Establishment and validation of an interactive artificial intelligence platform to predict postoperative ambulatory status for patients with metastatic spinal disease: a multicenter analysis.

BACKGROUND: Identification of patients with high-risk of experiencing inability to walk after surgery is important for surgeons to make therapeutic strategies for patients with metastatic spinal disease. However, there is a lack of clinical tool to assess postoperative ambulatory status for those patients. The emergence of artificial intelligence (AI) brings a promising opportunity to develop accurate prediction models. METHODS: This study collected 455 patients with metastatic spinal disease who underwent posterior decompressive surgery at three tertiary medical institutions. Of these, 220 patients were collected from one medical institution to form the model derivation cohort, while 89 and 146 patients were collected from two other medical institutions to form the external validation cohorts 1 and 2, respectively. Patients in the model derivation cohort were used to develop and internally validate models. To establish the interactive AI platform, machine learning techniques were used to develop prediction models, including logistic regression (LR), decision tree (DT), random forest (RF), extreme gradient boosting machine (eXGBM), support vector machine (SVM), and neural network (NN). Furthermore, to enhance the resilience of the study's model, an ensemble machine learning approach was employed using a soft-voting method by combining the results of the above six algorithms. A scoring system incorporating 10 evaluation metrics was used to comprehensively assess the prediction performance of the developed models. The scoring system had a total score of 0 to 60, with higher scores denoting better prediction performance. An interactive AI platform was further deployed via Streamlit. The prediction performance was compared between medical experts and the AI platform in assessing the risk of experiencing postoperative inability to walk among patients with metastatic spinal disease. RESULTS: Among all developed models, the ensemble model outperformed the six other models with the highest score of 57, followed by the eXGBM model (54), SVM model (50), and NN model (50). The ensemble model had the best performance in accuracy and calibration slope, and the second-best performance in precise, recall, specificity, area under the curve (AUC), Brier score, and log loss. The scores of the LR model, RF model, and DT model were 39, 46, and 26, respectively. External validation demonstrated that the ensemble model had an AUC value of 0.873 (95% CI: 0.809-0.936) in the external validation cohort 1 and 0.924 (95% CI: 0.890-0.959) in the external validation cohort 2. In the new ensemble machine learning model excluding the feature of the number of comorbidities, the AUC value was still as high as 0.916 (95% CI: 0.863-0.969). In addition, the AUC values of the new model were 0.880 (95% CI: 0.819-0.940) in the external validation cohort 1 and 0.922 (95% CI: 0.887-0.958) in the external validation cohort 2, indicating favorable generalization of the model. The interactive AI platform was further deployed online based on the final machine learning model, and it was available at https://postoperativeambulatory-izpdr6gsxxwhitr8fubutd.streamlit.app/ . By using the AI platform, researchers were able to obtain the individual predicted risk of postoperative inability to walk, gain insights into the key factors influencing the outcome, and find the stratified therapeutic recommendations. The AUC value obtained from the AI platform was significantly higher than the average AUC value achieved by the medical experts ( P <0.001), denoting that the AI platform obviously outperformed the individual medical experts. CONCLUSIONS: The study successfully develops and validates an interactive AI platform for evaluating the risk of postoperative loss of ambulatory ability in patients with metastatic spinal disease. This AI platform has the potential to serve as a valuable model for guiding healthcare professionals in implementing surgical plans and ultimately enhancing patient outcomes.

Precise Design of TiO2@CoOx Heterostructure via Atomic Layer Deposition for Synergistic Sono-Chemodynamic Oncotherapy.

Sonodynamic therapy (SDT), a tumor treatment modality with high tissue penetration and low side effects, is able to selectively kill tumor cells by producing cytotoxic reactive oxygen species (ROS) with ultrasound-triggered sonosensitizers. N-type inorganic semiconductor TiO2 has low ROS quantum yields under ultrasound irradiation and inadequate anti-tumor activity. Herein, by using atomic layer deposition (ALD) to create a heterojunction between porous TiO2 and CoOx, the sonodynamic therapy efficiency of TiO2 can be improved. Compared to conventional techniques, the high controllability of ALD allows for the delicate loading of CoOx nanoparticles into TiO2 pores, resulting in the precise tuning of the interfaces and energy band structures and ultimately optimal SDT properties. In addition, CoOx exhibits a cascade of H2O2→O2→·O2 - in response to the tumor microenvironment, which not only mitigates hypoxia during the SDT process, but also contributes to the effect of chemodynamic therapy (CDT). Correspondingly, the synergistic CDT/SDT treatment is successful in inhibiting tumor growth. Thus, ALD provides new avenues for catalytic tumor therapy and other pharmaceutical applications.

Operando Mobile Catalysis for Reverse Water Gas Shift Reaction.

Metal atoms on the support serve as active sites for many heterogeneous catalysts. However, the active metal sites on the support are conventionally described as static, and the intermediates adsorbed on the support far away from the active metal sites cannot be transformed. Herein, we report the first example of operando mobile catalysis to promote catalytic efficiency by enhancing the collision probability between active sites and reactants or reaction intermediates. Specifically, ligand-coordinated Pt single atoms (isolated MeCpPt- species) are bonded on CeO2 and transformed into mobile MeCpPt(H)CO complexes during the reverse water gas shift reaction for operando mobile catalysis. This strategy enables the conversion of inert carbonate intermediates on the CeO2 support. A turnover frequency (TOF) of 6358 mol CO2 molPt -1 ⋅ h-1 and 99 % CO selectivity at 300 °C is obtained for reverse water gas shift reaction, dramatically higher than those of Pt catalysts reported in the literature. Operando mobile catalysis presents a promising strategy for designing high-efficiency heterogeneous catalysts for various chemical reactions and applications.

Endovascular Denervation for the Improvement of Limb Ischemia in Patients with Peripheral Artery Disease: A Randomized Clinical Trial.

BACKGROUND: To evaluate the safety and efficacy of endovascular denervation (EDN) as an adjunct to percutaneous vascular intervention (PVI) for peripheral artery disease (PAD). METHODS: From August 2019 to April 2021, 38 eligible patients with PAD enrolled in this study were randomly and equally assigned into 2 groups: the PVI group and the PVI + EDN group treated with EDN at the iliac and femoral arteries before PVI. The primary endpoint was the improvement in the ankle brachial index at 6 months after the procedure. The secondary endpoints were transcutaneous oxygen pressure (TcPO2), Rutherford category, numerical rating scale score, and safety. RESULTS: The technical success rates of PVI and EDN were 100%, and no device-related or procedure-related major adverse events occurred in either group. Compared with PVI alone, PVI + EDN demonstrated a significant improvement in limb hemodynamics at 6 months (Δ ankle brachial index 0.44 ± 0.31 vs. 0.24 ± 0.15, P = 0.018). Microcirculatory perfusion of PAD was significantly better at 6 months in the PVI + EDN group (ΔTcPO2, 15.68 ± 16.72 vs. 4.95 ± 13.43, P = 0.036). The Rutherford category was significantly improved in the PVI + EDN group in comparison with the PVI group at the 3-month follow-up (100.00% vs. 68.42%, P = 0.02). The decrease in the numerical rating scale score in the PVI + EDN group was greater than that in the PVI group at 1 week following the procedure (3 [2-5] vs. 4 [4-6], P = 0.022). CONCLUSIONS: In this single-center pilot analysis of a heterogeneous cohort of patients with PAD, PVI with EDN demonstrated a significant improvement in limb ischemia at 6 months compared with PVI alone.

Overexpression of TMEM150A in glioblastoma multiforme patients correlated with dismal prognoses and compromised immune statuses.

Transmembrane proteins have exhibited a significant correlation with glioblastoma multiforme (GBM). The current study elucidates the roles of transmembrane protein 150A (TMEM150A) in GBM. Data on patients with GBM were collected from The Cancer Genome Atlas and Xena databases. The objective was to identify the expression levels of TMEM150A in patients with GBM, and evaluate its diagnostic and prognostic values, accomplished using the receiver operating characteristic and survival analyses. On a cellular level, Cell Counting Kit-8, Wound healing, and Transwell experiments were performed to gauge the impact of TMEM150A on cell growth and migration. The study further investigated the correlation between TMEM150A expression and immune status, along with ribonucleic acid (RNA) modifications in GBM. The findings demonstrated TMEM150A overexpression in the cancerous tissues of patients with GBM, with an area under the curve value of 0.95. TMEM150A overexpression was significantly correlated with poor prognostic indicators. TMEM150A overexpression and isocitrate dehydrogenase (IDH) mutation status were predictive of poor survival time among patients with GBM. In vitro experiments indicated that suppressing TMEM150A expression could inhibit GBM cell proliferation, migration, and invasion. Moreover, TMEM150A overexpression was associated with stromal, immune, and estimate scores, immune cells (such as the T helper (Th) 17 cells, Th2 cells, and regulatory T cells), cell markers, and RNA modifications. Therefore, TMEM150A overexpression might serve as a promising biomarker for predicting poor prognosis in patients with GBM. Inhibiting TMEM150A expression holds the potential for improving the survival time of patients with GBM.

Effect the accumulation of bioactive constituents of a medicinal plant (Salvia Miltiorrhiza Bge.) by arbuscular mycorrhizal fungi community.

BACKGROUND: Arbuscular mycorrhizal fungi (AMF) form symbiotic relationships with various terrestrial plants and have attracted considerable interest as biofertilizers for improving the quality and yield of medicinal plants. Despite the widespread distribution of AMFs in Salvia miltiorrhiza Bunge's roots, research on the impact of multiple AMFs on biomass and active ingredient accumulations has not been conducted. In this study, the effects of five native AMFs (Glomus formosanum, Septoglomus constrictum, Rhizophagus manihotis, Acaulospora laevis, and Ambispora gerdemannii) and twenty-six communities on the root biomass and active ingredient concentrations of S. miltiorrhiza were assessed using the total factor design method. RESULTS: Thirty-one treatment groups formed symbiotic relationships with S. miltiorrhiza based on the pot culture results, and the colonization rate ranged from 54.83% to 89.97%. AMF communities had higher colonization rates and total phenolic acid concentration than single AMF, and communities also appeared to have higher root fresh weight, dry weight, and total phenolic acid concentration than single inoculations. As AMF richness increased, there was a rising trend in root biomass and total tanshinone accumulations (ATTS), while total phenolic acid accumulations (ATP) showed a decreasing trend. This suggests that plant productivity was influenced by the AMF richness, with higher inoculation benefits observed when the communities contained three or four AMFs. Additionally, the affinities of AMF members were also connected to plant productivity. The inoculation effect of closely related AMFs within the same family, such as G. formosanum, S. constrictum, and R. manihotis, consistently yielded lower than that of mono-inoculation when any combinations were applied. The co-inoculation of S. miltiorrhiza with nearby or distant AMFs from two families, such as G. formosanum, R. manihotis, and Ac. laevis or Am. gerdemannii resulted in an increase of ATP and ATTS by more than 50%. AMF communities appear to be more beneficial to the yield of bioactive constituents than the single AMF, but overall community inoculation effects are related to the composition of AMFs and the relationship between members. CONCLUSION: This study reveals that the AMF community has great potential to improve the productivity and the accumulation of bioactive constituents in S. miltiorrhiza, indicating that it is an effective way to achieve sustainable agricultural development through using the AMF community.

Identification of a Prognostic Gene Signature Based on Lipid Metabolism-Related Genes in Esophageal Squamous Cell Carcinoma.

Background: Dysregulation of lipid metabolism is common in cancer. However, the molecular mechanism underlying lipid metabolism in esophageal squamous cell carcinoma (ESCC) and its effect on patient prognosis are not well understood. The objective of our study was to construct a lipid metabolism-related prognostic model to improve prognosis prediction in ESCC. Methods: We downloaded the mRNA expression profiles and corresponding survival data of patients with ESCC from the Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) databases. We performed differential expression analysis to identify differentially expressed lipid metabolism-related genes (DELMGs). We used Univariate Cox regression and least absolute shrinkage and selection operator (LASSO) analyses to establish a risk model in the GEO cohort and used data of patients with ESCC from the TCGA cohort for validation. We also explored the relationship between the risk model and the immune microenvironment via infiltrated immune cells and immune checkpoints. Results: The result showed that 132 unique DELMGs distinguished patients with ESCC from the controls. We identified four genes (ACAA1, ACOT11, B4GALNT1, and DDHD1) as prognostic gene expression signatures to construct a risk model. Patients were classified into high- and low-risk groups as per the signature-based risk score. We used the receiver operating characteristic (ROC) curve and the Kaplan-Meier (KM) survival analysis to validate the predictive performance of the 4-gene signature in both the training and validation sets. Infiltrated immune cells and immune checkpoints indicated a difference in the immune status between the two risk groups. Conclusion: The results of our study indicated that a prognostic model based on the 4-gene signature related to lipid metabolism was useful for the prediction of prognosis in patients with ESCC.

A Web-Based Calculator to Predict Early Death Among Patients With Bone Metastasis Using Machine Learning Techniques: Development and Validation Study.

BACKGROUND: Patients with bone metastasis often experience a significantly limited survival time, and a life expectancy of <3 months is generally regarded as a contraindication for extensive invasive surgeries. In this context, the accurate prediction of survival becomes very important since it serves as a crucial guide in making clinical decisions. OBJECTIVE: This study aimed to develop a machine learning-based web calculator that can provide an accurate assessment of the likelihood of early death among patients with bone metastasis. METHODS: This study analyzed a large cohort of 118,227 patients diagnosed with bone metastasis between 2010 and 2019 using the data obtained from a national cancer database. The entire cohort of patients was randomly split 9:1 into a training group (n=106,492) and a validation group (n=11,735). Six approaches-logistic regression, extreme gradient boosting machine, decision tree, random forest, neural network, and gradient boosting machine-were implemented in this study. The performance of these approaches was evaluated using 11 measures, and each approach was ranked based on its performance in each measure. Patients (n=332) from a teaching hospital were used as the external validation group, and external validation was performed using the optimal model. RESULTS: In the entire cohort, a substantial proportion of patients (43,305/118,227, 36.63%) experienced early death. Among the different approaches evaluated, the gradient boosting machine exhibited the highest score of prediction performance (54 points), followed by the neural network (52 points) and extreme gradient boosting machine (50 points). The gradient boosting machine demonstrated a favorable discrimination ability, with an area under the curve of 0.858 (95% CI 0.851-0.865). In addition, the calibration slope was 1.02, and the intercept-in-large value was -0.02, indicating good calibration of the model. Patients were divided into 2 risk groups using a threshold of 37% based on the gradient boosting machine. Patients in the high-risk group (3105/4315, 71.96%) were found to be 4.5 times more likely to experience early death compared with those in the low-risk group (1159/7420, 15.62%). External validation of the model demonstrated a high area under the curve of 0.847 (95% CI 0.798-0.895), indicating its robust performance. The model developed by the gradient boosting machine has been deployed on the internet as a calculator. CONCLUSIONS: This study develops a machine learning-based calculator to assess the probability of early death among patients with bone metastasis. The calculator has the potential to guide clinical decision-making and improve the care of patients with bone metastasis by identifying those at a higher risk of early death.

Catalyzing Generation and Stabilization of Oxygen Vacancies on CeO2-x Nanorods by Pt Nanoclusters as Nanozymes for Catalytic Therapy.

Although CeO2 nanomaterials have been widely explored as nanozymes for catalytic therapy, they still suffer from relatively low activities. Herein, the catalyzing generation and stabilization of oxygen vacancies on CeO2 nanorods by Pt nanoclusters via H2 gas reduction under mild temperature (350 °C) to obtain Pt/CeO2- x , which can serve as a highly efficient nanozyme for catalytic cancer therapy, is reported. The deposited Pt on CeO2 by the atomic layer deposition technique not only can serve as the catalyst to generate oxygen vacancies under mild temperature reduction through the hydrogen spillover effect, but also can stabilize the generated oxygen vacancies. Meanwhile, the oxygen vacancies also provide anchoring sites for Pt forming strong metal-support interactions and thus preventing their agglomerations. Importantly, the Pt/CeO2- x reduced at 350 °C (Pt/CeO2- x -350R) exhibits excellent enzyme-mimicking catalytic activity for generation of reactive oxygen species (e.g., ·OH) as compared to other control samples, including CeO2 , Pt/CeO2 , and Pt/CeO2- x reduced at other temperatures, thus achieving excellent performance for tumor-specific catalytic therapy to efficiently eliminate cancer cells in vitro and ablate tumors in vivo. The excellent enzyme-mimicking catalytic activity of Pt/CeO2- x -350R originates from the good catalytic activities of oxygen vacancy-rich CeO2- x and Pt nanoclusters.

Diversity-oriented synthesis of diterpenoid alkaloids yields a potent anti-inflammatory agent.

BACKGROUND: The diterpenoid alkaloids belong to a highly esteemed group of natural compounds, which display significant biological activities. It is a productive strategy to expand the chemical space of these intriguing natural compounds for drug discovery. METHODS: We prepared a series of new derivatives bearing diverse skeletons and functionalities from the diterpenoid alkaloids deltaline and talatisamine based on a diversity-oriented synthesis strategy. The anti-inflammatory activity of these derivatives was initially screened and evaluated by the release of nitric oxide (NO), tumor necrosis factor (TNF-α), and interleukin-6 (IL-6) in lipopolysaccharide (LPS)-activated RAW264.7 cells. Futhermore, the anti-inflammatory activity of the representative derivative 31a was validated in various inflammatory animal models, including phorbol ester 12-O-tetradecanoylphorbol-13-acetate (TPA)-induced mice ear edema, LPS-stimulated acute kidney injury, and collagen-induced arthritis (CIA). RESULTS: It was found that several derivatives were able to suppress the secretion of NO, TNF-α, and IL-6 in LPS-activated RAW264.7 cells. Compound 31a, one of the representative derivatives named as deltanaline, demonstrated the strongest anti-inflammatory effects in LPS-activated macrophages and three different animal models of inflammatory diseases by inhibiting nuclear factor kappa-B (NF-κB)/mitogen-activated protein kinase (MAPK) signaling and inducing autophagy. CONCLUSION: Deltanaline is a new structural compound derived from natural diterpenoid alkaloids, which may serve as a new lead compound for the treatment of inflammatory diseases.

The prognostic significance and immune correlation of SLC10A3 in low-grade gliomas revealed by bioinformatic analysis and multiple immunohistochemistry.

PURPOSE: Despite the fact that genetic risk factors contribute to low-grade gliomas (LGGs), the role of critical genes as prognostic and theraputic biomarkers is quite limited. This study is designed to comprehensively investigate the prognostic role and predictive ability of solute carrier family 10 member 3 (SLC10A3) for immunotherapy in LGGs. METHODS: We analyzed the prognostic value of SLC10A3 from multiple datasets of LGG patients, and explored its immune correlation via multiple algorithms. Finally, we independently confirmed the clinical significance and its immune correlation using the multiplex staining assay of LGG samples on the tissue microarray. RESULTS: SLC10A3 mRNA was up-regulated in LGGs compared with normal brain tissues, and correlated with tumor grade, histological type, IDH wide type and non-codel 1p19q. Up-regulation of SLC10A3 transcription was remarkably associated with shortened overall survival time compared with down-regulation in TCGA, CGGA and Rembrandt datasets, and SLC10A3 exhibited good predictive ability for survival outcomes among LGGs. Correlation analyses showed that SLC10A3 mRNA expression correlates well with the six immune check points and immune cells. When the expression and immune correlation of SLC10A3 at the translational level were verified via multiplex immunohistochemistry, expression of SLC10A3 protein was higher in LGG compared with normal tissues, and expression of SLC10A3 protein was correlated well with macrophage, CD4 + T cell and B cell. CONCLUSIONS: Up-regulation of SLC10A3 mRNA is statistically associated with adverse survival outcomes and immune infiltration among LGGs. SLC10A3 might be a reliable survival predictor and a promising immunotherapy target for LGG patients.

Development and validation of an ensemble machine-learning model for predicting early mortality among patients with bone metastases of hepatocellular carcinoma.

Purpose: Using an ensemble machine learning technique that incorporates the results of multiple machine learning algorithms, the study's objective is to build a reliable model to predict the early mortality among hepatocellular carcinoma (HCC) patients with bone metastases. Methods: We extracted a cohort of 124,770 patients with a diagnosis of hepatocellular carcinoma from the Surveillance, Epidemiology, and End Results (SEER) program and enrolled a cohort of 1897 patients who were diagnosed as having bone metastases. Patients with a survival time of 3 months or less were considered to have had early death. To compare patients with and without early mortality, subgroup analysis was used. Patients were randomly divided into two groups: a training cohort (n = 1509, 80%) and an internal testing cohort (n = 388, 20%). In the training cohort, five machine learning techniques were employed to train and optimize models for predicting early mortality, and an ensemble machine learning technique was used to generate risk probability in a way of soft voting, and it was able to combine the results from the multiply machine learning algorithms. The study employed both internal and external validations, and the key performance indicators included the area under the receiver operating characteristic curve (AUROC), Brier score, and calibration curve. Patients from two tertiary hospitals were chosen as the external testing cohorts (n = 98). Feature importance and reclassification were both operated in the study. Results: The early mortality was 55.5% (1052/1897). Eleven clinical characteristics were included as input features of machine learning models: sex (p = 0.019), marital status (p = 0.004), tumor stage (p = 0.025), node stage (p = 0.001), fibrosis score (p = 0.040), AFP level (p = 0.032), tumor size (p = 0.001), lung metastases (p < 0.001), cancer-directed surgery (p < 0.001), radiation (p < 0.001), and chemotherapy (p < 0.001). Application of the ensemble model in the internal testing population yielded an AUROC of 0.779 (95% confidence interval [CI]: 0.727-0.820), which was the largest AUROC among all models. Additionally, the ensemble model (0.191) outperformed the other five machine learning models in terms of Brier score. In terms of decision curves, the ensemble model also showed favorable clinical usefulness. External validation showed similar results; with an AUROC of 0.764 and Brier score of 0.195, the prediction performance was further improved after revision of the model. Feature importance demonstrated that the top three most crucial features were chemotherapy, radiation, and lung metastases based on the ensemble model. Reclassification of patients revealed a substantial difference in the two risk groups' actual probabilities of early mortality (74.38% vs. 31.35%, p < 0.001). Patients in the high-risk group had significantly shorter survival time than patients in the low-risk group (p < 0.001), according to the Kaplan-Meier survival curve. Conclusions: The ensemble machine learning model exhibits promising prediction performance for early mortality among HCC patients with bone metastases. With the aid of routinely accessible clinical characteristics, this model can be a trustworthy prognostic tool to predict the early death of those patients and facilitate clinical decision-making.

Inhibitory Effect of Hernandezine on the Proliferation of Hepatocellular Carcinoma.

Hepatocellular carcinoma (HCC) causes 830000 deaths every year and is becoming the third malignant tumor worldwide. One of the primary reasons is the lack of effective drugs. Hernandezine (HER), a bisbenzylisoquinoline alkaloid of Thalictrum simplex, has been confirmed to have antitumor activity. But there are few reports about its effect on HCC and the underlying mechanisms still remain unclear. Therefore, the antitumor effects and mechanisms of HER on HCC were evaluated in HepG2 and Hep3B cells. The in vitro experiments demonstrated that HER significantly induced G0/G1 phase arrest, inhibited the proliferation and promoted cell apoptosis in liver cancer cell lines. In the mechanisms, the antitumor effects of HER on liver cancer cells were mediated by phosphatidylinositol 3-kinase (PI3K)-protein kinase B (AKT) pathway and reactive oxygen species (ROS), simultaneously. In one way, HER inhibited the activities of PI3K-AKT pathway, which interrupt the dimer formation of cyclin-dependent kinase 4 (CDK4) and cyclin D1 (CCND1) and result to G0/G1 phase arrest. In another way, after HER treatment, ROS accumulated in liver cancer cells and caused mitochondria injury which further influenced the expression of apoptosis-related proteins and eventually resulted to HepG2 and Hep3B cell apoptosis. In addition, HER showed a tumor restrain function in HepG2 and Hep3B bearing nude mice. Overall, these findings indicated that HER is a promising antitumor drug, which may provide a new direction for clinical HCC treatment.