Development of a web-based tool for estimating individualized survival curves in glioblastoma using clinical, mRNA, and tumor microenvironment features with fusion techniques.

OBJECTIVE: Glioblastoma (GBM), one of the most common brain tumors, is known for its low survival rates and poor treatment responses. This study aims to create a robust predictive model that integrates multiple feature types, including clinical data, RNA expression, and tumor microenvironment data, using fusion techniques to enhance model performance. METHODS: We obtained data from the SEER database to assess the impact of nine demographic and clinical features on the survival of 58,495 GBM patients and built predictive machine learning models. Additionally, mRNA expression data from 600 GBM patients from TCGA, CGGA, and GEO were analyzed. We used Cox regression and LASSO to create a gene signature, which was compared against 13 published signatures for accuracy. Twenty-one machine learning models were applied to predict survival at multiple time points. Finally, we integrated multiple feature types using fusion techniques and developed a Shiny app to provide survival predictions for GBM patients. RESULTS: Using the SEER database, we constructed machine learning models based on nine clinical variables: age, gender, marital status, race, tumor site, laterality, surgery, chemotherapy, and radiation therapy. The best-performing model achieved AUC values of 0.775, 0.728, 0.692, and 0.683 for predicting survival at 6, 12, 18, and 24 months in the testing cohort. In the merged TCGA, CGGA, and GEO cohorts, we identified 11 genes to develop predictive models. These 11 genes outperformed 13 other published gene signatures in predicting the prognosis of GBM. When incorporating mRNA features, tumor microenvironment features, and clinical variables into the fusion models, the AUC values for predicting survival at 6, 12, 18, and 24 months were 0.641, 0.624, 0.655, and 0.637, respectively. A user-friendly tool for predicting the survival curve of individual GBM patients is available at https://zzubioinfo.shinyapps.io/mlGBM/ . CONCLUSIONS: Our study provides a web-based tool that includes two modules: one for predicting survival curves using only clinical variables, and another that integrates multiple feature types for more comprehensive predictions.

Retrospective clinical and microbiologic analysis of metagenomic next-generation sequencing in the microbiological diagnosis of cutaneous infectious granulomas.

BACKGROUND: Cutaneous infectious granulomas (CIG) are localized and chronic skin infection caused by a variety of pathogens such as protozoans, bacteria, worms, viruses and fungi. The diagnosis of CIG is difficult because microbiological examination shows low sensitivity and the histomorphological findings of CIG caused by different pathogens are commonly difficult to be distinguished. OBJECTIVE: The objective of this study is to explore the application of mNGS in tissue sample testing for CIG cases, and to compare mNGS with traditional microbiological methods by evaluating sensitivity and specificity. METHODS: We conducted a retrospective study at the Department of Dermatology of Sun Yat-sen Memorial Hospital, Sun Yat-sen University from January 1st, 2020, to May 31st, 2024. Specimens from CIG patients with a clinical presentation of cutaneous infection that was supported by histological examination were retrospectively enrolled. Specimens were delivered to be tested for microbiological examinations and mNGS. RESULTS: Our data show that mNGS detected Non-tuberculosis mycobacteria, Mycobacterium tuberculosis, fungi and bacteria in CIG. Compared to culture, mNGS showed a higher positive rate (80.77% vs. 57.7%) with high sensitivity rate (100%) and negative predictive value (100%). In addition, mNGS can detect more pathogens in one sample and can be used to detect variable samples including the samples of paraffin-embedded tissue with shorter detective time. Of the 21 patients who showed clinical improvement within a 30-day follow-up, eighteen had their treatments adjusted, including fifteen who continued treatment based on the results of mNGS. CONCLUSIONS: mNGS could provide a potentially rapid and effective alternative detection method for diagnosis of cutaneous infectious granulomas and mNGS results may affect the clinical prognosis resulting from enabling the patients to initiate timely treatment.

Role of Dectin-1 in immune response of macrophages induced by Fonsecaea monophora wild strain and melanin-deficient mutant strain.

Chromoblastomycosis is a chronic granulomatous subcutaneous fungal disease caused mainly by Fonsecaea monophora in southern China. Melanin is an important virulence factor in wild strain (Mel+), and the strains lack of the polyketide synthase gene is a melanin-deficient mutant strain (Mel-). We investigated the effect of melanin in F. monophora on Dectin-1 receptor-mediated immune responses in macrophages. Conidia and tiny hyphae of Mel+ and Mel- were co-cultured with THP-1 macrophages expressing normal or low levels of Dectin-1. Compare the killing rate, phagocytosis rate, and expression levels of the inflammatory cytokines tumour necrosis factor-α, interleukin-1ß, interleukin-6, and nitric oxide in each group. The results showed that the killing rate, phagocytosis rate, and pro-inflammatory factor levels of Mel+ infected macrophages with normal expression of Dectin-1 were lower than those of Mel-. And the knockdown of Dectin-1 inhibited the phagocytic rate, killing rate, and proinflammatory factor expression in macrophages infected with Mel+ and Mel-. And there was no significant difference in the above indexes between Mel+ and Mel- groups in Dectin-1 knockdown macrophages. In summary, the study reveals that melanin of F. monophora inhibits the immune response effect of the host by hindering its binding to Dectin-1 on the surface of macrophage, which may lead to persistent fungal infections.

Reproductive Toxicity and Teratogenicity of Fluorene-9-bisphenol on Chinese Medaka (Oryzias sinensis): A Study from Laboratory to Field.

Fluorene-9-bisphenol (BHPF), a bisphenol A (BPA) substitute, has been increasingly used as a material in syntheses of polymers that are widely used in road markings, artificial tracks, coating floors, building paints, etc., increasing the likelihood of BHPF contamination in the aquatic environment due to its release from the products. However, to date, it is unknown whether it may have actual impacts on fish in real environments. In this study, a 105-day exposure experiment of BHPF at various concentrations (0.01, 0.1, 1, and 10 µg/L) on Chinese medaka (Oryzias sinensis) was performed under laboratory conditions and found decreased fecundity, such as lower egg qualities and quantities, retarded oogenesis, and atretic follicles in the fish and deformed eyes and bodies in its F1 generation. Toxico-transcriptome analyses showed that estrogen-responsive genes were significantly suppressed by BHPF, indicating that antagonist properties of BHPF on estrogen receptors might be causes for the decreased fecundity. Field investigations (Beijing) demonstrated that BHPF was detectable in 60% surface waters, with a mean concentration of 10.49 ± 6.33 ng/L, by gas chromatography-mass spectrometry, and similar effects in wild Chinese medaka were also observed, some of which the parameters were found to be obviously correlated with the BHPF levels in corresponding waters.

Genome and transcriptome of Chinese medaka (Oryzias sinensis) and its uses as a model fish for evaluating estrogenicity of surface water.

Ecological toxicity assessments of contaminants in aquatic environments are of great concern. However, a dilemma in ecological toxicity assessments often arises when linking the effects found in model animals in the laboratory and the phenomena observed in wild fishes in the field due to species differences. Chinese medaka (Oryzias sinensis), widely distributed in East Asia, is a satisfactory model animal to assess aquatic environment in China. Here, we domesticated this species and assembled its genome (814 Mb) using next-generation sequencing (NGS). A total of 21,922 high-confidence genes with 41,306 transcripts were obtained and annotated, and their expression patterns in tissues were determined by RNA-sequencing. Six mostly sensitive biomarker genes, including vtg1, vtg3, vtg6, zp3a.2, zp2l1, and zp2.3 to estrogen exposure were screened and validated in the fish exposed to concentrations of estrone (E1), 17ß-estradiol (E2), and estriol (E3) under laboratory condition. Field investigations were then performed to evaluating the gene expression of biomarkers in wild Chinese medaka and levels of E1, E2, and E3 in the fish habitats. It was found that in 40 sampling sites, the biomarker genes were obviously highly expressed in the wild fish from about half sites, and the detection frequencies of E1, E2, and E3, were 97.5%, 42.5%, and 45% with mean concentrations of 82.48, 43.17, 52.69 ng/L, respectively. Correlation analyses of the biomarker gene expressions in the fish with the estrogens levels which were converted to EEQs showed good correlation, indicating that the environmental estrogens and estrogenicity of the surface water might adversely affect wild fishes. Finally, histologic examination of gonads in male wild Chinese medaka was performed and found the presence of intersex in the fish. This study facilitated the uses of Chinese medaka as a model animal for ecotoxicological studies.

Identification and Verification of Immune Subtype-Related lncRNAs in Clear Cell Renal Cell Carcinoma.

Background: According to clinical study results, immune checkpoint blockade (ICB) treatment enhances the survival outcome of patients with clear cell renal cell carcinoma (ccRCC). Previous research has divided ccRCC patients into immune subtypes with distinct ICB response rates. However, the study on the association between lncRNAs and ccRCC immune subtypes is lacking. Methods: Differentially expressed lncRNAs/mRNAs between two major immune subgroups were calculated. A weighted gene co-expression network analysis (WGCNA) was conducted to establish the lncRNA-mRNA co-expression network and select the key lncRNAs. Then, prognostic lncRNAs were selected from the network by the bioinformatics method. Next, the risk-score was estimated by lncRNA expression and their coefficients. Finally, a nomogram based on lncRNAs and clinical parameters was created to predict the prognosis of ccRCC. Results: LncRNAs and mRNAs associated with ccRCC immune subtypes were identified. The lncRNAs and mRNAs from a gene module closely linked to the immune subtype were used to construct a network. The KEGG pathways enriched in the network were related to immune system activation processes. These 8 lncRNAs (AL365361.1, LINC01934, AC090152.1, PCED1B-AS1, LINC00426, AC007728.2, AC243829.4, and LINC00158) were found to be positively correlated with immune cells of the tumor microenvironment. The C-index of the nomogram was 0.777, and the calibration curve data suggests that the nomogram has a high degree of discriminating capacity. Conclusion: In summary, we discovered core lncRNAs linked with immune subtypes and created corresponding lncRNA-mRNA networks. These lncRNAs are anticipated to have predictive significance for ccRCC and may provide insight into novel biomarkers for the disease.

Classification of Muscle Invasive Bladder Cancer to Predict Prognosis of Patients Treated with Immunotherapy.

Background: Recently, immunotherapies have been approved for advanced muscle invasive bladder cancer (MIBC) treatment, but only a small fraction of MIBC patients could achieve a durable drug response. Our study is aimed at identifying tumor microenvironment (TME) subtypes that have different immunotherapy response rates. Methods: The mRNA expression profiles of MIBC samples from seven discovery datasets (GSE13507, GSE31684, GSE32548, GSE32894, GSE48075, GSE48276, and GSE69795) were analyzed to identify TME subtypes. The identified TME subtypes were then validated by an independent dataset (TCGA-MIBC). The subtype-related biomarkers were discovered using computational analyses and then utilized to establish a random forest predictive model. The associations of TME subtypes with immunotherapy therapeutic responses were investigated in a group of patients who had been treated with immunotherapy. A prognostic index model was constructed using the subtype-related biomarkers. Two nomograms were built by the subtype-related biomarkers or the clinical parameters. Results: Two TME subtypes, including ECM-enriched class (EC) and immune-enriched class (IC), were found. EC was associated with greater extracellular matrix (ECM) pathways, and IC was correlated with immune pathways, respectively. Overall survival was significantly greater for tumors classified as IC, whereas the EC subtype had a worse prognosis. A total of nine genes (AKAP12, APOL3, CXCL13, CXCL9, GBP4, LRIG1, PEG3, PODN, and PTPRD) were selected by computational analyses to construct the random forest model. The area under the curve (AUC) values for this model were 0.827 and 0.767 in the testing and external validation datasets, respectively. Therapeutic response rates were greater in IC patients than in EC patients (28 percent vs. 18 percent). Patients with a high prognostic index had a poorer prognosis than those with a low prognostic index. The nomogram constructed from nine genes and stage achieved a C-index of 0.71. Conclusion: The present investigation defined two distinct TME subtypes and developed models to assess immunotherapeutic treatment outcomes.

PDGF signaling inhibits mitophagy in glioblastoma stem cells through N6-methyladenosine.

Dysregulated growth factor receptor pathways, RNA modifications, and metabolism each promote tumor heterogeneity. Here, we demonstrate that platelet-derived growth factor (PDGF) signaling induces N6-methyladenosine (m6A) accumulation in glioblastoma (GBM) stem cells (GSCs) to regulate mitophagy. PDGF ligands stimulate early growth response 1 (EGR1) transcription to induce methyltransferase-like 3 (METTL3) to promote GSC proliferation and self-renewal. Targeting the PDGF-METTL3 axis inhibits mitophagy by regulating m6A modification of optineurin (OPTN). Forced OPTN expression phenocopies PDGF inhibition, and OPTN levels portend longer survival of GBM patients; these results suggest a tumor-suppressive role for OPTN. Pharmacologic targeting of METTL3 augments anti-tumor efficacy of PDGF receptor (PDGFR) and mitophagy inhibitors in vitro and in vivo. Collectively, we define PDGF signaling as an upstream regulator of oncogenic m6A regulation, driving tumor metabolism to promote cancer stem cell maintenance, highlighting PDGF-METTL3-OPTN signaling as a GBM therapeutic target.

Etanercept treatment of Stevens-Johnson syndrome and toxic epidermal necrolysis.

BACKGROUND: Stevens-Johnson syndrome/toxic epidermal necrolysis (SJS/TEN) is a severe cutaneous adverse reaction to drugs with considerable morbidity and mortality. Immunomodulators for SJS/TEN including systemic corticosteroids and intravenous immunoglobulin (IVIG) have been widely used in clinical practice. Emerging evidence suggested the therapeutic effects of tumor necrosis factor-α antagonists on SJS/TEN. OBJECTIVE: To compare the efficacy and safety of IVIG and systemic steroids in conjunction with or without etanercept, a tumor necrosis factor-α inhibitor, for patients with SJS/TEN. METHODS: We undertook a retrospective review of 41 patients with SJS/TEN admitted to our institution from 2015 to February 2021. A total of 25 patients with integrated data were involved in this study, of which 14 patients were treated with IVIG and corticosteroids and 11 were in addition given etanercept. The clinical characteristics, duration of hospitalization, exposure time to high-dose steroids, and the total amount of systemic steroids were analyzed. RESULTS: In comparison to conventional therapy, conjunction with etanercept reduced the duration of hospitalization (13.5 vs 19.0 days; P = .01), the exposure time of high-dose steroids (7.1 vs 14.9 days; P = .01), and the overall amount of systemic steroid (925 mg vs 1412.5 mg; P = .03) in patients with SJS/TEN. No pronounced adverse effects were observed within 6 months of follow-up after the treatment. CONCLUSION: The add-in of etanercept at the time of initiating conventional therapy could be a superior option to accelerate disease recovery and reduce the high dose and total amount of systemic steroids without pronounced adverse events in patients with SJS/TEN.

The long non-coding RNA LIMT inhibits metastasis of hepatocellular carcinoma and is suppressed by EGF signaling.

BACKGROUND: The long non-coding RNA LIMT (lncRNA inhibiting metastasis) acts as a tumor suppressor factor in some cancers. However, the biological role of LIMT in hepatocellular carcinoma (HCC) has not been explored. METHODS AND RESULTS: Quantitative real-time PCR was performed to evaluate the expression of LIMT in HCC tissue. The effects of LIMT on tumor growth and metastasis were assessed by in vitro experiments, including colony formation and transwell assays, and in vivo in nude mouse models. Western blot analysis was used to evaluate the expression levels of proteins associated with epithelial-mesenchymal transition (EMT). LIMT expression was significantly lower in HCC than in normal liver tissue. Functionally, overexpression of LIMT repressed the proliferation, invasion, and EMT of HCC cells, while LIMT knockdown increased proliferation, invasion, and EMT of HCC cells in vitro. Furthermore, LIMT overexpression suppressed HCC growth and metastasis while silencing of LIMT had an opposite effect in vivo. Finally, LIMT overexpression reversed EGF-induced EMT. CONCLUSIONS: Our results suggest that LIMT could play an anti-cancer effect in HCC and might be a potential novel therapeutic target in HCC.

Identification of Cancer-Associated Fibroblast Subtype of Triple-Negative Breast Cancer.

Background: There is limited knowledge about the role of cancer-associated fibroblasts (CAF) in the tumor microenvironment of triple-negative breast cancer (TNBC). Methods: Three hundred and thirty-five TNBC samples from four datasets were retrieved and analyzed. In order to determine the CAF subtype by combining gene expression profiles, an unsupervised clustering analysis was adopted. The prognosis, enriched pathways, immune cells, immune scores, and tumor purity were compared between CAF subtypes. The genes with the highest importance were selected by bioinformatics analysis. The machine learning model was built to predict the TNBC CAF subtype by these selected genes. Results: TNBC samples were classified into two CAF subtypes (CAF+ and CAF-). The CAF- subtype of TNBC was linked to the longer overall survival and more immune cells than the CAF+ subtype. CAF- and CAF+ were enriched in immune-related pathways and extracellular matrix pathways, respectively. Bioinformatics analysis identified 9 CAF subtype-related markers (ADAMTS12, AEBP1, COL10A1, COL11A1, CXCL11, CXCR6, EDNRA, EPPK1, and WNT7B). We constructed a robust random forest model using these 9 genes, and the area under the curve (AUC) value of the model was 0.921. Conclusion: The current study identified CAF subtypes based on gene expression profiles and found that CAF subtypes have significantly different overall survival, immune cells, and immunotherapy response rates.

PSRR: A Web Server for Predicting the Regulation of miRNAs Expression by Small Molecules.

Background: MicroRNAs (miRNAs) play key roles in a variety of pathological processes by interacting with their specific target mRNAs for translation repression and may function as oncogenes (oncomiRs) or tumor suppressors (TSmiRs). Therefore, a web server that could predict the regulation relations between miRNAs and small molecules is expected to achieve implications for identifying potential therapeutic targets for anti-tumor drug development. Methods: Upon obtaining positive/known small molecule-miRNA regulation pairs from SM2miR, we generated a multitude of high-quality negative/unknown pairs by leveraging similarities between the small molecule structures. Using the pool of the positive and negative pairs, we created the Dataset1 and Dataset2 datasets specific to up-regulation and down-regulation pairs, respectively. Manifold machine learning algorithms were then employed to construct models of predicting up-regulation and down-regulation pairs on the training portion of pairs in Dataset1 and Dataset2, respectively. Prediction abilities of the resulting models were further examined by discovering potential small molecules to regulate oncogenic miRNAs identified from miRNA sequencing data of endometrial carcinoma samples. Results: The random forest algorithm outperformed four machine-learning algorithms by achieving the highest AUC values of 0.911 for the up-regulation model and 0.896 for the down-regulation model on the testing datasets. Moreover, the down-regulation and up-regulation models yielded the accuracy values of 0.91 and 0.90 on independent validation pairs, respectively. In a case study, our model showed highly-reliable results by confirming all top 10 predicted regulation pairs as experimentally validated pairs. Finally, our predicted binding affinities of oncogenic miRNAs and small molecules bore a close resemblance to the lowest binding energy profiles using molecular docking. Predictions of the final model are freely accessible through the PSRR web server at https://rnadrug.shinyapps.io/PSRR/. Conclusion: Our study provides a novel web server that could effectively predict the regulation of miRNAs expression by small molecules.

Analysis of synthetic cannabinoids in wastewater of major cities in China.

Synthetic cannabinoids (SCs) could pose serious health risks to its users. It is necessary to monitor its community consumption. Wastewater-based epidemiology is a potentially useful approach in this regard. However, limited research has been conducted to investigate the occurrence of SCs in wastewater. In this study, liquid chromatography-tandem mass spectrometry (LC-MS/MS) method was optimized to analyze 8 SCs and metabolites (in total 16 analytes) in wastewater. The limit of quantification for this method for certain analytes in wastewater was as low as 0.03 ng L-1. The validated method was used to examine the stability of the analytes under different conditions and to examine their occurrence in wastewater collected from 31 major cities across China. The overwhelming majority of the analytes were stable within 24 h, even at room temperature. However, 5-fluoro MDMB-PICA and MDMB-4en-PINACA butanoic acid metabolite showed significant degradation within 120 days even when stored at -20 °C or -80 °C. At least one cannabinoid or their metabolite was detected in 21 cities. In the city with the highest detection rate, at least one synthetic cannabinoid or metabolite was detected in 95% of samples of the city. MDMB-4en-PINACA butanoic acid metabolite had the highest detection frequency (in 13.4% of the samples). These results indicated that SCs were used in a significant number of Chinese cities. A few parent drugs (MDMB-4en-PINACA, ADB-BUTINACA, 5-fluoro MDMB-PICA, 4-fluoro MDMB-BUTINACA) were detected in a small fraction of wastewater samples, possibly due to release from manufacturing of these cannabinoids or illegal addition of electronic cigarettes.

Deep learning-based diagnosis models for onychomycosis in dermoscopy.

BACKGROUND: Onychomycosis is a common disease. Emerging noninvasive, real-time techniques such as dermoscopy and deep convolutional neural networks have been proposed for the diagnosis of onychomycosis. However, deep learning application in dermoscopic images has not been reported. OBJECTIVES: To explore the establishment of deep learning-based diagnostic models for onychomycosis in dermoscopy to improve the diagnostic efficiency and accuracy. METHODS: We evaluated the dermoscopic patterns of onychomycosis diagnosed at Sun Yat-sen Memorial Hospital, Guangzhou, China, from May 2019 to February 2021 and included nail psoriasis and traumatic onychodystrophy as control groups. Based on the dermoscopic images and the characteristic dermoscopic patterns of onychomycosis, we gain the faster region-based convolutional neural networks to distinguish between nail disorder and normal nail, onychomycosis and non-mycological nail disorder (nail psoriasis and traumatic onychodystrophy). The diagnostic performance is compared between deep learning-based diagnosis models and dermatologists. RESULTS: All of 1,155 dermoscopic images were collected, including onychomycosis (603 images), nail psoriasis (221 images), traumatic onychodystrophy (104 images) and normal cases (227 images). Statistical analyses revealed subungual keratosis, distal irregular termination, longitudinal striae, jagged edge, and marble-like turbid area, and cone-shaped keratosis were of high specificity (>82%) for onychomycosis diagnosis. The deep learning-based diagnosis models (ensemble model) showed test accuracy /specificity/ sensitivity /Youden index of (95.7%/98.8%/82.1%/0.809) and (87.5%/93.0%/78.5%/0.715) for nail disorder and onychomycosis. The diagnostic performance for onychomycosis using ensemble model was superior to 54 dermatologists. CONCLUSIONS: Our study demonstrated that onychomycosis had distinctive dermoscopic patterns, compared with nail psoriasis and traumatic onychodystrophy. The deep learning-based diagnosis models showed a diagnostic accuracy of onychomycosis, superior to dermatologists.

ISPRF: a machine learning model to predict the immune subtype of kidney cancer samples by four genes.

BACKGROUND: Clear cell renal cell carcinoma (ccRCC) is the most common type of renal cell carcinoma (RCC). Immunotherapy, especially anti-PD-1, is becoming a pillar of ccRCC treatment. However, precise biomarkers and robust models are needed to select the proper patients for immunotherapy. METHODS: A total of 831 ccRCC transcriptomic profiles were obtained from 6 datasets. Unsupervised clustering was performed to identify the immune subtypes among ccRCC samples based on immune cell enrichment scores. Weighted correlation network analysis (WGCNA) was used to identify hub genes distinguishing subtypes and related to prognosis. A machine learning model was established by a random forest (RF) algorithm and used on an open and free online website to predict the immune subtype. RESULTS: In the identified immune subtypes, subtype2 was enriched in immune cell enrichment scores and immunotherapy biomarkers. WGCNA analysis identified four hub genes related to immune subtypes, CTLA4, FOXP3, IFNG, and CD19. The RF model was constructed by mRNA expression of these four hub genes, and the value of area under the receiver operating characteristic curve (AUC) was 0.78. Subtype2 patients in the independent validation cohort had a better drug response and prognosis for immunotherapy treatment. Moreover, an open and free website was developed by the RF model (https://immunotype.shinyapps.io/ISPRF/). CONCLUSIONS: The current study constructs a model and provides a free online website that could identify suitable ccRCC patients for immunotherapy, and it is an important step forward to personalized treatment.

Gout-induced endothelial impairment: The role of SREBP2 transactivation of YAP.

Gout is a multifaceted inflammatory disease involving vascular impairments induced by hyperuricemia. Experiments using human umbilical vein endothelial cells treated with uric acid (UA), monosodium urate (MSU), or serum from gout patients showed increased expression of pro-inflammatory genes (ie, VCAM1, ICAM1, CYR61, CCNA1, and E2F1) with attendant increase in monocyte adhesion. Mechanistically, UA- or MSU-induced SREBP2 expression and its transcriptional activity. RNA sequencing analysis and real-time PCR showed the induction of YAP signaling and pro-inflammatory pathways in HUVECs transfected with adenovirus-SREBP2. The SREBP2 knockdown by siRNA partially abolished UA- or MSU-induced YAP activity, pro-inflammatory gene expression, and monocytes adhesion. Vascular intima from transgenic mice overexpressing SREBP2 in endothelium or mice with hyperuricemia exhibited activated YAP signaling and increased expression of pro-inflammatory genes. Betulin, an SREBP pharmacological inhibitor, attenuated the UA-, MSU-, or gout serum-induced endothelial cell inflammation and dysfunction. In the human study, endothelial cell function, assessed by EndoPAT, was negatively correlated with serum UA level among gouty patients and healthy controls. Collectively, UA or MSU causes endothelial dysfunction via SREBP2 transactivation of YAP. Betulin inhibition of SREBP2 may restrain gout-induced endothelial dysfunction.

Occurrence of areca alkaloids in wastewater of major Chinese cities.

Areca nut is a widely used psychoactive product that can cause multiple health problems, such as oral and pharyngeal cancers. Therefore, it is important to estimate areca nut use and the exposure levels of areca alkaloids that are responsible for its health effects. China is a major producer of areca nut and has a large number of areca nut chewers. In this study, occurrence of areca alkaloids and metabolites in wastewater of major cities across China was examined via wastewater-based epidemiology (WBE). Arecoline, arecaidine, and their metabolite, N-methylnipecotic acid (NMNA) were detected in the overwhelming majority of wastewater samples, with concentrations up to several µg/L. In contrast, guvacoline was only occasionally detected and guvacine was below detection limit in all samples, possibly due to their low contents in areca nut products, low excretion rates, and/or low stability in sewer systems. Strong positive correlations existed between arecoline, arecaidine, and NMNA concentrations. In addition, their loads were much higher in Central and Southern China. This geographic pattern is consistent with previous survey results on prevalence of areca nut chewing. These results indicate that WBE is a potentially useful method to monitor areca nut consumption and to estimate the exposure levels of areca alkaloids.

Long non­coding RNA H19 is involved in sorafenib resistance in hepatocellular carcinoma by upregulating miR­675.

Sorafenib is the first­line treatment for advanced hepatocellular carcinoma (HCC). Since many HCC patients experience drug resistance, there is an urgent need to discover more effective therapeutic strategies to overcome drug resistance. Long non­coding RNAs (lncRNAs) play an important role in tumor drug resistance. However, research on the role of lncRNA H19 in sorafenib resistance in HCC is quite limited. In the present study, CCK­8 assay, RT­qPCR, EdU staining, immunofluorescence staining, and western blot analysis were used to detect the effect of lncRNA H19 on sorafenib resistance of HCC cells. H19 expression was found to be negatively related to sorafenib sensitivity in HCC cells. Knockdown of lncRNA H19 elevated sorafenib sensitivity by suppressing epithelial­mesenchymal transition (EMT) in HCC cells. H19 upregulated miR­675 expression. miR­675 inhibitor decreased the cell viability in sorafenib­treated HCC cells, while miR­675 overexpression had the opposite effect on the treated cells. When the cells were pretreated with miR­675 mimic, H19 siRNA did not alter the effect of miR­675 on sorafenib sensitivity. In conclusion, our study provides new clues for further clinical treatment of sorafenib­resistant liver cancer patients.