Systematic analysis of the role of LDHs subtype in pan-cancer demonstrates the importance of LDHD in the prognosis of hepatocellular carcinoma patients.

BACKGROUND: Lactate dehydrogenase (LDHs) is an enzyme involved in anaerobic glycolysis, including LDHA, LDHB, LDHC and LDHD. Given the regulatory role in the biological progression of certain tumors, we analyzed the role of LDHs in pan-cancers. METHODS: Cox regression, Kaplan-Meier curves, Receiver Operating Characteristic (ROC) curves, and correlation of clinical indicators in tumor patients were used to assess the prognostic significance of LDHs in pan-cancer. The TCGA, HPA, TIMER, UALCAN, TISIDB, and Cellminer databases were used to investigate the correlation between the expression of LDHs and immune subtypes, immune checkpoint genes, methylation levels, tumor mutational load, microsatellite instability, tumor-infiltrating immune cells and drug sensitivity. The cBioPortal database was also used to identify genomic abnormalities of LDHs in pan-cancer. A comprehensive assessment of the biological functions of LDHs was performed using GSEA. In vitro, HepG2 and Huh7 cells were transfected with LDHD siRNA and GFP-LDHD, the proliferation capacity of cells was examined using CCK-8, EdU, and colony formation assays; the migration and invasion of cells was detected by wound healing and transwell assays; western blotting was used to detect the levels of MMP-2, MMP-9, E-cadherin, N-cadherin and Akt phosphorylation. RESULTS: LDHs were differentially expressed in a variety of human tumor tissues. LDHs subtypes can act as pro-oncogenes or anti-oncogenes in different types of cancer and have an impact on the prognosis of patients with tumors by influencing their clinicopathological characteristics. LDHs were differentially expressed in tumor immune subtypes and molecular subtypes. In addition, LDHs expression correlated with immune checkpoint genes, tumor mutational load, and microsatellite instability. LDHD was identified to play an important role in the prognosis of HCC patients, according to a comprehensive analysis of LDHs in pan-cancer. In HepG2 and Huh7 cells, knockdown of LDHD promoted cell proliferation, migration, and invasion, promoted the protein expression levels of MMP-2, MMP-9, N-cadherin, and Akt phosphorylation, but inhibited the protein expression level of E-cadherin. In addition, LDHD overexpression showed the opposite changes. CONCLUSION: LDHs subtypes can be used as potential prognostic markers for certain cancers. Prognostic and immunotherapeutic analysis indicated that LDHD plays an important role in the prognosis of HCC patients. In vitro experiments revealed that LDHD can affect HCC proliferation, migration, and invasion by regulating MMPs expression and EMT via Akt signaling pathway, which provides a new perspective on the anti-cancer molecular mechanism of LDHD in HCC.

Artificial intelligence for the prediction of acute kidney injury during the perioperative period: systematic review and Meta-analysis of diagnostic test accuracy.

BACKGROUND: Acute kidney injury (AKI) is independently associated with morbidity and mortality in a wide range of surgical settings. Nowadays, with the increasing use of electronic health records (EHR), advances in patient information retrieval, and cost reduction in clinical informatics, artificial intelligence is increasingly being used to improve early recognition and management for perioperative AKI. However, there is no quantitative synthesis of the performance of these methods. We conducted this systematic review and meta-analysis to estimate the sensitivity and specificity of artificial intelligence for the prediction of acute kidney injury during the perioperative period. METHODS: Pubmed, Embase, and Cochrane Library were searched to 2nd October 2021. Studies presenting diagnostic performance of artificial intelligence in the early detection of perioperative acute kidney injury were included. True positives, false positives, true negatives and false negatives were pooled to collate specificity and sensitivity with 95% CIs and results were portrayed in forest plots. The risk of bias of eligible studies was assessed using the PROBAST tool. RESULTS: Nineteen studies involving 304,076 patients were included. Quantitative random-effects meta-analysis using the Rutter and Gatsonis hierarchical summary receiver operating characteristics (HSROC) model revealed pooled sensitivity, specificity, and diagnostic odds ratio of 0.77 (95% CI: 0.73 to 0.81),0.75 (95% CI: 0.71 to 0.80), and 10.7 (95% CI 8.5 to 13.5), respectively. Threshold effect was found to be the only source of heterogeneity, and there was no evidence of publication bias. CONCLUSIONS: Our review demonstrates the promising performance of artificial intelligence for early prediction of perioperative AKI. The limitations of lacking external validation performance and being conducted only at a single center should be overcome. TRIAL REGISTRATION: This study was not registered with PROSPERO.

YTHDC1-mediated VPS25 regulates cell cycle by targeting JAK-STAT signaling in human glioma cells.

BACKGROUND: Glioma is a common type of malignant brain tumor with a high mortality and relapse rate. The endosomal sorting complex required for transport (ESCRT) has been reported to be involved in tumorigenesis. However, the molecular mechanisms have not been clarified. METHODS: Bioinformatics was used to screen the ESCRT subunits highly expressed in glioma tissues from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. The function of the ESCRT subunits in glioma cells was examined in vitro. Transcriptome sequencing analyzed the target genes and signaling pathways affected by the ESCRT subunit. Finally, the relationship between m6A (N6-methyladenosine) modification and high expression of the ESCRT subunit was studied. RESULTS: VPS25 was upregulated in glioma tissues, which was correlated with poor prognosis in glioma patients. Furthermore, VPS25 knockdown inhibited the proliferation, blocked the cell cycle, and promoted apoptosis in glioma cells. Meanwhile, VPS25 induced a G0/G1 phase arrest of the cell cycle in glioma cells by directly mediating p21, CDK2, and cyclin E expression, and JAK-signal transducer and activator of transcription (STAT) activation. Finally, YTHDC1 inhibited glioma proliferation by reducing the expression of VPS25. CONCLUSION: These results suggest that VPS25 is a promising prognostic indicator and a potential therapeutic target for glioma.

RGD-modified multifunctional nanoparticles encapsulating salvianolic acid A for targeted treatment of choroidal neovascularization.

BACKGROUND: The development of alternative anti-angiogenesis therapy for choroidal neovascularization (CNV) remains a great challenge. Nanoparticle systems have emerged as a new form of drug delivery in ocular diseases. Here, we report the construction and characterization of arginine-glycine-aspartic acid (RGD)-conjugated polyethyleneimine (PEI) as a vehicle to load antioxidant salvianolic acid A (SAA) for targeted anti-angiogenesis therapy of CNV. In this study, PEI was consecutively modified with polyethylene glycol (PEG) conjugated RGD segments, 3-(4'-hydroxyphenyl) propionic acid-Osu (HPAO), and fluorescein isothiocyanate (FI), followed by acetylation of the remaining PEI surface amines to generate the multifunctional PEI vehicle PEI.NHAc-FI-HPAO-(PEG-RGD) (for short, RGD-PEI). The formed RGD-PEI was utilized as an effective vehicle platform to load SAA. RESULTS: We showed that RGD-PEI/SAA complexes displayed desirable water dispersibility, low cytotoxicity, and sustainable release of SAA under different pH conditions. It could be specifically taken up by retinal pigment epithelium (RPE) cells which highly expressed ɑvß5 integrin receptors in vitro and selectively accumulated in CNV lesions in vivo. Moreover, the complexes displayed specific therapeutic efficacy in a mouse model of laser induced CNV, and the slow elimination of the complexes in the vitreous cavity was verified by SPECT imaging after 131I radiolabeling. The histological examinations further confirmed the biocompatibility of RGD-PEI/SAA. CONCLUSIONS: The results suggest that the designed RGD-PEI/SAA complexes may be a potential alternative anti-angiogenesis therapy for posterior ocular neovascular diseases.

Auditory Messages for Intersection Movement Assist (IMA) Systems: Effects of Speech- and Nonspeech-Based Cues.

OBJECTIVE: The objective of this study was to assess the effects of different warning messages for an Intersection Movement Assist (IMA) based on drivers' ability to avoid a potential safety hazard. BACKGROUND: An IMA system can detect hazards and warn drivers when it is unsafe to enter an intersection. The effects of different warning information conveyed by these systems are still unknown. METHOD: A driving simulator study with 80 participants was conducted with a red light running (RLR) scenario using a 5 (warnings) x 2 (training) between-subject design. IMA warnings included the messages "Danger," "Brake now," "Vehicle on your left," a beep, and no IMA warning. Training was provided to half of the participants. Analysis of variance and logistic regression models were used to examine differences in drivers' avoidance behavior. RESULTS: The analyses showed that all tested warning messages can significantly enhance drivers' avoidance performance. Significant differences were observed in crash occurrence, avoidance behavior (i.e., reaction time and speed change), and eye movements (i.e., fixation pattern and time to first fixation). The effects of training also differed given the warning message provided. CONCLUSION: The "Brake now" message performed best in reducing crash involvement and prompted better avoidance performance. The "Danger" and "Vehicle on your left" messages improved drivers' hazard detection ability. The training showed a potential to enhance the effectiveness of nonspeech warning messages. APPLICATION: The findings of this study can help designers and engineers better design IMA warning messages for RLR scenarios.

Tetramethylpyrazine protects mice retinas against sodium iodate-induced oxidative injury.

Purpose: To observe the effects of tetramethylpyrazine (TMP) on mice retinas injured by sodium iodate (NaIO3). Methods: Male mice (n = 45) were randomly divided into three groups: the control group (Group C), the NaIO3-degenerated group (Group I), and the TMP-treated group (TMP group). The Group I mice were intraperitoneally injected with 35 mg/kg NaIO3. The Group C mice were injected with similar volumes of PBS. The TMP group mice were intraperitoneally injected with 80 mg/kg TMP starting 24 h after NaIO3 administration once a day for 14 days. Fundus photography, optical coherence tomography (OCT), electroretinography (ERG), hematoxylin and eosin (H&E) staining, terminal deoxynucleotidyl transferase dUTP nick-end labeling (TUNEL) assay, and western blotting were used to assess the effects of TMP on mice retinas at day 3, 7, and 14 after NaIO3 administration. Results: TMP effectively prevented the decrease in the thicknesses of the retinas and the outer nuclear layer (ONL), and effectively alleviated the functional decline in the retinas after NaIO3 administration. TMP significantly decreased the number of TUNEL-positive cells in retinas. In addition, TMP rapidly increased the expression of Nrf2 and HO-1 and decreased BAX expression in mice retinas after NaIO3 injection. Conclusions: TMP alleviates morphological and functional retinal damage in mice exposed to NaIO3 and reduces retinal apoptosis.

Analyzing the lncRNA, miRNA, and mRNA-associated ceRNA networks to reveal potential prognostic biomarkers for glioblastoma multiforme.

BACKGROUND: Glioblastoma multiforme (GBM) is the most seriously brain tumor with extremely poor prognosis. Recent research has demonstrated that competitive endogenous RNA (ceRNA) network which long noncoding RNAs (lncRNAs) act as microRNA (miRNA) sponges to regulate mRNA expression were closely related to tumor development. However, the regulatory mechanisms and functional roles of ceRNA network in the pathogenesis of GBM are remaining poorly understood. METHODS: In this study, we systematically analyzed the expression profiles of lncRNA and mRNA (GSE51146 dataset) and miRNA (GSE65626 dataset) from GEO database. Then, we constructed a ceRNA network with the dysregulated genes by bioinformatics methods. The TCGA and GSE4290 dataset were used to confirm the expression and prognostic value of candidate mRNAs. RESULTS: In total, 3413 differentially expressed lncRNAs and mRNAs, 305 differentially expressed miRNAs were indentified in GBM samples. Then a ceRNA network containing 3 lncRNAs, 5 miRNAs, and 60 mRNAs was constructed. The overall survival analysis of TCGA databases indicated that two mRNAs (C1s and HSD3B7) were remarkly related with the prognosis of GBM. CONCLUSION: The ceRNA network may increase our understanding to the pathogenesis of GBM. In general, the candidate mRNAs from the ceRNA network can be predicted as new therapeutic targets and prognostic biomarkers for GBM.

7-Ketocholesterol disturbs RPE cells phagocytosis of the outer segment of photoreceptor and induces inflammation through ERK signaling pathway.

7-Ketocholesterol (7KCh), an oxidized form of cholesterol, is present at a high level in drusen and has been believed to play a role in the pathogenesis of age-related macular degeneration (AMD). Therefore, we developed a rat model to study the direct impact of 7KCh on retina. We delivered 7KCh to the rat retina by intravitreal injection using hydroxypropyl-ß-cyclodextrin as a vehicle. We observed that 7KCh mainly deposited in the retinal pigment epithelial (RPE) cells and induced marked photoreceptor apoptosis. Transmission electron microscope examination demonstrated cytoplasmic vacuoles in RPE cells and the microvilli detached from the outer segment after 7KCh treatment. In vitro experiments also revealed that RPE cells could take up 7KCh in culture. Moreover, 7KCh up-regulated IL-1ßmRNA, TNF-αmRNA, IL-6 mRNA, and IL-1ß secretion of RPE. U0126, a MEK1/2 inhibitor, down regulated the expression of these inflammation factors. Our findings may help elucidate the potential role of 7KCh in the pathogenesis of AMD.

Measurement of retinal function with flash-electroretinography in Chinese patients with hyperlipidemia.

BACKGROUND: We used flash electroretinography (F-ERG) to determine if retinal function was impaired in patients with hyperlipidemia, including visual acuity and fundus morphological changes, and to identify predictors of impaired retinal function in hyperlipidemia patients. METHODS: This was a prospective case-control study (Shanghai, China; February 2011 to January 2012) in 696 hyperlipidemia patients and 136 healthy controls. Exclusion criteria included best-corrected visual acuity <0.6, previous intraocular surgery, and chronic comorbidities. Each participant underwent a comprehensive series of ophthalmologic examinations, and standard F-ERG examination. Data were analyzed using t-tests and multivariate analysis. RESULTS: Six hundred and twenty-six hyperlipidemia patients (57.69 ± 14.01 years; 59.58% female) and 120 healthy controls (55.13 ± 14.03 years; 60% female) were included in the final analysis. After adjustment for age and gender using multivariate covariance analysis, F-ERG result revealed significantly lower response amplitudes in the hyperlipidemia group (P < 0.05), and longer implicit times (P < 0.05) than the control group. F-ERG parameters were significantly different between the two groups. Stepwise multiple linear regression analysis showed that serum LDL levels (P < 0.001), BMI (P < 0.001), duration of hyperlipidemia (P < 0.001), and serum HDL levels (P = 0.03) were negatively correlated with ΣOps. CONCLUSION: The present study suggests that the retinal function of hyperlipidemia patients was significantly lower than in healthy controls, even before the occurrence of pathological changes in the fundus.

Bioinformatics analysis identifies coagulation factor II receptor as a potential biomarker in stomach adenocarcinoma.

Coagulation factor 2 thrombin receptor (F2R), a member of the G protein-coupled receptor family, plays an important role in regulating blood clotting through protein hydrolytic cleavage mediated receptor activation. However, the underlying biological mechanisms by which F2R affects the development of gastric adenocarcinoma are not fully understood. This study aimed to systematically analyze the role of F2R in gastric adenocarcinoma. Stomach adenocarcinoma (STAD)-related gene microarray data and corresponding clinicopathological information were downloaded from the Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. Differential expression genes (DEGs) associated with F2R were analyzed using Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), gene set enrichment analysis (GSEA), and protein-protein interaction (PPI) networks. F2R mRNA expression data were utilized to estimate stromal cell and immune cell scores in gastric cancer tissue samples, including stromal score, immune score, and ESTIMATE score, derived from single-sample enrichment studies. Analysis of TCGA and GEO databases revealed significantly higher F2R expression in STAD tissues compared to normal tissues. Patients with high F2R expression had shorter survival times than those with low F2R expression. F2R expression was significantly correlated with tumor (T) stage, node (N) stage, histological grade and pathological stage. Enrichment analysis of F2R-related genes showed that GO terms were mainly related to circulation-mediated human immune response, immunoglobulin, cell recognition and phagocytosis. KEGG analysis indicated associations to extracellular matrix (ECM) receptor interactions, neuroactive ligand-receptor interactions, the phosphoinositide-3-kinase-protein kinase B/Akt (PI3K-AKT) signaling pathway, the Wnt signaling pathway and the transforming growth factor-beta (TGF-ß) signaling pathway. GSEA revealed connections to DNA replication, the Janus kinase/signal transducers and activators of transcription (JAK-STAT) signaling pathway, the mitogen-activated protein kinase (MAPK) signaling pathway and oxidative phosphorylation. Drug sensitivity analysis demonstrated positive correlations between F2R and several drugs, including BEZ235, CGP-60474, Dasatinib, HG-6-64-1, Aazopanib, Rapamycin, Sunitinib and TGX221, while negative correlation with CP724714, FH535, GSK1904529A, JNK-9L, LY317615, pyrimidine, rTRAIL and Vinorelbine. Knocking down F2R in GC cell lines resulted in slowed proliferation, migration, and invasion. All statistical analyses were performed using R software (version 4.2.1) and GraphPad Prism 9.0. p < 0.05 was considered statistically significant. In conclusion, this study underscores the significance of F2R as a potential biomarker in gastric adenocarcinoma, shedding light on its molecular mechanisms in tumorigenesis. F2R holds promise for aiding in the diagnosis, prognosis, and targeted therapy of STAD.

Analysis of m6A-related lncRNAs for prognostic and immunotherapeutic response in hepatocellular carcinoma.

Background: RNA methylation modifications are important post-translational modifications that are regulated in an epigenetic manner. Recently, N6-methyladenosine (m6A) RNA modifications have emerged as potential epigenetic markers in tumor biology. Methods: Gene expression and clinicopathological data of LIHC were obtained from the cancer genome atlas (TCGA) database. The relationship between long non-coding RNAs (lncRNAs) and m6A-related genes was determined by gene expression analysis using Perl and R software. Co-expression network of m6A-lncRNA was constructed, and the relevant lncRNAs associated with prognosis were identified using univariate Cox regression analysis. These lncRNAs were then divided into two clusters (cluster 1 and cluster 2) to determine the differences in survival, pathoclinical parameters, and immune cell infiltration between the different lncRNA subtypes. The least absolute shrinkage and selection operator (LASSO) was carried out for regression analysis and prognostic model. The HCC patients were randomly divided into a train group and a test group. According to the median risk score of the model, HCC patients were divided into high-risk and low-risk groups. We built models using the train group and confirmed them through the test group. The m6A-lncRNAs derived from the models were analyzed for the tumor mutational burden (TMB), immune evasion and immune function using R software. AL355574.1 was identified as an important m6A-associated lncRNA and selected for further investigation. Finally, in vitro experiments were conducted to confirm the effect of AL355574.1 on the biological function of HCC and the possible biological mechanisms. Huh7 and HepG2 cells were transfected with AL355574.1 siRNA and cell proliferation ability was measured by CCK-8, EdU and colony formation assays. Wound healing and transwell assays were used to determine the cell migration capacity. The expression levels of MMP-2, MMP-9, E-cadherin, N-cadherin and Akt/mTOR phosphorylation were all determined by Western blotting. Results: The lncRNAs with significant prognostic value were classified into two subtypes by a consistent clustering analysis. We found that the clinical features, immune cell infiltration and tumor microenvironment (TME) were significantly different between the lncRNA subtypes. Our analysis revealed significant correlations between these different lncRNA subtypes and immune infiltrating and stromal cells. We created the final risk profile using LASSO regression, which notably included three lncRNAs (AL355574.1, AL158166.1, TMCC1-AS1). A prognostic signature consisting of the three lncRNAs was constructed, and the model showed excellent prognostic predictive ability. The overall survival (OS) of the low-risk cohort was significantly higher than that of the high-risk cohort in both the train and test group. Both risk score [hazard ratio (HR)=1.062; P<0.001] and stage (HR=1.647; P< 0.001) were considered independent indicators of HCC prognosis by univariate and multivariate Cox regression analysis. In Huh7 and HepG2 cells, AL355574.1 knockdown inhibited cell proliferation and migration, suppressed the protein expression levels of MMP-2, MMP-9, N-cadherin and Akt/mTOR phosphorylation, but promoted the protein expression levels of E-cadherin. Conclusions: This study established a predictive model for the OS of HCC patients, and these OS-related m6A-lncRNAs, especially AL355574.1 may play a potential role in the progression of HCC. In vitro experiments also showed that AL355574.1 could enhance the expression of MMPs and EMT through the Akt/mTOR signaling pathway, thereby affected the proliferation and migration of HCC. This provides a new perspective on the anticancer molecular mechanism of AL355574.1 in HCC.

The role of EMG1 in lung adenocarcinoma progression: Implications for prognosis and immune cell infiltration.

BACKGROUND AND AIMS: Lung adenocarcinoma (LUAD) is the most common and aggressive cancer with a high incidence. N1-specific pseudouridine methyltransferase (EMG1), a highly conserved nucleolus protein, plays an important role in the biological development of ribosomes. However, the role of EMG1 in the progression of LUAD is still unclear. METHODS: The expression of EMG1 in LUAD cells, and LUAD tissues, and adjacent noncancerous tissues was quantified using real-time polymerase chain reaction (PCR) and western blotting. The roles of EMG1 in LUAD cell proliferation, migration, invasion and tumorigenicity were explored in vitro and in vivo. Western blot analysis to underlying molecular mechanism of EMG1 regulating the biological function of LUAD. EMG1 expression and its impact on tumor prognosis were analyzed using a range of databases including GEPIA, UALCAN, cBioPortal, LinkedOmics, and Kaplan-Meier Plotter. RESULTS: EMG1 expression was elevated in LUAD patients compared to normal tissues, and EMG1 expression was strongly correlated with prognosis in LUAD patients. EMG1 expression correlated with age, gender, N stage, T stage, and pathologic stage. EMG1 expression was strongly positively correlated with MRPL51, PHB2, SNRPG, ATP5MD, and TPI1, and strongly negatively correlated with MACF1, DOCK9, RAPGEF2, SYNJ1, and KIDINS220, the major enrichment pathways for EMG1 and related genes include Cell cycle, DNA Replication and Pathways in cancer signaling pathways. EMG1 expression level was significantly increased in LUAD cell lines and tissues. Knockdown of EMG1 could inhibit LUAD cell proliferation, migration, invasion, and tumorigenicity. Besides, EMG1 overexpression could promote LUAD cell proliferation, migration, and invasion. High expression of EMG1 predicts poor prognosis in LUAD patients, and EMG1 may play an oncogenic role in the tumor microenvironment by participating in the infiltration of LUAD immune cells. CONCLUSIONS: EMG1 regulated various functions in LUAD by directly mediating Akt/mTOR/p70s6k signaling pathways activation. The results suggest that EMG1 may be a novel biomarker for assessing prognosis and immune cell infiltration in LUAD.

m6A-Mediated Upregulation of lncRNA CHASERR Promotes the Progression of Glioma by Modulating the miR-6893-3p/TRIM14 Axis.

Long noncoding RNAs (lncRNAs) play crucial roles in tumor progression and are dysregulated in glioma. However, the functional roles of lncRNAs in glioma remain largely unknown. In this study, we utilized the TCGA (the Cancer Genome Atlas database) and GEPIA2 (Gene Expression Profiling Interactive Analysis 2) databases and observed the overexpression of lncRNA CHASERR in glioma tissues. We subsequently investigated this phenomenon in glioma cell lines. The effects of lncRNA CHASERR on glioma proliferation, migration, and invasion were analyzed using in vitro and in vivo experiments. Additionally, the regulatory mechanisms among PTEN/p-Akt/mTOR and Wnt/ß-catenin, lncRNA CHASERR, Micro-RNA-6893-3p(miR-6893-3p), and tripartite motif containing14 (TRIM14) were investigated via bioinformatics analyses, quantitative real-time PCR (qRT-PCR), western blot (WB), RNA immunoprecipitation (RIP), dual luciferase reporter assay, fluorescence in situ hybridization (FISH), and RNA sequencing assays. RIP and RT-qRCR were used to analyze the regulatory effect of N6-methyladenosine(m6A) on the aberrantly expressed lncRNA CHASERR. High lncRNA CHASERR expression was observed in glioma tissues and was associated with unfavorable prognosis in glioma patients. Further functional assays showed that lncRNA CHASERR regulates glioma growth and metastasis in vitro and in vivo. Mechanistically, lncRNA CHASERR sponged miR-6893-3p to upregulate TRIM14 expression, thereby facilitating glioma progression. Additionally, the activation of PTEN/p-Akt/mTOR and Wnt/ß-catenin pathways by lncRNA CHASERR, miR-6893-3p, and TRIM14 was found to regulate glioma progression. Moreover, the upregulation of lncRNA CHASERR was observed in response to N6-methyladenosine modification, which was facilitated by METTL3/YTHDF1-mediated RNA transcripts. This study elucidates the m6A/lncRNACHASERR/miR-6893-3p/TRIM14 pathway that contributes to glioma progression and underscores the potential of lncRNA CHASERR as a novel prognostic indicator and therapeutic target for glioma.

Risk prediction model based on machine learning for predicting miscarriage among pregnant patients with immune abnormalities.

Introduction: It is known that patients with immune-abnormal co-pregnancies are at a higher risk of adverse pregnancy outcomes. Traditional pregnancy risk management systems have poor prediction abilities for adverse pregnancy outcomes in such patients, with many limitations in clinical application. In this study, we will use machine learning to screen high-risk factors for miscarriage and develop a miscarriage risk prediction model for patients with immune-abnormal pregnancies. This model aims to provide an adjunctive tool for the clinical identification of patients at high risk of miscarriage and to allow for active intervention to reduce adverse pregnancy outcomes. Methods: Patients with immune-abnormal pregnancies attending Sichuan Provincial People's Hospital were collected through electronic medical records (EMR). The data were divided into a training set and a test set in an 8:2 ratio. Comparisons were made to evaluate the performance of traditional pregnancy risk assessment tools for clinical applications. This analysis involved assessing the cost-benefit of clinical treatment, evaluating the model's performance, and determining its economic value. Data sampling methods, feature screening, and machine learning algorithms were utilized to develop predictive models. These models were internally validated using 10-fold cross-validation for the training set and externally validated using bootstrapping for the test set. Model performance was assessed by the area under the characteristic curve (AUC). Based on the best parameters, a predictive model for miscarriage risk was developed, and the SHapley additive expansion (SHAP) method was used to assess the best model feature contribution. Results: A total of 565 patients were included in this study on machine learning-based models for predicting the risk of miscarriage in patients with immune-abnormal pregnancies. Twenty-eight risk warning models were developed, and the predictive model constructed using XGBoost demonstrated the best performance with an AUC of 0.9209. The SHAP analysis of the best model highlighted the total number of medications, as well as the use of aspirin and low molecular weight heparin, as significant influencing factors. The implementation of the pregnancy risk scoring rules resulted in accuracy, precision, and F1 scores of 0.3009, 0.1663, and 0.2852, respectively. The economic evaluation showed a saving of ¥7,485,865.7 due to the model. Conclusion: The predictive model developed in this study performed well in estimating the risk of miscarriage in patients with immune-abnormal pregnancies. The findings of the model interpretation identified the total number of medications and the use of other medications during pregnancy as key factors in the early warning model for miscarriage risk. This provides an important basis for early risk assessment and intervention in immune-abnormal pregnancies. The predictive model developed in this study demonstrated better risk prediction performance than the Pregnancy Risk Management System (PRMS) and also demonstrated economic value. Therefore, miscarriage risk prediction in patients with immune-abnormal pregnancies may be the most cost-effective management method.

A rat model for studying the biological effects of circulating LDL in the choriocapillaris-BrM-RPE complex.

Retention of apolipoprotein B-containing lipoproteins in Bruch's membrane (BrM) is believed to be important in early age-related macular degeneration (AMD). The origin of the lipoproteins in BrM is a hot topic in AMD research. Some studies hypothesize an intraocular origin. BrM is in direct contact to the choriocapillaris; a plasma origin has also been suggested for the low-density lipoprotein (LDL) particles. We developed an animal model to study the biological effects of circulating LDL on the retina. After injection of LDL for 7 days, our results showed evidence of circulating apolipoprotein B100 retention in BrM and showed induction of early AMD-like alterations in the rat retina, such as thickening of BrM, photoreceptor TUNEL-positive cells, and inflammatory cell infiltration. In vitro assays showed that oxidized LDL (ox-LDL) treatment decreased ARPE-19 cell viability in a dose-dependent manner and that 10 mg/L ox-LDL induced marked apoptosis. The ratio of matrix metalloproteinase-2 to tissue inhibitors of metalloproteinase-3 was dysregulated after LDL and ox-LDL treatment in ARPE-19 cells, which can produce profound changes in the extracellular matrix, including thickening of and deposit formation in BrM. The observation that circulating LDL may be a significant, but not complete, origin of the lipoprotein in BrM suggests that these findings can be readily exploited for the development of new model systems and the future benefit of patients with AMD.

Inverse relationship between macular pigment optical density and axial length in Chinese subjects with myopia.

BACKGROUND: Macular pigment (MP) has been the focus of much attention in recent years, due to its protective effect against macular degenerations. In this study, we investigated the association between macular pigment optical density (MPOD) and axial length (AL) in Chinese subjects with myopia. METHODS: In total, 173 myopes (mean spherical equivalent [MSE] ≤-1.00D) were recruited for this prospective observational study. MPOD was measured in both eyes of each subject using a macular metrics densitometer. AL was measured in eyes using an IOL-Master. A raw coefficient of correlation analysis and a partial correlation analysis were used to investigate the relationship between MPOD and AL. RESULTS: The age of the subjects ranged from 18 to 67 years. The overall mean MPOD for the cohort was 0.412 ± 0.119 (range, 0.105-0.812). The mean AL was 25.18 ± 1.08 mm (range, 23.14-28.19 mm). Using a raw coefficient of correlation, a significant inverse correlation was found between MPOD and AL (r= -0.134, p=0.012). When using a partial correlation analysis to eliminate the impact of covariant, a significant inverse correlation was also found between MPOD and AL (r= -0.142, p=0.008). Furthermore, when AL was divided into two groups: AL>26 mm and AL ≤ 26 mm, a significant inverse correlation was observed between MPOD and AL in the former (r= -0.253, p=0.029), but no significant relationship was observed between these in the latter (r=0.104, p=0.067). CONCLUSIONS: MPOD correlated inversely with AL in this sample of Chinese subjects with myopia.

CLEC5A regulates the proliferation and migration of colon cancer via the AKT/mTOR signaling pathway.

Background: The purpose of this study is to understand the CLEC5A mechanism in colon cancer's proliferation and migration. Methods: The CLEC5A expression levels in colon cancer tissues were analyzed using bioinformatics method based on Oncomine and The Cancer Genome Atlas (TCGA) databases, which were further tested by immunohistochemistry (IHC) and quantitative real-time polymerase chain reaction (qRT-PCR). The CLEC5A expression levels in 4 types of colon cancer cell lines (HCT116, SW620, HT29, and SW480) were also examined by qRT-PCR. We constructed CLEC5A knockdown cell lines and used colony formation, Cell Counting Kit-8 (CCK-8), 5-Ethynyl-2'-deoxyuridine (EdU), wound healing, and transwell assays for investigating the CLEC5A function in colon cancer's proliferation and migration. A CLEC5A silencing nude mice model was established to measure the scale, weight, and growth rate of tumor xenograft. In CLEC5A knockdown cell lines and xenograft tissues, the levels of cell cycle and epithelial-mesenchymal transition (EMT)-related proteins were detected using Western blot (WB), and the phosphorylation levels of AKT/mTOR pathway key proteins were also detected by WB. On the basis of gene expression data retrieved from TCGA database, a relevance between CLEC5A and AKT/mTOR pathway in colon cancer was examined by gene set enrichment analysis (GSEA), and correlation analysis of CLEC5A and COL1A1 was employed to confirm their interaction. Results: Bioinformatics analysis, IHC staining, and qRT-PCR assay results all showed the significant high levels of CLEC5A expression in colon cancer tissues and cells, and positive links between CLEC5A levels and lymph node metastasis, vascular metastasis, and tumor-node-metastasis (TNM) stages of colon cancer patients. The suppressive effects of CLEC5A knockdown on colon cancer's proliferation and migration were verified in cell function and nude mice tumorigenesis assays. WB analysis further indicated that CLEC5A knockdown could inhibit cell cycle, and EMT processes, as well as AKT/mTOR pathway phosphorylation in colon cancer. On the basis of TCGA data, CLEC5A's activation effect on AKT/mTOR pathway had been confirmed by GSEA analysis, and the interaction between CLEC5A and COL1A1 was also revealed through correlation analysis in colon cancer. Conclusions: CLEC5A may promote the development and migration of colon cancer by triggering the AKT/mTOR signaling pathway. Furthermore, COL1A1 could serve as the target gene of CLEC5A.

Development and economic assessment of machine learning models to predict glycosylated hemoglobin in type 2 diabetes.

Background: Glycosylated hemoglobin (HbA1c) is recommended for diagnosing and monitoring type 2 diabetes. However, the monitoring frequency in real-world applications has not yet reached the recommended frequency in the guidelines. Developing machine learning models to screen patients with poor glycemic control in patients with T2D could optimize management and decrease medical service costs. Methods: This study was carried out on patients with T2D who were examined for HbA1c at the Sichuan Provincial People's Hospital from April 2018 to December 2019. Characteristics were extracted from interviews and electronic medical records. The data (excluded FBG or included FBG) were randomly divided into a training dataset and a test dataset with a radio of 8:2 after data pre-processing. Four imputing methods, four screening methods, and six machine learning algorithms were used to optimize data and develop models. Models were compared on the basis of predictive performance metrics, especially on the model benefit (MB, a confusion matrix combined with economic burden associated with therapeutic inertia). The contributions of features were interpreted using SHapley Additive exPlanation (SHAP). Finally, we validated the sample size on the best model. Results: The study included 980 patients with T2D, of whom 513 (52.3%) were defined as positive (need to perform the HbA1c test). The results indicated that the model trained in the data (included FBG) presented better forecast performance than the models that excluded the FBG value. The best model used modified random forest as the imputation method, ElasticNet as the feature screening method, and the LightGBM algorithms and had the best performance. The MB, AUC, and AUPRC of the best model, among a total of 192 trained models, were 43475.750 (¥), 0.972, 0.944, and 0.974, respectively. The FBG values, previous HbA1c values, having a rational and reasonable diet, health status scores, type of manufacturers of metformin, interval of measurement, EQ-5D scores, occupational status, and age were the most significant contributors to the prediction model. Conclusion: We found that MB could be an indicator to evaluate the model prediction performance. The proposed model performed well in identifying patients with T2D who need to undergo the HbA1c test and could help improve individualized T2D management.

m6A-mediated upregulation of LINC01003 regulates cell migration by targeting the CAV1/FAK signaling pathway in glioma.

BACKGROUND: Long non-coding RNAs (lncRNAs) play important roles in the progression of glioma. Here, we examined the potential functions of a lncRNA, LINC01003, in glioma and characterized the underlying molecular mechanisms. METHODS: The GEIPA2 and Chinese Glioma Genome Atlas (CCGA) databases were employed to analyze gene expression and the overall survival curve in patients with glioma. The functions of LINC01003 in glioma growth and migration were assessed by loss-of-function experiments in vitro and in vivo. RNA sequencing was used to determine the signaling pathways effected by LINC01003. Bioinformatics analysis and RNA immunoprecipitation (RIP) assays were used to explore the mechanism underlying the N6-methyladenine (m6A) modification-dependent upregulation of LINC01003 in glioma. RESULTS: LINC01003 expression was upregulated in glioma cell lines and tissues. Higher LINC01003 expression predicted shorter overall survival time in glioma patients. Functionally, LINC01003 knockdown inhibited the cell cycle and cell proliferation and migration in glioma cells. Mechanistically, RNA sequencing revealed that LINC01003 mediated the focal adhesion signaling pathway. Furthermore, LINC01003 upregulation is induced by m6A modification regulated by METTL3. CONCLUSION: This study characterized LINC01003 as a lncRNA that contributes to tumorigenesis in glioma and demonstrated that the LINC01003-CAV1-FAK axis serves as a potential therapeutic target for glioma.

Construction of an Early Alert System for Intradialytic Hypotension before Initiating Hemodialysis Based on Machine Learning.

Introduction: Intradialytic hypotension (IDH) is prevalent and associated with high hospitalization and mortality rates. The purpose of this study was to explore the risk factors for IDH and use artificial intelligence to establish an early alert system before hemodialysis sessions to identify patients at high risk of IDH. Materials and Methods: We obtained data on 314,534 hemodialysis sessions conducted at Sichuan Provincial People's Hospital from the renal disease treatment information system. IDH was defined as a systolic blood pressure drop ≥20 mm Hg, a mean arterial pressure drop ≥10 mm Hg during dialysis, or the occurrence of clinical hypotensive events requiring nursing intervention. After pre-processing, the data were randomly divided into training (80%) and testing (20%) sets. Four interpolation methods, three feature selection methods, and 18 machine learning algorithms were used to construct predictive models. The area under the receiver operating characteristic curve (AUC) was the main indicator for evaluating the performance of the models, while Shapley Additive ExPlanation was used to explain the contribution of each variable to the best predictive model. Results: A total of 3,906 patients and 314,534 dialysis sessions were included, of which 142,237 cases showed IDH (incidence rate, 45.2%). Nineteen parameters were identified through artificial intelligence feature screening. They included age, pre-dialysis weight, dry weight, pre-dialysis blood pressure, heart rate, prescribed ultrafiltration, blood cell counts (neutrophil, lymphocyte, monocyte, eosinophil, lymphocyte, and platelet counts), hematocrit, serum calcium, creatinine, urea, glucose, and uric acid. Random forest, gradient boosting, and logistic regression were the three best models, and the AUCs were 0.812 (95% confidence interval [CI], 0.811-0.813), 0.748 (95% CI, 0.747-0.749), and 0.743 (95% CI, 0.742-0.744), respectively. Conclusion: Our dialysis software-based artificial intelligence alert system can be used to predict IDH occurrence, enabling the initiation of relevant interventions.