Dynamic changes of gut microbiota in mouse models of metabolic dysfunction-associated steatohepatitis and its transition to hepatocellular carcinoma.

Dysbiosis of gut microbiota may account for pathobiology in simple fatty liver (SFL), metabolic dysfunction-associated steatohepatitis (MASH), fibrotic progression, and transformation to MASH-associated hepatocellular carcinoma (MASH-HCC). The aim of the present study is to investigate gut dysbiosis in this progression. Fecal microbial rRNA-16S sequencing, absolute quantification, histopathologic, and biochemical tests were performed in mice fed high fat/calorie diet plus high fructose and glucose in drinking water (HFCD-HF/G) or control diet (CD) for 2, 16 weeks, or 14 months. Histopathologic examination verified an early stage of SFL, MASH, fibrotic, or MASH-HCC progression with disturbance of lipid metabolism, liver injury, and impaired gut mucosal barrier as indicated by loss of occludin in ileum mucosa. Gut dysbiosis occurred as early as 2 weeks with reduced α diversity, expansion of Kineothrix, Lactococcus, Akkermansia; and shrinkage in Bifidobacterium, Lactobacillus, etc., at a genus level. Dysbiosis was found as early as MAHS initiation, and was much more profound through the MASH-fibrotic and oncogenic progression. Moreover, the expansion of specific species, such as Lactobacillus johnsonii and Kineothrix alysoides, was confirmed by an optimized method for absolute quantification. Dynamic alterations of gut microbiota were characterized in three stages of early SFL, MASH, and its HCC transformation. The findings suggest that the extent of dysbiosis was accompanied with MASH progression and its transformation to HCC, and the shrinking or emerging of specific microbial species may account at least in part for pathologic, metabolic, and immunologic alterations in fibrogenic progression and malignant transition in the liver.

Metabolome Sequencing Reveals that Protein Arginine-N-Methyltransferase 1 Promotes the Progression of Invasive Micropapillary Carcinoma of the Breast and Predicts a Poor Prognosis.

Invasive micropapillary carcinoma (IMPC) of the breast is a special histopathologic type of cancer with a high recurrence rate and the biological features of invasion and metastasis. Previous spatial transcriptome studies indicated extensive metabolic reprogramming in IMPC, which contributes to tumor cell heterogeneity. However, the impact of metabolome alterations on IMPC biological behavior is unclear. Herein, endogenous metabolite-targeted metabolomic analysis was done on frozen tumor tissue samples from 25 patients with breast IMPC and 34 patients with invasive ductal carcinoma not otherwise specified (IDC-NOS) by liquid chromatography-mass spectrometry. An IMPC-like state, which is an intermediate transitional morphologic phenotype between IMPC and IDC-NOS, was observed. The metabolic type of IMPC and IDC-NOS was related to breast cancer molecular type. Arginine methylation modification and 4-hydroxy-phenylpyruvate metabolic changes play a major role in the metabolic reprogramming of IMPC. High protein arginine-N-methyltransferase (PRMT) 1 expression was an independent factor related to the poor prognosis of patients with IMPC in terms of disease-free survival. PRMT1 promoted H4R3me2a, which induced tumor cell proliferation via cell cycle regulation and facilitated tumor cell metastasis via the tumor necrosis factor signaling pathway. This study identified the metabolic type-related features and intermediate transition morphology of IMPC. The identification of potential targets of PRMT1 has the potential to provide a basis for the precise diagnosis and treatment of breast IMPC.

Melatonin antagonizes oxidative stress-induced apoptosis in retinal ganglion cells through activating the thioredoxin-1 pathway.

This study aimed to explore the role of melatonin in oxidative stress-induced injury on retinal ganglion cells and the underlying mechanisms. The immortalized RGC-5 cells were treated with H2O2 to induce oxidative injury. Cell viability was measured by Cell Counting Kit-8, and apoptosis was determined by flow cytometry and western blot assays. Reactive oxygen species (ROS), lactate dehydrogenase (LDH), and malondialdehyde (MDA) levels were examined to evaluate oxidative stress levels. In addition, Thioredoxin-1 (Trx1) was silenced in RGC-5 cells using small interfering RNA followed by signaling pathway examination to explore the underlying mechanisms of melatonin in alleviating oxidative injury. Melatonin pre-treatment significantly alleviated H2O2-induced apoptosis in RGC-5 cells. Melatonin also markedly reversed the upregulation of cleaved-caspase 3, cleaved-caspase 9, and Bax expression and downregulation of Bcl-2 expression induced by H2O2. Further analyses presented that melatonin significantly attenuated the increase of ROS, LDH, and MDA levels in RGC-5 cells after H2O2 treatment. Melatonin also abolished the downregulated expression of Superoxide dismutase type 1, Trx1, and Thioredoxin reductase 1, and the reduced activity of thioredoxin reductase in RGC-5 cells after H2O2 treatment. Notably, Trx1 knockdown significantly mitigated the protective effect of melatonin in alleviating H2O2-induced apoptosis and oxidative stress, while administration of compound C, a common inhibitor of c-Jun N-terminal kinase (JNK) signaling, partially reversed the effect of Trx1 silencing, thereby ameliorating the apoptosis and oxidative injury induced by H2O2 in RGC-5 cells. Melatonin could significantly alleviate oxidative stress-induced injury of retinal ganglion cells via modulating Trx1-mediated JNK signaling pathway.

Mycobacterial fatty acid catabolism is repressed by FdmR to sustain lipogenesis and virulence.

Host-derived fatty acids are an important carbon source for pathogenic mycobacteria during infection. How mycobacterial cells regulate the catabolism of fatty acids to serve the pathogenicity, however, remains unknown. Here, we identified a TetR-family transcriptional factor, FdmR, as the key regulator of fatty acid catabolism in the pathogen Mycobacterium marinum by combining use of transcriptomics, chromatin immunoprecipitation followed by sequencing, dynamic 13C-based flux analysis, metabolomics, and lipidomics. An M. marinum mutant deficient in FdmR was severely attenuated in zebrafish larvae and adult zebrafish. The mutant showed defective growth but high substrate consumption on fatty acids. FdmR was identified as a long-chain acyl-coenzyme A (acyl-CoA)-responsive repressor of genes involved in fatty acid degradation and modification. We demonstrated that FdmR functions as a valve to direct the flux of exogenously derived fatty acids away from ß-oxidation toward lipid biosynthesis, thereby avoiding the overactive catabolism and accumulation of biologically toxic intermediates. Moreover, we found that FdmR suppresses degradation of long-chain acyl-CoAs endogenously synthesized through the type I fatty acid synthase. By modulating the supply of long-chain acyl-CoAs for lipogenesis, FdmR controls the abundance and chain length of virulence-associated lipids and mycolates and plays an important role in the impermeability of the cell envelope. These results reveal that despite the fact that host-derived fatty acids are used as an important carbon source, overactive catabolism of fatty acids is detrimental to mycobacterial cell growth and pathogenicity. This study thus presents FdmR as a potentially attractive target for chemotherapy.

PRMT3 regulates the progression of invasive micropapillary carcinoma of the breast.

Invasive micropapillary carcinoma (IMPC) is a special histopathological subtype of breast cancer. Clinically, IMPC exhibits a higher incidence of lymphovascular invasion and lymph node metastasis compared with that of invasive ductal carcinoma (IDC), the most common type. However, the metabolic characteristics and related mechanisms underlying malignant IMPC biological behaviors are unknown. We performed large-scale targeted metabolomics analysis on resected tumors obtained from chemotherapy-naïve IMPC (n = 25) and IDC (n = 26) patients to investigate metabolic alterations, and we integrated mass spectrometry analysis, RNA sequencing, and ChIP-sequencing data to elucidate the potential molecular mechanisms. The metabolomics revealed distinct metabolic profiles between IMPC and IDC. For IMPC patients, the metabolomic profile was characterized by significantly high levels of arginine methylation marks, and protein arginine methyltransferase 3 (PRMT3) was identified as a critical regulator that catalyzed the formation of these arginine methylation marks. Notably, overexpression of PRMT3 was an independent risk factor for poor IMPC prognosis. Furthermore, we demonstrated that PRMT3 was a key regulator of breast cancer cell proliferation and metastasis both in vitro and in vivo, and treatment with a preclinical PRMT3 inhibitor decreased the xenograft tumorigenic capacity. Mechanistically, PRMT3 regulated the endoplasmic reticulum (ER) stress signaling pathway by facilitating histone H4 arginine 3 asymmetric dimethylation (H4R3me2a), which may endow breast cancer cells with great proliferative and metastatic capacity. Our findings highlight PRMT3 importance in regulating the malignant biological behavior of IMPC and suggest that small-molecule inhibitors of PRMT3 activity might be promising breast cancer treatments.

Individual Micron-Sized Aerosol Qualitative Analysis-Combined Raman Spectroscopy and Laser-Induced Breakdown Spectroscopy by Optical Trapping in Air.

The attribution of single particle sources of atmospheric aerosols is an essential problem in the study of air pollution. However, it is still difficult to qualitatively analyze the source of a single aerosol particle using noncontact in situ techniques. Hence, we proposed using optical trapping to combine gated Raman spectroscopy with laser-induced breakdown spectroscopy (LIBS) in a single levitated micron aerosol. The findings of the spectroscopic imaging indicated that the particle plasma formed by a single particle ablation with a pulsed laser within 7 ns deviates from the trapped particle location. The LIBS acquisition field of view was expanded using the 19-bundle fiber, which also reduces the fluctuation of a single particle signal. In addition, gated Raman was utilized to suppress the fluorescence and increase the Raman signal-to-noise ratio. Based on this, Raman can measure hard-to-ionize substances with LIBS, such as sulfates. The LIBS radical can overcome the restriction that Raman cannot detect ionic chemicals like fluoride and chloride in halogens. To test the capability of directly identifying distinctive feature compounds utilizing spectra, we detected anions using Raman spectroscopy and cations using LIBS. Four typical mineral aerosols are subjected to precise qualitative evaluations (marble, gypsum, baking soda, and activated carbon adsorbed potassium bicarbonate). To further validate the application potential for substances with indistinctive feature discrimination, we employed machine learning algorithms to conduct a qualitative analysis of the coal aerosol from ten different origin regions. Three data fusion methodologies (early fusion, intermediate fusion, and late fusion) for Raman and LIBS are implemented, respectively. The accuracy of the late fusion model prediction using StackingClassifier is higher than that of the LIBS data (66.7%) and Raman data (86.1%) models, with an average accuracy of 90.6%. This research has the potential to provide online single aerosol analysis as well as technical assistance for aerosol monitoring and early warning.

Association of Body Mass Index and the Risk of Gastro-Esophageal Cancer: A Mendelian Randomization Study in a Japanese Population.

There are growing concerns that body mass index (BMI) is related to cancer risk at various anatomical sites, including the upper gastrointestinal tract, and the existence of a causal relationship remains unclear. The Mendelian randomization (MR) method uses instrumental genetic variables of risk factors to explore whether a causal relationship exists while preventing confounding. In our study, genome-wide association study (GWAS) data from the BioBank Japan (BBJ) project were used. Genetic variants were chosen as instrumental variables using inverse-variance weighting (IVW), MR-Egger regression and weighted-median methods to estimate the causal relationship between BMI and the risk of gastro-esophageal cancer. We found no evidence to support a causal association between BMI and risk of gastric cancer [odds ratio (OR) =0.99 per standard deviation (SD) increase in BMI; 95% confidence interval (CI): (0.76-1.30); P = 0.96] or esophageal cancer [0.78(0.50-1.22); P = 0.28] using the IVW method. Sensitivity analysis did not reveal any sign of horizontal pleiotropy. Additionally, in the gender-stratified analysis, no causal association was found. Findings from this study do not support a causal effect of BMI on gastro-esophageal cancer risk. However, we cannot rule out a modest or nonlinear effect of BMI.

Triglyceride-glucose index as a valuable predictor for aged 65-years and above in critical delirium patients: evidence from a multi-center study.

BACKGROUND: The triglyceride-glucose index (TyG), an established indicator of insulin resistance, is closely correlated with the prognosis of several metabolic disorders. This study aims to investigate the association between the TyG index and the incidence of critical delirium in patients aged 65 years and older. METHODS: We focused on evaluating patients aged 65 years and older diagnosed with critical delirium. Data were obtained from the Medical Information Database for Intensive Care (MIMIC-IV) and the eICU Collaborative Research Database (eICU-CRD). Multivariate logistic regression and restricted cubic spline (RCS) regression were used to determine the relationship between the TyG index and the risk of delirium. RESULTS: Participants aged 65 years and older were identified from the MIMIC-IV (n = 4,649) and eICU-CRD (n = 1,844) databases. Based on optimal thresholds derived from RCS regression, participants were divided into two cohorts: Q1 (< 8.912), Q2 (≥ 8.912). The logistic regression analysis showed a direct correlation between the TyG index and an increased risk of critical delirium among ICU patients aged 65 and older. These findings were validated in the eICU-CRD dataset, and sensitivity analysis further strengthened our conclusions. In addition, the subgroup analysis revealed certain differences. CONCLUSION: This study highlights a clear, independent relationship between the TyG index and the risk of critical delirium in individuals aged 65 years and older, suggesting the importance of the TyG index as a reliable cardio-cerebrovascular metabolic marker for risk assessment and intervention.

Retinol binding protein 1-dependent activation of NF- κB signaling enhances the malignancy of non-glioblastomatous diffuse gliomas.

Nonglioblastomatous diffuse glioma (non-GDG) is a heterogeneous neuroepithelial tumor that exhibits a varied survival range from 4 to 13 years based on the diverse subtypes. Recent studies demonstrated novel molecular markers can predict prognosis for non-GDG patients; however, these findings as well as pathological classification strategies show obvious limitations on malignant transition due to the heterogeneity among non-GDGs. Therefore, developing reliable prognostic biomarkers and therapeutic targets have become an urgent need for precisely distinguishing non-GDG subtypes, illuminating the underlying mechanism. Nuclear factor κß (NF-κB) has been proved to be a significant nuclear transcriptional regulator with specific DNA-binding sequences to participate in multiple pathophysiological processes. However, the underlying mechanism of NF-κB activation still needs to be further investigated. Herein, our results indicated retinol-binding protein 1 (RBP1) was significantly upregulated in the IDHWT and 1p19qNon co-del non-GDG subtypes and enriched RBP1 expression was markedly correlated with more severe outcomes. Additionally, malignant signatures of the non-GDG cells including proliferation, migration, invasion, and self-renewal were significantly suppressed by lentiviral knockdown of RBP1. To further explore the underlying molecular mechanism, bioinformatics analysis was performed using databases, and the results demonstrated RBP1 was strongly correlated with tumor necrosis factor α (TNFα)-NF-κB signaling. Moreover, exogenous silencing of RBP1 reduced phosphorylation of IkB-kinase α (IKKα) and thus decreased NF-κB expression via decreasing the degradation of the IκBα protein. Altogether, these data suggested RBP1-dependent activation of NF-κB signaling promoted malignancy of non-GDG, indicating that RBP1 could be a reliable prognostic biomarker and potential therapeutic target for non-GDG.

Multi-element Quantitative Analysis of Single Micro-sized Suspended Particles in Air with High Accuracy Based on Random Forest and Variable Selection Strategies.

The chemical compositions of atmospheric particles have been studied for several decades, and the traditional techniques for particle analysis usually require time-consuming sample preparation. Within this study, simultaneous quantitative detection of multiple metallic species (Zn, Cu, and Ni) in single micro-sized suspended particles was investigated by combining random forest (RF) and variable selection strategies. Laser-induced breakdown spectra of 15 polluted black carbon samples were applied for establishing the RF model, and the movmean smoothing spectral pretreatment method and variable selection methods [variable importance measurement (VIM), genetic algorithm (GA), and variable importance projection (VIP)] were proposed. Finally, the optimized RF calibration model with the evaluation indicators of mean relative error (MRE), root-mean-square error (RMSE), and coefficient of determination (R2) was constructed based on the optimal input variables and model parameters. Compared with the univariate regression method, the VIP-RF (Zn) and VIM-RF (Cu and Ni) models showed a better correlation relationship (Rp2 = 0.9662 for Zn, Rp2 = 0.9596 for Cu, and Rp2 = 0.9548 for Ni). For Zn, Cu, and Ni, the values of RMSEP (RMSE of prediction) decreased by 116.44, 68.94, and 102.10 ppm, while the values of MREP (MRE of prediction) decreased by 67, 55, and 48%, respectively. The values of ratio of prediction to deviation (RPD) of VIP-RF (Zn), VIM-RF (Cu), and VIM-RF (Ni) models were 5.4, 5.0, and 4.7, respectively. The performance of this combined approach displays a notable accuracy improvement in the quantitative analysis of single particles, suggesting that it is a promising tool for real-time air particulate matter pollution monitoring and control in the future.

Dietary patterns and severity of symptom with the risk of esophageal squamous cell carcinoma and its histological precursor lesions in China: a multicenter cross-sectional latent class analysis.

BACKGROUND: Dietary patterns and symptoms research among Chinese with esophageal squamous cell carcinoma (ESCC) and its precursor lesions is limited, especially as it relates to multiple food consumption and multiple co-occurring symptoms. The aim of our study was to identify the dietary patterns and severity of symptom classes with the risk of esophageal squamous cell carcinoma and its histological precursor lesions, and develop a risk prediction model for different stages of esophageal disease. METHODS: We analyzed data from a multicenter cross-sectional study carried out in ESCC high incidence areas between 2017 and 2018, which included 34,707 individuals aged 40-69 years. Dietary patterns and severity of symptom classes were derived by applying a latent class analysis (LCA). A multiple logistic regression model was used to derive the odds ratio (ORs) and corresponding 95% confidence intervals (CIs) for ESCC and the different stages of esophageal disease according to the dietary patterns and severity of symptom classes identified. We built the risk prediction model by using a nomogram. RESULTS: We identified five dietary patterns and three severity of symptom classes. The dietary patterns were classified as follows: "Healthy", "Western", "Lower consumers-combination", "Medium consumers-combination" and "Higher consumers-combination" patterns based on the intake of foods such as red meat, vegetables and fruits. The severity of symptoms was categorized into "Asymptomatic", "Mild symptoms" and "Overt symptoms" classes based on health-related symptoms reported by the participants. Compared to the "Healthy" pattern, the other four patterns were all associated with an increased risk of esophageal disease. Similarly, the other two symptom classes present different degrees of increased risk of esophageal disease compared to the "Asymptomatic". The nomograms reflect the good predictive ability of the model. CONCLUSION: Among individuals aged 40-69 years in high incidence regions of upper gastrointestinal cancer, the results supplied that subjects with diets rich in livestock and poultry meat and low in fruits and vegetables and subjects with typical symptoms were at increased ESCC risk. The findings highlight the importance of considering food and symptom combinations in cancer risk evaluation.

The STING pathway: An uncharacterized angle beneath the gut-retina axis.

The gut-retina axis is an emerging concept that describes a close interaction between the gut host-microbiota interface and the retina. Stimulator of interferon genes (STING) is a universally expressed adaptor protein localized in the endoplasmic reticulum. When activated by the cytosolic DNA sensor cyclic GMP-AMP synthase (cGAS), STING induces the activation of the transcription factor interferon regulatory factor 3 (IRF3) and nuclear factor-κB (NF-κB). Downstream effects include inflammation, autophagy, and programmed cell death. Dysregulation of the STING pathway has emerged as a crucial pathogenic mechanism underpinning a broad range of inflammatory diseases, autoimmune diseases, and cancer. Recently, a positive feedback loop between dysbiosis and aberrant activation of the intestinal STING pathway has been demonstrated, concurrently related to increased intestinal permeability. Alternations in the STING pathway have also been reported in the retina of patients with ocular diseases and retinal cells treated with pathological stimuli. Collectively, there is a chance that dysbiosis in patients with retinal diseases disrupts intestinal homeostasis and exacerbates barrier dysfunction through the erroneous accumulation of STING in the gut. Subsequent translocation of microbial products into the bloodstream allows access to the eye via the impaired blood-retina barrier, inducing the chronic activation of the STING pathway in the retina to participate in the disease progression. In this review, we explore how the alterations in the STING pathway could contribute to the gut disturbance and retinal pathologies and discuss its potential as a therapeutic target to treat the gut-retina axis-related diseases, which sheds some light on the better understanding of the crosstalk between the gut and retina.

Novel approaches to intervene gut microbiota in the treatment of chronic liver diseases.

Recent investigations of gut microbiota have contributed to understanding of the critical role of microbial community in pathophysiology. Dysbiosis not only causes disturbance directly to the gastrointestinal tract but also affects the liver through gut-liver axis. Various types of dysbiosis have been documented in alcoholic liver disease (ALD), nonalcoholic fatty liver disease, autoimmune hepatitis (AIH), primary sclerosing cholangitis, and may be crucial for the initiation, progression, or deterioration to end-stage liver disease. A few microbial species have been identified as the causal factors leading to these chronic illnesses that either do not have clear etiologies or lack effective treatment. Notably, cytolysin-producing Enterococcus faecalis, Klebsiella pneumoniae and Enterococcus gallinarum were defined for ALD, NASH, and AIH, respectively. These groundbreaking discoveries drive a rapid development in innovative therapeutics, such as fecal microbial transplantation and implementation of specific bacteriophages in addition to prebiotics, probiotics, or synbiotics for intervention of dysbiosis. Although most emerging interventions are in preclinical development or early clinical trials, a better delineation of specific dysbiosis in these disorders at metabolic, immunogenic, or molecular levels in establishing particular causal effects aids in modulating or correcting the microbial community which is the part of daily life for human being.

Diagnostic Performance of SGA, PG-SGA and MUST for Malnutrition Assessment in Adult Cancer Patients: A Systematic Literature Review and Hierarchical Bayesian Meta-Analysis.

Because cancer-associated malnutrition is a major health complication, timely nutritional screening is of utmost importance. The aim of this study was to conduct a systematic literature review and meta-analysis of three tools in order to identify the method with the best diagnostic performance. PubMed, EMBASE and Cochrane central register of controlled trials were searched for articles published from database inception to January 2021. Studies that assessed the diagnostic accuracy of the SGA, PG-SGA or MUST in adult cancer patients were included. In order to evaluate the quality of each included study, the QUADAS-2 tool was used after which a meta-analysis was conducted using the hierarchical bivariate model. This model accounts for both within and between study variability. 16 studies (18 datasets) were included to evaluate these tools. The overall sensitivity and specificity for SGA was 0.69 and 0.80, 0.95 and 0.81 for PG-SGA, along with 0.83 and 0.83 for MUST respectively. An assessment of the likelihood ratios showed that PG-SGA had the highest LR + and the lowest LR-, it therefore has the best diagnostic performance to confirm malnutrition in adult cancer patients.

A novel material Cs2RbxAg1-xIn0.875Bi0.125Cl6 with a special blue shift and application for white light LED devices.

Perovskite microcrystals have attracted wide attention and have been applied in extensive optical applications. The CsPbX3 perovskite poses a great threat to the environment due to the presence of lead (Pb), and there is an urgent need to improve the photoluminescence quantum yield. Therefore, a lead-free perovskite microcrystal material Cs2RbxAg1-xIn0.875Bi0.125Cl6 with a high photoluminescence quantum yield (PLQY) was synthesized by a convenient hydrothermal method, with comprehensive characterization of both the structure and optical performance at varying Rb ratios. Optimal properties were observed at x = 0.15 with bright white emission and a PLQY of 32.15%. Superior stability of the novel material in ethanol was observed under the radiation of an excitation light of 365 nm. Interestingly, a blue shift of the emission peak occurred after exposure to humid air, possibly due to the reconstruction of the crystal structure. Moreover, a LED device packaged with this novel material was developed with a desirable color temperature of 3190 K, promising for further lighting applications.

Comparative analysis of malnutrition diagnosis methods in lung cancer patients using a Bayesian latent class model.

BACKGROUND AND OBJECTIVES: There are no consensus criteria for malnutrition diagnosis in clinical settings, the Global Leadership Initiative on Malnutrition (GLIM) criteria were developed to facilitate global comparisons of malnutrition prevalence, interventions and outcomes. Validation to assess usefulness in clinical practice is essential, however, the imperfect nature of reference standards used in concurrent validation may result in biased estimates of diagnostic accuracy. The Bayesian latent class model (BLCM) can assess the diagnostic performance when a "gold standard" is absent. This study's objective was to assess the diagnostic performance of the GLIM criteria in comparison with the Nutritional Risk Screening 2002 (NRS-2002) and the Patient Generated Subjective Global Assessment (PG-SGA) in lung cancer patients using a BLCM. We hypothesized that the GLIM criteria are more sensitive and specific for malnutrition diagnosis in lung cancer patients. METHODS AND STUDY DESIGN: 1,384 patient records retrospectively obtained from the "Investigation on Nutrition Status and its clinical outcome of common Cancers" (INSCOC) study were used to determine the prevalence of malnutrition, sensitivity (Se) and specificity (Sp) by applying a BLCM. RESULTS: The prevalence of malnutrition was 0.56. The sensitivity and specificity of the GLIM criteria were Se: 0.85 and Sp: 0.88; Se: 0.74 and Sp: 0.85 for NRS-2002 and Se: 0.96 and Sp: 0.89 for PG-SGA. CONCLUSIONS: Although the GLIM criteria were acceptable for malnutrition diagnosis, PG-SGA is superior for determining cancer-associated malnutrition. Because of its fair sensitivity, NRS-2002 was best equipped to screen out patients not at nutritional risk.

NLRP3 deficiency did not attenuate NASH development under high fat calorie diet plus high fructose and glucose in drinking water.

NOD-like receptor protein 3 (NLRP3) promotes the inflammatory response during progression of nonalcoholic fatty liver (NAFL) to nonalcoholic steatohepatitis (NASH). This study aimed to further delineate the role of NLRP3 in NASH development by abolishing its expression in mice. A high-fat and calorie diet plus high fructose and glucose in drinking water (HFCD-HF/G) was used to establish NASH in both wild-type (WT) and NLRP3 knock-out (KO) mice. Hepatocellular injury, hepatic steatosis and fibrosis, as well as inflammatory response and insulin resistance in the liver and epidydimal white adipose tissue (eWAT) were determined. Elevated body weight, liver weight and serum alanine transaminase level, increased hepatic triglyceride accumulation and collagen deposition, and worsened systemic insulin resistance were observed in Nlrp3-/- mice compared to WT mice under HFCD-HF/G feeding. Upregulated hepatic transcription of tumor necrosis factor-α (TNF-α) and monocyte chemotactic protein-1 (MCP-1), and enhanced infiltration of inducible nitric oxide synthase-positive (iNOS+) M1 macrophages were also documented in HFCD-HF/G-fed Nlrp3-/- mice in comparison to HFCD-HF/G-fed WT mice. Moreover, transcription of TNF-α and MCP-1 and infiltration of iNOS+ M1 macrophages were increased in the liver of Nlrp3-/- mice under control diet. NLRP3 deficiency did not attenuate, but instead aggravated NASH development under HFCD-HF/G feeding. The worsened extent of NASH might be attributed to enhanced hepatic MCP-1 expression and M1 macrophage infiltration in Nlrp3-/- mice. Our study points to additional caution when NLRP3 blockade is considered as a therapeutic strategy in the treatment of human NASH.

Novel Method Based on Hollow Laser Trapping-LIBS-Machine Learning for Simultaneous Quantitative Analysis of Multiple Metal Elements in a Single Microsized Particle in Air.

Elemental identification of individual microsized aerosol particles is an important topic in air pollution studies. However, simultaneous and quantitative analysis of multiple constituents in a single aerosol particle with the noncontact in situ manner is still a challenging task. In this work, we explore the laser trapping-LIBS-machine learning to analyze four elements (Zn, Ni, Cu, and Cr) absorbed in a single micro-carbon black particle in air. By employing a hollow laser beam for trapping, the particle can be restricted in a range as small as ∼1.72 µm, which is much smaller than the focal diameter of the flat-topped LIBS exciting laser (∼20 µm). Therefore, the particle can be entirely and homogeneously radiated, and the LIBS spectrum with a high signal-to-noise ratio (SNR) is correspondingly achieved. Then, two types of calibration models, i.e., the univariate method (calibration curve) and the multivariate calibration method (random forests (RF) regression), are employed for data processing. The results indicate that the RF calibration model shows a better prediction performance. The mean relative error (MRE), relative standard deviation (RSD), and root-mean-squared error (RMSE) are reduced from 0.1854, 363.7, and 434.7 to 0.0866, 179.8, and 216.2 ppm, respectively. Finally, simultaneous and quantitative determination of the four metal contents with high accuracy is realized based on the RF model. The method proposed in this work has the potential for online single aerosol particle analysis and further provides a theoretical basis and technical support for the precise prevention and control of composite air pollution.

Risk factors for esophageal squamous cell carcinoma and its histological precursor lesions in China: a multicenter cross-sectional study.

BACKGROUND: Despite research efforts, the causative factors that contribute to esophageal squamous cell carcinoma (ESCC) in high-risk areas have not yet been understood. In this study, we, therefore, aimed to describe the risk factors associated with ESCC and its precursor lesions. METHODS: We performed an endoscopic examination of 44,857 individuals aged 40-69 years from five high incidence regions of China in 2017-2018. Participants were classified as 4 groups of normal control, esophagitis, low-grade intraepithelial neoplasia (LGIN) and high-grade intraepithelial neoplasia/esophageal squamous cell carcinoma (HGIN/ESCC) using an unconditional logistic regression determine risk factors. RESULTS: We identified 4890 esophagitis, 1874 LGIN and 437 HGIN/ESCC cases. Crude odds ratios (ORs) and adjusted odds ratios were calculated using unconditional logistic regression. Drinking well and surface water, salty diet, and positive family history of cancer were the common risk factors for esophagitis, LGIN and HGIN/ESCC. History of chronic hepatitis/cirrhosis was the greatest risk factor of esophagitis (adjusted OR 2.96, 95%CI 2.52-3.47) and HGIN/ESCC (adjusted OR 1.91, 95%CI 1.03-3.22). Pesticide exposure (adjusted OR 1.20, 95%CI 1.05-1.37) was essential risk factor of LGIN. CONCLUSIONS: Among individuals aged 40-69 years in high incidence regions of upper gastrointestinal cancer, the results provided important epidemiological evidence for the prevention of different precancerous lesions of ESCC.

Machine learning may predict individual hand motor activation from resting-state fMRI in patients with brain tumors in perirolandic cortex.

OBJECTIVE: The study aimed to evaluate the predictive validity of the neural network (NN) method for presurgical mapping of motor areas using resting-state functional MRI (rs-fMRI) data of patients with brain tumor located in the perirolandic cortex (PRC). METHODS: A total of 109 patients with brain tumors occupying PRC underwent rs-fMRI and hand movement task-based fMRI (tb-fMRI) scans. Using a NN model trained on fMRI data of 47 healthy controls, individual task activation maps were predicted from their rs-fMRI data. NN-predicted maps were compared with task activation and independent component analysis (ICA)-derived maps. Spatial Pearson's correlation coefficients (CC) matrices and Dice coefficients (DC) between task activation and predicted activation using NN (DCNN_Act) and ICA (DCICA_Act) were calculated and compared using non-parametric tests. The effects of tumor types and head motion on predicted maps were demonstrated. RESULTS: The CC matrix of NN-predicted maps showed higher diagonal values compared with ICA-derived maps (p < 0.001). DCNN_Act were higher than DCICA_Act (p < 0.001) for patients with or without motor deficits. Lower DCs were found in subjects with head motion greater than one voxel. DCs were higher on the nontumor side than on the tumor side (p < 0.001), especially in the glioma group compared with meningioma and metastatic groups. CONCLUSIONS: This study indicated that the NN approach could predict individual motor activation using rs-fMRI data and could have promising clinical applications in brain tumor patients with anatomical and functional reorganizations. KEY POINTS: • The neural network machine learning approach successfully predicted hand motor activation in patients with a tumor in the perirolandic cortex, despite space-occupying effects and possible functional reorganization. • Compared to the conventional independent component analysis, the neural network approach utilizing resting-state fMRI data yielded a higher correlation to the active task hand activation data. • The Dice coefficient of machine learning-predicted activation vs. task fMRI activation was different between tumor and nontumor side, also between tumor types, which might indicate different effects of possible neurovascular uncoupling on resting-state and task fMRI.