Rapid quantitative analysis of petroleum coke properties by laser-induced breakdown spectroscopy combined with random forest based on a variable selection strategy.

Driven by the "double carbon" strategy, petroleum coke short-term demand is growing rapidly as a negative electrode material for artificial graphite. The analysis of petroleum coke physicochemical properties has always been an important part of its research, encompassing significant indicators such as ash content, volatile matter and calorific value. A strategy based on laser-induced breakdown spectroscopy (LIBS) in combination with chemometrics is proposed to realize the rapid and accurate quantification of the above properties. LIBS spectra of 46 petroleum coke samples were collected, and an original random forest (RF) calibration model was constructed by optimizing the pretreatment parameters. The RF calibration model was further optimized based on variable importance measures (VIM) and variable importance in projection (VIP) methods. After variable selection, the elemental spectral lines related to ash content, volatile matter and calorific value modeling were screened out, thus initially exploring the correlation between these properties and elements. Under the optimized spectral pretreatment method, VI threshold and model parameters, the mean relative error (MREP) of the prediction set of ash content, volatile matter and calorific value were 0.0881, 0.0527 and 0.006, the root mean square error (RMSEP) of the prediction set of ash content, volatile matter and calorific value were 0.0471%, 0.6178% and 0.2697 MJ kg-1, respectively, and the determination coefficient (RP2) of the prediction set was 0.9187, 0.9820 and 0.9510, respectively. The combination of LIBS technology and chemometric methods can provide powerful technical means for the analysis and evaluation of the physicochemical properties of petroleum coke.

Quantitative analysis of four PAHs in oily sludge by surface-enhanced Raman spectroscopy (SERS) combined with partial least squares regression (PLS) based on a novel nano-silver-silicon coupling substrate.

Polycyclic aromatic hydrocarbons (PAHs) present in oily sludge generated by the petroleum and petrochemical industries have emerged as a prominent concern within the realm of environmental conservation. The precise determination of PAHs holds immense significance in both petroleum geochemistry and environmental protection. In this study, a combination of surface-enhanced Raman spectroscopy (SERS) and solid-liquid extraction was employed for the screening of PAHs in oily sludge. Methanol was utilized as the extraction solvent for PAHs, while nanosilver-silicon coupling substrates were employed for their detection. The SERS spectrum was acquired using a portable Raman spectrometer. The nano silver-silicon coupling substrate exhibits excellent uniformity, with relative standard deviations (RSDs) of Phenanthrene, Fluoranthrene, Fluorene and Naphthalene (Phe, Flt, Flu and Nap) being 2.8%, 1.08%, 1.41%, and 5.44% respectively. Moreover, the limits of detection (LODs) achieved remarkable values of 0.542 µg/g, 0.342 µg/g, 0.541 µg/g, and 5.132 µg/g. The quantitative analysis of PAHs in oily sludge was investigated using SERS technology combined with partial least squares (PLS). The optimal PLS calibration model was optimized by combining spectral preprocessing methods and using the SiPLS (Synergy interval partial least squares)-VIP (Variable Importance in Projection) hybrid variable selection strategy. The prediction performance of the D1st (First derivative)-WT (Wavelet transform)-SiPLS-VIP-PLS model was deemed satisfactory, as evidenced by high R2P values of 0.9851, 0.9917, and 0.9925 for Phe, Flt, and Flu respectively; additionally, the corresponding MREP values were found to be 0.0580, 0.0668, and 0.0669 respectively. However, for Nap analysis, the D1st-WT-PLS model proved to be a better calibration model with an R2P value of 0.9864 and an MREP (Mean relative error of prediction) value of 0.0713. In summary, SERS technology combined with PLS based on different spectral pretreatment methods and mixed variable selection strategies is a promising method for quantitative analysis of PAHs in oily sludge, which will provide new ideas and methods for the quantitative analysis of PAHs in oily sludge.

Metformin Inhibits NLRP3 Inflammasome Expression and Regulates Inflammatory Microenvironment to Delay the Progression of Colorectal Cancer.

BACKGROUND: Colorectal cancer is a common malignant tumor, with about one million people diagnosed with it worldwide each year. Recent studies have found that metformin can inhibit the production of inflammatory factors and regulate the polarization of immune cells. However, whether metformin can regulate the inflammatory microenvironment and delay the progression of colorectal cancer by inhibiting the inflammatory response has not been deeply studied yet. OBJECTIVE: This study aimed to explore the molecular mechanism by which metformin inhibits the expression of NLRP3 inflammasome, regulates the inflammatory microenvironment, and delays the progression of colorectal cancer through in vitro cell experiments. METHODS: In this research, NLRP3 was knocked down in human colorectal cancer cells, and metformin was added to them. Cell proliferation ability was detected by CCK8, and cell migration and invasion abilities were assessed by Transwell assay. The apoptosis rate was determined by flow cytometry. In addition, the expression of NLRP3 inflammatory vesicles and inflammatory factors in each group of cells was studied by qRT-PCR and Western blotting. Finally, clinical colorectal cancer samples were analyzed by immunohistochemistry. RESULTS: The results of the study showed that NLRP3 expression was significantly increased in colorectal cancer cell lines and human colorectal cancer tissues. Knockdown of NLRP3 significantly inhibited tumor cell proliferation, migration, and invasion. In addition, the proliferation, migration and invasion of tumor cells were also significantly reduced by the addition of metformin intervention. Furthermore, qRT-PCR and WB results demonstrated that the expression of IL-1ß, IL-6, TNF- α, TGF-ß, and IL-10 was down-regulated in LS1034 tumor cells after NLRP3 knockdown. In addition, metformin intervention also resulted in different degrees of downregulation of NLRP3 and inflammatory factor expression (p <0.05). Notably, the reduction in inflammatory factors was more pronounced after the combination of NLRP3 knockdown and metformin intervention. CONCLUSION: Metformin can inhibit the expression of NLRP3 inflammasome, thereby suppressing the expression of inflammation-related factors, reducing the damage of the inflammatory microenvironment to normal cells, and delaying the progression of colorectal cancer.

Raman spectroscopy combined with partial least squares (PLS) based on hybrid spectral preprocessing and backward interval PLS (biPLS) for quantitative analysis of four PAHs in oil sludge.

Polycyclic aromatic hydrocarbons (PAHs) contained in a large amount of oily sludge produced in petroleum and petrochemical production has become one of the main environmental protection concerns in the industry. The accurate determination of PAHs is of great significance in the field of petroleum geochemistry and environmental protection. In this study, Raman spectroscopy combined with partial least squares (PLS) based on different hybrid spectral preprocessing methods and variable selection strategies was proposed for quantitative analysis of phenanthrene, fluoranthrene, fluorene and naphthalene (Phe, Flt, Flu and Nap) in oil sludge. At first, PAHs in oily sludge was extracted by solid-liquid extraction with methanol as extractant, and Raman spectra of 21 oily sludge samples were collected by portable Raman spectrometer. And then, the influence of first derivative (D1st), wavelet transform (WT) and their hybrid spectral preprocessing on the predictive performance of the PLS calibration model was discussed. Thirdly, biPLS (backward interval partial least squares) was used to optimize the input variables before and after the hybrid spectral preprocessing methods, and the influence of biPLS and the hybrid spectral preprocessing sequence on the predictive performance of the PLS calibration model was discussed. Finally, the predictive performance of the PLS calibration model was optimized according to the results of leave-one-out cross-validation (LOOCV) method. The results show that the biPLS-D1st-WT-PLS calibration model established by using biPLS first to select the characteristic variables, followed by hybrid spectral preprocessing of the characteristic variables, has better prediction performance for Flt (determination coefficient of prediction (R2P) = 0.9987, and the mean relative error of prediction (MREP) = 0.0606). For Phe, Flu and Nap, the WT-biPLS-PLS calibration model has a better predictive effect (R2P are 0.9995, 0.9996 and 0.9983, and MREP are 0.0426, 0.0719 and 0.0497, respectively). In general, portable Raman spectroscopy combined with PLS calibration model based on different hybrid spectral preprocessing and variable selection strategies has achieved good prediction results for quantitative analysis of four PAHs in oily sludge. It is a new strategy to firstly select the characteristic variables of the original spectra, and secondly to preprocess the characteristic variables by the hybrid spectral preprocessing, which will provide a new idea for the establishment of quantitative analysis methods for PAHs in oily sludge.

MicroRNA3650 Promotes Gastric Cancer Proliferation and Migration through the PTEN/PI3K-AKT-mTOR and Hippo Pathways.

BACKGROUND: Gastric cancer (GC) is a malignant tumor with seriously poor outcomes. Studies have shown that microRNAs (miRNAs) play an omnifarious regulatory effect in GC. However, the role of miR-3650 in the progression of GC is not well known. METHODS: In this study, miR-3650 expression and its clinical significance were determined using clinical specimens. The biological functions of miR-3650 were determined in gastric cancer cell lines through CCK-8, cell scratch, and transwell experiments. Bioinformatics predictions, combined with Western blot experiments, were employed to explore its downstream molecular targets. RESULTS: We observed that miR-3650 was overexpressed in GC specimens and most cell lines, i.e., 77.8% (MKN28, SNU1, AGS, MKN45, N87, BGC823 and SGC7901). The overexpression correlated with advanced T-stage, N-stage, M-stage, and TNM-stage. Furthermore, miR-3650 promoted the proliferation and migration of gastric cancer cells, and its overexpression promoted the PI3K-AKT-mTOR pathway and inhibited the PTEN and hippo pathways. The potassium ion signaling pathway was also involved in the biological process of miR-3650 promoting cancer. CONCLUSION: Therefore, we concluded that miR-3650/PTEN/PI3K-AKT-mTOR and miR-3650/hippo pathways are vital in the progression of GC and serve as novel targets for GC therapy.

Rapid quantitative analysis of rare earth elements Lu and Y in rare earth ores by laser induced breakdown spectroscopy combined with iPLS-VIP and partial least squares.

Rare earth ores are complex in composition and diverse in mineral composition, requiring high technical requirements for the selection of rare earth ores. It is of great significance to explore the on-site rapid detection and analysis methods of rare earth elements in rare earth ores. Laser induced breakdown spectroscopy (LIBS) is an important tool to detect rare earth ores, which can be used for in situ analyses without complicated sample preparation. In this study, a rapid quantitative analysis method for rare earth elements Lu and Y in rare earth ores was established by LIBS combined with an iPLS-VIP hybrid variable selection strategy and partial least squares (PLS) method. First, the LIBS spectra of 25 samples were studied using laser induced breakdown spectrometry. Second, taking the spectrum processed by wavelet transform (WT) as the input variables, PLS calibration models based on interval partial least squares (iPLS), variable importance projection (VIP) and iPLS-VIP hybrid variable selection were constructed to quantitatively analyze rare earth elements Lu and Y, respectively. The results show that the WT-iPLS-VIP-PLS calibration model has better prediction performance for rare earth elements Lu and Y, and the optimal coefficient of determination (R2) of Lu and Y were 0.9897 and 0.9833, the root mean square error (RMSE) were 0.8150 µg g-1 and 97.1047 µg g-1, and the mean relative error (MRE) were 0.0754 and 0.0766, respectively. It shows that LIBS technology combined with the iPLS-VIP and PLS calibration model provides a new method for in situ quantitative analysis of rare earth elements in rare earth ores.

Quantitative analysis of phenanthrene in soil by fluorescence spectroscopy coupled with the CARS-PLS model.

Polycyclic aromatic hydrocarbons (PAHs) are typical organic pollutants in soil and are teratogenic and carcinogenic. Therefore, rapid and accurate analysis of PAHs in soil can provide a theoretical basis and data support for soil contamination risk assessment. In this work, a fluorescence spectroscopy technique combined with partial least squares (PLS) was proposed for rapid quantitative analysis of phenanthrene (PHE) in soil. At first, the fluorescence spectra of 29 soil samples with different concentrations (0.3-10 mg g-1) of PHE were collected by RF-5301 PC fluorescence spectrophotometer. Secondly, the effects of different spectral preprocessing methods were investigated on the prediction performance of the PLS calibration model. And then, the influence of competitive adaptive reweighted sampling (CARS) wavelength points on the prediction performance of PLS calibration model was discussed. Finally, according to the selected wavelength points, a quantitative analytical model for PHE content in soil was constructed using the PLS calibration method. To further explore the predictive performance of the CARS-PLS calibration model, the predictive results were compared with those of the RAW spectrum-partial least squares calibration model (RAW-PLS) and the wavelet transform-standard normal variation (WT-SNV) calibration model. The CARS-PLS calibration model showed the optimal predictive performance and its coefficient of determination of cross-validation (R cv 2) and root mean square error of 10-fold cross-validation (RMSEcv) were 0.9957 and 18.98%, respectively. The coefficient of determination of prediction set (R p 2) and root mean square error of prediction set (RMSEp) were 0.9963 and 16.13%, respectively. Hence, the CARS algorithm based on fluorescence spectrum coupled with PLS can give a rapid and accurate quantitative analysis of the PHE content in soil.

Quantitative Analysis of the Cu Element Enhanced by AgNPs in a Single Microsized Suspended Particle Based on Optical Trapping-LIBS and Machine Learning.

Extremely severe and persistent particulate pollution caused by industrialization and urbanization impacts air quality, regional and global climates, and human health. The unstable and complex spectral signal of laser-induced breakdown spectroscopy (LIBS) with minimal feature information and interference signals considerably influences the accuracy of qualitative and quantitative analysis. In response to overcome this phenomenon, in this work, quantitative analysis of Cu element enhanced by silver nanoparticles (AgNPs) in a single microsized suspended particle was proposed herein using optical trapping-LIBS and machine learning method was proposed. Initially, the optimal AgNPs enhancement conditions were optimized. The LIBS spectra of 15 polluted black carbon samples were collected and various spectral pretreatment methods were compared to optimize the LIBS spectra. Variable selection methods include variable importance measurement (VIM), variable importance projection (VIP), VIM-successive projections algorithm (VIM-SPA), VIM-genetic algorithm (VIM-GA), and VIM-mutual information (VIM-MI). Finally, several hybrid variable selection methods were implemented in random forest (RF) calibration models. In particular, a wavelet transform (WT)-VIM-SPA-RF calibration model has constructed under the WT spectral pretreatment method and the selected and optimized input variables (VIM-SPA). Results elucidate that the WT-VIM-SPA-RF calibration model (R2P = 0.9858, MREP = 0.0396) have the best prediction performance than the WT-RF and Raw-RF models in predicting the Cu level in a single microsized black carbon particle. Compared to the WT-RF and Raw-RF models, MREP values decreased by 37% and 62%, respectively. The values of RSD, RPD, and RER of this calibration model are 2.8%, 8.39%, and 17.79%, respectively. The aforementioned results demonstrate that the WT-VIM-SPA-RF calibration model with accuracy, stability, and robustness is a promising approach for improving the quantitative accuracy of the Cu level in carbon black particles.

Novel Random Forest Ensemble Modeling Strategy Combined with Quantitative Structure-Property Relationship for Density Prediction of Energetic Materials.

With the further development of the concept of green chemistry, the new generation of energetic materials tends to exhibit detonation properties such as higher insensitivity, higher density, and higher energy. Therefore, the precise molecular design and green and efficient synthesis of energetic materials will be one of the serious challenges. For the purpose of accurate prediction of detonation performance of energetic materials, an ensemble modeling strategy based on the combination of Monte Carlo (MC) and variable importance measurement (VIM) improved random forest (RF) and quantitative structure-property relationship (QSPR) is proposed, which was successfully used for density prediction of energetic materials. First, the structure of 162 energetic compounds was optimized by Gaussian software, and the molecular descriptor data were calculated by CODESSA software based on the optimized molecular structure. Then, the MCVIMRF_Med ensemble model was constructed on the basis of the above molecular descriptor data and the corresponding energetic compound density index. The joint X-Y distance algorithm (SPXY) is used to partition the data set. And then, MC is used to further divide the calibration set data into multiple subsets for the construction of the ensemble model. The subset size and the number of iterations of the MCVIMRF_Med ensemble model were optimized through MC cross validation. The final output strategy of the ensemble model is optimized based on the optimized parameters, and an output optimization method based on median screening is proposed and successfully applied for the prediction performance optimization of the MCVIMRF_Med ensemble model. To further investigate the performance of the MCVIMRF_Med ensemble model, the performance of it was compared with partial least squares, RF, VIMRF, and MCVIMRF calibration models. It shows that the MCVIMRF_Med ensemble model can achieve a better prediction result for the density of energetic materials, with R 2 CV of 0.9596, RMSECV of 0.0437 g/cm3, R 2 P of 0.9768, RMSEP of 0.0578 g/cm3, and relative analysis deviation of prediction set of 3.951. Therefore, the MCVIMRF_Med ensemble modeling strategy combined with QSPR is an effective approach for the density prediction of energetic materials. This work is expected to provide new research ideas and technical support for accurate prediction of detonation performance of energetic materials.

On-line Raman spectroscopy combined with multivariate curve resolution-alternating least squares (MCR-ALS) to investigate the synthesis mechanism of 3,5-diamino-1,2,4-triazole (DAT).

The precise and accurate synthesis mechanism of typical energetic materials (EMs) intermediate is extremely important for the optimization of synthesis technology of EMs. In this research, on-line Raman spectroscopy technique combined with multivariate curve resolution-alternating least squares(MCR-ALS) method was proposed and used to investigate the synthesis mechanism of EMs intermediate (3,5-diamino-1,2,4-triazole, DAT). Initially, on-line Raman spectroscopy was applied to collect the Raman spectral data of DAT synthesis process. Secondly, principal component analysis (PCA), coupled with singular value decomposition (SVD) were used to determine the number of component of the reaction system and the components was 5. Thirdly, MCR-ALS was used to extract the pure Raman spectra and concentration curves of each substance of DAT synthesis process. During the MCR-ALS operation, evolving factor analysis (EFA) was choose to acquire the initial concentration estimation for MCR-ALS. Several constraints were selected to apply to ALS optimization including non-negative, closure, equality and correlation constraint. And the correlation coefficient between the Raman spectra and the actual Raman spectra of the hydrazine hydrochloride, dicyandiamide and DAT was calculated, their correlation coefficient R2 were 0.9522, 0.9446, 0.9908 respectively which showed a good data fit of MCR-ALS method. Finally, according to the results of MCR-ALS analysis, the structure of the synthetic intermediates was successfully deduced and the mechanism of DAT synthesis was proposed. Hence, a precise and comprehensive method for analyzing the DAT synthesis reaction mechanism is proposed, which is helpful to provide a new idea for the analysis of the synthesis reaction mechanism of energetic materials.

Vessels that encapsulate tumor clusters (VETC) pattern predicts the efficacy of adjuvant TACE in hepatocellular carcinoma.

PURPOSE: Postoperative adjuvant trans-catheter arterial chemoembolization (TACE) is regarded as a common strategy for hepatocellular carcinoma (HCC) patients at a high risk of recurrence. However, there are currently no clinically available biomarkers to predict adjuvant TACE response. Vessels that encapsulate tumor clusters (VETC) can be used as an independent predictor of HCC prognosis. In this study, we aimed to explore whether the VETC pattern could predict adjuvant TACE benefit. METHODS: Vascular pattern and HIF-1α expression were detected in immunohistochemistry. The survival benefit of adjuvant TACE therapy for patients with or without VETC pattern (VETC+ /VETC-) was evaluated. RESULTS: The adjuvant TACE therapy obviously improved the TTR and OS in VETC+ patients, while adjuvant TACE therapy could not benefit from VETC- patients. Univariate and multivariate analysis revealed that adjuvant TACE therapy significantly improved the TTR and OS in VETC+ patients, but not in VETC- patients. In addition, the VETC+ , but not VETC- , patients could benefit from adjuvant TACE therapy in patients with high-risk factors of vascular invasion, larger tumor or multiple tumor. The mechanistic investigations revealed that the favorable efficacy of adjuvant TACE on VETC+ patients, but not VETC- ones, may be not due to the activation of HIF-1α pathway. CONCLUSION: The VETC pattern may represent a novel and reliable factor for selecting HCC patients who may benefit from adjuvant TACE therapy, and the combination of VETC pattern and tumor characteristics may help stratify patients' outcomes and responses to adjuvant TACE therapy.

Quantitative analysis of polycyclic aromatic hydrocarbons (PAHs) in water by surface-enhanced Raman spectroscopy (SERS) combined with Random Forest.

Polycyclic aromatic hydrocarbons (PAHs) have strong carcinogenicity, teratogenicity, mutagenicity and other adverse effects on human beings. They are one of the most dangerous pollutants, which have attracted great attention in the past decades. In this work, aiming at the actual problems that water environment is polluted and human health is threatened by PAHs, surface enhanced Raman spectroscopy (SERS) combined with Random Forest (RF) calibration models were used to quantitative analysis of phenanthrene and fluoranthene in water. Firstly, the SERS data was collected after samples mixed with Ag NPs, after 31 PAHs samples were prepared. Secondly, it was discussed how spectral preprocessing integration strategies affect on the prediction performance of the RF calibration models. And then, the effect of mutual information (MI) variable selection method on the performance of RF calibration models was explored. Finally, the RF calibration models were established for phenanthrene and fluoranthene. For the prediction set, a lowest mean relative error (MRE) and a largest determination coefficient (R2) were obtained. For quantitative analysis of phenanthrene, the final prediction performance results show that R2p is 0.9780, and MREp is 0.0369 based on the D1st-WT-RF calibration model. For fluoranthene, WT-D1st-MI-RF is a better calibration model, and corresponding to R2p and MREp are 0.9770 and 0.0694, respectively. Hence, a rapid and accurate quantitative method of PAHs is established for the real-time detection of water environmental pollution, which is intended to provide new ideas and methods for the quantitative analysis of PAHs in water.

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.

Added Value of Quantitative Apparent Diffusion Coefficients for Identifying Small Hepatocellular Carcinoma from Benign Nodule Categorized as LI-RADS 3 and 4 in Cirrhosis.

BACKGROUND AND AIMS: Correct identification of small hepatocellular carcinomas (HCCs) and benign nodules in cirrhosis remains challenging, quantitative apparent diffusion coefficients (ADCs) have shown potential value in characterization of benign and malignant liver lesions. We aimed to explore the added value of ADCs in the identification of small (≤3 cm) HCCs and benign nodules categorized as Liver Imaging Reporting and Data System (LI-RADS) 3 (LR-3) and 4 (LR-4) in cirrhosis. METHODS: Ninety-seven cirrhosis patients with 109 small nodules (70 HCCs, 39 benign nodules) of LR-3 and 4 LR-4 based on major and ancillary magnetic resonance imaging features were included. Multiparametric quantitative ADCs of the lesions, including the mean ADC (ADCmean), minimum ADC (ADCmin), maximal ADC (ADCmax), ADC standard deviation (ADCstd), and mean ADC value ratio of lesion-to-liver parenchyma (ADCratio) were calculated. Regarding the joint diagnosis, a nomogram model was plotted using multivariate logistic regression analysis. The performance was assessed using the area under the receiver operating characteristic curve (AUC). RESULTS: The ADCmean, ADCmin, ADCratio, and ADCstd were significantly associated with the identification of small HCC and benign nodules (p<0.001). For the joint diagnosis, the LI-RADS category (odds ratio [OR]=12.50), ADCmin (OR=0.14), and ADCratio (OR=0.12) were identified as independent factors for distinguishing HCCs from benign nodules. The joint nomogram model showed good calibration and discrimination, with a C-index of 0.947. Compared with the LI-RADS category alone, this nomogram model demonstrated a significant improvement in diagnostic performance, with AUC increasing from 0.820 to 0.967 (p=0.001). CONCLUSIONS: The addition of quantitative ADCs could improve the identification of small HCC and benign nodules categorized as LR-3 and 4 LR-4 in patients with cirrhosis.

Tumor Mutational Burden Associated With Response to Hyperthermic Intraperitoneal Chemotherapy.

Background: Gastric cancer (GC) is one of the most common cancer types, especially in Asian countries. Hyperthermic intraperitoneal chemotherapy (HIPEC) has been shown to improve the progression-free survival among gastric cancer patients with peritoneal metastases; however, not all patients demonstrate response to HIPEC. Methods: Biomarkers are needed to select patients for effective treatment of HIPEC. Here, we performed whole-exome sequencing on tumor samples from 18 gastric cancer patients who received HIPEC treatment and assessed the association between genomic mutation features and progression-free survival. Exome sequencing was further conducted on tumor samples from additional 15 gastric cancer patients as a replication study. Results: The tumor mutational burden (TMB) was significantly higher in the group of patients with a better response to HIPEC treatment than that of the others. Kaplan-Meier survival curve showed that patients with high TMB had a significantly longer survival time than that in patients with low TMB. This discovery was validated in the replication cohort. Genes bearing mutations recurrently and selectively in patients with better response to HIPEC were found in the two cohorts. Conclusion: We found that higher TMB is significantly associated with better response to HIPEC. Our results provide useful hints for prognostic stratification of HIPEC treatment.

Noncoding RNAs and hyperthermic intraperitoneal chemotherapy in advanced gastric cancer.

Gastric cancer (GC) is one of the most common malignant tumors globally. About 20-30% of patients with gastric cancer show peritoneal implantation metastasis at the first diagnosis. Peritoneal metastasis is responsible for 70% of deaths of patients with advanced gastric cancer. Although there are many ways to treat advanced gastric cancer, the prognosis of patients with recurrence is unsatisfactory. An auxiliary treatment with hyperthermic intraperitoneal chemotherapy (HIPEC), is an internationally recognized recommended treatment for advanced gastric cancer. A series of clinical trials have shown that HIPEC significantly improves the overall survival of patients with cancer. Compared with the cytoreductive surgery (CRS) alone, HIPEC combined with CRS markedly reduced the rate of peritoneal metastasis in patients with ovarian cancer and colorectal cancer. It has been demonstrated that HIPEC alters transcription of many genes by affecting non-coding RNAs, which may contribute to the suppressive effect of HIPEC on the synthesis of nucleic acids and proteins in cancer cells. This paper reviews the recent advances in understanding the role of non-coding RNAs in tumor invasion and metastasis of advanced gastric cancer. We also consider changes in noncoding RNA levels and other molecules in advanced gastric cancer cases treated with HIPEC. We hope that our review will provide a reference for future research on molecular epidemiology and etiology of advanced gastric cancer and promote precise treatment of this malignancy using HIPEC.

HSP70 promotes tumor progression by stabilizing Skp2 expression in gastric cancer cells.

Gastric cancer (GC) has one of the highest tumor incidences worldwide. Heat shock protein 70 (HSP70) is highly expressed and plays a critical role in the occurrence, progression, metastasis, poor prognosis, and drug resistance of GC. However, the underlying mechanisms of HSP70 are not clear. To explore the regulatory role of HSP70 in GC, we performed cell counting kit-8 (CCK-8) and EdU staining assays to assess cell proliferation; immunohistochemistry and western blot analyses to assess protein expression; coimmunoprecipitation (Co-IP) assays to assess interactions between two proteins; and immunofluorescence to assess protein expression and localization. HSP70 was highly expressed in clinical samples from patients with GC and indicated a poor prognosis. HSP70 inhibition enhanced the sensitivity of GC cells to thermochemotherapy. Furthermore, we found that S phase kinase-associated protein 2 (Skp2) was highly expressed in GC and correlated with HSP70 in array data from The Cancer Genome Atlas (TCGA). Importantly, HSP70 inhibition promoted Skp2 degradation. Skp2 overexpression abrogated HSP70 inhibition-induced cell cycle arrest, suggesting that the role of HSP70 in GC depends on Skp2 expression. Our results illustrate a possible regulatory mechanism of HSP70 and may provide a therapeutic strategy for overcoming resistance to thermochemotherapy.

Pollution risk estimation of the Cu element in atmospheric sedimentation samples by laser induced breakdown spectroscopy (LIBS) combined with random forest (RF).

Laser-induced breakdown spectroscopy (LIBS) combined with the random forest (RF) algorithm was proposed to predict three pollution indexes (geo-accumulation index, enrichment factor, and potential ecological risk index) of the Cu element in atmospheric sedimentation samples to evaluate the pollution risk. To begin with, the LIBS spectra of 15 atmospheric sedimentation samples from different locations were collected and the copper element was identified using the National Institute of Standards and Technology (NIST) database. Then, the influence of different spectral pretreatment methods (MSC, WT and D1st) on the predictive performance of the RF was discussed according to the calibration set with the determination coefficient (Rc2) and mean relative error (MREC) as evaluation indexes. Next, in order to obtain a better RF calibration model, a variable importance (VI) measurement was applied to select input variables from LIBS spectral data based on the optimal spectral pretreatment method, and the optimal variable importance threshold was selected as the input variable to establish the RF calibration model. Finally, the predictive performance of the optimal RF calibration model was verified using the prediction set with the determination coefficient (Rp2) and the mean relative error (MREP). The results show that Rp2 of the geo-accumulation index, enrichment factor and potential ecological risk index is up to 0.9971, 0.9919 and 0.9290, respectively, and MREP of the three indexes is 0.0234, 0.1173 and 0.0810, respectively; the average relative standard deviation (RSD) of the prediction set for the three indexes is 2.16%, 5.78% and 0.71%, respectively. Furthermore, it can be inferred that Cu was at levels corresponding to serious pollution primarily because of anthropogenic activities based on the predictive Igeo, Er and RI values. Therefore, LIBS combined with the RF algorithm is a promising means which can achieve fast and simple estimation of the pollution risk degree of Cu in atmospheric sedimentation samples without complicated sample preparation to provide a basis for pollution prevention and control measures.

Quantitative analysis of polycyclic aromatic hydrocarbons in soil by infrared spectroscopy combined with hybrid variable selection strategy and partial least squares.

Infrared spectroscopy (IR) combined with multivariate calibration technology can be used as a potential method to quantitative analysis of polycyclic aromatic hydrocarbons (PAHs) in soil, which provides a rapid data support for soil risk assessment. However, IR spectrum contains lots of useless information, its predictive performance is poor. Variable selection is an effective strategy to eliminate irrelevant wavelengths and enhance predictive performance. In this study, IR combined with partial least squares (PLS) was proposed to quantify anthracene and fluoranthene in soil. In order to improve the predictive performance of the PLS calibration model, the synergy interval PLS (siPLS) method was first used for "rough selection" to select feature bands; on this basis, "fine selection" was performed to extract the feature variables. In "fine selection", three different feature variables selection methods, such as successive projection algorithm (SPA), genetic algorithm (GA), and particle swarm optimization (PSO), were compared for their performance in extracting effective variables. The results show that the siPLS-GA calibration model receive a lowest root mean square error (RMSE) and a largest determination coefficient (R2). Results of external validation demonstrate an excellent predictive performance of siPLS-GA calibration model, with the R2 = 0.9830, RMSE = 0.5897 mg/g and R2 = 0.9849, RMSE = 0.4739 mg/g for anthracene and fluoranthene, respectively. In summary, siPLS combined with GA can accurately extract the effective information of the target substance and improve the predictive performance of the PLS calibration model based on IR spectroscopy.

Differentiation of small (≤ 3 cm) hepatocellular carcinomas from benign nodules in cirrhotic liver: the added additive value of MRI-based radiomics analysis to LI-RADS version 2018 algorithm.

BACKGROUND: Accurate characterization of small nodules in a cirrhotic liver is challenging. We aimed to determine the additive value of MRI-based radiomics analysis to Liver Imaging Reporting and Data System version 2018 (LI-RADS v 2018) algorithm in differentiating small (≤ 3 cm) hepatocellular carcinomas (HCCs) from benign nodules in cirrhotic liver. METHODS: In this retrospective study, 150 cirrhosis patients with histopathologically confirmed small liver nodules (HCC, 112; benign nodules, 44) were evaluated from January 2013 to October 2018. Based on the LI-RADS algorithm, a LI-RADS category was assigned for each lesion. A radiomics signature was generated based on texture features extracted from T1-weighted, T2W, and apparent diffusion coefficient (ADC) images by using the least absolute shrinkage and selection operator regression model. A nomogram model was developed for the combined diagnosis. Diagnostic performance was assessed using receiver operating characteristic curve (ROC) analysis. RESULTS: A radiomics signature consisting of eight features was significantly associated with the differentiation of HCCs from benign nodules. Both LI-RADS algorithm (area under ROC [Az] = 0.898) and the MRI-Based radiomics signature (Az = 0.917) demonstrated good discrimination, and the nomogram model showed a superior classification performance (Az = 0.975). Compared with LI-RADS alone, the combined approach significantly improved the specificity (97.7% vs 81.8%, p = 0.030) and positive predictive value (99.1% vs 92.9%, p = 0.031) and afforded comparable sensitivity (97.3% vs 93.8%, p = 0.215) and negative predictive value (93.5% vs 83.7%, p = 0.188). CONCLUSIONS: MRI-based radiomics analysis showed additive value to the LI-RADS v 2018 algorithm for differentiating small HCCs from benign nodules in the cirrhotic liver.