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.

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.

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.

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.

Comparison of leaf transcriptome in response to Rhizoctonia solani infection between resistant and susceptible rice cultivars.

BACKGROUND: Sheath blight (SB), caused by Rhizoctonia solani, is a common rice disease worldwide. Currently, rice cultivars with robust resistance to R. solani are still lacking. To provide theoretic basis for molecular breeding of R. solani-resistant rice cultivars, the changes of transcriptome profiles in response to R. solani infection were compared between a moderate resistant cultivar (Yanhui-888, YH) and a susceptible cultivar (Jingang-30, JG). RESULTS: In the present study, 3085 differentially express genes (DEGs) were detected between the infected leaves and the control in JG, with 2853 DEGs in YH. A total of 4091 unigenes were significantly upregulated in YH than in JG before infection, while 3192 were significantly upregulated after infection. Further analysis revealed that YH and JG showed similar molecular responses to R. solani infection, but the responses were earlier in JG than in YH. Expression levels of trans-cinnamate 4-monooxygenase (C4H), ethylene-insensitive protein 2 (EIN2), transcriptome factor WRKY33 and the KEGG pathway plant-pathogen interaction were significantly affected by R. solani infection. More importantly, these components were all over-represented in YH cultivar than in JG cultivar before and/or after infection. CONCLUSIONS: These genes possibly contribute to the higher resistance of YH to R. solani than JG and were potential target genes to molecularly breed R. solani-resistant rice cultivar.

Hyperthermic intraperitoneal chemotherapy for gastric cancer with peritoneal metastasis: A multicenter propensity score-matched cohort study.

OBJECTIVE: Systemic chemotherapy has limited efficacy in the treatment of peritoneal metastasis (PM) in gastric cancer (GC). Hyperthermic intraperitoneal chemotherapy (HIPEC) combined with complete cytoreductive surgery (CRS) has shown promising outcomes but remains controversial. The present study aimed to evaluate the safety and efficacy of HIPEC without CRS in GC patients with PM. METHODS: This retrospective propensity score-matched multicenter cohort study included GC patients with PM treated with either chemotherapy alone (Cx group) or with HIPEC combined with chemotherapy (HIPEC-Cx group) in four Chinese high-volume gastric medical centers between 2010 and 2017. The primary outcomes were median survival time (MST) and 3-year overall survival (OS). Propensity score matching was performed to compensate for controlling potential confounding effects and selection bias. RESULTS: Of 663 eligible patients, 498 were matched. The MST in the Cx and HIPEC-Cx groups was 10.8 and 15.9 months, respectively [hazard ratio (HR)=0.71, 95% confidence interval (95% CI), 0.58-0.88; P=0.002]. The 3-year OS rate was 10.1% (95% CI, 5.4%-14.8%) and 18.4% (95% CI, 12.3%-24.5%) in the Cx and HIPEC-Cx groups, respectively (P=0.017). The complication rates were comparable. The time to first flatus and length of hospital stay for patients undergoing HIPEC combined with chemotherapy was longer than that of chemotherapy alone (4.6±2.4 dvs. 2.7±1.8 d, P<0.001; 14.2±5.8 dvs. 11.4±7.7 d, P<0.001), respectively. The median follow-up period was 33.2 months. CONCLUSIONS: Compared with standard systemic chemotherapy, HIPEC combined with chemotherapy revealed a statistically significant survival benefit for GC patients with PM, without compromising patient safety.

Long Non-Coding RNA XLOC_006753 Promotes the Development of Multidrug Resistance in Gastric Cancer Cells Through the PI3K/AKT/mTOR Signaling Pathway.

BACKGROUND/AIMS: The development of multidrug resistance (MDR), which results in disease recurrence and metastasis, is a crucial obstacle to successful chemotherapy for patients with gastric cancer (GC). Long non-coding RNAs (lncRNAs) have been found to play various roles in cancer. This study aimed to investigate the effect of XLOC_006753 on the development of MDR in GC cells. METHODS: The expression levels of XLOC_006753 in GC patients and MDR GC cell lines (SGC-7901/5-FU and SGC-7901/DDP cell line) were assessed by qRT-PCR. Statistical analyses were conducted to determine the relationship between XLOC_006753 expression and clinical features and to assess the prognostic value of XLOC_006753 for overall survival and progression-free survival. Then, a CCK-8 assay was used to detect cell proliferation ability and chemosensitivity. Flow cytometry was used to detect cell cycle and cell apoptosis. A wound-healing assay and transwell assay were used to detect cell migration. The expression of markers for MDR, G1/S transition, epithelial-mesenchymal transition (EMT) and PI3K/ AKT/mTOR signaling pathway were examined by western blot. RESULTS: XLOC_006753 was highly expressed in GC patients and MDR GC cell lines (SGC-7901/5-FU and SGC-7901/DDP cell lines), and its high expression was positively associated with metastasis, TNM stage, tumor size, and poor survival in GC patients. Moreover, XLOC_006753 was an independent prognostic biomarker of overall survival and progression-free survival for gastric cancer patients. Knocking down XLOC_006753 in the two MDR GC cell lines significantly inhibited cell proliferation, cell viability, cell cycle G1/S transition, and migration. XLOC_006753 knockdown also promoted apoptosis. Furthermore, western blots showed that XLOC_006753 knockdown decreased some markers of MDR, G1/S transition, and EMT expression, while increasing caspase9 expression and inhibiting the PI3K/AKT/mTOR signaling pathway in SGC-7901/5-FU and SGC-7901/DDP cells. CONCLUSION: High expression of XLOC_006753 promoted the development of MDR, which was activated by the PI3K/AKT/mTOR pathway in GC cells.

Cytoreductive surgery combined with hyperthermic intraperitoneal chemoperfusion for the treatment of gastric cancer: A single-centre retrospective study.

AIM: Cytoreductive surgery (CRS) combined with hyperthermic intraperitoneal chemoperfusion (HIPEC) is the treatment regime most likely to achieve prolonged survival in patients with peritoneal carcinomatosis from gastroenteric cancer. To date, few publications have focused on the treatment of patients with gastric cancer alone. Several controversies remain unsolved, including the safety and effectiveness of the CRS-HIPEC combination regime, particularly in cases where HIPEC is used as adjuvant treatment after CRS. Therefore, in the current study, we aimed to evaluate the safety and effectiveness of CRS combined with HIPEC in patients with gastric cancer. METHOD: Data from 231 patients with a median age of 55.1 years treated with the CRS-HIPEC combination regime between January 2009 and December 2014 were retrospectively reviewed. All patients underwent the combination therapy (mean of 2.4 cycles per patient, range, 1 to 4 cycles). RESULTS: Median overall survival was 37.0 months, with 1-, 2- and 3-year survival rates recorded as 83.4%, 68.5%, and 38.7%, respectively. The serum levels of carcinoembryonic antigen (CEA) and carbohydrate antigen 199 (CA199) were significantly decreased after combination therapy in the completeness of cytoreduction (CCR)-0 and CCR-1 groups, while no significant changes observed in marker levels were observed in the CC ≥2 group. The post-operative morbidity and mortality rates were 6.9% and 0.9%, respectively. Multivariate analysis revealed low TNM tumour stage, ascites condition and CCR score as independent predictors for better survival. CONCLUSION: In view of the acceptable morbidity and mortality rates we propose that CRS combined with HIPEC presents an effective and safe treatment modality for patients with gastric cancer, especially in cases where optimal cytoreduction is achieved before the HIPEC procedure.

Long noncoding RNA aberrant expression profiles after cytoreductive surgery and hyperthermic intraperitoneal chemotherapy of AGC ascertained by microarray analysis.

Long noncoding RNAs (lncRNAs) have been shown to be involved in the development and progression of advanced gastric cancer (AGC). However, the roles of lncRNAs in advanced gastric cancer during the process of cytoreductive surgery (CRS) and hyperthermic intraperitoneal chemotherapy (HIPEC) are not well understood. A high-throughput microarray analysis was performed to compare the expression profiles of lncRNAs and messenger RNAs (mRNAs) in AGC serum samples during the process of CRS + HIPEC. Several potentially AGC-associated lncRNAs were verified by real-time quantitative reverse transcription polymerase chain reaction (PCR) analysis. Using abundant and varied probes, we were able to assess 33,045 lncRNAs and 30,215 mRNAs in our microarray. We found that 566 lncRNAs were differentially expressed (2-fold change) in AGC serum samples, indicating the significantly up- or downregulated lncRNAs play important roles in AGC during the process of CRS + HIPEC. Quantitative PCR results further verified that eight lncRNAs were aberrantly expressed in AGC serum samples after CRS + HIEC compared with matched serum sample before CRS + HIPEC. Among them, BC031243 and RP11-356I2.2 were the most aberrantly expressed lncRNAs, as estimated by quantitative PCR in six pairs of AGC serum samples. Our study demonstrated the expression patterns of lncRNAs in AGC serums before and after CRS + HIPEC by microarray. These results revealed that lncRNAs were differentially expressed during the process of CRS + HIPEC, suggesting that they might play key roles in tumor development.

Thermo-chemotherapy Induced miR-218 upregulation inhibits the invasion of gastric cancer via targeting Gli2 and E-cadherin.

Thermo-chemotherapy has been proven to reduce the invasion capability of cancer cells. However, the molecular mechanism underlying this anti-invasion effect is still unclear. In this study, the role of thermo-chemotherapy in the inhibition of tumor invasion was studied. The results demonstrated that expression of miR-218 was downregulated in gastric cancer tissues, which had a positive correlation with tumor invasion and metastasis. In vitro thermo-chemotherapy increased miR-218 expression in SGC7901 cells and inhibited both proliferation and invasion of cancer cells. Gli2 was identified as a downstream target of miR-218, and its expression was negatively regulated by miR-218. The thermo-chemotherapy induced miR-218 upregulation was also accompanied by increasing of E-cadherin expression. In conclusion, the present study indicates that thermo-chemotherapy can effectively decrease the invasion capability of cancer cells and increase cell-cell adhesion. miR-218 and its downstream target Gli2, as well as E-cadherin, participate in the anti-invasion process.

Classification of steel materials by laser-induced breakdown spectroscopy coupled with support vector machines.

The feasibility of steel materials classification by support vector machines (SVMs), in combination with laser-induced breakdown spectroscopy (LIBS) technology, was investigated. Multi-classification methods based on SVM, the one-against-all and the one-against-one models, and a combination model, are applied to classify nine types of round steel. Due to the inhomogeneity of steel composition, the data obtained using the one-against-all and one-against-one models were ambiguous and difficult to discriminate; whereas, the combination model, was able to successfully distinguish most of the ambiguous data and control the computation cost within an acceptable range. The studies presented here demonstrate that LIBS-SVM is a useful technique for the identification and discrimination of steel materials, and would be very well-suited for process analysis in the steelmaking industry.

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.

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.

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.

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.

Multivariate comparison of B-ultrasound guided and laparoscopic continuous circulatory hyperthermic intraperitoneal perfusion chemotherapy for malignant ascites.

OBJECTIVE: Clinical efficacy of B-ultrasound-guided and laparoscopy-assisted continuous hyperthermic intraperitoneal perfusion chemotherapy (CHIPC) for treatment of malignant ascites was investigated. METHODS: Sixty-two patients with malignant ascites induced by ovarian or gastrointestinal cancers were randomly treated with B-ultrasound-guided CHIPC (therapeutic group) or laparoscopy-assisted CHIPC (control group) performed at the same center. Hospitalization costs and surgical duration were evaluated. Follow-up was conducted for 21 months with B-ultrasound or computed tomography at least once per month for assessment of ascites amount and tumor progression. Clinical efficacy was assessed by modified World Health Organization criteria. Survival time, Karnofsky performance score (KPS) of quality of life (QOL), and complications were recorded for all patients. RESULTS: Overall condition, primary disease type, and ascites amounts were comparable between groups. Significantly shorter mean duration of perfusion catheter placement (35 vs. 85 min) and mean hospitalization cost (36,000 vs. 55,000 ¥/patient) were observed in the therapeutic group than the control group (P < 0.01). Significantly different KPS scores were not observed before or after CHIPC (23.13 vs. 22.64 %) in both groups (P > 0.05). No significant differences in objective remission rates of malignant ascites (93.75 vs. 93.34 %), median survival times (9 vs. 8 months), or stamp hole metastasis rates (18.75 vs. 18.15 %) were observed between groups (P > 0.05). CONCLUSIONS: B-ultrasound-guided and laparoscopy-assisted CHIPC have similar clinical efficacy for improving QOL and prolonging patient survival. B-ultrasound-guided CHIPC may, however, shorten operation times and reduce hospitalization costs, making the treatment available to a broader patient population, although port hole metastasis remains an issue.

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.

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.

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.