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.

Percutaneous intramyocardial septal radiofrequency ablation after 5-year follow-up.

OBJECTIVE: The objective is to evaluate the 5-year follow-up results of percutaneous intramyocardial septal radiofrequency ablation (PIMSRA) for hypertrophic obstructive cardiomyopathy (HOCM), including clinical status, electrocardiographic and echocardiographic characteristics. METHODS: 27 patients (age: 44.3±15.5 years; 67% men, 33% women) with severely symptomatic HOCM who underwent PIMSRA from October 2016 to September 2017 were included. Their clinical status, resting and exercise stress echocardiography, electrocardiography and cardiac MRI (CMRI) after long-term follow-up were assessed. RESULTS: One patient died of intracerebral haemorrhage 1 year post procedurally. The New York Heart Association class, Canadian Cardiovascular Society class and exercise-induced syncopal attacks improved significantly in 26 patients (all p<0.01). Left ventricular (LV) outflow tract gradients revealed sustained reduction (resting: from 95.0 to 9.0 mm Hg, p<0.001; post exercise: from 130.5 to 21.0 mm Hg, p<0.001). The echocardiographic evaluation revealed decreased septal thickness, LV posterior wall thickness and left atrial (LA) diameter (all p<0.001). CMRI data revealed decrease in LV mass index and LA volume index and increase in LV end-diastolic volume index and stroke volume index between baseline and long-term follow-up (all p<0.05). The global longitudinal strain of LV improved from (-11.9%±3.7%) before the procedure to (-13.1%±3.9%) at the last check (p<0.001). Malignant ventricular arrhythmia and heart failure events were not observed. CONCLUSIONS: PIMSRA can effectively alleviate symptoms in patients with HOCM and improve their hemodynamics in the long term. TRIAL REGISTRATION NUMBER: NCT02888132.

Myocardial mechanics of percutaneous intramyocardial septal radiofrequency ablation.

OBJECTIVE: Echocardiography-guided Percutaneous IntraMyocardial Septal Radiofrequency Ablation (PIMSRA, Liwen procedure) is a novel treatment option for hypertrophic obstructive cardiomyopathy (HOCM). The impact of PIMSRA on myocardial mechanics is unknown. METHODS: Between October 2016 and June 2019, PIMSRA and 3-dimentional speckle tracking echocardiography were performed in 82 patients. Echocardiographic imaging, qualitative and quantitative clinical assessment were completed at baseline, immediately postprocedure and 1-year follow-up. RESULTS: There was a significant reduction in the peak left ventricular outflow tract (LVOT) gradients immediately following PIMSRA and at 1-year follow-up (resting gradients: from 83.50 (61.25) to 23.00 (41.75) mm Hg, p<0.001 and 13.50 (21.75) mm Hg, p<0.001, respectively; stress-induced gradients: from 118.25 (96.02) to 47.00 (74.50) mm Hg (1 year), p<0.001). There was an improvement in exercise time on stress echocardiography (p<0.001) and distance by 6 min walk test (p=0.034). Immediately after PIMSRA, there was a significant reduction in radial and circumferential strain in the ablated segments (p<0.001), with no change of longitudinal strain. At 1-year follow-up, the radial and circumferential strain recovered in the ablated segments. Meanwhile, left ventricular regional and global longitudinal strain had improved significantly (p<0.05). CONCLUSIONS: PIMSRA is an effective treatment for symptomatic HOCM that resulted in a sustained improvement in exercise capacity, a persistent decrease in LVOT gradient, and a measurable increase in myocardial contractile function. TRIAL REGISTRATION NUMBER: NCT04777188.

A novel hybrid filter/wrapper method for feature selection in archaeological ceramics classification by laser-induced breakdown spectroscopy.

Laser-induced breakdown spectroscopy (LIBS) has been appreciated as a valuable analytical tool in the cultural heritage field owing to its unique technological superiority, particularly in combination with chemometric methods. Feature selection (FS) as an indispensable pre-processing step in data optimization, for eliminating the redundant or irrelevant features from high-dimensional data to enhance the predictive capacity and result comprehensibility of multivariate classification based on LIBS technology. In this paper, a novel hybrid filter/wrapper method based on the MI-DBS algorithm was proposed to enhance the qualitative analysis performance of the LIBS technique. The proposed method combines the advantages of the mutual information (MI) algorithm based filter method and bi-directional selection (DBS) algorithm based wrapper method. The MI algorithm is the first to remove the redundant or uncorrelated features so that a simplified input subset can be established. Then, the DBS algorithm is used to further select the retained features and hence to seek an optimal feature subset with good predictive performance. To benefit the above feature selection process, the wavelet transform denoising (WTD) method was used to reduce the noise from LIBS spectra. LIBS experiments were performed using 35 archaeological ceramic samples. Besides, the proposed hybrid filter/wrapper method was implemented through a random forest (RF) based nonlinear multivariate classification method. Through a comparison between several other feature selection methods and the proposed method, it has been seen that the proposed method is the best regarding the predictive performance and number of the selected features. Finally, the MI-DBS algorithm is used to seek the optimal features from the full spectrum (220-720 nm); the corresponding sensitivity, specificity and accuracy acquired through the RF classifier for the test set were 0.9722, 0.9956 and 0.9850. It is shown from the general results that the MI-DBS algorithm is more effective in terms of improving the model performance and decreasing the redundant or uncorrelated features and computational time and serves as a good alternative for FS in multivariate classification.