Tumor marker-based RecistTM is superior to RECIST as criteria to predict the long-term benefits of targeted therapy in advanced non-small-cell lung cancer with driver gene mutations.

BACKGROUND: Tyrosine kinase inhibitors (TKIs) are standard first-line treatments for advanced non-small-cell lung cancer (NSCLC) with driver gene mutations. The Response Evaluation Criteria in Solid Tumors (RECIST) are limited in predicting long-term patient benefits. A tumour marker-based evaluation criteria, RecistTM, was used to investigate the potential for assessing targeted-therapy efficacy in lung cancer treatment. METHODS: We retrospectively analysed patients with stage IIIA-IV NSCLC and driver gene mutations, whose baseline tumour marker levels exceeded the pre-treatment cut-off value three-fold and who received TKI-targeted therapy as a first-line treatment. We compared efficacy, progression-free survival (PFS), and overall survival (OS) between RecistTM and RECIST. FINDINGS: The median PFS and OS differed significantly among treatment-response subgroups based on RecistTM but not RECIST. The predicted 1-, 2-, and 3-year disease-progression risk, according to area under the receiver operating characteristic curve, as well as the 1-, 3-, and 5-year mortality risk, differed significantly between RecistTM and RECIST. The median PFS and OS of tmCR according to RecistTM, was significantly longer than (CR+PR) according to RECIST. Imaging analysis revealed that the ΔPFS was 11.27 and 6.17 months in the intervention and non-intervention groups, respectively, suggesting that earlier intervention could extend patients' PFS. INTERPRETATION: RecistTM can assess targeted-therapy efficacy in patients with advanced NSCLC and driver gene mutations, along with tumour marker abnormalities. RecistTM surpasses RECIST in predicting short- and long-term patient benefits, and allows the early identification of patients resistant to targeted drugs, enabling prompt intervention and extending the imaging-demonstrated time to progression.

Fabrication of Superhydrophobic and Light-Absorbing Polyester Fabric Based on Caffeic Acid.

Caffeic acid (CA) was treated on the surface of polyester fabric (PET), and Fe2+ was used as an intermediate to form chelates with CA to increase the roughness of the polyester surface. With the addition of n-octadecyl mercaptan (SH), the mercapto group reacted with the carbon-carbon double bond of CA on the PET surface through enol click chemical reaction. Meanwhile, CA was polymerized under UV radiation, and thus CA-Fe-SH-PET was prepared. The introduction of SH with a long carbon chain reduced the surface energy of the PET, in order to endow the polyester fabric with a superhydrophobic/lipophilic function. Combined with XPS and FTIR tests, the new carbon-carbon double bond's binding energy and vibration peak were found on the fabric surface, indicating that CA was adsorbed on the PET fabric's surface. After adding SH, the double bond disappeared, demonstrating that SH and CA occurred a click chemical reaction and were grafted onto the PET fabric's surface. The water contact angle (WCA) of CA-Fe-SH-PET was about 156 ± 0.6°, and the scrolling angle (SA) was about 3.298°. The results showed that the modified polyester had a robust superhydrophobic stability in washing, mechanical friction, sun aging, seawater immersion, organic reagent, and acid-base erosion derived from the good adhesion of polymerized CA (PCA). At the same time, the modified polyester fabric had good self-cleaning, antifouling, and oil-water separation performance. It was found that the CA-Fe-SH-PET fabric had unique photothermal conversion characteristics, which can convert the absorbed ultraviolet light into thermal energy, providing a local warming effect due to rapid heating and improving the transmission speed of heavy oil (engine oil and diesel). The CA-Fe-SH-PET fabric can further prevent the transmission of ultraviolet rays, and the UV resistance of CA-Fe-SH-PET fabric is far higher than the UV resistance standard. The preparation method is simple, fast, efficient, and environmentally friendly, and it has better a potential application value in the oil-water separation field.

Application of Subwindow Factor Analysis and Mass Spectral information for accurate alignment of non-targeted metabolic profiling.

The peak shifts may lead to an incorrect statistical result for nontargeted metabolomics profiling, such as classification and discrimination in pattern recognition. In the paper, a more accurate alignment algorithm is developed based on Subwindow Factor Analysis and Mass Spectral information (SFA-MS). Compared with other methods, this new algorithm aligns the peaks more accurately without changing their shapes, especially for the overlapping peak clusters. To begin, the Continuous Wavelet Transform with Haar wavelet as the mother wavelet (Haar CWT) is used to determine the position and width of peaks. On this basis, the candidate drift points are confirmed by Fast Fourier Transform (FFT) cross correlation. Furthermore, the MS fitting degree of the common components between the reference chromatogram and the raw chromatogram is determined by the Subwindow Factor Analysis (SFA). When the MS information between reference and raw peaks is identical, the corresponding moving points are the optimum shifts. It is remarkable that all the peaks are moved through linear interpolation in the non-peak parts, so that the aligned chromatograms remain unchanged. The SFA-MS algorithm was implemented in the Matlab language and is available as an open source package.