Retrospective analysis of preoperative application of triple-modal pre-rehabilitation on postoperative recovery of colorectal cancer patients.

PURPOSE: To retrospectively analyze the difference between triple-modal pre-rehabilitation and common treatment in patients with colorectal cancer (CRC). METHODS: A total of 145 patients with CRC diagnosed by pathology and admitted to our hospital for surgery between June 2020 and June 2022 were included in the study. All patients were divided into two groups: the triple-modal pre-rehabilitation group (pre-rehabilitation group) and the common treatment group. The triple-modal pre-rehabilitation strategy included exercise (3-5 times per week, with each session lasting more than 50 min), nutritional support, and psychological support. The study was designed to assess the potential of the pre-rehabilitation intervention to accelerate postoperative recovery by assessing the 6-min walk test, nutritional indicators, and HADS score before and after surgery. RESULTS: The pre-rehabilitation intervention did not reduce the duration of initial postoperative recovery or the incidence of postoperative complications, but it did increase the patients' exercise capacity (as determined by the 6-min walk test), with the pre-rehabilitation group performing significantly better than the common group (433.0 (105.0) vs. 389.0 (103.5), P < 0.001). The study also found that triple-modal pre-rehabilitation was beneficial for the early recovery of nutritional status in surgical patients and improved anxiety and depression in patients after surgery, especially in those who had not received neoadjuvant therapy. CONCLUSION: The triple-modal pre-rehabilitation strategy is of significant importance for reducing stress and improving the functional reserve of patients with colorectal cancer (CRC) during the perioperative period. The results of our study provide further support for the integration of the triple-modal pre-rehabilitation strategy into the treatment and care of CRC patients.

Comprehensive Similarity Algorithm and Molecular Dynamics Simulation-Assisted Terahertz Spectroscopy for Intelligent Matching Identification of Quorum Signal Molecules (N-Acyl-Homoserine Lactones).

To investigate the mechanism of aquatic pathogens in quorum sensing (QS) and decode the signal transmission of aquatic Gram-negative pathogens, this paper proposes a novel method for the intelligent matching identification of eight quorum signaling molecules (N-acyl-homoserine lactones, AHLs) with similar molecular structures, using terahertz (THz) spectroscopy combined with molecular dynamics simulation and spectral similarity calculation. The THz fingerprint absorption spectral peaks of the eight AHLs were identified, attributed, and resolved using the density functional theory (DFT) for molecular dynamics simulation. To reduce the computational complexity of matching recognition, spectra with high peak matching values with the target were preliminarily selected, based on the peak position features of AHL samples. A comprehensive similarity calculation (CSC) method using a weighted improved Jaccard similarity algorithm (IJS) and discrete Fréchet distance algorithm (DFD) is proposed to calculate the similarity between the selected spectra and the targets, as well as to return the matching result with the highest accuracy. The results show that all AHL molecular types can be correctly identified, and the average quantization accuracy of CSC is 98.48%. This study provides a theoretical and data-supported foundation for the identification of AHLs, based on THz spectroscopy, and offers a new method for the high-throughput and automatic identification of AHLs.

Improving the Heat Resistance and Flame Retardancy of Epoxy Resin Composites by Novel Multifunctional Cyclophosphazene Derivatives.

A novel multiple-ring molecule containing P and N, called HCCP-SA, was successfully prepared by the nucleophilic substitution reaction of salicylamide (SA) and hexachlorocyclotriphosphazene (HCCP). Particularly, HCCP-SA possessed the dual functions of heat resistance and flame retardancy. The molecular structure of HCCP-SA was identified by Fourier transform infrared spectroscopy and nuclear magnetic resonance spectroscopy. HCCP-SA was bonded into the molecular chain of epoxy resin by the ring-opening curing reaction of epoxy resin, aiming to form a heat-resistant and flame-retardant composite (E-HS-x). In particular, the best-prepared E-HS-x composite with a 20 phr content of HCCP-SA (E-HS-20) presented excellent thermal stability, with an initial decomposition temperature of 267.94 °C and a max weight loss speed of only 0.95 mg·min-1. Moreover, E-HS-20 exhibited remarkable flame retardancy with a limiting oxygen index value of 27.1% and a V-2 rating in the UL94 flame retardancy test. The best-prepared E-HS-20 composite would be a suitable and potential candidate for heat-resistant and flame-retardant polymer materials.

Deep-Learning-Based Hair Damage Diagnosis Method Applying Scanning Electron Microscopy Images.

In recent years, with the gradual development of medicine and deep learning, many technologies have been developed. In the field of beauty services or medicine, it is particularly important to judge the degree of hair damage. Because people in modern society pay more attention to their own dressing and makeup, changes in the shape of their hair have become more frequent, e.g., owing to a perm or dyeing. Thus, the hair is severely damaged through this process. Because hair is relatively thin, a quick determination of the degree of damage has also become a major problem. Currently, there are three specific methods for this purpose. In the first method, professionals engaged in the beauty service industry make a direct judgement with the naked eye. The second way is to observe the damaged cuticle layers of the hair using a microscope, and then make a judgment. The third approach is to conduct experimental tests using physical and chemical methods. However, all of these methods entail certain limitations, inconveniences, and a high complexity and time consumption. Therefore, our proposed method is to use scanning electron microscope to collect various hair sample images, combined with deep learning to identify and judge the degree of hair damage. This method will be used for hair quality diagnosis. Experiment on the data set we made, compared with the experimental results of other lightweight networks, our method showed the highest accuracy rate of 94.8%.