The Effectiveness of Polyhexanide in Treating Wound Infections Due to Methicillin-Resistant Staphylococcus Aureus: A Prospective Analysis.

Purpose: Polyhexanide is a safe and effective wound care antiseptic commonly used in clinics as wound rinsing solution and gel. However, the efficacy of Polyhexanide in treatment of wound infected with MRSA (methicillin-resistant Staphylococcus aureus) is unknown. The aim of this study is to assess the effectiveness of polyhexanide with povidone iodine in treating wound infected with MRSA. Patients and Methods: A prospective analysis of 62 patients with wound infections, who were admitted to our department from 2016 to 2020, was conducted in order to assess the efficacy of different treatment approaches. The patients were divided into two groups: the experimental group and the control group. In the experimental group, 30 patients underwent treatment with a combination of diluted povidone iodine and polyhexanide immersion. Conversely, in the control group, 32 patients received treatment with diluted povidone iodine along with systemic antibiotic therapy. The time required for dressing changes, bacterial clearance rates, and the Bates-Jasen wound assessment tool (BWAT) scores were utilized as indicators to evaluate the effectiveness of the treatments. Results: In our study, the findings indicated that the experimental group exhibited a lesser number of days for the bacteria culture to turn negative compared to the control group, with statistical significance (p<0.05). Furthermore, the decline in the BWAT score was significantly greater in the experimental group than in the control group (p<0.05). However, no significant differences were observed in terms of dressing times and wound coverage between the two groups (p>0.05). Conclusion: Polyhexanide combined with povidone iodine can effectively remove MRSA infection in wounds and reduce antibiotic dosages.

Detection of Ningnanmycin Using Fluorescence Spectroscopy Combined with BP Neural Network.

BACKGROUND: Ningnanmycin is a new antibiotic pesticide with good bactericidal and antiviral efficacy, which is widely used in the control of fruit and vegetable diseases, and the excessive pesticide residues pose a serious threat to the environment and human health. METHODS: In this study, we used fluorescence spectrometer to scan the three-dimensional spectrum of ningnanmycin samples. We used a BP neural network to complete the regression analysis of content prediction based on the fluorescence spectra. After that, the prediction performance of the BP neural network was compared with the exponential fitting method. RESULTS: The results of the BP neural network modeling based on the obtained samples showed that the mean square error of the prediction results of the test set is less than 10-4, the R-square is greater than 0.99, the average recovery is 99.11%, and the model performance of the BP neural network is better than exponential fitting. CONCLUSION: Studies have shown that fluorescence spectroscopy combined with BP neural network can effectively predict the concentration of ningnanmycin.

Detection of Dihydrocoumarin in Coconut Juice via Photoelectric Detection System Based on Ultraviolet Absorption Spectrometry.

Based on ultraviolet absorption spectroscopy technology combined with stoichiometry, a portable photoelectric detection system with wireless transmission was designed with the advantages of simple operation, low cost, and quick response to realize the non-destructive detection of dihydrocoumarin content in coconut juice. Through the detection of a sample solution, the light intensity through the solution is measured and converted into absorbance. Particle swarm optimization (PSO) is applied to optimize support vector regression (SVR) to establish a corresponding concentration prediction model. At the same time, in order to solve the shortcomings of the conventional portable photoelectric detection equipment in data storage, data transmission, and other aspects, based on the optimal PSO-SVR model, we used Python language to develop a friendly graphical user interface (GUI), integrating data collection, storage, analysis, and prediction modeling in one, greatly simplifying the operation process. The experimental results show that, compared with the traditional methods, the system achieves the purpose of rapid and non-destructive detection and has a small gap compared with the detection results of the ultraviolet spectrophotometer. It provides a good method for the determination of dihydrocoumarin in coconut juice.

Stemness Subtypes and Scoring System Predict Prognosis and Efficacy of Immunotherapy in Soft Tissue Sarcoma.

Tumor stemness has been reported to play important roles in cancers. However, a comprehensive analysis of tumor stemness remains to be performed to investigate the specific mechanisms and practical values of stemness in soft tissue sarcomas (STS). Here, we applied machine learning to muti-omic data of patients from TCGA-SARC and GSE21050 cohorts to reveal important roles of stemness in STS. We demonstrated limited roles of existing mRNAsi in clinical application. Therefore, based on stemness-related signatures (SRSs), we identified three stemness subtypes with distinct stemness, immune, and metabolic characteristics using consensus clustering. The low-stemness subtype had better prognosis, activated innate and adaptive immunity (e.g., infiltrating B, DC, Th1, CD8+ T, activated NK, gamma delta T cells, and M1 macrophages), more enrichment of metabolic pathways, more sites with higher methylation level, higher gene mutations, CNA burdens, and immunogenicity indicators. Furthermore, the 16 SRS-based stemness prognostic index (SPi) was developed, and we found that low-SPi patients with low stemness had better prognosis and other characteristics similar to those in the low-stemness subtype. Besides, low-stemness subtype and low-SPi patients could benefit from immunotherapy. The predictive value of SPi in immunotherapy was more accurate after the addition of MSI into SPi. MSIlowSPilow patients might be more sensitive to immunotherapy. In conclusion, we highlighted mechanisms and practical values of the stemness in STS. We also recommended the combination of MSI and SPi which is a promising tool to predict prognosis and achieve precise treatments of immunotherapy in STS.

Detection of captan residues in apple juice using fluorescence spectroscopy combined with a genetic algorithm and support vector machines.

The captan residues in apple juice were detected by fluorescence spectrometry, and the content level of captan was predicted based on a genetic algorithm and support vector machines (GA-SVMs). According to the captan concentration in apple juice, the experimental samples were divided into four levels, including no excess, slight excess, moderate excess, and severe excess. A GA was used to select the characteristic wavelength and optimize SVM parameters, and SVM was applied to train the classification model. 50 characteristic wavelength points were selected from the original fluorescence spectra, which contained 401 wavelength points, and the classification accuracy of the training set and test set is 99.02% and 100%, respectively, which is higher than the traditional PLS method. The results show that a GA can effectively select the feature wavelengths, and an SVM model can accurately predict the content level of captan residues. A fast and non-destructive analysis method, combined with a GA and SVM based on fluorescence spectroscopy, was realized.

Analysis of pesticide residues by a support vector machine combined with fluorescence spectroscopy.

Pesticide residues enter a lake through the water cycle, causing harm to the water environment and human health. It is necessary to select highly sensitive fluorescence spectroscopy to detect pesticides (bifenthrin, prochloraz, and cyromazine), and a support vector machine (SVM) is used to analyze the concentration of pesticides. In addition, this paper adopts K-fold cross validation and a grid search to optimize the SVM algorithm. The performance evaluation index and running time prove the reliability of the results of this experiment. They show that fluorescence spectroscopy combined with SVM is efficient in predicting pesticide residue content.