Analysis of therapeutic effect of silver-based dressings on chronic wound healing.

Chronic wounds are susceptible to bacterial infections and at high risk of developing antibiotic-resistant bacterial infections. Silver is an antimicrobial by targeting almost all types of bacteria in chronic wounds to reduce the bacterial load in the infected area and further facilitate the healing process. This study focused on exploring whether silver-based dressings were superior to non-silver dressings in the treatment of chronic wounds. PubMed, Web of Science and Embase were comprehensively searched from inception to March 2024 for randomized clinical trials and observational studies. The endpoints in terms of wound healing rate, complete healing time, reduction on wound surface area and wound infection rate were analysed using Review Manager 5.4 software. A total of 15 studies involving 5046 patients were eventually included. The results showed that compared with patients provided with non-silver dressings, patients provided with silver-based dressings had higher wound healing rate (OR: 1.43, 95% CI: 1.10-1.85, p = 0.008), shorter complete healing time (MD: -0.96, 95% CI: -1.08 ~ -0.85, p < 0.00001) and lower wound infection rate (OR: 0.56, 95% CI: 0.40-0.79, p = 0.001); no significant difference in the reduction on wound surface area (MD: 12.41, 95% CI: -19.59-44.40, p = 0.45) was found. These findings suggested that the silver-based dressings were able to enhance chronic wound healing rate, shorten the complete healing time and reduce wound infection rate, but had no significant improvement in the reduction on wound surface area. Large-scale and rigorous studies are required to confirm the beneficial effects of silver-based dressings on chronic wound healing.

Identification of blurred terahertz images by improved cross-layer convolutional neural network.

Terahertz imaging technology has been gradually used in space communication, radar detection, aerospace and biomedical fields. Nevertheless, there are still some limits in terahertz image, such as single tone, fuzzy texture features, poor image resolution and less data, which seriously affect the application and popularization of Terahertz image technology in many fields. Traditional convolutional neural network (CNN) is an effective method for image recognition, but it is limited in highly blurred terahertz image recognition due to the great difference between terahertz image and traditional optical image. This paper presents a proven method for higher recognition rate of blurred terahertz images by using an improved Cross-Layer CNN model with different definition terahertz image dataset. Compared to employing clear image dataset, the accuracy of blurred image recognition can be improved from about 32% to 90% with different definition dataset. Meanwhile, the recognition accuracy of high blurred image can be improved by approximately 5% in contrast to the traditional CNN, which makes the higher recognition ability of neural network. It can be demonstrated that various types of blurred terahertz imaging data can be effectively identified by constructing different definition dataset combined with Cross-Layer CNN. A new method is proved to improve the recognition accuracy of terahertz imaging and application robustness in real scenarios.

Association Between the Diabetic Foot Ulcer and the Bacterial Colony of the Skin Based on 16S rRNA Gene Sequencing: An Observational Study.

Objective: Microorganisms have been the main cause of refractory and high recurrence of diabetic foot ulcer (DFU). This study attempted to observe the skin bacterial colony in healthy skin, diabetic skin and DFU skin. Methods: Forty-eight diabetes patients were recruited at Panyu Central Hospital from March 2021 to March 2022 and divided into DFU group (T group, n = 22), diabetes without foot ulcer group (TW group, n = 26). Besides, a healthy control group (H group, n = 10) was recruited at the same time. The swab samples of foot skin in the same position in the three groups were collected. The microorganisms obtained from the skin were analyzed by 16S rRNA gene sequencing. The composition of the skin microorganisms was determined, and the species diversity of the skin microbiota was analyzed by α and ß diversity. The species differences in the skin microbiota and the relative abundance of different operational taxonomic units (OUTs) with the most significant abundance were analyzed by linear discriminant analysis effect size (LEfSe). Results: Significant changes were found in the composition of the skin microbiota in the T and TW groups relative to the H group. However, the species diversity of the skin microbiota was significantly reduced in the T and TW groups, with the lowest one in the T group. The composition of microbial diversity in the T group was significantly different from that of the TW and H groups. Among the skin bacterial colonies, the abundance of Staphylococcus, Enhydrobacter, and Corynebacterium_1 was obviously reduced, while that of Escherichia coli and Pseudomonas was significantly increased. Conclusion: Changes in the abundance of Staphylococcus, Enhydrobacter, Corynebacterium_1, Escherichia coli and Pseudomonas in the skin bacterial colonies can be the main causative factors for DFU. This study indicates that altering the microbiota composition of wounds may help the treatment of DFU.

The impact of combined gene mutations in inhA and ahpC genes on high levels of isoniazid resistance amongst katG non-315 in multidrug-resistant tuberculosis isolates from China.

Whole-genome sequencing was used to analyze the profiles of isoniazid (INH) resistance-related mutations among 188 multidrug-resistant strains of Mycobacterium tuberculosis (MDR-TB) and mono-INH-resistant isolates collected in a recent Chinese national survey. Mutations were detected in 18 structural genes and two promoter regions in 96.8% of 188 resistant isolates. There were high mutation frequencies in katG, the inhA promoter, and ahpC-oxyR regulator regions in INH-resistant isolates with frequencies of 86.2%, 19.6%, and 18.6%, respectively. Moreover, a high diversity of mutations was identified as 102 mutants contained various types of single or combined gene mutations in the INH-resistant group of isolates. The cumulative frequencies of katG 315 or inhA-P/inhA mutations was 68.1% (128/188) for the INH-resistant isolates. Of these isolates, 46 isolates (24.5% of 188) exhibited a high level of resistance. A high level of resistance was also observed in 21 isolates (11.2% of 188) with single ahpC-oxyR mutations or a combination of ahpC-oxyR and katG non-315 mutations. The remaining 17 mutations occurred sporadically and emerged in isolates with combined katG mutations. Such development of INH resistance is likely due to an accumulation of mutations under the pressure of drug selection. Thus, these findings provided insights on the levels of INH resistance and its correlation with the combinatorial mutation effect resulting from less frequent genes (inhA and/or ahpC). Such knowledge of other genes (apart from katG) in high-level resistance will aid in developing better strategies for the diagnosis and management of TB.