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Surgical sutures are important components of surgical operation and protection against post-surgical infections. The current in vitro study is an effort to evaluate the effects of antibacterial finishes extracted from natural sources, i.e. plants and herbs on natural fiber-based suture materials. These bioactive compounds are derived from Azadirachta indica (AI), Cyamopsis tetragonoloba (CT), Calotropis procera (CP), Prunus persica (PP), and Berberis lycium (BL), etc. All finishes were obtained by direct extraction method, and their effect was studied in this research. Two types of natural fiber materials, e.g. silk suture (SS, non-absorbable) and catgut suture (CS, absorbable), were selected. Mechanical properties were evaluated after application of the finish with concentrations of 50% and 100%, respectively. Antibacterial testing was also conducted for different finishes on both types of sutures, which provide promising results. In both types of sutures, mechanical properties and antibacterial properties were improved after application of herbal finishes. This paper highlights the possibility of using natural fiber sutures and bioactive finishes for imparting antibacterial wound care properties. The natural fibers and finishes will be helpful for reducing toxic treatments of sutures, which are hazardous to the environment.

Objectives The COVID-19 pandemic has negatively impacted mental health worldwide but there is paucity of knowledge regarding the level of change in mental health in people with a medical condition (physical/psychiatric). The objectives of this study were (1) to compare the change in mental health in people with and without medical conditions, (2) to assess the change in various types of medical conditions, (3) to evaluate the association between change in mental health and number of comorbidities, and (4) to investigate the influence of receiving treatment and activity limitation imposed by the medical condition(s).Design Cross-sectional.Setting Online international survey.Participant English-speaking adults (age ≥18) were included in the study, with no exclusions based on sex/gender or location. 1276 participants (mean age 30.4, 77.7% female) were included.Primary and secondary outcome measures Pre and during COVID-19 pandemic symptoms of anxiety (Generalized Anxiety Disorder-2) and depression (Patient Health Questionnaire-9) were assessed. The Self-Administered Comorbidity Questionnaire was used to collect data regarding medical conditions.Repeated-measures analysis of covariance (objectives 1, 2 and 4) and Pearson’s correlation coefficient (objective 3).Results 50.1% of participants had a medical condition. During the COVID-19 pandemic, compared with people with no medical condition, people with both psychiatric and physical conditions experienced significantly higher symptoms of anxiety (12%, p=0.009) and depression (9.4%, p0.05).Conclusions During the COVID-19 pandemic, people who had a combination of psychiatric and physical conditions experienced greater symptoms of anxiety and depression. Patients with chronic diseases may need extra support to address their mental health as a result of the pandemic.

Malaria is a potentially fatal disease which is caused by Plasmodium parasite. These parasites are transmitted to humans through the bites of female Anopheles mosquitoes which play the role of disease vector. Five types of plasmodium cause malaria named P. Falciparum, P. Vivax, P. Ovale, P. Knowlesi, and P. Malariae, Among the Plasmodium parasites, Falciparum and Vivax are particularly lethal to humans. Therefore, early detection of malaria is mandatory to avoid the loss of human life. Different automatic/semi-automatic malaria detection techniques are available in the literature, which reduces the chance of human errors in the prognosis of malaria. In recent years, deep learning-based methods have proven to be effective for object detection. Therefore, such methods have caught the attention of researchers to use for the detection of malarial parasites in human blood. In this paper, we proposed a Convolutional Neural Network (CNN) model, which detects malarial parasites in microscopic images of human blood samples with high accuracy. The proposed model comprises 15 layers. It has 8 convolution layers with ReLu activation function, 4 max-pooling layers, 1 flattening layer, and 2 fully connected layers. The proposed method has been evaluated using various statistical measures against existing state-of-the-art methods. The quantitative measures show the effectiveness of the proposed model. It has a 97.42% testing accuracy, 97.42% sensitivity, 97.41% specificity, 97.70% precision, 97.42% recall , 97.97% F1-score , 97.41% Area Under Curve (AUC), and 94.82% Mathews correlation coefficient.

Objectives: The present work describes the application of Taguchi Grey Relational Analysis in order to optimize the durability of sheeting fabrics. The existing study investigates the multi-response optimization of certain parameters of yarn process including FL, RS and RT on tensile strength, tear strength and abrasion resistance of durable fabric sheets after washing. Methods: In the development process L9 orthogonal array in Taguchi design was used. The results have been analyzed by using statistical multi-response optimization technique, grey relational analysis to set the process parameters and to decide the simultaneous optimization of responses including tensile strength, tear strength and abrasion resistance. In addition, the analysis of variance (ANOVA) was used to determine the most significant factors. Reference sheeting fabric with same construction parameters was also tested. Results: The results show a greater improvement in parameters of developed samples quality. The experimental results show parameter fiber length has the most significant effect on the multiple performance characteristics. Conclusions: The developed fabric sheet having durable life would help to reduce consumption of resources of both consumers and manufacturers. Manufacturers can have great chance to establish their image and can increase the profit margin over products. Therefore, the integration of grey relational analysis and the Taguchi Method can be applicable for the optimization of process parameters and help to conserve resources by extending the lifetime of sheeting fabrics.