Synthesis and investigation on synergetic effect of activated carbon loaded silver nanoparticles with enhanced photocatalytic and antibacterial activities.

In this study, we synthesized silver nanoparticle-loaded cashew nut shell activated carbon (Ag/CNSAC). The synthesized samples were characterized by XRD, XPS, SEM with EDS, FT-IR, and BET analysis. The XRD, XPS, and EDS data provided convincing proof that Ag loaded on CNSAC is formed. The energy dispersive spectrum analysis and X-ray diffraction pattern both supported the face-centered cubic and amorphous structures of Ag/CNSAC. The SEM micrographs showed the inner surface development of Ag NPs and many tiny pores in CNSAC. The photodegradation of methylene blue (MB) dye by the Ag/CNSAC photocatalyst was investigated. This effective degradation of MB dye by Ag/CNSAC is attributed to the cooperative action of Ag as a photocatalyst and CNSAC as a catalytic support and adsorbent. In tests with gram-positive and negative bacteria including Escherichia coli (E. coli) and Staphylococcus aureus (S. aureus), the as-synthesized Ag/CNSAC showed outstanding antibacterial efficiency. Additionally, this study demonstrates a workable procedure for creating an affordable and efficient Ag/CNSAC for the photocatalytic eradication of organic contaminants.

Resilience as a protective factor on the quality of life (QoL) of Indian nursing students during the COVID-19 pandemic.

BACKGROUND: The COVID-19 pandemic negatively impacted the quality of life of individuals around the world, including health care professionals. There has been little research that examines the role of resilience concerning the impact of COVID-19 on the quality of life of nursing students. This study aimed to determine how resilience influenced the quality of life among nursing students during the COVID-19 pandemic. METHODS: A cross-sectional research design was adopted for this study. A total of two hundred sixty-eight nursing students from three universities, South India responded in the web-based survey. Data was collected using self-reported questionnaires in June 2021. RESULTS: Our findings revealed that the participants' resilience was normal, which had a moderate impact on the quality of life of nursing students during the COVID-19 pandemic. The COVID-19 impact on QoL significantly differed with year of education (F = 3.087; p < 0.02) and university (F = 6.697, p < 0.001). Bivariate analysis revealed significant inverse relationships between the impact of COVID-19 on quality of life with resilience (r = -0.259; p < 0.001) and perceived knowledge on COVID-19(r = -0.168; p < 0.006). CONCLUSION: In our study, we found that resilience had a moderate impact on the quality of life of nursing students during the COVID-19 pandemic. Therefore, it is important to promote students' resilience and improve their quality of life during stressful situations.

Optimized chest X-ray image semantic segmentation networks for COVID-19 early detection.

BACKGROUND: Although detection of COVID-19 from chest X-ray radiography (CXR) images is faster than PCR sputum testing, the accuracy of detecting COVID-19 from CXR images is lacking in the existing deep learning models. OBJECTIVE: This study aims to classify COVID-19 and normal patients from CXR images using semantic segmentation networks for detecting and labeling COVID-19 infected lung lobes in CXR images. METHODS: For semantically segmenting infected lung lobes in CXR images for COVID-19 early detection, three structurally different deep learning (DL) networks such as SegNet, U-Net and hybrid CNN with SegNet plus U-Net, are proposed and investigated. Further, the optimized CXR image semantic segmentation networks such as GWO SegNet, GWO U-Net, and GWO hybrid CNN are developed with the grey wolf optimization (GWO) algorithm. The proposed DL networks are trained, tested, and validated without and with optimization on the openly available dataset that contains 2,572 COVID-19 CXR images including 2,174 training images and 398 testing images. The DL networks and their GWO optimized networks are also compared with other state-of-the-art models used to detect COVID-19 CXR images. RESULTS: All optimized CXR image semantic segmentation networks for COVID-19 image detection developed in this study achieved detection accuracy higher than 92%. The result shows the superiority of optimized SegNet in segmenting COVID-19 infected lung lobes and classifying with an accuracy of 98.08% compared to optimized U-Net and hybrid CNN. CONCLUSION: The optimized DL networks has potential to be utilised to more objectively and accurately identify COVID-19 disease using semantic segmentation of COVID-19 CXR images of the lungs.

Clinico-demographic profile and outcomes of 25-gauge vitrectomy in advanced stage 5 retinopathy of prematurity.

BACKGROUND: Stage 5 retinopathy of prematurity is a difficult condition to treat despite technological advances in vitreous surgery. METHODS: A retrospective chart review of all consecutive cases of stage 5 ROP was performed between December 2016 and December 2018, and 21 babies were included for assessment of surgical outcomes using a modified vitrectomy technique. Data extracted from documents included demography, ROP screening status, preoperative prophylactic therapy, clinical presentation, surgery performed, and postsurgical outcomes. RESULTS: Out of the 21 babies, ophthalmologist screening was done in 42.9%. Mean birth weight was 1185 ± 222.4 g with a mean gestational age of 29.86 ± 2.0 weeks and mean post-menstrual age of 44.55 ± 9.82 weeks. Lesser than stage 5 disease was seen in 16.7% of eyes and they were managed accordingly. Seventy percent of babies had bilateral disease. 21 eyes underwent 25-gauge pars plicata vitrectomy using a modified technique. After an average follow-up duration of 6.33 ± 2.18 months, the final macular attachment rate was 19%. Anteriorly closed-posteriorly closed type configuration of retinal detachments had a poorer outcome. Fix and follow visual acuity was achieved in 23.8% of eyes, while 57.1% of eyes had a perception of light. CONCLUSIONS: Management of stage 5 ROP is mostly surgical; however, the risk of ending up with a poor vision or vision loss is high, irrespective of whether surgery is performed. The modified surgical technique with a spacer described in this study may help in better manipulation of instruments inside the vitreous cavity.

Substituting brown rice for white rice on diabetes risk factors in India: a randomised controlled trial.

India has the second largest number of people with type 2 diabetes (T2D) globally. Epidemiological evidence indicates that consumption of white rice is positively associated with T2D risk, while intake of brown rice is inversely associated. Thus, we explored the effect of substituting brown rice for white rice on T2D risk factors among adults in urban South India. A total of 166 overweight (BMI ≥ 23 kg/m2) adults aged 25-65 years were enrolled in a randomised cross-over trial in Chennai, India. Interventions were a parboiled brown rice or white rice regimen providing two ad libitum meals/d, 6 d/week for 3 months with a 2-week washout period. Primary outcomes were blood glucose, insulin, glycosylated Hb (HbA1c), insulin resistance (homeostasis model assessment of insulin resistance) and lipids. High-sensitivity C-reactive protein (hs-CRP) was a secondary outcome. We did not observe significant between-group differences for primary outcomes among all participants. However, a significant reduction in HbA1c was observed in the brown rice group among participants with the metabolic syndrome (-0·18 (se 0·08) %) relative to those without the metabolic syndrome (0·05 (se 0·05) %) (P-for-heterogeneity = 0·02). Improvements in HbA1c, total and LDL-cholesterol were observed in the brown rice group among participants with a BMI ≥ 25 kg/m2 compared with those with a BMI < 25 kg/m2 (P-for-heterogeneity < 0·05). We observed a smaller increase in hs-CRP in the brown (0·03 (sd 2·12) mg/l) compared with white rice group (0·63 (sd 2·35) mg/l) (P = 0·04). In conclusion, substituting brown rice for white rice showed a potential benefit on HbA1c among participants with the metabolic syndrome and an elevated BMI. A small benefit on inflammation was also observed.

In silico and in vivo analysis to identify the antidiabetic activity of beta sitosterol in adipose tissue of high fat diet and sucrose induced type-2 diabetic experimental rats.

Adipose tissue is the primary site of storage for excess energy as triglyceride and it helps in synthesizing a number of biologically active compounds that regulate metabolic homeostasis. Consumption of high dietary fat increases stored fat mass and is considered as a main risk factor for metabolic diseases. Beta-sitosterol (ß-sitosterol) is a plant sterol. It has the similar chemical structure like cholesterol. Clinical and experimental studies have shown that ß-sitosterol has anti-diabetic, hypolipidemic, anti-cancer, anti-arthritic, and hepatoprotective role. However, effect of ß-sitosterol on insulin signaling molecules and glucose oxidation has not been explored. Hence in the present study we aimed to discover the protective role of ß-sitosterol on the expression of insulin signaling molecules in the adipose tissue of high-fat diet and sucrose-induced type-2 diabetic experimental rats. Effect dose of ß-sitosterol (20 mg/kg b.wt, orally for 30 days) was given to high fat diet and sucrose-induced type-2 diabetic rats to study its anti-diabetic activity. Results of the study showed that the treatment with ß-sitosterol to diabetes-induced rats normalized the altered levels of blood glucose, serum insulin and testosterone, lipid profile, oxidative stress markers, antioxidant enzymes, insulin receptor (IR), and glucose transporter 4 (GLUT4) proteins. Our present findings indicate that ß-sitosterol improves glycemic control through activation of IR and GLUT4 in the adipose tissue of high fat and sucrose-induced type-2 diabetic rats. Insilico analysis also coincides with invivo results. Hence it is very clear that ß-sitosterol can act as potent antidiabetic agent.

Indian Nurses' Attitudes Toward Euthanasia: Gender Differences.

The debate about euthanasia continues worldwide, with nurses' attitudes becoming increasingly more important. The aim of this study is to investigate gender differences among nurses' perceptions of attitudes toward euthanasia. A nonprobability quantitative, cross-sectional design was carried out among nurses working at a tertiary care center. Data were collected through self-reported questionnaires at their work place. Significant differences were found between men and women to the items such as "Fear of death shows differences due to religious beliefs" (χ2 = 10.550, p < 0.05), "If patient wants euthanasia, nutrition support should be stopped" (χ2 = 12.209, p < 0.05), "CPR should not be applied in case of sudden respiration and heart stop" (χ2 = 9.591, p < 0.05), and "burden for relatives to take care of a patient who is in terminal period and who will die" (χ2 = 9.069, p < 0.05). The present study depicts that gender plays an important role in euthanasia; hence, the researchers strongly suggest that there is an urgent need to draft uniform guidelines after wide consultation with all the stake holders regarding nurses' role in taking care of patients who request euthanasia, to face these situations effectively and competently within professional boundaries.

High Permeate Recovery for Concentrate Reduction by Integrated Membrane Process in Textile Effluent.

The textile dyeing industry consumes a significant amount of high-quality water for processing, which stresses water resources. In recent decades, technologies have been developed to recover water from wastewater. This study describes the high recovery (greater than 92%) of reusable water from an industrial-scale hosiery dye-water recovery facility, consisting of three stages of reverse osmosis and ultrafiltration. The effluent was pre-treated before the membrane process was performed to prevent biofouling. The process performance results in the generation of a consistent water quality that is required for dyeing operations. An average feed flux of 15 l/m2h was maintained in the reverse osmosis membrane by regular chemical dosing and cleaning. The integrated membrane process achieved a permeate with a pH of 6.5 and total dissolved solids (TDS) of 160 mg/l, with no other contaminants, which is of sufficient quality for reuse in the cotton hosiery dyeing process.

Effect of Uoncept Mapping in Improving Nursing Students' Knowledge and Attitudes Related to Care of HIV/AIDS Patients.

This one group pre- and post-test study evaluated the effectiveness of concept mapping and lecture method of teaching among 73 under graduate nursing students. One group was exposed to concept mapping technique and the other group was exposed to lecture method of teaching about HIV/AIDS. Findings indicated a statistically significant increase in the knowledge and attitude of students towards HIV/ AIDS among students exposed to concept mapping than students who underwent lecture method. The results of this study indicated that moden teaching strategies may enhance learning among nursing students. However further evaluation is needed.

Surgical management of oculomotor nerve palsy: a comparison of lateral rectus deactivation combined with either medial rectus resection or medial rectus fixation to the medial palpebral ligament.

PURPOSE: To analyze and compare the outcome of two different surgical procedures in patients with complete oculomotor nerve palsy with large-angle exotropia. METHODS: The medical records of patients with total oculomotor nerve palsy and large-angle exotropia operated on at a single center from January 2006 to June 2020 were reviewed retrospectively. One group underwent lateral rectus deactivation with medial rectus resection (resection group); the other group underwent lateral rectus deactivation with medial rectus fixation to the medial palpebral ligament (fixation group). Surgical outcomes on the first postoperative day and at 6 months postoperatively were analyzed, including alignment and postoperative complications. All statistical analyses were performed using STATA version 14. A P value of <0.05 was considered significant. RESULTS: A total of 35 patients were included. There was a trend toward greater surgical success in the fixation group (93%) than in the resection group (65%), but these results were not statistically significant. Postoperative exotropic drifts were noted in both the procedures but tended to be more with patients in the resection group. Postoperative complications were noted only in the fixation group. CONCLUSIONS: Lateral rectus deactivation with medial rectus fixation to the medial palpebral ligament requires more time and greater surgical expertise but appears to better prevent postoperative exotropic drift compared with lateral rectus deactivation combined with medial rectus resection.

9-(2-Chloro-benzyl-idene)anthracen-10(9H)-one.

In the title compound, C21H13ClO, the central anthracene system is distorted towards a boat conformation and the outer rings are not coplanar with the central ring [dihedral angles = 7.79 (1) and 11.90 (1)°]. The crystal structure features inversion dimers with graph-set motif R 2 (2)(18) formed by C-H⋯O inter-actions.

MTMC-AUR2CNet: Multi-textural multi-class attention recurrent residual convolutional neural network for COVID-19 classification using chest X-ray images.

Coronavirus disease (COVID-19) has infected over 603 million confirmed cases as of September 2022, and its rapid spread has raised concerns worldwide. More than 6.4 million fatalities in confirmed patients have been reported. According to reports, the COVID-19 virus causes lung damage and rapidly mutates before the patient receives any diagnosis-specific medicine. Daily increasing COVID-19 cases and the limited number of diagnosis tool kits encourage the use of deep learning (DL) models to assist health care practitioners using chest X-ray (CXR) images. The CXR is a low radiation radiography tool available in hospitals to diagnose COVID-19 and combat this spread. We propose a Multi-Textural Multi-Class (MTMC) UNet-based Recurrent Residual Convolutional Neural Network (MTMC-UR2CNet) and MTMC-UR2CNet with attention mechanism (MTMC-AUR2CNet) for multi-class lung lobe segmentation of CXR images. The lung lobe segmentation output of MTMC-UR2CNet and MTMC-AUR2CNet are mapped individually with their input CXRs to generate the region of interest (ROI). The multi-textural features are extracted from the ROI of each proposed MTMC network. The extracted multi-textural features from ROI are fused and are trained to the Whale optimization algorithm (WOA) based DeepCNN classifier on classifying the CXR images into normal (healthy), COVID-19, viral pneumonia, and lung opacity. The experimental result shows that the MTMC-AUR2CNet has superior performance in multi-class lung lobe segmentation of CXR images with an accuracy of 99.47%, followed by MTMC-UR2CNet with an accuracy of 98.39%. Also, MTMC-AUR2CNet improves the multi-textural multi-class classification accuracy of the WOA-based DeepCNN classifier to 97.60% compared to MTMC-UR2CNet.

Impact of special software training on quality of life among people with visual impairment.

Purpose: To evaluate the impact of special software training in computer and smartphone apps as a form of rehabilitation to improve the quality of life of individuals with visual impairment (VI). Methods: The study utilized a one-group pretest-posttest design. A total of 50 individuals aged 15 years and older with VI participated in the training, which included special software (NVDA) and mobile app training for 60 hours at the Visual Rehabilitation Center. The Low Vision Quality of Life Questionnaire was administered before the start of training and six months after completion. The questionnaire covered the following domains: (1) mobility, distance vision, and lighting; (2) reading and fine work; (3) social well-being; (4) economic impact; (5) attitude toward life; and (6) activities of daily living. Statistical software STATA 14.0 (Texas, USA) was used for data analysis. Paired t-tests and Wilcoxon signed-rank tests were conducted to compare the mean differences before and after training. Results: Significant improvements were observed in five dimensions, namely mobility, distance vision and lighting, reading and fine work, and attitude toward life, at a highly significant level of 1% probability. The dimensions of social well-being and economic impact showed significant improvement at a 5% level of probability. Conclusion: Visual impairment, whether congenital or acquired later in life, affects independence in all aspects of life. To the best of our knowledge, this is the first study to investigate the impact of special software training on the quality of life of visually impaired individuals. The authors suggest that this form of rehabilitation enhances accessibility to mainstream living, promotes independence, and ultimately improves quality of life. Participants experienced an improved quality of life through increased access to mainstream resources, enhanced ability to navigate and manage daily activities independently, and reduced reliance on multiple low vision aids or assistance from sighted individuals.

Augmented loop myopexy with concurrent intraocular lens implantation for myopic strabismus fixus.

Purpose: To evaluate safety profile and surgical outcomes of loop myopexy with concurrent intra-ocular lens implantation in cases of myopic strabismus fixus (MSF). Methods: A retrospective chart review of patients who underwent loop myopexy with concurrent small incision cataract surgery with intra-ocular lens implantation between January 2017 and July 2021 for MSF at a tertiary eye care centre was undertaken. A minimum of 6 months of follow-up after surgery was required for inclusion. The main outcome measures were improvement in alignment postoperatively, improvement in extra-ocular motility postoperatively, intraoperative and postoperative complications and post-operative visual acuity. Results: 12 eyes of 7 patients (male (6): female (1)) underwent modified loop myopexy at a mean age of 46.86 years (range 32-65 years). 5 patients underwent bilateral loop myopexy with intra-ocular lens implantation whereas 2 patients underwent unilateral loop with intra-ocular lens implantation. All eyes underwent additional medial rectus (MR) recession with lateral rectus (LR) plication. At the last follow-up, mean esotropia improved to 16 prism dioptres (PD) (Range: 10-20 PD) from 80 PD (Range:60-90PD), P = 0.016; and success (deviation ≤20PD) was achieved in 73% (95% CI 48 to 89%). Mean hypotropia at presentation was 10 PD (range 6-14 PD), which improved to 0 PD (range 0-9 PD), P = 0.063. Mean BCVA improved from 1.08 LogMar to 0.3 LogMar units. Conclusion: Loop myopexy combined with intra-ocular lens implantation is a safe and effective procedure in the management of patients who have Myopic Strabismus Fixus with visually significant cataract and improves both visual acuity and ocular alignment significantly.

CXGNet: A tri-phase chest X-ray image classification for COVID-19 diagnosis using deep CNN with enhanced grey-wolf optimizer.

The coronavirus disease 2019 (COVID-19) epidemic had a significant impact on daily life in many nations and global public health. COVID's quick spread has become one of the biggest disruptive calamities in the world. In the fight against COVID-19, it's critical to keep a close eye on the initial stage of infection in patients. Furthermore, early COVID-19 discovery by precise diagnosis, especially in patients with no evident symptoms, may reduce the patient's death rate and can stop the spread of COVID-19. When compared to CT images, chest X-ray (CXR) images are now widely employed for COVID-19 diagnosis since CXR images contain more robust features of the lung. Furthermore, radiologists can easily diagnose CXR images because of its operating speed and low cost, and it is promising for emergency situations and therapy. This work proposes a tri-stage CXR image based COVID-19 classification model using deep learning convolutional neural networks (DLCNN) with an optimal feature selection technique named as enhanced grey-wolf optimizer with genetic algorithm (EGWO-GA), which is denoted as CXGNet. The proposed CXGNet is implemented as multiple classes, such as 4-class, 3-class, and 2-class models based on the diseases. Extensive simulation outcome discloses the superiority of the proposed CXGNet model with enhanced classification accuracy of 94.00% for the 4-class model, 97.05% of accuracy for the 3-class model, and 100% accuracy for the 2-class model as compared to conventional methods.