Exploring transfer learning in chest radiographic images within the interplay between COVID-19 and diabetes.

The intricate relationship between COVID-19 and diabetes has garnered increasing attention within the medical community. Emerging evidence suggests that individuals with diabetes may experience heightened vulnerability to COVID-19 and, in some cases, develop diabetes as a post-complication following the viral infection. Additionally, it has been observed that patients taking cough medicine containing steroids may face an elevated risk of developing diabetes, further underscoring the complex interplay between these health factors. Based on previous research, we implemented deep-learning models to diagnose the infection via chest x-ray images in coronavirus patients. Three Thousand (3000) x-rays of the chest are collected through freely available resources. A council-certified radiologist discovered images demonstrating the presence of COVID-19 disease. Inception-v3, ShuffleNet, Inception-ResNet-v2, and NASNet-Large, four standard convoluted neural networks, were trained by applying transfer learning on 2,440 chest x-rays from the dataset for examining COVID-19 disease in the pulmonary radiographic images examined. The results depicted a sensitivity rate of 98 % (98%) and a specificity rate of almost nightly percent (90%) while testing those models with the remaining 2080 images. In addition to the ratios of model sensitivity and specificity, in the receptor operating characteristics (ROC) graph, we have visually shown the precision vs. recall curve, the confusion metrics of each classification model, and a detailed quantitative analysis for COVID-19 detection. An automatic approach is also implemented to reconstruct the thermal maps and overlay them on the lung areas that might be affected by COVID-19. The same was proven true when interpreted by our accredited radiologist. Although the findings are encouraging, more research on a broader range of COVID-19 images must be carried out to achieve higher accuracy values. The data collection, concept implementations (in MATLAB 2021a), and assessments are accessible to the testing group.

Corrigendum: An advanced deep learning models-based plant disease detection: a review of recent research.

[This corrects the article DOI: 10.3389/fpls.2023.1158933.].

Evaluating the Sorption Kinetics of Polychlorinated Biphenyls in Powdered and Granular Activated Carbon.

Activated carbon (AC) has been applied widely in water treatment as a strong sorbent for organic contaminants and, more recently, in-situ treatment and capping for remediating legacy contaminants. In some sediment environments, the sorption kinetics onto AC may significantly impact remedial performance, particularly for large, highly hydrophobic contaminants such as PCBs, but there is limited kinetic data on such compounds. In this study, batch experiments were conducted over 52 weeks to measure PCB adsorption kinetics on 2 ACs in granular (1.1 mm diameter) and powdered (0.02 mm) form using polydimethylsiloxane (PDMS) fibers to measure aqueous concentrations over time. The experiment was conducted in glass containers with water at known PCB concentration and containing 10 mg/L natural organic matter (NOM) and activated carbon. Blanks without activated carbon were used to estimate kinetics and equilibrium uptake to PDMS and NOM. The PDMS measured aqueous concentration in AC containing slurries was then used to estimate kinetics and equilibrium uptake of the various PCBs onto the AC. Achieving equilibration of PCBs onto the powdered activated carbon (PAC) was accomplished in days to weeks, but granular activated carbon (GAC) uptake was not complete for some high molecular weight congeners in a year. The data were used to fit linear driving force models with both linear and Freundlich models of equilibrium. The models were then used to predict uptake onto powdered and granular AC during in-situ capping and treatment using the CapSim model. Slow kinetics can significantly limit the performance of granular AC in high upwelling (> 1-10 cm/day) environments. This study demonstrates the usage of polymeric passive samplers to explore sorption kinetics and equilibrium for low solubility compounds as well as the differences in performance of granular and powdered forms of AC for remediation of PCB contaminated sediment.

An advanced deep learning models-based plant disease detection: A review of recent research.

Plants play a crucial role in supplying food globally. Various environmental factors lead to plant diseases which results in significant production losses. However, manual detection of plant diseases is a time-consuming and error-prone process. It can be an unreliable method of identifying and preventing the spread of plant diseases. Adopting advanced technologies such as Machine Learning (ML) and Deep Learning (DL) can help to overcome these challenges by enabling early identification of plant diseases. In this paper, the recent advancements in the use of ML and DL techniques for the identification of plant diseases are explored. The research focuses on publications between 2015 and 2022, and the experiments discussed in this study demonstrate the effectiveness of using these techniques in improving the accuracy and efficiency of plant disease detection. This study also addresses the challenges and limitations associated with using ML and DL for plant disease identification, such as issues with data availability, imaging quality, and the differentiation between healthy and diseased plants. The research provides valuable insights for plant disease detection researchers, practitioners, and industry professionals by offering solutions to these challenges and limitations, providing a comprehensive understanding of the current state of research in this field, highlighting the benefits and limitations of these methods, and proposing potential solutions to overcome the challenges of their implementation.

An effective deep learning approach for the classification of Bacteriosis in peach leave.

Bacteriosis is one of the most prevalent and deadly infections that affect peach crops globally. Timely detection of Bacteriosis disease is essential for lowering pesticide use and preventing crop loss. It takes time and effort to distinguish and detect Bacteriosis or a short hole in a peach leaf. In this paper, we proposed a novel LightWeight (WLNet) Convolutional Neural Network (CNN) model based on Visual Geometry Group (VGG-19) for detecting and classifying images into Bacteriosis and healthy images. Profound knowledge of the proposed model is utilized to detect Bacteriosis in peach leaf images. First, a dataset is developed which consists of 10000 images: 4500 are Bacteriosis and 5500 are healthy images. Second, images are preprocessed using different steps to prepare them for the identification of Bacteriosis and healthy leaves. These preprocessing steps include image resizing, noise removal, image enhancement, background removal, and augmentation techniques, which enhance the performance of leaves classification and help to achieve a decent result. Finally, the proposed LWNet model is trained for leaf classification. The proposed model is compared with four different CNN models: LeNet, Alexnet, VGG-16, and the simple VGG-19 model. The proposed model obtains an accuracy of 99%, which is higher than LeNet, Alexnet, VGG-16, and the simple VGG-19 model. The achieved results indicate that the proposed model is more effective for the detection of Bacteriosis in peach leaf images, in comparison with the existing models.

CFP10-loaded PLGA nanoparticles as a booster vaccine confer protective immunity against Mycobacterium bovis.

Introduction: The limited efficacy of BCG (bacillus Calmette-Guérin) urgently requires new effective vaccination approaches for the control of tuberculosis. Poly lactic-co-glycolic acid (PLGA) is a prevalent drug delivery system. However, the effect of PLGA-based nanoparticles (NPs) against tuberculosis for the induction of mucosal immune response is no fully elucidated. In this study, we hypothesized that intranasal immunization with culture filtrate protein-10 (CFP10)-loaded PLGA NPs (CFP10-NPs) could boost the protective immunity of BCG against Mycobacterium bovis in mice. Methods: The recombinant protein CFP10 was encapsulated with PLGA NPs to prepare CFP10-NPs by the classical water-oil-water solvent-evaporation method. Then, the immunoregulatory effects of CFP10-NPs on macrophages in vitro and on BCG-immunized mice in vivo were investigated. Results: We used spherical CFP10-NPs with a negatively charged surface (zeta-potential -28.5 ± 1.7 mV) having a particle size of 281.7 ± 28.5 nm in diameter. Notably, CFP10-NPs significantly enhanced the secretion of tumor necrosis factor α (TNF-α) and interleukin (IL)-1ß in J774A.1 macrophages. Moreover, mucosal immunization with CFP10-NPs significantly increased TNF-α and IL-1ß production in serum, and immunoglobulin A (IgA) secretion in bronchoalveolar lavage fluid (BALF), and promoted the secretion of CFP10-specific interferon-γ (IFN-γ) in splenocytes of mice. Furthermore, CFP10-NPs immunization significantly reduced the inflammatory area and bacterial load in lung tissues at 3-week post-M. bovis challenge. Conclusion: CFP10-NPs markedly improve the immunogenicity and protective efficacy of BCG. Our findings explore the potential of the airway mucosal vaccine based on PLGA NPs as a vehicle for targeted lung delivery.

Deep learning-based segmentation and classification of leaf images for detection of tomato plant disease.

Plants contribute significantly to the global food supply. Various Plant diseases can result in production losses, which can be avoided by maintaining vigilance. However, manually monitoring plant diseases by agriculture experts and botanists is time-consuming, challenging and error-prone. To reduce the risk of disease severity, machine vision technology (i.e., artificial intelligence) can play a significant role. In the alternative method, the severity of the disease can be diminished through computer technologies and the cooperation of humans. These methods can also eliminate the disadvantages of manual observation. In this work, we proposed a solution to detect tomato plant disease using a deep leaning-based system utilizing the plant leaves image data. We utilized an architecture for deep learning based on a recently developed convolutional neural network that is trained over 18,161 segmented and non-segmented tomato leaf images-using a supervised learning approach to detect and recognize various tomato diseases using the Inception Net model in the research work. For the detection and segmentation of disease-affected regions, two state-of-the-art semantic segmentation models, i.e., U-Net and Modified U-Net, are utilized in this work. The plant leaf pixels are binary and classified by the model as Region of Interest (ROI) and background. There is also an examination of the presentation of binary arrangement (healthy and diseased leaves), six-level classification (healthy and other ailing leaf groups), and ten-level classification (healthy and other types of ailing leaves) models. The Modified U-net segmentation model outperforms the simple U-net segmentation model by 98.66 percent, 98.5 IoU score, and 98.73 percent on the dice. InceptionNet1 achieves 99.95% accuracy for binary classification problems and 99.12% for classifying six segmented class images; InceptionNet outperformed the Modified U-net model to achieve higher accuracy. The experimental results of our proposed method for classifying plant diseases demonstrate that it outperforms the methods currently available in the literature.

Perceived risk and perceptions of COVID-19 vaccine: A survey among general public in Pakistan.

BACKGROUND: The coronavirus disease has become a global pandemic, and it continues to wreak havoc on global health and the economy. The development of vaccines may offer a potential eradication of COVID-19. This study evaluated the general knowledge, attitude, and perception of COVID-19 vaccines in the Pakistani population. METHODS: A self-reporting e-survey and questionnaire-based survey from vaccination centers of different cities of Pakistan among 502 participants were conducted. The questionnaire comprised four sections inquiring demographics, vaccination status, and perception or attitude towards the vaccine. Univariate logistic regression was applied to predict the knowledge, attitude and behavior of participants. RESULTS: The mean age of participants was 50.8±20.3 years. 53% of the participants have both doses of vaccine administered. Pain on the site of injection (49.8%) was the most common symptom, followed by asthenia (43.0%), muscle pain (29.5%), and swelling (24.5%) on the site of vaccine administration. Females complain of more symptoms than males. More severe symptoms were reported after the first dose of vaccine administration; these symptoms subsided within a week for most participants. Overall, the respondents have a positive attitude towards the vaccine. 47.4% are sure about the vaccine's efficacy, 48.6% said getting vaccinated was their own decision, and 79.9% also recommended others to get vaccinated. CONCLUSION: The study concluded that the Pakistani population has a positive attitude but inadequate knowledge towards COVID-19 vaccines. Immediate awareness and vaccination education programs should be conducted by the authorities to complete the mass vaccination schedule.

A deep learning-based model for plant lesion segmentation, subtype identification, and survival probability estimation.

Plants are the primary source of food for world's population. Diseases in plants can cause yield loss, which can be mitigated by continual monitoring. Monitoring plant diseases manually is difficult and prone to errors. Using computer vision and artificial intelligence (AI) for the early identification of plant illnesses can prevent the negative consequences of diseases at the very beginning and overcome the limitations of continuous manual monitoring. The research focuses on the development of an automatic system capable of performing the segmentation of leaf lesions and the detection of disease without requiring human intervention. To get lesion region segmentation, we propose a context-aware 3D Convolutional Neural Network (CNN) model based on CANet architecture that considers the ambiguity of plant lesion placement in the plant leaf image subregions. A Deep CNN is employed to recognize the subtype of leaf lesion using the segmented lesion area. Finally, the plant's survival is predicted using a hybrid method combining CNN and Linear Regression. To evaluate the efficacy and effectiveness of our proposed plant disease detection scheme and survival prediction, we utilized the Plant Village Benchmark Dataset, which is composed of several photos of plant leaves affected by a certain disease. Using the DICE and IoU matrices, the segmentation model performance for plant leaf lesion segmentation is evaluated. The proposed lesion segmentation model achieved an average accuracy of 92% with an IoU of 90%. In comparison, the lesion subtype recognition model achieves accuracies of 91.11%, 93.01 and 99.04 for pepper, potato and tomato plants. The higher accuracy of the proposed model indicates that it can be utilized for real-time disease detection in unmanned aerial vehicles and offline to offer crop health updates and reduce the risk of low yield.

Mycobacterium bovis induces mitophagy to suppress host xenophagy for its intracellular survival.

Mitophagy is a selective autophagy mechanism for eliminating damaged mitochondria and plays a crucial role in the immune evasion of some viruses and bacteria. Here, we report that Mycobacterium bovis (M. bovis) utilizes host mitophagy to suppress host xenophagy to enhance its intracellular survival. M. bovis is the causative agent of animal tuberculosis and human tuberculosis. In the current study, we show that M. bovis induces mitophagy in macrophages, and the induction of mitophagy is impaired by PINK1 knockdown, indicating the PINK1-PRKN/Parkin pathway is involved in the mitophagy induced by M. bovis. Moreover, the survival of M. bovis in macrophages and the lung bacterial burden of mice are restricted by the inhibition of mitophagy and are enhanced by the induction of mitophagy. Confocal microscopy analysis reveals that induction of mitophagy suppresses host xenophagy by competitive utilization of p-TBK1. Overall, our results suggest that induction of mitophagy enhances M. bovis growth while inhibition of mitophagy improves growth restriction. The findings provide a new insight for understanding the intracellular survival mechanism of M. bovis in the host.Abbreviations: BMDM: mouse bone marrow-derived macrophage; BNIP3: BCL2/adenovirus E1B interacting protein 3; BNIP3L/NIX: BCL2/adenovirus E1B interacting protein 3-like; BCL2L13: BCL2-like 13 (apoptosis facilitator); CCCP: carbonyl cyanide m-cholorophenyl hydrazone; FUNDC1: FUN14 domain-containing 1; FKBP8: FKBP506 binding protein 8; HCV: hepatitis C virus; HBV: hepatitis B virus; IFN: interferon; L. monocytogenes: Listeria monocytogenes; M. bovis: Mycobacterium bovis; Mtb: Mycobacterium tuberculosis; Mdivi-1: mitochondrial division inhibitor 1; PINK1: PTEN-induced putative kinase 1; TBK1: TANK-binding kinase 1; TUFM: Tu translation elongation factor, mitochondrial; TEM: transmission electron microscopy.

Hybrid Nanofluids as Renewable and Sustainable Colloidal Suspensions for Potential Photovoltaic/Thermal and Solar Energy Applications.

The comparative utilization of solar thermal or photovoltaic systems has significantly increased to fulfill the requirement of electricity and heat since few decades. These hybrid systems produce both thermal and electrical energy simultaneously. In recent times, increasing interest is being redirected by researchers in exploiting variety of nanoparticles mixed with miscellaneous base fluids (hybrid nanofluid) for these hybrid systems. This new class of colloidal suspensions has many fascinating advantages as compared to conventional types of nanofluids because of their modified and superior rheological and thermophysical properties which makes them appealing for solar energy devices. Here, we have attempted to deliver an extensive overview of the synthetic methodologies of hybrid nanofluids and their potential in PV/T and solar thermal energy systems. A detailed comparison between conventional types of nanofluids and hybrid nanofluids has been carried out to present in-depth understanding of the advantages of the hybrid nanofluids. The documented reports reveal that enhanced thermal properties of hybrid nanofluids promise the increased performance of solar thermal PV/T systems. Additionally, the unique properties such as nanoparticles concentration and type of base fluid, etc. greatly influence the behavior of hybrid nanofluidic systems. Finally, the outlook, suggestions, and challenges for future research directions are discussed.

The Role of Stem Cells and Their Derived Extracellular Vesicles in Restoring Female and Male Fertility.

Infertility is a globally recognized issue caused by different reproductive disorders. To date, various therapeutic approaches to restore fertility have been attempted including etiology-specific medication, hormonal therapies, surgical excisions, and assisted reproductive technologies. Although these approaches produce results, however, fertility restoration is not achieved in all cases. Advances in using stem cell (SC) therapy hold a great promise for treating infertile patients due to their abilities to self-renew, differentiate, and produce different paracrine factors to regenerate the damaged or injured cells and replenish the affected germ cells. Furthermore, SCs secrete extracellular vesicles (EVs) containing biologically active molecules including nucleic acids, lipids, and proteins. EVs are involved in various physiological and pathological processes and show promising non-cellular therapeutic uses to combat infertility. Several studies have indicated that SCs and/or their derived EVs transplantation plays a crucial role in the regeneration of different segments of the reproductive system, oocyte production, and initiation of sperm production. However, available evidence triggers the need to testify the efficacy of SC transplantation or EVs injection in resolving the infertility issues of the human population. In this review, we highlight the recent literature covering the issues of infertility in females and males, with a special focus on the possible treatments by stem cells or their derived EVs.

Koumiss promotes Mycobacterium bovis infection by disturbing intestinal flora and inhibiting endoplasmic reticulum stress.

Mycobacterium bovis is the causative agent of bovine tuberculosis and also responsible for serious threat to public health. Koumiss is a fermented mare's milk product, used as traditional drink. Here, we explored the effect of koumiss on gut microbiota and the host immune response against M bovis infection. Therefore, mice were treated with koumiss and fresh mare milk for 14 days before M bovis infection and continue for 5 weeks after infection. The results showed a clear change in the intestinal flora of mice treated with koumiss, and the lungs of mice treated with koumiss showed severe edema, inflammatory infiltration, and pulmonary nodules in M bovis-infected mice. Notably, we found that the content of short-chain fatty acids was significantly lower in the koumiss-treated group compared with the control group. However, the expression of endoplasmic reticulum stress and apoptosis-related proteins in the lungs of koumiss-treated mice were significantly decreased. Collectively, these findings suggest that koumiss treatment disturb the intestinal flora of, which is associated with disease severity and the possible mechanism that induces lungs pathology. Our current findings can be exploited further to establish the "gut-lung" axis which might be a novel strategy for the control of tuberculosis.

Caspase-1 inhibits IFN-ß production via cleavage of cGAS during M. bovis infection.

Mycobacterium bovis (M. bovis) infection triggers cytokine production via pattern recognition receptors. These cytokines include type I interferons (IFNs) and interleukin-1ß (IL-1ß). Excessive type I IFN levels impair host resistance to M. bovis infection. Therefore, strict control of type I IFN production is helpful to reduce pathological damage and bacterial burden. Here, we found that a deficiency in caspase-1, which is the critical component of the inflammasome responsible for IL-1ß production, resulted in increased IFN-ß production upon M. bovis infection. Subsequent experiments demonstrated that caspase-1 activation reduced cyclic GMP-AMP synthase (cGAS) expression, thereby inhibiting downstream TANK-binding kinase 1 (TBK1)- interferon regulatory factor 3 (IRF3) signaling and ultimately reducing IFN production. A deficiency in caspase-1 activation enhanced the bacterial burden during M. bovis infection in vitro and in vivo and aggravated pathological lesion formation. Thus, caspase-1 activation reduced IFN-ß production upon M. bovis infection by dampening cGAS-TBK1-IRF3 signaling, suggesting that the inflammasome protects hosts by negatively regulating harmful cytokines.

Bio-interaction of nano and bulk lanthanum and ytterbium oxides in soil system: Biochemical, genetic, and histopathological effects on Eisenia fetida.

The massive application of rare earth elements (REEs) in electronic industries cause their inevitable release into the environment; however, its effects on soil biota remain largely unaddressed. We investigated the E. fetida detoxification potential of nano and bulk La2O3 and Yb2O3 and their potential impact on biochemical and genetic markers at 50, 100, 200, 500 and 1000 mg kg-1 concentration. We found that earthworms bioremediate 3-15% La2O3 and Yb2O3 contaminated soil at low and medium levels, while this potential was limited at higher levels. Nano and bulk La2O3 and Yb2O3 treatment induced neurotoxicity in earthworm by inhibiting acetylcholinesterase by 49-65% and 22-36% at 500 and 1000 mg kg-1, respectively. Nano La2O3 proved to be highly detrimental, mainly through oxidative stress and subsequent failure of antioxidant system. Nano La2O3 and Yb2O3 at 100 mg kg-1 significantly down-regulated the expression of annetocin mRNA in the parental and progeny earthworms by 50% and 20%, which is crucial for earthworm reproduction. Similarly, expression level of heat shock protein 70 (HSP70) and metallothionein was significantly upregulated in both generations at medium exposure level. Histological observations showed that nano REEs at 200 mg kg-1 induced drastic changes in the intestinal epithelium and typhlosole of E. fetida. To date, our results enhance the understanding of interaction between REEs and earthworms.

Effects of Flaxseed and Multi-Carbohydrase Enzymes on the Cecal Microbiota and Liver Inflammation of Laying Hens.

BACKGROUND: The use of wheat and flaxseed to produce omega-3 (ω-3) enriched poultry meat and eggs is very popular in the world. However, wheat and flaxseed contain some anti-nutritional factors (ANFs), and enzymes are usually used to alleviate the deleterious influence of ANFs. METHOD: A 2 × 3 two factors design was used in the experiment. A total of 540 twenty-week-old Nongda-3 laying hens were randomly allocated to six dietary treatments, two diets (corn/flaxseed and wheat/flaxseed), and three enzymes (enzyme-a contains neutral protease 10,000, xylanase 35,000, ß-mannanase 1500, ß-glucanase 2000, cellulose 500, amylase 100, and pectinase 10,000 (U g-1); enzyme-b contains alkaline protease 40,000 and neutral protease 10,000 (U g-1); enzyme-c contains alkaline protease 40,000, neutral protease 10,000, and cellulase 4000 (U g-1). RESULTS: There was an interaction between dietary treatment and supplemental enzymes for liver weight and liver inflammatory cytokines of broilers. A significant increase was observed in the fat weight of birds fed a corn diet as compared with a wheat diet. A corn diet and wheat diet with the addition of enzyme-a (p < 0.001) showed the highest level of liver fat followed by enzyme-c (p < 0.01) and enzyme-b. Moreover, a high level of secretory IL-1ß, IL-6, and IL-10 and comparatively higher inflammatory changes in the liver tissue were found in birds fed a corn diet as compared with a wheat diet, and enzyme-b showed more beneficial effects as compared with enzyme-a and -c. The gut microbial composition of hens fed a corn diet was significantly different than that of birds fed a wheat diet. Bacteroides were significantly (p < 0.05) abundant in the corn-fed birds as compared with wheat-fed birds. However, Firmicutes were less abundant in the wheat-fed birds than the corn-fed birds (16.99 vs. 31.80%, respectively). The microbial community at the genus level differed significantly in the dietary groups and we observed that Bacteroides are the predominant cecal microbiota. Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways of co-factors, carbohydrates, vitamins, protein, and energy were expressed at slightly higher levels in the microbiota of the wheat-fed birds, whereas, metabolic pathways for nucleotides, lipids, and glycine were expressed at higher levels in the wheat-fed birds. Furthermore, expression of the growth and cellular processes pathway and endocrine system pathway levels were predicted to be higher for the wheat-fed group as compared with the corn-fed group. CONCLUSIONS: In conclusion, our findings suggest that inflammatory changes in laying birds were mediated by a corn diet with flaxseed and enzymes instead of a wheat diet. Additionally, in the wheat-fed group, enzyme-b and -c showed more encouraging results as compared to enzyme-a.

Recombinant ArgF PLGA nanoparticles enhances BCG induced immune responses against Mycobacterium bovis infection.

Mycobacterium bovis (M. bovis) is a member of mycobacterium tuberculosis complex (MTBC), and a causative agent of chronic respiratory disease in a wide range of hosts. Bacillus Calmette-Guerin (BCG) vaccine is mostly used for the prevention of childhood tuberculosis. Further substantial implications are required for the development and evaluation of new tuberculosis (TB) vaccines as well as improving the role of BCG in TB control strategies. In this study, we prepared PLGA nanoparticles encapsulated with argF antigen (argF-NPs). We hypothesized, that argF nanoparticles mediate immune responses of BCG vaccine in mice models of M. bovis infection. We observed that mice vaccinated with argF-NPs exhibited a significant increase in secretory IFN-γ, CD4+ T cells response and mucosal secretory IgA against M. bovis infection. In addition, a marked increase was observed in the level of secretory IL-1ß, TNF-α and IL-10 both in vitro and in vivo upon argF-NPs vaccination. Furthermore, argF-NPs vaccination resulted in a significant reduction in the inflammatory lesions in the lung's tissues, minimized the losses in total body weight and reduced M. bovis burden in infected mice. Our results indicate that BCG prime-boost strategy might be a promising measure for the prevention against M. bovis infection by induction of CD4+ T cells responses and mucosal antibodies.

Electrically Tunable Lens (ETL)-Based Variable Focus Imaging System for Parametric Surface Texture Analysis of Materials.

Electrically tunable lenses (ETLs) are those with the ability to alter their optical power in response to an electric signal. This feature allows such systems to not only image the areas of interest but also obtain spatial depth perception (depth of field, DOF). The aim of the present study was to develop an ETL-based imaging system for quantitative surface analysis. Firstly, the system was calibrated to achieve high depth resolution, warranting the accurate measurement of the depth and to account for and correct any influences from external factors on the ETL. This was completed using the Tenengrad operator which effectively identified the plane of best focus as demonstrated by the linear relationship between the control current applied to the ETL and the height at which the optical system focuses. The system was then employed to measure amplitude, spatial, hybrid, and volume surface texture parameters of a model material (pharmaceutical dosage form) which were validated against the parameters obtained using a previously validated surface texture analysis technique, optical profilometry. There were no statistically significant differences between the surface texture parameters measured by the techniques, highlighting the potential application of ETL-based imaging systems as an easily adaptable and low-cost alternative surface texture analysis technique to conventional microscopy techniques.

Attitude, Perception, and Knowledge of COVID-19 Among General Public in Pakistan.

The World Health Organization has acknowledged coronavirus disease 2019 (COVID-19) disease as a pandemic. Efforts are being made all over the world to raise awareness to prevent the spread of the disease. The goal of this study was to assess the attitude, perception, and knowledge of Pakistani people toward COVID-19 disease. We conducted a cross-sectional survey in which a questionnaire of 17 questions was transformed online on Google forms and was sent to random individuals online. A total of 1,000 questionnaires from individuals throughout Pakistan were evaluated. The results revealed that 42.9% of the participants knew about COVID-19 through social media, the largest source of information. Most of the participants (48.3%) started working from home amid the lockdown; 39.9% of the participants reported that they wash their hands every hour, and 56.9% participants are using a surgical mask. About thermal scanners, 30.5% of the people answered they may be effective, and 46.0% of the people think COVID-19 is a bioweapon; 59% of the participants think everyone is susceptible, whereas 83.9% of the people recognize fever as a primary symptom; 65.2% of the people are practicing social distancing, whereas 85.1% of the people think social gatherings causes spread of the disease. In general, participants had a good knowledge about the disease and a positive attitude toward protective measures. The effective measures are being taken by the government and the public; still, there remains a need for further awareness campaigns and knowledge of safe interventions to combat the spread of disease.

Imaging of the Effect of Alcohol-Containing Media on the Performance of Hypromellose Hydrophilic Matrix Tablets: Comparison of Direct Compression and Regular Grades of Polymer.

As the ingestion of drug products with alcohol could have adverse effects on the release of drugs from dosage forms, it is important to understand the mechanisms underpinning the influence on drug release by evaluating the effect of alcohol-containing media on the behaviour of pharmaceutical excipients. In this work, the effect of hydroalcoholic media containing up to 40% v/v absolute ethanol was evaluated, employing both the regular (CR) and direct compression grades (DC) of hypromellose. X-ray microtomography (XµT) and magnetic resonance imaging (MRI) were used as complementary techniques in determining the influence of the media composition on the ability of the CR and DC polymers to form and evolve the gel layer that controls drug release. Particle and powder properties of the polymer were characterised to determine any relationship to performance in hydroalcoholic media. Triboelectrification results showed the CR grade formulation to charge electropositively whereas the DC grade charged electronegatively. The flow properties also showed the DC grade to have a superior flow as compared to its CR counterpart. Differences in particle morphology between the grades influenced charging and flow behaviour of the powders; however, it did not seem to impact significantly either on the mechanical strength or the drug release properties of the compacted formulation using the model drug propranolol HCl. XµT and MRI imaging were successfully used as complementary techniques in determining the gel layer/hydration layer thickness measurements as the layer developed, as well as following ingress of hydroalcoholic media and its impact on the dry core. The result showed that although differences were present in the gel layer thickness potentially due to differences in particle morphology, this also did not impact significantly on the dissolution process, especially in acidic and hydroalcoholic media.