Magnetofection approach for the transformation of okra using green iron nanoparticles

Climate change, pesticide resistance, and the need for developing new plant varieties have galvanized biotechnologists to find new solutions in order to produce transgenic plants. Over the last decade scientists are working on green metallic nanoparticles to develop DNA delivery systems for plants. In the current study, green Iron nanoparticles were synthesized using leaf extract of Camellia sinensis (green tea) and Iron Chloride (FeCl3), the characterization and Confirmation was done using UV-VIS Spectroscopy, FTIR, SEM, and TEM. Using these nanoparticles, a novel method of gene transformation in okra plants was developed, with a combination of different Magnetofection factors. Maximum gene transformation efficiency was observed at the DNA to Iron-nanoparticles ratio of 1:20, by rotation of mixture (Plasmid DNA, Iron-nanoparticles, and seed embryo) at 800 rpm for 5 h. Using this approach, the transformation of the GFP (green fluorescent protein) gene was successfully carried out in Abelmoschus esculentus (Okra plant). The DNA transformation was confirmed by observing the expression of transgene GFP via Laser Scanning Confocal Microscope (LSCM) and PCR. This method is highly economical, adaptable, genotype independent, eco-friendly, and time-saving as well. We infer that this approach can be a potential solution to combat the yield and immunity challenges of plants against pathogens.

Synergistic Activity of Tetrandrine and Colistin against mcr-1-Harboring Escherichia coli

Before the emergence of plasmid-mediated colistin resistance, colistin was once considered the last drug of choice for infections caused by carbapenem-resistant bacteria. Currently, researchers are relentlessly exploring possible alternative therapies that could efficiently curb the spread of drug resistance. In this study, we aim to investigate the synergistic antibacterial activity of tetrandrine in combination with colistin against mcr-1-harboring Escherichia coli. We examined the antibacterial activity of tetrandrine in combination with colistin in vivo and in vitro and examined the bacterial cells by fluorescence, scanning, and transmission electron microscopy (TEM) to explore their underlying mechanism of action. We further performed a computational analysis of MCR-1 protein and tetrandrine to determine the interaction interface of these two molecules. We confirmed that neither colistin nor tetrandrine could, on their own, inhibit the growth of mcr-1-positive E. coli. However, in combination, tetrandrine synergistically enhanced colistin activity to inhibit the growth of E. coli both in vivo and in vitro. Similarly, molecular docking showed that tetrandrine interacted with the three crucial amino acids of the MCR-1 protein in the active site, which might inhibit MCR-1 from binding to its substrates, cause MCR-1 to lose its ability to confer resistance. This study confirmed that tetrandrine and colistin have the ability to synergistically overcome the issue of colistin resistance in mcr-1-harboring E. coli.

Evolution of basic human values orientations: An application of monitoring changes in cluster solutions

This study enumerates the evolution of basic human values orientations and the dynamic relationship between them, computed from Schwartz's value survey conducted in European nations. For this purpose, eight datasets related to the human value scale were extracted from the European Social Survey; each corresponds to a single round conducted cross-sectionally every two years since 2001. Change detection algorithm was implemented to the cluster solutions of temporal datasets, and the evolution of important clusters was traced. Finding of the study reveals that Universalism and Benevolence values are on the rise in European societies in the last couple of decades. Most of the European inhabitants believe in the smooth group functioning and form the organismic needs of cooperation. The people prefer anxiety-free life, and love for nature, environment, humanity, and kindness to other beings in society are essential constructs for them. They avoid self-centred behaviour and prefer social physiognomies.

Biosorption potential and molecular characterization of metal-resistant autochthonous microbes from tannery solid waste

This study encompasses isolation and screening of heavy metal-resistant fungal and bacterial strains from tannery solid waste (TSW). Twelve fungal strains and 25 bacterial strains were isolated from TSW. The growth of fungal strains was observed against different heavy metals ranging from 10 to 1050 mg L^-1 and the growth of bacteria was observed in metal concentrations ranging from 10 to 1200 mg L^-1. Five multi-metal-resistant fungal isolates belonging to the genus Trichoderma and ten bacterial isolates belonging to the genus Bacillus showed good metal resistance and biosorption potential. They were identified through molecular techniques, fungi based on ITS region ribotyping, and bacteria based on 16S rRNA ribotyping. The fungal strains were characterized as T. hamatum (TSWF-06), T. harzianum (TSWF-11), T. lixii (TSWF-02), and T. pseudokoningii (TSWF-03, TSWF-10). The bacterial strains were characterized as Bacillus xiamenensis (TSW-02), B. velezensis (TSW-05), B. piscis (TSW-06), B. safensis (TSW-10), B. subtilis (TSW-14, TSW-15, TSW-17) B. licheniformis (TSW-19), B. cereus (TSW-20), and B. thuringiensis (TSW-22). The fungal strains, namely, T. pseudokoningii (TSWF-03) and T. harzianum, proved to be two multi-metal-resistant strains with good biosorption efficiency. Unlike fungi, bacterial strains showed metal-specific resistance. The strains Bacillus xiamenensis, B. subtilis (TSW-14), and B. subtilis (TSW-15) showed good biosorption efficiency against Cr, B. safensis against Cu, B. piscis, and B. subtilis (TSW-17) against Pb and B. licheniformis and B. thuringiensis against Zn. The autochthonous fungal and bacterial strains can therefore be employed to clean metal-contaminated environments.

E2EGI: End-to-End Gradient Inversion in Federated Learning

A plethora of healthcare data is produced every day due to the proliferation of prominent technologies such as Internet of Medical Things (IoMT). Digital-driven smart devices like wearable watches, wristbands and bracelets are utilized extensively in modern healthcare applications. Mining valuable information from the data distributed at the owners' level is useful, but it is challenging to preserve data privacy. Federated learning (FL) has swiftly surged in popularity due to its efficacy in dealing privacy vulnerabilities. Recent studies have demonstrated that Gradient Inversion Attack (GIA) can reconstruct the input data by leaked gradients, previous work demonstrated the achievement of GIA in very limited scenarios, such as the label repetition rate of the target sample being low and batch sizes being smaller than 48. In this paper, a novel method of End-to-End Gradient Inversion (E2EGI) is proposed. Compared to the state-of-the-art method, E2EGI's Minimum Loss Combinatorial Optimization (MLCO) has the ability to realize reconstructed samples with higher similarity, and the Distributed Gradient Inversion algorithm can implement GIA with batch sizes of 8 to 256 on deep network models (such as ResNet-50) and ImageNet datasets. A new Label Reconstruction algorithm is developed that relies only on the gradient information of the target model, which can achieve a label reconstruction accuracy of 81% in one batch sample with a label repetition rate of 96%, a 27% improvement over the state-of-the-art method. This proposed work can underpin data security assessments for healthcare federated learning. Code is available at https://github.com/zhaohuali/E2EGI.

Stochastic Recognition of Human Physical Activities via Augmented Feature Descriptors and Random Forest Model

Human physical activity recognition from inertial sensors is shown to be a successful approach for monitoring elderly individuals and children in indoor and outdoor environments. As a result, researchers have shown significant interest in developing state-of-the-art machine learning methods capable of utilizing inertial sensor data and providing key decision support in different scenarios. This paper analyzes data-driven techniques for recognizing human daily living activities. Therefore, to improve the recognition and classification of human physical activities (for example, walking, drinking, and running), we introduced a model that integrates data preprocessing methods (such as denoising) along with major domain features (such as time, frequency, wavelet, and time-frequency features). Following that, stochastic gradient descent (SGD) is used to improve the performance of the extracted features. The selected features are catered to the random forest classifier to detect and monitor human physical activities. Additionally, the proposed HPAR system was evaluated on five benchmark datasets, namely the IM-WSHA, PAMAP-2, UCI HAR, MobiAct, and MOTIONSENSE databases. The experimental results show that the HPAR system outperformed the present state-of-the-art methods with recognition rates of 90.18%, 91.25%, 91.83%, 90.46%, and 92.16% from the IM-WSHA, PAMAP-2, UCI HAR, MobiAct, and MOTIONSENSE datasets, respectively. The proposed HPAR model has potential applications in healthcare, gaming, smart homes, security, and surveillance.

Impact of workflow interruptions on baseline activities of the doctors working in the emergency department

Background

Workflow interruptions are common in the emergency department (ED) of the hospitals for physicians, leading to an increased risk of errors.

Purpose

This study aims to understand the baseline activities of the ED doctors and how these are affected by workflow interruptions.

Methods

The study was conducted in two phases to collect the doctor’s perspective (through questionnaire survey) and observer’s perspective (through workflow observation study) about ED doctors’ baseline activities and workflow interruptions. Two different perspectives were obtained to make the insights clearer and more valuable. The point of view of the 223 doctors working in ED of the hospitals was recorded through a questionnaire survey. In the second phase, the observer’s point of view (authors) was obtained through a workflow observation study, and 13 doctors were observed for 160 hours.

Results

Direct communication with patients (37.1%) and ‘documentation and prescription’ (22.7%) were found to be the most frequent activities. The most common interruptions were visual and auditory distractions, rumination (mind-wandering) and intrusion (by co-workers). Also, the time consumed on indirect patient care (6.6%) was higher than direct patient care (4. 2%). Interruptions increase the chances of errors by making it hard for a doctor to resume a primary task after facing interruptions.

Conclusion

Interruptions increase the chances of errors and make it difficult for the doctors to resume primary tasks (after facing such incidents).

Incidence and molecular characterization of ESBL-producing and colistin-resistant Escherichia coli isolates recovered from healthy food-producing animals in Pakistan

OBJECTIVES

To investigate the occurrence and molecular features of ESBL-producing and colistin-resistant Escherichia coli isolates recovered from healthy food-producing animals in Pakistan.

METHODS

A total of 153 E. coli isolates were recovered from 250 faecal samples collected from livestock and poultry. The antibiotic susceptibility, resistant determinants and mobile genetic elements were determined for all the isolates. The clonal relatedness was analysed by MLST. Plasmids harbouring, localization and transferability of mcr-1 gene were carried out by Southern hybridization, S1-PFGE and transconjugation.

RESULTS

Out of 153 E. coli strains, 49.01% isolates were ESBLs producers, whereas 18.95% were resistant to colistin and 84.31% of the isolates. Multidrug resistance was found in 84% of the isolates. The ESBL-producing E. coli in buffaloes, cattle, sheep, goat and broilers faecal samples were 60%, 74%, 54%, 50% and 68%, respectively. Among the ESBLs genes, bla_CTX-M was the most prevalent group detected in 98.66%, while only mcr-1 of the colistin-resistant genes could be PCR amplified in 29 isolates. The common MGEs found were ISECP1 (35.13%), ISCR1 (33.78%), ISApl1 (20.27%) and Inti1 (58.10%). The most predominant Inc. types found were IncFIB 46.66%, followed by IncFIA 30.66%, IncFIC 26.66%, IncFrepB 26.66%, IncHI2 26.66%, IncP 22.66% and IncX4 21.33%. The most frequent sequence type detected was ST58. Southern blot and S1-PFGE confirmed the plasmid harbouring of mcr-1 gene.

CONCLUSION

The co-occurrence of mcr-1 and ESBLs-encoding genes, along with MGEs in E. coli from healthy food animals in Pakistan, is a major concern.

SIGNIFICANCE AND IMPACT OF STUDY

Antimicrobial resistance can be transferred from animals to humans by direct contact or via the food chain and environment. The prevalence and co-occurrence of ESBL and colistin resistance genes from food-producing animals is rare in Pakistan. To our knowledge, this is the first report to find ESBLs and mcr-1-harbouring E. coli from the faecal samples of the healthy food-producing animals in Pakistan. The presence of ARGs in association with MGEs, co-harbouring the virulence factors, as determined in the current study, is a severe threat to livestock and the human community as it has horizontally and food web transferability.

Comprehensive Analysis of Network Slicing for the Developing Commercial Needs and Networking Challenges

Network slicing (NS) is one of the most prominent next-generation wireless cellular technology use cases, promising to unlock the core benefits of 5G network architecture by allowing communication service providers (CSPs) and operators to construct scalable and customized logical networks. This, in turn, enables telcos to reach the full potential of their infrastructure by offering customers tailored networking solutions that meet their specific needs, which is critical in an era where no two businesses have the same requirements. This article presents a commercial overview of NS, as well as the need for a slicing automation and orchestration framework. Furthermore, it will address the current NS project objectives along with the complex functional execution of NS code flow. A summary of activities in important standards development groups and industrial forums relevant to artificial intelligence (AI) and machine learning (ML) is also provided. Finally, we identify various open research problems and potential answers to provide future guidance.

Phosphate solubilizing microorganisms isolated from medicinal plants improve growth of mint

The current research project involves isolation and characterization of PSM (phosphate solubilizing microorganisms) from the rhizospheric soil of certain medicinal plants and to determine their effect on plant growth. Medicinal plants, Aloe vera, Bauhinia variegata, Cannabis sativa, Lantana camara and Mentha viridis were selected for the isolation of PSMs. Soil status of the selected medicinal plants was also checked. Phosphate solubilizing bacteria (PSB) were observed under stereomicroscope for their morphological characteristics and Gram's staining. Phosphate solubilizing fungi (PSF) were also identified microscopically. Colony diameter, halo zone diameter and solubilization index were determined on PVK agar plates. TLC results indicated that citric acid was the most common acid produced by PSM strains. All strains were found to be non-pathogenic in pathogenicity test. A positive plant growth response to PSM inoculation was observed in all studies. In study 1, individual inoculation of PSM showed a significant increased effect on plant growth parameter i.e., fresh and dry weight, plant height and root and shoot length as compared to control. In study2, composite inoculation of PSM along with different P sources revealed that rock phosphate (RP) with PSM increased growth of plants significantly. The present study suggests that PSM inoculation along with RP amendment can be used as biofertilizer.

Machine Learning-Based Multimodel Computing for Medical Imaging for Classification and Detection of Alzheimer Disease

Alzheimer is a disease that causes the brain to deteriorate over time. It starts off mild, but over the course of time, it becomes increasingly more severe. Alzheimer's disease causes damage to brain cells as well as the death of those cells. Memory in humans is especially susceptible to this. Memory loss is the first indication of Alzheimer's disease, but as the disease progresses and more brain cells die, additional symptoms arise. Medical image processing entails developing a visual portrayal of the inside of a body using a range of imaging technologies in order to discover and cure problems. This paper presents machine learning-based multimodel computing for medical imaging for classification and detection of Alzheimer disease. Images are acquired first. MRI images contain noise and contrast problem. Images are preprocessed using CLAHE algorithm. It improves image quality. CLAHE is better to other methods in its capacity to enhance the look of mammography in minute places. A white background makes the lesions more obvious to the naked eye. In spite of the fact that this method makes it simpler to differentiate between signal and noise, the images still include a significant amount of graininess. Images are segmented using the k-means algorithm. This results in the segmentation of images and identification of region of interest. Useful features are extracted using PCA algorithm. Finally, images are classified using machine learning algorithms.

Convergence Analysis of Path Planning of Multi-UAVs Using Max-Min Ant Colony Optimization Approach

Unmanned Aerial Vehicles (UAVs) seem to be the most efficient way of achieving the intended aerial tasks, according to recent improvements. Various researchers from across the world have studied a variety of UAV formations and path planning methodologies. However, when unexpected obstacles arise during a collective flight, path planning might get complicated. The study needs to employ hybrid algorithms of bio-inspired computations to address path planning issues with more stability and speed. In this article, two hybrid models of Ant Colony Optimization were compared with respect to convergence time, i.e., the Max-Min Ant Colony Optimization approach in conjunction with the Differential Evolution and Cauchy mutation operators. Each algorithm was run on a UAV and traveled a predetermined path to evaluate its approach. In terms of the route taken and convergence time, the simulation results suggest that the MMACO-DE technique outperforms the MMACO-CM approach.

Clinical outcomes of coronavirus disease 2019 in liver transplant recipients

BACKGROUND

Liver transplant patients are at higher risk of infection due to immunosuppression. Whether liver transplant recipients are also more susceptible to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and will have worse outcomes than the general population if they develop coronavirus disease 2019 (COVID-19) due to SARS-CoV-2 is a topic of ongoing studies, including ours.

AIM

To assess the clinical outcomes of COVID-19 in liver transplant recipients.

METHODS

This was a case-control study, with a database search performed (at the study site) from March 1, 2020 through February 28, 2021. Patients 18 years or older who tested positive for SARS-CoV-2 via polymerase chain reaction (PCR) were included in the study. Patients with infection other than pneumonia at the time of admission were excluded. After selection, patients who had been the recipient of liver transplant were considered cases and those without as controls. After being matched by age, sex, and obesity, two controls were randomly selected for each case. Death and hospitalization due to COVID-19 infection were the primary outcomes. Secondary outcomes were pertinent only to patients who were hospitalized, and they included duration of hospital stay, need for supplemental oxygen, presence of at least one type of end-organ damage, effects on liver enzymes, incidence of acute liver failure, effect on d-dimer levels, and incidence of venous thromboembolism (VTE). Chi-square or Fisher’s exact test was used to compare all primary and secondary outcomes with the exception of duration of hospital stay and d-dimer levels, which were compared using the Wilcoxon signed-rank test. Alpha criterion was set at 0.05. Logistic regression was performed for each primary outcome (as the dependent variable). Statistical analyses were performed using R software.

RESULTS

Of the 470 Liver transplant recipients who were tested for COVID-19 via the PCR test, 39 patients tested positive (8.3%). There was no significant difference between cases and controls regarding death [odds ratio (OR): 2.04, 95% confidence interval (CI): 0.14–29.17; P = 0.60] and hospitalization rates (OR: 1.38, 95%CI: 0.59–3.24; P = 0.46). There also was no significant difference between cases and controls with respect to all secondary outcomes. Among all patients who had elevated liver enzymes, their levels were either normalized, improving, or remained stable at the time of discharge. No patient developed acute liver failure. Of the 31 hospitalized patients, 27 received a prophylactic anticoagulation dose and no patient developed VTE in either group. Among cases who were hospitalized, immunosuppression was decreased in 5 patients and there was no change in immunosuppression among the remaining 7 patients. One patient died in each of these two subgroups. Logistic regression analysis was done, but all of the models had poor model predictions as well as insignificant predictors (independent variables). Therefore, they could not be used for either prediction or inference.

CONCLUSION

Clinical outcomes of COVID-19 in liver transplant recipients are not different than those without transplantation. COVID-19 should not impact timely health care access and immunosuppression continuation among these patients.

Synergistic effect of glucagon-like peptide-1 analogue liraglutide and ZnO on the antibacterial, hemostatic, and wound healing properties of nanofibrous dressings

Bacterial infections and poor vascularization delay wound healing, thus necessitating alternative strategies for functional wound dressings. Zinc oxide (ZnO) has been shown to exert a potent antibacterial effect against bacterial species. Similarly, Glucagon-like peptide-1 (GLP-1) analogue liraglutide (LG) has been shown to promote vascularization and improve wound healing. The objective of this research was to investigate the synergistic effect of ZnO nanoparticles (ZnO-NPs) and LG to simultaneously induce antibacterial, hemostatic, and vascularization effects for infected wound healing. Electrospun poly (l-lactide-co-glycolide)/gelatin (PLGA/Gel) membranes containing ZnO-NPs and LG displayed good biocompatibility and hemostatic ability. Both, ZnO-NPs and LG exhibited synergistic antibacterial effect against Staphylococcus aureus and Escherichia coli as well as improved the migration and tubule-like network formation of human umbilical vein endothelial cells (HUVECs) in vitro. Once evaluated in a bacterial-infected wound model in rats, the membranes loaded with ZnO-NPs and LG effectively promoted wound healing causing significant reduction in wound area and scar-like tissue formation. Therefore, ZnO-NPs/LG synergism may offer an invaluable solution for the treatment of poorly healing infected wounds.

Early-Stage Alzheimer's Disease Categorization Using PET Neuroimaging Modality and Convolutional Neural Networks in the 2D and 3D Domains

Alzheimer's Disease (AD) is a health apprehension of significant proportions that is negatively impacting the ageing population globally. It is characterized by neuronal loss and the formation of structures such as neurofibrillary tangles and amyloid plaques in the early as well as later stages of the disease. Neuroimaging modalities are routinely used in clinical practice to capture brain alterations associated with AD. On the other hand, deep learning methods are routinely used to recognize patterns in underlying data distributions effectively. This work uses Convolutional Neural Network (CNN) architectures in both 2D and 3D domains to classify the initial stages of AD into AD, Mild Cognitive Impairment (MCI) and Normal Control (NC) classes using the positron emission tomography neuroimaging modality deploying data augmentation in a random zoomed in/out scheme. We used novel concepts such as the blurring before subsampling principle and distant domain transfer learning to build 2D CNN architectures. We performed three binaries, that is, AD/NC, AD/MCI, MCI/NC and one multiclass classification task AD/NC/MCI. The statistical comparison revealed that 3D-CNN architecture performed the best achieving an accuracy of 89.21% on AD/NC, 71.70% on AD/MCI, 62.25% on NC/MCI and 59.73% on AD/NC/MCI classification tasks using a five-fold cross-validation hyperparameter selection approach. Data augmentation helps in achieving superior performance on the multiclass classification task. The obtained results support the application of deep learning models towards early recognition of AD.

Composite Superelastic Aerogel Scaffolds Containing Flexible SiO2 Nanofibers Promote Bone Regeneration

Repairing irregular-shaped bone defects poses enormous challenges. Scaffolds that can fully fit the defect site and simultaneously induce osteogenesis and angiogenesis hold great promise for bone defect healing. This study aimed to produce superelastic organic/inorganic composite aerogel scaffolds by blending silica nanofibers (SiO₂ ) and poly (lactic acid)/gelatin (PLA/gel) nanofibers; the content of SiO₂ nanofibers were varied from 0-60 wt% (e.g., PLA/gel, PLA/gel/SiO₂ -L, PLA/gel/SiO₂ -M, and PLA/gel/SiO₂ -H for 0%, 20%, 40%, and 60% of SiO₂ nanofibers, respectively) to produce a range of scaffolds. The PLA/gel/SiO₂ -M scaffold had excellent elasticity and good mechanical properties. In vitro experiments demonstrated that the silicon ions released from PLA/gel/SiO₂ -M scaffolds could promote the differentiation of rat bone marrow-derived mesenchymal stem cells (rBMSCs) into osteoblasts, thereby enhancing alkaline phosphatase activity and bone-related genes expressions. Meanwhile, the released silicon ions also promoted the proliferation of human umbilical vein endothelial cells (HUVECs) and the expression of vascular endothelial growth factors, thereby promoting angiogenesis. The assessment of these scaffolds in a calvarial defect model in rats showed good potential of PLA/gel/SiO₂ -M to induce bone regeneration as well as promote osteogenesis and angiogenesis. Overall, these superelastic scaffolds containing flexible SiO₂ nanofibers can simultaneously induce osteogenesis and angiogenesis, which may have broad applications for tissue engineering applications. This article is protected by copyright. All rights reserved.

Nrf2 Activation and NF-Kb & caspase/bax signaling inhibition by sodium butyrate alleviates LPS-induced cell injury in bovine mammary epithelial cells

Mastitis, an inflammation of the mammary gland, is a complex disease that affects the health of dairy cows worldwide. Sodium butyrate (SB) is a short-chain fatty acid that has recently been shown to have antioxidant, anti-inflammatory and anti-apoptotic potential in various cells types, although its role in bovine mammary epithelial cells (bMECs) has not been comprehensively reported. Therefore, the aim of this study was to assess the protective effect of sodium butyrate on Lipopolysaccharide (LPS)-induced mastitis model in vitro and to elucidate the possible underlying molecular mechanisms. The in vitro mastitis model was designed to investigate the regulatory effect of SB on LPS-induced inflammatory conditions in bMECs, with particular emphasis on oxidative stress, inflammatory response, apoptosis, and mitochondrial dysfunction. The results showed that SB co-treatment markedly prevented LPS-induced death of bMECs in a concentration-dependent manner. In addition, SB attenuated LPS-induced oxidative stress (OS) (Increased Intracellular ROS, MDA, and decreased SOD, GSH-Px and CAT activity), thereby reduced inflammation (increased expression of IL-6, IL-Iβ, and TNF-α), and apoptosis (Increased the expression of caspases and Bax and decreased Bcl-2) via inhibiting NF-kB and caspase/bax signaling pathways. Furthermore, the protective effect of SB was also associated with the activation of endogenous antioxidant system (Nrf2, Keap1, NQO-1 and HO-1). Nrf2 silencing significantly abolished the protective effect of SB on bMECs. In conclusion, our findings suggest that SB has a significant protective effect on LPS-induced OS, inflammatory responses and apoptosis by activating Nrf2 and inhibiting NF-kB and ROS-mediated mitochondrial dysfunction. These results propose that SB may be an important regulator of OS and its subsequent inflammatory responses, and thus could be used as a therapeutic agent for bovine mastitis.

Role of composted tannery solid waste and its autochthonous microbes in enhancing phytoextraction of toxic metals and stress abatement in sunflower

The excessive concentration of multiple heavy metals in the tannery solid waste (TSW) needs integrated process solutions for its decontamination. This study is aimed at deriving TSW compost and autochthonous microbe synergies for improving phytoextraction potential of sunflower. In-vessel composting of TSW was carried out by using fruit waste as an inoculum to achieve the optimized conditions. Autochthonous strains of Trichoderma viride and Bacilllus sp. isolated from TSW were utilized individually as well as in combination with TSWC amendments of 2.5, 5 and 10% (w/w) prepared in our pilot scale experiment. Analyses of TSW compost based on FTIR and SEM illustrated the wide range of functionality and porosity along the mesh of fungal hyphae and inorganic moieties present on the compost surface. Plant biomass and TMs uptake (Cr 540 mg kg^-1 > Cd 330 mg kg^-1 > Pb 285 mg kg^-1) were significantly pronounced in shoots of sunflower under combined treatments at 10% TSWC amended soils. However, in seeds, TMs were found below detection limit (BDL) through atomic absorption spectrophotometry. Biochemical assays of sunflower including total chlorophyll content (18%), total soluble protein (45%), superoxide dismutase (80%) and catalase (75%) activities were also increased significantly at higher level of amendment in combination with microbes than in the control. Despite being high in TMs, high biomass in sunflower and associated elevation in biochemical products demonstrate the potential of TSW for valorization.Novelty statement: This study identifies the cost-effective management of multi metal contaminated tannery solid waste through deriving its compost along with autochthonous microbes as phytoextraction assistants by yielding higher plant biomass. This study suggests the use of composted TSW inoculated with selected autochthonous fungi and bacteria for enhancing sunflower's biomass and enhancing the bioavailable fractions of toxic metals for phytoextraction.

A new approach based on CXCR4-targeted combination liposomes for the treatment of liver fibrosis

Liver fibrosis results from excessive extracellular matrix accumulation due to injury and leads to cirrhosis, cancer, and death. Herein, we propose a chemokine receptor 4 (CXCR4)-targeted combination (CTC) liposomal therapy to treat carbon tetrachloride (CCl₄)-induced liver fibrosis in a mouse model. This study aims to combine small molecules such as pirfenidone and AMD3100 in a single nanoplatform to investigate their synergistic antifibrotic effects in a setting of CCl₄-induced liver fibrosis. CTC liposomes (CTC lipo) were prepared using the thin-film hydration method. CTC lipo exhibited a spherical shape, and the particle size was recorded at the nanoscale which confirms its appropriateness for in vitro and in vivo applications. CTC lipo had good storage and serum stability. The entrapped drugs in CTC lipo showed reduced toxicity at higher concentrations. CTC lipo displayed CXCR4 mediated cell uptake and were internalized by caveolae-mediated endocytosis. CTC lipo showed CXCR4 targeting and stromal cell-derived factor 1α (SDF1-α)/CXCR4 axis blocking activity. CTC lipo reduced the elevated serum aspartate aminotransferase (AST), alanine transaminase (ALT), and hydroxyproline (HYP) levels. The histological studies showed improved liver architecture and reduced collagen deposition after treatment. Transforming growth factor β (TGFβ), alpha-smooth muscle actin (α-SMA), and collagen I were elevated by CCl₄ in comparison with the Sham. Upon CTC liposomal treatment, the quantitative score for the elevated fibrotic proteins such as TGFβ, α-SMA, and collagen I was normalized. CTC lipo displayed significant downregulation of the upregulated TGFβ, α-SMA, collagen I, and P-p38 expressions at the molecular level. The CXCR4 targeted liposomes showed prolonged biodistribution at 24 h. Our findings indicated that CTC lipo might be an alternative antifibrotic therapy that may offer new access to research and development. In a nutshell, the present study suggests that systemic administration of CTC lipo has efficient antifibrotic potential and deserves to be investigated for further clinical applications.

Match-Level Fusion of Finger-Knuckle Print and Iris for Human Identity Validation Using Neuro-Fuzzy Classifier

Biometrics is the term for measuring human characteristics. If the term is divided into two parts, bio means life, and metric means measurement. The measurement of humans through different computational methods is performed to authorize a person. This measurement can be performed via a single biometric or by using a combination of different biometric traits. The combination of multiple biometrics is termed biometric fusion. It provides a reliable and secure authentication of a person at a higher accuracy. It has been introduced in the UIDIA framework in India (AADHAR: Association for Development and Health Action in Rural) and in different nations to figure out which biometric characteristics are suitable enough to authenticate the human identity. Fusion in biometric frameworks, especially FKP (finger-knuckle print) and iris, demonstrated to be a solid multimodal as a secure framework. The proposed approach demonstrates a proficient and strong multimodal biometric framework that utilizes FKP and iris as biometric modalities for authentication, utilizing scale-invariant feature transform (SIFT) and speeded up robust features (SURF). Log Gabor wavelet is utilized to extricate the iris feature set. From the extracted region, features are computed using principal component analysis (PCA). Both biometric modalities, FKP and iris, are combined at the match score level. The matching is performed using a neuro-fuzzy neural network classifier. The execution and accuracy of the proposed framework are tested on the open database Poly-U, CASIA, and an accuracy of 99.68% is achieved. The accuracy is higher compared to a single biometric. The neuro-fuzzy approach is also tested in comparison to other classifiers, and the accuracy is 98%. Therefore, the fusion mechanism implemented using a neuro-fuzzy classifier provides the best accuracy compared to other classifiers. The framework is implemented in MATLAB 7.10.

Enumeration of citrus endophytic bacterial communities based on illumine metagenomics technique

Citrus is a valuable crop in Pakistan. It is rich in vitamin C, other nutrients and antioxidants. Huanglongbing (HLB) caused by a bacterium “Candidatus liberibacter asiaticus” (CLas), africanus and americanus has an influence on citrus production around the world. Beside HLB there exist several other bacterial species in citrus groves in Pakistan. The structure and diversity of bacterial species in various ecosystems can be quickly examined using NGS. This approach is considerably quicker and more precise than outdated methods. Healthy or citrus greening infected leaf samples of Grapefruit (Citrus paradisi), C. aurantifolia, and C. reticulata Blanco were used for diversity analysis. In this study high throughput, NGS technique was used to access the population of both cultivable and non-cultivable bacterial endophytes from citrus leaves, by using PCR amplicons of 16S rDNA sequences (V5–V7 regions) with Illumina Hi seq. As a result, a total number of 68,722 sequences were produced from the test samples. According to the NGS-based diversity classification, the most common genera of exploited bacterial endophytes were Proteobacteria, Firmicutes, Bacteroides, Cyanobacteria, and Actinobacteria. C. aurantifolia and C. paradisi showed almost equal diversity, whereas C. reticulata Blanco had a higher proportion of Proteobacteria and Cyanobacteria in their leaves. To determine alpha diversity (AD), additional data was analyzed using statistical indices such as Shannon, Chao1, and Simpson. According to the inverse Simpson diversity index, the abundance of the microbial population in six different citrus samples was 0.48, 0.567, and 0.163, respectively. The metagenomics of microbiota in plant tissues was successfully recorded by NGS technology, which can help us learn more about the interactions between plants and microbes. This research is the first step toward a better understanding of 16SrRNA-based metagenomics from citrus in Pakistan using Illumina (Hi seq) Technology.