Studies on Anticancer Effect of Methanolic Leaf Extract of Justicia gendarussa on Lung Cancer Cell Line.

Objective: The aim of study's goal was to look into the anticancer efficacy of a methanolic extract of Justicia gendarussa against a lung cancer cell line. Materials and Methods: Cell viability assays and cell and nuclear morphology examinations were used to evaluate the anticancer efficacy against methanolic extract of Justicia gendarussa on lung cancer cell lines. The IC50 doses were calculated using different concentrations of Justicia gendarussa extract (0, 10, 20, 40, 60, and 80 µg/mL). Results: The results of MTT (3-[4,5-dimethylthiazol-2-yl]-2,5-diphenyl-tetrazolium bromide) assay revealed that the percentage of viability in treated cells was significantly lower as compared with untreated control groups, which represented as 100%, and an inhibitory concentration of 40 µg/mL was observed. Under a phase-contrast microscope, morphological changes revealed cell shrinkage and cytoplasmic membrane blebbing. The apoptotic nuclei (intensely colored, broken nuclei, and compacted chromatin) were examined under a fluorescence microscope. Conclusions: The outcome of the research work on Justicia gendarussa was investigated for anticancer properties. The results revealed the proapoptotic and cytotoxic effects of Justicia gendarussa extract on lung cancer cell lines. From the above results and findings, it could be concluded that the Justicia gendarussa methanolic leaf extract exhibited potent anticancer activity against a lung cancer cell line. Further study needs to be conducted to investigate the active chemicals in the extract as well as the molecular mechanisms underlying its anticancer benefits.

STITCH and Molecular Docking Analysis of Selected Wood Apple (Limonia acidissima) Constituents as Anti-Dandruff and Anti-Acne Agents.

Background: Wood apple (Limonia acidissima) has been reported to possess various pharmacological activities. The present study aimed to evaluate the 11 selected constituents of Wood apple (L. acidissima) as potent anti-dandruff and anti-acne agents using a molecular docking approach. Materials and Methods: The 11 selected constituents of Wood apple were studied on the molecular docking behavior of Malassezia globosa Lipase-1 and Cutibacterium acnes beta-keto acyl synthase-III enzymes by using the patchdock method. Furthermore, STITCH analysis was carried out to determine the ligand-protein interactions. STITCH analysis reveals that two ligands, namely, psoralen and umbelliferone, have exhibited interactions with both the M. globosa and P. acnes KPA 171202 proteins. Results: The docking studies revealed that isopimpinellin and saponarin exhibited the highest (ACE) atomic contact energy (-162.32 and - 318.63 kcal/mol) with that of M. globosa Lipase-1 and C. acnes beta-ketoacyl synthase-III, respectively. Conclusion: Thus, the present finding provides new knowledge for understanding the 11 selected ligands of Wood apple (L. acidissima) as potent anti-dandruff and anti-acne agents.

Evaluation of In vitro Anti-Cancer Activity of Methanolic Leaf Extract of Phoenix pusilla on Colon Cancer Cell Line.

Background: Cancer rates continue to climb, owing largely to the world population's aging and growth, as well as economically developing countries, a surge in cancer-causing behavior, particularly smoking. The third or fourth most prevalent type of cancer is colon cancer. Cancer of the large intestine (colon) is one of the primary causes of death from cancer. Colorectal cancer prevention is mostly based on adenomatous disease screening approaches. The cytotoxic and pharmacological properties of Phoenix pusilla are widely documented. As a result, there is little recorded evidence of its cytotoxic activity against colon cancer cells. Therefore, we planned to study the efficacy of a methanolic leaf extract of Phoenix pusilla against in vitro colon cancer cells. Aim: To evaluate the anti-cancer effects of the methanolic leaf extract of Phoenix pusilla on colon cancer cell lines. Materials and Methods: In vitro screening and anti-cancer effects of the methanolic effect of Phoenix pusilla on colon cancer cell lines were assessed by cell viability assays and cell and nuclear morphological studies. For the in vitro cell culture study, different concentrations of Phoenix pusilla leaf extract (0, 25, 50, 75, 100, 150 µg/ml) were used, and IC50 doses were calculated. Results: The results of the MTT (3-[4,5-dimethylthiazol-2-yl]-2,5 diphenyl tetrazolium bromide) assay revealed that the fraction of viability cells significantly decreased in treated cells when compared to untreated control groups, was expressed as 100%, and an inhibitory concentration of µg/ml was identified. A phase-contrast microscope was used to observe cell shrinkage and cytoplasmic membrane blebbing. A fluorescent microscope was used to examine the apoptotic nuclei (internally dyed nuclei, shattered nuclei, and condensed chromatin). Conclusion: In conclusion, the present study results showed that the leaf extracts of Phoenix pusilla had a strong cytotoxic effect and induced significant apoptosis in the colon cancer cell lines at a concentration of 75 µg/ml in the 24 h incubation period. More research is needed to investigate the extract's active components as well as the molecular mechanisms underlying its anti-cancer properties.

Recent advances in nanotechnology-based modifications of micro/nano PET plastics for green energy applications.

Poly(ethylene terephthalate) (PET) plastic is an omnipresent synthetic polymer in our lives, which causes negative impacts on the ecosystem. It is crucial to take mandatory action to control the usage and sustainable disposal of PET plastics. Recycling plastics using nanotechnology offers potential solutions to the challenges associated with traditional plastic recycling methods. Nano-based degradation techniques improve the degradation process through the influence of catalysts. It also plays a crucial role in enhancing the efficiency and effectiveness of recycling processes and modifying them into value-added products. The modified PET waste plastics can be utilized to manufacture batteries, supercapacitors, sensors, and so on. The waste PET modification methods have massive potential for research, which can play major role in removing post-consumer plastic waste. The present review discusses the effects of micro/nano plastics in terrestrial and marine ecosystems and its impacts on plants and animals. Briefly, the degradation and bio-degradation methods in recent research were explored. The depolymerization methods used for the production of monomers from PET waste plastics were discussed in detail. Carbon nanotubes, fullerene, and graphene nanosheets synthesized from PET waste plastics were delineated. The reuse of nanotechnologically modified PET waste plastics for potential green energy storage products, such as batteries, supercapacitors, and sensors were presented in this review.

DNA vaccine incorporated poly (lactic-co-glycolic) acid (PLGA) microspheres offer enhanced protection against Aeromonas hydrophila infection.

Encapsulation of DNA vaccines onto carriers enhances the immunogenicity of an antigen. Specifically, biodegradable polymers offer sustained release of vaccines which is crucial for any targeted delivery approach. Poly (lactic-co-glycolic) acid (PLGA) microspheres were used to load a DNA vaccine having a targeted gene of outer membrane protein (OMP) of Aeromonas hydrophila to clone and construct a DNA vaccine using a eukaryotic expression vector system (pVAX1-OMP DNA) and delivery in Carassius auratus against A. hydrophila infection. PLGA microspheres were prepared by emulsion technique oil-in-water and characterized by a High-Resolution Scanning Electron Microscope (HR-SEM). The results of PLGA-pVAX1-OMP DNA microspheres shows that average of 100-150 µm particle size and a loading efficiency (LE) of 68.8 %. Results indicate that C. auratus fed with PLGA-pVAX1-OMP DNA microspheres revealed a significant improvement in innate immune response, which includes, myeloperoxidase activity, respiratory burst and total immunoglobulin level compared with control group fish. The immune-related gene, IL1ß, IL10, TGF, c-type, and g-type lysozyme also showed significantly higher expression after immunization. Furthermore, dietary supplementation of the PLGA-pVAX1-OMP DNA (G III) group exhibited a significantly higher survival rate (78 %) than the control group of fish. These results help us to understand the of mechanism of DNA vaccine administrated feed through PLGA nanoparticles resistance to infection by regulating systemic and innate immunity in Carassius auratus.

Fitness dependent optimizer with neural networks for COVID-19 patients.

The Coronavirus, known as COVID-19, which appeared in 2019 in China, has significantly affected the global health and become a huge burden on health institutions all over the world. These effects are continuing today. One strategy for limiting the virus's transmission is to have an early diagnosis of suspected cases and take appropriate measures before the disease spreads further. This work aims to diagnose and show the probability of getting infected by the disease according to textual clinical data. In this work, we used five machine learning techniques (GWO_MLP, GWO_CMLP, MGWO_MLP, FDO_MLP, FDO_CMLP) all of which aim to classify Covid-19 patients into two categories (Positive and Negative). Experiments showed promising results for all used models. The applied methods showed very similar performance, typically in terms of accuracy. However, in each tested dataset, FDO_MLP and FDO_CMLP produced the best results with 100% accuracy. The other models' results varied from one experiment to the other. It is concluded that the models on which the FDO algorithm was used as a learning algorithm had the possibility of obtaining higher accuracy. However, it is found that FDO has the longest runtime compared to the other algorithms. The link to the Covid 19 models is found here: https://github.com/Tarik4Rashid4/covid19models.

An Indigenous, Field-Deployable, Lateral Flow Immunochromatographic Assay Rapidly Detects Infectious Myonecrosis in Shrimp, Litopenaeus vannamei.

Shrimp farming is an important socioeconomic activity worldwide. Infectious myonecrosis virus (IMNV) is an important shrimp virus responsible for significant mortality (up to 70%) in Litopenaeus vannamei. We produced recombinant capsid protein (r-IMNV31) and obtained a highly specific antibody, anti-r-IMNV31, which was used in WOAH-approved ELISA and Western blot to detect IMNV. Further, anti-r-IMNV31 was employed in an indigenously developed lateral flow immunoassay (LFA) with gold nanoparticles as a visual label. Using LFA, IMNV could be detected rapidly (20 min) from tissue homogenate with high specificity, reproducibility, and sensitivity (LOD = 103 viral particles). LFA was validated with "gold standard" qRT-PCR using 60 samples with high sensitivity (100%), specificity (86%). A Cohen's kappa coefficient of 0.86 suggested "good agreement" between LFA and qRT-PCR. With a shelf-life of ~ 1 year at ambient temperature, the use of LFA in the on-site detection of IMNV by shrimp farmers will be a reality.

Kurdish Handwritten character recognition using deep learning techniques.

Handwriting recognition is regarded as a dynamic and inspiring topic in the exploration of pattern recognition and image processing. It has many applications including a blind reading aid, computerized reading, and processing for paper documents, making any handwritten document searchable and converting it into structural text form. High accuracy rates have been achieved by this technology when recognizing handwriting recognition systems for English, Chinese Arabic, Persian, and many other languages. However, there is not such a system for recognizing Kurdish handwriting. In this paper, an attempt is made to design and develop a model that can recognize handwritten characters for Kurdish alphabets using deep learning techniques. Kurdish (Sorani) contains 34 characters and mainly employs an Arabic/Persian based script with modified alphabets. In this work, a Deep Convolutional Neural Network model is employed that has shown exemplary performance in handwriting recognition systems. Then, a comprehensive database has been created for handwritten Kurdish characters which contain more than 40 thousand images. The created database has been used for training the Deep Convolutional Neural Network model for classification and recognition tasks. In the proposed system the experimental results show an acceptable recognition level. The testing results reported an 83% accuracy rate, and training accuracy reported a 96% accuracy rate. From the experimental results, it is clear that the proposed deep learning model is performing well and comparable to the similar to other languages handwriting recognition systems.

Harmony search: Current studies and uses on healthcare systems.

One of the popular metaheuristic search algorithms is Harmony Search (HS). It has been verified that HS can find solutions to optimization problems due to its balanced exploratory and convergence behavior and its simple and flexible structure. This capability makes the algorithm preferable to be applied in several real-world applications in various fields, including healthcare systems, different engineering fields, and computer science. The popularity of HS urges us to provide a comprehensive survey of the literature on HS and its variants on health systems, analyze its strengths and weaknesses, and suggest future research directions. In this review paper, the current studies and uses of harmony search are studied in four main domains. (i) The variants of HS, including its modifications and hybridization. (ii) Summary of the previous review works. (iii) Applications of HS in healthcare systems. (iv) And finally, an operational framework is proposed for the applications of HS in healthcare systems. The main contribution of this review is intended to provide a thorough examination of HS in healthcare systems while also serving as a valuable resource for prospective scholars who want to investigate or implement this method.

Improved Fitness-Dependent Optimizer for Solving Economic Load Dispatch Problem.

Economic load dispatch depicts a fundamental role in the operation of power systems, as it decreases the environmental load, minimizes the operating cost, and preserves energy resources. The optimal solution to economic load dispatch problems and various constraints can be obtained by evolving several evolutionary and swarm-based algorithms. The major drawback to swarm-based algorithms is premature convergence towards an optimal solution. Fitness-dependent optimizer is a novel optimization algorithm stimulated by the decision-making and reproductive process of bee swarming. Fitness-dependent optimizer (FDO) examines the search spaces based on the searching approach of particle swarm optimization. To calculate the pace, the fitness function is utilized to generate weights that direct the search agents in the phases of exploitation and exploration. In this research, the authors have used a fitness-dependent optimizer to solve the economic load dispatch problem by reducing fuel cost, emission allocation, and transmission loss. Moreover, the authors have enhanced a novel variant of the fitness-dependent optimizer, which incorporates novel population initialization techniques and dynamically employed sine maps to select the weight factor for the fitness-dependent optimizer. The enhanced population initialization approach incorporates a quasi-random Sabol sequence to generate the initial solution in the multidimensional search space. A standard 24-unit system is employed for experimental evaluation with different power demands. The empirical results obtained using the enhanced variant of the fitness-dependent optimizer demonstrate superior performance in terms of low transmission loss, low fuel cost, and low emission allocation compared to the conventional fitness-dependent optimizer. The experimental study obtained 7.94E-12, the lowest transmission loss using the enhanced fitness-dependent optimizer. Correspondingly, various standard estimations are used to prove the stability of the fitness-dependent optimizer in phases of exploitation and exploration.

Investigation of the one-step electrochemical deposition of graphene oxide-doped poly(3,4-ethylenedioxythiophene)-polyphenol oxidase as a dopamine sensor.

In this paper, we fabricated poly(3,4-ethylenedioxythiophene) (PEDOT)-graphene oxide-polyphenol oxidase (PEDOT-GO-PPO) as a dopamine sensor. The morphology of PEDOT-GO-PPO was observed using scanning electron microscopy. Cyclic voltammetry was conducted to study the oxidation-reduction characteristics of dopamine. To optimize the pH, potential and limit of detection of dopamine, the amperometric technique was employed. The found limit of detection was 8 × 10-9 M, and the linear range was from 5 × 10-8 to 8.5 × 10-5 M. The Michaelis-Menten constant (K m) was calculated to be 70.34 µM, and the activation energy of the prepared electrode was 32.75 kJ mol-1. The electrode shows no significant change in the interference study. The modified electrode retains up to 80% of its original activity after 2 months. In the future, the biosensor can be used for the quantification of dopamine in human urine samples. The present modified electrode constitutes a tool for the electrochemical analysis of dopamine.

Extreme learning machine and bayesian optimization-driven intelligent framework for IoMT cyber-attack detection.

The Internet of Medical Things (IoMT) is a bionetwork of allied medical devices, sensors, wearable biosensor devices, etc. It is gradually reforming the healthcare industry by leveraging its capabilities to improve personalized healthcare services by enabling seamless communication of medical data. IoMT facilitates prompt emergency responses and provides improved quality of medical services with minimum cost. With the advancement of modern technology, progressively ubiquitous medical devices raise critical security and data privacy concerns through resource constraints and open connectivity. Vulnerabilities in IoMT devices allow unauthorized access for potential entry into healthcare and sensitive personal data. In addition, the patient may experience severe physical damage with the attack on IoMT devices. To provide security to IoMT devices and privacy to patient data, we have proposed a novel IoMT framework with the hybridization of Bayesian optimization and extreme learning machine (ELM). The proposed model derives encouraging performance with enhanced accuracy in decision-making process compared to similar state-of-the-art methods.

Banana Plant Disease Classification Using Hybrid Convolutional Neural Network.

Banana cultivation is one of the main agricultural elements in India, while the common problem of cultivation is that the crop has been influenced by several diseases, while the pest indications have been needed for discovering the infections initially for avoiding the financial loss to the farmers. This problem will affect the entire banana productivity and directly affects the economy of the country. A hybrid convolution neural network (CNN) enabled banana disease detection, and the classification is proposed to overcome these issues guide the farmers through enabling fertilizers that have to be utilized for avoiding the disease in the initial stages, and the proposed technique shows 99% of accuracy that is compared with the related deep learning techniques.

Diagnosis and combating COVID-19 using wearable Oura smart ring with deep learning methods.

Since the coronavirus (COVID-19) outbreak keeps on spreading all through the world, scientists have been crafting varied technologies mainly focusing on AI for an approach to acknowledge the difficulties of the epidemic. In this current worldwide emergency, the clinical business is searching for new advancements to screen and combat COVID-19 contamination. Strategies used by artificial intelligence can stretch screen the spread of the infection, distinguish highly infected patients, and be compelling in supervising the illness continuously. The artificial intelligence anticipation can further be used for passing dangers by sufficiently dissecting information from past sufferers. International patient support with recommendations for population testing, medical care, notification, and infection control can help fight this deadly virus. We proposed the hybrid deep learning method to diagnose COVID-19. The layered approach is used here to measure the symptom level of the patients and to analyze the patient image data whether he/she is positive with COVID-19. This work utilizes smart AI techniques to predict and diagnose the coronavirus rapidly by the Oura smart ring within 24 h. In the laboratory, a coronavirus rapid test is prepared with the help of a deep learning model using the RNN and CNN algorithms to diagnose the coronavirus rapidly and accurately. The result shows the value 0 or 1. The result 1 indicates the person is affected with coronavirus and the result 0 indicates the person is not affected with coronavirus. X-Ray and CT image classifications are considered here so that the threshold value is utilized for identifying an individual's health condition from the initial stage to a severe stage. Threshold value 0.5 is used to identify coronavirus initial stage condition and 1 is used to identify the coronavirus severe condition of the patient. The proposed methods are utilized for four weighting parameters to reduce both false positive and false negative image classification results for rapid and accurate diagnosis of COVID-19.

Correction to: Diagnosis and combating COVID-19 using wearable Oura smart ring with deep learning methods.

[This corrects the article DOI: 10.1007/s00779-021-01541-4.].

Performance Evaluation of Multilayer Clustering Network Using Distributed Energy Efficient Clustering with Enhanced Threshold Protocol.

In this research, pure deterministic system has been established by a new Distributed Energy Efficient Clustering Protocol with Enhanced Threshold (DEECET) by clustering sensor nodes to originate the wireless sensor network. The DEECET is very dynamic, highly distributive, self-confessed and much energy efficient as compared to most of the other existing protocols. The MATLAB simulation provides aim proved result by means of energy dissipation being emulated in the networks lifespan for homogeneous as well as heterogeneous sensor network, which when contrasted for other traditional protocols. An enhanced result has been obtained for equitable energy dissipation for systematized networks using DEECET.

The Application of Convolutional Neural Network Model in Diagnosis and Nursing of MR Imaging in Alzheimer's Disease.

The disease Alzheimer is an irrepressible neurologicalbrain disorder. Earlier detection and proper treatment of Alzheimer's disease can help for brain tissue damage prevention. The study was intended to explore the segmentation effects of convolutional neural network (CNN) model on Magnetic Resonance (MR) imaging for Alzheimer's diagnosis and nursing. Specifically, 18 Alzheimer's patients admitted to Indira Gandhi Medical College (IGMC) hospital were selected as the experimental group, with 18 healthy volunteers in the Ctrl group. Furthermore, the CNN model was applied to segment the MR imaging of Alzheimer's patients, and its segmentation effects were compared with those of the fully convolutional neural network (FCNN) and support vector machine (SVM) algorithms. It was found that the CNN model demonstrated higher segmentation precision, and the experimental group showed a higher clinical dementia rating (CDR) score and a lower mini-mental state examination (MMSE) score (P < 0.05). The size of parahippocompalgyrus and putamen was bigger in the Ctrl (P < 0.05). In experimental group, the amplitude of low-frequency fluctuation (ALFF) was positively correlated with the MMSE score in areas of bilateral cingulum gyri (r = 0.65) and precuneus (r = 0.59). In conclusion, the grey matter structure is damaged in Alzheimer's patients, and hippocampus ALFF and regional homogeneity (ReHo) is involved in the neuronal compensation mechanism of hippocampal damage, and the caregivers should take an active nursing method.

Peptide-based epitope design on non-structural proteins of SARS-CoV-2

The SARS-CoV-2 virus has caused the severe pandemic, COVID19 and since then its been critical to produce a potent vaccine to prevent the quick transmission and also to avoid alarming deaths. Among all type of vaccines peptide based epitope design tend to outshine with respect to low cost production and more efficacy. Therefore, we started with obtaining the necessary protein sequences from NCBI database of SARS-CoV-2 virus and filtered with respect to antigenicity, virulency, pathogenicity and non-homologous nature with human proteome using different available online tools and servers. The promising proteins was checked for containing common B and T-cell epitopes. The structure for these proteins were modeled from I-TASSER server followed by its refinement and validation. The predicted common epitopes were mapped on modeled structures of proteins by using Pepitope server. The surface exposed epitopes were docked with the most common allele DRB1*0101 using the GalaxyPepDock server. The epitopes, ELEGIQYGRS from Leader protein (NSP1), YGPFVDRQTA from 3c-like proteinase (nsp5), DLKWARFPKS from NSP9 and YQDVNCTEVP from Surface glycoprotein (spike protein) are the epitopes which has more hydrogen bonds. Hence these four epitopes could be considered as a more promising epitopes and these epitopes can be used for future studies.

Quantitative correlation of lumbar foraminal stenosis with local morphological metrics.

PURPOSE: Clinical evaluation of lumbar foraminal stenosis typically includes qualitative assessments of perineural epidural fat content around the spinal nerve root and evaluation of nerve root impingement. The present study investigates the use of several morphological MRI-derived metrics as quantitative predictors of foraminal stenosis grade. METHODS: 62 adult patients that underwent lumbar spine MRI evaluation over a 1-month duration in 2018 were included in the analysis. Radiological gradings of stenosis were captured from the existing clinical electronic medical record. Clinical gradings were recorded using a 0-5 scale: 0 = no stenosis, 1 = mild stenosis, 2 = mild-moderate stenosis, 3 = moderate stenosis, 4 = moderate-severe stenosis, 5 = severe stenosis. Quantitative measures of perineural epidural fat volume, nerve root cross-sectional area, and lumbar pedicle length were derived from T1 weighted sagittal spine MRI on each side of all lumbar levels. Spearman correlations of each measured metric at each level were then computed against the stenosis gradings. RESULTS: A total of 347 volumetric segmentation and radiological foraminal stenosis grade sets were derived from the 62-subject study cohort. Statistical analysis revealed significant correlations (p < 0.001) between the volume of perineural fat and stenosis grades for all lumbar vertebral levels. CONCLUSION: The results of the study have demonstrated that segmented volumes of perineural fat predict the severity of clinically scored foraminal stenosis. This finding motivates further development of automated perineural fat segmentation methods, which could offer a quantitative imaging biometric that yields more reproducible diagnosis, assessment, and tracking of foraminal stenosis.

Draft Genome Analysis of Acinetobacter indicus Strain UBT1, an Efficient Lipase and Biosurfactant Producer.

Acinetobacter indicus strain UBT1 has shown efficient lipase (243 U ml-1) and biosurfactant (61.1% E24% emulsification and surface tension reduction to 37.7 mN m-1) production capabilities using agro-industrial waste as sole carbon source. We report here the draft genome sequence of A. indicus strain UBT1 having genome size of 2.97 Mb with 45.90% GC content. Total 2721 coding genes were predicted using National Center for Biotechnology Information-Prokaryotic Genome Annotation Pipeline (NCBI-PGAP). The whole genome shotgun project sequence data are accessible through NCBI Gene Bank under accession no. JABFOI000000000. PGAP annotation revealed the presence of the triacylglycerol lipase, phospholipase etc., that circuitously confers the oil consumption competency to the strain UBT1. Rapid Annotation using the Subsystem Technology (RAST) server used for mapping the genes to the subsystem resulted in 278 subsystem with 30% subsystem coverage. The draft genome data can be used to exploit the A. indicus strain UBT1 for its advance biotechnological application and also for further comparative genomic studies.