Mapping of 20 L capacity ultrasonic reactor using cavitation activity meter and dye degradation.

Mapping of a novel 20 L capacity ultrasonic (US) reactor having a total of 44 transducers was done by measuring the local cavitation intensity using a cavitation activity meter at different horizontal planes and subsequent validation based on dye degradation. A fixed frequency of 33 kHz and temperature of 30 °C was used during the mapping performed at two different power levels of 250 W and 400 W. In addition, the mapping of specific plane 2 was also performed with transducers operating on walls 1 and 3, while switching the transducers on walls 2 and 4 off and vice versa so as to establish the role of using multiple transducers. Degradation of RO4 dye was also measured at the plane 2 at various powers as 250 W, 400 W, and 1000 W. The degradation of the RO4 dye directly correlated to the cavitation intensity measured at the various location inside the US reactor. The average cavitation intensity was 265.38, 317.25, 185, and 300.5 Cavins for power dissipations of 250 W, 400 W, 250 W (wall 1 and 3 transducers in operation), and 400 W (wall 2 and 4 transducers in operation), respectively. Correspondingly, the average degradation was 10.35 %, 13.03 %, 5.52 %, and 8.9 % for same sequence of operational power and transducers. The investigation amply illustrated dependency of the cavitational activity on the location, power dissipation, and operating mode elucidating important design related information useful for scale up of sonochemical reactors.

A novel artificial intelligence approach to detect the breast cancer using KNNet technique with EPM gene profiling.

Women's most frequent type of cancer is breast cancer, second only to lung cancer. This paper summarizes changes in genomics and epigenetics and incremental biological activities. A tumour develops through a series of phases involving a separate abnormal gene. Even though many diseases cause DNA mutations, most treatments are designed to relieve symptoms rather than change the DNA. Clustering short palindromic repeats (CRISPR) or Cas9 is the primary approach for discovering and confirming tumorigenic genomic targets. A Kohonen neural network with an expression programming model was developed for gene selection. The main problem in genetic selection is reducing the number of features chosen while maintaining accuracy. This purpose is accomplished systematically. In the end, the approach method performed better than the existing quantum squirrel-inspired algorithm and the recurrent neural network oppositional call search algorithm for genetic selection. The KNNet-EPM model used an expression programming approach to identify gene biomarkers for breast cancer. This method was achieved with RAE of 42%, sensitivity of 93%, f1 score of 88%, accuracy of 98%, kappa score of 83%, specificity of 92% and MAE of 30%.

Physio-Biochemical Integrators and Transcriptome Analysis Reveal Nano-Elicitation Associated Response during Dendrocalamus asper (Schult. and Schult. F.) Backer ex K. Heyne Micropropagation.

Bamboos are perennial, arborescent, monocarpic and industrially important non-timber plants. They are important for various purposes, such as carbon sequestration, biodiversity support, construction, and food and fiber production. However, traditional vegetative propagation is insufficient for bamboo multiplication. Moreover, little is known about the mechanism of gold nanoparticles (AuNPs) in vitro proliferation and regulation of physiological and biochemical properties. In this study, we investigated the impacts of citrate and cetyltrimethylammonium bromide (CTAB) coated AuNPs on in vitro proliferation, photosynthetic pigment content and antioxidant potential of Dendrocalamus asper (Schult. and Schult. F.) Backer ex K. Heyne. Various morpho-physiological and biochemical parameters were differentially affected along the citrate- and CTAB-coated AuNPs concentration gradients (200-600 µM). In vitro shoot proliferation, photosynthetic pigment content and antioxidant activities were higher in D. asper grown on Murashige and Skoog medium supplemented with 2 mg·L-1 benzyladenine and 400 µM citrate-coated AuNPs than in those grown on Murashige and Skoog medium supplemented with 600 µM CTAB- coated AuNPs. Identification of genes regulating in vitro D. asper proliferation will help understand the molecular regulation of AuNPs-mediated elicitation for modulating various physiological and biochemical activities during micropropagation. Gene Ontology enrichment analysis and Kyoto Encyclopedia of Genes and Genomes pathway analyses identified differentially expressed genes associated with in vitro modulation of AuNPs-regulated biological processes and molecular functions. The findings of this study provide new insight into AuNPs-mediated elicitation of in vitro mass scale bamboo propagation.

Analyzing physical activity impact based on ubiquitous nonlinear dynamics and electroencephalography data.

BACKGROUND: Understanding complex systems is made easier with the tools provided by the theory of nonlinear dynamic systems. It provides novel ideas, algorithms, and techniques for signal processing, analysis, and classification. Presently, these ideas are being applied to the investigation of how physiological signals evolve. OBJECTIVE: The study applies nonlinear dynamics theory to electroencephalogram (EEG) signals to better comprehend the range of alcoholic mental states. One of the main contributions of this paper is an algorithm for automatically distinguishing between sober and drunken EEG signals based on their salient features. METHODS: The study utilized various entropy-based features, including ApEn, SampEn, Shannon and Renyi entropies, PE, TS, FE, WE, and KSE, to extract information from EEG signals. To identify the most relevant features, the study employed ranking methods like T-test, Wilcoxon, and Bhattacharyya, and trained SVM classifiers with the selected features. The Bhattacharyya ranking method was found to be the most effective in achieving high classification accuracy, sensitivity, and specificity. RESULTS: Classification accuracy of 95.89%, the sensitivity of 94.43%, and specificity of 96.67% are achieved by the SVM classifier with radial basis function (RBF) for polynomial Kernel using the Bhattacharyya ranking method. CONCLUSION: From the result, it is clear that the model serves as a cost-effective and accurate decision-support tool for doctors in diagnosing alcoholism and for rehabilitation centres to monitor the effectiveness of interventions aimed at mitigating or reversing brain damage caused by alcoholism.

Sensing, signalling, and regulatory mechanism of cold-stress tolerance in plants.

Cold stress is a crucial environmental factor influencing growth and distribution and possessing yield penalties. To survive in the cold, plants have evolved to use a range of molecular mechanisms. The major regulatory pathway under low-temperature stress involves the conversion of external stimulus into an internal signal that triggers a defence mechanism through a transcriptional cascade to counter stress. Cold-receptive mechanism and cell signalling involve cold-related signalling molecules, sensors, calcium signals, MAPK cascade, and ICE-COR-CBF pathway that modulate signal transduction in plants. Of these, the ICE-CBF-COR signalling is considered to be an important regulator for cold-stress acclimation. ICE stimulates acclimation to cold and plays a pivotal role in regulating CBF-mediated cold-tolerance mechanism. Thus, CBFs regulate COR gene expression by binding to its promoter. Similarly, the C-repeat binding factor-dependent signalling cascade also stimulates osmotic stress-regulatory gene expression. This review elucidates the regulatory mechanism underlying cold stress, i.e., signal molecules, cold receptors, signal-transduction pathways, metabolic regulation under cold stress, and crosstalk of regulatory pathways with other abiotic stresses in plants. The results may pave the way for crop improvement in low-temperature environments.

Enhanced Drug Delivery System Using Mesenchymal Stem Cells and Membrane-Coated Nanoparticles.

Mesenchymal stem cells (MSCs) have newly developed as a potential drug delivery system. MSC-based drug delivery systems (MSCs-DDS) have made significant strides in the treatment of several illnesses, as shown by a plethora of research. However, as this area of research rapidly develops, several issues with this delivery technique have emerged, most often as a result of its intrinsic limits. To increase the effectiveness and security of this system, several cutting-edge technologies are being developed concurrently. However, the advancement of MSC applicability in clinical practice is severely hampered by the absence of standardized methodologies for assessing cell safety, effectiveness, and biodistribution. In this work, the biodistribution and systemic safety of MSCs are highlighted as we assess the status of MSC-based cell therapy at this time. We also examine the underlying mechanisms of MSCs to better understand the risks of tumor initiation and propagation. Methods for MSC biodistribution are explored, as well as the pharmacokinetics and pharmacodynamics of cell therapies. We also highlight various promising technologies, such as nanotechnology, genome engineering technology, and biomimetic technology, to enhance MSC-DDS. For statistical analysis, we used analysis of variance (ANOVA), Kaplan Meier, and log-rank tests. In this work, we created a shared DDS medication distribution network using an extended enhanced optimization approach called enhanced particle swarm optimization (E-PSO). To identify the considerable untapped potential and highlight promising future research paths, we highlight the use of MSCs in gene delivery and medication, also membrane-coated MSC nanoparticles, for treatment and drug delivery.

Charged Gold Nanoparticles Promote In Vitro Proliferation in Nardostachys jatamansi by Differentially Regulating Chlorophyll Content, Hormone Concentration, and Antioxidant Activity.

Nardostachys jatamansi is a critically endangered medicinal plant and endemic to the Himalayas, having high commercial demand globally. The accumulation of various secondary metabolites in its shoots and roots with antioxidant potential are well-documented in traditional as well as modern medicine systems. In the present study, we first attempted to investigate the impact of citrate (-ve charge, 11.1 ± 1.9 nm) and CTAB (+ve charge, 19.5 ± 3.2 nm) coated gold nanoparticles (AuNPs) on the in vitro proliferation and antioxidant activities of N. jatamansi. Both the nanoparticles differentially affected the morphological and biochemical parameters, chlorophyll content, internal hormone concentration, and antioxidant activities in a concentration-dependent (10-100 µM) manner. Vigorous shooting was observed in half strength MS medium supplemented with IAA (1 mg/L) with 60 µM citrate-AuNPs (46.4 ± 3.7 mm) and 40 µM CTAB-AuNPs (42.2 ± 3.2 mm). Similarly, the maximum number of roots (5.00 ± 0.67 and 5.33 ± 0.58) and root length (29.9 ± 1.5 mm and 27.3 ± 4.8 mm) was reported in half-strength MS medium with IAA (1 mg/L) supplemented with 60 µM citrate-AuNPs and 40 µM CTAB-AuNPs, respectively. In addition, plants growing on MS medium supplemented with 60 µM citrate-AuNPs and 40 µM CTAB-AuNPs showed significantly enhanced photosynthetic pigments (chlorophyll a and b, carotenoids, and total chlorophyll), internal hormone concentration (GA3, IAA, and ABA), and antioxidant activities (total phenolics, flavonoids, DPPH, and SOD enzyme activity). Moreover, the transcript analysis of ANR1, ARF18, PLY9, SAUR28, GID1A, GRF1, SOD, and CAT further confirmed the role of 60 µM citrate-AuNPs and 40 µM CTAB-AuNPs in the improvement in the growth and antioxidant activities of N. jatamansi. Bearing in mind the urgent requirements of the effective conservation measures of this endangered species, the present findings suggest the elicitation of citrate-AuNPs and CTAB-AuNPs would significantly improve the potential applications of N. jatamansi in the medicinal plant-based industry.

Analysis of Smart Lung Tumour Detector and Stage Classifier Using Deep Learning Techniques with Internet of Things.

The use of artificial intelligence (AI) and the Internet of Things (IoT), which is a developing technology in medical applications that assists physicians in making more informed decisions regarding patients' courses of treatment, has become increasingly widespread in recent years in the field of healthcare. On the other hand, the number of PET scans that are being performed is rising, and radiologists are getting significantly overworked as a result. As a direct result of this, a novel approach that goes by the name "computer-aided diagnostics" is now being investigated as a potential method for reducing the tremendous workloads. A Smart Lung Tumor Detector and Stage Classifier (SLD-SC) is presented in this study as a hybrid technique for PET scans. This detector can identify the stage of a lung tumour. Following the development of the modified LSTM for the detection of lung tumours, the proposed SLD-SC went on to develop a Multilayer Convolutional Neural Network (M-CNN) for the classification of the various stages of lung cancer. This network was then modelled and validated utilising standard benchmark images. The suggested SLD-SC is now being evaluated on lung cancer pictures taken from patients with the disease. We observed that our recommended method gave good results when compared to other tactics that are currently being used in the literature. These findings were outstanding in terms of the performance metrics accuracy, recall, and precision that were assessed. As can be shown by the much better outcomes that were achieved with each of the test images that were used, our proposed method excels its rivals in a variety of respects. In addition to this, it achieves an average accuracy of 97 percent in the categorization of lung tumours, which is much higher than the accuracy achieved by the other approaches.

Long-acting formulation strategies for protein and peptide delivery in the treatment of PSED.

The invigoration of protein and peptides in serious eye disease includes age-related macular degeneration, choroidal neovascularization, retinal neovascularization, and diabetic retinopathy. The transportation of macromolecules like aptamers, recombinant proteins, and monoclonal antibodies to the posterior segment of the eye is challenging due to their high molecular weight, rapid degradation, and low solubility. Moreover, it requires frequent administration for prolonged therapy. The long-acting novel formulation strategies are helpful to overcome these issues and provide superior therapy. It avoids frequent administration, improves stability, high retention time, and avoids burst release. This review briefly enlightens posterior segments of eye diseases with their diagnosis techniques and treatments. This article mainly focuses on recent advanced approaches like intravitreal implants and injectables, electrospun injectables, 3D printed drug-loaded implants, nanostructure thin-film polymer devices encapsulated cell technology-based intravitreal implants, injectable and depots, microneedles, PDS with ranibizumab, polymer nanoparticles, inorganic nanoparticles, hydrogels and microparticles for delivering macromolecules in the eye for intended therapy. Furthermore, novel techniques like aptamer, small Interference RNA, and stem cell therapy were also discussed. It is predicted that these systems will make revolutionary changes in treating posterior segment eye diseases in future.

Ion transporters and their regulatory signal transduction mechanisms for salinity tolerance in plants.

Soil salinity is one of the most serious threats to plant growth and productivity. Due to global climate change, burgeoning population and shrinking arable land, there is an urgent need to develop crops with minimum reduction in yield when cultivated in salt-affected areas. Salinity stress imposes osmotic stress as well as ion toxicity, which impairs major plant processes such as photosynthesis, cellular metabolism, and plant nutrition. One of the major effects of salinity stress in plants includes the disturbance of ion homeostasis in various tissues. In the present study, we aimed to review the regulation of uptake, transport, storage, efflux, influx, and accumulation of various ions in plants under salinity stress. We have summarized major research advancements towards understanding the ion homeostasis at both cellular and whole-plant level under salinity stress. We have also discussed various factors regulating the function of ion transporters and channels in maintaining ion homeostasis and ionic interactions under salt stress, including plant antioxidative defense, osmo-protection, and osmoregulation. We further elaborated on stress perception at extracellular and intracellular levels, which triggers downstream intracellular-signaling cascade, including secondary messenger molecules generation. Various signaling and signal transduction mechanisms under salinity stress and their role in improving ion homeostasis in plants are also discussed. Taken together, the present review focuses on recent advancements in understanding the regulation and function of different ion channels and transporters under salt stress, which may pave the way for crop improvement.

Augmented Reality-Centered Position Navigation for Wearable Devices with Machine Learning Techniques.

People have always relied on some form of instrument to assist them to get to their destination, from hand-drawn maps and compasses to technology-based navigation systems. Many individuals these days have a smartphone with them at all times, making it a common part of their routine. Using GPS technology, these cellphones offer applications such as Google Maps that let people find their way around the outside world. Indoor navigation, on the other hand, does not offer the same level of precision. The development of indoor navigation systems is continuously ongoing. Bluetooth, Wi-Fi, RFID, and computer vision are some of the existing technologies used for interior navigation in current systems. In this article, we discuss the shortcomings of current indoor navigation solutions and offer an alternative approach based on augmented reality and ARCore. Navigating an indoor environment is made easier with ARCore, which brings augmented reality to your smartphone or tablet.

Glaucoma Detection Using Image Processing and Supervised Learning for Classification.

A difficult challenge in the realm of biomedical engineering is the detection of physiological changes occurring inside the human body, which is a difficult undertaking. At the moment, these irregularities are graded manually, which is very difficult, time-consuming, and tiresome due to the many complexities associated with the methods involved in their identification. In order to identify illnesses at an early stage, the use of computer-assisted diagnostics has acquired increased attention as a result of the requirement of a disease detection system. The major goal of this proposed work is to build a computer-aided design (CAD) system to help in the early identification of glaucoma as well as the screening and treatment of the disease. The fundus camera is the most affordable image analysis modality available, and it meets the financial needs of the general public. The extraction of structural characteristics from the segmented optic disc and the segmented optic cup may be used to characterize glaucoma and determine its severity. For this study, the primary goal is to estimate the potential of the image analysis model for the early identification and diagnosis of glaucoma, as well as for the evaluation of ocular disorders. The suggested CAD system would aid the ophthalmologist in the diagnosis of ocular illnesses by providing a second opinion as a judgment made by human specialists in a controlled environment. An ensemble-based deep learning model for the identification and diagnosis of glaucoma is in its early stages now. This method's initial module is an ensemble-based deep learning model for glaucoma diagnosis, which is the first of its kind ever developed. It was decided to use three pretrained convolutional neural networks for the categorization of glaucoma. These networks included the residual network (ResNet), the visual geometry group network (VGGNet), and the GoogLeNet. It was necessary to use five different data sets in order to determine how well the proposed algorithm performed. These data sets included the DRISHTI-GS, the Optic Nerve Segmentation Database (DRIONS-DB), and the High-Resolution Fundus (HRF). Accuracy of 91.11% for the PSGIMSR data set and the sensitivity of 85.55% and specificity of 95.20% for the suggested ensemble architecture on the PSGIMSR data set were achieved. Similarly, accuracy rates of 95.63%, 98.67%, 95.64%, and 88.96% were achieved using the DRIONS-DB, HRF, DRISHTI-GS, and combined data sets, respectively.

Detection of Breast Cancer Using Histopathological Image Classification Dataset with Deep Learning Techniques.

Cancer is one of the top causes of mortality, and it arises when cells in the body grow abnormally, like in the case of breast cancer. For people all around the world, it has now become a huge issue and a threat to their safety and wellbeing. Breast cancer is one of the major causes of death among females all over the globe, and it is particularly prevalent in the United States. It is possible to diagnose breast cancer using a variety of imaging modalities including mammography, computerized tomography (CT), magnetic resonance imaging (MRI), ultrasound, and biopsies, among others. To analyze the picture, a histopathology study (biopsy) is often performed, which assists in the diagnosis of breast cancer. The goal of this study is to develop improved strategies for various CAD phases that will play a critical role in minimizing the variability gap between and among observers. It created an automatic segmentation approach that is then followed by self-driven post-processing activities to successfully identify the Fourier Transform based Segmentation in the CAD system to improve its performance. When compared to existing techniques, the proposed segmentation technique has several advantages: spatial information is incorporated, there is no need to set any initial parameters beforehand, it is independent of magnification, it automatically determines the inputs for morphological operations to enhance segmented images so that pathologists can analyze the image with greater clarity, and it is fast. Extensive tests were conducted to determine the most effective feature extraction techniques and to investigate how textural, morphological, and graph characteristics impact the accuracy of categorization classification. In addition, a classification strategy for breast cancer detection has been developed that is based on weighted feature selection and uses an upgraded version of the Genetic Algorithm in conjunction with a Convolutional Neural Network Classifier. The practical application of the suggested improved segmentation and classification algorithms for the CAD framework may reduce the number of incorrect diagnoses and increase the accuracy of classification. So, it may serve as a second opinion tool for pathologists and aid in the early detection of diseases.

Cancer Categorization Using Genetic Algorithm to Identify Biomarker Genes.

In the microarray gene expression data, there are a large number of genes that are expressed at varying levels of expression. Given that there are only a few critically significant genes, it is challenging to analyze and categorize datasets that span the whole gene space. In order to aid in the diagnosis of cancer disease and, as a consequence, the suggestion of individualized treatment, the discovery of biomarker genes is essential. Starting with a large pool of candidates, the parallelized minimal redundancy and maximum relevance ensemble (mRMRe) is used to choose the top m informative genes from a huge pool of candidates. A Genetic Algorithm (GA) is used to heuristically compute the ideal set of genes by applying the Mahalanobis Distance (MD) as a distance metric. Once the genes have been identified, they are input into the GA. It is used as a classifier to four microarray datasets using the approved approach (mRMRe-GA), with the Support Vector Machine (SVM) serving as the classification basis. Leave-One-Out-Cross-Validation (LOOCV) is a cross-validation technique for assessing the performance of a classifier. It is now being investigated if the proposed mRMRe-GA strategy can be compared to other approaches. It has been shown that the proposed mRMRe-GA approach enhances classification accuracy while employing less genetic material than previous methods. Microarray, Gene Expression Data, GA, Feature Selection, SVM, and Cancer Classification are some of the terms used in this paper.

A Novel Diabetes Healthcare Disease Prediction Framework Using Machine Learning Techniques.

Diabetes is a chronic disease that continues to be a significant and global concern since it affects the entire population's health. It is a metabolic disorder that leads to high blood sugar levels and many other problems such as stroke, kidney failure, and heart and nerve problems. Several researchers have attempted to construct an accurate diabetes prediction model over the years. However, this subject still faces significant open research issues due to a lack of appropriate data sets and prediction approaches, which pushes researchers to use big data analytics and machine learning (ML)-based methods. Applying four different machine learning methods, the research tries to overcome the problems and investigate healthcare predictive analytics. The study's primary goal was to see how big data analytics and machine learning-based techniques may be used in diabetes. The examination of the results shows that the suggested ML-based framework may achieve a score of 86. Health experts and other stakeholders are working to develop categorization models that will aid in the prediction of diabetes and the formulation of preventative initiatives. The authors perform a review of the literature on machine models and suggest an intelligent framework for diabetes prediction based on their findings. Machine learning models are critically examined, and an intelligent machine learning-based architecture for diabetes prediction is proposed and evaluated by the authors. In this study, the authors utilize our framework to develop and assess decision tree (DT)-based random forest (RF) and support vector machine (SVM) learning models for diabetes prediction, which are the most widely used techniques in the literature at the time of writing. It is proposed in this study that a unique intelligent diabetes mellitus prediction framework (IDMPF) is developed using machine learning. According to the framework, it was developed after conducting a rigorous review of existing prediction models in the literature and examining their applicability to diabetes. Using the framework, the authors describe the training procedures, model assessment strategies, and issues associated with diabetes prediction, as well as solutions they provide. The findings of this study may be utilized by health professionals, stakeholders, students, and researchers who are involved in diabetes prediction research and development. The proposed work gives 83% accuracy with the minimum error rate.

Determinants of intrauterine contraceptive device discontinuation among Indian women.

OBJECTIVE: To determine intrauterine contraceptive device (IUCD) discontinuation rate and its causes and related factors among women attending the OPD/family planning clinic in Mahila Chikitasalaya, SMS Medical College, Jaipur from January 2012 to December 2012. METHODS: 387 women who had an intrauterine device (IUD) inserted during the last 1-5 years were interviewed during their visits to the OPD/family planning clinic. Sociodemographic characteristics for all women were described using frequency distribution. Life tables were used to describe the proportion of women who discontinued IUD at various time intervals. The main outcome measure was IUD discontinuation. RESULTS: The incidence of IUD discontinuation in the first year following insertion was 16.79 %. Approximately 31 % of the study sample continued using their devices after 5 years. The average duration of IUD use was 36 months. Of the 387 women, 56 % discontinued IUD use because of a desire to conceive, 27.7 % because of side effects, 15.36 % because of opposition from the woman's family, and 1.5 % because they were sexually inactive. The most common side effects reported as the reasons for discontinuation were bleeding, infection, and pain. Discontinuation was inversely related to the age at insertion, the number of living children, and the sex of children. Previous contraceptive users were significantly less likely to discontinue IUD use. CONCLUSIONS: The crude cumulative rate of IUD discontinuation was 16.79 % during the first year, suggesting a need to tackle the problem of discontinuation through effective educational strategies and counseling techniques. Desire to have a male child still predominates among Indian families. The average duration of IUD use in majority of the females was about 36 months (45 %), thereby fulfilling its objective of spacing between children as laid down by the WHO (2 years spacing between pregnancies). About 31 % of the women continued using IUCD even after 5 years. It is crucial to correct misconceptions and identify the lack of correct and complete information both among the providers and the acceptors, to improve the effectiveness of family planning programs.