Prototyping a wearable and stretchable graphene-on-PDMS sensor for strain detection on human body physiological and joint movements.

In the era of wearable electronic devices, which are quite popular nowadays, our research is focused on flexible as well as stretchable strain sensors, which are gaining humongous popularity because of recent advances in nanocomposites and their microstructures. Sensors that are stretchable and flexible based on graphene can be a prospective 'gateway' over the considerable biomedical speciality. The scientific community still faces a great problem in developing versatile and user-friendly graphene-based wearable strain sensors that satisfy the prerequisites of susceptible, ample range of sensing, and recoverable structural deformations. In this paper, we report the fabrication, development, detailed experimental analysis and electronic interfacing of a robust but simple PDMS/graphene/PDMS (PGP) multilayer strain sensor by drop casting conductive graphene ink as the sensing material onto a PDMS substrate. Electrochemical exfoliation of graphite leads to the production of abundant, fast and economical graphene. The PGP sensor selective to strain has a broad strain range of ⁓60%, with a maximum gauge factor of 850, detection of human physiological motion and personalized health monitoring, and the versatility to detect stretching with great sensitivity, recovery and repeatability. Additionally, recoverable structural deformation is demonstrated by the PGP strain sensors, and the sensor response is quite rapid for various ranges of frequency disturbances. The structural designation of graphene's overlap and crack structure is responsible for the resistance variations that give rise to the remarkable strain detection properties of this sensor. The comprehensive detection of resistance change resulting from different human body joints and physiological movements demonstrates that the PGP strain sensor is an effective choice for advanced biomedical and therapeutic electronic device utility.

TPGS-modified Chitosan Nanoparticles of EGFR Inhibitor: Physicochemical and In vitro Evaluation against HepG2 Cell Lines.

BACKGROUND: Gefitinib (GFN) is an Epithelial Growth Factor Receptor (EGFR) inhibitor, and Food and Drug Administration (FDA) has approved medication to treat lung cancer. However, this investigation aimed to produce and characterize Gefitinib (GFN)-loaded chitosan and soy lecithin nanoparticles (NPs) modified with D-α-tocopheryl polyethylene glycol 1000 succinate mono ester (TPGS) and assess their therapeutic potential against HepG2 liver cell lines. METHODS: Chitosan, a cationic polymer with biocompatible and biodegradable properties, was combined with soy lecithin to develop the NPs loaded with GFN using a self-organizing ionic interaction methodology. RESULTS: The entrapment efficiency and drug loading were found to be 59.04±4.63 to 87.37±3.82% and 33.46±3.76 to 49.50±4.35%, respectively, and results indicated the encapsulation of GEN in NPs. The pH of the formulations was observed between 4.48-4.62. Additionally, all the prepared NPs showed the size and PDI range of 89.2±15.9 nm to 799.2±35.8 nm and 0.179±0.065 to 0.455±0.097, respectively. The FTIR bands in optimized formulation (GFN-NP1) indicated that the drug might be contained within the NP's core. The SEM photograph revealed the spherical shape of NPs. The kinetic release model demonstrated the combination of diffusion and erosion mechanisms. The IC50 value of GFN and GFN-NP1 formulation against the HepG2 cell lines were determined and found to be 63.22±3.36 µg/ml and 45.80±2.53 µg/ml, respectively. DAPI and PI staining agents were used to detect nuclear morphology. CONCLUSION: It was observed that the optimized GFN-NP1 formulation successfully internalized and inhibited the growth of HepG2 cells. Hence, it can be concluded that the prepared NPs can be a new therapeutic option for treating liver cancer.

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.

An Intelligent Diabetic Patient Tracking System Based on Machine Learning for E-Health Applications.

BACKGROUND: Continuous surveillance helps people with diabetes live better lives. A wide range of technologies, including the Internet of Things (IoT), modern communications, and artificial intelligence (AI), can assist in lowering the expense of health services. Due to numerous communication systems, it is now possible to provide customized and distant healthcare. MAIN PROBLEM: Healthcare data grows daily, making storage and processing challenging. We provide intelligent healthcare structures for smart e-health apps to solve the aforesaid problem. The 5G network must offer advanced healthcare services to meet important requirements like large bandwidth and excellent energy efficacy. METHODOLOGY: This research suggested an intelligent system for diabetic patient tracking based on machine learning (ML). The architectural components comprised smartphones, sensors, and smart devices, to gather body dimensions. Then, the preprocessed data is normalized using the normalization procedure. To extract features, we use linear discriminant analysis (LDA). To establish a diagnosis, the intelligent system conducted data classification utilizing the suggested advanced-spatial-vector-based Random Forest (ASV-RF) in conjunction with particle swarm optimization (PSO). RESULTS: Compared to other techniques, the simulation's outcomes demonstrate that the suggested approach offers greater accuracy.

Short peptide amphiphile nanostructures facilitate sunlight-induced nanowelding of gold nanosheets.

An effortless thermoplasmonic welding of multi-shaped gold nanosheets is achieved by ordinary and simple sunlight irradiation. A light-matter interaction occurred via the nanogaps of smaller nanosheets, leading to the enhancement of the electromagnetic field and thus effectively concentrating the heat at the welding point. The sPA peptide nanostructure facilitates the nanowelding of small caged gold nanostructures.

Text-Based Emotion Recognition Using Deep Learning Approach.

Sentiment analysis is a method to identify people's attitudes, sentiments, and emotions towards a given goal, such as people, activities, organizations, services, subjects, and products. Emotion detection is a subset of sentiment analysis as it predicts the unique emotion rather than just stating positive, negative, or neutral. In recent times, many researchers have already worked on speech and facial expressions for emotion recognition. However, emotion detection in text is a tedious task as cues are missing, unlike in speech, such as tonal stress, facial expression, pitch, etc. To identify emotions from text, several methods have been proposed in the past using natural language processing (NLP) techniques: the keyword approach, the lexicon-based approach, and the machine learning approach. However, there were some limitations with keyword- and lexicon-based approaches as they focus on semantic relations. In this article, we have proposed a hybrid (machine learning + deep learning) model to identify emotions in text. Convolutional neural network (CNN) and Bi-GRU were exploited as deep learning techniques. Support vector machine is used as a machine learning approach. The performance of the proposed approach is evaluated using a combination of three different types of datasets, namely, sentences, tweets, and dialogs, and it attains an accuracy of 80.11%.

Improved Analysis of COVID-19 Influenced Pneumonia from the Chest X-Rays Using Fine-Tuned Residual Networks.

COVID-19 has remained a threat to world life despite a recent reduction in cases. There is still a possibility that the virus will evolve and become more contagious. If such a situation occurs, the resulting calamity will be worse than in the past if we act irresponsibly. COVID-19 must be widely screened and recognized early to avert a global epidemic. Positive individuals should be quarantined immediately, as this is the only effective way to prevent a global tragedy that has occurred previously. No positive case should go unrecognized. However, current COVID-19 detection procedures require a significant amount of time during human examination based on genetic and imaging techniques. Apart from RT-PCR and antigen-based tests, CXR and CT imaging techniques aid in the rapid and cost-effective identification of COVID. However, discriminating between diseased and normal X-rays is a time-consuming and challenging task requiring an expert's skill. In such a case, the only solution was an automatic diagnosis strategy for identifying COVID-19 instances from chest X-ray images. This article utilized a deep convolutional neural network, ResNet, which has been demonstrated to be the most effective for image classification. The present model is trained using pretrained ResNet on ImageNet weights. The versions of ResNet34, ResNet50, and ResNet101 were implemented and validated against the dataset. With a more extensive network, the accuracy appeared to improve. Nonetheless, our objective was to balance accuracy and training time on a larger dataset. By comparing the prediction outcomes of the three models, we concluded that ResNet34 is a more likely candidate for COVID-19 detection from chest X-rays. The highest accuracy level reached 98.34%, which was higher than the accuracy achieved by other state-of-the-art approaches examined in earlier studies. Subsequent analysis indicated that the incorrect predictions occurred with approximately 100% certainty. This uncovered a severe weakness in CNN, particularly in the medical area, where critical decisions are made. However, this can be addressed further in a future study by developing a modified model to incorporate uncertainty into the predictions, allowing medical personnel to manually review the incorrect predictions.

Human-Computer Interaction for Recognizing Speech Emotions Using Multilayer Perceptron Classifier.

Human-computer interaction (HCI) has seen a paradigm shift from textual or display-based control toward more intuitive control modalities such as voice, gesture, and mimicry. Particularly, speech has a great deal of information, conveying information about the speaker's inner condition and his/her aim and desire. While word analysis enables the speaker's request to be understood, other speech features disclose the speaker's mood, purpose, and motive. As a result, emotion recognition from speech has become critical in current human-computer interaction systems. Moreover, the findings of the several professions involved in emotion recognition are difficult to combine. Many sound analysis methods have been developed in the past. However, it was not possible to provide an emotional analysis of people in a live speech. Today, the development of artificial intelligence and the high performance of deep learning methods bring studies on live data to the fore. This study aims to detect emotions in the human voice using artificial intelligence methods. One of the most important requirements of artificial intelligence works is data. The Ryerson Audio-Visual Database of Emotional Speech and Song (RAVDESS) open-source dataset was used in the study. The RAVDESS dataset contains more than 2000 data recorded as speeches and songs by 24 actors. Data were collected for eight different moods from the actors. It was aimed at detecting eight different emotion classes, including neutral, calm, happy, sad, angry, fearful, disgusted, and surprised moods. The multilayer perceptron (MLP) classifier, a widely used supervised learning algorithm, was preferred for classification. The proposed model's performance was compared with that of similar studies, and the results were evaluated. An overall accuracy of 81% was obtained for classifying eight different emotions by using the proposed model on the RAVDESS dataset.

Human-Computer Interaction with Detection of Speaker Emotions Using Convolution Neural Networks.

Emotions play an essential role in human relationships, and many real-time applications rely on interpreting the speaker's emotion from their words. Speech emotion recognition (SER) modules aid human-computer interface (HCI) applications, but they are challenging to implement because of the lack of balanced data for training and clarity about which features are sufficient for categorization. This research discusses the impact of the classification approach, identifying the most appropriate combination of features and data augmentation on speech emotion detection accuracy. Selection of the correct combination of handcrafted features with the classifier plays an integral part in reducing computation complexity. The suggested classification model, a 1D convolutional neural network (1D CNN), outperforms traditional machine learning approaches in classification. Unlike most earlier studies, which examined emotions primarily through a single language lens, our analysis looks at numerous language data sets. With the most discriminating features and data augmentation, our technique achieves 97.09%, 96.44%, and 83.33% accuracy for the BAVED, ANAD, and SAVEE data sets, respectively.

Optimal Medical Image Size Reduction Model Creation Using Recurrent Neural Network and GenPSOWVQ.

Medical diagnosis is always a time and a sensitive approach to proper medical treatment. Automation systems have been developed to improve these issues. In the process of automation, images are processed and sent to the remote brain for processing and decision making. It is noted that the image is written for compaction to reduce processing and computational costs. Images require large storage and transmission resources to perform their operations. A good strategy for pictures compression can help minimize these requirements. The question of compressing data on accuracy is always a challenge. Therefore, to optimize imaging, it is necessary to reduce inconsistencies in medical imaging. So this document introduces a new image compression scheme called the GenPSOWVQ method that uses a recurrent neural network with wavelet VQ. The codebook is built using a combination of fragments and genetic algorithms. The newly developed image compression model attains precise compression while maintaining image accuracy with lower computational costs when encoding clinical images. The proposed method was tested using real-time medical imaging using PSNR, MSE, SSIM, NMSE, SNR, and CR indicators. Experimental results show that the proposed GenPSOWVQ method yields higher PSNR SSIMM values for a given compression ratio than the existing methods. In addition, the proposed GenPSOWVQ method yields lower values of MSE, RMSE, and SNR for a given compression ratio than the existing methods.

IntOPMICM: Intelligent Medical Image Size Reduction Model.

Due to the increasing number of medical imaging images being utilized for the diagnosis and treatment of diseases, lossy or improper image compression has become more prevalent in recent years. The compression ratio and image quality, which are commonly quantified by PSNR values, are used to evaluate the performance of the lossy compression algorithm. This article introduces the IntOPMICM technique, a new image compression scheme that combines GenPSO and VQ. A combination of fragments and genetic algorithms was used to create the codebook. PSNR, MSE, SSIM, NMSE, SNR, and CR indicators were used to test the suggested technique using real-time medical imaging. The suggested IntOPMICM approach produces higher PSNR SSIM values for a given compression ratio than existing methods, according to experimental data. Furthermore, for a given compression ratio, the suggested IntOPMICM approach produces lower MSE, RMSE, and SNR values than existing methods.

Electrostatic deposition and functionalization of CVD grown multiwalled carbon nanotubes for sensitive & selective detection of CO and NOx at room temperature.

Among the most toxic and suffocating gases in industries and mines are carbon monoxide (CO) and nitrogen oxides (NOx). The electrostatically functionalized self-assembled MWCNT (f-MWCNTs) were employed to develop a sensing device to selectively sense gases such as CO and NOx with high sensitivity and repeatability to as low as sub-ppm levels. The resistive gas sensor's operation is primarily based on changes in the electrical resistance of the f-MWCNT network as a result of its selective interaction with the specific target gas in a two-pole format. The degree to which the electrical resistance of the sensing film increases or decreases is determined by the concentration of the target gas to which it is exposed. As a result, the target gas can be detected both qualitatively and quantitatively. The sensitivity of 100 ppb and 300 ppb with the sensor response time of ∼30 s and ∼50 s for NOx and CO respectively were recorded using our gas sensor and was found noticeably efficient than conventional MOS-based solid-state gas detectors. It was also realized that the corona-assisted, electrostatic, self-assembled MWCNT based sensor fabrication technique is fast, simple, low-cost, and environmentally friendly for commercial-scale production of gas sensors. This approach also extends many technical merits such as simultaneous deposition and functionalization of MWCNTs at (RT = 30 °C) room temperature for specific target analyte detection. These unique characteristics make f-MWCNTs based devices very appealing for real-time commercial and domestic gas sensing applications.

Lactobacillus rhamnosus attenuates bone loss and maintains bone health by skewing Treg-Th17 cell balance in Ovx mice.

Osteoporosis is a systemic-skeletal disorder characterized by enhanced fragility of bones leading to increased rates of fractures and morbidity in large number of populations. Probiotics are known to be involved in management of various-inflammatory diseases including osteoporosis. But no study till date had delineated the immunomodulatory potential of Lactobacillus rhamnosus (LR) in bone-health. In the present study, we examined the effect of probiotic-LR on bone-health in ovariectomy (Ovx) induced postmenopausal mice model. In the present study, we for the first time report that LR inhibits osteoclastogenesis and modulates differentiation of Treg-Th17 cells under in vitro conditions. We further observed that LR attenuates bone loss under in vivo conditions in Ovx mice. Both the cortical and trabecular bone-content of Ovx+LR treated group was significantly higher than Ovx-group. Remarkably, the percentage of osteoclastogenic CD4+Rorγt+Th17 cells at distinct immunological sites such as BM, spleen, LN and PP were significantly reduced, whereas the percentage of anti-osteoclastogenic CD4+Foxp3+Tregs and CD8+Foxp3+Tregs were significantly enhanced in LR-treated group thereby resulting in inhibition of bone loss. The osteoprotective role of LR was further supported by serum cytokine data with a significant reduction in osteoclastogenic cytokines (IL-6, IL-17 and TNF-α) along with enhancement in anti-osteoclastogenic cytokines (IL-4, IL-10, IFN-γ) in LR treated-group. Altogether, the present study for the first time establishes the osteoprotective role of LR on bone health, thus highlighting the immunomodulatory potential of LR in the treatment and management of various bone related diseases including osteoporosis.

A rotation based regularization method for semi-supervised learning.

In manifold learning, the intrinsic geometry of the manifold is explored and preserved by identifying the optimal local neighborhood around each observation. It is well known that when a Riemannian manifold is unfolded correctly, the observations lying spatially near to the manifold, should remain near on the lower dimension as well. Due to the nonlinear properties of manifold around each observation, finding such optimal neighborhood on the manifold is a challenge. Thus, a sub-optimal neighborhood may lead to erroneous representation and incorrect inferences. In this paper, we propose a rotation-based affinity metric for accurate graph Laplacian approximation. It exploits the property of aligned tangent spaces of observations in an optimal neighborhood to approximate correct affinity between them. Extensive experiments on both synthetic and real world datasets have been performed. It is observed that proposed method outperforms existing nonlinear dimensionality reduction techniques in low-dimensional representation for synthetic datasets. The results on real world datasets like COVID-19 prove that our approach increases the accuracy of classification by enhancing Laplacian regularization.

Development of putrescine anchored nano-crystalsomes bearing doxorubicin and oleanolic acid: deciphering their role in inhibiting metastatic breast cancer.

Angiogenesis driven tumor initiation and progression calls for a targeted therapy. Moreover, combined chemotherapy supplements the therapy to act on the cause of concern. In this study, we aimed to develop a targeted crystalsomes approach to delineate tumor cells against normal cells. Self-assembled crystalline monodispersed nanosized polyethylene-polyethylene glycol (PE-PEG)-based hollow crystalsomes were modified with pluronylated putrescine (Put-PF) and loaded with doxorubicin (Dox), synergistically in combination with oleanolic acid (OA) to target the glypican-1 (gp-1) receptor on tumor cells. The developed crystalsomes (Put-D + O@NCs) showed increased intracellular accumulation of Dox and OA in a synergistic combination inside the MDA-MB-231 cell lines. The developed crystalsomes marked an enhanced depolarization of the mitochondrial membrane potential and cell cycle arrest leading to apoptosis. Furthermore, the proposed therapy has a greater anti-angiogenesis activity with vascular endothelial growth factor (VEGF) dependent modulation in the proliferation, invasion, migration and tube formation of human endothelial umbilical vein cells (HUVECs) in vitro and in vivo in a BALB/c mouse model. Interestingly, the perseverance of the tumor boundary, inhibiting the expression and activity of the matrix metalloproteinase (MMPs) (>5.2-fold) with suppressed degradation of the extracellular matrix paves the way for significant inhibition of metastases. However, an intravenously administered Put-D + O@NCs showed an improved pharmacokinetic profile and exquisite inhibition of the 4T1 induced tumor with a significantly lower toxicity. In a nutshell, these findings highlight the important role of Put in the gp-1 receptor for specific targeting and synergistic delivery of Dox and OA through crystalsomes as a potential approach for the treatment of metastatic breast cancer using combined chemotherapy.

Metal-free C-H Activation over Graphene Oxide toward Direct Syntheses of Structurally Different Amines and Amides in Water.

Unprecedented metal-free synthesis of a variety of amines and amides is reported via amination of C(sp3)-H and C(sp2)-H bonds. The strategy involves graphene-oxide/I2-catalyzed nitrene insertion using PhINTs as a nitrene (NT) source in water at room temperature. A wide range of structurally different substrates, viz., cyclohexane, cyclic ethers, arenes, alkyl aromatic systems, and aldehydes/ketones, having an α-phenyl ring have been employed successfully to afford the corresponding nitrene insertion product in good yield, albeit low in few cases. The envisaged method has superiority over others in terms of its operational simplicity, metal-free catalysis, use of water as a solvent, ambient reaction conditions, and reusability of the catalyst.

Exopolysaccharides directed embellishment of diatoms triggered on plastics and other marine litter.

In the present study, embellishment or beautification of diatoms on substrates like plastics, polydimethylsiloxane, graphite, glass plate, and titanium dioxide, triggered by exopolysaccharides was examined under laboratory conditions. Exopolysaccharides are secreted mainly by primary colonisers, bacteria, which is succeeded by secondary colonisers i.e. diatoms. Both diatom (Nitzschia sp.4) and bacteria (Bacillus subtilis) were exposed with substrates separately for 30 days. Diatoms adhere on substrates strongly, not only because of surface roughness of different substrates but also the nanoporous architecture of diatoms which enhanced their embellishment. This study attempted to identify the substrates that adhere to diatoms strongly and was mainly analyzed by scanning electron microscope and further the observations are well supported by math work software (MATLAB). The variation of diatom's binding on different substrates is due to the influence of marine litters on diatom population in ocean beds where they undergo slow degradation releasing macro, micro and nanoparticles besides radicals and ions causing cell death. Therefore a proof-of-concept model is developed to successfully deliver a message concerning benefit of using different diatom species.

Efficient prediction of drug-drug interaction using deep learning models.

A drug-drug interaction or drug synergy is extensively utilised for cancer treatment. However, prediction of drug-drug interaction is defined as an ill-posed problem, because manual testing is only implementable on small group of drugs. Predicting the drug-drug interaction score has been a popular research topic recently. Recently many machine learning models have proposed in the literature to predict the drug-drug interaction score efficiently. However, these models suffer from the over-fitting issue. Therefore, these models are not so-effective for predicting the drug-drug interaction score. In this work, an integrated convolutional mixture density recurrent neural network is proposed and implemented. The proposed model integrates convolutional neural networks, recurrent neural networks and mixture density networks. Extensive comparative analysis reveals that the proposed model significantly outperforms the competitive models.

Interpreting SVM for medical images using Quadtree.

In this paper, we propose a quadtree based approach to capture the spatial information of medical images for explaining nonlinear SVM prediction. In medical image classification, interpretability becomes important to understand why the adopted model works. Explaining an SVM prediction is difficult due to implicit mapping done in kernel classification is uninformative about the position of data points in the feature space and the nature of the separating hyperplane in the original space. The proposed method finds ROIs which contain the discriminative regions behind the prediction. Localization of the discriminative region in small boxes can help in interpreting the prediction by SVM. Quadtree decomposition is applied recursively before applying SVMs on sub images and model identified ROIs are highlighted. Pictorial results of experiments on various medical image datasets prove the effectiveness of this approach. We validate the correctness of our method by applying occlusion methods.

Cu(I)-Induced Activation of Furan for Inverse Electron Demand ADAR with Alkenes toward Regioselective Synthesis of Tetrahydropyridine.

The aza-Diels-Alder reaction of various alkenes and in situ formed 1-aza-1,3-dienes from the reaction of furan/pyrrole/thiophene and PhINTs for the regioselective synthesis of tetrahydropyridine (THP) derivatives was realized. The reaction was catalyzed by TpMe2Cu as a catalyst under very mild reaction conditions. Structurally different alkenes, along with an alkyne, have been utilized as dienophiles to afford a wide range of different THP derivatives up to 70% yield. The potential application of the envisaged method was also investigated by a gram-scale synthesis.