Simulation of transvascular transport of nanoparticles in tumor microenvironments for drug delivery applications.

Nanomedicine is a promising approach for tumor therapy but penetration is challenged by complex tumor microenvironments. The purpose of this study is to design nanoparticles and analyze their transport in two abnormal microenvironments through a 2-D simulation. Employing a Computational Fluid Dynamics (CFD) approach, tumor vascular-interstitial models were initially simulated, and the impact of nanoparticles on the velocity profile and pressure gradient within the tumor microenvironment was observed. Through meticulous mesh analysis, it was determined that optimal outcomes were achieved using a quadrilateral meshing method for pancreatic tumor and a quad/tri meshing method for hepatic tumor. Results showed an increase in vessel diameter correlated with elevated blood flow velocity, reaching a maximum of 1.40 × 10^-3 m/s with an expanding cell gap. The simulation results for pressure distribution show that as vessel diameter increases, the velocity of nanoparticles in blood increases and decreases the pressure of blood. Intriguingly, distinct fluid flow patterns in pancreatic and hepatic tumors, emphasize how microenvironmental differences, specifically cell pore size, profoundly impact therapeutic agent transport, with implications for drug delivery strategies in cancer therapy. These simulation-based insights enable researchers to anticipate nanofluid behavior in realistic settings. Future work, incorporating immune cells, will enhance the understanding of nanoparticle efficiency in cancer therapy.

Machine learning (ML) techniques as effective methods for evaluating hair and skin assessments: A systematic review.

Machine Learning (ML) techniques provide the ability to effectively evaluate and analyze human skin and hair assessments. The aim of this study is to systematically review the effectiveness of applying Machine Learning (ML) methods and Artificial Intelligence (AI) techniques in order to evaluate hair and skin assessments. PubMed, Web of Science, IEEE Xplore, and Science Direct were searched in order to retrieve research publications between 1 January 2010 and 31 March 2020 using appropriate keywords such as "hair and skin analysis." Following accurate screening, 20 peer-reviewed publications were selected for inclusion in this systematic review. The analysis demonstrated that prevalent Machine Learning (ML) methods comprised of Support Vector Machine (SVM), k-nearest Neighbor, and Artificial Neural Networks (ANN). ANN's were observed to yield the highest accuracy of 95% followed by SVM generating 90%. These techniques were most commonly applied for drafting framework assessments such as that of Melanoma. Values of parameters such as Sensitivity, Specificity, and Area under the Curve (AUC) were extracted from the studies and with the help of comparisons, relevant inferences were also made. ANN's were observed to yield the highest sensitivity of 82.30% as well as a 96.90% specificity. Hence, with this systematic review, a summarization of the studies was drafted that encapsulated how Machine Learning (ML) techniques have been employed for the analysis and evaluation of hair and skin assessments.

Intramuscular EMG feature extraction and evaluation at different arm positions and hand postures based on a statistical criterion method.

Prostheses control using electromyography signals have shown promising aspects in various fields including rehabilitation sciences and assistive technology controlled devices. Pattern recognition and machine learning methods have been observed to play a significant role in evaluating features and classifying different limb motions for enhanced prosthetic executions. This paper proposes feature extraction and evaluation method using intramuscular electromyography (iEMG) signals at different arm positions and hand postures based on the RES Index value statistical criterion method. Sixteen-time domain features were selected for the study at two main circumstances; fixed arm position (FAP) and fixed hand posture (FHP). Eight healthy male participants (30.62 ± 3.87 years) were asked to execute five motion classes including hand grip, hand open, rest, hand extension, and hand flexion at four different arm positions that comprise of 0°, 45°, 90°, and 135°. The classification process is accomplished via the application of the k-nearest neighbor (KNN) classifier. Then RES index was calculated to investigate the optimal features based on the proposed statistical criterion method. From the RES Index, we concluded that Variance (VAR) is the best feature while WAMP, Zero Crossing (ZC), and Slope Sign Change (SSC) are the worst ones in FAP conditions. On the contrary, we concluded that Average Amplitude Change (AAC) is the best feature while WAMP and Simple Square Integral (SSI) resulted in least RES Index values for FHP conditions. The proposed study has possible iEMG based applications such as assistive control devices, robotics. Also, working with the frequency domain features encapsulates the future scope of the study.

Smart face shield for the monitoring of COVID-19 physiological parameters: Personal protective equipment (PPE) for health-care workers (HCW's) and COVID-19 patients.

The COVID-19 pandemic has triggered instabilities in various aspects of daily life. This includes economic, social, financial, and health crisis. In addition, the COVID-19 pandemic with the evolution of different virus strains such as delta and omicron has led to frequent global lockdowns. These lockdowns have caused disruption of trade activities that in turn have led to the shortage of medical supplies, especially personal protective equipment's (PPE's). Health-care workers (HCW's) have been at the forefront of the fight against this pandemic and are responsible for saving millions of lives worldwide. However, the PPE's available to HCW's in the form of face shields and face masks only provide face and eye protection without encapsulating the ability to continuously monitor vital COVID-19 parameters including body temperature, heart rate, and SpO2. Hence, in this study, we propose the design and utilization of a PPE in the form of smart face shield. The device has been integrated with the MAX30102 sensor for measuring the heart rate and oxygen saturation (SpO2) and the DS18B20 body temperature measuring sensor. The readings of these sensors are analyzed by a NodeMCU ESP8266 and measurements are displayed on a laptop screen. Also, the Wi-Fi module of NodeMCU ESP8266 enables compatibility with the ThingSpeak mobile application and permits HCW's and patients recovering from COVID-19 to keep a track of their physiological parameters. Overall, this PPE has been observed to provide reliable readings and the results indicate that the designed prototype can be used for monitoring COVID-19 essential parameters.

Review on electromyography based intention for upper limb control using pattern recognition for human-machine interaction.

Upper limb myoelectric prosthetic control is an essential topic in the field of rehabilitation. The technique controls prostheses using surface electromyogram (sEMG) and intramuscular EMG (iEMG) signals. EMG signals are extensively used in controlling prosthetic upper and lower limbs, virtual reality entertainment, and human-machine interface (HMI). EMG signals are vital parameters for machine learning and deep learning algorithms and help to give an insight into the human brain's function and mechanisms. Pattern recognition techniques pertaining to support vector machine (SVM), k-nearest neighbor (KNN) and Bayesian classifiers have been utilized to classify EMG signals. This paper presents a review on current EMG signal techniques, including electrode array utilization, signal acquisition, signal preprocessing and post-processing, feature selection and extraction, data dimensionality reduction, classification, and ultimate application to the community. The paper also discusses using alternatives to EMG signals, such as force sensors, to measure muscle activity with reliable results. Future implications for EMG classification include employing deep learning techniques such as artificial neural networks (ANN) and recurrent neural networks (RNN) for achieving robust results.

Deep Learning Application for Effective Classification of Different Types of Psoriasis.

Psoriasis is a chronic inflammatory skin disorder mediated by the immune response that affects a large number of people. According to latest worldwide statistics, 125 million individuals are suffering from psoriasis. Deep learning techniques have demonstrated success in the prediction of skin diseases and can also lead to the classification of different types of psoriasis. Hence, we propose a deep learning-based application for effective classification of five types of psoriasis namely, plaque, guttate, inverse, pustular, and erythrodermic as well as the prediction of normal skin. We used 172 images of normal skin from the BFL NTU dataset and 301 images of psoriasis from the Dermnet dataset. The input sample images underwent image preprocessing including data augmentation, enhancement, and segmentation which was followed by color, texture, and shape feature extraction. Two deep learning algorithms of convolutional neural network (CNN) and long short-term memory (LSTM) were applied with the classification models being trained with 80% of the images. The reported accuracies of CNN and LSTM are 84.2% and 72.3%, respectively. A paired sample T-test exhibited significant differences between the accuracies generated by the two deep learning algorithms with a p < 0.001. The accuracies reported from this study demonstrate potential of this deep learning application to be applied to other areas of dermatology for better prediction.

Global COVID-19 Vaccine Acceptance: A Systematic Review of Associated Social and Behavioral Factors.

COVID-19 vaccines have met varying levels of acceptance and hesitancy in different parts of the world, which has implications for eliminating the COVID-19 pandemic. The aim of this systematic review is to examine how and why the rates of COVID-19 vaccine acceptance and hesitancy differ across countries and continents. PubMed, Web of Science, IEEE Xplore and Science Direct were searched between 1 January 2020 and 31 July 2021 using keywords such as "COVID-19 vaccine acceptance". 81 peer-reviewed publications were found to be eligible for review. The analysis shows that there are global variations in vaccine acceptance among different populations. The vaccine-acceptance rates were the highest amongst adults in Ecuador (97%), Malaysia (94.3%) and Indonesia (93.3%) and the lowest amongst adults in Lebanon (21.0%). The general healthcare workers (HCWs) in China (86.20%) and nurses in Italy (91.50%) had the highest acceptance rates, whereas HCWs in the Democratic Republic of Congo had the lowest acceptance (27.70%). A nonparametric one-way ANOVA showed that the differences in vaccine-acceptance rates were statistically significant (H (49) = 75.302, p = 0.009*) between the analyzed countries. However, the reasons behind vaccine hesitancy and acceptance were similar across the board. Low vaccine acceptance was associated with low levels of education and awareness, and inefficient government efforts and initiatives. Furthermore, poor influenza-vaccination history, as well as conspiracy theories relating to infertility and misinformation about the COVID-19 vaccine on social media also resulted in vaccine hesitancy. Strategies to address these concerns may increase global COVID-19 vaccine acceptance and accelerate our efforts to eliminate this pandemic.

A prelude to wearable technology for the measurement and restoration of core body temperature and heart rate in athletes suffering from hypothermia.

An individual who is in good physical health tends to exhibit an internal core temperature of 37°C and a heart rate of 60-100 beats per minute. Increase in the temperature of the surrounding environment can serve as the basis for the onset of the condition of Hypothermia. Hypothermia acts as one of the most significant barriers being faced by winter athletes and starts initially with an increase in the heart and breathing rate. However, if the condition persists it can lead to reduction in the heart and breathing rate and ultimately results in cardiac failure. Although, jackets are commercially available, they tend to operate manually and furthermore, do not serve the primary purpose of counteracting the condition of hypothermia, particularly experienced by athletes taking part in winter sports. The objective of this study is to design a heating jacket that enables effective counteraction of the condition of Hypothermia. It enables precise measurement of the of core body temperature with the aid of a pyroelectric sensor. Along with this, a pulse rate sensor for detecting the accurate heart rate has been incorporated on the index finger. Five heating pads would get activated to attain optimal temperature, in case the core body temperature of <37°C is detected. If the condition of hypothermia advances to the moderate stage, two additional heating pads will get activated and provide extra warmth to attain normal heart rate along with core body temperature. Overall, this wearable technology serves as a definitive solution to counteract the condition of hypothermia only when the internal parameters exhibit that you actually have it. The results of the study exhibited that this prototype can be utilized for detecting and treating the condition of Hypothermia.

Inter-classifier comparison for upper extremity EMG signal at different hand postures and arm positions using pattern recognition.

The utilization of surface EMG and intramuscular EMG signals has been observed to create significant improvement in pattern recognition approaches and myoelectric control. However, there is less data of different arm positions and hand postures available. Hand postures and arm positions tend to affect the combination of surface and intramuscular EMG signal acquisition in terms of classifier accuracy. Hence, this study aimed to find a robust classifier for two scenarios: (1) at fixed arm position (FAP) where classifiers classify different hand postures and (2) at fixed hand posture (FHP) where classifiers classify different arm positions. A total of 20 healthy male participants (30.62 ± 3.87 years old) were recruited for this study. They were asked to perform five motion classes including hand grasp, hand open, rest, hand extension, and hand flexion at four different arm positions at 0°, 45°, 90°, and 135°. SVM, KNN, and LDA classifier were deployed. Statistical analysis in the form of pairwise comparisons was carried out using SPSS. It is concluded that there is no significant difference among the three classifiers. SVM gave highest accuracy of 75.35% and 58.32% at FAP and FHP respectively for each motion classification. KNN yielded the highest accuracies of 69.11% and 79.04% when data was pooled and was classified at different arm positions and at different hand postures respectively. The results exhibited that there is no significant effect of changing arm position and hand posture on the classifier accuracy.

Classification Framework for Healthy Hairs and Alopecia Areata: A Machine Learning (ML) Approach.

Alopecia areata is defined as an autoimmune disorder that results in hair loss. The latest worldwide statistics have exhibited that alopecia areata has a prevalence of 1 in 1000 and has an incidence of 2%. Machine learning techniques have demonstrated potential in different areas of dermatology and may play a significant role in classifying alopecia areata for better prediction and diagnosis. We propose a framework pertaining to the classification of healthy hairs and alopecia areata. We used 200 images of healthy hairs from the Figaro1k dataset and 68 hair images of alopecia areata from the Dermnet dataset to undergo image preprocessing including enhancement and segmentation. This was followed by feature extraction including texture, shape, and color. Two classification techniques, i.e., support vector machine (SVM) and k-nearest neighbor (KNN), are then applied to train a machine learning model with 70% of the images. The remaining image set was used for the testing phase. With a 10-fold cross-validation, the reported accuracies of SVM and KNN are 91.4% and 88.9%, respectively. Paired sample T-test showed significant differences between the two accuracies with a p < 0.001. SVM generated higher accuracy (91.4%) as compared to KNN (88.9%). The findings of our study demonstrate potential for better prediction in the field of dermatology.