Silver nanoparticles (AgNPs) have recently gained interest in the medical field because of their biological features. The present study aimed at screening Rhizophora apiculata secondary metabolites, quantifying their flavonoids and total phenolics content, green synthesis and characterization of R. apiculata silver nanoparticles. In addition, an assessment of in vitro cytotoxic, antioxidant, anti-inflammatory and wound healing activity of R. apiculata and its synthesized AgNPs was carried out. The powdered plant material (leaves) was subjected to Soxhlet extraction to obtain R. apiculata aqueous extract. The R. apiculata extract was used as a reducing agent in synthesizing AgNPs from silver nitrate. The synthesized AgNPs were characterized by UV-Vis, SEM-EDX, XRD, FTIR, particle size analyzer and zeta potential. Further aqueous leaf extract of R. apiculata and AgNPs was subjected for in vitro antioxidant, anti-inflammatory, wound healing and cytotoxic activity against A375 (Skin cancer), A549 (Lung cancer), and KB-3-1 (Oral cancer) cell lines. All experiments were repeated three times (n = 3), and the results were given as the mean ± SEM. The flavonoids and total phenolics content in R. apiculata extract were 44.18 ± 0.086 mg/g of quercetin and 53.24 ± 0.028 mg/g of gallic acid, respectively. SEM analysis revealed R. apiculata AgNPs with diameters ranging from 35 to 100 nm. XRD confirmed that the synthesized silver nanoparticles were crystalline in nature. The cytotoxicity cell viability assay revealed that the AgNPs were less toxic (IC50 105.5 µg/mL) compared to the R. apiculata extract (IC50 47.47 µg/mL) against the non-cancerous fibroblast L929 cell line. Antioxidant, anti-inflammatory, and cytotoxicity tests revealed that AgNPs had significantly more activity than the plant extract. The AgNPs inhibited protein denaturation by a mean percentage of 71.65%, which was equivalent to the standard anti-inflammatory medication diclofenac (94.24%). The AgNPs showed considerable cytotoxic effect, and the percentage of cell viability against skin cancer, lung cancer, and oral cancer cell lines was 31.84%, 56.09% and 22.59%, respectively. R. apiculata AgNPs demonstrated stronger cell migration and percentage of wound closure (82.79%) compared to the plant extract (75.23%). The overall results revealed that R. apiculata AgNPs exhibited potential antioxidant, anti-inflammatory, wound healing, and cytotoxic properties. In future, R. apiculata should be further explored to unmask its therapeutic potential and the mechanistic pathways of AgNPs should be studied in detail in in vivo animal models.

Artificial Intelligence (AI) and Internet of Things (IoT) offer immense potential to transform conventional healthcare systems. The IoT and AI enabled smart systems can play a key role in driving the future of smart healthcare. Remote monitoring of critical and non-critical patients is one such field which can leverage the benefits of IoT and machine learning techniques. While some work has been done in developing paradigms to establish effective and reliable communications, there is still great potential to utilize optimized IoT network and machine learning technique to improve the overall performance of the communication systems, thus enabling fool-proof systems. This study develops a novel IoT framework to offer ultra-reliable low latency communications to monitor post-surgery patients. The work considers both critical and non-critical patients and is balanced between these to offer optimal performance for the desired outcomes. In addition, machine learning based regression analysis of patients’ sensory data is performed to obtain highly accurate predictions of the patients’ sensory data (patients’ vitals), which enables highly accurate virtual observers to predict the data in case of communication failures. The performance analysis of the proposed IoT based vital signs monitoring system for the post-surgery patients offers reduced delay and packet loss in comparison to IEEE low latency deterministic networks. The gradient boosting regression analysis also gives a highly accurate prediction for slow as well as rapidly varying sensors for vital sign monitoring.

Physical activity plays an important role in controlling obesity and maintaining healthy living. It becomes increasingly important during a pandemic due to restrictions on outdoor activities. Tracking physical activities using miniature wearable sensors and state-of-the-art machine learning techniques can encourage healthy living and control obesity. This work focuses on introducing novel techniques to identify and log physical activities using machine learning techniques and wearable sensors. Physical activities performed in daily life are often unstructured and unplanned, and one activity or set of activities (sitting, standing) might be more frequent than others (walking, stairs up, stairs down). None of the existing activities classification systems have explored the impact of such class imbalance on the performance of machine learning classifiers. Therefore, the main aim of the study is to investigate the impact of class imbalance on the performance of machine learning classifiers and also to observe which classifier or set of classifiers is more sensitive to class imbalance than others. The study utilizes motion sensors’ data of 30 participants, recorded while performing a variety of daily life activities. Different training splits are used to introduce class imbalance which reveals the performance of the selected state-of-the-art algorithms with various degrees of imbalance. The findings suggest that the class imbalance plays a significant role in the performance of the system, and the underrepresentation of physical activity during the training stage significantly impacts the performance of machine learning classifiers.

Approximately 30% of the global population is suffering from obesity and being overweight, which is approximately 2.1 billion people worldwide. The ratio is expected to surpass 40% by 2030 if the current balance continues to grow. The global pandemic due to COVID-19 will also impact the predicted obesity rates. It will cause a significant increase in morbidity and mortality worldwide. Multiple chronic diseases are associated with obesity and several threat elements are associated with obesity. Various challenges are involved in the understanding of risk factors and the ratio of obesity. Therefore, diagnosing obesity in its initial stages might significantly increase the patient’s chances of effective treatment. The Internet of Things (IoT) has attained an evolving stage in the development of the contemporary environment of healthcare thanks to advancements in information and communication technologies. Therefore, in this paper, we thoroughly investigated machine learning techniques for making an IoT-enabled system. In the first phase, the proposed system analyzed the performances of random forest (RF), K-nearest neighbor (KNN), support vector machine (SVM), decision tree (DT), logistic regression (LR), and naïve Bayes (NB) algorithms on the obesity dataset. The second phase, on the other hand, introduced an IoT-based framework that adopts a multi-user request system by uploading the data to the cloud for the early diagnosis of obesity. The IoT framework makes the system available to anyone (and everywhere) for precise obesity categorization. This research will help the reader understand the relationships among risk factors with weight changes and their visualizations. Furthermore, it also focuses on how existing datasets can help one study the obesity nature and which classification and regression models perform well in correspondence to others.

Obesity poses several challenges to healthcare and the well-being of individuals. It can be linked to several life-threatening diseases. Surgery is a viable option in some instances to reduce obesity-related risks and enable weight loss. State-of-the-art technologies have the potential for long-term benefits in post-surgery living. In this work, an Internet of Things (IoT) framework is proposed to effectively communicate the daily living data and exercise routine of surgery patients and patients with excessive weight. The proposed IoT framework aims to enable seamless communications from wearable sensors and body networks to the cloud to create an accurate profile of the patients. It also attempts to automate the data analysis and represent the facts about a patient. The IoT framework proposes a co-channel interference avoidance mechanism and the ability to communicate higher activity data with minimal impact on the bandwidth requirements of the system. The proposed IoT framework also benefits from machine learning based activity classification systems, with relatively high accuracy, which allow the communicated data to be translated into meaningful information.

Obesity is a critical health condition that severely affects an individual's quality of life and well-being. The occurrence of obesity is strongly associated with extreme health conditions, such as cardiac diseases, diabetes, hypertension, and some types of cancer. Therefore, it is vital to avoid obesity and or reverse its occurrence. Incorporating healthy food habits and an active lifestyle can help to prevent obesity. In this regard, artificial intelligence (AI) can play an important role in estimating health conditions and detecting obesity and its types. This study aims to see obesity levels in adults by implementing AIenabled machine learning on a real-life dataset. This dataset is in the form of electronic health records (EHR) containing data on several aspects of daily living, such as dietary habits, physical conditions, and lifestyle variables for various participants with different health conditions (underweight, normal, overweight, and obesity type I, II and III), expressed in terms of a variety of features or parameters, such as physical condition, food intake, lifestyle and mode of transportation. Three classifiers, i.e., eXtreme gradient boosting classifier (XGB), support vector machine (SVM), and artificial neural network (ANN), are implemented to detect the status of several conditions, including obesity types. The findings indicate that the proposed XGB-based system outperforms the existing obesity level estimation methods, achieving overall performance rates of 98.5% and 99.6% in the scenarios explored.

Background. When the skin and tissues within the body are injured, the healing process begins. Medicinal herbs have been used to cure wounds since time immemorial. The antimicrobial and antioxidant activity possessed by P. integrifolia may accelerate wound healing. Objectives. To assess the wound healing activity of Premna integrifolia extract (PIE) by employing in-vivo experimental animal models and an in-vitro migration scratch assay. Furthermore, to assess its cytotoxicity using the MTT assay. Methods. Wistar albino rats were used for the in vivo wound healing models. The animals were divided into four groups at random: Group I was untreated. Group II was vehicle control (ointment base). Group III was PIE ointment (5% W/W). Group IV was standard (povidone-iodine ointment) (5% W/W). The ointments were applied directly to the wounds as described above until they healed completely. The wound contraction percentage and tensile strength were calculated. The MTT test was used to determine the viability of the test extract against the fibroblast cells. The scratch assay was used in vitro to determine the wound healing potential of the test drug. P ≤ 0.05 values were considered statistically significant. Results. Premna integrifolia extract did not possess any noticeable cytotoxicity to the cell line and showed an IC50 of 185.98 μg/ml. The wound contraction potential of PIE ointment-treated animals was considerably greater ( P ≤ 0.001 ) on days 4, 8, 12, 16, and 20 when compared to the control group. The percentage of wound contraction on day 20 was 99.92% in PIE-treated animals compared to 83.23% in untreated animals. Compared to the untreated group, the duration of full epithelization was significantly ( P ≤ 0.01 ) shorter in the test group. When compared to the incision control group, the animals treated with PIE ointment had significantly higher ( P ≤ 0.001 ) tensile strength. In addition, animals given the test drug had a significant ( P ≤ 0.001 ) increase in total protein and hydroxyproline. In the in vitro scratch assay, test drug-treated cells demonstrated greater cell migration. Histology images confirmed that the test drug-treated group had epithelial tissue proliferation and keratinization. Conclusion. The current study found that Premna integrifolia improved wound healing activity both in vitro and in vivo. These findings indicate that Premna integrifolia extract has wound-healing potential and could be a viable source of nutraceuticals with wound-healing properties.

More than 5 million people require medical attention due to burn-related injuries annually. Significant research has been carried out in recent decades to develop approaches to improve the healing of burn wounds. The focus has also been on the development of natural product-based therapeutic remedies for the treatment of burn wounds. This has been done primarily due to multimodal mechanisms exhibited by some promising bioactive molecules of natural origin. Hesperetin is one such molecule that possesses strong anti-inflammatory and antioxidant properties. It is mainly obtained from citrus species. The goal of the current study was to assess how well chitosan gel that contains hesperetin may cure burn wounds. The advantage of using chitosan gel is that it could form a depot at the site and provide a protective therapeutic covering over burn wounds. In the present study, hesperetin-containing chitosan gel was prepared and evaluated for percentage hesperetin content, extrudability, spreadability, and rheological behavior. The preclinical wound healing activity was evaluated using an experimental burn wound model in Wistar rats. The results of the animal experiment showed early and better healing of burn wounds in animals treated with hesperetin-containing chitosan gel. There was 92.79% healing after 14 days of application of hesperetin-containing chitosan gel compared to 69.49% healing observed in the control group. Further, the histopathological evaluation suggested no inflammatory cell infiltration, normal epidermal growth, and normal collagen bundle arrangement in these animals. Overall the results provide proof of concept to establish the wound healing potential of hesperetin-containing chitosan gel against burn wounds.