Antibacterial conductive self-healing hydrogel wound dressing with dual dynamic bonds promotes infected wound healing.

In clinical applications, there is a lack of wound dressings that combine efficient resistance to drug-resistant bacteria with good self-healing properties. In this study, a series of adhesive self-healing conductive antibacterial hydrogel dressings based on oxidized sodium alginate-grafted dopamine/carboxymethyl chitosan/Fe3+ (OSD/CMC/Fe hydrogel)/polydopamine-encapsulated poly(thiophene-3-acetic acid) (OSD/CMC/Fe/PA hydrogel) were prepared for the repair of infected wound. The Schiff base and Fe3+ coordination bonds of the hydrogel structure are dynamic bonds that can be repaired automatically after the hydrogel network is disrupted. Macroscopically, the hydrogel exhibits self-healing properties, allowing the hydrogel dressing to adapt to complex wound surfaces. The OSD/CMC/Fe/PA hydrogel showed good conductivity and photothermal antibacterial properties under near-infrared (NIR) light irradiation. In addition, the hydrogels exhibit tunable rheological properties, suitable mechanical properties, antioxidant properties, tissue adhesion properties and hemostatic properties. Furthermore, all hydrogel dressings improved wound healing in the infected full-thickness defect skin wound repair test in mice. The wound size repaired by OSD/CMC/Fe/PA3 hydrogel + NIR was much smaller (12%) than the control group treated with Tegaderm™ film after 14 days. In conclusion, the hydrogels have high antibacterial efficiency, suitable conductivity, great self-healing properties, good biocompatibility, hemostasis and antioxidant properties, making them promising candidates for wound healing dressings for the treatment of infected skin wounds.

Manilkara zapota L. extract topical ointment application to skin wounds in rats speeds up the healing process.

Poor circulation, unresolved inflammation, neuropathy, and infection make wound care difficult. Manilkara zapota (M. zapota) antibacterial and antioxidant properties may help speed up the healing process. The present investigation aimed to evaluate the wound healing activity of M. zapota bark ethanolic extract (MZE) by employing in-vitro migration scratch assay and in-vivo animal models. Wistar albino rats were used for the in-vivo wound healing models. No treatment was given to Group I; Group II received povidone-iodine (5% W/W); Group III received MZE (5% W/W); and Group IV received MZE (10% W/W). Linear incision models and excision wound models were used to induce injury. The ointments were applied immediately to the wounds after causing the injury. The percentage of wound contraction, the length of the epithelization period, and the wound's tensile strength were all calculated. The scratch assay assessed the test drug's potential for wound healing in-vitro. H2O2 and DPPH scavenging assays were used to measure antioxidant activity. A p < 0.05 was used to define statistical significance. On days 4, 8, 12, 16, and 20, the wound contraction potential of animals treated with MZE ointment was significantly higher (p < 0.001) than that of the control group. On day 20, the proportion of wound contraction in MZE-treated animals was 99.88%, compared to 83.86% in untreated animals. The test group had a significantly (p < 0.01) faster time to full epithelization than the control group. In the incision model, the control group had considerably lower mechanical strength (p < 0.001) than animals treated with MZE. In addition, MZE caused a significant increase (p < 0.001) in total protein and hydroxyproline levels. In the scratch experiment, test drug-treated cells showed a higher rate of cell migration than untreated cells. Furthermore, animals treated with MZE showed increased levels of epithelial tissue, collagen proliferation, and keratinization. To summarize, the current study found that M. zapota improved wound healing activity both in vitro and in vivo, as evidenced by the study results. M. zapota extract has significant wound-healing potential and could be a viable source of wound-healing nutraceuticals.

IoT Framework for a Decision-Making System of Obesity and Overweight Extrapolation among Children, Youths, and Adults.

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.

Physical Activity Monitoring and Classification Using Machine Learning Techniques.

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.

Topical Application of Premna integrifolia Linn on Skin Wound Injury in Rats Accelerates the Wound Healing Process: Evidence from In Vitro and In Vivo Experimental Models.

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.

Machine Learning and Internet of Things Enabled Monitoring of Post-Surgery Patients: A Pilot Study.

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.

Circulating long non-coding RNAs NKILA, NEAT1, MALAT1, and MIAT expression and their association in type 2 diabetes mellitus.

BACKGROUND: Type 2 diabetes mellitus (T2DM) is a multifactorial disorder that leads to alterations in gene regulation. Long non-coding RNAs (lncRNAs) have become a major research topic as they are involved in metabolic disorders. METHODS: This study included a total of 400 study subjects; 200 were subjects with T2DM and 200 were healthy subjects. Extracted RNA was used to synthesize cDNA by quantitative real time. Serum analysis was carried out to determine differences in biochemical parameters. Recorded data were used to evaluate associations with expression of lncRNAs NF-kappaB interacting lncRNA (NKILA), nuclear enriched abundant transcript 1 (NEAT1), metastasis-associated lung adenocarcinoma transcript 1 (MALAT1), and myocardial infarction-associated transcript (MIAT) in T2DM cases. RESULTS: Compared with healthy controls, patients with T2DM showed an overall increase in expression of lncRNAs NKILA, NEAT, MALAT1, and MIAT by 3.94-fold, 5.28-fold, 4.46-fold, and 6.35-fold, respectively. Among patients with T2DM, higher expression of lncRNA NKILA was associated with hypertension (p=0.001), smoking (p<0.0001), and alcoholism (p<0.0001). Altered NEAT1 expression was significantly associated with weight loss (p=0.04), fatigue (p=0.01), slow wound healing (p=0.002), blurred vision (p=0.008), loss of appetite (p=0.007), smoking (p<0.0001), and alcoholism (p<0.0001). Higher expression of lncRNA MALAT1 was significantly linked with weight loss (p=0.003), blurred vision (p=0.01), smoking (p<0.0001), and alcoholism (p<0.0001). Expression of lncRNA MIAT was associated with only blurred vision (p<0.0001), smoking (p<0.0001), and alcoholism (p<0.0001). Positive correlations of lncRNA NKILA with lncRNAs NEAT1 (r=0.42, p<0.0001), MALAT (r=0.36, p<0.0001) and MIAT (r=0.42, p<0.0001) were observed among patients with T2DM. Significant positive correlations of lncRNA NEAT with lncRNAs MALAT and MIAT were observed among patients with T2DM. A positive correlation between lncRNAs MALAT and MIAT was also observed among patients with T2DM. CONCLUSION: Increased circulating NKILA, NEAT1, MALAT, and MIAT expression in patients with T2DM, which is linked with poor patient outcomes and significantly linked with alcoholism and smoking, may influence the degree and severity of disease among patients with T2DM. These lncRNAs may contribute to the progression of T2DM disease or other related diabetes-related complications.

Awareness of Breast Cancer among Female Students and Faculty from Najran University, Najran, Saudi Arabia.

BACKGROUND: Breast cancer (BC) is the leading malignancy among women in Najran, Saudi Arabia. However, not much is known about the public's awareness of BC. This study explored the general knowledge, early warning signs, risk factors and sources of information about BC. METHODS: An online-based, anonymous, self-rating, cross-sectional and survey-based study was conducted from March-2019 to April-2019. Three-hundred female students and/or faculty from College of Medicine, Najran University (Najran, Saudi Arabia) participated in the study. RESULTS: A total of 232 students (77.3%) and 68 faculty (22.7%) responded to the survey. Our study showed that nulliparity (83.8%) and early menarche before 12 years of age (29.7%) were the most pertinent obstetric risk factors of BC. Conversely, lack of physical activity (66.3%) and family history of BC (18%) were the most substantial non-obstetric risk factors of BC. According to pre-defined criteria, while the surveyed research subjects demonstrated 'good' general knowledge about BC (75.3%), they unfavorably exhibited 'poor' knowledge about the warning signs of BC (94.3%). The predictors of 'good' overall knowledge (general knowledge plus signs knowledge about BC) included age, marital status, educational level and family history (all p<0.05, two-tailed Chi-square test). Apart from the campaigns' educational materials (43%), the top source of knowledge about BC was internet (33%), whereas the lowest ones were healthcare professionals (11.3%) and training workshops (7.3%). CONCLUSIONS: The surveyed research subjects harbored risk factors of BC and demonstrated 'poor' knowledge about the warning early signs of BC. We call for rigorous and well-crafted educational campaigns geared toward improving the awareness level of BC among women in Najran province.