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This study focuses on the synthesis, characterization, and use of zinc oxide nanoparticles (ZnONPs) derived from W. bifurcata fruit peel extract. ZnONPs are frequently synthesized utilizing a green technique that is both cost-effective and ecologically friendly. ZnONPs were characterized utilizing analytical techniques. Ultra Violet visible (UV-Vis) spectra showed peaks at 364 nm, confirming the production of ZnONPs. Scanning Electron Microscope analysis indicated that the nanoparticles generated were spherical/agglomerated, with diameters ranging from 11 to 25 nm. FTIR spectroscopy was used to identify the particular functional groups responsible for the nanoparticles' reduction, stabilization, and capping. Phytochemical analysis of the extract revealed that flavonoids, saponins, steroids, triterpenoids, and resins were present. The antibacterial activity of W. bifurcata synthesised nanoparticles was evaluated against pathogenic bacteria. The ZnONPs antioxidant activity was assessed using DPPH assay. The in vitro cytotoxicity was assessed against prostate cancer PC3 cells. The wound healing potential was assessed by employing in vitro scratch assay and in vivo excision model in Wistar rats. Because of its environmentally benign production, low toxicity, and biocompatibility, ZnONPs exhibited potential antibacterial, antioxidant, anticancer, and wound healing activities, indicating that they could be used in cancer treatment and wound management. Further study is required to examine the fundamental mechanisms and evaluate the safety and effectiveness of the test sample in clinical situations. [ABSTRACT FROM AUTHOR]

Obesity is a critical health condition that severely affects an individual's quality of life andwell-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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Objectives: This research aimed to compare the incidence and severity of acute appendicitis (AA) before and during the COVID-19 pandemic. Materials and Methods: Two southern Saudi Arabian high-volume hospitals hosted this retrospective investigation. The COVID pandemic period (March-June 2020) and the pre-COVID period were identified (March-June 2019). The frequency, clinical manifestation, and severity of AA were compared for patients who underwent appendectomies during these periods. Results: In the study hospitals, total admissions decreased by 52.3% (95% confidence interval [CI]: 51.1%-53.3%) from 6231 patients during the pre-COVID period to 3261 patients during the COVID period. In the 2019 study period, 107 cases of AA were surgically treated, compared to 61 cases in 2020. The observed reduction rate was 57.1% (95% CI: 47.5%-66.8%). During the COVID period, the duration from the onset of symptoms to hospital admission and hospital admission to surgery was significantly shorter for patients. There were no significant differences between the COVID and pre-COVID periods in terms of the clinical picture or severity of AA. Conclusions: During the COVID era, there was a significant decrease in the number of AA patients hospitalized, which may indicate the efficacy of home treatment for mild cases. The successful measures taken by the Saudi Arabian government during the curfew, along with the optimal utilization of resources and logistics, resulted in the early presentation and management of AA, yielding results comparable to the pre-COVID era. [ABSTRACT FROM AUTHOR]

Introduction: Diabetic foot syndrome is a significant hurdle for the health-care profession, with substantial economic outcomes for diabetic patients, their families, and society, affecting the quality of care and quality of life. Aim: The aim of this study was to assess diabetic patients' knowledge, attitude, and practices regarding foot care. Methods: A cross-sectional study was carried out at various diabetic clinics in Najran, Saudi Arabia. All attending diabetic patients were subjects of the study. The investigators developed the survey instrument based on prior surveys and guidelines. The survey explored the sociodemographic characteristics of the patients and evaluated attitudes toward diabetic patients and the level of knowledge of foot self-care practices. Results: The mean awareness score was 9.68 (standard deviation [SD]: 2.65) out of 16 points, with low, satisfactory, and high awareness scores identified in 28.7%, 60.6%, and 10.7% of patients, respectively. With regard to attitude, the mean score was 3.18 (SD: 1.55) out of 6 points, where, negative, neutral, and positive attitudes were found in 34.9%, 41.1%, and 23.9% of patients, respectively. The most commonly known risk factors of diabetes mellitus (DM) were neuropathy, followed by retinopathy. Antiglycemic control, duration of diabetes, and educational level were associated with neuropathy, retinopathy, vasculopathy, and foot deformity. On the other hand, uneducated patients, on monotherapy with insulin, with no diet plan, those with uncontrolled glycosylated hemoglobin levels, and with more than 10 years of DM significantly had poor awareness and negative attitude toward foot self-care. Conclusion: There were moderate awareness and attitudes concerning foot self-care among diabetic patients. Diabetic patients and physicians have an essential role to play in preventing diabetic foot syndrome. The health-care institutions should nurture diabetic patients with better knowledge and a positive attitude toward foot self-care. [ABSTRACT FROM AUTHOR]

Introduction: Team-based learning (TBL) is gaining popularity because it allows students to achieve desired intellectual tasks through teamwork, discussion, and professionalism, in contrast to traditional (Trad) learning, which fails to keep the students' continued attention throughout the teaching session, resulting in low grades and retention rates. Aims: Comparing the outcomes between TBL and Trad learning sessions. Materials and Methods: A cross-sectional pilot study was carried out. Seventy-two undergraduate dental students were grouped as TBL and Trad. Each group consisted of 36 students and was made up of good, average, and below-average achievers based on their most recent exam results. The control group (Trad) received lecture-based teaching on a topic, while the case group (TBL) received discussion-based teaching on the same topic using the TBL approach. Allocated time slot for each method was equal. Following the completion of the teaching, a test was taken in the same session, consisting of 20 well-constructed multiple-choice questions (MCQs). The outcomes of the MCQ tests were subjected to item analysis. Chi-squared test was used to compare test parameters between the control and case groups using SPSS (v. 23). Results and Discussion: Overall, the TBL groups yielded significantly higher (58.33%) learning outcomes than the Trad groups (44.4%) (P = 0.01). In general, the TBL approach showed highly significant performance results (P = 0.000) compared to Trad approach. The Cronbach's alpha for TBL and trad learning was 0.967 and 0.949, respectively, indicating an excellent reliability of our findings. Conclusion: TBL approach has a potential to improve learning outcome by increasing knowledge and encouraging self-directed learning. [ABSTRACT FROM AUTHOR]

Introduction: The breast cancer diagnosis and treatment is based on a less invasive, more accurate and effective strategy but the key factor in respect of these new standards is to confirm cancer before going to the operating room. Closed biopsies by means of Tru-cut biopsy needle have replaced the more traditional incisional or excisional biopsies for the diagnosis of palpable breast lumps and have a definite role in the histological diagnosis of majority of palpable breast lumps. These biopsies can be performed under local anesthesia in the office or in outpatient setup. The technique is reliable, simple, and reproducible, and inexpensive; it can be adapted even for low-income developing countries. Objective: This study is designed mainly to compare the diagnostic accuracy of Tru-cut needle biopsy with that of excision biopsy. Materials and Methods: The original study is carried from July to December 2014 in Surgical Unit-1, Baqai Medical University Hospital Karachi, Pakistan. Among women with palpable breast lumps came to the outpatient, 140 patients selected and Tru-cut needle biopsy along with excision biopsy was performed and the result of both the modalities recorded and compared with each other. Results: The diagnosis given by Tru-cut needle biopsy is confirmed by means of excision biopsies. Therefore, it may be said that out of 49 malignant lumps 45 (91.83%) and out of 91 benign lumps 87 (95.60%) are correctly diagnosed with a total of 132 (94.28%) correct diagnosis. Conclusion: As the diagnostic accuracy of closed biopsies is considerable and very close to that of excision biopsies, the closed biopsies can be performed routinely on an outpatient basis on women with palpable breast lumps. [ABSTRACT FROM AUTHOR]