The emergence and global spread of COVID-19 has disrupted the traditional mechanisms of education throughout the world. Institutions of learning were caught unprepared and this jeopardised the face-to-face method of curriculum delivery and assessment. Teaching institutions have shifted to an asynchronous mode whilst attempting to preserve the principles of integrity, equity, inclusiveness, fairness, ethics, and safety. A framework of assessment that enables educators to utilise appropriate methods in measuring a student’s progress is crucial for the success of teaching and learning, especially in health education that demands high standards and comprises consistent scientific content. Within such a framework, this paper aims to present a narrative review of the currently utilised methods of assessment in health education and recommend selected modalities that could be administered in an asynchronous mode during the COVID-19 pandemic. Assessment methods such as open-ended short answer questions, problem-based questions, oral exams, and recorded objective structured clinical exams (OSCE) would be appropriate for use in an asynchronous environment to assess the knowledge and competence of health professional students during COVID-19. Fairness and integrity can be ensured by using technological tools such as video and audio recording surveillance.

The objective of this study is to conduct in-vitro quality control testing of diclofenac sodium tablets involves weight variation test, drug assay, friability test, and the disintegration and dissolution test. Two brands of diclofenac sodium tablets were used in the study, named Brand A and Brand B. Quality control (QC) test results for diclofenac sodium tablets show that both Brand A and Brand B conform to the United States Pharmacopeia (USP) standards. In terms of weight variation, Brand A and B have an above the mean weight limit variation of 2.79% and 2.05%, respectively. The lower mean weight limit variations are 1.21% and 1.27%, respectively, which are within the 10% standard limits of USP. Friability tests show that Brands A and B have an average friability of 0.062% and 0.01% mass loss, which are within the 1% mass loss limits of USP. In terms of drug assay, both Brands A and B fall under the USP parameter of 85%-115%, respectively. The disintegration test shows that Brand A and Brand B fall within a 15-minute time interval segment with disintegration time calculated as 6.69 min and 7.02 min for Brands A and B, respectively. Brand B of Diclofenac Sodium has a drug dissolution percentage of 90.7% within a 45-min sampling time interval. Brands A and B pass the pharmacopeia limits set under the USP standards. The friability test shows that the loss of mass for both Brands A and B was within the 1% standard limit. Similarly, with regard to weight variation, both brands conform under the normal limit of 10% above or lower the mean weight. In terms of drug assay, both brands' drug availability was within the specified 85%-115% standard range. They passed the disintegration and dissolution test within a time limit of less than 15 minutes and 45 minutes, respectively.

Background Atrial fibrillation (AFIB) is a common atrial arrhythmia that affects millions of people worldwide. However, most of the time, AFIB is paroxysmal and can pass unnoticed in medical exams; therefore, regular screening is required. This paper proposes machine learning (ML) methods to detect AFIB from short-term electrocardiogram (ECG) and photoplethysmography (PPG) signals. Aim Several experiments were conducted across five different databases, with three of them containing ECG signals and the other two consisting of only PPG signals. Experiments were conducted to investigate the hypothesis that an ML model trained to predict AFIB from ECG segments could be used to predict AFIB from PPG segments. Materials and methods A random forest (RF) ML algorithm achieved the best accuracy and achieved a 90% accuracy rate on the University of Mississippi Medical Center (UMMC) dataset (216 samples) and a 97% accuracy rate on the Medical Information Mart for Intensive Care (MIMIC)-III datasets (2,134 samples). Results A total of 269,842 signal segments were analyzed across all datasets (212,266 were of normal sinus rhythm (NSR) and 57,576 corresponded to AFIB segments). Conclusions The ability to detect AFIB with significant accuracy using ML algorithms from PPG signals, which can be acquired via non-invasive contact or contactless, is a promising step forward toward the goal of achieving large-scale screening for AFIB.

Regularly monitoring common physiological signs, including heart rate, blood pressure, and oxygen saturation, can effectively prevent or detect several potential conditions. In particular, cardiovascular diseases (CVDs) are a worldwide concern. According to the World Health Organization, 31% of all deaths worldwide are from CVDs. Recently, the coronavirus disease 2019 pandemic has increased the interest in remote monitoring. At present, contact devices are required to extract most of an individual’s physiological information, which can be inconvenient for users and may cause discomfort. However, remote photoplethysmography (rPPG) technology offers a solution for this issue, enabling contactless monitoring of the blood volume pulse signal using a regular camera. Ultimately, it can provide the same physiological information as a contact device. In this paper, we propose an evaluation of Vastmindz’s rPPG technology against medical devices in a clinical environment with a variety of subjects in a wide range of age, height, weight, and baseline vital signs. This study confirmed the findings that the contactless technology for the estimation of vitals proposed by Vastmindz was able to estimate heart rate, respiratory rate, and oxygen saturation with a mean error of ±3 units as well as ±10 mmHg for systolic and diastolic blood pressure. Reported results have shown that Vastmindz’s rPPG technology was able to meet the initial hypothesis and is acceptable for users who want to understand their general health and wellness.