Comparing educational and dissemination videos in a STEM YouTube channel: A six-year data analysis.

Didactic videos have proven to be particularly beneficial for Science, Technology, Engineering and Mathematics (STEM) education challenges. This is due to their contribution to the reduction of the intrinsic cognitive load, as well as fostering connections among subjects to promote generative processing. This fact, together with the wide presence of STEM dissemination videos on YouTube, creates an opportunity both for teachers to focus on dissemination channels to acquire quality materials, and for content creators to develop specific videos aiming to serve as pedagogical aid. Aligned with such reasoning, this paper intends to evaluate the use and performance of a STEM YouTube channel that already publishes didactic videos categorized by intentionality into dissemination or educational. Using owners' data, the channel has been analyzed, considering a six-year extension period, from 2017 to 2022, and distinguishing between the two categories of contents. This dual intention of the channel allows to evaluate the educational use of dissemination channels, compared to their use for curiosity satisfaction. Through Mann-Whitney U and correlation analyses, the main channels' metrics have been analyzed in terms of awareness (i.e., impressions, views, or subscribers), use (view duration) and interaction (comments, shares, likes or dislikes). Significant differences have been found in the performance of educational and dissemination contents, such as in views (p < 0.001), average view duration (p = 0.044) and likes (p < 0.001). Additionally, video length optimization has been found as a determining parameter influencing video performance, being educational videos more sensitive to this metric than dissemination videos. This study has shown that a STEM YouTube channel of these characteristics can benefit from publishing videos aiming to a pedagogical purpose, increasing its use compared with other STEM dissemination channels only aiming to an entertainment purpose. Additionally, this study supports previous findings that video length optimization is a determinant characteristic for audience retention rate.

Synthetic Patient Data Generation and Evaluation in Disease Prediction Using Small and Imbalanced Datasets.

The increasing prevalence of chronic non-communicable diseases makes it a priority to develop tools for enhancing their management. On this matter, Artificial Intelligence algorithms have proven to be successful in early diagnosis, prediction and analysis in the medical field. Nonetheless, two main issues arise when dealing with medical data: lack of high-fidelity datasets and maintenance of patient's privacy. To face these problems, different techniques of synthetic data generation have emerged as a possible solution. In this work, a framework based on synthetic data generation algorithms was developed. Eight medical datasets containing tabular data were used to test this framework. Three different statistical metrics were used to analyze the preservation of synthetic data integrity and six different synthetic data generation sizes were tested. Besides, the generated synthetic datasets were used to train four different supervised Machine Learning classifiers alone, and also combined with the real data. F1-score was used to evaluate classification performance. The main goal of this work is to assess the feasibility of the use of synthetic data generation in medical data in two ways: preservation of data integrity and maintenance of classification performance.

Curve-Based Classification Approach for Hyperspectral Dermatologic Data Processing.

This paper shows new contributions in the detection of skin cancer, where we present the use of a customized hyperspectral system that captures images in the spectral range from 450 to 950 nm. By choosing a 7 × 7 sub-image of each channel in the hyperspectral image (HSI) and then taking the mean and standard deviation of these sub-images, we were able to make fits of the resulting curves. These fitted curves had certain characteristics, which then served as a basis of classification. The most distinct fit was for the melanoma pigmented skin lesions (PSLs), which is also the most aggressive malignant cancer. Furthermore, we were able to classify the other PSLs in malignant and benign classes. This gives us a rather complete classification method for PSLs with a novel perspective of the classification procedure by exploiting the variability of each channel in the HSI.

Non-Invasive Skin Cancer Diagnosis Using Hyperspectral Imaging for In-Situ Clinical Support.

Skin cancer is one of the most common forms of cancer worldwide and its early detection its key to achieve an effective treatment of the lesion. Commonly, skin cancer diagnosis is based on dermatologist expertise and pathological assessment of biopsies. Although there are diagnosis aid systems based on morphological processing algorithms using conventional imaging, currently, these systems have reached their limit and are not able to outperform dermatologists. In this sense, hyperspectral (HS) imaging (HSI) arises as a new non-invasive technology able to facilitate the detection and classification of pigmented skin lesions (PSLs), employing the spectral properties of the captured sample within and beyond the human eye capabilities. This paper presents a research carried out to develop a dermatological acquisition system based on HSI, employing 125 spectral bands captured between 450 and 950 nm. A database composed of 76 HS PSL images from 61 patients was obtained and labeled and classified into benign and malignant classes. A processing framework is proposed for the automatic identification and classification of the PSL based on a combination of unsupervised and supervised algorithms. Sensitivity and specificity results of 87.5% and 100%, respectively, were obtained in the discrimination of malignant and benign PSLs. This preliminary study demonstrates, as a proof-of-concept, the potential of HSI technology to assist dermatologists in the discrimination of benign and malignant PSLs during clinical routine practice using a real-time and non-invasive hand-held device.

Underwater Electromagnetic Sensor Networks-Part I: Link Characterization.

Underwater Wireless Sensor Networks (UWSNs) using electromagnetic (EM) technology in marine shallow waters are examined, not just for environmental monitoring but for further interesting applications. Particularly, the use of EM waves is reconsidered in shallow waters due to the benefits offered in this context, where acoustic and optical technologies have serious disadvantages. Sea water scenario is a harsh environment for radiocommunications, and there is no standard model for the underwater EM channel. The high conductivity of sea water, the effect of seabed and the surface make the behaviour of the channel hard to predict. This justifies the need of link characterization as the first step to approach the development of EM underwater sensor networks. To obtain a reliable link model, measurements and simulations are required. The measuring setup for this purpose is explained and described, as well as the procedures used. Several antennas have been designed and tested in low frequency bands. Agreement between attenuation measurements and simulations at different distances was analysed and made possible the validation of simulation setups and the design of different communications layers of the system. This leads to the second step of this work, where data and routing protocols for the sensor network are examined.

Underwater Electromagnetic Sensor Networks, Part II: Localization and Network Simulations.

In the first part of the paper, we modeled and characterized the underwater radio channel in shallowwaters. In the second part,we analyze the application requirements for an underwaterwireless sensor network (U-WSN) operating in the same environment and perform detailed simulations. We consider two localization applications, namely self-localization and navigation aid, and propose algorithms that work well under the specific constraints associated with U-WSN, namely low connectivity, low data rates and high packet loss probability. We propose an algorithm where the sensor nodes collaboratively estimate their unknown positions in the network using a low number of anchor nodes and distance measurements from the underwater channel. Once the network has been self-located, we consider a node estimating its position for underwater navigation communicating with neighboring nodes. We also propose a communication system and simulate the whole electromagnetic U-WSN in the Castalia simulator to evaluate the network performance, including propagation impairments (e.g., noise, interference), radio parameters (e.g., modulation scheme, bandwidth, transmit power), hardware limitations (e.g., clock drift, transmission buffer) and complete MAC and routing protocols. We also explain the changes that have to be done to Castalia in order to perform the simulations. In addition, we propose a parametric model of the communication channel that matches well with the results from the first part of this paper. Finally, we provide simulation results for some illustrative scenarios.

Can the Anesthesiologist Use the Radial Artery for Monitoring After Transradial Artery Catheterization?

The use of transradial coronary angiography and intervention is growing because of its advantages over the femoral approach. However, the small size of the radial artery can contribute to complications. We present a case of an in situ access complication of transradial coronary artery catheterization. It is important for the anesthesiologist to know about the short-term and long-term consequences of this intervention, which could lead to narrowing of the artery even beyond the site of puncture. Understanding these changes could help anesthesiologists make better decisions about using the radial artery for monitoring after transradial coronary artery catheterization procedures.

Safety of liposome extended-release bupivacaine for postoperative pain control.

BACKGROUND: Ideal postoperative pain management requires a multidisciplinary approach in combination with a variety of dosage regimens. Approximately 21-30% of patients experience moderate to severe pain in the postoperative period, which may have a significant impact on recovery rate, standard of living, psychological health, and postoperative complications. OBJECTIVE: Analysis of the incidence and characterization of reported adverse effects with DepoFoam bupivacaine compared to conventional bupivacaine or placebo. METHODS: A systematic review of prospective studies on the use of DepoFoam versus bupivacaine or placebo was performed in order to answer the clinically relevant question: is DepoFoam a safer formulation in place of bupivacaine single injection or continuous local infusion techniques for postoperative pain management? Inclusion criteria required randomized, controlled, double-blind trials in patients 18 years old or older, single dose used for postoperative pain control, and a primary procedure performed. RESULTS: Six studies fitted the inclusion criteria for analysis, DepoFoam bupivacaine used in therapeutic doses was well-tolerated, had a higher safety margin, and showed a favorable safety profile compared to bupivacaine and control groups. CONCLUSION: Extended drug delivery system DepoFoam bupivacaine is a promising drug formulation that may significantly improve postoperative care and pain control in surgical patients.

Perioperative Outcome of Dyssomnia Patients on Chronic Methylphenidate Use.

Methylphenidate is frequently prescribed for attention deficit hyperactivity disorder, narcolepsy, and other sleep disorders requiring psychostimulants. Our report is based on 2 different clinical experiences of patients with chronic methylphenidate use, undergoing general anesthesia. These cases contrast different strategies of taking versus withholding the drug treatment on the day of surgery. From the standpoint of anesthetic management and patient safety, the concerns for perioperative methylphenidate use are mainly related to cardiovascular stability and possible counteraction of sedatives and anesthetics.