Switching between continuous glucose monitoring (CGM) device brands and application sites is common in clinical practice. We hypothesized that the differences between CGM device measurements are significant enough to impact glycemic control interpretation. Current evidence has identified a trend of clinically meaningful differences between right- and left-arm CGM readings in one CGM device. We also hypothesized that application sites can affect glycemic control interpretation. A case study in which two adult males each wore three Dexcom G6 (right arm, left arm, abdomen) and two FreeStyle Libre Pro (right arm, left arm) CGM devices simultaneously for 10 days was conducted to explore performance differences between CGM devices and application sites. Our case report was consistent with both hypotheses as clinically meaningful differences were observed between time-matched, same-arm Dexcom G6 versus FreeStyle Libre Pro glucose readings and between time-matched FreeStyle Libre Pro right-arm versus left-arm glucose readings. Glycemic control was not significantly impacted by the three Dexcom G6 application sites.
In healthcare settings, effective and timely interventions play a pivotal role in mitigating life-threatening critical diseases by providing the crucial time for issue identification and immediate troubleshooting. Optimizing this critical window depends on three key elements: hospital resources, clinical expertise, and efficient execution of critical medical interventions within specified timeframes. The crux lies in the timely application of these factors to ensure prompt intervention and resource utilization. The role of Digital Critical Care Medicine via tele-ICU technology comes from the command centre hub, where super-specialized ICU experts dedicated to these spoke sites are available around the clock. We report the case of a 16-year-old boy who presented to a spoke site tele-ICU with massive bleeding in the rectum and was in a gasping state with non-recordable blood pressure and feeble pulse during the late evening hours. The spoke site was continuously monitored by the Medanta Command Centre Hub. With prompt resuscitation and guidance from Medanta e-ICU intensivists, the remote-site ICU team was able to manage this case at his first point of contact, that is, utilizing the life-saving golden hours with the help of the tele-ICU facility, thereby creating survival.
The design and construction process of a non-invasive operating device aimed at regulating heart rate in patients with arrhythmias is presented. The methodology involved a combination of electronic devices, biomedical instrumentation and physiological procedures. Through the functioning of the sympathetic and parasympathetic systems, electrical stimulation of the vagus nerve and magnetic stimulation of cervical sympathetic ganglia are performed. Bioelectromagnetic stimulation induced activity in the sinus node or the cardiac nervous plexus, thereby achieving heart rhythm regulation. This stimulation must be performed on the right atrial branch of the vagus nerve in cases of tachycardia, whereas magnetic stimulation is applied to the cervical sympathetic ganglia in cases of bradycardia. The device consists of a real-time arrhythmia detector, which operates through an optical sensor. Preliminary results suggest an excellent option as a complement therapy in arrythmias. The sensor reading is recorded by a microcontroller with a comparison algorithm that correlates the previously stored stable rhythm to the rhythm a patient experiences during an arrhythmia episode. When the algorithm detects an irregularity, it generates a signal that activates one of the stimulators, depending on the type of arrhythmia occurring. Each stimulator has a microcontroller automated to generate stimulation based on the sensor’s reading.
In low- and middle-income countries (LMICs), digital health has repeatedly proven to play a promising role in optimizing the level of care available and accessible to vulnerable populations in a timely and cost-effective manner. Despite financial constraints, digital technologies have demonstrated unique potential in reaching typically inaccessible groups, including refugee and internally displaced populations. Given the potential of digital health to bridge gaps in healthcare coverage, enhance quality, and improve affordability, this study aimed to assess the effectiveness of the CoronaCheck mHealth application among people in LMICs. This analytical cross-sectional study spanned multiple LMICs, focusing on males and females aged 18 years and older in regions such as Pakistan, Afghanistan, Kenya, and Tajikistan. Participants were selected through convenient sampling. Knowledge change and self-reported behavior change were assessed. The p-value of <0.05 was considered statistically significant. A total of 1507 participants responded to the survey. A difference in knowledge among countries was observed with a statistically significant p-value of <0.001. An effect modification was observed between gender and refugees/migrants with a statistically significant p-value of <0.1. A substantial self-reported behavior change was identified among those residing in informal settlements with a significant p-value of <0.001. Moreover, the users expressed a high level of satisfaction with the application. The CoronaCheck application has demonstrated its effectiveness in promoting both knowledge change and self-reported behavior change among people in LMICs. The user-friendly nature of the application, coupled with its accessibility at no cost, represents an asset in promoting health education and awareness.
Managing pain when a patient cannot communicate, during anesthesia or critical illness, is a challenge many clinicians face. Numerous subjective methods of evaluating pain have been developed to address this, for instance, the visual analog and numerical rating scale. Intraoperatively, objective monitoring of pain in anesthetized patients is assessed through hemodynamic parameters; however, these parameters may not always accurately reflect pain perception. The high-frequency heart rate variability index (HFVI), also known as analgesia nociception index (ANI), is a commercially available device developed by MDoloris that objectively assesses nociception based on patient electrocardiogram, sympathetic tone, and parasympathetic tone. The monitor displays a value from 0–100, where <50 indicates nociception and >50 indicates anti-nociception. Given its potential to objectively monitor pain, numerous studies have utilized this device in clinical and non-clinical settings. As such, we conducted a literature review using various search terms in PubMed and selected HFVI studies based on our inclusion criteria for this review. In this review, we discuss the mechanisms by which numerous available nociception monitors assess pain along with the results of clinical and non-clinical HFVI studies to provide a comprehensive summary for clinicians interested in or considering the use of novel pain monitoring.
Hyperbaric Oxygen Therapy (HBOT), the use of pure oxygen (100% O2) at high pressure (2–3 ATM), is gaining prominence as a tool for managing persistent post-concussive symptoms, otherwise known as post-concussion syndrome (PCS). Recent research has emerged that elucidates the mechanisms by which HBOT improves PCS. This article reviews the progression and pathophysiology of PCS, challenges in diagnosis, and novel imaging solutions. It also delves into recent advancements in the understanding of HBOT mechanisms and the benefits observed from HBOT in PCS patients. The discussion concludes with an examination of innovative imaging techniques, novel biomarkers, the potential role of data sharing, machine learning, and how these developments can advance the use of HBOT in the management of PCS.
It is becoming clear that bulk gene expression measurements represent an average over very different cells. Elucidating the expression and abundance of each of the encompassed cells is key to disease understanding and precision medicine approaches. A first step in any such deconvolution is the inference of cell type abundances in the given mixture. Numerous approaches to cell-type deconvolution have been proposed, yet very few take advantage of the emerging discipline of deep learning and most approaches are limited to input data regarding the expression profiles of the cell types in question. Here we present DECODE, a deep learning method for the task that is data-driven and does not depend on input expression profiles. DECODE builds on a deep unfolded non-negative matrix factorization technique. It is shown to outperform previous approaches on a range of synthetic and real data sets, producing abundance estimates that are closer to and better correlated with the real values.
Cell migration is observed in various cases such as embryonic and lesion developments. The migration directly influences the phenomena around the migration path. Bright field microscopy, generally used for cell observation, is effective in tracking cell movement, but the detection of the cell outline via image processing methods partially fails. In this study, a simple method, utilizing the intensity fluctuation of the image caused by the passage of a cell as a parameter for evaluation of the cell movement, has been proposed to visualize the region where the cell passed and quantitatively evaluate its frequency by the fluctuation pattern. The map that depicts the activity of cell movement is created, and the geometrical characteristics of each region and fluctuation degree are quantitatively shown. Because detection of the cell outline is not necessary, this method is applicable to collective cells as well as single cells. When this method was applied to the images of hemocytes in Halocynthia roretzi (Ascidiacea), which were composed of single and collective cells and showed complex patterns in movement, the map and quantitative parameters for the fluctuation were successfully obtained. The method can be improved by the calibration of intensity distribution and applied to cells in various species.
Sports injuries are becoming increasingly widespread, and professional player injuries are having a negative impact on the field of sports. Preventing sports injuries is becoming more popular. Numerous machine learning (ML) techniques have been used in different sports injury fields since the birth of ML. In order to deal with the issue of karate injury treatment, rehabilitation, and prevention, this paper presents a new behavioral system to identify injuries and the rehabilitation process in karate utilizing hybrid models that mix unsupervised learning and supervised learning. In our scenario, we picked Autoencoder for unsupervised learning and CNN and DNN models for supervised learning. The experimental investigation shows that the suggested model is capable of yielding accurate outcomes. In fact, our model’s accuracy for DNN and CNN is 99.67% and 99.66%, respectively.
… [A] knowledge of sequences could contribute much to our understanding of living matter."[Frederick Sanger [
The present research was aimed at imaging predentine, structure of the walls of the dentinal tubules, and distribution of collagen fibres on which the dentinal tubules are built, using microtomography. Methodology Teeth were first demineralised and subsequently contrasted with uranyl acetate and osmium tetroxide. In the next stage, these contrasted teeth were analysed by X-rays with the use of Nanotom S. The Fiji Is Just ImageJ and VG Studio Max programs were used to conduct numerical analysis of the data. Then the 3D model was made. Results The teeth serving as reference material were not subjected to contrasting agents. The images obtained via microtomography were poorly differentiated. Teeth contrasted with uranyl acetate: the spatial image of the entire tooth became very clearly visible. Teeth contrasted with osmium: the preparations differ in terms of contrast. This preparation enables the differentiation of sharper details throughout the tooth model. Conclusions It was possible to show vessels and odontoblast spikes in the pulp chamber. It was also possible to follow the course of the dentinal tubules and to link the structures of the walls of the tubules with collagen fibres in the 3D image, with using Nanotom S microtomograph.
Introduction Telehealth pain management has become instrumental in managing patients with chronic pain (CP) since the onset of the COVID-19 pandemic. The primary aim of this study was to investigate whether various covert therapeutic variables aid in the efficacy of telehealth group-based pain management programs (GPMPs). The therapeutic alliance (TA), group dynamics (GDs), attendance and change in pain neuroscience knowledge were evaluated as potential predictor covert variables of change in pain outcome measures and readiness to change (RTC) maladaptive pain behaviors. Methods Telehealth GPMP groups met once a week for 3 hours via zoom software and ran over a course of 6 weeks in which CP self-management techniques were taught. Pain outcome measures were taken at baseline and after the final telehealth GPMP. In addition, the measures around pain neuroscience understanding were examined at baseline and post-intervention. Finally, the TA and GDs were examined at post-treatment using the Therapeutic Group Context Questionnaire (TGCQ). Various statistical procedures were utilized to determine the predictive nature between the specific variables. Results The TA and GDs showed statistically significant ( p < 0.05) predictive relationships with improved changes in maladaptive pain behaviors and pain self-efficacy. There was also a statistically significant ( p < 0.05) predictive relationship between maladaptive pain behavioral changes and improvements in pain self-efficacy, pain catastrophizing and pain kinesiophobia. Discussion This research suggested that covert components in a telehealth GPMP such as changes in readiness to change (RTC) maladaptive pain behaviors, the TA, and GDs are all strong predictors of improvements in pain outcome measures following such an intervention.
ChatGPT-4, BARD, and YOU.com are AI large language models (LLM) developed by OpenAI based on the GPT-3-4 architecture and Google. They were trained using unsupervised learning, which allows them to learn from vast amounts of text data without requiring explicit human labels. ChatGPT-4 was exposed to training information up to September 2021. By presenting prompts (queries) to ChatGPT-4, BARD, and YOU.com, including a typical case presentation (vignette) of a new patient with squamous cell tonsillar cancer, we uncovered several specific issues that raise concerns for the current application of this early phase of advanced LLM AI technology for clinical medicine. By prompting and comparing responses of three different LLMs (ChatGPT-4, BARD, and YOU.com) to identical prompts, we reveal several flaws in each AI that, if taken as factual, would affect clinical therapeutic suggestions and possible survival. The presented clinical vignette of a patient with newly diagnosed tonsillar cancer is presented to three LLMs readily available for free trial allowing comparison of results. We observed frequent changing responses to unchanging prompts over just hours and days within the same and between LLMs, critical errors of guideline-recommended drug therapy, and noted that several AI-supplied references presented by the AIs are bogus AI-generated references whose DOI and or PMID identifiers were either nonexistent or led to completely irrelevant manuscripts on other subjects.
Remote monitoring has been demonstrated to be an effective tool for decreasing costs and improving outcomes, however, both patients and providers have shown a reluctance to embrace technology. This survey-based, cross-sectional designed study assessed the barriers faced by patients and providers in the District of Columbia for technology adoption in remote monitoring. The patients had a diagnosis of either diabetes, hypertension, or both conditions, and utilized the technology of a home blood glucose monitor, continuous glucose monitor or ambulatory blood pressure monitor. The surveyed providers included staff engaged in chronic disease management of patients with diabetes and hypertension. An adapted version of the Barriers to Health Promoting Activities for Disabled Persons Scale (BHADP)was administered to study participants and statistically analyzed. Data analysis compared and contrasted demographics and survey responses and revealed that there is a discordance in the ways that patients perceive the barriers to technology as compared to the ways that providers perceive them. Data analysis also revealed significant system barriers that limited providers use of remote monitoring technologies. A model is proposed that identifies inputs and barriers experienced by the patient in their journey to initiate or continue a technology (adapted from Moore et al.’s 2021 Conceptual Model).
Aim The mechanization of today’s world and the recent developments about robots and its use in industry and medicine, as well as the replacement by these tools instead of human labor with the ability to make them intelligent, have made artificial intelligence (AI) and robots hot topics these days. Artificial intelligence is the ability of intelligent machines to predict unknown variables by using algorithms and internal statistical patterns and information structures. In the structure of AI, which are divided into two general categories, machine learning and deep learning, human neural patterns are in the form of neural networks. The working areas of AI in maxillofacial and plastic surgery are wide and in the fields of rhinoplasty, orthognathic surgery, cleft lip and palate, augmentation in implants, and diagnosis and determination of survival rate in cancer patients. In this review article the different functions of AI in the fields of maxillofacial surgery and the extent of its effectiveness in helping to improve the acceleration of work are discussed. Methods & Materials This study examines articles from 2000–2023. Google Scholar and PubMed databases were used for searching and keywords such as artificial intelligence-machine learning, deep learning were investigated. The inclusion criteria for this study were all the articles that were written and reviewed in the years in question, in English, and the field of research was maxillofacial or plastic surgery. Results Rhinoplasty: The application of artificial intelligence (AI) in the field of examining bone shape, examining the beauty of patients based on the evaluation of pre-treatment photographs and predicting the results of the operation based on radiographic interpretation. Orthognathic surgery: AI can be used in the field of lateral cephalometric tracing, scanning of patients’ occlusion, examination of periodontal diseases and dental problems, as well as making oral appliances and predicting the operation using machine learning (ML). Cleft palate and lip: Examining the success rate of bone grafting in the alveolar cleft area and predicting the results of grafting and the risk of infection and failure of grafting in the area is one of the applications of AI in this field. Oral cancer: Oral squamous cell cancer is one of the most common head and neck cancers and due to the high rate of recurrence, morbidity and mortality, it is of great concern in medical sciences today. The application of AI and the interpretation of risk factors and samples using complex neural algorithms can reduce the mortality rate through faster disease prediction and at earlier stages. Conclusion In this review article, the applications of AI and its sub-branches, including ML, deep learning, in various branches of maxillofacial surgery, including orthognathics, rhinoplasty, cleft lip and palate, and oral cancer are discussed. Making decisions smarter by using complex neural algorithms and its involvement in decisions can reduce human errors and increase patient satisfaction.
Today, digital dentistry has revolutionized the way dental professionals provide patient care. It refers to the use of digital technologies in all aspects of dentistry, including diagnosis, treatment planning, and restoration; encompassing a range of technologies, including computer-aided design/computer-aided manufacturing (CAD/CAM), three-dimensional (3D) printing, artificial intelligence (AI), augmented reality (AR), and teledentistry; a rapidly evolving and transformative field. This review article explores the evolution of digital dentistry, including advancements in imaging, CAD/CAM, 3D printing, and regenerative dentistry, amongst others. It discusses current and future applications of digital dentistry, such as AI, AR, and teledentistry. The potential benefits and challenges associated with these applications are also examined, including their impact on patient privacy, dental education, and the overall practice of dentistry and oral surgery. Indeed, digital dentistry has transformed the way we diagnose, plan, and treat our patients. In other words, the use of digital technologies in dentistry has allowed for greater precision, accuracy, and efficiency, while also improving patient outcomes. An overview of the history and current state of digital dentistry, as well as a discussion of future developments in the field is presented, in addition to examining benefits, limitations, ethical considerations, and the importance of staying up-to-date with the latest advancements in the rapidly evolving field. To simplify concepts and approaches, real-life examples of how digital dentistry is being used in modern dental practices are also provided to the reader.
A 79-year-old retired physician with type II diabetes mellitus and hypercholesterolemia presented to his physician complaining of recurrent severe unilateral T10 thoracic pain. This report demonstrates the effect of early high-dose oral acyclovir therapy as a diagnostic, therapeutic challenge to assist in the early diagnosis of zoster sine herpete, herpes zoster infection without dermatomal skin rash (ZSH), a clinically covert form of the more recognized herpes zoster infection with both dermatomal pain and skin rash (HZ).
Electrocardiography (ECG) has been a subject of research interest in human identification because it is a promising biometric trait that is believed to have discriminatory characteristics. However, features of ECGs that are recorded at different times are often likely to vary significantly. To address the variability of ECG features over multiple records, we propose a new methodology for human identification using ECGs recorded on different days. To demonstrate the applicability of our method, we use the publicly available ECG ID dataset. The main goal of this work is to extract the most significant and discriminative wavelet components of the ECG signal, followed by utilizing the ECG spectral change for human identification using multi-level filtering technique. Our proposed multi-channel identification system is based on using the Maximal Overlap Discrete Wavelet Transform (MODWT) and its inverse (the IMODWT) to create multiple filtered ECG signals. The discriminative feature that we utilize for human identification is based on modeling the dynamic change of the frequency components in these multiple filtered signals. To reach the best possible identification performance, we use the Weighted Majority Voting Method (WMVM) for ECG classification. We evaluated the robustness of our proposed method over several random experiments and obtained 92.29% average identification accuracy, 0.9495 precision, 0.9229 recall, 0.0771 FRR and 0.0013 FAR. These results indicate that filtering some of the ECG wavelet components along with performing data fusion technique can be utilized for human identification.
