Cor Triatriatum Dexter With a Sinus Venosus Atrial Septal Defect in a 50-Year-Old Woman: A Case Report.

The diagnosis of atrial septal defect (ASD) may be delayed until adulthood or even later in life as it is a well-tolerated congenital heart disease. If patients are not examined and investigated well in childhood, the diagnosis may be delayed until later in adulthood when patients present with palpitations and sometimes dyspnea due to the right chambers dilatation from right ventricular volume overload. In this report, we present a case of a 50-year-old female patient with symptoms of heart failure and atrial fibrillation who was found to have dilated right cardiac chambers, dilated pulmonary artery, severe tricuspid regurgitation, pulmonary hypertension, and a pulmonary-to-systemic flow ratio (Qp/Qs) of more than 1.5 by transthoracic echocardiography and Doppler, indicating left to right shunt at the atrial level. However, transthoracic echocardiography could not visualize the defect, and two-dimensional (2D) transesophageal echocardiography was done in this patient and documented the presence of a sinus venosus ASD with an incomplete cor triatriatum dexter membrane; all four pulmonary veins were identified going to the left atrium. Since the presence of an incomplete cor triatriatum dexter membrane (despite causing no symptoms) makes the percutaneous closure of the sinus venosus ASD and the percutaneous repair of tricuspid regurgitation very difficult, we decided to advise surgical ASD closure and tricuspid valve repair for the patient.

Reviewing Federated Machine Learning and Its Use in Diseases Prediction.

Machine learning (ML) has succeeded in improving our daily routines by enabling automation and improved decision making in a variety of industries such as healthcare, finance, and transportation, resulting in increased efficiency and production. However, the development and widespread use of this technology has been significantly hampered by concerns about data privacy, confidentiality, and sensitivity, particularly in healthcare and finance. The "data hunger" of ML describes how additional data can increase performance and accuracy, which is why this question arises. Federated learning (FL) has emerged as a technology that helps solve the privacy problem by eliminating the need to send data to a primary server and collect it where it is processed and the model is trained. To maintain privacy and improve model performance, FL shares parameters rather than data during training, in contrast to the typical ML practice of sending user data during model development. Although FL is still in its infancy, there are already applications in various industries such as healthcare, finance, transportation, and others. In addition, 32% of companies have implemented or plan to implement federated learning in the next 12-24 months, according to the latest figures from KPMG, which forecasts an increase in investment in this area from USD 107 million in 2020 to USD 538 million in 2025. In this context, this article reviews federated learning, describes it technically, differentiates it from other technologies, and discusses current FL aggregation algorithms. It also discusses the use of FL in the diagnosis of cardiovascular disease, diabetes, and cancer. Finally, the problems hindering progress in this area and future strategies to overcome these limitations are discussed in detail.

Smart Wearables for the Detection of Cardiovascular Diseases: A Systematic Literature Review.

Background: The advancement of information and communication technologies and the growing power of artificial intelligence are successfully transforming a number of concepts that are important to our daily lives. Many sectors, including education, healthcare, industry, and others, are benefiting greatly from the use of such resources. The healthcare sector, for example, was an early adopter of smart wearables, which primarily serve as diagnostic tools. In this context, smart wearables have demonstrated their effectiveness in detecting and predicting cardiovascular diseases (CVDs), the leading cause of death worldwide. Objective: In this study, a systematic literature review of smart wearable applications for cardiovascular disease detection and prediction is presented. After conducting the required search, the documents that met the criteria were analyzed to extract key criteria such as the publication year, vital signs recorded, diseases studied, hardware used, smart models used, datasets used, and performance metrics. Methods: This study followed the PRISMA guidelines by searching IEEE, PubMed, and Scopus for publications published between 2010 and 2022. Once records were located, they were reviewed to determine which ones should be included in the analysis. Finally, the analysis was completed, and the relevant data were included in the review along with the relevant articles. Results: As a result of the comprehensive search procedures, 87 papers were deemed relevant for further review. In addition, the results are discussed to evaluate the development and use of smart wearable devices for cardiovascular disease management, and the results demonstrate the high efficiency of such wearable devices. Conclusions: The results clearly show that interest in this topic has increased. Although the results show that smart wearables are quite accurate in detecting, predicting, and even treating cardiovascular disease, further research is needed to improve their use.

Smart Wearables for the Detection of Occupational Physical Fatigue: A Literature Review.

Today's world is changing dramatically due to the influence of various factors. Whether due to the rapid development of technological tools, advances in telecommunication methods, global economic and social events, or other reasons, almost everything is changing. As a result, the concepts of a "job" or work have changed as well, with new work shifts being introduced and the office no longer being the only place where work is done. In addition, our non-stop active society has increased the stress and pressure at work, causing fatigue to spread worldwide and becoming a global problem. Moreover, it is medically proven that persistent fatigue is a cause of serious diseases and health problems. Therefore, monitoring and detecting fatigue in the workplace is essential to improve worker safety in the long term. In this paper, we provide an overview of the use of smart wearable devices to monitor and detect occupational physical fatigue. In addition, we present and discuss the challenges that hinder this field and highlight what can be done to advance the use of smart wearables in workplace fatigue detection.

Retraction: Samaha et al. Effects of a Single Dose of Ivermectin on Viral and Clinical Outcomes in Asymptomatic SARS-CoV-2 Infected Subjects: A Pilot Clinical Trial in Lebanon. Viruses 2021, 13, 989.

The journal retracts the article, Effects of a Single Dose of Ivermectin on Viral and Clinical Outcomes in Asymptomatic SARS-CoV-2 Infected Subjects: A Pilot Clinical Trial in Lebanon [...].

Effects of a Single Dose of Ivermectin on Viral and Clinical Outcomes in Asymptomatic SARS-CoV-2 Infected Subjects: A Pilot Clinical Trial in Lebanon.

OBJECTIVE: This study was designed to determine the efficacy of ivermectin, an FDA-approved drug, in producing clinical benefits and decreasing the viral load of SARS-CoV-2 among asymptomatic subjects that tested positive for this virus in Lebanon. METHODS: A randomized controlled trial was conducted in 100 asymptomatic Lebanese subjects that have tested positive for SARS-CoV2. Fifty patients received standard preventive treatment, mainly supplements, and the experimental group received a single dose (according to body weight) of ivermectin, in addition to the same supplements the control group received. RESULTS: There was no significant difference (p = 0.06) between Ct-values of the two groups before the regimen was started (day zero), indicating that subjects in both groups had similar viral loads. At 72 h after the regimen started, the increase in Ct-values was dramatically higher in the ivermectin than in the control group. In the ivermectin group, Ct increased from 15.13 ± 2.07 (day zero) to 30.14 ± 6.22 (day three; mean ± SD), compared to the control group, where the Ct values increased only from 14.20 ± 2.48 (day zero) to 18.96 ± 3.26 (day three; mean ± SD). Moreover, more subjects in the control group developed clinical symptoms. Three individuals (6%) required hospitalization, compared to the ivermectin group (0%). CONCLUSION: Ivermectin appears to be efficacious in providing clinical benefits in a randomized treatment of asymptomatic SARS-CoV-2-positive subjects, effectively resulting in fewer symptoms, lower viral load and reduced hospital admissions. However, larger-scale trials are warranted for this conclusion to be further cemented.

Peripheral interleukin-1ß inhibits arcuate kiss1 cells and LH pulses in female mice.

Peripheral immune/inflammatory challenges rapidly disrupt reproductive neuroendocrine function. This inhibition is considered to be centrally mediated via suppression of gonadotropin-releasing hormone secretion, yet the neural pathway(s) for this effect remains unclear. We tested the hypothesis that interleukin-1ß inhibits pulsatile luteinizing hormone secretion in female mice via inhibition of arcuate kisspeptin cell activation, a population of neurons considered to be the gonadotropin-releasing hormone pulse generator. In the first experiment, we determined that the inhibitory effect of peripheral interleukin-1ß on luteinizing hormone secretion was enhanced by estradiol. We next utilized serial sampling and showed that interleukin-1ß reduced the frequency of luteinizing hormone pulses in ovariectomized female mice treated with estradiol. The interleukin-1ß-induced suppression of pulse frequency was associated with reduced kisspeptin cell activation, as determined by c-Fos coexpression, but not as a result of impaired responsiveness to kisspeptin challenge. Together, these data suggest an inhibitory action of interleukin-1ß upstream of kisspeptin receptor activation. We next tested the hypothesis that estradiol enhances the activation of brainstem nuclei responding to interleukin-1ß. We determined that the expression of interleukin-1 receptor was elevated within the brainstem following estradiol. Interleukin-1ß induced c-Fos in the area postrema, ventrolateral medulla, and nucleus of the solitary tract; however, the response was not increased by estradiol. Collectively, these data support a neural mechanism whereby peripheral immune/inflammatory stress impairs reproductive neuroendocrine function via inhibition of kisspeptin cell activation and reduced pulsatile luteinizing hormone secretion. Furthermore, these findings implicate the influence of estradiol on peripherally mediated neural pathways such as those activated by peripheral cytokines.

Deformable image tracking of the parotid gland for adaptive radiotherapy application.

Radiation therapy is a type of cancer treatment using radiation at different times defined as treatment sessions, distributed over different weeks. In each session, we have to determine and define the optimal treatment parameters for the patient. The aim of Adaptive Radiotherapy Treatment (ART) is to identify any change of initial parameters during the treatment course and modify the treatment plan for the purpose of maintaining optimal treatment objectives. In order to track the deformable image of biological organ such as the parotid gland, a 3D reconstruction is needed. 10 patients were scanned at the medical center of Oscar Lambret (Lille, France) using CT scan as imaging modality. The contours of the acquired images were extracted manually by the expert. Relaxed bi-cubic Bézier spline surface has been used in our study for the purpose of automatically reconstruction of the biological organ. Once the reconstruction is accomplished, the volume of the parotid gland at each session of treatment has been calculated for each patient. The obtained results show a decreasing of the volume of the parotid from one week to other one and a shifting of the detected center of gravity. These variations should be used to build a predictive model for adaptive robotized radiotherapy.