Examining sea levels forecasting using autoregressive and prophet models.

Global climate change in recent years has resulted in significant changes in sea levels at both global and local scales. Various oceanic and climatic factors play direct and indirect roles in influencing sea level changes, such as temperature, ocean heat, and Greenhouse gases (GHG) emissions. This study examined time series analysis models, specifically Autoregressive Moving Average (ARIMA) and Facebook's prophet, in forecasting the Global Mean Sea Level (GMSL). Additionally, Vector Autoregressive (VAR) model was utilized to investigate the influence of selected oceanic and climatic factors contributing to sea level rise, including ocean heat, air temperature, and GHG emissions. Moreover, the models were applied to regional sea level data from the Arabian Gulf, which experienced higher fluctuations compared to GMSL. Results showed the capability of autoregressive models in long-term forecasting, while the Prophet model excelled in capturing trends and patterns in the time series over extended periods of time.

A deep learning framework for noninvasive fetal ECG signal extraction.

Introduction: The availability of proactive techniques for health monitoring is essential to reducing fetal mortality and avoiding complications in fetal wellbeing. In harsh circumstances such as pandemics, earthquakes, and low-resource settings, the incompetence of many healthcare systems worldwide in providing essential services, especially for pregnant women, is critical. Being able to continuously monitor the fetus in hospitals and homes in a direct and fast manner is very important in such conditions. Methods: Monitoring the health of the baby can potentially be accomplished through the computation of vital bio-signal measures using a clear fetal electrocardiogram (ECG) signal. The aim of this study is to develop a framework to detect and identify the R-peaks of the fetal ECG directly from a 12 channel abdominal composite signal. Thus, signals were recorded noninvasively from 70 pregnant (healthy and with health conditions) women with no records of fetal abnormalities. The proposed model employs a recurrent neural network architecture to robustly detect the fetal ECG R-peaks. Results: To test the proposed framework, we performed both subject-dependent (5-fold cross-validation) and independent (leave-one-subject-out) tests. The proposed framework achieved average accuracy values of 94.2% and 88.8%, respectively. More specifically, the leave-one-subject-out test accuracy was 86.7% during the challenging period of vernix caseosa layer formation. Furthermore, we computed the fetal heart rate from the detected R-peaks, and the demonstrated results highlight the robustness of the proposed framework. Discussion: This work has the potential to cater to the critical industry of maternal and fetal healthcare as well as advance related applications.

LSTM-Modeling of Emotion Recognition Using Peripheral Physiological Signals in Naturalistic Conversations.

The automated recognition of human emotions plays an important role in developing machines with emotional intelligence. Major research efforts are dedicated to the development of emotion recognition methods. However, most of the affective computing models are based on images, audio, videos and brain signals. Literature lacks works that focus on utilizing only peripheral signals for emotion recognition (ER), which can be ideally implemented in daily life settings. Therefore, this paper present a framework for ER on the arousal and valence space, based on using multi-modal peripheral signals. The data used in this work were collected during a debate between two people using wearable devices. The emotions of the participants were rated by multiple raters and converted into classes in correspondence to the arousal and valence space. The use of a dynamic threshold for ratings conversion was investigated. An ER model is proposed that uses a Long Short-Term Memory (LSTM)-based architecture for classification. The model uses heart rate (HR), temperature (T), and electrodermal activity (EDA) signals as its inputs with emotional cues. Additionally, a post-processing prediction mechanism is introduced to enhance the recognition performance. The model is implemented to study the use of individual and different combinations of the peripheral signals, as well as utilizing annotations from different ratings. Additionally, it is employed for classification of valence and arousal in an independent and combined fashion, under subject dependent and independent scenarios. The experimental results have justified the efficient performance of the proposed framework, achieving classification accuracy 96% and 93% for the independent and combined classification scenarios, accordingly. The comparison of the achieved performance against the baseline methods shows the superiority of the proposed framework and the ability to recognize arousal-valance levels with high accuracy from peripheral signals, in real-life scenarios.

Automated recognition of major depressive disorder from cardiovascular and respiratory physiological signals.

Major Depressive Disorder (MDD) is a neurohormonal disorder that causes persistent negative thoughts, mood and feelings, often accompanied with suicidal ideation (SI). Current clinical diagnostic approaches are solely based on psychiatric interview questionnaires. Thus, a computational intelligence tool for the automated detection of MDD with and without suicidal ideation is presented in this study. Since MDD is proven to affect cardiovascular and respiratory systems, the aim of the study is to automatically identify the disorder severity in MDD patients using corresponding multi-modal physiological signals, including electrocardiogram (ECG), finger photoplethysmography (PPG) and respiratory signals (RSP). Data from 88 subjects were used in this study, out of which 25 were MDD patients without SI (MDDSI-), 18 MDD patients with SI (MDDSI+), and 45 normal subjects. Multi-modal physiological signals were acquired from each subject, including ECG, RSP, and PPG signals, and then pre-processed. Discrete wavelet transform (DWT) was applied to the signals, which were decomposed up to six levels, and then eleven nonlinear features were extracted. The features were ranked according to the analysis of variance test and Marginal Fisher Analysis was employed to reduce the feature set, after which the reduced features were ranked again to select the most discriminatory features. Support vector machine with polynomial radial basis function (SVM-RBF) as well as k-nearest neighbor (KNN) classifiers were used to classify the significant features. The performance of the classifiers was evaluated in a 10-fold cross validation scheme. The best performance achieved for the classification of MDDSI+ patients was up to 85.2%, by using selected features from the obtained multi-modal signals with SVM-RBF, while it was up to 96.6% for the detection of MDD patients against healthy subjects. This work is a step toward the utilization of automated tools in diagnostics and monitoring of MDD patients in a personalized and wearable healthcare system.

Trends in Smart Helmets With Multimodal Sensing for Health and Safety: Scoping Review.

BACKGROUND: As a form of the Internet of Things (IoT)-gateways, a smart helmet is one of the core devices that offers distinct functionalities. The development of smart helmets connected to IoT infrastructure helps promote connected health and safety in various fields. In this regard, we present a comprehensive analysis of smart helmet technology and its main characteristics and applications for health and safety. OBJECTIVE: This paper reviews the trends in smart helmet technology and provides an overview of the current and future potential deployments of such technology, the development of smart helmets for continuous monitoring of the health status of users, and the surrounding environmental conditions. The research questions were as follows: What are the main purposes and domains of smart helmets for health and safety? How have researchers realized key features and with what types of sensors? METHODS: We selected studies cited in electronic databases such as Google Scholar, Web of Science, ScienceDirect, and EBSCO on smart helmets through a keyword search from January 2010 to December 2021. In total, 1268 papers were identified (Web of Science: 87/1268, 6.86%; EBSCO: 149/1268, 11.75%; ScienceDirect: 248/1268, 19.55%; and Google Scholar: 784/1268, 61.82%), and the number of final studies included after PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) study selection was 57. We also performed a self-assessment of the reviewed articles to determine the quality of the paper. The scoring was based on five criteria: test environment, prototype quality, feasibility test, sensor calibration, and versatility. RESULTS: Smart helmet research has been considered in industry, sports, first responder, and health tracking scenarios for health and safety purposes. Among 57 studies, most studies with prototype development were industrial applications (18/57, 32%), and the 2 most frequent studies including simulation were industry (23/57, 40%) and sports (23/57, 40%) applications. From our assessment-scoring result, studies tended to focus on sensor calibration results (2.3 out of 3), while the lowest part was a feasibility test (1.6 out of 3). Further classification of the purpose of smart helmets yielded 4 major categories, including activity, physiological and environmental (hazard) risk sensing, as well as risk event alerting. CONCLUSIONS: A summary of existing smart helmet systems is presented with a review of the sensor features used in the prototyping demonstrations. Overall, we aimed to explore new possibilities by examining the latest research, sensor technologies, and application platform perspectives for smart helmets as promising wearable devices. The barriers to users, challenges in the development of smart helmets, and future opportunities for health and safety applications are also discussed. In conclusion, this paper presents the current status of smart helmet technology, main issues, and prospects for future smart helmet with the objective of making the smart helmet concept a reality.

Beyond Pathogen Filtration: Possibility of Smart Masks as Wearable Devices for Personal and Group Health and Safety Management.

Face masks are an important way to combat the COVID-19 pandemic. However, the prolonged pandemic has revealed confounding problems with the current face masks, including not only the spread of the disease but also concurrent psychological, social, and economic complications. As face masks have been worn for a long time, people have been interested in expanding the purpose of masks from protection to comfort and health, leading to the release of various "smart" mask products around the world. To envision how the smart masks will be extended, this paper reviewed 25 smart masks (12 from commercial products and 13 from academic prototypes) that emerged after the pandemic. While most smart masks presented in the market focus on resolving problems with user breathing discomfort, which arise from prolonged use, academic prototypes were designed for not only sensing COVID-19 but also general health monitoring aspects. Further, we investigated several specific sensors that can be incorporated into the mask for expanding biophysical features. On a larger scale, we discussed the architecture and possible applications with the help of connected smart masks. Namely, beyond a personal sensing application, a group or community sensing application may share an aggregate version of information with the broader population. In addition, this kind of collaborative sensing will also address the challenges of individual sensing, such as reliability and coverage. Lastly, we identified possible service application fields and further considerations for actual use. Along with daily-life health monitoring, smart masks may function as a general respiratory health tool for sports training, in an emergency room or ambulatory setting, as protection for industry workers and firefighters, and for soldier safety and survivability. For further considerations, we investigated design aspects in terms of sensor reliability and reproducibility, ergonomic design for user acceptance, and privacy-aware data-handling. Overall, we aim to explore new possibilities by examining the latest research, sensor technologies, and application platform perspectives for smart masks as one of the promising wearable devices. By integrating biomarkers of respiration symptoms, a smart mask can be a truly cutting-edge device that expands further knowledge on health monitoring to reach the next level of wearables.

Arousal-Valence Classification from Peripheral Physiological Signals Using Long Short-Term Memory Networks.

The automated recognition of human emotions plays an important role in developing machines with emotional intelligence. However, most of the affective computing models are based on images, audio, videos and brain signals. There is a lack of prior studies that focus on utilizing only peripheral physiological signals for emotion recognition, which can ideally be implemented in daily life settings using wearables, e.g., smartwatches. Here, an emotion classification method using peripheral physiological signals, obtained by wearable devices that enable continuous monitoring of emotional states, is presented. A Long Short-Term Memory neural network-based classification model is proposed to accurately predict emotions in real-time into binary levels and quadrants of the arousal-valence space. The peripheral sensored data used here were collected from 20 participants, who engaged in a naturalistic debate. Different annotation schemes were adopted and their impact on the classification performance was explored. Evaluation results demonstrate the capability of our method with a measured accuracy of >93% and >89% for binary levels and quad classes, respectively. This paves the way for enhancing the role of wearable devices in emotional state recognition in everyday life.

Spectrum of autoantibodies other than anti-desmoglein in pemphigus patients.

BACKGROUND: Pemphigus is a life-threatening autoimmune blistering disease mediated by autoantibodies against adhesion molecule of the skin. Its concurrence with systemic and organ-specific autoimmune disease was described in case reports. OBJECTIVES: To evaluate the presence of a broad spectrum of organ-specific and non-organ-specific autoantibodies other than anti-desmoglein antibodies in pemphigus patients. PATIENTS AND METHODS: Serum samples were obtained from 105 pemphigus foliaceus (PF) patients, 51 pemphigus vulgaris (PV) patients and 50 controls. Both indirect immunofluorescence assay and ELISA were used to assess the presence of autoantibodies related to connective tissue diseases, autoimmune hepatitis, vasculitis, rheumatoid arthritis, coeliac disease, diabetes and thyroiditis. RESULTS: Significant difference was observed between the three groups for anti-thyroglobulin antibodies in the pemphigus foliaceus group (18% vs. 4%, P=0.03). A significantly higher occurrence of IgM anti-cardiolipin (P=0.03), IgG anti-reticulin (P=0.01) and IgG anti-gliadin antibodies (P=0.008) were observed in the PV group. Cases with more than four autoantibodies were frequently positives for both anti-desmoglein 1 and anti-desmoglein 3. CONCLUSION: Autoantibodies other than anti-desmoglein antibodies are not rare in pemphigus patients. Clinical and serological follow-up of pemphigus patients with positive autoantibodies are needed to clarify their impact in disease evolution.

Ketosis-onset diabetes in Tunisian adults: immunological markers and beta-cell function.

The aim of this study in Tunisia was to classify ketosis-onset diabetes in adult patients. All patients aged > 30 years without known diabetes, presenting with ketosis and admitted to our department were studied. Patients with secondary or gestational diabetes and those on corticoid therapy or with coinciding infection were excluded. The data included clinical characteristics, immunological markers and beta-cell function. Of the 63 patients, islet-cell antibodies were present in 27.0%, glutamic acid decarboxylase antibodies in 25.4% and thyrosin phosphatase antibodies in 19.0%. Beta-cell functional reserve was preserved in 54.0%. Our results confirm that patients with ketosis-onset diabetes mellitus in adulthood are a heterogeneous group.

Ketosis-onset diabetes in Tunisian adults: immunological markers and beta-cell function

The aim of this study in Tunisia was to classify ketosis-onset diabetes in adult patients. All patients aged >/= 30 years without known diabetes, presenting with ketosis and admitted to our department were studied. Patients with secondary or gestational diabetes and those on corticoid therapy or with coinciding infection were excluded. The data included clinical characteristics, immunological markers and beta-cell function. Of the 63 patients, islet-cell antibodies were present in 27.0%, glutamic acid decarboxylase antibodies in 25.4% and thyrosin phosphatase antibodies in 19.0%. beta-cell functional reserve was preserved in 54.0%. Our results confirm that patients with ketosis-onset diabetes mellitus in adulthood are a heterogeneous group

Tunisian endemic pemphigus foliaceus is associated with the HLA-DR3 gene: anti-desmoglein 1 antibody-positive healthy subjects bear protective alleles.

BACKGROUND: Pemphigus foliaceus is an autoimmune blistering skin disease that partly results from genetic factors, especially human leucocyte antigen (HLA) class II genes. OBJECTIVES: The aim of the study was to determine the HLA DR/DQ markers of susceptibility and protection in the Tunisian endemic form. METHODS: Genomic DNA from 90 patients with pemphigus foliaceus recruited from all parts of the country and matched by age, sex and geographical origin with 270 healthy individuals, was genotyped. RESULTS: Firstly, when the whole patient population was studied, DRB1*03, DQB1*0302 and DRB1*04 alleles were significantly associated with the disease while a significant decrease of, in particular, DRB1*11 and DQB1*0301 was observed in patients compared with controls. DRB1*0301 was the dominant allele in DR3-positive patients and controls, while DRB1*0402 was found in 42% of DR4-positive patients. Secondly, when the HLA DR/DQ allele distribution was studied after dividing patients according to their geographical origin, the southern group, which consisted exclusively of patients with the endemic form of the disease, showed the same associations as the whole pemphigus foliaceus population, particularly with DRB1*03. In the northern group, only the DRB1*04 and DQB1*0301 alleles were found to be associated. Interestingly, anti-desmoglein 1 antibody-positive healthy controls did not carry susceptibility alleles but, in contrast, most carried negatively associated alleles. CONCLUSIONS: These observations indicate that a particular genetic background characterizes the Tunisian endemic form of pemphigus foliaceus and that HLA class II genes control the pathogenic properties of the autoimmune response rather than the initial breakage of B-cell tolerance.

Expression of Notch family members in cultured murine articular chondrocytes.

The Notch family is involved in cell differentiation during embryogenesis. Osteoarthritic chondrocytes undergo morphological and biochemical changes leading to the de-differentiation process. In the study reported here, we were interested in the involvement of the Notch pathway in murine articular chondrocyte de-differentiation. Articular chondrocytes were subjected to several cell culture passages and treated with or without a Notch inhibitor, N-[N-(3, 5-difluorophenacetyl-L-alanyl)]-S-phenylglycine t-Butyl Ester (DAPT). Chondrocyte morphology was studied using optical microscopy. Immunocytochemistry and immunoblot were performed to study the expression of collagens and Notch family members. Without DAPT treatment, chondrocyte de-differentiation resulted in fibroblast-like morphology. This was confirmed by immunocytochemical staining and immunoblot analysis, which showed an increase in collagen type I (col I) and a decrease in collagen type II (col II) expression. With DAPT treatment, de-differentiation was delayed. Immunocytochemistry and immunoblot analysis showed during the first passages inhibition of col II expression, which then was re-instituted during the last passage, suggesting chondrocyte re-differentiation. In the study reported here, we showed that inhibition of the Notch receptor not only delayed the de-differentiation process, but also chondrocyte re-differentiation, which confirms the involvement of the Notch pathway in chondrocyte de-differentiation.

Anti-desmoglein 1 antibodies in healthy related and unrelated subjects and patients with pemphigus foliaceus in endemic and non-endemic areas from Tunisia.

BACKGROUND: Pemphigus foliaceus is an autoimmune blistering skin disease characterized by the production of pathogenic IgG autoantibodies directed against desmoglein 1. AIM: To determine the prevalence of anti-desmoglein 1 antibodies in healthy subjects and their distribution in the different regions of Tunisia and to better identify endemic areas of pemphigus foliaceus. METHODS: We tested, by enzyme-linked immunoserbent assay, sera of 270 normal subjects recruited from different Tunisian areas and 203 related healthy relatives to 90 Tunisian pemphigus foliaceus patients. Results Seventy-six patients (84.4%), 20 healthy controls (7.4%), and 32 relatives (15.76%) had anti-desmoglein 1 antibodies. In southern regions where pemphigus foliaceus is associated with a significant sex ratio imbalance (9 female : 1 male in the south vs. 2.3 : 1 in the north) and a lower mean age of disease onset (33.5 in the south vs. 45 years in the north), a higher prevalence of anti-desmoglein 1 antibodies in healthy controls was observed (9.23% vs. 5.71% in the north). Interestingly, the highest prevalence of anti-desmoglein 1 antibodies in healthy relatives (up to 22%) was observed in the most rural southern localities. More than half anti-desmoglein 1-positive healthy controls were living in rural conditions with farming as occupation, which suggests that this activity may expose the subjects to particular environmental conditions. CONCLUSION: These results show that the endemic features of Tunisian pemphigus foliaceus are focused in these southern areas more than in other areas and that both environmental and genetic factors contribute to the disease.

[Recurrent rhinitis and pulmonary infections revealing celiac disease: case report]. / Atteinte rhino-pulmonaire révélatrice d'une maladie coeliaque de l'adulte: à propos d'un cas.

Celiac disease is associated with a number of extra-gastrointestinal features such as hepatitis, arthralgia, and recurrent foetal loss. However respiratory involvement is an extremely rare disorder. We report a case of celiac disease revealed by bronchiectasia in a 39-year-old man. The patient reported a history of recurrent pulmonary infections and intermittent intestinal symptoms in childhood. Wegner granulomatosis was initially suspected because of rhinopulmonary involvement. Serum's patient was tested for ANCA and anti-tissue antibodies. The latest test was performed on histological sections from rat and revealed the presence of anti-reticulin antibodies. Further testing, for anti-tissue transglutaminase and anti-endomysium antibodies, revealed positive results. Celiac disease was confirmed by histological examination of intestinal biopsy. Pulmonary symptoms were improved on a gluten free diet suggesting a causal relationship between celiac disease and respiratory symptoms.

Pemphigus vulgaris and pemphigus foliaceus: similar prognosis?

BACKGROUND: Pemphigus is a rare, life-threatening, acquired autoimmune bullous dermatosis. The prognosis of pemphigus foliaceus (PF) is usually regarded as more favourable than that of pemphigus vulgaris (PV). Our study aims to compare the clinical course of PV and PF in 37 patients. PATIENTS AND METHODS: We conducted a retrospective study over a period of eight years (1994-2001). The patients were referred during this period and were followed until December 2003. PF and PV were included based on clinical, histological and immunopathological criteria. RESULTS: In our study there was no significant difference between PF group and PV group concerning; age, sex, duration of the disease, presence of disseminated lesions, treatment, healing time, remission, relapse, complications, death and follows up duration. The survival graph showed no difference between the two groups for the first two relapses. There was a tendency to significance concerning an additional treatment and relapses frequency in the PF group. CONCLUSIONS: Few studies in the literature were interested in the evolution of the two forms of pemphigus. They showed that the two populations share the same clinical course; nevertheless they revealed the frequency of partial remission, failed treatment, relapses, necessity of high dose of corticosteroids, and difficulties of discontinuing treatment in PF. Our study, suggests that PF and PV may share the same clinical course.

[Physiopathology of osteoarthritis. From normal cartilage to osteoarthritic cartilage: risk factors and inflammatory mechanisms]. / Physiopathologie de l'arthrose. Du cartilage normal au cartilage arthrosique: facteurs de prédisposition et mécanismes inflammatoires.

INTRODUCTION: osteoarthritis is the most prevalent form of arthritis in the world. With the progressive ageing of the population, it becomes a major problem of public health. Osteoarthritis is a degenerative affection characterized by many disorders leading to a structural and functional defect of one or several joints. CURRENT KNOWLEDGE AND KEY POINTS: In this review, we focus on the main inflammatory mechanisms occurring in cartilage during primary osteoarthritis. We also describe some well established risk factors involved in the pathogenesis of this disease such as age, overload and genetic factors. Indeed, osteoarthritis is the result of an imbalance between the processes of degradation and the attempts of repair by the chondrocyte which is the exclusive cell type in cartilage. Degradation is induced by several chemical substances such as proteolytic enzymes (metalloproteinases) and pro-inflammatory cytokines especially interleukin 1beta. To face these events, the chondrocyte starts attempts of repair by secreting growth factors (Transforming Growth Factor and Insulin Growth Factor) or anti-inflammatory cytokines (interleukins 4 and 10) unsuccessfully. All these events will lead to the structural modifications observed in the osteoarthritic cartilage. PROSPECTS: A better comprehension of the physiopathology of osteoarthritis will allow an improvement of therapeutic strategies of this common and invalidating disease.