Approaches to Medical Emergencies on Commercial Flights.

In-flight medical incidents are becoming increasingly critical as passengers with diverse health profiles increase in the skies. In this paper, we reviewed how airlines, aviation authorities, and healthcare professionals respond to such emergencies. The analysis was focused on the strategies developed by the top ten airlines in the world by examining training in basic first aid, collaboration with ground-based medical support, and use of onboard medical equipment. Appropriate training of crew members, availability of adequate medical resources on board airplanes, and improved capabilities of dialogue between a flying plane and medical doctors on the ground will contribute to a positive outcome of the majority of medical issues on board airlines. In this respect, the adoption of advanced telemedicine solutions and the improvement of real-time teleconsultations between aircraft and ground-based professionals can represent the future of aviation medicine, offering more safety and peace of mind to passengers in case of medical problems during a flight.

Brain Tumor Detection and Categorization with Segmentation of Improved Unsupervised Clustering Approach and Machine Learning Classifier.

There is no doubt that brain tumors are one of the leading causes of death in the world. A biopsy is considered the most important procedure in cancer diagnosis, but it comes with drawbacks, including low sensitivity, risks during biopsy treatment, and a lengthy wait for results. Early identification provides patients with a better prognosis and reduces treatment costs. The conventional methods of identifying brain tumors are based on medical professional skills, so there is a possibility of human error. The labor-intensive nature of traditional approaches makes healthcare resources expensive. A variety of imaging methods are available to detect brain tumors, including magnetic resonance imaging (MRI) and computed tomography (CT). Medical imaging research is being advanced by computer-aided diagnostic processes that enable visualization. Using clustering, automatic tumor segmentation leads to accurate tumor detection that reduces risk and helps with effective treatment. This study proposed a better Fuzzy C-Means segmentation algorithm for MRI images. To reduce complexity, the most relevant shape, texture, and color features are selected. The improved Extreme Learning machine classifies the tumors with 98.56% accuracy, 99.14% precision, and 99.25% recall. The proposed classifier consistently demonstrates higher accuracy across all tumor classes compared to existing models. Specifically, the proposed model exhibits accuracy improvements ranging from 1.21% to 6.23% when compared to other models. This consistent enhancement in accuracy emphasizes the robust performance of the proposed classifier, suggesting its potential for more accurate and reliable brain tumor classification. The improved algorithm achieved accuracy, precision, and recall rates of 98.47%, 98.59%, and 98.74% on the Fig share dataset and 99.42%, 99.75%, and 99.28% on the Kaggle dataset, respectively, which surpasses competing algorithms, particularly in detecting glioma grades. The proposed algorithm shows an improvement in accuracy, of approximately 5.39%, in the Fig share dataset and of 6.22% in the Kaggle dataset when compared to existing models. Despite challenges, including artifacts and computational complexity, the study's commitment to refining the technique and addressing limitations positions the improved FCM model as a noteworthy advancement in the realm of precise and efficient brain tumor identification.

Automatic Detection and Classification of Hypertensive Retinopathy with Improved Convolution Neural Network and Improved SVM.

Hypertensive retinopathy (HR) results from the microvascular retinal changes triggered by hypertension, which is the most common leading cause of preventable blindness worldwide. Therefore, it is necessary to develop an automated system for HR detection and evaluation using retinal images. We aimed to propose an automated approach to identify and categorize the various degrees of HR severity. A new network called the spatial convolution module (SCM) combines cross-channel and spatial information, and the convolution operations extract helpful features. The present model is evaluated using publicly accessible datasets ODIR, INSPIREVR, and VICAVR. We applied the augmentation to artificially increase the dataset of 1200 fundus images. The different HR severity levels of normal, mild, moderate, severe, and malignant are finally classified with the reduced time when compared to the existing models because in the proposed model, convolutional layers run only once on the input fundus images, which leads to a speedup and reduces the processing time in detecting the abnormalities in the vascular structure. According to the findings, the improved SVM had the highest detection and classification accuracy rate in the vessel classification with an accuracy of 98.99% and completed the task in 160.4 s. The ten-fold classification achieved the highest accuracy of 98.99%, i.e., 0.27 higher than the five-fold classification accuracy and the improved KNN classifier achieved an accuracy of 98.72%. When computation efficiency is a priority, the proposed model's ability to quickly recognize different HR severity levels is significant.

Risk prediction model of self-reported hypertension for telemedicine based on the sociodemographic, occupational and health-related characteristics of seafarers: a cross-sectional epidemiological study.

OBJECTIVES: High blood pressure is a common health concern among seafarers. However, due to the remote nature of their work, it can be difficult for them to access regular monitoring of their blood pressure. Therefore, the development of a risk prediction model for hypertension in seafarers is important for early detection and prevention. This study developed a risk prediction model of self-reported hypertension for telemedicine. DESIGN: A cross-sectional epidemiological study was employed. SETTING: This study was conducted among seafarers aboard ships. Data on sociodemographic, occupational and health-related characteristics were collected using anonymous, standardised questionnaires. PARTICIPANTS: This study involved 8125 seafarers aged 18-70 aboard 400 vessels between November 2020 and December 2020. 4318 study subjects were included in the analysis. Seafarers over 18 years of age, active (on duty) during the study and willing to give informed consent were the inclusion criteria. OUTCOME MEASURES: We calculated the adjusted OR (AOR) with 95% CIs using multiple logistic regression models to estimate the associations between sociodemographic, occupational and health-related characteristics and self-reported hypertension. We also developed a risk prediction model for self-reported hypertension for telemedicine based on seafarers' characteristics. RESULTS: Among the 4318 participants, 55.3% and 44.7% were non-officers and officers, respectively. 20.8% (900) of the participants reported having hypertension. Multivariable analysis showed that age (AOR: 1.08, 95% CI 1.07 to 1.10), working long hours per week (AOR: 1.02, 95% CI 1.01 to 1.03), work experience at sea (10+ years) (AOR: 1.79, 95% CI 1.33 to 2.42), being a non-officer (AOR: 1.75, 95% CI 1.44 to 2.13), snoring (AOR: 3.58, 95% CI 2.96 to 4.34) and other health-related variables were independent predictors of self-reported hypertension, which were included in the final risk prediction model. The sensitivity, specificity and accuracy of the predictive model were 56.4%, 94.4% and 86.5%, respectively. CONCLUSION: A risk prediction model developed in the present study is accurate in predicting self-reported hypertension in seafarers' onboard ships.

Automatic Detection and Classification of Diabetic Retinopathy Using the Improved Pooling Function in the Convolution Neural Network.

Diabetic retinopathy (DR) is an eye disease associated with diabetes that can lead to blindness. Early diagnosis is critical to ensure that patients with diabetes are not affected by blindness. Deep learning plays an important role in diagnosing diabetes, reducing the human effort to diagnose and classify diabetic and non-diabetic patients. The main objective of this study was to provide an improved convolution neural network (CNN) model for automatic DR diagnosis from fundus images. The pooling function increases the receptive field of convolution kernels over layers. It reduces computational complexity and memory requirements because it reduces the resolution of feature maps while preserving the essential characteristics required for subsequent layer processing. In this study, an improved pooling function combined with an activation function in the ResNet-50 model was applied to the retina images in autonomous lesion detection with reduced loss and processing time. The improved ResNet-50 model was trained and tested over the two datasets (i.e., APTOS and Kaggle). The proposed model achieved an accuracy of 98.32% for APTOS and 98.71% for Kaggle datasets. It is proven that the proposed model has produced greater accuracy when compared to their state-of-the-art work in diagnosing DR with retinal fundus images.

The Use of Radio and Telemedicine by TMAS Centers in Provision of Medical Care to Seafarers: A Systematic Review.

Objective: From medicine via radio to telemedicine, personalized medical care at sea has improved significantly over the years. Currently, very little research has been conducted on telemedicine services and tools at sea. This study aims to review real-time case studies of seafarers' personalized treatment via telemedical devices published in medical journals. Methods: A literature search was conducted using three libraries such as PubMed (Medline), Cumulative Index to Nursing and Allied Health Literature (CINAHL), BioMed Central, and Google Scholar. The Medical Subject Headings (MeSH) were used for information retrieval and document selection was conducted based on the guidelines of preferred reporting items for systematic reviews and meta-analyses (PRISMA) 2020 flowchart. Selected articles were subjected to quality checks using the Newcastle-Ottawa scale (NOS). Results: The literature search produced 785 papers and documents. The selection was conducted in three stages such as selection, screening, and inclusion. After applying predefined inclusion and exclusion criteria, only three articles on real-time medical assistance with telemedical tools were identified. It is reported that medical attention is delivered to seafarers in real time thanks to advancements in telemedicine, satellite technology, and video conferencing. Conclusions: By improving the quality of medical care and reducing response times for medical emergencies at sea, lives have been saved. There are still several gaps despite these advancements. Medical assistance at sea should therefore be improved to address many of the still unsolved issues.

Artificial Intelligence Models in the Diagnosis of Adult-Onset Dementia Disorders: A Review.

Background: The progressive aging of populations, primarily in the industrialized western world, is accompanied by the increased incidence of several non-transmittable diseases, including neurodegenerative diseases and adult-onset dementia disorders. To stimulate adequate interventions, including treatment and preventive measures, an early, accurate diagnosis is necessary. Conventional magnetic resonance imaging (MRI) represents a technique quite common for the diagnosis of neurological disorders. Increasing evidence indicates that the association of artificial intelligence (AI) approaches with MRI is particularly useful for improving the diagnostic accuracy of different dementia types. Objectives: In this work, we have systematically reviewed the characteristics of AI algorithms in the early detection of adult-onset dementia disorders, and also discussed its performance metrics. Methods: A document search was conducted with three databases, namely PubMed (Medline), Web of Science, and Scopus. The search was limited to the articles published after 2006 and in English only. The screening of the articles was performed using quality criteria based on the Newcastle-Ottawa Scale (NOS) rating. Only papers with an NOS score ≥ 7 were considered for further review. Results: The document search produced a count of 1876 articles and, because of duplication, 1195 papers were not considered. Multiple screenings were performed to assess quality criteria, which yielded 29 studies. All the selected articles were further grouped based on different attributes, including study type, type of AI model used in the identification of dementia, performance metrics, and data type. Conclusions: The most common adult-onset dementia disorders occurring were Alzheimer's disease and vascular dementia. AI techniques associated with MRI resulted in increased diagnostic accuracy ranging from 73.3% to 99%. These findings suggest that AI should be associated with conventional MRI techniques to obtain a precise and early diagnosis of dementia disorders occurring in old age.

Maritime Telemedicine: Design and Development of an Advanced Healthcare System Called Marine Doctor.

Background: The availability of better healthcare services is critical for onboard seafarers. The development of expert systems can help ships with limited medical facilities, which allow the shipside doctors to properly refer symptoms to remote doctors. This allows clinicians to make a correct diagnosis from there, which leads to proper treatment. A software named Marine Doctor (M Doc) has been developed by incorporating computing technologies to address this objective. Methods: With the help of Information and Communication Technology (ICT) this application can support the provision of appropriate medical assistance to seafarers. The system was developed with Python Tkinter (frontend) and PHP (backend) languages. MySQL was used as a server database. Results: Seafarers can use M Doc to benefit from medical advice that can reduce complications due to misdiagnosis and help doctors to make better-informed decisions. By automatically collecting appropriate sequences of symptoms, doctors will be able to generate proper information for referral of patient symptoms and subsequent advice based on the data. Conclusions: Technology that supports experts on board ships in better interacting with Telemedical Maritime Assistance Services (TMAS) could define the future of medical assistance at sea.

A Survey on the Role of Artificial Intelligence in Biobanking Studies: A Systematic Review.

Introduction: In biobanks, participants' biological samples are stored for future research. The application of artificial intelligence (AI) involves the analysis of data and the prediction of any pathological outcomes. In AI, models are used to diagnose diseases as well as classify and predict disease risks. Our research analyzed AI's role in the development of biobanks in the healthcare industry, systematically. Methods: The literature search was conducted using three digital reference databases, namely PubMed, CINAHL, and WoS. Guidelines for preferred reporting elements for systematic reviews and meta-analyses (PRISMA)-2020 in conducting the systematic review were followed. The search terms included "biobanks", "AI", "machine learning", and "deep learning", as well as combinations such as "biobanks with AI", "deep learning in the biobanking field", and "recent advances in biobanking". Only English-language papers were included in the study, and to assess the quality of selected works, the Newcastle-Ottawa scale (NOS) was used. The good quality range (NOS ≥ 7) is only considered for further review. Results: A literature analysis of the above entries resulted in 239 studies. Based on their relevance to the study's goal, research characteristics, and NOS criteria, we included 18 articles for reviewing. In the last decade, biobanks and artificial intelligence have had a relatively large impact on the medical system. Interestingly, UK biobanks account for the highest percentage of high-quality works, followed by Qatar, South Korea, Singapore, Japan, and Denmark. Conclusions: Translational bioinformatics probably represent a future leader in precision medicine. AI and machine learning applications to biobanking research may contribute to the development of biobanks for the utility of health services and citizens.

Cascaded Deep Learning Frameworks in Contribution to the Detection of Parkinson's Disease.

Parkinson's disease (PD) is a progressive neurodegenerative disorder characterized by motor impairment, as well as tremors, stiffness, and rigidity. Besides the typical motor symptomatology, some Parkinsonians experience non-motor symptoms such as hyposmia, constipation, urinary dysfunction, orthostatic hypotension, memory loss, depression, pain, and sleep disturbances. The correct diagnosis of PD cannot be easy since there is no standard objective approach to it. After the incorporation of machine learning (ML) algorithms in medical diagnoses, the accuracy of disease predictions has improved. In this work, we have used three deep-learning-type cascaded neural network models based on the audial voice features of PD patients, called Recurrent Neural Networks (RNN), Multilayer Perception (MLP), and Long Short-Term Memory (LSTM), to estimate the accuracy of PD diagnosis. A performance comparison between the three models was performed on a sample of the subjects' voice biomarkers. Experimental outcomes suggested that the LSTM model outperforms others with 99% accuracy. This study has also presented loss function curves on the relevance of good-fitting models to the detection of neurodegenerative diseases such as PD.

LASSO Regression Modeling on Prediction of Medical Terms among Seafarers' Health Documents Using Tidy Text Mining.

Generally, seafarers face a higher risk of illnesses and accidents than land workers. In most cases, there are no medical professionals on board seagoing vessels, which makes disease diagnosis even more difficult. When this occurs, onshore doctors may be able to provide medical advice through telemedicine by receiving better symptomatic and clinical details in the health abstracts of seafarers. The adoption of text mining techniques can assist in extracting diagnostic information from clinical texts. We applied lexicon sentimental analysis to explore the automatic labeling of positive and negative healthcare terms to seafarers' text healthcare documents. This was due to the lack of experimental evaluations using computational techniques. In order to classify diseases and their associated symptoms, the LASSO regression algorithm is applied to analyze these text documents. A visualization of symptomatic data frequency for each disease can be achieved by analyzing TF-IDF values. The proposed approach allows for the classification of text documents with 93.8% accuracy by using a machine learning model called LASSO regression. It is possible to classify text documents effectively with tidy text mining libraries. In addition to delivering health assistance, this method can be used to classify diseases and establish health observatories. Knowledge developed in the present work will be applied to establish an Epidemiological Observatory of Seafarers' Pathologies and Injuries. This Observatory will be a collaborative initiative of the Italian Ministry of Health, University of Camerino, and International Radio Medical Centre (C.I.R.M.), the Italian TMAS.

Improved Alzheimer's Disease Detection by MRI Using Multimodal Machine Learning Algorithms.

Adult-onset dementia disorders represent a challenge for modern medicine. Alzheimer's disease (AD) represents the most diffused form of adult-onset dementias. For half a century, the diagnosis of AD was based on clinical and exclusion criteria, with an accuracy of 85%, which did not allow for a definitive diagnosis, which could only be confirmed by post-mortem evaluation. Machine learning research applied to Magnetic Resonance Imaging (MRI) techniques can contribute to a faster diagnosis of AD and may contribute to predicting the evolution of the disease. It was also possible to predict individual dementia of older adults with AD screening data and ML classifiers. To predict the AD subject status, the MRI demographic information and pre-existing conditions of the patient can help to enhance the classifier performance. In this work, we proposed a framework based on supervised learning classifiers in the dementia subject categorization as either AD or non-AD based on longitudinal brain MRI features. Six different supervised classifiers are incorporated for the classification of AD subjects and results mentioned that the gradient boosting algorithm outperforms other models with 97.58% of accuracy.

A Two-Year Comparative Evaluation of Clinical Performance of a Nanohybrid Composite Resin to a Flowable Composite Resin.

OBJECTIVE: This prospective in vivo study aimed to compare the clinical behavior of a flowable composite resin (Genial Universal Flo, GC) and a nanohybrid universal composite resin (Tetric Evo Ceram, Ivoclar Vivadent) used in Class I and II direct esthetic restorations in posterior teeth. METHODS: A total of 108 Class I and II direct restorations were performed in patients aged between 20 and 60 years. The originality of this study lies in the fact that both materials were placed in pairs, in the same clinical environment (i.e., the same patient and the same type of tooth). The evaluations were performed now of restoration and after 2-weeks, 6-, 12-, and 24-months intervals using clinical examination, clinical photographs, and radiological examination, according to modified USPHS criteria. Statistical analysis was performed using the Fisher exact test and chi-square analysis. RESULTS: At baseline, the universal composite resin showed better esthetic properties such as surface luster, surface staining marginal staining. Both materials regressed significantly over time with no significant difference between groups. CONCLUSIONS: Both flowable and nanohybrid composite resins exhibit acceptable clinical performance. The present 24 months of evaluation of different composites showed that the G-ænial Universal Flo could be an effective esthetic material for posterior restoration. No significant difference between both materials over time concerning surface luster, surface staining, and marginal staining.

Second wave of COVID-19 in Italy: Preliminary estimation of reproduction number and cumulative case projections.

The second wave of a novel coronavirus in Italy has caused 247,369 new cases and 1782 deaths only in October 2020. This significantly alarming infectious disease controlling board to impose again mitigation measures for controlling the epidemic growth. In this paper, we estimate the latest COVID-19 reproduction number (R_0) and project the epidemic size for the future 45 days. The R_0 value has calculated as 2.83 (95% CI: 1.5-4.2) and the cumulative incidences 100,015 (95% CI; 73,201-100,352), and daily incidences might be reached up to 15,012 (95% CI: 8234-16,197) respectively.

The Benefits of Telemedicine in Personalized Prevention of Cardiovascular Diseases (CVD): A Systematic Review.

INTRODUCTION: Adverse effects on personalized care and outcomes of cardiovascular diseases (CVD) could occur if health systems do not work in an efficient manner. The pandemic caused by COVID-19 has opened new perspectives for the execution and advancement of cardiovascular tests through telemedicine platforms. OBJECTIVE: This study aimed to analyze the usefulness of telemedical systems for providing personal care in the prevention of CVD. METHODS: A systematic review analysis was conducted on the literature available from libraries such as PubMed (Medline), Scopus (Embase), and Cumulative Index to Nursing and Allied Health Literature (CINAHL). Data available in the last 10 years (2011-2020) were also examined by PRISMA guidelines. The selected studies were divided into two categories: (1) benefits of telemedicine in CVD prevention, and (2) recent progress in telemedical services for personalized care of CVD. RESULTS: The literature search produced 587 documents, and 19 articles were considered in this review. Results highlighted that the timely delivery of preventive care for CVD which can be implemented virtually can benefit and modify morbidity and mortality. This could also reduce the pressure on hospitals by decreasing acute CVD occurrence among the general population. The use of these technologies can also help to reduce access to hospitals and other medical devices when not necessary. CONCLUSIONS: Telemedicine platforms can be used for regular checkups for CVD and contribute to preventing the occurrence of acute events and more in general the progression of CVD.

Google Trend Analysis and Paradigm Shift of Online Education Platforms during the COVID-19 Pandemic.

Objective: The largest pandemic in history, the COVID-19 pandemic, has been declared a doomsday globally. The second wave spreading worldwide has devastating consequences in every sector of life. Several measures to contain and curb its infection have forged significant challenges for the education community. With an estimated 1.6 billion learners, the closure of schools and other educational institutions has impacted more than 90% of students worldwide from the elementary to tertiary level. Methods: In a view to studying impacts on student's fraternity, this article aims at addressing alternative ways of educating-more specifically, online education-through the analysis of Google trends for the past year. The study analyzed the platforms of online teaching and learning systems that have been enabling remote learning, thereby limiting the impact on the education system. Thorough text analysis is performed on an existing dataset from Kaggle to retrieve insight on the clustering of words that are more often looked at during this pandemic to find the general patterns of their occurrence. Findings: The results show that the coronavirus patients are the most trending patterns in word search clustering, with the education system being at the control and preventive measures to bring equilibrium in the system of education. There has been significant growth in online platforms in the last year. Existing assets of educational establishments have effectively converted conventional education into new-age online education with the help of virtual classes and other key online tools in this continually fluctuating scholastic setting. The effective usage of teaching tools such as Microsoft Teams, Zoom, Google Meet, and WebEx are the most used online platforms for the conduction of classes, and whiteboard software tools and learning apps such as Vedantu, Udemy, Byju's, and Whitehat Junior have been big market players in the education system over the pandemic year, especially in India. Conclusions: The article helps to draw a holistic approach of ongoing online teaching-learning methods during the lockdown and also highlights changes that took place in the conventional education system amid the COVID pandemic to overcome the persisting disruption in academic activities and to ensure correct perception towards the online procedure as a normal course of action in the new educational system. To fill in the void of classroom learning and to minimize the virus spread over the last year, digital learning in various schools and colleges has been emphasized, leading to a significant increase in the usage of whiteboard software platforms.

Impact of Obesity-Induced Inflammation on Cardiovascular Diseases (CVD).

Overweight and obesity are key risk factors of cardiovascular disease (CVD). Obesity is currently presented as a pro-inflammatory state with an expansion in the outflow of inflammatory cytokines, such as interleukin-6 (IL-6) and tumor necrosis factor-alpha (TNF-α), alongside the expanded emission of leptin. The present review aimed to evaluate the relationship between obesity and inflammation and their impacts on the development of cardiovascular disease. A literature search was conducted by employing three academic databases, namely PubMed (Medline), Scopus (EMBASE), and the Cumulative Index to Nursing and Allied Health Literature (CINAHL). The search presented 786 items, and by inclusion and exclusion filterers, 59 works were considered for final review. The Newcastle-Ottawa Scale (NOS) method was adopted to conduct quality assessment; 19 papers were further selected based on the quality score. Obesity-related inflammation leads to a low-grade inflammatory state in organisms by upregulating pro-inflammatory markers and downregulating anti-inflammatory cytokines, thereby contributing to cardiovascular disease pathogenesis. Because of inflammatory and infectious symptoms, adipocytes appear to instigate articulation and discharge a few intense stage reactants and carriers of inflammation. Obesity and inflammatory markers are strongly associated, and are important factors in the development of CVD. Hence, weight management can help prevent cardiovascular risks and poor outcomes by inhibiting inflammatory mechanisms.

Sentimental Analysis of COVID-19 Tweets Using Deep Learning Models.

The novel coronavirus disease (COVID-19) is an ongoing pandemic with large global attention. However, spreading false news on social media sites like Twitter is creating unnecessary anxiety towards this disease. The motto behind this study is to analyses tweets by Indian netizens during the COVID-19 lockdown. The data included tweets collected on the dates between 23 March 2020 and 15 July 2020 and the text has been labelled as fear, sad, anger, and joy. Data analysis was conducted by Bidirectional Encoder Representations from Transformers (BERT) model, which is a new deep-learning model for text analysis and performance and was compared with three other models such as logistic regression (LR), support vector machines (SVM), and long-short term memory (LSTM). Accuracy for every sentiment was separately calculated. The BERT model produced 89% accuracy and the other three models produced 75%, 74.75%, and 65%, respectively. Each sentiment classification has accuracy ranging from 75.88-87.33% with a median accuracy of 79.34%, which is a relatively considerable value in text mining algorithms. Our findings present the high prevalence of keywords and associated terms among Indian tweets during COVID-19. Further, this work clarifies public opinion on pandemics and lead public health authorities for a better society.

Assessment of Awareness and Knowledge on Novel Coronavirus (COVID-19) Pandemic among Seafarers.

Background: The ongoing pandemic due to the novel coronavirus (COVID-19) is becoming a serious global threat. Experts suggest that the infection can be controlled by immediate prevention measures. Sailing is one of the occupational categories more vulnerable to this virus outbreak due to the proximity of the working conditions. Objective: Awareness and knowledge assessments of seafarers towards the current epidemic is mandatory to understand the effectiveness and success of the infection control measures adopted by shipping companies. Methods: In this study, we presented an online questionnaire survey to determine the knowledge levels of COVID-19 among seafarers. The data were collected by self-reported survey, and analysis was done by the analysis of variance (ANOVA). The t-test was used to understand the knowledge attitude differences to COVID-19 among different occupational groups of seafarers, and the p-value ≤ of 0.05 was considered statistically significant. Results: Among 1,458 responses received, 92.82% had a college or university degree. The results reported that the mean COVID-19 knowledge score was 5.82 (standard deviation = 0.51, range 0-6), and the overall correct percentage was 97%. There was a statistically significant difference between age groups (F (4, 1453) = 5.44, p < 0.001) and educational groups (F (4, 1453) = 1.52, p < 0.001). The knowledge score was not significantly different across the educational status of the participants (F (2, 1455) = 1.52, p = 0.220). Conclusions: The present study highlighted good knowledge and behaviours among sailors about COVID-19. However, shipping companies need to come up with new campaigns to hold optimistic practices and suitable guidelines on ships, including cruise boats, to keep sea workers always alert and collaborative in mitigating the spread of COVID-19.

Conceptual Framework and Designing for a Seafarers' Health Observatory (SHO) Based on the Centro Internazionale Radio Medico (C.I.R.M.) Data Repository.

BACKGROUND: Health observatory (HO) models are helpful in gathering, analyzing, interpreting, and circulating reliable and quality information on population health and health service delivery. In this study, we proposed an HO conceptual model to enhance seafarer's health, which subjects to disease trends. METHODS: Three methods were followed during the study: a systematic collection of seafarer's health data from the Centro Internazionale Radio Medico (C.I.R.M.) repository, an integrative review of existing seafarer's policy, and both open and closed questionnaires were distributed to stakeholders to develop clinical knowledge. C.I.R.M. is the Italian Telemedical Maritime Assistance Service (TMAS). Results and Discussion. A three-layer HO framework was developed, and each layer had its components and functionalities. The proposed HO model integrated with the outcomes of the mentioned methods was working as the origin of the framework. In this way, we can design a standard infrastructure in ships and risk assessment conduction.