Machine learning applications for COVID-19 outbreak management

A dynamic approach to support outbreak management using reinforcement learning and semi-connected SEIQR models

BACKGROUND

Containment measures slowed the spread of COVID-19 but led to a global economic crisis. We establish a reinforcement learning (RL) algorithm that balances disease control and economic activities.

METHODS

To train the RL agent, we design an RL environment with 4 semi-connected regions to represent the COVID-19 epidemic in Tokyo, Osaka, Okinawa, and Hokkaido, Japan. Every region is governed by a Susceptible-Exposed-Infected-Quarantined-Removed (SEIQR) model and has a transport hub to connect with other regions. The allocation of the synthetic population and inter-regional traveling is determined by population-weighted density. The agent learns the best policy from interacting with the RL environment, which involves obtaining daily observations, performing actions on individual movement and screening, and receiving feedback from the reward function. After training, we implement the agent into RL environments describing the actual epidemic waves of the four regions to observe the agent's performance.

RESULTS

For all epidemic waves covered by our study, the trained agent reduces the peak number of infectious cases and shortens the epidemics (from 165 to 35 cases and 148 to 131 days for the 5th wave). The agent is generally strict on screening but easy on movement, except for Okinawa, where the agent is easy on both actions. Action timing analyses indicate that restriction on movement is elevated when the number of exposed or infectious cases remains high or infectious cases increase rapidly, and stringency on screening is eased when the number of exposed or infectious cases drops quickly or to a regional low. For Okinawa, action on screening is tightened when the number of exposed or infectious cases increases rapidly.

CONCLUSIONS

Our experiments exhibit the potential of the RL in assisting policy-making and how the semi-connected SEIQR models establish an interactive environment for imitating cross-regional human flows.

Opportunities and challenges of artificial intelligence and distributed systems to improve the quality of healthcare service

The healthcare sector, characterized by vast datasets and many diseases, is pivotal in shaping community health and overall quality of life. Traditional healthcare methods, often characterized by limitations in disease prevention, predominantly react to illnesses after their onset rather than proactively averting them. The advent of Artificial Intelligence (AI) has ushered in a wave of transformative applications designed to enhance healthcare services, with Machine Learning (ML) as a noteworthy subset of AI. ML empowers computers to analyze extensive datasets, while Deep Learning (DL), a specific ML methodology, excels at extracting meaningful patterns from these data troves. Despite notable technological advancements in recent years, the full potential of these applications within medical contexts remains largely untapped, primarily due to the medical community's cautious stance toward novel technologies. The motivation of this paper lies in recognizing the pivotal role of the healthcare sector in community well-being and the necessity for a shift toward proactive healthcare approaches. To our knowledge, there is a notable absence of a comprehensive published review that delves into ML, DL and distributed systems, all aimed at elevating the Quality of Service (QoS) in healthcare. This study seeks to bridge this gap by presenting a systematic and organized review of prevailing ML, DL, and distributed system algorithms as applied in healthcare settings. Within our work, we outline key challenges that both current and future developers may encounter, with a particular focus on aspects such as approach, data utilization, strategy, and development processes. Our study findings reveal that the Internet of Things (IoT) stands out as the most frequently utilized platform (44.3 %), with disease diagnosis emerging as the predominant healthcare application (47.8 %). Notably, discussions center significantly on the prevention and identification of cardiovascular diseases (29.2 %). The studies under examination employ a diverse range of ML and DL methods, along with distributed systems, with Convolutional Neural Networks (CNNs) being the most commonly used (16.7 %), followed by Long Short-Term Memory (LSTM) networks (14.6 %) and shallow learning networks (12.5 %). In evaluating QoS, the predominant emphasis revolves around the accuracy parameter (80 %). This study highlights how ML, DL, and distributed systems reshape healthcare. It contributes to advancing healthcare quality, bridging the gap between technology and medical adoption, and benefiting practitioners and patients.

Concordance and generalization of an AI algorithm with real-world clinical data in the pre-omicron and omicron era

All viruses, including SARS-CoV-2, the virus responsible for COVID-19, continue to evolve, which can lead to new variants. The objective of this study is to assess the agreement between real-world clinical data and an algorithm that utilizes laboratory markers and age to predict the progression of disease severity in COVID-19 patients during the pre-Omicron and Omicron variant periods. The study evaluated the performance of a deep learning (DL) algorithm in predicting disease severity scores for COVID-19 patients using data from the USA, Spain, and Turkey (Ankara City Hospital (ACH) data set). The algorithm was developed and validated using pre-Omicron era data and was tested on both pre-Omicron and Omicron-era data. The predictions were compared to the actual clinical outcomes using a multidisciplinary approach. The concordance index values for all datasets ranged from 0.71 to 0.81. In the ACH cohort, a negative predictive value (NPV) of 0.78 or higher was observed for severe patients in both the pre-Omicron and Omicron eras, which is consistent with the algorithm's performance in the development cohort.

Applications of Machine Learning (ML) and Mathematical Modeling (MM) in Healthcare with Special Focus on Cancer Prognosis and Anticancer Therapy: Current Status and Challenges

The use of data-driven high-throughput analytical techniques, which has given rise to computational oncology, is undisputed. The widespread use of machine learning (ML) and mathematical modeling (MM)-based techniques is widely acknowledged. These two approaches have fueled the advancement in cancer research and eventually led to the uptake of telemedicine in cancer care. For diagnostic, prognostic, and treatment purposes concerning different types of cancer research, vast databases of varied information with manifold dimensions are required, and indeed, all this information can only be managed by an automated system developed utilizing ML and MM. In addition, MM is being used to probe the relationship between the pharmacokinetics and pharmacodynamics (PK/PD interactions) of anti-cancer substances to improve cancer treatment, and also to refine the quality of existing treatment models by being incorporated at all steps of research and development related to cancer and in routine patient care. This review will serve as a consolidation of the advancement and benefits of ML and MM techniques with a special focus on the area of cancer prognosis and anticancer therapy, leading to the identification of challenges (data quantity, ethical consideration, and data privacy) which are yet to be fully addressed in current studies.

Intolerance of uncertainty and mental health in China "Post-pandemic" age: The mediating role of difficulties in emotion regulation

The Chinese government adjusted its national epidemic prevention and control policy in December 2022 after the worldwide declaration of COVID-19 as a common influenza. After the policy adjustment, there has been widespread infection in China, which has brought a lot of uncertainty to the lives and studies of Chinese university students. This study focused on the impact of the intolerance of uncertainty for COVID-19 (IUC) on the emotional and mental health of college students in China "Post-pandemic" age. This study examined the mediating role of difficulties in emotion regulation (DER) between IUC and mental health (MH). 1,281 university students in China were surveyed using the intolerance of uncertainty for COVID-19 scale, the difficulties in emotion regulation scale and the mental health scale. A structural equation model was used to test the hypothesis model, and it was shown that IUC had a significant negative effect on the MH of college students and a significant positive effect on the DER. DER had a significant negative effect on the MH, and DER had a complete mediation effect between IUC and MH. The findings of this study enrich our understanding of the influencing factors of mental health of university students under the background of post-epidemic in China, and provide practical reference for universities on how to prevent mental health problems under the current uncertain environment in China.

Machine learning and deep learning-based approach in smart healthcare: Recent advances, applications, challenges and opportunities

In recent years, machine learning (ML) and deep learning (DL) have been the leading approaches to solving various challenges, such as disease predictions, drug discovery, medical image analysis, etc., in intelligent healthcare applications. Further, given the current progress in the fields of ML and DL, there exists the promising potential for both to provide support in the realm of healthcare. This study offered an exhaustive survey on ML and DL for the healthcare system, concentrating on vital state of the art features, integration benefits, applications, prospects and future guidelines. To conduct the research, we found the most prominent journal and conference databases using distinct keywords to discover scholarly consequences. First, we furnished the most current along with cutting-edge progress in ML-DL-based analysis in smart healthcare in a compendious manner. Next, we integrated the advancement of various services for ML and DL, including ML-healthcare, DL-healthcare, and ML-DL-healthcare. We then offered ML and DL-based applications in the healthcare industry. Eventually, we emphasized the research disputes and recommendations for further studies based on our observations.

Imaging and Non-imaging Analytical Techniques Used for Drug Nanosizing and their Patents: An Overview

BACKGROUND

Nanosizing is widely recognized as an effective technique for improving the solubility, dissolution rate, onset of action, and bioavailability of poorly water-soluble drugs. To control the execution and behavior of the output product, more advanced and valuable analytical techniques are required.

OBJECTIVE

The primary intent of this review manuscript was to furnish the understanding of imaging and non-imaging techniques related to nanosizing analysis by focusing on related patents. In addition, the study also aimed to collect and illustrate the information on various classical (laser diffractometry, photon correlation spectroscopy, zeta potential, laser Doppler electrophoresis, X-ray diffractometry, differential scanning calorimeter, scanning electron microscopy, transmission electron microscopy), new, and advanced analytical techniques (improved dynamic light scattering method, Brunauer-Emmett- Teller method, ultrasonic attenuation, biosensor), as well as commercial techniques, like inductively coupled plasma mass spectroscopy, aerodynamic particle sizer, scanning mobility particle sizer, and matrix- assisted laser desorption/ionization mass spectroscopy, which all relate to nano-sized particles.

METHODS

The present manuscript has taken a fresh look at the various aspects of the analytical techniques utilized in the process of nanosizing, and has achieved this through the analysis of a wide range of peer-reviewed literature. All summarized literature studies provide the information that can meet the basic needs of nanotechnology.

RESULTS

A variety of analytical techniques related to the nanosizing process have already been established and have great potential to weed out several issues. However, the current scenarios require more relevant, accurate, and advanced analytical techniques that can minimize the time and deviations associated with different instrumental and process parameters. To meet this requirement, some new and more advanced analytical techniques have recently been discovered, like ultrasonic attenuation technique, BET technique, biosensors, etc. Conclusion: The present overview certifies the significance of different analytical techniques utilized in the nanosizing process. The overview also provides information on various patents related to sophisticated analytical tools that can meet the needs of such an advanced field. The data show that the nanotechnology field will flourish in the coming future.

Flavonoid-metal ion Complexes as Potent Anticancer Metallodrugs: A Comprehensive Review

BACKGROUND

Flavonoids and their analogous are mainly found in pink lady apples, green and black tea (catechins), celery and red peppers, onions, broccoli and spinach, berries, cherries, soybean, citrus fruits, and fungi. The different derivatives of flavonoids belonging to polyphenolic compounds such as 3,4',5,7-Tetrahydroxyflavylium (pelargonidin), 2-(3,4-Dihydroxyphenyl)chromenylium-3,5,7-triol (cyanidin), 3,3',4',5,5',7-Hexahydroxyflavylium (delphinidin), 3,3',4',5,7-Pentahydroxy-5'-methoxyflavylium (petunidin), and 3,4',5,7-Tetrahydroxy-3',5'-dimethoxyflavylium (malvidin) can act as good chelating agents for metal-chelate complex formation. These flavonoid-metal complexes have been reported to have various biomedical and pharmacological activities.

OBJECTIVE

Flavonoid-metal ion complexes display a broad spectrum of biological properties such as antioxidant, anti-inflammatory, anti-allergic, antiviral, anticarcinogenic, and cytotoxic activity. The literature survey showed that flavonoid metal complexes have potential therapeutic properties against various cancerous cells. The objective is to gain insight into the current perspective and development of novel anticancer metallodrugs.

METHODS

The flavonoid-metal ion complexes can be prepared by reacting flavonoid ligand with appropriate metal salt in aqueous or alcoholic reaction medium under stirring or refluxing conditions. In this review article, the various reported methods for the synthesis of flavonoid-metal complexes have been included. The utility of synthetic methods for flavonoid-metal complexes will support the discovery of novel therapeutic drugs.

RESULTS

In this review study, short libraries of flavonoid-metal ion complexes were studied as potential anticancer agents against various human cancer cell lines. The review report reveals that metal ions such as Fe, Co, Ni, Cu, Zn, Rh, Ru, Ga, Ba, Sn etc., when binding to flavonoid ligands, enhance the anticancer activity compared to free ligands. This review study covered some important literature surveys for the last two decades.

CONCLUSION

It has been concluded that flavonoid metal complexes have been associated with a wide range of biological properties that could be noteworthy in the medicinal field. Therefore, to develop a new anticancer drug, it is essential to determine the primordial interaction of drug with DNA under physiological or anatomical conditions. The study of numerous flavonoid metal complexes mentioned in this paper could be the future treatment against various cancerous diseases.

Learner satisfaction with synchronous and asynchronous virtual learning systems during the COVID-19 pandemic in Tehran university of medical sciences: a comparative analysis

BACKGROUND

The need for electronic learning and its systems, especially during specific circumstances and crises, is crucial and fundamental for users in universities. However, what is even more important is the awareness and familiarity of learners with different systems and their appropriate use in e-learning. Therefore, the present study was conducted to determine the satisfaction of learners with synchronous and asynchronous electronic learning systems during the COVID-19 period at Tehran University of Medical Sciences.

METHODS

The present study was a descriptive-analytical study conducted cross-sectionally from the first semester of 2019-2020 academic year until the end of the second semester of 2021-2022 academic year, coinciding with the COVID-19 pandemic. The sample size was determined to be 370 students and 650 staff members using the Krejcie and Morgan table. The face validity and reliability of the research tool, which was a researcher-made questionnaire, was confirmed. Considering a response rate of 75%, 280 completed questionnaires were received from students, and 500 completed questionnaires were collected from employees. For data analysis, absolute and relative frequencies, as well as independent t-test, analysis of variance (ANOVA), and Post Hoc tests in the SPSS software were utilized.

RESULTS

During the COVID-19 pandemic, both students and staff members at Tehran University of Medical Sciences showed a relatively decreasing level of satisfaction with electronic learning. There was a significant difference in satisfaction between these two groups of learners regarding electronic learning (P = 0/031). Learners were relatively more satisfied with the offline system called "Navid" compared to online learning systems. Among the online systems, the highest level of satisfaction was observed with the Skype platform.

CONCLUSION

Although learners expressed relative satisfaction with electronic learning during the COVID-19 period, it is necessary to strengthen infrastructure and provide support services, technical assistance, and continuous updates for electronic learning platforms. This can contribute to more effective and efficient utilization of electronic learning, especially during particular circumstances and crises, or in hybrid models combining online and face to face education and training.

Abdul Hamid AA, Zainuddin NH, Aruchunan E, Abdullah MT. Enhancing COVID-19 Classification Accuracy with a Hybrid SVM-LR Model

Support ector achine (SVM) is a newer machine learning algorithm for classification, while logistic regression (LR) is an older statistical classification method. Despite the numerous studies contrasting SVM and LR, new improvements such as bagging and ensemble have been applied to them since these comparisons were made. This study proposes a new hybrid model based on SVM and LR for predicting small events per variable (EPV). The performance of the hybrid, SVM, and LR models with different EPV values was evaluated using COVID-19 data from December 2019 to May 2020 provided by the WHO. The study found that the hybrid model had better classification performance than SVM and LR in terms of accuracy, mean squared error (MSE), and root mean squared error (RMSE) for different EPV values. This hybrid model is particularly important for medical authorities and practitioners working in the face of future pandemics.

The applications of machine learning techniques in medical data processing based on distributed computing and the Internet of Things

Medical data processing has grown into a prominent topic in the latest decades with the primary goal of maintaining patient data via new information technologies, including the Internet of Things (IoT) and sensor technologies, which generate patient indexes in hospital data networks. Innovations like distributed computing, Machine Learning (ML), blockchain, chatbots, wearables, and pattern recognition can adequately enable the collection and processing of medical data for decision-making in the healthcare era. Particularly, to assist experts in the disease diagnostic process, distributed computing is beneficial by digesting huge volumes of data swiftly and producing personalized smart suggestions. On the other side, the current globe is confronting an outbreak of COVID-19, so an early diagnosis technique is crucial to lowering the fatality rate. ML systems are beneficial in aiding radiologists in examining the incredible amount of medical images. Nevertheless, they demand a huge quantity of training data that must be unified for processing. Hence, developing Deep Learning (DL) confronts multiple issues, such as conventional data collection, quality assurance, knowledge exchange, privacy preservation, administrative laws, and ethical considerations. In this research, we intend to convey an inclusive analysis of the most recent studies in distributed computing platform applications based on five categorized platforms, including cloud computing, edge, fog, IoT, and hybrid platforms. So, we evaluated 27 articles regarding the usage of the proposed framework, deployed methods, and applications, noting the advantages, drawbacks, and the applied dataset and screening the security mechanism and the presence of the Transfer Learning (TL) method. As a result, it was proved that most recent research (about 43%) used the IoT platform as the environment for the proposed architecture, and most of the studies (about 46%) were done in 2021. In addition, the most popular utilized DL algorithm was the Convolutional Neural Network (CNN), with a percentage of 19.4%. Hence, despite how technology changes, delivering appropriate therapy for patients is the primary aim of healthcare-associated departments. Therefore, further studies are recommended to develop more functional architectures based on DL and distributed environments and better evaluate the present healthcare data analysis models.

Natural and socio-environmental factors contribute to the transmissibility of COVID-19: evidence from an improved SEIR model

COVID-19 has ravaged Brazil, and its spread showed spatial heterogeneity. Changes in the environment have been implicated as potential factors involved in COVID-19 transmission. However, considerable research efforts have not elucidated the risk of environmental factors on COVID-19 transmission from the perspective of infectious disease dynamics. The aim of this study is to model the influence of the environment on COVID-19 transmission and to analyze how the socio-ecological factors affecting the probability of virus transmission in 10 states dramatically shifted during the early stages of the epidemic in Brazil. First, this study used a Pearson correlation to analyze the interconnection between COVID-19 morbidity and socio-ecological factors and identified factors with significant correlations as the dominant factors affecting COVID-19 transmission. Then, the time-lag effect of dominant factors on the morbidity of COVID-19 was investigated by constructing a distributed lag nonlinear model and standard two-stage meta-analytic model, and the results were considered in the improved SEIR model. Lastly, a machine learning method was introduced to explore the nonlinear relationship between the environmental propagation probability and socio-ecological factors. By analyzing the impact of environmental factors on virus transmission, it can be found that population mobility directly caused by human activities had a greater impact on virus transmission than temperature and humidity. The heterogeneity of meteorological factors can be accounted for by the diverse climate patterns in Brazil. The improved SEIR model was adopted to explore the interconnection of COVID-19 transmission and the environment, which revealed a new strategy to probe the causal links between them.

Potent multi-target natural inhibitors against SARS-CoV-2 from medicinal plants of the Himalaya: a discovery from hybrid machine learning, chemoinformatics, and simulation assisted screening

The emergence and immune evasion ability of SARS-CoV-2 Omicron strains, mainly BA.5.2 and BF.7 and other variants of concern have raised global apprehensions. With this context, the discovery of multitarget inhibitors may be proven more comprehensive paradigm than its one-drug-to-one target counterpart. In the current study, a library of 271 phytochemicals from 25 medicinal plants from the Indian Himalayan Region has been virtually screened against SARS-CoV-2 by targeting nine virus proteins, viz., papain-like protease, main protease, nsp12, helicase, nsp14, nsp15, nsp16, envelope, and nucleocapsid for screening of a multi-target inhibitor against the viral replication. Initially, 94 phytochemicals were screened by a hybrid machine learning model constructed by combining 6 confirmatory bioassays against SARS-CoV-2 replication using an instance-based learner lazy k-nearest neighbour classifier. Further, 25 screened compounds with excellent drug-like properties were subjected to molecular docking. The phytochemical Cepharadione A from the plant Piper longum showed binding potential against four proteins with the highest binding energy of -10.90 kcal/mol. The compound has acceptable absorption, distribution, metabolism, excretion, and toxicity properties and exhibits stable binding behaviour in terms of root mean square deviation (0.068 ± 0.05 nm), root-mean-square fluctuation, hydrogen bonds, solvent accessible surface area (83.88-161.89 nm2), and molecular mechanics Poisson-Boltzmann surface area during molecular dynamics simulation of 200 ns with selected target proteins. Concerning the utility of natural compounds in the therapeutics formulation, Cepharadione A could be further investigated as a remarkable lead candidate for the development of therapeutic drugs against SARS-CoV-2.Communicated by Ramaswamy H. Sarma.

Artificial Intelligence in Evaluation of Permanent Impairment: New Operational Frontiers

Artificial intelligence (AI) and machine learning (ML) span multiple disciplines, including the medico-legal sciences, also with reference to the concept of disease and disability. In this context, the International Classification of Diseases, Injuries, and Causes of Death (ICD) is a standard for the classification of diseases and related problems developed by the World Health Organization (WHO), and it represents a valid tool for statistical and epidemiological studies. Indeed, the International Classification of Functioning, Disability, and Health (ICF) is outlined as a classification that aims to describe the state of health of people in relation to their existential spheres (social, family, work). This paper lays the foundations for proposing an operating model for the use of AI in the assessment of impairments with the aim of making the information system as homogeneous as possible, starting from the main coding systems of the reference pathologies and functional damages. Providing a scientific basis for the understanding and study of health, as well as establishing a common language for the assessment of disability in its various meanings through AI systems, will allow for the improvement and standardization of communication between the various expert users.

Computational identification of differentially-expressed genes as suggested novel COVID-19 biomarkers: A bioinformatics analysis of expression profiles

COVID-19 was declared a pandemic in March 2020, and since then, it has not stopped spreading like wildfire in almost every corner of the world, despite the many efforts made to stem its spread. SARS-CoV-2 has one of the biggest genomes among RNA viruses and presents unique characteristics that differentiate it from other coronaviruses, making it even more challenging to find a cure or vaccine that is efficient enough. This work aims, using RNA sequencing (RNA-Seq) data, to evaluate whether the expression of specific human genes in the host can vary in different grades of disease severity and to determine the molecular origins of the differences in response to SARS-CoV-2 infection in different patients. In addition to quantifying gene expression, data coming from RNA-Seq allow for the discovery of new transcripts, the identification of alternative splicing events, the detection of allele-specific expression, and the detection of post-transcriptional alterations. For this reason, we performed differential expression analysis on different expression profiles of COVID-19 patients, using RNA-Seq data coming from NCBI public repository, and we obtained the lists of all differentially expressed genes (DEGs) emerging from 7 experimental conditions. We performed a Gene Set Enrichment Analysis (GSEA) on these genes to find possible correlations between DEGs and known disease phenotypes. We mainly focused on DEGs coming out from the analysis of the contrasts involving severe conditions to infer any possible relation between a worsening of the clinical picture and an over-representation of specific genes. Based on the obtained results, this study indicates a small group of genes that result up-regulated in the severe form of the disease. EXOSC5, MESD, REXO2, and TRMT2A genes are not differentially expressed or not present in the other conditions, being for that reason, good biomarkers candidates for the severe form of COVID-19 disease. The use of specific over-expressed genes, whether up-regulated or down-regulated, which have an individual role in each different condition of COVID-19 as a biomarker, can assist in early diagnosis.

BIO-CXRNET: a robust multimodal stacking machine learning technique for mortality risk prediction of COVID-19 patients using chest X-ray images and clinical data

Nowadays, quick, and accurate diagnosis of COVID-19 is a pressing need. This study presents a multimodal system to meet this need. The presented system employs a machine learning module that learns the required knowledge from the datasets collected from 930 COVID-19 patients hospitalized in Italy during the first wave of COVID-19 (March-June 2020). The dataset consists of twenty-five biomarkers from electronic health record and Chest X-ray (CXR) images. It is found that the system can diagnose low- or high-risk patients with an accuracy, sensitivity, and F1-score of 89.03%, 90.44%, and 89.03%, respectively. The system exhibits 6% higher accuracy than the systems that employ either CXR images or biomarker data. In addition, the system can calculate the mortality risk of high-risk patients using multivariate logistic regression-based nomogram scoring technique. Interested physicians can use the presented system to predict the early mortality risks of COVID-19 patients using the web-link: Covid-severity-grading-AI. In this case, a physician needs to input the following information: CXR image file, Lactate Dehydrogenase (LDH), Oxygen Saturation (O2%), White Blood Cells Count, C-reactive protein, and Age. This way, this study contributes to the management of COVID-19 patients by predicting early mortality risk.

Supplementary Information

The online version contains supplementary material available at 10.1007/s00521-023-08606-w.

Application of graph auto-encoders based on regularization in recommendation algorithms

Social networking has become a hot topic, in which recommendation algorithms are the most important. Recently, the combination of deep learning and recommendation algorithms has attracted considerable attention. The integration of autoencoders and graph convolutional neural networks, while providing an effective solution to the shortcomings of traditional algorithms, fails to take into account user preferences and risks over-smoothing as the number of encoder layers increases. Therefore, we introduce L1 and L2 regularization techniques and fuse them linearly to address user preferences and over-smoothing. In addition, the presence of a large amount of noisy data in the graph data has an impact on feature extraction. To our best knowledge, most existing models do not account for noise and address the problem of noisy data in graph data. Thus, we introduce the idea of denoising autoencoders into graph autoencoders, which can effectively address the noise problem. We demonstrate the capability of the proposed model on four widely used datasets and experimentally demonstrate that our model is more competitive by improving up to 1.3, 1.4, and 1.2, respectively, on the edge prediction task.

Analysis of Diabetic Retinopathy (DR) Based on the Deep Learning

If Diabetic Retinopathy (DR) patients do not receive quick diagnosis and treatment, they may lose vision. DR, an eye disorder caused by high blood glucose, is becoming more prevalent worldwide. Once early warning signs are detected, the severity of the disease must be validated before choosing the best treatment. In this research, a deep learning network is used to automatically detect and classify DR fundus images depending on severity using AlexNet and Resnet101-based feature extraction. Interconnected layers helps to identify the critical features or characteristics; in addition, Ant Colony systems also help choose the characteristics. Passing these chosen attributes through SVM with multiple kernels yielded the final classification model with promising accuracy. The experiment based on 750 features proves that the proposed approach has achieved an accuracy of 93%.

Computational Approaches to Designing Antiviral Drugs against COVID-19: A Comprehensive Review

The global impact of the COVID-19 pandemic caused by SARS-CoV-2 necessitates innovative strategies for the rapid development of effective treatments. Computational methodologies, such as molecular modelling, molecular dynamics simulations, and artificial intelligence, have emerged as indispensable tools in the drug discovery process. This review aimed to provide a comprehensive overview of these computational approaches and their application in the design of antiviral agents for COVID-19. Starting with an examination of ligand-based and structure-based drug discovery, the review has delved into the intricate ways through which molecular modelling can accelerate the identification of potential therapies. Additionally, the investigation extends to phytochemicals sourced from nature, which have shown promise as potential antiviral agents. Noteworthy compounds, including gallic acid, naringin, hesperidin, Tinospora cordifolia, curcumin, nimbin, azadironic acid, nimbionone, nimbionol, and nimocinol, have exhibited high affinity for COVID-19 Mpro and favourable binding energy profiles compared to current drugs. Although these compounds hold potential, their further validation through in vitro and in vivo experimentation is imperative. Throughout this exploration, the review has emphasized the pivotal role of computational biologists, bioinformaticians, and biotechnologists in driving rapid advancements in clinical research and therapeutic development. By combining state-of-the-art computational techniques with insights from structural and molecular biology, the search for potent antiviral agents has been accelerated. The collaboration between these disciplines holds immense promise in addressing the transmissibility and virulence of SARS-CoV-2.

A Multimodal Deep Learning Approach to Predicting Systemic Diseases from Oral Conditions

Background: It is known that oral diseases such as periodontal (gum) disease are closely linked to various systemic diseases and disorders. Deep learning advances have the potential to make major contributions to healthcare, particularly in the domains that rely on medical imaging. Incorporating non-imaging information based on clinical and laboratory data may allow clinicians to make more comprehensive and accurate decisions. Methods: Here, we developed a multimodal deep learning method to predict systemic diseases and disorders from oral health conditions. A dual-loss autoencoder was used in the first phase to extract periodontal disease-related features from 1188 panoramic radiographs. Then, in the second phase, we fused the image features with the demographic data and clinical information taken from electronic health records (EHR) to predict systemic diseases. We used receiver operation characteristics (ROC) and accuracy to evaluate our model. The model was further validated by an unseen test dataset. Findings: According to our findings, the top three most accurately predicted chapters, in order, are the Chapters III, VI and IX. The results indicated that the proposed model could predict systemic diseases belonging to Chapters III, VI and IX, with AUC values of 0.92 (95% CI, 0.90-94), 0.87 (95% CI, 0.84-89) and 0.78 (95% CI, 0.75-81), respectively. To assess the robustness of the models, we performed the evaluation on the unseen test dataset for these chapters and the results showed an accuracy of 0.88, 0.82 and 0.72 for Chapters III, VI and IX, respectively. Interpretation: The present study shows that the combination of panoramic radiograph and clinical oral features could be considered to train a fusion deep learning model for predicting systemic diseases and disorders.