Rescuing emergency cases of COVID-19 patients: An intelligent real-time MSC transfusion framework based on multicriteria decision-making methods

Functional heterogeneity of mesenchymal stem cells and their therapeutic potential in the K18-hACE2 mouse model of SARS-CoV-2 infection

BACKGROUND

Despite many years of investigation into mesenchymal stem cells (MSCs) and their potential for treating inflammatory conditions such as COVID-19, clinical outcomes remain variable due to factors like donor variability, different tissue sources, and diversity within MSC populations. Variations in MSCs' secretory and proliferation profiles, and their proteomic and transcriptional characteristics significantly influence their therapeutic potency, highlighting the need for enhanced characterization methods to better predict their efficacy. This study aimed to evaluate the biological characteristics of MSCs from different tissue origins, selecting the most promising line for further validation in a K18-hACE2 mouse model of SARS-CoV-2 infection.

METHODS

We studied nine MSC lines sourced from either bone marrow (hBMMSC), dental pulp (hDPMSC), or umbilical cord tissue (hUCMSC). The cells were assessed for their proliferative capacity, immunophenotype, trilineage differentiation, proteomic profile, and in vitro immunomodulatory potential by co-culture with activated lymphocytes. The most promising MSC line was selected for further experimental validation using the K18-hACE2 mouse model of SARS-CoV-2 infection.

RESULTS

The analyzed cells met the minimum criteria for defining MSCs, including the expression of surface molecules and differentiation capacity, showing genetic stability and proliferative potential. Proteomic analysis revealed distinct protein profiles that correlate with the tissue origin of MSCs. The immunomodulatory response exhibited variability, lacking a discernible pattern associated with their origin. In co-culture assays with lymphocytes activated with anti-CD3/CD28 beads, all MSC lines demonstrated the ability to inhibit TNF-α, to induce TGF-β and Indoleamine 2,3-dioxygenase (IDO), with varying degrees of inhibition observed for IFN-γ and IL-6, or induction of IL-10 expression. A module of proteins was found to statistically correlate with the potency of IL-6 modulation, leading to the selection of one of the hUCMSCs as the most promising line. Administration of hUCMSC to SARS-CoV-2-infected K18 mice expressing hACE2 was effective in improving lung histology and modulating of a panel of cytokines.

CONCLUSIONS

Our study assessed MSCs derived from various tissues, uncovering significant variability in their characteristics and immunomodulatory capacities. Particularly, hUCMSCs demonstrated potential in mitigating lung pathology in a SARS-CoV-2 infection model, suggesting their promising therapeutic efficacy.

Pharmacokinetic characteristics of mesenchymal stem cells in translational challenges

Over the past two decades, mesenchymal stem/stromal cell (MSC) therapy has made substantial strides, transitioning from experimental clinical applications to commercial products. MSC therapies hold considerable promise for treating refractory and critical conditions such as acute graft-versus-host disease, amyotrophic lateral sclerosis, and acute respiratory distress syndrome. Despite recent successes in clinical and commercial applications, MSC therapy still faces challenges when used as a commercial product. Current detection methods have limitations, leaving the dynamic biodistribution, persistence in injured tissues, and ultimate fate of MSCs in patients unclear. Clarifying the relationship between the pharmacokinetic characteristics of MSCs and their therapeutic effects is crucial for patient stratification and the formulation of precise therapeutic regimens. Moreover, the development of advanced imaging and tracking technologies is essential to address these clinical challenges. This review provides a comprehensive analysis of the kinetic properties, key regulatory molecules, different fates, and detection methods relevant to MSCs and discusses concerns in evaluating MSC druggability from the perspective of integrating pharmacokinetics and efficacy. A better understanding of these challenges could improve MSC clinical efficacy and speed up the introduction of MSC therapy products to the market.

Novel Federated Decision Making for Distribution of Anti-SARS-CoV-2 Monoclonal Antibody to Eligible High-Risk Patients

Context: When the epidemic first broke out, no specific treatment was available for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The urgent need to end this unusual situation has resulted in many attempts to deal with SARS-CoV-2. In addition to several types of vaccinations that have been created, anti-SARS-CoV-2 monoclonal antibodies (mAbs) have added a new dimension to preventative and treatment efforts. This therapy also helps prevent severe symptoms for those at a high risk. Therefore, this is one of the most promising treatments for mild to moderate SARS-CoV-2 cases. However, the availability of anti-SARS-CoV-2 mAb therapy is limited and leads to two main challenges. The first is the privacy challenge of selecting eligible patients from the distribution hospital networking, which requires data sharing, and the second is the prioritization of all eligible patients amongst the distribution hospitals according to dose availability. To our knowledge, no research combined the federated fundamental approach with multicriteria decision-making methods for the treatment of SARS-COV-2, indicating a research gap. Objective: This paper presents a unique sequence processing methodology that distributes anti-SARS-CoV-2 mAbs to eligible high-risk patients with SARS-CoV-2 based on medical requirements by using a novel federated decision-making distributor. Method: This paper proposes a novel federated decision-making distributor (FDMD) of anti-SARS-CoV-2 mAbs for eligible high-risk patients. FDMD is implemented on augmented data of 49,152 cases of patients with SARS-CoV-2 with mild and moderate symptoms. For proof of concept, three hospitals with 16 patients each are enrolled. The proposed FDMD is constructed from the two sides of claim sequencing: central federated server (CFS) and local machine (LM). The CFS includes five sequential phases synchronised with the LMs, namely, the preliminary criteria setting phase that determines the high-risk criteria, calculates their weights using the newly formulated interval-valued spherical fuzzy and hesitant 2-tuple fuzzy-weighted zero-inconsistency (IVSH2-FWZIC), and allocates their values. The subsequent phases are federation, dose availability confirmation, global prioritization of eligible patients and alerting the hospitals with the patients most eligible for receiving the anti-SARS-CoV-2 mAbs according to dose availability. The LM independently performs all local prioritization processes without sharing patients’ data using the provided criteria settings and federated parameters from the CFS via the proposed Federated TOPSIS (F-TOPSIS). The sequential processing steps are coherently performed at both sides. Results and Discussion: (1) The proposed FDMD efficiently and independently identifies the high-risk patients most eligible for receiving anti-SARS-CoV-2 mAbs at each local distribution hospital. The final decision at the CFS relies on the indexed patients’ score and dose availability without sharing the patients’ data. (2) The IVSH2-FWZIC effectively weighs the high-risk criteria of patients with SARS-CoV-2. (3) The local and global prioritization ranks of the F-TOPSIS for eligible patients are subjected to a systematic ranking validated by high correlation results across nine scenarios by altering the weights of the criteria. (4) A comparative analysis of the experimental results with a prior study confirms the effectiveness of the proposed FDMD. Conclusion: The proposed FDMD has the benefits of centrally distributing anti-SARS-CoV-2 mAbs to high-risk patients prioritized based on their eligibility and dose availability, and simultaneously protecting their privacy and offering an effective cure to prevent progression to severe SARS-CoV-2 hospitalization or death.

A Systematic Review of Using Deep Learning Technology in the Steady-State Visually Evoked Potential-Based Brain-Computer Interface Applications: Current Trends and Future Trust Methodology

The significance of deep learning techniques in relation to steady-state visually evoked potential- (SSVEP-) based brain-computer interface (BCI) applications is assessed through a systematic review. Three reliable databases, PubMed, ScienceDirect, and IEEE, were considered to gather relevant scientific and theoretical articles. Initially, 125 papers were found between 2010 and 2021 related to this integrated research field. After the filtering process, only 30 articles were identified and classified into five categories based on their type of deep learning methods. The first category, convolutional neural network (CNN), accounts for 70% (n = 21/30). The second category, recurrent neural network (RNN), accounts for 10% (n = 3/30). The third and fourth categories, deep neural network (DNN) and long short-term memory (LSTM), account for 6% (n = 30). The fifth category, restricted Boltzmann machine (RBM), accounts for 3% (n = 1/30). The literature's findings in terms of the main aspects identified in existing applications of deep learning pattern recognition techniques in SSVEP-based BCI, such as feature extraction, classification, activation functions, validation methods, and achieved classification accuracies, are examined. A comprehensive mapping analysis was also conducted, which identified six categories. Current challenges of ensuring trustworthy deep learning in SSVEP-based BCI applications were discussed, and recommendations were provided to researchers and developers. The study critically reviews the current unsolved issues of SSVEP-based BCI applications in terms of development challenges based on deep learning techniques and selection challenges based on multicriteria decision-making (MCDM). A trust proposal solution is presented with three methodology phases for evaluating and benchmarking SSVEP-based BCI applications using fuzzy decision-making techniques. Valuable insights and recommendations for researchers and developers in the SSVEP-based BCI and deep learning are provided.

Circular economy of medical waste: novel intelligent medical waste management framework based on extension linear Diophantine fuzzy FDOSM and neural network approach

Environmental pollution has been a major concern for researchers and policymakers. A number of studies have been conducted to enquire the causes of environmental pollution which suggested numerous policies and techniques as remedial measures. One such major source of environmental pollution, as reported by previous studies, has been the garbage resulting from disposed hospital wastes. The recent outbreak of the COVID-19 pandemic has resulted into mass generation of medical waste which seems to have further deteriorated the issue of environmental pollution. This necessitates active attention from both the researchers and policymakers for effective management of medical waste to prevent the harm to environment and human health. The issue of medical waste management is more important for countries lacking sophisticated medical infrastructure. Accordingly, the purpose of this study is to propose a novel application for identification and classification of 10 hospitals in Iraq which generated more medical waste during the COVID-19 pandemic than others in order to address the issue more effectively. We used the Multi-Criteria Decision Making (MCDM) method to this end. We integrated MCDM with other techniques including the Analytic Hierarchy Process (AHP), linear Diophantine fuzzy set decision by opinion score method (LDFN-FDOSM), and Artificial Neural Network (ANN) analysis to generate more robust results. We classified medical waste into five categories, i.e., general waste, sharp waste, pharmaceutical waste, infectious waste, and pathological waste. We consulted 313 experts to help in identifying the best and the worst medical waste management technique within the perspectives of circular economy using the neural network approach. The findings revealed that incineration technique, microwave technique, pyrolysis technique, autoclave chemical technique, vaporized hydrogen peroxide, dry heat, ozone, and ultraviolet light were the most effective methods to dispose of medical waste during the pandemic. Additionally, ozone was identified as the most suitable technique among all to serve the purpose of circular economy of medical waste. We conclude by discussing the practical implications to guide governments and policy makers to benefit from the circular economy of medical waste to turn pollutant hospitals into sustainable ones.

A Three-Dimensional Xeno-Free Culture Condition for Wharton's Jelly-Mesenchymal Stem Cells: The Pros and Cons

Xeno-free three-dimensional cultures are gaining attention for mesenchymal stem cell (MSCs) expansion in clinical applications. We investigated the potential of xeno-free serum alternatives, human serum and human platelet lysate, to replace the current conventional use of foetal bovine serum for subsequent MSCs microcarrier cultures. In this study, Wharton's Jelly MSCs were cultured in nine different media combinations to identify the best xeno-free culture media for MSCs culture. Cell proliferation and viability were identified, and the cultured MSCs were characterised in accordance with the minimal criteria for defining multipotent mesenchymal stromal cells by the International Society for Cellular Therapy (ISCT). The selected culture media was then used in the microcarrier culture of MSCs to determine the potential of a three-dimensional culture system in the expansion of MSCs for future clinical applications, and to identify the immunomodulatory potential of cultured MSCs. Low Glucose DMEM (LG) + Human Platelet (HPL) lysate media appeared to be good candidates for replacing conventional MSCs culture media in our monolayer culture system. MSCs cultured in LG-HPL achieved high cell yield, with characteristics that remained as described by ISCT, although the overall mitochondrial activity of the cells was lower than the control and the subsequent effects remained unknown. MSC microcarrier culture, on the other hand, showed comparable cell characteristics with monolayer culture, yet had stagnated cell proliferation, which is potentially due to the inactivation of FAK. Nonetheless, both the MSCs monolayer culture and the microcarrier culture showed high suppressive activity on TNF-α, and only the MSC microcarrier culture has a better suppression of IL-1 secretion. In conclusion, LG-HPL was identified as a good xeno-free media for WJMSCs culture, and although further mechanistic research is needed, the results show that the xeno-free three-dimensional culture maintained MSC characteristics and improved immunomodulatory activities, suggesting the potential of translating the monolayer culture into this culture system in MSC expansion for future clinical application.

Accuracy of Teledentistry for Diagnosis and Treatment Planning of Pediatric Patients during COVID-19 Pandemic

Teledentistry is a new technology in the dentistry field, which has great benefits during pandemic such as the coronavirus disease 2019 (COVID-19). The overall purpose of the study was to assess the diagnostic sensitivity and specificity of virtual (mobile phone teledentistry) compared with clinical examinations during COVID-19. The basic design of the study was based on the comparison treatment plans by the students and the gold standard (clinical treatment plan of an expert pedodontist with 10 years of clinical experience). This double-blind clinical trial was conducted on 20 children (aged 6 to 12 years) with a chief complaint of dental caries with or without pain. An appropriate radiograph and five standard intraoral photographs (frontal view occlusion, maxillary occlusal view, mandibular occlusal view, right lateral view, and left lateral view) were prescribed for each patient according to the guidelines of the American Association of Pediatric Dentistry. Then, the treatment plan for the carious teeth was recorded for each patient. Each patient underwent a clinical examination at first and was followed randomly by a virtual examination by two dental students. Then, the clinical and virtual treatment plans were compared with each other, and also with the gold standard. The sensitivity and specificity values were calculated for each group. The accuracy of the diagnosis was measured by applying Cohen's kappa. Interexaminer reliability was measured using the intraclass correlation coefficient (ICC) and Cronbach's alpha. The mean kappa coefficient for the interexaminer agreement (for 24 teeth) was 0.62 in clinical and 0.69 in virtual examinations. The results showed no significant difference in the treatment plans of students and the gold standard (P > 0.05). The diagnostic sensitivity and specificity were 73.22% and 95.8% for clinical and 76.44% and 92.9% for virtual treatment plans showing no significant differences between virtual (mobile phone teledentistry) and clinical examinations (P > 0.05). The intraexaminer reliability of the examiners was found to be 0.92 by calculating the ICC. Then, teledentistry can be considered as a supplement to clinical examinations of pediatric dentistry, finally resulting in better patient management. However, more studies are necessary for teledentistry.

Hospital selection framework for remote MCD patients based on fuzzy q-rung orthopair environment

This research proposes a novel mobile health-based hospital selection framework for remote patients with multi-chronic diseases based on wearable body medical sensors that use the Internet of Things. The proposed framework uses two powerful multi-criteria decision-making (MCDM) methods, namely fuzzy-weighted zero-inconsistency and fuzzy decision by opinion score method for criteria weighting and hospital ranking. The development of both methods is based on a Q-rung orthopair fuzzy environment to address the uncertainty issues associated with the case study in this research. The other MCDM issues of multiple criteria, various levels of significance and data variation are also addressed. The proposed framework comprises two main phases, namely identification and development. The first phase discusses the telemedicine architecture selected, patient dataset used and decision matrix integrated. The development phase discusses criteria weighting by q-ROFWZIC and hospital ranking by q-ROFDOSM and their sub-associated processes. Weighting results by q-ROFWZIC indicate that the time of arrival criterion is the most significant across all experimental scenarios with (0.1837, 0.183, 0.230, 0.276, 0.335) for (q = 1, 3, 5, 7, 10), respectively. Ranking results indicate that Hospital (H-4) is the best-ranked hospital in all experimental scenarios. Both methods were evaluated based on systematic ranking and sensitivity analysis, thereby confirming the validity of the proposed framework.

Hybrid Diagnosis Models for Autism Patients Based on Medical and Sociodemographic Features Using Machine Learning and Multicriteria Decision-Making (MCDM) Techniques: An Evaluation and Benchmarking Framework

Method

The three-phase framework integrated the MCDM and ML to develop the diagnosis models and evaluate and benchmark the best. Firstly, the new ASD-dataset-combined medical tests and sociodemographic characteristic features is identified and preprocessed. Secondly, developing the hybrid diagnosis models using the intersection process between three FS techniques and five ML algorithms introduces 15 models. The selected medical tests and sociodemographic features from each FS technique are weighted before feeding the five ML algorithms using the fuzzy-weighted zero-inconsistency (FWZIC) method based on four psychiatry experts. Thirdly, (i) formulate a dynamic decision matrix for all developed models based on seven evaluation metrics, including classification accuracy, precision, F1 score, recall, test time, train time, and AUC. (ii) The fuzzy decision by opinion score method (FDOSM) is used to evaluate and benchmark the 15 models concerning the seven evaluation metrics.

Results

Results reveal that (i) the three FS techniques have obtained a size different from the others in the number of the selected features; the sets were 39, 38, and 41 out of 48 features. Each set has its weights constructed by FWIZC. Considered sociodemographic features have been mostly selected more than medical tests within FS techniques. (ii) The first three best hybrid models were "ReF-decision tree," "IG-decision tree," and "Chi2-decision tree," with score values 0.15714, 0.17539, and 0.29444. The best diagnosis model (ReF-decision tree) has obtained 0.4190, 0.0030, 0.9946, 0.9902, 0.9902, 0.9902, 0.9902, and 0.9951 for the C1=train time, C2=test time, C3=AUC, C4=CA, C5=F1 score, C6=precision, and C7=recall, respectively. The developed framework would be beneficial in advancing, accelerating, and selecting diagnosis tools in therapy with ASD. The selected model can identify severity as light, medium, or intense based on medical tests and sociodemographic weighted features.

Based on neutrosophic fuzzy environment: a new development of FWZIC and FDOSM for benchmarking smart e-tourism applications

The task of benchmarking smart e-tourism applications based on multiple smart key concept attributes is considered a multi-attribute decision-making (MADM) problem. Although the literature review has evaluated and benchmarked these applications, data ambiguity and vagueness continue to be unresolved issues. The robustness of the fuzzy decision by opinion score method (FDOSM) and fuzzy weighted with zero inconsistency (FWZIC) is proven compared with that of other MADM methods. Thus, this study extends FDOSM and FWZIC under a new fuzzy environment to address the mentioned issues whilst benchmarking the applications. The neutrosophic fuzzy set is used for this purpose because of its high ability to handle ambiguous and vague information comprehensively. Fundamentally, the proposed methodology comprises two phases. The first phase adopts and describes the decision matrices of the smart e-tourism applications. The second phase presents the proposed framework in two sections. In the first section, the weight of each attribute of smart e-tourism applications is calculated through the neutrosophic FWZIC (NS-FWZIC) method. The second section employs the weights determined by the NS-FWZIC method to benchmark all the applications per each category (tourism marketing and smart-based tourism recommendation system categories) through the neutrosophic FDOSM (NS-FDOSM). Findings reveal that: (1) the NS-FWZIC method effectively weights the applications’ attributes. Real time receives the highest importance weight (0.402), whereas augmented reality has the lowest weight (0.005). The remaining attributes are distributed in between. (2) In the context of group decision-making, NS-FDOSM is used to uniform the variation found in the individual benchmarking results of the applications across all categories. Systematic ranking, sensitivity analysis and comparison analysis assessments are used to evaluate the robustness of the proposed work. Finally, the limitations of this study are discussed along with several future directions.

Multi-criteria decision-making for coronavirus disease 2019 applications: a theoretical analysis review

The influence of the ongoing COVID-19 pandemic that is being felt in all spheres of our lives and has a remarkable effect on global health care delivery occurs amongst the ongoing global health crisis of patients and the required services. From the time of the first detection of infection amongst the public, researchers investigated various applications in the fight against the COVID-19 outbreak and outlined the crucial roles of different research areas in this unprecedented battle. In the context of existing studies in the literature surrounding COVID-19, related to medical treatment decisions, the dimensions of context addressed in previous multidisciplinary studies reveal the lack of appropriate decision mechanisms during the COVID-19 outbreak. Multiple criteria decision making (MCDM) has been applied widely in our daily lives in various ways with numerous successful stories to help analyse complex decisions and provide an accurate decision process. The rise of MCDM in combating COVID-19 from a theoretical perspective view needs further investigation to meet the important characteristic points that match integrating MCDM and COVID-19. To this end, a comprehensive review and an analysis of these multidisciplinary fields, carried out by different MCDM theories concerning COVID19 in complex case studies, are provided. Research directions on exploring the potentials of MCDM and enhancing its capabilities and power through two directions (i.e. development and evaluation) in COVID-19 are thoroughly discussed. In addition, Bibliometrics has been analysed, visualization and interpretation based on the evaluation and development category using R-tool involves; annual scientific production, country scientific production, Wordcloud, factor analysis in bibliographic, and country collaboration map. Furthermore, 8 characteristic points that go through the analysis based on new tables of information are highlighted and discussed to cover several important facts and percentages associated with standardising the evaluation criteria, MCDM theory in ranking alternatives and weighting criteria, operators used with the MCDM methods, normalisation types for the data used, MCDM theory contexts, selected experts ways, validation scheme for effective MCDM theory and the challenges of MCDM theory used in COVID-19 studies. Accordingly, a recommended MCDM theory solution is presented through three distinct phases as a future direction in COVID19 studies. Key phases of this methodology include the Fuzzy Delphi method for unifying criteria and establishing importance level, Fuzzy weighted Zero Inconsistency for weighting to mitigate the shortcomings of the previous weighting techniques and the MCDM approach by the name Fuzzy Decision by Opinion Score method for prioritising alternatives and providing a unique ranking solution. This study will provide MCDM researchers and the wider community an overview of the current status of MCDM evaluation and development methods and motivate researchers in harnessing MCDM potentials in tackling an accurate decision for different fields against COVID-19.