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Coronavirus-specific antibodies can be detected in the blood of people who have recently recovered from coronavirus disease-2019 (COVID-19). Convalescent-Plasma (CP) transfusion process proved that it's among the most efficient protocols, and it's used in hospitals to treat various infections and diseases. Several medical issues have been addressed due to the growing interest in creating Artificial Intelligence (AI) applications. However, considering the virus's enormous potential harm to global public health, such uses are insufficient. This proposed systematic review and meta-analysis aims to obtain an overview of COVID-19, highlight the limits of decision-making approaches, and give healthcare professionals information about the technique's advantages. Between 2016 and 2021, five databases, namely IEEE Xplore, Web of Science, PubMed, Science Direct, and Scopus, were utilized to run four sequences of search queries. As a result, 477 studies are found to be relevant. Only six studies were thoroughly examined and included in this review after screening articles and using proper inclusion criteria, highlighting the lack of research on this crucial topic. Studies' findings were reviewed to identify the gaps in all the evaluated papers. Motivations, problems, constraints, suggestions, and case examples were thoroughly examined. This study seeks to answer how we support the researchers with collected information for managing transfusion of the highest quality CP to the most critical COVID-19 patients across telemedicine hospitals.

The problem complexity of multi-criteria decision-making (MCDM) has been raised in the distribution of coronavirus disease 2019 (COVID-19) vaccines, which required solid and robust MCDM methods. Compared with other MCDM methods, the fuzzy-weighted zero-inconsistency (FWZIC) method and fuzzy decision by opinion score method (FDOSM) have demonstrated their solidity in solving different MCDM challenges. However, the fuzzy sets used in these methods have neglected the refusal concept and limited the restrictions on their constants. To end this, considering the advantage of the T-spherical fuzzy sets (T-SFSs) in handling the uncertainty in the data and obtaining information with more degree of freedom, this study has extended FWZIC and FDOSM methods into the T-SFSs environment (called T-SFWZIC and T-SFDOSM) to be used in the distribution of COVID-19 vaccines. The methodology was formulated on the basis of decision matrix adoption and development phases. The first phase described the adopted decision matrix used in the COVID-19 vaccine distribution. The second phase presented the sequential formulation steps of T-SFWZIC used for weighting the distribution criteria followed by T-SFDOSM utilised for prioritising the vaccine recipients. Results revealed the following: (1) T-SFWZIC effectively weighted the vaccine distribution criteria based on several parameters including T = 2, T = 4, T = 6, T = 8, and T = 10. Amongst all parameters, the age criterion received the highest weight, whereas the geographic locations severity criterion has the lowest weight. (2) According to the T parameters, a considerable variance has occurred on the vaccine recipient orders, indicating that the existence of T values affected the vaccine distribution. (3) In the individual context of T-SFDOSM, no unique prioritisation was observed based on the obtained opinions of each expert. (4) The group context of T-SFDOSM used in the prioritisation of vaccine recipients was considered the final distribution result as it unified the differences found in an individual context. The evaluation was performed based on systematic ranking assessment and sensitivity analysis. This evaluation showed that the prioritisation results based on each T parameter were subject to a systematic ranking that is supported by high correlation results over all discussed scenarios of changing criteria weights values.

For optimal agricultural and other soil utilization, microbial structure analysis is essential. Sabkhat Madinah, Saudi Arabia, boasts high-salinity soil and thriving vegetation. The microbial makeup of the soil in this area is novel and has not been extensively studied. This study aimed to analyze the microbial structure of two locations in Sabkhat Madinah's soil and examine the correlation with soil properties. Soils microbiota were investigated using Illumina sequencing of bacterial 16S rRNA genes. The soil chemical properties were determined. The microbial composition of both sites was investigated at different taxonomic levels. There was a significant difference in salt concentration between the two sites derived from the same sabkha. The second sample had higher sodium content, leading to increased electrical conductivity (EC) measures. Moreover, the samples had different microbial compositions. The first sample was dominated by Bacteroidetes (18.37%), Firmicutes (13.57%), Proteobacteria (13.57%), and Actinobacteria (9.30%), while the second one contained Bacteroidetes (8.96%), Proteobacteria (25.01%), Actinobacteria (12.03%), and Firmicutes (11.36%). Firmicutes were newly recorded and found only in saline habitats in Saudi Arabia. Acidobacteria, Thermodesulfobacteria and Streptophyta were only in the first sample, while Verrucomicrobia sequences were identified only in the second sample which had higher salt content. On the genus level, 16 genera were found across both samples, with Bacillus being the most prevalent at 5.17% followed by Marinoscillum (4%), Fibrobacter (3.57%) and Rubrobacter (3.45%) in the first sample. The second soil sample had a dominant genus, Halomonas, making up 10.64% of the total sequences. Other genera present included Fibrobacter (3.96%), Nitrospira (3.92%), Rubrobacter (3.36%), and Methylophaga (3%). To conclude, the analysis of bacteria in the two sites revealed notable differences in soil properties and bacterial diversity. This study provides insight into the complex microbial compositions of saline soil and a foundation for future studies. Understanding these bacteria' genetics can improve soils or help crops thrive in saline environments. [ABSTRACT FROM AUTHOR]

Background: Autism spectrum disorder (ASD) symptoms and severity levels vary from patient to patient, so treatment and healthcare will vary. However, little attention has been given to developing an autistic triage method for ASD patients concerning four issues: hybrid triage criteria, multi-selection criteria problems, criteria importance, and trade-off based on the inverse relationship between autistic criteria. Therefore, this study aims to develop a new method for triaging ASD patients and classifying them according to their severity of disorder using Fuzzy Multi-Criteria Decision Making (fMCDM) methods. Methods: Two methodology phases have been conducted: the first phase is to identify and preprocess the ASD dataset, including 988 autistic patients with 42 medical and Sociodemographic criteria. In the second phase, two fMCDM methods were used to develop the triage method. The fuzzy Delphi Method (FDM) is used to select the most influential criteria among the 42 based on thirteen psychologists in the psychological field. Then Fuzzy-Weighted Zero-Inconsistency (FWZIC) is used to assign weights to the important criteria according to four psychologists' opinions. Accordingly, the Processes for Triaging Autism Patients (PTAP) method has been developed for the first time for triaging and classifying patients into three severity levels: minor, moderate, and urgent. Results: For the preprocessed phase, 538 out of 988 patients were obtained as a new ASD dataset underwent data cleaning to capture only autism patients. For the second phase, the FDM results have selected 19 out of 42 criteria and can control the bias of psychologists' opinions, FWZIC has assigned the appropriate weights for the 19 criteria, and the PTAP method triages the 538 patients into three severity levels: 36 minor injuries, 432 moderate injuries, and 70 urgent injuries. More complex statistical analyses have been presented using MedCalc statistical software. Three physicians in the psychological field gave their subjective judgements for the diagnosis of 46 random samples of patients. The sensitivity results were 86.67%, 80%, and 90.91%, while the specificity results were 93.55%, 88.46%, and 94.29% for urgent, moderate, and minor levels, respectively. In addition, the accuracy was 91.30% for urgent, 84.78% for moderate, and 93.48% for minor. This assessment led to a deduction that a proposed ASD triage method can be applied with high performance. Conclusions: The developed triage method can be used for early autism diagnosis application and support clinical treatment utilizing the advantages of fMCDM techniques of multidimensional criteria. Four medical criteria were selected from the psychologists, while Sociodemographic acquired high attention with 15 selected criteria. For the correlation analysis of the 19 used criteria, the ‘Wave’ criterion has the highest correlation with the triage level and obtained 0.4523. On the contrary, the “Pointing with the index finger” criterion has the lowest correlation and obtained −0.0542. Limitations and future works have also been reported in this study. The study confirms the efficacy of the proposed triage method compared with previous studies in five comparative points with 100%.

Mesenchymal stem cell (MSC) transfusion has shown promising results in treating COVID-19 cases despite the limited availability of these MSCs. The task of prioritizing COVID-19 patients for MSC transfusion based on multiple criteria is considered a multi-attribute decision-analysis (MADA) problem. Although literature reviews have assessed the prioritization of COVID-19 patients for MSCs, issues arising from imprecise, unclear and ambiguous information remain unresolved. Compared with the existing MADA methods, the robustness of the fuzzy decision by opinion score method (FDOSM) and fuzzy-weighted zero inconsistency (FWZIC) is proven. This study adopts and integrates FDOSM and FWZIC in a homogeneous Fermatean fuzzy environment for criterion weighting followed by the prioritization of the most eligible COVID-19 patients for MSC transfusion. The research methodology had two phases. The decision matrices of three COVID-19 emergency levels (moderate, severe, and critical) were adopted based on an augmented dataset of 60 patients and discussed in the first phase. The second phase was divided into two subsections. The first section developed Fermatean FWZIC (F-FWZIC) to weigh criteria across each emergency level of COVID-19 patients. These weights were fed to the second section on adopting Fermatean FDOSM (F-FDOSM) for the purpose of prioritizing COVID-19 patients who are the most eligible to receive MSCs. Three methods were used in evaluating the proposed works, and the results included systematic ranking, sensitivity analysis, and benchmarking checklist. [ABSTRACT FROM AUTHOR]

This study presents a systematic review of artificial intelligence (AI) techniques used in the detection and classification of coronavirus disease 2019 (COVID-19) medical images in terms of evaluation and benchmarking. Five reliable databases, namely, IEEE Xplore, Web of Science, PubMed, ScienceDirect and Scopus were used to obtain relevant studies of the given topic. Several filtering and scanning stages were performed according to the inclusion/exclusion criteria to screen the 36 studies obtained; however, only 11 studies met the criteria. Taxonomy was performed, and the 11 studies were classified on the basis of two categories, namely, review and research studies. Then, a deep analysis and critical review were performed to highlight the challenges and critical gaps outlined in the academic literature of the given subject. Results showed that no relevant study evaluated and benchmarked AI techniques utilised in classification tasks (i.e. binary, multi-class, multi-labelled and hierarchical classifications) of COVID-19 medical images. In case evaluation and benchmarking will be conducted, three future challenges will be encountered, namely, multiple evaluation criteria within each classification task, trade-off amongst criteria and importance of these criteria. According to the discussed future challenges, the process of evaluation and benchmarking AI techniques used in the classification of COVID-19 medical images considered multi-complex attribute problems. Thus, adopting multi-criteria decision analysis (MCDA) is an essential and effective approach to tackle the problem complexity. Moreover, this study proposes a detailed methodology for the evaluation and benchmarking of AI techniques used in all classification tasks of COVID-19 medical images as future directions; such methodology is presented on the basis of three sequential phases. Firstly, the identification procedure for the construction of four decision matrices, namely, binary, multi-class, multi-labelled and hierarchical, is presented on the basis of the intersection of evaluation criteria of each classification task and AI classification techniques. Secondly, the development of the MCDA approach for benchmarking AI classification techniques is provided on the basis of the integrated analytic hierarchy process and VlseKriterijumska Optimizacija I Kompromisno Resenje methods. Lastly, objective and subjective validation procedures are described to validate the proposed benchmarking solutions.