How plants respond to heavy metal contamination: a narrative review of proteomic studies and phytoremediation applications.

MAIN CONCLUSION: Heavy metal pollution caused by human activities is a serious threat to the environment and human health. Plants have evolved sophisticated defence systems to deal with heavy metal stress, with proteins and enzymes serving as critical intercepting agents for heavy metal toxicity reduction. Proteomics continues to be effective in identifying markers associated with stress response and metabolic processes. This review explores the complex interactions between heavy metal pollution and plant physiology, with an emphasis on proteomic and biotechnological perspectives. Over the last century, accelerated industrialization, agriculture activities, energy production, and urbanization have established a constant need for natural resources, resulting in environmental degradation. The widespread buildup of heavy metals in ecosystems as a result of human activity is especially concerning. Although some heavy metals are required by organisms in trace amounts, high concentrations pose serious risks to the ecosystem and human health. As immobile organisms, plants are directly exposed to heavy metal contamination, prompting the development of robust defence mechanisms. Proteomics has been used to understand how plants react to heavy metal stress. The development of proteomic techniques offers promising opportunities to improve plant tolerance to toxicity from heavy metals. Additionally, there is substantial scope for phytoremediation, a sustainable method that uses plants to extract, sequester, or eliminate contaminants in the context of changes in protein expression and total protein behaviour. Changes in proteins and enzymatic activities have been highlighted to illuminate the complex effects of heavy metal pollution on plant metabolism, and how proteomic research has revealed the plant's ability to mitigate heavy metal toxicity by intercepting vital nutrients, organic substances, and/or microorganisms.

Antiproliferative and cell cycle arrest potentials of 3-O-acetyl-11-keto-ß-boswellic acid against MCF-7 cells in vitro.

INTRODUCTION: Cancer is a major issue in medical science with increasing death cases every year worldwide. Therefore, searching for alternatives and nonorthodox methods of treatments with high efficiency, selectivity and less toxicity is the main goal in fighting cancer. Acetyl-11-keto-ß-boswellic acid (AKBA), is a derivative pentacyclic triterpenoid that exhibited various biological activities with potential anti-tumoral agents. In this research, AKBA was utilized to examine the potential cytotoxic activity against MCF-7 cells in vitro and monitor the cellular and morphological changes with a prospective impact on apoptosis induction. METHODS: The cytotoxic activity of AKBA was measured by 3(4,5dimethylthiazole- 2-yl)-2,5 diphyneltetrazolium bromide (MTT) assay. A dose-dependent inhibition in MCF-7 cell viability was detected. The clonogenicity of MCF-7 cells was significantly suppressed by AKBA increment in comparison with untreated cells. RESULT: Morphologically, exposure of MCF-7 cells to high AKBA concentrations caused changes in cell nuclear morphology which was indicated by increasing in nuclear size and cell permeability intensity. The mitochondrial membrane potential (ΔΨm) was reduced by increasing AKBA concentration with a significant release of cytochrome c. Acridine orange/ethidium bromide dual staining experiment confirmed that MCF-7 cells treated with AKBA (IC50 concentration) displayed a late stage of apoptosis indicated by intense and bright reddish colour. CONCLUSION: A significant increase in reactive oxygen species formation was observed. Caspase 8 and caspase 9 activities were estimated and AKBA activated the production of caspase 8 and caspase 9 in a dose-dependent pattern. Finally, the cell phase distribution analysis was conducted, and flow cytometric analysis showed that AKBA at 200 µg mL-1 significantly arrest MCF-7 cells at the G1 phase and triggered apoptosis.

Detection of orthodontically induced inflammatory root resorption-associated biomarkers from the gingival crevicular fluid by proteomics analysis: a randomized-controlled clinical trial.

Orthodontically induced inflammatory root resorption (OIIRR) is an undesirable complication of orthodontic treatment (OT) with an ambiguous aetiologic mechanism. This study aimed to identify OIIRR-associated biomarkers in the gingival crevicular fluid (GCF) using proteomic analysis. In this randomized clinical trial, the upper first premolars (UFP) were exposed either to light or heavy force. The GCF was collected at 1 h, 1 day, 7 days, 14 days, 21 days, and 28 days following force application. After extraction of UFP, roots were imaged and resorption premolar, was used to deliver either light forcecraters were measured. Proteomic analysis of GCF was performed using 2D gel electrophoresis with MALDI-TOF/TOF MS/MS. Results were further analyzed by bioinformatics analyses showing the biological functions and predicted pathways. The predicted canonical pathways showed that the expression of immunoglobulin kappa (IGKC), neutrophil gelatinase-associated lipocalin (NGAL), neurolysin mitochondrial (NEUL), keratin, type II cytoskeletal 1 (K2C1), S100-A9, and the extracellular calcium-sensing receptor (CASR) were significantly associated with a range of biological and inflammatory processes. In conclusion, up-regulation of S100A9, CASR, and K2C1 suggested a response to force-related inflammation, chemotactic activities, osteoclastogenesis, and epithelial cell breakdown. Meanwhile, the up-regulation of IGKC, NGAL, and K2C1 indicated a response to the inflammatory process, innate immunity activation, and epithelial cell breakdown. Supplementary Information: The online version contains supplementary material available at 10.1007/s13205-023-03572-5.

Cross-reactivity analysis of milk proteins from different goat breeds with cow's milk allergens using a proteomic approach.

INTRODUCTION: Goat's milk thought to be a good substitute for cow's milk protein allergic (CMPA) individuals. However, there is growing evidence that their proteins have cross-reactivities with cow's milk allergens. This study aimed to profile and compare milk proteins from different goat breeds that have cross-reactivity to cow's milk allergens. METHODOLOGY: Proteomics was used to compare protein extracts of skim milk from Saanen, Jamnapari, and Toggenburg. Cow's milk was used as a control. IgE-immunoblotting and mass spectrometry were used to compare and identify proteins that cross-reacted with serum IgE from CMPA patients (n = 10). RESULTS: The analysis of IgE-reactive proteins revealed that the protein spots identified with high confidence were proteins homologous to common cow's milk allergens such as α-S1-casein (αS1-CN), ß-casein (ß-CN), κ-casein (κ-CN), and beta-lactoglobulin (ß-LG). Jamnapari's milk proteins were found to cross-react with four major milk allergens: α-S1-CN, ß-CN, κ-CN, and ß-LG. Saanen goat's milk proteins, on the other hand, cross-reacted with two major milk allergens, α-S1-CN and ß-LG, whereas Toggenburg goat's milk proteins only react with one of the major milk allergens, κ-CN. CONCLUSION: These findings may help in the development of hypoallergenic goat milk through cross-breeding strategies of goat breeds with lower allergenic milk protein contents.

Role of Different Types of miRNAs in Some Cardiovascular Diseases and Therapy-Based miRNA Strategies: A Mini Review.

The role of microRNAs (miRNAs) in the pathogenesis of cardiovascular disease has been extensively studied. miRNAs have been highlighted as an important physiological regulator for activities like cardiac protection. miRNAs are present in the circulation, and they have been investigated as physiological markers, especially in the condition of heart failure. However, there is less compelling verification that miRNAs can outperform traditional biomarkers. However, clinical evidence is still required. In this review article, we explored the feasibility of miRNAs as diagnostic biomarkers for heart failure in a systematic study. Searching in the PubMed database to identify miRNA molecules that are differentially expressed between groups of patients with heart failure or heart disease and controls, throughout the investigation, we discovered no significant overlap in differentially expressed miRNAs. Only four miRNAs ("miR-126," "miR-150-5p," "hsa-miR-233," and "miR-423-5p") were differentially expressed. Results from our review show that there is not enough evidence to support the use of miRNAs as biomarkers in clinical settings.

The environmental, economic, and social development impact of desertification in Iraq: a review on desertification control measures and mitigation strategies.

The threat of desertification is considered a global concern that occurs in many environments in different parts of the world, where extensive lands are transformed gradually into desert or semi-desert areas, and this causes economic and health issues. Iraq and many other parts of the Middle East are facing desertification threats in the last twenty years. Despite the significance of this issue, relevant reviews are scarce. The removal of vegetation cover, overgrazing, deforestation in times of war, poor irrigation practices and water scarcity are some of the main causes of desertification in Iraq. Fighting desertification requires cooperative efforts including the utilization of innovative practices, biotechnological approaches, restoration of oases, continuous reforestation, and rehabilitation of agricultural lands. The objective of this review article is to discuss the causes of desertification and land degradation in Iraq, highlighting the main natural and human factors involved, and the consequent impact on the national security, economy, society, and health. In addition, it suggests recommendations for policies and actions that can be integrated to mitigate this problem.

Novel dynamic fuzzy Decision-Making framework for COVID-19 vaccine dose recipients.

Introduction: The vaccine distribution for the COVID-19 is a multicriteria decision-making (MCDM) problem based on three issues, namely, identification of different distribution criteria, importance criteria and data variation. Thus, the Pythagorean fuzzy decision by opinion score method (PFDOSM) for prioritising vaccine recipients is the correct approach because it utilises the most powerful MCDM ranking method. However, PFDOSM weighs the criteria values of each alternative implicitly, which is limited to explicitly weighting each criterion. In view of solving this theoretical issue, the fuzzy-weighted zero-inconsistency (FWZIC) can be used as a powerful weighting MCDM method to provide explicit weights for a criteria set with zero inconstancy. However, FWZIC is based on the triangular fuzzy number that is limited in solving the vagueness related to the aforementioned theoretical issues. Objectives: This research presents a novel homogeneous Pythagorean fuzzy framework for distributing the COVID-19 vaccine dose by integrating a new formulation of the PFWZIC and PFDOSM methods. Methods: The methodology is divided into two phases. Firstly, an augmented dataset was generated that included 300 recipients based on five COVID-19 vaccine distribution criteria (i.e., vaccine recipient memberships, chronic disease conditions, age, geographic location severity and disabilities). Then, a decision matrix was constructed on the basis of an intersection of the 'recipients list' and 'COVID-19 distribution criteria'. Then, the MCDM methods were integrated. An extended PFWZIC was developed, followed by the development of PFDOSM. Results: (1) PFWZIC effectively weighted the vaccine distribution criteria. (2) The PFDOSM-based group prioritisation was considered in the final distribution result. (3) The prioritisation ranks of the vaccine recipients were subject to a systematic ranking that is supported by high correlation results over nine scenarios of the changing criteria weights values. Conclusion: The findings of this study are expected to ensuring equitable protection against COVID-19 and thus help accelerate vaccine progress worldwide.

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.

Proteomic analysis revealed the biofilm-degradation abilities of the bacteriophage UPMK_1 and UPMK_2 against Methicillin-resistant Staphylococcus aureus.

OBJECTIVE: The degradation activity of two bacteriophages UPMK_1 and UPMK_2 against methicillin-resistant Staphylococcus aureus phages were examined using gel zymography. METHODS: The analysis was done using BLASTP to detect peptides catalytic domains. Many peptides that are related to several phage proteins were revealed. RESULTS: UPMK_1 and UPMK_2 custom sequence database were used for peptide identification. The biofilm-degrading proteins in the bacteriophage UPMK_2 revealed the same lytic activity towards polysaccharide intercellular adhesin-dependent and independent of Methicillin-resistant Staphylococcus aureus (MRSA) biofilm producers in comparison to UPMK_1, which had lytic activity restricted solely to its host. CONCLUSION: Both bacteriophage enzymes were involved in MRSA biofilm degradation during phage infection and they have promising enzybiotics properties against MRSA biofilm formation.

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

Mesenchymal stem cells (MSCs) have shown promising ability to treat critical cases of coronavirus disease 2019 (COVID-19) by regenerating lung cells and reducing immune system overreaction. However, two main challenges need to be addressed first before MSCs can be efficiently transfused to the most critical cases of COVID-19. First is the selection of suitable MSC sources that can meet the standards of stem cell criteria. Second is differentiating COVID-19 patients into different emergency levels automatically and prioritising them in each emergency level. This study presents an efficient real-time MSC transfusion framework based on multicriteria decision-making(MCDM) methods. In the methodology, the testing phase represents the ability to adhere to plastic surfaces, the upregulation and downregulation of specific surface protein markers and finally the ability to differentiate into different kinds of cells. In the development phase, firstly, two scenarios of an augmented dataset based on the medical perspective are generated to produce 80 patients with different emergency levels. Secondly, an automated triage algorithm based on a formal medical guideline is proposed for real-time monitoring of COVID-19 patients with different emergency levels (i.e. mild, moderate, severe and critical) considering the improvement and deterioration procedures from one level to another. Thirdly, a unique decision matrix for each triage level (except mild) is constructed on the basis of the intersection between the evaluation criteria of each emergency level and list of COVID-19 patients. Thereafter, MCDM methods (i.e. analytic hierarchy process [AHP] and vlsekriterijumska optimizcija i kaompromisno resenje [VIKOR]) are integrated to assign subjective weights for the evaluation criteria within each triage level and then prioritise the COVID-19 patients on the basis of individual and group decision-making(GDM) contexts. Results show that: (1) in both scenarios, the proposed algorithm effectively classified the patients into four emergency levels, including mild, moderate, severe and critical, taking into consideration the improvement and deterioration cases. (2) On the basis of experts' perspectives, clear differences in most individual prioritisations for patients with different emergency levels in both scenarios were found. (3) In both scenarios, COVID-19 patients were prioritised identically between the internal and external group VIKOR. During the evaluation, the statistical objective method indicated that the patient prioritisations underwent systematic ranking. Moreover, comparison analysis with previous work proved the efficiency of the proposed framework. Thus, the real-time MSC transfusion for COVID-19 patients can follow the order achieved in the group VIKOR results.

Differential Analysis of Mycelial Proteins and Metabolites From Rigidoporus Microporus During In Vitro Interaction With Hevea Brasiliensis.

Rigidoporus microporus is the fungus accountable for the white root rot disease that is detrimental to the rubber tree, Hevea brasiliensis. The pathogenicity mechanism of R. microporus and the identity of the fungal proteins and metabolites involved during the infection process remain unclear. In this study, the protein and metabolite profiles of two R. microporus isolates, Segamat (SEG) and Ayer Molek (AM), were investigated during an in vitro interaction with H. brasiliensis. The isolates were used to inoculate H. brasiliensis clone RRIM 2025, and mycelia adhering to the roots of the plant were collected for analysis. Transmission electron microscope (TEM) images acquired confirms the hyphae attachment and colonization of the mycelia on the root of the H. brasiliensis clones after 4 days of inoculation. The protein samples were subjected to 2-DE analysis and analyzed using MALDI-ToF MS/MS, while the metabolites were extracted using methanol and analyzed using LC/MS-QTOF. Based on the differential analyses, upregulation of proteins that are essential for fungal evolution such as malate dehydrogenase, fructose 1,6-biphosphate aldolase, and glyceraldehyde-3-phosphate dehydrogenase hints an indirect role in fungal pathogenicity, while metabolomic analysis suggests an increase in acidic compounds which may lead to increased cell wall degrading enzyme activity. Bioinformatics analyses revealed that the carbohydrate and amino acid metabolisms were prominently affected in response to the fungal pathogenicity. In addition to that, other pathways that were significantly affected include "Protein Ubiquitination Pathway," Unfolded Protein Response," "HIFα Signaling," and "Sirtuin Signaling Pathway." The identification of responsive proteins and metabolites from this study promotes a better understanding of mechanisms underlying R. microporus pathogenesis and provides a list of potential biological markers for early recognition of the white root rot disease.

Convalescent-plasma-transfusion intelligent framework for rescuing COVID-19 patients across centralised/decentralised telemedicine hospitals based on AHP-group TOPSIS and matching component.

As coronavirus disease 2019 (COVID-19) spreads across the world, the transfusion of efficient convalescent plasma (CP) to the most critical patients can be the primary approach to preventing the virus spread and treating the disease, and this strategy is considered as an intelligent computing concern. In providing an automated intelligent computing solution to select the appropriate CP for the most critical patients with COVID-19, two challenges aspects are bound to be faced: (1) distributed hospital management aspects (including scalability and management issues for prioritising COVID-19 patients and donors simultaneously), and (2) technical aspects (including the lack of COVID-19 dataset availability of patients and donors and an accurate matching process amongst them considering all blood types). Based on previous reports, no study has provided a solution for CP-transfusion-rescue intelligent framework during this pandemic that has addressed said challenges and issues. This study aimed to propose a novel CP-transfusion intelligent framework for rescuing COVID-19 patients across centralised/decentralised telemedicine hospitals based on the matching component process to provide an efficient CP from eligible donors to the most critical patients using multicriteria decision-making (MCDM) methods. A dataset, including COVID-19 patients/donors that have met the important criteria in the virology field, must be augmented to improve the developed framework. Four consecutive phases conclude the methodology. In the first phase, a new COVID-19 dataset is generated on the basis of medical-reference ranges by specialised experts in the virology field. The simulation data are classified into 80 patients and 80 donors on the basis of the five biomarker criteria with four blood types (i.e., A, B, AB, and O) and produced for COVID-19 case study. In the second phase, the identification scenario of patient/donor distributions across four centralised/decentralised telemedicine hospitals is identified 'as a proof of concept'. In the third phase, three stages are conducted to develop a CP-transfusion-rescue framework. In the first stage, two decision matrices are adopted and developed on the basis of the five 'serological/protein biomarker' criteria for the prioritisation of patient/donor lists. In the second stage, MCDM techniques are analysed to adopt individual and group decision making based on integrated AHP-TOPSIS as suitable methods. In the third stage, the intelligent matching components amongst patients/donors are developed on the basis of four distinct rules. In the final phase, the guideline of the objective validation steps is reported. The intelligent framework implies the benefits and strength weights of biomarker criteria to the priority configuration results and can obtain efficient CPs for the most critical patients. The execution of matching components possesses the scalability and balancing presentation within centralised/decentralised hospitals. The objective validation results indicate that the ranking is valid.

LC-MS/MS Proteomic Study of MCF-7 Cell Treated with Dox and Dox-Loaded Calcium Carbonate Nanoparticles Revealed Changes in Proteins Related to Glycolysis, Actin Signalling, and Energy Metabolism.

One of the most prevalent death causes among women worldwide is breast cancer. This study aimed to characterise and differentiate the proteomics profiles of breast cancer cell lines treated with Doxorubicin (DOX) and Doxorubicin-CaCO3-nanoparticles (DOX-Ar-CC-NPs). This study determines the therapeutic potential of doxorubicin-loaded aragonite CaCO3 nanoparticles using a Liquid Chromatography/Mass Spectrometry analysis. In total, 334 proteins were expressed in DOX-Ar-CC-NPs treated cells, while DOX treatment expressed only 54 proteins. Out of the 334 proteins expressed in DOX-CC-NPs treated cells, only 36 proteins showed changes in abundance, while in DOX treated cells, only 7 out of 54 proteins were differentially expressed. Most of the 30 identified proteins that are differentially expressed in DOX-CC-NPs treated cells are key enzymes that have an important role in the metabolism of carbohydrates as well as energy, including: pyruvate kinase, ATP synthase, enolase, glyceraldehyde-3-phosphate dehydrogenase, mitochondrial ADP/ATP carrier, and trypsin. Other identified proteins are structural proteins which included; Keratin, α- and ß-tubulin, actin, and actinin. Additionally, one of the heat shock proteins was identified, which is Hsp90; other proteins include Annexins and Human epididymis protein 4. While the proteins identified in DOX-treated cells were tubulin alpha-1B chain and a beta chain, actin cytoplasmic 1, annexin A2, IF rod domain-containing protein, and 78 kDa glucose-regulated protein. Bioinformatics analysis revealed the predicted canonical pathways linking the signalling of the actin cytoskeleton, ILK, VEGF, BAG2, integrin and paxillin, as well as glycolysis. This research indicates that proteomic analysis is an effective technique for proteins expression associated with chemotherapy drugs on cancer tumours; this method provides the opportunity to identify treatment targets for MCF-7 cancer cells, and a liquid chromatography-mass spectrometry (LC-MS/MS) system allowed the detection of a larger number of proteins than 2-DE gel analysis, as well as proteins with maximum pIs and high molecular weight.

Mycopharmaceuticals and Nutraceuticals: Promising Agents to Improve Human Well-Being and Life Quality.

Fungi, especially edible mushrooms, are considered as high-quality food with nutritive and functional values. They are of considerable interest and have been used in the synthesis of nutraceutical supplements due to their medicinal properties and economic significance. Specific fungal groups, including predominantly filamentous endophytic fungi from Ascomycete phylum and several Basidiomycetes, produce secondary metabolites (SMs) with bioactive properties that are involved in the antimicrobial and antioxidant activities. These beneficial fungi, while high in protein and important fat contents, are also a great source of several minerals and vitamins, in particular B vitamins that play important roles in carbohydrate and fat metabolism and the maintenance of the nervous system. This review article will summarize and discuss the abilities of fungi to produce antioxidant, anticancer, antiobesity, and antidiabetic molecules while also reviewing the evidence from the last decade on the importance of research in fungi related products with direct and indirect impact on human health.

Protein profile of MCF-7 breast cancer cell line treated with lectin delivered by CaCO3NPs revealed changes in molecular chaperones, cytoskeleton, and membrane-associated proteins.

The second most predominant cancer in the world and the first among women is breast cancer. We aimed to study the protein abundance profiles induced by lectin purified from the Agaricus bisporus mushroom (ABL) and conjugated with CaCO3NPs in the MCF-7 breast cancer cell line. Two-dimensional electrophoresis (2-DE) and orbitrap mass spectrometry techniques were used to reveal the protein abundance pattern induced by lectin. Flow cytometric analysis showed the accumulation of ABL-CaCO3NPs treated cells in the G1 phase than the positive control. Thirteen proteins were found different in their abundance in breast cancer cells after 24 h exposure to lectin conjugated with CaCO3NPs. Most of the identified proteins were showing a low abundance in ABL-CaCO3NPs treated cells in comparison to the positive and negative controls, including V-set and immunoglobulin domain, serum albumin, actin cytoplasmic 1, triosephosphate isomerase, tropomyosin alpha-4 chain, and endoplasmic reticulum chaperone BiP. Hornerin, tropomyosin alpha-1 chain, annexin A2, and protein disulfide-isomerase were up-regulated in comparison to the positive. Bioinformatic analyses revealed the regulation changes of these proteins mainly affected the pathways of 'Bcl-2-associated athanogene 2 signalling pathway', 'Unfolded protein response', 'Caveolar-mediated endocytosis signalling', 'Clathrin-mediated endocytosis signalling', 'Calcium signalling' and 'Sucrose degradation V', which are associated with breast cancer. We concluded that lectin altered the abundance in molecular chaperones/heat shock proteins, cytoskeletal, and metabolic proteins. Additionally, lectin induced a low abundance of MCF-7 cancer cell proteins in comparison to the positive and negative controls, including; V-set and immunoglobulin domain, serum albumin, actin cytoplasmic 1, triosephosphate isomerase, tropomyosin alpha-4 chain, and endoplasmic reticulum chaperone BiP.

Optimization of Protein Extraction Method for 2DE Proteomics of Goat's Milk.

Two-dimensional electrophoretic (2DE)-based proteomics remains a powerful tool for allergenomic analysis of goat's milk but requires effective extraction of proteins to accurately profile the overall causative allergens. However, there are several current issues with goat's milk allergenomic analysis, and among these are the absence of established standardized extraction method for goat's milk proteomes and the complexity of goat's milk matrix that may hamper the efficacy of protein extraction. This study aimed to evaluate the efficacies of three different protein extraction methods, qualitatively and quantitatively, for the 2DE-proteomics, using milk from two commercial dairy goats in Malaysia, Saanen, and Jamnapari. Goat's milk samples from both breeds were extracted by using three different methods: a milk dilution in urea/thiourea based buffer (Method A), a triphasic separation protocol in methanol/chloroform solution (Method B), and a dilution in sulfite-based buffer (Method C). The efficacies of the extraction methods were assessed further by performing the protein concentration assay and 1D and 2D SDS-PAGE profiling, as well as identifying proteins by MALDI-TOF/TOF MS/MS. The results showed that method A recovered the highest amount of proteins (72.68% for Saanen and 71.25% for Jamnapari) and produced the highest number of protein spots (199 ± 16.1 and 267 ± 10.6 total spots for Saanen and Jamnapari, respectively) with superior gel resolution and minimal streaking. Six milk protein spots from both breeds were identified based on the positive peptide mass fingerprinting matches with ruminant milk proteins from public databases, using the Mascot software. These results attest to the fitness of the optimized protein extraction protocol, method A, for 2DE proteomic and future allergenomic analysis of the goat's milk.

Helping doctors hasten COVID-19 treatment: Towards a rescue framework for the transfusion of best convalescent plasma to the most critical patients based on biological requirements via ml and novel MCDM methods.

CONTEXT: People who have recently recovered from the threat of deteriorating coronavirus disease-2019 (COVID-19) have antibodies to the coronavirus circulating in their blood. Thus, the transfusion of these antibodies to deteriorating patients could theoretically help boost their immune system. Biologically, two challenges need to be surmounted to allow convalescent plasma (CP) transfusion to rescue the most severe COVID-19 patients. First, convalescent subjects must meet donor selection plasma criteria and comply with national health requirements and known standard routine procedures. Second, multi-criteria decision-making (MCDM) problems should be considered in the selection of the most suitable CP and the prioritisation of patients with COVID-19. OBJECTIVE: This paper presents a rescue framework for the transfusion of the best CP to the most critical patients with COVID-19 on the basis of biological requirements by using machine learning and novel MCDM methods. METHOD: The proposed framework is illustrated on the basis of two distinct and consecutive phases (i.e. testing and development). In testing, ABO compatibility is assessed after classifying donors into the four blood types, namely, A, B, AB and O, to indicate the suitability and safety of plasma for administration in order to refine the CP tested list repository. The development phase includes patient and donor sides. In the patient side, prioritisation is performed using a contracted patient decision matrix constructed between 'serological/protein biomarkers and the ratio of the partial pressure of oxygen in arterial blood to fractional inspired oxygen criteria' and 'patient list based on novel MCDM method known as subjective and objective decision by opinion score method'. Then, the patients with the most urgent need are classified into the four blood types and matched with a tested CP list from the test phase in the donor side. Thereafter, the prioritisation of CP tested list is performed using the contracted CP decision matrix. RESULT: An intelligence-integrated concept is proposed to identify the most appropriate CP for corresponding prioritised patients with COVID-19 to help doctors hasten treatments. DISCUSSION: The proposed framework implies the benefits of providing effective care and prevention of the extremely rapidly spreading COVID-19 from affecting patients and the medical sector.

Role of biological Data Mining and Machine Learning Techniques in Detecting and Diagnosing the Novel Coronavirus (COVID-19): A Systematic Review.

Coronaviruses (CoVs) are a large family of viruses that are common in many animal species, including camels, cattle, cats and bats. Animal CoVs, such as Middle East respiratory syndrome-CoV, severe acute respiratory syndrome (SARS)-CoV, and the new virus named SARS-CoV-2, rarely infect and spread among humans. On January 30, 2020, the International Health Regulations Emergency Committee of the World Health Organisation declared the outbreak of the resulting disease from this new CoV called 'COVID-19', as a 'public health emergency of international concern'. This global pandemic has affected almost the whole planet and caused the death of more than 315,131 patients as of the date of this article. In this context, publishers, journals and researchers are urged to research different domains and stop the spread of this deadly virus. The increasing interest in developing artificial intelligence (AI) applications has addressed several medical problems. However, such applications remain insufficient given the high potential threat posed by this virus to global public health. This systematic review addresses automated AI applications based on data mining and machine learning (ML) algorithms for detecting and diagnosing COVID-19. We aimed to obtain an overview of this critical virus, address the limitations of utilising data mining and ML algorithms, and provide the health sector with the benefits of this technique. We used five databases, namely, IEEE Xplore, Web of Science, PubMed, ScienceDirect and Scopus and performed three sequences of search queries between 2010 and 2020. Accurate exclusion criteria and selection strategy were applied to screen the obtained 1305 articles. Only eight articles were fully evaluated and included in this review, and this number only emphasised the insufficiency of research in this important area. After analysing all included studies, the results were distributed following the year of publication and the commonly used data mining and ML algorithms. The results found in all papers were discussed to find the gaps in all reviewed papers. Characteristics, such as motivations, challenges, limitations, recommendations, case studies, and features and classes used, were analysed in detail. This study reviewed the state-of-the-art techniques for CoV prediction algorithms based on data mining and ML assessment. The reliability and acceptability of extracted information and datasets from implemented technologies in the literature were considered. Findings showed that researchers must proceed with insights they gain, focus on identifying solutions for CoV problems, and introduce new improvements. The growing emphasis on data mining and ML techniques in medical fields can provide the right environment for change and improvement.

Proteomics of Sago Palm Towards Identifying Contributory Proteins in Stress-Tolerant Cultivar.

Metroxylon sagu Rottb. or locally known as sago palm is a tropical starch crop grown for starch production in commercial plantations in Malaysia, especially in Sarawak, East Malaysia. This plant species accumulate the highest amount of edible starch compared to other starch-producing crops. However, the non-trunking phenomenon has been observed to be one of the major issues restricting the yield of sago palm starch. In this study, proteomics approach was utilised to discover differences between trunking and non-trunking proteomes in sago palm leaf tissues. Total protein from 16 years old trunking and non-trunking sago palm leaves from deep peat area were extracted with PEG fractionation extraction method and subjected to two-dimensional gel electrophoresis (2D PAGE). Differential protein spots were subjected to MALDI-ToF/ToF MS/MS. Proteomic analysis has identified 34 differentially expressed proteins between trunking and non-trunking sago samples. From these protein spots, all 19 proteins representing different enzymes and proteins have significantly increased in abundance in non-trunking sago plant when subjected to mass spectrometry. The identified proteins mostly function in metabolic pathways including photosynthesis, tricarboxylic acid cycle, glycolysis, carbon utilization and oxidative stress. The current study indicated that the several proteins identified through differentially expressed proteome contributed to physical differences in trunking and non-trunking sago palm.

Comparative Proteomic Analysis on Fruit Ripening Processes in Two Varieties of Tropical Mango (Mangifera indica).

Mango (Mangifera indica L.) is an economically important fruit. However, the marketability of mango is affected by the perishable nature and short shelf-life of the fruit. Therefore, a better understanding of the mango ripening process is of great importance towards extending its postharvest shelf life. Proteomics is a powerful tool that can be used to elucidate the complex ripening process at the cellular and molecular levels. This study utilized 2-dimensional gel electrophoresis (2D-GE) coupled with MALDI-TOF/TOF to identify differentially abundant proteins during the ripening process of the two varieties of tropical mango, Mangifera indica cv. 'Chokanan' and Mangifera indica cv 'Golden Phoenix'. The comparative analysis between the ripe and unripe stages of mango fruit mesocarp revealed that the differentially abundant proteins identified could be grouped into the three categories namely, ethylene synthesis and aromatic volatiles, cell wall degradation and stress-response proteins. There was an additional category for differential proteins identified from the 'Chokanan' variety namely, energy and carbohydrate metabolism. However, of all the differential proteins identified, only methionine gamma-lyase was found in both 'Chokanan' and 'Golden Phoenix' varieties. Six differential proteins were selected from each variety for validation by analysing their respective transcript expression using reverse transcription-quantitative PCR (RT-qPCR). The results revealed that two genes namely, glutathione S-transferase (GST) and alpha-1,4 glucan phosphorylase (AGP) were found to express in concordant with protein abundant. The findings will provide an insight into the fruit ripening process of different varieties of mango fruits, which is important for postharvest management.