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), β-casein (β-CN), κ-casein (κ-CN), and beta-lactoglobulin (β-LG). Jamnapari's milk proteins were found to cross-react with four major milk allergens: α-S1-CN, β-CN, κ-CN, and β-LG. Saanen goat's milk proteins, on the other hand, cross-reacted with two major milk allergens, α-S1-CN and β-LG, whereas Toggenburg goat's milk proteins only react with one of the major milk allergens, κ-CN.

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.

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.