OBJECTIVE: The present study aimed to measure the agreement between BAT and immunoassay in diagnosis of penicillin allergy.

METHOD: BAT was performed using penicillin G (Pen G), penicillin V (Pen V), penicilloyl-polylysine (PPL), minor determinant mix (MDM), amoxicillin (Amx) and ampicillin (Amp) in 25 patients. Immunoassay of total IgE (tIgE) and specific IgE (sIgE) antibodies to Pen G, Pen V, Amx and Amp were quantified. Skin prick test (SPT) using PPL-MDM, Amx, Amp and Clavulanic acid were also performed.

RESULTS: Minimal agreement was observed between BAT and immunoassay (k=0.25). Of two BAT-positive patients, one patient is positive to Amx (59.27%, SI=59) and Amp (82.32%, SI=82) but sIgE-negative to all drug tested. This patient is also SPT-positive to both drugs. Another patient is BAT-positive to Pen G (10.18%, SI=40), Pen V (25.07%, SI=100) and Amp (19.52%, SI=79). In sIgE immunoassay, four patients were sIgE-positive to at least one of the drugs tested. The sIgE level of three patients was between low and moderate and they were BAT-negative. One BAT-positive patient had a high level of sIgE antibodies (3.50-17.5kU/L) along with relatively high specific to total IgE ratio ≥0.002 (0.004-0.007).

MATERIALS AND METHODS: A literature search was carried out to gather eligible studies from the following widely sourced electronic databases such as Scopus, PubMed and Google Scholar using the combination of the following keywords: AD, MRS, brain metabolites, deep learning (DL), machine learning (ML) and artificial intelligence (AI); having the aim of taking the readers through the advancements in the usage of MRS analysis and related AI applications for the detection of AD.

RESULTS: We elaborate on the MRS data acquisition, processing, analysis, and interpretation techniques. Recommendation is made for MRS parameters that can obtain the best quality spectrum for fingerprinting the brain metabolomics composition in AD. Furthermore, we summarise ML and DL techniques that have been utilised to estimate the uncertainty in the machine-predicted metabolite content, as well as streamline the process of displaying results of metabolites derangement that occurs as part of ageing.

METHODOLOGY: A total of 700 maxillary premolars were examined using CBCT in an Egyptian subpopulation. The number of roots was identified, and root canal configurations were classified according to Vertucci's classification and a new system for classifying root and canal morphology. In addition, the position where roots bifurcated and the levels where canals merged or diverged were identified. Fisher's exact test and independent t-test were used for statistical analysis, and the level of significance was set at 0.05 (P = 0.05).