An investigation to choose the proper therapy technique in the management of autism spectrum disorder

A Multi-Criteria Decision Aid Tool for Radiopharmaceutical Selection in Tau PET Imaging

The accumulation of pathologically misfolded tau is a feature shared by a group of neurodegenerative disorders collectively referred to as tauopathies. Alzheimer's disease (AD) is the most prevalent of these tauopathies. Immunohistochemical evaluation allows neuropathologists to visualize paired-helical filaments (PHFs)-tau pathological lesions, but this is possible only after death and only shows tau in the portion of brain sampled. Positron emission tomography (PET) imaging allows both the quantitative and qualitative analysis of pathology over the whole brain of a living subject. The ability to detect and quantify tau pathology in vivo using PET can aid in the early diagnosis of AD, provide a way to monitor disease progression, and determine the effectiveness of therapeutic interventions aimed at reducing tau pathology. Several tau-specific PET radiotracers are now available for research purposes, and one is approved for clinical use. This study aims to analyze, compare, and rank currently available tau PET radiotracers using the fuzzy preference ranking organization method for enrichment of evaluations (PROMETHEE), which is a multi-criteria decision-making (MCDM) tool. The evaluation is based on relatively weighted criteria, such as specificity, target binding affinity, brain uptake, brain penetration, and rates of adverse reactions. Based on the selected criteria and assigned weights, this study shows that a second-generation tau tracer, [¹⁸F]RO-948, may be the most favorable. This flexible method can be extended and updated to include new tracers, additional criteria, and modified weights to help researchers and clinicians select the optimal tau PET tracer for specific purposes. Additional work is needed to confirm these results, including a systematic approach to defining and weighting criteria and clinical validation of tracers in different diseases and patient populations.

Supporting rehabilitation stakeholders in making service delivery decisions: a rapid review of multi-criteria decision analysis methods

PURPOSE

This review aimed to synthesize knowledge about multi-criteria decision analysis methods for supporting rehabilitation service design and delivery decisions, including: (1) describing the use of these methods within rehabilitation, (2) identifying decision types that can be supported by these methods, (3) describing client and family involvement, and (4) identifying implementation considerations.

METHODS

We conducted a rapid review in collaboration with a knowledge partner, searching four databases for peer-reviewed articles reporting primary research. We extracted relevant data from included studies and synthesized it descriptively and with conventional content analysis.

RESULTS

We identified 717 records, of which 54 met inclusion criteria. Multi-criteria decision analysis methods were primarily used to understand the strength of clients' and clinicians' preferences (n = 44), and five focused on supporting decision making. Shared decision making with stakeholders was evident in only two studies. Clients and families were mostly engaged in data collection and sometimes in selecting the relevant criteria. Good practices for supporting external validity were inconsistently reported. Implementation considerations included managing cognitive complexity and offering authentic choices.

CONCLUSIONS

Multi-criteria decision analysis methods are promising for better understanding client and family preferences and priorities across rehabilitation professions, contexts, and caseloads. Further work is required to use these methods in shared decision making, for which increased use of qualitative methods and stakeholder engagement is recommended. IMPLICATIONS FOR REHABILITATIONMulti-criteria decision analysis methods are promising for evidence-based, shared decision making for rehabilitation.However, most studies to date have focused on estimating stakeholder preferences, not supporting shared decision making.Cognitive complexity and modelling authentic and realistic decision choices are major barriers to implementation.Stakeholder-engagement and qualitative methods are recommended to address these barriers.

Breast Cancer Screening Based on Supervised Learning and Multi-Criteria Decision-Making

On average, breast cancer kills one woman per minute. However, there are more reasons for optimism than ever before. When diagnosed early, patients with breast cancer have a better chance of survival. This study aims to employ a novel approach that combines artificial intelligence and a multi-criteria decision-making method for a more robust evaluation of machine learning models. The proposed machine learning techniques comprise various supervised learning algorithms, while the multi-criteria decision-making technique implemented includes the Preference Ranking Organization Method for Enrichment Evaluations. The Support Vector Machine, having achieved a net outranking flow of 0.1022, is ranked as the most favorable model for the early detection of breast cancer. The net outranking flow is the balance between the positive and negative outranking flows. This indicates that the higher the net flow, the better the alternative. K-nearest neighbor, logistic regression, and random forest classifier ranked second, third, and fourth, with net flows of 0.0316, −0.0032, and −0.0541, respectively. The least preferred alternative is the naive Bayes classifier with a net flow of −0.0766. The results obtained in this study indicate the use of the proposed method in making a desirable decision when selecting the most appropriate machine learning model. This gives the decision-maker the option of introducing new criteria into the decision-making process.

The Efficiency of AuNPs in Cancer Cell Targeting Compared to Other Nanomedicine Technologies Using Fuzzy PROMETHEE

Cancer is a disease with rare, diverse symptoms, causing abnormal cell growth in an uncontrolled way, leading to cell damage, apoptosis, and eventually death of the patient. This study uses the Fuzzy PROMETHEE technique to develop a new path for cancer treatment based on nanoparticles (NPs) applications, used in controlled anticancer drug delivery (drug release, toxicity, and unspecific site targeting) to enhance patient safety. The different nanoparticles employed in the drug delivery analysis are gold nanoparticles (AuNPs), liposomes, dendrimers, polymeric micelles (PMs), and quantum dots (QDs). Fuzzy predictable preference organization mode and evaluation multicriteria choice were used as tactics in making the best decision using the data from the factors of cost, size, shape, surface charge, ligand type, pH and temperature stimuli, biocompatibility, accumulation ratio, toxicity, specificity, stability, efficacy, adverse effect, and safety factor of the NPs. The results obtained from the total net flow of the visual PROMETHEE scenario for anticancer drug delivery, based on NPs data analysis, show that AuNPs are ranked the highest among the other NPs. The Phi values obtained for the NPs are as follows: AuNPs (0.1428), PMs (0.0280), QDs (-0.0467), dendrimers (-0.0593), and liposomes (-0.0649). This study highlights the optimal choice of NPs as an intelligent drug delivery system that facilitates therapeutic efficiency, where cancer cells are accurately targeted to enhance treatment quality and patient safety.