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Introduction: The main obstacle in treating cancer patients is drug resistance. Lenvatinib treatment poses challenges due to loss of response and the common dose-limiting adverse events (AEs). The Constrained-disorder-principle (CDP)- based second-generation artificial intelligence (AI) systems introduce variability into treatment regimens and offer a potential strategy for enhancing treatment efficacy. This proof-of-concept clinical trial aimed to assess the impact of a personalized algorithm-controlled therapeutic regimen on lenvatinib effectiveness and tolerability. Methods: A 14-week open-label, non-randomized trial was conducted with five cancer patients receiving lenvatinib--an AI-assisted application tailored to a personalized therapeutic regimen for each patient, which the treating physician approved. The study assessed changes in tumor response through FDG-PET-CT and tumor markers and quality of life via the EORTC QLQ-THY34 questionnaire, AEs, and laboratory evaluations. The app monitored treatment adherence. Results: At 14 weeks of follow-up, the disease control rate (including the following outcomes: complete response, partial response, stable disease) was 80%. The FDG-PET-CT scan-based RECIST v1.1 and PERCIST criteria showed partial response in 40% of patients and stable disease in an additional 40% of patients. One patient experienced a progressing disease. Of the participants with thyroid cancer, 75% showed a reduction in thyroglobulin levels, and 60% of all the participants showed a decrease in neutrophil-to-lymphocyte ratio during treatment. Improvement in the median social support score among patients utilizing the system supports an ancillary benefit of the intervention. No grade 4 AEs or functional deteriorations were recorded. Summary: The results of this proof-of-concept open-labeled clinical trial suggest that the CDP-based second-generation AI system-generated personalized therapeutic recommendations may improve the response to lenvatinib with manageable AEs. Prospective controlled studies are needed to determine the efficacy of this approach. [ABSTRACT FROM AUTHOR]

Aim: Neurological manifestations are common in patients with chronic liver diseases. This study aimed to depict the association between liver cirrhosis and Parkinson's disease (PD) and propose a clinically relevant diagnostic scheme. Methods: We examined patients' medical records with PD and chronic liver impairment secondary to cirrhosis or liver metastases for temporal correlations between liver insult and Parkinsonian signs. Results: Thirty-five individuals with PD and chronic liver impairment were included due to either cirrhosis or liver metastases. In all 22 patients with PD and liver metastases, the diagnosis of PD preceded the diagnosis of cancer. Conversely, patients with cirrhosis were often diagnosed with liver impairment before diagnosing PD. Age at diagnosis did not account for this difference. Conclusions: This study reinforces the potential clinical association between cirrhosis and PD. We also provide a diagnostic scheme that may guide therapeutic interventions and prognostic assessments. [ABSTRACT FROM AUTHOR]

There is still controversy surrounding the definition and mechanisms of consciousness. The constrained disorder principle (CDP) defines complex systems by their dynamic borders, limiting their inherent disorder. In line with the CDP, the brain exhibits a disorder bounded by dynamic borders essential for proper function, efficient energy use, and life support under continuous perturbations. The brain's inherent variability contributes to its adaptability and flexibility. Neuronal signal variability challenges the association of brain structures with consciousness and methods for assessing consciousness. The present paper discusses some theories about consciousness, emphasizing their failure to explain the brain's variability. This paper describes how the CDP accounts for consciousness's variability, complexity, entropy, and uncertainty. Using newly developed second-generation artificial intelligence systems, we describe how CDP-based platforms may improve disorders of consciousness (DoC) by accounting for consciousness variability, complexity, entropy, and uncertainty. This platform could be used to improve response to current interventions and develop new therapeutic regimens for patients with DoC in future studies. [ABSTRACT FROM AUTHOR]

Antimicrobial resistance results from the widespread use of antimicrobial agents and is a significant obstacle to the effectiveness of these agents. Numerous methods are used to overcome this problem with moderate success. Besides efforts of antimicrobial stewards, several artificial intelligence (AI)-based technologies are being explored for preventing resistance development. These first-generation systems mainly focus on improving patients' adherence. Chronobiology is inherent in all biological systems. Host response to infections and pathogens activity are assumed to be affected by the circadian clock. This paper describes the problem of antimicrobial resistance and reviews some of the current AI technologies. We present the establishment of a second-generation AI chronobiology-based approach to help in preventing further resistance and possibly overcome existing resistance. An algorithm-controlled regimen that improves the long-term effectiveness of antimicrobial agents is being developed based on the implementation of variability in dosing and drug administration times. The method provides a means for ensuring a sustainable response and improved outcomes. Ongoing clinical trials determine the effectiveness of this second-generation system in chronic infections. Data from these studies are expected to shed light on a new aspect of resistance mechanisms and suggest methods for overcoming them. IMPORTANCE SECTION The paper presents the establishment of a second-generation AI chronobiology-based approach to help in preventing further resistance and possibly overcome existing resistance. Antimicrobial resistance results from the widespread use of antimicrobial agents and is a significant obstacle to the effectiveness of these agents. We present the establishment of a second-generation AI chronobiology-based approach to help in preventing further resistance and possibly overcome existing resistance. [ABSTRACT FROM AUTHOR]

This document is a special issue of the International Journal of Molecular Sciences focusing on Computer-Aided Drug Discovery and Treatment (CADDT). CADDT is a multidisciplinary approach that combines bioinformatics, chemoinformatics, structural biology, and artificial intelligence (AI) to facilitate drug development and optimize treatment strategies. The use of virtual screening allows researchers to computationally evaluate potential drug candidates, reducing the need for time-consuming and resource-intensive synthesis and testing. CADDT also incorporates structural biology and molecular dynamics simulations to understand how drugs interact with target proteins. Chemoinformatics plays a critical role in analyzing chemical data and predicting chemical properties. Artificial intelligence and machine learning enhance CADDT by analyzing vast datasets and predicting drug discovery and treatment outcomes. CADDT can optimize the pharmacokinetics and pharmacodynamics of drugs, leading to more effective and personalized treatments. The approach also considers ethical considerations and challenges, such as data reliability, privacy, transparency, and responsible AI use in healthcare. Overall, CADDT has transformed the approach to drug development and shows potential for further improvement with emerging technologies. [Extracted from the article]