Endocrine Entropy.

This communication defines and describes the novel concept of endocrine entropy. The authors share insights regarding the various facets of entropy in endocrine epidemiology, physiology, clinical presentation and management. The discussion opens up a new way of approaching endocrinology. Recent advances in artificial intelligence, assessment and addressal of entropy may become integral part of endocrine diagnostics and therapeutics.

Endocrine Entropy.

This communication defines and describes the novel concept of endocrine entropy. The authors share insights regarding the various facets of entropy in endocrine epidemiology, physiology, clinical presentation and management. The discussion opens up a new way of approaching endocrinology. Recent advances in artificial intelligence, assessment and addressal of entropy may become integral part of endocrine diagnostics and therapeutics.

Topological Spectra and Entropy of Chromatin Loop Networks.

The 3D folding of a mammalian gene can be studied by a polymer model, where the chromatin fiber is represented by a semiflexible polymer which interacts with multivalent proteins, representing complexes of DNA-binding transcription factors and RNA polymerases. This physical model leads to the natural emergence of clusters of proteins and binding sites, accompanied by the folding of chromatin into a set of topologies, each associated with a different network of loops. Here, we combine numerics and analytics to first classify these networks and then find their relative importance or statistical weight, when the properties of the underlying polymer are those relevant to chromatin. Unlike polymer networks previously studied, our chromatin networks have finite average distances between successive binding sites, and this leads to giant differences between the weights of topologies with the same number of edges and nodes but different wiring. These weights strongly favor rosettelike structures with a local cloud of loops with respect to more complicated nonlocal topologies. Our results suggest that genes should overwhelmingly fold into a small fraction of all possible 3D topologies, which can be robustly characterized by the framework we propose here.

Detecting tipping points of complex diseases by network information entropy.

The progression of complex diseases often involves abrupt and non-linear changes characterized by sudden shifts that trigger critical transformations. Identifying these critical states or tipping points is crucial for understanding disease progression and developing effective interventions. To address this challenge, we have developed a model-free method named Network Information Entropy of Edges (NIEE). Leveraging dynamic network biomarkers, sample-specific networks, and information entropy theories, NIEE can detect critical states or tipping points in diverse data types, including bulk, single-sample expression data. By applying NIEE to real disease datasets, we successfully identified critical predisease stages and tipping points before disease onset. Our findings underscore NIEE's potential to enhance comprehension of complex disease development.

Evaluation of the applicability of hospital-affiliated green spaces to patient recovery using the entropy weight method and grey relational analysis.

Introduction: Hospital affiliated green spaces can help patients recover and recover their physical functions, promote physical and mental relaxation, enhance health awareness, and improve overall health. However, there are still significant questions about how to scientifically construct hospital affiliated green spaces. This study examines the impact of hospital green spaces on patient rehabilitation through scientific evaluation methods, providing reference for the scientific construction of hospital affiliated green spaces. Applicability evaluation was conducted on the affiliated green spaces of three hospitals in Harbin. An evaluation system covering plants, space, accessibility, rehabilitation functions, and promotional and educational functions has been constructed. The entropy weight method is used to determine the weight of indicators, and the grey correlation analysis method is used to evaluate the suitability of green space for patient rehabilitation. Methods: The experimental results showed that the landscape accessibility index had the highest weight (0.3005) and the plant index had the lowest weight (0.1628), indicating that caring for special needs is the foundation of hospital landscapes, and plants have subtle and long-term effects on physical and mental health. In the evaluation of the rehabilitation applicability of the affiliated green spaces of various hospitals, the second hospital has the highest grey correlation degree (0.8525), followed by the tumor hospital (0.5306) and the fifth hospital (0.4846). It can be seen that the green space of the second hospital has high applicability for patient rehabilitation, but the green space of the tumor hospital and the fifth hospital needs to be improved and developed. Results and discussion: The evaluation criteria used in this study are comprehensive. The landscaping at the Third Hospital is well-planned with good plant configuration and reasonable spatial layout. However, there is insufficient consideration for accessibility in the landscape design, and the details are lacking. The rehabilitation and educational functions of the landscape are inadequate, with limited outdoor activities and low road safety. The hospital's affiliated green spaces should adhere to the principle of "appropriate scale, comprehensive functionality, and educational leisure," integrating rehabilitation and educational functions while increasing the variety of outdoor activities. In the future, emphasis should be placed on exploring the integration of landscape and rehabilitation to provide a functional site that is convenient for visiting, with improved rehabilitation facilities and an educational and enjoyable environment. The design should incorporate elements that contribute to a sense of well-being, including roads and.

Assembly-Induced Membrane Selectivity of Artificial Model Peptides through Entropy-Enthalpy Competition.

Peptide design and drug development offer a promising solution for combating serious diseases or infections. In this study, using an AI-human negotiation approach, we have designed a class of minimal model peptides against tuberculosis (TB), among which K7W6 exhibits potent efficacy attributed to its assembly-induced function. Comprising lysine and tryptophan with an amphiphilic α-helical structure, the K7W6 sequence exhibits robust activity against various infectious bacteria causing TB (including clinically isolated and drug-resistant strains) both in vitro and in vivo. Moreover, it synergistically enhances the effectiveness of the first-line antibiotic rifampicin while displaying low potential for inducing drug resistance and minimal toxicity toward mammalian cells. Biophysical experiments and simulations elucidate that K7W6's exceptional performance can be ascribed to its highly selective and efficient membrane permeabilization activity induced by its distinctive self-assembly behavior. Additionally, these assemblies regulate the interplay between enthalpy and entropy during K7W6-membrane interaction, leading to the peptide's two-step mechanism of membrane interaction. These findings provide valuable insights into rational design principles for developing advanced peptide-based drugs while uncovering the functional role played by assembly.

EEG/MEG source imaging of deep brain activity within the maximum entropy on the mean framework: Simulations and validation in epilepsy.

Electro/Magneto-EncephaloGraphy (EEG/MEG) source imaging (EMSI) of epileptic activity from deep generators is often challenging due to the higher sensitivity of EEG/MEG to superficial regions and to the spatial configuration of subcortical structures. We previously demonstrated the ability of the coherent Maximum Entropy on the Mean (cMEM) method to accurately localize the superficial cortical generators and their spatial extent. Here, we propose a depth-weighted adaptation of cMEM to localize deep generators more accurately. These methods were evaluated using realistic MEG/high-density EEG (HD-EEG) simulations of epileptic activity and actual MEG/HD-EEG recordings from patients with focal epilepsy. We incorporated depth-weighting within the MEM framework to compensate for its preference for superficial generators. We also included a mesh of both hippocampi, as an additional deep structure in the source model. We generated 5400 realistic simulations of interictal epileptic discharges for MEG and HD-EEG involving a wide range of spatial extents and signal-to-noise ratio (SNR) levels, before investigating EMSI on clinical HD-EEG in 16 patients and MEG in 14 patients. Clinical interictal epileptic discharges were marked by visual inspection. We applied three EMSI methods: cMEM, depth-weighted cMEM and depth-weighted minimum norm estimate (MNE). The ground truth was defined as the true simulated generator or as a drawn region based on clinical information available for patients. For deep sources, depth-weighted cMEM improved the localization when compared to cMEM and depth-weighted MNE, whereas depth-weighted cMEM did not deteriorate localization accuracy for superficial regions. For patients' data, we observed improvement in localization for deep sources, especially for the patients with mesial temporal epilepsy, for which cMEM failed to reconstruct the initial generator in the hippocampus. Depth weighting was more crucial for MEG (gradiometers) than for HD-EEG. Similar findings were found when considering depth weighting for the wavelet extension of MEM. In conclusion, depth-weighted cMEM improved the localization of deep sources without or with minimal deterioration of the localization of the superficial sources. This was demonstrated using extensive simulations with MEG and HD-EEG and clinical MEG and HD-EEG for epilepsy patients.

Adhesion dynamics of Janus nanocarriers to endothelial cells: A dissipative particle dynamics study.

Janus nanocarriers (NCs) provide promising features in interfacial applications such as targeted drug delivery. Herein, we use dissipative particle dynamics simulations to study the adhesion dynamics of NCs with Janus ligand compositions to the endothelial cell as a function of a series of effects, such as the initial orientation, ligand density, shape, and size of Janus NCs. The Janus NCs, with its long axis parallel to the endothelial glycocalyx (EG) layer, has the best penetration depth due to its lower potential energy and the lowest shell entropy loss. Among different shapes of Janus NCs, both the potential energy and the EG entropy loss control the penetration. In fact, at the parallel orientations, Janus shapes with a robust mechanical strength and larger surface area at the EG/water interface can rotate and penetrate more efficiently. An increase in the ligand density of Janus NCs increases entropy losses of both the hydrophilic and the hydrophobic ligands and decreases the potential energy. Thus, for a specific Janus NCs, functionalizing with an appropriate ligand density would help driving forces prevail over barriers of penetration into the EG layer. For a particular ligand density, once the radius of the Janus NCs exceeds the appropriate size, barriers such as hydrophobic ligands and shell entropy losses are also reinforced significantly and surpass driving forces. Our observations reveal that entropy losses for hydrophobic ligands of Janus NCs and for the shell of NCs are decisive for the adhesion and penetration of Janus NCs to endothelial cells.

Identifying vital nodes for yeast network by dynamic network entropy.

BACKGROUND: The progress of the cell cycle of yeast involves the regulatory relationships between genes and the interactions proteins. However, it is still obscure which type of protein plays a decisive role in regulation and how to identify the vital nodes in the regulatory network. To elucidate the sensitive node or gene in the progression of yeast, here, we select 8 crucial regulatory factors from the yeast cell cycle to decipher a specific network and propose a simple mixed K2 algorithm to identify effectively the sensitive nodes and genes in the evolution of yeast. RESULTS: Considering the multivariate of cell cycle data, we first utilize the K2 algorithm limited to the stationary interval for the time series segmentation to measure the scores for refining the specific network. After that, we employ the network entropy to effectively screen the obtained specific network, and simulate the gene expression data by a normal distribution approximation and the screened specific network by the partial least squares method. We can conclude that the robustness of the specific network screened by network entropy is better than that of the specific network with the determined relationship by comparing the obtained specific network with the determined relationship. Finally, we can determine that the node CDH1 has the highest score in the specific network through a sensitivity score calculated by network entropy implying the gene CDH1 is the most sensitive regulatory factor. CONCLUSIONS: It is clearly of great potential value to reconstruct and visualize gene regulatory networks according to gene databases for life activities. Here, we present an available algorithm to achieve the network reconstruction by measuring the network entropy and identifying the vital nodes in the specific nodes. The results indicate that inhibiting or enhancing the expression of CDH1 can maximize the inhibition or enhancement of the yeast cell cycle. Although our algorithm is simple, it is also the first step in deciphering the profound mystery of gene regulation.

Entropy drives the ligand recognition in G-protein-coupled receptor subtypes.

Achieving ligand subtype selectivity within highly homologous subtypes of G-protein-coupled receptor (GPCR) is critical yet challenging for GPCR drug discovery, primarily due to the unclear mechanism underlying ligand subtype selectivity, which hampers the rational design of subtype-selective ligands. Herein, we disclose an unusual molecular mechanism of entropy-driven ligand recognition in cannabinoid (CB) receptor subtypes, revealed through atomic-level molecular dynamics simulations, cryoelectron microscopy structure, and mutagenesis experiments. This mechanism is attributed to the distinct conformational dynamics of the receptor's orthosteric pocket, leading to variations in ligand binding entropy and consequently, differential binding affinities, which culminate in specific ligand recognition. We experimentally validated this mechanism and leveraged it to design ligands with enhanced or ablated subtype selectivity. One such ligand demonstrated favorable pharmacokinetic properties and significant efficacy in rodent inflammatory analgesic models. More importantly, it is precisely due to the high subtype selectivity obtained based on this mechanism that this ligand does not show addictive properties in animal models. Our findings elucidate the unconventional role of entropy in CB receptor subtype selectivity and suggest a strategy for rational design of ligands to achieve entropy-driven subtype selectivity for many pharmaceutically important GPCRs.

A predictive model combining connectomics and entropy biomarkers to discriminate long-term vagus nerve stimulation efficacy for pediatric patients with drug-resistant epilepsy.

AIMS: To predict the vagus nerve stimulation (VNS) efficacy for pediatric drug-resistant epilepsy (DRE) patients, we aim to identify preimplantation biomarkers through clinical features and electroencephalogram (EEG) signals and thus establish a predictive model from a multi-modal feature set with high prediction accuracy. METHODS: Sixty-five pediatric DRE patients implanted with VNS were included and followed up. We explored the topological network and entropy features of preimplantation EEG signals to identify the biomarkers for VNS efficacy. A Support Vector Machine (SVM) integrated these biomarkers to distinguish the efficacy groups. RESULTS: The proportion of VNS responders was 58.5% (38/65) at the last follow-up. In the analysis of parieto-occipital α band activity, higher synchronization level and nodal efficiency were found in responders. The central-frontal θ band activity showed significantly lower entropy in responders. The prediction model reached an accuracy of 81.5%, a precision of 80.1%, and an AUC (area under the receiver operating characteristic curve) of 0.838. CONCLUSION: Our results revealed that, compared to nonresponders, VNS responders had a more efficient α band brain network, especially in the parieto-occipital region, and less spectral complexity of θ brain activities in the central-frontal region. We established a predictive model integrating both preimplantation clinical and EEG features and exhibited great potential for discriminating the VNS responders. This study contributed to the understanding of the VNS mechanism and improved the performance of the current predictive model.

Bioinformatic Analysis of Topoisomerase IIα Reveals Interdomain Interdependencies and Critical C-Terminal Domain Residues.

DNA Topoisomerase IIα (Top2A) is a nuclear enzyme that is a cancer drug target, and there is interest in identifying novel sites on the enzyme to inhibit cancer cells more selectively and to reduce off-target toxicity. The C-terminal domain (CTD) is one potential target, but it is an intrinsically disordered domain, which prevents structural analysis. Therefore, we set out to analyze the sequence of Top2A from 105 species using bioinformatic analysis, including the PSICalc algorithm, Shannon entropy analysis, and other approaches. Our results demonstrate that large (10th-order) interdependent clusters are found including non-proximal positions across the major domains of Top2A. Further, CTD-specific clusters of the third, fourth, and fifth order, including positions that had been previously analyzed via mutation and biochemical assays, were identified. Some of these clusters coincided with positions that, when mutated, either increased or decreased relaxation activity. Finally, sites of low Shannon entropy (i.e., low variation in amino acids at a given site) were identified and mapped as key positions in the CTD. Included in the low-entropy sites are phosphorylation sites and charged positions. Together, these results help to build a clearer picture of the critical positions in the CTD and provide potential sites/regions for further analysis.

Multi-scale entropy assessment of magnetoencephalography signals in schizophrenia.

Schizophrenia is a severe disruption in cognition and emotion, affecting fundamental human functions. In this study, we applied Multi-Scale Entropy analysis to resting-state Magnetoencephalography data from 54 schizophrenia patients and 98 healthy controls. This method quantifies the temporal complexity of the signal across different time scales using the concept of sample entropy. Results show significantly higher sample entropy in schizophrenia patients, primarily in central, parietal, and occipital lobes, peaking at time scales equivalent to frequencies between 15 and 24 Hz. To disentangle the contributions of the amplitude and phase components, we applied the same analysis to a phase-shuffled surrogate signal. The analysis revealed that most differences originate from the amplitude component in the δ, α, and ß power bands. While the phase component had a smaller magnitude, closer examination reveals clear spatial patterns and significant differences across specific brain regions. We assessed the potential of multi-scale entropy as a schizophrenia biomarker by comparing its classification performance to conventional spectral analysis and a cognitive task (the n-back paradigm). The discriminative power of multi-scale entropy and spectral features was similar, with a slight advantage for multi-scale entropy features. The results of the n-back test were slightly below those obtained from multi-scale entropy and spectral features.

Decreasing Intracellular Entropy by Increasing Mitochondrial Efficiency and Reducing ROS Formation-The Effect on the Ageing Process and Age-Related Damage.

A hypothesis is presented to explain how the ageing process might be influenced by optimizing mitochondrial efficiency to reduce intracellular entropy. Research-based quantifications of entropy are scarce. Non-equilibrium metabolic reactions and compartmentalization were found to contribute most to lowering entropy in the cells. Like the cells, mitochondria are thermodynamically open systems exchanging matter and energy with their surroundings-the rest of the cell. Based on the calculations from cancer cells, glycolysis was reported to produce less entropy than mitochondrial oxidative phosphorylation. However, these estimations depended on the CO2 concentration so that at slightly increased CO2, it was oxidative phosphorylation that produced less entropy. Also, the thermodynamic efficiency of mitochondrial respiratory complexes varies depending on the respiratory state and oxidant/antioxidant balance. Therefore, in spite of long-standing theoretical and practical efforts, more measurements, also in isolated mitochondria, with intact and suboptimal respiration, are needed to resolve the issue. Entropy increases in ageing while mitochondrial efficiency of energy conversion, quality control, and turnover mechanisms deteriorate. Optimally functioning mitochondria are necessary to meet energy demands for cellular defence and repair processes to attenuate ageing. The intuitive approach of simply supplying more metabolic fuels (more nutrients) often has the opposite effect, namely a decrease in energy production in the case of nutrient overload. Excessive nutrient intake and obesity accelerate ageing, while calorie restriction without malnutrition can prolong life. Balanced nutrient intake adapted to needs/activity-based high ATP requirement increases mitochondrial respiratory efficiency and leads to multiple alterations in gene expression and metabolic adaptations. Therefore, rather than overfeeding, it is necessary to fine-tune energy production by optimizing mitochondrial function and reducing oxidative stress; the evidence is discussed in this paper.

Study on the risk assessment of Pedestrian-Vehicle conflicts in channelized Right-Turn lanes based on the Hierarchical-Grey Entropy-Cloud model.

Channelized right-turn lanes (CRTLs) in urban areas have been effective in improving the efficiency of right-turning vehicles but have also presented negative impacts on pedestrian movement. Pedestrians experience confusion regarding the allocation of road space when crossing crosswalks within these areas, leading to frequent conflicts between pedestrians and motor vehicles. In this paper, considering the characteristics of pedestrian-vehicle conflicts at channelized right-turn lanes as well as the ambiguity and uncertainty of the causes, a comprehensive assignment combined with a cloud model is proposed as a risk evaluation model for pedestrian-vehicle conflicts. The study established a risk indicator system based on three aspects of the transportation system: pedestrians, motor vehicles, and the road environment. Combining the analytic hierarchy process (AHP), grey relational analysis (GRA), and entropy weighting method (EWM) to get the weights of indicator combinations, and then using the cloud model to realize quantitative and qualitative language transformation to complete the risk evaluation. This study employs specific road segments in Qingdao as a validation case for model analysis. The results indicate that the model's evaluation outcomes exhibited a significant level of agreement with the findings from field investigations during both peak and off-peak periods. It is demonstrated that the model has good performance for the safety assessment of pedestrian-vehicle conflicts at CRTL, and it also reflects the ability of the model to assess fuzzy randomness problems. It provides participation value for urban pedestrian-vehicle safety problems as well as applications in other fields.

RNA's Dynamic Conformational Selection and Entropic Allosteric Mechanism in Controlling Cascade Protein Binding Events.

In the TAR RNA of immunodeficiency viruses, an allosteric communication exists between a distant loop and a bulge. The bulge interacts with the TAT protein vital for transactivating viral RNA, while the loop interacts with cyclin-T1, contingent on TAT binding. Through extensive atomistic and free energy simulations, we investigate TAR-TAT binding in nonpathogenic bovine immunodeficiency virus (BIV) and pathogenic human immunodeficiency virus (HIV). Thermodynamic analysis reveals enthalpically driven binding in BIV and entropically favored binding in HIV. The broader global basin in HIV is attributed to binding-induced loop fluctuation, corroborated by nuclear magnetic resonance (NMR), indicating classical entropic allostery onset. While this loop fluctuation affects the TAT binding affinity, it generates a binding-competent conformation that aids subsequent effector (cyclin-T1) binding. This study underscores how two structurally similar apo-RNA scaffolds adopt distinct conformational selection mechanisms to drive enthalpic and entropic allostery, influencing protein affinity in the signaling cascade.

Target recycle initiated entropy driven assembly strategy for sensitive, enzyme-free, and portable miRNA detection.

MicroRNA (miRNA) is a pivotal biomarker in the diagnosis of various cancers, including bladder cancer (BCa). Despite their significance, the low abundance of miRNA presents a substantial challenge for sensitive and reliable detection. We introduce an innovative, highly sensitive assay for miRNA expression quantification that is both enzyme-free and portable. This method leverages the synergy of target recycling and entropy-driven assembly (EDA) for enhanced sensitivity and specificity. The proposed method possesses several advantages, including i) dual signal amplification through target recycling and EDA, which significantly boosts sensitivity with a lower limit of detection of 2.54 fM; ii) elimination of enzyme requirements, resulting in a cost-effective and stable signal amplification process; and iii) utilization of a personal glucose meter (PGM) for signal recording, rendering the method portable and adaptable to diverse settings. In summary, this PGM-based approach holds promising potential for clinical molecular diagnostics, offering a practical and efficient solution for miRNA analysis in cancer detection.

Multiscale distribution entropy analysis of short epileptic EEG signals.

This paper proposes an information-theoretic measure for discriminating epileptic patterns in short-term electroencephalogram (EEG) recordings. Considering nonlinearity and nonstationarity in EEG signals, quantifying complexity has been preferred. To decipher abnormal epileptic EEGs, i.e., ictal and interictal EEGs, via short-term EEG recordings, a distribution entropy (DE) is used, motivated by its robustness on the signal length. In addition, to reflect the dynamic complexity inherent in EEGs, a multiscale entropy analysis is incorporated. Here, two multiscale distribution entropy (MDE) methods using the coarse-graining and moving-average procedures are presented. Using two popular epileptic EEG datasets, i.e., the Bonn and the Bern-Barcelona datasets, the performance of the proposed MDEs is verified. Experimental results show that the proposed MDEs are robust to the length of EEGs, thus reflecting complexity over multiple time scales. In addition, the proposed MDEs are consistent irrespective of the selection of short-term EEGs from the entire EEG recording. By evaluating the Man-Whitney U test and classification performance, the proposed MDEs can better discriminate epileptic EEGs than the existing methods. Moreover, the proposed MDE with the moving-average procedure performs marginally better than one with the coarse-graining. The experimental results suggest that the proposed MDEs are applicable to practical seizure detection applications.

Driving Cognitive Alertness Detecting Using Evoked Multimodal Physiological Signals Based on Uncertain Self-Supervised Learning.

Multimodal physiological signals play a pivotal role in drivers' perception of work stress. However, the scarcity of labels and the multitude of modalities render the utilization of physiological signals for driving cognitive alertness detection challenging. We thus propose a multimodal physiological signal detection model based on self-supervised learning. First, in order to mine the intrinsic information of data and enable data to highlight effective information, we introduce a multiscale entropy (MSE) evoked attention mechanism. Secondly, the multimodal patches undergo processing through a novel cascaded attention mechanism. This attention mechanism is rooted in patch-level interactions within each modality, progressively integrating and interacting with other modalities in a cascading manner, thereby mitigating computational complexity. Moreover, a multimodal uncertainty-aware module is devised to effectively cope with intricate variations in the data. This module enhances its generalization ability through the incorporation of uncertain resampling. Experiments were conducted on the DriveDB dataset and the CogPilot dataset with both the linear probing and the fine-tuning evaluation protocols. Experimental results in subject-dependent setting show that our model significantly outperforms previous competitive baselines. In the linear probing evaluation, our model achieves on average 6.26%, 6.64%, and 7.75% improvements in Accuracy (Acc), Recall (Rec), and F1 Score. It also outperforms other models by 7.96% in Acc, 9.13% in Rec, and 9.2% in F1 using the fine-tuning evaluation. Furthermore, our model also demonstrates robust performance in subject-independent setting.

Is EEG Entropy a Useful Measure for Alzheimer's Disease?

BACKGROUND: The number of individuals diagnosed with Alzheimer's disease (AD) has increased, and it is estimated to continue rising in the coming years. The diagnosis of this disease is challenging due to variations in onset and course, its diverse clinical manifestations, and the indications for measuring deposit biomarkers. Hence, there is a need to develop more precise and less invasive diagnostic tools. Multiple studies have considered using electroencephalography (EEG) entropy measures as an indicator of the onset and course of AD. Entropy is deemed suitable as a potential indicator based on the discovery that variations in its complexity can be associated with specific pathologies such as AD. METHODOLOGY: Following PRISMA guidelines, a literature search was conducted in 4 scientific databases, and 40 articles were analyzed after discarding and filtering. RESULTS: There is a diversity in entropy measures; however, Sample Entropy (SampEn) and Multiscale Entropy (MSE) are the most widely used (21/40). In general, it is found that when comparing patients with controls, patients exhibit lower entropy (20/40) in various areas. Findings of correlation with the level of cognitive decline are less consistent, and with neuropsychiatric symptoms (2/40) or treatment response less explored (2/40), although most studies show lower entropy with greater severity. Machine learning-based studies show good discrimination capacity. CONCLUSIONS: There is significant difficulty in comparing multiple studies due to their heterogeneity; however, changes in Multiscale Entropy (MSE) scales or a decrease in entropy levels are considered useful for determining the presence of AD and measuring its severity.