Brain Representation of Animal and Non-Animal Images in Patients with Mild Cognitive Impairment and Alzheimer's Disease.

Background: In early Alzheimer's disease (AD), high-level visual functions and processing speed are impacted. Few functional magnetic resonance imaging (fMRI) studies have investigated high-level visual deficits in AD, yet none have explored brain activity patterns during rapid animal/non-animal categorization tasks. Objective: To address this, we utilized the previously known Integrated Cognitive Assessment (ICA) to collect fMRI data and compare healthy controls (HC) to individuals with mild cognitive impairment (MCI) and mild AD. Methods: The ICA encompasses a rapid visual categorization task that involves distinguishing between animals and non-animals within natural scenes. To comprehensively explore variations in brain activity levels and patterns, we conducted both univariate and multivariate analyses of fMRI data. Results: The ICA task elicited activation across a range of brain regions, encompassing the temporal, parietal, occipital, and frontal lobes. Univariate analysis, which compared responses to animal versus non-animal stimuli, showed no significant differences in the regions of interest (ROIs) across all groups, with the exception of the left anterior supramarginal gyrus in the HC group. In contrast, multivariate analysis revealed that in both HC and MCI groups, several regions could differentiate between animals and non-animals based on distinct patterns of activity. Notably, such differentiation was absent within the mild AD group. Conclusions: Our study highlights the ICA task's potential as a valuable cognitive assessment tool designed for MCI and AD. Additionally, our use of fMRI pattern analysis provides valuable insights into the complex changes in brain function associated with AD. This approach holds promise for enhancing our understanding of the disease's progression.

Combined action of albumin and heparin regulates lipoprotein lipase oligomerization, stability, and ligand interactions.

Lipoprotein lipase (LPL), a crucial enzyme in the intravascular hydrolysis of triglyceride-rich lipoproteins, is a potential drug target for the treatment of hypertriglyceridemia. The activity and stability of LPL are influenced by a complex ligand network. Previous studies performed in dilute solutions suggest that LPL can appear in various oligomeric states. However, it was not known how the physiological environment, that is blood plasma, affects the action of LPL. In the current study, we demonstrate that albumin, the major protein component in blood plasma, has a significant impact on LPL stability, oligomerization, and ligand interactions. The effects induced by albumin could not solely be reproduced by the macromolecular crowding effect. Stabilization, isothermal titration calorimetry, and surface plasmon resonance studies revealed that albumin binds to LPL with affinity sufficient to form a complex in both the interstitial space and the capillaries. Negative stain transmission electron microscopy and raster image correlation spectroscopy showed that albumin, like heparin, induced reversible oligomerization of LPL. However, the albumin induced oligomers were structurally different from heparin-induced filament-like LPL oligomers. An intriguing observation was that no oligomers of either type were formed in the simultaneous presence of albumin and heparin. Our data also suggested that the oligomer formation protected LPL from the inactivation by its physiological regulator angiopoietin-like protein 4. The concentration of LPL and its environment could influence whether LPL follows irreversible inactivation and aggregation or reversible LPL oligomer formation, which might affect interactions with various ligands and drugs. In conclusion, the interplay between albumin and heparin could provide a mechanism for ensuring the dissociation of heparan sulfate-bound LPL oligomers into active LPL upon secretion into the interstitial space.

Designing humanitarian logistics network for managing epidemic outbreaks in disasters using Internet-of-Things. A case study: An earthquake in Salas-e-Babajani city.

Along with the destructive effects of catastrophes throughout the world, the COVID-19 outbreak has intensified the severity of disasters. Although the global aid organizations and philanthropists aim to alleviate the adverse impacts, many employed actions are not impactful in dealing with the epidemic outbreak in disasters. However, there is a gap in controlling the epidemic outbreak in the aftermath of disasters. Therefore, this paper proposes a novel humanitarian location-allocation-inventory model by focusing on preventing COVID-19 outbreaks with IoT-based technology in the response phase of disasters. In this study, IoT-based systems enable aid and health-related organizations to monitor people remotely, suspect detection, surveillance, disinfection, and transportation of relief items. The presented model consists of two stages; the first is defining infected cases, transferring patients to temporary hospitals promptly, and accommodating people in evacuation centers. Next, distribution centers are located in the second stage, and relief items are transferred to temporary hospitals and evacuation centers equally regarding shortage minimization. The model is solved by the LP-metric method and applied in a real case study in Salas-e-Babajani city, Kermanshah province. Then, sensitivity analysis on significant model parameters pertaining to the virus, relief items, and capacity has been conducted. Using an IoT-based system in affected areas and evacuation centers reduces the number of infected cases and relief item's shortages. Finally, several managerial insights are obtained from sensitivity analyses provided for healthcare managers.

Improvement of DBR routing protocol in underwater wireless sensor networks using fuzzy logic and bloom filter.

Routing protocols for underwater wireless sensor networks (UWSN) and underwater Internet of Things (IoT_UWSN) networks have expanded significantly. DBR routing protocol is one of the most critical routing protocols in UWSNs. In this routing protocol, the energy consumption of the nodes, the rate of loss of sent packets, and the rate of drop of routing packets due to node shutdown have created significant challenges. For this purpose, in a new scenario called FB-DBR, clustering is performed, and fuzzy logic and bloom filter are used in each cluster's new routing protocol in underwater wireless sensor networks. Due to the fuzzy nature of the parameters used in DBR, better results are obtained and bloom filters are used in routing tables to compensate for the deceleration. as the average number of accesses to routing table entries, dead nodes, Number of Packets Sent to Base Station (BS), Number of Packets Received at BS, Packet Dropped, and Remaining Energy has improved significantly.

Temporal dynamics of animacy categorization in the brain of patients with mild cognitive impairment.

Electroencephalography (EEG) has been commonly used to measure brain alterations in Alzheimer's Disease (AD). However, reported changes are limited to those obtained from using univariate measures, including activation level and frequency bands. To look beyond the activation level, we used multivariate pattern analysis (MVPA) to extract patterns of information from EEG responses to images in an animacy categorization task. Comparing healthy controls (HC) with patients with mild cognitive impairment (MCI), we found that the neural speed of animacy information processing is decreased in MCI patients. Moreover, we found critical time-points during which the representational pattern of animacy for MCI patients was significantly discriminable from that of HC, while the activation level remained unchanged. Together, these results suggest that the speed and pattern of animacy information processing provide clinically useful information as a potential biomarker for detecting early changes in MCI and AD patients.

Validity and Cultural Generalisability of a 5-Minute AI-Based, Computerised Cognitive Assessment in Mild Cognitive Impairment and Alzheimer's Dementia.

Introduction: Early detection and monitoring of mild cognitive impairment (MCI) and Alzheimer's Disease (AD) patients are key to tackling dementia and providing benefits to patients, caregivers, healthcare providers and society. We developed the Integrated Cognitive Assessment (ICA); a 5-min, language independent computerised cognitive test that employs an Artificial Intelligence (AI) model to improve its accuracy in detecting cognitive impairment. In this study, we aimed to evaluate the generalisability of the ICA in detecting cognitive impairment in MCI and mild AD patients. Methods: We studied the ICA in 230 participants. 95 healthy volunteers, 80 MCI, and 55 mild AD participants completed the ICA, Montreal Cognitive Assessment (MoCA) and Addenbrooke's Cognitive Examination (ACE) cognitive tests. Results: The ICA demonstrated convergent validity with MoCA (Pearson r=0.58, p<0.0001) and ACE (r=0.62, p<0.0001). The ICA AI model was able to detect cognitive impairment with an AUC of 81% for MCI patients, and 88% for mild AD patients. The AI model demonstrated improved performance with increased training data and showed generalisability in performance from one population to another. The ICA correlation of 0.17 (p = 0.01) with education years is considerably smaller than that of MoCA (r = 0.34, p < 0.0001) and ACE (r = 0.41, p < 0.0001) which displayed significant correlations. In a separate study the ICA demonstrated no significant practise effect over the duration of the study. Discussion: The ICA can support clinicians by aiding accurate diagnosis of MCI and AD and is appropriate for large-scale screening of cognitive impairment. The ICA is unbiased by differences in language, culture, and education.

Effective optimization using sample persistence: A case study on quantum annealers and various Monte Carlo optimization methods.

We present and apply a general-purpose, multistart algorithm for improving the performance of low-energy samplers used for solving optimization problems. The algorithm iteratively fixes the value of a large portion of the variables to values that have a high probability of being optimal. The resulting problems are smaller and less connected, and samplers tend to give better low-energy samples for these problems. The algorithm is trivially parallelizable since each start in the multistart algorithm is independent, and could be applied to any heuristic solver that can be run multiple times to give a sample. We present results for several classes of hard problems solved using simulated annealing, path-integral quantum Monte Carlo, parallel tempering with isoenergetic cluster moves, and a quantum annealer, and show that the success metrics and the scaling are improved substantially. When combined with this algorithm, the quantum annealer's scaling was substantially improved for native Chimera graph problems. In addition, with this algorithm the scaling of the time to solution of the quantum annealer is comparable to the Hamze-de Freitas-Selby algorithm on the weak-strong cluster problems introduced by Boixo et al. Parallel tempering with isoenergetic cluster moves was able to consistently solve three-dimensional spin glass problems with 8000 variables when combined with our method, whereas without our method it could not solve any.

Alpha particle vascular brachytherapy in the treatment of in-stent Restenosis.

In-stent Restenosis (ISR) is the Achilles heel for using of stent. Vascular brachytherapy (VBT) has been the principal scientifically investigated local therapy for coronary artery ISR. In our suggested method, a new type of stent, which is coated with Boron ((10)B) compound, is used. The coating layer of the stent consists of stable isotype of (10)B. Boron Neutron Capture Therapy produced alpha particle has enough energy to kill the cells adjacent to the stent, which is the site of boron deposition. It would prevent from ISR and intimal proliferation. After percutaneous coronary intervention (PCI) and insertion of stent, follow-up should be done continuously, and if ISR is detected, BNCT will be used to treat the recurrence. Using this modality of VBT would help us to overcome the pitfalls in en vogue radiotherapy methods.