A total of 3102 neurons were recorded before and following acute and chronic methylphenidate (MPD) administration. Acute MPD exposure elicits mainly increases in neuronal and behavioral activity in dose-response characteristics. The response to chronic MPD exposure, as compared to acute 0.6, 2.5, or 10.0 mg/kg MPD administration, elicits electrophysiological and behavioral sensitization in some animals and electrophysiological and behavioral tolerance in others when the neuronal recording evaluations were performed based on the animals\' behavioral responses, or amount of locomotor activity, to chronic MPD exposure. The majority of neurons recorded from those expressing behavioral sensitization responded to chronic MPD with further increases in firing rate as compared to the initial MPD responses. The majority of neurons recorded from animals expressing behavioral tolerance responded to chronic MPD with decreases in their firing rate as compared to the initial MPD exposures. Each of the six brain areas studied-the ventral tegmental area, locus coeruleus, dorsal raphe, nucleus accumbens, prefrontal cortex, and caudate nucleus (VTA, LC, DR, NAc, PFC, and CN)-responds significantly (p < 0.001) differently to MPD, suggesting that each one of the above brain areas exhibits different roles in the response to MPD. Moreover, this study demonstrates that it is essential to evaluate neuronal activity responses to psychostimulants based on the animals\' behavioral responses to acute and chronic effects of the drug from several brain areas simultaneously to obtain accurate information on each area\'s role in response to the drug.

Diabetic retinopathy (DR) poses a significant challenge in diabetes management, with its progression often asymptomatic until advanced stages. This underscores the urgent need for cost-effective and reliable screening methods. Consequently, the integration of artificial intelligence (AI) tools presents a promising avenue to address this need effectively. We provide an overview of the current state of the art results and techniques in DR screening using AI, while also identifying gaps in research for future exploration. By synthesizing existing database and pinpointing areas requiring further investigation, this paper seeks to guide the direction of future research in the field of automatic diabetic retinopathy screening. There has been a continuous rise in the number of articles detailing deep learning (DL) methods designed for the automatic screening of diabetic retinopathy especially by the year 2021. Researchers utilized various databases, with a primary focus on the IDRiD dataset. This dataset consists of color fundus images captured at an ophthalmological clinic situated in India. It comprises 516 images that depict various stages of DR and diabetic macular edema. Each of the chosen papers concentrates on various DR signs. Nevertheless, a significant portion primarily focused on detecting exudates, which remains insufficient to assess the overall presence of this disease. Various AI methods have been employed to identify DR signs. Among the chosen papers, 4.7 % utilized detection methods, 46.5 % employed classification techniques, 41.9 % relied on segmentation, and 7 % opted for a combination of classification and segmentation. Metrics calculated from 80 % of the articles employing preprocessing techniques demonstrated the significant benefits of this approach in enhancing results quality. In addition, multiple DL techniques, starting by classification, detection then segmentation. Researchers used mostly YOLO for detection, ViT for classification, and U-Net for segmentation. Another perspective on the evolving landscape of AI models for diabetic retinopathy screening lies in the increasing adoption of Convolutional Neural Networks for classification tasks and U-Net architectures for segmentation purposes; however, there is a growing realization within the research community that these techniques, while powerful individually, can be even more effective when integrated. This integration holds promise for not only diagnosing DR, but also accurately classifying its different stages, thereby enabling more tailored treatment strategies. Despite this potential, the development of AI models for DR screening is fraught with challenges. Chief among these is the difficulty in obtaining the high-quality, labeled data necessary for training models to perform effectively. This scarcity of data poses significant barriers to achieving robust performance and can hinder progress in developing accurate screening systems. Moreover, managing the complexity of these models, particularly deep neural networks, presents its own set of challenges. Additionally, interpreting the outputs of these models and ensuring their reliability in real-world clinical settings remain ongoing concerns. Furthermore, the iterative process of training and adapting these models to specific datasets can be time-consuming and resource-intensive. These challenges underscore the multifaceted nature of developing effective AI models for DR screening. Addressing these obstacles requires concerted efforts from researchers, clinicians, and technologists to develop new approaches and overcome existing limitations. By doing so, a full potential of AI may transform DR screening and improve patient outcomes.

Diabetic retinopathy (DR) is characterized by chronic, low-grade inflammation. This state may be related to the heightened production of neutrophil extracellular traps (NETs) induced by high glucose (HG). Human cathelicidin antimicrobial peptide (LL37) is an endogenous ligand of G protein-coupled chemoattractant receptor formyl peptide receptor 2 (FPR2), expressed on neutrophils and facilitating the formation and stabilization of the structure of NETs. In this study, we detected neutrophils cultured under different conditions, the retinal tissue of diabetic mice, and fibrovascular epiretinal membranes (FVM) samples of patients with proliferative diabetic retinopathy (PDR) to explore the regulating effect of LL37/FPR2 on neutrophil in the development of NETs during the process of DR. Specifically, HG or NG with LL37 upregulates the expression of FPR2 in neutrophils, induces the opening of mitochondrial permeability transition pore (mPTP), promotes the increase of reactive oxygen species and mitochondrial ROS, and then leads to the rise of NET production, which is mainly manifested by the release of DNA reticular structure and the increased expression of NETs-related markers. The PI3K/AKT signaling pathway was activated in neutrophils, and the phosphorylation level was enhanced by FPR2 agonists in vitro. In vivo, increased expression of NETs markers was detected in the retina of diabetic mice and in FVM, vitreous fluid, and serum of PDR patients. Transgenic FPR2 deletion led to decreased NETs in the retina of diabetic mice. Furthermore, in vitro, inhibition of the LL37/FPR2/mPTP axis and PI3K/AKT signaling pathway decreased NET production induced by high glucose. These results suggested that FPR2 plays an essential role in regulating the production of NETs induced by HG, thus may be considered as one of the potential therapeutic targets.

In this paper, a method of the energy management system (EMS) in multiple microgrids considering the constraints of power flow based on the three-objective optimization model is presented. The studied model specifications, the variable speed pumps in the water network as well and the storage tanks are optimally planned as flexible resources to reduce operating costs and pollution. The proposed method is implemented hierarchically through two primary and secondary control layers. At the primary control level, each microgrid performs local planning for its subscribers and energy generation resources, and their excess or unsupplied power is determined. Then, by sending this information to the central energy management system (CEMS) at the secondary level, it determines the amount of energy exchange, taking into account the limitations of power flow. Energy storage systems (ESS) are also considered to maintain the balance between power generation by renewable energy sources and consumption load. Also, the demand response (DR) program has been used to smooth the load curve and reduce operating costs. Finally, in this article, the multi-objective particle swarm optimization (MOPSO) is used to solve the proposed three-objective problem with three cost functions generation, pollution, and pump operation. Additionally, sensitivity analysis was conducted with uncertainties of 25 % and 50 % in network generation units, exploring their impact on objective functions. The proposed model has been tested on the microgrid of a 33-bus test distribution and 15-node test water system and has been investigated for different cases. The simulation results prove the effectiveness of the integration of water and power network planning in reducing the operating cost and emission of pollution in such a way that the proposed control scheme properly controls the performance of microgrids and the network in interactions with each other and has a high level of robustness, stable behavior under different conditions and high quality of the power supply. In such a way that improvements of 41.1 %, 52.2 %, and 20.4 % in the defined objective functions and the evaluation using DM, SM, and MID indices further confirms the algorithm\'s enhanced performance in optimizing the specified objective functions by 51 %, 11 %, and 5.22 %, respectively.

BACKGROUND: Diabetic retinopathy (DR) is one of the most common complications of diabetes mellitus. The global burden is immense with a worldwide prevalence of 8.5%. Recent advancements in artificial intelligence (AI) have demonstrated the potential to transform the landscape of ophthalmology with earlier detection and management of DR.
OBJECTIVE: This study seeks to provide an update and evaluate the accuracy and current diagnostic ability of AI in detecting DR versus ophthalmologists. Additionally, this review will highlight the potential of AI integration to enhance DR screening, management, and disease progression.
METHODS: A systematic review of the current landscape of AI\'s role in DR will be undertaken, guided by the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analysis) model. Relevant peer-reviewed papers published in English will be identified by searching 4 international databases: PubMed, Embase, CINAHL, and the Cochrane Central Register of Controlled Trials. Eligible studies will include randomized controlled trials, observational studies, and cohort studies published on or after 2022 that evaluate AI\'s performance in retinal imaging detection of DR in diverse adult populations. Studies that focus on specific comorbid conditions, nonimage-based applications of AI, or those lacking a direct comparison group or clear methodology will be excluded. Selected papers will be independently assessed for bias by 2 review authors (JS and DM) using the Quality Assessment of Diagnostic Accuracy Studies tool for systematic reviews. Upon systematic review completion, if it is determined that there are sufficient data, a meta-analysis will be performed. Data synthesis will use a quantitative model. Statistical software such as RevMan and STATA will be used to produce a random-effects meta-regression model to pool data from selected studies.
RESULTS: Using selected search queries across multiple databases, we accumulated 3494 studies regarding our topic of interest, of which 1588 were duplicates, leaving 1906 unique research papers to review and analyze.
CONCLUSIONS: This systematic review and meta-analysis protocol outlines a comprehensive evaluation of AI for DR detection. This active study is anticipated to assess the current accuracy of AI methods in detecting DR.
UNASSIGNED: DERR1-10.2196/57292.

Diabetic retinopathy (DR) is the leading cause of visual impairment globally. It occurs due to long-term diabetes with fluctuating blood glucose levels. It has become a significant concern for people in the working age group as it can lead to vision loss in the future. Manual examination of fundus images is time-consuming and requires much effort and expertise to determine the severity of the retinopathy. To diagnose and evaluate the disease, deep learning-based technologies have been used, which analyze blood vessels, microaneurysms, exudates, macula, optic discs, and hemorrhages also used for initial detection and grading of DR. This study examines the fundamentals of diabetes, its prevalence, complications, and treatment strategies that use artificial intelligence methods such as machine learning (ML), deep learning (DL), and federated learning (FL). The research covers future studies, performance assessments, biomarkers, screening methods, and current datasets. Various neural network designs, including recurrent neural networks (RNNs), generative adversarial networks (GANs), and applications of ML, DL, and FL in the processing of fundus images, such as convolutional neural networks (CNNs) and their variations, are thoroughly examined. The potential research methods, such as developing DL models and incorporating heterogeneous data sources, are also outlined. Finally, the challenges and future directions of this research are discussed.

The aim of this study is to evaluate the level of accuracy and precision of bone scan (BS), MRI, and digital radiography (DR) to measure long bone tumors to design custom-made prosthesis (CMP)/modular prosthesis (MP) in limb salvage surgery (LSS) with the help of phantom and patient\'s study. There are two separate groups: one is the phantom study and another one is the patient\'s study. The phantom study is done with the Jaszack Phantom for the Gamma camera and the indigenous phantom for the MRI and DR. Three independent imaging professionals (nuclear medicine physicians and radiologists) measured the distance between standardized, preselected points on the Jaszack phantom in the Gamma Camera (GC) and indigenous phantom on the coronal and sagittal view of the MRI scan and in digital radiography. The measured values were compared with the known values for phantom measurement. A total of 36 patients, which include 24 males and 12 females, 3 independent imaging professionals measured the patient\'s long bone in a bone scan, MRI and DR and compared it with histopathological specimen measurement after limb salvage surgery (LSS). Descriptive statistics using appropriate measures of central tendency and dispersion were employed to describe the data. Karl-Pearson correlation coefficient was used to establish the association between continuous covariates. Paired t-test was utilized to test the differences in paired values for statistical significance. A near-perfect positive correlation was evident between all three pairs of bone scan, MRI scan, and digital radiography values, and a positive agreement within 1 mm of the bone scan, MRI scan, and DR values of all three pairs was around 95%. For the phantom study, we conclude that Gamma camera and MRI measurements are equal in physical measurements (MCF-1). DR measurements were found to be near equal physical measurements and multiplication correction factor (MCF)-0.9104 and three observer\'s measurements values were also near normal. For the patient\'s study, we conclude that the bone scan, MRI, and DR measurements of 3 independent imaging professionals are near normal, and it was confirmed with pathological specimen after LSS, to confirm reliability, repeatability, reproducibility, and accuracy of the tumor length to do custom-made prosthesis or modular prosthesis for the patients who are affected by osteosarcoma and Ewing\'s sarcoma.

Approximately five million Ukrainians were displaced to the EU/EEA following the Russian invasion of Ukraine. While tuberculosis (TB) notification rates per 100,000 Ukrainians in the EU/EEA remained stable, the number of notified TB cases in Ukrainians increased almost fourfold (mean 2019-2021: 201; 2022: 780). In 2022, 71% cases were notified in three countries, and almost 20% of drug-resistant TB cases were of Ukrainian origin. Targeted healthcare services for Ukrainians are vital for early diagnosis and treatment, and preventing transmission.

This review addresses the complexities of type 1 diabetes (T1D) and its associated complications, with a particular focus on diabetic retinopathy (DR). This review outlines the progression from non-proliferative to proliferative diabetic retinopathy and diabetic macular edema, highlighting the role of dysglycemia in the pathogenesis of these conditions. A significant portion of this review is devoted to technological advances in diabetes management, particularly the use of hybrid closed-loop systems (HCLSs) and to the potential of open-source HCLSs, which could be easily adapted to different patients\' needs using big data analytics and machine learning. Personalized HCLS algorithms that integrate factors such as patient lifestyle, dietary habits, and hormonal variations are highlighted as critical to reducing the incidence of diabetes-related complications and improving patient outcomes.

OBJECTIVE: Using a common DR chest radiography system to realize a long bone stitching technology.
METHODS: Introduce the role of long bone stitching technology in medical diagnosis and treatment, and the principle of long bone stitching technology to make a long bone stitching radiographic device, and combine with the chest radiography system to take the long bone stitching image experiment.
RESULTS: The hospitals of class Ⅱ (or more lower levels) can realize the long bone stitching technology using a common DR chest radiography system.
CONCLUSIONS: Using this technology can save the hospital costs, reduce the burden on patients, achieve good social and economic benefits.