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Background: Humans often use co‐speech gestures to promote effective communication. Attention has been paid to the cortical areas engaged in the processing of co‐speech gestures. Aims: To investigate the neural network underpinned in the processing of co‐speech gestures and to observe whether there is a relationship between areas involved in language and gesture processing. Methods & Procedures: We planned to include studies with neurotypical and/or stroke participants who underwent a bimodal task (i.e., processing of co‐speech gestures with relative speech) and a unimodal task (i.e., speech or gesture alone) during a functional magnetic resonance imaging (fMRI) session. After a database search, abstract and full‐text screening were conducted. Qualitative and quantitative data were extracted, and a meta‐analysis was performed with the software GingerALE 3.0.2, performing contrast analyses of uni‐ and bimodal tasks. Main Contribution: The database search produced 1024 records. After the screening process, 27 studies were included in the review. Data from 15 studies were quantitatively analysed through meta‐analysis. Meta‐analysis found three clusters with a significant activation of the left middle frontal gyrus and inferior frontal gyrus, and bilateral middle occipital gyrus and inferior temporal gyrus. Conclusions: There is a close link at the neural level for the semantic processing of auditory and visual information during communication. These findings encourage the integration of the use of co‐speech gestures during aphasia treatment as a strategy to foster the possibility to communicate effectively for people with aphasia. WHAT THIS PAPER ADDS: What is already known on this subject: Gestures are an integral part of human communication, and they may have a relationship at neural level with speech processing. What this paper adds to the existing knowledge: During processing of bi‐ and unimodal communication, areas related to semantic processing and multimodal processing are activated, suggesting that there is a close link between co‐speech gestures and spoken language at a neural level. What are the potential or actual clinical implications of this work?: Knowledge of the functions related to gesture and speech processing neural networks will allow for the adoption of model‐based neurorehabilitation programs to foster recovery from aphasia by strengthening the specific functions of these brain networks. [ABSTRACT FROM AUTHOR]

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Background: Alzheimer's disease (AD) is a progressive neurodegenerative disease that primarily affects people above the age of 60 all around the world. As of now, the cause is unknown and there is no effective cure. The pathological changes of AD have occurred many years before the onset of the disease, and current treatment techniques can only delay the progression of the disease. Because disease-modifying therapies may be most beneficial in the early stages of AD, the clinical significance of an early diagnosis is emphasized. So far, a variety of imaging technologies and related biomarkers have been used to identify and monitor AD, but there are many imaging technologies; finding the most effective imaging technology can assist medical personnel in interpreting the early stages of AD and can also improve patient treatment opportunities. This is, therefore, the main purpose and background of this study. Methods: PubMed and other repositories were used in this study to conduct a literature search with various keywords, and relevant articles were reviewed. In this review, different neuroimaging techniques are reviewed which are considered advanced tools to help establish the diagnosis, and in addition, the diagnostic utility, advantages, and limitations of contemporary AD imaging techniques are discussed. Results: The results of the literature review and synthesis show that the prevalence of several in vivo biomarkers helps distinguish affected individuals from healthy controls in the early stages of the disease. Additionally, each current imaging method has its advantages and disadvantages, so no single imaging method is the best diagnostic modality. Conclusions: This article also reviews and draws conclusions on better ways to use the imaging techniques to improve the likelihood of an early diagnosis of AD. It is suggested that future research could focus on expanding the use of imaging technologies and on identifying novel biomarkers manifesting the earliest stages of AD pathology. [ABSTRACT FROM AUTHOR]

This case concentrates on the persistent left superior vena cava (PLSVC), a rare vascular anomaly which contributes to central venous catheter (CVC) misplacement. A 72-year-old woman with renal insufficiency presented to the hospital with recurrent bleeding from her permanent CVC device placed in the right common jugular vein. An initial attempt to replace the device was unsuccessful, necessitating the placement of a secondary catheter in the left jugular vein. Shortly after the procedure, the patient developed swelling of the face and neck. Further diagnostic imaging, including a chest radiograph and computed tomography (CT), revealed CVC misplacement in the PLSVC and coronary sinus, thrombosis of the common jugular vein, and a posterior mediastinal hematoma. Conservative therapy of the mediastinal hematoma was implemented and proved effective in this case. A temporary CVC was inserted into the left femoral vein. Two months later, the catheter underwent further dysfunction and a decision was made to place a long-term permanent CVC via the right femoral vein. The patient is currently awaiting an arteriovenous fistula for dialysis use. This case emphasizes the importance of radiological techniques for CVC procedural placement, as well as the detection of congenital abnormalities. Providers regularly placing CVCs should have an in-depth knowledge of the possible complications and potential anatomical variations, especially as seen in high-risk patients. [ABSTRACT FROM AUTHOR]

Digital Precision Agriculture (DPA) is a comprehensive approach to agronomic management that utilizes advanced technologies, such as sensor data analysis and automation, to optimize crop productivity, enhance farm income, and minimize environmental impacts. DPA encompasses various agricultural domains, including pest control, pest management, fertilization, irrigation management, sowing, transplanting, crop health monitoring, yield forecasting, harvesting, and post-harvest stages. Among the enabling technologies for DPA, Unmanned Aerial Vehicles (UAVs) have gained significant attention and market growth. The advancements in control systems, robotics, electronics, and artificial intelligence have led to the development of sophisticated agricultural drones. UAVs offer advantages such as versatility, quick and accurate remote sensing capabilities, and high-quality imaging at affordable prices. Furthermore, the miniaturization of sensors and advancements in nanotechnology enable UAVs to perform multiple operations simultaneously without compromising flight autonomy. However, various variables, including aircraft mass, payload capacity, size, battery characteristics, flight autonomy, cost, and environmental conditions, impact the performance and applicability of UAV systems in agriculture. The economic considerations involve the purchase of drones, equipment, and the expertise of trained pilots for flight management and data processing. Payload capacity, flight range, and financial factors influence agriculture’s choice and implementation of UAVs. The research and patent trends show the growing interest in UAVs for agricultural applications. This paper provides a general review of UAV types, construction architectures, and their diverse applications in agriculture until 2022.

Myocardial infarction is a serious medical condition that requires prompt and accurate diagnosis for effective treatment. In this paper, we present a novel approach for detecting and classifying MI in echocardiogram frames using an enhanced CNN algorithm and an ECV-3D network. The proposed method aims to improve the accuracy and efficiency of MI diagnosis by leveraging advanced deep learning techniques. Through extensive experimentation, we demonstrated the effectiveness of our approach in achieving high accuracy and robustness in MI detection and classification. These results indicate the potential of our method to aid in the early and precise diagnosis of MI, thereby contributing to improved patient outcomes and clinical decision-making. After conducting thorough experimentation, our proposed approach achieved an impressive accuracy of 97.05% in the detection and classification of myocardial infarction in echocardiogram frames. This shows the robustness and reliability of our method, indicating its potential to significantly impact the accurate diagnosis of MI and subsequently improve patient outcomes. Furthermore, the area under the curve attained by our model is 0.82, reaffirming the efficacy of the enhanced CNN algorithm and ECV-3D network in accurately detecting and classifying MI. It is noteworthy that all the parameters utilized in our approach have demonstrated a high level of accuracy, emphasizing the effectiveness of our deep learning techniques in enhancing the diagnostic process for MI. Moreover, the proposed method efficiently process large volumes of echocardiogram frames, making it suitable for real-time clinical applications.

Chronic kidney disease (CKD) is a global health issue with a rising prevalence, affecting 697.5 million people worldwide. It imposes a substantial burden, contributing to 35.8 million disability-adjusted life years (DALYs) and 1.2 million deaths in 2017. The mortality rate for CKD has increased by 41.5% between 1990 and 2017, positioning it as a significant cause of global mortality. CKD is associated with diverse health complications, impacting cardiovascular, neurological, nutritional, and endocrine aspects. One prominent complication is CKD–mineral and bone disorder (MBD), a complex condition involving dysregulation of bone turnover, mineralization, and strength, accompanied by soft tissue and vascular calcification. Alterations in mineral metabolism, including calcium, phosphate, parathyroid hormone (PTH), vitamin D, fibroblast growth factor-23 (FGF-23), and Klotho, play pivotal roles in CKD-MBD. These disturbances, observed early in CKD, contribute to the progression of bone disorders and renal osteodystrophy (ROD). Vascular calcification (VC) is a key component of CKD-MBD, accelerated by CKD. The pathophysiology involves complex processes in vascular smooth muscle cells and the formation of calciprotein particles (CPP). VC is closely linked to cardiovascular events and mortality, emphasizing its prognostic significance. Various serum markers and imaging techniques, including lateral plain X-ray, Kauppila Score, Adragao Score, and pulse wave velocity, aid in VC detection. Additionally, pQCT provides valuable information on arterial calcifications, offering an advantage over traditional scoring systems. CKD poses a substantial global health burden, and its complications, including CKD-MBD and VC, significantly contribute to morbidity and mortality. Understanding the intricate relationships between mineral metabolism, bone disorders, and vascular calcification is crucial for effective diagnosis and therapeutic interventions. [ABSTRACT FROM AUTHOR]

The pathways through which mature blood cells in the bone marrow (BM) enter the blood stream and exit the BM, hematopoietic stem cells in the peripheral blood return to the BM, and other substances exit the BM are referred to as the marrow–blood barrier (MBB). This barrier plays an important role in the restrictive sequestration of blood cells, the release of mature blood cells, and the entry and exit of particulate matter. In some blood diseases and tumors, the presence of immature cells in the blood suggests that the MBB is damaged, mainly manifesting as increased permeability, especially in angiogenesis. Some imaging methods have been used to monitor the integrity and permeability of the MBB, such as DCE-MRI, IVIM, ASL, BOLD-MRI, and microfluidic devices, which contribute to understanding the process of related diseases and developing appropriate treatment options. In this review, we briefly introduce the theory of MBB imaging modalities along with their clinical applications. [ABSTRACT FROM AUTHOR]

Gastrointestinal stromal tumors (GISTs) are rare mesenchymal subepithelial tumors originating from abnormal proliferation of interstitial cells of Cajal, with worldwide incidence of about 1–2 per 100,000. Herein, we report an unusual case of a 55-year-old man who presented a severe digestive hemorrhage as a rare post-surgical complication after intestinal GIST surgical removal. The patient was admitted to the Emergency Department of our center affected by abdominal epigastric pain. Different imaging techniques were performed leading to the final diagnosis of a GIST and surgical intervention planning. Immediately after intervention the patient developed a severe intestinal hemorrhage. Multidetector computed tomography (MDCT) confirmed the ongoing bleeding and the patient underwent a new intervention to control the hemorrhage. The aim of the paper is to show the different imaging techniques used to assess GIST. MDCT represents the gold standard for diagnosis and in the emergency setting is used to identify post-surgical complications.