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The evolution of Positron Emission Tomography (PET), culminating in the Total-Body PET (TB-PET) system, represents a paradigm shift in medical imaging. This paper explores the transformative role of Artificial Intelligence (AI) in enhancing clinical and research applications of TB-PET imaging. Clinically, TB-PET’s superior sensitivity facilitates rapid imaging, low-dose imaging protocols, improved diagnostic capabilities and higher patient comfort. In research, TB-PET shows promise in studying systemic interactions and enhancing our understanding of human physiology and pathophysiology. In parallel, AI’s integration into PET imaging workflows—spanning from image acquisition to data analysis—marks a significant development in nuclear medicine. This review delves into the current and potential roles of AI in augmenting TB-PET/CT’s functionality and utility. We explore how AI can streamline current PET imaging processes and pioneer new applications, thereby maximising the technology’s capabilities. The discussion also addresses necessary steps and considerations for effectively integrating AI into TB-PET/CT research and clinical practice. The paper highlights AI’s role in enhancing TB-PET’s efficiency and addresses the challenges posed by TB-PET’s increased complexity. In conclusion, this exploration emphasises the need for a collaborative approach in the field of medical imaging. We advocate for shared resources and open-source initiatives as crucial steps towards harnessing the full potential of the AI/TB-PET synergy. This collaborative effort is essential for revolutionising medical imaging, ultimately leading to significant advancements in patient care and medical research.

Epidemiological evidence has shown higher susceptibility of Children to the adverse effects of lead (Pb) exposure. However, experimental studies on Pb-induced neurotoxicity in prepubertal (PP) rats are limited. The present study aimed to examine the propensity of ferulic acid (FA), a commonly occurring phenolic acid in staple foods (fruits, vegetables, cereals, coffee etc.) to abrogate Pb-induced toxicity. Initially, we characterized Pb-induced adverse effects among PP rats exposed to Pb acetate (1,000–3,000 ppm in drinking water) for 5 weeks in terms of locomotor phenotype, activity of 5-aminolevulinic acid dehydratase (ALAD) in the blood, blood Pb levels and oxidative stress in brain regions. Further, the ameliorative effects of oral supplements of FA (25 mg/kg bw/day) were investigated in PP rats exposed to Pb (3,000 ppm). Pb intoxication increased the locomotor activity and FA supplements partially reversed the phenotype, while the reduced ALAD activity was also restored. FA significantly abrogated the enhanced oxidative stress in cerebellum (Cb) and hippocampus (Hc) as evidenced in terms of ROS generation, lipid peroxidation and protein carbonyls. Further, Pb-mediated perturbations in the glutathione levels and activity of enzymic antioxidants were also markedly restored. Furthermore, the protective effect of FA was discernible in striatum in terms of reduced oxidative stress, restored cholinergic activity and dopamine levels. Interestingly, reduced activity levels of mitochondrial complex I in Cb and enhanced levels in Hc among Pb-intoxicated rats were ameliorated by FA supplements. FA also decreased the number of damaged cells in cornu ammonis area CA1 and dentate gyrus as reflected by the histoarchitecture of Hc among Pb intoxicated rats. Collectively, our findings in the PP model allow us to hypothesize that ingestion of common phenolics such as FA may significantly alleviate the neurotoxic effects of Pb which may be largely attributed to its ability to abrogate oxidative stress.

Background Parkinson's disease (PD) is the fourth most common neurodegenerative disorder, characterized by degeneration of basal ganglia and a decrease in dopamine levels in the brain. Purinergic 2X7 receptors (P2X7R) serve as inflammation gatekeepers. They are found in both central and peripheral nervous systems (CNS, & PNS), and are activated in glial cells during inflammation. Purinergic 2X receptors (P2XRs) have been extensively studied in recent decades, particularly P2X7R, because of their important role in neuroinflammation caused by selective overexpression in glial cells. As P2X7R and its selective antagonists may provide neuroprotection by preventing the release of inflammatory mediators such as IL-1, they have become a research focus in PD. The review covers structure, signalling, molecular mechanisms, neuroprotective role, and current developments of P2X7R antagonists in PD. Methods A systematic analysis and review of the potential prospects of P2X7R antagonists in the treatment of PD were conducted by analyzing existing research data and reports published between 1996 and present. Results There is a substantial body of evidence linking P2X7R to pathology of PD. As a result, P2X7R antagonists may have therapeutic potential in treatment of PD. Conclusion P2X7R has been demonstrated as an efficacious target in PD. Recent advances in rational drug design have paved the way for development of therapeutically valuable P2X7R antagonists such as adamantylcyanoguanides, small molecular weight compounds, and PET ligands for the treatment of PD. However, the exact molecular mechanism and therapeutic potential of P2X7R antagonists in treatment of PD are yet to be fully explored.

Background: Seating comfort is one of the most important indicators of the performance of automotive seats. Seat is one of the places in the vehicle where most of the drivers spent driving. A good seat can prevent a lot of painful disorders including low back pain, which is typical of bad posture. Driver posture is one of the most important issues that need to be considered in the vehicle seat design process. Research Gap: Around the world, there have been many studies on seating comfort including car seats, truck seats, bus seats, train seats, etc. However, in India there are not many studies focusing on bus drivers seating comfort. Objective: This study aimed at investigating bus drivers seating comfort in private transportation using virtual ergonomics. Methods: We have considered a group of male bus drivers with different percentiles. And, we have selected a bus seat typically used in private transportation. The anthropometry of drivers and dimensions of the seat has been measured and modelled in the virtual environment (CATIA V6). For the seating comfort analysis, RULA (Rapid Upper Limb Assessment) analysis was performed. Results and Discussion: The RULA score revealed that the drivers with 77th to 94th percentile felt comfortable with the seat. The rest had higher RULA scores and felt discomfort. Conclusion: The bus seat design needs to be changed by considering Indian anthropometry. Also, this study only examined a few subjects; hence, further investigation would give better recommendations. Application: The benefit of virtual ergonomics is used in this project. The methodology used in this study could be used for other seat studies.

Oncological diseases account for a significant portion of the burden on public healthcare systems with associated costs driven primarily by complex and long-lasting therapies. Through the visualization of patient-specific morphology and functional-molecular pathways, cancerous tissue can be detected and characterized non-invasively, so as to provide referring oncologists with essential information to support therapy management decisions. Following the onset of stand-alone anatomical and functional imaging, we witness a push towards integrating molecular image information through various methods, including anato-metabolic imaging (e.g., PET/CT), advanced MRI, optical or ultrasound imaging. This perspective paper highlights a number of key technological and methodological advances in imaging instrumentation related to anatomical, functional, molecular medicine and hybrid imaging, that is understood as the hardware-based combination of complementary anatomical and molecular imaging. These include novel detector technologies for ionizing radiation used in CT and nuclear medicine imaging, and novel system developments in MRI and optical as well as opto-acoustic imaging. We will also highlight new data processing methods for improved non-invasive tissue characterization. Following a general introduction to the role of imaging in oncology patient management we introduce imaging methods with well-defined clinical applications and potential for clinical translation. For each modality, we report first on the status quo and, then point to perceived technological and methodological advances in a subsequent status go section. Considering the breadth and dynamics of these developments, this perspective ends with a critical reflection on where the authors, with the majority of them being imaging experts with a background in physics and engineering, believe imaging methods will be in a few years from now. Overall, methodological and technological medical imaging advances are geared towards increased image contrast, the derivation of reproducible quantitative parameters, an increase in volume sensitivity and a reduction in overall examination time. To ensure full translation to the clinic, this progress in technologies and instrumentation is complemented by advances in relevant acquisition and image-processing protocols and improved data analysis. To this end, we should accept diagnostic images as “data”, and – through the wider adoption of advanced analysis, including machine learning approaches and a “big data” concept – move to the next stage of non-invasive tumour phenotyping. The scans we will be reading in 10 years from now will likely be composed of highly diverse multi-dimensional data from multiple sources, which mandate the use of advanced and interactive visualization and analysis platforms powered by Artificial Intelligence (AI) for real-time data handling by cross-specialty clinical experts with a domain knowledge that will need to go beyond that of plain imaging.

A major source of revenue and a means of sustenance in India is agriculture. Rice is a staple meal that is farmed in the major areas of India. It has been discovered that diseases significantly harm rice harvests, causing considerable costs for the agricultural sector. Plant pathologists are searching for an accurate and reliable method of identifying the illness afflicting rice plants. One effective use of machine learning in crop remote sensing is the categorization of agricultural illnesses. A major area of research right now for detecting agricultural diseases is deep learning. In this study, an effective Convolution Neural Network (CNN) based technique for detecting leaf disease in rice plants was developed. The major subjects of this study are the three well-known rice illnesses brown spot, hispa and leaf blast. This approach for diagnosing and recognising rice plant disease is based on the size, shape, and colour of lesions in the leaf picture. In Otsu’s global thresholding, the background noise is removed from the picture by binarizing it. The Histogram of gradient image edges and features are then displayed to see whether the image contains vectors that can identify sick regions. This model is used to learn the features after the vectors have been validated. It then divides the rice leaf images into four categories: healthy, brown spot, hispa, and leaf blast. The necessary deep learning toolkit is constructed in MATLAB to use the CNN based rice leaf detection algorithm. The Support Vector Machine (SVM) based classifier is also examined for comparison.

In the past, determination of absolute values of cerebral metabolic rate of glucose (CMRGlc) in clinical routine was rarely carried out due to the invasive nature of arterial sampling. With the advent of combined PET/MR imaging technology, CMRGlc values can be obtained non-invasively, thereby providing the opportunity to take advantage of fully quantitative data in clinical routine. However, CMRGlc values display high physiological variability, presumably due to fluctuations in the intrinsic activity of the brain at rest. To reduce CMRGlc variability associated with these fluctuations, the objective of this study was to determine whether functional connectivity measures derived from resting-state fMRI (rs-fMRI) could be used to correct for these fluctuations in intrinsic brain activity. We studied 10 healthy volunteers who underwent a test-retest dynamic [18F]FDG-PET study using a fully integrated PET/MR system (Siemens Biograph mMR). To validate the non-invasive derivation of an image-derived input function based on combined analysis of PET and MR data, arterial blood samples were obtained. Using the arterial input function (AIF), parametric images representing CMRGlc were determined using the Patlak graphical approach. Both directed functional connectivity (dFC) and undirected functional connectivity (uFC) were determined between nodes in six major networks (Default mode network, Salience, L/R Executive, Attention, and Sensory-motor network) using either a bivariate-correlation (R coefficient) or a Multi-Variate AutoRegressive (MVAR) model. In addition, the performance of a regional connectivity measure, the fractional amplitude of low frequency fluctuations (fALFF), was also investigated. The average intrasubject variability for CMRGlc values between test and retest was determined as (14 ±8%) with an average inter-subject variability of 25% at test and 15% at retest. The average CMRGlc value (umol/100 g/min) across all networks was 39 ±10 at test and increased slightly to 43 ±6 at retest. The R, MVAR and fALFF coefficients showed relatively large test-retest variability in comparison to the inter-subjects variability, resulting in poor reliability (intraclass correlation in the range of 0.11-0.65). More importantly, no significant relationship was found between the R coefficients (for uFC), MVAR coefficients (for dFC) or fALFF and corresponding CMRGlc values for any of the six major networks. Measurement of functional connectivity within established brain networks did not provide a means to decrease the inter- or intrasubject variability of CMRGlc values. As such, our results indicate that connectivity measured derived from rs-fMRI acquired contemporaneously with PET imaging are not suited for correction of CMRGlc variability associated with intrinsic fluctuations of resting-state brain activity. Thus, given the observed substantial inter- and intrasubject variability of CMRGlc values, the relevance of absolute quantification for clinical routine is presently uncertain.

Background Cancer‐associated cachexia (CAC) is a metabolic syndrome contributing to therapy resistance and mortality in lung cancer patients (LCP). CAC is typically defined using clinical non‐imaging criteria. Given the metabolic underpinnings of CAC and the ability of [18F]fluoro‐2‐deoxy‐D‐glucose (FDG)‐positron emission tomography (PET)/computer tomography (CT) to provide quantitative information on glucose turnover, we evaluate the usefulness of whole‐body (WB) PET/CT imaging, as part of the standard diagnostic workup of LCP, to provide additional information on the onset or presence of CAC. Methods This multi‐centre study included 345 LCP who underwent WB [18F]FDG‐PET/CT imaging for initial clinical staging. A weight loss grading system (WLGS) adjusted to body mass index was used to classify LCP into ‘No CAC’ (WLGS‐0/1 at baseline prior treatment and at first follow‐up: N = 158, 51F/107M), ‘Dev CAC’ (WLGS‐0/1 at baseline and WLGS‐3/4 at follow‐up: N = 90, 34F/56M), and ‘CAC’ (WLGS‐3/4 at baseline: N = 97, 31F/66M). For each CAC category, mean standardized uptake values (SUV) normalized to aorta uptake ( ) and CT‐defined volumes were extracted for abdominal and visceral organs, muscles, and adipose‐tissue using automated image segmentation of baseline [18F]FDG‐PET/CT images. Imaging and non‐imaging parameters from laboratory tests were compared statistically. A machine‐learning (ML) model was then trained to classify LCP as ‘No CAC’, ‘Dev CAC’, and ‘CAC’ based on their imaging parameters. SHapley Additive exPlanations (SHAP) analysis was employed to identify the key factors contributing to CAC development for each patient. Results The three CAC categories displayed multi‐organ differences in . In all target organs, was higher in the ‘CAC’ cohort compared with ‘No CAC’ (P < 0.01), except for liver and kidneys, where in ‘CAC’ was reduced by 5%. The ‘Dev CAC’ cohort displayed a small but significant increase in of pancreas (+4%), skeletal‐muscle (+7%), subcutaneous adipose‐tissue (+11%), and visceral adipose‐tissue (+15%). In ‘CAC’ patients, a strong negative Spearman correlation (ρ = −0.8) was identified between and volumes of adipose‐tissue. The machine‐learning model identified ‘CAC’ at baseline with 81% of accuracy, highlighting of spleen, pancreas, liver, and adipose‐tissue as most relevant features. The model performance was suboptimal (54%) when classifying ‘Dev CAC’ versus ‘No CAC’. Conclusions WB [18F]FDG‐PET/CT imaging reveals groupwise differences in the multi‐organ metabolism of LCP with and without CAC, thus highlighting systemic metabolic aberrations symptomatic of cachectic patients. Based on a retrospective cohort, our ML model identified patients with CAC with good accuracy. However, its performance in patients developing CAC was suboptimal. A prospective, multi‐centre study has been initiated to address the limitations of the present retrospective analysis.

A raising population and to meet the raising needs there is an increasing demand for tall structure both for commercial use and industrial purpose. Wind behaviour is a key design parameter for such structures and need to be assessed accurately in the preliminary and secondary design stages. This study is aimed at prediction and analysis of deflection of hollow structure due to steady wind loads. Hollow structures typically represent chimneys that are used in the coal fired stream power plant. A hollow cylindrical part with base diameter of 6 cm is fabricated and tested in wind tunnel at constant speeds of 10, 15 and 20 m/s. An accelerometer is mounted on top of body to measure the deflection. Next, the deflection of the body is predicted numerically using commercial ANSYS software. Initially Computational Fluid Dynamics (CFD) simulations are performed to predict the flow field and associated wind force acting on the body. The wind load is transferred to the structural solver to predict the deflection of the body. The predicted deflection compared well with the wind tunnel experiments. Further FSI simulations are performed by changing the thickness of the hollow structure. The results are analysed to study the effect of wind speed and thickness on the deflection. A cubic polynomial curve-fit for the deflection, as a function of the wind speed is developed.