Current Limitations Research Articles

A critical review of electrochemical and optical Vitamin B6 sensing: evolution of biosensor platforms based on advanced nanosystems.

Vitamin B6 (VB6) is a member of the water-soluble B vitamins which have a vital performance in nervous system operating activities. VB6 is highly demanded to maintain excellent skin and immune systems in the human body. furthermore, VB6 is tremendously substantial in the functions of some enzymes that participate in the metabolism of proteins, amino acids, etc. The deficiency of VB6 will eventuate in anemic situations and may lead to permanent injuries in the brain. moreover, recent studies disclosed that adequate Vitamin B6 in the human body can decrease the intensity of illnesses such as diabetes, stress, etc., in patients with COVID-19 infections. Thus, the detection of VB6 from real samples is crucial to control the amount of this vitamin in biological fluids and to monitor the pharmaceutical dosage quality. Various analytical approaches have been employed for the VB6 detection in biological and pharmaceutical samples. Although biosensing and sensing approaches hold several obvious advantages such as simplicity, capability for miniaturization, quick response time, etc. from other analytical methods. Hence, through the last decades, designing and fabricating biosensors with sufficient sensitivity and selectivity have been investigated by many researchers in order to detect VB6. The purpose of this review is to illustrate the importance of diverse electrochemical and optical approaches for VB6 detection. Additionally, novel VB6 detection techniques based on electrochemical, optical, and conventional methods have been considerably discussed, and compared with each other. Furthermore, a comprehensive summary of the current limitations and future challenges in VB6 analysis are explained and also create a pathway for subsequent expansions and applications.

Evaluating multimodal AI in medical diagnostics

This study evaluates multimodal AI models’ accuracy and responsiveness in answering NEJM Image Challenge questions, juxtaposed with human collective intelligence, underscoring AI’s potential and current limitations in clinical diagnostics. Anthropic’s Claude 3 family demonstrated the highest accuracy among the evaluated AI models, surpassing the average human accuracy, while collective human decision-making outperformed all AI models. GPT-4 Vision Preview exhibited selectivity, responding more to easier questions with smaller images and longer questions.

The current therapeutic cancer vaccines landscape in non-small cell lung cancer.

Currently, conventional immunotherapies for the treatment of non-small cell lung cancer (NSCLC) have low response rates and benefit only a minority of patients, particularly those with advanced disease, so novel therapeutic strategies are urgent deeded. Therapeutic cancer vaccines, a form of active immunotherapy, harness potential to activate the adaptive immune system against tumor cells via antigen cross-presentation. Cancer vaccines can establish enduring immune memory and guard against recurrences. Vaccine-induced tumor cell death prompts antigen epitope spreading, activating functional T cells and thereby sustaining a cancer-immunity cycle. The success of vaccines against severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has rendered cancer vaccines a promising avenue, especially when combined with immunotherapy or chemoradiotherapy for NSCLC. This review delves into the intricate antitumor immune mechanisms underlying therapeutic cancer vaccines, enumerates the tumor antigen spectrum of NSCLC, discusses different cancer vaccines progress and summarizes relevant clinical trials. Additionally, we analyze the combination strategies, current limitations, and future prospects of cancer vaccines in NSCLC treatment, aiming to offer fresh insights for their clinical application in managing NSCLC. Overall, cancer vaccines offer promising potential for NSCLC treatment, particularly combining with chemoradiotherapy or immunotherapy could further improve survival in advanced patients. Exploring inhaled vaccines or prophylactic vaccines represents a crucial research avenue.

Optimizing pervious concrete with machine learning: Predicting permeability and compressive strength using artificial neural networks

This study makes a significant contribution to the field of pervious concrete by using machine learning to innovatively predict both mechanical and hydraulic performance. Unlike existing methods that rely on labor-intensive trial-and-error experiments, our proposed approach leverages a multilayer perceptron network. To develop this approach, we compiled a comprehensive dataset comprising 271 sets and 3,252 experimental data points. Our methodology involved evaluating 22,246 network configurations, employing Monte Carlo cross-validation over 20 iterations, and using 4 training algorithms, resulting in a total of 1,779,680 training iterations. This results in an optimized model that integrates diverse mix design parameters, enabling accurate predictions of permeability and compressive strength even in the absence of experimental data, achieving R² values of 0.97 and 0.98, respectively. Sensitivity analyses validate the model's alignment with established principles of pervious concrete behavior. By demonstrating the efficacy of machine learning as a complementary tool for optimizing pervious concrete mix designs, this research not only addresses current methodological limitations but also lays the groundwork for more efficient and effective approaches in the field.

Merkel cell polyomavirus small tumor antigen contributes to immune evasion by interfering with type I interferon signaling.

Merkel cell polyomavirus (MCPyV) is the causative agent of the majority of Merkel cell carcinomas (MCC). The virus has limited coding capacity, with its early viral proteins, large T (LT) and small T (sT), being multifunctional and contributing to infection and transformation. A fundamental difference in early viral gene expression between infection and MCPyV-driven tumorigenesis is the expression of a truncated LT (LTtr) in the tumor. In contrast, sT is expressed in both conditions and contributes significantly to oncogenesis. Here, we identified novel functions of early viral proteins by performing genome-wide transcriptome and chromatin studies in primary human fibroblasts. Due to current limitations in infection and tumorigenesis models, we mimic these conditions by ectopically expressing sT, LT or LTtr, individually or in combination, at different time points. In addition to its known function in cell cycle and inflammation modulation, we reveal a fundamentally new function of sT. We show that sT regulates the type I interferon (IFN) response downstream of the type I interferon receptor (IFNAR) by interfering with the interferon-stimulated gene factor 3 (ISGF3)-induced interferon-stimulated gene (ISG) response. Expression of sT leads to a reduction in the expression of interferon regulatory factor 9 (IRF9) which is a central component of the ISGF3 complex. We further show that this function of sT is conserved in BKPyV. We provide a first mechanistic understanding of which early viral proteins trigger and control the type I IFN response, which may influence MCPyV infection, persistence and, during MCC progression, regulation of the tumor microenvironment.

Suitability and Early Clinical Outcomes of Corneal Donor Tissue in the 76–80-Year-Old Age Group

Purpose: There is a significant global shortage of corneal donor tissue suitable for keratoplasty. One simple strategy for addressing this shortage is to increase the upper age limit for acceptable tissue over the current customary upper limit of age 75. We describe a pilot study completed at one eye bank procuring, processing, and distributing keratoplasty tissue from donors aged 76 to 80 years. Methods: This is a retrospective case series of donor tissues aged 76 to 80 years in comparison with donor tissues aged 71 to 75 years. Standard tissue parameters were evaluated for all tissues, including tissue suitability, mean endothelial cell density (ECD), death-to-procurement time, and cause of death. For the older tissues, clinical outcomes through 3 months were obtained from each surgeon, including intraoperative and postoperative complications. Results: Corneal tissues from donors aged 76 to 80 years had a similar suitability rate (57%) when compared with donors aged 71 to 75 years (59%) (P = 0.635). Both groups had comparable ECDs (P = 0.097). Early clinical outcomes of corneal grafts from donors older than 76 years were favorable, with no early graft failure or significant concerns for clinical safety or efficacy. Conclusions: Our findings suggest that corneal tissues aged 76 to 80 years can be safely and efficiently transplanted in the United States. While additional study is needed, our results strengthen the case that expanding the upper age limit for donor age has the potential to help meet the global shortage for suitable keratoplasty tissue.

Potential of Benchtop NMR for the Determination of Polymer Molar Masses, Molar Mass Distributions, and Chemical Composition Profiles by Means of Diffusion-Ordered Spectroscopy, DOSY.

The determination of molar masses and their distributions is crucial in polymer synthesis and design. This work presents the current performance and limitations of diffusion-ordered spectroscopy (DOSY) on a low-field (benchtop) NMR spectrometer (at 90MHz) as an alternative to size exclusion chromatography (SEC) for determining diffusion coefficient distributions (DCDs) and molar mass distributions (MMDs). After optimization for narrowly distributed homopolymers, MMDs obtained with inverse Laplace transformation (ILT) and log-normal distribution are compared with average molar masses obtained with mono- and bi-exponential fits, as well as MMDs obtained from SEC. This approach enables ILT to determine DCDs and MMDs even for bimodal homopolymers with fully spectrally overlapping signals and block copolymers with various chemical compositions, for which chemical composition profiles are determined. The feasibility of low-field diffusion NMR with samples dissolved in non-deuterated solvents is further demonstrated and methods for solvent suppression are discussed.

Illumination and Shadows in Head Rotation: Experiments with Denoising Diffusion Models

Accurately modeling the effects of illumination and shadows during head rotation is critical in computer vision for enhancing image realism and reducing artifacts. This study delves into the latent space of denoising diffusion models to identify compelling trajectories that can express continuous head rotation under varying lighting conditions. A key contribution of our work is the generation of additional labels from the CelebA dataset, categorizing images into three groups based on prevalent illumination direction: left, center, and right. These labels play a crucial role in our approach, enabling more precise manipulations and improved handling of lighting variations. Leveraging a recent embedding technique for Denoising Diffusion Implicit Models (DDIM), our method achieves noteworthy manipulations, encompassing a wide rotation angle of ±30°. while preserving individual distinct characteristics even under challenging illumination conditions. Our methodology involves computing trajectories that approximate clouds of latent representations of dataset samples with different yaw rotations through linear regression. Specific trajectories are obtained by analyzing subsets of data that share significant attributes with the source image, including light direction. Notably, our approach does not require any specific training of the generative model for the task of rotation; we merely compute and follow specific trajectories in the latent space of a pre-trained face generation model. This article showcases the potential of our approach and its current limitations through a qualitative discussion of notable examples. This study contributes to the ongoing advancements in representation learning and the semantic investigation of the latent space of generative models.

A Reconfigurable Ferroelectric Transistor as An Ultra‐Scaled Cell for Low‐Power In‐Memory Data Processing

AbstractCompact in‐memory computing architectures are desirable to embed artificial intelligence (AI) in resource‐restricted edge devices. However, current technologies face limitations in both the area and energy efficiency. Here, a reconfigurable ferroelectric tunnel field‐effect transistor (ferro‐TFET) is presented that can be used as an ultra‐scaled cell for low‐power in‐memory data processing. A gate‐all‐around ferroelectric film is integrated on a vertical nanowire TFET with a gate/source overlapped channel, enabling non‐volatilely reconfigurable anti‐ambipolarity by programming the ferroelectric polarization state. By considering the stored polarization state and reading voltage as inputs, an XNOR operation is achieved in a single‐gate ferro‐TFET. It is shown that the ferro‐TFETs can be implemented in a crossbar array for convolutional frequency filtering whose performance can be evaluated by an impulse‐response method considering the effect of device‐to‐device variation based on statistics. Benefiting from the miniaturized footprint, non‐volatility, and low‐power operation, ferro‐TFETs show promises as a one‐transistor in‐memory computing cell for area‐ and energy‐efficient edge AI applications.

Airborne particulate matter and diesel engine exhaust on infrastructure construction sites in the Copenhagen metropolitan area.

Diesel engine exhaust (DEE) is carcinogenic and potentially hazardous for those working in close proximity to diesel-powered machines. This study characterizes workplace exposure to DEE and its associated particulate matter (PM) during outdoor construction activities. We sampled at 4 construction sites in the Copenhagen metropolitan area. We used portable constant-flow pumps and quartz-fiber filters to quantify personal exposure to elemental carbon (EC), and used real-time instruments to collect activity-based information about particle number and size distribution, as well as black carbon (BC) concentration. Full-shift measurements of EC concentration ranged from < 0.3 to 6.4 µg/m3. Geometric mean (GM) EC exposure was highest for ground workers (3.4 µg/m3 EC; geometric standard deviation, GSD = 1.3), followed by drilling rig operators (2.6 µg/m3 EC; GSD = 1.4). Exposure for non-drilling-rig machine operators (1.2 µg/m3 EC; GSD = 2.9) did not differ significantly from background (0.9 µg/m3 EC; GSD = 1.7). The maximum 15-min moving average concentration of BC was 17 µg/m3, and the highest recorded peak concentration was 44 µg/m3. In numbers, the particle size distributions were dominated by ultrafine particles ascribed to DEE and occasional welding activities at the sites. The average total particle number concentrations (PNCs) measured in near-field and far-field positions across all worksites were 10,600 (GSD = 3.0) and 6,000 (GSD = 2.8)/cm3, respectively. Sites with active drilling rigs saw significantly higher average total PNCs at their near-field stations (13,600, 32,000, and 9,700/cm3; GSD = 2.4, 3.4, and 2.4) than sites without (4,700/cm3; GSD = 1.6). Overall, the DEE exposures at these outdoor construction sites were below current occupational exposure limits for EC (10 µg/m3 in Denmark; 50 µg/m3 in the European Union), but extended durations of exposure to the observed DEE levels may still be a health risk.

Head and neck cancer surveillance: The value of computed tomography and clinical exam

PurposeThe recurrence of head and neck cancer (HNC) is most prevalent during the initial two years following curative treatment, underscoring the criticality of regular surveillance for HNC survivors. This study aims to evaluate the effectiveness of computed tomography (CT) imaging and clinical physical examination (CE) in HNC surveillance, assessing whether these imaging protocols meet the current treatment limitations confronting HNC specialists. MethodsRetrospective chart review of a 9-year experience with head and neck cancer patients at a single, academic tertiary care center. Demographic data was collected along with data regarding whether the recurrences were detected primarily through CE, flexible endoscopic exam (scope exam), or CT or CT/PET scan. Subsets of the data were analyzed and compared by sensitivity, specificity, and negative predictive values. Results264 HNC patients were identified. 72 total recurrences (27 %) were noted. The method of initial detection spurring further investigation was imaging in 42 (58.3 %) patients, CE (33.3 %) in 24 patients, scope exam in 6 (8.4 %) patients. Overall, 65 (90.3 %) patients had imaging that showed recurrence regardless of method of initial detection. Sensitivity, (87.1 % vs 70.5 %), and specificity (93.95 % vs 96.9 %) were noted for CT and CE respectively. Combined sensitivity and specificity for CT and CE was 96.2 % and 91.05 % respectively. ConclusionThe data suggests that imaging could provide sufficient methods of HNC surveillance despite limitations the COVID-19 pandemic presents.

Solid-state NMR of active sites in TiO2 photocatalysis: a critical review

Titanium dioxide (TiO2) is one of the optimal semiconductor metal oxide photocatalysts with a wide range of application fields, such as heterogeneous catalysis, energy science, and environmental science. Solid-state nuclear magnetic resonance (NMR) spectroscopy is a powerful tool for characterizing both structure and dynamics at an atomic-molecular level in heterogeneous catalysts. In this review, we first provide a brief discussion on the progress in investigating the structures of titanium and oxygen in bulk and on the surface of TiO2 by using various solid-state NMR techniques. Advances in the understanding of electronic structure and properties of TiO2 with distinct surface features, including various crystal facets and heteroatomic adsorption by chemical probe-assisted NMR techniques, are secondly presented. The solid-state NMR characterization of heteroatom active sites (such as 13C, 15N, 11B, 27Al) and their function in TiO2 photocatalysts is described in detail. Finally, a critical discourse assesses the current limitations and prospects of solid-state NMR in its application to the optimization and design of advanced TiO2 photocatalysts.

A deep experimental analysis of energy‐proficient firewall policies and security practices for resource limited wireless networks

AbstractThe role of firewalls and security principles in resource‐limited wireless networks and Wireless Sensor Networks (WSN) is more expected than in any dedicated network environment. The advanced wireless technologies and emerging fashion of autonomous wireless network nodes are mostly expecting resilient firewall services with multi‐level security policies. Wireless networks are classified under sensor networks, Internet of Things (IoT) mobile networks, and so forth. According to the expectations, security frameworks are gradually invented around the communication platform using various ideologies. The novel ideology and respective implementation effort open better solutions against wireless network attacks. Anyhow, the minimal production of time complexity, energy complexity, and computation overhead from any novel security approach is always considered under the best practices. The complexity levels directly affect the wireless node's energy consumption ability and operational spans severely. The energy optimization techniques and load‐balancing techniques integrated into multi‐class firewall rules are extremely useful solutions for resource‐limited wireless networks. This article has been motivated to analyze the recent firewall techniques and secure data communication mechanisms used for securing wireless networks. Consequently, the practice of comparative literature analysis helps to improve the current limitations identified under the classified categories of security mechanisms such as energy‐optimized security principles and load‐balanced security principles. The establishment of secure and energy‐optimized multi‐class firewall rules in each wireless node assures a healthy focus on next‐generation networks. The experiment section shows the benefits of energy‐optimized secure network communication in terms of better accuracy and the benefits of load‐balanced secure network communication through minimal overhead in computing platforms.

Nanogel: A versatile drug delivery system for the treatment of various diseases and their future perspective.

Nanogel (NG) drug delivery systems have emerged as promising tools for targeted and controlled drug release, revolutionizing treatment approaches across various diseases.Their unique physicochemical properties, such as nano size, high surface area,biocompatibility, stability, and tunable drug release, make themideal carriers for a wide range of therapeutic agents. Nanogels (NGs), characterized by their 3D network of crosslinked polymers, offer unique edges like high drug loading capacity, controlled release, and targeted delivery. Additionally, the diverse applications of NGs in medical therapeutics highlight their versatility and potential impact on improving patient outcomes. Their application spans cancer treatment, infectious diseases, and chronic conditions, allowing for precise drug delivery to specific tissues or cells, minimizing side effects, and enhancing therapeutic efficacy. Despite their potential, challenges such as scalability, manufacturing reproducibility, and regulatory hurdles must be addressed. Achieving clinical translation requires overcoming these obstacles to ensure therapeutic payloads' safe and efficient delivery. Strategies such as surface modification and incorporating stimuli-responsive elements enhanced NG performance and addressed specific therapeutic challenges. Advances in nanotechnology, biomaterials, and targeted drug design offer opportunities to improve the performance of NGs and address current limitations. Tailoring NGs for exploring combination therapies and integrating diagnostics for real-time monitoring represent promising avenues for future research. In conclusion, NG drug delivery systems have demonstrated tremendous potential in diverse disease applications. Overcoming challenges and leveraging emerging technologies will pave the way for their widespread clinical implementation, ushering in a new era of precision medicine and improved patient care.

Ultra‐low loss 2‐core fibre for expanding submarine cable capacity

Abstract2‐core fibre for submarine cable is becoming the first commercial application of multi‐core fibre, which should enable to expand the capacity beyond the current limitation of submarine cable size. For successful commercial applications of 2‐core fibre, it is important to minimise the complexities in manufacturing, splicing, and fibre handling. The 2‐core fibre design for minimum complexity is reviewed.

The effect of an auxiliary spring on the decrease of contact resistance and contact temperature for power automotive connector

Power automotive connectors used to connect two electrical systems in an electric vehicle should resist the rising of electric power or electric current. The latter can cause a rise in contact temperature by the Joule effect and can damage a connector improperly designed. The purpose of this work is to study experimentally and numerically the influence of the addition of a second spring on the ability of a power connector to transmit a high current. For this reason, an experimental test has been held on a connector without a second spring to measure contact resistance and contact temperature; then, a finite element model FEM of this connector was developed to compare and validate the numerical results with the experimental results. In addition, another FE model with a second spring was used to minimize contact resistance and contact temperature. Moreover, a theoretical model has shown its effectiveness in contact temperature and contact area calculations; it provides good agreement with experimental and numerical results. Numerical results show that the effect of the addition of a 2nd spring on the decrease of the contact temperature and contact resistance is significant. In addition, the thermo-electrical analysis by finite element shows that the current limit supported by a power connector with an auxiliary spring will equal twice the current supported by a power connector with one spring. FE mechanical analysis shows that the maximum mechanical stress in the connector with an auxiliary spring remains lower than the 2nd spring material yield stress. Finally, experimental and numerical results are in good agreement.

Soluble ST2: A Novel Biomarker for Diagnosis and Prognosis of Cardiovascular Disease.

The increasing incidence of cardiovascular disease (CVD) is a significant global health concern, affecting millions of individuals each year. Accurate diagnosis of acute CVD poses a formidable challenge, as misdiagnosis can significantly decrease patient survival rates. Traditional biomarkers have played a vital role in the diagnosis and prognosis of CVDs, but they can be influenced by various factors, such as age, sex, and renal function. Soluble ST2 (sST2) is a novel biomarker that is closely associated with different CVDs. Its low reference change value makes it suitable for continuous measurement, unaffected by age, kidney function, and other confounding factors, facilitating risk stratification of CVDs. Furthermore, the combination of sST2 with other biomarkers can enhance diagnostic accuracy and prognostic value. This review aims to provide a comprehensive overview of sST2, focusing on its diagnostic and prognostic value as a myocardial marker for different types of CVDs and discussing the current limitations of sST2.

The prognostic role of MRI-based radiomics in tongue carcinoma: a multicentric validation study.

Radiomics is an emerging field that utilizes quantitative features extracted from medical images to predict clinically meaningful outcomes. Validating findings is crucial to assess radiomics applicability. We aimed to validate previously published magnetic resonance imaging (MRI) radiomics models to predict oncological outcomes in oral tongue squamous cell carcinoma (OTSCC). Retrospective multicentric study on OTSCC surgically treated from 2010 to 2019. All patients performed preoperative MRI, including contrast-enhanced T1-weighted (CE-T1), diffusion-weighted sequences and apparent diffusion coefficient map. We evaluated overall survival (OS), locoregional recurrence-free survival (LRRFS), cause-specific mortality (CSM). We elaborated different models based on clinical and radiomic data. C-indexes assessed the prediction accuracy of the models. We collected 112 consecutive independent patients from three Italian Institutions to validate the previously published MRI radiomic models based on 79 different patients. The C-indexes for the hybrid clinical-radiomic models in the validation cohort were lower than those in the training cohort but remained > 0.5 in most cases. CE-T1 sequence provided the best fit to the models: the C-indexes obtained were 0.61, 0.59, 0.64 (pretreatment model) and 0.65, 0.69, 0.70 (posttreatment model) for OS, LRRFS and CSM, respectively. Our clinical-radiomic models retain a potential to predict OS, LRRFS and CSM in heterogeneous cohorts across different centers. These findings encourage further research, aimed at overcoming current limitations, due to the variability of imaging acquisition, processing and tumor volume delineation.

Current limitations and future research needs for predicting soil precompression stress: A synthesis of available data

Precompression stress, compression index, and swelling index are used for characterizing the compressive behavior of soils, and are essential soil properties for establishing decision support tools to reduce the risk of soil compaction. Because measurements are time-consuming, soil compressive properties are often derived through pedotransfer functions. This study aimed to develop a comprehensive database of soil compressive properties with additional information on basic soil properties, site characteristics, and methodological aspects sourced from peer-reviewed literature, and to develop random forest models for predicting precompression stress using various subsets of the database. Our analysis illustrates that soil compressive properties data primarily originate from a limited number of countries. There is a predominance of precompression stress data, while little data on compression index or recompression index are available. Most precompression stress data were derived from the topsoils of conventionally tilled arable fields, which is not compatible with knowledge that subsoil compaction is a serious problem. The data compilation unveiled considerable variations in soil compression test procedures and methods for calculating precompression stress across different studies, and a concentration of data at soil moisture conditions at or above field capacity. The random forest models exhibited unsatisfactory predictive performance although they performed better than previously developed models. Models showed slight improvement in predictive power when the underlying data were restricted to a specific precompression stress calculation method. Although our database offers broader coverage of precompression stress data than previous studies, the lack of standardization in methodological procedures complicates the development of predictive models based on combined datasets. Methodological standardization and/or functions to translate results between methodologies are needed to ensure consistency and enable data comparison, to develop robust models for precompression stress predictions. Moreover, data across a wider range of soil moisture conditions are needed to characterize soil mechanical properties as a function of soil moisture, similar to soil hydraulic functions, and to develop models to predict the parameters of such soil mechanical functions.

Photon-Counting Detector CT Applications in Musculoskeletal Radiology.

Photon-counting detectors (PCDs) have emerged as one of the most influential technical developments for medical imaging in recent memory. Surpassing conventional systems with energy-integrating detector technology in many aspects, PCD-CT scanners provide superior spatial resolution and dose efficiency for all radiological subspecialities. Demanding detailed display of trabecular microarchitecture and extensive anatomical coverage frequently within the same scan, musculoskeletal (MSK) imaging in particular can be a beneficiary of PCD-CT's remarkable performance. Since PCD-CT provides users with a plethora of customization options for both image acquisition and reconstruction, however, MSK radiologists need to be familiar with the scanner to unlock its full potential. From filter-based spectral shaping for artifact reduction over full field-of-view ultra-high-resolution scans to postprocessing of single- or dual-source multienergy data, almost every imaging task can be met with an optimized approach in PCD-CT. The objectives of this review were to give an overview of the most promising applications of PCD-CT in MSK imaging to date, to state current limitations, and to highlight directions for future research and developments.