STING facilitates nuclear import of herpesvirus genome during infection.

Once inside the host cell, DNA viruses must overcome the physical barrier posed by the nuclear envelope to establish a successful infection. The mechanism underlying this process remains unclear. Here, we show that the herpesvirus exploits the immune adaptor stimulator of interferon genes (STING) to facilitate nuclear import of the viral genome. Following the entry of the viral capsid into the cell, STING binds the viral capsid, mediates capsid docking to the nuclear pore complex via physical interaction, and subsequently enables accumulation of the viral genome in the nucleus. Silencing STING in human cytomegalovirus (HCMV)-susceptible cells inhibited nuclear import of the viral genome and reduced the ensuing viral gene expression. Overexpressing STING increased the host cell's susceptibility to HCMV and herpes simplex virus 1 by improving the nuclear delivery of viral DNA at the early stage of infection. These observations suggest that the proviral activity of STING is conserved and exploited by the herpesvirus family. Intriguingly, in monocytes, which act as latent reservoirs of HCMV, STING deficiency negatively regulated the establishment of HCMV latency and reactivation. Our findings identify STING as a proviral host factor regulating latency and reactivation of herpesviruses.

Aicardi-Goutières syndrome-associated gene SAMHD1 preserves genome integrity by preventing R-loop formation at transcription-replication conflict regions.

The comorbid association of autoimmune diseases with cancers has been a major obstacle to successful anti-cancer treatment. Cancer survival rate decreases significantly in patients with preexisting autoimmunity. However, to date, the molecular and cellular profiles of such comorbidities are poorly understood. We used Aicardi-Goutières syndrome (AGS) as a model autoimmune disease and explored the underlying mechanisms of genome instability in AGS-associated-gene-deficient patient cells. We found that R-loops are highly enriched at transcription-replication conflict regions of the genome in fibroblast of patients bearing SAMHD1 mutation, which is the AGS-associated-gene mutation most frequently reported with tumor and malignancies. In SAMHD1-depleted cells, R-loops accumulated with the concomitant activation of DNA damage responses. Removal of R-loops in SAMHD1 deficiency reduced cellular responses to genome instability. Furthermore, downregulation of SAMHD1 expression is associated with various types of cancer and poor survival rate. Our findings suggest that SAMHD1 functions as a tumor suppressor by resolving R-loops, and thus, SAMHD1 and R-loop may be novel diagnostic markers and targets for patient stratification in anti-cancer therapy.

Development of a Semiquantitative Whole-Body MRI Scoring System for Multiple Myeloma.

Background In patients with multiple myeloma (MM), the serum marker ß2-microglobulin does not always accurately reflect tumor load. In contrast, whole-body (WB) MRI has shown high sensitivity for detecting bone lesions. Purpose To develop and validate a semiquantitative WB MRI scoring system for newly diagnosed MM and to compare it with the International Staging System (ISS) and Revised ISS (R-ISS). Materials and Methods This study included two retrospective groups (group 1, July 2015 to September 2021; group 2, February 2020 to September 2021) and one prospective group (group 3, October 2021 to February 2022) of patients with newly diagnosed MM. A new scoring system for MM was developed using spine MRI scans in group 1 and WB MRI scans in group 2 that integrated three features: (a) background marrow pattern, (b) number of focal bone lesions, and (c) presence of extramedullary or paramedullary lesions. The summed total score ranged from zero to nine. The interobserver agreement for each feature was assessed using Fleiss or Cohen weighted κ. WB MRI total scores in group 3 were compared across ISS and R-ISS stages using two-way analysis of variance. Results Groups 1, 2, and 3 included 103 patients (mean age, 62.1 years ± 9.1 [SD]; 60 men), 36 patients (mean age 65.4 years ± 11.3 [SD]; 19 women), and 39 participants (mean age, 62.0 years ± 11.7 [SD]; 20 men), respectively. The interobserver agreements for the three features composing the scoring system were substantial (κ range, 0.69-0.80). WB MRI total score increased with increasing ISS stage (mean score for ISS 1, 2, and 3 was 2.2, 4.2, and 5.8, respectively; P = .009) and R-ISS stage (mean score for R-ISS 1, 2, and 3 was 2.1, 3.8, and 5.9, respectively; P = .005). Conclusion The developed WB MRI scoring system for MM demonstrated substantial observer agreement and corresponded well with ISS and R-ISS stages. © RSNA, 2023 Supplemental material is available for this article. See also the editorial by Dragan and Messiou in this issue.

Digital Forensic Research for Analyzing Drone Pilot: Focusing on DJI Remote Controller.

Drones, also known as unmanned aerial vehicles (UAVs) and sometimes referred to as 'Mobile IoT' or 'Flying IoT', are widely adopted worldwide, with their market share continuously increasing. While drones are generally harnessed for a wide range of positive applications, recent instances of drones being employed as lethal weapons in conflicts between countries like Russia, Ukraine, Israel, Palestine, and Hamas have demonstrated the potential consequences of their misuse. Such misuse poses a significant threat to cybersecurity and human lives, thereby highlighting the need for research to swiftly and accurately analyze drone-related crimes, identify the responsible pilot, and establish when and what illegal actions were carried out. In contrast to existing research, involving limited data collection and analysis of the drone, our study focused on collecting and rigorously analyzing data without restrictions from the remote controller used to operate the drone. This comprehensive approach allowed us to unveil essential details, including the pilot's account information, the specific drone used, pairing timestamps, the pilot's operational location, the drone's flight path, and the content captured during flights. We developed methodologies and proposed artifacts to reveal these specifics, which were supported by real-world data. Significantly, this study is the pioneering digital forensic investigation of remote controller devices. We meticulously collected and analyzed all internal data, and we even employed reverse engineering to decrypt critical information files. These achievements hold substantial significance. The outcomes of this research are expected to serve as a digital forensic methodology for drone systems, thereby making valuable contributions to numerous investigations.

Fully Automated Abdominal CT Biomarkers for Type 2 Diabetes Using Deep Learning.

Background CT biomarkers both inside and outside the pancreas can potentially be used to diagnose type 2 diabetes mellitus. Previous studies on this topic have shown significant results but were limited by manual methods and small study samples. Purpose To investigate abdominal CT biomarkers for type 2 diabetes mellitus in a large clinical data set using fully automated deep learning. Materials and Methods For external validation, noncontrast abdominal CT images were retrospectively collected from consecutive patients who underwent routine colorectal cancer screening with CT colonography from 2004 to 2016. The pancreas was segmented using a deep learning method that outputs measurements of interest, including CT attenuation, volume, fat content, and pancreas fractal dimension. Additional biomarkers assessed included visceral fat, atherosclerotic plaque, liver and muscle CT attenuation, and muscle volume. Univariable and multivariable analyses were performed, separating patients into groups based on time between type 2 diabetes diagnosis and CT date and including clinical factors such as sex, age, body mass index (BMI), BMI greater than 30 kg/m2, and height. The best set of predictors for type 2 diabetes were determined using multinomial logistic regression. Results A total of 8992 patients (mean age, 57 years ± 8 [SD]; 5009 women) were evaluated in the test set, of whom 572 had type 2 diabetes mellitus. The deep learning model had a mean Dice similarity coefficient for the pancreas of 0.69 ± 0.17, similar to the interobserver Dice similarity coefficient of 0.69 ± 0.09 (P = .92). The univariable analysis showed that patients with diabetes had, on average, lower pancreatic CT attenuation (mean, 18.74 HU ± 16.54 vs 29.99 HU ± 13.41; P < .0001) and greater visceral fat volume (mean, 235.0 mL ± 108.6 vs 130.9 mL ± 96.3; P < .0001) than those without diabetes. Patients with diabetes also showed a progressive decrease in pancreatic attenuation with greater duration of disease. The final multivariable model showed pairwise areas under the receiver operating characteristic curve (AUCs) of 0.81 and 0.85 between patients without and patients with diabetes who were diagnosed 0-2499 days before and after undergoing CT, respectively. In the multivariable analysis, adding clinical data did not improve upon CT-based AUC performance (AUC = 0.67 for the CT-only model vs 0.68 for the CT and clinical model). The best predictors of type 2 diabetes mellitus included intrapancreatic fat percentage, pancreatic fractal dimension, plaque severity between the L1 and L4 vertebra levels, average liver CT attenuation, and BMI. Conclusion The diagnosis of type 2 diabetes mellitus was associated with abdominal CT biomarkers, especially measures of pancreatic CT attenuation and visceral fat. © RSNA, 2022 Online supplemental material is available for this article.

Psychiatric symptoms mediate the effect of resilience on health-related quality of life in patients with breast cancer: Longitudinal examination.

OBJECTIVE: Patients with breast cancer receiving neoadjuvant chemotherapy are at increased risk of poor health-related quality of life (HRQOL). This study examined clinical caseness on depression and anxiety mediate the relationship between resilience and HRQOL in patients with breast cancer. METHODS: A total of 193 patients with breast cancer undergoing neoadjuvant chemotherapy completed questionnaires including the Connor-Davidson Resilience Scale, Hospital Anxiety and Depression Scale (HADS), and Functional Assessment of Cancer Therapy-Breast before the first session (T0), before the start of the last session (T1), and 6 months after the end (T2) of chemotherapy. Mediation analyses using a bootstrapping method was performed. RESULTS: The indirect effect (IE) through T1 depression was significant (IE through depression = 0.043, 95% confidence interval [CI] [0.002-0.090]), while IE through T1 anxiety was not significant (IE through anxiety = 0.037, 95% CI [-0.010-0.097]) in the association between T0 resilience and T2 HRQOL. CONCLUSIONS: Clinical caseness on HADS depression subscale during chemotherapy was a mediating factor of the relationship between resilience before chemotherapy and HRQOL after chemotherapy in patients with breast cancer receiving neoadjuvant chemotherapy. Depression during chemotherapy in patients with breast cancer may be a target symptom of screening and intervention to maintain the HRQOL after chemotherapy. Also, patients with low resilience are more likely to develop depression during chemotherapy, and clinicians should carefully monitor whether depression occurs in these patients with low resilience.

Human cytomegalovirus induces and exploits Roquin to counteract the IRF1-mediated antiviral state.

RNA represents a pivotal component of host-pathogen interactions. Human cytomegalovirus (HCMV) infection causes extensive alteration in host RNA metabolism, but the functional relationship between the virus and cellular RNA processing remains largely unknown. Through loss-of-function screening, we show that HCMV requires multiple RNA-processing machineries for efficient viral lytic production. In particular, the cellular RNA-binding protein Roquin, whose expression is actively stimulated by HCMV, plays an essential role in inhibiting the innate immune response. Transcriptome profiling revealed Roquin-dependent global down-regulation of proinflammatory cytokines and antiviral genes in HCMV-infected cells. Furthermore, using cross-linking immunoprecipitation (CLIP)-sequencing (seq), we identified IFN regulatory factor 1 (IRF1), a master transcriptional activator of immune responses, as a Roquin target gene. Roquin reduces IRF1 expression by directly binding to its mRNA, thereby enabling suppression of a variety of antiviral genes. This study demonstrates how HCMV exploits host RNA-binding protein to prevent a cellular antiviral response and offers mechanistic insight into the potential development of CMV therapeutics.

Perspectives on Potential Fatty Acid Modulations of Motility Associated Human Sperm Ion Channels.

Human spermatozoan ion channels are specifically distributed in the spermatozoan membrane, contribute to sperm motility, and are associated with male reproductive abnormalities. Calcium, potassium, protons, sodium, and chloride are the main ions that are regulated across this membrane, and their intracellular concentrations are crucial for sperm motility. Fatty acids (FAs) affect sperm quality parameters, reproductive pathologies, male fertility, and regulate ion channel functions in other cells. However, to date the literature is insufficient to draw any conclusions regarding the effects of FAs on human spermatozoan ion channels. Here, we aimed to discern the possible effects of FAs on spermatozoan ion channels and direct guidance for future research. After investigating the effects of FAs on characteristics related to human spermatozoan motility, reproductive pathologies, and the modulation of similar ion channels in other cells by FAs, we extrapolated polyunsaturated FAs (PUFAs) to have the highest potency in modulating sperm ion channels to increase sperm motility. Of the PUFAs, the ω-3 unsaturated fatty acids have the greatest effect. We speculate that saturated and monounsaturated FAs will have little to no effect on sperm ion channel activity, though the possible effects could be opposite to those of the PUFAs, considering the differences between FA structure and behavior.

Covariate-driven factorization by thresholding for multiblock data.

Multiblock data, where multiple groups of variables from different sources are observed for a common set of subjects, are routinely collected in many areas of science. Methods for joint factorization of such multiblock data are being developed to explore the potentially joint variation structure of the data. While most of the existing work focuses on delineating joint components, shared across all data blocks, from individual components, which is only relevant to a single data block, we propose to model and estimate partially joint components across some, but not all, data blocks. If covariates, with potential multiblock structures, are available, then the components are further modeled to be driven by the covariate information. To estimate such a covariate-driven, block-structured factor model, we propose an iterative algorithm based on thresholding, by transforming the problem of signal segmentation into a grouped variable selection problem. The proposed factorization provides accurate estimation of individual and (partially) joint structures in multiblock data, as confirmed by simulation studies. In the analysis of a real multiblock genomic dataset from the Cancer Genome Atlas project, we demonstrate that the estimated block structures provide straightforward interpretation and facilitate subsequent analyses.

In situ, operando studies on the size and structure of supported Pt catalysts under supercritical conditions by simultaneous synchrotron-based X-ray techniques.

To control the size and structure of supported Pt catalysts, the influence of additional metal particles and the effect of supports were elucidated during the cracking reaction of n-dodecane under supercritical reaction conditions. The dynamical changes in nanocatalysts and catalytic activity are studied under realistic reaction conditions by using a combination of simultaneous temperature-programmed heating, in situ Small Angle X-ray Scattering (SAXS) and X-ray Absorption Near Edge Structure (XANES). In situ SAXS results indicate that the stability of the catalysts increases with Sn concentration. In situ XANES analysis reveals that the degree of oxidation and the electronic states of catalysts are dependent on the amount of Sn. Carbonaceous deposits over spent catalysts were characterized by Raman spectroscopy, indicating that the highest Sn loading inhibits the formation of disordered graphitic lattices, which leads to an increased catalytic activity. SiO2, γ-Al2O3 and Mg(Al)Ox were employed as supports to investigate the support effect on the stability of Pt catalysts. In situ SAXS and XANES results clearly show the improved stability of catalysts on γ-Al2O3 and Mg(Al)Ox supports compared to Pt catalysts on SiO2 and the electronic states of catalysts are strongly influenced by support materials.

Atorvastatin and rosuvastatin improve physiological parameters and alleviate immune dysfunction in metabolic disorders.

This study was designed to characterize the potential therapeutic effects of two statin drugs commonly used to treat dyslipidemia in inflammation-linked metabolic disorders related to type 2 diabetes. Atorvastatin (10 mg/kg/day) and rosuvastatin (3 mg/kg/day) were administered to mice with diet-induced obesity (DIO). The statins lowered serum total and LDL cholesterol levels, and improved the atherogenic index and cardiac risk index. Furthermore, the drugs decreased fasting glucose levels, improved glucose tolerance, and decreased fat tissue weight and adipocyte size; this was accompanied by an overall body weight loss tendency. The statins also improved antigen-specific immunity. The killing activity of cytotoxic T cells and exacerbation of IgG secretion levels were considerably normalized. Most importantly, serum tumor necrosis factor-α and interleukin 6 levels decreased, while their RNA expression levels in fat tissue were regulated by the statins as well. This study is the first to indicate that low doses of atorvastatin and rosuvastatin, the dosing regimen for which has been controversial, could significantly improve diabetes-related metabolic disorders, and could modulate pro-inflammatory cytokines, alleviating inflammation and simultaneously restoring overall humoral and cell-mediated immunity.

Modified Aloe Polysaccharide Restores Chronic Stress-Induced Immunosuppression in Mice.

Chronic stress generally experienced in our daily lives; is known to augment disease vulnerability by suppressing the host immune system. In the present study; the effect of modified Aloe polysaccharide (MAP) on chronic stress-induced immunosuppression was studied; this Aloe compound was characterized in our earlier study. Mice were orally administered with MAP for 24 days and exposed to electric foot shock (EFS; duration; 3 min; interval; 10 s; intensity; 2 mA) for 17 days. The stress-related immunosuppression and restorative effect of MAP were then analyzed by measuring various immunological parameters. MAP treatment alleviated lymphoid atrophy and body weight loss. The numbers of lymphocyte subsets were significantly normalized in MAP-treated mice. Oral administration of MAP also restored the proliferative activities of lymphocytes; ovalbumin (OVA)-specific T cell proliferation; antibody production; and the cell killing activity of cytotoxic T lymphocytes. In summary; oral administration of MAP ameliorated chronic EFS stress-induced immunosuppression.

Emission Projections for Long-Haul Freight Trucks and Rail in the United States through 2050.

This work develops an integrated model approach for estimating emissions from long-haul freight truck and rail transport in the United States between 2010 and 2050. We connect models of macroeconomic activity, freight demand by commodity, transportation networks, and emission technology to represent different pathways of future freight emissions. Emissions of particulate matter (PM), carbon monoxide (CO), nitrogen oxides (NOx), and total hydrocarbon (THC) decrease by 60%-70% from 2010 to 2030, as older vehicles built to less-stringent emission standards retire. Climate policy, in the form of carbon tax that increases apparent fuel prices, causes a shift from truck to rail, resulting in a 30% reduction in fuel consumption and a 10%-28% reduction in pollutant emissions by 2050, if rail capacity is sufficient. Eliminating high-emitting conditions in the truck fleet affects air pollutants by 20% to 65%; although these estimates are highly uncertain, they indicate the importance of durability in vehicle engines and emission control systems. Future infrastructure investment will be required both to meet transport demand and to enable actions that reduce emissions of air and climate pollutants. By driving the integrated model framework with two macroeconomic scenarios, we show that the effect of carbon tax on air pollution is robust regardless of growth levels.

MRI of the lumbar spine: comparison of 3D isotropic turbo spin-echo SPACE sequence versus conventional 2D sequences at 3.0 T.

BACKGROUND: Three-dimensional (3D) fast spin-echo sequence with variable flip-angle refocusing pulse allows retrospective alignments of magnetic resonance imaging (MRI) in any desired plane. PURPOSE: To compare isotropic 3D T2-weighted (T2W) turbo spin-echo sequence (TSE-SPACE) with standard two-dimensional (2D) T2W TSE imaging for evaluating lumbar spine pathology at 3.0 T MRI. MATERIAL AND METHODS: Forty-two patients who had spine surgery for disk herniation and had 3.0 T spine MRI were included in this study. In addition to standard 2D T2W TSE imaging, sagittal 3D T2W TSE-SPACE was obtained to produce multiplanar (MPR) images. Each set of MR images from 3D T2W TSE and 2D TSE-SPACE were independently scored for the degree of lumbar neural foraminal stenosis, central spinal stenosis, and nerve compression by two reviewers. These scores were compared with operative findings and the sensitivities were evaluated by McNemar test. Inter-observer agreements and the correlation with symptoms laterality were assessed with kappa statistics. RESULTS: The 3D T2W TSE and 2D TSE-SPACE had similar sensitivity in detecting foraminal stenosis (78.9% versus 78.9% in 32 foramen levels), spinal stenosis (100% versus 100% in 42 spinal levels), and nerve compression (92.9% versus 81.8% in 59 spinal nerves). The inter-observer agreements (κ = 0.849 vs. 0.451 for foraminal stenosis, κ = 0.809 vs. 0.503 for spinal stenosis, and κ = 0.681 vs. 0.429 for nerve compression) and symptoms correlation (κ = 0.449 vs. κ = 0.242) were better in 3D TSE-SPACE compared to 2D TSE. 3D TSE-SPACE with oblique coronal MPR images demonstrated better inter-observer agreements compared to 3D TSE-SPACE without oblique coronal MPR images (κ = 0.930 vs. κ = 0.681). CONCLUSION: Isotropic 3D T2W TSE-SPACE at 3.0 T was comparable to 2D T2W TSE for detecting foraminal stenosis, central spinal stenosis, and nerve compression with better inter-observer agreements and symptom correlation.

Artificial Intelligence in Spinal Imaging and Patient Care: A Review of Recent Advances.

Artificial intelligence (AI) is transforming spinal imaging and patient care through automated analysis and enhanced decision-making. This review presents a clinical task-based evaluation, highlighting the specific impact of AI techniques on different aspects of spinal imaging and patient care. We first discuss how AI can potentially improve image quality through techniques like denoising or artifact reduction. We then explore how AI enables efficient quantification of anatomical measurements, spinal curvature parameters, vertebral segmentation, and disc grading. This facilitates objective, accurate interpretation and diagnosis. AI models now reliably detect key spinal pathologies, achieving expert-level performance in tasks like identifying fractures, stenosis, infections, and tumors. Beyond diagnosis, AI also assists surgical planning via synthetic computed tomography generation, augmented reality systems, and robotic guidance. Furthermore, AI image analysis combined with clinical data enables personalized predictions to guide treatment decisions, such as forecasting spine surgery outcomes. However, challenges still need to be addressed in implementing AI clinically, including model interpretability, generalizability, and data limitations. Multicenter collaboration using large, diverse datasets is critical to advance the field further. While adoption barriers persist, AI presents a transformative opportunity to revolutionize spinal imaging workflows, empowering clinicians to translate data into actionable insights for improved patient care.

Negative Screening AI in Ankle Stress Radiography to Reduce Workload.

RATIONALE AND OBJECTIVES: Interpreting ankle stress radiographs is subjective and time-consuming. We aimed to train an AI model that efficiently screens negative cases, assess the agreement with expert with and without AI-assistance, and compare the workload reduction. MATERIAL AND METHODS: We collected anterior draw test (ADT) and talar tilt test (TTT) ankle stress radiographs from Seoul St. Mary's Hospital and St. Vincent's Hospital. Patients with prior surgery, severe joint fusion, or incomplete images were excluded. Expert measurements of tibio-talar distance (TTD) and tibio-talar angle (TTA) served as reference, defining positive labels as TTD ≥ 8.3 mm or TTA ≥ 6.2°. We trained a VGG16 model on data from hospital A and tested it on three separate test sets (testset1, 2 from St. Mary's Hospital, and testset3 from St. Vincent's Hospital). Three readers (expert, reader2, and the collective reading reports) evaluated the test sets, with and without AI-assistance (focusing only on AI-predicted positive cases). We measured agreement with the expert using Cohen's weighted Kappa and assessed the hypothetical workload reduction. RESULTS: AI-assistance did not significantly affect agreement with the expert for any reader in all test sets. Reader2 showed moderate-substantial agreement for all test sets, while collective reports reached fair agreement. The AI alone demonstrated fair to moderate agreement with the expert. AI-assistance reduced the hypothetical workload by 68.8-89.2% for ADT and 58.3-70.4% for TTT. CONCLUSION: We successfully trained an AI model for ankle stress radiography, achieving an average of 70% workload reduction while maintaining agreement with expert radiologists.

Universal detection and segmentation of lymph nodes in multi-parametric MRI.

PURPOSE: Reliable measurement of lymph nodes (LNs) in multi-parametric MRI (mpMRI) studies of the body plays a major role in the assessment of lymphadenopathy and staging of metastatic disease. Previous approaches do not adequately exploit the complementary sequences in mpMRI to universally detect and segment lymph nodes, and they have shown fairly limited performance. METHODS: We propose a computer-aided detection and segmentation pipeline to leverage the T2 fat-suppressed (T2FS) and diffusion-weighted imaging (DWI) series from a mpMRI study. The T2FS and DWI series in 38 studies (38 patients) were co-registered and blended together using a selective data augmentation technique, such that traits of both series were visible in the same volume. A mask RCNN model was subsequently trained for universal detection and segmentation of 3D LNs. RESULTS: Experiments on 18 test mpMRI studies revealed that the proposed pipeline achieved a precision of [Formula: see text]%, sensitivity of [Formula: see text]% at 4 false positives (FP) per volume, and dice score of [Formula: see text]%. This represented an improvement of [Formula: see text]% in precision, [Formula: see text]% in sensitivity at 4 FP/volume, and [Formula: see text]% in dice score, respectively, over current approaches evaluated on the same dataset. CONCLUSION: Our pipeline universally detected and segmented both metastatic and non-metastatic nodes in mpMRI studies. At test time, the input data used by the trained model could either be the T2FS series alone or a blend of co-registered T2FS and DWI series. Contrary to prior work, this eliminated the reliance on both the T2FS and DWI series in a mpMRI study.

Longitudinal follow-up of incidental renal calculi on computed tomography.

RATIONALE AND OBJECTIVES: Measuring small kidney stones on CT is a time-consuming task often neglected. Volumetric assessment provides a better measure of size than linear dimensions. Our objective is to analyze the growth rate and prognosis of incidental kidney stones in asymptomatic patients on CT. MATERIALS AND METHODS: This retrospective study included 4266 scans from 2030 asymptomatic patients who underwent two or more nonenhanced CT scans for colorectal screening between 2004 and 2016. The DL software identified and measured the volume, location, and attenuation of 883 stones. The corresponding scans were manually evaluated, and patients without follow-up were excluded. At each follow-up, the stones were categorized as new, growing, persistent, or resolved. Stone size (volume and diameter), attenuation, and location were correlated with the outcome and growth rates of the stones. RESULTS: The stone cohort comprised 407 scans from 189 (M: 124, F: 65, median age: 55.4 years) patients. The median number of stones per scan was 1 (IQR: [1, 2]). The median stone volume was 17.1 mm3 (IQR: [7.4, 43.6]) and the median peak attenuation was 308 HU (IQR: [204, 532]. The 189 initial scans contained 291stones; 91 (31.3%) resolved, 142 (48.8%) grew, and 58 (19.9) remained persistent at the first follow-up. At the second follow-up (for 27 patients with 2 follow-ups), 14/44 (31.8%) stones had resolved, 19/44 (43.2%) grew and 11/44 (25%) were persistent. The median growth rate of growing stones was 3.3 mm3/year, IQR: [1.4,7.4]. Size and attenuation had a moderate correlation (Spearman rho 0.53, P < .001 for volume, and 0.50 P < .001 for peak attenuation) with the growth rate. Growing and persistent stones had significantly greater maximum axial diameter (2.7 vs 2.3 mm, P =.047) and peak attenuation (300 vs 258 HU, P =.031) CONCLUSION: We report a 12.7% prevalence of incidental kidney stones in asymptomatic adults, of which about half grew during follow-up with a median growth rate of about 3.3 mm3/year.

Progress and Perspective in harnessing MXene-carbon-based composites (0-3D): Synthesis, performance, and applications.

MXene is recognized as a promising catalyst for versatile applications due to its abundant metal sites, physicochemical properties, and structural formation. This comprehensive review offers an in-depth analysis of the incorporation of carbon into MXene, resulting in the formation of MXene-carbon-based composites (MCCs). Pristine MXene exhibits numerous outstanding characteristics, such as its atomically thin 2D structure, hydrophilic surface nature, metallic electrical conductivity, and substantial specific surface area. The introduction of carbon guides the assembly of MCCs through electrostatic self-assembly, pairing positively charged carbon with negatively charged MXene. These interactions result in increased interlayer spacing, reduced ion/electron transport distances, and enhanced surface hydrophilicity. Subsequent sections delve into the synthesis methods for MCCs, focusing on MXene integrated with various carbon structures, including 0D, 1D, 2D, and 3D carbon. Comprehensive discussions explore the distinctive properties of MCCs and the unique advantages they offer in each application domain, emphasizing the contributions and advancements they bring to specific fields. Furthermore, this comprehensive review addresses the challenges encountered by MCCs across different applications. Through these analyses, the review promotes a deeper understanding of exceptional characteristics and potential applications of MCCs. Insights derived from this review can serve as guidance for future research and development efforts, promoting the widespread utilization of MCCs across a broad spectrum of disciplines and spurring future innovations.

Piezo strain-controlled phase transition in single-crystalline Mott switches for threshold-manipulated leaky integrate-and-fire neurons.

Electrical manipulation of the metal-insulator transition (MIT) in quantum materials has attracted considerable attention toward the development of ultracompact neuromorphic devices because of their stimuli-triggered transformations. VO2 is expected to undergo abrupt electronic phase transition by piezo strain near room temperature; however, the unrestricted integration of defect-free VO2 films on piezoelectric substrates is required to fully exploit this emerging phenomenon in oxide heterostructures. Here, we demonstrate the integration of single-crystalline VO2 films on highly lattice-mismatched PMN-PT piezoelectric substrates using a single-crystal TiO2-nanomembrane (NM) template. Using our strategy on heterogeneous integration, single-crystal-like steep transition was observed in the defect-free VO2 films on TiO2-NM-PMN-PT. Unprecedented TMI modulation (5.2 kelvin) and isothermal resistance of VO2 [ΔR/R (Eg) ≈ 18,000% at 315 kelvin] were achieved by the efficient strain transfer-induced MIT, which cannot be achieved using directly grown VO2/PMN-PT substrates. Our results provide a fundamental strategy to realize a single-crystalline artificial heterojunction for promoting the application of artificial neurons using emergent materials.