Effect of aluminum and ammonium perchlorate particle sizes on the condensed combustion products characteristics of aluminized NEPE propellants.

Aluminum (Al) is usually added to solid propellants to improve the combustion performance, however the condensed combustion products (CCPs) especially the large agglomerates generated from aluminum combustion can reduce the specific impulse of the engine, and result in two-phase loss, residue accumulation and throat liner ablation. Al and ammonium perchlorate (AP), as important components of NEPE propellants, can affect the formation process of the CCPs of aluminized NEPE propellants. To clarify the effect of Al and AP particle sizes on the properties of the CCPs of aluminized NEPE propellants, a constant-pressure quench vessel was adopted to collect the combustion products of four different formulations of NEPE propellants. It was found that the condensed combustion products are mainly divided into aluminum agglomerates and oxide particles, the diameter of the aluminum agglomerates of these four different formulations of NEPE propellants at 7 MPa was smaller than that in 3 MPa, and the shells of the aluminum agglomerates were smoother and the spherical shape was more perfect. X-ray diffraction analysis of the CCPs of the four NEPE propellants under 3 MPa revealed the presence of both Al and Al2O3. With the increase of the particle size of Al and AP, the oxidation degree of aluminum particles decreases. The particle size of the CCPs of the four different formulations of NEPE propellants under 1 and 3 MPa was analyzed by using a laser particle size analyzer, it is found that the increase of AP particle size is helpful to reduce the size of condensate combustion products. Based on the classical pocket theory, establishing a new agglomeration size prediction model, which can be used to predict the agglomeration size on the burning surface. Compared with the empirical model, the new agglomeration size prediction model is in good agreement with the experimental results.

Model based on the automated AI-driven CT quantification is effective for the diagnosis of refractory Mycoplasma pneumoniae pneumonia.

The prediction of refractory Mycoplasma pneumoniae pneumonia (RMPP) remains a clinically significant challenge. This study aimed to develop an early predictive model utilizing artificial intelligence (AI)-derived quantitative assessment of lung lesion extent on initial computed tomography (CT) scans and clinical indicators for RMPP in pediatric inpatients. A retrospective cohort study was conducted on patients with M. pneumoniae pneumonia (MP) admitted to the Children's Hospital of Nanjing Medical University, China from January 2019 to December 2020. An early prediction model was developed by stratifying the patients with Mycoplasma pneumoniae pneumonia (MPP) into two cohorts according to the presence or absence of refractory pneumonia. A retrospective cohort of 126 children diagnosed with Mycoplasma pneumoniae pneumonia (MPP) was utilized as a training set, with 85 cases classified as RMPP. Subsequently, a prospective cohort comprising 54 MPP cases, including 37 instances of RMPP, was assembled as a validation set to assess the performance of the predictive model for RMPP from January to December 2021. We defined a constant Φ which can combine the volume and CT value of pulmonary lesions and be further used to calculate the logarithm of Φ to the base of 2 (Log2Φ). A clinical-imaging prediction model was then constructed utilizing Log2Φ and clinical characteristics. Performance was evaluated by the area under the receiver operating characteristic curve (AUC). The clinical model demonstrated AUC values of 0.810 and 0.782, while the imaging model showed AUC values of 0.764 and 0.769 in the training and test sets, respectively. The clinical-imaging model, incorporating Log2Φ, temperature(T), aspartate aminotransferase (AST), preadmission fever duration (PFD), and preadmission macrolides therapy duration (PMTD), achieved the highest AUC values of 0.897 and 0.895 in the training and test sets, respectively. A prognostic model developed through automated quantification of lung disease on CT scans, in conjunction with clinical data in MPP may be utilized for the early identification of RMPP.

Assessing the Impact of an Artificial Intelligence-Based Model for Intracranial Aneurysm Detection in CT Angiography on Patient Diagnosis and Outcomes (IDEAL Study)-a protocol for a multicenter, double-blinded randomized controlled trial.

BACKGROUND: This multicenter, double-blinded, randomized controlled trial (RCT) aims to assess the impact of an artificial intelligence (AI)-based model on the efficacy of intracranial aneurysm detection in CT angiography (CTA) and its influence on patients' short-term and long-term outcomes. METHODS: Study design: Prospective, multicenter, double-blinded RCT. SETTINGS: The model was designed for the automatic detection of intracranial aneurysms from original CTA images. PARTICIPANTS: Adult inpatients and outpatients who are scheduled for head CTA scanning. Randomization groups: (1) Experimental Group: Head CTA interpreted by radiologists with the assistance of the True-AI-integrated intracranial aneurysm diagnosis strategy (True-AI arm). (2) Control Group: Head CTA interpreted by radiologists with the assistance of the Sham-AI-integrated intracranial aneurysm diagnosis strategy (Sham-AI arm). RANDOMIZATION: Block randomization, stratified by center, gender, and age group. PRIMARY OUTCOMES: Coprimary outcomes of superiority in patient-level sensitivity and noninferiority in specificity for the True-AI arm to the Sham-AI arm in intracranial aneurysms. SECONDARY OUTCOMES: Diagnostic performance for other intracranial lesions, detection rates, workload of CTA interpretation, resource utilization, treatment-related clinical events, aneurysm-related events, quality of life, and cost-effectiveness analysis. BLINDING: Study participants and participating radiologists will be blinded to the intervention. SAMPLE SIZE: Based on our pilot study, the patient-level sensitivity is assumed to be 0.65 for the Sham-AI arm and 0.75 for the True-AI arm, with specificities of 0.90 and 0.88, respectively. The prevalence of intracranial aneurysms for patients undergoing head CTA in the hospital is approximately 12%. To establish superiority in sensitivity and noninferiority in specificity with a margin of 5% using a one-sided α = 0.025 to ensure that the power of coprimary endpoint testing reached 0.80 and a 5% attrition rate, the sample size was determined to be 6450 in a 1:1 allocation to True-AI or Sham-AI arm. DISCUSSION: The study will determine the precise impact of the AI system on the detection performance for intracranial aneurysms in a double-blinded design and following the real-world effects on patients' short-term and long-term outcomes. TRIAL REGISTRATION: This trial has been registered with the NIH, U.S. National Library of Medicine at ClinicalTrials.gov, ID: NCT06118840 . Registered 11 November 2023.

Application of bolus tracking: The effect of ROI positions on the images quality of cervicocerebral CT angiography.

Background: Cervicocerebral CT angiography (CTA) using the bolus tracking technique has been widely used for the assessment of cerebrovascular diseases. Regions of interest (ROI) can be placed in the descending aorta, ascending aorta, and the aortic arch. However, no study has compared the arteries and veins display when when the region of interest (ROI) is placed at different sites. In this study, we showed the impact of ROI positions on the image quality of cervicocerebral CTA. Methods: Two hundred and seventy patients who underwent cervicocerebral CTA with bolus tracking technique were randomly divided into three groups based on the position of the ROI placement: ascending aorta (Group 1, n = 90), aortic arch (Group 2, n = 90), and descending aorta (Group 3, n = 90). The scanning parameters and contrast agent injection protocols were consistent across all groups. Three observers independently assessed the objective image quality, while two observers jointly assessed the subjective image quality using a grade scale: poor (grade 1), average (grade 2), good (grade 3), and excellent (grade 4). The differences in intravascular CT values, signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), AVCR (arterial venous contrast ratio), and subjective image quality scores were compared among the three groups. Results: The CT values of the intracranial veins (superior sagittal sinus, ethmoid sinus and great cerebral vein) in group 1 were significantly lower than those in group 3 (p < 0.001). However, no significant differences were observed in CT values, SNR and CNR in the internal carotid artery and middle cerebral artery among the three groups. The proportion of images with grade 4 was significantly higher in group 1 than group 2 and 3 (41.1% vs 15.6% and 13.3%, p < 0.001). The proportion of images with grade 1 was significantly lower in group 1 than group 2 and 3 (1.1% vs 6.6% and 17.8%, p < 0.001). Conclusion: The ROI positions for cervicocerebral CTA did not affect the arterial image quality, but venous structures imaging was affected when the ROI was placed in the ascending aorta.

Diagnostic and prognostic performance of artificial intelligence-based fully-automated on-site CT-FFR in patients with CAD.

Currently, clinically available coronary CT angiography (CCTA) derived fractional flow reserve (CT-FFR) is time-consuming and complex. We propose a novel artificial intelligence-based fully-automated, on-site CT-FFR technology, which combines the automated coronary plaque segmentation and luminal extraction model with reduced order 3 dimentional (3D) computational fluid dynamics. A total of 463 consecutive patients with 600 vessels from the updated China CT-FFR study in Cohort 1 undergoing both CCTA and invasive fractional flow reserve (FFR) within 90 d were collected for diagnostic performance evaluation. For Cohort 2, a total of 901 chronic coronary syndromes patients with index CT-FFR and clinical outcomes at 3-year follow-up were retrospectively analyzed. In Cohort 3, the association between index CT-FFR from triple-rule-out CTA and major adverse cardiac events in patients with acute chest pain from the emergency department was further evaluated. The diagnostic accuracy of this CT-FFR in Cohort 1 was 0.82 with an area under the curve of 0.82 on a per-patient level. Compared with the manually dependent CT-FFR techniques, the operation time of this technique was substantially shortened by 3 times and the number of clicks from about 60 to 1. This CT-FFR technique has a highly successful (> 99%) calculation rate and also provides superior prediction value for major adverse cardiac events than CCTA alone both in patients with chronic coronary syndromes and acute chest pain. Thus, the novel artificial intelligence-based fully automated, on-site CT-FFR technique can function as an objective and convenient tool for coronary stenosis functional evaluation in the real-world clinical setting.

Deep Learning for Chest X-ray Diagnosis: Competition Between Radiologists with or Without Artificial Intelligence Assistance.

This study aimed to assess the performance of a deep learning algorithm in helping radiologist achieve improved efficiency and accuracy in chest radiograph diagnosis. We adopted a deep learning algorithm to concurrently detect the presence of normal findings and 13 different abnormalities in chest radiographs and evaluated its performance in assisting radiologists. Each competing radiologist had to determine the presence or absence of these signs based on the label provided by the AI. The 100 radiographs were randomly divided into two sets for evaluation: one without AI assistance (control group) and one with AI assistance (test group). The accuracy, false-positive rate, false-negative rate, and analysis time of 111 radiologists (29 senior, 32 intermediate, and 50 junior) were evaluated. A radiologist was given an initial score of 14 points for each image read, with 1 point deducted for an incorrect answer and 0 points given for a correct answer. The final score for each doctor was automatically calculated by the backend calculator. We calculated the mean scores of each radiologist in the two groups (the control group and the test group) and calculated the mean scores to evaluate the performance of the radiologists with and without AI assistance. The average score of the 111 radiologists was 597 (587-605) in the control group and 619 (612-626) in the test group (P < 0.001). The time spent by the 111 radiologists on the control and test groups was 3279 (2972-3941) and 1926 (1710-2432) s, respectively (P < 0.001). The performance of the 111 radiologists in the two groups was evaluated by the area under the receiver operating characteristic curve (AUC). The radiologists showed better performance on the test group of radiographs in terms of normal findings, pulmonary fibrosis, heart shadow enlargement, mass, pleural effusion, and pulmonary consolidation recognition, with AUCs of 1.0, 0.950, 0.991, 1.0, 0.993, and 0.982, respectively. The radiologists alone showed better performance in aortic calcification (0.993), calcification (0.933), cavity (0.963), nodule (0.923), pleural thickening (0.957), and rib fracture (0.987) recognition. This competition verified the positive effects of deep learning methods in assisting radiologists in interpreting chest X-rays. AI assistance can help to improve both the efficacy and efficiency of radiologists.

Rapid Access to Chiral Spiro[2.3] Lactams: Stereoselective Hydroborylation and Hydrosilylation and Remote Control of Axial Chirality by Copper-Catalyzed Desymmetrization of Spirocyclopropenes.

Chiral spirocyclopropyl ß-lactams are common motifs in bioactive compounds and pharmaceuticals. Here we disclose a diastereoselective and enantioselective hydroborylation and hydrosilylation of spirocyclopropenes, via a Cu-catalyzed desymmetrization strategy, for the rapid preparation of enantio-enriched spirocyclopropyl ß-lactams. The efficient desymmetrization strategy allows the remote control of axial chirality, offering the borylated and silylated products bearing central, spiro, and axial chirality. The combination of multichiral elements would provide a novel motif for biological evaluation in potential drug discovery.

Coronary chronic total occlusion on coronary CT angiography: what radiologists should know?

Coronary chronic total occlusion (CTO) often occurs in patients with obstructive coronary artery disease, which remains one of the greatest challenges for interventional cardiologists. Coronary computed tomography angiography (CCTA) with its emerging post-processing techniques can provide a detailed assessment of CTO lesions before percutaneous coronary intervention (PCI), playing an important role in the clinical management of CTO PCI, from early diagnosis, pre-procedural outcome prediction, the crossing algorithm planning, intraprocedural guidance, and finally post-procedural assessment and follow-up. In addition, the feasibility of CT perfusion (CTP) in patients with CTO has been validated. Combined CCTA and CTP have the great potential to be the one-stop-shop imaging modality for assessing both anatomy and function of CTO lesions. This review aims to make radiologists understand the role of CCTA in the diagnosis and assessment of CTO lesions, thus assisting interventionalists in optimizing CTO PCI crossing strategies with the expertise of radiologists.Critical relevance statement The anatomical features of CTO on CCTA can reveal the complexity of CTO lesions and are associated with CTO PCI outcome, thus helping interventionalists optimize CTO PCI crossing strategies.Key points • CTO is the common lesion in invasive coronary angiography, and CTO PCI is technically difficult and its success rate is relatively low.• Length, collaterals, and attenuation-related signs can help distinguish CTO from subtotal occlusion.• The anatomical features of CTO lesions can help grade the difficulty of CTO PCI and predict procedural outcomes and long-term outcomes of CTO PCI.• The real-time fusion of CCTA with fluoroscopic angiography can be applied in highly complicated CTO lesions.• After CTO PCI, CCTA can help guide a second CTO PCI re-entry or follow up stent patency.

Edge-Type Hyperintense Intracranial Artery Plaque: A Potential MRI Biomarker of Stroke Recurrence.

BACKGROUND: Identifying patients at high risk of stroke recurrence is important for stroke prevention and treatment. PURPOSE: To explore the characteristics of T1 hyperintense plaques (HIP) and their relationship with stroke recurrence in patients with symptomatic intracranial atherosclerotic stenosis (sICAS). STUDY TYPE: Retrospective. POPULATION: One hundred fifty-seven patients with moderate-to-severe (≥50%) nonocclusive sICAS and MRI studies (42 females and 115 males, mean age 58.69 ± 10.68 years). FIELD STRENGTH/SEQUENCE: 3D higher-resolution black-blood T1-weighted fast-spin-echo sequence at 3.0 T. ASSESSMENT: HIP (signal intensity [SI] of plaque-to-adjacent gray matter >1.0 on non-contrast T1-weighted images) and non-HIP plaques were identified. HIP plaques were categorized as edge type (high SI adjacent to lumen) and non-edge type (high SI within plaque). Clinical and imaging features of different plaque types were compared. Stroke recurrence was assessed through telephone or medical records at 3 and 6 months, and then once a year post-MRI. The relationship between edge type and non-edge types HIP with stroke recurrence was analyzed. STATISTICAL TESTS: Student's t test, Mann-Whitney U-test, chi square test and Fisher's exact test to compare features between plaque types. Kaplan-Meier curves (with log-rank tests) and Cox proportional hazards regression to assess relationship between stroke recurrence and different plaque types. A two-tailed P-value of <0.05 was considered statistically significant. RESULTS: Of 157 culprit lesions, 87 (55%) were HIPs (43 edge type, 44 non-edge type) and 70 (45%) were non-HIPs. Plaque thickness, area, and volume were significantly higher for HIPs than for non-HIPs. Among patients with HIPs, edge type was significantly more likely in the posterior circulation (53.5% vs. 27.3%), and had significantly higher plaque thickness, length, area, volume, plaque burden, and remodeling index than non-edge type. Edge-type HIP was significantly more common than non-edge HIP in patients with diabetes mellitus (51.2% vs. 29.5%) and dyslipidemia (79.1% vs. 54.5%). During median follow-up of 27 months, 33 patients experienced stroke recurrence. Recurrence was associated with edge-type HIP (adjusted hazard ratio = 2.83; 95% confidence interval: 1.40-5.69), both in the overall cohort (34.9% vs. 15.8%) and in patients with HIP (34.9% vs. 9.0%). Age ≥60 years and edge-type HIP had a significant interaction. DATA CONCLUSIONS: Hyperintense plaque may be categorized as edge type or non-edge type. Edge-type HIP may be a potential MRI biomarker of stroke recurrence. EVIDENCE LEVEL: 3 TECHNICAL EFFICACY: Stage 2.

Immune characteristics of dedifferentiated retroperitoneal liposarcomas and the reliability of regional samples in evaluating their tumor immune microenvironments.

BACKGROUND: Tumor immunotherapy is a new treatment breakthrough for retroperitoneal liposarcoma (RPLS), which is highly invasive and has few effective treatment options other than tumor resection. However, the heterogeneity of the tumor immune microenvironment (TIME) leads to missed clinical diagnosis and inappropriate treatment. Therefore, it is crucial to evaluate whether the TIME of a certain part of the tumor reliably represents the whole tumor, particularly for very large tumors, such as RPLS. METHODS: We conducted a prospective study to evaluate the TIME in different regions of dedifferentiated RPLS (DDRPLS) by detecting the expressions of markers such as CD4+, CD8+, Foxp3+, CD20+, CD68+, LAMP3+, PD-1+ tumor-infiltrating lymphocytes (TILs), and PD-L1 in tumors and corresponding paratumor tissues via immunohistochemistry and RNA sequencing. RESULTS: In DDRPLS, very few TILs were observed. Differentially expressed genes were significantly enriched in cell part and cell functions, as well as the metabolic pathway and PI3K-Akt signaling pathway. In addition, for most tumors (70-80%), the TIME was similar in different tumor regions. CONCLUSIONS: For most tumors (70-80%), the TIME in any region of the tumor reliably represents the whole tumor. DDRPLS may regulate cell functions by modulating the metabolic and PI3K-Akt signaling pathways to promote its malignant behavior.

CT-derived fractional flow reserve in intracranial arterial stenosis: A pilot study based on computational fluid dynamics.

BACKGROUND: CT-derived fractional flow reserve (CT-FFR) has been widely applied in coronary hemodynamic assessment. However, the feasieablity and standardization measurement in intracranial artery stenosis (ICAS) remains to be defined. PURPOSE: To demonstrate the feasibility of CT-FFR in ICAS functional assessment and explore the optimal CT-FFR measurement position with invasive FFR as reference standard. MATERIALS AND METHODS: Nineteen patients (mean age, 58.6 years ± 1.9 [SD]; 13 men) with moderate to severe (≥50 %) ICAS undergoing guidewire-based pressure measurement and preoperative head CT angiography (CTA) were retrospectively enrolled. CT-FFR was measured in the following standard measurement positions, including the end of stenosis (D0), 1 cm distal to the stenosis (D1) and 2 cm distal to the stenosis (D2). Diagnostic performance of CT-FFR was assessed by the area under the curve (AUC) of receiver operating characteristic curves by assuming invasive FFR ≤ 0.80 or 0.75 as hemodynamically significant stenosis. RESULTS: Excellent intra- and inter-observer agreement (ICC range, 0.930-0.992) was observed for CT-FFR measurement in different positions. Under different FFR thresholds, the diagnostic performance of CT-FFRD1 showed perfect prediction with AUC values of 1.000 (95 % CI: 0.824, 1.000). The sensitivity, specificity and AUC of CT-FFRD1 ≤ 0.80 in detecting FFR ≤ 0.80 was 0.94 (95 % CI: 0.68, 1.00), 1.00 (95 % CI: 0.31, 1.00) and 0.969 (95 % CI: 0.772, 1.000), respectively. Similar performance of CT-FFRD1 ≤ 0.75 was obtained for identifying FFR ≤ 0.75 with the AUC of 0.964. The strongest correlation (r = 0.915, p < 0.001) and agreement (mean difference: 0.02, 95 % limits of agreement: -0.16 to 0.19) were observed between CT-FFRD1 and FFR. CONCLUSION: Cerebral CT-derived fractional flow reserve (CT-FFR) measured 1 cm distal to stenosis achieved the most comparable results with invasive FFR, which indicated its potentially promising clinical application for evaluating the functional relevance of intracranial artery stenosis.

Stent-specific fat attenuation index is associated with target vessel revascularization after PCI.

OBJECTIVES: To explore the clinical relevance of stent-specific perivascular fat attenuation index (FAI) in patients with stent implantation. METHODS: A total of 162 consecutive patients who underwent coronary computed tomography angiography (CCTA) following stent implantation were retrospectively included. The stent-specific FAI at 2 cm adjacent to the stent edge was calculated. The endpoints were defined as target vessel revascularization (TVR) on the stented vessel after CCTA and readmission times due to chest pain after stent implantation. Binary logistic regression analysis for TVR and ordinal regression models were conducted to identify readmission times (0, 1, and ≥ 2) with generalized estimating equations on a per-stent basis. RESULTS: On a per-stent basis, 9 stents (4.5%) experienced TVR after PCI at a median 30 months' follow-up duration. Stent-specific FAI differed significantly among subgroups of patients with stent implantation and different readmission times (p = 0.002); patients with at least one readmission had higher stent-specific FAI than those without readmission (p < 0.001). Bifurcated stents (odds ratio [OR]: 11.192, p = 0.001) and stent-specific FAI (OR: 1.189, p = 0.04) were independently associated with TVR. With no readmission as a reference, stent-specific FAI (OR: 0.984, p = 0.007) was an independent predictor for hospital readmission times ≥ 2 (p = 0.003). CONCLUSION: Non-invasive stent-specific FAI derived from CCTA was found to be associated with TVR, which was a promising imaging marker for functional assessment in patients who underwent stent implantation. CLINICAL RELEVANCE STATEMENT: Noninvasive fat attenuation index adjacent to the stents edge derived from CCTA, an imaging marker reflecting the presence of inflammation acting on the neointimal tissue at the sites of coronary stenting, might be relevant clinically with target vessel revascularization. KEY POINTS: • Non-invasive stent-specific FAI derived from CCTA was associated with TVR (OR: 1.189 [95% CI: 1.007-1.043], p = 0.04) in patients who underwent stent implantation. • Stent-specific FAI significantly differed among a subgroup of patients with chest pain after stent implantation and with different readmission times (p = 0.002); the patients with at least one readmission had higher stent-specific FAI than those without readmission (p < 0.001). • Non-invasive stent-specific FAI derived from CCTA could be used as an imaging maker for the functional assessment of patients following stent implantation.

Distinguishing Intracranial Diabetes-Related Atherosclerotic Plaques: A High-Resolution Magnetic Resonance Imaging-Based Radiomics Study.

INTRODUCTION: Diabetes markedly affects the formation and development of intracranial atherosclerosis. The study was aimed at evaluating whether radiomics features can help distinguish plaques primarily associated with diabetes. MATERIALS AND METHODS: We retrospectively analyzed patients who were admitted to our center because of acute ischemic stroke due to intracranial atherosclerosis between 2016 and 2022. Clinical data, blood biomarkers, conventional plaque features, and plaque radiomics features were collected for all patients. Odds ratios (ORs) with 95% confidence intervals (CIs) were determined from logistic regression models. The receiver operating characteristic (ROC) curve and area under the ROC curve (AUC) were used to describe diagnostic performance. The DeLong test was used to compare differences between models. RESULTS: Overall, 157 patients (115 men; mean age, 58.7 ± 10.7 years) were enrolled. Multivariate logistic regression analysis showed that plaque length (OR: 1.17; 95% CI: 1.07-1.28) and area (OR: 1.13; 95% CI: 1.02-1.24) were independently associated with diabetes. On combining plaque length and area as a conventional model, the AUCs of the training and validation cohorts for identifying diabetes patients were 0.789 and 0.720, respectively. On combining radiomics features on T1WI and contrast-enhanced T1WI sequences, a better diagnostic value was obtained in the training and validation cohorts (AUC: 0.889 and 0.861). The DeLong test showed the model combining radiomics and conventional plaque features performed better than the conventional model in both cohorts (p < 0.05). CONCLUSIONS: The use of radiomics features of intracranial plaques on high-resolution magnetic resonance imaging can effectively distinguish culprit plaques with diabetes as the primary pathological cause, which will provide new avenues of research into plaque formation and precise treatment.

Transition-metal-free and additive-free intermolecular hydroarylation of alkenes with indoles in hexafluoroisopropanol.

Hydroarylation of alkenes is one of the most straightforward and atom-economical strategy for the construction of multi-aryl-substituted alkanes, but systematic studies have been limited to transition metal catalysis. Here we report a hexafluoroisopropanol (HFIP)-promoted hydroarylation of alkenes with indoles without the presence of transition metal catalysts or any additive. HFIP was the only reagent used in this work, and could be easily removed via evaporation, and recovered via distillation in industry settings. This reaction was shown to provide an efficient, clean and operationally simple procedure with a remarkable substrate scope and versatile transformations, delivering a variety of multi-aryl alkanes incorporating the indole motif. In preliminary studies, several of these products showed biologically activity against cells from an array of human cancer cell lines. A mechanistic study was also carried out and suggested that the quinone methide might be the key intermediate. And in contrast to the conclusions of a previous report, the current work suggested that protonation by HFIP might not be the rate-determining step.

Silver(I)-Catalyzed Diastereoselective Hydroborylation of Cyclopropenes.

An effective (NHC)AgCl catalysis was developed in the hydroborylation of cyclopropenes with B2pin2, delivering a variety of cyclopylboronates in a stereoselective manner, which could be easily transformed for the construction of versatile cyclopropanes. This protocol works effectively under mild reaction conditions in an open-air atmosphere, and it was easy to apply on a gram scale. This novel method in detail was also explored by control experiments, providing a number of key insights. The kinetic process followed by 1H NMR indicated that the reaction was finished in 15 min. Furthermore, the mechanism of silver(I)-catalyzed hydroborylation of cyclopropenes was proposed, with the protonation by methanol as the rate-determining step.

Burden and distribution of monosodium urate deposition in patients with hyperuricemia and gout: a cross-sectional Chinese population-based dual-energy CT study.

Background: To investigate the distribution and burden of monosodium urate (MSU) deposition in hyperuricemia and gout patients with dual-energy computed tomography (DECT). Methods: A total of 1,936 consecutive patients from January 1, 2009, to September 15, 2017, underwent DECT examinations in Jinling Hospital. Of these, 1,294 patients were excluded due to other clinical diagnoses (n=1,041), inappropriate locations (n=82), poor-quality images (n=105), training cases (n=30) and duplicated data (n=36). Finally, 642 patients were included in this study. We retrospectively analyzed 1,127 DECT examinations in 642 consecutive patients (hyperuricemia group, n=121; gout group, n=521) and recorded the volume and number of MSU deposits. For each anatomical location, we recorded MSU deposition in the soft tissue and joint cavity. MSU deposition was analyzed and compared between groups. For normally distributed data, independent sample t-tests were used for comparison between the two groups. The independent samples nonparametric test was used to analyze nonnormally distributed data. Results: (I) The burden of MSU deposition in the gout group {volume [0.14 (0.04-1.36)] and numbers [10.00 (5.00-19.00)]} was significantly higher than that {volume [0.08 (0.02-0.47), P=0.003] and numbers [9.50 (2.00-16.00), P=0.01]} in the hyperuricemia group. (II) The burden of MSU deposition in the knees {volume [0.24 (0.01-1.79), P=0.002] and quantity [6.00 (2.00-12.00), P=0.04]} and feet {volume [0.10 (0.04-0.66)] and number [9.00 (5.00-15.00)]} was significantly higher in the gout group than those {knees: the volume [0.03 (0.00-0.27), P=0.002] and the quantity [4.00 (0.00-9.00), P=0.04]; feet: the volume [0.07 (0.02-0.19), P=0.003)] and number [8.00 (2.25-12.00), P=0.04]} in the hyperuricemia group, respectively. (III) In the hyperuricemia group, the volume of MSU deposition was significantly higher in the soft tissues of the knee (0.022±0.042) and ankle (0.062±0.305) than in those (knee: 0.001±0.005, P=0.02; ankle: 0.027±0.234, P=0.02) in the joint cavity. Conclusions: Although subclinical urate deposition can occur in patients with asymptomatic hyperuricemia, the burden of urate deposition is greater in patients with symptomatic gout, and the distribution is more pronounced in the foot/knee. Thus, more effective patient management and monitoring can be achieved by measuring the burden of MSU deposits in the patient's feet/knees. These data suggest that a threshold for urate crystal volume at typical sites may be required before symptomatic disease develops.

A CT-Based Radiomics Model for Prediction of Prognosis in Patients with Novel Coronavirus Disease (COVID-19) Pneumonia: A Preliminary Study.

This study aimed to develop a computed tomography (CT)-based radiomics model to predict the outcome of COVID-19 pneumonia. In total of 44 patients with confirmed diagnosis of COVID-19 were retrospectively enrolled in this study. The radiomics model and subtracted radiomics model were developed to assess the prognosis of COVID-19 and compare differences between the aggravate and relief groups. Each radiomic signature consisted of 10 selected features and showed good performance in differentiating between the aggravate and relief groups. The sensitivity, specificity, and accuracy of the first model were 98.1%, 97.3%, and 97.6%, respectively (AUC = 0.99). The sensitivity, specificity, and accuracy of the second model were 100%, 97.3%, and 98.4%, respectively (AUC = 1.00). There was no significant difference between the models. The radiomics models revealed good performance for predicting the outcome of COVID-19 in the early stage. The CT-based radiomic signature can provide valuable information to identify potential severe COVID-19 patients and aid clinical decisions.

Incremental value of enhanced plaque length for identifying intracranial atherosclerotic culprit plaques: a high-resolution magnetic resonance imaging study.

OBJECTIVES: Besides plaque enhancement grade, the incremental value of enhancement-related high-resolution MRI features in defining culprit plaques needs further evaluation. This study was focused on assessing whether plaque enhancement features contribute to culprit plaque identification and further risk stratification. METHODS: We retrospectively studied patients who experienced an acute ischaemic stroke and transient ischaemic attack due to intracranial atherosclerosis from 2016 to 2022. The enhancement features included enhancement grade, enhanced length, and enhancement quadrant. Associations between plaque enhancement features and culprit plaques, as well as diagnostic value, were investigated using logistic regression and receiver operating characteristic analyses. RESULTS: Overall, 287 plaques were identified, of which 231 (80.5%) and 56 (19.5%) were classified as culprit and non-culprit plaques, respectively. Comparison of the pre- and post-enhancement images revealed enhanced length longer than the plaque length in 46.32% of the culprit plaques. Multivariate logistic regression showed that enhanced length longer than plaque length (OR 6.77; 95% CI 2.47-18.51) and grade II enhancement (OR 7.00; 95% CI 1.69-28.93) were independently associated with culprit plaques. The area under the curve value for the combination of stenosis and plaque enhancement grade for the diagnosis of culprit plaques was 0.787, which increased significantly to 0.825 on the addition of enhanced length longer than the plaque length (p = 0.026 for DeLong's test). CONCLUSIONS: Enhanced length longer than the plaque length and grade II enhancement were independently associated with culprit plaques. The combination of the enhanced plaque features resulted in better culprit plaque identification.

Development and validation of a nonenhanced CT based radiomics model to detect brown adipose tissue.

Purpose: It has been reported that brown adipose tissue (BAT) has a protective effect regarding cardiovascular disease. Positron emission tomography-computed tomography (PET-CT) is the reference method for detecting active BAT; however, it is not feasible to screen for BAT due to the required radionuclides and high-cost. The purpose of this study is to develop and validate a nonenhanced CT based radiomics model to detect BAT and to explore the relationship between CT radiomics derived BAT and cardiovascular calcification. Patients and methods: 146 patients undergoing 18F-FDG PET-CT were retrospectively included from two centers for model development (n = 86) and external validation (n = 60). The data for the model development were randomly divided into a training cohort and an internal validation cohort with a 7:3 ratio, while the external validation data were divided 1:1 into a propensity score matching (PSM) cohort and a randomly sex matched cohort. Radiomics features of BAT and non-BAT depots were extracted from regions of interest (ROI) on nonenhanced CT corresponding to PET studies. Inter-class correlation coefficient (ICC) and Pearson's correlation analysis were performed to select radiomics features with high consistency. Next, least absolute shrinkage and selection operator (LASSO) with linear regression model was used to select radiomics features for model construction. Support vector machine (SVM) was used to develop the model and a radiomics score (RS) was calculated for each depot. The diagnostic performance of the radiomics model was evaluated both on a per-depot and per-patient basis by calculating the area under the receiver operating characteristic curve (AUROC). We further divided patients into BAT-RS group and non-BAT-RS group based on radiomics score and compared their cardiovascular calcification by calculating calcium volume and score. Results: A total of 22 radiomics features were selected for model construction. On a per-depot basis, the AUROCs were 0.87 (95% CI: 0.83-0.9), 0.85 (95% CI: 0.79-0.90), 0.72 (95% CI: 0.67-0.77) and 0.74 (95% CI: 0.69-0.79) for detecting BAT in the training, internal validation, external validation 1 and external validation 2 cohorts, respectively. On a per-patient basis, the radiomics model had high AUROCs of 0.91 (95% CI: 0.84-0.98), 0.77 (95% CI: 0.61-0.92) and 0.85 (95% CI: 0.72-0.98) in the training, external validation 1 and external validation 2 cohorts, respectively. When grouping based on the radiomics model, the BAT-RS group had lower odds of coronary artery calcium (CAC) and thoracic aorta calcium (TAC) compared with the non-BAT-RS group (CAC: 2.8% vs. 20.3%, p = 0.001; TAC: 19.4% vs. 39.2%, p = 0.009). The BAT-RS group had less CAC volume (4.1 ± 4.0 mm3 vs. 147.4 ± 274.3 mm3; p = 0.001), CAC score (2.8 ± 3.0 vs. 169.1 ± 311.5; p = 0.001), TAC volume (301.4 ± 450.2 mm3 vs. 635.3 ± 1100.7 mm3; p = 0.007) and TAC score (496.2 ± 132.6 vs. 749.2 ± 1297.3; p = 0.007) than the non-BAT-RS group. Conclusion: We developed and validated a nonenhanced CT based reliable radiomics model for detecting BAT with PET-CT findings as reference standard. Radiomics signatures from nonenhanced CT can reliably detect BAT and have promising potential to be used in routine clinical settings. Importantly, our study showed that patients with BAT had less cardiovascular calcification.

Keratin19 promotes pancreatic cancer progression and poor prognosis via activating the Hedgehog pathway.

Pancreatic cancer is a serious threat to human health, with strong invasiveness, rapid progression and poor prognosis. Tumors expressing keratin 19 (K19) have stronger invasiveness and a worse prognosis. However, the role and mechanism of K19 in pancreatic cancer have remained largely elusive. In the present study, K19 expression was detected in pancreatic cancer tissues, its effect on proliferation, apoptosis and metastasis of pancreatic cancer at the cellular, in vivo preclinical and clinical levels was evaluated and its effect on the Hedgehog pathway was analyzed. K19 was significantly overexpressed in pancreatic cancer, promoted pancreatic cancer proliferation and metastasis, inhibited tumor cell apoptosis and was associated with poor prognosis. Mechanistically, these effects were mediated through the activation of the Hedgehog pathway. In conclusion, K19 may be a novel target molecule for pancreatic cancer treatment.