Multi-Functional 2D Covalent Organic Frameworks with Diketopyrrolopyrrole as Electron Acceptor.

2D covalent organic framework (COF) materials with extended conjugated structure and periodic columnar π-arrays exhibit promising applications in organic optoelectronics. However, there is a scarcity of reports on optoelectronic COFs, mainly due to the lack of suitable π-skeletons. Here, two multi-functional optoelectronic 2D COFs DPP-TPP-COF and DPP-TBB-COF are constructed with diketopyrrolopyrrole as electron acceptor (A), and 1,3,6,8-tetraphenylpyrene and 1,3,5-triphenylbenzene as electron donor (D) through imine bonds. Both 2D COFs showed good crystallinities and AA stacking with a rhombic framework for DPP-TPP-COF and hexagonal one for DPP-TBB-COF, respectively. The electron D-A and ordered intermolecular packing structures endow the COFs with broad UV-vis absorptions and narrow bandgaps along with suitable HOMO/LUMO energy levels, resulting in multi-functional optoelectronic properties, including photothermal conversion, supercapacitor property, and ambipolar semiconducting behavior. Among them, DPP-TPP-COF exhibits a high photothermal conversion efficiency of 47% under 660 nm laser irradiation, while DPP-TBB-COF exhibits superior specific capacitance of 384 F g-1. Moreover, P-type doping and N-type doping are achieved by iodine and tetrakis(dimethylamino)ethylene on a single host COF, resulting in ambipolar semiconducting behavior. These results provide a paradigm for the application of multi-functional optoelectronic COF materials.

MRI-Based Radiomics and Deep Learning in Biological Characteristics and Prognosis of Hepatocellular Carcinoma: Opportunities and Challenges.

Hepatocellular carcinoma (HCC) is the fifth most common malignancy and the third leading cause of cancer-related death worldwide. HCC exhibits strong inter-tumor heterogeneity, with different biological characteristics closely associated with prognosis. In addition, patients with HCC often distribute at different stages and require diverse treatment options at each stage. Due to the variability in tumor sensitivity to different therapies, determining the optimal treatment approach can be challenging for clinicians prior to treatment. Artificial intelligence (AI) technology, including radiomics and deep learning approaches, has emerged as a unique opportunity to improve the spectrum of HCC clinical care by predicting biological characteristics and prognosis in the medical imaging field. The radiomics approach utilizes handcrafted features derived from specific mathematical formulas to construct various machine-learning models for medical applications. In terms of the deep learning approach, convolutional neural network models are developed to achieve high classification performance based on automatic feature extraction from images. Magnetic resonance imaging offers the advantage of superior tissue resolution and functional information. This comprehensive evaluation plays a vital role in the accurate assessment and effective treatment planning for HCC patients. Recent studies have applied radiomics and deep learning approaches to develop AI-enabled models to improve accuracy in predicting biological characteristics and prognosis, such as microvascular invasion and tumor recurrence. Although AI-enabled models have demonstrated promising potential in HCC with biological characteristics and prognosis prediction with high performance, one of the biggest challenges, interpretability, has hindered their implementation in clinical practice. In the future, continued research is needed to improve the interpretability of AI-enabled models, including aspects such as domain knowledge, novel algorithms, and multi-dimension data sources. Overcoming these challenges would allow AI-enabled models to significantly impact the care provided to HCC patients, ultimately leading to their deployment for clinical use. LEVEL OF EVIDENCE: 5 TECHNICAL EFFICACY: Stage 2.

Phosphorus deficiency-induced cell wall pectin demethylesterification enhances cadmium accumulation in roots of Salix caprea.

Regions affected by heavy metal contamination frequently encounter phosphorus (P) deficiency. Numerous studies highlight crucial role of P in facilitating cadmium (Cd) accumulation in woody plants. However, the regulatory mechanism by which P affects Cd accumulation in roots remains ambiguous. This study aims to investigate the effects of phosphorus (P) deficiency on Cd accumulation, Cd subcellular distribution, and cell wall components in the roots of Salix caprea under Cd stress. The results revealed that under P deficiency conditions, there was a 35.4% elevation in Cd content in roots, coupled with a 60.1% reduction in Cd content in shoots, compared to the P sufficiency conditions. Under deficient P conditions, the predominant response of roots to Cd exposure was the increased sequestration of Cd in root cell walls. The sequestration of Cd in root cell walls increased from 37.1% under sufficient P conditions to 66.7% under P deficiency, with pectin identified as the primary Cd binding site under both P conditions. Among cell wall components, P deficiency led to a significant 31.7% increase in Cd content within pectin compared to P sufficiency conditions, but did not change the pectin content. Notably, P deficiency significantly increased pectin methylesterase (PME) activity by regulating the expression of PME and PMEI genes, leading to a 10.4% reduction in the degree of pectin methylesterification. This may elucidate the absence of significant changes in pectin content under P deficiency conditions and the concurrent increase in Cd accumulation in pectin. Fourier transform infrared spectroscopy (FTIR) results indicated an increase in carboxyl groups in the root cell walls under P deficiency compared to sufficient P treatment. The results provide deep insights into the mechanisms of higher Cd accumulation in root mediated by P deficiency.

Association between Liver MRI Proton Density Fat Fraction and Liver Disease Risk.

Background A better understanding of the association between liver MRI proton density fat fraction (PDFF) and liver diseases might support the clinical implementation of MRI PDFF. Purpose To quantify the genetically predicted causal effect of liver MRI PDFF on liver disease risk. Materials and Methods This population-based prospective observational study used summary-level data mainly from the UK Biobank and FinnGen. Mendelian randomization analysis was conducted using the inverse variance-weighted method to explore the causal association between genetically predicted liver MRI PDFF and liver disease risk with Bonferroni correction. The individual-level data were downloaded between August and December 2020 from the UK Biobank. Logistic regression analysis was performed to validate the association between liver MRI PDFF polygenic risk score and liver disease risk. Mediation analyses were performed using multivariable mendelian randomization. Results Summary-level and individual-level data were obtained from 32 858 participants and 378 436 participants (mean age, 57 years ± 8 [SD]; 203 108 female participants), respectively. Genetically predicted high liver MRI PDFF was associated with increased risks of malignant liver neoplasm (odds ratio [OR], 4.5; P < .001), alcoholic liver disease (OR, 1.9; P < .001), fibrosis and cirrhosis of the liver (OR, 3.0; P < .004), fibrosis of the liver (OR, 3.6; P = .002), cirrhosis of the liver (OR, 3.8; P < .001), nonalcoholic steatohepatitis (OR, 7.7; P < .001), and nonalcoholic fatty liver disease (NAFLD) (OR, 4.4; P < .001). Individual-level evidence supported these associations after grouping participants based on liver MRI PDFF polygenic risk score (all P < .004). The mediation analysis indicated that genetically predicted high-density lipoprotein cholesterol, type 2 diabetes mellitus, and waist-to-hip ratio (mediation effects, 25.1%-46.3%) were related to the occurrence of fibrosis and cirrhosis of the liver, cirrhosis of the liver, and NAFLD at liver MRI PDFF (all P < .05). Conclusion This study provided evidence of the association between genetically predicted liver MRI PDFF and liver health. © RSNA, 2023 Supplemental material is available for this article. See also the editorials by Reeder and Starekova and Monsell in this issue.

Predicting Microvascular Invasion in Hepatocellular Carcinoma Using CT-based Radiomics Model.

Background Prediction of microvascular invasion (MVI) may help determine treatment strategies for hepatocellular carcinoma (HCC). Purpose To develop a radiomics approach for predicting MVI status based on preoperative multiphase CT images and to identify MVI-associated differentially expressed genes. Materials and Methods Patients with pathologically proven HCC from May 2012 to September 2020 were retrospectively included from four medical centers. Radiomics features were extracted from tumors and peritumor regions on preoperative registration or subtraction CT images. In the training set, these features were used to build five radiomics models via logistic regression after feature reduction. The models were tested using internal and external test sets against a pathologic reference standard to calculate area under the receiver operating characteristic curve (AUC). The optimal AUC radiomics model and clinical-radiologic characteristics were combined to build the hybrid model. The log-rank test was used in the outcome cohort (Kunming center) to analyze early recurrence-free survival and overall survival based on high versus low model-derived score. RNA sequencing data from The Cancer Image Archive were used for gene expression analysis. Results A total of 773 patients (median age, 59 years; IQR, 49-64 years; 633 men) were divided into the training set (n = 334), internal test set (n = 142), external test set (n = 141), outcome cohort (n = 121), and RNA sequencing analysis set (n = 35). The AUCs from the radiomics and hybrid models, respectively, were 0.76 and 0.86 for the internal test set and 0.72 and 0.84 for the external test set. Early recurrence-free survival (P < .01) and overall survival (P < .007) can be categorized using the hybrid model. Differentially expressed genes in patients with findings positive for MVI were involved in glucose metabolism. Conclusion The hybrid model showed the best performance in prediction of MVI. © RSNA, 2023 Supplemental material is available for this article. See also the editorial by Summers in this issue.

An imaging-based machine learning model outperforms clinical risk scores for prognosis of cirrhotic variceal bleeding.

OBJECTIVES: To develop and validate a machine learning model based on contrast-enhanced CT to predict the risk of occurrence of the composite clinical endpoint (hospital-based intervention or death) in cirrhotic patients with acute variceal bleeding (AVB). METHODS: This retrospective study enrolled 330 cirrhotic patients with AVB between January 2017 and December 2020 from three clinical centers. Contrast-enhanced CT and clinical data were collected. Centers A and B were divided 7:3 into a training set and an internal test set, and center C served as a separate external test set. A well-trained deep learning model was applied to segment the liver and spleen. Then, we extracted 106 original features of the liver and spleen separately based on the Image Biomarker Standardization Initiative (IBSI). We constructed the Liver-Spleen (LS) model based on the selected radiomics features. The performance of LS model was evaluated by receiver operating characteristics and calibration curves. The clinical utility of models was analyzed using decision curve analyses (DCA). RESULTS: The LS model demonstrated the best diagnostic performance in predicting the composite clinical endpoint of AVB in patients with cirrhosis, with an AUC of 0.782 (95% CI 0.650-0.882) and 0.789 (95% CI 0.674-0.878) in the internal test and external test groups, respectively. Calibration curves and DCA indicated the LS model had better performance than traditional clinical scores. CONCLUSION: A novel machine learning model outperforms previously known clinical risk scores in assessing the prognosis of cirrhotic patients with AVB CLINICAL RELEVANCE STATEMENT: The Liver-Spleen model based on contrast-enhanced CT has proven to be a promising tool to predict the prognosis of cirrhotic patients with acute variceal bleeding, which can facilitate decision-making and personalized therapy in clinical practice. KEY POINTS: • The Liver-Spleen machine learning model (LS model) showed good performance in assessing the clinical composite endpoint of cirrhotic patients with AVB (AUC ≥ 0.782, sensitivity ≥ 80%). • The LS model outperformed the clinical scores (AUC ≤ 0.730, sensitivity ≤ 70%) in both internal and external test cohorts.

Changes in Resting-State Neural Activity and Nerve Fibres in Ischaemic Stroke Patients with Hemiplegia.

Many neuroimaging studies have reported that stroke induces abnormal brain activity. However, little is known about resting-state networks (RSNs) and the corresponding white matter changes in stroke patients with hemiplegia. Here, we utilized functional magnetic resonance imaging (fMRI) to measure neural activity and related fibre tracts in 14 ischaemic stroke patients with hemiplegia and 12 healthy controls. Fractional amplitude of low-frequency fluctuations (fALFF) calculation and correlation analyses were used to assess the relationship between regional neural activity and movement scores. Tractography was performed using diffusion tensor imaging (DTI) data to analyse the fibres passing through the regions of interest. Compared with controls, stroke patients showed abnormal functional connectivity (FC) between some brain regions in the RSNs. The fALFF was increased in the contralesional parietal lobe, with the regional fALFF being correlated with behavioural scores in stroke patients. Additionally, the passage of fibres across regions with reduced FC in the RSNs was increased in stroke patients. This study suggests that structural remodelling of functionally relevant white matter tracts is probably an adaptive response that compensates for injury to the brain.

Clinical Value of a Novel Magnetic Resonance Imaging Protocol and Prognostic Model Establishment for Sudden Sensorineural Hearing Loss: A Prospective Study.

INTRODUCTION: Sudden sensorineural hearing loss (SSNHL) is one of the most common acute symptoms in the otolaryngology department. Etiological diagnosis is the premise of effective treatment of SSNHL, and prognostic evaluation is the key. However, most of the patients are diagnosed as idiopathic due to a lack of overall assessment, while prognostic factors of SSNHL are numerous and controversial. Our purpose was to validate the potential value of a novel three-dimensional fluid-attenuated inversion recovery (3D-FLAIR) MR protocol in SSNHL and to establish a clinical-image prognostic model for unilateral SSNHL. METHODS: This prospective study included consecutive patients from May 2019 to November 2021. Pathogenic diagnosis relied on expertise-based estimation and the associations of MR findings with clinical features of unilateral SSNHL were assessed. The prognostic evaluation of unilateral SSNHL was adopted for recovery and no recovery groups and complete and incomplete recovery groups. Significant clinical and MR features were compared and screened out by single-factor analyses. The primary clinical-image prognosis assessment model was built by multifactor logistic regression analyses. RESULTS: A total of 101 patients were enrolled in our study who acquired the correct etiological diagnosis based on the novel 3D-FLAIR MR combined with clinical examination. Among the 93 patients with unilateral SSNHL, 30.1% (28/93) showed labyrinthine abnormalities on 3D-FLAIR images. The severity of initial hearing loss in the MR+ group was worse than that in the MR- group (p < 0.05), and patients with positive MR findings tended to have poor recovery. An excellent prognostic model was built for hearing complete recovery and no recovery. The combination of three independent risk factors, including abnormal distortion products otoacoustic emission and transient evoked otoacoustic emission, the period from onset to treatment, and PTA at the onset, was adopted for hearing recovery/no recovery (accuracy = 90.2%, AUC = 0.820). Furthermore, adding the factor of positive MRI findings could improve the confidence for the judgment of hearing no recovery. The only independent risk factor, PTA at the onset, was adopted for complete/incomplete hearing recovery (accuracy = 86.1%, AUC = 0.874). CONCLUSION: The novel MR protocol had a good advantage in pathogenic diagnosis. Labyrinthine MR 3D-FLAIR signal abnormalities were related to the severity of an initial hearing loss and had a greater tendency to be found in patients with no recovery. A prognostic model with two main steps of unilateral SSNHL, mainly for SSNHL with no recovery and complete recovery, was built successfully and needed further verification by larger series of patients.

Allochthonous arbuscular mycorrhizal fungi promote Salix viminalis L.-mediated phytoremediation of polycyclic aromatic hydrocarbons characterized by increasing the release of organic acids and enzymes in soils.

Polycyclic aromatic hydrocarbons (PAHs) are well known persistent organic pollutants that have carcinogenic, teratogenic, and mutagenic effects on humans and animals. Arbuscular mycorrhizal fungi (AMF) that can infest plant hosts and form symbioses may help plants to enhance potential rhizosphere effects, thus contributing to the rhizodegradation of PAH-contaminated soils. The present study aimed to assess the effectiveness of AMF on enhancing Salix viminalis-mediated phytoremediation of PAH-polluted soil and clarify the plant enzymatic and organic acid mechanisms induced by AMF. Natural attenuation (NA), phytoremediation (P, Salix viminalis), S. viminalis-AMF combined remediation using willow inoculated with Funneliformis mosseae (PM), Laroideoglomus etunicatum (PE), and Rhizophagus intraradices (PI) were used as strategies for the remediation of PAH-polluted soils. The results showed that AMF inoculation contributed to the dissipation of the high-molecular-weight PAH benzo (α) pyrene that had concentrations in PM, PE, and PI treatments of 40.1 %, 24.49 %, and 36.28 % of the level in the NA treatment, and 62.32 %, 38.05 %, and 56.38 % of the level in the P treatment after 90 days. The mycorrhizal treatment also improved the removal efficiency of phenanthrene and pyrene, as their concentrations were sharply decreased after 30 days compared to the NA and P treatments. The research further clarified the changes in rhizosphere substances induced by AMF. Organic acids including arachidonic acid, octadecanedioic acid, α-linolenic acid, 10,12,14-octadecarachidonic acid and 5-methoxysalicylic acid that can act as co-metabolic substrates for certain microbial species to metabolize PAHs were significantly increased in AMF-inoculated treatments. AMF inoculation also elevated the levels of polyphenol oxidase, laccase, and dehydrogenase, that played crucial roles in PAHs biodegradation. These findings provide an effective strategy for using AMF-assisted S. viminalis to remediate PAH-polluted soils, and the results have confirmed the key roles of organic acids and soil enzymes in plant-AMF combined remediation of PAHs.

A Novel Approach for Screening Sericin-Derived Therapeutic Peptides Using Transcriptomics and Immunoprecipitation.

With the demand for more efficient and safer therapeutic drugs, targeted therapeutic peptides are well received due to their advantages of high targeting (specificity), low immunogenicity, and minimal side effects. However, the conventional methods of screening targeted therapeutic peptides in natural proteins are tedious, time-consuming, less efficient, and require too many validation experiments, which seriously restricts the innovation and clinical development of peptide drugs. In this study, we established a novel method of screening targeted therapeutic peptides in natural proteins. We also provide details for library construction, transcription assays, receptor selection, therapeutic peptide screening, and biological activity analysis of our proposed method. This method allows us to screen the therapeutic peptides TS263 and TS1000, which have the ability to specifically promote the synthesis of the extracellular matrix. We believe that this method provides a reference for screening other drugs in natural resources, including proteins, peptides, fats, nucleic acids, and small molecules.

Preoperative Microvascular Invasion Prediction to Assist in Surgical Plan for Single Hepatocellular Carcinoma: Better Together with Radiomics.

BACKGROUND: Prediction models with or without radiomic analysis for microvascular invasion (MVI) in hepatocellular carcinoma (HCC) have been reported, but the potential for model-predicted MVI in surgical planning is unclear. Therefore, we aimed to explore the effect of predicted MVI on early recurrence after anatomic resection (AR) and non-anatomic resection (NAR) to assist surgical strategies. METHODS: Patients with a single HCC of 2-5 cm receiving curative resection were enrolled from 2 centers. Their data were used to develop (n = 230) and test (n = 219) two prediction models for MVI using clinical factors and preoperative computed tomography images. The two prediction models, clinico-radiologic model and clinico-radiologic-radiomic (CRR) model (clinico-radiologic variables + radiomic signature), were compared using the Delong test. Early recurrence based on model-predicted high-risk MVI was evaluated between AR (n = 118) and NAR (n = 85) via propensity score matching using patient data from another 2 centers for external validation. RESULTS: The CRR model showed higher area under the curve values (0.835-0.864 across development, test, and external validation) but no statistically significant improvement over the clinico-radiologic model (0.796-0.828). After propensity score matching, difference in 2-year recurrence between AR and NAR was found in the CRR model predicted high-risk MVI group (P = 0.005) but not in the clinico-radiologic model predicted high-risk MVI group (P = 0.31). CONCLUSIONS: The prediction model incorporating radiomics provided an accurate preoperative estimation of MVI, showing the potential for choosing the more appropriate surgical procedure between AR and NAR.

Disturbed Interhemispheric Functional and Structural Connectivity in Type 2 Diabetes.

BACKGROUND: Type 2 diabetes mellitus (T2DM) is associated with cognitive decline and altered brain structure and function. However, the interhemispheric coordination of T2DM patients is unclear. PURPOSE: To investigate interhemispheric functional and anatomic connectivity in T2DM, and their associations with cognitive performance and endocrine parameters. STUDY TYPE: Prospective. SUBJECTS: 38 T2DM patients and 42 matched controls. FIELD STRENGTH/SEQUENCES: 3.0 T magnetic resonance imaging (MRI) scanner; magnetization-prepared rapid acquisition gradient echo sequence; fluid-attenuated inversion recovery sequence; single-shot, gradient-recalled echo-planar imaging sequence (resting-state functional MRI); and diffusion-weighted spin-echo-based echo-planar sequence (diffusion tensor imaging). ASSESSMENT: Voxel-mirrored homotopic connectivity (VMHC) value was calculated based on the functional images. Fibers passing through the regions with significant VMHC differences were identified using an atlas-guided track recognition. The mean fractional anisotropy (FA), mean diffusivity (MD), and fiber length were extracted and compared between the two groups. Finally, correlational analyses were performed to examine the relationships between abnormal interhemispheric connectivity, cognitive performances, and endocrine parameters. STATISTICAL TESTS: Two-sample t-tests were performed controlling for confounding factors, with partial correlation analysis. False discovery rate (FDR) correction was used for multiple comparisons. A P value <0.05 was considered statistically significant. RESULTS: T2DM patients exhibited significantly decreased VMHC between bilateral lingual gyrus and sensorimotor cortex. The fibers connecting lingual gyrus in patients showed significantly lower FA (P = 0.011) and shorter fiber length (P < 0.001), while the differences in sensorimotor fibers were insignificant (P = 0.096 for FA, P = 0.739 for fiber length and P = 0.150 for MD). The FA value in the lingual fibers was negatively correlated with insulin resistance (IR) level in T2DM group after FDR correction (R = -0.635). DATA CONCLUSION: We noted disruptions in interhemispheric coordination in T2DM patients, involving both functional and anatomical connectivities. IR might be a promising therapeutic target in the intervention of T2DM-related cognitive impairment. LEVEL OF EVIDENCE: 2 TECHNICAL EFFICACY STAGE: 2.

Beyond collaterals: brain frailty additionally improves prediction of clinical outcome in acute ischemic stroke.

OBJECTIVES: We aimed to investigate the additional significance of cerebral small vessel disease (SVD) beyond collaterals in determining the clinical outcome after acute ischemic stroke (AIS). METHODS: We retrospectively reviewed large vessel-involved stroke patients who had baseline CTA within 24 h after symptom onset and had an MRI scan 5 days after admission from October 1, 2018, to October 31, 2021. Collaterals and SVD markers (including atrophy, leukoaraiosis, lacunes, and perivascular space) were graded on CT angiography and MR images, respectively. Modified Rankin Scale (mRS) score at 90 days was recorded, and mRS ≤ 2 was regarded as a good clinical outcome. The associations between SVD markers, collaterals, and mRS were analyzed using logistic and causal mediation regression. RESULTS: We finally enrolled 119 patients (70 ± 13 years). The multivariable regression showed atrophy (evidence: OR 0.05 [95% CI 0.01-0.31], p = 0.002; severe: OR 0.08 [95% CI 0.01-0.44], p = 0.007) and evidence of lacune (OR 0.30 [95% CI 0.08-0.96], p = 0.049) were associated with poor clinical outcomes after correcting covariables. Collaterals mediated 25.74% of the effect of atrophy on poor clinical outcomes (p < 0.001), while lacune impacted clinical outcomes without collaterals' mediation effect (p = 0.54). The classification model with atrophy and lacune had a significantly higher AUC than without markers to distinguish good and poor outcomes (p = 0.036). CONCLUSIONS: Beyond collaterals, brain frailty, specifically assessed by atrophy and lacune, was essential in evaluating stroke patients and could additionally improve the stroke outcome prediction. KEY POINTS: • Beyond collaterals, brain frailty, specifically assessed by brain atrophy and lacune, was still an independent risk factor of unfavorable clinical outcomes after AIS. • Adding brain atrophy and lacune into the model has an extra benefit in predicting stroke outcomes. • The effect of atrophy on stroke outcomes was proportionally mediated through collaterals, but about three-quarters of the effect of brain atrophy and the total effect of lacune directly impacted stroke outcomes without a mediation effect of collaterals.

A Triple-Doped Phosphor Strategy for the Conversion of Cool and Warm White Light.

This work proposes a new type of Eu2+, Ce3+, Mn2+ codoped strategy that can be adapted to both ultraviolet (UV) and blue chips to achieve high-quality white light illumination. Primarily, the target sample was confirmed by X-ray diffraction (XRD) and Rietveld refinement, and the surface morphology and element distribution were observed by scanning electron microscopy (SEM). Second, the energy transfer behavior and mechanism were determined by studying double-doped samples. Lu2Mg2Al2Si2O12: Eu2+,Ce3+ (LMAS: Eu2+,Ce3+) can realize an emission color adjustment from blue to yellow. The emission color of LMAS: Ce3+,Mn2+ can be adjusted from light yellow to orange yellow. Afterward, the triple-doped sample exhibits full-spectrum emission under the excitation at 365 nm, and yellow emission under the excitation at 450 nm. When combined with a 365 nm chip, the obtained light-emitting diode (LED) devices can achieve warm white light with a color rendering index (Ra) of 96.6, light emission (LE) of 1.79 lm/W, and correlated color temperature (CCT) of 4874 K. When this phosphor was combined with a 460 nm chip, cold white light with Ra = 70, LE = 13.57 lm/W, and CCT = 5782 K can be achieved. Finally, according to the properties of the phosphor, a conceptual diagram of a new type parallel device was designed, which can easily and effectively realize the conversion of cold and warm white light. This work provides a new idea for the design of single-substrate white light phosphor and proposes a new parallel device concept, which is expected to be applied in the field of lighting.

2D Covalent Organic Frameworks as Photocatalysts for Solar Energy Utilization.

In the context of the energy crisis and global warming, developing clean and sustainable energy is receiving increasing attention. Photocatalytic processes including water splitting, CO2 reduction, coenzyme regeneration, etc., provide ideal ways to utilize renewable solar resources. The photocatalyst plays a central role in photocatalytic processes. Organic porous polymers have recently gained extensive attention in photocatalysis. Covalent organic frameworks (COFs), as one of the organic porous polymers, have the characteristics of high crystallinity, porosity, and structural designability that make them perfect platforms for photocatalysis. In this minireview, the recent progress of 2D COFs as photocatalysts is summarized including the recent work. The synthesis of the diversified structures of the COFs including the different linkages is first introduced. Then, the photocatalytic applications of the 2D COFs including photocatalytic hydrogen evolution, CO2 conversion, coenzyme regeneration, and other traditional organic reaction are then discussed. Finally, conclusions and prospects are provided in the last section.

Characterizing Diaschisis-Related Thalamic Perfusion and Diffusion After Middle Cerebral Artery Infarction.

Background and Purpose: Ipsilateral thalamic diaschisis (ITD) initially describes functional depression of the thalamus ipsilateral to a supratentorial lesion, but accumulating evidence has shown morphological changes also occur. Therefore, we aimed to characterize thalamic perfusion and diffusion related to ITD over time and their inter-relationships after middle cerebral artery infarction. Methods: Eighty-five patients with middle cerebral artery infarction who underwent diffusion kurtosis imaging and arterial spin labeling were retrospectively included. ITD was diagnosed as ipsilateral thalamic hypoperfusion present on ≥2 cerebral blood flow maps. The thalamic asymmetrical index was calculated as (ipsilateral value−contralateral value)/contralateral value×100%. Finally, the inter-relationships of thalamic perfusion and diffusion were analyzed. Results: ITD was present in 56/85 patients (65.9%, ITD+). In ITD+ patients, larger abnormal perfusion volume, higher perfusion-infarct mismatch and lower rates of focal hyperperfusion were observed than ITD− patients. Infarction affecting the corona radiata were more frequent among ITD+ patients. Mean kurtosis were slightly but significantly increased within the ipsilateral thalamus compared with the contralateral one in ITD+ patients of subacute and chronic groups, while fractional anisotropy was significantly increased in subacute group but decreased in chronic group for both ITD+ and ITD− patients. Mean diffusivity was significantly increased in ITD+ patients of chronic group. Furthermore, the AICBF was negatively and significantly correlated with AIMK and AIFA in ITD+ patients in subacute group, and AIMD, even after adjustment for abnormal perfusion volume and days from symptoms onset, in chronic group. ITD+ patients had significantly higher National Institutes of Health Stroke Scale and modified Rankin Scale scores at admission and discharge and also showed a trend to independent association with clinical outcome at discharge. Conclusions: The combination of arterial spin labeling and diffusion kurtosis imaging can reveal early, time-specific thalamic perfusion and diffusion changes after middle cerebral artery infarction. ITD-related hypoperfusion was significantly correlated with underlying microstructural alterations.

White matter degeneration in remote brain areas of stroke patients with motor impairment due to basal ganglia lesions.

Diffusion tensor imaging (DTI) studies have revealed distinct white matter (WM) characteristics of the brain following diseases. Beyond the lesion-symptom maps, stroke is characterized by extensive structural and functional alterations of brain areas remote to local lesions. Here, we further investigated the structural changes over a global level by using DTI data of 10 ischemic stroke patients showing motor impairment due to basal ganglia lesions and 11 healthy controls. DTI data were processed to obtain fractional anisotropy (FA) maps, and multivariate pattern analysis was used to explore brain regions that play an important role in classification based on FA maps. The WM structural network was constructed by the deterministic fiber-tracking approach. In comparison with the controls, the stroke patients showed FA reductions in the perilesional basal ganglia, brainstem, and bilateral frontal lobes. Using network-based statistics, we found a significant reduction in the WM subnetwork in stroke patients. We identified the patterns of WM degeneration affecting brain areas remote to the lesions, revealing the abnormal organization of the structural network in stroke patients, which may be helpful in understanding of the neural mechanisms underlying hemiplegia.

Measuring Brown Fat Using MRI and Implications in the Metabolic Syndrome.

Metabolic syndrome is presently becoming a global health concern. Brown adipose tissue (BAT) has the potential for managing the risk factors of metabolic syndrome by adjusting plasma lipids and glucose. Magnetic resonance imaging (MRI) is a noninvasive and radiation-free imaging modality for BAT research and clinical applications in both animals and humans. In the past decade, MRI technologies for detecting and characterizing BAT have developed rapidly, with progress in MRI sequencing and the emerging understanding of BAT. In this review, we focus on the main MRI methods for BAT including currently used imaging techniques and new methods and their implications for the symptoms and complications of metabolic syndrome. LEVEL OF EVIDENCE: 5 TECHNICAL EFFICACY STAGE: 2.

Comparison of MRI and CT for the Prediction of Microvascular Invasion in Solitary Hepatocellular Carcinoma Based on a Non-Radiomics and Radiomics Method: Which Imaging Modality Is Better?

BACKGROUND: Computed tomography (CT) and magnetic resonance imaging (MRI) are both capable of predicting microvascular invasion (MVI) in hepatocellular carcinoma (HCC). However, which modality is better is unknown. PURPOSE: To intraindividually compare CT and MRI for predicting MVI in solitary HCC and investigate the added value of radiomics analyses. STUDY TYPE: Retrospective. SUBJECTS: Included were 402 consecutive patients with HCC (training set:validation set = 300:102). FIELD STRENGTH/SEQUENCE: T2-weighted, diffusion-weighted, and contrast-enhanced T1-weighted imaging MRI at 3.0T and contrast-enhanced CT. ASSESSMENT: CT- and MR-based radiomics signatures (RS) were constructed using the least absolute shrinkage and selection operator regression. CT- and MR-based radiologic (R) and radiologic-radiomics (RR) models were developed by univariate and multivariate logistic regression. The performance of the RS/models was compared between two modalities. To investigate the added value of RS, the performance of the R models was compared with the RR models in HCC of all sizes and 2-5 cm in size. STATISTICAL TESTS: Model performance was quantified by the area under the receiver operating characteristic curve (AUC) and compared using the Delong test. RESULTS: Histopathologic MVI was identified in 161 patients (training set:validation set = 130:31). MRI-based RS/models tended to have a marginally higher AUC than CT-based RS/models (AUCs of CT vs. MRI, P: RS, 0.801 vs. 0.804, 0.96; R model, 0.809 vs. 0.832, 0.09; RR model, 0.835 vs. 0.872, 0.54). The improvement of RR models over R models in all sizes was not significant (P = 0.21 at CT and 0.09 at MRI), whereas the improvement in 2-5 cm was significant at MRI (P < 0.05) but not at CT (P = 0.16). DATA CONCLUSION: CT and MRI had a comparable predictive performance for MVI in solitary HCC. The RS of MRI only had significant added value for predicting MVI in HCC of 2-5 cm. LEVEL OF EVIDENCE: 3 TECHNICAL EFFICACY: Stage 2.

Sudden coma at the onset of severe refractory thrombotic thrombocytopenic purpura with successful treatment.

Most patients develop coma several days after the onset of thrombotic thrombocytopenic purpura (TTP) caused by microvascular occlusion. However, aggravated coma as the first symptom of TTP has rarely been reported. Although plasma exchange (PEX) and steroids have reduced mortality, the prognosis of patients with TTP is still poor. We reported a patient with refractory TTP presenting with aggravated coma on admission. After days of successful PEX, rituximab, and glucocorticoid therapy for clinical remission, the patient regained consciousness and returned to his normal life with a good outcome. Our case highlights that TTP should be considered when coma occurs as the first symptom.