Dynamics of epitranscriptomes uncover translational reprogramming directed by ac4C in rice during pathogen infection.

Messenger RNA modifications play pivotal roles in RNA biology, but comprehensive landscape changes of epitranscriptomes remain largely unknown in plant immune response. Here we report translational reprogramming directed by ac4C mRNA modification upon pathogen challenge. We first investigate the dynamics of translatomes and epitranscriptomes and uncover that the change in ac4C at single-base resolution promotes translational reprogramming upon Magnaporthe oryzae infection. Then by characterizing the specific distributions of m1A, 2'O-Nm, ac4C, m5C, m6A and m7G, we find that ac4Cs, unlike other modifications, are enriched at the 3rd position of codons, which stabilizes the Watson-Crick base pairing. Importantly, we demonstrate that upon pathogen infection, the increased expression of the ac4C writer OsNAT10/OsACYR (N-ACETYLTRANSFERASE FOR CYTIDINE IN RNA) promotes translation to facilitate rapid activation of immune responses, including the enhancement of jasmonic acid biosynthesis. Our study provides an atlas of mRNA modifications and insights into ac4C function in plant immunity.

Selective degradation of sulfonamide antibiotics by peracetic acid alone: Direct oxidation and radical mechanisms.

In this study, a peracetic acid (PAA) alone process was systematically demonstrated to give a high efficiency in the selective degradation of sulfonamide antibiotics (SAs). The employment of scavengers and probe compounds in this process demonstrates the predominant role of PAA in direct oxidation, and the limited role of carbon-centered radicals (R-O•) in the degradation of representative SA, sulfamethazine (SMT). The process also exhibits high tolerance towards solution pH and competing anions in wastewater, indicating its applicability in enhancing the biodegradation of SAs in wastewater. Furthermore, the relationships between the observed rate constants (kobs) and the molecule descriptors for ten SA compounds are demonstrated through the assessment of structure-activity relationships, calculated from density functional theory (DFT). This study gives new insights into the selectivity, performance and mechanism of PAA direct-oxidation in SA degradation.

Kinetics and mechanisms of non-radically and radically induced degradation of bisphenol A in a peroxymonosulfate-chloride system.

Bisphenol A, a hazardous endocrine disruptor, poses significant environmental and human health threats, demanding efficient removal approaches. Traditional biological methods struggle to treat BPA wastewater with high chloride (Cl-) levels due to the toxicity of high Cl- to microorganisms. While persulfate-based advanced oxidation processes (PS-AOPs) have shown promise in removing BPA from high Cl- wastewater, their widespread application is always limited by the high energy and chemical usage costs. Here we show that peroxymonosulfate (PMS) degrades BPA in situ under high Cl- concentrations. BPA was completely removed in 30 min with 0.3 mM PMS and 60 mM Cl-. Non-radical reactive species, notably free chlorine species, including dissolved Cl2(l), HClO, and ClO- dominate the removal of BPA at temperatures ranging from 15 to 60 °C. Besides, free radicals, including •OH and Cl2 •-, contribute minimally to BPA removal at 60 °C. Based on the elementary kinetic models, the production rate constant of Cl2(l) (32.5 M-1 s-1) is much higher than HClO (6.5 × 10-4 M-1 s-1), and its degradation rate with BPA (2 × 107 M-1 s-1) is also much faster than HClO (18 M-1 s-1). Furthermore, the degradation of BPA by Cl2(l) and HClO were enlarged by 10- and 18-fold at 60 °C compared to room temperature, suggesting waste heat utilization can enhance treatment performance. Overall, this research provides valuable insights into the effectiveness of direct PMS introduction for removing organic micropollutants from high Cl- wastewater. It further underscores the critical kinetics and mechanisms within the PMS/Cl⁻ system, presenting a cost-effective and environmentally sustainable alternative for wastewater treatment.

Deep learning-based 3D quantitative total tumor burden predicts early recurrence of BCLC A and B HCC after resection.

OBJECTIVES: This study aimed to evaluate the potential of deep learning (DL)-assisted automated three-dimensional quantitative tumor burden at MRI to predict postoperative early recurrence (ER) of hepatocellular carcinoma (HCC). MATERIALS AND METHODS: This was a single-center retrospective study enrolling patients who underwent resection for BCLC A and B HCC and preoperative contrast-enhanced MRI. Quantitative total tumor volume (cm3) and total tumor burden (TTB, %) were obtained using a DL automated segmentation tool. Radiologists' visual assessment was used to ensure the quality control of automated segmentation. The prognostic value of clinicopathological variables and tumor burden-related parameters for ER was determined by Cox regression analyses. RESULTS: A total of 592 patients were included, with 525 and 67 patients assigned to BCLC A and B, respectively (2-year ER rate: 30.0% vs. 45.3%; hazard ratio (HR) = 1.8; p = 0.007). TTB was the most important predictor of ER (HR = 2.2; p < 0.001). Using 6.84% as the threshold of TTB, two ER risk strata were obtained in overall (p < 0.001), BCLC A (p < 0.001), and BCLC B (p = 0.027) patients, respectively. The BCLC B low-TTB patients had a similar risk for ER to BCLC A patients and thus were reassigned to a BCLC An stage; whilst the BCLC B high-TTB patients remained in a BCLC Bn stage. The 2-year ER rate was 30.5% for BCLC An patients vs. 58.1% for BCLC Bn patients (HR = 2.8; p < 0.001). CONCLUSIONS: TTB determined by DL-based automated segmentation at MRI was a predictive biomarker for postoperative ER and facilitated refined subcategorization of patients within BCLC stages A and B. CLINICAL RELEVANCE STATEMENT: Total tumor burden derived by deep learning-based automated segmentation at MRI may serve as an imaging biomarker for predicting early recurrence, thereby improving subclassification of Barcelona Clinic Liver Cancer A and B hepatocellular carcinoma patients after hepatectomy. KEY POINTS: Total tumor burden (TTB) is important for Barcelona Clinic Liver Cancer (BCLC) staging, but is heterogenous. TTB derived by deep learning-based automated segmentation was predictive of postoperative early recurrence. Incorporating TTB into the BCLC algorithm resulted in successful subcategorization of BCLC A and B patients.

Apparent diffusion coefficient and tissue stiffness are associated with different tumor microenvironment features of hepatocellular carcinoma.

OBJECTIVES: To investigate associations between tissue diffusion, stiffness, and different tumor microenvironment features in resected hepatocellular carcinoma (HCC). METHODS: Seventy-two patients were prospectively included for preoperative magnetic resonance (MR) diffusion-weighted imaging and MR elastography examination. The mean apparent diffusion coefficient (ADC) and stiffness value were measured on the central three slices of the tumor and peri-tumor area. Cell density, tumor-stroma ratio (TSR), lymphocyte-rich HCC (LR-HCC), and CD8 + T cell infiltration were estimated in resected tumors. The interobserver agreement of MRI measurements and subjective pathological evaluation was assessed. Variables influencing ADC and stiffness were screened with univariate analyses, and then identified with multivariable linear regression. The potential relationship between explored imaging biomarkers and histopathological features was assessed with linear regression after adjustment for other influencing factors. RESULTS: Seventy-two patients (male/female: 59/13, mean age: 56 ± 10.2 years) were included for analysis. Inter-reader agreement was good or excellent regarding MRI measurements and histopathological evaluation. No correlation between tumor ADC and tumor stiffness was found. Multivariable linear regression confirmed that cell density was the only factor associated with tumor ADC (Estimate = -0.03, p = 0.006), and tumor-stroma ratio was the only factor associated with tumor stiffness (Estimate = -0.18, p = 0.03). After adjustment for fibrosis stage (Estimate = 0.43, p < 0.001) and age (Estimate = 0.04, p < 0.001) in the multivariate linear regression, intra-tumoral CD8 + T cell infiltration remained a significant factor associated with peri-tumor stiffness (Estimate = 0.63, p = 0.02). CONCLUSIONS: Tumor ADC surpasses tumor stiffness as a biomarker of cellularity. Tumor stiffness is associated with tumor-stroma ratio and peri-tumor stiffness might be an imaging biomarker of intra-tumoral immune microenvironment. CLINICAL RELEVANCE STATEMENT: Tissue stiffness could potentially serve as an imaging biomarker of the intra-tumoral immune microenvironment of hepatocellular carcinoma and aid in patient selection for immunotherapy. KEY POINTS: Apparent diffusion coefficient reflects cellularity of hepatocellular carcinoma. Tumor stiffness reflects tumor-stroma ratio of hepatocellular carcinoma and is associated with tumor-infiltrating lymphocytes. Tumor and peri-tumor stiffness might serve as imaging biomarkers of intra-tumoral immune microenvironment.

Procrastination in the Intention-Behaviour gap: Exercise procrastination and the moderating role of emotion.

Health behaviour procrastination is closely associated with the intention-behaviour gap. However, research on health behaviour procrastination has tended to focus on bedtime procrastination, with relatively few studies on exercise procrastination. This research examined the relationship between exercise procrastination and the intention-behaviour gap through three studies. Additionally, based on the temporal-affective self-regulation resource model, the moderating role of emotion as a self-regulatory resource in exercise procrastination was explored. Study 1 validated the Chinese version of the newly developed Procrastination in Exercise Scale in two Chinese adult samples (N = 2376 and N = 393). Study 2 collected two waves of data from 447 Chinese adults (Mage = 31.19) and examined the mediating role of exercise procrastination in the intention-behaviour gap. Using a sample of 453 Chinese adults (Mage = 20.39), Study 3 investigated the moderating role of positive and negative affect in the association between intention and exercise procrastination. Cross-lagged analyses revealed the predictive roles of Time 1 intention on Time 2 exercise procrastination and Time 1 exercise procrastination on Time 2 physical activity. Exercise procrastination mediated the relationship between intention and physical activity. Examining the moderating role of emotion between intention (Time 1) and exercise procrastination (Time 2), Study 3 found that negative affect buffered this association. Findings highlight the role of exercise procrastination in explaining the intention-behaviour gap and shed new light on physical activity interventions, with implications for promoting exercise behaviour.

New α-Glucosidase Inhibitors from the Whole Plant of Hypericum beanii Based on Ligand Fishing and Molecular Networking Analysis.

In this work, a new rapid and targeted method for screening α-glucosidase inhibitors from Hypericum beanii was developed and verified. Ten new polycyclic polyprenylated acylphloroglucinols (PPAPs), hyperlagarol A-J (1-10), and nine known PPAPs (11-19) were obtained from H. beanii. Their structures were identified by using comprehensive analyses involving mass spectrometry, ultraviolet spectroscopy, infrared spectroscopy, nuclear magnetic resonance spectroscopy, and electron capture dissociation calculations. 1 and 2 are two new rare 2,3-seco-spirocyclic PPAPs, 3 and 4 are two novel 12,13-seco-spirocyclic PPAPs, 5 and 6 are two novel spirocyclic PPAPs, 7 and 8 are two new unusual spirocyclic PPAPs with complex bridged ring systems, and 9 and 10 are two novel nonspirocyclic PPAPs. α-GC inhibitory activities of all isolated compounds were tested. Most of them displayed inhibitory activities against α-glucosidase, with the IC50 values ranging from 6.85 ± 0.65 to 112.5 ± 9.03 µM. Moreover, the inhibitory type and mechanism of the active compounds were further analyzed using kinetic studies and molecular docking.

MRI radiomics based on deep learning automated segmentation to predict early recurrence of hepatocellular carcinoma.

OBJECTIVES: To investigate the utility of deep learning (DL) automated segmentation-based MRI radiomic features and clinical-radiological characteristics in predicting early recurrence after curative resection of single hepatocellular carcinoma (HCC). METHODS: This single-center, retrospective study included consecutive patients with surgically proven HCC who underwent contrast-enhanced MRI before curative hepatectomy from December 2009 to December 2021. Using 3D U-net-based DL algorithms, automated segmentation of the liver and HCC was performed on six MRI sequences. Radiomic features were extracted from the tumor, tumor border extensions (5 mm, 10 mm, and 20 mm), and the liver. A hybrid model incorporating the optimal radiomic signature and preoperative clinical-radiological characteristics was constructed via Cox regression analyses for early recurrence. Model discrimination was characterized with C-index and time-dependent area under the receiver operating curve (tdAUC) and compared with the widely-adopted BCLC and CNLC staging systems. RESULTS: Four hundred and thirty-four patients (median age, 52.0 years; 376 men) were included. Among all radiomic signatures, HCC with 5 mm tumor border extension and liver showed the optimal predictive performance (training set C-index, 0.696). By incorporating this radiomic signature, rim arterial phase hyperenhancement (APHE), and incomplete tumor "capsule," a hybrid model demonstrated a validation set C-index of 0.706 and superior 2-year tdAUC (0.743) than both the BCLC (0.550; p < 0.001) and CNLC (0.635; p = 0.032) systems. This model stratified patients into two prognostically distinct risk strata (both datasets p < 0.001). CONCLUSION: A preoperative imaging model incorporating the DL automated segmentation-based radiomic signature with rim APHE and incomplete tumor "capsule" accurately predicted early postsurgical recurrence of a single HCC. CRITICAL RELEVANCE STATEMENT: The DL automated segmentation-based MRI radiomic model with rim APHE and incomplete tumor "capsule" hold the potential to facilitate individualized risk estimation of postsurgical early recurrence in a single HCC. KEY POINTS: A hybrid model integrating MRI radiomic signature was constructed for early recurrence prediction of HCC. The hybrid model demonstrated superior 2-year AUC than the BCLC and CNLC systems. The model categorized the low-risk HCC group carried longer RFS.

Working-Together Normative Appeals to Promote Pro-Environmental Donations.

Investigating the role of social norms in fostering pro-environmental behaviors is crucial for advancing human efforts toward environmental protection. This study employed a one-way, two-level, between-participants experimental design, focusing on the type of social norm as the independent variable and pro-environmental donations as the dependent variable. This study aimed to explore the impact of working-together normative appeals on pro-environmental donations and to understand the underlying mediating mechanism. In total, 128 Chinese university students participated in an online experiment. The findings indicated that working-together normative appeals significantly increased both the intention to donate and the actual amount of pro-environmental donations in the experimental group compared to those in the control group. Furthermore, the perceived behavioral control and intentions towards pro-environmental donations were identified as mediators in the relationship between social norm categorization and pro-environmental donations. Notably, intentions towards pro-environmental donations alone had a substantial mediating effect. These results underscore the positive influence of working-together normative appeals on pro-environmental donations and offer valuable insights into encouraging active participation in the creation of an eco-friendly society, particularly within a collectivist cultural context.

Deep learning-based compressed SENSE improved diffusion-weighted image quality and liver cancer detection: A prospective study.

PURPOSE: To assess whether diffusion-weighted imaging (DWI) with Compressed SENSE (CS) and deep learning (DL-CS-DWI) can improve image quality and lesion detection in patients at risk for hepatocellular carcinoma (HCC). METHODS: This single-center prospective study enrolled consecutive at-risk participants who underwent 3.0 T gadoxetate disodium-enhanced MRI. Conventional DWI was acquired using parallel imaging (PI) with SENSE (PI-DWI). In CS-DWI and DL-CS-DWI, CS but not PI with SENSE was used to accelerate the scan with 2.5 as the acceleration factor. Qualitative and quantitative image quality were independently assessed by two masked reviewers, and were compared using the Wilcoxon signed-rank test. The detection rates of clinically-relevant (LR-4/5/M based on the Liver Imaging Reporting and Data System v2018) liver lesions for each DWI sequence were independently evaluated by another two masked reviewers against their consensus assessments based on all available non-DWI sequences, and were compared by the McNemar test. RESULTS: 67 participants (median age, 58.0 years; 56 males) with 197 clinically-relevant liver lesions were enrolled. Among the three DWI sequences, DL-CS-DWI showed the best qualitative and quantitative image qualities (p range, <0.001-0.039). For clinically-relevant liver lesions, the detection rates (91.4%-93.4%) of DL-CS-DWI showed no difference with CS-DWI (87.3%-89.8%, p = 0.230-0.231) but were superior to PI-DWI (82.7%-85.8%, p = 0.015-0.025). For lesions located in the hepatic dome, DL-CS-DWI demonstrated the highest detection rates (94.8%-97.4% vs 76.9%-79.5% vs 64.1%-69.2%, p = 0.002-0.045) among the three DWI sequences. CONCLUSION: In patients at high-risk for HCC, DL-CS-DWI improved image quality and detection for clinically-relevant liver lesions, especially for the hepatic dome.

Application of the magnetic resonance 3D multiecho Dixon sequence for quantifying hepatic iron overload and steatosis in patients with thalassemia.

OBJECTIVE: To investigate the feasibility and diagnostic efficacy of a 3D multiecho Dixon (qDixon) research application for simultaneously quantifying the liver iron concentration (LIC) and steatosis in thalassemia patients. MATERIALS AND METHODS: This prospective study enrolled participants with thalassemia who underwent 3 T MRI of the liver for the evaluation of hepatic iron overload. The imaging protocol including qDixon and conventional T2* mapping based on 2D multiecho gradient echo (ME GRE) sequences respectively. Regions of interest (ROIs) were drawn in the liver on the qDixon maps to obtain R2* and proton density fat fraction (PDFF). The reference R2* value was measured and calculated on conventional T2* mapping using the CMRtools software. Correlation analysis, Linear regression analysis, and Bland-Altman analysis were performed. RESULTS: 84 patients were finally included in this study. The median R2*-ME-GRE was 366.97 (1/s), range [206.68 (1/s), 522.20 (1/s)]. 8 patients had normal hepatic iron deposition, 16 had Insignificant, 42 had mild, 18 had moderate. The median of R2*-qDixon was 376.88 (1/s) [219.33 (1/s), 491.75 (1/s)]. A strong correlation was found between the liver R2*-qDixon and the R2*-ME-GRE (r = 0.959, P < 0.001). The median value of PDFF was 1.76% (1.10%, 2.95%). 8 patients had mild fatty liver, and 1 had severe fatty liver. CONCLUSION: MR qDixon research sequence can rapidly and accurately quantify liver iron overload, that highly consistent with the measured via conventional GRE sequence, and it can also simultaneously detect hepatic steatosis, this has great potential for clinical evaluation of thalassemia patients.

Prediction of late recurrence after curative-intent resection using MRI-measured spleen volume in patients with hepatocellular carcinoma and cirrhosis.

BACKGROUND: Late recurrence of hepatocellular carcinoma (HCC) after liver resection is regarded as a de novo tumor primarily related to the severity of underlying liver disease. We aimed to investigate risk factors, especially spleen volume, associated with late recurrence in patients with HCC and cirrhosis. METHODS: We retrospectively analyzed 301 patients with HCC and cirrhosis who received curative resection and preoperative MRI. Patients were followed for late recurrence for at least 2 years. Spleen volume was automatically measured on MRI with artificial intelligence techniques, and qualitative MRI imaging features reflecting tumor aggressiveness were evaluated. Uni- and multivariable Cox regression analyses were performed to identify independent predictors and a risk score was developed to predict late recurrence. RESULTS: Eighty-four (27.9%) patients developed late recurrence during follow-up. Preoperative spleen volume was independently associated with late recurrence, and patients with a volume > 370 cm3 had significantly higher recurrence risk (hazard ratio 2.02, 95%CI 1.31-3.12, p = 0.002). Meanwhile, no qualitative imaging features were associated with late recurrence. A risk score was developed based on the APRI score, spleen volume, and tumor number, which had time-dependent area under the curve ranging from 0.700 to 0.751. The risk score at a cutoff of 0.42 allowed for the identification of two risk categories with distinct risk of late recurrence. CONCLUSIONS: Preoperative spleen volume on MRI was independently associated with late recurrence after curative-intent resection in patients with HCC and cirrhosis. A risk score was proposed for individualized risk prediction and tailoring of postoperative surveillance strategies. CRITICAL RELEVANCE STATEMENT: Spleen volume measured on MRI with the aid of AI techniques was independently predictive of late HCC recurrence after liver resection. A risk score based on spleen volume, APRI score, and tumor number was developed for accurate prediction of late recurrence. KEY POINTS: • Preoperative spleen volume measured on MRI was independently associated with late recurrence after curative-intent resection in patients with HCC and cirrhosis. • Qualitative MRI features reflecting tumor aggressiveness were not associated with late recurrence. • A risk score based on spleen volume was developed for accurate prediction of late recurrence and risk stratification.

Value of Magnetic Resonance T1 Mapping in Evaluating the Early Response to Treatment for Rheumatoid Arthritis.

BACKGROUND: Rheumatoid Arthritis Magnetic Resonance Imaging Score (RAMRIS) is usually used for the semi-quantitative evaluation of joint changes in Rheumatoid Arthritis (RA). However, this method cannot evaluate early changes in bone marrow edema (BME). OBJECTIVE: To determine whether T1 mapping of wrist BME predicts early treatment response in RA. METHODS: This study prospectively enrolled 48 RA patients administered oral anti-rheumatic drugs. MRI of the most severely affected wrist was performed before and after 4 (48 patients) and 8 weeks of treatment (38 patients). Mean T1 values of BME in the lunate, triangular, and capitate bones; RAMRIS for each wrist; Erythrocyte-Sedimentation Rate (ESR); and 28-joint Disease Activity Score (DAS28)-ESR score were analyzed. Patients were divided into responders (4 weeks, 30 patients; 8 weeks, 32 patients) and non-responders (4 weeks, 18 patients; 8 weeks, 6 patients), according to EULAR response criteria. Receiver operating characteristic (ROC) curves were used to evaluate the efficacy of T1 values. RESULTS: ESR and DAS28-ESR were not correlated with T1 value and RAMRIS at each examination (P > 0.05). Changes in T1 value and DAS28-ESR relative to the baseline were moderately positively correlated with each other at 4 and 8 weeks (r = 0.555 and 0.527, respectively; P < 0.05). At 4 weeks, the change and rate of change in T1 value significantly differed between responders and non-responders (-85.63 vs. -19.92 ms; -12.89% vs. -2.81%; P < 0.05). The optimal threshold of the rate of change in T1 value at 4 weeks for predicting treatment response was -5.32% (area under the ROC curve, 0.833; sensitivity, 0.900; specificity, 0.667). CONCLUSION: T1 mapping provides a new imaging method for monitoring RA lesions; changes in wrist BME T1 values reflect early treatment response.

Comparison contrast-enhanced CT with contrast-enhanced US in diagnosing combined hepatocellular-cholangiocarcinoma: a propensity score-matched study.

OBJECTIVES: To develop and compare noninvasive models for differentiating between combined hepatocellular-cholangiocarcinoma (cHCC-CCA) and HCC based on serum tumor markers, contrast-enhanced ultrasound (CEUS), and computed tomography (CECT). METHODS: From January 2010 to December 2021, patients with pathologically confirmed cHCC-CCA or HCC who underwent both preoperative CEUS and CECT were retrospectively enrolled. Propensity scores were calculated to match cHCC-CCA and HCC patients with a near-neighbor ratio of 1:2. Two predicted models, a CEUS-predominant (CEUS features plus tumor markers) and a CECT-predominant model (CECT features plus tumor markers), were constructed using logistic regression analyses. Model performance was evaluated by the area under the curve (AUC), sensitivity, specificity, and accuracy. RESULTS: A total of 135 patients (mean age, 51.3 years ± 10.9; 122 men) with 135 tumors (45 cHCC-CCA and 90 HCC) were included. By logistic regression analysis, unclear boundary in the intratumoral nonenhanced area, partial washout on CEUS, CA 19-9 > 100 U/mL, lack of cirrhosis, incomplete tumor capsule, and nonrim arterial phase hyperenhancement (APHE) volume < 50% on CECT were independent factors for a diagnosis of cHCC-CCA. The CECT-predominant model showed almost perfect sensitivity for cHCC-CCA, unlike the CEUS-predominant model (93.3% vs. 55.6%, p < 0.001). The CEUS-predominant model showed higher diagnostic specificity than the CECT-predominant model (80.0% vs. 63.3%; p = 0.020), especially in the ≤ 5 cm subgroup (92.0% vs. 70.0%; p = 0.013). CONCLUSIONS: The CECT-predominant model provides higher diagnostic sensitivity than the CEUS-predominant model for CHCC-CCA. Combining CECT features with serum CA 19-9 > 100 U/mL shows excellent sensitivity. CRITICAL RELEVANCE STATEMENT: Combining lack of cirrhosis, incomplete tumor capsule, and nonrim arterial phase hyperenhancement (APHE) volume < 50% on CECT with serum CA 19-9 > 100 U/mL shows excellent sensitivity in differentiating cHCC-CCA from HCC. KEY POINTS: 1. Accurate differentiation between cHCC-CCA and HCC is essential for treatment decisions. 2. The CECT-predominant model provides higher accuracy than the CEUS-predominant model for CHCC-CCA. 3. Combining CECT features and CA 19-9 levels shows a sensitivity of 93.3% in diagnosing cHCC-CCA.

Transformative Removal of Aqueous Micropollutants into Polymeric Products by Advanced Oxidation Processes.

This perspective presents the latest advancements in selective polymerization pathways in advanced oxidation processes (AOPs) for removal of featured organic pollutants in wastewater. In radical-based homogeneous reactions, SO4• --based systems exhibit superior oxidative activity toward aromatics with electron-donating substituents via single electron transfer and radical adduct formation (RAF). The produced organic radical cations subsequently undergo coupling and polymerization reactions to produce polymers. For •OH-based oxidation, metal ions facilitate the production of monomer radicals via RAF. Additionally, heterogeneous catalysts can mediate both coupling and polymerization reactions via persulfate activation without generating inorganic radicals. Metal-based catalysts will mediate a direct oxidation pathway toward polymerization. In contrast, carbon-based catalysts will induce coupling reactions to produce low-molecular-weight oligomers (≤4 units) via an electron transfer process. In comparison to mineralization, polymerization pathways remarkably reduce peroxide usage, quickly separate pollutants from the aqueous phase, and generate polymeric byproducts. Thus, AOP-driven polymerization systems hold significant promise in reducing carbon emission and realizing carbon recycling in water treatment processes.

The value of contrast-enhanced magnetic resonance imaging for diagnosis of extrahepatic cholangiocarcinoma.

Rationale and objectives: To establish a diagnostic model based on contrast-enhanced magnetic resonance imaging (MRI) and clinical characteristics for diagnosing extrahepatic cholangiocarcinoma (eCCA). Materials and methods: From April 2014 to September 2021, consecutive patients with extrahepatic bile duct lesions who underwent contrast-enhanced MRI within 1 month before pathological examination were retrospectively enrolled. Two radiologists blinded to clinicopathological information independently evaluated MR images. Univariable and multivariable logistic regression analyses were performed to identify significant clinicoradiological features associated with eCCA, which were subsequently incorporated into a diagnostic model. Model performance was assessed using the area under the receiver operating characteristic curve (AUC), calibration curve, and decision curve. Results: A total of 182 patients (mean age, 60.8 ± 10.0 years, 117 men) were included, 144 (79 %) of whom had pathologically confirmed eCCA. Diffusion restriction (odds ratio [OR], 8.32; 95 % confidence interval [CI]: 2.88, 25.82; P < 0.001), indistinct outer margin (OR, 4.01; 95 % CI: 1.40, 11.84; P = 0.010), cholelithiasis (OR, 0.34; 95 % CI: 0.12, 1.00; P = 0.049), serum ln(carbohydrate antigen 125) (OR, 4.95; 95 % CI: 1.61, 18.55; P = 0.010), and serum ln(direct bilirubin) (OR, 1.82; 95 % CI: 1.29, 2.63; P < 0.001) were independently associated with eCCA. Incorporating the above 5 variables, a diagnostic model achieved an AUC of 0.912 (95 % CI: 0.859, 0.965), with well-fitted calibration curve (P = 0.815) and good clinical utility. Additionally, the sensitivity, specificity and accuracy of the model were 83.33 %, 86.84 %, and 84.07 %, respectively. Conclusion: The proposed model integrating two MRI features (i.e., indistinct outer margin and diffusion restriction) and three clinical characteristics (i.e., cholelithiasis, lnCA125 and lnDBIL) enabled accurate diagnosis of eCCA. This tool holds the potential to facilitate an early diagnosis and thereby allow timely treatment interventions and improved clinical outcomes for patients with eCCA.

CT findings and clinical characteristics in distinguishing renal urothelial carcinoma mimicking renal cell carcinoma from clear cell renal cell carcinoma.

BACKGROUND: We aimed to characterize the clinical and multiphase computed tomography (CT) features, which can distinguish renal urothelial carcinoma (RUC) mimicking renal cell carcinoma (RCC) from clear cell renal cell carcinoma (ccRCC) with collecting system invasion (CSI). METHODS: Data from 56 patients with RUC (46 men and 10 women) and 366 patients with ccRCC (262 men and 104 women) were collected and assessed retrospectively. The median age was 65.50 (IQR: 56.25-69.75) and 53.50 (IQR: 42.25-62.5) years, respectively. Univariate and multivariate logistic regression analyses were performed on clinical and CT characteristics to determine independent factors for distinguishing RUC and ccRCC, and an integrated predictive model was constructed. Differential diagnostic performance was assessed using the area under the receiver operating characteristic curve (AUC). RESULTS: The independent predictors for differentiating RUC from ccRCC were infiltrative growth pattern, hydronephrosis, heterogeneous enhancement, preserving reniform contour, and hematuria. The differential diagnostic performance of the integrated predictive model-1 (AUC: 0.947, sensitivity: 89.07%, specificity: 89.29%) and model-2 (AUC: 0.960, sensitivity: 92.1%, specificity: 89.3%) were both better than that of the infiltrative growth pattern (AUC: 0.830, sensitivity: 71.9%, specificity: 92.9%), heterogeneous enhancement (AUC: 0.771, sensitivity: 86.3%, specificity: 67.9%), preserving reniform contour (AUC = 0.758, sensitivity: 85.5%, specificity: 66.1%), hydronephrosis (AUC: 0.733, sensitivity: 87.7%, specificity: 58.9%), or hematuria (AUC: 0.706, sensitivity: 79.5%, specificity: 51.8%). CONCLUSION: The CT and clinical characteristics showed extraordinary discriminative abilities in the differential diagnosis of RUC and ccRCC, which might provide helpful information for clinical decision-making.

Noninvasive prediction of insufficient biochemical response after ursodeoxycholic acid treatment in patients with primary biliary cholangitis based on pretreatment nonenhanced MRI.

OBJECTIVES: To explore the feasibility of pretreatment nonenhanced magnetic resonance imaging (MRI) in predicting insufficient biochemical response to ursodeoxycholic acid (UDCA) in patients with primary biliary cholangitis (PBC). METHODS: From January 2009 to April 2022, consecutive PBC patients who were treated with UDCA and underwent nonenhanced MRI within 30 days before treatment were retrospectively enrolled. All MR images were independently evaluated by two blinded radiologists. Uni- and multivariable logistic regression analyses were performed to develop a predictive model for 12-month insufficient biochemical response. Model performances were evaluated by computing the area under the receiver operating characteristic curve (AUC), sensitivity, and specificity. RESULTS: A total of 74 patients (50.6 ± 11.9 years; 62 females) were included. Three pretreatment MRI features, including hepatomegaly (odds ratio [OR]: 4.580; p = 0.011), periportal hyperintensity on T2-weighted imaging (T2WI) (OR: 4.795, p = 0.008), and narrowing of the bile ducts (OR: 3.491; p = 0.027) were associated with 12-month insufficient biochemical response in the multivariable analysis. A predictive model based on the above indicators had an AUC of 0.781, sensitivity of 85.4%, and specificity of 61.5% for predicting insufficient biochemical response. CONCLUSIONS: A noninvasive model based on three pretreatment MRI features could accurately predict 12-month insufficient biochemical response to UDCA in patients with PBC. Early identification of PBC patients at increased risk for insufficient response can facilitate the timely initiation of additional treatment. CLINICAL RELEVANCE STATEMENT: A noninvasive predictive model constructed by incorporating three pretreatment MRI features may help identify patients with primary biliary cholangitis at high risk of insufficient biochemical response to ursodeoxycholic acid and facilitate the timely initiation of additional treatment. KEY POINTS: • Noninvasive imaging features based on nonenhanced pretreatment MRI may predict an insufficient biochemical response to UDCA in PBC patients. • A combined model based on three MRI features (hepatomegaly, periportal hyperintensity on T2-weighted imaging, and narrowing of the bile ducts) further improved the predictive efficacy for an insufficient biochemical response to UDCA in PBC patients, with high sensitivity and specificity. • The nomogram of the combined model showed good calibration and predictive efficacy for an insufficient biochemical response to UDCA in PBC patients. In particular, the calibration curve visualised the clinical applicability of the prediction model.

The potential influences of professional identity for undergraduate nursing students during the COVID-19 pandemic: A cross-sectional study.

AIM: We aimed to investigate the level of undergraduate nursing students' professional identity (PI) during the COVID-19 pandemic and its potential influences. METHODS: This cross-sectional study included 742 undergraduate nursing students from Chengdu Medical College. Basic demographics, personal characteristics and PI score were collected. Chi-squared test, one-way analysis of variance, and multivariable logistic regression analysis were used to analyze the data. RESULTS: In the classes of 2019, 2020, and 2021, the PI score was 96.85 ± 18.26, 105.37 ± 17.46, 106.67 ± 22.08 (P < .001). PI score was the independent predictive factor for both "want to be a nurse" and "willingness to serve during COVID-19 or other pandemics". CONCLUSIONS: PI contributes greatly to nursing students' willingness to be a nurse and overcome pandemics. The challenge of culturing PI during COVID-19 pandemic revealed the necessity of developing individual management strategies for nursing education.

Development of a Model including MRI Features for Predicting Advanced-stage Recurrence of Hepatocellular Carcinoma after Liver Resection.

Background Identifying patients at high risk for advanced-stage hepatocellular carcinoma (HCC) recurrence after liver resection may improve patient survival. Purpose To develop a model including MRI features for predicting postoperative advanced-stage HCC recurrence. Materials and Methods This single-center, retrospective study includes consecutive adult patients who underwent preoperative contrast-enhanced MRI and curative-intent resection for early- to intermediate-stage HCC (from December 2011 to April 2021). Three radiologists evaluated 52 qualitative features on MRI scans. In the training set, Fine-Gray proportional subdistribution hazard analysis was performed to identify clinical, laboratory, imaging, pathologic, and surgical variables to include in the predictive model. In the test set, the concordance index (C-index) was computed to compare the developed model with current staging systems. The Kaplan-Meier survival curves were compared using the log-rank test. Results The study included 532 patients (median age, 54 years; IQR, 46-62 years; 465 male patients), 302 patients from the training set (median age, 54 years; IQR, 46-63 years; 265 male patients), and 128 patients from the test set (median age, 53 years; IQR, 46-63 years; 108 male patients). Advanced-stage recurrence was observed in 38 of 302 (12.6%) and 15 of 128 (11.7%) of patients from the training and test sets, respectively. Serum neutrophil count (109/L), tumor size (in centimeters), and arterial phase hyperenhancement proportion on MRI scans were associated with advanced-stage recurrence (subdistribution hazard ratio range, 1.16-3.83; 95% CI: 1.02, 7.52; P value range, <.001 to .02) and included in the predictive model. The model showed better test set prediction for advanced-stage recurrence than four staging systems (2-year C-indexes, 0.82 [95% CI: 0.74, 0.91] vs 0.63-0.68 [95% CI: 0.52, 0.82]; P value range, .001-.03). Patients at high risk for HCC recurrence (model score, ≥15 points) showed increased advanced-stage recurrence and worse all-stage recurrence-free survival (RFS), advanced-stage RFS, and overall survival than patients at low risk for HCC recurrence (P value range, <.001 to .02). Conclusion A model combining serum neutrophil count, tumor size, and arterial phase hyperenhancement proportion predicted advanced-stage HCC recurrence better than current staging systems and may identify patients at high risk. Published under a CC BY 4.0 license. Supplemental material is available for this article. See also the editorial by Tsai and Mellnick in this issue.