Canagliflozin alleviates pulmonary hypertension by activating PPARγ and inhibiting its S225 phosphorylation.

Pulmonary hypertension (PH) is a progressive fatal disease with no cure. Canagliflozin (CANA), a novel medication for diabetes, has been found to have remarkable cardiovascular benefits. However, few studies have addressed the effect and pharmacological mechanism of CANA in the treatment of PH. Therefore, our study aimed to investigate the effect and pharmacological mechanism of CANA in treating PH. First, CANA suppressed increased pulmonary artery pressure, right ventricular hypertrophy, and vascular remodeling in both mouse and rat PH models. Network pharmacology, transcriptomics, and biological results suggested that CANA could ameliorate PH by suppressing excessive oxidative stress and pulmonary artery smooth muscle cell proliferation partially through the activation of PPARγ. Further studies demonstrated that CANA inhibited phosphorylation of PPARγ at Ser225 (a novel serine phosphorylation site in PPARγ), thereby promoting the nuclear translocation of PPARγ and increasing its ability to resist oxidative stress and proliferation. Taken together, our study not only highlighted the potential pharmacological effect of CANA on PH but also revealed that CANA-induced inhibition of PPARγ Ser225 phosphorylation increases its capacity to counteract oxidative stress and inhibits proliferation. These findings may stimulate further research and encourage future clinical trials exploring the therapeutic potential of CANA in PH treatment.

Time-dependent CT score-based model for identifying severe/critical COVID-19 at a fever clinic after the emergence of Omicron variant.

Rationale and objectives: The computed tomography (CT) score has been used to evaluate the severity of COVID-19 during the pandemic; however, most studies have overlooked the impact of infection duration on the CT score. This study aimed to determine the optimal cutoff CT score value for identifying severe/critical COVID-19 during different stages of infection and to construct corresponding predictive models using radiological characteristics and clinical factors. Materials and methods: This retrospective study collected consecutive baseline chest CT images of confirmed COVID-19 patients from a fever clinic at a tertiary referral hospital from November 28, 2022, to January 8, 2023. Cohorts were divided into three subcohorts according to the time interval from symptom onset to CT examination at the hospital: early phase (0-3 days), intermediate phase (4-7 days), and late phase (8-14 days). The binary endpoints were mild/moderate and severe/critical infection. The CT scores and qualitative CT features were manually evaluated. A logistic regression analysis was performed on the CT score as determined by a visual assessment to predict severe/critical infection. Receiver operating characteristic analysis was performed and the area under the curve (AUC) was calculated. The optimal cutoff value was determined by maximizing the Youden index in each subcohort. A radiology score and integrated models were then constructed by combining the qualitative CT features and clinical features, respectively, using multivariate logistic regression with stepwise elimination. Results: A total of 962 patients (aged, 61.7 ± 19.6 years; 490 men) were included; 179 (18.6%) were classified as severe/critical COVID-19, while 344 (35.8%) had a typical Radiological Society of North America (RSNA) COVID-19 appearance. The AUCs of the CT score models reached 0.91 (95% confidence interval (CI) 0.88-0.94), 0.82 (95% CI 0.76-0.87), and 0.83 (95% CI 0.77-0.89) during the early, intermediate, and late phases, respectively. The best cutoff values of the CT scores during each phase were 1.5, 4.5, and 5.5. The predictive accuracies associated with the time-dependent cutoff values reached 88% (vs.78%), 73% (vs. 63%), and 87% (vs. 57%), which were greater than those associated with universal cutoff value (all P < 0.001). The radiology score models reached AUCs of 0.96 (95% CI 0.94-0.98), 0.90 (95% CI 0.87-0.94), and 0.89 (95% CI 0.84-0.94) during the early, intermediate, and late phases, respectively. The integrated models including demographic and clinical risk factors greatly enhanced the AUC during the intermediate and late phases compared with the values obtained with the radiology score models; however, an improvement in accuracy was not observed. Conclusion: The time interval between symptom onset and CT examination should be tracked to determine the cutoff value for the CT score for identifying severe/critical COVID-19. The radiology score combining qualitative CT features and the CT score complements clinical factors for identifying severe/critical COVID-19 patients and facilitates timely hierarchical diagnoses and treatment.

Clonal expansion of shared T cell receptors reveals the existence of immune commonality among different lesions of synchronous multiple primary lung cancer.

The increase in the detection rate of synchronous multiple primary lung cancer (MPLC) has posed remarkable clinical challenges due to the limited understanding of its pathogenesis and molecular features. Here, comprehensive comparisons of genomic and immunologic features between MPLC and solitary lung cancer nodule (SN), as well as different lesions of the same patient, were performed. Compared with SN, MPLC displayed a lower rate of EGFR mutation but higher rates of BRAF, MAP2K1, and MTOR mutation, which function exactly in the upstream and downstream of the same signaling pathway. Considerable heterogeneity in T cell receptor (TCR) repertoire exists among not only different patients but also among different lesions of the same patient. Invasive lesions of MPLC exhibited significantly higher TCR diversity and lower TCR expansion than those of SN. Intriguingly, different lesions of the same patient always shared a certain proportion of TCR clonotypes. Significant clonal expansion could be observed in shared TCR clonotypes, particularly in those existing in all lesions of the same patient. In conclusion, this study provided evidences of the distinctive mutational landscape, activation of oncogenic signaling pathways, and TCR repertoire in MPLC as compared with SN. The significant clonal expansion of shared TCR clonotypes demonstrated the existence of immune commonality among different lesions of the same patient and shed new light on the individually tailored precision therapy for MPLC.

Predicting Invasiveness of Lung Adenocarcinoma at Chest CT with Deep Learning Ternary Classification Models.

Background Preoperative discrimination of preinvasive, minimally invasive, and invasive adenocarcinoma at CT informs clinical management decisions but may be challenging for classifying pure ground-glass nodules (pGGNs). Deep learning (DL) may improve ternary classification. Purpose To determine whether a strategy that includes an adjudication approach can enhance the performance of DL ternary classification models in predicting the invasiveness of adenocarcinoma at chest CT and maintain performance in classifying pGGNs. Materials and Methods In this retrospective study, six ternary models for classifying preinvasive, minimally invasive, and invasive adenocarcinoma were developed using a multicenter data set of lung nodules. The DL-based models were progressively modified through framework optimization, joint learning, and an adjudication strategy (simulating a multireader approach to resolving discordant nodule classifications), integrating two binary classification models with a ternary classification model to resolve discordant classifications sequentially. The six ternary models were then tested on an external data set of pGGNs imaged between December 2019 and January 2021. Diagnostic performance including accuracy, specificity, and sensitivity was assessed. The χ2 test was used to compare model performance in different subgroups stratified by clinical confounders. Results A total of 4929 nodules from 4483 patients (mean age, 50.1 years ± 9.5 [SD]; 2806 female) were divided into training (n = 3384), validation (n = 579), and internal (n = 966) test sets. A total of 361 pGGNs from 281 patients (mean age, 55.2 years ± 11.1 [SD]; 186 female) formed the external test set. The proposed strategy improved DL model performance in external testing (P < .001). For classifying minimally invasive adenocarcinoma, the accuracy was 85% and 79%, sensitivity was 75% and 63%, and specificity was 89% and 85% for the model with adjudication (model 6) and the model without (model 3), respectively. Model 6 showed a relatively narrow range (maximum minus minimum) across diagnostic indexes (accuracy, 1.7%; sensitivity, 7.3%; specificity, 0.9%) compared with the other models (accuracy, 0.6%-10.8%; sensitivity, 14%-39.1%; specificity, 5.5%-17.9%). Conclusion Combining framework optimization, joint learning, and an adjudication approach improved DL classification of adenocarcinoma invasiveness at chest CT. Published under a CC BY 4.0 license. Supplemental material is available for this article. See also the editorial by Sohn and Fields in this issue.

[The mechanism of Xuebijing injection in preventing and treating lung injury induced by cardiopulmonary bypass by regulating the apoptosis of alveolar polymorphonuclear neutrophil].

OBJECTIVE: To investigate the protective effect of Xuebijing injection on acute lung injury (ALI) associated with cardiopulmonary bypass (CPB) by regulating the apoptosis of polymorphonuclear neutrophils (PMN). METHODS: Thirty male Sprague-Dawley (SD) rats were randomly divided into sham operation group (Sham group), CPB model group (CPB group) and Xuebijing pretreatment group (XBJ group) according to the random number table method, with 10 rats in each group. Rats in the CPB group and XBJ group undergoing CPB procedures for 60 minutes. Rats in the Sham group did not undergo CPB. Rats in the XBJ group received intraperitoneal injection of 4 mL/kg Xuebijing injection 2 hours before CPB. Rats in the Sham group and CPB group were injected with an equal amount of normal saline. 4 hours after CPB, arterial blood was collected for blood gas analysis to calculate respiratory index (RI), and lung tissue of rats was collected for determination of lung index (LI) and pulmonary water containing rate. PMN in bronchoalveolar lavage fluid (BALF) were collected and the activity of caspase-3 was detected. The apoptosis rate was detected by flow cytometry. The expressions of microRNA-142-3p (miR-142-3p) and FoxO1 mRNA were detected by real-time fluorescence quantitative polymerase chain reaction (RT-qPCR). The protein expression of FoxO1 was detected by Western blotting. In addition, HL-60 cells were divided into control oligonucleotide transfection group, miR-142-3p mimics transfection group, and miR-142-3p inhibitor transfection group. After 48 hours of transfection, the activity of miR-142-3p binding to FoxO1 was detected using dual luciferase reporter genes. RESULTS: Compared with Sham group, RI, LI and pulmonary water containing rate were significantly increased in CPB group. The caspase-3 activity and apoptosis rate of PMN obtained from BALF were significantly decreased, the expression of miR-142-3p was decreased, and the expression of FoxO1 protein was increased. However, compared with CPB group, RI, LI and pulmonary water containing rate were significantly decreased in XBJ group [RI: 0.281±0.066 vs. 0.379±0.071, LI: 4.50±0.26 vs. 5.71±0.42, pulmonary water containing rate: (80.31±32.50)% vs. (84.59±3.41)%, all P < 0.01]. The caspase-3 activity and apoptosis rate of PMN obtained from BALF were significantly increased [caspase-3 activity: 0.350±0.021 vs. 0.210±0.014, apoptosis rate: (15.490±1.382)% vs. (8.700±0.701)%, both P < 0.01], the expression of miR-142-3p was significantly up-regulated (2-ΔΔCt: 2.61±0.17 vs. 0.62±0.05, P < 0.01), and the protein expression of FoxO1 was decreased [FoxO1/GAPDH (relative expression level): 0.81±0.04 vs. 1.22±0.06, P < 0.01]. However, there was no statistically significant difference in FoxO1 mRNA expression among the three groups. The bioinformatics analysis results showed that miR-142-3p can bind to the FoxO1 3'untranslated region (3'UTR). In HL-60 cells, compared with control oligonucleotide transfection group, the transfection of miR-142-3p mimics could reduce the expression of FoxO1 protein [FoxO1/GAPDH (relative expression level): 0.48±0.06 vs. 1.00±0.05, P < 0.01], however, the transfection of miR-142-3p inhibitor increased the expression of FoxO1 protein [FoxO1/GAPDH (relative expression level): 1.37±0.21 vs. 1.00±0.05, P < 0.05]. But, transfection with miR-142-3p mimics or inhibitor had no effect on FoxO1 mRNA expression. The luciferase reporter gene showed that miR-142-3p could bind to the FoxO1 3'UTR to inhibit FoxO1 expression. CONCLUSIONS: Xuebijing injection may promote the apoptosis of pulmonary alveolar PMN through the miR-142-3p/FoxO1 axis, and play a role in the prevention and treatment of CPB-induced ALI.

Value of deep learning reconstruction of chest low-dose CT for image quality improvement and lung parenchyma assessment on lung window.

OBJECTIVES: To explore the performance of low-dose computed tomography (LDCT) with deep learning reconstruction (DLR) for the improvement of image quality and assessment of lung parenchyma. METHODS: Sixty patients underwent chest regular-dose CT (RDCT) followed by LDCT during the same examination. RDCT images were reconstructed with hybrid iterative reconstruction (HIR) and LDCT images were reconstructed with HIR and DLR, both using lung algorithm. Radiation exposure was recorded. Image noise, signal-to-noise ratio, and subjective image quality of normal and abnormal CT features were evaluated and compared using the Kruskal-Wallis test with Bonferroni correction. RESULTS: The effective radiation dose of LDCT was significantly lower than that of RDCT (0.29 ± 0.03 vs 2.05 ± 0.65 mSv, p < 0.001). The mean image noise ± standard deviation was 33.9 ± 4.7, 39.6 ± 4.3, and 31.1 ± 3.2 HU in RDCT, LDCT HIR-Strong, and LDCT DLR-Strong, respectively (p < 0.001). The overall image quality of LDCT DLR-Strong was significantly better than that of LDCT HIR-Strong (p < 0.001) and comparable to that of RDCT (p > 0.05). LDCT DLR-Strong was comparable to RDCT in evaluating solid nodules, increased attenuation, linear opacity, and airway lesions (all p > 0.05). The visualization of subsolid nodules and decreased attenuation was better with DLR than with HIR in LDCT but inferior to RDCT (all p < 0.05). CONCLUSION: LDCT DLR can effectively reduce image noise and improve image quality. LDCT DLR provides good performance for evaluating pulmonary lesions, except for subsolid nodules and decreased lung attenuation, compared to RDCT-HIR. CLINICAL RELEVANCE STATEMENT: The study prospectively evaluated the contribution of DLR applied to chest low-dose CT for image quality improvement and lung parenchyma assessment. DLR can be used to reduce radiation dose and keep image quality for several indications. KEY POINTS: • DLR enables LDCT maintaining image quality even with very low radiation doses. • Chest LDCT with DLR can be used to evaluate lung parenchymal lesions except for subsolid nodules and decreased lung attenuation. • Diagnosis of pulmonary emphysema or subsolid nodules may require higher radiation doses.

Runoff nitrogen losses under confluence and diverging drainage systems in the sloped plot scale: A comparative study.

The drainage system is a key measure for regulating runoff nutrient losses on sloping farmlands. Confluence and diverging drainage systems are two drainage layouts representing natural water network systems and are widely distributed in sloping farmlands; however, the effects of these drainage systems on runoff nutrient losses in the sloped plots remain unclear. This study investigated the effects of different drainage systems on the characteristics of runoff nitrogen (N) losses in sloped plots using laboratory rainfall simulations. Three treatments, including bare slope (without drainage system, CK), confluence drainage system (T1), and diverging drainage system (T2), were used to compare the changes in concentrations and losses of total nitrogen (TN), dissolved nitrogen (DN), and particulate nitrogen (PN), and the DN:TN ratio in runoff under a combination of 1.8 mm min-1 rainfall intensity and three slope gradients (5°, 10°, and 15°). The results showed that the time to runoff was significantly delayed in T2 compared with that in CK and T1 across all slopes (p < 0.05). Accumulated runoff depth was considerably lower in T1 and T2 than in CK across all slopes (p < 0.05). The TN and PN concentrations in T1 were markedly lower than those in T2 on the 10° and 15° slopes (p < 0.05). The DN concentration in T1 was lowest at the 5° slope (p < 0.05). TN loss in T1 was 14.7-33.9% and 17.9-30.3% lower than those in CK and T2 across all slopes, respectively (p < 0.05). The PN loss in T1 was 56.7% and 53.3% lower than that in T2 on the 10° and 15° slopes, respectively (p < 0.05). DN loss in T1 was 39.3-72.5% lower than that in CK for all slopes (p < 0.05). DN:TN in T2 was lower than that in CK and T1 at the 10° and 15° slopes (p < 0.05). Our results confirm the effectiveness of drainage systems in reducing runoff nutrient losses in a sloped plot and demonstrate that the confluence drainage system is better at reducing N losses in runoff than diverging drainage systems.

Microarray Expression Profile of Exosomal circRNAs from High Glucose Stimulated Human Renal Tubular Epithelial Cells.

Introduction: Circular RNA (circRNAs) are a type of non-coding RNA (ncRNAs) with a wealth of functions. Recently, circRNAs have been identified as important regulators of diabetic kidney disease (DKD), owing to their stability and enrichment in exosomes. However, the role of circRNAs in exosomes of tubular epithelial cells in DKD development has not been fully elucidated. Methods: In our study, microarray technology was used to analyze circRNA expression in cell supernatant exosomes isolated from HK-2 cells with or without high glucose (HG) treatment. The small interfering RNAs (siRNA) and plasmid overexpression were used to validate functions of differentially expressed circRNAs. Results: We found that exosome concentration was higher in HG-stimulated HK-2 cells than in controls. A total of 235 circRNAs were significantly increased and 458 circRNAs were significantly decreased in the exosomes of the HG group. In parallel with the microarray data, the qPCR results showed that the expression of circ_0009885, circ_0043753, and circ_0011760 increased, and the expression of circ_0032872, circ_0004716, and circ_0009445 decreased in the HG group. Rescue experiments showed that the effects of high glucose on regulation of CCL2, IL6, fibronetin, n cadherin, e cadherin and epcam expression can be reversed by inhibiting or overexpressing these circRNAs. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) biological pathway analyses indicated that circRNA parental genes are associated with glucose metabolism, lipid metabolism, and inflammatory processes, which are important in DKD development. Further analysis of circRNA/miRNA interactions indicated that 152 differentially expressed circRNAs with fold change (FC) ≥1.5 could be paired with 43 differentially expressed miRNAs, which are associated with diabetes or DKD. Discussion: Our results indicate that exosomal circRNAs may be promising diagnostic and therapeutic biomarkers, and may play a critical role in the progression of DKD.

Mechanism of Rhodiola rosea-Euonymus alatus drug pair against rheumatoid arthritis: Network pharmacology and experimental validation.

PURPOSE: The present study aimed to explore the potential components and mechanisms of Rhodiola rosea-Euonymus alatus drug pair (TY) that ameliorate rheumatoid arthritis (RA). METHODS: The main active components, core targets, and important pathways of TY against RA were predicted by network pharmacology analysis. The binding activity between the main active components and the core targets was verified by the molecular docking technique. Collagen-induced arthritis (CIA) rat model and tumor necrosis factor (TNF)-α-induced fibroblast-like synovial cells in human RA (HFLS-RA) model were established, respectively. The core targets were verified by cell counting kit-8 assay, hematoxylin eosin, enzyme-linked immunosorbent assay, real-time polymerase chain reaction, and Western blot analysis, and the therapeutic effect of TY was evaluated. RESULTS: A total of 18 possible components and 34 core targets were obtained by network pharmacology, among which inflammatory response, phosphatidylinositide 3-kinases (PI3K)-AKT and MAPK pathways were involved in the therapeutic effect of TY on RA. The results of molecular docking showed that kaempferol and quercetin had high binding affinity to interleukin (IL)-1ß, IL-6, matrix metalloproteinase (MMP)9, and TNF-α. In vivo and in vitro experiments showed that TY dose-dependently inhibited the proliferation of HFLS-RA cells induced by TNF-α, and significantly reduced the paw swelling and arthritis scores in CIA rats. At the same time, TY inhibited the production of inflammatory factors in CIA rat serum and TNF-α-induced HFLS-RA cells. It also decreased the expression of PI3K, phospho-protein kinase B, MMP1, MMP3, MMP9, and increased the protein and mRNA levels of tissue inhibitors of MMPs (TIMP)1 in synovial tissue. CONCLUSION: TY can inhibit the PI3K/AKT signaling pathway and regulate the balance between MMPs and TIMP, thus playing a therapeutic role in RA.

[CT-Based Weighted Radiomic Score Predicts Tumor Response to Immunotherapy in Non-Small Cell Lung Cancer].

Objective To develop a CT-based weighted radiomic model that predicts tumor response to programmed death-1(PD-1)/PD-ligand 1(PD-L1)immunotherapy in patients with non-small cell lung cancer.Methods The patients with non-small cell lung cancer treated by PD-1/PD-L1 immune checkpoint inhibitors in the Peking Union Medical College Hospital from June 2015 to February 2022 were retrospectively studied and classified as responders(partial or complete response)and non-responders(stable or progressive disease).Original radiomic features were extracted from multiple intrapulmonary lesions in the contrast-enhanced CT scans of the arterial phase,and then weighted and summed by an attention-based multiple instances learning algorithm.Logistic regression was employed to build a weighted radiomic scoring model and the radiomic score was then calculated.The area under the receiver operating characteristic curve(AUC)was used to compare the weighted radiomic scoring model,PD-L1 model,clinical model,weighted radiomic scoring + PD-L1 model,and comprehensive prediction model.Results A total of 237 patients were included in the study and randomized into a training set(n=165)and a test set(n=72),with the mean ages of(64±9)and(62±8)years,respectively.The AUC of the weighted radiomic scoring model reached 0.85 and 0.80 in the training set and test set,respectively,which was higher than that of the PD-L1-1 model(Z=37.30,P<0.001 and Z=5.69,P=0.017),PD-L1-50 model(Z=38.36,P<0.001 and Z=17.99,P<0.001),and clinical model(Z=11.40,P<0.001 and Z=5.76,P=0.016).The AUC of the weighted scoring model was not different from that of the weighted radiomic scoring + PD-L1 model and the comprehensive prediction model(both P>0.05).Conclusion The weighted radiomic scores based on pre-treatment enhanced CT images can predict tumor responses to immunotherapy in patients with non-small cell lung cancer.

Simulation video: a tool to evaluate communications skills in radiologist residents.

BACKGROUND: Effective communication is a crucial component of radiology resident training, and many different aspects need to be explored when teaching and evaluating communication skills. To ensure that radiology residents' communication skill levels can be measured accurately, a standardized evaluation tool has been introduced. In twenty hospitals in Beijing, simulation videos have been developed as a way to assess the communication skills of radiology residents during their certification exams, to minimize evaluating biases. This study aims to assess the performance of a simulation video model in evaluating communications skills compared to the standard patient model. METHODS: This is a retrospective observational study. The performance of standard patient and simulation video models was evaluated through an eight-year examination of communication skills in radiology residents. From 2014 to 2021, communications skill tests were administered to 1003 radiology residents in 20 hospitals in Beijing. The standardized patient (SP) model was applied in 2014, and simulation videos were used from 2015 to 2021. The difficulty and discrimination radio of the tests were evaluated. The subjective survey for candidates on two models of communication skills evaluation was performed and analyzed. RESULTS: The simulation video model evaluation demonstrated stable difficulty (ranging from 0.92 to 0.98) and discrimination ratio (ranging from 0.37 to 0.49), except for minor exceptions of discrimination in 2019 (0.58) and 2020 (0.20). Furthermore, the Kruskal-Wallis H test revealed no significant differences in average scores between 2016 (93.9 ± 4.6) and 2018 (94.5 ± 4.2), 2016 and 2019 (97.3 ± 3.9), 2017 (97.0 ± 5.6) and 2019, 2017 and 2020 (97.7 ± 4.7), as well as 2019 and 2020 exams (all p ≥ 0.05). In addition, candidates who responded to the survey preferred the simulation video model (with a 77.2% response rate), with 62.7% choosing it over the SP model for communication skills evaluation. CONCLUSION: The simulation video demonstrated a stable and better acceptable construct for assessing radiology residents' communication skills.

Predicting the impact of the COVID-19 pandemic on globalization.

The COVID-19 pandemic has significantly influenced the global economy, international travel, global supply chains, and how people interact, and subsequently affect globalization in coming years. In order to understand the impact of COVID-19 on globalization and provide potential guidance to policymakers, the present study predicted the globalization level of the world average and 14 specific countries in scenarios with and without COVID-19 based on a new Composite Indicator method which contains 15 indicators. Our results revealed that the world average globalization level is expected to decrease from 2017 to 2025 under the scenario without COVID-19 by -5.99%, while the decrease of globalization under the COVID-19 scenario is predicted to reach -4.76% in 2025. This finding implies that the impact of COVID-19 on globalization will not be as severe as expected in 2025. Nevertheless, the downward trend of globalization without COVID-19 is due to the decline of the Environmental indicators, whereas the decline under the COVID-19 scenario is attributed to Economic aspects (almost -50%). The impact of COVID-19 on globalization varies across individual countries. Among the countries investigated, COVID-19 had a positive impact on the globalization of Japan, Australia, the United States, the Russian Federation, Brazil, India and Togo. In contrast, the globalization in the United Kingdom, Switzerland, Qatar, Egypt, China and Gabon are expected to decrease. The variation of impact induced by COVID-19 on those countries is attributed to the weighting of economic, environmental and political aspects of globalization is different across these countries. Our results can help governments take suitable measures to balance economic, environmental and political policies, which may better support their decision-making.

Evaluating the spatial and temporal distribution of emerging contaminants in the Pearl River Basin for regulating purposes.

Little information is available on how the types, concentrations, and distribution of chemicals have evolved over the years. The objective of the present study is therefore to review the spatial and temporal distribution profile of emerging contaminants with limited toxicology data in the pearl river basin over the years to build up the emerging contaminants database in this region for risk assessment and regulatory purposes. The result revealed that seven groups of emerging contaminants were abundant in this region, and many emerging contaminants had been detected at much higher concentrations before 2011. Specifically, antibiotics, phenolic compounds, and acidic pharmaceuticals were the most abundant emerging contaminants detected in the aquatic compartment, while phenolic compounds were of the most profound concern in soil. Flame retardants and plastics were the most frequently studied chemicals in organisms. The abundance of the field concentrations and frequencies varied considerably over the years, and currently available data can hardly be used for regulation purposes. It is suggested that watershed management should establish a regular monitoring scheme and comprehensive database to monitor the distribution of emerging contaminants considering the highly condensed population in this region. The priority monitoring list should be formed in consideration of historical abundance, potential toxic effects of emerging contaminants as well as the distribution of heavily polluting industries in the region.

Infection in systemic lupus erythematosus-associated diffuse alveolar hemorrhage: a potential key to improve outcomes.

OBJECTIVES: This study aimed to investigate the clinical characteristics, outcomes, and associated factors of patients with systemic lupus erythematosus-associated diffusive alveolar hemorrhage (SLE-DAH) stratified by infection status in a national representative cohort. METHODS: This single-center retrospective study included 124 consecutive patients with SLE-DAH in a tertiary care center between 2006 and 2021. The diagnosis of DAH was made based on a comprehensive evaluation of clinical manifestations, laboratory and radiologic findings, and bronchoalveolar lavage. Demographics, clinical features, and survival curves were compared between patients with bacterial, non-bacterial, and non-infection groups. Univariate and multivariate logistic regression analyses were performed to determine the factors independently associated with bacterial infection in SLE-DAH. RESULTS: Fifty-eight patients with SLE-DAH developed bacterial infection after DAH occurrence, thirty-two patients developed fungal and/or viral infection, and thirty-four patients were categorized as non-infection. The bacterial infection group have a worse prognosis (OR 3.059, 95%CI 1.469-6.369, p = 0.002) compared with the other two groups, with a mortality rate of 60.3% within 180 days after DAH occurrence. Factors independently associated with bacterial infections in SLE-DAH included hematuria (OR 4.523, 95%CI 1.068-19.155, p = 0.040), hemoglobin drop in the first 24 h after DAH occurred (OR 1.056, 95%CI 1.001-1.115, p = 0.049), and anti-Smith antibody (OR 0.167, 95%CI 0.052-0.535, p = 0.003). Glucocorticoid pulse therapy and cyclophosphamide were administered in more than 50% of patients regardless of their infectious status. According to clinical experience at our hospital and in previous studies, we recommended a comprehensive management algorithm for SLE-DAH based on infection stratification. CONCLUSION: Infection, especially bacterial infection, is a severe complication and prognostic factor of SLE-DAH. Comprehensive management strategies, including diagnosis, evaluation, treatment, and monitoring, based on infection stratification may fundamentally improve outcomes of patients with SLE-DAH. Key Points • Bacterial infection is an important, but neglected, prognosis factor of systemic lupus erythematosus (SLE)-associated diffusive alveolar hemorrhage (DAH). • Hematuria, hemoglobin drop, and anti-Smith antibody can independently predict bacterial infections in SLE-DAH. • We put forward a comprehensive management algorithm based on infection stratification for SLE-DAH.

A pre-treatment CT-based weighted radiomic approach combined with clinical characteristics to predict durable clinical benefits of immunotherapy in advanced lung cancer.

OBJECTIVES: To develop a pre-treatment CT-based predictive model to anticipate inoperable lung cancer patients' progression-free survival (PFS) to immunotherapy. METHODS: This single-center retrospective study developed and cross-validated a radiomic model in 185 patients and tested it in 48 patients. The binary endpoint is the durable clinical benefit (DCB, PFS ≥ 6 months) and non-DCB (NDCB, PFS < 6 months). Radiomic features were extracted from multiple intrapulmonary lesions and weighted by an attention-based multiple-instance learning model. Aggregated features were then selected through L2-regularized ridge regression. Five machine-learning classifiers were conducted to build predictive models using radiomic and clinical features alone and then together. Lastly, the predictive value of the model with the best performance was validated by Kaplan-Meier survival analysis. RESULTS: The predictive models based on the weighted radiomic approach showed superior performance across all classifiers (AUCs: 0.75-0.82) compared with the largest lesion approach (AUCs: 0.70-0.78) and the average sum approach (AUCs: 0.64-0.80). Among them, the logistic regression model yielded the most balanced performance (AUC = 0.87 [95%CI 0.84-0.89], 0.75 [0.68-0.82], 0.80 [0.68-0.92] in the training, validation, and test cohort respectively). The addition of five clinical characteristics significantly enhanced the performance of radiomic-only model (train: AUC 0.91 [0.89-0.93], p = .042; validation: AUC 0.86 [0.80-0.91], p = .011; test: AUC 0.86 [0.76-0.96], p = .026). Kaplan-Meier analysis of the radiomic-based predictive models showed a clear stratification between classifier-predicted DCB versus NDCB for PFS (HR = 2.40-2.95, p < 0.05). CONCLUSIONS: The adoption of weighted radiomic features from multiple intrapulmonary lesions has the potential to predict long-term PFS benefits for patients who are candidates for PD-1/PD-L1 immunotherapies. KEY POINTS: • Weighted radiomic-based model derived from multiple intrapulmonary lesions on pre-treatment CT images has the potential to predict durable clinical benefits of immunotherapy in lung cancer. • Early line immunotherapy is associated with longer progression-free survival in advanced lung cancer.

Role of heteroaggregation and internalization in the toxicity of differently sized and charged plastic nanoparticles to freshwater microalgae.

The toxic effect of waterborne nanoplastics is a manifestation of bio-nano interfacial interactions. Although nanoplastics with different physicochemical characteristics are known to exhibit distinct toxicities, it remains poorly understood how the properties of nanoplastics affect the bio-nano interface interactions. Here, polystyrene nanoparticles (PSNPs) varying in size (50, 300, and 500 nm) and surface charge (negative and positive charge) were employed to explore the interplay between PSNPs and algal cells (Chlamydomonas reinhardtii), with special focus on the heteroaggregation of PSNPs and microalgae, PSNPs cellular internalization, and cellular physiological responses. Results showed that large-sized PSNPs (300 and 500 nm) caused apparent toxicity to C. reinhardtii, mainly due to light blockage resulting from the PSNPs-microalgae heteroaggregation and the shading effect of PSNPs, which was independent of PSNPs concentrations. However, the toxicity of small-sized PSNPs (50 nm) was controlled by both particle surface charge and particle concentration. The positively charged PS-NH2 was more readily heteroaggregated with microalgae than the negatively charged PS-COOH, leading to photosynthesis damage-induced toxicity. Increasing the concentration of small-sized PSNPs stimulated the secretion of extracellular polymeric substances, allowing more PSNPs to attach on the cell surface and further to enter the cell, which was responsible for the increased toxicity. These findings provide new insights into how nanoplastics induce contact toxicity in microalgae cells through specific biointerfacial interactions.

Discovery and validation of Ferroptosis-related molecular patterns and immune characteristics in Alzheimer's disease.

Background: To date, the pathogenesis of Alzheimer's disease is still not fully elucidated. Much evidence suggests that Ferroptosis plays a crucial role in the pathogenesis of AD, but little is known about its molecular immunological mechanisms. Therefore, this study aims to comprehensively analyse and explore the molecular mechanisms and immunological features of Ferroptosis-related genes in the pathogenesis of AD. Materials and methods: We obtained the brain tissue dataset for AD from the GEO database and downloaded the Ferroptosis-related gene set from FerrDb for analysis. The most relevant Hub genes for AD were obtained using two machine learning algorithms (Least absolute shrinkage and selection operator (LASSO) and multiple support vector machine recursive feature elimination (mSVM-RFE)). The study of the Hub gene was divided into two parts. In the first part, AD patients were genotyped by unsupervised cluster analysis, and the different clusters' immune characteristics were analysed. A PCA approach was used to quantify the FRGscore. In the second part: we elucidate the biological functions involved in the Hub genes and their role in the immune microenvironment by integrating algorithms (GSEA, GSVA and CIBERSORT). Analysis of Hub gene-based drug regulatory networks and mRNA-miRNA-lncRNA regulatory networks using Cytoscape. Hub genes were further analysed using logistic regression models. Results: Based on two machine learning algorithms, we obtained a total of 10 Hub genes. Unsupervised clustering successfully identified two different clusters, and immune infiltration analysis showed a significantly higher degree of immune infiltration in type A than in type B, indicating that type A may be at the peak of AD neuroinflammation. Secondly, a Hub gene-based Gene-Drug regulatory network and a ceRNA regulatory network were successfully constructed. Finally, a logistic regression algorithm-based AD diagnosis model and Nomogram diagram were developed. Conclusion: Our study provides new insights into the role of Ferroptosis-related molecular patterns and immune mechanisms in AD, as well as providing a theoretical basis for the addition of diagnostic markers for AD.

When Treatment of Pulmonary Emphysema with Endobronchial Valves Did Not Work: Evaluation of Quantitative CT Analysis and Pulmonary Function Tests Before and After Valve Explantation.

Purpose: To investigate changes in quantitative CT analysis (QCT) and pulmonary function tests (PFT) in pulmonary emphysema patients who required premature removal of endobronchial valves (EBV). Patients and Methods: Our hospital's medical records listed 274 patients with high-grade COPD (GOLD stages 3 and 4) and pulmonary emphysema who were treated with EBV to reduce lung volume. Prior to intervention, a complete evaluation was performed that included quantitative computed tomography analysis (QCT) of scans acquired at full inspiration and full expiration, pulmonary function tests (PFT), and paraclinical findings (6-minute walking distance test (6MWDT) and quality of life questionnaires). In 41 of these 274 patients, EBV treatment was unsuccessful and the valves had to be removed for various reasons. A total of 10 of these 41 patients ventured a second attempt at EBV therapy and underwent complete reevaluation. In our retrospective study, results from three time points were compared: Before EBV implantation (BL), after EBV implantation (TP2), and after EBV explantation (TP3). QCT parameters included lung volume, total emphysema score (TES, ie, the emphysema index) and the 15th percentile of lung attenuation (P15) for the whole lung and each lobe separately. Differences in these parameters between inspiration and expiration were calculated (Vol. Diff (%), TES Diff (%), P15 Diff (%)). The results of PFT and further clinical tests were taken from the patient's records. Results: We found persistent therapy effect in the target lobe even after valve explantation together with a compensatory hyperinflation of the rest of the lung. As a result of these two divergent effects, the volume of the total lung remained rather constant. Furthermore, there was a slight deterioration of the emphysema score for the whole lung, whereas the TES of the target lobe persistently improved. Conclusion: Interestingly, we found evidence that, contrary to our expectations, unsuccessful EBV therapy can have a persistent positive effect on target lobe QCT scores.

Proteomic analysis and identification reveal the anti-inflammatory mechanism of clofazimine on lipopolysaccharide-induced acute lung injury in mice.

BACKGROUND AND OBJECTIVE: Acute lung injury (ALI)/ acute respiratory distress syndrome (ARDS) was increasingly recognized as one of the most severe acute hyperimmune response of coronavirus disease 2019 (COVID-19). Clofazimine (CFZ) has attracted attention due to its anti-inflammatory property in immune diseases as well as infectious diseases. However, the role and potential molecular mechanism of CFZ in anti-inflammatory responses remain unclear. METHODS: We analyze the protein expression profiles of CFZ and LPS from Raw264.7 macrophages using quantitative proteomics. Next, the protective effect of CFZ on LPS-induced inflammatory model is assessed, and its underlying mechanism is validated by molecular biology analysis. RESULTS: LC-MS/MS-based shotgun proteomics analysis identified 4746 (LPS) and 4766 (CFZ) proteins with quantitative information. The key proteins and their critical signal transduction pathways including TLR4/NF-κB/HIF-1α signaling was highlighted, which was involved in multiple inflammatory processes. A further analysis of molecular biology revealed that CFZ could significantly inhibit the proliferation of Raw264.7 macrophages, decrease the levels of TNF-α and IL-1ß, alleviate lung histological changes and pulmonary edema, improve the survival rate, and down-regulate TLR4/NF-κB/HIF-1α signaling in LPS model. CONCLUSION: This study can provide significant insight into the proteomics-guided pharmacological mechanism study of CFZ and suggest potential therapeutic strategies for infectious disease.

The coupling effect between economic development and the urban ecological environment in Shanghai port.

With the rapid development of ports in the world today, the ecological environment pollution of port cities is becoming more and more serious. How to coordinate the port industry and the urban environment is an urgent problem to be solved. This article proposes a system to evaluate the coordination between port industry chain development and the urban ecological environment by taking Shanghai Port as an example. The Principal Component Analysis (PCA) method was applied to calculate the weights of various indexes, then the Coupling Coordination Degree Model (CCDM) was adopted to systematically study the coordination relationship between port industry development and the urban ecological environment. Our research finds the coupling coordination degree between Shanghai Port's economic development and the urban ecological environment shows an overall upward trend, changing from severely misaligned in 2000 (0.125) to barely coordinated in 2019 (0.691). Shanghai has achieved some results in constructing the ecological environment and green port, but there is still much room for improvement. Shanghai port needs to strengthen container transportation, enhance energy conservation and emission reduction, and vigorously promote the development of a green port. This study will provide a basis and reference for relevant institutions, government departments, and policymakers to formulate scientific and effective green port industry chain development strategies.