Inhibitory effects of Belamcanda extract on inflammatory response and antiviral mechanism in H9N2 Avian influenza virus: insights from in vitro and in vivo studies.

Avian influenza, particularly the H9N2 subtype, presents significant challenges to poultry health, underscoring the need for effective antiviral interventions. This study explores the antiviral capabilities of Belamcanda extract, a traditional Chinese medicinal herb, against H9N2 Avian influenza virus (AIV) in specific pathogen-free (SPF) chicks. Through a comprehensive approach, we evaluated the impact of the extract on cytokine modulation and crucial immunological signaling pathways, essential for understanding the host-virus interaction. Our findings demonstrate that Belamcanda extract significantly modulates the expression of key inflammatory cytokines, including tumor necrosis factor alpha (TNF-α), interleukin-1 (IL-1), interleukin-2 (IL-2), and interleukin-6 (IL-6), which are pivotal to the host's response to H9N2 AIV infection. Western blot analysis further revealed that the extract markedly reduces the expression of critical immune signaling molecules such as toll-like receptor 3 (TLR3), TIR-domain-containing adapter-inducing interferon-ß (TRIF), and nuclear factor kappa B (NF-κB). These insights into the mechanisms by which Belamcanda extract influences host immune responses and hinders viral replication highlight its potential as an innovative antiviral agent for poultry health management. The study advances our comprehension of natural compounds' antiviral mechanisms and lays the groundwork for developing strategies to manage viral infections in poultry. The demonstrated ability of Belamcanda extract to modulate immune responses and inhibit viral replication establishes it as a promising candidate for future antiviral therapy development, especially in light of the need for effective treatments against evolving influenza virus strains and the critical demand for enhanced poultry health management strategies.

Oxymatrine Modulation of TLR3 Signaling: A Dual-Action Mechanism for H9N2 Avian Influenza Virus Defense and Immune Regulation.

In our research, we explored a natural substance called Oxymatrine, found in a traditional Chinese medicinal plant, to fight against a common bird flu virus known as H9N2. This virus not only affects birds but can also pose a threat to human health. We focused on how this natural compound can help in stopping the virus from spreading in cells that line the lungs of birds and potentially humans. Our findings show that Oxymatrine can both directly block the virus and boost the body's immune response against it. This dual-action mechanism is particularly interesting because it indicates that Oxymatrine might be a useful tool in developing new ways to prevent and treat this type of bird flu. Understanding how Oxymatrine works against the H9N2 virus could lead to safer and more natural ways to combat viral infections in animals and humans, contributing to the health and well-being of society. The H9N2 Avian Influenza Virus (AIV) is a persistent health threat because of its rapid mutation rate and the limited efficacy of vaccines, underscoring the urgent need for innovative therapies. This study investigated the H9N2 AIV antiviral properties of Oxymatrine (OMT), a compound derived from traditional Chinese medicine, particularly focusing on its interaction with pulmonary microvascular endothelial cells (PMVECs). Employing an array of in vitro assays, including 50% tissue culture infectious dose, Cell Counting Kit-8, reverse transcription-quantitative polymerase chain reaction, enzyme-linked immunosorbent assay, and Western blot, we systematically elucidated the multifaceted effects of OMT. OMT dose-dependently inhibited critical antiviral proteins (PKR and Mx1) and modulated the expression of type I interferons and key cytokines (IFN-α, IFN-ß, IL-6, and TNF-α), thereby affecting TLR3 signaling and its downstream elements (NF-κB and IRF-3). OMT's antiviral efficacy extended beyond TLR3-mediated responses, suggesting its potential as a versatile antiviral agent. This study not only contributes to the growing body of research on the use of natural compounds as antiviral agents but also underscores the importance of further investigating the broader application of OMT for combating viral infections.

Efficient Biosynthesis of Salidroside via Artificial in Vivo enhanced UDP-Glucose System Using Cheap Sucrose as Substrate.

Salidroside, a valuable phenylethanoid glycoside, is obtained from plants belonging to the Rhodiola genus, known for its diverse biological properties. At present, salidroside is still far from large-scale industrial production due to its lower titer and higher process cost. In this study, we have for the first time increased salidroside production by enhancing UDP-glucose supply in situ. We constructed an in vivo UDP-glucose regeneration system that works in conjunction with UDP-glucose transferase from Rhodiola innovatively to improve UDP-glucose availability. And a coculture was formed in order to enable de novo salidroside synthesis. Confronted with the influence of tyrosol on strain growth, an adaptive laboratory evolution strategy was implemented to enhance the strain's tolerance. Similarly, salidroside production was optimized through refinement of the fermentation medium, the inoculation ratio of the two microbes, and the inoculation size. The final salidroside titer reached 3.8 g/L. This was the highest titer achieved at the shake flask level in the existing reports. And this marked the first successful synthesis of salidroside in an in situ enhanced UDP-glucose system using sucrose. The cost was reduced by 93% due to the use of inexpensive substrates. This accomplishment laid a robust foundation for further investigations into the synthesis of other notable glycosides and natural compounds.

Advances in succinic acid production: the enhancement of CO2 fixation for the carbon sequestration benefits.

Succinic acid (SA), one of the 12 top platform chemicals produced from biomass, is a precursor of various high value-added derivatives. Specially, 1 mol CO2 is assimilated in 1 mol SA biosynthetic route under anaerobic conditions, which helps to achieve carbon reduction goals. In this review, methods for enhanced CO2 fixation in SA production and utilization of waste biomass for SA production are reviewed. Bioelectrochemical and bioreactor coupling systems constructed with off-gas reutilization to capture CO2 more efficiently were highlighted. In addition, the techno-economic analysis and carbon sequestration benefits for the synthesis of bio-based SA from CO2 and waste biomass are analyzed. Finally, a droplet microfluidics-based high-throughput screening technique applied to the future bioproduction of SA is proposed as a promising approach.

Cecropin AD reduces viral load and inflammatory response against H9N2 avian influenza virus in chickens.

Introduction: This study focuses on evaluating the therapeutic efficacy of cecropin AD, an antimicrobial peptide, against H9N2 avian influenza virus (AIV) in chickens. Given the global impact of H9N2 AIV on poultry health, identifying effective treatments is crucial. Methods: To assess the impact of cecropin AD, we conducted in vivo experiments involving 108 5-week-old chickens divided into control, infected, and various treatment groups based on cecropin AD dosage levels (high, medium, and low). The methodologies included hemagglutination (HA) tests for viral titers, histopathological examination and toluidine blue (TB) staining for lung pathology, real-time PCR for viral detection, and enzyme-linked immunosorbent assays for measuring serum levels of inflammatory markers. Results: The findings revealed that cecropin AD substantially reduced lung pathology and viral load, especially at higher dosages, comparing favorably with the effects seen from conventional treatments. Moreover, cecropin AD effectively modulated mast cell activity and the levels of inflammatory markers such as IL-6, TNF-α, IFN-γ, and 5-HT, indicating its potential to diminish inflammation and viral spread. Discussion: Cecropin AD presents a significant potential as an alternative treatment for H9N2 AIV in chickens, as evidenced by its ability to lessen lung damage, decrease viral presence, and adjust immune responses. This positions cecropin AD as a promising candidate for further exploration in the management of H9N2 AIV infections in poultry.

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.

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.

SF3B3-regulated mTOR alternative splicing promotes colorectal cancer progression and metastasis.

BACKGROUND: Aberrant alternative splicing (AS) is a pervasive event during colorectal cancer (CRC) development. SF3B3 is a splicing factor component of U2 small nuclear ribonucleoproteins which are crucial for early stages of spliceosome assembly. The role of SF3B3 in CRC remains unknown. METHODS: SF3B3 expression in human CRCs was analyzed using publicly available CRC datasets, immunohistochemistry, qRT-PCR, and western blot. RNA-seq, RNA immunoprecipitation, and lipidomics were performed in SF3B3 knockdown or overexpressing CRC cell lines. CRC cell xenografts, patient-derived xenografts, patient-derived organoids, and orthotopic metastasis mouse models were utilized to determine the in vivo role of SF3B3 in CRC progression and metastasis. RESULTS: SF3B3 was upregulated in CRC samples and associated with poor survival. Inhibition of SF3B3 by RNA silencing suppressed the proliferation and metastasis of CRC cells in vitro and in vivo, characterized by mitochondria injury, increased reactive oxygen species (ROS), and apoptosis. Mechanistically, silencing of SF3B3 increased mTOR exon-skipped splicing, leading to the suppression of lipogenesis via mTOR-SREBF1-FASN signaling. The combination of SF3B3 shRNAs and mTOR inhibitors showed synergistic antitumor activity in patient-derived CRC organoids and xenografts. Importantly, we identified SF3B3 as a critical regulator of mTOR splicing and autophagy in multiple cancers. CONCLUSIONS: Our findings revealed that SF3B3 promoted CRC progression and metastasis by regulating mTOR alternative splicing and SREBF1-FASN-mediated lipogenesis, providing strong evidence to support SF3B3 as a druggable target for CRC therapy.

Combining directed evolution with high cell permeability for high-level cadaverine production in engineered Escherichia coli.

The biosynthesis of cadaverine from lysine is an environmentally promising technology, that could contribute to a more sustainable approach to manufacturing bio-nylon 5X. However, the titer of biosynthesized cadaverine has still not reached a sufficient level for industrial production. A powerful green cell factory was developed to enhance cadaverine production by regulating lipopolysaccharide (LPS) genes and improving membrane permeability. Firstly, 10 LPS mutant strains were constructed and the effect on the growth was investigated. Then, the lysine decarboxylase (CadA) was overexpressed in 10 LPS mutant strains of Escherichia coli MG1655 and the ability to produce cadaverine was compared. Using 20.0 g L-1 of L-lysine hydrochloride (L-lysine-HCl) as the substrate for the biotransformation reaction, Cad02 and Cad06 strains exhibited high production levels of cadaverine, with 8.95 g L-1 and 7.55 g L-1 respectively while the control strain Cad00 only 4.92 g L-1 . Directed evolution of CadA was also used to improve its stability under alkaline conditions. The cadaverine production of the Cad02-M mutant stain increased by 1.86 times at pH 8.0. Finally, the production process was scaled up using recombinant whole cells as catalysts, achieving a high titer of 211 g L-1 cadaverine (96.8%) by fed-batch bioconversion. This study demonstrates the potential role of LPS in enhancing the efficiency of mass transfer between substrate and enzymes in vivo by increasing cell permeability. The results indicate that the argumentation of cell permeability could not only significantly enhance the biotransformation efficiency of cadaverine, but also provide a universally applicable, straightforward, environment-friendly, and cost-effective method for the biosynthesis of other high-value chemicals.

Association of lower extremity peripheral arterial disease with quantitative muscle features from computed tomography angiography.

OBJECTIVES: To explore the association between lower extremity muscle features from CTA and peripheral arterial disease (PAD) severity using digital subtraction angiography (DSA) as reference standard. METHODS: Informed consent was waived for this Institutional Review Board approved retrospective study. PAD patients were recruited from July 2016 to September 2020. Two radiologists evaluated PAD severity on DSA and CTA using runoff score. The patients were divided into two groups: mild PAD (DSA score ≤ 7) vs. severe PAD (DSA score > 7). After segmenting lower extremity muscles from CTA, 95 features were extracted for univariable analysis, logistic regression model (LRM) analysis, and sub-dataset analysis (PAD prediction based on only part of the images). AUC of CTA score and LRMs for PAD prediction were calculated. Features were analyzed using Student's t test and chi-squared test. p < 0.05 was considered statistically significant. RESULTS: A total of 56 patients (69 ± 11 years; 38 men) with 56 lower legs were enrolled in this study. The lower leg muscles of mild PAD group (36 patients) showed higher CT values (44.6 vs. 39.5, p < 0.001) with smaller dispersion (35.6 vs. 41.0, p < 0.001) than the severe group (20 patients). The AUC of CTA score, LRM-I (constructed with muscle features), and LRM-II (constructed with muscle features and CTA score) for PAD severity prediction were 0.81, 0.84, and 0.89, respectively. The highest predictive performance was observed in the image subset of the middle and inferior segments of lower extremity (LRM-I, 0.83; LRM-II, 0.90). CONCLUSIONS: Lower extremity muscle features are associated with PAD severity and can be used for PAD prediction. CRITICAL RELEVANCE STATEMENT: Quantitative image features of lower extremity muscles are associated with the degree of lower leg arterial stenosis/occlusion and can be a beneficial supplement to the current imaging methods of vascular stenosis evaluation for the prediction of peripheral arterial disease severity. KEY POINTS: • Compared with severe PAD, lower leg muscles of mild PAD showed higher CT values (39.5 vs. 44.6, p < 0.001). • Models developed with muscle CT features had AUC = 0.89 for predicting PAD. • PAD severity prediction can be realized through the middle and inferior segment of images (AUC = 0.90).

Development of a novel prognostic signature based on single-cell combined bulk RNA analysis in breast cancer.

BACKGROUND: Breast cancer (BC), a malignant tumor, is a significant cause of death and disability among women globally. Recent research indicates that copy number variation plays a crucial role in tumor development. In this study, we employed the Single-Cell Variational Aneuploidy Analysis (SCEVAN) algorithm to differentiate between malignant and non-malignant cells, aiming to identify genetic signatures with prognostic relevance for predicting patient survival. METHODS: We analyzed gene expression profiles and associated clinical data from the Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) databases. Using the SCEVAN algorithm, we distinguished malignant from non-malignant cells and investigated cellular interactions within the tumor microenvironment (TME). We categorized TCGA samples based on differentially expressed genes (DEGs) between these cell types. Subsequent Kyoto Encyclopedia of Genes and Genomes pathway analysis was conducted. Additionally, we developed polygenic models for the DEGs using least absolute shrinkage and selection operator-penalized Cox regression analysis. To assess the prognostic accuracy of these characteristics, we generated Kaplan-Meier and receiver operating characteristic curves from training and validation datasets. We also monitored the expression variations of prognostic genes across the pseudotime of malignant cells. Patients were divided into high-risk and low-risk groups based on median risk scores to compare their TME and identify potential therapeutic agents. Lastly, polymerase chain reaction was used to validate seven pivotal genes. RESULTS: The SCEVAN algorithm identified distinct malignant and non-malignant cells in GSE180286. Cellchat analysis revealed significantly increased cellular communication, particularly between fibroblasts, endothelial cells and malignant cells. The DEGs were predominantly involved in immune-related pathways. TCGA samples were classified into clusters A and B based on these genes. Cluster A, enriched in immune pathways, was associated with poorer prognosis, whereas cluster B, predominantly involved in circadian rhythm pathways, showed better outcomes. We constructed a 14-gene prognostic signature, validated in a 1:1 internal TCGA cohort and external GEO datasets (GSE42568 and GSE146558). Kaplan-Meier analysis confirmed the prognostic signature's accuracy (p < 0.001). Receiver operating characteristic curve analysis demonstrated the predictive reliability of these prognostic features. Single-cell pseudotime analysis with monocle2 highlighted the distinct expression trends of these genes in malignant cells, underscoring the intratumoral heterogeneity. Furthermore, we explored the differences in TME between high- and low-risk groups and identified 16 significantly correlated drugs. CONCLUSION: Our findings suggest that the 14-gene prognostic signature could serve as a novel biomarker for forecasting the prognosis of BC patients. Additionally, the immune cells and pathways in different risk groups indicate that immunotherapy may be a crucial component of treatment strategies for BC patients.

Unenhanced computed tomography radiomics help detect endoleaks after endovascular repair of abdominal aortic aneurysm.

OBJECTIVES: To explore the feasibility of unenhanced CT images for endoleak detection of abdominal aortic aneurysm (AAA) after endovascular repair (EVAR). METHODS: Patients who visited our hospital after EVAR from July 2014 to September 2021 were retrospectively collected. Two radiologists evaluated the presence or absence of endoleaks using the combination of contrast-enhanced and unenhanced CT as the referenced standard. After segmenting the aneurysm sac of the unenhanced CT, the radiomic features were automatically extracted from the region of interest. Histogram features of patients with and without endoleak were statistically analyzed to explore the differences between the two groups. Twelve common machine learning (ML) models based on radiomic features were constructed to evaluate the performance of endoleak detection with unenhanced CT images. RESULTS: The study included 216 patients (69 ± 8 years; 191 men) with AAA, including 64 patients with endoleaks. A total of 1955 radiomic features of unenhanced CT were extracted. Compared with patients without endoleak, the aneurysm sac outside the stent of patients with endoleak had higher CT attenuation (41.7 vs. 33.6, p < 0.001) with smaller dispersion (51.5 vs. 58.8, p < 0.001). The average area under the curve (AUC) of the ML models constructed with unenhanced CT radiomics was 0.86 ± 0.05, the accuracy was 81% ± 4, the sensitivity was 88% ± 10, and the specificity was 78% ± 5. When fixing the sensitivity to > 90% (92% ± 2), the models retained specificity at 72% ± 10. CONCLUSIONS: Unenhanced CT features exhibit significant differences between patients with and without endoleak and can help detect endoleaks in AAA after EVAR with high sensitivity. CLINICAL RELEVANCE STATEMENT: Unenhanced CT radiomics can help provide an alternative method of endoleak detection in patients who have adverse reactions to contrast media. This study further exploits the value of unenhanced CT examinations in the clinical management and surveillance of postoperative abdominal aortic aneurysm. KEY POINTS: • Unenhanced CT features of the aneurysm sac outside the stent exhibit significant differences between patients with and without endoleak. The endoleak group showed higher unenhanced CT attenuation (41.7 vs 33.6, p < .001) with smaller dispersion (51.5 vs 58.8, p < .001) than the nonendoleak group. • Unenhanced CT radiomics can help detect endoleaks after intervention. The average area under the curve (AUC) of twelve common machine learning models constructed with unenhanced CT radiomics was 0.86 ± 0.05, the average accuracy was 81% ± 4. • When fixing the sensitivity to > 90% (92% ± 2), the machine learning models retained average specificity at 72% ± 10.

Prediction of Hepatic Encephalopathy After Transjugular Intrahepatic Portosystemic Shunt Based on CT Radiomic Features of Visceral Adipose Tissue.

RATIONALE AND OBJECTIVES: To evaluate the performance and clinical utility of CT radiomic features of visceral adipose tissue (VAT) in the prediction of hepatic encephalopathy (HE) after transjugular intrahepatic portosystemic shunt (TIPS). MATERIALS AND METHODS: This multi-center study was retrospectively designed. Patients with cirrhosis who underwent TIPS were recruited from January 2015 to December 2020. Pre-TIPS contrast-enhanced CT images were collected for VAT segmentation and radiomic feature extraction. Least absolute shrinkage and selection operator regression with ten-fold cross-validation was performed to reduce dimension. Logistic regression with regularization, support vector machine, and random forest were used for model construction. RESULTS: A total of 130 patients (90 men; mean age, 54 ± 11 years) were finally enrolled. The cohort was split into 85 patients for the training set (58 men; mean age, 53 ± 12 years) with 19 HE, 21 patients for the internal test set (17 men; mean age, 53 ± 11 years) with 5 HE, and 24 patients for the external test set (15 men; mean age, 55 ± 11 years). Ten radiomic features and C-reactive protein constituted radiomic-clinical models with the best performance. The average area under the receiver operating characteristic curve is 0.97 in the training set and 0.84 in the test sets. For a fixed sensitivity of 0.90, the specificity and negative predictive value of the model is 0.63 and 1.00, respectively; while for a fixed specificity of 0.90, the sensitivity and positive predictive value is 0.60 and 0.75, respectively. CONCLUSION: Machine learning models based on CT radiomic features extracted from VAT can predict post-TIPS HE with satisfactory performance. CLINICAL RELEVANCE STATEMENT: Our machine learning models based on CT radiomic features of visceral adipose tissue in patients with cirrhosis may assist in predicting hepatic encephalopathy after transjugular intrahepatic portosystemic shunt, indicating its potential in patient selection and clinical decision-making. KEY POINTS: Radiomics of visceral adipose tissue provide great help in predicting hepatic encephalopathy after transjugular intrahepatic portosystemic shunt. The clinical-radiomic models showed satisfactory performance with an average area under the receiver operating characteristic curve of 0.84. The model can hypothetically provide 90% sensitivity and 100% negative predictive value for guiding patients who are considering transjugular intrahepatic portosystemic shunt.

Characterization of a highly virulent Avibacterium paragallinarum isolate.

Infectious coryza (IC) is an important respiratory infectious disease in chickens. In this study, an Avibacterium paragallinarum Page serovar C strain, named ZJ-C, was isolated from a local layer flock that was routinely vaccinated with an inactivated trivalent vaccine, using reference strain Modesto as the serovar C immunogen. The pathogenicity, immunogenicity, and genetic characteristics of ZJ-C were studied. The minimum pathogenic dose of the isolate was 100 CFU, which was 1/1,000 of the dose of the serovar C reference strain Modesto. The vaccination-challenge trial in specific pathogen-free (SPF) chickens showed that the ZJ-C bacterin could provide 100% protection against challenge from both ZJ-C and Modesto strains, whereas Modesto provided 100% protection against challenge from itself, but only 70% protection against ZJ-C. Sequence analysis of the HMTp210 hypervariable region (region 2) showed that the homology of region 2 between ZJ-C and Modesto was 96.14%, whereas the homology between ZJ-C and the Kume serovar C-4 reference strain HP60 was 99.83%. Phylogenetic analysis of region 2 showed that ZJ-C was most closely related to cluster C-4, represented by HP60. The experimental data obtained in this study will help the selection of optimal vaccine strains and assist serotyping studies of Av. paragallinarum.

Vaccination with inactivated multivalent vaccines is a primary strategy to control Infectious coryza. Avibacterium paragallinarum serotyping is important for effective protection as inactivated whole-cell vaccines provide protection against only the serogroup or serovar from which the vaccine was derived. In this study, a novel serovar within the serogroup C Avibacterium paragallinarum isolate ZJ-C has been characterized first time in China. It was highly virulent and induced 100% cross-protection to Modesto bacterin vaccinated chickens, but not the other way around.

Anti-obesity effects exerted by Dioscorea opposita Thunb. polysaccharides in diet-induced obese mice.

Obesity is characterized by chronic inflammation, insulin resistance, and gut microbiota dysbiosis. Dioscorea opposita Thunb. is a traditional food and medicine homolog from China. In the present study, polysaccharides isolated from a water extract of Dioscorea opposita Thunb. (DOTPs) were prepared. We showed that DOTPs reduced body weight, accumulation of fat tissues, insulin resistance, and inflammation in high-fat diet (HFD)-fed mice. Further experiments showed that DOTPs could regulate the composition of the gut microbiota in HFD mice. DOTPs supplementation in HFD-fed mice resulted in the reduction of the Firmicutes-to-Bacteroidetes ratio. We further demonstrated that DOTPs supplementation enhanced bacterial levels of Akkermansia and reduced levels of Ruminiclostridium_9. A significant reduction of glycolysis metabolism related to obesity and gut microbiota dysbiosis was also observed upon administration of DOTPs. Our results suggest that DOTPs can produce significant anti-obesity effects, by inhibiting systematic inflammation and ameliorating gut microbiota dysbiosis in diet-induced obese mice.

Investigation of 60Co Irradiation on the Volatile Organic Compounds from Finger Citron (Citri Sarcodactylis Fructus) Using GC-IMS.

In recent years, as the desire for a healthy lifestyle has become more widespread, consumers are gaining an increasing appreciation for safe, high-quality food. Researchers are constantly seeking new ways to protect foods from insect pests and fungi. This study used GC-IMS to analyze the volatile organic compounds and flavor characteristics of Finger Citron in response to different doses of 60Co irradiation. The principal component analysis method was used to explore the overall differences in flavor spectra, and a total of 60 compounds were identified. The fingerprints of volatile organic compounds in the samples showed that the volatile organic compounds with doses of 60Co irradiation in about 0 kGy and 5 kGy are similar, while the 10 kGy samples are quite different. The PCA results showed that the similarity between 0 kGy and 5 kGy was slightly higher, and the difference between 10 kGy and other samples was greater. Therefore, it was determined that 60Co irradiation with a 10 kGy intensity has a significant influence on the content of volatile oils components, while 60Co irradiation with a 5 kGy intensity has little effect. Irradiation technology is demonstrated as a promising method of food sterilization, but the irradiation dose and chemical composition must be taken into consideration.

[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.

Lumican is a potential predictor on the efficacy of concurrent chemoradiotherapy in cervical squamous cell carcinoma.

Purpose: To identify new novel biomarkers for predicting the efficacy of concurrent chemoradiotherapy(CCRT) in cervical squamous cell carcinoma(CESC). Methods: Gene expression datasets GSE56363, GSE5787, and GSE168009 were analyzed to identify candidate genes to predict the efficacy of CCRT in CESC. Single-cell RNA sequencing (scRNA-seq) data from GSE168652 and CESC patients in The Cancer Genome Atlas(TCGA) were systematically analyzed to explore possible molecular mechanisms. Kaplan-Meier evaluated the correlation between LUM (Lumican) and prognostic significance. The expression of LUM protein in biopsy tissues before CCRT was detected by immunohistochemistry in 15 CESC patients. Results: LUM mRNA levels were significantly upregulated in nonresponders of CESC.patients receiving CCRT and positively correlated with poor therapeutic effect. Furthermore, high expression of LUM influenced the immune microenvironment in CESC patient-derived organoids treated with CCRT. LUM overexpression in CESC cells induced resistance to CCRT, potentially via immune landscape modulation. Gene Set Enrichment Analysis (GSEA) revealed that possible mechanisms underlying resistance to CCRT might involve the PARs and IL1 signaling pathway affecting the immune landscape. Conclusions: High LUM expression is correlated with poor efficacy in CESC patients receiving CCRT, possibly through the PARs and IL1 signaling pathway affecting the immune landscape.

Molecular Evolution and Genetic Variation of G2-Like Transcription Factor Genes in Wheat (Triticum aestivum L.).

The GOLDEN2-LIKE (G2-like) gene family members provide significant contributions to the growth and development of plants. In this study, a total of 76 wheat G2-like gene family members (TaG1-TaG76) were detected in the wheat genome and were categorized into three groups (including six subgroups) based on the gene structure and protein motif analyses. These genes were unevenly distributed in 19 of 21 wheat chromosomes. A total of 63 segmental duplication pairs of TaG2-like genes were identified in the wheat genome. The expression levels of all the TaG2-like genes indicated that TaG2-like genes showed different expression patterns in various organs and tissues. Moreover, the transcriptions of TaG2-like genes were significantly affected under abiotic stress (cold, ABA, NaCl, and PEG). This study offered valuable insights into the functional characterization of TaG2-like genes in wheat.