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Constructing bio-based composite hydrogel materials are receiving much interest, while regulating the interactions of the hydrogel components and integrating functions for multi-application meet various challenges. Herein, composite hydrogels were prepared by cross-linking of poly-acrylamide (PAM) and poly-N-[3-(Dimethylamino) propyl] acrylamide (PDMAPAA), assisted by natural galactomannan (GM) regulation. Even distribution and compatibility of GM in the three-dimensional materials were proved by a series of chemical and morphological characterizations, which favored the improvement of mechanical properties (~80 kPa) and flexibility. Besides, the hydrogels were well-connected with double networks of noncovalent intermolecular hydrogen bonding interactions and hydrophobic interactions, in addition to covalent-linked polymers. Due to great amount of inner hydrogen bond linkages, the hydrogels present satisfying anti-swelling capabilities (<15 %), exhibiting high potential for application in water treatment. Meanwhile, abundant surface functional groups provided possibilities to form interactive layer with the various substrates surface, exhibiting highly adhesive properties. Significant dyes adsorption capabilities were revealed on the hydrogels according to the electrostatic attraction with Congo red and hydrogen bond interactions with Brilliant green respectively. Thus, the proposed composite hydrogels integrated multi-functions due to the tuning the surface groups and cross-linking interactions, which provided deeper understanding on bio-based materials on fields of water treatment and environmental protection.

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RNA-binding proteins (RBPs) are attractive targets in human pathologies. Despite a number of efforts to target RBPs with small molecules, it is still difficult to develop RBP inhibitors, asking for a deeper understanding of how to chemically perturb RNA-binding activity. In this study, we found that the thiopurine drugs (6-mercaptopurine and 6-thioguanine) effectively disrupt CELF1-RNA interaction. The disrupting activity relies on the formation of disulfide bonds between the thiopurine drugs and CELF1. Mutating the cysteine residue proximal to the RNA recognition motifs (RRMs), or adding reducing agents, abolishes the disrupting activity. Furthermore, the 1,2,4-triazole-3-thione, a thiopurine analogue, was identified with 20-fold higher disrupting activity. Based on this analogue, we found that compound 9 disrupts CELF1-RNA interaction in living cells and ameliorates CELF1-mediated myogenesis deficiency. In summary, we identified a thiol-mediated binding mechanism for thiopurine drugs and their derivatives to perturb protein-RNA interaction, which provides novel insight for developing RBP inhibitors. Additionally, this work may benefit the pharmacological and toxicity research of thiopurine drugs.

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BACKGROUND: Sustained siRNA release from nanocarriers is difficult to achieve inside the cell after entry: typically, all nanocarriers exhibit burst release of the cargo into the cytoplasm. RESEARCH DESIGN AND METHODS: Layer by layer (LbL) nanoparticles (NPs) can be constructed so that they escape endosomes intact, and subsequently exhibit sustained release of the cargo. Our work quantifies intra-cellular siRNA release from multilayered NPs, evaluates mechanism behind the sustained release, and optimizes the duration of release. RESULTS: Intra-cellular studies showed that nanoparticles developed with 4 layers of polyL-arginine, alternated with 3 layers of siRNA layers was able to elicit effective and prolonged SPARC knockdown activity over 21 days with a single dose treatment. For the first time, we have quantified the amounts of released siRNA in the cytoplasm and the amount of siRNA remaining inside the nanoparticles at each timepoint. Furthermore, we have correlated the amount of released siRNA within cells by LbL NPs to the cellular knockdown efficiency of multilayered delivery system. CONCLUSIONS: This methodology may provide an excellent screening tool for assessing the duration of gene silencing by various nanocarrier formulations.

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The Editors of Medical Science Monitor wish to inform you that the above manuscript has been retracted from publication due to concerns with the credibility and originality of the study, the manuscript content, and the Figure images. Reference: Yihua Zhang, Yang Tan, Hao Wang, Minhui Xu, Lunshan Xu. Long Non-Coding RNA Plasmacytoma Variant Translocation 1 (PVT1) Enhances Proliferation, Migration, and Epithelial-Mesenchymal Transition (EMT) of Pituitary Adenoma Cells by Activating ß-Catenin, c-Myc, and Cyclin D1 Expression. Med Sci Monit, 2019; 25: 7652-7659. DOI: 10.12659/MSM.917110.

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Movimiento Celular , Proliferación Celular , Ciclina D1 , Transición Epitelial-Mesenquimal , Neoplasias Hipofisarias , Proteínas Proto-Oncogénicas c-myc , ARN Largo no Codificante , beta Catenina , ARN Largo no Codificante/genética , ARN Largo no Codificante/metabolismo , Humanos , Transición Epitelial-Mesenquimal/genética , beta Catenina/metabolismo , beta Catenina/genética , Proliferación Celular/genética , Movimiento Celular/genética , Neoplasias Hipofisarias/genética , Neoplasias Hipofisarias/metabolismo , Neoplasias Hipofisarias/patología , Ciclina D1/metabolismo , Ciclina D1/genética , Proteínas Proto-Oncogénicas c-myc/metabolismo , Proteínas Proto-Oncogénicas c-myc/genética , Línea Celular Tumoral , Adenoma/genética , Adenoma/metabolismo , Adenoma/patología , Regulación Neoplásica de la Expresión Génica

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Deep learning-based methods for generating functional proteins address the growing need for novel biocatalysts, allowing for precise tailoring of functionalities to meet specific requirements. This advancement leads to the development of highly efficient and specialized proteins with diverse applications across scientific, technological, and biomedical fields. This study establishes a pipeline for protein sequence generation with a conditional protein diffusion model, namely CPDiffusion, to create diverse sequences of proteins with enhanced functions. CPDiffusion accommodates protein-specific conditions, such as secondary structures and highly conserved amino acids. Without relying on extensive training data, CPDiffusion effectively captures highly conserved residues and sequence features for specific protein families. We applied CPDiffusion to generate artificial sequences of Argonaute (Ago) proteins based on the backbone structures of wild-type (WT) Kurthia massiliensis Ago (KmAgo) and Pyrococcus furiosus Ago (PfAgo), which are complex multi-domain programmable endonucleases. The generated sequences deviate by up to nearly 400 amino acids from their WT templates. Experimental tests demonstrated that the majority of the generated proteins for both KmAgo and PfAgo show unambiguous activity in DNA cleavage, with many of them exhibiting superior activity as compared to the WT. These findings underscore CPDiffusion's remarkable success rate in generating novel sequences for proteins with complex structures and functions in a single step, leading to enhanced activity. This approach facilitates the design of enzymes with multi-domain molecular structures and intricate functions through in silico generation and screening, all accomplished without the need for supervision from labeled data.

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The protein dynamical transition at ~200 K, where the biomolecule transforms from a harmonic, non-functional form to an anharmonic, functional state, has been thought to be slaved to the thermal activation of dynamics in its surface hydration water. Here, by selectively probing the dynamics of protein and hydration water using elastic neutron scattering and isotopic labeling, we found that the onset of anharmonicity in the two components around 200 K is decoupled. The one in protein is an intrinsic transition, whose characteristic temperature is independent of the instrumental resolution time, but varies with the biomolecular structure and the amount of hydration, while the one of water is merely a resolution effect.

Asunto(s)

Agua , Agua/química , Proteínas/química , Proteínas/metabolismo , Difracción de Neutrones , Temperatura , Marcaje Isotópico

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Protein language models (PLMs) play a dominant role in protein representation learning. Most existing PLMs regard proteins as sequences of 20 natural amino acids. The problem with this representation method is that it simply divides the protein sequence into sequences of individual amino acids, ignoring the fact that certain residues often occur together. Therefore, it is inappropriate to view amino acids as isolated tokens. Instead, the PLMs should recognize the frequently occurring combinations of amino acids as a single token. In this study, we use the byte-pair-encoding algorithm and unigram to construct advanced residue vocabularies for protein sequence tokenization, and we have shown that PLMs pre-trained using these advanced vocabularies exhibit superior performance on downstream tasks when compared to those trained with simple vocabularies. Furthermore, we introduce PETA, a comprehensive benchmark for systematically evaluating PLMs. We find that vocabularies comprising 50 and 200 elements achieve optimal performance. Our code, model weights, and datasets are available at https://github.com/ginnm/ProteinPretraining . SCIENTIFIC CONTRIBUTION: This study introduces advanced protein sequence tokenization analysis, leveraging the byte-pair-encoding algorithm and unigram. By recognizing frequently occurring combinations of amino acids as single tokens, our proposed method enhances the performance of PLMs on downstream tasks. Additionally, we present PETA, a new comprehensive benchmark for the systematic evaluation of PLMs, demonstrating that vocabularies of 50 and 200 elements offer optimal performance.

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OBJECTIVE: Breast cancer has become the most prevalent malignant tumor in women, and the occurrence of distant metastasis signifies a poor prognosis. Utilizing predictive models to forecast distant metastasis in breast cancer presents a novel approach. This study aims to utilize readily available clinical data and advanced machine learning algorithms to establish an accurate clinical prediction model. The overall objective is to provide effective decision support for clinicians. METHODS: Data from 239 patients from two centers were analyzed, focusing on clinical blood biomarkers (tumor markers, liver and kidney function, lipid profile, cardiovascular markers). Spearman correlation and the least absolute shrinkage and selection operator regression were employed for feature dimension reduction. A predictive model was built using LightGBM and validated in training, testing, and external validation cohorts. Feature importance correlation analysis was conducted on the clinical model and the comprehensive model, followed by univariate and multivariate regression analysis of these features. RESULTS: Through internal and external validation, we constructed a LightGBM model to predict de novo bone metastasis in newly diagnosed breast cancer patients. The area under the receiver operating characteristic curve values of this model in the training, internal validation test, and external validation test1 cohorts were 0.945, 0.892, and 0.908, respectively. Our validation results indicate that the model exhibits high sensitivity, specificity, and accuracy, making it the most accurate model for predicting bone metastasis in breast cancer patients. Carcinoembryonic Antigen, creatine kinase, albumin-globulin ratio, Apolipoprotein B, and Cancer Antigen 153 (CA153) play crucial roles in the model's predictions. Lipoprotein a, CA153, gamma-glutamyl transferase, α-Hydroxybutyrate dehydrogenase, alkaline phosphatase, and creatine kinase are positively correlated with breast cancer bone metastasis, while white blood cell ratio and total cholesterol are negatively correlated. CONCLUSION: This study successfully utilized clinical blood biomarkers to construct an artificial intelligence model for predicting distant metastasis in breast cancer, demonstrating high accuracy. This suggests potential clinical utility in predicting and identifying distant metastasis in breast cancer. These findings underscore the potential prospect of developing economically efficient and readily accessible predictive tools in clinical oncology.

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Inteligencia Artificial , Biomarcadores de Tumor , Neoplasias Óseas , Neoplasias de la Mama , Humanos , Neoplasias de la Mama/patología , Femenino , Neoplasias Óseas/secundario , Neoplasias Óseas/sangre , Persona de Mediana Edad , Biomarcadores de Tumor/sangre , Adulto , Anciano , Curva ROC , Aprendizaje Automático , Valor Predictivo de las Pruebas

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Fine-tuning pretrained protein language models (PLMs) has emerged as a prominent strategy for enhancing downstream prediction tasks, often outperforming traditional supervised learning approaches. As a widely applied powerful technique in natural language processing, employing parameter-efficient fine-tuning techniques could potentially enhance the performance of PLMs. However, the direct transfer to life science tasks is nontrivial due to the different training strategies and data forms. To address this gap, we introduce SES-Adapter, a simple, efficient, and scalable adapter method for enhancing the representation learning of PLMs. SES-Adapter incorporates PLM embeddings with structural sequence embeddings to create structure-aware representations. We show that the proposed method is compatible with different PLM architectures and across diverse tasks. Extensive evaluations are conducted on 2 types of folding structures with notable quality differences, 9 state-of-the-art baselines, and 9 benchmark data sets across distinct downstream tasks. Results show that compared to vanilla PLMs, SES-Adapter improves downstream task performance by a maximum of 11% and an average of 3%, with significantly accelerated convergence speed by a maximum of 1034% and an average of 362%, the training efficiency is also improved by approximately 2 times. Moreover, positive optimization is observed even with low-quality predicted structures. The source code for SES-Adapter is available at https://github.com/tyang816/SES-Adapter.

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Modelos Moleculares , Proteínas , Proteínas/química , Conformación Proteica , Procesamiento de Lenguaje Natural

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Breast cancer metastasis significantly impacts women's health globally. This study aimed to construct predictive models using clinical blood markers and ultrasound data to predict distant metastasis in breast cancer patients, ensuring clinical applicability, cost-effectiveness, relative non-invasiveness, and accessibility of these models. Analysis was conducted on data from 416 patients across two centers, focusing on clinical blood markers (tumor markers, liver and kidney function indicators, blood lipid markers, cardiovascular biomarkers) and maximum lesion diameter from ultrasound. Feature reduction was performed using Spearman correlation and LASSO regression. Two models were built using LightGBM: a clinical model (using clinical blood markers) and a combined model (incorporating clinical blood markers and ultrasound features), validated in training, internal test, and external validation (test1) cohorts. Feature importance analysis was conducted for both models, followed by univariate and multivariate regression analyses of these features. The AUC values of the clinical model in the training, internal test, and external validation (test1) cohorts were 0.950, 0.795, and 0.883, respectively. The combined model showed AUC values of 0.955, 0.835, and 0.918 in the training, internal test, and external validation (test1) cohorts, respectively. Clinical utility curve analysis indicated the combined model's superior net benefit in identifying breast cancer with distant metastasis across all cohorts. This suggests the combined model's superior discriminatory ability and strong generalization performance. Creatine kinase isoenzyme (CK-MB), CEA, CA153, albumin, creatine kinase, and maximum lesion diameter from ultrasound played significant roles in model prediction. CA153, CK-MB, lipoprotein (a), and maximum lesion diameter from ultrasound positively correlated with breast cancer distant metastasis, while indirect bilirubin and magnesium ions showed negative correlations. This study successfully utilized clinical blood markers and ultrasound data to develop AI models for predicting distant metastasis in breast cancer. The combined model, incorporating clinical blood markers and ultrasound features, exhibited higher accuracy, suggesting its potential clinical utility in predicting and identifying breast cancer distant metastasis. These findings highlight the potential prospects of developing cost-effective and accessible predictive tools in clinical oncology.

Asunto(s)

Biomarcadores de Tumor , Neoplasias de la Mama , Metástasis de la Neoplasia , Humanos , Neoplasias de la Mama/sangre , Neoplasias de la Mama/patología , Neoplasias de la Mama/diagnóstico por imagen , Femenino , Biomarcadores de Tumor/sangre , Persona de Mediana Edad , Adulto , Ultrasonografía/métodos , Anciano

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DNase I hypersensitive sites (DHSs) are chromatin regions highly sensitive to DNase I enzymes. Studying DHSs is crucial for understanding complex transcriptional regulation mechanisms and localizing cis-regulatory elements (CREs). Numerous studies have indicated that disease-related loci are often enriched in DHSs regions, underscoring the importance of identifying DHSs. Although wet experiments exist for DHSs identification, they are often labor-intensive. Therefore, there is a strong need to develop computational methods for this purpose. In this study, we used experimental data to construct a benchmark dataset. Seven feature extraction methods were employed to capture information about human DHSs. The F-score was applied to filter the features. By comparing the prediction performance of various classification algorithms through five-fold cross-validation, random forest was proposed to perform the final model construction. The model could produce an overall prediction accuracy of 0.859 with an AUC value of 0.837. We hope that this model can assist scholars conducting DNase research in identifying these sites.

Asunto(s)

Cromatina , Desoxirribonucleasa I , Genoma Humano , Humanos , Desoxirribonucleasa I/metabolismo , Desoxirribonucleasa I/genética , Desoxirribonucleasa I/química , Cromatina/genética , Cromatina/metabolismo , Cromatina/química , Biología Computacional/métodos , Algoritmos , Secuencias Reguladoras de Ácidos Nucleicos/genética

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INTRODUCTION: The overall reject rate (RR) of our newly set up Radiology department was an average of 14%, higher than the recommended 8% target and 10% threshold set by the American Association of Physicists in Medicine (AAPM). An analysis done to identify potential causes of a high RR suggested that radiographers might have been rejecting images of diagnostic value. A lack of consistency in the definition of a diagnostic value image amongst radiographers may be a possible cause in the higher overall RR. This study aims to investigate potential discrepancies among radiographers in defining a diagnostic radiograph. METHODS: An online survey composed of an image bank with a questionnaire was created, participants grade each image as either accepted or rejected. Fleiss Kappa was used to determine the level of agreement between the radiographers in accepting or rejecting the images in the image bank. RESULTS: Twenty radiographers with varying years of experience participated in this study. There was fair agreement amongst the radiographers' judgements, k=.277 (95% CI, .277 to .278), p < .005. Individual kappa for the "Accept" and "Reject" categories were both 0.277. There is no significant difference in the agreement level across the junior (k=.278), intermediate (k=.371) and senior (k=.275) radiographers. CONCLUSION: The result suggests that there is discrepancy in the radiographers' definition of a diagnostic radiograph and this misalignment of radiographers' perception might be one of the underlying causes of high RR. IMPLICATIONS FOR PRACTICE: This study has provided the researchers with a better insight on the underlying cause of the department high RR. By calibrating the radiographers' definition of a diagnostic radiograph, it will help realign the radiographer's agreement on when a radiograph should be rejected. This will reduce the overall RR and patient's overall dose. A lower RR translates to a more efficient turnaround time in General Radiography services, ensuring quality service is provided without further strain on our limited resources.

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Objective: This study aims to develop an artificial intelligence model utilizing clinical blood markers, ultrasound data, and breast biopsy pathological information to predict the distant metastasis in breast cancer patients. Methods: Data from two medical centers were utilized, Clinical blood markers, ultrasound data, and breast biopsy pathological information were separately extracted and selected. Feature dimensionality reduction was performed using Spearman correlation and LASSO regression. Predictive models were constructed using LR and LightGBM machine learning algorithms and validated on internal and external validation sets. Feature correlation analysis was conducted for both models. Results: The LR model achieved AUC values of 0.892, 0.816, and 0.817 for the training, internal validation, and external validation cohorts, respectively. The LightGBM model achieved AUC values of 0.971, 0.861, and 0.890 for the same cohorts, respectively. Clinical decision curve analysis showed a superior net benefit of the LightGBM model over the LR model in predicting distant metastasis in breast cancer. Key features identified included creatine kinase isoenzyme (CK-MB) and alpha-hydroxybutyrate dehydrogenase. Conclusion: This study developed an artificial intelligence model using clinical blood markers, ultrasound data, and pathological information to identify distant metastasis in breast cancer patients. The LightGBM model demonstrated superior predictive accuracy and clinical applicability, suggesting it as a promising tool for early diagnosis of distant metastasis in breast cancer.

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The utilization of deformed microcavities, such as elliptical microdisks, has been widely acknowledged as an effective solution for achieving free-space emission in microcavity lasers. However, the deformations introduced in the microcavity structure tend to decrease the quality factor (Q factor), resulting in weakened output intensity. To address this issue, one potential approach is to employ highly efficient laser gain media that can compensate for the negative impact of the structure on the output intensity. In this study, we employed the exceptional laser crystal material Nd:YAG as the laser gain medium and successfully fabricated an elliptical microdisk laser with a major semiaxis of 15 µm and an eccentricity ratio of 0.15. By utilizing an 808 nm laser for pumping, we were able to achieve free-space laser emission with a slope efficiency of 1.7% and a remarkable maximum output power of 58 µW. This work contributes toward the advancement of the application of deformation microcavity lasers.

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OBJECTIVES: To investigate the clinical character of differentiated thyroid cancer (DTC) coexisting with Hashimoto's thyroiditis (HT) and provide state-of-art evidence for personalized radioactive iodine-131 therapy (RAIT) for patients coexisting with HT. METHODS: From January 2000 to January 2023, PubMed, Embase, and Web of Science databases were searched for relevant original articles that published in English on the RAIT efficacy for DTC with HT. RevMan 5.4 and Stata 17.0 were used for data analysis. RESULTS: Eleven studies involving 16 605 DTC patients (3321 with HT) were included. HT was more frequent in female (OR: 2.90, 95% confidence interval [CI]: 1.77-4.76, P < .00001). The size of tumour (MD: -0.20, 95% CI: -0.30 to -0.11), extrathyroidal extension rate (OR: 0.77, 95% CI: 0.67-0.90), and metastasis rate (OR: 0.18, 95% CI: 0.08-0.41) were less in HT, but tumour, node, metastasis (TNM) stage had no significant difference among HT and non-HT group. Disease-free survival (DFS) rate (OR: 1.96, 95% CI: 1.57-2.44, P < .00001), 5-year DFS (OR: 1.73, 95% CI: 1.04-2.89, P = .04), and 10-year DFS (OR: 1.56, 95% CI: 1.17-2.09, P = .003) were higher in HT group. The recurrent (OR: 0.62, 95% CI: 0.45-0.83, P = .002), RAIT dosage (MD = -38.71, 95% CI: -60.86 to -16.56, P = .0006), and treatment (MD: -0.13, 95% CI: -0.22 to -0.03, P = .008) were less in HT group. CONCLUSIONS: DTC coexisting with HT was associated with less invasion. DFS of HT group was higher than non-HT group after RAIT. Low-dose treatment did not impair the efficacy of RAIT in DTC with HT. ADVANCES IN KNOWLEDGE: Hashimoto's thyroiditis is a risk for DTC, but it minimalizes the progression of cancer and enhance the efficacy of RAIT, which should be considered in personalizing RAIT.

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Enfermedad de Hashimoto , Radioisótopos de Yodo , Neoplasias de la Tiroides , Femenino , Humanos , Enfermedad de Hashimoto/complicaciones , Enfermedad de Hashimoto/radioterapia , Radioisótopos de Yodo/uso terapéutico , Neoplasias de la Tiroides/radioterapia , Neoplasias de la Tiroides/complicaciones , Masculino

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BACKGROUND: Drug-eluting bead transarterial chemoembolization (DEB-TACE) has shown efficacy for treating hepatocellular carcinoma (HCC) with portal vein tumor thrombus (PVTT). However, whether DEB-TACE is superior to conventional TACE (cTACE) remains unclear. OBJECTIVE: This randomized controlled trial aimed to compare the efficacy and safety of DEB-TACE versus cTACE in treating HCC with PVTT. METHODS: The study was conducted at a tertiary care center in Southeast China. HCC patients with PVTT were randomized at a 1:1 ratio into the DEB-TACE or cTACE groups. The primary endpoint was progression-free survival (PFS), and the secondary endpoints were overall survival (OS) and the incidence of adverse events (AEs). An independent review committee assessed the radiologic response according to the modified Response Evaluation Criteria in Solid Tumors (mRECIST). AEs were assessed by the Common Terminology Criteria for Adverse Events (CTCAE) version 4.0. Systemic therapies were not restricted. RESULTS: Between September 2018 and July 2020, 163 patients were randomized to undergo DEB-TACE ( n =82) or cTACE ( n =81). Nine patients were excluded, and 154 patients were included in the final analysis; the median age was 55 years (range, 24-78 years), and 140 (90.9%) were male. The median PFS in the DEB-TACE group was 6.0 months (95% CI, 5.0-10.0) versus 4.0 months (95% CI, 3.0-5.0) in the cTACE group (hazard ratio, 0.63; 95% CI, 0.42-0.95; P =0.027). The DEB-TACE group showed a higher response rate [51 (66.2%) vs. 36 (46.8%); P =0.0015] and a longer median OS [12.0 months (95% CI, 9.0-16.0) vs. 8.0 months (95% CI, 7.0-11.0), P =0.039] than the cTACE group. Multivariate analysis showed that the treatment group, ALBI score, distant metastasis and additional TKIs were the four independent prognostic factors correlated with PFS. In addition, the treatment group, PVTT group and combination with surgery were independently associated with OS. AEs were similar in the two groups, and postembolization syndrome was the most frequent AE. CONCLUSION: DEB-TACE is superior to cTACE in treating HCC patients with PVTT, demonstrating improved PFS and OS with an acceptable safety profile, and may thus emerge as a promising treatment strategy for HCC patients with PVTT. TRIAL REGISTRATION: Chinese Clinical Trial Registry ChiCTR1800018035.

Asunto(s)

Carcinoma Hepatocelular , Quimioembolización Terapéutica , Neoplasias Hepáticas , Vena Porta , Humanos , Carcinoma Hepatocelular/terapia , Carcinoma Hepatocelular/complicaciones , Quimioembolización Terapéutica/métodos , Masculino , Neoplasias Hepáticas/terapia , Neoplasias Hepáticas/complicaciones , Neoplasias Hepáticas/patología , Persona de Mediana Edad , Femenino , Anciano , Adulto , China , Trombosis de la Vena/terapia , Resultado del Tratamiento

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OBJECTIVE: To summary radiating blood flow signals and evaluate their diagnostic value in differentiating benign and malignant thyroid nodules. MATERIALS AND METHODS: We retrospectively recruited consecutive patients undergoing US at 4 hospitals from 2018 to 2022. In a training dataset, the correlations of US features with malignant thyroid nodules were assessed by multivariate logistic analysis. Multivariate logistic regression models involving the ACR TI-RADS score, radiating blood flow signals and their combination were built and validated internally and externally. The AUC with 95% asymptotic normal confidence interval as well as sensitivity, specificity, negative predictive value (NPV), and positive predictive value (PPV) with 95% exact binomial confidence intervals were calculated. RESULTS: Among 2475 patients (1818 women, age: 42.47 ± 11.57; 657 men, age: 42.16 ± 11.69), there were 3187 nodules (2342 malignant nodules and 845 benign nodules). Radiating blood flow signals were an independent risk factor for diagnosing thyroid carcinoma. In the training set, the AUC of the model using the combination of radiating blood flow signals and the ACR TI-RADS score (0.95 95 % CI: [0.94, 0.97]; P < 0.001) was significantly higher than that of the ACR TI-RADS model (0.91 [0.89, 0.93]). In the two internal validation sets and the external validation set, the AUCs of the combination model were 0.97 [0.96, 0.98], 0.92 [0.88, 0.96], and 0.91 [0.86, 0.95], respectively, and were all significantly higher than that of the ACR TI-RADS score (0.92 [0.90, 0.95], 0.86 [0.81, 0.91], 0.84 [0.79, 0.89]; P < 0.001). CONCLUSION: Radiating blood flow is a new US feature of thyroid carcinomas that can significantly improve the diagnostic performance vs. the ACR TI-RADS score.

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Sensibilidad y Especificidad , Neoplasias de la Tiroides , Ultrasonografía , Humanos , Masculino , Femenino , Neoplasias de la Tiroides/diagnóstico por imagen , Adulto , Estudios Retrospectivos , Ultrasonografía/métodos , Diagnóstico Diferencial , Persona de Mediana Edad , Nódulo Tiroideo/diagnóstico por imagen , Nódulo Tiroideo/irrigación sanguínea

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In the context of China's dual carbon target, reducing personal carbon emissions has been identified as a crucial strategy to achieve the target. The 2022 Digital China Development Report emphasizes the significance of implementing the Carbon Generalized System of Preferences (CGSP) in driving individual carbon reduction efforts in China. However, the psychological factors influencing public participation in CGSP are still unknown. Based on the Extended Theory of Planned Behavior (TPB), this study explored the psychological factors of different personality trait groups' participation in the CGSP and categorized 712 respondents into Compatible, Positive, Responsible, and Susceptible based on the MINI-IPIP scale and the K-means method. The results show that the strength of willingness to engage (WTE) in CGSP was ranked as: Compatible > Positive > Responsible > Susceptible and the WTE of compatible groups is more influenced by attitude, while Perceived Behavioral Control (PBC) plays a more crucial role in other groups. Personal Norms (PN) and Policy Awareness (PA) were significant for all specific personality groups except the Susceptible group. Surprisingly subjective norms had little to do with WTE. We believe that policymakers should consider the impact of PBC on WTE when formulating policies and raise the expectation of residents in terms of the value they can obtain from participating in CGSP. Additionally, promotional activities related to PN and PA in connection with CGSP should be conducted. These efforts may help individuals better engage in CGSP.

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Personalidad , Humanos , China , Actitud , Carbono , Teoría Psicológica , Teoría del Comportamiento Planificado

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The rs72613567:TA polymorphism in 17-beta hydroxysteroid dehydrogenase 13 (HSD17B13) has been found to reduce the progression from steatosis to nonalcoholic steatohepatitis. In this study, we sought to define the pathogenic role of HSD17B13 in triggering liver inflammation. Here we find that HSD17B13 forms liquid-liquid phase separation (LLPS) around lipid droplets in the livers of nonalcoholic steatohepatitis patients. The dimerization of HSD17B13 supports the LLPS formation and promotes its enzymatic function. HSD17B13 LLPS increases the biosynthesis of platelet activating factor (PAF), which in turn promotes fibrinogen synthesis and leukocyte adhesion. Blockade of PAFR or STAT3 pathway inhibited the fibrinogen synthesis and leukocyte adhesion. Importantly, adeno-associated viral-mediated xeno-expression of human HSD17B13 exacerbated western diet/carbon tetrachloride-induced liver inflammation in Hsd17b13-/- mice. In conclusion, our results suggest that HSD17B13 LLPS triggers liver inflammation by promoting PAF-mediated leukocyte adhesion, and targeting HSD17B13 phase transition could be a promising therapeutic approach for treating hepatic inflammation in chronic liver disease.