T-2 toxin induces cardiac fibrosis by causing metabolic disorders and up-regulating Sirt3/FoxO3α/MnSOD signaling pathway-mediated oxidative stress.

T-2 toxin, an omnipresent environmental contaminant, poses a serious risk to the health of humans and animals due to its pronounced cardiotoxicity. This study aimed to elucidate the molecular mechanism of cardiac tissue damage by T-2 toxin. Twenty-four male Sprague-Dawley rats were orally administered T-2 toxin through gavage for 12 weeks at the dose of 0, 10, and 100 nanograms per gram body weight per day (ng/(g·day)), respectively. Morphological, pathological, and ultrastructural alterations in cardiac tissue were meticulously examined. Non-targeted metabolomics analysis was employed to analyze alterations in cardiac metabolites. The expression of the Sirt3/FoxO3α/MnSOD signaling pathway and the level of oxidative stress markers were detected. The results showed that exposure to T-2 toxin elicited myocardial tissue disorders, interstitial hemorrhage, capillary dilation, and fibrotic damage. Mitochondria were markedly impaired, including swelling, fusion, matrix degradation, and membrane damage. Metabonomics analysis unveiled that T-2 toxin could cause alterations in cardiac metabolic profiles as well as in the Sirt3/FoxO3α/MnSOD signaling pathway. T-2 toxin could inhibit the expressions of the signaling pathway and elevate the level of oxidative stress. In conclusion, the T-2 toxin probably induces cardiac fibrotic impairment by affecting amino acid and choline metabolism as well as up-regulating oxidative stress mediated by the Sirt3/FoxO3α/MnSOD signaling pathway. This study is expected to provide targets for preventing and treating T-2 toxin-induced cardiac fibrotic injury.

Learning to Explain is a Good Biomedical Few-Shot Learner.

MOTIVATION: Significant progress has been achieved in biomedical text mining using deep learning methods, which rely heavily on large amounts of high-quality data annotated by human experts. However, the reality is that obtaining high-quality annotated data is extremely challenging due to data scarcity (e.g., rare or new diseases), data privacy and security concerns, and the high cost of data annotation. Additionally, nearly all researches focus on predicting labels without providing corresponding explanations. Therefore, in this paper, we investigate a more realistic scenario, biomedical few-shot learning, and explore the impact of interpretability on biomedical few-shot learning. RESULTS: We present LetEx-Learning to explain-a novel multi-task generative approach that leverages reasoning explanations from large language models (LLMs) to enhance the inductive reasoning ability of few-shot learning. Our approach includes (1) collecting high-quality explanations by devising a suite of complete workflow based on LLMs through CoT prompting and self-training strategies. (2) converting various biomedical NLP tasks into a text-to-text generation task in a unified manner, where collected explanations serve as additional supervision between text-label pairs by multi-task training. Experiments are conducted on 3 few-shot settings across 6 biomedical benchmark datasets. The results show that learning to explain improves the performances of diverse biomedical NLP tasks in low-resource scenario, outperforming strong baseline models significantly by up to 6.41%. Notably, the proposed method makes the 220M LetEx perform superior reasoning explanation ability against LLMs. AVAILABILITY: Our source code and data are available at https://github.com/cpmss521/LetEx. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Conditional survival estimates for ependymomas reveal the dynamic nature of prognostication.

BACKGROUND: Traditional survival analysis is frequently used to assess the prognosis of ependymomas (EPNs); however, it may not provide additional survival insights for patients who have survived for several years. Thus, the conditional survival (CS) pattern of this disease is yet to be further investigated. This study aimed to evaluate the improvement of survival over time using CS analysis and develop a CS-based nomogram model for real-time dynamic survival estimation for EPN patients. METHODS: Data on patients with EPN were collected from the Surveillance, Epidemiology, and End Results (SEER) database. In order to construct and validate the model effectively, the selected patients were randomly divided at 7:3 ratio. CS is defined as the probability of surviving for a specified time period (y years) after initial diagnosis, given that the patient has survived x years. The CS pattern of EPN patients were explored. Then, the least absolute shrinkage and selection operator (LASSO) regression method with tenfold cross-validation was employed to identify prognostic predictors. Multivariate Cox regression was employed to develop a CS-based nomogram model, and we used this model to quantify EPN patient risk. Finally, the performance of the prediction model was also evaluated and verified. RESULTS: In total, 1829 patients diagnosed with EPN were included in the study, with 1280 and 549 patients in the training and validation cohorts, respectively. The CS analysis demonstrated that patients' OS saw gradual improvements over time. With each additional year of survival post-diagnosis, the 10-year survival rate of EPN patients saw an increase, updating from 74% initially to 79%, 82%, 85%, 87%, 89%, 91%, 93%, 96%, and 98% (after surviving for 1-9 years, respectively). The LASSO regression model, which implements tenfold cross-validation, identified 7 significant predictors (age, tumor grade, tumor site, tumor extension, tumor size, surgery and radiotherapy) to develop a CS-based nomogram model. And further risk stratification was conducted based on nomogram model for these patients. Furthermore, this survival prediction model was successfully validated. CONCLUSION: This study described the CS pattern of EPN patients and highlighted the gradual improvement of survival observed over time for long-term survivors. We also developed the first novel CS-nomogram model that enabled individualized and real-time prognosis prediction. But patients must be counselled that individual circumstances may not always accurately reflect the findings of the nomogram.

METTL16 Promotes Stability of SYNPO2L mRNA and leading to Cancer Cell Lung Metastasis by Secretion of COL10A1 and attract the Cancer-Associated Fibroblasts.

The occurrence of metastasis is a major factor contributing to poor prognosis in colorectal cancer. Different stages of the disease play a crucial role in distant metastasis. Furthermore, m6A has been demonstrated to play a significant role in regulating tumor metastasis. Therefore, we conducted an analysis of transcriptome data from high-stage and low-stage colorectal cancer patients in The Cancer Genome Atlas (TCGA) to identify genes associated with m6A-related regulation. We identified SYNPO2L as a core gene regulated by m6A, and it is correlated with adverse prognosis and metastasis in patients. Additionally, we demonstrated that the m6A writer gene Mettl16 can regulate the stability of SYNPO2L through interaction with YTHDC1. Subsequently, using Weighted Gene Co-expression Network Analysis (WGCNA), we discovered that SYNPO2L can regulate COL10A1, mediating the actions of Cancer-Associated Fibroblasts. SYNPO2L promotes the secretion of COL10A1 and the infiltration of tumor-associated fibroblasts, thereby facilitating Epithelial-Mesenchymal Transition (EMT) in tumor cells and making them more prone to distant metastasis.

Conditional survival and changing risk profile in patients with gliosarcoma.

Background: Conditional survival (CS) considers the duration since the initial diagnosis and can provide supplementary informative insights. Our objective was to evaluate CS among gliosarcoma (GSM) patients and develop a CS-incorporated nomogram to predict the conditional probability of survival. Methods: This retrospective study using the Surveillance, Epidemiology, and End Results (SEER) database included patients with GSM between 2000 and 2017. The CS was defined as the probability of surviving additional y years after already surviving for x years. The formula utilized for CS was: CS(y|x) = S(y + x)/S(x), where S(x) denotes the overall survival at x years. Univariate Cox regression, best subset regression (BSR) and the least absolute shrinkage and selection operator (LASSO) were used for significant prognostic factors screening. Following this, backward stepwise multivariable Cox regression was utilized to refine predictor selection. Finally, a novel CS-integrated nomogram model was developed and we also employed diverse evaluation methods to assess its performance. Results: This study included a total of 1,015 GSM patients, comprising 710 patients in training cohort and 305 patients in validation cohort. CS analysis indicated a gradual increase in the probability of achieving a 5-year survival, ascending from 5% at diagnosis to 13, 31, 56, and 74% with each subsequent year survived after 1, 2, 3, and 4 years post-diagnosis, respectively. Following variable screening through univariate Cox regression, BSR, and LASSO analysis, five factors-age, tumor stage, tumor size, radiotherapy, and chemotherapy-were ultimately identified for constructing the CS-nomogram model. The performance of the nomogram model was validated through discrimination and calibration assessments in both the training and validation cohorts. Furthermore, we confirmed that the effectiveness of the CS-nomogram in stratifying GSM patient risk status. Conclusion: This nationwide study delineated the CS of patients diagnosed with GSM. Utilizing national data, a CS-nomogram could provide valuable guidance for patient counseling during follow-up and risk stratification.

A nomogram for individualized prediction of new-onset postoperative atrial fibrillation in acute type A aortic dissection patients: a retrospective study.

Objective: The objective of this study is to explore the risk factors associated with new-onset postoperative atrial fibrillation (POAF) following Sun's surgery(total arch replacement using a tetrafurcate graft with stented elephant trunk implantation) for acute type A aortic dissection(AAAD) and to develop a predictive model for assessing the likelihood of new-onset POAF in patients undergoing Sun's surgery for AAAD. Methods: We reviewed the clinical parameters of patients diagnosed with AAAD who underwent Sun's surgery at Qilu Hospital between December 1, 2017 and December 31, 2022. The data was analyzed through univariable and multivariable logistic regression analysis. Variance inflation factor was used to investigate for variable collinearity. A nomogram for predicting new-onset POAF was developed and verified by bootstrap resampling. In addition, the calibration of our model was evaluated by the calibration curve and Hosmer-Lemeshow test. Furthermore, the clinical utility of our model was evaluated using the net benefit curve. Results: This study focused on a cohort of 242 patients with AAAD, among whom 42 experienced new-onset POAF, indicating an incidence rate of 17.36%. Age, left atrial diameter (LA), right atrial diameter (RA), preoperative red blood cells (RBC), and previous acute coronary syndrome (preACS) emerged as independent influences on new-onset POAF following Sun's surgery, as identified by univariable and multivariable logistic regression analysis. Collinearity analysis with demonstrated no collinearity among the variables. A user-friendly prediction nomogram for new onset POAF following Sun's surgery was formulated. The model demonstrated commendable diagnostic accuracy with an area under the curve (AUC) of 0.7852. Validation of the model through bootstrapping (1,000 repetitions) yielded an AUC of 0.8080 (95% CI: 0.8056-0.8104). affirming its robustness. Additionally, the model exhibited favorable fit, calibration, and positive net benefits in decision curve analysis. Conclusions: Drawing upon these findings, we have developed a predictive model for the occurrence of new-onset POAF. These results suggest the potential efficacy of this prediction model for identifying patients at risk of developing POAF. The visualization of this model empowers healthcare professionals to conveniently and promptly assess the risk of AF in patients, thereby facilitating the timely intervention implementation.

A novel fungal sensor (Ngs1) of N-acetylglucosamine (GlcNAc) mediates the fungal response to GlcNAc in the interaction between entomopathogenic Beauveria bassiana and insect host.

As N-acetylglucosamine (GlcNAc) ubiquitously exists in both insect cuticle and fungal cell walls, the GlcNAc sensor (Ngs1) potentially plays important roles in the interactions between entomopathogenic fungi and their insect hosts. However, the roles of the Ngs1 derived from the entomopathogens in response to the host's cuticle remain completely unexplored. In this study, a putative Ngs1 homolog was identified in the entomopathogenic fungus Beauveria bassiana. Deletion of Ngs1 significantly reduced virulence towards Galleria mellonella larvae either through cuticle infection (by 23%) or by bypassing the cuticle (by 44%). To investigate the role of Ngs1 in fungal virulence, an analysis of the transcriptome induced by Locusta migratoria exoskeleton was conducted, highlighting the regulatory mechanism of Ngs1 in carbohydrate metabolic process, particularly chitin metabolism and GlcNAc metabolism. Consistent with the transcriptomic data, Ngs1-deletion mutants showed reduced activities of both secreted chitinase (17% reduction) and Pr1 protease (35% reduction). Loss of Ngs1 down-regulated the transcript levels of GlcNAc-catabolism genes, resulting in a 17% decrease in fungal growth on GlcNAc-supported media. Furthermore, Ngs1 deficiency attenuated the fungal response to GlcNAc, leading to the alteration of fungal resistance to diverse stress cues. All of these changes contribute to the reduction in virulence in Ngs1-deficient B. bassiana. These findings support that Ngs1 plays a critical role in responding to insect-derived GlcNAc, affecting the production of cuticle-degrading enzymes to penetrate insect epidermis, GlcNAc-induced changes of stress resistance, and contribute to the fungal virulence against insects.

Lignin-induced rapid polymerization of asymmetrical adhesion Janus gel for strain sensor.

Functional hydrogel sensors have shown explosive growth in the health and medical fields. However, the uniform adhesion and the complicated polymerization process of hydrogels seriously hinder their further development. Herein, inspired by the layered structure of human skin, we prepare a Janus gel using in-situ polymerization. Based on the lignin-Fe3+ dual catalytic system, the rapid polymerization of the gel was achieved at room temperature. By tailoring the mass ratio of lignin and Fe3+ in the precursor, the adhesion of the upper and bottom layers can be easily adjusted. In addition, hydrophobic association is introduced into the upper layer to improve the gel's mechanical properties. The obtained asymmetric bilayer gel has a significant difference in adhesion (7 times), and exhibits excellent mechanical properties in the elongation at break (1437 %) and the breaking strength (463.2 kPa). Moreover, the bilayer gel also has good freezing and UV resistance. We use the bilayer gel as a wearable strain sensor, which shows a wide strain detection range of 0-800 % (maximum gauge factor = 5.3). The proposed simple strategy avoids UV irradiation and heating processes, which provides a new idea for the rapid polymerization of multifunctional Janus hydrogels with adjustable performances.

Spatial and temporal attention embedded spatial temporal graph convolutional networks for skeleton based gait recognition with multiple IMUs.

Gait recognition is one of the key technologies for exoskeleton robot control, while the current IMU-based gait recognition methods only use inertial data and do not fully consider the interconnections of human spatial structure and human joints. In this regard, a skeleton-based gait recognition approach with inertial measurement units using spatial temporal graph convolutional networks with spatial and temporal attention is proposed. A human forward kinematics solver module was used for constructing different human skeleton models and a temporal attention module was added for capturing the more important time frames in the gait cycle. Moreover, the two-stream structure was used to construct spatial temporal graph convolutional networks with spatial and temporal attention for gait recognition, and an average accuracy of about 99% was obtained in user experiments, which is the best performance compared to other algorithms, provides certain reference for gait recognition and real-time control of exoskeleton robots.

SSGU-CD: A combined semantic and structural information graph U-shaped network for document-level Chemical-Disease interaction extraction.

Document-level interaction extraction for Chemical-Disease is aimed at inferring the interaction relations between chemical entities and disease entities across multiple sentences. Compared with sentence-level relation extraction, document-level relation extraction can capture the associations between different entities throughout the entire document, which is found to be more practical for biomedical text information. However, current biomedical extraction methods mainly concentrate on sentence-level relation extraction, making it difficult to access the rich structural information contained in documents in practical application scenarios. We put forward SSGU-CD, a combined Semantic and Structural information Graph U-shaped network for document-level Chemical-Disease interaction extraction. This framework effectively stores document semantic and structure information as graphs and can fuse the original context information of documents. Using the framework, we propose a balanced combination of cross-entropy loss function to facilitate collaborative optimization among models with the aim of enhancing the ability to extract Chemical-Disease interaction relations. We evaluated SSGU-CD on the document-level relation extraction dataset CDR and BioRED, and the results demonstrate that the framework can significantly improve the extraction performance.

Can items derived from international literature be used in national quality of life instruments? A qualitative study conceptualising the EQ-HWB in China.

INTRODUCTION: The EQ Health and Wellbeing (EQ-HWB) is a new questionnaire for measuring quality of life (QoL) from a broad perspective. The items of the EQ-HWB were derived based on a 'qualitative review' of literature, which reported primarily on Western studies. It can be argued that the QoL is a cultural-related concept and therefore people from China have a different understanding of the QoL. This study aimed to explore whether Chinese citizens could understand the EQ-HWB's candidate items and what they thought of those items. In doing so, we wanted to examine the face validity of the candidate items and explore if further cultural adaptation is necessary. METHODS: This research was part of the E-QALY project, in which 36 candidate items were selected for the EQ-HWB from a 97-item pool. In China, three interviewers investigated the face validity of these EQ-HWB candidate items in semi-structured qualitative face-to-face interviews. Respondents were invited to report 'problems' with regard to the interpretation of the items and these problems were grouped into themes. We explored to what extent those themes related to specific cultural aspects in China. We also classified the rates of reported problems for each item into three groups: 1) less than 20%, 2) from 20-50%, and 3) over 50%. RESULTS: For 17 items the rate of reported problems was less than 20%, 15 items fell into the second group (with 20 - 50%) and for 4 items the rate of problems reported was more than 50%. The thematic analysis revealed eight themes: ambiguous problems in the interpretation of 16 items; difficult to understand (11); contained a complex negative expression (10); examples used seemed inappropriate (7); misleading connotation in Chinese (2); long and complex (2); complex response options (1); and use of non-colloquial language (1). DISCUSSION: Our research shows that EQ-HWB candidate items require careful examination to make them more comprehensible. Most of the reported problem themes were generic problems related to the items, and only a few face validity issues appeared to relate to specific cultural aspects in China, even though most of the items were based on Western studies. Our findings are reassuring for the instrument's international application, especially in China.

Testing four cognition bolt-on items to the EQ-5D in a general Chinese population.

OBJECTIVES: This study aimed to evaluate the psychometric properties of four candidate cognition bolt-on items and their combinations to the EQ-5D-5L. METHODS: Four cognition items (concentration, memory, calculation, and learning) were developed as separate questionnaire items, and were administered with the standard EQ-5D-5L to 640 individuals in a general population survey in China. From the 4 items, 11 compound items were constructed, and the 'worse level counts' rule was used to calculate a compound item score. Psychometric performance of the cognition bolt-ons was assessed in terms of informativity, convergent validity, explanatory power, and discriminatory power. RESULTS: The tested four cognition bolt-on items improved the informativity, convergent validity, explanatory power, and discriminatory power of EQ-5D-5L, with calculation and learning yielding better psychometric performance. The compound bolt-on items that coverd a range of cognitive functions demonstrated superior psychometric performance compared to single-aspect bolt-on items, with those items covering calculation and learning resulting in better psychometric performance. CONCLUSION: This study confirmed the validity of the tested cognition bolt-ons in a general Chinese population. It supported the use of a compound bolt-on item covering a range of cognitive functions such as the ability to calculate and learn.

Uncovering the Interfacial Strengthening Mechanisms of α-Mg/Mg2Sn/ß-Li Interfaces Using First-Principle Calculations and HAADF-STEM.

Previously, we reported our new invention of an ultralight (ρ = 1.61 g/cm3) and super high modulus (E = 64.5 GPa) Mg-Li-Al-Zn-Mn-Gd-Y-Sn (LAZWMVT) alloy. Surprisingly, the minor additions of Sn contribute to significant strength and stiffness increases. In this study, we found that Mg2Sn was not only the simple precipitate but also acted as the glue to bind the α-Mg/ß-Li interface in a rather complicated way. To explore its mechanism, we have performed first-principle calculations and HAADF-STEM experiments on the interfacial structures. It was found that the interfacial structural models of α-Mg/ß-Li, α-Mg/Mg2Sn, and ß-Li/Mg2Sn composite interfaces prefer to form α-Mg/Mg2Sn/ß-Li ternary composite structures due to the stable formation enthalpy (ΔH: -1.95 eV/atom). Meanwhile, the interface cleavage energy and critical cleavage stress show that Mg2Sn contribute to the interfacial bond strength better than the ß-Li/α-Mg phase bond strength (σb(ß-Li/Mg2Sn): 0.82 GPa > σb(α-Mg/Mg2Sn): 0.78 GPa > σb(ß-Li/α-Mg): 0.62 GPa). Based on the interfacial electronic structure analysis, α-Mg/Mg2Sn and ß-Li/Mg2Sn were found to have a denser charge distribution and larger charge transfer at the interface, forming stronger chemical bonds. Additionally, according to the crystal orbital Hamiltonian population analysis, the bonding strength of the Mg-Sn atom pair was 2.61 eV, which was higher than the Mg-Li bond strength (0.39 eV). The effect of the Mg2Sn phase on the stability and interfacial bonding strength of the alloying system was dominated by the formation of stronger and more stable Mg-Sn metal covalent bonds, which mainly originated from the contribution of the Mg 3p-Sn 5p orbital bonding states.

Transitioning between the EQ-5D youth and adult descriptive systems in a group of adolescents.

PURPOSE: To investigate whether the same health state results in the same distribution of responses on the EQ-5D youth and adult descriptive systems. METHODS: Adolescents aged 13-18 years with a range of health conditions and from the general school going population were recruited in South Africa (ZA) and Ethiopia (ET). In ZA participants completed the English EQ-5D-3L, EQ-5D-Y-3L and EQ-5D-5L in parallel. Whereas in ET participants completed the Amharic EQ-5D-5L and EQ-5D-Y-5L in parallel. Analysis aimed to describe the transition between youth and adult instruments and not differences between countries. RESULTS: Data from 592 adolescents completing the EQ-5D-3L, EQ-5D-Y-3L and EQ-5D-5L (ZA) and 693 completing the EQ-5D-5L and EQ-5D-Y-5L (ET) were analysed. Adolescents reported more problems on the youth versions compared to the adult version for the dimension of mental health. 13% and 4% of adolescents who reported no problems on the EQ-5D-3L and EQ-5D-5L reported some problems on the EQ-5D-Y-3L respectively. This was less notable with transition between the five level versions with 4% of adolescents reporting more problems on the EQ-5D-Y-5L than the EQ-5D-5L. Very few adolescents reported severe problems (level 3 on the EQ-5D-3L or EQ-5D-Y-3L and level 4 and level 5 on the EQ-5D-5L or EQ-5D-5L) thus there was little variation between responses between the versions. In ZA, discriminatory power, measured on the Shannon's Index, was higher for Y-3L compared to 3L for pain/discomfort (ΔH'=0.11) and anxiety/depression (ΔH'=0.04) and across all dimensions for Y-3L compared to 5L. Similarly, in ET discriminatory power was higher for Y-5L than 5L (ΔH' range 0.05-0.09). Gwet's AC showed good to very good agreement across all paired (ZA) 3L and (ET) 5L dimensions. The summary score of all EQ-5D versions were able to differentiate between known disease groups. CONCLUSION: Despite the overall high levels of agreement between EQ-5D instruments for youth and for adults, they do not provide identical results in terms of health state, from the same respondent. The differences were most notable for anxiety/depression. These differences in the way individuals respond to the various descriptive systems need to be taken into consideration for descriptive analysis, when transitioning between instruments, and when comparing preference-weighted scores.

N-acetylglucosamine kinase (BbHxk1) has pleiotropic effects on vegetative growth, cell wall integrity, morphological transition, cuticle infection, and metabolic modulation in the biological pesticide Beauveria bassiana.

Beauveria bassiana is a popular and eco-friendly biopesticide. During its pathogen-pest interaction, both N-acetylglucosamine (GlcNAc) catabolism and anabolism are crucial for nutrient supply and cell-wall construction. The initiation of GlcNAc metabolism relies on the catalysis of GlcNAc kinase, which has been extensively studied in the human pathogen Candida albicans. However, the physiological function of GlcNAc kinase remains poorly understood in entomopathogenic fungi. In the present study, a GlcNAc kinase homolog was identified and designated as BbHxk1 in B. bassiana. Deletion of BbHxk1 resulted in viable but reduced vegetative growth on various carbon sources. ΔBbHxk1 mutants displayed severe defects in cell wall integrity, making them more susceptible to cell wall stress cues. Furthermore, the absence of BbHxk1 resulted in an increase in conidial yield and blastospore production, and a faster rate of germination and filamentation, potentially attributed to higher intracellular ATP levels. BbHxk1 deficiency led to a reduction in the activities of cuticle-degrading enzymes, which might contribute to the attenuated pathogenicity specifically through cuticle penetration rather than hemocoel infection towards Galleria mellonella larvae. Being different from C. albicans Hxk1, which facultatively acts as a catalyzing enzyme and transcriptional regulator, BbHxk1 primarily acts as a catalyzing enzyme and metabolic regulator. The altered metabolomic profiling correlated with the phenotypic defects in ΔBbHxk1 mutants, further implicating a potential metabolism-dependent mechanism of BbHxk1 in mediating physiologies of B. bassiana. These findings not only unveil a novel role for GlcNAc kinase in B. bassiana, but also provide a solid theoretical basis to guide metabolic reprogramming in order to maintain or even enhance the efficiency of fungi for practical applications.

Effects of dietary arginine supplementation on muscle structure, meat characteristics and lipid oxidation products in lambs and its potential mechanisms of action.

This study aimed to assess the effect of dietary arginine supplementation on muscle structure and meat characteristics of lambs also considering lipid oxidation products and to contribute to reveal its mechanisms of action using tandem mass tagging (TMT) proteomics. Eighteen lambs were allocated to two dietary treatment groups: control diet or control diet with the addition of 1% L-arginine. The results revealed that dietary arginine supplementation increased muscle fibre diameter and cross-sectional area (P < 0.05), which was attributable to protein deposition, as evidenced by increased RNA content, RNA/DNA ratio, inhibition of apoptotic enzyme activity, and alterations in the IGF-1/Akt signaling pathway (P < 0.05). In addition, dietary arginine elevated pH24h, a* values, and IMF content, decreased shear force value and backfat thickness (P < 0.05), as well as decreased the formation of lipid oxidation products involved in meat flavor including hexanal, heptanal, octanal, nonanal and 1-octen-3-ol by increasing the antioxidant capacity of the muscle (P < 0.05). The proteomics results suggested that seven enrichment pathways may be potential mechanisms by which arginine affected the muscle structure and meat characteristics of lambs. In summary, arginine supplementation in lamb diets provides a safe and effective way to improve meat quality, and antioxidant capacity of muscle of lamb.

Valuation of Multi-Dimensional Health States With a Bolt-On: Is There a Shortcut?

OBJECTIVES: To use the EQ-5D questionnaire with bolt-on dimensions in economic evaluation studies, new value sets are needed. In this study, we explored the feasibility of a new approach called the scaling factor model, which estimates bolt-on value sets using estimated EQ-5D dimensional weights. METHODS: We designed a 2-arm study, inviting university students to value health states with and without bolt-on items using the composite time trade-off method. We selected 25 health states from an orthogonal array and added the 5 mildest EQ-5D states in the design. In arm 1, EQ-5D without self-care and standard EQ-5D states were valued, and in arm 2, standard EQ-5D states and EQ-5D with vision were valued. By arm, we compared the mean observed values of health states with and without bolt-on item. Next, by arm, we estimated value sets for the EQ-5D with bolt-on states using both standard model and scaling factor model. Model performances were compared in terms of prediction accuracy and correlation with likelihood-based mean values. RESULTS: Adding a five-level bolt-on to EQ-5D resulted in statistically lower values. This effect was consistent across 2 arms and bolt-on items. The scaling factor models outperformed the standard models in all statistics. CONCLUSIONS: The scaling factor model offers a methodologically viable and low-cost option for producing value sets for EQ-5D supplemented with bolt-on items. Future studies should further test this method using other bolt-on items and more relevant study populations.

Sub-femtomolar vertical graphene field effect immunosensor for detection of lung tumor markers.

Lung cancer is the main cancer that endangers human life worldwide, with the highest mortality rate. The detection of lung tumor markers is of great significance for the early diagnosis and subsequent treatment of lung cancer. In this study, a vertical graphene field effect transistor (VGFET) immunosensor based on graphene/C60 heterojunction was created to offer quantitative detections for the lung tumor markers carcinoembryonic antigen (CEA), cytokeratin 19 fragment (Cyfra21-1), and neuron-specific enolase (NSE). The experimental results showed that the sensitive range for standard antigen is between 1 pg/ml to 100 ng/ml, with a limit of detection (LOD) of 5.6 amol/ml for CEA, 33.3 amol/ml for Cyfra 21-1 and 12.8 amol/ml for NSE (1 pg/ml for all). The detection accuracy for these tumor markers was compared with the clinically used method for clinical patients on serum samples. Results are highly consistent with clinically used immunoassay in its efficient diagnosis concentration range. Subsequently, the mesoporous silica nanospheres (MSNs) with an average size of 90 nm were surface modified with glutaraldehyde, and a second antibody was assembled on MSNs, which fixes nanospheres on the antigen and amplified the field effect. The LODs for three markers are 100 fg/ml (0.56 amol/ml for CEA) under optimal circumstances of detection. This result indicates that specific binding to MSNs enhances local field effects and can achieve higher sensing efficiency for tumor marker detection at extremely low concentrations, providing effective assistance for the early diagnosis of lung cancer.

Psychometric validation of the Chinese versions of EQ-5D-Y-3L and the experimental EQ-TIPS in children and adolescents with COVID-19.

OBJECTIVES: Respiratory infectious diseases like COVID-19 profoundly impacts the health of children and adolescents, but validated instruments to measure their impacts on health-related quality of life (HRQoL) are lacking. The EQ-5D-Y-3L, widely used for youth HRQoL, now features a Chinese value set. The experimental EQ-TIPS addresses HRQoL assessment for toddlers and infants. This study tested the psychometric properties of both instruments in paediatric COVID-19 patients, and compared the performance of self-complete and proxy EQ-5D-Y-3L. METHODS: This longitudinal study recruited 861 COVID-19 patients aged 0-18 years and their parental caregivers, with 311 dyads completing the follow-up. Digital administration included the EQ-TIPS, the EQ-5D-Y-3L, and Overall Health Assessment (OHA). Controls comprised 231 healthy children. Analysis encompassed known-group validity, child-parent agreement, and responsiveness to change in disease severity and OHA. RESULTS: COVID-19 children exhibited lower HRQoL than non-infected peers. The EQ-TIPS and the EQ-5D-Y-3L distinguished groups by disease presence, severity and symptoms, showing moderate to good known-group validity (ESs: 0.45-1.39 for EQ-TIPS, 0.44-1.91 for self-complete EQ-5D-Y-3L, and 0.32-1.67 for proxy EQ-5D-Y-3L). Child-parent agreement was moderate to good for EQ-5D-Y-3L (ICC: 0.653-0.823; Gwet's AC1: 0.470-0.738), and responsiveness was good for both EQ-TIPS Level Sum Score (LSS) (ESs: 1.21-1.39) and EQ-5D-Y-3L index scores (ESs: 1.00-1.16). CONCLUSIONS: This study demonstrates the reliability, validity, and responsiveness of the experimental EQ-TIPS and the EQ-5D-Y-3L in paediatric COVID-19 patients. It is the first evidence of the EQ-TIPS' responsiveness, supporting its use in assessing the impact of COVID-19 on paediatric HRQoL.

Drug-target interaction prediction using knowledge graph embedding.

The prediction of drug-target interactions (DTIs) is a critical phase in the sustainable drug development process, especially when the research focus is to capitalize on the repositioning of existing drugs. Computational approaches to predicting DTIs can provide important insights into drug mechanisms of action. However, current methods for predicting DTIs based on the structural information of the knowledge graph may suffer from the sparseness and incompleteness of the knowledge graph and neglect the latent type information of the knowledge graph. In this paper, we propose TTModel, a knowledge graph embedding model for DTI prediction. By exploiting biomedical text and type information, TTModel can learn latent text semantics and type information to improve the performance of representation learning. Comprehensive experiments on two public datasets demonstrate that our model outperforms the state-of-the-art methods significantly on the task of DTI prediction.