Advances in the study of acetaminophen-induced liver injury

Acetaminophen (APAP) overdose is a significant cause of drug-induced liver injury and acute liver failure. The diagnosis, screening, and management of APAP-induced liver injury (AILI) is challenging because of the complex mechanisms involved. Starting from the current studies on the mechanisms of AILI, this review focuses on novel findings in the field of diagnosis, screening, and management of AILI. It highlights the current issues that need to be addressed. This review is supposed to summarize the recent research progress and make recommendations for future research

Dairy Product Consumption and Changes in Cognitive Performance: Two-Year Analysis of the PREDIMED-Plus Cohort

Scope Dairy consumption has been suggested to impact cognition; however, evidence is limited and inconsistent. This study aims to longitudinally assess the association between dairy consumption with cognitive changes in an older Spanish population at high cardiovascular disease risk. Methods and results Four thousand six hundred sixty eight participants aged 55-75 years, completed a validated food frequency questionnaire at baseline and a neuropsychological battery of tests at baseline and 2-year follow-up. Multivariable linear regression models are used, scaled by 100 (i.e., the units of beta correspond to 1 SD/100), to assess associations between baseline tertile daily consumption and 2-year changes in cognitive performance. Participants in the highest tertile of total milk and whole-fat milk consumption have a greater decline in global cognitive function (beta: -4.71, 95% CI: -8.74 to -0.69, p-trend = 0.020 and beta: -6.64, 95% CI: -10.81 to -2.47, p-trend = 0.002, respectively) compared to those in the lowest tertile. No associations are observed between low fat milk, yogurt, cheese or fermented dairy consumption, and changes in cognitive performance. Conclusion Results suggest there are no clear prospective associations between consumption of most commonly consumed dairy products and cognition, although there may be an association with a greater rate of cognitive decline over a 2-year period in older adults at high cardiovascular disease risk for whole-fat milk.CIBER Fisiopatologia de la Obesidad y Nutricion (CIBEROBN)Instituto de Salud Carlos III (ISCIII), through the Fondo de Investigacion para la Salud (FIS) - European Regional Development Fund PI13/00673 PI13/00492 PI13/00272 PI13/01123 PI13/00462 PI13/00233 PI13/02184 PI13/00728 PI13/01090 PI13/01056 PI14/01722 PI14/00636 PI14/00618 PI14/00696 PI14/01206 PI14/01919 PI14/00853 PI14/01374 PI14/00972 PI14/00728 PI14/01471 PI16/00473Especial Action Project entitled: Implementacion y evaluacion de una intervencion intensiva sobre la actividad fisica Cohorte PREDIMED-Plus grantEuropean Research Council (ERC) European Commission 340918 2013ACUP00194Junta de Andalucia PI0458/2013 PS0358/2016 PI0137/2018Center for Forestry Research & Experimentation (CIEF)European Commission PROMETEO/2017/017 PROMETEO/2021/021 SEMERGEN grantJuan de la Cierva-Incorporacion research grant of the Spanish Ministry of Economy, Industry and Competitiveness IJC2019-042420-IEuropean Social Fund (ESF) EU-H2020 Grants Eat2beNICE/H2020-SFS-2016-2 Horizon 2020 PRIME study (Prevention and Remediation of Insulin Multimorbidity in Europe 847879Spanish Government FPU 20/00385 FPU 17/01925Canadian Institutes of Health Research (CIHR)University of Rovira I Virgili 2020PMF-PIPF-37ICREA under the ICREA Academia program'Instituto de Salud Carlos III (ISCIII), through the Fondo de Investigacion para la Salud (FIS) - European Regional Development Fund' PI16/00662 PI16/01873 PI16/01094 PI16/00501 PI16/00533 PI16/00381 PI16/00366 PI16/01522 PI16/01120 PI17/00764 PI17/01183 PI17/00855 PI17/01347 PI17/00525 PI17/01827 PI17/00532 PI17/00215 PI17/01441 PI17/00508 PI17/01732 PI17/00926 PI19/00957 PI19/00386 the Instituto de Salud Carlos III (ISCIII), through the Fondo de Investigacion para la Salud (FIS) - European Regional Development Fund PI19/00309 PI19/01032 PI19/00576 PI19/00017 PI19/01226 PI19/00781 PI19/01560 PI19/01332 PI20/01802 PI20/00138 PI20/01532 PI20/00456 PI20/00339 PI20/00557 PI20/00886 PI20/0115

Teaching Autonomous Vehicles to Express Interaction Intent during Unprotected Left Turns: A Human-Driving-Prior-Based Trajectory Planning Approach

Incorporating Autonomous Vehicles (AVs) into existing transportation systems necessitates examining their coexistence with Human-driven Vehicles (HVs) in mixed traffic environments. Central to this coexistence is the AVs' ability to emulate human-like interaction intentions within traffic scenarios. We introduce a novel framework for planning unprotected left-turn trajectories for AVs, designed to mirror human driving behaviors and effectively communicate social intentions. This framework consists of three phases: trajectory generation, evaluation, and selection.In the trajectory generation phase, we utilize real human-driving trajectory data to establish constraints for a predicted trajectory space, creating candidate motion trajectories that reflect intent. The evaluation phase incorporates maximum entropy inverse reinforcement learning (ME-IRL) to gauge human trajectory preferences, considering aspects like traffic efficiency, driving comfort, and interactive safety. During the selection phase, a Boltzmann distribution-based approach is employed to assign rewards and probabilities to the candidate trajectories, promoting human-like decision-making. We validate our framework using an authentic trajectory dataset and conduct a comparative analysis with various baseline methods. Our results, derived from simulator tests and human-in-the-loop driving experiments, affirm our framework's superiority in mimicking human-like driving, expressing intent, and computational efficiency. For additional information of this research, please visit https://shorturl.at/jqu35

Higher versus lower nut consumption and changes in cognitive performance over two years in a population at risk of cognitive decline: a cohort study

This work was supported by the official Spanish Institutions for funding scientific biomedical research, CIBER Fisiopatologia de la Obesidad y Nutricio?n (CIBEROBN) and Instituto de Salud Carlos III (ISCIII) , through the Fondo de Investigacion para la Salud (FIS) , which is co-funded by the European Regional Development Fund (6 coordinated FIS projects leaded by JS-S and JoV, including the following projects: PI13/00673, PI13/00492, PI13/00272, PI13/01123, PI13/00462, PI13/00233, PI13/02184, PI13/00728, PI13/01090, PI13/01056, PI14/01722, PI14/00636, PI14/00618, PI14/00696, PI14/01206, PI14/01919, PI14/00853, PI14/01374, PI14/00972, PI14/00728, PI14/01471, PI16/00473, PI16/00662, PI16/01873, PI16/01094, PI16/00501, PI16/00533, PI16/00381, PI16/00366, PI16/01522, PI16/01120, PI17/00764, PI17/01183, PI17/00855, PI17/01347, PI17/00525, PI17/01827, PI17/00532, PI17/00215, PI17/01441, PI17/00508, PI17/01732, PI17/00926, PI19/00957, PI19/00386, PI19/00309, PI19/01032, PI19/00576, PI19/00017, PI19/01226, PI19/00781, PI19/01560, PI19/01332, PI20/01802, PI20/00138, PI20/01532, PI20/00456, PI20/00339, PI20/00557, PI20/00886, PI20/01158) ; the Especial Action Project entitled: Implementacion y evaluacion de una intervencion intensiva sobre la actividad fisica Cohorte PREDIMED-Plus grant and the Recercaixa (number 2013ACUP00194) grant to JS-S; grants from the European Research Council (Advanced Research Grant 2014-2019; agreement #340918) ; grants from the Consejeria de Salud de la Junta de Andalucia (PI0458/2013, PS0358/2016, PI0137/2018) ; the PROMETEO/2017/017 and PROMETEO/2021/021 grants from the Conselleria de Innovacio?n, Universidades, Ciencia y Sociedad digital de la Generalitat Valenciana; Grant PID2019-108858RB-I00 funded by AEI 10.13039/501100011033 and by "ERDF A way of making Europe"; the SEMERGEN grant; the AICO/2021/347 grants from the Generalitat Valenciana. This research was also partially funded by EU-H2020 Grants (Eat2beNICE/H2020-SFS-2016-2) ; and the Horizon 2020 PRIME study (Prevention and Remediation of Insulin Multimorbidity in Europe; grant agreement #847879) . JN is supported by a predoctoral grant from Ministerio de Ciencia, Innovacion y Universidades (FPU 20/00385) . SKN is supported by a postdoctoral fellowship from the Canadian Institutes of Health Research (CIHR) . JS-S, the senior author of this paper, was partially supported by ICREA under the ICREA Academia program. None of the funding sources took part in the design,collection, analysis, interpretation of the data, writing the report, or in the decision to submit the manuscript for publication.Background: Tree nuts and peanuts (henceforth, nuts) are nutrient-dense foods rich in neuroprotective components; thus, their consumption could benefit cognitive health. However, evidence to date is limited and inconsistent regarding the potential benefits of nuts for cognitive function.Objective: To prospectively evaluate the association between nut consumption and 2-y changes in cognitive performance in older adults at cognitive decline risk.Methods: A total of 6,630 participants aged 55 to 75 y (mean age 65.0 & PLUSMN;4.9 y, 48.4% women) with overweight/obesity and metabolic syndrome completed a validated semi-quantitative food frequency questionnaire and a comprehensive battery of neuropsychological tests at baseline and a 2-y follow-up. Composite cognitive scores were used to assess global, general, attention, and executive function domains. Nut consumption was categorized as 1 to 3 to 7 servings/wk (1 serving=30 g). Multivariable-adjusted linear regression models were fitted to assess associations between baseline nut consumption and 2-y cognitive changes.Results: Nut consumption was positively associated with 2-y changes in general cognitive function (P-trend 3 to 7 servings/wk showed more favorable changes in general cognitive performance (& beta; z-score [95% CI] = 0.06 [0.00,0.12] and 0.13 [0.06,0.20], respectively). No significant changes were observed in the multivariableadjusted models for other cognitive domains assessed.Conclusion: Frequent nut consumption was associated with a smaller decline in general cognitive performance over 2 y in older adults at risk of cognitive decline. Randomized clinical trials to verify our findings are warranted.Official Spanish InstitutionsInstituto de Salud Carlos III (ISCIII) , Fondo de Investigacion para la Salud (FIS) - European Regional Development Fund PI13/00673, PI13/00492, PI13/00272, PI13/01123, PI13/00462, PI13/00233, PI13/02184, PI13/00728, PI13/01090, PI13/01056, PI14/01722, PI14/00636, PI14/00618, PI14/00696, PI14/01206, PI14/01919, PI14/00853, PI14/01374, PI14/00972, PI14/00728, PI14/01471, PI16/00473, PI16/00662, PI16/01873, PI16/01094, PI16/00501, PI16/00533, PI16/00381, PI16/00366, PI16/01522, PI16/01120, PI17/00764, PI17/01183, PI17/00855, PI17/01347, PI17/00525, PI17/01827, PI17/00532, PI17/00215, PI17/01441, PI17/00508, PI17/01732, PI17/00926, PI19/00957, PI19/00386, PI19/00309, PI19/01032, PI19/00576, PI19/00017, PI19/01226, PI19/00781, PI19/01560, PI19/01332, PI20/01802, PI20/00138, PI20/01532, PI20/00456, PI20/00339, PI20/00557, PI20/00886, PI20/01158La Caixa Foundation 2013ACUP00194Junta de Andalucía PI0458/2013, PS0358/2016, PI0137/2018, PROMETEO/2017/017Conselleria de Innovación, Universidades PID2019-108858RB-I00, AICO/2021/347Center for Forestry Research & Experimentation (CIEF) EU-H2020: Eat2beNICE/H2020-SFS-2016-2Horizon 2020 PRIME study 847879Spanish Government FPU 20/00385Ministerio de Ciencia, Innovación y Universidades FPU 20/00385Canadian Institutes of Health Research (CIHR)ICREA under the ICREA Academia progra

Coverage and Handoff Analysis of 5G Fractal Small Cell Networks

It is anticipated that much higher network capacity will be achieved by the fifth generation (5G) small cell networks incorporated with the millimeter wave (mmWave) technology. However, mmWave signals are more sensitive to blockages than signals in lower frequency bands, which highlights the effect of anisotropic path loss in network coverage. According to the fractal characteristics of cellular coverage, a multi-directional path loss model is proposed for 5G small cell networks, where different directions are subject to different path loss exponents. Furthermore, the coverage probability, association probability, and the handoff probability are derived for 5G fractal small cell networks based on the proposed multi-directional path loss model. Numerical results indicate that the coverage probability with the multi-directional path loss model is less than that with the isotropic path loss model, and the association probability with long link distance, e.g., 150m, increases obviously with the increase of the effect of anisotropic path loss in 5G fractal small cell networks. Moreover, it is observed that the anisotropic propagation environment is having a profound impact on the handoff performance. Meanwhile, we could conclude that the resulting heavy handoff overhead is emerging as a new challenge for 5G fractal small cell networks

The influence of labor education participation on the subjective well-being of college students: chain mediation effect of self-efficacy and healthy lifestyle

BackgroundIn the process of modernization, along with economic development, intensified social competition, and increasing mental health problems such as anxiety and depression, the issue of subjective well-being has received widespread attention. The level of subjective well-being of college students also affects whether society can achieve sustainable development. In philosophy, political science, economics, sociology and other disciplines, labor is regarded as an important factor affecting subjective well-being. Labor education is an educational activity carried out by Chinese universities in recent years. This further inspires the author to think, for the college students, will the labor education received on campus have an impact on the subjective well-being? What characteristics will its impact mechanism present? What are the characteristics of the influence on subjective well-being?.MethodsThis research adopts a cross-sectional design, specifically employing a random sampling approach. In this study, the questionnaire was distributed to the college’s students of 14 universities in China through the Internet. A total of 2100 questionnaires were collected.ResultsThis paper mainly used questionnaires to collect data, and on this basis, examined the relationship between labor education participation, self-efficacy, healthy lifestyle and subjective well-being of college students. The results showed that: (1) Labor education participation positively affected college students’ subjective well-being. (2) Self-efficacy partially mediated the relationship between labor education participation and college students’ subjective well-being. (3) Healthy lifestyle partially mediated the relationship between labor education participation and college students’ subjective well-being. (4) Self-efficacy and healthy lifestyle played a chain mediating role between labor education participation and college students’ subjective well-being

An Image Dataset for Benchmarking Recommender Systems with Raw Pixels

Recommender systems (RS) have achieved significant success by leveraging explicit identification (ID) features. However, the full potential of content features, especially the pure image pixel features, remains relatively unexplored. The limited availability of large, diverse, and content-driven image recommendation datasets has hindered the use of raw images as item representations. In this regard, we present PixelRec, a massive image-centric recommendation dataset that includes approximately 200 million user-image interactions, 30 million users, and 400,000 high-quality cover images. By providing direct access to raw image pixels, PixelRec enables recommendation models to learn item representation directly from them. To demonstrate its utility, we begin by presenting the results of several classical pure ID-based baseline models, termed IDNet, trained on PixelRec. Then, to show the effectiveness of the dataset's image features, we substitute the itemID embeddings (from IDNet) with a powerful vision encoder that represents items using their raw image pixels. This new model is dubbed PixelNet.Our findings indicate that even in standard, non-cold start recommendation settings where IDNet is recognized as highly effective, PixelNet can already perform equally well or even better than IDNet. Moreover, PixelNet has several other notable advantages over IDNet, such as being more effective in cold-start and cross-domain recommendation scenarios. These results underscore the importance of visual features in PixelRec. We believe that PixelRec can serve as a critical resource and testing ground for research on recommendation models that emphasize image pixel content. The dataset, code, and leaderboard will be available at https://github.com/westlake-repl/PixelRec

The tumor-associated fibrotic reactions in microenvironment aggravate glioma chemoresistance

Malignant gliomas are one of the most common and lethal brain tumors with poor prognosis. Most patients with glioblastoma (GBM) die within 2 years of diagnosis, even after receiving standard treatments including surgery combined with concomitant radiotherapy and chemotherapy. Temozolomide (TMZ) is the first-line chemotherapeutic agent for gliomas, but the frequent acquisition of chemoresistance generally leads to its treatment failure. Thus, it’s urgent to investigate the strategies for overcoming glioma chemoresistance. Currently, many studies have elucidated that cancer chemoresistance is not only associated with the high expression of drug-resistance genes in glioma cells but also can be induced by the alterations of the tumor microenvironment (TME). Numerous studies have explored the use of antifibrosis drugs to sensitize chemotherapy in solid tumors, and surprisingly, these preclinical and clinical attempts have exhibited promising efficacy in treating certain types of cancer. However, it remains unclear how tumor-associated fibrotic alterations in the glioma microenvironment (GME) mediate chemoresistance. Furthermore, the possible mechanisms behind this phenomenon are yet to be determined. In this review, we have summarized the molecular mechanisms by which tumor-associated fibrotic reactions drive glioma transformation from a chemosensitive to a chemoresistant state. Additionally, we have outlined antitumor drugs with antifibrosis functions, suggesting that antifibrosis strategies may be effective in overcoming glioma chemoresistance through TME normalization

Low-Protein Diet Supplemented with Keto Acids Is Associated with Suppression of Small-Solute Peritoneal Transport Rate in Peritoneal Dialysis Patients

Objective. We investigate whether low-protein diet would show benefits in suppressing peritoneal transport rate in peritoneal dialysis (PD) patients. Methods. This is a supplemented analysis of our previously published trial, which randomized 60 PD patients to receive low- (LP: dietary protein intake of 0.6–0.8 g/kg/d), keto-acid-supplemented low- (sLP: 0.6–0.8 g/kg/d with 0.12 g/kg/d of keto acids), or high- (HP: 1.0–1.2 g/kg/d) protein diet and lasted for one year. In this study, the variations of peritoneal transport rate were assessed. Results. While baseline D/Pcr (dialysate-to-plasma concentration ratio for creatinine at 4 hour) and D/D0glu (dialysate glucose at 4 hour to baseline dialysate glucose concentration ratio) were similar, D/Pcr in group sLP was lower, and D/D0glu was higher than those in the other two groups (P < 0.05) at 12th month. D/D0glu increased (P < 0.05), and D/Pcr tended to decrease, (P = 0.071) in group sLP. Conclusions. Low-protein diet with keto acids may benefit PD patients by maintaining peritoneum at a lower transport rate

NineRec: A Benchmark Dataset Suite for Evaluating Transferable Recommendation

Learning a recommender system model from an item's raw modality features (such as image, text, audio, etc.), called MoRec, has attracted growing interest recently. One key advantage of MoRec is that it can easily benefit from advances in other fields, such as natural language processing (NLP) and computer vision (CV). Moreover, it naturally supports transfer learning across different systems through modality features, known as transferable recommender systems, or TransRec. However, so far, TransRec has made little progress, compared to groundbreaking foundation models in the fields of NLP and CV. The lack of large-scale, high-quality recommendation datasets poses a major obstacle. To this end, we introduce NineRec, a TransRec dataset suite that includes a large-scale source domain recommendation dataset and nine diverse target domain recommendation datasets. Each item in NineRec is represented by a text description and a high-resolution cover image. With NineRec, we can implement TransRec models in an end-to-end training manner instead of using pre-extracted invariant features. We conduct a benchmark study and empirical analysis of TransRec using NineRec, and our findings provide several valuable insights. To support further research, we make our code, datasets, benchmarks, and leaderboards publicly available at https://github.com/westlake-repl/NineRec