Adiponectin protects against paraquat-induced lung injury by attenuating oxidative/nitrative stress.

The specific mechanisms underlying paraquat (PQ)-induced lung injury remain unknown, which limits understanding of its cytotoxic potential. Although oxidative stress has been established as an important mechanism underlying PQ toxicity, multiple antioxidants have proven ineffective in attenuating the deleterious effects of PQ. Adiponectin, which shows anti-oxidative and antinitrative effects, may have the potential to reduce PQ-mediated injury. The present study determined the protective action of globular domain adiponectin (gAd) on PQ-induced lung injury, and attempted to elucidate the underlying mechanism or mechanisms of action. BALB/c mice were administered PQ, with and without 12 or 36 h of gAd pre-treatment. The pulmonary oxidative/nitrative status was assessed by measuring pulmonary O2(•-), superoxide dismutase (SOD), malondialdehyde (MDA), nitric oxide (NO) and 8-hydroxy-2-dydeoxy guanosine (8-OHdG) production, and blood 3-Nitrotyrosine (3-NT). At a dose of 20 mg/kg, PQ markedly increased O2(•-), SOD, MDA, NO and 8-OHdG production 3 h post-administration, but did not significantly increase 3-NT levels until 12 h. gAd inhibited these changes in a dose-dependent manner, via transient activation of MDA, followed by attenuation of MDA formation from 6 h onwards. Histological analysis demonstrated that gAd decreased interstitial edema and inflammatory cell infiltration. These results suggest that gAd protects against PQ-induced lung injury by mitigating oxidative/nitrative stress. Furthermore, gAd may be a potential therapeutic agent for PQ-induced lung injury, and further pharmacological studies are therefore warranted

Jailbreaking ChatGPT via Prompt Engineering: An Empirical Study

Large Language Models (LLMs), like ChatGPT, have demonstrated vast potential but also introduce challenges related to content constraints and potential misuse. Our study investigates three key research questions: (1) the number of different prompt types that can jailbreak LLMs, (2) the effectiveness of jailbreak prompts in circumventing LLM constraints, and (3) the resilience of ChatGPT against these jailbreak prompts. Initially, we develop a classification model to analyze the distribution of existing prompts, identifying ten distinct patterns and three categories of jailbreak prompts. Subsequently, we assess the jailbreak capability of prompts with ChatGPT versions 3.5 and 4.0, utilizing a dataset of 3,120 jailbreak questions across eight prohibited scenarios. Finally, we evaluate the resistance of ChatGPT against jailbreak prompts, finding that the prompts can consistently evade the restrictions in 40 use-case scenarios. The study underscores the importance of prompt structures in jailbreaking LLMs and discusses the challenges of robust jailbreak prompt generation and prevention

Understanding Large Language Model Based Fuzz Driver Generation

Fuzz drivers are a necessary component of API fuzzing. However, automatically generating correct and robust fuzz drivers is a difficult task. Compared to existing approaches, LLM-based (Large Language Model) generation is a promising direction due to its ability to operate with low requirements on consumer programs, leverage multiple dimensions of API usage information, and generate human-friendly output code. Nonetheless, the challenges and effectiveness of LLM-based fuzz driver generation remain unclear. To address this, we conducted a study on the effects, challenges, and techniques of LLM-based fuzz driver generation. Our study involved building a quiz with 86 fuzz driver generation questions from 30 popular C projects, constructing precise effectiveness validation criteria for each question, and developing a framework for semi-automated evaluation. We designed five query strategies, evaluated 36,506 generated fuzz drivers. Furthermore, the drivers were compared with manually written ones to obtain practical insights. Our evaluation revealed that: while the overall performance was promising (passing 91% of questions), there were still practical challenges in filtering out the ineffective fuzz drivers for large scale application; basic strategies achieved a decent correctness rate (53%), but struggled with complex API-specific usage questions. In such cases, example code snippets and iterative queries proved helpful; while LLM-generated drivers showed competent fuzzing outcomes compared to manually written ones, there was still significant room for improvement, such as incorporating semantic oracles for logical bugs detection.Comment: 17 pages, 14 figure

Field-free spin-orbit torque switching enabled by interlayer Dzyaloshinskii-Moriya interaction

Perpendicularly magnetized structures that are switchable using a spin current under field-free conditions can potentially be applied in spin-orbit torque magnetic random-access memory(SOT-MRAM).Several structures have been developed;however,new structures with a simple stack structure and MRAM compatibility are urgently needed.Herein,a typical structure in a perpendicular spin-transfer torque MRAM,the Pt/Co multilayer and its synthetic antiferromagnetic counterpart with perpendicular magnetic anisotropy, was observed to possess an intrinsic interlayer chiral interaction between neighboring magnetic layers,namely the interlayer Dzyaloshinskii-Moriya interaction (DMI) effect. Furthermore, using a current parallel to the eigenvector of the interlayer DMI, we switched the perpendicular magnetization of both structures without a magnetic field, owing to the additional symmetry-breaking introduced by the interlayer DMI. This SOT switching scheme realized in the Pt/Co multilayer and its synthetic antiferromagnet structure may open a new avenue toward practical perpendicular SOT-MRAM and other SOT devices

Deep Learning for Automated Contouring of Gross Tumor Volumes in Esophageal Cancer

PurposeThe aim of this study was to propose and evaluate a novel three-dimensional (3D) V-Net and two-dimensional (2D) U-Net mixed (VUMix-Net) architecture for a fully automatic and accurate gross tumor volume (GTV) in esophageal cancer (EC)–delineated contours.MethodsWe collected the computed tomography (CT) scans of 215 EC patients. 3D V-Net, 2D U-Net, and VUMix-Net were developed and further applied simultaneously to delineate GTVs. The Dice similarity coefficient (DSC) and 95th-percentile Hausdorff distance (95HD) were used as quantitative metrics to evaluate the performance of the three models in ECs from different segments. The CT data of 20 patients were randomly selected as the ground truth (GT) masks, and the corresponding delineation results were generated by artificial intelligence (AI). Score differences between the two groups (GT versus AI) and the evaluation consistency were compared.ResultsIn all patients, there was a significant difference in the 2D DSCs from U-Net, V-Net, and VUMix-Net (p=0.01). In addition, VUMix-Net showed achieved better 3D-DSC and 95HD values. There was a significant difference among the 3D-DSC (mean ± STD) and 95HD values for upper-, middle-, and lower-segment EC (p<0.001), and the middle EC values were the best. In middle-segment EC, VUMix-Net achieved the highest 2D-DSC values (p<0.001) and lowest 95HD values (p=0.044).ConclusionThe new model (VUMix-Net) showed certain advantages in delineating the GTVs of EC. Additionally, it can generate the GTVs of EC that meet clinical requirements and have the same quality as human-generated contours. The system demonstrated the best performance for the ECs of the middle segment

Understanding surface melt in Antarctica and implications for future ice sheet evolution

Global mean sea level (GMSL) is projected to continue rising this century, potentially impacting up to 1 billion people by 2050 (Lee et al., 2023). Antarctica, as the Earth’s largest ice reservoir with a sea level equivalent volume of around 58 meters (Morlighem et al., 2020), could significantly impact the magnitude of future sea level rise. However, how much sea level rise will be caused by the Antarctic Ice Sheet (AIS) is highly uncertain (Rintoul et al., 2018), partly because of unclear future stability of Antarctic ice shelves. Surface melt has been identified as a crucial factor contributing to ice shelf collapse (Rott et al., 1996; van den Broeke, 2005; Trusel et al., 2015) through mechanisms of hydrofracturing (Lai et al., 2020). Projections have shown that the magnitude of surface melt will increase and the melt extent will be widespread (Trusel et al., 2015; Gilbert and Kittel, 2021). However, the distribution of future surface melt is not well known at high spatial resolutions. This is because climate models that employ comprehensive surface energy balance (SEB) schemes are too computationally expensive to run at fine resolutions (van den Broeke et al., 2023). By contrast, temperature-index models, such as the positive degree-day (PDD) model, are computationally efficient and have been utilized for snowmelt estimation for more than 90 years (Rango and Martinec, 1995), offering an alternative approach for future melt projections. However, the PDD parameters commonly used for AIS modelling are typically based on those derived for the Greenland Ice Sheet. An assessment of the viability of the PDD modelling approach for AIS surface melt projections has not yet been conducted, and the accuracy of the PDD model in estimating surface melting on the AIS remains unclear.This thesis first comprehensively assesses the PDD model for estimating surface melt on the AIS. The results from the assessment show that a PDD model with spatially-uniform parameters, when compared to estimates of surface melt days from satellites and surface melt rates from regional climate models over the past four decades, lacks accuracy in reconstructing AIS surface melt. Therefore, in order to improve the accuracy of the PDD model for AIS surface melt projections, I develop a novel grid-cell-level spatially-distributed PDD model by minimizing the error with respect to satellite estimates and SEB model outputs on each individual computing cell (minimal RMSE approach) for the past four decades. Evaluations of this PDD model demonstrate the robustness of the minimal RMSE approach and the applicability of the PDD model to warmer climate scenarios. To calculate future melting, I incorporate 100-meter-resolution topographic variability to downscale forcing temperature fields derived from ERA5, CMIP5, and CMIP6. The resultant 100-meter-resolution AIS surface melt projections show that the Larsen-C, Shackleton, Thwaites, and Totten ice shelves will all be at high risk of collapse this century due to increased surface melt if emissions follow the SSP3-7.0 pathway. Trajectories of latitudinal melt migration calculated from these high resolution AIS surface melt projections suggest that SSP1-2.6 is likely the only emissions pathway under which future AIS surface melt can be stabilized at present levels.</p

Clinical characteristics and oral care needs of perioperative patients with oral cancer

The treatment of oral cancer patients mainly involves surgery in combination with radiotherapy and che⁃ motherapy. This paper reviews the clinical features of perioperative patients with oral cancer, including oral flora imbal⁃ ance, oral complications after radiotherapy and chemotherapy, the presence of oral incisions (and flaps), special dietary needs, and airway management. In connection with the above characteristics, this article analyzes the necessity of three aspects of oral care to improve the patients comfort and prevent pulmonary and surgical site infections, with the goal of providing a reference for oral care research on patients undergoing oral cancer surgery and laying a foundation for the construction of comprehensive oral care programs during the perioperative period for patients with oral cancer

Admission homocysteine is an independent predictor of spontaneous reperfusion and early infarct-related artery patency before primary percutaneous coronary intervention in ST-segment elevation myocardial infarction

Abstract Background Spontaneous reperfusion (SR) and early infarct related artery (IRA) patency before primary percutaneous coronary intervention (PPCI) might bring extra benefit for patients with ST-segment elevation myocardial infarction (STEMI). This study premilinarily screened the independent predictors of SR, and assessed the relationship between SR and plasma homocysteine (HCY). Methods The medical records of 998 patients who were diagnosed as STEMI and underwent emergency coronary angiography were retrospectively studied, SR was defined as achievement of TIMI grade 3 flow in the IRA before PCI. The baseline characteristics, clinical manifestations and hematological variables were compared between SR and NSR group. Optimal cutoff point of HCY was calculated with receiving operating characteristics (ROC) analysis, multivariate logistic regression models were used to identify predictors of SR. Results 229 (22.95%) patients showed angiographic SR. For HCY, the area under the curve was 0.70 (95% CI: 0.63–0.77, P = 0.034), the optimized cut off point was 17.55 μmol/L. Preinfarct angina (95% CI: 1.61–5.65, P = 0.0005), plasma C-reactive protein (CRP) level (95% CI: 0.87–0.99, P = 0.016) and HCY < 17.55 μmol/L (95% CI: 2.43–8.72, P < 0.0001) were found to be independent predictors for SR. Conclusion In patients with STEMI, HCY < 17.55 μmol/L, preinfarct angina and plasma CRP level were independent predictors of SR