Explore the vast range of titles available.

p53, encoded by the tumor suppressor gene TP53, is one of the most important tumor suppressor factors in vivo and can be negatively regulated by MDM2 through p53–MDM2 negative feedback loop. Abnormal p53 can be observed in almost all tumors, mainly including p53 mutation and functional inactivation. Blocking MDM2 to restore p53 function is a hotspot in the development of anticancer candidates. Till now, nine MDM2 inhibitors with different structural types have entered clinical trials. However, no MDM2 inhibitor has been approved for clinical application. This review focused on the discovery, structural modification, preclinical and clinical research of the above compounds from the perspective of medicinal chemistry. Based on this, the possible defects in MDM2 inhibitors in clinical development were analyzed to suggest that the multitarget strategy or targeted degradation strategy based on MDM2 has the potential to reduce the dose-dependent hematological toxicity of MDM2 inhibitors and improve their anti-tumor activity, providing certain guidance for the development of agents targeting the p53–MDM2 interaction.

Malnutrition and insulin resistance are linked to complications like delirium, yet their impact on elderly heart failure patients remains underexplored. This study investigates how nutritional status and the triglyceride-glucose (TyG) index influence delirium in this population. We conducted a retrospective study involving patients aged 75 and older with decompensated heart failure. Delirium was assessed using the Confusion Assessment Method (CAM), and nutritional status was evaluated with the Mini Nutritional Assessment Scale-Short Form (MNA-SF). The TyG index was calculated as: TyG index = ln [triglycerides (mg/dL) × fasting glucose (mg/dL) / 2]. The study included 412 patients (mean age 84.30 ± 5.16 years; 56.31% male), with 24.03% experiencing delirium during hospitalization. After adjusting for confounders, a higher TyG index was significantly associated with increased delirium risk (OR = 1.549, 95% CI: 1.102–2.178, P  = 0.012), while a higher MNA-SF score correlated with reduced risk (OR = 0.793, 95% CI: 0.662–0.949, P  = 0.011). Kaplan-Meier analysis showed significant differences in cumulative survival based on nutritional status and TyG index (Log-Rank test: χ²= 65.604, P  < 0.001). Mediation analysis indicated that nutritional status partially mediated the relationship between the TyG index and delirium, and vice versa. Malnutrition and elevated TyG index levels increase delirium risk in elderly heart failure patients, underscoring the need for effective nutritional management and metabolic regulation.

Metasurfaces have exhibited excellent capabilities in controlling main characteristics of electromagnetic fields. Thus, a lot of significant achievements have been attained in many areas especially in the fields of hologram and near-field imaging. However, some of these designs are implemented in a manner of interleaved subarrays that complicates the design and makes them difficult to achieve integration. Here, an innovative stacking technique of metasurface is combined with vanadium dioxide (VO 2 ) to achieve independent imaging of six channels in terahertz band. Our research combines intensity modulation controlled by the Malus’s law and phase modulation of geometry and propagation to merge amplitude, phase, and polarization manipulation of electromagnetic wave. A “six-in-one” meta-device is constructed by combining phase change properties of VO 2 to realize simultaneous near-field grayscale imaging and far-field holography. This design has advantages of wide bandwidth and low crosstalk. Based on the advantage of low crosstalk, single-cell bilayer design allows the number of independent channels to be doubled within an acceptable error range. The proposed metasurface introduces a fresh viewpoint for the design of multi-purpose meta-devices, and has broad application prospects in information encryption and multi-channel image display.

Sharding blockchain is a technology designed to improve the performance and scalability of traditional blockchain systems. However, due to its design, communication between shards depends on shard leaders for transmitting information, while shard members are unable to detect communication activities between shards. Consequently, Byzantine nodes can act as shard leaders, engaging in malicious behaviors to disrupt message transmission. To address these issues, we propose the Cross shard leader accountability protocol (CSLAP), which is based on the two-phase atomic commit protocol (2PC). CSLAP employs byzantine broadcast/byzantine agreement (BB/BA) for Byzantine fault tolerance to generate cross-shard leader re-election certificates, thereby reducing the impact of shard leaders on inter-shard communication. It also uses Round-robin mechanism to facilitate leader re-election. Moreover, we demonstrate that CSLAP maintains the security and liveness of sharding transactions while providing lower communication latency. Finally, we conducted an experimental comparison between CSLAP and other cross-shard protocols. The results indicate that CSLAP exhibits superior performance in reducing communication latency.

ObjectiveTo develop a gastric cancer (GC) risk prediction rule as an initial prescreening tool to identify individuals with a high risk prior to gastroscopy.DesignThis was a nationwide multicentre cross-sectional study. Individuals aged 40–80 years who went to hospitals for a GC screening gastroscopy were recruited. Serum pepsinogen (PG) I, PG II, gastrin-17 (G-17) and anti-Helicobacter pylori IgG antibody concentrations were tested prior to endoscopy. Eligible participants (n=14 929) were randomly assigned into the derivation and validation cohorts, with a ratio of 2:1. Risk factors for GC were identified by univariate and multivariate analyses and an optimal prediction rule was then settled.ResultsThe novel GC risk prediction rule comprised seven variables (age, sex, PG I/II ratio, G-17 level, H. pylori infection, pickled food and fried food), with scores ranging from 0 to 25. The observed prevalence rates of GC in the derivation cohort at low-risk (≤11), medium-risk (12–16) or high-risk (17–25) group were 1.2%, 4.4% and 12.3%, respectively (p<0.001).When gastroscopy was used for individuals with medium risk and high risk, 70.8% of total GC cases and 70.3% of early GC cases were detected. While endoscopy requirements could be reduced by 66.7% according to the low-risk proportion. The prediction rule owns a good discrimination, with an area under curve of 0.76, or calibration (p<0.001).ConclusionsThe developed and validated prediction rule showed good performance on identifying individuals at a higher risk in a Chinese high-risk population. Future studies are needed to validate its efficacy in a larger population.

(1) Background: Numerous prions exist in the budding yeast, including [SWI+], the prion form of Swi1—a subunit of the chromatin-remodeling complex SWI/SNF. Despite decades of research, the molecular mechanisms underlying prion initiation and propagation are not fully understood. In this study, we aimed to identify endogenous cellular proteins that destabilize [SWI+]. (2) Methods: We screened the MoBY-ORF 2.0 library for proteins that destabilize [SWI+] upon overproduction. We further explored the effects of the identified candidates against other yeast prions and analyzed their potential prion-curing mechanisms. (3) Results: Eighty-two [SWI+] suppressors were identified, and their effects were shown to be [SWI+]-specific. Interestingly, a few documented [SWI+] suppressors were not among the identified hits. Further experiments indicate that, for some of these [SWI+] suppressors, their overproduction, and thus their prion-curing activities, are regulated by environmental conditions. Bioinformatics analyses show that our identified [SWI+] suppressors are involved in diverse biological functions, with gene ontology term enrichments specifically for transcriptional regulation and translation. Competition for Swi1 monomers between [SWI+] and Swi1 interactors, including the SWI/SNF complex, is a potential prion-curing mechanism. (4) Conclusions: We identified a number of [SWI+]-specific suppressors that highlight unique features of [SWI+] in maintaining its self-perpetuating conformations.

In intensive care unit (ICU) patients undergoing mechanical ventilation (MV), the occurrence of difficult weaning contributes to increased ventilator-related complications, prolonged hospitalization duration, and a significant rise in healthcare costs. Therefore, early identification of influencing factors and prediction of patients at risk of difficult weaning can facilitate early intervention and preventive measures. This study aimed to strengthen airway management for ICU patients by constructing a risk prediction model with comprehensive and individualized offline programs based on machine learning techniques. This study involved the collection of data from 487 patients undergoing MV in the ICU, with a total of 36 variables recorded. The dataset was divided into a training set (70% of the data) and a test set (30% of the data). Five machine learning models, namely logistic regression, random forest, support vector machine, light gradient boosting machine, and extreme gradient boosting, were compared to predict the risk of difficult weaning in ICU patients with MV. Significant influencing factors were identified based on the results of these models, and a risk prediction model for ICU patients with MV was established. When evaluating the models using AUC (Area under the Curve of ROC) and Accuracy as performance metrics, the Random Forest algorithm exhibited the best performance among the five machine learning algorithms. The area under the operating characteristic curve for the subjects was 0.805, with an accuracy of 0.748, recall (0.888), specificity (0.767) and F1 score (0.825). This study successfully developed a risk prediction model for ICU patients with MV using a machine learning algorithm. The Random Forest algorithm demonstrated the highest prediction performance. These findings can assist clinicians in accurately assessing the risk of difficult weaning in patients and formulating effective individualized treatment plans. Ultimately, this can help reduce the risk of difficult weaning and improve the quality of life for patients.

Background Stress hyperglycaemia is common among older adults hospitalised with community-acquired pneumonia (CAP) and is associated with delirium; however, the underlying inflammatory pathways remain unclear. We investigated whether the neutrophil-to-lymphocyte ratio (NLR) and procalcitonin (PCT) mediate the relation between the stress-hyperglycaemia ratio (SHR) and delirium. Methods This single-centre retrospective cohort included 412 patients aged ≥ 65 years admitted for CAP. Delirium was assessed daily with the Confusion Assessment Method. Logistic regression models yielded odds ratios (ORs) for delirium, whereas linear regression models for NLR and PCT provided standardised beta coefficients (β_std). All continuous variables (SHR, NLR and PCT) were z-scored prior to mediation analysis. Results Delirium developed in 99 patients (24.0%). After adjustment for age, sex and comorbidities, SHR (OR = 9.13, 95% CI 4.44–18.96), NLR (OR = 1.12 per unit, 95% CI 1.06–1.19) and PCT (OR = 1.14 per ng/mL, 95% CI 1.06–1.21) were independent predictors of delirium. The total standardised effect of SHR on delirium was β_std = 0.321, of which 80.6% was direct (β_std = 0.259, 95% CI 0.149–0.427). The NLR-mediated pathway accounted for 19.4% of the association (β_std = 0.062, 95% CI 0.024–0.107). Pathways involving PCT—alone or in sequence with NLR—were not significant. Conclusions In older CAP patients, stress hyperglycaemia substantially elevates delirium risk, predominantly through a direct mechanism only partly (≈ 20%) mediated by systemic inflammation reflected by NLR. Prompt detection and management of acute hyperglycaemia may therefore offer a practical approach to delirium prevention. Prospective multicentre studies are needed to confirm causality and to test glucose- and inflammation-modulating interventions.

Remote sensing has more advantages than the traditional methods of land surface water (LSW) mapping because it is a low-cost, reliable information source that is capable of making high-frequency and repeatable observations. The normalized difference water indexes (NDWIs), calculated from various band combinations (green, near-infrared (NIR), or shortwave-infrared (SWIR)), have been successfully applied to LSW mapping. In fact, new NDWIs will become available when Advanced Land Imager (ALI) data are used as the ALI sensor provides one green band (Band 4), two NIR bands (Bands 6 and 7), and three SWIR bands (Bands 8, 9, and 10). Thus, selecting the optimal band or combination of bands is critical when ALI data are employed to map LSW using NDWI. The purpose of this paper is to find the best performing NDWI model of the ALI data in LSW map. In this study, eleven NDWI models based on ALI, Thematic Mapper (TM), and Enhanced Thematic Mapper Plus (ETM+) data were compared to assess the performance of ALI data in LSW mapping, at three different study sites in the Yangtze River Basin, China. The contrast method, Otsu method, and confusion matrix were calculated to evaluate the accuracies of the LSW maps. The accuracies of LSW maps derived from eleven NDWI models showed that five NDWI models of the ALI sensor have more than an overall accuracy of 91% with a Kappa coefficient of 0.78 of LSW maps at three test sites. In addition, the NDWI model, calculated from the green (Band 4: 0.525–0.605 μm) and SWIR (Band 9: 1.550–1.750 μm) bands of the ALI sensor, namely NDWIA4,9, was shown to have the highest LSW mapping accuracy, more than the other NDWI models. Therefore, the NDWIA4,9 is the best indicator for LSW mapping of the ALI sensor. It can be used for mapping LSW with high accuracy.

Several single-center studies have evaluated the predictive performance of mechanical power (MP) on weaning outcomes in prolonged invasive mechanical ventilation (IMV) patients. The relationship between MP and weaning outcomes in all IMV patients has rarely been studied. A retrospective study was conducted on MIMIC-IV patients with IMV for more than 24 h to investigate the correlation between MP and weaning outcome using logistic regression model and subgroup analysis. The discriminative ability of MP, MP normalized to dynamic lung compliance (C dyn -MP) and MP normalized to predicted body weight (PBW-MP) on weaning outcome were evaluated by analyzing the area under the receiver-operating characteristic (AUROC). Following adjustment for confounding factors, compared with the reference group, the Odds Ratio of weaning failure in the maximum MP, C dyn -MP, and PBW-MP groups increased to 3.33 [95%CI (2.04–4.53), P  < 0.001], 3.58 [95%CI (2.27–5.56), P  < 0.001] and 5.15 [95%CI (3.58–7.41), P  < 0.001], respectively. The discriminative abilities of C dyn -MP (AUROC 0.760 [95%CI 0.745–0.776]) and PBW-MP (AUROC 0.761 [95%CI 0.744–0.779]) were higher than MP (AUROC 0.745 [95%CI 0.730–0.761]) ( P  < 0.05). MP is associated with weaning outcomes in IMV patients and is an independent predictor of the risk of weaning failure. C dyn -MP and PBW-MP showed higher ability in weaning failure prediction than MP.