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Objective To investigate the prognostic significance of the advanced lung cancer inflammation index (ALI) in patients with operable non-small-cell lung carcinoma (NSCLC). By constructing the nomogram model, it can provide a reference for clinical work. Methods A total of 899 patients with non-small cell lung cancer who underwent surgery in our hospital between January 2017 and June 2021 were retrospectively included. ALI was calculated by body mass index (BMI) × serum albumin/neutrophil to lymphocyte ratio (NLR). The optimal truncation value of ALI was obtained using the receiver operating characteristic (ROC) curve and divided into two groups. Survival analysis was represented by the Kaplan-Meier curve. The predictors of Overall survival (OS) were evaluated by the Cox proportional risk model using single factor and stepwise regression multifactor analysis. Based on the results of multi-factor Cox proportional risk regression analysis, a nomogram model was established using the R survival package. The bootstrap method (repeated sampling 1 000 times) was used for internal verification of the nomogram model. The concordance index (C-index) was used to represent the prediction performance of the nomogram model, and the calibration graph method was used to visually represent its prediction conformity. The application value of the model was evaluated by decision curve analysis (DCA). Results The optimal cut-off value of ALI was 70.06, and the low ALI group (ALI < 70.06) showed a poor survival prognosis. In multivariate analyses, tumor location, pathological stage, neuroaggression, and ALI were independently associated with operable NSCLC-specific survival. The C index of OS predicted by the nomogram model was 0.928 (95% CI: 0.904–0.952). The bootstrap self-sampling method (B = 1000) was used for internal validation of the prediction model, and the calibration curve showed good agreement between the prediction and observation results of 1-year, 2-year, and 3-year OS. The ROC curves for 1-year, 2-year, and 3-year survival were plotted according to independent factors, and the AUC was 0.952 (95% CI: 0.925–0.979), 0.951 (95% CI: 0.916–0.985), and 0.939 (95% CI: 0.913–0.965), respectively. DCA shows that this model has good clinical application value. Conclusion ALI can be used as a reliable indicator to evaluate the prognosis of patients with operable NSCLC, and through the construction of a nomogram model, it can facilitate better individualized treatment and prognosis assessment.

Heart failure (HF) is a complex and prevalent condition, particularly in the elderly, presenting symptoms like chest tightness, shortness of breath, and dyspnea. The study aimed to improve the classification of HF subtypes and identify potential drug targets by exploring the role of Immunogenic Cell Death (ICD), a process known for its role in tumor immunity but underexplored in HF research. Additionally, the study sought to apply deep learning models to enhance HF classification and identify diagnosis-related genes. Various deep learning encoder models were employed to evaluate their effectiveness in clustering HF based on ICD-related genes. Identified HF subtypes were further refined using differentially expressed genes, allowing for the assessment of immune infiltration and functional enrichment. Advanced machine learning techniques were used to identify diagnosis-related genes, and these genes were used to construct nomogram models. The study also explored gene interactions with miRNA and transcription factors. Distinct HF subtypes were identified through clustering based on ICD-related genes. Differentially expressed genes revealed significant variations in immune infiltration and functional enrichment across these subtypes. The diagnostic model showed excellent performance, with an AUC exceeding 0.99 in both internal and external test sets. Diagnosis-related genes were also identified, serving as the foundation for nomogram models and further exploration of their regulatory interactions. This study provides a novel insight into HF by combining the exploration of ICD, the application of deep learning models, and the identification of diagnosis-related genes. These findings contribute to a deeper understanding of HF subtypes and highlight potential therapeutic targets for improving HF classification and treatment.

Hyperspectral images have a special attribute with both spectral and spatial information, which is of great significance for the evaluation of the stealth performance of camouflaged targets. Aiming at the problems of a single evaluation index and the low credibility of traditional optical camouflage evaluation methods, this paper proposes a grayscale clustering camouflage effect evaluation method based on multifeature descriptions of hyperspectral images using similarity indicators that reflect different spectral characteristics of the target and background. From the perspective of spectrum and human visual contrast, a comprehensive evaluation index system including spectral distance feature, spectral derivative feature, curve shape feature and spatial texture feature is constructed by combining spatial–spectral multi-feature constraints. At the same time, an improved Delphi method is proposed to simulate the expert decision-making process, and better evaluation weights are obtained by comparison and screening. The comprehensive evaluation of camouflage effect based on whitening function gray clustering is realized. The proposed method can not only give the “excellent” and “bad” of camouflage effect qualitatively, but also calculate the comprehensive score of camouflage effect by model.

The cavity characteristics in liquid-filled containers caused by high-velocity impacts represent an important area of research in hydrodynamic ram phenomena. The dynamic expansion of the cavity induces liquid pressure variations, potentially causing catastrophic damage to the container. Current studies mainly focus on non-deforming projectiles, such as fragments, with limited exploration of shaped charge jets. In this paper, a uniquely experimental system was designed to record cavity profiles in behind-armor liquid-filled containers subjected to shaped charge jet impacts. The impact process was then numerically reproduced using the explicit simulation program ANSYS LS-DYNA with the Structured Arbitrary Lagrangian-Eulerian (S-ALE) solver. The formation mechanism, along with the dimensional and shape evolution of the cavity was investigated. Additionally, the influence of the impact kinetic energy of the jet on the cavity characteristics was analyzed. The findings reveal that the cavity profile exhibits a conical shape, primarily driven by direct jet impact and inertial effects. The expansion rates of both cavity length and maximum radius increase with jet impact kinetic energy. When the impact kinetic energy is reduced to 28.2 kJ or below, the length-to-diameter ratio of the cavity ultimately stabilizes at approximately 7.

Resistance to chemotherapy has been a major hurdle that limits therapeutic benefits for many types of cancer. Here we systematically identify genetic drivers underlying chemoresistance by performing 30 genome-scale CRISPR knockout screens for seven chemotherapeutic agents in multiple cancer cells. Chemoresistance genes vary between conditions primarily due to distinct genetic background and mechanism of action of drugs, manifesting heterogeneous and multiplexed routes towards chemoresistance. By focusing on oxaliplatin and irinotecan resistance in colorectal cancer, we unravel that evolutionarily distinct chemoresistance can share consensus vulnerabilities identified by 26 second-round CRISPR screens with druggable gene library. We further pinpoint PLK4 as a therapeutic target to overcome oxaliplatin resistance in various models via genetic ablation or pharmacological inhibition, highlighting a single-agent strategy to antagonize evolutionarily distinct chemoresistance. Our study not only provides resources and insights into the molecular basis of chemoresistance, but also proposes potential biomarkers and therapeutic strategies against such resistance. Chemoresistance limits the success of chemotherapy in patients with cancer. Here, the authors perform 30 genome wide CRISPR knockout screens to identify genes associated to resistance against commonly used chemotherapeutics across multiple cancer types, followed by 26 second round CRISPR screens to identify druggable targets of chemoresistance.

This study determined the physical and mechanical characteristics of fresh black fungus during the harvesting season to provide basic data for the development of mechanical equipment for black fungus harvesting and processing. We have conducted a comprehensive test of black fungus cultivars \"Heishan\". The mono-factor separation force experiments of black fungus and black fungus virgulate medium were conducted. It was noted that the tension angle was an important factor affecting the separation force, which was mainly distributed between 1.06 and 3.65 N. Besides, the average value of Poisson's ratio of black fungus was measured to be 0.445 in the tensile test of black fungus leaves using image recognition and analysis techniques, with a test error within 2.5%; and the average value of tensile elastic modulus and shear elastic modulus of black fungus leaves was 0.947 MPa and 0.327 MPa, respectively; we also found that the average tensile strength at the root of black fungus was not significantly different from that at the leaf, which was around 0.436 MPa. In addition, it was obtained that the height and thickness dimensions of black fungus in the picking season conformed to a normal distribution, and concentrated around 34.39mm and 0.92mm respectively.

This study determined the physical and mechanical characteristics of fresh black fungus during the harvesting season to provide basic data for the development of mechanical equipment for black fungus harvesting and processing. We have conducted a comprehensive test of black fungus cultivars “Heishan”. The mono-factor separation force experiments of black fungus and black fungus virgulate medium were conducted. It was noted that the tension angle was an important factor affecting the separation force, which was mainly distributed between 1.06 and 3.65 N. Besides, the average value of Poisson’s ratio of black fungus was measured to be 0.445 in the tensile test of black fungus leaves using image recognition and analysis techniques, with a test error within 2.5%; and the average value of tensile elastic modulus and shear elastic modulus of black fungus leaves was 0.947 MPa and 0.327 MPa, respectively; we also found that the average tensile strength at the root of black fungus was not significantly different from that at the leaf, which was around 0.436 MPa. In addition, it was obtained that the height and thickness dimensions of black fungus in the picking season conformed to a normal distribution, and concentrated around 34.39mm and 0.92mm respectively.

The impact of high-velocity penetrators into liquid-filled containers can generate hydrodynamic ram effects, potentially causing catastrophic structural damage to the container. Previous studies have primarily focused on undeformed penetrators, such as fragments or bullets, with limited attention directed toward shaped charge jets. This study investigates the penetration characteristics of shaped charge jets impacting behind-armor liquid-filled containers, with particular emphasis on jet–liquid interactions. A theoretical penetration model incorporating material compressibility and jet stretching was developed based on the virtual origin theory. A high-speed imaging experimental system was designed to capture the jet motion within the container. The impact process was numerically reproduced using ANSYS/LS-DYNA, and the effects of standoff and overmatch on jet penetration were analyzed. The results reveal that jet stretching induced by increased standoff enhances the penetration velocity of the jet. A proportional relationship between the stretching factor (λ) and the overmatch parameter (I) was identified, with λ ranging from approximately 1.22 to 1.38 times I across the studied standoff range (80–220 mm). The findings offer a basis for future studies on the pressure distribution in the liquid and the structural response of containers.

Dendritic cells (DCs) play pivotal roles in T-cell homeostasis and activation, and metabolic programing has been recently linked to DC development and function. However, the metabolic underpinnings corresponding to distinct DC functions remain largely unresolved. Here, we demonstrate a special metabolic-epigenetic coupling mechanism orchestrated by tuberous sclerosis complex subunit 1 (TSC1)-mechanistic target of rapamycin (mTOR) for homeostatic DC function. Specific ablation of Tsc1 in the DC compartment (Tsc1DC-KO) largely preserved DC development but led to pronounced reduction in naïve and memory-phenotype cluster of differentiation (CD)8+ T cells, a defect fully rescued by concomitant ablation of mTor or regulatory associated protein of MTOR, complex 1 (Rptor) in DCs. Moreover, Tsc1DC-KO mice were unable to launch efficient antigen-specific CD8+ T effector responses required for containing Listeria monocytogenes and B16 melanomas. Mechanistically, our data suggest that the steady-state DCs tend to tune down de novo fatty acid synthesis and divert acetyl-coenzyme A (acetyl-CoA) for histone acetylation, a process critically controlled by TSC1-mTOR. Correspondingly, TSC1 deficiency elevated acetyl-CoA carboxylase 1 (ACC1) expression and fatty acid synthesis, leading to impaired epigenetic imprinting on selective genes such as major histocompatibility complex (MHC)-I and interleukin (IL)-7. Remarkably, tempering ACC1 activity was able to divert cytosolic acetyl-CoA for histone acetylation and restore the gene expression program compromised by TSC1 deficiency. Taken together, our results uncover a crucial role for TSC1-mTOR in metabolic programing of the homeostatic DCs for T-cell homeostasis and implicate metabolic-coupled epigenetic imprinting as a paradigm for DC specification.

Fungal infection induces signaling downstream C-type lectin receptors through the activation of the tyrosine kinase Syk. Xiao and colleagues show that the phosphatase SHP-2 recruits Syk to dectin-1. Fungal infection stimulates the canonical C-type lectin receptor (CLR) signaling pathway via activation of the tyrosine kinase Syk. Here we identify a crucial role for the tyrosine phosphatase SHP-2 in mediating CLR-induced activation of Syk. Ablation of the gene encoding SHP-2 ( Ptpn11 ; called ' Shp-2 ' here) in dendritic cells (DCs) and macrophages impaired Syk-mediated signaling and abrogated the expression of genes encoding pro-inflammatory molecules following fungal stimulation. Mechanistically, SHP-2 operated as a scaffold, facilitating the recruitment of Syk to the CLR dectin-1 or the adaptor FcRγ, through its N-SH2 domain and a previously unrecognized carboxy-terminal immunoreceptor tyrosine-based activation motif (ITAM). We found that DC-derived SHP-2 was crucial for the induction of interleukin 1β (IL-1β), IL-6 and IL-23 and anti-fungal responses of the T H 17 subset of helper T cells in controlling infection with Candida albicans . Together our data reveal a mechanism by which SHP-2 mediates the activation of Syk in response to fungal infection.