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Lung cancer remains the primary cause of cancer-related mortality, with factors such as postoperative tumor recurrence, metastasis, and therapeutic drug resistance exacerbating patient outcomes. Immunotherapy has emerged as a transformative approach, challenging conventional treatment paradigms for lung cancer. Consequently, advancing research in lung cancer immunotherapy is imperative. Recent studies indicate that numerous regulators within the tumor microenvironment (TME) drive tumor angiogenesis and epithelial-mesenchymal transition (EMT); these processes are interdependent, reciprocal, and collectively contribute to tumor progression. Tumor angiogenesis not only supplies adequate oxygen and nutrients for cellular proliferation but also establishes pathways facilitating tumor metastasis and creating hypoxic regions that foster drug resistance. Concurrently, EMT enhances metastatic potential and reinforces drug-resistance genes within tumor cells, creating a reciprocal relationship with angiogenesis. This interplay ultimately results in tumor invasion, metastasis, and therapeutic resistance. This paper reviews key regulators of angiogenesis and EMT, examining their impact on lung cancer immunotherapy and progression, and investigates whether newly identified regulators could influence lung cancer treatment, thus offering valuable insights for developing future therapeutic strategies.

Gaussian steering in continuousvariable (CV) systems, as a quantum correlation between nonlocality and entanglement, is an important quantum resource. Rapid detection of Gaussian steering is a significant challenge in quantum information process. In this paper, we employ a combination of machine learning methods, including Support Vector Machine (SVM), Backpropagation Neural Network (BPNN) and Meta-Weight-Net Neural Network (MWN) to speed up the detection. An ensemble learning approach that integrates these methods is also utilized to increase the accuracy of detection. A computable Gaussian steering quantification introduced recently in [Phys. Rev. A 110, 052427] serves as a pivotal tool for labeling the samples. A key observation is that steerable Gaussian states are vastly outnumbered by unsteerable ones, particularly in configurations where the untrusted party possesses significantly more modes than the trusted party. This leads to a highly skewed distribution of sample states in the dataset. In response to this phenomenon and to make comparison, we propose the imbalance factor and prepare three types of datasets to be trained: balanced datasets, naturally generated datasets and augmented datasets with via a data augmentation strategy. Numerical experiments for seven low modes scenarios reveal that the classifiers obtained by utilizing the ensemble learning method training on augmented dataset have the best overall performance, significantly improving generalization capabilities with low cost and high test accuracy, achieving detection times as fast as seconds, at least 100 times faster than calculating . The speed advantage of machine learning detection will be more obvious in the case of higher modes. Thus the approach is both efficient and reliable, offering valuable insights into the broader potential of machine learning applications in quantum information science and providing a robust framework for machine learning utilized to classification tasks, especially in data-imbalanced scenarios.

The application of biochar-based fertilizer can improve soil properties in part by stimulating microbial activity and growth. Karst ecosystems, which make up large areas of Southwest China, are prone to degradation. Understanding the response of arbuscular mycorrhizal fungal (AMF) community structure to biochar-based fertilizer application is of great significance to karst soil restoration. A field experiment was conducted in a typical karst soil (calcareous sandy loam) in Southwest China. A high-throughput sequencing approach was used to investigate the effect of biochar-based fertilization on AMF community structure in the karst soil. With the control (CK), compost with NPK fertilizer (MF), biochar (B), a lower amount of biochar with compost and NPK fertilizer (B1MF), biochar with compost and NPK fertilizer (BMF), and a higher amount of biochar with compost and NPK fertilizer (B4MF), the field trials were set up for 24 months. Soil amendments increased soil nutrient content and AMF diversity. The composition and structure of the AMF community varied among the treatments. AMF community composition was significantly impacted by soil chemical properties such as TC (total carbon), TN (total nitrogen), TP (total phosphorus), and AP (available phosphorus). Furthermore, network analysis showed that biochar-based fertilization increased the scale and complexity of the microbial co-occurrence network. Biochar-based fertilization enabled more keystone species (such as order Diversisporales and Glomerales ) in the soil AMF network to participate in soil carbon resource management and soil nutrient cycling, indicating that biochar-based fertilizer is beneficial for the restoration of degraded karst soils.

Karst landforms are widely distributed around the world, and karst rocky desertification has occurred on a large scale in many countries and regions, causing significant adverse impacts on local natural environments and societies. The improvement and rational use of karst soil is a key aspect of rocky desertification governance. Karst soil science studies are of great value in karst regions and are essential for controlling karst rocky desertification and ecological restoration. In order to understand the research hotspots and the development directions in the field of vulnerable karst soil environment, we undertook bibliometrics citation analysis on 1913 contributions to the literature written in the range from 2001 to 2019 based on the “Web of Science” core collection citation index database. Hopefully, this work will help to set up a scientific foundation for further studies. Using CiteSpace visualization software, we analyzed the distribution of disciplinary categories, reference co-citation clusters, and keyword clusters in the literature. The results show the basic characteristics and evolution of the literature related to karst pedology. We then recognized the main intellectual bases in the domain of karst soil science. This study also revealed the research hotspots and trends in this field. Through a bibliometrics citation analysis of research on karst vulnerable soil environment, the present study provides a quantitative and objective understanding of development directions that have emerged in this field over the past 19 years, offering a reference for future research.

The classification of shale is commonly based on lithofacies structure, mineral content, organic carbon content, physical and chemical parameters, and element contents. A geochemical classification method based on the compositional data of shale is proposed which divides shale, sedimentary rocks, sediments, and soils into six types, named siliceous, felsic, silicate, calcsilicate, dolomitic, and calcareous components, and numbered from type 1 to type 6, respectively. Sedimentary rocks in China, including mudstone (shale), sandstone, carbonate rocks, and siliceous rocks, are selected to test the method, and the results show that the method can accurately classify not only shale but also other sedimentary rocks. Shale samples from a drill well in southern Sichuan area are classified based on the proposed method as an illustration in a line graph. Furthermore, the stream sediments and soils from geochemical survey projects in the southern Sichuan area are also classified based on their compositional data. Based on the classification results, a geochemical map is drawn and compared with the strata lithology. These results indicate that the new classification method is suitable for shale, sedimentary rocks, sediments, and soils, and illustrates clear geochemical properties based on their classified types.

The prompt and accurate prognostication of acute-on-chronic liver failure (ACLF) patients is crucial for clinical intervention and reducing mortality. This study aimed to develop a novel prognostic model based on pathological changes, with a specific focus on portal vein which is correlated with hepatic pathology. A cohort of 127 ACLF patients was enrolled to develop the prognostic model for 90-day mortality, which was validated in a prospective cohort of 105 ACLF patients. Demographic characteristics, laboratory indicators, and imaging factor portal vein diameter (PVD) were screened, and a nomogram prognostic model was developed using logistic regression. Patients with PVD ≥ 13.4 mm had significantly higher mortality ( P  = 0.047). PVD, age, neutrophil percentage, sex and total bilirubin were identified as independent predictors for the new PVD-based nomogram prognostic model, PANST. The C-index (0.878) of PANST score was higher than Chronic Liver Failure-Consortium-ACLF (CLIF-C ACLF), end-stage liver disease (MELD) and Child-Turcotte-Pugh (CTP) scores (0.691, 0.687, 0.639, respectively; P  < 0.001). The ROC and decision curves demonstrated that the PANST score was superior to CLIF-C ACLF, MELD and CTP scores. Furthermore, after subgroup analysis, the C-indices of PANST score for hepatitis B virus-related ACLF (HBV-ACLF) and non-HBV-ACLF patients (0.842,0.950) were significantly higher than those of CLIF-C ACLF score (0.772, 0.750; P  < 0.05), MELD score (0.730, 0.608; all P  < 0.05) and CTP score (0.701,0.513; all P  < 0.05). These results were confirmed in the validation cohort. PVD was an independent predictor, and the PANST score, a novel prognostic model based on PVD can accurately predict 90-day mortality in ACLF patients.

Background & Aims There is limited evidence on the efficacy and safety of nucleos(t) ide analogues (NAs) in the treatment of HBV-ACLF. Our objective was to evaluate the outcomes among TAF, TDF and ETV, three first-line antivirals against chronic hepatitis B, in patients with HBV-ACLF. Methods Patients with HBV-related ACLF were recruited and received daily TAF (25 mg/d), TDF (300 mg/d) and ETV (0.5 mg/d). They were prospectively followed-up. The primary endpoint was overall survival at week 12 and week 48, the secondary endpoints were virological response and biochemical response. Results Forty gender and age matched eligible subjects were recruited and divided into three groups: TAF group, TDF group and ETV group. By week 48, 8 (80%) patients in TAF group, 6 (60%) patients in TDF group and 17 (85%) patients in ETV group survived without liver transplantation ( P  = 0.251). After 4 weeks of NAs treatment, all three groups showed paralleling reduction of HBV DNA levels. All three groups presented similar biochemical responses at week 4, patients treated with TAF showed a priority in total bilirubin reduction, albumin and cholesterol maintenance. Additionally, although there was no significant difference in changes of serum urea, serum creatinine, serum cystatin C and estimated GFR among the three groups by treatment week 4, TDF showed unfavorable renal safety even in short -term treatment. The treatment using NAs was well-tolerated and there was no serious drug-related adverse event reported. Conclusions TAF, TDF and ETV are of similar efficacy and safety in short-term and long-term treatment of HBV-ACLF. Trial registration This study is ongoing and is registered with ClinicalTrials.gov , NCT03640728 (05/02/2019).

Quantum steering is an important quantum resource, which is intermediate between entanglement and Bell nonlocality. In this paper, we study steering witnesses for Gaussian states in continuous-variable systems. We give a definition of steering witnesses by covariance matrices of Gaussian states, and then obtain a steering criterion by steering witnesses to detect steerability of any (m+n)-mode Gaussian states. In addition, the conditions for two steering witnesses to be comparable and the optimality of steering witnesses are also discussed.

Introduction The incidence of Invasive pulmonary aspergillosis (IPA) in acute-on-chronic liver failure (ACLF) population is increasing, and its diagnosis and treatment is still a major clinical conundrum. This study aimed to characterize disease-stage-specific patterns and identify independent risk factors for IPA development in ACLF. Methods A cohort of 835 ACLF patients (Jan 2014-Aug 2023) was retrospectively analyzed. IPA diagnosis followed Invasive Fungal Infections Group of the European Organization for the Research and Treatment of Cancer/Mycoses Study Group (EORTC/MSG). Comparative analysis included 55 ACLF-IPA cases versus 163 pulmonary infection controls without IPA. Multivariate logistic regression established risk factors, with predictive performance evaluated by ROC analysis. Results The rate of clinical diagnosis of IPA in 835 ACLF patients was 6.6% (55/835). The median interval from ACLF diagnosis to IPA diagnosis was 31 days. The logistic regression model was C-Aged = 1.531 × [cirrhosis (yes:1, no:0)] + 1.511 × [diabetes mellitus (yes:1, no:0)] + 0.811 × [steroid exposure (yes:1, no:0)] + 0.033 × age + 1.520 × [types of broad-spectrum antibiotics ≥ 3 (yes:1, no:0)] -6.155. The derived predictive model demonstrated strong discrimination (AUROC = 0.828). Conclusions IPA predominantly manifests in advanced ACLF stages (≥ 28 days). Critical risk factors include cirrhosis, diabetes, glucocorticoid use, advanced age, and multi-class antibiotic exposure. These findings enable risk-stratified surveillance and early intervention.

The geochemical maps and geochemical anomaly maps produced based on the data in the databases of the Regional Geochemistry–National Reconnaissance (RGNR) and the National Multipurpose Regional Geochemistry Survey (NMPRGS) projects have played a crucial role in China’s geochemical exploration. A geochemical survey of the Mojiang area, Yunnan Province, China, has been completed and reveals potential new regions for Ni exploration related to occurrences of serpentinite melanges. The geochemical maps and geochemical anomaly maps need to be drawn in this area. Traditional geochemical maps, heavily dependent on data quantity, are less suitable for consistent comparisons across distinct regions and elements. Here, a fixed value method is proposed to contour the Ni geochemical map on 19 levels, which is convenient for the comparison among elements. On the geochemical maps, the two known Ni deposits are located in a region with Ni surely screening risk level (on the national standard of pollution risk of heavy metals in China) and a region with Ni economic level (Ni as an associate or main economic metal on the national standard of Ni deposit in China), respectively. In addition, we have determined that the Sn and Li levels in this area are at (low or high) background levels compared to other regions. Then, the method of seven levels of classification, which is also suitable for the comparison across different areas or elements, is used to draw the geochemical anomaly maps in the Mojiang area. On the anomaly maps, the two known Ni deposits are located in the regions with Ni anomaly levels not less than four, while the anomaly areas of Sn and Li are sporadic, with anomaly levels not larger than two in this area. These consistent results with the known facts of Ni, Sn, and Li deposits in the Mojiang area not only consolidate the roles of geochemical maps and geochemical anomaly maps but also illustrate the comparison among elements in mineral exploration. Furthermore, we predicted three Ni potential regions in the Mojiang area on the geochemical survey.