Search Results (147)

Sustainable irrigation planning under increasing water scarcity requires quantitative optimization tools to balance land and water resources. This study develops a linear programming (LP)-based framework to determine optimal cropping patterns under variable seasonal water availability in three irrigation projects in Lao PDR: Nam Tong 2 (1000 ha; ≈48.16 million m³ (MCM)), Nam Hin (80 ha; ≈0.73 MCM), and Xe Salalong (1530 ha; ≈30.80 MCM). Six major crops were analyzed for each project, with crop water requirements ranging from 4000 to 12,000 m³ ha⁻¹ and gross revenues from 1200 to 41,322 US$ ha⁻¹. Eight irrigation scenarios were constructed by combining land suitability (suitable vs. unsuitable), crop water requirement levels, and gross revenue assumptions. The model maximizes total gross revenue subject to seasonal water and land constraints. The results indicate that under limited water availability (e.g., 5.35–6.20 MCM in Nam Tong 2), crops with lower water demand (≤6000 m³ ha⁻¹) and higher economic return per unit of water are prioritized, improving water-use efficiency. As water availability increases, high-value but water-intensive crops expand until land suitability becomes the dominant constraint. Expanding irrigation on unsuitable land produces diminishing economic returns. The framework enhances the realism of irrigation planning and supports economically efficient, water-sustainable crop allocation in water-scarce regions. Full article

The need for a transition to a low-carbon economy has led to the growing demand for hydrogen as a clean energy source. Hence, ethanol steam reforming (ESR) is one of the promising technological pathways for hydrogen production. Ethanol, which is the major feedstock, can be obtained from abundant biomass. However, one of the major drawbacks is catalyst deactivation due to the high temperature requirement to start the reaction. This study therefore focused on employing a response surface approach to optimize the operating conditions (reaction temperature, steam-to-ethanol ratio and catalyst amount) of ethanol steam reforming over an Iridium-promoted Ni/MCM-41 catalyst. The Iridium-promoted Ni/MCM-41 catalyst was synthesized using the sequential wet impregnation method and characterized using field emission scanning electron microscopy (FESEM), energy dispersive X-ray spectroscopy (EDS), transmission electron microscopy (TEM), N₂ physisorption analysis, and X-ray diffraction (XRD). A central composite experiment design (CCD) was employed to study the effect of the variables on the hydrogen production from the ESR. The catalytic efficacy was ascertained by evaluating the H₂ yield under varied experimental conditions provided by the CCD. The characterization of the catalyst revealed well-dispersed Ir and Ni nanoparticles on a mesoporous MCM-41 support. Catalytic evaluations indicate that the H₂ yield was most influenced by the reaction temperature (correlation coefficient of 0.68), followed by the catalyst amount (correlation coefficient of 0.34) and steam-to-ethanol ratio (correlation coefficient of 0.28). A maximum H₂ yield of 5.82 mol/mol ethanol was obtained at 798.11 °C, a steam-to-ethanol ratio of 3.40, and 1.25 g of catalyst. These findings underscore the importance of Ir-promoted Ni/MCM-41 catalyst for efficient H₂ production, highlighting the reaction temperature as a critical parameter for process optimization. Full article

The interest in more sustainable energy sources has necessitated research in hydrogen production from various reliable pathways. This study investigates the potential of hydrogen production by ethanol-assisted CO₂ reforming over Al-MCM-41-supported Ni catalysts considering the effect on the catalytic performance and stability. The Ni/Al-MCM-41 catalysts were synthesized via wet impregnation method and characterized using different instruments and techniques. Evidence of the formation of well-crystallized Ni nanoparticles dispersed on the Al-MCM-41 support was confirmed by X-ray diffraction analysis and field emission scanning electron microscopy. The amount of Ni loading, which varied from 5 to 15%, was confirmed using energy-dispersive analysis, while the mesoporous nature of the Ni/Al-MCM-41 was ascertained using N₂ physisorption analysis. The performance of the Ni/Al-MCM-41 catalyst as a function of the ethanol conversion, CO₂ conversion, hydrogen and CO yield is strongly corrected with Ni loading and reaction temperature. The ethanol conversion and hydrogen yield increase with the increase in reaction temperature. At a reaction temperature of 550 °C the lowest ethanol conversion and hydrogen yield of 32.3% and 33.7% were obtained over the 5 wt% Ni/Al-MCM-41 catalyst, while the highest ethanol conversion of 87.4% and hydrogen yield of 75.5% were obtained over the 15% Ni/Al-MCM-41 at 700 °C. The 15 wt% catalyst achieves the most balanced syngas profile at 700 °C, where the H₂ and CO yields are optimized through the synergistic consumption of both ethanol and CO₂. It can be inferred that the reaction follows a bifunctional pathway whereby the Ni active sites are responsible for the ethanol dissociation while the CO₂ adsorption and activation are enhanced by the Al-MCM-41 support. Full article

Background: Familial prostate cancer (PCa) accounts for nearly 20% of all PCa cases and is associated with increased genetic susceptibility and earlier disease onset. However, early detection and risk stratification in genetically predisposed individuals remain challenging. Circulating cell-free DNA (cfDNA) provides a minimally invasive source of tumor-derived genomic and epigenomic information. Integrating multi-omic cfDNA analyses may enhance the discovery of biomarkers relevant to familial PCa biology. Methods: We conducted a pilot feasibility study employing whole-genome, strand-specific sequencing of cfDNA from eight patients with familial PCa. A unified analytical pipeline was used to jointly profile genomic alterations and epigenomic features. Variant calling, methylation mapping, and allele-specific methylation (ASM) analysis were performed to identify somatic mutations, characterize epigenetic dysregulation, and explore potential interactions between genetic and epigenetic mechanisms. Results: Sequencing revealed 18,878 genetic variants, including 2276 potentially pathogenic alterations. We identified 26 recurrent high-impact mutations, such as stop-gain and start-loss variants, in genes including MUC4, MCM9, and SKA3. Epigenomic profiling demonstrated widespread gene-specific hypermethylation, consistent with transcriptional repression in these loci. ASM events were detected in TTC22, TEX51, WDR89, LAIR2, and SKA3, suggesting coordinated interactions between somatic variation and epigenetic regulation in familial PCa. Conclusions: This proof-of-concept study highlights the feasibility and potential of integrating whole-genome and epigenome profiling of cfDNA to decode the molecular architecture of familial prostate cancer. By jointly capturing genomic alterations and epigenetic signatures, including allele-specific methylation, this multi-omic liquid biopsy approach supports a high-resolution exploration of biologically relevant molecular features. Moreover, this integrated profiling strategy provides a minimally invasive and clinically scalable tool that may substantially improve risk assessment. These findings offer a promising foundation for future validation studies in larger cohorts, with the aim of advancing multi-omic cfDNA analysis as a next-generation technology in the field of precision oncologic epigenetics. Full article

Urban water supply systems require considerable electrical energy inputs across all operational processes: raw water abstraction, treatment, transmission, and distribution. Consequently, water loss within these processes represents not merely a loss of water volume, but also additional energy consumption and an increase in carbon emissions, given that electricity generation relies predominantly on fossil fuels. This study applied two methodological approaches to analyze the role of water loss within the Water–Energy–Carbon (WEC) Nexus of the Metropolitan Waterworks Authority (MWA), Thailand, over the period 2017–2024. The first method utilized a detailed WEC linkage analysis to balance water inputs and outputs in each process to quantify specific losses: raw water, in-plant, transmission, and distribution losses. The second method applied the International Water Association’s Leakage Emissions Initiative framework, focusing specifically on potable real water loss in distribution process, which constituted the largest volume (64.85% of total losses) and embodied the highest specific energy consumption. Based on the first method, the average annual potable real water loss was 534.71 MCM/yr (23.58% of water supplied to distribution), corresponding to embedded energy and carbon emissions of 103.76 GWh/yr (24.89% of total energy consumption) and 49,562 tCO₂e/yr (24.89% of total carbon emission), respectively. Although the second method was considerably simplified, the estimated energy and carbon emission values were only slightly higher than those derived from the detailed method, demonstrating the second method’s effectiveness as a streamlined assessment tool. These findings underscored that water loss reduction initiatives are essential for minimizing energy consumption and carbon emissions, thereby supporting Thailand’s pathway toward Net Zero emissions by 2050. Full article

Xylaria karsticola NBIMCC 9097 is a recently described and rare fungicolous species originating from Bulgaria. Understanding its growth behavior and bioactive potential is essential for evaluating its biotechnological and pharmaceutical relevance. In the presented study, we model the in vitro growth kinetics of X. karsticola mycelium under submerged cultivation and assess its antimicrobial activity. Optimization of MCM and MYB media markedly increased biomass yields to 20.11 and 23.25 g/dm³, respectively, compared with non-optimized media (9.9 ± 0.21 and 10.8 ± 0.28 g/dm³). The maximum specific growth rate was higher in the MCM (0.803 ± 0.004 h⁻¹) in comparison with the MYB medium (0.711 ± 0.003 h⁻¹); however, the MYB medium supported greater biomass accumulation and more efficient substrate utilization, reflected by a higher utilization coefficient (0.9900 ± 0.001 versus 0.9644 ± 0.005). The antimicrobial activity was evaluated using agar disk diffusion and minimum inhibitory concentration assays against Gram-positive and Gram-negative bacteria and yeasts. Hexane and ethyl acetate extracts were most effective against Pseudomonas aeruginosa ATCC 9027 (MIC 0.067 and 0.059 mg/cm³), while notable anti-yeast activity was observed, particularly against Wickerhamomyces anomalus, Saccharomycodes ludwigii, and Pichia membranifaciens. The lowest MIC (0.02 mg/cm³) was recorded for the water biomass extract against S. ludwigii indicating potent antimicrobial activity against the tested microorganism. These findings identify X. karsticola as a potential source of antimicrobial metabolites and provide a strong motivation for comprehensive metabolomic profiling and systematic optimization of its cultivation. Full article

Triple-negative breast cancer (TNBC), particularly the androgen receptor-low (AR-low) subtype, is one of the most aggressive and hard-to-treat forms of BC, characterized by a high index of proliferation, chromosomal instability (CIN), and high prevalence of TP53 mutations. These features fuel therapy resistance, metastases, and poor clinical outcomes. An integrated framework describing the dysregulated molecular networks that support the pathobiology of AR-low TNBC is lacking. Multiple published studies in breast cancer have previously proposed mechanistic links between TP53 loss, AR-low states, and heightened FOXM1-driven G2/M transcriptional programs, potentially via deregulation of E2F activity, chromatin-associated co-regulators (e.g., ATAD2), and disruption of repressive networks involving p53–p21–DREAM and SPDEF. Additional reports suggest that FOXM1-associated circuitry may be reinforced by chromatin regulators such as WDR5 and by mitotic/spindle factors such as ASPM, including through feedback interactions and condensate-associated transcriptional organization. We previously showed that FOXM1, a master regulator transcription factor, is upregulated and is a biomarker of poor prognosis in AR-low TNBC. In this study, we filtered a set of “TNBC core genes” known to promote transcriptional chaos downstream of FoxM1. We identified a set of 15 cell cycle regulators—including mitotic kinesin motors (KIF14, KIF11, KIF4A, KIF2C, and KIF20A), centromeric proteins (CENPA, CENPO, CENPL, CENPF, and OIP5), and regulators of proteolysis (UBE2C, UBE2S, UBE2T, PSMD14, and TUBA1B). These 15 genes, which were ranked highly among genes overexpressed in TNBC featured prominently in gene signatures of chromosomal instability and were also overexpressed among AR-low TNBCs and TP53-mutant breast tumors. We show that expression of each of these 15 genes correlates positively with proliferation markers (Ki67, PCNA, and MCM2) in TNBC, and that the overexpression of this gene set is associated with shorter relapse-free survival and distinct immune/stromal infiltration patterns. In light of prior work, our findings point to a FOXM1-associated 15-gene signature enriched in AR-low TNBC and associated with the high-proliferation and high-CIN phenotypes of this clinically challenging tumor type. This 15-gene set represents an actionable vulnerability with therapeutic potential for AR-low TNBC and provides a framework for rethinking how to manage highly proliferative, genomically unstable BCs. Full article

The development of non-noble metal catalysts provides a cost-effective and sustainable route for glucose oxidation to gluconic acid. In this study, a series of catalysts based on inexpensive transition metals (Cr, Cu, Ni, Fe) and/or Au were synthesized using siliceous supports (SiO₂ and MCM-41) and systematically evaluated. The aim was to partially or fully replace noble metals with lower-cost alternatives, while maintaining high catalytic performance. Comprehensive characterization—including ICP-AES for composition, N₂ adsorption–desorption for porosity, XRD for structure, H₂-TPR for reducibility, and NH₃-TPD for acidity—was conducted to establish structure–property relationships. Among the tested catalysts, Ni- and Fe-based systems exhibited superior stability, with NiO/SiO₂ achieving gluconic acid yields comparable to Au. The bimetallic Au–Ni/SiO₂ catalyst displayed enhanced metal–support interactions and minimal leaching (<2%), while Au–Fe/SiO₂ improved selectivity, yielding up to 23% gluconic acid, surpassing 5Fe/SiO₂ (18%) and 0.3Au/SiO₂ (15%), albeit with lower stability. These results highlight the potential of low-cost transition-metal and bimetallic catalysts as efficient and economically viable systems for selective glucose oxidation, providing insights for rational catalyst design in sustainable carbohydrate valorization. Full article

Clear-cell renal cell carcinoma (ccRCC) accounts for the majority of kidney cancer diagnoses and exhibits widely variable clinical behaviour. The dataset described here was generated to support the discovery of robust biomarkers of tumour cell-cycle arrest and to inform the risk-stratified management of ccRCC. We assembled four independent cohorts including 480 patients from the UK arm of the SORCE adjuvant trial, 300 patients from a surgically treated series in Korea, 120 patients from a retrospective Scottish cohort, and a paired primary–metastatic cohort comprising 62 patients. Formalin-fixed paraffin-embedded nephrectomy specimens were processed for routine hematoxylin and eosin (H&E) histology, and for multiplex immunofluorescence (mIF). The mIF panels detect the cyclin-dependent kinase inhibitor p21^CDKN1a, the DNA replication licencing factor MCM2, endoglin/CD105, Lamin B1 and nuclear DNA (Hoechst). Whole-slide images (WSIs) were acquired at high resolution, and artificial-intelligence pipelines were used to segment nuclei, classify individual cells into arrested phenotypes, and calculate the fraction of cells. Accompanying metadata include demographics, tumour stage, grade, Leibovich score, treatment arm (sorafenib/placebo), relapse events, and disease-free survival. All images and derived tables are released under a CC0 licence via the BioImage Archive, ensuring unrestricted reuse. This multi-cohort dataset provides a rich resource for studying cell-cycle arrest and proliferation markers, training image-analysis algorithms, and developing prognostic signatures in RCC. Full article

Predicting dam inflow is critical for human life safety, water resource management, and hydroelectric power generation. While machine learning (ML) models address complex, nonlinear hydrological problems, quantum machine learning (QML) offers greater potential to overcome classical computational limits. This study compares a hybrid quantum neural network (HQNN) with the following two classical models: bidirectional CNN-LSTM and support vector regression (SVR). These models were evaluated to predict monthly inflow to the Mile Mughan Dam, a transboundary hydroelectric and irrigation dam located on the Aras River between Azerbaijan and Iran, using a 14-year dataset (2010–2023) under two scenarios. In total, 70% of data was used for training and 30% for testing. The first scenario encompassed meteorological variables plus three months of inflow lags, and the second included inflow lags only. Model performance was assessed using Coefficient of Determination (R²), Root Mean Squared Error (RMSE), Nash–Sutcliffe efficiency (NSE), Mean Absolute Percentage Error (MAPE), and graphical plots. HQNN showed superior performance across all metrics. In Scenario 1, HQNN achieved R² = 0.915, RMSE = 37.318 MCM, NSE = 0.908, MAPE = 8.343%; CNN-BiLSTM had R² = 0.867, RMSE = 46.506 MCM, NSE = 0.858, MAPE = 10.795%; SVR had R² = 0.846, RMSE = 52.372 MCM, NSE = 0.821, MAPE = 12.772%. In Scenario 2, HQNN maintained strong performance (R² = 0.855, RMSE = 48.56 MCM, NSE = 0.845, MAPE = 9.979%) and outperformed CNN-BiLSTM (R² = 0.810, RMSE = 56.126 MCM, NSE = 0.793, MAPE = 11.456%) and SVR (R² = 0.801, RMSE = 60.336 MCM, NSE = 0.761, MAPE = 12.901%). In Scenario 1 and Scenario 2, HQNN increased the prediction accuracy by 19.76% and 13.47%, respectively, compared to the CNN-BiLSTM model. These results confirm HQNN’s reliability in both multivariate and univariate modeling. Full article

Minute virus of canines (MVC), which is a member of the Bocaparvovirus genus, is a non-enveloped, single-stranded DNA virus that causes respiratory and gastrointestinal disease in canines, as well as causing infertility and fetal death in pregnant dogs. The non-structural small protein NP1 of bocaparvoviruses is a unique feature that distinguishes the bocaparvovirus subfamily from other parvovirus subfamilies. In the life cycle of the MVC, NP1 plays an indispensable role in viral DNA replication and pre-mRNA processing. Currently, there is a paucity of studies reporting the characterization of host cell proteins interacting with NP1 during MVC replication. In this study, we screened and identified host cell proteins interacting with MVC-NP1 through immunoprecipitation (IP) combined with liquid chromatography and tandem mass spectrometry (LC-MS/MS) analysis; MCM7 (Mini-chromosome Maintenance Protein 7) has been identified and confirmed to interact directly with NP1 through its N-terminal domain. Furthermore, functional studies reveal that MCM7 is essential in MVC replication. The knockdown of MCM7 decreased the expression of this MVC protein significantly, as well as suppressing MVC replication by arresting the cell cycle in the G0/G1 phase during infection. Conversely, up-regulating MCM7 can rehabilitate the expression of MVC proteins, as well as supporting MVC replication. In conclusion, this study elucidates the interaction between the NP1 protein of MVC and the host factor MCM7, demonstrating that MCM7 is a key factor in the replication process of MVC. These findings provide a potential target for future antiviral therapy. Full article

Background/Objectives: Inteins are mobile genetic elements invading highly conserved genes across all domains of life and viruses. Five active intein insertion sites (MCM-a through e) had previously been identified and studied in the archaeal replicative helicase minichromosome maintenance (MCM) subunit gene mcm, making MCM an ideal system for dissecting the dynamics of multi-intein genes. However, work in this system thus far has been limited to particular archaeal groups. To better understand the dynamics and diversity of these inteins, MCM homologs spanning all archaeal groups were extracted from NCBI’s non-redundant protein sequence database, and the distribution and structural architectures of their inteins were characterized. Methods: The amino acid sequences of 4243 archaeal MCM homologs were retrieved from NCBI’s non-redundant protein sequence database. These sequences were systematically assessed for their intein content through within-group multiple sequence alignments. To characterize the inteins present at each site, extensive intein structure predictions and comparisons were performed. Phylogenetic analyses were used to investigate intein relatedness between and within sites, as well as the distribution of different MCM inteins in geographically overlapping populations of archaea. Results: In total, 11 active MCM intein insertion sites were identified, expanding on the previously known five. The insertion sites have varied invasion activity levels across archaeal groups, with Nanobdellati (DPANN) being the only group with all 11 sites active. In all but two (Methanonatronarchaeia and Hadarchaeota) of the archaeal groups studied where inteins were present, at least one MCM homolog was invaded by more than one intein. With respect to intein structure, within-intein insertions bearing semblance to DNA-binding domains were identified, with varied presence between inteins. Additionally, a study of archaeal MCM sequences of samples collected from the Atacama Desert in June 2013 revealed high MCM intein diversity levels. Conclusions: We identified six new active intein insertion sites in archaeal MCM, more than doubling the five previously known sites. All eleven intein insertion sites were either close to the ATP binding site, or the lined the channel through which the single-stranded DNA is pulled during the catalytic cycle of the helicase. Many of the analyzed inteins contained insertions bearing similarity to DNA-binding helix-turn-helix domains suggesting potential involvement in the intein homing process. Additionally, the high levels of MCM intein diversity observed in archaea from the Atacama Desert provide novel and strong support for a co-existence model of intein persistence. Full article

The ongoing miniaturization of semiconductor devices necessitates continuous advancements in lithographic processes, which are critical for high-precision circuit formation. To prevent substrate damage during the etching step, a spin-on hardmask (SOH) layer is often introduced between the photoresist (PR) and the substrate. However, residual metal ions in SOH solutions can adversely affect integrated circuit performance, underscoring the need for efficient and chemically compatible removal strategies. This study investigates the adsorption of metal ions (Al³⁺, Cr³⁺, Cu²⁺, Fe³⁺, Ni²⁺, and Ti⁴⁺) from SOH solutions using mesoporous silica materials—MCM-41 and SBA-15—functionalized with various groups (–OH, –NH₂, –SH, and –CH₃). Adsorption performance was evaluated under solvent-only, monomer-containing, and polymer-containing conditions. Among the tested materials, amine-functionalized mesoporous silica exhibited the highest adsorption efficiency, with SBA-15-NH₂ showing relatively effective and uniform performance in polymer-containing systems. Isotherm analysis supported a monolayer chemical adsorption mechanism, suggesting the significance of surface functional groups in the adsorption process. These findings demonstrate the potential of functionalized mesoporous silica as a promising candidate for trace metal ion removal in semiconductor manufacturing, offering enhanced yield and improved process reliability. Full article

Wildfire detection in power transmission corridors is essential for providing timely warnings and ensuring the safe and stable operation of power lines. However, this task faces significant challenges due to the large number of smoke-like samples in the background, the complex and diverse target morphologies, and the difficulty of detecting small-scale smoke and flame objects. To address these issues, this paper proposed an improved Oriented R-CNN model enhanced with metric learning for wildfire detection in power transmission corridors. Specifically, a multi-center metric loss (MCM-Loss) module based on metric learning was introduced to enhance the model’s ability to differentiate features of similar targets, thereby improving the recognition accuracy in the presence of interference. Experimental results showed that the introduction of the MCM-Loss module increased the average precision (AP) for smoke targets by 2.7%. In addition, the group convolution-based network ResNeXt was adopted to replace the original backbone network ResNet, broadening the channel dimensions of the feature extraction network and enhancing the model’s capability to detect flame and smoke targets with diverse morphologies. This substitution led to a 0.6% improvement in mean average precision (mAP). Furthermore, an FPN-CARAFE module was designed by incorporating the content-aware up-sampling operator CARAFE, which improved multi-scale feature representation and significantly boosted performance in detecting small targets. In particular, the proposed FPN-CARAFE module improved the AP for fire targets by 8.1%. Experimental results demonstrated that the proposed model achieved superior performance in wildfire detection within power transmission corridors, achieving a mAP of 90.4% on the test dataset—an improvement of 6.4% over the baseline model. Compared with other commonly used object detection algorithms, the model developed in this study exhibited improved detection performance on the test dataset, offering research support for wildfire monitoring in power transmission corridors. Full article

Background: Lung adenocarcinoma (LUAD) is the most common subtype of non-small-cell lung cancer and is frequently diagnosed at advanced stages with metastasis, contributing to its poor prognosis. Identifying key metastasis-related biomarkers is critical for improving early diagnosis and therapeutic targeting. Methods: We analyzed four GEO microarray datasets (GSE32863, GSE27262, GSE40275, and GSE33356) and TCGA data to identify differentially expressed genes (DEGs) in LUAD. Functional enrichment of DEGs was analyzed using Gene Ontology, Kyoto Encyclopedia of Genes and Genomes analysis, and a Cancer Hallmark Enrichment Plot. Hub gene analysis was conducted using Cytoscape. Hub genes were evaluated for their expression, prognostic significance (via the Kaplan–Meier plotter), and clinical correlation using additional platforms (TCGA, Lung Cancer Explorer, TNMplot, and the Human Protein Atlas). Results: A total of 333 consistently dysregulated DEGs were identified, enriched in pathways related to metastasis, including angiogenesis, immune escape, and ECM interaction. Ten hub genes (AURKA, TOP2A, CCNB2, CENPF, MCM4, TPX2, KIF20A, ASPM, MELK, and NEK2) were identified through network analysis. Among these, MCM4 showed strong upregulation in LUAD and was significantly associated with poor overall survival. Notably, MCM4 expression also correlated with post-progression survival and markers of invasiveness. Immunohistochemistry and transcriptomic analyses confirmed MCM4 overexpression at both mRNA and protein levels. Additionally, MCM4 expression was positively correlated with various matrix metalloproteinases, supporting its role in promoting tumor invasiveness. Conclusions: MCM4 is a novel potential biomarker for LUAD metastasis and prognosis. Its consistent upregulation, association with metastatic markers, and clinical significance suggest it may serve as a candidate target for diagnostic use or therapeutic intervention in advanced LUAD. Full article