Relevant Information Research Articles

Comparison of Surgical and Colonoscopy Tissue to Establish Colorectal Patient-derived Organoids.

Patient-derived organoids (PDOs) are ex vivo models that retain the functions and characteristics of individualized source tissues, including a simulated tumor microenvironment. However, the potential impact of undiscovered differences between tissue sources on PDO growth and progression remains unclear. This study aimed to compare the growth and condition of PDO models originating from surgical resection and colonoscopy and to provide practical insights for PDO studies. Tissue samples and relevant patient clinical information were collected to establish organoid models. PDOs were derived from both surgical and colonoscopy tissues. The growth of the organoids, including their state, size, and success rate of establishment, was recorded and analyzed. The activity of the organoids at the end stage of growth was detected using calcein-AM fluorescence staining. The results showed that the early growth phase of 2/3 colonoscopy-derived organoids was faster compared to surgical PDOs, with a growth difference observed within 11-13 days of establishment. However, colonoscopy-derived organoids exhibited a diminished growth trend after this time. There were no significant differences observed in the terminal area and quantity between the two types of tissue-derived organoids. Immunofluorescence assays of the PDOs revealed that the surgical PDOs possessed a denser cell mass with relatively higher viability than colonoscopy-derived PDOs. In the establishment of colorectal patient-derived organoids, surgically derived organoids require a slightly longer establishment period, while colonoscopy-derived organoids should be passaged prior to growth inhibition to preserve organoid viability.

El efecto del tratamiento de hierro en pacientes con insuficiencia cardiaca e insuficiencia de hierro. Un metaanálisis de ensayos aleatorizados

IntroductionApproximately 45% of patients with heart failure had iron deficiency for the rest of their lives. ObjectiveTo determine the effect of iron treatment in patients with heart failure and iron deficiency or anemia. Materials and MethodsThe studies were selected in: PubMed, Scopus, Cochrane and Google Scholar, from the beginning of June to July 2023. The selected articles that met the inclusion criteria, the relevant information of the studies were collected according to the selection variables and were recorded in the Revman 5.0 program of the Cochrane collaboration. The primary outcome was composed of hospitalization for heart failure or cardiovascular death. ResultsOf the 7 included studies, 6717 patients were obtained, the average age was 70 years with a predominance of the male gender (57%). The primary outcome was lower in patients in the control group compared to the placebo group (OR, 0.62; 95%CI, 0.54-0.70). The 6-minute walk test (6-MWT) was improved in the iron-supplemented group compared to the placebo group (OR, 0.79; 95%CI, 0.64-0.98). Adverse events were fewer in the iron supplement group compared to the placebo group (OR, 0.78; 95%CI, 0.67-0.91). ConclusionIn this meta-analysis of randomized controlled trials, the effect of iron supplementation is found to reduce the incidence rate of hospitalization for heart failure or cardiovascular death. In addition to a reduction in adverse events, such as gastrointestinal and neurological disorders, it also improves 6-MWT.

Increased Ability to Perceive Relevant Sensory Information Minimizes Low Back Exposures in Lifting.

We have previously shown evidence that some individuals seem to consistently minimize low back loads when lifting, while others do not. However, it is unknown why. Individual differences in ability to perceive relevant sensory information may explain differences in minimization of low back loads during lifting, consistent with considering load reduction in one's movement objective in an optimal feedback control theory framework. The purpose of this study was to investigate whether individuals' ability to perceive proprioceptive information (both force- and posture-senses) at the low back was associated with peak low back loads when performing generic or occupation-specific lifts. Seventy-two participants were recruited to perform 10 barbell (generic) and backboard (occupation-specific) lifts, while whole-body kinematics and ground reaction forces were collected. Peak low back compression and anteroposterior shear forces normalized to body mass were calculated as dependent variables. Both posture matching ability and force matching ability at the heavier force targets were associated with lower means and variability of peak low-back loads in both lift types, albeit with small effect sizes (R2 ≤ .17). These findings support the utility of an optimal feedback control theory framework to explore factors explaining interindividual differences in low back loads during lifting. Further, this evidence suggests improving proprioceptive ability may be a useful strategy in lift training programs designed for workplace injury prevention.

Reliability of Accuracy and Precision Tests for Elite Para Table Tennis Players.

The primary purpose of this study was to adapt selected accuracy and precision tests in table tennis to the specific skills of elite table tennis players with impairment. The study included a sample of 23 Para table tennis players with an average age of 31.8 ± 12.22years (including 30.4% females) who belonged to the senior Polish Para table tennis team. A battery of six tests evaluating stroke accuracy and serve precision was assessed. The analysis of the reliability of these tests confirmed the legitimacy of using this battery of accuracy and precision tests to assess the skills of Para table tennis players in all three integrated sport classes: players in wheelchairs, in a standing position, and with intellectual impairment. Analysis of the data obtained from the tests could provide coaches with relevant information regarding elite Para table tennis players' level of performance and examine selectedaccuracy and precision elements of their individual technique.

Does Knowledge of Results Affect Motor Skill Learning and Adaptation in Interception-Like Tasks?

Knowledge of results (KR), particularly its informational role, has often been regarded as redundant for learning interception-like tasks, such as coincidence–anticipation timing tasks. However, it is possible that the KR’s guiding effect might be detrimental to motor adaptation, instead of only redundant, leading to a dependency on KR and steering the sensorimotor system away from relevant information of the task. In this study, we aimed to investigate KR’s effect on learning a coincidence–anticipation timing tasks and on the adaptation to unpredictable perturbations. Two groups of participants practiced a coincidence–anticipation timing tasks with or without KR on 1 day and underwent testing the next day for learning (Retention test) and for adaptation to unpredictable perturbations (Exposure phase). Both groups exhibited similar learning results but failed to adapt to the perturbations, contradicting the assumption of negative guidance effects of KR and the positive effects of relying solely on intrinsic information (no KR). These findings suggest that motor adaptation may require specific information during the acquisition process, highlighting for more systematic analyses to understand this phenomenon better. Such insights could have practical implications in contexts like sports and rehabilitation, by providing learners with appropriate information for acquiring adaptive internal representations of tasks.

Physical Education Teachers’ Perceptions of a Motor Competence Assessment Digital App

Purpose: To refine a motor competence assessment app prototype, enabling its redesign. Method: Workshops were conducted to inform the prototype development. App’s usability was evaluated on its ability to communicate relevant information to the teachers (n = 9). A “think out loud” protocol was applied by the users. Results: Both workshops refined the app, namely: interface, background, acceptability/feasibility, results, teachers themselves, and effectiveness. Competitor analysis revealed recommendations for the wireframe, visuals, and proposed skills to be assessed. The need to include a demonstration for first-time users, video guidance, and links to more information for each skill was also raised. There was a recommendation on the design brief features and experiences. Conclusions: This study will provide guidance in discovering how digital solutions may shape motor competence assessment. The usability testing process with the teachers provides insight on the essential/desirable features required on these apps, enabling the prototype’s redesign to meet the users’ needs.

Preventing Fraudulent Vehicle Listings: Digital ADS Detection

Abstract: With the increasing popularity of online selling platforms for vehicles, the risk of fraudulent advertisements has also risen significantly. This research paper addresses the challenge of detecting and preventing digital fraud ads, particularly those related to vehicle listings, on typical online vehicle selling web portals. The proposed approach leverages advanced algorithms to identify and filter out fraudulent vehicle ads, including unregistered vehicles and unnecessary spam ads posted by sellers. One of the key techniques employed in this research is pattern matching, which involves comparing the details of posted vehicles with the records from Regional Transport Offices (RTOs) to determine their registration status. By analyzing the vehicle details provided in the ads and cross-referencing them with RTO data, the system can flag unregistered vehicles, thus mitigating the risk of fraudulent listings. Additionally, the study incorporates the use of the You Only Look Once (YOLO) algorithm for object detection in the images accompanying the ads. By applying YOLO, the system can accurately identify vehicles in the images and extract relevant information, such as make, model, and license plate details. This allows for further validation of the authenticity of the listings and helps in filtering out spam ads containing irrelevant or misleading images.

Properties of Fine-Grained Cement Composites, with a Special Emphasis on Cement Screeds in Floor Constructions

Through experimental research and theoretical analysis, this study primarily aimed to compare the behavior of cement screeds made in a traditional manner with those made with the addition of microreinforcement. The study also explored the possibility of using electrofilter ash as a component of screeds, examining the advantages and disadvantages of partial substitution of cement with fly ash. The contribution of this article is the experimental research on the characteristics of fresh and hardened cement composites, as well as the parameters influencing the structure and behavior of cement screeds during their use. It has been determined that by using electrofilter ash as a partial replacement for cement, satisfactory values of physical–mechanical and deformation characteristics of fine-grained cement composite can be achieved. Through analysis of the obtained results and influential parameters of these composites, the optimal design approach has been explored. This relevant information could potentially provide reliable recommendations to designers and contractors for the production of quality and durable cement screeds.

Region-Focused Network for Dense Captioning

Dense captioning is a very critical but under-explored task, which aims to densely detect localized regions-of-interest (RoIs) and describe them with natural language in a given image. Although recent studies tried to fuse multi-scale features from different visual instances to generate more accurate descriptions, their methods still suffer from the lack of exploration of relation semantic information in images, leading to less informative descriptions. Furthermore, indiscriminately fusing all visual instance features will introduce redundant information, resulting in poor matching between descriptions and corresponding regions. In this work, we propose a Region-Focused Network (RFN) to address these issues. Specifically, to fully comprehend the images, we first extract the object-level features, and encode the interaction and position relations between objects to enhance the object representations. Then, to decrease the interference from redundant information about the target region, we extract the most relevant information to the region. Finally, a region-based Transformer is employed to compose and align the previous mined information and generate the corresponding descriptions. Extensive experiments on Visual Genome V1.0 and V1.2 datasets show that our RFN model outperforms the state-of-the-art methods, thus verifying its effectiveness. Our code is available at https://github.com/VILAN-Lab/DesCap .

The potential and limitations of large language models in identification of the states of motivations for facilitating health behavior change.

The study highlights the potential and limitations of the Large Language Models (LLMs) in recognizing different states of motivation to provide appropriate information for behavior change. Following the Transtheoretical Model (TTM), we identified the major gap of LLMs in responding to certain states of motivation through validated scenario studies, suggesting future directions of LLMs research for health promotion. The LLMs-based generative conversational agents (GAs) have shown success in identifying user intents semantically. Little is known about its capabilities to identify motivation states and provide appropriate information to facilitate behavior change progression. We evaluated 3 GAs, ChatGPT, Google Bard, and Llama 2 in identifying motivation states following the TTM stages of change. GAs were evaluated using 25 validated scenarios with 5 health topics across 5 TTM stages. The relevance and completeness of the responses to cover the TTM processes to proceed to the next stage of change were assessed. 3 GAs identified the motivation states in the preparation stage providing sufficient information to proceed to the action stage. The responses to the motivation states in the action and maintenance stages were good enough covering partial processes for individuals to initiate and maintain their changes in behavior. However, the GAs were not able to identify users' motivation states in the precontemplation and contemplation stages providing irrelevant information, covering about 20%-30% of the processes. GAs are able to identify users' motivation states and provide relevant information when individuals have established goals and commitments to take and maintain an action. However, individuals who are hesitant or ambivalent about behavior change are unlikely to receive sufficient and relevant guidance to proceed to the next stage of change. The current GAs effectively identify motivation states of individuals with established goals but may lack support for those ambivalent towards behavior change.

Decreasing alpha flow releases task-specific processing paths

Abstract Directing and maintaining attention toward relevant information and away from non-relevant information is a critical competence of higher-order nervous systems. Here, we used directed connectivity to investigate how the human brain prioritizes appropriate processing paths when participants are performing a behavioral task requiring attention in the visual or the auditory modality. We show that the efficient transfer of information relevant for the task is mediated by a region- and task-specific decrease of alpha band connectivity from the parietal and, in case a motor response is required, motor cortex to the relevant sensory cortices. Further, alpha-band connectivity modulations reliably predict alpha power modulations in the task-related sensory cortices, particularly where the task-irrelevant cortex is inhibited via local alpha power increases. We suggest that the task- and region-specific modulation of alpha-band connectivity in the present study is a basic neuronal mechanism orchestrating the allocation of task-relevant neuronal resources related to and possibly underlying the previously reported sensory alpha power modulations in association with the allocation of competing attentional resources.

Abstract 1536: Deciphering the crosstalk within the tumor microenvironment of NSCLC by a virtual microdissection approach

Abstract Introduction: Non-small cell lung cancer (NSCLC) is a highly heterogenous disease with the largest number of cancer-related mortality worldwide, one of the reasons for this is the complex and diverse tumor microenvironment (TME) comprising of numerous cell types. Several studies have already highlighted the importance of TME in dictating progression steps and response to therapies; however, a transcriptome-based molecular subtyping of patients in lung adenocarcinomas (LUADs) and lung squamous cell carcinomas (LUSCs) can further determine the distinct tumor immune microenvironment (TiME), which can eventually provide a systematic overview to improve the diagnosis and prognosis of patients. Material and method: To elucidate such nature of interactions between tumor cells and cells comprising the TME, we exploited the transcriptome of 300 early stages (Ib-IIIa) NSCLC recruited in the prospective observational clinical trial PROMOLE. With the help of a clustering approach, initially we performed a molecular-based virtual stratification/dissection on the NSCLC patients. Next, to elucidate the relative cell-type abundance, a deconvolution approach was applied to identify the possibility of tumor infiltrating immune cells within these subgroups. Immunohistochemistry (IHC) was then used to substantiate these predictions on tumor cells. Results and discussion: The resulting subgroups of LUADs and LUSCs are biologically well-characterized by mutational and gene expression profiles. Cell-type abundance approach identified samples which are enriched with tumor infiltrating immune cells like Neutrophils, Tcells, macrophages, etc. These findings were positively confirmed by IHC with multiple cell markers such as MPO, CD4, CD8, CD68, etc. Integrating these two results highlighted the proportion of TiME in the two different sub-populations along with shedding some light on the crosstalk happening between different cancer-/immune- cell lines. Conclusion: The in-silico predictions on bulk RNA data by virtual micro-dissection, distinguished the two distinct NSCLC subtypes, each associated with clinical and molecular features. Furthermore, the immune cells infiltration suggests a possible role of infiltrating tumor immune cells with the prognosis of patients. Our analysis successfully performed an intra-sample and inter-sample comparison, which can unveil new prognostic markers that can provide relevant information for cancer immunotherapy. Citation Format: Sushant Parab, Francesca Napoli, Davide Corà, Gabriella Doronzo, Valentina Communanza, Luisella Righi, Luca Primo, Valentina Monica, Lorenzo Manganaro, Bianco Selene, Paolo Bironzo, Giorgio Scagliotti, Federico Bussolino. Deciphering the crosstalk within the tumor microenvironment of NSCLC by a virtual microdissection approach [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 1536.

The differential impact of quantitative and qualitative information on outbound cross-border acquisition outcomes: evidence from emerging economies

PurposeThe purpose of this study is to examine the differential impact of qualitative and quantitative informational signals within the merger and acquisition (M&A) press releases on deal completion and duration. A significant percentage of deals by emerging market acquirers get abandoned before completion, and those that are completed have a longer duration. The limited information about the operations of acquirers from emerging markets creates suspicion among the stakeholders involved in deal resolution, hindering the completion of deals. Thus, using the signal-feedback paradigm, authors investigate how informational signals in the M&A press release impact the deal resolution.Design/methodology/approachThe study employs content analysis on M&A press releases announced by firms from five emerging economies: Brazil, Russia, India, China and South Africa. The technique is applied based on the exploration-exploitation framework developed by March (1991) to categorize the announced deal motives (qualitative information). Next, the authors identify the percentage of relevant quantitative information disclosed in the press release, following which results are obtained using logistic and ordinary least square regressions.FindingsThe study reports that deals with declared exploratory motives take longer to complete. Additionally, deals disclosing higher percentage of quantitative disclosure exhibit lower completion rate and increased deal duration.Originality/valueThis is the first study to provide evidence that familiarity bias impacts deal duration as relative to exploitation deals that are familiar to the stakeholders; exploratory deals take longer to conclude. Further, our analysis indicates that a greater percentage of quantitative disclosure may not always reduce information risk but rather be interpreted negatively in the form of the acquirer’s overconfidence in the deal’s potential.

Improving Automated Labeling for ATT&CK Tactics in Malware Threat Reports

Once novel malware is detected, threat reports are written by security companies that discover it. The reports often vary in the terminology describing the behavior of the malware making comparisons of reports of the same malware from different companies difficult. To aid in the automated discovery of novel malware, it was recently proposed that novel malware could be detected by identifying behaviors. This assumes that a core set of behaviors are present in most, if not all, malware variants. However, there is a lack of malware datasets that are labeled with behaviors. Motivated by a need to label malware with a common set of behaviors, this work examines automating the process of labeling malware with behaviors identified in malware threat reports despite the variability of terminology. To do so, we examine several techniques from the natural language processing (NLP) domain. We find that most state-of-the-art word embedding NLP methods require large amounts of data and are trained on generic corpora of text data—missing the nuances related to information security. To address this, we use simple feature selection techniques. We find that simple feature selection techniques generally outperform word embedding methods and achieve an increase of 6% in the F .5 -score over prior work when used to predict MITRE ATT&CK tactics in threat reports. Our work indicates that feature selection, which has commonly been overlooked by sophisticated methods in NLP tasks, is beneficial for information security related tasks, where more sophisticated NLP methodologies are not able to pick out relevant information security terms.

Discovering invariant spatial features in electron energy loss spectroscopy images on the mesoscopic and atomic levels

Over the last two decades, Electron Energy Loss Spectroscopy (EELS) imaging with a scanning transmission electron microscope has emerged as a technique of choice for visualizing complex chemical, electronic, plasmonic, and phononic phenomena in complex materials and structures. The availability of the EELS data necessitates the development of methods to analyze multidimensional data sets with complex spatial and energy structures. Traditionally, the analysis of these data sets has been based on analysis of individual spectra, one at a time, whereas the spatial structure and correlations between individual spatial pixels containing the relevant information of the physics of underpinning processes have generally been ignored and analyzed only via the visualization as 2D maps. Here, we develop a machine learning-based approach and workflows for the analysis of spatial structures in 3D EELS data sets using a combination of dimensionality reduction and multichannel rotationally invariant variational autoencoders. This approach is illustrated for the analysis of both the plasmonic phenomena in a system of nanowires and in the core excitations in functional oxides using low loss and core-loss EELS, respectively. The code developed in this manuscript is open sourced and freely available and provided as a Jupyter notebook for the interested reader.

Uranium accumulation in environmentally relevant microplastics and agricultural soil at acidic and circumneutral pH

The co-occurrence of microplastics (MPs) with potentially toxic metals in the environment stresses the need to address their physicochemical interactions and the potential ecological and human health implications. Here, we investigated the reaction of aqueous U with agricultural soil and high-density polyethylene (HDPE) through the integration of batch experiments, microscopy, and spectroscopy. The aqueous initial concentration of U (100 μM) decreased between 98.6 and 99.2 % at pH 5 and between 86.2 and 98.9 % at pH 7.5 following the first half hour of reaction with 10 g of soil. In similar experimental conditions but with added HDPE, aqueous U decreased between 98.6 and 99.7 % at pH 5 and between 76.1 and 95.2 % at pH 7.5, suggesting that HDPE modified the accumulation of U in soil as a function of pH. Uranium-bearing precipitates on the cracked surface of HDPE were identified by SEM/EDS after two weeks of agitation in water at both pH 5 and 7.5. Accumulation of U on the near-surface region of reacted HDPE was confirmed by XPS. Our findings suggest that the precipitation of U was facilitated by the weathering of the surface of HDPE. These results provide insights about surface-mediated reactions of aqueous metals with MPs, contributing relevant information about the mobility of metals and MPs at co-contaminated agricultural sites.

Serum tumor markers for response prediction and monitoring of advanced lung cancer: A review focusing on immunotherapy and targeted therapies.

The value of serum tumor markers (STMs) in the current therapeutic landscape of lung cancer is unclear. This scoping review gathered evidence of the predictive, prognostic, and monitoring value of STMs for patients with advanced lung cancer receiving immunotherapy (IT) or targeted therapy (TT). Literature searches were conducted (cut-off: May 2022) using PubMed and Cochrane CENTRAL databases. Medical professionals advised on the search strategies. Study heterogeneity limited the evidence and inferences from the 36 publications reviewed. While increased baseline levels of serum cytokeratin 19 fragment antigen (CYFRA21-1) and carcinoembryonic antigen (CEA) may predict IT response, results for TT were less clear. For monitoring IT-treated patients, STM panels (including CYFRA21-1, CEA, and neuron-specific enolase) may surpass the power of single analyses to predict non-response. CYFRA21-1 measurement could aid in monitoring TT-treated patients, but the value of CEA in this context requires further investigation. Overall, baseline and dynamic changes in individual or combined STM levels have potential utility to predict treatment outcome and for monitoring of patients with advanced lung cancer. In advanced lung cancer, STMs provide additional relevant clinical information by predicting treatment outcome, but further standardization and validation is warranted.

An improved custom convolutional neural network based hand sign recognition using machine learning algorithm

AbstractThe biggest challenge the deaf and dumb group faces is that individuals around them do not understand sign language, which they use to communicate with one another. Written communication is slower than face‐to‐face contact, despite the fact that it can be used. Many sign languages have been developed around the world because they are more effective in emergency situations than text‐based communication. India in‐spite of having the large deaf population of almost 18 million and having only around 250 trained/untrained; skilled interpreters. The proposed system can utilize a custom convolution neural networks (CCNNs) model to identify hand motions in order to resolve this issue. This system uses a filter to process the hand before sending it through a classifier to identify the type of hand movements. CCNN strategy employs two levels of algorithm to predict and evaluate symbols that are increasingly similar to one another in order to get as close to precisely recognizing the symbol presented as possible. Convolutional neural networks (CNNs) are able to precisely identify a variety of gestures after being trained on large datasets of hand sign photographs. As a result of their frequent usage of many layers of filters and pooling to extract relevant information from the input images, these networks can recognize hand signs with an accuracy rate of 99.95%, which is much greater than previously built models like SIGNGRAPH, SVM, KNN, CNN + Bi‐LSTM, 3D‐CNN and 2D CNN network and 1D CNN skeleton network. The simulation result shows that a suggested CCNN‐based learning approach is useful for hand sign detection and future usage research when compared with existing machine learning models.

Design of signage guidance system for tourist attractions based on computer vision technology

In order to improve tourists’ sense of touring experience, this research proposes a signage navigation system for mobile scenic spots. Firstly, the system uses a recurrent neural network algorithm incorporating convolutional neural network for image recognition function to obtain relevant information through image recognition. Then the target localization of the image is performed according to the single-stage target detection algorithm, and the location of the landscape in which the user is located is localized by the recognized image information. The results show that the algorithm can achieve 86.7% recognition accuracy, and it can recognize part of the image samples when the recognition time reaches the range of 0.8 min–1 min. The single-stage target detection algorithm has a localization accuracy of 97.2% with a minimum loss rate of 1.1%. And the algorithm has high average accuracy and full class average accuracy values. The system has good application value.

Development of a prognostic model for lung adenocarcinoma polarity-related genes and analysis of immune landscape.

Despite the progress made in the management of lung adenocarcinoma (LUAD), the overall prognosis for LUAD individuals remains suboptimal. While the role of cell polarity in tumor invasion and metastasis is well established, its prognostic significance in LUAD is still unknown. Differential analysis was performed on the Cancer Genome Atlas (TCGA)-LUAD and normal lung tissue, and candidate genes were identified by intersecting differentially expressed genes with polarity-related genes (PRGs). A prognostic model was constructed using univariate and multivariate Cox regression and LASSO regression. To enhance the robustness of the analysis, an independent prognostic analysis was conducted by incorporating relevant clinical information. The accuracy and sensitivity of the model were validated using survival analysis and ROC curves. Finally, immune landscape, immune therapy, tumor mutation burden, and drug sensitivity analysis were carried out on high- and low-risk patients. Ten prognostic genes were screened to divide LUAD patients into different risk groups. Survival analysis, ROC curves, and univariate/multivariate Cox regression analyses collectively demonstrated the favorable predictive performance of the model, which could be an independent prognostic factor. The nomogram, in conjunction with the calibration curve, demonstrated the model's compelling predictive capacity in prognosticating the overall survival of LUAD individuals. Low-risk LUAD patients exhibited heightened levels of immune cell infiltration, immune scores, and immune checkpoint expression compared to high-risk individuals. So, they may have a greater likelihood of benefiting from immune therapy. The high-risk group demonstrated a remarkably higher tumor mutation burden (TMB) in contrast with the low-risk group. XAV-939, Fulvestrant, and SR16157 may have potential value in the clinical use of LUAD. We revealed the potential linkage between PRGs and LUAD prognosis, and the application of these prognostic factors in risk stratification and prognosis prediction of LUAD patients may be of great significance.