Deep learning based CETSA feature prediction cross multiple cell lines with latent space representation.

Mass spectrometry-coupled cellular thermal shift assay (MS-CETSA), a biophysical principle-based technique that measures the thermal stability of proteins at the proteome level inside the cell, has contributed significantly to the understanding of drug mechanisms of action and the dissection of protein interaction dynamics in different cellular states. One of the barriers to the wide applications of MS-CETSA is that MS-CETSA experiments must be performed on the specific cell lines of interest, which is typically time-consuming and costly in terms of labeling reagents and mass spectrometry time. In this study, we aim to predict CETSA features in various cell lines by introducing a computational framework called CycleDNN based on deep neural network technology. For a given set of n cell lines, CycleDNN comprises n auto-encoders. Each auto-encoder includes an encoder to convert CETSA features from one cell line into latent features in a latent space [Formula: see text]. It also features a decoder that transforms the latent features back into CETSA features for another cell line. In such a way, the proposed CycleDNN creates a cyclic prediction of CETSA features across different cell lines. The prediction loss, cycle-consistency loss, and latent space regularization loss are used to guide the model training. Experimental results on a public CETSA dataset demonstrate the effectiveness of our proposed approach. Furthermore, we confirm the validity of the predicted MS-CETSA data from our proposed CycleDNN through validation in protein-protein interaction prediction.

Ultrasound-Guided Needle Release Combined with Corticosteroid Injection for the Treatment of Supinator Syndrome.

The two main branches of the radial nerve (RN) are the deep branch (DBRN) and the superficial branch (SBRN). The RN splits into two main branches at the elbow. The DBRN runs between the deep and shallow layers of the supinator. The DBRN can be easily compressed at the arcade of Frohse (AF) due to its anatomical features. This work focuses on a 42-year-old male patient who had injured his left forearm 1 month prior. Multiple muscles of the forearm (extensor digitorum, extensor digiti minimi, and extensor carpi ulnaris) were sutured in another hospital. After that, he had dorsiflexion limitations in his left ring and little fingers. The patient was reluctant to undergo another operation because he had undergone suture surgeries for multiple muscles 1 month prior. Ultrasound revealed that the deep branch of the radial nerve (DBRN) had edema and was thickened. The exit point of the DBRN had deeply adhered to the surrounding tissue. To relieve this, ultrasound-guided needle release plus a corticosteroid injection were performed on the DBRN. Nearly 3 months later, the dorsal extension in the patient's ring and little fingers was significantly improved (ring finger: -10°, little finger: -15°). Then, the same treatment was done for the second time. Nearly 1 month after that, the dorsal extension of the ring and the little finger was normal when the joints of the fingers were fully straightened. Ultrasound could evaluate the condition of the DBRN and its relationship with the surrounding tissues. Ultrasound-guided needle release combined with corticosteroid injection is an effective and safe treatment for DBRN adhesion.

Metabonomic analysis of tumor microenvironments: a mini-review.

Metabolomic analysis is a vital part of studying cancer progression. Metabonomic crosstalk, such as nutrient availability, physicochemical transformation, and intercellular interactions can affect tumor metabolism. Many original studies have demonstrated that metabolomics is important in some aspects of tumor metabolism. In this mini-review, we summarize the definition of metabolomics and how it can help change a tumor microenvironment, especially in pathways of three metabonomic tumors. Just as non-invasive biofluids have been identified as early biomarkers of tumor development, metabolomics can also predict differences in tumor drug response, drug resistance, and efficacy. Therefore, metabolomics is important for tumor metabolism and how it can affect oncology drugs in cancer therapy.

Comparison efficacy of ultrasound-guided needle release plus corticosteroid injection and mini-open surgery in patients with carpal tunnel syndrome.

This retrospective study was to compare clinical outcomes of ultrasound-guided needle release with corticosteroid injection vs. mini-open surgery in patients with carpal tunnel syndrome (CTS). From January 2021 to December 2021, 40 patients (40 wrists) with CTS were analyzed in this study. The diagnosis was based on clinical symptoms, electrophysiological imaging, and ultrasound imaging. A total of 20 wrists were treated with ultrasound-guided needle release plus corticosteroid injection (Group A), and the other 20 wrists were treated with mini-open surgery (Group B). We evaluated the Boston carpal tunnel questionnaire, electrophysiological parameters (distal motor latency, sensory conduction velocity, and sensory nerve action potential of the median nerve), and ultrasound parameters (cross-sectional area, flattening ratio, and the thicknesses of transverse carpal ligament) both before and 3 months after treatment. Total treatment cost, duration of treatment, healing time, and complications were also recorded for the two groups. The Boston carpal tunnel questionnaire and electrophysiological and ultrasound outcomes at preoperatively and 3 months postoperatively had a significant difference for each group (each with P < 0.05). There were no complications such as infection, hemorrhage, vascular, nerve, or tendon injuries in both groups. Ultrasound-guided needle release and mini-open surgery are both effective measures in treating CTS patients. Ultrasound-guided needle release plus corticosteroid injection provides smaller incision, less cost, less time of treatment, and faster recovery compared with mini-open surgery. Ultrasound-guided needle release plus corticosteroid injection is better for clinical application.

CoIn: Correlation Induced Clustering for Cognition of High Dimensional Bioinformatics Data.

Analysis of high dimensional biomedical data such as microarray gene expression data and mass spectrometry images, is crucial to provide better medical services including cancer subtyping, protein homology detection, etc. Clustering is a fundamental cognitive task which aims to group unlabeled data into multiple clusters based on their intrinsic similarities. However, for most clustering methods, including the most widely used K-means algorithm, all features of the high dimensional data are considered equally in relevance, which distorts the performance when clustering high-dimensional data where there exist many redundant variables and correlated variables. In this paper, we aim at addressing the problem of the high dimensional bioinformatics data clustering and propose a new correlation induced clustering, CoIn, to capture complex correlations among high dimensional data and guarantee the correlation consistency within each cluster. We evaluate the proposed method on a high dimensional mass spectrometry dataset of liver cancer tumor to explore the metabolic differences on tissues and discover the intra-tumor heterogeneity (ITH). By comparing the results of baselines and ours, it has been found that our method produces more explainable and understandable results for clinical analysis, which demonstrates the proposed clustering paradigm has the potential with application to knowledge discovery in high dimensional bioinformatics data.

LE-UDA: Label-Efficient Unsupervised Domain Adaptation for Medical Image Segmentation.

While deep learning methods hitherto have achieved considerable success in medical image segmentation, they are still hampered by two limitations: (i) reliance on large-scale well-labeled datasets, which are difficult to curate due to the expert-driven and time-consuming nature of pixel-level annotations in clinical practices, and (ii) failure to generalize from one domain to another, especially when the target domain is a different modality with severe domain shifts. Recent unsupervised domain adaptation (UDA) techniques leverage abundant labeled source data together with unlabeled target data to reduce the domain gap, but these methods degrade significantly with limited source annotations. In this study, we address this underexplored UDA problem, investigating a challenging but valuable realistic scenario, where the source domain not only exhibits domain shift w.r.t. the target domain but also suffers from label scarcity. In this regard, we propose a novel and generic framework called "Label-Efficient Unsupervised Domain Adaptation" (LE-UDA). In LE-UDA, we construct self-ensembling consistency for knowledge transfer between both domains, as well as a self-ensembling adversarial learning module to achieve better feature alignment for UDA. To assess the effectiveness of our method, we conduct extensive experiments on two different tasks for cross-modality segmentation between MRI and CT images. Experimental results demonstrate that the proposed LE-UDA can efficiently leverage limited source labels to improve cross-domain segmentation performance, outperforming state-of-the-art UDA approaches in the literature.

Efficient Perturbation Inference and Expandable Network for continual learning.

Although humans are capable of learning new tasks without forgetting previous ones, most neural networks fail to do so because learning new tasks could override the knowledge acquired from previous data. In this work, we alleviate this issue by proposing a novel Efficient Perturbation Inference and Expandable Network (EPIE-Net), which dynamically expands lightweight task-specific decoders for new classes and utilizes a mixed-label uncertainty strategy to improve the robustness. Moreover, we calculate the average probability of perturbed samples at inference, which can generally improve the performance of the model. Experimental results show that our method consistently outperforms other methods with fewer parameters in class incremental learning benchmarks. For example, on the CIFAR-100 10 steps setup, our method achieves an average accuracy of 76.33% and the last accuracy of 65.93% within only 3.46M average parameters.

Epidemiological characteristics and antimicrobial resistance of 151 cases of melioidosis in Hainan Province / 中国热带医学

@#Abstract: Objective　To analyze the epidemiological characteristics of 151 cases of melioidosis and the drug resistance of Burkholderia pseudomallei (BP), in order to provide the basis for diagnosis, treatment and reasonable prevention of melioidosis. Methods　A total of 151 inpatients and outpatients from the Second Affiliated Hospital of Hainan Medical University from January 1, 2013 to August 31, 2022 were collected, and clinical specimens were submitted for examination to isolate and identify BP strains. The clinical data of 151cases of melioidosis and the drug resistance characteristics of pathogenic bacteria were retrospectively analyzed, and using SPSS26.0 software for statistical analysis. Results　Among 151 cases with BP infection, there were 138 males (91.4%) and 13 females (8.6%); the most patients were aged from 45-<60 years old, accounting for 74 cases (49.0%); melioidosis incidence was concentrated in October (19.2%), November (19.2%), August (9.9%) and July (8.6%), and; the number of confirmed cases showed an increasing trend and the time for confirmation was <10 d; Internal medicine system (31.1%), surgery system (26.5%) and intensive care department (20.5%) were the common departments for treating melioidosis; blood (49.0%), sputum (9.9%) and wound secretion (8.6%) were the main clinical specimens for detecting BP; pulmonary infection (68.2%), sepsis (35.1%) and local suppurative infection (23.8%) were the top clinical manifestations in patients with BP infection; the effective rate of treating melioidosis was 74.8%; abnormal liver function was a risk factor for the curative effect of melioidosis (χ2=5.010, P<0.05); the sensitivity rates of BP strains to sulfamethoxazole-trimethoprim (SXT), doxycycline (DOX), imipenem(IPM), ceftazidime (CAZ), amoxicillin/clavulanate (AMC) and tetracycline (TCY) were generally more than 90%, with sensitivities of 98.7%, 97.2%, 96.7%, 94.0%, 93.2% and 90.7%, respectively. Conclusions　It can be concluded that misdiagnosis or missed diagnosis of melioidosis is easy to occur, and the understanding of the epidemiological characteristics and risk factors in this area should be strengthened. The sensitivity of BP to commonly used antibiotics has shown a certain downward trend, clinical use should be standardized, and drug resistance monitoring should be strengthened to improve the efficacy of melioidosis treatment.

Subclavian brachial plexus metastasis from breast cancer: A case report.

BACKGROUND: The common area of breast cancer metastases are bone, lung and liver. Brachial plexus metastasis from breast cancer is extremely rare. We report a case of subclavian brachial plexus metastasis from breast cancer 6 years postoperative, which were detected by ultrasound, magnetic resonance imaging (MRI) and 18F-fluorodeoxyglucose positron emission tomography and computed tomography (FDG-PET/CT). CASE SUMMARY: Our study reports a 64-year-old woman who had right breast cancer and underwent radical mastectomy 6 years before. Ultrasound first revealed a soft lesion measuring 38 mm × 37 mm which located on the right side of the clavicle to the armpit subcutaneously. The right subclavian brachial plexus (beam level) was significantly thickened, wrapped around by a hypoechoic lesion, the surrounded axillary artery and vein were pressed. MRI brachial plexus scan showed that the right side of brachial plexus was enlarged compared with the left side and brachial plexus bundle in the distance showed a flake shadow. FDG-PET/CT revealed that the right side of brachial plexus nodular appearance with increased FDG metabolism. These results supported brachial plexus metastasis from breast cancer. Ultrasound exam also found many lesions between pectoralis major, deltoid muscle and inner upper arm. The lesion puncture was performed under ultrasound guidance and the tissue was sent for pathology. Pathology showed large areas of tumor cells in fibroblast tissue. Immunohistochemistry showed the following results: A2-1: GATA3 (+), ER (+, strong, 90%), PR (+, moderate, 10%), HER-2 (3+), Ki67 (+15%), P120 (membrane+), P63 (-), E-cadherin (+), CK5/6 (-). These results were consistent with the primary right breast cancer characteristics, thus supporting lesion metastasis from breast cancer. CONCLUSION: The brachial plexus metastasis from breast cancer is uncommon. Ultrasound has great value in detecting brachial plexus metastasis of breast cancer. It is an easy, non-invasive and affordable method. Close attention should be paid to new grown out lesions in those patients who had a history of breast cancer when doing ultrasound review.

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Livestock wastewater is an important reservoir of antibiotic resistance genes (ARGs), with high environmental risks. We investigated the seasonal variations of distribution and removal of swine wastewater originated high-risk tetracycline resistance genes (TRGs) in horizontal subsurface flow constructed wetlands. The effects of exogenous addition of tetracycline (TC) and copper ion (Cu2+) on the abundance of TRGs in effluent with single and combined pollution of antibiotic and heavy metal were studied. The results showed that all the three high-risk TRGs (tetM, tetO and tetW) were detected in swine wastewater. Wetlands could effectively reduce the ARGs, with the absolute abundance of TRGs in effluent being decreased by 1.1-2.4 and 1.7-2.9 orders of magnitude in summer and winter compared with the influent, respectively. The abundance of TRGs in wetland soils showed the characte-ristics that the outflow side was lower than the inflow side, the non-rhizosphere area was lower than the rhizosphere area, and lower in winter than in summer. In summer and winter, single and combined pollution of TC and Cu2+ in swine wastewater would increase the abundance of TRGs in effluent compared with that in the control. The constructed wetland is suitable for controlling the environmental diffusion of ARGs in livestock wastewater.

Self-supervised Assisted Active Learning for Skin Lesion Segmentation.

Label scarcity has been a long-standing issue for biomedical image segmentation, due to high annotation costs and professional requirements. Recently, active learning (AL) strategies strive to reduce annotation costs by querying a small portion of data for annotation, receiving much traction in the field of medical imaging. However, most of the existing AL methods have to initialize models with some randomly selected samples followed by active selection based on various criteria, such as uncertainty and diversity. Such random-start initialization methods inevitably introduce under-value redundant samples and unnecessary annotation costs. For the purpose of addressing the issue, we propose a novel self-supervised assisted active learning framework in the cold-start setting, in which the segmentation model is first warmed up with self-supervised learning (SSL), and then SSL features are used for sample selection via latent feature clustering without accessing labels. We assess our proposed methodology on skin lesions segmentation task. Extensive experiments demonstrate that our approach is capable of achieving promising performance with substantial improvements over existing baselines. Clinical Relevance- The proposed method can smartly select samples to annotate without requiring labels for model initialization, which can save annotation costs in clinical practice.

CETSA Feature Based Clustering for Protein Outlier Discovery by Protein-to-Protein Interaction Prediction.

The Cellular Thermal Shift Assay (CETSA) is a biophysical assay based on the principle of ligand-induced thermal stabilization of target proteins. This technology has revolutionized cell-based target engagement studies and has been used as guidance for drug design. Although many ap-plications of CETSA data have been explored, the correlations between CETSA data and protein-protein interactions (PPI) have barely been touched. In this study, we conduct the first exploration study applying CETSA data for PPI prediction. We use a machine learning method, Decision Tree, to predict PPI scores using proteins' CETSA features. It shows promising results that the predicted PPI scores closely match the ground-truth PPI scores. Furthermore, for a small number of protein pairs, whose PPI score predictions mismatch the ground truth, we use iterative clustering strategy to gradually reduce the number of these pairs. At the end of iterative clustering, the remaining protein pairs may have some unusual properties and are of scientific value for further biological investigation. Our study has demonstrated that PPI is a brand-new application of CETSA data. At the same time, it also manifests that CETSA data can be used as a new data source for PPI exploration study.

CycleDNN - A Novel Deep Neural Network Model for CETSA Feature Prediction cross Cell Lines.

Cellular Thermal Shift Assay (CETSA) has been widely used in drug discovery, cancer cell biology, immunology, etc. One of the barriers for CETSA applications is that CETSA experiments have to be conducted on various cell lines, which is extremely time-consuming and costly. In this study, we make an effort to explore the translation of CETSA features cross cell lines, i.e., known CETSA feature of a given protein in one cell line, can we automatically predict the CETSA feature of this protein in another cell line, and vice versa? Inspired by pix2pix and CycleGAN, which perform well on image-to-image translation cross various domains in computer vision, we propose a novel deep neural network model called CycleDNN for CETSA feature translation cross cell lines. Given cell lines A and B, the proposed CycleDNN consists of two auto-encoders, the first one encodes the CETSA feature from cell line A into Z in the latent space [Formula: see text], then decodes Z into the CETSA feature in cell line B., Similarly, the second one translates the CETSA feature from cell line B to cell line A through the latent space [Formula: see text]. In such a way, the two auto-encoders form a cyclic feature translation between cell lines. The reconstructed loss, cycle-consistency loss, and latent vector regularization loss are used to guide the training of the model. The experimental results on a public CETSA dataset demonstrate the effectiveness of the proposed approach.

ADAN: An Adversarial Domain Adaptation Neural Network for Early Gastric Cancer Prediction.

Gastric cancer is a highly prevalent cancer world-wide. Accurate diagnosis of Early Gastric Cancer (EGC) is of great significance to improve the treatment and survival rate of patients. However, EGC and gastric ulcers have similar en-doscopic image characteristics, resulting in a high misdiagnosis rate. Most existing deep learning and machine learning models for EGC recognition have the disadvantages of cumbersome pre-processing steps and high leakage ratios. To address the above challenges, we propose an end-to-end Adversarial Do-main Adaptation Neural network (ADAN) for EGC prediction on endoscopic images. ADAN network consists of a source domain feature extractor, a source domain classifier, two target domain feature extractors, a target domain classifier, and a domain discriminator. A source domain feature extractor is designed to train the model on public gastrointestinal datasets, which effectively solves the problem of insufficient training data. In addition, an adaptive source-target domain mapping classifier is added to each target domain feature extractor for automatically adjusting the number of classification categories in the target domain. Experimental results show that the proposed ADAN network is superior to the most advanced methods and can accurately predict EGC in clinical practice. Clinical relevance-In this study, the EGC diagnosis model based on the adversarial domain adaptive construction will be more applicable to the real clinical scenario, with higher accuracy and sensitivity and assist the endoscopist to make more accurate diagnosis for EGC and reduce the workload.

Residual Channel Attention Network for Brain Glioma Segmentation.

A glioma is a malignant brain tumor that seriously affects cognitive functions and lowers patients' life quality. Segmentation of brain glioma is challenging because of inter-class ambiguities in tumor regions. Recently, deep learning approaches have achieved outstanding performance in the automatic segmentation of brain glioma. However, existing al-gorithms fail to exploit channel-wise feature interdependence to select semantic attributes for glioma segmentation. In this study, we implement a novel deep neural network that integrates residual channel attention modules to calibrate intermediate features for glioma segmentation. The proposed channel at-tention mechanism adaptively weights feature channel-wise to optimize the latent representation of gliomas. We evaluate our method on the established dataset BraTS2017. Experimental results indicate the superiority of our method. Clinical relevance - While existing glioma segmentation approaches do not leverage channel-wise feature dependence for feature selection our method can generate segmentation masks with higher accuracies and provide more insights on graphic patterns in brain MRI images for further clinical reference.

Deep Feature Fusion via Graph Convolutional Network for Intracranial Artery Labeling.

Intracranial arteries are critical blood vessels that supply the brain with oxygenated blood. Intracranial artery labels provide valuable guidance and navigation to numerous clinical applications and disease diagnoses. Various machine learning algorithms have been carried out for automation in the anatomical labeling of cerebral arteries. However, the task remains challenging because of the high complexity and variations of intracranial arteries. This study investigates a novel graph convolutional neural network with deep feature fusion for cerebral artery labeling. We introduce stacked graph convolutions in an encoder-core-decoder architecture, extracting high-level representations from graph nodes and their neighbors. Furthermore, we efficiently aggregate intermediate features from different hierarchies to enhance the proposed model's representation capability and labeling performance. We perform extensive experiments on public datasets, in which the results prove the superiority of our approach over baselines by a clear margin. Clinical relevance- The graph convolutions and feature fusion in our approach effectively extract graph information, which provides more accurate intracranial artery label predictions than existing methods and better facilitates medical research and disease diagnosis.

Malignancy Suspicious Region Guided Deep Neural Networks for Gastric Ulcer Classification.

Malignant transformation of gastric ulcer can result in gastric cancer, hence an accurate gastric ulcer classification method is of vital importance. Despite marvelous progress has been achieved in recent years, there are still many challenges in diagnosis of gastric ulcer. In this paper, we propose a mechanism to mimic gastroenterologist's behaviours based on deep learning techniques, by integrating the segmented malignancy suspicious masks with gastroscopic images for gastric ulcer classification, which instructs the model to focus on the area where symptoms occur for gastric ulcer diagnosis. Specifically, a U-Net-type deep neural network is built to segment the suspicious pathological regions from gastroscopic images, then the segmented regions are treated as an attention channel of gastroscopic images for the gastric ulcer classification by a ResNet-type deep neural network. Experiments on a real gastroscopic dataset with 900+ patient cases demonstrate that our proposed approach achieves much better performance for gastric ulcer diagnosis, compared with standard method with only gastroscopic images.

AdaSG: A Lightweight Feature Point Matching Method Using Adaptive Descriptor with GNN for VSLAM.

Feature point matching is a key component in visual simultaneous localization and mapping (VSLAM). Recently, the neural network has been employed in the feature point matching to improve matching performance. Among the state-of-the-art feature point matching methods, the SuperGlue is one of the top methods and ranked the first in the CVPR 2020 workshop on image matching. However, this method utilizes graph neural network (GNN), resulting in large computational complexity, which makes it unsuitable for resource-constrained devices, such as robots and mobile phones. In this work, we propose a lightweight feature point matching method based on the SuperGlue (named as AdaSG). Compared to the SuperGlue, the AdaSG adaptively adjusts its operating architecture according to the similarity of input image pair to reduce the computational complexity while achieving high matching performance. The proposed method has been evaluated through the commonly used datasets, including indoor and outdoor environments. Compared with several state-of-the-art feature point matching methods, the proposed method achieves significantly less runtime (up to 43× for indoor and up to 6× for outdoor) with similar or better matching performance. It is suitable for feature point matching in resource constrained devices.

Efficacy and Safety of Ultrasound-Guided Radiofrequency Ablation for Primary Hyperparathyroidism: A Prospective Study.

OBJECTIVE: To assess the efficacy and safety of ultrasound (US)-guided radiofrequency ablation (RFA) in patients with primary hyperparathyroidism (PHPT). MATERIALS AND METHODS: This prospective study enrolled 39 participants (14 male, 25 female; mean age, 59.5 ± 15.3 [range, 18-87] years) between September 1, 2018, and January 31, 2021. All participants had parathyroid lesions causing PHPT, proven biochemically and through imaging. The imaging features of the PHPT nodules, including the shape, margin, size, composition, and location, were evaluated before treatment. Serum intact parathyroid hormone, calcium, and phosphorus levels; parathyroid nodule volume; and PHPT-related symptoms were recorded before and after treatment. We calculated the technical success, biochemical cure, and clinical cure rates for these patients. Complications were evaluated during and after the ablation. RESULTS: Complete ablation was achieved in 38 of the 39 nodules in the 39 enrolled participants. All the patients were treated in one session. The technical success rate was 97.4% (38/39). The mean follow-up duration was 13.2 ± 4.6 (range, 6.0-24.9) months. At 6 and 12 months post-RFA, the biochemical cure rates were 82.1% (32/39) and 84.4% (27/32), respectively, and the clinical cure rates were 100% (39/39) and 96.9% (31/32), respectively. Only 2.6% (1/39) of the patients had recurrent PHPT. At 1, 3, 6, and 12 months after technically successful RFA, 44.7% (17/38), 34.3% (12/35), 15.8% (6/38), and 12.5% (4/32) of participants, respectively, had elevated eucalcemic parathyroid hormone levels. Recurrent laryngeal nerve paralysis occurred in 5.1% (2/39) of the patients, who recovered spontaneously within 1-3 months. CONCLUSION: US-guided RFA was effective and safe for PHPT patients. RFA may be an alternative treatment tool for patients who cannot tolerate or refuse to undergo surgery.

Immunoprognostic model of lung adenocarcinoma and screening of sensitive drugs.

Screening of mRNAs and lncRNAs associated with prognosis and immunity of lung adenocarcinoma (LUAD) and used to construct a prognostic risk scoring model (PRS-model) for LUAD. To analyze the differences in tumor immune microenvironment between distinct risk groups of LUAD based on the model classification. The CMap database was also used to screen potential therapeutic compounds for LUAD based on the differential genes between distinct risk groups. he data from the Cancer Genome Atlas (TCGA) database. We divided the transcriptome data into a mRNA subset and a lncRNA subset, and use multiple methods to extract mRNAs and lncRNAs associated with immunity and prognosis. We further integrated the mRNA and lncRNA subsets and the corresponding clinical information, randomly divided them into training and test set according to the ratio of 5:5. Then, we performed the Cox risk proportional analysis and cross-validation on the training set to construct a LUAD risk scoring model. Based on the risk scoring model, patients were divided into distinct risk group. Moreover, we evaluate the prognostic performance of the model from the aspects of Area Under Curve (AUC) analysis, survival difference analysis, and independent prognostic analysis. We analyzed the differences in the expression of immune cells between the distinct risk groups, and also discuss the connection between immune cells and patient survival. Finally, we screened the potential therapeutic compounds of LUAD in the Connectivity Map (CMap) database based on differential gene expression profiles, and verified the compound activity by cytostatic assays. We extracted 26 mRNAs and 74 lncRNAs related to prognosis and immunity by using different screening methods. Two mRNAs (i.e., KLRC3 and RAET1E) and two lncRNAs (i.e., AL590226.1 and LINC00941) and their risk coefficients were finally used to construct the PRS-model. The risk score positions of the training and test set were 1.01056590 and 1.00925190, respectively. The expression of mRNAs involved in model construction differed significantly between the distinct risk population. The one-year ROC areas on the training and test sets were 0.735 and 0.681. There was a significant difference in the survival rate of the two groups of patients. The PRS-model had independent predictive capabilities in both training and test sets. Among them, in the group with low expression of M1 macrophages and resting NK cells, LUAD patients survived longer. In contrast, the monocyte expression up-regulated group survived longer. In the CMap drug screening, three LUAD therapeutic compounds, such as resveratrol, methotrexate, and phenoxybenzamine, scored the highest. In addition, these compounds had significant inhibitory effects on the LUAD A549 cell lines. The LUAD risk score model constructed using the expression of KLRC3, RAET1E, AL590226.1, LINC00941 and their risk coefficients had a good independent prognostic power. The optimal LUAD therapeutic compounds screened in the CMap database: resveratrol, methotrexate and phenoxybenzamine, all showed significant inhibitory effects on LUAD A549 cell lines.