Benchmarking PathCLIP for Pathology Image Analysis.

Accurate image classification and retrieval are of importance for clinical diagnosis and treatment decision-making. The recent contrastive language-image pre-training (CLIP) model has shown remarkable proficiency in understanding natural images. Drawing inspiration from CLIP, pathology-dedicated CLIP (PathCLIP) has been developed, utilizing over 200,000 image and text pairs in training. While the performance the PathCLIP is impressive, its robustness under a wide range of image corruptions remains unknown. Therefore, we conduct an extensive evaluation to analyze the performance of PathCLIP on various corrupted images from the datasets of osteosarcoma and WSSS4LUAD. In our experiments, we introduce eleven corruption types including brightness, contrast, defocus, resolution, saturation, hue, markup, deformation, incompleteness, rotation, and flipping at various settings. Through experiments, we find that PathCLIP surpasses OpenAI-CLIP and the pathology language-image pre-training (PLIP) model in zero-shot classification. It is relatively robust to image corruptions including contrast, saturation, incompleteness, and orientation factors. Among the eleven corruptions, hue, markup, deformation, defocus, and resolution can cause relatively severe performance fluctuation of the PathCLIP. This indicates that ensuring the quality of images is crucial before conducting a clinical test. Additionally, we assess the robustness of PathCLIP in the task of image-to-image retrieval, revealing that PathCLIP performs less effectively than PLIP on osteosarcoma but performs better on WSSS4LUAD under diverse corruptions. Overall, PathCLIP presents impressive zero-shot classification and retrieval performance for pathology images, but appropriate care needs to be taken when using it.

Large Language Model-Based Natural Language Encoding Could Be All You Need for Drug Biomedical Association Prediction.

Analyzing drug-related interactions in the field of biomedicine has been a critical aspect of drug discovery and development. While various artificial intelligence (AI)-based tools have been proposed to analyze drug biomedical associations (DBAs), their feature encoding did not adequately account for crucial biomedical functions and semantic concepts, thereby still hindering their progress. Since the advent of ChatGPT by OpenAI in 2022, large language models (LLMs) have demonstrated rapid growth and significant success across various applications. Herein, LEDAP was introduced, which uniquely leveraged LLM-based biotext feature encoding for predicting drug-disease associations, drug-drug interactions, and drug-side effect associations. Benefiting from the large-scale knowledgebase pre-training, LLMs had great potential in drug development analysis owing to their holistic understanding of natural language and human topics. LEDAP illustrated its notable competitiveness in comparison with other popular DBA analysis tools. Specifically, even in simple conjunction with classical machine learning methods, LLM-based feature representations consistently enabled satisfactory performance across diverse DBA tasks like binary classification, multiclass classification, and regression. Our findings underpinned the considerable potential of LLMs in drug development research, indicating a catalyst for further progress in related fields.

Enhancing Chemical Reaction Monitoring with a Deep Learning Model for NMR Spectra Image Matching to Target Compounds.

In the synthetic laboratory, researchers typically rely on nuclear magnetic resonance (NMR) spectra to elucidate structures of synthesized products and confirm whether they match the desired target compounds. As chemical synthesis technology evolves toward intelligence and continuity, efficient computer-assisted structure elucidation (CASE) techniques are required to replace time-consuming manual analysis and provide the necessary speed. However, current CASE methods typically aim to derive precise chemical structures from spectroscopic data, yet they suffer from drawbacks such as low accuracy, high computational cost, and reliance on chemical libraries. In meticulously designed chemical synthesis reactions, researchers prioritize confirming the attainment of the target product based on NMR spectra, rather than focusing on identifying the specific product obtained. For this purpose, we innovatively developed a binary classification model, termed as MatCS, to directly predict the relationship between NMR spectra image (including 1H NMR and 13C NMR) and the molecular structure of the target compound. After evaluating various feature extraction methods, MatCS employs a combination of the Graph Attention Networks and Graph Convolutional Networks to learn the structural features of molecular graphs and the pretrained ResNet101 network with a Convolutional Block Attention Module to extract features from NMR spectra images. The results show that on a challenging Testsim data set, which poses difficulty in distinguishing spectra of similar molecular structures, MatCS achieves comprehensive evaluation metrics with an F1-score of 0.81 and an AUC value of 0.87. Simultaneously, it exhibited commendable performance on an external SDBS data set containing experimental NMR spectra, showcasing substantial potential for structural verification tasks in real automated chemical synthesis.

TGF-ß1 maintains the developmental potential of embryonic submandibular gland epithelia separated with mesenchyme.

Objective: The objective of this study was to investigate the impact of transforming growth factor ß1 (TGF-ß1) on epithelial development using an ex vivo model of submandibular gland (SMG) epithelial-mesenchymal separation. Materials and methods: The ex vivo model was established by separating E13 mouse SMG epithelia and mesenchyme, culturing them independently for 24 h, recombining them, and observing branching morphogenesis. Microarray analysis was performed to evaluate the transcriptome of epithelia treated with and without 1 ng/ml TGF-ß1. Differential gene expression, pathway enrichment, and protein-protein interaction networks were analyzed. Quantitative real-time polymerase chain reaction, Western blot, and immunofluorescence were employed to validate the mRNA and protein levels. Results: Recombined SMGs using separated epithelia and mesenchyme that were cultured for 24 h showed a significant inhibition of epithelial development compared to SMGs recombined immediately after separation. The level of TGF-ß1 decreased in the SMG epithelia after epithelia-mesenchyme separation. Epithelia that were separated from mesenchyme for 24 h and pretreated with 1 ng/ml TGF-ß1 continued to develop after recombination with mesenchyme, while epithelia without 1 ng/ml TGF-ß1 treatment did not. Microarray analysis suggested pathway enrichment related to epithelial development and an upregulation of Sox2 in the 1 ng/ml TGF-ß1-treated epithelia. Further experiments validated the phosphorylation of SMAD2 and SMAD3, upregulation of SOX2 and genes associated with epithelial development, including Prol1, Dcpp1, Bhlha15, Smgc, and Bpifa2. Additionally, 1 ng/ml TGF-ß1 inhibited epithelial apoptosis by improving the BCL2/BAX ratio and reducing cleaved caspase 3. Conclusions: The addition of 1 ng/ml TGF-ß1 maintained the developmental potential of embryonic SMG epithelia separated from mesenchyme for 24 h. This suggests that 1 ng/ml TGF-ß1 may partially compensate for the role of mesenchyme during the separation phase, although its compensation is limited in extent.

Computed tomography guided electromagnetic navigation system in percutaneous laser ablation for treating primary lung cancer: a case report.

Background: The majority of patients of lung cancer have already lost the chance of surgery at the time of diagnosis. Percutaneous local thermal ablation is a precise minimally invasive technique and a viable alternative to surgical treatment. Compared with radiofrequency ablation and microwave ablation, percutaneous laser ablation for the treatment of lung tumors is less commonly used and reported, especially for primary lung cancer. Case presentation: A 63-year-old male patient with mixed pulmonary nodules selected computed tomography-guided electromagnetic navigation system for percutaneous biopsy and laser ablation therapy. The puncture point was determined through Computed tomography scanning, along with the placement of the electromagnetic navigation system locators. After rapid on-site evaluation and pathological examination of the puncture tissue specimen, the diagnosis of lung adenocarcinoma was confirmed. A 980-nanometer wavelength semiconductor laser fiber was inserted into the appropriate position guided by the electromagnetic navigation system. Subsequently, a power of 7 watt was applied to ablate the tumor for 30 seconds, then pause for 60 seconds before repeating the procedure. Positron emission tomography-Computed tomography examination was performed 1 month after operation, suggesting complete response of the tumor. Conclusion: Here, we present a case of percutaneous laser ablation treatment for primary lung cancer guided by computed tomography-electromagnetic navigation system. As a more precise, shorter duration, impedance-independent, safe and effective minimally invasive thermal ablation method, it is expected to gain wider application and become a novel alternative for surgical treatment.

The ufmylation cascade controls COPII recruitment, anterograde transport, and sorting of nascent GPCRs at ER.

Ufmylation is implicated in multiple cellular processes, but little is known about its functions and regulation in protein trafficking. Here, we demonstrate that the genetic depletion of core components of the ufmylation cascade, including ubiquitin-fold modifier 1 (UFM1), UFM1 activation enzyme 5, UFM1-specific ligase 1 (UFL1), UFM1-specific protease 2, and UFM1-binding protein 1 (UFBP1) each markedly inhibits the endoplasmic reticulum (ER)-Golgi transport, surface delivery, and recruitment to COPII vesicles of a subset of G protein-coupled receptors (GPCRs) and UFBP1's function partially relies on UFM1 conjugation. We also show that UFBP1 and UFL1 interact with GPCRs and UFBP1 localizes at COPII vesicles coated with specific Sec24 isoforms. Furthermore, the UFBP1/UFL1-binding domain identified in the receptors effectively converts non-GPCR protein transport into the ufmylation-dependent pathway. Collectively, these data reveal important functions for the ufmylation system in GPCR recruitment to COPII vesicles, biosynthetic transport, and sorting at ER via UFBP1 ufmylation and interaction directly.

Development of Degraders of Cyclin-Dependent Kinases 4 and 6 Based on Rational Drug Design.

Degradation of target proteins has been considered to be a promising therapeutic approach, but the rational design of compounds for degradation remains a challenge. In this study, we reasonably designed and synthesized only 10 compounds to discover effective CDK4/6 protein degraders. Among the newly synthesized compounds, 7f achieved dual degradation of CDK4/6 protein, with DC50 values of 10.5 and 2.5 nM, respectively. Compound 7f also exhibited inhibitory proliferative activity against Jurkat cells with an IC50 value of 0.18 µM. Furthermore, 7f induced cell apoptosis and G1 phase cell cycle arrest in a dose-dependent manner in Jurkat cells. In conclusion, these findings demonstrate the potential of 7f as a CDK4/6 degrader and a potential therapeutic strategy against cancer, thereby expanding the potential of CDK4/6 dual PROTACs.

Clustering single-cell RNA sequencing data via iterative smoothing and self-supervised discriminative embedding.

Single-cell transcriptome sequencing (scRNA-seq) is a high-throughput technique used to study gene expression at the single-cell level. Clustering analysis is a commonly used method in scRNA-seq data analysis, helping researchers identify cell types and uncover interactions between cells. However, the choice of a robust similarity metric in the clustering procedure is still an open challenge due to the complex underlying structures of the data and the inherent noise in data acquisition. Here, we propose a deep clustering method for scRNA-seq data called scRISE (scRNA-seq Iterative Smoothing and self-supervised discriminative Embedding model) to resolve this challenge. The model consists of two main modules: an iterative smoothing module based on graph autoencoders designed to denoise the data and refine the pairwise similarity in turn to gradually incorporate cell structural features and enrich the data information; and a self-supervised discriminative embedding module with adaptive similarity threshold for partitioning samples into correct clusters. Our approach has shown improved quality of data representation and clustering on seventeen scRNA-seq datasets against a number of state-of-the-art deep learning clustering methods. Furthermore, utilizing the scRISE method in biological analysis against the HNSCC dataset has unveiled 62 informative genes, highlighting their potential roles as therapeutic targets and biomarkers.

Diagnostic implications of ubiquitination-related gene signatures in Alzheimer's disease.

The purpose of this study was to explore the diagnostic implications of ubiquitination-related gene signatures in Alzheimer's disease. In this study, we first collected 161 samples from the GEO database (including 87 in the AD group and 74 in the normal group). Subsequently, through differential expression analysis and the iUUCD 2.0 database, we obtained 3450 Differentially Expressed Genes (DEGs) and 806 Ubiquitin-related genes (UbRGs). After taking the intersection, we obtained 128 UbR-DEGs. Secondly, by conducting GO and KEGG enrichment analysis on these 128 UbR-DEGs, we identified the main molecular functions and biological pathways related to AD. Furthermore, through the utilization of GSEA analysis, we have gained insight into the enrichment of functions and pathways within both the AD and normal groups. Further, using lasso regression analysis and cross-validation techniques, we identified 22 characteristic genes associated with AD. Subsequently, we constructed a logistic regression model and optimized it, resulting in the identification of 6 RUbR-DEGs: KLHL21, WDR82, DTX3L, UBTD2, CISH, and ATXN3L. In addition, the ROC result showed that the diagnostic model we built has excellent accuracy and reliability in identifying AD patients. Finally, we constructed a lncRNA-miRNA-mRNA (competing endogenous RNA, ceRNA) regulatory network for AD based on six RUbR-DEGs, further elucidating the interaction between UbRGs and lncRNA, miRNA. In conclusion, our findings will contribute to further understanding of the molecular pathogenesis of AD and provide a new perspective for AD risk prediction, early diagnosis and targeted therapy in the population.

Effects of fuel and driving conditions on particle number emissions of China-VI gasoline vehicles: based on corrections to test results.

The increasing number of vehicles are emitting a large amount of particles into the atmosphere, causing serious harm to the ecological environment and human health. This study conducted the Worldwide Harmonized Light Vehicles Test Cycle (WLTC) to investigate the emission characteristics of particle number (PN) of China-VI gasoline vehicles with different gasoline. The gasoline with lower aromatic hydrocarbons and olefins reduced particulate matter (PM) and PN emissions by 24% and 52% respectively. The average PN emission rate of the four vehicles during the first 300 s (the cold start period) was 7.2 times that of the 300 s-1800s. Additionally, because the particle transmission time and instrument response time, the test results of instantaneous emissions of PN were not synchronized with vehicle specific power (VSP). By calculating the Spearman correlation coefficient between pre-average vehicle specific power (PAVSP) and the test results of PN instantaneous emissions, the delay time was determined as 10s. After the PN emissions results were corrected, the PN emissions were found to be more related to VSP. By analyzing the influence of driving status on emission, this study found that vehicles in acceleration mode increased PN emissions by 76% compared to those in constant speed mode.

Discovery of Novel Non-Nucleoside Inhibitors Interacting with Dizinc Ions of CD73.

High extracellular concentrations of adenosine triphosphate (ATP) in the tumor microenvironment generate adenosine by sequential dephosphorylation of CD39 and CD73, resulting in potent immunosuppression to inhibit T cell and natural killer (NK) cell function. CD73, as the determining enzyme for adenosine production, has been shown to correlate with poor clinical tumor prognosis. Conventional inhibitors as analogues of adenosine 5'-monophosphate (AMP) may have a risk of further metabolism to adenosine analogues. Here, we report a new series of malonic acid non-nucleoside inhibitors coordinating with zinc ions of CD73. Compound 12f was found to be a superior CD73 inhibitor (IC50 = 60 nM) by structural optimization, and its pharmacokinetic properties were investigated. In mouse tumor models, compound 12f showed excellent efficacy and reversal of immunosuppression in combination with chemotherapeutic agents or checkpoint inhibitors, suggesting that it deserves further development as a novel CD73 inhibitor.

Acupuncture for radicular pain: a review of analgesic mechanism.

Radicular pain, a common and complex form of neuropathic pain, presents significant challenges in treatment. Acupuncture, a therapy originating from ancient traditional Chinese medicine and widely utilized for various pain types, including radicular pain, has shown promising outcomes in the management of lumbar radicular pain, cervical radicular pain, and radicular pain due to spinal stenosis. Despite its efficacy, the exact mechanisms through which acupuncture achieves analgesia are not fully elucidated and are the subject of ongoing research. This review sheds light on the current understanding of the analgesic mechanisms of acupuncture for radicular pain, offering valuable perspectives for both clinical application and basic scientific research. Acupuncture is postulated to relieve radicular pain by several mechanisms: peripherally, it reduces muscle spasms, lessens mechanical pressure on nerve roots, and improves microcirculation; at the molecular level, it inhibits the HMGB1/RAGE and TLR4/NF-κB signaling pathways, thereby decreasing the release of pro-inflammatory cytokines; within the spinal cord, it influences synaptic plasticity; and centrally, it modulates brain function, particularly affecting the medial prefrontal cortex, anterior cingulate cortex, and thalamus within the default mode network. By acting across these diverse biological domains, acupuncture presents an effective treatment modality for radicular pain, and deepening our understanding of the underlying mechanisms regarding analgesia for radicular pain is crucial for enhancing its clinical efficacy and advancement in pain management.

Effects of lianas on forest biogeochemistry during their lives and afterlives.

Climate change and other anthropogenic disturbances are increasing liana abundance and biomass in many tropical and subtropical forests. While the effects of living lianas on species diversity, ecosystem carbon, and nutrient dynamics are receiving increasing attention, the role of dead lianas in forest ecosystems has been little studied and is poorly understood. Trees and lianas coexist as the major woody components of forests worldwide, but they have very different ecological strategies, with lianas relying on trees for mechanical support. Consequently, trees and lianas have evolved highly divergent stem, leaf, and root traits. Here we show that this trait divergence is likely to persist after death, into the afterlives of these organs, leading to divergent effects on forest biogeochemistry. We introduce a conceptual framework combining horizontal, vertical, and time dimensions for the effects of liana proliferation and liana tissue decomposition on ecosystem carbon and nutrient cycling. We propose a series of empirical studies comparing traits between lianas and trees to answer questions concerning the influence of trait afterlives on the decomposability of liana and tree organs. Such studies will increase our understanding of the contribution of lianas to terrestrial biogeochemical cycling, and help predict the effects of their increasing abundance.

Discovery of a novel SHP2 allosteric inhibitor using virtual screening, FMO calculation, and molecular dynamic simulation.

CONTEXT: SHP2 is a non-receptor protein tyrosine phosphatase to remove tyrosine phosphorylation. Functionally, SHP2 is an essential bridge to connect numerous oncogenic cell-signaling cascades including RAS-ERK, PI3K-AKT, JAK-STAT, and PD-1/PD-L1 pathways. This study aims to discover novel and potent SHP2 inhibitors using a hierarchical structure-based virtual screening strategy that combines molecular docking and the fragment molecular orbital method (FMO) for calculating binding affinity (referred to as the Dock-FMO protocol). For the SHP2 target, the FMO method prediction has a high correlation between the binding affinity of the protein-ligand interaction and experimental values (R2 = 0.55), demonstrating a significant advantage over the MM/PBSA (R2 = 0.02) and MM/GBSA (R2 = 0.15) methods. Therefore, we employed Dock-FMO virtual screening of ChemDiv database of ∼2,990,000 compounds to identify a novel SHP2 allosteric inhibitor bearing hydroxyimino acetamide scaffold. Experimental validation demonstrated that the new compound (E)-2-(hydroxyimino)-2-phenyl-N-(piperidin-4-ylmethyl)acetamide (7188-0011) effectively inhibited SHP2 in a dose-dependent manner. Molecular dynamics (MD) simulation analysis revealed the binding stability of compound 7188-0011 and the SHP2 protein, along with the key interacting residues in the allosteric binding site. Overall, our work has identified a novel and promising allosteric inhibitor that targets SHP2, providing a new starting point for further optimization to develop more potent inhibitors. METHODS: All the molecular docking studies were employed to identify potential leads with Maestro v10.1. The protein-ligand binding affinities of potential leads were further predicted by FMO calculations at MP2/6-31G* level using GAMESS v2020 system. MD simulations were carried out with AmberTools18 by applying the FF14SB force field. MD trajectories were analyzed using VMD v1.9.3. MM/GB(PB)SA binding free energy analysis was carried out with the mmpbsa.py tool of AmberTools18. The docking and MD simulation results were visualized through PyMOL v2.5.0.

Application of Oral Retractor for Lip Injury Protection in Oral and Maxillofacial Surgeries: A Randomized Controlled Trial.

PURPOSE: Iatrogenic lip injury may occur during oral and maxillofacial surgical procedures. This study aimed to evaluate the effect of oral retractors on iatrogenic lip injury prevention during intraoral procedures of oral and maxillofacial surgery. METHODS: We conducted a randomized controlled trial and included patients who underwent intraoral procedures of oral and maxillofacial surgery. Patients were randomly allocated to receive oral retractor (intervention group) or traditional procedure without lip protection (control group). The incidence of lip injury was the outcome variable. Other study variables included surgical time and satisfaction of patients and surgeons with treatment experience evaluated by visual analog scale (VAS). Student t test and χ 2 test were used to compare both groups' variables and measure the relationship between the predictor variable and the outcome variable. P <0.05 was considered significant for all analyses. RESULTS: A total of 114 patients were included, with 56 allocated to intervention group and 58 to control group. The results showed that the application of an oral retractor did not significantly increase surgical time ( P =0.318). A total of 12 patients had lip injury, with 1 in the intervention group and 11 in the control group ( P =0.003). For the assessment of satisfaction with treatment experience, the intervention group had significantly higher VAS scores for doctors and patients ( P <0.05). CONCLUSIONS: We found that the oral retractor was a good tool for iatrogenic lip injury prevention in oral and maxillofacial surgical procedures and could be considered in clinical treatment.

How Seeds Attract and Protect: Seed Coat Development of Magnolia.

Seeds are one of the most important characteristics of plant evolution. Within a seed, the embryo, which will grow into a plant, can survive harsh environments. When the seeds are mature, the mother plant will disperse them from its body, allowing them to be taken away to grow in a new place. Otherwise, if the young generation grows alongside the mother plants in the same place, they will compete for sunlight and nutrition. The mother plants use different strategies to send away their seeds. One of these strategies is endozoochory, which means that the seeds disperse via ingestion by animals. There is a conflict between the seeds' abilities to attract animals and protect the embryo within the digestion systems of animals. Magnolia seeds exhibit typical endozoochory. The seed coats of Magnolia feature sarcotestas and sclerotestas. The sarcotesta, which is fleshy, bright-colored, and edible, attracts animals. The sclerotesta is hard and woody, protecting the embryo from the digestive systems of animals. In this study, we used scanning electron and light microscopes to examine the development of the sarcotesta and sclerotesta of Magnolia stellata seed coats. The results showed that the sarcotesta and sclerotesta come from the outer integument. This result confirms the hypothesis of Asa Gray from 1848. The dependence of the seed dispersal strategy on structural development is discussed.

Estimation of Protein and Amino Acid Requirements in Layer Chicks Depending on Dynamic Model.

Four trials were conducted to establish a protein and amino acid requirement model for layer chicks over 0-6 weeks by using the analytical factorization method. In trial 1, a total of 90 one-day-old Jing Tint 6 chicks with similar body weight were selected to determine the growth curve, carcass and feather protein deposition, and amino acid patterns of carcass and feather proteins. In trials 2 and 3, 24 seven-day-old and 24 thirty-five-day-old Jing Tint 6 chicks were selected to determine the protein maintenance requirements, amino acid pattern, and net protein utilization rate. In trial 4, 24 ten-day-old and 24 thirty-eight-day-old Jing Tint 6 chicks were selected to determine the standard terminal ileal digestibility of amino acids. The chicks were fed either a corn-soybean basal diet, a low nitrogen diet, or a nitrogen-free diet throughout the different trials. The Gompertz equation showed that there is a functional relationship between body weight and age, described as BWt(g) = 2669.317 × exp(-4.337 × exp(-0.019t)). Integration of the test results gave a comprehensive dynamic model equation that could accurately calculate the weekly protein and amino acid requirements of the layer chicks. By applying the model, it was found that the protein requirements for Jing Tint 6 chicks during the 6-week period were 21.15, 20.54, 18.26, 18.77, 17.79, and 16.51, respectively. The model-predicted amino acid requirements for Jing Tint 6 chicks during the 6-week period were as follows: Aspartic acid (0.992-1.284), Threonine (0.601-0.750), Serine (0.984-1.542), Glutamic acid (1.661-1.925), Glycine (0.992-1.227), Alanine (0.909-0.961), Valine (0.773-1.121), Cystine (0.843-1.347), Methionine (0.210-0.267), Isoleucine (0.590-0.715), Leucine (0.977-1.208), Tyrosine (0.362-0.504), Phenylalanine (0.584-0.786), Histidine (0.169-0.250), Lysine (0.3999-0.500), Arginine (0.824-1.147), Proline (1.114-1.684), and Tryptophan (0.063-0.098). In conclusion, this study constructed a dynamic model for the protein and amino acid requirements of Jing Tint 6 chicks during the brooding period, providing an important insight to improve precise feeding for layer chicks through this dynamic model calculation.

Impact of electronic cigarette usage on the onset of respiratory symptoms and COPD among Chinese adults.

The prevalence of dual usage and the relatively low cessation rate among e-cigarette (EC) users suggest that ECs have not demonstrated significant effectiveness as a smoking cessation tool. Furthermore, there has been a substantial increase in the prevalence of EC usage in recent years. Therefore, the objective of this study is to investigate the association between EC use and the incidence of respiratory symptoms and chronic obstructive pulmonary disease (COPD). A total of 10,326 participants aged between 20 and 55 years, without any respiratory diseases or COPD, were recruited for the study. These individuals attended employee physical examinations conducted at 16 public hospitals in Hebei province, China from 2015 to 2020. Logistic regression models were utilized to assess the association between EC use and the risk of respiratory symptoms and COPD using risk ratios along with their corresponding 95% confidence intervals. Restricted cubic spline functions were employed to investigate the dose-response non-linear relationship. The robustness of the logistic regression models was evaluated through subgroup analyses, and sensitivity analyses. During the 5-year follow-up period, a total of 1071 incident cases of respiratory symptoms and 146 incident cases of COPD were identified in this cohort study. After adjusting for relevant confounding factors, EC users demonstrated a respective increase in the risk of reporting respiratory symptoms and COPD by 28% and 8%. Furthermore, dual users who used both ECs and combustible cigarettes exhibited an elevated risk of incident respiratory symptoms and COPD by 41% and 18%, respectively, compared to those who had never used non-users of any cigarette products. The association between daily EC consumption and the development of respiratory symptoms, as well as COPD, demonstrated a significant J-shaped pattern. The potential adverse association between the consumption of ECs, particularly when used in combination with combustible cigarettes, and the development of respiratory symptoms and COPD necessitates careful consideration. Policymakers should approach ECs cautiously as a prospective smoking cessation tool.