Factors associated with health behaviours among stroke survivors: A mixed-methods study using COM-B model.

AIMS: To identify factors associated with health behaviours among stroke survivors, through a multi-centre study. DESIGN: A sequential mixed methods design. METHODS: In the quantitative research phase, a total of 350 participants were recruited through multi-stage sampling from December 2022 to June 2023. General information questionnaires, The Stroke Prevention Knowledge Questionnaire (SPKQ), Short Form Health Belief Model Scale (SF-HBMS), Health Promoting Lifestyle Profile (HPLPII), and the WHOQOL-BREF (World Health Organization Quality of Life Questionnaire, Brief Version) were distributed across five tertiary hospitals in Henan province, China. For the qualitative research component, semi-structured interviews were conducted to explore the barriers and facilitators of health behaviour. This study adheres to the GRAMMS guidelines. RESULTS: A total of 315 participants (90.0%) completed the survey. Identified barriers to health behaviour included residing in rural areas, higher scores on the Charlson Comorbidity Index (CCI) and mRS, as well as lower scores on SPKQ, SF-HBMS and WHOQOL-BREF. Twenty-four individuals participated in qualitative interviews. Twenty-eight themes were identified and categorised by frequency, covering areas such as knowledge, skills, intentions, social influences, social/professional role and identity, environmental context and resources, beliefs about capabilities, beliefs about consequences and behavioural regulation. Both quantitative and qualitative data suggested that health behaviour among stroke survivors is at a moderate level, and the identified barrier factors can be mapped into the COM-B model (Capability, Opportunity, Motivation and Behaviour). CONCLUSION: The study indicates that key barriers to health behaviour among stroke survivors align with the COM-B model. These identified factors should be carefully considered in the planning of future systematic interventions aimed at improving health behaviours among stroke survivors. PATIENT OR PUBLIC CONTRIBUTION: Patients were invited to completed questionnaires in the study and semi-structured interviews. The investigators provided explanation of this study' content, purpose and addressed issues during the data collection.

DANCE: a deep learning library and benchmark platform for single-cell analysis.

DANCE is the first standard, generic, and extensible benchmark platform for accessing and evaluating computational methods across the spectrum of benchmark datasets for numerous single-cell analysis tasks. Currently, DANCE supports 3 modules and 8 popular tasks with 32 state-of-art methods on 21 benchmark datasets. People can easily reproduce the results of supported algorithms across major benchmark datasets via minimal efforts, such as using only one command line. In addition, DANCE provides an ecosystem of deep learning architectures and tools for researchers to facilitate their own model development. DANCE is an open-source Python package that welcomes all kinds of contributions.

Urolithin A Hijacks ERK1/2-ULK1 Cascade to Improve CD8+ T Cell Fitness for Antitumor Immunity.

According to the latest evidence, the microbial metabolite Urolithin A (UA), known for its role in promoting cellular health, modulates CD8+ T cell-mediated antitumor activity. However, the direct target protein of UA and its underlying mechanism remains unclear. Here, this research identifies ERK1/2 as the specific target crucial for UA-mediated CD8+ T cell activation. Even at low doses, UA markedly enhances the persistence and effector functions of primary CD8+ cytotoxic T lymphocytes (CTLs) and human chimeric antigen receptor (CAR) T cells both in vitro and in vivo. Mechanistically, UA interacts directly with ERK1/2 kinases, enhancing their activation and subsequently facilitating T cell activation by engaging ULK1. The UA-ERK1/2-ULK1 axis promotes autophagic flux in CD8+ CTLs, enhancing cellular metabolism and maintaining reactive oxygen species (ROS) levels, as evidenced by increased oxygen consumption and extracellular acidification rates. UA-treated CD8+ CTLs also display elevated ATP levels and enhanced spare respiratory capacity. Overall, UA activates ERK1/2, inducing autophagy and metabolic adaptation, showcasing its potential in tumor immunotherapy and interventions for diseases involving ERKs.

Backbone cyclization of Salmonella typhimurium diaminopropionate ammonia-lyase to enhance the activity and stability.

Diaminopropionate ammonia-lyase transforms D and L isomers of 2,3-diaminopropionate to pyruvate and ammonia. It catalyzes D- and l-serine less effectively. L-2,3-diaminopropionate is a precursor in the biosynthesis of oxalyl diaminopropionate as a neurotoxin in certain legume species. In this work, we cyclized the diaminopropionate ammonia-lyase from Salmonella typhimurium in vitro using the redox-responsive split intein, and identified that backbone cyclization afforded the enzyme with the improved activity, thermal stability and resistance to the exopeptidase proteolysis, different from effects of the incorporated sequence recognized by tobacco vein mottling virus protease at C-terminus. Using analyses of three fluorescent dyes including 8-anilino-1-naphthalenesulfonic acid, N-phenyl-1-naphthylamine, and thioflavin T, the same amounts of the cyclic protein displayed less fluorescence than those of the linear protein upon the heat treatment. The cyclic enzyme displayed the enhanced activity in Escherichia coli cells using the designed novel reporter. In this system, d-serine was added to the culture and transported into the cytoplasm. It was transformed by pre-overexpression of the diaminopropionate ammonia-lyase, and untransformed d-serine was oxidized by the coproduced human d-amino acid oxidase to generate hydrogen peroxide. This oxidant is monitored by the HyPer indicator. The current results presented that the cyclized enzyme could be applied as a better candidate to block the neurotoxin biosynthesis in certain plant species.

Microbiota-dependent activation of CD4+ T cells induces CTLA-4 blockade-associated colitis via Fcγ receptors.

Immune checkpoint inhibitors can stimulate antitumor immunity but can also induce toxicities termed immune-related adverse events (irAEs). Colitis is a common and severe irAE that can lead to treatment discontinuation. Mechanistic understanding of gut irAEs has been hampered because robust colitis is not observed in laboratory mice treated with checkpoint inhibitors. We report here that this limitation can be overcome by using mice harboring the microbiota of wild-caught mice, which develop overt colitis following treatment with anti-CTLA-4 antibodies. Intestinal inflammation is driven by unrestrained activation of IFNγ-producing CD4+ T cells and depletion of peripherally induced regulatory T cells through Fcγ receptor signaling. Accordingly, anti-CTLA-4 nanobodies that lack an Fc domain can promote antitumor responses without triggering colitis. This work suggests a strategy for mitigating gut irAEs while preserving antitumor stimulating effects of CTLA-4 blockade.

Single-Cell Multimodal Prediction via Transformers.

The recent development of multimodal single-cell technology has made the possibility of acquiring multiple omics data from individual cells, thereby enabling a deeper understanding of cellular states and dynamics. Nevertheless, the proliferation of multimodal single-cell data also introduces tremendous challenges in modeling the complex interactions among different modalities. The recently advanced methods focus on constructing static interaction graphs and applying graph neural networks (GNNs) to learn from multimodal data. However, such static graphs can be suboptimal as they do not take advantage of the downstream task information; meanwhile GNNs also have some inherent limitations when deeply stacking GNN layers. To tackle these issues, in this work, we investigate how to leverage transformers for multimodal single-cell data in an end-to-end manner while exploiting downstream task information. In particular, we propose a scMoFormer framework which can readily incorporate external domain knowledge and model the interactions within each modality and cross modalities. Extensive experiments demonstrate that scMoFormer achieves superior performance on various benchmark datasets. Remarkably, scMoFormer won a Kaggle silver medal with the rank of 24/1221 (Top 2%) without ensemble in a NeurIPS 2022 competition. Our implementation is publicly available at Github.

Antitumor activity and mechanisms of dual EGFR/DNA-targeting strategy for the treatment of lung cancer with EGFRL858R/T790M mutation.

Dual- or multi-targeted EGFR inhibitors as single drugs can overcome EGFR inhibitor resistance and circumvent many disadvantages of combination therapy. In this work, fifteen 4-anilinoquinazoline derivatives bearing nitrogen mustard or hemi mustard moieties were designed and synthesized as dual EGFR-DNA targeting anticancer agents. Structures of target molecules were confirmed by 1H NMR, 13C NMR and HR-MS, and evaluated for their in vitro anti-proliferative activities using MTT assay. Compound 6g emerged as the most potent derivative against mutant-type H1975 cells with IC50 value of 1.45 µM, which exhibited 4-fold stronger potency than Chl/Gef (equimolar combination of chlorambucil and gefitinib). Kinase inhibition studies indicated that 6g showed excellent inhibitory effect on EGFRL858R/T790M enzyme, which was 8.6 times more effective than gefitinib. Mechanistic studies indicated that 6g induced apoptosis of H1975 cells in a dose-dependent manner and caused DNA damage. Importantly, 6g could significantly inhibit the expression of p-EGFR and its downstream p-AKT and p-ERK in H1975 cells. Molecular docking was also performed to gain insights into the ligand-binding interactions of 6g inside EGFRWT and EGFRL858R/T790M binding sites. Moreover, 6g efficiently inhibited tumor growth in the H1975 xenograft model without side effects.

Saturated fatty acids dampen the immunogenicity of cancer by suppressing STING.

Oncogenes destabilize STING in epithelial cell-derived cancer cells, such as head and neck squamous cell carcinomas (HNSCCs), to promote immune escape. Despite the abundance of tumor-infiltrating myeloid cells, HNSCC presents notable resistance to STING stimulation. Here, we show how saturated fatty acids in the microenvironment dampen tumor response to STING stimulation. Using single-cell analysis, we found that obesity creates an IFN-I-deprived tumor microenvironment with a massive expansion of suppressive myeloid cell clusters and contraction of effector T cells. Saturated fatty acids, but not unsaturated fatty acids, potently inhibit the STING-IFN-I pathway in HNSCC cells. Myeloid cells from obese mice show dampened responses to STING stimulation and are more suppressive of T cell activation. In agreement, obese hosts exhibited increased tumor burden and lower responsiveness to STING agonist. As a mechanism, saturated fatty acids induce the expression of NLRC3, depletion of which results in a T cell inflamed tumor microenvironment and IFN-I-dependent tumor control.

Effect of laser-assisted reconstructive surgical therapy of peri-implantitis on protein biomarkers and bacterial load.

OBJECTIVES: This randomized clinical trial assessed changes in protein biomarker levels and bacterial profiles after surgical reconstructive therapy of peri-implantitis and investigated whether the adjunctive use of Er:YAG laser impacts protein biomarker and microbial outcomes. MATERIALS AND METHODS: Twenty-four patients received surgical reconstructive therapy for peri-implantitis with guided bone regeneration following mechanical debridement with (test) or without (control) the adjunctive irradiation of Er:YAG laser. Bacterial and peri-implant crevicular fluid (PICF) samples were collected over 6 months and analyzed with bacterial qPCR and luminex multiplex assays. RESULTS: Surgical reconstructive treatment significantly affected the concentration of PICF protein biomarkers, including a 50% reduction in IL-1ß between 2 and 4 weeks (p < .0001). Both MMP-9 (p < .001) and VEGF (p < .05) levels steadily decreased after treatment. In the laser group, the peak increase in IL-1ß was attenuated at 2 weeks, followed by significant reduction in MMP-9 (p < .01) and VEGF (p < .05) across all follow-up appointments compared with the control nonlaser group. The total bacterial load was reduced 2 weeks after treatment, especially in the laser group, but recolonized to presurgical levels after 4 weeks in both groups (p < .01). The composition of selective pathogens varied significantly over the follow-up, but recolonization patterns did not differ between groups. CONCLUSIONS: Reconstructive therapy of peri-implantitis significantly altered PICF protein biomarker and microbial levels during the healing process. The adjunctive use of Er:YAG laser significantly modulated the inflammatory response through reduced levels of MMP-9 and VEGF during the postsurgical period. The bacterial load was reduced immediately after therapy, but recolonization was observed by 4 weeks in both groups.

An analysis of classical multidimensional scaling with applications to clustering.

Classical multidimensional scaling is a widely used dimension reduction technique. Yet few theoretical results characterizing its statistical performance exist. This paper provides a theoretical framework for analyzing the quality of embedded samples produced by classical multidimensional scaling. This lays a foundation for various downstream statistical analyses, and we focus on clustering noisy data. Our results provide scaling conditions on the signal-to-noise ratio under which classical multidimensional scaling followed by a distance-based clustering algorithm can recover the cluster labels of all samples. Simulation studies confirm these scaling conditions are sharp. Applications to the cancer gene-expression data, the single-cell RNA sequencing data and the natural language data lend strong support to the methodology and theory.

An overview of deterministic and probabilistic forecasting methods of wind energy.

In recent years, a variety of wind forecasting models have been developed, prompting necessity to review the abundant methods to gain insights of the state-of-the-art development status. However, existing literature reviews only focus on a subclass of methods, such as multi-objective optimization and machine learning methods while lacking the full particulars of wind forecasting field. Furthermore, the classification of wind forecasting methods is unclear and incomplete, especially considering the rapid development of this field. Therefore, this article aims to provide a systematic review of the existing deterministic and probabilistic wind forecasting methods, from the perspectives of data source, model evaluation framework, technical background, theoretical basis, and model performance. It is expected that this work will provide junior researchers with broad and detailed information on wind forecasting for their future development of more accurate and practical wind forecasting models.

An epithelial-immune circuit amplifies inflammasome and IL-6 responses to SARS-CoV-2.

Elevated levels of cytokines IL-1ß and IL-6 are associated with severe COVID-19. Investigating the underlying mechanisms, we find that while primary human airway epithelia (HAE) have functional inflammasomes and support SARS-CoV-2 replication, they are not the source of IL-1ß released upon infection. In leukocytes, the SARS-CoV-2 E protein upregulates inflammasome gene transcription via TLR2 to prime, but not activate, inflammasomes. SARS-CoV-2-infected HAE supply a second signal, which includes genomic and mitochondrial DNA, to stimulate leukocyte IL-1ß release. Nuclease treatment, STING, and caspase-1 inhibition but not NLRP3 inhibition blocked leukocyte IL-1ß release. After release, IL-1ß stimulates IL-6 secretion from HAE. Therefore, infection alone does not increase IL-1ß secretion by either cell type. Rather, bi-directional interactions between the SARS-CoV-2-infected epithelium and immune bystanders stimulates both IL-1ß and IL-6, creating a pro-inflammatory cytokine circuit. Consistent with these observations, patient autopsy lungs show elevated myeloid inflammasome gene signatures in severe COVID-19.

OXPHOS promotes apoptotic resistance and cellular persistence in TH17 cells in the periphery and tumor microenvironment.

T cell proliferation and cytokine production are bioenergetically and biosynthetically costly. The inability to meet these metabolic demands results in altered differentiation, accompanied by impaired effector function, and attrition of the immune response. Interleukin-17-producing CD4 T cells (TH17s) are mediators of host defense, autoimmunity, and antitumor immunity in the setting of adoptive T cell therapy. TH17s are long-lived cells that require mitochondrial oxidative phosphorylation (OXPHOS) for effector function in vivo. Considering that TH17s polarized under standardized culture conditions are predominately glycolytic, little is known about how OXPHOS regulates TH17 processes, such as their ability to persist and thus contribute to protracted immune responses. Here, we modified standardized culture medium and identified a culture system that reliably induces OXPHOS dependence in TH17s. We found that TH17s cultured under OXPHOS conditions metabolically resembled their in vivo counterparts, whereas glycolytic cultures were dissimilar. OXPHOS TH17s exhibited increased mitochondrial fitness, glutamine anaplerosis, and an antiapoptotic phenotype marked by high BCL-XL and low BIM. Limited mitophagy, mediated by mitochondrial fusion regulator OPA-1, was critical to apoptotic resistance in OXPHOS TH17s. By contrast, glycolytic TH17s exhibited more mitophagy and an imbalance in BCL-XL to BIM, thereby priming them for apoptosis. In addition, through adoptive transfer experiments, we demonstrated that OXPHOS protected TH17s from apoptosis while enhancing their persistence in the periphery and tumor microenvironment in a murine model of melanoma. Together, our work demonstrates how metabolism regulates TH17 cell fate and highlights the potential for therapies that target OXPHOS in TH17-driven diseases.

Changes in salivary biomarkers associated with periodontitis and diabetic neuropathy in individuals with type 1 diabetes.

The objective of this pilot clinical study was to identify salivary biomarkers that are associated with periodontal disease and measures of diabetic autonomic dysfunction. Saliva samples from 32 participants were obtained from 3 groups: healthy (H), type 1 diabetes mellitus (DM), and type 1 diabetes mellitus with neuropathy (DMN). Based on the periodontal examination, individuals' mean Periodontal Screening and Recording scores were categorized into two groups (periodontally healthy and gingivitis), and correlated to specific salivary inflammatory biomarkers assessed by a customized protein array and enzyme assay. The mean salivary IgA level in DM was 9211.5 ± 4776.4 pg/ml, which was significantly lower than H (17,182.2 ± 8899.3 pg/ml). IgA in DMN with healthy periodontium was significantly lower (5905.5 ± 3124.8 pg/ml) compared to H, although IgA levels in DMN patients with gingivitis (16,894. 6 ± 7084.3) were not. According to the result of a logistic regression model, IgA and periodontal condition were the indicators of the binary response given by H versus DM, and H versus DMN, respectively. These data suggest that selected salivary biomarkers, such as IgA, combined with a periodontal examination prior to obtaining salivary samples can offer a non-invasive method to assess risk for developing diabetic neuropathy.

Theoretical modeling of ice lithography on amorphous solid water.

Due to the perfection of the nanofabrication in nanotechnology and nanoscience, ice lithography (IL) by patterning ice thin-films with a focused electron beam, as a significant derivative technology of electron beam lithography (EBL), is attracting growing attention, evoked by its advantages over traditional EBL with respects of in situ-fabrication, high efficiency, high accuracy, limited proximity effect, three-dimensional (3D) profiling capability, etc. However, theoretical modeling of ice lithography for replicated profiles on the ice resist (amorphous solid water, ASW) has rarely been reported so far. As the result, the development of ice lithography still stays at the experimental stage. The shortage of modeling methods limits our insight into the ice lithography capability, as well as theoretical anticipations for future developments of this emerging technique. In this work, an e-beam induced etching ice model based on the Monte Carlo algorithm for point/line spread functions is established to calculate the replicated profiles of the resist by ice lithography. To testify the fidelity of the modeling method, systematic simulations of the ice lithography property under the processing parameters of the resist thickness, electron accelerating voltage and actual patterns are performed. Theoretical comparisons between the IL on ASW and the conventional EBL on polymethyl methacrylate (PMMA) show superior properties of IL over EBL in terms of the minimum feature size, the highest aspect ratio, 3D nanostructure/devices, etc. The success in developing a modeling method for ice lithography, as reported in this paper, offers a powerful tool in characterizing ice lithography up to the theoretical level and down to molecular scales.

Supervised capacity preserving mapping: a clustering guided visualization method for scRNA-seq data.

MOTIVATION: The rapid development of scRNA-seq technologies enables us to explore the transcriptome at the cell level on a large scale. Recently, various computational methods have been developed to analyze the scRNAseq data, such as clustering and visualization. However, current visualization methods, including t-SNE and UMAP, are challenged by the limited accuracy of rendering the geometric relationship of populations with distinct functional states. Most visualization methods are unsupervised, leaving out information from the clustering results or given labels. This leads to the inaccurate depiction of the distances between the bona fide functional states. In particular, UMAP and t-SNE are not optimal to preserve the global geometric structure. They may result in a contradiction that clusters with near distance in the embedded dimensions are in fact further away in the original dimensions. Besides, UMAP and t-SNE cannot track the variance of clusters. Through the embedding of t-SNE and UMAP, the variance of a cluster is not only associated with the true variance but also is proportional to the sample size. RESULTS: We present supCPM, a robust supervised visualization method, which separates different clusters, preserves the global structure and tracks the cluster variance. Compared with six visualization methods using synthetic and real datasets, supCPM shows improved performance than other methods in preserving the global geometric structure and data variance. Overall, supCPM provides an enhanced visualization pipeline to assist the interpretation of functional transition and accurately depict population segregation. AVAILABILITY AND IMPLEMENTATION: The R package and source code are available at https://zenodo.org/record/5975977#.YgqR1PXMJjM. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Dual light-driven p-ZnFe2O4/n-TiO2 catalyst: Benzene-breaking reaction for malachite green.

Heterocyclic aromatic compounds such as malachite green can cause immense harm to the environment and mankind because of their toxic bio-accumulation and insufficient biodegradation. ZnFe2O4/TiO2 (ZF-T) has attracted attentions as a visible-light-driven catalyst because it can break and mineralize benzene through photolysis. Compared with TiO2, which photodegrades only 53.5% malachite green, anatase TiO2 loaded with ZnFe2O4 has greater photocatalytic activity and can degrade up to 90.1% malachite green. Furthermore, a photocatalytic efficiency above 80% can be obtained through five consecutive cycles with a duration of 4 h. In this study, ZF-T was characterized, and its photolytic parameters, including dosage, pH, time, and ionic strength, were optimized. The photolytic products of malachite green were analyzed by ultraviolet-visible spectroscopy and liquid chromatography-mass spectrometry, which confirmed that ZF-T can drive visible light to produce •O2- and H+ free radicals that can efficiently degrade heterocyclic aromatic hydrocarbons and cleave benzene rings. These outcomes deepen our understanding of the development and applications of visible-light-driven ZF-T composites in the field of wastewater purification.

Cancer-specific type-I interferon receptor signaling promotes cancer stemness and effector CD8+ T-cell exhaustion.

Type-I interferon (IFN-I) signaling is critical to maintaining antigen-presenting cell function for anti-tumor immunity. However, recent studies have suggested that IFN-I signaling may also contribute to more aggressive phenotypes, raising the possibility that IFN-I downstream signaling in cancer and myeloid cells may exert dichotomous functions.We analyzed the clinicopathologic correlation of cancer-specific IFN-I activation in 195 head and neck squamous cell carcinoma patients. We also characterized the immune impact of IFN-I receptor (IFNAR1)-deficiency in syngeneic tumor models using biochemistry, flow cytometry, and single-cell RNA-Seq. We stained HNSCC tissue microarrays with a sensitive IFN-I downstream signaling activation marker, MX1, and quantitated cancer cell-specific MX1 staining. Kaplan-Meier analysis revealed that MX1-high tumors exhibited worse survival, a phenotype that depends on the number of CD8+ intratumoral T-cells. We found that cancer-specific IFNAR1 engagement promotes cancer stemness and higher expression levels of suppressive immune checkpoint receptor ligands in cancer-derived exosomes. Notably, mice bearing Ifnar1-deficient tumors exhibited lower tumor burden, increased T-cell infiltration, reduced exhausted CD4+PD1high T-cells, and increased effector population CD8+IFN-γ+ T-cells. Then, we performed single-cell RNA-sequencing and discovered that cancer-specific IFN-I signaling not only restricts effector cells expansion but also dampens their functional fitness.The beneficial role of IFN-I activation is largely dependent on the myeloid compartment. Cancer-specific IFN-I receptor engagement promotes cancer stemness and the release of cancer-derived exosomes with high expression levels of immune checkpoint receptor ligands. Cancer-specific IFN-I activation is associated with poor immunogenicity and worse clinical outcomes in HNSCC.

Machine learning-assisted immune profiling stratifies peri-implantitis patients with unique microbial colonization and clinical outcomes.

Rationale: The endemic of peri-implantitis affects over 25% of dental implants. Current treatment depends on empirical patient and site-based stratifications and lacks a consistent risk grading system. Methods: We investigated a unique cohort of peri-implantitis patients undergoing regenerative therapy with comprehensive clinical, immune, and microbial profiling. We utilized a robust outlier-resistant machine learning algorithm for immune deconvolution. Results: Unsupervised clustering identified risk groups with distinct immune profiles, microbial colonization dynamics, and regenerative outcomes. Low-risk patients exhibited elevated M1/M2-like macrophage ratios and lower B-cell infiltration. The low-risk immune profile was characterized by enhanced complement signaling and higher levels of Th1 and Th17 cytokines. Fusobacterium nucleatum and Prevotella intermedia were significantly enriched in high-risk individuals. Although surgery reduced microbial burden at the peri-implant interface in all groups, only low-risk individuals exhibited suppression of keystone pathogen re-colonization. Conclusion: Peri-implant immune microenvironment shapes microbial composition and the course of regeneration. Immune signatures show untapped potential in improving the risk-grading for peri-implantitis.

Multiply robust estimation of causal quantile treatment effects.

In causal inference, often the interest lies in the estimation of the average causal effect. Other quantities such as the quantile treatment effect may be of interest as well. In this article, we propose a multiply robust method for estimating the marginal quantiles of potential outcomes by achieving mean balance in (a) the propensity score, and (b) the conditional distributions of potential outcomes. An empirical likelihood or entropy measure approach can be utilized for estimation instead of inverse probability weighting, which is known to be sensitive to the misspecification of the propensity score model. Simulation studies are conducted across different scenarios of correctness in both the propensity score models and the outcome models. Both simulation results and theoretical development indicate that our proposed estimator is consistent if any of the models are correctly specified. In the data analysis, we investigate the quantile treatment effect of mothers' smoking status on infants' birthweight.