Association of gene polymorphisms and the decreased expression of long non-coding RNA LOC553103 with rheumatoid arthritis.

BACKGROUND: Long non-coding RNAs (lncRNAs) are involved in many key bioprocesses, including the occurrence and development of rheumatoid arthritis (RA). We aimed to analyze the association of genetic variants of long non-coding RNA LOC553103 and its peripheral blood mononuclear cells (PBMC) expression with RA. METHODS: We enrolled 457 RA patients and 551 healthy controls and conducted a case-control study to analyze the relationship between LOC553103 gene rs272879 and the susceptibility of RA by TaqMan single nucleotide polymorphism genotyping. Among them, we sampled 92 cases and 92 controls, respectively, to detect the PBMC level of LOC553103 using quantitative real-time polymerase chain reaction technology. We explored the association between LOC553103 rs272879 and its PBMC expression levels in 71 RA patients. Mann-Whitney, Chi-square, and Spearman correlation analysis were used for statistical analysis and P-value <.05 was considered statistically significant. RESULTS: The genotype frequency of LOC553103 rs272879 CC was increased, and CG was decreased in RA patients compared to the control group (χ2 = 6.772, P = .034). The LOC553103 expression level in PBMC of RA patients was downregulated compared to healthy control (Z = -4.497, P < .001). Moreover, negative correlations were observed between the PBMC level of LOC553103 and erythrocyte sedimentation rate (rs = -0.262, P = .018), white blood cell count (rs = -0.382, P = .004), platelet (rs = -0.293, P = .030), and disease activity score in 28 joints (rs = -0.271, P = .016) in RA patients. CONCLUSIONS: This study provides the first evidence supporting an association between LOC553103 gene polymorphisms and susceptibility of RA and a relationship of PBMC level of LOC553103 with clinical manifestations and laboratory indicators of RA patients.

Long-term outcomes of endoscopic resection for well-differentiated nonampullary duodenal neuroendocrine tumors.

BACKGROUND & AIMS: Nonampullary duodenal neuroendocrine tumors (NAD-NETs) are rare with limited evidence regarding endoscopic treatment. The study aimed to investigate the efficacy and safety of endoscopic resection of well-differentiated NAD-NETs and evaluate long-term outcomes, including local recurrence and metastasis. METHODS: A total of 78 patients with NAD-NETs who underwent endoscopic resection between January 2011 and August 2022 were included. The clinicopathologic characteristics and treatment outcomes were collected and analyzed. RESULTS: En bloc resection was achieved for 74 of the tumors (94.9%) and R0 resection was obtained in 68 of the tumors (87.2%). Univariate analysis identified tumors in the second part of the duodenum, tumor size ≥ 10 mm and muscularis propria invasion as risk factors for non-curative resection. Two patients with R1 resection (vertical margin involvement) and two patients with lymphovascular invasion underwent additional surgery. Four patients experienced adverse events (5.1%), including two cases of delayed bleeding and two cases of perforation, all successfully managed conservatively. During a median follow-up period of 62.6 months, recurrence and lymph node metastasis were only detected in one patient with R1 resection 3 months after the original procedure. CONCLUSION: Endoscopic resection is safe and effective and provides a favorable long-term outcome for patients with well-differentiated NAD-NETs without regional lymph node or distant metastasis.

An evaluation of the replicability of analyses using synthetic health data.

Synthetic data generation is being increasingly used as a privacy preserving approach for sharing health data. In addition to protecting privacy, it is important to ensure that generated data has high utility. A common way to assess utility is the ability of synthetic data to replicate results from the real data. Replicability has been defined using two criteria: (a) replicate the results of the analyses on real data, and (b) ensure valid population inferences from the synthetic data. A simulation study using three heterogeneous real-world datasets evaluated the replicability of logistic regression workloads. Eight replicability metrics were evaluated: decision agreement, estimate agreement, standardized difference, confidence interval overlap, bias, confidence interval coverage, statistical power, and precision (empirical SE). The analysis of synthetic data used a multiple imputation approach whereby up to 20 datasets were generated and the fitted logistic regression models were combined using combining rules for fully synthetic datasets. The effects of synthetic data amplification were evaluated, and two types of generative models were used: sequential synthesis using boosted decision trees and a generative adversarial network (GAN). Privacy risk was evaluated using a membership disclosure metric. For sequential synthesis, adjusted model parameters after combining at least ten synthetic datasets gave high decision and estimate agreement, low standardized difference, as well as high confidence interval overlap, low bias, the confidence interval had nominal coverage, and power close to the nominal level. Amplification had only a marginal benefit. Confidence interval coverage from a single synthetic dataset without applying combining rules were erroneous, and statistical power, as expected, was artificially inflated when amplification was used. Sequential synthesis performed considerably better than the GAN across multiple datasets. Membership disclosure risk was low for all datasets and models. For replicable results, the statistical analysis of fully synthetic data should be based on at least ten generated datasets of the same size as the original whose analyses results are combined. Analysis results from synthetic data without applying combining rules can be misleading. Replicability results are dependent on the type of generative model used, with our study suggesting that sequential synthesis has good replicability characteristics for common health research workloads.

Correspondence attention for facial appearance simulation.

In orthognathic surgical planning for patients with jaw deformities, it is crucial to accurately simulate the changes in facial appearance that follow the bony movement. Compared with the traditional biomechanics-based methods like the finite-element method (FEM), which are both labor-intensive and computationally inefficient, deep learning-based methods offer an efficient and robust modeling alternative. However, current methods do not account for the physical relationship between facial soft tissue and bony structure, causing them to fall short in accuracy compared to FEM. In this work, we propose an Attentive Correspondence assisted Movement Transformation network (ACMT-Net) to predict facial changes by correlating facial soft tissue changes with bony movement through a point-to-point attentive correspondence matrix. To ensure efficient training, we also introduce a contrastive loss for self-supervised pre-training of the ACMT-Net with a k-Nearest Neighbors (k-NN) based clustering. Experimental results on patients with jaw deformities show that our proposed solution can achieve significantly improved computational efficiency over the state-of-the-art FEM-based method with comparable facial change prediction accuracy.

A refined deep-learning-based algorithm for harmful-algal-bloom remote-sensing recognition using Noctiluca scintillans algal bloom as an example.

Harmful algal blooms (HABs) are challenging to recognize because of their striped and uneven biomass distributions. To address this issue, a refined deep-learning algorithm termed HAB-Ne was developed for the recognition of HABs in GF-1 Wide Field of View (WFV) images using Noctiluca scintillans algal bloom as an example. First, a pretrained image super-resolution model was integrated to improve the spatial resolution of the GF-1 WFV images and minimize the impact of mixed pixels caused by the strip distribution. Side-window convolution was also explored to enhance the edge features of HABs and minimize the effects of uneven biomass distribution. In addition, a convolutional encoder-decoder network was constructed for threshold-free HAB recognition to address the dependence on thresholds in existing methods. HAB-Net effectively recognized HABs from GF-1 WFV images, achieving an average precision of 90.1% and an F1-score of 0.86. HAB-Net showed more fine-grained recognition results than those of existing methods, with over 4% improvement in the F1-Score, especially in the marginal areas of HAB distribution. The algorithm demonstrated its effectiveness in recognizing HABs in different marine environments, such as the Yellow Sea, East China Sea, and northern Vietnam. Additionally, the algorithm was proven suitable for detecting the macroalga Sargassum. This study demonstrates the potential of deep-learning-based fine-grained recognition of HABs, which can be extended to the recognition of other fine-scale and strip-distributed objects, such as oil spills and Ulva prolifera.

Study on Sensor Fault-Tolerant Control for Central Air-Conditioning Systems Using Bayesian Inference with Data Increments.

A lack of available information on heating, ventilation, and air-conditioning (HVAC) systems can affect the performance of data-driven fault-tolerant control (FTC) models. This study proposed an in situ selective incremental calibration (ISIC) strategy. Faults were introduced into the indoor air (Ttz1) thermostat and supply air temperature (Tsa) and chilled water supply air temperature (Tchws) sensors of a central air-conditioning system. The changes in the system performance after FTC were evaluated. Then, we considered the effects of the data quality, data volume, and variable number on the FTC results. For the Ttz1 thermostat and Tsa sensor, the system energy consumption was reduced by 2.98% and 3.72% with ISIC, respectively, and the predicted percentage dissatisfaction was reduced by 0.67% and 0.63%, respectively. Better FTC results were obtained using ISIC when the Ttz1 thermostat had low noise, a 7-day data volume, or sufficient variables and when the Tsa and Tchws sensors had low noise, a 14-day data volume, or limited variables.

Efficient estimation for left-truncated competing risks regression for case-cohort studies.

The case-cohort study design provides a cost-effective study design for a large cohort study with competing risk outcomes. The proportional subdistribution hazards model is widely used to estimate direct covariate effects on the cumulative incidence function for competing risk data. In biomedical studies, left truncation often occurs and brings extra challenges to the analysis. Existing inverse probability weighting methods for case-cohort studies with competing risk data not only have not addressed left truncation, but also are inefficient in regression parameter estimation for fully observed covariates. We propose an augmented inverse probability-weighted estimating equation for left-truncated competing risk data to address these limitations of the current literature. We further propose a more efficient estimator when extra information from the other causes is available. The proposed estimators are consistent and asymptotically normally distributed. Simulation studies show that the proposed estimator is unbiased and leads to estimation efficiency gain in the regression parameter estimation. We analyze the Atherosclerosis Risk in Communities study data using the proposed methods.

Blood urea nitrogen has a nonlinear association with 3-month outcomes with acute ischemic stroke: A second analysis based on a prospective cohort study.

BACKGROUND: Evidence regarding the relationship between blood urea nitrogen (BUN) and 3-month outcomes in acute ischemic stroke (AIS) patients is still scarce. Therefore, the present study was preformed to explore the link between the BUN and 3-month poor outcomes in patients with AIS. METHODS: A retrospective study of 1866 participants with AIS enrolled from January 2010 to December 2016 at a hospital in South Korea. Binary logistic regression, smooth curve fitting, and a set of sensitivity analyses were used to analyze the association between BUN and 3-month poor outcomes. RESULTS: After adjusting covariates, the results of the binary logistic regression model suggested that the relationship between the BUN and the risk of 3-month poor outcomes for AIS patients was not statistically significant. However, there was a special nonlinear relationship between them, and the inflection point of the BUN was 13 mg/dl. On the left side of the inflection point, every unit increase in the BUN reduces the risk of 3-month poor outcomes by 14.1 % (OR = 0.859, 95%CI: 0.780-0.945, p = 0.0019). On the right side of the inflection point, the relationship is not statistically significant. CONCLUSION: There is a nonlinear relationship with saturation effect between BUN level and 3-month poor outcomes in AIS patients. Maintaining the BUN at around 13 mg/dl can reduce the risk of 3-month poor outcome in AIS patients.

Identification of m6A-related lncRNAs-based signature for predicting the prognosis of patients with skin cutaneous melanoma.

BACKGROUND: Skin cutaneous melanoma (SKCM) is one of the fastest developing malignancies with strong aggressive ability and no proper curative treatments. Numerous studies illustrated the importance of N6-methyladenosine (m6A) RNA modification to tumorigenesis. The aim of this study was to identify novel prognostic signature by using m6A-related lncRNAs, thus to improve the survival for SKCM patients and guide SKCM therapy. METHODS: We downloaded the Presentational Matrix data from The Cancer Genome Atlas (TCGA) and analyzed all the expressed lncRNAs among 468 SKCM samples. Pearson correlation analysis was performed to assess the correlations between lncRNAs and 29 m6A-related genes. Least absolute shrinkage and selection operator (LASSO), univariate and multivariate Cox regression analysis were performed to construct m6A-related lncRNAs prognostic signature (m6A-LPS). The accuracy and prognostic value of this signature were validated by using receiver operating characteristic (ROC) curves, Kaplan-Meier (K-M) survival analysis, univariate COX or multivariate COX analyses. After calculating risk scores, patients were divided into low- and high-risk subgroups by the median value of risk scores. RESULTS: A total of 2973 lncRNAs were found expressed among SKCM tissues. Prognostic analysis showed that 98 lncRNAs had a significant effect on the survival of SKCM patients. The m6A-LPS was validated using K-M and ROC analysis and the predictive accuracy of the risk score was also high according to the AUC of the ROC curve in training and testing sets. A nomogram based on tumor stage, gender and risk score that had a strong ability to forecast the 1-, 2-, 3-, 5-year OS of SKCM patients confirmed by calibrations. Enrichment analysis indicated that malignancy-associated biological processes and pathways were more common in the high-risk subgroup. CONCLUSION: Collectively, m6A-related lncRNAs exert as potential biomarkers for prognostic stratification of SKCM patients and may assist clinicians achieving individualized treatment for SKCM.

Mediating effect of cumulative lipid profile burden on the effect of diet and obesity on hypertension incidence: a cohort study of people aged 35-65 in rural China.

BACKGROUND AND OBJECTIVES: Cumulative lipid profile burden is designed to dynamically measure lipid accumulation, and its effect on hypertension has been poorly studied. Our main purpose was to investigate the effect of cumulative lipid profile burden on the incidence of essential hypertension (EH) and to investigate whether cumulative lipid burden mediates the pathogenesis of the effects of diet and obesity on EH. SUBJECTS AND METHODS: A total of 1295 participants were included in the study, which started in 2017. The average follow-up time was 2.98 years. A total of 240 EH patients occurred during the follow-up period. RESULTS: The HR (95% CI) of the highest quartile in cumulative Total cholesterol (TC), triglyceride (TG) and high density lipoprotein (HDL) burden were 1.747 (1.145 - 2.664), 1.502 (1.038 - 2.173), 0.615 (0.413 - 0.917) for incidence of EH respectively, compared to the respective reference groups. Participants with EH consumed more red meat and refined grains, and red meat was positively associated with cumulative TC burden. BMI and Waist-To-Height Ratio (WHtR) increased the incidence of EH, and obesity was positively correlated with cumulative TG burden. Mediating analysis showed that cumulative TG had a partial mediating effect in the causal relationship between obesity and EH, and Mendelian randomization (MR) also proved this result. Diet was not found to influence EHn through cumulative lipid profile burden. CONCLUSIONS: The cumulative TG burden partially mediates the effect of obesity on EH.

MULTI-TASK DEEP LEARNING AND UNCERTAINTY ESTIMATION FOR PET HEAD MOTION CORRECTION.

Head motion occurring during brain positron emission tomography images acquisition leads to a decrease in image quality and induces quantification errors. We have previously introduced a Deep Learning Head Motion Correction (DL-HMC) method based on supervised learning of gold-standard Polaris Vicra motion tracking device and showed the potential of this method. In this study, we upgrade our network to a multi-task architecture in order to include image appearance prediction in the learning process. This multi-task Deep Learning Head Motion Correction (mtDL-HMC) model was trained on 21 subjects and showed enhanced motion prediction performance compared to our previous DL-HMC method on both quantitative and qualitative results for 5 testing subjects. We also evaluate the trustworthiness of network predictions by performing Monte Carlo Dropout at inference on testing subjects. We discard the data associated with a great motion prediction uncertainty and show that this does not harm the quality of reconstructed images, and can even improve it.

Evaluating the Utility and Privacy of Synthetic Breast Cancer Clinical Trial Data Sets.

PURPOSE: There is strong interest from patients, researchers, the pharmaceutical industry, medical journal editors, funders of research, and regulators in sharing clinical trial data for secondary analysis. However, data access remains a challenge because of concerns about patient privacy. It has been argued that synthetic data generation (SDG) is an effective way to address these privacy concerns. There is a dearth of evidence supporting this on oncology clinical trial data sets, and on the utility of privacy-preserving synthetic data. The objective of the proposed study is to validate the utility and privacy risks of synthetic clinical trial data sets across multiple SDG techniques. METHODS: We synthesized data sets from eight breast cancer clinical trial data sets using three types of generative models: sequential synthesis, conditional generative adversarial network, and variational autoencoder. Synthetic data utility was evaluated by replicating the published analyses on the synthetic data and assessing concordance of effect estimates and CIs between real and synthetic data. Privacy was evaluated by measuring attribution disclosure risk and membership disclosure risk. RESULTS: Utility was highest using the sequential synthesis method where all results were replicable and the CI overlap most similar or higher for seven of eight data sets. Both types of privacy risks were low across all three types of generative models. DISCUSSION: Synthetic data using sequential synthesis methods can act as a proxy for real clinical trial data sets, and simultaneously have low privacy risks. This type of generative model can be one way to enable broader sharing of clinical trial data.

Exploring the Action Mechanism and Validation of the Key Pathways of Dendrobium officinale Throat-clearing Formula for the Treatment of Chronic Pharyngitis Based on Network Pharmacology.

This study investigated the molecular action mechanism of a compound herb, also known as the Dendrobium officinale throat-clearing formula (QYF), by using network pharmacology and animal experimental validation methods to treat chronic pharyngitis (CP). The active ingredients and disease targets of QYF were determined by searching the Batman-TCM and GeneCards databases. Subsequently, the drug-active ingredient-target and protein-protein interaction networks were constructed, and the core targets were obtained through network topology. The Metascape database was screened, and the core targets were enriched with Gene Ontology and the Kyoto Encyclopedia of Genes and Genomes. In total, 1403 and 241 potential targets for drugs and diseases, respectively, and 81 intersecting targets were yielded. The core targets included TNF, IL-6, and IL-1ß, and the core pathways included PI3K-Akt. The QYF treatment group exhibited effectively improved general signs, enhanced anti-inflammatory ability in vitro, reduced serum and tissue expressions of TNF-α, IL-6, and IL-1ß inflammatory factors, and decreased blood LPS levels and Myd88, TLR4, PI3K, Akt, and NF-κB p65 protein expression in the tissues. QYF could inhibit LPS production, which regulated the expression of the TLR4/PI3K/Akt/NF-κB signaling pathway to suppress the expression of the related inflammatory factors (i.e., TNF-α, IL-6, and IL-1ß), thereby alleviating the CP process.

The analysis of multiple outcomes, multiple variables and variables selection in hematopoietic cell transplantation studies.

It is common to study time-to-event data in cancer research such as hematopoietic cell transplantation (HCT) for leukemia. The extensive work has been done for the univariate survival outcome, that is, one event type. However, in practice a subject is often exposed to multiple types of outcomes. In this article, we review various types of right-censored data with multiple outcome types including competing risks data, recurrent event data, and composite endpoints. We also provide hematopoietic cell transplantation data examples.

Climate change and daily outpatient visits for dermatomyositis in Hefei, China: a time-series study.

With the deepening of research on the correlation between meteorological factors and autoimmune diseases, the relationship between climate change and dermatomyositis (DM) has come to our attention. This study aimed to explore the short-term correlation between meteorological factors and DM outpatient visits. Daily records of hospital outpatient visits for DM, air pollutants, and meteorological factor data in Hefei from January 1, 2018 to December 31, 2021 were obtained. The mean temperature (MT), relative humidity (RH), diurnal temperature range (DTR), and temperature change between neighboring days (TCN) were used to quantify environmental temperature and humidity and their variations. And we performed a time series analysis using a generalized linear model (GLM) in combination with a distributed lag nonlinear model (DLNM). Furthermore, gender and age were further stratified for the analysis. The sensitivity analysis was also performed. A total of 4028 DM outpatient visits were recorded during this period. There were statistically significant associations of low temperature (5th, 1.5 °C), low RH (1st, 48.6%), high RH (99th, 99%), high DTR (75th, 12.6°c), and low TCN (10th, -2.7 °C) that were associated with risk of DM outpatient visits, with lag days of 30, 16, 16, 10, and 14, respectively. Moreover, women were more susceptible to high RH exposure and low TCN exposure, while the elderly were more susceptible to low temperature. This study concluded that exposure to low temperature, extreme RH, and temperature changes (especially high DTR and low TCN) was associated with an increased risk of DM outpatient visits.

Impact of thiotepa dose-intensity in primary diffuse large B-cell lymphoma of the central nervous system undergoing autologous hematopoietic cell transplant with thiotepa/carmustine conditioning.

Thiotepa/carmustine (TT-BCNU) is a commonly used autologous transplant (ASCT) conditioning regimen for primary DLBCL of the CNS (PCNSL). The total thiotepa dose varies among TT-BCNU recipients, with some centers administering a total dose of 20 mg/kg, while others using 10 mg/kg. We retrospectively assessed the impact of thiotepa dose intensity on ASCT outcomes in 218 adult PCNSL patients who underwent a first ASCT with TT-BCNU conditioning and received either a total thiotepa dose of 10 mg/kg (TT-10 group; N = 90), or 20 mg/kg (TT-20 group; N = 128). The median follow-up of survivors was 22 months. The cumulative incidence of 1-year non-relapse mortality (NRM) for TT-10 and TT-20 cohorts were 6% (95%CI = 2-12%) vs. 4% (95%CI = 1-8%), respectively (p = 0.66). The 3-year cumulative incidence of relapse (15% vs. 13%; p = 0.67), progression-free survival (PFS) (71% vs. 80%; p = 0.25) and overall survival (OS) (79% vs. 83%; p = 0.56) were similar in the TT-10 and TT-20 groups, respectively. On multivariate analysis compared to TT-10, the TT-20 cohort was not associated with significantly different risk of NRM (Hazard ration [HR] = 0.77; p = 0.64), relapse/progression (HR = 0.87; p = 0.74), PFS (HR = 0.80; p = 0.48) or OS (HR = 1.10; p = 0.80). In conclusion thiotepa dose-intensity in TT-BCNU conditioning does not impact ASCT outcomes of PCNSL patients.

Case Study: Impacts of Air-Conditioner Air Supply Strategy on Thermal Environment and Energy Consumption in Offices Using BES-CFD Co-Simulation.

Due to energy constraints and people's increasing requirements for indoor thermal comfort, improving energy efficiency while ensuring thermal comfort has become the focus of research in the design and operation of HVAC systems. This study took office rooms with few people occupying them in Wuhan as the research object. The EnergyPlus-Fluent co-simulation method was used to study the impact of 12 forms of air distribution on the thermal environment and air-conditioner energy consumption. The results indicate that 3 m/s supply air velocity and 45° supply air angle are more suitable for the case model in this study. The EnergyPlus-Fluent co-simulation method used in this paper provides a reference for the study of indoor environments in offices with few people occupying them.

Serum DKK-1 level in ankylosing spondylitis: insights from meta-analysis and Mendelian randomization.

Objective: The purpose of this study was to precisely evaluate the serum Dickkopf-1 (DKK-1) level in patients with ankylosing spondylitis (AS) relative to that in normal controls and to test the causal relationship between DKK-1 and the risk of AS. Methods: Embase, PubMed, Web of Science, WANFANG DATA, VIP, and China National Knowledge Infrastructure (CNKI) were comprehensively searched until July 2022 for pertinent studies. The pooled standardized mean difference (SMD) with a 95% confidence interval (CI) was calculated by the fixed or random-effect model. In Mendelian randomization (MR) analysis on the causal relationship between serum DKK-1 level and AS risk, the inverse variance weighting method (IVW), MR-Egger regression, weighted median method, and weighted pattern method were applied. Sensitivity analyses, including the horizontal pleiotropy test, heterogeneity test, and leave-one-out test, were also performed. Results: The meta-analysis of 40 studies containing 2,371 AS patients and 1,633 healthy controls showed that there was no significant difference in DKK-1 serum level between AS patients and normal controls (pooled SMD=0.207, 95% CI =-0.418-0.832, P=0.516). The subgroup analysis of the CRP ≤ 10 mg/L group showed that AS patients had higher serum DKK-1 concentration than the healthy controls (SMD=2.267, 95% CI = 0.102-4.432, P=0.040). Similarly, MR analysis also demonstrated no significant association between DKK-1 serum level and AS (IVW OR=0.999, 95% CI = 0.989-1.008, P=0.800). All sensitivity analyses revealed consistent results. Conclusions: There was no significant change in serum DKK-1 concentration between AS patients and healthy controls. In addition, no causal relationship exists between serum DKK-1 levels and AS risk.

Interaction of Filamin C With Actin Is Essential for Cardiac Development and Function.

BACKGROUND: FLNC (filamin C), a member of the filamin family predominantly expressed in striated muscles, plays a crucial role in bridging the cytoskeleton and ECM (extracellular matrix) in cardiomyocytes, thereby maintaining heart integrity and function. Although genetic variants within the N-terminal ABD (actin-binding domain) of FLNC have been identified in patients with cardiomyopathy, the precise contribution of the actin-binding capability to FLNC's function in mammalian hearts remains poorly understood. METHODS: We conducted in silico analysis of the 3-dimensional structure of mouse FLNC to identify key amino acid residues within the ABD that are essential for FLNC's actin-binding capacity. Subsequently, we performed coimmunoprecipitation and immunofluorescent assays to validate the in silico findings and assess the impact of these mutations on the interactions with other binding partners and the subcellular localization of FLNC. Additionally, we generated and analyzed knock-in mouse models in which the FLNC-actin interaction was completely disrupted by these mutations. RESULTS: Our findings revealed that F93A/L98E mutations completely disrupted FLNC-actin interaction while preserving FLNC's ability to interact with other binding partners ITGB1 (ß1 integrin) and γ-SAG (γ-sarcoglycan), as well as maintaining FLNC subcellular localization. Loss of FLNC-actin interaction in embryonic cardiomyocytes resulted in embryonic lethality and cardiac developmental defects, including ventricular wall malformation and reduced cardiomyocyte proliferation. Moreover, disruption of FLNC-actin interaction in adult cardiomyocytes led to severe dilated cardiomyopathy, enhanced lethality and dysregulation of key cytoskeleton components. CONCLUSIONS: Our data strongly support the crucial role of FLNC as a bridge between actin filaments and ECM through its interactions with actin, ITGB1, γ-SAG, and other associated proteins in cardiomyocytes. Disruption of FLN-actin interaction may result in detachment of actin filaments from the extracellular matrix, ultimately impairing normal cardiac development and function. These findings also provide insights into mechanisms underlying cardiomyopathy associated with genetic variants in FLNC ABD and other regions.