Psychometric Properties of the Iranian Version of the Invalidating Childhood Environment Scale.

Objective: To understand the consequences of an invalidating environment, it is essential to have a measurement tool with appropriate statistical properties. Thus, the primary aim of this study was to render the ICES (Invalidating Childhood Environment Scale) into Persian and subsequently evaluate the psychometric attributes of this translated version. Method : Data were collected from 1221 nonclinical participants, including 1053 females and 168 males, who were students at medical universities in Tehran, Iran. Several questionnaires, such as the ICES, CTQ (Childhood Trauma Questionnaire), DTS (Distress Tolerance Scale), BIS-11 (Barratt Impulsiveness Scale), Self-Compassion Questionnaire, Dutch Eating Behavior Questionnaire, and EAT-26 (Eating AttitudesTest) were used in the study. The data sets were investigated through SPSS and R language to evaluate the ICES' reliability and construct validity. Additionally, Item Response Theory (IRT) was employed with the Graded Response Model (GRM) to measure the psychometric properties of each item in terms of difficulty and discrimination parameters. Results: Confirmatory factor analysis indicated that both single-factor and two-factor models fit well for both maternal and paternal versions of the ICES. The internal consistency, as assessed by Cronbach's alpha, was high and satisfactory for both maternal (0.87) and paternal (0.87) versions. Notably, the IRT analysis revealed that item 9 performed poorly in both maternal and paternal versions. Compared to the one-factor model, the two-factor model demonstrated a superior fit. Additionally, the test-retest reliability of the ICES over two months demonstrated good reliability for both maternal and paternal versions (0.98). Divergent and convergent validity analysis revealed a significant negative relationship between childhood invalidation environment and distress tolerance (r = 0.175, P < 0.01) as well as self-compassion (r = 0.142, P < 0.01), which were inversely related to the ICES. Furthermore, there was a considerably positive correlation between the invalidating environment experienced during childhood and impulsivity, as evidenced by r = 0.196 and P < 0.01. Conclusion: This study established the favorable psychometric properties of the Persian version of the ICES, indicating that this version is reliable and valid to assess the Invalidating Childhood Environment in the Iranian population. However, further investigations are warranted to reevaluate its validity and reliability.

Highly sensitive label-free biosensor: graphene/CaF2 multilayer for gas, cancer, virus, and diabetes detection with enhanced quality factor and figure of merit.

One of the primary goals for the researchers is to create a high-quality sensor with a simple structure because of the urgent requirement to identify biomolecules at low concentrations to diagnose diseases and detect hazardous chemicals for health early on. Recently graphene has attracted much interest in the field of improved biosensors. Meanwhile, graphene with new materials such as CaF2 has been widely used to improve the applications of graphene-based sensors. Using the fantastic features of the graphene/CaF2 multilayer, this article proposes an improvement sensor in the sensitivity (S), the figure of merit (FOM), and the quality factor (Q). The proposed sensor is based on the five-layers graphene/dielectric grating integrated with a Fabry-Perot cavity. By tuning graphene chemical potential (µc), due to the semi-metal features of graphene, the surface plasmon resonance (SPR) waves excited at the graphene/dielectric boundaries. Due to the vertical polarization of the source to the gratings and the symmetry of the electric field, both corners of the grating act as electric dipoles, and this causes the propagation of plasmonic waves on the graphene surface to propagate towards each other. Finally, it causes Fabry-Perot (FP) interference on the surface of graphene in the proposed structure's active medium (the area where the sample is located). In this article, using the inherent nature of FP interference and its S to the environment's refractive index (RI), by changing a minimal amount in the RI of the sample, the resonance wavelength (interferometer order) shifts sharply. The proposed design can detect and sense some cancers, such as Adrenal Gland Cancer, Blood Cancer, Breast Cancer I, Breast Cancer II, Cervical Cancer, and skin cancer precisely. By optimizing the structure, we can achieve an S as high as 9000 nm/RIU and a FOM of about 52.14 for the first resonance order (M1). Likewise, the remarkable S of 38,000 nm/RIU and the FOM of 81 have been obtained for the second mode (M2). In addition, the proposed label-free SPR sensor can detect changes in the concentration of various materials, including gases and biomolecules, hemoglobin, breast cancer, diabetes, leukemia, and most alloys, with an accuracy of 0.001. The proposed sensor can sense urine concentration with a maximum S of 8500 nm/RIU and cancers with high S in the 6000 nm/RIU range to 7000 nm/RIU. Also, four viruses, such as M13 bacteriophage, HIV type one, Herpes simplex type 1, and influenza, have been investigated, showing Maximum S (for second resonance mode of λR(M2) of 8000 nm/RIU (λR(M2) = 11.2 µm), 12,000 nm/RIU (λR(M2) = 10.73 µm), 38,000 nm/RIU (λR(M2) = 11.78 µm), and 12,000 nm/RIU (λR(M2) = 10.6 µm), respectively, and the obtained S for first resonance mode (λR(M1)) for mentioned viruses are 4740 nm/RIU (λR(M1) = 8.7 µm), 8010 nm/RIU (λR(M1) = 8.44 µm), 8100 nm/RIU (λR(M1) = 10.15 µm), and 9000 (λR(M1) = 8.36 µm), respectively.

Smart Detecting and Versatile Wearable Electrical Sensing Mediums for Healthcare.

A rapidly expanding global population and a sizeable portion of it that is aging are the main causes of the significant increase in healthcare costs. Healthcare in terms of monitoring systems is undergoing radical changes, making it possible to gauge or monitor the health conditions of people constantly, while also removing some minor possibilities of going to the hospital. The development of automated devices that are either attached to organs or the skin, continually monitoring human activity, has been made feasible by advancements in sensor technologies, embedded systems, wireless communication technologies, nanotechnologies, and miniaturization being ultra-thin, lightweight, highly flexible, and stretchable. Wearable sensors track physiological signs together with other symptoms such as respiration, pulse, and gait pattern, etc., to spot unusual or unexpected events. Help may therefore be provided when it is required. In this study, wearable sensor-based activity-monitoring systems for people are reviewed, along with the problems that need to be overcome. In this review, we have shown smart detecting and versatile wearable electrical sensing mediums in healthcare. We have compiled piezoelectric-, electrostatic-, and thermoelectric-based wearable sensors and their working mechanisms, along with their principles, while keeping in view the different medical and healthcare conditions and a discussion on the application of these biosensors in human health. A comparison is also made between the three types of wearable energy-harvesting sensors: piezoelectric-, electrostatic-, and thermoelectric-based on their output performance. Finally, we provide a future outlook on the current challenges and opportunities.

A Dynamic Analysis of Smart and Nanomaterials for New Approaches to Structural Control and Health Monitoring.

During recent years, remarkable progress has been made in the development of new materials [...].

Structural Health Monitoring of Composite Pipelines Utilizing Fiber Optic Sensors and an AI-Based Algorithm-A Comprehensive Numerical Study.

In this paper, a structural health monitoring (SHM) system is proposed to provide automatic early warning for detecting damage and its location in composite pipelines at an early stage. The study considers a basalt fiber reinforced polymer (BFRP) pipeline with an embedded Fiber Bragg grating (FBG) sensory system and first discusses the shortcomings and challenges with incorporating FBG sensors for accurate detection of damage information in pipelines. The novelty and the main focus of this study is, however, a proposed approach that relies on designing an integrated sensing-diagnostic SHM system that has the capability to detect damage in composite pipelines at an early stage via implementation of an artificial intelligence (AI)-based algorithm combining deep learning and other efficient machine learning methods using an Enhanced Convolutional Neural Network (ECNN) without retraining the model. The proposed architecture replaces the softmax layer by a k-Nearest Neighbor (k-NN) algorithm for inference. Finite element models are developed and calibrated by the results of pipe measurements under damage tests. The models are then used to assess the patterns of the strain distributions of the pipeline under internal pressure loading and under pressure changes due to bursts, and to find the relationship of strains at different locations axially and circumferentially. A prediction algorithm for pipe damage mechanisms using distributed strain patterns is also developed. The ECNN is designed and trained to identify the condition of pipe deterioration so the initiation of damage can be detected. The strain results from the current method and the available experimental results in the literature show excellent agreement. The average error between the ECNN data and FBG sensor data is 0.093%, thus confirming the reliability and accuracy of the proposed method. The proposed ECNN achieves high performance with 93.33% accuracy (P%), 91.18% regression rate (R%) and a 90.54% F1-score (F%).

A Novel Computer-Vision Approach Assisted by 2D-Wavelet Transform and Locality Sensitive Discriminant Analysis for Concrete Crack Detection.

This study proposes FastCrackNet, a computationally efficient crack-detection approach. Instead of a computationally costly convolutional neural network (CNN), this technique uses an effective, fully connected network, which is coupled with a 2D-wavelet image transform for analyzing and a locality sensitive discriminant analysis (LSDA) for reducing the number of features. The algorithm described here is used to detect tiny concrete cracks in two noisy adverse conditions and image shadows. By combining wavelet-based feature extraction, feature reduction, and a rapid classifier based on deep learning, this technique surpasses other image classifiers in terms of speed, performance, and resilience. In order to evaluate the accuracy and speed of FastCrackNet, two prominent pre-trained CNN architectures, namely GoogleNet and Xception, are employed. Findings reveal that FastCrackNet has better speed and accuracy than the other models. This study establishes performance and computational thresholds for classifying photos in difficult conditions. In terms of classification efficiency, FastCrackNet outperformed GoogleNet and the Xception model by more than 60 and 80 times, respectively. Furthermore, FastCrackNet's dependability was proved by its robustness and stability in the presence of uncertainties produced by network characteristics and input images, such as input image size, batch size, and input image dimensions.

Studying Acoustic Behavior of BFRP Laminated Composite in Dual-Chamber Muffler Application Using Deep Learning Algorithm.

Over the last two decades, several experimental and numerical studies have been performed in order to investigate the acoustic behavior of different muffler materials. However, there is a problem in which it is necessary to perform large, important, time-consuming calculations particularly if the muffler was made from advanced materials such as composite materials. Therefore, this work focused on developing the concept of the indirect dual-chamber muffler made from a basalt fiber reinforced polymer (BFRP) laminated composite, which is a monitoring system that uses a deep learning algorithm to predict the acoustic behavior of the muffler material in order to save effort and time on muffler design optimization. Two types of deep neural networks (DNNs) architectures are developed in Python. The first DNN is called a recurrent neural network with long short-term memory blocks (RNN-LSTM), where the other is called a convolutional neural network (CNN). First, a dual-chamber laminated composite muffler (DCLCM) model is developed in MATLAB to provide the acoustic behavior datasets of mufflers such as acoustic transmission loss (TL) and the power transmission coefficient (PTC). The model training parameters are optimized by using Bayesian genetic algorithms (BGA) optimization. The acoustic results from the proposed method are compared with available experimental results in literature, thus validating the accuracy and reliability of the proposed technique. The results indicate that the present approach is efficient and significantly reduced the time and effort to select the muffler material and optimal design, where both models CNN and RNN-LSTM achieved accuracy above 90% on the test and validation dataset. This work will reinforce the mufflers' industrials, and its design may one day be equipped with deep learning based algorithms.

Closed-loop MOEMS accelerometer.

In this paper, a closed-loop micro-opto-electro-mechanical system (MOEMS) accelerometer based on the Fabry-Pérot (FP) interferometer is presented. The FP cavity is formed between the end of a cleaved single-mode optical fiber and the cross-section of a proof mass (PM) which is suspended by four U-shaped springs. The applied acceleration tends to move the PM in the opposite direction. The arrays of fixed and movable comb fingers produce an electrostatic force which keeps the PM in its resting position. The voltage that can provide this electrostatic force is considered as the output of the sensor. Using a closed-loop detection method it is possible to increase the measurement range without losing the resolution. The proposed sensor is fabricated on a silicon-on-insulator wafer using the bulk micromachining method. The results of the sensor characterization show that the accelerometer has a linear response in the range of ±5 g. In the closed-loop mode, the sensitivity and bias instability of the sensor are 1.16 V/g and 40 µg, respectively.

Sustainable Earthquake Resilience with the Versatile Shape Memory Alloy (SMA)-Based Superelasticity-Assisted Slider.

Earthquakes threaten humanity globally in complex ways that mainly include various socioeconomic consequences of life and property losses. Resilience against seismic risks is of high importance in the modern world and needs to be sustainable. Sustainable earthquake resilience (SER) from the perspective of structural engineering means equipping the built environment with appropriate aseismic systems. Shape memory alloys (SMAs) are a class of advanced materials well suited for fulfilling the SER demand of the built environment. This article explores how this capability can be realized by the innovative SMA-based superelasticity-assisted slider (SSS), recently proposed for next-generation seismic protection of structures. The versatility of SSS is first discussed as a critical advantage for an effective SER. Alternative configurations and implementation styles of the system are presented, and other advantageous features of this high-tech isolation system (IS) are studied. Results of shaking table experiments, focused on investigating the expected usefulness of SSS for seismic protection in hospitals and conducted at the structural earthquake engineering laboratory of the University of Bonab, are then reported. SSS is compared with currently used ISs, and it is shown that SSS provides the required SER for the built environments and outperforms other ISs by benefitting from the pioneered utilization of SMAs in a novel approach.

Probabilistic Seismic Response Prediction of Three-Dimensional Structures Based on Bayesian Convolutional Neural Network.

Seismic response prediction is a challenging problem and is significant in every stage during a structure's life cycle. Deep neural network has proven to be an efficient tool in the response prediction of structures. However, a conventional neural network with deterministic parameters is unable to predict the random dynamic response of structures. In this paper, a deep Bayesian convolutional neural network is proposed to predict seismic response. The Bayes-backpropagation algorithm is applied to train the proposed Bayesian deep learning model. A numerical example of a three-dimensional building structure is utilized to validate the performance of the proposed model. The result shows that both acceleration and displacement responses can be predicted with a high level of accuracy by using the proposed method. The main statistical indices of prediction results agree closely with the results from finite element analysis. Furthermore, the influence of random parameters and the robustness of the proposed model are discussed.

Visible Particle Series Search Algorithm and Its Application in Structural Damage Identification.

Identifying structural damage is an essential task for ensuring the safety and functionality of civil, mechanical, and aerospace structures. In this study, the structural damage identification scheme is formulated as an optimization problem, and a new meta-heuristic optimization algorithm, called visible particle series search (VPSS), is proposed to tackle that. The proposed VPSS algorithm is inspired by the visibility graph technique, which is a technique used basically to convert a time series into a graph network. In the proposed VPSS algorithm, the population of candidate solutions is regarded as a particle series and is further mapped into a visibility graph network to obtain visible particles. The information captured from the visible particles is then utilized by the algorithm to seek the optimum solution over the search space. The general performance of the proposed VPSS algorithm is first verified on a set of mathematical benchmark functions, and, afterward, its ability to identify structural damage is assessed by conducting various numerical simulations. The results demonstrate the high accuracy, reliability, and computational efficiency of the VPSS algorithm for identifying the location and the extent of damage in structures.

Misdiagnosis of Budd Chiari syndrome, a case report from Afghanistan.

INTRODUCTION: Budd-Chiari syndrome is a rare disease characterized by hepatic venous flow obstruction. The obstruction may be thrombotic or non-thrombotic anywhere along the venous course from the hepatic venules to the inferior vena cava (IVC) junction to the right atrium. In clinical practice, cases can be misdiagnosed, particularly in regions where resources are limited, unless the clinician pays special attention to such diagnosis. CASE REPORT AND CLINICAL DISCUSSION: Here, we would like to present a misdiagnosed case of Budd Chiari syndrome. This reported case is a case of 30 years old female patient complaining of dull abdominal pain and swelling. Initially, the patient consulted a local health facility where the patient was diagnosed with tuberculous peritonitis and subsequently treated with an anti-TB regimen empirically. Within a few days of taking medicine, she developed mild jaundice and lower limb edema. At this stage, the patient came to us, which after taking history, her physical examination unveiled mild jaundice, ascites, abdominal tenderness, and mild lower limb petting edema. The patient was recommended an abdominal CT scan with contrast, which revealed early enhancement and enlargement of the caudate lobe and non-opacification of hepatic veins with narrowing of the hepatic part of the inferior vena cava consistent with Budd-Chiari syndrome. The patient was started on warfarin and referred for a hepatic decongestive procedure. After four months of performing a transjugular portosystemic shunt, the patient came to us for follow-up. She had an excellent clinical improvement and was started on rivaroxaban 20 mg daily orally. CONCLUSION: The main takeaway lesson of this particular case is to consider the differential diagnosis of ascites from an etiologic point of view and not to overemphasize a single etiology.

Improvement of star identification algorithms using a database generation method based on spatial correlation of stars.

A star tracker, in lost-in-space (LIS) and tracking operation modes, applies an accurate algorithm in the star identification phase. The pattern-matching-based star identification algorithms apply patterns to search a prebuilt database. By applying this newly proposed database, it is possible to apply many LIS algorithms in the LIS and tracking modes. Modifying accurate LIS mode algorithms through this proposed method and applying them in the tracking mode would improve reliability, accuracy, and speed. The simulation results indicate that this proposed approach would reduce the search time for all applied algorithms by a tens factor based on the algorithm's specifications.

Prevalence of impaired renal function and determinants in the southwest of Iran.

BACKGROUND: Chronic kidney disease (CKD) is a growing global health problem with faster progression in developing countries such as Iran. Here we aimed to evaluate the prevalence and determinants of CKD stage III+. METHODS: This research is part of the Khuzestan Comprehensive Health Study (KCHS), a large observational population-based cross-sectional study in which 30,041 participants aged 20 to 65 were enrolled. CKD was determined with estimated glomerular filtration rate (eGFR) less than 60 ml/min/1.73m2, based on two equations of Modification of Diet in Renal Disease (MDRD) and Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI). The multivariate logistic regression was used to evaluate the CKD stage III+ determinants. RESULTS: Prevalence of CKD stage III+ is estimated to be 7.1, 5.5, and 5.4% based on MDRD, CKD-EPI, and combination of both equations, respectively. More than 89% of CKD subjects aged higher than 40 years. In regression analysis, age more than 40 years had the strongest association with CKD stage III+ probability (OR: 8.23, 95% CI: 6.91-9.18). Higher wealth score, hypertension, High-Density Lipoprotein levels less than 40 mg/dl, and higher waist to hip ratio were all associated with CKD stage III+ while Arab ethnicity showed a protective effect (OR: 0.69, 95% CI: 0.57-0.78). CONCLUSION: Our findings provide detailed information on the CKD stage III+ and its determinants in the southwest region of Iran. Due to strong association between age and CKD stage III+, within a few decades we might expect a huge rise in the CKD prevalence.

Impact of 2017 ACC/AHA guideline on prevalence, awareness, treatment, control, and determinants of hypertension: a population-based cross-sectional study in southwest of Iran.

BACKGROUND: In 2017, the American College of Cardiology/American Heart Association (ACC/AHA) provided a new guideline for hypertension prevention and management. We aimed to update the prevalence, awareness, control, and determinants of hypertension based on this guideline in Khuzestan province, southwest of Iran, and to estimate the number of people who are eligible for non-pharmacologic and pharmacologic intervention. METHODS: This population-based cross-sectional study was conducted in Khuzestan, a large province in the southwest of Iran. Comprehensive information about the potential relating factors of hypertension was collected, blood pressure was measured, and anthropometric measurements were obtained. Moreover, the dietary pattern was evaluated in 2830 individuals, using a qualitative food frequency questionnaire. RESULTS: Among 30,506 participants, 30,424 individuals aged 20-65 years were eligible for the study. In comparison with the previous guideline released by the Joint National Committee (JNC8), the prevalence of hypertension in Khuzestan dramatically increased from 15.81 to 42.85% after implementation of the ACC/AHA guideline, which was more dominant in the male population and the 45-54 age group. The sex and age adjustment of the hypertension prevalence was estimated to be 39.40%. The percentage of hypertension awareness, treatment, and control were 45.85%, 35.42%, and 59.63%, which dropped to 22.72%, 26.37%, and 28.94% after implementation of new guideline, respectively. CONCLUSIONS: In the ACC/AHA guideline, a higher number of individuals with the pre-hypertension condition were shifted into the hypertension category and the level of awareness, treatment, and control were dramatically decreased, which highlight a great need to expand the public health infrastructure for further managing the substantial increased burden on healthcare system. However, further studies with population over 65 years are required to estimate the eligibility for antihypertensive treatment in this province after implementation of new guideline.

An insight into the bioelectrochemical photoreduction of CO2 to value-added chemicals.

Goal of sustainable carbon neutral economy can be achieved by designing an efficient CO2 reduction system to generate biofuels, in particular, by mimicking the mechanism of natural photosynthesis using semiconducting nanomaterials interfaced with electroactive bacteria (EAB) in a photosynthetic microbial electrosynthesis (PMES) system. This review paper presents an overview of the recent advancements in the biohybrid photoanode and photocathode materials. We discuss the reaction mechanism observed at photoanode and photocathode to enhance our understanding on the solar driven MES. We extend the discussion by showcasing the potential activity of EABs toward high selectivity and production rates for desirable products by manipulating their genomic sequence. Additionally, the critical challenges associated in scaling up the PMES system including the strategies for diminution of reactive oxygen species, low solubility of CO2 in the typical electrolytes, low selectivity of product species are presented along with the suggestions of alternative strategies to achieve economically viable generation of (bio)commodities.

A new approach to the preeclampsia puzzle; MicroRNA-326 in CD4+ lymphocytes might be as a potential suspect.

BACKGROUND: Alongside many complications in understanding the etiology of Preeclampsia (PE), several determinants, such as the imbalanced proportion of anti-angiogenic/proangiogenic T-cell subsets, especially CD4+ (Th17/Treg), as well as alterations in the expression profile of related cytokines, miRNAs, and transcription factors might have been implicated in PE pathogenesis. MATERIAL AND METHOD: After sample collection and preparation, CD4+ cells were isolated from PE and non-PE pregnant woman and were cultured. Furthermore, analysis such as flow cytometry, real-time PCR, western blotting, and ELISA were performed to assess determinants related to PE manifestation, including sFlt-1, sEng, STAT-3, RORγt, SMAD-7, Foxp3, IL-17, IL-22, Ets-1, and miRNA-326. RESULTS: Our results showed that the miRNA-326 expression level increased in CD4+ Cells and Th17 in PE patients which downregulated Ets-1 expression that acts as a negative control for Th17 development. Furthermore, we showed that the number and expression level of Th17 s and transcription factor RORγt escalated, respectively. While Treg and its related transcription factor (Foxp3) demonstrated a decrease. Flow cytometry analysis illustrated that the Th17/Treg ratio increased in PE. Additionally, we demonstrated that expression and concentration levels of cytokines (IL-17 and IL22) and anti-angiogenic molecules (sEng and sFlt-1) soared in isolated CD4+ cells from PE patients, which could be correlated with PE pathogenicity. CONCLUSION: In conclusion, we comprehensively evaluated immunological factors and molecules involved in PE manifestation. Interestingly, the CD4+ T-cell subset could be an extra source of antiangiogenic factors for the maintenance of this hypertension disorder.

Association of anthropometric indices with cardiovascular disease risk factors among adults: a study in Iran.

AIMS: Cardiovascular diseases (CVDs) are the leading cause of death in the world. Many modifiable risk factors have been reported to synergistically act in the development of CVDs. We aimed to compare the predictive power of anthropometric indices, as well as to provide the best cut-off point for these indicators in a large population of Iranian people for the prediction of CVDs and CVD risk factors. METHODS AND RESULTS: All the data used in the present study were obtained from Khuzestan comprehensive health study (KCHS). Anthropometric indices, including BMI (body mass index), WC (waist circumference), HC (hip circumference), WHR (waist-to-hip ratio), WHtR (waist-to-height ratio), ABSI (a body shape index), as well as CVD risk factors [dyslipidaemia, abnormal blood pressure (BP), and hyperglycaemia] were recorded among 30 429 participants. WHtR had the highest adjusted odds ratios amongst anthropometric indices for all the risk factors and CVDs. WC had the highest predictive power for dyslipidaemia and hyperglycaemia [area under the curve (AUC) = 0.622, 0.563; specificity 61%, 59%; sensitivity 69%, 60%; cut-off point 87.95, 92.95 cm, respectively], while WHtR had the highest discriminatory power for abnormal BP (AUC = 0.585; specificity 60%; sensitivity 65%; cut-off point 0.575) and WHR tended to be the best predictor of CVDs (AUC = 0.527; specificity 58%; sensitivity 64%; cut-off point 0.915). CONCLUSION: In this study, we depicted a picture of the Iranian population in terms of anthropometric measurement and its association with CVD risk factors and CVDs. Different anthropometric indices showed different predictive power for CVD risk factors in the Iranian population.

Premature Coronary Artery Disease Is More Prevalent in People Who Go to Bed Late.

BACKGROUND: Little is known regarding the impact of quantity and quality of sleep on the incidence of cardiovascular disease. The aim of this study was to investigate the possible independent association of late bedtime and premature coronary artery disease (PCAD). METHODS: Between October 2016 and November 2019, we conducted a cross-sectional population-based study on 30101 participants aged 20-65 years in Khuzestan Comprehensive Health Study (KCHS). Data on major risk factors of cardiovascular disease, habit history, physical activity, and sleep behavior was gathered and participants underwent blood pressure, anthropometric, and serum lipid and glucose profile measurements. PCAD was defined as documented history of developing obstructive coronary artery disease before 45 years in men and before 55 years in women. RESULTS: Of a total of 30101 participants (64.1% female, mean age: 41.7±11.7 years) included in this study, 1602 (5.3%, 95% confidence interval: 5.1%-5.6%) had PCAD. Late bedtime was reported in 7613 participants (25.3%, 95% confidence interval: 24.9%-25.8%). Age-sex standardized prevalence for PCAD and late bedtime were 3.62 (3.43-3.82) and 27.8 (27.2-28.4), respectively. There was no significant difference (P=0.558) regarding prevalence of PCAD between those with late bedtime (5.5%, 95% CI: 4.9%-6.0%) and those with early bedtime (5.3%, 95% CI: 5.0%-5.6%). However, after adjustment for potential confounders, late bedtime was independently associated with PCAD (OR=1.136, 95% CI=1.002-1.288, P=0.046). CONCLUSION: In this study, late bedtime was significantly associated with presence of PCAD. Future prospective studies should elucidate the exact role of late bedtime in developing coronary atherosclerosis prematurely.