Shahed University

Over the past decade, electrochemical energy storage (EES) devices have greatly improved, as a wide variety of advanced electrode active materials and new device architectures have been developed. These new materials and devices should be evaluated against clear and rigorous metrics, primarily based on the evidence of real performances. A series of criteria are commonly used to characterize and report performance of EES systems in the literature. However, as advanced EES systems are becoming more and more sophisticated, the methodologies to reliably evaluate the performance of the electrode active materials and EES devices need to be refined to realize the true promise as well as the limitations of these fast-moving technologies, and target areas for further development. In the absence of a commonly accepted core group of metrics, inconsistencies may arise between the values attributed to the materials or devices and their real performances. Herein, we provide an overview of the energy storage devices from conventional capacitors to supercapacitors to hybrid systems and ultimately to batteries. The metrics for evaluation of energy storage systems are described, although the focus is kept on capacitive and hybrid energy storage systems. In addition, we discuss the challenges that still need to be addressed for establishing more sophisticated criteria for evaluating EES systems. We hope this effort will foster ongoing dialog and promote greater understanding of these metrics to develop an international protocol for accurate assessment of EES systems.

Introduction There are no determined treatment agents for severe COVID-19. It is suggested that methylprednisolone, as an immunosuppressive treatment, can reduce the inflammation of the respiratory system in COVID-19 patients. Methods We conducted a single-blind, randomised controlled clinical trial involving severe hospitalised patients with confirmed COVID-19 at the early pulmonary phase of the illness in Iran. The patients were randomly allocated in a 1:1 ratio by the block randomisation method to receive standard care with methylprednisolone pulse (intravenous injection, 250 mg·day −1 for 3 days) or standard care alone. The study end-point was the time of clinical improvement or death, whichever came first. Primary and safety analysis was done in the intention-to-treat (ITT) population. Results 68 eligible patients underwent randomisation (34 patients in each group) from April 20, 2020 to June 20, 2020. In the standard care group, six patients received corticosteroids by the attending physician before the treatment and were excluded from the overall analysis. The percentage of improved patients was higher in the methylprednisolone group than in the standard care group (94.1% versus 57.1%) and the mortality rate was significantly lower in the methylprednisolone group (5.9% versus 42.9%; p<0.001). We demonstrated that patients in the methylprednisolone group had a significantly increased survival time compared with patients in the standard care group (log-rank test: p<0.001; hazard ratio 0.293, 95% CI 0.154–0.556). Two patients (5.8%) in the methylprednisolone group and two patients (7.1%) in the standard care group showed severe adverse events between initiation of treatment and the end of the study. Conclusions Our results suggest that methylprednisolone pulse could be an efficient therapeutic agent for hospitalised severe COVID-19 patients at the pulmonary phase.

The increasing demand for energy has triggered tremendous research efforts for the development of lightweight and durable energy storage devices. Herein, we report a simple, yet effective, strategy for high-performance supercapacitors by building three-dimensional pseudocapacitive CuO frameworks with highly ordered and interconnected bimodal nanopores, nanosized walls (∼4 nm) and large specific surface area of 149 m(2) g(-1). This interesting electrode structure plays a key role in providing facilitated ion transport, short ion and electron diffusion pathways and more active sites for electrochemical reactions. This electrode demonstrates excellent electrochemical performance with a specific capacitance of 431 F g(-1) (1.51 F cm(-2)) at 3.5 mA cm(-2) and retains over 70% of this capacitance when operated at an ultrafast rate of 70 mA cm(-2). When this highly ordered CuO electrode is assembled in an asymmetric cell with an activated carbon electrode, the as-fabricated device demonstrates remarkable performance with an energy density of 19.7 W h kg(-1), power density of 7 kW kg(-1), and excellent cycle life. This work presents a new platform for high-performance asymmetric supercapacitors for the next generation of portable electronics and electric vehicles.

The search for faster, safer, and more efficient energy storage systems continues to inspire researchers to develop new energy storage materials with ultrahigh performance. Mesoporous nanostructures are interesting for supercapacitors because of their high surface area, controlled porosity, and large number of active sites, which promise the utilization of the full capacitance of active materials. Herein, highly ordered mesoporous CuCo2O4 nanowires have been synthesized by nanocasting from a silica SBA-15 template. These nanowires exhibit superior pseudocapacitance of 1210 F g–1 in the initial cycles. Electroactivation of the electrode in the subsequent 250 cycles causes a significant increase in capacitance to 3080 F g–1. An asymmetric supercapacitor composed of mesoporous CuCo2O4 nanowires for the positive electrode and activated carbon for the negative electrode demonstrates an ultrahigh energy density of 42.8 Wh kg–1 with a power density of 15 kW kg–1 plus excellent cycle life. We also show that two asymmetric devices in series can efficiently power 5 mm diameter blue, green, and red LED indicators for 60 min. This work could lead to a new generation of hybrid supercapacitors to bridge the energy gap between chemical batteries and double layer supercapacitors.

The development of symbioses between soil fungi, arbuscular mycorrhizae (AM), and most terrestrial plants can be very beneficial to both partners and hence to the ecosystem. Among such beneficial effects, the alleviation of soil stresses by AM is of especial significance. It has been found that AM fungi can alleviate the unfavourable effects on plant growth of stresses such as heavy metals, soil compaction, salinity and drought. In this article, such mechanisms are reviewed, in the hope that this may result in more efficient use of AM under different stress conditions.

An analysis of electric and magnetic fields radiated by lightning first and subsequent return strokes to tall towers is presented. The contributions of the various components of the fields, namely, static, induction, and radiation for the electric field, and induction and radiation for the magnetic field are illustrated and discussed. It is shown in particular that the presence of a tower tends, in general, to increase substantially the electric and magnetic field peaks and their derivatives. This increase is mainly caused by the presence of two oppositely propagating current wavefronts originating from the tower top and by the very high propagation velocity of current pulses within the tower, and depends essentially on the wavefront steepness of the channel-base current. Because of the last factor, the increase of the field magnitudes is found to be significantly higher for subsequent return strokes, which are characterized by much faster risetimes compared to first return strokes. The presented results are consistent with experimental observations of current in lightning strokes to the Toronto CN Tower and of the associated electric and magnetic fields measured 2 km away. These findings partially explain the fact that subsequent return strokes characterized by lower current peaks but higher front steepnesses and return stroke speeds may result in higher field peaks. The results obtained have important implications in electromagnetic (EM) compatibility. It is found that lightning strokes to tall metallic objects lead to increased EM field disturbances. Also, subsequent return strokes are to be considered an even more important source of EM interference than first return strokes. Indeed, EM fields from subsequent strokes are characterized by faster fronts and additionally, they may reach greater peaks than first strokes. Lastly, findings of this study emphasize the difficulty of extracting reliable lightning return stroke current information from remote EM field measurements using oversimplified formulae.

The need for enhanced energy storage and improved catalysts has led researchers to explore advanced functional materials for sustainable energy production and storage. Herein, we demonstrate a reductive electrosynthesis approach to prepare a layer-by-layer (LbL) assembled trimetallic Fe–Co–Ni metal–organic framework (MOF) in which the metal cations within each layer or at the interface of the two layers are linked to one another by bridging 2-amino-1,4-benzenedicarboxylic acid linkers. Tailoring catalytically active sites in an LbL fashion affords a highly porous material that exhibits excellent trifunctional electrocatalytic activities toward the hydrogen evolution reaction (ηj=10 = 116 mV), oxygen evolution reaction (ηj=10 = 254 mV), as well as oxygen reduction reaction (half-wave potential = 0.75 V vs reference hydrogen electrode) in alkaline solutions. The dispersion-corrected density functional theory calculations suggest that the prominent catalytic activity of the LbL MOF toward the HER, OER, and ORR is due to the initial negative adsorption energy of water on the metal nodes and the elongated O–H bond length of the H2O molecule. The Fe–Co–Ni MOF-based Zn–air battery exhibits a remarkable energy storage performance and excellent cycling stability of over 700 cycles that outperform the commercial noble metal benchmarks. When assembled in an asymmetric device configuration, the activated carbon||Fe–Co–Ni MOF supercapacitor provides a superb specific energy and a power of up to 56.2 W h kg–1 and 42.2 kW kg–1, respectively. This work offers not only a novel approach to prepare an LbL assembled multimetallic MOF but also provides a benchmark for a multifunctional electrocatalyst for water splitting and Zn–air batteries.

CuCo2O4 nanostructures were synthesized through a facile solution combustion method. Electrochemical investigations demonstrate a novel electrode material for supercapacitors with remarkable performance including high-rate capability, high-power density (22.11 kW kg(-1)) and desirable cycling stability at different current densities.

Motor learning is dependent upon plasticity in motor areas of the brain, but does it occur in isolation, or does it also result in changes to sensory systems? We examined changes to somatosensory function that occur in conjunction with motor learning. We found that even after periods of training as brief as 10 min, sensed limb position was altered and the perceptual change persisted for 24 h. The perceptual change was reflected in subsequent movements; limb movements following learning deviated from the prelearning trajectory by an amount that was not different in magnitude and in the same direction as the perceptual shift. Crucially, the perceptual change was dependent upon motor learning. When the limb was displaced passively such that subjects experienced similar kinematics but without learning, no sensory change was observed. The findings indicate that motor learning affects not only motor areas of the brain but changes sensory function as well.

Predictive analytics, or the use of electronic algorithms to forecast future events in real time, makes it possible to harness the power of big data to improve the health of patients and lower the cost of health care. However, this opportunity raises policy, ethical, and legal challenges. In this article we analyze the major challenges to implementing predictive analytics in health care settings and make broad recommendations for overcoming challenges raised in the four phases of the life cycle of a predictive analytics model: acquiring data to build the model, building and validating it, testing it in real-world settings, and disseminating and using it more broadly. For instance, we recommend that model developers implement governance structures that include patients and other stakeholders starting in the earliest phases of development. In addition, developers should be allowed to use already collected patient data without explicit consent, provided that they comply with federal regulations regarding research on human subjects and the privacy of health information.

Motor learning changes the activity of cortical motor and subcortical areas of the brain, but does learning affect sensory systems as well? We examined in humans the effects of motor learning using fMRI measures of functional connectivity under resting conditions and found persistent changes in networks involving both motor and somatosensory areas of the brain. We developed a technique that allows us to distinguish changes in functional connectivity that can be attributed to motor learning from those that are related to perceptual changes that occur in conjunction with learning. Using this technique, we identified a new network in motor learning involving second somatosensory cortex, ventral premotor cortex, and supplementary motor cortex whose activation is specifically related to perceptual changes that occur in conjunction with motor learning. We also found changes in a network comprising cerebellar cortex, primary motor cortex, and dorsal premotor cortex that were linked to the motor aspects of learning. In each network, we observed highly reliable linear relationships between neuroplastic changes and behavioral measures of either motor learning or perceptual function. Motor learning thus results in functionally specific changes to distinct resting-state networks in the brain.

Although self-reported measures play a central role in the assessment of pain and its treatment, it has long been recognized that interpretation of these measures is severely limited by the absence of normative data. Despite that, relatively few of the measures used in pain clinics or research studies have normative data for reference. Using a pain centre sample (n=6124), this paper describes the development of a normative dataset on a number of commonly used pain-related measures. The measures cover many of the key dimensions in pain assessment, including pain severity/quality, disability (physical functioning), and mood (emotional functioning). Measures of different cognitive and coping constructs are also included. Mean scores are reported for each measure according to age group, gender, pain site, as well as percentiles for different scores for patients with chronic low back pain. The potential uses for datasets of this type include the assessment and evaluation of individual cases, as well as the interpretation of published clinical trials. It is also argued that future systematic reviews of pain treatments should include consideration of such patient characteristics as pain levels, disability and mood in the studies reviewed rather than pain site and chronicity alone.

OBJECTIVE: To investigate the effect of exercise during pregnancy on the intensity of low back pain and kinematics of spine. METHOD: A prospective randomized study was designed. 107 women participated in an exercise program three times a week during second half of pregnancy for 12 weeks and 105 as control group. All filled a questionnaire between 17-22 weeks of gestation and 12 weeks later for assessment of their back pain intensity. Lordosis and flexibility of spine were measured by Flexible ruler and Side bending test, respectively, at the same times. Weight gain during pregnancy, Pregnancy length and neonatal weight were recorded. RESULT: Low back pain intensity was increased in the control group. The exercise group showed significant reduction in the intensity of low back pain after exercise (p<0.0001). Flexibility of spine decreased more in the exercise group (p<0.0001). Weight gain during pregnancy, pregnancy length and neonatal weight were not different between the two groups. CONCLUSION: Exercise during second half of the pregnancy significantly reduced the intensity of low back pain, had no detectable effect on lordosis and had significant effect on flexibility of spine.

BACKGROUND: Timely and comprehensive analyses of causes of death stratified by age, sex, and location are essential for shaping effective health policies aimed at reducing global mortality. The Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2023 provides cause-specific mortality estimates measured in counts, rates, and years of life lost (YLLs). GBD 2023 aimed to enhance our understanding of the relationship between age and cause of death by quantifying the probability of dying before age 70 years (70q0) and the mean age at death by cause and sex. This study enables comparisons of the impact of causes of death over time, offering a deeper understanding of how these causes affect global populations. METHODS: GBD 2023 produced estimates for 292 causes of death disaggregated by age-sex-location-year in 204 countries and territories and 660 subnational locations for each year from 1990 until 2023. We used a modelling tool developed for GBD, the Cause of Death Ensemble model (CODEm), to estimate cause-specific death rates for most causes. We computed YLLs as the product of the number of deaths for each cause-age-sex-location-year and the standard life expectancy at each age. Probability of death was calculated as the chance of dying from a given cause in a specific age period, for a specific population. Mean age at death was calculated by first assigning the midpoint age of each age group for every death, followed by computing the mean of all midpoint ages across all deaths attributed to a given cause. We used GBD death estimates to calculate the observed mean age at death and to model the expected mean age across causes, sexes, years, and locations. The expected mean age reflects the expected mean age at death for individuals within a population, based on global mortality rates and the population's age structure. Comparatively, the observed mean age represents the actual mean age at death, influenced by all factors unique to a location-specific population, including its age structure. As part of the modelling process, uncertainty intervals (UIs) were generated using the 2·5th and 97·5th percentiles from a 250-draw distribution for each metric. Findings are reported as counts and age-standardised rates. Methodological improvements for cause-of-death estimates in GBD 2023 include a correction for the misclassification of deaths due to COVID-19, updates to the method used to estimate COVID-19, and updates to the CODEm modelling framework. This analysis used 55 761 data sources, including vital registration and verbal autopsy data as well as data from surveys, censuses, surveillance systems, and cancer registries, among others. For GBD 2023, there were 312 new country-years of vital registration cause-of-death data, 3 country-years of surveillance data, 51 country-years of verbal autopsy data, and 144 country-years of other data types that were added to those used in previous GBD rounds. FINDINGS: The initial years of the COVID-19 pandemic caused shifts in long-standing rankings of the leading causes of global deaths: it ranked as the number one age-standardised cause of death at Level 3 of the GBD cause classification hierarchy in 2021. By 2023, COVID-19 dropped to the 20th place among the leading global causes, returning the rankings of the leading two causes to those typical across the time series (ie, ischaemic heart disease and stroke). While ischaemic heart disease and stroke persist as leading causes of death, there has been progress in reducing their age-standardised mortality rates globally. Four other leading causes have also shown large declines in global age-standardised mortality rates across the study period: diarrhoeal diseases, tuberculosis, stomach cancer, and measles. Other causes of death showed disparate patterns between sexes, notably for deaths from conflict and terrorism in some locations. A large reduction in age-standardised rates of YLLs occurred for neonatal disorders. Despite this, neonatal disorders remained the leading cause of global YLLs over the period studied, except in 2021, when COVID-19 was temporarily the leading cause. Compared to 1990, there has been a considerable reduction in total YLLs in many vaccine-preventable diseases, most notably diphtheria, pertussis, tetanus, and measles. In addition, this study quantified the mean age at death for all-cause mortality and cause-specific mortality and found noticeable variation by sex and location. The global all-cause mean age at death increased from 46·8 years (95% UI 46·6-47·0) in 1990 to 63·4 years (63·1-63·7) in 2023. For males, mean age increased from 45·4 years (45·1-45·7) to 61·2 years (60·7-61·6), and for females it increased from 48·5 years (48·1-48·8) to 65·9 years (65·5-66·3), from 1990 to 2023. The highest all-cause mean age at death in 2023 was found in the high-income super-region, where the mean age for females reached 80·9 years (80·9-81·0) and for males 74·8 years (74·8-74·9). By comparison, the lowest all-cause mean age at death occurred in sub-Saharan Africa, where it was 38·0 years (37·5-38·4) for females and 35·6 years (35·2-35·9) for males in 2023. Lastly, our study found that all-cause 70q0 decreased across each GBD super-region and region from 2000 to 2023, although with large variability between them. For females, we found that 70q0 notably increased from drug use disorders and conflict and terrorism. Leading causes that increased 70q0 for males also included drug use disorders, as well as diabetes. In sub-Saharan Africa, there was an increase in 70q0 for many non-communicable diseases (NCDs). Additionally, the mean age at death from NCDs was lower than the expected mean age at death for this super-region. By comparison, there was an increase in 70q0 for drug use disorders in the high-income super-region, which also had an observed mean age at death lower than the expected value. INTERPRETATION: We examined global mortality patterns over the past three decades, highlighting-with enhanced estimation methods-the impacts of major events such as the COVID-19 pandemic, in addition to broader trends such as increasing NCDs in low-income regions that reflect ongoing shifts in the global epidemiological transition. This study also delves into premature mortality patterns, exploring the interplay between age and causes of death and deepening our understanding of where targeted resources could be applied to further reduce preventable sources of mortality. We provide essential insights into global and regional health disparities, identifying locations in need of targeted interventions to address both communicable and non-communicable diseases. There is an ever-present need for strengthened health-care systems that are resilient to future pandemics and the shifting burden of disease, particularly among ageing populations in regions with high mortality rates. Robust estimates of causes of death are increasingly essential to inform health priorities and guide efforts toward achieving global health equity. The need for global collaboration to reduce preventable mortality is more important than ever, as shifting burdens of disease are affecting all nations, albeit at different paces and scales. FUNDING: Gates Foundation.

Currently in the world there is an alarming number of people who suffer from sleep disorders. A number of biomedical signals, such as EEG, EMG, ECG and EOG are used in sleep labs among others for diagnosis and treatment of sleep related disorders. The usual method for sleep stage classification is visual inspection by a sleep specialist. This is a very time consuming and laborious exercise. Automatic sleep stage classification can facilitate this process. The definition of sleep stages and the sleep literature show that EEG signals are similar in Stage 1 of non-rapid eye movement (NREM) sleep and rapid eye movement (REM) sleep. Therefore, in this work an attempt was made to classify four sleep stages consisting of Awake, Stage 1 + REM, Stage 2 and Slow Wave Stage based on the EEG signal alone. Wavelet packet coefficients and artificial neural networks were deployed for this purpose. Seven all night recordings from Physionet database were used in the study. The results demonstrated that these four sleep stages could be automatically discriminated from each other with a specificity of 94.4 +/- 4.5%, a of sensitivity 84.2+3.9% and an accuracy of 93.0 +/- 4.0%.

Cumin (Cuminum cyminum) is one of the commonly used spices in food preparations. It is also used in traditional medicine as a stimulant, a carminative, and an astringent. In this study, we characterized the antimicrobial, antioxidant, and cytotoxic activities of cumin. E. coli, S. aureus, and S. faecalis were sensitive to various oil dilutions. The total phenol content of the essential oil was estimated to be 33.43 microg GAE/mg of the oil. The oil showed higher antioxidant activity compared with that of BHT and BHA. The cumin essential oil exhibited a dose-dependent scavenging of DPPH radicals and 5.4 microg of the oil was sufficient to scavenge 50% of DPPH radicals/mL. At a concentration of 0.1 microL/mL, oil destructed Hela cells by 79%. The antioxidant activity of cumin essential oil might contribute to its cytotoxic activity. Acute and subchronic toxicity was studied in a 30-d oral toxicity study by administration to Wistar rats of the essential oil. A 17.38% decrease in WBCs count, and 25.77%, 14.24%, and 108.81% increase in hemoglobin concentration, hematocrit, and platelet count, respectively, were noted. LDL/HDL ratio was reduced to half, which adds to the nutritional effects of cumin. Thus, cumin with a high phenolic content and good antioxidant activity can be supplemented for both nutritional purposes and preservation of foods.

In recent years, wind speed forecasting is a challenging task required for the prediction of wind energy resources. As a highly varying data source, wind speed time series requires highly nonlinear temporal features for the prediction tasks. However, most forecasting approaches apply shallow supervised features extracted using architectures with few nonlinear hidden layers. Moreover, the exact features captured in such methodologies cannot decrease the wind data uncertainties. In this paper, an interval probability distribution learning (IPDL) model is proposed based on restricted Boltzmann machines and rough set theory to capture unsupervised temporal features from wind speed data. The proposed model contains a set of interval latent variables tuned to capture the probability distribution of wind speed time series data using contrastive divergence with Gibbs sampling. A real-valued interval deep belief network (IDBN) is further designed employing a stack of IPDLs with a fuzzy type II inference system (FT2IS) for the supervised regression of future wind speed values. In order to automatically learn meaningful unsupervised features from the underlying wind speed data, real-valued input units are designed inside IDBN to better approximate the wind speed probability distribution function compared to classic deep belief networks. The high generalization capability of our unsupervised feature learning model incorporated with the robustness of IPDLs and FT2IS leads to accurate predictions. Simulation results on the Western Wind Dataset reveal significant performance improvement in 1-h up to 24-h ahead predictions compared to single-model approaches including both shallow and deep architectures, as well as recently proposed hybrid methodologies.

INTRODUCTION: This review presents the current in vitro and in vivo animal and human research on the roles of IL-8 in ocular inflammatory diseases. MATERIALS AND METHODS: Data sources were a literature review using Pub Med, Medline, and ISI databases (from 1990 to 2011). Search items included interleukine-8 (IL-8), CXCL8, chemokines, cytokines, alone or in combination with the, serum, aqueous, vitreous, eye, ocular, ocular tissues, ophthalmic, and review. RESULTS: IL-8 may be involved in primary or secondary ocular inflammations. Ocular effects of IL-8 differ based on the source of the secretion and site of the action. The most important effects of IL-8 in the eyes are angiogenic activities and induction of ocular inflammation. CONCLUSION: IL-8 plays important roles in ocular inflammation and angiogenesis in conjunctiva, cornea, iris, retina, and orbit. Anti-IL-8 targeted immunotherapy has been introduced as an important treatment modality, provided that IL-8 signal blocking takes place in desired areas and tissues.

Abstract Discovering efficient pseudocapacitive charge storage materials has become one of the grand challenges to reduce the gap between high energy density batteries and high power density and durable electrical double‐layer capacitors. This research direction is facilitated by the introduction of redox‐active species that add Faradaic charge storage to the system. However, the astonishing abilities of organic redox species to increase energy density are insufficient to compensate for their poor electrical conductivity and inferior cyclability. Herein, it is proposed that these challenges can be simultaneously met by thoughtful selection of a redox species, thionine, that can be conjugated to a 3D graphene aerogel as a substrate via π–π interactions. The as‐fabricated metal‐free symmetric device exhibits a very high specific capacitance of 384 F g −1 at 1 A g −1 . Moreover, the device shows an ultrawide potential window of 2.0 V in pH‐neutral aqueous electrolytes and delivers a maximum specific energy of 32.6 Wh kg −1 , specific power of up to 12.8 kW kg −1 , outstanding flexibility, and an excellent capacitance retention of 91% after 10 000 charge–discharge cycles at 10 A g −1 . This device design provides an effective strategy to fabricate high‐performance aqueous supercapacitors and facilitates progress toward a sustainable energy future.

BACKGROUND: There is evidence that traumatic birth experiences are associated with psychological impairments. This study aimed to estimate the prevalence of childbirth-related post-traumatic stress symptoms and its obstetric and perinatal risk factors among a sample of Iranian women. METHODS: This was a cross-sectional study carried out in Bushehr, Iran during a 3-months period from July to September 2009. Data were collected from all women attending eleven healthcare centers for postnatal care 6 to 8 weeks after childbirth. Those who had a traumatic delivery were identified and entered into the study. In order to assess childbirth-related post-traumatic stress, the Post-traumatic Symptom Scale-Interview (PSS-I) was administered. Data on demographic, obstetric and perinatal characteristics also were collected. Multivariate logistic regression was performed to examine the association between childbirth-related post-traumatic stress and demographic and obstetric and perinatal variables. RESULTS: In all, 400 women were initially evaluated. Of these, 218 women (54.5%) had a traumatic delivery and overall, 80 women (20%) were found to be suffering from post-partum post-traumatic stress disorder (PTSD). Multiple logistic regression analysis revealed that post-partum PTSD was associated with educational level, gestational age at delivery, number of prenatal care visits, pregnancy complications, pregnancy intervals, labor duration, and mode of delivery. CONCLUSIONS: The findings indicated that the prevalence of traumatic birth experiences and post-partum PTSD were relatively high among Iranian women. The findings also indicated that obstetric and perinatal variables were independently the most significant contributing factors to women's post-partum PTSD. It seems that a better perinatal care and supportive childbirth might help to reduce the burden of post-partum PTSD among this population.