Number Of Conditions Research Articles

Ultra‐high frequency sensors positioning on the power transformer to mitigate the negative effects on the partial discharge localization accuracy

AbstractThis paper proposes a new method for improving the partial discharge (PD) localization accuracy by detecting the best places of ultra‐high frequency (UHF) sensors on the power transformer tank. In this way, firstly, the critical sites inside the power transformer that can produce PD are determined. Then the sensors’ positioning is done in such a way that if the PD happens at each point of these critical sites, the distance between the estimated PD location obtained by solving the time difference of arrival (TDOA) equations and the actual PD location is laid in an acceptable range. For this purpose, the TDOA equations are linearized around the critical PD coordinates as a matrix form A.X = B. The configuration of the sensor positions that minimizes the condition number of the matrix A is the best configuration. To evaluate the effectiveness of the proposed method, several simulations and evaluations are performed on a power transformer in the CST software. The obtained results and comparing them with the previous method and also conventional arbitrary sensors positioning confirm the effectiveness of this method.

Ultrasensitive fiber sensor with enhanced Vernier effect for simultaneous measurements of transverse load and temperature.

Based on enhanced Vernier effect, a compact fiber sensor with ultrahigh sensitivity is proposed for simultaneous transverse load (TL) and temperature measurements. A single mode fiber (SMF) is spliced with a segment of hollow-core fiber (HCF) coated with polydimethylsiloxane (PDMS), some PDMS is injected into the HCF, forming a Vernier sensor with an air cavity adjacent to a PDMS cavity. It is shown that TL and temperature changes give rise to opposite and remarkable different variations in lengths of the two cavities, thereby enhancing Vernier effect and in favor of simultaneous measurements of TL and temperature. Moreover, the limited sensitivity magnification due to the length mismatch between the two cavities is compensated for by reconstructing the Vernier envelope with a broadened free spectrum range (FSR) from output signal. As a result, the highest TL sensitivity reported so far of -2637.47 nm/N and a good condition number of 69.056 for the sensitivity coefficient matrix have been achieved.

Understanding the Effects of Trauma Exposure, Life Stress, Intimate Partner Violence, and Depression on Sexually Transmitted Infection Risk in Post-Conflict Liberia.

In sub-Saharan Africa, sexually transmitted infections (STIs) are a public health concern. The impact of STIs are exacerbated in post-conflict low- and middle-income countries, such as Liberia, where exposure to traumatic events is prevalent and access to mental health services are limited. Following a syndemics framework, this study used regression analyses to explore the independent, additive, and multiplicative effects of four psychosocial conditions (exposure to war-related traumatic events, intimate partner violence [IPV], stressful life events, and depressive symptoms) on self-reported STIs. Data were collected from 379 youth aged 18-30 years (n = 170 women; n = 179 men) in Montserrado County, Liberia. Results revealed that psychosocial variables correlated with each other and STI risk. In multivariable analysis, stressful life events, depressive symptoms, and IPV were statistically significant predictors of STI risk. We found support for an additive effect between the number of psychosocial conditions reported and STI risk, as well as a multiplicative effect (interaction) between IPV and depressive symptoms on STI risk. Our results suggest a synergy between experiencing psychosocial conditions and STI risk and point to the potential benefit of multi-level sexual health approaches that simultaneously address mental health and IPV among youth in Liberia.

Condition numbers of the generalized ridge regression and its statistical estimation

<abstract><p>In this paper, we considered the condition number theory of a new generalized ridge regression model. The explicit expressions of different types of condition numbers were derived to measure the ill-conditionness of the generalized ridge regression problem with respect to different circumstances. To overcome the computational difficulty of computing the exact value of the condition number, we employed the statistical condition estimation theory to design efficient condition number estimators, and the numerical examples were also given to illustrate its efficiency.</p></abstract>

Including the Horizontal Observation Error Correlation in the Ensemble Kalman Filter: Idealized Experiments with NICAM-LETKF

Abstract Densely observed remote sensing data such as radars and satellites generally contain significant spatial error correlations. In data assimilation, the observation error covariance matrix is usually assumed to be diagonal, and the dense data are thinned or spatially averaged to compensate for neglecting the spatial observation error correlation. However, in theory, including the spatial observation error correlation in data assimilation can make better use of the dense data. This study performs perfect model observing system simulation experiments (OSSEs) using the nonhydrostatic icosahedral atmospheric model (NICAM) and the local ensemble transform Kalman filter (LETKF) to assess the impact of assimilating horizontally dense and error-correlated observations. The condition number of the observation error covariance matrix, defined as the ratio of the largest to smallest eigenvalues, is important for the numerical stability of the LETKF computation. A large condition number makes it difficult to compute the ensemble transform matrix correctly. Reducing the condition number by reconditioning is found effective for stable computation. The results show that including the horizontal observation error correlation with reconditioning makes the analysis more accurate but requires 6 times more computations than the case with the diagonal observation error covariance matrix. Significance Statement It is important to effectively utilize observations in data assimilation for more accurate weather prediction. Spatially dense observations are known to have an error correlation that is ignored in the data assimilation. This study explores assimilating dense observations by explicitly including observation error correlations with an idealized experiment. The results shows that the analysis is improved by including the observation error correlations. Also, the condition number of the observation error covariance matrix is essential for stable computations.

The Problem of the Reliability of Bending Models for Composite Plates of Medium Thickness

Most refined bending models of medium-thick plates, which consider transverse shear and partial compression deformations, differ little. However, despite a significant increase in the order of the governing differential equations, the results obtained from their equations give mainly a small increase in accuracy compared to the existing theories. On the other hand, such an increase in the order of the constructed systems of differential equations requires a significant increase in the effort required to solve them, complicates their physical interpretation, and narrows the range of people who can use them, primarily engineers and designers. Therefore, developing a plate-bending model that incorporates all the above factors and is on par with previously applied theories regarding the complexity of the calculation equations remains relevant. For example, most of the applied theories that do not consider transverse compression cannot be used to solve problems of contact interaction with rigid and elastic dies and bases because it is impossible to satisfy the conditions at the contact boundary of the outer surface of the plate, as well as the boundary conditions at the edges of the plate. Therefore, to provide guaranteed accuracy of the results, some researchers of these problems have introduced such a concept as “energy consistency” between the functions of representation of the displacement vector components, their number, the order of equations, and the number of boundary conditions. The authors, based on the developed version of the model of orthotropic plates of medium thickness, investigate the problem of taking into account the so-called “energy consistency” effect of the bending model, depending on the order of the design equations and the number of boundary conditions, as well as its usefulness and disadvantages in practical calculations. The equations of equilibrium in displacements and expressions for stresses in terms of force and moment forces are recorded. For rectangular and circular plates of medium thickness, test problems are solved, and the numerical data are compared with those obtained using spatial problems of elasticity theory, as well as the refined Timoshenko and Reissner theories. An analysis of the obtained results is provided.

Participant completion of longitudinal assessments in an online cognitive aging registry: The role of medical conditions.

This study aimed to understand whether older adults' longitudinal completion of assessments in an online Alzheimer's disease and related dementias (ADRD)-related registry is influenced by self-reported medical conditions. Brain Health Registry (BHR) is an online cognitive aging and ADRD-related research registry that includes longitudinal health and cognitive assessments. Using logistic regressions, we examined associations between longitudinal registry completion outcomes and self-reported (1) number of medical conditions and (2) eight defined medical condition groups (cardiovascular, metabolic, immune system, ADRD, current psychiatric, substance use/abuse, acquired, other specified conditions) in adults aged 55+ (N = 23,888). Longitudinal registry completion outcomes were assessed by the completion of the BHR initial questionnaire (first questionnaire participants see at each visit) at least twice and completion of a cognitive assessment (Cogstate Brief Battery) at least twice. Models included ethnocultural identity, education, age, and subjective memory concern as covariates. We found that the likelihood of longitudinally completing the initial questionnaire was negatively associated with reporting a diagnosis of ADRD and current psychiatric conditions but was positively associated with reporting substance use/abuse and acquired medical conditions. The likelihood of longitudinally completing the cognitive assessment task was negatively associated with number of reported medical conditions, as well as with reporting cardiovascular conditions, ADRD, and current psychiatric conditions. Previously identified associations between ethnocultural identity and longitudinal assessment completion in BHR remained after accounting for the presence of medical conditions. This post hoc analysis provides novel, initial evidence that older adults' completion of longitudinal assessments in an online registry is associated with the number and types of participant-reported medical conditions. Our findings can inform future efforts to make online studies with longitudinal health and cognitive assessments more usable for older adults with medical conditions. The results need to be interpreted with caution due to selection biases, and the under-inclusion of minoritized communities.

A Bayesian approach to Mendelian randomization using summary statistics in the univariable and multivariable settings with correlated pleiotropy

Mendelian randomization uses genetic variants as instrumental variables to make causal inferences on the effect of an exposure on an outcome. Due to the recent abundance of high-powered genome-wide association studies, many putative causal exposures of interest have large numbers of independent genetic variants with which they associate, each representing a potential instrument for use in a Mendelian randomization analysis. Such polygenic analyses increase the power of the study design to detect causal effects; however, they also increase the potential for bias due to instrument invalidity. Recent attention has been given to dealing with bias caused by correlated pleiotropy, which results from violation of the "instrument strength independent of direct effect" assumption. Although methods have been proposed that can account for this bias, a number of restrictive conditions remain in many commonly used techniques. In this paper, we propose a Bayesian framework for Mendelian randomization that provides valid causal inference under very general settings. We propose the methods MR-Horse and MVMR-Horse, which can be performed without access to individual-level data, using only summary statistics of the type commonly published by genome-wide association studies, and can account for both correlated and uncorrelated pleiotropy. In simulation studies, we show that the approach retains type I error rates below nominal levels even in high-pleiotropy scenarios. We demonstrate the proposed approaches in applied examples in both univariable and multivariable settings, some with very weak instruments.

Towards improving the prognosis of stroke through targeting the circadian clock system.

Rhythmicity of the circadian system is a 24-hour period, driven by transcription-translation feedback loops of circadian clock genes. The central circadian pacemaker in mammals is located in the hypothalamic suprachiasmatic nucleus (SCN), which controls peripheral circadian clocks. In general, most physiological processes are regulated by the circadian system, which is modulated by environmental cues such as exposure to light and/or dark, temperature, and the timing of sleep/wake and food intake. The chronic circadian disruption caused by shift work, jetlag, and/or irregular sleep-wake cycles has long-term health consequences. Its dysregulation contributes to the risk of psychiatric disorders, sleep abnormalities, hypothyroidism and hyperthyroidism, cancer, and obesity. A number of neurological conditions may be worsened by changes in the circadian clock via the SCN pacemaker. For stroke, different physiological activities such as sleep/wake cycles are disrupted due to alterations in circadian rhythms. Moreover, the immunological processes that affect the evolution and recovery processes of stroke are regulated by the circadian clock or core-clock genes. Thus, disrupted circadian rhythms may increase the severity and consequences of stroke, while readjustment of circadian clock machinery may accelerate recovery from stroke. In this manuscript, we discuss the relationship between stroke and circadian rhythms, particularly on stroke development and its recovery process. We focus on immunological and/or molecular processes linking stroke and the circadian system and suggest the circadian rhythm as a target for designing effective therapeutic strategies in stroke.

Prediction of Transformer Oil Temperature Based on an Improved PSO Neural Network Algorithm

Abstract: In addressing the issue of power transformer oil temperature prediction, traditional back propagation (BP) neural network algorithms have been found to suffer from local optimization and slow convergence. This study proposes an oil temperature prediction model based on an improved particle swarm optimization (PSO) neural network algorithm, which introduces an asymmetric adjustment learning factor and a mutation operator. The BP neural network, genetic algorithm (GA) optimization neural network, and the improved PSO neural network are compared by considering various factors, such as ambient temperature, load changes, and the number of cooler groups under different working conditions. Results show that the proposed algorithm improves the actual change trend of oil surface temperature and makes the transformer operation more stable to a certain extent. Background: The mathematical model for predicting transformer oil temperature is clear, but the parameters in the model are uncertain and vary with time. When subjected to different operating conditions, such as ambient temperature, load changes, and the number of cooler groups acting independently or in combination, the prediction results of the oil temperature model vary with different system parameters. Objective: This paper aims to enhance the accuracy of transformer temperature prediction. In order to optimize the oil temperature prediction model, asymmetric adjustment learning factors and mutant operators are added to meet diverse system parameter requirements. Methods: The paper utilizes an oil temperature prediction model based on an improved PSO neural network algorithm, which introduces an asymmetric adjustment learning factor and a mutation operator to address the limitations of the standard PSO algorithm. Results: This paper has employed a fusion algorithm of the genetic algorithm of the BP neural network and the PSO algorithm, and conducted simulation and experimental analysis. The simulation and experimental results demonstrate the accuracy and effectiveness of the fusion algorithm. Conclusion: This study demonstrates enhanced prediction accuracy of transformer oil surface temperature using the improved particle swarm optimization neural network algorithm. This algorithm has less prediction error under different working conditions compared to other algorithms. By increasing population diversity and combining inertia weights, the algorithm not only greatly improves its search performance but also avoids local optimization.

Electrochemical detection and removal of brilliant blue dye via photocatalytic degradation and adsorption using phyto-synthesized nanoparticles.

Herein, we report a sensitive electrochemical platform prepared by modifying the electrode surface with copper-doped zinc oxide nanoparticles; these nanoparticles were prepared via a green synthetic approach using the extract of Cassia fistula leaves and multiwalled carbon nanotubes (MWCNTs). For the best response of the electrode modifier, a number of experimental conditions were optimized to obtain the most intense signal of the target analyte Coomassie brilliant blue using a rapid analysis technique square wave voltammetry. The designed sensor displayed remarkable sensitivity for Coomassie brilliant blue with a detection limit of 0.1 nM under the optimized conditions. Moreover, the repeatability, specificity and reproducibility of the designed sensor demonstrated its potential for practical applications. The sensing platform was also used for monitoring the degradation kinetics of the Coomassie brilliant blue dye. Catalytic degradation of the dye was performed using the synergistic effect of Cu-ZnO NPs together with Fenton reagent. The dye degraded by 96% in 60 minutes under neutral conditions, which is one of the main achievements of this work that has never been reported. The photocatalytic breakdown of Coomassie brilliant blue was also monitored using UV-visible spectroscopy. The degradation kinetics results of both techniques agreed well. The adsorption of Coomassie brilliant blue using ZnO NPs was monitored spectrophotometrically. The adsorption data were fitted in a pseudo-second order kinetic model by following the Langmuir isotherm at lower concentration and Freundlich isotherm at higher concentration.

Fabrication and application of glutathione biosensing SPCE strips with gold nanoparticle modification.

Glutathione (GSH) is a major antioxidant in organisms. An alteration in GSH concentration has been implicated in a number of pathological conditions. Therefore, GSH sensing has become a critical issue. In this study, a disposable strip used for tyrosinase-modified electrochemical testing was fabricated for the detection of GSH levels in vivo. The system is based on tyrosinase as a biorecognition element and a screen-printed carbon electrode (SPCE) as an amperometric transducer. On the tyrosinase-SPCE strips, the oxidation reaction from catechol to o-quinone was catalyzed by tyrosinase. The tyrosinase-SPCE strips were modified with gold nanoparticles (AuNPs). In the presence of AuNPs of 25 nm diameter, the cathodic peak current of cyclic voltammetry (CV) was significantly enhanced by 5.2 fold. Under optimized conditions (250 μM catechol, 50 mM phosphate buffer, and pH 6.5), the linear response of the tyrosinase-SPCE strips ranged from 31.25 to 500 μM GSH, with a detection limit of approximately 35 μM (S/N > 3). The tyrosinase-SPCE strips have been used to detect real samples of plasma and tissue homogenates in a mouse experiment. The mice were orally administrated with N-acetylcysteine (NAC) 100 mg kg-1 once a day for 7 days; the plasma GSH significantly enhanced 2.8 fold as compared with saline-treated mice (1123 vs. 480 μM μg-1 protein). NAC administration also could alleviate the adverse effect of GSH reduction in the mice treated with doxorubicin.

Health care barriers and perceived mental health among adults in Canada during the COVID-19 pandemic: a population-based cross-sectional study.

The perceived mental health of individuals in Canada who faced health care barriers during the COVID-19 pandemic is underexplored. We analyzed data collected March to June 2021 from adults who reported needing health care services within the past 12 months in the Survey on Access to Health Care and Pharmaceuticals during the Pandemic. Unadjusted and adjusted logistic regression analyses examined the associations between health care barriers (appointment scheduling problems, delaying contacting health care) and high self-rated mental health and perceived worsening mental health compared to before the pandemic, overall and stratified by gender, age group, number of chronic health conditions and household income tertile. Individuals who experienced pandemic-related appointment changes or had appointments not yet scheduled were less likely to have high self-rated mental health (aOR = 0.81 and 0.64, respectively) and more likely to have perceived worsening mental health (aOR = 1.50 and 1.94, respectively) than those with no scheduling problems. Adults who delayed contacting health care for pandemic-related reasons (e.g. fear of infection) or other reasons were less likely to have high self-rated mental health (aOR = 0.52 and 0.45, respectively) and more likely to have perceived worsening mental health (aOR = 2.31 and 2.43, respectively) than those who did not delay. Delaying contacting health care for pandemic-related reasons was associated with less favourable perceived mental health in all subgroups, while the association between perceived mental health and pandemic-related appointment changes was significant in some groups. Health care barriers during the pandemic were associated with less favourable perceived mental health. These findings could inform health care resource allocation and public health messaging.

Strategies and Impacts of COVID-19 Vaccine Rollout: A Deterministic SEIVR Model Approach

Immunization has come to play a key role in our primary health care as it has saved millions of lives yearly since it came into existence. The Corona virus disease (COVID-19) pandemic brought about an urgent need for Covid-19 vaccines. Various researches have been done and are still ongoing to help produce vaccines to help protect people by creating an immune response without the potentially severe illness or post-COVID conditions associated with COVID-19 infection. In this work, vaccine deployment strategies and their impact using a deterministic SEIVR model was examined. This consists of investigating the disease-free and endemic equilibria, basic reproduction number and stability. The local stability of the disease-free equilibrium was determined by solving the Jacobian matrix of the system of the system of differential equations. The study calculated the basic reproduction number, R0 , using the next generation matrix method and found it to be R0=1.1251426e−10. This low value suggests that vaccination efforts can be effective in reducing the spread of COVID-19.

Application of conjugate gradient least squares method in solving magnetic moments of magnetic targets based on magnetic dipole array model

In order to solve the problem of magnetic targets magnetic field modeling, a magnetic dipole array model was established, a modeling inversion equation system was constructed, and the degree of sickness of the solved equation system under different numbers of magnetic dipoles was quantitatively analyzed based on the coefficient matrix condition number. In order to solve the problem of the pathological system of magnetic field modeling equations, a regularization method based on the conjugate gradient least squares method was designed to invert the magnetic moment parameters of the magnetic dipole. In order to analyze the applicability of the regularization method in magnetic field modeling inversion, the magnetic moment solving accuracy, the magnetic moment solving robustness, calculation time, and other metrics are defined. A detailed test of the ship model was designed, and the magnetic field passing characteristics of the two types of ship models at different positive and horizontal conditions at two depths were measured. Under the condition of no interference and interference, the conjugate gradient least squares method is used to invert the magnetic field model, and the numerical test analysis shows that the conjugate gradient least squares method has higher applicability than the generalized inverse solution method and the stepwise regression method. Under the condition of interference, the relative error of magnetic field fitting of the array model with 15 magnetic dipoles is 0.1537, and the relative error of magnetic field extrapolation is 0.0868. The method proposed has the advantages of high accuracy and strong robustness in solving the magnetic moment of the magnetic dipole array model.

Clinical Use of a Mineral-Organic Resorbable Bone Adhesive: A First Human Case Report with a 3-Year Follow-up.

The ability for clinicians to adequately obtain primary stability in host bone is critical to the success of dental implants. Numerous conditions require dentists to perform multistage approaches to rebuild deficient bone volume prior to surgically placing implants. In many instances, implant placement cannot be achieved due to a lack of primary implant stability. Recently, a novel mineral-organic resorbable bone adhesive (MORBA) has demonstrated promising results in animal studies. MORBA is a synthetic, injectable, self-setting, load-bearing adhesive biomaterial that exhibits osteopromotive properties and bonds bone to bone and metal within 10 minutes and can fully resorb in 30 weeks. Its unique novel formulation was developed from biomimetic proteins found in marine animal creatures that possess distinct adhesive properties underwater. Excellent long-term results have shown its potential use for achieving primary stability in immediate implants. The present case report demonstrates the first use of MORBA in a human patient, utilized on a nonrestorable mandibular first molar. MORBA was utilized after placement of a mobile 5.8-mm implant to achieve stabilization. At 3 months postsurgery, both clinical and CBCT evaluations showed maintained implant stability. One year after implant placement, radiographic bone was seen on the buccal surface of the implant with continued long-term stabilization. This case report extends to 3 years whereby the use of MORBA, in an initially unstable situation, demonstrated an excellent long-term follow-up. MORBA provided immediate implant stability with resorbable characteristics, leading to successful long-term clinical outcomes up to 3 years. This innovative biomaterial offers a more efficient solution to a critical problem in implant dentistry, allowing optimal primary stability during immediate implant placement, thus reducing treatment times and costs.

Efficient estimates for matrix-inverse quadratic forms

In this paper we present two approaches for estimating matrix-inverse quadratic forms xTA−1x, where A is a symmetric positive definite matrix of order n, and x∈Rn. Using the first, analytic approach, we establish two families of estimates which are convenient for matrices with small condition number. Based on the second, heuristic approach, we derive two families of estimates which are suitable for matrices when vector x is close enough to an eigenvector. The low complexity and stability of the estimates is proved. Several numerical results illustrating the effectiveness of the methods are presented.

The determinants of actual place of death among noncancer patients with end-stage chronic health conditions: a scoping review.

A home is a preferred place of death by most people. Nurses play a key role in supporting end-of-life home care, yet less is known about the factors that determine home as a place of death. This scoping review describes the percentage of actual places of death and determines social factors related to home as the place of death among noncancer patients with end-stage chronic health conditions. Inclusion criteria included (1) noncancer chronic illness conditions, (2) outcomes of place of death, and (3) factors that determine home as a place of death. Sources of evidence included PubMed, CINAHL, and Web of Science databases, which were searched in May 2022, and additional searches from May 2022 to November 2023.The JBI scoping review guide (2020) and the Preferred Reporting Items for Systematic Reviews and Meta-Analyses Scoping Review extension were used. Twenty-eight studies were included in this analysis. The range of percentages is varied within the same place of death among the sample. Two major constructs that determine a home as a place of death were identified: preceding factors and social capital. The results suggest that the place of death among noncancer patients with end-stage chronic health conditions should be continued to be understood. Two constructs determined home as a place of death and are considered as a fundamental to increasing equal accessibility in the initiation of palliative care services to promote home death and meet end-of-life care goals.

The Relationship between Healthy Eating Index and Lipid Profile in Healthy Individuals: A Systematic Review.

The Healthy Eating Index (HEI) and Alternate Healthy Eating Index (AHEI) are instruments developed by competing American research teams, aiming to assess the level of adherence to a dietary pattern, claimed to prevent chronic illness conditions such as dyslipidemia. This systematic review evaluated cross-sectional studies examining the association between HEI/AHEI score and the lipid profile in healthy participants. The systematic review was Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) compliant, and a search process was conducted through Scopus, Web of Knowledge, Google Scholar, Cochrane, PubMed, and ScienceDirect up to November 2022. Studies assessing the relationship between HEI/AHEI and lipid profile (low-density lipoprotein cholesterol (LDL-C), total cholesterol (TC), high-density lipoprotein cholesterol (HDL-C), and triglyceride (TG)) were eligible for inclusion. The statistical differences in outcomes, anthropometric indices, and demographic data were extracted from the selected studies. Also, the quality assessment of studies was performed using the Newcastle-Ottawa scale. The systematic search presented 17 cross-sectional studies. Most of the studies revealed a significant correlation between HEI score and lipid profile (LDL-C, HDL-C, TG, and TC) (P < 0.05), while a few of them indicated a significant relationship between AHEI score and these factors. Overall, the elevation of HEI/AHEI score was associated with the improvement in lipid profile (P < 0.05), though this association was more obvious for HEI compared with AHEI. Overall, the results of the study indicated that an improved lipid profile in healthy individuals is associated with a higher score in either HEI or AHEI. Further research in the future is required to confirm the claim.

A Rare Case of Evans Syndrome Associated with Underlying Dermatomyositis Without Generalized Subcutaneous Edema: A Case Report

Background: Primary Evans Syndrome is a typically idiopathic systemic illness associated with a number of autoimmune conditions and lymphomas and characterized by Coombs-positive warm-agglutinin Autoimmune Hemolytic Anemia (AIHA) and immune thrombocytopenia. The disease can be severe and prolonged with multiple recurring disease flares. Its association with dermatomyositis remains exceedingly rare and all reported cases have been accompanied by generalized subcutaneous edema. Case Report: We report a case of a 24-year-old male with a decade-long medical history of Evans Syndrome who presented to the emergency room with a one-week history of shortness of breath and fatigue. An extensive workup revealed severe anemia, thrombocytopenia and a left hemothorax. Active intervention included video-assisted thoracoscopy with decortication, blood products and autoimmune investigation. He was ultimately diagnosed with dermatomyositis after an electromyograph showed findings consistent with demyelinating peripheral polyneuropathy of motor nerves. Conclusion: Evans syndrome associated with dermatomyositis is extremely rare and this is to our knowledge the first reported case of Evans syndrome associated with dermatomyositis without generalized subcutaneous edema.