Complicated Patterns Research Articles

Homogeneous–heterogeneous reactions in the colloidal investigation of Casson fluid

Abstract With particular attention to the effects of an electromagnetically induced resistive force on homogeneous–heterogeneous processes and the related homogeneous heat effects, the Casson fluid flow towards a stretching sheet at the magnetohydrodynamic stagnation point is investigated in detail. In this situation, the Laplace approach helps decipher the subtleties of the first-order kinetics governing the fluid’s motion. Notably, the fluid dynamics are largely determined by the homogeneous behaviour expected in the surrounding environment, forming a strong correlation between catalyst temperature on the wall and surface activity. Using conventional differential systems, our analysis gains a great deal from the modified Laplace decomposition method, which allows non-linear systems to be computed and examined. In order to improve understanding, numerical findings are included, and graphs are skillfully used to examine the subtleties of different factors. The in-depth examination also includes the complicated patterns of concentration and temperature, providing insightful information on the intricate interactions between forces and effects in this dynamic system.

Postoperative Complications and Factors Associated with Surgical Site Infection at Muhimbili National Hospital, Dar es Salaam, Tanzania: A cross-Sectional study.

Postoperative complications are a leading cause of morbidity and mortality to surgical patients. Different complications are encountered in clinical practice, however surgical site infection (SSI) appears to be the most common. To date, limited published information is known pertaining to the patterns of postoperative complications and factors associated with SSI among patients operated on in other hospitals in Tanzania and referred to Muhimbili national hospital for further management. Therefore, the purpose of this study was to identify patterns of postoperative complications and factors associated with SSI among the study participants. This was a hospital based cross-sectional study conducted at Muhimbili national hospital from August 2022 to January 2023, which included 181 patients. Obtained data was analysed using frequency tables, Pearson Chi-squared test and binary logistic regression model, at a level of significance of <0.05. One hundred eighty one (181) patients were included in this study, of whom 113(62.4%) were females; median age was 39 years. Cigarette smokers and alcohol consumers were 42(23.2%) and 90(49.7%) respectively. Diabetic patients were 8(4.4%), 35(19.3%) were HIV positive and 52(28.7%) had hypertension. Patients whose diagnosis was related to general surgery accounted for 50.2%, whereas 30.9% were obstetric cases. Patients who had undergone Caesarean section were 45(24.9%), whereas 20(11%) patients had undergone total abdominal hysterectomy. Bowel resection and primary anastomosis included 16(8.8%) patients and perforated peptic ulcer repair 8(4.4%) patients. Patients with infected peritoneal collection were 34(18.8%), postpartum haemorrhage 18(9.9%) and bowel perforation 10(5.5%). Patients who had undergone relaparotomy as part of treatment were 70(38.7%), whereas 30(16.6%) patients with SSIs were treated by serial wound dressing and 26(14.4%) patients were admitted and nursed in intensive care unit. None of the factors was found to have a statistically significant association with SSI. There is a large proportion of patients suffering from postoperative complications after gastroenterology and obstetric surgeries; and of all complications encountered in this study; SSI was the most common contributing 52%, followed by gastrointestinal complications at 31%. Despite the fact that multiple factors were associated with SSI, none of them was found to be statistically significant.

Scientific Justification of the Nature of Ultramudflow Disaster

Based on the research and analysis of numerous publications about the Genaldon disaster, the authors propose an ingenious, scientifically grounded approach to understanding the nature of the process. It is shown that the mechanism of displacement of large ice-waterrock (moraine-ice) masses has a complicated pattern and represents an ultramudflow displacement. Initially, several mechanisms were implemented in the process: 1) impact; 2) vibration and 3) water hammer. At the beginning, this led to the breakaway of the glacier, its movement, disintegration, and then the gliding mechanism turned on and caused stretching of the moving mass, its rocking and powerful blows against the sides of the valley in the Karmadon Gorge, then its compaction, squeezing and stopping at the Karmadon Gate. Below is the zone of low-power water-soil mass accumulation.

Clinical and epidemiological profile of herpes zoster cases and its complications: A prospective study

Background: The varicella-zoster virus, which is known to lie dormant in the dorsal root ganglia after primary chickenpox infection or immunization, and in rare instances, intrauterine infection, is the source of the common viral infection known as herpes zoster (HZ). In healthy people, the illness usually goes away on its own, but in severely immunocompromised patients, it can cause persistent debilitation through multi-dermatomal, severe, vesiculo-pustular, nodular, crusted, and ecthymatous manifestations. Aims and Objectives: The aim of this study was to better understand the clinical profile and epidemiological patterns of HZ and various postherpetic complications. Materials and Methods: A prospective cross-sectional study was conducted at a tertiary care hospital in Karnataka for 24 months during August 2013–August 2015. Seventy-five patients with HZ and postherpetic complications attending the dermatology outpatient department were included in the study. Pre-existing comorbidities and patient details were recorded. The Hayes and Patterson Visual Analog Scale (VAS) was used to record the kind, duration, and intensity of the pain. Results: The current cross-sectional study was done in a tertiary care hospital, and a total of 75 patients were considered for the study. The youngest patient was a 12-year-old male and the eldest patient was a 90-year-old male. The average age of patients was 52.45 ± 12.5 years. Cutaneous examination showed erythema (84%) and vesicles (84%). The intensity of pain was classified based on the VAS. About 54.6% of patients had a moderate degree of pain. Conclusion: Acute vesicles with erythema and radicular pain with a unilateral dermatomal distribution are the most typical presentation of HZ infection, and they are most frequently seen in the thoracic area.

REDUÇÃO ASSISTIDA POR GANCHO EM HASTE CEFALOMEDULAR SEM MESA DE TRAÇÃO

ABSTRACT Introduction: Proximal femoral nailing for intertrochanteric femur fracture is sometimes a challenging procedure without a traction table, especially if complicated fracture pattern. We aimed to overcome this difficulty with the hook. Materials and Methods: A retrospective study of 60 patients. 28 of the patients reduction was necessitated with a hook (group 1). The other patients did not need to use this technique (group 2, n=32). The collo-diaphyseal angle, lag screw placement, and tip-apex distance were measured using radiographs. Results: There were statistically significant differences between the two groups regarding the Garden Alignment Index, postoperative collo-diaphyseal angle measurements, and tip-apex distance. The Garden Alignment Index was found as 163.92 degrees (dg.) In the frontal plane in group 1, and 154.78 dg in group 2, respectively. In group 1, the tip-apex distance was 16.05 cm, whereas it was 25.32 cm in group 2. The collo-diaphyseal angle was 133.1º in group 1, and 128.65º in group 2. Conclusions: The hook-assisted reduction is beneficial when operating without a traction table; however, it can also be a part of the surgeons’ equipment even when operating on a traction table. When difficulties in obtaining an ideal anatomical reduction in displaced intertrochanteric femoral fractures, we suggest using the hook-assisted reduction technique. Level of Evidence III; Case-control Study.

Multicontrast MRI Super-Resolution via Transformer-Empowered Multiscale Contextual Matching and Aggregation.

Magnetic resonance imaging (MRI) possesses the unique versatility to acquire images under a diverse array of distinct tissue contrasts, which makes multicontrast super-resolution (SR) techniques possible and needful. Compared with single-contrast MRI SR, multicontrast SR is expected to produce higher quality images by exploiting a variety of complementary information embedded in different imaging contrasts. However, existing approaches still have two shortcomings: 1) most of them are convolution-based methods and, hence, weak in capturing long-range dependencies, which are essential for MR images with complicated anatomical patterns and 2) they ignore to make full use of the multicontrast features at different scales and lack effective modules to match and aggregate these features for faithful SR. To address these issues, we develop a novel multicontrast MRI SR network via transformer-empowered multiscale feature matching and aggregation, dubbed McMRSR ++ . First, we tame transformers to model long-range dependencies in both reference and target images at different scales. Then, a novel multiscale feature matching and aggregation method is proposed to transfer corresponding contexts from reference features at different scales to the target features and interactively aggregate them. Furthermore, a texture-preserving branch and a contrastive constraint are incorporated into our framework for enhancing the textural details in the SR images. Experimental results on both public and clinical in vivo datasets show that McMRSR ++ outperforms state-of-the-art methods under peak signal to noise ratio (PSNR), structure similarity index measure (SSIM), and root mean square error (RMSE) metrics significantly. Visual results demonstrate the superiority of our method in restoring structures, demonstrating its great potential to improve scan efficiency in clinical practice.

Structures of Lateral Flow and Turbulence in a Breaking Tidal Bore Rushing through a Curved Channel of the Qiantang Estuary

Abstract Tidal bores form in distinct funnel-shaped estuaries and bays initially and cause unique flow and turbulence dynamics when passing through meandering channels, such as in the Qiantang Estuary, Amazon River Estuary, and Seine River Estuary. To understand the lateral currents and turbulence processes of tidal bores in curved channels, velocity profiles and free-surface elevations are sampled for four semidiurnal tidal cycles in a curved channel located upstream of the Qiantang Estuary. During flood tides, the evolution of lateral currents experiences three distinct phases: first, there are inner-bank-toward lateral currents at the front of tidal bores, followed by two-layer helical currents in the middle of the flood tides, and finally, outer-bank-pointing lateral currents at the end of flood tides. Tidal bore breaking creates outbursts of turbulent kinetic energy. The enhanced turbulence emerges above the middle layers and persists for more than 10 minutes after the breaking front. The lateral momentum balance indicates that the decreasing lateral barotropic pressure gradient (LBTPG) and the increasing summation of centrifugal and Coriolis acceleration give rise to the variation in lateral currents. The phase lead of bores near the outer bank induced by shoal-channel topography generates an inner-bank-pointing LBTPG at the bore front and then gradually weakens it. Significant turbulence following bore breaking may be induced via the wave-induced turbulence mechanism by violent secondary waves. This research shows that complicated lateral currents are an important component of tidal bores flowing through meandering channels and that secondary waves after bore breaking can continually feed turbulence. Significance Statement The purpose of this study is to discern and explain the variation in lateral flows and turbulence when tidal bores rush through a curved estuarine channel. Lateral currents and turbulence play important roles in momentum exchange, scalar dispersion, and transverse topography evolution. Moreover, the discussion on lateral flows is necessary to extend the understanding of tidal bore hydrodynamics from the x–z dimension to the x–y–z dimension. In situ observations reveal that a complicated but regular pattern of lateral currents occurred in every tidal cycle and that there was significant turbulence at the tidal bore front. Our findings show that the lateral difference in tidal bore propagation speed caused by shoal-channel topography in the curved bend is an essential reason for the generation of lateral currents and that the secondary waves simulated by bore breaking can continuously feed tidal bore turbulence.

Boosting Financial Fraud Detection Using Parameter Tuned Ensemble Machine Learning Model

Fraud detection in the financial industry is a challenging area as financial transactions gradually shift to digital platforms. More and more businesses such as the financial industry are operationalizing their services online as the usage of the internet is growing exponentially. Accordingly, financial fraud can increase in number and forms worldwide leading to remarkable financial losses that make financial fraud a main challenge. Threats such as irregular attacks and unauthorized access must be identified through a financial fraud detection system. Over the past few years, data mining and machine learning (ML) approaches have been widely used to address these issues. However, this technique has yet to be enhanced in terms of speed computation, identifying unknown attack patterns, and dealing with big data. This study presents Financial Fraud Detection using the Parameter Tuned Ensemble Machine Learning (FFD-PTEML) method. The FFD-PTEML incorporates multiple advanced components, such as z-score normalization for feature scaling and ensemble classification employing Artificial Neural Networks (ANN), Multilayer Perceptron (MLP), and Radial Basis Function (RBF) networks. The use of z-score normalization ensures uniformity in feature distribution, improving the effectiveness and interpretability of the fraud detection technique. Furthermore, the ensemble classification model combines the strength of different neural network architectures to enhance the detection performance and resilience to complicated fraud patterns. FFD-PTEML demonstrates better performance than the classical technique through extensive experimentation on real-time financial datasets, exhibiting high sensitivity and specificity in fraudulent activity detection.

Leveraging Neutrosophic TOPSIS with Artificial Intelligence-Driven Tropical Cyclone Intensity Estimation for Weather Prediction

Tropical cyclones (TCs) are powerful, low-pressure weather systems attributed to heavy rainfall and strong winds, and have often resulted in extensive damage to coastal regions. TC intensity prediction, an essential aspect of meteorological forecasting, includes evaluating the strength of the storm to facilitate disaster preparedness and alleviate possible risks. Classical approaches for the prediction of TC intensity rely on different oceanic and atmospheric parameters, but the incorporation of artificial intelligence (AI) approaches, especially those leveraging image data, provides positive breakthroughs in efficiency and accuracy. By harnessing AI techniques like deep learning architectures and convolutional neural networks (CNNs), meteorologists could analyze radar data, satellite imagery, and other visual inputs to distinguish complicated patterns indicative of intensity changes and TC development. This combination of weather science and AI-driven image analysis enables more timely and precise predictions and improves our understanding of TC dynamics, eventually fortifying protection against the impacts of formidable storms. This article introduces Neutrosophic TOPSIS with Artificial Intelligence Driven Tropical Cyclone Intensity Estimation (NTOPSIS-TCIE) technique for Weather Prediction. The presented NTOPSIS-TCIE technique determines the intensities of the TC which in turn helps to forecast weather. In the NTOPSIS-TCIE technique, median filtering (MF) approach is used to remove the noise in the images. In addition, the features are extracted using deep convolutional neural network (CNN) model. To enhance the performance of the CNN model, Harris Hawks Optimization (HHO) algorithm is applied. Finally, the NTOPSIS model is employed for the prediction of TC intensities. The performance of the NTOPSIS-TCIE technique can be studied using TC image dataset and the results signify its promising results over other models

Leucosceptrane Sesterterpenoids as a New Type of Natural Immunosuppressive Agents in Treating Sepsis.

Intragastric administration of the total sesterterpenoid extract (TSE) of medicinal plant Leucosceptrum canum at 2.5 g/kg dose protected mice from LPS-induced sepsis. Phytochemical investigation led to the isolation and identification of 47 leucosceptrane sesterterpenoids (1-47) including 30 new compounds (1-30) with complicated oxygenation patterns. Biological screening indicated their immunosuppressive activity via inhibiting IFN-γ secretion and/or proliferation of T cells with different potencies. Mechanism study of compounds 9, 25, and 32 revealed that they inhibited the activations of AKT-mTOR, JNK, p38 MAPK or ERK pathway in T cells and macrophages. In addition, compounds 9 and 25 induced G0/G1 cell arrest of T cells. The major component, leucosceptroid N (32), significantly lowered the levels of IL-6 and TNF-α in peripheral blood serum, and ameliorated the multiorgan damages of LPS-induced sepsis mice at 25 mg/kg dose. These findings suggest that leucosceptrane sesterterpenoids are a new type of potential immunosuppressive agents for sepsis treatment.

Navigating Nonlinear Analysis and Artificial Intelligence Frontiers for Revolutionary Technology Solutions

The intersection of nonlinear analysis and artificial intelligence (AI) presents a promising frontier in the rapidly changing field of technology, with the potential to yield ground-breaking solutions in a multitude of fields. In order to investigate how different disciplines may work together to transform technological progress, this study explores the synthesis of these fields. Deeper comprehension of complicated patterns and behaviours is made possible by the intersection of AI and nonlinear analysis, which can both model complex systems and events. This combination has the potential to go beyond conventional linear thinking and open doors to solve complex problems. Through the utilisation of AI's learning skills to augment nonlinear models, a novel approach to problem-solving is revealed. The purpose of the paper is to examine the applications of this synergy in various disciplines. The combination of nonlinear analysis and AI holds the potential to transform a number of industries, from banking and predictive analytics to biological systems research and autonomous vehicle development. This investigation also includes the societal and ethical ramifications of using such cutting-edge technologies. It aims to provide a thorough understanding of the factors necessary for the ethical integration of these advances into our lives by delving into concerns of privacy, prejudice, and responsible use. This study tries to give academics, engineers, and innovators a full road map by looking at recent advances and possible future possibilities. It seeks to stimulate novel research directions, interdisciplinary teamwork, and the advancement of ground-breaking technical innovations that have the potential to fundamentally alter our understanding of and interactions with the environment around us.

Pattern and outcome of first cardiovascular disease complications in patients with type 2 diabetes mellitus in sub-Saharan Africa

Pattern and outcome of first cardiovascular disease complications in patients with type 2 diabetes mellitus in sub-Saharan Africa

Prevalence and Pattern of Complications Associated with Type 2 Diabetes Mellitus among Patients Presenting to a Tertiary Hospital in Zambia

Prevalence and Pattern of Complications Associated with Type 2 Diabetes Mellitus among Patients Presenting to a Tertiary Hospital in Zambia

Linking ecological niches to bacterial community structure and assembly in polluted urban aquatic ecosystems.

Bacterial communities play crucial roles in the functioning and resilience of aquatic ecosystems, and their responses to water pollution may be assessed from ecological niches. However, our understanding of such response patterns and the underlying ecological mechanisms remains limited. In this study, we comprehensively investigated the effects of water pollution on the bacterial structure and assembly within different ecological niches, including water, sediment, submerged plant leaf surfaces, and leaf surfaces, using a 16S high-throughput sequencing approach. Ecological niches had a greater impact on bacterial community diversity than pollution, with a distinct enrichment of unique dominant phyla in different niches. This disparity in diversity extends to the bacterial responses to water pollution, with a general reduction in α-diversity observed in the niches, excluding leaf surfaces. Additionally, the distinct changes in bacterial composition in response to pollution should be correlated with their predicted functions, given the enrichment of functions related to biogeochemical cycling in plant surface niches. Moreover, our study revealed diverse interaction patterns among bacterial communities in different niches, characterized by relatively simply associations in sediments and intricate or interconnected networks in water and plant surfaces. Furthermore, stochastic processes dominated bacterial community assembly in the water column, whereas selective screening of roots and pollution events increased the impact of deterministic processes. Overall, our study emphasizes the importance of ecological niches in shaping bacterial responses to water pollution. These findings improve our understanding of the complicated microbial response patterns to water pollution and have ecological implications for aquatic ecosystem health.

Utilizing predictive machine-learning modelling unveils feature-based risk assessment system for hyperinflammatory patterns and infectious outcomes in polytrauma.

Earlier research has identified several potentially predictive features including biomarkers associated with trauma, which can be used to assess the risk for harmful outcomes of polytraumatized patients. These features encompass various aspects such as the nature and severity of the injury, accompanying health conditions, immune and inflammatory markers, and blood parameters linked to organ functioning, however their applicability is limited. Numerous indicators relevant to the patients` outcome are routinely gathered in the intensive care unit (ICU) and recorded in electronic medical records, rendering them suitable predictors for risk assessment of polytraumatized patients. 317 polytraumatized patients were included, and the influence of 29 clinical and biological features on the complication patterns for systemic inflammatory response syndrome (SIRS), pneumonia and sepsis were analyzed with a machine learning workflow including clustering, classification and explainability using SHapley Additive exPlanations (SHAP) values. The predictive ability of the analyzed features within three days after admission to the hospital were compared based on patient-specific outcomes using receiver-operating characteristics. A correlation and clustering analysis revealed that distinct patterns of injury and biomarker patterns were observed for the major complication classes. A k-means clustering suggested four different clusters based on the major complications SIRS, pneumonia and sepsis as well as a patient subgroup that developed no complications. For classification of the outcome groups with no complications, pneumonia and sepsis based on boosting ensemble classification, 90% were correctly classified as low-risk group (no complications). For the high-risk groups associated with development of pneumonia and sepsis, 80% of the patients were correctly identified. The explainability analysis with SHAP values identified the top-ranking features that had the largest impact on the development of adverse outcome patterns. For both investigated risk scenarios (infectious complications and long ICU stay) the most important features are SOFA score, Glasgow Coma Scale, lactate, GGT and hemoglobin blood concentration. The machine learning-based identification of prognostic feature patterns in patients with traumatic injuries may improve tailoring personalized treatment modalities to mitigate the adverse outcomes in high-risk patient clusters.

Patterns of surgical complications after delayed fixation of peripartum pubic symphysis rupture: a report of 5 cases

BackgroundThe disruption of the pubic symphysis during the peripartum period is a rare injury to the pelvic ring. In most cases, conservative treatment is successful. Nonetheless, there are cases where surgical intervention is necessary. We analyzed five surgical cases treated in our department and performed a literature review.Case presentationsFive women, ranging in age from 25 to 38, who experienced peripartum symphysis rupture were primarily treated with a conservative approach. Patients who did not show improvement and met certain criteria, such as experiencing pain starting from childbirth, having a separation in the pubic bone of more than 10 mm, and/or having a vertical instability greater than 5 mm, were recommended to undergo surgery. The average length of time between childbirth and surgery was 5.6 months, ranging from 1 to 14 months. One patient was treated with an external fixator, another patient received a combination of an external fixator and an anterior plate, and three patients were treated with anterior plates. In four cases, we observed a failure in fixation and a partial or complete loss of reduction. The plate and screws were removed in one case, and in three cases, revision surgery was performed. One case involved using a larger plate, while the other used 90-90 plating, known as “box plate fixation.” The mean follow-up was 7.4 years. Two cases had good results, and two had excellent results on the Lindahl scale.ConclusionFor patients with peripartum pubic symphyseal dislocation, our case series and literature review demonstrated that early reduction and fixation correlate with improved clinical outcomes and lower implant failure. For patients with subacute/chronic injuries, there was a higher incidence of implant failure. Orthogonal plate fixation and/or pubic symphysiodesis was associated with improved clinical outcomes.

Deep-Learning-Based Arrhythmia Detection Using ECG Signals: A Comparative Study and Performance Evaluation.

Heart diseases is the world's principal cause of death, and arrhythmia poses a serious risk to the health of the patient. Electrocardiogram (ECG) signals can be used to detect arrhythmia early and accurately, which is essential for immediate treatment and intervention. Deep learning approaches have played an important role in automatically identifying complicated patterns from ECG data, which can be further used to identify arrhythmia. In this paper, deep-learning-based methods for arrhythmia identification using ECG signals are thoroughly studied and their performances evaluated on the basis of accuracy, specificity, precision, and F1 score. We propose the development of a small CNN, and its performance is compared against pretrained models like GoogLeNet. The comparative study demonstrates the promising potential of deep-learning-based arrhythmia identification using ECG signals.

Research on the Coordinated Development of National Defense Education and Ideological and Political Education in the New Era

Colleges and universities, as the highest educational institution and “cradle” of talent cultivation, assume broader responsibilities, with sacred missions. Facing the complicated and changeable world pattern and challenges, we should organically combine national defense education with ideological and political education, give full play to the role of ideological and political education, improve students’ awareness of national defense security and ideology and politics, face the realistic difficulties during the coordinated development of national defense education and ideological and political education in the new era, conduct a comprehensive analysis, provide solutions, and explore the integration of national defense education and ideological and political education, thus promoting the deep integration and development of national defense education and ideological and political education.

Invited Session V: Binocular vision and interactions: Harnessing interactions within binocular vision to treat amblyopia.

Inexpensive virtual reality (VR) headsets have enabled at-home therapy for binocular dysfunctions, including amblyopia. Healthy binocular vision is exquisite, which is achieved though interactions among various visual subsystems. As a result, however, binocular vision has multiple points of weakness, so development can go wrong in many different ways. Furthermore, the visual system has many ways it can adapt to dysfunctions to improve vision. It is therefore not surprising that amblyopias are idiosyncratic from one person to the next. Habitual interocular suppression is a typical adaption in amblyopia, and by treating suppression in VR, acuity can be improved by 0.1 to 0.2 logMAR. However, the amblyopic visual system shows complicated patterns of interaction between suppression, acuity, stereopsis, motor vergence, accommodation, and motion perception; and each of those subsystems is itself complex. Thus, to improve acuity and stereopsis in amblyopia, a multifaceted approach would seem more promising. In a VR headset, one can addresses suppression, stereopsis, vergence ability, and even accommodation, simultaneously. I will describe Vivid Vision's approach to this problem, along with preliminary results from independent researchers.

Executive Difficulties and Depressive Symptoms Predict Future Acute Pain Emergency Visits and Hospitalizations in Children with Sickle Cell Disease

Patients with sickle cell disease (SCD) commonly experience episodes of disabling vaso-occlusive pain leading to emergency department (ED) visits or hospitalizations. SCD is also associated with significant neurocognitive risk due to overt stroke, silent cerebral infarcts, and chronic insufficiencies in oxygen and/or glucose delivery to the brain. Neurocognitive deficits are observed early in life and are often characterized by deficits in executive functioning skills (e.g., planning, organizing, attention). The patterns of progressive chronic medical complications and functional limitations associated with SCD place children at higher risk of experiencing depressive symptoms. Research in patients with SCD has suggested that the experience of pain is associated with executive skills and depression in children with SCD. However, studies have yet to explore the bidirectional relationship between these constructs. It is important to understand the directionality and causal nature of this relationship using a longitudinal approach to optimize treatment approaches. The primary objective of this study was to investigate associations between pain-related ED visits or hospitalizations with future parent-reported executive functioning and depression; conversely we examined how executive difficulties and depression predicted subsequent pain-related visits. All patients were enrolled in the St. Jude longitudinal cohort study, the Sickle Cell Clinical Research Intervention Program (SCCRIP; Hankins et al., Pediatric Blood &amp; Cancer 2018). The number of ED visits or hospitalizations due to pain-related reasons in the year pre- and post-assessment were obtained retrospectively from chart review. This data was then cleaned, validated, and merged with other data sets. Parent ratings of executive skills were collected using the Behavior Rating Inventory of Executive Function (BRIEF) Global Executive Composite scale. Depressive symptoms were measured using the parent version of the Behavior Assessment System for Children 2 nd or 3 rd Edition (Depression scale). Multivariable generalized linear regression was used to examine associations between the number of pain-related hospitalizations with executive difficulties and depressive symptoms after adjusting for age, sex, sickle genotype, current hydroxyurea treatment, and geocoded social vulnerability. P-values &amp;lt;0.05 were considered significant. We included 374 patients with SCD (60% HbSS/HbSβ 0-thalassemia; see Table 1). The mean age of participants at the time of psychological evaluation was 11.84 years (SD=3.39; Range= 8-18). In the year prior to the assessment, patients experienced an average of 0.27 pain events requiring a visit to the ED (SD=0.97, Range=0-11). In the year post evaluation, there were an average of 0.36 events (SD=1.08, Range=0-9). Hospitalizations due to pain occurred an average of 0.09 times (SD=0.37, Range=0-3) in the year before the evaluation and 0.11 times (SD=0.35, Range=0-2) post-evaluation. The number of pain events leading to ED visits or hospitalization in the year prior to the evaluation were not associated with future depressive symptoms or executive difficulties. However, parent-rated executive difficulties and symptoms of depression were positively associated with future ED visits (p=0.033 and p=0.032) and hospitalizations (p=0.005 and p=0.009) in the year following the evaluation (Table 2). Executive functioning difficulties and depressive symptoms predicted greater frequency of future ED visits and hospitalizations among children with SCD. Hospital visits due to acute SCD pain did not predict future executive difficulties or depressive symptoms. Behavioral and pharmacological treatments targeting depressive symptoms and executive difficulties should be tested to decrease the frequency of acute care visits due to pain in SCD.