Analysis Of Continuous Data Research Articles

Atrial Fibrillation Detection Through ML Approach: A Comparitive Study

Atrial Fibrillation (AF) is a common “cardiac arrhythmia” with significant health implications. Traditional AF detection methods have limitations in continuous monitoring and data analysis. The emergence of machine learning (ML) offers promising solutions for accurate and timely AF detection. This study aims to explore and evaluate various ML techniques for AF detection, considering data quality, clinical validation, and algorithm performance. A diverse dataset of ECG signals and patient information is collected and pre-processed for training and testing ML models. The study implements supervised and unsupervised learning algorithms, deep learning (DL) architectures, and ensemble methods to compare their effectiveness in AF detection. Results demonstrate the potential of ML-based AF detection to revolutionize diagnosis and management, leading to improved patient care and healthcare outcomes in cardiology. The results of our comparative study demonstrate that all ML approaches achieved impressive results in detecting AF from ECG signals. “The logistic regression classifier achieved an accuracy of 92.48% and sensitivity of 91.89%. The Naïve Bayes classifier achieved an accuracy of 90.26% and sensitivity of 89.27%. The SVM classifier achieved an accuracy of 93.87% and sensitivity of 92.43%. The Decision tree achieved an accuracy of 93.87% and sensitivity of 90.63%. Finally, the Random Forest model attained an accuracy of 95.8% and sensitivity of 92.88%”.

A Flexible Probabilistic Framework With Concurrent Analysis of Continuous and Categorical Data for Industrial Fault Detection and Diagnosis

Operational data from industrial processes typically consist of continuous and categorical variables. They reveal different aspects of the operational conditions, and both are useful for comprehensive fault detection and diagnosis (FDD). However, due to the multiple production modes, the variables usually do not follow Gaussian or Bernoulli distributions. Furthermore, their correlations can be different across normal and various faulty classes. Thus, the main challenge lies in how to properly fuse the complementary information in the two types of variables and accurately characterize the complicated distribution for each class. This work proposes a flexible probabilistic framework that can concurrently analyze continuous and categorical variables for FDD. Our framework specifies a finite mixture model for each class. Thus, it can handle non-Gaussian and non-Bernoulli variables and capture their correlations under the conditional independence assumption. We then introduce the variational inference for parameter estimation, which makes our framework adaptive to the different distributions of various classes. Furthermore, a unified statistical index is designed for two types of variables, giving our method extra capability to distinguish unknown faults. Finally, the effectiveness of our method is validated on the Tennessee Eastman (TE) process and a practical industrial plant process. The average <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">F1</i> score of our method is improved by 3.4/3.9 percentage points in the single-mode/multimode situation on the TE process compared with traditional mixture discriminant analysis. In the industrial plant process, when unknown faults are added, the average <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">F1</i> score of our method is 91.1% and only 1.2 percentage points lower than that on the test set without unknown faults.

The Views of Classroom Teachers on the Instruction of Current Issues

The present study aimed to determine the views of classroom teachers on the inclusion or exclusion of current issues in classroom instruction, to investigate these views based on the definition of current issues, the methods and techniques employed in the instruction of current issues, the analysis of the instruction, and the positive and negative consequences of the instruction of these issues in the classroom. The present research was conducted with the case study method. The maximum diversity sampling method was employed to assign the study group members. The study group included 10 classroom teachers employed in seven primary schools in Battalgazi, Yesilyurt, Doğanşehir, and Hekimhan districts in Malatya province, Turkey. The study data were collected with a semi-structured interview form. Content analysis and continuous comparative data analysis were employed. The participants stated that the discussion of current issues in the classroom would lead to positive outcomes such as the development of student empathy, instruction of tolerance for different ideas, development of the problem-solving skills, sensibility, and communication skills, and negative outcomes such as problems in peer relations, behavioral disorders, negative reactions, and conflict. It was determined that classroom teachers mostly discussed controversial issues such as Covid 19, environmental pollution and natural disasters, and the student-initiated discussions on Covid 19 and natural disasters in the classroom. About 20% of the participating primary school teachers stated that the level of proficiency of their colleagues was low in the instruction of current events.

Development and Validation of The Anadolu University Music Perception Test

In Turkey, children are accepted to conservatory music departments after fourth grade and fine arts high school music departments after eighth grade by taking a musical talent test. For students with high musical aural skills to know about their potential and be directed to the related education institutions there needs to be a valid test. This study was, therefore, conducted to develop a valid internet-based test to assess music perception of children with design-based research. Design-based research includes a series of iterative stages that involve continuous data collection, analysis, and improvement, rather than a linear process in development and implementation activities. Voluntary-basis selected schools in the pilot city’s first through fourth grades (both public and private schools) were invited to join the preliminary test. A total of 433 students participated in the online test. The test has seven categories including pitch discrimination (single, two, and multiple tones), tempo, length, melody, and rhythm. For item analysis, two separate sequential samples were collected. The results were evaluated according to psychometric test development principles. The initial results show that the music perception test (AMAT) is a valid and reliable instrument. The test is open to public use and can be reached at: https://aummat.anadolu.edu.tr/amat/

The CBVE Model-Keystone and Stimulus for Educational Transformation in Veterinary Schools.

The AAVMC CBVE (American Association of Veterinary Medical Colleges Competency-Based Veterinary Education) model was developed in three parts and published in 2018-2019, providing an evidence-based foundation for use in all aspects of veterinary curricula management from review to redesign and continuous curricular improvement. The Ohio State University College of Veterinary Medicine (OSU CVM) recently undertook a comprehensive review and complete redesign of their curriculum, incorporating all the components of the CBVE model and, in the process, developed a continuous curricular improvement system that may serve other veterinary programs making similar changes. Anchoring the CBVE model within an adapted LEAN approach for systemic change created an outcomes-aligned system for faculty to engage with for curricular development, oversight, and modification based on continuous data collection and analysis. Even though the CBVE model has been in existence for 5 years, confusion remains as to how the three parts of the model best work together and how they can be used for much more than just curriculum redesign, and programs report struggle with how to effectively implement and manage the model. We share how the CBVE model has not only driven our college's curriculum redesign, but how it has also created an opportunity to develop a foundational educational system focused on competency, continual improvement, and innovation. This emerging system for managing curricular change acts in accordance with accreditation demands for ensuring faculty ownership and provides documented evidence-based processes for any changes undertaken.

A Bayesian framework for pathway-guided identification of cancer subgroups by integrating multiple types of genomic data.

In recent years, comprehensive cancer genomics platforms, such as The Cancer Genome Atlas (TCGA), provide access to an enormous amount of high throughput genomic datasets for each patient, including gene expression, DNA copy number alterations, DNA methylation, and somatic mutation. While the integration of these multi-omics datasets has the potential to provide novel insights that can lead to personalized medicine, most existing approaches only focus on gene-level analysis and lack the ability to facilitate biological findings at the pathway-level. In this article, we propose Bayes-InGRiD (Bayesian Integrative Genomics Robust iDentification of cancer subgroups), a novel pathway-guided Bayesian sparse latent factor model for the simultaneous identification of cancer patient subgroups (clustering) and key molecular features (variable selection) within a unified framework, based on the joint analysis of continuous, binary, and count data. By utilizing pathway (gene set) information, Bayes-InGRiD does not only enhance the accuracy and robustness of cancer patient subgroup and key molecular feature identification, but also promotes biological understanding and interpretation. Finally, to facilitate an efficient posterior sampling, an alternative Gibbs sampler for logistic and negative binomial models is proposed using Pólya-Gamma mixtures of normal to represent latent variables for binary and count data, which yields a conditionally Gaussian representation of the posterior. The R package "INGRID" implementing the proposed approach is currently available in our research group GitHub webpage (https://dongjunchung.github.io/INGRID/).

Investigating the value of glucodensity analysis of continuous glucose monitoring data in type 1 diabetes: an exploratory analysis.

Continuous glucose monitoring (CGM) devices capture longitudinal data on interstitial glucose levels and are increasingly used to show the dynamics of diabetes metabolism. Given the complexity of CGM data, it is crucial to extract important patterns hidden in these data through efficient visualization and statistical analysis techniques. In this paper, we adopted the concept of glucodensity, and using a subset of data from an ongoing clinical trial in pediatric individuals and young adults with new-onset type 1 diabetes, we performed a cluster analysis of glucodensities. We assessed the differences among the identified clusters using analysis of variance (ANOVA) with respect to residual pancreatic beta-cell function and some standard CGM-derived parameters such as time in range, time above range, and time below range. Distinct CGM data patterns were identified using cluster analysis based on glucodensities. Statistically significant differences were shown among the clusters with respect to baseline levels of pancreatic beta-cell function surrogate (C-peptide) and with respect to time in range and time above range. Our findings provide supportive evidence for the value of glucodensity in the analysis of CGM data. Some challenges in the modeling of CGM data include unbalanced data structure, missing observations, and many known and unknown confounders, which speaks to the importance of--and provides opportunities for--taking an approach integrating clinical, statistical, and data science expertise in the analysis of these data.

FTP7.9 Not all necrotising pancreatitis are the same: does aetiology affect the extent pancreatic necrosis? A single-centre retrospective study

Abstract Aim To assess the extent of pancreatic necrosis (PN) on abdominal computed tomography (CT) in patients with acute gallstone pancreatitis (GP) and alcohol-induced pancreatitis (AIP). Methods Patients ≥ 18-year-old with first-time onset of GP, AIP and PN on CT scan, from January 2010 to September 2018, were included. Extent of PN on CT, organ failure rate, admission to the intensive care unit (ICU), were compared between GP (group 1) and AIP (group 2). PN extent was described as: localised – right or left of the midline; diffuse – across the midline; more/less than 50% of the pancreas. Analysis of continuous and categorical data and multiple logistic regression were conducted. Significance was set at p&amp;lt;0.05. Results One hundred-two consecutive patients, 66 in group 1, 36 in group 2, were included. Overall, diffuse PN occurred in 34.3% of cases, more frequently in group 1 (42.4% vs 19.4%, p=0.03). Single/multiorgan failure developed in 50 patients, 57.6% belonged to group 1 (p=0.03). Predictors of organ failure were diffuse pattern of PN (OR 2.70, 95% CI 1.03 to 7.03, p=0.004) and PN ≥50% (OR 5.50, 95% CI 1.39 to 21.6, p=0.01). Admission to ICU was required in 27 subjects; PN ≥50% predicted ICU admission (OR 8.93, 95% CI 2.68 to 29.73, p=0.0004). The median hospital stay was 12 days (range 1-265), longer in group 1 (12.5 vs 10.5, p=0.02). Conclusions Diffuse PN occurred more frequently in patients with GP. A larger extent of PN predicted the onset of single/multiorgan failure and admission to the ICU.

The quest to develop automated systems for monitoring animal behavior

Automated behavior analysis (ABA) strategies are being researched at a rapid rate to detect an array of behaviors across a range of species. There is growing optimism that soon ethologists will not have to manually decode hours (and hours) of animal behavior videos, but that instead computers will process them for us. However, before we assume ABA is ready for practical use, it is important to take a realistic look at exactly what ABA is being developed, the expertise being used to develop it, and the context in which these studies occur. Once we understand common pitfalls occurring during ABA development and identify limitations, we can construct robust ABA tools to achieve automated (ultimately even continuous and real time) analysis of behavioral data, allowing for more detailed or longer-term studies of behavior on larger numbers of animals than ever before. ABA is only as good as it is trained to be. A key starting point is having manually annotated data for model training and assessment. However, most ABA developers are not trained in ethology. Often no formal ethogram is developed and descriptions of target behaviors in ABA publications are limited or inaccurate. In addition, ABA is also frequently developed using small datasets, which lack sufficient variability in animal morphometrics, activities, camera viewpoints, and environmental features to be generalizable. Thus, ABA often needs to be further validated before being used satisfactorily on different populations or under other conditions, even for research purposes. Multidisciplinary teams of researchers including ethologists and ethicists as well as computer scientists, data scientists, and engineers are needed to help address problems when applying computer vision ABA to measure behavior. Reference datasets that can be used for behavior detection should be generated and shared that include image data, annotations, and baseline analyses for benchmarking. Also critical is the development of standards for creating such reference datasets and descriptions of best practices for methods for validating results from detection tools to ensure they are robust and generalizable. At present, only a handful of publicly available datasets exist that can be used for development of ABA tools. As we work to realize the promise of ABA (and subsequent precision livestock farming technologies) to detect animal behavior, a clear understanding of best practices, access to accurately annotated datasets, and networking among ethologists and ABA developers will increase our chances for rapid and robust successes.

The February 2018 seismic swarm in São Miguel, Azores

The island of São Miguel is among the most seismically active areas of the Azores archipelago. This work focuses on the most significant recent swarm, which occurred on February 2018. We set up an automated procedure to process continuous full seismic waveform data from local stations to generate high-quality earthquake information on the volcano unrest episode. First, we applied an automated detector software, next we located the detected events and then classified the earthquakes based on their waveform similarity, identifying three families of seismic events. We then extended the catalog by template matching. Finally, we computed moment tensors to investigate the source mechanisms of the largest earthquakes. Our results image the ∼2-week swarm evolution. The activity started with a precursory phase with low rate and low magnitude (ML &amp;lt; 2.0) seismicity and the activation of a deeper structure (∼10-15 km). After ∼1 week, a new earthquake family emerged at shallower depths (∼8–12 km) reaching magnitudes up to ML 3.4. Finally, a third slightly shallower family was activated. Moment tensors show mostly normal faulting mechanisms, striking ∼NW-SE, compatible with the orientation of the regional stress field. A surface deformation transient was recorded by geodetic stations, starting with the swarm, and continuing over the following ∼17 months, corresponding to either inflation or extension around the swarm region. The prolonged surface deformation implies a process that was initiated during the swarm and subsequently accommodated mostly aseismically. We interpret the seismicity observed at the early stage of deformation as indicating episodic fluid injection through the crust, related to the local hydrothermal or magmatic systems. We conclude that the Fogo-Congro region continues to be seismo-volcanically active, with both seismic and aseismic deformation observed and requiring close multidisciplinary monitoring. The proposed methology based on the automated analysis of continuous waveform data provides high-quality imaging of the spatio-temporal evolution of seismicity, which can be used elsewhere in the operational monitoring of seismo-volcanic crises to gain insight into the ongoing deformation processes, improve hazard assessment and help in the development of effective mitigation strategies.

Overview and possible approach to street sweeping criticalities

In order to highlight the complexity in planning street sweeping in urban areas, an example is given based on the collection of the number of passages of sweeping vehicles during an observation period of one year. Data refer to a district of a European city whose street sweeping service was kept under monitoring with the aid of a software tracking sweeping vehicles and other operational parameters. The monitoring campaign pointed out the issues occurring during sweeping operations and the available margins of improvements. Two methodologies for optimization were proposed: a theoretical-analytical approach based on the attribution of weights to street characteristics and a hybrid approach, initially based on a previous experience in an urban area and later refined with the continuous analysis of data collected during sweeping operations. The present article shows that the second approach is the easier to adopt and allows for a flexible calibration of sweeping operations.

Surgical site infection following fasciotomy in patients with acute forearm compartment syndrome: a retrospective analysis of risk factors.

Fasciotomy, a standard therapy for acute forearm compartment syndrome (AFCS), can prevent serious complications, but there may be significant postoperative consequences. Surgical site infection (SSI) may cause fever, discomfort, and potentially fatal sepsis. This study aimed to identify risk factors for SSI in AFCS patients who had undergone fasciotomy. Patients with AFCS who had fasciotomies between November 2013 and January 2021 were recruited. We collected demographic information, comorbidities, and admission laboratory results. Analyses of continuous data were conducted using the t-test, the Mann-Whitney U test, and logistic regression analysis, while analyses of categorical data were conducted using the Chi-square and Fisher's exact tests. Sixteen AFCS patients (13.9%) suffered infections that need further therapy. Using the logistic regression analysis, we identified that patients with a history of diabetes (p = 0.028, OR = 16.353, 95% CI (1.357, 197.001)), open fractures (p = 0.026, OR = 5.239, 95% CI (1.223, 22.438)), and a higher level of TC (p = 0.004, OR = 4.871, 95% CI (1.654-14.350)) were the best predictors of SSI, while ALB levels (p = 0.004, OR = 0.776, 95% CI (0.653-0.924)) were protective for SSI in AFCS patients. Our results showed that open fractures, diabetes, and TC levels were relevent risk factors for SSI following fasciotomy in patients with AFCS, allowing us to personalize the risk assessment and apply early targeted interventions.

Emergency department-based injury surveillance information system: a conceptual model

BackgroundInjury data play a pivotal role in monitoring public health issues and Injury Surveillance Information Systems (ISIS) are useful for continuous data collection and analysis purposes. Since emergency department (ED) is usually the first place of referral for the injured people, the aim of this study was to develop a conceptual model for an ED-based ISIS.MethodsThis study was completed in 2020 and the Delphi technique (three rounds) was used to determine the main components of an ED-based ISIS. The participants were selected using the purposive sampling method. A 5-point Likert scale questionnaire was used for data collection and data were analyzed using descriptive statistics.ResultsIn the first, second, and third rounds of the Delphi study, 60, 44, and 28 experts participated, respectively. In the first and second rounds, most of the items including the personal data, clinical data, data sources, and system functions were found important. In the third round of the Delphi study, 13 items which did not reach a consensus in the previous rounds were questioned again and five items were removed from the final model.ConclusionAccording to the findings, various data elements and functions could be considered for designing an ED-based ISIS and a number of data sources should be taken into count to be integrated with this system. Although the conceptual model presented in the present study can facilitate designing the actual system, the final system needs to be implemented and used in practice to determine how it can meet users’ requirements.

Prediction Model based on Continuous Data for Student Performance using Principal Component Analysis and Support Vector Machine

Predicting student performance in higher education based on students’ self-efficacy and learning behaviour data is challenging, because the data is changing with time. The potential of using continuous data which is collected weekly needs to be investigated to identify the effectiveness in making predictions of low-performing students. Therefore, this paper presents the analysis of continuous data using the Principal Component Analysis (PCA) and Support Vector Machine (SVM) for predicting student performance. Firstly, we proposed three patterns of the Principal Component (PC) scores to predict the trends of behaviour within a semester. Secondly, we present an analysis of using different combinations of time frames in predicting the performance using the SVM. The obtained results show that three behaviour patterns have been extracted from the Hotelling’s T² values calculated using the PC scores which were fluctuating, ascending, and descending. The use of different time frames using SVM shows different accuracy results in prediction. The use of continuous data indicates that certain data can be predicted at the early stage using multiple time frames.

Mechanism Analysis of the Strong Coastal Current Zone and Abrupt Strong–Current Phenomena in Spring and Summer in the Yangjiang Sea Area of Western Guangdong in the Northwest of the South China Sea

Residual current analysis of multiple stations’ periodic observational data for sea currents, and multiple voyages, multiple seasons from 2018 to 2022, revealed the existence of a strong southwest current zone, 20–30 m underwater and within the coastal current area of Yangjiang, western Guangdong, in the northwest region of the South China Sea. The velocity of the residual currents in the strong–current zone was 38.2–100.3 cm/s. Observational data for wind, sea currents, salinity and tide from multiple coastal stations in the spring (from March 2019 to May 2019) and summer (from June 2019 to August 2019) of 2019 demonstrated the existence of abrupt strong currents in the coastal current sea area of Yangjiang, western Guangdong. Analyses of continuous sequence hydro meteorological data for the Yangjiang area indicated that wind stress was the main factor determining the direction of sea currents; increases in the near-shore water level produce a westward geostrophic current and a wind current in the shallow sea area; their joint effect was the key factor determining the velocity of the coastal current in this sea area. In spring and summer, when Pearl River runoff into the sea reaches a peak, and under the action of the northeast wind, the water level on the west coast of Guangdong rose. This created a barotropic pressure–gradient force from the shore to the outer sea, generating a southwestward geostrophic current in the same direction as the wind-driven current. The joint action of the wind-driven and geostrophic currents then generated a sudden southwestward coastal current.

A class-specific feature selection and classification approach using neighborhood rough set and K-nearest neighbor theories

Rough set theories are utilized in class-specific feature selection to improve the classification performance of continuous data while handling data uncertainty. However, most of those approaches are converting continuous data into discrete or fuzzy data before applying rough set theories. These data conversions can reduce or change the meaning of data, as well as introduce unnecessary complexity to the feature domain. Therefore, in this study, we use neighborhood rough sets in class-specific feature selection to improve the classification performance without data conversions. As the standard classification algorithms are capable of handling a single feature set, we propose a novel classification algorithm based on the K-Nearest Neighbour algorithm to use class-specific feature subsets. Experimental evaluations prove that the proposed approach outperforms most of the baselines, and the selected feature sets are more effective than using the full feature sets in classification. The approach highly reduces the number of selected features and hence, can be used for effective data analysis of continuous data with high performance.

Clinical Practice Patterns in Abdominoplasty: 16-Year Analysis of Continuous Certification Data from the American Board of Plastic Surgery.

The authors evaluated trends in practice patterns for abdominoplasty based on a 16-year review of tracer data collected by the American Board of Plastic Surgery as part of the continuous certification process. To facilitate comparison of an equal number of patients over time, tracer data from 2005 to 2021 were split into an early cohort (EC) (from 2005 to 2014) and a recent cohort (RC) (from 2015 to 2021). Fisher exact tests and two-sample t tests were used to compare patient demographics, surgical techniques, and complication rates. Data from 8990 abdominoplasties (EC, n = 4740; RC, n = 4250) were analyzed. RC abdominoplasties report a lower rate of complications (RC, 19%; EC, 22%; P < 0.001) and a lower rate of revision surgery (RC 8%; EC, 10%; P < 0.001). This has occurred despite the increased use of abdominal flap liposuction (RC, 25%; EC, 18%; P < 0.001). There has been a decline in the use of wide undermining (81% versus 75%; P < 0.001), vertical plication of the abdomen (89% versus 86%; P < 0.001), and surgical drains (93% versus 89%; P < 0.001). Abdominoplasty surgery is increasingly performed in an outpatient setting, with increased use of chemoprophylaxis for thrombosis prevention. Analysis of these American Board of Plastic Surgery tracer data highlights important trends in clinical practice over the past 16 years. Abdominoplasty continues to be a safe and effective procedure with similar complication and revision rates over the 16-year period.

Perspectives of patients on technology-supported community-based rehabilitation: a qualitative study

Introduction: The study's primary aim is to explore the perspectives and experiences of patients regarding technologysupported community-based rehabilitation in the Philippines. The secondary aim is to ascertain the utilization of technologysupported community-based rehabilitation based on the participants' responses. Methods: In this qualitative phenomenological study, purposive sampling for recruitment was utilized. Online focus group discussions were conducted with patients who have received rehabilitation in a technology-supported community-based rehabilitation setting. The recorded discussions were transcribed and coded using NVivo 12 software. The study employed an inductive approach of thematic analysis to extract significant themes. Continuous refinement and data analysis were done for data saturation. Results: Five themes emerged using Braun and Clarke’s thematic analysis: accessibility in technology-supported community-based rehabilitation, primary device and platforms used in technologysupported community-based rehabilitation, limitations of technology-supported community-based rehabilitation, perceived usefulness of technology in community-based rehabilitation, and patient satisfaction. Discussion: The facilitators and barriers found in the study affect the participants’ access and perceived usefulness of technology-based community-based rehabilitation which contributes to patient satisfaction. The study also found that patients perceived technology useful in community-based rehabilitation because it facilitates learning of exercises and enhances adherence to home exercise programs.

Clinical characteristics of pediatric allogeneic hematopoietic stem cell transplantation-associated thrombotic microangiopathy (TA-TMA): a retrospective single-center analysis

ObjectivesTo investigate the clinical features of thrombotic microangiopathy associated with allogeneic hematopoietic stem cell transplantation in children.MethodsA retrospective analysis of continuous clinical data from HSCT received in the Department of Hematology and Oncology of Wuhan Children's Hospital from August 1, 2016 to December 31, 2021.ResultsDuring this period, 209 patients received allo-HSCT in our department, 20 (9.6%) of whom developed TA-TMA. TA-TMA was diagnosed at a median of 94 (7–289) days post-HSCT. Eleven (55%) patients had early TA-TMA within 100 days post-HSCT, while the other 9 (45%) patients had TA-TMA thereafter. The most common symptom of TA-TMA was ecchymosis (55%), while the main signs were refractory hypertension (90%) and multi-cavity effusion (35%). Five (25%) patients had central nervous system symptoms (convulsions and lethargy). All 20 patients had progressive thrombocytopenia, with 16 patients receiving transfusion of platelets that was ineffective. Ruptured red blood cells were visible in only two patients with peripheral blood smears. Cyclosporine A or Tacrolimus (CNI) dose was reduced once TA-TMA was diagnosed. Nineteen cases were treated with low-molecular-weight heparin, 17 patients received plasma exchange, and 12 patients were treated with rituximab. TA-TMA-related mortality percentage in this study was 45% (9/20).ConclusionPlatelet decline and/or ineffective transfusion post-HSCT should be considered an early indicator of TA-TMA in pediatric patients. TA-TMA in pediatric patients may occur without evidence of peripheral blood schistocytes. Aggressive treatment is required once diagnosis is confirmed, but the long-term prognosis is poor.

Antimicrobial Resistance and Genomic Characterization of Salmonella Infantis from Different Sources

The epidemiology of Salmonella Infantis is complex in terms of its distribution and transmission. The continuous collection and analysis of updated data on the prevalence and antimicrobic resistance are essential. The present work aimed to investigate the antimicrobial resistance and the correlation among S. Infantis isolates from different sources through the multiple-locus variable-number of tandem repeat (VNTR) analysis (MLVA). A total of 562 Salmonella strains isolated from 2018 to 2020 from poultry, humans, swine, water buffalo, mussels, cattle, and wild boar were serotyped, and 185 S. Infantis strains (32.92%) were identified. S. Infantis was commonly isolated in poultry and, to a lesser extent, in other sources. The isolates were tested against 12 antimicrobials, and a high prevalence of resistant strains was recorded. S. Infantis showed high resistance against fluoroquinolones, ampicillin, and tetracycline, which are commonly used in human and veterinary medicine. From all S. Infantis isolates, five VNTR loci were amplified. The use of MLVA was not sufficient to understand the complexity of the epidemiological relationships between S. Infantis strains. In conclusion, an alternative methodology to investigate genetic similarities and differences among S. Infantis strains is needed.