Stratification of Sepsis Patients on Admission into the Intensive Care Unit According to Differential Plasma Metabolic Phenotypes.

Delayed diagnosis of patients with sepsis or septic shock is associated with increased mortality and morbidity. UPLC-MS and NMR spectroscopy were used to measure panels of lipoproteins, lipids, biogenic amines, amino acids, and tryptophan pathway metabolites in blood plasma samples collected from 152 patients within 48 h of admission into the Intensive Care Unit (ICU) where 62 patients had no sepsis, 71 patients had sepsis, and 19 patients had septic shock. Patients with sepsis or septic shock had higher concentrations of neopterin and lower levels of HDL cholesterol and phospholipid particles in comparison to nonsepsis patients. Septic shock could be differentiated from sepsis patients based on different concentrations of 10 lipids, including significantly lower concentrations of five phosphatidylcholine species, three cholesterol esters, one dihydroceramide, and one phosphatidylethanolamine. The Supramolecular Phospholipid Composite (SPC) was reduced in all ICU patients, while the composite markers of acute phase glycoproteins were increased in the sepsis and septic shock patients within 48 h admission into ICU. We show that the plasma metabolic phenotype obtained within 48 h of ICU admission is diagnostic for the presence of sepsis and that septic shock can be differentiated from sepsis based on the lipid profile.

A distributed data processing scheme based on Hadoop for synchrotron radiation experiments.

With the development of synchrotron radiation sources and high-frame-rate detectors, the amount of experimental data collected at synchrotron radiation beamlines has increased exponentially. As a result, data processing for synchrotron radiation experiments has entered the era of big data. It is becoming increasingly important for beamlines to have the capability to process large-scale data in parallel to keep up with the rapid growth of data. Currently, there is no set of data processing solutions based on the big data technology framework for beamlines. Apache Hadoop is a widely used distributed system architecture for solving the problem of massive data storage and computation. This paper presents a set of distributed data processing schemes for beamlines with experimental data using Hadoop. The Hadoop Distributed File System is utilized as the distributed file storage system, and Hadoop YARN serves as the resource scheduler for the distributed computing cluster. A distributed data processing pipeline that can carry out massively parallel computation is designed and developed using Hadoop Spark. The entire data processing platform adopts a distributed microservice architecture, which makes the system easy to expand, reduces module coupling and improves reliability.

Expression pattern of serum interleukin-7 in elderly septic patients and its prognostic value for predicting short-term mortality.

BACKGROUND: The identification of novel prognostic biomarkers in elderly septic patients are essential for the improvement of mortality in sepsis in the context of precision medicine. The purpose of this study was to explore the expression pattern and prognostic value of serum interleukin-7 (IL-7) in predicting 28-day mortality in elderly patients with sepsis. METHODS: Patients were retrospectively enrolled according to the sepsis-3.0 diagnostic criteria and divided into the survival group and non-survival group based on the clinical outcome at the 28-day interval. The baseline characteristic data, samples for the laboratory tests, and the SOFA, Acute Physiology and Chronic Health Evaluation (APACHE II), as well as Glasgow coma scale (GCS) scores, were recorded within 24 h after admission to the emergency department. Serum levels of IL-7 and TNF-α of the patients were quantified by the Luminex assay. Spearman correlation analysis, logistic regressive analysis and receiver operating characteristic curve (ROC) analysis were performed, respectively. RESULTS: Totally, 220 elderly patients with sepsis were enrolled, 151 of whom died in a 28-day period. Albumin (ALB), high-density lipoprotein (HDL), systolic pressure (SBP), and platelet (PLT) were found to be significantly higher in the survival group (p < 0.05). IL-7 was shown to be correlated with TNF-α in the non-survival group (p = 0.030) but not in the survival group (p = 0.194). No correlation was shown between IL-7 and other factors (p > 0.05). IL-7 and TNF-α were found to be independent risk factors associated with the 28-day mortality (OR = 1.215, 1.420). Combination of IL-7, SOFA and ALB can make an AUROC of 0.874 with the specificity of 90.77 %. Combination of IL-7 and TNF-α can make an AUROC of 0.901 with the sensitivity of 90.41 % while the combination of IL-7, TNF-α, and ALB can make an AUROC of 0.898 with the sensitivity of 94.52 %. CONCLUSIONS: This study highlights the importance of monitoring the serum level of IL-7 and TNF-α in elderly septic patients as well as evaluating the combinations with other routine risk factors which can be potentially used for the identification of elderly septic patients with higher risk of mortality.

Severity of critical illness correlates with CD3-low expression on iNKT-cells among septic surgical patients.

BACKGROUND: iNKT-cells are innate regulatory lymphocytes capable of directing immune and inflammatory responses to sepsis. Repeat stimulation of iNKT-cells leads to the induction of anergy with the emergence of a hyporesponsive CD3low iNKT-cell subpopulation. METHODS: iNKT-cells were isolated from critical ill surgical patients with sepsis and phenotyped for CD3 expression. This was correlated with degree of severity of illness, as denoted by APACHE-II score. RESULTS: Comparing healthy volunteers to critically ill septic patients, it was noted that increasing severity of sepsis was associated with increasing frequency of circulating CD3low-iNKT-cell populations. CONCLUSION: The emergence of CD3low -iNKT-cells may serve as a clinically translatable marker of degree of sepsis-induced immune dysfunction.

Prognostic evaluation of quick sequential organ failure assessment score in ICU patients with sepsis across different income settings.

BACKGROUND: There is conflicting evidence on association between quick sequential organ failure assessment (qSOFA) and sepsis mortality in ICU patients. The primary aim of this study was to determine the association between qSOFA and 28-day mortality in ICU patients admitted for sepsis. Association of qSOFA with early (3-day), medium (28-day), late (90-day) mortality was assessed in low and lower middle income (LLMIC), upper middle income (UMIC) and high income (HIC) countries/regions. METHODS: This was a secondary analysis of the MOSAICS II study, an international prospective observational study on sepsis epidemiology in Asian ICUs. Associations between qSOFA at ICU admission and mortality were separately assessed in LLMIC, UMIC and HIC countries/regions. Modified Poisson regression was used to determine the adjusted relative risk (RR) of qSOFA score on mortality at 28 days with adjustments for confounders identified in the MOSAICS II study. RESULTS: Among the MOSAICS II study cohort of 4980 patients, 4826 patients from 343 ICUs and 22 countries were included in this secondary analysis. Higher qSOFA was associated with increasing 28-day mortality, but this was only observed in LLMIC (p < 0.001) and UMIC (p < 0.001) and not HIC (p = 0.220) countries/regions. Similarly, higher 90-day mortality was associated with increased qSOFA in LLMIC (p < 0.001) and UMIC (p < 0.001) only. In contrast, higher 3-day mortality with increasing qSOFA score was observed across all income countries/regions (p < 0.001). Multivariate analysis showed that qSOFA remained associated with 28-day mortality (adjusted RR 1.09 (1.00-1.18), p = 0.038) even after adjustments for covariates including APACHE II, SOFA, income country/region and administration of antibiotics within 3 h. CONCLUSIONS: qSOFA was independently associated with 28-day mortality in ICU patients admitted for sepsis. In LLMIC and UMIC countries/regions, qSOFA was associated with early to late mortality but only early mortality in HIC countries/regions.

Improving 1-Year Mortality Following Intensive Care Unit Admission in Adults with HIV: A 20-Year Observational Study.

BACKGROUND: Despite widespread use of combination antiretroviral therapy, people with HIV (PWH) continue to have an increased risk of admission to and mortality in the intensive care unit (ICU). Mortality risk after hospital discharge is not well described. Using retrospective data on adult PWH (≥18 years) admitted to ICU from 2000-2019 in an HIV-referral centre, we describe trends in 1-year mortality after ICU admission. METHODS: One-year mortality was calculated from index ICU admission to date of death; with follow-up right-censored at day 365 for people remaining alive at 1 year, or day 7 after ICU discharge if lost-to-follow-up after hospital discharge. Cox regression was used to describe the association with calendar year before and after adjustment for patient characteristics (age, sex, Acute Physiology and Chronic Health Evaluation II [APACHE II] score, CD4+ T-cell count, and recent HIV diagnosis) at ICU admission. Analyses were additionally restricted to those discharged alive from ICU using a left-truncated design, with further adjustment for respiratory failure at ICU admission in these analyses. RESULTS: Two hundred and twenty-one PWH were admitted to ICU (72% male, median [interquartile range] age 45 [38-53] years) of whom 108 died within 1-year (cumulative 1-year survival: 50%). Overall, the hazard of 1-year mortality was decreased by 10% per year (crude hazard ratio (HR): 0.90 (95% confidence interval: 0.87-0.93)); the association was reduced to 7% per year (adjusted HR: 0.93 (0.89-0.98)) after adjustment. Conclusions were similar among the subset of 136 patients discharged alive (unadjusted: 0.91 (0.84-0.98); adjusted 0.92 (0.84, 1.02)). CONCLUSIONS: Between 2000 and 2019, 1-year mortality after ICU admission declined at this ICU. Our findings highlight the need for multi-centre studies and the importance of continued engagement in care after hospital discharge among PWH.

Application of machine learning for mortality prediction in patients with candidemia: Feasibility verification and comparison with clinical severity scores.

BACKGROUND: Clinical severity scores, such as acute physiology, age, chronic health evaluation II (APACHE II), sequential organ failure assessment (SOFA), Pitt Bacteremia Score (PBS), and European Confederation of Medical Mycology Quality (EQUAL) score, may not reliably predict candidemia prognosis owing to their prespecified scorings that can limit their adaptability and applicability. OBJECTIVES: Unlike those fixed and prespecified scorings, we aim to develop and validate a machine learning (ML) approach that is able to learn predictive models adaptively from available patient data to increase adaptability and applicability. METHODS: Different ML algorithms follow different design philosophies and consequently, they carry different learning biases. We have designed an ensemble meta-learner based on stacked generalisation to integrate multiple learners as a team to work at its best in a synergy to improve predictive performances. RESULTS: In the multicenter retrospective study, we analysed 512 patients with candidemia from January 2014 to July 2019 and compared a stacked generalisation model (SGM) with APACHE II, SOFA, PBS and EQUAL score to predict the 14-day mortality. The cross-validation results showed that the SGM significantly outperformed APACHE II, SOFA, PBS, and EQUAL score across several metrics, including F1-score (0.68, p < .005), Matthews correlation coefficient (0.54, p < .05 vs. SOFA, p < .005 vs. the others) and the area under the curve (AUC; 0.87, p < .005). In addition, in an independent external test, the model effectively predicted patients' mortality in the external validation cohort, with an AUC of 0.77. CONCLUSIONS: ML models show potential for improving mortality prediction amongst patients with candidemia compared to clinical severity scores.

Predictive value of soluble CD40L combined with APACHE II score in elderly patients with sepsis in the emergency department.

BACKGROUND: The prognostic performance of soluble CD40L (sCD40L) for illness severity in infectious diseases is rarely reported. We investigated the ability of sCD40L combined with Acute Physiology and Chronic Health Evaluation II (APACHE II) score to evaluate mortality in septic patients in the emergency department(ED). METHODS: We enrolled 222 septic patients in the ED of Beijing Chao-Yang Hospital from October 2020 to April 2021. Their serum sCD40L, PCT, lactate (Lac), Sequential Organ Failure Assessment (SOFA) score, Acute Physiology and Chronic Health Evaluation II (APACHE II) score were used to predict the prognosis of septic patients in terms of 28-day mortality. Serum sCD40L was detected by Human XL Cytokine Luminex. Logistic regression analysis and receiver operating characteristic (ROC) curves were used to assess the prognostic value of the variables. RESULTS: One hundred ninety-five patients met the inclusion criteria, divided into survival group (55 cases) and non-survival group (140 cases). sCD40L, PCT, Lac, SOFA and APACHE II score were found to independently predict 28-day mortality (P < 0.05). The AUC values of sCD40L, PCT, Lac, SOFA and APACHE II score were 0.662,0.727,0.704, 0.719 and 0.716, respectively. There was no difference in the diagnostic value of sCD40L compared with the PCT, Lac, SOFA score or APACHE II score (Z1 = 1.19, P = 0.234; Z2 = 0.77, P = 0.441; Z3 = 1.05, P = 0.294; Z4 = 0.97, P = 0.332). However, the combined evaluation of sCD40L + APACHE II (AUC:0.772, Z = 2.10, P = 0.036) was much better than sCD40L alone in predicting 28-day mortality. CONCLUSION: The predictive value of sCD40L + APACHE II is better than sCD40L alone for 28-day mortality. sCD40L combined with APACHE II score is valuable for predicting 28-day mortality in elderly patients with sepsis.

An ontology-based tool for modeling and documenting events in neurosurgery.

BACKGROUND: Intraoperative neurophysiological monitoring (IOM) plays a pivotal role in enhancing patient safety during neurosurgical procedures. This vital technique involves the continuous measurement of evoked potentials to provide early warnings and ensure the preservation of critical neural structures. One of the primary challenges has been the effective documentation of IOM events with semantically enriched characterizations. This study aimed to address this challenge by developing an ontology-based tool. METHODS: We structured the development of the IOM Documentation Ontology (IOMDO) and the associated tool into three distinct phases. The initial phase focused on the ontology's creation, drawing from the OBO (Open Biological and Biomedical Ontology) principles. The subsequent phase involved agile software development, a flexible approach to encapsulate the diverse requirements and swiftly produce a prototype. The last phase entailed practical evaluation within real-world documentation settings. This crucial stage enabled us to gather firsthand insights, assessing the tool's functionality and efficacy. The observations made during this phase formed the basis for essential adjustments to ensure the tool's productive utilization. RESULTS: The core entities of the ontology revolve around central aspects of IOM, including measurements characterized by timestamp, type, values, and location. Concepts and terms of several ontologies were integrated into IOMDO, e.g., the Foundation Model of Anatomy (FMA), the Human Phenotype Ontology (HPO) and the ontology for surgical process models (OntoSPM) related to general surgical terms. The software tool developed for extending the ontology and the associated knowledge base was built with JavaFX for the user-friendly frontend and Apache Jena for the robust backend. The tool's evaluation involved test users who unanimously found the interface accessible and usable, even for those without extensive technical expertise. CONCLUSIONS: Through the establishment of a structured and standardized framework for characterizing IOM events, our ontology-based tool holds the potential to enhance the quality of documentation, benefiting patient care by improving the foundation for informed decision-making. Furthermore, researchers can leverage the semantically enriched data to identify trends, patterns, and areas for surgical practice enhancement. To optimize documentation through ontology-based approaches, it's crucial to address potential modeling issues that are associated with the Ontology of Adverse Events.

Enhancing clinical data retrieval with Smart Watchers: a NiFi-based ETL pipeline for Elasticsearch queries.

BACKGROUND: The aim is to develop and deploy an automated clinical alert system to enhance patient care and streamline healthcare operations. Structured and unstructured data from multiple sources are used to generate near real-time alerts for specific clinical scenarios, with an additional goal to improve clinical decision-making through accuracy and reliability. METHODS: The automated clinical alert system, named Smart Watchers, was developed using Apache NiFi and Python scripts to create flexible data processing pipelines and customisable clinical alerts. A comparative analysis between Smart Watchers and the legacy Elastic Watchers was conducted to evaluate performance metrics such as accuracy, reliability, and scalability. The evaluation involved measuring the time taken for manual data extraction through the electronic patient record (EPR) front-end and comparing it with the automated data extraction process using Smart Watchers. RESULTS: Deployment of Smart Watchers showcased a consistent time savings between 90% to 98.67% compared to manual data extraction through the EPR front-end. The results demonstrate the efficiency of Smart Watchers in automating data extraction and alert generation, significantly reducing the time required for these tasks when compared to manual methods in a scalable manner. CONCLUSIONS: The research underscores the utility of employing an automated clinical alert system, and its portability facilitated its use across multiple clinical settings. The successful implementation and positive impact of the system lay a foundation for future technological innovations in this rapidly evolving field.

Prediction of hospital mortality among critically ill patients in a single centre in Asia: comparison of artificial neural networks and logistic regression-based model.

INTRODUCTION: This study compared the performance of the artificial neural network (ANN) model with the Acute Physiologic and Chronic Health Evaluation (APACHE) II and IV models for predicting hospital mortality among critically ill patients in Hong Kong. METHODS: This retrospective analysis included all patients admitted to the intensive care unit of Pamela Youde Nethersole Eastern Hospital from January 2010 to December 2019. The ANN model was constructed using parameters identical to the APACHE IV model. Discrimination performance was assessed using area under the receiver operating characteristic curve (AUROC); calibration performance was evaluated using the Brier score and Hosmer-Lemeshow statistic. RESULTS: In total, 14 503 patients were included, with 10% in the validation set and 90% in the ANN model development set. The ANN model (AUROC=0.88, 95% confidence interval [CI]=0.86-0.90, Brier score=0.10; P in Hosmer-Lemeshow test=0.37) outperformed the APACHE II model (AUROC=0.85, 95% CI=0.80-0.85, Brier score=0.14; P<0.001 for both comparisons of AUROCs and Brier scores) but showed performance similar to the APACHE IV model (AUROC=0.87, 95% CI=0.85-0.89, Brier score=0.11; P=0.34 for comparison of AUROCs, and P=0.05 for comparison of Brier scores). The ANN model demonstrated better calibration than the APACHE II and APACHE IV models. CONCLUSION: Our ANN model outperformed the APACHE II model but was similar to the APACHE IV model in terms of predicting hospital mortality in Hong Kong. Artificial neural networks are valuable tools that can enhance real-time prognostic prediction.

Elevating Smart Manufacturing with a Unified Predictive Maintenance Platform: The Synergy between Data Warehousing, Apache Spark, and Machine Learning.

The transition to smart manufacturing introduces heightened complexity in regard to the machinery and equipment used within modern collaborative manufacturing landscapes, presenting significant risks associated with equipment failures. The core ambition of smart manufacturing is to elevate automation through the integration of state-of-the-art technologies, including artificial intelligence (AI), the Internet of Things (IoT), machine-to-machine (M2M) communication, cloud technology, and expansive big data analytics. This technological evolution underscores the necessity for advanced predictive maintenance strategies that proactively detect equipment anomalies before they escalate into costly downtime. Addressing this need, our research presents an end-to-end platform that merges the organizational capabilities of data warehousing with the computational efficiency of Apache Spark. This system adeptly manages voluminous time-series sensor data, leverages big data analytics for the seamless creation of machine learning models, and utilizes an Apache Spark-powered engine for the instantaneous processing of streaming data for fault detection. This comprehensive platform exemplifies a significant leap forward in smart manufacturing, offering a proactive maintenance model that enhances operational reliability and sustainability in the digital manufacturing era.

The Role of Programmed Cell Death 1/Programmed Death Ligand 1 (PD-1/PD-L1) Axis in Sepsis-Induced Apoptosis.

Background and Objectives: Sepsis involves a dysregulated host response, characterized by simultaneous immunosuppression and hyperinflammation. Initially, there is the release of pro-inflammatory factors and immune system dysfunction, followed by persistent immune paralysis leading to apoptosis. This study investigates sepsis-induced apoptosis and its pathways, by assessing changes in PD-1 and PD-L1 serum levels, CD4+ and CD8+ T cells, and Sequential Organ Failure Assessment (SOFA) and Acute Physiology and Chronic Health Evaluation (APACHE II) severity scores. Materials and Methods: This prospective, observational, single-centre study enrolled 87 sepsis patients admitted to the intensive care unit at the County Emergency Clinical Hospital in Târgu Mureș, Romania. We monitored the parameters on day 1 (the day sepsis or septic shock was diagnosed as per the Sepsis-3 Consensus) and day 5. Results: Our study found a statistically significant variation in the SOFA score for the entirety of the patients between the studied days (p = 0.001), as well as for the studied patient groups: sepsis, septic shock, survivors, and non-survivors (p = 0.001, p = 0.003, p = 0.01, p = 0.03). On day 1, we found statistically significant correlations between CD8+ cells and PD-1 (p = 0.02) and PD-L1 (p = 0.04), CD4+ and CD8+ cells (p < 0.0001), SOFA and APACHE II scores (p < 0.0001), and SOFA and APACHE II scores and PD-L1 (p = 0.001 and p = 0.01). On day 5, we found statistically significant correlations between CD4+ and CD8+ cells and PD-L1 (p = 0.03 and p = 0.0099), CD4+ and CD8+ cells (p < 0.0001), and SOFA and APACHE II scores (p < 0.0001). Conclusions: The reduction in Th CD4+ and Tc CD8+ lymphocyte subpopulations were evident from day 1, indicating that apoptosis is a crucial factor in the progression of sepsis and septic shock. The increased expression of the PD-1/PD-L1 axis impairs costimulatory signalling, leading to diminished T cell responses and lymphopenia, thereby increasing the susceptibility to nosocomial infections.

Exploring Ventilator-Associated Pneumonia: Microbial Clues and Biomarker Insights from a Retrospective Study.

Background and Objectives: Ventilator-associated pneumonia (VAP) is a common complication in critically ill patients receiving mechanical ventilation. The incidence rates of VAP vary, and it poses significant challenges due to microbial resistance and the potential for adverse outcomes. This study aims to explore the microbial profile of VAP and evaluate the utility of biomarkers and illness severity scores in predicting survival. Materials and Methods: A retrospective cohort study was conducted involving 130 patients diagnosed with VAP. Microbial analysis of bronchoalveolar lavage (BAL) fluid, as well as measurements of C-reactive protein (CRP) and procalcitonin (PCT) levels, were performed. Sequential Organ Failure Assessment (SOFA) and Acute Physiology and Chronic Health Evaluation II (APACHE II) scores were calculated to assess illness severity. Statistical analyses were conducted to determine correlations and associations. Results: The study revealed that Klebsiella pneumoniae (K. pneumoniae) (50.7%) and Pseudomonas aeruginosa (P. aeruginosa) (27.69%) were the most identified microorganisms in VAP cases. SOFA (p-value < 0.0001) and APACHE II (p-value < 0.0001) scores were effective in assessing the severity of illness and predicting mortality in VAP patients. Additionally, our investigation highlighted the prognostic potential of CRP levels (odds ratio [OR]: 0.980, 95% confidence interval [CI] 0.968 to 0.992, p = 0.001). Elevated levels of CRP were associated with reduced survival probabilities in VAP patients. Conclusion: This study highlights the microbial profile of VAP and the importance of biomarkers and illness severity scores in predicting survival. Conclusions: The findings emphasize the need for appropriate management strategies to combat microbial resistance and improve outcomes in VAP patients.

[Wearable Smart Cardiac Care: Take Vipasyana for Example].

The most direct challenge to the healthcare system in Taiwan associated with population aging is the chronic and growing shortfall in medical and nursing practitioners. The integration of artificial intelligence, Internet of Things, and advanced 5G communication technologies is making healthcare more streamlined and intuitive to improve healthcare service efficiency, encourage cross-disciplinary cooperation to improve patient health management, and introduce and mainstream innovative service models such as telecare. To this end, the wearable smart cardiac care system 'Vipasyana', jointly developed by the interdisciplinary research team of the Departments of Electrical Engineering and Computer Science at National Yang Ming Chiao Tung University, not only has an innovative fully functional wireless wearable electrocardiograph but also uses machine learning-based detection/prediction methods to detect and monitor for emergencies such as myocardial infarction, ventricular tachycardia/ventricular fibrillation, and heart failure. To handle the real-time streaming sensing data generated during clinical trials, a big data real-time streaming framework based on Apache Storm and microservices has also been integrated. In addition, a mobile phone app and virtual reality 3D ward round system providing a user-friendly management and monitoring tool for medical staffs were developed and integrated into the system. The 'Vipasyana' system is capable of providing precision medical services to individual patients.

Synergizing Survival: Uniting Acute Gastrointestinal Injury Grade and Disease Severity Scores in Critical Care Prognostication.

How to cite this article: Patnaik RK, Karan N. Synergizing Survival: Uniting Acute Gastrointestinal Injury Grade and Disease Severity Scores in Critical Care Prognostication. Indian J Crit Care Med 2024;28(6):529-530.

Interleukin-6 in Sepsis-Promising but Yet to Be Proven.

How to cite this article: Sinha S. Interleukin-6 in Sepsis-Promising but Yet to Be Proven. Indian J Crit Care Med 2024;28(7):629-631.

SeQual-Stream: approaching stream processing to quality control of NGS datasets.

BACKGROUND: Quality control of DNA sequences is an important data preprocessing step in many genomic analyses. However, all existing parallel tools for this purpose are based on a batch processing model, needing to have the complete genetic dataset before processing can even begin. This limitation clearly hinders quality control performance in those scenarios where the dataset must be downloaded from a remote repository and/or copied to a distributed file system for its parallel processing. RESULTS: In this paper we present SeQual-Stream, a streaming tool that allows performing multiple quality control operations on genomic datasets in a fast, distributed and scalable way. To do so, our approach relies on the Apache Spark framework and the Hadoop Distributed File System (HDFS) to fully exploit the stream paradigm and accelerate the preprocessing of large datasets as they are being downloaded and/or copied to HDFS. The experimental results have shown significant improvements in the execution times of SeQual-Stream when compared to a batch processing tool with similar quality control features, providing a maximum speedup of 2.7[Formula: see text] when processing a dataset with more than 250 million DNA sequences, while also demonstrating good scalability features. CONCLUSION: Our solution provides a more scalable and higher performance way to carry out quality control of large genomic datasets by taking advantage of stream processing features. The tool is distributed as free open-source software released under the GNU AGPLv3 license and is publicly available to download at https://github.com/UDC-GAC/SeQual-Stream .

SAPS2, APACHE2, SOFA, and Core-10-TISS upon admission as risk indicators for ICU-acquired infections: a retrospective cohort study.

PURPOSE: Early identification of high-risk patients is an important component in improving infection prevention. The SAPS2, APACHE2, Core-10-TISS, and SOFA scores are already widely used to estimate mortality, morbidity and nursing workload, but this study evaluated their usefulness in assessing a patient's risk of ICU-acquired infection. METHODS: We conducted a retrospective cohort study by analyzing all patient admissions to seven ICUs at Charité Berlin, Germany in 2017 and 2018. The four scores were documented by physicians on the day of admission. The infection control staff monitored daily whether the patients experienced lower respiratory tract infections (LRTIs), urinary tract infections (UTIs), or primary blood stream infections (PBSIs). For each combination of scoring system and infection type, an adjusted Fine and Gray model was fitted. RESULTS: We analyzed 5053 ICU admissions and observed at least one ICU-acquired infection in N = 253 patients (incidence density: 4.73 per 1000 days). 59.0% (N = 2983) of the patients were male, median age was 66 years (IQR 55-77) and median length of stay was 6 days (IQR 4-12). All models showed that patients with a higher score value were at higher risk for ICU-acquired first PBSI, LRTI, or UTI, except for the model of APACHE2 and PBSI. Patients with a SAPS2 score of > 50 points showed an increased risk of infection of sHR = 2.34 for PBSIs (CI 1.06-5.17, p < 0.05), sHR = 2.33 for LRTIs (1.53-2.55, p < 0.001) and sHR = 2.25 for UTIs (1.23-4.13, p < 0.01) when compared to the reference group with 0-30 points. CONCLUSIONS: The result of this study showed that admission scores of SAPS2, Core-10-TISS, APACHE2, and SOFA might be adequate indicators for assessing a patient's risk of ICU-acquired infection.

Prolonged Intermittent Renal Replacement Therapy Combined with Hemoperfusion Can Improve Early Recovery of Moderate and Severe Acute Pancreatitis, Especially in Patients with Acute Kidney Injury.

INTRODUCTION: The aim of this study was to investigate the efficacy of prolonged intermittent renal replacement therapy (PIRRT) plus hemoperfusion (HP) in treating moderately severe acute pancreatitis (MSAP) and severe acute pancreatitis (SAP). METHODS: A total of 105 MSAP and SAP patients were enrolled. Sixty of them received routine internal medical therapy (control group), and 45 received PIRRT and HP in addition to routine internal medical therapy (PIRRT + HP group). The vital signs, laboratory results, and the Acute Physiology and Chronic Health Evaluation II (APACHE II) score were compared between the two groups before treatment and on the 3rd and 7th days of treatment. RESULTS: No deaths or treatment-related serious adverse reactions occurred in both groups. After 3 and 7 days of treatment, the APACHE II score decreased more significantly in the PIRRT + HP group than in the control group (3 days: 5.47 [±3.30] vs. 7.53 [±3.89], p = 0.005. 7 days: 4.82 [±3.49] vs. 6.87 [±3.54], p = 0.004). After 3 days of treatment, the inflammatory combination parameters systemic immune-inflammation index (SII) and neutrophil-to-lymphocyte ratio (NLR) in the PIRRT + HP group decreased more significantly than those in the control group (SII: 1,239.00 [737.80-1,769.00] vs. 2,013.00 [1,260.00-3,167.00], p = 0.001. NLR: 8.78 [±4.52] vs. 11.88 [±7.30], p = 0.009). After 7 days of treatment, SII, NLR, and hypersensitive C-reactive protein decreased significantly compared with baseline, but no statistical differences between the two groups were observed. AST in both groups remained stable with treatment. There was no significant difference in baseline creatinine between the two groups of AKI patients, but after 3 and 7 days of treatment, the proportion of acute kidney injury (AKI) patients in the PIRRT + HP group whose creatinine decreased by 50% from baseline or fell to the normal range was significantly higher than that in the control group (p < 0.05). CONCLUSION: PIRRT + HP therapy could not only improve the general conditions, as measured by APACHE II score, but also reduce the inflammatory cascade of patients with acute pancreatitis. For MSAP and SAP patients complicated with AKI, this therapy may accelerate the recovery of renal function.