Secreted Aspartic Proteinases: Key Factors in Candida Infections and Host-Pathogen Interactions.

Extracellular proteases are key factors contributing to the virulence of pathogenic fungi from the genus Candida. Their proteolytic activities are crucial for extracting nutrients from the external environment, degrading host defenses, and destabilizing the internal balance of the human organism. Currently, the enzymes most frequently described in this context are secreted aspartic proteases (Saps). This review comprehensively explores the multifaceted roles of Saps, highlighting their importance in biofilm formation, tissue invasion through the degradation of extracellular matrix proteins and components of the coagulation cascade, modulation of host immune responses via impairment of neutrophil and monocyte/macrophage functions, and their contribution to antifungal resistance. Additionally, the diagnostic challenges associated with Candida infections and the potential of Saps as biomarkers were discussed. Furthermore, we examined the prospects of developing vaccines based on Saps and the use of protease inhibitors as adjunctive therapies for candidiasis. Given the complex biology of Saps and their central role in Candida pathogenicity, a multidisciplinary approach may pave the way for innovative diagnostic strategies and open new opportunities for innovative clinical interventions against candidiasis.

Africa must create centres of educational excellence for innovation and development.

This paper explores the following development questions that perplex most Africans: "Why do African countries rely on foreign companies and foreign experts for almost all our development projects? Why can't we build our own roads, process our own food, and mine our own minerals, oil, and gas? Why don't we have world-class hospitals and industries? How can we have so much natural wealth and yet be so poor? Why do we invent so little?" The answer lies in our failure to implement idea number two. There are two major ideas in educational policy. Idea number one is the obligation to educate all children because it is their fundamental human right as enshrined in the 1948 United Nations Declaration of Human Rights. Idea number two is the strategy of establishing and sustaining world class schools and universities for the education of the most highly gifted and highly talented citizens. Developed countries deploy both ideas aggressively. Underdeveloped countries in Africa have not implemented idea number two. Countries that have deployed idea number two have at least one university ranked among the top 200 in the world. The presence of great universities (top 200) in a country is a 21st century indicator of the presence of high levels of innovation, technology, development and wealth in that country. According to the three major rankings of world universities (Shanghai-ARWU, THE, and QS-topuniversities.com), none of the world's top 100 great universities is in Africa. Although Africa was a pioneer among the continents in innovations such as human language, domestication of fire, making of tools, invention of agriculture, development of writing, and creation of great centers of learning in ancient times, it has fallen behind other continents over the last 500 years and it has been disrupted by enslavement and colonization, and the structural adjustment programs (SAPs) of the IMF and the World Bank. Ancient African centers of innovation included the Ancient City of Benin and Timbuktu in Western Africa, the Kingdom of Kush and ancient Egypt in northern Africa, Axum in Eastern Africa, Mapungubwe and Great Zimbabwe in Southern Africa, and the Kingdom of Kongo in central-Africa. An African renaissance will only occur when we implement idea number two by establishing world class schools and at least one great university per African country.

Cet article explore les questions de développement suivantes qui intriguent la plupart des Africains : « Pourquoi les pays africains s'appuient-ils sur des entreprises et des experts étrangers pour presque tous nos projets de développement ? Pourquoi ne pouvons-nous pas construire nos propres routes, transformer nos propres aliments et extraire nos propres minéraux, pétrole et gaz ? Pourquoi n'avons-nous pas d'hôpitaux et d'industries de classe mondiale ? Comment pouvons-nous avoir autant de richesses naturelles et pourtant être si pauvres ? Pourquoi inventons-nous si peu ? La réponse réside dans notre échec à mettre en œuvre l'idée numéro deux. Il y a deux idées majeures en politique éducative. L'idée numéro un est l'obligation d'éduquer tous les enfants, car il s'agit de leur droit humain fondamental tel que consacré dans la Déclaration des droits de l'homme des Nations Unies de 1948. L'idée numéro deux est la stratégie consistant à créer et à maintenir des écoles et des universités de classe mondiale pour l'éducation des citoyens les plus doués et les plus talentueux. Les pays développés déploient ces deux idées de manière agressive. Les pays sous-développés d'Afrique n'ont pas mis en œuvre l'idée numéro deux. Les pays qui ont déployé l'idée numéro deux comptent au moins une université classée parmi les 200 meilleures au monde. La présence de grandes universités (les 200 meilleures) dans un pays est un indicateur du XXIe siècle de la présence de niveaux élevés d'innovation, de technologie, de développement et de richesse dans ce pays. Selon les trois principaux classements des universités mondiales (Shanghai-ARWU, THE et QS-topuniversities.com), aucune des 100 meilleures universités mondiales ne se trouve en Afrique. Bien que l'Afrique ait été un continent pionnier en matière d'innovations telles que le langage humain, la domestication du feu, la fabrication d'outils, l'invention de l'agriculture, le développement de l'écriture et la création de grands centres d'apprentissage dans l'Antiquité, elle a pris du retard sur les autres continents au fil du temps. Ces 500 dernières années ont été perturbées par l'esclavage et la colonisation, ainsi que par les programmes d'ajustement structurel (PAS) du FMI et de la Banque mondiale. Les anciens centres d'innovation de l'Afrique comprenaient l'ancienne ville du Bénin et Tombouctou en Afrique de l'Ouest, le royaume de Kouch et l'Égypte ancienne en Afrique du Nord, Axum en Afrique de l'Est, Mapungubwe et le Grand Zimbabwe en Afrique australe et le royaume de Kongo en Afrique centrale. . Une renaissance africaine ne se produira que lorsque nous mettrons en œuvre l'idée numéro deux en créant des écoles de classe mondiale et au moins une grande université par pays africain.

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.

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.

Toxicity assessment of core-shell and superabsorbent polymers in cell-based systems.

The identification of health risks arising from occupational exposure to submicron/nanoscale materials is of particular interest and toxicological investigations designed to assess their hazardous properties can provide valuable insights. The core-shell polymers poly (methyl methacrylate)@poly (methacrylic acid-co-ethylene glycol dimethacrylate) [PMMA@P (MAA-co-EGDMA)] and poly (n-butyl methacrylate-co-ethylene glycol dimethacrylate)@poly (methyl methacrylate) [P (nBMA-co-EGDMA)@PMMA] could be utilized for the debonding of coatings and for the encapsulation and targeted delivery of various compounds. The hybrid superabsorbent core-shell polymers poly (methacrylic acid-co-ethylene glycol dimethacrylate)@silicon dioxide [P (MAA-co-EGDMA)@SiO2] could be utilized as internal curing agents in cementitious materials. Therefore, the characterization of their toxicological profile is essential to ensure their safety throughout manufacturing and the life cycle of the final products. Based on the above, the purpose of the present study was to assess the acute toxic effects of the above mentioned polymers on cell viability and on cellular redox state in EA. hy926 human endothelial cells and in RAW264.7 mouse macrophages. According to our results, the examined polymers did not cause any acute toxic effects on cell viability after any administration. However, the thorough evaluation of a panel of redox biomarkers revealed that they affected cellular redox state in a cell-specific manner. As regards EA. hy926 cells, the polymers disrupted redox homeostasis and promoted protein carbonylation. Concerning RAW264.7 cells, P (nBMA-co-EGDMA)@PMMA caused disturbances in redox equilibrium and special emphasis was placed on the triphasic dose-response effect detected in lipid peroxidation. Finally, P (MAA-co-EGDMA)@SiO2 activated cellular adaptive mechanisms in order to prevent from oxidative damage.

A first-level customization study of SAPS II with Norwegian Intensive Care and Pandemic Registry (NIPaR) data.

BACKGROUND: Severity scores and mortality prediction models (MPMs) are important tools for benchmarking and stratification in the intensive care unit (ICU) and need to be regularly updated using data from a local and contextual cohort. Simplified acute physiology score II (SAPS II) is widely used in European ICUs. METHODS: A first-level customization was performed on the SAPS II model using data from the Norwegian Intensive Care and Pandemic Registry (NIPaR). Two previous SAPS II models (Model A: the original SAPS II model and Model B: a SAPS II model based on NIPaR data from 2008 to 2010) were compared to the new Model C. Model C was based on patients from 2018 to 2020 (corona virus disease 2019 patients omitted; n = 43,891), and its performances (calibration, discrimination, and uniformity of fit) compared to the previous models (Model A and Model B). RESULTS: Model C was better calibrated than Model A with a Brier score 0.132 (95% confidence interval 0.130-0.135) versus 0.143 (95% confidence interval 0.141-0.146). The Brier score for Model B was 0.133 (95% confidence interval 0.130-0.135). In the Cox's calibration regression α ≈ 0 and ß ≈ 1 for both Model C and Model B but not for Model A. Uniformity of fit was similar for Model B and for Model C, both better than for Model A, across age groups, sex, length of stay, type of admission, hospital category, and days on respirator. The area under the receiver operating characteristic curve was 0.79 (95% confidence interval 0.79-0.80), showing acceptable discrimination. CONCLUSIONS: The observed mortality and corresponding SAPS II scores have significantly changed during the last decades and an updated MPM is superior to the original SAPS II. However, proper external validation is required to confirm our findings. Prediction models need to be regularly customized using local datasets in order to optimize their performances.

Association between nutritional status scores and the 30-day mortality in patients with acute kidney injury: an analysis of MIMIC-III database.

BACKGROUND: Studies have proven that the risk of acute kidney injury (AKI) increased in patients with malnutrition. Prognostic nutritional index (PNI) and geriatric nutritional risk index (GNRI) were general tools to predict the risk of mortality, but the prognostic value of them for in-hospital mortality among patients with AKI have not been validated yet. Herein, this study aims to explore the association between PNI and GNRI and 30-day mortality in patients with AKI. METHODS: Demographic and clinical data of 863 adult patients with AKI were extracted from the Medical Information Mart for Intensive Care III (MIMIC-III) database in 2001-2012 in this retrospective cohort study. Univariate and multivariate Cox proportional regression analyses were used to explore the association between PNI and GNRI and 30-day mortality. The evaluation indexes were hazard ratios (HRs) and 95% confidence intervals (CIs). Subgroup analyses of age, Sequential Organ Failure Assessment (SOFA) score and Simplified Acute Physiology (SAPS-II) score were also performed. RESULTS: Totally, 222 (26.71%) patients died within 30 days. After adjusting for covariates, PNI ≥ 28.5 [HR = 0.71, 95%CI: (0.51-0.98)] and GNRI ≥ 83.25 [HR = 0.63, 95%CI: (0.47-0.86)] were both associated with low risk of 30-day mortality. These relationships were also found in patients who aged ≥ 65 years old. Differently, high PNI level was associated with low risk of 30-day mortality among patients with SOFA score < 6 or SAPS-II score < 43, while high GNRI was associated with low risk of 30-day mortality among those who with SOFA score ≥ 6 or SAPS-II score ≥ 43 (all P < 0.05). CONCLUSION: PNI and GNRI may be potential predictors of 30-day mortality in patients with AKI. Whether the PNI is more recommended for patients with mild AKI, while GNRI for those with severe AKI is needed further exploration.

Which model is superior in predicting ICU survival: artificial intelligence versus conventional approaches.

BACKGROUND: A disease severity classification system is widely used to predict the survival of patients admitted to the intensive care unit with different diagnoses. In the present study, conventional severity classification systems were compared with artificial intelligence predictive models (Artificial Neural Network and Decision Tree) in terms of the prediction of the survival rate of the patients admitted to the intensive care unit. METHODS: This retrospective cohort study was performed on the data of the patients admitted to the ICU of Ghaemshahr's Razi Teaching Care Center from March 20th, 2017, to September 22nd, 2019. The required data for calculating conventional severity classification models (SOFA, SAPS II, APACHE II, and APACHE IV) were collected from the patients' medical records. Subsequently, the score of each model was calculated. Artificial intelligence predictive models (Artificial Neural Network and Decision Tree) were developed in the next step. Lastly, the performance of each model in predicting the survival of the patients admitted to the intensive care unit was evaluated using the criteria of sensitivity, specificity, accuracy, F-measure, and area under the ROC curve. Also, each model was validated externally. The R program, version 4.1, was used to create the artificial intelligence models, and SPSS Statistics Software, version 21, was utilized to perform statistical analysis. RESULTS: The area under the ROC curve of SOFA, SAPS II, APACHE II, APACHE IV, multilayer perceptron artificial neural network, and CART decision tree were 76.0, 77.1, 80.3, 78.5, 84.1, and 80.0, respectively. CONCLUSION: The results showed that although the APACHE II model had better results than other conventional models in predicting the survival rate of the patients admitted to the intensive care unit, the other conventional models provided acceptable results too. Moreover, the findings showed that the artificial neural network model had the best performance among all the studied models, indicating the discrimination power of this model in predicting patient survival compared to the other models.

Prognostic factors in patients treated with extracorporeal membrane oxygenation and continuous renal replacement therapy.

INTRODUCTION: Extracorporeal membrane oxygenation (ECMO) and Continuous renal replacement therapy (CRRT) are treatments for critically ill patients with respiratory failure and acute kidney injury. However, no reliable factors have been identified to predict survival in patients treated with both ECMO and CRRT. The aim of this study was to identify prognostic factors for discharging intensive care unit (ICU) patients who required CRRT during ECMO. METHODS: We retrospectively analyzed data from patients who required CRRT in addition to the ECMO, between April 2015 and March 2018. The patients were divided into two groups: patients who survived and patients who died during ICU hospitalization. We determined their demographic and clinical characteristics, Acute Physiology and Chronic Health Evaluation II (APACHE II) scores, Simplified Acute Physiology Score II (SAPS II) scores, and sequential organ failure assessment (SOFA) scores. Further, we assessed whether these characteristics differed between individuals who did or did not survive the ICU hospitalization. RESULTS: We found that the APACHE II and SAPS II scores differed significantly between both ECMO and CRRT treated patients who did or did not survive hospitalization. Further, intracranial hemorrhage during ECMO and CRRT therapy was associated with lower survival rate. CONCLUSIONS: Using APACHE II and SAPS II scores might be helpful in making treatment decisions for patients treated with ECMO and CRRT. Intracranial hemorrhage could be a poor prognostic factor. Our findings indicate the potential utility of APACHE II and SAPS II scores to predict mortality in patients treated with both ECMO and CRRT.

Outcome in patients with open abdomen treatment for peritonitis: a multidomain approach outperforms single domain predictions.

Numerous patient-related clinical parameters and treatment-specific variables have been identified as causing or contributing to the severity of peritonitis. We postulated that a combination of clinical and surgical markers and scoring systems would outperform each of these predictors in isolation. To investigate this hypothesis, we developed a multivariable model to examine whether survival outcome can reliably be predicted in peritonitis patients treated with open abdomen. This single-center retrospective analysis used univariable and multivariable logistic regression modeling in combination with repeated random sub-sampling validation to examine the predictive capabilities of domain-specific predictors (i.e., demography, physiology, surgery). We analyzed data of 1,351 consecutive adult patients (55.7% male) who underwent open abdominal surgery in the study period (January 1998 to December 2018). Core variables included demographics, clinical scores, surgical indices and indicators of organ dysfunction, peritonitis index, incision type, fascia closure, wound healing, and fascial dehiscence. Postoperative complications were also added when available. A multidomain peritonitis prediction model (MPPM) was constructed to bridge the mortality predictions from individual domains (demographic, physiological and surgical). The MPPM is based on data of n = 597 patients, features high predictive capabilities (area under the receiver operating curve: 0.87 (0.85 to 0.90, 95% CI)) and is well calibrated. The surgical predictor "skin closure" was found to be the most important predictor of survival in our cohort, closely followed by the two physiological predictors SAPS-II and MPI. Marginal effects plots highlight the effect of individual outcomes on the prediction of survival outcome in patients undergoing staged laparotomies for treatment of peritonitis. Although most single indices exhibited moderate performance, we observed that the predictive performance was markedly increased when an integrative prediction model was applied. Our proposed MPPM integrative prediction model may outperform the predictive power of current models.

Characterization of the Secreted Acid Phosphatase SapS Reveals a Novel Virulence Factor of Staphylococcus aureus That Contributes to Survival and Virulence in Mice.

Staphylococcus aureus possesses a large arsenal of immune-modulating factors, enabling it to bypass the immune system's response. Here, we demonstrate that the acid phosphatase SapS is secreted during macrophage infection and promotes its intracellular survival in this type of immune cell. In animal models, the SA564 sapS mutant demonstrated a significantly lower bacterial burden in liver and renal tissues of mice at four days post infection in comparison to the wild type, along with lower pathogenicity in a zebrafish infection model. The SA564 sapS mutant elicits a lower inflammatory response in mice than the wild-type strain, while S. aureus cells harbouring a functional sapS induce a chemokine response that favours the recruitment of neutrophils to the infection site. Our in vitro and quantitative transcript analysis show that SapS has an effect on S. aureus capacity to adapt to oxidative stress during growth. SapS is also involved in S. aureus biofilm formation. Thus, this study shows for the first time that SapS plays a significant role during infection, most likely through inhibiting a variety of the host's defence mechanisms.

Preoperative and ICU Scoring Models for Predicting the In-Hospital Mortality of Patients With Ruptured Abdominal Aortic Aneurysms.

OBJECTIVES: This study's objective was to compare several preoperative and intensive care unit (ICU) prognostic scoring systems for predicting the in-hospital mortality of ruptured abdominal aortic aneurysms (RAAAs). DESIGN: Retrospective cohort study. SETTING: Single tertiary university center. PARTICIPANTS: The study comprised 157 patients. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: A total of 157 patients (82% male) presented with RAAA at Charité University Hospital from January 2011 to December 2020. The mean age was 74 years (standard deviation ten years). In-hospital mortality was 29% (n = 45), of whom nine patients (6%) died en route to the operating room, 13 (8%) on the operating table, and 23 (15%) in the ICU. A total of 135 patients (86%) were admitted to the ICU. All six models demonstrated good discriminating performance between survivors and nonsurvivors. Overall, the area under the curve (AUC) for RAAA preoperative scores was greater than those for ICU scores. The largest AUC was achieved with the Vascular Study Group of New England (VSGNE) RAAA risk score (AUC = 0.87 for all patients, AUC = 0.84 for patients admitted to the ICU), followed by Hardman Index (AUC = 0.83 for all patients, AUC = 0.81 for patients admitted to the ICU), and Glasgow Aneurysm Score (AUC = 0.74 for all patients, AUC = 0.83 for patients admitted to the ICU). The largest AUC for ICU scores (only patients admitted to the ICU) was achieved with Simplified Acute Physiology Score II (0.75), followed by Sepsis-related Organ Failure Assessment (0.73), and Acute Physiology and Chronic Health Evaluation II (0.71). CONCLUSIONS: Preoperative and ICU scores can predict the mortality of patients presenting with RAAA. In addition, the discriminatory ability of preoperative scores between survivors and nonsurvivors was larger than that for ICU scores.

Systemic Severity and Organ Dysfunction in Subarachnoid Hemorrhage: A Large Retrospective Multicenter Cohort Study.

BACKGROUND AND PURPOSE: Acute physiologic derangements and multiple organ dysfunction are common after subarachnoid hemorrhage. We aimed to evaluate the simplified acute physiology score 3 (SAPS-3) and the sequential organ failure assessment (SOFA) scores for the prediction of in-hospital mortality in a large multicenter cohort of SAH patients. METHODS: This was a retrospective analysis of prospectively collected data from 45 ICUs in Brazil, during 2014 and 2015. Patients admitted with non-traumatic subarachnoid hemorrhage (SAH) were included. Clinical and outcome data were retrieved from an electronic ICU quality registry. SAPS-3 and SOFA scores, without the neurological components (i.e., nSAPS-3 and nSOFA, respectively) were recorded, as well as the World Federation of Neurological Surgeons (WFNS) scale. We used multilevel logistic regression analysis to identify factors associated with in-hospital mortality. We evaluated performance using the area under the receiver operating characteristic curve (AUROC), as well as calibration belts and precision-recall plots. RESULTS: The study included 997 patients, from which 426 (43%) had poor clinical grade (WFNS 4 or 5) and in-hospital mortality was 34%. Median nSAPS-3 and nSOFA score at admission were 46 (IQR: 38-55) and 2 (0-5), respectively. Non-survivors were older, had higher nSAPS-3 and nSOFA, and more often poor grade. After adjustment for age, poor grade and withdrawal of life sustaining therapies, multivariable analysis identified nSAPS-3 and nSOFA score as independent clinical predictors of in-hospital mortality. The AUROC curve that included nSAPS-3 and nSOFA scores significantly improved the already good discrimination and calibration of age and WFNS to predict in-hospital mortality (AUROC: 0.89 for the full final model vs. 0.85 for age and WFNS; P < 0.0001). CONCLUSIONS: nSAPS-3 and nSOFA scores were independently associated with in-hospital mortality after SAH. The addition of these scores improved early prediction of hospital mortality in our cohort and should be integrated to other specific prognostic indices in the early assessment of SAH.

Prognostic scores and early management of septic patients in the emergency department of a secondary hospital: results of a retrospective study.

BACKGROUND: Sequential Organ Failure Assessment (SOFA) and other illness prognostic scores predict adverse outcomes in critical patients. Their validation as a decision-making tool in the emergency department (ED) of secondary hospitals is not well established. The aim of this study was to compare SOFA, NEWS2, APACHE II, and SAPS II scores as predictors of adverse outcomes and decision-making tool in ED. METHODS: Data of 121 patients (age 73 ± 10 years, 58% males, Charlson Comorbidity Index 5.7 ± 2.1) with a confirmed sepsis were included in a retrospective study between January 2017 and February 2020. Scores were computed within the first 24 h after admission. Primary outcome was the occurrence of either in-hospital death or mechanical ventilation within 7 days. Secondary outcome was 30-day all-cause mortality. RESULTS: Patients older than 64 years (elderly) represent 82% of sample. Primary and secondary outcomes occurred in 40 and 44%, respectively. Median 30-day survival time of dead patients was 4 days (interquartile range 1-11). The best predictive score based on the area under the receiver operating curve (AUROC) was SAPS II (0.823, 95% confidence interval, CI, 0.744-0.902), followed by APACHE II (0.762, 95% CI 0.673-0.850), NEWS2 (0.708, 95% CI 0.616-0.800), and SOFA (0.650, 95% CI 0.548-0.751). SAPS II cut-off of 49 showed the lowest false-positive rate (12, 95% CI 5-20) and the highest positive predictive value (80, 95% CI 68-92), whereas NEWS2 cut-off of 7 showed the lowest false-negative rate (10, 95% CI 2-19) and the highest negative predictive value (86, 95% CI 74-97). By combining NEWS2 and SAPS II cut-offs, we accurately classified 64% of patients. In survival analysis, SAPS II cut-off showed the highest difference in 30-day mortality (Hazards Ratio, HR, 5.24, 95% CI 2.99-9.21, P < 0.001). Best independent negative predictors of 30-day mortality were body temperature, mean arterial pressure, arterial oxygen saturation, and hematocrit levels. Positive predictors were male sex, heart rate and serum sodium concentration. CONCLUSIONS: SAPS II is a good prognostic tool for discriminating high-risk patient suitable for sub-intensive/intensive care units, whereas NEWS2 for discriminating low-risk patients for low-intensive units. Our results should be limited to cohorts with a high prevalence of elderly or comorbidities.

Self-Assembling Peptides: From Design to Biomedical Applications.

Self-assembling peptides could be considered a novel class of agents able to harvest an array of micro/nanostructures that are highly attractive in the biomedical field. By modifying their amino acid composition, it is possible to mime several biological functions; when assembled in micro/nanostructures, they can be used for a variety of purposes such as tissue regeneration and engineering or drug delivery to improve drug release and/or stability and to reduce side effects. Other significant advantages of self-assembled peptides involve their biocompatibility and their ability to efficiently target molecular recognition sites. Due to their intrinsic characteristics, self-assembled peptide micro/nanostructures are capable to load both hydrophobic and hydrophilic drugs, and they are suitable to achieve a triggered drug delivery at disease sites by inserting in their structure's stimuli-responsive moieties. The focus of this review was to summarize the most recent and significant studies on self-assembled peptides with an emphasis on their application in the biomedical field.

Predicting 30-days mortality for MIMIC-III patients with sepsis-3: a machine learning approach using XGboost.

BACKGROUND: Sepsis is a significant cause of mortality in-hospital, especially in ICU patients. Early prediction of sepsis is essential, as prompt and appropriate treatment can improve survival outcomes. Machine learning methods are flexible prediction algorithms with potential advantages over conventional regression and scoring system. The aims of this study were to develop a machine learning approach using XGboost to predict the 30-days mortality for MIMIC-III Patients with sepsis-3 and to determine whether such model performs better than traditional prediction models. METHODS: Using the MIMIC-III v1.4, we identified patients with sepsis-3. The data was split into two groups based on death or survival within 30 days and variables, selected based on clinical significance and availability by stepwise analysis, were displayed and compared between groups. Three predictive models including conventional logistic regression model, SAPS-II score prediction model and XGBoost algorithm model were constructed by R software. Then, the performances of the three models were tested and compared by AUCs of the receiver operating characteristic curves and decision curve analysis. At last, nomogram and clinical impact curve were used to validate the model. RESULTS: A total of 4559 sepsis-3 patients are included in the study, in which, 889 patients were death and 3670 survival within 30 days, respectively. According to the results of AUCs (0.819 [95% CI 0.800-0.838], 0.797 [95% CI 0.781-0.813] and 0.857 [95% CI 0.839-0.876]) and decision curve analysis for the three models, the XGboost model performs best. The risk nomogram and clinical impact curve verify that the XGboost model possesses significant predictive value. CONCLUSIONS: Using machine learning technique by XGboost, more significant prediction model can be built. This XGboost model may prove clinically useful and assist clinicians in tailoring precise management and therapy for the patients with sepsis-3.

Coagulation tests on admission correlate with mortality and morbidity in general ICU patients: An observational study.

BACKGROUND: It is well known that low platelet count on admission to intensive care units (ICU) is associated with increased mortality. However, it is unknown whether prothrombin time (PT-INR) and activated partial thromboplastin time (APTT) on admission correlate with mortality and organ failure. Therefore, the aim of this study was to investigate whether PT-INR and APTT at admission can predict outcome in the critically ill patient after adjusting for severity of illness measured with Simplified Acute Physiology Score 3 (SAPS 3). MATERIALS AND METHODS: Data were retrospectively collected. APTT and PT-INR taken on admission and SAPS 3 score were independent variables in all regression analyses. Survival analysis was done with Cox regression. Organ failure was reported as days alive and free (DAF) of vasopressors and invasive ventilation, need of continuous renal replacement therapy (CRRT) and Acute Kidney Injury Network creatinine score (AKIN-crea). RESULTS: A total of 3585 ICU patients were included. Prolonged APTT correlated with mortality with 95% confidence interval (CI) of hazard ratio 1.001-1.010. Prolonged APTT also correlated with DAF vasopressor, CRRT and AKIN-crea with 95% CI of odds ratio (OR) 1.009-1.034, 1.016-1.037 and 1.009-1.028, respectively. Increased PT-INR correlated with DAF vasopressor and DAF ventilator with 95% CI of OR 1.112-2.014 and 1.135-1.847, respectively. CONCLUSIONS: Activated partial thromboplastin time prolongation was associated with mortality and all morbidity outcomes except the DAF ventilator. PT-INR increase at admission was associated with DAF vasopressor and DAF ventilator. APTT and PT-INR at admission correlate with morbidity, which is not accounted for in the SAPS 3 model.

Calprotectin is superior to procalcitonin as a sepsis marker and predictor of 30-day mortality in intensive care patients.

Sepsis is the most frequent cause of death in the intensive care unit (ICU). A rapid and correct diagnosis and initiation of therapy is crucial for improving patient outcomes. The aim of this study was to compare the performance of calprotectin with the more widely used sepsis biomarker procalcitonin (PCT) in ICU patients. The performance of calprotectin and PCT as sepsis and prognostic markers for 30-d mortality was compared in a prospective, observational study in an eight-bed ICU. We investigated concentrations of the biomarkers in plasma collected at admission from all ICU patients admitted during a year (2012-2013, n = 271) together with simplified acute physiology 3 scores (SAPS3) and sequential organ failure assessment (SOFA) scores. The receiver operating characteristic (ROC) analysis showed a higher area under the curve (AUC) value for calprotectin (0.79) than for PCT (0.49) when used as a sepsis marker. The calprotectin concentrations at admission were higher in non-survivors than in survivors at day 30. In our study, calprotectin was superior to PCT for distinguishing between ICU patients with sepsis and non-sepsis patients. Calprotectin also had higher predictive ability regarding 30-d mortality.

Performance of three prognostic models in critically ill patients with cancer: a prospective study.

BACKGROUND: The aim of the study was to evaluate the performance of "Acute Physiology and Chronic Health Evaluation II" (APACHE-II), "Simplified Acute Physiology Score 3" (SAPS-3), and "APACHE-II Score for Critically Ill Patients with a Solid Tumor" (APACHE-IICCP) models in cancer patients admitted to ICU. METHODS: Prospective cohort study of 414 patients with an active solid tumor. Discrimination was assessed by area under receiver operating characteristic (AROC) curves and calibration by Hosmer-Lemeshow goodness-of-fit C test (H-L). RESULTS: The hospital mortality rate was 32.6%. In the total cohort, discrimination for prognostic models were: APACHE-IICCP (AROC 0.98), APACHE-II (AROC 0.96), SAPS-3 for Central and South American countries (SAPS-3CSA) (AROC 0.95), and SAPS-3 (AROC 0.91). Calibration was good (p value of H-L test > 0.05) using APACHE-IICCP, APACHE-II and SAPS-3CSA models. Estimation of the probability of death was more precise with APACHE-IICCP (standardized mortality ratio, SMR = 1.03) and SAPS-3 (SMR = 1.08) models. Further analysis showed that discrimination was high with all prognostic model whether for patients with planned ICU admission (AROC APACHE-IICCP 0.97, APACHE-II 0.96, SAPS-3 0.95, SAPS-3CSA 0.95) or for patients with unplanned ICU admission (AROC APACHE-IICCP 0.97, APACHE-II 0.94, SAPS-3 0.86, SAPS-3CSA 0.95). Calibration was good for all predictive models in both subgroups (p value of H-L test > 0.05, except for APACHE-II model inpatients with planned ICU admission). CONCLUSIONS: In this prospective study, general predictive models (e.g., APACHE-II, SAPS-3) and cancer-specific models (e.g., APACHE-IICCP) are accurate in predicting hospital mortality. Other studies confirming these results are required.

Performance of SAPS II according to ICU length of stay: A Danish nationwide cohort study.

BACKGROUND: Intensive care unit (ICU) severity scores use data available at admission or shortly thereafter. There are limited contemporary data on how the prognostic performance of these scores is affected by ICU length of stay (LOS). METHODS: We conducted a nationwide cohort study using routinely collected health data from the Danish Intensive Care Database. We included adults with ICU admissions ≥24 hours between 1 January 2012 and 30 June 2016, who survived to ICU discharge and had valid ICU LOS and vital status data registered. We assessed discrimination of the Simplified Acute Physiology Score (SAPS) II for predicting mortality 90 days after ICU discharge, followed by recalibration of the model and assessment of standardized mortality ratios (SMRs) and calibration. Performance was assessed in the entire cohort and stratified by ICU LOS quartiles. RESULTS: We included 44 523 patients. Increasing SAPS II was associated with increasing ICU LOS. Overall discrimination (area under the receiver-operating characteristics curve) of SAPS II was 0.70 (95% CI: 0.70-0.71), with decreasing discrimination from the first (0.75, 95% CI: 0.73-0.76) to the last (0.64, 95% CI: 0.63-0.65) ICU LOS quartile. SMRs were lower (less deaths) than expected in the first ICU LOS quartile and higher (more deaths) than expected in the last two ICU LOS quartiles. Calibration decreased with increasing ICU LOS. CONCLUSIONS: We observed that discrimination and calibration of SAPS II decreased with increasing ICU LOS, and that this affected SMRs. These findings should be acknowledged when using SAPS II for clinical, research and administrative purposes.