Pediatric septic shock estimation using deep learning and electronic medical records.

BACKGROUND: Diagnosing pediatric septic shock is difficult due to the complex and often impractical traditional criteria, such as systemic inflammatory response syndrome (SIRS), which result in delays and higher risks. This study aims to develop a deep learning-based model using SIRS data for early diagnosis in pediatric septic shock cases. METHODS: The study analyzed data from pediatric patients (<18 years old) admitted to a tertiary hospital from January 2010 to July 2023. Vital signs, lab tests, and clinical information were collected. Septic shock cases were identified using SIRS criteria and inotrope use. A deep learning model was trained and evaluated using the area under the receiver operating characteristics curve (AUROC) and area under the precision-recall curve (AUPRC). Variable contributions were analyzed using the Shapley additive explanation value. RESULTS: The analysis, involving 9,616,115 measurements, identified 34,696 septic shock cases (0.4%). Oxygen supply was crucial for 41.5% of the control group and 20.8% of the septic shock group. The final model showed strong performance, with an AUROC of 0.927 and AUPRC of 0.879. Key influencers were age, oxygen supply, sex, and partial pressure of carbon dioxide, while body temperature had minimal impact on estimation. CONCLUSIONS: The proposed deep learning model simplifies early septic shock diagnosis in pediatric patients, reducing the diagnostic workload. Its high accuracy allows timely treatment, but external validation through prospective studies is needed.

Streamlining pediatric vital sign assessment: innovations and insights.

Accurate assessment of pediatric vital signs is critical for detecting abnormalities and guiding medical interventions, but interpretation is challenging due to age-dependent physiological variations. Therefore, this study aimed to develop age-specific centile curves for blood pressure, heart rate, and respiratory rate in pediatric patients and create a user-friendly web-based application for easy access to these data. We conducted a retrospective cross-sectional observational study analyzing 3,779,482 records from the National Emergency Department Information System of Korea, focusing on patients under 15 years old admitted between January 2016 and December 2017. After applying exclusion criteria to minimize the impact of patients' symptoms on vital signs, 1,369,608 records were used for final analysis. The box-cox power exponential distribution and Lambda-Mu-Sigma (LMS) method were used to generate blood pressure centile charts, while heart rate and respiratory rate values were drawn from previously collected LMS values. We developed comprehensive age-specific centile curves for systolic, diastolic, and mean blood pressure, heart rate, and respiratory rate. These were integrated into a web-based application ( http://centile.research.or.kr ), allowing users to input patient data and promptly obtain centile and z-score information for vital signs. Our study provides an accessible system for pediatric vital sign evaluation, addressing previous limitations and offering a practical solution for clinical assessment. Future research should validate these centile curves in diverse populations.

Discovery of Antimicrobial Agents Based on Structural and Functional Study of the Klebsiella pneumoniae MazEF Toxin-Antitoxin System.

Klebsiella pneumoniae causes severe human diseases, but its resistance to current antibiotics is increasing. Therefore, new antibiotics to eradicate K. pneumoniae are urgently needed. Bacterial toxin-antitoxin (TA) systems are strongly correlated with physiological processes in pathogenic bacteria, such as growth arrest, survival, and apoptosis. By using structural information, we could design the peptides and small-molecule compounds that can disrupt the binding between K. pneumoniae MazE and MazF, which release free MazF toxin. Because the MazEF system is closely implicated in programmed cell death, artificial activation of MazF can promote cell death of K. pneumoniae. The effectiveness of a discovered small-molecule compound in bacterial cell killing was confirmed through flow cytometry analysis. Our findings can contribute to understanding the bacterial MazEF TA system and developing antimicrobial agents for treating drug-resistant K. pneumoniae.

Lysine-homologue substitution: Impact on antimicrobial activity and proteolytic stability of cationic stapled heptapeptides.

Numerous natural antimicrobial peptides (AMPs) exhibit a cationic amphipathic helical conformation, wherein cationic amino acids, such as lysine and arginine, play pivotal roles in antimicrobial activity by aiding initial attraction to negatively charged bacterial membranes. Expanding on our previous work, which introduced a de novo design of amphipathic helices within cationic heptapeptides using an 'all-hydrocarbon peptide stapling' approach, we investigated the impact of lysine-homologue substitution on helix formation, antimicrobial activity, hemolytic activity, and proteolytic stability of these novel AMPs. Our results demonstrate that substituting lysine with ornithine enhances both the antimicrobial activity and proteolytic stability of the stapled heptapeptide AMP series, while maintaining low hemolytic activity. This finding underscores lysine-homologue substitution as a valuable strategy for optimizing the therapeutic potential of diverse cationic AMPs.

Metallo-ß-lactamase inhibitors: A continuing challenge for combating antibiotic resistance.

ß-lactam antibiotics are the most successful and commonly used antibacterial agents, but the emergence of resistance to these drugs has become a global health threat. The expression of ß-lactamase enzymes produced by pathogens, which hydrolyze the amide bond of the ß-lactam ring, is the major mechanism for bacterial resistance to ß-lactams. In particular, among class A, B, C and D ß-lactamases, metallo-ß-lactamases (MBLs, class B ß-lactamases) are considered crucial contributors to resistance in gram-negative bacteria. To combat ß-lactamase-mediated resistance, great efforts have been made to develop ß-lactamase inhibitors that restore the activity of ß-lactams. Some ß-lactamase inhibitors, such as diazabicyclooctanes (DBOs) and boronic acid derivatives, have also been approved by the FDA. Inhibitors used in the clinic can inactivate mostly serine-ß-lactamases (SBLs, class A, C, and D ß-lactamases) but have not been effective against MBLs until now. In order to develop new inhibitors particularly for MBLs, various attempts have been suggested. Based on structural and mechanical studies of MBL enzymes, several MBL inhibitor candidates, including taniborbactam in phase 3 and xeruborbactam in phase 1, have been introduced in recent years. However, designing potent inhibitors that are effective against all subclasses of MBLs is still extremely challenging. This review summarizes not only the types of ß-lactamase and mechanisms by which ß-lactam antibiotics are inactivated, but also the research finding on ß-lactamase inhibitors targeting these enzymes. These detailed information on ß-lactamases and their inhibitors could give valuable information for novel ß-lactamase inhibitors design.

Development of a deep learning model for predicting critical events in a pediatric intensive care unit.

BACKGROUND: Identifying critically ill patients at risk of cardiac arrest is important because it offers the opportunity for early intervention and increased survival. The aim of this study was to develop a deep learning model to predict critical events, such as cardiopulmonary resuscitation or mortality. METHODS: This retrospective observational study was conducted at a tertiary university hospital. All patients younger than 18 years who were admitted to the pediatric intensive care unit from January 2010 to May 2023 were included. The main outcome was prediction performance of the deep learning model at forecasting critical events. Long short-term memory was used as a deep learning algorithm. The five-fold cross validation method was employed for model learning and testing. RESULTS: Among the vital sign measurements collected during the study period, 11,660 measurements were used to develop the model after preprocessing; 1,060 of these data points were measurements that corresponded to critical events. The prediction performance of the model was the area under the receiver operating characteristic curve (95% confidence interval) of 0.988 (0.9751.000), and the area under the precision-recall curve was 0.862 (0.700-1.000). CONCLUSIONS: The performance of the developed model at predicting critical events was excellent. However, follow-up research is needed for external validation.

Individualized estimation of arterial carbon dioxide partial pressure using machine learning in children receiving mechanical ventilation.

BACKGROUND: Measuring arterial partial pressure of carbon dioxide (PaCO2) is crucial for proper mechanical ventilation, but the current sampling method is invasive. End-tidal carbon dioxide (EtCO2) has been used as a surrogate, which can be measured non-invasively, but its limited accuracy is due to ventilation-perfusion mismatch. This study aimed to develop a non-invasive PaCO2 estimation model using machine learning. METHODS: This retrospective observational study included pediatric patients (< 18 years) admitted to the pediatric intensive care unit of a tertiary children's hospital and received mechanical ventilation between January 2021 and June 2022. Clinical information, including mechanical ventilation parameters and laboratory test results, was used for machine learning. Linear regression, multilayer perceptron, and extreme gradient boosting were implemented. The dataset was divided into 7:3 ratios for training and testing. Model performance was assessed using the R2 value. RESULTS: We analyzed total 2,427 measurements from 32 patients. The median (interquartile range) age was 16 (12-19.5) months, and 74.1% were female. The PaCO2 and EtCO2 were 63 (50-83) mmHg and 43 (35-54) mmHg, respectively. A significant discrepancy of 19 (12-31) mmHg existed between EtCO2 and the measured PaCO2. The R2 coefficient of determination for the developed models was 0.799 for the linear regression model, 0.851 for the multilayer perceptron model, and 0.877 for the extreme gradient boosting model. The correlations with PaCO2 were higher in all three models compared to EtCO2. CONCLUSIONS: We developed machine learning models to non-invasively estimate PaCO2 in pediatric patients receiving mechanical ventilation, demonstrating acceptable performance. Further research is needed to improve reliability and external validation.

Polymorphic Self-Assembly with Procedural Flexibility for Monodisperse Quaternary Protein Structures of DegQ Enzymes.

As large molecular tertiary structures, some proteins can act as small robots that find, bind, and chaperone target protein clients, showing the potential to serve as smart building blocks in self-assembly fields. Instead of using such intrinsic functions, most self-assembly methodologies for proteins aim for de novo-designed structures with accurate geometric assemblies, which can limit procedural flexibility. Here, a strategy enabling polymorphic clustering of quaternary proteins, exhibiting simplicity and flexibility of self-assembling paths for proteins in forming monodisperse quaternary cage particles is presented. It is proposed that the enzyme protomer DegQ, previously solved at low resolution, may potentially be usable as a threefold symmetric building block, which can form polyhedral cages incorporated by the chaperone action of DegQ in the presence of protein clients. To obtain highly monodisperse cage particles, soft, and hence, less resistive client proteins, which can program the inherent chaperone activity of DegQ to efficient formations of polymorphic cages, depending on the size of clients are utilized. By reconstructing the atomic resolution cryogenic electron microscopy DegQ structures using obtained 12- and 24-meric clusters, the polymorphic clustering of DegQ enzymes is validated in terms of soft and rigid domains, which will provide effective routes for protein self-assemblies with procedural flexibility.

Effect of Divalent Metal Ions on the Ribonuclease Activity of the Toxin Molecule HP0894 from Helicobacter pylori.

Bacteria and archaea respond and adapt to environmental stress conditions by modulating the toxin-antitoxin (TA) system for survival. Within the bacterium Helicobacter pylori, the protein HP0894 is a key player in the HP0894-HP0895 TA system, in which HP0894 serves as a toxin and HP0895 as an antitoxin. HP0894 has intrinsic ribonuclease (RNase) activity that regulates gene expression and translation, significantly influencing bacterial physiology and survival. This activity is influenced by the presence of metal ions such as Mg2+. In this study, we explore the metal-dependent RNase activity of HP0894. Surprisingly, all tested metal ions lead to a reduction in RNase activity, with zinc ions (Zn2+) causing the most significant decrease. The secondary structure of HP0894 remained largely unaffected by Zn2+ binding, whereas structural rigidity was notably increased, as revealed using CD analysis. NMR characterized the Zn2+ binding, implicating numerous His, Asp, and Glu residues in HP0894. In summary, these results suggest that metal ions play a regulatory role in the RNase activity of HP0894, contributing to maintaining the toxin molecule in an inactive state under normal conditions.

Development of a deep learning model that predicts critical events of pediatric patients admitted to general wards.

Early detection of deteriorating patients is important to prevent life-threatening events and improve clinical outcomes. Efforts have been made to detect or prevent major events such as cardiopulmonary resuscitation, but previously developed tools are often complicated and time-consuming, rendering them impractical. To overcome this problem, we designed this study to create a deep learning prediction model that predicts critical events with simplified variables. This retrospective observational study included patients under the age of 18 who were admitted to the general ward of a tertiary children's hospital between 2020 and 2022. A critical event was defined as cardiopulmonary resuscitation, unplanned transfer to the intensive care unit, or mortality. The vital signs measured during hospitalization, their measurement intervals, sex, and age were used to train a critical event prediction model. Age-specific z-scores were used to normalize the variability of the normal range by age. The entire dataset was classified into a training dataset and a test dataset at an 8:2 ratio, and model learning and testing were performed on each dataset. The predictive performance of the developed model showed excellent results, with an area under the receiver operating characteristics curve of 0.986 and an area under the precision-recall curve of 0.896. We developed a deep learning model with outstanding predictive power using simplified variables to effectively predict critical events while reducing the workload of medical staff. Nevertheless, because this was a single-center trial, no external validation was carried out, prompting further investigation.

Development of a model to predict vancomycin serum concentration during continuous infusion of vancomycin in critically ill pediatric patients.

Vancomycin is a frequently used antibiotic in intensive care units, and the patient's renal clearance affects the pharmacokinetic characteristics of vancomycin. Several advantages have been reported for vancomycin continuous intravenous infusion, but studies on continuous dosing regimens based on patients' renal clearance are insufficient. The aim of this study was to develop a vancomycin serum concentration prediction model by factoring in a patient's renal clearance. Children admitted to our institution between July 1, 2021, and July 31, 2022 with records of continuous infusion of vancomycin were included in the study. Sex, age, height, weight, vancomycin dose by weight, interval from the start of vancomycin administration to the time of therapeutic drug monitoring sampling, and vancomycin serum concentrations were analyzed with the linear regression analysis of the mixed effect model. Univariable regression analysis was performed using the vancomycin serum concentration as a dependent variable. It showed that vancomycin dose (p < 0.001) and serum creatinine (p = 0.007) were factors that had the most impact on vancomycin serum concentration. Vancomycin serum concentration was affected by vancomycin dose (p < 0.001) and serum creatinine (p = 0.001) with statistical significance, and a multivariable regression model was obtained as follows: Vancomycin serum concentration (mg/l) = -1.296 + 0.281 × vancomycin dose (mg/kg) + 20.458 × serum creatinine (mg/dl) (adjusted coefficient of determination, R2 = 0.66). This prediction model is expected to contribute to establishing an optimal continuous infusion regimen for vancomycin.

Pediatric Extracorporeal Membrane Oxygenation in Korea: A Multicenter Retrospective Study on Utilization and Outcomes Spanning Over a Decade.

BACKGROUND: Over the last decade, extracorporeal membrane oxygenation (ECMO) use in critically ill children has increased and is associated with favorable outcomes. Our study aims to evaluate the current status of pediatric ECMO in Korea, with a specific focus on its volume and changes in survival rates based on diagnostic indications. METHODS: This multicenter study retrospectively analyzed the indications and outcomes of pediatric ECMO over 10 years in patients at 14 hospitals in Korea from January 2012 to December 2021. Four diagnostic categories (neonatal respiratory, pediatric respiratory, post-cardiotomy, and cardiac-medical) and trends were compared between periods 1 (2012-2016) and 2 (2017-2021). RESULTS: Overall, 1065 ECMO runs were performed on 1032 patients, with the annual number of cases remaining unchanged over the 10 years. ECMO was most frequently used for post-cardiotomy (42.4%), cardiac-medical (31.8%), pediatric respiratory (17.5%), and neonatal respiratory (8.2%) cases. A 3.7% increase and 6.1% decrease in pediatric respiratory and post-cardiotomy cases, respectively, were noted between periods 1 and 2. Among the four groups, the cardiac-medical group had the highest survival rate (51.2%), followed by the pediatric respiratory (46.4%), post-cardiotomy (36.5%), and neonatal respiratory (29.4%) groups. A consistent improvement was noted in patient survival over the 10 years, with a significant increase between the two periods from 38.2% to 47.1% (P = 0.004). Improvement in survival was evident in post-cardiotomy cases (30-45%, P = 0.002). Significant associations with mortality were observed in neonates, patients requiring dialysis, and those treated with extracorporeal cardiopulmonary resuscitation (P < 0.001). In pediatric respiratory ECMO, immunocompromised patients also showed a significant correlation with mortality (P < 0.001). CONCLUSION: Pediatric ECMO demonstrated a steady increase in overall survival in Korea; however, further efforts are needed since the outcomes remain suboptimal compared with global outcomes.

Eleven years of experience in operating a pediatric rapid response system at a children's hospital in South Korea.

BACKGROUND: Various rapid response systems have been developed to detect clinical deterioration in patients. Few studies have evaluated single-parameter systems in children compared to scoring systems. Therefore, in this study we evaluated a single-parameter system called the acute response system (ARS). METHODS: This retrospective study was performed at a tertiary children's hospital. Patients under 18 years old admitted from January 2012 to August 2023 were enrolled. ARS parameters such as systolic blood pressure, heart rate, respiratory rate, oxygen saturation, and whether the ARS was activated were collected. We divided patients into two groups according to activation status and then compared the occurrence of critical events (cardiopulmonary resuscitation or unexpected intensive care unit admission). We evaluated the ability of ARS to predict critical events and calculated compliance. We also analyzed the correlation between each parameter that activates ARS and critical events. RESULTS: The critical events prediction performance of ARS has a specificity of 98.5%, a sensitivity of 24.0%, a negative predictive value of 99.6%, and a positive predictive value of 8.1%. The compliance rate was 15.6%. Statistically significant increases in the risk of critical events were observed for all abnormal criteria except low heart rate. There was no significant difference in the incidence of critical events. CONCLUSIONS: ARS, a single parameter system, had good specificity and negative predictive value for predicting critical events; however, sensitivity and positive predictive value were not good, and medical staff compliance was poor.

Dynamics-Based Regulatory Switches of Type II Antitoxins: Insights into New Antimicrobial Discovery.

Type II toxin-antitoxin (TA) modules are prevalent in prokaryotes and are involved in cell maintenance and survival under harsh environmental conditions, including nutrient deficiency, antibiotic treatment, and human immune responses. Typically, the type II TA system consists of two protein components: a toxin that inhibits an essential cellular process and an antitoxin that neutralizes its toxicity. Antitoxins of type II TA modules typically contain the structured DNA-binding domain responsible for TA transcription repression and an intrinsically disordered region (IDR) at the C-terminus that directly binds to and neutralizes the toxin. Recently accumulated data have suggested that the antitoxin's IDRs exhibit variable degrees of preexisting helical conformations that stabilize upon binding to the corresponding toxin or operator DNA and function as a central hub in regulatory protein interaction networks of the type II TA system. However, the biological and pathogenic functions of the antitoxin's IDRs have not been well discussed compared with those of IDRs from the eukaryotic proteome. Here, we focus on the current state of knowledge about the versatile roles of IDRs of type II antitoxins in TA regulation and provide insights into the discovery of new antibiotic candidates that induce toxin activation/reactivation and cell death by modulating the regulatory dynamics or allostery of the antitoxin.

Structural basis for CEP192-mediated regulation of centrosomal AURKA.

Aurora kinase A (AURKA) performs critical functions in mitosis. Thus, the activity and subcellular localization of AURKA are tightly regulated and depend on diverse factors including interactions with the multiple binding cofactors. How these different cofactors regulate AURKA to elicit different levels of activity at distinct subcellular locations and times is poorly understood. Here, we identified a conserved region of CEP192, the major cofactor of AURKA, that mediates the interaction with AURKA. Quantitative binding studies were performed to map the interactions of a conserved helix (Helix-1) within CEP192. The crystal structure of Helix-1 bound to AURKA revealed a distinct binding site that is different from other cofactor proteins such as TPX2. Inhibiting the interaction between Helix-1 and AURKA in cells led to the mitotic defects, demonstrating the importance of the interaction. Collectively, we revealed a structural basis for the CEP192-mediated AURKA regulation at the centrosome, which is distinct from TPX2-mediated regulation on the spindle microtubule.

Domain swapping of the C-terminal helix promotes the dimerization of a novel ribonuclease protein from Mycobacterium tuberculosis.

Polyketide metabolism-associated proteins in Mycobacterium tuberculosis play an essential role in the survival of the bacterium, which makes them potential drug targets for the treatment of tuberculosis (TB). The novel ribonuclease protein Rv1546 is predicted to be a member of the steroidogenic acute regulatory protein-related lipid-transfer (START) domain superfamily, which comprises bacterial polyketide aromatase/cyclases (ARO/CYCs). Here, we determined the crystal structure of Rv1546 in a V-shaped dimer. The Rv1546 monomer consists of four α-helices and seven antiparallel ß-strands. Interestingly, in the dimeric state, Rv1546 forms a helix-grip fold, which is present in START domain proteins, via three-dimensional domain swapping. Structural analysis revealed that the conformational change of the C-terminal α-helix of Rv1546 might contribute to the unique dimer structure. Site-directed mutagenesis followed by in vitro ribonuclease activity assays was performed to identify catalytic sites of the protein. This experiment suggested that surface residues R63, K84, K88, and R113 are important in the ribonuclease function of Rv1546. In summary, this study presents the structural and functional characterization of Rv1546 and supplies new perspectives for exploiting Rv1546 as a novel drug target for TB treatment.

Aurora Kinase A Regulation by Cysteine Oxidative Modification.

Aurora kinase A (AURKA), which is a member of serine/threonine kinase family, plays a critical role in regulating mitosis. AURKA has drawn much attention as its dysregulation is critically associated with various cancers, leading to the development of AURKA inhibitors, a new class of anticancer drugs. As the spatiotemporal activity of AURKA critically depends on diverse intra- and inter-molecular factors, including its interaction with various protein cofactors and post-translational modifications, each of these pathways should be exploited for the development of a novel class of AURKA inhibitors other than ATP-competitive inhibitors. Several lines of evidence have recently shown that redox-active molecules can modify the cysteine residues located on the kinase domain of AURKA, thereby regulating its activity. In this review, we present the current understanding of how oxidative modifications of cysteine residues of AURKA, induced by redox-active molecules, structurally and functionally regulate AURKA and discuss their implications in the discovery of novel AURKA inhibitors.

Effect of Diurnal Variation of Heart Rate and Respiratory Rate on Activation of Rapid Response System and Clinical Outcome in Hospitalized Children.

Heart rate and respiratory rate display circadian variation. Pediatric single-parameter rapid response system is activated when heart rate or respiratory rate deviate from age-specific criteria, though activation criteria do not differentiate between daytime and nighttime, and unnecessary activation has been reported due to nighttime bradycardia. We evaluated the relationship between rapid response system activation and the patient's clinical outcome by separately applying the criteria to daytime and nighttime in patients < 18. The observation period was divided into daytime and nighttime (8:00−20:00, and 20:00 to 8:00), according to which measured heart rate and respiratory rate were divided and rapid response system activation criteria were applied. We classified lower nighttime than daytime values into the 'decreased group', and the higher ones into the 'increased group', to analyze their effect on cardiopulmonary resuscitation occurrence or intensive care unit transfer. Nighttime heart rate and respiratory rate were lower than the daytime ones in both groups (both p values < 0.001), with no significant association with cardiopulmonary resuscitation occurrence or intensive care unit transfer in either group. Heart rate and respiratory rate tend to be lower at nighttime; however, their effect on the patient's clinical outcome is not significant.