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Background Acute kidney injury (AKI) in pediatric critical care patients is diagnosed using elevated serum creatinine, which occurs only after kidney impairment. There are no treatments other than supportive care for AKI once it has developed, so it is important to identify patients at risk to prevent injury. This study develops a machine learning model to learn pre-disease patterns of physiological measurements and predict pediatric AKI up to 48 h earlier than the currently established diagnostic guidelines. Methods EHR data from 16,863 pediatric critical care patients between 1 month to 21 years of age from three independent institutions were used to develop a single machine learning model for early prediction of creatinine-based AKI using intelligently engineered predictors, such as creatinine rate of change, to automatically assess real-time AKI risk. The primary outcome is prediction of moderate to severe AKI (Stage 2/3), and secondary outcomes are prediction of any AKI (Stage 1/2/3) and requirement of renal replacement therapy (RRT). Predictions generate alerts allowing fast assessment and reduction of AKI risk, such as: “patient has 90% risk of developing AKI in the next 48 h” along with contextual information and suggested response such as “patient on aminoglycosides, suggest check level and review dose and indication”. Results The model was successful in predicting Stage 2/3 AKI prior to detection by conventional criteria with a median lead-time of 30 h at AUROC of 0.89. The model predicted 70% of subsequent RRT episodes, 58% of Stage 2/3 episodes, and 41% of any AKI episodes. The ratio of false to true alerts of any AKI episodes was approximately one-to-one (PPV 47%). Among patients predicted, 79% received potentially nephrotoxic medication after being identified by the model but before development of AKI. Conclusions As the first multi-center validated AKI prediction model for all pediatric critical care patients, the machine learning model described in this study accurately predicts moderate to severe AKI up to 48 h in advance of AKI onset. The model may improve outcome of pediatric AKI by providing early alerting and actionable feedback, potentially preventing or reducing AKI by implementing early measures such as medication adjustment.

Gardeniae Fructus (GF), a traditional Chinese medicine rich in iridoids, has demonstrated skin-improving effects. However, its mechanisms for enhancing epidermal barrier function remain unclear. In this study, the iridoids in GF were characterized using UPLC-MS/MS. The improvement in the barrier function by GF was assessed through in vitro experiments and a human efficacy assessment. In addition, the potential targets were predicted through proteomics analysis, molecular docking, and molecular dynamics (MD), and verified in HaCaT cells and three-dimensional epidermal models. Nine iridoids were identified in GF. In vitro, GF effectively promoted cell migration and reduced cell damage and oxidative stress. Proteomics analysis combined with molecular docking and MD simulations predicted that the primary iridoids in GF ameliorate barrier function by binding to the aryl hydrocarbon receptor (AHR) with high affinity and stability. Subsequent validation demonstrated that GF significantly upregulated AHR, filaggrin (FLG), loricrin (LOR), and involucrin (IVL) mRNA and protein expression. A 28-day randomized double-blind human efficacy assessment in subjects with sensitive skin showed that the gel with GF increased stratum corneum hydration, reduced transepidermal water loss (TEWL), and lowered erythema index and lactic acid tingling. These findings suggest that GF enhances the skin barrier via AHR activation-mediated upregulation of barrier proteins, supporting its cosmeceutical potential.

Background Timely recognition of hemodynamic instability in critically ill patients enables increased vigilance and early treatment opportunities. We develop the Hemodynamic Stability Index (HSI), which highlights situational awareness of possible hemodynamic instability occurring at the bedside and to prompt assessment for potential hemodynamic interventions. Methods We used an ensemble of decision trees to obtain a real-time risk score that predicts the initiation of hemodynamic interventions an hour into the future. We developed the model using the eICU Research Institute (eRI) database, based on adult ICU admissions from 2012 to 2016. A total of 208,375 ICU stays met the inclusion criteria, with 32,896 patients (prevalence = 18%) experiencing at least one instability event where they received one of the interventions during their stay. Predictors included vital signs, laboratory measurements, and ventilation settings. Results HSI showed significantly better performance compared to single parameters like systolic blood pressure and shock index (heart rate/systolic blood pressure) and showed good generalization across patient subgroups. HSI AUC was 0.82 and predicted 52% of all hemodynamic interventions with a lead time of 1-h with a specificity of 92%. In addition to predicting future hemodynamic interventions, our model provides confidence intervals and a ranked list of clinical features that contribute to each prediction. Importantly, HSI can use a sparse set of physiologic variables and abstains from making a prediction when the confidence is below an acceptable threshold. Conclusions The HSI algorithm provides a single score that summarizes hemodynamic status in real time using multiple physiologic parameters in patient monitors and electronic medical records (EMR). Importantly, HSI is designed for real-world deployment, demonstrating generalizability, strong performance under different data availability conditions, and providing model explanation in the form of feature importance and prediction confidence.

Gardeniae Fructus (GF), the desiccative mature fruitage of Gardenia jasminoides J. Ellis (G. jasminoides), is a traditional herbal medicine in China with potential value against skin photodamage. However, the phytochemical basis and mechanisms underlying GF’s anti-photodamage effects remain unclear. In this study, the chemical components in GF extract (GFE) were analyzed using ultra-high-performance liquid chromatography coupled with tandem mass spectrometry (UPLC-MS/MS), and iridoids were identified as the main components. The antioxidant, anti-inflammatory, and barrier-repair effects of GFE in UVB-induced photodamage were assessed through in vitro experiments. Additionally, the potential mechanisms of GFE against skin photodamage were predicted using proteomics and network pharmacology. The results showed that GFE significantly increased the levels of total superoxide dismutase (T-SOD), catalase (CAT), and glutathione peroxidase (GSH-Px) induced by UVB, while decreasing reactive oxygen species (ROS) and malondialdehyde (MDA) contents. GFE also inhibited the secretion of interleukin-6 (IL-6) and interleukin-1β (IL-1β). Additionally, GFE upregulated the expression of filaggrin (FLG), loricrin (LOR), and involucrin (IVL) in 3D epidermal skin models. Proteomic analysis and network pharmacology indicated that the iridoid components identified in GFE ameliorated UVB-induced damage probably by regulating cell cycle-related proteins and signaling pathways, though this requires further experimental confirmation. Overall, the results provide essential evidence to support the development of GFE as a skincare active ingredient.

BackgroundIntensivists target different blood pressure component values to manage intensive care unit (ICU) patients with sepsis. We aimed to evaluate the relationship between individual blood pressure components and organ dysfunction in critically ill septic patients.MethodsIn this retrospective observational study, we evaluated 77,328 septic patients in 364 ICUs in the eICU Research Institute database. Primary exposure was the lowest cumulative value of each component; mean, systolic, diastolic, and pulse pressure, sustained for at least 120 min during ICU stay. Primary outcome was ICU mortality and secondary outcomes were composite outcomes of acute kidney injury or death and myocardial injury or death during ICU stay. Multivariable logistic regression spline and threshold regression adjusting for potential confounders were conducted to evaluate associations between exposures and outcomes. Sensitivity analysis was conducted in 4211 patients with septic shock.ResultsLower values of all blood pressures components were associated with a higher risk of ICU mortality. Estimated change-points for the risk of ICU mortality were 69 mmHg for mean, 100 mmHg for systolic, 60 mmHg for diastolic, and 57 mmHg for pulse pressure. The strength of association between blood pressure components and ICU mortality as determined by slopes of threshold regression were mean (− 0.13), systolic (− 0.11), diastolic (− 0.09), and pulse pressure (− 0.05). Equivalent non-linear associations between blood pressure components and ICU mortality were confirmed in septic shock patients. We observed a similar relationship between blood pressure components and secondary outcomes.ConclusionBlood pressure component association with ICU mortality is the strongest for mean followed by systolic, diastolic, and weakest for pulse pressure. Critical care teams should continue to follow MAP-based resuscitation, though exploratory analysis focusing on blood pressure components in different sepsis phenotypes in critically ill ICU patients is needed.

Patients supported by mechanical ventilation require frequent invasive blood gas samples to monitor and adjust the level of support. We developed a transparent and novel blood gas estimation model to provide continuous monitoring of blood pH and arterial CO 2 in between gaps of blood draws, using only readily available noninvasive data sources in ventilated patients. The model was trained on a derivation dataset (1,883 patients, 12,344 samples) from a tertiary pediatric intensive care center, and tested on a validation dataset (286 patients, 4030 samples) from the same center obtained at a later time. The model uses pairwise non-linear interactions between predictors and provides point-estimates of blood gas pH and arterial CO 2 along with a range of prediction uncertainty. The model predicted within Clinical Laboratory Improvement Amendments of 1988 (CLIA) acceptable blood gas machine equivalent in 74% of pH samples and 80% of PCO 2 samples. Prediction uncertainty from the model improved estimation accuracy by 15% by identifying and abstaining on a minority of high-uncertainty samples. The proposed model estimates blood gas pH and CO 2 accurately in a large percentage of samples. The model’s abstention recommendation coupled with ranked display of top predictors for each estimation lends itself to real-time monitoring of gaps between blood draws, and the model may help users determine when a new blood draw is required and delay blood draws when not needed.

Our auditory systems are evolved to process a myriad of acoustic environments. In complex listening scenarios, we can tune our attention to one sound source (e.g., a conversation partner), while monitoring the entire acoustic space for cues we might be interested in (e.g., our names being called, or the fire alarm going off). While normal hearing listeners handle complex listening scenarios remarkably well, hearing-impaired listeners experience difficulty even when wearing hearing-assist devices. This thesis presents both theoretical work in understanding the neural mechanisms behind this process, as well as the application of neural models to segregate mixed sources and potentially help the hearing impaired population. On the theoretical side, auditory spatial processing has been studied primarily up to the midbrain region, and studies have shown how individual neurons can localize sounds using spatial cues. Yet, how higher brain regions such as the cortex use this information to process multiple sounds in competition is not clear. This thesis demonstrates a physiology-based spiking neural network model, which provides a mechanism illustrating how the auditory cortex may organize up-stream spatial information when there are multiple competing sound sources in space. Based on this model, an engineering solution to help hearing-impaired listeners segregate mixed auditory inputs is proposed. Using the neural model to perform sound-segregation in the neural domain, the neural outputs (representing the source of interest) are reconstructed back to the acoustic domain using a novel stimulus reconstruction method.

As more and more infection-specific machine learning models are developed and planned for clinical deployment, simultaneously running predictions from different models may provide overlapping or even conflicting information. It is important to understand the concordance and behavior of parallel models in deployment. In this study, we focus on two models for the early detection of hospital-acquired infections (HAIs): 1) the Infection Risk Index (IRI) and 2) the Ventilator-Associated Pneumonia (VAP) prediction model. The IRI model was built to predict all HAIs, whereas the VAP model identifies patients at risk of developing ventilator-associated pneumonia. These models could make important improvements in patient outcomes and hospital management of infections through early detection of infections and in turn, enable early interventions. The two models vary in terms of infection label definition, cohort selection, and prediction schema. In this work, we present a comparative analysis between the two models to characterize concordances and confusions in predicting HAIs by these models. The learnings from this study will provide important findings for how to deploy multiple concurrent disease-specific models in the future.

Our work focuses on the problem of predicting the transfer of pediatric patients from the general ward of a hospital to the pediatric intensive care unit. Using data collected over 5.5 years from the electronic health records of two medical facilities, we develop classifiers based on adaptive boosting and gradient tree boosting. We further combine these learned classifiers into an ensemble model and compare its performance to a modified pediatric early warning score (PEWS) baseline that relies on expert defined guidelines. To gauge model generalizability, we perform an inter-facility evaluation where we train our algorithm on data from one facility and perform evaluation on a hidden test dataset from a separate facility. We show that improvements are witnessed over the PEWS baseline in accuracy (0.77 vs. 0.69), sensitivity (0.80 vs. 0.68), specificity (0.74 vs. 0.70) and AUROC (0.85 vs. 0.73).

Cognitive impairment is a common dysfunction following stroke, significantly affecting patients' quality of life. Studies suggest that post-stroke cognitive impairment (PSCI) may be related to neural activity in specific brain regions. However, the neural mechanisms remain to be further explored. This study aimed to investigate the alterations in brain function in patients with PSCI. This was a case-control study. Thirty patients with PSCI, thirty with non-PSCI (NPSCI), and thirty age- and gender-matched healthy controls (HCs) were selected in a 1:1:1 ratio. Resting-state functional magnetic resonance imaging (rs-fMRI) were acquired from all participants to study the potential neural mechanisms of PSCI patients by comparing the differences in fractional amplitude of low-frequency fluctuation (fALFF), Kendall's coefficient of concordance-regional homogeneity (KCC-ReHo), and seed-based functional connectivity (FC). Additionally, the Montreal Cognitive Assessment (MoCA) scores of PSCI patients were collected, and Pearson correlation was used to analyze the correlation between functional indicators and cognitive performance in PSCI patients. fALFF analysis revealed that the PSCI group had decreased zfALFF values in the left caudate, right inferior temporal gyrus (ITG), anterior cingulate cortex (ACC), left putamen, and left superior temporal gyrus. In contrast, increased zfALFF values were observed in the right Cerebellum_6. KCC-ReHo analysis indicated that the PSCI group had decreased SzKCC-ReHo values in the right middle frontal gyrus (MFG) and left postcentral lobe, while increased SzKCC-ReHo values in the left cerebellum_ crus 1, and left cerebellum_4-5. Furthermore, seed-based FC analysis revealed decreased zFC values between brain regions in the PSCI group, especially between the angular gyrus and precuneus. Additionally, correlation analysis showed that the zfALFF value of ACC was positively correlated with MoCA scores in the PSCI group. This study demonstrated significant changes in the spontaneous neural activity intensity, regional homogeneity, and FC of multiple cognition-related brain regions in PSCI patients, shedding light on the underlying neural mechanisms of brain function in PSCI.