Non-invasive pulse arrival time is associated with cardiac index in pediatric heart transplant patients with normal ejection fraction.

OBJECTIVE: Cardiac Index (CI) is a key physiologic parameter to ensure end organ perfusion in the pediatric intensive care unit (PICU). Determination of CI requires invasive cardiac measurements and is not routinely done at the PICU bedside. To date, there is no gold standard non-invasive means to determine CI. This study aims to use a novel non-invasive methodology, based on routine continuous physiologic data, called Pulse Arrival Time (PAT) as a surrogate for CI in patients with normal Ejection Fraction. Approach: Electrocardiogram (ECG) and photoplethysmogram (PPG) signals were collected from beside monitors at a sampling frequency of 250 samples per second. Continuous PAT, derived from the ECG and PPG waveforms was averaged per patient. Pearson's correlation coefficient was calculated between PAT and CI, PAT and heart rate (HR), and PAT and ejection fraction (EF). Main Results: Twenty patients underwent right heart cardiac catheterization. The mean age of patients was 11.7±5.4 years old, ranging from 11 months old to 19 years old, the median age was 13.4 years old. HR in this cohort was 93.8±17.0 beats per minute. The average EF was 54.4±9.6%. The average CI was 3.51±0.72 L/min/m2, with ranging from 2.6 to 4.77 L/min/m2. The average PAT was 0.31±0.12 seconds. Pearson correlation analysis showed a positive correlation between PAT and CI (0.57, p < 0.01). Pearson correlation between HR and CI, and correlation between EF and CI was 0.22 (p = 0.35) and 0.03 (p = 0.23) respectively. The correlation between PAT, when indexed by HR (i.e. PAT × HR), and CI minimally improved to 0.58 (p < 0.01). Significance: This pilot study demonstrates that PAT may serve as a valuable surrogate marker for CI at the bedside, as a non-invasive and continuous modality in the PICU. The use of PAT in clinical practice remains to be thoroughly investigated. .

Suboptimal Cerebral Perfusion is Associated with Ischemia After Intracerebral Hemorrhage.

BACKGROUND: Remote ischemic lesions on diffusion-weighted imaging (DWI) occur in one third of patients with intracerebral hemorrhage (ICH) and are associated with worse outcomes. The etiology is unclear and not solely due to blood pressure reduction. We hypothesized that impaired cerebrovascular autoregulation and hypoperfusion below individualized lower limits of autoregulation are associated with the presence of DWI lesions. METHODS: This was a retrospective, single-center study of all primary ICH with intraparenchymal pressure monitoring within 10 days from onset and subsequent magnetic resonance imaging. Pressure reactivity index was calculated as the correlation coefficient between mean arterial pressure and intracranial pressure. Optimal cerebral perfusion pressure (CPPopt) is the cerebral perfusion pressure (CPP) with the lowest corresponding pressure reactivity index. The difference between CPP and CPPopt, time spent below the lower limit of autoregulation (LLA), and time spent above the upper limit of autoregulation (ULA) were calculated by using mean hourly physiologic data. Univariate associations between physiologic parameters and DWI lesions were analyzed by using binary logistic regression. RESULTS: A total of 505 h of artifact-free data from seven patients without DWI lesions and 479 h from six patients with DWI lesions were analyzed. Patients with DWI lesions had higher intracranial pressure (17.50 vs. 10.92 mm Hg; odds ratio 1.14, confidence interval 1.01-1.29) but no difference in mean arterial pressure or CPP compared with patients without DWI lesions. The presence of DWI lesions was significantly associated with a greater percentage of time spent below the LLA (49.85% vs. 14.70%, odds ratio 5.77, confidence interval 1.88-17.75). No significant association was demonstrated between CPPopt, the difference between CPP and CPPopt, ULA, LLA, or time spent above the ULA between groups. CONCLUSIONS: Blood pressure reduction below the LLA is associated with ischemia after acute ICH. Individualized, autoregulation-informed targets for blood pressure reduction may provide a novel paradigm in acute management of ICH and require further study.

Heart rate and heart rate variability as a prognosticating feature for functional outcome after cardiac arrest: A scoping review.

Background: Despite significant progress in cardiopulmonary resuscitation and post-cardiac arrest care, favorable outcome in out-of hospital sudden cardiac arrest patients remains low. One of the main reasons for mortality in these patients is withdrawal of life-sustaining treatment. There is a need for precise and equitable prognostication tools to support families in avoiding premature or inappropriate WLST. Heart rate (HR) and heart rate variability (HRV) have been noted for their association with outcome, and are positioned to be a useful modality for prognostication. Objectives: The aim of this scoping review is to rigorously explore which electrocardiography features have been shown to predict functional outcome in post-cardiac arrest patients. Methods: The search was performed in Pubmed, EMBASE, and SCOPUS for studies published from January 1, 2011, to September 29, 2022, including papers in English or Korean. Results: Seven studies were included with a total of 1359 patients. Four studies evaluated HR, one study evaluated RR inverval, and two studies evaluated HRV. All studies were retrospective, with 3 multi-center and 4 single-center studies. All seven studies were inclusive of patients who underwent targeted temperature management (TTM) after cardiac arrest, and two studies included patients without TTM. Five studies used cerebral performance category to assess functional outcome, two studies used Glasgow outcome score, and one study used modified Rankin scale. Three studies measured outcome at hospital discharge, one study measured outcome at 14 days after return of spontaneous circulation, two studies measured outcome after 3 months, and one after 1 year. In all studies that evaluated HR, lower HR was associated with favorable functional outcome. Two studies found that higher complexity of HRV was associated with favorable functional outcome. Conclusion: HR and HRV showed clear associations with functional outcome in patients after CA, but cinilcial utility for prognostication is uncertain.

The oxygen reactivity index indicates disturbed local perfusion regulation after aneurysmal subarachnoid hemorrhage: an observational cohort study.

BACKGROUND: Cerebral autoregulation (CA) can be impaired in patients with delayed cerebral ischemia (DCI) after aneurysmal subarachnoid hemorrhage (aSAH). The Pressure Reactivity Index (PRx, correlation of blood pressure and intracranial pressure) and Oxygen Reactivity Index (ORx, correlation of cerebral perfusion pressure and brain tissue oxygenation, PbtO2) are both believed to estimate CA. We hypothesized that CA could be poorer in hypoperfused territories during DCI and that ORx and PRx may not be equally effective in detecting such local variances. METHODS: ORx and PRx were compared daily in 76 patients with aSAH with or without DCI until the time of DCI diagnosis. The ICP/PbtO2-probes of DCI patients were retrospectively stratified by being in or outside areas of hypoperfusion via CT perfusion image, resulting in three groups: DCI + /probe + (DCI patients, probe located inside the hypoperfused area), DCI + /probe- (probe outside the hypoperfused area), DCI- (no DCI). RESULTS: PRx and ORx were not correlated (r = - 0.01, p = 0.56). Mean ORx but not PRx was highest when the probe was located in a hypoperfused area (ORx DCI + /probe + 0.28 ± 0.13 vs. DCI + /probe- 0.18 ± 0.15, p < 0.05; PRx DCI + /probe + 0.12 ± 0.17 vs. DCI + /probe- 0.06 ± 0.20, p = 0.35). PRx detected poorer autoregulation during the early phase with relatively higher ICP (days 1-3 after hemorrhage) but did not differentiate the three groups on the following days when ICP was lower on average. ORx was higher in the DCI + /probe + group than in the other two groups from day 3 onward. ORx and PRx did not differ between patients with DCI, whose probe was located elsewhere, and patients without DCI (ORx DCI + /probe- 0.18 ± 0.15 vs. DCI- 0.20 ± 0.14; p = 0.50; PRx DCI + /probe- 0.06 ± 0.20 vs. DCI- 0.08 ± 0.17, p = 0.35). CONCLUSIONS: PRx and ORx are not interchangeable measures of autoregulation, as they likely measure different homeostatic mechanisms. PRx represents the classical cerebrovascular reactivity and might be better suited to detect disturbed autoregulation during phases with moderately elevated ICP. Autoregulation may be poorer in territories affected by DCI. These local perfusion disturbances leading up to DCI may be more readily detected by ORx than PRx. Further research should investigate their robustness to detect DCI and to serve as a basis for autoregulation-targeted treatment after aSAH.

Automatic identification of intracranial pressure waveform during external ventricular drainage clamping: segmentation via wavelet analysis.

Objective. The objective of this study is to develop and validate a method for automatically identifying segments of intracranial pressure (ICP) waveform data from external ventricular drainage (EVD) recordings during intermittent drainage and closure.Methods. The proposed method uses time-frequency analysis through wavelets to distinguish periods of ICP waveform in EVD data. By comparing the frequency compositions of the ICP signals (when the EVD system is clamped) and the artifacts (when the system is open), the algorithm can detect short, uninterrupted segments of ICP waveform from the longer periods of non-measurement data. The method involves applying a wavelet transform, calculating the absolute power in a specific range, using Otsu thresholding to automatically identify a threshold, and performing a morphological operation to remove small segments. Two investigators manually graded the same randomly selected one-hour segments of the resulting processed data. Performance metrics were calculated as a percentage.Results. The study analyzed data from 229 patients who had EVD placed following subarachnoid hemorrhage between June 2006 and December 2012. Of these, 155 (67.7%) were female and 62 (27%) developed delayed cerebral ischemia. A total of 45 150 h of data were segmented. 2044 one-hour segments were randomly selected and evaluated by two investigators (MM and DN). Of those, the evaluators agreed on the classification of 1556 one-hour segments. The algorithm was able to correctly identify 86% (1338 h) of ICP waveform data. 8.2% (128 h) of the time the algorithm either partially or fully failed to segment the ICP waveform. 5.4% (84 h) of data, artifacts were mistakenly identified as ICP waveforms (false positives).Conclusion. The proposed algorithm automates the identification of valid ICP waveform segments of waveform in EVD data and thus enables the inclusion in real-time data analysis for decision support. It also standardizes and makes research data management more efficient.

A Deep Learning Framework for Deriving Noninvasive Intracranial Pressure Waveforms from Transcranial Doppler.

Increased intracranial pressure (ICP) causes disability and mortality in the neurointensive care population. Current methods for monitoring ICP are invasive. We designed a deep learning framework using a domain adversarial neural network to estimate noninvasive ICP, from blood pressure, electrocardiogram, and cerebral blood flow velocity. Our model had a mean of median absolute error of 3.88 ± 3.26 mmHg for the domain adversarial neural network, and 3.94 ± 1.71 mmHg for the domain adversarial transformers. Compared with nonlinear approaches, such as support vector regression, this was 26.7% and 25.7% lower. Our proposed framework provides more accurate noninvasive ICP estimates than currently available. ANN NEUROL 2023;94:196-202.

Optimal Cerebral Perfusion Pressure and Brain Tissue Oxygen in Aneurysmal Subarachnoid Hemorrhage.

BACKGROUND: Targeting a cerebral perfusion pressure optimal for cerebral autoregulation (CPPopt) has been gaining more attention to prevent secondary damage after acute neurological injury. Brain tissue oxygenation (PbtO2) can identify insufficient cerebral blood flow and secondary brain injury. Defining the relationship between CPPopt and PbtO2 after aneurysmal subarachnoid hemorrhage may result in (1) mechanistic insights into whether and how CPPopt-based strategies might be beneficial and (2) establishing support for the use of PbtO2 as an adjunctive monitor for adequate or optimal local perfusion. METHODS: We performed a retrospective analysis of a prospectively collected 2-center dataset of patients with aneurysmal subarachnoid hemorrhage with or without later diagnosis of delayed cerebral ischemia (DCI). CPPopt was calculated as the cerebral perfusion pressure (CPP) value corresponding to the lowest pressure reactivity index (moving correlation coefficient of mean arterial and intracranial pressure). The relationship of (hourly) deltaCPP (CPP-CPPopt) and PbtO2 was investigated using natural spline regression analysis. Data after DCI diagnosis were excluded. Brain tissue hypoxia was defined as PbtO2 <20 mmHg. RESULTS: One hundred thirty-one patients were included with a median of 44.0 (interquartile range, 20.8-78.3) hourly CPPopt/PbtO2 datapoints. The regression plot revealed a nonlinear relationship between PbtO2 and deltaCPP (P<0.001) with PbtO2 decrease with deltaCPP <0 mmHg and stable PbtO2 with deltaCPP ≥0mmHg, although there was substantial individual variation. Brain tissue hypoxia (34.6% of all measurements) was more frequent with deltaCPP <0 mmHg. These dynamics were similar in patients with or without DCI. CONCLUSIONS: We found a nonlinear relationship between PbtO2 and deviation of patients' CPP from CPPopt in aneurysmal subarachnoid hemorrhage patients in the pre-DCI period. CPP values below calculated CPPopt were associated with lower PbtO2. Nevertheless, the nature of PbtO2 measurements is complex, and the variability is high. Combined multimodality monitoring with CPP/CPPopt and PbtO2 should be recommended to redefine individual pressure targets (CPP/CPPopt) and retain the option to detect local perfusion deficits during DCI (PbtO2), which cannot be fulfilled by both measurements interchangeably.

A generalizable physiological model for detection of Delayed Cerebral Ischemia using Federated Learning.

Delayed cerebral ischemia (DCI) is a complication seen in patients with subarachnoid hemorrhage stroke. It is a major predictor of poor outcomes and is detected late. Machine learning models are shown to be useful for early detection, however training such models suffers from small sample sizes due to rarity of the condition. Here we propose a Federated Learning approach to train a DCI classifier across three institutions to overcome challenges of sharing data across hospitals. We developed a framework for federated feature selection and built a federated ensemble classifier. We compared the performance of FL model to that obtained by training separate models at each site. FL significantly improved performance at only two sites. We found that this was due to feature distribution differences across sites. FL improves performance in sites with similar feature distributions, however, FL can worsen performance in sites with heterogeneous distributions. The results highlight both the benefit of FL and the need to assess dataset distribution similarity before conducting FL.

Predicting Shunt Dependency from the Effect of Cerebrospinal Fluid Drainage on Ventricular Size.

BACKGROUND: Prolonged external ventricular drainage (EVD) in patients with subarachnoid hemorrhage (SAH) leads to morbidity, whereas early removal can have untoward effects related to recurrent hydrocephalus. A metric to help determine the optimal time for EVD removal or ventriculoperitoneal shunt (VPS) placement would be beneficial in preventing the prolonged, unnecessary use of EVD. This study aimed to identify whether dynamics of cerebrospinal fluid (CSF) biometrics can temporally predict VPS dependency after SAH. METHODS: This was a retrospective analysis of a prospective, single-center, observational study of patients with aneurysmal SAH who required EVD placement for hydrocephalus. Patients were divided into VPS-dependent (VPS+) and non-VPS dependent groups. We measured the bicaudate index (BCI) on all available computed tomography scans and calculated the change over time (ΔBCI). We analyzed the relationship of ΔBCI with CSF output by using Pearson's correlation. A k-nearest neighbor model of the relationship between ΔBCI and CSF output was computed to classify VPS. RESULTS: Fifty-eight patients met inclusion criteria. CSF output was significantly higher in the VPS+ group in the 7 days post EVD placement. There was a negative correlation between delta BCI and CSF output in the VPS+ group (negative delta BCI means ventricles become smaller) and a positive correlation in the VPS- group starting from days four to six after EVD placement (p < 0.05). A weighted k-nearest neighbor model for classification had a sensitivity of 0.75, a specificity of 0.70, and an area under the receiver operating characteristic curve of 0.80. CONCLUSIONS: The correlation of ΔBCI and CSF output is a reliable intraindividual biometric for VPS dependency after SAH as early as days four to six after EVD placement. Our machine learning model leverages this relationship between ΔBCI and cumulative CSF output to predict VPS dependency. Early knowledge of VPS dependency could be studied to reduce EVD duration in many centers (intensive care unit length of stay).

Vector Angle Analysis of Multimodal Neuromonitoring Data for Continuous Prediction of Delayed Cerebral Ischemia.

BACKGROUND: Dysfunctional cerebral autoregulation often precedes delayed cerebral ischemia (DCI). Currently, there are no data-driven techniques that leverage this information to predict DCI in real time. Our hypothesis is that information using continuous updated analyses of multimodal neuromonitoring and cerebral autoregulation can be deployed to predict DCI. METHODS: Time series values of intracranial pressure, brain tissue oxygenation, cerebral perfusion pressure (CPP), optimal CPP (CPPOpt), ΔCPP (CPP - CPPOpt), mean arterial pressure, and pressure reactivity index were combined and summarized as vectors. A validated temporal signal angle measurement was modified into a classification algorithm that incorporates hourly data. The time-varying temporal signal angle measurement (TTSAM) algorithm classifies DCI at varying time points by vectorizing and computing the angle between the test and reference time signals. The patient is classified as DCI+ if the error between the time-varying test vector and DCI+ reference vector is smaller than that between the time-varying test vector and DCI- reference vector. Finally, prediction at time point t is calculated as the majority voting over all the available signals. The leave-one-patient-out cross-validation technique was used to train and report the performance of the algorithms. The TTSAM and classifier performance was determined by balanced accuracy, F1 score, true positive, true negative, false positive, and false negative over time. RESULTS: One hundred thirty-one patients with aneurysmal subarachnoid hemorrhage who underwent multimodal neuromonitoring were identified from two centers (Columbia University: 52 [39.7%], Aachen University: 79 [60.3%]) and included in the analysis. Sixty-four (48.5%) patients had DCI, and DCI was diagnosed 7.2 ± 3.3 days after hemorrhage. The TTSAM algorithm achieved a balanced accuracy of 67.3% and an F1 score of 0.68 at 165 h (6.9 days) from bleed day with a true positive of 0.83, false positive of 0.16, true negative of 0.51, and false negative of 0.49. CONCLUSIONS: A TTSAM algorithm using multimodal neuromonitoring and cerebral autoregulation calculations shows promise to classify DCI in real time.

Machine Learning Models for Predicting the Occurrence of Respiratory Diseases Using Climatic and Air-Pollution Factors.

OBJECTIVES: Because climatic and air-pollution factors are known to influence the occurrence of respiratory diseases, we used these factors to develop machine learning models for predicting the occurrence of respiratory diseases. METHODS: We obtained the daily number of respiratory disease patients in Seoul. We used climatic and air-pollution factors to predict the daily number of patients treated for respiratory diseases per 10,000 inhabitants. We applied the relief-based feature selection algorithm to evaluate the importance of feature selection. We used the gradient boosting and Gaussian process regression (GPR) methods, respectively, to develop two different prediction models. We also employed the holdout cross-validation method, in which 75% of the data was used to train the model, and the remaining 25% was used to test the trained model. We determined the estimated number of respiratory disease patients by applying the developed prediction models to the test set. To evaluate the performance of each model, we calculated the coefficient of determination (R2) and the root mean square error (RMSE) between the original and estimated numbers of respiratory disease patients. We used the Shapley Additive exPlanations (SHAP) approach to interpret the estimated output of each machine learning model. RESULTS: Features with negative weights in the relief-based algorithm were excluded. When applying gradient boosting to unseen test data, R2 and RMSE were 0.68 and 13.8, respectively. For GPR, the R2 and RMSE were 0.67 and 13.9, respectively. SHAP analysis showed that reductions in average temperature, daylight duration, average humidity, sulfur dioxide (SO2), total solar insolation amount, and temperature difference increased the number of respiratory disease patients, whereas increases in atmospheric pressure, carbon monoxide (CO), and particulate matter ≤2.5 µm in aerodynamic diameter (PM2.5) increased the number of respiratory disease patients. CONCLUSION: We successfully developed models for predicting the occurrence of respiratory diseases using climatic and air-pollution factors. These models could evolve into public warning systems.

RNA-assisted self-assembly of monomeric antigens into virus-like particles as a recombinant vaccine platform.

Representing highly ordered repetitive structures of antigen macromolecular assemblies, virus-like particles (VLPs) serve as a high-priority vaccine platform against emerging viral infections, as alternatives to traditional cell culture-based vaccines. RNAs can function as chaperones (Chaperna) and are extremely effective in promoting protein folding. Beyond their canonical function as translational adaptors, tRNAs may moonlight as chaperones for the kinetic control of macromolecular antigen assembly. Capitalizing on genomic RNA co-assembly in infectious virions, we present the first report of a biomimetic assembly of viral capsids that was assisted by non-viral host RNAs into genome-free, non-infectious empty particles. Here, we demonstrate the assembly of bacterially-produced soluble norovirus VP1 forming VLPs (n = 180) in vitro. A tRNA-interacting domain (tRID) was genetically fused with the VP1 capsid protein, as a tRNA docking tag, in the bacterial host to transduce chaperna function for de novo viral antigen folding. tRID/tRNA removal prompted the in vitro assembly of monomeric antigens into highly ordered repetitive structures that elicited robust protective immune responses after immunization. The chaperna-based assembly of monomeric antigens will impact the development and deployment of VLP vaccines for emerging and re-emerging viral infections.

A machine learning-based diagnostic model associated with knee osteoarthritis severity.

Knee osteoarthritis (KOA) is characterized by pain and decreased gait function. We aimed to find KOA-related gait features based on patient reported outcome measures (PROMs) and develop regression models using machine learning algorithms to estimate KOA severity. The study included 375 volunteers with variable KOA grades. The severity of KOA was determined using the Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC). WOMAC scores were used to classify disease severity into three groups. A total of 1087 features were extracted from the gait data. An ANOVA and student's t-test were performed and only features that were significant were selected for inclusion in the machine learning algorithm. Three WOMAC subscales (physical function, pain and stiffness) were further divided into three classes. An ANOVA was performed to determine which selected features were significantly related to the subscales. Both linear regression models and a random forest regression was used to estimate patient the WOMAC scores. Forty-three features were selected based on ANOVA and student's t-test results. The following number of features were selected from each joint: 12 from hip, 1 feature from pelvic, 17 features from knee, 9 features from ankle, 1 feature from foot, and 3 features from spatiotemporal parameters. A significance level of < 0.0001 and < 0.00003 was set for the ANOVA and t-test, respectively. The physical function, pain, and stiffness subscales were related to 41, 10, and 16 features, respectively. Linear regression models showed a correlation of 0.723 and the machine learning algorithm showed a correlation of 0.741. The severity of KOA was predicted by gait analysis features, which were incorporated to develop an objective estimation model for KOA severity. The identified features may serve as a tool to guide rehabilitation and progress assessments. In addition, the estimation model presented here suggests an approach for clinical application of gait analysis data for KOA evaluation.

Two-Dimensional Image-Based Screening Tool for Infants with Positional Cranial Deformities: A Machine Learning Approach.

Positional cranial deformities are relatively common conditions, characterized by asymmetry and changes in skull shape. Although three-dimensional (3D) scanning is the gold standard for diagnosing such deformities, it requires expensive laser scanners and skilled maneuvering. We therefore developed an inexpensive, fast, and convenient screening method to classify cranial deformities in infants, based on single two-dimensional vertex cranial images. In total, 174 measurements from 80 subjects were recorded. Our screening software performs image processing and machine learning-based estimation related to the deformity indices of the cranial ratio (CR) and cranial vault asymmetry index (CVAI) to determine the severity levels of brachycephaly and plagiocephaly. For performance evaluations, the estimated CR and CVAI values were compared to the reference data obtained using a 3D cranial scanner. The CR and CVAI correlation coefficients obtained via support vector regression were 0.85 and 0.89, respectively. When the trained model was evaluated using the unseen test data for the three CR and three CVAI classes, an 86.7% classification accuracy of the proposed method was obtained for both brachycephaly and plagiocephaly. The results showed that our method for screening cranial deformities in infants could aid clinical evaluations and parental monitoring of the progression of deformities at home.

Publisher Correction: Conversion of a soluble protein into a potent chaperone in vivo.

A correction to this article has been published and is linked from the HTML and PDF versions of this paper. The error has been fixed in the paper.

Estimating Maximal Oxygen Uptake From Daily Activity Data Measured by a Watch-Type Fitness Tracker: Cross-Sectional Study.

BACKGROUND: Cardiorespiratory fitness (CRF), an important index of physical fitness, is the ability to inhale and provide oxygen to the exercising muscle. However, despite its importance, the current gold standard for measuring CRF is impractical, requiring maximal exercise from the participants. OBJECTIVE: This study aimed to develop a convenient and practical estimation model for CRF using data collected from daily life with a wristwatch-type device. METHODS: A total of 191 subjects, aged 20 to 65 years, participated in this study. Maximal oxygen uptake (VO2 max), a standard measure of CRF, was measured with a maximal exercise test. Heart rate (HR) and physical activity data were collected using a commercial wristwatch-type fitness tracker (Fitbit; Fitbit Charge; Fitbit) for 3 consecutive days. Maximal activity energy expenditure (aEEmax) and slope between HR and physical activity were calculated using a linear regression. A VO2 max estimation model was built using multiple linear regression with data on age, sex, height, percent body fat, aEEmax, and the slope. The result was validated with 2 different cross-validation methods. RESULTS: aEEmax showed a moderate correlation with VO2 max (r=0.50). The correlation coefficient for the multiple linear regression model was 0.81, and the SE of estimate (SEE) was 3.518 mL/kg/min. The regression model was cross-validated through the predicted residual error sum of square (PRESS). The PRESS correlation coefficient was 0.79, and the PRESS SEE was 3.667 mL/kg/min. The model was further validated by dividing it into different subgroups and calculating the constant error (CE) where a low CE showed that the model does not significantly overestimate or underestimate VO2 max. CONCLUSIONS: This study proposes a CRF estimation method using data collected by a wristwatch-type fitness tracker without any specific protocol for a wide range of the population.

Stabilization of Intrinsically Disordered DKK2 Protein by Fusion to RNA-Binding Domain.

Intrinsic disorders are a common feature of hub proteins in eukaryotic interactomes controlling the signaling pathways. The intrinsically disordered proteins (IDPs) are prone to misfolding, and maintaining their functional stability remains a major challenge in validating their therapeutic potentials. Considering that IDPs are highly enriched in RNA-binding proteins (RBPs), here we reasoned and confirmed that IDPs could be stabilized by fusion to RBPs. Dickkopf2 (DKK2), Wnt antagonist and a prototype IDP, was fused with lysyl-tRNA synthetase (LysRS), with or without the fragment crystallizable (Fc) domain of an immunoglobulin and expressed predominantly as a soluble form from a bacterial host. The functional competence was confirmed by in vitro Wnt signaling reporter and tube formation in human umbilical vein endothelial cells (HUVECs) and in vivo Matrigel plug assay. The removal of LysRS by site-specific protease cleavage prompted the insoluble aggregation, confirming that the linkage to RBP chaperones the functional competence of IDPs. While addressing to DKK2 as a key modulator for cancer and ischemic vascular diseases, our results suggest the use of RBPs as stabilizers of disordered proteinaceous materials for acquiring and maintaining the structural stability and functional competence, which would impact the druggability of a variety of IDPs from human proteome.

Conversion of a soluble protein into a potent chaperone in vivo.

Molecular chaperones play an important role in cellular protein-folding assistance and aggregation inhibition. As a different but complementary model, we previously proposed that, in general, soluble cellular macromolecules with large excluded volume and surface charges exhibit intrinsic chaperone activity to prevent aggregation of their connected polypeptides irrespective of the connection type, thereby contributing to efficient protein folding. As a proof of concept, we here demonstrated that a model recombinant protein with a specific sequence-binding domain robustly exerted chaperone activity toward various proteins harbouring a short recognition tag of 7 residues in Escherichia coli. The chaperone activity of this protein was comparable to that of representative E. coli chaperones in vivo. Furthermore, in vitro refolding experiments confirmed the in vivo results. Our findings reveal that a soluble protein exhibits the intrinsic chaperone activity to prevent off-pathway aggregation of its interacting proteins, leading to more productive folding while allowing them to fold according to their intrinsic folding pathways. This study gives new insights into the plausible chaperoning role of soluble cellular macromolecules in terms of aggregation inhibition and indirect folding assistance.

Quality Screening of Incorrectly Folded Soluble Aggregates from Functional Recombinant Proteins.

Solubility is the prime criterion for determining the quality of recombinant proteins, yet it often fails to represent functional activity due to the involvement of non-functional, misfolded, soluble aggregates, which compromise the quality of recombinant proteins. However, guidelines for the quality assessment of soluble proteins have neither been proposed nor rigorously validated experimentally. Using the aggregation-prone enhanced green-fluorescent protein (EGFP) folding reporter system, we evaluated the folding status of recombinant proteins by employing the commonly used sonication and mild lysis of recombinant host cells. We showed that the differential screening of solubility and folding competence is crucial for improving the quality of recombinant proteins without sacrificing their yield. These results highlight the importance of screening out incorrectly folded soluble aggregates at the initial purification step to ensure the functional quality of recombinant proteins.