Machine learning risk stratification for high-risk infant follow-up of term and late preterm infants.

BACKGROUND: Term and late preterm infants are not routinely referred to high-risk infant follow-up programs at neonatal intensive care unit (NICU) discharge. We aimed to identify NICU factors associated with abnormal developmental screening and develop a risk-stratification model using machine learning for high-risk infant follow-up enrollment. METHODS: We performed a retrospective cohort study identifying abnormal developmental screening prior to 6 years of age in infants born ≥34 weeks gestation admitted to a level IV NICU. Five machine learning models using NICU predictors were developed by classification and regression tree (CART), random forest, gradient boosting TreeNet, multivariate adaptive regression splines (MARS), and regularized logistic regression analysis. Performance metrics included sensitivity, specificity, accuracy, precision, and area under the receiver operating curve (AUC). RESULTS: Within this cohort, 87% (1183/1355) received developmental screening, and 47% had abnormal results. Common NICU predictors across all models were oral (PO) feeding, follow-up appointments, and medications prescribed at NICU discharge. Each model resulted in an AUC > 0.7, specificity >70%, and sensitivity >60%. CONCLUSION: Stratification of developmental risk in term and late preterm infants is possible utilizing machine learning. Applying machine learning algorithms allows for targeted expansion of high-risk infant follow-up criteria. IMPACT: This study addresses the gap in knowledge of developmental outcomes of infants ≥34 weeks gestation requiring neonatal intensive care. Machine learning methodology can be used to stratify early childhood developmental risk for these term and late preterm infants. Applying the classification and regression tree (CART) algorithm described in the study allows for targeted expansion of high-risk infant follow-up enrollment to include those term and late preterm infants who may benefit most.

Patients with juvenile idiopathic arthritis have decreased clonal diversity in the CD8+ T cell repertoire response to influenza vaccination.

Recurrent exposures to a pathogenic antigen remodel the CD8+ T cell compartment and generate a functional memory repertoire that is polyclonal and complex. At the clonotype level, the response to the conserved influenza antigen, M158-66 has been well characterized in healthy individuals, but not in patients receiving immunosuppressive therapy or with aberrant immunity, such as those with juvenile idiopathic arthritis (JIA). Here we show that patients with JIA have a reduced number of M158-66 specific RS/RA clonotypes, indicating decreased clonal richness and, as a result, have lower repertoire diversity. By using a rank-frequency approach to analyze the distribution of the repertoire, we found several characteristics of the JIA T cell repertoire to be akin to repertoires seen in healthy adults, including an amplified RS/RA-specific antigen response, representing greater clonal unevenness. Unlike mature repertoires, however, there is more fluctuation in clonotype distribution, less clonotype stability, and more variable IFNy response of the M158-66 specific RS/RA clonotypes in JIA. This indicates that functional clonal expansion is altered in patients with JIA on immunosuppressive therapies. We propose that the response to the influenza M158-66 epitope described here is a general phenomenon for JIA patients receiving immunosuppressive therapy, and that the changes in clonal richness and unevenness indicate a retarded and uneven generation of a mature immune response.

A Study on Predicting the Deviation of Jet Trajectory Falling Point under the Influence of Random Wind.

As one of the main external factors affecting the fire extinguishing accuracy of sprinkler systems, it is necessary to analyze and study random wind. However, in practical applications, there is little research on the impact of random wind on sprinkler fire extinguishing points. To address this issue, a new random wind acquisition system was constructed in this paper, and a method for predicting jet trajectory falling points in Random Forest (RF) under the influence of random wind was proposed, and compared with the commonly used prediction model Support Vector Machine (SVM). The method in this article reduces the error in the x direction of the 50 m prediction result from 2.11 m to 1.53 m, the error in the y direction from 0.64 m to 0.6 m, and the total mean absolute error (MAE) from 31.3 to 23.5. Simultaneously, predict the falling points of jet trajectory at different distances under the influence of random wind, to demonstrate the feasibility of the proposed method in practical applications. The experimental results show that the system and method proposed in this article can effectively improve the influence of random wind on the falling points of a jet trajectory. In summary, the image acquisition system and error prediction method proposed in this article have many potential applications in fire extinguishing.

Firefighting Water Jet Trajectory Detection from Unmanned Aerial Vehicle Imagery Using Learnable Prompt Vectors.

This research presents an innovative methodology aimed at monitoring jet trajectory during the jetting process using imagery captured by unmanned aerial vehicles (UAVs). This approach seamlessly integrates UAV imagery with an offline learnable prompt vector module (OPVM) to enhance trajectory monitoring accuracy and stability. By leveraging a high-resolution camera mounted on a UAV, image enhancement is proposed to solve the problem of geometric and photometric distortion in jet trajectory images, and the Faster R-CNN network is deployed to detect objects within the images and precisely identify the jet trajectory within the video stream. Subsequently, the offline learnable prompt vector module is incorporated to further refine trajectory predictions, thereby improving monitoring accuracy and stability. In particular, the offline learnable prompt vector module not only learns the visual characteristics of jet trajectory but also incorporates their textual features, thus adopting a bimodal approach to trajectory analysis. Additionally, OPVM is trained offline, thereby minimizing additional memory and computational resource requirements. Experimental findings underscore the method's remarkable precision of 95.4% and efficiency in monitoring jet trajectory, thereby laying a solid foundation for advancements in trajectory detection and tracking. This methodology holds significant potential for application in firefighting systems and industrial processes, offering a robust framework to address dynamic trajectory monitoring challenges and augment computer vision capabilities in practical scenarios.

Correction: Surface mobility gradient and emergent facilitation in glassy films.

Correction for 'Surface mobility gradient and emergent facilitation in glassy films' by Qiang Zhai et al., Soft Matter, 2024, https://doi.org/10.1039/D4SM00221K.

Aerosol sources and transport paths co-control the atmospheric bacterial diversity over the coastal East China Sea.

Airborne bacteria along with chemical composition of aerosols were investigated during five sampling seasons at an offshore island of the East China Sea. Bacterial diversity was the lowest in spring, the highest in winter, and similar between the autumns of 2019 and 2020, suggesting remarkably seasonal variation but little interannual change. Geodermatophilus (Actinobacteria) was the indicator genus of mineral dust (MD) showed higher proportion in spring than in other seasons. Mastigocladopsis_PCC-10914 (Cyanobacteria) as the indicator of sea salt (SS) demonstrated the highest percentages in both autumns, when the air masses mainly passed over the ocean prior to the sampling site. The higher proportions of soil-derived genera Rubellimicrobium and Craurococcus (both Proteobacteria) and extremophile Chroococcidiopsis_SAG_2023 (Cyanobacteria) were found in summer and winter, respectively. Our study explores the linkage between aerosol source and transport path and bacterial composition, which has implication to understanding of land-sea transmission of bacterial taxa.

Ultrasound-assisted deep eutectic solvents extraction of polysaccharides from Loquat leaf: Process optimization and bioactivity study.

Loquat leaves are the by-product of loquat fruit production. Polysaccharides are one of the main active ingredients in loquat leaves. In this study, polysaccharides were extracted from loquat leaves by ultrasonic-assisted deep eutectic solvents (DESs) extraction method. Further, the extracted crude loquat leaf polysaccharides (CLLP) were purified and separated via S-8 resin and DEAE-52 cellulose column chromatography, respectively. Additionally, the effects of polysaccharides on activity of sperm in boar semen preserved in medium at 17 °C, were evaluated preliminarily. DES, composed of choline chloride/ethylene glycol (1:6, molar ratio), was proved to be the suitable solvent for LLP extraction. The optimized extraction conditions were water content 44 %, liquid-solid ratio 1:29 (g/g), extraction temperature 61 °C and extraction time 98 min. Under these conditions, the LLP yield was 57.82 ± 1.50 mg/g. A homogeneous polysaccharide (LLP1-2, Mw: 2.17 × 104 Da) was isolated from CLLP. Its total sugar, uronic acid and protein contents were 76.31 ± 1.25 %, 14.19 ± 0.67 % and 3.28 ± 0.42 %, respectively. Further, 800 µg/mL LLP1-2 could effectively enhance the antioxidant activity of sperm. This study laid a foundation for DESs and column chromatography in the field of polysaccharide extraction and separation, proving that LLP can be used as a natural antioxidant for sperm preservation.

A cosine adaptive particle swarm optimization based long-short term memory method for urban green area prediction.

In the quest for sustainable urban development, precise quantification of urban green space is paramount. This research delineates the implementation of a Cosine Adaptive Particle Swarm Optimization Long Short-Term Memory (CAPSO-LSTM) model, utilizing a comprehensive dataset from Beijing (1998-2021) to train and test the model. The CAPSO-LSTM model, which integrates a cosine adaptive mechanism into particle swarm optimization, advances the optimization of long short-term memory (LSTM) network hyperparameters. Comparative analyses are conducted against conventional LSTM and Partical Swarm Optimization (PSO)-LSTM frameworks, employing mean absolute error (MAE), root mean square error (RMSE), and mean absolute percentage error (MAPE) as evaluative benchmarks. The findings indicate that the CAPSO-LSTM model exhibits a substantial improvement in prediction accuracy over the LSTM model, manifesting as a 66.33% decrease in MAE, a 73.78% decrease in RMSE, and a 57.14% decrease in MAPE. Similarly, when compared to the PSO-LSTM model, the CAPSO-LSTM model demonstrates a 58.36% decrease in MAE, a 65.39% decrease in RMSE, and a 50% decrease in MAPE. These results underscore the efficacy of the CAPSO-LSTM model in enhancing urban green space area prediction, suggesting its significant potential for aiding urban planning and environmental policy formulation.

Outcomes of a Dexamethasone-Prednisone Combination Treatment Course for Status Asthmaticus.

ObjectivesDexamethasone has become the standard of care for pediatric patients with status asthmaticus in the emergency department (ED) setting. Inpatient providers often must decide between continuing the second dose of dexamethasone or transitioning to prednisone. The effectiveness of receiving dexamethasone followed by prednisone (combination therapy) compared to only prednisone or dexamethasone remains unclear. This study compares patient characteristics and ED reutilization/hospital readmission outcomes of dexamethasone, prednisone, and combination therapy for inpatient asthma management.MethodsA retrospective study was conducted at our tertiary children's hospital of children aged 2 to 18 years hospitalized between March 2016 and December 2018 with a primary discharge diagnosis of asthma, reactive airway disease, or bronchospasm. The differences between steroid groups were compared using Fisher's exact or Chi-square tests for categorical variables, and a Kruskal-Wallis test for continuous variables. A multivariable logistic regression was performed to analyze ED reutilization and hospital readmission rates.Results1697 subjects met inclusion criteria. 115 (6.8%) patients received dexamethasone, 597 (35.2%) received prednisone, and 985 (58.0%) received combination therapy. Patients prescribed combination therapy had a lower exacerbation severity than patients prescribed prednisone, but higher severity than patients prescribed dexamethasone (p < 0.001, p = 0.001 respectively). Dexamethasone and combination therapy were not associated with increased 30-day ED reutilization/hospital readmissions compared to prednisone (p > 0.05).ConclusionsIn our study, most patients hospitalized for status asthmaticus received combination therapy. Despite the differences in severity between steroid groups, outcomes of combination therapy and dexamethasone monotherapy, as measured by frequency of ED reutilizations/hospital readmissions, are comparable to prednisone monotherapy.

Surface mobility gradient and emergent facilitation in glassy films.

Confining glassy polymers into films can substantially modify their local and film-averaged properties. We present a lattice model of film geometry with void-mediated facilitation behaviors but free from any elasticity effect. We analyze the spatially varying viscosity to delineate the transport properties of glassy films. The film mobility measurements reported by Yang et al., Science, 2010, 328, 1676 are successfully reproduced. The flow exhibits a crossover from a simple viscous flow to a surface-dominated regime as the temperature decreases. The propagation of a highly mobile front induced by the free surface is visualized in real space. Our approach provides a microscopic treatment of the observed glassy phenomena.

Effect of voltage gradients on EK-PRB remediation: Experimental and molecular dynamics simulations.

Electrokinetic-Permeable Reaction Barrier (EK-PRB) coupled remediation technology can effectively treat heavy metal-contaminated soil near coal mines. This study was conducted on cadmium (Cd), a widely present element in the soil of the mining area. To investigate the impact of the voltage gradient on the remediation effect of EK-PRB, the changes in current, power consumption, pH, and Cd concentration content during the macroscopic experiment were analyzed. A three-dimensional visualized kaolinite-heavy metal-water simulation system was constructed and combined with the Molecular Dynamics (MD) simulations to elucidate the migration mechanism and binding active sites of Cd on the kaolinite (001) crystalline surface at the microscopic scale. The results showed that the voltage gradient positively correlates with the current, power consumption, and Cd concentration during EK-PRB remediation, and the average removal efficiency increases non-linearly with increasing voltage gradient. Considering power consumption, average removal efficiency, and cost-effectiveness, the voltage range is between 1.5 and 3.0 V/cm, with 2.5 V/cm being the optimal value. The results of MD simulations and experiments correspond to each other. Cd2+ formed a highly stable adsorption structure in contrast to the Al-O sheet on the kaolinite (001) crystalline surface. The mean square displacement (MSD) curve of Cd2+ under the electric field exhibits anisotropy, the total diffusion coefficient DTotal increases and the Cd2+ migration rate accelerates. The electric field influences the microstructure of Cd2+ complexes. With the enhancement of the voltage gradient, the complexation between Cd2+ and water molecules is enhanced, and the interaction between Cd2+ and Cl- in solution is weakened.

Optimizing an English text reading recommendation model by integrating collaborative filtering algorithm and FastText classification method.

To comprehend the genuine reading habits and preferences of diverse user cohorts and furnish tailored reading recommendations, this study introduces an English text reading recommendation model designed specifically for long-tail users. This model integrates collaborative filtering algorithms with the FastText classification method. Initially, the integrated collaborative filtering algorithm is explicated, followed by the calculation of the user's interest distribution across various types of English texts, achieved through an enhanced Ebbinghaus forgetting curve and analysis of user reading behaviors. Subsequently, an intelligent English text reading recommendation is generated by amalgamating collaborative filtering algorithms with association rule-based recommendation algorithms. Through optimization of the recommendation generation process, the model's recommendation accuracy is enhanced, thereby augmenting the performance and user satisfaction of the recommendation system. Finally, a comparative analysis is conducted with respect to the Top-N algorithm model, matrix factorization-based algorithm model, and FastText classification model, illustrating the superior recommendation accuracy and F-Measure value of the proposed model. The study findings indicate that when the recommendation list contains 10, 30, 50, and 70 texts, the recommendation accuracy of the proposed algorithm model is 0.75, 0.79, 0.8, and 0.74, respectively, outperforming other algorithms. Furthermore, as the number of texts increases, the F-Measure of all four models gradually improves, with the final F-Measure of the proposed model reaching 0.81. Notably, the F-Measure of the English text reading recommendation model proposed in this study significantly surpasses that of the other three recommendation methods. Demonstrating commendable performance in recall rate, root mean square error, normalized cumulative gain, precision, and accuracy, the model adeptly reflects user reading interests, thereby enhancing the accuracy of text recommendations and the overall system performance. The study findings offer crucial insights and guidance for enhancing the accuracy and overall efficacy of English text recommendation systems.

Accelerating PROTACs Discovery Through a Direct-to-Biology Platform Enabled by Modular Photoclick Chemistry.

Proteolysis targeting chimeras (PROTACs) have emerged as a promising strategy for drug discovery and exploring protein functions, offering a revolutionary therapeutic modality. Currently, the predominant approach to PROTACs discovery mainly relies on an empirical design-synthesis-evaluation process involving numerous cycles of labor-intensive synthesis-purification and bioassay data collection. Therefore, the development of innovative methods to expedite PROTAC synthesis and exploration of chemical space remains highly desired. Here, a direct-to-biology strategy is reported to streamline the synthesis of PROTAC libraries on plates, enabling the seamless transfer of reaction products to cell-based bioassays without the need for additional purification. By integrating amide coupling and light-induced primary amines and o-nitrobenzyl alcohols cyclization (PANAC) photoclick chemistry into a plate-based synthetic process, this strategy produces PROTAC libraries with high efficiency and structural diversity. Moreover, by employing this platform for PROTACs screening, we smoothly found potent PROTACs effectively inhibit triple-negative breast cancer (TNBC) cell growth and induce rapid, selective targeted degradation of cyclin-dependent kinase 9 (CDK9). The study introduces a versatile platform for assembling PROTACs on plates, followed by direct biological evaluation. This approach provides a promising opportunity for high-throughput synthesis of PROTAC libraries, thereby enhancing the efficiency of exploring chemical space and accelerating the discovery of PROTACs.

Research progress of photo-crosslink hydrogels in ophthalmology: A comprehensive review focus on the applications.

Hydrogel presents a three-dimensional polymer network with high water content. Over the past decade, hydrogel has developed from static material to intelligent material with controllable response. Various stimuli are involved in the formation of hydrogel network, among which photo-stimulation has attracted wide attention due to the advantages of controllable conditions, which has a good application prospect in the treatment of ophthalmic diseases. This paper reviews the application of photo-crosslink hydrogels in ophthalmology, focusing on the types of photo-crosslink hydrogels and their applications in ophthalmology, including drug delivery, tissue engineering and 3D printing. In addition, the limitations and future prospects of photo-crosslink hydrogels are also provided.

Functional vertical connectivity of microbial communities in the ocean.

Sinking particles are a critical conduit for the transport of surface microbes to the ocean's interior. Vertical connectivity of phylogenetic composition has been shown; however, the functional vertical connectivity of microbial communities has not yet been explored in detail. We investigated protein and taxa profiles of both free-living and particle-attached microbial communities from the surface to 3000 m depth using a combined metaproteomic and 16S rRNA amplicon sequencing approach. A clear compositional and functional vertical connectivity of microbial communities was observed throughout the water column with Oceanospirillales, Alteromonadales, and Rhodobacterales as key taxa. The surface-derived particle-associated microbes increased the expression of proteins involved in basic metabolism, organic matter processing, and environmental stress response in deep waters. This study highlights the functional vertical connectivity between surface and deep-sea microbial communities via sinking particles and reveals that a considerable proportion of the deep-sea microbes might originate from surface waters and have a major impact on the biogeochemical cycles in the deep sea.

Mechanism of dexmedetomidine protection against cisplatin induced acute kidney injury in rats.

OBJECTIVE: This study explored the molecular mechanisms by which dexmedetomidine (Dex) alleviates cisplatin (CP)-induced acute kidney injury (AKI) in rats. METHODS: CP-induced AKI models were established, and Dex was intraperitoneally injected at different concentrations into rats in the model groups. Subsequently, rats were assigned to the control, CP, CP + Dex 10 µg/kg, and CP + Dex 25 µg/kg groups. After weighing the kidneys of the rats, the kidney arterial resistive index was calculated, and CP-induced AKI was evaluated. In addition, four serum biochemical indices were measured: histopathological damage in rat kidneys was detected; levels of inflammatory factors, interleukin (IL)-1ß, IL-18, IL-6, and tumor necrosis factor alpha, in kidney tissue homogenate of rats were assessed through enzyme-linked immunosorbent assay (ELISA); and levels of NLRP-3, caspase-1, cleaved caspase-1, gasdermin D (GSDMD), and GSDMD-N in kidney tissues of rats were determined via western blotting. RESULTS: Dex treatment reduced nephromegaly and serum clinical marker upregulation caused by CP-induced AKI. In addition, hematoxylin and eosin staining revealed that Dex treatment relieved CP-induced kidney tissue injury in AKI rats. ELISA analyses demonstrated that Dex treatment reduced the upregulated levels of proinflammatory cytokines in the kidney tissue of AKI rats induced by CP, thereby alleviating kidney tissue injury. Western blotting indicated that Dex alleviated CP-induced AKI by inhibiting pyroptosis mediated by NLRP-3 and caspase-1. CONCLUSION: Dex protected rats from CP-induced AKI, and the mechanism may be related to NLRP-3/Caspase-1-mediated pyroptosis.

TPG-functionalized PLGA/PCL nanofiber membrane facilitates periodontal tissue regeneration by modulating macrophages polarization via suppressing PI3K/AKT and NF-κB signaling pathways.

Traditional fibrous membranes employed in guided tissue regeneration (GTR) in the treatment of periodontitis have limitations of bioactive and immunomodulatory properties. We fabricated a novel nTPG/PLGA/PCL fibrous membrane by electrospinning which exhibit excellent hydrophilicity, mechanical properties and biocompatibility. In addition, we investigated its regulatory effect on polarization of macrophages and facilitating the regeneration of periodontal tissue both in vivo and in vitro. These findings showed the 0.5%TPG/PLGA/PCL may inhibit the polarization of RAW 264.7 into M1 phenotype by suppressing the PI3K/AKT and NF-κB signaling pathways. Furthermore, it directly up-regulated the expression of cementoblastic differentiation markers (CEMP-1 and CAP) in periodontal ligament stem cells (hPDLSCs), and indirectly up-regulated the expression of cementoblastic (CEMP-1 and CAP) and osteoblastic (ALP, RUNX2, COL-1, and OCN) differentiation markers by inhibiting the polarization of M1 macrophage. Upon implantation into a periodontal bone defect rats model, histological assessment revealed that the 0.5%TPG/PLGA/PCL membrane could regenerate oriented collagen fibers and structurally intact epithelium. Micro-CT (BV/TV) and the expression of immunohistochemical markers (OCN, RUNX-2, COL-1, and BMP-2) ultimately exhibited satisfactory regeneration of alveolar bone, periodontal ligament. Overall, 0.5%TPG/PLGA/PCL did not only directly promote osteogenic effects on hPDLSCs, but also indirectly facilitated cementoblastic and osteogenic differentiation through its immunomodulatory effects on macrophages. These findings provide a novel perspective for the development of materials for periodontal tissue regeneration.

In-situ texturing hollow carbon host anchored with Fe single atoms accelerating solid-phase redox for Li-Se batteries.

Se-based cathodes have caught tremendous attention owing to their comparable volumetric capacity and better electronic conductivity to S cathodes. However, its low utilization ratio and sluggish redox kinetics due to the high reaction barrier of solid-phase transformation from Se to Li2Se limit its practical application. Herein, an in-situ texturing hollow carbon host by gas-solid interface reaction anchored with Fe single-atomic catalyst is designed and prepared for advanced Li-Se batteries. This Se host presents high pore volume of 1.49 cm3 g-1, Fe single atom content of 1.53 wt%, and its specific structure protects single-atomic catalyst from the destructive reaction environment, thus balancing catalytic activity and durability. After Se loading by reduction of H2SeO3, this homogenous Se-based cathode delivers a superior rate capacity of 431.3 mA h g-1 at 4C, and great discharge capacity of 301.8 mA h g-1 after 1000 cycles at 10C, with high Li-ion diffusion coefficient and capacitance-contributed ratio. The distribution of relaxation times analysis verifies solid-phase transformation mechanism of this cathode and density functional theory calculations confirm the adsorption and bidirectionally catalysis effect of Fe single-atomic catalyst. This work provides a new strategy to prepare high-efficient Se cathode associated with non-noble metal single atoms for high-performance Li-Se batteries.

Two-dimensional chiral metal-organic framework nanosheets L-hyp-Ni/Fe@SiO2 composite for HPLC separation.

In this study, we have synthesised a chiral l-hyp-Ni/Fe@SiO2 composite as a chiral stationary phase (CSP) for high-performance liquid chromatography (HPLC) for the first time. This was achieved by coating two-dimensional (2D) chiral metal-organic framework nanosheets (MONs) l-hyp-Ni/Fe onto the surface of activated SiO2 microspheres using the "wrapped in net" method. The separation efficiency of the l-hyp-Ni/Fe chromatographic column was systematically evaluated in normal-phase HPLC (NP-HPLC) and reversed-phase HPLC (RP-HPLC) configurations, employing various racemates as analytes. The findings revealed that 16 chiral compounds were separated using NP-HPLC, and five were separated using RP-HPLC, encompassing alcohols, amines, ketones, esters, alkanes, ethers, amino acids and sulfoxides. Notably, the resolution (Rs) of nine chiral compounds exceeded 1.5, indicating baseline separation. Furthermore, the resolution performance of the l-hyp-Ni/Fe@SiO2-packed column was compared with that of Chiralpak AD-H. It was observed that certain enantiomers, which either could not be resolved or were inadequately separated on the Chiralpak AD-H column, attained separation on the 2D chiral MONs column. These findings suggest a complementary relationship between the two columns in racemate separation, with their combined application facilitating the resolution of a broader spectrum of chiral compounds. In addition, baseline separation was achieved for five positional isomers on the l-hyp-Ni/Fe@SiO2-packed column. The effects of the analyte mass and column temperature on the resolution were also examined. Moreover, during HPLC analysis, the l-hyp-Ni/Fe columns demonstrated commendable repeatability, stability and reproducibility in enantiomer separation. This research not only advances the utilisation of 2D chiral MONs as CSPs but also expands their applications in the separation sciences.

Accuracy of Early Neuroprognostication in Pediatric Severe Traumatic Brain Injury.

BACKGROUND: Children with severe traumatic brain injury (sTBI) are at risk for neurological sequelae impacting function. Clinicians are tasked with neuroprognostication to assist in decision-making. We describe a single-center study assessing clinicians' neuroprognostication accuracy. METHODS: Clinicians of various specialties caring for children with sTBI were asked to predict their patients' functioning three to six months postinjury. Clinicians were asked to participate in the study if their patient had survived but not returned to baseline between day 4 and 7 postinjury. The outcome tool utilized was the functional status scale (FSS), ranging from 6 to 30 (best-worst function). Predicted scores were compared with actual scores three to six months postinjury. Lin concordance correlation coefficients were used to estimate agreement between predicted and actual FSS. Outcome was dichotomized as good (FSS 6 to 8) or poor (FSS ≥9). Positive and negative predictive values for poor outcome were calculated. Pessimistic prognostic prediction was defined as predicted worse outcome by ≥3 FSS points. Demographic and clinical variables were collected. RESULTS: A total of 107 surveys were collected on 24 patients. Two children died. Fifteen children had complete (FSS = 6) or near-complete (FSS = 7) recovery. Mean predicted and actual FSS scores were 10.8 (S.D. 5.6) and 8.6 (S.D. 4.1), respectively. Predicted FSS scores were higher than actual scores (P < 0.001). Eight children had collective pessimistic prognostic prediction. CONCLUSIONS: Clinicians predicted worse functional outcomes, despite high percentage of patients with near-normal function at follow-up clinic. Certain patient and provider factors were noted to impact accuracy and need to be studied in larger cohorts.