High performance molecular iodine optical reference using an unsaturated vapor cell.

We describe a high-performance molecular iodine optical frequency reference that is referenced to the R(56)32-0: a1 hyperfine transition of molecular iodine based on modulation transfer spectroscopy. We design an unsaturated iodine vapor cell with a gas pressure equivalent to the saturation pressure at -17 °C. Using this cell, we developed a compact, frequency-stabilized laser. The iodine cell operates at room temperature and is not actively temperature stabilized. We demonstrate a laser with fractional frequency instability of 1.4 × 10-14 at 1 s and 1.7 × 10-15 at 104 s. To our knowledge, the level of frequency instability at 104 s is comparable to the previously reported best results for an iodine stabilized laser. These results suggest that using an unsaturated iodine vapor cell is a valid approach for the development of long-term, stable iodine-based optical references.

Noise characterization of an ultra-stable laser for optical clocks.

We report on the development and performance evaluation of an ultra-stable laser for an 27Al+ optical clock. After a series of noise suppressions, especially the vibrational and temperature fluctuation noise, the 30 cm long cavity stabilized laser obtains a frequency instability of 1.3 × 10-16 @1 s. This result is predicted by noise summation and confirmed by the three-cornered hat method. The 27Al+ optical clock transition is also used to characterize the laser frequency noise, and consistent results are yielded. This is the first reported instance of using single ion optical clocks to measure the frequency noise of ultra-stable lasers, as far as we know. With the implementation of the ultra-stable clock laser, an ultra-narrow linewidth clock transition of 2.8 Hz is obtained.

Electrochemical Generation of Te Vacancy Pairs in PtTe for Efficient Hydrogen Evolution.

Two-dimensional (2D) van der Waals materials are increasingly seen as potential catalysts due to their unique structures and unmatched properties. However, achieving precise synthesis of these remarkable materials and regulating their atomic and electronic structures at the most fundamental level to enhance their catalytic performance remain a significant challenge. In this study, we synthesized single-crystal bulk PtTe crystals via chemical vapor transport and subsequently produced atomically thin, large PtTe nanosheets (NSs) through electrochemical cathode intercalation. These NSs are characterized by a significant presence of Te vacancy pairs, leading to undercoordinated Pt atoms on their basal planes. Experimental and theoretical studies together reveal that Te vacancy pairs effectively optimize and enhance the electronic properties (such as charge distribution, density of states near the Fermi level, and d-band center) of the resultant undercoordinated Pt atoms. This optimization results in a significantly higher percentage of dangling O-H water, a decreased energy barrier for water dissociation, and an increased binding affinity of these Pt atoms to active hydrogen intermediates. Consequently, PtTe NSs featuring exposed and undercoordinated Pt atoms demonstrate outstanding electrocatalytic activity in hydrogen evolution reactions, significantly surpassing the performance of standard commercial Pt/C catalysts.

Development of Taste Sensor with Lipid/Polymer Membranes for Detection of Umami Substances Using Surface Modification.

A taste sensor employs various lipid/polymer membranes with specific physicochemical properties for taste classification and evaluation. However, phosphoric acid di(2-ethylhexyl) ester (PAEE), employed as one of the lipids for the taste sensors, exhibits insufficient selectivity for umami substances. The pH of sample solutions impacts the dissociation of lipids to influence the membrane potential, and the response to astringent substances makes accurate measurement of umami taste difficult. This study aims to develop a novel taste sensor for detecting umami substances like monosodium L-glutamate (MSG) through surface modification, i.e., a methodology previously applied to taste sensors for non-charged bitter substance measurement. Four kinds of modifiers were tested as membrane-modifying materials. By comparing the results obtained from these modifiers, the modifier structure suitable for measuring umami substances was identified. The findings revealed that the presence of carboxyl groups at para-position of the benzene ring, as well as intramolecular H-bonds between the carboxyl group and hydroxyl group, significantly affect the effectiveness of a modifier in the umami substance measurement. The taste sensor treated with this type of modifier showed excellent selectivity for umami substances.

Quantitative risk assessment of China's first liquid hydrogen refueling station.

Hydrogen refueling stations (HRSs) are among the most important infrastructures for fuel cell vehicles. However, the safety issue of HRSs has become a key constraint to the wide application and development of hydrogen energy. This article presents a quantitative risk assessment of the first liquid HRS (LHRS) in China and conducts a comprehensive assessment in terms of both individual (IR) and societal risks (SRs). The results showed that both the IRs and SRs related to the LHRS exceeded the risk acceptance criteria. The rupture of the flexible hose of the dispenser and the leak from the compressor are the main contributors to these risks. On the other hand, implementing appropriate mitigation measures on the level of the LHRS dispenser and compressor, including the addition of breakaway couplings in the flexible hose of the dispenser, the installation of hydrogen detection sensors, the arrangement of automatic and manual emergency shutdown buttons, and the elevation of the compressor, is capable of reducing the risk of the LHRS to be within the risk acceptance criteria.

Multi-channel EEG-based sleep staging using brain functional connectivity and domain adaptation.

Objective.Sleep stage recognition has essential clinical value for evaluating human physical/mental condition and diagnosing sleep-related diseases. To conduct a five-class (wake, N1, N2, N3 and rapid eye movement) sleep staging task, twenty subjects with recorded six-channel electroencephalography (EEG) signals from the ISRUC-SLEEP dataset is used.Approach.Unlike the exist methods ignoring the channel coupling relationship and non-stationarity characteristics, we developed a brain functional connectivity method to provide a new insight for multi-channel analysis. Furthermore, we investigated three frequency-domain features: two functional connectivity estimations, i.e. synchronization likelihood (SL) and wavelet-based correlation (WC) among four frequency bands, and energy ratio (ER) related to six frequency bands, respectively. Then, the Gaussian support vector machine (SVM) method was used to predict the five sleep stages. The performance of the applied features is evaluated in both subject dependence experiment by ten-fold cross validation and subject independence experiment by leave-one-subject-out cross-validation, respectively.Main results.In subject dependence experiment, the results showed that the fused feature (fusion of SL, WC and ER features) contributes significant gain the performance of SVM classifier, where the mean of classification accuracy can achieve 83.97% ± 1.04%. However, in subject-independence experiment, the individual differences EEG patterns across subjects leads to inferior accuracy. Five typical domain adaptation (DA) methods were applied to reduce the discrepancy of feature distributions by selecting the optimal subspace dimension. Results showed that four DA methods can significantly improve the mean accuracy by 1.89%-5.22% compared to the baseline accuracy 57.44% in leave-one-subject-out cross-validation.Significance.Compared with traditional time-frequency and nonlinear features, brain functional connectivity features can capture the correlation between different brain regions. For the individual EEG response differences, domain adaptation methods can transform features to improve the performance of sleep staging algorithms.

DBF-Net: a semi-supervised dual-task balanced fusion network for segmenting infected regions from lung CT images.

Accurate segmentation of infected regions in lung computed tomography (CT) images is essential to improve the timeliness and effectiveness of treatment for coronavirus disease 2019 (COVID-19). However, the main difficulties in developing of lung lesion segmentation in COVID-19 are still the fuzzy boundary of the lung-infected region, the low contrast between the infected region and the normal trend region, and the difficulty in obtaining labeled data. To this end, we propose a novel dual-task consistent network framework that uses multiple inputs to continuously learn and extract lung infection region features, which is used to generate reliable label images (pseudo-labels) and expand the dataset. Specifically, we periodically feed multiple sets of raw and data-enhanced images into two trunk branches of the network; the characteristics of the lung infection region are extracted by a lightweight double convolution (LDC) module and fusiform equilibrium fusion pyramid (FEFP) convolution in the backbone. According to the learned features, the infected regions are segmented, and pseudo-labels are made based on the semi-supervised learning strategy, which effectively alleviates the semi-supervised problem of unlabeled data. Our proposed semi-supervised dual-task balanced fusion network (DBF-Net) creates pseudo-labels on the COVID-SemiSeg dataset and the COVID-19 CT segmentation dataset. Furthermore, we perform lung infection segmentation on the DBF-Net model, with a segmentation sensitivity of 70.6% and specificity of 92.8%. The results of the investigation indicate that the proposed network greatly enhances the segmentation ability of COVID-19 infection.

Dual-frequency fundamental-mode NPRO laser for low-noise microwave generation.

Monolithic nonplanar ring oscillators (NPROs) have achieved great success in industry, scientific applications and space missions due to their excellent narrow-linewidth, low-noise, high beam-quality, lightweight and compact performances. Here, we show that stable dual-frequency or multi-frequency fundamental-mode (DFFM or MFFM) laser can be stimulated directly by tunning pump divergence-angle and beam-waist injected to NPRO. The DFFM laser has a frequency deviation of one free spectral range of the resonator and thus can be utilized for pure microwave generation by common-mode-rejection. To demonstrate the purity of the microwave signal, a theoretical phase noise model is established, and the phase noise and the frequency tunability of the microwave signal are experimentally studied. Single sideband phase noise for a 5.7 GHz carrier is measured as low as -112 dBc/Hz at 10 kHz offset, and -150 dBc/Hz at 10 MHz offset in the free running condition of the laser, which outperforms its counterparts from dual-frequency Laguerre-Gaussian (LG) modes. The frequency of the microwave signal can be efficiently tunned through two channels, with frequency tunning coefficients of 15 Hz/V by piezo, and -60.5 kHz/K by temperature, respectively. We expect that such compact, tunable, low-cost and low-noise microwave sources can facilitate multiple applications including miniaturized atomic clocks, communication and radar, etc.

AP-HTPB propellant combustion under strain conditions with laser absorption spectroscopy.

Understanding the combustion behaviors of solid propellant with different levels of strains is of practical interest. In this work, an experimental study of the effects of static and dynamic strains on the burning rate, temperature, CO, and C O 2 formation of aluminized ammonium perchlorate (AP)-hydroxyl terminated poly-butadiene (HTPB) propellant combustion was presented at initial pressures of 0.1 MPa, 0.2 MPa, and 0.5 MPa. The strains were being applied onto solid propellant by exerting static and cyclic loadings. The propellant burning rate was acquired by a 4 kHz high-speed photography system, and the combustion temperature, CO, and C O 2 column densities were measured at 10 kHz through laser absorption spectroscopy (LAS). At atmospheric pressure, it was demonstrated that the propellant burning rate increased with tensile stress and decreased with compressive stress. The measured flame temperature showed a similar correlation with strains as compared to the propellant burning rate. At elevated pressures, the increase of the propellant burning rate due to tensile stress was more evident, while the difference in combustion temperatures was less significant. For the cyclic strain condition, the variations of the measured C O 2 and CO column densities were consistent with the static strain condition.

A Mechanistic Study of HZSM-5-Catalyzed Guaiacol Amination Using Photoionization Time-of-Flight Mass Spectrometry.

Thermal-catalytic conversion and amination (TCC-A) of lignin and lignin derivates over zeolites is a promising and renewable method to produce aromatic amines, but suffers from product diversity. Currently, no unambiguous mechanism could fully describe the chemistry of this process. In this work, the TCC-A mechanism of guaiacol, a typical lignin model compound, with ammonia over HZSM-5 was investigated by online photoionization time-of-flight mass spectrometry combined with density functional theory. Various products including amines, pyrroles, and pyridines were identified. The formation of methylamine and aminophenol below 400 °C via nucleophilic substitutions is attributed to the strong adsorption of ammonia on the active site of HZSM-5. Aniline is the major product above 400 °C coproduced with pyrroles and pyridines. It is suggested that the reactions among radical intermediates (•CH3 and •NH2) and molecules (guaiacol and catechol) lead to poor aniline selectivity via transmethylation, amination, and partial deoxygenation reactions. Hydroamination is proposed as the main formation mechanism of pyrroles and pyridines. The maximum yield of aniline can be achieved at 650 °C owing to the enhancement of amination and deoxygenation and the suppression of transmethylation reactions.

FCKDNet: A Feature Condensation Knowledge Distillation Network for Semantic Segmentation.

As a popular research subject in the field of computer vision, knowledge distillation (KD) is widely used in semantic segmentation (SS). However, based on the learning paradigm of the teacher-student model, the poor quality of teacher network feature knowledge still hinders the development of KD technology. In this paper, we investigate the output features of the teacher-student network and propose a feature condensation-based KD network (FCKDNet), which reduces pseudo-knowledge transfer in the teacher-student network. First, combined with the pixel information entropy calculation rule, we design a feature condensation method to separate the foreground feature knowledge from the background noise of the teacher network outputs. Then, the obtained feature condensation matrix is applied to the original outputs of the teacher and student networks to improve the feature representation capability. In addition, after performing feature condensation on the teacher network, we propose a soft enhancement method of features based on spatial and channel dimensions to improve the dependency of pixels in the feature maps. Finally, we divide the outputs of the teacher network into spatial condensation features and channel condensation features and perform distillation loss calculation with the student network separately to assist the student network to converge faster. Extensive experiments on the public datasets Pascal VOC and Cityscapes demonstrate that our proposed method improves the baseline by 3.16% and 2.98% in terms of mAcc, and 2.03% and 2.30% in terms of mIoU, respectively, and has better segmentation performance and robustness than the mainstream methods.

PDRF-Net: a progressive dense residual fusion network for COVID-19 lung CT image segmentation.

The lungs of patients with COVID-19 exhibit distinctive lesion features in chest CT images. Fast and accurate segmentation of lesion sites from CT images of patients' lungs is significant for the diagnosis and monitoring of COVID-19 patients. To this end, we propose a progressive dense residual fusion network named PDRF-Net for COVID-19 lung CT segmentation. Dense skip connections are introduced to capture multi-level contextual information and compensate for the feature loss problem in network delivery. The efficient aggregated residual module is designed for the encoding-decoding structure, which combines a visual transformer and the residual block to enable the network to extract richer and minute-detail features from CT images. Furthermore, we introduce a bilateral channel pixel weighted module to progressively fuse the feature maps obtained from multiple branches. The proposed PDRF-Net obtains good segmentation results on two COVID-19 datasets. Its segmentation performance is superior to baseline by 11.6% and 11.1%, and outperforming other comparative mainstream methods. Thus, PDRF-Net serves as an easy-to-train, high-performance deep learning model that can realize effective segmentation of the COVID-19 lung CT images.

Probing combustion and catalysis intermediates by synchrotron vacuum ultraviolet photoionization molecular-beam mass spectrometry: recent progress and future opportunities.

Soft photoionization molecular-beam mass spectrometry (PI-MBMS) using synchrotron vacuum ultraviolet (SVUV) light has been significantly developed and applied in various fields in recent decades. Particularly, the tunability of SVUV light enables two-dimensional measurements, i.e. mass spectrum and photoionization efficiency spectrum measurements, affording isomer distinguishment in complex reaction processes. Many key intermediates have been successfully detected in combustion and catalysis reactions with the help of the state-of-the-art SVUV-PI-MBMS, promoting the understanding of the chemical mechanisms. Herein, we present a brief review of the instrumentation of beamline and PI-MBMS machines at the current synchrotron user facility Hefei Light Source II and exemplify the advantages of the SVUV-PI-MBMS method with recent applications in combustion and catalysis research, especially in probing key reaction intermediates. Future opportunities with the next generation synchrotron light source and bench-top light source have also been discussed.

Insights into the Decomposition and Oxidation Chemistry of p-Xylene in Laminar Premixed Flames.

This work reports an experimental and kinetic modeling investigation on laminar premixed flame of p-xylene at 0.04 atm and equivalence ratios of 0.75, 1.0, and 1.79. Intermediates such as the p-xylyl radical, p-xylylene, and styrene, as well as polycyclic aromatic hydrocarbons (PAHs), were detected by using synchrotron vacuum ultraviolet photoionization mass spectrometry. Based on our previous aromatic kinetic model, a detailed kinetic model of p-xylene combustion was developed, and the model was validated against the present flame structure data. Model analysis work was also performed in order to reveal the important reactions in p-xylene decomposition and oxidation. The H-abstraction reactions leading to the p-xylyl radical are found to control the consumption of p-xylene in all the three flames. In the rich flame, p-xylyl mainly suffers the H-elimination and isomerization reactions, which produce p-xylylene and the o-xylyl radical, respectively. The further decomposition reactions of the o-xylyl radical contribute to the production of styrene, which is another important C8 intermediate observed in the rich flame. In the stoichiometric and lean flames, p-xylyl mainly suffers the oxidation reactions by O, which give p-methylbenzaldehyde as major product. The growth pathways of PAHs in the rich flame were also investigated in this work. Indenyl, indene, naphthalene, and phenanthrene were observed as the abundantly produced bicyclic and tricyclic PAHs due to the existence of direct formation pathways from the decomposition of p-xylyl radical.

Short-term developmental outcomes in neonates born to mothers with COVID-19 from Wuhan, China.

BACKGROUND: Coronavirus disease 2019 (COVID-19) caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is an emerging disease. The consequences of SARS-CoV-2 exposure in infants remain unknown. Therefore, this study aims to investigate whether neonates born to mothers with COVID-19 have adverse brain development. METHODS: This multicenter observational study was conducted at two designated maternal and children's hospitals in Hubei Province, mainland China from February 1, 2020 to May 15, 2020. Neonates born to mothers with COVID-19 were enrolled. Brain magnetic resonance imaging (MRI) findings, and volumes of grey and white matters, and physical growth parameters were observed at 44 weeks corrected gestational age. RESULTS: Of 72 neonates born to mothers with COVID-19, 8 (11%) were diagnosed with COVID-19, 8 (11%) were critically ill, and no deaths were reported. Among the eight neonates that underwent brain MRI at corrected gestational age of 44 weeks, five neonates were diagnosed with COVID-19. Among these five neonates, three presented abnormal MRI findings including abnormal signal in white matter and delayed myelination in newborn 2, delayed myelination and brain dysplasia in newborn 3, and abnormal signal in the bilateral periventricular in newborn 5. The other three neonates without COVID-19 presented no significantly changes of brain MRI findings and the volumes of grey matter and white matter compared to those of healthy newborns at the equivalent age (P > 0.05). Physical growth parameters for weight, length, and head circumference at gestational age of 44 weeks were all above the 3rd percentile for all neonates. CONCLUSIONS: Some of the neonates born to mothers with COVID-19 had abnormal brain MRI findings but these neonates did not appear to have poor physical growth. These findings may provide the information on the follow-up schedule on the neonates exposed to SARS-CoV-2, but further study is required to evaluate the association between the abnormal MRI findings and the exposure to SARS-CoV-2.

Effects of SARS-CoV-2 infection on neuroimaging and neurobehavior in neonates.

BACKGROUND: We collected neonatal neurological, clinical, and imaging data to study the neurological manifestations and imaging characteristics of neonates with coronavirus disease 2019 (COVID-19). METHODS: This case-control study included newborns diagnosed with COVID-19 in Wuhan, China from January 2020 to July 2020. All included newborns had complete neurological evaluations and head magnetic resonance imaging. We normalized the extracted T2-weighted imaging data to a standard neonate template space, and segmented them into gray matter, white matter, and cerebrospinal fluid. The comparison of gray matter volume was conducted between the two groups. RESULTS: A total of five neonates with COVID-19 were included in this study. The median reflex scores were 2 points lower in the infected group than in the control group (P = 0.0094), and the median orientation and behavior scores were 2.5 points lower in the infected group than in the control group (P = 0.0008). There were also significant differences between the two groups in the total scale score (P = 0.0426). The caudate nucleus, parahippocampal gyrus, and thalamus had the strongest correlations with the Hammersmith neonatal neurologic examination (HNNE) score, and the absolute correlation coefficients between the gray matter volumes and each part of the HNNE score were all almost greater than 0.5. CONCLUSIONS: We first compared the neurological performance of neonates with and without COVID-19 by quantitative neuroimaging and neurological examination methods. Considering the limited numbers of patients, more studies focusing on the structural or functional aspects of the virus in the central nervous system in different age groups will be carried out in the future.

Silencing of Long Non-Coding RNA X Inactive Specific Transcript (Xist) Contributes to Suppression of Bronchopulmonary Dysplasia Induced by Hyperoxia in Newborn Mice via microRNA-101-3p and the transforming growth factor-beta 1 (TGF-ß1)/Smad3 Axis.

BACKGROUND Bronchopulmonary dysplasia (BPD) is a chronic lung disease mostly affecting premature infants. Long non-coding RNA (lncRNA) X inactive specific transcript (Xist) is actively involved in pulmonary disease development. The present study explored the potential mechanism of Xist in BPD development. MATERIAL AND METHODS First, newborn BPD mouse models were successfully established. lncRNAs and genes with differential expression were identified using microarray analysis. Various injuries and radial alveolar counts of lung tissues of BPD mice were detected by hematoxylin-eosin staining. Functional assays were utilized to detect alterations of superoxide dismutase (SOD), malondialdehyde (MDA), vascular endothelial growth factor, collagen I, alpha-smooth muscle Actin, TGF-ß1, and Smad3. Then, dual-luciferase reporter gene assay and RNA pull-down assay were performed to clarify the targeting relationship between Xist and miR-101-3p and between miR-101-3p and high-mobility group protein B3 (HMGB3). RESULTS In BPD mice, radial alveolar counts value and SOD activity declined while MDA level increased. Results of microarray analysis found that Xist and HMGB3 were highly expressed in BPD mice. Next, silenced Xist alleviated lung damage in BPD mice. Xist competitively bound to miR-101-3p to activate HMGB3, and overexpressed miR-101-3p mitigated lung damage in BPD mice. Additionally, silenced Xist downregulated the TGF-ß1/Smad3 axis. CONCLUSIONS Our study demonstrated that silencing of Xist suppressed BPD development by binding to miR-101-3p and downregulating HMGB3 and the TGF-b1/Smad3 axis. Our results may provide novel insights for BPD treatment.

Experimental and kinetic modeling investigation of rich premixed toluene flames doped with n-butanol.

n-Butanol is a promising renewable biofuel and has a lot of advantages as a gasoline additive compared with ethanol. Though the combustion of pure n-butanol has been extensively investigated, the chemical structures of large hydrocarbons doped with n-butanol, especially for aromatic fuels, are still insufficiently understood. In this work, rich premixed toluene/n-butanol/oxygen/argon flames were investigated at 30 Torr with synchrotron vacuum ultraviolet photoionization mass spectrometry (SVUV-PIMS). The blending ratio of n-butanol was varied from 0 to 50%, while the equivalence ratio was maintained at a quite rich value (1.75) for the purpose of studying the influence of n-butanol on the aromatic growth process. Flame species including radicals, reactive molecules, isomers and polycyclic aromatic hydrocarbons (PAHs) were identified and their mole fraction profiles were measured. A kinetic model of toluene/n-butanol combustion was developed from our recently reported toluene and n-butanol models. It is observed that the production of most toluene decomposition products and larger aromatics was suppressed as the blending ratio of n-butanol increases. Meanwhile, the addition of n-butanol generally enhanced the formation of most observed C2-C4 hydrocarbons and C1-C4 oxygenated species. The rate of production (ROP) analysis and experimental observations both indicate that the interaction between toluene and n-butanol in their decomposition processes mainly occurs at the formation of small intermediates, e.g. acetylene and methyl. In particular, the interaction between toluene and n-butanol in methyl formation influences the formation of large monocyclic aromatics such as ethylbenzene, styrene and phenylacetylene, making their maximum mole fractions decay slowly upon increasing the blending ratio of n-butanol compared with toluene and benzyl. The increase of the blending ratio of n-butanol reduces the formation of key PAH precursors such as benzyl, fulvenallenyl, benzene, phenyl and propargyl, which leads to a remarkable reduction in the formation of PAHs.

[Value of arterial blood lactic acid in the evaluation of disease severity and prognosis in neonatal shock].

OBJECTIVE: To evaluate the value of blood lactic acid (BLA) as a predictor for the severity and prognosis of neonatal shock. METHODS: A total of 326 neonates with shock were enrolled and divided into three groups based on the severity, namely mild group (n=147), moderate group (n=105), and severe group (n=74). BLA level was measured during and early after (about 6 hours later) fluid resuscitation, and lactate clearance rate (LCR) was calculated. The receiver operating characteristic (ROC) curve was applied to evaluate the predictive value of BLA in neonatal shock. RESULTS: BLA level was high in all subjects prior to treatment, and was highest in the severe group and lowest in the mild group (P<0.01). BLA level was significantly higher among patients with septic shock than among those with hypovolemic, cardiogenic, and asphyxiating shock (P<0.05). BLA level was significantly reduced in patients in recovery after treatment (P<0.05). Mortality was significantly lower in patients with BLA level ≤4 mmol/L or LCR ≥10% than in those with BLA level >4 mmol/L or LCR <10% (P<0.01). BLA at 11.15 mmol/L had 100% sensitivity and 96.8% specificity in predicting severe shock. BLA at 10.65 mmol/L had 88.9% sensitivity and 74.1% specificity in predicting the prognosis (survival or dead) of newborns with shock. CONCLUSIONS: In neonates with shock, arterial BLA level increases as the disease severity increases and is associated with prognosis, so it is a useful predictor of the severity and prognosis of neonatal shock.