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It has proved that the auscultation of respiratory sound has advantage in early respiratory diagnosis. Various methods have been raised to perform automatic respiratory sound analysis to reduce subjective diagnosis and physicians' workload. However, these methods highly rely on the quality of respiratory sound database. In this work, we have developed the first open-access paediatric respiratory sound database, SPRSound. The database consists of 2,683 records and 9,089 respiratory sound events from 292 participants. Accurate label is important to achieve a good prediction for adventitious respiratory sound classification problem. A custom-made sound label annotation software (SoundAnn) has been developed to perform sound editing, sound annotation, and quality assurance evaluation. A team of 11 experienced paediatric physicians is involved in the entire process to establish golden standard reference for the dataset. To verify the robustness and accuracy of the classification model, we have investigated the effects of different feature extraction methods and machine learning classifiers on the classification performance of our dataset. As such, we have achieved a score of 75.22%, 61.57%, 56.71%, and 37.84% for the four different classification challenges at the event level and record level.

This study examined the effect on pediatric nursing handover quality and efficiency when a standardized e-handover system was implemented.Handover quality is an important aspect of nursing quality management; however, handover quality among nursing staff is poor.A prospective interventional study was carried out in a general pediatrics ward from December 2019 to November 2020. The tools included a standardized e-handover system. The intervention strategies included workflow remodeling and employee training on oral handover using the standardized e-handover system.The omission frequency of critical handover elements decreased from 47.32% to 2.94% (p .01), among which the omission frequencies of nine out of 16 key elements significantly decreased. Integrity also showed improvement. Specifically, the integrity of five types of critical information was significantly improved, including vital signs, signs and symptoms, laboratory test results, radiologic examination results, and treatment regimen (2.00 vs. 5.00, p .01; 3.00 vs. 5.00, p .01; 3.00 vs. 5.00, p .01; 5.00 vs. 5.00, p = .009; 3.00 vs. 4.00, p .01, respectively). Information accuracy was 100%. Workflow and efficiency significantly improved, communication duration with patient/family during work hours significantly increased (24.00 vs. 56.00, p .01), and prehandover preparation duration significantly decreased (32.00 vs. 2.50, p .01). Nurse handover satisfaction showed improvement (56.88 ± 15.08 vs. 74.31 ± 9.22, p .01).The standardized e-handover system effectively improved nurse handover quality, optimized workflow, increased work efficiency, and promoted teamwork.Standardized e-handover systems have great potential for ensuring the safety of pediatric patients and improving the quality of handover.

Currently, most epidemiological studies on haze focus on respiratory diseases, cardiovascular diseases, etc. However, the relationship between haze and mental health has not been adequately explored. The purpose of this study was to investigate the influence of hazes on schizophrenia admissions and to further explore the potential interaction effect with the combined atmospheric oxidative indices (OWe collected 5328 cases during the cold season from 2013 to 2015 in Hefei, China. By integrating the Poisson Generalized Linear Models with the Distributed Lag Non-linear Models, the association between haze and schizophrenia admissions was evaluated. The interaction between hazes and two combined oxidation indexes was tested by stratifying hazes and OHaze was found to be significantly linked to an increased risk of hospitalization for schizophrenia, and a 9-day lag effect on schizophrenia (lag 3-lag 11), with the largest effect on lag 6 (RR = 1.080, 95% confidence interval (CI): 1.046-1.116). Males, females, and40 y (people under 40 years old) were sensitive to hazes. Furthermore, in the stratified analysis, we found synergies between two combined oxidation indexes and hazes. The interaction relative risk (IRR) and relative excess risk due to interaction (RERI) between OOur findings suggest that exposure to haze significantly increases the risk of hospitalization for schizophrenia. More significant public health benefits can be obtained by prioritizing haze periods with high combined atmospheric oxidation capacity.

IntroductionComplicated outpatient procedures are associated with excessive paperwork and long waiting times. We aimed to shorten queuing times and improve visiting satisfaction.MethodsWe developed an artificial intelligence (AI)-assisted program named Smart-doctor. A randomized controlled trial was conducted at Shanghai Children’s Medical Center. Participants were randomly divided into an AI-assisted and conventional group. Smart-doctor was used as a medical assistant in the AI-assisted group. At the end of the visit, an e-medical satisfaction questionnaire was asked to be done. The primary outcome was the queuing time, while secondary outcomes included the consulting time, test time, total time, and satisfaction score. Wilcoxon rank sum test, multiple linear regression and ordinal regression were also used.ResultsWe enrolled 740 eligible patients (114 withdrew, response rate: 84.59%). The median queuing time was 8.78 (interquartile range [IQR] 3.97,33.88) minutes for the AI-assisted group versus 21.81 (IQR 6.66,73.10) minutes for the conventional group (p < 0.01), and the AI-assisted group had a shorter consulting time (0.35 [IQR 0.18, 0.99] vs. 2.68 [IQR 1.82, 3.80] minutes, p < 0.01), and total time (40.20 [IQR 26.40, 73.80] vs. 110.40 [IQR 68.40, 164.40] minutes, p < 0.01). The overall satisfaction score was increased by 17.53% (p < 0.01) in the AI-assisted group. In addition, multiple linear regression and ordinal regression showed that the queuing time and satisfaction were mainly affected by group (p < 0.01), and missing the turn (p < 0.01).ConclusionsUsing AI to simplify the outpatient service procedure can shorten the queuing time of patients and improve visit satisfaction.

In the context of frequent global extreme weather events, there are few studies on the effects of sequential extreme precipitation (EP) and heatwaves (HW) events on schizophrenia. We aimed to quantify the effects of the events on hospitalizations for schizophrenia and compare them with EP and HW alone to explore the amplification effect of successive extremes on health loss.A time-series Poisson regression model combined with a distributed lag non-linear model was applied to estimate the association between sequential EP and HW events (EP-HW) and schizophrenia hospitalizations. The effects of EP-HW with different intervals and intensities on the admission of schizophrenia were compared. In addition, we calculated the mean attributable fraction (AF) and attributable numbers (AN) per exposure of extreme events to reflect the amplification effect of sequential extreme events on health hazards compared with individual extreme events.EP-HW increased the risk of hospitalization for schizophrenia, with significant effects lasting from lag0 (RR and 95% CI: 1.150 (1.041-1.271)) to lag11 (1.046 (1.000-1.094)). Significant associations were found in the subgroups of male, female, married people, and those aged≥ 40 years old. Shorter-interval (0-3days) or higher-intensity EP-HW (both precipitation ≥ P97.5 and mean temperature ≥ P97.5) had a longer lag effect compared to EP-HW with longer intervals or lower intensity. We found that the mean AF and AN caused by each exposure to EP-HW (AF: 0.074% (0.015%-0.123%); AN: 4.284 (0.862-7.118)) were higher than those induced by each exposure to HW occurring alone (AF:0.032% (0.004%-0.058%); AN:1.845 (0.220-3.329)).Sequential extreme precipitation-heatwaves events significantly increase the risk of hospitalizations for schizophrenia, with greater impact and disease burden than independently occurring extremes. The impact of consecutive extremes is supposed to be considered in local sector early warning systems for comprehensive public health decision-making.

This meta-analysis aimed to explore the association between short-term exposure to air pollution and schizophrenia (SCZ)A systematic review and meta-analysis was conducted by searching PubMed, Cochrane, Web of Sciences, and CNKI for relevant literature published up to 28 Feb 2022. Meta-analysis was performed separately to investigate the association of ambient particulates (diameter ≤ 2.5 μm (PMWe identified 17 articles mainly conducted in Asia, of which 13 were included in the meta-analysis. Increased risk of SCZ was associated with short-term exposure to PMThe present meta-analysis suggests that short-term exposure to PM

Aims Computer-assisted auscultation has become available to assist clinicians with physical examinations to detect congenital heart disease (CHD). However, its accuracy and effectiveness remain to be evaluated. This study seeks to evaluate the accuracy of auscultations of abnormal heart sounds of an artificial intelligence-assisted auscultation (AI-AA) platform we create. Methods and results Initially, 1397 patients with CHD were enrolled in the study. The samples of their heart sounds were recorded and uploaded to the platform using a digital stethoscope. By the platform, both remote auscultation by a team of experienced cardiologists from Shanghai Children’s Medical Center and automatic auscultation of the heart sound samples were conducted. Samples of 35 patients were deemed unsuitable for the analysis; therefore, the remaining samples from 1362 patients (mean age—2.4 ± 3.1 years and 46% female) were analysed. Sensitivity, specificity, and accuracy were calculated for remote auscultation compared to experts’ face-to-face auscultation and for artificial intelligence automatic auscultation compared to experts’ face-to-face auscultation. Kappa coefficients were measured. Compared to face-to-face auscultation, remote auscultation detected abnormal heart sound with 98% sensitivity, 91% specificity, 97% accuracy, and kappa coefficient 0.87. AI-AA demonstrated 97% sensitivity, 89% specificity, 96% accuracy, and kappa coefficient 0.84. Conclusions The remote auscultations and automatic auscultations, using the AI-AA platform, reported high auscultation accuracy in detecting abnormal heart sound and showed excellent concordance to experts’ face-to-face auscultation. Hence, the platform may provide a feasible way to screen and detect CHD.

BACKGROUND: The infection rate of Coronavirus Disease 2019 (COVID-19) in children was less than that in adults. However, the underlining reason is not well known. METHODS: Children with COVID-19 were recruited from two Children’s Hospitals in Wuhan and Shanghai in this case-control study. The associations of initial symptoms with age, vaccinations of Bacillus Calmette Guerin (BCG), and influenza and pathogens were determined by Chi-square t-test. RESULTS: We evaluated 248 confirmed cases, and 56 suspected cases with COVID-19. The median age was 6.82 years old, and 118 cases (38.82%) were girls. Furthermore, 30.26% of all patients were asymptomatic cases. The percentage of asymptomatic cases vaccinated with BCG was not significantly higher than that without BCG vaccination [86/280 (30.71%) vs. 6/13 (46.15%), P=0.203], and initial symptoms were not related with immunized influenza vaccine (P=0.267). Compared to parameters in pediatric patients with normal body temperatures, patients with fever had higher C reactive protein (CRP) (P

Secundum atrial septal defect (ASD) is one of the most common congenital heart diseases (CHDs). This study aims to evaluate the feasibility and accuracy of automatic detection of ASD in children based on color Doppler echocardiographic images using convolutional neural networks. In this study, we propose a fully automatic detection system for ASD, which includes three stages. The first stage is used to identify four target echocardiographic views (that is, the subcostal view focusing on the atrium septum, the apical four-chamber view, the low parasternal four-chamber view, and the parasternal short-axis view). These four echocardiographic views are most useful for the diagnosis of ASD clinically. The second stage aims to segment the target cardiac structure and detect candidates for ASD. The third stage is to infer the final detection by utilizing the segmentation and detection results of the second stage. The proposed ASD detection system was developed and validated using a training set of 4,031 cases containing 370,057 echocardiographic images and an independent test set of 229 cases containing 203,619 images, of which 105 cases with ASD and 124 cases with intact atrial septum. Experimental results showed that the proposed ASD detection system achieved accuracy, recall, precision, specificity, and F1 score of 0.8833, 0.8545, 0.8577, 0.9136, and 0.8546, respectively on the image-level averages of the four most clinically useful echocardiographic views. The proposed system can automatically and accurately identify ASD, laying a good foundation for the subsequent artificial intelligence diagnosis of CHDs.

The environmental conditions in estuaries display distinct variability along the river-ocean mixing continuum from turbid, eutrophic freshwater to clear, oligotrophic offshore oceanic water. In order to understand the effects of suspended particulate matter (SPM), nutrient, and salinity on phytoplankton growth, this study investigated the response of a harmful dinoflagellate (Amphidinium carterae Hulburt) to the ecological gradients in estuary environments. Rapid nutrient uptake and growth of A. carterae were detected in the nutrient-rich clear water, while nutrient concentration had little impact on the cellular chlorophyll a (Chl-a) content at the stationary phase. Light attenuation caused by SPM not only inhibited the specific growth rate of A. carterae but also prolonged its adaption period in turbid water, resulting in a delayed and weakened growth response. The elevated cellular Chl-a content under high SPM conditions resulting from photo-acclimation led to the decoupling of cell density and Chl-a concentration, indicating that Chl-a is not a reliable indicator for phytoplankton abundance in turbid environments. The combined effect of SPM and nutrient on specific growth rate of A. carterae can be explained by the comparative effect model, while the multiplicative effect model better predicted their interactive effect on the growth inhibitory rate (GIR). There is a transit of dominant limiting factor for phytoplankton growth along the salinity gradient in estuary environments. Salinity (for marine phytoplankton cannot survive under low salinity condition) and SPM are the dominant limiting factors at low salinities in nearshore turbid environments, while nutrient depletion exerts the dominant inhibitory effect in high salinity offshore water. Depending on the balance between enhancing nutrient limitation and reducing light limitation with increasing salinity, blooms most likely occur in the “optimal growth region” at intermediate salinities where light and nutrient are both suitable for phytoplankton growth.

Two efficient transition-metal-free highly regioselective pathways for constructing sulfonylated imidazoles via three-component reactions of amidines, ynals, and sodium sulfonates have been developed. The generations of different sulfonylated imidazoles were simply controlled by additives. In addition, this method features environmental friendliness, good functional group tolerance, and high atom economy, which makes it practical.

Knowledge of the surface ocean dynamics and the underlying controlling mechanisms is critical to understand the natural variability of the ocean and to predict its future response to climate change. In this paper, we highlight the potential use of Volunteer Observing Ship (VOS), as carrier for automatic underway measuring system and as platform for sample collection, to enhance the observing capacity for the surface ocean. We review the concept, history, present status and future development of the VOS-based in situ surface ocean observation. The successes of various VOS projects demonstrate that, along with the rapid advancing sensor techniques, VOS is able to improve the temporal resolution and spatial coverage of the surface ocean observation in a highly cost-effective manner. A sustained and efficient marine monitoring system in the future should integrate the advantages of various observing platforms including VOS.

Objective. Auscultation of lung sound plays an important role in the early diagnosis of lung diseases. This work aims to develop an automated adventitious lung sound detection method to reduce the workload of physicians.Approach. We propose a deep learning architecture, LungAttn, which incorporates augmented attention convolution into ResNet block to improve the classification accuracy of lung sound. We adopt a feature extraction method based on dual tunableQ-factor wavelet transform and triple short-time Fourier transform to obtain a multi-channel spectrogram. Mixup method is introduced to augment adventitious lung sound recordings to address the imbalance dataset problem.Main results. Based on the ICBHI 2017 challenge dataset, we implement our framework and compare with the state-of-the-art works. Experimental results show that LungAttn has achieved theSensitivity, Se,Specificity, SpandScoreof 36.36%, 71.44% and 53.90%, respectively. Of which, our work has improved theScoreby 1.69% compared to the state-of-the-art models based on the official ICBHI 2017 dataset splitting method.Significance. Multi-channel spectrogram based on different oscillatory behavior of adventitious lung sound provides necessary information of lung sound recordings. Attention mechanism is introduced to lung sound classification methods and has proved to be effective. The proposed LungAttn model can potentially improve the speed and accuracy of lung sound classification in clinical practice.

Background Many studies suggest that patient satisfaction is significantly negatively correlated with the waiting time. A well-designed healthcare system should not keep patients waiting too long for an appointment and consultation. However, in China, patients spend notable time waiting, and the actual time spent on diagnosis and treatment in the consulting room is comparatively less. Methods We developed an artificial intelligence (AI)-assisted module and name it XIAO YI. It could help outpatients automatically order imaging examinations or laboratory tests based on their chief complaints. Thus, outpatients could get examined or tested before they went to see the doctor. People who saw the doctor in the traditional way were allocated to the conventional group, and those who used XIAO YI were assigned to the AI-assisted group. We conducted a retrospective cohort study from August 1, 2019 to January 31, 2020. Propensity score matching was used to balance the confounding factor between the two groups. And waiting time was defined as the time from registration to preparation for laboratory tests or imaging examinations. The total cost included the registration fee, test fee, examination fee, and drug fee. We used Wilcoxon rank-sum test to compare the differences in time and cost. The statistical significance level was set at 0.05 for two sides. Results Twelve thousand and three hundred forty-two visits were recruited, consisting of 6171 visits in the conventional group and 6171 visits in the AI-assisted group. The median waiting time was 0.38 (interquartile range: 0.20, 1.33) hours for the AI-assisted group compared with 1.97 (0.76, 3.48) hours for the conventional group (p p Conclusions Using XIAO YI can significantly reduce the waiting time of patients, and thus, improve the outpatient service process of hospitals.

Background: Many studies suggest that patient satisfaction is significantly negatively correlated with waiting time. A well-designed healthcare system should not keep patients waiting too long for appointment and consultation. However, in China, patients spend notable time waiting, and the actual time spent on diagnosis and treatment in the consulting room is comparatively less.Methods: We developed an artificial intelligence (AI)-assisted module and name it XIAO YI. It could help outpatients automatically order imaging examinations or laboratory tests based on their chief complaints. Thus, outpatients could get examined or tested before they went to see the doctor. People who saw a doctor in the traditional way were assigned to the conventional group, and those who used XIAO YI were assigned to the AI-assisted group. We conducted a retrospective cohort study that used data from HIS of Shanghai Children’s Medical Center for the period of August 1, 2019 to January 31, 2020. Propensity score matching was used to balance the confounding factor between the two groups. And waiting time was defined as the time from registration to preparation for laboratory tests or imaging examinations. The total cost included the registration fee, test fee, examination fee, and drug fee. We used Wilcoxon rank-sum test to compare the differences in time and cost between the AI-assisted group and the conventional group. The statistical significance level was set at 0.05 for two sides.Results: 12,342 visits were recruited, consisting of 6,171 visits in the conventional group and 6,171 visits in the AI-assisted group. The median waiting time was 0.38 (interquartile range: 0.20, 1.33) hours for the AI-assisted group compared with 1.97 (0.76, 3.48) hours for the conventional group (p < 0.05).Conclusions: Using XIAO YI can significantly reduce the waiting time of patients, and thus, improve the outpatient service process of hospitals.

STUDY DESIGN A retrospective study of incomplete cervical spinal cord injury (SCI) treated with and without hyperbaric oxygen (HBO) therapy after operation. OBJECTIVE To investigate the effects of hyperbaric oxygen therapy on patients' postoperative recovery after incomplete cervical spinal cord injury. SETTING Shulan Hangzhou Hospital, Hangzhou, China. METHODS We analyzed the clinical data of 78 patients admitted in the Orthopedic Department of our hospital from June 2014 to June 2016, due to trauma-induced incomplete cervical spinal cord injury. All study subjects underwent nerve decompression and internal fixation procedures within 2 weeks of injury. The patients were divided into hyperbaric oxygen therapy (HBO) group (n = 40) and non-hyperbaric oxygen therapy (NHBO) group (n = 38) according to the chosen treatment option. The NHBO group only receive the conventional treatment regimen while the HBO group received a combination of conventional treatment and hyperbaric oxygen therapy. The subsequent changes in spinal functions and activities of daily living (ADL) were assessed by The American Spinal Injury Association (ASIA) scale and the Barthel Index at different time points (pretreatment, 1 month and 3 months of treatment, as well as 6 months, 1 year, 2 years, and 3 years after the surgical procedure). RESULTS There were no significant differences in age, gender, injury site, and disease condition between patients (p > 0.05). The results showed a significant difference in treatment total effectiveness rate between the HBO and NHBO groups (p

Background: Many studies indicate that patient satisfaction is significantly negatively correlated with waiting time. A well-designed healthcare system should not keep patients waiting too long for appointment and consultation. However, in China, patients spend considerable time waiting, and the actual time spent on diagnosis and treatment in the consulting room is comparatively less.Methods: We developed an artificial intelligence (AI)-assisted module that is embedded in hospital information systems. Through its use, outpatients were automatically recommended an imaging examination or a laboratory test based on their symptoms and chief complaint. Thus, they could get examined or tested before they went to see the doctor. People who saw a doctor in the traditional way were assigned to the conventional group, and those who used the AI-assisted system were assigned to the AI-assisted group. We conducted a 1:1 case–control study that applied propensity score matching to pair the data from patients in a pediatric tertiary hospital between August 1, 2019 and January 31, 2020. Waiting time was defined as the time from registration to preparation for a laboratory test or an imaging examination. The total cost included the registration fee, test fee, examination fee, and drug fee. The Wilcoxon rank-sum test was used to compare the differences in time and cost between the AI-assisted group and the conventional group. The statistical significance level was set at 0.05 for two sides.Results: A total of 12,342 visits were recruited for this study, consisting of 6,171 visits in the conventional group and 6,171 visits in the AI-assisted group. The median waiting time was 0.38 (inter-quartile range: 0.20, 1.33) hours for the AI-assisted group compared with 1.97 (0.76, 3.48) hours for the conventional group (p < 0.05).Conclusions: Using AI can significantly reduce the waiting time of patients for outpatient procedures, and thus, enhance the outpatient process of hospitals.

An impressive clinical success has been observed in treating a variety of cancers using immunotherapy with programmed cell death-1 (PD-1) checkpoint blockade. However, limited response in most patients treated with anti-PD-1 antibodies remains a challenge, requiring better understanding of molecular mechanisms limiting immunotherapy. In colorectal cancer (CRC) resistant to immunotherapy, mismatch-repair-proficient or microsatellite instability-low (pMMR-MSI-L) tumors have low mutation burden and constitute ~85% of patients. Here, we show that inhibition of N6 -methyladenosine (m6 A) mRNA modification by depletion of methyltransferases, Mettl3 and Mettl14, enhanced response to anti-PD-1 treatment in pMMR-MSI-L CRC and melanoma. Mettl3- or Mettl14-deficient tumors increased cytotoxic tumor-infiltrating CD8+ T cells and elevated secretion of IFN-γ, Cxcl9, and Cxcl10 in tumor microenvironment in vivo. Mechanistically, Mettl3 or Mettl14 loss promoted IFN-γ-Stat1-Irf1 signaling through stabilizing the Stat1 and Irf1 mRNA via Ythdf2. Finally, we found a negative correlation between METTL3 or METTL14 and STAT1 in 59 patients with pMMR-MSI-L CRC tumors. Altogether, our findings uncover a new awareness of the function of RNA methylation in adaptive immunity and provide METTL3 and METTL14 as potential therapeutic targets in anticancer immunotherapy.

An impressive clinical success has been observed in treating a variety of cancers using immunotherapy with programmed cell death‐1 (PD‐1) checkpoint blockade. However, limited response in most patients treated with anti‐PD‐1 antibodies remains a challenge, requiring better understanding of molecular mechanisms limiting immunotherapy. In colorectal cancer (CRC) resistant to immunotherapy, mismatch‐repair‐proficient or microsatellite instability‐low (pMMR‐MSI‐L) tumors have low mutation burden and constitute ~85% of patients. Here, we show that inhibition of N 6‐methyladenosine (m6A) mRNA modification by depletion of methyltransferases, Mettl3 and Mettl14, enhanced response to anti‐PD‐1 treatment in pMMR‐MSI‐L CRC and melanoma. Mettl3‐ or Mettl14‐deficient tumors increased cytotoxic tumor‐infiltrating CD8+ T cells and elevated secretion of IFN‐γ, Cxcl9, and Cxcl10 in tumor microenvironment in vivo. Mechanistically, Mettl3 or Mettl14 loss promoted IFN‐γ‐Stat1‐Irf1 signaling through stabilizing the Stat1 and Irf1 mRNA via Ythdf2. Finally, we found a negative correlation between METTL3 or METTL14 and STAT1 in 59 patients with pMMR‐MSI‐L CRC tumors. Altogether, our findings uncover a new awareness of the function of RNA methylation in adaptive immunity and provide METTL3 and METTL14 as potential therapeutic targets in anticancer immunotherapy., Disruption of m6A methyltransferases leads to enhanced immunotherapy response in colorectal cancer and melanoma cells due to enhanced IFN‐γ‐Stat1‐Irf1 signaling and modulation of the tumor microenvironment.

Lattice Boltzmann Method (LBM) plays an important role in CFD applications. Accelerating LBM computation indicates the decrease of simulation costs for many industries. However, the loop-carried dependencies in LBM kernels prevent the vectorization of loops and general compilers therefore have missed many opportunities of vectorization. This paper proposes a SIMD-aware loop transformation algorithm to fully expose vectorizable loops for LBM kernels. The proposed algorithm identifies most potential vectorizable loops according to a defined dependence table. Then, it performs appropriate loop transformations and array copying techniques to legalize loop-carried dependencies and makes the identified loops automatically vectorized by compiler. Experiments carried on an Intel Xeon Gold 6140 server show that the proposed algorithm significantly raises the ratio of number of vectorized loops to number of all loops in LBM kernels. And our algorithm also achieves a better performance than an Intel C++ compiler and a polyhedral optimizer, accelerating LBM computation by 147% and 120% on average lattice update speed, respectively.

Background: The aim of the present study was to explore the association of vaccinations and clinical manifestations in pediatric patients with Coronavirus Disease 2019 (COVID–19). Methods: Pediatric patients with COVID–19 were recruited from Wuhan Children’s Hospital and Shanghai Children’s Medical Center, including 248 laboratory–confirmed and 56 suspected cases from January 21 to March 12, 2020. Suspected and confirmed cases were diagnosed basing on the Novel Coronavirus 2019 Diagnosis and Treatment Protocol, seventh version. The associations of initial symptoms with age, vaccinations, pathogens, and cytokines were determined via Chi–square tests, Spearman correlations, and logistic regressions. Findings: We assessed 248 (81.58%) laboratory–confirmed cases and 56 (18.42%) suspected cases of COVID–19. The median age of all patients was 6.82 years (interquartile range: 2.08–10.20), and 186 cases (61.18%) were boys; furthermore, 30.26% of all patients were asymptomatic cases. The percentage of asymptomatic cases vaccinated with Bacillus Calmette Guerin (BCG) was not significantly higher than that without BCG vaccination (86/280 [30.71%] vs 6/13 [46.15%], P = 0.203), initial symptoms was not corrected with immunized influenza vaccine ( P = 0.267). Significant correlations were found between age and immunoglobulin (Ig) A (P < 0.001), IgE (P = 0.021), IgG (P < 0.001), complement C4 (P = 0.031), interleukin (IL)–10 (P = 0.003), and tumor necrosis factor (TNF)–α (P = 0.044). Compared to parameters in pediatric patients with normal body temperatures, patients with fever had higher high sensitivity C reactive protein (hs-CRP) (P < 0.001), IL–4 (P = 0.005), IL–10 (P = 0.038), TNF–α (P = 0.004), and TNF–γ (P = 0.036). Interpretation: Pediatric COVID–19 patients with BCG vaccinations exhibit similar clinical manifestations compared to those without BCG vaccinations, and the severity of symptoms in pediatric patients may be related to the maturity of immune function. Funding Statement: This study was partly supported by Top Medical Young Talents of Hubei Province, the National Science Foundation of China [81872637], Shanghai Municipal Commission of Health and Family Planning[201840324], Science and Technology Development Program of Pudong Shanghai New District [PKJ2017–Y01], Shanghai Professional and Technical Services Platform [18DZ2294100], and Shanghai Science and Technology Commission of Shanghai Municipality (17411965300, 17XD1402800,19441904400]. Declaration of Interests: The authors declare no competing interests. Ethics Approval Statement: This study was approved by the Institutional Review Board of Wuhan Children’s Hospital (IEC–2020R003–E01) and Shanghai Children’s Medical Center (SCMCIRB– K2020019–1).

Void nucleation, growth, and coalescence at extreme strain rates in ductile metals with weak mechanical anisotropies, e.g., copper, iron, and aluminum, have been extensively investigated. However, the atomic-scale fracture properties of strongly anisotropic metals, especially hexagonal close-packed (HCP) metals, at ultrahigh strain rates have rarely been studied. We have investigated the nucleation, growth, and coalescence of voids in HCP-Zr under isotropic-triaxial tension using molecular dynamics (MD) and void nucleation and growth (NAG) models. The effects of temperature were also examined by MD. The void evolution predicted by MD corresponded to that predicted by the NAG model and is divided into three stages, i.e., an initial nucleation stage, an exponential growth stage, and a linear stage. The nucleation threshold Pn0 is very sensitive to temperature, while the growth threshold Pg0 decreases slightly with increasing temperature. The initial NAG parameters were evaluated by an improved optimized genetic algorithm. In addition, we adjusted the NAG parameters until the history of the void volume fraction calculated by these parameters was exactly the same as that calculated by MD. This study predicts comprehensive NAG parameters for HCP-Zr under extreme conditions, providing a valuable reference for future studies of dynamic damage in HCP materials.