Liver Immune Profiling Reveals Pathogenesis and Therapeutics for Biliary Atresia.

Biliary atresia (BA) is a severe cholangiopathy that leads to liver failure in infants, but its pathogenesis remains to be fully characterized. By single-cell RNA profiling, we observed macrophage hypo-inflammation, Kupffer cell scavenger function defects, cytotoxic T cell expansion, and deficiency of CX3CR1+effector T and natural killer (NK) cells in infants with BA. More importantly, we discovered that hepatic B cell lymphopoiesis did not cease after birth and that tolerance defects contributed to immunoglobulin G (IgG)-autoantibody accumulation in BA. In a rhesus-rotavirus induced BA model, depleting B cells or blocking antigen presentation ameliorated liver damage. In a pilot clinical study, we demonstrated that rituximab was effective in depleting hepatic B cells and restoring the functions of macrophages, Kupffer cells, and T cells to levels comparable to those of control subjects. In summary, our comprehensive immune profiling in infants with BA had educed that B-cell-modifying therapies may alleviate liver pathology.

Mucosal Profiling of Pediatric-Onset Colitis and IBD Reveals Common Pathogenics and Therapeutic Pathways.

Pediatric-onset colitis and inflammatory bowel disease (IBD) have significant effects on the growth of infants and children, but the etiopathogenesis underlying disease subtypes remains incompletely understood. Here, we report single-cell clustering, immune phenotyping, and risk gene analysis for children with undifferentiated colitis, Crohn's disease, and ulcerative colitis. We demonstrate disease-specific characteristics, as well as common pathogenesis marked by impaired cyclic AMP (cAMP)-response signaling. Specifically, infiltration of PDE4B- and TNF-expressing macrophages, decreased abundance of CD39-expressing intraepithelial T cells, and platelet aggregation and release of 5-hydroxytryptamine at the colonic mucosae were common in colitis and IBD patients. Targeting these pathways by using the phosphodiesterase inhibitor dipyridamole restored immune homeostasis and improved colitis symptoms in a pilot study. In summary, comprehensive analysis of the colonic mucosae has uncovered common pathogenesis and therapeutic targets for children with colitis and IBD.

Identifying Medical Diagnoses and Treatable Diseases by Image-Based Deep Learning.

The implementation of clinical-decision support algorithms for medical imaging faces challenges with reliability and interpretability. Here, we establish a diagnostic tool based on a deep-learning framework for the screening of patients with common treatable blinding retinal diseases. Our framework utilizes transfer learning, which trains a neural network with a fraction of the data of conventional approaches. Applying this approach to a dataset of optical coherence tomography images, we demonstrate performance comparable to that of human experts in classifying age-related macular degeneration and diabetic macular edema. We also provide a more transparent and interpretable diagnosis by highlighting the regions recognized by the neural network. We further demonstrate the general applicability of our AI system for diagnosis of pediatric pneumonia using chest X-ray images. This tool may ultimately aid in expediting the diagnosis and referral of these treatable conditions, thereby facilitating earlier treatment, resulting in improved clinical outcomes. VIDEO ABSTRACT.

Precise detection of awareness in disorders of consciousness using deep learning framework.

Diagnosis of disorders of consciousness (DOC) remains a formidable challenge. Deep learning methods have been widely applied in general neurological and psychiatry disorders, while limited in DOC domain. Considering the successful use of resting-state functional MRI (rs-fMRI) for evaluating patients with DOC, this study seeks to explore the conjunction of deep learning techniques and rs-fMRI in precisely detecting awareness in DOC. We initiated our research with a benchmark dataset comprising 140 participants, including 76 unresponsive wakefulness syndrome (UWS), 25 minimally conscious state (MCS), and 39 Controls, from three independent sites. We developed a cascade 3D EfficientNet-B3-based deep learning framework tailored for discriminating MCS from UWS patients, referred to as "DeepDOC", and compared its performance against five state-of-the-art machine learning models. We also included an independent dataset consists of 11 DOC patients to test whether our model could identify patients with cognitive motor dissociation (CMD), in which DOC patients were behaviorally diagnosed unconscious but could be detected conscious by brain computer interface (BCI) method. Our results demonstrate that DeepDOC outperforms the five machine learning models, achieving an area under curve (AUC) value of 0.927 and accuracy of 0.861 for distinguishing MCS from UWS patients. More importantly, DeepDOC excels in CMD identification, achieving an AUC of 1 and accuracy of 0.909. Using gradient-weighted class activation mapping algorithm, we found that the posterior cortex, encompassing the visual cortex, posterior middle temporal gyrus, posterior cingulate cortex, precuneus, and cerebellum, as making a more substantial contribution to classification compared to other brain regions. This research offers a convenient and accurate method for detecting covert awareness in patients with MCS and CMD using rs-fMRI data.

EBD: an eye biomarker database.

MOTIVATION: Many ophthalmic disease biomarkers have been identified through comprehensive multiomics profiling, and hold significant potential in advancing the diagnosis, prognosis, and management of diseases. Meanwhile, the eye itself serves as a natural biomarker for several systemic diseases including neurological, renal, and cardiovascular systems. We aimed to collect and standardize this eye biomarkers information and construct the eye biomarker database (EBD) to provide ophthalmologists with a platform to search, analyze, and download these eye biomarker data. RESULTS: In this study, we present the EBD , a world-first online compilation comprising 889 biomarkers for 26 ocular diseases and 939 eye biomarkers for 181 systemic diseases. The EBD also includes the information of 78 "nonbiomarkers"-the objects that have been proven cannot be biomarkers. Biological function and network analysis were conducted for these ocular disease biomarkers, and several hub pathways and common network topology characteristics were newly identified, which may promote future ocular disease biomarker discovery and characterizes the landscape of biomarkers for eye diseases at the pathway and network level. The EBD is expected to yield broader utility among developmental biologists and clinical scientists in and outside of the eye field by assisting in the identification of biomarkers linked to eye disorders and related systemic diseases. AVAILABILITY AND IMPLEMENTATION: EBD is available at http://www.eyeseeworld.com/ebd/index.html.

A novel image deep learning-based sub-centimeter pulmonary nodule management algorithm to expedite resection of the malignant and avoid over-diagnosis of the benign.

OBJECTIVES: With the popularization of chest computed tomography (CT) screening, there are more sub-centimeter (≤ 1 cm) pulmonary nodules (SCPNs) requiring further diagnostic workup. This area represents an important opportunity to optimize the SCPN management algorithm avoiding "one-size fits all" approach. One critical problem is how to learn the discriminative multi-view characteristics and the unique context of each SCPN. METHODS: Here, we propose a multi-view coupled self-attention module (MVCS) to capture the global spatial context of the CT image through modeling the association order of space and dimension. Compared with existing self-attention methods, MVCS uses less memory consumption and computational complexity, unearths dimension correlations that previous methods have not found, and is easy to integrate with other frameworks. RESULTS: In total, a public dataset LUNA16 from LIDC-IDRI, 1319 SCPNs from 1069 patients presenting to a major referral center, and 160 SCPNs from 137 patients from three other major centers were analyzed to pre-train, train, and validate the model. Experimental results showed that performance outperforms the state-of-the-art models in terms of accuracy and stability and is comparable to that of human experts in classifying precancerous lesions and invasive adenocarcinoma. We also provide a fusion MVCS network (MVCSN) by combining the CT image with the clinical characteristics and radiographic features of patients. CONCLUSION: This tool may ultimately aid in expediting resection of the malignant SCPNs and avoid over-diagnosis of the benign ones, resulting in improved management outcomes. CLINICAL RELEVANCE STATEMENT: In the diagnosis of sub-centimeter lung adenocarcinoma, fusion MVCSN can help doctors improve work efficiency and guide their treatment decisions to a certain extent. KEY POINTS: • Advances in computed tomography (CT) not only increase the number of nodules detected, but also the nodules that are identified are smaller, such as sub-centimeter pulmonary nodules (SCPNs). • We propose a multi-view coupled self-attention module (MVCS), which could model spatial and dimensional correlations sequentially for learning global spatial contexts, which is better than other attention mechanisms. • MVCS uses fewer huge memory consumption and computational complexity than the existing self-attention methods when dealing with 3D medical image data. Additionally, it reaches promising accuracy for SCPNs' malignancy evaluation and has lower training cost than other models.

[Longitudinal study on catch-up growth in preterm infants with small for gestational age at corrected ages 0-24 months]. / æ—©äº§å°äºŽèƒŽé¾„å„¿æ ¡æ­£0~24æœˆé¾„è¿½èµ¶ç”Ÿé•¿çš„çºµå‘ç ”ç©¶.

OBJECTIVES: To understand the growth and development status and differences between small for gestational age (SGA) and appropriate for gestational age (AGA) preterm infants during corrected ages 0-24 months, and to provide a basis for early health interventions for preterm infants. METHODS: A retrospective study was conducted, selecting 824 preterm infants who received regular health care at the Guangzhou Women and Children's Medical Center from July 2019 to July 2022, including 144 SGA and 680 AGA infants. The growth data of SGA and AGA groups at birth and corrected ages 0-24 months were analyzed and compared. RESULTS: The SGA group had significantly lower weight and length than the AGA group at corrected ages 0-18 months (P<0.05), while there were no significant differences between the two groups at corrected age 24 months (P>0.05). At corrected age 24 months, 85% (34/40) of SGA and 79% (74/94) of AGA preterm infants achieved catch-up growth. Stratified analysis by gestational age showed that there were significant differences in weight and length at corrected ages 0-9 months between the SGA subgroup with gestational age <34 weeks and the AGA subgroups with gestational age <34 weeks and 34 weeks (P<0.05). In addition, the weight and length of the SGA subgroup with gestational age 34 weeks showed significant differences compared to the AGA subgroups with gestational age <34 weeks and 34 weeks at corrected ages 0-18 months and corrected ages 0-12 months, respectively (P<0.05). Catch-up growth for SGA infants with gestational age <34 weeks and 34 weeks mainly occurred at corrected ages 0-12 months and corrected ages 0-18 months, respectively. CONCLUSIONS: SGA infants exhibit delayed early-life physical growth compared to AGA infants, but can achieve a higher proportion of catch-up growth by corrected age 24 months than AGA infants. Catch-up growth can be achieved earlier in SGA infants with a gestational age of <34 weeks compared to those with 34 weeks.

Combining deep learning and intelligent biometry to extract ultrasound standard planes and assess early gestational weeks.

OBJECTIVES: To develop and validate a fully automated AI system to extract standard planes, assess early gestational weeks, and compare the performance of the developed system to sonographers. METHODS: In this three-center retrospective study, 214 consecutive pregnant women that underwent transvaginal ultrasounds between January and December 2018 were selected. Their ultrasound videos were automatically split into 38,941 frames using a particular program. First, an optimal deep-learning classifier was selected to extract the standard planes with key anatomical structures from the ultrasound frames. Second, an optimal segmentation model was selected to outline gestational sacs. Third, novel biometry was used to measure, select the largest gestational sac in the same video, and assess gestational weeks automatically. Finally, an independent test set was used to compare the performance of the system with that of sonographers. The outcomes were analyzed using the area under the receiver operating characteristic curve (AUC), sensitivity, specificity, and mean similarity between two samples (mDice). RESULTS: The standard planes were extracted with an AUC of 0.975, a sensitivity of 0.961, and a specificity of 0.979. The gestational sacs' contours were segmented with a mDice of 0.974 (error less than 2 pixels). The comparison showed that the relative error of the tool in assessing gestational weeks was 12.44% and 6.92% lower and faster (min, 0.17 vs. 16.6 and 12.63) than that of the intermediate and senior sonographers, respectively. CONCLUSIONS: This proposed end-to-end tool allows automatic assessment of gestational weeks in early pregnancy and may reduce manual analysis time and measurement errors. CLINICAL RELEVANCE STATEMENT: The fully automated tool achieved high accuracy showing its potential to optimize the increasingly scarce resources of sonographers. Explainable predictions can assist in their confidence in assessing gestational weeks and provide a reliable basis for managing early pregnancy cases. KEY POINTS: • The end-to-end pipeline enabled automatic identification of the standard plane containing the gestational sac in an ultrasound video, as well as segmentation of the sac contour, automatic multi-angle measurements, and the selection of the sac with the largest mean internal diameter to calculate the early gestational week. • This fully automated tool combining deep learning and intelligent biometry may assist the sonographer in assessing the early gestational week, increasing accuracy and reducing the analyzing time, thereby reducing observer dependence.

Association of fresh vegetable and salt-preserved vegetable consumptions with estimated glomerular filtration rate.

OBJECTIVE: This study aimed to investigate the relationship between the consumption of fresh and salt-preserved vegetables and the estimated glomerular filtration rate (eGFR), which requires further research. METHODS: For this purpose, the data of those subjects who participated in the 2011-2012 and 2014 surveys of the Chinese Longitudinal Healthy Longevity Survey (CLHLS) and had biomarker data were selected. Fresh and salt-preserved vegetable consumptions were assessed at each wave. eGFR was assessed using the Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) equation based on plasma creatinine. Furthermore, a linear mixed model was used to evaluate associations between fresh/salt-preserved vegetables and eGFR. RESULTS: The results indicated that the median baseline and follow-up eGFRs were 72.47 mL/min/1.73 m² and 70.26 mL/min/1.73 m², respectively. After applying adjusted linear mixed model analysis to the data, the results revealed that compared to almost daily intake, occasional consumption of fresh vegetables was associated with a lower eGFR (ß=-2.23, 95% CI: -4.23, -0.23). Moreover, rare or no consumption of salt-preserved vegetables was associated with a higher eGFR (ß = 1.87, 95% CI: 0.12, 3.63) compared to individuals who consumed salt-preserved vegetables daily. CONCLUSION: Fresh vegetable consumption was direct, whereas intake of salt-preserved vegetables was inversely associated with eGFR among the oldest subjects, supporting the potential benefits of diet-rich fresh vegetables for improving eGFR.

Metabolomic analyses reveal new stage-specific features of COVID-19.

The current pandemic of coronavirus disease 2019 (COVID-19) has affected >160 million individuals to date, and has caused millions of deaths worldwide, at least in part due to the unclarified pathophysiology of this disease. Identifying the underlying molecular mechanisms of COVID-19 is critical to overcome this pandemic. Metabolites mirror the disease progression of an individual and can provide extensive insights into their pathophysiological significance at each stage of disease. We provide a comprehensive view of metabolic characterisation of sera from COVID-19 patients at all stages using untargeted and targeted metabolomic analysis. As compared with the healthy controls, we observed different alteration patterns of circulating metabolites from the mild, severe and recovery stages, in both the discovery cohort and the validation cohort, which suggests that metabolic reprogramming of glucose metabolism and the urea cycle are potential pathological mechanisms for COVID-19 progression. Our findings suggest that targeting glucose metabolism and the urea cycle may be a viable approach to fight COVID-19 at various stages along the disease course.

Global Incidence, Progression, and Risk Factors of Age-Related Macular Degeneration and Projection of Disease Statistics in 30 Years: A Modeling Study.

OBJECTIVE: Age-related macular degeneration(AMD) has become a major cause of visual impairment worldwide, especially in the elderly. Estimates of incidence, progression rates, and risk factors of AMD vary among studies, complicating the understanding of its epidemiology. METHODS: For this systematic review and meta-analysis, literature published up to March 1, 2021, was searched in both English and Chinese databases. Hierarchical Bayesian approaches were used to estimate pooled incidence, progression, and 95% credible intervals (CrIs). RESULTS: Thirty studies were included. The pooled annual early and late AMD incidence rates were 1.59 (95% CrI: 1.18-2.11) and 0.23 (95% CrI: 0.14-0.34) per 100 person-years, respectively. The annual progression rate of AMD was 5.5 (95% CrI: 2.3-8.8) per 100 person-years. Smoking was an independent risk factor for both early and late AMD, whereas age, high-density lipoprotein cholesterol, and alcohol consumption were risk factors for early AMD incidence only. The projected number of new cases of early and late AMD in 2050 would be 39.05 million (95% CrI: 23.12-63.57) and 6.41 million (95% CrI: 3.37-13.22), respectively. CONCLUSION: The prediction the number of new cases of AMD is not equal across the globe. Our findings indicate the need for more rigorous control and prevention measures in AMD focus on its risk factors for early intervention. The epidemiological estimates reported in this study could inform to identify effective strategies for preventing AMD worldwide.

Myristicin regulates proliferation and apoptosis in oxidized low-density lipoprotein-stimulated human vascular smooth muscle cells and human umbilical vein endothelial cells by regulating the PI3K/Akt/NF-κB signalling pathway.

CONTEXT: Atherosclerosis (AS) is a chronic inflammatory disease. Human vascular smooth muscle cell (hVSMC) accumulation and human umbilical vein endothelial cell (HUVEC) dysfunction are associated with the pathogenesis of AS. This study explores whether myristicin plays a protective role in AS. MATERIALS AND METHODS: hVSMCs and HUVECs were stimulated with 100 µg/mL oxidized low-density lipoprotein (ox-LDL) to establish a cellular model of AS. Cell viability, lactate dehydrogenase (LDH) release and cell apoptosis were evaluated using MTT, LDH and flow cytometry assays, respectively. Cell migration and inflammatory cytokine release were assessed using Transwell assay and ELISA. RESULTS: Myristicin (5, 10, 25, and 50 µM) had no obvious effect on cell viability or the activity of LDH in hVSMCs, while 100 and 200 µM myristicin markedly suppressed hVSMCs viability and increased LDH release. Myristicin had no obvious effect on cell viability or the activity of LDH in HUVECs. Myristicin inhibited viability and increased apoptosis in ox-LDL-treated hVSMCs, but was associated with increased proliferation and inhibited apoptosis in HUVECs stimulated by ox-LDL. Additionally, myristicin markedly suppressed ox-LDL-induced hVSMCs migration and the release of inflammatory cytokines, including MCP-1, IL-6, VCAM-1 and ICAM-1, in HUVECs. Results also demonstrated that the promoting effects of ox-LDL on the PI3K/Akt and NF-κB signalling pathway in both hVSMCs and HUVECs were abolished by treatment with myristicin. DISCUSSION AND CONCLUSIONS: Myristicin regulated proliferation and apoptosis by regulating the PI3K/Akt/NF-κB signalling pathway in ox-LDL-stimulated hVSMCs and HUVECs. Thus, myristicin may be used as a new potential drug for AS treatment.

Distinct Disease Severity Between Children and Older Adults With Coronavirus Disease 2019 (COVID-19): Impacts of ACE2 Expression, Distribution, and Lung Progenitor Cells.

BACKGROUND: Children and older adults with coronavirus disease 2019 (COVID-19) display a distinct spectrum of disease severity yet the risk factors aren't well understood. We sought to examine the expression pattern of angiotensin-converting enzyme 2 (ACE2), the cell-entry receptor for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), and the role of lung progenitor cells in children and older patients. METHODS: We retrospectively analyzed clinical features in a cohort of 299 patients with COVID-19. The expression and distribution of ACE2 and lung progenitor cells were systematically examined using a combination of public single-cell RNA-seq data sets, lung biopsies, and ex vivo infection of lung tissues with SARS-CoV-2 pseudovirus in children and older adults. We also followed up patients who had recovered from COVID-19. RESULTS: Compared with children, older patients (>50 years.) were more likely to develop into serious pneumonia with reduced lymphocytes and aberrant inflammatory response (P = .001). The expression level of ACE2 and lung progenitor cell markers were generally decreased in older patients. Notably, ACE2 positive cells were mainly distributed in the alveolar region, including SFTPC positive cells, but rarely in airway regions in the older adults (P < .01). The follow-up of discharged patients revealed a prolonged recovery from pneumonia in the older (P < .025). CONCLUSIONS: Compared to children, ACE2 positive cells are generally decreased in older adults and mainly presented in the lower pulmonary tract. The lung progenitor cells are also decreased. These risk factors may impact disease severity and recovery from pneumonia caused by SARS-Cov-2 infection in older patients.

Comparison of clinical features on admission between coronavirus disease 2019 and influenza a among children: a retrospective study in China.

BACKGROUND: Coronavirus disease 2019 (COVID-19) share similar symptoms with influenza A (IA), but it is more worthwhile to understand the disparities of the two infections regarding their clinical characteristics on admission. METHODS: A total of 71 age-matched pediatric IA and COVID-19 patient pairs were formed and their clinical data on admission were compared. RESULTS: Fever, cough, nasal congestion and nausea/vomiting were the most common symptoms on admission for both infections but occurred less often in COVID-19. The IA patients were more likely to have lower-than-normal levels of lymphocyte count and percentage and to have higher-than-normal levels of activated partial thromboplastin time, prothrombin time, serum C-reactive protein, and serum procalcitonin, while the COVID-19 patients had higher odds of having lower-than-normal levels of neutrophil count and percentage. CONCLUSIONS: This study suggests that influenza A is more symptomatic than COVID-19 for children and might be an overall more severe infection at the time of admission.

Effectiveness of analgesia with hydromorphone hydrochloride for postoperative pain following surgical repair of structural congenital malformations in children: a randomized controlled trial.

BACKGROUND: Effective postoperative analgesia is needed to prevent the negative effects of postoperative pain on patient outcomes. To compare the effectiveness of hydromorphone hydrochloride and sufentanil, combined with flurbiprofen axetil, for postoperative analgesia in pediatric patients. METHODS: This prospective randomized controlled trial included 222 pediatric patients scheduled for repair of a structural congenital malformation under general anesthesia. Patients were randomized into 3 groups: hydromorphone hydrochloride 0.1 mg/kg (H1), hydromorphone hydrochloride 0.2 mg/kg; (H2) or sufentanil 1.5 µg/kg (S). Analgesics were diluted in 0.9% saline to 100 ml and infused continuously at a basic flow rate of 2 mL per h. The primary outcome measure was the Face, Legs, Activity, Cry, and Consolability (FLACC) pain score. Secondary outcomes included heart rate (HR), respiration rate (RR), SpO2, Ramsay sedation scores, scores on the Paediatric Anaesthesia Emergence Delirium (PAED) scale, adverse reactions, parent satisfaction with analgesia. RESULTS: The FLACC score was significantly lower in H1 and H2 groups compared to S. The Ramsay sedation score was significantly higher in H1 and H2 groups compared to S. Recovery time was shorter in H1 group compared to patients H2 group or S group. There were no significant differences in the PAED scale, HR, RR, SpO2, adverse reactions, satisfaction of parents with analgesia, or length and cost of hospital stay. CONCLUSIONS: Hydromorphone hydrochloride is a more effective analgesic than sufentanil for postoperative pain in pediatric patients following surgical repair of a structural congenital malformation, however, hydromorphone hydrochloride and sufentanil had similar safety profiles in this patient population. TRIAL REGISTRATION: Chinese Clinical Trial Register ChiCTR-INR-17013935). Clinical trial registry URL: Date of registration: December 14, 2017.

Modified endometriosis fertility index is more accurate to predict the non-ART pregnancy rate following surgery: a cohort of Chinese women.

OBJECTIVE: To examine whether a modified endometriosis fertility index (EFI) can better predict the rate of pregnancy without assisted reproductive technologies (ART) after laparoscopic surgery in infertile Chinese women with endometriosis. METHODS: 564 infertile women undergoing laparoscopy for endometriosis were retrospectively collected from January 2014 to December 2018. 472 patients were used to modify the EFI based on new, optimal cutoffs for its predictor variables. The predictive accuracy of the modified EFI was examined in the other 92 patients. RESULTS: Among the patients for the EFI modification, the multivariable Cox regression results showed that historical factors made more contribution in predicting non-ART pregnancy rate than surgical factors both in modified EFI (C-index: historical factors 0.617 vs surgical factors 0.558) and original EFI (C-index: historical factors 0.600 vs surgical factors 0.549). No significant relationship between the prior pregnancy and post-operative non-ART pregnancy rates was detected by both modified EFI and original EFI (p = 0.530 and 0.802, respectively). To assess the predictive effect of modified EFI, the two versions of modified EFI not only had higher predictive accuracy (C-index: 0.627 and 0.632) for non-ART pregnancy rates than that of the original EFI (C-index: 0.602) in the patients undergoing surgery during 2014-2017, but also higher than that of the original EFI (C-index: 0.638 and 0.612 vs 0.560) in the externally validated population in 2018. CONCLUSIONS: A modified EFI based on population-specific optimal cutoffs and weights might be more suitable for estimating the rate of non-ART pregnancy after laparoscopic surgery in infertile women with endometriosis.

A model for predicting 7-day pressure injury outcomes in paediatric patients: A machine learning approach.

AIMS: We sought to explore factors associated with early pressure injury progression and build a model for predicting these outcomes using a machine learning approach. DESIGN: A retrospective cohort study. METHODS: In this study, we recruited paediatric patients, with hospital-acquired stage I pressure injury or suspected deep tissue injury, who met the inclusion criteria between 1 January 2015-31 October 2018. We divided patients into two groups, namely healing or delayed healing, then followed them up for 7 days. We analysed patient pressure injury characteristics, demographics, treatment, clinical situation, vital signs, and blood test results, then build prediction models using the Random Forest and eXtreme Gradient Boosting approaches. RESULTS: The best prediction model, trained and tested using Random Forest with 10 variables, achieved an accuracy, sensitivity, specificity, and area under the curve of 0.82 (SD 0.06), 0.80 (SD 0.08), 0.84 (SD 0.08), and 0.89 (SD 0.06), respectively. The most contributing variables, in order of importance, included serum creatinine, red blood cell, and haematocrit. CONCLUSION: An awareness of specific conditions and areas that could lead to delayed healing pressure injury in paediatric patients is needed. IMPACT: This evidence-based prediction model, coupled with the aforementioned clinical indicators, is expected to enhance early prediction of outcomes in paediatric patients thereby improve the quality of care and the outcome of children with PIs.

Molecular simulation of SARS-CoV-2 spike protein binding to pangolin ACE2 or human ACE2 natural variants reveals altered susceptibility to infection.

We constructed complex models of SARS-CoV-2 spike protein binding to pangolin or human ACE2, the receptor for virus transmission, and estimated the binding free energy changes using molecular dynamics simulation. SARS-CoV-2 can bind to both pangolin and human ACE2, but has a significantly lower binding affinity for pangolin ACE2 due to the increased binding free energy (9.5 kcal mol-1). Human ACE2 is among the most polymorphous genes, for which we identified 317 missense single-nucleotide variations (SNVs) from the dbSNP database. Three SNVs, E329G (rs143936283), M82I (rs267606406) and K26R (rs4646116), had a significant reduction in binding free energy, which indicated higher binding affinity than wild-type ACE2 and greater susceptibility to SARS-CoV-2 infection for people with them. Three other SNVs, D355N (rs961360700), E37K (rs146676783) and I21T (rs1244687367), had a significant increase in binding free energy, which indicated lower binding affinity and reduced susceptibility to SARS-CoV-2 infection.

New Criterion to Evaluate Acute-on-Chronic Kidney Injury Based on the Creatinine Reference Change.

BACKGROUND: The lack of consensus criteria of acute on chronic kidney injury (ACKI) affects the judgment for its clinical prognosis. METHODS: In this study, we analyzed the data from 711,615 hospitalized adults who had at least 2 serum creatinine (SCr) tests within 30 days. We estimated the reference change value (RCV) of SCr given initial SCr level in adults without known risks of acute kidney injury other than chronic kidney disease (CKD). We proposed a criterion for ACKI based on the RCV of SCr (cROCK), which defined ACKI as a ≥25% increase in SCr in 7 days. We validated cROCK by its association with the risks of in-hospital mortality, death after discharge, and CKD progression in a large cohort of patients with CKD stage 3. RESULTS: In 21,661 patients with CKD stage 3, a total of 3,145 (14.5%), 1,512 (7.0%), and 221 (1.0%) ACKI events were detected by both cROCK and Kidney Disease Improving Global Outcomes (KDIGO), cROCK only, and KDIGO only, respectively. cROCK detected 40% more ACKI events than KDIGO. Compared with patients without ACKI by both definitions, those with cROCK- but not KDIGO-defined ACKI had a significantly increased risk of in-hospital mortality (hazard ratio [HR] 5.53; 95% CI 3.75-8.16), death after discharge (HR 1.51; 95% CI 1.21-1.83), and CKD progression (OR 5.65; 95% CI 3.05-10.48). CONCLUSIONS: RCV-based criterion (cROCK) for ACKI is clinically valid in that it has a substantially improved sensitivity in identifying patients with high risk of adverse outcomes.

Comparative analysis of clinical features of SARS-CoV-2 and adenovirus infection among children.

BACKGROUND: The new emerging coronavirus disease 2019 (COVID-19) overall shares similar symptoms with other common respiratory viral infections. We aimed in this study to compare COVID-19 and human adenovirus (HAdV) infections in pediatric patients regarding the frequencies of major clinical symptoms and the potential disparities in laboratory and imaging parameters. METHODS: Following a case-control-like design, we built 72 age-matched pediatric COVID-19 and HAdV patient pairs. Their early symptoms and laboratory and imaging characteristics were then retrieved and compared. RESULTS: Fever and cough were the most common symptoms for both infections but were seen more often in HAdV than in COVID-19 patients (92% vs. 66% and 60% vs. 18%, respectively). Compared with COVID-19 patients, children with HAdV infection had statistically significantly higher values of neutrophil count, neutrophil percentage, activated partial thromboplastin time, prothrombin time, lactate dehydrogenase, C-reactive protein, procalcitonin but lower values of lymphocyte percentage, total bilirubin, potassium and sodium. Thoracic computed tomography also revealed more anomalies in HAdV patients than in COVID-19 patients (95% vs. 67%). CONCLUSIONS: COVID-19 is an overall less symptomatic and less severe infection at admission compared to HAdV respiratory infection in pediatric population.