Novel Classification of Cardiovascular Disease Subtypes Reveals Associations Between Mortality and Polyunsaturated Fatty Acids: Insights from the United Kingdom Biobank Study.

Background: Traditional association studies of cardiovascular disease (CVD) categorizations and polyunsaturated fatty acids (PUFAs) yielded conflicting findings. We propose a novel classification system based on fundamental characteristics of cardiovascular patients, such as age, body mass index, waist-hip ratio, to more accurately assess the impact of PUFAs (plasma measures) such as omega (ω)-3 (n-3) and ω-6 on mortality in cardiovascular patients. Methods: Principal component analysis and k-means clustering were used to determine the CVD subtype. Variables included age, body mass index, waist-hip ratio, diastolic blood pressure, systolic blood pressure, total cholesterol, total triglycerides, high-density lipoprotein-cholesterol, apolipoprotein B:apolipoprotein A1, glycated hemoglobin, creatinine, albumin, C-reactive protein, white blood cell count, platelet count, and hemoglobin concentration. The association of PUFAs with all-cause, cardiovascular, and ischemic heart disease (IHD) mortality in patients with CVD was prospectively evaluated using restricted cubic splines and Cox proportional risk models. Results: Among the 35,096 participants, 3,786 fatalities occurred. Three distinct CVD subtypes were identified, with cluster 3 characterized by older age, male gender, and low high-density lipoprotein-cholesterol, having the highest risk of mortality. Clusters 2 and 3 had the highest DHA and ω-6/ω-3 ratios, respectively, compared with Cluster 1. The protective effects of total PUFAs, ω-3, and DHA were mainly reflected in all-cause mortality and were more significant in clusters 2 and 3. Furthermore, the ω-6/ω-3 ratio of the highest quartile increased risk of all-cause [Q3: hazard ratio (HR): 1.14, 95% confidence interval [CI]: 1.00, 1.29; Q4: HR: 1.41, 95% CI: 1.24, 1.61], CVD (Q4: HR: 1.36, 95% CI: 1.07, 1.75), and IHD mortality (Q4: HR: 1.17, 95% CI: 1.12, 2.03) in cluster 3 compared with the first quartile. Conclusions: Our findings highlight the heterogeneity of associations observed for the same type of PUFAs across distinct clusters. This association may be elucidated by the intricate interplay of various factors, encompassing inflammation, lipid metabolism, and cardiovascular health.

A multimodal physiological and psychological dataset for human with mental stress induced myocardial ischemia.

Accurate differentiation between angina with no obstructive coronary arteries (ANOCA) and mental stress-induced myocardial ischemia (MSIMI) is crucial for tailored treatment strategies, yet public data scarcity hampers understanding. Given the higher incidence of both conditions in women, this study prospectively enrolled 80 female ANOCA and 39 age-matched female controls, subjecting them to three types of mental stress tasks. ECGs were continuously monitored across Rest, Stress, and Recover stages of the mental stress tasks, with PET/CT imaging during the Stress stage to evaluate myocardial perfusion. With PET/CT serving as the gold standard for MSIMI diagnosis, 35 of the 80 ANOCA patients were diagnosed as MSIMI. Using ECG variables from different stages of mental stress tasks, we developed five machine learning models to diagnose MSIMI. The results showed that ECG data from different stages provide valuable information for MSIMI classification. Additionally, the dataset encompassed demographic details, physiological, and blood sample test results of the patients. We anticipate this new dataset will significantly push further progress in ANOCA and MSIMI research.

Effects of different early cardiac rehabilitation exercise treatments on the prognosis of acute myocardial infarction patients receiving percutaneous coronary intervention.

OBJECTIVES: Exercise rehabilitation is the core of Cardiac Rehabilitation (CR) and will improve the prognosis of patients receiving Percutaneous Coronary Intervention (PCI surgery). The current study retrospectively analyzed the effects of different exercise-based CR strategies on the prognosis of AMI patients receiving PCI treatment. METHODS: Clinicopathological information from 127 patients was collected and divided into different groups based on the exercise-based CR received, including Continuous Resistance Exercise (COR), Continuous Aerobic Exercise (COA), Interval Resistance Exercise (IVR), Interval Aerobic Exercise (IVA), Inspiratory Muscle Exercises (ITM), and Control. The differences regarding cardio-pulmonary function, hemodynamics, and life quality were analyzed against different CR strategies. RESULTS: All the exercise-based CR strategies showed improving effects compared with patients in the Control group regarding cardio-pulmonary parameters, with IVR showing the strongest improving effects (IVR > ITM > COR > IVA > COA) (p < 0.05) at the first recoding point. However, the improving effects of exercise-based CR declined with time. Regarding the effects on hemodynamics parameters, the improving effects of exercise-based CR were only observed regarding LVEF, and the effects of IVR were also the strongest (IVR > COR > ITM > COA > IVA) (p < 0.05). Similar improving effects were also observed for 6MWT and life quality (IVR showing the strongest improving effects) (p < 0.05), which all declined three months after the surgery. CONCLUSIONS: The current study showed that exercise-based CRs had better improving effects than the normal nursing strategy on the prognosis of AMI patients receiving PCI surgery.

A deep-learning model for intracranial aneurysm detection on CT angiography images in China: a stepwise, multicentre, early-stage clinical validation study.

BACKGROUND: Artificial intelligence (AI) models in real-world implementation are scarce. Our study aimed to develop a CT angiography (CTA)-based AI model for intracranial aneurysm detection, assess how it helps clinicians improve diagnostic performance, and validate its application in real-world clinical implementation. METHODS: We developed a deep-learning model using 16 546 head and neck CTA examination images from 14 517 patients at eight Chinese hospitals. Using an adapted, stepwise implementation and evaluation, 120 certified clinicians from 15 geographically different hospitals were recruited. Initially, the AI model was externally validated with images of 900 digital subtraction angiography-verified CTA cases (examinations) and compared with the performance of 24 clinicians who each viewed 300 of these cases (stage 1). Next, as a further external validation a multi-reader multi-case study enrolled 48 clinicians to individually review 298 digital subtraction angiography-verified CTA cases (stage 2). The clinicians reviewed each CTA examination twice (ie, with and without the AI model), separated by a 4-week washout period. Then, a randomised open-label comparison study enrolled 48 clinicians to assess the acceptance and performance of this AI model (stage 3). Finally, the model was prospectively deployed and validated in 1562 real-world clinical CTA cases. FINDINGS: The AI model in the internal dataset achieved a patient-level diagnostic sensitivity of 0·957 (95% CI 0·939-0·971) and a higher patient-level diagnostic sensitivity than clinicians (0·943 [0·921-0·961] vs 0·658 [0·644-0·672]; p<0·0001) in the external dataset. In the multi-reader multi-case study, the AI-assisted strategy improved clinicians' diagnostic performance both on a per-patient basis (the area under the receiver operating characteristic curves [AUCs]; 0·795 [0·761-0·830] without AI vs 0·878 [0·850-0·906] with AI; p<0·0001) and a per-aneurysm basis (the area under the weighted alternative free-response receiver operating characteristic curves; 0·765 [0·732-0·799] vs 0·865 [0·839-0·891]; p<0·0001). Reading time decreased with the aid of the AI model (87·5 s vs 82·7 s, p<0·0001). In the randomised open-label comparison study, clinicians in the AI-assisted group had a high acceptance of the AI model (92·6% adoption rate), and a higher AUC when compared with the control group (0·858 [95% CI 0·850-0·866] vs 0·789 [0·780-0·799]; p<0·0001). In the prospective study, the AI model had a 0·51% (8/1570) error rate due to poor-quality CTA images and recognition failure. The model had a high negative predictive value of 0·998 (0·994-1·000) and significantly improved the diagnostic performance of clinicians; AUC improved from 0·787 (95% CI 0·766-0·808) to 0·909 (0·894-0·923; p<0·0001) and patient-level sensitivity improved from 0·590 (0·511-0·666) to 0·825 (0·759-0·880; p<0·0001). INTERPRETATION: This AI model demonstrated strong clinical potential for intracranial aneurysm detection with improved clinician diagnostic performance, high acceptance, and practical implementation in real-world clinical cases. FUNDING: National Natural Science Foundation of China. TRANSLATION: For the Chinese translation of the abstract see Supplementary Materials section.

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.

Congenital heart disease detection by pediatric electrocardiogram based deep learning integrated with human concepts.

Early detection is critical to achieving improved treatment outcomes for child patients with congenital heart diseases (CHDs). Therefore, developing effective CHD detection techniques using low-cost and non-invasive pediatric electrocardiogram are highly desirable. We propose a deep learning approach for CHD detection, CHDdECG, which automatically extracts features from pediatric electrocardiogram and wavelet transformation characteristics, and integrates them with key human-concept features. Developed on 65,869 cases, CHDdECG achieved ROC-AUC of 0.915 and specificity of 0.881 on a real-world test set covering 12,000 cases. Additionally, on two external test sets with 7137 and 8121 cases, the overall ROC-AUC were 0.917 and 0.907 while specificities were 0.937 and 0.907. Notably, CHDdECG surpassed cardiologists in CHD detection performance comparison, and feature importance scores suggested greater influence of automatically extracted electrocardiogram features on CHD detection compared with human-concept features, implying that CHDdECG may grasp some knowledge beyond human cognition. Our study directly impacts CHD detection with pediatric electrocardiogram and demonstrates the potential of pediatric electrocardiogram for broader benefits.

[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.

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.

Effects of different early cardiac rehabilitation exercise treatments on the prognosis of acute myocardial infarction patients receiving percutaneous coronary intervention

Abstract Objectives: Exercise rehabilitation is the core of Cardiac Rehabilitation (CR) and will improve the prognosis of patients receiving Percutaneous Coronary Intervention (PCI surgery). The current study retrospectively analyzed the effects of different exercise-based CR strategies on the prognosis of AMI patients receiving PCI treatment. Methods: Clinicopathological information from 127 patients was collected and divided into different groups based on the exercise-based CR received, including Continuous Resistance Exercise (COR), Continuous Aerobic Exercise (COA), Interval Resistance Exercise (IVR), Interval Aerobic Exercise (IVA), Inspiratory Muscle Exercises (ITM), and Control. The differences regarding cardio-pulmonary function, hemodynamics, and life quality were analyzed against different CR strategies. Results: All the exercise-based CR strategies showed improving effects compared with patients in the Control group regarding cardio-pulmonary parameters, with IVR showing the strongest improving effects (IVR > ITM > COR > IVA > COA) (p < 0.05) at the first recoding point. However, the improving effects of exercise-based CR declined with time. Regarding the effects on hemodynamics parameters, the improving effects of exercise-based CR were only observed regarding LVEF, and the effects of IVR were also the strongest (IVR > COR > ITM > COA > IVA) (p < 0.05). Similar improving effects were also observed for 6MWT and life quality (IVR showing the strongest improving effects) (p < 0.05), which all declined three months after the surgery. Conclusions: The current study showed that exercise-based CRs had better improving effects than the normal nursing strategy on the prognosis of AMI patients receiving PCI surgery.

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.

A cardiologist-like computer-aided interpretation framework to improve arrhythmia diagnosis from imbalanced training datasets.

Arrhythmias can pose a significant threat to cardiac health, potentially leading to serious consequences such as stroke, heart failure, cardiac arrest, shock, and sudden death. In computer-aided electrocardiogram interpretation systems, the inclusion of certain classes of arrhythmias, which we term "aggressive" or "bullying," can lead to the underdiagnosis of other "vulnerable" classes. To address this issue, a method for arrhythmia diagnosis is proposed in this study. This method combines morphological-characteristic-based waveform clustering with Bayesian theory, drawing inspiration from the diagnostic reasoning of experienced cardiologists. The proposed method achieved optimal performance in macro-recall and macro-precision through hyperparameter optimization, including spliced heartbeats and clusters. In addition, with increasing bullying by aggressive arrhythmias, our model obtained the highest average recall and the lowest average drop in recall on the nine vulnerable arrhythmias. Furthermore, the maximum cluster characteristics were found to be consistent with established arrhythmia diagnostic criteria, lending interpretability to the proposed method.

Diagnosis of generalized joint hypermobility with gait patterns using a deep neural network.

Generalized joint hypermobility (GJH) describes the situation that the range of joint motion exceeds the normal range. GJH is found to increase the risk of knee-related injury and osteoarthritis, challenging the athletic ability of the population. Gait signals are directly related to hip and knee athletic conditions, and have been shown to have significant changes with GJH by our previous research. But gait data are noisy, and vary with age, gender, weight, and ethnicity, which makes them hard to analyze with traditional statistical methods. In this study, we proposed an end-to-end deep learning model to recognize the patterns of the gait signals. The model consists of convolutional network blocks, residual network blocks, and attention blocks. Our dataset is composed of 452 samples of gait data obtained by a three-dimension motion capture system, with the six-degree-of-freedom kinematic data of hip, knee, and ankle joints during level walking, downhill, and uphill walking. The model achieves 95.77% accuracy and 98.68% specificity with a recall of 76.84% while is more efficient than traditional machine learning methods. The trained model can be run on economical friendly devices, and provide help for immediate and precise diagnosis of GJH. It is also meaningful to consider its application in large-scale GJH screening, which can contribute to sports medicine.

MRI-based brain age prediction model for children under 3 years old using deep residual network.

Early identification and intervention of abnormal brain development individual subjects are of great significance, especially during the earliest and most active stage of brain development in children aged under 3. Neuroimage-based brain's biological age has been associated with health, ability, and remaining life. However, the existing brain age prediction models based on neuroimage are predominantly adult-oriented. Here, we collected 658 T1-weighted MRI scans from 0 to 3 years old healthy controls and developed an accurate brain age prediction model for young children using deep learning techniques with high accuracy in capturing age-related changes. The performance of the deep learning-based model is comparable to that of the SVR-based model, showcasing remarkable precision and yielding a noteworthy correlation of 91% between the predicted brain age and the chronological age. Our results demonstrate the accuracy of convolutional neural network (CNN) brain-predicted age using raw T1-weighted MRI data with minimum preprocessing necessary. We also applied our model to children with low birth weight, premature delivery history, autism, and ADHD, and discovered that the brain age was delayed in children with extremely low birth weight (less than 1000 g) while ADHD may cause accelerated aging of the brain. Our child-specific brain age prediction model can be a valuable quantitative tool to detect abnormal brain development and can be helpful in the early identification and intervention of age-related brain disorders.

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.

The Association between Emergency Department Length of Stay and In-Hospital Mortality in Older Patients Using Machine Learning: An Observational Cohort Study.

The association between emergency department (ED) length of stay (EDLOS) with in-hospital mortality (IHM) in older patients remains unclear. This retrospective study aims to delineate the relationship between EDLOS and IHM in elderly patients. From the ED patients (n = 383,586) who visited an urban academic tertiary care medical center from January 2010 to December 2016, 78,478 older patients (age ≥60 years) were identified and stratified into three age subgroups: 60-74 (early elderly), 75-89 (late elderly), and ≥90 years (longevous elderly). We applied multiple machine learning approaches to identify the risk correlation trends between EDLOS and IHM, as well as boarding time (BT) and IHM. The incidence of IHM increased with age: 60-74 (2.7%), 75-89 (4.5%), and ≥90 years (6.3%). The best area under the receiver operating characteristic curve was obtained by Light Gradient Boosting Machine model for age groups 60-74, 75-89, and ≥90 years, which were 0.892 (95% CI, 0.870-0.916), 0.886 (95% CI, 0.861-0.911), and 0.838 (95% CI, 0.782-0.887), respectively. Our study showed that EDLOS and BT were statistically correlated with IHM (p < 0.001), and a significantly higher risk of IHM was found in low EDLOS and high BT. The flagged rate of quality assurance issues was higher in lower EDLOS ≤1 h (9.96%) vs. higher EDLOS 7 h

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.

Identification of Distinct Clinical Phenotypes of Heterogeneous Mechanically Ventilated ICU Patients Using Cluster Analysis.

This retrospective study aimed to derive the clinical phenotypes of ventilated ICU patients to predict the outcomes on the first day of ventilation. Clinical phenotypes were derived from the eICU Collaborative Research Database (eICU) cohort via cluster analysis and were validated in the Medical Information Mart for Intensive Care (MIMIC-IV) cohort. Four clinical phenotypes were identified and compared in the eICU cohort (n = 15,256). Phenotype A (n = 3112) was associated with respiratory disease, had the lowest 28-day mortality (16%), and had a high extubation success rate (~80%). Phenotype B (n = 3335) was correlated with cardiovascular disease, had the second-highest 28-day mortality (28%), and had the lowest extubation success rate (69%). Phenotype C (n = 3868) was correlated with renal dysfunction, had the highest 28-day mortality (28%), and had the second-lowest extubation success rate (74%). Phenotype D (n = 4941) was associated with neurological and traumatic diseases, had the second-lowest 28-day mortality (22%), and had the highest extubation success rate (>80%). These findings were validated in the validation cohort (n = 10,813). Additionally, these phenotypes responded differently to ventilation strategies in terms of duration of treatment, but had no difference in mortality. The four clinical phenotypes unveiled the heterogeneity of ICU patients and helped to predict the 28-day mortality and the extubation success rate.

The predictive value of YAP-1 and POU2F3 for the efficacy of immuno-chemotherapy in extensive-stage SCLC patients.

INTRODUCTION: Recently, several clinical trials of immunotherapy for extensive-stage small-cell lung cancer (ES-SCLC) have shown limited benefits because of unselected patients. Thus, we aimed to explore whether YES-associated protein 1 (YAP-1) and POU domain class 2 transcription factor 3 (POU2F3) could identify SCLC patients with durable benefits from immunotherapy as potential biomarkers. METHODS: We performed IHC of YAP-1 and POU2F3, and RNA-seq on tissues of ES- SCLC patients. An open-source plugin based on IHC-profiler was conducted to calculate the expression levels of YAP-1 and POU2F3. RESULTS: Patients with ES-SCLC were retrospectively investigated in the Guangdong Provincial People's Hospital from January 2018 to July 2021, and 21 patients whoever received atezolizumab plus etoposide/carboplatin (ECT) regimen also had tissue samples reachable. The median IHC-score of YAP-1 in responders (CR/PR patients) was significantly lower than in nonresponders (SD/PD patients) at 13.97 (95% CI: 8.97-16.30) versus 23.72 (95% CI: 8.13-75.40). The IHC-score of YAP-1 and PFS showed a negative correlation by Spearman (r=-0.496). However, POU2F3 did not show a correlation with efficacy. Besides, patients with YAP-1 high expression had IL6, MYCN, and MYCT1 upregulated, while analysis of immune cell infiltration only showed that M0 macrophages were significantly higher. CONCLUSIONS: The expression of YAP-1 negatively correlated with the efficacy of ECT in ES-SCLC patients while POU2F3 did not reveal the predictive value. However, prospective investigations with a large sample size are needed.

Development and application of a detection method for varicella-zoster virus neutralizing antibody titer based on complement dependence / 中国生物制品学杂志

@#ObjectiveTo develop and apply a method for detecting the titer of varicella-zoster virus(VZV)neutralizing antibodies based on complement dependence,so as to improve the sensitivity of traditional plaque reduction neutralization assay for detection of the titer of VZV antibody.MethodsThe antigen(live attenuated varicella vaccine)and antibody(human VZV immunoglobulin)were mixed in different proportions and different incubation times. After neutralization,the antigen-antibody mixture was inoculated into human diploid cell 2BS strain cultured in a six-well plate. After 7 ～ 10 d of culture,the number of plaques was counted by Coomassie brilliant blue staining,and the 50% neutralizing antibody titer was calculated by Karber′s formula. Under the optimal neutralization conditions obtained,the effect of complement on the sensitivity of neutralization experiment was explored by changing the addition amount of complement(lyophilized guinea pig serum)to evaluate the optimal addition amount of complement. According to the determined neutralization test parameters,the neutralizing antibody titers of 12 anti-VZV mouse sera and 14 anti-VZV human sera were detected by using traditional plaque method and complement-dependent plaque method respectively.ResultsThe key parameters of the detection method were determined:the titer of VZV standard antigen was 500 ～ 1 000 PFU/mL;the proportion of complement added to the antigen-antibody neutralization system was 1∶10(v/v),and the neutralization condition was 37 ℃ for 1 h. Both the complement-dependent plaque method and the traditional plaque method were positive for anti-VZV mouse serum antibody,while the antibody titer detected by the traditional plaque method was generally lower,and the antibody level of mice inoculated with 2 doses of live attenuated varicella vaccine was significantly higher than that of mice inoculated with 1 dose(t = 0. 45,P < 0. 05);Both of the two methods were positive for anti-VZV human serum antibody.ConclusionA complement-dependent detection method for neutralizing antibody titer of VZV was established. The addition of complement significantly improved the sensitivity of neutralization detection. The evaluation of the titers of neutralizing antibodies in mouse serum with different immunization strategies by the method suggested that the immune effect of two doses of vaccine was better than that of one dose.