Computer-Aided Diagnosis of Duchenne Muscular Dystrophy Based on Texture Pattern Recognition on Ultrasound Images Using Unsupervised Clustering Algorithms and Deep Learning

OBJECTIVE

The feasibility of using deep learning in ultrasound imaging to predict the ambulatory status of patients with Duchenne muscular dystrophy (DMD) was previously explored for the first time. The present study further used clustering algorithms for the texture reconstruction of ultrasound images of DMD data sets and analyzed the difference in echo intensity between disease stages.

METHODS

k-means (Kms) and fuzzy c-means (FCM) clustering algorithms were used to reconstruct the DMD data-set textures. Each image was reconstructed using seven texture-feature categories, six of which were used as the primary analysis items. The task of automatically identifying the ambulatory function and DMD severity was performed by establishing a machine-learning model.

RESULTS

The experimental results indicated that the Gaussian Naïve Bayes and k-nearest neighbors classification models achieved an accuracy of 86.78% in ambulatory function classification. The decision-tree model achieved an identification accuracy of 83.80% in severity classification. A deep convolutional neural network model was established as the main structure of the deep-learning model while automatic auxiliary interpretation tasks of ambulatory function and severity were performed, and data augmentation was used to improve the recognition performance of the trained model. Both the visual geometry group (VGG)-16 and VGG-19 models achieved 98.53% accuracy in ambulatory-function classification. The VGG-19 model achieved 92.64% accuracy in severity classification.

CONCLUSION

Regarding the overall results, the Kms and FCM clustering algorithms were used in this study to reconstruct the characteristic texture of the gastrocnemius muscle group in DMD, which was indeed helpful in quantitatively analyzing the deterioration of the gastrocnemius muscle group in patients with DMD at different stages. Subsequent combination of machine-learning and deep-learning technologies can automatically and accurately assist in identifying DMD symptoms and tracking DMD deterioration for long-term observation.

Outcome prediction of cardiac arrest with automatically computed gray-white matter ratio on computed tomography images

BACKGROUND

This study aimed to develop an automated method to measure the gray-white matter ratio (GWR) from brain computed tomography (CT) scans of patients with out-of-hospital cardiac arrest (OHCA) and assess its significance in predicting early-stage neurological outcomes.

METHODS

Patients with OHCA who underwent brain CT imaging within 12 h of return of spontaneous circulation were enrolled in this retrospective study. The primary outcome endpoint measure was a favorable neurological outcome, defined as cerebral performance category 1 or 2 at hospital discharge. We proposed an automated method comprising image registration, K-means segmentation, segmentation refinement, and GWR calculation to measure the GWR for each CT scan. The K-means segmentation and segmentation refinement was employed to refine the segmentations within regions of interest (ROIs), consequently enhancing GWR calculation accuracy through more precise segmentations.

RESULTS

Overall, 443 patients were divided into derivation N=265, 60% and validation N=178, 40% sets, based on age and sex. The ROI Hounsfield unit values derived from the automated method showed a strong correlation with those obtained from the manual method. Regarding outcome prediction, the automated method significantly outperformed the manual method in GWR calculation (AUC 0.79 vs. 0.70) across the entire dataset. The automated method also demonstrated superior performance across sensitivity, specificity, and positive and negative predictive values using the cutoff value determined from the derivation set. Moreover, GWR was an independent predictor of outcomes in logistic regression analysis. Incorporating the GWR with other clinical and resuscitation variables significantly enhanced the performance of prediction models compared to those without the GWR.

CONCLUSIONS

Automated measurement of the GWR from non-contrast brain CT images offers valuable insights for predicting neurological outcomes during the early post-cardiac arrest period.

A self-driven, microfluidic, integrated-circuit biosensing chip for detecting four cardiovascular disease biomarkers

Cardiovascular diseases (CVDs) claimed the lives of nearly 21 million people worldwide in 2021, accounting for 30% of global deaths. However, one in five CVD patients is unaware that they have the disease, emphasizing the need for accurate biomarker monitoring. Herein we developed an integrated microfluidic system (IMS) for rapid quantification of four CVD biomarkers, including N-terminal pro B-type natriuretic peptide (NT-proBNP), fibrinogen, cardiac troponin I (cTnI), and C-reactive protein (CRP)- via aptamer-coated interdigitated electrodes (IDE) with integrated circuits (IC) and a self-driven IMS for sample treatment. The device was composed of plasma filtration, metering, and fluidic delay modules, and the former could extract 45% of plasma from a 20-μL blood sample; the metering module could quantify 5 μL of plasma within 90 s. Subsequently, the plasma was transported to a detection chamber, where IC-based IDE sensors made measurements within 5 min. The entire 15-min process allowed us to evaluate biomarkers across a wide dynamic range: NT-proBNP (0.1-10,000 pg/mL), fibrinogen (50-1,000 mg/dL), cTnI (0.1-10,000 pg/mL), and CRP (0.5-9 mg/L). Given that spiked blood samples were measured with reasonable accuracy (>80%), the IMS could see utility in CVD risk assessment and personalized medicine.

Deep Learning-based Diagnosis of Pulmonary Tuberculosis on Chest X-ray in the Emergency Department: A Retrospective Study

Prompt and correct detection of pulmonary tuberculosis (PTB) is critical in preventing its spread. We aimed to develop a deep learning-based algorithm for detecting PTB on chest X-ray (CXRs) in the emergency department. This retrospective study included 3498 CXRs acquired from the National Taiwan University Hospital (NTUH). The images were chronologically split into a training dataset, NTUH-1519 (images acquired during the years 2015 to 2019; n = 2144), and a testing dataset, NTUH-20 (images acquired during the year 2020; n = 1354). Public databases, including the NIH ChestX-ray14 dataset (model training; 112,120 images), Montgomery County (model testing; 138 images), and Shenzhen (model testing; 662 images), were also used in model development. EfficientNetV2 was the basic architecture of the algorithm. Images from ChestX-ray14 were employed for pseudo-labelling to perform semi-supervised learning. The algorithm demonstrated excellent performance in detecting PTB (area under the receiver operating characteristic curve [AUC] 0.878, 95% confidence interval [CI] 0.854-0.900) in NTUH-20. The algorithm showed significantly better performance in posterior-anterior (PA) CXR (AUC 0.940, 95% CI 0.912-0.965, p-value < 0.001) compared with anterior-posterior (AUC 0.782, 95% CI 0.644-0.897) or portable anterior-posterior (AUC 0.869, 95% CI 0.814-0.918) CXR. The algorithm accurately detected cases of bacteriologically confirmed PTB (AUC 0.854, 95% CI 0.823-0.883). Finally, the algorithm tested favourably in Montgomery County (AUC 0.838, 95% CI 0.765-0.904) and Shenzhen (AUC 0.806, 95% CI 0.771-0.839). A deep learning-based algorithm could detect PTB on CXR with excellent performance, which may help shorten the interval between detection and airborne isolation for patients with PTB.

Nanoscale sorting of extracellular vesicles via optically-induced dielectrophoresis on an integrated microfluidic system

We reported a microfluidic system for sorting of extracellular vesicles (EVs), which can house DNAs, RNAs, lipids, proteins, and metabolites that are important in intercellular communication. Their presence within bodily fluids has demonstrated potential in both clinical diagnostic and therapeutic applications. Furthermore, EVs exhibit distinct subtypes categorized by their sizes, each endowed with unique biophysical properties. Despite several existing techniques for EV isolation and purification, diminished purity and prolonged processing times still hamper clinical utility; comprehensive capture of EVs remains an ongoing pursuit. To address these challenges, we devised an innovative method for automated sorting of nano-scale EVs employing optically-induced dielectrophoresis on an integrated microfluidic chip. With this approach, EVs of three distinct size categories (small: 100-150 nm, medium-sized: 150-225 nm, and large: 225-350 nm) could be isolated at a purity of 86%. This new method has substantial potential in expediting EV research and diagnostics.

Prognostic value of arterial carbon dioxide tension during cardiopulmonary resuscitation in out-of-hospital cardiac arrest patients receiving extracorporeal resuscitation

BACKGROUND

Current guidelines on extracorporeal cardiopulmonary resuscitation (ECPR) recommend careful patient selection, but precise criteria are lacking. Arterial carbon dioxide tension (PaCO2) has prognostic value in out-of-hospital cardiac arrest (OHCA) patients but has been less studied in patients receiving ECPR. We studied the relationship between PaCO2 during cardiopulmonary resuscitation (CPR) and neurological outcomes of OHCA patients receiving ECPR and tested whether PaCO2 could help ECPR selection.

METHODS

This single-centre retrospective study enrolled 152 OHCA patients who received ECPR between January 2012 and December 2020. Favorable neurological outcome (FO) at discharge was the primary outcome. We used multivariable logistic regression to determine the independent variables for FO and generalised additive model (GAM) to determine the relationship between PaCO2 and FO. Subgroup analyses were performed to test discriminative ability of PaCO2 in subgroups of OHCA patients.

RESULTS

Multivariable logistic regression showed that PaCO2 was independently associated with FO after adjusting for other favorable resuscitation characteristics (Odds ratio [OR] 0.23, 95% Confidence Interval [CI] 0.08-0.66, p-value = 0.006). GAM showed a near-linear reverse relationship between PaCO2 and FO. PaCO2 < 70 mmHg was the cutoff point for predicting FO. PaCO2 also had prognostic value in patients with less favorable characteristics, including non-shockable rhythm (OR, 3.78) or low flow time > 60 min (OR, 4.66).

CONCLUSION

PaCO2 before ECMO implementation had prognostic value for neurological outcomes in OHCA patients. Patients with PaCO2 < 70 mmHg had higher possibility of FO, even in those with non-shockable rhythm or longer low-flow duration. PaCO2 could serve as an ECPR selection criterion.

[Correlation analysis between Pirani score and talo-navicular angle,calcaneo-cuboid angle and tibio-calcaneall angle of infant clubfoot under ultrasound]

Objective: To explore the evaluation effect of ultrasonography and Pirani score on tarsal deformity, treatment effect and pseudo-correction of congenital clubfoot in infants and young children, and the correlation between the two methods. Methods: This is a retrospective case series study. The clinical data of 26 children (40 feet) with congenital clubfoot who were evaluated by ultrasonography in the Third Affiliated Hospital of Zhengzhou University from January 2020 to January 2023 were retrospectively collected. There were 16 males and 10 females. The age at the first ultrasound examination was (M(IQR)) 9.0 (18.0) days (range: 1 to 46 days). All patients were treated with Ponseti method by the same physician. The Pirani scores before and after treatment and at the last examination, and the talonavicular angle, calcaneocuboid angle and tibiocalcaneal angle measured by ultrasound were collected, and the treatment and follow-up were recorded. Paired sample t test, repeated measures analysis of variance or Kruskal-Wallis test were used for data comparison, and Spearman correlation analysis was used for correlation analysis. The receiver operating characteristic curve was used to calculate the efficacy of ultrasound in evaluating different Pirani scores. Results: The number of plaster fixation in 26 children was 4.0 (1.0) times (range: 2 to 8 times). The medial talonavicular angle and posterior tibiocalcaneal angle were significantly improved after treatment and at the last follow-up compared with those before treatment, and the differences were statistically significant (all P<0.01). There was no difference in lateral calcaneocuboid angle before and after treatment and at the last follow-up (F=1.971, P>0.05). Pseudo-correction occurred in 2 cases (2 feet) during the treatment, with an incidence of 5%. Correlation analysis showed that there was a moderate positive correlation between talonavicular angle and Pirani midfoot score (r=0.480, P<0.01). There was no correlation between calcaneocuboid angle and Pirani midfoot score (r=0.114, P=0.105). There was a moderate negative correlation between tibial heel angle and Pirani hindfoot score (r=-0.566, P<0.01). The cut-off point of Pirani midfoot score of 1.5 was 38.78°, the sensitivity was 0.90, the specificity was 0.56, and the area under the curve was 0.75. The cut-off value of angle was 27.51 °, the sensitivity was 0.16, the specificity was 0.92, and the area under the curve was 0.44.The cut-off points of Pirani midfoot score of 3.0 were 45.08°and 9.96°, the sensitivity was 0.94 and 0.91, the specificity was 0.37 and 0.42, and the area under the curve was 0.59 and 0.62, respectively. The cut-off values of Pirani hindfoot score of 2.0 and 3.0 were 167.46° and 160.15°, respectively. The sensitivity was 0.75 and 0.67, the specificity was 0.81 and 0.83, and the area under the curve was 0.78 and 0.71, respectively. Conclusion: Ultrasound can complement with Pirani score, visually and dynamically observe the morphology and position changes of talonavicular joint, calcaneocuboid joint and tibiotalocalcaneal joint, monitor the recovery and pseudo-correction of tarsal bones, and better evaluate the therapeutic effect.

Prognostic implication of heart failure stage and left ventricular ejection fraction for patients with in-hospital cardiac arrest: a 16-year retrospective cohort study

BACKGROUND

The 2022 AHA/ACC/HFSA guidelines for the management of heart failure (HF) makes therapeutic recommendations based on HF status. We investigated whether the prognosis of in-hospital cardiac arrest (IHCA) could be stratified by HF stage and left ventricular ejection fraction (LVEF).

METHODS

This single-center retrospective study analyzed the data of patients who experienced IHCA between 2005 and 2020. Based on admission diagnosis, past medical records, and pre-arrest echocardiography, patients were classified into general IHCA, at-risk for HF, pre-HF, HF with preserved ejection fraction (HFpEF), and HF with mildly reduced ejection fraction or HF with reduced ejection fraction (HFmrEF-or-HFrEF) groups.

RESULTS

This study included 2,466 patients, including 485 (19.7%), 546 (22.1%), 863 (35.0%), 342 (13.9%), and 230 (9.3%) patients with general IHCA, at-risk for HF, pre-HF, HFpEF, and HFmrEF-or-HFrEF, respectively. A total of 405 (16.4%) patients survived to hospital discharge, with 228 (9.2%) patients achieving favorable neurological recovery. Multivariable logistic regression analysis indicated that pre-HF and HFpEF were associated with better neurological (pre-HF, OR: 2.11, 95% confidence interval [CI]: 1.23-3.61, p = 0.006; HFpEF, OR: 1.90, 95% CI: 1.00-3.61, p = 0.05) and survival outcomes (pre-HF, OR: 2.00, 95% CI: 1.34-2.97, p < 0.001; HFpEF, OR: 1.91, 95% CI: 1.20-3.05, p = 0.007), compared with general IHCA.

CONCLUSION

HF stage and LVEF could stratify patients with IHCA into different prognoses. Pre-HF and HFpEF were significantly associated with favorable neurological and survival outcomes after IHCA. Further studies are warranted to investigate whether HF status-directed management could improve IHCA outcomes.

Host-Pathogen Interactions in K. pneumoniae Urinary Tract Infections: Investigating Genetic Risk Factors in the Taiwanese Population

BACKGROUND

Klebsiella pneumoniae (K. pneumoniae) urinary tract infections pose a significant challenge in Taiwan. The significance of this issue arises because of the growing concerns about the antibiotic resistance of K. pneumoniae. Therefore, this study aimed to uncover potential genomic risk factors in Taiwanese patients with K. pneumoniae urinary tract infections through genome-wide association studies (GWAS).

METHODS

Genotyping data are obtained from participants with a history of urinary tract infections enrolled at the Tri-Service General Hospital as part of the Taiwan Precision Medicine Initiative (TPMI). A case-control study employing GWAS is designed to detect potential susceptibility single-nucleotide polymorphisms (SNPs) in patients with K. pneumoniae-related urinary tract infections. The associated genes are determined using a genome browser, and their expression profiles are validated via the GTEx database. The GO, Reactome, DisGeNET, and MalaCards databases are also consulted to determine further connections between biological functions, molecular pathways, and associated diseases between these genes.

RESULTS

The results identified 11 genetic variants with higher odds ratios compared to controls. These variants are implicated in processes such as adhesion, protein depolymerization, Ca2+-activated potassium channels, SUMOylation, and protein ubiquitination, which could potentially influence the host immune response.

CONCLUSIONS

This study implies that certain risk variants may be linked to K. pneumoniae infections by affecting diverse molecular functions that can potentially impact host immunity. Additional research and follow-up studies are necessary to elucidate the influence of these risk variants on infectious diseases and develop targeted interventions for mitigating the spread of K. pneumoniae urinary tract infections.

Deep Learning-Based Localization and Detection of Malpositioned Endotracheal Tube on Portable Supine Chest Radiographs in Intensive and Emergency Medicine: A Multicenter Retrospective Study

OBJECTIVES

We aimed to develop a computer-aided detection (CAD) system to localize and detect the malposition of endotracheal tubes (ETTs) on portable supine chest radiographs (CXRs).

DESIGN

This was a retrospective diagnostic study. DeepLabv3+ with ResNeSt50 backbone and DenseNet121 served as the model architecture for segmentation and classification tasks, respectively.

SETTING

Multicenter study.

PATIENTS

For the training dataset, images meeting the following inclusion criteria were included: 1) patient age greater than or equal to 20 years; 2) portable supine CXR; 3) examination in emergency departments or ICUs; and 4) examination between 2015 and 2019 at National Taiwan University Hospital (NTUH) (NTUH-1519 dataset: 5,767 images). The derived CAD system was tested on images from chronologically (examination during 2020 at NTUH, NTUH-20 dataset: 955 images) or geographically (examination between 2015 and 2020 at NTUH Yunlin Branch [YB], NTUH-YB dataset: 656 images) different datasets. All CXRs were annotated with pixel-level labels of ETT and with image-level labels of ETT presence and malposition.

INTERVENTIONS

None.

MEASUREMENTS AND MAIN RESULTS

For the segmentation model, the Dice coefficients indicated that ETT would be delineated accurately (NTUH-20: 0.854; 95% CI, 0.824-0.881 and NTUH-YB: 0.839; 95% CI, 0.820-0.857). For the classification model, the presence of ETT could be accurately detected with high accuracy (area under the receiver operating characteristic curve [AUC]: NTUH-20, 1.000; 95% CI, 0.999-1.000 and NTUH-YB: 0.994; 95% CI, 0.984-1.000). Furthermore, among those images with ETT, ETT malposition could be detected with high accuracy (AUC: NTUH-20, 0.847; 95% CI, 0.671-0.980 and NTUH-YB, 0.734; 95% CI, 0.630-0.833), especially for endobronchial intubation (AUC: NTUH-20, 0.991; 95% CI, 0.969-1.000 and NTUH-YB, 0.966; 95% CI, 0.933-0.991).

CONCLUSIONS

The derived CAD system could localize ETT and detect ETT malposition with excellent performance, especially for endobronchial intubation, and with favorable potential for external generalizability.

Application of Machine Learning to Ultrasonography in Identifying Anatomical Landmarks for Cricothyroidotomy Among Female Adults: A Multi-center Prospective Observational Study

We aimed to develop machine learning (ML)-based algorithms to assist physicians in ultrasound-guided localization of cricoid cartilage (CC) and thyroid cartilage (TC) in cricothyroidotomy. Adult female volunteers were prospectively recruited from two hospitals between September and December, 2020. Ultrasonographic images were collected via a modified longitudinal technique. You Only Look Once (YOLOv5s), Faster Regions with Convolutional Neural Network features (Faster R-CNN), and Single Shot Detector (SSD) were selected as the model architectures. A total of 488 women (mean age: 36.0 years) participated in the study, contributing to a total of 292,053 frames of ultrasonographic images. The derived ML-based algorithms demonstrated excellent discriminative performance for the presence of CC (area under the receiver operating characteristic curve [AUC]: YOLOv5s, 0.989, 95% confidence interval [CI]: 0.982-0.994; Faster R-CNN, 0.986, 95% CI: 0.980-0.991; SSD, 0.968, 95% CI: 0.956-0.977) and TC (AUC: YOLOv5s, 0.989, 95% CI: 0.977-0.997; Faster R-CNN, 0.981, 95% CI: 0.965-0.991; SSD, 0.982, 95% CI: 0.973-0.990). Furthermore, in the frames where the model could correctly indicate the presence of CC or TC, it also accurately localized CC (intersection-over-union: YOLOv5s, 0.753, 95% CI: 0.739-0.765; Faster R-CNN, 0.720, 95% CI: 0.709-0.732; SSD, 0.739, 95% CI: 0.726-0.751) or TC (intersection-over-union: YOLOv5s, 0.739, 95% CI: 0.722-0.755; Faster R-CNN, 0.709, 95% CI: 0.687-0.730; SSD, 0.713, 95% CI: 0.695-0.730). The ML-based algorithms could identify anatomical landmarks for cricothyroidotomy in adult females with favorable discriminative and localization performance. Further studies are warranted to transfer this algorithm to hand-held portable ultrasound devices for clinical use.

[Analyses of the risk factors for the progression of primary antiphospholipid syndrome to systemic lupus erythematosus]

Objectives: Analyze the clinical characteristics of patients with primary antiphospholipid syndrome (PAPS) progressing to systemic lupus erythematosus (SLE).Explore the risk factors for the progression from PAPS to SLE. Methods: The clinical data of 262 patients with PAPS enrolled in Peking Union Medical College Hospital from February 2005 to September 2021 were evaluated. Assessments included demographic data, clinical manifestations, laboratory tests (serum levels of complement, anti-nuclear antibodies, anti-double-stranded DNA antibodies), treatment, and outcomes. Kaplan-Meier analysis was used to calculate the prevalence of SLE in patients with PAPS. Univariate Cox regression analysis was employed to identify the risk factors for PAPS progressing to SLE. Results: Among 262 patients with PAPS, 249 had PAPS (PAPS group) and 13 progressed to SLE (5.0%) (PAPS-SLE group). Univariate Cox regression analysis indicated that cardiac valve disease (HR=6.360), positive anti-double-stranded DNA antibodies (HR=7.203), low level of complement C3 (HR=25.715), and low level of complement C4 (HR=10.466) were risk factors for the progression of PAPS to SLE, whereas arterial thrombotic events (HR=0.109) were protective factors (P<0.05 for all). Kaplan-Meier analysis showed that the prevalence of SLE in patients suffering from PAPS with a disease course>10 years was 9%-15%. Hydroxychloroquine treatment had no effect on the occurrence of SLE in patients with PAPS (HR=0.753, 95%CI 0.231-2.450, P=0.638). Patients with≥2 risk factors had a significantly higher prevalence of SLE compared with those with no or one risk factor (13-year cumulative prevalence of SLE 48.7% vs. 0 vs. 6.2%, P<0.001 for both). Conclusions: PAPS may progress to SLE in some patients. Early onset, cardiac-valve disease, positive anti-dsDNA antibody, and low levels of complement are risk factors for the progression of PAPS to SLE (especially in patients with≥2 risk factors). Whether application of hydroxychloroquine can delay this transition has yet to be demonstrated.

Artificial Intelligence-enabled Chest X-ray Classifies Osteoporosis and Identifies Mortality Risk

A deep learning model was developed to identify osteoporosis from chest X-ray (CXR) features with high accuracy in internal and external validation. It has significant prognostic implications, identifying individuals at higher risk of all-cause mortality. This Artificial Intelligence (AI)-enabled CXR strategy may function as an early detection screening tool for osteoporosis. The aim of this study was to develop a deep learning model (DLM) to identify osteoporosis via CXR features and investigate the performance and clinical implications. This study collected 48,353 CXRs with the corresponding T score according to Dual energy X-ray Absorptiometry (DXA) from the academic medical center. Among these, 35,633 CXRs were used to identify CXR- Osteoporosis (CXR-OP). Another 12,720 CXRs were used to validate the performance, which was evaluated by the area under the receiver operating characteristic curve (AUC). Furthermore, CXR-OP was tested to assess the long-term risks of mortality, which were evaluated by Kaplan‒Meier survival analysis and the Cox proportional hazards model. The DLM utilizing CXR achieved AUCs of 0.930 and 0.892 during internal and external validation, respectively. The group that underwent DXA with CXR-OP had a higher risk of all-cause mortality (hazard ratio [HR] 2.59, 95% CI: 1.83-3.67), and those classified as CXR-OP in the group without DXA also had higher all-cause mortality (HR: 1.67, 95% CI: 1.61-1.72) in the internal validation set. The external validation set produced similar results. Our DLM uses CXRs for early detection of osteoporosis, aiding physicians to identify those at risk. It has significant prognostic implications, improving life quality and reducing mortality. AI-enabled CXR strategy may serve as a screening tool.

Development and External Validation of Clinical Features-based Machine Learning Models for Predicting COVID-19 in the Emergency Department

Introduction

Timely diagnosis of patients affected by an emerging infectious disease plays a crucial role in treating patients and avoiding disease spread. In prior research, we developed an approach by using machine learning (ML) algorithms to predict serious acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection based on clinical features of patients visiting an emergency department (ED) during the early coronavirus 2019 (COVID-19) pandemic. In this study, we aimed to externally validate this approach within a distinct ED population.

Methods

To create our training/validation cohort (model development) we collected data retrospectively from suspected COVID-19 patients at a US ED from February 23-May 12, 2020. Another dataset was collected as an external validation (testing) cohort from an ED in another country from May 12-June 15, 2021. Clinical features including patient demographics and triage information were used to train and test the models. The primary outcome was the confirmed diagnosis of COVID-19, defined as a positive reverse transcription polymerase chain reaction test result for SARS-CoV-2. We employed three different ML algorithms, including gradient boosting, random forest, and extra trees classifiers, to construct the predictive model. The predictive performances were evaluated with the area under the receiver operating characteristic curve (AUC) in the testing cohort.

Results

In total, 580 and 946 ED patients were included in the training and testing cohorts, respectively. Of them, 98 (16.9%) and 180 (19.0%) were diagnosed with COVID-19. All the constructed ML models showed acceptable discrimination, as indicated by the AUC. Among them, random forest (0.785, 95% confidence interval [CI] 0.747-0.822) performed better than gradient boosting (0.774, 95% CI 0.739-0.811) and extra trees classifier (0.72, 95% CI 0.677-0.762). There was no significant difference between the constructed models.

Conclusion

Our study validates the use of ML for predicting COVID-19 in the ED and demonstrates its potential for predicting emerging infectious diseases based on models built by clinical features with temporal and spatial heterogeneity. This approach holds promise for scenarios where effective diagnostic tools for an emerging infectious disease may be lacking in the future.

Optimal inhaled oxygen and carbon dioxide concentrations for post-cardiac arrest cerebral reoxygenation and neurological recovery

Prolonged cerebral hypoperfusion after the return of spontaneous circulation (ROSC) from cardiac arrest (CA) may lead to poor neurological recovery. In a 7-min asphyxia-induced CA rat model, four combinations of inhaled oxygen (iO2) and carbon dioxide (iCO2) were administered for 150 min post-ROSC and compared in a randomized animal trial. At the end of administration, the partial pressure of brain tissue oxygenation (PbtO2) monitored in the hippocampal CA1 region returned to the baseline for the 88% iO2 [ΔPbtO2, median: -0.39 (interquartile range: 5.6) mmHg] and 50% iO2 [ΔpbtO2, -2.25 (10.9) mmHg] groups; in contrast, PbtO2 increased substantially in the 88% iO2+12% iCO2 [ΔpbtO2, 35.05 (16.0) mmHg] and 50% iO2+12% iCO2 [ΔpbtO2, 42.03 (31.7) mmHg] groups. Pairwise comparisons (post hoc Dunn's test) indicated the significant role of 12% iCO2 in augmenting PbtO2 during the intervention and improving neurological recovery at 24 h post-ROSC. Facilitating brain reoxygenation may improve post-CA neurological outcomes.

Opportunistic Screening for Asymptomatic Left Ventricular Dysfunction With the Use of Electrocardiographic Artificial Intelligence: A Cost-Effectiveness Approach

BACKGROUND

The burden of asymptomatic left ventricular dysfunction (LVD) is greater than that of heart failure; however, a cost-effective tool for asymptomatic LVD screening has not been well validated. We aimed to prospectively validate an artificial intelligence (AI)-enabled electrocardiography (ECG) algorithm for asymptomatic LVD detection and evaluate its cost-effectiveness for opportunistic screening.

METHODS

In this prospective observational study, patients undergoing ECG at outpatient clinics or health check-ups were enrolled in 2 hospitals in Taiwan. Patients were stratified into LVD (left ventricular ejection fraction ≤ 40%) risk groups according to a previously developed ECG algorithm. The performance of AI-ECG was used to conduct a cost-effectiveness analysis of LVD screening compared with no screening. Incremental cost-effectiveness ratio (ICER) and sensitivity analyses were used to examine the cost-effectiveness and robustness of the results.

RESULTS

Among the 29,137 patients, the algorithm demonstrated areas under the receiver operating characteristic curves of 0.984 and 0.945 for detecting LVD within 28 days in the 2 hospital cohorts. For patients not initially scheduled for ECG, the algorithm predicted future echocardiograms (high-risk, 46.2%; medium-risk, 31.4%; low-risk, 14.6%) and LVD (high-risk, 26.2%; medium-risk, 3.4%; low-risk, 0.1%) at 12 months. Opportunistic screening with AI-ECG could result in a negative ICER of -$7,439 for patients aged 65 years, with consistent cost-savings across age groups and particularly in men. Approximately 91.5% of the cases were found to be cost-effective at the willingness-to-pay threshold of $30,000 in the probabilistic analysis.

CONCLUSIONS

The use of AI-ECG for asymptomatic LVD risk stratification is promising, and opportunistic screening in outpatient clinics has the potential to reduce costs.

Deep Learning-based Diagnosis and Localization of Pneumothorax on Portable Supine Chest X-ray in Intensive and Emergency Medicine: A Retrospective Study

PURPOSE

To develop two deep learning-based systems for diagnosing and localizing pneumothorax on portable supine chest X-rays (SCXRs).

METHODS

For this retrospective study, images meeting the following inclusion criteria were included: (1) patient age ≥ 20 years; (2) portable SCXR; (3) imaging obtained in the emergency department or intensive care unit. Included images were temporally split into training (1571 images, between January 2015 and December 2019) and testing (1071 images, between January 2020 to December 2020) datasets. All images were annotated using pixel-level labels. Object detection and image segmentation were adopted to develop separate systems. For the detection-based system, EfficientNet-B2, DneseNet-121, and Inception-v3 were the architecture for the classification model; Deformable DETR, TOOD, and VFNet were the architecture for the localization model. Both classification and localization models of the segmentation-based system shared the UNet architecture.

RESULTS

In diagnosing pneumothorax, performance was excellent for both detection-based (Area under receiver operating characteristics curve [AUC]: 0.940, 95% confidence interval [CI]: 0.907-0.967) and segmentation-based (AUC: 0.979, 95% CI: 0.963-0.991) systems. For images with both predicted and ground-truth pneumothorax, lesion localization was highly accurate (detection-based Dice coefficient: 0.758, 95% CI: 0.707-0.806; segmentation-based Dice coefficient: 0.681, 95% CI: 0.642-0.721). The performance of the two deep learning-based systems declined as pneumothorax size diminished. Nonetheless, both systems were similar or better than human readers in diagnosis or localization performance across all sizes of pneumothorax.

CONCLUSIONS

Both deep learning-based systems excelled when tested in a temporally different dataset with differing patient or image characteristics, showing favourable potential for external generalizability.

GRAIL gene knockout mice protect against aging-related and noise-induced hearing loss

BACKGROUND

Hearing loss is a global health issue and its etiopathologies involve complex molecular pathways. The ubiquitin-proteasome system has been reported to be associated with cochlear development and hearing loss. The gene related to anergy in lymphocytes ( GRAIL ), as an E3 ubiquitin ligase, has not, as yet, been examined in aging-related and noise-induced hearing loss mice models.

METHODS

This study used wild-type (WT) and GRAIL knockout (KO) mice to examine cochlear hair cells and synaptic ribbons using immunofluorescence staining. The hearing in WT and KO mice was detected using auditory brainstem response. Gene expression patterns were compared using RNA-sequencing to identify potential targets during the pathogenesis of noise-induced hearing loss in WT and KO mice.

RESULTS

At the 12-month follow-up, GRAIL KO mice had significantly less elevation in threshold level and immunofluorescence staining showed less loss of outer hair cells and synaptic ribbons in the hook region compared with GRAIL WT mice. At days 1, 14, and 28 after noise exposure, GRAIL KO mice had significantly less elevation in threshold level than WT mice. After noise exposure, GRAIL KO mice showed less loss of outer hair cells in the cochlear hook and basal regions compared with WT mice. Moreover, immunofluorescence staining showed less loss of synaptic ribbons in the hook regions of GRAIL KO mice than of WT mice. RNA-seq analysis results showed significant differences in C-C motif chemokine ligand 19 ( CCL19 ), C-C motif chemokine ligand 21 ( CCL21 ), interleukin 25 ( IL25 ), glutathione peroxidase 6 ( GPX6 ), and nicotinamide adenine dinucleotide phosphate (NADPH) oxidase 1 ( NOX1 ) genes after noise exposure.

CONCLUSION

The present data demonstrated that GRAIL deficiency protects against aging-related and noise-induced hearing loss. The mechanism involved needs to be further clarified from the potential association with synaptic modulation, inflammation, and oxidative stress.

Treatment efficacy of low-dose 5-fluorouracil with ultrasound in mediating 5-fluorouracil-loaded microbubble cavitation in head and neck cancer

Over the past 50 years, 5-fluorouracil (5-FU) has played a critical role in the systemic chemotherapy of cancer patients. Bolus intravenous (IV) 5-FU infusion has been used due to the limitation of its extremely short half-life (10-15 min). This study used ultrasound (US) mediating 5-FU-loaded microbubbles (MBs) cavitation as a tool to increase local intratumoral 5-FU levels with a reduced dose of 5-FU (a single IV injection of 2.5 mg/kg instead of a single intraperitoneal injection of 25-200 mg/kg as used in previous studies in mice). The 5-FU-MBs were prepared with a 132 mg/mL albumin solution and a 0.30 mg/mL 5-FU solution. The diameters of the MBs and 5-FU-MBs were 1.24 ± 0.85 and 2.00 ± 0.53 µm (mean ± SEM), respectively, and the maximum loading efficiency of 5-FU on MBs was 19.04 ± 0.25%. In the in vitro study, the cell viabilities of 5-FU and 5-FU-MBs did not differ significantly, but compared with the 5-FU-MBs treatment-alone group, cell toxicity increased to 31% in the 5-FU-MBs + US group (p < 0.001). The biodistribution results indicated that the 5-FU levels of the tumors in small animals were significant higher for the 5-FU-MBs + US treatment than for either the 5-FU-MBs or 5-FU treatment with low 5-FU systemic treatment doses (2.5 mg/kg 5-FU IV). In small-animal treatment, 2.5 mg/kg 5-FU therapeutic IV doses injected into mice caused a more-significant reduction in tumor growth in the 5-FU-MBs + US group (65.9%) than in the control group after 34 days of treatment.

[Clinical characteristics analysis of patients with severe immune checkpoint inhibitors related myocarditis]

Objective: To analyze the clinical characteristics of patients with severe immune checkpoint inhibitors (ICIs) related myocarditis. Methods: A retrospective study was conducted on the 50 patients with ICIs-related myocarditis in the multidisciplinary cardio-oncology clinic of Zhongshan Hospital affiliated to Fudan University from April 2020 to April 2022. The age of patients was (63.7±10.8) years old, including 37 males and 13 females. The patients were divided into the mild group (n=37) and the severe group (n=13) according to severity. The differences of basic characteristics, clinical manifestations, laboratory tests, auxiliary examination, combined irAEs, treatment and outcomes between the two groups of patients were analyzed. Results: The immunotherapy time [M(Q1,Q3)] of patients in the mild group and severe group were 81 (49, 134) and 24 (20, 116) days, respectively (P<0.05). In the severe group, the levels of cTnT [0.605 (0.317, 1.072) μg/L], NT-proBNP [1 126 (386, 1 744) ng/L], CK-MB [78 (48, 238) U/L], and CK-MM [240 (45, 6 543) U/L] were higher than those in the mild group [0.104 (0.045, 0.189) μg/L, 237 (39, 785) ng/L, 24 (20, 33) U/L, 108 (72, 168) U/L, respectively] (all P<0.05). The left ventricular ejection fraction of the severe group [64% (57%, 65%)] was lower than that of the mild group [66% (63%, 69%)] (P<0.05), and the incidence of conduction block (n=4, 4/13) and abnormal ventricular wall motion (n=4, 4/13), the incidence of ICIs-related myositis (n=10, 10/13), ICIs-related hepatitis (n=4, 4/13) and ICIs-related neurotoxicity (n=4, 4/13) were higher than those in the mild group (n=1, 2.7%; n=2, 5.4%; n=16, 43.2%; n=2, 5.4%; n=1, 2.7%, respectively) (all P<0.05). The proportion of patients receiving intensified immunosuppressive therapy and mortality rate in the severe group were 12/13 (n=12) and 4/13 (n=4), which were both higher than those in the mild group [10.8% (n=4) and 0] (both P<0.05). Conclusions: The incidence of ICIs-related myocarditis is not high, but the severe rate and mortality are high. The differential diagnosis of severe ICIs related myocarditis should be combined with myocardial markers, electrocardiogram and echocardiogram, and early diagnosis and treatment can improve the prognosis of patients.

Deep Learning Can Predict Bevacizumab Therapeutic Effect and Microsatellite Instability Directly from Histology in Epithelial Ovarian Cancer

Epithelial ovarian cancer (EOC) remains a significant cause of mortality among gynecologic cancers, with the majority of cases being diagnosed at an advanced stage. Before targeted therapies were available, EOC treatment relied largely on debulking surgery and platinum-based chemotherapy. Vascular endothelial growth factors have been identified as inducing tumor angiogenesis. According to several clinical trials, anti-vascular endothelial growth factor-targeted therapy with bevacizumab was effective in all phases of EOC treatment. However, there are currently no biomarkers accessible for regular therapeutic use despite the importance of patient selection. Microsatellite instability (MSI), caused by a deficiency of the DNA mismatch repair system, is a molecular abnormality observed in EOC associated with Lynch syndrome. Recent evidence suggests that angiogenesis and MSI are interconnected. Developing predictive biomarkers, which enable the selection of patients who might benefit from bevacizumab-targeted therapy or immunotherapy, is critical for realizing personalized precision medicine. In this study, we developed 2 improved deep learning methods that eliminate the need for laborious detailed image-wise annotations by pathologists and compared them with 3 state-of-the-art methods to not only predict the efficacy of bevacizumab in patients with EOC using mismatch repair protein immunostained tissue microarrays but also predict MSI status directly from histopathologic images. In prediction of therapeutic outcomes, the 2 proposed methods achieved excellent performance by obtaining the highest mean sensitivity and specificity score using MSH2 or MSH6 markers and outperformed 3 state-of-the-art deep learning methods. Moreover, both statistical analysis results, using Cox proportional hazards model analysis and Kaplan-Meier progression-free survival analysis, confirm that the 2 proposed methods successfully differentiate patients with positive therapeutic effects and lower cancer recurrence rates from patients experiencing disease progression after treatment (P < .01). In prediction of MSI status directly from histopathology images, our proposed method also achieved a decent performance in terms of mean sensitivity and specificity score even for imbalanced data sets for both internal validation using tissue microarrays from the local hospital and external validation using whole section slides from The Cancer Genome Atlas archive.

Upper Airway Surgery or Weight Control? Modified Drug-Induced Sleep Endoscopy for Obstructive Sleep Apnea

OBJECTIVE

To identify the value of head rotation in the supine position and oral appliance (OA) use in drug-induced sleep endoscopy (DISE).

STUDY DESIGN

Eighty-three sleep apnea adults undergoing target-controlled infusion-DISE (TCI-DISE) were recruited from a tertiary academic medical center.

SETTING

During DISE, 4 positions were utilized: supine position (position 1), head rotation (position 2), mandibular advancement using an OA (position 3), and head rotation with an OA (position 4).

METHODS

Polysomnography (PSG) data and anthropometric variables during DISE were analyzed.

RESULTS

Eighty-three patients (65 men and 18 women; mean [standard deviation, SD], 48.5 [11.0] years) who underwent PSG and TCI-DISE were included. The mean (SD) apnea-hypopnea index (AHI) was 35.5 (22.4) events/h. Twenty-three patients had persistent complete concentric velopharyngeal collapse in the supine position, even with concurrent head rotation and OA (position 4). Their mean (SD) AHI was 54.7 (24.6) events/h, significantly higher than that of the 60 patients without such collapse in position 4 (p < .001). Their mean (SD) body mass index (BMI) was 29.0 (4.1) kg/m2 , also significantly higher (p = .005). After adjustment for age, BMI, tonsil size, and tongue position, the degree of velum and tongue base obstruction was significantly associated with sleep apnea severity in positions 2, 3, and 4.

CONCLUSION

We showed the feasibility, safety, and usefulness of using simple edge-to-edge, reusable OA in DISE. Patients who are not responsive to head rotation and OA during TCI-DISE may need upper airway surgery and/or weight control.

[Hsa_circ_0000392 affects the radiation sensitivity of cervical cancer by targeting the miR-145-5p/CRKL/MAPK pathway]

Objective: To investigate the effect of hsa_circ_0000392 (circ_0000392) on the radiosensitivity of cervical cancer cells and explore its potential mechanism. Methods: Cervical cancer tissues and adjacent normal tissues of 42 patients with cervical cancer who were confirmed pathologically for the first time in Huaihe Hospital of Henan University from 2016 to 2019 were collected. According to the patients' response to radiotherapy, the cancer tissues were divided into radio-sensitive tissues and radio-resistant tissues. The expressions of circ_0000392, miR-145-5p, and CRKL in radiation-sensitive, radiation-resistant cervical cancer tissues and Hela, SiHa cells were detected by reverse transcription-quantitative real-time polymerase chain reaction (RT-qPCR) and western blot. SiRNA circ_0000392, miR-145-5p mimic, miR-145-5p inhibitor, pcDNA 3.1-CRKL and its negative control were transfected into HeLa and Siha cells, respectively. After radiation induction, the survival fraction of cells was detected by clone formation assay, apoptosis was detected by flow cytometry, and the expressions of apoptosis-related proteins Bax and Bcl-2 and ERK pathway protein p-ERK1/2 and ERK1/2 were detected by western blot. The targeting relationship between circ_0000392, miR-145-5p and CRKL was verified by dual luciferase reporter gene assay. The effect of circ_0000392 on radiotherapy sensitivity of cervical cancer in vivo was observed in the tumor formation experiment in nude mice. Results: circ_0000392 and CRKL were upregulated in radiation-resistant tissues and cancer cells of cervical cancer, while miR-145-5p was downregulated. The clone formation numbers of Hela and SiHa cells in si-circ_0000392#1+ 6 Gy group were (78.67±10.97) and (71.00±9.54), respectively, which were lower than those in si-Ctrl+ 6 Gy group [(176.00±22.27) and (158.33±17.56), respectively]. The apoptosis rates were (41.55±3.40)% and (31.41±3.29)%, respectively, which were higher than those in si-Ctrl+ 6 Gy group [(15.91±1.37)% and (13.70±1.89)%, P<0.05]. The protein expression of Bax was higher than that of si-Ctrl+ 6 Gy group, and the protein expressions of Bcl2 was lower than those of si-Ctrl+ 6 Gy group. The clone formation numbers of Hela and SiHa cells in si-circ_0000392#1+ miR-145-5p inhibitor+ 6 Gy group were (171.33±25.01) and (137.00±21.66), higher than those in si-circ_0000392#1+ inhibitor NC+ 6 Gy group [(84.67±17.79) vs (71.00±11.00), P<0.05]. The apoptosis rates were (17.41±2.58) % and (15.96±1.25) %, lower than those of si-circ_0000392 #1+ inhibitor NC+ 6 Gy [(40.29±2.92)% and (30.82±2.34)%, respectively, P<0.05]. The expression of Bax protein was lower than that of si-circ_0000392#1+ inhibitor NC+ 6 Gy group, and the expressions of Bcl2 protein were higher than those of si-circ_0000392#1+ inhibitor NC+ 6 Gy group. Circ_0000392 can target miR-145-5p, and CRKL is the downstream target gene of miR-145-5p. The clone formation numbers of Hela and SiHa cells in miR-145-5p mimic+ 6 Gy group were (74.33±10.02) and (66.00±12.17), respectively, which were lower than those of mimic NC+ 6 Gy group [(197.67±17.21) vs (157.67±11.59), respectively, P<0.05]. The apoptosis rates were (45.58±2.16)% and (32.10±3.55)%, higher than those of mimic NC+ 6 Gy group [(15.85±2.45)% and (13.99±1.69)%, respectively, P<0.05]. The expression of Bax protein was higher than that of the mimic NC+ 6 Gy mimic group, and the expression of Bcl2 protein was lower than that of the mimic NC+ 6 Gy group. The clone formation numbers of Hela and SiHa cells in miR-145-5p mimic+ pcDNA-CRKL+ 6 Gy group were (158.00±15.88) and (122.33±13.65), respectively, which were higher than those of miR-145-5p mimic+ pcDNA+ 6 Gy group [(71.33±8.02) vs (65.67±12.22), P<0.05]. The apoptosis rates were (19.50±3.45)% and (17.04±0.94)%, respectively, which were lower than those of miR-145-5p mimic+ pcDNA+ 6 Gy group [(44.33±2.36)% and (32.05±2.76)%, respectively, P<0.05]. The expression of Bax protein was lower than that of miR-145-5p mimic+ pcDNA group+ 6 Gy group, and the expression of Bcl2 protein was higher than that of miR-145-5p mimic+ pcDNA+ 6 Gy group. Sh-circ_0000392 group had smaller tumor volume and decreased tumor weight (P<0.05). The relative mRNA expression levels of circ_0000392, miR-145-5p and CRKL and the relative protein expression levels of CRKL, Bcl-2 and p-ERK1/2 were decreased, while the relative expression level of Bax protein was increased (P<0.05). Conclusion: Circ_0000392 could enhance the radiosensitivity of cervical cancer cells, and its mechanism may be related to the regulation of CRKL/ERK signaling pathway by targeting miR-145-5p, which provides a new reference for enhancing the radiosensitivity of cervical cancer cells.

Highly-specific aptamer targeting SARS-CoV-2 S1 protein screened on an automatic integrated microfluidic system for COVID-19 diagnosis

Variants of the severe acute respiratory syndrome coronavirus (SARS-CoV-2) have evolved such that it may be challenging for diagnosis and clinical treatment of the pandemic coronavirus disease-19 (COVID-19). Compared with developed SARS-CoV-2 diagnostic tools recently, aptamers may exhibit some advantages, including high specificity/affinity, longer shelf life (vs. antibodies), and could be easily prepared. Herein an integrated microfluidic system was developed to automatically carry out one novel screening process based on the systematic evolution of ligands by exponential enrichment (SELEX) for screening aptamers specific with SARS-CoV-2. The new screening process started with five rounds of positive selection (with the S1 protein of SARS-CoV-2). In addition, including non-target viruses (influenza A and B), human respiratory tract-related cancer cells (adenocarcinoma human alveolar basal epithelial cells and dysplastic oral keratinocytes), and upper respiratory tract-related infectious bacteria (including methicillin-resistant Staphylococcus aureus, Pseudomonas aeruginosa, Acinetobacter baumannii, and Klebsiella pneumoniae), and human saliva were involved to increase the specificity of the screened aptamer during the negative selection. Totally, all 10 rounds could be completed within 20 h. The dissociation constant of the selected aptamer was determined to be 63.0 nM with S1 protein. Limits of detection for Wuhan and Omicron clinical strains were found to be satisfactory for clinical applications (i.e. 4.80 × 10¹ and 1.95 × 10² copies/mL, respectively). Moreover, the developed aptamer was verified to be capable of capturing inactivated SARS-CoV-2 viruses, eight SARS-CoV-2 pseudo-viruses, and clinical isolates of SARS-CoV-2 viruses. For high-variable emerging viruses, this developed integrated microfluidic system can be used to rapidly select highly-specific aptamers based on the novel SELEX methods to deal with infectious diseases in the future.

Artificial Intelligence-Enabled Electrocardiography Detects B-Type Natriuretic Peptide and N-Terminal Pro-Brain Natriuretic Peptide

BACKGROUND: The B-type natriuretic peptide (BNP) and N-terminal pro-brain natriuretic peptide (pBNP) are predictors of cardiovascular morbidity and mortality. Since the artificial intelligence (AI)-enabled electrocardiogram (ECG) system is widely used in the management of many cardiovascular diseases (CVDs), patients requiring intensive monitoring may benefit from an AI-ECG with BNP/pBNP predictions. This study aimed to develop an AI-ECG to predict BNP/pBNP and compare their values for future mortality. METHODS: The development, tuning, internal validation, and external validation sets included 47,709, 16,249, 4001, and 6042 ECGs, respectively. Deep learning models (DLMs) were trained using a development set for estimating ECG-based BNP/pBNP (ECG-BNP/ECG-pBNP), and the tuning set was used to guide the training process. The ECGs in internal and external validation sets belonging to nonrepeating patients were used to validate the DLMs. We also followed-up all-cause mortality to explore the prognostic value. RESULTS: The DLMs accurately distinguished mild (≥500 pg/mL) and severe (≥1000 pg/mL) an abnormal BNP/pBNP with AUCs of ≥0.85 in the internal and external validation sets, which provided sensitivities of 68.0-85.0% and specificities of 77.9-86.2%. In continuous predictions, the Pearson correlation coefficient between ECG-BNP and ECG-pBNP was 0.93, and they were both associated with similar ECG features, such as the T wave axis and correct QT interval. ECG-pBNP provided a higher all-cause mortality predictive value than ECG-BNP. CONCLUSIONS: The AI-ECG can accurately estimate BNP/pBNP and may be useful for monitoring the risk of CVDs. Moreover, ECG-pBNP may be a better indicator to manage the risk of future mortality.

[Clinical features and antimicrobial resistance of invasive non-typhoid Salmonella infection in children at Xiamen]

Objective: To investigate the clinical characteristics, serogroups and antimicrobial resistance of invasive non-typhoid Salmonella infection in children at Xiamen. Methods: Retrospective cohort study. The clinical manifestations, treatment, prognosis, serogroups and antimicrobial resistance of 29 hospitalized children with invasive non-typhoid Salmonella infection confirmed by blood, cerebrospinal fluid, bone marrow and other sterile body fluids or deep pus culture at the Department of Infectious Diseases, the Department of Orthopedics and the Department of General Surgery in Xiamen Children's Hospital from January 2016 to December 2021 were analyzed. According to the clinical diagnosis criteria, the patients were divided into sepsis group and non-sepsis group (bacteremia and local suppurative infection). The inflammatory markers, serogroups distribution and drug resistance were compared between the two groups. Comparison between groups using Mann-Whitney U test and χ² test. Results: Among the 29 cases, there were 17 males and 12 females, with an onset age of 14 (9, 25) months, and 10 cases (34%) of patients were younger than 1 year old, 15 cases (52%) under 1 to 3 years old, and 4 cases (14%) greater than or equal 3 years old. The onset time of 25 cases (86%) was from April to September. The diseases included 19 cases (66%) septicemia (2 of which were combined with suppurative meningitis), 10 cases (34%) non-sepsis group, including 7 cases bacteremia and 3 cases local suppurative infection (2 cases of osteomyelitis, 1 case of appendicitis with peritonitis). The clinical manifestations were fever in 29 cases (100%), diarrhea and abdominal pain in 18 cases (62%), cough and runny nose in 10 cases (34%). Eighteen cases (62%) were cured and 11 cases (38%) were improved by effective antibiotics treatment. C-reactive protein in sepsis group was significantly higher than that in non-sepsis group (25.2 (16.1, 56.4) vs. 3.4 (0.5, 7.5) mg/L, Z=-3.81, P<0.001).The serogroups of C, B and E were the most prevalent among non-typhoid Salmonella isolates, accounting for 10 cases (34%), 9 cases (31%) and 7 cases (24%) respectively. Antibacterial drug sensitivity test showed that the sensitivity rates of imipenem, ertapenem and piperaciratazobactam were all 100% (31/31), those of ceftazidime, ceftriaxone, and cefepime were 94% (29/31), 94% (29/31) and 97% (30/31) respectively. The drug resistance rates of ampicillin, ampicillin-sulbactam and trimethoprim-sulfamethoxazole were 51% (16/31), 48% (15/31) and 48% (15/31) respectively, those of cefazolin, cefotetan, tobramycin, gentamicin and amikacinwere all 100% (31/31). There were no significant differences in the drug resistance rates of ceftazidime, ceftriaxone, aztreonam, ampicillin-sulbactam, ampicillin, trimethoprim-sulfamethoxazole and ciprofloxacin between the sepsis group and the non-sepsis group (χ²=0.31,0.31,0.00,0.02,0.02,0.02,0.26, all P>0.05). Conclusions: Invasive non-typhoid Salmonella infection in children at Xiamen mainly occurred in infants younger than 3 years old.The main clinical manifestations are fever, abdominal pain and diarrhea. C-reactive protein can be served as the laboratory indicators for indicating sepsis. The third generation of cephalosporins is recommended as the first choice for treatment.

The role of carrier spectral composition in the perception of musical pitch

Temporal envelope fluctuations of natural sounds convey critical information to speech and music processing. In particular, musical pitch perception is assumed to be primarily underlined by temporal envelope encoding. While increasing evidence demonstrates the importance of carrier fine structure to complex pitch perception, how carrier spectral information affects musical pitch perception is less clear. Here, transposed tones designed to convey identical envelope information across different carriers were used to assess the effects of carrier spectral composition to pitch discrimination and musical-interval and melody identifications. Results showed that pitch discrimination thresholds became lower (better) with increasing carrier frequencies from 1k to 10k Hz, with performance comparable to that of pure sinusoids. Musical interval and melody defined by the periodicity of sine- or harmonic complex envelopes across carriers were identified with greater than 85% accuracy even on a 10k-Hz carrier. Moreover, enhanced interval and melody identification performance was observed with increasing carrier frequency up to 6k Hz. Findings suggest a perceptual enhancement of temporal envelope information with increasing carrier spectral region in musical pitch processing, at least for frequencies up to 6k Hz. For carriers in the extended high-frequency region (8-20k Hz), the use of temporal envelope information to music pitch processing may vary depending on task requirement. Collectively, these results implicate the fidelity of temporal envelope information to musical pitch perception is more pronounced than previously considered, with ecological implications.

An AI-Enabled Dynamic Risk Stratification for Emergency Department Patients with ECG and CXR Integration

Emergency department (ED) triage scale determines the priority of patient care and foretells the prognosis. However, the information retrieved from the initial assessment is limited, hindering the risk identification accuracy of triage. Therefore, we sought to develop a 'dynamic' triage system as secondary screening, using artificial intelligence (AI) techniques to integrate information from initial assessment data and subsequent examinations. This retrospective cohort study included 134,112 ED visits with at least one electrocardiography (ECG) and chest X-ray (CXR) in a medical center from 2012 to 2022. Additionally, an independent community hospital provided 45,614 ED visits as an external validation set. We trained an eXtreme gradient boosting (XGB) model using initial assessment data to predict all-cause mortality in 7 days. Two deep learning models (DLMs) using ECG and CXR were trained to stratify mortality risks. The dynamic triage levels were based on output from the XGB-triage and DLMs from ECG and CXR. During the internal and external validation, the area under the receiver operating characteristic curve (AUC) of the XGB-triage model was >0.866; furthermore, the AUCs of DLMs using ECG and CXR were >0.862 and >0.886, respectively. The dynamic triage scale provided a higher C-index (0.914-0.920 vs. 0.827-0.843) than the original one and demonstrated better predictive ability for 5-year mortality, 30-day ED revisit, and 30-day discharge. The AI-based risk scale provides a more accurate and dynamic stratification of mortality risk in ED patients, particularly in identifying patients who tend to be overlooked due to atypical symptoms.

Applying a Smartwatch to Predict Work-related Fatigue for Emergency Healthcare Professionals: Machine Learning Method

INTRODUCTION

Healthcare professionals frequently experience work-related fatigue, which may jeopardize their health and put patient safety at risk. In this study, we applied a machine learning (ML) approach based on data collected from a smartwatch to construct prediction models of work-related fatigue for emergency clinicians.

METHODS

We conducted this prospective study at the emergency department (ED) of a tertiary teaching hospital from March 10-June 20, 2021, where we recruited physicians, nurses, and nurse practitioners. All participants wore a commercially available smartwatch capable of measuring various physiological data during the experiment. Participants completed the Multidimensional Fatigue Inventory (MFI) web form before and after each of their work shifts. We calculated and labeled the before-and-after-shift score differences between each pair of scores. Using several tree-based algorithms, we constructed the prediction models based on features collected from the smartwatch. Records were split into training/validation and testing sets at a 70:30 ratio, and we evaluated the performances using the area under the curve (AUC) measure of receiver operating characteristic on the test set.

RESULTS

In total, 110 participants were included in this study, contributing to a set of 1,542 effective records. Of these records, 85 (5.5%) were labeled as having work-related fatigue when setting the MFI difference between two standard deviations as the threshold. The mean age of the participants was 29.6. Most of the records were collected from nurses (87.7%) and females (77.5%). We selected a union of 31 features to construct the models. For total participants, CatBoost classifier achieved the best performances of AUC (0.838, 95% confidence interval [CI] 0.742-0.918) to identify work-related fatigue. By focusing on a subgroup of nurses <35 years in age, XGBoost classifier obtained excellent performance of AUC (0.928, 95% CI 0.839-0.991) on the test set.

CONCLUSION

By using features derived from a smartwatch, we successfully built ML models capable of classifying the risk of work-related fatigue in the ED. By collecting more data to optimize the models, it should be possible to use smartwatch-based ML models in the future to predict work-related fatigue and adopt preventive measures for emergency clinicians.

Accelerating pandemic response with the emergency Omicron RT-PCR test: A comprehensive solution for COVID-19 diagnosis and tracking

The Omicron variant of concern (VOC) has surged in many countries and replaced the previously reported VOC. To identify different Omicron strains/sublineages on a rapid, convenient, and precise platform, we report a novel multiplex real-time reverse transcriptase polymerase chain reaction (RT-PCR) method in one tube based on the Omicron lineage sequence variants' information. Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) subvariants were used in a PCR-based assay for rapid identification of Omicron sublineage genotyping in 1000 clinical samples. Several characteristic mutations were analyzed using specific primers and probes for the spike gene, del69-70, and F486V. To distinguish Omicron sublineages (BA.2, BA.4, and BA.5), the NSP1:141-143del in the ORF1a region and D3N mutation in membrane protein occurring outside the spike protein region were analyzed. Results from the real-time PCR assay for one-tube accuracy were compared to those of whole genome sequencing. The developed PCR assay was used to analyze 400 SARS-CoV-2 positive samples. Ten samples determined as BA.4 were positive for NSP1:141-143del, del69-70, and F486V mutations; 160 BA.5 samples were positive for D3N, del69-70, and F486V mutations, and 230 BA.2 samples were without del69-70. Screening these samples allowed the identification of epidemic trends at different time intervals. Our novel one-tube multiplex PCR assay was effective in identifying Omicron sublineages.

Acute exacerbation of chronic obstructive pulmonary disease in United States emergency departments, 2010-2018

OBJECTIVES

Little is known about the recent status of acute exacerbation of chronic obstructive pulmonary disease (AECOPD) in the U.S. emergency department (ED). This study aimed to describe the disease burden (visit and hospitalization rate) of AECOPD in the ED and to investigate factors associated with the disease burden of AECOPD.

METHODS

Data were obtained from the National Hospital Ambulatory Medical Care Survey (NHAMCS), 2010-2018. Adult ED visits (aged 40 years or above) with AECOPD were identified using International Classification of Diseases codes. Analysis used descriptive statistics and multivariable logistic regression accounting for NHAMCS's complex survey design.

RESULTS

There were 1,366 adult AECOPD ED visits in the unweighted sample. Over the 9-year study period, there were an estimated 7,508,000 ED visits for AECOPD, and the proportion of AECOPD visits in the entire ED population remained stable at approximately 14 per 1,000 visits. The mean age of these AECOPD visits was 66 years, and 42% were men. Medicare or Medicaid insurance, presentation in non-summer seasons, the Midwest and South regions (vs. Northeast), and arrival by ambulance were independently associated with a higher visit rate of AECOPD, whereas non-Hispanic black or Hispanic race/ethnicity (vs. non-Hispanic white) was associated with a lower visit rate of AECOPD. The proportion of AECOPD visits that were hospitalized decreased from 51% to 2010 to 31% in 2018 (p = 0.002). Arrival by ambulance was independently associated with a higher hospitalization rate, whereas the South and West regions (vs. Northeast) were independently associated with a lower hospitalization rate. The use of antibiotics appeared to be stable over time, but the use of systemic corticosteroids appeared to increase with near statistical significance (p = 0.07).

CONCLUSIONS

The number of ED visits for AECOPD remained high; however, hospitalizations for AECOPD appeared to decrease over time. Some patients were disproportionately affected by AECOPD, and certain patient and ED factors were associated with hospitalizations. The reasons for decreased ED admissions for AECOPD deserve further investigation.

Using point-of-care testing for adult patients with out-of-hospital cardiac arrest resuscitated at the emergency department to predict return of spontaneous circulation: Development and external validation of POC-ED-ROSC model

BACKGROUND AND IMPORTANCE

Most prediction models, like return of spontaneous circulation (ROSC) after cardiac arrest (RACA) or Utstein-based (UB)-ROSC score, were developed for prehospital settings to predict the probability of ROSC in patients with out-of-hospital cardiac arrest (OHCA). A prediction model has been lacking for the probability of ROSC in patients with OHCA at emergency departments (EDs).

OBJECTIVE

In the present study, a point-of-care (POC) testing-based model, POC-ED-ROSC, was developed and validated for predicting ROSC of OHCA at EDs.

DESIGN, SETTINGS AND PARTICIPANTS

Prospectively collected data for adult OHCA patients between 2015 and 2020 were analysed. POC blood gas analysis obtained within 5 min of ED arrival was used.

OUTCOMES MEASURE AND ANALYSIS

The primary outcome was ROSC. In the derivation cohort, multivariable logistic regression was used to develop the POC-ED-ROSC model. In the temporally split validation cohort, the discriminative performance of the POC-ED-ROSC model was assessed using the area under the receiver operating characteristic (ROC) curve (AUC) and compared with RACA or UB-ROSC score using DeLong test.

MAIN RESULTS

The study included 606 and 270 patients in the derivation and validation cohorts, respectively. In the total cohort, 471 patients achieved ROSC. Age, initial cardiac rhythm at ED, pre-hospital resuscitation duration, and POC testing-measured blood levels of lactate, potassium and glucose were significant predictors included in the POC-ED-ROSC model. The model was validated with fair discriminative performance (AUC: 0.75, 95% confidence interval [CI]: 0.69-0.81) with no significant differences from RACA (AUC: 0.68, 95% CI: 0.62-0.74) or UB-ROSC score (AUC: 0.74, 95% CI: 0.68-0.79).

CONCLUSION

Using only six easily accessible variables, the POC-ED-ROSC model can predict ROSC for OHCA resuscitated at ED with fair accuracy.

[Abnormal changes of static and dynamic functional connectivity of dopaminergic midbrain in patients with first-episode schizophrenia and their correlations with clinical symptoms]

Objective: To investigate the abnormal changes of static functional connectivity (sFC) and dynamic functional connectivity (dFC) in the dopaminergic midbrain (ventral dorsal tegmental area and bilateral substantia nigra compacta, VTA/SNc) in patients with first-episode schizophrenia(SCH), and their correlation with the Positive and Negative Symptom Scale (PANSS). Methods: The data of 198 first-episode untreated schizophrenia patients and 199 healthy controls (HC) matched by age, sex and years of education who were admitted to the First Affiliated Hospital of Zhengzhou University from January 2019 to May 2022 were prospectively collected. All subjects underwent high resolution structural MRI and resting state functional magnetic resonance imaging (rs-fMRI) scanning. The dopaminergic midbrain (VTA/SNc) was defined as three regions of interest (ROI). The sFC and dFC analyses with VTA/SNc as seeds were performed to produce a whole-brain diagram initially, which subsequently were compared between schizophrenia group and HC group. Finally, the correlation analysis of sFC and dFC values with the PANSS scores were performed, including the positive scale score, negative scale score, general psychopathology scale score, total score and symptom scores. Results: There were 86 males and 112 females in SCH group, and aged (23±9) years. Meanwhile, there were 95 males and 104 females in HC group, and aged (22±5) years. In the SCH group, the positive (P), the negative (N) and the general psychopathology (G) scale scores and the total score (T) of the PANSS scale was 20±7, 21±7, 41±11 and 82±22, respectively. Compared with the HC group, the VTA showed decreased sFC with four clusters including cerebellar vermis 7/9, left putamen, right thalamus and left middle cingulate gyrus in the schizophrenia group (peak center, t=-4.35, -4.81, -4.35 and -4.65; voxel P<0.005; cluster P<0.05), the right SNc showed decreased sFC with four clusters including left cerebellar hemisphere 4/5/8, right putamen, right medial orbitofrontal gyrus and the left putamen in the schizophrenia group (peak center, t=-4.91, -5.15, -4.77 and -5.21; voxel P<0.005; cluster P<0.05), and the left SNc showed decreased sFC with four clusters including the left putamen, right putamen, right medial orbitofrontal gyrus and left middle cingulate gyrus in the schizophrenia group (peak center, t=-5.82, -4.83 and -4.65; voxel P<0.005; cluster P<0.05). Compared with the HC group, the VTA showed decreased dFC with the right inferior parietal gyrus, right angular gyrus and right superior parietal gyrus in schizophrenia group (t=-4.17). In the schizophrenia group, the sFC value of cluster 2 (left putamen) with VTA as seed and cluster 4 (left putamen) with right SNc as seed were positively correlated with the positive scale scores in PANSS (r=0.141, 0.169, both P<0.05). The sFC and dFC values of significant regions were also correlated with hallucination, delusion, suspicion, hostility, communication disorder, passivity/indifference, lack of communication, stereotyped thinking, depression, non-cooperation, lack of judgment and insight, impulse control disorder, active social avoidance (all P<0.05). Conclusion: The static and dynamic functional connectivity (stability) of VTA/SNc to cerebellum, thalamus, striatum, prefrontal lobe and cingulate gyrus in first-episode schizophrenia patients were decreased, which were closely related to the positive and negative symptoms of schizophrenia.

Pulmonary embolism in United States emergency departments, 2010-2018

Little is known about pulmonary embolism (PE) in the United States emergency department (ED). This study aimed to describe the disease burden (visit rate and hospitalization) of PE in the ED and to investigate factors associated with its burden. Data were obtained from the National Hospital Ambulatory Medical Care Survey (NHAMCS) from 2010 to 2018. Adult ED visits with PE were identified using the International Classification of Diseases codes. Analyses used descriptive statistics and multivariable logistic regression accounting for the NHAMCS's complex survey design. Over the 9-year study period, there were an estimated 1,500,000 ED visits for PE, and the proportion of PE visits in the entire ED population increased from 0.1% in 2010-2012 to 0.2% in 2017-2018 (P for trend = 0.002). The mean age was 57 years, and 40% were men. Older age, obesity, history of cancer, and history of venous thromboembolism were independently associated with a higher proportion of PE, whereas the Midwest region was associated with a lower proportion of PE. The utilization of chest computed tomography (CT) scan appeared stable, which was performed in approximately 43% of the visits. About 66% of PE visits were hospitalized, and the trend remained stable. Male sex, arrival during the morning shift, and higher triage levels were independently associated with a higher hospitalization rate, whereas the fall and winter months were independently associated with a lower hospitalization rate. Approximately 8.8% of PE patients were discharged with direct-acting oral anticoagulants. The ED visits for PE continued to increase despite the stable trend in CT use, suggesting a combination of prevalent and incident PE cases in the ED. Hospitalization for PE remains common practice. Some patients are disproportionately affected by PE, and certain patient and hospital factors are associated with hospitalization decisions.

Improved Measurement of the Evolution of the Reactor Antineutrino Flux and Spectrum at Daya Bay

Reactor neutrino experiments play a crucial role in advancing our knowledge of neutrinos. In this Letter, the evolution of the flux and spectrum as a function of the reactor isotopic content is reported in terms of the inverse-beta-decay yield at Daya Bay with 1958 days of data and improved systematic uncertainties. These measurements are compared with two signature model predictions: the Huber-Mueller model based on the conversion method and the SM2018 model based on the summation method. The measured average flux and spectrum, as well as the flux evolution with the ^{239}Pu isotopic fraction, are inconsistent with the predictions of the Huber-Mueller model. In contrast, the SM2018 model is shown to agree with the average flux and its evolution but fails to describe the energy spectrum. Altering the predicted inverse-beta-decay spectrum from ^{239}Pu fission does not improve the agreement with the measurement for either model. The models can be brought into better agreement with the measurements if either the predicted spectrum due to ^{235}U fission is changed or the predicted ^{235}U, ^{238}U, ^{239}Pu, and ^{241}Pu spectra are changed in equal measure.

First Measurement of the B^{+}→π^{+}π^{0}π^{0} Branching Fraction and CP Asymmetry

We study B^{+}→π^{+}π^{0}π^{0} using 711  fb^{-1} of data collected at the ϒ(4S) resonance with the Belle detector at the KEKB asymmetric-energy e^{+}e^{-} collider. We measure an inclusive branching fraction of (19.0±1.5±1.4)×10^{-6} and an inclusive CP asymmetry of (9.2±6.8±0.7)%, where the first uncertainties are statistical and the second are systematic, and a B^{+}→ρ(770)^{+}π^{0} branching fraction of (11.2±1.1±0.9_{-1.6}^{+0.8})×10^{-6}, where the third uncertainty is due to possible interference with B^{+}→ρ(1450)^{+}π^{0}. We present the first observation of a structure around 1  GeV/c^{2} in the π^{0}π^{0} mass spectrum, with a significance of 6.4σ, and measure a branching fraction to be (6.9±0.9±0.6)×10^{-6}. We also report a measurement of local CP asymmetry in this structure.

Design and rationale of re-energize fontan: Randomized exercise intervention designed to maximize fitness in fontan patients

In this manuscript, we describe the design and rationale of a randomized controlled trial in pediatric Fontan patients to test the hypothesis that a live-video-supervised exercise (aerobic+resistance) intervention will improve cardiac and physical capacity; muscle mass, strength, and function; and endothelial function. Survival of children with single ventricles beyond the neonatal period has increased dramatically with the staged Fontan palliation. Yet, long-term morbidity remains high. By age 40, 50% of Fontan patients will have died or undergone heart transplantation. Factors that contribute to onset and progression of heart failure in Fontan patients remain incompletely understood. However, it is established that Fontan patients have poor exercise capacity which is associated with a greater risk of morbidity and mortality. Furthermore, decreased muscle mass, abnormal muscle function, and endothelial dysfunction in this patient population is known to contribute to disease progression. In adult patients with 2 ventricles and heart failure, reduced exercise capacity, muscle mass, and muscle strength are powerful predictors of poor outcomes, and exercise interventions can not only improve exercise capacity and muscle mass, but also reverse endothelial dysfunction. Despite these known benefits of exercise, pediatric Fontan patients do not exercise routinely due to their chronic condition, perceived restrictions to exercise, and parental overprotection. Limited exercise interventions in children with congenital heart disease have demonstrated that exercise is safe and effective; however, these studies have been conducted in small, heterogeneous groups, and most had few Fontan patients. Critically, adherence is a major limitation in pediatric exercise interventions delivered on-site, with adherence rates as low as 10%, due to distance from site, transportation difficulties, and missed school or workdays. To overcome these challenges, we utilize live-video conferencing to deliver the supervised exercise sessions. Our multidisciplinary team of experts will assess the effectiveness of a live-video-supervised exercise intervention, rigorously designed to maximize adherence, and improve key and novel measures of health in pediatric Fontan patients associated with poor long-term outcomes. Our ultimate goal is the translation of this model to clinical application as an "exercise prescription" to intervene early in pediatric Fontan patients and decrease long-term morbidity and mortality.

[Clinical value of nomogram model in evaluating the prognosis of cholangiocarcinoma after interventional therapy]

Objective: To investigate the clinical value and efficacy of the nomogram model in evaluating the prognosis of cholangiocarcinoma after interventional therapy. Methods: The clinical data of 259 patients with cholangiocarcinoma who received interventional therapy at the First Affiliated Hospital of zhengzhou University from January 2014 to June 2021 were retrospectively analyzed, including 148 males and 111 females, aged from 26 to 91 (65±12) years. They were randomly divided into a training group (181 cases) and a validation group (78 cases) in a ratio of 7∶3. Cox regression analysis was performed in the training group, independent risk factors affecting the prognosis of patients were screened, and a nomogram for 6-month, 1-year, and 2-year survival was constructed. The performance of the nomogram was analyzed by calculating the area under the receiver operating characteristic curve (AUC) value, calibration curve, and decision curve, and the predictive efficacy of the model was evaluated in the validation group. Results: There was no significant difference in baseline data between the training group and the validation group, which was comparable. Regression analysis showed that T stage (T2: HR=0.147,95%CI: 0.077-0.281;T3: HR=0.207,95%CI: 0.122-0.351;T4: HR=0.864,95%CI: 0.537-1.393), tumor diameter (17-33 mm: HR=0.201,95%CI: 0.119-0.341;≥33 mm: HR=0.795,95%CI: 0.521-1.211) and differentiation degree(middle differentiation: HR=3.318,95%CI: 2.082-5.289;highly differentiation: HR=1.842,95%CI: 1.184-2.867) were risk factors affecting the prognosis of interventional therapy for cholangiocarcinoma. The AUC values of the survival curve prediction models were generally consistent between the training and validation groups, and the AUC values of the training group at 6 months, 1 year, and 2 years were 0.925 (95%CI: 0.888-0.963), 0.921 (95%CI: 0.877-0.964) and 0.974 (95%CI: 0.957-0.993), respectively. In the validation group, the 6-month, 1-year, and 2-year AUC values were 0.951 (95%CI: 0.911-0.991), 0.917 (95%CI: 0.857-0.977) and 0.848 (95%CI: 0.737-0.959), respectively, and the AUC values were all greater than 0.8, suggesting that the nomogram had better discrimination ability. The calibration curves of the prediction models of the two groups were basically consistent, and the shape of the calibration curves at 6 months and 1 year fitted the ideal curve, while the fitting degree of the calibration curves at 2 years was relatively poor. The decision curve showed the high clinical utility of this nomogram in predicting the 6-month, 1-year survival of patients with cholangiocarcinoma. Conclusions: T stage, tumor diameter, and differentiation are independent risk factors affecting the prognosis of patients with interventional cholangiocarcinoma, and the nomogram model proposed in this study has good distinguishing ability and exact clinical value for prognosis evaluation.

Precision Measurement of Reactor Antineutrino Oscillation at Kilometer-Scale Baselines by Daya Bay

We present a new determination of the smallest neutrino mixing angle θ_{13} and the mass-squared difference Δm_{32}^{2} using a final sample of 5.55×10^{6} inverse beta-decay (IBD) candidates with the final-state neutron captured on gadolinium. This sample is selected from the complete dataset obtained by the Daya Bay reactor neutrino experiment in 3158 days of operation. Compared to the previous Daya Bay results, selection of IBD candidates has been optimized, energy calibration refined, and treatment of backgrounds further improved. The resulting oscillation parameters are sin^{2}2θ_{13}=0.0851±0.0024, Δm_{32}^{2}=(2.466±0.060)×10^{-3}  eV^{2} for the normal mass ordering or Δm_{32}^{2}=-(2.571±0.060)×10^{-3}  eV^{2} for the inverted mass ordering.

Interpretable attention-based deep learning ensemble for personalized ovarian cancer treatment without manual annotations

Inhibition of pathological angiogenesis has become one of the first FDA approved targeted therapies widely tested in anti-cancer treatment, i.e. VEGF-targeting monoclonal antibody bevacizumab, in combination with chemotherapy for frontline and maintenance therapy for women with newly diagnosed ovarian cancer. Identification of the best predictive biomarkers of bevacizumab response is necessary in order to select patients most likely to benefit from this therapy. Hence, this study investigates the protein expression patterns on immunohistochemical whole slide images of three angiogenesis related proteins, including Vascular endothelial growth factor, Angiopoietin 2 and Pyruvate kinase isoform M2, and develops an interpretable and annotation-free attention based deep learning ensemble framework to predict the bevacizumab therapeutic effect on patients with epithelial ovarian cancer or peritoneal serous papillary carcinoma using tissue microarrays (TMAs). In evaluation with five-fold cross validation, the proposed ensemble model using the protein expressions of both Pyruvate kinase isoform M2 and Angiopoietin 2 achieves a notably high F-score (0.99±0.02), accuracy (0.99±0.03), precision (0.99±0.02), recall (0.99±0.02) and AUC (1.00±0). Kaplan-Meier progression free survival analysis confirms that the proposed ensemble is able to identify patients in the predictive therapeutic sensitive group with low cancer recurrence (p<0.001), and the Cox proportional hazards model analysis further confirms the above statement (p=0.012). In conclusion, the experimental results demonstrate that the proposed ensemble model using the protein expressions of both Pyruvate kinase isoform M2 and Angiopoietin 2 can assist treatment planning of bevacizumab targeted therapy for patients with ovarian cancer.

Machine learning to predict in-hospital cardiac arrest from patients presenting to the emergency department

In-hospital cardiac arrest (IHCA) in the emergency department (ED) is not uncommon but often fatal. Using the machine learning (ML) approach, we sought to predict ED-based IHCA (EDCA) in patients presenting to the ED based on triage data. We retrieved 733,398 ED records from a tertiary teaching hospital over a 7 year period (Jan. 1, 2009-Dec. 31, 2015). We included only adult patients (≥ 18 y) and excluded cases presenting as out-of-hospital cardiac arrest. Primary outcome (EDCA) was identified via a resuscitation code. Patient demographics, triage data, and structured chief complaints (CCs), were extracted. Stratified split was used to divide the dataset into the training and testing cohort at a 3-to-1 ratio. Three supervised ML models were trained and performances were evaluated and compared to the National Early Warning Score 2 (NEWS2) and logistic regression (LR) model by the area under the receiver operating characteristic curve (AUC). We included 316,465 adult ED records for analysis. Of them, 636 (0.2%) developed EDCA. Of the constructed ML models, Random Forest outperformed the others with the best AUC result (0.931, 95% CI 0.911-0.949), followed by Gradient Boosting (0.930, 95% CI 0.909-0.948) and Extra Trees classifier (0.915, 95% CI 0.892-0.936). Although the differences between each of ML models and LR (AUC: 0.905, 95% CI 0.882-0.926) were not significant, all constructed ML models performed significantly better than using the NEWS2 scoring system (AUC 0.678, 95% CI 0.635-0.722). Our ML models showed excellent discriminatory performance to identify EDCA based only on the triage information. This ML approach has the potential to reduce unexpected resuscitation events if successfully implemented in the ED information system.

The deep learning algorithm estimates chest radiograph-based sex and age as independent risk factors for future cardiovascular outcomes

Objectives

Chest X-rays (CXRs) convey much illegible physiological information that deep learning model (DLM) has been reported interpreting successfully. Since the electrocardiogram age established by DLM was revealed as a heart biological marker, we hypothesize that CXR age has similar potential to describe the heart and lung states. Therefore, we developed a DLM to predict sex and age through CXR and analyzed its relation with future cardiovascular diseases (CVD).

Methods

A total of 90,396 CXRs aged 20 to 90 were collected and separated into a development set with 53,102 CXRs and demographic information pairs, a tuning set with 7073 pairs, an internal validation set with 17,364 pairs, and an external validation set with 12,857 pairs. The study trained DLM with development set for estimating age and sex and compared them to actual information.

Results

The mean absolute errors of predicted age were 4.803 and 4.313 years in the internal and external validation sets, respectively. The area under the curve of sex analysis was 0.9993 and 0.9988 in the internal and external validation sets, respectively. Patients whose CXR age was 5 years older than chronologic age lead to higher risk of all-cause mortality (hazard ratio (HR): 2.42, 95% confidence interval (CI): 2.00-2.92), cardiovascular (CV)-cause mortality (HR: 7.57, 95% CI: 4.55-12.60), new-onset heart failure (HR: 2.07, 95% CI: 1.56-2.76), new-onset chronic kidney disease (HR: 1.73, 95% CI: 1.46-2.05), new-onset acute myocardial infarction (HR: 1.80, 95% CI: 1.12-2.92), new-onset stroke (HR: 1.45, 95% CI: 1.10-1.90), new-onset coronary artery disease (HR: 1.26, 95% CI: 1.04-1.52), and new-onset atrial fibrillation (HR: 1.43, 95% CI: 1.01-2.02).

Conclusions

Using DLM to predict CXR age provided additional information for future CVDs. Older CXR age is an accessible risk classification tool for clinician use.

Mortality risk prediction of the electrocardiogram as an informative indicator of cardiovascular diseases

Background

The electrocardiogram (ECG) may be the most popular test in the management of cardiovascular disease (CVD). Although wide applications of artificial intelligence (AI)-enabled ECG have been developed, an integrating indicator for CVD risk stratification was not investigated. Since mortality may be the most important global outcome, this study aimed to develop a survival deep learning model (DLM) to establish a critical ECG value and explore the associations with various CVD events.

Methods

We trained a DLM with 451,950 12-lead resting ECGs obtained from 210,552 patients, for whom 23,592 events occurred. The internal validation set included 27,808 patients with one ECG for each patient. The external validations were performed in a community hospital with 33,047 patients and two transnational data sets with 233,647 and 1631 ECGs. We distinguished the cause of mortality and additionally investigated CVD-related outcomes, including new-onset acute myocardial infarction (AMI), stroke (STK), and heart failure (HF).

Results

The DLM achieved C-indices of 0.858/0.836 in internal/external validation sets by using ECG over a 10-year period. The high-mortality-risk group identified by the proposed DLM presented a hazard ratio (HR) of 14.16 (95% confidence interval (CI): 11.33-17.70) compared to the low-risk group in the internal validation and presented a higher risk of cardiovascular (CV) mortality (HR: 18.50, 95% CI: 9.82-34.84), non-CV mortality (HR: 13.68, 95% CI: 10.76-17.38), AMI (HR: 4.01, 95% CI: 2.24-7.17), STK (HR: 2.15, 95% CI: 1.70-2.72), and HF (HR: 6.66, 95% CI: 4.54-9.77), which was consistent in an independent community hospital. The transnational validation also revealed HRs of 4.91 (95% CI: 2.63-9.16) and 2.29 (95% CI: 2.15-2.44) for all-cause mortality in the SaMi-Trop and Clinical Outcomes in Digital Electrocardiography 15% (CODE15) cohorts.

Conclusions

The mortality risk by AI-enabled ECG may be applied in passive electronic-health-record-based CVD risk screening, which may identify more asymptomatic and unaware high-risk patients.

Development and validation of a dynamic deep learning algorithm using electrocardiogram to predict dyskalaemias in patients with multiple visits

Aims

Deep learning models (DLMs) have shown superiority in electrocardiogram (ECG) analysis and have been applied to diagnose dyskalaemias. However, no study has explored the performance of DLM-enabled ECG in continuous follow-up scenarios. Therefore, we proposed a dynamic revision of DLM-enabled ECG to use personal pre-annotated ECGs to enhance the accuracy in patients with multiple visits.

Methods and results

We retrospectively collected 168 450 ECGs with corresponding serum potassium (K⁺) levels from 103 091 patients as development samples. In the internal/external validation sets, the numbers of ECGs with corresponding K⁺ were 37 246/47 604 from 13 555/20 058 patients. Our dynamic revision method showed better performance than the traditional direct prediction for diagnosing hypokalaemia [area under the receiver operating characteristic curve (AUC) = 0.730/0.720-0.788/0.778] and hyperkalaemia (AUC = 0.884/0.888-0.915/0.908) in patients with multiple visits.

Conclusion

Our method has shown a distinguishable improvement in DLMs for diagnosing dyskalaemias in patients with multiple visits, and we also proved its application in ejection fraction prediction, which could further improve daily clinical practice.

Critical pediatric neurological illness associated with COVID-19 (Omicron BA.2.3.7 variant) infection in Taiwan: immunological assessment and viral genome analysis in tertiary medical center

OBJECTIVES

Since April 2022, another wave of the Omicron epidemic has struck Taiwanese society, and children with severe neurological complications have been reported frequently. A few cases even developed acute fulminant encephalitis. To investigate the possible causes of the increased incidence of such complications in Taiwan, we reviewed several cases of pediatric patients with severe neurological symptoms.

METHODS

We collected the medical records of pediatric patients with COVID-19 infection who presented with severe neurological symptoms. The COVID-19 infection was diagnosed by nasal swab reverse transcriptase-polymerase chain reaction. The remaining samples were sent for whole genome sequencing and spike (S) protein amino acid variation mapping.

RESULTS

The increase of several inflammatory markers was observed in all patients included in this study. However, none of the cerebrospinal fluid samples tested positive for SARS-CoV-2. The result of whole genome sequencing showed that all the sequences belonged to the lineage BA.2.3.7. However, the sequences had a K97E mutation in the S protein that differed from other BA.2.3.7 lineage strains, which was located at the S protein N-terminal domain.

CONCLUSION

The new mutation in the S protein, which had not previously been observed but was discovered in this study, potentially explains the sudden increase in incidence of extremely adverse neurological symptoms in pediatric patients.

Curbing COVID-19 progression and mortality with traditional Chinese medicine among hospitalized patients with COVID-19: A propensity score-matched analysis

Background

Viral- and host-targeted traditional Chinese medicine (TCM) formulae NRICM101 and NRICM102 were administered to hospitalized patients with COVID-19 during the mid-2021 outbreak in Taiwan. We report the outcomes by measuring the risks of intubation or admission to intensive care unit (ICU) for patients requiring no oxygen support, and death for those requiring oxygen therapy.

Methods

This multicenter retrospective study retrieved data of 840 patients admitted to 9 hospitals between May 1 and July 26, 2021. After propensity score matching, 302 patients (151 received NRICM101 and 151 did not) and 246 patients (123 received NRICM102 and 123 did not) were included in the analysis to assess relative risks.

Results

During the 30-day observation period, no endpoint occurred in the patients receiving NRICM101 plus usual care while 14 (9.27%) in the group receiving only usual care were intubated or admitted to ICU. The numbers of deceased patients were 7 (5.69%) in the group receiving NRICM102 plus usual care and 27 (21.95%) in the usual care group. No patients receiving NRICM101 transitioned to a more severe status; NRICM102 users were 74.07% less likely to die than non-users (relative risk= 25.93%, 95% confidence interval 11.73%-57.29%).

Conclusion

NRICM101 and NRICM102 were significantly associated with a lower risk of intubation/ICU admission or death among patients with mild-to-severe COVID-19. This study provides real-world evidence of adopting broad-spectrum oral therapeutics and shortening the gap between outbreak and effective response. It offers a new vision in our preparation for future pandemics.

Electromagnetically-driven integrated microfluidic platform using reverse transcription loop-mediated isothermal amplification for detection of severe acute respiratory syndrome coronavirus 2

Rapid, sensitive and accurate diagnosis of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is of great need for effective quarantining and treatment. Real-time reverse-transcription polymerase chain reaction requiring thermocyling has been commonly used for diagnosis of SARS-CoV-2 though it may take two to 4 h before lengthy sample pretreatment process and require bulky apparatus and well-trained personnel. Since multiple reverse transcription loop-mediated isothermal amplification (multiple RT-LAMP) process without thermocycling is sensitive, specific and fast, an electromagnetically-driven microfluidic chip (EMC) was developed herein to lyse SARS-CoV-2 viruses, extract their RNAs, and perform qualitative analysis of three marker genes by on-chip multiple RT-LAMP in an automatic format within 82 min at a limit of detection of only ∼5000 copies per reaction (i.e. 200 virus/ μL). This compact EMC may be especially promising for SARS-CoV-2 diagnostics in resource-limited countries.

[Recommendations for diagnosis and treatment of psoriatic arthritis in China]

Psoriatic arthritis is a chronic systemic autoimmune disease, characterized by psoriasis skin lesions and inflammation of the spine and joint. It has complicated clinical manifestations and individual variations. Nearly half of the patients will have joints erosion in two years, which is crippling. The severity of the skin and joint disease frequently do not correlate with each other. Currently, the understanding of the disease is insufficient in China with the lack of standardized diagnosis and treatment. Therefore, researchers from the Chinese Rheumatology Association formulated this specification based on the diagnosis and management experience together with guidelines at home and abroad. The specification summarizes the present situation of domestic diagnosis and treatment, aiming to standardize the diagnosis process and treatment protocols of psoriatic arthritis. Furthermore, it can reduce misdiagnosis and missed diagnosis, as well as improve the prognosis.

Inflammatory Pseudotumour of Temporal Bone in a Multiple-Comorbidity Patient

Inflammatory pseudotumour (IPT) of the temporal bone is relatively rare in the head and neck, but it is important for clinicians to be aware of this emerging entity. A 39-year man presented with a protruding reddish mass over the left external ear. High-resolution Computed Tomography and Magnetic Resonance Imaging of temporal bone showed soft tissue collection in the left external ear, middle ear, and mastoid cavity with bony erosion. The patient first received modified radical mastoidectomy. Several surgeries were required for recurrences with eventual intracranial invasion within 2 years. The pathology showed chronic inflammation without malignancy, autoimmune or infectious pathologies. Based on the clinical manifestations, IPT was diagnosed. Finally, radiation therapy (RT) with 30 Gy was given. There was no recurrence following the RT course. Early recognition of IPT presenting as a recurrent and locally aggressive inflammatory lesion in the temporal bone is necessary to achieve favorable outcomes. Key Words: Inflammatory pseudotumour, Temporal bone, Radiation therapy.