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.

A Delphi Method for Development of a Barrett's Esophagus Question Prompt List as a Communication Tool for Optimal Patient-physician Communication.

BACKGROUND METHODS: The question prompt list content was derived through a modified Delphi process consisting of 3 rounds. In round 1, experts provided 5 answers to the prompts "What general questions should patients ask when given a new diagnosis of Barrett's esophagus" and "What questions do I not hear patients asking, but given my expertise, I believe they should be asking?" Questions were reviewed and categorized into themes. In round 2, experts rated questions on a 5-point Likert scale. In round 3, experts rerated questions modified or reduced after the previous rounds. Only questions rated as "essential" or "important" were included in Barrett's esophagus question prompt list (BE-QPL). To improve usability, questions were reduced to minimize redundancy and simplified to use language at an eighth-grade level (Fig. 1). RESULTS: Twenty-one esophageal medical and surgical experts participated in both rounds (91% males; median age 52 years). The expert panel comprised of 33% esophagologists, 24% foregut surgeons, and 24% advanced endoscopists, with a median of 15 years in clinical practice. Most (81%), worked in an academic tertiary referral hospital. In this 3-round Delphi technique, 220 questions were proposed in round 1, 122 (55.5%) were accepted into the BE-QPL and reduced down to 76 questions (round 2), and 67 questions (round 3). These 67 questions reached a Flesch Reading Ease of 68.8, interpreted as easily understood by 13 to 15 years olds. CONCLUSIONS: With multidisciplinary input, we have developed a physician-derived BE-QPL to optimize patient-physician communication. Future directions will seek patient feedback to distill the questions further to a smaller number and then assess their usability.

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.

C-Phycocyanin Attenuates Noise-Induced Cochlear Synaptopathy via the Inhibition of Oxidative Stress and Intercellular Adhesion Molecule-1 in the Cochlea.

The synapses between inner hair cells (IHCs) and spiral ganglion neurons (SGNs) are the most vulnerable structures in the noise-exposed cochlea. Cochlear synaptopathy results from the disruption of these synapses following noise exposure and is considered the main cause of poor speech understanding in noisy environments, even when audiogram results are normal. Cochlear synaptopathy leads to the degeneration of SGNs if damaged IHC-SGN synapses are not promptly recovered. Oxidative stress plays a central role in the pathogenesis of cochlear synaptopathy. C-Phycocyanin (C-PC) has antioxidant and anti-inflammatory activities and is widely utilized in the food and drug industry. However, the effect of the C-PC on noise-induced cochlear damage is unknown. We first investigated the therapeutic effect of C-PC on noise-induced cochlear synaptopathy. In vitro experiments revealed that C-PC reduced the H2O2-induced generation of reactive oxygen species in HEI-OC1 auditory cells. H2O2-induced cytotoxicity in HEI-OC1 cells was reduced with C-PC treatment. After white noise exposure for 3 h at a sound pressure of 118 dB, the guinea pigs intratympanically administered 5 µg/mL C-PC exhibited greater wave I amplitudes in the auditory brainstem response, more IHC synaptic ribbons and more IHC-SGN synapses according to microscopic analysis than the saline-treated guinea pigs. Furthermore, the group treated with C-PC had less intense 4-hydroxynonenal and intercellular adhesion molecule-1 staining in the cochlea compared with the saline group. Our results suggest that C-PC improves cochlear synaptopathy by inhibiting noise-induced oxidative stress and the inflammatory response in the cochlea.

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.

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.

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.

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.

Point-of-care testing for adult out-of-hospital cardiac arrest resuscitated at the ED to predict ROSC: 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.

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.

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.

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.

Integrated Microfluidic System for Cell-Free DNA Extraction from Plasma for Mutant Gene Detection and Quantification.

Ovarian cancer (OvCa) is among the most severe gynecologic cancers, yet individuals may be asymptomatic during its early stages. Routine, early screening for genetic abnormalities associated with OvCa could improve prognoses, and this can be achieved by detecting mutant genes in cell-free DNA (cfDNA). Herein, we developed an integrated microfluidic chip (IMC) that could extract cfDNA from plasma and automatically detect and quantify mutations in the OvCa biomarker BRCA1. The cfDNA extraction module relied on a vortex-type micromixer to mix cfDNA with magnetic beads surface-coated with cfDNA probes and could isolate 76% of molecules from a 200 µL plasma sample in 45 min. The cfDNA quantification module, which comprised a micropump that evenly distributed 4.5 µL of purified cfDNA into the on-chip, allele-specific quantitative polymerase chain reaction (qPCR) zones, was capable of quantifying mutant genes within 90 min. By automating the cfDNA extraction and qPCR processes, this IMC could be used for clinical screening for OvCa-associated mutations.

Isolation and Quantification of Methylated Cell-Free DNA in Plasma on an Integrated Microfluidic System.

Methylated cell-free DNA (cfDNA) has been deemed a promising biomarker for ovarian cancer (OvCa) prognosis and therapy selection. However, exploring the methylation profiles of tumor suppressor genes in cfDNA remains a challenge due to their extremely low concentrations and complicated protocols, as well as methodological constraints. In this study, an integrated microfluidic system was developed to automatically (1) capture methylated cfDNA in plasma by magnetic beads coated with the methyl-CpG-binding domain and (2) quantify the methylation level of tumor suppressor genes by on-chip quantitative polymerase chain reaction (qPCR). For capturing methylated cfDNA from a very small amount of plasma, samples along with beads were mixed in a new micromixer to enhance the capture rate. With a high capture rate (72%) and a limit of quantification of 0.1 pg/µL (3 orders of magnitude lower than that of the benchtop method), the compact system could detect the methylated cfDNA from only 20 µL of plasma sample in 2 h. Furthermore, the dynamic range, from 0.1 to 2000 pg/µL of methylated cfDNA, spans the physiological range in plasma, signifying that this device has great potential for personalized medicine in OvCa.

Genome-wide association study reveals ethnicity-specific SNPs associated with ankylosing spondylitis in the Taiwanese population.

BACKGROUND: Ankylosing spondylitis (AS) is an autoimmune disease affecting mainly spine and sacroiliac joints and adjacent soft tissues. Genome-wide association studies (GWASs) are used to evaluate genetic associations and to predict genetic risk factors that determine the biological basis of disease susceptibility. We aimed to explore the race-specific SNP susceptibility of AS in Taiwanese individuals and to investigate the association between HLA-B27 and AS susceptibility SNPs in Taiwan. METHODS: Genotyping data were collected from a medical center participating in the Taiwan Precision Medicine Initiative (TPMI) in the northern district of Taiwan. We designed a case-control study to identify AS susceptibility SNPs through GWAS. We searched the genome browser to find the corresponding susceptibility genes and used the GTEx database to confirm the regulation of gene expression. A polygenic risk score approach was also applied to evaluate the genetic variants in the prediction of developing AS. RESULTS: The results showed that the SNPs located on the sixth chromosome were related to higher susceptibility in the AS group. There was no overlap between our results and the susceptibility SNPs found in other races. The 12 tag SNPs located in the MHC region that were found through the linkage disequilibrium method had higher gene expression. Furthermore, Taiwanese people with HLA-B27 positivity had a higher proportion of minor alleles. This might be the reason that the AS prevalence is higher in Taiwan than in other countries. We developed AS polygenic risk score models with six different methods in which those with the top 10% polygenic risk had a fivefold increased risk of developing AS compared to the remaining group with low risk. CONCLUSION: A total of 147 SNPs in the Taiwanese population were found to be statistically significantly associated with AS on the sixth pair of chromosomes and did not overlap with previously published sites in the GWAS Catalog. Whether those genes mapped by AS-associated SNPs are involved in AS and what the pathogenic mechanism of the mapped genes is remain to be further studied.

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.

An integrated microfluidic platform featuring real-time reverse transcription loop-mediated isothermal amplification for detection of COVID-19.

An integrated microfluidic platform (IMP) utilizing real-time reverse-transcription loop-mediated isothermal amplification (RT-LAMP) was developed here for detection and quantification of three genes of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2; i.e., coronavirus diseases 2019 (COVID-19)): RNA-dependent RNA polymerase, the envelope gene, and the nucleocapsid gene for molecular diagnosis. The IMP comprised a microfluidic chip, a temperature control module, a fluidic control module that collectively carried out viral lysis, RNA extraction, RT-LAMP, and the real-time detection within 90 min in an automatic format. A limit of detection of 5 × 103 copies/reaction for each gene was determined with three samples including synthesized RNAs, inactive viruses, and RNAs extracted from clinical samples; this compact platform could be a useful tool for COVID-19 diagnostics.

Associations among quality of life, activities, and participation in elderly residents with joint contractures in long-term care facilities: a cross-sectional study.

BACKGROUND: Joint contractures and degenerative osteoarthritis are the most common joint diseases in the elderly population, can lead to limited mobility in elderly individuals, can exacerbate symptoms such as pain, stiffness, and disability, and can interfere with social participation and quality of life, thus affecting mental health. However, relevant studies on this topic are very limited. This study describes the associations of joint contracture categories and sites in elderly residents in long-term care facilities with their quality of life, activities, and participation. METHODS: Elderly individuals with joint contractures who were residents in long-term care facilities were recruited. The World Health Organization (WHO) Quality of Life and the WHO Disability Assessment Schedule 2.0 were used to survey the participants. Correlations, multiple linear regressions, and multiple analyses of variance, with joint contractures as the response variable, were used in the statistical analysis. RESULTS: The final statistical analysis included 232 participants. The explanatory power of contracture sites on activities and participation had a moderate strength of association (η2 = .113). Compared with elderly residents with joint contractures and osteoarthritis isolated to the upper limbs, those with joint contractures and osteoarthritis in both the upper and lower limbs had significantly worse activity and participation limitations. No significant differences in activity and participation were found between elderly residents with joint contractures affecting only the upper limbs and those with joint contractures affecting only the lower limbs (F1,226 = 2.604 and F1,226 = 0.674, nonsignificant). Osteoarthritis had the greatest impact on activity limitations and participation restrictions among elderly residents with joint contractures affecting both the upper and lower limbs (F1,226 = 6.251, p = .014). CONCLUSIONS: Elderly residents in long-term care facilities belonging to minority groups, with a history of stroke, and with osteoarthritis are at a high risk of developing activity limitations and participation restrictions. Moreover, compared with other contraction sites, regardless of osteoarthritis, joint contractures affecting both the upper and lower limbs were associated with the greatest activity limitations and participation restrictions. TRIAL REGISTRATION: This study has been registered in the Chinese Clinical Trial Registry, registration number and date: ChiCTR2000039889 (13/11/2020).

Multivessel versus Culprit-Only Revascularization Strategies in Cardiac Arrest Survivors.

Background: Whether multivessel revascularization or culprit-only revascularization is more beneficial in cardiac arrest survivors with multivessel coronary artery disease remains unclear. We aimed to retrospectively evaluate whether multivessel or culprit-only revascularization following cardiac arrest was associated with a reduced incidence of in-hospital mortality. Methods: A total of 273 adult nontraumatic cardiac arrest survivors (aged ≥ 18 years) who underwent emergent coronary angiography (CAG) within 24 h following cardiac arrest were retrospectively recruited from three hospitals. Patients without definite coronary artery stenosis (n = 72), one-vessel stenosis (n = 74), or failed percutaneous coronary intervention (PCI; n = 37) were excluded. A total of 90 patients were enrolled for the final analysis and classified into multivessel (revascularization of more than one major vessel during the index CAG; n = 45) and culprit-only (revascularization of the infarct-related artery alone; n = 45) groups. Results: Twenty-five patients (55.6%) in the culprit-only group and 17 patients (37.8%) in the multivessel group failed to survive to discharge [adjusted hazard ratio (HR) = 0.47, 95% confidence interval (CI) = 0.24-0.95, p = 0.035]. The benefit of multivessel revascularization on survival was obvious among those with a prolonged cardiopulmonary resuscitation duration (> 10 min) (47.82% vs. 76.92%, adjusted HR = 0.27, 95% CI = 0.08-0.93, p = 0.03). No difference in neurological outcomes (favorable = cerebral performance category scores 1-2; poor = 3-5) between groups was observed (60.0% vs. 55.6%, adjusted OR = 1.22, 95% CI = 0.35-4.26, p = 0.753). Conclusions: Compared with culprit-only revascularization, multivessel revascularization was associated with lower in-hospital mortality among cardiac arrest survivors with multivessel lesions. Owing to the retrospective design and small sample size, the current study should be interpreted as observational and exploratory.

Neuroprognostic Accuracy of Quantitative Versus Standard Pupillary Light Reflex for Adult Postcardiac Arrest Patients: A Systematic Review and Meta-Analysis.

OBJECTIVES: An automated infrared pupillometer measures quantitative pupillary light reflex using a calibrated light stimulus. We examined whether the timing of performing quantitative pupillary light reflex or standard pupillary light reflex may impact its neuroprognostic performance in postcardiac arrest comatose patients and whether quantitative pupillary light reflex may outperform standard pupillary light reflex in early postresuscitation phase. DATA SOURCES: PubMed and Embase databases from their inception to July 2020. STUDY SELECTION: We selected studies providing sufficient data of prognostic values of standard pupillary light reflex or quantitative pupillary light reflex to predict neurologic outcomes in adult postcardiac arrest comatose patients. DATA EXTRACTION: Quantitative data required for building a 2 × 2 contingency table were extracted, and study quality was assessed using standard criteria. DATA SYNTHESIS: We used the bivariate random-effects model to estimate the pooled sensitivity and specificity of standard pupillary light reflex or quantitative pupillary light reflex in predicting poor neurologic outcome during early (< 72 hr), middle (between 72 and 144 hr), and late (≧ 145 hr) postresuscitation periods, respectively. We included 39 studies involving 17,179 patients. For quantitative pupillary light reflex, the cut off points used in included studies to define absent pupillary light reflex ranged from 0% to 13% (median: 7%) and from zero to 2 (median: 2) for pupillary light reflex amplitude and Neurologic Pupil index, respectively. Late standard pupillary light reflex had the highest area under the receiver operating characteristic curve (0.98, 95% CI [CI], 0.97-0.99). For early standard pupillary light reflex, the area under the receiver operating characteristic curve was 0.80 (95% CI, 0.76-0.83), with a specificity of 0.91 (95% CI, 0.85-0.95). For early quantitative pupillary light reflex, the area under the receiver operating characteristic curve was 0.83 (95% CI, 0.79-0.86), with a specificity of 0.99 (95% CI, 0.91-1.00). CONCLUSIONS: Timing of pupillary light reflex examination may impact neuroprognostic accuracy. The highest prognostic performance was achieved with late standard pupillary light reflex. Early quantitative pupillary light reflex had a similar specificity to late standard pupillary light reflex and had better specificity than early standard pupillary light reflex. For postresuscitation comatose patients, early quantitative pupillary light reflex may substitute for early standard pupillary light reflex in the neurologic prognostication algorithm.