Optimal methods of vitamin D supplementation to prevent acute respiratory infections: a systematic review, dose-response and pairwise meta-analysis of randomized controlled trials.

BACKGROUND: Vitamin D supplementation may prevent acute respiratory infections (ARIs). This study aimed to identify the optimal methods of vitamin D supplementation. METHODS: PubMed, Embase, Cochrane Central Register of Controlled Trials, Web of Science, and the ClinicalTrials.gov registry were searched from database inception through July 13, 2023. Randomized-controlled trials (RCTs) were included. Data were pooled using random-effects model. The primary outcome was the proportion of participants with one or more ARIs. RESULTS: The analysis included 43 RCTs with 49320 participants. Forty RCTs were considered to be at low risk for bias. The main pairwise meta-analysis indicated there were no significant preventive effects of vitamin D supplementation against ARIs (risk ratio [RR]: 0.99, 95% confidence interval [CI]: 0.97 to 1.01, I2 = 49.6%). The subgroup dose-response meta-analysis indicated that the optimal vitamin D supplementation doses ranged between 400-1200 IU/day for both summer-sparing and winter-dominant subgroups. The subgroup pairwise meta-analysis also revealed significant preventive effects of vitamin D supplementation in subgroups of daily dosing (RR: 0.92, 95% CI: 0.85 to 0.99, I2 = 55.7%, number needed to treat [NNT]: 36), trials duration < 4 months (RR: 0.81, 95% CI: 0.67 to 0.97, I2 = 48.8%, NNT: 16), summer-sparing seasons (RR: 0.85, 95% CI: 0.74 to 0.98, I2 = 55.8%, NNT: 26), and winter-dominant seasons (RR: 0.79, 95% CI: 0.71 to 0.89, I2 = 9.7%, NNT: 10). CONCLUSION: Vitamin D supplementation may slightly prevent ARIs when taken daily at doses between 400 and 1200 IU/d during spring, autumn, or winter, which should be further examined in future clinical trials.

A Deep-Learning-Enabled Electrocardiogram and Chest X-Ray for Detecting Pulmonary Arterial Hypertension.

The diagnosis and treatment of pulmonary hypertension have changed dramatically through the re-defined diagnostic criteria and advanced drug development in the past decade. The application of Artificial Intelligence for the detection of elevated pulmonary arterial pressure (ePAP) was reported recently. Artificial Intelligence (AI) has demonstrated the capability to identify ePAP and its association with hospitalization due to heart failure when analyzing chest X-rays (CXR). An AI model based on electrocardiograms (ECG) has shown promise in not only detecting ePAP but also in predicting future risks related to cardiovascular mortality. We aimed to develop an AI model integrating ECG and CXR to detect ePAP and evaluate their performance. We developed a deep-learning model (DLM) using paired ECG and CXR to detect ePAP (systolic pulmonary artery pressure > 50 mmHg in transthoracic echocardiography). This model was further validated in a community hospital. Additionally, our DLM was evaluated for its ability to predict future occurrences of left ventricular dysfunction (LVD, ejection fraction < 35%) and cardiovascular mortality. The AUCs for detecting ePAP were as follows: 0.8261 with ECG (sensitivity 76.6%, specificity 74.5%), 0.8525 with CXR (sensitivity 82.8%, specificity 72.7%), and 0.8644 with a combination of both (sensitivity 78.6%, specificity 79.2%) in the internal dataset. In the external validation dataset, the AUCs for ePAP detection were 0.8348 with ECG, 0.8605 with CXR, and 0.8734 with the combination. Furthermore, using the combination of ECGs and CXR, the negative predictive value (NPV) was 98% in the internal dataset and 98.1% in the external dataset. Patients with ePAP detected by the DLM using combination had a higher risk of new-onset LVD with a hazard ratio (HR) of 4.51 (95% CI: 3.54-5.76) in the internal dataset and cardiovascular mortality with a HR of 6.08 (95% CI: 4.66-7.95). Similar results were seen in the external validation dataset. The DLM, integrating ECG and CXR, effectively detected ePAP with a strong NPV and forecasted future risks of developing LVD and cardiovascular mortality. This model has the potential to expedite the early identification of pulmonary hypertension in patients, prompting further evaluation through echocardiography and, when necessary, right heart catheterization (RHC), potentially resulting in enhanced cardiovascular outcomes.

Deep Learning-Based Localization and Detection of Malpositioned Nasogastric Tubes on Portable Supine Chest X-Rays in Intensive Care and Emergency Medicine: A Multi-center Retrospective Study.

Malposition of a nasogastric tube (NGT) can lead to severe complications. We aimed to develop a computer-aided detection (CAD) system to localize NGTs and detect NGT malposition on portable chest X-rays (CXRs). A total of 7378 portable CXRs were retrospectively retrieved from two hospitals between 2015 and 2020. All CXRs were annotated with pixel-level labels for NGT localization and image-level labels for NGT presence and malposition. In the CAD system, DeepLabv3 + with backbone ResNeSt50 and DenseNet121 served as the model architecture for segmentation and classification models, respectively. The CAD system was tested on images from chronologically different datasets (National Taiwan University Hospital (National Taiwan University Hospital)-20), geographically different datasets (National Taiwan University Hospital-Yunlin Branch (YB)), and the public CLiP dataset. For the segmentation model, the Dice coefficients indicated accurate delineation of the NGT course (National Taiwan University Hospital-20: 0.665, 95% confidence interval (CI) 0.630-0.696; National Taiwan University Hospital-Yunlin Branch: 0.646, 95% CI 0.614-0.678). The distance between the predicted and ground-truth NGT tips suggested accurate tip localization (National Taiwan University Hospital-20: 1.64 cm, 95% CI 0.99-2.41; National Taiwan University Hospital-Yunlin Branch: 2.83 cm, 95% CI 1.94-3.76). For the classification model, NGT presence was detected with high accuracy (area under the receiver operating characteristic curve (AUC): National Taiwan University Hospital-20: 0.998, 95% CI 0.995-1.000; National Taiwan University Hospital-Yunlin Branch: 0.998, 95% CI 0.995-1.000; CLiP dataset: 0.991, 95% CI 0.990-0.992). The CAD system also detected NGT malposition with high accuracy (AUC: National Taiwan University Hospital-20: 0.964, 95% CI 0.917-1.000; National Taiwan University Hospital-Yunlin Branch: 0.991, 95% CI 0.970-1.000) and detected abnormal nasoenteric tube positions with favorable performance (AUC: 0.839, 95% CI 0.807-0.869). The CAD system accurately localized NGTs and detected NGT malposition, demonstrating excellent potential for external generalizability.

Artificial Intelligence-Enabled Electrocardiography Predicts Future Pacemaker Implantation and Adverse Cardiovascular Events.

Medical advances prolonging life have led to more permanent pacemaker implants. When pacemaker implantation (PMI) is commonly caused by sick sinus syndrome or conduction disorders, predicting PMI is challenging, as patients often experience related symptoms. This study was designed to create a deep learning model (DLM) for predicting future PMI from ECG data and assess its ability to predict future cardiovascular events. In this study, a DLM was trained on a dataset of 158,471 ECGs from 42,903 academic medical center patients, with additional validation involving 25,640 medical center patients and 26,538 community hospital patients. Primary analysis focused on predicting PMI within 90 days, while all-cause mortality, cardiovascular disease (CVD) mortality, and the development of various cardiovascular conditions were addressed with secondary analysis. The study's raw ECG DLM achieved area under the curve (AUC) values of 0.870, 0.878, and 0.883 for PMI prediction within 30, 60, and 90 days, respectively, along with sensitivities exceeding 82.0% and specificities over 81.9% in the internal validation. Significant ECG features included the PR interval, corrected QT interval, heart rate, QRS duration, P-wave axis, T-wave axis, and QRS complex axis. The AI-predicted PMI group had higher risks of PMI after 90 days (hazard ratio [HR]: 7.49, 95% CI: 5.40-10.39), all-cause mortality (HR: 1.91, 95% CI: 1.74-2.10), CVD mortality (HR: 3.53, 95% CI: 2.73-4.57), and new-onset adverse cardiovascular events. External validation confirmed the model's accuracy. Through ECG analyses, our AI DLM can alert clinicians and patients to the possibility of future PMI and related mortality and cardiovascular risks, aiding in timely patient intervention.

Ultrasound-Mediated Lysozyme Microbubbles Targeting NOX4 Knockdown Alleviate Cisplatin-Exposed Cochlear Hair Cell Ototoxicity.

The nicotinamide adenine dinucleotide phosphate (NADPH) oxidase 4 (NOX4) protein plays an essential role in the cisplatin (CDDP)-induced generation of reactive oxygen species (ROS). In this study, we evaluated the suitability of ultrasound-mediated lysozyme microbubble (USMB) cavitation to enhance NOX4 siRNA transfection in vitro and ex vivo. Lysozyme-shelled microbubbles (LyzMBs) were constructed and designed for siNOX4 loading as siNOX4/LyzMBs. We investigated different siNOX4-based cell transfection approaches, including naked siNOX4, LyzMB-mixed siNOX4, and siNOX4-loaded LyzMBs, and compared their silencing effects in CDDP-treated HEI-OC1 cells and mouse organ of Corti explants. Transfection efficiencies were evaluated by quantifying the cellular uptake of cyanine 3 (Cy3) fluorescein-labeled siRNA. In vitro experiments showed that the high transfection efficacy (48.18%) of siNOX4 to HEI-OC1 cells mediated by US and siNOX4-loaded LyzMBs significantly inhibited CDDP-induced ROS generation to almost the basal level. The ex vivo CDDP-treated organ of Corti explants of mice showed an even more robust silencing effect of the NOX4 gene in the siNOX4/LyzMB groups treated with US sonication than without US sonication, with a marked abolition of CDDP-induced ROS generation and cytotoxicity. Loading of siNOX4 on LyzMBs can stabilize siNOX4 and prevent its degradation, thereby enhancing the transfection and silencing effects when combined with US sonication. This USMB-derived therapy modality for alleviating CDDP-induced ototoxicity may be suitable for future clinical applications.

End-tidal Carbon Dioxide Trajectory-based Prognostication of Out-of-hospital Cardiac Arrest.

Background: During cardiopulmonary resuscitation (CPR), end-tidal carbon dioxide (EtCO2) is primarily determined by pulmonary blood flow, thereby reflecting the blood flow generated by CPR. We aimed to develop an EtCO2 trajectory-based prediction model for prognostication at specific time points during CPR in patients with out-of-hospital cardiac arrest (OHCA). Methods: We screened patients receiving CPR between 2015-2021 from a prospectively collected database of a tertiary-care medical center. The primary outcome was survival to hospital discharge. We used group-based trajectory modeling to identify the EtCO2 trajectories. Multivariable logistic regression analysis was used for model development and internally validated using bootstrapping. We assessed performance of the model using the area under the receiver operating characteristic curve (AUC). Results: The primary analysis included 542 patients with a median age of 68.0 years. Three distinct EtCO2 trajectories were identified in patients resuscitated for 20 minutes (min): low (average EtCO2 10.0 millimeters of mercury [mm Hg]; intermediate (average EtCO2 26.5 mm Hg); and high (average EtCO2: 51.5 mm Hg). Twenty-min EtCO2 trajectory was fitted as an ordinal variable (low, intermediate, and high) and positively associated with survival (odds ratio 2.25, 95% confidence interval [CI] 1.07-4.74). When the 20-min EtCO2 trajectory was combined with other variables, including arrest location and arrest rhythms, the AUC of the 20-min prediction model for survival was 0.89 (95% CI 0.86-0.92). All predictors in the 20-min model remained statistically significant after bootstrapping. Conclusion: Time-specific EtCO2 trajectory was a significant predictor of OHCA outcomes, which could be combined with other baseline variables for intra-arrest prognostication. For this purpose, the 20-min survival model achieved excellent discriminative performance in predicting survival to hospital discharge.

The Risk of Aircraft-Acquired SARS-CoV-2 Transmission during Commercial Flights: A Systematic Review.

The aircraft-acquired transmission of SARS-CoV-2 poses a public health risk. Following PRISMA guidelines, we conducted a systematic review and analysis of articles, published prior to vaccines being available, from 24 January 2020 to 20 April 2021 to identify factors important for transmission. Articles were included if they mentioned index cases and identifiable flight duration, and excluded if they discussed non-commercial aircraft, airflow or transmission models, cases without flight data, or that were unable to determine in-flight transmission. From the 15 articles selected for in-depth review, 50 total flights were analyzed by flight duration both as a categorical variable-short (<3 h), medium (3-6 h), or long flights (>6 h)-and as a continuous variable with case counts modeled by negative binomial regression. Compared to short flights without masking, medium and long flights without masking were associated with 4.66-fold increase (95% CI: [1.01, 21.52]; p < 0.0001) and 25.93-fold increase in incidence rates (95% CI: [4.1, 164]; p < 0.0001), respectively; long flights with enforced masking had no transmission reported. A 1 h increase in flight duration was associated with 1.53-fold (95% CI: [1.19, 1.66]; p < 0.001) increase in the incidence rate ratio (IRR) of cases. Masking should be considered for long flights.

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.

Prognostic Impact of Heart Rhythm Shockability Trajectory in Out-of-Hospital Cardiac Arrest: A Multicenter Retrospective Study.

BACKGROUND: This study aimed to investigate the association between the temporal transitions in heart rhythms during cardiopulmonary resuscitation (CPR) and outcomes after out-of-hospital cardiac arrest. METHODS: This was an analysis of the prospectively collected databases in 3 academic hospitals in northern and central Taiwan. Adult patients with out-of-hospital cardiac arrest transported by emergency medical service between 2015 and 2022 were included. Favorable neurological recovery and survival to hospital discharge were the primary and secondary outcomes, respectively. Time-specific heart rhythm shockability was defined as the probability of shockable rhythms at a particular time point during CPR. The temporal changes in the time-specific heart rhythm shockability were calculated by group-based trajectory modeling. Multivariable logistic regression analyses were performed to examine the association between the trajectory group and outcomes. Subgroup analyses examined the effects of extracorporeal CPR in different trajectories. RESULTS: The study comprised 2118 patients. The median patient age was 69.1 years, and 1376 (65.0%) patients were male. Three distinct trajectories were identified: high-shockability (52 patients; 2.5%), intermediate-shockability (262 patients; 12.4%), and low-shockability (1804 patients; 85.2%) trajectories. The median proportion of shockable rhythms over the course of CPR for the 3 trajectories was 81.7% (interquartile range, 73.2%-100.0%), 26.7% (interquartile range, 16.7%-37.5%), and 0% (interquartile range, 0%-0%), respectively. The multivariable analysis indicated both intermediate- and high-shockability trajectories were associated with favorable neurological recovery (intermediate-shockability: adjusted odds ratio [aOR], 4.98 [95% CI, 2.34-10.59]; high-shockability: aOR, 5.40 [95% CI, 2.03-14.32]) and survival (intermediate-shockability: aOR, 2.46 [95% CI, 1.44-4.18]; high-shockability: aOR, 2.76 [95% CI, 1.20-6.38]). The subgroup analysis further indicated extracorporeal CPR was significantly associated with favorable neurological outcomes (aOR, 4.06 [95% CI, 1.11-14.81]) only in the intermediate-shockability trajectory. CONCLUSIONS: Heart rhythm shockability trajectories were associated with out-of-hospital cardiac arrest outcomes, which may be a supplementary factor in guiding the allocation of medical resources, such as extracorporeal CPR.

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.

AI-enabled electrocardiography alert intervention and all-cause mortality: a pragmatic randomized clinical trial.

The early identification of vulnerable patients has the potential to improve outcomes but poses a substantial challenge in clinical practice. This study evaluated the ability of an artificial intelligence (AI)-enabled electrocardiogram (ECG) to identify hospitalized patients with a high risk of mortality in a multisite randomized controlled trial involving 39 physicians and 15,965 patients. The AI-ECG alert intervention included an AI report and warning messages delivered to the physicians, flagging patients predicted to be at high risk of mortality. The trial met its primary outcome, finding that implementation of the AI-ECG alert was associated with a significant reduction in all-cause mortality within 90 days: 3.6% patients in the intervention group died within 90 days, compared to 4.3% in the control group (4.3%) (hazard ratio (HR) = 0.83, 95% confidence interval (CI) = 0.70-0.99). A prespecified analysis showed that reduction in all-cause mortality associated with the AI-ECG alert was observed primarily in patients with high-risk ECGs (HR = 0.69, 95% CI = 0.53-0.90). In analyses of secondary outcomes, patients in the intervention group with high-risk ECGs received increased levels of intensive care compared to the control group; for the high-risk ECG group of patients, implementation of the AI-ECG alert was associated with a significant reduction in the risk of cardiac death (0.2% in the intervention arm versus 2.4% in the control arm, HR = 0.07, 95% CI = 0.01-0.56). While the precise means by which implementation of the AI-ECG alert led to decreased mortality are to be fully elucidated, these results indicate that such implementation assists in the detection of high-risk patients, prompting timely clinical care and reducing mortality. ClinicalTrials.gov registration: NCT05118035 .

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.

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.

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.

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 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.

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.

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.

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.

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.