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.

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.

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.

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.

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.

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.

pH-Responsive Polymeric Micelle Dynamic Complexes for Selective Killing of Helicobacter pylori.

Helicobacter pylori, the world's most common chronic infection-causing pathogen, is responsible for causing gastric ulcers, the fourth-leading cause of cancer-related death globally in 2020. In recent years, the effectiveness of the current treatment regimen (two antibiotics and one proton pump inhibitor) has often been plagued with problems such as resistance and the undesired elimination of commensal bacteria. Herein, we report the synthesis of block and random copolycarbonates, functionalized with cationic guanidinium and anionic acetate functional groups, aimed at selectively killing H. pylori in the acidic environment of the stomach, while remaining nontoxic to the commensal bacteria in the gut. The compositions of the polymers were fine-tuned so that the polymers were readily dispersed in water without any difficulty at both pH 3.0 and 7.4. The self-assembly behavior of the polymers at different pH values by dynamic light scattering showed that the random and block copolymers formed stable micelles in a simulated gastric environment (pH 3.0) while aggregated at pH 7.4. Both polymers demonstrated stronger antibacterial activity against H. pylori than the guanidinium-functionalized homopolymer without any acetate functional group at pH 3.0. The block copolymer was significantly more bactericidal at pH 3.0 across the concentrations tested, as compared to the random copolymer, while it did not show significant toxicity toward rat red blood cells (rRBCs) and HK-2 cells or bactericidal effect toward E. coli (a common gut bacterium) and nor caused aggregation of rRBCs at its effective concentration and at physiological pH of 7.4. Additionally, both the block and random copolymers were much more stable against hydrolysis at pH 3.0 than at pH 7.4. This study provides insight into the influence of both polymer architecture and dynamic assembly on the bioactivities of antimicrobial polymers, where the disassembly of coacervates into narrowly dispersed micelles at pH 3 make them potent antimicrobials aided by the protonated carboxylic acid block.

End-tidal Carbon Dioxide + Return of Spontaneous Circulation After Cardiac Arrest (RACA) Score to Predict Outcomes After Out-of-hospital Cardiac Arrest.

INTRODUCTION: The return of spontaneous circulation after cardiac arrest (RACA) score is a well-validated model for estimating the probability of return of spontaneous circulation (ROSC) in patients with out-of-hospital cardiac arrest (OHCA) by incorporating several variables, including gender, age, arrest aetiology, witness status, arrest location, initial cardiac rhythms, bystander cardiopulmonary resuscitation (CPR), and emergency medical services (EMS) arrival time. The RACA score was initially designed for comparisons between different EMS systems by standardising ROSC rates. End-tidal carbon dioxide (EtCO2) is a quality indicator of CPR. We aimed to improve the performance of the RACA score by adding minimum EtCO2 measured during CPR to develop the EtCO2 + RACA score for OHCA patients transported to an emergency department (ED). METHODS: This was a retrospective analysis using prospectively collected data for OHCA patients resuscitated at an ED during 2015-2020. Adult patients with advanced airways inserted and available EtCO2 measurements were included. We used the EtCO2 values recorded in the ED for analysis. The primary outcome was ROSC. In the derivation cohort, we used multivariable logistic regression to develop the model. In the temporally split validation cohort, we assessed the discriminative performance of the EtCO2 + RACA score by the area under the receiver operating characteristic curve (AUC) and compared it with the RACA score using the DeLong test. RESULTS: There were 530 and 228 patients in the derivation and validation cohorts, respectively. The median measurements of EtCO2 were 8.0 times (interquartile range [IQR] 3.0-12.0 times), with the median minimum EtCO2 of 15.5 millimeters of mercury (mm Hg) (IQR 8.0-26.0 mm Hg). The median RACA score was 36.4% (IQR 28.9-48.0%), and a total of 393 patients (51.8%) achieved ROSC. The EtCO2 + RACA score was validated with good discriminative performance (AUC, 0.82, 95% CI 0.77-0.88), outperforming the RACA score (AUC, 0.71, 95% CI 0.65-0.78) (DeLong test: P < 0.001). CONCLUSION: The EtCO2 + RACA score may facilitate the decision-making process regarding allocations of medical resources in EDs for OHCA resuscitation.

Preparation of Medical Supply for Prehospital Emergencies and Disasters: An Internet-Based Simulation Drill.

Background: Mass casualties caused by natural disasters and man-made events may overwhelm local emergency medical services and healthcare systems. Logistics is essential to a successful emergency medical response. Drills have been used in disaster preparedness to validate plans, policies, procedures, and agreements, and identify resource gaps. The application of the internet to facilitate the conduct of exercise was still limited. This study aimed to investigate the optimal preparation of medical supplies by medical emergency response teams (MERTs) during emergencies and disasters using an internet-based drill. Methods: An internet-based drill based on real-life mass casualty incidents (MCIs) was developed and conducted in Taiwan from June 2017 to July 2018. The drill involved an MCI with 50 events delivered under two scenarios: (1) reduced transfer capacity and well-functioning local healthcare facilities (emergency module); (2) severely reduced transfer capacity and dysfunctional local healthcare facilities (disaster module). For each event, medical supplies commonly prepared by local MERTs in Taiwan were listed in structured questionnaires and participants selected the supplies they would use. Results: Forty-three senior medical emergency responders participated in the survey (responding rate of 47.3%). Resuscitation-related supplies increased from emergency to disaster module (e.g., intubation from 9.1% to 13.9%; dopamine from 3.2% to 5.0%; all p < 0.001). In the subgroup analysis of events with life-threatening injuries, the utilization of resuscitation-related supplies (e.g., intubation from 46.6% to 65.3%; p < 0.001) remained higher in the disaster than in the emergency module. Compared to emergency medical technicians, physicians and nurses are more likely to use intravenous/intramuscular analgesics. Conclusions: The severity of scenarios and the professional background of emergency responders have a different utilization of medical supplies in the simulation drill. The internet-based drill may contribute to optimizing the preparedness of medical response to prehospital emergencies and disasters.

Nanocomplexes of Biodegradable Anticancer Macromolecules: Prolonged Plasma Half-Life, Reduced Toxicity, and Increased Tumor Targeting.

Anticancer drug resistance is a large contributing factor to the global mortality rate of cancer patients. Anticancer macromolecules such as polymers have been recently reported to overcome this issue. Anticancer macromolecules have unselective toxicity because they are highly positively charged. Herein, an anionic biodegradable polycarbonate carrier is synthesized and utilized to form nanocomplexes with an anticancer polycarbonate via self-assembly to neutralize its positive charges. Biotin is conjugated to the anionic carrier and serves as cancer cell-targeting moiety. The nanoparticles have sizes of < 130 nm with anticancer polymer loading levels of 38-49%. Unlike the small molecular anticancer drug doxorubicin, the nanocomplexes effectively inhibit the growth of both drug-susceptible MCF7 and drug-resistant MCF7/ADR human breast cancer cell lines with low half maximal inhibitory concentration (IC50 ). The nanocomplexes increase the anticancer polymer's in vivo half-life from 1 to 6-8 h, and rapidly kill BT474 human breast cancer cells primarily via an apoptotic mechanism. The nanocomplexes significantly increase the median lethal dose (LD50 ) and reduce the injection site toxicity of the anticancer polymer. They suppress tumor growth by 32-56% without causing any damage to the liver and kidneys. These nanocomplexes may potentially be used for cancer treatment to overcome drug resistance.

Association between trajectories of end-tidal carbon dioxide and return of spontaneous circulation among emergency department patients with out-of-hospital cardiac arrest.

BACKGROUND: We aimed to identify distinct trajectories of end-tidal carbon dioxide (EtCO2) during cardiopulmonary resuscitation in patients with out-of-hospital cardiac arrest (OHCA) and to investigate the association between EtCO2 trajectories and OHCA outcomes. METHODS: This was a secondary analysis of a prospectively collected database on adult patients with OHCA who had been resuscitated in the emergency department of a tertiary medical center between 2015 and 2020. The primary outcome was the return of spontaneous circulation (ROSC). Group-based trajectory modelling was used to identify the EtCO2 trajectories. Multivariable logistic regression analysis was performed to evaluate the association between EtCO2 trajectories and ROSC. The predictive performance of the EtCO2 trajectories was assessed using the area under the receiver operating characteristic curve (AUC). RESULTS: The study comprised 655 patients with OHCA. In the primary analysis, three distinct EtCO2 trajectories, including 10-mmHg, 30-mmHg, and 50-mmHg trajectories, were identified. Compared with the 10-mmHg trajectory, both 30-mmHg (odds ratio [OR]: 4.66, 95% confidence interval [CI]: 3.15-6.90) and 50-mmHg (OR: 7.58, 95% CI: 4.30-13.35) trajectories were associated with a higher likelihood of ROSC. In a sensitivity analysis of excluding EtCO2 measured before tracheal intubation or after sodium bicarbonate administration, the predictive ability of the identified EtCO2 trajectories remained. As a single predictor of ROSC, EtCO2 trajectories had an acceptable discriminative performance (AUC: 0.69, 95% CI: 0.66-0.73). CONCLUSION: Three distinct EtCO2 trajectories during cardiopulmonary resuscitation were identified and significantly associated with outcomes. Early identification of these EtCO2 trajectories could potentially guide the ongoing resuscitation efforts.

Drug-free neutrally charged polypeptide nanoparticles as anticancer agents.

Cationic synthetic anticancer polymers and peptides have attracted increasing attention for advancing cancer treatment without causing drug resistance development. To circumvent in vivo instability and toxicity caused by cationic charges of the anticancer polymers/peptides, we report, for the first time, a nanoparticulate delivery system self-assembled from a negatively charged pH-sensitive polypeptide poly(ethylene glycol)-b-poly(ʟ-lysine)-graft-cyclohexene-1,2-dicarboxylic anhydride and a cationic anticancer polypeptide guanidinium-functionalized poly(ʟ-lysine) (PLL-Gua) via electrostatic interaction. The formation of nanoparticles (Gua-NPs) neutralized the positive charges of PLL-Gua. Both PLL-Gua and Gua-NPs killed cancer cells in a dose- and time-dependent manner, and induced cell death via apoptosis. Confocal microscopic studies demonstrated that PLL-Gua and Gua-NPs readily entered cancer cells, and Gua-NPs were taken up by the cells via endocytosis. Notably, Gua-NPs and PLL-Gua exhibited similar in vitro anticancer efficacy against MCF-7 and resistant MCF-7/ADR. PLL-Gua and Gua-NPs also induced similar morphological changes in MCF-7/ADR cells compared to MCF-7 cells, further indicating their ability to bypass drug resistance mechanisms in the MCF-7/ADR cells. More importantly, Gua-NPs with higher LD50 and enhanced tumor accumulation significantly inhibited tumor growth with negligible side effects in vivo. Our findings shed light on the in vivo delivery of anticancer peptides and opened a new avenue for cancer treatment.

Pain trajectories in the emergency department: Patient characteristics and clinical outcomes.

BACKGROUND: Little is known about pain trajectories in the emergency department (ED), which could inform the heterogeneous response to pain treatment. We aimed to identify clinically relevant subphenotypes of pain resolution in the ED and their relationships with clinical outcomes. METHODS: This retrospective cohort study used electronic clinical warehouse data from a tertiary medical center. We retrieved data from 733,398 ED visits over a 7-year period. We selected one ED visit per person and retrieved data including patient demographics, triage data, repeated pain scores evaluated on a numeric rating scale, pain characteristics, laboratory markers, and patient disposition. The primary outcome measures were hospitalization and ED revisit. RESULTS: 28,105 adult ED patients were included with a total of 154,405 pain measurements. Three distinct pain trajectory groups were identified: no pain (57.1%); moderate-to-severe pain, fast resolvers (17.9%); and moderate pain, slow resolvers (24.9%). The fast resolvers responded well to treatment and were independently associated with a lower risk of hospitalization (adjusted odds ratio [aOR], 0.75; 95% confidence interval [CI], 0.70-0.81). By contrast, the slow resolvers had lingering pain in the ED and were independently associated with a higher risk of ED revisit (aOR, 2.65; 95%CI, 1.85-3.69). This group also had higher levels of inflammatory markers, including a higher leukocyte count and a higher level of C-reactive protein. CONCLUSIONS: We identified three novel pain subphenotypes with distinct patterns in clinical characteristics and patient outcomes. A better understanding of the pain trajectories may help with the personalized approach to pain management in the ED.

Broad-Spectrum Antiviral Peptides and Polymers.

As the human cost of the pandemic caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is still being witnessed worldwide, the development of broad-spectrum antiviral agents against emerging and re-emerging viruses is seen as a necessity to hamper the spread of infections. Various targets during the viral life-cycle can be considered to inhibit viral infection, from viral attachment to viral fusion or replication. Macromolecules represent a particularly attractive class of therapeutics due to their multivalency and versatility. Although several antiviral macromolecules hold great promise in clinical applications, the emergence of resistance after prolonged exposure urges the need for improved solutions. In the present article, the recent advancement in the discovery of antiviral peptides and polymers with diverse structural features and antiviral mechanisms is reviewed. Future perspectives, such as, the development of virucidal peptides/polymers and their coatings against SARS-CoV-2 infection, standardization of antiviral testing protocols, and use of artificial intelligence or machine learning as a tool to accelerate the discovery of antiviral macromolecules, are discussed.

Elucidating the anticancer activities of guanidinium-functionalized amphiphilic random copolymers by varying the structure and composition in the hydrophobic monomer.

Rationale: Use of traditional anticancer chemotherapeutics has been hindered by the multifactorial nature of multi-drug resistance (MDR) development and metastasis. Recently, cationic polycarbonates were reported as novel unconventional anticancer agents that mitigated MDR and inhibited metastasis. The aim of this study is to explore structure-anticancer activity relationship. Specifically, a series of cationic guanidinium-based random copolymers of varying hydrophobicity was synthesized with a narrow polydispersity (Ð = 1.12-1.27) via organocatalytic ring-opening polymerization (OROP) of functional cyclic carbonate monomers, and evaluated for anticancer activity, killing kinetics, degradability and functional mechanism. Methods: Linear, branched and aromatic hydrophobic side chain units, such as ethyl, benzyl, butyl, isobutyl and hexyl moieties were explored as comonomer units for modulating anticancer activity. As hydrophobicity/hydrophilicity balance of the polymers determines their anticancer efficacy, the feed ratio between the two monomers was varied to tune their hydrophobicity. Results: Notably, incorporating the hexyl moiety greatly enhanced anticancer efficiency and killing kinetics on cancer cells. Degradation studies showed that the polymers degraded completely within 4-6 days. Flow cytometry and lactate dehydrogenase (LDH) release analyses demonstrated that anticancer mechanism of the copolymers containing a hydrophobic co-monomer was concentration dependent, apoptosis at IC50, and both apoptosis and necrosis at 2 × IC50. In contrast, the homopolymer without a hydrophobic comonomer killed cancer cells predominantly via apoptotic mechanism. Conclusion: The hydrophobicity of the polymers played an important role in anticancer efficacy, killing kinetics and anticancer mechanism. This study provides valuable insights into designing novel anticancer agents utilizing polymers.

Early versus Late Surgical Decompression for Traumatic Spinal Cord Injury on Neurological Recovery: A Systematic Review and Meta-Analysis.

This study aimed to investigate whether early surgical decompression was associated with favorable neurological recovery in patients with traumatic spinal cord injury (tSCI). We searched PubMed and Embase from the database inception through December 2020 and selected studies comparing the impact of early versus late surgical decompression on neurological recovery as assessed by American Spinal Injury Association Impairment Scale (AIS) for adult patients sustaining tSCI. We pooled the effect estimates in random-effects models and quantified the heterogeneity by the I2 statistics. Subgroup analysis and meta-regression analysis was conducted to identify significant outcome moderator. We included 26 studies involving 3574 patients in the meta-analysis. The pooled results demonstrated significant association between early surgical decompression and an improvement of at least one AIS grade (odds ratio [OR], 1.85; 95% confidence interval [CI], 1.41-2.41; I2, 48.06%). The benefits of early surgical decompression were consistently observed across different subgroups, including patients with cervical or thoracolumbar injury and patients with complete or incomplete injury. The meta-regression analysis indicated that cut-off timing defining early versus late decompression was a significant effect moderator, with early decompression performed before post-tSCI 8 or 12 h associated with greatest benefits (OR, 3.37; 95% CI, 1.74-6.50; I2, 53.52%). No obvious publication bias was detected by the funnel plot. In conclusion, early surgical decompression was associated with favorable neurological recovery for tSCI patients. However, there was a lack of high-quality evidence and the results need further examination.

A Double Triage and Telemedicine Protocol to Optimize Infection Control in an Emergency Department in Taiwan During the COVID-19 Pandemic: Retrospective Feasibility Study.

BACKGROUND: Frontline health care workers, including physicians, are at high risk of contracting coronavirus disease (COVID-19) owing to their exposure to patients suspected of having COVID-19. OBJECTIVE: The aim of this study was to evaluate the benefits and feasibility of a double triage and telemedicine protocol in improving infection control in the emergency department (ED). METHODS: In this retrospective study, we recruited patients aged ≥20 years referred to the ED of the National Taiwan University Hospital between March 1 and April 30, 2020. A double triage and telemedicine protocol was developed to triage suggested COVID-19 cases and minimize health workers' exposure to this disease. We categorized patients attending video interviews into a telemedicine group and patients experiencing face-to-face interviews into a conventional group. A questionnaire was used to assess how patients perceived the quality of the interviews and their communication with physicians as well as perceptions of stress, discrimination, and privacy. Each question was evaluated using a 5-point Likert scale. Physicians' total exposure time and total evaluation time were treated as primary outcomes, and the mean scores of the questions were treated as secondary outcomes. RESULTS: The final sample included 198 patients, including 93 cases (47.0%) in the telemedicine group and 105 cases (53.0%) in the conventional group. The total exposure time in the telemedicine group was significantly shorter than that in the conventional group (4.7 minutes vs 8.9 minutes, P<.001), whereas the total evaluation time in the telemedicine group was significantly longer than that in the conventional group (12.2 minutes vs 8.9 minutes, P<.001). After controlling for potential confounders, the total exposure time in the telemedicine group was 4.6 minutes shorter than that in the conventional group (95% CI -5.7 to -3.5, P<.001), whereas the total evaluation time in the telemedicine group was 2.8 minutes longer than that in the conventional group (95% CI -1.6 to -4.0, P<.001). The mean scores of the patient questionnaire were high in both groups (4.5/5 to 4.7/5 points). CONCLUSIONS: The implementation of the double triage and telemedicine protocol in the ED during the COVID-19 pandemic has high potential to improve infection control.

Synthetic macromolecules as therapeutics that overcome resistance in cancer and microbial infection.

Synthetic macromolecular antimicrobials have shown efficacy in the treatment of multidrug resistant (MDR) pathogens. These synthetic macromolecules, inspired by Nature's antimicrobial peptides (AMPs), mitigate resistance by disrupting microbial cell membrane or targeting multiple intracellular proteins or genes. Unlike AMPs, these polymers are less prone to degradation by proteases and are easier to synthesize on a large scale. Recently, various studies have revealed that cancer cell membrane, like that of microbes, is negatively charged, and AMPs can be used as anticancer agents. Nevertheless, efforts in developing polymers as anticancer agents has remained limited. This review highlights the recent advancement in the development of synthetic biodegradable antimicrobial polymers (e.g. polycarbonates, polyesters and polypeptides) and anticancer macromolecules including peptides and polymers. Additionally, strategies to improve their in vivo bioavailability and selectivity towards bacteria and cancer cells are examined. Lastly, future perspectives, including use of artificial intelligence or machine learning, in the development of antimicrobial and anticancer macromolecules are discussed.

A conserved face of the Jagged/Serrate DSL domain is involved in Notch trans-activation and cis-inhibition.

The Notch receptor and its ligands are key components in a core metazoan signaling pathway that regulates the spatial patterning, timing and outcome of many cell-fate decisions. Ligands contain a disulfide-rich Delta/Serrate/LAG-2 (DSL) domain required for Notch trans-activation or cis-inhibition. Here we report the X-ray structure of a receptor binding region of a Notch ligand, the DSL-EGF3 domains of human Jagged-1 (J-1(DSL-EGF3)). The structure reveals a highly conserved face of the DSL domain, and we show, by functional analysis of Drosophila melanogster ligand mutants, that this surface is required for both cis- and trans-regulatory interactions with Notch. We also identify, using NMR, a surface of Notch-1 involved in J-1(DSL-EGF3) binding. Our data imply that cis- and trans-regulation may occur through the formation of structurally distinct complexes that, unexpectedly, involve the same surfaces on both ligand and receptor.