Electrochemical control of the morphological evolution of PEDOT on a Ni-Co(OH)2/carbon cloth surface to modulate the performance of wearable H2O2 sensors.

The slow redox rate of hydrogen peroxide (H2O2) in neutral environments makes the H2O2 sensor inadequate for the detection of low levels of signalling molecules. The aim of this study is to fabricate a flexible sensing electrode by hydrothermally loading micro-nanometer Ni and Co(OH)2 on carbon cloth (CC) and electrochemically depositing poly(3,4-ethylenedioxythiophene) (PEDOT) on the surface of the electrode. The sensor presented high sensitivity (10.43 mA mM-1 cm-2), a wide detection range (0.033-120.848 mM), a low detection limit (0.92 nM), high stability, and excellent anti-interference performance in neutral solutions. Ni-Co(OH)2 provides abundant active sites while CC solves their agglomeration phenomenon and conductivity. The PEDOT film offers heightened conductivity, hydrophilicity, interfacial stability, and an electrochemically active surface area (ECSA). The side area of the chrysanthemum petal like PEDOT is 39 ± 7 times the bottom area, and PEDOT increases the ECSA of the composite to six times that of CC. Electrochemical precise control of PEDOT morphology to improve sensor performance provides a new strategy for the application of PEDOT in sensors.

Improving product quality and productivity of an antibody-based biotherapeutic using inverted frustoconical shaking bioreactors.

The Chinese hamster ovarian (CHO) cells serve as a common choice in biopharmaceutical production, traditionally cultivated in stirred tank bioreactors (STRs). Nevertheless, the pursuit of improved protein quality and production output for commercial purposes demand exploration into new bioreactor types. In this context, inverted frustoconical shaking bioreactors (IFSB) present unique physical properties distinct from STRs. This study aims to compare the production processes of an antibody-based biotherapeutic in both bioreactor types, to enhance production flexibility. The findings indicate that, when compared to STRs, IFSB demonstrates the capability to produce an antibody-based biotherapeutic with either comparable or enhanced bioprocess performance and product quality. IFSB reduces shear damage to cells, enhances viable cell density (VCD), and improves cell state at a 5-L scale. Consequently, this leads to increased protein expression (3.70 g/L vs 2.56 g/L) and improved protein quality, as evidenced by a reduction in acidic variants from 27.0% to 21.5%. Scaling up the culture utilizing the Froude constant and superficial gas velocity ensures stable operation, effective mixing, and gas transfer. The IFSB maintains a high VCD and cell viability at both 50-L and 500-L scales. Product expression levels range from 3.0 to 3.6 g/L, accompanied by an improved acidic variants attribute of 20.6%-22.7%. The IFSB exhibits superior productivity and product quality, underscoring its potential for incorporation into the manufacturing process for antibody-based biotherapeutics. These results establish the foundation for IFSB to become a viable option in producing antibody-based biotherapeutics for clinical and manufacturing applications.

Cardiometabolic Improvements After Metabolic Surgery and Related Presurgery Factors.

Context: Metabolic surgery remains the most effective and durable treatment for severe obesity and related metabolic diseases. Objective: We examined cardiometabolic improvements after metabolic surgery and associated presurgery demographic and clinical factors in a large multiracial cohort. Methods: Included were 7804 patients (20-79 years) undergoing first-time metabolic surgery at Vanderbilt University Medical Center from 1999 to 2022. Pre- and 1-year postsurgery cardiometabolic profiles were extracted from medical records, including body mass index (BMI), blood pressure, blood lipids, glucose, and hemoglobin A1c. The 10-year atherosclerotic cardiovascular disease (ASCVD) risk was estimated per American College of Cardiology/American Heart Association equations. Pre- to postsurgery cardiometabolic profiles were compared by paired t-test, and associated factors were identified by multivariable linear and logistic regression. Results: Among 7804 patients, most were women and White, while 1618 were men and 1271 were Black; median age and BMI were 45 years [interquartile range (IQR): 37-53] and 46.4 kg/m2 (IQR: 42.1-52.4). At 1-year postsurgery, patients showed significant decreases in systolic blood pressure (10.5 [95% confidence interval: 10.1, 10.9] mmHg), total cholesterol (13.5 [10.3, 16.7] mg/dL), glucose (13.6 [12.9, 14.4] mg/dL), hemoglobin A1c (1.13% [1.06, 1.20]), and 10-year ASCVD risk (absolute reduction: 1.58% [1.22, 1.94]; relative reduction: 34.4% [29.4, 39.3]); all P < .0001. Older, male, or Black patients showed less reduction in 10-year ASCVD risk and lower odds of diabetes/hypertension/dyslipidemia remission than younger, female, or White patients. Patients with a history of diabetes, hypertension, dyslipidemia, or cardiovascular disease showed less cardiometabolic improvements than those without. Results were similar with or without further adjusting for weight loss and largely sustained at 2-year postsurgery. Conclusion: Metabolic surgery results in significant cardiometabolic improvements, particularly among younger, female, or White patients and those without comorbidities.

Conjugated Oligoelectrolyte with DNA Affinity for Enhanced Nuclear Imaging and Precise DNA Quantification.

Precise DNA quantification and nuclear imaging are pivotal for clinical testing, pathological diagnosis, and drug development. The detection and localization of mitochondrial DNA serve as crucial indicators of cellular health. We introduce a novel conjugated oligoelectrolyte (COE) molecule, COE-S3, featuring a planar backbone composed of three benzene rings and terminal side chains. This unique amphiphilic structure endows COE-S3 with exceptional water solubility, a high quantum yield of 0.79, and a significant fluorescence Stokes shift (λex = 366 nm, λem = 476 nm), alongside a specific fluorescence response to DNA. The fluorescence intensity correlates proportionally with DNA concentration. COE-S3 interacts with double-stranded DNA (dsDNA) through an intercalation binding mode, exhibiting a binding constant (K) of 1.32 × 106 M-1. Its amphiphilic nature and strong DNA affinity facilitate its localization within mitochondria in living cells and nuclei in apoptotic cells. Remarkably, within 30 min of COE-S3 staining, cell vitality can be discerned through real-time nuclear fluorescence imaging of apoptotic cells. COE-S3's high DNA selectivity enables quantitative intracellular DNA analysis, providing insights into cell proliferation, differentiation, and growth. Our findings underscore COE-S3, with its strategically designed, shortened planar backbone, as a promising intercalative probe for DNA quantification and nuclear imaging.

Spectral reflectance reconstruction based on wideband multi-illuminant imaging and a modified particle swarm optimization algorithm.

A method for spectral reflectance factor reconstruction based on wideband multi-illuminant imaging was proposed, using a programmable LED lighting system and modified Bare Bones Particle Swarm Optimization algorithms. From a set of 16 LEDs with different spectral power distributions, nine light sources with correlated color temperatures in the range of 1924 K - 15746 K, most of them daylight simulators, were generated. Samples from three color charts (X-Rite ColorChecker Digital SG, SCOCIE ScoColor paint chart, and SCOCIE ScoColor textile chart), were captured by a color industrial camera under the nine light sources, and used in sequence as training and/or testing colors. The spectral reconstruction models achieved under multi-illuminant imaging were trained and tested using the canonical Bare Bones Particle Swarm Optimization and its proposed modifications, along with six additional and commonly used algorithms. The impacts of different illuminants, illuminant combinations, algorithms, and training colors on reconstruction accuracy were studied comprehensively. The results indicated that training colors covering larger regions of color space give more accurate reconstructions of spectral reflectance factors, and combinations of two illuminants with a large difference of correlated color temperature achieve more than twice the accuracy of that under a single illuminant. Specifically, the average reconstruction error by the method proposed in this paper for patches from two color charts under A + D90 light sources was 0.94 and 1.08 CIEDE2000 color difference units. The results of the experiment also confirmed that some reconstruction algorithms are unsuitable for predicting spectral reflectance factors from multi-illuminant images due to the complexity of optimization problems and insufficient accuracy. The proposed reconstruction method has many advantages, such as being simple in operation, with no requirement of prior knowledge, and easy to implement in non-contact color measurement and color reproduction devices.

Enhanced Patient Portal Engagement Associated with Improved Weight Loss Outcomes in Post-Bariatric Surgery Patients.

Background: Bariatric surgery is an effective intervention for obesity, but it requires comprehensive postoperative self-management to achieve optimal outcomes. While patient portals are generally seen as beneficial in engaging patients in health management, the link between their use and post-bariatric surgery weight loss remains unclear. Objective: This study investigated the association between patient portal engagement and postoperative body mass index (BMI) reduction among bariatric surgery patients. Methods: This retrospective longitudinal study included patients who underwent Roux-en-Y gastric bypass (RYGB) or sleeve gastrectomy (SG) at Vanderbilt University Medical Center (VUMC) between January 2018 and March 2021. Using generalized estimating equations, we estimated the association between active days of postoperative patient portal use and the reduction of BMI percentage (%BMI) at 3, 6, and 12 months post-surgery. Covariates included duration since surgery, the patient's age at the time of surgery, gender, race and ethnicity, type of bariatric surgery, severity of comorbid conditions, and socioeconomic disadvantage. Results: The study included 1,415 patients, mostly female (80.9%), with diverse racial and ethnic backgrounds. 805 (56.9%) patients underwent RYGB and 610 (43.1%) underwent SG. By one-year post-surgery, the mean (SD) %BMI reduction was 31.1% (8.3%), and the mean (SD) number of patient portal active days was 61.0 (41.2). A significantly positive association was observed between patient portal engagement and %BMI reduction, with variations revealed over time. Each 10-day increment of active portal use was associated with a 0.57% ([95% CI: 0.42- 0.72], P < .001) and 0.35% ([95% CI: 0.22- 0.49], P < .001) %BMI reduction at 3 and 6 months postoperatively. The association was not statistically significant at 12 months postoperatively (ß=-0.07, [95% CI: -0.24- 0.09], P = .54). Various portal functions, including messaging, visits, my record, medical tools, billing, resources, and others, were positively associated with %BMI reduction at 3- and 6-months follow-ups. Conclusions: Greater patient portal engagement, which may represent stronger adherence to postoperative instructions, better self-management of health, and enhanced communication with care teams, was associated with improved postoperative weight loss. Future investigations are needed to identify important portal features that contribute to the long-term success of weight loss management.

Recent Advances in Engineering Carriers for siRNA Delivery.

RNA interference (RNAi) technology has been a promising treatment strategy for combating intractable diseases. However, the applications of RNAi in clinical are hampered by extracellular and intracellular barriers. To overcome these barriers, various siRNA delivery systems have been developed in the past two decades. The first approved RNAi therapeutic, Patisiran (ONPATTRO) using lipids as the carrier, for the treatment of amyloidosis is one of the most important milestones. This has greatly encouraged researchers to work on creating new functional siRNA carriers. In this review, the recent advances in siRNA carriers consisting of lipids, polymers, and polymer-modified inorganic particles for cancer therapy are summarized. Representative examples are presented to show the structural design of the carriers in order to overcome the delivery hurdles associated with RNAi therapies. Finally, the existing challenges and future perspective for developing RNAi as a clinical modality will be discussed and proposed. It is believed that the addressed contributions in this review will promote the development of siRNA delivery systems for future clinical applications.

Jointing Achievement and Performance Evaluation of Bogie Crossmember Ring Joint Welded via Inertia Friction Welding.

As a major load-bearing component of trains, the weld quality of the bogie beam is critical to the safety of railway operations. This study specifically investigates the inertia friction welding process of S355 bogie crosshead tubes, with the aim of improving the weld quality and achieving one-time formation of the crosshead tube and tube seat. The microstructural features and mechanical properties of S355 inertia-welded joints were also compared with the base metal. Research indicates that inertia friction welds have no visible defects, and that the microstructure of the welding seam (WS) consists of granular bainite, acicular ferrite and little pearlite. The thermo-mechanically affected zone (TMAZ) consists of granular bainite bands and ferrite + pearlite bands. The hot work strengthening mechanism of inertia friction welding results in a higher level of hardness for both WS and TMAZ. The tensile property of the welded joints can be compared to the base metal. The yield strength, tensile strength and elongation of the welded joints, respectively, reach 87.5%, 100% and 79.5% of S355. However, the impact toughness of the welds at room temperature is lower than that of the base material, particularly in the TMAZ zone. Conversely, in an environment with a temperature of -40 °C, WS's impact toughness surpasses that of the parent material.

Differences in Health Professionals' Engagement With Electronic Health Records Based on Inpatient Race and Ethnicity.

Importance: US health professionals devote a large amount of effort to engaging with patients' electronic health records (EHRs) to deliver care. It is unknown whether patients with different racial and ethnic backgrounds receive equal EHR engagement. Objective: To investigate whether there are differences in the level of health professionals' EHR engagement for hospitalized patients according to race or ethnicity during inpatient care. Design, Setting, and Participants: This cross-sectional study analyzed EHR access log data from 2 major medical institutions, Vanderbilt University Medical Center (VUMC) and Northwestern Medicine (NW Medicine), over a 3-year period from January 1, 2018, to December 31, 2020. The study included all adult patients (aged ≥18 years) who were discharged alive after hospitalization for at least 24 hours. The data were analyzed between August 15, 2022, and March 15, 2023. Exposures: The actions of health professionals in each patient's EHR were based on EHR access log data. Covariates included patients' demographic information, socioeconomic characteristics, and comorbidities. Main Outcomes and Measures: The primary outcome was the quantity of EHR engagement, as defined by the average number of EHR actions performed by health professionals within a patient's EHR per hour during the patient's hospital stay. Proportional odds logistic regression was applied based on outcome quartiles. Results: A total of 243 416 adult patients were included from VUMC (mean [SD] age, 51.7 [19.2] years; 54.9% female and 45.1% male; 14.8% Black, 4.9% Hispanic, 77.7% White, and 2.6% other races and ethnicities) and NW Medicine (mean [SD] age, 52.8 [20.6] years; 65.2% female and 34.8% male; 11.7% Black, 12.1% Hispanic, 69.2% White, and 7.0% other races and ethnicities). When combining Black, Hispanic, or other race and ethnicity patients into 1 group, these patients were significantly less likely to receive a higher amount of EHR engagement compared with White patients (adjusted odds ratios, 0.86 [95% CI, 0.83-0.88; P < .001] for VUMC and 0.90 [95% CI, 0.88-0.92; P < .001] for NW Medicine). However, a reduction in this difference was observed from 2018 to 2020. Conclusions and Relevance: In this cross-sectional study of inpatient EHR engagement, the findings highlight differences in how health professionals distribute their efforts to patients' EHRs, as well as a method to measure these differences. Further investigations are needed to determine whether and how EHR engagement differences are correlated with health care outcomes.

Biodegradable Carbon Dioxide-Derived Non-Viral Gene Vectors for Osteosarcoma Gene Therapy.

Osteosarcoma often occurs in children and adolescents with high invasiveness and high mortality. Polo-like kinase 1 (PLK1) overexpressed in most tumors promotes cancer cell proliferation and transformation. PLK1 is considered as a therapeutic target for osteosarcoma. RNA interference-based therapies are employed to combat osteosarcoma through silencing PLK1 gene expression. However, the treatment results remain unsatisfactory due to the lack of a safe and efficient nonviral gene vector. To tackle this hurdle, biodegradable and CO2 -derivative cationic poly(vinylcyclohexene carbonates) (CPCHCs) are used as gene vectors to perform a siPLK1 therapeutic strategy for osteosarcoma treatment. Of those CPCHCs, CPCHC60 demonstrates the most excellent performance in gene transfection efficiency, endo-lysosome escaping, biodegradability, and biosafety. With the treatment of CPCHCs/siRNA nanoparticles, the expression level of PLK1 gene in osteosarcoma cells is significantly down-regulated. Subsequently, cells are arrested in the G2 /M phase and subsequently dead in the form of apoptosis, resulting in significant tumor regression both in vitro and in vivo. This study brings a new insight into the development of superior nonviral gene vectors for practical cancer treatment. Based on the results, the resulting nanoparticle-based gene drug formation is considered to have a highly successful chance in further translational nanomedicine applications.

Split Learning for Distributed Collaborative Training of Deep Learning Models in Health Informatics.

Deep learning continues to rapidly evolve and is now demonstrating remarkable potential for numerous medical prediction tasks. However, realizing deep learning models that generalize across healthcare organizations is challenging. This is due, in part, to the inherent siloed nature of these organizations and patient privacy requirements. To address this problem, we illustrate how split learning can enable collaborative training of deep learning models across disparate and privately maintained health datasets, while keeping the original records and model parameters private. We introduce a new privacy-preserving distributed learning framework that offers a higher level of privacy compared to conventional federated learning. We use several biomedical imaging and electronic health record (EHR) datasets to show that deep learning models trained via split learning can achieve highly similar performance to their centralized and federated counterparts while greatly improving computational efficiency and reducing privacy risks.

Image classification and identification for rice leaf diseases based on improved WOACW_SimpleNet.

In view of the problem that manual selection of hyperparameters may lead to low performance and large consumption of manpower cost of the convolutional neural network (CNN), this paper proposes a nonlinear convergence factor and weight cooperative self-mapping chaos optimization algorithm (WOACW) to optimize the hyperparameters in the identification and classification model of rice leaf disease images, such as learning rate, training batch size, convolution kernel size and convolution kernel number. Firstly, the opposition-based learning is added to the whale population initialization with improving the diversity of population initialization. Then the algorithm improves the convergence factor, increases the weight coefficient, and calculates the self-mapping chaos. It makes the algorithm have a strong ability to find optimization in the early stage of iteration and fast convergence rate. And disturbance is carried out to avoid falling into local optimal solution in the late stage of iteration. Next, a polynomial mutation operator is introduced to correct the current optimal solution with a small probability, so that a better solution can be obtained in each iteration, thereby enhancing the optimization performance of the multimodal objective function. Finally, eight optimized performance benchmark functions are selected to evaluate the performance of the algorithm, the experiment results show that the proposed WOACW outperforms than 5 other common improved whale optimization algorithms. The WOACW_SimpleNet is used to identify rice leaf diseases (rice blast, bacterial leaf blight, brown spot disease, sheath blight and tungro disease), and the experiment results show that the identification average recognition accuracy rate reaches 99.35%, and the F1-score reaches 99.36%.

Highly biocompatible chlorin e6-poly(dopamine) core-shell nanoparticles for enhanced cancer phototherapy.

Cancer is a life-threatening disease worldwide. Although several approaches, such as surgery, chemotherapy, and radiotherapy, have been proven effective for many patients in clinics, they usually suffer from drug resistance, severe toxic-side effects, patient discomfort, and sometimes, unsatisfactory efficacies. In recent years, phototherapy, as a less invasive but effective therapeutic method, has brought hope for cancer treatment. However, most reported photo-therapeutic agents are constructed using complex components with non-negligible toxicity risk, thus retarding the start of their clinical trials. To address this issue, herein, biocompatible photothermal/photodynamic dual-mode therapeutic nanoparticles (CBP NPs) were successfully designed and constructed based on the Food and Drug Administration (FDA)-approved ingredients, chlorin e6 (Ce6) and poly(dopamine) (PDA). Upon light irradiation, hyperthermia was induced and reactive oxygen species (ROS) were generated simultaneously by CBP NPs, contributing to synergistic phototherapy toward cancer. The in vitro and in vivo experiments have demonstrated well the antitumor effect of CBP NPs. More importantly, CBP NPs are completely harmless and degradable in vivo. Together, the CBP NPs developed by us are an ideal candidate for the enhanced phototherapy of tumors, which holds great potential for future clinical translation.

Recent advances in exosome-mediated nucleic acid delivery for cancer therapy.

Cancer is a leading public health problem worldwide. Its treatment remains a daunting challenge, although significant progress has been made in existing treatments in recent years. A large concern is the poor therapeutic effect due to lack of specificity and low bioavailability. Gene therapy has recently emerged as a powerful tool for cancer therapy. However, delivery methods limit its therapeutic effects. Exosomes, a subset of extracellular vesicles secreted by most cells, have the characteristics of good biocompatibility, low toxicity and immunogenicity, and great designability. In the past decades, as therapeutic carriers and diagnostic markers, they have caught extensive attention. This review introduced the characteristics of exosomes, and focused on their applications as delivery carriers in DNA, messenger RNA (mRNA), microRNA (miRNA), small interfering RNA (siRNA), circular RNA (circRNA) and other nucleic acids. Meanwhile, their application in cancer therapy and exosome-based clinical trials were presented and discussed. Through systematic summarization and analysis, the recent advances and current challenges of exosome-mediated nucleic acid delivery for cancer therapy are introduced, which will provide a theoretical basis for the development of nucleic acid drugs.

Color tunable of Ln-MOFs (Ln = Tb, Eu) and excellent stability for white light-emitting diode.

White light-emitting diodes (WLEDs) possess the advantages of environmental friendliness, long lifetime, and energy saving. Recently, metal-organic frameworks (MOFs) have become one of the hot candidates for LEDs. However, the tunable color and thermal stability of MOFs are urgent problems for their actual applications. In this work, Ln-MOFs (Ln = Eu, Tb) were synthesized by a facile wet chemical route. A series of Ln-MOFs phosphors with tunable luminescence color showed potential applications in white LEDs. The emission color of the phosphors can be easily modulated by changing the molar ratio of the raw materials. The luminescence intensities of Ln-MOFs retained over 90.6% of the initial value, showing excellent thermal stability of Ln-MOFs. In order to explore the potential applications of Ln-MOFs in WLEDs, we mixed them with two kinds of blue phosphors and packaged them to obtain WLEDs. The CIE coordinates of both were (0.31, 0.33) and (0.31, 0.34), which were able to achieve white light emission. The peak shape and peak position in the EL spectrum of the WLEDs device remained stable when increasing the applied current of the device. Meanwhile, the white light with excellent color quality and visual performance was achieved. The results show that Ln-MOFs are potential materials for white light LED, and provide a novel idea for the application of Ln-MOFs materials in the luminescence field.

Forecasting the future clinical events of a patient through contrastive learning.

OBJECTIVE: Deep learning models for clinical event forecasting (CEF) based on a patient's medical history have improved significantly over the past decade. However, their transition into practice has been limited, particularly for diseases with very low prevalence. In this paper, we introduce CEF-CL, a novel method based on contrastive learning to forecast in the face of a limited number of positive training instances. MATERIALS AND METHODS: CEF-CL consists of two primary components: (1) unsupervised contrastive learning for patient representation and (2) supervised transfer learning over the derived representation. We evaluate the new method along with state-of-the-art model architectures trained in a supervised manner with electronic health records data from Vanderbilt University Medical Center and the All of Us Research Program, covering 48 000 and 16 000 patients, respectively. We assess forecasting for over 100 diagnosis codes with respect to their area under the receiver operator characteristic curve (AUROC) and area under the precision-recall curve (AUPRC). We investigate the correlation between forecasting performance improvement and code prevalence via a Wald Test. RESULTS: CEF-CL achieved an average AUROC and AUPRC performance improvement over the state-of-the-art of 8.0%-9.3% and 11.7%-32.0%, respectively. The improvement in AUROC was negatively correlated with the number of positive training instances (P < .001). CONCLUSION: This investigation indicates that clinical event forecasting can be improved significantly through contrastive representation learning, especially when the number of positive training instances is small.

Inferring EHR Utilization Workflows through Audit Logs.

Electronic health records (EHRs) usage and clinical workflows are intrinsically linked. To accommodate the complex care settings (e.g., emergency departments), EHR utilization workflows dynamically change in clinical practice, which in turn shapes the clinical workflows. Learning EHR workflows would provide an opportunity for healthcare organizations to enhance clinical workflows in the context of EHRs. However, very few studies investigated HER utilization workflows executed in clinical practice. We develop a network analysis framework and apply it to EHR audit logs to infer EHR workflows. We then measure the differences in the workflows between patient subgroups divided by races via differential network analysis. We apply our framework to trauma patients admitted to the emergency department, which is one of the clinical settings that need timely support from EHR utilizations. Our results show five core EHR workflows related to Narrator, Navigator, SmartTools, Chart Review, and ED workup activities in the ED. We find EHR workflows involving Narrator, SmartTools, and BPA are different when comparing patient subgroups.

Predicting next-day discharge via electronic health record access logs.

OBJECTIVE: Hospital capacity management depends on accurate real-time estimates of hospital-wide discharges. Estimation by a clinician requires an excessively large amount of effort and, even when attempted, accuracy in forecasting next-day patient-level discharge is poor. This study aims to support next-day discharge predictions with machine learning by incorporating electronic health record (EHR) audit log data, a resource that captures EHR users' granular interactions with patients' records by communicating various semantics and has been neglected in outcome predictions. MATERIALS AND METHODS: This study focused on the EHR data for all adults admitted to Vanderbilt University Medical Center in 2019. We learned multiple advanced models to assess the value that EHR audit log data adds to the daily prediction of discharge likelihood within 24 h and to compare different representation strategies. We applied Shapley additive explanations to identify the most influential types of user-EHR interactions for discharge prediction. RESULTS: The data include 26 283 inpatient stays, 133 398 patient-day observations, and 819 types of user-EHR interactions. The model using the count of each type of interaction in the recent 24 h and other commonly used features, including demographics and admission diagnoses, achieved the highest area under the receiver operating characteristics (AUROC) curve of 0.921 (95% CI: 0.919-0.923). By contrast, the model lacking user-EHR interactions achieved a worse AUROC of 0.862 (0.860-0.865). In addition, 10 of the 20 (50%) most influential factors were user-EHR interaction features. CONCLUSION: EHR audit log data contain rich information such that it can improve hospital-wide discharge predictions.

Carbon Dioxide-Derived Biodegradable and Cationic Polycarbonates as a New siRNA Carrier for Gene Therapy in Pancreatic Cancer.

Pancreatic cancer is an aggressive malignancy associated with poor prognosis and a high tendency in developing infiltration and metastasis. K-ras mutation is a major genetic disorder in pancreatic cancer patient. RNAi-based therapies can be employed for combating pancreatic cancer by silencing K-ras gene expression. However, the clinical application of RNAi technology is appreciably limited by the lack of a proper siRNA delivery system. To tackle this hurdle, cationic poly (cyclohexene carbonate) s (CPCHCs) using widely sourced CO2 as the monomer are subtly synthesized via ring-opening copolymerization (ROCOP) and thiol-ene functionalization. The developed CPCHCs could effectively encapsulate therapeutic siRNA to form CPCHC/siRNA nanoplexes (NPs). Serving as a siRNA carrier, CPCHC possesses biodegradability, negligible cytotoxicity, and high transfection efficiency. In vitro study shows that CPCHCs are capable of effectively protecting siRNA from being degraded by RNase and promoting a sustained endosomal escape of siRNA. After treatment with CPCHC/siRNA NPs, the K-ras gene expression in both pancreatic cancer cell line (PANC-1 and MiaPaCa-2) are significantly down-regulated. Subsequently, the cell growth and migration are considerably inhibited, and the treated cells are induced into cell apoptotic program. These results demonstrate the promising potential of CPCHC-mediated siRNA therapies in pancreatic cancer treatment.

Collaboration Structures in COVID-19 Critical Care: Retrospective Network Analysis Study.

BACKGROUND: Few intensive care unit (ICU) staffing studies have examined the collaboration structures of health care workers (HCWs). Knowledge about how HCWs are connected to the care of critically ill patients with COVID-19 is important for characterizing the relationships among team structures, care quality, and patient safety. OBJECTIVE: We aimed to discover differences in the teamwork structures of COVID-19 critical care by comparing HCW collaborations in the management of critically ill patients with and without COVID-19. METHODS: In this retrospective study, we used network analysis methods to analyze the electronic health records (EHRs) of 76 critically ill patients (with COVID-19: n=38; without COVID-19: n=38) who were admitted to a large academic medical center, and to learn about HCW collaboration. We used the EHRs of adult patients who were admitted to the COVID-19 ICU at the Vanderbilt University Medical Center (Nashville, Tennessee, United States) between March 17, 2020, and May 31, 2020. We matched each patient according to age, gender, and their length of stay. Patients without COVID-19 were admitted to the medical ICU between December 1, 2019, and February 29, 2020. We used two sociometrics-eigencentrality and betweenness-to quantify HCWs' statuses in networks. Eigencentrality characterizes the degree to which an HCW is a core person in collaboration structures. Betweenness centrality refers to whether an HCW lies on the path of other HCWs who are not directly connected. This sociometric was used to characterize HCWs' broad skill sets. We measured patient staffing intensity in terms of the number of HCWs who interacted with patients' EHRs. We assessed the statistical differences in the core and betweenness statuses of HCWs and the patient staffing intensities of COVID-19 and non-COVID-19 critical care, by using Mann-Whitney U tests and reporting 95% CIs. RESULTS: HCWs in COVID-19 critical care were more likely to frequently work with each other (eigencentrality: median 0.096) than those in non-COVID-19 critical care (eigencentrality: median 0.057; P<.001). Internal medicine physicians in COVID-19 critical care had higher core statuses than those in non-COVID-19 critical care (P=.001). Nurse practitioners in COVID-19 care had higher betweenness statuses than those in non-COVID-19 care (P<.001). Compared to HCWs in non-COVID-19 settings, the EHRs of critically ill patients with COVID-19 were used by a larger number of internal medicine nurse practitioners (P<.001), cardiovascular nurses (P<.001), and surgical ICU nurses (P=.002) and a smaller number of resident physicians (P<.001). CONCLUSIONS: Network analysis methodologies and data on EHR use provide a novel method for learning about differences in collaboration structures between COVID-19 and non-COVID-19 critical care. Health care organizations can use this information to learn about the novel changes that the COVID-19 pandemic has imposed on collaboration structures in urgent care.