Prediction of Time to Hemodynamic Stabilization of Unstable Injured Patient Encounters Using Electronic Medical Record Data.

BACKGROUND: This study sought to predict time to patient hemodynamic stabilization during trauma resuscitations of hypotensive patient encounters using electronic medical records (EMR) data. METHODS: This observational cohort study leveraged EMR data from a nine-hospital academic system composed of Level I, Level II and non-trauma centers. Injured, hemodynamically unstable (initial systolic blood pressure < 90 mmHg) emergency encounters from 2015-2020 were identified. Stabilization was defined as documented subsequent systolic blood pressure > 90 mmHg. We predicted time to stabilization testing random forests, gradient boosting and ensembles using patient, injury, treatment, EPIC Trauma Narrator and hospital features from the first four hours of care. RESULTS: Of 177,127 encounters, 1347 (0.8%) arrived hemodynamically unstable; 168 (12.5%) presented to Level I trauma centers, 853 (63.3%) to Level II, and 326 (24.2%) to non-trauma centers. Of those, 747 (55.5%) were stabilized with a median of 50 minutes (IQR 21-101 min). Stabilization was documented in 94.6% of unstable patient encounters at Level I, 57.6% at Level II and 29.8% at non-trauma centers (p < 0.001). Time to stabilization was predicted with a C-index of 0.80. The most predictive features were EPIC Trauma Narrator measures; documented patient arrival, provider exam, and disposition decision. In-hospital mortality was highest at Level I, 3.0% vs. 1.2% at Level II, and 0.3% at non-trauma centers (p < 0.001). Importantly, non-trauma centers had the highest re-triage rate to another acute care hospital (12.0%) compared to Level II centers (4.0%, p < 0.001). CONCLUSION: Time to stabilization of unstable injured patients can be predicted with EMR data.

Prediction of intra-abdominal injury using natural language processing of electronic medical record data.

BACKGROUND: This study aimed to use natural language processing to predict the presence of intra-abdominal injury using unstructured data from electronic medical records. METHODS: This was a random-sample retrospective observational cohort study leveraging unstructured data from injured patients taken to one of 9 acute care hospitals in an integrated health system between 2015 and 2021. Patients with International Classification of Diseases External Cause of Morbidity codes were identified. History and physical, consult, progress, and radiology report text from the first 8 hours of care were abstracted. Annotator dyads independently annotated encounters' text files to establish ground truth regarding whether intra-abdominal injury occurred. Features were extracted from text using natural language processing techniques, bag of words, and principal component analysis. We tested logistic regression, random forests, and gradient boosting machine to determine accuracy, recall, and precision of natural language processing to predict intra-abdominal injury. RESULTS: A random sample of 7,000 patient encounters of 177,127 was annotated. Only 2,951 had sufficient information to determine whether an intra-abdominal injury was present. Among those, 84 (2.9%) had an intra-abdominal injury. The concordance between annotators was 0.989. Logistic regression of features identified with bag of words and principal component analysis had the best predictive ability, with an area under the receiver operating characteristic curve of 0.9, recall of 0.73, and precision of 0.17. Text features with greatest importance included "abdomen," "pelvis," "spleen," and "hematoma." CONCLUSION: Natural language processing could be a screening decision support tool, which, if paired with human clinical assessment, can maximize precision of intra-abdominal injury identification.

Hospital variation in adoption of balanced transfusion practices among injured patients requiring blood transfusions.

BACKGROUND: This study sought to measure hospital variability in adoption of balanced transfusion following the Pragmatic, Randomized Optimal Platelet and Plasma Ratios (PROPPR) guidelines. We hypothesized hospital adoption rates of balanced transfusion would be low, and vary significantly among hospitals after controlling for patient, injury and hospital characteristics. STUDY DESIGN AND METHODS: This was an observational cohort study of injured adult patients (≥16 years) in Trauma Quality Improvement Program hospitals 2016-2021. Inclusion criteria were hypotensive patients receiving one transfusion of packed red blood cells, fresh frozen plasma, platelets, or cryoprecipitate. Balanced transfusion was defined as ≥1 ratio of plasma to packed red blood cells or platelets to packed red blood cells or whole blood use at 4 hours. Hierarchical multivariable logistic regression quantified residual hospital-level variability in balanced transfusion rates after adjusting for patient and hospital characteristics. RESULTS: Among 172,457 injured patients who received transfusions, 30,386 (17.6%) underwent balanced transfusion. Patient-level balanced transfusion rates were 11% in 2016, rose to 14.0% in 2019, and jumped up once whole blood transfusions were measured to 24.0% in 2020 and to 25.9% in 2021. Approximately 26% of the variability in balanced transfusion rates was attributable to the hospital. Verified level I hospitals had a 2.09 increased adjusted odds of balanced transfusion (95% CI 1.88-2.21) compared to nonverified hospitals. University teaching status had a 1.29 increased adjusted odds of balanced transfusion (95% CI 1.08-1.54) compared with community hospitals. Overall, 150 (23.5%) hospitals were high outliers (high performing) in balanced transfusion adoption and 124 (19.4%) hospitals were low outliers. CONCLUSION: There was significant variability in hospital adoption of balanced transfusion.

Generation of Rapid and High-Quality Serum by Recombinant Prothrombin Activator Ecarin (RAPClot™).

We recently reported the potential application of recombinant prothrombin activator ecarin (RAPClot™) in blood diagnostics. In a new study, we describe RAPClot™ as an additive to develop a novel blood collection prototype tube that produces the highest quality serum for accurate biochemical analyte determination. The drying process of the RAPClot™ tube generated minimal effect on the enzymatic activity of the prothrombin activator. According to the bioassays of thrombin activity and plasma clotting, γ-radiation (>25 kGy) resulted in a 30-40% loss of the enzymatic activity of the RAPClot™ tubes. However, a visual blood clotting assay revealed that the γ-radiation-sterilized RAPClot™ tubes showed a high capacity for clotting high-dose heparinized blood (8 U/mL) within 5 min. This was confirmed using Thrombelastography (TEG), indicating full clotting efficiency under anticoagulant conditions. The storage of the RAPClot™ tubes at room temperature (RT) for greater than 12 months resulted in the retention of efficient and effective clotting activity for heparinized blood in 342 s. Furthermore, the enzymatic activity of the RAPClot™ tubes sterilized with an electron-beam (EB) was significantly greater than that with γ-radiation. The EB-sterilized RAPClot™ tubes stored at RT for 251 days retained over 70% enzyme activity and clotted the heparinized blood in 340 s after 682 days. Preliminary clinical studies revealed in the two trials that 5 common analytes (K, Glu, lactate dehydrogenase (LD), Fe, and Phos) or 33 analytes determined in the second study in the γ-sterilized RAPClot™ tubes were similar to those in commercial tubes. In conclusion, the findings indicate that the novel RAPClot™ blood collection prototype tube has a significant advantage over current serum or lithium heparin plasma tubes for routine use in measuring biochemical analytes, confirming a promising application of RAPClot™ in clinical medicine.

Seizure prediction in stroke survivors who experienced an infection at skilled nursing facilities-a machine learning approach.

Background: Infections and seizures are some of the most common complications in stroke survivors. Infections are the most common risk factor for seizures and stroke survivors that experience an infection are at greater risk of experiencing seizures. A predictive model to determine which stroke survivors are at the greatest risk for a seizure after an infection can be used to help providers focus on prevention of seizures in higher risk residents that experience an infection. Methods: A predictive model was generated from a retrospective study of the Long-Term Care Minimum Data Set (MDS) 3.0 (2014-2018, n = 262,301). Techniques included three data balancing methods (SMOTE for up sampling, ENN for down sampling, and SMOTEENN for up and down sampling) and three feature selection methods (LASSO, Recursive Feature Elimination, and Principal Component Analysis). One balancing and one feature selection technique was applied, and the resulting dataset was then trained on four machine learning models (Logistic Regression, Random Forest, XGBoost, and Neural Network). Model performance was evaluated with AUC and accuracy, and interpretation used SHapley Additive exPlanations. Results: Using data balancing methods improved the prediction performances of the machine learning models, but feature selection did not remove any features and did not affect performance. With all models having a high accuracy (76.5%-99.9%), interpretation on all four models yielded the most holistic view. SHAP values indicated that therapy (speech, physical, occupational, and respiratory), independence (activities of daily living for walking, mobility, eating, dressing, and toilet use), and mood (severity score, anti-anxiety medications, antidepressants, and antipsychotics) features contributed the most. Meaning, stroke survivors who received fewer therapy hours, were less independent, had a worse overall mood were at a greater risk of having a seizure after an infection. Conclusion: The development of a tool to predict seizure following an infection in stroke survivors can be interpreted by providers to guide treatment and prevent complications long term. This promotes individualized treatment plans that can increase the quality of resident care.

A Programmable Peptidic Hydrogel Adjuvant for Personalized Immunotherapy in Resected Stage Tumors.

Adjuvant treatment after surgical resection usually plays an important role in delaying disease recurrence. Immunotherapy displays encouraging results in increasing patients' chances of staying cancer-free after surgery, as reported by recent clinical trials. However, the clinical outcomes of current immunotherapy need to be improved due to the limited responses, patient heterogeneity, nontargeted distribution, and immune-related adverse effects. This work describes a programmable hydrogel adjuvant for personalized immunotherapy after surgical resection. By filling the hydrogel in the cavity, this system aims to address the limited secretion of granzyme B (GrB) during immunotherapy and improve the low immunotherapy responses typically observed, while minimizing immune-related side effects. The TLR7/8 agonist imidazoquinoline (IMDQ) is linked to the self-assembling peptide backbone through a GrB-responsive linkage. Its release could enhance the activation and function of immune cells, which will lead to increased secretion of GrB and enhance the effectiveness of immunotherapy together. The hydrogel adjuvant recruits immune cells, initiates dendritic cell maturation, and induces M1 polarized macrophages to reverse the immunosuppressive tumor microenvironment in situ. In multiple murine tumor models, the hydrogel adjuvant suppresses tumor growth, increases animal survival and long-term immunological memory, and protects mice against tumor rechallenge, leading to effective prophylactic and therapeutic responses. This work provides a potential chemical strategy to overcome the limitations associated with immunotherapy.

An Improved Statistical Modeling Approach to Individual Anticholinergic Drug Use Trend Analysis.

Anticholinergic (AC) drugs are commonly prescribed to older adults for treating diseases and chronic conditions, such as chronic obstructive pulmonary disease, urinary incontinence, gastrointestinal disorder, or simply pain and allergy. The high prevalence of AC drug use can have a detrimental effect on the mental health of older adults. We aim to improve the prediction of future trends of AC drug use at the individual level, with pharmacy refill data. The individual drug use data presents challenges in the modeling, such as data being discrete-valued with excess zeros and having significant unobserved heterogeneity in the trend pattern. To address these challenges, we propose a statistical model of hierarchical structure and an EM scheme for the model parameter estimation. We evaluate the proposed modeling approach through a numerical study with synthetic data and a case study with real-world pharmacy refill data. The simulation study show that our analysis method outperforms the existing ones (e.g., reducing MSE significantly), particularly in terms of accurately predicting the trend pattern. The real-world case study further verifies the out-performance and demonstrate the advantageous features of our method. We expect the prediction tool developed based on our study can assist pharmacists' decision on initiating or strengthening behavioral interventions with the hope of discontinuing AC drug misuse.

Untrained Bystanders Administering Drone-Delivered Naloxone: An Exploratory Study.

Introduction: Opioid overdose deaths continue to climb in the United States. Administering naloxone to an overdose victim can save their life, but rapid access to naloxone remains a barrier. Delivering naloxone to a bystander using a drone has potential to increase naloxone availability but there are still many uncertainties about this mode of delivery. Can an untrained bystander to an opioid overdose successfully administer drone delivered naloxone after viewing video instructions on the drone and how long does it take? Methods: This mixed-methods observational study, conducted in a controlled outdoor environment, simulated an opioid overdose using a mannequin (overdose victim) and panicked bystander. Untrained and medically naïve participants were instructed to call for help, move the drone from the landing spot to the mannequin, and follow the instructions provided by the drone to administer naloxone. Data was collected using video recordings, interviews, and an online survey. Time stamp data was extracted from the video for 2 time points: time for removing the naloxone from the drone and time to administer the naloxone. Interviews were audio recorded and analyzed using an inductive concept analysis approach. One interview question was coded as a binary response of anxiety/no anxiety and added to the demographic data. Results: The average time to remove and administer naloxone was 62 seconds. Anxiety during the activity was reported by 59% of the participants but there was no correlation between anxiety and time. The design of our drone and instructional video can be improved. Conclusions: We have established baseline times for completing steps in administering naloxone delivered by drone. We were able to successfully induce anxiety and have a baseline measure for what percentage of untrained bystanders may experience anxiety when involved with an emergency situation. Design of instructional materials and drone construction can contribute to anxiety and successful administration of naloxone.

Learning in a crisis moment: a randomized controlled trial in emergency bystander intervention.

BACKGROUND: Opioid overdose is the leading cause of injury-related death in the United States. Individuals who overdose outside of clinical settings have more positive clinical outcomes when they receive naloxone, an opioid antagonist, from a bystander as an early intervention before emergency personnel arrive. However, there is a gap in knowledge about successful instantaneous learning and intervention in a real-life stressful environment. The objective of this study is to explore the efficacy of different instructional delivery methods for bystanders in a stressful environment. We aim to evaluate which methods are most effective for instantaneous learning, successful intervention, and improved clinical outcomes. METHODS: To explore instantaneous learning in a stressful environment, we conducted a quantitative randomized controlled trial to measure how accurately individuals responded to memory-based survey questions guided by different instructional methods. Students from a large university in the Midwest (n = 157) were recruited in a public space on campus and accessed the six-question survey on their mobile devices. The intervention group competed the survey immediately while the research team created a distracting environment. The control group was asked to complete the survey later in a quiet environment. RESULTS: The intervention group correctly answered 0.72 questions fewer than the control group (p = .000, CI [0.337, 1.103]). Questions Q1 and Q5 contained direct instructions with a verbal component and showed the greatest accuracy with over 90% correct for both stressful and controlled environments. CONCLUSIONS: The variability in the responses suggests that there are environmental factors as well as instructional design features which influence instantaneous learning. The findings of this study begin to address the gap in knowledge about the effects of stress on instantaneous learning and the most effective types of instruction for untrained bystanders in emergency situations.

Dynamic Control of Cyclic Peptide Assembly to Form Higher-Order Assemblies.

Chirality correction, asymmetry, ring-chain tautomerism and hierarchical assemblies are fundamental phenomena in nature. They are geometrically related and may impact the biological roles of a protein or other supermolecules. It is challenging to study those behaviors within an artificial system due to the complexity of displaying these features. Herein, we design an alternating D,L peptide to recreate and validate the naturally occurring chirality inversion prior to cyclization in water. The resulting asymmetrical cyclic peptide containing a 4-imidazolidinone ring provides an excellent platform to study the ring-chain tautomerism, thermostability and dynamic assembly of the nanostructures. Different from traditional cyclic D,L peptides, the formation of 4-imidazolidinone promotes the formation of intertwined nanostructures. Analysis of the nanostructures confirmed the left-handedness, representing chirality induced self-assembly. This proves that a rationally designed peptide can mimic multiple natural phenomena and could promote the development of functional biomaterials, catalysts, antibiotics, and supermolecules.

Accelerating the prediction and discovery of peptide hydrogels with human-in-the-loop.

The amino acid sequences of peptides determine their self-assembling properties. Accurate prediction of peptidic hydrogel formation, however, remains a challenging task. This work describes an interactive approach involving the mutual information exchange between experiment and machine learning for robust prediction and design of (tetra)peptide hydrogels. We chemically synthesize more than 160 natural tetrapeptides and evaluate their hydrogel-forming ability, and then employ machine learning-experiment iterative loops to improve the accuracy of the gelation prediction. We construct a score function coupling the aggregation propensity, hydrophobicity, and gelation corrector Cg, and generate an 8,000-sequence library, within which the success rate of predicting hydrogel formation reaches 87.1%. Notably, the de novo-designed peptide hydrogel selected from this work boosts the immune response of the receptor binding domain of SARS-CoV-2 in the mice model. Our approach taps into the potential of machine learning for predicting peptide hydrogelator and significantly expands the scope of natural peptide hydrogels.

Interpretable machine learning models for hospital readmission prediction: a two-step extracted regression tree approach.

BACKGROUND: Advanced machine learning models have received wide attention in assisting medical decision making due to the greater accuracy they can achieve. However, their limited interpretability imposes barriers for practitioners to adopt them. Recent advancements in interpretable machine learning tools allow us to look inside the black box of advanced prediction methods to extract interpretable models while maintaining similar prediction accuracy, but few studies have investigated the specific hospital readmission prediction problem with this spirit. METHODS: Our goal is to develop a machine-learning (ML) algorithm that can predict 30- and 90- day hospital readmissions as accurately as black box algorithms while providing medically interpretable insights into readmission risk factors. Leveraging a state-of-art interpretable ML model, we use a two-step Extracted Regression Tree approach to achieve this goal. In the first step, we train a black box prediction algorithm. In the second step, we extract a regression tree from the output of the black box algorithm that allows direct interpretation of medically relevant risk factors. We use data from a large teaching hospital in Asia to learn the ML model and verify our two-step approach. RESULTS: The two-step method can obtain similar prediction performance as the best black box model, such as Neural Networks, measured by three metrics: accuracy, the Area Under the Curve (AUC) and the Area Under the Precision-Recall Curve (AUPRC), while maintaining interpretability. Further, to examine whether the prediction results match the known medical insights (i.e., the model is truly interpretable and produces reasonable results), we show that key readmission risk factors extracted by the two-step approach are consistent with those found in the medical literature. CONCLUSIONS: The proposed two-step approach yields meaningful prediction results that are both accurate and interpretable. This study suggests a viable means to improve the trust of machine learning based models in clinical practice for predicting readmissions through the two-step approach.

Assessing the Impact of Indiana Public Law 194 on Curbing the Concurrent Opioid Prescribing for Indiana Medicaid Enrollees.

Background: Several US states have introduced legislation to support the legitimate medical use of opioids while limiting misuse and diversion. One concern which has been addressed through legislation is preventing individuals from seeking opioid prescriptions concurrently from multiple providers. However, the impact of this legislation on the incidence of patients receiving concurrent prescriptions remains relatively unexplored. This study examines this phenomenon based on claims data from Medicaid enrollees and the enactment of legislation in Indiana. Methods: Indiana Medicaid claims data over the period of January 2014 to December 2019 were used to determine the changes in the percentage of individuals receiving opioid prescriptions from multiple providers within a 30-day period, that is, concurrent opioid prescription (COP) individuals. Indiana Medicaid enrollees with a diagnosis of opioid use disorder (OUD) receiving opioid prescriptions, that is, the OUD-group, were identified and separated from the enrollees without a diagnosis but receiving opioid prescriptions, that is, the non-OUD group. The mean percentages of COP individuals (with or without an OUD diagnosis) within the subset of individuals that received opioid prescriptions were compared before and after the passage of Indiana Public Law 194. Results: There were 5336 who met the criteria of COP individuals, and 2050 of those were in the OUD-group. In either group, there was a significant difference in the change in percentages (slope) before and after Indiana Public Law 194 passed. In addition, there was a significant decrease in the mean percentage of COP individuals in the non-OUD group, while the difference was not significant in the OUD group. Conclusion: Our study suggests that Indiana Public Law 194 had a positive impact on curbing COP. This study is limited by the level of details available from claims data and suggests additional studies to evaluate prescription use and prescribing practices are warranted.

Factors influencing user acceptance of weight management apps among Chinese obese individuals during the COVID-19 pandemic.

Background: During the COVID-19 epidemic, the number of obesities increased rapidly in China. Weight management apps have potential value in controlling obesity. Objective: Explore the mechanisms behind the adoption of weight management applications by overweight and obese individuals, including psychological factors and demographic variables. Methods: The theoretical model was extended from the technology acceptance model (TAM), and the structural equation model was used for hypothesis testing. From January 2020 to December 2021, we conducted a cross-sectional survey in six megacities in mainland China during the COVID-19 pandemic by an online questionnaire. Results: 1364 participants completed the questionnaire, and the proposed theoretical model explained 55.7% of the variance in behavioral intention. Perceived usefulness was predicted by perceived ease of use (ß = 0. 290), attitude was jointly predicted by perceived usefulness (ß = 0.118) and perceived ease of use (ß = 0.159). Behavioral intention was predicted by perceived usefulness (ß = 0. 256), perceived ease of use (ß = 0. 463), attitude (ß = 0. 293), and perceived risk (ß = -0.136). Health awareness (ß=0.016) did not significantly affect behavioral intention. Four demographic variables gender, age, education, and residence exerted significant moderating effects in theoretical model. Conclusions: During the COVID-19 epidemic, the health awareness and behavior patterns of obese people have changed significantly. Psychological factors and demographic characteristics dynamically interact to generate user behavioral intentions of weight management applications. Weight management application developers and marketers should focus on perceived usefulness, safety, ease of use, and health awareness.

Opioid-Related Treatment Disparities Among Medicaid Enrollees in Indiana.

Introduction: Health care disparities based on race/ethnicity and sex can be found in a variety of settings. Our aim is to determine if there are disparities in treatment provided to Indiana Medicaid enrollees who have medically documented opioid use. Study Data and Methods: We used Medicaid reimbursement claims data to extract patients who were diagnosed with opioid use disorder (OUD) or had other medical event related to opioid use between January 2018 and March 2019. We used a two-proportion Z-test to verify the difference in the proportion of treatment provided between population subgroups. The study was approved by the Purdue University Institutional Review Board (2019-118). Study Results: Over the study period, there were 52,994 Indiana Medicaid enrollees diagnosed with OUD or documentation of another opioid related event. Only 5.41% of them received at least one type of treatment service (detoxification, psychosocial, medication assisted treatment, or comprehensive). Discussion: Although Medicaid began covering treatment services for enrollees with an OUD in Indiana at the start of 2018, very few received evidence-based services. Men and White enrollees with an OUD were in general more likely to receive services compared to women and non-White enrollees.

Application of EfficientNet-B0 and GRU-based deep learning on classifying the colposcopy diagnosis of precancerous cervical lesions.

BACKGROUND: Colposcopy is indispensable for the diagnosis of cervical lesions. However, its diagnosis accuracy for high-grade squamous intraepithelial lesion (HSIL) is at about 50%, and the accuracy is largely dependent on the skill and experience of colposcopists. The advancement in computational power made it possible for the application of artificial intelligence (AI) to clinical problems. Here, we explored the feasibility and accuracy of the application of AI on precancerous and cancerous cervical colposcopic image recognition and classification. METHODS: The images were collected from 6002 colposcopy examinations of normal control, low-grade squamous intraepithelial lesion (LSIL), and HSIL. For each patient, the original, Schiller test, and acetic-acid images were all collected. We built a new neural network classification model based on the hybrid algorithm. EfficientNet-b0 was used as the backbone network for the image feature extraction, and GRU(Gate Recurrent Unit)was applied for feature fusion of the three modes examinations (original, acetic acid, and Schiller test). RESULTS: The connected network classifier achieved an accuracy of 90.61% in distinguishing HSIL from normal and LSIL. Furthermore, the model was applied to "Trichotomy", which reached an accuracy of 91.18% in distinguishing the HSIL, LSIL and normal control at the same time. CONCLUSION: Our results revealed that as shown by the high accuracy of AI in the classification of colposcopic images, AI exhibited great potential to be an effective tool for the accurate diagnosis of cervical disease and for early therapeutic intervention in cervical precancer.

Clinical significance of cylindromatosis expression in primary hepatocellular carcinoma.

BACKGROUND AND STUDY AIM: There is currently a lack of sensitive biomarkers for the diagnosis of hepatocellular carcinoma (HCC). Low expression of cylindromatosis (CYLD), a tumor suppressor gene that encodes a deubiquitinase, is associated with the development of HCC. The present study, therefore, aimed to determine the clinical utility of measuring CYLD expression in the early diagnosis of HCC. PATIENTS AND METHODS: The present study comprised 257 patients from the Affiliated Hospital of Qingdao University including 90 patients with HCC, 41 patients with liver cirrhosis (LC), 46 patients with hepatitis B (HB), and 80 healthy controls. qPCR was used to measure the amounts of CYLD mRNA in stored blood samples. The sensitivity and specificity of CYLD mRNA in diagnosing HCC was analyzed using receiver operator characteristic (ROC) curves. We also obtained HCC data from the Oncomine database to further verify our results. RESULTS: The relative levels of CYLD mRNA in peripheral blood from patients with HCC (median, 0.060; interquartile range [IQR], 0.019-0.260) was significantly lower than in blood from patients with LC (median, 3.732; IQR, 0.648-14.573), HB (median, 0.419; IQR, 0.255-1.809) and healthy controls (median, 1.262; IQR, 0.279-3.537; P < 0.05). CYLD mRNA levels in peripheral blood were significantly higher in patients with LC compared to healthy controls and patients with HB. Oncomine data demonstrated that CYLD mRNA expression levels in HCC tissues were significantly lower than in normal liver tissues. ROC analysis demonstrated that the combined use of peripheral blood levels of CYLD and AFP had the greatest diagnostic accuracy for HCC (area under the curve (AUC), 0.897; 95 % confidence interval [CI], 0.853-0.942). CYLD had utility as a supplementary marker to AFP for diagnosing HCC. CONCLUSION: Circulating levels of CYLD mRNA are significantly decreased in patients with HCC, indicating CYLD may have utility as a biomarker of HCC. Combined measurement of CYLD mRNA and AFP protein had the greatest diagnostic accuracy.

Confinement of Assemblies of Peptides by Chemical Reactions in Living Cells.

The self-assembly of peptides plays an important role in optics, catalysis, medicine, and disease treatment. In recent years, peptide-based materials have exhibited great potential for cancer therapy and disease imaging due to their excellent biocompatibility, structural tenability, and ease of functionality. Peptides could self-assemble into diverse nanostructures in vivo triggered by endogenous stimuli, which initiated chemical reactions and self-assembled to achieve desired biological functions in the tumor microenvironment. This concept introduces the utilization of endogenous triggers to construct functional nanostructures in vivo and their corresponding biological applications. After briefly discussing the representative example of chemical reactions induced self-assembly of peptides in the living system, we describe the several stimuli triggered self-assembly for constructing therapeutic assemblies and serving as an imaging probe. Finally, we give a brief outlook to discuss the future direction of this exciting new field.

The Health and Economic Impact of Using a Sugar Sweetened Beverage Tax to Fund Fruit and Vegetable Subsidies in New York City: A Modeling Study.

Low fruit and vegetable (FV) intake and high sugar-sweetened beverage (SSB) consumption are independently associated with an increased risk of developing cardiovascular disease (CVD). Many people in New York City (NYC) have low FV intake and high SSB consumption, partly due to high cost of fresh FVs and low cost of and easy access to SSBs. A potential implementation of an SSB tax and an FV subsidy program could result in substantial public health and economic benefits. We used a validated microsimulation model for predicting CVD events to estimate the health impact and cost-effectiveness of SSB taxes, FV subsidies, and funding FV subsidies with an SSB tax in NYC. Population demographics and health profiles were estimated using data from the NYC Health and Nutrition Examination Survey. Policy effects and price elasticity were derived from recent meta-analyses. We found that funding FV subsidies with an SSB tax was projected to be the most cost-effective policy from the healthcare sector perspective. From the societal perspective, the most cost-effective policy was SSB taxes. All policy scenarios could prevent more CVD events and save more healthcare costs among men compared to women, and among Black vs. White adults. Public health practitioners and policymakers may want to consider adopting this combination of policy actions, while weighing feasibility considerations and other unintended consequences.

Potential Application of Recombinant Snake Prothrombin Activator Ecarin in Blood Diagnostics.

We describe here the purification and cloning of a codon-optimized form of the snake prothrombin activator ecarin from the saw scaled viper (Echis carinatus) expressed in mammalian cells. Expression of recombinant ecarin (rEcarin) was carried out in human embryonic kidney cells (HEK) cells under conditions for the development and performance of a novel and scalable recombinant snake ecarin to industry standards. Clotting performance of the rEcarin was established in recalcified citrated whole blood, plasma, and fresh whole blood and found to be comparable to native ecarin (N-Ecarin). Furthermore, hemolysis was observed with N-Ecarin at relatively high doses in both recalcified citrated and fresh whole blood, while clotting was not observed with rEcarin, providing an important advantage for the recombinant form. In addition, rEcarin effectively clotted both recalcified citrated whole blood and fresh whole blood containing different anticoagulants including heparin, warfarin, dabigatran, Fondaparinux, rivaroxaban and apixaban, forming firm clots in the blood collection tubes. These results demonstrate that rEcarin efficiently clots normal blood as well as blood spiked with high concentrations of anticoagulants and has great potential as an additive to blood collection tubes to produce high quality serum for analyte analysis in diagnostic medicine.