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BackgroundUnfractionated heparin is widely used in the intensive care unit as an anticoagulant. However, weight-based heparin dosing has been shown to be suboptimal and may place patients at unnecessary risk during their intensive care unit stay. ObjectiveIn this study, we intended to develop and validate a machine learning–based model to predict heparin treatment outcomes and to provide dosage recommendations to clinicians. MethodsA shallow neural network model was adopted in a retrospective cohort of patients from the Multiparameter Intelligent Monitoring in Intensive Care III (MIMIC III) database and patients admitted to the Peking Union Medical College Hospital (PUMCH). We modeled the subtherapeutic, normal, and supratherapeutic activated partial thromboplastin time (aPTT) as the outcomes of heparin treatment and used a group of clinical features for modeling. Our model classifies patients into 3 different therapeutic states. We tested the prediction ability of our model and evaluated its performance by using accuracy, the kappa coefficient, precision, recall, and the F1 score. Furthermore, a dosage recommendation module was designed and evaluated for clinical decision support. ResultsA total of 3607 patients selected from MIMIC III and 1549 patients admitted to the PUMCH who met our criteria were included in this study. The shallow neural network model showed results of F1 scores 0.887 (MIMIC III) and 0.925 (PUMCH). When compared with the actual dosage prescribed, our model recommended increasing the dosage for 72.2% (MIMIC III, 1240/1718) and 64.7% (PUMCH, 281/434) of the subtherapeutic patients and decreasing the dosage for 80.9% (MIMIC III, 504/623) and 76.7% (PUMCH, 277/361) of the supratherapeutic patients, suggesting that the recommendations can contribute to clinical improvements and that they may effectively reduce the time to optimal dosage in the clinical setting. ConclusionsThe evaluation of our model for predicting heparin treatment outcomes demonstrated that the developed model is potentially applicable for reducing the misdosage of heparin and for providing appropriate decision recommendations to clinicians.

BackgroundMonitoring critically ill patients in intensive care units (ICUs) in real time is vitally important. Although scoring systems are most often used in risk prediction of mortality, they are usually not highly precise, and the clinical data are often simply weighted. This method is inefficient and time-consuming in the clinical setting. ObjectiveThe objective of this study was to integrate all medical data and noninvasively predict the real-time mortality of ICU patients using a gradient boosting method. Specifically, our goal was to predict mortality using a noninvasive method to minimize the discomfort to patients. MethodsIn this study, we established five models to predict mortality in real time based on different features. According to the monitoring, laboratory, and scoring data, we constructed the feature engineering. The five real-time mortality prediction models were RMM (based on monitoring features), RMA (based on monitoring features and the Acute Physiology and Chronic Health Evaluation [APACHE]), RMS (based on monitoring features and Sequential Organ Failure Assessment [SOFA]), RMML (based on monitoring and laboratory features), and RM (based on all monitoring, laboratory, and scoring features). All models were built using LightGBM and tested with XGBoost. We then compared the performance of all models, with particular focus on the noninvasive method, the RMM model. ResultsAfter extensive experiments, the area under the curve of the RMM model was 0.8264, which was superior to that of the RMA and RMS models. Therefore, predicting mortality using the noninvasive method was both efficient and practical, as it eliminated the need for extra physical interventions on patients, such as the drawing of blood. In addition, we explored the top nine features relevant to real-time mortality prediction: invasive mean blood pressure, heart rate, invasive systolic blood pressure, oxygen concentration, oxygen saturation, balance of input and output, total input, invasive diastolic blood pressure, and noninvasive mean blood pressure. These nine features should be given more focus in routine clinical practice. ConclusionsThe results of this study may be helpful in real-time mortality prediction in patients in the ICU, especially the noninvasive method. It is efficient and favorable to patients, which offers a strong practical significance.

BackgroundPrevious studies have shown that electronic cigarette (e-cigarette) users might be more vulnerable to COVID-19 infection and could develop more severe symptoms if they contract the disease owing to their impaired immune responses to viral infections. Social media platforms such as Twitter have been widely used by individuals worldwide to express their responses to the current COVID-19 pandemic. ObjectiveIn this study, we aimed to examine the longitudinal changes in the attitudes of Twitter users who used e-cigarettes toward the COVID-19 pandemic, as well as compare differences in attitudes between e-cigarette users and nonusers based on Twitter data. MethodsThe study dataset containing COVID-19–related Twitter posts (tweets) posted between March 5 and April 3, 2020, was collected using a Twitter streaming application programming interface with COVID-19–related keywords. Twitter users were classified into two groups: Ecig group, including users who did not have commercial accounts but posted e-cigarette–related tweets between May 2019 and August 2019, and non-Ecig group, including users who did not post any e-cigarette–related tweets. Sentiment analysis was performed to compare sentiment scores towards the COVID-19 pandemic between both groups and determine whether the sentiment expressed was positive, negative, or neutral. Topic modeling was performed to compare the main topics discussed between the groups. ResultsThe US COVID-19 dataset consisted of 4,500,248 COVID-19–related tweets collected from 187,399 unique Twitter users in the Ecig group and 11,479,773 COVID-19–related tweets collected from 2,511,659 unique Twitter users in the non-Ecig group. Sentiment analysis showed that Ecig group users had more negative sentiment scores than non-Ecig group users. Results from topic modeling indicated that Ecig group users had more concerns about deaths due to COVID-19, whereas non-Ecig group users cared more about the government’s responses to the COVID-19 pandemic. ConclusionsOur findings show that Twitter users who tweeted about e-cigarettes had more concerns about the COVID-19 pandemic. These findings can inform public health practitioners to use social media platforms such as Twitter for timely monitoring of public responses to the COVID-19 pandemic and educating and encouraging current e-cigarette users to quit vaping to minimize the risks associated with COVID-19.

BackgroundInstagram is a popular social networking platform for users to upload pictures sharing their experiences. Instagram has been widely used by vaping companies and stores to promote electronic cigarettes (e-cigarettes), as well as by public health entities to communicate the risks of e-cigarette use (vaping) to the public. ObjectiveWe aimed to characterize current vaping-related content on Instagram through descriptive analyses. MethodsFrom Instagram, 42,951 posts were collected using vaping-related hashtags in November 2019. The posts were grouped as (1) pro-vaping, (2) vaping warning, (3) neutral to vaping, and (4) not related to vaping based on the attitudes to vaping expressed within the posts. From these Instagram posts and the corresponding 18,786 unique Instagram user accounts, 200 pro-vaping and 200 vaping-warning posts as well as 200 pro-vaping and 200 vaping-warning user accounts were randomly selected for hand coding. Furthermore, follower counts and media counts of the Instagram user accounts as well as the “like” counts and hashtags of the posts were compared between pro-vaping and vaping-warning groups. ResultsThere were more posts in the pro-vaping group (41,412 posts) than there were in the vaping-warning group (1539 posts). The majority of pro-vaping images were product display images (163/200, 81.5%), and the most popular image type in vaping-warning posts was educational (95/200, 47.5%). The highest proportion of pro-vaping user account type was vaping store (110/189, 58.1%), and the store account type had the highest mean number of posts (10.33 posts/account). The top 3 vaping-warning user account types were personal (79/155, 51%), vaping-warning community (37/155, 23.9%), and community (35/155, 22.6%), of which the vaping-warning community had the highest mean number of posts (3.68 posts/account). Pro-vaping user accounts had more followers (median 850) and media (median 232) than vaping-warning user accounts had (follower count: median 191; media count: 92). Pro-vaping posts had more “likes” (median 22) and hashtags (mean 20.39) than vaping-warning posts had (“like” count: median 12; hashtags: mean 7.16). ConclusionsInstagram is dominated by pro-vaping content, and pro-vaping posts and user accounts seem to have more user engagement than vaping-warning accounts have. These results highlight the importance of regulating e-cigarette posts on social media and the urgency of identifying effective communication content and message delivery methods with the public about the health effects of e-cigarettes to ameliorate the epidemic of vaping in youth.

BackgroundIn late December 2019, a pneumonia caused by SARS-CoV-2 was first reported in Wuhan and spread worldwide rapidly. Currently, no specific medicine is available to treat infection with COVID-19. ObjectiveThe aims of this study were to summarize the epidemiological and clinical characteristics of 175 patients with SARS-CoV-2 infection who were hospitalized in Renmin Hospital of Wuhan University from January 1 to January 31, 2020, and to establish a tool to identify potential critical patients with COVID-19 and help clinical physicians prevent progression of this disease. MethodsIn this retrospective study, clinical characteristics of 175 confirmed COVID-19 cases were collected and analyzed. Univariate analysis and least absolute shrinkage and selection operator (LASSO) regression were used to select variables. Multivariate analysis was applied to identify independent risk factors in COVID-19 progression. We established a nomogram to evaluate the probability of progression of the condition of a patient with COVID-19 to severe within three weeks of disease onset. The nomogram was verified using calibration curves and receiver operating characteristic curves. ResultsA total of 18 variables were considered to be risk factors after the univariate regression analysis of the laboratory parameters (P

BackgroundIn recent years, flavored electronic cigarettes (e-cigarettes) have become popular among teenagers and young adults. Discussions about e-cigarettes and e-cigarette use (vaping) experiences are prevalent online, making social media an ideal resource for understanding the health risks associated with e-cigarette flavors from the users’ perspective. ObjectiveThis study aimed to investigate the potential associations between electronic cigarette liquid (e-liquid) flavors and the reporting of health symptoms using social media data. MethodsA dataset consisting of 2.8 million e-cigarette–related posts was collected using keyword filtering from Reddit, a social media platform, from January 2013 to April 2019. Temporal analysis for nine major health symptom categories was used to understand the trend of public concerns related to e-cigarettes. Sentiment analysis was conducted to obtain the proportions of positive and negative sentiment scores for all reported health symptom categories. Topic modeling was applied to reveal the topics related to e-cigarettes and health symptoms. Furthermore, generalized estimating equation (GEE) models were used to quantitatively measure potential associations between e-liquid flavors and the reporting of health symptoms. ResultsTemporal analysis showed that the Respiratory category was consistently the most discussed health symptom category among all categories related to e-cigarettes on Reddit, followed by the Throat category. Sentiment analysis showed higher proportions of positive sentiment scores for all reported health symptom categories, except for the Cancer category. Topic modeling conducted on all health-related posts showed that 17 of the top 100 topics were flavor related. GEE models showed different associations between the reporting of health symptoms and e-liquid flavor categories, for example, lower association of the Beverage flavors with Respiratory compared with other flavors and higher association of the Fruit flavors with Cardiovascular than other flavors. ConclusionsThis study identified different potential associations between e-liquid flavors and the reporting of health symptoms using social media data. The results of this study provide valuable information for further investigation of the health effects associated with different e-liquid flavors.

BackgroundThe number of electronic cigarette (e-cigarette) users has been increasing rapidly in recent years, especially among youth and young adults. More e-cigarette products have become available, including e-liquids with various brands and flavors. Various e-liquid flavors have been frequently discussed by e-cigarette users on social media. ObjectiveThis study aimed to examine the longitudinal prevalence of mentions of electronic cigarette liquid (e-liquid) flavors and user perceptions on social media. MethodsWe applied a data-driven approach to analyze the trends and macro-level user sentiments of different e-cigarette flavors on social media. With data collected from web-based stores, e-liquid flavors were classified into categories in a flavor hierarchy based on their ingredients. The e-cigarette–related posts were collected from social media platforms, including Reddit and Twitter, using e-cigarette–related keywords. The temporal trend of mentions of e-liquid flavor categories was compiled using Reddit data from January 2013 to April 2019. Twitter data were analyzed using a sentiment analysis from May to August 2019 to explore the opinions of e-cigarette users toward each flavor category. ResultsMore than 1000 e-liquid flavors were classified into 7 major flavor categories. The fruit and sweets categories were the 2 most frequently discussed e-liquid flavors on Reddit, contributing to approximately 58% and 15%, respectively, of all flavor-related posts. We showed that mentions of the fruit flavor category had a steady overall upward trend compared with other flavor categories that did not show much change over time. Results from the sentiment analysis demonstrated that most e-liquid flavor categories had significant positive sentiments, except for the beverage and tobacco categories. ConclusionsThe most updated information about the popular e-liquid flavors mentioned on social media was investigated, which showed that the prevalence of mentions of e-liquid flavors and user perceptions on social media were different. Fruit was the most frequently discussed flavor category on social media. Our study provides valuable information for future regulation of flavored e-cigarettes.

BackgroundHeparin is one of the most commonly used medications in intensive care units. In clinical practice, the use of a weight-based heparin dosing nomogram is standard practice for the treatment of thrombosis. Recently, machine learning techniques have dramatically improved the ability of computers to provide clinical decision support and have allowed for the possibility of computer generated, algorithm-based heparin dosing recommendations. ObjectiveThe objective of this study was to predict the effects of heparin treatment using machine learning methods to optimize heparin dosing in intensive care units based on the predictions. Patient state predictions were based upon activated partial thromboplastin time in 3 different ranges: subtherapeutic, normal therapeutic, and supratherapeutic, respectively. MethodsRetrospective data from 2 intensive care unit research databases (Multiparameter Intelligent Monitoring in Intensive Care III, MIMIC-III; e–Intensive Care Unit Collaborative Research Database, eICU) were used for the analysis. Candidate machine learning models (random forest, support vector machine, adaptive boosting, extreme gradient boosting, and shallow neural network) were compared in 3 patient groups to evaluate the classification performance for predicting the subtherapeutic, normal therapeutic, and supratherapeutic patient states. The model results were evaluated using precision, recall, F1 score, and accuracy. ResultsData from the MIMIC-III database (n=2789 patients) and from the eICU database (n=575 patients) were used. In 3-class classification, the shallow neural network algorithm performed the best (F1 scores of 87.26%, 85.98%, and 87.55% for data set 1, 2, and 3, respectively). The shallow neural network algorithm achieved the highest F1 scores within the patient therapeutic state groups: subtherapeutic (data set 1: 79.35%; data set 2: 83.67%; data set 3: 83.33%), normal therapeutic (data set 1: 93.15%; data set 2: 87.76%; data set 3: 84.62%), and supratherapeutic (data set 1: 88.00%; data set 2: 86.54%; data set 3: 95.45%) therapeutic ranges, respectively. ConclusionsThe most appropriate model for predicting the effects of heparin treatment was found by comparing multiple machine learning models and can be used to further guide optimal heparin dosing. Using multicenter intensive care unit data, our study demonstrates the feasibility of predicting the outcomes of heparin treatment using data-driven methods, and thus, how machine learning–based models can be used to optimize and personalize heparin dosing to improve patient safety. Manual analysis and validation suggested that the model outperformed standard practice heparin treatment dosing.

The optimum choice of the slopes used to perform the encoding process in a projection code always in maximizing the minimum Hamming distance so that d = [2.sup.r]. Index Terms - FEC coding.

BackgroundPrior research has documented the effect of concurrent mobile phone use on medical care. This study examined the extent of hospital registered nurses’ awareness of their mobile-phone-associated performance decrements. ObjectiveThe objective of this study was to compare self-reported performance with reported observed performance of others with respect to mobile phone use by hospital registered nurses. MethodsIn March 2014, a previously validated survey was emailed to the 10,978 members of the Academy of Medical Surgical Nurses. The responses were analyzed using a two-proportion z test (alpha=.05, two-tailed) to examine whether self-reported and observed rates of error were significantly different. All possible demographic and employment confounders which could potentially contribute to self-reported and observed performance errors were tested for significance. ResultsOf the 950 respondents, 825 (8.68%, 825/950) met the inclusion criteria for analysis. The representativeness of the sample relative to the US nursing workforce was assessed using a two-proportion z test. This indicated that sex and location of primary place of employment (urban/rural) were represented appropriately in the study sample. Respondents in the age groups 55 years old were overrepresented. Whites, American Indians/Alaskan natives, and Native Hawaiian or Pacific Islanders were underrepresented, while Hispanic and multiple/other ethnicities were overrepresented. It was decided to report the unweighted, rather than the weighted survey data, with the recognition that the results, while valuable, may not be generalizable to the entire US registered nursing workforce. A significant difference was found between registered nurses’ self-reported and observed rates of errors associated with concurrent mobile phone use in following three categories (1) work performance (z=−26.6142, P

BackgroundPersonal mobile phones and other personal communication devices (smartphones and tablet computers) provide users with an ever-increasing number and diversity of non-work-related activities while at work. In hospitals, where the vigilance of health care workers is essential for patient care, the potential distraction of these devices could be hazardous to patients. ObjectiveThe objective of this study was to determine the frequency of non-work-related use of personal mobile phones and other personal communication devices among hospital registered nurses. MethodsIn March 2014, a previously validated 30-question survey was emailed to the 10,978 members of the Academy of Medical Surgical Nurses. There were 825 respondents who met the inclusion criteria. ResultsThe use of a personal mobile phone or other personal communication device while working (excluding meal times and breaks) was reported by 78.1% (644/825) of respondents. Nurses reported regularly (sometimes, often, or always) sending personal emails and text messages (38.6%, 318/825), reading news (25.7%, 212/825), checking/posting on social networking sites (20.8%, 172/825), shopping (9.6%, 79/825), and playing games (6.5%, 54/825) while working. ConclusionsThis study found that hospital nurses frequently use their personal mobile phones or other personal communication devices for non-work-related activities at work. The primary activity reported was to send personal emails and text messages to family and friends.

BackgroundThe use of personal communication devices (such as basic cell phones, enhanced cell phones or smartphones, and tablet computers) in hospital units has risen dramatically in recent years. The use of these devices for personal and professional activities can be beneficial, but also has the potential to negatively affect patient care, as clinicians may become distracted by these devices. ObjectiveNo validated questionnaire examining the impact of the use of these devices on patient care exists; thus, we aim to develop and validate an online questionnaire for surveying the views of registered nurses with experience of working in hospitals regarding the impact of the use of personal communication devices on hospital units. MethodsA 50-item, four-domain questionnaire on the views of registered nursing staff regarding the impact of personal communication devices on hospital units was developed based on a literature review and interviews with such nurses. A repeated measures pilot study was conducted to examine the psychometrics of a survey questionnaire and the feasibility of conducting a larger study. Psychometric testing of the questionnaire included examining internal consistency reliability and test-retest reliability in a sample of 50 registered nurses. ResultsThe response rate for the repeated measures was 30%. Cronbach coefficient alpha was used to examine the internal consistency and reliability, and in three of the four question groups (utilization, impact, and opinions), the correlation was observed to be very high. This suggests that the questions were measuring a single underlying theme. The Cronbach alpha value for the questions in the performance group, describing the use of personal communication devices while working, was lower than those for the other question groups. These values may be an indication that the assumptions underlying the Cronbach alpha calculation may have been violated for this group of questions. A Spearman rho correlation was used to determine the test-retest reliability. There was a strong test-retest reliability between the two tests for the majority of the questions. The average test-retest percent of agreement for the Likert scale responses was 74% (range 43-100%). Accounting for responses within the 1 SD range on the Likert scale increased the agreement to 96% (range 87-100%). Missing data were in the range of 0 to 7%. ConclusionsThe psychometrics of the questionnaire showed good to fair levels of internal consistency and test-retest reliability. The pilot study demonstrated that our questionnaire may be useful in exploring registered nurses’ perceptions of the impact of personal electronic devices on hospital units in a larger study.

An enantiocontrolled route to aziridinomitosenes had been developed from L-serine methyl ester hydrochloride. The tetracyclic target ring system was assembled by an internal azomethine ylide cycloaddition reaction based on silver ion-assisted intramolecular oxazole alkylation and cyanide-induced ylide generation via a labile oxazoline intermediate (62 to 66). Other key steps include reductive detritylation of 26, methylation of the N-H aziridine 56, oxidation of the sensitive cyclohexenedione 68 to quinone 70, and carbamoylation using Fmoc-NCO. Although the aziridinomitosene tetracycle is sensitive, a range of protecting group manipulations and redox chemistry can be performed if suitable precautions are taken. A study of DNA alkylation by the first C-6,C-7-unsubstitute aziridinomitosene 11a has been carried out, and evidence for DNA cross-link formation involving nucleophilic addition to the quinone subunit is described. [ABSTRACT FROM AUTHOR]

Background: The primary goal of the present study was to investigate injury to the deep medial collateral ligament (MCL), specifically the meniscofemoral ligament (MFL) portion, and its association with medial femoral condyle (MFC) bone marrow edema in acute anterior cruciate ligament (ACL) ruptures. The secondary goal was to examine the association between MFL injury and medial meniscal tears (MMTs) in these same patients., Methods: Preoperative magnetic resonance imaging (MRI) scans of 55 patients who underwent ACL reconstruction surgery were retrospectively reviewed by 2 board-certified musculoskeletal radiologists. MRI scans were examined for MFC edema at the insertion site of the MFL. This site on the MFC was referred to as the central-femoral-medial-medial (C-FMM) zone based on the coronal and sagittal locations on MRI. The presence or absence of bone marrow edema within this zone was noted. The prevalence, grade, and location of superficial MCL and MFL injuries were also recorded on MRI. The correlations between MFL injuries and the presence of MFC bone marrow edema were examined. Lastly, the presence and location of MMTs were also recorded on MRI and were confirmed on arthroscopy, according to the operative notes., Results: On MRI, 40 (73%) of the 55 patients had MFL injuries. MFL injuries were significantly more common than superficial MCL injuries (p = 0.0001). Of the 27 patients with C-FMM bruising, 93% (25 patients) had MFL tears (p < 0.00001). In addition, of the 40 patients with an MFL injury, 63% (25 patients) had C-FMM bruising (p = 0.0251). Chi-square testing showed that MMTs and MFL injuries were significantly associated, with 12 (100%) of 12 patients with MMTs also having a concomitant MFL injury (p = 0.0164)., Conclusions: The prevalence of MFL injury in ACL ruptures is high and MFC bone marrow edema at the MFL insertion site should raise suspicion of injury. MFL injuries can present with clinically normal medial ligamentous laxity in ACL ruptures. Additionally, MFL injuries were significantly associated with posterior horn MMTs, which have been shown in the literature to be a potential risk factor for ACL graft failure., Clinical Relevance: As deep MCL injuries are difficult to detect on physical examination, our findings suggest that the reported MFC edema in ACL ruptures can act as an indirect sign of MFL injury and may aid in the clinical detection. Additionally, due to the anatomical connection of the deep MCL and the meniscocapsular junction of the posterior horn of the medial meniscus, if an MFL injury is suspected through indirect MFC edema at the insertion site, the posterior horn of the medial meniscus should also be assessed for injury, as there is an association between the 2 injuries in ACL ruptures., Competing Interests: Disclosure: The Disclosure of Potential Conflicts of Interest forms are provided with the online version of the article (http://links.lww.com/JBJSOA/A340)., (Copyright © 2021 The Authors. Published by The Journal of Bone and Joint Surgery, Incorporated. All rights reserved.)

Fat and muscle cells contain a specialized, intracellular organelle known as the GLUT4 storage vesicle (GSV). Insulin stimulation mobilizes GSVs, so that these vesicles fuse at the cell surface and insert GLUT4 glucose transporters into the plasma membrane. This example is likely one instance of a broader paradigm for regulated, non-secretory exocytosis, in which intracellular vesicles are translocated in response to diverse extracellular stimuli. GSVs have been studied extensively, yet these vesicles remain enigmatic. Data support the view that in unstimulated cells, GSVs are present as a pool of preformed small vesicles, which are distinct from endosomes and other membrane-bound organelles. In adipocytes, GSVs contain specific cargoes including GLUT4, IRAP, LRP1, and sortilin. They are formed by membrane budding, involving sortilin and probably CHC22 clathrin in humans, but the donor compartment from which these vesicles form remains uncertain. In unstimulated cells, GSVs are trapped by TUG proteins near the endoplasmic reticulum - Golgi intermediate compartment (ERGIC). Insulin signals through two main pathways to mobilize these vesicles. Signaling by the Akt kinase modulates Rab GTPases to target the GSVs to the cell surface. Signaling by the Rho-family GTPase TC10α stimulates Usp25m-mediated TUG cleavage to liberate the vesicles from the Golgi. Cleavage produces a ubiquitin-like protein modifier, TUGUL, that links the GSVs to KIF5B kinesin motors to promote their movement to the cell surface. In obesity, attenuation of these processes results in insulin resistance and contributes to type 2 diabetes and may simultaneously contribute to hypertension and dyslipidemia in the metabolic syndrome.