Gastrocnemius recession: Discrepancies in the literature.

A gastrocnemius contracture is a common problem that results in decreased ankle dorsiflexion that contributes to an array of foot and ankle ailments. A common surgical treatment for this condition is a gastrocnemius recession (GR). Many adaptations of the original procedure have been described. Misinterpretations of proper GR procedures have potentially caused confusion when selecting a treatment. This paper proposes to identify errors between the use of GR and gastrocnemius-soleus recession (GSR) procedure techniques in the current literature. A systematic literature review was performed in June 2021, using the PubMed database and select orthopedic texts. Only studies that met the established criteria and either correctly or incorrectly described a GR or GSR procedure were included. After applying exclusion criteria, 108 publications were included. These articles and texts were reviewed for surgical technique and terminology errors in accordance with established parameters. The articles were classified as either: "Correct" or "Incorrect." Of the 108 publications and texts included, 18 articles incorrectly described either a GR or a GSR (16.67%). Ninety articles correctly described either a GR or a GSR (83.33%). The literature supports the use of a GR to treat a gastrocnemius contracture. Inaccurate articles create confusion as to what exactly a GR entails. Sources of ambiguity included terminology, inconsistent anatomical zone definition, and technique selection. Due to this confusion, it is suspected that patient outcomes can be impacted. Postoperative outcomes of GSR patients are worse than GR patients. Further investigation is necessary to determine if performing the incorrect procedure negatively affects patient outcomes.

Synthesis and Detection of BODIPY-, Biotin-, and 19 F- Labeled Single-Entity Dendritic Heparan Sulfate Mimetics.

Heparin and heparan sulfate (HS) are naturally occurring mammalian glycosaminoglycans, and their synthetic and semi-synthetic mimetics have attracted significant interest as potential therapeutics. However, understanding the mechanism of action by which HS, heparin, and HS mimetics have a biological effect is difficult due to their highly charged nature, broad protein interactomes, and variable structures. To address this, a library of novel single-entity dendritic mimetics conjugated to BODIPY, Fluorine-19 (19 F), and biotin was synthesized for imaging and localization studies. The novel dendritic scaffold allowed for the conjugation of labeling moieties without reducing the number of sulfated capping groups, thereby better mimicking the multivalent nature of HS-protein interactions. The 19 F labeled mimetics were assessed in phantom studies and were detected at concentrations as low as 5 mM. Flow cytometric studies using a fluorescently labeled mimetic showed that the compound associated with immune cells from tumors more readily than splenic counterparts and was directed to endosomal-lysosomal compartments within immune cells and cancer cells. Furthermore, the fluorescently labeled mimetic entered the central nervous system and was detectable in brain-infiltrating immune cells 24 hours after treatment. Here, we report the enabling methodology for rapidly preparing various labeled HS mimetics and molecular probes with diverse potential therapeutic applications.

The novel HS-mimetic, Tet-29, regulates immune cell trafficking across barriers of the CNS during inflammation.

BACKGROUND: Disruption of the extracellular matrix at the blood-brain barrier (BBB) underpins neuroinflammation in multiple sclerosis (MS). The degradation of extracellular matrix components, such as heparan sulfate (HS) proteoglycans, can be prevented by treatment with HS-mimetics through their ability to inhibit the enzyme heparanase. The heparanase-inhibiting ability of our small dendrimer HS-mimetics has been investigated in various cancers but their efficacy in neuroinflammatory models has not been evaluated. This study investigates the use of a novel HS-mimetic, Tet-29, in an animal model of MS. METHODS: Neuroinflammation was induced in mice by experimental autoimmune encephalomyelitis, a murine model of MS. In addition, the BBB and choroid plexus were modelled in vitro using transmigration assays, and migration of immune cells in vivo and in vitro was quantified by flow cytometry. RESULTS: We found that Tet-29 significantly reduced lymphocyte accumulation in the central nervous system which, in turn, decreased disease severity in experimental autoimmune encephalomyelitis. The disease-modifying effect of Tet-29 was associated with a rescue of BBB integrity, as well as inhibition of activated lymphocyte migration across the BBB and choroid plexus in transwell models. In contrast, Tet-29 did not significantly impair in vivo or in vitro steady state-trafficking under homeostatic conditions. CONCLUSIONS: Together these results suggest that Tet-29 modulates, rather than abolishes, trafficking across central nervous system barriers.

Suppression of Fibrinolysis and Hypercoagulability, Severity of Hypoxemia, and Mortality in COVID-19 Patients: A Retrospective Cohort Study.

BACKGROUND: COVID-19 causes hypercoagulability, but the association between coagulopathy and hypoxemia in critically ill patients has not been thoroughly explored. This study hypothesized that severity of coagulopathy would be associated with acute respiratory distress syndrome severity, major thrombotic events, and mortality in patients requiring intensive care unit-level care. METHODS: Viscoelastic testing by rotational thromboelastometry and coagulation factor biomarker analyses were performed in this prospective observational cohort study of critically ill COVID-19 patients from April 2020 to October 2020. Statistical analyses were performed to identify significant coagulopathic biomarkers such as fibrinolysis-inhibiting plasminogen activator inhibitor 1 and their associations with clinical outcomes such as mortality, extracorporeal membrane oxygenation requirement, occurrence of major thrombotic events, and severity of hypoxemia (arterial partial pressure of oxygen/fraction of inspired oxygen categorized into mild, moderate, and severe per the Berlin criteria). RESULTS: In total, 53 of 55 (96%) of the cohort required mechanical ventilation and 9 of 55 (16%) required extracorporeal membrane oxygenation. Extracorporeal membrane oxygenation-naïve patients demonstrated lysis indices at 30 min indicative of fibrinolytic suppression on rotational thromboelastometry. Survivors demonstrated fewer procoagulate acute phase reactants, such as microparticle-bound tissue factor levels (odds ratio, 0.14 [0.02, 0.99]; P = 0.049). Those who did not experience significant bleeding events had smaller changes in ADAMTS13 levels compared to those who did (odds ratio, 0.05 [0, 0.7]; P = 0.026). Elevations in plasminogen activator inhibitor 1 (odds ratio, 1.95 [1.21, 3.14]; P = 0.006), d-dimer (odds ratio, 3.52 [0.99, 12.48]; P = 0.05), and factor VIII (no clot, 1.15 ± 0.28 vs. clot, 1.42 ± 0.31; P = 0.003) were also demonstrated in extracorporeal membrane oxygenation-naïve patients who experienced major thrombotic events. Plasminogen activator inhibitor 1 levels were significantly elevated during periods of severe compared to mild and moderate acute respiratory distress syndrome (severe, 44.2 ± 14.9 ng/ml vs. mild, 31.8 ± 14.7 ng/ml and moderate, 33.1 ± 15.9 ng/ml; P = 0.029 and 0.039, respectively). CONCLUSIONS: Increased inflammatory and procoagulant markers such as plasminogen activator inhibitor 1, microparticle-bound tissue factor, and von Willebrand factor levels are associated with severe hypoxemia and major thrombotic events, implicating fibrinolytic suppression in the microcirculatory system and subsequent micro- and macrovascular thrombosis in severe COVID-19.

Political Alliance Formation and Cooperation Networks in the Utah State Legislature.

Social network analysis has become an increasingly important tool among political scientists for understanding legislative cooperation in modern, democratic nation-states. Recent research has demonstrated the influence that group affinity (homophily) and mutual exchanges (reciprocity) have in structuring political relationships. However, this literature has typically focused on political cooperation where costs are low, relationships are not exclusive, and/or partisan competition is high. Patterns of legislative behavior in alternative contexts are less clear and remain largely unexamined. Here, we compare theoretical expectations of cooperation in these contexts from the political and biosocial sciences and implement the first assessment of political alliance formation in a novel legislative environment where costs to cooperation are high and party salience low. We implement a stochastic actor-oriented model (SAOM) to examine bill floor sponsorship, a process in which a "floor sponsor" becomes the exclusive advocate for a colleague's piece of legislation, in the Utah state legislature from 2005 to 2008-a context in which gender (male) and political party (Republican) supermajorities exist. We find that (1) party and gender homophily predict who legislators recruit as floor sponsors, whereas seniority does not, and (2) legislators frequently engage in reciprocal exchanges of floor sponsorship. In addition, whereas gender homophily increases the likelihood of reciprocity, party homophily decreases it. Our findings suggest that when the cost of cooperation is high, political actors use in-group characteristics for initiating alliances, but once a cooperative relationship is established with an out-group political member, it is reinforced through repeated exchanges. These findings may be useful for understanding the rise of political polarization and gridlock in democracies internationally.

Variational Disentanglement for Rare Event Modeling.

Combining the increasing availability and abundance of healthcare data and the current advances in machine learning methods have created renewed opportunities to improve clinical decision support systems. However, in healthcare risk prediction applications, the proportion of cases with the condition (label) of interest is often very low relative to the available sample size. Though very prevalent in healthcare, such imbalanced classification settings are also common and challenging in many other scenarios. So motivated, we propose a variational disentanglement approach to semi-parametrically learn from rare events in heavily imbalanced classification problems. Specifically, we leverage the imposed extreme-distribution behavior on a latent space to extract information from low-prevalence events, and develop a robust prediction arm that joins the merits of the generalized additive model and isotonic neural nets. Results on synthetic studies and diverse real-world datasets, including mortality prediction on a COVID-19 cohort, demonstrate that the proposed approach outperforms existing alternatives.

NDVI predicts birth seasonality in historical Baja California Sur, Mexico: adaptive responses to arid ecosystems and the North American Monsoon.

Birth seasonality is a phenomenon whereby populations can be characterized by a single month or season in which births peak. While non-human animal research suggests that seasonal birth-pulses are related to variation in climate and local energy availability, social scientists debate the mechanisms responsible for it in humans. Here we investigate the role of precipitation, temperature, and energy availability on seasonal conception and birth pulses using a historical dataset from the Baja California peninsula - a hot, arid desert that experiences seasonal climatic fluctuations associated with the North American Monsoon. Analyses suggest that 1) local energy availability had a negative relationship with conception pulses; and 2) birth pulses had a positive relationship with local energy availability and a negative relationship with temperature. Taken together, our analyses suggest that women timed conceptions when local energy availability was lowest (challenging expectations of conception rates as simply reflecting ecological influences on female fecundity), so that children were born during the seasonal "green-up" associated with the North American Monsoon. Given our results, we speculate that birth seasonality represents a form of traditional ecological knowledge to improve neonate health and wellbeing.

Development and Validation of Machine Learning Models to Predict Admission From Emergency Department to Inpatient and Intensive Care Units.

STUDY OBJECTIVE: This study aimed to develop and validate 2 machine learning models that use historical and current-visit patient data from electronic health records to predict the probability of patient admission to either an inpatient unit or ICU at each hour (up to 24 hours) of an emergency department (ED) encounter. The secondary goal was to provide a framework for the operational implementation of these machine learning models. METHODS: Data were curated from 468,167 adult patient encounters in 3 EDs (1 academic and 2 community-based EDs) of a large academic health system from August 1, 2015, to October 31, 2018. The models were validated using encounter data from January 1, 2019, to December 31, 2019. An operational user dashboard was developed, and the models were run on real-time encounter data. RESULTS: For the intermediate admission model, the area under the receiver operating characteristic curve was 0.873 and the area under the precision-recall curve was 0.636. For the ICU admission model, the area under the receiver operating characteristic curve was 0.951 and the area under the precision-recall curve was 0.461. The models had similar performance in both the academic- and community-based settings as well as across the 2019 and real-time encounter data. CONCLUSION: Machine learning models were developed to accurately make predictions regarding the probability of inpatient or ICU admission throughout the entire duration of a patient's encounter in ED and not just at the time of triage. These models remained accurate for a patient cohort beyond the time period of the initial training data and were integrated to run on live electronic health record data, with similar performance.

Predicting Hospital Utilization and Inpatient Mortality of Patients Tested for COVID-19.

Using structured elements from Electronic Health Records (EHR), we seek to: i) build predictive models to stratify patients tested for COVID-19 by their likelihood for hospitalization, ICU admission, mechanical ventilation and inpatient mortality, and ii) identify the most important EHR-based features driving the predictions. We leveraged EHR data from the Duke University Health System tested for COVID-19 or hospitalized between March 11, 2020 and August 24, 2020, to build models to predict hospital admissions within 4 weeks. Models were also created for ICU admissions, need for mechanical ventilation and mortality following admission. Models were developed on a cohort of 86,355 patients with 112,392 outpatient COVID-19 tests or any-cause hospital admissions between March 11, 2020 and June 4, 2020. The four models considered resulted in AUROC=0.838 (CI: 0.832-0.844) and AP=0.272 (CI: 0.260-0.287) for hospital admissions, AUROC=0.847 (CI: 0.839-855) and AP=0.585 (CI: 0.565-0.603) for ICU admissions, AUROC=0.858 (CI: 0.846-0.871) and AP=0.434 (CI: 0.403-0.467) for mechanical ventilation, and AUROC=0.0.856 (CI: 0.842-0.872) and AP=0.243 (CI: 0.205-0.282) for inpatient mortality. Patient history abstracted from the EHR has the potential for being used to stratify patients tested for COVID-19 in terms of utilization and mortality. The dominant EHR features for hospital admissions and inpatient outcomes are different. For the former, age, social indicators and previous utilization are the most important predictive features. For the latter, age and physiological summaries (pulse and blood pressure) are the main drivers.

Variational Disentanglement for Rare Event Modeling.

Combining the increasing availability and abundance of healthcare data and the current advances in machine learning methods have created renewed opportunities to improve clinical decision support systems. However, in healthcare risk prediction applications, the proportion of cases with the condition (label) of interest is often very low relative to the available sample size. Though very prevalent in healthcare, such imbalanced classification settings are also common and challenging in many other scenarios. So motivated, we propose a variational disentanglement approach to semi-parametrically learn from rare events in heavily imbalanced classification problems. Specifically, we leverage the imposed extreme-distribution behavior on a latent space to extract information from low-prevalence events, and develop a robust prediction arm that joins the merits of the generalized additive model and isotonic neural nets. Results on synthetic studies and diverse real-world datasets, including mortality prediction on a COVID-19 cohort, demonstrate that the proposed approach outperforms existing alternatives.

Synthesis of 4-substituted oxazolo[4,5-c]quinolines by direct reaction at the C-4 position of oxazoles.

A facile synthesis of 4-aryl substituted oxazolo[4,5-c]quinolines has been described via a modified Pictet-Spengler method and using Cu(TFA)2 as a catalyst. The developed methodology directly functionalizes the C-4 position of oxazoles without the aid of any prefunctionalization, in the presence of the more reactive C-2 position in good yields. The versatility of the established method has been demonstrated by its application in the synthesis of 4-substituted oxazolo-[1,8]naphthyridine ring systems.