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Objective: Determine how each organ component of the SOFA score differs in its contribution to mortality risk and how that contribution may change over time., Methods: We performed multivariate logistic regression analysis to assess the contribution of each organ component to mortality risk on Days 1 and 7 of an intensive care unit stay. We used data from two publicly available datasets, eICU Collaborative Research Database (eICU-CRD) (208 hospitals) and Medical Information Mart for Intensive Care IV (MIMIC-IV) (1 hospital). The odds ratio of each SOFA component that contributed to mortality was calculated. Mortality was defined as death either in the intensive care unit or within 72 hours of discharge from the intensive care unit., Results: A total of 7,871 intensive care unit stays from eICU-CRD and 4,926 intensive care unit stays from MIMIC-IV were included. Liver dysfunction was most predictive of mortality on Day 1 in both cohorts (OR 1.3; 95%CI 1.2 - 1.4; OR 1.3; 95%CI 1.2 - 1.4, respectively). In the eICU-CRD cohort, central nervous system dysfunction was most predictive of mortality on Day 7 (OR 1.4; 95%CI 1.4 - 1.5). In the MIMIC-IV cohort, respiratory dysfunction (OR 1.4; 95%CI 1.3 - 1.5) and cardiovascular dysfunction (OR 1.4; 95%CI 1.3 - 1.5) were most predictive of mortality on Day 7., Conclusion: The SOFA score may be an oversimplification of how dysfunction of different organ systems contributes to mortality over time. Further research at a more granular timescale is needed to explore how the SOFA score can evolve and be ameliorated.

Competing Interests: Conflicts of interest None.

Currently, numerous glycosides have been synthesized and used in clinical applications, neutraceuticals, cosmetics, and food processing. Structurally, a glycoside is composed of aglycone attaching to one or several sugar moieties so-called glycone. It is found that biochemical or biopharmaceutical properties of glycoside are mainly determined by its sugar part and thereby alternation of this glycone resulting in novel structure and characteristics as well. The use of traditional production methods of glycosides such as direct extraction and purification from plants, animals, or microorganisms is very challenging (laborious, time-consuming, technique, high price, low yield, etc.). Alternatively, the use of enzymatic methods for the biosynthesis of glycosides has become a highly promising tool. Particularly, the diverse structure of glycosides can be obtained using the promiscuous catalytic activity of glycosyltransferases (GT) mined from bioresources (plants, fungi, microorganisms, etc.). In addition, the exploration of GT catalytic promiscuity toward diverse aglycones, and glycones has indeed been interesting and played a key role in the production of novel glycosides. This review described the recent advances in glycosyltransferase-mediated glycodiversification of small molecules (flavonoids, steroids, terpenoids, etc.). Mostly, references were collected from 2014 to 2023., Competing Interests: Conflict of interestThe authors declare that they have no conflict of interest in the publication., (© King Abdulaziz City for Science and Technology 2024. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.)

Abstract: Venous thromboembolism (VTE) is a leading cause of preventable in-hospital mortality. Monitoring VTE cases is limited by the challenges of manual medical record review and diagnosis code interpretation. Natural language processing (NLP) can automate the process. Rule-based NLP methods are effective but time consuming. Machine learning (ML)-NLP methods present a promising solution. We conducted a systematic review and meta-analysis of studies published before May 2023 that use ML-NLP to identify VTE diagnoses in the electronic health records. Four reviewers screened all manuscripts, excluding studies that only used a rule-based method. A meta-analysis evaluated the pooled performance of each study's best performing model that evaluated for pulmonary embolism and/or deep vein thrombosis. Pooled sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) with confidence interval (CI) were calculated by DerSimonian and Laird method using a random-effects model. Study quality was assessed using an adapted TRIPOD (Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis) tool. Thirteen studies were included in the systematic review and 8 had data available for meta-analysis. Pooled sensitivity was 0.931 (95% CI, 0.881-0.962), specificity 0.984 (95% CI, 0.967-0.992), PPV 0.910 (95% CI, 0.865-0.941) and NPV 0.985 (95% CI, 0.977-0.990). All studies met at least 13 of the 21 NLP-modified TRIPOD items, demonstrating fair quality. The highest performing models used vectorization rather than bag-of-words and deep-learning techniques such as convolutional neural networks. There was significant heterogeneity in the studies, and only 4 validated their model on an external data set. Further standardization of ML studies can help progress this novel technology toward real-world implementation., (© 2024 by The American Society of Hematology. Licensed under Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0), permitting only noncommercial, nonderivative use with attribution. All other rights reserved.)

Objective: Leverage electronic health record (EHR) audit logs to develop a machine learning (ML) model that predicts which notes a clinician wants to review when seeing oncology patients., Materials and Methods: We trained logistic regression models using note metadata and a Term Frequency Inverse Document Frequency (TF-IDF) text representation. We evaluated performance with precision, recall, F1, AUC, and a clinical qualitative assessment., Results: The metadata only model achieved an AUC 0.930 and the metadata and TF-IDF model an AUC 0.937. Qualitative assessment revealed a need for better text representation and to further customize predictions for the user., Discussion: Our model effectively surfaces the top 10 notes a clinician wants to review when seeing an oncology patient. Further studies can characterize different types of clinician users and better tailor the task for different care settings., Conclusion: EHR audit logs can provide important relevance data for training ML models that assist with note-writing in the oncology setting., (© The Author(s) 2024. Published by Oxford University Press on behalf of the American Medical Informatics Association. All rights reserved. For permissions, please email: journals.permissions@oup.com.)

Venous thromboembolism (VTE) is the leading cause of preventable death in hospitalized patients. Artificial intelligence (AI) and machine learning (ML) can support guidelines recommending an individualized approach to risk assessment and prophylaxis. We conducted electronic surveys asking clinician and healthcare informaticians about their perspectives on AI/ML for VTE prevention and management. Of 101 respondents to the informatician survey, most were 40 years or older, male, clinicians and data scientists, and had performed research on AI/ML. Of the 607 US-based respondents to the clinician survey, most were 40 years or younger, female, physicians, and had never used AI to inform clinical practice. Most informaticians agreed that AI/ML can be used to manage VTE (56.0%). Over one-third were concerned that clinicians would not use the technology (38.9%), but the majority of clinicians believed that AI/ML probably or definitely can help with VTE prevention (70.1%). The most common concern in both groups was a perceived lack of transparency (informaticians 54.4%; clinicians 25.4%). These two surveys revealed that key stakeholders are interested in AI/ML for VTE prevention and management, and identified potential barriers to address prior to implementation., (© 2024. The Author(s).)

Background: Accurate diagnostic and prognostic predictions of venous thromboembolism (VTE) are crucial for VTE management. Artificial intelligence (AI) enables autonomous identification of the most predictive patterns from large complex data. Although evidence regarding its performance in VTE prediction is emerging, a comprehensive analysis of performance is lacking., Aims: To systematically review the performance of AI in the diagnosis and prediction of VTE and compare it to clinical risk assessment models (RAMs) or logistic regression models., Methods: A systematic literature search was performed using PubMed, MEDLINE, EMBASE, and Web of Science from inception to April 20, 2021. Search terms included "artificial intelligence" and "venous thromboembolism." Eligible criteria were original studies evaluating AI in the prediction of VTE in adults and reporting one of the following outcomes: sensitivity, specificity, positive predictive value, negative predictive value, or area under receiver operating curve (AUC). Risks of bias were assessed using the PROBAST tool. Unpaired t-test was performed to compare the mean AUC from AI versus conventional methods (RAMs or logistic regression models)., Results: A total of 20 studies were included. Number of participants ranged from 31 to 111 888. The AI-based models included artificial neural network (six studies), support vector machines (four studies), Bayesian methods (one study), super learner ensemble (one study), genetic programming (one study), unspecified machine learning models (two studies), and multiple machine learning models (five studies). Twelve studies (60%) had both training and testing cohorts. Among 14 studies (70%) where AUCs were reported, the mean AUC for AI versus conventional methods were 0.79 (95% CI: 0.74-0.85) versus 0.61 (95% CI: 0.54-0.68), respectively (p < .001). However, the good to excellent discriminative performance of AI methods is unlikely to be replicated when used in clinical practice, because most studies had high risk of bias due to missing data handling and outcome determination., Conclusion: The use of AI appears to improve the accuracy of diagnostic and prognostic prediction of VTE over conventional risk models; however, there was a high risk of bias observed across studies. Future studies should focus on transparent reporting, external validation, and clinical application of these models., (© 2023 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.)

Background: Venous thromboembolism (VTE) is a leading cause of preventable mortality among hospitalized patients, but appropriate risk assessment and thromboprophylaxis remain underutilized or misapplied., Objectives: We conducted an electronic survey of US health care providers to explore attitudes, practices, and barriers related to thromboprophylaxis in adult hospitalized patients and at discharge., Results: A total of 607 US respondents completed the survey: 63.1% reported working in an academic hospital, 70.7% identified as physicians, and hospital medicine was the most frequent specialty (52.1%). The majority of respondents agreed that VTE prophylaxis is important (98.8%; 95% CI: 97.6%-99.5%) and that current measures are safe (92.6%; 95% CI: 90.2%-94.5%) and effective (93.8%; 95% CI: 91.6%-95.6%), but only half (52.0%; 95% CI: 47.9%-56.0%) believed that hospitalized patients at their institution are on appropriate VTE prophylaxis almost all the time. One-third (35.4%) reported using a risk assessment model (RAM) to determine VTE prophylaxis need; 44.9% reported unfamiliarity with RAMs. The most common recommendation for improving rates of appropriate thromboprophylaxis was to leverage technology. A majority of respondents (84.5%) do not reassess a patient's need for VTE prophylaxis at discharge, and a minority educates patients about the risk (16.2%) or symptoms (18.9%) of VTE at discharge., Conclusion: Despite guideline recommendations to use RAMs, the majority of providers in our survey do not use them. A majority of respondents believed that technology could help improve VTE prophylaxis rates. A majority of respondents do not reassess the risk of VTE at discharge or educate patients about this risk of VTE at discharge., (© 2023 The Authors.)

Background: COVID-19 infection delays therapy and in-person evaluation for oncology patients, but clinic clearance criteria are not clearly defined., Methods: We conducted a retrospective review of oncology patients with COVID-19 at a tertiary care center during the Delta and Omicron waves and compared clearance strategies., Results: Median clearance by two consecutive negative tests was 32.0 days (Interquartile Range [IQR] 22.0-42.5, n = 153) and was prolonged in hematologic malignancy versus solid tumors (35.0 days for hematologic malignancy, 27.5 days for solid tumors, p = 0.01) and in patients receiving B-cell depletion versus other therapies. Median clearance by single negative test was reduced to 23.0 days (IQR 16.0-33.0), with recurrent positive rate 25.4% in hematologic malignancy versus 10.6% in solid tumors (p = 0.02). Clearance by a predefined waiting period required 41 days until an 80% negative rate., Conclusions: COVID-19 clearance remains prolonged in oncology patients. Single-negative test clearance can balance delays in care with risk of infection in patients with solid tumors., (© 2023 The Authors. Cancer Medicine published by John Wiley & Sons Ltd.)

Competing Interests: None declared.

Objectives: Federated learning (FL) allows multiple institutions to collaboratively develop a machine learning algorithm without sharing their data. Organizations instead share model parameters only, allowing them to benefit from a model built with a larger dataset while maintaining the privacy of their own data. We conducted a systematic review to evaluate the current state of FL in healthcare and discuss the limitations and promise of this technology., Methods: We conducted a literature search using PRISMA guidelines. At least two reviewers assessed each study for eligibility and extracted a predetermined set of data. The quality of each study was determined using the TRIPOD guideline and PROBAST tool., Results: 13 studies were included in the full systematic review. Most were in the field of oncology (6 of 13; 46.1%), followed by radiology (5 of 13; 38.5%). The majority evaluated imaging results, performed a binary classification prediction task via offline learning (n = 12; 92.3%), and used a centralized topology, aggregation server workflow (n = 10; 76.9%). Most studies were compliant with the major reporting requirements of the TRIPOD guidelines. In all, 6 of 13 (46.2%) of studies were judged at high risk of bias using the PROBAST tool and only 5 studies used publicly available data., Conclusion: Federated learning is a growing field in machine learning with many promising uses in healthcare. Few studies have been published to date. Our evaluation found that investigators can do more to address the risk of bias and increase transparency by adding steps for data homogeneity or sharing required metadata and code., Competing Interests: I have read the journal’s policy and the authors of this manuscript have the following competing interests: Leo Anthony Celi is the Editor-in Chief of PLOS Digital Health., (Copyright: © 2022 Crowson et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

In 2018-2019, at the Keck School of Medicine of the University of Southern California (KSOM), we developed and piloted a narrative-based health systems science intervention for patients living with HIV and medical students in which medical students co-wrote patients' life narratives for inclusion in the electronic health record. The pilot study aimed to assess the acceptability of the "life narrative protocol" (LNP) from multiple stakeholder positions and characterize participants' experiences of the clinical and pedagogical implications of the LNP. Students were recruited from KSOM. Patients and staff were recruited from the Maternal, Child, and Adolescent/Adult Center for Infectious Disease and Virology (MCA) at Los Angeles County+USC Medical Center. Ten patients, seventeen students, and ten MCA staff participated in the pilot study. Qualitative methods were used to gather data from students', patients', and staff's perspectives. Three themes emerged from the thematic analysis: (1) patients' life narratives conveyed their unique life experiences and voices; (2) the protocol could result in wide-ranging effects on HIV care; (3) the LNP enabled students to contribute value to patients' healthcare. Across groups, participants considered the LNP an acceptable intervention. The LNP, its limitations, and implications for HIV care, narrative medicine, and health information technology are presented., (© 2021. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.)

The Cures Act made access to electronic visit notes ('open notes') nearly universal across the USA, and efforts to share open notes with patients are underway worldwide. This landmark policy change provides an opportunity to engage adolescents and young adults (AYA) early in their care, yet little is known about their attitudes related to reading notes. We compared the responses of 332 AYA (13-25 years old) and 6,914 adults (>25 years old) in a 2016 survey at two USA academic adult and paediatric hospitals. Over 85% of AYA and adults with available notes reported reading at least one note in the prior year. AYA reported similar benefits from note-reading to adults in 15 outcomes related to engagement, relational effects and safety behaviours, supporting efforts to engage AYA as partners in their care using open notes., (© Royal College of Physicians 2021. All rights reserved.)

Background: This retrospective review evaluated the causes of severe eosinophilia (≥5000 eosinophils/L). Higher eosinophilia levels are more likely to cause tissue damage and may reflect disease severity., Methods: We reviewed 193 cases of patients seen at Beth Israel Deaconess Medical Center in Boston, Massachusetts, and at the University of Vermont Medical Center in Burlington, Vermont, between January 2015 to May 2020 who had a peak absolute eosinophil count of at least 5000/μL., Results: Thirty-nine percent of cases were attributable to a hematologic or oncologic cause. These cases had the highest mean peak absolute eosinophil count at 11,698/μL. Twenty percent of cases were secondary to drug reactions, of which 90% took place in an inpatient setting. Three percent of cases were from helminthic infection, the majority of which were in returning travelers., Conclusions: In our region of study, hematologic and oncologic cases are important causes of severe eosinophilia, drug reactions are a common etiology in the inpatient setting, and infections are a rare cause., (Copyright © 2020 Elsevier Inc. All rights reserved.)

Background: Open notes invite patients and families to read ambulatory visit notes through the patient portal. Little is known about the extent to which they identify and speak up about perceived errors. Understanding the barriers to speaking up can inform quality improvements., Objective: To describe patient and family attitudes, experiences, and barriers related to speaking up about perceived serious note errors., Methods: Mixed method analysis of a 2016 electronic survey of patients and families at 2 northeast US academic medical centers. Participants had active patient portal accounts and at least 1 note available in the preceding 12 months., Results: 6913 adult patients (response rate 28%) and 3672 pediatric families (response rate 17%) completed the survey. In total, 8724/9392 (93%) agreed that reporting mistakes improves patient safety. Among 8648 participants who read a note, 1434 (17%) perceived ≥1 mistake. 627/1434 (44%) reported the mistake was serious and 342/627 (56%) contacted their provider. Participants who self-identified as Black or African American, Asian, "other," or "multiple" race(s) (OR 0.50; 95% CI (0.26,0.97)) or those who reported poorer health (OR 0.58; 95% CI (0.37,0.90)) were each less likely to speak up than white or healthier respondents, respectively. The most common barriers to speaking up were not knowing how to report a mistake (61%) and avoiding perception as a "troublemaker" (34%). Qualitative analysis of 476 free-text suggestions revealed practical recommendations and proposed innovations for partnering with patients and families., Conclusions: About half of patients and families who perceived a serious mistake in their notes reported it. Identified barriers demonstrate modifiable issues such as establishing clear mechanisms for reporting and more challenging issues such as creating a supportive culture. Respondents offered new ideas for engaging patients and families in improving note accuracy., (Published by Oxford University Press on behalf of the American Medical Informatics Association 2020.)

Competing Interests: Competing interests None declared.

Background & Aims: Dietary saturated fatty acids contribute to the development of fatty liver and have pathogenic link to systemic inflammation. We investigated the effects of dietary fat towards the pathogenesis of non-alcoholic fatty liver disease by longitudinal in vivo magnetic resonance spectroscopy (MRS) and in vitro liquid chromatography coupled with mass spectrometry (LC-MS)., Methods: All measurements were performed on rats fed with high fat diet (HFD) and chow diet for twenty four weeks. Longitudinal MRS measurements were performed at the 12th, 18th and 24th weeks. Liver tissues were analyzed by LC-MS, histology and gene transcription studies after terminal in vivo experiments., Results: Liver fat content of HFD rats for all ages was significantly (P<0.05) higher compared to their respective chow diet fed rats. Unsaturation indices estimated from MRS and LC-MS data of chow diet fed rats were significantly higher (P<0.05) than HFD fed rats. The concentration of triglycerides 48∶1, 48∶2, 50∶1, 50∶2, 50∶3, 52∶1, 52∶2, 52∶3, 54∶3 and 54∶2 was significantly higher (P<0.05) in HFD rats. The concentration for some polyunsaturated triglycerides 54∶7, 56∶8, 56∶7, 58∶11, 58∶10, 58∶9, 58∶8 and 60∶10 was significantly higher (P<0.05) in chow diet fed rats compared to HFD rats. Lysophospholipid concentrations including LPC and LPE were higher in HFD rats at 24 weeks indicating the increased risk of diabetes. The expression of CD36, PPARα, SCD1, SREBF1 and UCP2 were significantly upregulated in HFD rats., Conclusions: We demonstrated the early changes in saturated and unsaturated lipid composition in fatty liver by in vivo MRS and ex vivo LC-MS. The higher LPC concentration in HFD rats indicated a higher risk of developing diabetes. Early metabolic perturbations causing changes in lipid composition can be evaluated by the unsaturation index and correlated to the non alcoholic fatty liver disease.

Study Objectives: During normal sleep several neuroplasticity changes occur, some of which are considered to be fundamental to strengthen memories. Given the evidence linking sleep to neuroplasticity, it is conceivable that individuals with chronic sleep disruption, such as patients with chronic insomnia (CI), would experience abnormalities in neuroplastic processes during daytime. Protocols testing use-dependent plasticity (UDP), one of the mechanisms underlying formation of motor memories traces, provide a sensitive measure to assess neuroplasticity in the context of motor training., Design and Participants: A well-established transcranial magnetic stimulation (TMS) paradigm was used to evaluate the ability of patients with CI and age-matched good sleeper controls to undergo UDP. We also investigated the effect of insomnia on intracortical motor excitability measures reflecting GABAergic and glutamatergic mechanisms., Setting: Human Brain Physiology Laboratory, Johns Hopkins Medical Institutions., Measurements and Results: We found that patients with CI experienced increased UDP changes relative to controls. This effect was not due to differences in motor training. In addition, patients with CI showed enhanced intracortical facilitation relative to controls, in the absence of changes in intracortical inhibitory measures., Conclusion: This study provides the first evidence that patients with chronic insomnia have an increased plasticity response to physical exercise, possibly due to larger activation of glutamatergic mechanisms. This suggests a heightened state of neuroplasticity, which may reflect a form of maladaptive plasticity, similar to what has been described in dystonia patients and chronic phantom pain after amputation. These results could lead to development of novel treatments for chronic insomnia.

The circadian system regulates daily rhythms in lipid metabolism and adipose tissue function. Although disruption of circadian clock function is associated with negative cardiometabolic end points, very little is known about interindividual variation in circadian-regulated metabolic pathways. Here, we used targeted lipidomics-based approaches to profile the time course of 263 lipids in blood plasma in 20 healthy individuals. Over a span of 28 h, blood was collected every 4 h and plasma lipids were analyzed by HPLC/MS. Across subjects, about 13% of lipid metabolites showed circadian variation. Rhythmicity spanned all metabolite classes examined, suggesting widespread circadian control of lipid-mediated energy storage, transport, and signaling. Intersubject agreement for lipids identified as rhythmic was only about 20%, however, and the timing of lipid rhythms ranged up to 12 h apart between individuals. Healthy subjects therefore showed substantial variation in the timing and strength of rhythms across different lipid species. Strong interindividual differences were also observed for rhythms of blood glucose and insulin, but not cortisol. Using consensus clustering with iterative feature selection, subjects clustered into different groups based on strength of rhythmicity for a subset of triglycerides and phosphatidylcholines, suggesting that there are different circadian metabolic phenotypes in the general population. These results have potential implications for lipid metabolism disorders linked to circadian clock disruption.

This work is to explore the potential of producing biodiesel and valuable co-products from the biomass of Schizochytrium mangrovei PQ6 which was isolated from Phu Quoc Island, Kien Giang province, Vietnam. This microalga contains high lipid content (up to 70% of dry cell weight) and high level of total fatty acids, which is ideal for making biodiesel. The production of fatty acid methyl esters (FAME) from this marine microalga resulted in a yield of 88% based on algal oil and 44% based on algal biomass. The process of separating the obtained FAME into a first fraction enriched with saturated FAME (SFAME) and a second fraction enriched with unsaturated FAME (UFAME) was then investigated to exploit the valuable co-products. The obtained results shown that the mass fraction of SFAME and UFAME were 70% and 30%, respectively. The UFAME fraction contains a high content of DHA (accounting for 69.000% of TFAs). The test results of the SFAME fraction indicated that specific gravity at 15°C, flash point, water and sediment, kinematic viscosity at 40°C, sulfated ash, sulfur, copper strip corrosion at 50°C, cetane number, carbon residue, iodine number, workmanship meet Vietnam Biodiesel B100 Standard. Moreover, the utilization of waste glycerol from biodiesel process as carbon source for the cultivation of the microalgae S. mangrovei PQ6 and Spirulina platensis was also investigated., (Copyright © 2013 The Society for Biotechnology, Japan. Published by Elsevier B.V. All rights reserved.)

Cellular signaling by small GTPases is critically dependent on proper spatio-temporal orchestration of activation and output. In addition to their core G (guanine nucleotide binding)-domain, small GTPases comprise a hypervariable region (HVR) and a lipid anchor that are generally accepted to control subcellullar localization. The HVR encodes in many small GTPases a polybasic region (PBR) that permits charge-mediated association to the inner leaflet of the plasma membrane or to intracellular organelles. Over the past 15-20 years, evidence has accumulated for specific protein-protein interactions, mediated by the HVR, that control both targeting and signaling specificity of small GTPases. Using the RhoGTPase Rac1 as a paradigm we here review a series of protein partners that require the Rac1 HVR for association and that control various aspects of localized Rac1 signaling. Some of these proteins represent Rac1 activators, whereas others mediate Rac1 inactivation and degradation and yet others potentiate Rac1 downstream signaling. Finally, evidence is discussed which shows that the HVR of Rac1 also contributes to effector interactions, co-operating with the N-terminal effector domain. The complexity of localized Rac1 signaling, reviewed here, is most likely exemplary for many other small GTPases as well, representing a challenge to identify and define similar mechanisms controlling the specific signaling induced by small GTPases.

The RhoGTPase Rac1 is activated in a polarised fashion and controls cell motility. We previously showed that Rac1 binds the PP2A inhibitor SET and recruits nuclear SET to the cytosol. We show that a SET mutant, lacking a nuclear localization signal, SET(ΔNLS), promotes cell spreading and motility. This was accompanied by an increase in the number and frequency of membrane ruffles. Pharmacological inhibition of PP2A did not mimic the effects of SET(ΔNLS), however, we found that expression of SET and SET(ΔNLS) increases the levels of the MAP kinases ERK1 and ERK2., (Copyright © 2012 Federation of European Biochemical Societies. Published by Elsevier B.V. All rights reserved.)

SET/I2PP2A is a nuclear protein that was initially identified as an oncogene in human undifferentiated acute myeloid leukemia, fused to the nuclear porin Nup-214. In addition, SET is a potent inhibitior of the phosphatase PP2A. Previously, we proposed a model in which the small GTPase Rac1 recruits SET from the nucleus to the plasma membrane to promote cell migration. This event represents an entirely novel concept in the field of cell migration. Now, fluorescent versions of the SET protein are generated to analyze its nucleo-cytoplasmic shuttling in live cells. Our studies showed that under steady-state conditions a fraction of the SET protein, which is primarily localized in the nucleus, translocates to the cytosol in an apparently random fashion. SET exiting the nucleus was also seen in spreading as well as dividing cells. We designed an image analysis method to quantify the frequency of nuclear exit of the SET proteins, based on 4D confocal imaging. This straightforward method was validated by analysis of SET wild-type and mutant proteins. This showed that the frequency of nuclear exit of a Ser-9 phosphomimetic mutant (S9E) is enhanced compared to wild-type SET or a S9A mutant. Thus, we have developed a novel method to analyze the nucleo-cytoplasmic shuttling of the proto-oncogene SET dynamics in live cells. This method will also be applicable to monitor dynamic localization of other nuclear and/or cytoplasmic signaling proteins., (Copyright © 2011 International Society for Advancement of Cytometry.)

Background: Non-human primates (NHP) are now being considered as models for investigating human metabolic diseases including diabetes. Analyses of cholesterol and triglycerides in plasma derived from NHPs can easily be achieved using methods employed in humans. Information pertaining to other lipid species in monkey plasma, however, is lacking and requires comprehensive experimental analysis., Methodologies/principal Findings: We examined the plasma lipidome from 16 cynomolgus monkey, Macaca fascicularis, using liquid chromatography coupled with mass spectrometry (LC/MS). We established novel analytical approaches, which are based on a simple gradient elution, to quantify polar lipids in plasma including (i) glycerophospholipids (phosphatidylcholine, PC; phosphatidylethanolamine, PE; phosphatidylinositol, PI; phosphatidylglycerol, PG; phosphatidylserine, PS; phosphatidic acid, PA); (ii) sphingolipids (sphingomyelin, SM; ceramide, Cer; Glucocyl-ceramide, GluCer; ganglioside mannoside 3, GM3). Lipidomic analysis had revealed that the plasma of human and cynomolgus monkey were of similar compositions, with PC, SM, PE, LPC and PI constituting the major polar lipid species present. Human plasma contained significantly higher levels of plasmalogen PE species (p<0.005) and plasmalogen PC species (p<0.0005), while cynomolgus monkey had higher levels of polyunsaturated fatty acyls (PUFA) in PC, PE, PS and PI. Notably, cynomolgus monkey had significantly lower levels of glycosphingolipids, including GluCer (p<0.0005) and GM(3) (p<0.0005), but higher level of Cer (p<0.0005) in plasma than human. We next investigated the biochemical alterations in blood lipids of 8 naturally occurring diabetic cynomolgus monkeys when compared with 8 healthy controls., Conclusions: For the first time, we demonstrated that the plasma of human and cynomolgus monkey were of similar compositions, but contained different mol distribution of individual molecular species. Diabetic monkeys exhibited decreased levels of sphingolipids, which are microdomain-associated lipids and are thought to be associated with insulin sensitivity. Significant increases in PG species, which are precursors for cardiolipin biosynthesis in mitochondria, were found in fasted diabetic monkeys (n = 8).