RESUMEN

Computational modeling has emerged as a time-saving and cost-effective alternative to traditional animal testing for assessing chemicals for their potential hazards. However, few computational modeling studies for immunotoxicity were reported, with few models available for predicting toxicants due to the lack of training data and the complex mechanisms of immunotoxicity. In this study, we employed a data-driven quantitative structure-activity relationship (QSAR) modeling workflow to extensively enlarge the limited training data by revealing multiple targets involved in immunotoxicity. To this end, a probe data set of 6,341 chemicals was obtained from a high-throughput screening (HTS) assay testing for the activation of the aryl hydrocarbon receptor (AhR) signaling pathway, a key event leading to immunotoxicity. Searching this probe data set against PubChem yielded 3,183 assays with testing results for varying proportions of these 6,341 compounds. 100 assays were selected to develop QSAR models based on their correlations to AhR agonism. Twelve individual QSAR models were built for each assay using combinations of four machine-learning algorithms and three molecular fingerprints. 5-fold cross-validation of the resulting models showed good predictivity (average CCR = 0.73). A total of 20 assays were further selected based on QSAR model performance, and their resulting QSAR models showed good predictivity of potential immunotoxicants from external chemicals. This study provides a computational modeling strategy that can utilize large public toxicity data sets for modeling immunotoxicity and other toxicity endpoints, which have limited training data and complicated toxicity mechanisms.

RESUMEN

Developing mechanistic non-animal testing methods based on the adverse outcome pathway (AOP) framework must incorporate molecular and cellular key events associated with target toxicity. Using data from an in vitro assay and chemical structures, we aimed to create a hybrid model to predict hepatotoxicants. We first curated a reference dataset of 869 compounds for hepatotoxicity modeling. Then, we profiled them against PubChem for existing in vitro toxicity data. Of the 2560 resulting assays, we selected the mitochondrial membrane potential (MMP) assay, a high-throughput screening (HTS) tool that can test chemical disruptors for mitochondrial function. Machine learning was applied to develop quantitative structure-activity relationship (QSAR) models with 2536 compounds tested in the MMP assay for screening new compounds. The MMP assay results, including QSAR model outputs, yielded hepatotoxicity predictions for reference set compounds with a Correct Classification Ratio (CCR) of 0.59. The predictivity improved by including 37 structural alerts (CCR = 0.8). We validated our model by testing 37 reference set compounds in human HepG2 hepatoma cells, and reliably predicting them for hepatotoxicity (CCR = 0.79). This study introduces a novel AOP modeling strategy that combines public HTS data, computational modeling, and experimental testing to predict chemical hepatotoxicity.

Asunto(s)

RESUMEN

Traditional methodologies for assessing chemical toxicity are expensive and time-consuming. Computational modeling approaches have emerged as low-cost alternatives, especially those used to develop quantitative structure-activity relationship (QSAR) models. However, conventional QSAR models have limited training data, leading to low predictivity for new compounds. We developed a data-driven modeling approach for constructing carcinogenicity-related models and used these models to identify potential new human carcinogens. To this goal, we used a probe carcinogen dataset from the US Environmental Protection Agency's Integrated Risk Information System (IRIS) to identify relevant PubChem bioassays. Responses of 25 PubChem assays were significantly relevant to carcinogenicity. Eight assays inferred carcinogenicity predictivity and were selected for QSAR model training. Using 5 machine learning algorithms and 3 types of chemical fingerprints, 15 QSAR models were developed for each PubChem assay dataset. These models showed acceptable predictivity during 5-fold cross-validation (average CCR = 0.71). Using our QSAR models, we can correctly predict and rank 342 IRIS compounds' carcinogenic potentials (PPV = 0.72). The models predicted potential new carcinogens, which were validated by a literature search. This study portends an automated technique that can be applied to prioritize potential toxicants using validated QSAR models based on extensive training sets from public data resources.

Asunto(s)

RESUMEN

Advances in high-throughput screening (HTS) revolutionized the environmental and health sciences data landscape. However, new compounds still need to be experimentally synthesized and tested to obtain HTS data, which will still be costly and time-consuming when a large set of new compounds need to be studied against many tests. Quantitative structure-activity relationship (QSAR) modeling is a standard method to fill data gaps for new compounds. The major challenge for many toxicologists, especially those with limited computational backgrounds, is efficiently developing optimized QSAR models for each assay with missing data for certain test compounds. This chapter aims to introduce a freely available and user-friendly QSAR modeling workflow, which trains and optimizes models using five algorithms without the need for a programming background.

Asunto(s)

RESUMEN

Glucocorticoids (GCs) and ß2-adrenergic receptor (ß2AR) agonists improve asthma outcomes in most patients. GCs also modulate gene expression in human airway smooth muscle (HASM), thereby attenuating airway inflammation and airway hyperresponsiveness that define asthma. Our previous studies showed that the pro-fibrotic cytokine, transforming growth factor- ß1 (TGF-ß1) increases phosphodiesterase 4D (PDE4D) expression that attenuates agonist-induced levels of intracellular cAMP. Decreased cAMP levels then diminishes ß2 agonist-induced airway relaxation. In the current study, we investigated whether glucocorticoids reverse TGF-ß1-effects on ß2-agonist-induced bronchodilation and modulate pde4d gene expression in HASM. Dexamethasone (DEX) reversed TGF-ß1 effects on cAMP levels induced by isoproterenol (ISO). TGF-ß1 also attenuated G protein-dependent responses to cholera toxin (CTX), a Gαs stimulator downstream from the ß2AR receptor. Previously, we demonstrated that TGF-ß1 treatment increased ß2AR phosphorylation to induce hyporesponsiveness to a ß2 agonist. Our current data shows that expression of grk2/3, kinases associated with attenuation of ß2AR function, are not altered with TGF-ß1 stimulation. Interestingly, DEX also attenuated TGF-ß1-induced pde4d gene expression. These data suggest that steroids may be an effective therapy for treatment of asthma patients whose disease is primarily driven by elevated TGF-ß1 levels.

Asunto(s)

RESUMEN

BACKGROUND: The prevalence of nuisance (technical) alarms is the leading cause of alarm fatigue resulting in decreased awareness and a reduction in effective care. The Joint Commission identified in their National Patient Safety goals alarm fatigue as a major safety issue. The introduction of noninvasive respiratory volume monitoring (RVM) has implications for effective perioperative respiratory status management. We evaluated this within the Kaiser Permanente health system. METHODS: This observational study was conducted at 4 hospitals in the Kaiser Permanente system. Standard data from RVM, pulse oximetry, and capnography were collected postoperatively in the post-anesthesia care unit (PACU) and/or on the general hospital floor. Device-specific alarm types, rates, and respective actions were recorded and analyzed by non-study staff. RESULTS: RVM was applied to 247 subjects (143 females, body mass index 32.3 ± 8.7 kg/m2, age 60.9 ± 13.9 y) providing 2,321 h. RVM alarms occurred 605 times (0.25 alarms/h); 64% were actionable and addressed, 17% were not addressed, 13% were self-resolved, and only 6% were nuisance. In a subgroup, RVM completed all 127 h of monitoring, whereas oximetry with capnography only completed 51 h with 12.9 alarms/h (73% nuisance). The overall RVM alarm rate was significantly lower than with either pulse oximeters or capnography monitors. We saw a nearly 1,000-fold reduction in nuisance alarms compared to capnography and a 20-50-fold reduction in nuisance alarms compared to pulse oximetry. CONCLUSIONS: Our study indicates that alarm fatigue due to nuisance alarms continues to be a clinical challenge in perioperative settings. Among the 3 common technologies for respiratory function monitoring, RVM had the lowest rate of overall technical alarms and the highest rate of compliance. Furthermore, with early interventions, none of the subjects monitored with RVM suffered any negative outcomes.

Asunto(s)

RESUMEN

Helper T effector cytokines implicated in asthma modulate the contractility of human airway smooth muscle (HASM) cells. We have reported recently that a profibrotic cytokine, transforming growth factor (TGF)-ß1, induces HASM cell shortening and airway hyperresponsiveness. Here, we assessed whether TGF-ß1 affects the ability of HASM cells to relax in response to ß2-agonists, a mainstay treatment for airway hyperresponsiveness in asthma. Overnight TGF-ß1 treatment significantly impaired isoproterenol (ISO)-induced relaxation of carbachol-stimulated, isolated HASM cells. This single-cell mechanical hyporesponsiveness to ISO was corroborated by sustained increases in myosin light chain phosphorylation. In TGF-ß1-treated HASM cells, ISO evoked markedly lower levels of intracellular cAMP. These attenuated cAMP levels were, in turn, restored with pharmacological and siRNA inhibition of phosphodiesterase 4 and Smad3, respectively. Most strikingly, TGF-ß1 selectively induced phosphodiesterase 4D gene expression in HASM cells in a Smad2/3-dependent manner. Together, these data suggest that TGF-ß1 decreases HASM cell ß2-agonist relaxation responses by modulating intracellular cAMP levels via a Smad2/3-dependent mechanism. Our findings further define the mechanisms underlying ß2-agonist hyporesponsiveness in asthma, and suggest TGF-ß1 as a potential therapeutic target to decrease asthma exacerbations in severe and treatment-resistant asthma.

Asunto(s)

RESUMEN

BACKGROUND: Postoperative mortality and complications after geriatric hip fracture surgery remain high despite efforts to improve perioperative care for these patients. One factor of particular interest is anesthetic technique, but prior studies on this are limited by sample selection, competing risks, and incomplete followup. QUESTIONS/PURPOSES: (1) Among older patients undergoing surgery for hip fracture, does 90-day mortality differ depending on the type of anesthesia received? (2) Do 90-day emergency department returns and hospital readmissions differ based on anesthetic technique after geriatric hip fracture repairs? (3) Do 90-day Agency for Healthcare Research and Quality (AHRQ) outcomes differ according to anesthetic techniques used during hip fracture surgery? METHODS: We conducted a retrospective study on geriatric patients (65 years or older) with hip fractures between 2009 and 2014 using the Kaiser Permanente Hip Fracture Registry. A total of 1995 (11%) of the surgically treated patients with hip fracture were excluded as a result of missing anesthesia information. The final study sample consisted of 16,695 patients. Of these, 2027 (12%) died and 98 (< 1%) terminated membership during followup, which were handled as competing events and censoring events, respectively. Ninety-day mortality, emergency department returns, hospital readmission, deep vein thrombosis (DVT) or pulmonary embolism (PE), myocardial infarction (MI), and pneumonia were evaluated using multivariable competing risk proportional subdistribution hazard regression according to type of anesthesia technique: general anesthesia, regional anesthesia, or conversion from regional to general. Of the 16,695 patients, 58% (N = 9629) received general anesthesia, 40% (N = 6597) received regional anesthesia, and 2.8% (N = 469) patients were converted from regional to general. RESULTS: Compared with regional anesthesia, patients treated with general anesthesia had a higher likelihood of overall 90-day mortality (hazard ratio [HR], 1.22; 95% confidence interval [CI], 1.11-1.35; p < 0.001); however, when stratified by before and after hospital discharge but within 90 days of surgery, this higher risk was only observed during the inpatient stay (HR, 3.83; 95% CI, 3.18-4.61; p < 0.001); no difference was observed after hospital discharge (HR, 1.04; 95% CI, 0.94-1.16; p = 0.408). Patients undergoing conversion from regional to general also had a higher overall mortality risk compared with those undergoing regional anesthesia (HR, 1.34; 95% CI 1.04-1.74; p = 0.026), but this risk was only observed during their inpatient stay (HR, 6.84; 95% CI, 4.21-11.11; p < 0.001) when stratifying by before and after hospital discharge. Patients undergoing general anesthesia had a higher risk for all-cause readmission when compared with regional anesthesia (HR, 1.09; 95% CI, 1.01-1.19; p = 0.026). No differences according to anesthesia type were observed for risk of 90-day AHRQ outcomes, including DVT/PE, MI, and pneumonia. CONCLUSIONS: We found the use of general anesthesia and conversion from regional to general anesthesia were associated with a higher risk of mortality during the in-hospital stay compared with regional anesthetic techniques, but this higher risk did not persist after hospital discharge. We also found general anesthesia to be associated with a higher risk of all-cause readmission compared with regional, but no other differences were observed in risk for complications. Our findings suggest regional anesthetic techniques may be preferred when possible in this patient population. LEVEL OF EVIDENCE: Level III, therapeutic study.

Asunto(s)

RESUMEN

Hemodynamic monitoring and optimization improve postoperative outcome during high-risk surgery. However, hemodynamic management practices among Chinese anesthesiologists are largely unknown. This study sought to evaluate the current intraoperative hemodynamic management practices for high-risk surgery patients in China. From September 2010 to November 2011, we surveyed anesthesiologists working in the operating rooms of 265 hospitals representing 28 Chinese provinces. All questionnaires were distributed to department chairs of anesthesiology or practicing anesthesiologists. Once completed, the 29-item questionnaires were collected and analyzed. Two hundred and 10 questionnaires from 265 hospitals in China were collected. We found that 91.4% of anesthesiologists monitored invasive arterial pressure, 82.9% monitored central venous pressure (CVP), 13.3% monitored cardiac output (CO), 10.5% monitored mixed venous saturation, and less than 2% monitored pulse pressure variation (PPV) or systolic pressure variation (SPV) during high-risk surgery. The majority (88%) of anesthesiologists relied on clinical experience as an indicator for volume expansion and more than 80% relied on blood pressure, CVP and urine output. Anesthesiologists in China do not own enough attention on hemodynamic parameters such as PPV, SPV and CO during fluid management in high-risk surgical patients. The lack of CO monitoring may be attributed largely to the limited access to technologies, the cost of the devices and the lack of education on how to use them. There is a need for improving access to these technologies as well as an opportunity to create guidelines and education for hemodynamic optimization in China.

RESUMEN

Blood pressure monitoring has come a long way from the initial observations made by Reverend Hales in the 18th century. There are none that deny the importance of monitoring perioperative blood pressure; however, the limited ability of the current prevalent technology (oscillometric blood pressure monitoring) to offer continuous blood pressure measurements leaves room for improvement. Invasive monitoring is able to detect beat-to-beat blood pressure measurement, but the risks inherent to the procedure make it unsuitable for routine use except when this risk is outweighed by the benefits. This review focuses on the discoveries which have led up to the current blood pressure monitoring technologies, and especially the creation of those offering non-invasive but continuous blood pressure monitoring capabilities, including their methods of measurement and limitations.

Asunto(s)

RESUMEN

OBJECTIVE: The authors compared the performance of a group of anesthesia providers to closed-loop (Learning Intravenous Resuscitator [LIR]) management in a simulated hemorrhage scenario using cardiac output monitoring. DESIGN: A prospective cohort study. SETTING: In silico simulation. PARTICIPANTS: University hospital anesthesiologists and the LIR closed-loop fluid administration system. INTERVENTIONS: Using a patient simulator, a 90-minute simulated hemorrhage protocol was run, which included a 1,200-mL blood loss over 30 minutes. Twenty practicing anesthesiology providers were asked to manage this scenario by providing fluids and vasopressor medication at their discretion. The simulation program was also run 20 times with the LIR closed-loop algorithm managing fluids and an additional 20 times with no intervention. MEASUREMENTS AND MAIN RESULTS: Simulated patient weight, height, heart rate, mean arterial pressure, and cardiac output (CO) were similar at baseline. The mean stroke volume, the mean arterial pressure, CO, and the final CO were higher in the closed-loop group than in the practitioners group, and the coefficient of variance was lower. The closed-loop group received slightly more fluid (2.1 v 1.9 L, p < 0.05) than the anesthesiologist group. CONCLUSIONS: Despite the roughly similar volumes of fluid given, the closed-loop maintained more stable hemodynamics than the practitioners primarily because the fluid was given earlier in the protocol and CO optimized before the hemorrhage began, whereas practitioners tended to resuscitate well but only after significant hemodynamic change indicated the need. Overall, these data support the potential usefulness of this closed-loop algorithm in clinical settings in which dynamic predictors are not available or applicable.

Asunto(s)

Asunto(s)

RESUMEN

The Nexfin device allows for non-invasive beat-to-beat blood pressure monitoring (BP(NXF)). Perioperative hypotension and hypertension have been shown to be associated with poor clinical outcomes. The goal of the present study was to assess the ability of this device to decrease the duration of significant intraoperative hypo- or hypertension compared to standard BP monitoring by cuff (BP(CUFF)). We studied25 patients (ASA I-III) undergoing either abdominal or orthopedic surgery. BP(CUFF) was monitored every 5 min from the introduction of anesthesia, while BP(NXF) was monitored continuously on the opposite arm. When systolic BP(NXF) (SBP(NXF)) decreased or increased more than 20% relative to baseline SBP(NXF), a standard BP(CUFF) measurement was taken to compare values. In addition, the time interval between the 20% change in SBP(NXF) and the next scheduled standard SBP(CUFF) measurement was recorded for each event. The mean length of surgery was 3.0 ± 0.3 h. Patients presented with 11 ± 4 episodes of hypotension and 12 ± 4 episodes of hypertension during the surgery. If BP(CUFF) had been used, this would have resulted in 21 ± 7 min of hypotension and 20 ± 10 min of hypertension. If hemodynamic changes seen by SBP(NXF) were appropriately treated, an average of 7 ± 1 min/h of hypotension time, 7 ± 2 min/h of hypertension time and 14 ± 3 min per hour of hypo- or hypertension time may have been identified. The Nexfin BP has the potential to decrease the time of hypotension and hypertension compared to conventional intermittent BP(CUFF) monitoring. Therefore, this device has the potential to positively impact clinical outcomes.

Asunto(s)