Clinical effectiveness of the psychological therapy Mental Health Intervention for Children with Epilepsy in addition to usual care compared with assessment-enhanced usual care alone: a multicentre, randomised controlled clinical trial in the UK.

BACKGROUND: Mental health difficulties are common in children and young people with chronic health conditions, but many of those in need do not access evidence-based psychological treatments. The study aim was to evaluate the clinical effectiveness of integrated mental health treatment for children and young people with epilepsy, a common chronic health condition known to be associated with a particularly high rate of co-occurring mental health difficulties. METHODS: We conducted a parallel group, multicentre, open-label, randomised controlled trial of participants aged 3-18 years, attending epilepsy clinics across England and Northern Ireland who met diagnostic criteria for a common mental health disorder. Participants were randomised (1:1; using an independent web-based system) to receive the Mental Health Intervention for Children with Epilepsy (MICE) in addition to usual care, or assessment-enhanced usual care alone (control). Children and young people in both groups received a full diagnostic mental health assessment. MICE was a modular psychological intervention designed to treat common mental health conditions in children and young people using evidence-based approaches such as cognitive behaviour therapy and behavioural parenting strategies. Usual care for mental health disorders varied by site but typically included referral to appropriate services. Participants, along with their caregivers, and clinicians were not masked to treatment allocation but statisticians were masked until the point of analysis. The primary outcome, analysed by modified intention-to-treat, was the parent-report Strengths and Difficulties Questionnaire (SDQ) at 6 months post-randomisation. The study is complete and registered with ISRCTN (57823197). FINDINGS: 1401 young people were potentially deemed eligible for study inclusion. Following the exclusion of 531 young people, 870 participants were assessed for eligibility and completed the SDQ, and 480 caregivers provided consent for study inclusion between May 20, 2019, and Jan 31, 2022. Between Aug 28, 2019, and Feb 21, 2022, 334 participants (mean ages 10·5 years [SD 3·6] in the MICE group vs 10·3 [4·0] in control group at baseline) were randomly assigned to an intervention using minimisation balanced by age, primary mental health disorder, diagnosis of intellectual disability, and autistic spectrum disorder at baseline. 168 (50%) of the participants were female and 166 (50%) were male. 166 participants were randomly assigned to the MICE group and 168 were randomly assigned to the control group. At 6 months, the mean SDQ difficulties for the 148 participants in the MICE group was 17·6 (SD 6·3) and 19·6 (6·1) for the 148 participants in the control group. The adjusted effect of MICE was -1·7 (95% CI -2·8 to -0·5; p=0·0040; Cohen's d, 0·3). 14 (8%) patients in the MICE group experienced at least one serious adverse event compared with 24 (14%) in the control group. 68% percent of serious adverse events (50 events) were admission due to seizures. INTERPRETATION: MICE was superior to assessment-enhanced usual care in improving symptoms of emotional and behavioural difficulties in young people with epilepsy and common mental health disorders. The trial therefore shows that mental health comorbidities can be effectively and safely treated by a variety of clinicians, utilising an integrated intervention across ages and in the context of intellectual disability and autism. The evidence from this trial suggests that such a model should be fully embedded in epilepsy services and serves as a model for other chronic health conditions in young people. FUNDING: UK National Institute for Health Research Programme Grants for Applied Research programme and Epilepsy Research UK Endeavour Project Grant.

Enhancer sequence variants and transcription-factor deregulation synergize to construct pathogenic regulatory circuits in B-cell lymphoma.

Most B-cell lymphomas arise in the germinal center (GC), where humoral immune responses evolve from potentially oncogenic cycles of mutation, proliferation, and clonal selection. Although lymphoma gene expression diverges significantly from GC B cells, underlying mechanisms that alter the activities of corresponding regulatory elements (REs) remain elusive. Here we define the complete pathogenic circuitry of human follicular lymphoma (FL), which activates or decommissions REs from normal GC B cells and commandeers enhancers from other lineages. Moreover, independent sets of transcription factors, whose expression was deregulated in FL, targeted commandeered versus decommissioned REs. Our approach revealed two distinct subtypes of low-grade FL, whose pathogenic circuitries resembled GC B or activated B cells. FL-altered enhancers also were enriched for sequence variants, including somatic mutations, which disrupt transcription-factor binding and expression of circuit-linked genes. Thus, the pathogenic regulatory circuitry of FL reveals distinct genetic and epigenetic etiologies for GC B-cell transformation.

Modeling the Bioactivation and Subsequent Reactivity of Drugs.

Electrophilically reactive drug metabolites are implicated in many adverse drug reactions. In this mechanism-termed bioactivation-metabolic enzymes convert drugs into reactive metabolites that often conjugate to nucleophilic sites within biological macromolecules like proteins. Toxic metabolite-product adducts induce severe immune responses that can cause sometimes fatal disorders, most commonly in the form of liver injury, blood dyscrasia, or the dermatologic conditions toxic epidermal necrolysis and Stevens-Johnson syndrome. This study models four of the most common metabolic transformations that result in bioactivation: quinone formation, epoxidation, thiophene sulfur-oxidation, and nitroaromatic reduction, by synthesizing models of metabolism and reactivity. First, the metabolism models predict the formation probabilities of all possible metabolites among the pathways studied. Second, the exact structures of these metabolites are enumerated. Third, using these structures, the reactivity model predicts the reactivity of each metabolite. Finally, a feedfoward neural network converts the metabolism and reactivity predictions to a bioactivation prediction for each possible metabolite. These bioactivation predictions represent the joint probability that a metabolite forms and that this metabolite subsequently conjugates to protein or glutathione. Among molecules bioactivated by these pathways, we predicted the correct pathway with an AUC accuracy of 89.98%. Furthermore, the model predicts whether molecules will be bioactivated, distinguishing bioactivated and nonbioactivated molecules with 81.06% AUC. We applied this algorithm to withdrawn drugs. The known bioactivation pathways of alclofenac and benzbromarone were identified by the algorithm, and high probability bioactivation pathways not yet confirmed were identified for safrazine, zimelidine, and astemizole. This bioactivation model-the first of its kind that jointly considers both metabolism and reactivity-enables drug candidates to be quickly evaluated for a toxicity risk that often evades detection during preclinical trials. The XenoSite bioactivation model is available at http://swami.wustl.edu/xenosite/p/bioactivation.

Medically unexplained symptoms in children: an experimental investigation of the impact of internet searching on parental responses.

BACKGROUND: Medically unexplained symptoms (MUS) are symptoms for which no medical cause can be identified. For children and adolescents, symptoms can be maintained through parental responses. AIMS: The present study investigated the impact that internet searching of symptoms has on parental responses to MUS. METHOD: One hundred and twenty-seven adult participants read a vignette in which they were asked to imagine they were a parent of a young person with MUS and completed visual analogue scales (VAS) reporting their beliefs, emotions and behavioural intentions about the MUS. Participants were then randomly assigned to one of three conditions: searching reputable websites for further information about the symptoms (n = 47), free search of any websites for further information about the symptoms (n = 38) or a control condition (n = 42) during which participants spent 10 minutes doing their usual behaviour on the internet, for example checking email and social media. Participants then completed the VAS for a second time. RESULTS: Searching reputable websites led to a significantly greater decrease in behaviour VAS scores compared with the free search condition [F (1,123) = 11.374, p < .001], indicating that participants were less likely to seek a second opinion and to advise the child to avoid usual activities. CONCLUSIONS: This study demonstrated that internet searching reputable sites for information regarding physical symptoms can be positive and it may therefore be advisable for health professionals meeting children with MUS to provide the family with information links to reputable sources.

Experimental realization of arbitrary activation functions for optical neural networks.

We experimentally demonstrate an on-chip electro-optic circuit for realizing arbitrary nonlinear activation functions for optical neural networks (ONNs). The circuit operates by converting a small portion of the input optical signal into an electrical signal and modulating the intensity of the remaining optical signal. Electrical signal processing allows the activation function circuit to realize any optical-to-optical nonlinearity that does not require amplification. Such line shapes are not constrained to those of conventional optical nonlinearities. Through numerical simulations, we demonstrate that the activation function improves the performance of an ONN on the MNIST image classification task. Moreover, the activation circuit allows for the realization of nonlinearities with far lower optical signal attenuation, paving the way for much deeper ONNs.

The Metabolic Rainbow: Deep Learning Phase I Metabolism in Five Colors.

Metabolism of drugs affects their absorption, distribution, efficacy, excretion, and toxicity profiles. Metabolism is routinely assessed experimentally using recombinant enzymes, human liver microsome, and animal models. Unfortunately, these experiments are expensive, time-consuming, and often extrapolate poorly to humans because they fail to capture the full breadth of metabolic reactions observed in vivo. As a result, metabolic pathways leading to the formation of toxic metabolites are often missed during drug development, giving rise to costly failures. To address some of these limitations, computational metabolism models can rapidly and cost-effectively predict sites of metabolism-the atoms or bonds which undergo enzymatic modifications-on thousands of drug candidates, thereby improving the likelihood of discovering metabolic transformations forming toxic metabolites. However, current computational metabolism models are often unable to predict the specific metabolites formed by metabolism at certain sites. Identification of reaction type is a key step toward metabolite prediction. Phase I enzymes, which are responsible for the metabolism of more than 90% of FDA approved drugs, catalyze highly diverse types of reactions and produce metabolites with substantial structural variability. Without knowledge of potential metabolite structures, medicinal chemists cannot differentiate harmful metabolic transformations from beneficial ones. To address this shortcoming, we propose a system for simultaneously labeling sites of metabolism and reaction types, by classifying them into five key reaction classes: stable and unstable oxidations, dehydrogenation, hydrolysis, and reduction. These classes unambiguously identify 21 types of phase I reactions, which cover 92.3% of known reactions in our database. We used this labeling system to train a neural network model of phase I metabolism on a literature-derived data set encompassing 20 736 human phase I metabolic reactions. Our model, Rainbow XenoSite, was able to identify reaction-type specific sites of metabolism with a cross-validated accuracy of 97.1% area under the receiver operator curve. Rainbow XenoSite with five-color and combined output is available for use free and online through our secure server at http://swami.wustl.edu/xenosite/p/phase1_rainbow.

Metabolic Forest: Predicting the Diverse Structures of Drug Metabolites.

Adverse drug metabolism often severely impacts patient morbidity and mortality. Unfortunately, drug metabolism experimental assays are costly, inefficient, and slow. Instead, computational modeling could rapidly flag potentially toxic molecules across thousands of candidates in the early stages of drug development. Most metabolism models focus on predicting sites of metabolism (SOMs): the specific substrate atoms targeted by metabolic enzymes. However, SOMs are merely a proxy for metabolic structures: knowledge of an SOM does not explicitly provide the actual metabolite structure. Without an explicit metabolite structure, computational systems cannot evaluate the new molecule's properties. For example, the metabolite's reactivity cannot be automatically predicted, a crucial limitation because reactive drug metabolites are a key driver of adverse drug reactions (ADRs). Additionally, further metabolic events cannot be forecast, even though the metabolic path of the majority of substrates includes two or more sequential steps. To overcome the myopia of the SOM paradigm, this study constructs a well-defined system-termed the metabolic forest-for generating exact metabolite structures. We validate the metabolic forest with the substrate and product structures from a large, chemically diverse, literature-derived dataset of 20 736 records. The metabolic forest finds a pathway linking each substrate and product for 79.42% of these records. By performing a breadth-first search of depth two or three, we improve performance to 88.43 and 88.77%, respectively. The metabolic forest includes a specialized algorithm for producing accurate quinone structures, the most common type of reactive metabolite. To our knowledge, this quinone structure algorithm is the first of its kind, as the diverse mechanisms of quinone formation are difficult to systematically reproduce. We validate the metabolic forest on a previously published dataset of 576 quinone reactions, predicting their structures with a depth three performance of 91.84%. The metabolic forest accurately enumerates metabolite structures, enabling promising new directions such as joint metabolism and reactivity modeling.

Computational Investigations of Dispersion Interactions between Small Molecules and Graphene-like Flakes.

We investigate dispersion interactions in a selection of atomic, molecular, and molecule-surface systems, comparing high-level correlated methods with empirically corrected density functional theory (DFT). We assess the efficacy of functionals commonly used for surface-based calculations, with and without the D3 correction of Grimme. We find that the inclusion of the correction is essential to get meaningful results, but there is otherwise little to distinguish between the functionals. We also present coupled-cluster quality interaction curves for H2, NO2, H2O, and Ar interacting with large carbon flakes, acting as models for graphene surfaces, using novel absolutely localized molecular orbital based methods. These calculations demonstrate that the problems with empirically corrected DFT when investigating dispersion appear to compound as the system size increases, with important implications for future computational studies of molecule-surface interactions.

Is the American College of Surgeons Online Communities a safe and useful venue to ask for surgical advice?

BACKGROUND: Many surgeons rely on the American College of Surgeons (ACS) Community Forums for advice on managing complex patients. Our objective was to assess the safety and usefulness of advice provided on the most popular surgical forum. METHODS: Overall, 120 consecutive, deidentified clinical threads were extracted from the General Surgery community in reverse chronological order. Three groups of three surgeons (mixed academic and community perspectives) evaluated the 120 threads for unsafe or dangerous posts. Positive and negative controls for safe and unsafe answers were included in 20 threads, and reviewers were blinded to their presence. Reviewers were free to access all online and professional resources. RESULTS: There were 855 unique responses (median 7, 2-15 responses per thread) to the 120 clinical threads/scenarios. The review teams correctly identified all positive and negative controls for safety. While 58(43.3%) of threads contained unsafe advice, the majority (33, 56.9%) were corrected. Reviewers felt that a there was a standard of care response for 62/120 of the threads of which 50 (80.6%) were provided by the responses. Of the 855 responses, 107 (12.5%) were considered unsafe/dangerous. CONCLUSION: The ACS Community Forums are generally a safe and useful resource for surgeons seeking advice for challenging cases. While unsafe or dangerous advice is not uncommon, other surgeons typically correct it. When utilizing the forums, advice should be taken as a congregate, and any single recommendation should be approached with healthy skepticism. However, social media such as the ACS Forums is self-regulating and can be an appropriate method for surgeons to communicate challenging problems.

Are online surgical discussion boards a safe and useful venue for surgeons to ask for advice? A review of the International Hernia Collaboration Facebook Group.

BACKGROUND: Social media is a growing medium for disseminating information among surgeons. The International Hernia Collaboration Facebook Group (IHC) is a widely utilized social media platform to share ideas and advice on managing patients with hernia-related diseases. Our objective was to assess the safety and utility of advice provided. METHODS: Overall, 60 consecutive de-identified clinical threads were extracted from the IHC in reverse chronological order. A group of three hernia specialists evaluated all threads for unsafe posts, unhelpful comments, and if an established evidence-based management strategy was provided. Positive and negative controls for safe and unsafe answers were included in seven threads and reviewers were blinded to their presence. Reviewers were free to access all online and professional resources (except the IHC). RESULTS: There were 598 unique responses (median 10, 1-26 responses per thread) to the 60 clinical threads/scenarios. The review team correctly identified all seven positive and negative controls. Most responses were safe (96.6%) but some were unhelpful (28.4%). For sixteen threads, the reviewers believed there was an established evidence-based answer; however, only six were provided. In addition, 14 responses were considered unsafe, but only four were corrected. CONCLUSIONS: The vast majority of responses were considered helpful; however, evidence-based management is typically not provided and unsafe recommendations often go uncontested. While the IHC allows wide dissemination of hernia-related surgical advice/discussions, surgeons should be cautious when using the IHC for clinical advice. Mechanisms to provide evidence-based management strategies and to identify unsafe advice are needed to improve quality within online forums and to prevent patient harm.

Silicon nitride waveguide as a power delivery component for on-chip dielectric laser accelerators.

We study the weakly guided silicon nitride waveguide as an on-chip power delivery solution for dielectric laser accelerators (DLAs). We focus on the two main limiting factors on the waveguide network for DLAs: the optical damage and nonlinear characteristics. The typical delivered fluence at the onset of optical damage is measured to be ∼0.19 J/cm2 at a 2 µm central wavelength and 250 fs pulse width. This damage fluence is lower than that of the bulk Si3N4 (∼0.65 J/cm2), but higher than that of bulk silicon (∼0.17 J/cm2). We also report the nonlinearity-induced spectrum and phase variance of the output pulse at this pulse duration. We find that a total waveguide length within 3 mm is sufficient to avoid significant self-phase modulation effects when operating slightly below the damage threshold. We also estimate that one SiNx waveguide can power 70 µm silicon dual pillar DLAs from a single side, based on the results from the recent free-space DLA experiment.

Adjoint-based optimization of active nanophotonic devices.

We show that the adjoint variable method can be combined with the multi-frequency finite-difference frequency-domain method for efficient sensitivity calculations, enabling the systematic optimization of active nanophotonic devices. As a proof of principle demonstration, we have optimized a dynamic isolator structure in two-dimensions, resulting in the reduction of the length of the modulated regions by a factor of two, while retaining good performance in the isolation ratio and insertion loss.

Design of a tapered slot waveguide dielectric laser accelerator for sub-relativistic electrons.

We propose a dielectric laser accelerator design based on a tapered slot waveguide structure for sub-relativistic electron acceleration. This tapering scheme allows for straightforward tuning of the phase velocity of the accelerating field along the propagation direction, which is necessary for maintaining synchronization with electrons as their velocities increase. Furthermore, the non-resonant nature of this design allows for better tolerance to experimental errors. We also introduce a method to design this continuously tapered structure based on the eikonal approximation, and give a working example based on realistic experimental parameters.

Computationally Assessing the Bioactivation of Drugs by N-Dealkylation.

Cytochromes P450 (CYPs) oxidize alkylated amines commonly found in drugs and other biologically active molecules, cleaving them into an amine and an aldehyde. Metabolic studies usually neglect to report or investigate aldehydes, even though they can be toxic. It is assumed that they are efficiently detoxified into carboxylic acids and alcohols. Nevertheless, some aldehydes are reactive and escape detoxification pathways to cause adverse events by forming DNA and protein adducts. Herein, we modeled N-dealkylations that produce both amine and aldehyde metabolites and then predicted the reactivity of the aldehyde. This model used a deep learning approach previously developed by our group to predict other types of drug metabolism. In this study, we trained the model to predict N-dealkylation by human liver microsomes (HLM), finding that including isozyme-specific metabolism data alongside HLM data significantly improved results. The final HLM model accurately predicted the site of N-dealkylation within metabolized substrates (97% top-two and 94% area under the ROC curve). Next, we combined the metabolism, metabolite structure prediction, and previously published reactivity models into a bioactivation model. This combined model predicted the structure of the most likely reactive metabolite of a small validation set of drug-like molecules known to be bioactivated by N-dealkylation. Applying this model to approved and withdrawn medicines, we found that aldehyde metabolites produced from N-dealkylation may explain the hepatotoxicity of several drugs: indinavir, piperacillin, verapamil, and ziprasidone. Our results suggest that N-dealkylation may be an under-appreciated bioactivation pathway, especially in clinical contexts where aldehyde detoxification pathways are inhibited. Moreover, this is the first report of a bioactivation model constructed by combining a metabolism and reactivity model. These results raise hope that more comprehensive models of bioactivation are possible. The model developed in this study is available at http://swami.wustl.edu/xenosite/ .

Modeling Small-Molecule Reactivity Identifies Promiscuous Bioactive Compounds.

Scientists rely on high-throughput screening tools to identify promising small-molecule compounds for the development of biochemical probes and drugs. This study focuses on the identification of promiscuous bioactive compounds, which are compounds that appear active in many high-throughput screening experiments against diverse targets but are often false-positives which may not be easily developed into successful probes. These compounds can exhibit bioactivity due to nonspecific, intractable mechanisms of action and/or by interference with specific assay technology readouts. Such "frequent hitters" are now commonly identified using substructure filters, including pan assay interference compounds (PAINS). Herein, we show that mechanistic modeling of small-molecule reactivity using deep learning can improve upon PAINS filters when modeling promiscuous bioactivity in PubChem assays. Without training on high-throughput screening data, a deep learning model of small-molecule reactivity achieves a sensitivity and specificity of 18.5% and 95.5%, respectively, in identifying promiscuous bioactive compounds. This performance is similar to PAINS filters, which achieve a sensitivity of 20.3% at the same specificity. Importantly, such reactivity modeling is complementary to PAINS filters. When PAINS filters and reactivity models are combined, the resulting model outperforms either method alone, achieving a sensitivity of 24% at the same specificity. However, as a probabilistic model, the sensitivity and specificity of the deep learning model can be tuned by adjusting the threshold. Moreover, for a subset of PAINS filters, this reactivity model can help discriminate between promiscuous and nonpromiscuous bioactive compounds even among compounds matching those filters. Critically, the reactivity model provides mechanistic hypotheses for assay interference by predicting the precise atoms involved in compound reactivity. Overall, our analysis suggests that deep learning approaches to modeling promiscuous compound bioactivity may provide a complementary approach to current methods for identifying promiscuous compounds.

A simple model predicts UGT-mediated metabolism.

MOTIVATION: Uridine diphosphate glucunosyltransferases (UGTs) metabolize 15% of FDA approved drugs. Lead optimization efforts benefit from knowing how candidate drugs are metabolized by UGTs. This paper describes a computational method for predicting sites of UGT-mediated metabolism on drug-like molecules. RESULTS: XenoSite correctly predicts test molecule's sites of glucoronidation in the Top-1 or Top-2 predictions at a rate of 86 and 97%, respectively. In addition to predicting common sites of UGT conjugation, like hydroxyl groups, it can also accurately predict the glucoronidation of atypical sites, such as carbons. We also describe a simple heuristic model for predicting UGT-mediated sites of metabolism that performs nearly as well (with, respectively, 80 and 91% Top-1 and Top-2 accuracy), and can identify the most challenging molecules to predict on which to assess more complex models. Compared with prior studies, this model is more generally applicable, more accurate and simpler (not requiring expensive quantum modeling). AVAILABILITY AND IMPLEMENTATION: The UGT metabolism predictor developed in this study is available at http://swami.wustl.edu/xenosite/p/ugt CONTACT: : swamidass@wustl.eduSupplementary information: Supplementary data are available at Bioinformatics online.

Method for computationally efficient design of dielectric laser accelerator structures.

Dielectric microstructures have generated much interest in recent years as a means of accelerating charged particles when powered by solid state lasers. The acceleration gradient (or particle energy gain per unit length) is an important figure of merit. To design structures with high acceleration gradients, we explore the adjoint variable method, a highly efficient technique used to compute the sensitivity of an objective with respect to a large number of parameters. With this formalism, the sensitivity of the acceleration gradient of a dielectric structure with respect to its entire spatial permittivity distribution is calculated by the use of only two full-field electromagnetic simulations, the original and 'adjoint'. The adjoint simulation corresponds physically to the reciprocal situation of a point charge moving through the accelerator gap and radiating. Using this formalism, we perform numerical optimizations aimed at maximizing acceleration gradients, which generate fabricable structures of greatly improved performance in comparison to previously examined geometries.

Deep Learning to Predict the Formation of Quinone Species in Drug Metabolism.

Many adverse drug reactions are thought to be caused by electrophilically reactive drug metabolites that conjugate to nucleophilic sites within DNA and proteins, causing cancer or toxic immune responses. Quinone species, including quinone-imines, quinone-methides, and imine-methides, are electrophilic Michael acceptors that are often highly reactive and comprise over 40% of all known reactive metabolites. Quinone metabolites are created by cytochromes P450 and peroxidases. For example, cytochromes P450 oxidize acetaminophen to N-acetyl-p-benzoquinone imine, which is electrophilically reactive and covalently binds to nucleophilic sites within proteins. This reactive quinone metabolite elicits a toxic immune response when acetaminophen exceeds a safe dose. Using a deep learning approach, this study reports the first published method for predicting quinone formation: the formation of a quinone species by metabolic oxidation. We model both one- and two-step quinone formation, enabling accurate quinone formation predictions in nonobvious cases. We predict atom pairs that form quinones with an AUC accuracy of 97.6%, and we identify molecules that form quinones with 88.2% AUC. By modeling the formation of quinones, one of the most common types of reactive metabolites, our method provides a rapid screening tool for a key drug toxicity risk. The XenoSite quinone formation model is available at http://swami.wustl.edu/xenosite/p/quinone .

Computational Approach to Structural Alerts: Furans, Phenols, Nitroaromatics, and Thiophenes.

Structural alerts are commonly used in drug discovery to identify molecules likely to form reactive metabolites and thereby become toxic. Unfortunately, as useful as structural alerts are, they do not effectively model if, when, and why metabolism renders safe molecules toxic. Toxicity due to a specific structural alert is highly conditional, depending on the metabolism of the alert, the reactivity of its metabolites, dosage, and competing detoxification pathways. A systems approach, which explicitly models these pathways, could more effectively assess the toxicity risk of drug candidates. In this study, we demonstrated that mathematical models of P450 metabolism can predict the context-specific probability that a structural alert will be bioactivated in a given molecule. This study focuses on the furan, phenol, nitroaromatic, and thiophene alerts. Each of these structural alerts can produce reactive metabolites through certain metabolic pathways but not always. We tested whether our metabolism modeling approach, XenoSite, can predict when a given molecule's alerts will be bioactivated. Specifically, we used models of epoxidation, quinone formation, reduction, and sulfur-oxidation to predict the bioactivation of furan-, phenol-, nitroaromatic-, and thiophene-containing drugs. Our models separated bioactivated and not-bioactivated furan-, phenol-, nitroaromatic-, and thiophene-containing drugs with AUC performances of 100%, 73%, 93%, and 88%, respectively. Metabolism models accurately predict whether alerts are bioactivated and thus serve as a practical approach to improve the interpretability and usefulness of structural alerts. We expect that this same computational approach can be extended to most other structural alerts and later integrated into toxicity risk models. This advance is one necessary step toward our long-term goal of building comprehensive metabolic models of bioactivation and detoxification to guide assessment and design of new therapeutic molecules.

Rural surgeons would embrace surgical telementoring for help with difficult cases and acquisition of new skills.

BACKGROUND: Surgical telementoring, consisting of an expert surgeon guiding a less experienced surgeon through advanced or novel cases from a remote location, is an evolving technology which has potential to become an integral part of surgical practice. This study sought to apprise the attitudes of rural general surgeons toward the possible benefits and applications of surgical telementoring in their practices. METHODS: A survey assessing demographics and attitudes toward telementoring was e-mailed to members of the American College of Surgeons (ACS) Advisory Council for Rural Surgery and posted to the ACS website in areas targeting rural surgeons. A link to a webpage with a description of surgical telementoring and brief demonstrative video were included with the survey. RESULTS: There were 159 respondents, with 82.3 % of them practicing in communities smaller than 50,000 people. Overall, 78.6 % felt that telementoring would be useful to their practice, and 69.8 % thought it would benefit their hospitals. There was no correlation between years of practice and perceived usefulness of surgical telementoring. When asked the single most useful, or primary, application of surgical telementoring there was a split between learning new techniques (46.5 %) and intraoperative assistance with unexpected findings (39.0 %). When asked to select all applications in which they would be interested in using telementoring from a list of possible uses, surgeons most frequently selected: intraoperative consultation for unexpected findings (67.7 %), trauma consultation (32.9 %), and laparoscopic colectomy (32.9 %). CONCLUSIONS: Surgical telementoring is on the verge of widespread use but industry and surgical societies remain ambivalent about supporting its implementation due to concerns over lack of interest. This study demonstrates interest among rural surgeons. While there are differing opinions regarding compensation of the telementoring, the most common, single interest in the use of surgical telementoring was for learning new techniques or skill sets.