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.

A cohort study on blood coagulation in childhood cancer survivors.

Cancer survivors are at an increased risk of thromboembolism compared to the general pediatric population. Anticoagulant therapy decreases the risk of thromboembolism in cancer patients. We hypothesized that pediatric cancer survivors are in a chronically hypercoagulable state compared to healthy controls. Children who survived for more than five years from cancer diagnosis at the UT Health Science Center at San Antonio Cancer Survivorship Clinic were compared to healthy controls. The exclusion criteria were recent NSAID use or a history of coagulopathy. Coagulation analysis included platelet count, thrombin-antithrombin complexes (TAT), plasminogen activator inhibitor (PAI), routine coagulation assays, and thrombin generation with and without thrombomodulin. We enrolled 47 pediatric cancer survivors and 37 healthy controls. Platelet count was significantly lower in cancer survivors at a mean of 254 × 109/L (95%CI: 234-273 × 109/L) compared at 307 × 109/L (283-331 × 109/L) in healthy controls (p < 0.001), although not outside the normal range. Routine coagulation assays showed no differences, except for a significantly lower prothrombin time (PT) in cancer survivors (p < 0.004). Cancer survivors has significantly elevated biomarkers of the procoagulant state, such as TAT and PAI, compared to healthy controls (p < 0.001). A multiple logistic regression model controlling for age, BMI, gender, and race/ethnicity documented that a low platelet count, short prothrombin clot time, and higher procoagulant biomarkers (TAT and PAI) were significantly associated with past cancer therapy. Survivors of childhood cancer have a persistent procoagulant imbalance for more than five years after diagnosis. Further studies are needed to establish whether procoagulant imbalance increases the risk of thromboembolism in childhood cancer survivors.

Experimentally realized in situ backpropagation for deep learning in photonic neural networks.

Integrated photonic neural networks provide a promising platform for energy-efficient, high-throughput machine learning with extensive scientific and commercial applications. Photonic neural networks efficiently transform optically encoded inputs using Mach-Zehnder interferometer mesh networks interleaved with nonlinearities. We experimentally trained a three-layer, four-port silicon photonic neural network with programmable phase shifters and optical power monitoring to solve classification tasks using "in situ backpropagation," a photonic analog of the most popular method to train conventional neural networks. We measured backpropagated gradients for phase-shifter voltages by interfering forward- and backward-propagating light and simulated in situ backpropagation for 64-port photonic neural networks trained on MNIST image recognition given errors. All experiments performed comparably to digital simulations ([Formula: see text]94% test accuracy), and energy scaling analysis indicated a route to scalable machine learning.

The University of Alabama at Birmingham Surgery Community Network: Tackling the Challenges of Rural Surgery.

Over the past 5 years, The University of Alabama at Birmingham (UAB) Department of Surgery has taken a keen interest in the practice of surgery in rural Alabama and has established the UAB surgery community network. Our goal is to improve the delivery of surgical care in rural areas through active recruitment of rural surgeons, the development of research around rural surgery practice, and the expansion of a surgery network throughout the state. Here, we will present the challenges faced by rural surgery, our early work to address these challenges, and offer a plan for moving forward.

Endoscopic Reconstruction of the Anterior Skull Base Following Tumor Resection: Application of a Novel Bioabsorbable Plate.

OBJECTIVE: Endoscopic repair of skull base defects is required following resection of intracranial pathology via the endoscopic endonasal approach (EEA). Many closure techniques have been described, but choosing between techniques remains controversial. We report outcomes of 560 EEA procedures of skull base reconstruction performed on 508 patients over a 15-year-period. Halfway through this period, we adopted the use of a rigid, bioabsorbable extrasellar plate for reconstruction, enabling a comparison between this technique and those used previously. METHODS: All patients undergoing EEA from 2005 to 2019 at our institution were retrospectively reviewed. Demographic information, surgical pathology, tumor dimensions and radiographic features, reconstructive technique, and patient-related outcomes were collected and analyzed with univariate and multivariate statistical modeling. RESULTS: Five-hundred sixty procedures were performed on 508 patients. The series complication rate was 8.2%. Overall, cerebrospinal fluid (CSF) leak rate was 5.0% but varied significantly across closure techniques (p < 0.001). Critically, the CSF leak rate in the 272 cases prior to our 2013 adoption of the Resorb-X Plate (RXP) was 8.5%, whereas leak rate in the subsequent 288 cases was 1.7%. RXP was protective against CSF leak (p = 0.001), whereas gross total resection (GTR) correlated with increased leak rate (p = 0.001). Patient BMI was significantly associated with risk of leak (p = 0.047). Other variables did not impact leak risk. CONCLUSION: Reconstructive technique, extent of resection, and patient BMI significantly contributed to CSF leak rate. GTR was associated with increased leak risk while the RXP was protective. The bioabsorbable RXP is an effective option for rigid skull base repair with comparatively few complications. LEVEL OF EVIDENCE: 3 Laryngoscope, 133:1092-1098, 2023.

Dynamic ESPI Evaluation of Deformation and Fracture Mechanism of 7075 Aluminum Alloy.

The deformation and fracture mechanism in 7075 aluminum alloy is discussed based on a field theoretical approach. A pair of peak-aged and overaged plate specimens are prepared under the respective precipitation conditions, and their plastic deformation behaviors are visualized with two-dimensional electronic speckle pattern interferometry (ESPI). The in-plane velocity field caused by monotonic tensile loading is monitored continuously via the contour analysis method of ESPI. In the plastic regime, the peak-aged specimen exhibits a macroscopically uniform deformation behavior, while the annealed specimen exhibits non-uniform deformation characterized by a localized shear band. The occurrence of the shear band is explained by the transition of the material's elastic resistive mechanism from the longitudinal force dominant to shear force dominant mode. The shear force is interpreted as the frictional force that drives mobile dislocations along the shear band. The dynamic behavior of the shear band is explained as representing the motion of a solitary wave. The observed decrease in the solitary wave's velocity is accounted for by the change in the acoustic impedance with the advancement of plastic deformation.

Examining the Surgical Life.

Debriefing after a major event is a key component in ongoing improvement in performance. Likewise, reflecting on one's career at the time of leaving the operating room environment is an opportunity to transmit the lessons learned from decades of surgical practice. The authors, recently retired from daily operating and leaders in American surgery, reflect on the impact of surgical life on surgeons and their personal lives. Observations regarding selection of medical students, surgical trainees and practice models are presented from this perspective.

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.

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.

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.

Current surgeon practices for postoperative activity restrictions after abdominal surgery vary widely: A survey from the communities on the ACS website.

BACKGROUND: Surgeons often impose restrictions on patient activities after an abdominal operation in an effort to prevent complications such as incisional hernia. This study addresses the current recommendations concerning the restriction of activities given by a diverse group of surgeons to their patients after abdominal surgery. METHODS: A 14-item survey was posted on surgeon-specific social media platforms, primarily the American College of Surgeons Communities. This survey included questions about demographics, practice type, and activity recommendations after open and minimally invasive abdominal surgery. Descriptive, multivariable, and qualitative analyses were performed. RESULTS: A total of 420 surgeons completed the survey. The majority of respondents identified as general surgeons (76.2%). Practice types included private (37.6%), academic (34.3%), underserved (10.1%), and Veterans Affairs (5.6%). After an open laparotomy, the majority of respondents (53.1%) recommended that patients refrain from heavy lifting or strenuous activity for 6 weeks. For a minimally invasive abdominal operation, recommendations were even more variable, restricting activity for 2 weeks (34.4%), 4 weeks (23.8%), 6 weeks (15.5%), or no restrictions (12.6%). On average, participating surgeons recommended an earlier return to activity by 2.3 weeks for patients undergoing minimally invasive surgery compared with an open operation (95% confidence interval 2.1-2.5, P < .001). Qualitative analysis provided additional information regarding surgeons' rationale for decision making. Only 23.8% of the respondents indicated that their recommendations were based on evidence in literature. CONCLUSION: This survey on surgeon recommendations for convalescence after an abdominal operation indicates the wide variation in practices with insufficient evidence to guide decision making. Future clinical trials examining various durations and intensities of postoperative restrictions will be important to determine a safe and patient-centered approach for recovery after an abdominal operation.

Drawbacks of the Current Algorithm for Patients With Symptoms of Gastroesophageal Reflux Disease and the Impact of Adding Wireless pH Study to the Initial Diagnostic Endoscopy: Data From High-Volume Rural Antireflux Center.

BACKGROUND: Patients with symptoms of gastroesophageal reflux disease (GERD) are often given a trial of proton pump inhibitors (PPIs). When they respond, patients usually continue PPI therapy. If this empiric treatment fails, esophagogastroduodenoscopy (EGD) is recommended. When EGD findings are equivocal, pH study is warranted. We hypothesize that this algorithm results in prolonged PPI therapy, repetition of EGDs and patient dissatisfaction. This study evaluates the impact of placing a pH probe at the time of the initial EGD. METHODS: IRB approval was obtained for retrospective chart review of patients who presented with GERD symptoms between August 2015 and March 2019. Patients were included if they underwent EGD with placement of wireless pH probe. RESULTS: A total of 379 patients (260 females, 119 males) with average age was 56.7±14.2 years. There were 253/379 (66.7%) patients who had previous EGDs (1-10). Health Satisfaction Survey was completed by 357/379 (94.2%) patients and 250/357 (70%) reported dissatisfaction with GERD control. PPI use was noted in 299/379 (78.8%) patients with average duration of 10.9±9.1 years. Testing off antisecretory medication was performed in 360/379 (94.9%). The average time interval between the clinic visit and performing EGD and pH study was 22±25 days. CONCLUSION: The current GERD algorithm results in prolonged PPI therapy, repeated endoscopies and patient dissatisfaction. Placing a pH probe at the time of initial endoscopy is safe and expedient in a rural setting. Positive pH studies avoid repeating EGDs and negative pH studies warrant a search for potential alternative diagnosis.

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.

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.

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.

Enriching Medical Student Learning Experiences.

OBJECTIVE: Medical students should develop skills in assessing their own learning needs and developing strategies to meet those needs. Medical curricula should be designed to provide active and enriching ways to explore medicine beyond the classroom. The program should enrich the elements of motivation, discovery, innovation, social services, cultural exploration, and personal development. The University of Kansas School of Medicine instituted a new curriculum in 2017 called ACE (Active, Competency-based, and Excellence-driven). Eight 1-week courses of enrichment experiences are embedded within the first 2 years of the curriculum. METHODS: After each of 8 medical content blocks, students are required to participate in a 1-week, nongraded enrichment experience according to their own learning needs and interests. Students choose the type of enrichment activities including clinical experiences, professional development, leadership development, research and scholarly activity, and community engagement. Students select their top enrichment choices and a computer lottery makes the assignments from their designations. Students engaged in research and scholarly activity are guided to appropriate research mentors. RESULTS: A total of 196 enrichment activities at 3 campuses were developed for 211 students during the first 2 years of medical school. Most students selected clinical experiences with enrichments available in most medical specialties and subspecialties. Students also use enrichment weeks to conduct research/scholarly activity, particularly those students pursuing the Honors Track. A total of 2071 enrichment experiences were completed in the first 2 years. CONCLUSIONS: Most enrichments involved clinical experiences, although research/scholarly activity and professional development enrichments also were popular. Evaluations from students and antidotal data suggested enrichments are popular among students and a good change of pace from the usual rigorous activities of the curriculum. Because of the large number of experiences required to conduct the enrichment weeks, a continuous process of evaluation is required to maintain the program. Therefore, flexibility is required to administer the program.