Laparoscopic management of short-interval interstitial pregnancy after recent tubal ectopic pregnancy: A case report.

A 24-year-old woman, G6P2032, initially presented with a right-sided ruptured tubal ectopic pregnancy. Salpingectomy was performed with care to completely remove the fallopian tube. The patient then presented with ipsilateral interstitial pregnancy 11 weeks later and initially underwent systemic methotrexate injection, which failed to resolve the pregnancy. She then underwent laparoscopic cornuostomy and dilation and curettage. Cornuostomy was performed with injection of dilute vasopressin around the gestational sac to help minimize blood loss, followed by hydrodissection and sharp dissection to remove the pregnancy. Judicial electrocautery was used and the myometrium and serosa were closed in layers. Attention was given to preserve uterine myometrial tissue integrity at the cornua. The patient recovered and was discharged. Her beta-hCG level trended from 11,902 mIU/mL pre-surgery to 7726 on postoperative day 0, and 289 on postoperative day 7. Pathology from the interstitial region showed fragments of chorionic villi, and the dilation and curettage pathology demonstrated decidualized secretory endometrium. Short-interval interstitial ectopic pregnancies after prior salpingectomy for tubal ectopic pregnancy are extremely rare. This case demonstrated successful management with a minimally invasive laparoscopic cornuostomy. This case also displays that ipsilateral interstitial pregnancy can occur after salpingectomy even after care is taken to remove all visible portions of the fallopian tube. Thus, patients should be counseled carefully about the risks of short-interval pregnancy after a recent tubal ectopic pregnancy.

The Role of Preoperative Antibiotics in Surgical Site Infection (SSI) Rate after Class I/Clean Gynaecologic Surgery at an Academic Safety Net Hospital.

OBJECTIVES: Examine whether preoperative antibiotics in class I/clean abdominal gynaecologic surgery decrease the incidence of surgical site infections (SSI). METHODS: Retrospective cohort study at academic safety net hospital of patients undergoing class I laparoscopic or open gynaecologic surgery between November 2013 and September 2017. Performance improvement initiative to administer preoperative antibiotics to all surgical patients starting July 2016. RESULTS: In total, 510 patients were included: 283 in the antibiotic group and 227 in the no-antibiotic group. PRIMARY OUTCOME: incidence of SSI. Baseline characteristics were similar between groups once balanced by propensity score method. In unweighted analysis, incidence of SSI decreased from 9.3% (21/227) in the no-antibiotics group to 4.9% (14/283) in antibiotics group, but this was not statistically significant (odds ratio (OR) 0.51 CI 0.25-1.03, P = 0.0598). Following of inverse probability of treatment weighting adjustments in weighted analysis, incidence of SSI was found to be significantly lower in patients who received antibiotics compared to patients who did not receive antibiotics across entry types (4.6% vs. 9.8%, OR 0.45; CI 0.22-0.90, P = 0.023). Weighted analysis demonstrated in the exploratory laparotomy group patients who received antibiotics had a lower incidence of SSI compared to patients who did not receive antibiotics (5.1% vs. 18.7%, OR 0.23; CI 0.08-0.68, P = 0.008). In the laparoscopy group, there was no difference between groups (4.4% vs. 5.4%, OR 0.81; CI 0.3-2.16, P = 0.675). CONCLUSIONS: There is limited literature on SSI prevention/preoperative antibiotic use in class I gynaecologic surgeries. This study demonstrates antibiotics in class I procedures decrease SSI rates, specifically in open procedures. There was a lack of demonstrated benefit in laparoscopy.

Disease characteristics, treatments, and outcomes of patients with pulmonary arterial hypertension treated with selexipag in real-world settings from the SPHERE registry (SelexiPag: tHe usErs dRug rEgistry).

BACKGROUND: Selexipag is an oral prostacyclin receptor agonist, indicated for pulmonary arterial hypertension to delay disease progression and reduce the risk of pulmonary arterial hypertension-related hospitalization. SelexiPag: tHe usErs dRug rEgistry (NCT03278002) was a US-based, prospective, real-world registry of selexipag-treated patients. METHODS: Adults with pulmonary hypertension (enrolled 2016-2020) prescribed selexipag were followed for ≤18 months, with data collected at routine clinic visits. Patients were defined as newly or previously initiated if they had started selexipag ≤60 days or >60 days, respectively, before enrollment. RESULTS: The registry included 829 patients (430 newly initiated, 399 previously initiated; 759 with pulmonary arterial hypertension), of whom 55.6% were World Health Organization functional class (FC) 3/4; 57.3% were intermediate or high risk per Registry to Evaluate Early and Long-Term PAH Disease Management (REVEAL) 2.0. In patients with pulmonary arterial hypertension, 18-month discontinuation rates for adverse events were 22.0%, 32.0%, and 11.9%, and 18-month survival rates were 89.4%, 84.2%, and 94.5% in the overall, newly, and previously initiated patient populations, respectively. From baseline to month 18, most patients had stable or improved FC and stable or improved REVEAL 2.0 risk category status. Discontinuation for adverse events, hospitalization, and survival were similar regardless of patients' individually tolerated selexipag maintenance dose. No new safety signals were identified. CONCLUSIONS: In this real-world analysis of patients initiating selexipag, most patients had stable or improved FC and REVEAL 2.0 risk category. Similar to the GRIPHON trial, outcomes with selexipag in this real-world study were comparable across maintenance dose strata, with no new safety signals.

Use of a point-of-care test to rapidly assess levels of SARS-CoV-2 nasal neutralising antibodies in vaccines and breakthrough infected individuals.

Despite SARS-CoV-2 vaccines eliciting systemic neutralising antibodies (nAbs), breakthrough infections still regularly occur. Infection helps to generate mucosal immunity, possibly reducing disease transmission. Monitoring mucosal nAbs is predominantly restricted to lab-based assays, which have limited application to the public. In this multi-site study, we used lateral-flow surrogate neutralisation tests to measure mucosal and systemic nAbs in vaccinated and breakthrough infected individuals in Australia and Singapore. Using three lateral flow assays to detect SARS-CoV-2 nAbs, we demonstrated that nasal mucosal nAbs were present in 71.4 (95% CI 56.3-82.9%) to 85.7% (95% CI 71.8-93.7%) of individuals with breakthrough infection (positivity rate was dependent upon the type of test), whereas only 20.7 (95% CI 17.1-49.4%) to 34.5% (95% CI 19.8-52.7%) of vaccinated individuals without breakthrough infection had detectible nasal mucosal nAbs. Of the individuals with breakthrough infection, collective mucosal anti-S antibody detection in confirmatory assays was 92.9% (95% CI 80.3-98.2%) of samples, while 72.4% (95% CI 54.1-85.5%) of the vaccinated individuals who had not experienced a breakthrough infection were positive to anti-S antibody. All breakthrough infected individuals produced systemic anti-N antibodies; however, these antibodies were not detected in the nasal cavity. Mucosal immunity is likely to play a role in limiting the transmission of SARS-CoV-2 and lateral flow neutralisation tests provide a rapid readout of mucosal nAbs at the point-of-care.

Early Onset Diffusion Abnormalities in Refractory Headache Disorders.

Objective: This study sought to determine if individuals with medically refractory migraine headache have volume or diffusion abnormalities on neuroimaging compared to neurotypical individuals. Background: Neuroimaging biomarkers in headache medicine continue to be limited. Early prediction of medically refractory headache and migraine disorders could result in earlier administration of high efficacy therapeutics. Methods: A single-center, retrospective, case control study was performed. All patients were evaluated clinically between 2014 and 2018. Individuals with medically refractory migraine headache (defined by ICDH-3 criteria) without any other chronic medical diseases were enrolled. Patients had to have failed more than two therapeutics and aura was not exclusionary. The initial MRI study for each patient was reviewed. Multiple brain regions were analyzed for volume and apparent diffusion coefficient values. These were compared to 81 neurotypical control patients. Results: A total of 79 patients with medically refractory migraine headache were included and compared to 74 neurotypical controls without headache disorders. Time between clinical diagnosis and neuroimaging was a median of 24 months (IQR: 12.0-37.0). Comparison of individuals with medically refractory migraine headache to controls revealed statistically significant differences in median apparent diffusion coefficient (ADC) in multiple brain subregions (p < 0.001). Post-hoc pair-wise analysis comparing individuals with medically refractory migraine headache to control patients revealed significantly decreased median ADC values for the thalamus, caudate, putamen, pallidum, amygdala, brainstem, and cerebral white matter. No volumetric differences were observed between groups. Conclusions: In individuals with medically refractory MH, ADC changes are measurable in multiple brain structures at an early age, prior to the failure of multiple pharmacologic interventions and the diagnosis of medically refractory MH. This data supports the hypothesis that structural connectivity issues may predispose some patients toward more medically refractory pain disorders such as MH.

MRI Radiogenomics of Pediatric Medulloblastoma: A Multicenter Study.

Background Radiogenomics of pediatric medulloblastoma (MB) offers an opportunity for MB risk stratification, which may aid therapeutic decision making, family counseling, and selection of patient groups suitable for targeted genetic analysis. Purpose To develop machine learning strategies that identify the four clinically significant MB molecular subgroups. Materials and Methods In this retrospective study, consecutive pediatric patients with newly diagnosed MB at MRI at 12 international pediatric sites between July 1997 and May 2020 were identified. There were 1800 features extracted from T2- and contrast-enhanced T1-weighted preoperative MRI scans. A two-stage sequential classifier was designed-one that first identifies non-wingless (WNT) and non-sonic hedgehog (SHH) MB and then differentiates therapeutically relevant WNT from SHH. Further, a classifier that distinguishes high-risk group 3 from group 4 MB was developed. An independent, binary subgroup analysis was conducted to uncover radiomics features unique to infantile versus childhood SHH subgroups. The best-performing models from six candidate classifiers were selected, and performance was measured on holdout test sets. CIs were obtained by bootstrapping the test sets for 2000 random samples. Model accuracy score was compared with the no-information rate using the Wald test. Results The study cohort comprised 263 patients (mean age ± SD at diagnosis, 87 months ± 60; 166 boys). A two-stage classifier outperformed a single-stage multiclass classifier. The combined, sequential classifier achieved a microaveraged F1 score of 88% and a binary F1 score of 95% specifically for WNT. A group 3 versus group 4 classifier achieved an area under the receiver operating characteristic curve of 98%. Of the Image Biomarker Standardization Initiative features, texture and first-order intensity features were most contributory across the molecular subgroups. Conclusion An MRI-based machine learning decision path allowed identification of the four clinically relevant molecular pediatric medulloblastoma subgroups. © RSNA, 2022 Online supplemental material is available for this article. See also the editorial by Chaudhary and Bapuraj in this issue.

Experimental application of a zero-point charge based on pH as a simple indicator of microplastic particle aggregation.

Micro/nanoplastics - a useful but threatening material - continuously require fundamental research on its behaviors and properties for aggregation. Zeta potential (ζ) has been using as an indicator to determine the optimal aggregation for particle removal in water treatment processes. In the field work, however, an alternative method for streamlining these tasks and reducing the variability in processing efficiency is necessary. To improve practical utility in the field work, this study aimed at investigating applicability of the zero-point charge (ZPC) of the isoelectric point (IEP; ψpI) as an alternative indicator for aggregation in place of ζ. For the purpose, this study conducted laboratory experiments and model simulations. The experiments measured ψpI of microplastics in a trivalent-electrolyte aqueous solution using various concentrations of polyaluminum chloride (PAC) for reproducing the behavior of microplastics in natural water environments. As a result, ψpI for polyethylene (PE) and polyvinylchloride (PVC) were found to be pH 6.59 and 6.43, respectively. The removal rates (r) depended on the aggregation at the initial pH and optimal PAC concentration. The experimental attachment efficiency (αE), 0.14 to 0.4, showed a good correlation of over 95% with r, 0.04 to 0.84, both based on the pH change and PAC concentration and differing slightly with the type and size of the plastic. The highest αE was achieved with the highest r when ψpI was close to zero in the pH range of 6-8 using the optimized PAC concentration. Based on the experimental results, the model confirmed the applicability of ψpI instead of ζ as an indicator of the aggregation by simulating αE based on ψpI and ionic strength, which are themselves based on the change in pH. Therefore, this study provides some insights into behaviors of microplastics by using the isoelectric point (IEP, ψpI) as an indicator of aggregation of microplastics in place of ζ. The IEP method is limited by initial pH, optimal dosage of coagulant, and type and size of microplastics, but it will increase practical utility in the field.

Radiomic signatures of posterior fossa ependymoma: Molecular subgroups and risk profiles.

BACKGROUND: The risk profile for posterior fossa ependymoma (EP) depends on surgical and molecular status [Group A (PFA) versus Group B (PFB)]. While subtotal tumor resection is known to confer worse prognosis, MRI-based EP risk-profiling is unexplored. We aimed to apply machine learning strategies to link MRI-based biomarkers of high-risk EP and also to distinguish PFA from PFB. METHODS: We extracted 1800 quantitative features from presurgical T2-weighted (T2-MRI) and gadolinium-enhanced T1-weighted (T1-MRI) imaging of 157 EP patients. We implemented nested cross-validation to identify features for risk score calculations and apply a Cox model for survival analysis. We conducted additional feature selection for PFA versus PFB and examined performance across three candidate classifiers. RESULTS: For all EP patients with GTR, we identified four T2-MRI-based features and stratified patients into high- and low-risk groups, with 5-year overall survival rates of 62% and 100%, respectively (P < .0001). Among presumed PFA patients with GTR, four T1-MRI and five T2-MRI features predicted divergence of high- and low-risk groups, with 5-year overall survival rates of 62.7% and 96.7%, respectively (P = .002). T1-MRI-based features showed the best performance distinguishing PFA from PFB with an AUC of 0.86. CONCLUSIONS: We present machine learning strategies to identify MRI phenotypes that distinguish PFA from PFB, as well as high- and low-risk PFA. We also describe quantitative image predictors of aggressive EP tumors that might assist risk-profiling after surgery. Future studies could examine translating radiomics as an adjunct to EP risk assessment when considering therapy strategies or trial candidacy.

Machine Assist for Pediatric Posterior Fossa Tumor Diagnosis: A Multinational Study.

BACKGROUND: Clinicians and machine classifiers reliably diagnose pilocytic astrocytoma (PA) on magnetic resonance imaging (MRI) but less accurately distinguish medulloblastoma (MB) from ependymoma (EP). One strategy is to first rule out the most identifiable diagnosis. OBJECTIVE: To hypothesize a sequential machine-learning classifier could improve diagnostic performance by mimicking a clinician's strategy of excluding PA before distinguishing MB from EP. METHODS: We extracted 1800 total Image Biomarker Standardization Initiative (IBSI)-based features from T2- and gadolinium-enhanced T1-weighted images in a multinational cohort of 274 MB, 156 PA, and 97 EP. We designed a 2-step sequential classifier - first ruling out PA, and next distinguishing MB from EP. For each step, we selected the best performing model from 6-candidate classifier using a reduced feature set, and measured performance on a holdout test set with the microaveraged F1 score. RESULTS: Optimal diagnostic performance was achieved using 2 decision steps, each with its own distinct imaging features and classifier method. A 3-way logistic regression classifier first distinguished PA from non-PA, with T2 uniformity and T1 contrast as the most relevant IBSI features (F1 score 0.8809). A 2-way neural net classifier next distinguished MB from EP, with T2 sphericity and T1 flatness as most relevant (F1 score 0.9189). The combined, sequential classifier was with F1 score 0.9179. CONCLUSION: An MRI-based sequential machine-learning classifiers offer high-performance prediction of pediatric posterior fossa tumors across a large, multinational cohort. Optimization of this model with demographic, clinical, imaging, and molecular predictors could provide significant advantages for family counseling and surgical planning.

Discovery of IACS-9779 and IACS-70465 as Potent Inhibitors Targeting Indoleamine 2,3-Dioxygenase 1 (IDO1) Apoenzyme.

Indoleamine 2,3-dioxygenase 1 (IDO1), a heme-containing enzyme that mediates the rate-limiting step in the metabolism of l-tryptophan to kynurenine, has been widely explored as a potential immunotherapeutic target in oncology. We developed a class of inhibitors with a conformationally constrained bicyclo[3.1.0]hexane core. These potently inhibited IDO1 in a cellular context by binding to the apoenzyme, as elucidated by biochemical characterization and X-ray crystallography. A SKOV3 tumor model was instrumental in differentiating compounds, leading to the identification of IACS-9779 (62) and IACS-70465 (71). IACS-70465 has excellent cellular potency, a robust pharmacodynamic response, and in a human whole blood assay was more potent than linrodostat (BMS-986205). IACS-9779 with a predicted human efficacious once daily dose below 1 mg/kg to sustain >90% inhibition of IDO1 displayed an acceptable safety margin in rodent toxicology and dog cardiovascular studies to support advancement into preclinical safety evaluation for human development.

MRI-based radiomics for prognosis of pediatric diffuse intrinsic pontine glioma: an international study.

BACKGROUND: Diffuse intrinsic pontine gliomas (DIPGs) are lethal pediatric brain tumors. Presently, MRI is the mainstay of disease diagnosis and surveillance. We identify clinically significant computational features from MRI and create a prognostic machine learning model. METHODS: We isolated tumor volumes of T1-post-contrast (T1) and T2-weighted (T2) MRIs from 177 treatment-naïve DIPG patients from an international cohort for model training and testing. The Quantitative Image Feature Pipeline and PyRadiomics was used for feature extraction. Ten-fold cross-validation of least absolute shrinkage and selection operator Cox regression selected optimal features to predict overall survival in the training dataset and tested in the independent testing dataset. We analyzed model performance using clinical variables (age at diagnosis and sex) only, radiomics only, and radiomics plus clinical variables. RESULTS: All selected features were intensity and texture-based on the wavelet-filtered images (3 T1 gray-level co-occurrence matrix (GLCM) texture features, T2 GLCM texture feature, and T2 first-order mean). This multivariable Cox model demonstrated a concordance of 0.68 (95% CI: 0.61-0.74) in the training dataset, significantly outperforming the clinical-only model (C = 0.57 [95% CI: 0.49-0.64]). Adding clinical features to radiomics slightly improved performance (C = 0.70 [95% CI: 0.64-0.77]). The combined radiomics and clinical model was validated in the independent testing dataset (C = 0.59 [95% CI: 0.51-0.67], Noether's test P = .02). CONCLUSIONS: In this international study, we demonstrate the use of radiomic signatures to create a machine learning model for DIPG prognostication. Standardized, quantitative approaches that objectively measure DIPG changes, including computational MRI evaluation, could offer new approaches to assessing tumor phenotype and serve a future role for optimizing clinical trial eligibility and tumor surveillance.

Deep COVID DeteCT: an international experience on COVID-19 lung detection and prognosis using chest CT.

The Coronavirus disease 2019 (COVID-19) presents open questions in how we clinically diagnose and assess disease course. Recently, chest computed tomography (CT) has shown utility for COVID-19 diagnosis. In this study, we developed Deep COVID DeteCT (DCD), a deep learning convolutional neural network (CNN) that uses the entire chest CT volume to automatically predict COVID-19 (COVID+) from non-COVID-19 (COVID-) pneumonia and normal controls. We discuss training strategies and differences in performance across 13 international institutions and 8 countries. The inclusion of non-China sites in training significantly improved classification performance with area under the curve (AUCs) and accuracies above 0.8 on most test sites. Furthermore, using available follow-up scans, we investigate methods to track patient disease course and predict prognosis.

Artificial intelligence for automatic cerebral ventricle segmentation and volume calculation: a clinical tool for the evaluation of pediatric hydrocephalus.

OBJECTIVE: Imaging evaluation of the cerebral ventricles is important for clinical decision-making in pediatric hydrocephalus. Although quantitative measurements of ventricular size, over time, can facilitate objective comparison, automated tools for calculating ventricular volume are not structured for clinical use. The authors aimed to develop a fully automated deep learning (DL) model for pediatric cerebral ventricle segmentation and volume calculation for widespread clinical implementation across multiple hospitals. METHODS: The study cohort consisted of 200 children with obstructive hydrocephalus from four pediatric hospitals, along with 199 controls. Manual ventricle segmentation and volume calculation values served as "ground truth" data. An encoder-decoder convolutional neural network architecture, in which T2-weighted MR images were used as input, automatically delineated the ventricles and output volumetric measurements. On a held-out test set, segmentation accuracy was assessed using the Dice similarity coefficient (0 to 1) and volume calculation was assessed using linear regression. Model generalizability was evaluated on an external MRI data set from a fifth hospital. The DL model performance was compared against FreeSurfer research segmentation software. RESULTS: Model segmentation performed with an overall Dice score of 0.901 (0.946 in hydrocephalus, 0.856 in controls). The model generalized to external MR images from a fifth pediatric hospital with a Dice score of 0.926. The model was more accurate than FreeSurfer, with faster operating times (1.48 seconds per scan). CONCLUSIONS: The authors present a DL model for automatic ventricle segmentation and volume calculation that is more accurate and rapid than currently available methods. With near-immediate volumetric output and reliable performance across institutional scanner types, this model can be adapted to the real-time clinical evaluation of hydrocephalus and improve clinician workflow.

Discovery of IPN60090, a Clinical Stage Selective Glutaminase-1 (GLS-1) Inhibitor with Excellent Pharmacokinetic and Physicochemical Properties.

Inhibition of glutaminase-1 (GLS-1) hampers the proliferation of tumor cells reliant on glutamine. Known glutaminase inhibitors have potential limitations, and in vivo exposures are potentially limited due to poor physicochemical properties. We initiated a GLS-1 inhibitor discovery program focused on optimizing physicochemical and pharmacokinetic properties, and have developed a new selective inhibitor, compound 27 (IPN60090), which is currently in phase 1 clinical trials. Compound 27 attains high oral exposures in preclinical species, with strong in vivo target engagement, and should robustly inhibit glutaminase in humans.

A do-it-yourself 3D-printed thoracic spine model for anesthesia resident simulation.

Central line placement, cricothyroidotomy, and lumbar epidural placement are common procedures for which there are simulators to help trainees learn the procedures. However, a model or a simulator for thoracic epidurals is not commonly used by anesthesia training programs to help teach the procedure. This brief technical report aims to share the design and fabrication process of a low-cost and do-it-yourself (DIY) 3D-printed thoracic spine model. Ten expert anesthesiology attendings and fifteen novice anesthesiology residents practiced with the model and were subsequently surveyed to assess their attitudes towards its fidelity and usefulness as a teaching tool. Responses were recorded with a Likert scale and found to be positive for both groups. Design files and an assembly manual were developed and made public through an open-source website.

Deep Learning for Automated Classification of Inferior Vena Cava Filter Types on Radiographs.

PURPOSE: To demonstrate the feasibility and evaluate the performance of a deep-learning convolutional neural network (CNN) classification model for automated identification of different types of inferior vena cava (IVC) filters on radiographs. MATERIALS AND METHODS: In total, 1,375 cropped radiographic images of 14 types of IVC filters were collected from patients enrolled in a single-center IVC filter registry, with 139 images withheld as a test set and the remainder used to train and validate the classification model. Image brightness, contrast, intensity, and rotation were varied to augment the training set. A 50-layer ResNet architecture with fixed pre-trained weights was trained using a soft margin loss over 50 epochs. The final model was evaluated on the test set. RESULTS: The CNN classification model achieved a F1 score of 0.97 (0.92-0.99) for the test set overall and of 1.00 for 10 of 14 individual filter types. Of the 139 test set images, 4 (2.9%) were misidentified, all mistaken for other filter types that appear highly similar. Heat maps elucidated salient features for each filter type that the model used for class prediction. CONCLUSIONS: A CNN classification model was successfully developed to identify 14 types of IVC filters on radiographs and demonstrated high performance. Further refinement and testing of the model is necessary before potential real-world application.

Altered cerebral perfusion in children with Langerhans cell histiocytosis after chemotherapy.

BACKGROUND AND PURPOSE: Children with Langerhans cell histiocytosis (LCH) may develop a wide array of neurological symptoms, but associated cerebral physiologic changes are poorly understood. We examined cerebral hemodynamic properties of pediatric LCH using arterial spin-labeling (ASL) perfusion magnetic resonance imaging (MRI). MATERIALS AND METHODS: A retrospective study was performed in 23 children with biopsy-proven LCH. Analysis was performed on routine brain MRI obtained before or after therapy. Region of interest (ROI) methodology was used to determine ASL cerebral blood flow (CBF) (mL/100 g/min) in the following bilateral regions: angular gyrus, anterior prefrontal cortex, orbitofrontal cortex, dorsal anterior cingulate cortex, and hippocampus. Quantile (median) regression was performed for each ROI location. CBF patterns were compared between pre- and posttreatment LCH patients as well as with age-matched healthy controls. RESULTS: Significantly reduced CBF was seen in posttreatment children with LCH compared to age-matched controls in angular gyrus (P = .046), anterior prefrontal cortex (P = .039), and dorsal anterior cingulate cortex (P = .023). Further analysis revealed dominant perfusion abnormalities in the right hemisphere. No significant perfusion differences were observed in the hippocampus or orbitofrontal cortex. CONCLUSION: Perfusion in specific cerebral regions may be consistently reduced in children with LCH, and may represent effects of underlying disease physiology and/or sequelae of chemotherapy. Studies that combine a formal cognitive assessment and hemodynamic data may further provide insight into perfusion deficits associated with the disease and the potential neurotoxic effects in children treated by chemotherapy.

Effectiveness, treatment durability, and treatment costs of canagliflozin and glucagon-like peptide-1 receptor agonists in patients with type 2 diabetes in the USA.

Introduction: This real-world study compared glycemic effectiveness, treatment durability, and treatment costs with canagliflozin 300 mg versus any dose of glucagon-like peptide-1 (GLP-1) receptor agonists in patients with type 2 diabetes mellitus (T2DM) in the USA. Research design and methods: A retrospective cohort study using administrative claims and laboratory data (1 April 2012 to 28 February 2017) from the HealthCore Integrated Research Database were used to assess mean HbA1c at 3-month intervals, achievement of HbA1c thresholds (<7.0%, <8.0%, <9.0%), and treatment durability (ie, adherence, discontinuation, switching, treatment failure (ie, exceeding threshold (7.0%, 8.0%, 9.0%), having a prescription for a new antihyperglycemic agent)) in adults with T2DM who initiated canagliflozin 300 mg or any dose of a GLP-1 receptor agonist. Medication costs were calculated for adherent patients. Results: There were no significant differences in the primary outcome of HbA1c levels at 3-month intervals (≤12 months) in the canagliflozin 300 mg versus any dose GLP-1 receptor agonist cohort. The likelihood of achieving HbA1c<8.0% was not different (p=0.666), the likelihood of achieving HbA1c<7.0% was lower (p=0.016), and the likelihood of achieving HbA1c<9.0% was higher (p=0.020) in the canagliflozin 300 mg versus any dose GLP-1 receptor agonist cohort. The likelihood of treatment failure after reaching any HbA1c target was not different between cohorts. A higher proportion of patients were adherent to treatment (p<0.0001) and a lower proportion discontinued (p<0.0001) or switched medication (p=0.023) in the canagliflozin 300 mg versus any dose GLP-1 receptor agonist cohort. Over 1 year, medication costs were $1421 (p<0.001) lower with canagliflozin 300 mg than any dose of GLP-1 receptor agonists. Conclusions: This real-world, US-based study found that initiation of canagliflozin 300 mg versus any dose of a GLP-1 receptor agonist in patients with T2DM was not associated with significant differences in the primary outcome of HbA1c levels at 3-month intervals for up to 12 months after index, but showed better adherence, less discontinuation, and lower drug acquisition costs compared with initiation of any dose of a GLP-1 receptor agonist.

Posterior fossa syndrome and increased mean diffusivity in the olivary bodies.

OBJECTIVE: Posterior fossa syndrome (PFS) is a common postoperative complication following resection of posterior fossa tumors in children. It typically presents 1 to 2 days after surgery with mutism, ataxia, emotional lability, and other behavioral symptoms. Recent structural MRI studies have found an association between PFS and hypertrophic olivary degeneration, which is detectable as T2 hyperintensity in the inferior olivary nuclei (IONs) months after surgery. In this study, the authors investigated whether immediate postoperative diffusion tensor imaging (DTI) of the ION can serve as an early imaging marker of PFS. METHODS: The authors retrospectively reviewed pediatric brain tumor patients treated at their institution, Lucile Packard Children's Hospital at Stanford, from 2004 to 2016. They compared the immediate postoperative DTI studies obtained in 6 medulloblastoma patients who developed PFS to those of 6 age-matched controls. RESULTS: Patients with PFS had statistically significant increased mean diffusivity (MD) in the left ION (1085.17 ± 215.51 vs 860.17 ± 102.64, p = 0.044) and variably increased MD in the right ION (923.17 ± 119.2 vs 873.67 ± 60.16, p = 0.385) compared with age-matched controls. Patients with PFS had downward trending fractional anisotropy (FA) in both the left (0.28 ± 0.06 vs 0.23 ± 0.03, p = 0.085) and right (0.29 ± 0.06 vs 0.25 ± 0.02, p = 0.164) IONs compared with age-matched controls, although neither of these values reached statistical significance. CONCLUSIONS: Increased MD in the ION is associated with development of PFS. ION MD changes may represent an early imaging marker of PFS.