Acute and Long-Term Toxicity after Planned Intraoperative Boost and Whole Breast Irradiation in High-Risk Patients with Breast Cancer-Results from the Targeted Intraoperative Radiotherapy Boost Quality Registry (TARGIT BQR).

In the context of breast cancer treatment optimization, this study prospectively examines the feasibility and outcomes of utilizing intraoperative radiotherapy (IORT) as a boost in combination with standard external beam radiotherapy (EBRT) for high-risk patients. Different guidelines recommend such a tumor bed boost in addition to whole breast irradiation with EBRT for patients with risk factors for local breast cancer recurrence. The TARGIT BQR (NCT01440010) is a prospective, multicenter registry study aimed at ensuring the quality of clinical outcomes. It provides, for the first time, data from a large cohort with a detailed assessment of acute and long-term toxicity following an IORT boost using low-energy X-rays. Inclusion criteria encompassed tumors up to 3.5 cm in size and preoperative indications for a boost. The IORT boost, administered immediately after tumor resection, delivered a single dose of 20 Gy. EBRT and systemic therapy adhered to local tumor board recommendations. Follow-up for toxicity assessment (LENT SOMA criteria: fibrosis, teleangiectasia, retraction, pain, breast edema, lymphedema, hyperpigmentation, ulceration) took place before surgery, 6 weeks to 90 days after EBRT, 6 months after IORT, and then annually using standardized case report forms (CRFs). Between 2011 and 2020, 1133 patients from 10 centers were preoperatively enrolled. The planned IORT boost was conducted in 90%, and EBRT in 97% of cases. Median follow-up was 32 months (range 1-120, 20.4% dropped out), with a median age of 61 years (range 30-90). No acute grade 3 or 4 toxicities were observed. Acute side effects included erythema grade 1 or 2 in 4.4%, palpable seroma in 9.1%, punctured seroma in 0.3%, and wound healing disorders in 2.1%. Overall, chronic teleangiectasia of any grade occurred in 16.2%, fibrosis grade ≥ 2 in 14.3%, pain grade ≥ 2 in 3.4%, and hyperpigmentation in 1.1%. In conclusion, a tumor bed boost through IORT using low-energy X-rays is a swift and feasible method that demonstrates low rates in terms of acute or long-term toxicity profiles in combination with whole breast irradiation.

Empathy in schizophrenia: neural alterations during emotion recognition and affective sharing.

Introduction: Deficits in emotion recognition and processing are characteristic for patients with schizophrenia [SCZ]. Methods: We targeted both emotion recognition and affective sharing, one in static and one in dynamic facial stimuli, during functional magnetic resonance imaging [fMRI] in 22 SCZ patients and 22 matched healthy controls [HC]. Current symptomatology and cognitive deficits were assessed as potential influencing factors. Results: Behaviorally, patients only showed a prolonged response time in age-discrimination trials. For emotion-processing trials, patients showed a difference in neural response, without an observable behavioral correlate. During emotion and age recognition in static stimuli, a reduced activation of the bilateral anterior cingulate cortex [ACC] and the right anterior insula [AI] emerged. In the affective sharing task, patients showed a reduced activation in the left and right caudate nucleus, right AI and inferior frontal gyrus [IFG], right cerebellum, and left thalamus, key areas of empathy. Discussion: We conclude that patients have deficits in complex visual information processing regardless of emotional content on a behavioral level and that these deficits coincide with aberrant neural activation patterns in emotion processing networks. The right AI as an integrator of these networks plays a key role in these aberrant neural activation patterns and, thus, is a promising candidate area for neurofeedback approaches.

Sex Differences in the Association of Depression Symptoms and Cardiovascular Disease in Adults in the United States.

PURPOSE: This study explores the relationship between depression and cardiovascular disease (CVD) in the US adult population, focusing on sex differences. DESIGN: Cross-sectional study. SETTING: National Health and Nutrition Examination Survey data (2013-2018). PARTICIPANTS: A total of 14 699 community-dwelling adults (≥20 years). MEASURE: The Patient Health Questionnaire (PHQ-9) depression screening tool assessed depressive symptoms. CVD events included heart failure, coronary heart disease, angina, heart attack, or stroke. ANALYSIS: Adjusted prevalence ratios were estimated using a Poisson regression model. RESULTS: The study finds a positive association between CVD incidents and both mild to moderate depressive symptoms (aPR:1.42, P = .002) and moderately severe to severe depression (aPR:1.72, P = .024). Overall, females exhibit a 47% lower likelihood of CVD incidents compared to males. However, in a subgroup analysis, increased depressive symptoms correlate with higher CVD incidents in females (aPRs range: 2.09 to 3.43, P < .001) compared to males (aPRs range: 1.45 to 1.77, P < .001). CONCLUSION: Depression is associated with increased cardiovascular disease (CVD) risk. Females generally have a lower CVD risk than males, but more severe depressive symptoms elevate CVD risk in females. These findings emphasize the significance of considering sex differences. Further research is needed to understand the underlying mechanisms.

How can we quantify, explain, and apply the uncertainty of complex soil maps predicted with neural networks?

Artificial neural networks (ANNs) have proven to be a useful tool for complex questions that involve large amounts of data. Our use case of predicting soil maps with ANNs is in high demand by government agencies, construction companies, or farmers, given cost and time intensive field work. However, there are two main challenges when applying ANNs. In their most common form, deep learning algorithms do not provide interpretable predictive uncertainty. This means that properties of an ANN such as the certainty and plausibility of the predicted variables, rely on the interpretation by experts rather than being quantified by evaluation metrics validating the ANNs. Further, these algorithms have shown a high confidence in their predictions in areas geographically distant from the training area or areas sparsely covered by training data. To tackle these challenges, we use the Bayesian deep learning approach "last-layer Laplace approximation", which is specifically designed to quantify uncertainty into deep networks, in our explorative study on soil classification. It corrects the overconfident areas without reducing the accuracy of the predictions, giving us a more realistic uncertainty expression of the model's prediction. In our study area in southern Germany, we subdivide the soils into soil regions and as a test case we explicitly exclude two soil regions in the training area but include these regions in the prediction. Our results emphasize the need for uncertainty measurement to obtain more reliable and interpretable results of ANNs, especially for regions far away from the training area. Moreover, the knowledge gained from this research addresses the problem of overconfidence of ANNs and provides valuable information on the predictability of soil types and the identification of knowledge gaps. By analyzing regions where the model has limited data support and, consequently, high uncertainty, stakeholders can recognize the areas that require more data collection efforts.

Obstructive Sleep Apnea and Its Cardiac Implications in the United States: An Age-Stratified Analysis Between Young and Older Adults.

BACKGROUND: Obstructive sleep apnea (OSA) is a common sleep disorder associated with cardiovascular risks. This study aimed to assess the prevalence of probable OSA and its relationship with cardiovascular risks and diseases focusing on age-stratified young adults (20-40 years) and older (>40 years). METHODS AND RESULTS: The study used a cross-sectional design, analyzing data from the National Health and Nutrition Examination Survey conducted between 2013 and 2018, comprising 9887 community-dwelling adults aged ≥20 years. Probable OSA was determined on the basis of self-report of OSA-related symptoms (eg, snoring, gasping/breath cessation while sleeping). Cardiovascular risk factors, including hypertension, diabetes, hyperlipidemia, and metabolic syndrome, were evaluated according to established guidelines. Cardiovascular diseases (CVDs) included self-reported heart conditions, including congestive heart failure, coronary heart disease, angina, heart attacks, and strokes. Individuals with probable OSA showed a significantly higher prevalence of health conditions, including hypertension (adjusted prevalence ratio [aPR], 1.19; P<0.001), diabetes (aPR, 1.17; P: 0.01), metabolic syndrome (aPR, 1.14; P<0.001), heart attack (aPR, 1.63; P<0.01), stroke (aPR, 1.41; P: 0.03), and any CVD event (aPR, 1.36; P: 0.01) after adjusting for relevant factors. Young adults with probable OSA showed higher prevalence rates of any CVD events (aPR, 3.44; P<0.001), hypertension (aPR, 1.45; P<0.001), metabolic syndrome (aPR, 1.25; P<0.001), and angina (aPR, 10.39; P<0.001). CONCLUSIONS: The study suggests early identification and management of OSA in individuals at risk for CVD. While cross-sectional, it emphasizes that health care providers should recognize OSA as significantly associated with CVDs and its precursor risks in young adults, stressing proactive care and screening to reduce CVD risk in this population.

Multiomic analysis of uterine leiomyomas in self-described Black and White women: molecular insights into health disparities.

BACKGROUND: Black women are at an increased risk of developing uterine leiomyomas and experiencing worse disease prognosis than White women. Epidemiologic and molecular factors have been identified as underlying these disparities, but there remains a paucity of deep, multiomic analysis investigating molecular differences in uterine leiomyomas from Black and White patients. OBJECTIVE: To identify molecular alterations within uterine leiomyoma tissues correlating with patient race by multiomic analyses of uterine leiomyomas collected from cohorts of Black and White women. STUDY DESIGN: We performed multiomic analysis of uterine leiomyomas from Black (42) and White (47) women undergoing hysterectomy for symptomatic uterine leiomyomata. In addition, our analysis included the application of orthogonal methods to evaluate fibroid biomechanical properties, such as second harmonic generation microscopy, uniaxial compression testing, and shear-wave ultrasonography analyses. RESULTS: We found a greater proportion of MED12 mutant uterine leiomyomas from Black women (>35% increase; Mann-Whitney U, P<.001). MED12 mutant tumors exhibited an elevated abundance of extracellular matrix proteins, including several collagen isoforms, involved in the regulation of the core matrisome. Histologic analysis of tissue fibrosis using trichrome staining and secondary harmonic generation microscopy confirmed that MED12 mutant tumors are more fibrotic than MED12 wild-type tumors. Using shear-wave ultrasonography in a prospectively collected cohort, Black patients had fibroids that were firmer than White patients, even when similar in size. In addition, these analyses uncovered ancestry-linked expression quantitative trait loci with altered allele frequencies in African and European populations correlating with differential abundance of several proteins in uterine leiomyomas independently of MED12 mutation status, including tetracoidpeptide repeat protein 38. CONCLUSION: Our study shows that Black women have a higher prevalence of uterine leiomyomas harboring mutations in MED12 and that this mutational status correlates with increased tissue fibrosis compared with wild-type uterine leiomyomas. Our study provides insights into molecular alterations correlating with racial disparities in uterine leiomyomas and improves our understanding of the molecular etiology underlying uterine leiomyoma development within these populations.

The Journey to Improve the College of American Pathologists Cancer Biomarker Reporting Protocols.

CONTEXT.­: Biomarker reporting has increasingly become a key component of pathology reporting, providing diagnostic, prognostic, and actionable therapeutic data for patient care. OBJECTIVE.­: To expand and improve the College of American Pathologists (CAP) biomarker protocols. DESIGN.­: We surveyed CAP members to better understand the limitations they experienced when reporting cancer biomarker results. A Biomarker Workgroup reviewed the survey results and developed a strategy to improve and standardize biomarker reporting. Drafts of new and revised biomarker protocols were reviewed in both print and electronic template formats during interactive webinars presented to the CAP House of Delegates. Feedback was collected, and appropriate revisions were made to finalize the protocols. RESULTS.­: The first phase of the CAP Biomarker Workgroup saw the development of (1) a new stand-alone general Immunohistochemistry Biomarker Protocol that includes reporting for ER (estrogen receptor), PR (progesterone receptor), Ki-67, HER2 (human epidermal growth factor receptor 2), PD-L1 (programmed death ligand-1), and mismatch repair; (2) a new Head and Neck Biomarker Protocol that updates the prior 2017 paper-only version into an electronic template, adding new diagnostic and theranostic markers; (3) a major revision to the Lung Biomarker Protocol to streamline it and add in pan-cancer markers; and (4) a revision to the Colon and Rectum Biomarker Protocol to add HER2 reporting. CONCLUSIONS.­: We have taken a multipronged approach to improving biomarker reporting in the CAP cancer protocols. We continue to review current biomarker reporting protocols to reduce and eliminate unnecessary methodologic details and update with new markers as needed. The biomarker templates will serve as standardized modular units that can be inserted into cancer-reporting protocols.

Culturally sensitive stepped care for adolescent refugees: efficacy and cost-utility of a multicentric randomized controlled trial.

Adolescent refugees and asylum seekers (ARAS) are highly vulnerable to mental health problems. Stepped care models (SCM) and culturally sensitive therapies offer promising treatment approaches to effectively provide necessary medical and psychological support. To our knowledge, we were the first to investigate whether a culturally sensitive SCM will reduce symptoms of depression and PTSD in ARAS more effectively and efficiently than treatment as usual (TAU). We conducted a multicentric, randomized, controlled and rater-blinded trial across Germany with ARAS between the ages of 14 to 21 years. Participants (N = 158) were stratified by their level of depressive symptom severity and then equally randomized to either SCM or TAU. Depending on their severity level, SCM participants were allocated to tailored interventions. Symptom changes were assessed for depression (PHQ) and PTSD (CATS) at four time points, with the primary end point at post-intervention after 12 weeks. Based on an intention-to-treat sample, we used a linear mixed model approach for the main statistical analyses. Further evaluations included cost-utility analyses, sensitivity analyses, follow-up-analyses, response and remission rates and subgroup analysis. We found a significant reduction of PHQ (d = 0.52) and CATS (d = 0.27) scores in both groups. However, there was no significant difference between SCM and TAU. Cost-utility analyses indicated that SCM generated greater cost-utility when measured as quality-adjusted life years compared to TAU. Subgroup analysis revealed different effects for the SCM interventions depending on the outcome measure. Although culturally sensitive, SCMs did not prove to be more effective in symptom change and represent a more cost-effective treatment alternative for mentally burdened ARAS. Our research contributes to the optimization of clinical productivity and the improvement of therapeutic care for ARAS. Disorder-specific interventions should be further investigated.

Step away from depression-results from a multicenter randomized clinical trial with a pedometer intervention during and after inpatient treatment of depression.

Evidence for the effectiveness of physical activity (PA) in the treatment of depression prevails for outpatients with mild and moderate symptom levels. For inpatient treatment of severe depression, evidence-based effectiveness exists only for structured and supervised group PA interventions. The Step Away from Depression (SAD) study investigated the effectiveness of an individual pedometer intervention (PI) combined with an activity diary added to inpatient treatment as usual (TAU). In this multicenter randomized controlled trial, 192 patients were randomized to TAU or TAU plus PI. The two primary outcomes at discharge were depression-blindly rated with the Montgomery-Åsberg Depression Rating Scale (MADRS)-and average number of daily steps measured by accelerometers. Secondary outcomes were self-rated depression and PA, anxiety, remission and response rates. Multivariate analysis of variance (MANOVA) revealed no significant difference between both groups for depression and daily steps. Mean MADRS scores at baseline were 29.5 (SD = 8.3) for PI + TAU and 28.8 (SD = 8.1) for TAU and 16.4 (SD = 10.3) and 17.2 (SD = 9.9) at discharge, respectively. Daily steps rose from 6285 (SD = 2321) for PI + TAU and 6182 (SD = 2290) for TAU to 7248 (SD = 2939) and 7325 (SD = 3357). No differences emerged between groups in secondary outcomes. For severely depressed inpatients, a PI without supervision or further psychological interventions is not effective. Monitoring, social reinforcement and motivational strategies should be incorporated in PA interventions for this population to reach effectiveness.

Standardized Classification of Lung Adenocarcinoma Subtypes and Improvement of Grading Assessment Through Deep Learning.

The histopathologic distinction of lung adenocarcinoma (LADC) subtypes is subject to high interobserver variability, which can compromise the optimal assessment of patient prognosis. Therefore, this study developed convolutional neural networks capable of distinguishing LADC subtypes and predicting disease-specific survival, according to the recently established LADC tumor grades. Consensus LADC histopathologic images were obtained from 17 expert pulmonary pathologists and one pathologist in training. Two deep learning models (AI-1 and AI-2) were trained to predict eight different LADC classes. Furthermore, the trained models were tested on an independent cohort of 133 patients. The models achieved high precision, recall, and F1 scores exceeding 0.90 for most of the LADC classes. Clear stratification of the three LADC grades was reached in predicting the disease-specific survival by the two models, with both Kaplan-Meier curves showing significance (P = 0.0017 and 0.0003). Moreover, both trained models showed high stability in the segmentation of each pair of predicted grades with low variation in the hazard ratio across 200 bootstrapped samples. These findings indicate that the trained convolutional neural networks improve the diagnostic accuracy of the pathologist and refine LADC grade assessment. Thus, the trained models are promising tools that may assist in the routine evaluation of LADC subtypes and grades in clinical practice.

The associations of Positive and Negative Valence Systems, Cognitive Systems and Social Processes on disease severity in anxiety and depressive disorders.

Background: Anxiety and depressive disorders share common features of mood dysfunctions. This has stimulated interest in transdiagnostic dimensional research as proposed by the Research Domain Criteria (RDoC) approach by the National Institute of Mental Health (NIMH) aiming to improve the understanding of underlying disease mechanisms. The purpose of this study was to investigate the processing of RDoC domains in relation to disease severity in order to identify latent disorder-specific as well as transdiagnostic indicators of disease severity in patients with anxiety and depressive disorders. Methods: Within the German research network for mental disorders, 895 participants (n = 476 female, n = 602 anxiety disorder, n = 257 depressive disorder) were recruited for the Phenotypic, Diagnostic and Clinical Domain Assessment Network Germany (PD-CAN) and included in this cross-sectional study. We performed incremental regression models to investigate the association of four RDoC domains on disease severity in patients with affective disorders: Positive (PVS) and Negative Valance System (NVS), Cognitive Systems (CS) and Social Processes (SP). Results: The results confirmed a transdiagnostic relationship for all four domains, as we found significant main effects on disease severity within domain-specific models (PVS: ß = -0.35; NVS: ß = 0.39; CS: ß = -0.12; SP: ß = -0.32). We also found three significant interaction effects with main diagnosis showing a disease-specific association. Limitations: The cross-sectional study design prevents causal conclusions. Further limitations include possible outliers and heteroskedasticity in all regression models which we appropriately controlled for. Conclusion: Our key results show that symptom burden in anxiety and depressive disorders is associated with latent RDoC indicators in transdiagnostic and disease-specific ways.

Effects of electroconvulsive therapy on cerebral A1 adenosine receptor availability: a PET study in patients suffering from treatment-resistant major depressive disorder.

Introduction: Sleep deprivation and electroconvulsive therapy (ECT) effectively ameliorate symptoms in major depressive disorder (MDD). In rodents, both are associated with an enhancement of cerebral adenosine levels, which in turn likely influence adenosinergic receptor expression. The aim of the current study was to investigate cerebral A1 adenosine receptor (A1AR) availability in patients with MDD as a potential mediating factor of antidepressant effects of ECT using [18F]CPFPX and positron emission tomography (PET). Methods: Regional A1AR availability was determined before and after a series of ECT applications (mean number ± SD 10.4 ± 1.2) in 14 subjects (4 males, mean age 49.5 ± 11.8 years). Clinical outcome, measured by neuropsychological testing, and ECT parameters were correlated with changes in A1AR availability. Results: ECT had a strong antidepressive effect (p < 0.01) while on average cerebral A1AR availability remained unaltered between pre-and post-ECT conditions (F = 0.65, p = 0.42, mean difference ± SD 3.93% ± 22.7%). There was no correlation between changes in clinical outcome parameters and regional A1AR availability, although individual patients showed striking bidirectional alterations of up to 30-40% in A1AR availability after ECT. Solely, for the mean seizure quality index of the applied ECTs a significant association with changes in A1AR availability was found (rs = -0.6, p = 0.02). Discussion: In the present study, therapeutically effective ECT treatment did not result in coherent changes of A1AR availability after a series of ECT treatments. These findings do not exclude a potential role for cerebral A1ARs in ECT, but shift attention to rather short-termed and adaptive mechanisms during ECT-related convulsive effects.

Transmission probability filter optimization for Agility MLC in Monaco treatment planning system.

In the Monte Carlo-based treatment planning system (TPS) Monaco, transmission probability filters (TPF) are utilized to describe the transmission through the multi leaf collimator (MLC). By having knowledge of the TPF parameters for various photon beam energies, adjusting the MLC transmission parameters becomes easier, enhancing the accuracy of the Monte Carlo algorithm in achieving a dose distribution that closely aligns with the irradiated dose at the Versa HD linear accelerator (linac). The objective of this study was to determine the TPF parameters for 6MV, 10MV, 6MV flattening filter free (FFF) and 10MV FFF for a Versa HD linac equipped with Agility MLC. The TPF parameters were adjusted using point dose measurements and vendor-provided fields specifically designed to fine-tune the MLC. After adjusting the TPF parameters, a gamma passing rate (GPR) analysis was conducted on 25 treatment plans to ensure that the Monte Carlo model, with the updated TPF parameters, accurately matched the actual linac delivery. The TPF values ranged from 0.0018 to 0.0032 for leaf transmission and 1.15 to 1.25 for Leaf Tip leakage across the different energies. The average GPR ranged from 97.8% for 10MV FFF to 98.5% for 6MV photon energies. Additionally, the TPF parameters for 6MV obtained in this study were consistent with previously published TPF values for 6MV photon energy. Hence, it was concluded that optimizing the TPF does not need to be performed for every individual Versa HD linac with Agility MLC. Instead, the published parameters can be applied to other Versa HD linacs to enhance clinical accuracy. In conclusion, this study determined the TPF parameters for 6MV and previously unpublished photon energies 10MV, 6MV FFF and 10MV FFF. These parameters can be easily transferred to other facilities, resulting in improved agreement between the dose distribution from the TPS and the linac.

Cognitive Stress Regulation in Schizophrenia Patients and Healthy Individuals: Brain and Behavior.

Stress is an important factor in the development, triggering, and maintenance of psychotic symptoms. Still, little is known about the neural correlates of cognitively regulating stressful events in schizophrenia. The current study aimed at investigating the cognitive down-regulation of negative, stressful reactions during a neuroimaging psychosocial stress paradigm (non-regulated stress versus cognitively regulated stress). In a randomized, repeated-measures within-subject design, we assessed subjective reactions and neural activation in schizophrenia patients (SZP) and matched healthy controls in a neuroimaging psychosocial stress paradigm. In general, SZP exhibited an increased anticipation of stress compared to controls (p = 0.020). During non-regulated stress, SZP showed increased negative affect (p = 0.033) and stronger activation of the left parietal operculum/posterior insula (p < 0.001) and right inferior frontal gyrus/anterior insula (p = 0.005) than controls. Contrarily, stress regulation compared to non-regulated stress led to increased subjective reactions in controls (p = 0.003) but less deactivation in SZP in the ventral anterior cingulate cortex (p = 0.027). Our data demonstrate stronger reactions to and anticipation of stress in patients and difficulties with cognitive stress regulation in both groups. Considering the strong association between mental health and stress, the investigation of cognitive regulation in individuals vulnerable to stress, including SZP, has crucial implications for improving stress intervention trainings.

Multiplatform analyses reveal distinct drivers of systemic pathogenesis in adult versus pediatric severe acute COVID-19.

The pathogenesis of multi-organ dysfunction associated with severe acute SARS-CoV-2 infection remains poorly understood. Endothelial damage and microvascular thrombosis have been identified as drivers of COVID-19 severity, yet the mechanisms underlying these processes remain elusive. Here we show alterations in fluid shear stress-responsive pathways in critically ill COVID-19 adults as compared to non-COVID critically ill adults using a multiomics approach. Mechanistic in-vitro studies, using microvasculature-on-chip devices, reveal that plasma from critically ill COVID-19 adults induces fibrinogen-dependent red blood cell aggregation that mechanically damages the microvascular glycocalyx. This mechanism appears unique to COVID-19, as plasma from non-COVID sepsis patients demonstrates greater red blood cell membrane stiffness but induces less significant alterations in overall blood rheology. Multiomics analyses in pediatric patients with acute COVID-19 or the post-infectious multi-inflammatory syndrome in children (MIS-C) demonstrate little overlap in plasma cytokine and metabolite changes compared to adult COVID-19 patients. Instead, pediatric acute COVID-19 and MIS-C patients show alterations strongly associated with cytokine upregulation. These findings link high fibrinogen and red blood cell aggregation with endotheliopathy in adult COVID-19 patients and highlight differences in the key mediators of pathogenesis between adult and pediatric populations.

Prevention of First-Episode Psychosis in People at Clinical High Risk: A Randomized Controlled, Multicentre Trial Comparing Cognitive-Behavioral Therapy and Clinical Management Plus Low-Dose Aripiprazole or Placebo (PREVENT).

BACKGROUND: There is limited knowledge of whether cognitive-behavioral therapy (CBT) or second-generation antipsychotics (SGAs) should be recommended as the first-line treatment in individuals at clinical high risk for psychosis (CHRp). HYPOTHESIS: To examine whether individual treatment arms are superior to placebo and whether CBT is non-inferior to SGAs in preventing psychosis over 12 months of treatment. STUDY DESIGN: PREVENT was a blinded, 3-armed, randomized controlled trial comparing CBT to clinical management plus aripiprazole (CM + ARI) or plus placebo (CM + PLC) at 11 CHRp services. The primary outcome was transition to psychosis at 12 months. Analyses were by intention-to-treat. STUDY RESULTS: Two hundred eighty CHRp individuals were randomized: 129 in CBT, 96 in CM + ARI, and 55 in CM + PLC. In week 52, 21 patients in CBT, 19 in CM + ARI, and 7 in CM + PLC had transitioned to psychosis, with no significant differences between treatment arms (P = .342). Psychopathology and psychosocial functioning levels improved in all treatment arms, with no significant differences. CONCLUSIONS: The analysis of the primary outcome transition to psychosis at 12 months and secondary outcomes symptoms and functioning did not demonstrate significant advantages of the active treatments over placebo. The conclusion is that within this trial, neither low-dose aripiprazole nor CBT offered additional benefits over clinical management and placebo.

Organ absorbed doses in the IORT treatment of breast cancer with the INTRABEAM device: a Monte-Carlo study.

Purpose: The current prescription and the assessment of the delivered absorbed dose in intraoperative radiation therapy (IORT) with the INTRABEAM system rely mainly on depth-dose measurements in water. The accuracy of this approach is limited because tissue heterogeneity is ignored. It is also difficult to accurately determine the dose delivered to the patient experimentally as the steep dose gradient is highly sensitive to geometric errors. Our goal is to determine the dose to the target volume and the organs at risk of a clinical breast cancer patient from treatment with the system.Methods: A homogeneous water-equivalent CT dataset was derived from the preoperative CT scan of a patient by setting all materials in the patient volume as water-equivalent. This homogeneous CT data represents the current assumption of a homogenous patient, while the original CT data is considered the ground truth. An in-house Monte Carlo algorithm was used to simulate the delivered dose in both setups for a prescribed treatment dose of 20 Gy to the surface of the 3.5 cm diameter spherical applicator.Results: The doses received by 2% (D2%) of the target volume for the homogeneous and heterogeneous geometries are 16.26 Gy and 9.33 Gy, respectively. The D2% for the heart are 0.035 Gy and 0.119 Gy for the homogeneous and heterogeneous geometries, respectively. This trend is also observed for the other organs at risk.Conclusions: The assumption of a homogeneous patient overestimates the dose to the target volume and underestimates the doses to the organs at risk.

Functional connectivity signatures of NMDAR dysfunction in schizophrenia-integrating findings from imaging genetics and pharmaco-fMRI.

Both, pharmacological and genome-wide association studies suggest N-methyl-D-aspartate receptor (NMDAR) dysfunction and excitatory/inhibitory (E/I)-imbalance as a major pathophysiological mechanism of schizophrenia. The identification of shared fMRI brain signatures of genetically and pharmacologically induced NMDAR dysfunction may help to define biomarkers for patient stratification. NMDAR-related genetic and pharmacological effects on functional connectivity were investigated by integrating three different datasets: (A) resting state fMRI data from 146 patients with schizophrenia genotyped for the disease-associated genetic variant rs7191183 of GRIN2A (encoding the NMDAR 2 A subunit) as well as 142 healthy controls. (B) Pharmacological effects of the NMDAR antagonist ketamine and the GABA-A receptor agonist midazolam were obtained from a double-blind, crossover pharmaco-fMRI study in 28 healthy participants. (C) Regional gene expression profiles were estimated using a postmortem whole-brain microarray dataset from six healthy donors. A strong resemblance was observed between the effect of the genetic variant in schizophrenia and the ketamine versus midazolam contrast of connectivity suggestive for an associated E/I-imbalance. This similarity became more pronounced for regions with high density of NMDARs, glutamatergic neurons, and parvalbumin-positive interneurons. From a functional perspective, increased connectivity emerged between striato-pallido-thalamic regions and cortical regions of the auditory-sensory-motor network, while decreased connectivity was observed between auditory (superior temporal gyrus) and visual processing regions (lateral occipital cortex, fusiform gyrus, cuneus). Importantly, these imaging phenotypes were associated with the genetic variant, the differential effect of ketamine versus midazolam and schizophrenia (as compared to healthy controls). Moreover, the genetic variant was associated with language-related negative symptomatology which correlated with disturbed connectivity between the left posterior superior temporal gyrus and the superior lateral occipital cortex. Shared genetic and pharmacological functional connectivity profiles were suggestive of E/I-imbalance and associated with schizophrenia. The identified brain signatures may help to stratify patients with a common molecular disease pathway providing a basis for personalized psychiatry.

Mapping Research Domain Criteria using a transdiagnostic mini-RDoC assessment in mental disorders: a confirmatory factor analysis.

This study aimed to build on the relationship of well-established self-report and behavioral assessments to the latent constructs positive (PVS) and negative valence systems (NVS), cognitive systems (CS), and social processes (SP) of the Research Domain Criteria (RDoC) framework in a large transnosological population which cuts across DSM/ICD-10 disorder criteria categories. One thousand four hundred and thirty one participants (42.1% suffering from anxiety/fear-related, 18.2% from depressive, 7.9% from schizophrenia spectrum, 7.5% from bipolar, 3.4% from autism spectrum, 2.2% from other disorders, 18.4% healthy controls, and 0.2% with no diagnosis specified) recruited in studies within the German research network for mental disorders for the Phenotypic, Diagnostic and Clinical Domain Assessment Network Germany (PD-CAN) were examined with a Mini-RDoC-Assessment including behavioral and self-report measures. The respective data was analyzed with confirmatory factor analysis (CFA) to delineate the underlying latent RDoC-structure. A revised four-factor model reflecting the core domains positive and negative valence systems as well as cognitive systems and social processes showed a good fit across this sample and showed significantly better fit compared to a one factor solution. The connections between the domains PVS, NVS and SP could be substantiated, indicating a universal latent structure spanning across known nosological entities. This study is the first to give an impression on the latent structure and intercorrelations between four core Research Domain Criteria in a transnosological sample. We emphasize the possibility of using already existing and well validated self-report and behavioral measurements to capture aspects of the latent structure informed by the RDoC matrix.