Childhood maltreatment as predictor and moderator for treatment outcome in patients with major depressive disorders treated with metacognitive therapy or behavioral activation.

BACKGROUND: Childhood maltreatment (CM) is a risk factor for developing and maintaining depression. It is unclear whether CM influences the effect of treatments for depression. This study examined CM's predictor and moderator effect in Behavioral Activation (BA) and Metacognitive Therapy (MCT). METHOD: CM was analyzed in a trial comparing a six months treatment program of either BA or MCT for 122 outpatients with major depressive disorder (MDD). Depression was assessed by the Hamilton Rating Scale for Depression (HRSD-24). CM was assessed using the Childhood Trauma Questionnaire (CTQ). RESULTS: Linear mixed models showed no predictor or moderator effects for the CTQ total score (all p > .58) but revealed a moderator effect for 'sexual abuse' on the reduction of depressive symptoms (ß = 10.98, SE = 4.48, p = .015) indicating that patients with experiences of childhood sexual abuse benefited more from BA. There also was a predictor effect for 'physical neglect' (ß = -3.35, SE = 1.70, p = .049): patients without the experience of physical neglect benefited more from treatment regardless of condition. Exploratory analyses indicated no predictor or moderator effects for the onset or persistence of depression, comorbid anxiety disorders or Cluster-C PDs (all p > .28). LIMITATIONS: Limitations include small sample sizes for some of the subsamples. CONCLUSION: Childhood sexual abuse seems to moderate treatment effectiveness in a sample of severely affected outpatients with MDD treated with MCT or BA. If confirmed in further trials, a history of sexual abuse might guide the choice between MCT and BA. TRIAL REGISTRATION: German Clinical Trials Register DRKS-ID: DRKS00011536 (retrospectively registered on February 13, 2017, without changes to the study protocol).

Predicting the outcome of psychotherapy for chronic depression by person-specific symptom networks.

Psychotherapies are efficacious in the treatment of depression, albeit only with a moderate effect size. It is hoped that personalization of treatment can lead to better outcomes. The network theory of psychopathology offers a novel approach suggesting that symptom interactions as displayed in person-specific symptom networks could guide treatment planning for an individual patient. In a sample of 254 patients with chronic depression treated with either disorder-specific or non-specific psychotherapy for 48 weeks, we investigated if person-specific symptom networks predicted observer-rated depression severity at the end of treatment and one and two years after treatment termination. Person-specific symptom networks were constructed based on a time-varying multilevel vector autoregressive model of patient-rated symptom data. We used statistical parameters that describe the structure of these person-specific networks to predict therapy outcome. First, we used symptom centrality measures as predictors. Second, we used a machine learning approach to select parameters that describe the strength of pairwise symptom associations. We found that information on person-specific symptom networks strongly improved the accuracy of the prediction of observer-rated depression severity at treatment termination compared to common covariates recorded at baseline. This was also shown for predicting observer-rated depression severity at one- and two-year follow-up. Pairwise symptom associations were better predictors than symptom centrality parameters for depression severity at the end of therapy and one year later. Replication and external validation of our findings, methodological developments, and work on possible ways of implementation are needed before person-specific networks can be reliably used in clinical practice. Nevertheless, our results indicate that the structure of person-specific symptom networks can provide valuable information for the personalization of treatment for chronic depression.

Childhood Trauma Questionnaire-based child maltreatment profiles to predict efficacy of the Cognitive Behavioral Analysis System of Psychotherapy versus non-specific psychotherapy in adults with early-onset chronic depression: cluster analysis of data from a randomised controlled trial.

BACKGROUND: Child maltreatment is a broadly confirmed risk factor for mental and physical illness. Some psychological treatments specifically target mental health conditions associated with child maltreatment. For example, the Cognitive Behavioral Analysis System of Psychotherapy (CBASP) focuses on maladaptive interpersonal behaviours in chronic depression. However, how the assessment of child maltreatment could inform personalised treatment is unclear. We used data from a previously published clinical trial to investigate whether a pre-established child maltreatment clustering approach predicts differential outcomes after CBASP versus non-specific supportive psychotherapy in patients with early-onset chronic depression. METHODS: We did a cluster analysis of data from a previous randomised controlled trial of unmedicated adult outpatients with early-onset chronic depression who were treated at eight university clinics and psychological institutes in Germany with 32 sessions of CBASP or non-specific supportive psychotherapy. Participants were eligible for the original trial if they were aged 18-65 years; had major depressive disorder (MDD) with an early onset and duration of at least 2 years, current MDD superimposed on a pre-existing dysthymic disorder, or recurrent MDD with incomplete remission between episodes as defined by DSM-IV; and had a score of at least 20 points on the 24-item Hamilton Rating Scale for Depression (HRSD-24). Participants were included in the current study if they had completed the short form of the Childhood Trauma Questionnaire (CTQ) at trial baseline. We used an agglomerative hierarchical clustering approach to derive child maltreatment clusters from individual patterns across the five domains of the CTQ. We used linear mixed models to investigate whether clustering could predict differential clinical outcomes (change in symptom severity on the HRSD-24) up to 2 years after treatment onset. People with lived experience were involved in the current study. FINDINGS: 253 patients (129 [51%] treated with CBASP and 124 [49%] with supportive psychotherapy) had complete CTQ records and were included in the analysis. 169 (67%) participants were women, 84 (33%) were men, and the mean age was 45·9 years (SD 11·7). We identified seven child maltreatment clusters and found significant differences in treatment effects of CBASP and supportive psychotherapy between the clusters (F(6,948·76)=2·47; p=0·023); differences were maintained over the 2-year follow-up. CBASP was superior in distinct clusters of co-occurring child maltreatment: predominant emotional neglect (change in ß -6·02 [95% CI -11·9 to -0·13]; Cohen's d=-0·98 [95% CI -1·94 to -0·02]; p=0·045), predominant emotional neglect and abuse (-6·39 [-10·22 to -2·56]; -1·04 [-1·67 to -0·42]; p=0·0011), and emotional neglect and emotional and physical abuse (-9·41 [-15·91 to -2·91]; -1·54 [-2·6 to -0·47]; p=0·0046). INTERPRETATION: CTQ-based cluster analysis can facilitate identification of patients with early-onset chronic depression who would specifically benefit from CBASP. Child maltreatment clusters could be implemented in clinical assessments and serve to develop and personalise trauma-informed care in mental health. FUNDING: The German Research Foundation and the German Federal Ministry of Education and Research.

Generative adversarial network-based reconstruction of healthy anatomy for anomaly detection in brain CT scans.

Purpose: To help radiologists examine the growing number of computed tomography (CT) scans, automatic anomaly detection is an ongoing focus of medical imaging research. Radiologists must analyze a CT scan by searching for any deviation from normal healthy anatomy. We propose an approach to detecting abnormalities in axial 2D CT slice images of the brain. Although much research has been done on detecting abnormalities in magnetic resonance images of the brain, there is little work on CT scans, where abnormalities are more difficult to detect due to the low image contrast that must be represented by the model used. Approach: We use a generative adversarial network (GAN) to learn normal brain anatomy in the first step and compare two approaches to image reconstruction: training an encoder in the second step and using iterative optimization during inference. Then, we analyze the differences from the original scan to detect and localize anomalies in the brain. Results: Our approach can reconstruct healthy anatomy with good image contrast for brain CT scans. We obtain median Dice scores of 0.71 on our hemorrhage test data and 0.43 on our test set with additional tumor images from publicly available data sources. We also compare our models to a state-of-the-art autoencoder and a diffusion model and obtain qualitatively more accurate reconstructions. Conclusions: Without defining anomalies during training, a GAN-based network was used to learn healthy anatomy for brain CT scans. Notably, our approach is not limited to the localization of hemorrhages and tumors and could thus be used to detect structural anatomical changes and other lesions.

Adverse Events of Psychological Interventions: Definitions, Assessment, Current State of the Research and Implications for Research and Clinical Practice.

BACKGROUND: The effectiveness of psychological interventions is undisputed. But while in other fields of health care the safety of interventions is studied alongside effectiveness, adverse events (AEs) have only recently been assessed in clinical studies of psychological interventions. This critical review summarizes the definition, assessment and current research status of AEs of psychological interventions. SUMMARY: AEs are defined as any untoward event or unfavorable change that occurs in the course of a psychological intervention. AEs that are caused by the intervention can be classified into side effects of correctly applied treatment, malpractice (i.e., incorrectly applied treatment) and unethical conduct (e.g., sexual abuse). Ideally, they are assessed by independent raters or alternatively by self-report questionnaires that should also cover serious adverse events (SAEs, e.g., suicide attempts or self-injurious behaviors). About 1 to 2 in 3 patients report at least 1 AE and results of meta-analyses suggest that treatments might differ in frequency and/or severity of AE and in treatment acceptability (measured as dropout rates). KEY MESSAGES: Measures of AEs and SAEs as well as more nuanced descriptions of dropout should be included in all clinical studies of psychological interventions. If this happens, we might learn that psychological interventions differ with respect to AEs, SAEs and acceptability. As many psychological interventions are about equally effective, they might one day be chosen based on differences in their safety profile rather than their differential effectiveness. Ideally, reducing AEs might also lead to more effective interventions.

The Effectiveness of Dialectical Behavior Therapy Compared to Schema Therapy for Borderline Personality Disorder: A Randomized Clinical Trial.

INTRODUCTION: In the treatment of borderline personality disorder (BPD), there is empirical support for both dialectical behavior therapy (DBT) and schema therapy (ST); these treatments have never been compared directly. This study examines whether either of them is more effective than the other in treating patients with BPD. METHODS: In this randomized, parallel-group, rater-blind clinical trial, outpatients aged between 18 and 65 years with a primary diagnosis of BPD were recruited in a tertiary outpatient treatment center (Lübeck, Germany). Participants were randomized to DBT or ST with one individual and one group session per week over 1.5 years. The primary outcome was the BPD symptom severity assessed with the mean score of the Borderline Personality Disorder Severity Index at 1-year naturalistic follow-up. RESULTS: Between November 26, 2014, and December 14, 2018, we enrolled 164 patients (mean age = 33.7 [SD = 10.61] years). Of these, 81 (49.4%) were treated with ST and 83 (50.6%) with DBT, overall, 130 (79.3%) were female. Intention-to-treat analysis with generalized linear mixed models did not show a significant difference at 1-year naturalistic follow-up between DBT and ST for the BPDSI total score (mean difference 3.32 [95% CI: -0.58-7.22], p = 0.094, d = -24 [-0.69; 0.20]) with lower scores for DBT. Pre-to-follow-up effect sizes were large in both groups (DBT: d = 2.45 [1.88-3.02], ST: d = 1.78 [1.26-2.29]). CONCLUSION: Patients in both treatment groups showed substantial improvements indicating that even severely affected patients with BPD and various comorbid disorders can be treated successfully with DBT and ST. An additional non-inferiority trial is needed to show if both treatments are equally effective. The trial was retrospectively registered on the German Clinical Trials Register, DRKS00011534 without protocol changes.

The long shadow of early-change patterns: a 3-year follow-up after the use of a web-based intervention for mild to moderate depressive symptoms.

Web-based interventions can be effective in treating depressive symptoms. Patients with risk not responding to treatment have been identified by early change patterns. This study aims to examine whether early changes are superior to baseline parameters in predicting long-term outcome. In a randomized clinical trial with 409 individuals experiencing mild to moderate depressive symptoms using the web-based intervention deprexis, three latent classes were identified (early response after registration, early response after screening and early deterioration) based on early change in the first four weeks of the intervention. Baseline variables and these classes were included in a Stepwise Cox Proportional Hazard Multiple Regression to identify predictors associated with the onset of remission over 36-months. Early change class was a significant predictor of remission over 36 months. Compared to early deterioration after screening, both early response after registration and after screening were associated with a higher likelihood of remission. In sensitivity and secondary analyses, only change class consistently emerged as a predictor of long-term outcome. Early improvement in depression symptoms predicted long-term outcome and those showing early improvement had a higher likelihood of long-term remission. These findings suggest that early changes might be a robust predictor for long-term outcome beyond baseline parameters.

Symptom-specific improvement across therapies and their putative mediators: A mediation network intervention analysis.

OBJECTIVE: We evaluated differential treatment effects on specific symptoms and their mediators for Cognitive Behavioral Analysis System of Psychotherapy (CBASP) and Supportive Psychotherapy (SP) in persistently depressed patients. METHOD: We conducted a Bayesian mediation network intervention analysis with data from a randomized controlled trial comparing CBASP and SP. Three networks were calculated to investigate (1) differential treatment effects on specific symptoms, (2) differential treatment effects on the potential mediators interpersonal problems and social functioning, and (3) associations between change in symptoms and change in the potential mediators. RESULTS: First, we found no evidence that CBASP more strongly improves most depressive symptoms specifically, except minimal evidence of symptom-specific effects on sleeping problems and self-esteem. Second, no and minimal evidence for differential treatment effects on interpersonal problems and social functioning was shown, respectively. Third, interpersonal problems and social functioning were strongly related to depressive symptoms. CONCLUSION: While CBASP showed superior treatment effects for overall symptom severity, this treatment might not be superior in improving specific symptoms and the potential mediators interpersonal problems and social functioning. Still, interpersonal problems and social functioning seem to play an important role for depression symptoms. Future research needs to further investigate potential working mechanisms of CBASP.Trial registration: ClinicalTrials.gov identifier: NCT00970437.

Robotic Ureter Reimplantation After Urinary Diversion.

INTRODUCTION: Benign ureterointestinal anastomotic stricture (UIAS) is a recognized long-term complication following radical cystectomy with urinary diversion (UD). The incidence of UIAS following robotic-assisted radical cystectomy varies, with reported rates ranging from 6.5%-25.3%.1 Although endourologic treatments have been employed, their overall success rate is relatively low, ranging from 26%-50%. In contrast, open surgical revision has demonstrated higher success rates, between 80% and 91%.2,3 Given the morbidity associated with open surgery, there has been a shift toward minimally invasive approaches. The robotic approach offers a minimally invasive alternative to open surgery that is not inferior, with similar outcomes for UIAS reconstruction.4 In this video, we demonstrate a robotic technique for the revision of UIAS, which aims to combine the effectiveness of open surgery with the reduced morbidity of a minimally invasive approach. MATERIALS AND METHODS: From May 2020-March 2023, 6 patients underwent surgery. The mean age was 62 years (range 49-68 years). Among these, 2 patients received conduits in open technique and 4 were provided with robotic neobladders. The strictures were located as follows: 2 on the left side, 2 on the right, and 2 on both sides. The average time to stricture formation in the series was 4.5 months. The case presented involves a 49-year-old man who developed a left ureteroileal anastomotic stricture (UIAS) 6 months following robot-assisted radical cystectomy and neobladder creation. The obstruction was managed initially with nephrostomy tube drainage. The surgical technique employed is demonstrated in a step-by-step manner. Standard Da Vinci surgical instruments were used. The patient was positioned in a 30° Trendelenburg position, with port placement similar to that in robotic prostatectomy. The pneumoperitoneum was established through a supraumbilical mini-laparotomy using the Hasson technique. Adhesions around the neobladder were carefully freed. Subsequently, the affected ureter and the stricture were identified and localized. This was achieved by intraluminal application of 10 mL of indocyanine green solution (2.5 mg/mL concentration) through the nephrostomy catheter. The ureter was mobilized as needed. The ureteral stricture was identified and then fully excised. To exclude any malignancy at the ureteral margin, a frozen section analysis was conducted. The ureter was then spatulated. Reanastomosis between the ureter and neobladder was performed using a continuous 4-0 Stratafix suture. A double-J ureteral catheter was inserted to secure the anastomosis, and the anastomosis was completed over this catheter. RESULTS: The mean operative time at the robotic console was 122 minutes, ranging from 80-160 minutes, and the mean blood loss was 42 mL, within a range of 50-100 mL. Intraoperative frozen sections revealed no evidence of malignancy in all cases. No postoperative complications exceeding Clavien-Dindo grade 3 were observed. Two patients were treated for symptomatic urinary tract infections. The median length of stay in the hospital was 4 days, with a range of 2-7 days. Median times for cystography with transurethral catheter removal and double-J catheter removal were 15 postoperative days (range: 12-27) and 23 postoperative days (range: 17-37), respectively. No recurrence of the condition was observed during a mean follow-up period of 23 months (range 6-40 months). CONCLUSION: The robotic approach represents a viable, minimally invasive alternative to conventional open surgery for the reconstruction of UIAS following urinary diversion. The surgical outcomes are comparable to those of open surgery, with the added benefits of a minimally invasive approach, including reduced blood loss and shorter hospital stays.

[Imaging in nephroureterolithasis]. / Bildgebung bei Nephroureterolithiasis.

In the acute diagnostics of a suspected nephroureterolithiasis, ultrasonography should be the examination modality of choice. In cases of suspected urolithiasis, unclear flank pain with fever or in cases of a solitary kidney, a noncontrast computed tomography (CT) scan should always subsequently be performed. If the sonography findings are inconclusive in pregnant women a magnetic resonance imaging (MRI) examination can be considered. If there are indications for urinary diversion, a retrograde imaging study should be performed as part of the urinary diversion. This or CT imaging is also suitable for preinterventional imaging before shock wave lithotripsy, percutaneous nephrolithotomy or ureteroscopy. Postinterventional imaging is not always necessary and sonography is often sufficient. In a conservative treatment approach an abdominal plain X­ray can be used for follow-up assessment.

[Efficacy of internet-based interventions for depression available in Germany-A systematic review and meta-analysis]. / Wirksamkeit in Deutschland verfügbarer internetbasierter Interventionen für Depressionen ­ ein systematisches Review mit Metaanalyse.

BACKGROUND: Internet-based interventions (IBIs) for the treatment of depression have been found to have positive effects in international meta-analyses; however, it is unclear whether these effects also extend to IBIs specifically available in Germany. The aim of this meta-analysis was to estimate the immediate effects and the long-term effects of IBIs available in Germany free of charge or available on prescription and covered by the public health insurances as so-called digital health applications (DiGAs) and to compare the efficacy of DiGAs and freely available IBIs. METHOD: A systematic literature search and random-effects meta-analysis were performed (preregistration: INPLASY202250070). Randomized controlled trials (RCTs) of IBIs freely available in Germany or as DiGA in adults with elevated depressive symptoms were compared with active and inactive controls available at the time of the survey in May 2022. RESULTS: A total of six interventions were identified: COGITO, deprexis, iFightDepression, moodgym, Novego, and Selfapy. The pooled effect size of a total of 28 studies with 13,413 participants corresponded to an effect of Cohen's d = 0.42, (95% confidence interval, CI: 0.31-0.54, I2 = 81%). The analysis of long-term effects showed a smaller effect size of d = 0.29, (95% CI: 0.21-0.37, I2 = 22%, N = 10). Subgroup analyses indicated a possible superiority of the three interventions listed in the DiGA directory (d = 0.56, 95% CI: 0.38-0.74, I2 = 83%, N = 15) compared to the three freely available IBIs (d = 0.24, 95% CI: 0.14-0.33, I2 = 44%, N = 13, p = 0.002). CONCLUSION: The IBIs for depressive disorders available in Germany are effective and can therefore be used in the treatment of people with a depressive disorder; however, it is possible that not all interventions are equally effective.

Automatic planning of MR-guided transcranial focused ultrasound treatment for essential tremor.

Introduction: Transcranial focused ultrasound therapy (tcFUS) offers precise thermal ablation for treating Parkinson's disease and essential tremor. However, the manual fine-tuning of fiber tracking and segmentation required for accurate treatment planning is time-consuming and demands expert knowledge of complex neuroimaging tools. This raises the question of whether a fully automated pipeline is feasible or if manual intervention remains necessary. Methods: We investigate the dependence on fiber tractography algorithms, segmentation approaches, and degrees of automation, specifically for essential tremor therapy planning. For that purpose, we compare an automatic pipeline with a manual approach that requires the manual definition of the target point and is based on FMRIB software library (FSL) and other open-source tools. Results: Our findings demonstrate the high feasibility of automatic fiber tracking and the automated determination of standard treatment coordinates. Employing an automatic fiber tracking approach and deep learning (DL)-supported standard coordinate calculation, we achieve anatomically meaningful results comparable to a manually performed FSL-based pipeline. Individual cases may still exhibit variations, often stemming from differences in region of interest (ROI) segmentation. Notably, the DL-based approach outperforms registration-based methods in producing accurate segmentations. Precise ROI segmentation proves crucial, surpassing the importance of fine-tuning parameters or selecting algorithms. Correct thalamus and red nucleus segmentation play vital roles in ensuring accurate pathway computation. Conclusion: This study highlights the potential for automation in fiber tracking algorithms for tcFUS therapy, but acknowledges the ongoing need for expert verification and integration of anatomical expertise in treatment planning.

The Brainstem Cavernoma Case Series: A Formula for Surgery and Surgical Technique.

Background and Objectives: Cavernous malformations (CM) are vascular malformations with low blood flow. The removal of brainstem CMs (BS) is associated with high surgical morbidity, and there is no general consensus on when to treat deep-seated BS CMs. The aim of this study is to compare the surgical outcomes of a series of deep-seated BS CMs with the surgical outcomes of a series of superficially located BS CMs operated on at the Department of Neurosurgery, College of Tuebingen, Germany. Materials and Methods: A retrospective evaluation was performed using patient charts, surgical video recordings, and outpatient examinations. Factors were identified in which surgical intervention was performed in cases of BS CMs. Preoperative radiological examinations included MRI and diffusion tensor imaging (DTI). For deep-seated BS CMs, a voxel-based 3D neuronavigation system and electrophysiological mapping of the brainstem surface were used. Results: A total of 34 consecutive patients with primary superficial (n = 20/58.8%) and deep-seated (n = 14/41.2%) brainstem cavernomas (BS CM) were enrolled in this comparative study. Complete removal was achieved in 31 patients (91.2%). Deep-seated BS CMs: The mean diameter was 14.7 mm (range: 8.3 to 27.7 mm). All but one of these lesions were completely removed. The median follow-up time was 5.8 years. Two patients (5.9%) developed new neurologic deficits after surgery. Superficial BS CMs: The median diameter was 14.9 mm (range: 7.2 to 27.3 mm). All but two of the superficial BS CMs could be completely removed. New permanent neurologic deficits were observed in two patients (5.9%) after surgery. The median follow-up time in this group was 3.6 years. Conclusions: The treatment of BS CMs remains complex. However, the results of this study demonstrate that with less invasive posterior fossa approaches, brainstem mapping, and neuronavigation combined with the use of a blunt "spinal cord" dissection technique, deep-seated BS CMs can be completely removed in selected cases, with good functional outcomes comparable to those of superficial BS CM.

Remote blended treatment for individuals with suicidal ideation: A single-arm proof-of-concept trial.

INTRODUCTION: New digital treatment formats may reduce barriers to treatment for individuals with suicidal ideation. This study aimed to investigate the feasibility of a remote blended care programme for this population, defined as acceptability, demand, practicality, adaptation, indications of efficacy and safety. METHODS: We conducted a mixed-methods single-arm trial for proof-of-concept. Participants were eligible if they were at least 18 years old, had sufficient German proficiency, a Beck Scale for Suicidal Ideation score ≥2, internet access and lived near the outpatient clinic. The treatment consisted of 12 sessions of cognitive-behavioural videotherapy combined with online modules over 6 weeks. RESULTS: We included 10 participants. All patients were satisfied with the treatment; most patients (80%) reported unpleasant memories resurfacing. All patients completed all therapy sessions and a mean of 13.7 modules (SD = 5.7); three patients switched to face-to-face treatment, in one case due to safety concerns. All patients and most therapists (83.3%) found the treatment overall practicable. Most patients (66.7%) and therapists (66.7%) considered remote treatment equivalent to face-to-face therapy. There were no serious adverse events. CONCLUSION: While promising, the results suggest changes to the programme might be needed, particularly for patients' safety. A controlled feasibility trial should investigate temporary deteriorations.

Implications of the Network Theory for the Treatment of Mental Disorders: A Secondary Analysis of a Randomized Clinical Trial.

Importance: Conceptualizing mental disorders as latent entities has been challenged by the network theory of mental disorders, which states that psychological problems are constituted by a network of mutually interacting symptoms. While the implications of the network approach for planning and evaluating treatments have been intensively discussed, empirical support for the claims of the network theory regarding treatment effects is lacking. Objective: To assess the extent to which specific hypotheses derived from the network theory regarding the (interindividual) changeability of symptom dynamics in response to treatment align with empirical data. Design, Setting, and Participants: This secondary analysis entails data from a multisite randomized clinical trial, in which 254 patients with chronic depression reported on their depressive symptoms at every treatment session. Data collection was conducted between March 5, 2010, and October 14, 2013, and this analysis was conducted between November 1, 2021, and May 31, 2022. Intervention: Thirty-two sessions of either disorder-specific or nonspecific psychotherapy for chronic depression. Main Outcomes and Measures: Longitudinal associations of depressive symptoms with each other and change of these associations through treatment estimated by a time-varying longitudinal network model. Results: In a sample of 254 participants (166 [65.4%] women; mean [SD] age, 44.9 [11.9] years), symptom interactions changed through treatment, and this change varied across treatments and individuals. The mean absolute (ie, valence-ignorant) strength of symptom interactions (logarithmic odds ratio scale) increased from 0.40 (95% CI, 0.36-0.44) to 0.60 (95% CI, 0.52-0.70) during nonspecific psychotherapy and to 0.56 (95% CI, 0.48-0.64) during disorder-specific psychotherapy. In contrast, the mean raw (ie, valence-sensitive) strength of symptom interactions decreased from 0.32 (95% CI, 0.28-0.36) to 0.26 (95% CI, 0.20-0.32) and to 0.09 (95% CI, 0.02-0.16), respectively. Changing symptom severity could be explained to a large extent by symptom interactions. Conclusions and Relevance: These findings suggest that specific treatment-related hypotheses of the network theory align well with empirical data. Conceptualizing mental disorders as symptom networks and treatments as measures that aim to change these networks is expected to give further insights into the working mechanisms of mental health treatments, leading to the improvement of current and the development of new treatments. Trial Registration: ClinicalTrials.gov Identifier: NCT00970437.

Deep learning-based segmentation of brain parenchyma and ventricular system in CT scans in the presence of anomalies.

Introduction: The automatic segmentation of brain parenchyma and cerebrospinal fluid-filled spaces such as the ventricular system is the first step for quantitative and qualitative analysis of brain CT data. For clinical practice and especially for diagnostics, it is crucial that such a method is robust to anatomical variability and pathological changes such as (hemorrhagic or neoplastic) lesions and chronic defects. This study investigates the increase in overall robustness of a deep learning algorithm that is gained by adding hemorrhage training data to an otherwise normal training cohort. Methods: A 2D U-Net is trained on subjects with normal appearing brain anatomy. In a second experiment the training data includes additional subjects with brain hemorrhage on image data of the RSNA Brain CT Hemorrhage Challenge with custom reference segmentations. The resulting networks are evaluated on normal and hemorrhage test casesseparately, and on an independent test set of patients with brain tumors of the publicly available GLIS-RT dataset. Results: Adding data with hemorrhage to the training set significantly improves the segmentation performance over an algorithm trained exclusively on normally appearing data, not only in the hemorrhage test set but also in the tumor test set. The performance on normally appearing data is stable. Overall, the improved algorithm achieves median Dice scores of 0.98 (parenchyma), 0.91 (left ventricle), 0.90 (right ventricle), 0.81 (third ventricle), and 0.80 (fourth ventricle) on the hemorrhage test set. On the tumor test set, the median Dice scores are 0.96 (parenchyma), 0.90 (left ventricle), 0.90 (right ventricle), 0.75 (third ventricle), and 0.73 (fourth ventricle). Conclusion: Training on an extended data set that includes pathologies is crucial and significantly increases the overall robustness of a segmentation algorithm for brain parenchyma and ventricular system in CT data, also for anomalies completely unseen during training. Extension of the training set to include other diseases may further improve the generalizability of the algorithm.

Occurrence and conversion of progestogens and androgens are conserved in land plants.

Progestogens and androgens have been found in many plants, but little is known about their biosynthesis and the evolution of steroidogenesis in these organisms. Here, we show that the occurrence and biosynthesis of progestogens and androgens are conserved across the viridiplantae lineage. An UHPLC-ESI-MS/MS method allowed high-throughput analysis of the occurrence and chemical conversion of progestogens and androgens in 41 species across the green plant lineage. Dehydroepiandrosterone, testosterone, and 5α-dihydrotestosterone are plants' most abundant mammalian-like steroids. Progestogens are converted into 17α-hydroxyprogesterone and 5α-pregnane-3,20-dione. Androgens are converted into testosterone and 5α-dihydrotestosterone. 17,20-Lyases, essential for converting progestogens to androgens, seem to be most effective in monocot species. Our data suggest that the occurrence of progestogens and androgens is highly conserved in plants, and their biosynthesis might favor a route using the Δ4 pathway.

Overexpression and RNAi-mediated Knockdown of Two 3ß-hydroxy-Δ5-steroid dehydrogenase Genes in Digitalis lanata Shoot Cultures Reveal Their Role in Cardenolide Biosynthesis.

3ß-hydroxy-Δ5-steroid dehydrogenases (3ßHSDs) are supposed to be involved in 5ß-cardenolide biosynthesis. Here, a novel 3ßHSD (Dl3ßHSD2) was isolated from Digitalis lanata shoot cultures and expressed in E. coli. Recombinant Dl3ßHSD1 and Dl3ßHSD2 shared 70% amino acid identity, reduced various 3-oxopregnanes and oxidised 3-hydroxypregnanes, but only rDl3ßHSD2 converted small ketones and secondary alcohols efficiently. To explain these differences in substrate specificity, we established homology models using borneol dehydrogenase of Salvia rosmarinus (6zyz) as the template. Hydrophobicity and amino acid residues in the binding pocket may explain the difference in enzyme activities and substrate preferences. Compared to Dl3ßHSD1, Dl3ßHSD2 is weakly expressed in D. lanata shoots. High constitutive expression of Dl3ßHSDs was realised by Agrobacterium-mediated transfer of Dl3ßHSD genes fused to the CaMV-35S promotor into the genome of D. lanata wild type shoot cultures. Transformed shoots (35S:Dl3ßHSD1 and 35S:Dl3ßHSD2) accumulated less cardenolides than controls. The levels of reduced glutathione (GSH), which is known to inhibit cardenolide formation, were higher in the 35S:Dl3ßHSD1 lines than in the controls. In the 35S:Dl3ßHSD1 lines cardenolide levels were restored after adding of the substrate pregnane-3,20-dione in combination with buthionine-sulfoximine (BSO), an inhibitor of GSH formation. RNAi-mediated knockdown of the Dl3ßHSD1 yielded several shoot culture lines with strongly reduced cardenolide levels. In these lines, cardenolide biosynthesis was fully restored after addition of the downstream precursor pregnan-3ß-ol-20-one, whereas upstream precursors such as progesterone had no effect, indicating that no shunt pathway could overcome the Dl3ßHSD1 knockdown. These results can be taken as the first direct proof that Dl3ßHSD1 is indeed involved in 5ß-cardenolide biosynthesis.

MR-CT multi-atlas registration guided by fully automated brain structure segmentation with CNNs.

PURPOSE: Computed tomography (CT) is widely used to identify anomalies in brain tissues because their localization is important for diagnosis and therapy planning. Due to the insufficient soft tissue contrast of CT, the division of the brain into anatomical meaningful regions is challenging and is commonly done with magnetic resonance imaging (MRI). METHODS: We propose a multi-atlas registration approach to propagate anatomical information from a standard MRI brain atlas to CT scans. This translation will enable a detailed automated reporting of brain CT exams. We utilize masks of the lateral ventricles and the brain volume of CT images as adjuvant input to guide the registration process. Besides using manual annotations to test the registration in a first step, we then verify that convolutional neural networks (CNNs) are a reliable solution for automatically segmenting structures to enhance the registration process. RESULTS: The registration method obtains mean Dice values of 0.92 and 0.99 in brain ventricles and parenchyma on 22 healthy test cases when using manually segmented structures as guidance. When guiding with automatically segmented structures, the mean Dice values are 0.87 and 0.98, respectively. CONCLUSION: Our registration approach is a fully automated solution to register MRI atlas images to CT scans and thus obtain detailed anatomical information. The proposed CNN segmentation method can be used to obtain masks of ventricles and brain volume which guide the registration.