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Large-scale molecular profiling studies in recent years have shown that central nervous system (CNS) tumors display a much greater heterogeneity in terms of molecularly distinct entities, cellular origins and genetic drivers than anticipated from histological assessment. DNA methylation profiling has emerged as a useful tool for robust tumor classification, providing new insights into these heterogeneous molecular classes. This is particularly true for rare CNS tumors with a broad morphological spectrum, which are not possible to assign as separate entities based on histological similarity alone. Here, we describe a molecularly distinct subset of predominantly pediatric CNS neoplasms (n = 60) that harbor PATZ1 fusions. The original histological diagnoses of these tumors covered a wide spectrum of tumor types and malignancy grades. While the single most common diagnosis was glioblastoma (GBM), clinical data of the PATZ1-fused tumors showed a better prognosis than typical GBM, despite frequent relapses. RNA sequencing revealed recurrent MN1:PATZ1 or EWSR1:PATZ1 fusions related to (often extensive) copy number variations on chromosome 22, where PATZ1 and the two fusion partners are located. These fusions have individually been reported in a number of glial/glioneuronal tumors, as well as extracranial sarcomas. We show here that they are more common than previously acknowledged, and together define a biologically distinct CNS tumor type with high expression of neural development markers such as PAX2, GATA2 and IGF2. Drug screening performed on the MN1:PATZ1 fusion-bearing KS-1 brain tumor cell line revealed preliminary candidates for further study. In summary, PATZ1 fusions define a molecular class of histologically polyphenotypic neuroepithelial tumors, which show an intermediate prognosis under current treatment regimens.
Background: Deep surgical site infections (dSSIs) after instrumented spinal surgery pose major therapeutic challenges. Standard treatment involves surgical debridement, wound drainage, and long-term antibiotic administration. Autologous platelet-rich fibrin (PRF) constitutes a biomaterial obtained from patients’ own blood that contains leukocytes, chemokines and growth factors boosting cicatrization. Due to favorable results reported from other surgical disciplines such as dentistry, orthopedics, maxillofacial and plastic surgery using PRF, the authors hypothesized that PRF augmentation will promote wound healing in dSSIs. Objective: To report our preliminary results on the safety and efficacy of autologous-PRF as an add-on therapy on a pilot case series of persistent dSSI after instrumented spinal surgery. Methods: Among the 293 patients who underwent dorsal decompression and stabilization of the cervical, thoracic, and lumbar spine due to degenerative diseases in our department, 12 patients (4%) presented persisting dSSI after standard wound debridement and antibiotic treatment. PRF augmentation was used during a second surgical revision as an add-on therapy to standard debridement. In all cases, the wound was primarily closed without drains. Results: Wound healing was completed between 14 and 21 days after the second surgical revision in all patients. At a median follow-up of 8 months (range: 6 to 18 months), no recurrence of dSSI nor complications were encountered in any case. Conclusions: Our preliminary results suggest that PRF augmentation in persistent dSSI after instrumented spinal surgery appears to be a safe and effective strategy to promote wound healing. Prospective controlled studies are required to define the efficiency of PRF more clearly in both treating and preventing dSSI.
Objectives: The aim of this study was to develop a prognostic tool to estimate long-term tooth retention in periodontitis patients at the beginning of active periodontal therapy (APT). Material and methods: Tooth-related factors (type, location, bone loss (BL), infrabony defects, furcation involvement (FI), abutment status), and patient-related factors (age, gender, smoking, diabetes, plaque control record) were investigated in patients who had completed APT 10 years before. Descriptive analysis was performed, and a generalized linear-mixed model-tree was used to identify predictors for the main outcome variable tooth loss. To evaluate goodness-of-fit, the area under the curve (AUC) was calculated using cross-validation. A bootstrap approach was used to robustly identify risk factors while avoiding overfitting. Results: Only a small percentage of teeth was lost during 10 years of supportive periodontal therapy (SPT; 0.15/year/patient). The risk factors abutment function, diabetes, and the risk indicator BL, FI, and age (≤ 61 vs. > 61) were identified to predict tooth loss. The prediction model reached an AUC of 0.77. Conclusion: This quantitative prognostic model supports data-driven decision-making while establishing a treatment plan in periodontitis patients. In light of this, the presented prognostic tool may be of supporting value. Clinical relevance: In daily clinical practice, a quantitative prognostic tool may support dentists with data-based decision-making. However, it should be stressed that treatment planning is strongly associated with the patient’s wishes and adherence. The tool described here may support establishment of an individual treatment plan for periodontally compromised patients.
Background: No simple classification system has emerged for ‘advanced basal cell carcinomas’, and more generally for all difficult-to-treat BCCs (DTT-BCCs), due to the heterogeneity of situations, TNM inappropriateness to BCCs, and different approaches of different specialists. Objective: To generate an operational classification, using the unconscious ability of experts to simplify the great heterogeneity of the clinical situations into a few relevant groups, which drive their treatment decisions. Method: Non-supervised independent and blinded clustering of real clinical cases of DTT-BCCs was used. Fourteen international experts from different specialties independently partitioned 199 patient cases considered ‘difficult to treat’ into as many clusters they want (≤10), choosing their own criteria for partitioning. Convergences and divergences between the individual partitions were analyzed using the similarity matrix, K-mean approach, and average silhouette method. Results: There was a rather consensual clustering of cases, regardless of the specialty and nationality of the experts. Mathematical analysis showed that consensus between experts was best represented by a partition of DTT-BCCs into five clusters, easily recognized a posteriori as five clear-cut patterns of clinical situations. The concept of ‘locally advanced’ did not appear consistent between experts. Conclusion: Although convergence between experts was not granted, this experiment shows that clinicians dealing with BCCs all tend to work by a similar pattern recognition based on the overall analysis of the situation. This study thus provides the first consensual classification of DTT-BCCs. This experimental approach using mathematical analysis of independent and blinded clustering of cases by experts can probably be applied to many other situations in dermatology and oncology.
Resilience has been defined as the maintenance or quick recovery of mental health during and after times of adversity. How to operationalize resilience and to determine the factors and processes that lead to good long-term mental health outcomes in stressor-exposed individuals is a matter of ongoing debate and of critical importance for the advancement of the field. One of the biggest challenges for implementing an outcome-based definition of resilience in longitudinal observational study designs lies in the fact that real-life adversity is usually unpredictable and that its substantial qualitative as well as temporal variability between subjects often precludes defining circumscribed time windows of inter-individually comparable stressor exposure relative to which the maintenance or recovery of mental health can be determined. To address this pertinent issue, we propose to frequently and regularly monitor stressor exposure (E) and mental health problems (P) throughout a study's observation period [Frequent Stressor and Mental Health Monitoring (FRESHMO)-paradigm]. On this basis, a subject's deviation at any single monitoring time point from the study sample's normative E–P relationship (the regression residual) can be used to calculate that subject's current mental health reactivity to stressor exposure (“stressor reactivity,” SR). The SR score takes into account the individual extent of experienced adversity and is comparable between and within subjects. Individual SR time courses across monitoring time points reflect intra-individual temporal variability in SR, where periods of under-reactivity (negative SR score) are associated with accumulation of fewer mental health problems than is normal for the sample. If FRESHMO is accompanied by regular measurement of potential resilience factors, temporal changes in resilience factors can be used to predict SR time courses. An increase in a resilience factor measurement explaining a lagged decrease in SR can then be considered to index a process of adaptation to stressor exposure that promotes a resilient outcome (an allostatic resilience process). This design principle allows resilience research to move beyond merely determining baseline predictors of resilience outcomes, which cannot inform about how individuals successfully adjust and adapt when confronted with adversity. Hence, FRESHMO plus regular resilience factor monitoring incorporates a dynamic-systems perspective into resilience research.
Hirschsprung’s disease (HD) is a congenital disorder, characterized by aganglionosis in the distal part of the gastrointestinal tract. Despite complete surgical resection of the aganglionic segment, both constipation and fecal incontinence persist in a considerable number of patients with limited treatment options. There is growing evidence for structural abnormalities in the ganglionic bowel proximal to the aganglionosis in both humans and animals with HD, which may play a role in persistent bowel dysfunction. These abnormalities include: (1) Histopathological abnormalities of enteric neural cells; (2) Imbalanced expression of neurotransmitters and neuroproteins; (3) Abnormal expression of enteric pacemaker cells; (4) Abnormalities of smooth muscle cells; and (5) Abnormalities within the extracellular matrix. Hence, a better understanding of these previously unrecognized neuropathological abnormalities may improve follow-up and treatment in patients with HD suffering from persistent bowel dysfunction following surgical correction. In the long term, further combination of clinical and neuropathological data will hopefully enable a translational step towards more individual treatment for HD.
Background: Intensive Care Resources are heavily utilized during the COVID-19 pandemic. However, risk stratification and prediction of SARS-CoV-2 patient clinical outcomes upon ICU admission remain inadequate. This study aimed to develop a machine learning model, based on retrospective & prospective clinical data, to stratify patient risk and predict ICU survival and outcomes. Methods: A Germany-wide electronic registry was established to pseudonymously collect admission, therapeutic and discharge information of SARS-CoV-2 ICU patients retrospectively and prospectively. Machine learning approaches were evaluated for the accuracy and interpretability of predictions. The Explainable Boosting Machine approach was selected as the most suitable method. Individual, non-linear shape functions for predictive parameters and parameter interactions are reported. Results: 1039 patients were included in the Explainable Boosting Machine model, 596 patients retrospectively collected, and 443 patients prospectively collected. The model for prediction of general ICU outcome was shown to be more reliable to predict “survival”. Age, inflammatory and thrombotic activity, and severity of ARDS at ICU admission were shown to be predictive of ICU survival. Patients’ age, pulmonary dysfunction and transfer from an external institution were predictors for ECMO therapy. The interaction of patient age with D-dimer levels on admission and creatinine levels with SOFA score without GCS were predictors for renal replacement therapy. Conclusions: Using Explainable Boosting Machine analysis, we confirmed and weighed previously reported and identified novel predictors for outcome in critically ill COVID-19 patients. Using this strategy, predictive modeling of COVID-19 ICU patient outcomes can be performed overcoming the limitations of linear regression models. Trial registration “ClinicalTrials” (clinicaltrials.gov) under NCT04455451.
The development of binocular vision is an active learning process comprising the development of disparity tuned neurons in visual cortex and the establishment of precise vergence control of the eyes. We present a computational model for the learning and self-calibration of active binocular vision based on the Active Efficient Coding framework, an extension of classic efficient coding ideas to active perception. Under normal rearing conditions with naturalistic input, the model develops disparity tuned neurons and precise vergence control, allowing it to correctly interpret random dot stereograms. Under altered rearing conditions modeled after neurophysiological experiments, the model qualitatively reproduces key experimental findings on changes in binocularity and disparity tuning. Furthermore, the model makes testable predictions regarding how altered rearing conditions impede the learning of precise vergence control. Finally, the model predicts a surprising new effect that impaired vergence control affects the statistics of orientation tuning in visual cortical neurons.
Importance Surgery is a mainstay in the management of hidradenitis suppurativa (HS). Adalimumab is the first drug approved for HS. Objective To investigate the efficacy and safety of adalimumab in combination with wide-excision surgery followed by secondary intention healing. Design, Setting, and Participants The Safety and Efficacy of Adalimumab for Hidradenitis Suppurativa Peri-Surgically (SHARPS) trial was a phase 4, randomized, double-blind, placebo-controlled study of adalimumab in conjunction with surgery. Patients were enrolled in 45 sites across 20 countries from July 18, 2016, to February 2, 2019, with the last patient visit on October 16, 2019. Eligible patients (aged 18-65 years) had moderate to severe HS that required radical surgery in an axillary or inguinal region and had 2 other anatomical regions affected, with 1 or more regions at Hurley stage II or III. Analysis was conducted in November 2019. Interventions Patients were randomized 1:1 to receive continuous adalimumab, 40 mg, or placebo during presurgery (12 weeks), perioperative (2 weeks), and postoperative (10 weeks) periods. Main Outcomes and Measures The primary end point was the proportion of patients achieving HS clinical response across all body regions at week 12. Results Overall, 103 patients were randomized to adalimumab and 103 to matching placebo. Among all patients, 51% (n = 106) were women, 94% (n = 193) were White, and the mean (SD) age was 37.6 (11.3) years. At week 12, significantly more patients receiving adalimumab (49 of 103 [48%]) vs placebo (35 of 103 [34%]; P = .049) achieved HS clinical response across all body regions (treatment difference, 14% [95% CI, 0%-27%]). Treatment-emergent adverse events were reported in 74 of 103 patients (72%) and 69 of 103 patients (67%) in the adalimumab and placebo groups, respectively. No increased risk of postoperative wound infection, complication, or hemorrhage was observed with adalimumab vs placebo. Two deaths occurred in the adalimumab group; neither was considered as having a reasonable possibility of relationship to study drug. Conclusions and Relevance Adalimumab was efficacious in conjunction with wide-excision surgery followed by secondary intention healing, with no need to interrupt treatment prior to surgery. These data support further investigation of adalimumab as an adjuvant therapy to surgery in patients with moderate to severe HS. Trial Registration ClinicalTrials.gov Identifier: NCT02808975
Objectives: To compare dual-energy CT (DECT) and MRI for assessing presence and extent of traumatic bone marrow edema (BME) and fracture line depiction in acute vertebral fractures. Methods: Eighty-eight consecutive patients who underwent dual-source DECT and 3-T MRI of the spine were retrospectively analyzed. Five radiologists assessed all vertebrae for presence and extent of BME and for identification of acute fracture lines on MRI and, after 12 weeks, on DECT series. Additionally, image quality, image noise, and diagnostic confidence for overall diagnosis of acute vertebral fracture were assessed. Quantitative analysis of CT numbers was performed by a sixth radiologist. Two radiologists analyzed MRI and grayscale DECT series to define the reference standard. Results: For assessing BME presence and extent, DECT showed high sensitivity (89% and 84%, respectively) and specificity (98% in both), and similarly high diagnostic confidence compared to MRI (2.30 vs. 2.32; range 0–3) for the detection of BME (p = .72). For evaluating acute fracture lines, MRI achieved high specificity (95%), moderate sensitivity (76%), and a significantly lower diagnostic confidence compared to DECT (2.42 vs. 2.62, range 0–3) (p < .001). A cutoff value of − 0.43 HU provided a sensitivity of 89% and a specificity of 90% for diagnosing BME, with an overall AUC of 0.96. Conclusions: DECT and MRI provide high diagnostic confidence and image quality for assessing acute vertebral fractures. While DECT achieved high overall diagnostic accuracy in the analysis of BME presence and extent, MRI provided moderate sensitivity and lower confidence for evaluating fracture lines.