Prostate cancer and elective nodal radiation therapy for cN0 and pN0-a never ending story? : Recommendations from the prostate cancer expert panel of the German Society of Radiation Oncology (DEGRO).

For prostate cancer, the role of elective nodal irradiation (ENI) for cN0 or pN0 patients has been under discussion for years. Considering the recent publications of randomized controlled trials, the prostate cancer expert panel of the German Society of Radiation Oncology (DEGRO) aimed to discuss and summarize the current literature. Modern trials have been recently published for both treatment-naïve patients (POP-RT trial) and patients after surgery (SPPORT trial). Although there are more reliable data to date, we identified several limitations currently complicating the definitions of general recommendations. For patients with cN0 (conventional or PSMA-PET staging) undergoing definitive radiotherapy, only men with high-risk factors for nodal involvement (e.g., cT3a, GS ≥ 8, PSA ≥ 20 ng/ml) seem to benefit from ENI. For biochemical relapse in the postoperative situation (pN0) and no PSMA imaging, ENI may be added to patients with risk factors according to the SPPORT trial (e.g., GS ≥ 8; PSA > 0.7 ng/ml). If PSMA-PET/CT is negative, ENI may be offered for selected men with high-risk factors as an individual treatment approach.

Predicting cellular responses to complex perturbations in high-throughput screens.

Recent advances in multiplexed single-cell transcriptomics experiments facilitate the high-throughput study of drug and genetic perturbations. However, an exhaustive exploration of the combinatorial perturbation space is experimentally unfeasible. Therefore, computational methods are needed to predict, interpret, and prioritize perturbations. Here, we present the compositional perturbation autoencoder (CPA), which combines the interpretability of linear models with the flexibility of deep-learning approaches for single-cell response modeling. CPA learns to in silico predict transcriptional perturbation response at the single-cell level for unseen dosages, cell types, time points, and species. Using newly generated single-cell drug combination data, we validate that CPA can predict unseen drug combinations while outperforming baseline models. Additionally, the architecture's modularity enables incorporating the chemical representation of the drugs, allowing the prediction of cellular response to completely unseen drugs. Furthermore, CPA is also applicable to genetic combinatorial screens. We demonstrate this by imputing in silico 5,329 missing combinations (97.6% of all possibilities) in a single-cell Perturb-seq experiment with diverse genetic interactions. We envision CPA will facilitate efficient experimental design and hypothesis generation by enabling in silico response prediction at the single-cell level and thus accelerate therapeutic applications using single-cell technologies.

Quality of long standing radiographs assessment of the patella position.

BACKGROUND: The gold standard for evaluating leg alignment is a long leg standing radiograph (LSR). The research states that a correct LSR should have a patella that is centered and facing forward as well as a fibula head superimposition (FHS) with a tibia that is 1/3 larger than the fibula. The purpose of this study was to determine levels of quality for LSR by quantifying and correlating the patella position and fibular head superimposition. METHOD: 741 lower limbs were included using two distinct measurement techniques, we calculated the patella position's (PD) departure from the center of the knee joint (M1 and M2). To measure the inter-rater dependability in assessing PD and FHS, intraclass correlation coefficients were determined. The Bland-Altman approach was used to compare M1 with M2's performance. We created three quality groups based on the average quantity of PD. RESULTS: The mean PD was 3.5 mm for M1 and 4.1 mm for M2, respectively. Three quality categories were created: group A for PD ≤ 5 mm, group B for PD 5-10 mm, and group C for PD of ≥10 mm. Group A takes up 70.9% of the LSR. Interestingly, group A's FHS was 21.3% than the typical value of 1/3. CONCLUSIONS: The patella's center should be centered within a 5 mm range and the fibular head should be 1/5 covered from the tibia. This study is the first to define quantitative metrics based on LSR analysis. LEVEL OF EVIDENCE: Level IV (diagnostic retrospective case series).

Pain levels during distraction osteogenesis with lengthening nails in 168 cases.

PURPOSE: To firstly examine the pain levels during distraction osteogenesis (DO) with lengthening nails (LN) in a large sample. METHODS: A total of 168 cases underwent DO of the tibia or femur with five different models of LN. Under a standardized medical regime, daily pain levels were noted as nominal rating scale (NRS) score (0-10) during the distraction phase. NRS scores and several potential influence factors (LN model, bone, approach, side, age, gender) were evaluated. RESULTS: The mean distraction length was 39.1 ± 14.4 mm. The average NRS score decreased from postoperative day 1 with 2.84 nonlinearly by 1.03 points (36.3%) over the course of 62 days to an average score of 1.81. The mean decrease during the first thirty days was 0.67(23.6%). Subgroup analysis did not reveal any influence factors. CONCLUSION: Pain levels during the distraction phase are overall low, continuously decreasing, and well manageable with mostly non-opioid analgesics.

The Femoral Vein as a Long-Term Aorto-Iliac Graft for Aortic Infection and Aortitis.

BACKGROUND: Reconstruction of the aorto-iliac segment with femoral vein (FV) as substitute for infected synthetic grafts or mycotic aneurysms constitutes the most sustainably convenient alternative. The aim of this study was to evaluate the long-term outcome of up to 16 years of follow-up, analysing the morphologic adaption of the FV with special emphasis on the distal and proximal anastomoses. METHODS: We conducted a retrospective study of 22 patients with 109 computed tomography angiograms (CTAs) treated between August 2001 and January 2020 in case of aortic infection/aortitis. Morphologic changes like anastomotic dilatation/stenosis as well as changes of FV wall thickness were retrospectively analysed in pre- and postoperative CTAs. RESULTS: Elective procedure was done in 17/22 (77%) cases, and 5/22 (23%) patients required emergent surgery. The median follow-up was 91.5 months (P25;P75 = 21;117). Cross-sectional diameter of proximal (20.38 ± 3.77 vs 22.04 ± 3.97 mm, p = 0.007) and distal anastomoses (13.05 ± 4.23 vs 14.61 ± 5.19 mm, p = 0.05) increased significantly, as well as the proximal and distal anastomotic areas (3.36 ± 1.29 vs 4.32 ± 1.63 mm2, p = 0.04 and 0.99 ± 0.48 vs 1.25 ± 0.72 mm2, p = 0.023, respectively). Venous wall thickness was significantly reduced at the anastomotic site (1.74 ± 0.46 vs 1.24 ± 0.31 mm, p = 0.001). The upper thigh diameter did not differ before and after harvesting of the FV (161.6 ± 29.1 vs. 178.2 ± 23.3 mm, p = 0.326, respectively). CONCLUSION: This long-term CTA follow-up study showed that the FV wall becomes thinner at the anastomotic site, and the anastomoses dilate with time without rupture. The FV is a durable conductor after replacement of the aorto-iliac segment due to aortic infection. Further CTA studies from more centres are warranted to evaluate the risk of vein rupture.

Machine learning for perturbational single-cell omics.

Cell biology is fundamentally limited in its ability to collect complete data on cellular phenotypes and the wide range of responses to perturbation. Areas such as computer vision and speech recognition have addressed this problem of characterizing unseen or unlabeled conditions with the combined advances of big data, deep learning, and computing resources in the past 5 years. Similarly, recent advances in machine learning approaches enabled by single-cell data start to address prediction tasks in perturbation response modeling. We first define objectives in learning perturbation response in single-cell omics; survey existing approaches, resources, and datasets (https://github.com/theislab/sc-pert); and discuss how a perturbation atlas can enable deep learning models to construct an informative perturbation latent space. We then examine future avenues toward more powerful and explainable modeling using deep neural networks, which enable the integration of disparate information sources and an understanding of heterogeneous, complex, and unseen systems.

[Interactions between radical prostatovesiculectomy and diagnosis of prostate cancer : A medical-historical inventory on the occasion of 20 years of robot-assisted treatment]. / Wechselwirkungen von radikaler Prostatovesikulektomie und Diagnostik des Prostatakarzinoms : Eine medizinhistorische Bestandsaufnahme anlässlich 20 Jahre robotisch assistierter Therapie.

The question of what came first-in this case the diagnosis of prostate cancer or its therapy-seems absurd at first glance and is reminiscent of the classic metaphor-like problem that preoccupied the Greek writer Plutarch (45-125). Today it is a matter of course that a reliable diagnosis is made before treating a disease, but this must be viewed as inconsistent in medical history. The beginnings of radical prostatectomy for the treatment of prostate cancer, like the first surgical therapies for kidney and bladder tumors, can be located in the pioneering period of organ surgery in the German Empire (1871-1918). The establishment of this procedure in its current form with larger numbers of cases is in turn thanks to the Nestor of American urology, Hugh Hampton Young, who carried out the first perineal prostatovesiculectomy, which from today's perspective can be described as complete. Although the indication has remained largely unchanged since then, this intervention has undergone extensive changes in recent decades. But how has the diagnosis of prostate cancer developed in this period? Of course, much more dynamic. While the procedure prostatovesiculectomy was already established, development of prostate cancer diagnosis began first slowly in the course of the 20th century, then more dynamically. The following article uses medical (historical) original sources to present not only the basics and further developments of the established and, at the same time, subject to constant intervention in urology, but also the essential developments in the environment of neighboring medical disciplines, for example, think of laboratory medicine, radiology, nuclear medicine or rehabilitation medicine, but especially pathology. Incidentally, it was only these developments that created the basis for the correct setting of indications and the identification of alternatives to radical prostatovesiculectomy.

Conditional out-of-distribution generation for unpaired data using transfer VAE.

MOTIVATION: While generative models have shown great success in sampling high-dimensional samples conditional on low-dimensional descriptors (stroke thickness in MNIST, hair color in CelebA, speaker identity in WaveNet), their generation out-of-distribution poses fundamental problems due to the difficulty of learning compact joint distribution across conditions. The canonical example of the conditional variational autoencoder (CVAE), for instance, does not explicitly relate conditions during training and, hence, has no explicit incentive of learning such a compact representation. RESULTS: We overcome the limitation of the CVAE by matching distributions across conditions using maximum mean discrepancy in the decoder layer that follows the bottleneck. This introduces a strong regularization both for reconstructing samples within the same condition and for transforming samples across conditions, resulting in much improved generalization. As this amount to solving a style-transfer problem, we refer to the model as transfer VAE (trVAE). Benchmarking trVAE on high-dimensional image and single-cell RNA-seq, we demonstrate higher robustness and higher accuracy than existing approaches. We also show qualitatively improved predictions by tackling previously problematic minority classes and multiple conditions in the context of cellular perturbation response to treatment and disease based on high-dimensional single-cell gene expression data. For generic tasks, we improve Pearson correlations of high-dimensional estimated means and variances with their ground truths from 0.89 to 0.97 and 0.75 to 0.87, respectively. We further demonstrate that trVAE learns cell-type-specific responses after perturbation and improves the prediction of most cell-type-specific genes by 65%. AVAILABILITY AND IMPLEMENTATION: The trVAE implementation is available via github.com/theislab/trvae. The results of this article can be reproduced via github.com/theislab/trvae_reproducibility.

Generalizing RNA velocity to transient cell states through dynamical modeling.

RNA velocity has opened up new ways of studying cellular differentiation in single-cell RNA-sequencing data. It describes the rate of gene expression change for an individual gene at a given time point based on the ratio of its spliced and unspliced messenger RNA (mRNA). However, errors in velocity estimates arise if the central assumptions of a common splicing rate and the observation of the full splicing dynamics with steady-state mRNA levels are violated. Here we present scVelo, a method that overcomes these limitations by solving the full transcriptional dynamics of splicing kinetics using a likelihood-based dynamical model. This generalizes RNA velocity to systems with transient cell states, which are common in development and in response to perturbations. We apply scVelo to disentangling subpopulation kinetics in neurogenesis and pancreatic endocrinogenesis. We infer gene-specific rates of transcription, splicing and degradation, recover each cell's position in the underlying differentiation processes and detect putative driver genes. scVelo will facilitate the study of lineage decisions and gene regulation.

The prognostic importance of MRI detected extramural vascular invasion (mrEMVI) in locally advanced rectal cancer.

BACKGROUND: MRI detected extramural vascular invasion (mrEMVI) is a poor prognostic factor in rectal cancer patients. The objectives of this study were to assess survival outcomes in patients with and without mrEMVI and to compare the prognostic value of mrEMVI with other rectal cancer features. METHODS: In a Dutch high volume rectal cancer center cohort of sixty-seven locally advanced rectal cancer patients, an independent radiologist reviewed all primary staging MRI scans. The presence of mrEMVI was correlated to tumor specific and survival outcomes. RESULTS: 20/67 patients had mrEMVI positive rectal cancer. 55% (11/20) developed metachronous metastases, compared with 23% (11/47) in the mrEMVI negative group (OR 4.0, p = 0.01). Overall survival was also decreased with a Hazard ratio of 3.3 (p = 0.01). A multivariable logistic regression with a backward selection procedure was conducted including cT-stage, c-N-stage, extramural tumor invasion depth, mesorectal fascia involvement, distance to anorectal junction, tumor length, mrEMVI, CEA level, and synchronous metastases. After stepwise removal based on p value, only positive mrEMVI remained as a single significant predictor for metachronous metastases (OR: 4.16 , p < 0.05). CONCLUSION: Positive mrEMVI is a poor prognostic factor in locally advanced rectal cancer with a 4-fold increased risk of developing metachronous metastases after surgery and a worsened overall survival. mrEMVI also appeared an independent risk factor, with a stronger prediction for metachronous metastases than other MRI-detectable tumor characteristics. mrEMVI should be incorporated in all risk stratification guidelines for rectal cancer.

Intraoperative customization of intramedullary nails - First results.

INTRODUCTION: Intramedullary nailing is a valuable treatment option for many types of fractures. Furthermore nailing is applicable for osteosynthesis following osteotomy in deformity correction. For intraoperative fine-tuning of alignment in intramedullary (IM) nailing procedures, a bending device for customization of IM nails under sterile conditions was developed. We have performed a retrospective clinical study to analyze and describe the technical requirements, indications and limitations for intraoperative customization of IM nails. MATERIALS AND METHODS: In 41 cases of deformity correction with IM nailing, we applied intraoperative sterile bending of IM nails. The patient age ranged from 13 to 64 years. We evaluated the radiological outcome (precision of the intervention) of 31 completed cases, comparing the preoperative planning with the final result on long-standing radiographs (LSR). The diameter of the nails ranged from 8,5mm to 13mm. Cases with fracture or non-union treatment with intraoperative application of the bending device were excluded and analyzed separately. RESULTS: All removed implants were examined - none of them showed any signs of material fatigue. The amount of intraoperative bending of the nails was 1° to 12°. A high level of precision was achieved, with a median postoperative axis deviation to the preoperative planning of 3,5mm. In a polio patient with limited bone quality, the implant removal caused an undisplaced cortical crack. There were no other complications. There was uneventful and fast bone healing in all patients. CONCLUSIONS: Intraoperative customization of intramedullary nails is a valuable technique for precise alignment control with IM nailing. With this technique, the benefits of IM nailing can be used for a wide range of indications, including deformity correction. The sterile bending device is safe and easy to handle. It is strong enough to bend all commercially available IM nails. Monofocal or linear bending in multiple planes is possible. However, when defining the site of bending, one must consider the removal of the implant in the future.

scGen predicts single-cell perturbation responses.

Accurately modeling cellular response to perturbations is a central goal of computational biology. While such modeling has been based on statistical, mechanistic and machine learning models in specific settings, no generalization of predictions to phenomena absent from training data (out-of-sample) has yet been demonstrated. Here, we present scGen (https://github.com/theislab/scgen), a model combining variational autoencoders and latent space vector arithmetics for high-dimensional single-cell gene expression data. We show that scGen accurately models perturbation and infection response of cells across cell types, studies and species. In particular, we demonstrate that scGen learns cell-type and species-specific responses implying that it captures features that distinguish responding from non-responding genes and cells. With the upcoming availability of large-scale atlases of organs in a healthy state, we envision scGen to become a tool for experimental design through in silico screening of perturbation response in the context of disease and drug treatment.

Impacted Steel Sleeves for a minimally invasive approach in intramedullary nailing.

A custom-made instrument set of Steel Sleeves was developed to assist the insertion of reamers and intramedullary devices for fixation of long bone fractures or lengthening procedures with intramedullary nails. By use of the Steel Sleeves, migration of the entry point is prevented and protection of the bone and soft tissue at the entry point is guaranteed. In addition, the principle of a closed working channel for trans-articular approaches can be provided. In this article, a description of properties and clinical application of custom-made steel sleeve instrument set is provided.

Combining hippocampal volume metrics to better understand Alzheimer's disease progression in at-risk individuals.

To date nearly all clinical trials of Alzheimer's disease (AD) therapies have failed. These failures are, at least in part, attributable to poor endpoint choice and to inadequate recruitment criteria. Recently, focus has shifted to targeting at-risk populations in the preclinical stages of AD thus improved predictive markers for identifying individuals likely to progress to AD are crucial to help inform the sample of individuals to be recruited into clinical trials. We focus on hippocampal volume (HV) and assess the added benefit of combining HV and rate of hippocampal atrophy over time in relation to disease progression. Following the cross-validation of previously published estimates of the predictive value of HV, we consider a series of combinations of HV metrics and show that a combination of HV and rate of hippocampal atrophy characterises disease progression better than either measure individually. Furthermore, we demonstrate that the risk of disease progression associated with HV metrics does not differ significantly between clinical states. HV and rate of hippocampal atrophy should therefore be used in tandem when describing AD progression in at-risk individuals. Analyses also suggest that the effects of HV metrics are constant across the continuum of the early stages of the disease.

PAGA: graph abstraction reconciles clustering with trajectory inference through a topology preserving map of single cells.

Single-cell RNA-seq quantifies biological heterogeneity across both discrete cell types and continuous cell transitions. Partition-based graph abstraction (PAGA) provides an interpretable graph-like map of the arising data manifold, based on estimating connectivity of manifold partitions ( https://github.com/theislab/paga ). PAGA maps preserve the global topology of data, allow analyzing data at different resolutions, and result in much higher computational efficiency of the typical exploratory data analysis workflow. We demonstrate the method by inferring structure-rich cell maps with consistent topology across four hematopoietic datasets, adult planaria and the zebrafish embryo and benchmark computational performance on one million neurons.

A test metric for assessing single-cell RNA-seq batch correction.

Single-cell transcriptomics is a versatile tool for exploring heterogeneous cell populations, but as with all genomics experiments, batch effects can hamper data integration and interpretation. The success of batch-effect correction is often evaluated by visual inspection of low-dimensional embeddings, which are inherently imprecise. Here we present a user-friendly, robust and sensitive k-nearest-neighbor batch-effect test (kBET; https://github.com/theislab/kBET ) for quantification of batch effects. We used kBET to assess commonly used batch-regression and normalization approaches, and to quantify the extent to which they remove batch effects while preserving biological variability. We also demonstrate the application of kBET to data from peripheral blood mononuclear cells (PBMCs) from healthy donors to distinguish cell-type-specific inter-individual variability from changes in relative proportions of cell populations. This has important implications for future data-integration efforts, central to projects such as the Human Cell Atlas.

Mef2 induction of the immediate early gene Hr38/Nr4a is terminated by Sirt1 to promote ethanol tolerance.

Drug naïve animals given a single dose of ethanol show changed responses to subsequent doses, including the development of ethanol tolerance and ethanol preference. These simple forms of behavioral plasticity are due in part to changes in gene expression and neuronal properties. Surprisingly little is known about how ethanol initiates changes in gene expression or what the changes do. Here we demonstrate a role in ethanol plasticity for Hr38, the sole Drosophila homolog of the mammalian Nr4a1/2/3 class of immediate early response transcription factors. Acute ethanol exposure induces transient expression of Hr38 and other immediate early neuronal activity genes. Ethanol activates the Mef2 transcriptional activator to induce Hr38, and the Sirt1 histone/protein deacetylase is required to terminate Hr38 induction. Loss of Hr38 decreases ethanol tolerance and causes precocious but short-lasting ethanol preference. Similarly, reduced Mef2 activity in all neurons or specifically in the mushroom body α/ß neurons decreases ethanol tolerance; Sirt1 promotes ethanol tolerance in these same neurons. Genetically decreasing Hr38 expression levels in Sirt1 null mutants restores ethanol tolerance, demonstrating that both induction and termination of Hr38 expression are important for behavioral plasticity to proceed. These data demonstrate that Hr38 functions as an immediate early transcription factor that promotes ethanol behavioral plasticity.