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Background: Kidney autotransplantation is a useful technique to be reserved for cases in which kidney function is compromised by a complex anatomical configuration, such as long ureteral strictures and renal vascular anomalies not suitable for in situ reconstruction. Robot-assisted kidney autotransplantation (RAKAT) presents a novel, minimally invasive, and highly accurate approach. Objective: The aim of this study is to present the largest cohort of patients who underwent either extracorporeal (eRAKAT) or intracorporeal (iRAKAT) RAKAT, to confirm safety and feasibility and to compare the two approaches. Design, setting, and participants: We retrospectively analyzed prospectively followed patients undergoing eRAKAT and totally intracorporeal RAKAT in a total of three institutions. Surgical procedure: Extracorporeal RAKAT and iRAKAT. Measurements: Surgical and functional outcomes of patients subjected to eRAKAT and iRAKAT were measured. Results and limitations: Between January 2017 and February 2021, 29 patients underwent RAKAT: 15 eRAKAT and 14 iRAKAT. No statistical difference in the preoperative data was recorded. The analysis of intraoperative variables showed a statistically significant difference between eRAKAT and iRAKAT in cold ischemia time (median [interquartile range {IQR}]: 151 [125-199] vs 27.5 [20-55]; p < 0.001) and total ischemia time (median [IQR]: 196.2 [182-241] vs 81.5 [73-88]; p < 0.001). However, faster renal function recovery in favor of eRAKAT was observed during the first 90 d, with comparable renal function at 1 yr. The 90-d Clavien-Dindo >2 complications were 13.8%. It is important to stress that RAKAT, and above all iRAKAT, should be performed by surgeons with experience in robotic renal, vascular, and transplant surgery. Conclusions: Both eRAKAT and iRAKAT represent promising minimally invasive techniques in selected cases with acceptable ischemia time and comparable longterm operative outcomes. Patient summary: In selected patients, both extra-and intracorporeal robot-assisted kidney autotransplantation represent valid alternatives in case of long ureteral strictures and renal vascular anomalies not suitable for in situ reconstruction. (c) 2021 European Association of Urology. Published by Elsevier B.V. All rights reserved.

Combination treatments of s.c. 4T1 tumors with Clec9A-mAFN do not cause toxicity.

An ideal generic cancer immunotherapy should mobilize the immune system to destroy tumor cells without harming healthy cells and remain active in case of recurrence. Furthermore, it should preferably not rely on tumor-specific surface markers, as these are only available in a limited set of malignancies. Despite approval for treatment of various cancers, clinical application of cytokines is still impeded by their multiple toxic side effects. Type I IFN has a long history in the treatment of cancer, but its multifaceted activity pattern and complex side effects prevent its clinical use. Here we develop AcTakines (Activity-on-Target cytokines), optimized (mutated) immunocytokines that are up to 1,000-fold more potent on target cells, allowing specific signaling in selected cell types only. Type I IFN-derived AcTaferon (AFN)-targeting Clec9A+ dendritic cells (DC) displayed strong antitumor activity in murine melanoma, breast carcinoma, and lymphoma models and against human lymphoma in humanized mice without any detectable toxic side effects. Combined with immune checkpoint blockade, chemotherapy, or low-dose TNF, complete tumor regression and long-lasting tumor immunity were observed, still without adverse effects. Our findings indicate that DC-targeted AFNs provide a novel class of highly efficient, safe, and broad-spectrum off-the-shelf cancer immunotherapeutics with no need for a tumor marker.Significance: Targeted type I interferon elicits powerful antitumor efficacy, similar to wild-type IFN, but without any toxic side effects. Cancer Res; 78(2); 463–74. ©2017 AACR.

CD70 is an attractive target for chimeric antigen receptor (CAR) T cell therapy as treatment for both solid and liquid malignancies. However, functionality of CD70-specific CARs is only modest. Here, we optimized a CD70-specific VHH based CAR (nanoCAR). We evaluated the nanoCARs in clinically relevant modelsin vitro, using co-cultures of CD70-specific nanoCAR T cells with malignant rhabdoid tumor organoids, andin vivoby using a diffuse large B cell lymphoma (DLBCL) patient-derived xenograft (PDX) model. Whereas the nanoCAR T cells were highly efficient in organoid co-cultures, they showed only modest efficacy in the PDX model. Knocking out CD70 expression by the nanoCAR T cells resulted in dramatically enhanced functionality in the PDX model, suggesting that endogenous CD70 interaction with the nanoCAR induces exhaustion. Through single-cell transcriptomics, we obtained evidence that CD70KO CD70-specific nanoCAR T cells are protected from antigen induced exhaustion. Our data shows that CARs targeted to endogenous T cell antigens, negatively affect CAR T cell functionality by inducing an exhausted state which can be overcome by knocking out the specific target, in this case CD70.

Following usage-based views on language learning, input that learners are exposed to is the foundation of the linguistic features that are acquired. Inspired by the essential role of vocabulary for comprehension, studies have determined the vocabulary demands of various sources of input (e.g., novels, movies), but many questions remain regarding L2 textbooks, a central component of L2 classroom input. Moreover, current research into L2 textbooks has focused exclusively on English as L2. Flanders presents a unique case in that there are two main foreign languages: English and French. Therefore, a cross-lingual corpus consisting of English and French L2 textbook reading materials was compiled (ca. 300,000 words per L2) in order to investigate what the vocabulary demands are (= RQ1), how the demands evolve across secondary education (= RQ2), and how target language influences the (evolution of) demands (= RQ3). A custom Python script was developed to calculate lexical profiles, i.e., categorisations of the textbooks' vocabulary into word frequency lists. Typically, lexical profiling research relies on word families as lexical unit, but we opted for lemmas instead in light of the higher morphological demands of French. Results showed that knowledge of the 11,000 most frequent lemmas was required to reach unassisted comprehension of the first grade English materials, as opposed to 18,000 for French. From there, English shows a systematic and fairly gradual increase in vocabulary demands across grade levels, whereas French demands seem to fluctuate, an evolution which could hinder French L2 development. Pedagogical implications will be addressed.

In data-driven optimization, sample average approximation is known to suffer from the so-called optimizer's curse that causes optimistic bias in evaluating the solution performance. This can be tackled by adding a "margin" to the estimated objective value, or via distributionally robust optimization (DRO), a fast-growing approach based on worst-case analysis, which gives a protective bound on the attained objective value. However, in all these existing approaches, a statistically guaranteed bound on the true solution performance either requires restrictive conditions and knowledge on the objective function complexity, or otherwise exhibits an over-conservative rate that depends on the distribution dimension. We argue that a special type of DRO offers strong theoretical advantages in regard to these challenges: It attains a statistical bound on the true solution performance that is the tightest possible in terms of exponential decay rate, for a wide class of objective functions that notably does not hinge on function complexity. Correspondingly, its calibration also does not require any complexity information. This DRO uses an ambiguity set based on a KL-divergence smoothed by the Wasserstein or Levy-Prokhorov distance via a suitable distance optimization. Computationally, we also show that such a DRO, and its generalized version using smoothed $f$-divergence, is not much harder than standard DRO problems using the $f$-divergence or Wasserstein distance, thus supporting the strengths of such DRO as both statistically optimal and computationally viable.

Introduction Hereditary transthyretin-mediated (hATTR) amyloidosis, a genetic disease caused by mutations in the transthyretin gene, leads to progressive sensory and autonomic neuropathy and/or cardiomyopathy and is associated with renal and ophthalmologic manifestations and a poor prognosis. Methods This is a retrospective study based on data collected from the medical records of patients with hATTR amyloidosis treated with patisiran between 01 July 2018 and 01 February 2021. Six Belgian neuromuscular reference centers participated, covering all patisiran-treated hATTR amyloidosis patients at the study time. This study was conducted to collect data requested in the context of the reimbursement of patisiran in Belgium. Results Thirty-one patients were diagnosed with hATTR amyloidosis with polyneuropathy, Coutinho stage 1 or 2, and eligible for active treatment during the data collection period. Of the hATTR amyloidosis patients treated with patisiran (n = 12), seven and five had polyneuropathy stages 1 and 2, respectively. Six patients had cardiac symptoms (New York Heart Association class 2 or above). Follow-up information was available for nine patients. Following patisiran treatment, eight patients showed stable or improved assessments for most neurological or cardiological parameters. Only one patient presented with worsening statuses at the end of the data collection period. Conclusions The patients with hATTR amyloidosis in Belgium have similar baseline demographics and disease characteristics to those studied in the patisiran APOLLO study and show a similar therapeutic response in the real-world, altering the expected disease progression in most patients.

We propose a data-driven technique to automatically learn the uncertainty sets in robust optimization. Our method reshapes the uncertainty sets by minimizing the expected performance across a family of problems while guaranteeing constraint satisfaction. We learn the uncertainty sets using a novel stochastic augmented Lagrangian method that relies on differentiating the solutions of the robust optimization problems with respect to the parameters of the uncertainty set. We show sublinear convergence to stationary points under mild assumptions, and finite-sample probabilistic guarantees of constraint satisfaction using empirical process theory. Our approach is very flexible and can learn a wide variety of uncertainty sets while preserving tractability. Numerical experiments show that our method outperforms traditional approaches in robust and distributionally robust optimization in terms of out of sample performance and constraint satisfaction guarantees. We implemented our method in the open-source package LROPT.

Traumatic triceps tendon ruptures are rare and known to result in substantial disability unless appropriate surgical treatment is performed. A traumatic rupture can occur due to a fall onto the outstretched hand. Tearing of the triceps tendon results in a valgus load onto the elbow, which can lead to injuries to the radial head/neck and/or the ulnar collateral ligament. Hence, attention must be paid to associated pathologies after diagnosis of rupture to the distal triceps tendon. Our surgical procedure in these cases includes diagnostic arthroscopy to detect concomitant injuries. In the following, we present three cases where we performed an open suture bridge repair of the triceps tendon followed by suture repair of the ulnar collateral ligament. All patients recovered well and reported no elbow pain or limited range of motion. The ASES and Mayo Elbow Performance scores were 100 at the 1-year follow-up.Traumatische distale Trizepssehnenverletzungen sind seltene Verletzungen. Nichtsdestotrotz ist die korrekte Therapie entscheidend für die Funktion des Armes. Der Sturz auf den ausgestreckten Arm kann die Ursache sein. Die Ruptur der Sehne im Unfallmoment verändert die Krafteinwirkung, sodass Begleitverletzungen wie die Radiuskopffraktur oder der Riss des medialen, ulnaren Kollateralbandes nicht selten sind. Daher sind mögliche Begleitverletzungen bei einer distalen Trizepssehnenruptur dezidiert zu prüfen. Gemäß unserem Vorgehen kann neben der MRT-Diagnostik eine Arthroskopie hilfreich sein, um mögliche Begleitpathologie adäquat zu erfassen und zu adressieren. Im Folgenden werden drei Fälle vorgestellt, die operativ mittels Trizepssehnen-Naht sowie Refixation des medialen ulnaren Kollateralbandes versorgt wurden. Das 1-Jahres-Followup zeigte keine Bewegungseinschränkungen oder Instabilitäten, der ASES- und Mayo Elbow Score waren 100.

We address the problem of prescribing an optimal decision in a framework where the cost function depends on uncertain problem parameters that need to be learned from data. Earlier work proposed prescriptive formulations based on supervised machine learning methods. These prescriptive methods can factor in contextual information on a potentially large number of covariates to take context specific actions which are superior to any static decision. When working with noisy or corrupt data, however, such nominal prescriptive methods can be prone to adverse overfitting phenomena and fail to generalize on out-of-sample data. In this paper we combine ideas from robust optimization and the statistical bootstrap to propose novel prescriptive methods which safeguard against overfitting. We show indeed that a particular entropic robust counterpart to such nominal formulations guarantees good performance on synthetic bootstrap data. As bootstrap data is often a sensible proxy to actual out-of-sample data, our robust counterpart can be interpreted to directly encourage good out-of-sample performance. The associated robust prescriptive methods furthermore reduce to convenient tractable convex optimization problems in the context of local learning methods such as nearest neighbors and Nadaraya-Watson learning. We illustrate our data-driven decision-making framework and our novel robustness notion on a small newsvendor problem.

Patients with obsessive-compulsive disorder (OCD) are likely to be affected by the COVID-19 crisis since fear of contamination is highly prevalent in this illness and disease reminders are omnipresent during this crisis. The current study aimed to investigate the impact of the pandemic and the lockdown on the mental health, well-being and coping abilities of OCD patients and their families in order to increase our understanding of the underlying mechanisms of the disorder. Twenty-two patients and 13 family members were interviewed one-to-one about their experiences and challenges caused by the pandemic and home-confinement directives. Verbatim transcripts of the interviews were analyzed using inductive thematic analysis. Five overarching themes were identified: (1) changing point of reference: confusion and legitimization of OCD behavior, (2) coping strategies were challenged: too much or too little exposure to obsessional concerns, (3) distress but also relief in some areas, (4) developing a new equilibrium within the family, (5) changes in accessibility and nature of therapy: perils and merits of online treatment. These findings make clear the importance of the accessibility of mental health services during this pandemic through direct patient contacts or in a remote format. In therapy therapists should focus on challenging the changing point of reference, providing practical advice on coping, stimulating to engage in exposure and encouraging patients to seek social support. Furthermore, it is important to involve family members in therapy to support and coach them to be validating, supportive and encouraging, without accommodating to the OCD behaviour.