[Prioritized appointment allocation in new patients, what is really decisive? : Comparative analysis of manual appointment allocation with automated and AI-assisted approaches].

Background: The timely allocation of appointments for new patients is a daily challenge in rheumatological practice, which can be supported by digital solutions. The question is to find the simplest and most effective possible method for prioritization when allocating appointments.

Methods: Using a registration form for new patients, standardized symptoms and laboratory results were collated.

Distinct pulmonary patterns in ANCA-associated vasculitides: insights from a retrospective single center cohort study.

ANCA-associated vasculitides (AAV) comprise granulomatosis with polyangiitis (GPA), microscopic polyangiitis (MPA) and eosinophilic granulomatosis with polyangiitis. All forms may involve different organ systems, yet kidney and lung involvement are common and fatal in many cases. Here, we aimed to determine the predictive value of pulmonary disease manifestation and individual CT findings in AAV patients.

Discriminative machine learning for maximal representative subsampling.

Biased population samples pose a prevalent problem in the social sciences. Therefore, we present two novel methods that are based on positive-unlabeled learning to mitigate bias. Both methods leverage auxiliary information from a representative data set and train machine learning classifiers to determine the sample weights.

Multiple instance learning to predict immune checkpoint blockade efficacy using neoantigen candidates.

Previous studies showed that the neoantigen candidate load is an imperfect predictor of immune checkpoint blockade (ICB) efficacy. Further studies provided evidence that the response to ICB is also affected by the qualitative properties of a few or even single candidates, limiting the predictive power based on candidate quantity alone. Here, we predict ICB efficacy based on neoantigen candidates and their neoantigen features in the context of the mutation type, using Multiple-Instance Learning via Embedded Instance Selection (MILES).

The use of an educational video on forensic autopsy in curricular teaching for medical students-is it worth the effort? : A field report from lecturers and a survey among students.

Purpose: The purpose of this work is to share our experience with an educational video on forensic autopsy. Using questionnaires, we attempted to answer the following questions: Does watching the video trigger emotions in students? Does the autopsy meet the expectations that they had before? Does the video help to prepare them for their subsequent autopsy participation?

Methods: A total of 365 medical students who attended their classes during the COVID-19 pandemic measures were provided with the video on an online platform. Links leading to questionnaires were positioned before and after the video.

[Vasculitides].

In everyday clinical practice, immunologically mediated systemic vasculitides are among the rare diseases, meaning that basic knowledge of major symptoms and indicative laboratory findings is crucial for the inclusion of these complex clinical entities in differential diagnostic considerations. For many years, systemic vasculitides have been classified according to the primarily affected vessel size, distinguishing large, medium-sized, and small vessels. Pain is very often one of the main complaints of these diseases, be it, for example, the temporally accentuated headache in giant cell arteritis, the early morning myalgias in the shoulder and hip girdle in polymyalgia rheumatica, or the mononeuritis multiplex in eosinophilic granulomatosis with polyangiitis.

Relapsing TIPIC Syndrome after Administration of an mRNA-Based COVID-19 Vaccine.

Reported vascular complications following mRNA-based COVID-19 vaccines are consisting of myocarditis, cerebral venous thrombosis, cerebral vascular thrombosis, and vaccine-induced thrombocytopenia. Here, we describe a case of a 49-year-old woman with left-sided pain above the middle common carotid artery (carotidynia) starting a few days after her second vaccination with an mRNA-based COVID-19 vaccine (Spikevax). Imaging was indicative of transient perivascular inflammation of the carotid artery (TIPIC) syndrome.

An ultra-high-throughput screen for the evaluation of peptide HLA-Binder interactions.

Peptide human leukocyte antigen (pHLA) targeting therapeutics like T-cell receptor based adoptive cell therapy or bispecific T cell engaging receptor molecules hold great promise for the treatment of cancer. Comprehensive pre-clinical screening of therapeutic candidates is important to ensure patient safety but is challenging because of the size of the potential off-target space. By combining stabilized peptide-receptive HLA molecules with microarray printing and screening, we have developed an ultra-high-throughput screening platform named ValidaTe that enables large scale evaluation of pHLA-binder interactions.

Four-dimensional trapped ion mobility spectrometry lipidomics for high throughput clinical profiling of human blood samples.

Lipidomics encompassing automated lipid extraction, a four-dimensional (4D) feature selection strategy for confident lipid annotation as well as reproducible and cross-validated quantification can expedite clinical profiling. Here, we determine 4D descriptors (mass to charge, retention time, collision cross section, and fragmentation spectra) of 200 lipid standards and 493 lipids from reference plasma via trapped ion mobility mass spectrometry to enable the implementation of stringent criteria for lipid annotation. We use 4D lipidomics to confidently annotate 370 lipids in reference plasma samples and 364 lipids in serum samples, and reproducibly quantify 359 lipids using level-3 internal standards.

A fair experimental comparison of neural network architectures for latent representations of multi-omics for drug response prediction.

Background: Recent years have seen a surge of novel neural network architectures for the integration of multi-omics data for prediction. Most of the architectures include either encoders alone or encoders and decoders, i.e.

Learning to rank Higgs boson candidates.

In the extensive search for new physics, the precise measurement of the Higgs boson continues to play an important role. To this end, machine learning techniques have been recently applied to processes like the Higgs production via vector-boson fusion. In this paper, we propose to use algorithms for learning to rank, i.

Imaging patterns of cerebral ischemia in hypereosinophilic syndrome: case report and systematic review.

Introduction: Ischemic stroke is a potential complication of hypereosinophilic syndromes (HES), and little is known about underlying pathophysiological mechanisms. We aimed to describe the imaging patterns of cerebral ischemia in patients with HES.

Methods: An individual case is reported.

Deep neural networks to recover unknown physical parameters from oscillating time series.

Deep neural networks are widely used in pattern-recognition tasks for which a human-comprehensible, quantitative description of the data-generating process, cannot be obtained. While doing so, neural networks often produce an abstract (entangled and non-interpretable) representation of the data-generating process. This may be one of the reasons why neural networks are not yet used extensively in physics-experiment signal processing: physicists generally require their analyses to yield quantitative information about the system they study.

Difficulties of Breast Reconstruction - Problems That No One Likes to Face.

Reconstructive breast surgery following total or partial mastectomy can be performed using autologous tissues or breast implants, and each has its own set of complications. Most women do not experience significant complications and are highly satisfied but breast reconstruction must consider potential complications from surgical techniques, as well as additional complications that may arise from oncological treatment modalities such as radiation therapy and chemotherapy. The aim of this article is to provide a systemic overview of possible complications that may arise in the course of reconstructive breast surgery.

Rule Extraction From Binary Neural Networks With Convolutional Rules for Model Validation.

Classification approaches that allow to extract logical rules such as decision trees are often considered to be more interpretable than neural networks. Also, logical rules are comparatively easy to verify with any possible input. This is an important part in systems that aim to ensure correct operation of a given model.

The dignity of the human corpse in forensic medicine.

Working with the dead is a very specific kind of work. Although a dignified handling of the corpses is demanded by the legislator and by the general public, neither the legal status of the corpse is undisputed nor is it obvious what a dignified handling of the deceased should consist of. In our hypothesis generating pilot study, we asked which concrete considerations are involved in daily practice of forensic specialists.

SARS-CoV-2 neutralizing human recombinant antibodies selected from pre-pandemic healthy donors binding at RBD-ACE2 interface.

COVID-19 is a severe acute respiratory disease caused by SARS-CoV-2, a new recently emerged sarbecovirus. This virus uses the human ACE2 enzyme as receptor for cell entry, recognizing it with the receptor binding domain (RBD) of the S1 subunit of the viral spike protein. We present the use of phage display to select anti-SARS-CoV-2 spike antibodies from the human naïve antibody gene libraries HAL9/10 and subsequent identification of 309 unique fully human antibodies against S1.

Effect of Core-Crosslinking on Protein Corona Formation on Polymeric Micelles.

Most nanomaterials acquire a protein corona upon contact with biological fluids. The magnitude of this effect is strongly dependent both on surface and structure of the nanoparticle. To define the contribution of the internal nanoparticle structure, protein corona formation of block copolymer micelles with poly(N-2-hydroxypropylmethacrylamide) (pHPMA) as hydrophilic shell, which are crosslinked-or not-in the hydrophobic core is comparatively analyzed.

Identifying volatile organic compounds used for olfactory navigation by homing pigeons.

Many bird species have the ability to navigate home after being brought to a remote, even unfamiliar location. Environmental odours have been demonstrated to be critical to homeward navigation in over 40 years of experiments, yet the chemical identity of the odours has remained unknown. In this study, we investigate potential chemical navigational cues by measuring volatile organic compounds (VOCs): at the birds' home-loft; in selected regional forest environments; and from an aircraft at 180 m.

Machine learning for a combined electroencephalographic anesthesia index to detect awareness under anesthesia.

Spontaneous electroencephalogram (EEG) and auditory evoked potentials (AEP) have been suggested to monitor the level of consciousness during anesthesia. As both signals reflect different neuronal pathways, a combination of parameters from both signals may provide broader information about the brain status during anesthesia. Appropriate parameter selection and combination to a single index is crucial to take advantage of this potential.

Polymeric Nanoparticles with Neglectable Protein Corona.

The current understanding of nanoparticle-protein interactions indicates that they rapidly adsorb proteins upon introduction into a living organism. The formed protein corona determines thereafter identity and fate of nanoparticles in the body. The present study evaluates the protein affinity of three core-crosslinked polymeric nanoparticles with long circulation times, differing in the hydrophilic polymer material forming the particle surface, namely poly(N-2-hydroxypropylmethacrylamide) (pHPMA), polysarcosine (pSar), and poly(ethylene glycol) (PEG).

Digital DNA microarray generation on glass substrates.

In this work we show how DNA microarrays can be produced batch wise on standard microscope slides in a fast, easy, reliable and cost-efficient way. Contrary to classical microarray generation, the microarrays are generated via digital solid phase PCR. We have developed a cavity-chip system made of a PDMS/aluminum composite which allows such a solid phase PCR in a scalable and easy to handle manner.