The protective effect of tumor necrosis factor-alpha inhibitors in COVID-19 in patients with inflammatory rheumatic diseases compared to the general population-A comparison of two German registries

Objectives

To investigate, whether inflammatory rheumatic diseases (IRD) inpatients are at higher risk to develop a severe course of SARS-CoV-2 infections compared to the general population, data from the German COVID-19 registry for IRD patients and data from the Lean European Survey on SARS-CoV-2 (LEOSS) infected patients covering inpatients from the general population with SARS-CoV-2 infections were compared.

Methods

4310 (LEOSS registry) and 1139 cases (IRD registry) were collected in general. Data were matched for age and gender. From both registries, 732 matched inpatients (LEOSS registry: n = 366 and IRD registry: n = 366) were included for analyses in total.

Results

Regarding the COVID-19 associated lethality, no significant difference between both registries was observed. Age > 65°years, chronic obstructive pulmonary disease, diabetes mellitus, rheumatoid arthritis, spondyloarthritis and the use of rituximab were associated with more severe courses of COVID-19. Female gender and the use of tumor necrosis factor-alpha inhibitors (TNF-I) were associated with a better outcome of COVID-19.

Conclusion

Inflammatory rheumatic diseases (IRD) patients have the same risk factors for severe COVID-19 regarding comorbidities compared to the general population without any immune-mediated disease or immunomodulation. The use of rituximab was associated with an increased risk for severe COVID-19. On the other hand, the use of TNF-I was associated with less severe COVID-19 compared to the general population, which might indicate a protective effect of TNF-I against severe COVID-19 disease.

The effect of speech pathology on automatic speaker verification: a large-scale study

Navigating the challenges of data-driven speech processing, one of the primary hurdles is accessing reliable pathological speech data. While public datasets appear to offer solutions, they come with inherent risks of potential unintended exposure of patient health information via re-identification attacks. Using a comprehensive real-world pathological speech corpus, with over n[Formula: see text]3800 test subjects spanning various age groups and speech disorders, we employed a deep-learning-driven automatic speaker verification (ASV) approach. This resulted in a notable mean equal error rate (EER) of [Formula: see text], outstripping traditional benchmarks. Our comprehensive assessments demonstrate that pathological speech overall faces heightened privacy breach risks compared to healthy speech. Specifically, adults with dysphonia are at heightened re-identification risks, whereas conditions like dysarthria yield results comparable to those of healthy speakers. Crucially, speech intelligibility does not influence the ASV system's performance metrics. In pediatric cases, particularly those with cleft lip and palate, the recording environment plays a decisive role in re-identification. Merging data across pathological types led to a marked EER decrease, suggesting the potential benefits of pathological diversity in ASV, accompanied by a logarithmic boost in ASV effectiveness. In essence, this research sheds light on the dynamics between pathological speech and speaker verification, emphasizing its crucial role in safeguarding patient confidentiality in our increasingly digitized healthcare era.

Deep Learning in Surgical Workflow Analysis: A Review of Phase and Step Recognition

OBJECTIVE

In the last two decades, there has been a growing interest in exploring surgical procedures with statistical models to analyze operations at different semantic levels. This information is necessary for developing context-aware intelligent systems, which can assist the physicians during operations, evaluate procedures afterward or help the management team to effectively utilize the operating room. The objective is to extract reliable patterns from surgical data for the robust estimation of surgical activities performed during operations. The purpose of this article is to review the state-of-the-art deep learning methods that have been published after 2018 for analyzing surgical workflows, with a focus on phase and step recognition.

METHODS

Three databases, IEEE Xplore, Scopus, and PubMed were searched, and additional studies are added through a manual search. After the database search, 343 studies were screened and a total of 44 studies are selected for this review.

CONCLUSION

The use of temporal information is essential for identifying the next surgical action. Contemporary methods used mainly RNNs, hierarchical CNNs, and Transformers to preserve long-distance temporal relations. The lack of large publicly available datasets for various procedures is a great challenge for the development of new and robust models. As supervised learning strategies are used to show proof-of-concept, self-supervised, semi-supervised, or active learning methods are used to mitigate dependency on annotated data.

SIGNIFICANCE

The present study provides a comprehensive review of recent methods in surgical workflow analysis, summarizes commonly used architectures, datasets, and discusses challenges.

Enhancing student understanding of rheumatic disease pathologies through augmented reality: findings from a multicentre trial

OBJECTIVE

The possibility of combining real and virtual environments is driving the increased use of augmented reality (AR) in education, including medical training. The aim of this multicentre study was to evaluate the students' perspective on the AR-based Rheumality GO!® app as a new teaching concept, presenting six real anonymised patient cases with rheumatoid arthritis (RA), psoriatic arthritis (PsA), and axial spondyloarthritis (axSpA).

METHODS

The study encompassed 347 undergraduate medical students (232 women and 115 men) from four medical universities in Germany (Jena, Bad Nauheim/Gießen, Nuremberg, Erlangen). The course was divided into a theoretical refresher lecture followed by six AR-based cases in each of the three indications presented in the Rheumality GO!® app. All participants evaluated the course after completion, assessing the benefit of the app from a student´s perspective using a questionnaire with 16 questions covering six subject areas.

RESULTS

The use of the AR-based app Rheumality GO!® improved the understanding of pathologies in RA, PsA, and axSpA for 99% of the participants. For 98% of respondents, the concept of AR with real patient data has made a positive impact on the teaching environment. On the other hand, 82% were in favour of the use of virtual tools (e.g. AR) in addition to this conventional approach.

CONCLUSION

The results of our survey showed that from medical students' perspective, an AR-based concept like the Rheumality GO!® app can complement rheumatology teaching in medical school as an effective and attractive tool though not replace bedside teaching.

Distribution patterns of arterial affection and the influence of glucocorticoids on 18F-fluorodeoxyglucose positron emission tomography/CT in patients with giant cell arteritis

Background

Giant cell arteritis (GCA) with the involvement of extracranial vessels is increasingly coming into focus. Isolated aortic involvement in the acute phase of GCA is probably more frequent than estimated because only a minority of patients show typical symptoms. ¹⁸F-fluorodeoxyglucose positron emission tomography/CT (PET/CT) is a reliable imaging tool to diagnose patients with extracranial GCA. The aim of this retrospective study was to quantify arterial involvement at the onset of a newly diagnosed GCA by PET/CT and to evaluate the influence of glucocorticoid (GC) treatment on the diagnostic performance of this imaging technique.

Methods

The study included 60 patients with GCA at the onset of a GCA. All patients had undergone a PET/CT scan. 44 patients were GC naïve and 16 patients received GC.

Results

The most affected arteries were the ascending aorta (72%), followed by the brachiocephalic trunk (62%), aortic arch (60%) and descending aorta (60%). The aorta and its branches showed an inflammatory involvement in 83.3% of patients. A singular affection of the aorta and the brachiocephalic trunk was revealed in 20% of cases. GC-naïve patients (95.5%) had more frequently affected arteries compared with GC-treated patients (50%).

Conclusion

Our study showed the frequent involvement of the thoracic aorta and brachiocephalic trunk in patients with GCA using PET/CT. Since these vascular compartments cannot be visualised by ultrasound, we advocate screening imaging of the aorta with PET/CT when GCA is suspected. Because the use of GC is associated with a marked decrease in the inflamed vascular segment in GCA, PET/CT should be performed as soon as possible.

Experimental and Model-Based Analysis to Optimize Microalgal Biomass Productivity in a Pilot-Scale Tubular Photobioreactor

A dynamic coarse-grained model of microalgal growth considering light availability and temperature under discontinuous bioprocess operation was parameterized using experimental data from 15 batch cultivations of Nannochloropsis granulata in a pilot-scale tubular photobioreactor. The methodology applied consists of a consecutive two-step model parameter estimation using pooled, clustered and reorganized data to obtain initial estimates and multi-experiment fitting to obtain the final estimates, which are: maximum specific growth rate μ_max = 1.56 d⁻¹, specific photon half-saturation constant K_S,ph = 1.89 molphgX-1d-1, specific photon maintenance coefficient m_ph = 0.346 molphgX-1d-1 and the cardinal temperatures T_min = 2.3°C, T_opt = 27.93°C and T_max = 32.59°C. Biomass productivity prediction proved highly accurate, expressed by the mean absolute percent error MAPE = 7.2%. Model-based numerical optimization of biomass productivity for repeated discontinuous operation with respect to the process parameters cultivation cycle time, inoculation biomass concentration and temperature yielded productivity gains of up to 35%. This optimization points to best performance under continuous operation. The approach successfully applied here to small pilot-scale confirms an earlier one to lab-scale, indicating its transferability to larger scale tubular photobioreactors.

The Transcription Factor Stp2 Is Important for Candida albicans Biofilm Establishment and Sustainability

The fungal pathogen Candida albicans forms polymorphic biofilms where hyphal morphogenesis and metabolic adaptation are tightly coordinated by a complex intertwined network of transcription factors. The sensing and metabolism of amino acids play important roles during various phases of biofilm development – from adhesion to maturation. Stp2 is a transcription factor that activates the expression of amino acid permease genes and is required for environmental alkalinization and hyphal growth in vitro and during macrophage phagocytosis. While it is well established that Stp2 is activated in response to external amino acids, its role in biofilm formation remains unknown. In addition to widely used techniques, we applied newly developed approaches for automated image analysis to quantify Stp2-regulated filamentation and biofilm growth. Our results show that in the stp2Δ deletion mutant adherence to abiotic surfaces and initial germ tube formation were strongly impaired, but formed mature biofilms with cell density and morphological structures comparable to the control strains. Stp2-dependent nutrient adaptation appeared to play an important role in biofilm development: stp2Δ biofilms formed under continuous nutrient flow displayed an overall reduction in biofilm formation, whereas under steady conditions the mutant strain formed biofilms with lower metabolic activity, resulting in increased cell survival and biofilm longevity. A deletion of STP2 led to increased rapamycin susceptibility and transcriptional activation of GCN4, the transcriptional regulator of the general amino acid control pathway, demonstrating a connection of Stp2 to other nutrient-responsive pathways. In summary, the transcription factor Stp2 is important for C. albicans biofilm formation, where it contributes to adherence and induction of morphogenesis, and mediates nutrient adaption and cell longevity in mature biofilms.

Species, interindividual, and tissue specificity in endocrine signaling

The activity of endocrine-active agents exhibits specificity at many levels. Differential responsiveness to these agents has been observed between different species and extends to interindividual differences within a species and between different tissues as well. In cases where they have been identified, the biologic and molecular mechanisms underlying this specificity are quite diverse. Determinants of species specificity include differences that exist in receptor binding, gene transcription, and cellular responses to endocrine-active compounds between species. Interindividual differences in responsiveness may be determined at the level of genetic polymorphisms in hormone-metabolizing enzymes, hormone receptors, and in those genes that are transactivated by these receptors, as well as during changing windows of susceptibility that occur as a function of age, such as prenatal and postmenopausal exposures. Extrinsic factors such as diet can also impact individual susceptibility to endocrine-active agents. Tissue-specific determinants of susceptibility are well documented, but little is known regarding the mechanisms underlying these different responses. Differences in the expression of accessory proteins for steroid hormone receptors and different patterns of receptor expression, estrogen receptor alpha and estrogen receptor beta; for example, may contribute to tissue specificity, as may differences in the pattern of expression of other genes such as hormone-metabolizing enzymes. The use of animal model systems and development of appropriate mathematical models has the potential to yield additional valuable information for elucidating the role of these determinants of specificity at low-dose exposures and for improved risk assessments for the adverse health effects of endocrine-active compounds.