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Undetected infection and delayed isolation of infected individuals are key factors driving the monkeypox virus (now termed mpox virus or MPXV) outbreak. To enable earlier detection of MPXV infection, we developed an image-based deep convolutional neural network (named MPXV-CNN) for the identification of the characteristic skin lesions caused by MPXV. We assembled a dataset of 139,198 skin lesion images, split into training/validation and testing cohorts, comprising non-MPXV images (n = 138,522) from eight dermatological repositories and MPXV images (n = 676) from the scientific literature, news articles, social media and a prospective cohort of the Stanford University Medical Center (n = 63 images from 12 patients, all male). In the validation and testing cohorts, the sensitivity of the MPXV-CNN was 0.83 and 0.91, the specificity was 0.965 and 0.898 and the area under the curve was 0.967 and 0.966, respectively. In the prospective cohort, the sensitivity was 0.89. The classification performance of the MPXV-CNN was robust across various skin tones and body regions. To facilitate the usage of the algorithm, we developed a web-based app by which the MPXV-CNN can be accessed for patient guidance. The capability of the MPXV-CNN for identifying MPXV lesions has the potential to aid in MPXV outbreak mitigation., Stanford Data Science and Biomedical Informatics Training Program at Stanford 2T15LM007033, Spanish Government RTI-2018-101674-B-I00 PID2021-128317OB-I00, J.de Andalucia P20-00163, Fulbright Spanish Commission, United States Department of Health & Human Services, National Institutes of Health (NIH) - USA DP2AI171011 5R25AI147369-03, German Federal Ministry for Economic Affairs and Climate Action (BMWi) BMWi 01MK21009E, Berlin Institute of Health, German Research Foundation (DFG) German Federal Ministry for Economic Affairs and Climate Action and Studienstiftung des deutschen Volkes (German Academic Scholarship Foundation), Charite-Universitaetsmedizin Berlin

Since 2020, digital health applications (DiGA) can be prescribed at the expense of the German statutory health insurance (SHI) system after undergoing an approval procedure by the Federal Institute for Drugs and Medical Devices (BfArM). DiGA can be approved provisionally for 1 year (with the option of extension) or permanently. The latter is dependent on scientific evidence of a positive effect on care, which can be a medical benefit or a patient-relevant structural and procedural improvement in care. However, it is apparent that the investigation of DiGA in scientific studies is challenging, as they are often complex interventions whose success also includes user and prescriber factors. In addition, health services research data underpinning the benefits of DiGA are lacking to date. In the current article, methodological considerations for DiGA research are presented, and a selection of internal medicine DiGAs is used to critically discuss current research practice.Seit 2020 sind digitale Gesundheitsanwendungen (DiGA) nach Durchlaufen eines Zulassungsverfahrens durch das Bundesinstitut für Arzneimittel und Medizinprodukte (BfArM) zulasten der gesetzlichen Krankenversicherung (GKV) verordnungsfähig. Dabei können DiGA vorläufig für ein Jahr (mit der Option der Verlängerung) oder dauerhaft zugelassen werden. Letzteres ist vom wissenschaftlichen Nachweis eines positiven Versorgungseffekts abhängig, der ein medizinischer Nutzen oder eine patientenrelevante Struktur- und Verfahrensverbesserung in der Versorgung sein kann. Es zeigt sich jedoch, dass die Untersuchung von DiGA in wissenschaftlichen Studien herausfordernd ist, da es sich häufig um komplexe Interventionen handelt, in deren Erfolg auch Faktoren der Anwender und Verordner einfließen. Darüber hinaus fehlen bislang Versorgungsforschungsdaten, die den Nutzen der DiGA unterlegen. Im aktuellen Beitrag werden methodische Überlegungen zur DiGA-Forschung dargelegt. Anhand einer Auswahl internistischer DiGA wird die bisherige Forschungspraxis kritisch diskutiert.

Since 2020 physicians can prescribe digital health applications (DiGA), also colloquially known as apps on prescription, which are reimbursed by the statutory health insurance when they are approved by the Federal Institute for Drugs and Medical Devices (BfArM) and are included in the DiGA Ordinance. Currently, there is one approved DiGA (indication obesity) for internal medicine. There are many questions on the practical use of the DiGA, ranging from the prescription, the effectiveness, the complexities and reimbursement as well as the liability risks. The DiGA are innovative new means, which maybe support internal medicine physicians in the diagnostics and treatment in the future. The benefits in this field of indications are limited by unclarified issues, especially on the prescription practice and the currently low number of DiGA available in internal medicine.Seit September 2020 können Ärzte digitale Gesundheitsanwendungen (DiGA), umgangssprachlich auch „App auf Rezept“ genannt, zulasten der gesetzlichen Krankenversicherung verordnen, wenn diese vom Bundesinstitut für Arzneimittel und Medizinprodukte (BfArM) zugelassen und in das DiGA-Verzeichnis aufgenommen wurden. Aktuell gibt es eine zugelassene internistische DiGA (Indikation Adipositas). Zur praktischen Anwendung der DiGA stellen sich zahlreiche Fragen, von der Verordnung über die Wirksamkeit bis hin zu Aufwänden und Vergütung sowie Haftungsrisiken. DiGA sind innovative, neue Mittel, die Internisten zukünftig in der Diagnostik und Therapie unterstützen könnten. Ungeklärte Fragen, insbesondere zur Verordnungspraxis, und das aktuell geringe Angebot internistischer DiGA schränken den Nutzen in unserem Indikationsgebiet noch ein.

Lay Summary In 2022, the majority of clinical documents are still written as free text. Assuming that these records are consistently and correctly transformed into structured data, they present an opportunity for optimized health-economic purposes as well as personalized patient care. Deep-learning methods, particularly transformer-based models, have recently received much attention as they excel in a variety of fields; however, the majority of applications are currently only available in English. Although there are general-language models in German, none have been developed specifically for biomedical or clinical documents. In this context, this study systematically compared 8 previously published general-language models and 3 newly trained biomedical domain models in information extraction and document classification tasks. Our findings show that while training entirely new models with currently available data has proven ineffective, adapting existing models for biomedical language holds a lot of promise. Furthermore, we found out that even models that have not been specifically developed for biomedical applications can achieve excellent results in the specified fields. Objective Healthcare data such as clinical notes are primarily recorded in an unstructured manner. If adequately translated into structured data, they can be utilized for health economics and set the groundwork for better individualized patient care. To structure clinical notes, deep-learning methods, particularly transformer-based models like Bidirectional Encoder Representations from Transformers (BERT), have recently received much attention. Currently, biomedical applications are primarily focused on the English language. While general-purpose German-language models such as GermanBERT and GottBERT have been published, adaptations for biomedical data are unavailable. This study evaluated the suitability of existing and novel transformer-based models for the German biomedical and clinical domain. Materials and Methods We used 8 transformer-based models and pre-trained 3 new models on a newly generated biomedical corpus, and systematically compared them with each other. We annotated a new dataset of clinical notes and used it with 4 other corpora (BRONCO150, CLEF eHealth 2019 Task 1, GGPONC, and JSynCC) to perform named entity recognition (NER) and document classification tasks. Results General-purpose language models can be used effectively for biomedical and clinical natural language processing (NLP) tasks, still, our newly trained BioGottBERT model outperformed GottBERT on both clinical NER tasks. However, training new biomedical models from scratch proved ineffective. Discussion The domain-adaptation strategy's potential is currently limited due to a lack of pre-training data. Since general-purpose language models are only marginally inferior to domain-specific models, both options are suitable for developing German-language biomedical applications. Conclusion General-purpose language models perform remarkably well on biomedical and clinical NLP tasks. If larger corpora become available in the future, domain-adapting these models may improve performances.

Introduction Prospective and longitudinal data on pulmonary injury over one year after acute coronavirus disease 2019 (COVID-19) are sparse. We aim to determine reductions in pulmonary function and respiratory related quality of life up to 12 months after acute COVID-19. Methods Patients with acute COVID-19 were enrolled into an ongoing single-centre, prospective observational study and prospectively examined 6 weeks, 3, 6 and 12 months after onset of COVID-19 symptoms. Chest CT-scans, pulmonary function and symptoms assessed by St. Georges Respiratory Questionnaire were used to evaluate respiratory limitations. Patients were stratified according to severity of acute COVID-19. Results Median age of all patients was 57 years, 37.8% were female. Higher age, male sex and higher BMI were associated with acute-COVID-19 severity (p, Graphical abstract Image 1

Cardiopulmonary Exercise Testing (CPET) provides a comprehensive assessment of pulmonary, cardiovascular and musculosceletal function. Reduced CPET performance could be an indicator for chronic morbidity after COVID-19.Patients ≥18 years with confirmed PCR positive SARS-CoV-2 infection were offered to participate in a prospective observational study of clinical course and outcomes of COVID-19. 54 patients completed CPET, questionnaires on respiratory quality of life and performed pulmonary function tests 12 months after SARS-CoV-2 infection.At 12 months after SARS-CoV-2 infection, 46.3% of participants had a peak performance and 33.3% a peak oxygen uptake of80% of the predicted values, respectively. Further impairments were observed in diffusion capacity and ventilatory efficiency. Functional limitations were particularly pronounced in patients after invasive mechanical ventilation and extracorporeal membrane oxygenation treatment. Ventilatory capacity was reduced80% of predicted values in 55.6% of participants, independent from initial clinical severity. Patient reported dyspnea and respiratory quality of life after COVID-19 correlated with CPET performance and parameters of gas exchange. Risk factors for reduced CPET performance 12 months after COVID-19 were prior intensive care treatment (OR 5.58, p = 0.004), SGRQ outcome25 points (OR 3.48, p = 0.03) and reduced DFunctional limitations causing chronic morbidity in COVID-19 survivors persist over 12 months after SARS-CoV-2 infection. These limitations were particularly seen in parameters of overall performance and gas exchange resulting from muscular deconditioning and lung parenchymal changes. Patient reported reduced respiratory quality of life was a risk factor for adverse CPET performance.

BackgroundProspective and longitudinal data on pulmonary injury over one year after acute coronavirus disease 2019 (COVID-19) are sparse.Research questionWith this study, we aim to investigate pulmonary outcome following SARS-CoV-2 infection including pulmonary function, computed chest tomography, respiratory symptoms and quality of life over 12 months.Study design and Methods180 patients after acute COVID-19 were enrolled into a single-centre, prospective observational study and examined 6 weeks, 3, 6 and 12 months after onset of COVID-19 symptoms. Chest CT-scans, pulmonary function and symptoms assessed by St. Georges Respiratory Questionnaire were used to evaluate objective and subjective respiratory limitations. Patients were stratified according to acute COVID-19 disease severity.ResultsOf 180 patients enrolled, 42/180 were not hospitalized during acute SARS-CoV-2 infection, 29/180 were hospitalized without need for oxygen, 43/180 with need for low-flow and 24/180 with high-flow oxygen, 26/180 required invasive mechanical ventilation and 16/180 were treated with ECMO. After acute COVID-19, pulmonary restriction and reduced carbon monoxide diffusion capacity was associated with disease severity after the acute phase and improved over 12 months except for those requiring ECMO treatment. Patients with milder disease showed a predominant reduction of ventilated area instead of simple restriction. The CT score of lung involvement in the acute phase increased significantly with COVID-19 severity and was associated with restriction and reduction in diffusion capacity in follow-up. Respiratory symptoms improved for patients in higher severity groups during follow-up, but not for patients with mild initially disease.InterpretationSeverity of respiratory failure during COVID-19 correlates with the degree of pulmonary function impairment and respiratory quality of life in the year after acute infection. Patients with mild vs. severe disease show different patterns of lung involvement and symptom resolution.Clinical Trial RegistrationThe study is registered at the German registry for clinical studies (DRKS00021688)

Purpose Adequate patient allocation is pivotal for optimal resource management in strained healthcare systems, and requires detailed knowledge of clinical and virological disease trajectories. The purpose of this work was to identify risk factors associated with need for invasive mechanical ventilation (IMV), to analyse viral kinetics in patients with and without IMV and to provide a comprehensive description of clinical course. Methods A cohort of 168 hospitalised adult COVID-19 patients enrolled in a prospective observational study at a large European tertiary care centre was analysed. Results Forty-four per cent (71/161) of patients required invasive mechanical ventilation (IMV). Shorter duration of symptoms before admission (aOR 1.22 per day less, 95% CI 1.10–1.37, p p p Conclusions Our results indicate a short duration of symptoms before admission as a risk factor for severe disease that merits further investigation and different viral load kinetics in severely affected patients. Median duration of hospitalisation of IMV patients was longer than described for acute respiratory distress syndrome unrelated to COVID-19.

COVID-19 is highly variable in its clinical presentation, ranging from asymptomatic infection to severe organ damage and death. We characterized the time-dependent progression of the disease in 139 COVID-19 inpatients by measuring 86 accredited diagnostic parameters, such as blood cell counts and enzyme activities, as well as untargeted plasma proteomes at 687 sampling points. We report an initial spike in a systemic inflammatory response, which is gradually alleviated and followed by a protein signature indicative of tissue repair, metabolic reconstitution, and immunomodulation. We identify prognostic marker signatures for devising risk-adapted treatment strategies and use machine learning to classify therapeutic needs. We show that the machine learning models based on the proteome are transferable to an independent cohort. Our study presents a map linking routinely used clinical diagnostic parameters to plasma proteomes and their dynamics in an infectious disease., Graphical abstract, Demichev, Tober-Lau et al., present a time-resolved molecular map of the COVID-19, measuring plasma proteomes of patients with COVID-19 along with an extensive panel of clinical diagnostic parameters at 687-time points. They describe the specificity and dynamics, as well as the predictive and prognostic power of the molecular signatures in COVID-19.

Limbal stem cell (LSC) deficiency is a frequent and severe complication after chemical injury to the eye. Previous studies have assumed that this is mediated directly by the caustic agent. Here we show that LSC damage occurs through immune cell mediators, even without direct injury to LSCs. In particular, pH elevation in the anterior chamber (AC) causes acute uveal stress, release of inflammatory cytokines at the basal limbal tissue, and subsequent LSC loss. Peripheral CCR2+ CX3CR1- monocytes are the key mediators of LSC damage, through the upregulation of tumor necrosis factor alpha (TNF-α) at the limbus. In contrast to peripherally-derived monocytes, CX3CR1+ CCR2- tissue-resident macrophages have a protective role and their depletion prior to injury exacerbates LSC loss and increases LSC vulnerability to TNF-α-mediated apoptosis independently of CCR2+ cell infiltration into the tissue. Consistently, repopulation of the tissue by new resident macrophages not only restores the protective M2-like phenotype of macrophages but also suppresses LSC loss after exposure to inflammatory signals. These findings may have clinical implication in patient with chemical burns.SignificanceLimbal stem cell (LSC) loss after chemical burn is a significant clinical problem that has long been thought to be the result of direct chemical injury. This study demonstrates the existence of competing inflammatory mechanism responsible for LSC death, mediated by infiltrating peripheral monocytes that antagonize and overcome the protective role of tissue-resident macrophages. Thus, enhancing the role of tissue-resident macrophages while suppressing that of infiltrating monocytes could be a novel therapeutic strategy for LSC survival.

Global healthcare systems are challenged by the COVID-19 pandemic. There is a need to optimize allocation of treatment and resources in intensive care, as clinically established risk assessments such as SOFA and APACHE II scores show only limited performance for predicting the survival of severely ill COVID-19 patients. Comprehensively capturing the host physiology, we speculated that proteomics in combination with new data-driven analysis strategies could produce a new generation of prognostic discriminators. We studied two independent cohorts of patients with severe COVID-19 who required intensive care and invasive mechanical ventilation. SOFA score, Charlson comorbidity index and APACHE II score were poor predictors of survival. Plasma proteomics instead identified 14 proteins that showed concentration trajectories different between survivors and non-survivors. A proteomic predictor trained on single samples obtained at the first time point at maximum treatment level (i.e. WHO grade 7) and weeks before the outcome, achieved accurate classification of survivors in an exploratory (AUROC 0.81) as well as in the independent validation cohort (AUROC of 1.0). The majority of proteins with high relevance in the prediction model belong to the coagulation system and complement cascade. Our study demonstrates that predictors derived from plasma protein levels have the potential to substantially outperform current prognostic markers in intensive care.Trial registrationGerman Clinical Trials Register DRKS00021688

BackgroundAdequate patient allocation is pivotal for optimal resource management in strained healthcare systems, and requires detailed knowledge of clinical and virological disease trajectories.MethodsA cohort of 168 hospitalized adult COVID-19 patients enrolled in a prospective observational study at a large European tertiary care center was analyzed.ResultsForty-four percent (71/161) of patients required invasive mechanical ventilation (IMV). Shorter duration of symptoms before admission (aOR 1.22 per day less, 95%CI 1.10-1.37, pConclusionOur results indicate a short duration of symptoms before admission as a risk factor for severe disease and different viral load kinetics in severely affected patients.

COVID-19 is highly variable in its clinical presentation, ranging from asymptomatic infection to severe organ damage and death. There is an urgent need for predictive markers that can guide clinical decision-making, inform about the effect of experimental therapies, and point to novel therapeutic targets. Here, we characterize the time-dependent progression of COVID-19 through different stages of the disease, by measuring 86 accredited diagnostic parameters and plasma proteomes at 687 sampling points, in a cohort of 139 patients during hospitalization. We report that the time-resolved patient molecular phenotypes reflect an initial spike in the systemic inflammatory response, which is gradually alleviated and followed by a protein signature indicative of tissue repair, metabolic reconstitution and immunomodulation. Further, we show that the early host response is predictive for the disease trajectory and gives rise to proteomic and diagnostic marker signatures that classify the need for supplemental oxygen therapy and mechanical ventilation, and that predict the time to recovery of mildly ill patients. In severely ill patients, the molecular phenotype of the early host response predicts survival, in two independent cohorts and weeks before outcome. We also identify age-specific molecular response to COVID-19, which involves increased inflammation and lipoprotein dysregulation in older patients. Our study provides a deep and time resolved molecular characterization of COVID-19 disease progression, and reports biomarkers for risk-adapted treatment strategies and molecular disease monitoring. Our study demonstrates accurate prognosis of COVID-19 outcome from proteomic signatures recorded weeks earlier.

Purpose Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has spread worldwide causing a global health emergency. Pa-COVID-19 aims to provide comprehensive data on clinical course, pathophysiology, immunology and outcome of COVID-19, to identify prognostic biomarkers, clinical scores, and therapeutic targets for improved clinical management and preventive interventions. Methods Pa-COVID-19 is a prospective observational cohort study of patients with confirmed SARS-CoV-2 infection treated at Charité - Universitätsmedizin Berlin. We collect data on epidemiology, demography, medical history, symptoms, clinical course, and pathogen testing and treatment. Systematic, serial blood sampling will allow deep molecular and immunological phenotyping, transcriptomic profiling, and comprehensive biobanking. Longitudinal data and sample collection during hospitalization will be supplemented by long-term follow-up. Results Outcome measures include the WHO clinical ordinal scale on day 15 and clinical, functional, and health-related quality-of-life assessments at discharge and during follow-up. We developed a scalable dataset to (i) suit national standards of care, (ii) facilitate comprehensive data collection in medical care facilities with varying resources, and (iii) allow for rapid implementation of interventional trials based on the standardized study design and data collection. We propose this scalable protocol as blueprint for harmonized data collection and deep phenotyping in COVID-19 in Germany. Conclusion We established a basic platform for harmonized, scalable data collection, pathophysiological analysis, and deep phenotyping of COVID-19, which enables rapid generation of evidence for improved medical care and identification of candidate therapeutic and preventive strategies. The electronic database accredited for interventional trials allows fast trial implementation for candidate therapeutic agents. Trial registration Registered at the German registry for clinical studies (DRKS00021688)

PurposeSevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has spread worldwide causing a global health emergency. Pa-COVID-19 aims to provide comprehensive data on clinical course, pathophysiology, immunology and outcome of COVID-19, in order to identify prognostic biomarkers, clinical scores, and therapeutic targets for improved clinical management and preventive interventions.MethodsPa-COVID-19 is a prospective observational cohort study of patients with confirmed SARS-CoV-2 infection treated at Charité - Universitätsmedizin Berlin. We collect data on epidemiology, demography, medical history, symptoms, clinical course, pathogen testing and treatment. Systematic, serial blood sampling will allow deep molecular and immunological phenotyping, transcriptomic profiling, and comprehensive biobanking. Longitudinal data and sample collection during hospitalization will be supplemented by long-term follow-up.ResultsOutcome measures include the WHO clinical ordinal scale on day 15 and clinical, functional and health-related quality of life assessments at discharge and during follow-up. We developed a scalable dataset to (i) suit national standards of care (ii) facilitate comprehensive data collection in medical care facilities with varying resources and (iii) allow for rapid implementation of interventional trials based on the standardized study design and data collection. We propose this scalable protocol as blueprint for harmonized data collection and deep phenotyping in COVID-19 in Germany.ConclusionWe established a basic platform for harmonized, scalable data collection, pathophysiological analysis, and deep phenotyping of COVID-19, which enables rapid generation of evidence for improved medical care and identification of candidate therapeutic and preventive strategies. The electronic database accredited for interventional trials allows fast trial implementation for candidate therapeutic agents.

Objectives Dexamethasone has become the standard of care for severe coronavirus disease 2019 (COVID-19), but its virological impact is poorly understood. The objectives of this work were to characterize the kinetics of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) concentration in the upper respiratory tract (URT) and the antibody response in patients with (D+) and without (D–) dexamethasone treatment. Methods Data and biosamples from hospitalized patients with severe COVID-19, enrolled between 4th March and 11th December 2020 in a prospective observational study, were analysed. SARS-CoV-2 virus concentration in serial URT samples was measured using RT-PCR. SARS-CoV-2-specific immunoglobulins A and G (IgA and IgG) were measured in serum samples using S1-ELISA. Results We compared 101 immunocompetent patients who received dexamethasone (according to the inclusion criteria and dosage determined in the RECOVERY trial) to 93 immunocompetent patients with comparable disease severity from the first months of the pandemic, who had not been treated with dexamethasone or other glucocorticoids. We found no inter-group differences in virus concentration kinetics, duration of presence of viral loads >106 viral copies/mL (D+ median 17 days (IQR 13–24), D– 19 days (IQR 13–29)), or time from symptom onset until seroconversion (IgA: D+ median 11.5 days (IQR 11–12), D– 14 days (IQR 11.5–15.75); IgG: D+ 13 days (IQR 12–14.5), D– 12 days (IQR 11–15)). Conclusion Dexamethasone does not appear to lead to a change in virus clearance or a delay in antibody response in immunocompetent patients hospitalized with severe COVID-19.

Background: In face of the global spread of Corona Virus Disease (COVID)-19, best practice for mechanical ventilation in COVID-19 associated Acute Respiratory

COVID-19-induced ‘acute respiratory distress syndrome’ (ARDS) is associated with prolonged respiratory failure and high mortality, but the mechanistic basis of lung injury remains incompletely understood. Here, we analyzed pulmonary immune responses and lung pathology in two cohorts of patients with COVID-19 ARDS using functional single cell genomics, immunohistology and electron microscopy. We describe an accumulation of CD163-expressing monocyte-derived macrophages that acquired a profibrotic transcriptional phenotype during COVID-19 ARDS. Gene set enrichment and computational data integration revealed a significant similarity between COVID-19-associated macrophages and profibrotic macrophage populations identified in idiopathic pulmonary fibrosis. COVID-19 ARDS was associated with clinical, radiographic, histopathological, and ultrastructural hallmarks of pulmonary fibrosis. Exposure of human monocytes to SARS-CoV-2, but not Influenza A virus or viral RNA analogs, was sufficient to induce a similar profibrotic phenotype in vitro. In conclusion, we demonstrate that SARS-CoV-2 triggers profibrotic macrophage responses and pronounced fibroproliferative ARDS., SARS-CoV-2 infection, but not influenza A, triggers immunological and pathological changes in the lung that are hallmarks of pulmonary fibrosis. A subset of CD163+ macrophages are found to drive this fibroproliferative acute respiratory distress.