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Despite the ubiquity of prerequisites in undergraduate science, technology, engineering, and mathematics curricula, there has been minimal effort to assess their value in a data-driven manner. Using both quantitative and qualitative data, we examined the impact of prerequisites in the context of a microbiology lecture and lab course pairing. Through interviews and an online survey, students highlighted a number of positive attributes of prerequisites, including their role in knowledge acquisition, along with negative impacts, such as perhaps needlessly increasing time to degree and adding to the cost of education. We also identified a number of reasons why individuals do or do not enroll in prerequisite courses, many of which were not related to student learning. In our particular curriculum, students did not believe the microbiology lecture course impacted success in the lab, which agrees with our analysis of lab course performance using a previously established "familiarity" scale. These conclusions highlight the importance of soliciting and analyzing student feedback, and triangulating these data with quantitative performance metrics to assess the state of science, technology, engineering, and mathematics curricula.

Despite the ubiquity of prerequisites in undergraduate science, technology, engineering, and mathematics curricula, there has been minimal effort to assess their value in a data-driven manner. Using both quantitative and qualitative data, we examined the impact of prerequisites in the context of a microbiology lecture and lab course pairing. Through interviews and an online survey, students highlighted a number of positive attributes of prerequisites, including their role in knowledge acquisition, along with negative impacts, such as perhaps needlessly increasing time to degree and adding to the cost of education. We also identified a number of reasons why individuals do or do not enroll in prerequisite courses, many of which were not related to student learning. In our particular curriculum, students did not believe the microbiology lecture course impacted success in the lab, which agrees with our analysis of lab course performance using a previously established "familiarity" scale. These conclusions highlight the importance of soliciting and analyzing student feedback, and triangulating these data with quantitative performance metrics to assess the state of science, technology, engineering, and mathematics curricula.

The hnRNP C heterotetramer [(C1(3))C2] binds RNA polymerase II transcripts in the nucleus, along with other proteins of the core hnRNP complex, and plays an important role in mRNA biogenesis and transport. Infection of HeLa cells with poliovirus causes hnRNP C to re-localize from the nucleus, where it is normally retained during interphase, to the cytoplasm. We have proposed that in the cytoplasm, the protein isoforms of hnRNP C participate in the recognition of viral specific RNAs by the poliovirus replication proteins and/or in the assembly of membrane-bound RNA replication complexes. In SK-OV-3 cells, which express reduced levels of hnRNP C compared to HeLa cells or 293 cells, the kinetics of poliovirus replication are delayed. hnRNP C is also re-localized from the nucleus to the cytoplasm in SK-OV-3 cells infected with poliovirus. Increased expression of hnRNP C in SK-OV-3 cells by transient transfection increases the rate of virus production and overall yield over that seen in mock-transfected cells. We propose that hnRNP C interacts with poliovirus RNA and replication proteins to increase the efficiency of viral genomic RNA synthesis.

We had previously demonstrated that a cellular protein specifically interacts with the 3' end of poliovirus negative-strand RNA. We now report the identity of this protein as heterogeneous nuclear ribonucleoprotein (hnRNP) C1/C2. Formation of an RNP complex with poliovirus RNA was severely impaired by substitution of a lysine, highly conserved among vertebrates, with glutamine in the RNA recognition motif (RRM) of recombinant hnRNP C1, suggesting that the binding is mediated by the RRM in the protein. We have also shown that in a glutathione S-transferase (GST) pull-down assay, GST/hnRNP C1 binds to poliovirus polypeptide 3CD, a precursor to the viral RNA-dependent RNA polymerase, 3D(pol), as well as to P2 and P3, precursors to the nonstructural proteins. Truncation of the auxiliary domain in hnRNP C1 (C1DeltaC) diminished these protein-protein interactions. When GST/hnRNP C1DeltaC was added to in vitro replication reactions, a significant reduction in RNA synthesis was observed in contrast to reactions supplemented with wild-type fusion protein. Indirect functional depletion of hnRNP C from in vitro replication reactions, using poliovirus negative-strand cloverleaf RNA, led to a decrease in RNA synthesis. The addition of GST/hnRNP C1 to the reactions rescued RNA synthesis to near mock-depleted levels. Furthermore, we demonstrated that poliovirus positive-strand and negative-strand RNA present in cytoplasmic extracts prepared from infected HeLa cells coimmunoprecipitated with hnRNP C1/C2. Our findings suggest that hnRNP C1 has a role in positive-strand RNA synthesis in poliovirus-infected cells, possibly at the level of initiation.

We have previously described the RNA replication properties of poliovirus transcripts harboring chimeric RNA polymerase sequences representing suballelic exchanges between poliovirus type 1 (PV1) and coxsackievirus B3 (CVB3) utilizing an in vitro translation and RNA replication assay (C. Cornell, R. Perera, J. E. Brunner, and B. L. Semler, J. Virol. 78:4397-4407, 2004). We showed that three of the seven chimeras were capable of RNA replication in vitro, although replication levels were greatly reduced compared to that of wild-type transcripts. Interestingly, one of the replication-competent transcripts displayed a strand-specific RNA synthesis defect suggesting (i) a differential replication complex assembly mechanism involving 3D and/or precursor molecules (i.e., 3CD) required for negative- versus positive-strand RNA synthesis or (ii) effect(s) on the ability of the 3D polymerase to form higher-ordered structures required for positive-strand RNA synthesis. In this study, we have attempted to rescue defective RNA replication in vitro by cotranslating nonstructural proteins from a transcript encoding a large precursor polyprotein (P3) to complement 3D polymerase and/or precursor polypeptide functions altered in each of the chimeric constructs. Utilization of a wild-type P3 construct revealed that all transcripts containing chimeric PV1/CVB3 polymerase sequences can be complemented in trans for both negative- and positive-strand RNA synthesis. Furthermore, data from experiments utilizing genetically modified forms of the P3 polyprotein, containing mutations within 3C or 3D sequences, strongly suggest the existence of different protein-protein and protein-RNA interactions required for positive- versus negative-strand RNA synthesis. These results, combined with data from in vitro RNA elongation assays, indicate that the delivery of active 3D RNA polymerase to replication complexes requires a series of macromolecular interactions that rely on the presence of specific 3D amino acid sequences.

The viral RNA-dependent RNA polymerase (3D(pol)) is highly conserved between the closely related enteroviruses poliovirus type 1 (PV1) and coxsackievirus B3 (CVB3). In this study, we generated PV1/CVB3 chimeric polymerase sequences in the context of full-length poliovirus transcripts to determine the role of different subdomains within the RNA-dependent RNA polymerase of PV1 that are required for functions critical for RNA replication in vitro and in cell culture. The substitution of CVB3 sequences in the carboxy-terminal portion (thumb subdomain) of the polymerase resulted in transcripts incapable of RNA replication. In contrast, three of the seven chimeras were capable of synthesizing RNA, albeit to reduced levels compared to that of wild-type PV1 RNA. Interestingly, one of the replication-competent chimeras (CPP) displayed an inability to generate positive strands, indicating the presence of amino-terminal sequences within the 3D polymerase and/or the 3D domain of the 3CD precursor polypeptide that are necessary for the assembly of strand-specific RNA synthesis complexes. In some constructs, the partial reestablishment of PV1 amino acid sequences in this region was capable of rescuing RNA replication in vitro and in cell culture.

Abstract Background The Gram negative anaerobic bacterium Porphyromonas gingivalis has long been recognized as a causative agent of periodontitis. Periodontitis is a chronic infectious disease of the tooth supporting tissues eventually leading to tooth-loss. Capsular polysaccharide (CPS) of P. gingivalis has been shown to be an important virulence determinant. Seven capsular serotypes have been described. Here, we used micro-array based comparative genomic hybridization analysis (CGH) to analyze a representative of each of the capsular serotypes and a non-encapsulated strain against the highly virulent and sequenced W83 strain. We defined absent calls using Arabidopsis thaliana negative control probes, with the aim to distinguish between aberrations due to mutations and gene gain/loss. Results Our analyses allowed us to call aberrant genes, absent genes and divergent regions in each of the test strains. A conserved core P. gingivalis genome was described, which consists of 80% of the analyzed genes from the sequenced W83 strain. The percentage of aberrant genes between the test strains and control strain W83 was 8.2% to 13.7%. Among the aberrant genes many CPS biosynthesis genes were found. Most other virulence related genes could be found in the conserved core genome. Comparing highly virulent strains with less virulent strains indicates that hmuS, a putative CobN/Mg chelatase involved in heme uptake, may be a more relevant virulence determinant than previously expected. Furthermore, the description of the 39 W83-specific genes could give more insight in why this strain is more virulent than others. Conclusion Analyses of the genetic content of the P. gingivalis capsular serotypes allowed the description of a P. gingivalis core genome. The high resolution data from three types of analysis of triplicate hybridization experiments may explain the higher divergence between P. gingivalis strains than previously recognized.

Abstract Background Periodontitis is a bacterial infection of the periodontal tissues. The Gram-negative anaerobic bacterium Porphyromonas gingivalis is considered a major causative agent. One of the virulence factors of P. gingivalis is capsular polysaccharide (CPS). Non-encapsulated strains have been shown to be less virulent in mouse models than encapsulated strains. Results To examine the role of the CPS in host-pathogen interactions we constructed an insertional isogenic P. gingivalis knockout in the epimerase-coding gene epsC that is located at the end of the CPS biosynthesis locus. This mutant was subsequently shown to be non-encapsulated. K1 capsule biosynthesis could be restored by in trans expression of an intact epsC gene. We used the epsC mutant, the W83 wild type strain and the complemented mutant to challenge human gingival fibroblasts to examine the immune response by quantification of IL-1β, IL-6 and IL-8 transcription levels. For each of the cytokines significantly higher expression levels were found when fibroblasts were challenged with the epsC mutant compared to those challenged with the W83 wild type, ranging from two times higher for IL-1β to five times higher for IL-8. Conclusions These experiments provide the first evidence that P. gingivalis CPS acts as an interface between the pathogen and the host that may reduce the host's pro-inflammatory immune response. The higher virulence of encapsulated strains may be caused by this phenomenon which enables the bacteria to evade the immune system.

Deep-learning-based classification of pollen grains has been a major driver towards automatic monitoring of airborne pollen. Yet, despite an abundance of available datasets, little effort has been spent to investigate which aspects pose the biggest challenges to the (often black-box- resembling) pollen classification approaches. To shed some light on this issue, we conducted a sample-level difficulty analysis based on the likelihood for one of the largest automatically-generated datasets of pollen grains on microscopy images and investigated the reason for which certain airborne samples and specific pollen taxa pose particular problems to deep learning algorithms. It is here concluded that the main challenges lie in A) the (partly) co-occurring of multiple pollen grains in a single image, B) the occlusion of specific markers through the 2D capturing of microscopy images, and C) for some taxa, a general lack of salient, unique features. Our code is publicly available under https://github.com/millinma/SDPollen., Competing Interests: The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Corresponding author, Athanasios Damialis, serves as Section Editor in Heliyon Agriculture. If there are other authors, they declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2025 Published by Elsevier Ltd.)

Objective: The explicit prohibition of discontinuing intensive care unit (ICU) treatment that has already begun by the newly established German Triage Act in favor of new patients with better prognoses (tertiary triage) under crisis conditions may prevent saving as many patients as possible and therefore may violate the international well-accepted premise of undertaking the "best for the most" patients. During the COVID-19 pandemic, authorities set up lockdown measures and infection-prevention strategies to avoid an overburdened health-care system. In cases of situational overload of ICU resources, when transporting options are exhausted, the question of a tertiary triage of patients arises., Methods: We provide data-driven analyses of score- and non-score-based tertiary triage policies using simulation and real-world electronic health record data in a COVID-19 setting. Ten different triage policies, for example, based on the Simplified Acute Physiology Score (SAPS II), are compared based on the resulting mortality in the ICU and inferential statistics., Results: Our study shows that score-based tertiary triage policies outperform non-score-based tertiary triage policies including compliance with the German Triage Act. Based on our simulation model, a SAPS II score-based tertiary triage policy reduces mortality in the ICU by up to 18 percentage points. The longer the queue of critical care patients waiting for ICU treatment and the larger the maximum number of patients subject to tertiary triage, the greater the effect on the reduction of mortality in the ICU., Conclusion: A SAPS II score-based tertiary triage policy was superior in our simulation model. Random allocation or "first come, first served" policies yield the lowest survival rates, as will adherence to the new German Triage Act. An interdisciplinary discussion including an ethical and legal perspective is important for the social interpretation of our data-driven results., (© 2024. The Author(s).)

The COVID-19 pandemic has given rise to a broad range of research from fields alongside and beyond the core concerns of infectiology, epidemiology, and immunology. One significant subset of this work centers on machine learning-based approaches to supporting medical decision-making around COVID-19 diagnosis. To date, various challenges, including IT issues, have meant that, notwithstanding this strand of research on digital diagnosis of COVID-19, the actual use of these methods in medical facilities remains incipient at best, despite their potential to relieve pressure on scarce medical resources, prevent instances of infection, and help manage the difficulties and unpredictabilities surrounding the emergence of new mutations. The reasons behind this research-application gap are manifold and may imply an interdisciplinary dimension. We argue that the discipline of AI ethics can provide a framework for interdisciplinary discussion and create a roadmap for the application of digital COVID-19 diagnosis, taking into account all disciplinary stakeholders involved. This article proposes such an ethical framework for the practical use of digital COVID-19 diagnosis, considering legal, medical, operational managerial, and technological aspects of the issue in accordance with our diverse research backgrounds and noting the potential of the approach we set out here to guide future research., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Authors. Published by Elsevier B.V. All rights reserved.)

The significant increase in patients during the COVID-19 pandemic presented the healthcare system with a variety of challenges. The intensive care unit is one of the areas particularly affected in this context. Only through extensive infection control measures as well as an enormous logistical effort was it possible to treat all patients requiring intensive care in Germany even during peak phases of the pandemic, and to prevent triage even in regions with high patient pressure and simultaneously low capacities. Regarding pandemic preparedness, the German Parliament passed a law on triage that explicitly prohibits ex post (tertiary) triage. In ex post triage, patients who are already being treated are included in the triage decision and treatment capacities are allocated according to the individual likelihood of success. Legal, ethical, and social considerations for triage in pandemics can be found in the literature, but there is no quantitative assessment with respect to different patient groups in the intensive care unit. This study addressed this gap and applied a simulation-based evaluation of ex ante (primary) and ex post triage policies in consideration of survival probabilities, impairments, and pre-existing conditions. The results show that application of ex post triage based on survival probabilities leads to a reduction in mortality in the intensive care unit for all patient groups. In the scenario close to a real-world situation, considering different impaired and prediseased patient groups, a reduction in mortality of approximately 15% was already achieved by applying ex post triage on the first day. This mortality-reducing effect of ex post triage is further enhanced as the number of patients requiring intensive care increases., (© 2023. The Author(s).)

Airborne pollen monitoring has been conducted for more than a century now, as knowledge of the quantity and periodicity of airborne pollen has diverse use cases, like reconstructing historic climates and tracking current climate change, forensic applications, and up to warning those affected by pollen-induced respiratory allergies. Hence, related work on automation of pollen classification already exists. In contrast, detection of pollen is still conducted manually, and it is the gold standard for accuracy. So, here we used a new-generation, automated, near-real-time pollen monitoring sampler, the BAA500, and we used data consisting of both raw and synthesised microscope images. Apart from the automatically generated, commercially-labelled data of all pollen taxa, we additionally used manual corrections to the pollen taxa, as well as a manually created test set of bounding boxes and pollen taxa, so as to more accurately evaluate the real-life performance. For the pollen detection, we employed two-stage deep neural network object detectors. We explored a semi-supervised training scheme to remedy the partial labelling. Using a teacher-student approach, the model can add pseudo-labels to complete the labelling during training. To evaluate the performance of our deep learning algorithms and to compare them to the commercial algorithm of the BAA500, we created a manual test set, in which an expert aerobiologist corrected automatically annotated labels. For the novel manual test set, both the supervised and semi-supervised approaches clearly outperform the commercial algorithm with an F1 score of up to 76.9 % compared to 61.3 %. On an automatically created and partially labelled test dataset, we obtain a maximum mAP of 92.7 %. Additional experiments on raw microscope images show comparable performance for the best models, which potentially justifies reducing the complexity of the image generation process. Our results bring automatic pollen monitoring a step forward, as they close the gap in pollen detection performance between manual and automated procedure., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 Elsevier B.V. All rights reserved.)

The Covid-19 pandemic has pushed many hospitals to their capacity limits. Therefore, a triage of patients has been discussed controversially primarily through an ethical perspective. The term triage contains many aspects such as urgency of treatment, severity of the disease and pre-existing conditions, access to critical care, or the classification of patients regarding subsequent clinical pathways starting from the emergency department. The determination of the pathways is important not only for patient care, but also for capacity planning in hospitals. We examine the performance of a human-made triage algorithm for clinical pathways which is considered a guideline for emergency departments in Germany based on a large multicenter dataset with over 4,000 European Covid-19 patients from the LEOSS registry. We find an accuracy of 28 percent and approximately 15 percent sensitivity for the ward class. The results serve as a benchmark for our extensions including an additional category of palliative care as a new label, analytics, AI, XAI, and interactive techniques. We find significant potential of analytics and AI in Covid-19 triage regarding accuracy, sensitivity, and other performance metrics whilst our interactive human-AI algorithm shows superior performance with approximately 73 percent accuracy and up to 76 percent sensitivity. The results are independent of the data preparation process regarding the imputation of missing values or grouping of comorbidities. In addition, we find that the consideration of an additional label palliative care does not improve the results., (© 2023. The Author(s).)

The significant increase in patients during the COVID-19 pandemic presented the healthcare system with a variety of challenges. The intensive care unit is one of the areas particularly affected in this context. Only through extensive infection control measures as well as an enormous logistical effort was it possible to treat all patients requiring intensive care in Germany even during peak phases of the pandemic, and to prevent triage even in regions with high patient pressure and simultaneously low capacities. Regarding pandemic preparedness, the German Parliament passed a law on triage that explicitly prohibits ex post (tertiary) triage. In ex post triage, patients who are already being treated are included in the triage decision and treatment capacities are allocated according to the individual likelihood of success. Legal, ethical, and social considerations for triage in pandemics can be found in the literature, but there is no quantitative assessment with respect to different patient groups in the intensive care unit. This study addressed this gap and applied a simulation-based evaluation of ex ante (primary) and ex post triage policies in consideration of survival probabilities, impairments, and pre-existing conditions. The results show that application of ex post triage based on survival probabilities leads to a reduction in mortality in the intensive care unit for all patient groups. In the scenario close to a real-world situation, considering different impaired and prediseased patient groups, a reduction in mortality of approximately 15% was already achieved by applying ex post triage on the first day. This mortality-reducing effect of ex post triage is further enhanced as the number of patients requiring intensive care increases., (© 2023. The Author(s).)

With the coming into force of § 5c of the Infection Protection Act (IfSG), the so-called Triage Act, on 14 December 2022, a protracted discussion has come to a provisional conclusion, the result of which physicians and social associations but also lawyers and ethicists are equally dissatisfied. The explicit exclusion of the discontinuation of treatment that has already begun in favor of new patients with better chances of success (so-called tertiary or ex-post triage) prevents allocation decisions with the aim of enabling as many patients as possible to beneficially participate in medical care under crisis conditions. The result of the new regulation is de facto a first come first served allocation, which is associated with the highest mortality even among individuals with limitations or disabilities and was rejected by a large margin as unfair in a population survey. Mandating allocation decisions based on the likelihood of success but which are not permitted to be consistently implemented and prohibiting, for example the use of age and frailty as prioritization criteria, although both factors most strongly determine the short-term probability of survival according to evident data, shows the contradictory and dogmatic nature of the regulation. The only remaining possibility is the consistent termination of treatment that is no longer indicated or desired by the patient, regardless of the current resource situation; however, if a different decision is made in a crisis situation than in a situation without a lack of resources, this practice would not be justified and would be punishable. Accordingly, the highest efforts must be set on legally compliant documentation, especially in the stage of decompensated crisis care in a region. The goal of enabling as many patients as possible to beneficially participate in medical care under crisis conditions is in any case thwarted by the new German Triage Act., (© 2023. The Author(s), under exclusive licence to Springer Medizin Verlag GmbH, ein Teil von Springer Nature.)

Objectives: The aim is to quantitatively evaluate different infection prevention strategies in the context of hospital visitor management during pandemics and to provide a decision support system for strategic and operational decisions based on this evaluation., Methods: A simulation-based cost-effectiveness analysis is applied to the data of a university hospital in Southern Germany and published COVID-19 research. The performance of different hospital visitor management strategies is evaluated by several decision-theoretic methods with varying objective functions., Results: Appropriate visitor restrictions and infection prevention measures can reduce additional infections and costs caused by visitors of healthcare institutions by >90%. The risk of transmission of severe acute respiratory syndrome coronavirus 2 by visitors of terminal care (ie, palliative care) patients can be reduced almost to 0 if appropriate infection prevention measures are implemented. Antigen tests do not seem to be beneficial from both a cost and an effectiveness perspective., Conclusions: Hospital visitor management is crucial and effectively prevents infections while maintaining cost-effectiveness. For terminal care patients, visitor restrictions can be omitted if appropriate infection prevention measures are taken. Antigen testing plays a subordinate role, except in the case of a pure focus on additional infections caused by visitors of healthcare institutions. We provide decision support to authorities and hospital visitor managers to identify appropriate visitor restriction and infection prevention strategies for specific local conditions, incidence rates, and objectives., (Copyright © 2022. Published by Elsevier Inc.)

Performance modeling of hospitals using data envelopment analysis (DEA) has received steadily increasing attention in the literature. As part of the traditional DEA framework, hospitals are generally assumed to be functionally similar and therefore homogenous. Accordingly, any identified inefficiency is supposedly due to the inefficient use of inputs to produce outputs. However, the disparities in DEA efficiency scores may be a result of the inherent heterogeneity of hospitals. Additionally, traditional DEA models lack predictive capabilities despite having been frequently used as a benchmarking tool in the literature. To address these concerns, this study proposes a framework for analyzing hospital performance by combining two complementary modeling approaches. Specifically, we employ a self-organizing map artificial neural network (SOM-ANN) to conduct a cluster analysis and a multilayer perceptron ANN (MLP-ANN) to perform a heterogeneity analysis and a best practice analysis. The applicability of the integrated framework is empirically shown by an implementation to a large dataset containing more than 1,100 hospitals in Germany. The framework enables a decision-maker not only to predict the best performance but also to explore whether the differences in relative efficiency scores are ascribable to the heterogeneity of hospitals., (© 2022. The Author(s).)

When scheduling surgeries in the operating theater, not only the resources within the operating theater have to be considered but also those in downstream units, e.g., the intensive care unit and regular bed wards of each medical specialty. We present an extension to the master surgery schedule, where the capacity for surgeries on ICU patients is controlled by introducing downstream-dependent block types - one for both ICU and ward patients and one where surgeries on ICU patients must not be performed. The goal is to provide better control over post-surgery patient flows through the hospital while preserving each medical specialty's autonomy over its operational surgery scheduling. We propose a mixed-integer program to determine the allocation of the new block types within either a given or a new master surgery schedule to minimize the maximum workload in downstream units. Using a simulation model supported by seven years of data from the University Hospital Augsburg, we show that the maximum workload in the intensive care unit can be reduced by up to 11.22% with our approach while maintaining the existing master surgery schedule. We also show that our approach can achieve up to 79.85% of the maximum workload reduction in the intensive care unit that would result from a fully centralized approach. We analyze various hospital setting instances to show the generalizability of our results. Furthermore, we provide insights and data analysis from the implementation of a quota system at the University Hospital Augsburg., (© 2022. The Author(s).)

Airborne pollen monitoring has been an arduous task, making ecological applications and allergy management virtually disconnected from everyday practice. Over the last decade, intensive research has been conducted worldwide to automate this task and to obtain real-time measurements. The aim of this study was to evaluate such an automated biomonitoring system vs. the conventional 'gold-standard' Hirst-type technique, attempting to assess which may more accurately provide the genuine exposure to airborne pollen. Airborne pollen was monitored in Augsburg since 2015 with two different methods, a novel automatic Bio-Aerosol Analyser, and with the conventional 7-day recording Hirst-type volumetric trap, in two different sites. The reliability, performance, accuracy, and comparability of the BAA500 Pollen Monitor (PoMo) vs. the conventional device were investigated, by use of approximately 2.5 million particles sampled during the study period. The observations made by the automated PoMo showed an average accuracy of approximately 85%. However, it also exhibited reliability problems, with information gaps within the main pollen season of between 17 to 19 days. The PoMo automated algorithm had identification issues, mainly confusing the taxa of Populus , Salix and Tilia . Hirst-type measurements consistently exhibited lower pollen abundances (median of annual pollen integral: 2080), however, seasonal traits were more comparable, with the PoMo pollen season starting slightly later (median: 3 days), peaking later (median: 5 days) but also ending later (median: 14 days). Daily pollen concentrations reported by Hirst-type traps vs. PoMo were significantly, but not closely, correlated ( r = 0.53-0.55), even after manual classification. Automatic pollen monitoring has already shown signs of efficiency and accuracy, despite its young age; here it is suggested that automatic pollen monitoring systems may be more effective in capturing a larger proportion of the airborne pollen diversity. Even though reliability issues still exist, we expect that this new generation of automated bioaerosol monitoring will eventually change the aerobiological era, as known for almost 70 years now.

The intensive care unit (ICU) is one of the most crucial and expensive resources in a health care system. While high fixed costs usually lead to tight capacities, shortages have severe consequences. Thus, various challenging issues exist: When should an ICU admit or reject arriving patients in general? Should ICUs always be able to admit critical patients or rather focus on high utilization? On an operational level, both admission control of arriving patients and demand-driven early discharge of currently residing patients are decision variables and should be considered simultaneously. This paper discusses the trade-off between medical and monetary goals when managing intensive care units by modeling the problem as a Markov decision process. Intuitive, myopic rule mimicking decision-making in practice is applied as a benchmark. In a numerical study based on real-world data, we demonstrate that the medical results deteriorate dramatically when focusing on monetary goals only, and vice versa. Using our model, we illustrate the trade-off along an efficiency frontier that accounts for all combinations of medical and monetary goals. Coming from a solution that optimizes monetary costs, a significant reduction of expected mortality can be achieved at little additional monetary cost., (© 2021. The Author(s).)

Allergic diseases have been the epidemic of the century among chronic diseases. Particularly for pollen allergies, and in the context of climate change, as airborne pollen seasons have been shifting earlier and abundances have been becoming higher, pollen monitoring plays an important role in generating high-risk allergy alerts. However, this task requires labour-intensive and time-consuming manual classification via optical microscopy. Even new-generation, automatic, monitoring devices require manual pollen labelling to increase accuracy and to advance to genuinely operational devices. Deep Learning-based models have the potential to increase the accuracy of automated pollen monitoring systems. In the current research, transfer learning-based convolutional neural networks were employed to classify pollen grains from microscopic images. Given a high imbalance in the dataset, we incorporated class weighted loss, focal loss and weight vector normalisation for class balancing as well as data augmentation and weight penalties for regularisation. Airborne pollen has been routinely recorded by a Bio-Aerosol Analyzer (BAA500, Hund GmbH) located in Augsburg, Germany. Here we utilised a database referring to manually classified airborne pollen images of the whole pollen diversity throughout an annual pollen season. By using the cropped pollen images collected by this device, we achieved an unweighted average F1 score of 93.8% across 15 classes and an unweighted average F1 score of 75.9% across 31 classes. The majority of taxa (9 of 15), being also the most abundant and allergenic, showed a recall of at least 95%, reaching up to a remarkable 100% in pollen from Taxus and Urticaceae. The recent introduction of novel pollen monitoring devices worldwide has pointed to the necessity for real-time, automatic measurements of airborne pollen and fungal spores. Thus, we may improve everyday clinical practice and achieve the most efficient prophylaxis of allergic patients., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2021 Elsevier B.V. All rights reserved.)

Background: Since operating rooms are a major bottleneck resource and an important revenue driver in hospitals, it is important to use these resources efficiently. Studies estimate that between 60 and 70% of hospital admissions are due to surgeries. Furthermore, staffing cannot be changed daily to respond to changing demands. The resulting high complexity in operating room management necessitates perpetual process evaluation and the use of decision support tools. In this study, we evaluate several management policies and their consequences for the operating theater of the University Hospital Augsburg., Methods: Based on a data set with 12,946 surgeries, we evaluate management policies such as parallel induction of anesthesia with varying levels of staff support, the use of a dedicated emergency room, extending operating room hours reserved as buffer capacity, and different elective patient sequencing policies. We develop a detailed simulation model that serves to capture the process flow in the entire operating theater: scheduling surgeries from a dynamically managed waiting list, handling various types of schedule disruptions, rescheduling and prioritizing postponed and deferred surgeries, and reallocating operating room capacity. The system performance is measured by indicators such as patient waiting time, idle time, staff overtime, and the number of deferred surgeries., Results: We identify significant trade-offs between expected waiting times for different patient urgency categories when operating rooms are opened longer to serve as end-of-day buffers. The introduction of parallel induction of anesthesia allows for additional patients to be scheduled and operated on during regular hours. However, this comes with a higher number of expected deferrals, which can be partially mitigated by employing additional anesthesia teams. Changes to the sequencing of elective patients according to their expected surgery duration cause expectable outcomes for a multitude of performance indicators., Conclusions: Our simulation-based approach allows operating theater managers to test a multitude of potential changes in operating room management without disrupting the ongoing workflow. The close collaboration between management and researchers in the design of the simulation framework and the data analysis has yielded immediate benefits for the scheduling policies and data collection efforts at our practice partner.

Background: The quality of life of chronically ill individuals, such as hay fever sufferers, is significantly dependent on their health behavior. This survey aimed to explain the health-related behavior of allergic individuals using the protection motivation theory (PMT) and the transtheoretical model (TTM)., Methods: The influencing variables stated by PMT were operationalized based on data from semistructured pilot interviews and a pretest with 12 individuals from the target population. The final questionnaire inquired perceived seriousness and severity of hay fever, response efficacy, response costs, self-efficacy, and the use of various hay fever management measures in relation to the TTM stages. Multivariate logistic regression was performed to investigate the relationships between the PMT constructs and the examined health behavior., Results: A total of 569 allergic individuals completed the online questionnaire. Only 33.26% of allergic individuals were in the maintenance stage for treatment under medical supervision, and almost 60% preferred hay fever self-management. A total of 67.56% had a well-established habit of taking anti-allergic medication, but only 25.31% had undergone specific immunotherapy. The likelihood of seeking medical supervision was positively influenced by perceived severity (OR = 1.35, 95% CI: 1.02-1.81), perceived seriousness (OR = 2.12, 95% CI: 1.56-2.89), and self-efficacy (OR = 4.52, 95% CI: 3.11-6.65). The perceived severity of symptoms predicted the practice of hay fever self-management (OR = 1.60, 95% CI: 1.21-2.11), as well as anti-allergic medication intake (OR = 1.65, 95% CI: 1.16-2.35). The latter measure was also positively influenced by self-efficacy (OR = 1.52, 95% CI: 1.01-2.28) and hay fever self-management (OR = 4.76, 95% CI: 2.67-7.49). Undergoing specific immunotherapy was significantly predicted only by medical supervision (OR = 9.80, 95% CI: 8.16-13.80). Allergen avoidance was a strategy used by allergic individuals who preferred hay fever self-management (OR = 2.56, 95% CI: 1.87-3.52) and experienced notable symptom severity (OR = 2.12, 95% CI: 1.60-2.81)., Conclusion: Educational interventions that increase the awareness of health risks associated with inadequate hay fever management and measures to increase self-efficacy might be beneficial for the promotion of appropriate hay fever management among allergic individuals.

Background: Following the regional outbreak in China, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has spread all over the world, presenting the healthcare systems with huge challenges worldwide. In Germany the coronavirus diseases 2019 (COVID-19) pandemic has resulted in a slowly growing demand for health care with a sudden occurrence of regional hotspots. This leads to an unpredictable situation for many hospitals, leaving the question of how many bed resources are needed to cope with the surge of COVID-19 patients., Objective: In this study we created a simulation-based prognostic tool that provides the management of the University Hospital of Augsburg and the civil protection services with the necessary information to plan and guide the disaster response to the ongoing pandemic. Especially the number of beds needed on isolation wards and intensive care units (ICU) are the biggest concerns. The focus should lie not only on the confirmed cases as the patients with suspected COVID-19 are in need of the same resources., Material and Methods: For the input we used the latest information provided by governmental institutions about the spreading of the disease, with a special focus on the growth rate of the cumulative number of cases. Due to the dynamics of the current situation, these data can be highly variable. To minimize the influence of this variance, we designed distribution functions for the parameters growth rate, length of stay in hospital and the proportion of infected people who need to be hospitalized in our area of responsibility. Using this input, we started a Monte Carlo simulation with 10,000 runs to predict the range of the number of hospital beds needed within the coming days and compared it with the available resources., Results: Since 2 February 2020 a total of 306 patients were treated with suspected or confirmed COVID-19 at this university hospital. Of these 84 needed treatment on the ICU. With the help of several simulation-based forecasts, the required ICU and normal bed capacity at Augsburg University Hospital and the Augsburg ambulance service in the period from 28 March 2020 to 8 June 2020 could be predicted with a high degree of reliability. Simulations that were run before the impact of the restrictions in daily life showed that we would have run out of ICU bed capacity within approximately 1 month., Conclusion: Our simulation-based prognosis of the health care capacities needed helps the management of the hospital and the civil protection service to make reasonable decisions and adapt the disaster response to the realistic needs. At the same time the forecasts create the possibility to plan the strategic response days and weeks in advance. The tool presented in this study is, as far as we know, the only one accounting not only for confirmed COVID-19 cases but also for suspected COVID-19 patients. Additionally, the few input parameters used are easy to access and can be easily adapted to other healthcare systems.

Background: Pollen exposure induces local and systemic allergic immune responses in sensitized individuals, but nonsensitized individuals also are exposed to pollen. The kinetics of symptom expression under natural pollen exposure have never been systematically studied, especially in subjects without allergy., Objective: We monitored the humoral immune response under natural pollen exposure to potentially uncover nasal biomarkers for in-season symptom severity and identify protective factors., Methods: We compared humoral immune response kinetics in a panel study of subjects with seasonal allergic rhinitis (SAR) and subjects without allergy and tested for cross-sectional and interseasonal differences in levels of serum and nasal, total, and Betula verrucosa 1-specific immunoglobulin isotypes; immunoglobulin free light chains; cytokines; and chemokines. Nonsupervised principal component analysis was performed for all nasal immune variables, and single immune variables were correlated with in-season symptom severity by Spearman test., Results: Symptoms followed airborne pollen concentrations in subjects with SAR, with a time lag between 0 and 13 days depending on the pollen type. Of the 7 subjects with nonallergy, 4 also exhibited in-season symptoms whereas 3 did not. Cumulative symptoms in those without allergy were lower than in those with SAR but followed the pollen exposure with similar kinetics. Nasal eotaxin-2, CCL22/MDC, and monocyte chemoattactant protein-1 (MCP-1) levels were higher in subjects with SAR, whereas IL-8 levels were higher in subjects without allergy. Principal component analysis and Spearman correlations identified nasal levels of IL-8, IL-33, and Betula verrucosa 1-specific IgG 4 (sIgG 4 ) and Betula verrucosa 1-specific IgE (sIgE) antibodies as predictive for seasonal symptom severity., Conclusions: Nasal pollen-specific IgA and IgG isotypes are potentially protective within the humoral compartment. Nasal levels of IL-8, IL-33, sIgG 4 and sIgE could be predictive biomarkers for pollen-specific symptom expression, irrespective of atopy., (Copyright © 2020. Published by Elsevier Inc.)

The original version of this article unfortunately contained errors. The first column of Tables 5 and 6 in the Appendix section should contain the year of publication instead of the reference number in brackets. The reference citations were then placed in the second column.

This study analyzes the effect of economies of scale and scope on the optimal case mix of a hospital or hospital system. With respect to the ideal volume and patient composition, the goal is to evaluate (i) the impact of changes in the efficiency of resource use with increasing scale, and (ii) to determine the potential effects of spreading fixed costs over a greater number of patients. The problem is formulated as a non-linear mixed integer program. It turns out that this non-linear program is too difficult to be solved with standard software. As an alternative, an iterative procedure using piecewise linear approximations to derive lower and upper bounds is proposed and shown to converge to the optimum. The procedure is applied using a public database on German hospital costs and performance statistics. Results indicate that changes in the efficiency of resource use with increasing scale have a considerable impact if similar services can be consolidated, e.g., among different departments. However, if the scope for decision-making regarding the case mix of a hospital is limited, such changes may be negligible.

Physicians are a scarce resource in hospitals. In order to minimize physician attrition, schedulers incorporate individual physician preferences when creating the physicians' duty roster. The manual creation of a roster is very time-consuming and often produces suboptimal results. Many schedulers therefore use model-based software to assist in planning. The planning horizon for duty schedules is usually a single month. Many models optimize the plan for the current planning horizon, without taking into account data on preference fulfillment and work load distribution from previous months. It is therefore possible that, when looking at a longer time horizon, some physicians are disadvantaged in terms of preference fulfillment more often than their peers, simply because this generates better results for the individual months. This may be perceived as unfair by the disadvantaged physicians. In order to eliminate this imbalance, we introduce a satisfaction indicator for preference fulfillment in physician scheduling. This indicator is computed for each physician on each monthly plan and is then used to inform decisions regarding preference fulfillment on the current and future plans. As a result, a more equal distribution of preference fulfillment among physicians is achieved. We run a computational study with three different update strategies for our satisfaction indicator. Our study uses 24 months of data from a German university hospital and derives additional generated data from it. Results indicate that our satisfaction indicator, combined with the right update strategy, can achieve an equal distribution of satisfaction over all physicians within a peer group, as well as stable satisfaction levels for each individual physician over a longer time horizon. As our main contribution, we identify that our satisfaction indicator is more effective in creating equal distribution of long-term satisfaction the higher the rate of conflicting preferences is.

Pollen allergies are considered as a global epidemic nowadays, as they influence more than a quarter of the worldwide population, with this percentage expected to rapidly increase because of ongoing climate change. To date, alerts on high-risk allergenic pollen exposure have been provided only via forecasting models and conventional monitoring methods that are laborious. The aim of this study is to develop and evaluate our own pollen classification model based on deep neural networks. Airborne allergenic pollen have been monitored in Augsburg, Bavaria, Germany, since 2015, using a novel automatic Bio-Aerosol Analyzer (BAA 500, Hund GmbH). The automatic classification system is compared and evaluated against our own, newly developed algorithm. Our model achieves an unweighted average precision of 83.0 % and an unweighted average recall of 77.1 % across 15 classes of pollen taxa. Automatic, real-time information on concentrations of airborne allergenic pollen will significantly contribute to the implementation of timely, personalized management of allergies in the future. It is already clear that new methods and sophisticated models have to be developed so as to successfully switch to novel operational pollen monitoring techniques serving the above need.

The healthcare sector in general and hospitals in particular represent a main application area for Data Envelopment Analysis (DEA). This paper reviews 262 papers of DEA applications in healthcare with special focus on hospitals and therefore closes a gap of over ten years that were not covered by existing review articles. Apart from providing descriptive statistics of the papers, we are the first to examine the research purposes of the publications. These research goals can be grouped into four distinct clusters according to our proposed framework. The four clusters are (1) "Pure DEA efficiency analysis", i.e. performing a DEA on hospital data, (2) "Developments or applications of new methodologies", i.e. applying new DEAy approaches on hospital data, (3) "Specific management question", i.e. analyzing the effects of managerial specification, such as ownership, on hospital efficiency, and (4) "Surveys on the effects of reforms", i.e. researching the impact of policy making, such as reforms of health systems, on hospital efficiency. Furthermore, we analyze the methodological settings of the studies and describe the applied models. We analyze the chosen inputs and outputs as well as all relevant downstream techniques. A further contribution of this paper is its function as a roadmap to important methodological literature and publications, which provide crucial information on the setup of DEA studies. Thus, this paper should be of assistance to researchers planning to apply DEA in a hospital setting by providing information on a) what has been published between 2005 and 2016, b) possible pitfalls when setting up a DEA analysis, and c) possible ways to apply the DEA analysis in practice. Finally, we discuss what could be done to advance DEA from a scientific tool to an instrument that is actually utilized by managers and policymakers.

The intensive care unit (ICU) is a crucial and expensive resource largely affected by uncertainty and variability. Insufficient ICU capacity causes many negative effects not only in the ICU itself, but also in other connected departments along the patient care path. Operations research/management science (OR/MS) plays an important role in identifying ways to manage ICU capacities efficiently and in ensuring desired levels of service quality. As a consequence, numerous papers on the topic exist. The goal of this paper is to provide the first structured literature review on how OR/MS may support ICU management. We start our review by illustrating the important role the ICU plays in the hospital patient flow. Then we focus on the ICU management problem (single department management problem) and classify the literature from multiple angles, including decision horizons, problem settings, and modeling and solution techniques. Based on the classification logic, research gaps and opportunities are highlighted, e.g., combining bed capacity planning and personnel scheduling, modeling uncertainty with non-homogenous distribution functions, and exploring more efficient solution approaches.

The case mix planning problem deals with choosing the ideal composition and volume of patients in a hospital. With many countries having recently changed to systems where hospitals are reimbursed for patients according to their diagnosis, case mix planning has become an important tool in strategic and tactical hospital planning. Selecting patients in such a payment system can have a significant impact on a hospital's revenue. The contribution of this article is to provide the first literature review focusing on the case mix planning problem. We describe the problem, distinguish it from similar planning problems, and evaluate the existing literature with regard to problem structure and managerial impact. Further, we identify gaps in the literature. We hope to foster research in the field of case mix planning, which only lately has received growing attention despite its fundamental economic impact on hospitals.

We present a strategic model to solve the long-term staffing problem of physicians in hospitals using flexible shifts. The objective is to minimize the total number of staff subject to several labor agreements. A wide range of legal restrictions and facility-specific staffing policies are considered. In general, the model is capable to incorporate different experience levels. In the simplest version the model decides about the number of staff for two experience levels, i.e. the number of residents (low experience) versus specialists (high experience). Shifts are constructed implicitly by the model and may have different starting times and several lengths. This allows more flexibility in the scheduling process. We formulate the problem as a mixed-integer program and solve it applying a column generation based heuristic. Using data provided by an anesthesia department of an 1100-bed hospital, computational results demonstrate the usage of the model as decision supporting tool when staffing decision are made by hospital management.

This research addresses a shift scheduling problem in which physicians at a German university hospital are assigned to demand periods over a planning horizon that can extend up to several weeks. When performing the scheduling it is necessary to take into account a variety of legal and institutional constraints that are imposed by a national labor agreement, which governs all physicians in German university hospitals. Currently, most medical departments develop their staff schedules manually at great cost and time. To solve the problem, a new modeling approach is developed that requires shifts to be generated implicitly. Rather than beginning with a predetermined number of shift types and start times, shifts are allowed to start at every pre-defined period in the planning horizon and extend up to 13 h with an hour-long break included. The objective is to find an assignment such that the total hours that have to be paid out as overtime are minimal under the restrictions given by the labor agreement. The problem is formulated as a mixed-integer program and solved with CPLEX. During the solution process individual lines-of-work are constructed for each physician. Using data from an anesthesia department, computational results indicate that high quality schedules can be obtained much more quickly than by current practice.