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Science and technology-based entrepreneurship education (SBEE) is crucial for the valorisation of newly developed fundamental knowledge and innovative technology in science faculties. This is an important factor in enabling science to respond to societal challenges related to sustainability. However, its didactics are currently underdeveloped. Experiential learning plays an important role in SBEE. Against this background, the current study addresses the following research question: What are the specifications of experiential learning for further developing the didactics of SBEE, as identified through systematic assessment? The study reveals that significant improvement is possible when systematic attention is paid to the implementation of four core activities of experiential learning: bringing real-worldness into the learning setting, recognising the ill-defined nature of management problems and entrepreneurial challenges, encouraging involvement in the execution of management interventions, and highlighting the importance of reflection. Furthermore, we draw upon the literature stating that for a transformative experiential learning effect, the sole focus on a business logic in current SBEE needs to be transcended to enable such entrepreneurship and sustainability education to contribute to the kind of out-of-the-box technological innovation solutions required for the current, pressing sustainability challenges that society faces. This study provides the first evidence that dedicated attention to critical reflection is a crucial component in the design of the experiential learning process for science- and technology-based entrepreneurship and sustainability education.

Digital maturity models are not widely adopted or implemented in business practice, despite the interest in these models. It is unclear which dimensions and parameters are most crucial in measuring the level of digitalization within small and medium-sized enterprises. In this paper, we present a systematic literature review of studies on digital maturity models published from 2011 to 2021. The guiding research question of this study is: Which dimensions are included in digital maturity models to measure the level of digitalization in business models of SMEs? Existing research on the dimensions and parameters included in digital maturity models was reviewed. The review identified nine dimensions: (a) strategy, (b) leadership, (c) culture, (d) organization, (e) people/employees, (f) technology, (g) processes, (h) products, and (i) customers. Additionally, 16 parameters were identified that measure these dimensions. This paper concludes with a discussion of the implications of this review for future research and practice.

Abstract Infection with the novel pandemic SARS-CoV-2 virus has been shown to elicit a cross-reactive immune response that could lead to a back-boost of memory recall to previously encountered seasonal (endemic) coronaviruses (eCoVs). Whether this response is associated with a fatal clinical outcome in patients with severe COVID-19 remains unclear. In a cohort of hospitalized patients, we have previously shown that heterologous immune responses to eCoVs can be detected in severe COVID-19. Here, we report that COVID-19 patients with fatal disease have decreased SARS-CoV-2 neutralizing antibody titers at hospital admission, which correlated with lower SARS-CoV-2 spike-specific IgG and was paralleled by a relative abundance of IgG against spike protein of eCoVs of the genus Betacoronavirus. Additional research is needed to assess if eCoV-specific back-boosted IgG is a bystander phenomenon in severe COVID-19, or a factor that influences the development of an efficient anti-viral immune response.

Public-private partnerships (PPPs) for neglected tropical diseases (NTDs) are often studied as an organizational form that facilitates the management and control of the huge costs of drug research and development. Especially the later stages of drug development, including clinical trials, become very expensive. This present study investigates whether and how the type of PPPs influences the initiation and duration of NTD clinical trials. Using the ClinicalTrials.gov database, a dataset of 1175 NTD clinical studies that started between 2000 and 2021 is analyzed based on affiliation information and project duration. For the NTD clinical trials that resulted from PPPs, the collaborating types were determined and analyzed, including the public sector-, private sector-, governmental sector-, and nongovernmental organization-led collaborations. The determinants for the discontinuation of all stopped clinical trials were categorized into scientific-, funding-, political-, and logistic dimensions. The results reveal that public sector-led PPPs were the most common collaborative types, and logistic and scientific issues were the most frequent determinants of stopped clinical trials. Trial registration: ClinicalTrials.gov.

Purpose - The primary purpose of this paper is to investigate which internal organizational factors drive digital transformation for business model innovation in small and medium-sized enterprises (SMEs). Design/methodology/approach – The paper builds on a Delphi study from both a South African and Dutch scholarly and industry perspective. The Delphi study uses a questionnaire to query the commonly cited internal organizational factors that drive digital transformation for business model innovation in SMEs in an emerging and a developed economy. Findings – The paper identifies two important internal organizational factors driving digital transformation for business model innovation in SMEs: (a) a renewal of business strategy and (b) a renewal of business culture. It identifies various subfactors within these two internal organizational factors. Originality/value – The study contributes to the current understanding of the internal organizational factors that drive digital transformation for business model innovation in SMEs. Researchers can use these factors and associated subfactors as a basis for future research. To practitioners, the findings provide a guideline on which business activities in SMEs to (re)arrange to enable digital transformation-induced business model innovation.

Technological learning curve models have been continuously used to estimate the cost development of solar photovoltaics (PV) for climate mitigation targets over time. They can integrate several technical sources that influence the learning process. Yet, the accurate and realistic learning curve that reflects the cost estimations of PV development is still challenging to determine. To address this question, we develop four hypothetical-alternative learning curve models by proposing different combinations of technological learning sources, including both local and global technological experience and knowledge stock. We specifically adopt the system dynamics approach to focus on the non-linear relationship and dynamic interaction between the cost development and technological learning source. By applying this approach to Chinese PV systems, the results reveal that the suitability and accuracy of learning curve models for cost estimation are dependent on the development stages of PV systems. At each stage, different models exhibit different levels of closure in cost estimation. Furthermore, our analysis underscores the critical role of incorporating global technical sources into learning curve models.

Cellular immune responses are of pivotal importance to understand SARS-CoV-2 pathogenicity. Using an enzyme-linked immunosorbent spot (ELISpot) interferon-γ release assay with wild-type spike, membrane and nucleocapsid peptide pools, we longitudinally characterized functional SARS-CoV-2 specific T-cell responses in a cohort of patients with mild, moderate and severe COVID-19. All patients were included before emergence of the Omicron (B.1.1.529) variant. Our most important finding was an impaired development of early IFN-γ-secreting virus-specific T-cells in severe patients compared to patients with moderate disease, indicating that absence of virus-specific cellular responses in the acute phase may act as a prognostic factor for severe disease. Remarkably, in addition to reactivity against the spike protein, a substantial proportion of the SARS-CoV-2 specific T-cell response was directed against the conserved membrane protein. This may be relevant for diagnostics and vaccine design, especially considering new variants with heavily mutated spike proteins. Our data further strengthen the hypothesis that dysregulated adaptive immunity plays a central role in COVID-19 immunopathogenesis.

IntroductionIn the core of a brain infarct, perfusion is severely impeded, and neuronal death occurs within minutes. In the penumbra, an area near the core with more remaining perfusion, cells initially remain viable, but activity is significantly reduced. In principle, the penumbra can be saved if reperfusion is established on time, making it a promising target for treatment. In vitro models with cultured neurons on microelectrode arrays (MEAs) provide a useful tool to investigate how ischemic stroke affects neuronal functioning. These models tend to be uniform, focusing on the isolated penumbra, and typically lack adjacent regions such as a core and unaffected regions (normal perfusion). However, processes in these regions may affect neuronal functioning and survival in the penumbra.Materials and methodsHere, we designed, fabricated, and characterized a cytocompatible device that generates an oxygen gradient across in vitro neuronal cultures to expose cells to hypoxia of various depths from near anoxia to near normoxia. This marks a step in the path to mimic core, penumbra, and healthy tissue, and will facilitate better in vitro modeling of ischemic stroke.ResultsThe generator forms a stable and reproducible gradient within 30 min. Oxygen concentrations at the extremes are adjustable in a physiologically relevant range. Application of the generator did not negatively affect electrophysiological recordings or the viability of cultures, thus confirming the cytocompatibility of the device.DiscussionThe developed device is able to impose an oxygen gradient on neuronal cultures and may enrich in vitro stroke models.

Abstract Integrated valves enable automated control in microfluidic systems, as they can be applied for mixing, pumping and compartmentalization purposes. Such automation would be highly valuable for applications in organ-on-chip (OoC) systems. However, OoC systems typically have channel dimensions in the range of hundreds of micrometers, which is an order of magnitude larger than those of typical microfluidic valves. The most-used fabrication process for integrated, normally open polydimethylsiloxane (PDMS) valves requires a reflow photoresist that limits the achievable channel height. In addition, the low stroke volumes of these valves make it challenging to achieve flow rates of microliters per minute, which are typically required in OoC systems. Herein, we present a mechanical ‘macrovalve’ fabricated by multilayer soft lithography using micromilled direct molds. We demonstrate that these valves can close off rounded channels of up to 700 µm high and 1000 µm wide. Furthermore, we used these macrovalves to create a peristaltic pump with a pumping rate of up to 48 µL/min and a mixing and metering device that can achieve the complete mixing of a volume of 6.4 µL within only 17 s. An initial cell culture experiment demonstrated that a device with integrated macrovalves is biocompatible and allows the cell culture of endothelial cells over multiple days under continuous perfusion and automated medium refreshment.

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Within the university-industry ecosystem, improvement and innovation of technology transfer involve implementing appropriate dynamic capabilities. To answer the question-What are the micro-foundations of dynamic capabilities in university technology transfer?-this study investigates in-depth organizational-level dynamic capabilities in transferring university-based knowledge to business and society. Two qualitative case studies were deployed at organizational entities at Vrije Universiteit Amsterdam: the Industry Alliance Office, and the Demonstrator Lab. These two organizations stimulate science- and business-oriented university technology transfer. In this context, the micro-foundations of the dynamic capabilities "sensing", "seizing" and "reconfiguring" are identified and discussed. For "sensing", which is the university's ability to explore the opportunities in the ecosystem, the micro-foundations are "selecting internal competency" and "sensing external partners". For "seizing", which supports universities in managing complementarity with industry and society, micro-foundations include "resource co-allocation" and "collaborative business model". The micro-foundations of "reconfiguring", through which universities maintain evolutionary fitness in the innovation ecosystem, are "strategic renewal", "establishing a university technology transfer-friendly environment", and "asset orchestration". This study provides researchers with a better understanding of how dynamic capabilities facilitate university technology transfer. Industrial practitioners and policymakers can consider the suggestions of the present study when pursuing collaboration with universities.

Defining immune correlates of disease severity is important to better understand the immunopathogenesis in COVID-19. Here we made use of a protein microarray platform to detect IgG- and IgA-reactive antibodies in sera and saliva respectively, and assess cross-reactivity between SARS-CoV-2 and endemic coronaviruses (eCoVs). IgG responses against the full protein of spike, but not the S1 subunit, were significantly higher in convalescent sera of patients with severe disease compared to mild disease and healthy controls. In addition, we detected reactivity of secretory IgA to eCoVs in saliva of patients with severe disease, not present in patients with moderate disease or seropositive healthy controls. These heterologous immune responses are in line with non-protective cross-reactivity, and support a potential role for immune imprinting in the pathogenesis of severe COVID-19.

Objective To evaluate the perceived quality of follow-up telephone consultations (TCs) from the perspective of patients and healthcare professionals (HCPs) of multiple medical disciplines during the COVID-19 pandemic.Design A qualitative study using semi-structured interviews and reflexive thematic analysis.Setting Seven medical disciplines (general dermatology, dermato-oncology, head and neck oncology, internal medicine, medical oncology, gynaecological oncology and surgical oncology) at a large university hospital in the Netherlands.Participants Patients who received and HCPs who provided TCs as a substitute for outpatient follow-up appointments during the COVID-19 pandemic.Results Eighty-two patients and 58 HCPs were interviewed. Predominantly, patients and HCPs were satisfied with the quality of care by TCs. They regarded TCs as efficient, accessible and of acceptable quality, provided there was an established patient-HCP relationship, medical complaints were absent and physical examination was not indicated. However, most patients were worried about the accuracy of their health assessment in the absence of physical examination and non-verbal communication. Both patients and HCPs wish to use TCs in the future alternatively with face-to-face consultations.Conclusion This study concludes that TCs seem a valuable contribution to the context of follow-up care and could partially replace face-to-face consultations. TCs can be performed in stable, chronic patients with whom a doctor-patient relationship has already been established. Face-to-face consultations are considered more appropriate in the case of new patients, challenging or emotionally charged consultations and when clinically relevant physical examination is indicated. Due to the context-dependent nature of experiences of patients and HCPs, TCs should be used with an individually customised approach based on patient and disease specifics, in which shared decision-making plays an extensive role. Before major implementation is considered, sufficient data on the safety regarding missed diagnoses or cancer recurrences should be assembled first.

With the advent of the Fourth Industrial Revolution, businesses are adapting to the use of digitalisation which requires the digital transformation of their existing business models. However, there is limited empirical research on this phenomenon. The purpose of this study is twofold: (i) to develop a framework for businesses to digitally transform their business models and (ii) to examine literature in order to identify and analyse the constructs underlying the three concepts of Digitalisation, Digital Transformation and Business Model Innovation. The study is qualitative in nature and is based on a narrative review. Relevant articles were identified by using international bibliographic databases and scrutinised using thematic analysis. The findings reveal that the first two constructs require digital capabilities and a digital strategy. The third construct requires digital transformation in the realm of customercentricity, resources, processes and profit. A set of propositions was formulated and the commonalities were mapped. Based upon this map, a conceptual framework was developed. The findings will assist in the development of future instruments that can guide businesses to digitally transform existing business model elements. This study aims to fill the gap on how business model innovation should be pursued through digital transformation by developing a conceptual framework. Keywords: digitalisation, digital transformation, business model innovation, 1. INTRODUCTION The emergence of digital technologies has radically transformed businesses. Digital technologies have become the driver of the Fourth Industrial Revolution, largely through digital business transformation (Nwaiwu, 2018). Digital [...]

With the advent of the Fourth Industrial Revolution, businesses are adapting to the use of digitalisation which requires the digital transformation of their existing business models. However, there is limited empirical research on this phenomenon. The purpose of this study is twofold: (i) to develop a framework for businesses to digitally transform their business models and (ii) to examine literature in order to identify and analyse the constructs underlying the three concepts of Digitalisation, Digital Transformation and Business Model Innovation. The study is qualitative in nature and is based on a narrative review. Relevant articles were identified by using international bibliographic databases and scrutinised using thematic analysis. The findings reveal that the first two constructs require digital capabilities and a digital strategy. The third construct requires digital transformation in the realm of customer-centricity, resources, processes and profit. A set of propositions was formulated and the commonalities were mapped. Based upon this map, a conceptual framework was developed. The findings will assist in the development of future instruments that can guide businesses to digitally transform existing business model elements. This study aims to fill the gap on how business model innovation should be pursued through digital transformation by developing a conceptual framework.

BackgroundThe ability to accurately distinguish bacterial from viral infection would help clinicians better target antimicrobial therapy during suspected lower respiratory tract infections (LRTI). Although technological developments make it feasible to rapidly generate patient-specific microbiota profiles, evidence is required to show the clinical value of using microbiota data for infection diagnosis. In this study, we investigated whether adding nasal cavity microbiota profiles to readily available clinical information could improve machine learning classifiers to distinguish bacterial from viral infection in patients with LRTI.ResultsVarious multi-parametric Random Forests classifiers were evaluated on the clinical and microbiota data of 293 LRTI patients for their prediction accuracies to differentiate bacterial from viral infection. The most predictive variable was C-reactive protein (CRP). We observed a marginal prediction improvement when 7 most prevalent nasal microbiota genera were added to the CRP model. In contrast, adding three clinical variables, absolute neutrophil count, consolidation on X-ray, and age group to the CRP model significantly improved the prediction. The best model correctly predicted 85% of the 'bacterial' patients and 82% of the 'viral' patients using 13 clinical and 3 nasal cavity microbiota genera (Staphylococcus, Moraxella, and Streptococcus).ConclusionsWe developed high-accuracy multi-parametric machine learning classifiers to differentiate bacterial from viral infections in LRTI patients of various ages. We demonstrated the predictive value of four easy-to-collect clinical variables which facilitate personalized and accurate clinical decision-making. We observed that nasal cavity microbiota correlate with the clinical variables and thus may not add significant value to diagnostic algorithms that aim to differentiate bacterial from viral infections.

IntroductionThere is an urgent medical need to differentiate active tuberculosis (ATB) from latent tuberculosis infection (LTBI) and prevent undertreatment and overtreatment. The aim of this study was to identify biomarker profiles that may support the differentiation between ATB and LTBI and to validate these signatures.Materials and MethodsThe discovery cohort included adult individuals classified in four groups: ATB (n = 20), LTBI without prophylaxis (untreated LTBI; n = 20), LTBI after completion of prophylaxis (treated LTBI; n = 20), and healthy controls (HC; n = 20). Their sera were analyzed for 40 cytokines/chemokines and activity of adenosine deaminase (ADA) isozymes. A prediction model was designed to differentiate ATB from untreated LTBI using sparse partial least squares (sPLS) and logistic regression analyses. Serum samples of two independent cohorts (national and international) were used for validation.ResultssPLS regression analyses identified C-C motif chemokine ligand 1 (CCL1), C-reactive protein (CRP), C-X-C motif chemokine ligand 10 (CXCL10), and vascular endothelial growth factor (VEGF) as the most discriminating biomarkers. These markers and ADA(2) activity were significantly increased in ATB compared to untreated LTBI (p ≤ 0.007). Combining CCL1, CXCL10, VEGF, and ADA2 activity yielded a sensitivity and specificity of 95% and 90%, respectively, in differentiating ATB from untreated LTBI. These findings were confirmed in the validation cohort including remotely acquired untreated LTBI participants.ConclusionThe biomarker signature of CCL1, CXCL10, VEGF, and ADA2 activity provides a promising tool for differentiating patients with ATB from non-treated LTBI individuals.

Background: People with profound intellectual and multiple disabilities (PIMD) are at risk of being motorically inactive. This study investigated the degree and type of motor activation in daily practice and its relationship to personal and contextual factors. Method: A total of 58 participants with PIMD participated in the study. Data concerning the motor activation were collected by means of a diary over a period of 14 days. Relationships to personal and contextual factors were analysed using multilevel analyses. Results: The mean number of transfers was 3.1 times per day (range: 0-9, SD = 1.4), the mean number of relocations was 7.7 times per day (range: 2-13, SD = 2.5), and the mean number of motor activities offered was 1.5 per day (range: 0-10, SD = 1.9). Relationships to age, gender, location, and day of week were found. Conclusion: Motor activation seems to be a minor part of the support provided to people with PIMD.

One of the benefits of physical activity in people with profound intellectual and multiple disabilities (PIMD) is an increase in alertness. This study investigated the effect of a power-assisted exercise intervention on alertness and the relationship of this effect to the level of additional motor and visual impairments in people with PIMD. A randomized controlled trial design (N = 37) was used with five measurements. Using individual plots and multilevel analysis, differences in change of alertness over time were analyzed between the intervention and control group, as was the relationship of changes to additional impairments. Considerable variation in alertness over time was found. The results showed no difference between the control and intervention groups in terms of alertness. No relationship with additional impairments was found. This study underlines the importance of looking at the effectiveness of interventions for people with PIMD because those interventions may not be as effective as expected.

The cancer xenograft model in which human cancer cells are implanted in a mouse is one of the most used preclinical models to test the efficacy of novel cancer drugs. However, the model is imperfect; animal models are ethically burdened, and the imperfect efficacy predictions contribute to high clinical attrition of novel drugs. If microfluidic cancer-on-chip models could recapitulate key elements of the xenograft model, then these models could substitute the xenograft model and subsequently surpass the xenograft model by reducing variation, increasing sensitivity and scale, and adding human factors. Here, we exposed HCT116 colorectal cancer spheroids to dynamic, in vivo-like, concentrations of oxaliplatin, including a 5 day drug-free period, on-chip. Growth inhibition on-chip was comparable to existing xenograft studies. Furthermore, immunohistochemistry showed a similar response in proliferation and apoptosis markers. While small volume changes in xenografts are hard to detect, in the chip-system, we could observe a temporary growth delay. Lastly, histopathology and a pharmacodynamic model showed that the cancer spheroid-on-chip was representative of the proliferating outer part of a HCT116 xenograft, thereby capturing the major driver of the drug response of the xenograft. Hence, the cancer-on-chip model recapitulated the response of HCT116 xenografts to oxaliplatin and provided additional drug efficacy information.