Systemic chronic microinflammation and altered cytokine signaling, with adjunct cardiovascular disease (CVD), endothelial maladaptation and dysfunction is common in dialysis patients suffering from end-stage renal disease and associated with increased morbidity and mortality. New hemodialysis filters might offer improvements. We here studied the impact of novel improved molecular cut-off hemodialysis filters on systemic microinflammation, uremia and endothelial dysfunction. Human endothelial cells (ECs) were incubated with uremic serum obtained from patients treated with two different hemodialysis regimens in the Permeability Enhancement to Reduce Chronic Inflammation (PERCI-II) crossover clinical trial, comparing High-Flux (HF) and Medium Cut-Off (MCO) membranes, and then assessed for their vascular endothelial growth factor (VEGF) production and angiogenesis. Compared to HF membranes, dialysis with MCO membranes lead to a reduction in proinflammatory mediators and reduced endothelial VEGF production and angiogenesis. Cytokine multiplex screening identified tumor necrosis factor (TNF) superfamily members as promising targets. The influence of TNF-α and its soluble receptors (sTNF-R1 and sTNF-R2) on endothelial VEGF promoter activation, protein release, and the involved signaling pathways was analyzed, revealing that this detrimental signaling was indeed induced by TNF-α and mediated by AP-1/c-FOS signaling. In conclusion, uremic toxins, in particular TNF-signaling, promote endothelial maladaptation, VEGF expression and aberrant angiogenesis, which can be positively modulated by dialysis with novel MCO membranes.Systemic microinflammation, altered cytokine signaling, cardiovascular disease, and endothelial maladaptation/dysfunction are common clinical complications in dialysis patients suffering from end-stage renal disease. We studied the impact of novel improved medium-cut-off hemodialysis filters on uremia and endothelial dysfunction. We can show that uremic toxins, especially TNF-signaling, promote endothelial maladaptation, VEGF expression and aberrant angiogenesis, which can be positively modulated by dialysis with novel improved medium-cut-off membranes. Kidney disease is a major public health burden , 2. The Although HD has been effectively employed in the management of ESRD in the past, patients still suffer from considerable side effects, such as greatly enhanced cardiovascular morbidity and mortality , 13, 14.in vivo validation of these mechanistic findings in a cross-over randomized multi-center study, employing novel medium-cut-off  dialyzers in comparison to current standard of care high-flux (HF) hemodialyzers  production and maladaptive angiogenesis , 32–34. In this study, we have investigated the effect of HF and novel MCO dialyzer obtained uremic serum on endothelial VEGF production and maladaptive angiogenesis. We identified the role of TNF superfamily members in uremia-induced endothelial activation and adjunct changes in angiogenic homeostasis, with in-depth deciphering of the concomitant signaling pathways. Importantly, the new MCO dialysis membranes with improved porosity were found to positively modulate endothelial maladaptation and dysfunction.PERCI-II-MCO; ClinicalTrials.gov NCT02084381;https://clinicaltrials.gov/ct2/show/NCT02084381)  . The stu2084381) . The ser2084381) , 37. For84381) –. All cheThe ECs  were provided by Dr. Cora-Jean Edgell  –44, and Gene expression was assessed with reverse transcription and quantitative real-time polymerase chain reaction (qRT-PCR) , 46, 47.http://alggen.lsi.upc.es/cgi-bin/promo_v3/promo/promoinit.cgi?dirDB=TF_8.3) for the presence and location of potential transcription factor binding sites.Progressive VEGF 5’-deletion luciferase plasmid constructs  were provided by A. Scholz (Charité) as reported previously , and cheNuclear extracts were prepared using the NE-PER Nuclear and Cytoplasmic Extraction Kit and oligonucleotide probes labeled with Biotin 3’ End DNA Labeling Kit (ThermoFisher). For the EMSA , the fol®, San Diego, US) and R (version 3.5.1). Analyses of multiple variables were performed by one-way analysis of variance with Student–Newman–Keuls post- or Kruskal–Wallis with Müller–Dunn post-test. A P-value < 0.05 was considered statistically significant.Data are expressed as mean ± SEM. Non-parametric data are presented as medians. Statistical analysis and visualization was performed using GraphPad Prism  associated with the beneficial anti-inflammatory shift reported with the use of novel improved molecular cut-off dialyzers in the PERCI-II trial 2A. The in vitro exposure model of ECs with uremic serum samples obtained from chronic HD patients during the PERCI-II-MCO trial , employing VEGF expression and production as markers for endothelial activation and endothelial tube formation as a functional readout. This was followed by mechanistic validation, employing healthy and uremic serum pools  , with a further rise in VEGF release after a total of 12 weeks on HF dialysis . After 12 weeks of MCO dialysis there was no difference to VEGF baseline levels.In contrast, exposure of ECs to serum samples collected after four weeks of HF dialysis initially led to an increased VEGF expression , while incubation with healthy or MCO serum led to a reduction in endothelial tube formation (P<0.05). In analogy to the VEGF expression data, we found that this was a time-dependent effect, with a stronger reduction of angiogenesis after longer periods of expanded MCO dialysis therapy.Concomitantly, More than 30 soluble analytes were screened with multiplex technology in patient serum obtained at different study time points . UnsuperImportantly, pre and post-treatment groups separated into two major clusters  and thereby promote improved uremic toxin removal and reduction of chronic inflammation. We thus reanalysed and substratified our preliminary data according to the individual study phases and found that patients undergoing MCO dialysis in particular for a longer duration (12 vs. 4 weeks), show a beneficial anti-inflammatory shift in multiple soluble mediators  , 22.). Kinetic profiling documented a differential peak of 1.75-fold increase in VEGF mRNA after 3-hours of serum incubation , thus indicating stability of the TNF-α/sTNF-R1-ration during serial dilution.In line with prior identification of TNF signaling Figure 3), but decreased when adding 100 and 1000 pg/ml TNF-α (P<0.01 and P<0.001).To substantiate the role of TNF-α in endothelial VEGF induction, we assessed whether incubation of ECs with TNF-α affects endothelial VEGF promoter activity Figure 5via gel shift assay as the specific VEGF promoter-binding site in ECs .Next, we analyzed the VEGF promoter sequences responsive to TNF-α by employing ECs transiently transfected with step-wise 5’-deleted VEGF-A promoter-driven luciferase constructs (We quantified c-FOS mRNA expression relative to TNF-α concentration and the effect of sTNF-R1 on c-FOS mRNA expression in low and high TNF-α environment was assessed (in vitro angiogenesis . Addition of 1 pg/ml TNF-α led to an increased VEGF-expression and angiogenesis (P<0.05), which was reduced to baseline by addition of c-FOS inhibitor SR-11302 and Infliximab (P<0.05 to P<0.01). In contrast, high TNF-α concentrations suppressed VEGF mRNA expression and angiogenesis (both P<0.01), which could be partially reversed by blocking AP-1/c-FOS (P<0.05) and completely reversed through the blockade of TNF-α with Infliximab (P<0.01).Next, we linked the TNF-α- and s-TFN-R1-mediated regulation of AP-1/c-FOS-signaling to endothelial VEGF-expression and In conclusion, endothelial VEGF expression and angiogenesis are strongly dependent on both the TNF-α and sTNF-R1 concentration in their environment and can be blocked by targeting TNF-α and AP-1/c-FOS signaling pathways.Hemodialysis is a widely employed blood cleansing technique, but the use of conventional hemodialyzers is associated with a high cardiovascular morbidity and mortality resulting in part from the underlying chronic inflammation associated with this treatment. Thus, there is a high medical need to reduce inflammation and adverse cardiovascular events, which may be achieved with novel improved molecular cut-off MCO dialyzers in both, the chronic CKD/ESRD and the acute COVID-19 associated renal failure and RRT setting , 9, 27.In the current study, we have explored the molecular and biological mechanisms underlying this antiinflammatory shift by evaluating promising leads identified during the first screen . We founVascular disease and endothelial dysfunction are common in dialysis patients –22. One VEGF is a key regulator of angiogenesis in various settings, such as inflammation, and targeting VEGF has shown fist beneficial effects e.g. in vascular disease –52. In ain vitro and in vivo marker of endothelial dysfunction  is expressed in many cell types, sTNF-R2 (75 kDa) is expressed in more restricted fashion and the differences in their structure also suggest that they act in part through different downstream signaling pathways . While, Our manuscript focuses on readout of the TNFa, sTNF-R1/R2, AP1/cFOS, and VEGF signaling axis in the context of detrimental effects of uremia on microvascular ECs and amelioration thereof by employing novel dialysis filters with improved molecular cutoff. For better understanding to readers, we have focused on these main mediators, but in principle the list of studied markers could be expanded to many other markers indicative of endothelial maladaptation/injury/damage markers, such as endothelial E-Selectin (ELAM-1), ICAM-1, VCAM-1, IL-6, IL-8, Tie-2 and the production of reactive oxygen species (ROS), as outlines in other studies on endothelial inflammation , 57–59.in vivo reservoir for endothelial repair in the periphery. Here, the TNF-TNF-R2 axis may be involved in the triggering of immunosuppressive effects and thus also be of interest for clinical use . The modulatory aspect of the two employed dialysis regimens is mainly due to differential cutoff properties on the blood-interphase in the medium cut-off range, which leads to the specific differential elimination/retention of various proinflammatory mediators, but also many other medium sized molecular components that accumulate in blood of uremic patients  and endothelial progenitor cells (EPCs), which are an ical use . In our patients . Thus, tironment   , which is registered at ClinicalTrials.gov NCT02084381 2084381) . The curThe authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


These clinics allow employers to offer convenient care to employees at their workplaces, which can lead to reduced healthcare expenditure and improved productivity. The objective of this study was to build basic data by qualitatively exploring employees’ experiences and perspectives on onsite clinics in a semiconductor company, as one part of the project to examine and improve the health management system of a large semiconductor company in Korea. (2) Methods: This study adopted the methodology of “Consolidated Criteria for Reporting Qualitative Research” (COREQ-32 checklist). Semi-structured interviews were conducted for this study over a two-month period. For data analysis, a codebook was developed and the constant comparative method was used. (3) Results: Most employees perceived convenience and a sense of belonging as the benefits of onsite clinics, while barriers to the use of onsite clinics included a lack of communication, concerns about confidentiality, and a provider-centered system. Promotion of onsite clinic services and affiliated physicians, employee-centered service provisions, and trust-building in healthcare information privacy were considered necessary to strengthen the role of onsite clinics as a primary care provider in the workplace. (4) Conclusions: The results of this qualitative study help us to gain a better understanding of employees’ perspectives on the onsite clinic’s service and roles. Health promotion is the process of improving the health of an individual or a community by facilitating better management of health-determining factors . The worWorldwide, onsite clinics are increasingly common features of WHPPs and have become more prevalent in recent years. Currently, there are a total of 250 onsite clinics in the Republic of Korea, which are continuously increasing from 222 and 241 in 2017 and 2019, respectively ,11. In tClinic services should meet the objectives of the employer’s benefit strategies and employees’ medical needs. The success of the WHPP and onsite clinics depends on a thorough understanding of the factors that motivate employees to actively use the clinic for health promotion as well as the barriers to their involvement . In a suThis study adopted the methodology of “Consolidated Criteria for Reporting Qualitative Research” (COREQ-32 checklist)  18]. Se. Se18]. The company that is the background of this study is a large semiconductor company with 5 major domestic business sites and has been operating onsite clinics for each business site. The study population consisted of workers from those business sites. In consideration of the demographic characteristics or diversity of experience, maximum variation purposeful sampling was used for participant recruitment . PotentiThe recommended sample size is at least 30–60 people to obtain the rich data needed for qualitative analysis using semi-structured interviews . TherefoA faculty member in pharmacy  with prior training in interviewing and research in health practice led the interviews. No previous personal relationship existed between the interviewer and the participants existed.The interviews took place between September and November 2017. Each interview group were composed of three to five people, taking account of the affiliated business site, type of work, age, and gender. An interview guide was developed beforehand and reviewed by specialists from the related fields. It included interview questions on experiences and perspectives on the current onsite clinic services . We perfAll interviews were audio-recorded and transcribed verbatim by research staff members. To ensure quality, the interviewer checked the accuracy of the interview transcripts.For the qualitative thematic analysis, this study developed a codebook as recommended by DeCuir-Gunby et al. , utiliziFor reporting, the original language was translated into English by an independent bilingual translator. For validation purposes, the data were double-checked by back-translating English to Korean. The reference at the end of each quotation indicates the participant number followed by the paragraph numbers where the quotation occurs in the transcript, for example, “Participant #3, 109–111”.A total of 72 employees via 18 interview groups participated in this study. Three main themes  and nine codes emerged from analyzing the participant interviews .For the theme “perceived benefits to using onsite clinics”, participant thoughts fell into two main categories: (1) convenience and (2) sense of belonging.n = 42). The participants believed that the geographical proximity of onsite clinics was a significant advantage, making visits easier and less restricted by time.High convenience was the most frequently mentioned positive factor of using onsite clinics, mentioned by approximately 58% of all participants “It’s close, so I frequently use an onsite clinic. I prefer the onsite clinics over outside ones since the latter would require me to go out during my work hours. It’s a little cumbersome.”Participants responded that they felt respected as an employee and felt a sense of belonging when using onsite clinics and answered that this was a positive factor promoting their use. These feelings were linked to the expectation that the onsite clinic would consistently manage their health and to the trust in onsite clinics. Participants described feeling free of any psychological barriers and comfortable using onsite clinics, resulting from this sense of belonging.“In outside clinics or university hospitals, physicians tend to be authoritative. At the onsite clinics, however, I feel respected and cared for as an employee. (…) It feels like the clinic is exclusive to me because they have records of all the treatments and medications I have received since I joined the company.”“I definitely feel close to the physicians, something like solidarity?”“I don’t think there were any difficulties or inconveniences. People around me have no problem visiting the onsite clinics freely.”The perceived role of onsite clinics, based on participant experiences, included “primary diagnosis,” “acute illness treatment,” and “chronic disease management.” Although most of the employees found it helpful and a positive experience, not all were happy with their experience.n = 46) responded that the role of onsite clinics was to provide a one-time or short-term treatment for acute or mild diseases. Most participants expected onsite clinics to treat minor ailments, such as a cold, indigestion, or burns. They used onsite clinics only after assessing their symptoms independently and deeming they were treatable there.Sixty-four percent of all participants (“I would go to the onsite clinics for something like a cold. (…) My colleagues all seem to believe that the onsite clinics are for something as mild as a cold. They only visit the onsite clinics for a temporary symptom or else they some take time off to go to an outside hospital.”“I go to an onsite clinic for something treatable with medication, such as mild otorhinolaryngologic symptoms or indigestion. If I think my symptoms require more professional treatment, then I would not go to an onsite clinic.”A significant contributor to these opinions regarding the role of onsite clinics was their old facilities and equipment. More women than men mentioned this factor.“The medical equipment looks rather old. I think they have been using the same equipment since I joined the company.”The second most frequently mentioned role of the onsite clinic was for primary diagnosis. Participants liked how they could receive a primary diagnosis from the onsite clinic and be referred to another hospital if necessary. Many participants were referred to outside hospitals while receiving treatment for a mild condition at an onsite clinic. Such experiences led to trust and satisfaction with onsite clinics as primary care providers.“A piece of advice as simple as ‘you need to go to this hospital because this has to do with this condition’ was very useful to me. I did not know what my condition was and where to visit on my own. I am personally satisfied with the onsite clinics.”“I once visited an onsite clinic for throat pain. Since the pain worsened, they told me to go to an outside hospital. I ended up visiting a regular hospital last year to get it treated. I think the onsite clinics provide a good primary medical care.”n = 10) believed onsite clinics can prevent and manage chronic diseases. Those who believed onsite clinics played a role in chronic disease prevention and management did so due to the quality care and positive support they received from the clinic related to chronic disease. For example, they managed their blood pressure or glucose levels through the onsite clinic, so they believed onsite clinics effectively managed chronic diseases.All participants received a health risk appraisal recommending chronic disease risk management at their workplaces. However, only 14%  I’ve come to trust the onsite clinics since then. Now, I regularly go for a consultation and blood test every three months.”Some participants mentioned the nurse’s office in schools or the medical office of an army as an analogy to onsite clinics and believed that onsite clinics are incapable of providing continuous treatment for chronic diseases. This opinion was expressed more often by shift workers than office workers.“I do not believe that the onsite clinics can provide continuous treatment. (…) We all think of it as the medical office of an army.”Participants described a wide range of barriers to using onsite clinics properly: the lack of communication about their services, worries related to confidentiality, and the lack of choice in their provider-centered system.There was a significant discrepancy in participant awareness on the information needed to use the onsite clinics. Some participants were highly familiar with the range of treatments offered, hours of operation, reservation methods, and medical staff at their onsite clinics. Most participants (74%) were utterly unaware. They responded that they were unfamiliar with the services provided by onsite clinics and their medical staff and did not know how to find information about them. Most participants received information about onsite clinics from their colleagues. They commented that the scarcity of information about the medical staff made them skeptical about staff professionalism. The participants mentioned that they have low expectations about and trust toward medication prescriptions and treatments given at onsite clinics since they cannot ascertain the professionalism of the medical staff due to a lack of information on their qualifications and work experience. They agreed that the information on the onsite clinic and the medical staff should be delivered so that the employees could fully recognize it.“I don’t think there’s any way to obtain objective information. The most I can get is just stories from people who visited the clinic.”“I do not think there’s information available about what treatment the onsite clinic provides or the physicians who work there. (…) We don’t know what backgrounds the physicians come from. While advertising the background of the physician is not necessary, I hope that information can be more accessible. (…) It is unclear what treatments are provided at the onsite clinic. In outside hospitals, they have specialized divisions. This is why I think it might be better to just go to an outside hospital that can treat my symptoms. (…) If there are two physicians at an onsite clinic, it’s not like each of them has special expertise. I also don’t know much about them, so I feel afraid and reluctant to go to an onsite clinic.”n = 25) mentioned that feeling psychologically burdened about the possibility of having their healthcare information disclosed to the company or their colleagues after using an onsite clinic reduces their willingness to use the onsite clinic and the value of the onsite clinic. This psychological burden was common, felt by participants visiting an onsite clinic to treat an acute or mild condition and those visiting for chronic disease management. Participants believed that having their healthcare information disclosed to their company would negatively affect their work. They expressed feeling psychologically burdened by this and felt negatively toward onsite clinics. They also expressed concerns about having their healthcare information disclosed to their colleagues.Thirty-five percent of all participants “I want to keep my job as long as I can. I felt that using the onsite clinic would make people think I’m a sick person, and that would interfere with my career. (…) It’s not like people denounce me for going to the clinic, but I just feel my self-esteem draining and feel withdrawn, and the stress would, in turn, make me even sicker.”Participants responded that the provider-centered management of onsite clinics decreases their willingness to use onsite clinics. The participants felt inconvenienced by onsite clinics providing universal medical services to patients regardless of gender, occupational group (office workers or shift workers), and treatment purpose . Furthermore, patients had no way of choosing their physicians. Eleven out of 72 (15%) mentioned that the onsite clinics were either relatively far from their worksite or only operational during the daytime. Shift workers expressed this opinion more frequently. Shift workers explained the difficulty of using onsite clinics for night shift workers since the onsite clinics are only open during the daytime.“(At our business site) there is a factory that runs for 24 h with three shifts. Onsite clinics are only open from 8 a.m. to 5 p.m.”“Since patients are randomly taken to the examination rooms in the order that they arrive, some employees, such as young female employees, may feel uncomfortable when they see a male doctor, especially if they are visiting for a female-specific condition. The clinic doesn’t ask patients how they feel about this, nor does it care.” After discussing the research questions, the focus group facilitator asked for suggestions on improvements to the onsite clinic services. Common responses included coordination, communication, and accessibility. The most mentioned suggestion was sharing more basic information about onsite clinics and their activities. Participants said that implementing and expanding a system that continuously informs employees about the news related to onsite clinic management and medical staff/onsite clinic activities would be a significant improvement. They believed that such a system would remove negative preconceptions about onsite clinics and increase trust toward them. Participants also suggested that onsite clinic services should be accessed and improved in conjunction with the entire WHPP. Participants responded that onsite clinics must establish connectivity with related departments  and provide comprehensive care instead of managing and preventing chronic disease on their own. Additionally, they suggested methods to increase accessibility to onsite clinics, such as improving a reservation system for employees who work far from onsite clinics and investigating and providing the medical services needed by employees.“Wouldn’t letting employees know that the clinic is doing some specific activities help gain trust among the employees?”“It’s understandable that such misunderstanding exists since not many people are aware of the hard work that the medical staff commit themselves to the onsite clinic. Activities, like advertising, are needed to get rid of the misunderstanding, but currently, there are none. That’s why employees do not feel that the medical staff are working hard.”“They are all separate from one another. Workplace cafeteria, onsite clinics, fitness centers.… They all have different administrative divisions that are not connected. To manage chronic diseases, treatments, exercise, and diet should all go together.”Managing employee health and preventing diseases is critical for the health and welfare of workers and the economic and competitive values of companies and countries ,5,6,7,23All interviewees had received health risk appraisals recommending chronic disease risk management in a health assessment and visited an onsite clinic. Interviewees in their 20s to 40s participated in the interviews, and the mean age of the interviewees was 34 years. Since more than 90% of all the employees of the company that was the background of this study were in their 20s to 40s, the age range of the interviewees was deemed suitable ,25. MoreThe perceived benefits of onsite clinics reported by the participants were convenience, in terms of geographical proximity, time, and use of service, and a sense of belonging felt by employees when using the medical services provided by their company. Convenience was previously reported as the main advantage of onsite clinics in a systematic review . A senseThe factors that interfered with the use of onsite clinics were a lack of information about the provided services and medical staff of onsite clinics, concerns about having personal healthcare information disclosed to the company, and the provider-oriented management system, which is consistent with the literature . Bright It is noteworthy that employees were concerned about having their healthcare information disclosed to their company or colleagues while recognizing a sense of belonging and solidarity with medical staff as a benefit of onsite clinics. A trust relationship between healthcare providers and employees is a critical factor in health-promoting behaviors . Having Treatment of acute or mild ailments was the most common perceived role of onsite clinics among the interviewees. A few participants believed onsite clinics could manage chronic diseases. This is a phenomenon that can be seen at a rather early stage in terms of the development stage of the onsite clinic. Many onsite clinics started as occupational health clinics, treating minor injuries and serving workplace health and safety needs, but today, they have expanded into primary care services, involving management of chronic diseases, preventive care, and other areas . Onsite In this study, shift workers showed less satisfaction with the accessibility of onsite clinics than office workers. They repeatedly mentioned that the role of onsite clinics is limited to providing treatment for acute or mild ailments. Several studies have already reported that shift workers have poorer health compared to office workers. There are reports associating shiftwork with coronary heart disease, and the risk of cardiovascular disease is 40% higher among shift workers than non-shift workers ,39. ThesIt was reported that there were pillars of an effective WHPP such as multilevel leadership, alignment, scope, relevance, quality, accessibility, partnership, and communications . The resThis study has the following limitations. First, we included 72 employees from a single semiconductor company, which may not be sufficiently representative of onsite clinics from other companies with different environments or cultures. Nevertheless, this company has long been a leading provider of onsite clinics as part of the WHPP. Regarding the number of participants, we applied a maximum variation purposeful sampling method and the constant comparative method, which ensured a sufficient number of participants to reach a state of saturation. In a similar study in which focus group interviews were conducted on health promotion issues in the past, 8–10 focus groups with approximately 60–80 workers were carried out ,41. We bThe effectiveness and importance of onsite clinics in worksite health promotion are increasingly emphasized. In this study, employees recognized convenience and a sense of belonging as benefits of onsite clinics. On the other hand, factors that hindered employees from using onsite clinics were the lack of information on services and medical staff of the onsite clinic, concerns about the confidentiality of their medical information, and a provider-oriented management system. The results of this qualitative study provide basic data to help us better understand employees’ perspectives on the onsite clinic and its role. Based on the exploratory results of this study, additional research can be conducted to identify important factors for establishing and activating the role of onsite clinics in the future.


Nine papers have been published between 26 February 2021 and 4 January 2022 due to the COVID-19 pandemic. These papers are listed hereafter in the inverse order of date of publication. Thanks to all the authors for the various viewpoints expressed that unveil fundamental and application aspects of the Carnot cycle and heat engines.Authors are from Europe (four papers) and China (five papers). Each paper has been viewed by 400 to 1100 persons, except the last published one. Four papers have been presently cited 8 to 20 times.Five papers address heat engines and Carnot configurations ,2,3,4,5.The paper that discusses the Lenoir cycle is from a more conventional point of view. It deals with the steady flow . Objectives are power and first law efficiency. The corresponding allocation of heat transfer conductance is proposed, due to finite size constraints .-Maximum energy efficiency differs from maximum power through different variable piston speed values;-Results obtained through the FST method are different from those obtained from the Curzon–Ahlborn model (with time duration), due to the steady-state hypothesis.In , the autPaper  concernsPapers by ,7 extendThe study by  is, in fPapers of ,9 are spIn , the autPaper by  is relatSystematic consideration of irreversibility (more than endo-reversibility);Two ways of optimization—namely, sequential  and multiobjective  approaches;Various objectives including energy, power, and first law efficiency for the most used approach;Various constraints; from a general point of view, the use of what we introduce as finite physical dimensions of optimal thermodynamics (FDOT) with finite constraints ;The evolution of research from basic cycle to complex systems.To conclude, this second Special Issue confirms and improves the preceding one in terms of the following aspects:Perhaps these features could pave the way toward a third Special Issue, to expand and build upon concepts and approaches presented thus far.


Blockchain technology is a part of Industry 4.0’s new Internet of Things applications: decentralized systems, distributed ledgers, and immutable and cryptographically secure technology. This technology entails a series of transaction lists with identical copies shared and retained by different groups or parties. One field where blockchain technology has tremendous potential is health care, due to the more patient-centric approach to the health care system as well as blockchain’s ability to connect disparate systems and increase the accuracy of electronic health records.The aim of this study was to systematically review studies on the use of blockchain technology in health care and to analyze the characteristics of the studies that have implemented blockchain technology.EBSCOhost. A quality assessment of literature was performed on the 22 selected papers by assessing their trustworthiness and relevance.This study used a systematic review methodology to find literature related to the implementation aspect of blockchain technology in health care. Relevant papers were searched for using PubMed, SpringerLink, IEEE Xplore, Embase, Scopus, and After full screening, 22 papers were included. A table of evidence was constructed, and the results of the selected papers were interpreted. The results of scoring for measuring the quality of the publications were obtained and interpreted. Out of 22 papers, a total of 3 (14%) high-quality papers, 9 (41%) moderate-quality papers, and 10 (45%) low-quality papers were identified.Blockchain technology was found to be useful in real health care environments, including for the management of electronic medical records, biomedical research and education, remote patient monitoring, pharmaceutical supply chains, health insurance claims, health data analytics, and other potential areas. The main reasons for the implementation of blockchain technology in the health care sector were identified as data integrity, access control, data logging, data versioning, and nonrepudiation. The findings could help the scientific community to understand the implementation aspect of blockchain technology. The results from this study help in recognizing the accessibility and use of blockchain technology in the health care sector. Health informatics (HI) is an extension of medical informatics that concentrates on the clinical sector and implementation of technology in the distribution of health care . ChangesElectronic health record (EHR) systems and hospital information systems (HISs) are widely used across the world. However, the current HISs are mainly cloud based, are stored by one particular data contractor, and have several disadvantages, such as a lack of sufficient security measures. This has led to innumerable breaches of data, as well as issues of data validity and data sharing, which have left patients exposed to economic threats and possible social stigma. Centralized data or information is an appealing target for cyberattacks, and issues arise due to establishing a persistent view of the patient data across a network .Taking these issues into consideration, an improved tamperproof and hackproof database management system is much needed to replace the current system that has been used for the past several decades. The new innovative system should have better data security and be able to integrate with other information technology (IT) systems, such as finance and admission systems. When blockchain technology was introduced in 2008, it largely fulfilled all of these criteria, alongside its versatility for applications in banking and finance. Blockchain is a decentralized database that maintains an uninterrupted, growing list of data records that are established by the nodes involved. The information is recorded in a public ledger that includes data from every completed transaction . Along wBlockchain is a decentralized database that is not owned by anybody and is simultaneously owned by everybody, as the contents are available to all parties involved. For example, with Bitcoin, since all transactions are processed by users via a particular pseudonym, the information contained in the blockchain is completely anonymous . This reThis study aimed to systematically review studies on the use of blockchain technology in health care. We also analyzed the characteristics of the studies that have implemented blockchain technology. This study will be impactful in helping the scientific community to understand the use aspect of blockchain technology based on the findings of completed studies. The results from this study will help to identify the accessibility as well as the implementation of blockchain technology in the health care sector.The methodology used in this research was a systematic review, modeled on a recent systematic review about blockchain reported by Böhme et al .EBSCOhost databases were chosen to search for all the relevant literature. The search strings were constructed in accordance with the research domains and research questions and are listed in A search was conducted for scientific papers on the research topics. All papers that were relevant for these topics were gathered by using a search protocol that was developed for each scientific database. Possible keywords were tested, and appropriate terms were chosen for the search string. The Medical Subject Headings (MeSH) database was used to derive keywords and search term combinations. PubMed, SpringerLink, IEEE Xplore, Scopus, Embase, and Online digital libraries were used to search for relevant papers from January 2008 to September 2019. The year 2008 was chosen as the beginning of the range for this research study because the first published application of blockchain technology  was introduced in that year, so no blockchain-related studies were conducted before 2008. In this systematic review, the search query was purposely made broad, in order to identify many papers related to the research question. However, when “Bitcoin” was used as a search term, a large number of papers were identified, but the papers were mainly about economic applications rather than applications in the health care sector.Because the aim of this research was based on finding and mapping the papers related to blockchain technology in the health care sector, “Bitcoin” was dropped as a search term. By using “blockchain” and “health care” as search terms, the majority of Bitcoin-related papers with a technical perspective on blockchain were still included. A manual search was carried out for papers that were published at workshops, at conferences, in journals, and at symposiums.The next step in the process was screening relevant papers, wherein the papers that had been found during the previous step were assessed for actual relevance. The screening process started with all of the publications gathered from online digital libraries. A process inspired by Yli-Huumo et al  was usedThe iterative approach for title, abstract, and full-text searches was used, and the results were exported to Microsoft Excel 2013. The titles and abstracts of the searched papers were screened first to determine the relevance or appropriateness for this systematic review. At this stage, studies that were clearly not about the use of blockchain technology in health care were excluded. The titles and abstracts were screened by two reviewers based on the inclusion and exclusion criteria.The papers that had passed the previous screening phase were screened based on their abstract. In addition, the following specific inclusion and exclusion criteria were used to screen each paper:Inclusion criteria:Original research study.Study in English.Publication on blockchain technology in the health care sector.Publication including sufficient explanation of the research findings.Exclusion criteria:Papers without full-text availability.Papers for which English was not the main language.Papers that had some other focus instead of the use of blockchain in the health care sector.Papers that were duplicates.Search results that were editorials, prefaces, paper summaries, summaries of tutorials, interviews, news items, correspondences, discussions, comments, readers’ letters, workshops, panels, and poster sessions.Publications indicating ideas, magazine publications, and discussion papers.Screening based on keywords, as defined by Dyba and Dingsøyr , was donThe keywords were used to cluster and form categories. All the selected papers were read after the categories had been clustered. The categories were updated after reading each paper, or if the paper revealed something new, then a new category was created. This step resulted in clustered categories being formed from all the relevant papers based on this research topic. Papers with poor, misleading, or lost abstracts were excluded due to irrelevant information.After the title-, abstract-, and keyword-screening process, each remaining paper underwent full-text screening based on the same eligibility criteria. Two reviewers resolved discrepancies through discussion, and no adjudication by a third reviewer was required.A template was designed to collect the information required to address the research question. Basic metadata about the publication were collected, such as author name and country, year of publication, source type, and type of publisher.To categorize the 22 selected papers, further data were extracted. Each full paper was read to extract the keywords or outcomes related to our research question; these were then sorted into the identified categories, as follows:Use cases of blockchain technology in health care that indicate the specific health care area, such as electronic medical records (EMRs), biomedical research and education, remote patient monitoring, drug or pharmaceutical supply chains, health insurance claims, health data analytics, or other areas.Reasons for using blockchain technology in health care, such as data integrity, access control, logging, data versioning, and nonrepudiation.An assessment of literature quality was performed. All of the final 22 publications were independently reviewed and scored by two reviewers. The assessment tool for blockchain-related studies proposed by Petersen et al  was usedIn accordance with Hölbl et al , the quaThis tool was used to assess the trustworthiness, relevance, and results of the published papers. These led to the decision of which papers were believable and useful and could be used for the research. A three-tier scale was used to rank the quality of all four questions. A value of 0 (“barely” or “no”) was assigned when the criterion was addressed very poorly or not at all, a value of 1  was assigned when a criterion was partially addressed, and a value of 2 (“satisfactorily” or “yes”) was assigned when the reviewer felt that the publication had successfully satisfied the criterion.Two reviewers assessed each query from question 1 (Q1) to question 4 (Q4), which resulted in a minimum of 0 points to a maximum of 4 points per query. The minimum score for the sum of responses to question 2 (Q2), question 3 (Q3), and Q4 was 0 points, and the maximum score was 12 points. The score of the response to Q1 was converted to 40% of the total value, and the total summed score of responses to Q2, Q3, and Q4 was converted to 60% of the total value, with Q2, Q3, and Q4 each contributing 20% of the total points.So, the overall score was the sum of responses to Q1 to Q4, which is presented as a percentage to enhance readability and comprehension. An explanation of scoring of responses to queries Q1 to Q4 is given in To find the percentage score of the response to Q1, the following equation applies:To find the percentage score for the sum of responses to Q2 to Q4, the following equation applies:The overall score is represented by the following equation: Overall score = Percentage score of response to Q1 (%) + Percentage score of sum of responses to Q2 to Q4 (%) .From the calculation, the publications that have an overall score of 90% and above are high-quality papers. An overall score between 80% and 89% indicates a moderate-quality paper, and low-quality papers are represented by an overall score of 79% or less.All data have been reported in this manuscript.EBSCOhost. Then, the first screening was done based on the titles of the retrieved papers. All of the paper titles were examined independently by one reviewer based on the inclusion and exclusion criteria, which led to the selection of 175 papers. In the first screening, a total of 52 papers were excluded because they were not related to the research topic . Meanwhile, 25 papers related to other scientific areas, such as mathematics and chemistry, were excluded from the first screening, as the term “blockchain” had other meanings apart from the technology used in computer science and IT. Through a manual search and using references from the included papers, an additional 10 papers were collected.A total of 271 papers were initially retrieved as a result of implementation of the search protocol that was designed for searching the selected scientific databases. Of the 271 papers, 34 (12.5%) were from PubMed, 52 (19.2%) were from SpringerLink, 40 (14.8%) were from IEEE Xplore, 56 (20.7%) were from Embase, 45 (16.6%) were from Scopus, and 44 (16.2%) were from After the selection of 185 papers from the first screening, 87 duplicate papers were removed using Endnote X8. This resulted in 98 papers, which underwent further screening based on abstracts where, in some cases, the introduction and conclusion of the full text were analyzed. The abstracts of all the selected papers were read by two reviewers. Some of the papers were removed because the abstracts indicated no relevance to the research topic. The unclear or grey-area abstracts or papers were moved to the next screening step for more in-depth analysis.A total of 35 papers were identified for full-paper analysis, which was the last stage of paper selection for this systematic review. Each paper was read in full, independently, and assessed for eligibility using the inclusion and exclusion criteria. This resulted in the selection of 22 primary papers. Of the 35 papers identified for full-text analysis, 3 (9%) were dropped because they focused on the economic perspective of Bitcoin and not the health care setting. Of the 35 papers, 5 14%) were removed as they only described blockchain and how it works, without discussing any actual blockchain implementation in a real health care environment. Furthermore, 3 out of 35 (9%) papers identified as review papers and 2 (6%) papers identified as proposal papers were removed. % were reAll the selected papers were published since 2016. This indicates that blockchain technology in health care settings is very new. It is noted that from the 22 selected papers, the majority  were published in 2018, 7 (32%) papers were published in 2017, 2 (9%) papers were published in 2016, and 1 (5%) paper was published in 2019.The locations  of the institutions of the authors of the selected primary papers were used to distinguish the geographical distribution of the research community members who were involved in the research. If a paper had authors from different countries, the country of the corresponding author was used. It is noted that the authors, universities, and companies in the United States and China were leading, having 4 (18%) papers each. This was followed by the United Kingdom with 3 (14%) papers and the Netherlands with 2 (9%) papers. The rest of the countries, namely Ukraine, India, Italy, Portugal, Switzerland, Denmark, Japan, France, and Canada, contributed 1 (5%) paper each. This geographical distribution of the 22 selected papers indicates that blockchain technology in the health care sector has gathered research interest around the world.The channels where the papers were published determined the publication type. The two publication types included in this systematic review were conferences and journals. The majority  of the 22 selected primary papers were published in journals, while 7 (32%) were published as conference proceedings.The 22 selected primary papers were studied, and the data or keywords related to this systematic review’s question were extracted. A classification scheme was constructed based on the iterative identification of data, charting keywords extracted from the selected papers. The papers were then sorted into identified categories.Each of the selected primary papers addressed one or more different aspects of the use cases of blockchain technology in the health care sector. Therefore, the identified use cases were used to further classify the selected papers. Out of 22 selected papers, 10 (45%) addressed the application of blockchain in the management of EMRs, 5 (23%) addressed the use of blockchain technology in biomedical research and education, and 3 (14%) demonstrated the use of blockchain technology in remote patient monitoring. The remaining papers addressed the use of blockchain technology in drug or pharmaceutical supply chains , health insurance claims , health data analytics , and other applications .In the selected primary papers, blockchain was implemented in the real health care environment to address several information security components. The use of blockchain technology or the main reason it was implemented in health care was classified. From this data, it was noted that each of the selected primary papers addressed one or more reasons, out of a total of 34 reasons or benefits, for using blockchain technology in health care. Most papers addressed the application of blockchain in health care for data integrity . The next largest purpose of blockchain application was access control, which contributed 11 out of 34 (32%) reasons in the papers. Meanwhile, data logging was addressed 6 (18%) times, data versioning was addressed 2 (6%) times, and nonrepudiation was addressed 1 (3%) time.The setting of the studies, specifically the type of hospital used for the implementation of blockchain technology, was analyzed among the 22 selected papers. Only 3 out of 22 14%) papers gave the name of hospital where the study was carried out. Maslove et al % papers  at HospiInformation regarding the blockchain platform that was used was gathered from the 22 selected papers. Ethereum was the most commonly used blockchain platform , followed by Hyperledger Fabric , while the least used platform was the New Economy Movement (NEM) blockchain platform . The rest of the studies did not state which blockchain platform was used.The final and crucial part of this systematic review involved reviewers scoring the 22 papers to evaluate their quality and the relevance of the blockchain usage. The scoring results are shown in The 2 (9%) papers published in 2016 each had a relatively high average overall score. In 2017, the average overall score of the papers was 80%, which was slightly lower than that of 2016. This dropped to 78% in 2018 and rose again to 80% in 2019.The identified themes are summarized in The results from this systematic review show that the majority of the research regarding blockchain technology in health care environments was focused on the management of EMRs, followed by biomedical research and education, remote patient monitoring, pharmaceutical supply chains, health insurance claims, health data analytics, and other potential areas.Out of 22 selected papers, 10 (45%) concentrated on the management of EMRs. EMRs, similar to EHRs or personal health records, involve electronic modeling, storage, and management of patients’ personal, medical, or health-related data. Traditionally, different systems have been used to store patients’ records separately across different service providers, where the service providers have control over the records, which may limit data sharing with other health care stakeholders.The application of blockchain in the management of EHRs will make data sharing among health care stakeholders easier, more transparent, and more trustworthy, and patients will have control over their own data. This is because the characteristics of blockchain technology, such as decentralization, immutability, data provenance, reliability, robustness, smart contracts, security, and privacy, make it suitable for the management and storage of patient EHRs .Azaria et al  presenteThe second application that would integrate EMRs is blockchain-based privacy-preserving data sharing (BPDS), which was developed by Liu et al . This usFan et al  developeLi et al  presenteA total of 23% (5/22) of the selected papers in this study indicated that blockchain could be applied in biomedical research and education fields. Blockchain technology has been used extensively in biomedical research and education to preserve data privacy, integrity, sharing, record sharing, and record keeping, especially in clinical trials  presenteAngeletti et al  proposedKleinaki et al  presenteRemote patient monitoring was another blockchain use case in the health care sector. Generally, remote patient monitoring includes the gathering of biomedical data from the body and mobile devices to enable the monitoring of patient status remotely outside of traditional health care environments, such as hospitals.Liang et al  presenteCichosz et al  proposedDrug or pharmaceutical supply chains are one of the use cases of blockchain technology in the health care sector, particularly health-related supply chain management. Drug or pharmaceutical supply chains involve the introduction of new drugs into the market, ensuring the safety and validity of medical products sold to end customers . BlockchIn this systematic review, only 1 paper out of 22 (5%) presented the implementation of a blockchain-based application for pharmaceutical supply chain management. Bocek et al  presenteHealth insurance is necessary for everyone to get affordable medical treatment. Blockchain’s characteristics, such as immutability, decentralization, transparency, and auditability of records, can benefit the process of health insurance claims in the health care sector. Nevertheless, only 1 paper from the 22 (5%) selected primary papers focused on this application. Zhou et al  developeOnly 1 paper out of 22 (5%) presented the use of blockchain technology in health data analytics. Blockchain in collaboration with other emerging technologies, such as deep- and transfer-learning techniques, was used to identify predictive analytics of health care data. Kotsiuba et al  stated tOf the 22 selected primary papers, 1 (5%) study by Talukder et al  includedThe main reasons (N=34) for the application or implementation of blockchain technology in the health care sector in this study’s selected papers were identified and categorized into the following groups: data integrity , access control , data logging , data versioning , and nonrepudiation .Data integrity is defined as the accuracy and consistency of the data or information stored in a system, which acts as an important component of information security. Data integrity was achieved by using blockchain technology in the health care sector. Li et al  implemenKotsiuba et al  also preCichosz et al  implemenThe proof-of-concept implementation of patient-facing and researcher-facing systems using blockchain technology to enhance data integrity was demonstrated by Maslove et al  and AngeAngeletti et al  stated tBlockchain in digital health technologies has also been particularly used in mHealth, which includes remote patient monitoring to ensure the safe and precise preservation of medical information to improve data integrity. Ichikawa et al  concludeAccording to Azaria et al , access Fan et al  used a pA study by Cunningham and Ainsworth  found thFor access control and the preservation of data, Fan et al  used theAccording to Cichosz et al , patientAccording to Liu et al , health Data access by health care professionals can be achieved through smart contracts of blockchain technology with cryptographic security. Using blockchain technology, Saravanan et al  implemenData logging is defined as an operation of gathering and storing information over a period of time. It allows tracking of all types of interactions, such as storage, access, or modification of data, files, or applications in a system. Data logging can be achieved by the application of blockchain technology in the health care sector.In clinical trials, Nugent et al  demonstrBocek et al  stated iZhou et al  stated tData versioning is defined as saving new copies of the data when any modification is made to the existing data. This helps to keep track of the data and ensure easy retrieval of any specific version of the respective stored data in a system. Kleinaki et al  implemenNonrepudiation guarantees the validity of data in a particular health care system, which cannot not be denied by anyone and ensures the originality and integrity of data. A study by Angeletti et al  used bloOne limitation of this systematic review study was that there were no published studies on the safety of blockchain technology in health care, so the safety aspect of blockchain technology cannot be reviewed. In addition, there were few papers published on the negative aspects of implementation of blockchain technology in health care. Most studies only published the positive aspects, which may have led to bias.Blockchain technology is still a new technology that has not been widely implemented in the health care sector. This study can be a guide for future research, implementation, and evaluation of blockchain technology in this sector. More research should be carried out regarding the implementation of blockchain technology in real health care environments for better understanding, characterization, and evaluation. Researchers should also focus on carrying out research on the safety of implementing blockchain technology in health care.This systematic review has presented an overview of the use and characteristics of blockchain technology in the health care sector. The findings show that blockchain technology research and application in the health care sector is still in its infancy but growing rapidly. Blockchain technology has started to develop from cryptocurrencies, such as Bitcoin, into various general-purpose technologies in many industries, including health care. According to the selected papers in this study, EMRs, biomedical research and education, remote patient monitoring, drug or pharmaceutical supply chains, health insurance claims, and health data analytics are the most common uses of blockchain technology in health care. The main reasons for the application of blockchain technology are to enhance data integrity, access control, logging, data versioning, and nonrepudiation of patient health records or other health information in health care settings.


The elucidated molecular mechanism of action had an accuracy of 94%. Empagliflozin’s pharmacological action mainly affected cardiomyocyte oxidative stress modulation, and greatly influenced cardiomyocyte stiffness, myocardial extracellular matrix remodelling, heart concentric hypertrophy, and systemic inflammation. Validation of these in silico data was performed in vivo in patients with HFpEF by measuring the declining plasma concentrations of NOS2, the NLPR3 inflammasome, and TGF-β1 during 12 months of empagliflozin treatment. Using AI modelling, we identified that the main effect of empagliflozin in HFpEF treatment is exerted via NHE1 and is focused on cardiomyocyte oxidative stress modulation. These results support the potential use of empagliflozin in HFpEF.The use of sodium-glucose co-transporter 2 inhibitors to treat heart failure with preserved ejection fraction (HFpEF) is under investigation in ongoing clinical trials, but the exact mechanism of action is unclear. Here we aimed to use artificial intelligence (AI) to characterize the mechanism of action of empagliflozin in HFpEF at the molecular level. We retrieved information regarding HFpEF pathophysiological motifs and differentially expressed genes/proteins, together with empagliflozin target information and bioflags, from specialized publicly available databases. Artificial neural networks and deep learning AI were used to model the molecular effects of empagliflozin in HFpEF. The model predicted that empagliflozin could reverse 59% of the protein alterations found in HFpEF. The effects of empagliflozin in HFpEF appeared to be predominantly mediated by inhibition of NHE1 (Na Current therapy for HF with reduced ejection fraction (HFrEF) is based on modulation of the neurohormonal activation, i.e. inhibiting deleterious activation of the renin–angiotensin–aldosterone and sympathetic nervous systems and amplifying the protective natriuretic peptide system. However, such strategies have not been so successful in several landmark trials in HFpEF4 and Emperor-Reduced with empagliflozin5. At this time, pivotal clinical trials are underway with both dapagliflozin and empagliflozin in HFpEF.Sodium-glucose co-transporter 2 inhibitors (SGLT2i) are a new class of drugs under evaluation for use in HF. They have yielded better than anticipated clinical benefit in HFrEF, with and without diabetes, in two expedited landmark clinical trials—namely, the DAPA-HF with dapagliflozin7. However, SGLT2i-induced glycaemic control alone seems insufficient to explain the reported cardiovascular benefits, and a comprehensive mechanistic explanation of the extra-renal cardioprotective effects of SGLT2i remains elusive. Recent evidence suggests that the benefit of SGLT2i in HFrEF may also be mediated by the sodium-hydrogen exchanger (NHE)9.The mechanism of action of SGLT2i includes blockade of the SGLT2 receptor at the proximal renal tubule. SGLT2 receptors are responsible for 90% of glucose reabsorption into the bloodstream, and their inhibition leads to glycosuria and reduced glycaemic levels10 and is particularly well-suited for investigating the mechanisms underlying drug effects. Our group has previously used AI to enhance our understanding of the mechanisms of action of sacubitril/valsartan11 and empagliflozin in HFrEF9. In the present study, we used AI to unravel the effects of empagliflozin in HFpEF at the molecular level.Artificial intelligence (AI) is an interdisciplinary field in which computational and mathematical models are used to disentangle key interactions within complex biological networksFigure +/H+ exchanger 1), with less prominent roles played by the other targets (SGLT2 and NHE3) , interlukin-1 β (IL-1β), intercellular adhesion molecule-1 (ICAM1), and the NACHT, LRR, and PYD domains-containing protein 3 (NLRP3) inflammasome, leading to reduced inflammation.Lastly, downstream of empagliflozin-mediated NHE1 inhibition, we observed strong modulation of cardiomyocyte stiffness, myocardial extracellular matrix remodelling, and heart concentric hypertrophy mechanisms. These empagliflozin-inhibited myocardial alterations are mediated by vascular endothelial growth factor A (VEGFA), NFκB-induced collagen alpha-1 chain (CO1A1), transforming growth factor β1 (TGF-β1), NFATs, and interleukin-10 (IL-10). In the models, these events were identified as downstream consequences of the more direct effects of NHE1 inhibition on oxidative stress mechanisms Fig. .To validate the selected protein effectors identified in silico, we measured their circulating concentrations in a prospective cohort of patients with HFpEF, before and 12 months after empagliflozin treatment . The patients’ characteristics are displayed in Supplementary material online, Table We evaluated the kinetics of NOS2, NLPR3, and TGF-β1 during the 12 months of empagliflozin treatment. The mean delta changes observed were: − 14% for NOS2 , − 6% for NLPR3 , and − 17% for TGF-β1 . These findings were in line with the AI-generated data.ANN analyses revealed that HFpEF modulation by empagliflozin is similar to the effectiveness shown by ACEI, ARNI, β-blockers, and MRAs, and superior to ARBs Table . Systemi12. In 2013, Paulus and Tschope13 postulated a novel paradigm for HFpEF, proposing that the impaired myocardial relaxation is triggered by systemic inflammation and elevated oxidative stress, which has recently been experimentally validated by others.HFpEF is characterized by normal systolic function and altered diastolic function. Diastolic dysfunction is mainly caused by defects in myocardium relaxation, ventricular filling, and distensibility, all of which are associated with myocardial extracellular matrix remodelling, cardiac stiffness, and concentric cardiomyocyte hypertrophy14.In the present study, we examined empagliflozin’s mechanism of action using state-of-the-art AI modelling, considering all of the pathways involved in HFpEF progression Fig. . Our ana15, which is required for proper NHE1 activity16. This leads to decreased Ca2+-induced mitochondrial swelling and a reduced release of reactive oxygen species (ROS) in isolated cardiomyocytes17.Empagliflozin has profound effects on the modulation of oxidative stress-related mechanisms in HFpEF, and NHE1 inhibition is the most prominent mediator of these effects through several mechanisms Fig. . First, 18. Blockage of AKT prevents stimulation of inducible NOS2 production, thus reducing oxidative stress19. In turn, AKT-mediated NHE1 activation is cell-dependent, with AKT activating NHE1 in fibroblasts but inhibiting NHE1 in the myocardium20, thereby further modulating this ion exchanger.Second, inhibited NHE1 cannot initiate the signalling cascade that leads to activation of the AKT1/AKT2/AKT3 module22. Finally, NHE1 inhibition also interrupts the activation of HDAC1 (histone deacetylase 1) by NFκB (nuclear factor κ B) downstream of AKT, thus further reducing the oxidative stress produced by HDAC1-mediated acetylation defects24.Third, functional blockage of NHE1 also prevents activation of the NFAT1/NFAT2/NFAT3 pathway, which would result in the induction of NOS2 expression and nitric oxide production through activation of interleukin-10 (IL-10)25 leads to lower production of VEGFA  and reduced NFκB-induced expression of CO1A1 .NHE1 inactivation not only affects oxidative stress, but its effects are also reflected in myocardial extracellular matrix remodelling, cardiomyocyte stiffness and heart concentric hypertrophy through multiple mechanisms Fig. . Impaire20, which is known to be involved in cytoskeletal connections. This loop makes TLN-1 unable to trigger integrin β-3 (IT-β3) induction, which regulates cellular senescence32, and inhibits further production of TGF-β1, which is essential for myocardial extracellular matrix development34.On the other hand, NHE1 acts in a loop with talin-1 (TLN-1)33. Moreover, lack of activation of both NFAT2 and NFAT3 could have beneficial effects in HFpEF since both proteins are reportedly involved in cardiac concentric hypertrophy35.Blockade of NFAT1 activation also leads to reduced IT-β3-TGF-β1 signalling, and downregulation of the pro-fibrotic and pro-stiffness cytokine IL-1033. TNF-α and ICAM1 are also involved in the inflammatory process, and also cannot be activated since their activators (NFAT2 and NFAT3) will no longer be activated by NHE137.Through the inhibition of several inflammatory factors Fig. , empagliIn our present analysis, we also examined the molecular effects of other therapies that have been studied for use in HFpEF, but that have not shown incontrovertible clinical benefit. Empagliflozin displayed a more intense anti-oxidative stress effect than the other studied drugs, which were more impactful in the other studied motifs. Prospective clinical validation is required to determine whether empagliflozin’s effects could be further boosted by its combination with additional treatments, which is beyond the aims of the present study.38. Collectively, our present data provide appropriate proof-of-principle validation of the identified in silico data at the clinical level.This study has several limitations. First, the presently applied AI modelling systems only utilize information that has already been described or uploaded in public repositories and demonstrated experimentally. As knowledge on SGLT2 is rapidly evolving, it is likely that the mechanism of action is even more multifactorial than currently envisioned. Second, the pharmacological effect of empagliflozin upon HFpEF  could encompass, and be affected by, other molecular signalling cascades, as well as by a wide variety of physiological affected pathways. Although our models show that oxidative stress signalling is the most robust pathway affected, it is likely that other pathways may also be involved. Finally, the clinical validation of selected bioflags was conducted in a small prospective cohort of ambulatory mildly symptomatic  patients with HFpEF, all of whom had a background of diabetes, which is currently the only indication for clinical use of empagliflozin. There remains a need for further investigation of the kinetics of such protein effectors under empagliflozin treatment in larger cohorts of patients with HFpEF, who have more severe symptoms (and likely more inflammation and oxidation), and with and without diabetes. The recent SOLOIST-WHF Trial showed benefit of sotagliflozin (an SGLT1 and SGLT2 inhibitor) in patients with diabetes and worsening HF (both HFrEF and HFpEF)Here we used AI modelling to examine the effects of empagliflozin in HFpEF. We found that the main effect of empagliflozin’s pharmacological action is exerted through inhibition of NHE1, and is focused on cardiomyocyte oxidative stress modulation. These effects could improve mitochondrial homeostasis and reduce ROS production in cardiomyocytes, thus potentially reducing downstream damaging mechanisms related to cardiac hypertrophy, stiffness, and remodelling. We also found that empagliflozin modulates systemic inflammation. The identified mechanism of action of empagliflozin is cogent within the context of current scientific knowledge and could prove to be an important asset in understanding the clinical benefit of SGLT2i in HFpEF patients.We first performed a thorough review of the current scientific evidence regarding specific proteins/genes relevant to HFpEF development, using PubMed (only considering articles in English). A summary of the pathways or motifs involved in HFpEF, and the number of effector proteins found in each motif, is presented in Supplementary material online, Table 40 and ArrayExpress41 public repositories, considering only studies performed in humans (Organism: Homo sapiens) using expression or protein arrays (Series type: Expression profiling by array and protein profiling by protein array)42. Transcriptomics data were analysed using the following tests: the Lilliefors (4 < n < 20) method to test the normality of samples; Student’s t-test or Wilcoxon rank-sum test to determine whether genes were differentially expressed between cohorts when samples showed a normal or non-normal distribution, respectively; and a multi-test correction approach with the false discovery rate (FDR), corrected using the Benjamini–Hochberg method. Genes displaying differential expression with an FDR < 0.05 in the dataset were deemed statistically significant. The genes/proteins that were differentially expressed in patients with HFpEF compared to healthy controls were incorporated into the mathematical models, and are presented in Supplementary material online, Table To better model HFpEF pathophysiology, we also searched for gene expression data in the Gene Expression Omnibus (GEO)43, to generate an efficacy benchmark for the use of empagliflozin in HFpEF treatment. The studied drugs included ACEI , ARBs , ARNI , beta-blockers (bisoprolol), and MRA (spironolactone and eplerenone) .FDA—Food and Drug Administration: Multidisciplinary review and Chemistry review.Product Monograph.In order to characterize empaglifozin and incorporate this information into its mechanism of action, an in-depth review of the following official documents:http://www.drugbank.ca/)43, Stitch (http://stitch.embl.de/)44, and SuperTarget (http://insilico.charite.de/supertarget/)45. Currently available publications regarding known targets and bioflags of empaglifozin were identified in PubMed (https://www.ncbi.nlm.nih.gov/pubmed/) on February, 2020. The specific search performed was the following:("Empagliflozin" [Title]) AND ("heart failure" [Title/Abstract] AND "preserved".[Title/Abstract] AND "ejection" [Title/Abstract]) OR "HFpEF" [Title/Abstract]).Next, when available, target information was retrieved from specialised databases, including DrugBank  and other proposed targets, such as sodium/hydrogen exchangers 1 and 3 (NHE1 and NHE3)47, by inferring the probability that a specific relationship exists between two or more protein sets. The system attempts to find the shortest distance between two evaluated protein sets, thus generating a list of proteins ranked by their association with the defined pathophysiology. In this study, ANNs were used for the following analyses: (1) comparison of individual targets of empagliflozin in HFpEF (as a whole) and in each pathophysiological motif; (2) identification of empagliflozin bioflags in HFpEF; and (3) efficacy benchmarking of empagliflozin and other drugs that have been studied in HFpEF , including analyses of each drug’s relationship with the disease and the efficacy of empagliflozin compared to the other tested drugs.ANNs are supervised algorithms that identify relationships between proteins (e.g. drug targets) and clinical elements of a network11.Finally, we developed mathematical models that integrated all available biological and pharmacological knowledge to simulate the behaviour of HFpEF and the predicted effects of empagliflozin, in terms of changes in protein activity within the human protein network. Briefly, the following steps were taken. First, input models were generated, which included information about empagliflozin and HfpEF and the protein/gene relationships identified in public databases. These models served to evaluate the inhibition or activation of one or more nodes within the protein network, and to determine how the signalling cascade was affected. Second, output models were generated, which included experimental RNAseq data (upregulated or downregulated genes/proteins) to frame the mathematical models with specific and direct information describing the disease  predicting the therapeutic intensity of empagliflozin in HfpEF , and (2) generating a detailed description of the molecular pathways involved in empagliflozin’s mechanism of action. The graphical representation was monitored to ensure representation of the proteins most modulated by empagliflozin in HfpEF. This approximation ensured in-depth visualization of the downstream effects of the drug over the different processes involved in HfpEF . A drug (empagliflozin) is expected to revert the conditions of a disease phenotype. Consequently, a drug should inactivate the active protein effectors of a patho-phenotype and activate the inactive. Thus, the number of effectors of the pathology achieving an output signal with the opposite sign as that defined in the condition characterization is measured. Using Dirac’s d (i.e. d(0) = 1, and zero otherwise):The “intensity” of the response is defined to measure the effect of a mechanism of action and compare it with others. The intensity is defined in this project as the amount of protein effectors or motives (#Eff) achieving the expected signal to ameliorate a disease. Assuming Ambulatory patients with diagnosis of HFpEF (according to ESC guideline criteria) and diabetes mellitus were prospectively included from November 2018 to May 2019 and followed for 12 months. At baseline, empagliflozin (fixed dose of 10 mg/day) was introduced in the treatment regimen. A blood sample was obtained at baseline (before empagliflozin initiation) and after 12 months with empagliflozin.During the baseline visit, patients provided written consent for biomarker analyses and for the use of their clinical data for research purposes. The study was performed in compliance with the law protecting personal data in accordance with the international guidelines on clinical investigations from the World Medical Association’s Declaration of Helsinki. Informed consent was obtained from all participants. The ethics committee for research with medicinal products of the Germans Trias I Pujol University Hospital  approved the study .Blood samples were collected, and serum was obtained by centrifugation for 10 min at 1500 rcf and stored at − 80 °C. All samples were obtained between 09:00 am and noon using the same protocol at the first visit and at 12 months. All samples were processed in their first freeze–thaw cycle.Commercially available ELISA kits were used, following the instructions of the manufacturer, for each validated protein in all patients. Data expressed as mean ± SEM.NOS2 was evaluated with the Human Nitric Oxide Synthase 2, Inducible ELISA kit . The range of the assay is between 15.6 and 1000 pg/mL. Sensitivity is < 5.5 pg/mL.NLRP3 was measured by the Human NACHT, LRP and PYD domains containing protein 3 ELISA kit . The assay range is 156–10,000 pg/mL and sensitivity is < 0.08 ng/mL.TGF-β1 was analysed by the Human Transforming Growth Factor Beta ELISA kit . The range of the assay is between 15.6 and 1000 pg/mL.Supplementary Information.


Unfortunately, direct experimental characterization of new viral variants lags their sequence-based identification. Here we help address this challenge by aggregating deep mutational scanning data into an “escape calculator” that estimates the antigenic effects of arbitrary combinations of mutations to the virus’s spike receptor-binding domain (RBD). The calculator can be used to intuitively visualize how mutations impact polyclonal antibody recognition, and score the expected antigenic effect of combinations of mutations. These scores correlate with neutralization assays performed on SARS-CoV-2 variants, and emphasize the ominous antigenic properties of the recently described Omicron variant. An interactive version of the calculator is at  Human coronaviruses undergo antigenic evolution that erodes antibody-based neutralization . This anprospectively measure how viral mutations impact antibody binding or neutralization. Deep mutational scanning can systematically measure the antigenic impacts of all possible amino-acid mutations in key regions of spike on monoclonal antibodies , which is the primary target of neutralizing antibodies to SARS-CoV-2  to zero .For each set of mutated RBD sites, we can define a quantitative score that represents the polyclonal antibody binding that remains after mutating these sites. This score is defined using the same principle as the site-wise escape calculator in the previous section: we reduce the contribution of each antibody by an amount that scales with how strongly it is escaped by each mutated site, and define the overall score as the fraction of all antibody contributions that remain  simply by clicking (or shift-clicking) on sites. This interactivity provides an intuitive understanding of the antigenic structure of the RBD, and shows how the calculator is performing simple transformations directly on experimental data. We encourage interactive use of the calculator so it acts as an aid to augment human interpretation, rather than a black-box computational method. However, we note that in addition to the interactive calculator at There are caveats that should be kept in mind when using the escape calculator. First, the calculator only considers sites in the RBD, and ignores mutations to other regions of spike. Second, the calculator assumes the neutralizing activity of human polyclonal serum is represented by an equipotent mix of the monoclonal antibodies that happen to have been previously characterized by deep mutational scanning. Third, the calculator simply averages site-level escape measurements across antibodies, and does not yet implement a real biophysical model of the combined activity of multiple antibodies . FinallyDespite all these caveats, the escape calculator yields binding scores that correlate with experimentally measured neutralization titers. In addition, the actual antigenic evolution of SARS-CoV-2 seems to follow the principles captured by the escape calculator: variants of concern generally have combinations of mutations calculated to have additive effects on antibody escape  rather than combinations calculated to have redundant effects . We suspect the calculator works well because the RBD is the dominant target of neutralizing activity  were elicited by infection with SARS-CoV-1 and so are excluded from the datasets used for the calculations in this paper, although the calculator has an option (eliciting_virus) that allows optional inclusion of these antibodies. The majority of the antibodies were originally isolated from cohorts of individuals infected with SARS-CoV-2 in the first half of 2020, and were initially characterized by the Crowe lab (https://jbloomlab.github.io/SARS2_RBD_Ab_escape_maps/ and available in raw form at https://raw.githubusercontent.com/jbloomlab/SARS2_RBD_Ab_escape_maps/main/processed_data/escape_data.csv (see https://github.com/jbloomlab/SARS2_RBD_Ab_escape_maps/blob/bioRxiv_v1/processed_data/escape_data.csv for a stable version of the raw data corresponding to that used in this paper).The experimental data used by the escape calculator are drawn from seven previously published deep mutational scanning studies : essentially this corresponds to scaling the site escape values for each antibody so that a value of one corresponds to the larger of the maximum escape at a site or 20 times the median value across sites. The results reported in this paper use the normalized site-level metrics, although the calculator has an option  to also use the non-normalized values.The deep mutational scanning measures an escape fraction for each tolerated RBD mutation against each antibody, which represents an estimate of how completely that mutation escapes antibody binding . The variable s represents how dramatically binding is lost for mutations at sites of escape that are not the strongest: larger values of s means mutations at even moderate sites of escape reduce binding a lot. In this paper we report calculations with s = 2, although the calculator has an option (mutation_escape_strength) to choose other values. We then define the escape map after the mutations Let The overall antibody binding scores represent the fraction of antibodies that still bind, and are calculated simply as https://jbloomlab.github.io/SARS2_RBD_Ab_escape_maps/escape-calc/ is imlemented using Altair  (The interactive calculator at https://github.com/jbloomlab/SARS2_RBD_Ab_escape_maps/, and has all the same options as the interactive calculator.A Python module that implements the calculations is at https://github.com/jbloomlab/RBD_escape_calculator_paper/tree/main/results/neut_studies.For For


Triggering Receptor Expressed on Myeloid cells (TREM)-1 has been described as a predictive marker for anti-Tumor Necrosis Factor (TNF)-α monoclonal antibody (mAb) therapy responsiveness in patients with inflammatory bowel disease (IBD). Here we investigated expression of TREM-1 specifically in CD14+ monocytes in relation to anti-TNF response. The pretreatment TREM-1 expression levels of CD14+ monocytes of Crohn’s disease (CD) patients were predictive of outcome to anti-TNF mAb therapy, with low TREM-1 expression associated with response to anti-TNF. FACSorting of CD14+ monocytes with different TREM-1 levels showed that differentiation towards regulatory CD206+ M2 type macrophages by anti-TNF was suppressed in CD14+ monocytes with high TREM-1 expression. Activity of the Fcγ-Receptor and autophagy pathway, both necessary for M2 type differentiation and the response to anti-TNF, were decreased in CD14+ monocytes with high expression of TREM-1. We confirmed that the activity of the Fcγ-Receptor pathway was decreased in the CD patients that did not respond to anti-TNF therapy and that it was negatively correlated with TREM-1 expression levels in the CD patient cohort. In conclusion, our results indicate that TREM-1 expression levels in CD14+ monocytes associate with decreased autophagy and FcγR activity resulting in decreased differentiation to M2 type regulatory macrophages upon anti-TNF mAb treatment, which may explain anti-TNF non-response in IBD patients with high expression levels of TREM-1.The expression of  Triggering Receptor Expressed on Myeloid cells (TREM)-1 with response to anti-TNF therapy, Gaujoux et al.  levels in serum and anti-TNF response, with high levels being associated with anti-TNF non-response (Despite the considerable efficacy of anti-Tumor Necrosis Factor (TNF)-α monoclonal antibody (mAb) therapy in the treatment of inflammatory bowel disease (IBD), about one third of patients do not respond . The reax et al.  investigt et al. , 10 reporesponse . AlthougTREM-1 expression in CD14+ monocytes is related to future anti-TNF response, and what could be the functional link between the two.Over the last decade we have shown that anti-TNF mAbs interact with Fc Receptors on myeloid cells, and that this interaction is crucial for the therapeutic efficacy in IBD –13. Thro®, AbbVie, Wavre, Belgium) or isotype control IgG1  was added (both 10 µg/ml) and cultured for another 3 days. Similar, in the mixed lymphocyte reaction (MLR) PBMCs of two different donors were mixed in a 1:1 ratio and the agonistic mouse monoclonal TREM-1 antibody  or isotype control , both 10 µg/ml, was added together with anti-TNF or IgG after two days. In the MLR with the TREM-1 inhibitor LR12 , twelve hours before adding anti-TNF (after 2 days of culture) LR12 (50 µg/ml), was added. Upon the addition of anti-TNF or isotype control (10 µg/ml) the LR12 was refreshed and every 24 hours afterwards until a total of 5 days of culture. 6-thioguanin  was dissolved in dimethylsulfoxide (DMSO) and added together with the anti-TNF with DMSO as control as reported before  from healthy volunteers  were isolated by Ficoll Paque density-gradient centrifugation. After washing, monocytes were isolated by Percoll density-gradient centrifugation. TREM-1 low and high sorted CD14+ monocytes were cultured in RPMI supplemented with 10% heat-inactivated FCSin a 1:5 ratio with untouched T-cells isolated from PBMCs of a different donor by negative isolation . After 2 days the full monoclonal anti-TNF adalimumab , Twenty-four anti-TNF naïve patients with Crohn’s disease (CD), initiating anti-TNF therapy because of active endoscopic disease (presence of ulcerations), were enrolled. Prior to treatment initiation, a 20 ml blood sample was taken, and PBMCs were isolated by density centrifugation. After cell isolation, samples were cryopreserved with DMSO using Mr Frosty  for 24 hours and afterwards stored in liquid nitrogen. Endoscopic remission was assessed at 6 months after therapy initiation, and defined as a complete absence of ulcerations . PatientFrozen PBMCs from the CD patients were thawed and incubated with αCD3-APC(17-0038), αCD8-APCeF780(47-0087), CD14-PerCP-Cy5.5(45-0149), αCD19-PE(12-0199-80), αCD45-AF700 (56-9459) and αCD4-BB515  at 4°C for 30 minutes. Before acquisition, samples were filtered through a 40 µm mesh cell strainer, and DAPI added to a final concentration of 0.1 µg/ml. CD14+ cells were sorted (BD FACSAria III), lysed  and stored at -80°C until RNA extraction. Freshly isolated PBMCs from healthy individuals were stained for αCD14-PE  and αTREM-1-AF647 and CD14 TREM-1 low and high monocytes were sorted on a SONY SH800S or BD FACS ARIA III as shown in Lysed CD14+ sorted cells (obtained from CD patients initiating anti-TNF therapy) were processed for RNA extraction using the AllPrep DNA/RNA Mini kit , according to the manufacturer’s instructions. The mRNA from the TREM-1 low and TREM-1 high CD14+ monocytes from the healthy individuals was isolated by the ISOLATE II RNA Mini Kit  or the Agencourt RNAdvance Cellv2 kit  according to manufacturer’s conditions.TREM-1, FCGR2B, FCGR3A , TREM-1_MB, TREM-1_SV, TREM-1_X2 and bACTIN . mRNA lFor microarray analysis of the TREM-1 low and TREM-1 high CD14+ monocytes the RNA quality (integrity and concentration) was measured on an Agilent 2100 Bioanalyzer. All samples had a RNA integrity number (RIN) above 9,0 and concentration above 250 pg/µl. Microarray analysis was performed using a Clariom S Pico Human Assay  using a Gene Titan MC system (Affymetrix) according to the standard protocols of the Dutch Genomics Service and Support Provider . The data was normalized using robust multiarray analysis and analyzed by Transcriptome Analysis Console software (Thermo Fisher Scientific).TREM-1 high and low FACSorted cells were lysed in RIPA buffer , run on 15% SDS-PAGE gels under reducing conditions and transferred to an Immobilon-P PVDF Membrane . The membrane was blocked by incubation in 5% non-fat milk  in TBST (TBS + 0.1% Tween-20) for 2 hours at room temperature (RT) and subsequently incubated in LC3  or Cathepsin S  antibodies in 2% milk/TBST overnight at 4° C. After incubation membranes were washed 3 times with TBST, incubated with HRP conjugated secondary antibodies  in 2% milk/PBST for 2 hours at RT. Expression was detected by Lumilight Plus . Afterwards blots were stripped in stripping buffer (ThermoFischer Scientific) for 10 min at RT and incubated with β-actin  as a loading control. The Optical Density (OD) of the protein bands was determined using ImageJ software.post hoc test, analysis of variance (ANOVA) followed by Sidak’s post hoc, Mann Whitney test, paired t-test, unpaired t-tests or Wilcoxon signed rank test using Graphpad Prism 8.3.0 , an allogenic immune reaction created by mixing peripheral blood mononuclear cells (PBMCs) of two different donors, as extensively reported earlier  is/are responsible for the found difference in anti-TNF-induced differentiation between low versus high TREM-1 CD14+ monocytes. Therefore we performed gene expression analysis on FACSorted TREM-1 low versus TREM-1 high CD14+monocytes , and as FCGR3A, essential for the induction of regulatory type macrophages by anti-TNF  ratio, and showed that this FcγR A:I ratio is altered in IBD. Calculating this FcγR A:I ratio showed it was significantly increased in TREM-1 low versus TREM-1 high CD14+ monocytes . In conclusion, our results indicate that TREM-1 expression levels in CD14+ monocytes associate with both decreased FcγR activity and autophagy levels resulting in decreased differentiation to M2 type regulatory macrophages upon anti-TNF mAb treatment, which may explain anti-TNF non-response in IBD patients with high expression levels of TREM-1.The whole blood with IBD , 9, 10 Iwith IBD  and Verst et al.  that shoundantly  and is iundantly , 14, 17.escribed , and is escribed . In addiescribed . Moreoveresponse , 13, whiresponse . Transmeresponse . Althoughttps://www.ncbi.nlm.nih.gov/, GSE165522.The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found below: The studies involving human participants were reviewed and approved by Institutional Review Board approved IBD Biobank of University Hospitals Leuven, Belgium (B322201213950/S53684) and The Medical Ethical Committee of the Amsterdam UMC, METC 2009_113). The patients/participants provided their written informed consent to participate in this study.MP, BV, MW and PK performed experiments and analyzed and discussed data. MF and SV provided patient samples and discussed data. PK and MW supervised the study. MP and PK wrote the manuscript with input of all authors. All authors contributed to the article and approved the submitted version.Part of this research has been funded by the European Crohn’s and Colitis Organization Research Grant and by the Belgian IBD Research and Development group Research Grant (both to BV).MF received research grants from Amgen, Biogen, Janssen, Pfizer, Takeda, consultancy fees from Abbvie, Boehringer-Ingelheim, Janssen, MSD, Pfizer, Sandoz, Takeda, and speakers fees from Abbvie, Amgen, Biogen, Boehringer-Ingelheim, Falk, Ferring, Janssen, Lamepro, MSD, Mylan, Pfizer, Takeda. SV received research grants from MSD, AbbVie, Takeda, Pfizer, Janssen, consultancy fees from AbbVie, MSD, Takeda, Ferring, Genentech/Roche, Shire, Pfizer Inc, Galapagos, Mundipharma, Hospira, Celgene, Second Genome, Progenity, Lilly, Arena, GSK, Amgen, Ferring, Gilead and Janssen, and speakers fees from AbbVie, MSD, Takeda, Ferring, Hospira, Pfizer, Janssen, and Tillots. BV reports financial support for research from Pfizer; lecture fees from Abbvie, Biogen, Chiesi, Falk, Ferring, Galapagos, Janssen, MSD, Pfizer, R-Biopharm, Takeda and Truvion; consultancy fees from Janssen, Guidepont and Sandoz; all outside of the submitted work. MW received research grants from GlaxoSmithKline, Boehringer Ingelheim and Stryker and speakers fees from Takeda and Janssen.The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.