Individually obtained expert’s AUROCs were significantly lower . Conclusions: Laguna ONhE’s agreement with the experts is high, particularly where the diagnosis may be more obvious by the appearance of the ONH.Background: Optic nerve head (ONH) interpretation is a glaucoma screening method which may be influenced by criteria variability. Laguna ONhE software is a low-cost and non-invasive method of ONH analysis. Methods: We analysed the results of the Laguna ONhE application, interpreting 552 ONH images from the ACRIMA database, publicly available on the Internet, and compared them with the opinion of five experts. Diagnostic agreement was investigated using Cohen’s kappa (κ) with 95% confidence. Results: The kappa concordance index obtained with Laguna ONhE and the majority of the experts’ criterion (0.77) was significantly higher compared to that obtained with ACRIMA and the majority of the experts’ criterion (0.61). In 44.7% of the cases there was absolute agreement among the 5 experts and the Laguna ONhE program. Removing borderline cases from the analysis yielded increased diagnostic agreement (0.81). The area under the receiver operating characteristic (AUROC) of the Laguna ONhE program (0.953,  Glaucoma is a group of pathologies affecting the optic nerve and the leading cause of irreversible blindness. Predictions estimated that this disease may affect 80 million people worldwide by 2020 and 111.8 million by 2040 [In 2019, a clinical database of images was published to help evaluate a diagnostic method for glaucoma using artificial intelligence (AI) . The stuThe ACRIMA database’s images were graded by two glaucoma experts and no other clinical information was considered while labelling the images. As a result, the authors do not guarantee that the classification is correct and admit that the automatic procedure they designed has poor results against this classification, obtaining a poor area under the curve (AUROCs): AUROC = 0.7678  = 68.41–81.81%), with sensitivity a of 0.689 and specificity of 0.702.The Laguna ONhE program is a colourimetric technique used to estimate the presence of haemoglobin and its effects on the optic disc ,5,6. In Some differences between the authors’ methodology (ACRIMA) and the Laguna ONhE program are that ACRIMA automatically crops and resizes the optic disc screenings to a similar size. Hence, there is no quality control of the images and the size of the disc. On the contrary, the Laguna ONhE program identifies the position of the optic disc and segments it. Furthermore, if there are enough previous images of the same patient available, it compares the optic nerve size and takes it into account for the analysis of the case . A finalThe main purpose of this study is to evaluate the concordance of five glaucoma experts and the Laguna ONhE program in the analysis of images published on this ACRIMA database.As a secondary objective, the diagnostic capability of the Laguna ONhE is evaluated against the consensus of the experts.This was a cross-sectional study, approved by the Institutional Review Board of our university hospital, the Hospital Clinico San Carlos of Madrid, and carried out in accordance with the principles of the Declaration of Helsinki.ACRIMA is a public database including glaucoma or normality labelled images that can be used for the evaluation of glaucoma classification methods. The images of this database derive from the ACRIMA project (TIN2013-46751-R) funded by the Ministry of Economy and Competitiveness of Spain, which aims for the development of automatic algorithms for retinal disease assessment.® capture System. These series of images are public and the authors of this study were not involved in their production .The ACRIMA database is composed of 705 fundus images  obtained from patients who have previously given their consent and collected in accordance with the ethical standards of the Declaration of Helsinki. All patients were selected based on their criteria and with no other clinical information by two glaucoma experts with at least fifteen years of experience. Fundus images from the ACRIMA database, focusing on the optic disc, were taken using the Topcon TRC retinal camera and IMAGEnetFirstly, the 705 images were evaluated using the Laguna ONhE program. The application has an automatic image quality assessment system which is applied before performing the optic nerve head colourimetric analysis. From this pre-quality analysis, 153 images were eliminated from the study.Overall, 552 optic nerve head images were suitable to analyse, 236 of which were normal and 316 glaucomatous, according to the original classification of the ACRIMA study.The images were then analysed using the Laguna ONhE program, considering glaucomatous those which had a value of less than −15 on the Globin Discriminant Factor (GDF) index, the calculated cut-off value to achieve a specificity of 99% when identifying normal cases. In routine use when screening for Laguna ONhE, cases with a GDF value above 0 (specificity 95%) are considered normal, while borderline cases between the values of 0 and −15 are considered doubtful. In a first analysis, the doubtful images for Laguna ONhE were considered as normal and in a second analysis they were evaluated separately from those with higher diagnostic certainty.At the same time, five experts in the diagnosis and treatment of glaucoma were asked to visually classify these cases as normal or glaucoma according to their criteria. The majority of the results were then calculated; i.e., the percentage of three or more concurring opinions. To evaluate the concordance between the five glaucoma experts and the Laguna ONhE program, an analysis of inter-rater agreement was performed using Cohen’s kappa (κ) with a 95% confidence interval.Subsequently, Receiver Operating Characteristics (ROC) curves were assembled and Areas Under the ROC curves (AUROCs) were used to assess the diagnostic capacity of each procedure. AUROCS were compared using the DeLong method .The agreement between the original ACRIMA classification, the five glaucoma experts, and the Laguna ONhE program were analysed. AUROCs were calculated by taking into consideration two different gold standards: the majority opinion of the experts and the results obtained using the Laguna ONhE program. In order to assess Laguna ONhE’s diagnostic capability, the majority opinion of the experts was considered as the gold standard. We evaluated the number of experts who agreed on the diagnosis of glaucoma considering the −15 limit for the Laguna ONhE GDF index as the gold standard.For the second calculation, the cut-off point GDF −15 was established as the gold standard. The optic nerve heads were classified as normal when the GDF index was >−15, and as pathological when the GDF value was <−15. Doubtful cases, i.e., those with a GDF index value between 0 and −15, were evaluated separately.p-values were <0.05.The clinical statistical analyses were performed using the Excel 2016 program  and MedCalc . Significant In summary, the agreement between the original ACRIMA classification, the experts, and Laguna ONhE can be considered moderate, and as substantial between Laguna ONhE and the majority opinion of the experts.The Kappa index obtained using Laguna ONhE and the majority of the experts’ criterion  was significantly higher compared to that obtained with ACRIMA and the majority of the experts’ criterion .p = 0.052. Therefore, the Laguna ONhE criterion is not inferior to the majority opinion of the experts. However, it was significantly higher than that the ROC area obtained using the individual experts’ classification .A comparison of ROC curves yielded a In 247 cases (44.77%), there was absolute agreement among the 5 experts and the Laguna ONhE , but majority opinion apparently discriminates better than individual experts .The largest discrepancies were observed in 6 cases considered to be normal by ACRIMA and three experts, whilst Laguna ONhE and two of the five experts categorized them as glaucoma  which is considered “Almost perfect agreement”. Particularly relevant is the fact that, when the GDF was above 0, the majority of experts’ criterion indicated normality in 95.3% of the cases, in line with the theoretical specificity of 95% with which this cut-off has been designed.Achieving a completely reliable gold standard for the diagnosis of glaucoma is virtually impossible and continues to be a cause for debate . There aIf the subjective interpretation of the optic disc stated by an expert was a sufficient criterion, other measurements such as the visual field, the optic nerve fibre layers thickness using optical coherence tomography (OCT), or the analysis of the optic disc with colourimetry using programs like Laguna ONhE would not be needed. However, expert opinion does not seem to be enough to reliably establish glaucoma diagnosis and, in any case, the sensitivity and specificity of this criterion are not always optimal in addressing the issue.Laguna ONhE is a method for the colourimetric evaluation of the distribution of haemoglobin at the optic nerve head. By colorimetry, the nerve tissue is compared with the vessels of the central retinal artery and vein branches which are used as a standard or reference value. This allows for the estimation of the presence of haemoglobin in each area of the optic disc. The distribution of haemoglobin in the inferior and superior zones of the nerve in relation to the nasal and temporal zones, and the relationships between the size and shape of the cupping, weighted according to the size of the optic disc, as well as other factors such as the presence of peripapillary atrophy provide the globin or glaucoma discriminant function, the GDF index, in which the deep learning glaucoma vs. normal classifier has a high influence, with an approximate range of −100 to +85 units .The application is fully automated using several AI mechanisms, including deep learning convolutional neural networks, to determine the quality of the image, segment the limits of the optic nerve head, identify its laterality, establish the presence or absence of the entire optic disc and its surroundings, achieve the segmentation of its vessels, and, finally, identify the existence of normal or glaucoma characteristics . Other aAI and convolutional neural networks have been used in image recognition. Some recent studies use optic nerve head imaging, developing an objective machine learning classification pattern for glaucomatous optic disc classification ,17,18. IBy analysing 1542 retinographies using the convulsive neural network, Deep Convolution Network and Resnet, another study conducted by Ahn et al. correctly detected both early and advanced glaucoma with an accuracy of 92.2% .The Laguna ONhE program is commercially available and has been applied in glaucoma screening in optometry and ophthalmology centres mainly in Central Europe and Scandinavia. It has been used since 2013  in the sThe application has been compared with other diagnostic methods such as OCT and angio-OCT. Its usefulness in the diagnosis of early glaucoma and its power associating its information with the irregularity of the visual field have been demonstrated ,8,22. NoConfirming that a patient has glaucoma is highly complex. It is believed that half of glaucoma patients remain undiagnosed and many other patients with glaucoma are incorrectly diagnosed . In the The question to be considered is as follows: which criteria should be taken into account to efficiently identify such a prevalent disease in order to correctly diagnose a glaucoma patient?The most important criteria for a glaucoma screening procedure are that the method should be both fast and cost-effective so as many people as possible can be screened. Additionally, the method should be very specific in order to not overburden the health care system. As glaucoma is generally a slowly progressing disease, it is preferable to choose a procedure with high specificity rather than one with high sensitivity. By repeated application of the screening method, previously undetected cases could be distinguished before advanced stages of the disease are reached. If such a screening approach was not chosen, mass diagnoses would be unfeasible owing to saturation of the health care system. It should also be considered that diagnostic confirmation requires demonstrating the worsening of the patient’s condition, even within the limits of normality, and in these cases an isolated examination is not enough. Therefore, the chosen method should allow for proof of the progression of the disease.Intraocular pressure (IOP) is the main risk factor for developing glaucoma. However, IOP measurement cannot be the criterion for mass screening because it bypasses low-tension glaucoma. Furthermore, it requires training the examiners and the use of devices that can only be legally used by ophthalmologists and optometrists. Moreover, corneal thickness is a confounding factor that affects the results. As a result, the above factors make this method an unfeasible initial diagnosing criterion.Visual field testing is also not an adequate option because it requires complex instruments that are not always available and qualified personnel. In addition, since this method has low specificity, it requires good patient cooperation or else several training tests are needed until the learning effect is ruled out. Even if equipment to perform perimetry becomes cheaper, for example, by using virtual reality equipment, its mass application with high specificity is unattainable.Of course, more sophisticated techniques, such as OCT, are not available to every ophthalmological clinic because of their cost, the technical difficulties, and the complex interpretation of the images.The observation of a suspicious optic disc could be a suitable initial evaluation if experts competent to interpret the retinography images and establish a consensus diagnostic criterion were available everywhere. The latter method is not perfect because its specificity does not exceed 90%, meaning that many subjects would have to be revaluated. It also requires glaucoma experts with extensive experience which is impossible since they are not always available for mass screening of the population.Moreover, the results may be limited and variable depending on level of expertise. Our results, obtained by glaucoma experts with extensive experience, are among the most concordant of all published studies ,31,32,33The unlikelihood of having available experts for mass evaluations is itself enough to exclude this as a population-wide method. Instead, a system that is easy to use, automatic, cheap, fast, with high specificity, and with a sensitivity at least equivalent to the best expert or a pool of experts, could help with the establishment of mass screening.The Laguna ONhE program can be used on optic nerve head images, obtained using simple fundus cameras, with minimal training. If a retinography network system, such as the ones used in retinal monitoring of diabetic patients is also available, the feasibility of mass population screening is even greater.The level of specificity of Laguna ONhE to identify glaucoma cases GDF < −15) is 99%. This has been verified in several previous studies and is currently being verified in retrospective and prospective studies of diabetics  is 99%. . Our expFrom the above, it can be deduced that the balance between sensitivity and specificity in the Laguna ONhE program is superior from that of isolated experts. Indeed, we have found that the Laguna ONhE results are similar to the majority opinion of experts, and also exceeds it when other less subjective information is taken into account. The Laguna ONhE program offers quantitative data not only related to the morphology of the nerve , but alsAdditionally, it allows images to be monitored which can lead to a diagnosis if significant changes, even at the limits of normality, are observed, and to compare the changes throughout the follow-up period .The Laguna ONhE program could be used as a large-scale glaucoma screening method in optometry and ophthalmology centres.Those patients suspected of having glaucoma should be referred to ophthalmology clinics to complete the ophthalmological examination with intraocular pressure and pachymetry measurements, retinal nerve fibre layer analysis using SD-OCT, and perimetry. Thus, automated retinography analysis using the Laguna ONhE program would be the first step in the diagnosis of glaucoma.It should be emphasized that Laguna ONhE program should only be applied for glaucoma screening to identify glaucomatous changes in the ONH. To confirm the diagnosis, it would also be necessary to perform applanation tonometry applying a correction factor according to the central corneal thickness, daily curve of intraocular pressure, and optic nerve fibre layer measurements using SD-OCT to determine a diagnosis of pre-perimetric glaucoma, as well as computerised perimetry in already established functional damage.In summary, Laguna ONhE’s agreement with experts on the assessment of the optic nerve head is high. Its diagnostic classification agrees with that of the majority of experts in cases where the diagnosis may be made more obvious by the appearance of the optic nerve head; in cases where Laguna ONhE classifies as doubtful, the agreement between experts is low. Therefore, the Laguna ONhE program is an automated optic nerve head assessment system that could be useful in glaucoma screening and shows high agreement with the assessment of the glaucoma experts.Retinography analysis in primary care centres, ophthalmological clinics, and optometric centres can be an effective glaucoma screening system owing to its high specificity. Despite the need for future studies implementing the Laguna ONhE application in combination with a retinography network system, perimetry, and intraocular pressure measurement, it may eventuate as a mass glaucoma screening system, particularly considering its current specificity and cost-effectiveness.


Dingle Peninsula 2030 has emerged as an international case study of a collaborative regional sustainability project, whereby a wide range of initiatives, beyond the initial remit of the project, have emerged in the area. This holistic scale of action is required for effective climate action. Using the Sustainable Development Goals (SDGs) as a framing, the interrelated nature of climate action has been shown through this study. In setting out to undergo energy projects a wide range of new initiatives emerged as community members became engaged in the process. Initiatives have emerged related to energy, transport, agriculture, education, tourism and employment, in what we have coined the ‘diffusion of sustainability’.Instilling a collaborative approach can widen participation to a range of stakeholders, enabling the diffusion of sustainability and increasing local capacity to meet decarbonisation targets to mitigate against climate change.  Dingle Peninsula 2030, this paper highlights how community initiatives can offer an insight into the future potential of such projects to elicit an acceleration of the transition to a low-carbon society. In Ireland, community-led initiatives have gained less attention than their European counterparts , as well as Ireland’s national electricity distribution system operator (ESB Networks) and our research institute (MaREI). This transdisciplinary configuration is grounded within the local community, whilst also having capacity at a national level. From a research perspective the Dingle Peninsula 2030 initiative has been investigated with relation to social network analysis [The Dingle Peninsula has a population of 12,500. As outlined by McGookin et al. [analysis , the enganalysis  and partDingle Peninsula 2030 acts as a community-based, transdisciplinary collaboration that has had impact both at a local community level, and on a wider national and international stage. It offers valuable learnings on the real-world transformation process to guide efforts elsewhere [Despite emerging as recently as 2018, lsewhere . The casNiche social and technical innovations are emerging that differ radically from the prevailing socio-technical system and regime but are able to gain a foothold in particular applications, geographical areas or with the help of targeting policy support. Community contexts offer forth the experimental space in which niche innovations can emerge. They can be technical, social or governance related. The global challenge of climate change is experienced within local, community contexts, and as such, these contexts often bring about novel solutions that operate outside of global multi-lateral deliberations, with the potential to provide insights towards policy implementation. While niche innovations often act within specific and unique community contexts, they can be extrapolated from to provide wider learnings. While community action can assist in informing policy developments, there is a need to develop mechanisms which can represent instances of success. Here, the diffusion of sustainability is offered as a framing through which the emergence of novel innovations can be outlined in real time, using the analysis of a case study context.Dingle Peninsula 2030.Recent work on the diffusion of transformative innovations for sustainability  has highAs such, the diffusion of sustainability is investigated in this study, through an analysis of a collaborative, transdisciplinary approach to sustainable transitions in the community context. Nineteenth century social theorist Gabriel Tarde illustrated the idea of logical laws, whereby innovations which are aligned with the rational logic of a given society are more likely to spread than those which are not . KinnuneDiffusion can be seen to occur through three mechanisms: internal influence, external influence and mixed influence. At a most cursory level, internal influence for the diffusion of innovation is concerned with the spread of new ideas and practices through interpersonal communication between interpersonal contacts ,24. ExteThe last number of years has seen a growth in the emergence of research investigating the diffusion of social innovations related to transformations towards sustainability. The investigation of social innovations with relation to isolated rural locations, similar to the case study context under investigation, has recognised the importance of both local and external actors to processes of change in rural locations, but emphasised the central importance of local knowledge and local activities ,26. The Diffusion, in keeping with the foundational work of Rogers , can be “a process that helps create a vibrant economy and a high quality of life, while respecting the need to sustain natural resources and protect the environment. It expresses the principle that future generations should live in a world that the present generation has enjoyed but not diminished.” , p. 30.Our definition for the diffusion of sustainability is understood to be aligned with the definitions of both Palloni  and ClouThe SDGs offer a useful mechanism for capturing social innovations, with nine socially-orientated goals represented within the framework  . Our ownDingle Peninsula 2030. The aim is to outline the different emergent projects and initiatives which have developed in light of Dingle Peninsula 2030 through a process of diffusion. The list of projects is based on those that the four core partners in Dingle Peninsula 2030 were involved in between January 2018 to June 2021. In addition, effort has been made to map the various spin-off projects led by key local actors that took part in Dingle Peninsula 2030 events or initiatives and have since sought to champion sustainability projects of their own.Within this piece of research, diffusion has been investigated and highlighted through a qualitative approach combining exploratory interviews, ethnographic attendance of events and meetings, and secondary desk research. Also, once compiled, the diffusions outlined have been cross referenced with the primary project manager for A key challenge associated with this task is the fact it may take years for transformations to take place. In the long term, a more in-depth data-centric approach to diffusion research would be insightful. The development of retrospective historical analysis of social diffusion is an area in which new mechanisms and methods are being developed, informed by contemporary network concepts and models . Such apA range of different diffusion models has been developed within sociology and applied to a range of different social situations. There is, however, an issue with regards to the suitability of these models in certain situations, due to the need to internalise theories, assumptions and constraints within the models with relation to a range of factors such as socio-economic status, preferences, decision-making processes and individual constraints amongst other factors. As has been noted, ‘the conditions for identification of a diffusion process from observables are quite hard to meet, much harder than what is normally implied in traditional applications of diffusion theories and models to sociological and demographic analysis’ .Studying the diffusion of sustainability, in relation to environmental issues, is a well-developed field within tourism –38. For Dingle Peninsula 2030, the range of different diffusions leads to a number of issues with relation to the theories, assumptions and constraints which would be needed within the model. For example, the factors influencing the diffusion of technology are vastly different from the diffusion of climate education. Alongside this, due to the increased national and international attention given to decarbonisation and sustainability, the external factors placed upon the diffusion process would also need to be attributed within the model. Further research, detached from the active nature of this research project’s relationship with Dingle Peninsula 2030, may prove insightful with relation to the diffusion of sustainability in the region. The need for a broader time frame for an in-depth investigation of diffusion must be noted here. With this in mind, the different aspects of the diffusion process have been highlighted in a visual contained in the results section . Despite this, the concept of mixed influence [There are, however, several limitations that have been encountered that must be acknowledged. Firstly, the issue of attribution must be addressed. Aligning with the multi-level perspective , transitnfluence , must beDingle Peninsula 2030, is restricted. For example, the impact  associated with key diffusions such as the community energy groups  installations, savings resulting from home energy improvements or electric vehicles [The third key methodological challenge is determining the extent of the impact for each of the various initiatives. For energy and climate goals, the COvehicles . HoweverDingle Peninsula 2030 will continue to develop or dissolve over time, and a range of initiatives will emerge and/or fade away. What we have set out here is an active understanding of the diffusion of sustainability in real time, which lays a foundation for continued study in this area both concerning the project in question and other similar initiatives nationally and internationally.Through outlining the different diffusions, the foundation for further investigations which can build on what is presented here, analysing the diffusion of sustainability in a retrospective manner, has been set. Alongside the diffusions, more initiatives and projects will emerge, with the importance of mixed influence as outlined through the theoretical insights from studies of relevant diffusions of innovation. The landscape will change, Dingle Peninsula 2030 as it relates to the emergence of a range of sustainability initiatives both locally and nationally is self-evident. It was initiated as a collaborative project aiming to transition a geographical region in the Southwest of Ireland to a low-carbon, climate resilient, sustainable society by 2030. The project has sought to facilitate and support the emergence of different initiatives, and through this took an organic approach, allowing locally led initiatives to emerge rather than take a prescriptive approach.The pitfalls of attribution and the need to incorporate theoretical understandings on mixed influence with regards to the diffusion of innovations are clear. Still, the importance of Dingle Peninsula 2030 partnership, while supporting these different projects, also allowed for the emergence of local actors and organisations to involve themselves in different aspects of the initiatives. The diffusion of sustainability, as a core idea across the different projects, was influenced by Dingle Peninsula 2030, with sustainability central to its vision. From the initial partnership a large number of groups have formed around specific projects, with many of the diffusions outlined in Dingle Peninsula 2030, whilst acting within their own agenda and vision, can operate as intermediaries between top-down policy and bottom-up initiative with relation to the diffusion of sustainability.Several different local efforts have emerged over the 3-year time frame of the project such as the anaerobic digestion project, the community energy mentor course and the West Kerry Dairy Farm Sustainable Energy Community (SEC) see . The DinDingle Peninsula 2030 which combines local groups (Dingle Hub and NEWKD) with a national body and research institute (ESB Networks and MaREI) builds the capacity to play a role between the top-down and bottom-up with relation to sustainable transitions. The diffusion of sustainability locally can be supported by the intermediary role played by regional collaborative projects such as Dingle Peninsula 2030. From a bottom-up perspective, intermediaries play a crucial role in stimulating and facilitating the emergence and continuity of grassroots initiatives [Dingle Peninsula 2030, the intermediate role is not the primary function, but the capacities of the collaborative grouping [Despite the embedded nature within the local regional context, the organisational structure of tiatives . Furthertiatives . In relagrouping  enable igrouping .Dingle Peninsula 2030, driven by the desire for a more environmentally and economically sustainable future on the Dingle Peninsula, means intermediate functions which facilitate initiatives aligned with this vision are mutually beneficial. By way of example, Dingle Hub’s support of the West Kerry Dairy Farm SEC in developing their application to Sustainable Energy Authority of Ireland or MaREI’s support with relation to the initiation of the anaerobic digester feasibility study represent two amongst many when it comes to the provision of intermediary supports for emergent initiatives. The diffusion of sustainability is predicated on this intermediary role.The overarching vision of Dingle Peninsula 2030 both nationally and internationally [The national diffusion of sustainability has been referenced within the results with relation to the education initiatives and their emergent influence within the national system. Alongside this, there have been a number of outreach activities and events which seek to disseminate the story of tionally . Within The project has received extensive national exposure, including:Change Makers in Irish Universities television series.Being one of six case studies featured in the National Broadcaster’s Engaged Research: Train the Trainer’ course.A central case study in the Irish University Association Campus Engage ‘Better together: knowledge co-production for a sustainable society’An exemplar engaged research project in the Royal Irish Academy Workshop and White Paper entitled ‘Knowledge sharing with the Sustainable Communities Training Programme (SECAD) (3 × training sessions between September and December 2021).Presenting at Ireland’s first energy planning conference organised by Codema in November 2021.Launch by the Minister for Community, Joe O’Brien, of the Demographic and Socio-Economic Profile of the Dingle Peninsula.Recognised as a leading case study for transdisciplinary, action-orientated research in Science Foundation Ireland’s annual report.It has also made significant contributions to the international science–policy interface:Participation in events at the UNFCCC COP26 Climate Conference, Glasgow 2021 including travelling in an electric vehicle to Glasgow for COP26 and documenting the journey via a blog, social media posts and a video.EPRI European Workshop Week 2021 on Advancing Energy Communities.Being showcased at the Facilitating sustainable transitions through innovative governance’, and convened a number of panellists to share experiences from different viewpoints. These included the manager of the Dingle Hub (Dingle Hub), an expert in social innovations and a representative from the UN.A dialogue session was held at International Sustainable Transitions (IST) conference 2020. The title of the session was ‘Dingle Peninsula 2030 group to discuss their experiences with the project to date. The event was held in November 2020 and was attended by over 200 people, many from within the national SEC Network.Hosting an armchair chat with members of the Dingle Peninsula 2030 project was outlined by the UN Regional Information Centre for Western Europe (UNRIC) as an exemplar project on their website.The A significant contribution to knowledge sharing is the innovative ‘learning brief’ template developed as a way to collaboratively document reflections and learnings. These emerged as a useful means to capture important learnings on different projects or elements of the initiative in order to inform policy and community action elsewhere. They cover a variety of topics such as the ‘Climate Hacks’ with schools , value oDingle Peninsula 2030 partnership began with a very clear focus on the electrification of heating and transport, which is a central part of climate mitigation policy in Ireland. However, as illustrated through the mapping of emerging local initiatives to the SDGs, this has diffused into a rich diversity of sectors. The multi-stakeholder collaboration has acted as a springboard for local efforts, which stands to highlight the important role of intermediaries to bridge top-down and bottom-up objectives. The initial assessment of the diffusion of sustainability can provide a useful starting point for understanding the impact of such projects. Several challenges associated with this assessment of an emergent and evolving project have also been outlined. Finally, the ways in which the project has attempted to diffuse beyond the case study region demonstrates the valuable contribution of such efforts to policy and action elsewhere. Again, here the importance of intermediaries, and in particular researchers, is highlighted. The holistic approach to sustainability as outlined through the SDGs has been well represented through the Dingle Peninsula 2030 project. The diffusion of sustainability can act as a concept which enables collaborators to track emergent initiatives across a wide scope of sectors as outlined by the SDGs. Community led climate-action projects have potential to meet a wider spectrum of sustainability topics due to the diversity of interests and values contained within community contexts.This paper has demonstrated the wide variety of sustainability initiatives that can emerge from community-based collaborations, through outlining a case study. The


This paper presents a comprehensive study on the development of models and soft sensors required for the implementation of the automated bioreactor feeding of Chinese hamster ovary (CHO) cells using Raman spectroscopy and chemometric methods. This study integrates various methods, such as partial least squares regression and variable importance in projection and competitive adaptive reweighted sampling, and highlights their effectiveness in overcoming challenges such as high dimensionality, multicollinearity and outlier detection in Raman spectra. This paper emphasizes the importance of data preprocessing and the relationship between independent and dependent variables in model construction. It also describes the development of a simulation environment whose core is a model of CHO cell kinetics. The latter allows the development of advanced control algorithms for nutrient dosing and the observation of the effects of different parameters on the growth and productivity of CHO cells. All developed models were validated and demonstrated to have a high robustness and predictive accuracy, which were reflected in a 40% reduction in the root mean square error compared to established methods. The results of this study provide valuable insights into the practical application of these methods in the field of monitoring and automated cell feeding and make an important contribution to the further development of process analytical technology in the bioprocess industry. Chemometrics, which deals with the application of various mathematical and statistical methods, could be described by a broad definition in which the most important part is the application of a multivariate data analysis to data relevant to chemistry . The mulIn analytical chemistry, molecular spectroscopic methods, including infrared, near-infrared and Raman spectroscopy, are widely used to determine the molecular structure of various substances ,5,6. TheRaman spectroscopy is an optical method that enables the non-destructive investigation of molecular structures and chemical compositions. However, due to its low intensity, the study of Raman scattering requires the use of sophisticated instruments . The datIn bioprocess literature, spectroscopic sensors are sometimes referred to as soft sensors  because k-th order Savitzky–Golay derivative [Chemometric techniques, including the commonly used Partial Least Squares (PLS) ,14 methorivative , while mThe inherent complexity of spectral data derived from vibrational spectroscopic techniques, including IR, NIR and Raman, has sparked debates on the topic of variable selection in PLS regression models ,21. ThisCertain Raman spectra obtained from the same sample may differ from the group due to factors such as instrumental artefacts and variations in the sample. These spectra are often referred to as unwanted spectra or outliers. Omitting these spectra is considered crucial before applying multivariate techniques to obtain the desired results.Raman spectroscopy, known for its precise spectral features that correlate with the molecular structure of a sample, has demonstrated its strengths in a non-destructive analysis and its ability to work with aqueous systems. These properties make it particularly suitable for the study of cell cultures and tissues . It is wAlthough mammalian cell cultures are widely used in the pharmaceutical industry to produce biological products such as antibodies and growth factors, the full potential of advances in process monitoring and control has not yet been realised ,27. ConvBy using non-invasive real-time measurements PAT in conjunction with closed-loop feedback control, feeding strategies can be optimised to improve yield ,34,35. RThis manuscript represents a significant advance in the field of bioprocess technology by providing a comprehensive PLS model construction procedure for Raman spectroscopy that incorporates data preprocessing and outlier removal, thereby improving the understanding and control of bioprocess behaviour. In addition, the development of a simulator that incorporates CHO cell kinetics is an important contribution to the field. It paves the way for the development of a model predictive control system for the automated feeding of CHO cells, revolutionising the way we approach the automation and control of bioprocesses.The paper is organized as follows. The extensive research began with the systematic compilation of measurements and data obtained from the cultivation of CHO cells in a stainless steel bioreactor. The local pharmaceutical company, which was in charge of designing the experiment, played an important role. Our task, on the other hand, was to analyse the collected data, create the necessary models and establish a suitable simulation environment, which is described in this paper.The cultivation of the CHO cells took place in a bioreactor with a volume of 10 L. To collect measurements (Raman spectra), the probe of a Kaiser RamanRXN2 spectrometer was inserted into the bioreactor. The RamanRXN2 spectrometer is a sophisticated analytical device that uses laser light with a wavelength of 532 nm. The resulting Raman spectrum is collected over a period of at least 30 min, a measure that improves the signal-to-noise ratio. It is important to note that Raman scattering, which is essentially inelastic photon scattering, is a rather small fraction compared to its elastic counterpart.For data storage, a desktop computer with a Windows operating system was used, which was directly connected to the Raman spectrometer. Four different experiments were performed to grow the cells in the bioreactor, with each batch lasting about two weeks. The bioreactor contained CHO-S cell lines. This cell line is a sub-line of the original CHO-K1, with adaptations for suspension culture. CHO-S cells are commonly used in the industrial production of therapeutic proteins.To maintain the optimal environment of the bioreactor, the pH and temperature were strictly controlled and nutrient dosing (glucose and glutamine) was conducted manually on a daily basis using reference measurements. A Roche Cedex Bio Analyzer, known for its reliability and precision, was used to record these reference measurements daily. This allowed for the accurate monitoring of parameters such as glucose and glutamine concentration, viable cell count and others.x-data) have a strong relationship with the dependent variables (y-data) to be modelled [The development of useful models depends on appropriate methods, but even more important is the selection of appropriate data. In our case, the raw data consist of the Raman spectra shown in modelled . The choIn cases where the x-data for objects represent time series or digitised data from a continuous spectrum  on each side of j and For both differentiation and smoothing, the Savitzky–Golay method is used. This is a method widely used in chemistry. This technique, a local polynomial regression using the method of least linear squares, requires x-values that are both exact and uniformly distributed. For each point, symbolised as 2, −1, 2 . Figure The process of pre-processing includes both filtering and normalisation, with the latter playing an important role. The reason for this is that even spectra recorded for the same material may demonstrate differences due to different recording times or unequal instrument conditions such as laser power and alignment. These variations can lead to different intensity values for spectra of the same material.To compensate for these intensity differences, normalisation comes into play. This process ensures a maximum similarity of the intensity of a given Raman band of a given material when the spectra were taken under the same experimental parameters; however, some conditions are slightly different. Various normalisation methods are explored in the literature, including min-max normalisation, vector normalisation and Standard Normal Variate (SNV) normalisation. Of these methods, SNV normalisation is the most commonly used ,40. SNV The construction of predictive models for bioprocesses, particularly for the cultivation of CHO cells in bioreactors, has made significant progress through the application of chemometric methods to Raman spectroscopic data . These mModel construction begins with a calibration phase in which known samples are analysed using Raman spectroscopy and appropriate laboratory tests. This process generates a set of reference data that includes Raman spectra and associated concentrations of glucose, glutamine, lactate and cell counts. Another way to collect reference measurements is to use a device such as Roche’s Cedex Analyzer. Once the reference data are prepared, multivariate analysis techniques such as Partial Least Squares Regression (PLSR) are used to build the predictive model. These methods work by identifying correlation patterns within the Raman spectra and relating them to the biochemical parameters of interest.For more complex data sets or non-linear relationships, machine learning techniques such as Random Forest or SVM can be used. Advanced deep learning techniques such as Convolutional Neural Networks (CNN) are particularly effective for processing high-dimensional spectral data, as they can automatically extract meaningful features and improve prediction accuracy . HoweverThis approach not only improves our understanding of the bioprocess, but also our control over it. The real-time predictive capability of the model leads to optimised and consistent bioproduction outcomes by enabling rapid, data-driven decision-making and process adjustments, thereby increasing bioprocess performance, reducing costs and improving product quality. The model is continuously refined as more data become available, improving its predictive power over time.Partial Least Squares (PLS) is a statistical method that finds a linear regression model by projecting the predicted variables and the observable variables onto a new space. The method was first developed by Swedish statistician Herman Wold and has since been widely used in fields such as chemometrics, neuroimaging, bioinformatics and social sciences ,42.PLS simultaneously accounts for the covariance of both the independent variables (predictors) and the dependent variables (responses). This approach is advantageous when dealing with complex, multivariate data sets where the predictors are highly collinear or where there are more predictors than observations. The method can handle noisy and missing data, which makes it robust and flexible.Standardisation of data: The first step in PLS regression is to standardise the predictor and response matrices. This ensures that the model is not overly influenced by variables that have large values or a large range of values.Extraction of PLS components: PLS decomposes the predictor and response matrices into a set of orthogonal components. These are linear combinations of the original variables that explain the maximum covariance between the predictors and the responses. The number of PLS components is chosen to optimise the predictive power of the model.Estimation of the PLS model: The PLS regression coefficients are estimated by relating the PLS components to the responses. These coefficients show the relationship between the changes in the predictor variables and the changes in the response variables.Prediction and validation: The PLS model can then be used to predict responses for new data. Cross-validation is often used to assess the predictive performance of the model and to determine the optimal number of PLS components.Partial Least Squares (PLS) regression is a multivariate technique that combines features of principal component analysis (PCA) and multiple linear regression. Although PCA is not explicitly used in the PLS method, the concept of extracting principal components or latent variables is central to both methods. In PCA, the goal is to find a small number of uncorrelated variables, called principal components, that explain most of the variation in the data. Each principal component is a linear combination of the original variables and is orthogonal to all other components. PLS works in a similar way, but instead of trying to explain as much of the variance in the predictor variables as possible, PLS tries to extract components that explain as much of the covariance between the predictor and response variables as possible. Essentially, PLS looks for directions in which the predictors not only explain a large part of their own variance (as in PCA), but are also highly correlated with the response. PLS regression can be summarised in the following steps:In terms of its statistical properties, PLS is a form of regularised regression. Like other forms of regularisation, it can prevent overfitting by introducing some bias into the model, but it reduces the variance of the model and thus improves its predictive performance.PLS has been extended to handle different types of data and different modelling scenarios. The most popular versions of PLS include PLS-DA   for clasX is a predictor matrix and Y is a response matrix.Initialization:Y matrix represents the vector u:Selection of an initial column vector. Typically, the first column of the w and t until convergence:Iteratively compute the weights Normalize the weights:Compute the score vector:u as:Reassign The iteration continues until the difference between the new and old score vectors falls below a certain threshold, indicating convergence.X and Y:Deflate t and p (the loading vector for the X), then subtract it from X. Do the same for Y with t and q (the loading vector for the Y):Calculate the outer product of X (or Y) can no longer be deflated or when the number of extracted latent variables is enough to describe the data according to some criterion.The iterations end when B can be calculated as:W is the matrix of weight vectors, P is the loading matrix of X.Calculate the regression coefficients. Once all the latent vectors are extracted, the regression coefficients The Nonlinear Iterative Partial Least Squares (NIPALS) algorithm is a common method for calculating PLS components. The goal is to find a set of components  that capture the covariance between the predictors and the responses. The algorithm of the simplified NIPALS method can be summarised in the following five points:The Root Mean Square Error of Cross-Validation (RMSECV), which is calculated during the creation of the PLS model, can be used as a criterion to find the right number of latent variables and prevent overfitting. For example, To further improve the PLS models and reduce the possibility of overfitting, the Variable Importance in Projection (VIP) and Competitive Adaptive Reweighted Sampling—Partial Least Squares (CARS-PLS) methods were used.Variable Importance in the Projection is a popular method for assessing the importance of variables in a Partial Least Squares (PLS) regression model. PLS is a statistical approach used in predictive modelling where the prediction of a set of dependent variables from a set of independent variables is conducted through latent variable regression.Overemphasis on highly collinear variables: If variables are highly collinear, the VIP score can overestimate the importance of those variables and result in a model that may not be as accurate as possible. This can be problematic in areas where variables may be highly correlated, such as genomics or metabolomics.Unreliable with small data sets: The VIP method can be unreliable with small data sets because it depends on having enough data to estimate the PLS model accurately.The VIP score for a variable is a measure of that variable’s contribution to the model, taking into account both its contribution to explaining the dependent variable and its contribution to explaining the independent variable. A high VIP score indicates that the variable is highly significant in the model (Competitive Adaptive Reweighted Sampling: This is a Monte Carlo-based sampling technique that helps identify relevant variables (wavelengths) for building the model. Initially, CARS assigns equal weights to all variables. Then, a set of subsets of variables is generated, each subset containing each variable with a probability proportional to its weight. A PLS model is created for each subset and its performance is evaluated. Based on the evaluation, the weights of the variables are updated—variables that frequently contribute to good models are given higher weights, while those that contribute to poor models are given lower weights. This process is repeated many times  until the best subset of variables is found.Partial Least Squares (PLS): After identifying the best subset of variables with CARS, a PLS model is built using only these selected variables . This moOn the other hand, Competitive Adaptive Reweighted Sampling—Partial Least Squares is a more recent technique used for variable selection in spectroscopic multivariate calibration. It has gained considerable attention in the field of chemometrics. CARS-PLS was developed to overcome two major challenges in the analysis of spectroscopic data: high dimensionality and multicollinearity. These problems can lead to overfitting of the model, poor generalisation ability and difficulties in interpretation. The method CARS-PLS consists of two main stages:The CARS-PLS method has been used successfully in many areas where spectroscopic data are used, such as pharmaceutical analysis, food quality control and environmental monitoring. However, like all methods, it has its limitations and assumptions. It assumes that there is a linear relationship between predictors and responses, and it may not work well if this assumption is not met. In addition, the performance of CARS-PLS may depend on the initial weights of the variables and the number of Monte Carlo iterations. Therefore, it is often advisable to make several runs of CARS-PLS with different initial settings and determine the consensus of the results.Better handling of collinearity: In contrast to the method VIP, CARS can better handle the problem of collinearity between variables.Simplicity and interpretability: CARS tends to lead to simpler and more interpretable models, which is of great importance in practical applications.Better performance on small data sets: CARS is not as reliant on large data sets as VIP and is therefore a more reliable method for variable selection on small data sets.More robustness: CARS is less prone to overfitting because it focuses on a subset of particularly relevant variables instead of considering all variables in the model.Adaptive: CARS is an adaptive method, able to adjust its selection as more data becomes available or the nature of the data changes.Compared to VIP, CARS offers the following advantages:Partitioning: first, the original training dataset is randomly partitioned into a training dataset and a test dataset. For example, the partitioning could be 4:1, i.e., 80% of the data are used for training and 20% for testing. This partitioning is conducted many times, which is characteristic of a Monte Carlo approach.PLS modelling and prediction: A Partial Least Squares (PLS) regression model is built using the training data. This model is then used to make predictions for the test subset.Error calculation: The prediction errors for each spectrum in the test set are then calculated. Each spectrum will occur multiple times in different test sets; thus, an average error and standard deviation can be calculated for each spectrum across all iterations.Identification of outliers: Spectra that consistently produce high prediction errors (based on their average error or a combination of average error and standard deviation) can be considered outliers. These outliers represent spectra that are not well modelled by the PLS and thus affect the accuracy of the model. In Removal of outliers: The identified outlier spectra are removed from the original dataset, hopefully improving the robustness and accuracy of the model.Iterating: This entire process can be repeated as needed, each time recalculating the errors for each spectrum and identifying and removing outliers.The PLS model can be further improved by searching for spectra representing outliers. Therefore, a resampling method commonly used in statistics and machine learning was used, which can also be referred to as Monte Carlo cross-validation or repeated random sub-sampling validation. The outlier detection method consists of the following five steps:The advantage of this method is that it can help to increase the robustness of the PLS models by removing outliers that would otherwise distort the model parameters. It is a relatively simple and intuitive approach that combines the robustness of resampling with the ability to identify and remove problematic data points. This method helps to further reduce the Root Mean Square Error of Prediction (RMSEP) and thus improve the overall performance of the model.However, as with any method, it should be used judiciously. Removing outliers too aggressively can lead to over-fitting, where the model becomes over-fitted to the “typical” data points and performs poorly on new, unknown data. This method is most useful if you have a large enough dataset so that removing some data points does not significantly reduce the overall size of the dataset.In order to develop a predictive control algorithm for automated nutrient feeding in a bioreactor, a simulation environment based on a dynamic model was implemented. The latter describes the kinetics of the growth of a CHO cell culture in a fed-batch bioreactor. It is well known that the process parameters  have a significant impact on cell growth and especially on the quality of the monoclonal antibodies (mAbs) produced . TherefoChinese Hamster Ovary cells are the most commonly used mammalian hosts for the industrial production of therapeutic proteins, due to their capacity to perform human-like post-translational modifications. The growth kinetics of CHO cells can be studied using a mechanistic model . A mechaS and However, despite their complexity, mechanistic models can provide valuable insights into the cell growth process and can be helpful in optimising cell culture conditions for maximum productivity. Many authors ,46,47,48S phase:k, where, e.g., S phase. The transition factors between subpopulations depend mainly on the growth rate, which in turn is determined by the times , total cell count (TCC), glutamine, glutamate, lactate and ammonium.Data from four different batches were available to us for the development of PLS models. Raman spectra were collected every half hour and reference measurements (offline) were performed once or twice a day with Cedex Analyzer. Thus, the first step was to find the pairs of spectra and reference measurements that matched best in terms of acquisition time. The Raman measurement takes about half an hour to obtain a good signal-to-noise ratio and to remove fluorescence interference.As described in As described in Characteristic independent variables of the spectrum  at which a spike occurs can be extracted from the literature for individual observed variables. Taking these characteristic energy shifts into account when calculating PLS models is therefore considered useful as it further weighs the individual independent variables of the spectrum and improves the model in this way. If these characteristic energy shifts are not known, various methods are available to identify the more important independent variables and take them into account to a greater extent.The Variable Importance in the Projection method, described in It turned out that the Competitive Adaptive Reweighted Sampling method, which is also discussed in The reference values in Assuming that Cedex’s offline measurements are reliable, the training set was examined for spectra representing outliers that could affect the parameters of the PLS model during the learning phase and consequently affect the prediction accuracy. Applying the Monte Carlo sampling method and calculating the mean error and standard deviation for each PLS model led to the identification of spectra within the dataset that represent outliers, as shown in The learning process for the PLS models depended on a single offline measurement (Cedex) of each variable  per day. Therefore, only the Raman spectroscopy spectra that matched the offline measurements in time could be used. However, once the PLS models were built, all spectra collected every half hour could be used, giving an informative representation of the time course of each variable see . These dThe problem can be solved by implementing an automated feeding system that continuously doses the nutrients according to a predefined reference signal. However, such a system requires not only the application of the previously developed soft sensors (PLS models), but also a simulation environment. In this environment, a control algorithm can be developed and different scenarios such as different feeding regimes, the removal of inhibitors and the observation of important process variables can be investigated. The heart of the simulator, represented by the Simulink schema in Based on known process parameters (temperature and pH) and time series signals of the main process variables , it is possible to perform the optimisation of the parameters of the dynamic model of CHO cell kinetics  method was used, which makes it possible to find the global minimum of the chosen criterion function while optimising a large number of parameters. In this case, the criterion function was given as RMSE, with the final values presented in the A comparison of glucose concentration measurements from one of the batches with a glucose concentration prediction derived from a mechanistic model of CHO cell kinetics is shown in Furthermore, The predictions for the other process variables, as shown in In developing models that allow the use of soft sensors to monitor key process variables  in the bioreactor, it was found that using the PLS method alone did not provide the required accuracy and robustness of the models. In particular, with a limited data set (a few batches), the model can be overfitted, leading to a sharp drop in predictive performance compared to what the validation with limited data promises.Since in our work only about 100 spectra with reference measurements were available during the learning phase and Raman spectra contain more than 3000 components, the phase of selecting key variables became crucial for model construction. By using the CARS method, better handling of collinearity between variables was observed, as well as better performance on small data sets and higher robustness compared to the VIP method. As a result, the RMSE was reduced by up to 30%.It was found that the VIP method further impaired the predictive ability of the models in certain cases, indicating an overfitting problem, as the number of key variables selected was significantly larger than required by the CARS method. The VIP method also had stability problems, as the results may have become unstable with small samples. Minor variations in the data can lead to significant shifts in the scores, making it difficult to extrapolate the results to other data sets. When calculating the VIP scores based on the weighted sum of squares of the PLS loadings, high variability was found in small data sets.In Raman spectroscopy, it is important to understand that outlier spectra can occur, influenced by various factors. For example, if the sample in the bioreactor is not evenly mixed, this can lead to deviations in the spectra obtained. Raman spectroscopy derives its readings from the average properties of the area illuminated by the laser. Therefore, a lack of homogeneity in the sample can lead to inconsistent measurements.Moreover, the components of the sample can play an important role. If components fluoresce under the laser light of the Raman spectrometer, the resulting fluorescence could overshadow the Raman signal and distort the spectra. Additionally, bubbles or particles in the bioreactor can cause scattering or absorption of the laser light, resulting in unpredictable spectra.Given these potential sources of error, it is important to carefully identify and remove outlier spectra during the modelling phase, as described in The efficient growth and production of desired products by CHO cells requires specific, strictly controlled conditions in the bioreactor. These conditions include the regulation of pH and temperature, which affect cell metabolic rate, protein folding and expression levels. Equally important is the careful control of nutrient content, especially glucose and glutamine, according to a predetermined profile for the duration of the batch.Another critical factor is the control of inhibitor concentrations. Metabolic by-products such as ammonia and lactate can potentially inhibit cell growth and protein production if they reach high concentrations. Since glucose is the primary source of energy, its concentration directly affects cell metabolism. Too little glucose can starve cells and inhibit growth, while too much glucose can cause osmotic stress or trigger overproduction of waste products such as lactate.Given these complexities, the use of an automated bioreactor control system is essential for CHO cell cultivation. Such a system offers several advantages, including maintaining consistent conditions, real-time monitoring, reducing human error and improving efficiency and scalability. Given the significant costs associated with realistic bioreactor experiments, the development of a simulation environment is essential. This environment enables the creation of control algorithms and the evaluation of the effects of different parameters on cell growth and productivity.The main reason for the lack of advanced automated control techniques in cell culture bioprocesses and bioreactor operations is that these techniques require robust and reliable measurement methods that are available on site. Concentrations of nutrients and metabolites, cell densities and viability are not measured and are uncontrolled or are only controlled manually with long sampling times  model parameters will be facilitated. This adjustment is made possible by an optimisation method that uses the measurements of the current batch to facilitate the online parameterisation.Future efforts include the development of a model predictive control algorithm based on the simplified model of CHO cell kinetics. Subsequently, the monitoring and control system will be integrated into a real bioreactor. Finally, a practical test of the implemented system will be carried out.This study demonstrates the significant advances in fully automated feeding of CHO cells achieved through the development of advanced models, soft sensors and a novel simulation environment. The research has required a thorough understanding of various chemometric methods and demonstrated their context-specific application in combination with Raman spectroscopy. It has demonstrated the effectiveness of CARS-PLS and an outlier removal method in overcoming difficult challenges such as high dimensionality, multicollinearity and outlier detection. The models created are versatile and scalable and can be applied to a wide range of products, media and cell lines based on CHO host cells. They can be conveniently scaled up for use in large pilot studies and extensive manufacturing processes. However, the success of these methods depends not only on the right choice of techniques, but also crucially on the quality of the input data. Therefore, the preprocessing of the data to remove interfering signals is of the utmost importance. Raman spectra have no inherent value, but when integrated with the appropriate models, they allow for the creation of a sophisticated measurement system. This system, which consists of soft sensors, is used for real-time monitoring and control of important process variables. The measurements reconstructed with these soft sensors play a crucial role in the design of the simulation environment, which significantly speeds up and cheapens the development of control algorithms and thus the automated nutrient dosing system. In essence, this study provides essential insights into the pragmatic application of Raman spectroscopy and innovative methods that form a solid foundation for further research and development in the field of automated cell feeding.


Objective To describe and evaluate the use of a simple, low-cost, and reproducible simulator for teaching the repair of obstetric anal sphincter injuries (OASIS).Methods Twenty resident doctors in obstetrics and gynecology and four obstetricians participated in the simulation. A fourth-degree tear model was created using low-cost materials . The simulator was initially assembled with the aid of anatomical photos to study the anatomy and meaning of each component of the model. The laceration was created and repaired, using end-to-end or overlapping application techniques.Results The model cost less than R$ 10.00 and was assembled without difficulty, which improved the knowledge of the participants of anatomy and physiology. The sutures of the layers  were performed in keeping with the surgical technique. All participants were satisfied with the simulation and felt it improved their knowledge and skills. Between 3 and 6 months after the training, 7 participants witnessed severe lacerations in their practice and reported that the simulation was useful for surgical correction.Conclusion The use of a simulator for repair training in OASIS is affordable (low-cost and easy to perform). The simulation seems to improve the knowledge and surgical skills necessary to repair severe lacerations. Further systematized studies should be performed for evaluation. Severe perineal laceration involving the anal sphincter is an important complication of vaginal delivery. Its incidence is used as a safety marker in childbirth, and it can be used to evaluate an institution or region.An obstetrician should be able to diagnosis and adequately correct obstetric anal sphincter injuries (OASIS).The use of simulators and simulation environments for teaching health professionals is well established.Overall, the quality of the evidence about simulation-based learning (SBL) is low, but it suggests that the method is effective and leads to better and longer-lasting results compared with traditional teaching.While simulated environments and high-fidelity simulators have proved to be useful, there are barriers to their use in teaching, mainly concerning their cost.The objective of the present study was to describe and evaluate a simple, low-cost and reproducible simulator, adapted to the Brazilian reality, for teaching OASIS repair.http://saudesimuladores.paginas.ufsc.br/).This is an observational qualitative-quantitative research. The research is part of the project “handmade simulators for teaching in obstetrics”, which was developed by the authors and seeks to create, discover, compile, and disseminate the possibilities of using simulators and accessible simulations . The internal anal sphincter is a bright, fibrous structure that, when completely torn, generally retracts laterally. Suturing this structure separately from the external anal sphincter improves the posterior results,The practical aspects of diagnosing and suturing severe lacerations include the need to evaluate the sphincter and the rectal mucosa after the delivery, adequate anesthesia, positioning of the patient, illumination, a good surgical field, and antisepsis.All the participants answered a questionnaire three to six months after the simulation. The questionnaire sought to evaluate the experience, satisfaction, and learning with the simulator and to determine if the participants had encountered any cases of severe perineal laceration after the simulation and whether they had noticed changes in their surgical performance.The quantitative variables were analyzed with descriptive statistics, and the qualitative variables were categorized according to their content. The local ethics committee approved the research project.The simulator was created at a cost of approximately R$10.00. Twenty resident doctors and four expert obstetricians participated in the simulations. Only one resident who participated did not respond to the questionnaire. The mean age of the participants was 30.83 years old (standard deviation [SD] = 6.99), and the time since graduation in medicine was 4.5 years (SD = 5.64). Among the participants, five were in the first year of residence; seven were in the second; and eight were in the third. The time of experience of the experts varied between 10 and 20 years at the time of the simulation.All the participants were satisfied with the training and considered that the simulation improved their knowledge and skills for correction of severe perineal lacerations. In the open questions, greater security and confidence in the case of necessity to perform the suture were the most cited categories.The majority (78%) of the participants considered that the simulator was effective in replicating the anatomical structures, with inherent limitations to the model.“The thickness of the layers is very reliable and simulates the technical difficulties of the actual tear.” (Expert 3)“It allows visualizing the anatomy, mainly the texture/thickness of the external anal sphincter.” (Resident 14)“I have done training with 100% synthetic material, and this is closer to reality.” (Resident 8)“It is very difficult to simulate the anatomy; the model is very simplified.” (Resident 4)The majority (69%) of the participants also considered that they were not immersed in the experience .“The class was relaxed; we played, made mistakes, and we did it again. In practice, nervousness and responsibility weigh heavily on the procedure.” (Resident 18)“Remember step-by-step in case of necessity, but far from being a real situation.” (Resident 3).Four resident doctors attended cases of severe perineal rupture after participating in the simulation and considered that the training helped them remain calm and know how to proceed, in addition to having improved their surgical skills. Of the four experts, three attended serious lacerations after participating in the simulation and also considered that they were more confident and calmer when performing the procedure.“I felt more confident; I was able to better identify the structures involved.” (Resident 5)“... the suture becomes more automatic.” (Expert 1).There are several models for training the repair of severe perineal lacerations, the most described being synthetic,There is no need for the simulation to be ultra-realistic; a more simplified scenario can achieve the same objectives and is more accessible and reproducible at low cost.The format of the simulation  did not allow an immersion in the experience; the students did not feel the simulation as real. Although the immersion in the simulation is important in some contexts ,The improvement in surgical skills was achieved and assessed only by the self-evaluation of the participants, a method also used in other researches.A simple, accessible, and easily reproducible simulator for suture training for severe perineal laceration repair was created and used. All the participants enjoyed the simulation and assessed that their knowledge and skills improved. At least seven of the participants had to attend serious lacerations after participating in the simulation and reported feeling more confident and secure. Improved self-confidence to care for a case is described in other studies.The simulation was done in class time, with no need for a specific environment, which on the one hand is a disadvantage, since it did not allow students to immerse in the simulation. On the other hand, it can be seen as an advantage, since it can be done in all institutions, without the need of more a complex preparation.The present study has some limitations. Only the apprentices themselves evaluated the knowledge and skills acquired in a single moment. The teachers who guided the simulation belong to the institution and are known to the resident doctors. Although the questionnaires are anonymous, there may be a courtesy bias in the answers. For future investigations, a pre- and postsimulation evaluation is suggested, either with a theoretical test or with an OSATS and evaluation sometime later, to evaluate the retention of knowledge. It was possible, however, to notice changes in the behavior of the learners .The use of a simulator for OASIS repair is affordable (low-cost and easy to perform) and can be an alternative for resident doctors and expert training. The simulation seems to improve the knowledge and surgical skills to suture severe lacerations. Further systematized studies should be performed for evaluation.


Approximately 10% of antimicrobials used by humans in low- and middle-income countries are estimated to be substandard or falsified. In addition to their negative impact on morbidity and mortality, they may also be important drivers of antimicrobial resistance. Despite such concerns, our understanding of this relationship remains rudimentary. Substandard and falsified medicines have the potential to either increase or decrease levels of resistance, and here we discuss a range of mechanisms that could drive these changes. Understanding these effects and their relative importance will require an improved understanding of how different drug exposures affect the emergence and spread of resistance and of how the percentage of active pharmaceutical ingredients in substandard and falsified medicines is temporally and spatially distributed. Substandard and falsified medicines are a problem, particularly in low- and middle-income countries, and effects on antimicrobial resistance development aren’t well understood. Here, the authors discuss mechanisms by which they can increase or decrease levels of resistance and the need for improved data collection and analytical approaches. Falsified medicines ‘deliberately/fraudulently misrepresent their identity, composition or source’1. Both types of poor quality product can contain too much or not enough active pharmaceutical ingredient (API), no API, an API different to the stated one, and/or fail dissolution testing1. Several studies suggest that around 10% of antimicrobials taken by humans may be substandard or falsified in low- and middle-income countries4. In surveys in parts of Southeast Asia and sub-Saharan Africa, local prevalences >50% have been reported, though those figures should be interpreted with caution4. Knowing the true extent of this problem is difficult, as sampling is uneven and potentially unrepresentative5. This is in part because the majority of surveys use a ‘convenience’ sampling strategy, meaning there is no specific guidance on which locations to sample, potentially leading to biased sampling and some areas not being sampled5. There have been very few published multicountry studies of antimicrobial quality using a common protocol6. It can also be very challenging to sample in remote locations and from unlicensed outlets. SF antimicrobials can lead to worse patient outcomes9, putting an increased burden on already strained healthcare systems and economies. They may also be an important driver of the emergence and spread of antimicrobial resistance (AMR)10. AMR is a global public health problem, with resistance to antibiotics causing an estimated 1.3 million deaths in 201911, which is likely to get worse in the decades to come.Substandard and falsified (SF) antimicrobials are a neglected public health problem. Substandard medicines are ‘authorized medical products that fail to meet either their quality standards or their specifications, or both,’ due to within-factory errors or degradation in supply chainsEscherichia coli and Mycobacterium smegmatis12 and that subtherapeutic levels of dihydroartemisinin  can favor resistant strains of Plasmodium falciparum13. There remain many gaps in our understanding of how SF medicines affect AMR. For instance, how might SF medicines affect the emergence of AMR in human hosts, and how is this affected by the level of API in the medicine and its bioavailability? How much does this affect transmission? What are the population-level impacts of these effects on the prevalence of AMR? In this paper, we first discuss the ways in which SF antimicrobials could impact AMR, and highlight literature relevant to these mechanisms. We end the paper by discussing what types of data and analyses would help us to better understand the relationship between SF medicines and AMR. We focus throughout on the impact on AMR, but it should be re-emphasized that SF antimicrobials will also have other direct and indirect negative effects on morbidity and mortality independent of their effect on resistance. For example, one study found substandard antimalarials were associated with a high treatment failure rate of 28.5% during a malaria epidemic in a refugee camp in Pakistan9. Another study found that 3.75% of all under-5 deaths in sub-Saharan Africa could be associated with SF antimalarials, albeit with much uncertainty14. Regardless of their effect on AMR, SF antimicrobials remain a severe problem that we must do our utmost to reduce.Despite the recognition that SF medicines might be an important driver of AMR, there has been little research attempting to understand and quantify the strength of this link. In vitro studies have demonstrated that degraded rifampicin (a first-line tuberculosis medicine) can select for resistance genes in 15. This approach was originally formulated as a way to reduce morbidity and mortality due to the infection16. It can also be justified, however, as a way to address resistance by ensuring that the dose is high enough to kill even partially resistant microbes19. Additionally, by shortening the length of treatment the aggressive chemotherapy approach reduces the selection window available for a resistant mutant to emerge18.The traditional approach to antibiotic dosing regimens has been based on the principle “Hit hard and hit early”, first formulated by Paul Ehrlich in 191320. This trade-off between the density of microbes, which should decline with increasing dose, and the selective advantage of resistance, which should increase with increasing dose, implies that an intermediate dose will lead to a greater rate of emergence of resistance. This has been conceptualized as an inverted-U shape , the mutant prevention concentration (MPC), and the mutation selection window (MSW). The MIC is defined as the lowest concentration above which growth of sensitive organisms is inhibited, while the MPC is the lowest concentration above which growth of the least susceptible single-step mutants (i.e. those for which resistance is conferred by a single mutation) is inhibited24. The MSW is the difference between the MIC and MPC. The MPC will typically correspond to the right-hand side of the inverted-U, where neither wild-type nor single-step organisms can grow and so the rate of emergence of resistance is zero , then SF medicines could lead to an increased rate of emergence of resistance  of API in the standard course with high-quality antimicrobial, and how this differs in the SF medicine . This can be thought of as moving left or right along the inverted-U - if the bioavailable API . If an individual is infected with a resistant pathogen at a low level, but then receives treatment which gives the resistant organism a competitive advantage or enables it to grow to a higher population size, then this may result in increased density of resistant microbes, which in turn could lead to increased onward transmission. But if the patient received a course of medicines that had little API (or so much that it killed even the resistant microbes), and hence did not give a competitive advantage to resistant microbes, then treatment with SF medicines may result in a lower density of resistance relative to the standard course.The key factors that will determine how both substandard and falsified medicines impact on drug-resistance are % API and bioavailability. These factors affect the transmission of resistant microbes via several mechanisms, modifying the mechanisms proposed by Lipsitch & Samore30.Second, treatment with SF medicines that have a reduced level of API or bioavailability would likely lead to a longer period of infection than the standard course, as pathogens are cleared more slowly, increasing the amount of onward transmission, potentially with resistant organisms if they are present Fig. . LikewisThird, relative to the standard treatment course, treatment with SF medicines could reduce susceptibility to infection with a resistant strain Fig. , and canWhere there is little heterogeneity, the population-level distribution of resistance might be expected to also follow an inverted-U relationship with the average level of API found in medicines. In practice, however, transmission will be moderated by the frequency of AMR in the community and the spatial distribution of SF medicines, and the extent to which these overlap in space and time. In places where AMR is relatively common, this will make the effects described in 2D and 2E relatively more important, as there will be more opportunities for transmission of resistant strains. It is unclear what the impacts of a heterogeneous distribution of SF medicines might be, but if it was such that it led to many patients frequently switching between good quality and SF antibiotics, this could lead to a combination of higher pathogen densities from treatment failure and selection pressure in favor of treatment from the intermittent standard therapy. There are also likely to be lagged effects of substandard and falsified antimicrobials on the future frequency of AMR due to transmission of resistant strains.31. This exposure creates a selective pressure in favor of resistant bacteria, known as bystander selection. Failure to account for this effect can lead to mistaken inferences about the effect of interventions to reduce antibiotic use32. In addition to resistance in the bystander microbes, this resistance could in some cases be transferred to other microbes via horizontal gene transfer33.For many potential pathogens, such as those that often form part of the normal gut flora, most antibiotic exposure that they receive occurs when they are not the target of treatment34. Unless they have little or no API, SF broad-spectrum antibiotics will have a greater effect on non-target bacteria, and will affect a wider range of bacteria. Bystander effects are further complicated by antibiotic-induced microbiome dysbiosis, that is, when a course of antibiotics reduces the diversity and frequency of commensal bacteria in the gut35. If later infection with pathogenic bacteria then occurs, it can lead to more effective establishment and growth of that bacteria due to reduced competition35.In the case of SF antimicrobials, we might expect a similar dynamic to that shown in Fig. 39. For each of the ‘big three’ infectious diseases of malaria, tuberculosis, and HIV/AIDS, the standard regimens are combination therapies: artemisinin-combination therapy for malaria38, a four-drug combination of antibiotics for tuberculosis40, and a number of regimens for HIV/AIDS42. As combination therapy often has the intention of making the development of resistance more difficult, consequently the advantages of aggressive therapy increase18 and with it the risks of treatment with SF medicines with an insufficient amount of API or poor dissolution features. If one or more of the medicines involved in such combinations are SF, then it could lead to a negation of the synergistic effects or even inadvertent monotherapy. For example, if the pathogen was already resistant to one of the medicines in a two-drug combination, and the second one was substandard or falsified, this could help generate resistance to both medicines, negating the whole combination  is SF, the artemisinin derivative is always protected by the partner API in vivo, as artemisinin is only ever present when there are high levels of the partner drug. Later in the course of infection, once the artemisinin derivative has been eliminated, the partner drug is left unprotected73. Protection in this context means that when both drugs are only present together at the right concentration, the probability of a resistant strain emerging is reduced by orders of magnitude, as resistance needs to develop to both drugs simultaneously, and is a key benefit of combination therapies74. There have been reports of both drugs in artemether-lumefantrine combination therapy containing reduced concentrations of both APIs, both separately and together, in Uganda75, as well as high rates of substandard combination therapies in Ghana76. When either of the APIs in an artemisinin-based combination are substandard, this could lead to no or reduced protection of the other drug.The standard treatments for Plasmodium falciparum malaria are artemisinin-based combination therapies. These treatments include one fast-acting potent drug to clear parasites quickly (an artemisinin derivative) and one slower-acting partner drug to eliminate residual parasites and prevent recrudescence17. If the artemisinin derivative is substandard or falsified it may have a low API or low bioavailability, both of which would increase the potential for hyperparasitaemia (Box 1B), and hence the emergence of resistance genes, which could then be selected for by the slower-acting complementary antimalarial. It will also lead to a greater risk of recrudescence, as the lower level of artemisinin derivative fails to reduce parasite numbers to a low enough level17 (Box 1C). When the complementary antimalarial is SF, we may again see higher parasite numbers and consequently higher rates of recrudescence and onward transmission, including of resistant parasites (Box 1C). In this case, the artemisinin-derivative could also be left unprotected (Box 1D), increasing the chance of artemisinin-resistance emerging, which could be devastating given its key role in malaria treatment and the lack of alternatives in the production pipeline. In the case that both APIs in the combination are in limited amount or not present, there is an increased risk of hyperparasitaemia due to inadequate treatment, which could then lead to an increased risk of resistance emergence if followed with treatment with a standard course, or a course in which just one of the APIs is SF 49, which collates scientific articles which report the frequency of SF medicines, is one such resource, and includes only 30 random surveys of the prevalence of SF antibiotics conducted between 1992 and 2020, and many more surveys using convenience samples and other sampling approaches4. The World Health Organization is also currently undertaking large random surveys of the prevalence of SF medicines in several countries in sub-Saharan Africa50. Studies involving dissolution testing to predict the bioavailability and therapeutic efficacy of medicines are key to understanding the overall impact of SF medicines on AMR, but are currently scarce in the literature48. It is also important to undertake longitudinal studies of SF medicine frequency, % API, and bioavailability; most current studies are cross-sectional5. These data on SF medicines should then be complemented with data on AMR prevalence. The Global Research on AntiMicrobial resistance project  has compiled a database on AMR prevalence that could be a key resource in this regard11. It will also be necessary to take a One Health perspective and consider the frequency of SF veterinary medicines and their impact on human, animal and environmental health52.Coupling the understanding gained about within-host processes with epidemiological models could help understand the population-level effects of SF medicines. An important data source to inform such models will be objective random surveys of the frequency of SF medicines, including on the % API present and the neglected aspect of bioavailability, as this will be a key determinant of the direction and strength of the effect on AMR54. Such heterogeneities can be explicitly incorporated into mathematical models. For example, mathematical models have previously been used to show how within-host heterogeneity in antibiotic concentrations could affect the emergence of resistance in cancer55, HIV56, and M. tuberculosis57, finding that such heterogeneity can both facilitate and reduce the emergence of resistance. Another important heterogeneity is the frequency of resistant strains in the community, which will have an important impact on the within-host dynamics of SF medicines29 and in turn on the transmission of resistant strains. Data on the frequency of resistance, and the level of resistance , and coupled within- and between-host models could help understand this feedback across scales58. The level of resistance in a community is also likely to follow a continuous distribution, rather than being a binary trait, and models in the past have been used to understand this distribution59, which is likely to be relevant for the impact of SF medicines. There has been minimal laboratory work published to provide evidence to inform these discussions60. Further such work, including exploration of the use of hollow fiber systems61 may help inform discussions of the impact of SF antimicrobials on AMR in humans and other vertebrates with in vitro modeling.Epidemiological models could also help us understand the impact of spatio-temporal clustering of the distribution of SF medicines. For instance, certain places will likely have higher burdens of SF medicines, and their use will change through time, perhaps when an entire batch is determined to be substandard or when a covert sale of falsified medicines has recently been made5. Surveillance could be improved through empowering medicine inspectors with the deployment of affordable and easy-to-use technologies that can detect both substandard and falsified medicines63. Improved regulation and monitoring of the manufacturing of medicines is also critical, including drug registration and WHO prequalification of medicines, for example64. WHO prequalification involves evaluation of certain classes of medicines according to standards of safety, efficacy, and quality, and has been associated with more than ten times lower antimalarial failure rates6. It is currently planned to expand prequalification to other classes of medicines65. A related aspect is reducing degradation during the transportation of medicines, for instance through improved supply chain logistics and storage66. A final important aspect is improving our understanding of the manufacture and trade of falsified medicines to inform and prioritize actions and interventions to reduce their occurrence. Part of this could involve innovative forensic approaches using environmental DNA and stable isotope analysis to understand the origins of falsified medicines68. Social network analysis could then help identify common trade routes and manufacturing hotspots, and hence where interventions might be most effectively targeted69. There have been at least two previous reviews of strategies to reduce the burden of falsified medicines70.Reducing the burden of SF medicines will require an international and multifaceted approach. Key will be catalyzing political will and change so that all countries have functional medicine regulatory authorities, tasking with ensuring the quality of their medicine supply. Improved risk-based post-market surveillance for SF medicines will help inform where interventions should be targeted, within the WHO Prevent, Detect and Respond strategySubstandard and falsified medicines represent a scourge on health systems through large parts of the global South, and will likely lead to large health and economic costs if we do not invest in research and systems to reduce their prevalence. This includes efforts to understand their burden, their direct impact on health outcomes, the trade-routes of falsified medicines, and what types of mechanisms could ensure the quality of antimicrobials. Solving the mystery of their impact on the burden of AMR is one critical part of this endeavor.Further information on research design is available in the Supplementary InformationReporting Summary