Mutual Synergetic Folding (MSF) proteins belong to a recently discovered class of proteins. These proteins are disordered in their monomeric but ordered in their oligomeric forms. Their amino acid composition is more similar to globular proteins than to disordered ones. Our preceding work shed light on important structural aspects of the structural organization of these proteins, but the background of this behavior is still unknown. We suggest that solvent accessibility is an important factor, especially solvent accessibility of the peptide bonds can be accounted for this phenomenon. The side chains of the amino acids which form a peptide bond have a high local contribution to the shielding of the peptide bond from the solvent. During the oligomerization step, other non-local residues contribute to the shielding. We investigated these local and non-local effects of shielding based on Shannon information entropy calculations. We found that MSF and globular homodimeric proteins have different local contributions resulting from different amino acid pair frequencies. Their non-local distribution is also different because of distinctive inter-subunit contacts. The class of Mutual Synergetic Folding (MSF) proteins is a relatively newly discovered distinct class of oligomeric proteins, where a single polypeptide chain of an MSF protein is disordered, but all chains become ordered upon oligomerization ,2. In thAt the time of the discovery of the 3D structure of transmembrane proteins ,5, our kThe tertiary structures of experimentally validated MSF proteins have been collected to the Mutual Folding Induced by Binding (MFIB) database. This database contains 205 MSF protein structures ,7 of thiSequence and structural studies of protein interactions have revealed that sequential and spatial neighboring residues often play important roles in the environmental hydrophobicity and long-term binding site interactions, thus determining the structural and functional behavior of proteins ,13,14. BIn this work, we studied the solvent accessibility of peptide bonds in MSF and globular homodimeric proteins in light of relationship between dipeptide frequencies, and the ordered/disordered nature of the monomeric protein forms. Furthermore, we compared Shannon information entropy calculated from frequencies of local and spatial neighboring residues in MSF and globular proteins, which may indicate sequential differences between the two groups of proteins.We calculated the relative solvent accessible surface area (SASA) values for all peptide bonds in our MSF and globular homodimeric (MHOD and GHOD) protein datasets for both monomeric and dimeric forms using the FreeSASA program . The monA higher number of peptides bonds become buried during dimerization in the case of MSF homodimers, than in the case of globular homodimers. Next, we calculated the solvent accessibility of peptide bonds averaged within individual proteins. We calculated the ratio of the SASA values summed over all peptide bonds divided with the sum of the reference values, thus representing the average solvent accessibility of peptide bonds within a protein. Results are presented in We created a measure of the increase of peptide bond shielding upon dimerization using the following protocol. A peptide bond was identified as accessible, that is not properly shielded if its relative solvent accessibility was larger or equal than a threshold value of 10%. It was identified as buried if its relative SASA value was below 10%. We found that there are 2.6 times more not properly shielded peptide bonds in monomeric MSF proteins, than in globular proteins, which become buried upon dimerization. The number of buried (B) and accessible (A) residues were counted and we calculated the B/A ratio for both monomeric and dimeric forms. Then we calculated the dimeric B/A over the monomeric B/A quotient. This value represents the increase of peptide bond burial upon dimerization. A value of 1 would indicate that the buried/accessible residue count ratio is the same in both dimeric and monomeric forms, a value of 2 means that in the dimeric form the buried/accessible ratio is twice the monomeric value. Results can be seen in These results implicate, that solvent accessibility of the peptide bonds is an important factor in the destabilization of the monomeric form of MSF proteins. We investigated if amino acid composition plays a role in the above-presented findings. Instead of the peptide bond relative SASA value, which involves two amino acids, the relative main-chain SASA values were compared for the 20 residue types. The data presented in Glycine and proline residues have the highest average main-chain accessibility in both MSF and globular proteins, while cysteine, leucine, valine, and isoleucine residues have the lowest average values in both datasets. Traditional disordered proteins can be distinguished from globular ones based on their amino acid composition. Because of the similar amino acid composition of MSF and globular proteins ,7,8, staith amino acid typeiN is the number of the ith amino acid type and totN is the total number of amino acids in the actual dataset.Equation (1): Calculation of the frequencies of the iP amino acid probability distribution differs from a reference iQ probability distribution. We are using reference iQ values obtained from a non-redundant subset of the PDB database [For calculating entropy values of individual proteins, we decided to use a normalized version of the relative Shannon information entropy, frequently referred to as the Kullback–Leibler divergence  using Eqdatabase  (see PDBjN is the number of amino acids in protein j.Equation (2): Calculation of the normalized Shannon information entropy for an individual protein based on the amino acid compositionEntropy values were calculated for entries in our homodimeric protein datasets. Results are presented as a histogram in There is a difference between the distributions obtained on MSF and globular homodimers, entropy values of the MSF proteins are shifted toward positive values. This result is somewhat unexpected in light of our previous results. Recently  we compaAs we have found a significant difference in the burial of peptide bonds between MSF and globular homodimers, we started to look for a possible reason. Since local shielding of the peptide bonds is mainly provided by the two amino acids creating the peptide bond, we calculated the dipeptide frequencies in our MHOD and GHOD protein datasets by dividing the count number of a specific dipeptide with the total number of peptide bonds using Equation (3).ij dipeptidespbN is the total number of peptide bonds.Equation (3): Calculation of the frequencies of the ijP values obtained on the MHOD protein dataset, and reference ijQ values obtained on the GHOD protein dataset. The highest and lowest 10 values are plotted in To pinpoint the differences, we calculated relative entropy-like There are only a handful of dipeptides that have a strong preference for MSF or globular proteins, but most dipeptides have rather weak preference values. Though the amino acid composition of MSF homodimeric proteins is close to that of globular proteins ,8, diffeijQ and ijP values obtained on the GHOD and MHOD protein datasets can be found in Based on this observation dipeptide distribution might discriminate MSF proteins from globular ones, so we investigated the information content of the protein sequences. We calculated Shannon information entropy values based on dipeptide frequencies, similar to the previous case of amino acid compositions. The kN is the number of peptide bonds in protein k.Equation (4): Calculation of the Shannon information entropy for an individual protein based on the dipeptide frequencies:We plotted the entropy value distribution of individual proteins as histograms see . We can To investigate the effect of the non-local long-range shielding of peptide bonds upon dimerization we created the following protocol. We identified inter-subunit residue contacts based on a simple distance criterion. Residues pairs were identified as important for long range shielding, if the participating residues are part of different protein chains and they have at least one heavy-atom pair with a distance shorter than 4 Ångströms. We identified all these residue pairs and calculated their frequencies using Equation (5).ij residue pairstcN is the total number of contacting residue pairs.Equation (5): Calculation of the frequencies of the ijQ values can be derived only from dimeric structures, the GHOD protein dataset was used for this purpose. In order to produce entropy values also for globular proteins, both MHOD and GHOD protein datasets were scored using the MHOD derived ijP values. The Since reference cN is the number of contacts within protein k.Equation (6): Calculation of the Shannon information entropy for an individual protein based on inter-subunit contacts:ijP matrix was used for both datasets, there is a shift in the distribution of the MSF protein values towards positive values. There is an overlap of the two distributions, but more than a quarter of the globular proteins have negative values, while all MSF proteins have positive values. About one fifth of the globular proteins has a value larger than 0.01, while more than half of the MSF proteins are found in this range.Despite the same MSF proteins are a relatively new class of proteins with little knowledge about their folding. Our current comparison of MSF and globular homodimeric proteins provided the following results. MSF proteins have a higher average relative solvent accessibility of the peptide bonds in their monomeric form. Upon dimerization, a higher proportion of accessible peptide bonds become buried in the case of MSF homodimers, when compared to globular ones. A significant increase in the number of both buried peptide bonds and buried residues upon oligomerization is characteristic for MSF proteins. Zhou et al. recently analyzed the normalized monomer surface area versus normalized interfacial surface area in a recent publication . Their fThe burial of the peptide bonds from solvent molecules is established by shielding through local and non-local residues, relative to the actual peptide bond. We found differences in both local and non-local dipeptide frequencies between MSF and globular homodimers using Shannon information entropy calculations. This behavior originates from the different dipeptide frequencies locally and different inter-subunit contacts non-locally.Zhou et al. also emphasized the importance of intrinsic disorder in complex formation. Previously we found  that on For the database analyses, we wrote our own Python programs using Biopython extensions  and creaIn our previous publications, the solvent accessibility of the main chain was handled at the residue level ,7. In thWhen available, modified PDB files were used from the MFIB database. In the case of NMR structures, the representative model structure was selected based on the OLDERADO NMR resource found in PDBe .


In primary health care, multi-stakeholder partnerships between clinicians, policy makers, academic representatives and other stakeholders to improve service delivery are becoming more common. Literature on processes and approaches that enhance partnership effectiveness is growing. However, evidence on the performance of the measures of partnership functioning and the achievement of desired outcomes is still limited, due to the field’s definitional ambiguity and the challenges inherent in measuring complex and evolving collaborative processes. Reliable measures are needed for external or self-assessment of partnership functioning, as intermediate steps in the achievement of desired outcomes. We adapted the Partnership Self-Assessment Tool (PSAT) and distributed it to multiple stakeholders within five partnerships in Canada and Australia. The instrument contained a number of partnership functioning sub-scales. New sub-scales were developed for the domains of communication and external environment. Partnership synergy was assessed using modified Partnership Synergy Processes and Partnership Synergy Outcomes sub-scales, and a combined Partnership Synergy scale. Ranking by partnership scores was compared with independent ranks based on a qualitative evaluation of the partnerships’ development. 55 (90%) questionnaires were returned. Our results indicate that the instrument was capable of discriminating between different levels of dimensions of partnership functioning and partnership synergy even in a limited sample. The sub-scales were sufficiently reliable to have the capacity to discriminate between individuals, and between partnerships. There was negligible difference in the correlations between different partnership functioning dimensions and Partnership Synergy sub-scales. The Communication and External Environment sub-scales did not perform well metrically. The adapted partnership assessment tool is suitable for assessing the achievement of partnership synergy and specific indicators of partnership functioning. Further development of Communication and External Environment sub-scales is warranted. The instrument could be applied to assess internal partnership performance on key indicators across settings, in order to determine if the collaborative process is working well. Multi-stakeholder partnerships have been defined as a “voluntary and collaborative relationship … between various [stakeholders from different organizations] … in which all participants agree to work together to achieve a common purpose or undertake a specific task and to share risks, responsibilities, resources, competencies and benefits” . PartnerIn practice, however, partnerships can generate a great deal of frustration, as different organizations and individuals frequently struggle to achieve measurable and mutually beneficial outcomes ,5,7–9. TThis article presents findings from a survey of five multi-stakeholder partnerships involving decision makers, academic representatives, clinicians and organizational representatives with an interest in improving primary health care (PHC) accessibility. This quantitative study was part of a larger mixed methods sequential exploratory study to understand the processes that enhance multi-stakeholder partnerships in PHC service delivery. The qualitative phase entailed a comparative qualitative case study exploring, in a smaller sample of partnerships, the different manifestations of partnership synergy, different types of partnership resources and broad categories of partnership processes relevant to multi-stakeholder partnerships in PHC. Based on qualitative findings, approximately three-to-four years into the partnerships, we adapted an existing quantitative tool to measure different functions of partnerships and specifically to measure the concept of partnership synergy as an intermediate measure of partnership effectiveness. This article reports on the metric performance of the adapted measures of partnership functioning and partnership synergy.Some scholars and practitioners have argued that various dimensions of partnership functioning—including partner relationships, governance, leadership, management, use of resources, and the external environment–influence the achievement of the planned partnership outcomes ,13,14. TThe notion of “partnership synergy” has recently been proposed as an intermediate outcome between partnership functioning and the achievement of the planned ultimate outcomes . Synergyet al. scale [Two validated scales of partnership synergy have been developed. Both scales purport to assess the degree to which the partnership has maximized its synergy but each measures partnership synergy differently. The Weiss l. scale  conceptul. scale , whereasl. scale . Both syl. scale ,19,21.et al. partnership synergy scale. The PSAT also measures the various dimensions of partnership functioning including leadership, efficiency, administration and management, non-financial resources, financial and other capital resources, decision-making, benefits and drawbacks of participation and overall satisfaction with participation [et al. and the Jones partnership synergy scales (Partner Questionnaire) [We selected as the study instrument the Partnership Self-Assessment Tool (PSAT)  which cocipation . The PSAcipation ,23,24. Aonnaire) , we primonnaire) . The PSAet al. and the Jones partnership synergy scales [We made some adaptations to the PSAT. First, we modified the Partnership Synergy scale, incorporating items from the Weiss y scales ,22. SecoWe used a cross-sectional survey design, distributing a self-administered questionnaire to multiple stakeholders within five collaborative local partnerships in three Canadian provinces  and two Australian states . The survey took place between September 2018 and March 2019. Ethics approval for the study was obtained from the St. Mary’s Hospital Center Research Ethics Committee (No. SMHC-13-30C). All participants provided consent prior to data collection.The five partnerships were established in 2014 within a Canada-Australia research program entitled “Innovative Models Promoting Access-to-Care Transformation” (IMPACT). The program aimed at implementing organizational interventions to improve access to appropriate PHC for vulnerable populations . Using aEach partnership involved a combination of the following stakeholders: policy makers, clinicians and other health and community service providers, health system administrators, university researchers and research coordinators, and, in some cases, members and representatives of vulnerable populations . The preThe partnerships met the following criteria for the PSAT use: the partnerships had been in existence for at least six months, had at least five active partners, had continually worked together to develop and modify strategies in order to achieve their goals, and had begun to implement their plans . We usedWe adapted the validated Partnership Self-Assessment Tool (PSAT) ,24 as ouWe added elements that emerged in qualitative data but were not part of the PSAT: new proposed sub-scales for Communication (three items) and External Environment (two items). The case studies spoke to the importance of communication as an integral dimension of partnership functioning and the critical role of external influences.et al [et al. synergy scale as they were deemed more relevant in light of our overall aim to understand the processes and approaches of successful partnerships. In addition, they were more appropriate in our sample of partnerships that were underway and that exhibited varied levels of engagement of community-based stakeholders representing the target population. The partnerships were at a stage in their development when it was premature or impossible to evaluate elements of the Weiss et al. scale, such as the ability “to respond to the needs and problems of the community” or “to implement strategies that are most likely to work in the community” [Partnership Synergy was assessed using two sub-scales: 1) the adapted Partnership Synergy Processes sub-scale incorporating five items from the eight-item synergy scale developed by Jones and Barry ; and 2) et al . The itemmunity” . RespondThe majority of questions offered five-point Likert response options. We changed the order of response options to present the most negative response first, in order to reduce the commonly observed positive bias in responding . The useFinally, the questionnaire included questions about perceived Benefits (11 items) and Drawbacks (5 items) of participation to their respective organizations, and an overall assessment of benefits compared to drawbacks (1 item). It also elicited descriptive information about the stakeholder .The questionnaire was tested through cognitive interviewing  with thrQualtrics [The questionnaire was self-administered according to existing guidelines in the survey literature . Both paualtrics  online sThe unit of analysis for analysing the metric properties of the instrument was the individual respondent (n = 54), whereas the unit of analysis for partnership functioning and synergy was the aggregate of individual responses within a given partnership (n = 5). We preferred the median and IQR as the most appropriate way of representing central tendency and spread given the categorical nature of the data.We used descriptive statistics to characterize the sample and the distribution of the items and sub-scale scores. The scores for each sub-scale were derived through unweighted mean and medians of all the component items; scores for a respondent were calculated only if at least 50% of the items in the scale had been completed. Another method for handling the missing data was pairwise deletion. The small sample size precluded classical factor analysis. Instead, the analysis focused on examining some of the metric properties that may compromise precision and reliability. We looked for floor and ceiling effects, using a threshold ≥ 45% respondents in the lowest or highest response option. These items were tagged for exclusion as they indicated reduced capacity to discriminate between respondents. We used Spearman correlation coefficients to estimate the correlation between items within purported sub-scales, and between sub-scales, specifically dimensions of partnership functioning and partnership synergy. We examined internal consistency of the purported sub-scales by calculating the Cronbach’s Alpha.At the partnership level, the score for each scale was calculated by aggregating the scale scores of the component member respondents within each partnership. For partnership synergy, the two sub-scales of Partnership Synergy Processes and Partnership Synergy Outcomes were analysed separately, at the level of individual respondents. However, given that they were highly correlated with each other and demonstrated similar correlations with dimensions of partnership functioning, both sub-scales were combined at the level of the partnerships to provide a single score of Partnership Synergy. We used the non-parametric Kruskal–Wallis test to determine if we were able to detect statistically significant differences in sub-scale scores between any of the partnerships. The ranks of the scores were also derived. SPSS 23 for Windows  was usedIn lieu of statistical testing of predictive and convergent validity, we used consensus ranking from two external content reviewers who had conducted the independent longitudinal developmental evaluation of the five IMPACT partnerships as part of the IMPACT program’s effort to support the partnerships. They ranked the five partnerships based on the operational definitions and item content of the Partnership Synergy sub-scales and of the following partnership functioning sub-scales: Communication, Decision-making, Problem-solving, Resource Utilization and External Environment. The qualitative and quantitative ranks were compared. Finally, we correlated the score of Partnership Synergy with the partnership aggregate assessment of the extent to which developing a meaningful partnership had been achieved.The response rate was 90% (55/61), of which 62% (34) were online and 38% (21) were in paper format; 82% (45) in English and 18% (10) in French. One respondent was subsequently excluded from analysis due to comments on the questionnaire admitting that they picked answers at random. Our final sample was therefore 54 participants , 14 (academic representative), 15 (academic representative), 21  and 23 (patient representative) of a total of 76 questions). The missing data rates per question were calculated only on the sample of respondents who completed the questionnaire to that point. Missing data varied from 0% to 18% and the median was 2%, which was under the conventional missing rate of 10% . The misFinally, reliability of a scale is assumed if each item has an approximately equivalent correlation within the hypothesized sub-scale and contributes approximately the same proportion of information to the total score. The low and variable correlations in Communication and External Environment translated into a low internal consistency that failed to meet the criterion of ≥0.70 . CronbacIn addition, we observed that the shortened Partnership Synergy Outcomes and Partnership Synergy Processes sub-scales were strongly correlated with each other (r = 0.61), and there were few differences in how each of them correlated with the different dimensions of partnership functioning. We consequently chose to collapse the two synergy sub-scales together to a single Partnership Synergy scale (Partnership Synergy) for the analysis of associations between Partnership Synergy and functioning dimensions at the partnership level.P = .09) and Partnership Synergy (P = .07). The differences in medians were statistically significant between the following partnerships: Primary Care Connection-Service Linkage, Community Outreach-Community Health Resources, Community Outreach-Diabetes Self-management and Community Outreach-Service Linkage (in decreasing order of magnitude of difference). To further explore the extent to which differences in aggregate scores were meaningful, we calculated and found low to moderate variation for partnership synergy in all partnerships, suggesting that the statistically significant Kruskal-Wallis test of difference between at least two partnerships was not spurious. There was some variation in the content reviewers’ ranking of partnership synergy processes and partnership synergy outcomes, that was not reflected in the quantitative scores . We were unable to detect a significant distinction between the two Partnership Synergy sub-scales in terms of how strongly each one of them correlated with other dimensions of partnership functioning. This finding is consistent with prior research that suggested that the Weiss et al. and Jones synergy scales could be used interchangeably [We assessed partnership synergy with two constructs with a limited number of items  achieved on average the highest scores for partnership functioning on most dimensions. However, the results in the case of partnerships with lower scores on the Partnership Synergy and partnership functioning sub-scales were mixed. One partnership (Primary Care Connection) achieved a high synergy score despite a number of lower scores on the partnership functioning sub-scales, the most noticeable one of which was Resource Utilization. The sub-scale Resource Utilization was referred to as “Efficiency” in the 2002 version of the PSAT . EfficieThe PSAT is a generic tool that has demonstrated robustness in assessing the quality of partnership processes in multi-stakeholder partnerships in several areas, including public health . The PSAThe PSAT may need to be tailored to different partnership types and local conditions. For example, community-driven partnerships with fewer resources, a decentralized partnership structure and more distributed decision-making may need to adapt the Leadership and Administration & Management sub-scales. Alternatively, partnerships may prioritise different aspects for evaluation altogether. There is a variety of other partnership assessment tools that may be more or less suitable depending on the purpose of evaluation and the intended users of its results. For example, the Human Services Integration Measure  will be The utility of the PSAT as a longitudinal measure of change has not yet been established . We contOnce the partnership composition is established, and the partners have had the chance to work together (including face-to-face meetings), the revised PSAT can be administered in order to assess how the partnership is evolving and identify any areas for improvement. It may take some partnerships longer than six months to create momentum and therefore the timing of initial administration needs to be determined based on the specific circumstances of each partnership. At the end of the project the tool would benefit from additional sub-scales on effectiveness and sustainability to assess the likelihood of sustainability and impact on the target populations. In longer projects, if time and resources permit, we recommend that the tool be also administered at the mid-project point, to quantify progress related to how the collaboration is working, and identify strengths and areas that would require more work. In multi-partnership projects the tool would have to be administered at relatively similar intervals to ensure comparability across sites.Partnership self-assessment, including the administration of the tool, its analysis and sharing of results can be time-consuming, costly in terms of resources, and may contribute to participant burden ,39. ThesOur results should be interpreted in light of the study’s limitations. The first limitation concerns the generalizability of the study’s results. The sample of partnerships is small and the scope is narrow. All participants were sampled from a single international program of research with a targeted scope of implementing organizational interventions to improve the performance of accessibility to PHC, for a particular vulnerable population. Academic representatives constituted the largest single group of respondents in each partnership, which might have influenced our results. Despite the fact that most survey questions were fairly generic, they may have elicited different meaning for different participant groups . For exaWe considered and employed a number of mitigation strategies to overcome some of these limitations. In order to address the issue of small sample size, we used recommended statistical procedures and supplemented our analyses with qualitative information by independent content reviewers. In terms of the scope of partnership activities, we supplemented our survey data with information generated throughout the qualitative phase that preceded this quantitative study. Despite the specificity of partnerships under investigation, there was enough heterogeneity among them, in terms of their contexts, their composition and how the partnerships evolved, to assess the utility of the tool in comparing partnership performance across various settings. In terms of possible translation limitations, we ensured the accuracy of terminology through cognitive testing. Enriching our analyses with qualitative data from the first phase of this larger study has also allowed us to counter some of the challenges involved in using standardized self-assessment tools to assess partnerships. With regards to the causal relationships between partnership functioning and synergy, the administration of a scale to assess the effectiveness of both processes and outcomes would be warranted as all partnerships have now completed their work. This would allow us to determine whether higher synergy actually leads to better sustainability of interventions and better outcomes for the target populations.We adapted an existing partnership self-assessment tool and administered it to a small sample of developing partnerships. Our findings demonstrate that the tool is suitable for assessing the achievement of partnership synergy and the performance of each partnership along specific indicators of partnership functioning. The tool allowed us to detect meaningful differences between partnerships in how they performed on various dimensions, and corresponded overall to independent qualitative evaluations. The instrument demonstrates good capacity to discriminate between partnerships, and could be applied to assess internal partnership performance on these key indicators across settings, in order to determine if their collaborative process is working well. Therefore, it may have broader applicability to other partnerships in PHC, and arguably beyond PHC. We also propose that additional constructs of quality of partner relationships, communication and external environment could be developed and validated as part of partnership assessment.In conclusion, this study adds to the growing body of literature on assessing partnership synergy as a predictor of effectiveness of multi-stakeholder partnerships in health care, with a specific focus on PHC. In general, this work has contributed to the evolving body of knowledge on partnership synergy as a useful framework for studying collaborative ventures and identifying the main ingredients or requirements for synergistic partnerships. These requirements should be considered in order to determine if working in partnership should and can reasonably be pursued.S1 Appendix(DOCX)Click here for additional data file.S2 Appendix(XLSX)Click here for additional data file.S3 Appendix(DOCX)Click here for additional data file.S1 Dataset(XLSX)Click here for additional data file.


Organ transplantation survival rates have continued to improve over the last decades, mostly due to reduction of mortality early after transplantation. The advancement of the field is facilitating a liberalization of the access to organ transplantation with more patients with higher risk profile being added to the waiting list. At the same time, the persisting organ shortage fosters strategies to rescue organs of marginal donors. In this regard, hypothermic and normothermic machine perfusion are recognized as one of the most important developments in the modern era. Owing to these developments, novel non-invasive tools for the assessment of organ quality are on the horizon. Hyperspectral imaging represents a potentially suitable method capable of evaluating tissue morphology and organ perfusion prior to transplantation. Considering the changing environment, we here discuss the hypothetical combination of organ machine perfusion and hyperspectral imaging as a prospective feasibility concept in organ transplantation. The organ preservation process has been an important focus of research over the last years. In the context of transplantation, this procedure is needed in order to maintain organ quality from the point at which organs are removed from the donor, during storage and transportation, and until transplantation into the recipient . PreservThe era of SCS began in the 1960s, when Collins defined a method in order to perform kidney transport on ice using a preservation solution, with the result that organs could be conserved in a box after retrieval without damage after 30 h . However2), tissue perfusion , tissue hemoglobin index (THI), and tissue water index (TWI) [The digitization of pre-and intraoperatively imaging procedures offers the possibility of both the real-time analysis of acquired image data during the medical procedure and the visualization of the automatically extracted information for the surgeon . In thisex (TWI) . Previouex (TWI) . Variousex (TWI) .In light of the emerging technology of MP, focusing on organ restoration and viability assessment before transplantation, the introduction of a non-invasive, simple, cost-effective additional tool, capable of measuring organ perfusion and eventually able to predict graft dysfunction or surgical complications, is still warranted. During solid organ transplantation, adequate delivery of oxygen at the tissue level is critical for maintaining graft viability and promoting aerobic metabolism . PrincipReal-time monitoring of vascular flow and tissue oxygenation indicates viable tissue and facilitates early detection of impaired perfusion. In contrast to commonly used methods for the determination of the oxygenation status, HSI allows a pixel-wise analysis of chemical changes. The experimental results of HSI-based oxygen saturation calculation showed that HSI is suitable for the monitoring of the oxygen saturation distribution and the identification of areas with a reduced oxygen supply prior to transplantation . Given tPreclinical studies in porcine kidney grafts showed that using HSI during MP, by formulating an oxygenation map, could provide a precise differentiation between perfused and non-perfused tissue regions. Moreover, detection of arterial occlusions is possible . This siThe clear advantages of an eventual combination of the HSI technology with MP are the rapid applicability with reproducible results within ten minutes and its reliable information about the perfusion state of the organs. Furthermore, the organ can be monitored throughout the perfusion period, and a possible negative impact on posttransplant outcome may be anticipated before transplantation. Combining HSI with MP is technically feasible. However, the future challenge of integrating a real-time imaging procedure into a clinical MP setting might primarily regard technical features. An optimal acquisition distance and the automated use under sterile conditions should allow the best possible reproduction and interpretation of the spectral information. Data analysis based on HSI is limited by the low penetration depth of the propagating light in the tissue, depending on the attenuation coefficient of the analyzed tissue. Hence, tissue injuries in deeper regions cannot always be detected . The appCamera-based measurements such as HSI allowing a quick and contactless real-time perfusion imaging, founded on chemical tissue characteristics could be of great interest regarding pre-transplant viability assessment. Based on the statement, that ischemia has a relevant impact on graft injury, monitoring of oxygen supply might provide a potential marker for assessment and early identification of functional limitations, particularly in case of marginal organs . Hence,


Brain gliomas are characterized by remarkably intense invasive growth and the ability to create new blood vessels. Angiogenesis is a key process in the progression of these tumors. Coagulation and fibrinolysis factors play a role in promoting angiogenesis. The aim of the study was to evaluate the expression of proangiogenic proteins (VEGF and bFGF) and hemostatic proteins  associated with neoplastic cells and vascular endothelial cells in brain gliomas of various degrees of malignancy. Immunohistochemical tests were performed using the ABC method with the use of mono- and polyclonal antibodies. The obtained results indicated that both neoplastic cells and vascular endothelial cells in gliomas of various degrees of malignancy are characterized by heterogeneous expression of proteins of the hemostatic system and angiogenesis markers. The strongest expression of proangiogenic factors and procoagulant factors was demonstrated in gliomas of higher-grade malignancy. Since 1971, when J. Folkman promulgated the hypothesis that the growth of solid tumors depends on angiogenesis, and that therapeutic measures aimed at inhibiting this process may be an effective element of anticancer therapy , the era3 and the development of metastases [Glial tumors are characterized by remarkably intense invasive growth and the ability to create new blood vessels. In glioblastoma multiforme, vascular density is decidedly higher than in tumors with lower histological malignancy . Studiestastases . Activattastases .Among the proangiogenic factors, the main contributing mediators in vascularization of the neoplastic tumor are the growth factors: vascular endothelial growth factor (VEGF) and basic fibroblast growth factor (bFGF). Studies on glioblastoma multiforme have demonstrated that VEGF production is particularly induced in tumor cells close to the foci of necrosis. The coexistence of glomerular proliferation with areas of extensive necrosis indicates that the angiogenic response is a secondary effect of increased VEGF production by hypoxic tumor tissue . It is kin vitro, induces angiogenesis in vivo, and is often present in areas of vascular growth [An increase in VEGF expression under the influence of bFGF has been demonstrated, as well as a synergistic effect of both factors in the angiogenesis process . Despiter growth , 12. bFGr growth . FGF recr growth , 14.Tissue Factor (TF) expression has been demonstrated on the surface of many normal cells as well as neoplastic cells . The proThe procoagulant activity of thrombin leads to the conversion of fibrinogen into fibrin. Increased fibrinogen turnover and shortened plasma half-life are often found in cancer patients . The preThe aim of the study was to evaluate the expression of proangiogenic proteins (VEGF and bFGF) and coagulation/fibrinolysis proteins  associated with neoplastic cells and vascular endothelial cells in brain gliomas of various degrees of malignancy.Glioma tissues and tissues from the margin of these tumors were obtained at surgical resection of 40 cancer patients. The material consisted of 13 lower-grade and 27 higher-grade malignant tumors.Immunohistochemical (IHC) studies were performed on G2-grade gliomas  and high-grade gliomas , as well as control fragments of respective normal tissues, which were derived from the neoplasm-free surgical margins.The study protocol was approved by the local Ethics Committee of the Medical University in Bialystok, Poland . Informed consent was obtained from the patients.Antigens were detected with avidin-biotin complex technique (ABC) using reagents  which have been described previously .• 1-8C6 antibody to fibrinogen and fibrin-I, which requires intact 14Arg-15Gly binding on the Bβ chain of fibrinogen and therefore does not react with fibrin II .• T2G1 antibody to fibrin-II, which reacts with the amino-terminal part of the Bβ chain of fibrinogen only after cleavage by thrombin of fibrinopeptide B  and therefore does not react with fibrinogen and fibrin I.• GC4 antibody to D-dimers• Antibody to human recombinant TF – kindly provided by Dr. Walter Kisiel, University of New Mexico, Dept. of Pathology, School of Medicine, Albuquerque, United States. The antibody was employed in our earlier studies .The following mouse monoclonal antibodies were used:Antibodies  were kindly provided by Dr. Bohdan Kudryk, Lindsley F. Kimball Research, Blood Center, NY, United States and used in our earlier studies .• Goat antibody to human recombinant VEGF 121 and VEGF 165–R&D Systems, United States• Goat antibody to human recombinant bFGF-R&D Systems, United StatesThe following polyclonal antibodies were used:The results of staining of the glioma tissues were compared with matched normal tissues, which were processed simultaneously. Antigens of the proteins tested were detected as the brown reaction product of the avidin-biotin complex with the substrate. Visual assessment of protein expression was performed in 10 random high-power fields by two independent observers.Intensity of IHC reactions was evaluated acc. to Hirsch et al  in modifp value of <0.05 was considered statistically significant.χ2 test was employed for statistical analysis. The strongest expression of the VEGF antigen was obtained in glioblastoma multiforme cells and the endothelium of their blood vessels, with weaker but distinct expression in G3 tumors (anaplastic gliomas) and the weakest in endothelial cells and a few G2 glioma neoplastic cells .Clear and consistent expression of bFGF was found in tumor cells and in lower-grade malignant glioma vascular endothelial cells, while in gliomas of higher-grade, bFGF expression was less pronounced .A positive color reaction to the presence of TF antigen was demonstrated in neoplastic cells and vascular endothelial cells in high-grade glioma tissues ; Table 1A clear, positive reaction to fibrinogen antigens was above all demonstrated in the lumen of the blood vessels of all the examined tumors , as wellFibrin antigens were located in the neuropil around small blood vessels, in necrotic foci and in individual tumor cells . No systThe strongest expression of D-dimer antigens was found in higher-grade gliomas .The importance of angiogenesis as an independent prognostic factor in brain gliomas has long been recognized. An inverse correlation has been found between the intensity of angiogenesis and the survival time of patients with gliomas . DiffereOn the basis of our own research and data from the literature , 33, it One of the most exciting developments is the discovery, that autocrine and paracrine VEGF signaling contributes to vital aspects of tumorigenesis, namely cancer stem cells (CSCs) function, independently of angiogenesis. An alternative form of blood supply is vasculogenic mimicry, a process resembling embryonic vascular network, which is carried out by CSCs . The celThe neuropilins (NRPs), in addition to VEGF receptor tyrosine kinases, are fundamental for mediating the effects of VEGF on CSCs, due to their ability to affect the function of growth factor receptors and integrins . VEGF siin vivo. The expression of these growth factors correlates with tumor progression in high-grade tumors expressing higher levels of growth factors when compared to low-grade tumors. Yet other studies have revealed that bFGF is implicated in brain tumor progression and is localized in the microvasculature as well as in tumor cells in human gliomas [in vivo model and lead to reduced blood vessel densities in glioma tumors of treated animals [The expression of bFGF in the conducted studies was demonstrated in glioma cells of various degrees of malignancy, as well as in vascular endothelial cells. There was no correlation between bFGF and VEGF expression in glioma tissues. Other authors have also failed to demonstrate a correlation between bFGF expression and the intensity of angiogenesis in gliomas . Additio gliomas –52. bFGF gliomas . It has  animals .In our own studies, the presence of TF antigens was demonstrated in neoplastic cells and vascular endothelial cells of all higher-grade malignant gliomas, but only in a few lower-grade malignant glioma neoplastic cells. TF antigens were also demonstrated in neuropil, and the intensity of the color reaction was directly correlated with the tumor grade, which is consistent with studies by Hamada et al. 1996 . SimilarIn our own research, we have also demonstrated a correlation between the expression of TF and VEGF antigens, which is indicative of a relationship between TF and the intensity of angiogenesis in gliomas. The results obtained by other researchers have also confirmed this relationship , 58. In TF is believed to indirectly influence tumor angiogenesis through its procoagulant activity leading to thrombin generation and the formation of proangiogenic fibrin –61. The Thrombin generation, formation of a fibrin network and secondary fibrinolysis are evidenced by the presence of fibrin breakdown products found around small blood vessels and in association with glioblastoma cancer cells. Expression of D-dimers was clearly stronger in higher-grade gliomas. Compared to lower-grade malignant gliomas, anaplastic and multiforme gliomas are characterized by a stronger expression of proangiogenic factors and procoagulant factors involved in the processes of tumor growth and progression. When analyzing the expression of D-dimers and fibrin in the tissues, an inverse correlation was observed between these antigens: low fibrin expression was accompanied by high D-dimer expression, while strong fibrin expression was accompanied by low D-dimer expression.The above results indicate that neoplastic cells and vascular endothelial cells in gliomas of various degrees of malignancy are characterized by heterogeneous expression of proteins of the hemostatic system and angiogenesis markers. Compared to lower-grade gliomas, higher-grade gliomas express more proangiogenic factors and procoagulant factors involved in tumor angiogenesis and progression. In anaplastic oligodendrogliomas, the expression and distribution of the tested antigens is similar to that in low-grade gliomas.There are multiple links between coagulation activation and angiogenesis in cancer. Coagulation activation with subsequent fibrin formation takes place in the extravascular compartment of glial tumors. This solid phase coagulopathy may, at least in part, account for more pronounced angiogenesis observed in higher-grade gliomas, and thus contribute to more malignant course of the tumors.


The SARS-CoV-2 infection has resulted in massive loss of valuable human lives, extensive destruction of livelihoods and financial crisis of unprecedented levels across the globe. Kerala, a province in India, like the rest of the country, launched preventive and control measures to mitigate the impact of COVID-19 early in 2020. The Government of Kerala started 1206 Ayur Raksha Clinics and associated Task Forces across the state in April 2020 to improve the reach and penetration of Ayurvedic preventive, therapeutic and convalescent care strategies for the COVID-19 pandemic. The implementation framework of the strategy was properly designed, and had a decentralized, people-centered, and participatory approach. Kerala has robust public health machinery with adequate human resource and infrastructure in the conventional medicine sector. This community case study examines how the decentralized organizational framework was effectively utilized for facilitating the delivery of Ayurvedic services in the COVID-19 situation. Key observations from the study are: Ayurvedic programs implemented systematically, under an organized framework with social participation enables wider utilization of the services. Such a framework is easily replicable even in resource-poor settings. Rather than a pluralistic approach, an integrative health system approach may be more viable in the Kerala scenario in public health emergencies. The World Health Organization declared the COVID-19 outbreak as a global pandemic in March 2020 . HoweverThe traditional medicine systems across the globe, based on the available practices and knowledge, designed protocols for the prevention and management of early COVID-19 –20. The Kerala is a small state in the southern part of India with a dense population of 34 million . DespiteEver since the first Indian case of the SARS-CoV-2 infection was reported from Kerala, the state government was on alert to handle any emerging crisis. With a significant influx of COVID-19 positive persons from other states and countries in the early phases of the pandemic, Kerala built up an active COVID-19 containment strategy . The staKerala's robust COVID-19 containment strategy yielded good results right from the early days of the pandemic, as evident from published data. The seven-day average of the newly reported cases and deaths in Kerala as of 31 July 2020 was 945 and three respectively , 32, 33.Kerala has a vibrant public health machinery in the conventional medicine sector with over 50,000 beds in 1284 healthcare facilities. The state has also deployed over 6,000 doctors and approximately 21,000 paramedical staff and other skilled and unskilled workers under the above machinery to implement and report various healthcare activities . FurtherIn the early phase of the pandemic, Government of Kerala (GOK) constituted a seven-member Task Force to prepare the blue-print of an Ayurvedic strategy for prevention and mitigation of COVID-19 , 39. GOKThis community case study examines how a policy-oriented, decentralized, people-centered and participatory framework helped effective service delivery through Ayurveda and facilitated systematic reporting during the initial wave of the pandemic in Kerala. The paper also observes the impact of such an organized approach in the coverage of the Ayurvedic preventive program–Amritham and its preliminary effect on COVID-19 quarantined individuals in Kerala from 21 May 2020 to 8 July 2020.The community case study looks into a pre-designed Ayurvedic strategy comprising a well-defined organizational framework to execute diverse Ayurvedic programs for the COVID-19 pandemic in Kerala. The authors were responsible for successfully operationalizing these programs and acquired first-hand experience and access to primary data of Ayurvedic Covid programs.The key stakeholder is the GOK represented by the Department of Ayurveda, Yoga and Naturopathy, Unani, Siddha and Homeopathy (Ayush) with the main players being Department of Ayurveda Medical Education, Department of Indian Systems of Medicine, and the National Ayush Mission (State Mission Office). The State Ayurveda COVID-19 Response Cell (SACRC) coordinated activities of various departments, and LSGs for implementing the approved programs.The implementation framework broadly encompassed Ayurveda COVID-19 Response Cells at various levels with the support and participation of the key stakeholders. The SACRC was the coordinating body entrusted with the responsibility to design, implement, evaluate and report different Covid related activities . The staThe government operationalized the Ayur Raksha Clinics (ARC) and the Ayur Raksha Task Force (ARTF) as early as mid-April 2020. The ARTF included Ayurveda Medical Officer of the concerned ARC, the designated officials from the LSGs, Accredited Social Health Activists (ASHA), local health volunteers, private Ayurvedic practitioners and students, faculty and other staff of Ayurveda Medical Colleges and the elected representatives from the community. The ARTF was responsible for delivery of Ayurvedic medicines and services, follow-up, data collection and reporting.From the onset, the GOK implemented various Ayurvedic Preventive Strategies (APS) to strengthen the innate immunity of the public against COVID-19. The strategy covered different categories of the population based on the age and risk of exposure. These were Swasthyam  for general population below 60 years, Sukhayushyam (which means heathy old age) for general population above 60 years, and Amritham (which means nectar) for the Covid quarantined population. The strategy of Punarjani (which means rejuvenation) intended to ensure a speedy return to normal health in individuals in the post-Covid phase without any lingering Covid sequelae. Further, the government implemented the Bheshajam (which means free from disease fear) program, providing Ayurvedic treatments to asymptomatic or mildly symptomatic COVID-19 patients who had given consent for the same. The state cell circulated specific Standard Operating Procedures for implementation of the above-mentioned programs. The life clinics and task forces actively engaged with local social media groups and propagated Ayurvedic tips on daily living activities and diet. The Ayurvedic COVID-19 response cells also maintained social media platforms for propagating the Ayurvedic programs on healthy lifestyles and dietary tips. Oushadhi , the Pharmaceutical Corporation Kerala Limited, a Good Manufacturing Practice (GMP) certified company under the Government of Kerala manufactured and supplied the Ayurvedic medicines used for the above programs.The State Ayurveda COVID-19 Response Cell organized training programs on data collection, processing and management and various epidemiological methods for the district data management team and Ayurvedic physicians in charge of the life clinics. It also delivered necessary instructions as video tutorials. The life clinics and their task forces collected primary data from the targeted populations. They reported data to the district Covid cells, where the data management team verified and subsequently shared it with the state cell to evaluate and generate consolidated reports with recommendations.We conducted a preliminary analysis of the data in respect of the Ayurvedic quarantine care offered through life clinics across the state Amritham program). The analysis assessed the coverage of the program among the COVID-19 quarantined individuals in Kerala from 21 May 2020 to 8 July 2020 when delivered through the approved implementation framework. We also conducted a preliminary analysis of the preventive strategies on the quarantined individuals during the same period. The study considered the comprehensive data regarding the relevant sociodemographic status, comorbidity status, and quarantine course of all the individuals who registered under Amritham and the number of quarantined individuals who tested positive for SARS-CoV-2 infection from the program. The total number of quarantined individuals and the number of individuals who tested positive for SARS-CoV-2 virus was collected directly from the local health authorities by the district data management team and verified against data from authentic government sources, and the same was confirmed and compiled at the state level , 41.During the study period, the declared quarantine duration for an individual who travelled to Kerala or for a primary or secondary contact of a SARS-CoV-2 infected person was 14 days . Any perThe APS included polyherbal Ayurvedic formulations in varying dosage forms, including decoctions, tablets, pills, and powders, for internal use and as herbal fumigations . The AyuThe life clinics and task forces were responsible for delivering Ayurvedic medicines to the individuals under quarantine and for collecting data regarding the compliance of quarantined individual, the individual's health status, and the development of any Covid symptoms during the quarantine course. They obtained informed consent for consumption of Ayurvedic medicines from the quarantined individuals and gave personalized instructions regarding the use of Ayurvedic medicines, dietary advice, and lifestyle modifications and did tele-follow-up. The life clinics adhered to specific criteria for reporting the successful completion of quarantine under Amritham by an individual to the state cell, as follows;The individuals should have started Ayurvedic medicines within three days of the commencement of their quarantine period.The quarantined individuals should have consumed the APS medicines continuously for a minimum period of three days before undergoing tests for detecting SARS-CoV-2 infection.The quarantined individuals should not have been under any prophylactic cover against SARS-CoV-2 infection with medicines from other medical disciplines or Ayurvedic medicines not recommended as per the essential drug list.The quarantined individuals should not have any serious comorbidities or should not be debilitated.Ayurveda, a legitimized and well-accepted traditional medical system in India is integrated into the health system in Kerala through an extensive network of primary, secondary and tertiary institutions. Despite the integrated network, problems such as inadequate documentation and reporting of the clinical and public health outcomes have been the impediments towards building an evidence-based policy approach in Ayurvedic protocols, hitherto. The government has started focusing on building scientific and evidence-based data systems to assess the efficacy of promotive, prophylactic and therapeutic protocols of Ayurveda in various disease conditions.The Ayush Department worked out a decentralized, people-centered and participatory organizational framework for implementing the Ayurvedic preventive and therapeutic strategies during the COVID-19 pandemic, with a view to strengthening the reach, penetration, sustainability, and scalability of Ayurvedic services and for establishing robust reporting mechanisms. The current approach in the Kerala setting seems novel and does not seem to have been attempted in other Indian states.We have found that such a systematized approach facilitated the delivery of Ayurvedic medicines to Covid-19 quarantined individuals and facilitated the collection and dissemination of data. The Amritham program reached out to 25.7% of quarantined individuals in Kerala from 21 May 2020 to 8 July 2020. Further, only 0.34% of these individuals tested positive for SARS-CoV-2 infection. These individuals later recovered without developing any severe complications or Covid sequelae. However, a detailed analysis has not been done to assess the impact of other co-morbidities and pre-existing health conditions on the general health outcome of quarantined individuals.Government of India (GOI) approved the use of Ayurvedic regimen in boosting the immunity of the population against COVID-19 in the initial stage of the pandemic itself  GovernmeThe 1,206 life clinics cover all the 941villages with a jurisdiction of over six million households across Kerala . These cThere were seasonal epidemic outbreaks, earlier too, in Kerala, such as dengue and chikungunya , 57. AltWe overcame the scarcity of skilled human resources and infrastructure for service delivery and data-collection through a public-private-participatory model, with active involvement of the LSG bodies and local community. The method also enhanced the awareness of the general public about various Ayurvedic programs available for COVID-19. Apart from providing substantial financial assistance, the support from the LSG bodies helped the department overcome several operational hurdles in reaching the targeted population and in delivering medicines. The awareness generated through social media created social linkages necessary for pandemic response. Coordination between various government departments and professional organizations was crucial to improving communication and subsequent stakeholders' involvement.Globally there is convincing data regarding positive collaboration between health- care and non-healthcare organizations in improving public health , 59. OurWe have identified few lacunae in the implementation of the framework. Lack of prior experience in handling a public health crisis of such magnitude through an integrated multi-sectoral approach, lockdowns and subsequent closure of academic institutions were the critical hurdles in the implementation. The other identified obstacles were the scarcity of skilled human resources in epidemiological and field-level activities and lack of adequate infrastructure for precise delivery of medicines and for collection and dissemination of data. We have relied on secondary data from authentic government sources to gather information on the Covid positivity of quarantined individuals. Further, extensive data gathered from the field related to Amritham and its consolidation might have attracted various biases. The program implementation has continued with an ongoing in-depth analysis of the gathered data, which would help identify and resolve some of these biases. Succinctly, the single most barrier to the development, implementation and dissemination of Ayurvedic strategies for Covid was the lack of resources. The prominent facilitators to the strategy were the support from the local self-governance system, active involvement of the Ayurvedic private sector and social participation.Altogether, the lessons learnt from the reported community interventions are as follows;Policy-oriented, decentralized and well-planned implementation processes can improve the reach, penetration, scalability and sustainability of services even in resource-poor settings in an epidemic.Kerala's decentralized governance, social democracy and social participation can facilitate robust community-level activities.An implementation system with social participation can create awareness about services and improve the system's functioning.The scalability of implemented programs and constant reporting of community interventions during a pandemic can bring sustainability in public health activities and aid formulation of new policies.Rather than promoting a pluralistic approach, an integrated approach to public health delivery where Ayurveda/traditional medicine systems' services can be provided to the public with the collaboration of the local self-governments may be a better alternative.Broadly, the findings of this community study can work as a model for developing a well-organized integrative health system and as reference material for building a stronger science-policy interface. The model can also help governments mainstream evidence-based traditional medicine components to existing public health strategies and practices for better outcomes. A detailed understanding and critical analysis of such models and their elements can enrich traditional medicine and public health education and research at large. We recommend that national and state governments adopt the health system model we have reported for facilitating the delivery of traditional medicine services in resource-poor settings. While doing this, good data collection, monitoring, and reporting mechanisms are indispensable for timely evaluation of the mechanisms in place and for bringing about necessary corrective measures to the system. Identification of critical personnel with well-defined duties and responsibilities coupled with standard operating procedures will enable smooth functioning of the system. Regular discussions among various stakeholders and responsible persons can help build trust. Various capacity building and training programs are essential for delivering services at the grassroots and for the proper functioning of the operational framework. Rigorously designed, ready-to-implement health system research strategies are necessary for creating sustainable public health management models in the traditional medicine sector. Therefore, we also recommend extensive translational research in Ayurveda that can pave the way for developing clinically viable products for community use. At the same time, exploring valuable clinical and therapeutic information from Ayurvedic treatises is also required for integrating Ayurveda into the existing public health system.Since the evolution of the COVID-19 pandemic, we have focused on the synthesis of existing practice-based evidence and the development of evidence-based practice. The Government of Kerala worked out a feasible decentralized and participatory framework model for delivering and reporting Ayurvedic services replicable in resource-poor settings. Better utilization of these services can occur with an integrative health system approach. The establishment of accurate scientific evidence on the efficacy of the Ayurvedic management protocols would accelerate such integration and mainstreaming of Ayurveda.The original contributions presented in the study are included in the article/Ethical review and approval was not required for the study on human participants in accordance with the local legislation and institutional requirements. Written informed consent for participation was not required for this study in accordance with the national legislation and the institutional requirements.RP created the initial draft manuscript. SJ and DI edited and developed the manuscript. All authors contributed equally to the research and submission process.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


Lockdowns implemented to address the COVID-19 pandemic have disrupted human mobility flows around the globe to an unprecedented extent and with economic consequences which are unevenly distributed across territories, firms and individuals. Here we study socioeconomic determinants of mobility disruption during both the lockdown and the recovery phases in Italy. For this purpose, we analyze a massive data set on Italian mobility from February to October 2020 and we combine it with detailed data on pre-existing local socioeconomic features of Italian administrative units. Using a set of unsupervised and supervised learning techniques, we reliably show that the least and the most affected areas persistently belong to two different clusters. Notably, the former cluster features significantly higher income per capita and lower income inequality than the latter. This distinction persists once the lockdown is lifted. The least affected areas display a swift (V-shaped) recovery in mobility patterns, while poorer, most affected areas experience a much slower (U-shaped) recovery: as of October 2020, their mobility was still significantly lower than pre-lockdown levels. These results are then detailed and confirmed with a quantile regression analysis. Our findings show that economic segregation has, thus, strengthened during the pandemic. Among NPIs, mobility restrictions proved to play a central role in decreasing social contacts8, effectively contributing to control the diffusion of several pathogens across social systems and motivating their wide adoption for the containment also of the COVID-1913.Public health strategies extensively adopted by governments at the outbreak of the COVID-19 pandemic required the implementation of non-pharmaceutical interventions (NPIs) to reduce the transmission rate of the virus17, while restrictions targeted to specific geographic areas and for shorter periods constituted the preferred strategy during the second half of 202025.Specifically, full national lockdowns, i.e. periods of severe mobility restrictions and social distancing for almost all citizens, were widely adopted during the first wave of the COVID-19 pandemic26. However, the adoption of generalized mobility restrictions across entire national territories severely affected the whole population, causing the disruption of national mobility networks29 with relevant consequences for the socioeconomic context. The impact of the contraction of mobility flows on business activities and economic output thus sparked a vivid debate on the direct and indirect consequences of such policy measures32, stimulating the investigation of the socioeconomic effects of NPIs adoption34.The spread of the COVID-19 was unevenly distributed among geographic areas and clustered in specific zones during the first wave of contagion36 and loss of Gross Domestic Production (GDP)37 during the pandemic.This work aims to assess the impact of NPIs by evaluating how mobility restrictions interacted with socioeconomic characteristics. In so doing, we focus on Italian territories and we rely on the assumption that a reduction in mobility flows represents a first-order proxy for the contraction of economic activities. This is plausible since a wide share of the population relies on mobility to reach workplaces, and a significant portion of productive establishments depends on direct contact with consumers to carry on their activities. For example, mobility restrictions have been shown to be strongly correlated with consumption reduction40. In this work, we take advantage of a large-scale social network dataset comprising near real-time observations for over 4 million individuals, provided by Facebook through its “Data For Good” program, to measure Italian mobility patterns and assess how restrictions disrupted the corresponding mobility network. Given the severity of the COVID-19 pandemic, we follow the literature on the management of natural disasters and other disruptive events44 in which digital data have been exploited to evaluate the impact of shocks, even though these sources of information may not represent perfect survey instruments.Official measurements on the economic response of individuals and firms are typically available only several months after the events take place, and often with insufficient geographic detail. Digital traces of individuals, including mobility data, are instead widely diffused, readily available and contain information on the geographic position of users46, although the speed and strength of recovery after restrictions lifting have relevant consequences on the overall impact of the crisis, with potentially long-lasting effects which could modify the socioeconomic structure of a country. On a macroeconomic perspective, the study of the effects of restrictions on economic output has resulted in competing recovery scenarios from the COVID-19 pandemic52, distinguishing between a long and slow recovery (U-shaped or L-shaped), which signals the existence of long-run negative effects, and a short and fast dynamics (V-shaped), which indicates temporary disruptions.Extant literature on the negative impacts of NPIs on economic activities has focused mostly on the lockdown phaseAgainst this background, we investigate the existence of persistent effects from mobility restrictions in Italian territories by analyzing their recovery patterns and heterogeneous responses. In fact, some territories might limit the negative consequences of the lockdown, promptly reversing their mobility patterns once restrictions are lifted (a V-shaped recovery). Conversely, other territories might be persistently penalized by restrictions, suggesting long-lasting negative effects (a U-shaped recovery).To investigate the existence and persistence of these heterogeneous patterns, we analyze the evolution of mobility in Italy from March to October 2020, i.e., covering both the lockdown and the recovery phases, and we construct an indicator measuring the level of mobility disruption. By ranking territories according to their mobility performance, we provide evidence on the existence of two distinct classes characterized by different behaviors. The most affected territories exhibit a slow U-shaped recovery, and at the end of our period of analysis their mobility levels are still nearly 20% lower than their pre-lockdown levels. The least affected territories have a quicker, V-shaped, recovery, with mobility reaching pre-lockdown levels around mid-July, hence only 2 months after mobility restrictions are lifted.54. Such findings are confirmed with a quantile regression analysis to assess the individual effect of different socioeconomic dimensions.Then, to understand the extent to which pre-existing socioeconomic characteristics of Italian territories are informative to understand such mobility responses, we investigate the presence of commonalities among members of the two classes with both supervised and unsupervised machine learning techniques. We find significant evidence of persistent economic segregation induced by the lockdown, with most affected territories being characterized by lower income per capita and higher income inequality, in line with other recent studiesOur main variable of interest is income, but we also account for other relevant socioeconomic variables, such as the distribution of revenues per employee by productive sector, the share of employed individuals capable to work from remote, or the North-South geographic divide. Thus, we provide evidence that the effects of mobility restrictions have been unequally distributed across territories, with stronger and persistent effects where income per capita is lower.These findings are relevant for the decision process of policymakers. The existence of two separate behaviours of recovery among territories signals the urgency of a differentiated policy response, targeting the most affected territories with intervention reducing the persistence of the effects of mobility restrictions. Moreover, our results show the relevance of pre-existing economic and social conditions for the resilience of territories during disrupting events and support governments and policymakers in tailoring effective economic relief schemes. In fact, our analysis does not simply reveal how recovery behaves differently among territories, but it pinpoints which socio-economic characteristics are reliable indicators to assess heterogeneous recovery performances.56. Measurements are recorded with an 8-h frequency by aggregating individual trips of Facebook users moving between Bing map tiles57. Mobility flows are constructed with proprietary mechanisms to ensure privacy and anonymization, e.g., adding random noise and discarding flows with less than 10 individuals58. Specifically, we collect observations of traffic flows within about 5,000 distinct tiles produced by an average daily number of almost 4 million individuals, representing Italian users who enabled Facebook geolocation on their mobile phones during the period of observation.We collect near real-time observations of individuals moving in the Italian peninsula in a period of 32 weeks, from February 24th to October 4th, 2020, based on data of the “Disease Prevention Movement Maps” provided by Facebook through their “Data for Good” program60 , i.e., territorial divisions made by the Italian Institute of Statistics (Istat) to identify uniform sub-regional areas in which the labour force lives and works, and where local firms usually employ most of their workersinside LLMs. Our data, however, measure movements across fixed time windows of several hours and hence are more likely to represent long-range commuting patterns of individuals45, i.e., among LLMs, rather than shorter ones.Using LLMs as units of analysis is particularly suitable for our study. In fact, LLMs are defined based on labor markets, i.e., areas with a homogeneous productive structure in which, therefore, we can assume internal consistency in the distribution of labor forces and commuting flows. For these reasons, we can exploit them to infer differences among comparable units. Using lower-level territorial units  would have required, instead, a more complicated process of attribution of the economic productive structure since local labor markets overlap over administrative boundaries of municipalities. On the other hand, aggregating at the level of LLMs reduces our ability to capture short-range mobility flows, i.e., mobility flows before, (24/2-8/3), during (23/3-5/4) and after (15/6-29/6) the national lockdown of March 8th. Middle panels show the evolution of the total number of individuals flows, aggregating either over bi-weekly windows (D) or daily (E). Note how during the lockdown period there was a strong reduction in mobility flows, consistent with the hypothesis of the disruption of the Italian mobility network.We then build mobility networks using a weighted undirected graph formulation, where LLMs stand for nodes, while edges are weighted by the number of individuals traveling between two nodes in a given period of time. Given the large variability of daily measurements, for instance during the weekends, we study human mobility by aggregating observations using non-overlapping windows of 14 days over the interval from February 24th to October 4th. Figure 62. More specifically, we focus on the global efficiency (see Methods) along the literature on the resilience of networks against disruptive events65. We observe a pronounced variation of several network metrics . The evolution over time of the distribution of node centralities mirrors the trend of the global network statistics: a sharp decreasing trend consequent to the application of lockdown and then a gradual recovery during the summer period . We consider such proxy as particularly suitable in our setting, where a complex system undergoes external stress and then recovers, because it allows us to quantify the contribution of different territories to the configuration of the network. To model the shock in mobility at node level, a good candidate is also strength centrality, which simply sums the number of individuals moving from a location to all its immediate neighbors, and it is positively correlated with nodal efficiency. However, we employ nodal efficiency because it takes the overall topology of the graph into account, thus providing a system-wide evaluation of the mobility network, while strength centrality considers only direct connections.i be an Italian LLM in our sample, we define the relative variation in nodal efficiency t represents a 14-day window and Hence, let mentclass2pt{minimWe next examine whether the emergence of different mobility variations of Italian LLMs uncovers the presence of socioeconomic commonalities among those territories which instead share the same mobility performance in both the lockdown and recovery phases. Since socioeconomic variables do not cover the entire set of LLMs present in the mobility sample, at this stage the total number of observations in our analyses is reduced from 562 to 495 cf. Fig. .We attempt to answer the following question: do LLMs that exhibit different mobility performance, both during and after lockdown, also differ in their socioeconomic characteristics? To provide an answer, we rely on two measurements of mobility performance, one for the lockdown phase and one for the recovery phase, and we focus on those LLMS placed in the tails of the distribution of mobility performance, to identify the most and the least affected LLMs. By leveraging both unsupervised and supervised machine learning techniques, we test whether socioeconomic characteristics of LLMs are reliable predictors of their mobility performance.lockdown as the mean percentage variation over the first two 14-day windows during the lockdown phase, and recovery as the mean percentage variation over the first two 14-day windows after lockdown lifting. Specifically, let We introduce two indicators that allow us to identify the most and the least affected LLMs during the lockdown and the recovery phases. We consider the nodal efficiency and denote Figure most and the least affected LLMs as those territories respectively in the bottom and in the topK% of the distribution of the aforementioned mobility performance indicators, where K is a threshold chosen among the bottom/top percentiles of the distribution. As shown in the Supplementary Information  We then define the lockdown indicator , whereas Panel B provides the temporal evolution of their mobility as expressed by the relative percentage variation in nodal efficiency. Note that the two classes experience different dynamics: the least affected LLMs exhibit a V-shaped recovery, with a steep drop followed by a fast upturn, while the most affected LLMs present a more smoothed and U-shaped pattern, with a long-lasting period of reduction in mobility and with a lag of 3 months in the recovery after the end of the lockdown. This shows the absence of a catch-up process for the most affected LLMs during recovery. Hence, not only mobility restrictions have unevenly impacted LLMs, but their effects are lasting longer in those LLMs where mobility disruption during the lockdown has been stronger. Interestingly, the dynamics of the relative percentage variation in nodal efficiency is able to capture the fact that at the end of the summer Italy experienced a strong rebound in mobility patterns, which attenuated during the early stages of Autumn showing again more penalized trajectories for the most affected territories.Panel A of Fig. Unsupervised clustering of territories. We employ unsupervised hierarchical clustering (see Methods) to investigate whether the most and least affected LLMs manifest distinct peculiarities in terms of socioeconomic variables.46, we consider income per capita, inequality in the distribution of income and population density as potential drivers of variations in mobility. Furthermore, the market structure and the sectoral composition of the local economy are considered as relevant factors that might affect the impact of restrictions, given the different requirements of each sector in terms of mobility and direct contact with customers70. We control for overall market structure by introducing an aggregated measure representing the concentration of revenues among sectors (Revenue Concentration) in each LLM. We account for sectoral distribution of activities by including the number of employees by sector. Moreover, we control for the sectoral distribution of revenues by including the revenues per employee in each sector, i.e., a proxy of the local productivity of each sector in every LLM. Finally, we account for features affecting the response of LLMs to government interventions. Indeed, on the one hand, the Italian Government distinguished among industries having the possibility to maintain their activities open even during the lockdown  and others whose reopening was postponed. On the other hand, differences among firms in their capacity to implement work from remote influence the effect of restrictions. Hence, we account for the presence of employees in essential sectors and in sectors with higher attitudes to remote work using two aggregated variables, Essential Workers and Remote Workers, which measure the share of employees in these categories for each LLM. We use 16 aggregated NACE Rev. 2 categories to define sectors  assigning each observation to the class which is most frequent in the cluster, and (2) counting the number of correctly assigned observations within each cluster and dividing by the total number of observations. We note high values of purity score in the clustering (about 90%) when extracting more than 50 clusters, with values around 70–80% when extracting as few as 2 clusters . We use a stratified shuffle split cross-validation (with 10 folds and 20% of observations as test sample) to evaluate the performance of different models. We show in Panel D of Fig. lockdown and recovery indicators to extract the two classes, and different values for K. We note a great performance in the classification task, with AUROC values in the range 82–97%, with lockdown indicator performing slightly worse than recovery, and lower performance for both when considering larger samples . We observe that several variables exhibit statistically significant different distributions , but it does not indicate how these factors affect variation in nodal efficiency. In this section, we propose a framework to evaluate the magnitude and, more importantly, the direction of the interplay between local mobility performances and the socioeconomic characteristics of LLMs.within the two classes of peculiar relationships between the variation of mobility and the corresponding socioeconomic characteristics. For instance, among LLMs in one class the response of mobility performance to a certain variable might be negative, while among LLMs in the other class the relation might be reversed or with a reduced magnitude. Alternatively, it may happen that the correlation remains the same within the two classes, thus signaling a socioeconomic trait which strongly characterizes the mobility response of all LLMs, regardless of classes. Furthermore, by analyzing both lockdown and recovery performance of LLMs, we are interested in understanding the extent to which the impact of socioeconomic characteristics on mobility performance is stable over time, thus affecting LLMs both before and after the implementation of mobility restrictions.The unexplored hypothesis we want to test here is the emergence 73 in which the dependent variable corresponds, in turn, to the lockdown and recovery indicators defined in the previous section. The quantile regression method allows us to estimate specific coefficients for the explanatory variables in each quantile of the distribution of variation in mobility, investigating the existence of different effects involving the tails of the distribution. In contrast, a traditional Ordinary Least Squares (OLS) regression, assumes a uniform relationship between the distributions of the predictors and the one of the dependent variable, providing only a single coefficient.To do so, we perform a quantile regressionOur purpose is to analyze the role of socioeconomic variables on the resilience of territories. We focus, in particular, on the relationship between income per capita and mobility variation. To account for the role of other socioeconomic dimensions, we include the economic structure of territories in the econometric model and we employ a proxy of sectoral productivity (revenues per employee of each sector) to control for the influence of local productivity on the mobility performance of territories. Finally, territorial controls at the level of macroarea account for remaining unobserved local effects.K equal to 10%, 20% and 30%, we focus on the corresponding quantiles of the distribution of the two indicators as illustrative cases. Hence, for each quantile As we obtain a clear separation between the most and the least affected classes in correspondence of a threshold For the most relevant variables, Table Main socioeconomic variables. We observe that the coefficient of Income per Capita is positive and statistically significant for both the lower , and both during the Moreover, we find evidence that during the recovery phase, in the upper quantiles of the distribution of mobility the coefficient for Inequality is negative and statistically significant . Since we are not able to capture the higher mobility induced by essential sectors, we do not see a significant relation during lockdown. The same phenomenon yields a negative coefficient during recovery because it causes LLMs where the share of Essential Workers is higher to show lower levels of mobility in comparison to other LLMs.We also observe that the number of workers employed in essential sectors is not a key driver for mobility during the lockdown. This evidence is consistent with the results of our previous tests, where we showed that the Essential Workers variable was not statistically different between the most and the least affected LLMs See Fig. D. IntereRegarding the last aggregated measure, Revenue Concentration, we find a positive and significant coefficient during recovery  with G are represented by the weighted adjacency matrix W with elements i, j. In our analysis, we use the reciprocal of the number of individuals traveling between two locations to specify weights only when computing the efficiency measure of the network (and the nodes). Given that our network resembles a commuting network, we assume that two locations are closer if many individuals travel between them. The global network efficiency is thus computed as:i and j. When nodes i and j cannot be connected by any path, then Given a weighted undirected network 63 , Following the methodology of Latora et al.i to i as In addition, we define the nodal efficiency as the contribution of each node The Italian National Registry of Active Firms (ASIA) is managed by Istat and reports information on all economic units on the national territory exercising industrial, commercial and services activities. It collects and updates annually identification information (name and address) and structure  of firms within the register. Even though the data is collected at a granular level, for privacy reasons it has been provided to us by Istat at the province level and for each NACE Rev. 2 business sector which we aggregate in 21 macrocategories  which are comparable sub-regional geographical areas defined by the Italian National Institute of Statistics (Istat). As a result, our economic data cover different dimensions of the local socioeconomic context of each Italian LLM, in total 495 areas. Our three main variables of interest are Income per Capita, Inequality in the distributions of incomes and Population Density. These variables have been collected at the municipal level and then aggregated at the level of LLMs mapping each Italian municipality to a unique LLM. We then integrate these variables with sector related ones (employees and revenues per employee) obtained from the ASIA database at the province level. Using publicly available data from Istat on the number of employee at municipal level, we were able to redistribute province level data to the LLM level of aggregation using the share of province employees for each municipality as partition coefficient and the municipality-to-LLM mapping to aggregate the municipality data.Declared income. This variable is the tax base of the personal income tax declared by tax payers in the 2018 tax return to the Revenue Agency for the 2017 financial year. The distribution at the municipal level is provided by the Ministry of Economic and Finances (MEF) and can be recovered from Istat. We calculate Income per Capita as the ratio of total declared taxable income in a municipality divided by number of declarants with taxable income.Income inequality. We measure income inequality at the municipal level as the ratio between average and median values of the distribution of the declared income, without taking into account cadastral incomes. If the distribution of income is skewed, as is the case for 98% of Italian municipalities, the average municipal income will be consistently greater than the median municipal income. Hence municipalities with greater mean to median ratio will have a more unequal distribution than municipalities with lower ratio.Density of population. We use population density to account for concentration individuals in a certain territory. It is calculated as the ratio of total population calculated at the first of January divided by the extension of the territory in squared kilometers.Concentration of revenues by sector. We calculate this variable as the Herfindahl–Hirschman concentration index of the revenues of sectors in each LLMs. Let us define the revenue of LLM i in sector j as Percentage of workers who are able to work from remote. We use estimates of remote workers by sector from74 and adapt them to Italian firms. We obtain the LLMs share of potential remote worker as the weighted sum of each sector employees number multiplied by each sector-specific remote share estimate. The obtained estimates are also in line with those of75.Percentage of workers in sectors declared essential by the government . Essential sectors are obtained from official Government decrees. We obtain the LLMs share of essential sector workers as the percentage of employee from essential sectors over all employees from all sectors.Territorial controls. Our regression sample provides a good representation of the entire set of Italian municipalities, both in terms of population size and geographical location, including 2,387 observations corresponding to 30% of Italian municipalities. Nevertheless, to exclude spatial spillovers effect we control for territorial confounding factors using macroarea controls corresponding to NUTS1 areas, which represent the most aggregated geographical classification of national administrative units provide by Eurostat  and we assign them a binary label, based on the scipy76 to carry out a hierarchical clustering of LLMs based on their standardized socioeconomic variables, using a cosine distance and average linkage to obtain clusters. We report similar results when using euclidean distance or other linkage approaches, e.g., complete or Ward linkage.We employ scikit-learn77 to train several binary classifiers, namely Logistic Regression, Support Vector Machine, Random Forest and K-Nearest Neighbors, in the task of classifying most vs least affected LLMs based on their socioeconomic variables. We apply standardization of features and use default hyperparameters as specified in the package, and we use stratified shuffle-split cross-validation to evaluate their performance.We use scikit-posthocs78 to perform multiple test comparisons and assess statistical differences in the distribution of features of the most and the least affected LLMs (and remaining ones). In particular, we apply Kruskal–Wallis test with Conover procedure and Bonferroni correction for p-values.We employ quantreg R package73 on standardized variables with default options. Results in the Supplementary Information are obtained with heteroskedastic robust standard errors obtained via wild bootstrap with 1000 iterations.Finally, all quantile regression results have been performed with the Supplementary Information 1.