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Abstract Automation of surveillance of infectious diseases—where algorithms are applied to routine care data to replace manual decisions—likely reduces workload and improves quality of surveillance. However, various barriers limit large-scale implementation of automated surveillance (AS). Current implementation strategies for AS in surveillance networks include central implementation (i.e. collecting all data centrally, and central algorithm application for case ascertainment) or local implementation (i.e. local algorithm application and sharing surveillance results with the network coordinating center). In this perspective, we explore whether current challenges can be solved by federated AS. In federated AS, scripts for analyses are developed centrally and applied locally. We focus on the potential of federated AS in the context of healthcare associated infections (AS-HAI) and of severe acute respiratory illness (AS-SARI). AS-HAI and AS-SARI have common and specific requirements, but both would benefit from decreased local surveillance burden, alignment of AS and increased central and local oversight, and improved access to data while preserving privacy. Federated AS combines some benefits of a centrally implemented system, such as standardization and alignment of an easily scalable methodology, with some of the benefits of a locally implemented system including (near) real-time access to data and flexibility in algorithms, meeting different information needs and improving sustainability, and allowance of a broader range of clinically relevant case-definitions. From a global perspective, it can promote the development of automated surveillance where it is not currently possible and foster international collaboration.The necessary transformation of source data likely will place a significant burden on healthcare facilities. However, this may be outweighed by the potential benefits: improved comparability of surveillance results, flexibility and reuse of data for multiple purposes. Governance and stakeholder agreement to address accuracy, accountability, transparency, digital literacy, and data protection, warrants clear attention to create acceptance of the methodology. In conclusion, federated automated surveillance seems a potential solution for current barriers of large-scale implementation of AS-HAI and AS-SARI. Prerequisites for successful implementation include validation of results and evaluation requirements of network participants to govern understanding and acceptance of the methodology.

Research into explainable artificial intelligence (XAI) methods has exploded over the past five years. It is essential to synthesize and categorize this research and, for this purpose, multiple systematic reviews on XAI mapped out the landscape of the existing methods. To understand how these methods have developed and been applied and what evidence has been accumulated through model training and analysis, we carried out a tertiary literature review that takes as input systematic literature reviews published between 1992 and 2023. We evaluated 40 systematic literature review papers and presented binary tabular overviews of researched XAI methods and their respective characteristics, such as the scope, scale, input data, explanation data, and machine learning models researched. We identified seven distinct characteristics and organized them into twelve specific categories, culminating in the creation of comprehensive research grids. Within these research grids, we systematically documented the presence or absence of research mentions for each pairing of characteristic and category. We identified 14 combinations that are open to research. Our findings reveal a significant gap, particularly in categories like the cross-section of feature graphs and numerical data, which appear to be notably absent or insufficiently addressed in the existing body of research and thus represent a future research road map.

Introduction: Raising a child with social communication and emotion regulation challenges, such as those experienced in the context of an autism spectrum disorder (ASD), is associated with family disruption and parental stress. Research shows that parents of children and young people with ASD can experience challenges in managing their child’s behaviour and experience a sense of disconnectedness, a lack of support from the “system”, and a broader lack of understanding within the community. Methods: A therapeutic parent group was held in parallel to a young person group working on social skills. The young person cohort included both boys and girls in early adolescence with neurodevelopmental diagnoses. The parent group provided a space for participants to focus on themselves, tune into their own emotions, and receive support in relation to their lived experiences of parenting. Parental outcomes were assessed using standard measures, and the experiences of the groups were captured qualitatively through focus groups with both parents and staff. Results: The project found that the therapeutic parent group was effective in supporting parents to feel less stressed and more in control, as well as less alone in their parenting journey. Qualitative data indicated that the groups filled an identified need within the service and were engaging and acceptable to parents.

Abstract Background Detection of pathogen-related clusters within a hospital is key to early intervention to prevent onward transmission. Various automated surveillance methods for outbreak detection have been implemented in hospital settings. However, direct comparison is difficult due to heterogenicity of data sources and methodologies. In the hospital setting, we assess the performance of three different methods for identifying microbiological clusters when applied to various pathogens with distinct occurrence patterns. Methods In this retrospective cohort study we use WHONET-SaTScan, CLAR (CLuster AleRt system) and our currently used percentile-based system (P75) for the means of cluster detection. The three methods are applied to the same data curated from 1st January 2014 to 31st December 2021 from a tertiary care hospital. We show the results for the following case studies: the introduction of a new pathogen with subsequent endemicity, an endemic species, rising levels of an endemic organism, and a sporadically occurring species. Results All three cluster detection methods showed congruence only in endemic organisms. However, there was a paucity of alerts from WHONET-SaTScan (n = 9) compared to CLAR (n = 319) and the P75 system (n = 472). WHONET-SaTScan did not pick up smaller variations in baseline numbers of endemic organisms as well as sporadic organisms as compared to CLAR and the P75 system. CLAR and the P75 system revealed congruence in alerts for both endemic and sporadic organisms. Conclusions Use of statistically based automated cluster alert systems (such as CLAR and WHONET-Satscan) are comparable to rule-based alert systems only for endemic pathogens. For sporadic pathogens WHONET-SaTScan returned fewer alerts compared to rule-based alert systems. Further work is required regarding clinical relevance, timelines of cluster alerts and implementation.

Abstract Introduction To promote the nation-wide implementation of semi-automated surveillance (AS) of surgical site infection after hip and knee arthroplasty, the Dutch National Institute for Public Health and the Environment (RIVM) deployed a decentralised multifaceted implementation strategy. This strategy consisted of a protocol specifying minimum requirements for an AS system, supported by a user manual, education module, individual guidance for hospitals and user-group meetings. This study describes an effect evaluation and process evaluation of the implementation strategy for AS in five frontrunner hospitals. Methods To evaluate the effect of the implementation strategy, the achieved phase of implementation was determined in each frontrunner hospital at the end of the study period. The process evaluation consisted of (1) an evaluation of the feasibility of strategy elements, (2) an evaluation of barriers and facilitators for implementation and (3) an evaluation of the workload for implementation. Interviews were performed as a basis for a subsequent survey quantifying the results regarding the feasibility as well as barriers and facilitators. Workload was self-monitored per profession. Qualitative data were analysed using a framework analysis, whereas quantitative data were analysed descriptively. Results One hospital finished the complete implementation process in 240 person-hours. Overall, the elements of the implementation strategy were often used, positively received and overall, the strategy was rated effective and feasible. During the implementation process, participants perceived the relative advantage of AS and had sufficient knowledge about AS. However, barriers regarding complexity of AS data extraction, data-infrastructure, and validation, lack of capacity and motivation at the IT department, and difficulties with assigning roles and responsibilities were experienced. Conclusion A decentralised multifaceted implementation strategy is suitable for the implementation of AS in hospitals. Effective local project management, including clear project leadership and ownership, obtaining commitment of higher management levels, active involvement of stakeholders, and appropriate allocation of roles and responsibilities is important for successful implementation and should be facilitated by the implementation strategy. Sufficient knowledge about AS, its requirements and the implementation process should be available among stakeholders by e.g. an education module. Furthermore, exchange of knowledge and experiences between hospitals should be encouraged in user-group meetings.

Abstract Background Despite the advent of neoadjuvant chemoradiotherapy (CRT), overall survival rates of esophageal adenocarcinoma (EAC) remain low. A readily induced mesenchymal transition of EAC cells contributes to resistance to CRT. Methods In this study, we aimed to chart the heterogeneity in cell state transition after CRT and to identify its underpinnings. A panel of 12 esophageal cultures were treated with CRT and ranked by their relative epithelial-mesenchymal plasticity. RNA-sequencing was performed on 100 pre-treatment biopsies. After RNA-sequencing, Ridge regression analysis was applied to correlate gene expression to ranked plasticity, and models were developed to predict mesenchymal transitions in patients. Plasticity score predictions of the three highest significant predictive models were projected on the pre-treatment biopsies and related to clinical outcome data. Motif enrichment analysis of the genes associated with all three models was performed. Results This study reveals NANOG as the key associated transcription factor predicting mesenchymal plasticity in EAC. Expression of NANOG in pre-treatment biopsies is highly associated with poor response to neoadjuvant chemoradiation, the occurrence of recurrences, and median overall survival difference in EAC patients (>48 months). Perturbation of NANOG reduces plasticity and resensitizes cell lines, organoid cultures, and patient-derived in vivo grafts. Conclusions In conclusion, NANOG is a key transcription factor in mesenchymal plasticity in EAC and a promising predictive marker for outcome.

The pericellular matrix (PCM) surrounding chondrocytes is essential for articular cartilage tissue engineering. As the current isolation methods to obtain chondrocytes with their PCM (chondrons) result in a heterogeneous mixture of chondrocytes and chondrons, regenerating the PCM using a tissue engineering approach could prove beneficial. In this study, we aimed to discern the behavior of articular chondrocytes (ACs) in regenerating the PCM in such an approach and whether this would also be true for articular cartilage-derived progenitor cells (ACPCs), as an alternative cell source. Bovine ACs and ACPCs were encapsulated in agarose microgels using droplet-based microfluidics. ACs were stimulated with TGF-β1 and dexamethasone and ACPCs were sequentially stimulated with BMP-9 followed by TGF-β1 and dexamethasone. After 0, 3, 5, and 10 days of culture, PCM components, type-VI collagen and perlecan, and ECM component, type-II collagen, were assessed using flow cytometry and fluorescence microscopy. Both ACs and ACPCs synthesized the PCM before the ECM. It was seen for the first time that synthesis of type-VI collagen always preceded perlecan. While the PCM synthesized by ACs resembled native chondrons after only 5 days of culture, ACPCs often made less well-structured PCMs. Both cell types showed variations between individual cells and donors. On one hand, this was more prominent in ACPCs, but also a subset of ACPCs showed superior PCM and ECM regeneration, suggesting that isolating these cells may potentially improve cartilage repair strategies.

Background The origin of autoantibodies in patients with antiphospholipid syndrome (APS) is unknown. The gut microbiome contributes to autoimmunity and contains peptide homologues to the main APS autoantigen, which affect disease activity in animal models. Alteration of the gut microbiota with vancomycin diminishes disease activity in mice but no data on the effect of gut microbiota alteration in APS patients are available to date.

Objective Gut-residing bacteria, such as Escherichia coli, can acetylate their proteome under conditions of amine starvation. It is postulated that the (gut) microbiome is involved in the breach of immune tolerance to modified self-proteins leading to the anti-modified protein antibodies (AMPAs), hallmarking seropositive rheumatoid arthritis (RA). Our aim was to determine whether acetylated bacterial proteins can induce AMPA responses cross-reactive to modified self-proteins and be recognised by human AMPA (hAMPA).Methods E. coli bacteria were grown under amine starvation to generate endogenously acetylated bacterial proteins. Furthermore, E. coli proteins were acetylated chemically. Recognition of these proteins by hAMPA was analysed by western blotting and ELISA; recognition by B cells carrying a modified protein-reactive B cell receptor (BCR) was analysed by pSyk (Syk phosphorylation) activation assay. C57BL/6 mice were immunised with (modified) bacterial protein fractions, and sera were analysed by ELISA.Results Chemically modified bacterial protein fractions contained high levels of acetylated proteins and were readily recognised by hAMPA and able to activate B cells carrying modified protein-reactive BCRs. Likely due to substantially lower levels of acetylation, endogenously acetylated protein fractions were not recognised by hAMPA or hAMPA-expressing B cells. Immunising mice with chemically modified protein fractions induced a strong cross-reactive AMPA response, targeting various modified antigens including citrullinated proteins.Conclusions Acetylated bacterial proteins are recognisable by hAMPA and are capable of inducing cross-reactive AMPA in mice. These observations provide the first conceptual evidence for a novel mechanism involving the (endogenous) acetylation of the bacterial proteome, allowing a breach of tolerance to modified proteins and the formation of cross-reactive AMPA.

Summary: Human genetic studies of critical COVID-19 pneumonia have revealed the essential role of type I interferon-dependent innate immunity to SARS-CoV-2 infection. Conversely, an association between the HLA-B∗15:01 allele and asymptomatic SARS-CoV-2 infection in unvaccinated individuals was recently reported, suggesting a contribution of pre-existing T cell-dependent adaptive immunity. We report a lack of association of classical HLA alleles, including HLA-B∗15:01, with pre-omicron asymptomatic SARS-CoV-2 infection in unvaccinated participants in a prospective population-based study in the United States (191 asymptomatic vs. 945 symptomatic COVID-19 cases). Moreover, we found no such association in the international COVID Human Genetic Effort cohort (206 asymptomatic vs. 574 mild or moderate COVID-19 cases and 1,625 severe or critical COVID-19 cases). Finally, in the Human Challenge Characterisation study, the three HLA-B∗15:01 individuals infected with SARS-CoV-2 developed symptoms. As with other acute primary infections studied, no classical HLA alleles favoring an asymptomatic course of SARS-CoV-2 infection were identified.

Abstract Background In patients who underwent colorectal surgery, an existing semi-automated surveillance algorithm based on structured data achieves high sensitivity in detecting deep surgical site infections (SSI), however, generates a significant number of false positives. The inclusion of unstructured, clinical narratives to the algorithm may decrease the number of patients requiring manual chart review. The aim of this study was to investigate the performance of this semi-automated surveillance algorithm augmented with a natural language processing (NLP) component to improve positive predictive value (PPV) and thus workload reduction (WR). Methods Retrospective, observational cohort study in patients who underwent colorectal surgery from January 1, 2015, through September 30, 2020. NLP was used to detect keyword counts in clinical notes. Several NLP-algorithms were developed with different count input types and classifiers, and added as component to the original semi-automated algorithm. Traditional manual surveillance was compared with the NLP-augmented surveillance algorithms and sensitivity, specificity, PPV and WR were calculated. Results From the NLP-augmented models, the decision tree models with discretized counts or binary counts had the best performance (sensitivity 95.1% (95%CI 83.5–99.4%), WR 60.9%) and improved PPV and WR by only 2.6% and 3.6%, respectively, compared to the original algorithm. Conclusions The addition of an NLP component to the existing algorithm had modest effect on WR (decrease of 1.4–12.5%), at the cost of sensitivity. For future implementation it will be a trade-off between optimal case-finding techniques versus practical considerations such as acceptability and availability of resources.

Abstract Background Automated surveillance methods that re-use electronic health record data are considered an attractive alternative to traditional manual surveillance. However, surveillance algorithms need to be thoroughly validated before being implemented in a clinical setting. With semi-automated surveillance patients are classified as low or high probability of having developed infection, and only high probability patients subsequently undergo manual record review. The aim of this study was to externally validate two existing semi-automated surveillance algorithms for deep SSI after colorectal surgery, developed on Spanish and Dutch data, in a Swedish setting. Methods The algorithms were validated in 225 randomly selected surgeries from Karolinska University Hospital from the period January 1, 2015 until August 31, 2020. Both algorithms were based on (re)admission and discharge data, mortality, reoperations, radiology orders, and antibiotic prescriptions, while one additionally used microbiology cultures. SSI was based on ECDC definitions. Sensitivity, specificity, positive predictive value, negative predictive value, and workload reduction were assessed compared to manual surveillance. Results Both algorithms performed well, yet the algorithm not relying on microbiological culture data had highest sensitivity (97.6, 95%CI: 87.4–99.6), which was comparable to previously published results. The latter algorithm aligned best with clinical practice and would lead to 57% records less to review. Conclusions The results highlight the importance of thorough validation before implementation in other clinical settings than in which algorithms were originally developed: the algorithm excluding microbiology cultures had highest sensitivity in this new setting and has the potential to support large-scale semi-automated surveillance of SSI after colorectal surgery.

ABSTRACT: During the transition phase, dairy cows are susceptible to develop postpartum diseases. Cows that stay healthy or recover rapidly can be considered to be more resilient in comparison to those that develop postpartum diseases. An indication of loss of resilience will allow for early intervention with preventive and supportive measures before the onset of disease. We investigated which quantitative behavioral characteristics during the dry period could be used as indicators of reduced resilience after calving, using noninvasive Smart Tag neck and Smart Tag leg sensors in dairy cows (Nedap N.V.). We followed 180 cows during 2 wk before until 6 wk after parturition at 4 farms in the Netherlands. Serving as proxy for loss of resilience, as defined by the duration and severity of disease, a clinical assessment was performed twice weekly and blood samples were taken in the first and fifth week after parturition. For each cow, clinical and serum value deviations were aggregated into a total deficit score (TDS total). We also calculated TDS values relating to inflammation, locomotion, or metabolic problems, which were further divided into macro-mineral and liver-related deviations. Smart Tag neck and leg sensors provided continuous behavioral activity signals of which we calculated the average, variance, and autocorrelation during the dry period. Diurnal patterns in the behavioral activity signals were derived by fast Fourier transformation and the calculation of the nonperiodicity. To select significant predictors of resilience, we first performed a univariate analysis with TDS as dependent variable and the behavioral characteristics that were measured during the dry period, as potential predictors with cow as experimental unit. We included parity group as fixed effect and farm as random effect. Next, we performed multivariable analysis with only significant predictors, followed by a variable selection procedure to obtain a final linear mixed model with an optimal subset of predictors with parity group as fixed effect and farm as random effect. The TDS total was best predicted by average inactive time, nonperiodicity ruminating, nonperiodicity of bouts standing up and fast Fourier transformation stand still. Average inactive time was negatively correlated with average eating time, and these 2 predictors could be exchanged with only little difference in model performance. Our best performing model predicted TDS total at a cutoff level of 60 points, with a sensitivity of 79.5% and a specificity of 73.2% with a positive predicted value of 0.69 and a negative predicted value of 0.83. The models to predict the other TDS categories showed a lower predictive performance as compared with the TDS total model, which could be related to the limited sample size and therefore, low occurrence of problems within a specific TDS category. Furthermore, more resilient dairy cows are characterized by high averages of eating time with high regularity in rumination and low averages of inactive time. They reveal high regularity in standing time and transitions from lying to standing, in the dry period. These behaviors can be used as indicators of resilience and allow for preventive intervention during the dry period in vulnerable dairy cattle. However, further examination is still required to find clues for adequate intervention strategies.

BackgroundTumors in the distal esophagus (EAC), gastro-esophageal junction including cardia (GEJAC), and stomach (GAC) develop in close proximity and show strong similarities on a molecular and cellular level. However, recent clinical data showed that the effectiveness of chemo-immunotherapy is limited to a subset of GEAC patients and that EACs and GEJACs generally benefit less from checkpoint inhibition compared to GACs. As the composition of the tumor immune microenvironment drives response to (immuno)therapy we here performed a detailed immune analysis of a large series of GEACs to facilitate the development of a more individualized immunomodulatory strategy.MethodsExtensive immunophenotyping was performed by 14-color flow cytometry in a prospective study to detail the immune composition of untreated gastro-esophageal cancers (n=104) using fresh tumor biopsies of 35 EACs, 38 GEJACs and 31 GACs. The immune cell composition of GEACs was characterized and correlated with clinicopathologic features such as tumor location, MSI and HER2 status. The spatial immune architecture of a subset of tumors (n=30) was evaluated using multiplex immunohistochemistry (mIHC) which allowed us to determine the tumor infiltration status of CD3+, CD8+, FoxP3+, CD163+ and Ki67+ cells.ResultsImmunophenotyping revealed that the tumor immune microenvironment of GEACs is heterogeneous and that immune suppressive cell populations such as monocytic myeloid-derived suppressor cells (mMDSC) are more abundant in EACs compared to GACs (p

Abstract Background Sustainable Development Goal (SDG) indicator 3.b.3 monitors progress in medicines’ accessibility for adults and has significant limitations when applying to medicines for children. An adapted indicator methodology was developed to fill this gap, but no proof of its robustness exists. We provide this evidence through sensitivity analyses. Methods Data on availability and prices of child medicines from ten historical datasets were combined to create datasets for analysis: Dataset 1 (medicines selected at random) and Dataset 2 (preference given to available medicines, to better capture affordability of medicines). A base case scenario and univariate sensitivity analyses were performed to test critical components of the methodology, including the new variable of number of units needed for treatment (NUNT), disease burden (DB) weighting, and the National Poverty Line (NPL) limits. Additional analyses were run on a continuously smaller basket of medicines to explore the minimum number of medicines required. Mean facility scores for access were calculated and compared. Results The mean facility score for Dataset 1 and Dataset 2 within the base case scenario was 35.5% (range 8.0–58.8%) and 76.3% (range 57.2–90.6%). Different NUNT scenarios led to limited variations in mean facility scores of + 0.1% and -0.2%, or differences of + 4.4% and -2.1% at the more critical NPL of $5.50 (Dataset 1). For Dataset 2, variations to the NUNT generated differences of + 0.0% and -0.6%, at an NPL of $5.50 the differences were + 5.0 and -2.0%. Different approaches for weighting for DB induced considerable fluctuations of 9.0% and 11.2% respectively. Stable outcomes with less than 5% change in mean facility score were observed for a medicine basket down to 12 medicines. For smaller baskets, scores increased more rapidly with a widening range. Conclusion This study has confirmed that the proposed adaptations to make SDG indicator 3.b.3 appropriate for children are robust, indicating that they could be an important addition to the official Global Indicator Framework. At least 12 child-appropriate medicines should be surveyed to obtain meaningful outcomes. General concerns that remain about the weighting of medicines for DB and the NPL should be considered at the 2025 planned review of this framework.

Abstract Quantum information carriers, just like most physical systems, naturally occupy high-dimensional Hilbert spaces. Instead of restricting them to a two-level subspace, these high-dimensional (qudit) quantum systems are emerging as a powerful resource for the next generation of quantum processors. Yet harnessing the potential of these systems requires efficient ways of generating the desired interaction between them. Here, we experimentally demonstrate an implementation of a native two-qudit entangling gate up to dimension 5 in a trapped-ion system. This is achieved by generalizing a recently proposed light-shift gate mechanism to generate genuine qudit entanglement in a single application of the gate. The gate seamlessly adapts to the local dimension of the system with a calibration overhead that is independent of the dimension.

Background: Theoretically, reductions of self-esteem among people who stutter (PWS) are often explained by individual negative cognitions or emotions of the PWS or their conversation partners. We propose that the flow of a conversation can be seen as a representation of the relationship between speakers, and that by disrupting this flow, a stutter may directly threaten this relationship, and in turn affect self-esteem. Methodologically, we present a new, event-contingent, method that assesses fluctuations in self-esteem over time and thereby allows one to assess the predictive value of specific conversational experiences. Aims: To have both a theoretical and a methodological contribution on conversational disfluency. Methods & Procedures: Our focus is not on stable individual factors, but we expect that fluctuations within the flow of everyday conversations predict changes in self-esteem. We studied these fluctuations with an experience sampling method which prompted participants to answer a brief survey after each of 10 conversations in a 2-week period. Self-identified PWS (n = 58) and people who do not stutter (PWNS; n = 53) reported on their individual negative cognitions and emotions and experienced relational quality and state self-esteem in more than 1000 conversations. By assessing fluctuations in self-esteem over time, this method allows one to assess the predictive value of specific conversational experiences. Outcomes & Results: Correlational evidence demonstrates that flow disruptions are associated with temporary reductions in self-esteem. This association is mediated by increased individual negative cognitions and emotions, as well as threatened social relationships. This appeared to be true for both PWS and PWNS, although PWS experienced on average less conversational flow and lower state self-esteem. On average, PWS did not experience lower relational quality than PWNS. Conclusions & Implications: An event-contingent recording method is a useful way to assess momentary fluctuations in self-esteem. Findings are consistent with the notion that people monitor their relationship by attending to fluctuations in conversational flow; whereas a smooth conversational flow indicates a strong relationship, disruptions of flow (e.g., as caused by a stutter or other factors) signal a threat to the relationship.

Abstract Memory, one of the hallmarks of human cognition, can be modified when humans voluntarily modulate neural population activity using neurofeedback. However, it is currently unknown whether neurofeedback can influence the integration of memories, and whether memory is facilitated or impaired after such neural perturbation. In this study, participants memorized objects while we provided them with abstract neurofeedback based on their brain activity patterns in the ventral visual stream. This neurofeedback created an implicit face or house context in the brain while memorizing the objects. The results revealed that participants created associations between each memorized object and its implicit context solely due to the neurofeedback manipulation. Our findings shed light onto how memory formation can be influenced by synthetic memory tags with neurofeedback and advance our understanding of mnemonic processing.