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Abstract Digital biomarkers that remotely monitor symptoms have the potential to revolutionize outcome assessments in future disease-modifying trials in Parkinson’s disease (PD), by allowing objective and recurrent measurement of symptoms and signs collected in the participant’s own living environment. This biomarker field is developing rapidly for assessing the motor features of PD, but the non-motor domain lags behind. Here, we systematically review and assess digital biomarkers under development for measuring non-motor symptoms of PD. We also consider relevant developments outside the PD field. We focus on technological readiness level and evaluate whether the identified digital non-motor biomarkers have potential for measuring disease progression, covering the spectrum from prodromal to advanced disease stages. Furthermore, we provide perspectives for future deployment of these biomarkers in trials. We found that various wearables show high promise for measuring autonomic function, constipation and sleep characteristics, including REM sleep behavior disorder. Biomarkers for neuropsychiatric symptoms are less well-developed, but show increasing accuracy in non-PD populations. Most biomarkers have not been validated for specific use in PD, and their sensitivity to capture disease progression remains untested for prodromal PD where the need for digital progression biomarkers is greatest. External validation in real-world environments and large longitudinal cohorts remains necessary for integrating non-motor biomarkers into research, and ultimately also into daily clinical practice.

Abstract Background Due to changes in testing policy and increased use of rapid tests, other indicators for SARS-CoV-2 infections are needed to monitor vaccine effectiveness (VE). We aimed to estimate VE against COVID-19 sick leave (> 3 days, certified by a medical professional) among employed individuals (25–64-years-old) in Norway. Methods We performed a nationwide cohort study by collating data from the Emergency preparedness register for COVID-19. We used adjusted Cox proportional hazard models with vaccine status as a time-varying covariate and presented results as adjusted hazard ratios (aHRs) with corresponding 95% confidence intervals. Separate models were run against sick leave and against SARS-CoV-2 infections during the Delta period (June-December 2021), and against sick leave during the Omicron period (January-December 2022) when SARS-CoV-2 PCR-testing was replaced by rapid self-tests and infections were underreported. Results We included 2,236,419 individuals during the Delta period, of whom 73,776 (3.3%) had a reported infection and 54,334 (2.4%) were registered with sick leave. Of the 2,206,952 included individuals in the Omicron period, 300,140 (13.6%) were registered with sick leave. During the Delta period, 55% (26,611) of individuals who had registered sick leave also had a positive test, compared to 32% (96,445) during the Omicron period. The VE against sick leave during the Delta period followed a similar waning pattern to that against SARS-CoV-2 infections. After the second and third dose, the lowest aHRs were estimated for 2–7 days after vaccination for both sick leave (0.25; 95%CI 0.24–0.26 and 0.26; 95% CI 0.24–0.29) and infection ( 0.16; 95% CI 0.15–0.17 and 0.18; 95% CI 0.16–0.19) respectively. During the Omicron period, aHRs for sick leave were higher than during the Delta period, but the lowest aHRs were still found in 2–7 weeks after receiving the second (0.61; 95% CI 0.59–0.64) or third dose (0.63; 95% CI 0.62–0.64). Conclusion Our results showed that sick leave could be a relevant indicator for VE in the surveillance of COVID-19 and a finding that may be important in the surveillance of other respiratory infection.

Abstract Background During the COVID-19 pandemic, provision of non-COVID healthcare was recurrently severely disrupted. The objective was to determine whether disruption of non-COVID hospital use, either due to cancelled, postponed, or forgone care, during the first pandemic year of COVID-19 impacted socioeconomic groups differently compared with pre-pandemic use. Methods National population registry data, individually linked with data of non-COVID hospital use in the Netherlands (2017–2020). in non-institutionalised population of 25–79 years, in standardised household income deciles (1 = low, 10 = high) as proxy for socioeconomic status. Generic outcome measures included patients who received hospital care (dichotomous): outpatient contact, day treatment, inpatient clinic, and surgery. Specific procedures were included as examples of frequently performed elective and acute procedures, e.g.: elective knee/hip replacement and cataract surgery, and acute percutaneous coronary interventions (PCI). Relative risks (RR) for hospital use were reported as outcomes from generalised linear regression models (binomial) with log-link. An interaction term was included to assess whether income differences in hospital use during the pandemic deviated from pre-pandemic use. Results Hospital use rates declined in 2020 across all income groups. With baseline (2019) higher hospital use rates among lower than higher income groups, relatively stronger declines were found for lower income groups. The lowest income groups experienced a 10% larger decline in surgery received than the highest income group (RR 0.90, 95% CI 0.87 – 0.93). Patterns were similar for inpatient clinic, elective knee/hip replacement and cataract surgery. We found small or no significant income differences for outpatient clinic, day treatment, and acute PCI. Conclusions Disruption of non-COVID hospital use in 2020 was substantial across all income groups during the acute phases of the pandemic, but relatively stronger for lower income groups than could be expected compared with pre-pandemic hospital use. Although the pandemic’s impact on the health system was unprecedented, healthcare service shortages are here to stay. It is therefore pivotal to realise that lower income groups may be at risk for underuse in times of scarcity.

ABSTRACTIn order to tackle biodiversity decline, various Agri-Environmental Programmes (AEPs) have been established in intensive farming countries. Farmers play a crucial role in restoring biodiversity. Therefore, understanding farmers’ participation in AEPs is of vital importance. Yet, a theory-driven understanding of what AEP strategies need to be implemented in which context to motivate farmers to work on AEPs is lacking. We aimed to close this gap by identifying Programme Theories (PTs) for the design and implementation of successful AEPs. We performed a realist review to identify causal relationships between agri-environmental strategies (S), their outcomes (O), and the contextual factors (C) and motivational mechanisms (M) that explain how, when and why AEP outcomes were achieved. The identified strategy-context-mechanism-outcome (SCMO) configurations and underlying theories were clustered on the C–M relationship to develop PTs. 47 studies were included. Based on the available evidence on more than 60 AEPs in 17 intensive farming countries, 10 interrelated PTs were identified. These 10 PTs form a theory-driven framework that summarizes the insights into how, when and why farmers work on successful AEPs. Each PT provides practical insights into the agri-environmental strategies and necessary contextual factors and mechanisms to guide farmers’ behavioural change toward biodiversity conservation.

Abstract Twenty percent of children with T‐cell lymphoblastic lymphoma (T‐LBL) will relapse and have an extremely poor outcome. Currently, we can identify a genetically low‐risk subgroup in pediatric T‐LBL, yet these high‐risk patients who need intensified or alternative treatment options remain undetected. Therefore, there is an urgent need to recognize these high‐risk T‐LBL patients through identification of molecular characteristics and biomarkers. By using RNA sequencing which was performed in 29/49 T‐LBL patients who were diagnosed in the Princess Maxima Center for Pediatric Oncology between 2018 and 2023, we discovered a previously unknown high‐risk biological subgroup of children with T‐LBL. This subgroup is characterized by NOTCH1 gene fusions, found in 21% of our T‐LBL cohort (6/29). All patients presented with a large mediastinal mass, pleural/pericardial effusions, and absence of blasts in the bone marrow, blood, and central nervous system. Blood CCL17 (C‐C Motif Chemokine Ligand 17, TARC) levels were measured at diagnosis in 26/29 patients, and all six patients with NOTCH1 gene fusions patients exclusively expressed highly elevated blood CCL17 levels, defining a novel and previously not known clinically relevant biomarker for T‐cell lymphoblastic lymphoma. Four out of these six patients relapsed during therapy, a fifth developed a therapy‐related acute myeloid leukemia during maintenance therapy. These data indicate that T‐LBL patients with a NOTCH1 fusion have a high risk of relapse which can be easily identified using a blood CCL17 screening at diagnosis. Further molecular characterization through NOTCH1 gene fusion analysis offers these patients the opportunity for treatment intensification or new treatment strategies.

Abstract Background During the COVID-19 response in Norway, many municipalities used the Fiks contact tracing tool (FiksCT) to register positive individuals and follow-up contacts. This tool is based on DHIS2, an open source, web-based platform. In this study we examined if data completeness in FiksCT improved after integration with national registers between May 2020 and September 2021. Methods Data from municipalities using FiksCT was extracted from the Norwegian Emergency Preparedness Register for COVID-19 (Beredt C19). We linked FiksCT data to the Norwegian Surveillance System for Communicable Diseases (MSIS), the National Population Register (FREG), and the Norwegian Vaccine Registry (SYSVAK) using unique identification numbers (ID). Completeness for each variable linked with a national register was calculated before and after integration with these registers. Results Of the 125 municipalities using FiksCT, 87 (69.6%) agreed to share and upload their data to Beredt C19. Data completeness for positive individuals improved after integration with national registers. After integration with FREG, the proportion of missing values decreased from 12.5 to 1.6% for ID, from 4.5 to 0.9% for sex, and from 1.2 to 0.4% for date of birth. Missing values for vaccine type decreased from 63.0 to 15.2% and 39.3–36.7% for first and second dose, respectively. In addition, direct reporting from FiksCT to MSIS increased the proportion of complete records in MSIS (on the selected variables) from 68.6% before to 77.0% after integration. Conclusion The completeness of local contact tracing data can be improved by enabling integration with established national registers. In addition, providing the option to submit local data to the national registers could ease workload and reduce the need to collect duplicate data.

Abstract Combining wide bandgap 2D inorganic materials and blue-light-emitting 0D carbon dots in 0D/2D heterojunction nanocomposites was shown to give rise to unique optical properties and a multifunctional prototype device was developed, capable of polarized light luminescence, modulation and detection.

This paper reflects on the paradigmatic assumptions and ideologies that have shaped algorithmic management research. We identify two sets of assumptions: one about the "ontology of algorithms" (which holds that human resource management [HRM] algorithms are non‐human entities with material agency) and one about the "ontology of management" that HRM algorithms afford (which understands algorithmic management as a form of control for maximizing economic/shareholder value). We explain how these core assumptions underpin existing research of HRM algorithms, causing blind spots that hinder new ways of understanding and studying algorithmic management. After identifying and unpacking the assumptions and blind spots, we offer avenues to overcome these blind spots, allowing for future research based on new ideological assumption grounds that will help move algorithmic management scholarship further in significant ways. [ABSTRACT FROM AUTHOR]

Abstract Background Gene fusions are important cancer drivers in pediatric cancer and their accurate detection is essential for diagnosis and treatment. Clinical decision-making requires high confidence and precision of detection. Recent developments show RNA sequencing (RNA-seq) is promising for genome-wide detection of fusion products but hindered by many false positives that require extensive manual curation and impede discovery of pathogenic fusions. Methods We developed Fusion-sq to overcome existing disadvantages of detecting gene fusions. Fusion-sq integrates and “fuses” evidence from RNA-seq and whole genome sequencing (WGS) using intron–exon gene structure to identify tumor-specific protein coding gene fusions. Fusion-sq was then applied to the data generated from a pediatric pan-cancer cohort of 128 patients by WGS and RNA sequencing. Results In a pediatric pan-cancer cohort of 128 patients, we identified 155 high confidence tumor-specific gene fusions and their underlying structural variants (SVs). This includes all clinically relevant fusions known to be present in this cohort (30 patients). Fusion-sq distinguishes healthy-occurring from tumor-specific fusions and resolves fusions in amplified regions and copy number unstable genomes. A high gene fusion burden is associated with copy number instability. We identified 27 potentially pathogenic fusions involving oncogenes or tumor-suppressor genes characterized by underlying SVs, in some cases leading to expression changes indicative of activating or disruptive effects. Conclusions Our results indicate how clinically relevant and potentially pathogenic gene fusions can be identified and their functional effects investigated by combining WGS and RNA-seq. Integrating RNA fusion predictions with underlying SVs advances fusion detection beyond extensive manual filtering. Taken together, we developed a method for identifying candidate gene fusions that is suitable for precision oncology applications. Our method provides multi-omics evidence for assessing the pathogenicity of tumor-specific gene fusions for future clinical decision making.

Introduction The empty pelvis syndrome is a significant source of morbidity following pelvic exenteration surgery. It remains poorly defined with research in this field being heterogeneous and of low quality. Furthermore, there has been minimal engagement with patient representatives following pelvic exenteration with respect to the empty pelvic syndrome. ‘PelvEx—Beating the empty pelvis syndrome’ aims to engage both patient representatives and healthcare professionals to achieve an international consensus on a core outcome set, pathophysiology and mitigation of the empty pelvis syndrome.Methods and analysis A modified-Delphi approach will be followed with a three-stage study design. First, statements will be longlisted using a recent systematic review, healthcare professional event, patient and public engagement, and Delphi piloting. Second, statements will be shortlisted using up to three rounds of online modified Delphi. Third, statements will be confirmed and instruments for measurable statements selected using a virtual patient-representative consensus meeting, and finally a face-to-face healthcare professional consensus meeting.Ethics and dissemination The University of Southampton Faculty of Medicine ethics committee has approved this protocol, which is registered as a study with the Core Outcome Measures in Effectiveness Trials Initiative. Publication of this study will increase the potential for comparative research to further understanding and prevent the empty pelvis syndrome.Trial registration number NCT05683795.

BackgroundHigh-impact surgery imposes a significant physiological and functional burden and is associated with substantial postoperative morbidity. Multimodal prehabilitation has demonstrated a reduction in postoperative complications and enhanced functional recovery, mainly in abdominal cancer surgery. Common preoperative risk factors shared among patients undergoing high-impact surgery, extending beyond abdominal cancer surgery procedures, suggest the relevance of multimodal prehabilitation to a broader patient population. This stepped wedge trial primarily aims to examine the hospital-wide effect of multimodal prehabilitation, compared to standard preoperative care, on the occurrence and severity of postoperative complications. Secondary and tertiary endpoints include length of hospital stay, physical fitness, nutritional status, mental health, intoxications, and cost-effectiveness of the intervention.MethodsThe Fit4Surgery (F4S) PREHAB trial is a monocenter stepped wedge trial in an academic hospital. Adult patients, divided into 20 health clusters based on specific diagnoses, will be assessed for eligibility and receive usual preoperative care or multimodal prehabilitation. Patient enrollment commenced in March 2021 and continues up to and including April 2024. The intervention consists of a high-intensity exercise program, a nutritional intervention, psychological support, and smoking and alcohol cessation. The primary outcome will be measured by the Clavien-Dindo classification (grade II or higher) and the Comprehensive Complication Index (CCI).DiscussionMultimodal prehabilitation potentially reduces postoperative complications and enhances functional recovery. This is the first study to determine the hospital-wide effect and cost-effectiveness of multimodal prehabilitation in patients across various surgical specialties.