Predictors of response to axicabtagene‐ciloleucel CAR T cells in aggressive B cell lymphomas: A real‐world study

Chimeric antigen receptor T‐cell (CAR T) therapy has shown promising efficacy in relapsed and refractory diffuse large B cell lymphoma (DLBCL). While most patients undergo CAR T infusion with active disease, the impact of some clinical variables, such as responsiveness to the pre‐CAR T chemotherapy on the response to CAR T, is unknown. In this single‐institution study, we studied the impact of several pre‐CAR T variables on the post‐CAR outcomes. Sixty patients underwent apheresis for axicabtagene‐ciloleucel (axi‐cel) and 42 of them (70.0%) had primary refractory disease. Bridging therapy between apheresis and lymphodepletion was given in 34 patients (56.7%). After axi‐cel, the overall response rate was 63.3%. Responsiveness to the immediate pre‐CAR T therapy did not show a significant association with response to axi‐cel, progression‐free (PFS) or overall (OS) survival. Multivariable analysis determined that bulky disease before lymphodepletion was independently associated with inferior outcomes, and patients that presented with high‐burden disease unresponsive to immediate pre‐CAR T therapy had a dismal outcome. This data supports proceeding with treatment in CAR T candidates regardless of their response to immediate pre‐CAR T therapy. Interim therapeutic interventions should be considered in patients who have known risk factors for poor outcomes (bulky disease, high LDH).     

A real‐world implementation of a nationwide,long‐term monitoring program to assess the impact of agrochemicals and agricultural practices on biodiversity

Biodiversity has undergone a major decline throughout recent decades, particularly in farmland. Agricultural practices are recognized to be an important pressure on farmland biodiversity, and pesticides are suspected to be one of the main causes of this decline in biodiversity. As part of the national plan for reduction of pesticides use (Ecophyto), the French ministry of agriculture launched the 500 ENI (nonintended effects) monitoring program in 2012 in order to assess the unintended effects of agricultural practices, including pesticide use, on biodiversity represented by several taxonomic groups of interest for farmers. This long‐term program monitors the biodiversity of nontargeted species (earthworms, plants, coleoptera, and birds), together with a wide range of annual data on agricultural practices (crop rotation, soil tillage, weed control, fertilizers, chemical treatments, etc.). Other parameters (e.g., landscape and climatic characteristics) are also integrated as covariates during the analyses. This monitoring program is expected to improve our understanding of the relative contribution of the different drivers of population and community trends. Here, we present the experience of setting up the 500 ENI network for this ambitious and highly complex monitoring program, as well as the type of data it collects. The issue of data quality control and some first results are discussed. With the aim of being useful to readers who would like to set up similar monitoring schemes, we also address some questions that have arisen following the first five years of the implementation phase of the program.     

Spatial early warning signals for impending regime shifts: A practical framework for application in real‐world landscapes

Prediction of ecosystem response to global environmental change is a pressing scientific challenge of major societal relevance. Many ecosystems display nonlinear responses to environmental change, and may even undergo practically irreversible ‘regime shifts’ that initiate ecosystem collapse. Recently, early warning signals based on spatiotemporal metrics have been proposed for the identification of impending regime shifts. The rapidly increasing availability of remotely sensed data provides excellent opportunities to apply such model‐based spatial early warning signals in the real world, to assess ecosystem resilience and identify impending regime shifts induced by global change. Such information would allow land‐managers and policy makers to interfere and avoid catastrophic shifts, but also to induce regime shifts that move ecosystems to a desired state. Here, we show that the application of spatial early warning signals in real‐world landscapes presents unique and unexpected challenges, and may result in misleading conclusions when employed without careful consideration of the spatial data and processes at hand. We identify key practical and theoretical issues and provide guidelines for applying spatial early warning signals in heterogeneous, real‐world landscapes based on literature review and examples from real‐world data. Major identified issues include (1) spatial heterogeneity in real‐world landscapes may enhance reversibility of regime shifts and boost landscape‐level resilience to environmental change (2) ecosystem states are often difficult to define, while these definitions have great impact on spatial early warning signals and (3) spatial environmental variability and socio‐economic factors may affect spatial patterns, spatial early warning signals and associated regime shift predictions. We propose a novel framework, shifting from an ecosystem perspective towards a landscape approach. The framework can be used to identify conditions under which resilience assessment with spatial remotely sensed data may be successful, to support well‐informed application of spatial early warning signals, and to improve predictions of ecosystem responses to global environmental change.     

A tale of two multi‐focal therapies for glioblastoma: An antibody targeting ELTD1 and nitrone‐based OKN‐007

Glioblastoma (GBM) is the most common primary malignant brain tumour in adults. Despite a multimodal treatment response, survival for GBM patients remains between 12 and 15 months. Anti‐ELTD1 antibody therapy is effective in decreasing tumour volumes and increasing animal survival in an orthotopic GBM xenograft. OKN‐007 is a promising chemotherapeutic agent that is effective in various GBM animal models and is currently in two clinical trials. In this study, we sought to compare anti‐ELTD1 and OKN‐007 therapies, as single agents and combined, against bevacizumab, a commonly used therapeutic agent against GBM, in a human G55 xenograft mouse model. MRI was used to monitor tumour growth, and immunohistochemistry (IHC) was used to assess tumour markers for angiogenesis, cell migration and proliferation in the various treatment groups. OKN and anti‐ELTD1 treatments significantly increased animal survival, reduced tumour volumes and normalized the vasculature. Additionally, anti‐ELTD1 was also shown to significantly affect other pro‐angiogenic factors such as Notch1 and VEGFR2. Unlike bevacizumab, anti‐ELTD1 and OKN treatments did not induce a pro‐migratory phenotype within the tumours. Anti‐ELTD1 treatment was shown to be as effective as OKN therapy. Both OKN and anti‐ELTD1 therapies show promise as potential single‐agent multi‐focal therapies for GBM patients.     

QuoVidi: An open‐source web application for the organization of large‐scale biological treasure hunts

Learning biology, and in particular systematics, requires learning a substantial amount of specific vocabulary, both for botanical and zoological studies. While crucial, the precise identification of structures serving as evolutionary traits and systematic criteria is not per se a highly motivating task for students. Teaching this in a traditional teaching setting is quite challenging especially with a large crowd of students to be kept engaged. This is even more difficult if, as during the COVID‐19 crisis, students are not allowed to access laboratories for hands‐on observation on fresh specimens and sometimes restricted to short‐range movements outside their home. Here, we present QuoVidi, a new open‐source web platform for the organization of large‐scale treasure hunts. The platform works as follows: students, organized in teams, receive a list of quests that contain morphologic, ecologic, or systematic terms. They have to first understand the meaning of the quests, then go and find them in the environment. Once they find the organism corresponding to a quest, they upload a geotagged picture of their finding and submit this on the platform. The correctness of each submission is evaluated by the staff. During the COVID‐19 lockdown, previously validated pictures were also submitted for evaluation to students that were locked in low‐biodiversity areas. From a research perspective, the system enables the creation of large image databases by the students, similar to citizen science projects. Beside the enhanced motivation of students to learn the vocabulary and perform observations on self‐found specimens, this system allows instructors to remotely follow and assess the work performed by large numbers of students. The interface is freely available, open‐source and customizable. Unlike existing naturalist platforms, allows the educators to fully customize the quests of interest. This enables the creation of multiple teaching scenarios, without being bound to a fixed scope. QuoVidi can be used in other disciplines with adapted quests and we expect it to be of interest in many classroom settings.     

Different factors limit early‐ and late‐season windows of opportunity for monarch development

Seasonal windows of opportunity are intervals within a year that provide improved prospects for growth, survival, or reproduction. However, few studies have sufficient temporal resolution to examine how multiple factors combine to constrain the seasonal timing and extent of developmental opportunities. Here, we document seasonal changes in milkweed (Asclepias fascicularis)–monarch (Danaus plexippus) interactions with high resolution throughout the last three breeding seasons prior to a precipitous single‐year decline in the western monarch population. Our results show early‐ and late‐season windows of opportunity for monarch recruitment that were constrained by different combinations of factors. Early‐season windows of opportunity were characterized by high egg densities and low survival on a select subset of host plants, consistent with the hypothesis that early‐spring migrant female monarchs select earlier‐emerging plants to balance a seasonal trade‐off between increasing host plant quantity and decreasing host plant quality. Late‐season windows of opportunity were coincident with the initiation of host plant senescence, and caterpillar success was negatively correlated with heatwave exposure, consistent with the hypothesis that late‐season windows were constrained by plant defense traits and thermal stress. Throughout this study, climatic and microclimatic variations played a foundational role in the timing and success of monarch developmental windows by affecting bottom‐up, top‐down, and abiotic limitations. More exposed microclimates were associated with higher developmental success during cooler conditions, and more shaded microclimates were associated with higher developmental success during warmer conditions, suggesting that habitat heterogeneity could buffer the effects of climatic variation. Together, these findings show an important dimension of seasonal change in milkweed–monarch interactions and illustrate how different biotic and abiotic factors can limit the developmental success of monarchs across the breeding season. These results also suggest the potential for seasonal sequences of favorable or unfavorable conditions across the breeding range to strongly affect monarch population dynamics.     

Risk and protective factors for mental disorders beyond genetics: an evidence‐based atlas

Decades of research have revealed numerous risk factors for mental disorders beyond genetics, but their consistency and magnitude remain uncer­tain. We conducted a “meta‐umbrella” systematic synthesis of umbrella reviews, which are systematic reviews of meta‐analyses of individual studies, by searching international databases from inception to January 1, 2021. We included umbrella reviews on non‐purely genetic risk or protective factors for any ICD/DSM mental disorders, applying an established classification of the credibility of the evidence: class I (convincing), class II (highly suggestive), class III (suggestive), class IV (weak). Sensitivity analyses were conducted on prospective studies to test for temporality (reverse causation), TRANSD criteria were applied to test transdiagnosticity of factors, and A Measurement Tool to Assess Systematic Reviews (AMSTAR) was employed to address the quality of meta‐analyses. Fourteen eligible umbrella reviews were retrieved, summarizing 390 meta‐analyses and 1,180 associations between putative risk or protective factors and mental disorders. We included 176 class I to III evidence associations, relating to 142 risk/protective factors. The most robust risk factors (class I or II, from prospective designs) were 21. For dementia, they included type 2 diabetes mellitus (risk ratio, RR from 1.54 to 2.28), depression (RR from 1.65 to 1.99) and low frequency of social contacts (RR=1.57). For opioid use disorders, the most robust risk factor was tobacco smoking (odds ratio, OR=3.07). For non‐organic psychotic disorders, the most robust risk factors were clinical high risk state for psychosis (OR=9.32), cannabis use (OR=3.90), and childhood adversities (OR=2.80). For depressive disorders, they were widowhood (RR=5.59), sexual dysfunction (OR=2.71), three (OR=1.99) or four‐five (OR=2.06) metabolic factors, childhood physical (OR=1.98) and sexual (OR=2.42) abuse, job strain (OR=1.77), obesity (OR=1.35), and sleep disturbances (RR=1.92). For autism spectrum disorder, the most robust risk factor was maternal overweight pre/during pregnancy (RR=1.28). For attention‐deficit/hyperactivity disorder (ADHD), they were maternal pre‐pregnancy obesity (OR=1.63), maternal smoking during pregnancy (OR=1.60), and maternal overweight pre/during pregnancy (OR=1.28). Only one robust protective factor was detected: high physical activity (hazard ratio, HR=0.62) for Alzheimer’s disease. In all, 32.9% of the associations were of high quality, 48.9% of medium quality, and 18.2% of low quality. Transdiagnostic class I‐III risk/protective factors were mostly involved in the early neurodevelopmental period. The evidence‐based atlas of key risk and protective factors identified in this study represents a benchmark for advancing clinical characterization and research, and for expanding early intervention and preventive strategies for mental disorders.     

Digitally supported public health interventions through the lens of structural injustice: The case of mobile apps responding to violence against women and girls

Mobile applications (apps) have gained significant popularity as a new intervention strategy responding to violence against women and girls. Despite their growing relevance, an assessment from the perspective of public health ethics is still lacking. Here, we base our discussion on the understanding of violence against women and girls as a multidimensional, global public health issue on structural, societal and individual levels and situate it within the theoretical framework of structural injustice, including epistemic injustice. Based on a systematic app review we previously conducted, we evaluate the content and functions of apps through the lens of structural injustice. We argue that technological solutions such as apps may be a useful tool in the fight against violence against women and girls but have to be situated within the broader frame of public health that considers the structural dimensions of such violence. Ultimately, the concerns raised by structural injustice are—alongside key concerns of safety, data privacy, importance of human supportive contact, and so forth—crucial dimensions in the ethical assessment of such apps. However, research on the role and relevance of apps as strategies to address the structural and epistemic dimensions of violence remains scarce. This article aims to provide a foundation for further discussion in this area and could be applicable to other areas in public health policy and practice.     

Cell‐to‐cell and type‐to‐type heterogeneity of signaling networks: insights from the crowd

Recent technological developments allow us to measure the status of dozens of proteins in individual cells. This opens the way to understand the heterogeneity of complex multi‐signaling networks across cells and cell types, with important implications to understand and treat diseases such as cancer. These technologies are, however, limited to proteins for which antibodies are available and are fairly costly, making predictions of new markers and of existing markers under new conditions a valuable alternative. To assess our capacity to make such predictions and boost further methodological development, we organized the Single Cell Signaling in Breast Cancer DREAM challenge. We used a mass cytometry dataset, covering 36 markers in over 4,000 conditions totaling 80 million single cells across 67 breast cancer cell lines. Through four increasingly difficult subchallenges, the participants predicted missing markers, new conditions, and the time‐course response of single cells to stimuli in the presence and absence of kinase inhibitors. The challenge results show that despite the stochastic nature of signal transduction in single cells, the signaling events are tightly controlled and machine learning methods can accurately predict new experimental data.     

Study on the development and integration of 3D‐printed optics in small‐scale productions of single‐use cultivation vessels

Integrating optical sensors and 3D‐printed optics into single‐use (SU) cultivation vessels for customized, tailor‐made equipment could be a next big step in the bioreactor and screening platform development enabling online bioprocess monitoring. Many different parameters such as pH, oxygen, carbon dioxide and optical density (OD) can be monitored more easily using online measuring instruments compared to offline sampling. Space‐saving integrated sensors in combination with adapted optics such as prisms open up vastly new possibilities to precisely guide light through SU vessels. This study examines how optical prisms can be 3D‐printed with a 3D‐inkjet printer, modified and then evaluated in a custom made optical bench. The prisms are coated or bonded with thin cover glasses. For the examination of reflectance performance and conformity prisms are compared on the basis of measured characteristics of a conventional glass prism. In addition, the most efficient and reproducible prism geometry and modification technique is applied to a customized 3D‐printed cultivation vessel. The vessel is evaluated on a commercial sensor‐platform, a shake flask reader (SFR) vario, to investigate its sensing‐characteristics while monitoring scattered light with the turbidity standard formazine and a cell suspension of Saccharomyces cerevisiae as model organism. It is demonstrated that 3D‐printed prisms can be used in combination with commercial scattered light sensor‐platforms to determine OD of a microbial culture and that a 3D‐printed unibody design with integrated optics in a cultivation vessel is feasible. In the range of OD₆₀₀ 0–1.16 rel.AU a linear correlation between sensor amplitude and offline determined OD can be achieved. Thus, enabling for the first time a measurement of low cell densities with the SFR vario platform. Moreover, sensitivity is also at least three times higher compared to the commonly used method.     

Estimating fish swimming metrics and metabolic rates with accelerometers: the influence of sampling frequency

Accelerometry is growing in popularity for remotely measuring fish swimming metrics, but appropriate sampling frequencies for accurately measuring these metrics are not well studied. This research examined the influence of sampling frequency (1–25 Hz) with tri‐axial accelerometer biologgers on estimates of overall dynamic body acceleration (ODBA), tail‐beat frequency, swimming speed and metabolic rate of bonefish Albula vulpes in a swim‐tunnel respirometer and free‐swimming in a wetland mesocosm. In the swim tunnel, sampling frequencies of ≥ 5 Hz were sufficient to establish strong relationships between ODBA, swimming speed and metabolic rate. However, in free‐swimming bonefish, estimates of metabolic rate were more variable below 10 Hz. Sampling frequencies should be at least twice the maximum tail‐beat frequency to estimate this metric effectively, which is generally higher than those required to estimate ODBA, swimming speed and metabolic rate. While optimal sampling frequency probably varies among species due to tail‐beat frequency and swimming style, this study provides a reference point with a medium body‐sized sub‐carangiform teleost fish, enabling researchers to measure these metrics effectively and maximize study duration.     

TSGIT: An N‐ and C‐terminal tandem tag system for purification of native and intein‐mediated ligation‐ready proteins

A large variety of fusion tags have been developed to improve protein expression, solubilization, and purification. Nevertheless, these tags have been combined in a rather limited number of composite tags and usually these composite tags have been dictated by traditional commercially‐available expression vectors. Moreover, most commercially‐available expression vectors include either N‐ or C‐terminal fusion tags but not both. Here, we introduce TSGIT, a fusion‐tag system composed of both N‐ and a C‐terminal composite fusion tags. The system includes two affinity tags, two solubilization tags and two cleavable tags distributed at both termini of the protein of interest. Therefore, the N‐ and the C‐terminal composite fusion tags in TSGIT are fully orthogonal in terms of both affinity selection and cleavage. For using TSGIT, we streamlined the cloning, expression, and purification procedures. Each component tag is selected to maximize its benefits toward the final construct. By expressing and partially purifying the protein of interest between the components of the TSGIT fusion, the full‐length protein is selected over truncated forms, which has been a long‐standing problem in protein purification. Moreover, due to the nature of the cleavable tags in TSGIT, the protein of interest is obtained in its native form without any additional undesired N‐ or C‐terminal amino acids. Finally, the resulting purified protein is ready for efficient ligation with other proteins or peptides for downstream applications. We demonstrate the use of this system by purifying a large amount of native fluorescent mRuby3 protein and bacteriophage T7 gp2.5 ssDNA‐binding protein.     

Development of novel real‐time TaqMan® PCR assays for the species and sex identification of otter (Lutra lutra) and their application to noninvasive genetic monitoring

Developing strategies to maintain biodiversity requires baseline information on the current status of each individual species. The development of genetic techniques and their application to noninvasively collected samples have the potential to yield information on the structure of elusive animal populations and so are important tools in conservation management. Using DNA isolated from faecal samples can be challenging owing to low quantity and quality. This study, however, presents the development of novel real‐time polymerase chain reaction assays using fluorescently labelled TaqMan^® MGB probes enabling species and sex identification of Eurasian otter (Lutra lutra) spraints (faeces). These assays can also be used in determining an optimum microsatellite panel and can be employed as cost‐saving screening tools for downstream genetic testing including microsatellite genotyping and haplotype analysis. The techniques are shown to work efficiently with L. lutra DNA isolated from tissue, hair, spraint, blood and anal jelly samples.