shinyExprPortal: a configurable 'shiny' portal for sharing analysis of molecular expression data.

MOTIVATION: The scale of omics research presents many obstacles to full sharing and access to analysis results. Current publication models impose limits on the number of pages and figures, requiring careful preparation and selection of content. At the same time, depositing data in open repositories significantly shifts the burden of access and reproduction to readers, who may include people who are not programmers or analysts. RESULTS: We introduce shinyExprPortal, an R package that implements omics web portals with minimal coding effort. The portals allow exploration of transcriptomic or proteomic expression data and phenotypes, showcasing results of various types of analysis including differential expression, co-expression and pathways analysis. The integration with bioinformatics workflows enables researchers to focus on their results and share findings using interactive and publication-quality plots. AVAILABILITY AND IMPLEMENTATION: The shinyExprPortal package is available to download and install from CRAN and https://github.com/C4TB/shinyExprPortal.

Investigating the Poverty-Reducing Effects of SNAP on Non-nutritional Family Outcomes: A Scoping Review.

INTRODUCTION/PURPOSE: Poverty-reduction efforts that seek to support households with children and enable healthy family functioning are vital to produce positive economic, health, developmental, and upward mobility outcomes. The Supplemental Nutrition Assistance Program (SNAP) is an effective poverty-reduction policy for individuals and families. This study investigated the non-nutritional effects that families experience when receiving SNAP benefits. METHODS: We conducted a scoping review using the PRISMA Guidelines and strategic search terms across seven databases from 01 January 2008 to 01 February 2023 (n=2456). Data extraction involved two researchers performing title-abstract reviews. Full-text articles were assessed for eligibility (n=103). Forty articles were included for data retrieval. RESULTS: SNAP positively impacts family health across the five categories of the Family Stress Model (Healthcare utilization for children and parents, Familial allocation of resources, Impact on child development and behavior, Mental health, and Abuse or neglect). DISCUSSION/CONCLUSION: SNAP is a highly effective program with growing evidence that it positively impacts family health and alleviates poverty. Four priority policy actions are discussed to overcome the unintentional barriers for SNAP: distributing benefits more than once a month; increasing SNAP benefits for recipients; softening the abrupt end of benefits when wages increase; and coordinating SNAP eligibility and enrollment with other programs.

A high-fidelity, dual site-specific integration system in CHO cells by a Bxb1 recombinase.

Site-specific integration (SSI) via recombinase mediated cassette exchange (RMCE) has shown advantages over random integration methods for expression of biotherapeutics. As an extension of our previous work developing SSI host cells, we developed a dual-site SSI system having two independent integration sites at different genomic loci, each containing a unique landing pad (LP). This system was leveraged to generate and compare two RMCE hosts, one (dFRT) compatible with the Flp recombinase, the other (dBxb1) compatible with the Bxb1 recombinase. Our comparison demonstrated that the dBxb1 host was able to generate stable transfectant pools in a shorter time frame, and cells within the dBxb1 transfectant pools were more phenotypically and genotypically stable. We further improved process performance of the dBxb1 host, resulting in desired fed batch performance attributes. Clones derived from this improved host (referred as 41L-11) maintained stable expression profiles over extended generations. While the data represents a significant improvement in the efficiency of our cell line development process, the dual LP architecture also affords a high degree of flexibility for development of complex protein modalities.

Expression quantitative trait loci analysis in rheumatoid arthritis identifies tissue specific variants associated with severity and outcome.

OBJECTIVE: Genome-wide association studies have successfully identified more than 100 loci associated with susceptibility to rheumatoid arthritis (RA). However, our understanding of the functional effects of genetic variants in causing RA and their effects on disease severity and response to treatment remains limited. METHODS: In this study, we conducted expression quantitative trait locus (eQTL) analysis to dissect the link between genetic variants and gene expression comparing the disease tissue against blood using RNA-Sequencing of synovial biopsies (n=85) and blood samples (n=51) from treatment-naïve patients with RA from the Pathobiology of Early Arthritis Cohort. RESULTS: This identified 898 eQTL genes in synovium and genes loci in blood, with 232 genes in common to both synovium and blood, although notably many eQTL were tissue specific. Examining the HLA region, we uncovered a specific eQTL at HLA-DPB2 with the critical triad of single-nucleotide polymorphisms (SNPs) rs3128921 driving synovial HLA-DPB2 expression, and both rs3128921 and HLA-DPB2 gene expression correlating with clinical severity and increasing probability of the lympho-myeloid pathotype. CONCLUSIONS: This analysis highlights the need to explore functional consequences of genetic associations in disease tissue. HLA-DPB2 SNP rs3128921 could potentially be used to stratify patients to more aggressive treatment immediately at diagnosis.

IPEM topical report: results of a 2022 UK survey on the use of linac manufacturer integrated quality control (MIQC).

In recent years Radiotherapy linear accelerator (linac) vendors have developed their own integrated quality control (QC) systems. Such manufacturer-integrated-quality-control (MIQC) has the potential to improve both the quality and efficiency of linac QC but is currently being developed and utilised in the absence of specific best-practice guidance. An Institute of Physics and Engineering in Medicine working party was commissioned with a view to develop guidance for the commissioning and implementation of MIQC. This study is based upon a survey of United Kingdom (UK) radiotherapy departments performed by the working party. The survey was distributed to all heads of radiotherapy physics in the UK and investigated availability and uptake, community beliefs and opinions, utilisation, user experience and associated procedures. The survey achieved a 95% response rate and demonstrated strong support (>95%) for its use and further development. MIQC systems are available in 79% of respondents' centres, and are in clinical use in 66%. The most common MIQC system was Varian MPC, in clinical use in 58% of responding centres, with CyberKnife AQA\E2E in 11%, TomoTherapy TQA in 8% and no users of Elekta Machine QA. A majority of users found their MIQC to be easy to use, reliable, and had five or more years of experience. Most users reported occasions of discrepancy in results between MIQC and conventional testing, but the majority considered this acceptable, indicating a false reporting frequency of quarterly or less. MIQC has shown value in preventative maintenance and early detection of machine deviations. There were inconsistent approaches in the utilisation and commissioning tests performed. Fewer than half of users perform QC of MIQC. 45% of responders have modified their QC processes with the introduction of MIQC, via replacement of conventional tests or reduction in their frequency. Future guidance is recommended to assist in the implementation of MIQC.

Ethnic differences in early onset multimorbidity and associations with health service use, long-term prescribing, years of life lost, and mortality: A cross-sectional study using clustering in the UK Clinical Practice Research Datalink.

BACKGROUND: The population prevalence of multimorbidity (the existence of at least 2 or more long-term conditions [LTCs] in an individual) is increasing among young adults, particularly in minority ethnic groups and individuals living in socioeconomically deprived areas. In this study, we applied a data-driven approach to identify clusters of individuals who had an early onset multimorbidity in an ethnically and socioeconomically diverse population. We identified associations between clusters and a range of health outcomes. METHODS AND FINDINGS: Using linked primary and secondary care data from the Clinical Practice Research Datalink GOLD (CPRD GOLD), we conducted a cross-sectional study of 837,869 individuals with early onset multimorbidity (aged between 16 and 39 years old when the second LTC was recorded) registered with an English general practice between 2010 and 2020. The study population included 777,906 people of White ethnicity (93%), 33,915 people of South Asian ethnicity (4%), and 26,048 people of Black African/Caribbean ethnicity (3%). A total of 204 LTCs were considered. Latent class analysis stratified by ethnicity identified 4 clusters of multimorbidity in White groups and 3 clusters in South Asian and Black groups. We found that early onset multimorbidity was more common among South Asian (59%, 33,915) and Black (56% 26,048) groups compared to the White population (42%, 777,906). Latent class analysis revealed physical and mental health conditions that were common across all ethnic groups (i.e., hypertension, depression, and painful conditions). However, each ethnic group also presented exclusive LTCs and different sociodemographic profiles: In White groups, the cluster with the highest rates/odds of the outcomes was predominantly male (54%, 44,150) and more socioeconomically deprived than the cluster with the lowest rates/odds of the outcomes. On the other hand, South Asian and Black groups were more socioeconomically deprived than White groups, with a consistent deprivation gradient across all multimorbidity clusters. At the end of the study, 4% (34,922) of the White early onset multimorbidity population had died compared to 2% of the South Asian and Black early onset multimorbidity populations (535 and 570, respectively); however, the latter groups died younger and lost more years of life. The 3 ethnic groups each displayed a cluster of individuals with increased rates of primary care consultations, hospitalisations, long-term prescribing, and odds of mortality. Study limitations include the exclusion of individuals with missing ethnicity information, the age of diagnosis not reflecting the actual age of onset, and the exclusion of people from Mixed, Chinese, and other ethnic groups due to insufficient power to investigate associations between multimorbidity and health-related outcomes in these groups. CONCLUSIONS: These findings emphasise the need to identify, prevent, and manage multimorbidity early in the life course. Our work provides additional insights into the excess burden of early onset multimorbidity in those from socioeconomically deprived and diverse groups who are disproportionately and more severely affected by multimorbidity and highlights the need to ensure healthcare improvements are equitable.

The benefits and pitfalls of machine learning for biomarker discovery.

Prospects for the discovery of robust and reproducible biomarkers have improved considerably with the development of sensitive omics platforms that can enable measurement of biological molecules at an unprecedented scale. With technical barriers to success lowering, the challenge is now moving into the analytical domain. Genome-wide discovery presents a problem of scale and multiple testing as standard statistical methods struggle to distinguish signal from noise in increasingly complex biological systems. Machine learning and AI methods are good at finding answers in large datasets, but they have a tendency to overfit solutions. It may be possible to find a local answer or mechanism in a specific patient sample or small group of samples, but this may not generalise to wider patient populations due to the high likelihood of false discovery. The rise of explainable AI offers to improve the opportunity for true discovery by providing explanations for predictions that can be explored mechanistically before proceeding to costly and time-consuming validation studies. This review aims to introduce some of the basic concepts of machine learning and AI for biomarker discovery with a focus on post hoc explanation of predictions. To illustrate this, we consider how explainable AI has already been used successfully, and we explore a case study that applies AI to biomarker discovery in rheumatoid arthritis, demonstrating the accessibility of tools for AI and machine learning. We use this to illustrate and discuss some of the potential challenges and solutions that may enable AI to critically interrogate disease and response mechanisms.

The successes and challenges of harmonising juvenile idiopathic arthritis (JIA) datasets to create a large-scale JIA data resource.

BACKGROUND: CLUSTER is a UK consortium focussed on precision medicine research in JIA/JIA-Uveitis. As part of this programme, a large-scale JIA data resource was created by harmonizing and pooling existing real-world studies. Here we present challenges and progress towards creation of this unique large JIA dataset. METHODS: Four real-world studies contributed data; two clinical datasets of JIA patients starting first-line methotrexate (MTX) or tumour necrosis factor inhibitors (TNFi) were created. Variables were selected based on a previously developed core dataset, and encrypted NHS numbers were used to identify children contributing similar data across multiple studies. RESULTS: Of 7013 records (from 5435 individuals), 2882 (1304 individuals) represented the same child across studies. The final datasets contain 2899 (MTX) and 2401 (TNFi) unique patients; 1018 are in both datasets. Missingness ranged from 10 to 60% and was not improved through harmonisation. CONCLUSIONS: Combining data across studies has achieved dataset sizes rarely seen in JIA, invaluable to progressing research. Losing variable specificity and missingness, and their impact on future analyses requires further consideration.

Using technology to reduce critical deterioration (the DETECT study): a cost analysis of care costs at a tertiary children's hospital in the United Kingdom.

BACKGROUND: Electronic early warning systems have been used in adults for many years to prevent critical deterioration events (CDEs). However, implementation of similar technologies for monitoring children across the entire hospital poses additional challenges. While the concept of such technologies is promising, their cost-effectiveness is not established for use in children. In this study we investigate the potential for direct cost savings arising from the implementation of the DETECT surveillance system. METHODS: Data were collected at a tertiary children's hospital in the United Kingdom. We rely on the comparison between patients in the baseline period (March 2018 to February 2019) and patients in the post-intervention period (March 2020 to July 2021). These provided a matched cohort of 19,562 hospital admissions for each group. From these admissions, 324 and 286 CDEs were observed in the baseline and post-intervention period, respectively. Hospital reported costs and Health Related Group (HRG) National Costs were used to estimate overall expenditure associated with CDEs for both groups of patients. RESULTS: Comparing post-intervention with baseline data we found a reduction in the total number of critical care days, driven by an overall reduction in the number of CDEs, however without statistical significance. Using hospital reported costs adjusted for the Covid-19 impact, we estimate a non-significant reduction of total expenditure from £16.0 million to £14.3 million (corresponding to £1.7 million of savings - 11%). Additionally, using HRG average costs, we estimated a non-significant reduction of total expenditure from £8.2 million to £ 7.2 million (corresponding to £1.1 million of savings - 13%). DISCUSSION AND CONCLUSION: Unplanned critical care admissions for children not only impose a substantial burden on patients and families but are also costly for hospitals. Interventions aimed at reducing emergency critical care admissions can be crucial to contribute to the reduction of these episodes' costs. Even though cost reductions were identified in our sample, our results do not support the hypothesis that reducing CDEs using technology leads to a significant reduction on hospital costs. TRIAL REGISTRATION: Current Controlled Trials ISRCTN61279068, date of registration 07/06/2019, retrospectively registered.

The impact of expression vector position on transgene transcription allows for rational expression vector design in a targeted integration system.

Site-specific integration (SSI) technology has emerged as an effective approach by the pharmaceutical industry for the development of recombinant Chinese hamster ovary (CHO) cell lines. While SSI systems have been demonstrated to be effective for the development of CHO cell lines, they can be limiting in terms of both transgene expression and in the case of multi-specifics, the ability to generate the correct product of interest. To maximize the performance of Pfizer's dual SSI expression system for expressing monoclonal and multi-specific antibodies, we used a novel approach to investigate the positional effect of transgenes within expression vectors by engineering nucleotide polymorphisms (NP)s to use as biomarkers to track the level of transcript output from each expression vector position. We observed differences in transcript level for two different transgenes across all four expression vector positions interrogated. We then applied these learnings to rationally design expression vectors for six different mAbs and a multi-specific antibody. We showed enhanced productivity and optimal product quality when compared to a conventional expression vector topology. The learnings gained here can potentially aid in the determination of optimal vector topologies for several IgG-like multi-specific formats.

nestedcv: an R package for fast implementation of nested cross-validation with embedded feature selection designed for transcriptomics and high-dimensional data.

Motivation: Although machine learning models are commonly used in medical research, many analyses implement a simple partition into training data and hold-out test data, with cross-validation (CV) for tuning of model hyperparameters. Nested CV with embedded feature selection is especially suited to biomedical data where the sample size is frequently limited, but the number of predictors may be significantly larger (P ≫ n). Results: The nestedcv R package implements fully nested k × l-fold CV for lasso and elastic-net regularized linear models via the glmnet package and supports a large array of other machine learning models via the caret framework. Inner CV is used to tune models and outer CV is used to determine model performance without bias. Fast filter functions for feature selection are provided and the package ensures that filters are nested within the outer CV loop to avoid information leakage from performance test sets. Measurement of performance by outer CV is also used to implement Bayesian linear and logistic regression models using the horseshoe prior over parameters to encourage a sparse model and determine unbiased model accuracy. Availability and implementation: The R package nestedcv is available from CRAN: https://CRAN.R-project.org/package=nestedcv.

International Interlaboratory Comparison of Thermogravimetric Analysis of Graphene-Related Two-Dimensional Materials.

Research on graphene-related two-dimensional (2D) materials (GR2Ms) in recent years is strongly moving from academia to industrial sectors with many new developed products and devices on the market. Characterization and quality control of the GR2Ms and their properties are critical for growing industrial translation, which requires the development of appropriate and reliable analytical methods. These challenges are recognized by International Organization for Standardization (ISO 229) and International Electrotechnical Commission (IEC 113) committees to facilitate the development of these methods and standards which are currently in progress. Toward these efforts, the aim of this study was to perform an international interlaboratory comparison (ILC), conducted under Versailles Project on Advanced Materials and Standards (VAMAS) Technical Working Area (TWA) 41 "Graphene and Related 2D Materials" to evaluate the performance (reproducibility and confidence) of the thermogravimetric analysis (TGA) method as a potential new method for chemical characterization of GR2Ms. Three different types of representative and industrially manufactured GR2Ms samples, namely, pristine few-layer graphene (FLG), graphene oxide (GO), and reduced graphene oxide (rGO), were used and supplied to ILC participants to complete the study. The TGA method performance was evaluated by a series of measurements of selected parameters of the chemical and physical properties of these GR2Ms including the number of mass loss steps, thermal stability, temperature of maximum mass change rate (Tp) for each decomposition step, and the mass contents (%) of moisture, oxygen groups, carbon, and impurities (organic and non-combustible residue). TGA measurements determining these parameters were performed using the provided optimized TGA protocol on the same GR2Ms by 12 participants across academia, industry stakeholders, and national metrology institutes. This paper presents these results with corresponding statistical analysis showing low standard deviation and statistical conformity across all participants that confirm that the TGA method can be satisfactorily used for characterization of these parameters and the chemical characterization and quality control of GR2Ms. The common measurement uncertainty for each parameter, key contribution factors were identified with explanations and recommendations for their elimination and improvements toward their implementation for the development of the ISO/IEC standard for chemical characterization of GR2Ms.

The effects of COVID-19 stressors and family life on anxiety and depression one-year into the COVID-19 pandemic.

The purpose of this study was to examine the effects of Coronavirus (COVID-19)-related stressors and family health on adult anxiety and depressive symptoms 1 year into the pandemic. The sample consisted of 442 adults living in the United States who were recruited via Amazon Mechanical Turk. Data were analyzed using multiple logistic regression. Results indicated that compared to a sample 1 month into the pandemic, participants in the current sample reported worse family health and increases in both positive and negative perceptions of the pandemic on family life and routines. COVID-19 stressors and perceived negative effects of the pandemic on family life increased the odds for moderate-to-severe depression and anxiety while having more family health resources decreased the odds for depression and anxiety symptoms. Participants reported lower odds for worse depression and anxiety since the beginning of the pandemic when they reported more positive family meaning due to the pandemic. The results suggest a need to consider the impact of family life on mental health in pandemics and other disasters.

Local delivery of nitric oxide prevents endothelial dysfunction in periodontitis.

AIMS: Increased cardiovascular disease risk underlies elevated rates of mortality in individuals with periodontitis. A key characteristic of those with increased cardiovascular risk is endothelial dysfunction, a phenomenon synonymous with deficiencies of bioavailable nitric oxide (NO), and prominently expressed in patients with periodontitis. Also, inorganic nitrate can be reduced to NO in vivo to restore NO levels, leading us to hypothesise that its use may be beneficial in reducing periodontitis-associated endothelial dysfunction. Herein we sought to determine whether inorganic nitrate improves endothelial function in the setting of periodontitis and if so to determine the mechanisms underpinning any responses seen. METHODS AND RESULTS: Periodontitis was induced in mice by placement of a ligature for 14 days around the second molar. Treatment in vivo with potassium nitrate, either prior to or following establishment of experimental periodontitis, attenuated endothelial dysfunction, as determined by assessment of acetylcholine-induced relaxation of aortic rings, compared to control (potassium chloride treatment). These beneficial effects were associated with a suppression of vascular wall inflammatory pathways (assessed by quantitative-PCR), increases in the anti-inflammatory cytokine interleukin (IL)-10 and reduced tissue oxidative stress due to attenuation of xanthine oxidoreductase-dependent superoxide generation. In patients with periodontitis, plasma nitrite levels were not associated with endothelial function indicating dysfunction. CONCLUSION: Our results suggest that inorganic nitrate protects against, and can partially reverse pre-existing, periodontitis-induced endothelial dysfunction through restoration of nitrite and thus NO levels. This research highlights the potential of dietary nitrate as adjunct therapy to target the associated negative cardiovascular outcomes in patients with periodontitis.

COVID-19 Health Beliefs Regarding Mask Wearing and Vaccinations on Twitter: Deep Learning Approach.

Background: Amid the global COVID-19 pandemic, a worldwide infodemic also emerged with large amounts of COVID-19-related information and misinformation spreading through social media channels. Various organizations, including the World Health Organization (WHO) and the Centers for Disease Control and Prevention (CDC), and other prominent individuals issued high-profile advice on preventing the further spread of COVID-19. Objective: The purpose of this study is to leverage machine learning and Twitter data from the pandemic period to explore health beliefs regarding mask wearing and vaccines and the influence of high-profile cues to action. Methods: A total of 646,885,238 COVID-19-related English tweets were filtered, creating a mask-wearing data set and a vaccine data set. Researchers manually categorized a training sample of 3500 tweets for each data set according to their relevance to Health Belief Model (HBM) constructs and used coded tweets to train machine learning models for classifying each tweet in the data sets. Results: In total, 5 models were trained for both the mask-related and vaccine-related data sets using the XLNet transformer model, with each model achieving at least 81% classification accuracy. Health beliefs regarding perceived benefits and barriers were most pronounced for both mask wearing and immunization; however, the strength of those beliefs appeared to vary in response to high-profile cues to action. Conclusions: During both the COVID-19 pandemic and the infodemic, health beliefs related to perceived benefits and barriers observed through Twitter using a big data machine learning approach varied over time and in response to high-profile cues to action from prominent organizations and individuals.

Global hypo-methylation in a proportion of glioblastoma enriched for an astrocytic signature is associated with increased invasion and altered immune landscape.

We describe a subset of glioblastoma, the most prevalent malignant adult brain tumour, harbouring a bias towards hypomethylation at defined differentially methylated regions. This epigenetic signature correlates with an enrichment for an astrocytic gene signature, which together with the identification of enriched predicted binding sites of transcription factors known to cause demethylation and to be involved in astrocytic/glial lineage specification, point to a shared ontogeny between these glioblastomas and astroglial progenitors. At functional level, increased invasiveness, at least in part mediated by SRPX2, and macrophage infiltration characterise this subset of glioblastoma.

Predicting post-contrast information from contrast agent free cardiac MRI using machine learning: Challenges and methods.

Objectives: Currently, administering contrast agents is necessary for accurately visualizing and quantifying presence, location, and extent of myocardial infarction (MI) with cardiac magnetic resonance (CMR). In this study, our objective is to investigate and analyze pre- and post-contrast CMR images with the goal of predicting post-contrast information using pre-contrast information only. We propose methods and identify challenges. Methods: The study population consists of 272 retrospectively selected CMR studies with diagnoses of MI (n = 108) and healthy controls (n = 164). We describe a pipeline for pre-processing this dataset for analysis. After data feature engineering, 722 cine short-axis (SAX) images and segmentation mask pairs were used for experimentation. This constitutes 506, 108, and 108 pairs for the training, validation, and testing sets, respectively. We use deep learning (DL) segmentation (UNet) and classification (ResNet50) models to discover the extent and location of the scar and classify between the ischemic cases and healthy cases (i.e., cases with no regional myocardial scar) from the pre-contrast cine SAX image frames, respectively. We then capture complex data patterns that represent subtle signal and functional changes in the cine SAX images due to MI using optical flow, rate of change of myocardial area, and radiomics data. We apply this dataset to explore two supervised learning methods, namely, the support vector machines (SVM) and the decision tree (DT) methods, to develop predictive models for classifying pre-contrast cine SAX images as being a case of MI or healthy. Results: Overall, for the UNet segmentation model, the performance based on the mean Dice score for the test set (n = 108) is 0.75 (±0.20) for the endocardium, 0.51 (±0.21) for the epicardium and 0.20 (±0.17) for the scar. For the classification task, the accuracy, F1 and precision scores of 0.68, 0.69, and 0.64, respectively, were achieved with the SVM model, and of 0.62, 0.63, and 0.72, respectively, with the DT model. Conclusion: We have presented some promising approaches involving DL, SVM, and DT methods in an attempt to accurately predict contrast information from non-contrast images. While our initial results are modest for this challenging task, this area of research still poses several open problems.

Implication of cell culture methods and biases on UV inactivation of viruses.

Inactivation of human respiratory viruses in air and on surfaces is important to control their spread. Exposure to germicidal ultraviolet (UV-C) light damages viral nucleic acid rendering them non-infectious. Most of the recent viral inactivation studies have not considered potential artifacts caused by interactions between UV-C light and culture media used to suspend and deposit virus on surfaces. We show that the reactive oxygen and nitrogen species (ROS and RNS) form when commonly used virus culture media is exposed to 265 nm irradiation from light emitting diodes (LEDs) at UV-C doses (4 or 40 mJ/cm2) commonly considered to achieve multiple log-inactivation of virus. Surface viral inactivation values were enhanced from 0.49 to 2.92 log10 of viruses in DMEM, EMEM or EMEM-F as compared to absence of culture media (only suspended in Tris-buffer). The mechanisms responsible for the enhanced surface inactivate is hypothesized to involve photo-activation of vitamins and dyes present in the culture media, deposited with the virus on surfaces to be disinfected, which produce ROS and RNS. Given the rapidly growing research and commercial markets for UV-C disinfecting devices, there is a need to establish surface disinfecting protocols that avoid viral inactivation enhancement artifacts associated with selection and use of common cell culture media in the presence of UV-C light. This study addresses this weak link in the literature and highlights that inadequate selection of virus suspension media may cause a bias (i.e., over-estimation) for the UV-C dosages required for virus inactivation on surfaces.

Gyrokinetic electrostatic turbulence close to marginality in the Wendelstein 7-X stellarator.

The transition from strong (fluidlike) to nearly marginal (Floquet-type) regimes of ion-temperature-gradient (ITG) driven turbulence is studied in the stellarator Wendelstein 7-X by means of numerical simulations. Close to marginality, extended (along magnetic field lines) linearly unstable modes are dominant, even in the presence of kinetic electrons, and provide a drive that results in finite turbulent transport. A total suppression of turbulence above the linear stability threshold of the ITG modes, commonly present in tokamaks and known as the "Dimits shift," is not observed. We show that this is mostly due to the peculiar radial structure of marginal turbulence, which is more localized than in the fluid case and therefore less likely to be stabilized by shearing flows.

Letter to the editor on "Geotemporospatial and causal inferential epidemiological overview and survey of USA cannabis, cannabidiol and cannabinoid genotoxicity expressed in cancer incidence 2003-2017, parts 1-3".

We would like to thank authors Reece and Hulse (2022) for their three-part article titled "Geotemporospatial and causal inferential epidemiological overview and survey of USA cannabis, cannabidiol and cannabinoid genotoxicity expressed in cancer incidence 2003-2017", in which the authors infer that cannabis use has a causal role in the development of various cancer types. While the authors use reputable datasets and a well-established epidemiological methodology, the authors' conclusion of a causal association is limited due to biases inherent in ecological epidemiological studies. Though the researchers attempt to overcome these biases through validation and statistical manipulations, their approaches are insufficient to create conditions suitable for causal inferencing upon examination. There are also concerns in the practical and conceptual application of the studies' dataset that further question the validity of the authors' inferences. Further research exploring the potential benefits and harm of cannabinoids in the context of cancer must be performed before a distinct relationship can be defined.