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© (in press) The Authors. Published by Universitetsforlaget. This is an open access article available under a Creative Commons licence. The published version can be accessed at the following link on the publisher’s website: [DOI/weblink]

Mucosal vaccines offer the potential to trigger robust protective immune responses at the predominant sites of pathogen infection. In principle, the induction of adaptive immunity at mucosal sites, involving secretory antibody responses and tissue-resident T cells, has the capacity to prevent an infection from becoming established in the first place, rather than only curtailing infection and protecting against the development of disease symptoms. Although numerous effective mucosal vaccines are in use, the major advances seen with injectable vaccines (including adjuvanted subunit antigens, RNA and DNA vaccines) have not yet been translated into licensed mucosal vaccines, which currently comprise solely live attenuated and inactivated whole-cell preparations. The identification of safe and effective mucosal adjuvants allied to innovative antigen discovery and delivery strategies is key to advancing mucosal vaccines. Significant progress has been made in resolving the mechanisms that regulate innate and adaptive mucosal immunity and in understanding the crosstalk between mucosal sites, and this provides valuable pointers to inform mucosal adjuvant design. In particular, increased knowledge on mucosal antigen-presenting cells, innate lymphoid cell populations and resident memory cells at mucosal sites highlights attractive targets for vaccine design. Exploiting these insights will allow new vaccine technologies to be leveraged to facilitate rational mucosal vaccine design for pathogens including severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and for cancer., Here, Ed Lavelle and Ross Ward discuss the unique aspects of mucosal immunity that must be considered when developing effective mucosal vaccines. The authors highlight the key immune cell populations that are targeted by mucosal vaccination strategies and explain how innovative adjuvant and delivery approaches should lead to new vaccines for infectious diseases and cancers.

Alzheimer’s disease (AD) is increasingly prevalent worldwide, and disease‐modifying treatments may soon be at hand; hence, now, more than ever, there is a need to develop techniques that allow earlier and more secure diagnosis. Current biomarker‐based guidelines for AD diagnosis, which have replaced the historical symptom‐based guidelines, rely heavily on neuroimaging and cerebrospinal fluid (CSF) sampling. While these have greatly improved the diagnostic accuracy of AD pathophysiology, they are less practical for application in primary care, population‐based and epidemiological settings, or where resources are limited. In contrast, blood is a more accessible and cost‐effective source of biomarkers in AD. In this review paper, using the recently proposed amyloid, tau and neurodegeneration [AT(N)] criteria as a framework towards a biological definition of AD, we discuss recent advances in biofluid‐based biomarkers, with a particular emphasis on those with potential to be translated into blood‐based biomarkers. We provide an overview of the research conducted both in CSF and in blood to draw conclusions on biomarkers that show promise. Given the evidence collated in this review, plasma neurofilament light chain (N) and phosphorylated tau (p‐tau; T) show particular potential for translation into clinical practice. However, p‐tau requires more comparisons to be conducted between its various epitopes before conclusions can be made as to which one most robustly differentiates AD from non‐AD dementias. Plasma amyloid beta (A) would prove invaluable as an early screening modality, but it requires very precise tests and robust pre‐analytical protocols.

Following the observation of interleukin 3 receptor α chain (IL-3Rα; CD123) upregulation on leukemia stem cells (LSCs) almost two decades ago, targeted treatment via CD123-diptheria toxin conjugates has now been tested in patients with diverse myeloid malignancies. Targeted eradication of LSCs could result in effective treatments for many challenging diseases initiated by these cells. Consequently, considerable effort has been directed toward targeting CD123 as a potential strategy for treating patients with hematologic malignancies in which CD123 is overexpressed. However, these therapies have had limited success so far, highlighting the need for suitable criteria to identify patients who could benefit from them. Given the diversity in CD123 expression across different hematologic malignancies, understanding CD123 expression patterns and the functional pathogenetic significance is crucial. Here, we review the methodologies available for CD123 assessment and discuss the biological and clinical characteristics of patients for whom CD123-targeting therapies may have a clinical impact.

The ability of machines to make truly independent and autonomous decisions is a goal of many, not least of military leaders who wish to take the human out of the loop as much as possible, claiming that autonomous military weaponry—most notably drones—can make decisions more quickly and with greater accuracy. However, there is no clear understanding of how autonomous weapons should be conceptualized and of the implications that their “autonomous” nature has on them as ethical agents. It will be argued that autonomous weapons are not full ethical agents due to the restrictions of their coding. However, the highly complex machine-learning nature gives the impression that they are making their own decisions and creates the illusion that their human operators are protected from the responsibility of the harm they cause. Therefore, it is important to distinguish between autonomous AI weapons and an AI with autonomy, a distinction that creates two different ethical problems for their use. For autonomous weapons, their limited agency combined with machine-learning means their human counterparts are still responsible for their actions while having no ability to control or intercede in the actual decisions made. If, on the other hand, an AI could reach the point of autonomy, the level of critical reflection would make its decisions unpredictable and dangerous in a weapon.

In recent years the power and reach of prominent hacker groups such as Anonymous and LulzSec has been clearly demonstrated. However, in a world where hackers are able to wield significant online power, can they do so ethically as legitimate agents? To answer this question this paper will develop an ethical framework based on the premise that hackers have exhibited instances where they have acted to protect people from harm at a time when there was no one else to do so. At its core this paper will argue that political hacking can be justified when it is done to protect the vital interests of oneself or others. Moreover, it will also argue that just because hackers are outside the state does not automatically discount them as ethical actors and that when the state fails to protect people – whether it is due to a lack of ability, political will or because the state is the source of the threat – hackers can fill the void. In order to achieve this, first it is necessary to highlight the space for hackers to operate; second, guide hacker activity by creating an ethical framework detailing what actions are justified towards what end; third, to offer mechanisms that can aid in reaching these ethically justified decisions; and as a result, inform further ethical debates on how to react to these political hackers. This means that the framework can be used to both justify and condemn hacking depending on the circumstances, allowing those on the outside to distil and evaluate a political hack, both past and present, while guiding hacker collectives by providing clearer ethical tools for determining the appropriate agendas and methods.

Objectives We examined whether providing a quantitative report (QReport) of regional brain volumes improves radiologists’ accuracy and confidence in detecting volume loss, and in differentiating Alzheimer’s disease (AD) and frontotemporal dementia (FTD), compared with visual assessment alone. Methods Our forced-choice multi-rater clinical accuracy study used MRI from 16 AD patients, 14 FTD patients, and 15 healthy controls; age range 52–81. Our QReport was presented to raters with regional grey matter volumes plotted as percentiles against data from a normative population (n = 461). Nine raters with varying radiological experience (3 each: consultants, registrars, ‘non-clinical image analysts’) assessed each case twice (with and without the QReport). Raters were blinded to clinical and demographic information; they classified scans as ‘normal’ or ‘abnormal’ and if ‘abnormal’ as ‘AD’ or ‘FTD’. Results The QReport improved sensitivity for detecting volume loss and AD across all raters combined (p = 0.015* and p = 0.002*, respectively). Only the consultant group’s accuracy increased significantly when using the QReport (p = 0.02*). Overall, raters’ agreement (Cohen’s κ) with the ‘gold standard’ was not significantly affected by the QReport; only the consultant group improved significantly (κs 0.41➔0.55, p = 0.04*). Cronbach’s alpha for interrater agreement improved from 0.886 to 0.925, corresponding to an improvement from ‘good’ to ‘excellent’. Conclusion Our QReport referencing single-subject results to normative data alongside visual assessment improved sensitivity, accuracy, and interrater agreement for detecting volume loss. The QReport was most effective in the consultants, suggesting that experience is needed to fully benefit from the additional information provided by quantitative analyses. Key Points • The use of quantitative report alongside routine visual MRI assessment improves sensitivity and accuracy for detecting volume loss and AD vs visual assessment alone. • Consultant neuroradiologists’ assessment accuracy and agreement (kappa scores) significantly improved with the use of quantitative atrophy reports. • First multi-rater radiological clinical evaluation of visual quantitative MRI atrophy report for use as a diagnostic aid in dementia.

No biomarker of Parkinson’s disease exists that allows clinicians to adjust chronic therapy, either medication or deep brain stimulation, with real-time feedback. Consequently, clinicians rely on time-intensive, empirical, and subjective clinical assessments of motor behaviour and adverse events to adjust therapies. Accumulating evidence suggests that hypokinetic aspects of Parkinson’s disease and their improvement with therapy are related to pathological neural activity in the beta band (beta oscillopathy) in the subthalamic nucleus. Additionally, effectiveness of deep brain stimulation may depend on modulation of the dorsolateral sensorimotor region of the subthalamic nucleus, which is the primary site of this beta oscillopathy. Despite the feasibility of utilizing this information to provide integrated, biomarker-driven precise deep brain stimulation, these measures have not been brought together in awake freely moving individuals. We sought to directly test whether stimulation-related improvements in bradykinesia were contingent on reduction of beta power and burst durations, and/or the volume of the sensorimotor subthalamic nucleus that was modulated. We recorded synchronized local field potentials and kinematic data in 16 subthalamic nuclei of individuals with Parkinson’s disease chronically implanted with neurostimulators during a repetitive wrist-flexion extension task, while administering randomized different intensities of high frequency stimulation. Increased intensities of deep brain stimulation improved movement velocity and were associated with an intensity-dependent reduction in beta power and mean burst duration, measured during movement. The degree of reduction in this beta oscillopathy was associated with the improvement in movement velocity. Moreover, the reduction in beta power and beta burst durations was dependent on the theoretical degree of tissue modulated in the sensorimotor region of the subthalamic nucleus. Finally, the degree of attenuation of both beta power and beta burst durations, together with the degree of overlap of stimulation with the sensorimotor subthalamic nucleus significantly explained the stimulation-related improvement in movement velocity. The above results provide direct evidence that subthalamic nucleus deep brain stimulation-related improvements in bradykinesia are related to the reduction in beta oscillopathy within the sensorimotor region. With the advent of sensing neurostimulators, this beta oscillopathy combined with lead location could be used as a marker for real-time feedback to adjust clinical settings or to drive closed-loop deep brain stimulation in freely moving individuals with Parkinson’s disease.

Sporadic Creutzfeldt-Jakob disease (sCJD) presents as a rapidly progressive dementia which is usually fatal within six months. No clinical blood tests are available for diagnosis or disease monitoring. Here, we profile blood microRNA (miRNA) expression in sCJD. Sequencing of 57 sCJD patients, and healthy controls reveals differential expression of hsa-let-7i-5p, hsa-miR-16-5p, hsa-miR-93-5p and hsa-miR-106b-3p. Downregulation of hsa-let-7i-5p, hsa-miR-16-5p and hsa-miR-93-5p replicates in an independent cohort using quantitative PCR, with concomitant upregulation of four mRNA targets. Absence of correlation in cross-sectional analysis with clinical phenotypes parallels the lack of association between rate of decline in miRNA expression, and rate of disease progression in a longitudinal cohort of samples from 21 patients. Finally, the miRNA signature shows a high level of accuracy in discriminating sCJD from Alzheimer’s disease. These findings highlight molecular alterations in the periphery in sCJD which provide information about differential diagnosis and improve mechanistic understanding of human prion diseases., Sporadic Creutzfeldt-Jakob disease (sCJD) is a rapidly progressive dementia. No clinical blood tests are available for diagnosis. The authors identified three miRNAs in whole-blood that are downregulated in sCJD patients, and discriminate sCJD from Alzheimer’s disease patients and healthy controls.

Objective The study sought to examine the association between clinician burnout and measures of electronic health record (EHR) workload and efficiency, using vendor-derived EHR action log data. Materials and Methods We combined data from a statewide clinician survey on burnout with Epic EHR data from the ambulatory sites of 2 large health systems; the combined dataset included 422 clinicians. We examined whether specific EHR workload and efficiency measures were independently associated with burnout symptoms, using multivariable logistic regression and controlling for clinician characteristics. Results Clinicians with the highest volume of patient call messages had almost 4 times the odds of burnout compared with clinicians with the fewest (adjusted odds ratio, 3.81; 95% confidence interval, 1.44-10.14; P = .007). No other workload measures were significantly associated with burnout. No efficiency variables were significantly associated with burnout in the main analysis; however, in a subset of clinicians for whom note entry data were available, clinicians in the top quartile of copy and paste use were significantly less likely to report burnout, with an adjusted odds ratio of 0.22 (95% confidence interval, 0.05-0.93; P = .039). Discussion High volumes of patient call messages were significantly associated with clinician burnout, even when accounting for other measures of workload and efficiency. In the EHR, “patient calls” encompass many of the inbox tasks occurring outside of face-to-face visits and likely represent an important target for improving clinician well-being. Conclusions Our results suggest that increased workload is associated with burnout and that EHR efficiency tools are not likely to reduce burnout symptoms, with the exception of copy and paste.

This positioning article explores a reimagining within the field of applied theatre where through the medium of puppetry, the art and artist may become one as a way of healing. Building upon conceptual principles of animism, transference and embodiment, it is proposed that puppeteer training be usefully integrated into the higher education applied arts and health curriculum as an extension to existing programmes. Value is given to the metaphorical use of the puppet in both education and therapy. It is proposed that puppeteers may gain value from engaging with cross-disciplinary art-based research as a way to further understand puppetry’s uses and furthering their own practices.

BACKGROUND: There is limited data on cerebrospinal fluid (CSF) biomarkers in sporadic amyloid-β (Aβ) cerebral amyloid angiopathy (CAA). OBJECTIVE: To determine the profile of biomarkers relevant to neurodegenerative disease in the CSF of patients with CAA. METHODS: We performed a detailed comparison of CSF markers, comparing patients with CAA, Alzheimer’s disease (AD), and control (CS) participants, recruited from the Biomarkers and Outcomes in CAA (BOCAA) study, and a Specialist Cognitive Disorders Service. RESULTS: We included 10 CAA, 20 AD, and 10 CS participants (mean age 68.6, 62.5, and 62.2 years, respectively). In unadjusted analyses, CAA patients had a distinctive CSF biomarker profile, with significantly lower (p

Application of the 231Pa/235U radiochronometer for nuclear forensic investigations is challenged by a lack of certified reference materials with 231Pa/235U model purification dates. The Japan Atomic Energy Agency, Los Alamos National Laboratory, and Lawrence Livermore National Laboratory completed an interlaboratory study measuring 231Pa/235U model ages of New Brunswick Laboratory CRM U100. Results from independent laboratories were combined to calculate a consensus 231Pa/235U model purification date for CRM U100 of March 26, 1959 ± 237 days. This 231Pa/235U consensus date for CRM U100 may be used by the nuclear forensic community for quality control of 231Pa/235U radiochronometry measurements of unknown materials.

Myeloid-derived suppressor cells (MDSCs) are potent suppressors of antitumor immunity and are commonly associated with poor outcomes in melanoma patients treated with immune checkpoint inhibitors. Inducing the differentiation of MDSCs using all-trans retinoic acid (ATRA) alters their activity and reduces MDSC frequency. This trial seeks to assess the safety and efficacy of combining ATRA and pembrolizumab in metastatic melanoma patients. In 24 stage IV melanoma patients, treatment with pembrolizumab Q3W plus the supplemental treatment of ATRA orally for three days surrounding each of the first four pembrolizumab infusions effectively lowered the frequency of circulating PMN-MDSCs and enhanced melanoma-specific T cell activity. The combination was well tolerated. Median progression free survival was 20.3 months, and the overall response rate was 71%, with 50% of patients experiencing a complete response. Targeting MDSCs remains a promising mechanism to enhance the efficacy of anti-PD-1 therapies and this combination merits further investigation.

Germanium-on-silicon (Ge-on-Si) based single-photon avalanche diodes (SPADs) have recently emerged as a promising detector candidate for ultra-sensitive and picosecond resolution timing measurement of short-wave infrared (SWIR) photons. Many applications benefit from operating in the SWIR spectral range, such as long distance light detection and ranging, however, there are few single-photon detectors exhibiting the high-performance levels obtained by all-silicon SPADs commonly used for single-photon detection at wavelengths µm. This paper first details the advantages of operating at SWIR wavelengths, the current technologies, and associated issues, and describes the potential of Ge-on-Si SPADs as a single-photon detector technology for this wavelength region. The working principles, fabrication and characterisation processes of such devices are subsequently detailed. We review the research in these single-photon detectors and detail the state-of-the-art performance. Finally, the challenges and future opportunities offered by Ge-on-Si SPAD detectors are discussed.

BackgroundResting state beta band (13 – 30 Hz) oscillations represent pathological neural activity in Parkinson’s disease (PD). It is unknown how the peak frequency or dynamics of beta oscillations may change among fine, limb and axial movements and different disease phenotypes. This will be critical for the development of personalized closed loop deep brain stimulation (DBS) algorithms during different activity states.MethodsSubthalamic (STN) local field potentials (LFPs) were recorded from a sensing neurostimulator (Activa® PC+S, Medtronic PLC.,) in fourteen PD participants (six tremor-dominant, 8 akinetic-rigid) off medication/off STN DBS during thirty seconds of repetitive alternating finger tapping, wrist-flexion extension, stepping in place, and free walking. Beta power peaks and beta burst dynamics were identified by custom algorithms and were compared among movement tasks and between tremor-dominant and akinetic-rigid groups.ResultsBeta power peaks were evident during fine, limb, and axial movements in 98% of movement trials; the peak frequencies were similar during each type of movement. Burst power and duration were significantly larger in the high beta band, but not in the low beta band, in the akinetic-rigid group compared to the tremor-dominant group.ConclusionsThe conservation of beta peak frequency during different activity states supports the feasibility of patient-specific closed loop DBS algorithms driven by the dynamics of the same beta band during different activities. Akinetic-rigid participants had greater power and longer burst durations in the high beta band than tremor-dominant participants during movement, which may relate to the difference in underlying pathophysiology between phenotypes.

Objective. To investigate the progression of neural and motor features of Parkinson's disease in a longitudinal study, after washout of medication and bilateral subthalamic nucleus deep brain stimulation (STN DBS). Methods. Participants with clinically established Parkinson's disease underwent bilateral implantation of DBS leads (18 participants, 13 male) within the STN using standard functional frameless stereotactic technique and multi-pass microelectrode recording. Both DBS leads were connected to an implanted investigative sensing neurostimulator (Activa™ PC + S, Medtronic, PLC). Resting state STN local field potentials (LFPs) were recorded and motor disability, (the Movement Disorder Society-Unified Parkinson's Disease Rating Scale – motor subscale, MDS-UPDRS III) was assessed off therapy at initial programming, and after 6 months, 1 year, and yearly out to 5 years of treatment. The primary endpoint was measured at 3 years. At each visit, medication had been held for over 12/24 h and DBS was turned off for at least 60 min, by which time LFP spectra reached a steady state. Results. After 3 years of chronic DBS, there were no increases in STN beta band dynamics (p = 0.98) but there were increases in alpha band dynamics (p = 0.0027, 25 STNs). Similar results were observed in a smaller cohort out to 5 years. There was no increase in the MDS-UPDRS III score. Interpretation. These findings provide evidence that the beta oscillopathy does not substantially progress following combined STN DBS plus medication in moderate to advanced Parkinson's disease.

Single-photon avalanche diode (SPAD) detectors are of significant interest for numerous applications, including light detection and ranging (LIDAR), and quantum technologies such as quantum-key distribution and quantum information processing. Here we present a record low noise-equivalent-power (NEP) for Ge-on-Si SPADs using a pseudo-planar design, showing high detection efficiency in the short-wave infrared; a spectral region which is key for quantum technologies and hugely beneficial for LIDAR. These devices can leverage the benefits of Si avalanche layers, with lower afterpulsing compared to InGaAs/InP, and reduced cost due to Si foundry compatibility. By scaling the SPAD pixels down to 26μm diameter, a step change in performance has been demonstrated, with significantly reduced dark count rates (DCRs), and low jitter (134ps). Ge-on-Si SPADs were fabricated using photolithography techniques and characterised using time-correlated single-photon counting. The DCR reaches as low as kilocount/s at 100K for excess bias up to ~5%. This reduction in DCR enables higher temperature operation; e.g. the DCR of a 26μm diameter pixel at 150 K is approximately equivalent to a 100 μm diameter pixel at 77 K (100s of kilocounts/s). These low values of DCR, coupled with the relatively temperature independent single photon detection efficiencies (SPDE) of ~29% (at 1310nm wavelength) leads to a record low NEP of 7.7×10−17WHz−1/2. This is approximately 2 orders of magnitude lower than previous similarly sized mesa-geometry Ge-on-Si SPADs. This technology can potentially offer a lowcost, Si foundry compatible SPAD operating at short-wave infrared wavelengths, with potential applications in quantum technologies and autonomous vehicle LIDAR.

Background Previous studies suggest that use of artificial intelligence (AI) algorithms as diagnostic aids may improve the quality of skeletal age assessment, though these studies lack evidence from clinical practice. Purpose To compare the accuracy and interpretation time of skeletal age assessment on hand radiograph examinations with and without the use of an AI algorithm as a diagnostic aid. Materials and Methods In this prospective randomized controlled trial, the accuracy of skeletal age assessment on hand radiograph examinations was performed with (

Background: A high prevalence of antiphospholipid antibodies has been reported in case series of patients with neurological manifestations and COVID-19; however, the pathogenicity of antiphospholipid antibodies in COVID-19 neurology remains unclear. Methods: This single-centre cross-sectional study included 106 adult patients: 30 hospitalised COVID-neurological cases, 47 non-neurological COVID-hospitalised controls, and 29 COVID-non-hospitalised controls, recruited between March and July 2020. We evaluated nine antiphospholipid antibodies: anticardiolipin antibodies [aCL] IgA, IgM, IgG; anti-beta-2 glycoprotein-1 [aβ2GPI] IgA, IgM, IgG; anti-phosphatidylserine/prothrombin [aPS/PT] IgM, IgG; and anti-domain I β2GPI (aD1β2GPI) IgG. Findings: There was a high prevalence of antiphospholipid antibodies in the COVID-neurological (73.3%) and non-neurological COVID-hospitalised controls (76.6%) in contrast to the COVID-non-hospitalised controls (48.2%). aPS/PT IgG titres were significantly higher in the COVID-neurological group compared to both control groups (p

Mid-infrared spectroscopy in the fingerprint region of the electromagnetic spectrum is demonstrated using a Ge on Si photonic platform technology. Components that can be integrated into complete lab-on-a-chip sensors for spectroscopic identification of healthcare and security analytes is presented capable of parts per billion sensitivity.

We present a pseudo-planar geometry 26µm diameter Ge-on-Si single-photon avalanche diode (SPAD) detector with temperature insensitive single photon detection efficiency of 29.4% at 1310nm wavelength for applications including free-space LIDAR. A record low dark count rate of 104 counts/s at 125K at an excess bias of 6.6% is demonstrated, with temporal jitter reaching 134ps. The noise-equivalent power is measured to be 7.7x10-17WHz-12 which is a 2 orders of magnitude reduction when compared to comparable 25µm mesa devices. This device represents the state-of-the-art for Ge-on-Si SPADs, and highlights that these Si foundry compatible devices have enormous potential for SWIR single-photon applications.

Freezing of gait (FOG), a devastating symptom of Parkinson’s disease (PD), can be refractory to current treatments such as medication and open-loop deep brain stimulation (olDBS). Recent evidence suggests that closed-loop DBS (clDBS), using beta local field potential power from the subthalamic nucleus (STN) as the control variable, can improve tremor and bradykinesia; however, no study has investigated the use of clDBS for the treatment of FOG. In this study, we provide preliminary evidence that clDBS was superior to olDBS in reducing percent time freezing and in reducing freezing behavior (gait arrhythmicity) in a person with PD and FOG. These findings warrant further investigation into the use of clDBS to treat FOG while also minimizing the total energy delivered to maintain a therapeutic effect.

Pneumothorax is a potentially life-threatening condition that requires prompt recognition and often urgent intervention. In the ICU setting, large numbers of chest radiographs are performed and must be interpreted on a daily basis which may delay diagnosis of this entity. Development of artificial intelligence (AI) techniques to detect pneumothorax could help expedite detection as well as localize and potentially quantify pneumothorax. Open image analysis competitions are useful in advancing state-of-the art AI algorithms but generally require large expert annotated datasets. We have annotated and adjudicated a large dataset of chest radiographs to be made public with the goal of sparking innovation in this space. Because of the cumbersome and time-consuming nature of image labeling, we explored the value of using AI models to generate annotations for review. Utilization of this machine learning annotation (MLA) technique appeared to expedite our annotation process with relatively high sensitivity at the expense of specificity. Further research is required to confirm and better characterize the value of MLAs. Our adjudicated dataset is now available for public consumption in the form of a challenge.

Cerebrospinal fluid (CSF) tau and phospho-tau are well established biomarkers of Alzheimer’s disease. While these measures are conventionally referred to as ‘total tau’ (T-tau) and ‘phospho-tau’ (P-tau), several truncated and modified tau forms exist that may relay additional diagnostic information. We evaluated the diagnostic performance of an endogenous tau peptide in CSF, tau 175–190, in the phosphorylated and non-phosphorylated state. A liquid chromatography-mass spectrometry (LC-MS) method was established to measure these peptides in CSF and was used to analyze two independent clinical cohorts; the first cohort included patients with Alzheimer’s disease (AD, n = 15), Parkinson’s disease (PD, n = 15), progressive supranuclear palsy (PSP, n = 15), and healthy controls (n = 15), the second cohort included AD patients (n = 16), and healthy controls (n = 24). In both cohorts T-tau and P-tau concentrations were determined by immunoassay. While tau 175–190 and P-tau 175–190 did not differentiate the study groups, the separation of AD and controls by T-tau (area under the ROC Curve (AUC) = 95%) and P-tau (AUC = 92%) was improved when normalizing the ELISA measurements to the concentrations of the endogenous peptides: T-tau/tau 175–190 (AUC = 100%), P-tau/P-tau 175–190 (AUC = 95%). The separation between patients and controls by T-tau (AUC = 88%) and P-tau (AUC = 82%) was similarly improved in the second cohort by taking the ratios of T-tau/tau 175–190 (AUC = 97%) and P-tau/P-tau 175–190 (AUC = 98%). In conclusion, our results suggest that the performance of the AD biomarkers T-tau and P-tau could be improved by normalizing their measurements to the endogenous peptides tau 175–190 and P-tau 175–190, possibly because these endogenous tau peptides serve to normalize for physiological, and disease-independent, secretion of tau from neurons to the extracellular space and the CSF. Finally, the observations made here add to the general applicability of mass spectrometry as a tool for rapid identification and accurate quantification of biomarker candidates.

We examined the mediating role of self-control in the relation between helicopter parenting and college student school burnout and whether the relation between helicopter parenting and college student school burnout varied by parental gender. Specifically, we hypothesized that (1) there would be a positive association between helicopter parenting and school burnout through lower reports of self-control and (2) perceptions of paternal helicopter parenting would have a greater negative impact on school burnout compared to maternal helicopter parenting. In an online survey, college students (N= 427) reported on both maternal and paternal helicopter parenting, self-control, school burnout, and demographics. Results from structural equation modeling suggested that self-control fully mediated the relation between perceptions of maternal helicopter parenting and feelings of school burnout, and partially mediated the relation between perceptions of paternal helicopter parenting and school burnout. Further, perceptions of paternal helicopter parenting had a stronger direct association with college student school burnout compared to perceptions of maternal helicopter parenting. The results of our study suggest that helicopter parenting behaviors may hinder the development of self-control skills among emerging adult college students, which are associated with feelings of school burnout. Further, helicopter fathers may have a more direct negative impact on college students’ feelings of school burnout than helicopter mothers due to violating their child’s expectations of the typical fathering role. The implications of the findings for practices in higher education were also discussed.

Purpose Radiology-pathology correlation has long been foundational to continuing education, peer learning, quality assurance, and multidisciplinary patient care. The objective of this study was to determine whether modern deep-learning language-modeling techniques could reliably match pathology reports to pertinent radiology reports. Methods The recently proposed Universal Language Model Fine-Tuning for Text Classification methodology was used. Two hundred thousand radiology and pathology reports were used for adaptation to the radiology-pathology space. One hundred thousand candidate radiology-pathology pairs, evenly split into match and no-match categories, were used for training the final binary classification model. Matches were defined by a previous-generation artificial intelligence anatomic concept radiology-pathology correlation system. Results The language model rapidly adapted very closely to the prior anatomic concept-matching approach, with 100% specificity, 65.1% sensitivity, and 73.7% accuracy. For comparison, the previous methodology, which was intentionally designed to be specific at the expense of sensitivity, had 98.0% specificity, 65.1% sensitivity, and 73.2% accuracy. Conclusions Modern deep-learning language-modeling approaches are promising for radiology-pathology correlation. Because of their rapid adaptation to underlying training labels, these models advance previous artificial intelligence work in that they can be continuously improved and tuned to improve performance and adjust to user and site-level preference.

A Bloom filter is a special case of an artificial neural network with two layers. Traditionally, it is seen as a simple data structure supporting membership queries on a set. The standard Bloom filter does not support the delete operation, and therefore, many applications use a counting Bloom filter to enable deletion. This paper proposes a generalization of the counting Bloom filter approach, called “autoscaling Bloom filters”, which allows adjustment of its capacity with probabilistic bounds on false positives and true positives. Thus, by relaxing the requirement on perfect true positive rate, the proposed autoscaling Bloom filter addresses the major difficulty of Bloom filters with respect to their scalability. In essence, the autoscaling Bloom filter is a binarized counting Bloom filter with an adjustable binarization threshold. We present the mathematical analysis of its performance and provide a procedure for minimizing its false positive rate., Neural Computing and Applications, 32 (8), ISSN:1433-3058, ISSN:0941-0643

Exposure of the lenses to direct ionizing radiation during computed tomography (CT) examinations predisposes patients to cataract formation and should be avoided when possible. Avoiding such exposure requires positioning and other maneuvers by technologists that can be challenging. Continuous feedback has been shown to sustain quality improvement and can remind and encourage technologists to comply with these methods. Previously, for use cases such as this, cumbersome manual techniques were required for such feedback. Modern deep learning methods utilizing convolutional neural networks (CNNs) can be used to develop models that can detect lenses in CT examinations. These models can then be used to facilitate automatic and continuous feedback to sustain technologist performance for this task, thus contributing to higher quality patient care. This continuous evaluation for quality purposes also surfaces other operational or process-based challenges that can be addressed. Given high-performance characteristics, these models could also be used for other tasks such as population health research.

Alzheimer disease (AD) is one of several neurodegenerative diseases characterized by dysregulation, misfolding and accumulation of specific proteins in the CNS. The stable isotope labelling kinetics (SILK) technique is based on generating amino acids labelled with naturally occurring stable (that is, nonradioactive) isotopes of carbon and/or nitrogen. These labelled amino acids can then be incorporated into proteins, enabling rates of protein production and clearance to be determined in vivo and in vitro without the use of radioactive or chemical labels. Over the past decade, SILK studies have been used to determine the turnover of key pathogenic proteins amyloid-β (Aβ), tau and superoxide dismutase 1 (SOD1) in the cerebrospinal fluid of healthy individuals, patients with AD and those with other neurodegenerative diseases. These studies led to the identification of several factors that alter the production and/or clearance of these proteins, including age, sleep and disease-causing genetic mutations. SILK studies have also been used to measure Aβ turnover in blood and within brain tissue. SILK studies offer the potential to elucidate the mechanisms underlying various neurodegenerative disease mechanisms, including neuroinflammation and synaptic dysfunction, and to demonstrate target engagement of novel disease-modifying therapies.

In the first multicentre, international longitudinal investigation of disease progression in posterior cortical atrophy, Firth et al. reveal distinct trajectories of cognitive decline and patterns of tissue loss compared to typical Alzheimer’s disease. Mapping this heterogeneity provides a basis for understanding the factors underlying variability in neurodegenerative disease expression., Posterior cortical atrophy is a clinico-radiological syndrome characterized by progressive decline in visual processing and atrophy of posterior brain regions. With the majority of cases attributable to Alzheimer’s disease and recent evidence for genetic risk factors specifically related to posterior cortical atrophy, the syndrome can provide important insights into selective vulnerability and phenotypic diversity. The present study describes the first major longitudinal investigation of posterior cortical atrophy disease progression. Three hundred and sixty-one individuals (117 posterior cortical atrophy, 106 typical Alzheimer’s disease, 138 controls) fulfilling consensus criteria for posterior cortical atrophy-pure and typical Alzheimer’s disease were recruited from three centres in the UK, Spain and USA. Participants underwent up to six annual assessments involving MRI scans and neuropsychological testing. We constructed longitudinal trajectories of regional brain volumes within posterior cortical atrophy and typical Alzheimer’s disease using differential equation models. We compared and contrasted the order in which regional brain volumes become abnormal within posterior cortical atrophy and typical Alzheimer’s disease using event-based models. We also examined trajectories of cognitive decline and the order in which different cognitive tests show abnormality using the same models. Temporally aligned trajectories for eight regions of interest revealed distinct (P < 0.002) patterns of progression in posterior cortical atrophy and typical Alzheimer’s disease. Patients with posterior cortical atrophy showed early occipital and parietal atrophy, with subsequent higher rates of temporal atrophy and ventricular expansion leading to tissue loss of comparable extent later. Hippocampal, entorhinal and frontal regions underwent a lower rate of change and never approached the extent of posterior cortical involvement. Patients with typical Alzheimer’s disease showed early hippocampal atrophy, with subsequent higher rates of temporal atrophy and ventricular expansion. Cognitive models showed tests sensitive to visuospatial dysfunction declined earlier in posterior cortical atrophy than typical Alzheimer’s disease whilst tests sensitive to working memory impairment declined earlier in typical Alzheimer’s disease than posterior cortical atrophy. These findings indicate that posterior cortical atrophy and typical Alzheimer’s disease have distinct sites of onset and different profiles of spatial and temporal progression. The ordering of disease events both motivates investigation of biological factors underpinning phenotypic heterogeneity, and informs the selection of measures for clinical trials in posterior cortical atrophy.

Develop a highly accurate deep learning model to reliably classify radiographs by laterality. Digital Imaging and Communications in Medicine (DICOM) data for nine body parts was extracted retrospectively. Laterality was determined directly if encoded properly or inferred using other elements. Curation confirmed categorization and identified inaccurate labels due to human error. Augmentation enriched training data to semi-equilibrate classes. Classification and object detection models were developed on a dedicated workstation and tested on novel images. Receiver operating characteristic (ROC) curves, sensitivity, specificity, and accuracy were calculated. Study-level accuracy was determined and both were compared to human performance. An ensemble model was tested for the rigorous use-case of automatically classifying exams retrospectively. The final classification model identified novel images with an ROC area under the curve (AUC) of 0.999, improving on previous work and comparable to human performance. A similar ROC curve was observed for per-study analysis with AUC of 0.999. The object detection model classified images with accuracy of 99% or greater at both image and study level. Confidence scores allow adjustment of sensitivity and specificity as needed; the ensemble model designed for the highly specific use-case of automatically classifying exams was comparable and arguably better than human performance demonstrating 99% accuracy with 1% of exams unchanged and no incorrect classification. Deep learning models can classify radiographs by laterality with high accuracy and may be applied in a variety of settings that could improve patient safety and radiologist satisfaction. Rigorous use-cases requiring high specificity are achievable.

In modern times, scholars have widely regarded early Elizabethan tragedy, like Thomas Norton and Thomas Sackville’s Gorboduc (1561/62) and its successors at the Inns of Court, as verbose and unlyrical. Those criticisms may reflect an incomplete understanding of the original performance tradition, however. Like Senecan tragedies from this period, those plays include act-ending choruses, mostly in pentameter and in various stanza configurations. This study proposes that in the English tragedies, at least, those choruses were very likely sung, most probably to tunes from the emerging repertoire of metrical psalms. These findings would significantly affect the character of such plays and how they are perceived by scholars and audiences alike.

Single-Photon Avalanche Diode (SPAD) detectors are of significant interest for a range of applications [1] , in particular for quantum technologies (e.g. quantum-key distribution, quantum information processing), and light detection and ranging (LIDAR) for defence, terrain mapping, and autonomous vehicles. These applications either require, or benefit from, operation at wavelengths in the short-wave infrared (SWIR). Previous SWIR single-photon LIDAR has typically used InGaAs/InP SPAD detector technology, which has relatively low efficiency and suffers from afterpulsing. Previously, a pseudo-planar design for a Ge-on-Si SPAD was demonstrated [2] , yielding a huge improvement in performance for Ge-on-Si SPADs at 1310 nm and demonstrating the potential for Si foundry compatible SWIR SPADs. Furthermore, reduced afterpulsing was demonstrated compared to a commercial InGaAs/InP device when measured in nominally identical conditions. Here we present a further step change in performance, with reduced dark count rate (DCR), record low noise-equivalent-power (NEP) and low jitter by scaling the technology and developing 26 µm diameter pixels [3] .

Reports of Guillain-Barré syndrome (GBS) have emerged during the Coronavirus disease 2019 (COVID-19) pandemic. This epidemiological and cohort study sought to investigate any causative association between COVID-19 infection and GBS. The epidemiology of GBS cases reported to the UK National Immunoglobulin Database was studied from 2016 to 2019 and compared to cases reported during the COVID-19 pandemic. Data were stratified by hospital trust and region, with numbers of reported cases per month. UK population data for COVID-19 infection were collated from UK public health bodies. In parallel, but separately, members of the British Peripheral Nerve Society prospectively reported incident cases of GBS during the pandemic at their hospitals to a central register. The clinical features, investigation findings and outcomes of COVID-19 (definite or probable) and non-COVID-19 associated GBS cases in this cohort were compared. The incidence of GBS treated in UK hospitals from 2016 to 2019 was 1.65–1.88 per 100 000 individuals per year. GBS incidence fell between March and May 2020 compared to the same months of 2016–19. GBS and COVID-19 incidences during the pandemic also varied between regions and did not correlate with one another (r = 0.06, 95% confidence interval: −0.56 to 0.63, P = 0.86). In the independent cohort study, 47 GBS cases were reported (COVID-19 status: 13 definite, 12 probable, 22 non-COVID-19). There were no significant differences in the pattern of weakness, time to nadir, neurophysiology, CSF findings or outcome between these groups. Intubation was more frequent in the COVID-19 affected cohort (7/13, 54% versus 5/22, 23% in COVID-19-negative) attributed to COVID-19 pulmonary involvement. Although it is not possible to entirely rule out the possibility of a link, this study finds no epidemiological or phenotypic clues of SARS-CoV-2 being causative of GBS. GBS incidence has fallen during the pandemic, which may be the influence of lockdown measures reducing transmission of GBS inducing pathogens such as Campylobacter jejuni and respiratory viruses.

Objectives: The coronavirus disease 2019 (COVID-19) pandemic has created several challenges for residency programs and prospective interns alike during the upcoming application cycle, including the cancellation of away sub-internships and in-person interviews. Given prior research documenting that applicants' application and ranking decisions are significantly influenced by residency webpages, a potential solution to the loss of in-person experiences during the pandemic is the expansion of residency programs' online presence through their program websites, provision of virtual grand rounds and pseudo-away rotations, and enhancement of virtual interviews. This study seeks to summarize the existing literature on these areas and provide concrete suggestions for improving programs' virtual presence.Methods: The authors summarize earlier literature querying the content of program websites across 14 medical specialties, which documented significant gaps in the content of interest to applicants.Results: Among 14 analyzed specialties, the majority of programs had a functional website (>90%), with the exception of interventional radiology (73.9%). However, significant gaps in content were documented, with the percentage of content variables contained on websites ranging from 33.3% to 70.5% (median = 47.0%, interquartile range = 37.8-52.6%). Program websites were also limited by underrepresentation of content most valued by applicants as well as potential areas of inaccurate or outdated information.Conclusions: There are several interventions programs can undertake to address existing gaps in online presence. During an application cycle facing unprecedented resource strain, bolstering the online presence of programs may facilitate an improved fit between programs and future residents.

Preliminary pathological and biomarker data suggest that SARS-CoV-2 infection can damage the nervous system. To understand what, where and how damage occurs, we collected serum and CSF from patients with COVID-19 and characterised neurological syndromes involving the peripheral and central nervous system (n = 34). We measured biomarkers of neuronal damage and neuroinflammation, and compared these with non-neurological control groups, which included patients with (n = 94) and without (n = 24) COVID-19. We detected increased concentrations of neurofilament light, a dynamic biomarker of neuronal damage, in the CSF of those with central nervous system inflammation (encephalitis and acute disseminated encephalomyelitis) (14800pg/mL [400, 32400]), compared to those with encephalopathy (1410pg/mL [756, 1446], peripheral syndromes (GBS) (740pg/mL [507, 881]) and controls (872pg/mL [654,1200]). Serum neurofilament light levels were elevated across patients hospitalised with COVID-19, irrespective of neurological manifestations. There was not the usual close correlation between CSF and serum neurofilament light, suggesting serum neurofilament light elevation in the non-neurological patients may reflect peripheral nerve damage in response to severe illness. We did not find significantly elevated levels of serum neurofilament light in community cases of COVID-19 arguing against significant neurological damage. Glial fibrillary acidic protein, a marker of astrocytic activation, was not elevated in the CSF or serum of any group, suggesting astrocytic activation is not a major mediator of neuronal damage in COVID-19., Graphical Abstract Graphical Abstract

In the last decade, silicon photonics has undergone an impressive development driven by an increasing number of technological applications. Plasmonics has not yet made its way to the microelectronic industry, mostly because of the lack of compatibility of typical plasmonic materials with foundry processes. In this framework, we have developed a plasmonic platform based on heavily n-doped Ge grown on silicon substrates. We developed growth protocols to reach n-doping levels exceeding 1020 cm-3, allowing us to tune the plasma wavelength of Ge in the 3-15 μm range. The plasmonic resonances of Ge-on-Si nanoantennas have been predicted by simulations, confirmed by experimental spectra and exploited for molecular sensing. Our work represents a benchmark for group-IV mid-IR plasmonics.

A broad diversity of biological organisms and systems interact with soil in ways that facilitate their growth and survival. These interactions are made possible by strategies that enable organisms to accomplish functions that can be analogous to those required in geotechnical engineering systems. Examples include anchorage in soft and weak ground, penetration into hard and stiff subsurface materials and movement in loose sand. Since the biological strategies have been ‘vetted’ by the process of natural selection, and the functions they accomplish are governed by the same physical laws in both the natural and engineered environments, they represent a unique source of principles and design ideas for addressing geotechnical challenges. Prior to implementation as engineering solutions, however, the differences in spatial and temporal scales and material properties between the biological environment and engineered system must be addressed. Current bio-inspired geotechnics research is addressing topics such as soil excavation and penetration, soil–structure interface shearing, load transfer between foundation and anchorage elements and soils, and mass and thermal transport, having gained inspiration from organisms such as worms, clams, ants, termites, fish, snakes and plant roots. This work highlights the potential benefits to both geotechnical engineering through new or improved solutions and biology through understanding of mechanisms as a result of cross-disciplinary interactions and collaborations.

An analysis of fentanyl analog exposures in Cook County, Illinois recently published in this journal by Chhabra et al. found that more than half of urine drug screens detected at least one fentanyl...

The performance of planar geometry Ge-on-Si single-photon avalanche diode detectors of 26 µ m diameter is presented. Record low dark count rates are observed, remaining less than 100 K counts per second at 6.6% excess bias and 125 K. Single-photon detection efficiencies are found to be up to 29.4%, and are shown to be temperature insensitive. These performance characteristics lead to a significantly reduced noise equivalent power (NEP) of 7.7 × 10 − 17 W H z − 1 2 compared to prior planar devices, and represent a two orders of magnitude reduction in NEP compared to previous Ge-on-Si mesa devices of a comparable diameter. Low jitter values of 134 ± 10 p s are demonstrated.

Background Patients with opioid use disorder (OUD) frequently present to the emergency department for acute treatment of overdose and withdrawal. Case presentation A 29-year-old male presented to the emergency room after intravenous heroin use followed by accidental ingestion of naltrexone. He was treated with buprenorphine with significant improvement in his Clinical Opioid Withdrawal Score, from moderately severe to mild withdrawal symptoms within a few hours. Conclusion Buprenorphine and minimal supportive care can be used to treat acute withdrawal precipitated by oral naltrexone in patients with OUD.

The addition of germanium to Si-based single-photon avalanche diode (SPAD) detectors can significantly increase the spectral range of these devices into the into the strategically important short-wave infrared (SWIR) region. We present the performance characteristics of small area (26 μm and 50 µm diameter) planar geometry Ge-on-Si SPAD detectors. There are many advantages for operating such SPAD detection in the SWIR region, these include: reduced eye-safety laser threshold, longer measurable ranges, improved depth resolution in range finding applications; and improved capability for imaging through obscurants such as precipitation and smoke. The time-correlated single-photon counting (TCSPC) technique has been utilized for the measurement of record low dark count rates (DCRs) and high single-photon detection efficiency. Specifically, the 26 µm diameter devices maintained DCR values < 100 kHz up to a temperature of 125 K for excess biases up to 6.6 %. The 50 µm diameter device consistently demonstrated DCRs a factor of approximately 4 times greater than 26 µm diameter devices, under identical operating conditions of excess bias and temperature, illustrating a dark count rate in proportion to the device volume. Single-photon detection efficiencies were found to reach a maximum of ~ 29 %, measured at a wavelength of 1310 nm and a temperature of 125 K. Due the record low dark currents observed, noise equivalent power values (NEP) down to 7.7 × 10-17 WHz-1/2 are obtained, significantly reduced when compared to both previous mesa geometry and larger area planar geometry Ge-on-Si SPADs, indicating much improved optical sensitivity levels attainable with these planar geometry devices. In addition to this, high speed operation was demonstrated, quantified by jitter values down to 134 ± 10 ps at a temperature of 100 K. These results demonstrate the potential of these devices for highly sensitive and high-speed LIDAR imaging in the SWIR.

BackgroundPoor diet and lack of physical activity are strongly linked to non-communicable disease risk, but modifying them is challenging. There is increasing recognition that adolescence is an important time to intervene; habits formed during this period tend to last, and physical and psychological changes during adolescence make it an important time to help individuals form healthier habits. Improving adolescents’ health behaviours is important not only for their own health now and in adulthood, but also for the health of any future children. Building on LifeLab - an existing, purpose-built educational facility at the University of Southampton - we have developed a multi-component intervention for secondary school students called Engaging Adolescents in Changing Behaviour (EACH-B) that aims to motivate and support adolescents to eat better and be more physically active.MethodsA cluster randomised controlled trial is being conducted to evaluate the effectiveness of the EACH-B intervention. The primary outcomes of the intervention are self-reported dietary quality and objectively measured physical activity (PA) levels, both assessed at baseline and at 12-month follow-up. The EACH-B intervention consists of three linked elements: professional development for teachers including training in communication skills to support health behaviour change; the LifeLab educational module comprising in-school teaching of nine science lessons linked to the English National Curriculum and a practical day visit to the LifeLab facility; and a personalised digital intervention that involves social support and game features that promote eating better and being more active. Both the taught module and the LifeLab day are designed with a focus on the science behind the messages about positive health behaviours, such as diet and PA, for the adolescents now, in adulthood and their future offspring, with the aim of promoting personal plans for change. The EACH-B research trial aims to recruit approximately 2,300 secondary school students aged 12-13 years from 50 schools (the clusters) from Hampshire and neighbouring counties. Participating schools will be randomised to either the control or intervention arm. The intervention will be run during two academic years, with continual recruitment of schools throughout the school year until the sample size is reached. The schools allocated to the control arm will receive normal schooling but will be offered the intervention after data collection for the trial is complete. An economic model will be developed to assess the cost-effectiveness of the EACH-B intervention compared with usual schooling.DiscussionAdolescents’ health needs are often ignored and they can be difficult to engage in behaviour change. Building a cheap, sustainable way of engaging them in making healthier choices will benefit their long-term health and that of their future children.Trial registrationEACH-B is a cluster randomised controlled trial (ISRCTN 74109264, registered 30th August 2019), funded by the National Institute for Health Research (RP-PG-0216-20004).

BackgroundReports of Guillain-Barré Syndrome (GBS) have emerged during the Coronavirus Disease 2019 (COVID-19) pandemic. This epidemiological and cohort study sought to investigate any causative association between COVID-19 infection and GBS.MethodsThe epidemiology of GBS cases reported via the UK National Immunoglobulin Database were studied from 2016-2019 and compared to cases reported during the COVID-19 pandemic. For the cohort study, members of the British Peripheral Nerve Society reported all cases of GBS during the pandemic. The clinical features, investigation findings and outcomes of COVID-19 (definite or probable) and non-COVID-19 associated GBS cases were compared.ResultsThe UK GBS incidence from 2016-2019 was 1.65-1.88 per 100,000 people per year. GBS and COVID-19 incidence varied between regions and did not correlate (r = 0.06, 95% CI −0.56 to 0.63, p=0.86). GBS incidence fell between March and May 2020 compared to the same months of 2016-2019. Forty-seven GBS cases were included in the cohort study (13 definite, 12 probable COVID-19 and 22 non-COVID-19). There were no significant differences in the pattern of weakness, time to nadir, neurophysiology, CSF findings or outcome. Intubation was more frequent in the COVID-19+ve cohort (7/13, 54% vs 5/22, 23% in COVID negative) thought to be related directly to COVID-19 pulmonary involvement.ConclusionsThis study finds no epidemiological or phenotypic clues of SARS-CoV-2 being causative of GBS. GBS incidence has fallen during the pandemic which may be the influence of lockdown measures reducing transmission of GBS inducing pathogens such as Campylobacter jejuni and respiratory viruses.