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ObjectivesInterest in less than full-time training (LTFT) is growing among doctors in training. LTFT applications have previously been limited to fulfilment of specific criteria such as childcare or health reasons, but Health Education for England (HEE) has recently completed a pilot into LTFT for a third category: lifestyle choice. This was recognised as an opportunity to canvas trainee perspectives and intentions on LTFT and implications for workforce planning.DesignA cross-sectional study of UK trainees via an online questionnaire.Setting/participantsThe survey was distributed via email to trainees in all specialities and stages of training across the UK. The survey focused on three key themes: experiences of current LTFT trainees, perspectives of trainees considering LTFT in the future and experience of working with LTFT colleagues.ResultsResponses were received from 783 trainees across the UK, with most responses received from physician trainees (76%). Current LTFT trainees represented one-third of respondents. Of those not currently working LTFT, 75% expressed an intention to do so in future with lifestyle being the most common reason. Almost half of this group were concerned about the impact on their training. Stigma, reduced training opportunities, prolonged training and the application process were commonly cited barriers. These difficulties were experienced by several current LTFT trainees, 32% of whom described a negative impact on their training. Almost two-thirds (62%) of respondents stated they wish to work LTFT as a consultant.ConclusionSystems must adapt to increase access to LTFT training to promote trainee well-being and retention. Progress is being made and we suggest HEE’s category three pilot be rolled out across the UK as a priority. Workforce planning needs to consider the substantial rise in popularity of LTFT among trainees to offset any shortfalls in the present and future workforce.

Background A specialist neurological infectious disease service has been run jointly by the departments of infectious disease and neurology at the Royal Liverpool University Hospital since 2005. We sought to describe the referral case mix and outcomes of the first six years of referrals to the service. Methods Retrospective service review. Results Of 242 adults referred to the service, 231 (95 %) were inpatients. Neurological infections were confirmed in 155 (64 %), indicating a high degree of selection before referral. Viral meningitis (35 cases), bacterial meningitis (33) and encephalitis (22) accounted for 38 % of referrals and 61 % of confirmed neurological infections. Although an infrequent diagnosis ( n  = 19), neurological TB caused the longest admission (median 23, range 5 – 119 days). A proven or probable microbiological diagnosis was found in 100/155 cases (64.5 %). For the whole cohort, altered sensorium, older age and longer hospital stay were associated with poor outcome (death or neurological disability); viral meningitis was associated with good outcome. In multivariate analysis altered sensorium remained significantly associated with poor outcome, adjusted odds ratio 3.04 (95 % confidence interval 1.28 – 7.22, p  = 0.01). Conclusions A service of this type provides important specialist care and a focus for training and clinical research on complex neurological infections.

Reframing beliefs about illness, along with specialist rehabilitation, can help recovery in people with severe ME/CFS, write Alastair Miller, Fiona Symington, Paul Garner, and Maria Pedersen

BackgroundTumor Immuno-MicroEnvironment (TIME) is characterized by a heterogeneous interplay of cellular and molecular components. For the TIME of cancers caused by virus infection, the comparison of immune cells close to and far from viral infection, crucial for understanding localized immune response and developing targeted therapies, has not been not possible to investigate until the advent of spatial transcriptomic techniques. Here, we proposed a methodology to localize viral infection sites using SpaTial Enhanced REsolution Omics-sequencing (Stereo-seq, figure 1)1 data of virus-associated cancers. We used Epstein-Barr virus (EBV)-associated Nasophayngeal Carcinoma (NPC)2 3 and human Hepatitis B Virus (HBV)-associated hepatocellular carcinoma (HCC)4 5 as two paradigmatic examples to show the robustness of this methodology.MethodsWe ran Stereo-seq experimental protocol separately for fresh-frozen NPC and LELC samples. In each run, we added one virus-free cancer fresh-frozen sample as control Given the low read depth per Nanoball, a grid of 100 x 100 Nanoballs (BIN100) were used as the unit of analysis to ensure that there is sufficient read depth (figure 1A). For each BIN100, we used Stereo-Seq Analysis Workflow (SAW) pipeline v5.5.2 to align its reads to human genome GRCh38.p13 for Seurat cell clustering and cell type annotation using Bioconductor packages EasyCellType and SingleR. We used STAR v2.7.10b to map reads unaligned from human genome to EBV-1 or HBV genome from NCBI RefSeq. Finally, we superimposed BIN100 with high viral reads onto SAW-registered ssDNA fluorescent-staining image. In addition, we performed Hematoxylin and Eosin (H&E) staining, and QuPath tissue annotation.ResultsThe overlaid images of virus-positive BIN100s and ssDNA tissue-staining illustrated a clear contrast between virus-positive and virus-negative cancer samples (figures 2A, 3A). Most EBV1-positive BIN100s are in the invasive margin indicated by QuPath annotation of H&E staining (figure 2A-B), which is as expected. Most cells surrounding viral infection sites are B cell/plasma cell clusters 5,9,14 for NPC, and monocyte clusters 13,14,15 for HCC (figures 2,3). The clear separation of these clusters from other immune cell clusters illustrates that the distance to viral infection site significantly alters the gene expression profile of immune cells.ConclusionsOur study proposes a powerful virus localization method to uncover the fine structure of TIME contributed by virus infection. In future, we will test this method with more NPC and HCC samples, and more virus-free cancer samples to validate its robustness. We will continue differential gene expression analysis for immune cell clusters with different distance to viral infection sites.ReferencesChen A, Liao S, Cheng M, Ma K, Wu L, Lai Y, et al. Spatiotemporal transcriptomic atlas of mouse organogenesis using DNA nanoball-patterned arrays. Cell. 2022;185:1777–1792.Young L, Yap L, Murray P. Epstein-Barr virus: more than 50 years old and still providing surprises. Nat Rev Cancer. 2016;16:789–802.Png YT, Yang AZY, Lee MY, Chua MJM, Lim CM. The Role of NK Cells in EBV Infection and EBV-Associated NPC. Viruses. 2021 Feb 15;13(2):300.Chen CJ, Yang HI, Su J, Jen CL, You SL, Lu SN, Huang GT, Iloeje UH; REVEAL-HBV Study Group. Risk of hepatocellular carcinoma across a biological gradient of serum hepatitis B virus DNA level. JAMA. 2006 Jan 4;295(1):65–73.Liu Z, Jiang Y, Yuan H, Fang Q, Cai N, Suo C, Jin L, Zhang T, Chen X. The trends in incidence of primary liver cancer caused by specific etiologies: Results from the Global Burden of Disease Study 2016 and implications for liver cancer prevention. J Hepatol. 2019 Apr;70(4):674–683.Ethics ApprovalThis study was approved by the Agency of Science, Technology and Research (A*STAR) Human Biomedical Research Office (A*STAR IRB: 2021-161, 2021-188, 2021-112).ConsentDe-identified patient data was used in our work. Samples were collected with consent from patients.Abstract 1512 Figure 1Briefing of Stereo-seq technology and applying Stereo-seq experimental workflow. (A) Graphical illustration of Stereo-seq chip - a DNA nanoball (DNB)-patterned array with sub-cellular resolution. Circular templates with different coordinate identifier (CID) are amplified separately by rolling circle amplification (RCR) using Phi29 DNA polymerase to form DNA nanoballs in separate spots. The diameter of each spot is 220nm, and the spot center-center distance is 500nm. In SAW data analysis, a square bin (BIN1) contains spot, sequencing reads in the spot and surrounding area. To decrease sequencing error in one single spot due to low read counts in each BIN, users typically need to combine adjacent square bins into a large square bin (BIN3 = 3x3 BIN1 spots in this illustration). Most studies use BIN50, BIN100 or BIN200. (B) The mechanism of Stereo-seq experimental protocol. First, oligonucleotide probes with CID, UMI and Poly-T are fixed onto the spot by hybridizing with circular single-stranded DNAs in the fixed DNB. Next, fresh-frozen tissue slide is placed onto the Stereo-seq chip, and tissue poly(A) mRNAs are hybridized with fixed oligoucleotide probes. The subsequent steps include reverse transcription, amplification, cDNA library preparation and sequencing. The sequencing machine will export the raw results as fastq files. Finally, Stereo-seq Analysis Workflow (SAW) will use a chip-specific mask file to convert CIDs to x and y coordinates on the chip to enable spatial localization of sequencing reads. (C) Stereo-seq results are pair-ended. For each read pair, read 1 contains CID and UMI information. Read 2 contains 100nt sequence of the cDNA of interest. (D) To date, the Stereo-seq experimental protocol does not allow applying hematoxylin and eosin (H&E) staining and in-situ sequencing on the same tissue section. Instead, H&E staining needs to be done on a nearby tissue section. The caveat of this two-layer protocol is that for some fragile tissue blocks, the outline of the tissue may be discordant (see figure 3). The next version of experiment protocol will resolve this issue and allows applying H&E staining and in-situ sequencing on the same tissue sectionAbstract 1512 Figure 2Applying Stereo-seq experimental protocol, Stereo-seq Bioinformatics workflow (SAW) to an Epstein-Barr Virus (EBV)-positive Nasopharyngeal Carcinoma (NPC) sample. (A) Localization of EBV-1 reads on Stereo-seq chip indicated clear contrast between EBV-positive and EBV-negative cancers. We did Stereo-seq analysis for the EBV-positive NPC sample and an EBV- negative Oral Squamous Cell Carcinoma (OSCC). Red circle illustrates 10 BIN100 with the highest number of reads (≥100) reads mapped to EBV-1 genome. The background image is the ssDNA fluorescence image from the tissue section for in-situ sequencing, instead of the tissue section undergone H&E staining (figure 1D). (B) QuPath annotation (bottom) of the H&E image (top) of the NPC sample. Tumor region is colored as red in QuPath image and dark purple in H&E staining; while stroma is colored as green in QuPath image and light pink in H&E staining. (C) 18 clusters of BIN100s were found using function ‘Seurat::FindClusters(resolution=2.2)’. All clusters were contiguous in coordinate space. (D) Seurat clusters of NPC sample show clear localization relative to QuPath annotation. All the clusters are only or mostly located in tumor, stroma or invasive margin: Cluster 2,3,4,15 primarily represented stroma (colored in green); clusters 5,9,14 which were primarily plasma cells were primarily represented invasive margin (boundary between stroma and tumor area, colored in orange); the rest of the 4 clusters were colored in red. (E) t-SNE plot showed 18 clusters generated by Seurat were contiguous in the t-SNE space. Contiguity in both coordinate space (panel C) and t-SNE space (this panel) indicated high quality of the clustering result. (F) t-SNE plot of the clusters located in tumor, stroma, and invasive margin (tumor - red, invasive margin - orange, stroma - green)Abstract 1512 Figure 3Applying Stereo-seq experimental protocol, Stereo-seq Bioinformatics workflow (SAW) to a Human Hepatitis B Virus (HBV)-positive HepatoCellular Carcinoma (HCC) sample. (A) Localization of HBV reads on Stereo-seq chip indicated clear contrast between HBV-positive and HBV-negative cancers. We did Stereo-seq analysis for the HBV-positive HCC sample and an HBV-negative ColoRectal Carcinoma (CRC). Red circle illustrates the BIN100 with top 9 reads (≥360) mapped to HBV genome. The background image is ssDNA fluorescence image from the tissue section for in-situ sequencing. We accept some BIN100s to be outside of the tissue contour of the ssDNA image at the top-right of the panel, because tissue morphologies are different on the two sections. Panels A and C together showed that clusters 13,15 (monocyte) and cluster 14 (hepatocyte) are most proximal to HBV infection sites. The concentration of cluster 13 and 15 around HBV infection site illustrated that Seurat clustering successfully distinguishes monocytes proximate to and remote to HBV site. (B) QuPath annotation (left) of the H&E image (right) of the HCC sample. Tumor region is colored as red in QuPath image and dark purple in H&E staining; while stroma is colored as green in QuPath image and light pink in H&E staining. The outline of the HCC tissue on the H&E image is different from the outline of HCC tissue on the Stereo-seq chip due to the limitation of the current experiment protocol (figure 1D). (C) We found 19 clusters of BIN100s using function ‘Seurat::FindClusters(resolution= 2.2)’. Cell types annotation was done by Bioconductor packages EasyCellType and SlngleR. All clusters were contiguous in coordinate space. (D) The localization BIN100 clusters with respect to the QuPath annotation is not so apparent as what it is in NPC sample. Here we did a manual localization: clusters 1,5,9–12,16–18 primarily represented str

Chronic fatigue syndrome/myalgic encephalomyelitis (CFS/ME) is a disabling long-term condition of unknown cause. The National Institute for Health and Care Excellence (NICE) published a guideline in 2021 that highlighted the seriousness of the condition, but also recommended that graded exercise therapy (GET) should not be used and cognitive–behavioural therapy should only be used to manage symptoms and reduce distress, not to aid recovery. This U-turn in recommendations from the previous 2007 guideline is controversial.We suggest that the controversy stems from anomalies in both processing and interpretation of the evidence by the NICE committee. The committee: (1) created a new definition of CFS/ME, which ‘downgraded’ the certainty of trial evidence; (2) omitted data from standard trial end points used to assess efficacy; (3) discounted trial data when assessing treatment harm in favour of lower quality surveys and qualitative studies; (4) minimised the importance of fatigue as an outcome; (5) did not use accepted practices to synthesise trial evidence adequately using GRADE (Grading of Recommendations, Assessment, Development and Evaluations trial evidence); (6) interpreted GET as mandating fixed increments of change when trials defined it as collaborative, negotiated and symptom dependent; (7) deviated from NICE recommendations of rehabilitation for related conditions, such as chronic primary pain and (8) recommended an energy management approach in the absence of supportive research evidence.We conclude that the dissonance between this and the previous guideline was the result of deviating from usual scientific standards of the NICE process. The consequences of this are that patients may be denied helpful treatments and therefore risk persistent ill health and disability.

Creatine phosphokinase (CPK) estimations are done routinely in some HIV clinics, irrespective of patient symptoms. We studied patients attending the Worcestershire HIV clinic between 1987 and 2001 to identify whether routine elevations in serum levels of CPK in patients with HIV were associated with clinical features of muscle disease (CFMD), and whether such elevations influenced patient management. There was no association between CFMD and a rise in CPK. Major rises of CPK >400 IU/L were significantly associated with CFMD. No individual had a persistent CPK rise >200 IU/L without CFMD. In the great majority of cases, there was no change in management consequent to enzyme rises. In patients with HIV infection and no CFMD, the chance of finding a major and persistent CPK rise is low. This study does not support the practice of routine monitoring of CPK in asymptomatic patients attending HIV clinics.

BackgroundSurvivors of bacterial meningitis are known to suffer neuropsychological deficits after their acute illness. Previous studies have suggested that viral meningitis may also cause cognitive problems.Primary and ObjectiveWe aimed to determine what the neuropsychological problems encountered by adults with viral meningitis were compared to healthy patients without meningitis and how long their problems lasted.MethodsPatients with viral meningitis and healthy controls completed the ‘Aldenkamp and Baker Neuropsychological Assessment Schedule (ABNAS)’, a 24 item self-administered questionnaire. Patients completed the ABNAS at 4 time points – 6, 12, 24 and 48 weeks post acute illness. Higher ABNAS scores correspond to greater levels of neuropsychological dysfunction, with a worst score possible of 72.ResultsHealthy controls (n=224) had a mean total ABNAS score of 7. Comparatively, the patients with viral meningitis had significantly worse scores at all 4 time points. At 6 weeks scoring 22 (p<0.001) (n=73), at 12 Weeks 19.5 (p<0.001) (n=102), at 24 weeks 13.5 (p 0.002) (n=86) and at 48 weeks 16.5 (p<0.001) (n=76).ConclusionsPatients with viral meningitis have significantly worse neuropsychological deficits compared to healthy controls. The deficits showed some improvement initially but failed to improve significantly beyond 24 weeks.

BackgroundAs bacterial meningitis decreases in incidence, viral meningitis becomes relatively more important. The incidence and costs of viral meningitis in adults are unknown.MethodsAn epidemiological study of adults with suspected meningitis in England, was carried out between 2011 and 2014. We estimated incidence and healthcare costs using patient level data from the Northwest of England, and extrapolated to estimate resource use throughout the UK.ResultsAmong 1117 patients enrolled, 638 (57%) had meningitis. 231/638 (36%) had viral meningitis, 99/638 (16%) bacterial and 267/638 (42%) unknown aetiology. Estimated annual incidences of viral and bacterial meningitis were 2.73 and 1.24 per 1 00 000 respectively. The yearly healthcare cost of viral and bacterial meningitis were similar: £3,220,343 (95% CI £1,206,963 – £4,418,424) and £4,860,218 (95% CI £3,728,598 – £6,358,419) respectively, p=0.57. The median length of stay for patients with viral meningitis was 4 days, increasing to 8 days in those treated with antivirals, which are of no proven benefit. Hospitalisation accounted for 79% of the cost.ConclusionsViral meningitis is the predominant cause of meningitis in adults in the UK. The total annual healthcare costs could be reduced by earlier discharge. This might be achieved through speedier diagnostics and avoiding unnecessary treatments.