Modulation of TRPV4 protects against degeneration induced by sustained loading and promotes matrix synthesis in the intervertebral disc

While it is well known that mechanical signals can either promote or disrupt intervertebral disc (IVD) homeostasis, the molecular mechanisms for transducing mechanical stimuli are not fully understood. The transient receptor potential vanilloid 4 (TRPV4) ion channel activated in isolated IVD cells initiates extracellular matrix (ECM) gene expression, while TRPV4 ablation reduces cytokine production in response to circumferential stretching. However, the role of TRPV4 on ECM maintenance during tissue-level mechanical loading remains unknown. Using an organ culture model, we modulated TRPV4 function over both short- (hours) and long-term (days) and evaluated the IVDs\u27 response. Activating TRPV4 with the agonist GSK101 resulted in a C

Pleiotropic effects of simvastatin and losartan in preclinical models of post-traumatic elbow contracture

Elbow trauma can lead to post-traumatic joint contracture (PTJC), which is characterized by loss of motion associated with capsule/ligament fibrosis and cartilage damage. Unfortunately, current therapies are often unsuccessful or cause complications. This study aimed to determine the effects of prophylactically administered simvastatin (SV) and losartan (LS) in two preclinical models of elbow PTJC: a

The Open Source GAITOR Suite for Rodent Gait Analysis

Locomotive changes are often associated with disease or injury, and these changes can be quantified through gait analysis. Gait analysis has been applied to preclinical studies, providing quantitative behavioural assessment with a reasonable clinical analogue. However, available gait analysis technology for small animals is somewhat limited. Furthermore, technological and analytical challenges can limit the effectiveness of preclinical gait analysis. The Gait Analysis Instrumentation and Technology Optimized for Rodents (GAITOR) Suite is designed to increase the accessibility of preclinical gait analysis to researchers, facilitating hardware and software customization for broad applications. Here, the GAITOR Suite’s utility is demonstrated in 4 models: a monoiodoacetate (MIA) injection model of joint pain, a sciatic nerve injury model, an elbow joint contracture model, and a spinal cord injury model. The GAITOR Suite identified unique compensatory gait patterns in each model, demonstrating the software’s utility for detecting gait changes in rodent models of highly disparate injuries and diseases. Robust gait analysis may improve preclinical model selection, disease sequelae assessment, and evaluation of potential therapeutics

Seasonality and the effects of weather on Campylobacter infections

Background Campylobacteriosis is a major public health concern. The weather factors that influence spatial and seasonal distributions are not fully understood. Methods To investigate the impacts of temperature and rainfall on Campylobacter infections in England and Wales, cases of Campylobacter were linked to local temperature and rainfall at laboratory postcodes in the 30 days before the specimen date. Methods for investigation included a comparative conditional incidence, wavelet, clustering, and time series analyses. Results The increase of Campylobacter infections in the late spring was significantly linked to temperature two weeks before, with an increase in conditional incidence of 0.175 cases per 100,000 per week for weeks 17 to 24; the relationship to temperature was not linear. Generalized structural time series model revealed that changes in temperature accounted for 33.3% of the expected cases of Campylobacteriosis, with an indication of the direction and relevant temperature range. Wavelet analysis showed a strong annual cycle with additional harmonics at four and six months. Cluster analysis showed three clusters of seasonality with geographic similarities representing metropolitan, rural, and other areas. Conclusions The association of Campylobacteriosis with temperature is likely to be indirect. High-resolution spatial temporal linkage of weather parameters and cases is important in improving weather associations with infectious diseases. The primary driver of Campylobacter incidence remains to be determined; other avenues, such as insect contamination of chicken flocks through poor biosecurity should be explored

Multidisciplinary Service Utilization Pattern by Advanced Head and Neck Cancer Patients: A Single Institution Study

Purpose. To analyze the patterns and associations of adjunctive service visits by head and neck cancer patients receiving primary, concurrent chemoradiation therapy. Methods. Retrospective chart review of patients receiving adjunctive support during a uniform chemoradiation regimen for stages III-IV head and neck squamous cell carcinoma. Univariate and multivariate models for each outcome were obtained from simple and multivariate linear regression analyses. Results. Fifty-two consecutive patients were assessed. Female gender, single marital status, and nonprivate insurance were factors associated with an increased number of social work visits. In a multivariate analysis, female gender and marital status were related to increased social work services. Female gender and stage IV disease were significant for increased nursing visits. In a multivariate analysis for nursing visits, living greater than 20 miles between home and hospital was a negative predictive factor. Conclusion. Treatment of advanced stage head and neck cancer with concurrent chemoradiation warrants a multidisciplinary approach. Female gender, single marital status, and stage IV disease were correlated with increased utilization of social work and nursing services. Distance over 20 miles from the center was a negative factor. This information may help guide the treatment team to allocate resources for the comprehensive care of patients

Prognostic model to predict postoperative acute kidney injury in patients undergoing major gastrointestinal surgery based on a national prospective observational cohort study.

Background: Acute illness, existing co-morbidities and surgical stress response can all contribute to postoperative acute kidney injury (AKI) in patients undergoing major gastrointestinal surgery. The aim of this study was prospectively to develop a pragmatic prognostic model to stratify patients according to risk of developing AKI after major gastrointestinal surgery. Methods: This prospective multicentre cohort study included consecutive adults undergoing elective or emergency gastrointestinal resection, liver resection or stoma reversal in 2-week blocks over a continuous 3-month period. The primary outcome was the rate of AKI within 7 days of surgery. Bootstrap stability was used to select clinically plausible risk factors into the model. Internal model validation was carried out by bootstrap validation. Results: A total of 4544 patients were included across 173 centres in the UK and Ireland. The overall rate of AKI was 14·2 per cent (646 of 4544) and the 30-day mortality rate was 1·8 per cent (84 of 4544). Stage 1 AKI was significantly associated with 30-day mortality (unadjusted odds ratio 7·61, 95 per cent c.i. 4·49 to 12·90; P < 0·001), with increasing odds of death with each AKI stage. Six variables were selected for inclusion in the prognostic model: age, sex, ASA grade, preoperative estimated glomerular filtration rate, planned open surgery and preoperative use of either an angiotensin-converting enzyme inhibitor or an angiotensin receptor blocker. Internal validation demonstrated good model discrimination (c-statistic 0·65). Discussion: Following major gastrointestinal surgery, AKI occurred in one in seven patients. This preoperative prognostic model identified patients at high risk of postoperative AKI. Validation in an independent data set is required to ensure generalizability

Whole-genome sequencing reveals host factors underlying critical COVID-19

Critical COVID-19 is caused by immune-mediated inflammatory lung injury. Host genetic variation influences the development of illness requiring critical care1 or hospitalization2–4 after infection with SARS-CoV-2. The GenOMICC (Genetics of Mortality in Critical Care) study enables the comparison of genomes from individuals who are critically ill with those of population controls to find underlying disease mechanisms. Here we use whole-genome sequencing in 7,491 critically ill individuals compared with 48,400 controls to discover and replicate 23 independent variants that significantly predispose to critical COVID-19. We identify 16 new independent associations, including variants within genes that are involved in interferon signalling (IL10RB and PLSCR1), leucocyte differentiation (BCL11A) and blood-type antigen secretor status (FUT2). Using transcriptome-wide association and colocalization to infer the effect of gene expression on disease severity, we find evidence that implicates multiple genes—including reduced expression of a membrane flippase (ATP11A), and increased expression of a mucin (MUC1)—in critical disease. Mendelian randomization provides evidence in support of causal roles for myeloid cell adhesion molecules (SELE, ICAM5 and CD209) and the coagulation factor F8, all of which are potentially druggable targets. Our results are broadly consistent with a multi-component model of COVID-19 pathophysiology, in which at least two distinct mechanisms can predispose to life-threatening disease: failure to control viral replication; or an enhanced tendency towards pulmonary inflammation and intravascular coagulation. We show that comparison between cases of critical illness and population controls is highly efficient for the detection of therapeutically relevant mechanisms of disease

The impact of viral mutations on recognition by SARS-CoV-2 specific T cells.

We identify amino acid variants within dominant SARS-CoV-2 T cell epitopes by interrogating global sequence data. Several variants within nucleocapsid and ORF3a epitopes have arisen independently in multiple lineages and result in loss of recognition by epitope-specific T cells assessed by IFN-γ and cytotoxic killing assays. Complete loss of T cell responsiveness was seen due to Q213K in the A∗01:01-restricted CD8+ ORF3a epitope FTSDYYQLY207-215; due to P13L, P13S, and P13T in the B∗27:05-restricted CD8+ nucleocapsid epitope QRNAPRITF9-17; and due to T362I and P365S in the A∗03:01/A∗11:01-restricted CD8+ nucleocapsid epitope KTFPPTEPK361-369. CD8+ T cell lines unable to recognize variant epitopes have diverse T cell receptor repertoires. These data demonstrate the potential for T cell evasion and highlight the need for ongoing surveillance for variants capable of escaping T cell as well as humoral immunity.This work is supported by the UK Medical Research Council (MRC); Chinese Academy of Medical Sciences(CAMS) Innovation Fund for Medical Sciences (CIFMS), China; National Institute for Health Research (NIHR)Oxford Biomedical Research Centre, and UK Researchand Innovation (UKRI)/NIHR through the UK Coro-navirus Immunology Consortium (UK-CIC). Sequencing of SARS-CoV-2 samples and collation of data wasundertaken by the COG-UK CONSORTIUM. COG-UK is supported by funding from the Medical ResearchCouncil (MRC) part of UK Research & Innovation (UKRI),the National Institute of Health Research (NIHR),and Genome Research Limited, operating as the Wellcome Sanger Institute. T.I.d.S. is supported by a Well-come Trust Intermediate Clinical Fellowship (110058/Z/15/Z). L.T. is supported by the Wellcome Trust(grant number 205228/Z/16/Z) and by theUniversity of Liverpool Centre for Excellence in Infectious DiseaseResearch (CEIDR). S.D. is funded by an NIHR GlobalResearch Professorship (NIHR300791). L.T. and S.C.M.are also supported by the U.S. Food and Drug Administration Medical Countermeasures Initiative contract75F40120C00085 and the National Institute for Health Research Health Protection Research Unit (HPRU) inEmerging and Zoonotic Infections (NIHR200907) at University of Liverpool inpartnership with Public HealthEngland (PHE), in collaboration with Liverpool School of Tropical Medicine and the University of Oxford.L.T. is based at the University of Liverpool. M.D.P. is funded by the NIHR Sheffield Biomedical ResearchCentre (BRC – IS-BRC-1215-20017). ISARIC4C is supported by the MRC (grant no MC_PC_19059). J.C.K.is a Wellcome Investigator (WT204969/Z/16/Z) and supported by NIHR Oxford Biomedical Research Centreand CIFMS. The views expressed are those of the authors and not necessarily those of the NIHR or MRC