Asymptomatic Fluid Collections Following Distal Pancreatectomy – Is Intervention Warranted?

Objective To determine the incidence, associated factors, natural history, and interventions for FCs

Assessment of a Wearable Device for Minute Ventilation in Detecting Different States of Ventilation

Introduction: Minute ventilation (MV) and breathing status are valuable vital signs to measure in patients clinically such as in detecting opioid induced respiratory depression. However, there are few devices capable of continuously monitoring MV in an accurate fashion. RTM Vital Signs, LLC and TJU are developing a non-invasive wearable Tracheal Sound Sensor to determine if a device can accurately and continuously measure respiratory rate (RR), tidal volume (TV), MV, and changes in ventilation patterns based on sound recordings of breathing. Methods: Tracheal breathing sounds were recorded in six researchers using a prototype sensor placed on the skin above the sternal notch. Simultaneously, researcher’s RR and MV were recorded in minute long intervals using a pneumotach. Researchers were asked to mimic various breathing patterns by adjusting breathing rate and breathing depth. A variety of signal processing methods and algorithms were used to analyze the data to produce RR, TV, and breathing pattern estimates. Results: Researchers tolerated use of the sensor and breathing apparatus system without difficulty and data was successfully obtained. Initial signal processing and analysis methods applied to this data were able to accurately measure the respiratory rate (~ 98% sensitivity/specificity), and accurately characterize normal breathing from hyperventilation and hypoventilation (~ 98% sensitivity/specificity). The sensor’s algorithm estimated tidal volume with ± 100 ml accuracy compared with the commercial pneumotach. Discussion: Based on the results, a non-invasive wearable device could obtain accurate measures of RR and classify breathing patterns based solely on measurements of breathing sounds. Although the TV results were not as accurate as we expected, this may be due in part to systematic error from the pneumotach device used for the reference TV. With the satisfactory sensor and data acquisition system, future trials are planned in volunteers and hospitalized patients using this system with more accurate pneumotach devices

Detecting Different States of Ventilation with a Wearable Device through Minute Ventilation

Introduction: Detecting changes in respiration are essential to monitoring a patient’s vital signs. Few devices accomplish this in a non-invasive manner. We are developing a wearable Trachea Sound Sensor that measures respiratory rate (RR), tidal volume (TV), minute ventilation (MV = RR x TV). A prototypical Trachea Sound Sensor (TSS) was created and compared to a reference pneumotachometer. Both were used to record the sounds of breathing with research team members. Methods:The TSS recording device was tested on six research team members and breath sounds were recorded. Simultaneously, the member’s RR and MV was recorded using a calibrated pneumotachometer. The researchers were instructed to adjust their breathing rate and depth while intervals were recorded. Signal processing techniques were used to analyze and produce measurements of RR, TV, and characterize hyperventilatory or hypoventilatory states. Results: Based on the results, we found that it is possible to obtain accurate measures of RR and identify breathing patterns through the TSS. Signal processing and analysis calculated RR, states of hyperventilation and hypoventilation with 98% sensitivity and specificity. Results obtained for measuring TV were less accurate (±100 mL). Discussion: Our results suggest that it is viable to obtain accurate measures of RR and classify breathing sounds solely on measurements of breathing sounds from the TSS. The inaccuracy in TV measurements may be partly due to the systematic error from the pneumotachometer used. The prototypical TSS are suitable for upcoming NIH-funded clinical trials to test the TSS in volunteers and hospitalized patients

Connective Tissue Growth Factor Overexpression in Cardiomyocytes Promotes Cardiac Hypertrophy and Protection against Pressure Overload

Connective tissue growth factor (CTGF) is a secreted protein that is strongly induced in human and experimental heart failure. CTGF is said to be profibrotic; however, the precise function of CTGF is unclear. We generated transgenic mice and rats with cardiomyocyte-specific CTGF overexpression (CTGF-TG). To investigate CTGF as a fibrosis inducer, we performed morphological and gene expression analyses of CTGF-TG mice and rat hearts under basal conditions and after stimulation with angiotensin II (Ang II) or isoproterenol, respectively. Surprisingly, cardiac tissues of both models did not show increased fibrosis or enhanced gene expression of fibrotic markers. In contrast to controls, Ang II treated CTGF-TG mice displayed preserved cardiac function. However, CTGF-TG mice developed age-dependent cardiac dysfunction at the age of 7 months. CTGF related heart failure was associated with Akt and JNK activation, but not with the induction of natriuretic peptides. Furthermore, cardiomyocytes from CTGF-TG mice showed unaffected cellular contractility and an increased Ca2+ reuptake from sarcoplasmatic reticulum. In an ischemia/reperfusion model CTGF-TG hearts did not differ from controls

Incidence and Risk Factors for Retinal Detachment and Retinal Tear after Cataract Surgery

Objective: To report the incidence of and evaluate demographic, ocular comorbidities, and intraoperative factors for rhegmatogenous retinal detachment (RRD) and retinal tear (RT) after cataract surgery in the American Academy of Ophthalmology IRIS® Registry (Intelligent Research in Sight). Design: Retrospective cohort study. Participants: Patients aged ≥ 40 years who underwent cataract surgery between 2014 and 2017. Methods: Multivariable logistic regression was used to evaluate demographic, comorbidity, and intraoperative factors associated with RRD and RT after cataract surgery. Main Outcome Measures: Incidence and risk factors for RRD or RT within 1 year of cataract surgery. Results: Of the 3 177 195 eyes of 1 983 712 patients included, 6690 (0.21%) developed RRD and 5489 (0.17%) developed RT without RRD within 1 year after cataract surgery. Multivariable logistic regression odds ratios (ORs) showed increased risk of RRD and RT, respectively, among men (OR 3.15; 95% confidence interval [CI], 2.99–3.32; P 70, peaking at age 40 to 50 for RRD (8.61; 95% CI, 7.74–9.58; P 40 years within 1 year of surgery. The presence of LD conferred the highest odds for RRD and RT after surgery. Additional risk factors for RRD included male gender, younger age, hypermature cataract, PVD, and high myopia. These data may be useful during the informed consent process for cataract surgery and help identify patients at a higher risk of retinal complications. Financial Disclosure(s): The author(s) have no proprietary or commercial interest in any materials discussed in this article

Characterizations of myosin essential light chain’s N-terminal truncation mutant Δ43 in transgenic mouse papillary muscles by using tension transients in response to sinusoidal length alterations

Cross-bridge kinetics were studied at 20 °C in cardiac muscle strips from transgenic (Tg) mice expressing N-terminal 43 amino acid truncation mutation (Δ43) of myosin essential light chain (ELC), and the results were compared to those from Tg-wild type (WT) mice. Sinusoidal length changes were applied to activated skinned papillary muscle strips to induce tension transients, from which two exponential processes were deduced to characterize the cross-bridge kinetics. Their two rate constants were studied as functions of ATP, phosphate (Pi), ADP, and Ca(2+) concentrations to characterize elementary steps of the cross-bridge cycle consisting of six states. Our results demonstrate for the first time that the cross-bridge kinetics of Δ43 are accelerated owing to an acceleration of the rate constant k(2) of the cross-bridge detachment step, and that the number of strongly attached cross-bridges are decreased because of a reduction of the equilibrium constant K(4) of the force generation step. The isometric tension and stiffness of Δ43 are diminished compared to WT, but the force per cross-bridge is not changed. Stiffness measurement during rigor induction demonstrates a reduction in the stiffness in Δ43, indicating that the N-terminal extension of ELC forms an extra linkage between the myosin cross-bridge and actin. The tension-pCa study demonstrates tht there is no Ca(2+) sensitivity change with Δ43, but the cooperativity is diminished. These results demonstrate the importance of the N-terminal extension of ELC in maintaining the myosin motor function during force generation and optimal cardiac performance

GWAS and meta-analysis identifies 49 genetic variants underlying critical COVID-19

Data availability: Downloadable summary data are available through the GenOMICC data site (https://genomicc.org/data). Summary statistics are available, but without the 23andMe summary statistics, except for the 10,000 most significant hits, for which full summary statistics are available. The full GWAS summary statistics for the 23andMe discovery dataset will be made available through 23andMe to qualified researchers under an agreement with 23andMe that protects the privacy of the 23andMe participants. For further information and to apply for access to the data, see the 23andMe website (https://research.23andMe.com/dataset-access/). All individual-level genotype and whole-genome sequencing data (for both academic and commercial uses) can be accessed through the UKRI/HDR UK Outbreak Data Analysis Platform (https://odap.ac.uk). A restricted dataset for a subset of GenOMICC participants is also available through the Genomics England data service. Monocyte RNA-seq data are available under the title ‘Monocyte gene expression data’ within the Oxford University Research Archives (https://doi.org/10.5287/ora-ko7q2nq66). Sequencing data will be made freely available to organizations and researchers to conduct research in accordance with the UK Policy Framework for Health and Social Care Research through a data access agreement. Sequencing data have been deposited at the European Genome–Phenome Archive (EGA), which is hosted by the EBI and the CRG, under accession number EGAS00001007111.Extended data figures and tables are available online at https://www.nature.com/articles/s41586-023-06034-3#Sec21 .Supplementary information is available online at https://www.nature.com/articles/s41586-023-06034-3#Sec22 .Code availability: Code to calculate the imputation of P values on the basis of SNPs in linkage disequilibrium is available at GitHub (https://github.com/baillielab/GenOMICC_GWAS).Acknowledgements: We thank the members of the Banco Nacional de ADN and the GRA@CE cohort group; and the research participants and employees of 23andMe for making this work possible. A full list of contributors who have provided data that were collated in the HGI project, including previous iterations, is available online (https://www.covid19hg.org/acknowledgements).Change history: 11 July 2023: A Correction to this paper has been published at: https://doi.org/10.1038/s41586-023-06383-z. -- In the version of this article initially published, the name of Ana Margarita Baldión-Elorza, of the SCOURGE Consortium, appeared incorrectly (as Ana María Baldion) and has now been amended in the HTML and PDF versions of the article.Copyright © The Author(s) 2023, Critical illness in COVID-19 is an extreme and clinically homogeneous disease phenotype that we have previously shown1 to be highly efficient for discovery of genetic associations2. Despite the advanced stage of illness at presentation, we have shown that host genetics in patients who are critically ill with COVID-19 can identify immunomodulatory therapies with strong beneficial effects in this group3. Here we analyse 24,202 cases of COVID-19 with critical illness comprising a combination of microarray genotype and whole-genome sequencing data from cases of critical illness in the international GenOMICC (11,440 cases) study, combined with other studies recruiting hospitalized patients with a strong focus on severe and critical disease: ISARIC4C (676 cases) and the SCOURGE consortium (5,934 cases). To put these results in the context of existing work, we conduct a meta-analysis of the new GenOMICC genome-wide association study (GWAS) results with previously published data. We find 49 genome-wide significant associations, of which 16 have not been reported previously. To investigate the therapeutic implications of these findings, we infer the structural consequences of protein-coding variants, and combine our GWAS results with gene expression data using a monocyte transcriptome-wide association study (TWAS) model, as well as gene and protein expression using Mendelian randomization. We identify potentially druggable targets in multiple systems, including inflammatory signalling (JAK1), monocyte–macrophage activation and endothelial permeability (PDE4A), immunometabolism (SLC2A5 and AK5), and host factors required for viral entry and replication (TMPRSS2 and RAB2A).GenOMICC was funded by Sepsis Research (the Fiona Elizabeth Agnew Trust), the Intensive Care Society, a Wellcome Trust Senior Research Fellowship (to J.K.B., 223164/Z/21/Z), the Department of Health and Social Care (DHSC), Illumina, LifeArc, the Medical Research Council, UKRI, a BBSRC Institute Program Support Grant to the Roslin Institute (BBS/E/D/20002172, BBS/E/D/10002070 and BBS/E/D/30002275) and UKRI grants MC_PC_20004, MC_PC_19025, MC_PC_1905 and MRNO2995X/1. A.D.B. acknowledges funding from the Wellcome PhD training fellowship for clinicians (204979/Z/16/Z), the Edinburgh Clinical Academic Track (ECAT) programme. This research is supported in part by the Data and Connectivity National Core Study, led by Health Data Research UK in partnership with the Office for National Statistics and funded by UK Research and Innovation (grant MC_PC_20029). Laboratory work was funded by a Wellcome Intermediate Clinical Fellowship to B.F. (201488/Z/16/Z). We acknowledge the staff at NHS Digital, Public Health England and the Intensive Care National Audit and Research Centre who provided clinical data on the participants; and the National Institute for Healthcare Research Clinical Research Network (NIHR CRN) and the Chief Scientist’s Office (Scotland), who facilitate recruitment into research studies in NHS hospitals, and to the global ISARIC and InFACT consortia. GenOMICC genotype controls were obtained using UK Biobank Resource under project 788 funded by Roslin Institute Strategic Programme Grants from the BBSRC (BBS/E/D/10002070 and BBS/E/D/30002275) and Health Data Research UK (HDR-9004 and HDR-9003). UK Biobank data were used in the GSMR analyses presented here under project 66982. The UK Biobank was established by the Wellcome Trust medical charity, Medical Research Council, Department of Health, Scottish Government and the Northwest Regional Development Agency. It has also had funding from the Welsh Assembly Government, British Heart Foundation and Diabetes UK. The work of L.K. was supported by an RCUK Innovation Fellowship from the National Productivity Investment Fund (MR/R026408/1). J.Y. is supported by the Westlake Education Foundation. SCOURGE is funded by the Instituto de Salud Carlos III (COV20_00622 to A.C., PI20/00876 to C.F.), European Union (ERDF) ‘A way of making Europe’, Fundación Amancio Ortega, Banco de Santander (to A.C.), Cabildo Insular de Tenerife (CGIEU0000219140 ‘Apuestas científicas del ITER para colaborar en la lucha contra la COVID-19’ to C.F.) and Fundación Canaria Instituto de Investigación Sanitaria de Canarias (PIFIISC20/57 to C.F.). We also acknowledge the contribution of the Centro National de Genotipado (CEGEN) and Centro de Supercomputación de Galicia (CESGA) for funding this project by providing supercomputing infrastructures. A.D.L. is a recipient of fellowships from the National Council for Scientific and Technological Development (CNPq)-Brazil (309173/2019-1 and 201527/2020-0)