143174 research outputs found
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Utilising routinely collected clinical data through time series deep learning to improve identification of bacterial bloodstream infections: a retrospective cohort study
No full textBackground:
Blood cultures are the gold standard for diagnosing bacterial bloodstream infections, but test results are only available 24–48 h after sampling. We aimed to develop and evaluate models using health-care data to predict bloodstream infections in patients admitted to hospital.
Methods:
In this retrospective cohort study, we used routinely collected blood biomarkers and demographic data from patients who underwent blood sample collection for testing via culture between March 3, 2014, and Dec 1, 2021, at Imperial College Healthcare NHS Trust (London, UK) as model features. Data up to 14 days before blood sample collection were provided to long short-term memory (LSTM) or static logistic regression models. The primary outcome was prediction of blood culture results, defined as a pathogenic bloodstream infection (ie, isolation of pathogenic bacteria of interest) or no bloodstream infection (ie, no growth or contamination). Data collected up to Feb 28, 2021 (n=15 212) comprised the training set and were evaluated against a temporal hold-out test set comprising patients who were sampled after March 1, 2021 (n=5638).
Findings:
Among 20 850 patients with available data, pathogenic bacteria were observed in the cultured blood samples of 3866 (18·5%) patients. 2920 (62·2%) of 4897 patients who had their blood samples taken more than 48 h after admission to hospital had pathogenic bloodstream infections, and so were defined as having hospital-acquired bloodstream infections. Including data from the 7 days before admission (7-day window approach) and using five-fold cross validation in the training set gave an area under receiver operator curve (AUROC) of 0·75 (IQR 0·68–0·82) and an area under the precision recall curve (AUPRC) of 0·58 (0·46–0·77) for static models and an AUROC of 0·92 (0·91–0·93) and AUPRC of 0·75 (0·72–0·76) for the LSTM model. In the hold-out test set performances were: AUROC of 0·74 (95% CI 0·70–0·78) and AUPRC of 0·48 (0·43–0·53) for static models and AUROC of 0·97 (0·96–0·97) and AUPRC of 0·65 (0·60–0·70) for LSTM. Removal of time series information resulted in lower model performance, particularly for hospital-acquired bloodstream infections. Dynamics of C-reactive protein concentration, eosinophil count, and platelet count were important features for prediction of blood culture results.
Interpretation:
Deep learning models accounting for longitudinal changes could support individualised clinical decision making for patients at risk of bloodstream infections. Appropriate implementation into existing diagnostic pathways could enhance diagnostic stewardship and reduce unnecessary antimicrobial prescribing.
Funding:
UK Department of Health and Social Care, the National Institute for Health and Care Research, and the Wellcome Trust
Risk factors for the recurrence of instability after operative treatment of chronic lateral ankle instability: a systematic review
No full textPurpose
To identify, review and summarize risk factors for failure of lateral ankle ligament operative treatment for chronic lateral ankle instability (CLAI).
Methods
A Systematic review according to PRISMA guidelines was performed. In July 2023, a bibliographic search of the PubMed, Medline, CINAHL, Cochrane, and Embase databases was performed. Articles were included if they were quantitative studies published in English and reported risk factors for recurrence of instability.
Results
A total of 496 articles were identified using the search strategy, and nine articles were included. All were low-quality cohort studies (level 3 or 4 evidence). These nine studies comprising 762 participants met the criteria for inclusion. Eighty-nine patients (11%) had treatment failure as defined by recurrence of instability, with rates ranging from 5.7% to 28.5%. Six risk factors were divided into three categories: patient demographics (generalized joint laxity [GJL], high-level sports activities and female sex), imaging features (varus hindfoot alignment), and surgical findings (poor quality of the remnant lateral ligaments, intraoperative syndesmosis widening).
Conclusion
The presence of risk factors such as GJL, high-level sports activities, female sex, varus hindfoot alignment, poor ligament quality, and intraoperative syndesmosis widening should guide surgical strategy to reduce the risk of treatment failure in lateral ankle ligament repair for CLAI
How COVID-19 affected academic publishing: a three-year study of 17 million research papers
No full textConflict-associated wounds and burns infected with GLASS pathogens in the Eastern Mediterranean region: a systematic review
No full textBackground: While the relationship between conflict-associated injuries and antimicrobial resistance is increasingly being elucidated, data concerning civilian casualties is sparse. This systematic review assesses literature focused on Global Antimicrobial Resistance Surveillance System (GLASS) Priority Pathogens causing infections in civilian wounds and burns in conflict-affected countries within the World Health Organisation’s Eastern Mediterranean Region Office (EMRO).
Methods: A systematic literature review was conducted following Preferred Reporting Items for Systematic Review and Meta-Analyses guidelines. Five databases and grey literature were searched, identifying studies published from January 2010 to June 2024. Search terms included “wounds”, “burns,” “antimicrobial resistance”, and the twelve countries of interest. Included studies reported resistance of GLASS pathogens. Two reviewers used Covidence to assess papers for inclusion. Data were extracted into a spreadsheet for analysis. Where quantitative data were available, medians, interquartile ranges and percentages were calculated by pathogen and country.
Results: 621 records were identified; 19 studies met inclusion criteria. Nine of the papers were from Iraq, three from Libya, three from Lebanon, one each from Yemen and Gaza; two reported on conflict affected refugees in Jordan. A total of 1,942 distinct microbiological isolates were reported, representing all four critical and high priority GLASS pathogen categories. Among the isolates, Staphylococcus aureus was the most prevalent (36.3%). Median resistances identified: Methicillin resistant Staphylococcus aureus (n=680): 55.6% (IQR:49.65-90.3%); carbapenem resistant Pseudomonas aeruginosa (n=372): 22.14% (7.43-52.22%); carbapenem resistant Acinetobacter baumannii (n=366): 60.3% (32.1-85%); carbapenem resistant Klebsiella pneumoniae (n=75): 12.65% (9.73-34.25%); ceftriaxone resistant Escherichia coli (n=63): 76% (69-84.65%); ceftriaxone resistant Klebsiella pneumoniae (n=40): 81.45% (76.73-86.18%). Only three studies had a low risk of bias.
Discussion: Findings imply high rates of GLASS priority pathogens among wounded civilians in conflict-affected EMRO countries. However, evidence was heterogeneous, low quality and sparse in certain countries, highlighting the necessity of effective surveillance including standardised data collection. Improving primary data will facilitate the production of large, high-quality studies throughout the EMRO, including under-represented countries.
Conclusion: Laboratory diagnostic capacity building and improved surveillance in conflict-affected settings in the Eastern Mediterranean Region are required to assess the burden of GLASS priority pathogens in vulnerable non-combatant populations
The N-terminal ELR+ motif of the neutrophil attractant CXCL8 confers susceptibility to degradation by the Group A Streptococcal protease, SpyCEP
No full textStreptococcus pyogenes (Group A Streptococcus or GAS) is a major human pathogen for which an effective vaccine is highly desirable. Invasive S. pyogenes strains evade the host immune response in part by producing a cell envelope protease, SpyCEP. This neutralizes chemokines containing an N-terminal Glu-Leu-Arg motif (ELR+ chemokines) by cleavage at a distal C-terminal site within the chemokine. SpyCEP is a component of several S. pyogenes vaccines, yet the molecular determinants underlying substrate selectivity are poorly understood. We hypothesized that chemokine recognition and cleavage is a multistep process involving distinct domains of both substrate and enzyme. We generated a panel of recombinant CXCL8 variants where domains of the chemokine were exchanged or mutated. Chemokine degradation by SpyCEP was assessed by SDS-PAGE, Western blot, and ELISA. Extension of the CXCL8 N-terminus was found to inhibit chemokine cleavage. Reciprocal exchanges of the N-termini of CXCL8 with that of the ELR- chemokine CXCL4 resulted in the generation of loss of function and gain of function substrates. This suggested a key role for the ELR motif in substrate recognition, which was supported directly by alanine substitution of the ELR motif of CXCL8, impairing the parameters, KM, Vmax, and Kcat in kinetic assays with SpyCEP. Collectively, our findings identify the N-terminal ELR motif as a major determinant for recognition by SpyCEP and expose a vulnerability in the mechanism by which the protease recognises its substrates. This likely presents potential avenues for therapeutic intervention via targeted vaccine design and small molecule inhibition
Unconventional reactivity of sulfonyl fluorides
No full textSulfonyl fluorides are now established click reagents that find broad applications in synthesis, chemical biology and drug discovery. Sulfonyl fluorides owe their popularity to their increased stability compared to their sulfonyl chloride cousins and to their high selectivity for the reaction pathway of fluoride exchange (SuFEx reaction). Other reaction pathways were unavailable to sulfonyl fluorides. However, recent reports have challenged this paradigm, showing unconventional leaving group capabilities of the whole SO2F group as part of Suzuki–Miyaura or defluorosulfonylative (deFS) couplings. This review highlights such early developments, discusses the key mechanistic aspects responsible for the alternative reactivity and recognises potential avenues for future research in this exciting new area
A compact orthosis compliance monitoring device using pressure sensors and accelerometers: design and proof-of-concept testing
No full textMonitoring orthosis compliance using patient diaries is subjective, as patients can overes-timate their levels of device use. An objective way to monitor compliance is required be-cause if an orthotic prescription is not followed the orthosis will not work as intended. This study aimed to develop and validate a device that monitors orthosis compliance ob-jectively using pressure and acceleration. Fifteen participants were recruited to test the de-vice’s ability to estimate wear time during the performance of several grip patterns and whilst completing selected activities of daily living. Sensor threshold values were used to discern whether users were wearing their orthosis or not. No differences between pressure sensor and accelerometer-based wear time estimations were found. The device’s pres-sure-based wear time estimations were found to have a specificity of 92.7 ± 16.4% and sen-sitivity of 74.0 ± 41.3%, whilst accelerometer-based wear time estimates had a specificity of 66.1 ± 34.7% and sensitivity of 86.2 ± 8.0%. This study successfully demonstrated the fea-sibility of monitoring hand orthosis compliance using pressure or acceleration. This de-vice has the potential to provide insight into the effectiveness of both existing and novel orthotics, benefitting both clinical practice and research
Hyperspectral sensing of aboveground biomass and species diversity in a longrunning grassland experiment
No full textVegetation properties can be assessed through analysis of canopy reflectance spectra. Early techniques relied on
simple two-band vegetation indices (VIs) that exploit leaf reflectance properties at key wavelengths. As the
technology matures it is now possible to gather and test hyperspectral data. Little evidence exists on how
different management regimes, such as nutrient addition, might affect hyperspectral reflectance and thus influence derived estimates of plant diversity and productivity. At a grassland experiment in southern England, we used a portable spectroradiometer to sample 96 plots exposed to multifactorial treatments combining herbivory, plant competition, soil pH and fertility. Our objective was to compare the predictive performance of popular two-band VIs with a multivariate partial least square regression (PLSR) model that uses all available wavelengths. We found that the PLSR models showed higher predictive power than the best performing VIs – that was especially true for our measure of species diversity (R2cv = 0.36 compared with a Pearson correlation of 0.21). The predictive power for our PLSR model of biomass (R2cv = 0.54) compares favourably with values reported in earlier grassland studies. These results confirm that hyperspectral measurement combined with multivariate regression techniques is a promising approach for monitoring grassland properties. There is evidence of particular benefit in capturing narrow bands associated with the red edge region of the spectrum (700–750 nm). Remotely sensed hyperspectral images at a fine spatial scale offer the prospect for matching with sampling units as small as the 2 × 2 m nutrient subplots measured here
Multilingual speaker-invariant dysarthria severity assessment using adversarial domain adaptation and self-supervised learning
No full textTraditional assessments for dysarthria are subjective and time-consuming, highlighting the need for automated, objective approaches that can be scaled for remote and resource-constrained environments. This paper introduces an adversarial domain adaptation framework tailored for dysarthria severity assessment, addressing the challenges of high intra-class variability and limited availability of dysarthric speech data. By framing speaker variability as a domain adaptation problem, we utilise an adversarially trained feature extractor to derive speaker-invariant yet discriminatively powerful representations utilising speech features learned through self-supervised learning. Experiments on previously unseen and diverse speakers reveal that the proposed approach yields a 7.86% average improvement across multilingual datasets compared to traditional severity-discriminative training, outperforming competitive baselines by 12.33% on average. Additionally, the method inherently supports privacy-preserving applications by minimising reliance on speaker-specific information. The results demonstrate strong alignment with clinical assessments, reinforcing our model’s clinical relevance and effectiveness
Increasing diagnoses per patient admission at a specialist children’s hospital: a retrospective study
No full textObjective
In adult practice there is recognition that average patient complexity is increasing, with a greater proportion of patients having multiple diagnoses or comorbidities. This study aims to examine whether there has been a change in number of recorded coexisting diagnoses per patient over a 24-year period for children attending as in-patients to a specialist children’s hospital in England.
Methods
Following all in-patient admissions, patient episodes are allocated specific diagnosis codes (ICD-10) by a specialist clinical coding team according to standard NHS criteria and guidance. We examine the number of coexisting diagnoses allocated per patient admission over a 24-year period.
Results
From a total of 278,579 overnight in-patient admissions during the study period (2000–2023) there were 1,023,276 ICD-10 patient diagnoses. The mean number of diagnoses per admission increased from 2.72 to 10.43 over the period (Kendall’s tau statistic of 0.93; p-value < 0.001), an increase of 284% (95% confidence interval 275% - 293%).
Conclusions
Over recent decades, the recorded complexity of patients attending a specialist children’s hospital appear to have increased significantly, with an almost 3-fold increase in the number of coexisting diagnoses present per admission. The cause of this finding cannot be determined from the data; however, it appears to be gradual and consistent, and across all speciality areas suggesting biological or referral factors rather than artefactual coding issues. Recognition of such a trend is important when interpreting retrospective data for AI, research, and planning purposes