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David A. Smith

University of Oxford

ORCID: 0000-0001-7778-7137

Publishes on Hepatitis C virus research, Hepatitis B Virus Studies, Liver Disease Diagnosis and Treatment. 116 papers and 4.1k citations.

116Publications
4.1kTotal Citations

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Top publicationsby citations

Unilateral Transforaminal Posterior Lumbar Interbody Fusion (TLIF): Indications, Technique, and 2-Year Results
Thomas G. Lowe, A. David Tahernia, Michael O’Brien et al.|Journal of Spinal Disorders & Techniques|2002
Cited by 246

A prospective analysis of consecutive cases of lumbar fusion using the unilateral transforaminal posterior lumbar interbody fusion (TLIF) technique with pedicle screw fixation. The objective of the study was to assess the clinical and radiographic outcome of TLIF and describe the technique and indications in the treatment of degenerative disease of the lumbar spine. Forty patients treated with TLIF for degenerative diseases of the lumbar spine were followed up for a minimum of 2.5 years (mean: 36 months; range: 30-42 months). Twenty-three patients had degenerative disc disease alone, 13 had associated isthmic or degenerative spondylolisthesis, and 4 had recurrent disc herniations at the L4-L5 level. Thirty-six (90%) had solid fusions radiographically at latest follow-up. Seventy-nine percent had excellent or good clinical outcomes. Our patients demonstrated high fusion rates and patient satisfaction.

An immune dysfunction score for stratification of patients with acute infection based on whole-blood gene expression
Eddie Cano-Gamez, Katie L. Burnham, Cyndi Goh et al.|Science Translational Medicine|2022
Cited by 102Open Access

Dysregulated host responses to infection can lead to organ dysfunction and sepsis, causing millions of global deaths each year. To alleviate this burden, improved prognostication and biomarkers of response are urgently needed. We investigated the use of whole-blood transcriptomics for stratification of patients with severe infection by integrating data from 3149 samples from patients with sepsis due to community-acquired pneumonia or fecal peritonitis admitted to intensive care and healthy individuals into a gene expression reference map. We used this map to derive a quantitative sepsis response signature (SRSq) score reflective of immune dysfunction and predictive of clinical outcomes, which can be estimated using a 7- or 12-gene signature. Last, we built a machine learning framework, SepstratifieR, to deploy SRSq in adult and pediatric bacterial and viral sepsis, H1N1 influenza, and COVID-19, demonstrating clinically relevant stratification across diseases and revealing some of the physiological alterations linking immune dysregulation to mortality. Our method enables early identification of individuals with dysfunctional immune profiles, bringing us closer to precision medicine in infection.