The Effect of Diabetes on Complications after Spinal Fusion: A Systematic Review and Meta-Analysis.

OBJECTIVE: Spinal fusion procedures are used to treat a wide variety of spinal pathologies. Diabetes mellitus (DM) has been shown to be a significant risk factor for several complications following these procedures in previous studies. To the authors' knowledge, this is the first systematic review and meta-analysis elucidating the relationship between DM and complications occurring after spinal fusion procedures. METHODS: Systematic literature searches of PubMed and EMBASE were performed from their inception to October 1, 2022, to identify studies that directly compared postfusion complications in patients with and without DM. Studies met the prespecified inclusion criteria if they reported the following data for patients with and without DM: (1) demographics; (2) postspinal fusion complication rates; and (3) postoperative clinical outcomes. The included studies were then pooled and analyzed. RESULTS: Twenty-eight studies, with a cumulative total of 18,853 patients (2695 diabetic patients), were identified that met the inclusion criteria. Analysis showed that diabetic patients had significantly higher rates of total number of postoperative complications (odds ratio [OR] = 1.33; 95% confidence interval [CI] = 1.12-1.58; P = 0.001), postoperative pulmonary complications (OR=2.01; 95%CI=1.31-3.08; P = 0.001), postoperative renal complications (OR=2.20; 95%CI=1.27-3.80; P = 0.005), surgical site infection (OR=2.65; 95%CI=2.19-3.20; P < 0.001), and prolonged hospital stay (OR=1.67; 95%CI=1.47-1.90; P < 0.001). CONCLUSIONS: Patients with DM had a significantly higher risk of developing complications after spinal fusion, particularly pulmonary and renal complications, in addition to surgical site infections and had a longer length of stay. These findings are important for informed discussions of surgical risks with patients and families before surgery.

Vaccines for immunoprevention of DNA mismatch repair deficient cancers.

The development of cancer vaccines to induce tumor-antigen specific immune responses was sparked by the identification of antigens specific to or overexpressed in cancer cells. However, weak immunogenicity and the mutational heterogeneity in many cancers have dampened cancer vaccine successes. With increasing information about mutational landscapes of cancers, mutational neoantigens can be predicted computationally to elicit strong immune responses by CD8 +cytotoxic T cells as major mediators of anticancer immune response. Neoantigens are potentially more robust immunogens and have revived interest in cancer vaccines. Cancers with deficiency in DNA mismatch repair have an exceptionally high mutational burden, including predictable neoantigens. Lynch syndrome is the most common inherited cancer syndrome and is caused by DNA mismatch repair gene mutations. Insertion and deletion mutations in coding microsatellites that occur during DNA replication include tumorigenesis drivers. The induced shift of protein reading frame generates neoantigens that are foreign to the immune system. Mismatch repair-deficient cancers and Lynch syndrome represent a paradigm population for the development of a preventive cancer vaccine, as the mutations induced by mismatch repair deficiency are predictable, resulting in a defined set of frameshift peptide neoantigens. Furthermore, Lynch syndrome mutation carriers constitute an identifiable high-risk population. We discuss the pathogenesis of DNA mismatch repair deficient cancers, in both Lynch syndrome and sporadic microsatellite-unstable cancers. We review evidence for pre-existing immune surveillance, the three mechanisms of immune evasion that occur in cancers and assess the implications of a preventive frameshift peptide neoantigen-based vaccine. We consider both preclinical and clinical experience to date. We discuss the feasibility of a cancer preventive vaccine for Lynch syndrome carriers and review current antigen selection and delivery strategies. Finally, we propose RNA vaccines as having robust potential for immunoprevention of Lynch syndrome cancers.

Pre-treatment letter fluency performance predicts antidepressant response to transcranial direct current stimulation.

BACKGROUND: Transcranial direct current stimulation (tDCS) is a promising new treatment for depression, however, clinical trials to-date indicate variable efficacy, thereby raising the need to identify inter-individual predictors of response. In the current study we aimed to investigate pre-treatment neurocognitive performance as a predictor of antidepressant response to tDCS. METHODS: Data was pooled from five clinical trials, including two randomised controlled trials (RCTs), which investigated the antidepressant effects of anodal tDCS administered to the prefrontal cortex. Data from 57 patients were included in the analysis. Mood was assessed before and after an acute course of treatment using the Montgomery-Åsberg Depression Rating Scale. The following neurocognitive tests were administered prior to treatment: Simple and choice reaction time, the Symbol Digit Modalities Test (SDMT), Rey Auditory Verbal Memory Task (RAVLT), Digit Span, and the Controlled Oral Word Association Test (COWAT). RESULTS: Better pre-treatment letter fluency performance measured using the COWAT predicted antidepressant response to tDCS after controlling for confounds. LIMITATIONS: Small sample size and analysis included data from both RCTs and open label studies. CONCLUSION: Pre-treatment letter fluency performance, an ability subserved by the left prefrontal cortex, the primary site of stimulation, is a predictor of response for tDCS treatment for depression. This study highlights the importance of inter-individual neurobiological differences in mediating tDCS antidepressant effects.

Cistrome: an integrative platform for transcriptional regulation studies.

The increasing volume of ChIP-chip and ChIP-seq data being generated creates a challenge for standard, integrative and reproducible bioinformatics data analysis platforms. We developed a web-based application called Cistrome, based on the Galaxy open source framework. In addition to the standard Galaxy functions, Cistrome has 29 ChIP-chip- and ChIP-seq-specific tools in three major categories, from preliminary peak calling and correlation analyses to downstream genome feature association, gene expression analyses, and motif discovery. Cistrome is available at http://cistrome.org/ap/.