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Early post-operative delirium is a common perioperative complication in the post anesthesia care unit. To date it is unknown if a specific anesthetic regime can affect the incidence of delirium after surgery. Our objective was to examine the effect of volatile anesthetics on post-operative delirium. Single Center Observational Study. Post Anesthesia Care Units at a German tertiary medical center. 30,075 patients receiving general anesthesia for surgery. Delirium was assessed with the Nursing Delirium Screening Scale at the end of the recovery period. Subgroup-specific effects of volatile anesthetics on post-operative delirium were estimated using generalized-linear-model trees with inverse probability of treatment weighting. We further assessed the age-specific effect of volatiles using logistic regression models. Out of 30,075 records, 956 patients (3.2%) developed delirium in the post anesthesia care unit. On average, patients who developed delirium were older than patients without delirium. We found volatile anesthetics to increase the risk (Odds exp. (B) for delirium in the elderly 1.8-fold compared to total intravenous anesthesia. Odds increases with unplanned surgery 3.0-fold. In the very old (87 years or older), the increase in delirium is 6.2-fold. This result was confirmed with internal validation and in a logistic regression model. Our exploratory study indicates that early postoperative delirium is associated with the use of volatile anesthetics especially in the sub-cohort of patients aged 75 years and above. Further studies should include both volatile and intravenous anesthetics to find the ideal anesthetic in elderly patients. •Early post-operative delirium is a common perioperative complication.•Though many risk factors are known, the ideal anesthetic regime is unknown.•In a big data approach, the association of volatiles with delirium was examined.•Use of volatile anesthetics in the very old is associated with delirium.

Background Blood transfusion (BT) is a critical aspect of medical care for surgical patients in the Intensive Care Unit (ICU). Timely and accurate identification of BT needs can enhance patient outcomes and healthcare resource management. Methods This study aims to determine whether a machine learning (ML) model can be trained to predict the need for blood transfusion (BT) in patients on the ICU after a wide range of surgeries, utilizing only data from the ICU. Results This retrospective study analyzed data from 9,118 surgical ICU patients from the Amsterdam University Medical Centers database (UMCdb). The study included a primary analysis using data from 6 h before ICU admission up to 1, 2, 3, and 6 h after admission, and a secondary analysis using only the data from 6 h before ICU admission and only the data from the first hour after admission. The model integrated 32 relevant clinical variables and compared the performance of XGBoost and logistic regression (LR) algorithms. Conclusions The model demonstrated an effective BT prediction, with XGBoost outperforming LR, particularly for a 12-hour prediction window. Notable differences in patient characteristics were observed among those who received BT and those who did not receive BT. The study establishes the feasibility of using ML for the prediction of BT in surgical ICU patients. It underlines the potential of ML models as decision support tools in healthcare, enabling early identification of BT needs.

Rationale Steroid profiles in combination with a corticotropin stimulation test provide information about steroidogenesis and its functional reserves in critically ill patients. Objectives We investigated whether steroid profiles before and after corticotropin stimulation can predict the risk of in-hospital death in sepsis. Methods An exploratory data analysis of a double blind, randomized trial in sepsis (HYPRESS [HYdrocortisone for PRevention of Septic Shock]) was performed. The trial included adult patients with sepsis who were not in shock and were randomly assigned to placebo or hydrocortisone treatment. Corticotropin tests were performed in patients prior to randomization and in healthy subjects. Cortisol and precursors of glucocorticoids (17-OH-progesterone, 11-desoxycortisol) and mineralocorticoids (11-desoxycorticosterone, corticosterone) were analyzed using the multi-analyte stable isotope dilution method (LC–MS/MS). Measurement results from healthy subjects were used to determine reference ranges, and those from placebo patients to predict in-hospital mortality. Measurements and main results Corticotropin tests from 180 patients and 20 volunteers were included. Compared to healthy subjects, patients with sepsis had elevated levels of 11-desoxycorticosterone and 11-desoxycortisol, consistent with activation of both glucocorticoid and mineralocorticoid pathways. After stimulation with corticotropin, the cortisol response was subnormal in 12% and the corticosterone response in 50% of sepsis patients. In placebo patients ( n  = 90), a corticotropin-stimulated cortisol-to-corticosterone ratio > 32.2 predicted in-hospital mortality (AUC 0.8 CI 0.70–0.88; sensitivity 83%; and specificity 78%). This ratio also predicted risk of shock development and 90-day mortality. Conclusions In this exploratory analysis, we found that in sepsis mineralocorticoid steroidogenesis was more frequently impaired than glucocorticoid steroidogenesis. The corticotropin-stimulated cortisol-to-corticosterone ratio predicts the risk of in-hospital death. Trial registration Clinical trial registered with www.clinicaltrials.gov Identifier: NCT00670254. Registered 1 May 2008, https://clinicaltrials.gov/ct2/show/NCT00670254 .

When different researchers study the same research question using the same dataset they may obtain different and potentially even conflicting results. This is because there is often substantial flexibility in researchers’ analytical choices, an issue also referred to as “researcher degrees of freedom”. Combined with selective reporting of the smallest p -value or largest effect, researcher degrees of freedom may lead to an increased rate of false positive and overoptimistic results. In this paper, we address this issue by formalizing the multiplicity of analysis strategies as a multiple testing problem. As the test statistics of different analysis strategies are usually highly dependent, a naive approach such as the Bonferroni correction is inappropriate because it leads to an unacceptable loss of power. Instead, we propose using the “minP” adjustment method, which takes potential test dependencies into account and approximates the underlying null distribution of the minimal p -value through a permutation-based procedure. This procedure is known to achieve more power than simpler approaches while ensuring a weak control of the family-wise error rate. We illustrate our approach for addressing researcher degrees of freedom by applying it to a study on the impact of perioperative p a O 2 on post-operative complications after neurosurgery. A total of 48 analysis strategies are considered and adjusted using the minP procedure. This approach allows to selectively report the result of the analysis strategy yielding the most convincing evidence, while controlling the type 1 error—and thus the risk of publishing false positive results that may not be replicable.

Background and Objectives Brain tissue oxygenation is usually inferred from arterial partial pressure of oxygen (paO 2 ), which is in turn often inferred from pulse oximetry measurements or other non-invasive proxies. Our aim was to evaluate the feasibility of continuous paO 2 prediction in an intraoperative setting among neurosurgical patients undergoing craniotomies with modern machine learning methods. Methods Data from routine clinical care of lung-healthy neurosurgical patients were extracted from databases of the respective clinical systems and normalized. We used recursive feature elimination to identify relevant features for the prediction of paO 2 . Six machine learning regression algorithms (gradient boosting, k-nearest neighbors, random forest, support vector, neural network, linear model with stochastic gradient descent) and a multivariable linear regression were then tuned and fitted to the selected features. A performance matrix consisting of standard deviation of absolute errors ( σ ae ), mean absolute percentage error (MAPE), adjusted R 2 , root mean squared error (RMSE), mean absolute error (MAE) and Spearman’s ρ was finally computed based on the test set, and used to compare and rank each algorithm. Results We analyzed N  = 4,581 patients with n  = 17,821 observations. Between 5 and 22 features were selected from the analysis of the training dataset comprising 3,436 patients with 13,257 observations. The best algorithm, a regularized linear model with stochastic gradient descent, could predict paO 2 values with σ ae  = 86.4 mmHg, MAPE = 16 %, adjusted R 2  = 0.77, RMSE = 44 mmHg and Spearman’s ρ = 0.83. Further improvement was possible by calibrating the algorithm with the first measured paO 2 /FiO 2 (p/F) ratio during surgery. Conclusion PaO 2 can be predicted by perioperative routine data in neurosurgical patients even before blood gas analysis. The prediction improves further when including the first measured p/F ratio, realizing quasi-continuous paO 2 monitoring.

Background We analyzed prospectively whether MGMT (O6-methylguanine-DNA methyltransferase) mRNA expression gains prognostic/predictive impact independent of MGMT promoter methylation in malignant glioma patients undergoing radiotherapy with concomitant and adjuvant temozolomide or temozolomide alone. As DNA-methyltransferases (DNMTs) are the enzymes responsible for setting up and maintaining DNA methylation patterns in eukaryotic cells, we analyzed further, whether MGMT promoter methylation is associated with upregulation of DNMT expression. Methodology/Principal Findings Adult patients with a histologically proven malignant astrocytoma (glioblastoma: N = 53, anaplastic astrocytoma: N = 10) were included. MGMT promoter methylation was determined by methylation-specific PCR (MSP) and sequencing analysis. Expression of MGMT and DNMTs mRNA were analysed by real-time qPCR. Prognostic factors were obtained from proportional hazards models. Correlation between MGMT mRNA expression and MGMT methylation status was validated using data from the Cancer Genome Atlas (TCGA) database (N = 229 glioblastomas). Low MGMT mRNA expression was strongly predictive for prolonged time to progression, treatment response, and length of survival in univariate and multivariate models (p<0.0001); the degree of MGMT mRNA expression was highly correlated with the MGMT promoter methylation status (p<0.0001); however, discordant findings were seen in 12 glioblastoma patients: Patients with methylated tumors with high MGMT mRNA expression (N = 6) did significantly worse than those with low transcriptional activity (p<0.01). Conversely, unmethylated tumors with low MGMT mRNA expression (N = 6) did better than their counterparts. A nearly identical frequency of concordant and discordant findings was obtained by analyzing the TCGA database (p<0.0001). Expression of DNMT1 and DNMT3b was strongly upregulated in tumor tissue, but not correlated with MGMT promoter methylation and MGMT mRNA expression. Conclusions/Significance MGMT mRNA expression plays a direct role for mediating tumor sensitivity to alkylating agents. Discordant findings indicate methylation-independent pathways of MGMT expression regulation. DNMT1 and DNMT3b are likely to be involved in CGI methylation. However, their exact role yet has to be defined.

Favorable outcome after chemotherapy of glioblastomas cannot unequivocally be linked to promoter hypermethylation of the O 6 -methylguanine-DNA methyltransferase ( MGMT ) gene encoding a DNA repair enzyme associated with resistance to alkylating agents. This indicates that molecular mechanisms determining MGMT expression have not yet been fully elucidated. We here show that glioblastomas are capable to downregulate MGMT expression independently of promoter methylation by elongation of the 3′-UTR of the mRNA, rendering the alternatively polyadenylated transcript susceptible to miRNA-mediated suppression. While the elongated transcript is poorly expressed in normal brain, its abundance in human glioblastoma specimens is inversely correlated with MGMT mRNA expression. Using a bioinformatically guided experimental approach, we identified miR-181d, miR-767-3p, and miR-648 as significant post-transcriptional regulators of MGMT in glioblastomas; the first two miRNAs induce MGMT mRNA degradation, the latter affects MGMT protein translation. A regression model including the two miRNAs influencing MGMT mRNA expression and the MGMT methylation status reliably predicts The Cancer Genome Atlas MGMT expression data. Responsivity of MGMT expressing T98G glioma cells to temozolomide was significantly enhanced after transfection of miR-181d, miR-767-3p, and miR-648. Taken together, our results uncovered alternative polyadenylation of the MGMT 3′-UTR and miRNA targeting as new mechanisms of MGMT silencing.

We analyzed prospectively whether MGMT (O(6)-methylguanine-DNA methyltransferase) mRNA expression gains prognostic/predictive impact independent of MGMT promoter methylation in malignant glioma patients undergoing radiotherapy with concomitant and adjuvant temozolomide or temozolomide alone. As DNA-methyltransferases (DNMTs) are the enzymes responsible for setting up and maintaining DNA methylation patterns in eukaryotic cells, we analyzed further, whether MGMT promoter methylation is associated with upregulation of DNMT expression. ADULT PATIENTS WITH A HISTOLOGICALLY PROVEN MALIGNANT ASTROCYTOMA (GLIOBLASTOMA: N = 53, anaplastic astrocytoma: N = 10) were included. MGMT promoter methylation was determined by methylation-specific PCR (MSP) and sequencing analysis. Expression of MGMT and DNMTs mRNA were analysed by real-time qPCR. Prognostic factors were obtained from proportional hazards models. Correlation between MGMT mRNA expression and MGMT methylation status was validated using data from the Cancer Genome Atlas (TCGA) database (N = 229 glioblastomas). Low MGMT mRNA expression was strongly predictive for prolonged time to progression, treatment response, and length of survival in univariate and multivariate models (p<0.0001); the degree of MGMT mRNA expression was highly correlated with the MGMT promoter methylation status (p<0.0001); however, discordant findings were seen in 12 glioblastoma patients: Patients with methylated tumors with high MGMT mRNA expression (N = 6) did significantly worse than those with low transcriptional activity (p<0.01). Conversely, unmethylated tumors with low MGMT mRNA expression (N = 6) did better than their counterparts. A nearly identical frequency of concordant and discordant findings was obtained by analyzing the TCGA database (p<0.0001). Expression of DNMT1 and DNMT3b was strongly upregulated in tumor tissue, but not correlated with MGMT promoter methylation and MGMT mRNA expression. MGMT mRNA expression plays a direct role for mediating tumor sensitivity to alkylating agents. Discordant findings indicate methylation-independent pathways of MGMT expression regulation. DNMT1 and DNMT3b are likely to be involved in CGI methylation. However, their exact role yet has to be defined.

Background Reverse-transcribed gene copies (retrocopies) have emerged as major sources of evolutionary novelty. MicroRNAs (miRNAs) are small and highly conserved RNA molecules that serve as key post-transcriptional regulators of gene expression. The origin and subsequent evolution of miRNAs have been addressed but not fully elucidated. Results In this study, we performed a comprehensive investigation of miRNA origination through retroduplicated mRNA sequences (retro-miRs). We identified 17 retro-miRs that emerged from the mRNA retrocopies. Four of these retro-miRs had de novo origins within retrocopied sequences, while 13 retro-miRNAs were located within exon regions and duplicated along with their host mRNAs. We found that retro-miRs were primate-specific, including five retro-miRs conserved among all primates and two human-specific retro-miRs. All retro-miRs were expressed, with predicted and experimentally validated target genes except miR-10527. Notably, the target genes of retro-miRs are involved in key biological processes such as metabolic processes, cell signaling, and regulation of neurotransmitters in the central nervous system. Additionally, we found that these retro-miRs play a potential oncogenic role in cancer by targeting key cancer genes and are overexpressed in several cancer types, including liver hepatocellular carcinoma and stomach adenocarcinoma. Conclusions Our findings demonstrated that mRNA retrotransposition is a key mechanism for the generation of novel miRNAs (retro-miRs) in primates. These retro-miRs are expressed, conserved, have target genes with important cellular functions, and play important roles in cancer.

Background Accumulating evidence indicates that neuropathic pain is a neuro-immune disorder with enhanced activation of the immune system. Recent data provided proof that neuropathic pain patients exhibit increased numbers of immunosuppressive regulatory T cells (Tregs), which may represent an endogenous attempt to limit inflammation and to reduce pain levels. We here investigate the molecular mechanisms underlying these alterations. Methods Our experimental approach includes functional analyses of primary human T cells, 3′-UTR reporter assays, and expression analyses of neuropathic pain patients’ samples. Results We demonstrate that microRNAs (miRNAs) are involved in the differentiation of Tregs in neuropathic pain. We identify miR-124a and miR-155 as direct repressors of the histone deacetylase sirtuin1 (SIRT1) in primary human CD4 + cells. Targeting of SIRT1 by either specific siRNA or by these two miRNAs results in an increase of Foxp3 expression and, consecutively, of anti-inflammatory Tregs (siRNA: 1.7 ± 0.4; miR-124a: 1.5 ± 0.4; miR-155: 1.6 ± 0.4; p  < 0.01). As compared to healthy volunteers, neuropathic pain patients exhibited an increased expression of miR-124a (2.5 ± 0.7, p  < 0.05) and miR-155 (1.3 ± 0.3; p  < 0.05) as well as a reduced expression of SIRT1 (0.5 ± 0.2; p  < 0.01). Moreover, the expression of these two miRNAs was inversely correlated with SIRT1 transcript levels. Conclusions Our findings suggest that in neuropathic pain, enhanced targeting of SIRT1 by miR-124a and miR-155 induces a bias of CD4 + T cell differentiation towards Tregs, thereby limiting pain-evoking inflammation. Deciphering miRNA-target interactions that influence inflammatory pathways in neuropathic pain may contribute to the discovery of new roads towards pain amelioration. Trial registration German Clinical Trial Register DRKS00005954