Risk factors of serious postoperative outcomes in patients aged ≥90 years undergoing surgical intervention

Objective: We aimed to identify preoperative and intraoperative factors associated with serious postoperative outcomes, which may help patients and clinicians make better-informed decisions. Methods: We conducted a retrospective study including all patients aged ≥90 years who underwent surgery between January 1, 2011, and January 1, 2021, at Chongqing University Central Hospital. We assessed 30 pre- and intraoperative demographic and clinical variables. Logistic regression was used to identify the independent risk factors for serious postoperative outcomes in patients aged ≥90 years. Results: A total of 428 patients were included in our analysis. The mean age was 92.6 years (SD ± 2.6). There were 240 (56.1%) females and 188 (43.9%) males. The most common comorbidities were hypertension (44.9%) and arrhythmias (34.8%). The 30-day hospital mortality was 5.6%, and severe morbidity was 33.2%. Based on the multivariate logistic regression classification analysis of the American Society of Anesthesiologists (ASA)≥ Ⅳ [odds ratio (OR), 5.39, 95% confidence interval (CI), 2.06–14.16, P = .001], emergency surgery (OR, 5.02, 95% CI, 2.85–15.98, P = .001) and chronic heart failure (OR, 6.11, 95% CI, 1.93–13.06, P = .001) were identified as independent risk factors for 30-day hospital mortality, and ASA≥ Ⅳ (OR, 4.56, 95%CI, 2.56–8.15, P < .001), Barthel index (BI) < 35 (OR, 2.28, 95%CI, 1.30–3.98, P = .001), chronic heart failure (OR, 3.67, 95%CI, 1.62–8.31, P = .002), chronic kidney disease (OR, 4.24, 95%CI, 1.99–9.05, P < .001), general anesthesia (OR, 3.31, 95%CI, 1.91–5.76, P < .001), emergency surgery (OR, 3.72, 95%CI, 1.98–6.99, P < .001), and major surgery (OR, 3.44, 95%CI, 1.90–6.22, P < .001) were identified as independent risk factors for serious postoperative complications. Conclusions: Patients aged ≥90 years with ASA≥ Ⅳ, BI < 35, combined with chronic heart failure or chronic kidney disease, undergoing emergency surgery, major surgery or general anesthesia have a higher risk of serious postoperative outcomes. Identifying these risk factors in an early stage may contribute to our clinical decision-making and improve the quality of treatments

The effect of operational parameters on the characteristics of gas–solid flow inside the COREX shaft furnace

The COREX shaft furnace is of great importance to the whole C-3000 process. There are many problems with the operation of the COREX shaft furnace, especially with gas and burden distribution, that have as yet been little studied. The present work establishes a three-dimensional quarter model. After validation by operating data in Baosteel, the model is used to investigate the gas utilization rate and the metallization rate of the COREX shaft furnace. The parameters, including the reducing gas flow, the volume fraction of gas phase, and the multilayered burden, are systematically investigated. The results show that the reducing gas flow has a great influence on the gas utilization rate and the metallization rate, while the volume fraction of gas phase has a more significant effect on the metallization rate than on the gas utilization rate. In order to obtain a higher metallization rate, the reducing gas flow needs to be adjusted step by step and the volume fraction of gas phase needs to be increased. In addition, ore and coke need to be discharged separately in order to increase the solid metallization rate

Lithofacies palaeogeography of Middle Permian in the Sichuan Basin and its petroleum geological significance

Exploration discoveries have been made in the Middle Permian of the Sichuan Basin, indicating great exploration potential. However, the Middle Permian has obvious tectonic-sedimentary variation, resulting in complexity of hydrocarbon accumulation mechanism. The reconstruction of sequence lithofacies paleogeography is of great significance to understand the tectonic-sedimentary evolution, sedimentary formation and hydrocarbon exploration. In this paper, the lithofacies palaeogeography during the Middle Permian Qixia-Maokou period in the Sichuan Basin was reconstructed with the third order sequence as the minimum longitudinal mapping unit based on oil and gas exploration results as well as typical drilling and outcropping data. Five third-order sequences for the Middle Permian in the Sichuan Basin were supposed in this study including: SQq1 (from the Liangshan Formation to the section B of the first member of the Qixia Formation), SQq2 (from the section A of the first member to the second member of the Qixia Formation), SQm1 (from the first member to the section C of the second member of the Maokou Formation), SQm2 (from the section B to the section A of the second member of the Maokou Formation), and SQm3 (from the third to the fourth member of Maokou Formation). During the deposition of SQq1 and SQm1, transgression and filling were dominant, and spot and banded beach bodies in some palaeo-highlands of the basin were discovered. During the deposition of SQq2, SQm2 and SQm3, continuous regression and geomorphic differentiation were dominant, the carbonate platforms depositional system with edge were developed in the margin parts of the basin, and large scale multi-stage and multi-zone beach bodies could be formed in the platform. SQq2 was the main shoal-forming stage in the Middle Permian. The relatively long regressive process [JP+1]and local block uplift promoted the formation of large scale multi-stage and multi-zone shoals in the platform margin of the western Sichuan Basin and the southern part of the central Sichuan Basin. During SQm3 period, the sedimentary differentiation in the northern Sichuan Basin was obviously intensified, and it was transformed into an alternate uplift and sag pattern with platform margin, slope and shelf. The platform margin and intra-platform shoal of the HST during the deposition of SQq2 and SQm3 provided material basis for dolomitization, dissolution and other later constructive diagenetic transformation. Therefore, the delineation of slope break zone geomorphology and the prediction of platform margin and intra-platform shoal should be regarded as the focus of further exploration

Integrated Analysis of Drug-Induced Gene Expression Profiles Predicts Novel hERG Inhibitors

<div><p>Growing evidence suggests that drugs interact with diverse molecular targets mediating both therapeutic and toxic effects. Prediction of these complex interactions from chemical structures alone remains challenging, as compounds with different structures may possess similar toxicity profiles. In contrast, predictions based on systems-level measurements of drug effect may reveal pharmacologic similarities not evident from structure or known therapeutic indications. Here we utilized drug-induced transcriptional responses in the Connectivity Map (CMap) to discover such similarities among diverse antagonists of the human <i>ether-à-go-go</i> related (hERG) potassium channel, a common target of promiscuous inhibition by small molecules. Analysis of transcriptional profiles generated in three independent cell lines revealed clusters enriched for hERG inhibitors annotated using a database of experimental measurements (hERGcentral) and clinical indications. As a validation, we experimentally identified novel hERG inhibitors among the unannotated drugs in these enriched clusters, suggesting transcriptional responses may serve as predictive surrogates of cardiotoxicity complementing existing functional assays.</p> </div

Expression and structural similarity of hERG inhibitor-enriched clusters.

<p>(<b>A</b>) Chemical similarity (Tanimoto coefficient = TC) computed from FCFP_6 circular fingerprints versus expression similarity (Pearson coefficient = PC) computed from drug-induced transcriptional response for selected hERG inhibitor-enriched clusters for MCF7 (top) PC3 (middle) and HL60 (bottom). Cluster in drug expression networks are highlighted, with example compounds outlined in black in inset (left column). Chemical structures are illustrated with corresponding chemical and expression similarity values. (<b>B</b>) Distribution of pairwise expression response similarities within hERG inhibitor-enriched clusters and between drugs in enriched and non-enriched clusters from <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0069513#pone-0069513-g002" target="_blank">Figure <b>2B</b></a>. (<b>C</b>) As (<b>B</b>), comparing distribution of chemical similarities.</p

Pipeline for construction and analysis of drug transcriptional response network.

<p>Raw microarray data for drugs profiled in three cancer cell lines in the Connectivity Map (left) are normalized and clustered using affinity propagation (top center) based on similarities in drug-induced gene expression profiles (nodes) to yield clusters with a characteristic “exemplar” (highlighted by red) representing the expression profile shared by cluster members. The resulting clusters (middle center) are annotated for experimental and clinical evidence of hERG inhibition (bottom center), and enrichment analysis conducted to find clusters with a statistically significant fraction of hERG inhibitors. Unannotated compounds in these enriched clusters (top right) are then experimentally assessed for hERG inhibition in a high-throughput electrophysiology assay (middle right) to yield potency values (bottom right).</p