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BackgroundThe assembly of metagenomes decomposes members of complex microbe communities and allows the characterization of these genomes without laborious cultivation or single-cell metagenomics. Metagenome assembly is a process that is memory intensive and time consuming. Multi-terabyte sequences can become too large to be assembled on a single computer node, and there is no reliable method to predict the memory requirement due to data-specific memory consumption pattern. Currently, out-of-memory (OOM) is one of the most prevalent factors that causes metagenome assembly failures.ResultsIn this study, we explored the possibility of using Persistent Memory (PMem) as a less expensive substitute for dynamic random access memory (DRAM) to reduce OOM and increase the scalability of metagenome assemblers. We evaluated the execution time and memory usage of three popular metagenome assemblers (MetaSPAdes, MEGAHIT, and MetaHipMer2) in datasets up to one terabase. We found that PMem can enable metagenome assemblers on terabyte-sized datasets by partially or fully substituting DRAM. Depending on the configured DRAM/PMEM ratio, running metagenome assemblies with PMem can achieve a similar speed as DRAM, while in the worst case it showed a roughly two-fold slowdown. In addition, different assemblers displayed distinct memory/speed trade-offs in the same hardware/software environment.ConclusionsWe demonstrated that PMem is capable of expanding the capacity of DRAM to allow larger metagenome assembly with a potential tradeoff in speed. Because PMem can be used directly without any application-specific code modification, these findings are likely to be generalized to other memory-intensive bioinformatics applications.

The coordinated optimization of energy conservation, efficiency improvement, and pollution reduction in the sintering production process is vital for the efficient and sustainable development of the sintering department. However, previous studies have shown shortcomings in the multi-objective collaborative optimization of sintering systems and the quantification of pollutant impacts. To address these, this paper proposes a multi-objective optimization method integrated with the NSGA-III algorithm and establishes an integrated system optimization model for sintered ore production and high-temperature waste heat recovery. The results demonstrate significant improvements: energy utilization efficiency increased by 0.67%, energy consumption decreased by 17.3 MJ/t, production costs were reduced by 11.45 CNY/t, and the emissions of CO2, SO2, and NOx were reduced by 0.464 kg/t, 0.034 kg/t, and 0.008 kg/t, respectively. Additionally, the study identified optimal configuration parameters and analyzed the quantitative impact of several key factors on multiple indicators. The results also show that reducing the water content of the mixture, decreasing the middling coal content in the fuel, and increasing the thickness of the material layer are effective strategies to reduce energy consumption and pollutant emissions in the sintering process. Overall, implementing these optimizations can bring significant economic and environmental benefits to the steel industry. [ABSTRACT FROM AUTHOR]

Background: Esketamine, the first Food and Drug Administration-approved fast-acting antidepressant, has limited use because of its addictive properties. Although the combination of dextromethorphan and bupropion partially addresses the limitations of esketamine, concerns remain regarding neurologic side effects related to dextromethorphan metabolites, and seizure risks associated with high-dose bupropion. SAL0114, a novel formulation combining deuterated dextromethorphan (in which hydrogen atoms are replaced with deuterium) with bupropion, seeks to enhance dextromethorphan stability through deuteration of its metabolic sites. This approach is expected to increase antidepressant efficacy, reduce metabolite-induced safety issues, and allow for lower bupropion dosages. Methods: Radioligand competition binding assays were used to evaluate the impact of deuterium substitution on the in vitro activity of dextromethorphan and its metabolite, dextrorphan. In vitro hepatic microsomal stability and in vivo mouse pharmacokinetic assays were performed to assess the effects of deuteration on dextromethorphan stability. Two mouse models of behavioral despair were used to determine the antidepressant and synergistic effects of deuterated dextromethorphan and bupropion. Additionally, a reserpine-induced hypothermia rat model and an ammonia-induced cough mouse model were used to assess the in vivo effects from a pathological perspective. Results: Deuterated dextromethorphan maintained the same in vitro activity as dextromethorphan while exhibiting twice the metabolic stability both in vitro and in vivo. Combination with bupropion further improved its in vivo stability, increasing the exposure by 2.4 times. The combination demonstrated efficacy and synergistic effects in all tested animal models, showing superior efficacy compared with the dextromethorphan-bupropion combination. Conclusion: Deuteration improved dextromethorphan metabolic stability without altering its in vitro activity. Bupropion enhanced this stability and synergistically boosted the antidepressant effect by increasing deuterated dextromethorphan exposure in vivo. This enhanced metabolic stability suggests a reduction in dextromethorphan metabolites associated with clinical neurological side effects. Consequently, SAL0114 is hypothesized to offer improved efficacy and safety compared with the non-deuterated combination, potentially allowing for lower bupropion dosages. Further clinical studies are required to confirm these preclinical findings. [ABSTRACT FROM AUTHOR]

To analyze the influence of diabetes mellitus on the clinical outcomes of moderately severe acute pancreatitis (MSAP) and severe acute pancreatitis (SAP). Methods: This retrospective study included patients diagnosed with MSAP and SAP at Shanxi Bethune Hospital from January 1, 2017, to December 31, 2021. Clinical data were collected, including patient demographics, 24-hour laboratory indicators, and inflammation indices. Propensity score matching (PSM) was used to compare outcomes before and after matching. Patients were randomized into training and validation sets (7:3) to develop and validate a clinical prediction model for infected pancreatic necrosis (IPN). Results: Among 421 patients, 79 had diabetes at admission. Before PSM, diabetic patients had higher incidences of peripancreatic fluid (71% vs 47%, p< 0.001) and IPN (48% vs 10%, p< 0.001), higher surgical intervention rates (24% vs 12%, p=0.008), and significant differences in abdominocentesis (22% vs 11%, p=0.014). After PSM, 174 patients were matched, and the diabetes group still showed higher incidences of peripancreatic fluid (69% vs 47%, p=0.008), IPN (48% vs 11%, p< 0.001), and surgical intervention rates (27% vs 13%, p=0.037). Diabetes, modified CT severity index (MCTSI), serum calcium, and HDL-c were identified as independent risk factors for IPN. The prediction model demonstrated good predictive value. Conclusion: In MSAP and SAP patients, diabetes mellitus can exert an influence on their clinical outcome and is an independent risk factor for IPN. The alignment diagram and web calculator constructed on the basis of diabetes mellitus, modified CT severity index (MCTSI), serum calcium and high-density lipoprotein cholesterol (HDL-c) have good predictive value and clinical guidance for the occurrence of IPN in MSAP and SAP. [ABSTRACT FROM AUTHOR]

Liver cancer is one of the most common malignant tumors in the world, and its incidence and mortality are increasing year by year. The prognosis of liver cancer depends on the stage of liver cancer, the treatment method, the liver function, and individual differences. The prognosis of liver cancer mainly worsens with the progression of the stage. The prediction and staging system of liver cancer prognosis plays a very important role in the outcome of liver cancer prognosis, providing some guidance for clinical practice and bringing benefits for patients. This article reports on the prediction models and staging systems that have been applied in the field of liver cancer in the past 5 years, objectively analyzes the advantages and disadvantages, applicable population of each model and staging system, and searches for other patient and clinical characteristics that need to be considered for successfully establishing a prediction model, aiming to improve the specificity, sensitivity, and accuracy of liver cancer prediction and increase the overall survival rate of liver cancer. [ABSTRACT FROM AUTHOR]