Metagenomic next-generation sequencing, instead of procalcitonin, could guide antibiotic usage in patients with febrile acute necrotizing pancreatitis: a multicenter, prospective cohort study.

BACKGROUNDS: The effectiveness of procalcitonin-based algorithms in guiding antibiotic usage for febrile acute necrotizing pancreatitis (ANP) remains controversial. Metagenomic next-generation sequencing (mNGS) has been applied to diagnose infectious diseases. The authors aimed to evaluate the effectiveness of blood mNGS in guiding antibiotic stewardship for febrile ANP. MATERIALS AND METHODS: The prospective multicenter clinical trial was conducted at seven hospitals in China. Blood samples were collected during fever (T ≥38.5°C) from ANP patients. The effectiveness of blood mNGS, procalcitonin, and blood culture in diagnosing pancreatic infection was evaluated and compared. Additionally, the real-world utilization of antibiotics and the potential mNGS-guided antimicrobial strategy in febrile ANP were also analyzed. RESULTS: From May 2023 to October 2023, a total of 78 patients with febrile ANP were enrolled and 30 patients (38.5%) were confirmed infected pancreatic necrosis (IPN). Compared with procalcitonin and blood culture, mNGS showed a significantly higher sensitivity rate (86.7% vs. 56.7% vs. 26.7%, P <0.001). Moreover, mNGS outperformed procalcitonin (89.5 vs. 61.4%, P <0.01) and blood culture (89.5 vs. 69.0%, P <0.01) in terms of negative predictive value. Blood mNGS exhibited the highest accuracy (85.7%) in diagnosing IPN and sterile pancreatic necrosis, significantly superior to both procalcitonin (65.7%) and blood culture (61.4%). In the multivariate analysis, positive blood mNGS (OR=60.2, P <0.001) and lower fibrinogen level (OR=2.0, P <0.05) were identified as independent predictors associated with IPN, whereas procalcitonin was not associated with IPN, but with increased mortality (Odds ratio=11.7, P =0.006). Overall, the rate of correct use of antibiotics in the cohort was only 18.6% (13/70) and would be improved to 81.4% (57/70) if adjusted according to the mNGS results. CONCLUSION: Blood mNGS represents important progress in the early diagnosis of IPN, with particular importance in guiding antibiotic usage for patients with febrile ANP.

Prediction of survival in patients with infected pancreatic necrosis: a prospective cohort study.

BACKGROUND: Infected pancreatic necrosis (IPN) is a severe complication of acute pancreatitis, with mortality rates ranging from 15 to 35%. However, limited studies exist to predict the survival of IPN patients and nomogram has never been built. This study aimed to identify predictors of mortality, estimate conditional survival (CS), and develop a CS nomogram and logistic regression nomogram for real-time prediction of survival in IPN patients. METHODS: A prospective cohort study was performed in 335 IPN patients consecutively enrolled at a large Chinese tertiary hospital from January 2011 to December 2022. The random survival forest method was first employed to identify the most significant predictors and capture clinically relevant nonlinear threshold effects. Instantaneous death risk and CS was first utilized to reveal the dynamic changes in the survival of IPN patients. A Cox model-based nomogram incorporating CS and a logistic regression-based nomogram were first developed and internally validated with a bootstrap method. RESULTS: The random survival forest model identified seven foremost predictors of mortality, including the number of organ failures, duration of organ failure, age, time from onset to first intervention, hemorrhage, bloodstream infection, and severity classification. Duration of organ failure and time from onset to first intervention showed distinct thresholds and nonlinear relationships with mortality. Instantaneous death risk reduced progressively within the first 30 days, and CS analysis indicated gradual improvement in real-time survival since diagnosis, with 90-day survival rates gradually increasing from 0.778 to 0.838, 0.881, 0.974, and 0.992 after surviving 15, 30, 45, 60, and 75 days, respectively. After further variables selection using step regression, five predictors (age, number of organ failures, hemorrhage, time from onset to first intervention, and bloodstream infection) were utilized to construct both the CS nomogram and logistic regression nomogram, both of which demonstrated excellent performance with 1000 bootstrap. CONCLUSION: Number of organ failures, duration of organ failure, age, time from onset to first intervention, hemorrhage, bloodstream infection, and severity classification were the most crucial predictors of mortality of IPN patients. The CS nomogram and logistic regression nomogram constructed by these predictors could help clinicians to predict real-time survival and optimize clinical decisions.

Novel insights into biomarkers of progression in Desmoid tumor.

Desmoid tumor (DT) is a rare neoplasm characterized by the proliferation of myofibroblastic cells that infiltrates and invades adjacent tissues. Due to its locally aggressive and recurrent nature, DT often causes local symptoms and can be challenging to manage clinically. Therefore, identifying biomarkers that can predict the progression of DT and guide treatment decisions is critical. This review summarizes several biomarkers that have been implicated in active surveillance (AS) and the prediction of postoperative recurrence and attempts to elucidate their underlying mechanisms. Some of these novel markers could provide prognostic value for clinicians, and ultimately help facilitate optimal and accurate therapeutic decisions for DT.

Investigation into surface composition of nitrogen-doped niobium for superconducting RF cavities.

Systematic analysis of the surface morphology, crystalline phase, chemical composition and elemental distribution along depth for nitrogen-doped niobium was carried out using different methods of characterization, including Scanning Electron Microscopy (SEM), Atomic-Force Microscopy (AFM), Grazing Incidence X-ray Diffraction (GIXRD), Rutherford Backscattering Spectrometry (RBS) and layer-by-layer X-ray Photoelectron Spectroscopy (XPS) analysis. The results showed that, after nitrogen doping, the surface was covered by densely distributed trigonal precipitates with an average crystallite size of 32 ± 8 nm, in line with the calculation result (29.9 nm) of nitrogen-enrichedß-Nb2N from GIXRD, demonstrating the phase composition of trigonal precipitates. The depth analysis through RBS and XPS indicated thatß-Nb2N was dominant in the topmost 9.7 nm and extended to a depth of 575 nm, with gradually decreased content. In addition, the successive change along depth in the naturally oxidized states of niobium after nitrogen doping, was revealed. It was interesting to find that the oxygen diffusion depth could be moderately enhanced by the nitridation process. These results established the near-surface phase composition of nitrided niobium, which is of great significance in evaluating the effect of nitrogen doping and further understanding the Q improvement of the superconducting radio frequency cavities.

Genome-wide identification, characterization and evolution of cuticular protein genes in the malaria vector Anopheles sinensis (Diptera: Culicidae).

Thirteen cuticular protein (CP) families have been recognized in arthropods. In this study, 250 Anopheles sinensis CP genes were identified and named based on genome and transcriptome sequences. They were classified into 10 families based on motifs and phylogenetic analyses. In 11 other insect species, nine had CP numbers > 150 while Apis mellifera and Tribolium castaneum had CP numbers less than 52. The CPs of eight species occupied > 1.4% of the total genomic gene number, whereas in three species the CPs occupied < 1%. The phylogenies for each CP family in An. sinensis were constructed and discussed. The 250 CPs each had 1-8 exons with 144 CPs (57.6%) having two exons. The intron length ranged from 66-3888 bp with 174 introns (54.0%) being 66-100 bp long. Except for two CPs on two contigs, 248 CPs were mapped onto 28 scaffolds with 136 genes (54.4%) restricted to five scaffolds. A total of 107 CPs were clustered and located at 27 loci. The CPR family had the conserved motif GSYSLVEPDGTVRTV. The RR-1 subfamily had an additional 21 amino acid (aa) motifs with the YVADENGF sequence that is common in insects. The RR-2 subfamily had an additional 50 aa motifs with two additional regions RDGDVVKG and G-x(3)-VV. A comparison with 115 orthologous counterparts of An. gambiae CPs suggested purifying selection for all of these genes. This study provides basic information useful for further studies on biological functions of An. sinensis CPs as well as for comparative genomics of insect CPs.

Modeling and optimizing of the random atomic spin gyroscope drift based on the atomic spin gyroscope.

In order to improve the atom spin gyroscope's operational accuracy and compensate the random error caused by the nonlinear and weak-stability characteristic of the random atomic spin gyroscope (ASG) drift, the hybrid random drift error model based on autoregressive (AR) and genetic programming (GP) + genetic algorithm (GA) technique is established. The time series of random ASG drift is taken as the study object. The time series of random ASG drift is acquired by analyzing and preprocessing the measured data of ASG. The linear section model is established based on AR technique. After that, the nonlinear section model is built based on GP technique and GA is used to optimize the coefficients of the mathematic expression acquired by GP in order to obtain a more accurate model. The simulation result indicates that this hybrid model can effectively reflect the characteristics of the ASG's random drift. The square error of the ASG's random drift is reduced by 92.40%. Comparing with the AR technique and the GP + GA technique, the random drift is reduced by 9.34% and 5.06%, respectively. The hybrid modeling method can effectively compensate the ASG's random drift and improve the stability of the system.