Status and influencing factors of nurses' burnout: A cross-sectional study during COVID-19 regular prevention and control in Jiangsu Province, China.

Background: Chinese nurses working with immense stress may have issues with burnout during COVID-19 regular prevention and control. There were a few studies investigating status of burnout and associated factors among Chinese nurses. However, the relationships remained unclear. Objectives: To investigate status and associated factors of nurses' burnout during COVID-19 regular prevention and control. Methods: 784 nurses completed questionnaires including demographics, Generalized Anxiety Disorder-7, Patient Health Questionnaire-9, Insomnia Severity Index, Impact of Event Scale-revised, Perceived Social Support Scale, Connor-Davidson Resilience Scale, General Self-efficacy Scale and Maslach Burnout Inventory. Results: 310 (39.5%), 393 (50.1%) and 576 (73.5%) of respondents were at high risk of emotional exhaustion (EE), depersonalization (DP) and reduced personal accomplishment (PA). The risk of EE, DP and reduced PA were moderate, high and high. Nurses with intermediate and senior professional rank and title and worked >40 h every week had lower scores in EE. Those worked in low-risk department reported lower scores in PA. Anxiety, post-traumatic stress disorder (PTSD), self-efficacy and social support were influencing factors of EE and DP, while social support and resilience were associated factors of PA. Conclusion: Chinese nurses' burnout during COVID-19 regular prevention and control was serious. Professional rank and title, working unit, weekly working hours, anxiety, PTSD, self-efficacy, social support and resilience were associated factors of burnout.

Mechanism and deterioration pattern of sandstone surrounding rock voiding at bottom of heavy-haul railway tunnel.

This study combines laboratory experiments and discrete element simulation methods to analyze the mechanism and deterioration patterns of sandstone surrounding rock voiding the bottom of a heavy-haul railway tunnel. It is based on previously acquired measurement data from optical fiber grating sensors installed in the Taihangshan Mountain Tunnel of the Wari Railway. By incorporating rock particle wastage rate results, a method for calculating the peak strength and elastic modulus attenuation of surrounding rock is proposed. Research indicates that the operation of heavy-haul trains leads to an instantaneous increase in the dynamic water pressure on the bottom rock ranging 144.4-390.0%, resulting in high-speed water flow eroding the rock. After 1-2 years of operation, the bottom water and soil pressures increase by 526.5% and 390.0%, respectively. Focusing on sandstone surrounding rock with high observability, laboratory experiments were conducted to monitor the degradation stages of infiltration, particle loss, and voiding of rock under the action of dynamic water flow. The impact of water flow on the "cone-shaped" bottom rock deformation was also clarified. The extent of rock deterioration and voiding was determined using miniature water and soil pressure sensors in conjunction with discrete element numerical simulations. The measured rock particle loss was used as a criterion. Finally, a fitting approach is derived to calculate the peak strength and elastic modulus attenuation of surrounding rock, gaining insight into and providing a reference for the maintenance and disposal measures for the bottom operation of heavy-haul railway tunnels.

Nomogram predicting survival in patients with lymph node-negative hepatocellular carcinoma based on the SEER database and external validation.

BACKGROUND: The relationship between lymph node (LN) status and survival outcome in hepatocellular carcinoma (HCC) is a highly controversial topic. The aim of this study was to investigate the prognostic factors in patients without LN metastasis (LNM) and to construct a nomogram to predict cancer-specific survival (CSS) in this group of patients. METHODS: We screened 6840 eligible HCC patients in the Surveillance, Epidemiology and End Results（SEER）database between 2010 and 2019 and randomized them into a training cohort and an internal validation cohort, and recruited 160 patients from Zhongnan Hospital of Wuhan University as an external validation cohort. Independent prognostic factors obtained from univariate and multivariate analysis were used to construct a nomogram prediction model. The concordance index (C-index), area under curve (AUC), calibration plots and decision curve analysis (DCA) were used to assess the predictive power and clinical application of the model. RESULTS: Univariate and multivariate analysis revealed age, gender, bone metastasis, lung metastasis, AFP, T stage, surgery and chemotherapy as independent prognostic factors. The C-index of the constructed nomogram for the training cohort, internal validation cohort and external validation cohort are 0.746, 0.740, and 0.777, respectively. In the training cohort, the AUC at 1-, 3-, and 5-year were 0.81, 0.800, and 0.800, respectively. Calibration curves showed great agreement between the actual observations and predictions for the three cohorts. The DCA results suggest that the nomogram model has more clinical application potential. CONCLUSION: We constructed a nomogram to predict CSS in HCC patients without LNM. The model has been internally and externally validated to have excellent predictive performance and can help clinicians determine prognosis and make treatment decisions.

LncRNA FRMD6-AS1/miR-491-5p/USP13 pathway attenuated ferroptosis and contributed to liver fibrosis.

Liver fibrosis is an invertible pathophysiologic process featured by excessive accumulation of extracellular matrix (ECM) which injures liver cells and activates hepatic stellate cells (HSCs). Besides, inducing ferroptosis in activated HSCs can alleviate liver fibrosis. LncRNAs modulate ferroptosis in activated HSCs and ECM deposition in liver fibrosis. However, the role of lncRNA FRMD6-AS1 in liver fibrosis is not discovered. In this study, lncRNA FRMD6-AS1 was dramatically up-regulated in activated HSCs. Knockdown of FRMD6-AS1 markedly increased iron ion, ROS and MDA levels, decreased GSH level, SLC7A11 and GPX4 protein expressions in activated HSCs. In addition, HSCs activation markers α-SMA and COL1α1 expressions were up-regulated in activated HSCs; knockdown of FRMD6-AS1 markedly down-regulated α-SMA and COL1α1 expressions in HSCs. Besides, lncRNA FRMD6-AS1 could interact with miR-491-5p, and negatively modulate miR-491-5p expression. USP13 was a target of miR-491-5p, and could be negatively modulated by miR-491-5p. Moreover, FRMD6-AS1 knockdown increased iron ion and ROS levels, decreased SLC7A11 and GPX4 protein expressions, facilitated HSCs viability, and up-regulated α-SMA and COL1α1 expressions via miR-491-5p/USP13 pathway. Finally, FRMD6-AS1 knockdown restored liver tissue structure and abrogated fibrosis in livers in a CCL4 liver fibrosis mouse model. Hence, lncRNA FRMD6-AS1/miR-491-5p/USP13 pathway repressed ferroptosis, promoted ECM deposition and facilitated liver fibrosis in vitro and in vivo models.

[Research progress of traditional Chinese medicines as quorum sensing inhibitors in collaboration with antibiotics to inhibit drug-resistant bacteria].

Quorum sensing system regulates the expression of genes related to bacterial growth, metabolism and other behaviors by sensing bacterial density, and controls the unified action of the entire bacterial population. This mechanism can ensure the normal secretion of bacterial metabolites and the stability of the biofilm microenvironment, providing protection for the formation of biofilms and the normal growth and reproduction of bacteria. Traditional Chinese medicine, capable of quorum sensing inhibition, can inhibit the formation of bacterial biofilms, reduce bacterial resistance, and enhance the anti-infection ability of antibiotics when combined with antibiotics. In recent years, the combination of traditional Chinese and Western medicine in the treatment of drug-resistant bacterial infections has become a research hotspot. Starting with the associations between quorum sensing, biofilm and drug-resistant bacteria, this paper reviews the relevant studies about the combined application of traditional Chinese medicines as quorum sensing inhibitors with antibiotics in the treatment of drug-resistant bacteria. This review is expected to provide ideas for the development of new clinical treatment methods and novel anti-infection drugs.

Acetylation of Rec8 cohesin complexes regulates reductional chromosome segregation in meiosis.

For establishing sister chromatid cohesion and proper chromosome segregation in mitosis in fission yeast, the acetyltransferase Eso1 plays a key role. Eso1 acetylates cohesin complexes, at two conserved lysine residues K105 and K106 of the cohesin subunit Psm3. Although Eso1 also contributes to reductional chromosome segregation in meiosis, the underlying molecular mechanisms have remained elusive. Here, we purified meiosis-specific Rec8 cohesin complexes localized at centromeres and identified a new acetylation at Psm3-K1013, which largely depends on the meiotic kinetochore factor meikin (Moa1). Our molecular genetic analyses indicate that Psm3-K1013 acetylation cooperates with canonical acetylation at Psm3-K105 and K106, and plays a crucial role in establishing reductional chromosome segregation in meiosis.

External quality assessment for molecular detection of Ureaplasma urealyticum in China.

BACKGROUND AND AIMS: A pilot external quality assessment (EQA) scheme for molecular detection of Ureaplasma urealyticum (UU) was conducted by the National Center for Clinical Laboratories (NCCL) to evaluate the testing capabilities of clinical laboratories and the actual performance of DNA-based nucleic acid amplification tests (NAAT) and RNA-based NAATs when applied in clinical settings. MATERIALS AND METHODS: The EQA panel contained twelve lyophilized samples, including positive samples containing inactivated cell culture supernatants of UU at different concentrations and sterile saline for negative samples. The positive samples were further divided into three groups of high, moderate and low concentrations. The panels were distributed to the participants and the datasets were analyzed according to the qualitative results. RESULTS: A total of 365 laboratories participated in the EQA scheme, and 360 results submitted by 338 laboratories were collected, of which 96.11 % (346/360) of the returned results and 95.86 % (324/338) of the laboratories were deemed competent. The positive percentage agreement (PPA) was ≥ 97.5 % for high and moderate concentration samples, but varied significantly for low concentration samples, decreasing from 86.94 % to 51.94 % as the sample concentration decreased. Additionally, for low concentration samples, RNA-based NAAT showed higher PPAs than DNA-based NAATs, but these results were specific to UU supernatants used in this study. CONCLUSION: Most of UU detection assays employed by the participants were generally consistent with their estimated limit of detection (LOD), and the majority of participants can reliably detect UU samples with high and moderate concentrations, while the poor analytical performance for low concentration samples requires further improvement and optimization.

An [18F]FDG PET/3D-ultrashort echo time MRI-based radiomics model established by machine learning facilitates preoperative assessment of lymph node status in non-small cell lung cancer.

OBJECTIVES: To develop an [18F]FDG PET/3D-UTE model based on clinical factors, three-dimensional ultrashort echo time (3D-UTE), and PET radiomics features via machine learning for the assessment of lymph node (LN) status in non-small cell lung cancer (NSCLC). METHODS: A total of 145 NSCLC patients (training, 101 cases; test, 44 cases) underwent whole-body [18F]FDG PET/CT and chest [18F]FDG PET/MRI were enrolled. Preoperative clinical factors and 3D-UTE, CT, and PET radiomics features were analyzed. The Mann-Whitney U test, LASSO regression, and SelectKBest were used for feature extraction. Five machine learning algorithms were used to establish prediction models, which were evaluated by the area under receiver-operator characteristic (ROC), DeLong test, calibration curves, and decision curve analysis (DCA). RESULTS: A prediction model based on random forest, consisting of four clinical factors, six 3D-UTE, and six PET radiomics features, was used as the final model for PET/3D-UTE. The AUCs of this model were 0.912 and 0.791 in the training and test sets, respectively, which not only showed different degrees of improvement over individual models such as clinical, 3D-UTE, and PET (AUC-training = 0.838, 0.834, and 0.828, AUC-test = 0.756, 0.745, and 0.768, respectively) but also achieved the similar diagnostic efficacy as the optimal PET/CT model (AUC-training = 0.890, AUC-test = 0.793). The calibration curves and DCA indicated good consistency (C-index, 0.912) and clinical utility of this model, respectively. CONCLUSION: The [18F]FDG PET/3D-UTE model based on clinical factors, 3D-UTE, and PET radiomics features using machine learning methods could noninvasively assess the LN status of NSCLC. CLINICAL RELEVANCE STATEMENT: A machine learning model of 18F-fluorodeoxyglucose positron emission tomography/ three-dimensional ultrashort echo time could noninvasively assess the lymph node status of non-small cell lung cancer, which provides a novel method with less radiation burden for clinical practice. KEY POINTS: • The 3D-UTE radiomics model using the PLS-DA classifier was significantly associated with LN status in NSCLC and has similar diagnostic performance as the clinical, CT, and PET models. • The [18F]FDG PET/3D-UTE model based on clinical factors, 3D-UTE, and PET radiomics features using the RF classifier could noninvasively assess the LN status of NSCLC and showed improved diagnostic performance compared to the clinical, 3D-UTE, and PET models. • In the assessment of LN status in NSCLC, the [18F]FDG PET/3D-UTE model has similar diagnostic efficacy as the [18F]FDG PET/CT model that incorporates clinical factors and CT and PET radiomics features.

Artificial Neural Network Analysis of Determinants of Tacrolimus Pharmacokinetics in Liver Transplant Recipients.

BACKGROUND: The efficacy and toxicity of tacrolimus are closely related to its trough blood concentrations. Identifying the influencing factors of pharmacokinetics of tacrolimus in the early postoperative period is conducive to the optimization of the individualized tacrolimus administration protocol and to help liver transplant (LT) recipients achieve the target blood concentrations. OBJECTIVE: This study aimed to develop an artificial neural network (ANN) for predicting the blood concentration of tacrolimus soon after liver transplantation and for identifying determinants of the concentration based on Shapley additive explanation (SHAP). METHODS: In this retrospective study, we enrolled 31 recipients who were first treated with liver transplantation from the Department of Liver Transplantation and Hepatic Surgery, the First Affiliated Hospital of Shandong First Medical University (Shandong Provincial Qianfoshan Hospital) from November 2020 to May 2021. The basic information, biochemical indexes, use of concomitant drugs, and genetic factors of organ donors and recipients were used for the ANN model inputs, and the output was the steady-state trough concentration (C0) of tacrolimus after oral administration in LT recipients. The ANN model was established to predict C0 of tacrolimus, SHAP was applied to the trained model, and the SHAP value of each input was calculated to analyze quantitatively the influencing factors for the output C0. RESULTS: A back-propagation ANN model with 3 hidden layers was established using deep learning. The mean prediction error was 0.27 ± 0.75 ng/mL; mean absolute error, 0.60 ± 0.52 ng/mL; correlation coefficient between predicted and actual C0 values, 0.9677; and absolute prediction error of all blood concentrations obtained by the ANN model, ≤3.0 ng/mL. The results indicated that the following factors had the most significant effect on C0: age, daily drug dose, genotype at CYP3A5 polymorphism rs776746 in both recipient and donor, and concomitant use of caspofungin. The predicted C0 value of tacrolimus in LT recipients increased in a dose-dependent manner when the daily dose exceeded 3 mg, whereas it decreased with age when LT recipients were older than 48 years. The predicted C0 was higher when recipients and donors had the genotype CYP3A5*3*3 than when they had the genotype CYP3A5*1. The predicted C0 value also increased with the use of caspofungin or Wuzhi capsule. CONCLUSION AND RELEVANCE: The established ANN model can be used to predict the C0 value of tacrolimus in LT recipients with high accuracy and good predictive ability, serving as a reference for personalized treatment in the early stage after liver transplantation.

Active role of amino acid metabolism in early diagnosis and treatment of diabetic kidney disease.

Diabetic Kidney Disease (DKD) is one of the significant microvascular consequences of type 2 diabetes mellitus with a complex etiology and protracted course. In the early stages of DKD, the majority of patients experience an insidious onset and few overt clinical symptoms and indicators, but they are prone to develop end-stage renal disease in the later stage, which is life-threatening. The abnormal amino acid metabolism is tightly associated with the development of DKD, which involves several pathological processes such as oxidative stress, inflammatory response, and immune response and is also closely related to autophagy, mitochondrial dysfunction, and iron death. With a focus on taurine, branched-chain amino acids (BCAAs) and glutamine, we explored the biological effects of various amino acid mechanisms linked to DKD, the impact of amino acid metabolism in the early diagnosis of DKD, and the role of amino acid metabolism in treating DKD, to offer fresh objectives and guidelines for later early detection and DKD therapy.

Sleep Deprivation Impairs Human Cognitive Reappraisal Ability: A Randomized Controlled Trial.

Purpose: This study aims to examine the impact of sleep deprivation on individual cognitive reappraisal ability using a standardized behavioral paradigm. Methods: A randomized pretest-posttest control group design was conducted. Thirty-nine participants were eventually enrolled and randomly assigned to receive either the sleep control (SC: n = 17) or the sleep deprivation (SD: n = 22). Both of them were required to perform a standardized behavioral paradigm of measuring cognitive reappraisal ability one time under sleep-rested condition and another time under the condition of different sleep manipulation a week later. Results: Mean valence ratings of SD group were more negative than SC group's (p < 0.05) and mean arousal ratings of SD group were higher than SC group's (p < 0.01). Conclusion: Sleep deprivation may impair individual cognitive reappraisal ability and could potentially undermine the efficacy of cognitive therapy in terms of emotion regulation.

A novel subspace outlier detection method by entropy-based clustering algorithm.

Subspace outlier detection has emerged as a practical approach for outlier detection. Classical full space outlier detection methods become ineffective in high dimensional data due to the "curse of dimensionality". Subspace outlier detection methods have great potential to overcome the problem. However, the challenge becomes how to determine which subspaces to be used for outlier detection among a huge number of all subspaces. In this paper, firstly, we propose an intuitive definition of outliers in subspaces. We study the desirable properties of subspaces for outlier detection and investigate the metrics for those properties. Then, a novel subspace outlier detection algorithm with a statistical foundation is proposed. Our method selectively leverages a limited set of the most interesting subspaces for outlier detection. Through experimental validation, we demonstrate that identifying outliers within this reduced set of highly interesting subspaces yields significantly higher accuracy compared to analyzing the entire feature space. We show by experiments that the proposed method outperforms competing subspace outlier detection approaches on real world data sets.

PTSD of Chinese nurses in the normalisation of COVID-19 pandemic prevention and control: Prevalence and correlates.

Background: Though the severe prevention and control measures faced by Chinese nurses had changed during the normalisation stage of the coronavirus 2019 (COVID-19) pandemic, they still worked under great stress. Due to a lack of related evidence, we aimed to investigate the prevalence and correlates of post-traumatic stress disorder (PTSD) among Chinese nurses during the normalisation of COVID-19 pandemic prevention and control measures. Methods: Using convenience sampling, we recruited 784 nurses in Jiangsu province, China to complete a survey via their mobile devices. We used a demographic questionnaire, the Perceived Social Support Scale, the Connor-Davidson Resilience Scale, the General Self-Efficacy Scale, and The Impact of Event Scale-Revised to collect data and applied binary logistic regression analysis to identify factors associated with PTSD. Results: The prevalence of PTSD was 26.4%. Married nurses were less likely to experience PTSD than unmarried ones (odds ratio (OR) = 0.573; 95% confidence interval (CI) = 0.33-0.99, P = 0.046). Social support (OR = 0.96; 95% CI = 0.94-0.98, P = 0.000) and resilience (OR = 0.98; 95% CI = 0.97-0.99, P = 0.004) were significant predictors of PTSD. Conclusions: PTSD remained prevalent among Chinese nurses as COVID-19 pandemic prevention and control measures became normalised, with an incidence rate of 26.4%. Resilience, social support, and marital status were factors associated with PTSD. Chinese hospital management must intervene to improve resilience and social support for nurses to reduce symptoms of PTSD.

NOP2-mediated m5C Modification of c-Myc in an EIF3A-Dependent Manner to Reprogram Glucose Metabolism and Promote Hepatocellular Carcinoma Progression.

Mitochondrial dysfunction and glycolysis activation are improtant hallmarks of hepatocellular carcinoma (HCC). NOP2 is an S-adenosyl-L-methionine-dependent methyltransferase that regulates the cell cycle and proliferation activities. In this study, found that NOP2 contributes to HCC progression by promoting aerobic glycolysis. Our results revealed that NOP2 was highly expressed in HCC and that it was associated with unfavorable prognosis. NOP2 knockout in combination with sorafenib enhanced sorafenib sensitivity, which, in turn, led to marked tumor growth inhibition. Mechanistically, we identified that NOP2 regulates the c-Myc expression in an m5C-modification manner to promote glycolysis. Moreover, our results revealed that m5C methylation induced c-Myc mRNA degradation in an eukaryotic translation initiation factor 3 subunit A (EIF3A)-dependent manner. In addition, NOP2 was found to increase the expression of the glycolytic genes LDHA, TPI1, PKM2, and ENO1. Furthermore, MYC associated zinc finger protein (MAZ) was identified as the major transcription factor that directly controlled the expression of NOP2 in HCC. Notably, in a patient-derived tumor xenograft (PDX) model, adenovirus-mediated knockout of NOP2 maximized the antitumor effect and prolonged the survival of PDX-bearing mice. Our cumulative findings revealed the novel signaling pathway MAZ/NOP2/c-Myc in HCC and uncovered the important roles of NOP2 and m5C modifications in metabolic reprogramming. Therefore, targeting the MAZ/NOP2/c-Myc signaling pathway is suggested to be a potential therapeutic strategy for the treatment of HCC.

A Turn-On Fluorescence Sensor Based on Nitrogen-Doped Carbon Dots and Cu2+ for Sensitively and Selectively Sensing Glyphosate.

Glyphosate has excellent herbicidal activity, and its extensive use may induce residue in the environment and enter into humans living through the food chain, causing negative impact. Here, water-soluble 1.55 nm size nitrogen-doped carbon quantum dots (NCDs) with strong blue fluorescence were synthesized using sodium citrate and adenine. The maximum excitation and emission wavelengths of NCDs were 380 nm and 440 nm, respectively. The above synthesized NCDs were first used for the construction of a fluorescence sensor for glyphosate detection. It was found that Cu2+ could quench the fluorescence of NCDs effectively through the photoinduced electron transfer (PET) process, which was confirmed using fluorescence lifetime measurements. Additionally, the fluorescence was restored with the addition of glyphosate. Hence, a sensitive turn-on fluorescence sensor based on NCDs/Cu2+ for glyphosate analysis was developed. The LODs of glyphosate for water and rice samples were recorded as 0.021 µg/mL and 0.049 µg/mL, respectively. The sensor was applied successfully for ultrasensitive and selective detection of glyphosate in environmental water and rice samples with satisfied recoveries from 82.1% to 113.0% using a simple sample pretreatment technique. The proposed strategy can provide a significant potential for monitoring glyphosate residue in water and agricultural product samples.

Advanced root mean square propagation with the warm-up algorithm for fiber coupling.

Fiber coupling plays an important role in applications such as free-space optical communication (FSOC) and self-referencing interferometry. However, the coupling efficiency is often affected by turbulence and platform vibrations, which requires dynamic coupling with optimization algorithms. In this paper, to further mitigate the effects of sudden disturbances and to expand the effective range of fiber coupling systems, we propose a new method called the advanced root mean square propagation with warm-up (ARW) algorithm. By adaptively adjusting both the gain rate and the perturbation rate with warm-up operations, the ARW algorithm can achieve higher effective range and acceptable steady-state coupling efficiency simultaneously. Simulation and experimental results demonstrate that the proposed method requires only 36.4% of the iterations of the SPGD algorithm to deal with sudden disturbances. Moreover, the effective range of the ARW algorithm is 530.50µrad in the specific coupling platform, which is 20% higher than the effective range of SPGD.

Avoidance-motivational intensity modulated the effect of negative emotion on working memory.

Although many studies have explored the association between negative emotion and working memory, the findings remain controversial. The present study investigated the role of avoidance-motivational intensity in modulating the effect of negative emotion on different processes (maintenance versus manipulation) of verbal and spatial working memory. Two experiments employed the modified delayed match-to-sample paradigms to separate the two processes of verbal and spatial working memory under different emotional states, respectively. In Experiment 1, participants were asked to perform the delayed match-to-sample task with or without reordering the characters (manipulation process of verbal working memory). In Experiment 2, mental rotation was used as the manipulation process of spatial working memory. The results showed that negative emotion only affected the manipulation process, but not the maintenance process. Relative to neutral and low avoidance-motivated negative conditions, the manipulation processes of both types of working memory were impaired under the high avoidance-motivated negative condition. No significant difference was observed between low avoidance-motivated negative condition and neutral condition. Our results are discussed in relation to efficiency processing theory and motivational dimensional model of affect. We conclude that negative emotional states with high avoidance-motivational intensity impair the manipulation process of verbal and spatial working memory.

A broad-host-range lytic phage vB_VhaS-R18L as a candidate against vibriosis.

Vibriosis is one of the most common bacterial diseases that cause high rates of mortality and considerable economic losses in aquaculture. Phage therapy has been considered as a promising alternative method to antibiotics in the biocontrol of infectious diseases. Genome sequencing and characterization of the phage candidates are prerequisites before field applications to ensure environmental safety. In this study, a lytic phage, named vB_VhaS-R18L (R18L), was isolated from the coastal seawater of Dongshan Island, China. The phage was characterized in terms of morphology, genetic content, infection kinetics, lytic profile, and virion stability. Transmission electronic microscopy indicated that R18L is siphovirus-like, comprising an icosahedral head (diameter 88.6 ± 2.2 nm) and a long noncontractile tail (225 × 11 nm). Genome analysis indicated R18L to be a double-stranded DNA virus with a genome size of 80,965 bp and a G + C content of 44.96%. No genes that encode known toxins or genes implicated in lysogeny control were found in R18L. A one-step growth experiment showed that R18L had a latent period of approximately 40 min and a burst size of 54 phage particles per infected cell. R18L showed lytic activity against a wide range of at least five Vibrio species (V. alginolyticus, V. cholerae, V. harveyi, V. parahemolyticus, and V. proteolyticus). R18L was relatively stable at pH 6-11 and at temperatures ranging from 4°C to 50°C. The broad lytic activity across Vibrio species and the stability in the environment make R18L a potential candidate for phage therapy in controlling vibriosis in aquaculture systems.

Performance evaluation of SARS-CoV-2 antigen detection in the post-pandemic era: multi-laboratory assessment.

OBJECTIVES: Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) antigen detection is an indispensable tool for epidemic surveillance in the post-pandemic era. Faced with irregular performance, a comprehensive external quality assessment (EQA) scheme was conducted by the National Center for Clinical Laboratories (NCCL) to evaluate the analytical performance and status of SARS-CoV-2 antigen tests. METHODS: The EQA panel included ten lyophilized samples containing serial 5-fold dilutions of inactivated SARS-CoV-2-positive supernatants of the Omicron BA.1 and BA.5 strains and negative samples, which were classified into "validating" samples and "educational" samples. Data were analyzed according to qualitative results for each sample. RESULTS: A total of 339 laboratories in China participated in this EQA scheme, and 378 effective results were collected. All validating samples were correctly reported by 90.56 % (307/339) of the participants and 90.21 % (341/378) of the datasets. The positive percent agreement (PPA) was >99 % for samples with concentrations of 2 × 107 copies/mL but was 92.20 % (697/756) for 4 × 106 copies/mL and 25.26 % (382/1,512) for 8 × 105 copies/mL samples. Colloidal gold was the most frequently used (84.66 %, 320/378) but showed the lowest PPAs (57.11 %, 1,462/2,560) for positive samples compared with fluorescence immunochromatography (90 %, 36/40) and latex chromatography (79.01 %, 335/424). Among 11 assays used in more than 10 clinical laboratories, ACON showed a higher sensitivity than other assays. CONCLUSIONS: The EQA study can help to validate whether it's necessary to update antigen detection assays for manufacturers and provide participants with information about the performance of assays to take the first step toward routine post-market surveillance.

Clinical Characteristics and Survival Analysis of Patients With Second Primary Malignancies After Hepatocellular Carcinoma Liver Transplantation: A SEER-based Analysis.

BACKGROUND: Second primary malignancies (SPMs) after liver transplantation (LT) are becoming the leading causes of death in LT recipients. The purpose of this study was to explore prognostic factors for SPMs and to establish an overall survival nomogram. METHODS: A retrospective analysis was conducted of data from the Surveillance, Epidemiology, and End Results (SEER) database on adult patients with primary hepatocellular carcinoma who had undergone LT between 2004 and 2015. Cox regression analysis was used to explore the independent prognostic factors for SPMs. Nomogram was constructed using R software to predict the overall survival at 2, 3, and 5 years. The concordance index, calibration curves, and decision curve analysis were used to evaluate the clinical prediction model. RESULTS: Data from a total of 2078 patients were eligible, of whom 221 (10.64%) developed SPMs. A total of 221 patients were split into a training cohort (n=154) or a validation cohort (n=67) with a 7:3 ratio. The 3 most common SPMs were lung cancer, prostate cancer, and non-Hodgkin lymphoma. Age at initial diagnosis, marital status, year of diagnosis, T stage, and latency were the prognostic factors for SPMs. The C-index of the nomogram for overall survival in the training and validation cohorts were 0.713 and 0.729, respectively. CONCLUSIONS: We analyzed the clinical characteristics of SPMs and developed a precise prediction nomogram, with a good predictive performance. The nomogram we developed may help clinicians provide personalized decisions and clinical treatment for LT recipients.