First case of bloodstream infection caused by NDM-positive Escherichia hermannii.

BACKGROUND: Escherichia hermannii (E. hermanni) is always accompanied by other bacterial infections in humans. In previous reports, most E. hermannii-related infections were caused by sensitive strains. Here, for the first time, we report the case of a patient with New Delhi metallo-ß-lactamase (NDM)-positive E. hermannii bloodstream infection. CASE PRESENTATION: The patient was a 70-year-old male admitted to our hospital due to a 4-day fever, with a history of malignant tumor, liver cirrhosis, and chronic obstructive pulmonary disease. After admission, his blood culture tested positive for E. hermannii. The drug resistance analysis showed positive for NDM resistance, with susceptibility to aztreonam, levofloxacin, and amikacin. The blood culture turned negative after 8 days of aztreonam treatment. The patient's symptoms improved, and he was discharged after 14 days of hospitalization. CONCLUSIONS: This is the first report of a bloodstream infection caused by an NDM-positive E. hermannii strain. The anti-infection regimen used in this case provides a new reference regimen for clinical practice.

Establishment and validation of a prediction nomogram for heart failure risk in patients with acute myocardial infarction during hospitalization.

BACKGROUND: Acute myocardial infarction (AMI) with consequent heart failure is one of the leading causes of death in humans. The aim of this study was to develop a prediction model to identify heart failure risk in patients with AMI during hospitalization. METHODS: The data on hospitalized patients with AMI were retrospectively collected and divided randomly into modeling and validation groups at a ratio of 7:3. In the modeling group, the independent risk factors for heart failure during hospitalization were obtained to establish a logistic prediction model, and a nomogram was constructed. The receiver operating characteristic (ROC) curve, calibration curve, and decision curve analysis (DCA) were used to evaluate the predictive performance and clinical value. Machine learning models with stacking method were also constructed and compared to logistic model. RESULTS: A total of 1875 patients with AMI were enrolled in this study, with a heart failure rate of 5.1% during hospitalization. The independent risk factors for heart failure were age, heart rate, systolic blood pressure, troponin T, left ventricular ejection fraction and pro-brain natriuretic peptide levels. The area under the curve (AUC) of the model in modeling group and validation group were 0.829 and 0.846, respectively. The calibration curve showed high prediction accuracy and the DCA curve showed good clinical value. The AUC value of the ensemble model by the stacking method in the validation group were 0.821, comparable to logistic prediction model. CONCLUSIONS: This model, combining laboratory and clinical factors, has good efficacy in predicting heart failure during hospitalization in AMI patients.

A case of Elizabethkingia meningoseptica septicemia. / 脑膜脓毒性伊丽莎白金菌败血症一例.

A 82-year-old man was admitted to hospital with fever, unresponsiveness, elevated hypersensitive C-reactive protein and neutrophile granulocyte. Ceftriaxone was administrated by intravenous dripping in the emergency room, but the effect was not satisfactory. Following his admission to the ward, cefoperazone sulbactam were given. Elizabethkingia meningoseptica was identified by blood culture and further confirmed by 16S rRNA sequencing. The lumbar puncture showed that cerebrospinal fluid pressure was 80 mmH2O (1 mmH2O=0.0098 kPa) and biochemical results were normal. After 11 days of cefoperazone sulbactam treatment, the patient was discharged with negative blood culture. The hypersensitive C-reactive protein and neutrophile granulocyte had also declined. The patient received levofloxacin tablets for anti-infection treatment for 14 d after discharge. No signs of infection were observed in three months' following up.

Potential biomarkers that discriminate rheumatoid arthritis and osteoarthritis based on the analysis and validation of datasets.

BACKGROUND: Rheumatoid arthritis (RA) and osteoarthritis (OA) share some similar arthritic symptoms, but different mechanisms underlie the pathogenesis of these two diseases. Analysis of differentially expressed molecules in rheumatoid arthritis and osteoarthritis may assist in improving diagnosis and treatment strategies in clinical practice. METHODS: Microarray and RNA-seq data were acquired from the gene expression omnibus database. Differentially expressed genes (DEGs) were identified using Bioconductor packages. Receiver operating characteristic curves were plotted to assess performance. Gene ontology enrichment analysis was conducted using the clusterProfiler application. During validation, synovial fluid was harvested from patients who had undergone in-hospital joint replacement, in which the expression of proteins was measured using enzyme-linked immunosorbent assays. RESULTS: Compared with OA samples, RA samples showed 14 genes to be upregulated and 3 to be downregulated. Gene ontology analysis indicated that DEGs principally included molecules responsible for the regulation of a synovial tissue inflammatory response. Seven genes displayed a good discriminatory power with an AUC higher than 0.90. ADAMDEC1 was the biomarker that most clearly discriminated RA from OA in the database, exhibiting an AUC of 0.999, a sensitivity of 100%, and a specificity of 97.8%. Following validation, the expression levels of ADAMDEC1 in the synovial fluid from RA patients were significantly higher than those in the synovial fluid from OA patients (P < 0.05). At the cut-off value of 1957 pg/mL, ADAMDEC1 expression in the synovial fluid discriminated RA from OA with an AUC of 0.951, a specificity of 88.6%, and a sensitivity of 92.9%. CONCLUSION: The differential expression of genes in RA compared with OA indicates potential targets for molecular diagnosis and treatment. The presence of ADAMDEC1 in synovial fluid is a good biomarker of RA.

A 7-year surveillance of the drug resistance in Klebsiella pneumoniae from a primary health care center.

BACKGROUND: The increased prevalence of Klebsiella pneumoniae infections and resistance rates are a current cause for concern. However, data for resistance rates in K. pneumoniae strains from primary hospitals and the resistance distribution among the different isolate sample sources are scarce. METHODS: All the K. pneumoniae strains were isolated from patients who visited a primary health care center located in Central Zhejiang Province from January 2011 to December 2017. The specimens included blood, sputum, cervical secretions and urine. The species were identified by the Vitek 2 Compact Bacterial Identification and Monitoring System or VITEK-MS and the extended spectrum ß-lactamase (ESBL) and drug resistance profiles were identified using the AST-GN13 Gram negative susceptibility card (VITEK-2). The genotype of strains from urine sources was analyzed by detecting TEM and SHV genes. Finally, the drug resistance rates among the isolates from different sample sources were analyzed using the Chi square test with SPSS software. RESULTS: A total of 5319 K. pneumoniae strains were isolated in this study. Among the 20 antimicrobial drugs studied, the resistance rates of K. pneumoniae strains varied from 1.4% (ertapenem) to 23.1% (nitrofurantoin). The antibiotic resistance rates varied significantly among the isolate samples sources for all, with the highest rates for all antibiotics except for nitrofurantoin found in urine samples. In addition, the ESBL-positive rate in urine samples was 27.1%, significantly higher than that of cervical secretions (20.2%), blood (16.5%) and sputum (15.2%). Compared to the ESBL-negative strains, higher resistance rates were detected in the ESBL-positive strains. The most common genotype of isolates from urine was SHV (28%, 23/82), following by TEM (14.6%, 12/82). CONCLUSION: The highest resistance rates of K. pneumoniae strains to most antibiotics found in urine samples are partly due to the ESBLs, indicating that a special attention should be paid in the treatment of urinary tract infection.

Persistently high prevalence of primary resistance and multidrug resistance of tuberculosis in Heilongjiang Province, China.

BACKGROUND: The spread of multidrug-resistant tuberculosis (MDR-TB) Mycobacterium tuberculosis (M. tuberculosis) strains has been a big challenge to the TB control and prevention in China. Knowledge about patterns of drug resistance in TB high-burden areas of China is crucial to develop appropriate control strategies. We conducted a comprehensive investigation of the resistance pattern of M. tuberculosis in Heilongjiang Province. METHODS: 1427 M. tuberculosis clinical strains were isolated from pulmonary TB patients hospitalized between 2007 and 2012. The susceptibility of the isolates to the first-line anti-TB drugs and the resistance of MDR M. tuberculosis to fluoroquinolones were examined. We also performed a statistical analysis to identify the correlated risk factors for high burden of MDR-TB. RESULTS: The global resistance rates of 2007-2012 to the first-line drugs and MDR were 57.0 and 22.8 %, respectively. Notably, the primary MDR-TB and pan-resistance rates were as high as 13.6 and 5.0 %, respectively. Of MDR M. tuberculosis isolates (2009), approximately 13 % were not susceptible to any of the fluoroquinolones tested. Being age of 35 to 54, high re-treatment proportion, the presence of cavity lesion, and high proportion of shorter hospitalization are correlated with the development of MDR-TB. CONCLUSIONS: The high prevalence of drug resistant, MDR-TB, and fluoroquinolone-resistant MDR-TB is a big concern for TB control. More importantly, in order to control the development of MDR-TB effectively, we need to pay more attention to the primary resistance. Targeting reducing the prevalence of the risk factors may lead to better TB control in China.

A nomogram for predicting mortality risk within 30 days in sepsis patients admitted in the emergency department: A retrospective analysis.

OBJECTIVE: To establish and validate an individualized nomogram to predict mortality risk within 30 days in patients with sepsis from the emergency department. METHODS: Data of 1205 sepsis patients who were admitted to the emergency department in a tertiary hospital between Jun 2013 and Sep 2021 were collected and divided into a training group and a validation group at a ratio of 7:3. The independent risk factors related to 30-day mortality were identified by univariate and multivariate analysis in the training group and used to construct the nomogram. The model was evaluated by receiver operating characteristic (ROC) curve, calibration chart and decision curve analysis. The model was validated in patients of the validation group and its performance was confirmed by comparing to other models based on SOFA score and machine learning methods. RESULTS: The independent risk factors of 30-day mortality of sepsis patients included pro-brain natriuretic peptide, lactic acid, oxygenation index (PaO2/FiO2), mean arterial pressure, and hematocrit. The AUCs of the nomogram in the training and verification groups were 0.820 (95% CI: 0.780-0.860) and 0.849 (95% CI: 0.783-0.915), respectively, and the respective P-values of the calibration chart were 0.996 and 0.955. The DCA curves of both groups were above the two extreme curves, indicating high clinical efficacy. The AUC values were 0.847 for the model established by the random forest method and 0.835 for the model established by the stacking method. The AUCs of SOFA model in the model and validation groups were 0.761 and 0.753, respectively. CONCLUSION: The sepsis nomogram can predict the risk of death within 30 days in sepsis patients with high accuracy, which will be helpful for clinical decision-making.

A nomogram model to predict the risk of drug-induced liver injury in patients receiving anti-tuberculosis treatment.

Objectives: To establish an individualized nomogram to predict the probability of drug-induced liver injury (DILI) in tuberculosis patients receiving anti-tuberculosis treatment. Methods: The clinical information of patients admitted to a tertiary hospital between January 2010 and December 2022 was retrospectively reviewed from the clinical records. Patients with baseline liver diseases (hepatis B or C infection and fatty liver) or taking liver protective drugs were excluded. The maximum values in liver function test within 180 days after anti-tuberculosis treatment were collected to determine the occurrence of DILI. The candidate variables used for establishing prediction model in this study were the last results within the 30 days before the treatment onset. The final variables were included after univariate and multivariate logistic regression analyses and applied to establish the nomogram model. The discrimination power and prediction accuracy of the prediction model were assessed using the area under the receiver operating characteristic (AUC) curve and a calibration chart. The clinical effectiveness was assessed via decision curve analysis (DCA). The established model was validated in two validation groups. Results: A total of 1979 patients with 25 variables were enrolled in this study, and the incidence of DILI was 4.2% (n = 83). The patients with complete variables were divided into training group (n = 1,121), validation group I (n = 492) and validation group II (n = 264). Five variables were independent factors for DILI and included in the final prediction model presented as nomogram: age (odds ratio [OR] 1.022, p = 0.023), total bilirubin ≥17.1 µmol/L (OR 11.714, p < 0.001), uric acid (OR 0.977, p = 0.047), neutrophil count (OR 2.145, 0.013) and alcohol consumption (OR 3.209, p = 0.002). The AUCs of the prediction model in the training group, validation group I and validation group II were 0.833, 0.668, and 0.753, respectively. The p-values of calibration charts in the three groups were 0.800, 0.996, and 0.853. The DCA curves of the prediction model were above the two extreme curves. Conclusion: The nomogram model in this study could effectively predict the DILI risk among patients under anti-tuberculosis drug treatment.

Role of Genetic Polymorphism and Expression of Angiopoietin-2 in Patients with Primary Liver Cancer Among the Southeastern Chinese Hans Population.

Background: Angiopoietin-2 (Ang2)-mediated angiogenesis plays a crucial role in the pathogenesis of vascular-rich cancers. However, the genetic polymorphism and expression level of Ang2 in patients with primary liver cancer remain unknown. Methods: This study included 234 primary liver cancer patients and 199 healthy controls. The expression levels of Ang2 in liver cancer tissues and plasma were determined. Peripheral blood samples were collected to test five ANGPT2 single nucleotide polymorphisms (rs2442598, rs734701, rs1823375, rs11137037, and rs12674822). Results: Plasma Ang2 levels in patients with liver cancer were upregulated compared with that in healthy controls. The upregulation of plasma Ang2 levels was significantly associated with vascular invasion, metastasis, and clinical stage. Notably, the transcription level of ANGPT2 was elevated in tumor tissues compared with para-carcinoma tissues. Individuals with the TT genotype at rs2442598 and genotype AC and AC+CC at rs11137037 had higher liver cancer risk compared with healthy controls. Conclusions: Upregulated Ang2 levels in blood plasma and cancer tissues of liver cancer patients confirm that Ang2 plays a vital role in the pathogenesis of liver cancer. ANGPT2 rs2442588 and rs11137037 are associated with liver cancer risk, thereby highlighting their role in screening individuals susceptible to liver cancer.

Antibiotic resistance spectrum of E. coli strains from different samples and age-grouped patients: a 10-year retrospective study.

OBJECTIVE: Escherichia coli (E. coli) is the most common opportunistic clinical micro-organism with high drug resistance. This study aimed to analyse the resistance pattern of E. coli according to patient age and clinical sample type. DESIGN AND SETTING: This retrospective observational study was conducted in a tertiary hospital in southeastern China. PARTICIPANTS: E. coli strains were isolated from blood, urine and sputum of infected inpatients. The patients were divided into four age groups: children (0-14 years old, including neonatal and non-neonatal groups), youths (15-40 years old), middle-aged (41-60 years old) and old (>60 years old). RESULTS: A total of 7165 E. coli strains were collected from all samples. Compared with urine and blood isolates, more sputum isolates were resistant against 12 tested antibiotics. Furthermore, urine isolates were more resistant to levofloxacin than sputum and blood isolates. Although the patients' age was not associated with resistance rates of E. coli strains isolated from blood, a larger proportion of urine-derived strains from youths were resistant to sulfamethoxazole-trimethoprim and piperacillin-tazobactam than those from old people. The sputum strains from the elderly were more resistant to most of the tested antibiotics compared with sputum strains isolated from children. CONCLUSIONS: The resistance profile of E. coli is different among age groups and specimen sources and should be considered during E. coli infection treatment.

An Individualized Nomogram for Predicting Mortality Risk of Septic Shock Patients During Hospitalization: A ten Years Retrospective Analysis.

Purpose: We intend to develop a nomogram for predicting the mortality risk of hospitalized septic shock patients. Patients and Methods: Data were collected from patients hospitalized with septic shock in Affiliated Dongyang Hospital of Wenzhou Medical University in China, over 10 years between January 2013 and January 2023. The eligible study participants were divided into modeling and validation groups. Factors independently related to the mortality in the modeling group were obtained by stepwise regression analysis. A logistic regression model and a nomogram were built. The model was evaluated based on the discrimination power (the area under the curve of the receiver operating characteristic, AUC), the calibration degree and decision curve analysis. In the validation group, the discrimination powers of the logistic regression model, the sequential organ failure assessment (SOFA) scoring model and machine learning model were compared. Results: A total of 1253 patients, including 878 patients in the modeling group and 375 patients in the validation group, were included in this study. Age, respiratory failure, serum cholinesterase, lactic acid, blood phosphorus, blood magnesium, total bilirubin, and pH were independent risk factors related to the mortality risk of septic shock. The AUCs of the prediction model for the modeling and validation groups were 0.881 and 0.868, respectively. The models had a good calibration degree and clinical applicability. The AUC of the SOFA model for the validation population was 0.799, significantly lower than that of our model. The AUCs of the random forest and ensemble models were 0.865 and 0.863, respectively, comparable to that of our logistical prediction model. Conclusion: The model established in this study can effectively predict the mortality risk in patients hospitalized with septic shock. Thus, the model could be used clinically to determine the best therapy or management for patients with septic shock.

Development of a Nomogram for Predicting Mortality Risk in Sepsis Patients During Hospitalization: A Retrospective Study.

Purpose: We attempted to establish a model for predicting the mortality risk of sepsis patients during hospitalization. Patients and Methods: Data on patients with sepsis were collected from a clinical record mining database, who were hospitalized at the Affiliated Dongyang Hospital of Wenzhou Medical University between January 2013 and August 2022. These included patients were divided into modeling and validation groups. In the modeling group, the independent risk factors of death during hospitalization were determined using univariate and multi-variate regression analyses. After stepwise regression analysis (both directions), a nomogram was drawn. The discrimination ability of the model was evaluated using the area under the curve (AUC) of the receiver operating characteristic (ROC) curve, and the GiViTI calibration chart assessed the model calibration. The Decline Curve Analysis (DCA) was performed to evaluate the clinical effectiveness of the prediction model. Among the validation group, the logistic regression model was compared to the models established by the SOFA scoring system, random forest method, and stacking method. Results: A total of 1740 subjects were included in this study, 1218 in the modeling population and 522 in the validation population. The results revealed that serum cholinesterase, total bilirubin, respiratory failure, lactic acid, creatinine, and pro-brain natriuretic peptide were the independent risk factors of death. The AUC values in the modeling group and validation group were 0.847 and 0.826. The P values of calibration charts in the two population sets were 0.838 and 0.771. The DCA curves were above the two extreme curves. Moreover, the AUC values of the models established by the SOFA scoring system, random forest method, and stacking method in the validation group were 0.777, 0.827, and 0.832, respectively. Conclusion: The nomogram model established by combining multiple risk factors could effectively predict the mortality risk of sepsis patients during hospitalization.

Bloodstream infection, peritonitis, and pneumonia caused by Pasteurella multocida in a patient with liver cirrhosis despite no animal contact: case report and literature review.

Pasteurella multocida is an opportunistic pathogen. Previously reported infections associated with P. multocida have often been linked to contact with cats, dogs, and other animals. Cases of systemic multiple-site infections following P. multocida infection are rare. This case study presents a 49-year-old middle-aged man with post-hepatitis B cirrhosis and no history of animal contact. The patient was admitted with symptoms of fever accompanied by diarrhea, abdominal distension, and cough. Blood tests showed elevated levels of CRP, PCT, and IL-6, and blood culture revealed the growth of P. multocida. CT scans revealed a large amount of abdominal effusion, a small amount of pleural effusion, and pulmonary infection foci. The patient's condition improved after successive administration of ceftriaxone and levofloxacin to fight the infection, and abdominal puncture and drainage. Multiple-site infections caused by P. multocida are rarely encountered in patients with liver cirrhosis but without animal contact, which could be regarded as serious conditions warranting careful attention in terms of clinical diagnosis and treatment.

Molecular identification and clinical significance of Mycobacterium seoulense strains from patients with nontuberculous mycobacterium infections.

PURPOSE: To investigate the clinical significance of Mycobacterium seoulense (M. seoulense) and the ideal gene for species determination. METHODS: Clinical symptoms, laboratory examinations, and radiological examinations were retrospectively reviewed. The hsp65, 16S rRNA, rpoB and ITS region of M. seoulense, were sequenced and phylogenetic trees of mycobacterium strains were constructed. RESULTS: Four M. seoulense strains isolated from 4 patients caused pulmonary infections based on the symptoms and radiological results. The 16S rRNA sequence identified 2 strains as M. intracellulare and the other 2 as Stenotrophomonas maltophilia. In contrast, the rpoB, 16S-23S inter-region (ITS), and hsp65 sequences shared high identity with M. seoulense. Notably, the phylogenetic tree based on the ITS, hsp65, and rpoB sequences clustered 4 M. seoulense strains identified in this study with M. seoulense strains in the database. CONCLUSION: M. seoulense strains can cause infection in humans. They can be identified by sequencing ITS, hsp65, and rpoB genes.

Case Report: The First Case of Bloodstream Infection Complicated with Psoas Abscess and Pulmonary Infection Caused by Aggregatibacter aphrophilus in China.

Aggregatibacter aphrophilus is part of the normal flora in the oropharynx and upper respiratory tract, which causes invasive bacteremia in rare cases. However, the culture and identification of Aggregatibacter aphrophilus are challenging, hence easily misdiagnosed or undetected in clinical practice. In this case, a 73-year-old male patient was admitted to the hospital with a fever and right hip pain. Routine blood and C-reactive protein tests showed abnormal inflammatory markers. Positive blood culture revealed the presence of Aggregatibacter aphrophilus through mass spectrometry. The computed tomography examination further revealed the presence of psoas abscess, pulmonary infection, and pleural effusion, which was relieved by ceftriaxone combined with levofloxacin therapy, the drainage of psoas abscess and pleural effusion. Therefore, since multiple anatomic sites infection, including bloodstream, psoas abscess and pulmonary infection caused by Aggregatibacter aphrophilus, is rare, sufficient attention should be paid to its clinical diagnosis and treatment.

Elucidation of the mechanism of action of Runyan Mixture in the treatment of pharyngitis using a network pharmacological approach.

This study aimed to elucidate the mechanism of action of Runyan Mixture in treating pharyngitis using a network pharmacological approach. The active components of the Runyan Mixture were obtained from the traditional chinese medicine systems pharmacology database and evaluated using Lipinski's rules. The SwissTargetPrediction database was used to predict the action targets of the Runyan Mixture, and a protein-protein interaction network was constructed using the STRING database. Moreover, the anti-inflammatory effect of Runyan Mixture was validated in vitro using the lipopolysaccharide induced inflammation in macrophages. The Runyan Mixture was the liquid preparation from 8 traditional Chinese medicine. A total of 89 types of active components, 53 core targets, and 98 signaling pathways (P < .001) were identified for the Runyan Mixture. The main action targets were EGFR, MAPK1, AKT1, PIK3CA, NFKB1, SRC, TNF, MAPK8, MET, and PTGS2. Among the identified signaling pathways, 20 were associated with microbial infection and 24 were related to the immune-inflammatory response. Experimental results in vitro showed that Runyan Mixture could significantly inhibit the expression of interleukin-1, interleukin-6, and tumor necrosis factor-α (P < .05) in macrophages by lipopolysaccharide stimulation. Based on the results of the protein-protein interaction network analysis and the anti-inflammatory effect in vitro, the efficiency of the Runyan Mixture in pharyngitis treatment could be attributed to the inhibition of the inflammatory response.

Retrospective analysis: The application of human menopausal gonadotropin combined with letrozole for IUI in patients undergoing artificial insemination by husband due to unexplained or mild male factors.

Objective: To compare the effects of human menopausal gonadotropin (HMG) combined with letrozole (LE) to HMG only for ovarian stimulation on pregnancy outcome of infertile patients undergoing artificial insemination by husband (AIH) due to unexplained or mild male factors. Materials and methods: Infertile patients with unexplained or mild male factors treated from July 2015 to December 2021 were selected as subjects. The patients were divided into two groups according to the ovarian stimulation schemes they received, namely HMG combined with LE or HMG only. We analyzed the laboratory examination results before drug treatment (baseline) and during ovarian stimulation and compared the pregnancy outcomes of the two groups using univariable analysis and multivariable logistic regression analysis. Results: In total, 526 cycles of 372 couples were included. The univariate analysis showed that the clinical pregnancy rate of the HMG combined with LE group was 24.8%, significantly higher than that of the HMG group (14.8%, P = 0.007). The live birth rate (19.9%) of the HMG combined with LE group were also significantly higher than those of the HMG group (11.2%, respectively). In multivariate logistic analysis, the age of males was negatively associated with the clinical pregnancy rate (OR 0.874, 95% CI 0.793~0.963, P=0.006) and live birth (OR0.875, 95% CI 0.783~0.977, P=0.018). Moreover, ovarian stimulation with HMG+LE was the only beneficial factor significantly associated with clinical pregnancy (OR 1.929, 95% CI 1.068~3.485, P=0.029) and live birth (OR 2.255, 95% CI 1.188~4.282, P=0.013). Conclusion: Ovarian stimulation using HMG combined with LE can increase the clinical outcomes (live birth and clinical pregnancy) among infertile patients undergoing AIH due to explained or mild male factors.

Behavioral Intentions and Factors Influencing Nurses' Care of COVID-19 Patients: A Cross-Sectional Study.

Objective: Behavioral intentions to care for patients with infectious diseases are crucial for improving quality of care. However, there have been few studies of the behavioral intentions and factors influencing patient care by clinical nurses during the COVID-19 pandemic. This study aims to explore cognition, attitudes, subjective norms, self-efficacy, and behavioral intentions of clinical nurses while caring for COVID-19 patients and to explore any influencing factors. Method: A cross-sectional survey was conducted of nurses through convenience sampling in southeast China from February 2020 to March 2020. The questionnaire was developed based on the theory of planned behavior and self-efficacy. Results: A total of 774 nurses completed the survey. Of these, 69.12% (535/774) reported positive behavioral intentions, 75.58% (585/774) reported a positive attitude, and 63.82% (494/774) reported having the confidence to care for patients. However, the lack of support from family and friends and special allowance affected their self-confidence. Attitude, self-efficacy, subjective norms, and ethical cognition were significantly positively correlated with behavioral intentions (r = 0.719, 0.690, 0.603, and 0.546, respectively, all P < 0.001). Structural equation model showed that self-efficacy, attitude, ethical cognition, and subjective norms had positive effects on behavioral intentions (ß = 0.402, 0.382, 0.091, and 0.066, respectively, P < 0.01). The total effect of behavioral intentions was influenced by attitude, ethical cognition, self-efficacy, and subjective norms (ß = 0.656, 0.630, 0.402, and 0.157, respectively, P < 0.01). In addition, ethical cognition had a positive mediating effect on behavioral intentions (ß = 0.539, P < 0.001). Conclusion: The study results indicated that attitude, ethical cognition, and self-efficacy were the main factors influencing nurses' behavioral intention. Efforts should be made to improve nurses' attitude and self-efficacy through ethical education and training to increase behavioral intentions to care for patients with infectious diseases, which will improve the quality of nursing care.

Whole genome sequence of a non-toxigenic Corynebacterium diphtheriae strain from a hospital in southeastern China.

BACKGROUND: Sporadic cases of infection with non-toxigenic Corynebacterium diphtheriae (C. diphtheriae) isolates have been reported in regions covered by the Diphtheria-Tetanus-Pertussis vaccine, but no information describing the whole genome of non-toxigenic strains collected in China is available. Therefore, in this work, the complete genome of a non-toxigenic strain of C. diphtheriae from a hospital located in southeastern China was performed. RESULTS: This non-toxigenic isolate belonged to the belfanti biotype and possessed a unique ST (assigned as ST799 in pubMLST). ErmX was present in the genome sequence and this isolate owned the resistance to erythromycin and clindamycin. Genes coding for virulence factors involved in adherence, iron-uptake and regulation of diphtheria toxin were also found. Two genes were involved in the interaction between pathogen and host. The phylogenetic analysis revealed that this newly isolated strain was similar to the strain NCTC10838, CMCNS703 and CHUV2995. CONCLUSION: Non-toxigenic C. diphtheriae strain contained virulence factors, thus it is able to cause an infectious disease, aspect that could be clarified by performing the whole genome sequencing analysis.