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Background: Group B streptococcus (GBS) is a leading cause of serious infection in infants. Understanding its regional molecular epidemiology is helpful for regulating efficient prevention practice. Methods: A retrospective study was conducted to collected data from infants and pregnant women with culture-proven GBS disease in the largest women and children's medical center in Shanxi between January 2017 and September 2019. All GBS isolates were analyzed by multi-locus sequence typing (MLST) as well as distribution of pilus island (PI) genes. Results: A total of 54 GBS isolates were obtained from 36 (66.7%) pregnant women and 18 (33.3%) infants with invasive disease. Among invasive GBS strains, the most common sequence type was ST10 (72.2%, P

Background Human errors during operations may seriously threaten patient recovery and safety and affect the doctor-patient relationship. Therefore, risk evaluation of the surgical process is critical. Risk evaluation by failure mode and effect analysis (FMEA) is a prospective technology that can identify and evaluate potential failure modes in the surgical process to ensure surgical quality and patient safety. In this study, a hybrid surgical risk-evaluation model was proposed using FMEA and multiobjective optimization on the basis of ratio analysis plus full multiplicative form (MULTIMOORA) method under a single-valued trapezoidal neutrosophic environment. This work aimed to determine the most critical risk points during the surgical process and analyze corresponding solutions. Methods A team for FMEA was established from domain experts from different departments in a hospital in Hunan Province. Single-valued trapezoidal neutrosophic numbers (SVTNNs) were used to evaluate potential risk factors in the surgical process. Cmprehensive weights combining subjective and objective weights were determined by the best-worst method and entropy method to differentiate the importance of risk factors. The SVTNN-MULTIMOORA method was utilized to calculate the risk-priority order of failure modes in a surgical process. Results The hybrid FMEA model under the SVTNN-MULTIMOORA method was used to calculate the ranking of severity of 21 failure modes in the surgical process. An unclear diagnosis is the most critical failure in the surgical process of a hospital in Hunan Province. Conclusion The proposed model can identify and evaluate the most critical potential failure modes of the surgical process effectively. In addition, such a model can help hospitals to reduce surgical risk and improve the safety of surgery.

Cholecystitis is a common disease with a high incidence, and attracts much attention. It not only harms human health, but also affects quality of work and life. Therefore, the choice of a suitable treatment is badly important for patients. In this paper, a novel selection model of treatments for cholecystitis based on hybrid multiple-criteria group decision-making (MCGDM), which is helpful to choose the most suitable treatment in the case of asymmetric information between doctors and patients. Subsequently, subjective and objective criteria are comprehensively taken into account in the index system of the selection model for cholecystitis, and combines 2-tuple linguistic with quantitative data analysis. Besides, the evaluation information obtained from the patient's conditions, the treatment and the hospital's medical status, etc., including real numbers, interval numbers, and linguistic labels with multi-granularity, is more complete and real. And the 2-tuple linguistic model is used to unify the non-homogeneous information, so the treatment selection is accurate and reliable. Simultaneously, for the unknown index and criteria weight, the improved entropy weight method and the BWM (best-worst-method) are utilized to figure out the index weight and criteria weight, respectively. Further, TODIM (an acronym in Portuguese for interactive and multi-criteria decision-making model) method based on the prospect theory is applied to solve the prioritization of cholecystitis treatments, and give full consideration to the decision maker of risk aversion. Eventually, an empirical study of treatment selection for cholecystitis is conducted. Sensitivity analysis and comparative analysis indicate that the proposed selection model of treatments for cholecystitis patients is reliable and effective.

The aim of this study was to analyze and compare procalcitonin (PCT) levels in serum and cerebrospinal fluid (CSF) as tools for detecting bacterial meningitis (BM) in children. Serum and CSF PCT levels as well as albumin index (AI = CSF albumin/serum albumin × 1000) were measured from 29 BM, 25 viral meningitis (VM), and 47 non-meningitis patients. Differences between groups only for CSF PCT were significant. A stronger positive correlation between CSF PCT level and AI was observed in the BM patients ( R = 0.68, P < .001). As a predictor of BM, the area under the receiver operating characteristics curve for CSF PCT was greater than that of serum PCT (0.76 vs 0.67, P < .05) and a cutoff of ⩾0.085 ng/mL achieved 55.17% sensitivity and 95.83% specificity. High levels of CSF PCT may indicate loss of integrity of the blood-brain barrier; only CSF PCT has a diagnostic value for BM in children suspected meningitis.

Tenascin-C (TNC) is involved in aortic disease pathophysiology. This study aims to evaluate TNC's value for predicting in-hospital death in acute aortic dissection (AD).We prospectively enrolled consecutive patients with suspected acute AD within 48 hours from symptom onset. Serum TNC and C-reactive protein (CRP) levels were examined on admission. Their baseline clinical characteristics and serum D-Dimer (DD) were collected. The endpoint was in-hospital death from AD.In the study cohort,78 survivors and 31 non-survivors with acute AD were enrolled. Compared to survivors, elevated median levels of serum TNC (141.10 pg/mL versus 75.30 pg/mL, P < 0.001), DD (8.74 μg/mL versus 4.58 μg/mL, P < 0.001), and CRP (19.20 mg/L versus 13.40 mg/L, P < 0.001) were found in non-survivors. Multiple logistic regressions revealed TNC, DD, and CRP were independent predictors of in-hospital death from acute AD. The OR and 95% CI were 1.038, 1.017-1.055; 1.084, 1.009-1.165 and 1.386, 1.107-1.643, respectively. Furthermore, TNC's sensitivity and specificity in predicting in-hospital death in acute AD were 83.87% and 83.33%. The combination of TNC and DD can improve the sensitivity and specificity to 90.30% and 88.46%.TNC is a valuable biomarker for predicting in-hospital death from acute AD. The combination of TNC and DD can improve predictions of in-hospital death from acute AD.