Lophatheri Herba (Danzhuye in Chinese) is derived from the dried stems and leaves of Lophatherum gracile and has a long history of use as a medicinal and food source. Flavonoids and phenolic acids are the main active ingredients in Lophatheri Herba, which produce diuretic, anti-inflammatory, and antipyretic effects. Flavonoid glycosides and hydroxybenzoic acids are respectively the main structure in 44 flavonoids and 16 phenolic acids obtained from Lophatheri Herba. Modern pharmacological studies have found that the main chemical constituents of Lophatheri Herba play important roles in anti-inflammatory, cardioprotective, hepatoprotective and hypoglycaemic effects. Studies have demonstrated that flavonoid monomers, for example, luteolin, isoorientin, luteolin-7-O-β-D-glucoside and apigenin are more effective in exerting the above pharmacological effects. In addition, Lophatheri Herba is used in different food products as the main ingredient or as an accessory. This review describes Lophatheri Herba in terms of its chemical composition, pharmacological effects and efficacy, food development and applications, and clinical utility, and discusses the problems facing its use. This study provides valuable ideas and a scientific basis for the future development and use of L. gracile.

Objective To investigate the clinical value of combined red blood cell distribution width (RDW), mean platelet volume (MPV), mean corpuscular volume (MCV) and procalcitonin (PCT) levels in the early diagnosis of acute pancreatitis (AP). Methods A total of 86 patients with AP were enrolled in AP group. and divided into mild acute pancreatitis (MAP) group (n=41), moderately severe acute pancreatitis (MSAP) group (n=27) and severe acute pancreatitis (SAP) group (n=18) based on the severity of their condition. An additional 80 healthy individuals undergoing routine physical examinations during the same period were recruited as control group. Peripheral blood levels of PCT, lipase (LIP), interleukin (IL)-17 and tumor necrosis factor-α (TNF-α) were measured using enzyme-linked immunosorbent assay (ELISA). RDW, MPV, MCV, triglyceride, lactate dehydrogenase and urea nitrogen levels were determined using a fully automated biochemical analyzer. Pearson correlation analysis was employed to assess the correlations between RDW, MPV, MCV, PCT levels and other indicators in AP patients. Receiver operating characteristic (ROC) curve analysis was performed to evaluate the clinical value of peripheral blood RDW, MPV, MCV and PCT in the early diagnosis of AP. Results The levels of body mass index, triglyceride, lactate dehydrogenase, urea nitrogen, LIP, IL-17, TNF-α, RDW, MPV, MCV and PCT in the AP group were significantly higher than those in the control group (P < 0.05). The RDW, MPV, MCV and PCT levels were significantly higher in the MSAP and SAP groups than in the MAP group, and the RDW, MPV, MCV and PCT levels in the SAP group were significantly higher than those in the MSAP group (P < 0.05). Pearson correlation analysis revealed positive correlations between RDW, MPV, MCV as well as PCT levels and body mass index, triglyceride, lactate dehydrogenase, urea nitrogen, LIP, IL-17 as well as TNF-α levels in the AP patients (P < 0.05). ROC curve analysis indicated that the areas under the curve (AUCs) for early diagnosis of AP using peripheral blood RDW, MPV, MCV and PCT were 0.780, 0.873, 0.818 and 0.830, respectively. The AUC for combined diagnosis using these four markers was 0.972, with a specificity of 92.50% and a sensitivity of 93.02%. The combined diagnosis of RDW, MPV, MCV and PCT demonstrated superior diagnostic value compared to individual markers (Z=5.060, 3.250, 4.162, 4.272, P < 0.05). Conclusion The RDW, MPV, MCV and PCT levels were elevated in the AP patients, and their combined diagnosis of early AP was of high clinical value.

Objective:To establish a disease risk prediction model for the newborn screening system of inherited metabolic diseases by artificial intelligence technology.Methods:This was a retrospectively study. Newborn screening data ( n=5 907 547) from February 2010 to May 2019 from 31 hospitals in China and verified data ( n=3 028) from 34 hospitals of the same period were collected to establish the artificial intelligence model for the prediction of inherited metabolic diseases in neonates. The validity of the artificial intelligence disease risk prediction model was verified by 360 814 newborns ' screening data from January 2018 to September 2018 through a single-blind experiment. The effectiveness of the artificial intelligence disease risk prediction model was verified by comparing the detection rate of clinically confirmed cases, the positive rate of initial screening and the positive predictive value between the clinicians and the artificial intelligence prediction model of inherited metabolic diseases. Results:A total of 3 665 697 newborns ' screening data were collected including 3 019 cases ' positive data to establish the 16 artificial intelligence models for 32 inherited metabolic diseases. The single-blind experiment ( n=360 814) showed that 45 clinically diagnosed infants were detected by both artificial intelligence model and clinicians. A total of 2 684 cases were positive in tandem mass spectrometry screening and 1 694 cases were with high risk in artificial intelligence prediction model of inherited metabolic diseases, with the positive rates of tandem 0.74% (2 684/360 814)and 0.46% (1 694/360 814), respectively. Compared to clinicians, the positive rate of newborns was reduced by 36.89% (990/2 684) after the application of the artificial intelligence model, and the positive predictive values of clinicians and artificial intelligence prediction model of inherited metabolic diseases were 1.68% (45/2 684) and 2.66% (45/1 694) respectively. Conclusion:An accurate, fast, and the lower false positive rate auxiliary diagnosis system for neonatal inherited metabolic diseases by artificial intelligence technology has been established, which may have an important clinical value.

Objective To discuss the diagnostic value of a diagnostic strategy combining D-dimer and aortic dissection detection risk score (ADDRS) for patients with acute aortic dissection (AAD). Methods The clinical data of 750 patients with suspected AAD in emergency department of Zhongda Hospital Affiliated to Southeast University from January 2016 to January 2018 were retrospectively analyzed, including medical history, gender, age, chief complaint, physical examination, diagnostic imaging data and D-dimer levels on admission. ADDRS = 0 was defined as low risk group, ADDRS = 1 as medium risk group, ADDRS≤1 as non-high risk group,whereas ADDRS > 1 as high risk group. The clinical characteristics of AAD and non-AAD patients, ADDRS, D-dimer, and the diagnostic ability of D-dimer (the cutoff value of 500 μg/L) for AAD in different risk groups were observed. Results AAD was diagnosed in 79 of 750 (10.53%) patients. Of the 256 (34.13%) patients in low risk group, 5 patients were diagnosed with AAD. The medium risk group had 337 (44.93%) patients, including 44 cases with AAD. The high risk group had 157 (20.93%) patients, including 30 cases with AAD. In AAD patients, the proportion of male and hypertension, the incidence of ADDRS risk markers (including abrupt onset of pain, severe pain intensity, ripping or tearing pain, pulse deficit or systolic blood pressure differential of upper limb, focal neurological deficit, recent aortic manipulation, known thoracic aortic aneurysm) and the D-dimer levels in AAD group were significantly higher than those of non-AAD patients [male: 82.28% (65/79) vs. 59.76% (401/671), hypertension: 81.01% (64/79) vs. 41.43% (278/671), abrupt onset of pain: 78.48% (62/79) vs. 39.94% (268/671), severe pain intensity: 78.48% (62/79) vs. 50.52% (339/671), ripping or tearing pain: 32.91% (26/79) vs. 0.75% (5/671), pulse deficit or systolic blood pressure differential of upper limb: 15.19% (12/79) vs. 0.15% (1/671), focal neurological deficit: 7.59% (6/79) vs. 1.64% (11/671), recent aortic manipulation: 6.33% (5/79) vs. 0.30% (2/671), known thoracic aortic aneurysm: 15.19% (12/79) vs. 0.30% (2/671), D-dimer (μg/L): 1 160 (588, 3 340) vs 135 (56, 478), all P < 0.05], the proportion of diabetics was significantly lower than that of non-AAD patients [7.59% (6/79) vs. 18.78% (126/671), P < 0.05]. The positive predictive values of D-dimer for AAD diagnosis in the low risk group and the non-high-risk groups (including low and medium risk groups) were lower than that in the high risk group (8.62%, 26.32% vs. 40.91%), the negative predictive values of D-dimer were higher in the low risk group and non-high-risk groups than that in the high risk group (100.00%, 99.05% vs. 96.70%), missed diagnosis rates were higher than that in high risk group (0, 0.95%, vs. 3.30%). Conclusion In the high risk group, D-dimer≥500 μg/L is helpful for diagnosis of AAD; and in low risk group or non-high-risk group, D-dimer < 500 μg/L can efficiently and accurately exclude AAD.

ObjectiveTo explore the nursing students' experience of participation in group resuscitation workshop. MethodsFrom July to September of 2018,12 nursing interns in the Emergency Department of a Class Ⅲ Grade A hospital in Zhengzhou were recruited to participate in the study by purposive sampling and semi-structured interviews were conducted. The data obtained were analyzed by Colaizzi method. ResultsNursing students' experience of participation in the team recovery workshop can be summarized into three themes:teamwork awareness, independent learning ability and confidence,and stress and response. ConclusionsThe workshop promotes nursing students' sense of teamwork and stimulates their subjective initiative and ability to learn. It is suggested that nursing clinical education workers should attach importance to the various pressures faced by students and provide support from various angles.