Objective To investigate the clinical application of SWI in detecting calcifications of vertebral artery wall.Methods 1 95 patients who accepted craniocerebral CT scans,and MRI scans (includingT1 WI,T2 WI,T2 GFLAIR,SWI)in recent three years in the Affiliated Hospital of Binzhou Medical University were reviewed.Taking CT as a standard,the calcification of intracranial vertebral artery wall was analyzed using conventional MRI and SWI sequences,and their sensitivities and specificities were calculated.Correlations among various imaging modalities were assessed by measuring the maximum diameter of calcifications.Results The sensitivity of SWI was 93%, and the specificity of SWI was 9 9%.The sensitivity of conventional MRI was 3 1%,and the specificity of conventional MRI was 9 1%. The correlation between SWI and CT was R2＝0.77 (0.60－0.89),while the correlation between conventional MRI and CT was R2＝0.22 (0.02－0.80).Conclusion SWI has high sensitivity and specificity in detecting calcification of intracranial vertebral artery wall,and has a good correlation with CT in measuring calcification,which can be a inspection method to detect calcification of intracranial vertebral artery wall.

Objective:This paper examines the access control mechanisms of a big data platform and explores its integration with the ChatGPT artificial intelligence platform for nursing management. The aim was to pilot a self-monitoring and follow-up big data platform for valve disease patients in the Northeastern region of China and assess its effectiveness, providing healthcare professionals with a more practical follow-up tool.Methods:Convenience sampling was used to select 32 patients who underwent mechanical valve replacement surgery or postoperative follow-up at the affiliated hospital of North Sichuan Medical College between January and October 2022 by a retrospective study, were taking oral warfarin anticoagulant therapy, and were willing to use the platform. Based on their platform usage data from November to December 2022, the 32 patients were divided into two groups according to their INR compliance rates: a high compliance group (16 patients) and a low compliance group (16 patients). Evaluate the operational effectiveness of the platform and its impact on patient anticoagulation efficacy based on its usage frequency and INR value compliance rate.Results:The number of login times and INR values written by patients in the high-standard-rate group were (11.31 ± 3.38) and (7.00 ± 1.63) times respectively, which were higher than those in the low-standard-rate group (9.44 ± 3.39) and (6.06 ± 1.88) times, the difference were not statistically significant (all P>0.05). The number of INR values written within the normal range and the number of occurrences of warning values by patients in the high-standard-rate group were (6.38 ± 1.50) and 1.00(0, 2.00) times, which were different than that in the low-standard-rate group (4.05 ± 1.57) and 2.00(2.00, 3.50) times, the differences were statistically significant ( t = 4.26, Z = - 2.22, P<0.05). Conclusions:The self-monitoring and follow-up big data platform for patients after artificial mechanical valve replacement equipped with ChatGPT can optimize and standardize the nursing follow-up workflow, improve nursing work efficiency, reduce the workload of medical staff. At the same time, it provides a better self-management platform for patients after artificial mechanical valve replacement. Assist patients in monitoring INR values and predicting possible changes in their condition, providing corresponding warnings and recommendations helps patients better participate in self-anticoagulation management, and improves the quality of life of patients.

Objective:To explore the change characteristics of amino acid levels in neonates, so as to provide theoretical basis for accurate clinical interpretation.Methods:By preliminary screening and diagnosis from 32 855 newborns, 32 843 samples were collected using tandem mass spectrometry to inherited metabolic disease (IMD) scree-ning in Sichuan Province from January to December 2018.Afterwards, according to gestational age (1 363 premature infants, 31 468 full-term infants and 12 overdue infants), blood collection time (3-7 days old, 3 095 cases, 8-28 days old, 1 637 cases, and more than 28 days old, 248 cases) and season (7 737 cases in the first quarter, 11 428 cases in the second quarter, 5 482 cases in the third quarter, and 8 196 cases in the fourth quarter), neonates were divided into different study groups.The difference of amino acid level in each group was compared, and the correlation between various influencing factors and metabolic index was analyzed.Results:(1) The distribution of 11 amino acids [alanine(ALA), arginine(ARG), citrulline(CIT), glycine(GLY), leucine+ isoleucine+ hydroxyproline (LEU+ ILE+ PRO-OH), methionine(MET), ornithine(ORN), phenylalanine(PHE), proline(PRO), tyrosine(TYR), and valine(VAL)] in neonates showed non-normally distribution.(2)The distribution of 11 amino acids in different gestational age were tested by nonparametric test, except for PHE( H=0.61, P>0.05)and TYR( H=2.02, P>0.05), and other indicators were significantly different [ALA( H=187.11, P<0.05), ARG( H=23.60, P<0.05), CIT( H=22.90, P<0.05), GLY( H=85.18, P<0.05), LEU( H=56.42, P<0.05), MET( H=18.74, P<0.05), ORN( H=129.27, P<0.05), PRO( H=344.40, P<0.05), and VAL( H=272.92, P<0.05)]. (3) The distribution of 11 amino acids in different blood collection time were significantly different [ALA( H=65.19, P<0.05), ARG( H=404.48, P<0.05), CIT( H=502.13, P<0.05), GLY( H=1 719.44, P<0.05), LEU( H=396.41, P<0.05), MET( H=199.39, P<0.05), ORN( H=31.26, P<0.05), PHE( H=325.49, P<0.05), PRO( H=70.09, P<0.05), TYR( H=159.29, P<0.05), and VAL( H=102.52, P<0.05)]. (4) The distribution of 11 amino acids in different birth seasons were significantly different [ALA( H=401.37, P<0.05), ARG( H=3 229.03, P<0.05), CIT( H=65.45, P<0.05), GLY( H=597.82, P<0.05), LEU( H=1 120.42, P<0.05), MET( H=10 515.18, P<0.05), ORN( H=1 275.23, P<0.05), PHE( H=2 260.17, P<0.05), PRO( H=319.57, P<0.05), TYR( H=884.37, P<0.05), and VAL( H=1 824.49, P<0.05)]. Conclusion:According to different gestational age, season and blood collection time, the metabolism of amino acids in neonates was different.When using tandem mass spectrometry for detection, appropriate interpretation criteria should be selected based on different conditions.

Objective:To detective the cut-off values of amino acid levels in premature infants in Sichuan.Methods:Data of newborns screening for inherited metabolic diseases (IMD) by tandem mass spectrometry in Sichuan Province from January 2018 to December 2019 were retrospectively analyzed.They were divided into premature infant group ( n=2 264, 1 312 males and 952 females) and full-term infant group ( n=53 275, 28 269 males and 25 006 females). The cut-off values of amino acids in dry blood spots were expressed as percentage ( P0.5 - P99.5), and rank sum test was used for comparison between preterm and full-term infants. Results:(1) The distribution of 11 amino acids [alanine (ALA), arginine (ARG), citrulline (CIT), glycine(GLY), leucine (LEU), methionine (MET), ornithine (ORN), phenylalanine (PHE), proline (PRO), tyrosine (TYR) and valine (VAL)] in premature infants were abnormal.(2) The cut-off values of amino acids in premature infants were as follows: ALA: 135.20-552.33 μmol/L, ARG: 1.34-47.04 μmol/L, CIT: 5.66-32.02 μmol/L, GLY: 181.48-909.93 μmol/L, LEU : 71.10-283.29 μmol/L, MET: 4.21-34.51 μmol/L, ORN: 40.58-293.76 μmol/L, PHE: 23.60-106.30 μmol/L, PRO: 77.76-358.24 μmol/L, TYR: 27.52-352.91 μmol/L, VAL: 53.74-228.37 μmol/L.(3) The cut-off values of amino acid in full-term infants were as follows: ALA: 135.20-552.33 μmol/L, ARG: 1.30-42.73 μmol/L, CIT: 5.92-30.35 μmol/L, GLY: 208.17-980.09 μmol/L, LEU: 72.91-287.49 μmol/L, MET: 4.27-33.90 μmol/L, ORN: 48.40-305.59 μmol/L, PHE: 27.63-92.27 μmol/L, PRO: 97.38-372.75 μmol/L, TYR: 40.19-276.54 μmol/L, VAL: 65.75-237.92 μmol/L.(4) Except for PHE ( Z=-0.58, P>0.05), the other indicators were significantly different between 2 groups [ALA ( Z=-15.32, P<0.05), ARG ( Z=-5.62, P<0.05), CIT ( Z=-5.86, P<0.05), GLY ( Z=-14.52, P<0.05), LEU ( Z=-5.62, P<0.05), MET ( Z=-5.22, P<0.05), ORN ( Z=-13.01, P<0.05), PRO ( Z=-22.09, P<0.05), TRY ( Z=-2.09, P<0.05), VAL ( Z=-17.82, P<0.05)]. Conclusions:The establishment of the cut-off values of amino acids in premature infants in Sichuan provides a theoretical basis for laboratory diagnosis of IMD screening, which enhances the accuracy of diagnosis and avoids excessive medical treatment.