In order to accurately capture the respiratory muscle movement and extract the synchronization signals corresponding to the breathing phases, a comprehensive signal sensing system for sensing the movement of the respiratory muscle was developed with applying the thin-film varistor FSR402 IMS-C07A in this paper. The system integrated a sensor, a signal processing circuit, and an application program to collect, amplify and denoise electronic signals. Based on the respiratory muscle movement sensor and a STM32F107 development board, an experimental platform was designed to conduct experiments. The respiratory muscle movement data and respiratory airflow data were collected from 3 healthy adults for comparative analysis. In this paper, the results demonstrated that the method for determining respiratory phase based on the sensing the respiratory muscle movement exhibited strong real-time performance. Compared to traditional airflow-based respiratory phase detection, the proposed method showed a lead times ranging from 33 to 210 ms [(88.3 ± 47.9) ms] for expiration switched into inspiration and 17 to 222 ms [(92.9 ± 63.8) ms] for inspiration switched into expiration, respectively. When this system is applied to trigger the output of the ventilator, it will effectively improve the patient-ventilator synchrony and facilitate the ventilation treatment for patients with respiratory diseases.
Humans ; Respiratory Muscles/physiology* ; Signal Processing, Computer-Assisted ; Movement/physiology* ; Respiration ; Monitoring, Physiologic/methods* ; Adult

Purpose/Significance To explore the changes,challenges,key application scenarios and future development directions of generative artificial intelligence(AI)represented by ChatGPT in the field of hospital management,and to provide references for the ap-plication of AI natural language processing(NLP)technology in the field of hospital management in China.Method/Process Through literature review and analysis,the changes and challenges brought about by the rapid development of generative AI in the field of hospital management are sorted out,its key application scenarios and future development directions in the field of hospital management are empha-sized and explored.Result/Conclusion AI has broad application prospects in the field of hospital management,and it should focus on exploring its practical application scenarios and strategic directions to provide reference and guidance for promoting the high-quality de-velopment of public hospitals.

Objective:To assess the quality of low-keV monoenergetic images using deep learning image reconstruction (DLIR) algorithm combined with dual energy CT (DECT) in gastric cancer and to compare them with images from the conventional adaptive statistical iterative reconstruction (ASiR-V) algorithm.Methods:In this cross-sectional study, DECT images of 31 gastric cancer patients in the First Affiliated Hospital of Zhengzhou University were prospectively collected from September 2022 to March 2023. The 55 keV monoenergy images were reconstructed using the DLIR algorithm at low-, medium-, and high-intensity levels (DLIR-L, DLIR-M, and DLIR-H) based on arterial phase and venous phase images, respectively. The 70 keV 40% mixing coefficient (ASiR-V40%) images were reconstructed using the ASiR-V algorithm. In the objective evaluation of images, the signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) for both lesions and muscle were calculated across four sets of reconstructed images. In the subjective evaluation of images, scores were assigned to the overall image quality, lesion visibility, and diagnostic confidence for each set of reconstructed images. Comparisons of SNR and CNR between the 4 groups were made by One-way repeated-measures ANOVA or Friedman′s test. Comparisons of scores were made by Friedman′s test. The P value of pairwise comparison was adjusted using Bonferroni correction methods. Results:In the objective evaluations, CNR lesion, SNR lesion and SNR muscle were highest on the 55 keV DLIR-H images in the arterial and venous phases, and showed a gradually increasing trend on the 70 keV ASiR-V40%, 55 keV DLIR-L, DLIR-M, DLIR-H images ( P<0.05). In subjective evaluations, compared to the 70 keV ASiR-V40% images, overall image quality scores were numerically higher for the 55 keV DLIR-H ( P>0.05), similar or slightly worse for the 55 keV DLIR-M, and significantly lower for the 55 keV DLIR-L ( P<0.05). The lesion visibility and diagnostic confidence on the 55 keV DLIR reconstruction images were higher in both arterial and venous phases than 70 keV ASiR-V40% images ( P<0.05). Conclusions:Compared to the conventional 70 keV ASiR-V40% images, the 55 keV DLIR-H images had higher lesion contrast and diagnostic confidence with lower image noise. The 55 keV DLIR-M images had comparable overall image quality to 70 keV ASiR-V40% images, but the former had higher lesion contrast and diagnostic confidence. The 55 keV DLIR-L was unable to improve image quality to the level of 70 keV ASiR-V40%.

Objective To investigate the levels of plasma bile acids(BA)in patients with primary liver cancer(PLC)or metastatic liver cancer(MLC)and their correlation with clinical indicators,as well as the value of plasma BAs combined with alpha-fetoprotein(AFP)in the diagnosis of PLC.Methods This study was conducted among 75 patients with PLC and 79 patients with MLC who attended Shuguang Hospital Affiliated to Shanghai University of Traditional Chinese Medicine from August 2020 to September 2021 and had a confirmed diagnosis based on histopathological and imaging findings.Peripheral blood samples were collected from all patients,and serum and plasma were separated.Colorimetry and chromatography were used to measure biochemical parameters;electrochemiluminescence immunoassay was used to measure the levels of tumor markers;liquid chromatography-tandem mass spectrometry was used to measure the content of BA.The t-test was used for comparison of normally distributed continuous data,and the Mann-Whitney U test was used for comparison of non-normally distributed continuous data;the Spearman's coefficient was used for correlation analysis;the receiver operating characteristic(ROC)curve was used to evaluate clinical diagnostic efficacy.Results The PLC group had significantly lower levels of total cholesterol,triglyceride,low-density lipoprotein cholesterol,and apolipoprotein B than the MLC group(U=1 598,1 255,909,and 889,all P＜0.05).Compared with the MLC group,the PLC group had a significantly higher level of AFP and a significantly lower level of carcinoembryonic antigen(U=1 873 and 926,both P＜0.05).Compared with the MLC group,the PLC group had significantly higher levels of TBA,CA,CDCA,UDCA,TCA,TCDCA,GCA,GCDCA,TUDCA,and GUDCA and a significantly lower level of DCA(all P＜0.05).In the total population,the levels of TBA,CDCA,GCA,GCDCA,GUDCA,TCA,TCDCA,and TUDCA were significantly positively correlated with the level of AFP(all P＜0.05).In the patients with PLC,the levels of GCA,TCA,TCDCA,and TUDCA were significantly positively correlated with the level of AFP(all P＜0.05).Combined measurement of AFP+TCA+GCA+TCDCA had an area under the ROC curve of 0.822(95%confidence interval:0.746-0.898,P＜0.000 1),suggesting that it had the highest diagnostic efficacy.Conclusion There are significant differences in the levels of plasma BA between the patients with PLC and those with MLC,and the differentially expressed BAs are closely associated with liver function impairment and the increase in AFP.BAs combined with AFP has a better clinical value in the diagnosis of PLC.

Objective To observe the value of dual-energy CT(DECT)radiomics combined with clinical and CT features for predicting differentiation degree of gastric adenocarcinoma(GAC).Methods Totally 254 patients with GAC were prospectively analyzed and divided into high-grade group(low differentiation GAC,n=88)and low-grade group(middle-high differentiation GAC,n=166)according to pathological results.The patients were also divided into training set(n=203,including 70 high-grade and 133 low-grade GAC)and verification set(n=51,including 18 high-grade and 33 low-grade GAC)at the ratio of 8∶2.Radiomics features were extracted and screened based on venous phase single-level(40,70,100 and 140 keV)DECT,and a multi-energy radiomics model was constructed to predict GAC classification.Univariate analysis and multivariate logistic regression were used to analyze clinical and CT features as well as DECT parameters in training set to construct a clinic-CT model.Then a combined model was constructed through combining clinic-CT model with radiomics model.The predictive efficacy of the models were evaluated,and the calibration degree of the combined model was assessed.Results The area under the curve(AUC)of clinic-CT model,multi-energy radiomics model and combined model was 0.74,0.75 and 0.78 in training set,and 0.73,0.77 and 0.78 in verification set,respectively.Except for AUC of combined model was higher than that of clinic-CT model in training set(P＜0.05),no significant difference of AUC was found among models in training set nor verification set(all P＞0.05).The calibration degree of combined model was good in both training set and verification set(both P＞0.05).Conclusion DECT radiomics combined with clinical and CT features could effectively predict differentiation degree of GAC.

Objective To observe the value of spectral CT parameters for evaluating T staging of advanced gastric cancer(AGC).Methods Totally 155 AGC patients were collected and divided into T2 stage(n=40)and T3/4a stage(n=115)according to postoperative pathology.CT values,water concentration(WC)and iodine concentration(IC)of AGC lesions on 40-140 keV arteriovenous phase single energy level images were measured,and the standardized IC(nIC)and spectral curve slopes k1 and k2 were calculated.Clinical variables and spectral quantitative parameters were compared between groups,and receiver operating characteristic curve was plotted,the area under the curve(AUC)was calculated to evaluate the value of each parameter and model for identifying T2 and T3/4a stage AGC.Results Tumor thickness,proportion of low differentiation degree,CT100kev,CT140kev,and WC values in T3/4a group were all significantly higher than those in T2 group(all P＜0.05).CT140keV of AGC lesions on venous phase images presented the highest discrimination efficacy among single parameters,with AUC of 0.782.AUC of clinical-arterial phase-venous phase model was 0.848,higher than that of clinical model and arterial phase model alone(both P＜0.05)but not significantly different compared with AUC of venous phase model(P＞0.05).Conclusion Spectral CT quantitative parameters,especially venous phase parameters could be used to effectively identify T stage of AGC.Multi-parameter combined models had higher diagnostic value.

Objective To observe the value of CT radiomics-based nomogram for predicting difference of Lauren types of gastric cancers between endoscopic biopsy and postoperative pathology.Methods Totally 126 patients with gastric cancer diagnosed by surgical pathology were retrospectively analyzed.The patients were divided into concordant group(n=77)and inconsistent group(n=49)according to the concordance between endoscopic biopsy and postoperative pathology results or not,also divided into training set and validation set at the ratio of 2∶1.Clinical predictors were screened,then a clinical prediction model was constructed.Radiomics features were extracted based on venous-phase CT images and screened using L1 regularization.Radiomics models were constructed using 3 machine learning(ML)algorithms,i.e.decision trees,random forests and logistic regression.The nomogram based on clinical and the best ML radiomics model was constructed,and the efficacy and clinical utility of the above models and nomogram for predicting inconsistency of Lauren types of gastric cancers between endoscopic biopsy and postoperative pathology were evaluated.Results Patients'age,platelet count,and arterial-phase CT values of tumors were all independent predictors of inconsistency between endoscopic biopsy and postoperative pathology of Lauren types of gastric cancer.CT radiomics model using random forests algorithm showed better predictive efficacy among 3 ML models,with the area under the curve(AUC)of 0.835 in training set and 0.724 in validation set,respectively.The AUC of clinical model,radiomics model and the nomogram in training set was 0.764,0.835 and 0.884,while was 0.760,0.724 and 0.841 in validation set,respectively.In both training set and validation set,the nomogram showed a good fit and considerable clinical utility.Conclusion CT radiomics-based nomogram had potential clinical application value for predicting inconsistency of Lauren types of gastric cancers between endoscopic biopsy and postoperative pathology.

Objective To observe the value of spectral CT multi-parameter imaging for preoperative predicting lymph node metastasis(LNM)of gastric cancer.Methods Totally 136 patients with gastric adenocarcinoma were retrospectively enrolled.The patients were further divided into LNM group(n=74)and non-LNM group(n=62)according to postoperative pathological findings of lymph nodes status.Clinical data,conventional CT findings and spectral CT parameters were compared between groups.Factors being significant different between groups were included in multivariate logistic regression analysis to screen independent predictors of gastric cancer LNM.Clinical+conventional CT model(model 1),spectrum CT model(model 2)and combined model(model 3)were constructed based on the above independent predictors,respectively.Receiver operating characteristic curve was drawn,and the area under the curve(AUC)was calculated to evaluate the efficacy of each model for preoperative predicting LNM of gastric cancer.Results CT-N stage,CT-T stage,70,100 and 140 keV CT valuestumor at arterial phase(AP),arterial enhancement fraction(AEF)and normalized iodine concentration at venous phase(NICVP)were all independent predictors of gastric cancer LNM(all P＜0.05).AUC of model 3 was 0.846,higher than that of model 1 and model 2(AUC=0.767,0.774,Z=-0.368,-2.373,both P＜0.05)for preoperative predicting LNM of gastric cancer,while the latter two were not significantly different(Z=-0.152,P=0.879).Conclusion Spectral CT multi-parameter imaging could effectively predict LNM of gastric cancer preoperatively.

Objective:To establish and internally validate a nomogram model for predicting complicated acute appendicitis (CA).Methods:The clinical data from 663 acute appendicitis patients from the First Affiliated Hospital of Anhui University of Traditional Chinese Medicine from October 2015 to October 2022 were retrospectively analyzed. There were 411 males and 252 females, aged ( M (IQR)) 41 (22) years (range: 18 to 84 years). There were 516 cases of CA and 147 cases of uncomplicated acute appendicitis. The minimum absolute contraction and selection operator regression model was used to screen the potential relative factors of CA, and the screened factors were included in the Logistic regression model for multivariate analysis. Software R was used to establish a preoperative CA nomogram prediction model, the receiver operating characteristic curve of the model was drawn, and the value of area under the curve (AUC) was compared to evaluate its identification ability, and the Bootstrap method was used for internal verification. Results:The elderly (age≥60 years) ( OR=2.428, 95% CI: 1.295 to 4.549), abdominal pain time (every rise of 1 hour) ( OR=1.089, 95% CI: 1.072 to 1.107), high fever (body temperature≥39 ℃) ( OR=1.122, 95% CI: 1.078 to 1.168), total bilirubin (every rise of 1 μmol/L) ( OR=2.629, 95% CI: 1.227 to 5.635) were independent relative factors of CA (all P<0.05). The AUC of this model was 0.935 (95% CI: 0.915 to 0.956). After internal verification using the Bootstrap method, the model still had a high discrimination ability (AUC=0.933), and the predicted CA curve was still in good agreement with the actual clinical CA curve. Conclusion:The clinical prediction model based on the elderly (age≥60 years), prolonged abdominal pain time, high fever (body temperature≥39 ℃), and increased total bilirubin can help clinicians effectively identify CA.

OBJECTIVE: This study was aimed at investigating the carrier rate of, and molecular variation in, α- and β-globin gene mutations in Hunan Province. METHODS: We recruited 25,946 individuals attending premarital screening from 42 districts and counties in all 14 cities of Hunan Province. Hematological screening was performed, and molecular parameters were assessed. RESULTS: The overall carrier rate of thalassemia was 7.1%, including 4.83% for α-thalassemia, 2.15% for β-thalassemia, and 0.12% for both α- and β-thalassemia. The highest carrier rate of thalassemia was in Yongzhou (14.57%). The most abundant genotype of α-thalassemia and β-thalassemia was -α ^3.7/αα (50.23%) and β ^IVS-II-654/β ^N (28.23%), respectively. Four α-globin mutations [CD108 (ACC>AAC), CAP +29 (G>C), Hb Agrinio and Hb Cervantes] and six β-globin mutations [CAP +8 (C>T), IVS-II-848 (C>T), -56 (G>C), beta nt-77 (G>C), codon 20/21 (-TGGA) and Hb Knossos] had not previously been identified in China. Furthermore, this study provides the first report of the carrier rates of abnormal hemoglobin variants and α-globin triplication in Hunan Province, which were 0.49% and 1.99%, respectively. CONCLUSION Our study demonstrates the high complexity and diversity of thalassemia gene mutations in the Hunan population. The results should facilitate genetic counselling and the prevention of severe thalassemia in this region.
Humans ; beta-Thalassemia/genetics* ; alpha-Thalassemia/genetics* ; Hemoglobinopathies/genetics* ; China/epidemiology* ; High-Throughput Nucleotide Sequencing