OBJECTIVE: To observe the clinical efficacy of Xujiang Xie's bloodletting therapy combined with Qingyan Lige decoction on acute pharyngitis with lung-stomach heat accumulation. METHODS: A total of 88 patients with acute pharyngitis of lung-stomach heat accumulation were randomly divided into an observation group (44 cases, 4 cases dropped out) and a control group (44 cases, 4 cases dropped out). The control group was treated with oral Qingyan Lige decoction, 150 mL each time, twice a day for 6 continuous days. On the basis of the treatment in the control group, Xujiang Xie's bloodletting therapy was applied at bilateral Shaoshang (LU11), Shangyang (LI1), and Erjian (EX-HN6) in the observation group, 0.1-0.5 mL of bloodletting per site, once every other day for 3 times in total. The TCM symptom and sign score, complete blood count (white blood cell [WBC] count, neutrophilic granulocyte percentage [NE%]), inflammation indexes (serum levels of C-reactive protein[CRP], interleukin[IL]-1β, IL-6, tumor necrosis factor [TNF]-α) and immune indexes (？？, ？？, ？？) of the two groups were observed before treatment and after 6 days of treatment, and the clinical efficacy was evaluated. RESULTS: After 6 days of treatment, the sore throat scores, redness and swelling scores of pharyngeal mucosa and uvula, pharyngeal dry and burning scores, hyperemia scores of posterior pharyngeal lymphoid follicles, chill and fever scores, total scores of TCM symptom and sign, WBC count, NE%, CRP, IL-1β, IL-6, TNF-α and ？？ in both groups were decreased compared with those before treatment (P<0.05), the above indexes in the observation group were lower than those in the control group (P<0.01, P<0.05, P<0.001). After 6 days of treatment, the levels of ？？ and ？？ in both groups were increased compared with those before treatment (P<0.05), and the above indexes in the observation group were higher than those in the control group (P<0.001). The total effective rate of the observation group was 95.0% (38/40), which was higher than 90.0% (36/40) in the control group (P<0.001). CONCLUSION Xujiang Xie's bloodletting therapy combined with Qingyan Lige decoction could improve the symptoms in patients with acute pharyngitis of lung-stomach heat accumulation, inhibit inflammatory response and improve immune function.
Humans ; Drugs, Chinese Herbal/administration & dosage* ; Male ; Female ; Pharyngitis/drug therapy* ; Adult ; Middle Aged ; Bloodletting ; Young Adult ; Lung/drug effects* ; Combined Modality Therapy ; Interleukin-6 ; Adolescent ; Tumor Necrosis Factor-alpha ; Acute Disease/therapy* ; Treatment Outcome

Objective:To explore the assessment value of multimodal imaging technique for the hepatic fibrosis(HF)of rats.Methods:Forty-eight SD rats were selected,and they were divided into control group,M1 model group,M2 model group and M3 model group according to random number table,with 12 rats in each group.The three model groups were induced by combination method of carbon tetrachloride(CCl4)plus ethanol to establish HF model.At the 4th,6th and 8th week of modelling,the M1 model group underwent ultrasound examination,and the M2 model group underwent examination of magnetic resonance,and the M3 model group adopted the examinations of conventional ultrasound,ultrasound elastography and dynamically contrast enhanced magnetic resonance imaging(DCE-MRI).The rats of control group were randomly divided into three subgroups,with four rats in each subgroup,which were controlled with the rats in the M1,M2 and M3 groups,respectively.At the end of the imaging examinations,the HE staining were conducted on the liver tissues so as to observe the HF degree of rats.The conventional ultrasound was used to observe the morphology of the rat liver,and to measure the inner diameter of the main trunk of the portal vein.colour Doppler ultrasound was used to measure the blood flow velocity of portal vein.Ultrasound elastography was used to observe the color distribution of liver parenchymal stiffness,and to measure the elasticity value of liver parenchyma.The initial area under curve(iAUC)of the receiver operating characteristic(ROC)was adopted to analyze the volume transit constant(Ktrans),rate constant(Kep)and extravascular extracellular space volume fraction(Ve)of the hemodynamic parameters in enhanced magnetic resonance examination.Results:The conventional ultrasound showed there was no abnormal changes in the livers of rats in control group,and the morphology of the liver of M1 group occurred changes.Ultrasound elastography showed that the elasticity values of the liver of rats in M1 group appeared upward trend with the aggravation of the HF degree than control group.Conventional MRI of group M2 showed that the liver morphology was normal in the control group,and the changes of the liver morphologies of rats in M2 group at the 6th and 8th week post modeling were more obvious than that at the 4th week post modeling.The ROC curve analysis of DCE-MRI results showed that Ktrans and Ve had higher diagnostic efficacy for HF,and their iAUC values were>0.9.The results of intergroup comparison of rats among M1 model group,M2 model group and M3 model group indicated that the sensitivity,accuracy,positive predictive value and negative predictive value of multimodal imaging technique were significantly higher than those of single examination in diagnosing HF at early stage of rats,and the differences were statistically significant(x2=0.634,0.644,0.621,0.543,P<0.05),respectively.HE staining results showed that the liver tissues of the rats in the model groups appeared fibroplasia of different degrees.Conclusion:Multimodal imaging technique can be used as an effective test method of evaluating hepatic fibrosis.

Objective:To investigate the predictive value of clinical factor model, deep learning model based on baseline plain CT images, and combination of both for predicting hematoma expansion in cerebral hemorrhage.Methods:The study was cross-sectional. Totally 471 cerebral hemorrhage patients who were firstly diagnosed in the Second Affiliated Hospital of Soochow University from January 2017 to December 2021 were collected retrospectively. These patients were randomly divided into a training dataset ( n=330) and a validation dataset ( n=141) at a ratio of 7∶3 by using the random function. All patients underwent two noncontrast CT examinations within 24 h and an increase in hematoma volume of >33% or an absolute increase in hematoma volume of >6 ml was considered hematoma enlargement. According to the presence or absence of hematoma enlargement, all patients were divided into hematoma enlargement group and hematoma non-enlargement group.Two-sample t test, Mann-Whitney U test or χ2 test were used for univariate analysis. The factors with statistically significant differences were included in multivariate logistic regression analysis, and independent influences related to hematoma enlargement were screened out to establish a clinical factor model. ITK-SNAP software was applied to manually label and segment the cerebral hemorrhage lesions on plain CT images to train and build a deep learning model based on ResNet50 architecture. A combination model for predicting hematoma expansion in cerebral hemorrhage was established by combining independent clinical influences with deep learning scores. The value of the clinical factor model, the deep learning model, and the combination model for predicting hematoma expansion in cerebral hemorrhage was evaluated using receiver operating characteristic (ROC) curves and decision curves in the training and validation datasets. Results:Among 471 cerebral hemorrhage patients, 136 cases were in the hematoma enlargement group and 335 cases were in the hematoma non-enlargement group. Regression analyses showed that male ( OR=1.790, 95% CI 1.136-2.819, P=0.012), time of occurrence ( OR=0.812, 95% CI 0.702-0.939, P=0.005), history of oral anticoagulants ( OR=2.157, 95% CI 1.100-4.229, P=0.025), admission Glasgow Coma Scale score ( OR=0.866, 95% CI 0.807-0.929, P<0.001) and red blood cell distribution width ( OR=1.045, 95% CI 1.010-1.081, P=0.011) were the independent factors for predicting hematoma expansion in cerebral hemorrhage. ROC curve analysis showed that in the training dataset, the area under the curve (AUC) of clinical factor model, deep learning model and combination model were 0.688 (95% CI 0.635-0.738), 0.695 (95% CI 0.642-0.744) and 0.747 (95% CI 0.697-0.793) respectively. The AUC of the combination model was better than that of the clinical model ( Z=0.54, P=0.011) and the deep learning model ( Z=2.44, P=0.015). In the validation dataset, the AUC of clinical factor model, deep learning model and combination model were 0.687 (95% CI 0.604-0.763), 0.683 (95% CI 0.599-0.759) and 0.736 (95% CI 0.655-0.806) respectively, with no statistical significance. Decision curves showed that the combination model had the highest net benefit rate and strong clinical practicability. Conclusions:Both the deep learning model and the clinical factor model established in this study have some predictive value for hematoma expansion in cerebral hemorrhage; the combination model established by the two together has the highest predictive value and can be applied to predict hematoma expansion.

Objective:To explore the value of the Nomogram model established by CT radiomics combined with clinical indicators for prediction of the severity of early acute pancreatitis (AP).Methods:From January 2016 to March 2023, the AP patients in the Second Affiliated Hospital of Soochow University were retrospectively collected. According to the revised Atlanta classification and definition of acute pancreatitis in 2012, all patients were divided into the severe group and the non-severe group. All patients were first diagnosed, and abdominal CT plain scan and enhanced scan were completed within 1 week. Patients were randomly (random number) divided into training and validation groups at a ratio of 7:3. The pancreatic parenchyma was delineated as the region of interest on each phase CT images, and the radiomics features were extracted by python software. LASSO regression and 10-fold cross-validation were used to reduce the dimension and select the optimal features to establish the radiomics signature. Multivariate Logistic regression was used to select the independent predictors of severe acute pancreatitis (SAP), and a clinical model was established. A Nomogram model was established by combining CT radiomics signature and clinical independent predictors. Receiver operating characteristic (ROC) curve and decision curve analysis (DCA) were used to evaluate the predictive efficacy of each model.Results:Total of 205 AP patients were included (59 cases in severe group, 146 cases in non-severe group). 3, 5, 5 and 5 optimal radiomics features were selected from the plain CT scan, arterial phase, venous phase and delayed phase images of all patients, and the radiomics models were established. Among them, the arterial phase radiomics model had relatively better performance in predicting SAP, with an area under curve (AUC) of 0.937 in the training group and 0.913 in the validation group. Multivariate Logistic regression showed that C-reactive protein (CRP) and lactate dehydrogenase (LDH) were independent predictors of SAP, and they were used to establish a clinical model. The AUC in the training and validation groups were 0.879 and 0.889, respectively. The Nomogram model based on arterial phase CT radiomics signature, CRP and LDH was established, and the AUC was 0.956 and 0.947 in the training group and validation group, respectively. DCA showed that the net benefit of Nomogram model was higher than that of clinical model or radiomics model alone.Conclusions:The Nomogram model established by CT radiomics combined with clinical indicators has high application value for early prediction of the severity of AP, which is conducive to the formulation of clinical treatment plans and prognosis evaluation.

Objective:To investigate the value of dual-detector spectral CTA in distinguishing infarct core from penumbra in patients with acute ischemic stroke(AIS), and to further explore the risk factors associated with infarct core and their predictive value.Methods:The imaging and clinical data of 163 patients with AIS who met the inclusion criteria admitted to the Second Affiliated Hospital of Soochow University from March 2022 to May 2023 were retrospectively analyzed. Patients from March 2022 to December 2022 were used as the training group, and patients from January 2023 to May 2023 were used as the validation group for internal validation. The head and neck spectral CTA and brain CT perfusion imaging with dual-layer detector spectral CT were all carried out on all patients. Using CTP as reference, the patients were divided into infarct core group and non-infarct core group according to whether an infarct core occurred in the hypoperfusion regions of brain tissue. Multivariate logistic regression analysis was used to screen predictors related to the infarct core. The receiver operating characteristic (ROC) curve was used to evaluate the predictive efficacy.Results:A total of 163 patients were included in the study, including 112 in the training group and 51 in the validation group. There were significant differences in iodine density, effective atomic number, hypertension, triglyceride and neutrophils between the two groups ( P< 0.05). The cutoff values for iodine density values and effective atomic number values were 0.215 mg/mL and 7.405, respectively. Multivariate logistic regression analysis showed that iodine density and hypertension were independent risk factors for infarct core in AIS, and triglyceride was an independent protective factor. The area under the ROC curve (AUC) of iodine density value was the largest (0.859), with a sensitivity of 70.27%, and a specificity of 90.67%, which had a good predictive value. The ROC curve analysis results for the validation group were consistent with the training group. Conclusions:Spectral CT parameters iodine density values and effective atomic number values have the potential to distinguish the infarct core area from the penumbra area in patients with AIS. Iodine density and hypertension were independent risk factors of infarct core in AIS, triglyceride was an independent protective factor, and iodine density values obtained by dual-layer spectral detector CT had a high predictive value.

Objective:To explore potential predictors of refractory Mycoplasma pneumoniae pneumonia (RMPP) in early stage. Methods:The prospective multicenter study was conducted in Zhejiang, China from May 1 st, 2019 to January 31 st, 2020. A total of 1 428 patients with fever >48 hours to <120 hours were studied. Their clinical data and oral pharyngeal swab samples were collected; Mycoplasma pneumoniae DNA in pharyngeal swab specimens was detected. Patients with positive Mycoplasma pneumoniae DNA results underwent a series of tests, including chest X-ray, complete blood count, C-reactive protein, lactate dehydrogenase (LDH), and procalcitonin. According to the occurrence of RMPP, the patients were divided into two groups, RMPP group and general Mycoplasma pneumoniae pneumonia (GMPP) group. Measurement data between the 2 groups were compared using Mann-Whitney U test. Logistic regression analyses were used to examine the associations between clinical data and RMPP. Receiver operating characteristic (ROC) curves were used to analyse the power of the markers for predicting RMPP. Results:A total of 1 428 patients finished the study, with 801 boys and 627 girls, aged 4.3 (2.7, 6.3) years. Mycoplasma pneumoniae DNA was positive in 534 cases (37.4%), of whom 446 cases (83.5%) were diagnosed with Mycoplasma pneumoniae pneumonia, including 251 boys and 195 girls, aged 5.2 (3.3, 6.9) years. Macrolides-resistant variation was positive in 410 cases (91.9%). Fifty-five cases were with RMPP, 391 cases with GMPP. The peak body temperature before the first visit and LDH levels in RMPP patients were higher than that in GMPP patients (39.6 (39.1, 40.0) vs. 39.2 (38.9, 39.7) ℃, 333 (279, 392) vs. 311 (259, 359) U/L, both P<0.05). Logistic regression showed the prediction probability π=exp (-29.7+0.667×Peak body temperature (℃)+0.004×LDH (U/L))/(1+exp (-29.7+0.667×Peak body temperature (℃)+0.004 × LDH (U/L))), the cut-off value to predict RMPP was 0.12, with a consensus of probability forecast of 0.89, sensitivity of 0.89, and specificity of 0.67; and the area under ROC curve was 0.682 (95% CI 0.593-0.771, P<0.01). Conclusion:In MPP patients with fever over 48 to <120 hours, a prediction probability π of RMPP can be calculated based on the peak body temperature and LDH level before the first visit, which can facilitate early identification of RMPP.

Objective To construct the machine learning models based on the radiomic features of non-contrast and enhanced CT and to evaluate the predictive value in the risk stratification of gastrointestinal stromal tumor(GIST).Methods A total of 182 patients with pathologically confirmed GIST were randomly divided into a training set and a validation set at a ratio of 7∶3.The volume of interest(VOI)was outlined in the non-contrast phase,arterial phase and venous phase,and its radiomic features were extracted.The most valuable radiomic features were selected using the least absolute shrinkage and selection operator(LASSO)algorithm.The logistic regression(LR)classifier was used to construct the prediction models based on single-phase or multi-phase images.The predictive efficacy of the different models was compared by using receiver operating characteristic(ROC)curves.Results Four,three,and four radiomic features were selected in the non-contrast phase,arterial phase and venous phase,and 4 models were constructed in total.Among the single-phase models,the venous phase had better predictive efficacy,with the area under the curve(AUC)of 0.932[95％confidence interval(CI)0.873-0.969]and 0.924(95％CI 0.819-0.979)in the training and validation sets.The predictive efficacy of the combined model was improved,with the AUC of 0.946(95％CI 0.891-0.978)and 0.938(95％CI 0.838-0.986).Conclusion The venous phase model can predict the risk stratification of GIST accurately,and the prediction efficacy can be improved by combining the non-contrast and arterial phases.

Objective To explore the feasibility of predicting expression level of Ki-67 in lung adenocarcinoma via multi-parameter of spectral CT.Methods The data of 226 patients with lung adenocarcinoma confirmed by pathology were analyzed retrospectively.The conventional and spectral CT parameters of the lesions were analyzed.According to Ki-67 expression level,all patients were divided into low expression group and high expression group.The parameters with statistical significance were identified as independent variables for multivariate logistic regression analysis to establish a logistic regression model for predicting the expression level of Ki-67.Receiver operating characteristic(ROC)curve was used to assess the diagnostic performance for each model,respectively.Results There were significant differences in the clinical factors of gender,smoking and chest pain between high and low Ki-67 expression groups.In spectral CT parameters,CT40 keV,CT100 keV,Z-effective(Zeff)and iodine concentration(IC)in the high expression group in arterial phase were significantly higher than those in the low expression group.Logistic regression analysis showed that CT100 keV was the independent risk factor for Ki-67 expression level.Both the spectral CT model and the combined model had high value in predicting the expression level of Ki-67 in lung adenocarcinoma,and the combined model had better diagnostic efficacy.Conclusion Spectral CT parameters combined with clinical factors have a certain value in predicting the expression level of Ki-67 in lung adenocarcinoma.

Objective:To investigate the differences in clinical and electrocardiographic characteristics between carriers of SCN5A mutations and non-SCN5A mutations in fever-induced Brugada syndrome.Methods:This study is a retrospective cohort study. A total of 263 patients with fever-induced Brugada syndrome who were admitted to Renmin Hospital of Wuhan University from January 2000 to December 2023 were selected. Their clinical manifestations, electrocardiographic characteristics, and major adverse cardiovascular events (MACE) at the time of diagnosis and during the follow-up period were collected. Among them, 200 patients underwent next-generation sequencing. Based on the genetic variation results, after excluding other mutations, they were divided into SCN5A mutation group, non-SCN5A sodium-related mutation group, potassium/calcium mutation group, and no mutation group. Comparisons were made among these groups in terms of their clinical and electrocardiographic characteristics.Results:Among the 263 patients with fever-induced Brugada syndrome, the mean age was (41.9±17.6) years, with 80.6% (212/263) being male. The median follow-up duration was 53.0 months, and 13.7% (36/263) of the patients experienced MACE. The rate of SCN5A mutation was 34.5% (69/200), while the rates of non-SCN5A sodium-related mutations and potassium/calcium-related mutations were 4.5% (9/200) and 3.5% (7/200), respectively. The SCN5A mutation group was younger than the non-SCN5A sodium-related mutation group and the no mutation group (ages were (33.8±14.7), (49.8±11.6), (44.6±15.7) years, respectively, P<0.001). The SCN5A mutation group also had a longer PR interval than the no mutation group ((176.8±32.3) ms vs. (163.9±28.6) ms, P=0.034). The incidence of MACE was higher in the non-SCN5A sodium-related mutation group than that in the no mutation group (55.6% (5/9) vs. 9.1% (9/99), P=0.002). Conclusions:Fever-induced Brugada syndrome patients carrying non-SCN5A mutations exhibit distinct clinical and electrocardiographic characteristics compared to those with SCN5A mutations. These differences warrant attention in clinical practice.

Objective To investigate the prevalence of Cryptosporidium infection and the distribution of parasite species and genotypes among HIV-positive individuals in Jiangxi Province. Methods HIV-positive individuals' sociodemographic and clinical data were collected from three AIDS designated hospitals in Jiangxi Province from January 2022 to March 2023. Subjects' stool samples were collected, and genomic DNA was extracted from stool samples. Nested PCR assay was performed based on the small subunit ribosomal RNA (SSU rRNA) gene of Cryptosporidium, and Cryptosporidium gp60 gene was amplified in stool samples positive for the SSU rRNA gene. The second-round PCR amplification product was checked with 1.5% agarose gel electrophoresis, and the products of suspected positive amplifications were sequenced, followed by sequence alignment. The phylogenetic tree was created using the Neighbor-Joining method with the software MEGA 11.0, to characterize the species, genotypes and sub-genotypes of Cryptosporidium. Results A total of 382 HIV-positive individuals were enrolled, with two cases identified with Cryptosporidium infection (0.52% prevalence), and both cases had no abdominal pain or diarrhea. Following sequencing and sequence alignment, the gene sequences of these two Cryptosporidium isolates shared 99.76% and 99.88% similarity with the gene sequence of C. meleagridis isolates. Phylogenetic analysis based on the Cryptosporidium SSU rRNA gene sequence identified the species of these two Cryptosporidium-positive stool samples as C. meleagridis. Following nested PCR amplification of the Cryptosporidium gp60 gene, sequencing and sequence alignment, the two C. meleagridis isolates were characterized as III eA17G2R1 and III bA25G1R1a sub-genotypes, and the sub-genotype III bA25G1R1a was firstly described in humans. Conclusion The prevalence of Cryptosporidium is low among HIV-positive individuals in Jiangxi Province. The likelihood of Cryptosporidium infection cannot be neglected among HIV-positive individuals without diarrhea.