Objective To explore the conditions for the restoration of competitive dysbacteriosis with antibiotics. Methods The mathematical model of the two competitive floras was analyzed by the Qualitative Theory of Ordinary Differential Equations. Results Three different types of dysbacteriosis and their restoring conditions were obtained. Conclusion Different restoring schemes should be applied for the regulation of different types of dysbacteriosis. Misuse of antibiotics can not result in satisfactory therapeutic effect.

Objective To investigate the difference of apparent diffusion coefficients (ADCs) changes in three major salivary glands after gustatory stimulation using two different stimuli. Methods Thirty healthy volunteers were examined with a 1.5 T MR unit. A diffusion-weighted MR imaging (MR DWI) sequence was performed once at rest and continuously repeated 13 times after gustatory stimulation using a commercially available lemon juice and vitamin C tablets in the same volunteer by using self-controlled method. The subsequence of two stimuli was random. In addition, the salivary flow rates at rest and after stimulation were measured. Characteristics and differences in ADCs curves of three salivary glands before and after stimulation between two stimuli were analyzed. Comparison of maximum ADCs, maximum ADCs increase rates (IRs) and times to maximum ADCs(Tmax) between two stimuli was performed by using independent-samples t test. Correlation analysis between rest salivary flow rates and rest ADCs, the maximum salivary flow rates and ADCs after stimulation, the maximum salivary flow IRs and ADC IRs after stimulation were performed by using Pearson correlation test. Results In lemon juice stimulation group, the mean ADCs mostly showed a steady increase to peak values during the first DW MRI scan after stimulation in all glands, followed by a gradually decrease fluctuating slightly around the baseline values. In vitamin C stimulation group, the mean ADCs were significantly increased in all glands during the first DW MRI scan after stimulation, followed by a gradual upward trend till peak values. In lemon juice stimulation group, the mean Tmax of submandibular and sublingual glands[(184±122)s, (345±232)s, respectively] were significantly earlier than those[(454 ± 301)s, (528 ± 297)s, respectively] in vitamin C stimulation group (t=-3.517 and-2.548 respectively, P<0.01 for all). The mean maximum ADCs of three glands in lemon juice stimulation group[(1.05 ± 0.12) × 10-3 mm2/s, (1.22 ± 0.10) × 10-3 mm2/s and (1.26 ± 0.21) × 10-3 mm2/s, respectively] were all lower than those in vitamin C stimulation group[(1.13±0.13) ×10-3 mm2/s, (1.32±0.25) × 10-3 mm2/s and (1.57 ± 0.36) × 10-3 mm2/s, respectively], and the differences in parotid and sublingual glands between two groups were significant(t=-2.894 and-3.681 respectively, P<0.01 for all). The mean maximum ADC IRs of three glands in lemon juice stimulation group[(11.35±4.07)%, (8.81±5.40)%, (34.08±21.66)%, respectively] were significantly lower than those[(17.80 ± 12.72)%, (18.16 ± 18.93)%, (67.49 ± 46.04)% , respectively] in vitamin C stimulation group (t=-2.252,-2.330 and-3.432 respectively, P<0.05 for all) . In two groups, the mean maximum ADC IRs of parotid and submandibular gland were all significantly lower than sublingual gland (t=-5.994 and-6.443 respectively, P<0.01 for all). No correlation was observed between ADCs and salivary flow rates, ADC IRs and salivary flow rate IRs in two groups (P>0.05). Conclusion MR DWI with transient stimulation using lemon juice is more stable for evaluating the physiologic changes of salivary glands in vivo.

Objective To explore the MRI features of patients with multiple system atrophy (MSA) and Parkinson's disease (PD) for providing early evidence in differential diagnosis. Methods The MRI features of 24 patients with MSA, 30 patients with PD and 30 healthy people as controls were retrospectively analyzed. Abnormal intensity in MRI included the hot-cross bun sign and the slitlike changes. The atrophies of brain included cerebellar, middle cerebellar peduncles, medulla oblongata and pon. Cerebral ventricle dilatation included fourth ventricle and cisterna pontis. The midbrain area, pons area and middle cerebellar peduncles width were measured. Results All patients with MSA had at least one of the features observed on MR images, and there were some differences in the subtypes of MSA. The high sensitive features were the atrophies of middle cerebellar peduncles (79.2%), the atrophies of pons (79.2%) and the hot-cross bun sign (75.0%). The parameters with high specificity and high positive predictive value were hot-cross bun sign (both 100%), the slit-like sign (both 100%), the atrophies of middle cerebellar peduncles (93.3% and 90.1%), and the atrophies of pons (96.7% and 95.0%). MSA group had the statistically significantly decreased values of pons area, midbrain area and middle cerebellar peduncles width [(288. 7±75. 4) mm2, (127.8±25.8) mm2 and (10. 7±2.8) mm, respectively], as compared with PD group [(477. 5 ± 54. 3) mm2, (145.9±21.6) mm2 and (16.2±1.3) mm, respectively] and healthy group [(454. 5±36. 8) mm2 , (146.4±17.4) mm2 and (16.7±1.2) mm, respectively] (all P ＜0. 05). Conclusions The routine MRI is helpful in differential diagnosis between MSA and PD and has some values in diagnosing the subtypes of MSA.

OBJECTIVE： To prepare etoposide－ bovine serum albumin－ microspheres (VP－ BSA－ MS)and to study the distribution and pharmacokinetics of VP－ BSA－ MS in mice. METHODS: The drug concentrations in various tissues were determined by high－ performance liquid chromatograph (HPLC). RESULTS: The VP－ BSA－ MS was injected into mice and (47.88± 2.56 )％ of the total dosage was detected in lung tissue 15min after administration， the pharmacokinetical equation was C=149.0 897e－ 1.7 780t＋ 3.9 627e－ 0.0 398t — 153.0 524e－ 3.5 054t. CONCLUSION： The VP－ BSA－ MS showed remakable targeting action to the lung and the pharmacokinetic regularity could be discribed as two－ compartment model

OBJECTIVE:To prepare etoposide-bovine serum albumin-microspheres (VP-BSA-MS)and to study the distribution and pharmacokinetics of VP-BSA-MS in mice. METHODS: The drug concentrations in various tissues were determined by high-performance liquid chromatograph (HPLC). RESULTS: The VP-BSA-MS was injected into mice and (47.88?2.56 )% of the total dosage was detected in lung tissue 15min after administration,the pharmacokinetical equation was C=149.0 897e-1.7 780t+3.9 627e-0.0 398t —153.0 524e-3.5 054t. CONCLUSION:The VP-BSA-MS showed remakable targeting action to the lung and the pharmacokinetic regularity could be discribed as two-compartment model

Objective To compare the predictive values of swirl sign and black hole sign on CT scanning in early hematoma expansion in spontaneous intracerebral hemorrhage (SICH) patients.Methods Two hundred and ten firstly diagnosed SICH patients,admitted to our hospital from January 2012 to December 2018,were enrolled in the study.All patients were divided into hematoma expansion and non-hematoma expansion group according to whether early hematoma expansion appeared;and they were also divided into positive imaging sign group and negative imaging sign group according to whether imaging signs appeared;the clinical and imaging data were compared between these groups,respectively.The accuracies of swirl sign and black hole sign in predicting early hematoma expansion were analyzed using receiver operator characteristic (ROC) curve.Multivariate Logistic regression analysis was performed to determine the independent risk factors for early hematoma expansion.Results (1) In the 57 patients with early hematoma expansion,21 (36.8％) had swirl sign,and 17 (29.8％) had black hole sign;in the 153 patients without hematoma expansion,12 (7.8％) had swirl sign and 22 (14.4％) had black hole sign;the differences between the two groups were statistically significant (P＜0.05).As compared with those in the non-hematoma expansion group,the admission systolic blood pressure increased significantly and number of patients with intraventricular hemorrhage was significantly larger in the hematoma expansion group (P＜0.05).(2) There were no statistical differences in clinical and imaging data between the patients with swirl sign (n=33) and patients without swirl sign (n=177,P＞0.05);the hematoma volume in patients with black hole sign (n=39) was significantly increased as compared with that in patients without black hole sign (n=171,P＜0.05),and there were no statistical differences in other clinical and imaging data between patients with and without black hole sign (P＞0.05).(3) The areas under ROC curve of swirl sign,black hole sign,and "swirl sign combined with black hole sign" were 0.645,0.577,and 0.570,respectively.(4) Multivariate Logistic regression analysis showed that admission systolic blood pressure,swirl sign and black hole sign were independent risk factors for early hematoma expansion (P＜0.05).Conclusion In comparison to black hole sign and "swirl sign combined with black hole sign",the swirl sign has higher predictive value in early hematoma expansion in ICH patients.

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 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.

The diagnosis and treatment resources for rare diseases in China are highly imbalanced. The basic diagnosis and treatment capabilities are weak, the diagnosis period for patients is long, and the rates of missed diagnosis and misdiagnosis are relatively high. The establishment of a hierarchical diagnosis and treatment system is the inevitable approach to enhancing the diagnosis and treatment standards of rare diseases. Currently, the implementation of the domestic hierarchical diagnosis and treatment system for rare diseases still confronts numerous challenges, such as ambiguous referral standards and processes of primary medical institutions, and ineffective information interaction among institutions at all levels. Thus, it is essential to facilitate high-level information construction for the hierarchical diagnosis and treatment of rare diseases. This paper explores the process of constructing a multidisciplinary joint remote diagnosis and treatment platform and a health management platform through informatization, with the hope of establishing two closed loops of digital diagnosis and treatment services and health follow-up management for patients with rare diseases, as well as achieving timely diagnosis and lifelong health management for patients. It integrates and optimizes auxiliary diagnostic tools, promotes the rapid dissemination of rare disease diagnosis and treatment experiences to the grassroots, enhances the information construction level of the hierarchical diagnosis and treatment system, and endeavors to address the practical predicament of weak diagnosis and treatment capabilities of rare diseases in grassroots medical institutions. Additionally, this paper proposes an essential approach for multi-dimensional independent innovation to guide the popularization of efficient and high-quality rare disease diagnosis and treatment services. By encompassing innovating the rare disease diagnosis and treatment collaboration network and multidisciplinary diagnosis and treatment model, facilitating the application of the latest biomedical and informatics technologies to the grassroots, and constructing a national intelligent data platform for rare disease innovation, a new model for rare disease services with Chinese characteristics will be established. This will significantly enhance the medical treatment level of rare diseases in China and strive for more benefits for patients.