Electronic Health Record-Oriented Knowledge Graph System for Collaborative Clinical Decision Support Using Multicenter Fragmented Medical Data: Design and Application Study.

BACKGROUND: The medical knowledge graph provides explainable decision support, helping clinicians with prompt diagnosis and treatment suggestions. However, in real-world clinical practice, patients visit different hospitals seeking various medical services, resulting in fragmented patient data across hospitals. With data security issues, data fragmentation limits the application of knowledge graphs because single-hospital data cannot provide complete evidence for generating precise decision support and comprehensive explanations. It is important to study new methods for knowledge graph systems to integrate into multicenter, information-sensitive medical environments, using fragmented patient records for decision support while maintaining data privacy and security. OBJECTIVE: This study aims to propose an electronic health record (EHR)-oriented knowledge graph system for collaborative reasoning with multicenter fragmented patient medical data, all the while preserving data privacy. METHODS: The study introduced an EHR knowledge graph framework and a novel collaborative reasoning process for utilizing multicenter fragmented information. The system was deployed in each hospital and used a unified semantic structure and Observational Medical Outcomes Partnership (OMOP) vocabulary to standardize the local EHR data set. The system transforms local EHR data into semantic formats and performs semantic reasoning to generate intermediate reasoning findings. The generated intermediate findings used hypernym concepts to isolate original medical data. The intermediate findings and hash-encrypted patient identities were synchronized through a blockchain network. The multicenter intermediate findings were collaborated for final reasoning and clinical decision support without gathering original EHR data. RESULTS: The system underwent evaluation through an application study involving the utilization of multicenter fragmented EHR data to alert non-nephrology clinicians about overlooked patients with chronic kidney disease (CKD). The study covered 1185 patients in nonnephrology departments from 3 hospitals. The patients visited at least two of the hospitals. Of these, 124 patients were identified as meeting CKD diagnosis criteria through collaborative reasoning using multicenter EHR data, whereas the data from individual hospitals alone could not facilitate the identification of CKD in these patients. The assessment by clinicians indicated that 78/91 (86%) patients were CKD positive. CONCLUSIONS: The proposed system was able to effectively utilize multicenter fragmented EHR data for clinical application. The application study showed the clinical benefits of the system with prompt and comprehensive decision support.

A Personal Healthcare Knowledge Graph Framework for Diagnosis of Pelvic Masses Diseases.

A Personal Health Knowledge Graph (PHKG) facilitates the efficient integration of potential diagnostic clues from patients' electronic health records with medical knowledge, establishing diagnostic reasoning paths and ensuring accurate, individually interpretable results in the diagnosis of pelvic masses.

A few-shot disease diagnosis decision making model based on meta-learning for general practice.

BACKGROUND: Diagnostic errors have become the biggest threat to the safety of patients in primary health care. General practitioners, as the "gatekeepers" of primary health care, have a responsibility to accurately diagnose patients. However, many general practitioners have insufficient knowledge and clinical experience in some diseases. Clinical decision making tools need to be developed to effectively improve the diagnostic process in primary health care. The long-tailed class distributions of medical datasets are challenging for many popular decision making models based on deep learning, which have difficulty predicting few-shot diseases. Meta-learning is a new strategy for solving few-shot problems. METHODS AND MATERIALS: In this study, a few-shot disease diagnosis decision making model based on a model-agnostic meta-learning algorithm (FSDD-MAML) is proposed. The MAML algorithm is applied in a knowledge graph-based disease diagnosis model to find the optimal model parameters. Moreover, FSDD-MAML can learn learning rates for all modules of the knowledge graph-based disease diagnosis model. For n-way, k-shot learning tasks, the inner loop of FSDD-MAML performs multiple gradient update steps to learn internal features in disease classification tasks using n×k examples, and the outer loop of FSDD-MAML optimizes the meta-objective to find the associated optimal parameters and learning rates. FSDD-MAML is compared with the original knowledge graph-based disease diagnosis model and other meta-learning algorithms based on an abdominal disease dataset. RESULT: Meta-learning algorithms can greatly improve the performance of models in top-1 evaluation compared with top-3, top-5, and top-10 evaluations. The proposed decision making model FSDD-MAML outperforms all the other models, with a precision@1 of 90.02 %. We achieve state-of-the-art performance in the diagnosis of all diseases, and the prediction performance for few-shot diseases is greatly improved. For the two groups with the fewest examples of diseases, FSDD-MAML achieves relative increases in precision@1 of 29.13 % and 21.63 % compared with the original knowledge graph-based disease diagnosis model. In addition, we analyze the reasoning process of several few-shot disease predictions and provide an explanation for the results. CONCLUSION: The decision making model based on meta-learning proposed in this paper can support the rapid diagnosis of diseases in general practice and is especially capable of helping general practitioners diagnose few-shot diseases. This study is of profound significance for the exploration and application of meta-learning to few-shot disease assessment in general practice.

[Response of PM2.5 and O3 to Emission Reductions in Nanjing Based on Random Forest Algorithm].

High levels of fine particulate matter (PM2.5) and ozone (O3) in ambient air affect climate change and also endanger human health and ecosystems. Air pollution in Nanjing has been improving since the implementation of the "Air Pollution Prevention and Control Action Plan" in 2013. However, Nanjing still faces PM2.5 and O3 pollution. Evaluating the response of pollutant concentrations to the reductions in precursor emissions is helpful to obtain effective strategies of emission reduction to improve pollution levels. The sensitive simulations of emission perturbation in atmospheric chemistry models directly demonstrate the response of pollution to the reductions in emissions. Nevertheless, these sensitive simulations are limited in computing time and resources. The random forest algorithm was trained by using the simulation results of the atmospheric chemical transport model (GEOS-Chem) in 2015. The changes in daily PM2.5 and daily maximum eight-hour O3 (MDA8 O3) concentrations in Nanjing in 2019 were efficiently predicted under different reduction scenarios of anthropogenic emissions. The simulations showed that the seasonal average of ρ(PM2.5) in Nanjing would decrease by 2-4 µg·m-3 with the reduction in anthropogenic emissions of 10% in 2019 in China. In the case of controlling only local emissions in Nanjing, the concentrations of PM2.5 in Nanjing decreased significantly without local anthropogenic emissions. Additionally, the simulations showed that the annual average of ρ(PM2.5) in Nanjing could be lower than the national secondary limit (35 µg·m-3) when the anthropogenic emission reduction in China was higher than 20% in 2019. For ozone, the equal proportional emission reductions in nitrogen oxides (NOx) and volatile organic pollutants (VOCs) of O3 precursors in China likely led to the increase in seasonal average concentrations of O3 in Nanjing. For the proportional reduction of anthropogenic emissions by 10%-50% in China, the seasonal average of ρ(MDA8 O3) in Nanjing in 2019 would increase by 1-3 µg·m-3 in spring, 1-4 µg·m-3 in autumn, and 3-11 µg·m-3 in winter, respectively, compared with that in the base simulation. With the reduction in anthropogenic NOx emission by 10% and VOCs by 20%, the seasonal average of ρ(MDA8 O3) in Nanjing would decrease by 3-6 µg·m-3. On this basis, further increasing the proportion (30%) of VOCs emission reduction could reduce the annual average of ρ(MDA8 O3) in Nanjing by 7 µg·m-3. However, the annual average of ρ(MDA8 O3) of Nanjing in 2019 increased by 1 µg·m-3, with the local emission reduction of NOx by 10% and VOCs by 30%. Therefore, this showed that the key to alleviate ozone pollution in Nanjing is a reasonable control ratio of ozone precursor emissions and the implementation of regional joint prevention and control. In order to effectively reduce the O3 pollution in Nanjing, the emission reduction ratio of NOx and VOCs in China should be less than 1:2. The response of pollutant concentrations to reductions in precursor emissions were efficiently obtained by the random forest algorithm and GEOS-Chem model. The simulations would provide the scientific basis for the emission control strategy to alleviate air pollution.

Causal knowledge graph construction and evaluation for clinical decision support of diabetic nephropathy.

BACKGROUND: Many important clinical decisions require causal knowledge (CK) to take action. Although many causal knowledge bases for medicine have been constructed, a comprehensive evaluation based on real-world data and methods for handling potential knowledge noise are still lacking. OBJECTIVE: The objectives of our study are threefold: (1) propose a framework for the construction of a large-scale and high-quality causal knowledge graph (CKG); (2) design the methods for knowledge noise reduction to improve the quality of the CKG; (3) evaluate the knowledge completeness and accuracy of the CKG using real-world data. MATERIAL AND METHODS: We extracted causal triples from three knowledge sources (SemMedDB, UpToDate and Churchill's Pocketbook of Differential Diagnosis) based on rule methods and language models, performed ontological encoding, and then designed semantic modeling between electronic health record (EHR) data and the CKG to complete knowledge instantiation. We proposed two graph pruning strategies (co-occurrence ratio and causality ratio) to reduce the potential noise introduced by SemMedDB. Finally, the evaluation was carried out by taking the diagnostic decision support (DDS) of diabetic nephropathy (DN) as a real-world case. The data originated from a Chinese hospital EHR system from October 2010 to October 2020. The knowledge completeness and accuracy of the CKG were evaluated based on three state-of-the-art embedding methods (R-GCN, MHGRN and MedPath), the annotated clinical text and the expert review, respectively. RESULTS: This graph included 153,289 concepts and 1,719,968 causal triples. A total of 1427 inpatient data were used for evaluation. Better results were achieved by combining three knowledge sources than using only SemMedDB (three models: area under the receiver operating characteristic curve (AUC): p < 0.01, F1: p < 0.01), and the graph covered 93.9 % of the causal relations between diseases and diagnostic evidence recorded in clinical text. Causal relations played a vital role in all relations related to disease progression for DDS of DN (three models: AUC: p > 0.05, F1: p > 0.05), and after pruning, the knowledge accuracy of the CKG was significantly improved (three models: AUC: p < 0.01, F1: p < 0.01; expert review: average accuracy: + 5.5 %). CONCLUSIONS: The results demonstrated that our proposed CKG could completely and accurately capture the abstract CK under the concrete EHR data, and the pruning strategies could improve the knowledge accuracy of our CKG. The CKG has the potential to be applied to the DDS of diseases.

Multi-band spatial feature extraction and classification for motor imaging EEG signals based on OSFBCSP-GAO-SVM model : EEG signal processing.

Electroencephalogram (EEG) is a non-stationary random signal with strong background noise, which makes its feature extraction difficult and recognition rate low. This paper presents a feature extraction and classification model of motor imagery EEG signals based on wavelet threshold denoising. Firstly, this paper uses the improved wavelet threshold algorithm to obtain the denoised EEG signal, divides all EEG channel data into multiple partially overlapping frequency bands, and uses the common spatial pattern (CSP) method to construct multiple spatial filters to extract the characteristics of EEG signals. Secondly, EEG signal classification and recognition are realized by the support vector machine algorithm optimized by a genetic algorithm. Finally, the dataset of the third brain-computer interface (BCI) competition and the dataset of the fourth BCI competition is selected to verify the classification effect of the algorithm. The highest accuracy of this method for two BCI competition datasets is 92.86% and 87.16%, respectively, which is obviously superior to the traditional algorithm model. The accuracy of EEG feature classification is improved. It shows that an overlapping sub-band filter banks common spatial pattern-genetic algorithms optimization-support vector machines (OSFBCSP-GAO-SVM) model is an effective model for feature extraction and classification of motor imagination EEG signals.

Risk Factors of Contrast Agent Intravasation With Sulfur Hexafluoride Microbubble During Hysterosalpingo-Contrast Sonography.

OBJECTIVES: To analyze the risk factors of sulfur hexafluoride microbubble contrast agent intravasation during hysterosalpingo-contrast sonography (HyCoSy), and to explore a simple prediction model by the obvious clinical history. METHODS: This was a retrospective study included 299 infertility women who had undergone HyCoSy examination from July 1, 2018 to June 31, 2019. The factors were recorded, including age, endometrial thickness, balloon length, infertility type, history of intrauterine surgery, history of pelvic surgery, and tubal patency. The method of multivariate logistic regression analysis was adopted to analyze the risk factors affecting the contrast agent intravasation, and the receiver operating characteristic curves were plotted to test their efficacy. RESULTS: Secondary infertility, a history of intrauterine surgery, thin endometrial thickness, and tubal obstruction were all risk factors of the occurrence of intravasation (P < .05). And the area under the receiver operating characteristic curves of the multifactor-combined prediction model of the intravasation was significantly larger than that of single-factor. CONCLUSIONS: Sonographers and gynecologists should be familiar with the risk factors of intravasation and select the appropriate timing of HyCoSy toward reducing the occurrence of intravasation and other complications after thoroughly explaining and communicating with the patients.

An 'AND'-based ratiometric fluorescence probe for the sequential detection of biothiols and hypochlorous acid.

An 'AND'-based ratiometric fluorescence probe for the sequential detection of biothiols and hypochlorous acid was developed. FRET was observed only when RSHClO reacted with biothiols before reacting with hypochlorous acid, a phenomenon that has been confirmed in aqueous solutions and cells. This feature enables the probe to mimic biological processes and is particularly suitable for imaging oxidizing and reducing substances that cannot coexist.

Operator's Influence on Contrast Agent Intravasation During Hysterosalpingo-Contrast Sonography: Explanation Based on a Physics Model.

Objective: To analyze the risk factors of the operators on contrast agent intravasation during hysterosalpingo-contrast sonography (HyCoSy). Methods: We retrospectively collected 399 infertile women who underwent HyCoSy by the same sonographer. These patients were divided into two groups according to the way how the assistants connected the syringe to the uterus radiography catheter to inject the contrast agent. We analyzed whether the use of different contrast bolus injection methods had any influence on the incidence of intravasation during HyCoSy. Results: There was no significant difference between the two groups with different cross-sectional areas of the syringe outflow tract in the risk variables for intravasation, but the intravasation rates of the two groups were different, 26.4% in group A and 17.1% in group B, P <0.05. The primary manifestation was that when both fallopian tubes were unobstructed, the intravasation rate of group B with smaller cross-sectional area of the outflow tract of the syringe was lower, and the difference was statistically significant. The inferences drawn from our physics model were also in line with the clinical results. Conclusion: The influence of different operators on the contrast agent intravasation rate of HyCoSy cannot be ignored. The assistants of HyCoSy examination should inject the contrast agent slowly and steady, and a needle can be used as a flow restrictor to control the flow into the uterine cavity per unit time, slow down the rising speed of intrauterine pressure, and avoid the accumulation of contrast agent in the uterine cavity, so as to reduce the intravasation caused by operator factors.

Further sesquiterpenoids from Pittosporum qinlingense and their anti-inflammatory activity.

Four new sesquiterpenoid glycoside esters, Pitqinlingoside N-Q (1-4), together with eleven known metabolites (5-15), were isolated from 95% EtOH extract of the twigs, fruits and leaves of P. qinlingense. The structures of new compounds were elucidated on the basis of extensive spectroscopic analyses, including IR, UV, HRMS, NMR and electronic circular dichroism spectra. Unusal glycoside esters are characterized by the presence of polyacylated ß-D-fucopyranosyl and ß-d-glucopyranosyl units. Pitqinlingoside N (1), O (2), P (3), boscialin (5) and arvoside C (6) showed significant nitric oxide production inhibition in lipopolysaccharide (LPS)-induced BV-2 microglial cells with IC50 values ranging from 1.58 to 28.74 µM. Structure-activity relationships of the isolated compounds are discussed.

A New Stemness-Related Prognostic Model for Predicting the Prognosis in Pancreatic Ductal Adenocarcinoma.

OBJECTIVE: This study is aimed at identifying stemness-related genes in pancreatic ductal adenocarcinoma (PDAC). METHODS: The RNA-seq data of PADC patients were downloaded from The Cancer Genome Atlas (TCGA) and Genotype-Tissue Expression (GTEx) databases. The mRNA expression-based stemness index (mRNAsi) and epigenetically regulated mRNAsi (EREG-mRNAsi) of PADC patients were evaluated. The mRNAsi-related gene sets in PADC were identified by weighted gene coexpression network analysis (WGCNA). The key genes were further analyzed using functional enrichment analysis. The Kaplan-Meier survival analysis and the Cox proportional hazards model were used to evaluate the prognostic value of the key genes. Prognostic hub genes were used to establish nomograms. The receiver operating characteristic (ROC) curves, concordance index (C-index), and calibration curves were used to assess the discrimination and accuracy of the nomogram. Finally, these results were validated in the Gene Expression Omnibus (GEO) database. RESULTS: A total of 36 key genes related to mRNAsi were identified by WGCNA. A prognostic gene signature compromising seven genes (TPX2, ZWINT, UBE2C, CCNB2, CDK1, BUB1, and BIRC5) was established to predict the overall survival (OS) of PADC patients. The Cox regression analysis revealed that the risk score was an independent prognostic factor for PADC. Patients were then divided into the high-risk and low-risk groups. The ROC curves, C-index, and calibration curves indicated good performance of the prognostic signature in the TCGA and GEO datasets. Moreover, the nomogram incorporating clinical parameters showed better sensitivity and specificity for predicting the OS of PADC patients. CONCLUSION: The stemness-related prognostic model successfully predicted the OS of PADC patients and could be used for the treatment of PADC.

EHR-Oriented Knowledge Graph System: Toward Efficient Utilization of Non-Used Information Buried in Routine Clinical Practice.

Non-used clinical information has negative implications on healthcare quality. Clinicians pay priority attention to clinical information relevant to their specialties during routine clinical practices but may be insensitive or less concerned about information showing disease risks beyond their specialties, resulting in delayed and missed diagnoses or improper management. In this study, we introduced an electronic health record (EHR)-oriented knowledge graph system to efficiently utilize non-used information buried in EHRs. EHR data were transformed into a semantic patient-centralized information model under the ontology structure of a knowledge graph. The knowledge graph then creates an EHR data trajectory and performs reasoning through semantic rules to identify important clinical findings within EHR data. A graphical reasoning pathway illustrates the reasoning footage and explains the clinical significance for clinicians to better understand the neglected information. An application study was performed to evaluate unconsidered chronic kidney disease (CKD) reminding for non-nephrology clinicians to identify important neglected information. The study covered 71,679 patients in non-nephrology departments. The system identified 2,774 patients meeting CKD diagnosis criteria and 10,377 patients requiring high attention. A follow-up study of 5,439 patients showed that 82.1% of patients who met the diagnosis criteria and 61.4% of patients requiring high attention were confirmed to be CKD positive during follow-up research. The application demonstrated that the proposed approach is feasible and effective in clinical information utilization. Additionally, it's valuable as an explainable artificial intelligence to provide interpretable recommendations for specialist physicians to understand the importance of non-used data and make comprehensive decisions.

Diversity-oriented functionalization of 2-pyridones and uracils.

Heterocycles 2-pyridone and uracil are privileged pharmacophores. Diversity-oriented synthesis of their derivatives is in urgent need in medicinal chemistry. Herein, we report a palladium/norbornene cooperative catalysis enabled dual-functionalization of iodinated 2-pyridones and uracils. The success of this research depends on the use of two unique norbornene derivatives as the mediator. Readily available alkyl halides/tosylates and aryl bromides are utilized as ortho-alkylating and -arylating reagents, respectively. Widely accessible ipso-terminating reagents, including H/DCO2Na, boronic acid/ester, terminal alkene and alkyne are compatible with this protocol. Thus, a large number of valuable 2-pyridone derivatives, including deuterium/CD3-labeled 2-pyridones, bicyclic 2-pyridones, 2-pyridone-fenofibrate conjugate, axially chiral 2-pyridone (97% ee), as well as uracil and thymine derivatives, can be quickly prepared in a predictable manner (79 examples reported), which will be very useful in new drug discovery.

Half-Brain Delineation for Prediction of Radiation-Induced Temporal Lobe Injury in Nasopharyngeal Carcinoma Receiving Intensity-Modulated Radiotherapy.

PURPOSE: To investigate the role of half-brain delineation in the prediction of radiation-induced temporal lobe injury (TLI) in nasopharyngeal carcinoma (NPC) receiving intensity-modulated radiotherapy (IMRT). METHODS AND MATERIALS: A total of 220 NPC cases treated with IMRT and concurrent platinum-based chemotherapy were retrospectively analyzed. Dosimetric parameters of temporal lobes, half-brains, and brains included maximum dose (Dmax), doses covering certain volume (DV) from 0.03 to 20 cc and absolute volumes receiving specific dose (VD) from 40 to 80 Gy. Inter-structure variability was assessed by coefficients of variation (CV) and paired samples t-tests. Receiver operating characteristic curve (ROC) and Youden index were used for screening dosimetric parameters to predict TLI. Dose/volume response curve was calculated using the logistic dose/volume response model. RESULTS: CVs of brains, left/right half-brains, and left/right temporal lobes were 9.72%, 9.96%, 9.77%, 27.85%, and 28.34%, respectively. Each DV in temporal lobe was significantly smaller than that in half-brain (P < 0.001), and the reduction ranged from 3.10% to 45.98%. The area under the curve (AUC) of DV and VD showed an "increase-maximum-decline" behavior with a peak as the volume or dose increased. The maximal AUCs of DVs in brain, half-brain and temporal lobe were 0.808 (D2cc), 0.828 (D1.2cc) and 0.806 (D0.6cc), respectively, and the maximal AUCs of VDs were 0.818 (D75Gy), 0.834 (V72Gy) and 0.814 (V70Gy), respectively. The cutoffs of V70Gy (0.86 cc), V71Gy (0.72 cc), V72Gy (0.60 cc), and V73Gy (0.45 cc) in half-brain had better Youden index. TD5/5 and TD50/5 of D1.2cc were 58.7 and 80.0 Gy, respectively. The probability of TLI was higher than >13% when V72Gy>0 cc, and equal to 50% when V72Gy = 7.66 cc. CONCLUSION: Half-brain delineation is a convenient and stable method which could reduce contouring variation and could be used in NPC patients. D1.2cc and V72Gy of half-brain are feasible for TLI prediction model. The dose below 70 Gy may be relatively safe for half-brain. The cutoff points of V70-73Gy could be considered when the high dose is inevitable.

Effectiveness of contrast-enhanced ultrasound for detecting the staging and grading of bladder cancer: a systematic review and meta-analysis.

AIM: The study retrospectively analysed the accuracy of preoperative contrast-enhanced ultrasound (CEUS) in differenti-ating stage Ta-T1 or low-grade bladder cancer (BC) from stage T2 or high-grade bladder cancer. MATERIAL AND METHODS: We systematically searched the literature indexed in PubMed, Embase, and the Cochrane Library for original diagnostic articles of bladder cancer. The diagnostic accuracy of CEUS was compared with cystoscopy and/or transurethral resection of bladder tumors (TURBT). The bivariate logistic regression model was used for data pooling, couple forest plot, diagnostic odds ratio (DOR) and summary receiver operating characteristic (SROC). RESULTS: Five studies met the selection criteria; the overall number of reported bladder cancers patients were 436. The pooled-sensitivity (P-SEN), pooled-specificity (P-SPE), pooled-positive likelihood ratio (PLR+), pooled-negative likelihood ratio (PLR-), DOR, and area under the SROC curve were 94.0% (95%CI: 85%-98%), 90% (95%CI: 83%-95%), 9.5 (95%CI: 5.1-17.6), 0.06 (95%CI: 0.02-0.17), 147 (95%CI: 35-612) and 97% (95% CI: 95%-98%) respectively. CONCLUSION: CEUS reaches a high efficiency in discriminating Ta-T1 or low-grade bladder cancer from stage T2 or high-grade bladder cancer. It can be a promising method in patients to distinguish T staging and grading of bladder cancer because of its high sensitivity, specificity and diagnostic accuracy.

Contrast-enhanced ultrasound for the preoperative assessment of laryngeal carcinoma: a preliminary study.

BACKGROUND: Contrast-enhanced ultrasound (CEUS) is considered an attractive imaging technique to evaluate tumor microcirculation. However, the validity of CEUS for assessing laryngeal carcinoma is unclear. PURPOSE: To compare the performance of CEUS with conventional US and contrast-enhanced computed tomography (CECT) in the diagnosis and preoperative T-staging of laryngeal carcinoma. MATERIAL AND METHODS: Forty-one consecutive patients with laryngeal carcinoma underwent conventional high-frequency US, CEUS, and CECT before surgery. The CEUS characteristics of laryngeal carcinoma were recorded. The imaging findings of CEUS and conventional US were compared with CECT findings and the postoperative pathological examination. RESULTS: CEUS showed hyperenhancement in 38 cases and isoenhancement in three cases. Homogeneous distribution of contrast agent was found in 20 cases and heterogeneous distribution in 21 cases, of which 16 cases showed local perfusion defects. In the enhanced phase, rapid entry was observed in 37 cases, synchronous entry was observed in two cases, and slow entry was observed in two cases. Rapid exit was observed in 25 cases and slow exit was observed in 16 cases. The pretherapeutic T-staging accuracy was not significantly different between conventional US, CEUS, and CECT (P ≥ 0.500). A high sensitivity and specificity were achieved by CEUS in the evaluation of involvement of thyroid cartilage. CONCLUSION: Compared with conventional US and CECT, CEUS has a reliable initial T-staging accuracy and diagnostic properties for detecting laryngeal cartilage invasion.

Shikonin Inhibits Der p 2-Induced Cytokine and Chemokine Expression in Dendritic Cells in Patients with Atopic Dermatitis.

Atopic dermatitis (AD) is a common inflammatory skin disorder. Shikonin, the active component of Lithospermum erythrorhizon extract, exhibits anti-inflammatory effects. The objective of the present study was to investigate the effect of shikonin on proinflammatory cytokines and chemokine in patients with AD. Ten patients with AD who were allergic to house dust mite (HDM) and seven healthy controls were recruited in this study. Peripheral blood mononuclear cells were isolated, and CD14+ cells were further selected and differentiated to dendritic cells. Dendritic cells stimulated using Der p 2, the major HDM allergen, were cotreated with shikonin for 24 hours, and dexamethasone was used as a control. Culture supernatants were collected, and proinflammatory cytokine and chemokine concentrations were analyzed using a multiplex assay system. Shikonin significantly inhibited Der p 2-induced expression of interleukin (IL)-6, IL-9, and IL-17A; monocyte chemoattractant protein (MCP)-1; macrophage inflammatory protein (MIP)-1α; MIP-1ß; and Chemokine (C-C motif) ligand 5 (RANTES). The inhibitory effects of shikonin on IL-9, MIP-1ß, and RANTES expression were stronger than those of dexamethasone. Therefore, Shikonin can be considered a promising drug for AD treatment because it inhibits different inflammatory cytokines expression.

Evaluation of efficacy and safety of DPP-4 inhibitors for Chinese elderly patients with type 2 diabetes mellitus.

BACKGROUND: The safety of hypoglycemic drugs should be paid more attention to in elderly patients with type 2 diabetes mellitus due to their concomitant diseases, physiological decline of liver and kidney function and cognitive decline. The aim of this study was to evaluate the efficacy and safety of DPP-4 inhibitors in elderly patients with type 2 diabetes mellitus. METHODS: From January 2010 to November 2018, 300 patients with type 2 diabetes mellitus who were over 60 years old were enrolled in the outpatient clinic of Geriatric Medical Center. Their medication records and follow-up medical records were used for retrospective analysis. The duration of treatment with DPP-4 inhibitors was more than 3 months. The changes of fasting blood glucose (GLU), glycosylated hemoglobin (HbA1C), body weight, body mass index (BMI) and liver and kidney function were compared before and after treatment. RESULTS: The average age of 300 patients (212 males and 88 females) was 73.7 ± 9.1 years old, BMI was 26.5 ± 2.8 kg/m2 and the duration of diabetes was 10.7 ± 8.2 years. The results of retrospective analysis showed that HbA1C decreased by 0.27% after treatment (P < 0.001). In the group of DPP-4 inhibitors used for less than 12 months, there was no difference in liver transaminase (ALT and AST) between before and after treatment, whereas in the group of DPP-4 inhibitors used formore than 12 months, liver transaminase decreased statistically compared with after treatment (P < 0.001). The incidence of fatty liver in elderly diabetic patients decreased after using DPP-4 inhibitors. There was no significant change in serum creatinine level and creatinine clearance rate in elderly patients with type 2 diabetes mellitus after treatment of DPP-4 inhibitor. In addition, the body weight and BMI of the patients decreased significantly (P < 0.001). No hypoglycemic reaction and gastrointestinal discomfort were found in the medical records. CONCLUSION: After DPP-4 inhibitors were used in elderly patients with type 2 diabetes mellitus, the elevated glycosylated hemoglobin could be controlled with improved safety of liver and kidney, and might have the effect of weight loss.