The Impact of a Multidisciplinary Experiential Training Model on Knowledge, Attitude and Practice of Healthcare Workers in Maternity Health Management: A Preliminary Study.

Background: Maternity health management has always been the area of concern and considering, and considering its complexity and multidisciplinary, it is necessary to provide effective training for healthcare workers. Purpose: To evaluate the impact of a multidisciplinary experiential training model on the knowledge, attitude, and practice of healthcare workers in maternity health management. Patients and Methods: We conducted a novel educational model, Multidisciplinary Maternity Health Experiential Training based on Knowledge, Attitude and Practice (MMHET), which combined theoretical knowledge, practical skills, and human-centred humanistic care, offering a comprehensive offline education program supported by online teaching materials structured around knowledge graphs. Pre- and post-test surveys were used to assess the changes in participants' knowledge, attitudes, and practices. Results: From May to July 2023, a total of 322 participants attended the course, and only a small percentage had participated in experiential training. For all topics, the vast majority of participants endorsed the course, and the attitude content had the highest percentage of participants who said they agreed. Among the groups with different years of working life, the highest percentage of participants in the >20 years group strongly endorsed the course. Conclusion: The preliminary findings indicate that the MMHET model is well-received and feasible, demonstrating its potential to enhance maternity health management education.

A new prognostic model of esophageal squamous cell carcinoma based on Cloud-least squares support vector machine.

Background: In view of the low accuracy of the prognosis model of esophageal squamous cell carcinoma (ESCC), this study aimed to optimize the least squares support vector machine (LSSVM) algorithm to determine the uncertain prognostic factors using a Cloud model, and consequently, to establish a new high-precision prognosis model of ESCC. Methods: We studied 4,771 ESCC patients(training samples) from the Surveillance, Epidemiology, and End Results (SEER) database and 635 ESCC patients(validation samples) from the Henan Provincial Center for Disease Control and Prevention (HCDC) database, with the same exclusion criteria and inclusion criteria for both databases, and obtained permission to obtain a research data file in the SEER database from the National Cancer Institute. The independent risk factors were analyzed using the log-rank method, survival curves, univariate and multivariate Cox analysis. Finally, the independent prognostic factors were used to construct the nomogram, random forest and Cloud-LSSVM prognostic models were utilized for validation. Results: The overall median survival time of the SEER database was 14 months (HCDC samples was 46 months), the mean survival time was 26.5 months (HCDC samples was 36.8 months), and the 3-year survival rate was 65.8%. This is because most of the patients with Henan samples are early ESCC, and most of the Seer patients are T3 and T4 people. The multivariate Cox analysis showed that age at diagnosis (P<0.001), sex (P=0.001), race (P=0.002), differentiation grade (P<0.001), pathologic T category (P<0.001), and pathologic M category (P<0.001) were the factors affecting the prognosis of ESCC patients. The SEER data and HCDC database results showed that the accuracy of the Cloud-LSSVM (C-index =0.71, 0.689) model is higher than the differentiation grade (C-index =0.548, 0.506), random forest (C-index =0.649, 0.498), and nomogram (C-index =0.659, 0.563). This new model can realize the unity of the randomness and fuzziness of the Cloud model and utilize the powerful learning and non-linear mapping abilities of LSSVM. Conclusions: Due to the difference of clans between training samples and test samples, the accuracy of prediction is generally not high, but the accuracy of Cloud-LSSVM model is much higher than other models. The new model provides a clear prognostic superiority over the random forest, nomogram, and other models.

Increasing Accessible Active Site Density of Non-Precious Metal Oxygen Reduction Reaction Catalysts through Ionic Liquid Modification.

Non-precious metal catalysts show great promise to replace the state-of-the-art Pt-based catalysts for catalyzing the oxygen reduction reaction (ORR), while their catalytic activity still needs to be greatly improved before their broad-based application. Here, we report a facile approach to improving the performance of zeolitic imidazolate framework-derived carbon (ZDC) toward the ORR by incorporating a small amount of ionic liquid (IL). The IL would preferentially fill the micropores of ZDC and greatly enhance the utilization of the active sites within the micropores, which are initially not accessible due to insufficient surface wetting. It is also disclosed that the ORR activity in terms of kinetic current at 0.85 V depends on the loading amount of the IL, and the maximum activity is obtained at a mass ratio of IL to ZDC at 1.2. The optimum stems from the counterbalance between the enhanced utilization of the active sites within the micropores and the retarded diffusion of the reactants within the IL phase due to its high viscosity.

Protective Effect and Mechanism of Placenta Extract on Liver.

The placenta contains multiple biologically active substances, which exert antioxidation, anti-inflammatory, immunomodulatory, and delayed aging effects. Its extract can improve hepatic morphology and function: on the one hand, it can reduce liver interstitial collagen deposition, lipogenesis, and inflammatory cell infiltration and improve fibrosis; on the other hand, it can prevent hepatocellular degeneration by scavenging reactive oxygen species (ROS) and inhibiting inflammatory cytokine production, further improve hepatocyte apoptosis and necrosis, and promote hepatocyte regeneration, making it a promising liver-protective agent. Current research on placenta extract (PE) mainly focuses on treating a specific type of liver injury, and there are no systematic reports. Therefore, this review comprehensively summarizes the treatment reports of PE on liver injury and analyzes its mechanism of action.

Effect of anemoside B4 on milk whey in clinical mastitis-affected cows elucidated using tandem mass tag (TMT)-based quantitative proteomics.

Intramuscular injection of anemoside B4 (AB4) has a superior therapeutic effect on clinical mastitis in lactating cows. Here, we explored AB4's effect on milk whey in clinical mastitis-affected cows using proteomics. Among fifty clinical mastitis cows received AB4 administration (0.05 ml/kg/day, for 7 days), twelve healed cows were selected and marked as group T. Twelve clinically heathy cows received the same dose of saline for 7 days, marked as group C. Collected milk whey of group T before and after AB4 administration marked as T1 and T2, respectively. The milk whey of group C after saline injection marked as C1. Milk whey protein changes were detected using tandem mass tag-based quantitative proteomic. We identified 872 quantifiable proteins in the samples. Among them, 511 proteins between T1 and C1, and 361 proteins between T2 and T1 were significantly altered. T1 than C1 had significantly more proteins associated with inflammatory damage and trans-endothelial migration of leukocytes, whereas these proteins were reduced in T2 treated with AB4. Compared with C, proteins associated with fibrin clot degradation and complement system activation were downregulated in T1 but upregulated in T2. In summary, AB4 can exert its therapeutic effect on clinical mastitis in cows mainly by reducing inflammatory damage, activating the complement system, inhibiting trans-endothelial migration of leukocytes, and promoting degradation of milk fibrin clots.

Association between Serum Oxytocin, Bone Mineral Density and Body Composition in Chinese Adult Females.

Background and Objectives: Oxytocin (OT) is a neuropeptide hormone which is known for its classical effects in pregnancy and lactation. Recently, growing evidence demonstrated a close relation between OT and bone. The present study aimed to explore the relationship between OT, bone and osteoporosis risk in Chinese adult females. Materials and Methods: in total, 149 adult females were enrolled. The serum OT levels were measured using ELISA kits. Bone mineral density (BMD) and body composition were measured by dual-energy X-ray absorptiometry (DXA). The study subjects were divided into two groups according to their menopause status and then divided into tertiles based on their serum OT level. Results: Serum OT, serum estradiol and BMD at three skeletal sites were significantly higher in the premenopausal group than in the postmenopausal group (p < 0.001, p = 0.008 and p < 0.001, respectively). In the tertile analysis, relative to tertile 1, significant associations were found for tertile 3 for OT levels and higher BMD in the femoral neck and total hip, in both pre- and postmenopausal groups. Using logistic regression analysis, tertile 3 appeared less likely to have low-BMD osteoporosis than tertile 1 (OR = 0.257, 95% CI = 0.073, 0.910). In multivariate stepwise regression analysis, OT and total lean mass were two positive determinants of BMD in the femoral neck and total hip in the premenopausal group (adjusted R2 for the model = 0.232 and 0.199, respectively; both p < 0.001). Conclusion: Our study demonstrated positive associations between serum OT levels and BMD in a Chinese (non-Caucasian) population. OT appeared to be more strongly associated with hip BMD in premenopausal females. These results may suggest a protective role and potential therapeutic use of OT in osteoporosis, especially for premenopausal women.

Antioxidant Capacity and Protective Effect of Cow Placenta Extract on D-Galactose-Induced Skin Aging in Mice.

Placental extract has been used for skin care and delaying skin aging. Cow placenta is an abundant resource with a large mass, which has not been harnessed effectively. Cow placenta extract (CPE) has the functions of antioxidation, anti-inflammatory, promoting growth and development, and promoting hair growth. However, little is known about the effect of oral administration of cow placenta extract on skin conditions. Therefore, the present study aimed to investigate the antioxidant capacity of CPE in vitro and in vivo and its protective effect on d-galactose (D-gal) induced skin aging in mice. The results showed that CPE had strong free radical scavenging, reducing and metal chelating activities. CPE can increase the activity of catalase (CAT), glutathione peroxidase (GSH-Px), peroxidase (POD), superoxide dismutase (SOD), and the content of glutathione (GSH), decrease the content of malondialdehyde (MDA). Moreover, CPE can decrease the gene and protein expression of matrix metalloproteinase 1a (MMP-1a) and matrix metalloproteinase 3 (MMP-3) and increase the expression of transforming growth factor-ß (TGF-ß) and tissue inhibitor of metalloproteinase 1 (TIMP-1) of mouse skin. Histopathological analysis showed CPE reduced the collagen damage caused by D-gal, increased collagen synthesis and reduced its degradation to delay skin aging.

Synthesis of polychloromethylated and halogenated spiro[5,5]trienones via dearomative spirocyclization of biaryl ynones.

We disclosed a selective polychloromethylation and halogenation reaction of alkynes via a radical addition/spirocyclization cascade sequence, in which polyhaloalkanes were used as the precursor for polyhalomethyl and halogen radicals. Using this strategy, a series of valuable halogen-, CHCl2- or CCl3-containing spiro[5,5]trienones were synthesized in good yields with good functional group tolerance in one pot under simple and mild conditions. It is noted that an unprecedented halogenation instead of dibromomethylation was achieved when CH2Br2 was used in this work.

Novel Diagnostic and Prognostic Tools for Lung Cancer Cachexia: Based on Nutritional and Inflammatory Status.

Background: Cachexia is one of the most common complications affecting lung cancer patients that seriously affects their quality-of-life and survival time. This study aimed to analyze the predictors and prognostic factors of lung cancer cachexia as well as to develop a convenient and accurate clinical prediction tool for oncologists. Methods: In this multicenter cohort study, 4022 patients with lung cancer were retrospectively analyzed. The patients were randomly categorized into training and verification sets (7:3 ratio). Univariate and multivariate logistic regression analyses were performed to determine the risk factors of cachexia in patients with lung cancer. Cox regression analysis was applied to determine independent prognostic factors in the patients with lung cancer cachexia. Meanwhile, two nomograms were established and evaluated by time-dependent receiver operating characteristic curve, calibration curve, and decision curve analysis (DCA). Results: Stage, serum albumin, ALI, anemia, and surgery were independent risk factors for cachexia in patients with lung cancer. Patients with lung cancer cachexia have a shorter survival time. Sex, stage, serum albumin, ALI, KPS score, and surgery served as independent prognostic factors for patients with lung cancer cachexia. The area under the curves (AUCs) of diagnostic nomogram in the training and validation sets were 0.702 and 0.688, respectively, the AUCs of prognostic nomogram in the training set for 1-, 3-, and 5-year were 0.70, 0.72, and 0.75, respectively, while in the validation set the AUCs were 0.71, 0.75, and 0.79, respectively. The calibration curves and DCA of the two nomograms were consistent and the clinical benefit rate was high. Conclusion: Cachexia brings an additional economic burden and worsens the prognosis of lung cancer patients. The two nomograms can accurately screen and predict the probability of occurrence of cachexia in lung cancer and the prognosis of patients with lung cancer cachexia, and guide clinical work.

Developing a framework for understanding health information behavior change from avoidance to acquisition: a grounded theory exploration.

BACKGROUND: Health information avoidance is common in real life, but because it is not always conducive to health promotion and maintenance, people often actively switch to health information acquisition. Understanding this process of active change can facilitate intervention in unreasonable avoidance behaviors. However, studies so far have mostly focused on why and how avoidance takes place, little is known about the process of active change from avoidance to acquisition. We thus use a grounded theory approach (GT) to explore how the active change takes place, and to generate a grounded theoretical framework capable of illustrating stages and influencing factors involved in the active change process. METHODS: Straussian grounded theory (Corbin & Strauss, 2015) was used to analyze data collected through semi-structured interviews with 30 adults (14 in good health, 11 with disease, 5 in other health status) who had experienced health information behavior change from avoidance to acquisition. These interviews focused on how the change occurred and what effected the change. RESULTS: The core category of Health Information Avoidance Change and 12 categories were identified and integrated to form a theoretical framework termed the Health Information Avoidance Change Model (HIACM). This model describes the process using five non-linear stage variables (initiation, preparation, action, maintenance, and abandonment) and seven moderating factor variables (cognitive change, social stimulus, beliefs and attitudes, intrapsychic literacy, social resources, information source, time and material resources). CONCLUSIONS: HIACM can be used to explain the process of active change from health information avoidance to health information acquisition. HIAC is a non-linear and holistic process, and it is necessary to dynamically analyze the impact of relevant factors and take targeted intervention measures in stages. HIAC is usually not only an individual behavior, but also a socialized behavior requiring the collaboration of individuals, families, health information providers, healthcare providers, and governments.

A Prognostic Model Based on mRNA Expression Analysis of Esophageal Squamous Cell Carcinoma.

Background: The aim of this study was to identify prognostic markers for esophageal squamous cell carcinoma (ESCC) and build an effective prognostic nomogram for ESCC. Methods: A total of 365 patients with ESCC from three medical centers were divided into four cohorts. In the discovery phase of the study, we analyzed transcriptional data from 179 cancer tissue samples and identified nine marker genes using edgeR and rbsurv packages. In the training phase, penalized Cox regression was used to select the best marker genes and clinical characteristics in the 179 samples. In the verification phase, these marker genes and clinical characteristics were verified by internal validation cohort (n = 58) and two external cohorts (n = 81, n = 105). Results: We constructed and verified a nomogram model based on multiple clinicopathologic characteristics and gene expression of a patient cohort undergoing esophagectomy and adjuvant radiochemotherapy. The predictive accuracy for 4-year overall survival (OS) indicated by the C-index was 0.75 (95% CI, 0.72-0.78), which was statistically significantly higher than that of the American Joint Committee on Cancer (AJCC) seventh edition (0.65). Furthermore, we found two marker genes (TM9SF1, PDZK1IP) directly related to the OS of esophageal cancer. Conclusion: The nomogram presented in this study can accurately and impersonally predict the prognosis of ESCC patients after partial resection of the esophagus. More research is required to determine whether it can be applied to other patient populations. Moreover, we found two marker genes directly related to the prognosis of ESCC, which will provide a basis for future research.

COVID term: a bilingual terminology for COVID-19.

BACKGROUND: The coronavirus disease (COVID-19), a pneumonia caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has shown its destructiveness with more than one million confirmed cases and dozens of thousands of death, which is highly contagious and still spreading globally. World-wide studies have been conducted aiming to understand the COVID-19 mechanism, transmission, clinical features, etc. A cross-language terminology of COVID-19 is essential for improving knowledge sharing and scientific discovery dissemination. METHODS: We developed a bilingual terminology of COVID-19 named COVID Term with mapping Chinese and English terms. The terminology was constructed as follows: (1) Classification schema design; (2) Concept representation model building; (3) Term source selection and term extraction; (4) Hierarchical structure construction; (5) Quality control (6) Web service. We built open access for the terminology, providing search, browse, and download services. RESULTS: The proposed COVID Term include 10 categories: disease, anatomic site, clinical manifestation, demographic and socioeconomic characteristics, living organism, qualifiers, psychological assistance, medical equipment, instruments and materials, epidemic prevention and control, diagnosis and treatment technique respectively. In total, COVID Terms covered 464 concepts with 724 Chinese terms and 887 English terms. All terms are openly available online (COVID Term URL: http://covidterm.imicams.ac.cn ). CONCLUSIONS: COVID Term is a bilingual terminology focused on COVID-19, the epidemic pneumonia with a high risk of infection around the world. It will provide updated bilingual terms of the disease to help health providers and medical professionals retrieve and exchange information and knowledge in multiple languages. COVID Term was released in machine-readable formats (e.g., XML and JSON), which would contribute to the information retrieval, machine translation and advanced intelligent techniques application.

New tirucallane-type triterpenoids from the resin of Boswellia carteriiand their NO inhibitory activities / 中国天然药物

Six new tirucallane-type triterpenoids (1-6), along with ten known triterpenoids, were isolated from methylene chloride extract of the resin of Boswellia carterii Birdw. By the application of the comprehensive spectroscopic data, the structures of the compounds were clarified. The experimental electronic circular dichroism spectra were compared with those calculated, which allowed to assign the absolute configurations. Compounds 5 and 6 possesed a 2, 3-seco tirucallane-type triterpenoid skeleton, which were first reported. Their inhibitory activity against NO formation in LPS-activated BV-2 cells were evaluated. Compound 9 showed appreciable inhibitory effect, with an IC

Review of Computer Modeling and Simulation of Spinal Thermal Ablation / 中国医疗器械杂志

Thermal ablation surgery can effectively eliminate bone tumors in the spine and meanwhile reduce damage to the human body. To realize the computer modeling and simulation of spine thermal ablation surgery, it is necessary to ensure the accuracy of both spine modeling and simulation temperature. This review summarizes the research progress of this field and analyzes the prospects from two aspects: computer modeling based on spine segmentation from medical images and simulation calculation of temperature field in ablation surgery. The research on spine segmentation has made great progress, but there are still some problems that prevent it from being applied in clinical simulation. Related research has been trying to solve the problems. For the ablation surgery of the spine, some researchers have tried ablation simulation and obtained simulation results that are relatively consistent with the actual temperature value.

Building a Pharmacogenomics Knowledge Model Toward Precision Medicine: Case Study in Melanoma.

BACKGROUND: Many drugs do not work the same way for everyone owing to distinctions in their genes. Pharmacogenomics (PGx) aims to understand how genetic variants influence drug efficacy and toxicity. It is often considered one of the most actionable areas of the personalized medicine paradigm. However, little prior work has included in-depth explorations and descriptions of drug usage, dosage adjustment, and so on. OBJECTIVE: We present a pharmacogenomics knowledge model to discover the hidden relationships between PGx entities such as drugs, genes, and diseases, especially details in precise medication. METHODS: PGx open data such as DrugBank and RxNorm were integrated in this study, as well as drug labels published by the US Food and Drug Administration. We annotated 190 drug labels manually for entities and relationships. Based on the annotation results, we trained 3 different natural language processing models to complete entity recognition. Finally, the pharmacogenomics knowledge model was described in detail. RESULTS: In entity recognition tasks, the Bidirectional Encoder Representations from Transformers-conditional random field model achieved better performance with micro-F1 score of 85.12%. The pharmacogenomics knowledge model in our study included 5 semantic types: drug, gene, disease, precise medication (population, daily dose, dose form, frequency, etc), and adverse reaction. Meanwhile, 26 semantic relationships were defined in detail. Taking melanoma caused by a BRAF gene mutation into consideration, the pharmacogenomics knowledge model covered 7 related drugs and 4846 triples were established in this case. All the corpora, relationship definitions, and triples were made publically available. CONCLUSIONS: We highlighted the pharmacogenomics knowledge model as a scalable framework for clinicians and clinical pharmacists to adjust drug dosage according to patient-specific genetic variation, and for pharmaceutical researchers to develop new drugs. In the future, a series of other antitumor drugs and automatic relation extractions will be taken into consideration to further enhance our framework with more PGx linked data.

PMO: A knowledge representation model towards precision medicine.

With the rapid development of biomedical technology, amounts of data in the field of precision medicine (PM) are growing exponentially. Valuable knowledge is included in scattered data in which meaningful biomedical entities and their semantic relationships are buried. Therefore, it is necessary to develop a knowledge representation model like ontology to formally represent the relationships among diseases, phenotypes, genes, mutations, drugs, etc. and achieve effective integration of heterogeneous data. On basis of existing work, our study focus on solving the following issues: (i) Selecting the primary entities in PM domain; (ii) collecting and integrating biomedical vocabularies related to the above entities; (iii) defining and normalizing semantic relationships among these entities. We proposed a semi-automated method which improved the original Ontology Development 101 method to build the Precision Medicine Ontology (PMO), including defining the scope of the PMO according to the definition of PM, collecting terms from different biomedical resources, integrating and normalizing the terms by a combination of machine and manual work, defining the annotation properties, reusing existing ontologies and taxonomies, defining semantic relationships, evaluating PMO and creating the PMO website. Finally, the Precision Medicine Vocabulary (PMV) contains 4.53 million terms collected from 62 biomedical vocabularies, and the PMO includes eleven branches of PM concepts such as disease, chemical and drug, phenotype, gene, mutation, gene product and cell, described by 93 semantic relationships among them. PMO is an open, extensible ontology of PM, all of the terms and relationships in which could be obtained from the PMO website (http://www.phoc.org.cn/pmo/). Compared to existing project, our work has brought a broader and deeper coverage of mutation, gene and gene product, which enriches the semantic type and vocabulary in PM domain and benefits all users in terms of medical literature annotation, text mining and knowledge base construction.

Probing CO2 Reduction Pathways for Copper Catalysis Using an Ionic Liquid as a Chemical Trapping Agent.

The key to fully leveraging the potential of the electrochemical CO2 reduction reaction (CO2RR) to achieve a sustainable solar-power-based economy is the development of high-performance electrocatalysts. The development process relies heavily on trial and error methods due to poor mechanistic understanding of the reaction. Demonstrated here is that ionic liquids (ILs) can be employed as a chemical trapping agent to probe CO2RR mechanistic pathways. This method is implemented by introducing a small amount of an IL ([BMIm][NTf2 ]) to a copper foam catalyst, on which a wide range of CO2RR products, including formate, CO, alcohols, and hydrocarbons, can be produced. The IL can selectively suppress the formation of ethylene, ethanol and n-propanol while having little impact on others. Thus, reaction networks leading to various products can be disentangled. The results shed new light on the mechanistic understanding of the CO2RR, and provide guidelines for modulating the CO2RR properties. Chemical trapping using an IL adds to the toolbox to deduce the mechanistic understanding of electrocatalysis and could be applied to other reactions as well.

Paper-Based Microfluidics for Electrochemical Applications.

Paper-based microfluidics is characteristic of fluid transportation through spontaneous capillary action of paper and has exhibited great promise for a variety of applications especially for sensing. Furthermore, paper-based microfluidics enables the design of miniaturized electrochemical devices to be applied in the energy sector, which is especially attractive for the rapid growing market of small size disposable electronics. This review gives a brief summary on the basics of paper chemistry and capillary-driven microfluidic behavior, and highlights recent advances of paper-based microfluidics in developing electrochemical sensing devices and miniaturized energy storage/conversion devices. Their structural features, working principles and exemplary applications are comprehensively elaborated and discussed. Additionally, this review also points out the existing challenges and future opportunities of paper-based microfluidic electronics.

Comparative iTRAQ Proteomics Reveals Multiple Effects of Selenium Yeast on Dairy Cows in Parturition.

The effects of prepartum dietary supplementation with selenium yeast on low abundant plasma proteins in postpartum dairy cows are not known. In this study, 24 healthy parturient dairy cows were divided into two groups (group C, a control group, and group T, a selenium treatment group). Low abundance proteins were extracted from plasma samples of calving cows, and 542 proteins were identified by isobaric tags for relative and absolute quantitation (iTRAQ) proteomic analysis. Dietary supplementation with selenium yeast caused differential abundance of 48 proteins with a fold change of more than 1.2 or less than 0.83 (p < 0.05); 14 proteins were upregulated and 34 were downregulated. The top five gene ontology (GO) enrichment terms for the differentially expressed proteins were protein homotetramerization (or tetramerization), defense response to bacteria or fungus, acute-phase reactions, nucleotide catabolic process, and positive regulation of lipid metabolic process. All proteins involved in acute-phase reactions were downregulated, indicating that selenium ameliorates systemic inflammation. The vast majority of proteins involved in the defense response to microorganisms were downregulated, thereby affecting innate immunity. The decreased abundance of apolipoprotein A-I and apolipoprotein C-II, critical proteins for positive regulation of lipid metabolism, indicated that selenium may optimize lipid metabolism. The iTRAQ results showed that prenatal supplementation with yeast selenium can relieve systemic inflammation after parturition. Moreover, selenium may reduce the effects of metabolic diseases, which can improve glyconeogenesis and prevent ketosis and fatty liver.

Early and mid-term results of type Ⅱ hybrid arch repair with zone 0 stent graft deployment for acute type A aortic dissection / 中华胸心血管外科杂志

Objective:By analyzing the early- and mid-term clinical results of type Ⅱ hybrid aortic arch repair(HAAR) for the treatment of acute type A aortic dissection(ATAAD), the efficacy of hybridization technique in the application of acute type A aortic dissection was evaluated systematically.Methods:We retrospectively studied the records of patients with ATAAD who were admitted to the Beijing Fuwai Hospital between January 2016 and December 2018. 504 patients were included for analysis , 383 men and 121 women, mean age(50.0±11.7) years, the rate of hypertension was 79.6%, the average time of onset to operation was(69.5±40.0) hours. During this period, 110 continuous patients underwent hybrid aortic arch repair(HAAR) without deep hypothermic circulatory arrest, the landing zone of stent was located in ascending aorta(zone 0); 394 consecutive patients with ATAAD involving the aortic arch received traditional total aortic arch replacement with frozen elephant trunk(FET). A propensity score-matching analysis was applied to adjust for age and gender.The preoperative, perioperative and postoperative data in the HAAR group and FET group were compared.Results:For all 504 patients , male patients accounted for 75.9%. There were 24 cases of early death(4.8%), 20 cases of stroke(4.0%) and 7 cases of paralysis(1.4%). HAAR group showed more age[(62.1±6.8) years vs (46.6±10.4) years, P<0.001)], hypertension(87.3% vs 77.4%, P=0.033), coronary artery disease(13.6% vs 6.1%, P=0.016) and chronic obstructive pulmonary diseases(12.7% vs 3.6%, P=0.001) than the FET group. HAAR group showed shorter cardiopulmonary bypass time[(143.7±53.7) min vs (175.3±52.7) min, P<0.001] and aortic cross-clamping time[(78.5±33.6) min vs (106.9±37.8) min, P<0.001] than the FET group. Between the two groups there was no significant difference in operative mortality, rate of reoperation, and late mortality. Follow-up period ranged from 2 to 43 months, averaged(22.6±11.1) months. During folloe-up period , there were 5 cases of death(4.9%) and 3 cases of reoperation(3.1%) in HAAR group, 10 cases of death(2.7%) and 11 cases of reoperation(3.3%) in FET group. 3 patients lost follow-up because of not been rechecked in the outpatient department or the phone was not connected. Conclusion:This single -stage hybrid arch procedure offers an alternative approach to acute type A dissection and associated with acceptable early and midterm major morbidity and mortality in old-age patients. Future further researches are required to confirm the long-term outcomes.