Evaluation of LPS-Induced Acute Lung Injury Attenuation in Rats by Aminothiazole-Paeonol Derivatives.

Paeonol is a key phenolic compound in the root bark of Moutan Cortex Radicis that has been used in traditional Chinese Medicine to ameliorate inflammation. A series of aminothiazole-paeonol derivatives (APDs) were synthesized in this work and subjected to preliminary evaluation in cells followed by verification in animals. Quantification of monocyte chemotactic protein-1 (MCP-1) and interleukin-6 (IL-6) in culture media of LPS-activated A549 cells, a lung epithelial adenocarcinoma cell line, were used to investigate the anti-inflammatory capability of APDs. ALI-bearing rats were employed to verify therapeutic efficacy of APDs according to observations of total cells, protein amounts, MCP-1 and IL-6 in bronchoalveolar lavage fluid (BALF). Histopathological examinations of lung tissues were consequently applied for validation of APDs. Among these compounds, 2-(2-aminothiazol-4-yl)-5-methoxyphenol (4) had the most potent activity, showing comparable inhibition of MCP-1/IL-6 and superior elimination of neutrophil infiltration and protein exudation in lungs compared to others as well as dexamethasone. This study demonstrated a comprehensive strategy to evaluate APDs through integration of cell-based screening and animal-based verification. In order to fulfill unmet needs of treating acute lung injury (ALI) and acute respiratory distress syndrome (ARDS), APDs introduced in this work could be promising lead compounds to develop high potent anti-inflammation agents.

A New Molecular Design Based on Thermally Activated Delayed Fluorescence for Highly Efficient Organic Light Emitting Diodes.

Two benzoylpyridine-carbazole based fluorescence materials DCBPy and DTCBPy, bearing two carbazolyl and 4-(t-butyl)carbazolyl groups, respectively, at the meta and ortho carbons of the benzoyl ring, were synthesized. These molecules show very small ΔEST of 0.03 and 0.04 eV and transient PL characteristics indicating that they are thermally activated delayed fluorescence (TADF) materials. In addition, they show extremely different photoluminescent quantum yields in solution and in the solid state: in cyclohexane the value are 14 and 36%, but in the thin films, the value increase to 88.0 and 91.4%, respectively. The OLEDs using DCBPy and DTCBPy as dopants emit blue and green light with EQEs of 24.0 and 27.2%, respectively, and with low efficiency roll-off at practical brightness level. The crystal structure of DTCBPy reveals a substantial interaction between the ortho donor (carbazolyl) and acceptor (4-pyridylcarbonyl) unit. This interaction between donor and acceptor substituents likely play a key role to achieve very small ΔEST with high photoluminescence quantum yield.

Safety and efficacy of intracoronary artery administration of human bone marrow-derived mesenchymal stem cells in STEMI of Lee-Sung pigs-A preclinical study for supporting the feasibility of the OmniMSC-AMI phase I clinical trial.

Background: This study tested whether early left intracoronary arterial (LAD) administration of human bone marrow-derived mesenchymal stem cells (hBMMSCs, called OmniMSCs) in acute ST-segment elevation myocardial infarction (STEMI) of Lee-Sung pigs induced by 90 min balloon-occluded LAD was safe and effective. Methods and results: Young male Lee-Sung pigs were categorized into SC (sham-operated control, n = 3), AMI-B (STEMI + buffer/21 cc/administered at 90 min after STEMI, n = 6), and AMI-M [acute myocardial infarction (AMI) + hBMMSCs/1.5 × 107/administered at 90 min after STEMI, n = 6] groups. By 2 and 5 months after STEMI, the cardiac magnetic resonance imaging demonstrated that the muscle scar score (MSS) and abnormal cardiac muscle exercise score in the infarct region were significantly increased in the AMI-B than in the SC group that were significantly reversed in the AMI-M group, whereas the left ventricular ejection function by each month (from 1 to 5) displayed an opposite pattern of MSS among the groups (all p < 0.001). By 5 months, histopathological findings of infarct and fibrosis areas and isolectin-B4 exhibited an identical pattern, whereas the cellular expressions of troponin-I/troponin-T/von Willebrand factor exhibited an opposite pattern of MSS among the groups (all p < 0.001). The ST-segment resolution (>80%) was significantly earlier (estimated after 6-h AMI) in the AMI-M group than in the AMI-B group (p < 0.001). The protein expressions of inflammation (IL-1ß/TNF-α/NF-κB)/oxidative stress (NOX-1/NOX-2/oxidized protein)/apoptosis (cleaved caspase-3/cleaved PARP)/DNA damage (γ-H2AX) displayed an identical pattern to MSS among the groups, whereas the protein expressions of angiogenesis factors (SDF-1α/VEGF) were significantly and progressively increased from SC, AMI-B, to AMI-M groups (all p < 0.001). Conclusion: Early intra-LAD transfusion of OmniMSC treatment effectively reduced the infarct size and preserved LV function in porcine STEMI.

Detection of asymptomatic paroxysmal atrial fibrillation with the trans-telephonic electrocardiograph system.

Paroxysmal atrial fibrillation (PAF) carries an equally high annual stroke rate as chronic atrial fibrillation (AF). Furthermore, the frequency and duration of PAF are thought to be associated with stroke risk. In this pilot study, a trans-telephonic electrocardiograph (TTE) monitoring system was used to detect asymptomatic PAF and to study the relationship between ischemic stroke and the frequency of PAF. Between December 2004 and April 2006, 70 patients enrolled in the TTE monitoring program. Patients either transmitted electrocardiograms (ECGs) daily or upon experiencing cardiac symptoms. Of the 70 patients included, 25 were diagnosed with PAF. In total, 11% (855/7,768) of the recordings were diagnosed as PAF, yet less than 2% of total calls collected and less than 17% of all the calls with PAF were associated with obvious symptoms. Four patients developed five ischemic strokes resulting in a calculated annual stroke rate of 0.56%. Patients with stroke had more episodes of AF (56.5±106.3 versus 6.7±85.9, p=0.685) and symptomatic AF episodes (9.8±17.5 versus 4.9±8.1, p=0.381) than the patients who did not have a stroke, but the differences were not statistically significant because of the low numbers of patients and episodes. Most PAF episodes were asymptomatic, and the TTE system could easily detect these episodes. Furthermore, these four patients tended to have more episodes of PAF and more symptomatic attacks of PAF than patients who did not have a stroke.

Probiotics' Efficacy in Preventing Asthmatic Allergic Reaction Induced by Air Particles: An Animal Study.

Global air pollution and diesel exhaust particles (DEPs) generated by intratracheal instillation aggravate asthma. In this study, we evaluated the effect of probiotics via tracheal- or oral-route administration on allergies or asthma. We continuously perfused rats daily, using the oral and tracheal routes, with approximately 106-108 CFU probiotics, for 4 weeks. During this period, we used OVA-sensitized rats to build the asthma models. We orally or intratracheally administered Lactobacillus paracasei 33 (LP33) to the rats, which reduced the number of total inflammatory cells, lymphocytes, and eosinophils in the bronchoalveolar-lavage fluid, the IgE concentration, and the cytokine levels of TH2 cells, but we found no significant difference in the cytokine levels of TH1 cells. LP33 can be used to prevent asthmatic allergic reactions induced by aerosol particles. Nevertheless, the dosage form or use of LP33 needs to be adjusted to reduce the irritation of lung tissues, which may produce lesions of the trachea. We observed that DEP dosage can alleviate emphysema, and that LP33 has a substantial effect on improving or slowing allergic asthma.

Predicting Postoperative Mortality With Deep Neural Networks and Natural Language Processing: Model Development and Validation.

BACKGROUND: Machine learning (ML) achieves better predictions of postoperative mortality than previous prediction tools. Free-text descriptions of the preoperative diagnosis and the planned procedure are available preoperatively. Because reading these descriptions helps anesthesiologists evaluate the risk of the surgery, we hypothesized that deep learning (DL) models with unstructured text could improve postoperative mortality prediction. However, it is challenging to extract meaningful concept embeddings from this unstructured clinical text. OBJECTIVE: This study aims to develop a fusion DL model containing structured and unstructured features to predict the in-hospital 30-day postoperative mortality before surgery. ML models for predicting postoperative mortality using preoperative data with or without free clinical text were assessed. METHODS: We retrospectively collected preoperative anesthesia assessments, surgical information, and discharge summaries of patients undergoing general and neuraxial anesthesia from electronic health records (EHRs) from 2016 to 2020. We first compared the deep neural network (DNN) with other models using the same input features to demonstrate effectiveness. Then, we combined the DNN model with bidirectional encoder representations from transformers (BERT) to extract information from clinical texts. The effects of adding text information on the model performance were compared using the area under the receiver operating characteristic curve (AUROC) and the area under the precision-recall curve (AUPRC). Statistical significance was evaluated using P<.05. RESULTS: The final cohort contained 121,313 patients who underwent surgeries. A total of 1562 (1.29%) patients died within 30 days of surgery. Our BERT-DNN model achieved the highest AUROC (0.964, 95% CI 0.961-0.967) and AUPRC (0.336, 95% CI 0.276-0.402). The AUROC of the BERT-DNN was significantly higher compared to logistic regression (AUROC=0.952, 95% CI 0.949-0.955) and the American Society of Anesthesiologist Physical Status (ASAPS AUROC=0.892, 95% CI 0.887-0.896) but not significantly higher compared to the DNN (AUROC=0.959, 95% CI 0.956-0.962) and the random forest (AUROC=0.961, 95% CI 0.958-0.964). The AUPRC of the BERT-DNN was significantly higher compared to the DNN (AUPRC=0.319, 95% CI 0.260-0.384), the random forest (AUPRC=0.296, 95% CI 0.239-0.360), logistic regression (AUPRC=0.276, 95% CI 0.220-0.339), and the ASAPS (AUPRC=0.149, 95% CI 0.107-0.203). CONCLUSIONS: Our BERT-DNN model has an AUPRC significantly higher compared to previously proposed models using no text and an AUROC significantly higher compared to logistic regression and the ASAPS. This technique helps identify patients with higher risk from the surgical description text in EHRs.

Automatic International Classification of Diseases Coding System: Deep Contextualized Language Model With Rule-Based Approaches.

BACKGROUND: The tenth revision of the International Classification of Diseases (ICD-10) is widely used for epidemiological research and health management. The clinical modification (CM) and procedure coding system (PCS) of ICD-10 were developed to describe more clinical details with increasing diagnosis and procedure codes and applied in disease-related groups for reimbursement. The expansion of codes made the coding time-consuming and less accurate. The state-of-the-art model using deep contextual word embeddings was used for automatic multilabel text classification of ICD-10. In addition to input discharge diagnoses (DD), the performance can be improved by appropriate preprocessing methods for the text from other document types, such as medical history, comorbidity and complication, surgical method, and special examination. OBJECTIVE: This study aims to establish a contextual language model with rule-based preprocessing methods to develop the model for ICD-10 multilabel classification. METHODS: We retrieved electronic health records from a medical center. We first compared different word embedding methods. Second, we compared the preprocessing methods using the best-performing embeddings. We compared biomedical bidirectional encoder representations from transformers (BioBERT), clinical generalized autoregressive pretraining for language understanding (Clinical XLNet), label tree-based attention-aware deep model for high-performance extreme multilabel text classification (AttentionXLM), and word-to-vector (Word2Vec) to predict ICD-10-CM. To compare different preprocessing methods for ICD-10-CM, we included DD, medical history, and comorbidity and complication as inputs. We compared the performance of ICD-10-CM prediction using different preprocesses, including definition training, external cause code removal, number conversion, and combination code filtering. For the ICD-10 PCS, the model was trained using different combinations of DD, surgical method, and key words of special examination. The micro F1 score and the micro area under the receiver operating characteristic curve were used to compare the model's performance with that of different preprocessing methods. RESULTS: BioBERT had an F1 score of 0.701 and outperformed other models such as Clinical XLNet, AttentionXLM, and Word2Vec. For the ICD-10-CM, the model had an F1 score that significantly increased from 0.749 (95% CI 0.744-0.753) to 0.769 (95% CI 0.764-0.773) with the ICD-10 definition training, external cause code removal, number conversion, and combination code filter. For the ICD-10-PCS, the model had an F1 score that significantly increased from 0.670 (95% CI 0.663-0.678) to 0.726 (95% CI 0.719-0.732) with a combination of discharge diagnoses, surgical methods, and key words of special examination. With our preprocessing methods, the model had the highest area under the receiver operating characteristic curve of 0.853 (95% CI 0.849-0.855) and 0.831 (95% CI 0.827-0.834) for ICD-10-CM and ICD-10-PCS, respectively. CONCLUSIONS: The performance of our model with the pretrained contextualized language model and rule-based preprocessing method is better than that of the state-of-the-art model for ICD-10-CM or ICD-10-PCS. This study highlights the importance of rule-based preprocessing methods based on coder coding rules.

Training a Deep Contextualized Language Model for International Classification of Diseases, 10th Revision Classification via Federated Learning: Model Development and Validation Study.

BACKGROUND: The automatic coding of clinical text documents by using the International Classification of Diseases, 10th Revision (ICD-10) can be performed for statistical analyses and reimbursements. With the development of natural language processing models, new transformer architectures with attention mechanisms have outperformed previous models. Although multicenter training may increase a model's performance and external validity, the privacy of clinical documents should be protected. We used federated learning to train a model with multicenter data, without sharing data per se. OBJECTIVE: This study aims to train a classification model via federated learning for ICD-10 multilabel classification. METHODS: Text data from discharge notes in electronic medical records were collected from the following three medical centers: Far Eastern Memorial Hospital, National Taiwan University Hospital, and Taipei Veterans General Hospital. After comparing the performance of different variants of bidirectional encoder representations from transformers (BERT), PubMedBERT was chosen for the word embeddings. With regard to preprocessing, the nonalphanumeric characters were retained because the model's performance decreased after the removal of these characters. To explain the outputs of our model, we added a label attention mechanism to the model architecture. The model was trained with data from each of the three hospitals separately and via federated learning. The models trained via federated learning and the models trained with local data were compared on a testing set that was composed of data from the three hospitals. The micro F1 score was used to evaluate model performance across all 3 centers. RESULTS: The F1 scores of PubMedBERT, RoBERTa (Robustly Optimized BERT Pretraining Approach), ClinicalBERT, and BioBERT (BERT for Biomedical Text Mining) were 0.735, 0.692, 0.711, and 0.721, respectively. The F1 score of the model that retained nonalphanumeric characters was 0.8120, whereas the F1 score after removing these characters was 0.7875-a decrease of 0.0245 (3.11%). The F1 scores on the testing set were 0.6142, 0.4472, 0.5353, and 0.2522 for the federated learning, Far Eastern Memorial Hospital, National Taiwan University Hospital, and Taipei Veterans General Hospital models, respectively. The explainable predictions were displayed with highlighted input words via the label attention architecture. CONCLUSIONS: Federated learning was used to train the ICD-10 classification model on multicenter clinical text while protecting data privacy. The model's performance was better than that of models that were trained locally.

Sustained release of transgenic human factor IX: preparation, characterization, and in vivo efficacy.

The current regimen of factor IX (FIX) injection is of an episodic format, which leads to limited efficacy. A sustained release dosage form is beneficial in terms of reducing the injection frequency and improving the therapeutic effectiveness. The aim of this study was to formulate a new microsphere form of a FIX-containing preparation to diminish these shortcomings. Using the water-in-oil-in-water (W/O/W) double emulsion technique, injectable long-acting FIX microspheres were prepared with transgenic recombinant human FIX (rhFIX) and poly(lactic-co-glycolic acid) (PLGA) polymer. The rhFIX microspheres prepared had diameters ranging between 25-350 µm and easily passed through a small-gauge-number needle for subcutaneous injection. In in vitro release testing, the microspheres had a sustained release profile featuring an initial burst and sustained release spanning a 5-day period. In in vivo pharmacodynamic testing, normalization of the bleeding of hemophilic mice was maintained for 5 days with microsphere injection as compared with 2 days with native rhFIX. Taken together, these results indicated that long-acting FIX microspheres were successfully prepared for potential use in hemophilic prophylaxis.

Utilizing Xenogeneic Cells As a Therapeutic Agent for Treating Diseases.

The utilization of biologically produced cells to treat diseases is a revolutionary invention in modern medicine after chemically synthesized small molecule drugs and biochemically made protein drugs. Cells are basic units of life with diverse functions in mature and developing organs, which biological properties could be utilized as a promising therapeutic approach for currently intractable and incurable diseases. Xenogeneic cell therapy utilizing animal cells other than human for medicinal purpose has been studied as a new way of treating diseases. Xenogeneic cell therapy is considered as a potential regenerative approach to fulfill current unmet medical needs because xenogeneic cells could be isolated from different animal organs and expanded ex vivo as well as maintain the characteristics of original organs, providing a versatile and plenty cell source for cell-based therapeutics beside autologous and allogeneic sources. The swine species is considered the most suitable source because of the similarity with humans in size and physiology of many organs in addition to the economic and ethical reasons plus the possibility of genetic modification. This review discusses the old proposed uses of xenogeneic cells such as xenogeneic pancreatic islet cells, hepatocytes and neuronal cells as a living drug for the treatment of degenerative and organ failure diseases. Novel applications of xenogeneic mesenchymal stroma cells and urothelial cells are also discussed. There are formidable immunological barriers toward successful cellular xenotransplantation in clinic despite major progress in the development of novel immunosuppression regimens and genetically multimodified donor pigs. However, immunological barriers could be turn into immune boosters by using xenogeneic cells of specific tissue types as a novel immunotherapeutic agent to elicit bystander antitumor immunity due to rejection immune responses. Xenogeneic cells have the potential to become a safe and efficacious option for intractable diseases and hard-to-treat cancers, adding a new class of cellular medicine in our drug armamentarium.

A resorbable hyaluronic acid hydrogel to prevent adhesion in porcine model under laparotomy pelvic surgery.

Surgical post-operative adhesions can lead to serious clinical complications. Barrier agent is the broad usage for the prevention of post-operative adhesions. This study aimed to evaluate the reducing adhesion efficacy of non-animal hyaluronic acid (HA) hydrogel in pigs undergoing conventional laparotomy pelvic surgery. HA hydrogel was applied to eighteen female pigs who underwent conventional laparotomy. The adhesion degrees and histopathology were evaluated in bilateral uterine horns as well as peritoneal sidewall excision. In the present study, all animals survived and had no complications after the surgery. The histopathological observations were demonstrated that HA obviously improved laparotomy pelvic surgery-induced adhesion in peritoneal sidewall and uterine horn. The anastomotic healing score of injury + HA group was significantly lower than the injury alone group. We conclude HA hydrogel can attenuate the post-operative adhesions in porcine.

Automatic ICD-10 Coding and Training System: Deep Neural Network Based on Supervised Learning.

BACKGROUND: The International Classification of Diseases (ICD) code is widely used as the reference in medical system and billing purposes. However, classifying diseases into ICD codes still mainly relies on humans reading a large amount of written material as the basis for coding. Coding is both laborious and time-consuming. Since the conversion of ICD-9 to ICD-10, the coding task became much more complicated, and deep learning- and natural language processing-related approaches have been studied to assist disease coders. OBJECTIVE: This paper aims at constructing a deep learning model for ICD-10 coding, where the model is meant to automatically determine the corresponding diagnosis and procedure codes based solely on free-text medical notes to improve accuracy and reduce human effort. METHODS: We used diagnosis records of the National Taiwan University Hospital as resources and apply natural language processing techniques, including global vectors, word to vectors, embeddings from language models, bidirectional encoder representations from transformers, and single head attention recurrent neural network, on the deep neural network architecture to implement ICD-10 auto-coding. Besides, we introduced the attention mechanism into the classification model to extract the keywords from diagnoses and visualize the coding reference for training freshmen in ICD-10. Sixty discharge notes were randomly selected to examine the change in the F1-score and the coding time by coders before and after using our model. RESULTS: In experiments on the medical data set of National Taiwan University Hospital, our prediction results revealed F1-scores of 0.715 and 0.618 for the ICD-10 Clinical Modification code and Procedure Coding System code, respectively, with a bidirectional encoder representations from transformers embedding approach in the Gated Recurrent Unit classification model. The well-trained models were applied on the ICD-10 web service for coding and training to ICD-10 users. With this service, coders can code with the F1-score significantly increased from a median of 0.832 to 0.922 (P<.05), but not in a reduced interval. CONCLUSIONS: The proposed model significantly improved the F1-score but did not decrease the time consumed in coding by disease coders.

Biodistribution and pharmacokinetics of transgenic pig-produced recombinant human factor IX (rhFIX) in rats.

BACKGROUND: Recombinant human factor IX (rhFIX) is a 56 kDa glycoprotein with full biological activity providing a guarantee of freedom from blood-borne viral contamination in the therapy of hemophilia B, but no data are available on the distribution of transgenic pig-produced rhFIX post injection (p.i.). Therefore, an 131I-radiolabeled rhFIX was developed to evaluate the distribution of rhFIX in rats. MATERIALS AND METHODS: rhFIX was labeled with the lodogen method. 131I-rhFIX (25 microCi/25 microg/200 microl/rat) was intravenously injected through the tail vein in normal Sprague-Dawley (SD) rats and the biodistribution was examined from 5 min to 72 h p.i.. The pharmacokinetics were also evaluated from 5 min to 96 h p.i. RESULTS: The radiolabeled efficiency and radiochemical purity of 131I-rhFIX was over 96% and 98%, respectively. The biodistribution study showed that the rhFIX chiefly accumulated in the liver. The distribution and elimination half-life (t(1/2alpha) and t(1/2beta)) of 131I-rhFIX were 0.82 and 9.34 h, respectively. The maximum concentration in the plasma (Cmax) and the area under the concentration versus time curve (AUC(INF)) of 131I-rhFIX in rats were 3.09% injected dose (ID)/g and 15.3 h x % ID/g. CONCLUSION: The transgenic pig-produced rhFIX is mostly retained in the liver and the preclinical biodistribution and pharmacokinetic studies of 131I radiolabeled rhFIX are helpful for researching its biological effect in vivo.

Hair growth is promoted by BeauTop via expression of EGF and FGF­7.

Minoxidil and finasteride have been approved to treat hair loss by the Food and Drug Administration. However, the further elucidation of treatments for hair loss, including those using Chinese herbal medicine, remains important clinically. BeauTop (BT) is a health food supplement which contains Ginseng radix, Astragali radix, Radix Angelicae sinensis, Ligustri fructus, Rehmannia glutinosa and Eclipta prostrata (Linn). Susbsequent to oral administration of BT at 0.6 g/kg/day to wax/rosin­induced alopecia in C57BL/6 mice, BT significantly induced hair growth at day 8 compared with control treatment (P<0.05). The expression levels of epidermal growth factor (EGF), and fibroblast growth factor (FGF)­7 were increased compared with control animals on day 8. In contrast, levels of FGF­5 of the BT group were reduced compared with the control on day 12. There were no effects on the expression of insulin­like growth factor 1. The results demonstrated that the mechanism of BT improving alopecia is potentially associated with modulation of EGF and FGF­7 levels. Taken together, it is suggested that BT may have a potential effect of the promotion of hair growth.

Angiotensin receptor blockers are associated with lower mortality than ACE inhibitors in predialytic stage 5 chronic kidney disease: A nationwide study of therapy with renin-angiotensin system blockade.

Dual renin angiotensin system (RAS) blockade using angiotensin-receptor blockers (ARBs) in combination with angiotensin converting enzyme inhibitors (ACEIs) is reported to improve proteinuria in both diabetic and non-diabetic patients. However, its renoprotective effect and safety remain uncertain in patients with advanced chronic kidney disease (CKD). From January 1, 2000 through June 30, 2009, we enrolled 14,117 pre-dialytic stage 5 CKD patients with serum creatinine >6mg/dL and hematocrit <28% under the treatment with erythropoiesis stimulating agents and RAS blockade. We used Cox proportional hazards regression models to estimate the hazard ratios (HRs) against the commencement of long-term dialysis and all-cause mortality for ACEI/ARB users. Over a median follow-up of 7 months, 9,867 patients (69.9%) required long-term dialysis and 2,805 (19.9%) died before progression to end-stage renal disease requiring dialysis. In comparison with the ARB-only users, dual blockade with ACEIs and ARBs was associated with a significantly higher risk of (1) death in all CKD patients (HR = 1.49, [95%CI, 1.30-1.71]; P = 0.02) and in diabetic subgroup (HR = 1.58, [95%CI, 1.34-1.86]; P = 0.02); (2) composite endpoint of long-term dialysis or death in diabetic subgroup (HR = 1.10, [95%CI, 1.01-1.20]; P = 0.04); (3) hyperkalemia-associated hospitalization in non-diabetic subgroup (HR, 2.74, [95%CI, 1.05-7.15]; P = 0.04). However, ACEIs users were associated with higher mortality than ARBs users in all CKD patients (HR = 1.17, [95%CI, 1.07-1.27]; P = 0.03) and in diabetic subgroup (HR = 1.32, [95%CI, 1.18-1.48]; P = 0.03). Monotherapy of RAS blockade, especially ARB, is more effective and safer than dual RAS blockade in pre-dialytic stage 5 CKD patients.

Increased high sensitivity C-reactive protein and neutrophil count are related to increased standard cardiovascular risk factors in healthy Chinese men.

BACKGROUND: Epidemiologic data regarding the role of inflammation in atherosclerosis have been based mainly on Caucasian populations. Thus, we analyzed the cross-sectional relationships of inflammatory biomarkers to cardiovascular risk factors (CVRF) and related variables in a large group of healthy Chinese men, a population with a markedly lower heart disease mortality rate compared with Western populations. METHODS: The study consisted of 8374 men aged 20-80 who attended a voluntary health examination at a metropolitan university center between 1997 and 2002. The relationships between serum high sensitivity C-reactive protein (hsCRP), total white blood cell (WBC), neutrophil, and monocyte counts to CVRF were analyzed using multivariate linear and logistic regression analyses. Whether a dose-response effect existed between elevated levels of each marker and increasing numbers of CVRF was also evaluated. RESULTS: The distribution of hsCRP was similar to Western studies. Both multivariate regression analyses showed all four markers to have significant correlations with body mass index, triglycerides, and adverse high density lipoprotein/low density lipoprotein ratio. Smoking was associated with increased levels of all four markers. Elevated neutrophil count had the most markedly progressive dose-response effect with increasing numbers of CVRF, whereas elevated monocyte count showed a drop in risk with CVRF of five and above. CONCLUSION: We show in our study that with increasing numbers of standard CVRF, healthy Chinese men have progressive and increasing risks of having elevated levels of hsCRP, total WBC, and neutrophil counts. While the inflammatory markers surveyed were largely correlated with CVRF, the similar values of CRP between populations with divergent mortality rates suggest that a more complex relationship may exist between CRP and disease outcome. The possible utility of neutrophil count as a marker for cardiovascular disease risk in healthy men awaits further evaluation in prospective studies.

Carthamus tinctorius L. ameliorates brain injury followed by cerebral ischemia-reperfusion in rats by antioxidative and anti-inflammatory mechanisms.

OBJECTIVES: Carthamus tinctorius L. (CT) or safflower is widely used in traditional Chinese medicine. This study investigated the effects of CT extract (CTE) on ischemia-reperfusion (I/R) brain injury and elucidated the underlying mechanism. MATERIALS AND METHODS: The I/R model was conducted by occlusion of both common carotid arteries and right middle cerebral artery for 90 min followed by 24 hr reperfusion in Sprague-Dawley rats. CTE (0.2-0.6 g/kg) was administered intraperitoneally before and during ischemia, and during reperfusion period. The cerebral infarction area, neurological deficit scores, free radicals (lucigenin chemiluminescence counts) and pro-inflammatory cytokines expression were measured. RESULTS: Pretreatment and treatment with CTE significantly reduced the cerebral infarction area and neurological deficits. CTE (0.4 g/kg) also reduced blood levels of free radicals and expression of tumor necrosis factor-α and interleukin-1ß in the cerebral infarction area. CONCLUSION: The reduction in I/R cerebral infarction caused by CTE is possibly associated with its antioxidation and anti-inflammatory properties.

Major gene effects in systolic and diastolic blood pressure in families receiving a health examination in Taiwan.

BACKGROUND: Genetic factors play an important role in determining blood pressure values. Strong familial aggregation and estimated heritability values around 0.4-0.6 were reported in a white population. OBJECTIVES: To investigate the genetic components and mode of inheritance of blood pressure in Taiwan. DESIGN: A cross-sectional family study based on an adult population undergoing a health examination. SETTING AND PARTICIPANTS: A total of 1313 adults and their spouses or first-degree relatives, or both, underwent a health examination in a tertiary university hospital from August 1998 to September 1999. MAIN OUTCOME MEASURES: Genetic analyses, including comingling analysis, familial correlation and complex segregation analysis, were used to detect the genetic components and the mode of inheritance of blood pressures. RESULTS: In both systolic and diastolic blood pressure, the comingling analyses demonstrated that a more than one-component distribution provided the best fit for the data. The familial correlation coefficients showed significant parent-offspring and sibling correlation. Complex segregation analyses showed major gene effects in controlling systolic and diastolic blood pressure. CONCLUSIONS: There were significant genetic components in blood pressure among Taiwanese. Further investigation of genomic loci for the control of blood pressure is indicated.

Consistency of genetic inheritance mode and heritability patterns of triglyceride vs. high density lipoprotein cholesterol ratio in two Taiwanese family samples.

BACKGROUND: Triglyceride/HDL cholesterol ratio (TG/HDL-C) is considered as a risk factor for cardiovascular events. Genetic components were important in controlling the variation in western countries. But the mode of inheritance and family aggregation patterns were still unknown among Asian-Pacific countries. This study, based on families recruited from community and hospital, is aimed to investigate the mode of inheritance, heritability and shared environmental factors in controlling TG/HDL-C. RESULTS: Two populations, one from community-based families (n = 988, 894 parent-offspring and 453 sibling pairs) and the other from hospital-based families (n = 1313, 76 parent-offspring and 52 sibling pairs) were sampled. The population in hospital-based families had higher mean age values than community-based families (54.7 vs. 34.0). Logarithmic transformed TG/ HDL-C values, after adjusted by age, gender and body mass index, were for genetic analyses. Significant parent-offspring and sibling correlations were also found in both samples. The parent-offspring correlation coefficient was higher in the hospital-based families than in the community-based families. Genetic heritability was higher in community-based families (0.338 +/- 0.114, p = 0.002), but the common shared environmental factor was higher in hospital-based families (0.203 +/- 0.042, p < 0.001). Commingling analyses showed that more than one-component distribution models were the best-fit models to explain the variance in both populations. Complex segregation analysis by regressive models revealed that in both samples the best-fit model of TG/HDL-C was the model of environmental effects plus familial correlation, in which significant parent-offspring and sibling correlations were demonstrated. Models of major gene effects were rejected in both samples. CONCLUSION: Variations of TG/HDL-C in the normal ranges were likely to be influenced by multiple factors, including environmental and genetic components. Higher genetic factors were proved in younger community-based families than in older hospital-based families.