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Accuracy and speed are the most important indexes for evaluating many object tracking algorithms. However, when constructing a deep fully convolutional neural network (CNN), the use of deep network feature tracking will cause tracking drift due to the effects of convolution padding, receptive field (RF), and overall network step size. The speed of the tracker will also decrease. This article proposes a fully convolutional siamese network object tracking algorithm that combines the attention mechanism with the feature pyramid network (FPN), and uses heterogeneous convolution kernels to reduce the amount of calculations (FLOPs) and parameters. The tracker first uses a new fully CNN to extract image features, and introduces a channel attention mechanism in the feature extraction process to improve the representation ability of convolutional features. Then use the FPN to fuse the convolutional features of high and low layers, learn the similarity of the fused features, and train the fully CNNs. Finally, the heterogeneous convolutional kernel is used to replace the standard convolution kernel to improve the speed of the algorithm, thereby making up for the efficiency loss caused by the feature pyramid model. In this article, the tracker is experimentally verified and analyzed on the VOT-2017, VOT-2018, OTB-2013, and OTB-2015 datasets. The results show that our tracker has achieved better results than the state-of-the-art trackers.
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BACKGROUND: The aim of the present study was to enhance understanding of the diagnosis and treatment of atypical hereditary spherocytosis (HS), and to broaden the diagnostic thoughts of physicians for patients with jaundice. CASE SUMMARY: A 28-year-old male presented with jaundice, bile duct stone, and splenomegaly, but without anemia. Other causes of jaundice were excluded, and gene sequencing revealed a novel heterozygous variant of c.1801C>T (p.Q601X) in exon 14 of the SPTB (NM_01355436) gene on chromosome 14 (chr14: 65260580) in the patient's blood; the biological parents and child of the patient did not have similar variants. A splenectomy was performed on the patient and his bilirubin levels returned to normal after surgery. Thus, a novel gene variant causing HS was identified. This variant may result in the truncation of ß-hemoglobin in the erythrocyte membrane, leading to loss of normal function, jaundice, and hemolytic anemia. The clinical manifestations of the patient were hyperjaundice and an absence of typical hemolysis during the course of the disease, which caused challenges for diagnosis by the clinicians. CONCLUSION: Following a definitive diagnosis, genetic testing and response to treatment identified a gene variant site for a novel hemolytic anemia.
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BACKGROUND: Hidradenitis suppurativa (HS) is a chronic, inflammatory and debilitating dermatosis characterized by painful nodules, sinus tracts and abscesses in apocrine gland-bearing areas that predominantly affect women worldwide. New therapeutic interventions based on the clinical manifestations of patients have recently been introduced in numerous articles. However, which countries, journals, subject categories, and articles have the ultimate influence remain unknown. This study aimed to display influential entities in 100 top-cited HS-related articles (T100HS) and investigate whether medical subject headings (i.e., MeSH terms) can be used to predict article citations. METHODS: T100HS data were extracted from PubMed since 2013. Subject categories were classified by MeSH terms using social network analysis. Sankey diagrams were applied to highlight the top 10 influential entities in T100HS from the three aspects of publication, citations, and the composited score using the hT index. The difference in article citations across subject categories and the predictive power of MeSH terms on article citations in T100HS were examined using one-way analysis of variance and regression analysis. RESULTS: The top three countries (the US, Italy, and Spain) accounts for 54% of the T100HS. The T100HS impact factor (IF) is 12.49 (IFâ =â citations/100). Most articles were published in J Am Acad Dermatol (15%; IFâ =â 18.07). Eight subject categories were used. The "methods" was the most frequent MeSH term, followed by "surgery" and "therapeutic use". Saunte et al, from Roskilde Hospital, Denmark, had 149 citations in PubMed for the most cited articles. Sankey diagrams were used to depict the network characteristics of the T100HS. Article citations did not differ by subject category (F(7, 92)â =â 1.97, Pâ =â .067). MeSH terms were evident in the number of article citations predicted (F(1, 98)â =â 129.1106; Pâ <â .001). CONCLUSION: We achieved a breakthrough by displaying the characteristics of the T100HS network on the Sankey diagrams. MeSH terms may be used to classify articles into subject categories and predict T100HS citations. Future studies can apply the Sankey diagram to the bibliometrics of the 100 most-cited articles.
Subject(s)
Hidradenitis Suppurativa , Journal Impact Factor , Humans , Female , Hidradenitis Suppurativa/therapy , Bibliometrics , Medical Subject Headings , PubMedABSTRACT
AIMS: A computerized tool and interdisciplinary care were implemented to develop a novel model for older patients with delirium in the emergency department (ED). METHODS: We developed a computerized tool using a delirium triage screen and brief confusion assessment in the hospital information system, performed education for the healthcare providers, and developed a continuous care protocol. Comparisons for outcomes between pre- and post-intervention periods were performed. RESULTS: Compared with the pre-intervention period, patients in the post-intervention period had shorter hospitalization stay, lower expenditure of hospitalization, more likely to return home, lower ED revisits of ≤ 3 days, re-hospitalization of ≤ 14 days, and mortality of ≤ 1 month. All mentioned differences were not statistically significant. CONCLUSIONS: A novel model was successfully developed for delirium management in older patients in the ED. Outcome differences were not significant; however, the result is promising, which gives us an important reference in the future.
Subject(s)
Delirium , Humans , Aged , Delirium/diagnosis , Delirium/therapy , Taiwan , Emergency Service, Hospital , Triage , HospitalizationABSTRACT
Particulate matter (PM) pollutants impose a certain degree of destruction and toxicity to the skin. Mast cells in the skin dermis could be activated by PMs that diffuse across the blood vessel after being inhaled. Mast cell degranulation in the dermis provides a kind of inflammatory insult to local fibroblasts. In this study, we evaluated human dermal fibroblast responses to conditioned medium from KU812 cells primed with PM. We found that PM promoted the production of proinflammatory cytokines in mast cells and that the cell secretome induced reactive oxygen species and mitochondrial reactive oxygen species production in dermal fibroblasts. Nicotinamide mononucleotide or coenzyme Q10 alleviated the generation of excessive ROS and mitochondrial ROS induced by the conditioned medium from PM-activated KU812 cells. PM-conditioned medium treatment increased the NF-κB expression in dermal fibroblasts, whereas NMN or Q10 inhibited p65 upregulation by PM. The reduced sirtuin 1 (SIRT 1) and nuclear factor erythroid 2-related Factor 2 (Nrf2) expression induced by PM-conditioned medium was reversed by NMN or Q10 in HDFs. Moreover, NMN or Q10 attenuated the expression of senescent ß-galactosidase induced by PM-conditioned KU812 cell medium. These findings suggest that NMN or Q10 ameliorates PM-induced inflammation by improving the cellular oxidative status, suppressing proinflammatory NF-κB, and promoting the levels of the antioxidant and anti-inflammatory regulators Nrf2 and SIRT1 in HDFs. The present observations help to understand the factors that affect HDFs in the dermal microenvironment and the therapeutic role of NMN and Q10 as suppressors of skin aging.
Subject(s)
Cellular Senescence , Nicotinamide Mononucleotide , Ubiquinone , Cellular Senescence/drug effects , Culture Media, Conditioned/metabolism , Culture Media, Conditioned/pharmacology , Fibroblasts/drug effects , Fibroblasts/metabolism , Humans , Mast Cells/metabolism , NF-E2-Related Factor 2/metabolism , NF-kappa B/metabolism , Nicotinamide Mononucleotide/metabolism , Nicotinamide Mononucleotide/pharmacology , Oxidative Stress , Particulate Matter/toxicity , Reactive Oxygen Species/metabolism , Sirtuin 1/metabolism , Ubiquinone/analogs & derivatives , Ubiquinone/pharmacologyABSTRACT
BACKGROUND: Web-based computerized adaptive testing (CAT) implementation of the skin cancer (SC) risk scale could substantially reduce participant burden without compromising measurement precision. However, the CAT of SC classification has not been reported in academics thus far. OBJECTIVE: We aim to build a CAT-based model using machine learning to develop an app for automatic classification of SC to help patients assess the risk at an early stage. METHODS: We extracted data from a population-based Australian cohort study of SC risk (N=43,794) using the Rasch simulation scheme. All 30 feature items were calibrated using the Rasch partial credit model. A total of 1000 cases following a normal distribution (mean 0, SD 1) based on the item and threshold difficulties were simulated using three techniques of machine learning-naïve Bayes, k-nearest neighbors, and logistic regression-to compare the model accuracy in training and testing data sets with a proportion of 70:30, where the former was used to predict the latter. We calculated the sensitivity, specificity, receiver operating characteristic curve (area under the curve [AUC]), and CIs along with the accuracy and precision across the proposed models for comparison. An app that classifies the SC risk of the respondent was developed. RESULTS: We observed that the 30-item k-nearest neighbors model yielded higher AUC values of 99% and 91% for the 700 training and 300 testing cases, respectively, than its 2 counterparts using the hold-out validation but had lower AUC values of 85% (95% CI 83%-87%) in the k-fold cross-validation and that an app that predicts SC classification for patients was successfully developed and demonstrated in this study. CONCLUSIONS: The 30-item SC prediction model, combined with the Rasch web-based CAT, is recommended for classifying SC in patients. An app we developed to help patients self-assess SC risk at an early stage is required for application in the future.
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BACKGROUND: Pemphigus vulgaris (PV) is a rare autoimmune blistering disease characterized by intraepithelial and mucocutaneous blister formation and erosion. Numerous articles related to PV have been published. However, which articles have a tremendous influence is still unknown, and factors affecting article citation numbers remain unclear. We aimed to visualize the prominent entities using the top 100 most-cited articles on the topic of PV (T100PV), and investigate whether medical subject headings (i.e., MeSH terms) can be used to predict article citations. METHODS: By searching the PubMed Central (PMC) database, the T100PV abstracts since 2011 were downloaded. Citation analysis was performed to compare the dominant entities in article topics, authors, and research institutes using social network analysis (SNA) and Kano diagrams. We examined the MeSH prediction power against article citations using correlation coefficients (CCs). RESULTS: The most cited article (125 times) was authored by Ellebrecht from the University of Pennsylvania in the US. The most productive countries were Germany (28%) and the US (25%). Most articles were published in J Invest Dermatol (16%) and Br J Dermatol (10%). Kasperkiewicz (Germany) and the Normandie University (France) were the most cited authors and research institutes, respectively. The most frequently occurred MeSH terms were administration and dosage, immunology, and metabolism. MeSH terms were evident in the prediction power on the number of article citations (Fâ=â19.77; Pâ<â.001). CONCLUSION: A breakthrough was achieved by developing dashboards to display the T100PV. MeSH terms can be used to predict the T100PV citations. These T100PV visualizations can be applied in future studies.
Subject(s)
Journal Impact Factor , Medical Subject Headings , Pemphigus , Bibliography of Medicine , Biomedical Research/standards , Humans , Meta-Analysis as Topic , Periodicals as Topic/standards , Periodicals as Topic/statistics & numerical data , Research Design , Systematic Reviews as TopicABSTRACT
Mast cells play a crucial role in the pathogenesis of type 1 allergic reactions by binding to IgE and allergen complexes and initiating the degranulation process, releasing pro-inflammatory mediators. Recently, research has focused on finding a stable and effective anti-allergy compound to prevent or treat anaphylaxis. Dihydromyricetin (DHM) is a flavonoid compound with several pharmacological properties, including free radical scavenging, antithrombotic, anticancer, and anti-inflammatory activities. In this study, we investigated the anti-allergic inflammatory effects and the underlying molecular mechanism of DHM in the DNP-IgE-sensitized human mast cell line, KU812. The cytokine levels and mast cell degranulation assays were determined by enzyme-linked immunosorbent assay (ELISA). The possible mechanism of the DHM-mediated anti-allergic signaling pathway was analyzed by western blotting. It was found that treatment with DHM suppressed the levels of inflammatory cytokines TNF-α and IL-6 in DNP-IgE-sensitized KU812 cells. The anti-allergic inflammatory properties of DHM were mediated by inhibition of NF-κB activation. In addition, DHM suppressed the phosphorylation of signal transducer and activator of transcription 5 (STAT5) and mast cell-derived tryptase production. Our study shows that DHM could mitigate mast cell activation in allergic diseases.
Subject(s)
Cell Degranulation/immunology , Flavonols/immunology , Immunoglobulin E/immunology , Mast Cells/immunology , NF-kappa B/immunology , Signal Transduction/immunology , Cell Line , HumansABSTRACT
BACKGROUND: The COVID-19 pandemic occurred and rapidly spread around the world. Some online dashboards have included essential features on a world map. However, only transforming data into visualizations for countries/regions is insufficient for the public need. This study aims to (1) develop an algorithm for classifying countries/regions into four quadrants inn GSM and (2) design an app for a better understanding of the COVID-19 situation. METHODS: We downloaded COVID-19 outbreak numbers daily from the Github website, including 189 countries/regions. A four-quadrant diagram was applied to present the classification of each country/region using Google Maps run on dashboards. A novel presentation scheme was used to identify the most struck entities by observing (1) the multiply infection rate (MIR) and (2) the growth trend in the recent 7 days. Four clusters of the COVID-19 outbreak were dynamically classified. An app based on a dashboard aimed at public understanding of the outbreak types and visualizing of the COVID-19 pandemic with Google Maps run on dashboards. The absolute advantage coefficient (AAC) was used to measure the damage hit by COVID-19 referred to the next two countries severely hit by COVID-19. RESULTS: We found that the two hypotheses were supported: India (i) is in the increasing status as of April 28, 2021; (ii) has a substantially higher ACC(= 0.81 > 0.70), and (iii) has a substantially higher ACC(= 0.66 < 0.70) as of May 17, 2021. CONCLUSION: Four clusters of the COVID-19 outbreak were dynamically classified online on an app making the public understand the outbreak types of COVID-19 pandemic shown on dashboards. The app with GSM and AAC is recommended for researchers in other disease outbreaks, not just limited to COVID-19.
Subject(s)
Algorithms , COVID-19/epidemiology , COVID-19/transmission , Global Health/statistics & numerical data , Models, Statistical , SARS-CoV-2/isolation & purification , Humans , India/epidemiologyABSTRACT
The prediction of whether active NBA players can be inducted into the Hall of Fame (HOF) is interesting and important. However, no such research have been published in the literature, particularly using the artificial neural network (ANN) technique. The aim of this study is to build an ANN model with an app for automatic prediction and classification of HOF for NBA players. We downloaded 4728 NBA players' data of career stats and accolades from the website at basketball-reference.com. The training sample was collected from 85 HOF members and 113 retired Non-HOF players based on completed data and a longer career length (≥15 years). Featured variables were taken from the higher correlation coefficients (<0.1) with HOF and significant deviations apart from the two HOF/Non-HOF groups using logistical regression. Two models (i.e., ANN and convolutional neural network, CNN) were compared in model accuracy (e.g., sensitivity, specificity, area under the receiver operating characteristic curve, AUC). An app predicting HOF was then developed involving the model's parameters. We observed that (1) 20 feature variables in the ANN model yielded a higher AUC of 0.93 (95% CI 0.93-0.97) based on the 198-case training sample, (2) the ANN performed better than CNN on the accuracy of AUC (= 0.91, 95% CI 0.87-0.95), and (3) an ready and available app for predicting HOF was successfully developed. The 20-variable ANN model with the 53 parameters estimated by the ANN for improving the accuracy of HOF has been developed. The app can help NBA fans to predict their players likely to be inducted into the HOF and is not just limited to the active NBA players.
Subject(s)
Basketball , Neural Networks, Computer , ROC Curve , RetirementABSTRACT
Severe dengue outbreaks (DOs) affect the majority of Asian and Latin American countries. Whether all DOs always occurred in sub-tropical and tropical areas (STTA) has not been verified. We downloaded abstracts by searching keywords "dengue (MeSH Major Topic)" from Pubmed Central since 1950, including three collections: country names in abstracts (CNA), no abstracts (WA), and no country names in abstracts (Non-CNA). Visualizations were created to present the DOs across countries/areas in STTA. The percentages of mentioned country names and authors' countries in STTA were computed on the CNA and Non-CNA bases. The social network analysis was applied to highlight the most cited articles and countries. We found that (1) three collections are 3427 (25.48%), 3137 (23.33%), and 6884 (51.19%) in CNA, WA, and Non-CNA, respectively; (2) the percentages of 94.3% and 79.9% were found in the CNA and Non-CNA groups; (3) the most mentioned country in abstracts were India, Thailand, and Brazil; (4) most authors in the Non-CNA collections were from the United States, Brazil, and China; (5) the most cited article (PMID = 23563266) authored by Bhatt et al. had 2604 citations since 2013. Our findings provide in-depth insights into the DO knowledge. The research approaches are recommended for authors in research on other infectious diseases in the future, not just limited to the DO topic.
Subject(s)
Dengue , Tropical Climate , Bibliometrics , Brazil , China/epidemiology , Dengue/epidemiology , Disease Outbreaks , Humans , India , Thailand , United StatesABSTRACT
Dictamnine is an active pharmaceutical ingredient in Dictamnus dasycarpus, a Chinese herbal medicine widely used for the treatment of skin inflammations such as atopic dermatitis (AD). Oxazolone has been demonstrated to induce significant skin inflammation and produce inflammatory cytokine expression identical to that of AD. An in vitro HaCaT inflammation model treated with dictamnine, which efficiently scavenged the reactive oxygen species (ROS) and mitochondrial ROS (mROS), and it reduced interleukin-1ß (IL-1ß), tumor necrosis factor-α (TNF-α) expression, NLRP3 inflammasome activation, and NF-κB expression. To explore the anti-inflammatory mechanism of dictamnine and enhance sustained drug release and penetration into epidermal structures in a dermatitis mouse model, we prepared PLGA-nanocarrier-encapsulated dictamnine (Dic-PLGA-NC) in a specifically designed bioreactor, namely an ultrasound composite streams-impinging mixer (U-SiM). Mouse dermatitis model was treated with Dic-PLGA-NC medication, spleens were collected to evaluate body weight ratio, and skin was retrieved for histological examination and two-photon microscopy. The data demonstrate that Dic-PLGA-NC efficiently penetrated the dermal layer, making it superior to naked dictamnine; moreover, it ameliorated the dermatitis symptoms and inflammatory cytokine expression in vivo. Dic-PLGA-NC produced using the U-SiM bioreactor could be used in new manufacturing processes for drugs to treat AD.
Subject(s)
Dermatitis, Atopic , Quinolines , Animals , Cytokines , Dermatitis, Atopic/drug therapy , Disease Models, Animal , Inflammation , Mice , NF-kappa B , Oxazolone , Skin , Tumor Necrosis Factor-alphaABSTRACT
Visibility forecasting in offshore areas faces the problems of low observational data and complex weather. This paper proposes an intelligent prediction method of offshore visibility based on temporal convolutional network (TCN) and transfer learning to solve the problem. First, preprocess the visibility data sets of the source and target domains to improve the quality of the data. Then, build a model based on temporal convolutional network and transfer learning (TCN_TL) to learn the visibility data of the source domain. Finally, after transferring the knowledge learned from a large amount of data in the source domain, the model learns the small data set in the target domain. After completing the training, the model data of the European Mid-Range Weather Forecast Center (ECMWF) meteorological field were selected to test the model performance. The method proposed in this paper has achieved relatively good results in the visibility forecast of Qiongzhou Strait. Taking Haikou Station in the spring and winter of 2018 as an example, the forecast error is significantly lower than that before the transfer learning, and the forecast score is increased by 0.11 within the 0-1 km level and the 24 h forecast period. Compared with the CUACE forecast results, the forecast error of TCN_TL is smaller than that of the former, and the TS score is improved by 0.16. The results show that under the condition of small data sets, transfer learning improves the prediction performance of the model, and TCN_TL performs better than other deep learning methods and CUACE.
Subject(s)
Research Design , Weather , Forecasting , Machine Learning , SeasonsABSTRACT
Dictamni dasycarpus is a type of Chinese medicine made from the root bark of D. dasycarpus. It has been reported to show a wide spectrum of biological and pharmacological effects, for example, it has been used widely for the treatment of rheumatism, nettle rash, itching, jaundice, chronic hepatitis and skin diseases. In the current study, D. dasycarpus extract was investigated for its antioxidant and anti-inflammatory effects, as well as its capability to alleviate oxazolone-induced skin damage in mice. The possible anti-inflammatory mechanism of D. dasycarpus extract against oxidative challenge was elucidated by measuring the levels of reactive oxygen species (ROS) production, interleukin-6, Tumor necrosis factor-α, NLRP3 (NACHT, LRR and PYD domains-containing protein 3 (NALP3)) inflammasome and interleukin-1β in HaCaT cells. D. dasycarpus extract did not affect cell viability in basal conditions. The extract significantly reduced oxazolone-induced epidermal swelling compared to untreated animal in the hairless albino mice (ICR mice) model. At the molecular level, Western blot assays indicated that the D. dasycarpus extract attenuated oxazolone-induced activation of apoptosis-associated speck-like protein containing CARD (ASC), procaspase-1, NF-κB and mitogen-activated protein kinase (MAPKs) such as c-Jun N-terminal protein kinase (JNK) and p38. This study demonstrates that D. dasycarpus extract could protect skin cells against oxidative and inflammatory insult by modulating the intracellular levels of ROS, TNF-α, interleukin-1, interleukin-6, NLR family pyrin domain containing 3 (NLRP3) inflammasome generation, antioxidant enzyme activity and cell signaling pathways. D. dasycarpus extract also attenuated the expression of NF-κB in HaCaT keratinocytes and thereby effectively downregulated inflammatory responses in the skin. Furthermore, D. dasycarpus extract alleviated oxazolone-induced damage in mice. Our results suggest the potential application of D. dasycarpus extract in preventing inflammatory processes in dermatitis.
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Periostracum cicadae is widely used for the treatment of skin diseases such as eczema, pruritus, and itching. The current study sought to evaluate the effect of P. cicadae extract on ultraviolet B (UVB) irradiation and identify the mechanisms involved. Photodamage-protective activity of P. cicadae extracts against oxidative challenge was screened using HaCaT keratinocytes. P. cicadae extracts did not affect cell viability but decreased reactive oxygen species (ROS) production. The extract attenuates the expression of interleukin-6 (IL-6), matrix metalloproteinase-2 (MMP-2), and MMP-9 in UVB-treated HaCaT cells. Also, P. cicadae abrogated UVB-induced activation of NF-κB, p53, and activator protein-1 (AP-1). The downmodulation of IL-6 by P. cicadae was inhibited by the p38 inhibitor (SB203580) or JNK inhibitor (SP600125). Moreover, the extract attenuated the expression of NF-κB and induced thrombomodulin in keratinocytes and thereby effectively downregulated inflammatory responses in the skin. The nuclear accumulation and expression of NF-E2-related factor (Nrf2) were increased by P. cicadae treatment. Furthermore, treatment with P. cicadae remarkably ameliorated the skin's structural damage induced by irradiation. This study demonstrates that P. cicadae may protect skin cells against oxidative insult by modulating ROS concentration, IL-6, MMPs generation, antioxidant enzymes activity, and cell signaling pathways.
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Native konjac glucomannan was used as the substrate for thermophilic actinomycetes, Thermobifida fusca BCRC19214, to produce ß-mannanase. The ß-mannanase was purified and five internal amino acid sequences were determined by LC-MS/MS. These sequences had high homology with the ß-mannanase from T. fusca YX. The tfm gene which encoded the ß-mannanase was cloned, sequenced and heterologous expressed in Yarrowia lipolytica P01 g expression system. Recombinant heterologous expression resulted in extracellular ß-mannanase production at levels as high as 3.16 U/ml in the culture broth within 48 h cultivation. The recombinant ß-mannanase from Y. lipolytica transformant had superior thermal property. The optimal temperature of the recombinant ß-mannanase from Y. lipolytica transformant (pYLSC1-tfm) was 80°C. When native konjac glucomannan was incubated with the recombinant ß-mannanase from Y. lipolytica transformant (pYLSC1-tfm) at 50°C, there was a fast decrease of viscosity happen during the initial phase of reaction. This viscosity reduction was accompanied by an increase of reducing sugars. The surface of konjac glucomannan film became smooth. After 24h of treatment, the DPw of native konjac glucomannan decreased from 6,435,139 to 3089.
Subject(s)
Mannans/chemistry , Yeasts/enzymology , beta-Mannosidase/chemistry , Enzyme Activation , Enzyme Stability , Fermentation , Hydrolysis , Recombinant Proteins/chemistry , Temperature , Yeasts/genetics , beta-Mannosidase/biosynthesis , beta-Mannosidase/isolation & purificationABSTRACT
BACKGROUND: The objective of this study was to explore the pharmacokinetics of vancomycin and determine an appropriate dosage regimen for vancomycin in adult neurosurgical intensive care unit (ICU) patients. METHODS: First, a 20-month therapeutic drug monitoring database at a medical center was used to retrospectively analyze the pharmacokinetic parameters of vancomycin in adult neurosurgical patients. Significant covariates were selected through Pearson or Spearman correlation tests and multiple linear regressions. Pharmacokinetic models were built using significant covariates to predict vancomycin clearance. Second, a 12-month prospective cohort of neurosurgical ICU patients was recruited to validate the models. Urine and cerebrospinal fluid samples were collected, and vancomycin concentrations were determined using a high-performance liquid chromatography assay. The relation between the model-predicted and observed pharmacokinetic parameters was assessed by Pearson correlation. RESULTS: In the retrospective cohort, 98 sets of peak/trough serum concentrations obtained from 73 patients were analyzed. These patients had a mean age of 54 ± 16 years, an estimated creatinine clearance (eClCr) of 83 ± 29 mL/min, a total vancomycin clearance (ClVan) of 101 ± 41 mL/min, and a volume of distribution (Vd) of 0.93 ± 0.27 L/kg. In a subgroup analysis, the ClVan of ICU patients was higher than the ClVan of non-ICU patients (1.57 ± 0.34-fold versus 1.16 ± 0.32-fold of eClCr, P < 0.05). Fifteen patients enrolled in the prospective cohort had an average age of 67 ± 12 years, an eClCr of 108 ± 44 mL/min, a ClVan of 112 ± 29 mL/min, and a Vd of 1.03 ± 0.55 L/kg. CONCLUSIONS: Adult neurosurgical ICU patients have a significantly elevated ClVan. In this study, 2 dosing equations were derived to achieve optimal serum vancomycin concentrations for this special population.
Subject(s)
Anti-Bacterial Agents/pharmacokinetics , Vancomycin/pharmacokinetics , Adult , Aged , Female , Humans , Intensive Care Units , Male , Metabolic Clearance Rate , Middle Aged , Neurosurgical Procedures , Retrospective StudiesABSTRACT
BACKGROUND: Uric acid measurement has become increasingly important, and electrochemically modified detection method based portable devices hold a dominant position in the market for point of care and self-monitoring of uric acid blood levels. However, there has been a lack of detailed performance evaluation of the electrochemical detection devices that are currently being used in professional health care facilities and for home self-monitoring of uric acid. METHODS: A commercially available uric acid monitoring system that is chemically modified to reduce interference was evaluated via clinical evaluation for its performance and interference as compared to a centralized laboratory instrument. RESULTS: Precision was within ±3.1% for 3 levels of control solutions and whole blood samples. A range from 30 to 55% was acceptable for the measurement of hematocrit levels in whole blood samples. There was no interference for the potential substances at their high therapeutic levels. Hemolyzed samples of up to 75 g/l showed no interference with test results obtained by the BeneCheck system, while a -45.9% bias% was obtained during testing of the same samples by a spectrophotometer. Clinical evaluation showed that >95% of tests were within ±20% bias% compared to a centralized instrument in hospitals. CONCLUSION: The uric acid monitoring system was suitable for use in monitoring or screening uric acid concentration for home users or professionals.
Subject(s)
Biosensing Techniques/methods , Uric Acid/blood , Artifacts , Electrochemistry , Hematocrit , Humans , TemperatureABSTRACT
Synthesis of ultrasmall water-soluble fluorescent gold nanoclusters is reported. The clusters have a decent quantum yield, high colloidal stability, and can be readily conjugated with biological molecules. Specific staining of cells and nonspecific uptake by living cells is demonstrated.