Biomarker Assessment of a High-Risk, Data-Driven Pediatric Sepsis Phenotype Characterized by Persistent Hypoxemia, Encephalopathy, and Shock.

OBJECTIVES: Identification of children with sepsis-associated multiple organ dysfunction syndrome (MODS) at risk for poor outcomes remains a challenge. We sought to the determine reproducibility of the data-driven "persistent hypoxemia, encephalopathy, and shock" (PHES) phenotype and determine its association with inflammatory and endothelial biomarkers, as well as biomarker-based pediatric risk strata. DESIGN: We retrained and validated a random forest classifier using organ dysfunction subscores in the 2012-2018 electronic health record (EHR) dataset used to derive the PHES phenotype. We used this classifier to assign phenotype membership in a test set consisting of prospectively (2003-2023) enrolled pediatric septic shock patients. We compared profiles of the PERSEVERE family of biomarkers among those with and without the PHES phenotype and determined the association with established biomarker-based mortality and MODS risk strata. SETTING: Twenty-five PICUs across the United States. PATIENTS: EHR data from 15,246 critically ill patients with sepsis-associated MODS split into derivation and validation sets and 1,270 pediatric septic shock patients in the test set of whom 615 had complete biomarker data. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: The area under the receiver operator characteristic curve of the modified classifier to predict PHES phenotype membership was 0.91 (95% CI, 0.90-0.92) in the EHR validation set. In the test set, PHES phenotype membership was associated with both increased adjusted odds of complicated course (adjusted odds ratio [aOR] 4.1; 95% CI, 3.2-5.4) and 28-day mortality (aOR of 4.8; 95% CI, 3.11-7.25) after controlling for age, severity of illness, and immunocompromised status. Patients belonging to the PHES phenotype were characterized by greater degree of systemic inflammation and endothelial activation, and were more likely to be stratified as high risk based on PERSEVERE biomarkers predictive of death and persistent MODS. CONCLUSIONS: The PHES trajectory-based phenotype is reproducible, independently associated with poor clinical outcomes, and overlapped with higher risk strata based on prospectively validated biomarker approaches.

Single-cell impedance cytometry of anticancer drug-treated tumor cells exhibiting mitotic arrest state to apoptosis using low-cost silver-PDMS microelectrodes.

Chemotherapeutic drugs such as paclitaxel and vinblastine interact with microtubules and thus induce complex cell states of mitosis arrest at the G2/M phase followed by apoptosis dependent on drug exposure time and concentration. Microfluidic impedance cytometry (MIC), as a label-free and high-throughput technology for single-cell analysis, has been applied for viability assay of cancer cells post drug exposure at fixed time and dosage, yet verification of this technique for varied tumor cell states after anticancer drug treatment remains a challenge. Here we present a novel MIC device and for the first time perform impedance cytometry on carcinoma cells exhibiting progressive states of G2/M arrest followed by apoptosis related to drug concentration and exposure time, after treatments with paclitaxel and vinblastine, respectively. Our results from impedance cytometry reveal increased amplitude and negative phase shift at low frequency as well as higher opacity for HeLa cells under G2/M mitotic arrest compared to untreated cells. The cells under apoptosis, on the other hand, exhibit opposite changes in these electrical parameters. Therefore, the impedance features differentiate the HeLa cells under progressive states post anticancer drug treatment. We also demonstrate that vinblastine poses a more potent drug effect than paclitaxel especially at low concentrations. Our device is fabricated using a unique sacrificial layer-free soft lithography process as compared to the existing MIC device, which gives rise to readily aligned parallel microelectrodes made of silver-PDMS embedded in PDMS channel sidewalls with one molding step. Our results uncover the potential of the MIC device, with a fairly simple and low-cost fabrication process, for cellular state screening in anticancer drug therapy.

External validation and biomarker assessment of a high-risk, data-driven pediatric sepsis phenotype characterized by persistent hypoxemia, encephalopathy, and shock.

Objective: Identification of children with sepsis-associated multiple organ dysfunction syndrome (MODS) at risk for poor outcomes remains a challenge. Data-driven phenotyping approaches that leverage electronic health record (EHR) data hold promise given the widespread availability of EHRs. We sought to externally validate the data-driven 'persistent hypoxemia, encephalopathy, and shock' (PHES) phenotype and determine its association with inflammatory and endothelial biomarkers, as well as biomarker-based pediatric risk-strata. Design: We trained and validated a random forest classifier using organ dysfunction subscores in the EHR dataset used to derive the PHES phenotype. We used the classifier to assign phenotype membership in a test set consisting of prospectively enrolled pediatric septic shock patients. We compared biomarker profiles of those with and without the PHES phenotype and determined the association with established biomarker-based mortality and MODS risk-strata. Setting: 25 pediatric intensive care units (PICU) across the U.S. Patients: EHR data from 15,246 critically ill patients sepsis-associated MODS and 1,270 pediatric septic shock patients in the test cohort of whom 615 had biomarker data. Interventions: None. Measurements and Main Results: The area under the receiver operator characteristic curve (AUROC) of the new classifier to predict PHES phenotype membership was 0.91(95%CI, 0.90-0.92) in the EHR validation set. In the test set, patients with the PHES phenotype were independently associated with both increased odds of complicated course (adjusted odds ratio [aOR] of 4.1, 95%CI: 3.2-5.4) and 28-day mortality (aOR of 4.8, 95%CI: 3.11-7.25) after controlling for age, severity of illness, and immuno-compromised status. Patients belonging to the PHES phenotype were characterized by greater degree of systemic inflammation and endothelial activation, and overlapped with high risk-strata based on PERSEVERE biomarkers predictive of death and persistent MODS. Conclusions: The PHES trajectory-based phenotype is reproducible, independently associated with poor clinical outcomes, and overlap with higher risk-strata based on validated biomarker approaches.

Risk assessment based on dose-responsive and time-responsive genes to build PLS-DA models for exogenously induced lung injury.

Xenobiotics can easily harm human lungs owing to the openness of the respiratory system. Identifying pulmonary toxicity remains challenging owing to several reasons: 1) no biomarkers for pulmonary toxicity are available that might help to detect lung injury; 2) traditional animal experiments are time-consuming; 3) traditional detection methods solely focus on poisoning accidents; 4) analytical chemistry methods hardly achieve universal detection. An in vitro testing system able to identify the pulmonary toxicity of contaminants from food, the environment, and drugs is urgently needed. Compounds are virtually infinite, whereas toxicological mechanisms are countable. Therefore, universal methods to identify and predict the risks of contaminants can be designed based on these well-known toxicity mechanisms. In this study, we established a dataset based on transcriptome sequencing of A549 cells upon treatment with different compounds. The representativeness of our dataset was analyzed using bioinformatics methods. Artificial intelligence methods, namely partial least squares discriminant analysis (PLS-DA) models, were employed for toxicity prediction and toxicant identification. The developed model predicted the pulmonary toxicity of compounds with a 92 % accuracy. These models were submitted to an external validation using highly heterogeneous compounds, which supported the accuracy and robustness of our developed methodology. This assay exhibits universal potential applications for water quality monitoring, crop pollution detection, food and drug safety evaluation, as well as chemical warfare agent detection.

Multimodal machine learning in precision health: A scoping review.

Machine learning is frequently being leveraged to tackle problems in the health sector including utilization for clinical decision-support. Its use has historically been focused on single modal data. Attempts to improve prediction and mimic the multimodal nature of clinical expert decision-making has been met in the biomedical field of machine learning by fusing disparate data. This review was conducted to summarize the current studies in this field and identify topics ripe for future research. We conducted this review in accordance with the PRISMA extension for Scoping Reviews to characterize multi-modal data fusion in health. Search strings were established and used in databases: PubMed, Google Scholar, and IEEEXplore from 2011 to 2021. A final set of 128 articles were included in the analysis. The most common health areas utilizing multi-modal methods were neurology and oncology. Early fusion was the most common data merging strategy. Notably, there was an improvement in predictive performance when using data fusion. Lacking from the papers were clear clinical deployment strategies, FDA-approval, and analysis of how using multimodal approaches from diverse sub-populations may improve biases and healthcare disparities. These findings provide a summary on multimodal data fusion as applied to health diagnosis/prognosis problems. Few papers compared the outputs of a multimodal approach with a unimodal prediction. However, those that did achieved an average increase of 6.4% in predictive accuracy. Multi-modal machine learning, while more robust in its estimations over unimodal methods, has drawbacks in its scalability and the time-consuming nature of information concatenation.

Assessment of respiratory drive with esophageal diaphragmatic electromyography in patients with acute respiratory distress syndrome treated with prone position ventilation.

BACKGROUND: Prone position ventilation (PPV) is an important strategy for patients with severe acute respiratory distress syndrome (ARDS). This prospective study investigated the use of electromyography of the diaphragm (EMGdi) for monitoring respiratory drive in patients with moderate to severe ARDS during long-term PPV. METHODS: An integrated nostril-gastric feeding tube containing an esophageal electrode and balloon was placed in 14 patients with severe ARDS prior to PPV. EMGdi and trans-pulmonary pressure (∆PL) data were collected before PPV (baseline), every 2 h during PPV, and 2 h after the restoration of supine position ventilation (post-2 h SPV). RESULTS: In ARDS patients, the static compliance of the chest wall was significantly decreased after PPV. EMGdi levels were slightly lower in the early, middle, and late stages of PPV compared with baseline. Patients who received neuromuscular blocker experienced a greater drop in EMGdi from baseline than those who did not. CONCLUSIONS: For ARDS patients, EMGdi was slightly decreased after prolonged PPV. This is contrary to the change in diaphragm electromyography during normal body position changes. Monitoring EMGdi regularly during PPV in ARDS patients is feasible and can be used as a reference for lung protective ventilation strategies.

Quick Sequential Organ Failure Assessment as a prognostic factor for infected patients outside the intensive care unit: a systematic review and meta-analysis.

Quick Sequential Organ Failure Assessment (qSOFA) was proposed to replace SIRS as a new screening tool for the identification of septic patients at high mortality. However, researches from infected patients outside of ICU especially in Emergency Department (ED) drew contradictory conclusions on the prognostic value of qSOFA. This systematic review evaluated qSOFA as a prognostic marker of infected patients outside of ICU. The primary outcome was hospital mortality or 28- or 30-day mortality. Data were pooled based on sensitivity and specificity. Twenty-four trials with 121,237 participants were included. qSOFA had a poor sensitivity (0.58 [95% CI 0.47-0.67], 0.54 [95% CI 0.43-0.65]) and moderate specificity (0.69 [95% CI 0.48-0.84], 0.77 [95% CI 0.66-0.86]) for prediction of mortality in patients outside of ICU and ED patients only. Studies that used in-hospital mortality showed a higher sensitivity (0.61 [95% CI 0.50-0.71] vs 0.32 [95% CI 0.15-0.49]) and lower specificity (0.70 [95% CI 0.59-0.82] vs 0.92 [95% CI 0.85-0.99]) than studies that used 28 or 30-day mortality. Studies with overall mortality < 10% showed higher specificity (0.89 [95% CI 0.82-0.95] vs 0.62 [95% CI 0.48-0.76]) than studies with overall mortality ≥ 10%. There is no difference in the accuracy of diagnosis of sepsis between positive qSOFA scores and SIRS criteria. qSOFA was poor sensitivity and moderate specificity in predicting mortality of infected patients outside of ICU especially in ED. Combining qSOFA and SIRS may be helpful in predicting mortality.

Predicting ICU readmission using grouped physiological and medication trends.

BACKGROUND: Patients who are readmitted to an intensive care unit (ICU) usually have a high risk of mortality and an increased length of stay. ICU readmission risk prediction may help physicians to re-evaluate the patient's physical conditions before patients are discharged and avoid preventable readmissions. ICU readmission prediction models are often built based on physiological variables. Intuitively, snapshot measurements, especially the last measurements, are effective predictors that are widely used by researchers. However, methods that only use snapshot measurements neglect predictive information contained in the trends of physiological and medication variables. Mean, maximum or minimum values take multiple time points into account and capture their summary statistics, however, these statistics are not able to catch the detailed picture of temporal trends. In this study, we find strong predictors with ability of capturing detailed temporal trends of variables for 30-day readmission risk and build prediction models with high accuracy. METHODS: We study physiological measurements and medications from the Multiparameter Intelligent Monitoring in Intensive Care II (MIMIC-II) clinical dataset. Time series of each variable are converted into trend graphs with nodes being discretized measurements of each variable. Then we extract important temporal trends by applying frequent subgraph mining on the trend graphs. The frequency of a subgraph is a good cue to find important temporal trends since similar patients often share similar trends regarding their pathophysiological evolution under medical interventions. Important temporal trends are then grouped automatically by non-negative matrix factorization. The grouped trends could be considered as an approximate representation of patients' pathophysiological states and medication profiles. We train a logistic regression model to predict 30-day ICU readmission risk based on snapshot measurements, grouped physiological trends and medication trends. RESULTS: Our dataset consists of 1170 patients who are alive 30 days after discharge from ICU and have at least 12 h of data. In the dataset, 860 patients were not readmitted and 310 were readmitted, within 30 days after discharge. Our model outperforms all comparison models, and shows an improvement in the area under the receiver operating characteristic curve (AUC) of almost 4% from the best comparison model. CONCLUSIONS: Grouped physiological and medication trends carry predictive information for ICU readmission risk. In order to build predictive models with higher accuracy, we should add grouped physiological and medication trends as complementary features to snapshot measurements.

Big Data and Data Science in Critical Care.

The digitalization of the health-care system has resulted in a deluge of clinical big data and has prompted the rapid growth of data science in medicine. Data science, which is the field of study dedicated to the principled extraction of knowledge from complex data, is particularly relevant in the critical care setting. The availability of large amounts of data in the ICU, the need for better evidence-based care, and the complexity of critical illness makes the use of data science techniques and data-driven research particularly appealing to intensivists. Despite the increasing number of studies and publications in the field, thus far there have been few examples of data science projects that have resulted in successful implementations of data-driven systems in the ICU. However, given the expected growth in the field, intensivists should be familiar with the opportunities and challenges of big data and data science. The present article reviews the definitions, types of algorithms, applications, challenges, and future of big data and data science in critical care.

Latent classes of resilience and psychological response among only-child loss parents in China.

Only-child loss parents in China recently gained extensive attention as a newly defined social group. Resilience could be a probable solution out of the psychological dilemma. Using a sample of 185 only-child loss people, this study employed latent class analysis (a) to explore whether different classes of resilience could be identified, (b) to determine socio-demographic characteristics of each class, and (c) to compare the depression and the subjective well-being of each class. The results supported a three-class solution, defined as 'high tenacity-strength but moderate optimism class', 'moderate resilience but low self-efficacy class' and 'low tenacity but moderate adaption-dependence class'. Parents with low income and medical insurance of low reimbursement type and without endowment insurance occupied more proportions in the latter two classes. The latter two classes also had a significant higher depression scores and lower subjective well-being scores than high tenacity-strength but moderate optimism class. Future work should care those socio-economically vulnerable bereaved parents, and an elastic economic assistance policy was needed. To develop targeted resilience interventions, the emphasis of high tenacity-strength but moderate optimism class should be the optimism. Moderate resilience but low self-efficacy class should be self-efficacy, and low tenacity but moderate adaption-dependence class should be tenacity.

Health impact of the 2008 cold spell on mortality in subtropical China: the climate and health impact national assessment study (CHINAs).

BACKGROUND: Many studies have investigated heat wave related mortality, but less attention has been given to the health effects of cold spells in the context of global warming. The 2008 cold spell in China provided a unique opportunity to estimate the effects of the 2008 cold spell on mortality in subtropical regions, spatial heterogeneity of the effects, stratification effect and added effects caused by sustained cold days. METHODS: Thirty-six study communities were selected from 15 provinces in subtropical China. Daily mortality and meteorological data were collected for each community from 2006 to 2010. A distributed lag linear non-linear model (DLNM) with a lag structure of up to 27 days was used to analyze the association between the 2008 cold spell and mortality. Multivariate meta-analyses were used to combine the cold effects across each community. RESULTS: The 2008 cold spell increased mortality by 43.8% (95% CI: 34.8% ~ 53.4%) compared to non-cold spell days with the highest effects in southern and central China. The effects were more pronounced for respiratory mortality (RESP) than for cardiovascular (CVD) or cerebrovascular mortality (CBD), for females more than for males, and for the elderly aged ≥75 years old more than for younger people. Overall, 148,279 excess deaths were attributable to the 2008 cold spell. The cold effect was mainly from extreme low temperatures rather than sustained cold days during this 2008 cold spell. CONCLUSIONS: The 2008 cold spell increased mortality in subtropical China, which was mainly attributable to the low temperature rather than the sustained duration of the cold spell. The cold effects were spatially heterogeneous and modified by individual-specific characteristics such as gender and age.

[Analysis of the impact of the socio-economic factors on temperature-mortality association in southern China].

OBJECTIVE: To explore the impact of the socio-economic factors on the temperature-mortality association in different cities in southern China. METHODS: Daily mortality registration data, meteorological data and air pollution data of the cities as Changsha and Kunming during 2006-2009, and cities as Guangzhou and Zhuhai during 2006-2010, were collected to explore modifying effects, stratified by age, gender, education and place of death, of socio-economic factors on the association between temperature and mortality, by distributed lag non-linear model. The accumulative effect of temperature-mortality were separately analyzed in each city, under the high temperature (0-3 days) and low temperature (0-20 days) situation. The association between temperature and mortality was evaluated by general linear threshold model. The above process was firstly adopted to analyze the impact in single city and then Meta analysis was applied to analyze the impact in several cities by effect-combine. RESULTS: The relationship between temperature and mortality in the four cities showed nonlinearity. The minimum mortality risk was separately 23.5 °C, 20.5 °C, 25.0 °C and 26.0 °C in Changsha, Kunming, Guangzhou and Zhuhai. The results of effect-combine showed that low-temperature (RR = 1.67, 95%CI:1.54-1.80) has a higher gross effect than high-temperature (RR = 1.11, 95%CI:1.01-1.18) on population. With the age increasing, risk of death increased both under high and low temperature situation, and the effect of low temperature was greater (RR = 1.83, 95%CI:1.65-2.04) for the elderly than it of high temperature (RR = 1.17, 95%CI:1.03-1.33). The mortality risk among females (cold and hot effects(95%CI) were 1.75(1.57-1.97) and 1.11(0.99-1.25), respectively)was higher than it among males (cold and hot effects(95%CI) were 1.59(1.45-1.77) and 1.11(1.03-1.19), respectively). Whereas the mortality risk on higher education population was significantly higher (cold and hot effects (95%CI) were 1.89(1.48-2.45)and 1.34(1.19-1.48), respectively) than it on other educated people. CONCLUSION: Age, gender, educational level and place of death showed modifying effects on the association between temperature and mortality. The elderly, women and highly educated people were vulnerable to the temperature influence on mortality.

[Assessment of neural respiratory drive in humans].

OBJECTIVE: Assessment of neural respiratory drive is useful for diagnosis of dyspnea and respiratory failure with unknown causes. The purpose of the study was to compare the sensitivity of trandiaphragmatic pressure (Pdi) and diaphragm electromyogram (EMGdi) in assessment of neural respiratory drive. METHODS: A combined catheter with 10 electrodes and 2 balloons was used to record EMGdi and Pdi during CO2 rebreathing. Three different inspiratory maneuvers-inspiration from functional residual capacity to total lung capacity (TLC), deep inspiration from functional residual capacity against closed airway (MIP), and short sharp inspiration through the nose (Sniff) were performed. Ten healthy subjects [male 4 and female 6; age (26 ± 4) years] were studied. RESULTS: Linear relationship between EMGdi and end-tidal CO2 (r = 0.83-0.98, all P < 0.01) was better than that between Pdi and end-tidal CO2 (r = 0.48-0.96, all P < 0.01) during CO2 rebreathing, Z = -2.731, P < 0.05. The slope of linear relation between EMGdi and end-tidal CO2 (16.3-32.5) was significantly higher than that between Pdi and end-tidal CO2 (0.4-11.1), Z = -3.780, P < 0.01. The maximal EMGdi derived from TLC maneuver (211 ± 48) µV was larger than those from the MIP maneuver (161 ± 48) µV and the Sniff maneuver (145 ± 37) µV, F = 5.931, P < 0.05, whereas the maximal Pdi derived from TLC maneuver (58 ± 27) cm H2O (1 cm H2O = 0.098 kPa) was significantly lower than those from the MIP maneuver (92 ± 32) cm H2O and the Sniff maneuver (95 ± 27) cm H2O, F = 5.155, P < 0.05. CONCLUSION: EMGdi is more sensitive than Pdi in the assessment of neural respiratory drive.

Large daily stock variation is associated with cardiovascular mortality in two cities of Guangdong, China.

OBJECTIVE: The current study aimed to examine the effects of daily change of the Shenzhen Stock Exchange Index on cardiovascular mortality in Guangzhou and Taishan, China. METHODS: Daily mortality and stock performance data during 2006-2010 were collected to construct the time series for the two cities. A distributed lag non-linear model was utilized to examine the effect of daily stock index changes on cardiovascular mortality after controlling for potential confounding factors. RESULTS: We observed a delayed non-linear effect of the stock index change on cardiovascular mortality: both rising and declining of the stock index were associated with increased cardiovascular deaths. In Guangzhou, the 15-25 lag days cumulative relative risk of an 800 index drop was 2.08 (95% CI: 1.38-3.14), and 2.38 (95% CI: 1.31-4.31) for an 800 stock index increase on the cardiovascular mortality, respectively. In Taishan, the cumulative relative risk over 15-25 days lag was 1.65 (95% CI: 1.13-2.42) for an 800 index drop and 2.08 (95% CI: 1.26-3.42) for an 800 index rising, respectively. CONCLUSIONS: Large ups and downs in daily stock index might be important predictor of cardiovascular mortality.

[The assessment of vulnerability to floods in Guangdong province at district level].

OBJECTIVE: To evaluate the vulnerability to floods in Guangdong province at district level. METHODS: Data were collected from the sixth census, the 2010 Statistical Yearbook of Guangdong, the 2010 Health Statistics Yearbook of Guangdong and China Disease Prevention and Control information systems, etc. The weight of each indicator was determined based on subjective method and objective method respectively; and finally the results of the two methods were compared. RESULTS: 13 indicators were selected for the assessment of vulnerability to floods, including 6 sensitivity indicators, 5 adaptability indicators and 2 exposure indicators. Indicators with large weight (subjective weight/objective weight) were the proportion of population older than 65 years old (0.31/0.30), the proportion of population older than 65 years old (0.16/0.23), infant mortality rate (0.18/0.20), the total Gross Domestic Product (GDP) per capita (0.33/0.21), the proportion of illiterate in the population older than 15 years old (0.19/0.28), history frequency of floods (0.75/0.75). The mean vulnerability index (VI) calculated by subjective method was 0.35 with the standard deviation of 0.10; the mean vulnerability index calculated by objective method was 0.31 with the standard deviation of 0.08. The two weighting methods showed consistent results of vulnerability index (ICC = 0.975, P < 0.01). VI of most districts dropped in the interval of 0.30 - 0.39. Districts with subjective VI > 0.50 or objective VI > 0.40 should pay more attention to floods, including parts of the coastal areas, Beijiang River Basin, the eastern tributary area of Dongjiang River and the northern part of Pearl River Delta. Dapu district of Meizhou (0.55/0.45), Dianbai district and Maogang district of Maoming (0.54/0.48) were most vulnerable. Districts of Heyuan, Dongguan, Zhaoqing and Huizhou were less vulnerable, Yuancheng district of Heyuan showed least vulnerable to floods (0.15/0.12) followed by Dongguan (0.18/0.16), Duanzhou district (0.18/0.16) and Guangning (0.17/0.15) district of Zhaoqing. The score of indicators differed among different level of vulnerability (P < 0.05). CONCLUSION: Different regions of Guangdong province showed different vulnerability to floods, vulnerable areas should be priority in the prevention and control of floods.

A wireless blood pressure monitoring system for personal health management.

In this paper, we developed a wireless blood pressure monitoring system which provides a useful tool for users to measure and manage their daily blood pressure values. This system includes an ARM-based blood pressure monitor with a ZigBee wireless transmission module and a PC-based management unit with graphic user interface and database. The wireless blood pressure monitor can measure the blood pressure and heart rate and then store and forward the measuring information to the management unit through the ZigBee wireless transmission. On the management unit, user can easy to see their blood pressure variation in the past using a line chart. Accuracy of blood pressure measurement has been verified by a commercial blood pressure simulator and shown the bias of systolic blood pressure is ≤ 1 mmHg and the bias of diastolic blood pressure is ≤ 1.4 mmHg.

Seasonal influenza vaccine supply and target vaccinated population in China, 2004-2009.

To better understand the gap between limited influenza vaccine supply and the target population for vaccination in China, we conducted a retrospective survey to quantify the production capacity, supply and sale of seasonal trivalent inactive vaccine (TIV) from the 2004-2005 through the 2008-2009 season, and estimated the target population who should receive annual influenza vaccine. The maximum domestic capacity to produce TIV was 126 million doses in 2009. A total of 32.5 million doses of TIV were supplied in 2008-2009, with an average annual increase rate of 18% from 16.9 million in 2004-2005. This represents an amount sufficient to vaccinate 1.9% of Chinese population. The average number of doses of TIV for sale by province ranged from <5 to 108 per 1000 people. The differences are explained in part by level of economic development but also influenced by local reimbursement policies in some provinces. Based on national recommendations, we estimated a target population of 570.6 million or 43% of the total population. Supply and domestic production capacity for influenza vaccine is currently insufficient to vaccinate the estimated target population in China. The Government of China should consider measures to improve domestic production capacity of influenza vaccine, expand successful promotional campaigns, and add cost subsidies in high risk groups to further encourage influenza vaccine usage.

[Comprehensive assessment on control measures of Ambrosia artemisiifolia L].

Three control measures of Ambrosia artemisiifolia, including biological control, chemical control, and CK without any treatment, were evaluated by analytic hierarchy process (AHP). Corresponding contributions of the three control measures to comprehensive profit (CP) and comprehensive cost (CC) were calculated and ranked, which were regarded as the assessment criteria of the control measures. The results showed that among the three control measures, biological control had the highest CP and the lowest CC, CK was in adverse; and chemical control was in intervenient. Biological control had the highest ratio of profit to cost, and suggesting that this control measure is an optimal and recommendable measure in controlling A. artemisiifolia.

Evaluating the state-of-the-art in automatic de-identification.

To facilitate and survey studies in automatic de-identification, as a part of the i2b2 (Informatics for Integrating Biology to the Bedside) project, authors organized a Natural Language Processing (NLP) challenge on automatically removing private health information (PHI) from medical discharge records. This manuscript provides an overview of this de-identification challenge, describes the data and the annotation process, explains the evaluation metrics, discusses the nature of the systems that addressed the challenge, analyzes the results of received system runs, and identifies directions for future research. The de-indentification challenge data consisted of discharge summaries drawn from the Partners Healthcare system. Authors prepared this data for the challenge by replacing authentic PHI with synthesized surrogates. To focus the challenge on non-dictionary-based de-identification methods, the data was enriched with out-of-vocabulary PHI surrogates, i.e., made up names. The data also included some PHI surrogates that were ambiguous with medical non-PHI terms. A total of seven teams participated in the challenge. Each team submitted up to three system runs, for a total of sixteen submissions. The authors used precision, recall, and F-measure to evaluate the submitted system runs based on their token-level and instance-level performance on the ground truth. The systems with the best performance scored above 98% in F-measure for all categories of PHI. Most out-of-vocabulary PHI could be identified accurately. However, identifying ambiguous PHI proved challenging. The performance of systems on the test data set is encouraging. Future evaluations of these systems will involve larger data sets from more heterogeneous sources.