Causal fairness assessment of treatment allocation with electronic health records.

OBJECTIVE: Healthcare continues to grapple with the persistent issue of treatment disparities, sparking concerns regarding the equitable allocation of treatments in clinical practice. While various fairness metrics have emerged to assess fairness in decision-making processes, a growing focus has been on causality-based fairness concepts due to their capacity to mitigate confounding effects and reason about bias. However, the application of causal fairness notions in evaluating the fairness of clinical decision-making with electronic health record (EHR) data remains an understudied domain. This study aims to address the methodological gap in assessing causal fairness of treatment allocation with electronic health records data. In addition, we investigate the impact of social determinants of health on the assessment of causal fairness of treatment allocation. METHODS: We propose a causal fairness algorithm to assess fairness in clinical decision-making. Our algorithm accounts for the heterogeneity of patient populations and identifies potential unfairness in treatment allocation by conditioning on patients who have the same likelihood to benefit from the treatment. We apply this framework to a patient cohort with coronary artery disease derived from an EHR database to evaluate the fairness of treatment decisions. RESULTS: Our analysis reveals notable disparities in coronary artery bypass grafting (CABG) allocation among different patient groups. Women were found to be 4.4%-7.7% less likely to receive CABG than men in two out of four treatment response strata. Similarly, Black or African American patients were 5.4%-8.7% less likely to receive CABG than others in three out of four response strata. These results were similar when social determinants of health (insurance and area deprivation index) were dropped from the algorithm. These findings highlight the presence of disparities in treatment allocation among similar patients, suggesting potential unfairness in the clinical decision-making process. CONCLUSION: This study introduces a novel approach for assessing the fairness of treatment allocation in healthcare. By incorporating responses to treatment into fairness framework, our method explores the potential of quantifying fairness from a causal perspective using EHR data. Our research advances the methodological development of fairness assessment in healthcare and highlight the importance of causality in determining treatment fairness.

Assessing Covariate Balance with Small Sample Sizes.

Propensity score adjustment addresses confounding by balancing covariates in subject treatment groups through matching, stratification, inverse probability weighting, etc. Diagnostics ensure that the adjustment has been effective. A common technique is to check whether the standardized mean difference for each relevant covariate is less than a threshold like 0.1. For small sample sizes, the probability of falsely rejecting the validity of a study because of chance imbalance when no underlying balance exists approaches 1. We propose an alternative diagnostic that checks whether the standardized mean difference statistically significantly exceeds the threshold. Through simulation and real-world data, we find that this diagnostic achieves a better trade-off of type 1 error rate and power than standard nominal threshold tests and not testing for sample sizes from 250 to 4000 and for 20 to 100,000 covariates. In network studies, meta-analysis of effect estimates must be accompanied by meta-analysis of the diagnostics or else systematic confounding may overwhelm the estimated effect. Our procedure for statistically testing balance at both the database level and the meta-analysis level achieves the best balance of type-1 error rate and power. Our procedure supports the review of large numbers of covariates, enabling more rigorous diagnostics.

Similar Risk of Kidney Failure among Patients with Blinding Diseases Who Receive Ranibizumab, Aflibercept, and Bevacizumab: An Observational Health Data Sciences and Informatics Network Study.

PURPOSE: To characterize the incidence of kidney failure associated with intravitreal anti-VEGF exposure; and compare the risk of kidney failure in patients treated with ranibizumab, aflibercept, or bevacizumab. DESIGN: Retrospective cohort study across 12 databases in the Observational Health Data Sciences and Informatics (OHDSI) network. SUBJECTS: Subjects aged ≥ 18 years with ≥ 3 monthly intravitreal anti-VEGF medications for a blinding disease (diabetic retinopathy, diabetic macular edema, exudative age-related macular degeneration, or retinal vein occlusion). METHODS: The standardized incidence proportions and rates of kidney failure while on treatment with anti-VEGF were calculated. For each comparison (e.g., aflibercept versus ranibizumab), patients from each group were matched 1:1 using propensity scores. Cox proportional hazards models were used to estimate the risk of kidney failure while on treatment. A random effects meta-analysis was performed to combine each database's hazard ratio (HR) estimate into a single network-wide estimate. MAIN OUTCOME MEASURES: Incidence of kidney failure while on anti-VEGF treatment, and time from cohort entry to kidney failure. RESULTS: Of the 6.1 million patients with blinding diseases, 37 189 who received ranibizumab, 39 447 aflibercept, and 163 611 bevacizumab were included; the total treatment exposure time was 161 724 person-years. The average standardized incidence proportion of kidney failure was 678 per 100 000 persons (range, 0-2389), and incidence rate 742 per 100 000 person-years (range, 0-2661). The meta-analysis HR of kidney failure comparing aflibercept with ranibizumab was 1.01 (95% confidence interval [CI], 0.70-1.47; P = 0.45), ranibizumab with bevacizumab 0.95 (95% CI, 0.68-1.32; P = 0.62), and aflibercept with bevacizumab 0.95 (95% CI, 0.65-1.39; P = 0.60). CONCLUSIONS: There was no substantially different relative risk of kidney failure between those who received ranibizumab, bevacizumab, or aflibercept. Practicing ophthalmologists and nephrologists should be aware of the risk of kidney failure among patients receiving intravitreal anti-VEGF medications and that there is little empirical evidence to preferentially choose among the specific intravitreal anti-VEGF agents. FINANCIAL DISCLOSURES: Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.

A novel discrete linkage-type electrode for radiofrequency-induced intestinal anastomosis.

INTRODUCTION: For decades, radiofrequency (RF)-induced tissue fusion has garnered great attention due to its potential to replace sutures and staples for anastomosis of tissue reconstruction. However, the complexities of achieving high bonding strength and reducing excessive thermal damage present substantial limitations of existing fusion devices. MATERIALS AND METHODS: This study proposed a discrete linkage-type electrode to carry out ex vivo RF-induced intestinal anastomosis experiments. The anastomotic strength was examined by burst pressure and shear strength test. The degree of thermal damage was monitored through an infrared thermal imager. And the anastomotic stoma fused by the electrode was further investigated through histopathological and ultrastructural observation. RESULTS: The burst pressure and shear strength of anastomotic tissue can reach 62.2 ± 3.08 mmHg and 8.73 ± 1.11N, respectively, when the pressure, power and duration are 995 kPa, 160 W and 13 s, and the thermal damage can be controlled within limits. Histopathological and ultrastructural observation indicate that an intact and fully fused stomas with collagenic crosslink can be formed. CONCLUSION: The discrete linkage-type electrode presents favorable efficiency and security in RF-induced tissue fusion, and these results are informative to the design of electrosurgical medical devices with controllable pressure and energy delivery.

Improved accuracy and efficiency of primary care fall risk screening of older adults using a machine learning approach.

BACKGROUND: While many falls are preventable, they remain a leading cause of injury and death in older adults. Primary care clinics largely rely on screening questionnaires to identify people at risk of falls. Limitations of standard fall risk screening questionnaires include suboptimal accuracy, missing data, and non-standard formats, which hinder early identification of risk and prevention of fall injury. We used machine learning methods to develop and evaluate electronic health record (EHR)-based tools to identify older adults at risk of fall-related injuries in a primary care population and compared this approach to standard fall screening questionnaires. METHODS: Using patient-level clinical data from an integrated healthcare system consisting of 16-member institutions, we conducted a case-control study to develop and evaluate prediction models for fall-related injuries in older adults. Questionnaire-derived prediction with three questions from a commonly used fall risk screening tool was evaluated. We then developed four temporal machine learning models using routinely available longitudinal EHR data to predict the future risk of fall injury. We also developed a fall injury-prevention clinical decision support (CDS) implementation prototype to link preventative interventions to patient-specific fall injury risk factors. RESULTS: Questionnaire-based risk screening achieved area under the receiver operating characteristic curve (AUC) up to 0.59 with 23% to 33% similarity for each pair of three fall injury screening questions. EHR-based machine learning risk screening showed significantly improved performance (best AUROC = 0.76), with similar prediction performance between 6-month and one-year prediction models. CONCLUSIONS: The current method of questionnaire-based fall risk screening of older adults is suboptimal with redundant items, inadequate precision, and no linkage to prevention. A machine learning fall injury prediction method can accurately predict risk with superior sensitivity while freeing up clinical time for initiating personalized fall prevention interventions. The developed algorithm and data science pipeline can impact routine primary care fall prevention practice.

Evaluating and Improving the Performance and Racial Fairness of Algorithms for GFR Estimation.

Data-driven clinical prediction algorithms are used widely by clinicians. Understanding what factors can impact the performance and fairness of data-driven algorithms is an important step towards achieving equitable healthcare. To investigate the impact of modeling choices on the algorithmic performance and fairness, we make use of a case study to build a prediction algorithm for estimating glomerular filtration rate (GFR) based on the patient's electronic health record (EHR). We compare three distinct approaches for estimating GFR: CKD-EPI equations, epidemiological models, and EHR-based models. For epidemiological models and EHR-based models, four machine learning models of varying computational complexity (i.e., linear regression, support vector machine, random forest regression, and neural network) were compared. Performance metrics included root mean squared error (RMSE), median difference, and the proportion of GFR estimates within 30% of the measured GFR value (P30). Differential performance between non-African American and African American group was used to assess algorithmic fairness with respect to race. Our study showed that the variable race had a negligible effect on error, accuracy, and differential performance. Furthermore, including more relevant clinical features (e.g., common comorbidities of chronic kidney disease) and using more complex machine learning models, namely random forest regression, significantly lowered the estimation error of GFR. However, the difference in performance between African American and non-African American patients did not decrease, where the estimation error for African American patients remained consistently higher than non-African American patients, indicating that more objective patient characteristics should be discovered and included to improve algorithm performance.

Reproducible variability: assessing investigator discordance across 9 research teams attempting to reproduce the same observational study.

OBJECTIVE: Observational studies can impact patient care but must be robust and reproducible. Nonreproducibility is primarily caused by unclear reporting of design choices and analytic procedures. This study aimed to: (1) assess how the study logic described in an observational study could be interpreted by independent researchers and (2) quantify the impact of interpretations' variability on patient characteristics. MATERIALS AND METHODS: Nine teams of highly qualified researchers reproduced a cohort from a study by Albogami et al. The teams were provided the clinical codes and access to the tools to create cohort definitions such that the only variable part was their logic choices. We executed teams' cohort definitions against the database and compared the number of subjects, patient overlap, and patient characteristics. RESULTS: On average, the teams' interpretations fully aligned with the master implementation in 4 out of 10 inclusion criteria with at least 4 deviations per team. Cohorts' size varied from one-third of the master cohort size to 10 times the cohort size (2159-63 619 subjects compared to 6196 subjects). Median agreement was 9.4% (interquartile range 15.3-16.2%). The teams' cohorts significantly differed from the master implementation by at least 2 baseline characteristics, and most of the teams differed by at least 5. CONCLUSIONS: Independent research teams attempting to reproduce the study based on its free-text description alone produce different implementations that vary in the population size and composition. Sharing analytical code supported by a common data model and open-source tools allows reproducing a study unambiguously thereby preserving initial design choices.

Data assimilation on mechanistic models of glucose metabolism predicts glycemic states in adolescents following bariatric surgery.

Type 2 diabetes mellitus is a complex and under-treated disorder closely intertwined with obesity. Adolescents with severe obesity and type 2 diabetes have a more aggressive disease compared to adults, with a rapid decline in pancreatic ß cell function and increased incidence of comorbidities. Given the relative paucity of pharmacotherapies, bariatric surgery has become increasingly used as a therapeutic option. However, subsets of this population have sub-optimal outcomes with either inadequate weight loss or little improvement in disease. Predicting which patients will benefit from surgery is a difficult task and detailed physiological characteristics of patients who do not respond to treatment are generally unknown. Identifying physiological predictors of surgical response therefore has the potential to reveal both novel phenotypes of disease as well as therapeutic targets. We leverage data assimilation paired with mechanistic models of glucose metabolism to estimate pre-operative physiological states of bariatric surgery patients, thereby identifying latent phenotypes of impaired glucose metabolism. Specifically, maximal insulin secretion capacity, σ, and insulin sensitivity, SI, differentiate aberrations in glucose metabolism underlying an individual's disease. Using multivariable logistic regression, we combine clinical data with data assimilation to predict post-operative glycemic outcomes at 12 months. Models using data assimilation sans insulin had comparable performance to models using oral glucose tolerance test glucose and insulin. Our best performing models used data assimilation and had an area under the receiver operating characteristic curve of 0.77 (95% confidence interval 0.7665, 0.7734) and mean average precision of 0.6258 (0.6206, 0.6311). We show that data assimilation extracts knowledge from mechanistic models of glucose metabolism to infer future glycemic states from limited clinical data. This method can provide a pathway to predict long-term, post-surgical glycemic states by estimating the contributions of insulin resistance and limitations of insulin secretion to pre-operative glucose metabolism.

Sphingosine-1-Phosphate Alleviates Irradiation Induced Salivary Gland Hypofunction through Preserving Endothelial Cells and Resident Macrophages.

Radiotherapy for head-and-neck cancers frequently causes long-term hypofunction of salivary glands that severely compromises quality of life and is difficult to treat. Here, we studied effects and mechanisms of Sphingosine-1-phosphate (S1P), a versatile signaling sphingolipid, in preventing irreversible dry mouth caused by radiotherapy. Mouse submandibular glands (SMGs) were irradiated with or without intra-SMG S1P pretreatment. The saliva flow rate was measured following pilocarpine stimulation. The expression of genes related to S1P signaling and radiation damage was examined by flow cytometry, immunohistochemistry, quantitative RT-PCR, Western blotting, and/or single-cell RNA-sequencing. S1P pretreatment ameliorated irradiation-induced salivary dysfunction in mice through a decrease in irradiation-induced oxidative stress and consequent apoptosis and cellular senescence, which is related to the enhancement of Nrf2-regulated anti-oxidative response. In mouse SMGs, endothelial cells and resident macrophages are the major cells capable of producing S1P and expressing the pro-regenerative S1P receptor S1pr1. Both mouse SMGs and human endothelial cells are protected from irradiation damage by S1P pretreatment, likely through the S1pr1/Akt/eNOS axis. Moreover, intra-SMG-injected S1P did not affect the growth and radiosensitivity of head-and-neck cancer in a mouse model. These data indicate that S1P signaling pathway is a promising target for alleviating irradiation-induced salivary gland hypofunction.

Adjusting for indirectly measured confounding using large-scale propensity score.

Confounding remains one of the major challenges to causal inference with observational data. This problem is paramount in medicine, where we would like to answer causal questions from large observational datasets like electronic health records (EHRs) and administrative claims. Modern medical data typically contain tens of thousands of covariates. Such a large set carries hope that many of the confounders are directly measured, and further hope that others are indirectly measured through their correlation with measured covariates. How can we exploit these large sets of covariates for causal inference? To help answer this question, this paper examines the performance of the large-scale propensity score (LSPS) approach on causal analysis of medical data. We demonstrate that LSPS may adjust for indirectly measured confounders by including tens of thousands of covariates that may be correlated with them. We present conditions under which LSPS removes bias due to indirectly measured confounders, and we show that LSPS may avoid bias when inadvertently adjusting for variables (like colliders) that otherwise can induce bias. We demonstrate the performance of LSPS with both simulated medical data and real medical data.

Predicting hospitalization of COVID-19 positive patients using clinician-guided machine learning methods.

OBJECTIVES: The coronavirus disease 2019 (COVID-19) is a resource-intensive global pandemic. It is important for healthcare systems to identify high-risk COVID-19-positive patients who need timely health care. This study was conducted to predict the hospitalization of older adults who have tested positive for COVID-19. METHODS: We screened all patients with COVID test records from 11 Mass General Brigham hospitals to identify the study population. A total of 1495 patients with age 65 and above from the outpatient setting were included in the final cohort, among which 459 patients were hospitalized. We conducted a clinician-guided, 3-stage feature selection, and phenotyping process using iterative combinations of literature review, clinician expert opinion, and electronic healthcare record data exploration. A list of 44 features, including temporal features, was generated from this process and used for model training. Four machine learning prediction models were developed, including regularized logistic regression, support vector machine, random forest, and neural network. RESULTS: All 4 models achieved area under the receiver operating characteristic curve (AUC) greater than 0.80. Random forest achieved the best predictive performance (AUC = 0.83). Albumin, an index for nutritional status, was found to have the strongest association with hospitalization among COVID positive older adults. CONCLUSIONS: In this study, we developed 4 machine learning models for predicting general hospitalization among COVID positive older adults. We identified important clinical factors associated with hospitalization and observed temporal patterns in our study cohort. Our modeling pipeline and algorithm could potentially be used to facilitate more accurate and efficient decision support for triaging COVID positive patients.

In situ diagnosis of mature HLB-asymptomatic citrus fruits by laser-induced breakdown spectroscopy.

Laser-induced breakdown spectroscopy (LIBS) is a promising alternative to conventional methods in classifying citrus huanglongbing (HLB). Mature citrus fruits with similar features were picked and divided into healthy and HLB-asymptomatic groups. LIBS spectra and images were collected by focusing a laser on fresh fruit surfaces without sample preparation. The pH value and soluble solids content of juice as the indicators of acidity and sugar were detected, and the content of Ca, Zn, and K in peel and pulp was analyzed. The characteristic lines from LIBS spectra were extracted by continuous wavelet transform and principal component analysis (PCA). The t-test of these indicators displayed significant difference between the two groups. Fisher discriminant analysis and multilayer perception neural network (MLP) were applied to identify the disease. The classification accuracy reached 100% by PCA-MLP. The results show that LIBS can realize in situ detection of citrus HLB fruits.

Predicting pressure injury using nursing assessment phenotypes and machine learning methods.

OBJECTIVE: Pressure injuries are common and serious complications for hospitalized patients. The pressure injury rate is an important patient safety metric and an indicator of the quality of nursing care. Timely and accurate prediction of pressure injury risk can significantly facilitate early prevention and treatment and avoid adverse outcomes. While many pressure injury risk assessment tools exist, most were developed before there was access to large clinical datasets and advanced statistical methods, limiting their accuracy. In this paper, we describe the development of machine learning-based predictive models, using phenotypes derived from nurse-entered direct patient assessment data. METHODS: We utilized rich electronic health record data, including full assessment records entered by nurses, from 5 different hospitals affiliated with a large integrated healthcare organization to develop machine learning-based prediction models for pressure injury. Five-fold cross-validation was conducted to evaluate model performance. RESULTS: Two pressure injury phenotypes were defined for model development: nonhospital acquired pressure injury (N = 4398) and hospital acquired pressure injury (N = 1767), representing 2 distinct clinical scenarios. A total of 28 clinical features were extracted and multiple machine learning predictive models were developed for both pressure injury phenotypes. The random forest model performed best and achieved an AUC of 0.92 and 0.94 in 2 test sets, respectively. The Glasgow coma scale, a nurse-entered level of consciousness measurement, was the most important feature for both groups. CONCLUSIONS: This model accurately predicts pressure injury development and, if validated externally, may be helpful in widespread pressure injury prevention.

A scoping review of clinical decision support tools that generate new knowledge to support decision making in real time.

OBJECTIVE: A growing body of observational data enabled its secondary use to facilitate clinical care for complex cases not covered by the existing evidence. We conducted a scoping review to characterize clinical decision support systems (CDSSs) that generate new knowledge to provide guidance for such cases in real time. MATERIALS AND METHODS: PubMed, Embase, ProQuest, and IEEE Xplore were searched up to May 2020. The abstracts were screened by 2 reviewers. Full texts of the relevant articles were reviewed by the first author and approved by the second reviewer, accompanied by the screening of articles' references. The details of design, implementation and evaluation of included CDSSs were extracted. RESULTS: Our search returned 3427 articles, 53 of which describing 25 CDSSs were selected. We identified 8 expert-based and 17 data-driven tools. Sixteen (64%) tools were developed in the United States, with the others mostly in Europe. Most of the tools (n = 16, 64%) were implemented in 1 site, with only 5 being actively used in clinical practice. Patient or quality outcomes were assessed for 3 (18%) CDSSs, 4 (16%) underwent user acceptance or usage testing and 7 (28%) functional testing. CONCLUSIONS: We found a number of CDSSs that generate new knowledge, although only 1 addressed confounding and bias. Overall, the tools lacked demonstration of their utility. Improvement in clinical and quality outcomes were shown only for a few CDSSs, while the benefits of the others remain unclear. This review suggests a need for a further testing of such CDSSs and, if appropriate, their dissemination.

Transient Activation of the Hedgehog-Gli Pathway Rescues Radiotherapy-Induced Dry Mouth via Recovering Salivary Gland Resident Macrophages.

Irreversible hypofunction of salivary glands is a common side effect of radiotherapy for head and neck cancer and is difficult to remedy. Recent studies indicate that transient activation of Hedgehog signaling rescues irradiation-impaired salivary function in animal models, but the underlying mechanisms are largely unclear. Here, we show in mice that activation of canonical Gli-dependent Hedgehog signaling by Gli1 gene transfer is sufficient to recover salivary function impaired by irradiation. Salivary gland cells responsive to Hedgehog/Gli signaling comprised small subsets of macrophages, epithelial cells, and endothelial cells, and their progeny remained relatively rare long after irradiation and transient Hedgehog activation. Quantities and activities of salivary gland resident macrophages were substantially and rapidly impaired by irradiation and restored by Hedgehog activation. Conversely, depletion of salivary gland macrophages by clodronate liposomes compromised the restoration of irradiation-impaired salivary function by transient Hedgehog activation. Single-cell RNA sequencing and qRT-PCR of sorted cells indicated that Hedgehog activation greatly enhances paracrine interactions between salivary gland resident macrophages, epithelial progenitors, and endothelial cells through Csf1, Hgf, and C1q signaling pathways. Consistently, expression of these paracrine factors and their receptors in salivary glands decreased following irradiation but were restored by transient Hedgehog activation. These findings reveal that resident macrophages and their prorepair paracrine factors are essential for the rescue of irradiation-impaired salivary function by transient Hedgehog activation and are promising therapeutic targets of radiotherapy-induced irreversible dry mouth. SIGNIFICANCE: These findings illuminate a novel direction for developing effective treatment of irreversible dry mouth, which is common after radiotherapy for head and neck cancer and for which no effective treatments are available. GRAPHICAL ABSTRACT: http://cancerres.aacrjournals.org/content/canres/80/24/5531/F1.large.jpg.See related commentary by Coppes, p. 5462.

Characterizing physicians' information needs related to a gap in knowledge unmet by current evidence.

OBJECTIVE: The study sought to explore information needs arising from a gap in clinicians' knowledge that is not met by current evidence and identify possible areas of use and target groups for a future clinical decision support system (CDSS), which will guide clinicians in cases where no evidence exists. MATERIALS AND METHODS: We interviewed 30 physicians in a large academic medical center, analyzed transcripts using deductive thematic analysis, and developed a set of themes of information needs related to a gap in knowledge unmet by current evidence. We conducted additional statistical analyses to identify the correlation between clinical experience, clinical specialty, settings of clinical care, and the characteristics of the needs. RESULTS: This study resulted in a set of themes and subthemes of information needs arising from a gap in current evidence. Experienced physicians and inpatient physicians had more questions and the number of questions did not decline with clinical experience. The main areas of information needs included patients with comorbidities, elderly and children, new drugs, and rare disorders. To address these questions, clinicians most often used a commercial tool, guidelines, and PubMed. While primary care physicians preferred the commercial tool, specialty physicians sought more in-depth knowledge. DISCUSSION: The current medical evidence appeared to be inadequate in covering specific populations such as patients with multiple comorbidities and elderly, and was sometimes irrelevant to complex clinical scenarios. Our findings may suggest that experienced and inpatient physicians would benefit from a CDSS that generates evidence in real time at the point of care. CONCLUSIONS: We found that physicians had information needs, which arose from the gaps in current medical evidence. This study provides insights on how the CDSS that aims at addressing these needs should be designed.

Second Step Aging on Nanosized Precipitates and Properties of Al-Zn-Mg-Cu-Cr Spray-Deposited Alloys.

The effects of second step aging (T76, T74, T73) on nano-sized precipitates and properties of Al-Zn-Mg-Cu-Cr spray-deposited alloys were explored through tensile tester, impact testing machine, metallographic microscope (OM), eddy-current device, scanning electron microscopy (SEM), twin-jet electro-polishing machine and transmission electron microscopy (TEM). Fine grain size (compared with as-deposited billet) and directional microstructures were obtained. T76 heat treatment of the alloy provided higher tensile strength, yield strength, impact toughness and hardness which were 767 MPa, 708 MPa, 39.41 J/cm1/2 99.1 HRB, respectively in comparison with T74 and T73 samples. However, they provided lower elongation and electrical conductivity which were 7.6% and 31.1% IACS, respectively in comparison with T74 and T73 samples. This resulted from the larger quantity and volume of tiny ' precipitates that distribute homogeneously in matrix. However, coarse precipitates with increasing second step aging time (T74, T73) made wider grain boundary width and discontinuous precipitates boosted conductivity of the Al-Zn-Mg-Cu-Cr alloy. Furthermore, proportion of white precipitated phase in the matrix decreased slightly and volume became larger with increasing second step aging time.

Rapid identification of rice species by laser-induced breakdown spectroscopy combined with pattern recognition.

Laser-induced breakdown spectroscopy (LIBS) combined with pattern recognition was proposed to discriminate rice species. LIBS spectra in the range of 210-480 nm wavelength from 11 different rice species were collected and preprocessed. Principal component analysis was applied to extract the characteristic variables from LIBS spectral data. Three pattern recognition methods, discriminant analysis, radial basis function neural network, and multi-layer perceptron neural network (MLP) were performed to compare the precision in identifying rice species. The results showed that the performance of the MLP model was better. The average identification rate of rice species reached 100% and 97.9% in the training and test sets, respectively, with MLP. The highest and lowest percentages for correct identification were 100% for early indica rice, Huai rice 5, Yan japonica 6, Lian japonica 8, Xuhan 1, Lvhan 1, Sheng rice 16, Yang japonica 687, and Fenghan 30, and 77.8% for Wuyu japonica rice in test sets. The overall results demonstrate that LIBS combined with MLP could be utilized to rapidly discriminate rice species.

Methods to Identify and Characterize siRNAs Targeting Serpin A1 In Vitro and In Vivo Using RNA Interference.

RNAi is a powerful tool that can be used to probe gene function as well as for therapeutic intervention. We describe a workflow and methods to identify screen and select potent and specific siRNAs in vitro and in vivo using qPCR-based methods as well as an AAT activity assay. We apply these techniques to a set of siRNAs targeting rat AAT, and use this set to exemplify the cell-based and in vivo data that can be generated using these methods.

[Treatment of Rheumatoid Arthritis by Hebi Recipe: an Efficacy Observation].

OBJECTIVE: To observe the auxiliary efficacy and safety of Hebi Recipe (HR)in treating early rheumatoid arthritis (RA). METHODS: Totally 63 early RA patients with Gan-Pi disharmony were randomly assigned to the treatment group [32 cases, treated by HR (one dose per day, taken in two portions for 24 successive weeks) plus Methotrexate (MTX)] and the control group (31 cases, treated by MTX alone). The dosage of MTX was increased from 7.5 mg to 12.5 mg, once per week, 24 weeks as one course of treatment. Efficacy for Chinese medical syndromes, American College of Rheumatology 20 (ACR20) improvement rate, disease activity score in 28 joints (DAS28), laboratory related indices [ESR, rheumatoid factor (RF), C-reactive protein (CRP), anti-cyclic citrullinated peptide (CCP)], and related ultrasonic inspection items (synovium thickness, synovium blood flow classification, effusion of joint), and adverse reactions were observed. RESULTS: The total effective rate (83.9%, 26/31 cases) and ACR20 improvement rate (74.2%, 23/31 cases) were higher in the treatment group than in the control group [60.7% (17/28 cases), 46.4% (13/28 cases); P < 0.05]. Compared with before treatment in the same group, DAS28 score, ESR, RF, CRP, CCP, synovium thickness, synovium blood flow classification, effusion of joint all decreased in the two groups after treatment (P < 0.01, P < 0.05). Compared with the control group after treatment, ACR20 improvement rate, DAS28 score, ESR, RF, CRP, CCP, synovium thickness, synovium blood flow classification, effusion of joint all decreased in the treatment group (P < 0.01, P < 0.05). Liver dysfunction occurred in 1 case of the treatment group. One leucopenia and 2 liver dysfunction occurred in the control group. CONCLUSION: HR could effectively improve joints and systemic symptoms of early RA patients with Gan-Pi disharmony.