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"Hong, Kan"
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Classification of emotional stress and physical stress using a multispectral based deep feature extraction model
A classification model (Stress Classification-Net) of emotional stress and physical stress is proposed, which can extract classification features based on multispectral and tissue blood oxygen saturation (StO
2
) characteristics. Related features are extracted on this basis, and the learning model with frequency domain and signal amplification is proposed for the first time. Given that multispectral imaging signals are time series data, time series StO
2
is extracted from spectral signals. The proper region of interest (ROI) is obtained by a composite criterion, and the ROI source is determined by the universality and robustness of the signal. The frequency-domain signals of ROI are further obtained by wavelet transform. To fully utilize the frequency-domain characteristics, the multi-neighbor vector of locally aggregated descriptors (MN-VLAD) model is proposed to extract useful features. The acquired time series features are finally put into the long short-term memory (LSTM) model to learn the classification characteristics. Through SC-NET model, the classification signals of emotional stress and physical stress are successfully obtained. Experiments show that the classification result is encouraging, and the accuracy of the proposed algorithm is over 90%.
Journal Article
Exploring the use of machine learning for risk adjustment: A comparison of standard and penalized linear regression models in predicting health care costs in older adults
Payers and providers still primarily use ordinary least squares (OLS) to estimate expected economic and clinical outcomes for risk adjustment purposes. Penalized linear regression represents a practical and incremental step forward that provides transparency and interpretability within the familiar regression framework. This study conducted an in-depth comparison of prediction performance of standard and penalized linear regression in predicting future health care costs in older adults.
This retrospective cohort study included 81,106 Medicare Advantage patients with 5 years of continuous medical and pharmacy insurance from 2009 to 2013. Total health care costs in 2013 were predicted with comorbidity indicators from 2009 to 2012. Using 2012 predictors only, OLS performed poorly (e.g., R2 = 16.3%) compared to penalized linear regression models (R2 ranging from 16.8 to 16.9%); using 2009-2012 predictors, the gap in prediction performance increased (R2:15.0% versus 18.0-18.2%). OLS with a reduced set of predictors selected by lasso showed improved performance (R2 = 16.6% with 2012 predictors, 17.4% with 2009-2012 predictors) relative to OLS without variable selection but still lagged behind the prediction performance of penalized regression. Lasso regression consistently generated prediction ratios closer to 1 across different levels of predicted risk compared to other models.
This study demonstrated the advantages of using transparent and easy-to-interpret penalized linear regression for predicting future health care costs in older adults relative to standard linear regression. Penalized regression showed better performance than OLS in predicting health care costs. Applying penalized regression to longitudinal data increased prediction accuracy. Lasso regression in particular showed superior prediction ratios across low and high levels of predicted risk. Health care insurers, providers and policy makers may benefit from adopting penalized regression such as lasso regression for cost prediction to improve risk adjustment and population health management and thus better address the underlying needs and risk of the populations they serve.
Journal Article
Mental health problems and social supports in the COVID-19 healthcare workers: a Chinese explanatory study
Background
Coronavirus disease 2019 (COVID-19) has spread rapidly in China and other overseas areas, which has aroused widespread concern. The sharp increase in the number of patients has led to great psychological pressure on health care workers. The purpose of this study was to understand their mental health status and needs, so as to provide a scientific basis for alleviating the psychological pressure of health care workers.
Methods
Using a cross-sectional study design, 540 health care workers were randomly selected from two designated tuberculosis medical institutions in Anhui Province. The basic situation, perceived social support, depression level, loneliness and COVID-19 related knowledge were collected and analyzed by questionnaire.
Results
A total of 511 valid questionnaires were finally retrieved. There were 139 people in epidemic prevention and control positions (27.20%). Depression level: People in isolation ward, fever clinic and pre-check triage were at the level of mild to moderate depression. Female was higher than male; nurse was higher than doctor; middle and junior job titles were higher than senior titles; junior college degree or below were higher than bachelor’s degree, master’s degree and above; isolation ward, fever clinic and pre-check triage were significantly higher than those of non-prevention and control positions (
p
< 0.05). Loneliness scores: Doctors were higher than that of medical technicians, and isolation ward, fever clinic and pre-check triage were higher than those of other medical departments (
p
< 0.05). Social support: Doctors were lower than that of medical technicians, and isolation ward, fever clinic and pre-check triage were significantly lower than those of other departments (
p
< 0.05). The score of social support was negatively correlated with depression and loneliness (
p
< 0.001), while depression was positively correlated with loneliness (
p
< 0.001). Health care workers most want to receive one-to-one psychological counseling (29.75%), and provide crisis management (24.07%). The awareness rate of health care workers on COVID-19’s knowledge was relatively high.
Conclusions
The psychological problems of health care workers, especially women, nurses with low educational background, low professional title, and staff in the epidemic prevention and control positions are relatively serious.
Journal Article
Evaluating the Impact of Prescription Fill Rates on Risk Stratification Model Performance
BACKGROUND:Risk adjustment models are traditionally derived from administrative claims. Prescription fill rates—extracted by comparing electronic health record prescriptions and pharmacy claims fills—represent a novel measure of medication adherence and may improve the performance of risk adjustment models.
OBJECTIVE:We evaluated the impact of prescription fill rates on claims-based risk adjustment models in predicting both concurrent and prospective costs and utilization.
METHODS:We conducted a retrospective cohort study of 43,097 primary care patients from HealthPartners network between 2011 and 2012. Diagnosis and/or pharmacy claims of 2011 were used to build 3 base models using the Johns Hopkins ACG system, in addition to demographics. Model performances were compared before and after adding 3 types of prescription fill ratesprimary 0–7 days, primary 0–30 days, and overall. Overall fill rates utilized all ordered prescriptions from electronic health record while primary fill rates excluded refill orders.
RESULTS:The overall, primary 0–7, and 0–30 days fill rates were 72.30%, 59.82%, and 67.33%. The fill rates were similar between sexes but varied across different medication classifications, whereas the youngest had the highest rate. Adding fill rates modestly improved the performance of all models in explaining medical costs (improving concurrent R by 1.15% to 2.07%), followed by total costs (0.58% to 1.43%), and pharmacy costs (0.07% to 0.65%). The impact was greater for concurrent costs compared with prospective costs. Base models without diagnosis information showed the highest improvement using prescription fill rates.
CONCLUSIONS:Prescription fill rates can modestly enhance claims-based risk prediction models; however, population-level improvements in predicting utilization are limited.
Journal Article
Gypenosides Prevent H2O2-Induced Retinal Ganglion Cell Apoptosis by Concurrently Suppressing the Neuronal Oxidative Stress and Inflammatory Response
Our previous study demonstrated that gypenosides (Gp) exert protective effects on retinal nerve fibers and axons in a mouse model of experimental autoimmune optic neuritis. However, the therapeutic mechanisms remain unclear. Thus, in this study, a model of oxidative damage in retinal ganglion cells (RGCs) was established to investigate the protective effect of Gp, and its possible influence on oxidative stress in RGCs. Treatment of cells with H2O2 induced RGC injury owing to the generation of intracellular reactive oxygen species (ROS). In addition, the activities of antioxidative enzymes decreased and the expression of inflammatory factors increased, resulting in an increase in cellular apoptosis. Gp helped RGCs to become resistant to oxidation damage by directly reducing the amount of ROS in cells and exerting protective effects against H2O2-induced apoptosis. Treatment with Gp also reduced the generation of inducible nitric oxide synthase (iNOS) and cyclooxygenase-2 (COX-2), and increased nuclear respiratory factor 2 (Nrf-2) levels so as to increase the levels of heme oxygenase-1 (HO-1) and glutathione peroxidase 1/2 (Gpx1/2), which can enhance antioxidation in RGCs. In conclusion, our data indicate that neuroprotection by Gp involves its antioxidation and anti-inflammation effects. Gp prevents apoptosis through a mitochondrial apoptotic pathway. This finding might provide novel insights into understanding the mechanism of the neuroprotective effects of gypenosides in the treatment of optic neuritis.
Journal Article
Association between organ damage and mortality in systemic lupus erythematosus: a systematic review and meta-analysis
ObjectiveAt least half of patients with systemic lupus erythematosus (SLE) develop organ damage as a consequence of autoimmune disease or long-term therapeutic steroid use. This study synthesised evidence on the association between organ damage and mortality in patients with SLE.DesignSystematic review and meta-analysis.MethodsElectronic searches were performed in PubMed, Embase, Cochrane Library and Latin American and Caribbean Health Sciences Literature for observational (cohort, case-control and cross-sectional) studies published between January 2000 and February 2017. Included studies reported HRs or ORs on the association between organ damage (measured by the Systemic Lupus International Collaborating Clinics/American College of Rheumatology Damage Index (SDI) score) and mortality. Study quality was assessed using the modified Newcastle-Ottawa assessment. Pooled HRs were obtained using the DerSimonian and Laird random-effects model. Heterogeneity was assessed using the Cochrane Q (Q) and I2 statistics.ResultsThe search yielded 10 420 articles, from which 21 longitudinal studies were selected. Most studies (85%) were of high quality. For 10 studies evaluating organ damage (SDI) as a continuous variable and reporting HR as a measure of association, a 1-unit increase in SDI was associated with increased mortality; pooled HR was 1.34 (95% CI: 1.24 to 1.44, p<0.001; Q p=0.027, I2=52.1%). Exclusion of one potential outlying study reduced heterogeneity with minimal impact on pooled HR (1.33 (95% CI: 1.25 to 1.42), p<0.001, Q p=0.087, I2=42.0%). The 11 remaining studies, although they could not be aggregated because of their varying patient populations and analyses, consistently demonstrated that greater SDI was associated with increased mortality.ConclusionsOrgan damage in SLE is consistently associated with increased mortality across studies from various countries. Modifying the disease course with effective therapies and steroid-sparing regimens may reduce organ damage, improve outcomes and decrease mortality for patients with SLE.
Journal Article
Akkermansia muciniphila Exerts Strain-Specific Effects on DSS-Induced Ulcerative Colitis in Mice
Akkermansia muciniphila is a commensal bacterium of the gut mucus layer. Although both in vitro and in vivo data have shown that A. muciniphila strains exhibit strain-specific modulation of gut functions, its ability to moderate immunity to ulcerative colitis have not been verified. We selected three isolated human A. muciniphila strains (FSDLZ39M14, FSDLZ36M5 and FSDLZ20M4) and the A. muciniphila type strain ATCC BAA-835 to examine the effects of different A. muciniphila strains on dextran sulfate sodium-induced colitis. All of the A. muciniphila strains were cultured anaerobically in brain heart infusion medium supplemented with 0.25% type II mucin from porcine stomach. To create animal models, colitis was established in C57BL/6 mice which randomly divided into six groups with 10 mice in each group by adding 3% dextran sulfate sodium to drinking water for 7 days. A. muciniphila strains were orally administered to the mice at a dose of 1 × 10 9 CFU. Only A. muciniphila FSDLZ36M5 exerted significant protection against ulcerative colitis (UC) by increasing the colon length, restoring body weight, decreasing gut permeability and promoting anti-inflammatory cytokine expression. However, the other strains (FSDLZ39M14, ATCC BAA-835 and FSDLZ20M4) failed to provide these effects. Notably, A. muciniphila FSDLZ20M4 showed a tendency to exacerbate inflammation according to several indicators. Gut microbiota sequencing showed that A. muciniphila FSDLZ36M5 supplementation recovered the gut microbiota of mice to a similar state to that of the control group. A comparative genomic analysis demonstrated that the positive effects of A. muciniphila FSDLZ36M5 compared with the FSDLZ20M4 strain may be associated with specific functional genes that are involved in immune defense mechanisms and protein synthesis. Our results verify the efficacy of A. muciniphila in improving UC and provide gene targets for the efficient and rapid screening of A. muciniphila strains with UC-alleviating effects.
Journal Article
Exploitation of HPLC Analytical Method for Simultaneous Determination of Six Principal Unsaturated Fatty Acids in Oviductus Ranae Based on Quantitative Analysis of Multi-Components by Single-Marker (QAMS)
As one of the featured products in northeast China, Oviductus Ranae has been widely used as a nutritious food, which contains a variety of bioactive unsaturated fatty acids (UFAs). It is necessary to establish a scientific and reliable determination method of UFA contents in Oviductus Ranae. In this work, six principal UFAs in Oviductus Ranae, namely eicosapentaenoic acid (EPA), linolenic acid (ALA), docosahexaenoic acid (DHA), arachidonic acid (ARA), linoleic acid (LA) and oleic acid (OA), were identified using UPLC-MS/MS. The UFAs identified in Oviductus Ranae were further separated based on the optimized RP-HPLC conditions. Quantitative analysis of multi-components by single-marker (QAMS) method was implemented in content determination of EPA, ALA, DHA, ARA and OA, where LA was used as the internal standard. The experiments based on Taguchi design verified the robustness of the QAMS method on different HPLC instruments and chromatographic columns. The QAMS and external standard method (ESM) were used to calculate the UFA content of 15 batches of Oviductus Ranae samples from different regions. The relative error (r < 0.73%) and cosine coefficient showed that the two methods obtained similar contents, and the method validations met the requirements. The results showed that QAMS can comprehensively and effectively control the quality of UFAs in Oviductus Ranae which provides new ideas and solutions for studying the active components in Oviductus Ranae.
Journal Article
The diversity of gut microbiota in type 2 diabetes with or without cognitive impairment
BackgroundDiabetes is associated with a high risk of developing cognitive impairment, but the underlying mechanism remains unclear. Recent studies have found that gut microbiota may be involved in the progression of diabetes-associated cognitive impairment.AimsTo analyze the diversity of gut microbiota in type 2 diabetes with or without cognitive impairmentMethods16S rRNA sequencing was used to detect the gut microbiota composition in 154 type 2 diabetes (T2DM) subjectsResultsAmong 154 elderly T2DM participants included in our study, 73 with normal and 81 with impaired cognition. Lower levels of hemoglobin and HDL were observed in subjects with cognitive impairment. Patients with cognitive impairment had a lower abundance of Tenericutes. Comparison at the genus level revealed that T2DM patients with cognitive impairment had a decreased abundance of Bifidobacterium and unranked-RF39 and an increased abundance of Peptococcus and unranked-Leuconostocaceae. Additionally, the relative abundance of Veillonella and Pediococcus were decreased in subjects with cognitive impairment. Furthermore, the relative abundance of 7 sub-functions was significantly changed in the group with cognitive impairment. Calcium signaling pathways and the Renin-angiotensin system were upregulated in the cognitive impairment group while GnRH signaling, Fc gamma R-mediated phagocytosis, endocytosis, isoflavonoid biosynthesis, and cytochrome P450 were deregulated.ConclusionBifidobacterium may be associated with cognition in T2DM. Calcium signaling and renin-angiotensin system were shown to be associated with diabetes-associated cognitive impairment through gut microbiota.
Journal Article