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"Yan, Ting"
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A practical guide to survey questionnaire design and evaluation
This text summarizes practical principles, guidelines, and best practices for developing and testing survey questionnaires driven and supported by theoretical and empirical research. It provides a broad overview of literature on questionnaire design, drawing on both theoretical and empirical research.
BOOK
Current advances and challenges in CAR T-Cell therapy for solid tumors: tumor-associated antigens and the tumor microenvironment
The past decade has witnessed ongoing progress in immune therapy to ameliorate human health. As an emerging technique, chimeric antigen receptor (CAR) T-cell therapy has the advantages of specific killing of cancer cells, a high remission rate of cancer-induced symptoms, rapid tumor eradication, and long-lasting tumor immunity, opening a new window for tumor treatment. However, challenges remain in CAR T-cell therapy for solid tumors due to target diversity, tumor heterogeneity, and the complex microenvironment. In this review, we have outlined the development of the CAR T-cell technique, summarized the current advances in tumor-associated antigens (TAAs), and highlighted the importance of tumor-specific antigens (TSAs) or neoantigens for solid tumors. We also addressed the challenge of the TAA binding domain in CARs to overcome off-tumor toxicity. Moreover, we illustrated the dominant tumor microenvironment (TME)-induced challenges and new strategies based on TME-associated antigens (TMAs) for solid tumor CAR T-cell therapy.
Journal Article
Correction: Detecting underreporters of abortions and miscarriages in the national study of family growth, 2011–2015
[This corrects the article DOI: 10.1371/journal.pone.0271288.].
Journal Article
Detecting underreporters of abortions and miscarriages in the national study of family growth, 2011–2015
This paper draws on individual-level data from the National Study of Family Growth (NSFG) to identify likely underreporters of abortion and miscarriage and examine their characteristics. The NSFG asks about abortion and miscarriage twice, once in the computer-assisted personal interviewing (CAPI) part of the questionnaire and the other in the audio computer-assisted self-interviewing (ACASI) part. We used two different methods to identify likely underreporters of abortion and miscarriage: direct comparison of answers obtained from CAPI and ACASI and latent class models. The two methods produce very similar results. Although miscarriages are just as prone to underreporting as abortions, characteristics of women underreporting abortion differ somewhat from those misreporting miscarriages. Underreporters of abortions tended to be older, poorer, less likely to be Hispanic or Black, and more likely to have no religion. They also reported more traditional attitudes toward sexual behavior. By contrast, underreporters of miscarriage also tended to be older, poorer, and more likely to be Hispanic or Black, but were also more likely to have children in the household, had fewer pregnancies, and held less traditional attitudes toward marriage.
Journal Article
Surface ligand controls silver ion release of nanosilver and its antibacterial activity against Escherichia coli
Understanding the mechanism of nanosilver-dependent antibacterial activity against microorganisms helps optimize the design and usage of the related nanomaterials. In this study, we prepared four kinds of 10 nm-sized silver nanoparticles (AgNPs) with dictated surface chemistry by capping different ligands, including citrate, mercaptopropionic acid, mercaptohexanoic acid, and mercaptopropionic sulfonic acid. Their surface-dependent chemistry and antibacterial activities were investigated. Owing to the weak bond to surface Ag, short carbon chain, and low silver ion attraction, citrate-coated AgNPs caused the highest silver ion release and the strongest antibacterial activity against
, when compared to the other tested AgNPs. The study on the underlying antibacterial mechanisms indicated that cellular membrane uptake of Ag, NAD
/NADH ratio increase, and intracellular reactive oxygen species (ROS) generation were significantly induced in both AgNP and silver ion exposure groups. The released silver ions from AgNPs inside cells through a Trojan-horse-type mechanism were suggested to interact with respiratory chain proteins on the membrane, interrupt intracellular O
reduction, and induce ROS production. The further oxidative damages of lipid peroxidation and membrane breakdown caused the lethal effect on
. Altogether, this study demonstrated that AgNPs exerted antibacterial activity through the release of silver ions and the subsequent induction of intracellular ROS generation by interacting with the cell membrane. The findings are helpful in guiding the controllable synthesis through the regulation of surface coating for medical care purpose.
Journal Article
Tracking of menstrual cycles and prediction of the fertile window via measurements of basal body temperature and heart rate as well as machine-learning algorithms
Background
Fertility awareness and menses prediction are important for improving fecundability and health management. Previous studies have used physiological parameters, such as basal body temperature (BBT) and heart rate (HR), to predict the fertile window and menses. However, their accuracy is far from satisfactory. Additionally, few researchers have examined irregular menstruators. Thus, we aimed to develop fertile window and menstruation prediction algorithms for both regular and irregular menstruators.
Methods
This was a prospective observational cohort study conducted at the International Peace Maternity and Child Health Hospital in Shanghai, China. Participants were recruited from August 2020 to November 2020 and followed up for at least four menstrual cycles. Participants used an ear thermometer to assess BBT and wore the Huawei Band 5 to record HR. Ovarian ultrasound and serum hormone levels were used to determine the ovulation day. Menstruation was self-reported by women. We used linear mixed models to assess changes in physiological parameters and developed probability function estimation models to predict the fertile window and menses with machine learning.
Results
We included data from 305 and 77 qualified cycles with confirmed ovulations from 89 regular menstruators and 25 irregular menstruators, respectively. For regular menstruators, BBT and HR were significantly higher during fertile phase than follicular phase and peaked in the luteal phase (all
P
< 0.001). The physiological parameters of irregular menstruators followed a similar trend. Based on BBT and HR, we developed algorithms that predicted the fertile window with an accuracy of 87.46%, sensitivity of 69.30%, specificity of 92.00%, and AUC of 0.8993 and menses with an accuracy of 89.60%, sensitivity of 70.70%, and specificity of 94.30%, and AUC of 0.7849 among regular menstruators. For irregular menstruators, the accuracy, sensitivity, specificity and AUC were 72.51%, 21.00%, 82.90%, and 0.5808 respectively, for fertile window prediction and 75.90%, 36.30%, 84.40%, and 0.6759 for menses prediction.
Conclusions
By combining BBT and HR recorded by the Huawei Band 5, our algorithms achieved relatively ideal performance for predicting the fertile window and menses among regular menstruators. For irregular menstruators, the algorithms showed potential feasibility but still need further investigation.
Trial registration
ChiCTR2000036556. Registered 24 August 2020.
Journal Article
Online State of Charge Estimation of Lithium-Ion Cells Using Particle Filter-Based Hybrid Filtering Approach
Filtering based state of charge (SOC) estimation with an equivalent circuit model is commonly extended to Lithium-ion (Li-ion) batteries for electric vehicle (EV) or similar energy storage applications. During the last several decades, different implementations of online parameter identification such as Kalman filters have been presented in literature. However, if the system is a moving EV during rapid acceleration or regenerative braking or when using heating or air conditioning, most of the existing works suffer from poor prediction of state and state estimation error covariance, leading to the problem of accuracy degeneracy of the algorithm. On this account, this paper presents a particle filter-based hybrid filtering method particularly for SOC estimation of Li-ion cells in EVs. A sampling importance resampling particle filter is used in combination with a standard Kalman filter and an unscented Kalman filter as a proposal distribution for the particle filter to be made much faster and more accurate. Test results show that the error on the state estimate is less than 0.8% despite additive current measurement noise with 0.05 A deviation.
Journal Article
Population Assessment of Tobacco and Health (PATH) reliability and validity study: selected reliability and validity estimates
IntroductionThis paper reports a study done to estimate the reliability and validity of answers to the Youth and Adult questionnaires of the Population Assessment of Tobacco and Health (PATH) Study.Methods407 adults and 117 youth respondents completed the wave 4 (2016–2017) PATH Study interview twice, 6–24 days apart. The reinterview data were used to estimate the reliability of answers to the questionnaire. Kappa statistics, gross discrepancy rates and correlations between answers to the initial interview and the reinterview were used to measure reliability. We examined every item in the questionnaire for which there were at least 100 observations. After the reinterview, most respondents provided a saliva sample that allowed us to assess the accuracy of their answers to the tobacco use questions.ResultsThere was generally a very high level of agreement between answers in the interview and reinterview. On the key current tobacco use items, the average kappa (the agreement rate adjusted for chance agreement) was 0.79 for adult respondents (age 18 or older). Youth respondents exhibited equally high levels of agreement across interviews. The items on current tobacco use also exhibited high levels of agreement with saliva test results (kappa=0.72). Rating scale items showed lower levels of exact agreement across interviews but the answers were generally within one scale point or category.ConclusionsThe PATH Study questions were developed using a careful protocol and the results indicate the answers provide reliable and valid information about tobacco use.
Journal Article
Early Prediction of Gestational Diabetes Mellitus in the Chinese Population via Advanced Machine Learning
Abstract
Context
Accurate methods for early gestational diabetes mellitus (GDM) (during the first trimester of pregnancy) prediction in Chinese and other populations are lacking.
Objectives
This work aimed to establish effective models to predict early GDM.
Methods
Pregnancy data for 73 variables during the first trimester were extracted from the electronic medical record system. Based on a machine learning (ML)-driven feature selection method, 17 variables were selected for early GDM prediction. To facilitate clinical application, 7 variables were selected from the 17-variable panel. Advanced ML approaches were then employed using the 7-variable data set and the 73-variable data set to build models predicting early GDM for different situations, respectively.
Results
A total of 16 819 and 14 992 cases were included in the training and testing sets, respectively. Using 73 variables, the deep neural network model achieved high discriminative power, with area under the curve (AUC) values of 0.80. The 7-variable logistic regression (LR) model also achieved effective discriminate power (AUC = 0.77). Low body mass index (BMI) (≤ 17) was related to an increased risk of GDM, compared to a BMI in the range of 17 to 18 (minimum risk interval) (11.8% vs 8.7%, P = .09). Total 3,3,5′-triiodothyronine (T3) and total thyroxin (T4) were superior to free T3 and free T4 in predicting GDM. Lipoprotein(a) was demonstrated a promising predictive value (AUC = 0.66).
Conclusions
We employed ML models that achieved high accuracy in predicting GDM in early pregnancy. A clinically cost-effective 7-variable LR model was simultaneously developed. The relationship of GDM with thyroxine and BMI was investigated in the Chinese population.
Journal Article
Identification of TMAO-producer phenotype and host–diet–gut dysbiosis by carnitine challenge test in human and germ-free mice
ObjectiveThe gut microbiota-derived metabolite, trimethylamine N-oxide (TMAO) plays an important role in cardiovascular disease (CVD). The fasting plasma TMAO was shown as a prognostic indicator of CVD incident in patients and raised the interest of intervention targeting gut microbiota. Here we develop a clinically applicable method called oral carnitine challenge test (OCCT) for TMAO-related therapeutic drug efforts assessment and personalising dietary guidance.DesignA pharmacokinetic study was performed to verify the design of OCCT protocol. The OCCT was conducted in 23 vegetarians and 34 omnivores to validate gut microbiota TMAO production capacity. The OCCT survey was integrated with gut microbiome, host genotypes, dietary records and serum biochemistry. A humanised gnotobiotic mice study was performed for translational validation.ResultsThe OCCT showed better efficacy than fasting plasma TMAO to identify TMAO producer phenotype. The omnivores exhibited a 10-fold higher OR to be high TMAO producer than vegetarians. The TMAO-associated taxa found by OCCT in this study were consistent with previous animal studies. The TMAO producer phenotypes were also reproduced in humanised gnotobiotic mice model. Besides, we found the faecal CntA gene was not associated with TMAO production; therefore, other key relevant microbial genes might be involved. Finally, we demonstrated the urine TMAO exhibited a strong positive correlation with plasma TMAO (r=0.92, p<0.0001) and improved the feasibility of OCCT.ConclusionThe OCCT can be used to identify TMAO-producer phenotype of gut microbiota and may serve as a personal guidance in CVD prevention and treatment.Trial registration number NCT02838732; Results.
Journal Article