Tartronic Acid as a Potential Inhibitor of Pathological Calcium Oxalate Crystallization.

Kidney stones are a pervasive disease with notoriously high recurrence rates that require more effective treatment strategies. Herein, tartronic acid is introduced as an efficient inhibitor of calcium oxalate monohydrate (COM) crystallization, which is the most prevalent constituent of human kidney stones. A combination of in situ experimental techniques and simulations are employed to compare the inhibitory effects of tartronic acid with those of its molecular analogs. Tartronic acid exhibits an affinity for binding to rapidly growing apical surfaces of COM crystals, thus setting it apart from other inhibitors such as citric acid, the current preventative treatment for kidney stones. Bulk crystallization and in situ atomic force microscopy (AFM) measurements confirm the mechanism by which tartronic acid interacts with COM crystal surfaces and inhibits growth. These findings are consistent with in vivo studies that reveal the efficacy of tartronic acid is similar to that of citric acid in mouse models of hyperoxaluria regarding their inhibitory effect on stone formation and alleviating stone-related physical harm. In summary, these findings highlight the potential of tartronic acid as a promising alternative to citric acid for the management of calcium oxalate nephropathies, offering a new option for clinical intervention in cases of kidney stones.

Effects of a Nurse-led Survivorship Care Program on the Health and Resilience of Primary Caregivers of Patients With Advanced Head and Neck Cancer: A Randomized Controlled Trial.

OBJECTIVES: The present study aimed to evaluate the effects of a nurse-led survivorship care program (SCP) on emotional distress, social support, physical health, mental health, and resilience in primary caregivers of patients with advanced head and neck cancer. DATA SOURCES: A randomized controlled trial was conducted. One hundred patient-primary caregiver dyads were randomized into the nurse-led SCP group (experimental) or the usual care group (control). Participants completed a self-reported questionnaire, including measures of emotional distress, social support, physical health, mental health, and resilience. After 6 months, the experimental group reported a significant improvement in emotional distress, social support, physical health, mental health, and resilience. Compared with the control group, measures of emotional distress, physical health, overall resilience, and the resilience aspects of equanimity and perseverance improved in the experimental group. CONCLUSION: An SCP may feasibly help alleviate emotional distress, improve social support, increase physical and mental health, and strengthen resilience in the primary caregivers of patients with head and neck cancer. Health care providers should encourage primary caregivers to join an SCP. IMPLICATIONS FOR NURSING PRACTICE: The nurse-led SCP can be applied before patients complete treatment, which may increase the positive effect on physical health and adaptation.

A Gibbs sampler for the multidimensional four-parameter logistic item response model via a data augmentation scheme.

The four-parameter logistic (4PL) item response model, which includes an upper asymptote for the correct response probability, has drawn increasing interest due to its suitability for many practical scenarios. This paper proposes a new Gibbs sampling algorithm for estimation of the multidimensional 4PL model based on an efficient data augmentation scheme (DAGS). With the introduction of three continuous latent variables, the full conditional distributions are tractable, allowing easy implementation of a Gibbs sampler. Simulation studies are conducted to evaluate the proposed method and several popular alternatives. An empirical data set was analysed using the 4PL model to show its improved performance over the three-parameter and two-parameter logistic models. The proposed estimation scheme is easily accessible to practitioners through the open-source IRTlogit package.

A note on monotonicity of item response functions for ordered polytomous item response theory models.

A monotone relationship between a true score (τ) and a latent trait level (θ) has been a key assumption for many psychometric applications. The monotonicity property in dichotomous response models is evident as a result of a transformation via a test characteristic curve. Monotonicity in polytomous models, in contrast, is not immediately obvious because item response functions are determined by a set of response category curves, which are conceivably non-monotonic in θ. The purpose of the present note is to demonstrate strict monotonicity in ordered polytomous item response models. Five models that are widely used in operational assessments are considered for proof: the generalized partial credit model (Muraki, 1992, Applied Psychological Measurement, 16, 159), the nominal model (Bock, 1972, Psychometrika, 37, 29), the partial credit model (Masters, 1982, Psychometrika, 47, 147), the rating scale model (Andrich, 1978, Psychometrika, 43, 561), and the graded response model (Samejima, 1972, A general model for free-response data (Psychometric Monograph no. 18). Psychometric Society, Richmond). The study asserts that the item response functions in these models strictly increase in θ and thus there exists strict monotonicity between τ and θ under certain specified conditions. This conclusion validates the practice of customarily using τ in place of θ in applied settings and provides theoretical grounds for one-to-one transformations between the two scales.

A Comparison of Constrained Item Selection Methods in Multidimensional Computerized Adaptive Testing.

The construction of assessments in computerized adaptive testing (CAT) usually involves fulfilling a large number of statistical and non-statistical constraints to meet test specifications. To improve measurement precision and test validity, the multidimensional priority index (MPI) and the modified MPI (MMPI) can be used to monitor many constraints simultaneously under a between-item and a within-item multidimensional framework, respectively. As both item selection methods can be implemented easily and computed efficiently, they are important and useful for operational CATs; however, no thorough simulation study has compared the performance of these two item selection methods under two different item bank structures. The purpose of this study was to investigate the efficiency of the MMPI and the MPI item selection methods under the between-item and within-item multidimensional CAT through simulations. The MMPI and the MPI item selection methods yielded similar performance in measurement precision for both multidimensional pools and yielded similar performance in exposure control and constraint management for the between-item multidimensional pool. For the within-item multidimensional pool, the MPI method yielded slightly better performance in exposure control but yielded slightly worse performance in constraint management than the MMPI method.

[Identification of antigenic epitope regions recognized by anti-synuclein-gamma monoclonal antibodies].

OBJECTIVE: To identify the antigenic epitope regions recognized by anti-synuclein-gamma monoclonal antibodies. METHODS: Six expression vectors of truncated SNCG were constructed and the truncated SNCG-GST fusion proteins were expressed in E.coli. The binding activities of anti-synuclein-gamma (SNCG) monoclonal antibodies (mAbs) with truncated SNCG were analyzed by ELISA and Western blot. According to the speculative epitope regions, expression vectors of potential epitopes were constructed and the GST-epitope fusion proteins were expressed in E.coli. And the fusion proteins were analyzed by ELISA and Western blot with mAbs to confirm the epitope regions. RESULTS: Epitope regions recognized by eleven anti-SNCG mAbs were identified. The epitopes recognized by 9#, 42#, and 3E4 were located in the region from 1st to 21st amino acids of SNCG, 11#, 22# in the region from 78th to 92nd amino acids, 1#, 14#, 18#, 31#, 36# in the region from 93rd to 110th amino acids, and 6C8 in the region from 111th to 127th amino acids. CONCLUSION: The epitope regions recognized by eleven anti-SNCG mAbs were identified. It will benefit the research on SNCG and application of anti-SNCG monoclonal antibodies.

Computing confidence intervals of item fit statistics in the family of Rasch models using the bootstrap method.

The item infit and outfit mean square errors (MSE) and their t-transformed statistics are widely used to screen poorly fitting items. The t-transformed statistics, however, do not follow the standard normal distribution so that hypothesis testing of item fit based on the conventional critical values is likely to be inaccurate (Wang and Chen, 2005). The MSE statistics are effect-size measures of misfit and have an expected value of unity when the data fit the model's expectation. Unfortunately, most computer programs for item response analysis do not report confidence intervals of the item infit and outfit MSE, mainly because their sampling distributions are analytically intractable. Hence, the user is left without interval estimates of the magnitudes of misfit. In this study, we developed a FORTRAN 90 computer program in conjunction with the commercial program WINSTEPS (Linacre, 2001) that yields confidence intervals of the item infit and outfit MSE using the bootstrap method. The utility of the program is demonstrated through three illustrations of simulated data sets.

Using SAS PROC NLMIXED to fit item response theory models.

Researchers routinely construct tests or questionnaires containing a set of items that measure personality traits, cognitive abilities, political attitudes, and so forth. Typically, responses to these items are scored in discrete categories, such as points on a Likert scale or a choice out of several mutually exclusive alternatives. Item response theory (IRT) explains observed responses to items on a test (questionnaire) by a person's unobserved trait, ability, or attitude. Although applications of IRT modeling have increased considerably because of its utility in developing and assessing measuring instruments, IRT modeling has not been fully integrated into the curriculum of colleges and universities, mainly because existing general purpose statistical packages do not provide built-in routines with which to perform IRT modeling. Recent advances in statistical theory and the incorporation of those advances into general purpose statistical software such as the Statistical Analysis System (SAS) allow researchers to analyze measurement data by using a class of models known as generalized linear mixed effects models (McCulloch & Searle, 2001), which include IRT models as special cases. The purpose of this article is to demonstrate the generality and flexibility of using SAS to estimate IRT model parameters. With real data examples, we illustrate the implementations of a variety of IRT models for dichotomous, polytomous, and nominal responses. Since SAS is widely available in educational institutions, it is hoped that this article will contribute to the spread of IRT modeling in quantitative courses.

Preliminary study on proteomics of gastric carcinoma and its clinical significance.

AIM: To explore the preliminary identification of serum protein pattern models that may be novel potential biomarkers in the detection of gastric cancer. METHODS: A total of 130 serum samples, including 70 from patients with gastric cancer and 60 from healthy adults, were detected by surface-enhanced laser desorption and ionization time-of-flight mass spectrometry (SELDI-TOF-MS). The data of spectra were analyzed by Biomarker Patterns Software (BPS). Thirty serum samples of gastric cancer patients and 30 serum samples of healthy adults were grouped into the training group to build models, and the other 70 samples were used to test and evaluate the models. The samples of the test group were judged only with their peaks' height and were separated into cancer group or healthy control group by BPS automatically and the judgments were checked with the histopathologic diagnosis of the samples. RESULTS: Sixteen mass peaks were found to be potential biomarkers with a significant level of P< 0.01. Among them, nine mass peaks showed increased expression in patients with gastric cancer. Analyzed by BPS, two peaks were chosen to build the model for gastric cancer detection. The sensitivity, specificity, and accuracy of the model were 90%, 36/40, 86.7%, 26/30, and 88.6%, 62/70, respectively, which were greatly higher than those of clinically used serum biomarkers CEA (carcinoembryonic antigen), CA19-9 and CA72-4. Stage I/II gastric cancer samples of the test group were all judged correctly. CONCLUSION: The novel biomarkers in serum and the established model could be potentially used in the detection of gastric cancer. However, large-scale studies should be carried on to further explore the clinical impact on the model.