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BackgroundDepression and anxiety have become increasingly prevalent across the globe. The rising need for treatment and the lack of clinicians has resulted in prolonged waiting times for patients to receive their first session. Responding to this gap, digital mental health interventions (DMHIs) have been found effective in treating depression and anxiety and are potentially promising pretreatments for patients who are awaiting face-to-face psychotherapy. Nevertheless, whether digital interventions effectively alleviate symptoms for patients on waiting lists for face-to-face psychotherapy remains unclear. ObjectiveThis review aimed to synthesize the effectiveness of DMHIs for relieving depression and anxiety symptoms of patients on waiting lists for face-to-face therapy. This review also investigated the features, perceived credibility, and usability of DMHIs during waiting times. MethodsIn this systematic review, we searched PubMed, PsycINFO, Cochrane, and Web of Science for research studies investigating the effectiveness of DMHIs in reducing either depression or anxiety symptoms among individuals waiting for face-to-face psychotherapy. The search was conducted in June 2024, and we have included the studies that met the inclusion criteria and were published before June 6, 2024. ResultsOf the 9267 unique records identified, 8 studies met the eligibility criteria and were included in the systematic review. Five studies were randomized controlled trials (RCTs), and 3 studies were not. Among the RCTs, we found that digital interventions reduced depression and anxiety symptoms, but the majority of interventions were not more effective compared to the control groups where participants simply waited or received a self-help book. For the non-RCTs, the interventions also reduced symptoms, but without control groups, the interpretation of the findings is limited. Finally, participants in the included studies perceived the digital interventions to be credible and useful, but high dropout rates raised concerns about treatment adherence. ConclusionsDue to the lack of effective interventions among the reviewed studies, especially among the RCTs, our results suggest that waiting list DMHIs are not more effective compared to simply waiting or using a self-help book. However, more high-quality RCTs with larger sample sizes are warranted in order to draw a more robust conclusion. Additionally, as this review revealed concerns regarding the high dropout rate in digital interventions, future studies could perhaps adopt more personalized and human-centered functions in interventions to increase user engagement, with the potential to increase treatment adherence and effectiveness.

Due to a high degree of symptom overlap in the early stages, with movement disorders predominating, Parkinson's disease (PD) and multiple system atrophy (MSA) may exhibit a similar decline in motor areas, yet they differ in their spread throughout the brain, ultimately resulting in two distinct diseases. Drawing upon neuroimaging analyses and altered motor cortex excitability, potential diffusion mechanisms were delved into, and comparisons of correlations across distinct disease groups were conducted in a bid to uncover significant pathological disparities. We recruited thirty-five PD, thirty-seven MSA, and twenty-eight matched controls to conduct clinical assessments, electromyographic recording, and magnetic resonance imaging scanning during the ''on medication'' state. Patients with neurodegeneration displayed a widespread decrease in electrophysiology in bilateral M1. Brain function in early PD was still in the self-compensatory phase and there was no significant change. MSA patients demonstrated an increase in intra-hemispheric function coupled with a decrease in diffusivity, indicating a reduction in the spread of neural signals. The level of resting motor threshold in healthy aged showed broad correlations with both clinical manifestations and brain circuits related to left M1, which was absent in disease states. Besides, ICF exhibited distinct correlations with functional connections between right M1 and left middle temporal gyrus in all groups. The present study identified subtle differences in the functioning of PD and MSA related to bilateral M1. By combining clinical information, cortical excitability, and neuroimaging intuitively, we attempt to bring light on the potential mechanisms that may underlie the development of neurodegenerative disease.

Assessment practices are, among other things, concerned with issues of fairness and appropriate score interpretation, in particular when making claims about subgroup differences in performance are of interest. In order to make such claims, a psychometric concept of measurement invariance or differential item functioning (DIF) ought to be considered and met. Over the last decades, researchers have proposed and developed a plethora of methods aimed at detecting DIF. However, DIF detection methods that allow multilevel data structures to be modeled are limited and understudied. In the current study, we evaluated the performance of four methods, including the model-based multilevel Wald and the score-based multilevel Mantel–Haenszel (MH), and two well-established single-level methods, the model-based single-level Lord and the score-based single-level MH. We conducted a simulation study that mimics real-world scenarios. Our results suggested that when data were generated as multilevel, mixed results regarding performances were observed, and not one method consistently outperformed the others. Single-level Lord and multilevel Wald yielded best control of the Type I error rates, in particular in conditions when latent means were generated as equal for the two groups. Power rates were low across all four methods in conditions with small number of between- and within-level units and when small DIF was modeled. However, in those conditions, single-level MH and multilevel MH yielded higher power rates than either single-level Lord or multilevel Wald. This suggests that current practices in detecting DIF should strongly consider adopting one of the more recent methods only in certain contexts as the tradeoff between power and complexity of the method may not warrant a blanket recommendation in favor of a single method. Limitations and future research directions are also discussed.

Abstract The occurrence of diabetic nephropathy (DN) and diabetic retinopathy (DR) are closely associated in patients with diabetes. However, the cellular and molecular linkage of DN and DR has not been elucidated, and further revelations are needed to improve mutual prognostic decisions and management. Here, we generate and integrate single-cell RNA sequencing profiles of kidney and retina to explore the cellular and molecular association of kidney and retina in both physiological and pathological conditions. We find renal mesangial cells and retinal pericytes share molecular features and undergo similar molecular transitions under diabetes. Furthermore, we uncover that chemokine regulation shared by the two cell types is critical for the co-occurrence of DN and DR, and the chemokine score can be used for the prognosis of DN complicated with DR. These findings shed light on the mechanism of the co-occurrence of DN and DR and could improve the prevention and treatments of diabetic microvascular complications.

Abstract Background and objective Hepatocellular carcinoma (HCC), which has a complex pathogenesis and poor prognosis, is one of the most common malignancies worldwide. Hepatitis virus B infection is the most common cause of HCC in Asian patients. Autophagy is the process of digestion and degradation, and studies have shown that autophagy-associated effects are closely related to the development of HCC. In this study, we aimed to construct a prognostic model based on autophagy-related genes (ARGs) for the Asian HCC population to provide new ideas for the clinical management of HCC in the Asian population. Methods The clinical information and transcriptome data of Asian patients with HCC were downloaded from The Cancer Genome Atlas (TCGA) database, and 206 ARGs were downloaded from the human autophagy database (HADB). We performed differential and Cox regression analyses to construct a risk score model. The accuracy of the model was validated by using the Kaplan–Meier (K–M) survival curve, receiver operating characteristic (ROC) curve, and univariate and multivariate Cox independent prognostic analyses. The results Thirteen ARGs that were significantly associated with prognosis were finally identified by univariate and multivariate Cox regression analyses. The K–M survival curves showed that the survival rate of the low-risk group was significantly higher than that of the high-risk group (p

Abstract Background The breast-conserving surgery and reconstruction rate in China is relatively low when compared with those in Western countries. Moreover, predictors of surgical choices for women with breast cancer in China have not yet been explored. This study aims to explore differences in the surgical choices of women with different demographic and clinical characteristics and the predictors that influence surgical choices of women with early-stage breast cancer. Methods This retrospective study included women with early-stage (0-II) breast cancer who underwent surgeries at one of two Xiamen University-affiliated hospitals between 2009 and 2017. Using medical records, eleven variables were collected: the woman's age, year of diagnosis, hospital, marital status, payment method, cancer stage, presence of positive axillary lymph node, histology, neoadjuvant chemotherapy, radiotherapy, and the type(s) of surgery they chose. Binary logistic regression was used to analyse predictors of surgical choice. Results A total of 1,787 cases were included in this study. Of the total number of women with breast cancer, 61.3% underwent mastectomy without breast reconstruction, 26.4% underwent mastectomy with breast reconstruction, and the remaining 12.2% chose breast-conserving surgery. Women with different demographic and clinical characteristics underwent different types of surgery. Cancer stage, neoadjuvant chemotherapy, radiotherapy, and the choice of hospital were found to be predictors of breast-conserving surgery. Meanwhile, age, year of diagnosis, payment method, neoadjuvant chemotherapy, and the choice of hospital were found to be predictors of reconstruction after mastectomy in women with early-stage breast cancer. Conclusions In China, surgical choices for women with breast cancer have diversified. Healthcare workers should understand the surgical preferences of women of different ages. For early detection of breast cancer, knowledge of breast self-examination and breast cancer screening should be provided. Adequate information about the safety of reconstruction and advocacy for medical insurance coverage of reconstruction should be offer. Breast surgeons need specialised training and standardising protocols towards different types of breast surgery. These actions will help women make better, well-informed decisions about their breast surgeries.

The aim of this study was to investigate the association between masturbation frequency and sexual dysfunction among men and women, focusing on individuals with and without regular sexual partners, and to determine whether sexual compatibility (e.g., similar sexual desire levels and a match between desired behaviors and behaviors one’s partner is willing to engage in) in the relationship affects masturbation frequency. Here, 12,271 Finnish men and women completed an online survey about masturbation frequency, sexual function, and sexual compatibility with their partner for those who were in a relationship. The results indicated that masturbation frequency was positively associated with overall sexual function for women. This was moderated by relationship status, meaning that more frequent masturbation was associated with better orgasmic function and sexual satisfaction in single women, whereas the opposite was true for women who were in a relationship. For men, more frequent masturbation was associated with better erectile function for single men, and better ejaculatory latency but worse orgasmic function, intercourse satisfaction, and more symptoms of delayed ejaculation for men who were in a relationship. Lower sexual compatibility and sexual dysfunctions in the partner were associated with more frequent masturbation in both sexes. The associations between masturbation frequency and sexual function vary for single and partnered individuals, and are, for the latter group, further affected by sexual compatibility.

A polarimeter for optical rotation (OR) detection and optical active media measurement based on m=1 Zero-Order Liquid Crystal Polymer Vortex Half-Wave Retarder (LCPVR) and Digital Image Processing (DIP) was proposed in this letter. In this research, we demonstrate the principle of measuring optical rotation with LCPVR polarimeter system which is based on Jones vectors and matrices. Two opposing wedge-shaped dark areas will appear in the output image when the uniform distribution polarized light passes the LCPVR. DIP is used to find the darkest line in the opposing wedge-shaped dark areas, then the optical rotation angle is calculated, which is from −90 degree to +90 degree. The polarimeter is used to measure the concentration of sucrose solutions at the range of 1000-10000mg/dl L. We also present the fabricated method of LCPVR by Digital Micro-mirror Device (DMD) system, and the LCPVR will be low price in the fabricated processing. Compared with existed polarimeter, our polarimeter with LCPVR has simpler optical structure and less size. The key innovation of this system is that the optical rotation angle can be displayed with visualized image except digital. And the rotation angle is calculated by DIP without mechanical adjust a 1/4 wave plate, which lead to higher detection speed and stronger stability.

IntroductionObservational studies demonstrated controversial effect of polyunsaturated fatty acids (PUFAs) on Parkinson’s disease (PD) with limited causality evidence. Randomized control trials showed possible improvement in PD symptoms with PUFA supplement but had small study population and limited intervention time.MethodsA two-sample Mendelian randomization was designed to evaluate the causal relevance between PUFAs and PD, using genetic variants of PUFAs as instrumental variables and PD data from the largest genome-wide association study as outcome. Inverse variance weighted (IVW) method was applied to obtain the primary outcome. Mendelian randomization Egger regression, weighted median and weighted mode methods were exploited to assist result analyses. Strict Mendelian randomization and multivariable Mendelian randomization (MVMR) were used to estimate direct effects of PUFAs on PD, eliminating pleiotropic effect. Debiased inverse variance weighted estimator was implemented when weak instrument bias was introduced into the analysis. A variety of sensitivity analyses were utilized to assess validity of the results.ResultsOur study included 33,674 PD cases and 449,056 controls. Higher plasma level of arachidonic acid (AA) was associated with a 3% increase of PD risk per 1-standard deviation (SD) increase of AA (IVW; Odds ratio (OR)=1.03 [95% confidence interval (CI) 1.01-1.04], P = 2.24E-04). After MVMR (IVW; OR=1.03 [95% CI 1.02-1.04], P =6.15E-08) and deletion of pleiotropic single-nucleotide polymorphisms overlapping with other lipids (IVW; OR=1.03 [95% CI 1.01-1.05], P =5.88E-04), result was still significant. Increased level of eicosapentaenoic acid (EPA) showed possible relevance with increased PD risk after adjustment of pleiotropy (MVMR; OR=1.05 [95% CI 1.01-1.08], P =5.40E-03). Linoleic acid (LA), docosahexaenoic acid (DHA), docosapentaenoic acid (DPA) and alpha-linolenic acid (ALA) were found not causally relevant to PD risk. Various sensitivity analyses verified the validity of our results. In conclusion, our findings from Mendelian randomization suggested that elevated levels of AA and possibly EPA might be linked to a higher risk of PD. No association between PD risk and LA, DHA, DPA, or ALA was found.DiscussionThe odds ratio for plasma AA and PD risk was weak. It is important to approach our results with caution in clinical practice and to conduct additional studies on the relationship between PUFAs and PD risk.

Abstract The occurrence of metastasis is a serious risk for renal cell carcinoma (RCC) patients. In order to develop novel therapeutic approaches to control the progression of metastatic RCC, it is of urgent need to understand the molecular mechanisms underlying RCC metastasis and identify prognostic markers of metastatic risk. Matrix metalloproteinases (MMPs) and tissue inhibitors of metalloproteinases (TIMPs) have been known to be closely associated with extracellular matrix (ECM) turnover, which plays a highly active role in tumor metastasis. Recent studies have shown that immunophilin FK-506-binding protein 51 (FKBP51) may be important for the regulation of ECM function, and exert effects on the invasion and migration of tumor cells. However, the mechanisms underlying these activities remain unclear. The present study detected the role of FKBP51 in clear cell renal cell carcinoma (ccRCC), the most common subtype of RCC, and found that FKBP51 significantly promotes ccRCC invasion and migration by binding with the TIMP3, connecting TIMP3 with Beclin1 complex and increasing autophagic degradation of TIMP3. Given the important roles that TIMPs/MMPs play in ECM regulation and remodeling, our findings will provide new perspective for future investigation of the regulation of metastasis of kidney cancer and other types of cancer.

Cross-classified random effects models (CCREMs) have been developed for appropriately analyzing data with a cross-classified structure. Despite its flexibility and the prevalence of cross-classified data in social and behavioral research, CCREMs have been under-utilized in applied research. In this article, we present CCREMs as a general and flexible modeling framework, and present a wide range of existing models designed for different purposes as special instances of CCREMs. We also introduce several less well-known applications of CCREMs. The flexibility of CCREMs allows these models to be easily extended to address substantive questions. We use the free R package PLmixed to illustrate the estimation of these models, and show how the general language of the CCREM framework can be translated into specific modeling contexts.

Abstract Multi-omics data are good resources for prognosis and survival prediction; however, these are difficult to integrate computationally. We introduce DeepProg, a novel ensemble framework of deep-learning and machine-learning approaches that robustly predicts patient survival subtypes using multi-omics data. It identifies two optimal survival subtypes in most cancers and yields significantly better risk-stratification than other multi-omics integration methods. DeepProg is highly predictive, exemplified by two liver cancer (C-index 0.73–0.80) and five breast cancer datasets (C-index 0.68–0.73). Pan-cancer analysis associates common genomic signatures in poor survival subtypes with extracellular matrix modeling, immune deregulation, and mitosis processes. DeepProg is freely available at https://github.com/lanagarmire/DeepProg

Telomeres attach to the nuclear envelope to facilitate homolog pairing during meiosis prophase I. Here, the authors show that SUN1 and SPDYA interact, and demonstrate that this interaction is important for telomere structure and function, and essential to mice gametogenesis.

Experimental and quasi-experimental designs have been increasingly employed in education. Mediation analysis has long been used to measure the role of mediators. Causal mediation analysis provides a modern approach to evaluate potential causal roles of mediators. Compared with conventional mediation analysis, causal mediation analysis has several advantages, e.g., by enabling us to evaluate necessary assumptions to establish a valid causal role of the mediator of interest. Despite these advantages and the availability of various software programs, causal mediation analysis has not been employed frequently in educational research. In this paper, we provide a step-by-step guide to causal mediation analysis using the free R package mediation in order to promote the more frequent application of causal mediation analysis in education, with an accessible data example from a Comprehensive College Transition Program (CCTP).

Plasma separation from whole blood is oftent required as an essential first step when performing blood tests with a viral assay. However, developing a point-of-care plasma extraction device with a large output and high virus recovery remains a significant obstacle to the success of on-site viral load tests. Here, we report a portable, easy-to-use, cost-efficient, membrane-filtration-based plasma separation device that enables rapid large-volume plasma extraction from whole blood, designed for point-of-care virus assays. The plasma separation is realized by a low-fouling zwitterionic polyurethane-modified cellulose acetate (PCBU-CA) membrane. The zwitterionic coating on the cellulose acetate membrane can decrease surface protein adsorption by 60% and increase plasma permeation by 46% compared with a pristine membrane. The PCBU-CA membrane, with its ultralow-fouling properties, enables rapid plasma separation. The device can yield a total of 1.33 mL plasma from 10 mL whole blood in 10 min. The extracted plasma is cell-free and exhibits a low hemoglobin level. In addition, our device demonstrated a 57.8% T7 phage recovery in the separated plasma. The results of real-time polymerase chain reaction analysis confirmed that the nucleic acid amplification curve of the plasma extracted by our device is comparable to that obtained by centrifugation. With its high plasma yield and good phage recovery, our plasma separation device provides an excellent replacement for traditional plasma separation protocols for point-of-care virus assays and a broad spectrum of clinical tests.

BackgroundAccurate prediction of survival is crucial for both physicians and women with breast cancer to enable clinical decision making on appropriate treatments. The currently available survival prediction tools were developed based on demographic and clinical data obtained from specific populations and may underestimate or overestimate the survival of women with breast cancer in China. ObjectiveThis study aims to develop and validate a prognostic app to predict the overall survival of women with breast cancer in China. MethodsNine-year (January 2009-December 2017) clinical data of women with breast cancer who received surgery and adjuvant therapy from 2 hospitals in Xiamen were collected and matched against the death data from the Xiamen Center of Disease Control and Prevention. All samples were randomly divided (7:3 ratio) into a training set for model construction and a test set for model external validation. Multivariable Cox regression analysis was used to construct a survival prediction model. The model performance was evaluated by receiver operating characteristic (ROC) curve and Brier score. Finally, by running the survival prediction model in the app background thread, the prognostic app, called iCanPredict, was developed for women with breast cancer in China. ResultsA total of 1592 samples were included for data analysis. The training set comprised 1114 individuals and the test set comprised 478 individuals. Age at diagnosis, clinical stage, molecular classification, operative type, axillary lymph node dissection, chemotherapy, and endocrine therapy were incorporated into the model, where age at diagnosis (hazard ratio [HR] 1.031, 95% CI 1.011-1.051; P=.002), clinical stage (HR 3.044, 95% CI 2.347-3.928; P

Abstract Background Overfeeding reduces laying performance in broiler breeder hens, which is associated with obesity, hepatic steatosis and systemic inflammation. To unravel the underlying mechanisms governing the effect of feeding regimes on energy metabolism and egg production, a transcriptomics approach was carried out for screening differentially expressed genes (DEGs) in ovary, liver and adipose tissues of broiler chickens under ad libitum and restricted feeding. Results It showed that 289, 388 and 204 DEGs were identified in the adipose, liver and ovary, respectively. These DEGs were significantly enriched in phagosome pathway, lipid transport, activity and nutrient reservoir activity in ovary; steroid hormone biosynthesis and metabolism of xenobiotics by cytochrome P450 pathways in adipose tissue; and the metabolic pathways, peroxisome proliferator-activated receptor (PPAR) and Jak-STAT signaling pathway in liver. Estrogen receptor 1, identified as one of important hubs by constructing PPI network, was up-regulated in ad libitum group, which would make more apolipoproteins be transferred to ovary. Conclusions High expression of VTGs, APOB, CYBB and CTSS in ovary would induce excess lipid deposit, oxidative stress and potential damage to ovulation. Our results contribute to understanding effects of feeding regimes on metabolic regulation during egg production of broiler breeder hens and also provide new evidence of metabolic regulation from integrated multi-tissue processes.

As a new wireless access technology, Narrow Band IoT (NB-IoT) enables low-power wide-area coverage services and can be applied to many domains. Emerging applications such as smart traffic, shared bicycles and shared cars have put forward more stringent requirements for connectivity. In order to get higher QoS, an enhancement access algorithm is proposed for the complex service scenario of NB-IoT, in which the network terminals are clustered based the distance from base station and terminal traffic intensity, and allocated reuse preamble code to improve network access capacity. The capacity is modeled and analyzed to evaluate the enhanced access algorithm based on clustering-reuse preamble allocation (ERA-CRPA). The experimental results show that the ERA-CRPA can effectively reduce the probability of random access preamble collision, further improve the access efficiency and increase the equivalent capacity of NB-IoT network.

In this study, we introduced a cross-classified multidimensional nominal response model (CC-MNRM) to account for various response styles (RS) in the presence of cross-classified data. The proposed model allows slopes to vary across items and can explore impacts of observed covariates on latent constructs. We applied a recently developed variant of the Metropolis-Hastings Robbins-Monro (MH-RM) algorithm to address the computational challenge of estimating the proposed model. To demonstrate our new approach, we analyzed empirical student evaluation of teaching (SET) data collected from a large public university with three models: a CC-MNRM with RS, a CC-MNRM with no RS, and a multilevel MNRM with RS. Results indicated that the three models led to different inferences regarding the observed covariates. Additionally, in the example, ignoring/incorporating RS led to changes in student substantive scores, while the instructor substantive scores were less impacted. Misspecifying the cross-classified data structure resulted in apparent changes on instructor scores. To further evaluate the proposed modeling approach, we conducted a preliminary simulation study and observed good parameter and score recovery. We concluded this study with discussions of limitations and future research directions.

Several validated adult autism symptom screening tools exist; however, there are concerns about the validity of instruments in adults who self-identify and those who have an autism diagnosis. This study evaluated the psychometric properties of the RAADS-R and RAADS-14 across gender, autism diagnosis and autistic identity, and age among a sample of 839 adults. Participants included individuals who reported a prior diagnosis of autism, self-identified as autistic without a diagnosis, were exploring a diagnosis of autism, or neurotypical. Psychometric evaluation included confirmation of scale factor structure, and examination of item performance, including bias and discrimination, across the target sociodemographic characteristics. The RAADS-R and RAADS-14 were found to be psychometrically sound, unidimensional instruments in the present sample without systematic bias by age, diagnosis/identity, or gender. The use of dichotomous response options would improve measure parsimony. Close examination of extant item-level bias is also recommended in the development of new measures. Notably, few psychometric differences between diagnosed and self-identifying individuals were identified. There was a distinction, however, between diagnosed individuals and those exploring autistic identification. Four items representing the consequences of adverse sensory experiences and the hidden rulebook of social interaction were most informative for differentiating autistic individuals from neurotypical.

Abstract It has become increasingly clear that both normal and cancer tissues are composed of heterogeneous populations. Genetic variation can be attributed to the downstream effects of inherited mutations, environmental factors, or inaccurately resolved errors in transcription and replication. When lesions occur in regions that confer a proliferative advantage, it can support clonal expansion, subclonal variation, and neoplastic progression. In this manner, the complex heterogeneous microenvironment of a tumour promotes the likelihood of angiogenesis and metastasis. Recent advances in next-generation sequencing and computational biology have utilized single-cell applications to build deep profiles of individual cells that are otherwise masked in bulk profiling. In addition, the development of new techniques for combining single-cell multi-omic strategies is providing a more precise understanding of factors contributing to cellular identity, function, and growth. Continuing advancements in single-cell technology and computational deconvolution of data will be critical for reconstructing patient specific intra-tumour features and developing more personalized cancer treatments.

Long intergenic noncoding RNAs (lincRNAs) are a relatively new class of non-coding RNAs that have the potential as cancer biomarkers. To seek a panel of lincRNAs as pan-cancer biomarkers, we have analyzed transcriptomes from over 3300 cancer samples with clinical information. Compared to mRNA, lincRNAs exhibit significantly higher tissue specificities that are then diminished in cancer tissues. Moreover, lincRNA clustering results accurately classify tumor subtypes. Using RNA-Seq data from thousands of paired tumor and adjacent normal samples in The Cancer Genome Atlas (TCGA), we identify six lincRNAs as potential pan-cancer diagnostic biomarkers (PCAN-1 to PCAN-6). These lincRNAs are robustly validated using cancer samples from four independent RNA-Seq data sets, and are verified by qPCR in both primary breast cancers and MCF-7 cell line. Interestingly, the expression levels of these six lincRNAs are also associated with prognosis in various cancers. We further experimentally explored the growth and migration dependence of breast and colon cancer cell lines on two of the identified lncRNAs. In summary, our study highlights the emerging role of lincRNAs as potentially powerful and biologically functional pan-cancer biomarkers and represents a significant leap forward in understanding the biological and clinical functions of lincRNAs in cancers.

The cross-classified data structure is ubiquitous in education, psychology, and health outcome sciences. In these areas, assessment instruments that are made up of multiple items are frequently used to measure latent constructs. The presence of both the cross-classified structure and multivariate categorical outcomes leads to the so-called item-level data with cross-classified structure. An example of such data structure is the routinely collected student evaluation of teaching (SET) data. Motivated by the lack of research on multilevel IRT modeling with crossed random effects and the need of an approach that can properly handle SET data, this study proposed a cross-classified IRT model, which takes into account both the cross-classified data structure and properties of multiple items in an assessment instrument. A new variant of the Metropolis-Hastings Robbins-Monro (MH-RM) algorithm was introduced to address the computational complexities in estimating the proposed model. A preliminary simulation study was conducted to evaluate the performance of the algorithm for fitting the proposed model to data. The results indicated that model parameters were well recovered. The proposed model was also applied to SET data collected at a large public university to answer empirical research questions. Limitations and future research directions were discussed.

Identifying items with differential item functioning (DIF) in an assessment is a crucial step for achieving equitable measurement. One critical issue that has not been fully addressed with existing studies is how DIF items can be detected when data are multilevel. In the present study, we introduced a Lord's Wald X[superscript 2] test-based procedure for detecting both uniform and non-uniform DIF with polytomous items in the presence of the ubiquitous multilevel data structure. The proposed approach is a multilevel extension of a two-stage procedure, which identifies anchor items in its first stage and formally evaluates candidate items in the second stage. We applied the Metropolis-Hastings Robbins-Monro (MH-RM) algorithm to estimate multilevel polytomous item response theory (IRT) models and to obtain accurate covariance matrices. To evaluate the performance of the proposed approach, we conducted a preliminary simulation study that considered various conditions to mimic real-world scenarios. The simulation results indicated that the proposed approach has great power for identifying DIF items and well controls the Type I error rate. Limitations and future research directions were also discussed.

BackgroundMany comparability problems appear in the process of the performance assessment of medical service. When comparing medical evaluation indicators across hospitals, or even within the same hospital, over time, the differences in the population composition such as types of diseases, comorbidities, demographic characteristics should be taken into account. This study aims to introduce a standardization technique for medical service indicators and provide a new insight on the comparability of medical data.MethodsThe medical records of 142592 inpatient from three hospitals in 2017 were included in this study. Chi-square and Kruskal-Wallis tests were used to explore the compositions of confounding factors among populations. The procedure of stratified standardization technique was applied to compare the differences of the average length of stay and the average hospitalization expense among three hospitals.ResultsAge, gender, comorbidity, and principal diagnoses category were considered as confounding factors. After correcting all factors, the average length of stay of hospital A and C were increased by 0.21 and 1.20 days, respectively, while that of hospital B was reduced by 1.54 days. The average hospitalization expenses of hospital A and C were increased by 1494 and 660 Yuan, whilst that of hospital B was decreased by 810 Yuan.ConclusionsStandardization method will be helpful to improve the comparability of medical service indicators in hospital administration. It could be a practical technique and worthy of promotion.

Multi-omics data integration is one of the major challenges in the era of precision medicine. Considerable work has been done with the advent of high-throughput studies, which have enabled the data access for downstream analyses. To improve the clinical outcome prediction, a gamut of software tools has been developed. This review outlines the progress done in the field of multi-omics integration and comprehensive tools developed so far in this field. Further, we discuss the integration methods to predict patient survival at the end of the review.

The recent surge of machine learning (ML) has impacted many disciplines, including educational and psychological measurement (hereafter shortened as "measurement"). The measurement literature has seen rapid growth in applications of ML to solve measurement problems. However, as we emphasize in this article, it is imperative to critically examine the potential risks associated with involving ML in measurement. The MxML project aims to explore the relationship between measurement and ML, so as to identify and address the risks and better harness the power of ML to serve measurement missions. This paper describes the first study of the MxML project, in which we summarize the state of the field of applications, extensions, and discussions about ML in measurement contexts with a systematic review of the recent 10 years' literature. We provide a snapshot of the literature in (1) areas of measurement where ML is discussed, (2) types of articles (e.g., applications, conceptual, etc.), (3) ML methods discussed, and (4) potential risks associated with involving ML in measurement, which result from the differences between what measurement tasks need versus what ML techniques can provide.

Breast cancer is the most common malignancy in women worldwide. With the increasing awareness of heterogeneity in breast cancers, better prediction of breast cancer prognosis is much needed for more personalized treatment and disease management. Towards this goal, we have developed a novel computational model for breast cancer prognosis by combining the Pathway Deregulation Score (PDS) based pathifier algorithm, Cox regression and L1-LASSO penalization method. We trained the model on a set of 236 patients with gene expression data and clinical information, and validated the performance on three diversified testing data sets of 606 patients. To evaluate the performance of the model, we conducted survival analysis of the dichotomized groups, and compared the areas under the curve based on the binary classification. The resulting prognosis genomic model is composed of fifteen pathways (e.g., P53 pathway) that had previously reported cancer relevance, and it successfully differentiated relapse in the training set (log rank p-value = 6.25e-12) and three testing data sets (log rank p-value < 0.0005). Moreover, the pathway-based genomic models consistently performed better than gene-based models on all four data sets. We also find strong evidence that combining genomic information with clinical information improved the p-values of prognosis prediction by at least three orders of magnitude in comparison to using either genomic or clinical information alone. In summary, we propose a novel prognosis model that harnesses the pathway-based dysregulation as well as valuable clinical information. The selected pathways in our prognosis model are promising targets for therapeutic intervention.

Bio-based carboxylic acids are some of the most available renewable chemicals, but since they are solids with high melting temperatures, they cannot be directly used as liquid resins. To this end, we report the formation of supramolecular complexes between an amino methacrylate and various solid carboxylic acids. The ionically bonded methacrylates exhibit low viscosities and rapid reaction kinetics for free-radical mediated polymerization, showing quantitative methacrylate conversions within one minute of irradiation at 5 mW cm−2 405 nm light. We demonstrate the implementation of these acid-base complexes as a neat resin system that comprises orthogonal polymerization reactions (free-radical methacrylate polymerization and epoxy-acid polymerization reactions), which yields high-strength network polymer materials. [ABSTRACT FROM AUTHOR]

Assessment practices are, among other things, concerned with issues of fairness and appropriate score interpretation, in particular when making claims about subgroup differences in performance are of interest. In order to make such claims, a psychometric concept of measurement invariance or differential item functioning (DIF) ought to be considered and met. Over the last decades, researchers have proposed and developed a plethora of methods aimed at detecting DIF. However, DIF detection methods that allow multilevel data structures to be modeled are limited and understudied. In the current study, we evaluated the performance of four methods, including the model-based multilevel Wald and the score-based multilevel Mantel-Haenszel (MH), and two well-established single-level methods, the model-based single-level Lord and the score-based single-level MH. We conducted a simulation study that mimics real-world scenarios. Our results suggested that when data were generated as multilevel, mixed results regarding performances were observed, and not one method consistently outperformed the others. Single-level Lord and multilevel Wald yielded best control of the Type I error rates, in particular in conditions when latent means were generated as equal for the two groups. Power rates were low across all four methods in conditions with small number of between- and within-level units and when small DIF was modeled. However, in those conditions, single-level MH and multilevel MH yielded higher power rates than either single-level Lord or multilevel Wald. This suggests that current practices in detecting DIF should strongly consider adopting one of the more recent methods only in certain contexts as the tradeoff between power and complexity of the method may not warrant a blanket recommendation in favor of a single method. Limitations and future research directions are also discussed. [ABSTRACT FROM AUTHOR]