Subarachnoid Hemorrhage Trials: Cutting, Sliding, or Keeping mRS Scores and WFNS Grades.

Rigorous evidence generation with randomized controlled trials has lagged for aneurysmal subarachnoid hemorrhage (SAH) compared with other forms of acute stroke. Besides its lower incidence compared with other stroke subtypes, the presentation and outcome of patients with SAH also differ. This must be considered and adjusted for in designing pivotal randomized controlled trials of patients with SAH. Here, we show the effect of the unique expected distribution of the SAH severity at presentation (World Federation of Neurological Surgeons grade) on the outcome most used in pivotal stroke randomized controlled trials (modified Rankin Scale) and, consequently, on the sample size. Furthermore, we discuss the advantages and disadvantages of different options to analyze the outcome and control the expected distribution of the World Federation of Neurological Surgeons grades in addition to showing their effects on the sample size. Finally, we offer methods that investigators can adapt to more precisely understand the effect of common modified Rankin Scale analysis methods and trial eligibility pertaining to the World Federation of Neurological Surgeons grade in designing their large-scale SAH randomized controlled trials.

Species-level characterization of the cervicovaginal microbiota and its role in human papillomavirus-associated cervical carcinogenesis.

The cervicovaginal microbiome may contribute to human papillomavirus (HPV)-associated cervical carcinogenesis, but studies have been limited by low-resolution analysis methods. Using a high-resolution bioinformatics pipeline, we evaluated the relationship of the cervicovaginal microbiome with HPV and cervical intraepithelial neoplasia (CIN). The cervicovaginal microbiome of 186 women was characterized by sequencing 16S rRNA regions (V3-V4 and V5-V6) and annotated with the high-resolution ANCHOR pipeline. Samples were genotyped for HPV using the Roche-Cobas 4800 assay. We fitted logistic regression models using stepwise forward selection to select species (presence/absence) as correlates of CIN1+ and constructed a linear microbiome-based score using the regression coefficients. An HPV-based score was calculated from a separate logistic regression model to detect CIN1+ . Receiver operating characteristic curve analyses were performed; the area under the curve (AUC) and 95% confidence intervals (CI) were compared between scores. Overall, 66.7% of participants were HPV-positive. 77 unique species were identified: 8 using V3-V4, 48 using V5-V6, and 21 shared. Twelve species were retained via stepwise selection. The AUCs for the microbiome-, and HPV-based scores were 0.7656 (95% CI 0.6885-0.8426), and 0.7529 (95% CI 0.6855-0.8204), respectively. Bacterial species may be involved in cervical carcinogenesis as the microbiome- and HPV-based scores performed similarly for CIN1+ detection.

Integrating Fréchet distance and AI reveals the evolutionary trajectory and origin of SARS-CoV-2.

A genome, composed of a precisely ordered sequence of four nucleotides (ATCG), encompasses a multitude of specific genome features like AAA motif. Mutations occurring within a genome disrupt the sequential order and composition of these features, thereby influencing the evolutionary trajectories and yielding variants. The evolutionary relatedness between a variant and its ancestor can be estimated by assessing evolutionary distances across a spectrum of genome features. This study develops a novel, alignment-free algorithm that considers both the sequential order and composition of genome features, enabling computation of the Fréchet distance (Fr) across multiple genome features to quantify the evolutionary status of a variant. Integrating this algorithm with an artificial recurrent neural network (RNN) reveals the quantitative evolutionary trajectory and origin of SARS-CoV-2, a puzzle unsolved by alignment-based phylogenetics. The RNN generates the evolutionary trajectory from Fr data at two levels: genome sequence mutations and organism variants. At the genome sequence level, SARS-CoV-2 evolutionarily shortens its genome to enhance its infectious capacity. Mutating signature features, such as TTA and GCT, increases its infectious potential and drives its evolution. At the organism level, variants mutating a single biomarker possess low infectious potential. However, mutating multiple markers dramatically increases their infectious capacity, propelling the COVID-19 pandemic. SARS-CoV-2 likely originates from mink coronavirus variants, with its origin trajectory traced as follows: mink, cat, tiger, mouse, hamster, dog, lion, gorilla, leopard, bat, and pangolin. Together, mutating multiple signature features and biomarkers delineates the evolutionary trajectory of mink-origin SARS-CoV-2, leading to the COVID-19 pandemic.

Detection of SARS-CoV-2 virus in middle ear effusions and its association with otitis media with effusion.

A large-scale outbreak of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) occurred in Shanghai, China, in early December 2022. To study the incidence and characteristics of otitis media with effusion (OME) complicating SARS-CoV-2, we collected 267 middle ear effusion (MEE) samples and 172 nasopharyngeal (NP) swabs from patients. The SARS-CoV-2 virus was detected by RT-PCR targeting. The SARS-CoV-2 virus, angiotensin-converting enzyme 2 (ACE2), and transmembrane serine protease 2 (TMPRSS2) expression in human samples was examined via immunofluorescence. During the COVID-19 epidemic in 2022, the incidence of OME (3%) significantly increased compared to the same period from 2020 to 2022. Ear symptoms in patients with SARS-CoV-2 complicated by OME generally appeared late, even after a negative NP swab, an average of 9.33 ± 6.272 days after COVID-19 infection. The SARS-CoV-2 virus was detected in MEE, which had a higher viral load than NP swabs. The insertion rate of tympanostomy tubes was not significantly higher than in OME patients in 2019-2022. Virus migration led to high viral loads in MEE despite negative NP swabs, indicating that OME lagged behind respiratory infections but had a favorable prognosis. Furthermore, middle ear tissue from adult humans coexpressed the ACE2 receptor for the SARS-CoV-2 virus and the TMPRSS2 cofactors required for virus entry.

Looking beyond the mean: quantile regression for comparative physiologists.

Statistical analyses that physiologists use to test hypotheses predominantly centre on means, but the tail ends of the response distribution can behave quite differently and underpin important scientific phenomena. We demonstrate that quantile regression (QR) offers a way to bypass some limitations of least squares regression (LSR) by building a picture of independent variable effects across the whole distribution of a dependent variable. We used LSR and QR with simulated and real datasets. With simulated data, LSR showed no change in the mean response but missed significant effects in the tails of the distribution found using QR. With real data, LSR showed a significant change in the mean response but missed a lack of response in the upper quantiles which was biologically revealing. Together, this highlights that QR can help to ask and answer more questions about variation in nature.

Decline in Explanatory Power of Trauma Mortality Models With Age: Varying Contribution of Glasgow Coma Scale, Injury Severity Score, Comorbidities, and Frailty.

INTRODUCTION: Adjusting for confounding variables is critical for objective comparison of outcomes. The explanatory power of variables used in adjusted models for injury and their relative utility across age groups has not been well-defined. This study aimed to assess the explanatory power of covariates commonly adjusted in injury research and their relative performance across age groups. METHODS: Inpatients 18-100 y (2017-2022) were selected from 90 hospital trauma registries. Patients were grouped into sequential 5-y age blocks. Mortality was defined as the proportion of patients "expired + hospice". Dominance analysis was used to determine the average contribution (McFadden's R2) for covariates commonly included in multivariable logistic regressions. RESULTS: Three hundred seventeen-thousand one hundred thirty-six patients were included (51.1% male, mean age: 63, mean injury severity score [ISS]: 9.8, mean Glasgow Coma Scale: 14.3, 93.5% blunt). Total explanatory power (McFadden's R2) for mortality was highest in youngest age group (52.7% in 18-24 group) and decreased with age, with the lowest R2 (19.6%) in 95-100 group. Regardless of age, the Glasgow Coma Scale was the most important covariate (R2 ranging from 9.0% to 20.4%). At age 18-24 y, ISS was a more dominant contributor than Elixhauser Score, but beyond 55 y, Elixhauser Score became more dominant than ISS. CONCLUSIONS: The explanatory power of adjustment models including common covariates is limited and varies significantly across age groups, decreasing linearly with age. Adjusting for outcomes using these covariates may limit objective comparisons especially for older adults. Additional research is needed to identify covariates that enhance the explanatory power of adjustment models to allow for more objective comparisons across all ages.

A Review of Meta-Analyses in Plastic Surgery: Need for Adequate Assessment of Publication Bias.

INTRODUCTION: Publication bias describes a phenomenon in which significant positive results have a higher likelihood of being published compared to negative or nonsignificant results. Publication bias can confound the estimated therapeutic effect in meta-analyses and needs to be adequately assessed in the surgical literature. METHODS: A review of meta-analyses published in five plastic surgery journals from 2002 to 2022 was conducted. The inclusion criteria for meta-analyses were factors that demonstrated an obligation to assess publication bias, such as interventions with comparable treatment groups and enough power for statistical analysis. Acknowledgment of publication bias risk, quality of bias assessment, methods used in assessment, and individual article factors were analyzed. RESULTS: 318 unique meta-analyses were identified in literature search, and after full-text reviews, 143 met the inclusion criteria for obligation to assess publication bias. 64% of eligible meta-analyses acknowledged the confounding potential of publication bias, and only 46% conducted a formal assessment. Of those who conducted an assessment, 49% used subjective inspection of funnel plots alone, while 47% used any statistical testing in analysis. Overall, only 9/143 (6.3%) assessed publication bias and attempted to correct for its effect. Journals with a higher average impact factor were associated with mention and assessment of publication bias, but more recent publication year and higher number of primary articles analyzed were not. CONCLUSIONS: This review identified low rates of proper publication bias assessment in meta-analyses published in five major plastic surgery journals. Assessment of publication bias using objective statistical testing is necessary to ensure quality literature within surgical disciplines.

Modelling time-varying risk factors of tooth loss: Results from joint model compared with extended Cox regression model.

AIM: To illustrate the use of joint models (JMs) for longitudinal and survival data in estimating risk factors of tooth loss as a function of time-varying endogenous periodontal biomarkers (probing pocket depth [PPD], alveolar bone loss [ABL] and mobility [MOB]). MATERIALS AND METHODS: We used data from the Veterans Affairs Dental Longitudinal Study, a longitudinal cohort study of over 30 years of follow-up. We compared the results from the JM with those from the extended Cox regression model which assumes that the time-varying covariates are exogenous. RESULTS: Our results showed that PPD is an important risk factor of tooth loss, but each model produced different estimates of the hazard. In the tooth-level analysis, based on the JM, the hazard of tooth loss increased by 4.57 (95% confidence interval [CI]: 2.13-8.50) times for a 1-mm increase in maximum PPD, whereas based on the extended Cox model, the hazard of tooth loss increased by 1.60 (95% CI: 1.37-1.87) times. CONCLUSIONS: JMs can incorporate time-varying periodontal biomarkers to estimate the hazard of tooth loss. As JMs are not commonly used in oral health research, we provide a comprehensive set of R codes and an example dataset to implement the method.

Optimizing the doses of cancer drugs after usual dose finding.

Since the middle of the 20th century, oncology's dose-finding paradigm has been oriented toward identifying a drug's maximum tolerated dose, which is then carried forward into phase 2 and 3 trials and clinical practice. For most modern precision medicines, however, maximum tolerated dose is far greater than the minimum dose needed to achieve maximal benefit, leading to unnecessary side effects. Regulatory change may decrease maximum tolerated dose's predominance by enforcing dose optimization of new drugs. Dozens of already approved cancer drugs require re-evaluation, however, introducing a new methodologic and ethical challenge in cancer clinical trials. In this article, we assess the history and current landscape of cancer drug dose finding. We provide a set of strategic priorities for postapproval dose optimization trials of the future. We discuss ethical considerations for postapproval dose optimization trial design and review three major design strategies for these unique trials that would both adhere to ethical standards and benefit patients and funders. We close with a discussion of financial and reporting considerations in the realm of dose optimization. Taken together, we provide a comprehensive, bird's eye view of the postapproval dose optimization trial landscape and offer our thoughts on the next steps required of methodologies and regulatory and funding regimes.

Designing AI for mental health diagnosis: challenges from sub-Saharan African value-laden judgements on mental health disorders.

Recently clinicians have become more reliant on technologies such as artificial intelligence (AI) and machine learning (ML) for effective and accurate diagnosis and prognosis of diseases, especially mental health disorders. These remarks, however, apply primarily to Europe, the USA, China and other technologically developed nations. Africa is yet to leverage the potential applications of AI and ML within the medical space. Sub-Saharan African countries are currently disadvantaged economically and infrastructure-wise. Yet precisely, these circumstances create significant opportunities for the deployment of medical AI, which has already been deployed in some places in the continent. However, while AI and ML have come with enormous promises in Africa, there are still challenges when it comes to successfully applying AI and ML designed elsewhere within the African context, especially in diagnosing mental health disorders. We argue, in this paper, that there ought not to be a homogeneous/generic design of AI and ML used in diagnosing mental health disorders. Our claim is grounded on the premise that mental health disorders cannot be diagnosed solely on 'factual evidence' but on both factual evidence and value-laden judgements of what constitutes mental health disorders in sub-Saharan Africa. For ML to play a successful role in diagnosing mental health disorders in sub-Saharan African medical spaces, with a precise focus on South Africa, we allude that it ought to understand what sub-Saharan Africans consider as mental health disorders, that is, the value-laden judgements of some conditions.

The eye of the beholder: how do public health researchers interpret regression coefficients? A qualitative study.

BACKGROUND: Calls for improved statistical literacy and transparency in population health research are widespread, but empirical accounts describing how researchers understand statistical methods are lacking. To address this gap, this study aimed to explore variation in researchers' interpretations and understanding of regression coefficients, and the extent to which these statistics are viewed as straightforward statements about health. METHODS: Thematic analysis of qualitative data from 45 one-to-one interviews with academics from eight countries, representing 12 disciplines. Three concepts from the sociology of scientific knowledge and science studies aided analysis: Duhem's Paradox, the Agonistic Field, and Mechanical Objectivity. RESULTS: Some interviewees viewed regression as a process of discovering 'real' relationships, while others indicated that regression models are not direct representations, and others blended these perspectives. Regression coefficients were generally not viewed as being mechanically objective, instead interpretation was described as iterative, nuanced, and sometimes depending on prior understandings. Researchers reported considering numerous factors when interpreting and evaluating regression results, including: knowledge from outside the model, whether results are expected or unexpected, 'common-sense', technical limitations, study design, the influence of the researcher, the research question, data quality and data availability. Interviewees repeatedly highlighted the role of the analyst, reinforcing that it is researchers who answer questions and assign meaning, not models. CONCLUSIONS: Regression coefficients were generally not viewed as complete or authoritative statements about health. This contrasts with teaching materials wherein statistical results are presented as straightforward representations, subject to rule-based interpretations. In practice, it appears that regression coefficients are not understood as mechanically objective. Attempts to influence conduct and presentation of regression models in the population health sciences should be attuned to the myriad factors which inform their interpretation.

Data Distribution: Normal or Abnormal?

Determining if the frequency distribution of a given data set follows a normal distribution or not is among the first steps of data analysis. Visual examination of the data, commonly by Q-Q plot, although is acceptable by many scientists, is considered subjective and not acceptable by other researchers. One-sample Kolmogorov-Smirnov test with Lilliefors correction (for a sample size ≥ 50) and Shapiro-Wilk test (for a sample size < 50) are common statistical tests for checking the normality of a data set quantitatively. As parametric tests, which assume that the data distribution is normal (Gaussian, bell-shaped), are more robust compared to their non-parametric counterparts, we commonly use transformations (e.g., log-transformation, Box-Cox transformation, etc.) to make the frequency distribution of non-normally distributed data close to a normal distribution. Herein, I wish to reflect on presenting how to practically work with these statistical methods through examining of real data sets.

Enhancing biostatistics education for medical students in Poland: factors influencing perception and educational recommendations.

BACKGROUND: A number of recommendations for the teaching of biostatistics have been published to date, however, student opinion on them has not yet been studied. For this reason, the aim of the manuscript was to find out the opinions of medical students at universities in Poland on two forms of teaching biostatistics, namely traditional and practical, as well as to indicate, on the basis of the results obtained, the related educational recommendations. METHODS: The study involved a group of 527 students studying at seven medical faculties in Poland, who were asked to imagine two different courses. The traditional form of teaching biostatistics was based on the standard teaching scheme of running a test from memory in a statistical package, while the practical one involved reading an article in which a particular test was applied and then applying it based on the instruction provided. Other aspects related to the teaching of the subject were assessed. RESULTS: According to the students of each course, the practical form of teaching biostatistics reduces the stress level associated with teaching and the student exam (p < 0.001), as well as contributing to an increased level of elevated knowledge (p < 0.001), while the degree of satisfaction after passing the exam is higher (p < 0.001). A greater proportion of students (p < 0.001) believe that credit for the course could be given by doing a statistical review of an article or conducting a survey, followed by the tests learned in class. More than 95% also said that the delivery of the courses should be based on the field of study they were taking, during which time they would also like to have the opportunity to take part in optional activities and hear lectures from experts. CONCLUSION: It is recommended that more emphasis be placed on practical teaching the subject of biostatistics.

Assessing attitudes towards biostatistics education among medical students: adaptation and preliminary evaluation of the Chinese version survey of attitudes towards statistics (SATS-36).

BACKGROUND: Despite the numerous advantages of mastering biostatistics, medical students generally perceive biostatistics as a difficult and challenging subject and even experience anxiety during the courses. Evidence for the correlation between students' academic achievements and their attitudes, indicating that attitudes at the beginning of the biostatistics course may affect cognitive competence at the end of the course and subsequently influence student academic performance. However, there are current disagreements regarding the measurement and evaluation of attitudes related to statistics. Thus, there is a need for standard instruments to assess them. This study was conducted to develop a Chinese version of the Survey of Attitudes Toward Statistics (SATS-36) in order to acquire a valid instrument to measure medical students' attitudes toward biostatistics under Chinese medical educational background. METHODS: The Chinese version SATS-36 was developed through translation and back-translation of the original scale, with subsequent revisions based on expert advice to ensure the most appropriate item content. The local adaption was performed with a cohort of 1709 Chinese-speaking medical undergraduate and graduate students enrolled in biostatistics courses. And then, the reliability, validity and discrimination of the questionnaires were evaluated through correlation coefficient calculation, factor analysis, parallel analysis and other methods. RESULTS: The Chinese version SATS-36 consisted of 36 items and loaded a five-factor structure by factor analysis, which offered an alternative similar but not equal to that original six-factor structure. The cumulative variance contribution rate was 62.20%, the Cronbach's α coefficient was 0.908, the Guttman split-half reliability coefficient was 0.905 and the test-retest reliability coefficient was 0.752. Discriminant analysis revealed small to large significant differences in the five attitude subscales. CONCLUSIONS: The Chinese version SATS-36 with good validity and reliability in this study can be used to evaluate the learning framework of Chinese medical students.

Phases of methodological research in biostatistics-Building the evidence base for new methods.

Although new biostatistical methods are published at a very high rate, many of these developments are not trustworthy enough to be adopted by the scientific community. We propose a framework to think about how a piece of methodological work contributes to the evidence base for a method. Similar to the well-known phases of clinical research in drug development, we propose to define four phases of methodological research. These four phases cover (I) proposing a new methodological idea while providing, for example, logical reasoning or proofs, (II) providing empirical evidence, first in a narrow target setting, then (III) in an extended range of settings and for various outcomes, accompanied by appropriate application examples, and (IV) investigations that establish a method as sufficiently well-understood to know when it is preferred over others and when it is not; that is, its pitfalls. We suggest basic definitions of the four phases to provoke thought and discussion rather than devising an unambiguous classification of studies into phases. Too many methodological developments finish before phase III/IV, but we give two examples with references. Our concept rebalances the emphasis to studies in phases III and IV, that is, carefully planned method comparison studies and studies that explore the empirical properties of existing methods in a wider range of problems.

Bayesian estimation in veterinary pharmacology: A conceptual and practical introduction.

Sophisticated mathematical and computational tools have become widespread and important in veterinary pharmacology. Although the theoretical basis and practical applications of these have been widely explored in the literature, statistical inference in the context of these models has received less attention. Optimization methods, often with frequentist statistical inference, have been predominant. In contrast, Bayesian statistics have not been widely applied, but offer both practical utility and arguably greater interpretability. Veterinary pharmacology applications are generally well supported by relevant prior information, from either existing substantive knowledge, or an understanding of study and model design. This facilitates practical implementation of Bayesian analyses that can take advantage of this knowledge. This essay will explore the specification of Bayesian models relevant to veterinary pharmacology, including demonstration of prior selection, and illustrate the capability of these models to generate practically useful statistics, including uncertainty statements, that are difficult or impossible to obtain otherwise. Case studies using simulated data will describe applications in clinical trials, pharmacodynamics, and pharmacokinetics, all including multilevel modeling. This content may serve as a suitable starting point for researchers in veterinary pharmacology and related disciplines considering Bayesian estimation for their applied work.

Defining the key intrahepatic gene networks in HCV infection driven by sex.

OBJECTIVE: The transcriptional response in the liver during HCV infection is critical for determining clinical outcomes. This issue remains relatively unexplored as tissue access to address this at scale is usually limited. We aimed to profile the transcriptomics of HCV-infected livers to describe the expression networks involved and assess the effect on them of major predictors of clinical outcome such as IFNL4 (interferon lambda 4) host genotype and sex. DESIGN: We took advantage of a large clinical study of HCV therapy accompanied by baseline liver biopsy to examine the drivers of transcription in tissue samples in 195 patients also genotyped genome-wide for host and viral single nucleotide polymorphisms. We addressed the role of host factors (disease status, sex, genotype, age) and viral factors (load, mutation) on transcriptional responses. RESULTS: We observe key modules of transcription which can be impacted differentially by host and viral factors. Underlying cirrhotic state had the most substantial impact, even in a stable, compensated population. Notably, sex had a major impact on antiviral responses in concert with IL28B (interleukin 28B)/IFNL4 genotype, with stronger interferon and humoral responses in females. Males tended towards a dominant cellular immune response. In both sexes, there was a strong influence of the underlying host disease status and of specific viral mutations, and sex-specific expression quantitative trait loci were also observed. CONCLUSION: These features help define the major influences on tissue responses in HCV infection, impacting on the response to treatment and with broader implications for responses in other sex-biased infections.

Power Analysis for Ordinal Analyses of the Modified Rankin Scale and an Online and Downloadable Tool for Practical Use.

BACKGROUND: Several methods for conducting power analysis of studies with outcomes across the full ordinal modified Rankin Scale are proposed in the literature. No systematic comparison of accuracy, agreement, and sensitivity to small changes in hypothesized effect sizes for these methods is available. Our aim is to conduct such a systematic comparative analysis and to introduce a comprehensive freely available online tool to facilitate appropriate power analyses for ordinal outcomes. METHODS: We performed simulation studies utilizing the control arm modified Rankin Scale distributions from the AVERT (A Very Early Rehabilitation Trial), EXTEND (Extending the Time for Thrombolysis in Emergency Neurological Deficits), and HERMES (Highly Effective Reperfusion Evaluated in Multiple Endovascular Stroke Trials) studies, as well as a uniform distribution, in combination with hypothetical treatment effects. We systematically evaluated published power formulas for Ordinal Logistic Regression and Tournament Methods (generalized odds ratio; Win Probability; Win Ratio; and Wilcoxon-Mann-Whitney U test). We also developed an online and downloadable Shiny R app facilitating sample size calculation for, and ordinal analysis of, modified Rankin Scale data. RESULTS: Power formulas for Tournament Methods performed well, while the formula for ordinal logistic regression was inaccurate. Tang's Wilcoxon-Mann-Whitney U test sample size formula exhibited the highest accuracy. All methods, including ordinal logistic regression, had almost identical empirical power for a given sample size. All power methods exhibited sensitivity to small changes in hypothesized effect size. The developed freely available online app supports analytical and visualization requirements for all investigated methods for power and statistical analyses of ordinal modified Rankin Scale outcomes. CONCLUSIONS: As Tournament Method sample size formulas are assumption-free and accurately calculate power, stroke researchers should use these methods when designing studies with outcomes measured on the full or partially collapsed modified Rankin Scale as well as other ordinal scales, even if they intend to use ordinal logistic regression for analysis. Conducting sensitivity analyses of the effect size assumptions are essential for appropriate sample size estimation. Our developed tool supports both of these recommendations (https://www.thembc.com.au/tournamentmethods).

Virome analysis provides new insights into the association between viruses and Parkinson's disease.

Parkinson's disease (PD) is a kind of neurodegenerative disease that causes a huge burden to society. Previous studies have suggested the association between PD and multiple viruses. However, there is still a lack of a virome study about PD. This study systematically identified viruses from the public RNA-sequencing data of more than 700 samples from both PD patients and the control group (most were healthy people). Only nine viruses such as human betaherpesvirus 5 and Merkel cell polyomavirus have been detected in several human brain tissues of the central nervous system, the appendix, and blood of PD patients, and all of these viruses were also detected in the control group. Most viruses were observed to have low abundance in no more than three tissues. No statistically significant differences were observed between the virus abundance in the PD patients and the control group for all viruses. The positive rates of most viruses in PD patients were higher or similar to that in the control group, although those were less than 5% for most viruses. Overall, this is the first study to systematically investigate the virome in PD patients, and provides new insights into the association between viruses and PD.

Proteomics profiling of nontumor liver tissues identifies prognostic biomarkers in hepatitis B-related hepatocellular carcinoma.

Hepatocellular carcinoma (HCC) often occurs following chronic hepatitis B virus (HBV) infection, leading to high recurrence and a low 5-year survival rate. We developed an overall survival (OS) prediction model based on protein expression profiles in HBV-infected nontumor liver tissues. We aimed to demonstrate the feasibility of using protein expression profiles in nontumor liver tissues for survival prediction. A univariate Cox and differential expression analysis were performed to identify candidate prognostic factors. A multivariate Cox analysis was performed to develop the liver gene prognostic index (LGPI). The survival differences between the different risk groups in the training and validation cohorts were also estimated. A total of 363 patients, 159 in the training cohort, and 204 in the validation cohort were included. Of the 6478 proteins extracted from nontumor liver tissues, we identified 1275 proteins altered between HCC and nontumor liver tissues. A total of 1090 out of 6478 proteins were significantly related to OS. The prognostic values of the proteins in nontumor tissues were mostly positively related to those in the tumor tissues. Protective proteins were mainly enriched in the metabolism-related pathways. From the differentially expressed proteins, the top 10 most significant prognosis-related proteins were submitted for LGPI construction. In the training and validation cohorts, this LGPI showed a great ability for distinguishing patients' OS risk stratifications. After adjusting for clinicopathological features, the LGPI was an independent prognostic factor in the training and validation cohorts. We demonstrated the prognostic value of protein expression profiling in nontumor liver tissues. The proposed LGPI was a promising predictive model for estimating OS in HBV-related HCC.