Alerta Cannabis: A Tailored-Computer Web-Based Program for the Prevention of Cannabis Use in Adolescents: A Cluster-Randomized Controlled Trial Protocol.

BACKGROUND: The growing use of cannabis in adolescence is a public health problem that must be addressed through prevention. In Spain, the average age of initiation of cannabis use in the adolescent population is 14.8 years. At 14 years, the lifetime prevalence of cannabis use is 11.7%, which increases to 51.,5% at the age of 18; the prevalence of cannabis use in the population aged 14 to 18 years is 28.6%, a figure that must be tried to reduce, that is why this school prevention program is proposed: Alerta Cannabis. METHODS: The Alerta Cannabis research project consists of design, implementation, and evaluation. In the first phase, a computer-tailored eHealth program (Alerta Cannabis) is developed based on the I-Change Model, an integrated model based on three main behavioral change processes: awareness, motivation, and action. This program consists of four 30-minute sessions that will provide culturally adapted and personalized advice to motivate students not to use cannabis through text feedback, animations, and gamification techniques. This phase will also include usability testing. In the implementation phase, secondary school students from Western Andalusia, Spain (Seville, Cádiz, Huelva, and Córdoba) and Eastern Andalusia (Jaén, Málaga, and Granada) will be randomized to an experimental condition (EC) or a control condition (CC) for a cluster randomized clinical trial (CRCT). Each condition will have 35 classes within 8 schools. GI will receive the online intervention Alerta Cannabis. EC and CC will have to fill out a questionnaire at baseline, six months, and twelve months of follow-up. In the last phase, the effect of Alerta Cannabis is evaluated. The primary outcomes are the lifetime prevalence of cannabis use and its use in the last 30 days and at 6 months. At 12 months of follow-up, the prevalence in the last 12 months will also be assessed. The secondary outcome is the intention to use cannabis. DISCUSSION: The study tests the effect of the innovative program specifically aimed to reduce the use of cannabis in the adolescent population through eHealth in Spain. The findings aim to develop and implement evidence-based cannabis prevention interventions, which could support school prevention, for instance, the assistance of school nurses. If the program proves to be effective, it could be useful to prevent cannabis use on a national and international scale. TRIAL REGISTRATION: NCT05849636. Date of registration: March 16, 2023.

Temporal pattern classification of PM2.5 chemical compositions in Seoul, Korea using K-means clustering analysis.

Particulate matter with a diameter ≤ 2.5 µm (PM2.5) is a complex mixture of particles with a variety of compositions potentially including sulfate ions (SO42-), nitrate ions (NO3-), ammonium ions (NH4+), organic and inorganic elemental carbon, and metals. Here, the temporal composition evolution of PM2.5 was analyzed to characterize its emission source, origin, and external influences. The concentrations of wintertime PM2.5 chemical compositions in Seoul, Korea during the period of 2012-2021 were classified into four representative clusters using a K-means cluster analysis method. Cluster 1 exhibited high concentrations of NO3- and NH4+ ions mainly due to the prevalence of emissions from domestic manure and fertilizer sources in the northeast. High concentrations of these two ions are conducive to generation of ammonium nitrate (NH4NO3) through atmospheric chemical reactions, resulting in relatively long-lasting high PM2.5 concentrations in Seoul. In cluster 2, high concentrations of SO42-, vanadium, and nickel were observed in frequent south-westerly winds, indicating the domestic influence of industrial facilities. Cluster 3 showed high concentrations of potassium ions and organic carbon, highlighting a pronounced external influence transported from China via prevailing westerly winds. Cluster 4 showed low PM2.5 concentrations accompanied by strong winds in warm environments, which are uncommon in winter. This study revealed that the air quality in Seoul, which was influenced by many factors, could be classified into four representative patterns. Our results provide insights into the emission sources, major influences, and responsible mechanisms of high PM2.5 concentrations in Seoul, which can help with air quality policies.

A Novel Approach to Analyze Disparities in Colorectal Cancer Screening and Mortality.

INTRODUCTION: Reducing disparities in colorectal cancer (CRC) screening rates and mortality remains a priority. Mitigation strategies to reduce these disparities have largely been unsuccessful. The primary aim is to determine variables in models of healthcare utilization and their association with CRC screening and mortality in North Carolina. METHODS: A cross-sectional analysis of publicly available data across North Carolina using variable reduction techniques with clustering to evaluate association of CRC screening rates and mortality was performed. RESULTS: Three million sixty-five thousand five hundred thirty-seven residents (32.1%) were aged 50 y or more. More than two-thirds (68.8%) were White, while 20.5% were Black. Approximately 61% aged 50 y or more underwent CRC screening (range: 44.0%-80.5%) and had a CRC mortality of 44.8 per 100,000 (range 22.8 to 76.6 per 100,000). Cluster analysis identified two factors, designated social economic education index (factor 1) and rural provider index (factor 2) for inclusion in the multivariate analysis. CRC screening rates were associated with factor 1, consisting of socioeconomic and education variables, and factor 2, comprised of the number of providers per 10,000 individuals aged 50 y or more and rurality. An increase in both factors 1 and 2 by one point would result in an increase in CRC screening rated by 6.8%. CRC mortality was associated with factor 2. An increase in one point in factor 1 results in a decrease in mortality risk by 10.9%. CONCLUSIONS: In North Carolina, using variable reduction with clustering, CRC screening rates were associated with the inter-relationship of the number of providers and rurality, while CRC mortality was associated with the inter-relationship of social, economic, and education variables.

Multivariate Cluster Analyses to Characterize Asthma Heterogeneity and Benralizumab Responsiveness.

BACKGROUND: An improved understanding of how severe asthma heterogeneity affects response could inform treatment decisions. OBJECTIVES: Characterize heterogeneity and benralizumab responsiveness in patients grouped by predefined Severe Asthma Research Program clusters using a multivariate approach. METHODS: In post-hoc analyses of the randomized, double-blind, placebo-controlled phase III SIROCCO (NCT01928771) and CALIMA (NCT01914757) studies, patients with severe asthma who received benralizumab or placebo were assigned to clusters using an established discriminant function to simultaneously analyze 11 clinical characteristics. Annualized asthma exacerbation rate, exacerbation incidence, and lung function were analyzed across clusters. RESULTS: Patients (N = 2,281) met criteria for 4 of 5 clusters: Cluster 2 (early-onset moderate asthma, n = 393), Cluster 4 (early-onset severe, n = 386), Cluster 3 (late-onset severe, n = 641), and Cluster 5 (late-onset severe, obstructed, n = 861); no patients met Cluster 1 criteria. Exacerbation rate reductions were significant in late-onset severe (-48% [95% CI: -61%, -31%], P<.0001) and late-onset severe, obstructed asthma (-50% [95% CI: -59%, -38%], P<.0001), with non-significant reductions in early-onset clusters. These differences could not be fully explained by blood eosinophil count differences. Forced expiratory volume in 1 second improvements were significant in late-onset severe (+133 mL [95% CI: 66 mL, 200 mL], P=.0001) and late-onset severe, obstructed asthma (+160 mL [95% CI: 85 mL, 235 mL], P<.0001) while maintaining acute bronchodilator responsiveness. CONCLUSIONS: Benralizumab reduced exacerbations and improved lung function, primarily in late-onset asthma clusters. This multivariate approach to identify subphenotypes, potentially reflecting pathobiological mechanisms, can guide therapy beyond univariate approaches.

Identifying subgroups in heart failure patients with multimorbidity by clustering and network analysis.

This study presents a workflow for identifying and characterizing patients with Heart Failure (HF) and multimorbidity utilizing data from Electronic Health Records. Multimorbidity, the co-occurrence of two or more chronic conditions, poses a significant challenge on healthcare systems. Nonetheless, understanding of patients with multimorbidity, including the most common disease interactions, risk factors, and treatment responses, remains limited, particularly for complex and heterogeneous conditions like HF. We conducted a clustering analysis of 3745 HF patients using demographics, comorbidities, laboratory values, and drug prescriptions. Our analysis revealed four distinct clusters with significant differences in multimorbidity profiles showing differential prognostic implications regarding unplanned hospital admissions. These findings underscore the considerable disease heterogeneity within HF patients and emphasize the potential for improved characterization of patient subgroups for clinical risk stratification through the use of EHR data.

MRI radiomics in head and neck cancer from reproducibility to combined approaches.

The clinical applicability of radiomics in oncology depends on its transferability to real-world settings. However, the absence of standardized radiomics pipelines combined with methodological variability and insufficient reporting may hamper the reproducibility of radiomic analyses, impeding its translation to clinics. This study aimed to identify and replicate published, reproducible radiomic signatures based on magnetic resonance imaging (MRI), for prognosis of overall survival in head and neck squamous cell carcinoma (HNSCC) patients. Seven signatures were identified and reproduced on 58 HNSCC patients from the DB2Decide Project. The analysis focused on: assessing the signatures' reproducibility and replicating them by addressing the insufficient reporting; evaluating their relationship and performances; and proposing a cluster-based approach to combine radiomic signatures, enhancing the prognostic performance. The analysis revealed key insights: (1) despite the signatures were based on different features, high correlations among signatures and features suggested consistency in the description of lesion properties; (2) although the uncertainties in reproducing the signatures, they exhibited a moderate prognostic capability on an external dataset; (3) clustering approaches improved prognostic performance compared to individual signatures. Thus, transparent methodology not only facilitates replication on external datasets but also advances the field, refining prognostic models for potential personalized medicine applications.

Morphological and molecular characterizations of Pratylenchus coffeae infecting Ming aralia and coffee in Vietnam.

Pratylenchus coffeae, belonging to the root-lesion nematode group, is a highly prevalent and destructive plant-parasitic nematode that is able to infest a wide range of host plants. Although this species' devastating impacts on coffee plantations across the world are widely known on other host plants, its association with Ming aralia has never been reported. Our study characterized two popu lations of P. coffeae (associated with Ming aralia and coffee) and compared them with other popu lations from previous studies in Vietnam and other countries in the world. The identification of P. coffeae in our study was confirmed by the comprehensive analysis encompassing morphological examination, morphometric data, and molecular characterizations of the COI mtDNA and D2D3 of 28S rRNA regions. The cluster and MDS analyses revealed that the two populations of P. coffeae from Vietnam are closely related to those from Japan and Indonesia. The D2-D3 sequences of 28S rRNA and COI mtDNA regions exhibited high similarity among these populations, indicating a stable genetic profile. Our research contributes to a better understanding of the distribution and genetic characterizations of P. coffeae by offering new morphological and molecular insights into the presence of this nematode in Vietnam. Additionally, this nematode species was found to be associated with host plant's symptoms such as chlorotic leaves, stunted growth and root lesion in both hosts. Given the economic significance of both Ming aralia and coffee crops in Vietnam, as well as the damaging potential of P. coffeae, this study emphasizes the need of proactive nematode management measures to control this destructive pest.

Herpes Zoster Ophthalmicus Initially Diagnosed As Cluster Headache, Complicated by Delayed Eruption.

Herpes zoster ophthalmicus (HZO) manifests as a consequence of the reactivation of the Varicella-zoster virus (VZV) and primarily affects the ophthalmic division of the trigeminal nerve. Identification of the vesicular eruption is central to the diagnostic process; however, the delayed manifestation of this cutaneous phenomenon poses a challenge to timely and accurate diagnosis. This report elucidates the case of a 61-year-old Japanese male with painful trigeminal neuropathy attributed to VZV that was initially diagnosed as cluster headache, mainly due to the delayed cutaneous eruption. Contrary to the expected pattern of cluster headache presentations, there was no discernible fluctuation in headache severity. The transient improvement of symptoms following interventions tailored for cluster headache management, including pure oxygen inhalation and subcutaneous sumatriptan injection, inadvertently contributed to a delay in accurate diagnosis. The importance of distinguishing HZO from cluster headache is emphasized, particularly in cases involving elderly patients or those with persistent cephalo-ophthalmalgia without the characteristic fluctuation of symptoms. In cases where clinical suspicion of HZO is raised, cerebrospinal fluid analysis should be performed. This approach is consistent with the overall goal of facilitating a prompt and accurate diagnosis.

Bradyrhizobium ontarionense sp. nov., a novel bacterial symbiont isolated from Aeschynomene indica (Indian jointvetch), harbours photosynthesis, nitrogen fixation and nitrous oxide (N2O) reductase genes.

A novel bacterial symbiont, strain A19T, was previously isolated from a root-nodule of Aeschynomene indica and assigned to a new lineage in the photosynthetic clade of the genus Bradyrhizobium. Here data are presented for the detailed genomic and taxonomic analyses of novel strain A19T. Emphasis is placed on the analysis of genes of practical or ecological significance (photosynthesis, nitrous oxide reductase and nitrogen fixation genes). Phylogenomic analysis of whole genome sequences as well as 50 single-copy core gene sequences placed A19T in a highly supported lineage distinct from described Bradyrhizobium species with B. oligotrophicum as the closest relative. The digital DNA-DNA hybridization and average nucleotide identity values for A19T in pair-wise comparisons with close relatives were far lower than the respective threshold values of 70% and ~ 96% for definition of species boundaries. The complete genome of A19T consists of a single 8.44 Mbp chromosome and contains a photosynthesis gene cluster, nitrogen-fixation genes and genes encoding a complete denitrifying enzyme system including nitrous oxide reductase implicated in the reduction of N2O, a potent greenhouse gas, to inert dinitrogen. Nodulation and type III secretion system genes, needed for nodulation by most rhizobia, were not detected. Data for multiple phenotypic tests complemented the sequence-based analyses. Strain A19T elicits nitrogen-fixing nodules on stems and roots of A. indica plants but not on soybeans or Macroptilium atropurpureum. Based on the data presented, a new species named Bradyrhizobium ontarionense sp. nov. is proposed with strain A19T (= LMG 32638T = HAMBI 3761T) as the type strain.

Genetic and Functional Diversity Help Explain Pathogenic, Weakly Pathogenic, and Commensal Lifestyles in the Genus Xanthomonas.

The genus Xanthomonas has been primarily studied for pathogenic interactions with plants. However, besides host and tissue-specific pathogenic strains, this genus also comprises nonpathogenic strains isolated from a broad range of hosts, sometimes in association with pathogenic strains, and other environments, including rainwater. Based on their incapacity or limited capacity to cause symptoms on the host of isolation, nonpathogenic xanthomonads can be further characterized as commensal and weakly pathogenic. This study aimed to understand the diversity and evolution of nonpathogenic xanthomonads compared to their pathogenic counterparts based on their cooccurrence and phylogenetic relationship and to identify genomic traits that form the basis of a life history framework that groups xanthomonads by ecological strategies. We sequenced genomes of 83 strains spanning the genus phylogeny and identified eight novel species, indicating unexplored diversity. While some nonpathogenic species have experienced a recent loss of a type III secretion system, specifically the hrp2 cluster, we observed an apparent lack of association of the hrp2 cluster with lifestyles of diverse species. We performed association analysis on a large data set of 337 Xanthomonas strains to explain how xanthomonads may have established association with the plants across the continuum of lifestyles from commensals to weak pathogens to pathogens. Presence of distinct transcriptional regulators, distinct nutrient utilization and assimilation genes, transcriptional regulators, and chemotaxis genes may explain lifestyle-specific adaptations of xanthomonads.

Clusters of lifestyle behavioral risk factors and their associations with depressive symptoms and stress: evidence from students at a university in Finland.

BACKGROUND: No previous research of university students in Finland assessed lifestyle behavioral risk factors (BRFs), grouped students into clusters, appraised the relationships of the clusters with their mental well-being, whilst controlling for confounders. The current study undertook this task. METHODS: Students at the University of Turku (n = 1177, aged 22.96 ± 5.2 years) completed an online questionnaire that tapped information on sociodemographic variables (age, sex, income sufficiency, accommodation during the semester), four BRFs [problematic alcohol consumption, smoking, food consumption habits, moderate-to-vigorous physical activity (MVPA)], as well as depressive symptoms and stress. Two-step cluster analysis of the BRFs using log-likelihood distance measure categorized students into well-defined clusters. Two regression models appraised the associations between cluster membership and depressive symptoms and stress, controlling for sex, income sufficiency and accommodation during the semester. RESULTS: Slightly more than half the study participants (56.8%) had always/mostly sufficient income and 33% lived with parents/partner. Cluster analysis of BRFs identified three distinct student clusters, namely Cluster 1 (Healthy Group), Cluster 2 (Smokers), and Cluster 3 (Nonsmokers but Problematic Drinkers). Age, sex and MVPA were not different across the clusters, but Clusters 1 and 3 comprised significantly more respondents with always/mostly sufficient income and lived with their parents/partner during the semester. All members in Clusters 1 and 3 were non-smokers, while all Cluster 2 members comprised occasional/daily smokers. Problematic drinking was significantly different between clusters (Cluster 1 = 0%, Cluster 2 = 54%, Cluster 3 = 100%). Cluster 3 exhibited significantly healthier nutrition habits than both other clusters. Regression analysis showed: (1) males and those with sufficient income were significantly less likely to report depressive symptoms or stress; (2) those living with parents/partner were significantly less likely to experience depressive symptoms; (3) compared to Cluster 1, students in the two other clusters were significantly more likely to report higher depressive symptoms; and (4) only students in Cluster 2 were more likely to report higher stress. CONCLUSIONS: BRFs cluster together, however, such clustering is not a clear-cut, all-or-none phenomenon. Students with BRFs consistently exhibited higher levels of depressive symptoms and stress. Educational and motivational interventions should target at-risk individuals including those with insufficient income or living with roommates or alone.

Methodological issues of retrospective surveys for measuring mortality of highly clustered diseases: case study of the 2014-16 Ebola outbreak in Bo District, Sierra Leone.

BACKGROUND: There is a lack of empirical data on design effects (DEFF) for mortality rate for highly clustered data such as with Ebola virus disease (EVD), along with a lack of documentation of methodological limitations and operational utility of mortality estimated from cluster-sampled studies when the DEFF is high. OBJECTIVES: The objectives of this paper are to report EVD mortality rate and DEFF estimates, and discuss the methodological limitations of cluster surveys when data are highly clustered such as during an EVD outbreak. METHODS: We analysed the outputs of two independent population-based surveys conducted at the end of the 2014-2016 EVD outbreak in Bo District, Sierra Leone, in urban and rural areas. In each area, 35 clusters of 14 households were selected with probability proportional to population size. We collected information on morbidity, mortality and changes in household composition during the recall period (May 2014 to April 2015). Rates were calculated for all-cause, all-age, under-5 and EVD-specific mortality, respectively, by areas and overall. Crude and adjusted mortality rates were estimated using Poisson regression, accounting for the surveys sample weights and the clustered design. RESULTS: Overall 980 households and 6,522 individuals participated in both surveys. A total of 64 deaths were reported, of which 20 were attributed to EVD. The crude and EVD-specific mortality rates were 0.35/10,000 person-days (95%CI: 0.23-0.52) and 0.12/10,000 person-days (95%CI: 0.05-0.32), respectively. The DEFF for EVD mortality was 5.53, and for non-EVD mortality, it was 1.53. DEFF for EVD-specific mortality was 6.18 in the rural area and 0.58 in the urban area. DEFF for non-EVD-specific mortality was 1.87 in the rural area and 0.44 in the urban area. CONCLUSION: Our findings demonstrate a high degree of clustering; this contributed to imprecise mortality estimates, which have limited utility when assessing the impact of disease. We provide DEFF estimates that can inform future cluster surveys and discuss design improvements to mitigate the limitations of surveys for highly clustered data.

Main findings: For humanitarian organizations it is imperative to document the methodological limitations of cluster surveys and discuss the utility.Added knowledge: This paper adds new knowledge on cluster surveys for highly clustered data such us in Ebola virus disease.Global health impact of policy and action: We provided empirical estimates and discuss design improvements to inform future study.

A Timeline of Biosynthetic Gene Cluster Discovery in Aspergillus fumigatus: From Characterization to Future Perspectives.

In 1999, the first biosynthetic gene cluster (BGC), synthesizing the virulence factor DHN melanin, was characterized in Aspergillus fumigatus. Since then, 19 additional BGCs have been linked to specific secondary metabolites (SMs) in this species. Here, we provide a comprehensive timeline of A. fumigatus BGC discovery and find that initial advances centered around the commonly expressed SMs where chemical structure informed rationale identification of the producing BGC (e.g., gliotoxin, fumigaclavine, fumitremorgin, pseurotin A, helvolic acid, fumiquinazoline). Further advances followed the transcriptional profiling of a ΔlaeA mutant, which aided in the identification of endocrocin, fumagillin, hexadehydroastechrome, trypacidin, and fumisoquin BGCs. These SMs and their precursors are the commonly produced metabolites in most A. fumigatus studies. Characterization of other BGC/SM pairs required additional efforts, such as induction treatments, including co-culture with bacteria (fumicycline/neosartoricin, fumigermin) or growth under copper starvation (fumivaline, fumicicolin). Finally, four BGC/SM pairs were discovered via overexpression technologies, including the use of heterologous hosts (fumicycline/neosartoricin, fumihopaside, sphingofungin, and sartorypyrone). Initial analysis of the two most studied A. fumigatus isolates, Af293 and A1160, suggested that both harbored ca. 34-36 BGCs. However, an examination of 264 available genomes of A. fumigatus shows up to 20 additional BGCs, with some strains showing considerable variations in BGC number and composition. These new BGCs present a new frontier in the future of secondary metabolism characterization in this important species.

Molecular Phylogenetic and Comparative Genomic Analysis of Pleurocordyceps fusiformispora sp. nov. and Perennicordyceps elaphomyceticola in the Family Polycephalomycetaceae.

Several Pleurocordyceps species have been reported as hyperparasitic fungi. A new species, Pleurocordyceps fusiformispora, and a known species, Perennicordyceps elaphomyceticola, are described here based on morphology and phylogenetic evidence from six genes (ITS, SSU, LSU, TET1-α, RPB1, and RPB2). Pl. fusiformispora differed from the other Pleurocordyceps species by producing flaky colonies, ovoid or elliptic α-conidia, and fusiform or long fusiform ß-conidia. Both full genomes of Pe. elaphomyceticola and Pl. fusiformispora were sequenced, annotated, and compared. The antiSMASH and local BLAST analyses revealed significant differences in the number and types of putative secondary metabolite biosynthetic gene clusters, i.e., NPPS, PKS, and hybrid PKS-NRPS domains, between the two species. In addition, the putative BGCs of six compounds, namely ε-poly lysine, 4-epi-15-epi-brefeldin A, Monorden D/monocillin IV/monocillin VII/pochonin M/monocillin V/monocillin II, Tolypyridone, Piperazine, and Triticone DABFC, were excavated in the present study. This study motivates the use of heterologous expression and gene knockout methods to discover novel biologically active SMs from Polycephalomycetaceae.

Dietary vitamin intake and cancer risk in patients with chronic kidney disease: results from the National Health and Nutrition Examination Survey (2007-2018).

PURPOSE: The relationship between vitamin intake and cancer risk in the chronic kidney disease (CKD) population is unknown. For this reason, we investigated the relationship between dietary vitamin intake and cancer risk in CKD patients and looked for effective vitamin dietary patterns. METHODS: This study included 3518 CKD patients from 2007 to 2018 National Health and Nutrition Examination Survey database. All participants were categorized into four groups based on vitamin intake by K-mean clustering. The data were collected and analyzed from June 2023 to December 2023. RESULTS: A total of 3518 CKD patients with a mean age of (61.8 ± 16.3) years were included in the study. During a median follow-up of 7.3 years, 137 participants died of cancer. In the multivariate adjusted cox proportional hazards model for single vitamin intake, vitamin E Q4 intake (reference Q1) reduced cancer mortality (HR (95% CI) = 0.45 (0.24-0.87), P = 0.018). Further plotting of the restricted cubic spline curve revealed a linearly decreasing relationship between vitamin E intake and cancer mortality (Poverall = 0.010 Pnon-linear = 0.163). In the multivariate adjusted cox proportional hazards model for multivitamin co-intake, the vitamin C/K intake group reduced cancer mortality compared to the low vitamin intake group (HR (95% CI) = 0.42 (0.20-0.88), P = 0.022). CONCLUSION: Increased vitamin C intake was independently associated with reduced cancer risk in CKD patients, and a vitamin dietary pattern with high vitamin C/K intake was also effective in reducing cancer risk.

A clinical prognostic model of oxidative stress-related genes linked to tumor immune cell infiltration and the prognosis of ovarian cancer patients.

Background: According to statistics, ovarian cancer (OV) is the most prevalent type of gynecologic malignancy and has the highest mortality rate of all gynecologic tumors. Although several studies have shown that oxidative stress (OS) contributes significantly to the onset and progression of cancer, the role of OS in OV needs to be investigated further. Thus, it is critical to comprehend the function of OS-related genes in OV. Methods: In this study, all data related to the transcriptome and clinical status of the patients were retrieved from "The Cancer Genome Atlas" (TCGA) and "Gene Expression Omnibus" (GEO) databases. Using the unsupervised cluster analysis technique, all patients with OV were classified into two different subtypes (categories) based on the OS gene. All hub genes were screened using the weighted gene co-expression network analysis (WGCNA). Since the hub genes and the differentially expressed genes (DEGs) in both categories were found to intersect, the univariate Cox regression analysis was implemented. A multivariate Cox analysis was also performed to construct a novel clinical prognosis model, which was validated using data from the GEO cohort. In addition, the relationship between risk score and immune cell infiltration level was evaluated using CIBERSORT. Finally, qRT-PCR was used to confirm the expression of the genes used to construct the model. Results: Two subtypes of OS were obtained. The findings indicated that OS-C1 had a better survival outcome than OS-C2. The results of WGCNA yielded 112 hub genes. For univariate COX regression analyses, 49 OS-related trait genes were obtained. Finally, a clinical prognostic model containing two genes was constructed. This model could differentiate between patients with OV having varying years of survival in the TCGA and GEO cohorts. The model risk score was verified as an independent prognostic indicator. According to the results of CIBERSORT, many tumor-infiltrating immune cells were found to be significantly related to the risk score. Furthermore, the results revealed that patients with low-risk OV in the CTLA4 treatment group had a high likelihood of benefiting from immunotherapy. qRT-PCR results also showed that the expression of MARVELD1 and VSIG4 was high in the OV samples. Conclusions: Analysis of the results suggested that the newly developed model, which contained two characteristic OS-related genes, could successfully predict the survival outcomes of all patients with OV. The findings of this study could offer valuable information and insights into the refinement of personalized therapy and immunotherapy for OV in the future.

Assessing treatment effect heterogeneity in the presence of missing effect modifier data in cluster-randomized trials.

Understanding whether and how treatment effects vary across subgroups is crucial to inform clinical practice and recommendations. Accordingly, the assessment of heterogeneous treatment effects based on pre-specified potential effect modifiers has become a common goal in modern randomized trials. However, when one or more potential effect modifiers are missing, complete-case analysis may lead to bias and under-coverage. While statistical methods for handling missing data have been proposed and compared for individually randomized trials with missing effect modifier data, few guidelines exist for the cluster-randomized setting, where intracluster correlations in the effect modifiers, outcomes, or even missingness mechanisms may introduce further threats to accurate assessment of heterogeneous treatment effect. In this article, the performance of several missing data methods are compared through a simulation study of cluster-randomized trials with continuous outcome and missing binary effect modifier data, and further illustrated using real data from the Work, Family, and Health Study. Our results suggest that multilevel multiple imputation and Bayesian multilevel multiple imputation have better performance than other available methods, and that Bayesian multilevel multiple imputation has lower bias and closer to nominal coverage than standard multilevel multiple imputation when there are model specification or compatibility issues.

Profiling the socioeconomic characteristics, dietary intake, and health status of Korean older adults for nutrition plan customization: a comparison of principal component, factor, and cluster analyses.

Objectives: This study was conducted to establish profiles of socioeconomic characteristics, dietary intake, and health status among Korean older adults by employing 3 multivariate analysis techniques. Methods: Data were obtained from 1,352 adults aged 65 years and older who participated in the 2019 Korea National Health and Nutrition Examination Survey. Principal component analysis (PCA), factor analysis (FA), and cluster analysis (CA) were utilized for profiling, with data preprocessing undertaken to facilitate these approaches. Results: PCA, FA, and CA yielded similar results, reflecting the high common variance among the variables. PCA identified 4 components, accounting for 71.6% of the accumulated variance. FA revealed 5 factors, displaying a Kaiser-Meyer-Olkin value of 0.51 and explaining 74.3% of the total variance. Finally, CA grouped the participants into 4 clusters (R2=0.465). Both PCA and FA identified dietary intake (energy, protein, carbohydrate, etc.), social support from family (incorporating family structure, number of family numbers, and engagement in social eating), and health status (encompassing oral, physical, and subjective health) as key factors. CA classified Korean older adults into 4 distinct typologies, with significant differences observed in dietary intake, health status, and household income (p<0.01). Conclusion: The study utilized PCA, FA, and CA to analyze profiling domains and derive characteristics of older adults in Korea, followed by a comparison of the results. The variables defining the clusters in CA were consistent with those identified by PCA and FA.

Data-driven classification of cognitively normal and mild cognitive impairment subtypes predicts progression in the NACC dataset.

INTRODUCTION: Data-driven neuropsychological methods can identify mild cognitive impairment (MCI) subtypes with stronger associations to dementia risk factors than conventional diagnostic methods. METHODS: Cluster analysis used neuropsychological data from participants without dementia (mean age = 71.6 years) in the National Alzheimer's Coordinating Center (NACC) Uniform Data Set (n = 26,255) and the "normal cognition" subsample (n = 16,005). Survival analyses examined MCI or dementia progression. RESULTS: Five clusters were identified: "Optimal" cognitively normal (oCN; 13.2%), "Typical" CN (tCN; 28.0%), Amnestic MCI (aMCI; 25.3%), Mixed MCI-Mild (mMCI-Mild; 20.4%), and Mixed MCI-Severe (mMCI-Severe; 13.0%). Progression to dementia differed across clusters (oCN < tCN < aMCI < mMCI-Mild < mMCI-Severe). Cluster analysis identified more MCI cases than consensus diagnosis. In the "normal cognition" subsample, five clusters emerged: High-All Domains (High-All; 16.7%), Low-Attention/Working Memory (Low-WM; 22.1%), Low-Memory (36.3%), Amnestic MCI (16.7%), and Non-amnestic MCI (naMCI; 8.3%), with differing progression rates (High-All < Low-WM = Low-Memory < aMCI < naMCI). DISCUSSION: Our data-driven methods outperformed consensus diagnosis by providing more precise information about progression risk and revealing heterogeneity in cognition and progression risk within the NACC "normal cognition" group.