C4 cereal and biofuel crop microbiomes.

Microbiomes provide multiple life-support functions for plants, including nutrient acquisition and tolerance to abiotic and biotic stresses. Considering the importance of C4 cereal and biofuel crops for food security under climate change conditions, more attention has been given recently to C4 plant microbiome assembly and functions. Here, we review the current status of C4 cereal and biofuel crop microbiome research with a focus on beneficial microbial traits for crop growth and health. We highlight the importance of environmental factors and plant genetics in C4 crop microbiome assembly and pinpoint current knowledge gaps. Finally, we discuss the potential of foxtail millet as a C4 model species and outline future perspectives of C4 plant microbiome research.

Clinical outcomes and phenotypes of respiratory failure in older subjects admitted to an acute care geriatric hospital ward.

Respiratory failure (RF) is frequent in hospitalized older patients, but was never systematically investigated in large populations of older hospitalized patients. We conducted a retrospective administrative study based on hospitalizations of a Geriatrics Unit regarding 2014, 2015, and 2016. Patients underwent daily screening for hypoxia. Hospital discharge records were coded through a standardized methodology. RF, defined as documented hypoxia on room air, was always coded, whenever present. We investigated how RF affected clinical outcomes, whether RF grouped into specific comorbidity phenotypes, and how phenotypes associated with the outcomes. RF was coded in 48.6% of the 1,810 hospitalizations. RF patients were older and more frequently had congestive heart failure (CHF: 49 vs 23%), chronic obstructive pulmonary disease (COPD: 27 vs 6%), pneumonia (14 vs 4%), sepsis (12 vs 7%), and pleural effusion (6 vs 3%), than non-RF patients. RF predicted longer length of stay (a-Beta 2.05, 95% CI 1.4-2.69; p < 0.001) and higher in-hospital death/intensive care units (ICU) need (aRR 7.12, 5-10.15; p < 0.001) after adjustment for confounders (linear and Poisson regression with robust error variance). Among RF patients, cerebrovascular disease, cancer, electrolyte disturbances, sepsis, and non-invasive ventilation predicted increased, while CHF and COPD predicted decreased in-hospital death/ICU need. The ONCO (cancer) and Mixed (cerebrovascular disease, dementia, pneumonia, sepsis, electrolyte disturbances, bedsores) phenotypes displayed higher in-hospital death/ICU need than CARDIO (CHF) and COPD phenotypes. In this study, RF predicted increased hospital death/ICU need and longer hospital stay, but also reflected diverse underlying conditions and clinical phenotypes that accounted for different clinical courses.

The Relationship between Dietary Patterns and Metabolic Phenotypes: A Cross-Sectional Study in a Representative Sample of Iranian Industrial Employees.

Background and Aim: There is limited evidence to support the relationship between dietary patterns and metabolic phenotypes. Therefore, this study aimed to assess the association of dietary patterns with metabolic phenotypes among a large sample of Iranian industrial employees. Methods: This cross-sectional study was conducted among 3,063 employees of Esfahan Steel Company, Iran. Using exploratory factor analysis, major dietary patterns were obtained from a validated short form of food frequency questionnaire. The metabolic phenotypes were defined according to Adult Treatment Panel III guidelines. The independent-sample t-test, one-way analysis of variance, χ2 test, and multivariable logistic regression were applied to analyze data. Results: Three major dietary patterns were identified by factor analysis: the Western dietary pattern, the healthy dietary pattern, and the traditional dietary pattern. After controlling for potential confounders, subjects in the highest tertile of Western dietary pattern score had a higher odds ratio (OR) for metabolically healthy obese (MHO; OR 1.58, 95% confidence interval [CI]: 1.29-1.94), metabolically unhealthy normal weight (OR 1.93, 95% CI 1.08-3.45), and metabolically unhealthy obese (MUHO) phenotypes (OR 2.87, 95% CI 2.05-4.03) than those in the lowest tertile. Also, higher adherence to traditional dietary pattern was positively associated with a higher risk of MHO (OR 1.91, 95% CI 1.56-2.34) and MUHO phenotypes (OR 2.33, 95% CI 1.69-3.22) in the final model. Conclusion: There were significant associations between dietary patterns and metabolic phenotypes, suggesting the necessity of nutritional interventions in industrial employees to improve metabolic phenotype, health outcomes, and, therefore, job productivity in the workforce population.

Using machine learning to discover traumatic brain injury patient phenotypes: national concussion surveillance system Pilot.

OBJECTIVE: The objective is to determine whether unsupervised machine learning identifies traumatic brain injury (TBI) phenotypes with unique clinical profiles. METHODS: Pilot self-reported survey data of over 10,000 adults were collected from the Centers for Disease Control and Prevention (CDC)'s National Concussion Surveillance System (NCSS). Respondents who self-reported a head injury in the past 12 months (n = 1,364) were retained and queried for injury, outcome, and clinical characteristics. An unsupervised machine learning algorithm, partitioning around medoids (PAM), that employed Gower's dissimilarity matrix, was used to conduct a cluster analysis. RESULTS: PAM grouped respondents into five TBI clusters (phenotypes A-E). Phenotype C represented more clinically severe TBIs with a higher prevalence of symptoms and association with worse outcomes. When compared to individuals in Phenotype A, a group with few TBI-related symptoms, individuals in Phenotype C were more likely to undergo medical evaluation (odds ratio [OR] = 9.8, 95% confidence interval[CI] = 5.8-16.6), have symptoms that were not currently resolved or resolved in 8+ days (OR = 10.6, 95%CI = 6.2-18.1), and more likely to report at least moderate impact on social (OR = 54.7, 95%CI = 22.4-133.4) and work (OR = 25.4, 95%CI = 11.2-57.2) functioning. CONCLUSION: Machine learning can be used to classify patients into unique TBI phenotypes. Further research might examine the utility of such classifications in supporting clinical diagnosis and patient recovery for this complex health condition.

From Organelle Morphology to Whole-Plant Phenotyping: A Phenotypic Detection Method Based on Deep Learning.

The analysis of plant phenotype parameters is closely related to breeding, so plant phenotype research has strong practical significance. This paper used deep learning to classify Arabidopsis thaliana from the macro (plant) to the micro level (organelle). First, the multi-output model identifies Arabidopsis accession lines and regression to predict Arabidopsis's 22-day growth status. The experimental results showed that the model had excellent performance in identifying Arabidopsis lines, and the model's classification accuracy was 99.92%. The model also had good performance in predicting plant growth status, and the regression prediction of the model root mean square error (RMSE) was 1.536. Next, a new dataset was obtained by increasing the time interval of Arabidopsis images, and the model's performance was verified at different time intervals. Finally, the model was applied to classify Arabidopsis organelles to verify the model's generalizability. Research suggested that deep learning will broaden plant phenotype detection methods. Furthermore, this method will facilitate the design and development of a high-throughput information collection platform for plant phenotypes.

A scoping review evaluating physical and cognitive functional outcomes in cancer survivors treated with chemotherapy: charting progress since the 2018 NCI think tank on cancer and aging phenotypes.

PURPOSE: The primary goal of this scoping review was to summarize the literature published after the 2018 National Cancer Institute think tank, "Measuring Aging and Identifying Aging Phenotypes in Cancer Survivors," on physical and cognitive functional outcomes among cancer survivors treated with chemotherapy. We focused on the influence of chemotherapy on aging-related outcomes (i.e., physical functional outcomes, cognitive functional outcomes, and frailty), given the known associations between chemotherapy and biologic mechanisms that affect aging-related physiologic processes. METHODS: A search was conducted across electronic databases, including PubMed, Scopus, and Web of Science, for manuscripts published between August 2018 and July 2023. Eligible studies: 1) included physical function, cognitive function, and/or frailty as outcomes; 2) included cancer survivors (as either the whole sample or a subgroup); 3) reported on physical or cognitive functional outcomes and/or frailty related to chemotherapy treatment (as either the whole sample or a subgroup); and 4) were observational in study design. RESULTS: The search yielded 989 potentially relevant articles, of which 65 met the eligibility criteria. Of the 65 studies, 49 were longitudinal, and 16 were cross-sectional; 30 studies (46%) focused on breast cancer, 20 studies (31%) focused on the age group 60 + years, and 17 (26%) focused on childhood cancer survivors. With regards to outcomes, 82% of 23 studies reporting on physical function showed reduced physical function, 74% of 39 studies reporting on cognitive functional outcomes found reduced cognitive function, and 80% of 15 studies reporting on frailty found increasing frailty among cancer survivors treated with chemotherapy over time and/or compared to individuals not treated with chemotherapy. Fourteen studies (22%) evaluated biologic mechanisms and their relationship to aging-related outcomes. Inflammation was consistently associated with worsening physical and cognitive functional outcomes and epigenetic age increases. Further, DNA damage was consistently associated with worse aging-related outcomes. CONCLUSION: Chemotherapy is associated with reduced physical function, reduced cognitive function, and an increase in frailty in cancer survivors; these associations were demonstrated in longitudinal and cross-sectional studies. Inflammation and epigenetic age acceleration are associated with worse physical and cognitive function; prospective observational studies with multiple time points are needed to confirm these findings. IMPLICATIONS FOR CANCER SURVIVORS: This scoping review highlights the need for interventions to prevent declines in physical and cognitive function in cancer survivors who have received chemotherapy.

Towards personalized medicine for the treatment of equine asthma.

Although horses with asthma share similar clinical signs, the heterogeneity of the disease in terms of severity, triggering factors, inflammatory profile, and pathological features has hindered our ability to define biologically distinct subgroups. The recognition of phenotypes and endotypes could enable the development of precision medicine, including personalized, targeted therapy, to benefit affected horses. While in its infancy in horses, this review outlines the phenotypes of equine asthma and discusses how knowledge gained from targeted therapy in human medicine can be applied to evaluate the potential opportunities for personalized medicine in equine asthma and to suggest avenues for research to advance this emerging field.

Multidimensional phenotyping to distinguish among distinct obstructive sleep apnea, chronic obstructive pulmonary disease, and overlap syndrome phenotypes.

OBJECTIVE/BACKGROUND: Chronic obstructive pulmonary disease (COPD), obstructive sleep apnea (OSA) and their comorbid association called Overlap Syndrome (OS) are frequent chronic diseases with high individual and societal burdens. Precise descriptions of the respective symptoms, comorbidities, and medications associated with these three conditions are lacking. We used a multidimensional phenotyping approach to identify relevant phenotypes characterizing these 3 disorders. PATIENTS/METHODS: 308 patients with OSA, COPD and OS were prospectively assessed using a combination of body shape measurements and multidimensional questionnaires evaluating sleep, fatigue, depression and respiratory symptoms. Comorbidities and medications were confirmed by physicians. Patients made home blood pressure self-measurements using a connected wearable device to identify undiagnosed or uncontrolled hypertension. RESULTS: Three distinct relevant phenotypes were identified. OSA patients were round in shape with a balanced waist-to-hip ratio, frequent witnessed apneas, nocturia, daytime sleepiness, depression, and high diastolic blood pressure. COPD patients had a thinner body shape with a high waist-to-hip ratio, complained mainly of fatigue, and exhibited a higher resting heart rate. OS patients were round in shape with a balanced waist-to-hip ratio, reported little sleepiness and depression, but had impaired sleep and the highest rate of cardio-metabolic comorbidities. Diminished fitness-to-drive was most apparent in patients with OSA and OS. Home blood pressure measurements identified undiagnosed hypertension in 80 % of patients and in nearly 80 % of those with hypertension it was uncontrolled by their current medications. CONCLUSIONS: Our systematic multidimensional phenotyping approach identified distinct body shapes, symptoms, and comorbidity profiles among patients with OSA, COPD, and OS.

Comparing GDF9 in mature follicles and clinical outcomes across different PCOS phenotype.

Background: Polycystic ovary syndrome (PCOS) is main cause of anovulatory infertility in women with gestational age. There are currently four distinct phenotypes associated with individualized endocrinology and metabolism. Growth differentiation factor 9 (GDF9) is a candidate as potential biomarker for the assessment of oocyte competence. The effect on oocyte capacity has not been evaluated and analyzed in PCOS phenotypes. Objective: We aimed to screen the expression levels of GDF9 in mature follicles of women with controlled ovarian hyperstimulation (COS) with different PCOS phenotypes. To determine the correlation between the expression level of GDF9 and oocyte development ability. Methods: In Part 1, we conducted a retrospective study comparing the clinical outcomes and endocrine characteristics of patients with PCOS according to different subgroups (depending on the presence or absence of the main features of polycystic ovarian morphology (PCOM), hyperandrogenism (HA), and oligo-anovulation (OA)) and non-PCOS control group. We stratified PCOS as phenotype A (n = 29), phenotype B (n = 18) and phenotype D (n = 24). In Part 2, the expression of GDF9 in follicular fluid (FF) and cumulus cells (CCs) were detected by enzyme-linked immunosorbent assay (ELISA) and immunohistochemistry, respectively. Results: In Part 1, the baseline clinical, hormonal, and ultrasonographic characteristics of the study population were matched with the presence or absence of the cardinal features of each PCOS phenotypes showed a clear difference. Phenotypes A and D had statistically significant associations with blastocyst formation and clinical pregnancy compared with phenotypes B (p < 0.001). In Part 2, the levels of GDF9 in FF and CCs for phenotype A and B were significantly were higher than those of phenotype D (P = 0.019, P = 0.0015, respectively). Multivariate logistic regression analysis showed that GDF9 was an important independent predictor of blastocyst formation (P＜0.001). The blastocyst formation rate of phenotype A was higher than that of phenotype B and D (P＜0.001). Combining the results of the two parts, GDF9 appears to play a powerful role in the development of embryos into blastocysts. Conclusions: GDF9 expression varies with different PCOS phenotypes. Phenotype A had higher GDF9 levels and blastocyst formation ability.

Exploring the link between obesity and hypothyroidism in autoimmune thyroid diseases: a metabolic perspective.

Background: The management of primary hypothyroidism demands a comprehensive approach that encompasses both the implications of autoimmune thyroid disease and the distinct effects posed by obesity and metabolic irregularities. Despite its clinical importance, the interplay between obesity and hypothyroidism, especially in the context of metabolic perspectives, is insufficiently explored in existing research. This study endeavors to classify hypothyroidism by considering the presence of autoimmune thyroid disease and to examine its correlation with various metabolic obesity phenotypes. Method: This research was conducted by analyzing data from 1,170 individuals enrolled in the Thyroid Disease Database of Shandong Provincial Hospital. We assessed four distinct metabolic health statuses among the participants: Metabolically Healthy No Obese Metabolically Healthy Obese Metabolically Unhealthy No Obese and Metabolically Unhealthy Obese Utilizing logistic regression, we investigated the association between various metabolic obesity phenotypes and hypothyroidism. Results: The study revealed a significant correlation between the Metabolically Unhealthy Obese (MUO) phenotype and hypothyroidism, particularly among women who do not have thyroid autoimmunity. Notably, the Metabolically Unhealthy No Obese (MUNO) phenotype showed a significant association with hypothyroidism in individuals with thyroid autoimmunity, with a pronounced prevalence in women. Furthermore, elevated levels of triglycerides and blood glucose were found to be significantly associated with hypothyroidism in men with thyroid autoimmunity and in women without thyroid autoimmunity. Conclusion: Effective treatment of hypothyroidism requires a thorough understanding of the process of thyroid autoimmune development. In patients without concurrent thyroid autoimmunity, there is a notable correlation between obesity and metabolic issues with reduced thyroid function. Conversely, for patients with thyroid autoimmunity, a focused approach on managing metabolic abnormalities, especially triglyceride levels, is crucial.

Comparative transcriptomics analysis reveals defense mechanisms of Manihot esculenta Crantz against Sri Lanka Cassava MosaicVirus.

BACKGROUND: Cassava mosaic disease (CMD), caused by Sri Lankan cassava mosaic virus (SLCMV) infection, has been identified as a major pernicious disease in Manihot esculenta Crantz (cassava) plantations. It is widespread in Southeast Asia, especially in Thailand, which is one of the main cassava supplier countries. With the aim of restricting the spread of SLCMV, we explored the gene expression of a tolerant cassava cultivar vs. a susceptible cassava cultivar from the perspective of transcriptional regulation and the mechanisms underlying plant immunity and adaptation. RESULTS: Transcriptomic analysis of SLCMV-infected tolerant (Kasetsart 50 [KU 50]) and susceptible (Rayong 11 [R 11]) cultivars at three infection stages-that is, at 21 days post-inoculation (dpi) (early/asymptomatic), 32 dpi (middle/recovery), and 67 dpi (late infection/late recovery)-identified 55,699 expressed genes. Differentially expressed genes (DEGs) between SLCMV-infected KU 50 and R 11 cultivars at (i) 21 dpi to 32 dpi (the early to middle stage), and (ii) 32 dpi to 67 dpi (the middle stage to late stage) were then identified and validated by real-time quantitative PCR (RT-qPCR). DEGs among different infection stages represent genes that respond to and regulate the viral infection during specific stages. The transcriptomic comparison between the tolerant and susceptible cultivars highlighted the role of gene expression regulation in tolerant and susceptible phenotypes. CONCLUSIONS: This study identified genes involved in epigenetic modification, transcription and transcription factor activities, plant defense and oxidative stress response, gene expression, hormone- and metabolite-related pathways, and translation and translational initiation activities, particularly in KU 50 which represented the tolerant cultivar in this study.

Unveiling Promising Neuroimaging Biomarkers for Schizophrenia Through Clinical and Genetic Perspectives.

Schizophrenia is a complex and serious brain disorder. Neuroscientists have become increasingly interested in using magnetic resonance-based brain imaging-derived phenotypes (IDPs) to investigate the etiology of psychiatric disorders. IDPs capture valuable clinical advantages and hold biological significance in identifying brain abnormalities. In this review, we aim to discuss current and prospective approaches to identify potential biomarkers for schizophrenia using clinical multimodal neuroimaging and imaging genetics. We first described IDPs through their phenotypic classification and neuroimaging genomics. Secondly, we discussed the applications of multimodal neuroimaging by clinical evidence in observational studies and randomized controlled trials. Thirdly, considering the genetic evidence of IDPs, we discussed how can utilize neuroimaging data as an intermediate phenotype to make association inferences by polygenic risk scores and Mendelian randomization. Finally, we discussed machine learning as an optimum approach for validating biomarkers. Together, future research efforts focused on neuroimaging biomarkers aim to enhance our understanding of schizophrenia.

Aneuploidy is Linked to Neurological Phenotypes Through Oxidative Stress.

Aneuploidy, having an aberrant genome, is gaining increasing attention in neurodegenerative diseases. It gives rise to proteotoxic stress as well as a stereotypical oxidative shift which makes these cells sensitive to internal and environmental stresses. A growing body of research from numerous laboratories suggests that many neurodegenerative disorders, especially Alzheimer's disease and frontotemporal dementia, are characterised by neuronal aneuploidy and the ensuing apoptosis, which may contribute to neuronal loss. Using Drosophila as a model, we investigated the effect of induced aneuploidy in GABAergic neurons. We found an increased proportion of aneuploidy due to Mad2 depletion in the third-instar larval brain and increased cell death. Depletion of Mad2 in GABAergic neurons also gave a defective climbing and seizure phenotype. Feeding animals an antioxidant rescued the climbing and seizure phenotype. These findings suggest that increased aneuploidy leads to higher oxidative stress in GABAergic neurons which causes cell death, climbing defects, and seizure phenotype. Antioxidant feeding represents a potential therapy to reduce the aneuploidy-driven neurological phenotype.

A genomic compendium of cultivated human gut fungi characterizes the gut mycobiome and its relevance to common diseases.

The gut fungal community represents an essential element of human health, yet its functional and metabolic potential remains insufficiently elucidated, largely due to the limited availability of reference genomes. To address this gap, we presented the cultivated gut fungi (CGF) catalog, encompassing 760 fungal genomes derived from the feces of healthy individuals. This catalog comprises 206 species spanning 48 families, including 69 species previously unidentified. We explored the functional and metabolic attributes of the CGF species and utilized this catalog to construct a phylogenetic representation of the gut mycobiome by analyzing over 11,000 fecal metagenomes from Chinese and non-Chinese populations. Moreover, we identified significant common disease-related variations in gut mycobiome composition and corroborated the associations between fungal signatures and inflammatory bowel disease (IBD) through animal experimentation. These resources and findings substantially enrich our understanding of the biological diversity and disease relevance of the human gut mycobiome.

Atypical familial hemophagocytic lymphohistiocytosis type 3 in children: A report of cases and literature review.

BACKGROUND: Familial hemophagocytic lymphohistiocytosis type 3 (FHL3) is caused by UNC13D variants. The clinical manifestations of FHL3 are highly diverse and complex. Some patients exhibit atypical or incomplete phenotypes, making accurate diagnosis difficult. Our study aimed to broaden the understanding of the atypical FHL3 clinical spectrum. METHODS: In our study, we analyzed in detail the clinical features of four Chinese patients with UNC13D variants. Additionally, we conducted a comprehensive review of the existing literature on previously reported atypical manifestations and summarized the findings. RESULTS: Two of our patients presented with muscle involvement, while the other two had hematological involvement; none of them met the diagnostic criteria for hemophagocytic lymphohistiocytosis (HLH). However, protein expression and functional analysis ultimately confirmed diagnostic criteria for FHL3 in all patients. From the literature we reviewed, many atypical FHL3 patients had neurological involvement, especially isolated neurological manifestations. At the same time, arthritis and hypogammaglobulinemia were also prone to occur. CONCLUSION: Our study highlights that the expression of the Munc13-4 protein may not fully indicate the pathogenicity of UNC13D variants, whereas CD107a analysis could be more sensitive for disease diagnosis. These findings contribute to a broader understanding of the FHL3 clinical spectrum and may offer new insights into the underlying pathogenesis of UNC13D variants. It is crucial to prioritize the timely and accurate diagnosis of atypical patients, as they may often be overlooked among individuals with rheumatic or hematological diseases.

The biomedical knowledge graph of symptom phenotype in coronary artery plaque: machine learning-based analysis of real-world clinical data.

A knowledge graph can effectively showcase the essential characteristics of data and is increasingly emerging as a significant means of integrating information in the field of artificial intelligence. Coronary artery plaque represents a significant etiology of cardiovascular events, posing a diagnostic challenge for clinicians who are confronted with a multitude of nonspecific symptoms. To visualize the hierarchical relationship network graph of the molecular mechanisms underlying plaque properties and symptom phenotypes, patient symptomatology was extracted from electronic health record data from real-world clinical settings. Phenotypic networks were constructed utilizing clinical data and protein‒protein interaction networks. Machine learning techniques, including convolutional neural networks, Dijkstra's algorithm, and gene ontology semantic similarity, were employed to quantify clinical and biological features within the network. The resulting features were then utilized to train a K-nearest neighbor model, yielding 23 symptoms, 41 association rules, and 61 hub genes across the three types of plaques studied, achieving an area under the curve of 92.5%. Weighted correlation network analysis and pathway enrichment were subsequently utilized to identify lipid status-related genes and inflammation-associated pathways that could help explain the differences in plaque properties. To confirm the validity of the network graph model, we conducted coexpression analysis of the hub genes to evaluate their potential diagnostic value. Additionally, we investigated immune cell infiltration, examined the correlations between hub genes and immune cells, and validated the reliability of the identified biological pathways. By integrating clinical data and molecular network information, this biomedical knowledge graph model effectively elucidated the potential molecular mechanisms that collude symptoms, diseases, and molecules.

Heterogeneity, Bayesian thinking, and phenotyping in critical care: A primer.

DISCLAIMER: In an effort to expedite the publication of articles, AJHP is posting manuscripts online as soon as possible after acceptance. Accepted manuscripts have been peer-reviewed and copyedited, but are posted online before technical formatting and author proofing. These manuscripts are not the final version of record and will be replaced with the final article (formatted per AJHP style and proofed by the authors) at a later time. PURPOSE: To familiarize clinicians with the emerging concepts in critical care research of Bayesian thinking and personalized medicine through phenotyping and explain their clinical relevance by highlighting how they address the issues of frequent negative trials and heterogeneity of treatment effect. SUMMARY: The past decades have seen many negative (effect-neutral) critical care trials of promising interventions, culminating in calls to improve the field's research through adopting Bayesian thinking and increasing personalization of critical care medicine through phenotyping. Bayesian analyses add interpretive power for clinicians as they summarize treatment effects based on probabilities of benefit or harm, contrasting with conventional frequentist statistics that either affirm or reject a null hypothesis. Critical care trials are beginning to include prospective Bayesian analyses, and many trials have undergone reanalysis with Bayesian methods. Phenotyping seeks to identify treatable traits to target interventions to patients expected to derive benefit. Phenotyping and subphenotyping have gained prominence in the most syndromic and heterogenous critical care disease states, acute respiratory distress syndrome and sepsis. Grouping of patients has been informative across a spectrum of clinically observable physiological parameters, biomarkers, and genomic data. Bayesian thinking and phenotyping are emerging as elements of adaptive clinical trials and predictive enrichment, paving the way for a new era of high-quality evidence. These concepts share a common goal, sifting through the noise of heterogeneity in critical care to increase the value of existing and future research. CONCLUSION: The future of critical care medicine will inevitably involve modification of statistical methods through Bayesian analyses and targeted therapeutics via phenotyping. Clinicians must be familiar with these systems that support recommendations to improve decision-making in the gray areas of critical care practice.

Identification of sleep phenotypes in COPD using machine learning-based cluster analysis.

BACKGROUND: Disturbed sleep in patients with COPD impact quality of life and predict adverse outcomes. RESEARCH QUESTION: To identify distinct phenotypic clusters of patients with COPD using objective sleep parameters and evaluate the associations between clusters and all-cause mortality to inform risk stratification. STUDY DESIGN AND METHODS: A longitudinal observational cohort study using nationwide Veterans Health Administration data of patients with COPD investigated for sleep disorders. Sleep parameters were extracted from polysomnography physician interpretation using a validated natural language processing algorithm. We performed cluster analysis using an unsupervised machine learning algorithm (K-means) and examined the association between clusters and mortality using Cox regression analysis, adjusted for potential confounders, and visualized with Kaplan-Meier estimates. RESULTS: Among 9992 patients with COPD and a clinically indicated baseline polysomnogram, we identified five distinct clusters based on age, comorbidity burden and sleep parameters. Overall mortality increased from 9.4 % to 42 % and short-term mortality (<5.3 years) ranged from 3.4 % to 24.3 % in Cluster 1 to 5. In Cluster 1 younger age, in 5 high comorbidity burden and in the other three clusters, total sleep time and sleep efficiency had significant associations with mortality. INTERPRETATION: We identified five distinct clinical clusters and highlighted the significant association between total sleep time and sleep efficiency on mortality. The identified clusters highlight the importance of objective sleep parameters in determining mortality risk and phenotypic characterization in this population.

Immune cell signatures and inflammatory mediators: unraveling their genetic impact on chronic kidney disease through Mendelian randomization.

Prior research has established associations between immune cells, inflammatory proteins, and chronic kidney disease (CKD). Our Mendelian randomization study aims to elucidate the genetic causal relationships among these factors and CKD. We applied Mendelian randomization using genetic variants associated with CKD from a large genome-wide association study (GWAS) and inflammatory markers from a comprehensive GWAS summary. The causal links between exposures (immune cell subtypes and inflammatory proteins) and CKD were primarily analyzed using the inverse variance-weighted, supplemented by sensitivity analyses, including MR-Egger, weighted median, weighted mode, and MR-PRESSO. Our analysis identified both absolute and relative counts of CD28 + CD45RA + CD8 + T cell (OR = 1.01; 95% CI = 1.01-1.02; p < 0.001, FDR = 0.018) (OR = 1.01; 95% CI = 1.00-1.01; p < 0.001, FDR = 0.002), CD28 on CD39 + CD8 + T cell(OR = 0.97; 95% CI = 0.96-0.99; p < 0.001, FDR = 0.006), CD16 on CD14-CD16 + monocyte (OR = 1.02; 95% CI = 1.01-1.03; p < 0.001, FDR = 0.004) and cytokines, such as IL-17A(OR = 1.11, 95% CI = 1.06-1.16, p < 0.001, FDR = 0.001), and LIF-R(OR = 1.06, 95% CI = 1.02-1.10, p = 0.005, FDR = 0.043) that are genetically predisposed to influence the risk of CKD. Moreover, the study discovered that CKD itself may causatively lead to alterations in certain proteins, including CST5(OR = 1.16, 95% CI = 1.09-1.24, p < 0.001, FDR = 0.001). No evidence of reverse causality was found for any single biomarker and CKD. This comprehensive MR investigation supports a genetic causal nexus between certain immune cell subtypes, inflammatory proteins, and CKD. These findings enhance the understanding of CKD's immunological underpinnings and open avenues for targeted treatments.