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Animal : An International Journal of... Jun 2024Feed efficiency is an important trait of dairy production. However, assessing feed efficiency is constrained by the associated cost and difficulty in measuring...
Feed efficiency is an important trait of dairy production. However, assessing feed efficiency is constrained by the associated cost and difficulty in measuring individual feed intake, especially on pastures. The objective of this study was to investigate short-term feed efficiency traits of herbage-fed dairy cows and screening of potential biomarkers (n = 238). Derived feed efficiency traits were ratio-based (i.e., feed conversion ratio (FCR) and N use efficiency (NUE)) or residual-based (i.e., residual feed intake (RFI), residual energy intake (REI), and residual N intake (RNI)). Thirty-eight Holstein and 16 Swiss Fleckvieh dairy cows underwent a 7-d measurement period during mid- and/or late-lactation. The experimental data (n = 100 measurement points) covered different lactational and herbage-fed system situations: mid-lactation grazing (n = 56), late-lactation grazing (n = 28), and late-lactation barn feeding (n = 16). During each measuring period, the individual herbage intake of each cow was estimated using the n-alkane marker technique. For each cow, biomarkers representing milk constituents (n = 109), animal characteristics (n = 13), behaviour, and activity (n = 46), breath emissions (n = 3), blood constituents (n = 35), surface, and rectal temperature (n = 29), hair cortisol (n = 1), and near-infrared (NIR) spectra of faeces and milk (n = 2) were obtained. The relationships between biomarkers and efficiency traits were statistically analysed with univariate linear regression and for NIR spectra using partial least squares regression with feed efficiency traits. The feed efficiency traits were interrelated with each other (r: -0.57 to -0.86 and 0.49-0.81). The biomarkers showed varying R values in explaining the variability of feed efficiency traits (FCR: 0.00-0.66, NUE: 0.00-0.74, RFI: 0.00-0.56, REI: 0.00-0.69, RNI: 0.00-0.89). Overall, the feed efficiency traits were best explained by NIR spectral characteristics of milk and faeces (R: 0.25-0.89). Biomarkers show potential for predicting feed efficiency in herbage-fed dairy cows. NIR spectra data analysis of milk and faeces presents a promising method for estimating individual feed efficiency upon further validation of prediction models. Future applications will depend on the ability to improve the robustness of biomarkers to predict feed efficiency in a greater variety of environments (locations), managing conditions, feeding systems, production intensities, and other aspects.
PubMed: 38935984
DOI: 10.1016/j.animal.2024.101211 -
JMIR Bioinformatics and Biotechnology Jun 2024Health care is at a turning point. We are shifting from protocolized medicine to precision medicine, and digital health systems are facilitating this shift. By providing...
Health care is at a turning point. We are shifting from protocolized medicine to precision medicine, and digital health systems are facilitating this shift. By providing clinicians with detailed information for each patient and analytic support for decision-making at the point of care, digital health technologies are enabling a new era of precision medicine. Genomic data also provide clinicians with information that can improve the accuracy and timeliness of diagnosis, optimize prescribing, and target risk reduction strategies, all of which are key elements for precision medicine. However, genomic data are predominantly seen as diagnostic information and are not routinely integrated into the clinical workflows of electronic medical records. The use of genomic data holds significant potential for precision medicine; however, as genomic data are fundamentally different from the information collected during routine practice, special considerations are needed to use this information in a digital health setting. This paper outlines the potential of genomic data integration with electronic records, and how these data can enable precision medicine.
PubMed: 38935958
DOI: 10.2196/55632 -
Journal of Medical Internet Research Jun 2024Artificial intelligence, particularly chatbot systems, is becoming an instrumental tool in health care, aiding clinical decision-making and patient engagement. (Comparative Study)
Comparative Study
BACKGROUND
Artificial intelligence, particularly chatbot systems, is becoming an instrumental tool in health care, aiding clinical decision-making and patient engagement.
OBJECTIVE
This study aims to analyze the performance of ChatGPT-3.5 and ChatGPT-4 in addressing complex clinical and ethical dilemmas, and to illustrate their potential role in health care decision-making while comparing seniors' and residents' ratings, and specific question types.
METHODS
A total of 4 specialized physicians formulated 176 real-world clinical questions. A total of 8 senior physicians and residents assessed responses from GPT-3.5 and GPT-4 on a 1-5 scale across 5 categories: accuracy, relevance, clarity, utility, and comprehensiveness. Evaluations were conducted within internal medicine, emergency medicine, and ethics. Comparisons were made globally, between seniors and residents, and across classifications.
RESULTS
Both GPT models received high mean scores (4.4, SD 0.8 for GPT-4 and 4.1, SD 1.0 for GPT-3.5). GPT-4 outperformed GPT-3.5 across all rating dimensions, with seniors consistently rating responses higher than residents for both models. Specifically, seniors rated GPT-4 as more beneficial and complete (mean 4.6 vs 4.0 and 4.6 vs 4.1, respectively; P<.001), and GPT-3.5 similarly (mean 4.1 vs 3.7 and 3.9 vs 3.5, respectively; P<.001). Ethical queries received the highest ratings for both models, with mean scores reflecting consistency across accuracy and completeness criteria. Distinctions among question types were significant, particularly for the GPT-4 mean scores in completeness across emergency, internal, and ethical questions (4.2, SD 1.0; 4.3, SD 0.8; and 4.5, SD 0.7, respectively; P<.001), and for GPT-3.5's accuracy, beneficial, and completeness dimensions.
CONCLUSIONS
ChatGPT's potential to assist physicians with medical issues is promising, with prospects to enhance diagnostics, treatments, and ethics. While integration into clinical workflows may be valuable, it must complement, not replace, human expertise. Continued research is essential to ensure safe and effective implementation in clinical environments.
Topics: Humans; Clinical Decision-Making; Artificial Intelligence
PubMed: 38935937
DOI: 10.2196/54571 -
PloS One 2024Expert opinion is widely used in clinical guidelines. No research has ever been conducted investigating the use of expert opinion in international infectious disease... (Meta-Analysis)
Meta-Analysis
INTRODUCTION
Expert opinion is widely used in clinical guidelines. No research has ever been conducted investigating the use of expert opinion in international infectious disease guidelines. This study aimed to create an analytical map by describing the prevalence and utilization of expert opinion in infectious disease guidelines and analyzing the methodological aspects of these guidelines.
METHODS
In this meta-epidemiological study, systematic searches in PubMed and Trip Medical Database were performed to identify clinical guidelines on infectious diseases, published between January 2018 and May 2023 in English, by international organizations. Data extracted included guideline characteristics, expert opinion utilization, and methodological details. Prevalence and rationale of expert opinion use were analyzed descriptively. Methodological differences between groups were analyzed with Chi-square and Mann-Whitney U Test.
RESULTS
The analysis covered 66 guidelines with 2296 recommendations, published/endorsed by 136 organizations. Most guidelines (79%) used systematic literature searches, 42% provided search strategies, and 38% presented screening flow diagrams and conducted risk of bias assessments. 48.5% of the guidelines allowed expert opinion, most of which included expert opinion as part of the evidence hierarchy within the grading system. Guidelines allowing expert opinion, compared to those which do not, issued more recommendations per guideline (48.82 vs.19.13, p<0.001), and reported fewer screening flow diagrams (25% vs. 65%, p = 0.002), and less risk of bias assessments (19% vs.78%, p<0.001).
CONCLUSIONS
Expert opinion is utilized in half of assessed guidelines, often integrated into the evidence hierarchy within the grading system. Its utilization varies considerably in methodology, form, and terminology between guidelines. These findings highlight a pressing need for additional research and guidance, to improve and advance the standardization of infectious disease guidelines.
Topics: Humans; Expert Testimony; Communicable Diseases; Practice Guidelines as Topic; Epidemiologic Studies
PubMed: 38935698
DOI: 10.1371/journal.pone.0306098 -
PloS One 2024Analysis of stable isotopes in consumers is used commonly to study their ecological and/or environmental niche. There is, however, considerable debate regarding how...
Analysis of stable isotopes in consumers is used commonly to study their ecological and/or environmental niche. There is, however, considerable debate regarding how isotopic values relate to diet and how other sources of variation confound this link, which can undermine the utility. From the analysis of a simple, but general, model of isotopic incorporation in consumer organisms, we examine the relationship between isotopic variance among individuals, and diet variability within a consumer population. We show that variance in consumer isotope values is directly proportional to variation in diet (through Simpson indices), to the number of isotopically distinct food sources in the diet, and to the baseline variation within and among the isotope values of the food sources. Additionally, when considering temporal diet variation within a consumer we identify the interplay between diet turnover rates and tissue turnover rates that controls the sensitivity of stable isotopes to detect diet variation. Our work demonstrates that variation in the stable isotope values of consumers reflect variation in their diet. This relationship, however, can be confounded with other factors to the extent that they may mask the signal coming from diet. We show how simple quantitative corrections can recover a direct 1:1 correlation in some situations, and in others we can adjust our interpretation in light of the new understanding arising from our models. Our framework provides guidance for the design and analysis of empirical studies where the goal is to infer niche width from stable isotope data.
Topics: Diet; Animals; Carbon Isotopes; Isotopes
PubMed: 38935686
DOI: 10.1371/journal.pone.0301900 -
JMIR Mental Health Jun 2024Text-based digital media platforms have revolutionized communication and information sharing, providing valuable access to knowledge and understanding in the fields of... (Review)
Review
BACKGROUND
Text-based digital media platforms have revolutionized communication and information sharing, providing valuable access to knowledge and understanding in the fields of mental health and suicide prevention.
OBJECTIVE
This systematic review aimed to determine how machine learning and data analysis can be applied to text-based digital media data to understand mental health and aid suicide prevention.
METHODS
A systematic review of research papers from the following major electronic databases was conducted: Web of Science, MEDLINE, Embase (via MEDLINE), and PsycINFO (via MEDLINE). The database search was supplemented by a hand search using Google Scholar.
RESULTS
Overall, 19 studies were included, with five major themes as to how data analysis and machine learning techniques could be applied: (1) as predictors of personal mental health, (2) to understand how personal mental health and suicidal behavior are communicated, (3) to detect mental disorders and suicidal risk, (4) to identify help seeking for mental health difficulties, and (5) to determine the efficacy of interventions to support mental well-being.
CONCLUSIONS
Our findings show that data analysis and machine learning can be used to gain valuable insights, such as the following: web-based conversations relating to depression vary among different ethnic groups, teenagers engage in a web-based conversation about suicide more often than adults, and people seeking support in web-based mental health communities feel better after receiving online support. Digital tools and mental health apps are being used successfully to manage mental health, particularly through the COVID-19 epidemic, during which analysis has revealed that there was increased anxiety and depression, and web-based communities played a part in reducing isolation during the pandemic. Predictive analytics were also shown to have potential, and virtual reality shows promising results in the delivery of preventive or curative care. Future research efforts could center on optimizing algorithms to enhance the potential of text-based digital media analysis in mental health and suicide prevention. In addressing depression, a crucial step involves identifying the factors that contribute to happiness and using machine learning to forecast these sources of happiness. This could extend to understanding how various activities result in improved happiness across different socioeconomic groups. Using insights gathered from such data analysis and machine learning, there is an opportunity to craft digital interventions, such as chatbots, designed to provide support and address mental health challenges and suicide prevention.
Topics: Humans; Machine Learning; Suicide Prevention; Mental Health; Social Media; Data Analysis
PubMed: 38935419
DOI: 10.2196/55747 -
Journal of the American Heart... Jun 2024The aim of this study was to evaluate the association between early postpartum weight change and (1) hospital readmission and (2) 2-week blood pressure trajectory.
BACKGROUND
The aim of this study was to evaluate the association between early postpartum weight change and (1) hospital readmission and (2) 2-week blood pressure trajectory.
METHODS AND RESULTS
This retrospective study cohort included 1365 individuals with a hypertensive disorder of pregnancy enrolled in a postpartum hypertension remote monitoring program. Exposure was percentage weight change from delivery to first weight recorded within 10 days postpartum. We first modeled likelihood of hospital readmission within 8 weeks postpartum using logistic regression adjusting for age, race, insurance, type of hypertensive disorder of pregnancy, early body mass index, gestational weight gain, mode of delivery, and any discharge antihypertensive medications. We then performed case-control analysis additionally matching in a 1:3 ratio on breastfeeding, early body mass index, discharge on antihypertensive medications, and days between weight measurements. Both analytic approaches were repeated, limiting to readmissions attributable to hypertension or heart failure. Finally, we compared blood pressure trajectories over first 2 weeks postpartum. Individuals who did not lose weight in the early postpartum period had more admissions compared with weight loss groups (group 3: 14.1% versus group 2: 5.8% versus group 1: 4.5%). These individuals had 4 times the odds of postpartum readmissions (adjusted odds ratio [aOR], 3.9 [95% CI, 1.8-8.6]) to 7 (aOR, 7.8 [95% CI, 2.3-26.5]) compared with those with the most weight loss. This association strengthened when limited to hypertension or heart failure readmissions. These individuals also had more adverse postpartum blood pressure trajectories, with significant differences by weight change group.
CONCLUSIONS
Weight change is readily accessible and may identify individuals at high risk for postpartum readmission following a hypertensive disorder of pregnancy who could benefit from targeted interventions.
PubMed: 38934854
DOI: 10.1161/JAHA.123.032820 -
Indian Journal of Public Health Oct 2023To trigger quit intention and practice of preventive measures for COVID-19 among tobacco users; it is imperative for them to be well aware of the fact that they are at...
BACKGROUND
To trigger quit intention and practice of preventive measures for COVID-19 among tobacco users; it is imperative for them to be well aware of the fact that they are at higher risk of COVID-19 infection and should be at higher efficacy to practice preventive measures for the disease as compared to nonusers of tobacco.
OBJECTIVES
This community-based cross-sectional analytical study was conducted from April 2020 to May 2020 among 1203 adult participants to compare the threat and efficacy perception among users and nonusers of tobacco.
MATERIALS AND METHODS
Perception of threat was assessed using three questions on perceived threat and one question on perceived susceptibility; whereas perception of efficacy was assessed using four questions each on self-efficacy and response efficacy through telephonic interview.
RESULTS
There was no significant difference in the overall threat perception among users and nonusers of tobacco. However, state-wise analysis showed that tobacco users had higher perception of threat for SARS-CoV-2 infection in all the states except Telangana. The overall perception of efficacy among tobacco users was significantly higher as compared to nonusers of tobacco.
CONCLUSION
The study calls for active collaboration between tobacco control enthusiasts and the Government to promote awareness of a higher risk of COVID-19 disease among tobacco users. In essence, the study's implications extend beyond COVID-19 and can guide targeted efforts to promote awareness, behavior change, and collaboration in the context of other infectious diseases among tobacco users.
Topics: Humans; COVID-19; Cross-Sectional Studies; India; Male; Female; Adult; Middle Aged; Health Knowledge, Attitudes, Practice; SARS-CoV-2; Self Efficacy; Young Adult; Tobacco Use
PubMed: 38934829
DOI: 10.4103/ijph.ijph_1438_22 -
MSystems Jun 2024Airway microbiota are known to contribute to lung diseases, such as cystic fibrosis (CF), but their contributions to pathogenesis are still unclear. To improve our...
Airway microbiota are known to contribute to lung diseases, such as cystic fibrosis (CF), but their contributions to pathogenesis are still unclear. To improve our understanding of host-microbe interactions, we have developed an integrated analytical and bioinformatic mass spectrometry (MS)-based metaproteomics workflow to analyze clinical bronchoalveolar lavage (BAL) samples from people with airway disease. Proteins from BAL cellular pellets were processed and pooled together in groups categorized by disease status (CF vs. non-CF) and bacterial diversity, based on previously performed small subunit rRNA sequencing data. Proteins from each pooled sample group were digested and subjected to liquid chromatography tandem mass spectrometry (MS/MS). MS/MS spectra were matched to human and bacterial peptide sequences leveraging a bioinformatic workflow using a metagenomics-guided protein sequence database and rigorous evaluation. Label-free quantification revealed differentially abundant human peptides from proteins with known roles in CF, like neutrophil elastase and collagenase, and proteins with lesser-known roles in CF, including apolipoproteins. Differentially abundant bacterial peptides were identified from known CF pathogens (e.g., ), as well as other taxa with potentially novel roles in CF. We used this host-microbe peptide panel for targeted parallel-reaction monitoring validation, demonstrating for the first time an MS-based assay effective for quantifying host-microbe protein dynamics within BAL cells from individual CF patients. Our integrated bioinformatic and analytical workflow combining discovery, verification, and validation should prove useful for diverse studies to characterize microbial contributors in airway diseases. Furthermore, we describe a promising preliminary panel of differentially abundant microbe and host peptide sequences for further study as potential markers of host-microbe relationships in CF disease pathogenesis.IMPORTANCEIdentifying microbial pathogenic contributors and dysregulated human responses in airway disease, such as CF, is critical to understanding disease progression and developing more effective treatments. To this end, characterizing the proteins expressed from bacterial microbes and human host cells during disease progression can provide valuable new insights. We describe here a new method to confidently detect and monitor abundance changes of both microbe and host proteins from challenging BAL samples commonly collected from CF patients. Our method uses both state-of-the art mass spectrometry-based instrumentation to detect proteins present in these samples and customized bioinformatic software tools to analyze the data and characterize detected proteins and their association with CF. We demonstrate the use of this method to characterize microbe and host proteins from individual BAL samples, paving the way for a new approach to understand molecular contributors to CF and other diseases of the airway.
PubMed: 38934598
DOI: 10.1128/msystems.00929-23 -
MSystems Jun 2024The application of fecal metaproteomics to large-scale studies of the gut microbiota requires high-throughput analysis and standardized experimental protocols. Although...
The application of fecal metaproteomics to large-scale studies of the gut microbiota requires high-throughput analysis and standardized experimental protocols. Although high-throughput protein cleanup and digestion methods are increasingly used in shotgun proteomics, no studies have yet critically compared such protocols using human fecal samples. In this study, human fecal protein extracts were processed using several different protocols based on three main approaches: filter-aided sample preparation (FASP), solid-phase-enhanced sample preparation (SP3), and suspension trapping (S-Trap). These protocols were applied in both low-throughput (i.e., microtube-based) and high-throughput (i.e., microplate-based) formats, and the final peptide mixtures were analyzed by liquid chromatography coupled to high-resolution tandem mass spectrometry. The FASP-based methods and the combination of SP3 with in-StageTips (iST) yielded the best results in terms of the number of peptides identified through a database search against gut microbiome and human sequences. The efficiency of protein digestion, the ability to preserve hydrophobic peptides and high molecular weight proteins, and the reproducibility of the methods were also evaluated for the different protocols. Other relevant variables, including interindividual variability of stool, duration of protocols, and total costs, were considered and discussed. In conclusion, the data presented here can significantly contribute to the optimization and standardization of sample preparation protocols in human fecal metaproteomics. Furthermore, the promising results obtained with the high-throughput methods are expected to encourage the development of automated workflows and their application to large-scale gut microbiome studies.IMPORTANCEFecal metaproteomics is an experimental approach that allows the investigation of gut microbial functions, which are involved in many different physiological and pathological processes. Standardization and automation of sample preparation protocols in fecal metaproteomics are essential for its application in large-scale studies. Here, we comparatively evaluated different methods, available also in a high-throughput format, enabling two key steps of the metaproteomics analytical workflow (namely, protein cleanup and digestion). The results of our study provide critical information that may be useful for the optimization of metaproteomics experimental pipelines and their implementation in laboratory automation systems.
PubMed: 38934547
DOI: 10.1128/msystems.00661-24