Tag: pubmed

Proc Natl Acad Sci U S A. 2024 Dec 24;121(52):e2419650121. doi: 10.1073/pnas.2419650121. Epub 2024 Dec 16.

ABSTRACT

For human infants, producing recognizable speech is more than a cognitive process. It is a motor skill that requires infants to learn to coordinate multiple muscles of varying functions across their body. This coordination is directly linked to ongoing fluctuations in heart rate; a physiological process that can scaffold behavior. We investigated whether ongoing fluctuations in heart rate coincide with vocal production and word formation in 24-mo-old infants. Infants were most likely to produce a vocalization when heart rate fluctuations reached a peak (local maximum) or trough (local minimum). Vocalizations produced at the peak were longer than expected by chance. Functionally, vocalizations produced just before the trough, while heart rate is decelerating, were more likely to be recognized as a word by naive listeners. Thus, for the developing infant, heart rate fluctuations align with the timing of vocal productions and are associated with their duration and the likelihood of producing recognizable speech. Our results have broad and immediate implications for understanding normative language development, the evolutionary basis and physiological process of vocal production, and potential early indicators of speech and communication disorders.

PMID:39680757 | DOI:10.1073/pnas.2419650121

Brief Bioinform. 2024 Nov 22;26(1):bbae649. doi: 10.1093/bib/bbae649.

ABSTRACT

Single-cell multi-omics refers to the various types of biological data at the single-cell level. These data have enabled insight and resolution to cellular phenotypes, biological processes, and developmental stages. Current advances hold high potential for breakthroughs by integrating multiple different omics layers. However, singlecell multi-omics data usually have different feature dimensions and direct or indirect relationships. How to keep the data structure of these different data and extract hidden relationships is a major challenge for omics data integration, and effective integration models are urgently needed. In this paper, we propose an irregular tensor decomposition model (GSTRPCA) based on tensor robust principal component analysis (TRPCA). We developed a weighted threshold model for the decomposition of irregular tensor data by combining low-rank and sparsity constraints, which requires that the low-dimensional embeddings of the data remain lowrank and sparse. The major advantage of the GSTRPCA algorithm is its ability to keep the original data structure and explore hidden related features among omics data. For GSTRPCA, we also designed an effective algorithm that theoretically guarantees global convergence for the tensor decomposition. The computational experiments on irregular tensor datasets demonstrate that GSTRPCA significantly outperformed the state-of-the-art methods and hence confirm the superiority of GSTRPCA in clustering single-cell multiomics data. To our knowledge, this is the first tensor decomposition method for irregular tensor data to keep the data structure and hence improve the clustering performance for single-cell multi-omics data. GSTRPCA is a Matlabbased algorithm, and the code is available from https://github.com/GGL-B/GSTRPCA.

PMID:39680741 | DOI:10.1093/bib/bbae649

J Bras Nefrol. 2025 Apr-Jun;47(2):e20240146. doi: 10.1590/2175-8239-JBN-2024-0146en.

ABSTRACT

INTRODUCTION: Visual Abstract is a visual summary of the most relevant information from a scientific article, presented as an infographic. Despite the growing use of Visual Abstracts by journals around the world, studies evaluating their components to guide their development remain scarce.

OBJECTIVE: The primary objective of this study is to identify the aesthetic perceptions of Visual Abstracts components by physicians and medical students.

METHODS: Cross-sectional study, using a virtual questionnaire sent via email to a convenience sample comprising physicians and medical students. Data were analyzed using descriptive statistics, with means and standard deviation or median and interquartile range, depending on the type of the variable distribution. Categorical variables are presented in absolute and relative numbers.

RESULT: The research sample consisted mainly of medical students (65%), who were female (57.2%), with a median age of 23.5 years (IQR 21-42.25). The majority of respondents declared no prior knowledge on Visual Abstracts (61.7%). Of the analyzed variables, preferences included icons (56.7%), in a monochrome style (36.7%), second-dimensional (81.1%), and moderately detailed layout (56.7%), using the “original” color (91.7%), and structured in IMRaD format (73.9%).

CONCLUSION: Several visual components influence the aesthetic perception of physicians and medical students regarding Visual Abstracts, with particular emphasis on textual objectivity, clarity of colors, and the use of icons.

PMID:39680739 | DOI:10.1590/2175-8239-JBN-2024-0146en

JMIR Med Educ. 2024 Dec 16;10:e56132. doi: 10.2196/56132.

ABSTRACT

BACKGROUND: Biochemistry is a cornerstone of medical education. Its knowledge is integral to the understanding of complex biological processes and how they are applied in several areas in health care. Also, its significance is reflected in the way it informs the practice of medicine, which can guide and help in both diagnosis and treatment. However, the retention of biochemistry knowledge over time remains a dilemma. Long-term retention of such crucial information is extremely important, as it forms the foundation upon which clinical skills are developed and refined. The effectiveness of biochemistry education, and consequently its long-term retention, is influenced by several factors. Educational methods play a critical role; interactional and integrative teaching approaches have been suggested to enhance retention compared with traditional didactic methods. The frequency and context in which biochemistry knowledge is applied in clinical settings can significantly impact its retention. Practical application reinforces theoretical understanding, making the knowledge more accessible in the long term. Prior knowledge (familiarity) of information suggests that it is stored in long-term memory, which makes its retention in the long term easier to recall.

OBJECTIVES: This investigation was conducted at King Saud bin Abdulaziz University for Health Sciences in Riyadh, Saudi Arabia. The aim of the study is to understand the dynamics of long-term retention of biochemistry among medical students. Specifically, it looks for the association between students’ familiarity with biochemistry content and actual knowledge retention levels.

METHODS: A cross-sectional correlational survey involving 240 students from King Saud bin Abdulaziz University for Health Sciences was conducted. Participants were recruited via nonprobability convenience sampling. A validated biochemistry assessment tool with 20 questions was used to gauge students’ retention in biomolecules, catalysis, bioenergetics, and metabolism. To assess students’ familiarity with the knowledge content of test questions, each question is accompanied by options that indicate students’ prior knowledge of the content of the question. Statistical analyses tests such as Mann-Whitney U test, Kruskal-Wallis test, and chi-square tests were used.

RESULTS: Our findings revealed a significant correlation between students’ familiarity of the content with their knowledge retention in the biomolecules (r=0.491; P<.001), catalysis (r=0.500; P<.001), bioenergetics (r=0.528; P<.001), and metabolism (r=0.564; P<.001) biochemistry knowledge domains.

CONCLUSIONS: This study highlights the significance of familiarity (prior knowledge) in evaluating the retention of biochemistry knowledge. Although limited in terms of generalizability and inherent biases, the research highlights the crucial significance of student’s familiarity in actual knowledge retention of several biochemistry domains. These results might be used by educators to customize instructional methods in order to improve students’ long-term retention of biochemistry information and boost their clinical performance.

PMID:39680441 | DOI:10.2196/56132

JMIR Public Health Surveill. 2024 Dec 16;10:e58391. doi: 10.2196/58391.

ABSTRACT

BACKGROUND: China is a country with a high burden of hepatitis B (Hep B) but a low treatment rate. One of the key reasons for the low treatment rate is the inadequate health literacy (HL) of the people, which may affect the awareness and knowledge of Hep B and its treatment, as well as the ability to actively and correctly seek medical resources.

OBJECTIVE: This study analyzed how HL contributed to the scale-up of antiviral treatment of Hep B in China. We expect that the findings of this study could be used to inform resource allocation for health education and other approaches intending to improve the HL of the Chinese population, thus facilitating the nationwide scale-up of Hep B treatment and contributing to the achievement of the 2030 goal of eliminating viral hepatitis as a major public health threat in China.

METHODS: We used the two-stage least squares regression method and adopted the mobile phone penetration rate as the instrumental variable to estimate the effect of improved HL on the number of 12-month standard Hep B antiviral treatments in China based on the panel data of 31 provinces from 2013 to 2020.

RESULTS: In the cross-sectional dimension, the higher the HL, the higher the number of treatments in the provinces in a specific year. In the time series dimension, the number of treatments in a specific province increased with the improvement of HL over time. After controlling the time-invariant inherent attributes of provinces, the instrumental variable estimation with two-stage least squares regression based on the province fixed effect model found that for every 1% increase of HL in each province, the number of treatments increased by 7.15% (0.0715 = e0.0691 – 1; P<.001). Such an increase turned to 5.19% (0.0519 = e0.0506 – 1; P<.001) for the analysis targeting the observation time from 2013 to 2019, as the data of 2020 were removed when the COVID-19 pandemic started. The study found no statistically significant effect of HL on the number of Hep B treatments in the provinces with higher newly reported Hep B incidence and lower gross domestic product per capita.

CONCLUSIONS: Our findings suggest that improved HL of the population is an important favorable facilitator for the scale-up of Hep B treatment in China. Building awareness and knowledge of Hep B and its treatment can help individuals understand their health status, ensuring a healthier lifestyle and appropriate health care-seeking behaviors and health care service utilization, so that people can be diagnosed and treated timely and appropriately. Enhancing resource allocation to improve the overall HL of the population and sending Hep B-specific messages to the infected people would be a feasible and effective approach to scale-up the treatment of Hep B in low- and middle-income settings with limited resources, and contribute to achieving the 2030 global goal of eliminating viral hepatitis as a major public health threat.

PMID:39680440 | DOI:10.2196/58391

Elife. 2024 Dec 16;13:RP96566. doi: 10.7554/eLife.96566.

ABSTRACT

Studying and understanding the code of large neural populations hinge on accurate statistical models of population activity. A novel class of models, based on learning to weigh sparse nonlinear Random Projections (RP) of the population, has demonstrated high accuracy, efficiency, and scalability. Importantly, these RP models have a clear and biologically plausible implementation as shallow neural networks. We present a new class of RP models that are learned by optimizing the randomly selected sparse projections themselves. This ‘reshaping’ of projections is akin to changing synaptic connections in just one layer of the corresponding neural circuit model. We show that Reshaped RP models are more accurate and efficient than the standard RP models in recapitulating the code of tens of cortical neurons from behaving monkeys. Incorporating more biological features and utilizing synaptic normalization in the learning process, results in accurate models that are more efficient. Remarkably, these models exhibit homeostasis in firing rates and total synaptic weights of projection neurons. We further show that these sparse homeostatic reshaped RP models outperform fully connected neural network models. Thus, our new scalable, efficient, and highly accurate population code models are not only biologically plausible but are actually optimized due to their biological features. These findings suggest a dual functional role of synaptic normalization in neural circuits: maintaining spiking and synaptic homeostasis while concurrently optimizing network performance and efficiency in encoding information and learning.

PMID:39680435 | DOI:10.7554/eLife.96566

JMIR Bioinform Biotechnol. 2024 Dec 16;5:e65506. doi: 10.2196/65506.

ABSTRACT

BACKGROUND: Health care students often experience high levels of stress, anxiety, and mental health issues, making it crucial to address these challenges. Variations in stress levels may be associated with changes in dehydroepiandrosterone sulfate (DHEA-S) and interleukin-6 (IL-6) levels and gene expression. Meditative practices have demonstrated effectiveness in reducing stress and improving mental well-being.

OBJECTIVE: This study aims to assess the effects of Heartfulness meditation on mental well-being, DHEA-S, IL-6, and gene expression profile.

METHODS: The 78 enrolled participants were randomly assigned to the Heartfulness meditation (n=42, 54%) and control (n=36, 46%) groups. The participants completed the Perceived Stress Scale (PSS) and Depression Anxiety Stress Scale (DASS-21) at baseline and after week 12. Gene expression with messenger RNA sequencing and DHEA-S and IL-6 levels were also measured at baseline and the completion of the 12 weeks. Statistical analysis included descriptive statistics, paired t test, and 1-way ANOVA with Bonferroni correction.

RESULTS: The Heartfulness group exhibited a significant 17.35% reduction in PSS score (from mean 19.71, SD 5.09 to mean 16.29, SD 4.83; P<.001) compared to a nonsignificant 6% reduction in the control group (P=.31). DASS-21 scores decreased significantly by 27.14% in the Heartfulness group (from mean 21.15, SD 9.56 to mean 15.41, SD 7.87; P<.001) while it increased nonsignificantly by 17% in the control group (P=.04). For the DASS-21 subcomponents-the Heartfulness group showed a statistically significant 28.53% reduction in anxiety (P=.006) and 27.38% reduction in stress (P=.002) versus an insignificant 22% increase in anxiety (P=.02) and 6% increase in stress (P=.47) in the control group. Further, DHEA-S levels showed a significant 20.27% increase in the Heartfulness group (from mean 251.71, SD 80.98 to mean 302.74, SD 123.56; P=.002) compared to an insignificant 9% increase in the control group (from mean 285.33, SD 112.14 to mean 309.90, SD 136.90; P=.10). IL-6 levels showed a statistically significant difference in both the groups (from mean 4.93, SD 1.35 to mean 3.67, SD 1.0; 28.6%; P<.001 [Heartfulness group] and from mean 4.52, SD 1.40 to mean 2.72, SD 1.74; 40%; P<.001 [control group]). Notably, group comparison at 12 weeks revealed a significant difference in perceived stress, DASS-21 and its subcomponents, and IL-6 (all P<.05/4). The gene expression profile with messenger RNA sequencing identified 875 upregulated genes and 1539 downregulated genes in the Heartfulness group compared to baseline, and there were 292 upregulated genes and 1180 downregulated genes in the Heartfulness group compared to the control group after the intervention.

CONCLUSIONS: Heartfulness practice was associated with decreased depression, anxiety, and stress scores and improved health measures in DHEA-S and IL-6 levels. The gene expression data point toward possible mechanisms of alleviation of symptoms of stress, anxiety and depression.

TRIAL REGISTRATION: ISRCTN Registry ISRCTN82860715; https://doi.org/10.1186/ISRCTN82860715.

PMID:39680432 | DOI:10.2196/65506

Microsc Res Tech. 2024 Dec 16. doi: 10.1002/jemt.24767. Online ahead of print.

ABSTRACT

Brain tumor is a most dangerous disease and requires accurate diagnosis in a short period to ensure the best treatment. Traditional methods for brain tumor classification (BTC) are quite effective, even though usually resulting in clinical manual analysis, which takes more time and prone to errors. Initially, the input image is collected from Brain Tumor dataset. The gathered image is given to preprocessing. In preprocessing stage, trust-based distributed set-membership filtering (TDSF) is used to remove the noise. The preprocessed output is fed to the quaternion offset linear canonical transform (QOLCT) for Grayscale statistic and Haralick texture features extraction. Then the extracted features are fed to the Semantic-Preserved Generative Adversarial Network (SPGAN) for classifying the brain tumor into Glioma, Meningioma and Pituitary. Finally, Hunger Games Search Optimization (HGSO) is used to enhance the weight parameters of SPGAN. The proposed BTC-SPGAN-HGSO method attains the accuracies of 99.72% for Glioma, 99.65% for Meningioma, 99.52% for Pituitary and lowest MSE values across all tumor types, with 0.45% for Glioma, 0.39% for Meningioma, and 0.5% for Pituitary, which performs better than existing models. The simulation results highlight the effectiveness of the proposed BTC-SPGAN-HGSO approach in improving the accuracy of BTC and assist neurologists and physicians make exact decisions of diagnostic.

PMID:39680418 | DOI:10.1002/jemt.24767

JAMA Netw Open. 2024 Dec 2;7(12):e2451217. doi: 10.1001/jamanetworkopen.2024.51217.

ABSTRACT

IMPORTANCE: Evidence from randomized clinical trials of transcutaneous auricular vagus nerve stimulation (taVNS) for chronic insomnia disorder is lacking.

OBJECTIVE: To evaluate the efficacy and safety of taVNS for chronic insomnia compared with the sham taVNS.

DESIGN, SETTING, AND PARTICIPANTS: This randomized clinical trial was conducted from October 2021 to December 2022 at a single center in Beijing, China. Patients with chronic insomnia disorder with a Pittsburgh Sleep Quality Index (PSQI) of at least 8 were enrolled. Statistical analysis was performed from June to September 2023.

INTERVENTIONS: Patients were allocated to the active taVNS group or sham taVNS group with a 1:1 ratio. Both groups received the stimulation for 30 minutes each time, twice a day, 5 consecutive days a week, with an 8-week treatment and a 12-week follow-up.

MAIN OUTCOMES AND MEASURES: The primary end point was the mean change from baseline through week 8 in PSQI scores. Minimal clinically important difference was 2.5 points. Secondary outcomes included mental health, sleepiness, and fatigue. Safety was also evaluated.

RESULTS: A total of 72 participants were randomized to either active taVNS group (36 participants; mean [SD] age, 45.2 [14.5] years; 27 [75.0%] female) or the sham taVNS group (36 participants; mean [SD] age, 44.6 [13.9] years; 31 [86.1%] female); 68 participants completed the 8-week intervention. The least-square mean changes from baseline to week 8 in PSQI were -8.2 (95% CI, -9.3 to -7.0) points in the taVNS group and -3.9 (95% CI, -5.1 to -2.7) points in the sham group. Both groups experienced statistically significant improvements from before to after the intervention. However, active taVNS showed a clinically meaningful 4.2-point greater reduction (95% CI, -5.9 to -2.6 points; P < .001; Cohen d effect size, 1.2) in PSQI compared with the sham group (minimal clinically important difference = 2.5 points). Secondary outcomes, including mental health and fatigue, showed similar favorable results. The efficacy of taVNS was sustained throughout the 20-week study period.

CONCLUSIONS AND RELEVANCE: In this randomized clinical trial, taVNS significantly reduced insomnia severity. Clinically meaningful enhancements in PSQI scores were observed compared with sham stimulation, with the benefits of taVNS sustained over a 20-week period. Future multicenter clinical trials with large sample sizes are needed to validate its effectiveness across diverse populations.

TRIAL REGISTRATION: Chinese Clinical Trial Registry: ChiCTR2100051319.

PMID:39680406 | DOI:10.1001/jamanetworkopen.2024.51217

Ann Bot. 2024 Dec 16:mcae217. doi: 10.1093/aob/mcae217. Online ahead of print.

ABSTRACT

BACKGROUND AND AIMS: The absence of a modern plant-based ‘dicotyledon’ phytolith reference baseline impedes the accurate interpretation of fossil phytolith records in archaeological and palaeoecological research within North-western Europe. This study aims to fill this gap by documenting and analysing the phytolith record from modern dicotyledon taxa occurring in this region.

METHODS: Phytoliths were extracted from several plant parts of 117 plant specimens representing 74 species (1-2 specimens/species). The study employed light microscopy to examine phytolith production (non-producer, trace, common, or abundant) and phytolith assemblage composition. The data were analysed statistically to (a) determine the influence of taxonomy and plant part on phytolith presence (absent/present) using a Mixed Model, (b) assess phytolith assemblage variation using a Permutational Multivariate Analysis of Variance (PerMANOVA), and (c) identify patterns among sample groups including segregation for plant part, life form (forbs vs shrubs/trees), and order using a Linear Discriminant Analyses (LDA).

KEY RESULTS: Morphotype analysis reveals diagnostic morphotypes and features for specific plant families, genera, and plant parts. LDA effectively segregated plant parts and life forms, though taxonomic groupings showed limited segregation. Phytolith presence (absent/present) was found to vary, influenced by both plant part and taxonomy. For species examined through two specimens, although phytolith production varied considerably, phytolith assemblage composition was consistent.

CONCLUSIONS: This study establishes a ‘dicotyledon’ phytolith baseline for North-western Europe, showing that the phytolith record can be informative in terms of plant part and life form and that several phytolith morphotypes and/or features are taxonomically diagnostic below ‘dicotyledon’ level. The findings constitute a foundation upon which future research can build, refining and expanding our knowledge of the North-western European region.

PMID:39680404 | DOI:10.1093/aob/mcae217