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Frontiers in Plant Science 2024The production of highly vigorous seeds with high longevity is an important lever to increase crop production efficiency, but its acquisition during seed maturation is...
INTRODUCTION
The production of highly vigorous seeds with high longevity is an important lever to increase crop production efficiency, but its acquisition during seed maturation is strongly influenced by the growth environment.
METHODS
An association rule learning approach discovered MtABI4, a known longevity regulator, as a gene with transcript levels associated with the environmentally-induced change in longevity. To understand the environmental sensitivity of transcription, Yeast One-Hybrid identified a class I BASIC PENTACYSTEINE (MtBPC1) transcription factor as a putative upstream regulator. Its role in the regulation of was further characterized.
RESULTS AND DISCUSSION
Overexpression of MtBPC1 led to a modulation of transcripts and its downstream targets. We show that MtBPC1 represses transcription at the early stage of seed development through binding in the CT-rich motif in its promoter region. To achieve this, MtBPC1 interacts with SWINGER, a sub-unit of the PRC2 complex, and Sin3-associated peptide 18, a sub-unit of the Sin3-like deacetylation complex. Consistent with this, developmental and heat stress-induced changes in transcript levels correlated with H3K27me3 and H3ac enrichment in the promoter. Our finding reveals the importance of the combination of histone methylation and histone de-acetylation to silence at the early stage of seed development and during heat stress.
PubMed: 38916028
DOI: 10.3389/fpls.2024.1395379 -
BioRxiv : the Preprint Server For... Jun 2024The classic output pathways of the basal ganglia are known as the direct-D1 and indirect-D2, or "Go/No-Go", pathways. Balance of the activity in these canonical...
The classic output pathways of the basal ganglia are known as the direct-D1 and indirect-D2, or "Go/No-Go", pathways. Balance of the activity in these canonical direct-indirect pathways is considered a core requirement for normal movement control, and their imbalance is a major etiologic factor in movement disorders including Parkinson's disease. We present evidence for a conceptually equivalent parallel system of direct-D1 and indirect-D2 pathways that arise from striatal projection neurons (SPNs) of the striosome compartment rather than from the matrix. These striosomal direct (S-D1) and indirect (S-D2) pathways, as a pair, target dopamine-containing neurons of the substantia nigra (SNpc) instead of the motor output nuclei of the basal ganglia. The novel anatomically and functionally distinct indirect-D2 striosomal pathway targets dopaminergic SNpc cells indirectly via a core region of the external pallidum (GPe). We demonstrate that these S-D1 and S-D2 pathways oppositely modulate striatal dopamine release in freely behaving mice under open-field conditions and oppositely modulate locomotor and other movements. These S-D1 and S-D2 pathways further exhibit different, time-dependent responses during performance of a probabilistic decision-making maze task and respond differently to rewarding and aversive stimuli. These contrasts depend on mediolateral and anteroposterior striatal locations of the SPNs as are the classic direct and indirect pathways. The effects of S-D1 and S-D2 stimulation on striatal dopamine release and voluntary locomotion are nearly opposite. The parallelism of the direct-indirect circuit design motifs of the striosomal S-D and S-D2 circuits and canonical matrix M-D1 and M-D2, and their contrasting behavioral effects, call for a major reformulation of the classic direct-indirect pathway model of basal ganglia function. Given that some striosomes receive limbic and association cortical inputs, the S-D1 and S-D2 circuits likely influence motivation for action and behavioral learning, complementing and possibly reorienting the motoric activities of the canonical matrix pathways. At a fundamental level, these findings suggest a unifying framework for aligning two sets of circuits that share the organizational motif of opponent D1 and D2 regulation, but that have different outputs and can even have opposite polarities in their targets and effects, albeit conditioned by striatal topography. Our findings further delineate a potentially therapeutically important set of pathways influencing dopamine, including a D2 receptor-linked S-D2 pathway likely unknowingly targeted by administration of many therapeutic drugs including those for Parkinson's disease. The novel parallel pathway model that we propose here could help to account for the normally integrated modulatory influence of the basal ganglia on motivation for actions as well as the actions themselves.
PubMed: 38915684
DOI: 10.1101/2024.06.01.596922 -
Clinical Kidney Journal Jun 2024In recent years, a number of predictive models have appeared to predict the risk of medium-term mortality in hemodialysis patients, but only one, limited to patients...
BACKGROUND
In recent years, a number of predictive models have appeared to predict the risk of medium-term mortality in hemodialysis patients, but only one, limited to patients aged over 70 years, has undergone sufficiently powerful external validation. Recently, using a national learning database and an innovative approach based on Bayesian networks and 14 carefully selected predictors, we have developed a clinical prediction tool to predict all-cause mortality at 2 years in all incident hemodialysis patients. In order to generalize the results of this tool and propose its use in routine clinical practice, we carried out an external validation using an independent external validation database.
METHODS
A regional, multicenter, observational, retrospective cohort study was conducted to externally validate the tool for predicting 2-year all-cause mortality in incident and prevalent hemodialysis patients. This study recruited a total of 142 incident and 697 prevalent adult hemodialysis patients followed up in one of the eight Association pour l'Utilisation du Rein Artificiel dans la région Lyonnaise (AURAL) Alsace dialysis centers.
RESULTS
In incident patients, the 2-year all-cause mortality prediction tool had an area under the receiver curve (AUC-ROC) of 0.73, an accuracy of 65%, a sensitivity of 71% and a specificity of 63%. In prevalent patients, the performance for the external validation were similar in terms of AUC-ROC, accuracy and specificity, but was lower in term of sensitivity.
CONCLUSION
The tool for predicting all-cause mortality at 2 years, developed using a Bayesian network and 14 routinely available explanatory variables, obtained satisfactory external validation in incident patients, but sensitivity was insufficient in prevalent patients.
PubMed: 38915433
DOI: 10.1093/ckj/sfae095 -
Pneumonia (Nathan Qld.) Jun 2024There exists consistent empirical evidence in the literature pointing out ample heterogeneity in terms of the clinical evolution of patients with COVID-19. The...
BACKGROUND
There exists consistent empirical evidence in the literature pointing out ample heterogeneity in terms of the clinical evolution of patients with COVID-19. The identification of specific phenotypes underlying in the population might contribute towards a better understanding and characterization of the different courses of the disease. The aim of this study was to identify distinct clinical phenotypes among hospitalized patients with SARS-CoV-2 pneumonia using machine learning clustering, and to study their association with subsequent clinical outcomes as severity and mortality.
METHODS
Multicentric observational, prospective, longitudinal, cohort study conducted in four hospitals in Spain. We included adult patients admitted for in-hospital stay due to SARS-CoV-2 pneumonia. We collected a broad spectrum of variables to describe exhaustively each case: patient demographics, comorbidities, symptoms, physiological status, baseline examinations (blood analytics, arterial gas test), etc. For the development and internal validation of the clustering/phenotype models, the dataset was split into training and test sets (50% each). We proposed a sequence of machine learning stages: feature scaling, missing data imputation, reduction of data dimensionality via Kernel Principal Component Analysis (KPCA), and clustering with the k-means algorithm. The optimal cluster model parameters -including k, the number of phenotypes- were chosen automatically, by maximizing the average Silhouette score across the training set.
RESULTS
We enrolled 1548 patients, each of them characterized by 92 clinical attributes (d=109 features after variable encoding). Our clustering algorithm identified k=3 distinct phenotypes and 18 strongly informative variables: Phenotype A (788 cases [50.9% prevalence] - age 57, Charlson comorbidity 1, pneumonia CURB-65 score 0 to 1, respiratory rate at admission 18 min, FiO 21%, C-reactive protein CRP 49.5 mg/dL [median within cluster]); phenotype B (620 cases [40.0%] - age 75, Charlson 5, CURB-65 1 to 2, respiration 20 min, FiO 21%, CRP 101.5 mg/dL); and phenotype C (140 cases [9.0%] - age 71, Charlson 4, CURB-65 0 to 2, respiration 30 min, FiO 38%, CRP 152.3 mg/dL). Hypothesis testing provided solid statistical evidence supporting an interaction between phenotype and each clinical outcome: severity and mortality. By computing their corresponding odds ratios, a clear trend was found for higher frequencies of unfavourable evolution in phenotype C with respect to B, as well as more unfavourable in phenotype B than in A.
CONCLUSION
A compound unsupervised clustering technique (including a fully-automated optimization of its internal parameters) revealed the existence of three distinct groups of patients - phenotypes. In turn, these showed strong associations with the clinical severity in the progression of pneumonia, and with mortality.
PubMed: 38915125
DOI: 10.1186/s41479-024-00132-0 -
BMC Psychology Jun 2024Video games have become a prevalent source of entertainment, especially among children. Furthermore, the amount of time spent playing video games has grown dramatically....
BACKGROUND
Video games have become a prevalent source of entertainment, especially among children. Furthermore, the amount of time spent playing video games has grown dramatically. The purpose of this research was to examine the mediation effects of attention and child memory on the relationship between video games addiction and cognitive and learning abilities in Egyptian children.
METHODS
A cross-sectional research design was used in the current study in two schools affiliated with Dakahlia District, Egypt. The study included 169 children aged 9 to 13 who met the inclusion criteria, and their mothers provided the questionnaire responses. The data collection methods were performed over approximately four months from February to May. Data were collected using different tools: Socio-demographic Interview, Game Addiction Scale for Children (GASC), Children's Memory Questionnaire (CMQ), Clinical Attention Problems Scale, Learning, Executive, and Attention Functioning (LEAF) Scale.
RESULTS
There was a significant indirect effect of video game addiction on cognitive and learning skills through attention, but not child memory. Video game addiction has a significant impact on children's attention and memory. Both attention and memory have a significant impact on a child's cognitive and learning skills.
CONCLUSIONS
These results revealed the significant effect of video game addiction on cognitive and learning abilities in the presence of mediators. It also suggested that attention-focused therapies might play an important role in minimizing the harmful effects of video game addiction on cognitive and learning abilities.
Topics: Humans; Child; Female; Male; Attention; Video Games; Adolescent; Cross-Sectional Studies; Memory; Learning; Cognition; Behavior, Addictive; Egypt; Internet Addiction Disorder; Executive Function
PubMed: 38915089
DOI: 10.1186/s40359-024-01849-9 -
PloS One 2024With a globally aging population, there is a need to better understand how brain structure relates to function in healthy older and younger adults. (Comparative Study)
Comparative Study
OBJECTIVE
With a globally aging population, there is a need to better understand how brain structure relates to function in healthy older and younger adults.
METHODS
34 healthy participants divided into older (17; Mean = 70.9, SD = 5.4) and younger adults (17; Mean = 28.1, SD = 2.8) underwent diffusion-weighted imaging and neuropsychological assessment, including the California Verbal Learning Test 2nd Edition and the Trail Making Test (TMT-A and TMT-B). Differences in white matter microstructure for older and younger adults and the association between DTI metrics (fractional anisotropy, FA; mean diffusivity, MD) and cognitive performance were analyzed using tract-based spatial statistics (p < 0.05, corrected).
RESULTS
Older adults had significantly lower FA and higher MD than younger adults in widespread brain regions. There was a significant negative correlation between executive function (TMT-B) and MD for older adults in the right superior/anterior corona radiata and the corpus callosum. No significant relationship was detected between DTI metrics and executive function in younger adults or with memory performance in either group.
CONCLUSIONS
The findings underscore the need to examine brain-behaviour relationships as a function of age. Future studies should include comprehensive assessments in larger lifespan samples to better understand the aging brain.
Topics: Humans; White Matter; Aged; Male; Female; Adult; Neuropsychological Tests; Diffusion Tensor Imaging; Aging; Middle Aged; Executive Function; Cognition; Young Adult; Diffusion Magnetic Resonance Imaging; Brain; Aged, 80 and over; Anisotropy
PubMed: 38913655
DOI: 10.1371/journal.pone.0305818 -
Biology Open Jun 2024Changes in mitochondrial distribution are a feature of numerous age-related neurodegenerative diseases. In Drosophila, reducing the activity of Cdk5 causes a...
Changes in mitochondrial distribution are a feature of numerous age-related neurodegenerative diseases. In Drosophila, reducing the activity of Cdk5 causes a neurodegenerative phenotype and is known to affect several mitochondrial properties. Therefore, we investigated whether alterations of mitochondrial distribution are involved in Cdk5-associated neurodegeneration. We find that reducing Cdk5 activity does not alter the balance of mitochondrial localization to the somatodendritic vs. axonal neuronal compartments of the mushroom body, the learning and memory center of the Drosophila brain. We do, however, observe changes in mitochondrial distribution at the axon initial segment (AIS), a neuronal compartment located in the proximal axon involved in neuronal polarization and action potential initiation. Specifically, we observe that mitochondria are partially excluded from the AIS in wild-type neurons, but that this exclusion is lost upon reduction of Cdk5 activity, concomitant with the shrinkage of the AIS domain that is known to occur in this condition. This mitochondrial redistribution into the AIS is not likely due to the shortening of the AIS domain itself but rather due to altered Cdk5 activity. Furthermore, mitochondrial redistribution into the AIS is unlikely to be an early driver of neurodegeneration in the context of reduced Cdk5 activity.
PubMed: 38912559
DOI: 10.1242/bio.060335 -
Heliyon Jun 2024This research examines the function of protein associated with topoisomerase II homolog 1 () in nasal-type natural killer/T-cell lymphoma (NKTCL) and head and neck...
This research examines the function of protein associated with topoisomerase II homolog 1 () in nasal-type natural killer/T-cell lymphoma (NKTCL) and head and neck squamous cell carcinoma (HNSCC). We analyzed bulk RNA-seq data from NKTCL, nasal polyps, and normal nasal mucosa, identifying 439 differentially expressed genes. Machine learning algorithms highlighted as a hub gene. exhibited significant upregulation in NKTCL and HNSCC tumor samples in comparison to normal tissues, showing high diagnostic accuracy (AUC = 1.000) for NKTCL. Further analysis of local hospital data identified as an independent prognostic risk factor for NKTCL. Data analysis of TCGA and GEO datasets revealed that high expression correlated with poorer prognosis in HNSCC patients ( < 0.05). We also constructed a -based nomogram, which emerged as an independent prognostic predictor for HNSCC after addressing missing values. Additionally, we found a strong correlation between and various immune cell infiltrates (e.g., activated.CD4 T cell), and a significant association with the expression of 37 immune checkpoints genes (e.g., , ) and 20 N6-methyladenosine-related genes (e.g., , ) (all < 0.05). Both TCIA and TIDE algorithms suggested that could potentially predict immunotherapy efficacy ( < 0.05). Cellular experiments demonstrated that transfection with a silencing plasmid of significantly inhibited the malignant behaviors of SNK6 and FaDu cell lines( < 0.05). In conclusion, our findings suggest that may serve as a valuable prognostic and predictive biomarker in NKTCL and HNSCC, highlighting its significant role in these cancers.
PubMed: 38912458
DOI: 10.1016/j.heliyon.2024.e32158 -
SSM. Mental Health Jun 2024Depression is a major global health concern especially among mothers of young children in low- and middle-income countries (LMICs). While various risk and protective...
Depression is a major global health concern especially among mothers of young children in low- and middle-income countries (LMICs). While various risk and protective factors have been well-established, the role of fathers in potentially mitigating maternal depression remains understudied. This study aimed to investigate the association between father involvement and maternal depressive symptoms in rural Western Kenya. We used cross-sectional baseline data collected in February-March 2023 from a cluster-randomized controlled trial evaluating the effectiveness of a community-based parenting program for improving early childhood development. Primary caregivers with children 0-18 months of age were enrolled into the trial across 51 villages in Nyamira and Vihiga counties. We analyzed data from 413 mothers who were in a relationship with a male partner (i.e., father of the young child). Maternal depressive symptoms were measured using the CESD-10. Father involvement was reported using a multidimensional measure of men's engagement in childcare activities, household chores, early learning activities, and affection towards their child. We used multilevel regression models to estimate the adjusted associations between father involvement (overall score and by specific domains) and maternal depressive symptoms. We also conducted exploratory subgroup analyses to assess whether this association differed by child age. Overall, greater father involvement was associated with fewer maternal depressive symptoms. Specifically, fathers' engagement in household chores and childcare activities had the strongest protective associations. Exploratory subgroup analyses revealed larger associations for mothers with younger children under 6 months. Our findings suggest that father involvement is a protective factor for maternal mental health. Engaging fathers in early childhood interventions and encouraging men's involvement in caregiving activities may potentially benefit maternal well-being.
PubMed: 38910840
DOI: 10.1016/j.ssmmh.2024.100318 -
Progress in Orthodontics Jun 2024Determining the right time for orthodontic treatment is one of the most important factors affecting the treatment plan and its outcome. The aim of this study is to...
INTRODUCTION
Determining the right time for orthodontic treatment is one of the most important factors affecting the treatment plan and its outcome. The aim of this study is to estimate the mandibular growth stage based on cervical vertebral maturation (CVM) in lateral cephalometric radiographs using artificial intelligence. Unlike previous studies, which use conventional CVM stage naming, our proposed method directly correlates cervical vertebrae with mandibular growth slope.
METHODS AND MATERIALS
To conduct this study, first, information of people achieved in American Association of Orthodontics Foundation (AAOF) growth centers was assessed and after considering the entry and exit criteria, a total of 200 people, 108 women and 92 men, were included in the study. Then, the length of the mandible in the lateral cephalometric radiographs that were taken serially from the patients was calculated. The corresponding graphs were labeled based on the growth rate of the mandible in 3 stages; before the growth peak of puberty (pre-pubertal), during the growth peak of puberty (pubertal) and after the growth peak of puberty (post-pubertal). A total of 663 images were selected for evaluation using artificial intelligence. These images were evaluated with different deep learning-based artificial intelligence models considering the diagnostic measures of sensitivity, specificity, accuracy, positive predictive value (PPV), and negative predictive value (NPV). We also employed weighted kappa statistics.
RESULTS
In the diagnosis of pre-pubertal stage, the convolutional neural network (CNN) designed for this study has the higher sensitivity and NPV (0.84, 0.91 respectively) compared to ResNet-18 model. The ResNet-18 model had better performance in other diagnostic measures of the pre-pubertal stage and all measures in the pubertal and post-pubertal stages. The highest overall diagnostic accuracy was also obtained using ResNet-18 model with the amount of 87.5% compared to 81% in designed CNN.
CONCLUSION
The artificial intelligence model trained in this study can receive images of cervical vertebrae and predict mandibular growth status by classifying it into one of three groups; before the growth spurt (pre-pubertal), during the growth spurt (pubertal), and after the growth spurt (post-pubertal). The highest accuracy is in post-pubertal stage with the designed networks.
Topics: Humans; Cephalometry; Mandible; Male; Female; Cervical Vertebrae; Artificial Intelligence; Child; Adolescent; Puberty; Deep Learning
PubMed: 38910180
DOI: 10.1186/s40510-024-00527-1