Correlation of weight-adjusted waist index with diabetic retinopathy in US adults aged ≥ 40 years: the NHANES 2005-2008.

The effect of obesity on diabetic retinopathy (DR) has been under scrutiny in recent years. The weight-adjusted waist index (WWI) has been reported to better assess the degree of centripetal obesity in humans, with a higher WWI indicating a higher amount of body fat. The present study is the first to investigate the relationship between WWI and DR and to assess the difference in the predictive ability of WWI and other obesity indices for DR. This cross-sectional study utilized data from the National Health and Nutrition Examination Survey (NHANES) from 2005 to 2008. Researchers collected complete anthropometric data (weight and waist circumference), dilated fundus images, and adult baseline information. Independent interactions between WWI and DR were investigated using multivariate regression and subgroup analyses. In addition, nonlinear associations and threshold effects between WWI and DR were searched for by smoothed curve fitting and by two-stage linear regression modeling. Finally, the receiver operating characteristic curve (ROC) was plotted to compare the predictive power of WWI with other indices of obesity for DR. A total of 1228 eligible patients with diabetes were collected in this study. There were 631 (51.38%) males and 597 (48.62%) females. Among them, 545 (44.38%) were without diabetic retinopathy, 555 (45.20%) had mild diabetic retinopathy, 100 (8.14%) had moderate/severe diabetic retinopathy and 28 (2.28%) had proliferative diabetic retinopathy (PDR). In the fully adjusted model, each unit increase in WWI was associated with a corresponding 31% reduction in the prevalence of DR [OR (95% CI) = 0.69 (0.58, 0.83)]. Compared with subjects in the lowest quartile (quartile 1), subjects in the highest quartile of WWI levels (quartile 4) were 45% less likely to have DR [OR (95% CI) = 0.55 (0.38, 0.78)]. In the case of female participants, a U-shaped correlation was observed between WWI and DR with an inflection point of 11.49. WWI also possesses a better predictive ability for DR compared to obesity indicators such as weight, BMI, and ABSI. This study showed a negative association between WWI and DR in the U.S. population aged 40 years and older.

Study on the relationship between berry, grape, red wine consumption and cognitive impairment in middle-aged and elderly people in China.

Introduction: Some evidence suggests that fruit and alcohol consumption may be related to cognitive impairment. Methods: This study conducted a cross-sectional study on the "correlation between eating habits and cognitive function of the middle-aged and elderly population in China." The purpose of this study is to explore the relationship between fruit consumption, drinking habits and cognitive impairment in Chinese people over 50 years old. Results: The results show that the protective factors of cognitive impairment are the preference for berries and the daily intake of 100-200 grams grapes in Chinese middle-aged and elderly people with objective cognitive unimpaired. The habit of drinking red wine is a protective factor for cognitive impairment in Chinese middle-aged and elderly people with mild cognitive impairment (MCI). However, this study did not find the relationship between white wine, beer, yellow rice wine, liquor and cognitive impairment. Discussion: Therefore, we believe that berries, grapes and red wine consumption can protect the cognitive function of the middle-aged and elderly people in China, and the protective function is related to the basic cognitive state.

Machine learning in soil nutrient dynamics of alpine grasslands.

As a terrestrial ecosystem, alpine grasslands feature diverse vegetation types and play key roles in regulating water resources and carbon storage, thus shaping global climate. The dynamics of soil nutrients in this ecosystem, responding to regional climate change, directly impact primary productivity. This review comprehensively explored the effects of climate change on soil nitrogen (N), phosphorus (P), and their balance in the alpine meadows, highlighting the significant roles these nutrients played in plant growth and species diversity. We also shed light on machine learning utilization in soil nutrient evaluation. As global warming continues, alongside shifting precipitation patterns, soil characteristics of grasslands, such as moisture and pH values vary significantly, further altering the availability and composition of soil nutrients. The rising air temperature in alpine regions substantially enhances the activity of soil organisms, accelerating nutrient mineralization and the decomposition of organic materials. Combined with varied nutrient input, such as increased N deposition, plant growth and species composition are changing. With the robust capacity to use and integrate diverse data sources, including satellite imagery, sensor-collected spectral data, camera-captured videos, and common knowledge-based text and audio, machine learning offers rapid and accurate assessments of the changes in soil nutrients and associated determinants, such as soil moisture. When combined with powerful large language models like ChatGPT, these tools provide invaluable insights and strategies for effective grassland management, aiming to foster a sustainable ecosystem that balances high productivity and advanced services with reduced environmental impacts.

A Two-color Single-molecule Sequencing Platform and Its Clinical Applications.

DNA sequencers have become increasingly important research and diagnostic tools over the past 20 years. In this study, we developed a single-molecule desktop sequencer, GenoCare 1600 (GenoCare), which utilizes amplification-free library preparation and two-color sequencing-by-synthesis chemistry, making it more user-friendly compared with previous single-molecule sequencing platforms for clinical use. Using the GenoCare platform, we sequenced an Escherichia coli standard sample and achieved a consensus accuracy exceeding 99.99%. We also evaluated the sequencing performance of this platform in microbial mixtures and coronavirus disease 2019 (COVID-19) samples from throat swabs. Our findings indicate that the GenoCare platform allows for microbial quantitation, sensitive identification of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) virus, and accurate detection of virus mutations, as confirmed by Sanger sequencing, demonstrating its remarkable potential in clinical application.

The epithelial C15ORF48/miR-147-NDUFA4 axis is an essential regulator of gut inflammation, energy metabolism, and the microbiome.

Chronic inflammation is epidemiologically linked to the pathogenesis of gastrointestinal diseases, including inflammatory bowel disease (IBD) and colorectal cancer (CRC). However, our understanding of the molecular mechanisms controlling gut inflammation remains insufficient, hindering the development of targeted therapies for IBD and CRC. In this study, we uncovered C15ORF48/miR-147 as a negative regulator of gut inflammation, operating through the modulation of epithelial cell metabolism. C15ORF48/miR-147 encodes two molecular products, C15ORF48 protein and miR-147-3p microRNA, which are predominantly expressed in the intestinal epithelium. C15ORF48/miR-147 ablation leads to gut dysbiosis and exacerbates chemically induced colitis in mice. C15ORF48 and miR-147-3p work together to suppress colonocyte metabolism and inflammation by silencing NDUFA4, a subunit of mitochondrial complex IV (CIV). Interestingly, the C15ORF48 protein, a structural paralog of NDUFA4, contains a unique C-terminal α-helical domain crucial for displacing NDUFA4 from CIV and its subsequent degradation. NDUFA4 silencing hinders NF-κB signaling activation and consequently attenuates inflammatory responses. Collectively, our findings have established the C15ORF48/miR-147-NDUFA4 molecular axis as an indispensable regulator of gut homeostasis, bridging mitochondrial metabolism and inflammation.

The associations between nutritional status and physical frailty and Alzheimer's disease plasma biomarkers in older cognitively unimpaired adults with positive of amyloid-ß PET.

BACKGROUND & AIMS: It has been revealed good nutritional status and no physical frailty, which are modifiable lifestyle factors, are linked to less cognitive decline and a lower risk of Alzheimer's disease (AD). We aimed to investigate the associations between nutritional status and physical frailty and plasma AD biomarkers, especially the Tau-associated biomarkers in older cognitively unimpaired (CU) adults with higher ß-amyloid (Aß) burden. METHODS: The nutritional status and physical frailty were assessed via Mini-Nutritional Assessment Short-Form (MNA-SF) and Fried frailty index. The participants underwent the examination of plasma AD biomarkers and 18F-florbetapir PET scan as well as 18F-MK6240 PET in the validation cohort. Correlation and multiple linear regression analyses were used to investigate the associations between nutritional status and frailty and AD biomarkers. RESULTS: Two cohorts were included in our study. A total of 129 participants with Aß-PET positive were enrolled in the development cohort. Multiple linear regression analysis showed MNA-SF scores, normal nutritional status, Fried frailty index scores, frailty and some domains of frailty including weight loss, maximal grip strength and exhaustion were associated with plasma p-Tau-181. Furthermore, weight loss, Fried frailty index scores and frailty were associated with higher Aß-PET standard uptake value ratio. We further performed subgroup analyses stratified by age, sex and apolipoprotein E ε4 genotype to investigate the beneficial characteristics of nutrition and frailty in the special subgroups. Validation cohort contained 38 Aß-PET positive participants. MNA-SF scores, normal nutritional status, Fried frailty index scores and frailty were associated with Tau burden evaluated by 18F-MK6240 PET Braak-like stages. CONCLUSIONS: Our data indicates that normal nutritional status and no physical frailty may be associated with expected trend of plasma AD biomarkers, especially less Tau pathology in older CU adults with Aß deposition. Adjusting to these characteristics of nutrition and physical frailty may help reduce the risk of AD development.

Advancing stroke therapy: the potential of MOF-based nanozymes in biomedical applications.

In this study, we explored the growing use of metal-organic framework (MOF)-based Nanozymes in biomedical research, with a specific emphasis on their applications in stroke therapy. We have discussed the complex nature of stroke pathophysiology, highlighting the crucial role of reactive oxygen species (ROS), and acknowledging the limitations of natural enzymes in addressing these challenges. We have also discussed the role of nanozymes, particularly those based on MOFs, their structural similarities to natural enzymes, and their potential to improve reactivity in various biomedical applications. The categorization of MOF nanozymes based on enzyme-mimicking activities is discussed, and their applications in stroke therapy are explored. We have reported the potential of MOF in treating stroke by regulating ROS levels, alleviation inflammation, and reducing neuron apoptosis. Additionally, we have addressed the challenges in developing efficient antioxidant nanozyme systems for stroke treatment. The review concludes with the promise of addressing these challenges and highlights the promising future of MOF nanozymes in diverse medical applications, particularly in the field of stroke treatment.

Community composition of phytopathogenic fungi significantly influences ectomycorrhizal fungal communities during subtropical forest succession.

Ectomycorrhizal fungi (EMF) can form symbiotic relationships with plants, aiding in plant growth by providing access to nutrients and defense against phytopathogenic fungi. In this context, factors such as plant assemblages and soil properties can impact the interaction between EMF and phytopathogenic fungi in forest soil. However, there is little understanding of how these fungal interactions evolve as forests move through succession stages. In this study, we used high-throughput sequencing to investigate fungal communities in young, intermediate, and old subtropical forests. At the genus level, EMF communities were dominated by Sebacina, Russula, and Lactarius, while Mycena was the most abundant genus in pathogenic fungal communities. The relative abundances of EMF and phytopathogenic fungi in different stages showed no significant difference with the regulation of different factors. We discovered that interactions between phytopathogenic fungi and EMF maintained a dynamic balance under the influence of the differences in soil quality attributed to each forest successional stage. The community composition of phytopathogenic fungi is one of the strong drivers in shaping EMF communities over successions. In addition, the EMF diversity was significantly related to plant diversity, and these relationships varied among successional stages. Despite the regulation of various factors, the positive relationship between the diversity of phytopathogenic fungi and EMF remained unchanged. However, there is no significant difference in the ratio of the abundance of EMF and phytopathogenic fungi over the course of successions. These results will advance our understanding of the biodiversity-ecosystem functioning during forest succession. KEY POINTS: •Community composition of both EMF and phytopathogenic fungi changed significantly over forest succession. •Phytopathogenic fungi is a key driver in shaping EMF community. •The effect of plant Shannon's diversity on EMF communities changed during the forest aging process.

A difficulty-aware and task-augmentation method based on meta-learning model for few-shot diabetic retinopathy classification.

Background: Accurate classification techniques are essential for the early diagnosis and treatment of patients with diabetic retinopathy (DR). However, the limited amount of annotated DR data poses a challenge for existing deep-learning models. This article proposes a difficulty-aware and task-augmentation method based on meta-learning (DaTa-ML) model for few-shot DR classification with fundus images. Methods: The difficulty-aware (Da) method operates by dynamically modifying the cross-entropy loss function applied to learning tasks. This methodology has the ability to intelligently down-weight simpler tasks, while simultaneously prioritizing more challenging tasks. These adjustments occur automatically and aim to optimize the learning process. Additionally, the task-augmentation (Ta) method is used to enhance the meta-training process by augmenting the number of tasks through image rotation and improving the feature-extraction capability. To implement the expansion of the meta-training tasks, various task instances can be sampled during the meta-training stage. Ultimately, the proposed Ta method was introduced to optimize the initialization parameters and enhance the meta-generalization performance of the model. The DaTa-ML model showed promising results by effectively addressing the challenges associated with few-shot DR classification. Results: The Asia Pacific Tele-Ophthalmology Society (APTOS) 2019 blindness detection data set was used to evaluate the DaTa-ML model. The results showed that with only 1% of the training data (5-way, 20-shot) and a single update step (training time reduced by 90%), the DaTa-ML model had an accuracy rate of 89.6% on the test data, which is a 1.7% improvement over the transfer-learning method [i.e., residual neural network (ResNet)50 pre-trained on ImageNet], and a 16.8% improvement over scratch-built models (i.e., ResNet50 without pre-trained weights), despite having fewer trainable parameters (the parameters used by the DaTa-ML model are only 0.47% of the ResNet50 parameters). Conclusions: The DaTa-ML model provides a more efficient DR classification solution with little annotated data and has significant advantages over state-of-the-art methods. Thus, it could be used to guide and assist ophthalmologists to determine the severity of DR.