Browse Articles

Estimating the dietary and health impact of implementing mandatory front-of-package nutrient disclosures in the US: A policy scenario modeling analysis

Background Recognized as a cost-effective policy to promote healthier diets, mandatory front-of-package labeling (FOPL) identifying foods high in sodium, sugar, and saturated fat has been adopted and implemented in ten countries, and is currently under consideration in several others including the US. However, its potential impact on dietary intake and health have not yet been estimated in the US context. Objectives To estimate (1) the potential dietary impact of implementing mandatory nutrient-specific ‘high in’ FOPL among US adults; and (2) the number of diet related non-communicable disease (NCD) deaths that could be averted or delayed due to estimated dietary changes. Methods Baseline and counterfactual dietary intakes of sodium, sugars, saturated fats, and calories were estimated among US adults (n = 7,572) using both available days of 24h recall data from the 2017–2020 National Health and Nutrition Examination Survey (NHANES). The National Cancer Institute method was used to estimate usual intakes and distributions, adjusting for age, sex, misreporting status, weekend/weekday, and sequence of recall. To estimate counterfactual dietary intakes, we modeled two reduction scenarios observed in experimental and observational studies that examined changes in sodium, sugars, saturated fat and calorie content of food and beverage purchases due to nutrient-specific ‘high in’ FOPL. This study used the Preventable Risk Integrated ModEl (PRIME) to estimate potential health impacts. Results Estimated mean dietary reductions of 156 mg and 259 mg/day of sodium, 10.1 g and 7.2 g/day of sugars, 1.08 g and 4.49 g/day of saturated fats, and 38 kcal and 57 kcal/day of calories were observed under the two policy scenarios tested. Between 96,926 (95% UI 89,011–105,284) and 137,261 (95% UI 125,534–148,719) diet related NCD deaths, primarily from cardiovascular diseases (74%), could potentially be averted or delayed by implementing mandatory nutrient-specific FOPL in the US. Overall, more lives would be saved in males than females. Conclusions Findings suggest that implementing mandatory nutrient-specific ‘high in’ FOPL in the US could significantly reduce sodium and total sugar intakes among US adults, resulting in a substantial number of NCD related deaths that could be averted or delayed. Our results can inform current food policy developments in the US regarding the adoption and implementation of FOPL regulations.

Optimizing the zymogram of exogenous proteases in broiler diets by in vitro simulated gastrointestinal digestion and response surface methodology

The alteration of uterine microbiota participated in the activation of the decidual inflammatory response in early spontaneous abortion

Background Early spontaneous abortion (ESA) is one of the most common clinically recognized pregnancy complications. While multiple factors such as embryo abnormalities and maternal conditions may contribute to ESA, early identification and screening of maternal risk factors are increasingly important to explore the potential etiologies and improve prevention and treatment strategies for ESA. This study investigates the changes in uterine microbiota and the decidual immune response in ESA patients without embryo abnormalities. Methods ESA patients without embryo abnormality and artificial abortion (AA) controls were enrolled for clinical characteristics analysis. The decidual endometrium was subsequently collected for histological evaluation and inflammatory indicator detection. Moreover, 16S rRNA gene sequencing of uterine secretions was performed to investigate the differences in uterine microorganisms between the ESA and AA groups. Results Clinical analysis showed higher inflammatory response with elevated neutrophil counts in ESA patients. The increase in leukocytes, including neutrophils, was positively correlated with ESA. ESA patients presented significantly increased IL-1β expression in decidual stromal cells. 16S rRNA gene sequencing revealed greater diversity in the uterine microbiota of the ESA group, which presented decreased Lactobacillus abundance and increased abundance of other bacteria at the genus and species levels. Conclusions Changes in the uterine microbiome are likely related to inflammatory response and lead to early pregnancy loss.

Research on optimal control design of displaced left turn signal at one two way and three one way traffic intersections

Abstract As the nodes of urban road traffic network, intersections serve as an effective means to improve traffic flow efficiency and alleviate traffic congestion through the optimization of reasonable traffic organization and signal schemes for intersections. In recent years, domestic research on unconventional intersections has attracted increasing attention. This paper first summarized the types of single-double traffic intersections and preliminarily discussed the applicability of displaced left-turn (DLT) signal organization. We then constructed a calculation method for determining the important design parameters in the DLT signal traffic organization, based on the design of the channelization form of the intersection of Haihe Road and Xuanqing Street in Harbin City, China. Based on the average delay of vehicles, we established a signal optimization control model with two components: the main signal delay and the pre-signal delay, aiming to minimize the delay of vehicles. The signal optimization control model was established considering both the main signal delay and the pre-signal delay. Finally, the selected examples were compared and verified using VISSIM simulation. The simulation results show that through the design of one two-way and three one-way traffic intersections with DLT signals and signal optimization control, the average vehicle delay at the intersection is significantly reduced, and traffic efficiency is enhanced.

Vertical distribution of methanotrophic archaea in an iron-rich groundwater discharge zone

Anaerobic oxidation of methane coupled to iron reduction (Fe-AOM) is a crucial process for methane removal in terrestrial environments. However, the occurrence of Fe-AOM in natural environments is rare, and the mechanisms behind the direct coupling of methane oxidation and iron reduction remain poorly understood. In this study, we investigated the environmental factors influencing the distribution of methanotrophic archaea in an iron-rich zone of a freshwater pond in Hiroshima Prefecture, Japan. High concentration of dissolved ferrous iron supplied by groundwater discharge led to considerable ferrihydrite precipitation. Pore water methane increased with sediment depth, while nitrate and sulfate concentrations were near detection limits throughout the sediment column. The coexistence of ferric iron and methane suggests the ongoing process of Fe-AOM. Tracer-based experiments using 14C showed potential Fe-AOM rates up to 110 pmol mL-1 day-1. Throughout the sediment core, except at the surface, PCR-based molecular ecological analyses of the 16S rRNA gene and functional genes for anaerobic oxidation of methane revealed abundant sequences belonging to the family “Candidatus Methanoperedenaceae”. These geochemical and microbiological findings suggest that Fe-AOM plays a key role in biogeochemical cycles of iron and methane, positioning this environment as a modern analogue of early Earth conditions.

FmCFA: a feature matching method for critical feature attention in multimodal images

Enhancing communication in healthcare: Supports for interactions between adults with hearing loss and healthcare providers

Background Communication between a healthcare provider and an adult with hearing loss can be challenging and potentially contribute to health outcomes. To overcome this challenge, researchers have called for urgent multidisciplinary action on communication support in hearing healthcare. Objective This scoping review examines the existing literature on communication support that can be provided to adults with hearing loss during their interactions with healthcare professionals. Methods An online search was conducted using EMBASE, MEDLINE, CINAHL, and PsycINFO. All studies were imported into the Covidence platform for screening. The studies included in this analysis were published in English between 2013 and 2024. Results Twelve articles were included in this review, and seven themes were identified: supportive nonverbal strategies, supportive verbal strategies, human support, environmental and physical adaptations, technological support, education and training, and psychological support and counseling. Conclusions When communicating with a healthcare provider, adults with hearing loss can be assisted through supportive communication strategies.

Correlation between gut microbiota dysbiosis, metabolic syndrome and breast cancer

Abstract Breast cancer is a widespread cancer with a high death rate globally. The incidence of breast cancer is expected to increase, particularly in low and middle-income countries due to environmental factors and lifestyle changes. Several risk factors, such as age, family history, hormonal and reproductive factors, have been identified to influence breast cancer development. Metabolic syndrome, is a metabolic disorder that has also been linked to breast cancer risk. The gut microbiome has been suggested as one of the environmental factors leading to breast cancer. The human microbiome is mainly colonized in the intestine by various bacterial species, including Lactobacillus, Bifidobacterium, and Streptococcus and protect the host against pathogenic microorganisms and regulate the immune system. This study included 50 female breast cancer patients and 50 healthy controls with matched ages. Stool fresh samples were taken from test and control groups and stored at − 20 °C until further investigations. DNA of the bacteria in stool samples was extracted using reverse transcription-quantitative polymerase chain reaction to check for the bacterial 16s rRNA gene. The exclusion criteria included other malignancies, recent intestinal surgery, infectious diarrhea, prolonged use of antibiotics, substance addiction, and pregnancy or lactation. Our findings exhibited that breast cancer patients had a higher incidence of metabolic syndrome (60%) compared to cancer-free controls (40%). Furthermore, breast cancer patients had significantly lower Bifidobacterium and Lactobacillus counts than the controls. No significant difference was found in Streptococcus counts between groups. These findings support the relationship between breast cancer and metabolic syndrome and suggest the potential involvement of Lactobacillus and Bifidobacterium in breast cancer pathophysiology. Our study supports the relation between breast cancer and disorder of metabolic syndrome and suggests the potential involvement of Lactobacillus and Bifidobacterium in breast cancer pathophysiology. Further research is necessary to investigate the complex interactions between genes, the environment, and the gut microbiome in breast cancer development. Understanding these interactions could lead to the progress of novel strategies for breast cancer prevention and treatment.

Optimizing Bi-LSTM networks for improved lung cancer detection accuracy

Lung cancer remains a leading cause of cancer-related deaths worldwide, with low survival rates often attributed to late-stage diagnosis. To address this critical health challenge, researchers have developed computer-aided diagnosis (CAD) systems that rely on feature extraction from medical images. However, accurately identifying the most informative image features for lung cancer detection remains a significant challenge. This study aimed to compare the effectiveness of both hand-crafted and deep learning-based approaches for lung cancer diagnosis. We employed traditional hand-crafted features, such as Gray Level Co-occurrence Matrix (GLCM) features, in conjunction with traditional machine learning algorithms. To explore the potential of deep learning, we also optimized and implemented a Bidirectional Long Short-Term Memory (Bi-LSTM) network for lung cancer detection. The results revealed that the highest performance using hand-crafted features was achieved by extracting GLCM features and utilizing Support Vector Machine (SVM) with different kernels, reaching an accuracy of 99.78% and an AUC of 0.999. However, the deep learning Bi-LSTM network surpassed both methods, achieving an accuracy of 99.89% and an AUC of 1.0000. These findings suggest that the proposed methodology, combining hand-crafted features and deep learning, holds significant promise for enhancing early lung cancer detection and ultimately improving diagnosis systems.

Synthesis and application of diazenyl sulfonamide-based schiff bases as potential BRCA2 active inhibitors against MCF-7 breast cancer cell line

Systematic identification and characterization of high efficiency Cas9 guide RNAs for therapeutic targeting of ADAR

Therapeutic targeting of the adenosine deaminase ADAR has great potential in cancer and other indications; however, it remains unclear what approach can enable effective and selective therapeutic inhibition. Herein, we conduct multi-staged guide RNA screening and identify high efficiency Cas9 guide RNAs to enable a CRISPR/Cas-based approach for ADAR knockout. Through characterization in human primary immune cell systems we observe similar activity with two-part guide RNA and single guide RNA, dose responsive activity, similar guide activity rank order across different cell types, and favorable computational off-target profiles of candidate guide RNAs. We determine that knockout of ADAR using these guide RNAs induces pharmacodynamic responses primarily consisting of immunological responses such as a type I interferon response, consistent with the known function of ADAR as a key regulator of dsRNA sensing. We observe similar biological effects with targeting only the p150 isoform or both p110 and p150 isoforms of ADAR, indicating that at least in the contexts evaluated, loss of p150 ADAR mediates the primary response. These findings provide a resource of well-characterized, high efficiency ADAR-targeting Cas9 guide RNAs suitable for genomic medicines utilizing different delivery modalities and addressing different therapeutic areas.

Unstable foveation’s impact on reading, object tracking, and its implications for diagnosing and intervening in reading difficulties

Evaluation of stroke sequelae and rehabilitation effect on brain tumor by neuroimaging technique: A comparative study

This study aims at the limitations of traditional methods in the evaluation of stroke sequelae and rehabilitation effect monitoring, especially for the accurate identification and tracking of brain injury areas. To overcome these challenges, we introduce an advanced neuroimaging technology based on deep learning, the SWI-BITR-UNet model. This model, introduced as novel Machine Learning (ML) model, combines the SWIN Transformer’s local receptive field and shift mechanism, and the effective feature fusion strategy in the U-Net architecture, aiming to improve the accuracy of brain lesion region segmentation in multimodal MRI scans. Through the application of a 3-D CNN encoder and decoder, as well as the integration of the CBAM attention module and jump connection, the model can finely capture and refine features, to achieve a level of segmentation accuracy comparable to that of manual segmentation by experts. This study introduces a 3D CNN encoder-decoder architecture specifically designed to enhance the processing capabilities of 3D medical imaging data. The development of the 3D CNN model utilizes the ADAM optimization algorithm to facilitate the training process. The Bra2020 dataset is utilized to assess the accuracy of the proposed deep learning neural network. By employing skip connections, the model effectively integrates the high-resolution features from the encoder with the up-sampling features from the decoder, thereby increasing the model’s sensitivity to 3D spatial characteristics. To assess both the training and testing phases, the SWI-BITR-Unet model is trained using reliable datasets and evaluated through a comprehensive array of statistical metrics, including Recall (Rec), Precision (Pre), F1 test score, Kappa Coefficient (KC), mean Intersection over Union (mIoU), and Receiver Operating Characteristic-Area Under Curve (ROC-AUC). Furthermore, various machine learning models, such as Random Forest (RF), Support Vector Machine (SVM), Extreme Gradient Boosting (XGBoost), Categorical Boosting (CatBoost), Adaptive Boosting (AdaBoost), and K-Nearest Neighbor (KNN), have been employed to analyze tumor progression in the brain, with performance characterized by Hausdorff distance. In From the performance of ML models, the SWI-BITR-Unet model was more accurate than other models. Subsequently, regarding DICE coefficient values, the segmentation maps (annotation maps of brain tumor distributions) generated by the ML models indicated the models’s capability to autonomously delineate areas such as the tumor core (TC) and the enhancing tumor (ET). Moreover, the efficacy of the proposed machine learning models demonstrated superiority over existing research in the field. The computational efficiency and the ability to handle long-distance dependencies of the model make it particularly suitable for applications in clinical Settings. The results showed that the SNA-BITR-UNet model can not only effectively identify and monitor the subtle changes in the stroke injury area, but also provided a new and efficient tool in the rehabilitation process, providing a scientific basis for developing personalized rehabilitation plans.

Optimizing irrigation and nitrogen levels to achieve sustainable rice productivity and profitability

Abstract The global scarcity of irrigation water poses a significant challenge to the sustainable production of rice and its availability worldwide. With a growing population driving increased demand for rice, it is crucial to enhance rice production while minimizing water usage. Achieving this requires a comprehensive understanding of the complex interactions between water and nitrogen dynamics and the formulation of strategies to optimize the application of irrigation water and nitrogen fertilizers. This study aims to investigate the impact of varying irrigation regimes and nitrogen application rates on rice growth attributes, yield performance, overall crop productivity, and economic returns. In the 2021 and 2022 rice growing season, two field experiments were carried out in split plot design with four nitrogen levels in sub plots [N0: Control, N1: 75% RDN (Recommended dose of nitrogen; @ 120 kg N ha−1), N2: 100% RDN, and N3: 125% RDN] and four irrigation treatments in main plots [I1: recommended irrigation scheduling, I2: at field capacity (20 L m−2), I3: 10% depletion from field capacity (20 L m−2), and I4: 20% depletion from field capacity (20 L m−2). The experiments were replicated three times. The suggested irrigation scheduling treatment (flooded) showed improved growth characteristics, such as plant height, dry matter accumulation, leaf area index, tiller count, SPAD (Soil Plant Analysis Development) value, NDVI (Normalized Difference Vegetation Index) value, leaf relative water content, and yield attributes; however, these were comparable to the application of irrigation water at field capacity. Due to improved plant growth and yield-attributing characteristics, the I1 treatment recorded the highest grain yield of 8.58 t ha−1 and 8.4 t ha−1, although it was comparable to the I2 treatment, which had grain yields of 8.27 t ha−1 and 8.15 t ha−1 in 2021 and 2022. The grain yield reported by the N3 treatment were significantly greater than those of the N2 treatment, IN 2021 and 2022 respectively. Applying nitrogen at 125% RDN (Recommended dose of nitrogen) and irrigation water at field capacity produced the highest benefit–cost ratio (1.64), which was closely followed by the same irrigation regime and 100% RDN application (1.60 BC ratio). Comparable to irrigation at field capacity, the suggested irrigation schedule demonstrated enhanced growth features, yield attributes, productivity, and profitability. The best way to achieve the optimum growth, productivity, and profitability in transplanted rice was to provide irrigation water at field capacity and nitrogen @ 100% RDN.

Trekking across Nepal to bridge its gender gap in the sciences

Pathways to sustainability: Higher education and green productivity

The research conducted theoretical analysis and empirical testing on the relationship between higher education and regional green productivity based on panel data from 30 Chinese provinces from 2003 to 2021. The study’s findings demonstrate that higher education can have a major impact on local green production. In order to determine whether industrial structure upgrading and the digital economy work together to promote the development of green productivity, higher education is added to these factors at the same time as the new economic growth mode transformation in the digital economy era. The research hypothesis aligns with the results, suggesting that higher education and the digital economy collaborate to enhance green productivity levels. Higher education has a more significant impact on green productivity the greater the level of regional economic growth, according to a further nonlinear test utilizing the partial linear function coefficient (PLFC) model. Higher education’s influence on green production varies by place and period, becoming more pronounced as time passes and the degree of regional economic growth rises. In order to fully utilize higher education’s capacity for scientific research, innovation, and talent, as well as to increase the direct contribution of its scientific and technological innovations to the advancement of national industry and production promotion, it is imperative that people actively promote the new type of industrialization, develop the digital economy, and work in tandem with higher education.

Cone beam computed tomography based upper airway measurement after orthognathic surgery: a comparative evaluation of different imaging software

Abstract Cone-beam computed tomography (CBCT) enhances understanding of the upper airway (UA). This study compared three software products’ abilities in visualizing and quantifying specific upper airway changes using CBCT. We conducted a retrospective analysis of pre- and post-operative CBCT images from 29 patients using Dolphin (Do), Romexis 5 (Ro5), and Romexis 6 (Ro6) software, focusing on alterations in oropharyngeal volume and minimum cross-sectional area as key indicators of orthognathic surgery outcomes. ANOVA analysis showed significant differences in volume measurements between Do/Ro5 (p = 0.034) and Do/Ro6 (p = 0.047), but no difference between Ro5 and Ro6 (p = 0.685). No significant differences were found in minimum cross-sectional area parameters. Despite standardized protocols, interpretation discrepancies exist between Do and Ro 5/6, possibly due to program-specific properties. Further studies on threshold value comparability are needed for data standardization. Direct comparisons of clinical data from Do, Ro5, and Ro6 are limited due to methodological disparities. Nonetheless, these programs allow reproducible and quantifiable measurements for clinical assessments of these specific airway changes following orthognathic surgery.

Mutational alterations in the QRDR regions associated with fluoroquinolone resistance in Pseudomonas aeruginosa of clinical origin from Savar, Dhaka

Bacterial DNA gyrase and topoisomerase IV are the major targets of quinolone antibiotic, and mutational alterations in quinolone resistance determining regions (QRDR) serve as major mechanism of resistance in most bacterial species, including P. aeruginosa. The present investigation was aimed to study the molecular mechanism of fluoroquinolone resistance among clinical P. aeruginosa isolated from Dhaka, Bangladesh, including alterations in target sites of the antimicrobial action. Laboratory collection of 53 P. aeruginosa were subjected to conventional cultural and biochemical characterization, followed by molecular identification using 16S rDNA sequencing. Susceptibility to ciprofloxacin and levofloxacin was tested by disc diffusion method followed by MIC assay. Resistant isolates were analyzed for mutation in their QRDR regions of gyrA and parC, and subjected to PCR detection of plasmid mediated quinolone resistance (PMQR) genes qnrA, qnrS and qnrB. Among the isolates, 28% were found to be resistant to both fluoroquinolones tested. All of the fluoroquinolone resistant isolates carried a single mutation in gyrA (Thr-83-Ile), while 20% carried a single parC mutation (Ser-87-Leu). Higher level of MIC was observed in isolates carrying alterations at both sites. None of the isolates harbored any PMQR genes investigated, suggesting that chromosomal mutations in QRDR regions to be the major contributing factor for quinolone resistance in P. aeruginosa under investigation.

Genetic analyses reveal association between rosacea and cardiovascular diseases

Time-sensitive effects of quercetin on rat basophilic leukemia (RBL-2H3) cell responsiveness and intracellular signaling

Quercetin is known for its ability to inhibit mast cell degranulation and reduce the release of inflammatory mediators. However, it has also been reported to sensitize mast cells, potentially leading to hyperresponsiveness. This necessitates careful optimization of its use in the treatment of chronic inflammatory diseases. To fully harness quercetin’s therapeutic potential, this study investigated the effects of quercetin on rat basophilic leukemia (RBL-2H3) cells responsiveness over varying durations of exposure. We employed comprehensive transcriptome analysis and subsequent functional validation of key signaling pathways. Our findings revealed that quercetin initially reduced cell activity with short-term treatment. However, with prolonged exposure, quercetin transiently enhanced both IgE cross-linkage-mediated and non-IgE-mediated responses. Specifically, prolonged quercetin treatment downregulated IgE-mediated degranulation and FcεRI expression, while potentially sensitizing RBL-2H3 cells to other non-IgE secretagogues through enhanced PKC activity. Given quercetin’s multifaceted effects on intracellular signaling pathways, it is crucial to further investigate its efficacy and potential risk of adverse effects. Future studies should focus on a deeper understanding of these mechanisms to optimize quercetin’s therapeutic applications while mitigating any possible negative outcomes.