Fourier Diffusion for Sparse CT Reconstruction.

Sparse CT reconstruction continues to be an area of interest in a number of novel imaging systems. Many different approaches have been tried including model-based methods, compressed sensing approaches, and most recently deep-learning-based processing. Diffusion models, in particular, have become extremely popular due to their ability to effectively encode rich information about images and to allow for posterior sampling to generate many possible outputs. One drawback of diffusion models is that their recurrent structure tends to be computationally expensive. In this work we apply a new Fourier diffusion approach that permits processing with many fewer time steps than the standard scalar diffusion model. We present an extension of the Fourier diffusion technique and evaluate it in a simulated breast cone-beam CT system with a sparse view acquisition.

Clinical and genetic evaluations of Zimmermann-Laband syndrome with gingival fibromatosis: a rare case report.

Zimmermann-Laband Syndrome (ZLS; MIM 135500) is a rare genetic disorder with the main clinical manifestations of gingival fibromatosis and finger/toe nail hypoplasia. KCNH1 (potassium channel, voltage-gated, subfamily H, member-1), KCNN3 (potassium channel, voltage-gated, subfamily H, member-3) and ATP6V1B2 (ATPase H+ transporting V1 subunit B2) genes are considered causative genes for ZLS. However, there are limited reports about the diverse clinical presentation and genetic heterogeneity. Reporting more information on phenotype-genotype correlation and the treatment of ZLS is necessary. This case reported a 2-year-old patient with gingival enlargement that failure of eruption of the deciduous teeth and severe hypoplasia of nails. Based on a systemic examination and a review of the relevant literature, we made an initial clinical diagnosis of ZLS. A novel pathogenic variant in the KCNH1 gene was identified using whole-exome sequencing to substantiate our preliminary diagnosis. The histopathological results were consistent with gingival fibromatosis. Gingivectomy and gingivoplasty were performed under general anesthesia. After surgery, the gingival appearance improved significantly, and the masticatory function of the teeth was restored. After 2-year follow-up, the gingival showed slightly hyperplasia. Systemic examination and gene sequencing firstly contribute to provide information for an early diagnosis for ZLS, then timely removal of the hyperplastic gingival facilitates the establishment of a normal occlusal relationship and improves oral aesthetics.

ResSAT: Enhancing Spatial Transcriptomics Prediction from H&E- Stained Histology Images with Interactive Spot Transformer.

Spatial transcriptomics (ST) revolutionizes RNA quantification with high spatial resolution. Hematoxylin and eosin (H&E) images, the gold standard in medical diagnosis, offer insights into tissue structure, correlating with gene expression patterns. Current methods for predicting spatial gene expression from H&E images often overlook spatial relationships. We introduce ResSAT (Residual networks - Self-Attention Transformer), a framework generating spatially resolved gene expression profiles from H&E images by capturing tissue structures and using a self-attention transformer to enhance prediction.Benchmarking on 10× Visium datasets, ResSAT significantly outperformed existing methods, promising reduced ST profiling costs and rapid acquisition of numerous profiles.

Variability in performance of genetic-enhanced DXA-BMD prediction models across diverse ethnic and geographic populations: A risk prediction study.

BACKGROUND: Osteoporosis is a major global health issue, weakening bones and increasing fracture risk. Dual-energy X-ray absorptiometry (DXA) is the standard for measuring bone mineral density (BMD) and diagnosing osteoporosis, but its costliness and complexity impede widespread screening adoption. Predictive modeling using genetic and clinical data offers a cost-effective alternative for assessing osteoporosis and fracture risk. This study aims to develop BMD prediction models using data from the UK Biobank (UKBB) and test their performance across different ethnic and geographical populations. METHODS AND FINDINGS: We developed BMD prediction models for the femoral neck (FNK) and lumbar spine (SPN) using both genetic variants and clinical factors (such as sex, age, height, and weight), within 17,964 British white individuals from UKBB. Models based on regression with least absolute shrinkage and selection operator (LASSO), selected based on the coefficient of determination (R2) from a model selection subset of 5,973 individuals from British white population. These models were tested on 5 UKBB test sets and 12 independent cohorts of diverse ancestries, totaling over 15,000 individuals. Furthermore, we assessed the correlation of predicted BMDs with fragility fractures risk in 10 years in a case-control set of 287,183 European white participants without DXA-BMDs in the UKBB. With single-nucleotide polymorphism (SNP) inclusion thresholds at 5×10-6 and 5×10-7, the prediction models for FNK-BMD and SPN-BMD achieved the highest R2 of 27.70% with a 95% confidence interval (CI) of [27.56%, 27.84%] and 48.28% (95% CI [48.23%, 48.34%]), respectively. Adding genetic factors improved predictions slightly, explaining an additional 2.3% variation for FNK-BMD and 3% for SPN-BMD over clinical factors alone. Survival analysis revealed that the predicted FNK-BMD and SPN-BMD were significantly associated with fragility fracture risk in the European white population (P < 0.001). The hazard ratios (HRs) of the predicted FNK-BMD and SPN-BMD were 0.83 (95% CI [0.79, 0.88], corresponding to a 1.44% difference in 10-year absolute risk) and 0.72 (95% CI [0.68, 0.76], corresponding to a 1.64% difference in 10-year absolute risk), respectively, indicating that for every increase of one standard deviation in BMD, the fracture risk will decrease by 17% and 28%, respectively. However, the model's performance declined in other ethnic groups and independent cohorts. The limitations of this study include differences in clinical factors distribution and the use of only SNPs as genetic factors. CONCLUSIONS: In this study, we observed that combining genetic and clinical factors improves BMD prediction compared to clinical factors alone. Adjusting inclusion thresholds for genetic variants (e.g., 5×10-6 or 5×10-7) rather than solely considering genome-wide association study (GWAS)-significant variants can enhance the model's explanatory power. The study highlights the need for training models on diverse populations to improve predictive performance across various ethnic and geographical groups.

Hosts manipulate lifestyle switch and pathogenicity heterogeneity of opportunistic pathogens in the single-cell resolution.

Host-microbe interactions are virtually bidirectional, but how the host affects their microbiome is poorly understood. Here, we report that the host is a critical modulator to regulate the lifestyle switch and pathogenicity heterogeneity of the opportunistic pathogens Serratia marcescens utilizing the Drosophila and bacterium model system. First, we find that Drosophila larvae efficiently outcompete S. marcescens and typically drive a bacterial switch from pathogenicity to commensalism toward the fly. Furthermore, Drosophila larvae reshape the transcriptomic and metabolic profiles of S. marcescens characterized by a lifestyle switch. More importantly, the host alters pathogenicity and heterogeneity of S. marcescens in the single-cell resolution. Finally, we find that larvae-derived AMPs are required to recapitulate the response of S. marcescens to larvae. Altogether, our findings provide an insight into the pivotal roles of the host in harnessing the life history and heterogeneity of symbiotic bacterial cells, advancing knowledge of the reciprocal relationships between the host and pathogen.

Genomic structural variations link multiple genes to bone mineral density in a multi-ethnic cohort study: Louisiana osteoporosis study.

Osteoporosis, characterized by low BMD, is a highly heritable metabolic bone disorder. Although single nucleotide variations (SNVs) have been extensively studied, they explain only a fraction of BMD heritability. Although genomic structural variations (SVs) are large-scale genomic alterations that contribute to genetic diversity in shaping phenotypic variations, the role of SVs in osteoporosis susceptibility remains poorly understood. This study aims to identify and prioritize genes that harbor BMD-related SVs. We performed whole genome sequencing on 4982 subjects from the Louisiana Osteoporosis Study. To obtain high-confidence SVs, the detection of SVs was performed using an ensemble approach. The SVs were tested for association with BMD variation at the hip (HIP), femoral neck (FNK), and lumbar spine (SPN), respectively. Additionally, we conducted co-occurrence analysis using multi-omics approaches to prioritize the identified genes based on their functional importance. Stratification was employed to explore the sex- and ethnicity-specific effects. We identified significant SV-BMD associations: 125 for FNK-BMD, 99 for SPN-BMD, and 83 for HIP-BMD. We observed SVs that were commonly associated with both FNK and HIP BMDs in our combined and stratified analyses. These SVs explain 13.3% to 19.1% of BMD variation. Novel bone-related genes emerged, including LINC02370, ZNF family genes, and ZDHHC family genes. Additionally, FMN2, carrying BMD-related deletions, showed associations with FNK or HIP BMDs, with sex-specific effects. The co-occurrence analysis prioritized an RNA gene LINC00494 and ZNF family genes positively associated with BMDs at different skeletal sites. Two potential causal genes, IBSP and SPP1, for osteoporosis were also identified. Our study uncovers new insights into genetic factors influencing BMD through SV analysis. We highlight BMD-related SVs, revealing a mix of shared and specific genetic influences across skeletal sites and gender or ethnicity. These findings suggest potential roles in osteoporosis pathophysiology, opening avenues for further research and therapeutic targets.

Multi-scale variational autoencoder for imputation of missing values in untargeted metabolomics using whole-genome sequencing data.

BACKGROUND: Missing data is a common challenge in mass spectrometry-based metabolomics, which can lead to biased and incomplete analyses. The integration of whole-genome sequencing (WGS) data with metabolomics data has emerged as a promising approach to enhance the accuracy of data imputation in metabolomics studies. METHOD: In this study, we propose a novel method that leverages the information from WGS data and reference metabolites to impute unknown metabolites. Our approach utilizes a multi-scale variational autoencoder to jointly model the burden score, polygenetic risk score (PGS), and linkage disequilibrium (LD) pruned single nucleotide polymorphisms (SNPs) for feature extraction and missing metabolomics data imputation. By learning the latent representations of both omics data, our method can effectively impute missing metabolomics values based on genomic information. RESULTS: We evaluate the performance of our method on empirical metabolomics datasets with missing values and demonstrate its superiority compared to conventional imputation techniques. Using 35 template metabolites derived burden scores, PGS and LD-pruned SNPs, the proposed methods achieved R2-scores > 0.01 for 71.55 % of metabolites. CONCLUSION: The integration of WGS data in metabolomics imputation not only improves data completeness but also enhances downstream analyses, paving the way for more comprehensive and accurate investigations of metabolic pathways and disease associations. Our findings offer valuable insights into the potential benefits of utilizing WGS data for metabolomics data imputation and underscore the importance of leveraging multi-modal data integration in precision medicine research.

Long Mu Qing Xin mixture improves behavioral performance in spontaneously hypertensive rats (SHR/NCrl) by upregulating catecholamine neurotransmitters in prefrontal cortex and striatum via DRD1/cAMP/PKA-CREB signaling pathway.

Background: Attention deficit hyperactivity disorder (ADHD), a prevalent neurodevelopmental disorder in children, can be effectively alleviated by the herbal preparation Long Mu Qing Xin Mixture (LMQXM), but its mechanism has not been fully elucidated. Objective: To scrutinize the potential pharmacological mechanisms by which LMQXM improves behavior in spontaneously hypertensive rats (SHR/NCrl). Methods: The SHR/NCrl rats were randomly stratified into the model (SHR) group, the methylphenidate hydrochloride (MPH) group, and groups subjected to varying dosages of LMQXM into the medium dose (MD) group with a clinically effective dose, the low dose (LD) group with 0.5 times the clinically effective dose, and high dose (HD) group with 2 times the clinically effective dose. Furthermore, the WKY/NCrl rats constituted the control group. The evaluation of behavior involved the open field test and the Morris water maze test. HPLC, LC-MS, ELISA, immunohistochemistry, Western blot, and RT-qPCR were utilized to scrutinize the catecholamine neurotransmitter content and the expression of proteins and genes associated with the dopamine receptor D1 (DRD1)/cAMP/protein kinase A (PKA)-cAMP response element-binding (CREB) pathway in prefrontal cortex (PFC) and striatum. Results: MPH and LMQXM ameliorated hyperactivity and learning and memory deficits of SHR/NCrl rats. Among them, LMQXM-MD and MPH also upregulated dopamine (DA), norepinephrine (NE), adenylate cyclase (AC) and cAMP levels, and the expression of proteins and genes associated with the DRD1/cAMP/PKA-CREB pathway in PFC and striatum of SHR/NCrl rats. PFC and striatum DA levels were also upregulated in the LMQXM-LD group as well as the striatum DA levels in the LMQXM-HD group, but there were no statistically significant differences in their NE levels compared to the SHR group. LMQXM-LD and LMQXM-HD also upregulated some DRD1/cAMP/PKA-CREB pathway-related proteins and gene expression, but the effects were discernibly disparate in PFC and striatum. Upon comprehensive analysis, LMQXM-MD appeared to be the most effective dose. Conclusion: Our study tentatively suggests that LMQXM may rectify hyperactivity and learning and memory deficits of SHR/NCrl rats by elevating catecholamine neurotransmitters in the PFC and striatum. This effect may be attributed to the potential activation of the DRD1/cAMP/PKA-CREB signaling pathway, which appears to achieve an optimal response at moderate doses.

The pathogenic mechanism of monosodium urate crystal-induced kidney injury in a rat model.

Objective: (MSU) crystals usually in the kidney tubules especially collecting ducts in the medulla. Previous animal models have not fully reproduced the impact of MSU on kidneys under non-hyperuricemic conditions. Methods: In the group treated with MSU, the upper pole of the rat kidney was injected intrarenally with 50 mg/kg of MSU, while the lower pole was injected with an equivalent volume of PBS solution. The body weight and kidney mass of the rats were observed and counted. H&E staining was used to observe the pathological damage of the kidney and to count the number of inflammatory cells. Masoon staining was used to observe the interstitial fibrosis in the kidneys of the rat model. Flow cytometric analysis was used for counting inflammatory cells in rats. ElISA was used to measure the concentration of serum and urine uric acid, creatinine and urea nitrogen in rats. Results: At the MSU injection site, a significantly higher infiltration of inflammatory cells and a substantial increase in the area of interstitial fibrosis compared to the control group and the site of PBS injection were observed. The serum creatinine level was significantly increased in the MSU group. However, there were no significant differences in the rats' general conditions or blood inflammatory cell counts when compared to the control group. Conclusion: The injection of urate crystals into the kidney compromised renal function, caused local pathological damage, and increased inflammatory cell infiltration and interstitial fibrosis. Intrarenal injection of MSU crystals may result in urate nephropathy. The method of intrarenal injection did not induce surgical infection or systemic inflammatory response.

MS-YOLOv8-Based Object Detection Method for Pavement Diseases.

Detection of pavement diseases is crucial for road maintenance. Traditional methods are costly, time-consuming, and less accurate. This paper introduces an enhanced pavement disease recognition algorithm, MS-YOLOv8, which modifies the YOLOv8 model by incorporating three novel mechanisms to improve detection accuracy and adaptability to varied pavement conditions. The Deformable Large Kernel Attention (DLKA) mechanism adjusts convolution kernels dynamically, adapting to multi-scale targets. The Large Separable Kernel Attention (LSKA) enhances the SPPF feature extractor, boosting multi-scale feature extraction capabilities. Additionally, Multi-Scale Dilated Attention in the network's neck performs Spatially Weighted Dilated Convolution (SWDA) across different dilatation rates, enhancing background distinction and detection precision. Experimental results show that MS-YOLOv8 increases background classification accuracy by 6%, overall precision by 1.9%, and mAP by 1.4%, with specific disease detection mAP up by 2.9%. Our model maintains comparable detection speeds. This method offers a significant reference for automatic road defect detection.

Herbacetin Inhibits Asthma Development by Blocking the SGK1/NF-κB Signaling Pathway.

Background: Asthma severely interferes with people's lives through coughing, wheezing and inflammation of the lungs. Herbacetin is a class of natural compounds that inhibit the development of inflammation. However, whether Herbacetin inhibits asthma has not been definitively studied. Methods: Lipopolysaccharides (LPS)-induced lung epithelial (BASE-2B) cells injury model was established, and then the relief of damaged BASE-2B cells with different concentrations of Herbacetin was examined. The cell counting kit (CCK8) was used to detect the effect of Herbacetin on the proliferation ability in ovalbumin (OVA)-induced asthma mice model, and Western Blot and flow cytometry were used to detect the effect of Herbacetin on the apoptosis in OVA-induced asthma mice model. Additionally, pulmonary pathology was detected by HE and Masson staining, and serum inflammatory factors were detected by alveolar lavage fluid. Results: Herbacetin reduces BESA-2B cells induced by LPS level of inflammation, and reactive oxygen species (ROS) generation, inhibits cell apoptosis, promotes cell proliferation, OVA-induced mice lung histopathology test HE staining, serum inflammatory factors show the same results. Western Blot shows that Herbacetin regulates the expression of Caspase-3, Bax, and Bcl-2. SGK1 overexpression increased the rate of apoptosis, and Herbacetin reversed this phenomenon. By silencing the expression of SGK1, it was found that Herbacetin was an inhibitor of SGK1, which could inhibit the NF-κB/p-P65 pathway in asthmatic airway inflammation. Conclusion: Herbacetin reduces pro-inflammatory cytokine levels by inhibiting the SGK1/NF-κB pathway. Our data suggest that Herbacetin has a significant anti-inflammatory effect on asthma and can be used as a potential therapeutic agent.

Spatial Dissection of the Distinct Cellular Responses to Normal Aging and Alzheimer's Disease in Human Prefrontal Cortex at Single-Nucleus Resolution.

Aging significantly elevates the risk for Alzheimer's disease (AD), contributing to the accumulation of AD pathologies, such as amyloid-ß (Aß), inflammation, and oxidative stress. The human prefrontal cortex (PFC) is highly vulnerable to the impacts of both aging and AD. Unveiling and understanding the molecular alterations in PFC associated with normal aging (NA) and AD is essential for elucidating the mechanisms of AD progression and developing novel therapeutics for this devastating disease. In this study, for the first time, we employed a cutting-edge spatial transcriptome platform, STOmics® SpaTial Enhanced Resolution Omics-sequencing (Stereo-seq), to generate the first comprehensive, subcellular resolution spatial transcriptome atlas of the human PFC from six AD cases at various neuropathological stages and six age, sex, and ethnicity matched controls. Our analyses revealed distinct transcriptional alterations across six neocortex layers, highlighted the AD-associated disruptions in laminar architecture, and identified changes in layer-to-layer interactions as AD progresses. Further, throughout the progression from NA to various stages of AD, we discovered specific genes that were significantly upregulated in neurons experiencing high stress and in nearby non-neuronal cells, compared to cells distant from the source of stress. Notably, the cell-cell interactions between the neurons under the high stress and adjacent glial cells that promote Aß clearance and neuroprotection were diminished in AD in response to stressors compared to NA. Through cell-type specific gene co-expression analysis, we identified three modules in excitatory and inhibitory neurons associated with neuronal protection, protein dephosphorylation, and negative regulation of Aß plaque formation. These modules negatively correlated with AD progression, indicating a reduced capacity for toxic substance clearance in AD subject samples. Moreover, we have discovered a novel transcription factor, ZNF460, that regulates all three modules, establishing it as a potential new therapeutic target for AD. Overall, utilizing the latest spatial transcriptome platform, our study developed the first transcriptome-wide atlas with subcellular resolution for assessing the molecular alterations in the human PFC due to AD. This atlas sheds light on the potential mechanisms underlying the progression from NA to AD.

Investigating the angular distortion impact on vehicular optical camera communication (OCC) systems.

Optical camera communication (OCC) shows promise for optical wireless communication (OWC) in vehicular networks. However, vehicle mobility-induced angular distortions hinder system throughput by degrading non-isotropic vehicular OCC channel gain. Few of the prior works have ever made a comprehensive analysis of their impact, especially based on the pixel value which reflects the camera imaging features. To address this knowledge gap, a pixel value-described vehicular OCC system model accounting for transmitter imaging location and intensity from the geometry and radiometry aspects is presented in this paper with common types of the offset and rotation angles included. We integrate a MATLAB-based simulated vehicular OCC system with an experimentally designed testbed for validation and performance analysis. For a single-time snapshot, we investigate the impacts of common angular distortion types in vehicular OCC systems on maximum pixel value, imaging location, and communication-related metrics. Furthermore, we statistically analyze their influences by considering two driving scenarios with respective angular distributions. The angular distortion characterization from this work is expected to lay a stepping stone to addressing mobility in vehicular OCC systems.

Odontogenesis-Empowered Extracellular Vesicles Safeguard Donor-Recipient Stem Cell Interplay to Support Tooth Regeneration.

Harnessing the developmental events of mesenchymal condensation to direct postnatal dental stem cell aggregation represents a cutting-edge and promising approach to tooth regeneration. Tooth avulsion is among the most prevalent and serious dental injuries, and odontogenic aggregates assembled by stem cells from human exfoliated deciduous teeth (SHED) have proven effective in revitalizing avulsed teeth after replantation in the clinical trial. However, whether and how SHED aggregates (SA) communicate with recipient components and promote synergistic tissue regeneration to support replanted teeth remains elusive. Here, it is shown that SA-mediated avulsed tooth regeneration involves periodontal restoration and recovery of recipient Gli1+ stem cells, which are mobilized and necessarily contribute to the reestablishment of the tooth-periodontal ligament-bone interface. Mechanistically, the release of extracellular vesicles (EVs) is revealed indispensable for the implanted SA to mobilize recipient Gli1+ cells and regenerate avulsed teeth. Furthermore, SHED aggregates-released EVs (SA-EVs) are featured with odontogenic properties linked to tissue regeneration, which enhance migration, proliferation, and differentiation of Gli1+ cells. Importantly, local application of SA-EVs per se empowers recipient Gli1+ cells and safeguards regeneration of avulsed teeth. Collectively, the findings establish a paradigm in which odontogenesis-featured EVs govern donor-recipient stem cell interplay to achieve tooth regeneration, inspiring cell-free translational regenerative strategies.

A Demographic-Conditioned Variational Autoencoder for fMRI Distribution Sampling and Removal of Confounds.

Objective: fMRI and derived measures such as functional connectivity (FC) have been used to predict brain age, general fluid intelligence, psychiatric disease status, and preclinical neurodegenerative disease. However, it is not always clear that all demographic confounds, such as age, sex, and race, have been removed from fMRI data. Additionally, many fMRI datasets are restricted to authorized researchers, making dissemination of these valuable data sources challenging. Methods: We create a variational autoencoder (VAE)-based model, DemoVAE, to decorrelate fMRI features from demographics and generate high-quality synthetic fMRI data based on user-supplied demographics. We train and validate our model using two large, widely used datasets, the Philadelphia Neurodevelopmental Cohort (PNC) and Bipolar and Schizophrenia Network for Intermediate Phenotypes (BSNIP). Results: We find that DemoVAE recapitulates group differences in fMRI data while capturing the full breadth of individual variations. Significantly, we also find that most clinical and computerized battery fields that are correlated with fMRI data are not correlated with DemoVAE latents. An exception are several fields related to schizophrenia medication and symptom severity. Conclusion: Our model generates fMRI data that captures the full distribution of FC better than traditional VAE or GAN models. We also find that most prediction using fMRI data is dependent on correlation with, and prediction of, demographics. Significance: Our DemoVAE model allows for generation of high quality synthetic data conditioned on subject demographics as well as the removal of the confounding effects of demographics. We identify that FC-based prediction tasks are highly influenced by demographic confounds.

A Demographic-Conditioned Variational Autoencoder for fMRI Distribution Sampling and Removal of Confounds.

Objective: fMRI and derived measures such as functional connectivity (FC) have been used to predict brain age, general fluid intelligence, psychiatric disease status, and preclinical neurodegenerative disease. However, it is not always clear that all demographic confounds, such as age, sex, and race, have been removed from fMRI data. Additionally, many fMRI datasets are restricted to authorized researchers, making dissemination of these valuable data sources challenging. Methods: We create a variational autoencoder (VAE)-based model, DemoVAE, to decorrelate fMRI features from demographics and generate high-quality synthetic fMRI data based on user-supplied demographics. We train and validate our model using two large, widely used datasets, the Philadelphia Neurodevel-opmental Cohort (PNC) and Bipolar and Schizophrenia Network for Intermediate Phenotypes (BSNIP). Results: We find that DemoVAE recapitulates group differences in fMRI data while capturing the full breadth of individual variations. Significantly, we also find that most clinical and computerized battery fields that are correlated with fMRI data are not correlated with DemoVAE latents. An exception are several fields related to schizophrenia medication and symptom severity. Conclusion: Our model generates fMRI data that captures the full distribution of FC better than traditional VAE or GAN models. We also find that most prediction using fMRI data is dependent on correlation with, and prediction of, demographics. Significance: Our DemoVAE model allows for generation of high quality synthetic data conditioned on subject demographics as well as the removal of the confounding effects of demographics. We identify that FC-based prediction tasks are highly influenced by demographic confounds.

Ultrasensitive Fluorescent Microsensors Based on Aptamers Modified with SYBR Green I for Visual Quantitative Detection of Organophosphate Pesticides.

Organophosphate pesticides (OPs) are widely utilized in agricultural production, and the residues threaten public health and environmental safety due to their toxicity. Herein, a novel and simple DNA aptamer-based sensor has been fabricated for the rapid, visual, and quantitative detection of profenofos and isocarbophos. The proposed DNA aptamers with a G-quadruplex spatial structure could be recognized by SYBR Green I (SG-I), resulting in strong green fluorescence emitted by SG-I. The DNA aptamers exhibit a higher specific binding ability to target OP molecules through aromatic ring stacking, disrupting the interaction between SG-I and DNA aptamers to induce green fluorescence quenching. Meanwhile, the fluorescence wavelength of G-quadruplex fluorescence emission peaks changes, accompanied by an obvious fluorescence variation from green to blue. SG-I-modified aptasensor without any additive reference fluorescence units for use in multicolor fluorescence assay for selective monitoring of OPs was first developed. The developed aptasensor provides a favorable linear range from 0 to 200 nM, with a low detection limit of 2.48 and 3.01 nM for profenofos and isocarbophos, respectively. Moreover, it offers high selectivity and stability in real sample detection with high recoveries. Then, a self-designed portable smartphone sensing platform was successfully used for quantitative result outputs, demonstrating experience in designing a neotype sensing strategy for point-of-care pesticide monitoring.

Types of Septic Cardiomyopathy: Prognosis and Influencing Factors - A Clinical Study.

Objective: To explore the prognostic outcomes associated with different types of septic cardiomyopathy and analyze the factors that exert an influence on these outcomes. Methods: The data collected within 24 hours of ICU admission included cardiac troponin I (cTnI), N-terminal pro-Brain Natriuretic Peptide (NT-proBNP); SOFA (sequential organ failure assessment) scores, and the proportion of vasopressor use. Based on echocardiographic outcomes, septic cardiomyopathy was categorized into left ventricular (LV) systolic dysfunction, LV diastolic dysfunction, and right ventricular (RV) systolic dysfunction. Differences between the mortality and survival groups, as well as between each cardiomyopathy subgroup and the non-cardiomyopathy group were compared, to explore the influencing factors of cardiomyopathy. Results: A cohort of 184 patients were included in this study, with LV diastolic dysfunction having the highest incidence rate (43.5%). The mortality group had significantly higher SOFA scores, vasopressor use, and cTnI levels compared to the survival group; the survival group had better LV diastolic function than the mortality group (p < 0.05 for all). In contrast to the non-cardiomyopathy group, each subgroup within the cardiomyopathy category exhibited elevated levels of cTnI. The subgroup with left ventricular diastolic dysfunction demonstrated a higher prevalence of advanced age, hypertension, diabetes mellitus, coronary artery disease, and an increased mortality rate; the RV systolic dysfunction subgroup had higher SOFA scores and NT-proBNP levels, and a higher mortality rate (P < 0.05 for all); the LV systolic dysfunction subgroup had a similar mortality rate (P > 0.05). Conclusion: Patients with advanced age, hypertension, diabetes mellitus, or coronary artery disease are more prone to develop LV diastolic dysfunction type of cardiomyopathy; cardiomyopathy subgroups had higher levels of cTnI. The RV systolic dysfunction cardiomyopathy subgroup had higher SOFA scores and NT-proBNP levels. The occurrence of RV systolic dysfunction in patients with sepsis significantly increased the mortality rate.

Influence of comorbidity of chronic diseases on basic activities of daily living among older adults in China: a propensity score-matched study.

Rationale: With the accelerating process of population aging, the comorbidity of chronic disease (CCD) has become a major public health problem that threatens the health of older adults. Objective: This study aimed to assess whether CCD is associated with basic activities of daily living (BADL) and explore the factors influencing BADL in older adults. Method: A cross-sectional community health survey with stratified random sampling among older residents (≥60 years old) was conducted in 2022. A questionnaire was used to collect information on BADL, chronic diseases, and other relevant aspects. Propensity score matching (PSM) was used to match the older adults with and without CCD. Univariate and multivariate logistic regression analyses were used to explore the factors influencing BADL. PSM was used to match participants with single-chronic disease (SCD) and CCD. Results: Among the 47,720 participants, those with CCD showed a higher prevalence of BADL disability (13.07%) than those with no CCD (6.33%) and SCD (7.39%). After adjusting for potential confounders with PSM, 6,513 pairs of cases with and without CCD were matched. The univariate analysis found that the older adults with CCD had a significantly higher prevalence of BADL disability (13.07%, 851 of 6,513) than those without CCD (9.83%, 640 of 6,513, P < 0.05). The multivariate logistic regression analysis revealed that CCD was a risk factor for BADL in older adults [OR = 1.496, 95% CI: 1.393-1.750, P < 0.001]. In addition, age, educational level, alcohol intake, social interaction, annual physical examination, retirement benefits, depression, weekly amount of exercise, and years of exercise were related to BADL disability (P < 0.05). PSM matching was performed on participants with CCD and SCD and showed that the older adults with CCD had a significantly higher prevalence of BADL disability (13.07%, 851 of 6,513) than those with SCD (11.39%, 742 of 6,513, P < 0.05). Conclusion: The older adults with CCD are at a higher risk of BADL disability than their counterparts with no CCD or SCD. Therefore, we advocate paying attention to and taking measures to improve the health and quality of life of these individuals.

A rapid and sensitive aptamer-based biosensor for beta-lactoglobulin in milk.

Beta-lactoglobulin (ß-Lg), a prominent milk protein, is a major contributor to milk allergies. The quantitative assessment of ß-Lg is a valuable method for assessing the allergenic potential of dairy products. In this study, a specific aptamer, ß-Lg-01, with an affinity constant (KD) of 28.6 nM for ß-Lg was screened through seven rounds of magnetic bead SELEX (MB-SELEX). A novel bio-layer interferometry (BLI)-based aptasensor was developed, which had a limit of detection (LOD) of 0.3 ng mL-1, a linear range of 1.5 ng mL-1-15 µg mL-1, and a recovery rate of 102-116% among the milk samples. This aptasensor provides a potential tool for the detection and risk assessment of ß-Lg within 10 min.