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Composed of obesity and lipid parameters, the cardiometabolic index (CMI) has emerged as a novel diagnostic tool. Originally developed for diabetes diagnosis, its application has expanded to identifying patients with cardiovascular diseases, such as atherosclerosis and hypertension. However, the relationship between CMI and non-alcoholic fatty liver disease (NAFLD) and liver fibrosis in the US population remains unclear. This cross-sectional study analyzed data from the National Health and Nutrition Examination Survey (NHANES) spanning 2017–2020, involving 2996 participants aged 20 years or older. Vibration controlled transient elastography using a FibroScan® system (model 502, V2 Touch) with controlled attenuation parameter measurements identified NAFLD at a threshold of ≥ 274 dB/m, while liver stiffness measurement (LSM) results (median, ≥ 8.2 kPa) indicated fibrosis. A multifactorial logistic regression model explored the relationship between CMI and NAFLD and fibrosis. The effectiveness of CMI in detecting NAFLD and liver fibrosis was assessed through receiver operating characteristic curve analysis. Controlling for potential confounders, CMI showed a significant positive association with NAFLD (adjusted OR = 1.44, 95% CI 1.44–1.45) and liver fibrosis (adjusted OR = 1.84, 95% CI 1.84–1.85). The Areas Under the Curve for predicting NAFLD and fibrosis were 0.762 (95% CI 0.745 ~ 0.779) and 0.664(95% CI 0.633 ~ 0.696), respectively, with optimal cut-off values of 0.462 and 0.527. There is a positive correlation between CMI and NAFLD and fibrosis, which is a suitable and simple predictor of NAFLD and fibrosis.

The platelet/high-density lipoprotein ratio (PHR) has been identified as a significant indicator of inflammation and a hypercoagulable state, demonstrating a strong link with the severity of nonalcoholic fatty liver disease (NAFLD) and metabolic syndrome (MetS). However, its correlation with hyperuricemia has not yet been documented. This study utilized a cross-sectional design, analyzing data collected from the National Health and Nutrition Examination Survey (NHANES) between 2007 and 2016 in the United States. The platelet/high-density lipoprotein ratio (PHR) was determined by dividing the number of platelets (PLT) by the level of high-density lipoprotein cholesterol (HDL-C). We employed multivariable logistic regression analyses, generalized additive models, and subgroup analyses to investigate the correlation between PHR and hyperuricemia. The study revealed a hyperuricemia prevalence of 18.56%. Analysis indicated a significant positive correlation between PHR and the risk of hyperuricemia (OR 1.11, 95% CI 1.08, 1.14). This correlation remained consistent across different subgroups including age, ethnicity, gender, and body mass index (BMI). Smooth curve fitting demonstrated a saturation effect between PHR and the risk of hyperuricemia. PHR is positively correlated with hyperuricemia and may serve as a novel biomarker for predicting the onset of this condition. Additionally, targeted interventions to improve PHR might help reduce the incidence of hyperuricemia.

There are few studies on the relationship between dietary habits and asthma–COPD overlap (ACO). In this study, we aimed to investigate the association between dietary inflammation index (DII) score and ACO. Data from the National Health and Nutrition Examination Survey (NHANES) from 1999 to 2020. The DII score was first calculated and the demographic characteristics of the grouping based on the DII quartile were assessed. The weighted logistic regression model was used to study the relationship between DII and ACO. Subgroup analysis was used to further explore the differences in different subgroups. Restricted cubic spline (RCS) plot was used to show the general trend of DII score and disease risk, and threshold effect analysis was used to determine the inflection point. In a comparison of baseline characteristics, the highest ACO prevalence was found in the fourth quartile array of people in DII. An adjusted weighted logistic regression model showed that DII was positively correlated with the incidence of ACO. Subgroup analysis showed that the association was more pronounced in women, non-Hispanics, people with cardiovascular disease, and people without diabetes. The RCS graph shows that overall, the risk of ACO increases with the increase of DII score. Threshold effect analysis showed that the inflection point was 3.779, and the risk was more significant after the DII score was greater than the inflection point value (OR 2.001, 95% CI 1.334–3.001, P < 0.001). Higher DII scores were positively associated with ACO risk. These results further support diet as an intervention strategy for ACO prevention and treatment.

Gene therapy aims to modify or manipulate gene expression and change the biological characteristics of living cells to achieve the purpose of treating diseases. The safe, efficient, and stable expression of exogenous genes in cells is crucial for the success of gene therapy, which is closely related to the vectors used in gene therapy. Currently, gene therapy vectors are mainly divided into two categories: viral vectors and non-viral vectors. Viral vectors are widely used due to the advantages of persistent and stable expression, high transfection efficiency, but they also have certain issues such as infectivity, high immunological rejection, randomness of insertion mutation, carcinogenicity, and limited vector capacity. Non-viral vectors have the advantages of non-infectivity, controllable chemical structure, and unlimited vector capacity, but the transfection efficiency is low. With the rapid development of nanotechnology, the unique physicochemical properties of nanomaterials have attracted increasing attention in the field of drug and gene delivery. Among many nanomaterials, iron-based nanomaterials have attracted much attention due to their superior physicochemical properties, such as Fenton reaction, magnetic resonance imaging, magnetothermal therapy, photothermal therapy, gene delivery, magnetically-assisted drug delivery, cell and tissue targeting, and so on. In this paper, the research progress of iron-based nanomaterials in gene delivery and tumor gene therapy is reviewed, and the future application direction of iron-based nanomaterials is further prospected.

This study aimed to investigate body physical parameters as substitutes for water equivalent diameter (Dw) while calculating size-specific dose estimates (SSDEs) during adult chest computed tomography (CT). A retrospective analysis was conducted on 776 patients. Patients were divided into training set (542 patients) and validation set (234 patients) according to a ratio of 7:3. The correlations between physical parameters and Dw were analyzed. The differences between SSDE substitutes and the reference SSDE (SSDE reference ) were compared. Strong positive correlations were observed between body mass index (BMI) and Dw as well as between weight and Dw in overall, male, and female patients (all p  < 0.001). The correlations between BMI and Dw were stronger than those between weight and Dw in overall, male, and female subjects (all p  < 0.001). SSDE weight and SSDE BMI were not significantly different from SSDE reference ( p  > 0.05). The RMSEs of overall patients between SSDE weight and SSDE reference as well as between SSDE BMI and SSDE reference were 0.237 and 0.2, respectively. The use of sex-specific regression equations for BMI caused a slightly reduction in RMSE. Weight and BMI can be used as surrogate parameters for Dw when calculating SSDE in adult chest CT exams, with BMI being the preferred substitute parameter.

This study aimed to compare the volume computed tomography dose index (CTDIvol), dose length product (DLP), and size-specific dose estimate (SSDE), with the China and updated 2017 American College of Radiology (ACR) diagnostic reference levels (DRLs) in chest CT examinations of adults based on the water-equivalent diameter (Dw). All chest CT examinations conducted without contrast administration from January 2020 to July 2020 were retrospectively included in this study. The Dw and SSDE of all examinations were calculated automatically by \"teamplay\". The CTDIvol and DLP were displayed on the DICOM-structured dose report in the console based on a 32cm phantom.The differences in patient CTDIvol, DLP, and SSDE values between groups were examined by the one-way ANOVA. The differences in patient CTDIvol, DLP, and SSDE values between the updated 2017 ACR and the China DRLs were examined with one sample t-tests. In total 14666 chest examinations were conducted in our study. Patients were divided into four groups based on Dw:270 (1.84%) in 15-20 cm group, 10287 (70.14%) in the 21-25 cm group, 4097 (27.94%) in the 26-30 cm group, and 12 (0.08%) patients had sizes larger than 30 cm. CTDIvol, DLP, and SSDE increased as a function of Dw (p<0.05). CTDIvol was smaller than SSDE among groups (p<0.05). The mean CTDIvol and DLP values were lower than the 25.sup.th, 50.sup.th, and 75.sup.th percentile of the China DRLs (p <0.05). The CTDIvol, DLP, and SSDE were lower than the 50.sup.th and 75.sup.th percentiles of the updated 2017 ACR DRLs (p <0.05) among groups. SSDE takes into account the influence of the scanning parameters, patient size, and X-ray attenuation on the radiation dose, which can give a more realistic estimate of radiation exposure dose for patients undergoing CT examinations. Establishing hospital's own DRL according to CTDIvol and SSDE is very important even though the radiation dose is lower than the national DRLs.

Microglia have been shown to contribute to the clearance of brain amyloid β peptides (Aβ), the major component of amyloid plaques, in Alzheimer’s disease (AD). However, it is not known whether microglia play a similar role in the clearance of tau, the major component of neurofibrillary tangles (NFTs). We now report that murine microglia rapidly internalize and degrade hyperphosphorylated pathological tau isolated from AD brain tissue in a time-dependent manner in vitro . We further demonstrate that microglia readily degrade human tau species released from AD brain sections and eliminate NFTs from brain sections of P301S tauopathy mice. The anti-tau monoclonal antibody MC1 enhances microglia-mediated tau degradation in an Fc-dependent manner. Our data identify a potential role for microglia in the degradation and clearance of pathological tau species in brain and provide a mechanism explaining the potential therapeutic actions of passively administered anti-tau monoclonal antibodies.

Prohibitin 1 (PHB1) and prohibitin 2 (PHB2) are highly conserved proteins belonging to the stomatin-prohibitin flotillin-HflC/K (SPFH) protein superfamily. They are ubiquitously expressed and implicated in the regulation of cell proliferation, migration, and survival. However, the expression and biological functions of PHB1/PHB2 in atherosclerosis (AS) remain unclear. In the present study, an enzyme-linked immunosorbent assay was used to detect PHB1/PHB2 expression in the serum of patients with hyperlipidemia. The potential effect and mechanism of PHB1/PHB2 in apolipoprotein E-deficient ( ApoE −/− ) mice were also investigated. shRNA-PHB1 and shRNA-PHB2 lentiviruses were engineered and tail vein-injected into ApoE −/− mice fed a high-fat diet. IL-8, a proatherogenic cytokine, was used as an inducer in vitro . The effects of a PHB1/PHB2 knockdown on vascular smooth muscle cell (VSMC) proliferation, migration, and autophagy and endothelial cell (EC) adhesion were evaluated using methyl thiazolyl tetrazolium (MTT), Transwell migration, Boyden chamber, and monocyte adhesion assays, as well as transmission electron microscopy. Compared with the healthy subjects, PHB1/PHB2 expression was elevated in the serum of patients with hyperlipidemia. Animal experiments showed that downregulation of PHBs reduced the area of atherosclerotic lesions, and the expression of cyclinD1, MMP9, and LC3. In addition, in vitro experiments showed that downregulating PHB1/PHB2 expression under inflammatory stimulation reduced the adhesion, proliferation, migration, and autophagy of ECs and VSMCs by inhibiting the PI3K/Akt/mTOR pathway activation. Collectively, our findings showed that PHBs are activly associated with AS progression.

As an important part of environmental science and water resources management, water quality prediction is of great importance. In order to improve the efficiency and accuracy of predicting dissolved oxygen (DO) at the outlet of a reservoir, this paper proposes an improved Seahorse Optimizer to enhance the hybrid kernel extreme learning machine model for water quality prediction. Firstly, the circle chaotic map is used to initialize the hippocampus population to improve the diversity and quality of the population, and then the sine and cosine strategy is used to replace the predation behavior of the hippocampus to improve the global search ability. Finally, the lens imaging reverse learning strategy is used to expand the search range and prevent it from falling into the local optimal solution. By introducing two kernel functions, a global kernel function (Poly) and a local kernel function (RBF), a new hybrid kernel function extreme learning machine is formed by linearly combining these two kernel functions. The parameters of this HKELM are optimized with the improved Seahorse Optimizer, and the water quality prediction model of CZTSHO-HKELM is constructed. The simulation results show that the operating efficiency and prediction accuracy of the model are better than those of the ELM, CZTSHO-ELM, CZTSHO-KELM, and SHO-HKELM models, with the correlation coefficients increased by 5.5%, 3.3%, 3.4%, and 7.4%, respectively. The dissolved oxygen prediction curve is close to the actual dissolved oxygen change, which can better meet the requirements of reservoir water quality prediction. The above method can be applied to further accurately predict the water quality of the reservoir.

Background Accurate measurement of hemoglobin concentration is essential for various medical scenarios, including preoperative evaluations and determining blood loss. Traditional invasive methods are inconvenient and not suitable for rapid, point-of-care testing. Moreover, current models, due to their complex parameters, are not well-suited for mobile medical settings, which limits the ability to conduct frequent and rapid testing. This study aims to introduce a novel, compact, and efficient system that leverages deep learning and smartphone technology to accurately estimate hemoglobin levels, thereby facilitating rapid and accessible medical assessments. Methods The study employed a smartphone application to capture images of the eye, which were subsequently analyzed by a deep neural network trained on data from invasive blood test data. Specifically, the EGE-Unet model was utilized for eyelid segmentation, while the DHA(C3AE) model was employed for hemoglobin level prediction. The performance of the EGE-Unet was evaluated using statistical metrics including mean intersection over union (MIOU), F1 Score, accuracy, specificity, and sensitivity. The DHA(C3AE) model’s performance was assessed using mean absolute error (MAE), mean-square error (MSE), root mean square error (RMSE), and R^2. Results The EGE-Unet model demonstrated robust performance in eyelid segmentation, achieving an MIOU of 0.78, an F1 Score of 0.87, an accuracy of 0.97, a specificity of 0.98, and a sensitivity of 0.86. The DHA(C3AE) model for hemoglobin level prediction yielded promising outcomes with an MAE of 1.34, an MSE of 2.85, an RMSE of 1.69, and an R^2 of 0.34. The overall size of the model is modest at 1.08 M, with a computational complexity of 0.12 FLOPs (G). Conclusions This system presents a groundbreaking approach that eliminates the need for supplementary devices, providing a cost-effective, swift, and accurate method for healthcare professionals to enhance treatment planning and improve patient care in perioperative environments. The proposed system has the potential to enable frequent and rapid testing of hemoglobin levels, which can be particularly beneficial in mobile medical settings. Trial Registration The clinical trial was registered on the Chinese Clinical Trial Registry (No. ChiCTR2100044138) on 20/02/2021.