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With the increasing research and development (R&D) difficulty of new molecular entities (NMEs), novel drug delivery systems (DDSs) are attracting widespread attention. This review investigated the current distribution of Food and Drug Administration (FDA)-approved pharmaceutical products and evaluated the technical barrier for the entry of generic drugs and highlighted the success and failure of advanced drug delivery systems. According to the ratio of generic to new drugs and the four-quadrant classification scheme for evaluating the commercialization potential of DDSs, the results showed that the traditional dosage forms (e.g., conventional tablets, capsules and injections) with a lower technology barrier were easier to reproduce, while advanced drug delivery systems (e.g., inhalations and nanomedicines) with highly technical barriers had less competition and greater market potential. Our study provides a comprehensive insight into FDA-approved products and deep analysis of the technical barriers for advanced drug delivery systems. In the future, the R&D of new molecular entities may combine advanced delivery technologies to make drug candidates into more therapeutically effective formulations.

Ultralight bosons can condense to form the so-called scalar clouds around rotating black holes (BHs) through superradiant instabilities. When quantum effects near the Planck scale of the event horizon are considered, the classical BH is replaced by an exotic compact object, such as an area-quantized BH. In this work, we examine the superradiant instabilities of massive scalar fields around area-quantized BHs. We model the frequency-dependent reflectivity function of area-quantized BHs for massive scalar fields, which reflects the distinctive selection property of these BHs for massive scalar fields. We then utilize this model to investigate the case that the scalar fields can be superradiated by area-quantized BHs. We find that the area quantization of BHs can affect the formation and effective radius, and suppress the total mass of scalar clouds. Especially, the energy gap of area-quantized BHs can break the growth continuity of scalar clouds between different modes. These are distinct from the case of classical BHs.

For the full cross-sectional profile measurement of high-aspect-ratio micro-grooves, traditional measurement methods have blind measurement areas in the vertical sidewall and its intersection area with the bottom. This paper proposes a deflection-based scanning method that utilizes a large length-to-diameter ratio probe to achieve a full cross-sectional profile measurement of micro-grooves. Blind measurement areas were eliminated by a deflection-based scanning method. The complete groove profile was obtained by stitching the positive and reversal deflection-based measurement results. The optimal deflection angle of the probe was calculated by considering the profile-stitching setting and the principle of minimizing the probe deformation during the measurement process. A four-axis measurement system was established to measure high-aspect-ratio micro-grooves, which incorporated a force feedback mechanism to maintain a constant contact force during the measurement and an integrated error separation module to modify the measurement results. The measurement method and system were experimentally validated to achieve a full cross-sectional profile measurement of micro-grooves with a width of 50 μm and an aspect ratio of no less than 3. The standard deviation of the measurement results was 82 nm, and the expanded uncertainty was 108 nm.

Puerarin is a predominant component of Radix Puerarin . Despite its anti-tumor and anti-virus effects and efficacy in improving cardiovascular or cerebrovascular diseases and preventing osteoporosis, it has been shown to protect against diabetes and its complications. This review summarizes the current knowledge on Puerarin in diabetes and related complications, aiming to provide an overview of antidiabetic mechanisms of Puerarin and new targets for treatment.

Selenium (Se) and its compounds have been reported to have great potential in the prevention and treatment of Alzheimer’s disease (AD). However, little is known about the functional mechanism of Se in these processes, limiting its further clinical application. Se exerts its biological functions mainly through selenoproteins, which play vital roles in maintaining optimal brain function. Therefore, selenoproteins, especially brain function-associated selenoproteins, may be involved in the pathogenesis of AD. Here, we analyze the expression and distribution of 25 selenoproteins in the brain and summarize the relationships between selenoproteins and brain function by reviewing recent literature and information contained in relevant databases to identify selenoproteins (GPX4, SELENOP, SELENOK, SELENOT, GPX1, SELENOM, SELENOS, and SELENOW) that are highly expressed specifically in AD-related brain regions and closely associated with brain function. Finally, the potential functions of these selenoproteins in AD are discussed, for example, the function of GPX4 in ferroptosis and the effects of the endoplasmic reticulum (ER)-resident protein SELENOK on Ca 2+ homeostasis and receptor-mediated synaptic functions. This review discusses selenoproteins that are closely associated with brain function and the relevant pathways of their involvement in AD pathology to provide new directions for research on the mechanism of Se in AD.

Neuroinflammation is known to have a pivotal role in the pathogenesis of Alzheimer's disease (AD), and curcumin has been reported to have therapeutical effects on AD because of its anti-inflammatory effects. Curcumin is not only a potent PPARγ agonist, but also has neuroprotective effects on cerebral ischemic injury. However, whether PPARγ activated by curcumin is responsible for the anti-neuroinflammation and neuroprotection on AD remains unclear, and needs to be further investigated. Here, using both APP/PS1 transgenic mice and beta-amyloid-induced neuroinflammation in mixed neuronal/glial cultures, we showed that curcumin significantly alleviated spatial memory deficits in APP/PS1 mice and promoted cholinergic neuronal function in vivo and in vitro. Curcumin also reduced the activation of microglia and astrocytes, as well as cytokine production and inhibited nuclear factor kappa B (NF-κB) signaling pathway, suggesting the beneficial effects of curcumin on AD are attributable to the suppression of neuroinflammation. Attenuation of these beneficial effects occurred when co-administrated with PPARγ antagonist GW9662 or silence of PPARγ gene expression, indicating that PPARγ might be involved in anti-inflammatory effects. Circular dichroism and co-immunoprecipitation analysis showed that curcumin directly bound to PPARγ and increased the transcriptional activity and protein levels of PPARγ. Taking together, these data suggested that PPARγ might be a potential target of curcumin, acting to alleviate neuroinflammation and improve neuronal function in AD.

Background Drug-target interaction (DTI) prediction has become a crucial prerequisite in drug design and drug discovery. However, the traditional biological experiment is time-consuming and expensive, as there are abundant complex interactions present in the large size of genomic and chemical spaces. For alleviating this phenomenon, plenty of computational methods are conducted to effectively complement biological experiments and narrow the search spaces into a preferred candidate domain. Whereas, most of the previous approaches cannot fully consider association behavior semantic information based on several schemas to represent complex the structure of heterogeneous biological networks. Additionally, the prediction of DTI based on single modalities cannot satisfy the demand for prediction accuracy. Methods We propose a multi-modal representation framework of ‘DeepMPF’ based on meta-path semantic analysis, which effectively utilizes heterogeneous information to predict DTI. Specifically, we first construct protein–drug-disease heterogeneous networks composed of three entities. Then the feature information is obtained under three views, containing sequence modality, heterogeneous structure modality and similarity modality. We proposed six representative schemas of meta-path to preserve the high-order nonlinear structure and catch hidden structural information of the heterogeneous network. Finally, DeepMPF generates highly representative comprehensive feature descriptors and calculates the probability of interaction through joint learning. Results To evaluate the predictive performance of DeepMPF, comparison experiments are conducted on four gold datasets. Our method can obtain competitive performance in all datasets. We also explore the influence of the different feature embedding dimensions, learning strategies and classification methods. Meaningfully, the drug repositioning experiments on COVID-19 and HIV demonstrate DeepMPF can be applied to solve problems in reality and help drug discovery. The further analysis of molecular docking experiments enhances the credibility of the drug candidates predicted by DeepMPF. Conclusions All the results demonstrate the effectively predictive capability of DeepMPF for drug-target interactions. It can be utilized as a useful tool to prescreen the most potential drug candidates for the protein. The web server of the DeepMPF predictor is freely available at http://120.77.11.78/DeepMPF/ , which can help relevant researchers to further study.

This study analyzed different contact theories and models for simulating rail wear in the turnout areas. The typical cross-sections of the high-speed turnout switch and frog areas are selected. Normal and tangential contact models of wheel-rail are compared. A vehicle-turnout coupling dynamic model is developed in the turnout area considering the actual rail profile, the continuous traction force, and the running resistance. In addition, different wear models, update steps, wear superposition, and filtering methods are investigated in the turnout area. A simulation program in the turnout area for rail wear damage is established and compared with experimental data. The results demonstrate that the normal semi-Hertz model and the tangential semi-Hertz-based FASTSIM algorithm suit the wheel-rail contact in the turnout area. The wear model based on wear work is selected for simulating wear in the turnout area with a updating wear step size of 0.05 mm. The proposed model provides accurate rail wear values in the turnout area compared to the traditional models.

Objective This study aims to assess the relationship between NHHR (non-high-density lipoprotein cholesterol to high-density lipoprotein cholesterol ratio) and Type 2 diabetes mellitus (T2DM) in US adults, using National Health and Nutrition Examination Survey (NHANES) data from 2007 to 2018. Methods This study explored the connection between NHHR and T2DM by analyzing a sample reflecting the adult population of the United States ( n  = 10,420; NHANES 2007–2018). NHHR was characterized as the ratio of non-high-density lipoprotein cholesterol to high-density lipoprotein cholesterol. T2DM was defined based on clinical guidelines. This research used multivariable logistic models to examine the connection between NHHR and T2DM. Additionally, it included subgroup and interaction analyses to assess variations among different groups. Generalized additive models, smooth curve fitting, and threshold effect analysis were also employed to analyze the data further. Results The study included 10,420 subjects, with 2160 diagnosed with T2DM and 8260 without. The weighted multivariate logistic regression model indicated an 8% higher probability of T2DM for each unit increase in NHHR (OR: 1.08, 95% CI: 1.01–1.15) after accounting for all covariates. Subgroup analysis outcomes were uniform across various categories, demonstrating a significant positive relationship between NHHR and T2DM. Interaction tests showed that the positive link between NHHR and T2DM remained consistent regardless of age, body mass index, smoking status, moderate recreational activities, hypertension, or stroke history, with all interaction P -values exceeding 0.05. However, participants’ sex appeared to affect the magnitude of the connection between NHHR and T2DM (interaction P -value < 0.05). Also, a nonlinear association between NHHR and T2DM was discovered, featuring an inflection point at 1.50. Conclusions Our study suggests that an increase in NHHR may be correlated with a heightened likelihood of developing T2DM. Consequently, NHHR could potentially serve as a marker for estimating the probability of T2DM development.

Advances in the \"omics\" field bring about the need for a high number of good quality samples. Many omics studies take advantage of biobanked samples to meet this need. Most of the laboratory errors occur in the pre-analytical phase. Therefore evidence-based standard operating procedures for the pre-analytical phase as well as markers to distinguish between 'good' and 'bad' quality samples taking into account the desired downstream analysis are urgently needed. We studied concentration changes of metabolites in serum samples due to pre-storage handling conditions as well as due to repeated freeze-thaw cycles. We collected fasting serum samples and subjected aliquots to up to four freeze-thaw cycles and to pre-storage handling delays of 12, 24 and 36 hours at room temperature (RT) and on wet and dry ice. For each treated aliquot, we quantified 127 metabolites through a targeted metabolomics approach. We found a clear signature of degradation in samples kept at RT. Storage on wet ice led to less pronounced concentration changes. 24 metabolites showed significant concentration changes at RT. In 22 of these, changes were already visible after only 12 hours of storage delay. Especially pronounced were increases in lysophosphatidylcholines and decreases in phosphatidylcholines. We showed that the ratio between the concentrations of these molecule classes could serve as a measure to distinguish between 'good' and 'bad' quality samples in our study. In contrast, we found quite stable metabolite concentrations during up to four freeze-thaw cycles. We concluded that pre-analytical RT handling of serum samples should be strictly avoided and serum samples should always be handled on wet ice or in cooling devices after centrifugation. Moreover, serum samples should be frozen at or below -80°C as soon as possible after centrifugation.