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Peroxisome proliferator-activated receptor (PPAR) α, β/δ, and γ modulate lipid homeostasis. PPARα regulates lipid metabolism in the liver, the organ that largely controls whole-body nutrient/energy homeostasis, and its abnormalities may lead to hepatic steatosis, steatohepatitis, steatofibrosis, and liver cancer. PPARβ/δ promotes fatty acid β-oxidation largely in extrahepatic organs, and PPARγ stores triacylglycerol in adipocytes. Investigations using liver-specific PPAR-disrupted mice have revealed major but distinct contributions of the three PPARs in the liver. This review summarizes the findings of liver-specific PPAR-null mice and discusses the role of PPARs in the liver.

Effective and accurate diagnosis of Alzheimer's disease (AD), as well as its prodromal stage (i.e., mild cognitive impairment (MCI)), has attracted more and more attention recently. So far, multiple biomarkers have been shown to be sensitive to the diagnosis of AD and MCI, i.e., structural MR imaging (MRI) for brain atrophy measurement, functional imaging (e.g., FDG-PET) for hypometabolism quantification, and cerebrospinal fluid (CSF) for quantification of specific proteins. However, most existing research focuses on only a single modality of biomarkers for diagnosis of AD and MCI, although recent studies have shown that different biomarkers may provide complementary information for the diagnosis of AD and MCI. In this paper, we propose to combine three modalities of biomarkers, i.e., MRI, FDG-PET, and CSF biomarkers, to discriminate between AD (or MCI) and healthy controls, using a kernel combination method. Specifically, ADNI baseline MRI, FDG-PET, and CSF data from 51AD patients, 99 MCI patients (including 43 MCI converters who had converted to AD within 18months and 56 MCI non-converters who had not converted to AD within 18months), and 52 healthy controls are used for development and validation of our proposed multimodal classification method. In particular, for each MR or FDG-PET image, 93 volumetric features are extracted from the 93 regions of interest (ROIs), automatically labeled by an atlas warping algorithm. For CSF biomarkers, their original values are directly used as features. Then, a linear support vector machine (SVM) is adopted to evaluate the classification accuracy, using a 10-fold cross-validation. As a result, for classifying AD from healthy controls, we achieve a classification accuracy of 93.2% (with a sensitivity of 93% and a specificity of 93.3%) when combining all three modalities of biomarkers, and only 86.5% when using even the best individual modality of biomarkers. Similarly, for classifying MCI from healthy controls, we achieve a classification accuracy of 76.4% (with a sensitivity of 81.8% and a specificity of 66%) for our combined method, and only 72% even using the best individual modality of biomarkers. Further analysis on MCI sensitivity of our combined method indicates that 91.5% of MCI converters and 73.4% of MCI non-converters are correctly classified. Moreover, we also evaluate the classification performance when employing a feature selection method to select the most discriminative MR and FDG-PET features. Again, our combined method shows considerably better performance, compared to the case of using an individual modality of biomarkers. ► We propose to combine MRI, FDG-PET, and CSF biomarkers, to discriminate between AD (or MCI) and healthy controls, using a kernel combination method. ► A high accuracy of 93.2% for AD classification and a high sensitivity of 91.5% (for MCI converters) for MCI classification. ► Each modality is indispensable for achieving good classification. ► CSF and PET have the highest complementary information and MRI and PET have the highest similar information for classification.

Learning engagement and academic emotions are critical factors influencing the academic performance of university students. Previous studies have shown that undergraduate nursing students generally exhibit lower levels of learning engagement, along with a higher prevalence of negative low-arousal academic emotions compared to their non-nursing peers. This study aimed to examine the effects of reflective diary writing on learning engagement and academic emotions among undergraduate nursing students. A quasi-experimental design was adopted, with the entire cohort of nursing students enrolled in 2020 at a selected university serving as participants. Following a four-week instructional period, a reflective diary writing intervention was implemented. Changes in learning engagement and academic emotions were assessed using validated questionnaires before and after the intervention. The results indicated a significant improvement in learning engagement post-intervention (t = − 9.92, 95% CI [− 4.36, − 2.89], p  < .001, d = 1.24). Regarding academic emotions, significant increases were observed in both positive activity orientation (t = − 8.64, 95% CI [− 1.90, − 1.18], p  < .001, d = 1.07) and positive outcome orientation (t = − 6.42, 95% CI [− 1.12, − 0.59], p  < .001, d = 0.80). Conversely, significant reductions were found in negative activity orientation (t = 5.74, 95% CI [1.11, 2.31], p  < .001, d = − 0.71) and negative outcome orientation (t = 4.35, 95% CI [0.81, 2.19], p  < .001, d = − 0.54). In conclusion, reflective diary writing effectively enhances learning engagement, promotes positive academic emotions, and reduces negative emotions among nursing students. These findings support the use of reflective diary writing as an evidence-based educational intervention in nursing education.

DNA modifications such as 5-methylcytosine （5mC） and 5-hydroxymethylcytosine （5hmC） are epigenetic marks known to affect global gene efpression in mammals. Given their prevalence in the human genome, close correlation with gene expression and high chemical stability, these DNA epigenetic marks could serve as ideal biomarkers for cancer diagnosis. Taking advantage of a highly sensitive and selective chemical labeling technology, we report here the genome-wide profiling of 5hmC in circulating cell-free DNA （cfDNA） and in genomic DNA （gDNA） of paired tumor and adjacent tissues collected from a cohort of 260 patients recently diagnosed with colorectal, gastric, pancreatic, liv- er or thyroid cancer and normal tissues from 90 healthy individuals. 5hmC was mainly distributed in transcriptionally active regions coincident with open chromatin and permissive histone modifications. Robust cancer-associated 5hmC signatures were identified in cfDNA that were characteristic for specific cancer types. 5hmC-based biomarkers of cir- culating cfDNA were highly predictive of colorectal and gastric cancers and were superior to conventional biomarkers and comparable to 5hmC biomarkers from tissue biopsies. Thus, this new strategy could lead to the development of effective, minimally invasive methods for diagnosis and prognosis of cancer from the analyses of blood samples.

Mechanosensitive ion channels are widely expressed in the cardiovascular system. They translate mechanical forces including shear stress and stretch into biological signals. The most prominent biological signal through which the cardiovascular physiological activity is initiated or maintained are intracellular calcium ions (Ca2+). Growing evidence show that the Ca2+ entry mediated by mechanosensitive ion channels is also precisely regulated by a variety of key proteins which are distributed in the cell membrane or endoplasmic reticulum. Recent studies have revealed that mechanosensitive ion channels can even physically interact with Ca2+ regulatory proteins and these interactions have wide implications for physiology and pathophysiology. Therefore, this paper reviews the cross-talk between mechanosensitive ion channels and some key Ca2+ regulatory proteins in the maintenance of calcium homeostasis and its relevance to cardiovascular health and disease.

Background Chemotherapy can induce premature ovarian insufficiency (POI) and reduce fertility in young female patients. Currently, there is no effective therapy for POI. Human amnion-derived mesenchymal stem cells (hAD-MSCs) may be a promising seed cell for regenerative medicine. This study investigated the effects and mechanisms of hAD-MSC transplantation on chemotherapy-induced POI in rats. Methods Chemotherapy-induced POI rat models were established by intraperitoneal injection of cyclophosphamide. Seventy-two female SD rats were randomly divided into control, POI, and hAD-MSC-treated groups. hAD-MSCs were labeled with PKH26 and injected into the tail veins of POI rats. To examine the underlying mechanisms, the differentiation of transplanted hAD-MSCs in the POI ovaries was analyzed by immunofluorescent staining. The in vitro expression of growth factors secreted by hAD-MSCs in hAD-MSC-conditioned media (hAD-MSC-CM) was analyzed by ELISA. Sixty female SD rats were divided into control, POI, and hAD-MSC-CM-treated groups, and hAD-MSC-CM was injected into the bilateral ovaries of POI rats. After hAD-MSC transplantation or hAD-MSC-CM injection, serum sex hormone levels, estrous cycles, ovarian pathological changes, follicle counts, granulosa cell (GC) apoptosis, and Bcl-2, Bax, and VEGF expression in ovaries were examined. Results PKH26-labeled hAD-MSCs mainly homed to ovaries after transplantation. hAD-MSC transplantation reduced ovarian injury and improved ovarian function in rats with POI. Transplanted hAD-MSCs were only located in the interstitium of ovaries, rather than in follicles, and did not express the typical markers of oocytes and GCs, which are ZP3 and FSHR, respectively. hAD-MSCs secreted FGF2, IGF-1, HGF, and VEGF, and those growth factors were detected in the hAD-MSC-CM. hAD-MSC-CM injection improved the local microenvironment of POI ovaries, leading to a decrease in Bax expression and an increase in Bcl-2 and endogenous VEGF expression in ovarian cells, which inhibited chemotherapy-induced GC apoptosis, promoted angiogenesis and regulated follicular development, thus partly reducing ovarian injury and improving ovarian function in rats with POI. Conclusions hAD-MSC transplantation can improve ovarian function in rats with chemotherapy-induced POI at least partly through a paracrine mechanism. The presence of a paracrine mechanism accounting for hAD-MSC-mediated recovery of ovarian function might be attributed to the growth factors secreted by hAD-MSCs.

In-memory computing provides an opportunity to meet the growing demands of large data-driven applications such as machine learning, by colocating logic operations and data storage. Despite being regarded as the ultimate solution for high-density integration and low-power manipulation, the use of spin or electric dipole at the single-molecule level to realize in-memory logic functions has yet to be realized at room temperature, due to their random orientation. Here, we demonstrate logic-in-memory operations, based on single electric dipole flipping in a two-terminal single-metallofullerene (Sc 2 C 2 @ C s (hept)-C 88 ) device at room temperature. By applying a low voltage of ±0.8 V to the single-metallofullerene junction, we found that the digital information recorded among the different dipole states could be reversibly encoded in situ and stored. As a consequence, 14 types of Boolean logic operation were shown from a single-metallofullerene device. Density functional theory calculations reveal that the non-volatile memory behaviour comes from dipole reorientation of the [Sc 2 C 2 ] group in the fullerene cage. This proof-of-concept represents a major step towards room-temperature electrically manipulated, low-power, two-terminal in-memory logic devices and a direction for in-memory computing using nanoelectronic devices. Single-molecule electronics provide the potential solution for high-density integration and low-power consumption in massive data-driven applications, but have yet to be explored. Here, the authors report low-power logic-in-memory operations, based on single electric dipole flipping in the two-terminal single-metallofullerene device at room temperature.

Senescence is a hot topic nowadays, which shows the accumulation of senescent cells and inflammatory factors, leading to the occurrence of various senescence-related diseases. Although some methods have been identified to partly delay senescence, such as strengthening exercise, restricting diet, and some drugs, these only slow down the process of senescence and cannot fundamentally delay or even reverse senescence. Stem cell-based therapy is expected to be a potential effective way to alleviate or cure senescence-related disorders in the coming future. Mesenchymal stromal cells (MSCs) are the most widely used cell type in treating various diseases due to their potentials of self-replication and multidirectional differentiation, paracrine action, and immunoregulatory effects. Some biological characteristics of MSCs can be well targeted at the pathological features of aging. Therefore, MSC-based therapy is also a promising strategy to combat senescence-related diseases. Here we review the recent progresses of MSC-based therapies in the research of age-related diseases and the challenges in clinical application, proving further insight and reference for broad application prospects of MSCs in effectively combating senesce in the future.

The study of drug–target interaction plays an important role in the process of drug development. The subject of DTI forecasting has advanced significantly in the last several years, yielding numerous significant research findings and methodologies. Heterogeneous data sources provide richer information and comprehensive perspectives for drug–target interaction prediction, so many existing methods rely on heterogeneous networks, and graph embedding technology becomes an important technology to extract information from heterogeneous networks. These approaches, however, are less concerned with potential noisy information in heterogeneous networks and more focused on the extent of information extraction in those networks. Based on this, a potential DTI predictive network model called FBRWPC is proposed in this paper. It uses a fine-grained similarity selection program to first integrate similarity on similar networks and then a bidirectional random walk graph embedding learning method with restart to obtain an updated drug target interaction matrix. Through the use of similarity selection and fine-grained selection similarity integration, the framework can effectively filter out the noise present in heterogeneous networks and enhance the model's prediction performance. The experimental findings demonstrate that, even after being split up into four distinct types of data sets, FBRWPC can still retain great prediction performance, a sign of the model's resilience and good generalization.

In complex industrial environments, the vibration signal of the rolling bearing is covered by noise, which makes fault diagnosis inaccurate. In order to overcome the effect of noise on the signal, a rolling bearing fault diagnosis method based on the WOA-VMD (Whale Optimization Algorithm-Variational Mode Decomposition) and the GAT (Graph Attention network) is proposed to deal with end effect and mode mixing issues in signal decomposition. Firstly, the WOA is used to adaptively determine the penalty factor and decomposition layers in the VMD algorithm. Meanwhile, the optimal combination is determined and input into the VMD, which is used to decompose the original signal. Then, the Pearson correlation coefficient method is used to select IMF (Intrinsic Mode Function) components that have a high correlation with the original signal, and selected IMF components are reconstructed to remove the noise in the original signal. Finally, the KNN (K-Nearest Neighbor) method is used to construct the graph structure data. The multi-headed attention mechanism is used to construct the fault diagnosis model of the GAT rolling bearing in order to classify the signal. The results show an obvious noise reduction effect in the high-frequency part of the signal after the application of the proposed method, where a large amount of noise was removed. In the diagnosis of rolling bearing faults, the accuracy of the test set diagnosis in this study was 100%, which is higher than that of the four other compared methods, and the diagnosis accuracy rate of various faults reached 100%.