HDAC inhibitor regulates the tumor immune microenvironment via pyroptosis in triple negative breast cancer.

Pyroptosis, an inflammatory form of cell death, promotes the release of immunogenic substances and stimulates immune cell recruitment, a process, which could turn cold tumors into hot ones. Thus, instigating pyroptosis in triple-negative breast cancer (TNBC) serves as a viable method for restoring antitumor immunity. We analyzed the effects of Histone Deacetylase Inhibitors (HDACi) on TNBC cells using the Cell Counting Kit-8 and colony formation assay. Apoptosis and lactate dehydrogenase (LDH) release assays were utilized to determine the form of cell death. The pyroptotic executor was validated by quantitative real-time polymerase chain reaction and western blot. Transcriptome was analyzed to investigate pyroptosis-inducing mechanisms. A subcutaneously transplanted tumor model was generated in BALB/c mice to evaluate infiltration of immune cells. HDACi significantly diminished cell proliferation, and pyroptotic "balloon"-like cells became apparent. HDACi led to an intra and extracellular material exchange, signified by the release of LDH and the uptake of propidium iodide. Among the gasdermin family, TNBC cells expressed maximum quantities of GSDME, and expression of GSDMA, GSDMB, and GSDME were augmented post HDACi treatment. Pyroptosis was instigated via the activation of the caspase 3-GSDME pathway with the potential mechanisms being cell cycle arrest and altered intracellular REDOX balance due to aberrant glutathione metabolism. In vivo experiments demonstrated that HDACi can activate pyroptosis, limit tumor growth, and escalate CD8+ lymphocyte and CD11b+ cell infiltration along with an increased presence of granzyme B in tumors. HDACi can instigate pyroptosis in TNBC, promoting infiltration of immune cells and consequently intensifying the efficacy of anticancer immunity.

Membrane-Based Preparation Process and Antioxidant and Anti-AGEs Activities of a Novel Propolis Ultrafiltrate.

Propolis is one functional supplement with hundreds of years of usage. However, it's rarely consumed directly for its resinous property. Herein, a pre-treated process which can remove the impurity while preserve its bioactivities is needed to maximise its therapeutic opportunities. In the present study, a membrane-based ultrafiltration process was developed on a KM1812-NF experimental instrument. Using Brazilian green propolis as testing material, all experimental steps and parameters were sequentially optimized. In addition, a mathematical model was developed to fit the process. As a result, the optimum solvent was 60 % ethanol adjusted to pH 8-9, while the optimum MWCO (molecular weight cut-off) value of membrane was 30 KDa. The membrane filtration dynamic model fitted with the function y=(ax+b)/(1+cx+dx2 ). The resulting propolis ultrafiltrate from Brazilian green propolis, termed P30K, contains the similar profile of flavonoids and phenolic acids as raw propolis. Meanwhile, the ORAC (oxygen radical absorbance capacity) value of P30K is 11429.45±1557.58 µM TE/g and the IC50 value of inhibition of fluorescent AGEs (advanced glycation end products) formation is 0.064 mg/mL. Our work provides an innovative alternative process for extraction of active compounds from propolis and reveals P30K as an efficient therapeutic antioxidant.

A Novel Ultrafiltrate Extract of Propolis Exerts Anti-inflammatory Activity through Metabolic Rewiring.

Thousands of years ago, humans started to use propolis because of its medicinal properties, and modern science has successfully identified several bioactive molecules within this resinous bee product. However, a natural propolis extract which has been removed the adhesive glue and preserved propolis bioactive compounds is urgently needed to maximise the therapeutic opportunities. In this study, a novel ultrafiltrate fraction from Brazilian green propolis, termed P30K, was demonstrated with anti-inflammatory properties, both in vitro and in vivo. Total flavonoids and total phenolic acids content in P30K were 244.6 mg/g and 275.8 mg/g respectively, while the IC50 value of inhibition of cyclooxygenase-2 (COX-2) was 8.30 µg/mL. The anti-inflammatory activity of P30K was furtherly corroborated in experimental models of lipopolysaccharides (LPS)-induced acute liver and lung injury. Mechanistically, integrated GC-MS and LC-MS based serum metabolomics analysis revealed that P30K modulated citrate cycle (TCA), pyruvate, glyoxylate and dicarboxylate metabolism pathways to inhibit secretion of pro-inflammatory cytokines. Results of network pharmacology and molecular docking suggested that P30K targeted catechol-O-methyltransferases (COMT), 11ß-hydroxysteroid dehydrogenases (HSD11B1), and monoamine oxidases (MAOA and MAOB) to promote cellular metabolomic rewiring. Collectively, our work reveals P30K as an efficient therapeutic agent against inflammatory conditions and its efficacy is related to metabolic rewiring.

Targeting the inward rectifier potassium channel 5.1 in thyroid cancer: artificial intelligence-facilitated molecular docking for drug discovery.

BACKGROUND: Recurrent and metastatic thyroid cancer is more invasive and can transform to dedifferentiated thyroid cancer, thus leading to a severe decline in the 10-year survival. The thyroid-stimulating hormone receptor (TSHR) plays an important role in differentiation process. We aim to find a therapeutic target in redifferentiation strategies for thyroid cancer. METHODS: Our study integrated the differentially expressed genes acquired from the Gene Expression Omnibus database by comparing TSHR expression levels in the Cancer Genome Atlas database. We conducted functional enrichment analysis and verified the expression of these genes by RT-PCR in 68 pairs of thyroid tumor and paratumor tissues. Artificial intelligence-enabled virtual screening was combined with the VirtualFlow platform for deep docking. RESULTS: We identified five genes (KCNJ16, SLC26A4, TG, TPO, and SYT1) as potential cancer treatment targets. TSHR and KCNJ16 were downregulated in the thyroid tumor tissues, compared with paired normal tissues. In addition, KCNJ16 was lower in the vascular/capsular invasion group. Enrichment analyses revealed that KCNJ16 may play a significant role in cell growth and differentiation. The inward rectifier potassium channel 5.1 (Kir5.1, encoded by KCNJ16) emerged as an interesting target in thyroid cancer. Artificial intelligence-facilitated molecular docking identified Z2087256678_2, Z2211139111_1, Z2211139111_2, and PV-000592319198_1 (-7.3 kcal/mol) as the most potent commercially available molecular targeting Kir5.1. CONCLUSION: This study may provide greater insights into the differentiation features associated with TSHR expression in thyroid cancer, and Kir5.1 may be a potential therapeutic target in the redifferentiation strategies for recurrent and metastatic thyroid cancer.

Sleep postures monitoring based on capacitively coupled electrodes and deep recurrent neural networks.

BACKGROUND: Capacitively coupled electrode (CC electrode), as a non-contact and unobtrusive technology for measuring physiological signals, has been widely applied in sleep monitoring scenarios. The most common implementation is capacitive electrocardiogram (cECG) that could provide useful clinical information for assessing cardiac function and detecting cardiovascular diseases. In the current study, we sought to explore another potential application of cECG in sleep monitoring, i.e., sleep postures recognition. METHODS: Two sets of experiments, the short-term experiment, and the overnight experiment, were conducted. The cECG signals were measured by a smart mattress based on flexible CC electrodes and sleep postures were recorded simultaneously. Then, a classifier model based on a deep recurrent neural network (RNN) was proposed to distinguish sleep postures (supine, left lateral and right lateral). To verify the reliability of the proposed model, leave-one-subject-out cross-validation was introduced. RESULTS: In the short-term experiment, the overall accuracy of 96.2% was achieved based on 30-s segment, while the overall accuracy was 88.8% using one heart beat segment. For the unconstrained overnight experiment, the accuracy of 91.0% was achieved based on 30-s segment, while the accuracy was 81.4% using one heart beat segment. CONCLUSIONS: The results suggest that cECG could render valuable information about sleep postures detection and potentially be helpful for sleep disorder diagnosis.

Purification Process and In Vitro and In Vivo Bioactivity Evaluation of Pectolinarin and Linarin from Cirsium japonicum.

Pectolinarin and linarin are two major flavone O-glycosides of Cirsium japonicum, which has been used for thousands of years in traditional Chinese medicine. Pharmacological research on pectolinarin and linarin is meaningful and necessary. Here, a process for the purification of pectolinarin and linarin from C. japonicum was established using macroporous resin enrichment followed by prep-HPLC separation. The results show the purity of pectolinarin and linarin reached 97.39% and 96.65%, respectively. The in vitro bioactivities result shows the ORAC values of pectolinarin and linarin are 4543 and 1441 µmol TE/g, respectively, meanwhile their inhibition rate of BSA-MGO-derived AGEs is 63.58% and 19.31% at 2 mg/mL, which is 56.03% and 30.73% in the BSA-fructose system, respectively. The COX-2 inhibition rate at 50 µg/mL of linarin and pectolinarin reached 55.35% and 40.40%, respectively. Furthermore, the in vivo bioassay combining of histopathologic evaluation and biochemical analysis of liver glutamic oxaloacetic transaminase, serum creatinine and TNF-α show pectolinarin can alleviate lipopolysaccharide (LPS)-induced acute liver and kidney injury in mice. Metabolomics analysis shows that pectolinarin attenuates LPS-challenged liver and kidney stress through regulating the arachidonic acid metabolism and glutathione synthesis pathways. Collectively, our work presents a solid process for pectolinarin and linarin purification and has discovered a promising natural therapeutic agent-pectolinarin.

[Research progress on the application of novel sensing technologies for sleep-related breathing disorder monitoring at home].

Sleep-related breathing disorder (SRBD) is a sleep disease with high incidence and many complications. However, patients are often unaware of their sickness. Therefore, SRBD harms health seriously. At present, home SRBD monitoring equipment is a popular research topic to help people get aware of their health conditions. This article fully compares recent state-of-art research results about home SRBD monitors to clarify the advantages and limitations of various sensing techniques. Furthermore, the direction of future research and commercialization is pointed out. According to the system design, novel home SRBD monitors can be divided into two types: wearable and unconstrained. The two types of monitors have their own advantages and disadvantages. The wearable devices are simple and portable, but they are not comfortable and durable enough. Meanwhile, the unconstrained devices are more unobtrusive and comfortable, but the supporting algorithms are complex to develop. At present, researches are mainly focused on system design and performance evaluation, while high performance algorithm and large-scale clinical trial need further research. This article can help researchers understand state-of-art research progresses on SRBD monitoring quickly and comprehensively and inspire their research and innovation ideas. Additionally, this article also summarizes the existing commercial sleep respiratory monitors, so as to promote the commercialization of novel home SRBD monitors that are still under research.

A novel dengue virus serotype 1 vaccine candidate based on Japanese encephalitis virus vaccine strain SA14-14-2 as the backbone.

To develop a potential dengue vaccine candidate, a full-length cDNA clone of a novel chimeric virus was constructed using recombinant DNA technology, with Japanese encephalitis virus (JEV) vaccine strain SA14-14-2 as the backbone, with its premembrane (prM) and envelope (E) genes substituted by their counterparts from dengue virus type 1 (DENV1). The chimeric virus (JEV/DENV1) was successfully recovered from primary hamster kidney (PHK) cells by transfection with the in vitro transcription products of JEV/DENV1 cDNA and was identified by complete genome sequencing and immunofluorescent staining. No neuroinvasiveness of this chimeric virus was observed in mice inoculated by the subcutaneous route (s.c.) or by the intraperitoneal route (i.p.), while some neurovirulence was displayed in mice that were inoculated directly by the intracerebral route (i.c.). The chimeric virus was able to stimulate high-titer production of antibodies against DENV1 and provided protection against lethal challenge with neuroadapted dengue virus in mice. These results suggest that the chimeric virus is a promising dengue vaccine candidate.

Phloretin prolongs lifespan of Caenorhabditis elegans via inhibition of NDUFS1 and NDUFS6 at mitochondrial complex â .

Phloretin has been widely perceived as an antioxidant. However, the bioavailability of phloretin in vivo is generally far too low to elicit a direct antioxidant effect by scavenging reactive oxygen species (ROS). Here we showed that administration of phloretin of apple polyphenols extended lifespan of Caenorhabditis elegans and promoted fitness. Specially phloretin enhanced the survival rates of nematodes under oxidants in an inverted U-shaped dose-response manner. The lifespan-extending effects of phloretin were mediated by ROS via mitochondrial complex I inhibition. The increase of ROS stimulated p38 MAPK/PMK-1 as well as transcription factors of NRF2/SKN-1 and FOXO/DAF-16. Consistent with the involvement of NRF2/SKN-1 and FOXO/DAF-16 in lifespan-extending effects, activities of superoxide dismutase (SOD) and catalase (CAT) were enhanced by phloretin. The exogenous application of antioxidants butylated hydroxyanisole and N-acetylcysteine abolished the increase of ROS, the enhancement of SOD and CAT activities, and the lifespan extending effects of phloretin. Meanwhile, with the inhibition of mitochondrial complex I, ATP was instantly decreased. Both energy sensors of AMPK/AAK-2 and SIRT1/SIR-2.1 were involved in the lifespan extension by phloretin. Transcriptomic, real-time qPCR and molecular docking analyses demonstrated that the binding of phloretin at complex I located at NDUFS1/NUO-5, NDUFS2/GAS-1, and NDUFS6/NDUF-6. The molecular dynamic simulation and binding free energy calculations showed that phloretin had high binding affinities towards NDUFS1 (-7.21 kcal/mol) and NDUFS6 (-7.02 kcal/mol). Collectively, our findings suggested phloretin had effects of life expectancy enhancement and fitness promotion via redox regulations in vivo. NDUFS1/NUO-5 and NDUFS6/NDUF-6 might be new targets in the lifespan and wellness regulations.

Polygenic risk score predicts all-cause death in East Asian patients with prior coronary artery disease.

Introduction: Coronary artery disease (CAD) is a highly heritable and multifactorial disease. Numerous genome-wide association studies (GWAS) facilitated the construction of polygenic risk scores (PRS) for predicting future incidence of CAD, however, exclusively in European populations. Furthermore, identifying CAD patients with elevated risks of all-cause death presents a critical challenge in secondary prevention, which will contribute largely to reducing the burden for public healthcare. Methods: We recruited a cohort of 1,776 Chinese CAD patients and performed medical follow-up for up to 11 years. A pruning and thresholding method was used to calculate PRS of CAD and its 14 risk factors. Their correlations with all-cause death were computed via Cox regression. Results and discussion: We found that the PRS for CAD and its seven risk factors, namely myocardial infarction, ischemic stroke, angina, heart failure, low-density lipoprotein cholesterol, total cholesterol and C-reaction protein, were significantly associated with death (P ≤ 0.05), whereas the PRS of body mass index displayed moderate association (P < 0.1). Elastic-net Cox regression with 5-fold cross-validation was used to integrate these nine PRS models into a meta score, metaPRS, which performed well in stratifying patients at different risks for death (P < 0.0001). Combining metaPRS with clinical risk factors further increased the discerning power and a 4% increase in sensitivity. The metaPRS generated from the genetic susceptibility to CAD and its risk factors can well stratify CAD patients by their risks of death. Integrating metaPRS and clinical risk factors may contribute to identifying patients at higher risk of poor prognosis.

Towards Real-Time Sleep Stage Prediction and Online Calibration Based on Architecturally Switchable Deep Learning Models.

Despite the recent advances in automatic sleep staging, few studies have focused on real-time sleep staging to promote the regulation of sleep or the intervention of sleep disorders. In this paper, a novel network named SwSleepNet, that can handle both precisely offline sleep staging, and online sleep stages prediction and calibration is proposed. For offline analysis, the proposed network coordinates sequence broadening module (SBM), sequential CNN (SCNN), squeeze and excitation (SE) block, and sequence consolidation module (SCM) to balance the operational efficiency of the network and the comprehensive feature extraction. For online analysis, only SCNN and SE are involved in predicting the sleep stage within a short-time segment of the recordings. Once more than two successive segments have disparate predictions, the calibration mechanism will be triggered, and contextual information will be involved. In addition, to investigate the appropriate time of the segment that is suitable to predict a sleep stage, segments with five-second, three-second, and two-second data are analyzed. The performance of SwSleepNet is validated on two publicly available datasets Sleep-EDF Expanded and Montreal Archive of Sleep Studies (MASS), and one clinical dataset Huashan Hospital Fudan University (HSFU), with the offline accuracy of 84.5%, 86.7%, and 81.8%, respectively, which outperforms the state-of-the-art methods. Additionally, for the online sleep staging, the dedicated calibration mechanism allows SwSleepNet to achieve high accuracy over 80% on three datasets with the short-time segments, demonstrating the robustness and stability of SwSleepNet. This study presents a real-time sleep staging architecture, which is expected to pave the way for accurate sleep regulation and intervention.

A Sequential End-to-End Neonatal Sleep Staging Model with Squeeze and Excitation Blocks and Sequential Multi-Scale Convolution Neural Networks.

Automatic sleep staging offers a quick and objective assessment for quantitatively interpreting sleep stages in neonates. However, most of the existing studies either do not encompass any temporal information, or simply apply neural networks to exploit temporal information at the expense of high computational overhead and modeling ambiguity. This limits the application of these methods to multiple scenarios. In this paper, a sequential end-to-end sleep staging model, SeqEESleepNet, which is competent for parallelly processing sequential epochs and has a fast training rate to adapt to different scenarios, is proposed. SeqEESleepNet consists of a sequence epoch generation (SEG) module, a sequential multi-scale convolution neural network (SMSCNN) and squeeze and excitation (SE) blocks. The SEG module expands independent epochs into sequential signals, enabling the model to learn the temporal information between sleep stages. SMSCNN is a multi-scale convolution neural network that can extract both multi-scale features and temporal information from the signal. Subsequently, the followed SE block can reassign the weights of features through mapping and pooling. Experimental results exhibit that in a clinical dataset, the proposed method outperforms the state-of-the-art approaches, achieving an overall accuracy, F1-score, and Kappa coefficient of 71.8%, 71.8%, and 0.684 on a three-class classification task with a single channel EEG signal. Based on our overall results, we believe the proposed method could pave the way for convenient multi-scenario neonatal sleep staging methods.

GlmS plays a key role in the virulence factor expression and biofilm formation ability of Staphylococcus aureus promoted by advanced glycation end products.

Staphylococcus aureus (S. aureus) is well known for its biofilm formation ability and is responsible for serious, chronic refractory infections worldwide. We previously demonstrated that advanced glycation end products (AGEs), a hallmark of chronic hyperglycaemia in diabetic tissues, enhanced biofilm formation by promoting eDNA release via sigB upregulation in S. aureus, contributing to the high morbidity and mortality of patients presenting a diabetic foot ulcer infection. However, the exact regulatory network has not been completely described. Here, we used pull-down assay and LC-MS/MS to identify the GlmS as a candidate regulator of sigB in S. aureus stimulated by AGEs. Dual-luciferase assays and electrophoretic mobility shift assays (EMSAs) revealed that GlmS directly upregulated the transcriptional activity of sigB. We constructed NCTC 8325 ∆glmS for further validation. qRT-PCR analysis revealed that AGEs promoted both glmS and sigB expression in the NCTC 8325 strain but had no effect on NCTC 8325 ∆glmS. NCTC 8325 ∆glmS showed a significant attenuation in biofilm formation and virulence factor expression, accompanied by a decrease in sigB expression, even under AGE stimulation. All of the changes, including pigment deficiency, decreased haemolysis ability, downregulation of hla and hld expression, and less and sparser biofilms, indicated that sigB and biofilm formation ability no longer responded to AGEs in NCTC 8325 ∆glmS. Our data extend the understanding of GlmS in the global regulatory network of S. aureus and demonstrate a new mechanism by which AGEs can upregulate GlmS, which directly regulates sigB and plays a significant role in mediating biofilm formation and virulence factor expression.

Causal associations of genetically predicted gut microbiota and blood metabolites with inflammatory states and risk of infections: a Mendelian randomization analysis.

Background: Inflammation serves as a key pathologic mediator in the progression of infections and various diseases, involving significant alterations in the gut microbiome and metabolism. This study aims to probe into the potential causal relationships between gut microbial taxa and human blood metabolites with various serum inflammatory markers (CRP, SAA1, IL-6, TNF-α, WBC, and GlycA) and the risks of seven common infections (gastrointestinal infections, dysentery, pneumonia, bacterial pneumonia, bronchopneumonia and lung abscess, pneumococcal pneumonia, and urinary tract infections). Methods: Two-sample Mendelian randomization (MR) analysis was performed using inverse variance weighted (IVW), maximum likelihood, MR-Egger, weighted median, and MR-PRESSO. Results: After adding other MR models and sensitivity analyses, genus Roseburia was simultaneously associated adversely with CRP (Beta IVW = -0.040) and SAA1 (Beta IVW = -0.280), and family Bifidobacteriaceae was negatively associated with both CRP (Beta IVW = -0.034) and pneumonia risk (Beta IVW = -0.391). After correction by FDR, only glutaroyl carnitine remained significantly associated with elevated CRP levels (Beta IVW = 0.112). Additionally, threonine (Beta IVW = 0.200) and 1-heptadecanoylglycerophosphocholine (Beta IVW = -0.246) were found to be significantly associated with WBC levels. Three metabolites showed similar causal effects on different inflammatory markers or infectious phenotypes, stearidonate (18:4n3) was negatively related to SAA1 and urinary tract infections, and 5-oxoproline contributed to elevated IL-6 and SAA1 levels. In addition, 7-methylguanine showed a positive correlation with dysentery and bacterial pneumonia. Conclusion: This study provides novel evidence confirming the causal effects of the gut microbiome and the plasma metabolite profile on inflammation and the risk of infection. These potential molecular alterations may aid in the development of new targets for the intervention and management of disorders associated with inflammation and infections.

sEMG-Based Inter-Session Hand Gesture Recognition via Domain Adaptation with Locality Preserving and Maximum Margin.

Surface electromyography (sEMG)-based gesture recognition can achieve high intra-session performance. However, the inter-session performance of gesture recognition decreases sharply due to the shift in data distribution. Therefore, developing a robust model to minimize the data distribution difference is crucial to improving the user experience. In this work, based on the inter-session gesture recognition task, we propose a novel algorithm called locality preserving and maximum margin criterion (LPMM). The LPMM algorithm integrates three main modules, including domain alignment, pseudo-label selection, and iteration result selection. Domain alignment is designed to preserve the neighborhood structure of the feature and minimize the overlap of different classes. The pseudo-label selection and iteration result selection can avoid the decrease in accuracy caused by mislabeled samples. The proposed algorithm was evaluated on two of the most widely used EMG databases. It achieves a mean accuracy of 98.46% and 71.64%, respectively, which is superior to state-of-the-art domain adaptation methods.

Directed differentiation of human embryonic stem cells into parathyroid cells and establishment of parathyroid organoids.

Differentiation of human embryonic stem cells (hESCs) into human embryonic stem cells-derived parathyroid-like cells (hESC-PT) has clinical significance in providing new therapies for congenital and acquired parathyroid insufficiency conditions. However, a highly reproducible, well-documented method for parathyroid differentiation remains unavailable. By imitating the natural process of parathyroid embryonic development, we proposed a new hypothesis about the in vitro differentiation of parathyroid-like cells. Transcriptome, differentiation marker protein detection and parathyroid hormone (PTH) secretion assays were performed after the completion of differentiation. To optimize the differentiation protocol and further improve the differentiation rate, we designed glial cells missing transcription factor 2 (GCM2) overexpression lentivirus transfection assays and constructed hESCs-derived parathyroid organoids. The new protocol enabled hESCs to differentiate into hESC-PT. HESC-PT cells expressed PTH, GCM2 and CaSR proteins, low extracellular calcium culture could stimulate hESC-PT cells to secrete PTH. hESC-PT cells overexpressing GCM2 protein secreted PTH earlier than their counterpart hESC-PT cells. Compared with the two-dimensional cell culture environment, hESCs-derived parathyroid organoids secreted more PTH. Both GCM2 lentiviral transfection and three-dimensional cultures could make hESC-PT cells functionally close to human parathyroid cells. Our study demonstrated that hESCs could differentiate into hESC-PT in vitro, which paves the road for applying the technology to treat hypoparathyroidism and introduces new approaches in the field of regenerative medicine.

Physiological measurements for driving drowsiness: A comparative study of multi-modality feature fusion and selection.

A large number of traffic accidents were caused by drowsiness while driving. In-vehicle alert system based on physiological signals was one of the most promising solutions to monitor driving fatigue. However, different physiological modalities can be used, and many relative studies compared different modalities without considering the implementation feasibility of portable or wearable devices. Moreover, evaluations of each modality in previous studies were based on inconsistent choices of fatigue label and signal features, making it hard to compare the results of different studies. Therefore, the modality comparison and fusion for continuous drowsiness estimation while driving was still unclear. This work sought to comprehensively compare widely-used physiological modalities, including forehead electroencephalogram (EEG), electrooculogram (EOG), R-R intervals (RRI) and breath, in a hardware setting feasible for portable or wearable devices to monitor driving fatigue. Moreover, a more general conclusion on modality comparison and fusion was reached based on the regression of features or their combinations and the awake-to-drowsy transition. Finally, the feature subset of fused modalities was produced by feature selection method, to select the optimal feature combination and reduce computation consumption. Considering practical feasibility, the most effective combination with the highest correlation coefficient was using forehead EEG or EOG, along with RRI and RRI-derived breath. If more comfort and convenience was required, the combination of RRI and RRI-derived breath was also promising.

MKL-1 suppresses ferroptosis by activating system Xc- and increasing glutathione synthesis.

Chemotherapy is a standard method in traditional treatment for gastric cancer. It is well known that the anti-tumor effects of chemotherapy are achieved mainly through the direct killing of cancer cells via apoptosis. However, chemotherapy often fails due to drug resistance. Therefore, non-apoptotic cell death induction by ferroptosis has recently been proposed as a new therapeutic modality to ablate cancer. In this study, we determined the role of MKL-1 in ferroptosis. In vitro and in vivo experiments showed that inhibition of MKL-1 expression significantly enhanced cell sensitivity to ferroptosis-inducing agents. It functions by targeting system Xc- to affect the synthesis of GSH in cells. Therefore, we developed an exosome-based therapeutic approach targeting MKL-1, which provides a novel insight into the treatment of gastric cancer.

SDE-YOLO: A Novel Method for Blood Cell Detection.

This paper proposes an improved target detection algorithm, SDE-YOLO, based on the YOLOv5s framework, to address the low detection accuracy, misdetection, and leakage in blood cell detection caused by existing single-stage and two-stage detection algorithms. Initially, the Swin Transformer is integrated into the back-end of the backbone to extract the features in a better way. Then, the 32 × 32 network layer in the path-aggregation network (PANet) is removed to decrease the number of parameters in the network while increasing its accuracy in detecting small targets. Moreover, PANet substitutes traditional convolution with depth-separable convolution to accurately recognize small targets while maintaining a fast speed. Finally, replacing the complete intersection over union (CIOU) loss function with the Euclidean intersection over union (EIOU) loss function can help address the imbalance of positive and negative samples and speed up the convergence rate. The SDE-YOLO algorithm achieves a mAP of 99.5%, 95.3%, and 93.3% on the BCCD blood cell dataset for white blood cells, red blood cells, and platelets, respectively, which is an improvement over other single-stage and two-stage algorithms such as SSD, YOLOv4, and YOLOv5s. The experiment yields excellent results, and the algorithm detects blood cells very well. The SDE-YOLO algorithm also has advantages in accuracy and real-time blood cell detection performance compared to the YOLOv7 and YOLOv8 technologies.

The regulatory effects of fucoidan and laminarin on functional dyspepsia mice induced by loperamide.

Gastrointestinal dysmotility is a common cause of functional dyspepsia. As two kinds of polysaccharides derived from brown algae, fucoidan and laminarin possess many physiological properties; however, their relative abilities in regulating gastrointestinal motility have not been illustrated yet. In this study, we aimed to investigate the regulatory effect of fucoidan and laminarin on functional dyspepsia mice induced by loperamide. Mice with gastrointestinal dysmotility were treated with fucoidan (100 and 200 mg per kg bw) and laminarin (50 and 100 mg per kg bw). As a result, fucoidan and laminarin reversed the dysfunction mainly through regulating gastrointestinal hormones (motilin and ghrelin), the cholinergic pathway, the total bile acid level, c-kit protein expression, and gastric smooth muscle contraction-related gene expression (ANO1 and RYR3). Moreover, fucoidan and laminarin intervention modulated the gut microbiota profile including the altered richness of Muribaculaceae, Lachnospiraceae, and Streptococcus. The results indicated that fucoidan and laminarin may restore the rhythm of the migrating motor complex and regulate gut microecology. In conclusion, we provided evidence to support that fucoidan and laminarin might have potential abilities to regulate gastrointestinal motility.