Search

Danshen has been widely used for the treatment of central nervous system diseases. We investigated the effect of dihydroisotanshinone I (DT), a compound extracted from Danshen, as well as the corresponding mechanisms in an in vitro-based 6-OHDA-induced Parkinson’s disease (PD) model. SH-SY5Y human neuroblastoma cell lines were pretreated with 6-hydroxydopamine (6-OHDA) and challenged with DT. Subsequently, the cell viability and levels of reactive oxygen species (ROS) and caspase-3 were analyzed. The effect of DT on the 6-OHDA-treated SH-SY5Y cells and the expression of the core circadian clock genes were measured using a real-time quantitative polymerase chain reaction. Our results indicated that DT attenuated the 6-OHDA-induced cell death in the SH-SY5Y cells and suppressed ROS and caspase-3. Moreover, DT reversed both the RNA and protein levels of BMAL1 and SIRT1 in the 6-OHDA-treated SH-SY5Y cells. Additionally, the SIRT1 inhibitor attenuated the effect of DT on BMAL1 and reduced the cell viability. The DT and SIRT1 activators activated SIRT1 and BMAL1, and then reduced the death of the SH-SY5Y cells damaged by 6-OHDA. SIRT1 silencing was enhanced by DT and resulted in a BMAL1 downregulation and a reduction in cell viability. In conclusion, our investigation suggested that DT reduces cell apoptosis, including an antioxidative effect due to a reduction in ROS, and regulates the circadian genes by enhancing SIRT1 and suppressing BMAL1. DT may possess novel therapeutic potential for PD in the future, but further in vivo studies are still needed.

BackgroundAdult asthma is phenotypically heterogeneous with unclear aetiology. We aimed to evaluate the potential contribution of environmental exposure and its ensuing response to asthma and its heterogeneity.MethodsEnvironmental risk was evaluated by assessing the records of National Health Insurance Research Database (NHIRD) and residence-based air pollution (particulate matter with diameter less than 2.5 micrometers (PM2.5) and PM2.5-bound polycyclic aromatic hydrocarbons (PAHs)), integrating biomonitoring analysis of environmental pollutants, inflammatory markers and sphingolipid metabolites in case–control populations with mass spectrometry and ELISA. Phenotypic clustering was evaluated by t-distributed stochastic neighbor embedding (t-SNE) integrating 18 clinical and demographic variables.FindingsIn the NHIRD dataset, modest increase in the relative risk with time-lag effect for emergency (N=209 837) and outpatient visits (N=638 538) was observed with increasing levels of PM2.5and PAHs. Biomonitoring analysis revealed a panel of metals and organic pollutants, particularly metal Ni and PAH, posing a significant risk for current asthma (ORs=1.28–3.48) and its severity, correlating with the level of oxidative stress markers, notably Nε-(hexanoyl)-lysine (r=0.108–0.311, p2.5exposure. Further, levels of circulating sphingosine-1-phosphate and ceramide-1-phosphate were found to discriminate asthma (pInterpretationThese results suggest the potential contribution of environmental factors from multiple sources, their ensuing oxidative stress and sphingolipid remodeling to adult asthma and its phenotypic heterogeneity.

Aging is the largest risk factor for neurodegenerative disorders, and commonly associated with compromised cerebrovasculature and pericytes. However, we do not know how normal aging differentially impacts the vascular structure and function in different brain areas. Here we utilize mesoscale microscopy methods (serial two-photon tomography and light sheet microscopy) and in vivo imaging (wide field optical spectroscopy and two-photon imaging) to determine detailed changes in aged cerebrovascular networks. Whole-brain vascular tracing showed an overall ~10% decrease in vascular length and branching density, and light sheet imaging with 3D immunolabeling revealed increased arteriole tortuosity in aged brains. Vasculature and pericyte densities showed significant reductions in the deep cortical layers, hippocampal network, and basal forebrain areas. Moreover, in vivo imaging in awake mice identified delays in neurovascular coupling and disrupted blood oxygenation. Collectively, we uncover regional vulnerabilities of cerebrovascular network and physiological changes that can mediate cognitive decline in normal aging.

Cardiovascular diseases (CVDs) are a leading cause of death worldwide. For early diagnosis, intervention and management of CVDs, it is highly desirable to frequently monitor blood pressure (BP), a vital sign closely related to CVDs, during people’s daily life, including sleep time. Towards this end, wearable and cuffless BP extraction methods have been extensively researched in recent years as part of the mobile healthcare initiative. This review focuses on the enabling technologies for wearable and cuffless BP monitoring platforms, covering both the emerging flexible sensor designs and BP extraction algorithms. Based on the signal type, the sensing devices are classified into electrical, optical, and mechanical sensors, and the state-of-the-art material choices, fabrication methods, and performances of each type of sensor are briefly reviewed. In the model part of the review, contemporary algorithmic BP estimation methods for beat-to-beat BP measurements and continuous BP waveform extraction are introduced. Mainstream approaches, such as pulse transit time-based analytical models and machine learning methods, are compared in terms of their input modalities, features, implementation algorithms, and performances. The review sheds light on the interdisciplinary research opportunities to combine the latest innovations in the sensor and signal processing research fields to achieve a new generation of cuffless BP measurement devices with improved wearability, reliability, and accuracy.

Heart rate variability (HRV) is an important metric with a variety of applications in clinical situations such as cardiovascular diseases, diabetes mellitus, and mental health. HRV data can be potentially obtained from electrocardiography and photoplethysmography signals, then computational techniques such as signal filtering and data segmentation are used to process the sampled data for calculating HRV measures. However, uncertainties arising from data acquisition, computational models, and physiological factors can lead to degraded signal quality and affect HRV analysis. Therefore, it is crucial to address these uncertainties and develop advanced models for HRV analysis. Although several reviews of HRV analysis exist, they primarily focus on clinical applications, trends in HRV methods, or specific aspects of uncertainties such as measurement noise. This paper provides a comprehensive review of uncertainties in HRV analysis, quantifies their impacts, and outlines potential solutions. To the best of our knowledge, this is the first study that presents a holistic review of uncertainties in HRV methods and quantifies their impacts on HRV measures from an engineer's perspective. This review is essential for developing robust and reliable models, and could serve as a valuable future reference in the field, particularly for dealing with uncertainties in HRV analysis.

The image-based high-content screening platform

Chemotherapy-induced neurotoxicity is a common adverse effect of cancer treatment. No medication has been shown to be effective in the prevention or treatment of chemotherapy-induced neurotoxicity. This study aimed to discover potential neuroprotective drugs for paclitaxel-induced neurotoxicity. An image-based high-content platform was first developed to screen for potential neuroprotective drugs. The screening system comprised of automated image acquisition and multiparameter analysis, including neuronal viability, neurite outgrowth, and synaptogenesis. By this platform, we obtained a candidate list from compound libraries. In the drug screening from compound libraries of ion channel ligands, REDOX and GABAergic ligands, 5-hydroxydecanoate (5-HD) exhibited the most significant neuroprotective effects against paclitaxel-induced neurotoxicity in both cortical and dorsal root ganglion (DRG) neurons. In mouse behavioral tests, 5-HD restored the thermal sensitivity and alleviated mechanical allodynia induced by paclitaxel. Electron micrographs of sciatic nerve revealed that 5-HD reduced the damages caused by paclitaxel in the nonmyelinated and smaller myelinated fibers. The mechanistic study on DRG neurons suggested that 5-HD rescued the dysregulation of intracellular calcium homeostasis provoked by paclitaxel. Importantly, 5-HD did not jeopardize the antitumor effect of paclitaxel in tumor xenograft models. In conclusion, we established an imaged-based high-content screening platform and a protocol for verifying the neuroprotective effect in vivo, by which 5-HD was identified and validated as a potential neuroprotective drug for paclitaxel-induced neuropathy. Mol Cancer Ther; 14(10); 2206–14. ©2015 AACR.

Antibody information

Information of drug library

Flow chart for drug discovery

The therapeutic potential of 5-HD in mouse model

Neuroprotective drugs screened from the image-based high-content platform

The neuroprotective effects of 5-HD in mouse model

Aims To investigate the capability, properties, and molecular mechanism of inulin fermentation by lactic acid bacteria (LAB) from Sichuan pickle. Methods and results A total of 79 LAB strains were purified from 30 aged Sichuan pickle brine samples, and only 21 Lactiplantibacillus plantarum strains (26.58%, 21/79) derived from 15 samples grew well through utilizing inulin as a carbon source. The fermentation tests through using long-chain inulin (lc-inulin) as a carbon source showed that only 6 L. plantarum strains grew well, while other 15 strains could only utilize short-chain oligofructose (FOS), and thin-layer chromatography analysis evidenced a strain specificity of inulin consumption patterns. Lactiplantibacillus plantarum YT041 is a vigorous inulin fermenter, and whole genome sequencing data revealed that sacPTS1 and fosRABCDXE operons might be associated with the fermentation of FOS and lc-inulin, respectively. Conclusions The phenotype of inulin consumption is commonly present in LAB from Sichuan pickle, which is strain-specific and largely depends on their specific ecological niche and degree of polymerization.

Oxytocin (Oxt) neurons regulate diverse physiological responses via direct connections with different neural circuits. However, the lack of comprehensive input-output wiring diagrams of Oxt neurons and their quantitative relationship with Oxt receptor (Oxtr) expression presents challenges to understanding circuit-specific Oxt functions. Here, we establish a whole-brain distribution and anatomic connectivity map of Oxt neurons, and their relationship with Oxtr expression using high-resolution 3D mapping methods in adult male and female mice. We use a flatmap to describe Oxt neuronal expression in four hypothalamic domains including under-characterized Oxt neurons in the tuberal nucleus (TU). Oxt neurons in the paraventricular hypothalamus (PVH) broadly project to nine functional circuits that control cognition, brain state, and somatic visceral response. In contrast, Oxt neurons in the supraoptic (SO) and accessory (AN) nuclei have limited central projection to a small subset of the nine circuits. Surprisingly, quantitative comparison between Oxt output and Oxtr expression showed no significant correlation across the whole brain, suggesting abundant indirect Oxt signaling in Oxtr-expressing areas. Unlike output, Oxt neurons in both the PVH and SO receive similar monosynaptic inputs from a subset of the nine circuits mainly in the thalamic, hypothalamic, and cerebral nuclei areas. Our results suggest that PVH-Oxt neurons serve as a central modulator to integrate external and internal information via largely reciprocal connection with the nine circuits while the SO-Oxt neurons act mainly as unidirectional Oxt hormonal output. In summary, our Oxt wiring diagram provides anatomic insights about distinct behavioral functions of Oxt signaling in the brain.

Tingting Han, Ting Yuan, Xinyue Liang, Ningxin Chen, Jia Song, Xin Zhao, Yurong Weng, Yaomin Hu Department of Geriatrics, Ren Ji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200127, People’s Republic of ChinaCorrespondence: Yaomin Hu, Department of Geriatrics, Ren Ji Hospital, Shanghai Jiao Tong University School of Medicine, No. 160 Pujian Road, Shanghai, 200127, People’s Republic of China, Tel +86 02168383815, Email amin1031@hotmail.comObjective: Data are limited regarding how body composition is linked to insulin resistance in elderly patients with type 2 diabetes mellitus (T2DM). We examined the association between body composition and insulin resistance in elderly T2DM patients.Methods: The cross-sectional study included 488 Chinese elderly patients wth T2DM. Subjects were classified into four groups based on body composition: normal body composition (NBC), low muscle mass alone (LMM), high body fat alone (HBF), both low muscle mass and high body fat (LMMHBF).Results: The percentage of subjects with LMMHBF was 14.5% (11.9% in men and 17.7% in women). Homeostasis model assessment of insulin resistance (HOMA2-IR) was higher in the LMMHBF group than in the HBF group (p = 0.045), and was also significantly higher in the LMMHBF or HBF group than in the NBC or LMM group. The HBF group showed the highest body mass index (BMI) of the four groups of different body compositions, and the LMMHBF group showed lower BMI than the HBF group; however, there was no significant difference in BMI or waist to hip ratio (WHR) between the LMMHBF group and the NBC group. The LMMHBF and HBF groups were significantly associated with increased risk of insulin resistance compared to the NBC group, with odds ratios (ORs) of 4.47 [95% confidence interval (CI) 2.06– 9.68, p < 0.001] and 1.76 (95% CI 1.02– 3.02, p = 0.041) respectively, even after the adjustment for covariates.Conclusion: In China, though elderly T2DM patients with the body composition of sarcopenic obesity (as defined by coexistence of low muscle mass and high body fat) seemed to have normal body size, they exhibited the most severe degree and the highest risk of insulin resistance.Keywords: sarcopenic obesity, type 2 diabetes mellitus, elderly, insulin resistance, body composition

Strawberry (Fragaria x ananassa Duch.) is an important crop worldwide. Tontonaka, Aroma and Benihoppe, are most popular cultivars in Taiwan, especially cv. Aroma is dominated in the market. In September 2021, the target spot outbreak occurred on the leaves of cv. Aroma and Benihoppe in Nantou County. In a greenhouse, the target spot incidents were estimated at 90-100% and 40-50% in Aroma and Benihoppe, respectively, and caused 2~5% plants lost. Between April and June of 2022, the target spot occurred in another greenhouse where the target spot incidents were 90% and 5-8% in Aroma and Benihoppe, respectively. Early symptoms were small and circular to irregular brown spot on the leaves with its diameter at 1-2 mm. Then the brown lesion expanded to 2-5 mm in diameter with pale green halo. Some lesions appeared with gray center, and 2-3 spots might merge into one lesion, and some lesions were surrounded with yellow tissues later. The round to oval brown spots were also observed on stems. Ten symptomatic leaves and stems each were collected for pathogen isolation. Pieces of tissue from the edge of the brown lesion on leaf were cut and disinfested with 0.6% NaOCl for 30 sec, and rinsed three times with sterile distilled water (SDW) followed by being placed on 2% water agar. The isolates obtained from symptomatic leaves/stems of Aroma and Benihoppe showed same colonies with 100% isolation rate. Isolates from cv. Benihoppe (Cos21-1) and Aroma (Cos21-2) were selected for further observation and tests. Colonies on potato dextrose agar exhibited gray aerial mycelium at 28 °C in dark after 7-day. Conidiophores were brown, single or in clustered, unbranched, 2 to 11 septa. Conidia were 5.6-6.7× 28.1-270.0 μm (n=50) in size with obclavate to cylindrical shape, 1 to 16 septa, and olivaceous to dark brown. Based on the morphology, two fungal isolates were identified as Corynespora cassiicola. Four regions, internal transcribed spacer (ITS), β-tubulin, translation elongation factor (TEF), and actin, were used to confirm the two isolates. Sequences of ITS and β-tululin shared 100% identity to ITS (MZ093622) and β-tululin (MW961419) of C. cassiicola in GenBank. Sequences of TEF and actin shared 99.60% and 99.70% identity to C. cassiicola (MK882240 and FJ853005), respectively. For the pathogenicity test, the conidial suspension (1x105 spores/ ml) of Cos21-1 and Cos21-2 was sprayed on leaves of two-month-old strawberry cv. Benihoppe and Aroma without wounds, respectively. Three plants each with more than two leaves were spray-inoculated with the selected isolates whereas three plants with SDW as controls and the test was repeated once. Inoculated plants were covered with plastic bags in the greenhouse, then removed when the initial symptoms were observed on leaves 5 days after inoculation whereas symptoms on stems were observed within 7 days. Re-isolation of the pathogens from the symptomatic leaves/stems demonstrated that the pathogen was C. cassiicola. The leaf spot or target spot caused by C. cassiicola on strawberry has been reported in Mainland China and North America. To our knowledge, this is the first report of target spot disease of strawberry caused by C. cassiicola in Taiwan.

A protocol of systematic review and meta-analysis on professional identity of nursing students in the post-epidemic era in China.

Additional file 6: Supplemental Table S2. The laboratory life cycle of Ixodes persulcatus and Dermacentor silvarum.

Modern hierarchical vision transformers have added several vision-specific components in the pursuit of supervised classification performance. While these components lead to effective accuracies and attractive FLOP counts, the added complexity actually makes these transformers slower than their vanilla ViT counterparts. In this paper, we argue that this additional bulk is unnecessary. By pretraining with a strong visual pretext task (MAE), we can strip out all the bells-and-whistles from a state-of-the-art multi-stage vision transformer without losing accuracy. In the process, we create Hiera, an extremely simple hierarchical vision transformer that is more accurate than previous models while being significantly faster both at inference and during training. We evaluate Hiera on a variety of tasks for image and video recognition. Our code and models are available at https://github.com/facebookresearch/hiera., Comment: ICML 2023 Oral version. Code+Models: https://github.com/facebookresearch/hiera

Additional file 3: Figure S3. PCoA analysis of R. raoultii-positive and -negative groups in second generation.

Additional file 7: Supplemental Table S3. Results of tick fitness post-rickettsial exposure in Ixodes persulcatus and Dermacentor silvarum.

Electrocardiography (ECG) is a non-invasive tool for predicting cardiovascular diseases (CVDs). Current ECG-based diagnosis systems show promising performance owing to the rapid development of deep learning techniques. However, the label scarcity problem, the co-occurrence of multiple CVDs and the poor performance on unseen datasets greatly hinder the widespread application of deep learning-based models. Addressing them in a unified framework remains a significant challenge. To this end, we propose a multi-label semi-supervised model (ECGMatch) to recognize multiple CVDs simultaneously with limited supervision. In the ECGMatch, an ECGAugment module is developed for weak and strong ECG data augmentation, which generates diverse samples for model training. Subsequently, a hyperparameter-efficient framework with neighbor agreement modeling and knowledge distillation is designed for pseudo-label generation and refinement, which mitigates the label scarcity problem. Finally, a label correlation alignment module is proposed to capture the co-occurrence information of different CVDs within labeled samples and propagate this information to unlabeled samples. Extensive experiments on four datasets and three protocols demonstrate the effectiveness and stability of the proposed model, especially on unseen datasets. As such, this model can pave the way for diagnostic systems that achieve robust performance on multi-label CVDs prediction with limited supervision.

Additional file 5: Supplemental Table S1. Reads counts of data analyzed in the study.

Electroencephalography (EEG) is an objective tool for emotion recognition and shows promising performance. However, the label scarcity problem is a main challenge in this field, which limits the wide application of EEG-based emotion recognition. In this paper, we propose a novel semi-supervised learning framework (EEGMatch) to leverage both labeled and unlabeled EEG data. First, an EEG-Mixup based data augmentation method is developed to generate more valid samples for model learning. Second, a semi-supervised two-step pairwise learning method is proposed to bridge prototype-wise and instance-wise pairwise learning, where the prototype-wise pairwise learning measures the global relationship between EEG data and the prototypical representation of each emotion class and the instance-wise pairwise learning captures the local intrinsic relationship among EEG data. Third, a semi-supervised multi-domain adaptation is introduced to align the data representation among multiple domains (labeled source domain, unlabeled source domain, and target domain), where the distribution mismatch is alleviated. Extensive experiments are conducted on two benchmark databases (SEED and SEED-IV) under a cross-subject leave-one-subject-out cross-validation evaluation protocol. The results show the proposed EEGmatch performs better than the state-of-the-art methods under different incomplete label conditions (with 6.89% improvement on SEED and 1.44% improvement on SEED-IV), which demonstrates the effectiveness of the proposed EEGMatch in dealing with the label scarcity problem in emotion recognition using EEG signals. The source code is available at https://github.com/KAZABANA/EEGMatch.

Additional file 8: Supplemental Table S4. Primers used in this study.

Additional file 4: Figure S4. The abundance correlation between Chlamydia and Actinobacteria in skin.

Additional file 1: Figure S1. Phylogenetic analysis of sequences from SFGR specific PCR with other Rickettsial specieses. Phylogenetic trees of ompA and gltA genes of SFGR identified from I. persulcatus cohort A) ompA and B) gltA and from D. silvarum C）ompA, D) gltA, respectively.

Additional file 2: Figure S2. Composition and similarity of bacteria on bitten and un-bitten skins. A) Relative abundance of bacteria between tick-bitten and un-bitten skins. B) Bray−Curtis dissimilarity within bitten or unbitten group and between unbitten and bitten group.

Target of Rapamycin Complex I (TORC1) is a central regulator of metabolism in eukaryotes that responds to a wide array of negative and positive inputs. The GTPase-activating protein toward Rags (GATOR) signaling pathway acts upstream of TORC1 and is comprised of two subcomplexes. The trimeric GATOR1 complex inhibits TORC1 activity in response to amino acid limitation by serving as a GTPase-activating protein (GAP) for the TORC1 activator RagA/B, a component of the lysosomally located Rag GTPase. The multi-protein GATOR2 complex inhibits the activity of GATOR1 and thus promotes TORC1 activation. Here we report that Wdr59, originally assigned to the GATOR2 complex based on studies performed in tissue culture cells, unexpectedly has a dual function in TORC1 regulation in Drosophila . We find that in the ovary and the eye imaginal disc brain complex, Wdr59 inhibits TORC1 activity by opposing the GATOR2-dependent inhibition of GATOR1. Conversely, in the Drosophila fat body, Wdr59 promotes the accumulation of the GATOR2 component Mio and is required for TORC1 activation. Similarly, in mammalian HeLa cells, Wdr59 prevents the proteolytic destruction of GATOR2 proteins Mio and Wdr24. Consistent with the reduced levels of the TORC1-activating GATOR2 complex, Wdr59KOs HeLa cells have reduced TORC1 activity which is restored along with GATOR2 protein levels upon proteasome inhibition. Taken together, our data support the model that the Wdr59 component of the GATOR2 complex functions to promote or inhibit TORC1 activity depending on cellular context.

A 75-year-old female patient presented with signs and symptoms of a right hemispheric syndrome. A CT scan of the brain with angiogram showed an acute infarct in the right middle cerebral artery (MCA) territory secondary to thromb-occlusion of the M1 segment of the right MCA. A follow-up CT scan 2 weeks later showed a large hyperdense region in the infarcted area. With the aid of a dual-energy CT scan, this was eventually shown to be due to contrast staining from an earlier administration of iodinated contrast on the same day, rather than frank haemorrhagic conversion of the recent right MCA infarct.

Heart rate variability (HRV) is an important non-invasive parameter to assess the cardiac autonomic nervous system. In particular, spectrum matrices of HRV data have been widely used for physical and mental health monitoring. However, measurement uncertainties from data acquisition and physiological factors can easily affect the HRV spectrum and degrade outcomes of health monitoring. In this paper, we propose a new model for incomplete spectrum estimation of the HRV data based on matrix completion (MC). We show that our model performs efficiently when estimating missing entries for HRV spectra. Moreover, a refined model of matrix completion (RMC) is proposed that can be derived from correlation analysis of the HRV spectra. Two benchmark electrocardiography (ECG) datasets are retrieved and used to derive the HRV data, which are employed to evaluate the performance of our RMC method on the estimation of missing entries in the spectra. Furthermore, four different types of deep recurrent neural networks and the traditional MC method are used for a comparison study, and our RMC method obtains the least estimation error with different masking ratios. The experimental studies and comparison results demonstrate the advantages and robustness of our developed method for the estimation of incomplete HRV spectra.