To tackle the challenges posed by low resolution,weak contrast,and positional variations in liver tumor ultrasound images,which affects the diagnostic efficiency and accuracy,a novel method based on prototype generation and contrastive learning is proposed for liver tumor segmentation in ultrasound images.The core of this method is a weighted mask attention Transformer structure which utilizes the probabilities of real categories in the predicted probability distribution to weight image feature vectors,generates class prototypes with category discrimination,and thereby effectively captures key features while enhancing spatial information representation.By combining contrastive loss with Dice cross-entropy loss,the model achieves significant improvements in both category discrimination capability and segmentation accuracy,and overcomes the limitations of traditional models related to insufficient spatial information and intra-class pixel distribution imbalance.Comprehensive evaluations of the proposed method are conducted on a collected dataset of 253 ultrasound images,and the experimental results reveal that the proposed method attains a mean intersection-over-union of 78.44%and a Dice similarity coefficient of 87.41%,validating its superiority in liver tumor segmentation from ultrasound image.

Rapid developments in biotechnology have led researchers to seek new method to improve the efficiency and accuracy of biomedical research and drug development,promoting interdisciplinary integration.Recent advancements in artificial intelligence(AI)technologies have brought unprecedented opportunities to this field.The integration of various AI models allows researchers to better utilize multi-omics data,identify disease phenotypes,interpret animal behavior,assess treatment effects,improve experimental designs,reduce the use of experimental animals,enhance animal facility management,and improve animal welfare.This article reviews the advancements in AI biomedical research over the past decade and discusses its contributions to disease phenotype identification,the selection and design of experimental animal models,animal behavior analysis,and animal facility management.It also points out the challenges related to data standardization,AI model selection and interpretability,the extrapolation process from AI models to animal experiments and clinical practice,as well as ethical considerations in using AI in sensitive areas involving human genetics and personalized medicine.This review aims to help researchers and practitioners in relevant fields understand the current state and opportunities of AI development,thus providing support for its broader application.

To tackle the challenges posed by low resolution,weak contrast,and positional variations in liver tumor ultrasound images,which affects the diagnostic efficiency and accuracy,a novel method based on prototype generation and contrastive learning is proposed for liver tumor segmentation in ultrasound images.The core of this method is a weighted mask attention Transformer structure which utilizes the probabilities of real categories in the predicted probability distribution to weight image feature vectors,generates class prototypes with category discrimination,and thereby effectively captures key features while enhancing spatial information representation.By combining contrastive loss with Dice cross-entropy loss,the model achieves significant improvements in both category discrimination capability and segmentation accuracy,and overcomes the limitations of traditional models related to insufficient spatial information and intra-class pixel distribution imbalance.Comprehensive evaluations of the proposed method are conducted on a collected dataset of 253 ultrasound images,and the experimental results reveal that the proposed method attains a mean intersection-over-union of 78.44%and a Dice similarity coefficient of 87.41%,validating its superiority in liver tumor segmentation from ultrasound image.

Rapid developments in biotechnology have led researchers to seek new method to improve the efficiency and accuracy of biomedical research and drug development,promoting interdisciplinary integration.Recent advancements in artificial intelligence(AI)technologies have brought unprecedented opportunities to this field.The integration of various AI models allows researchers to better utilize multi-omics data,identify disease phenotypes,interpret animal behavior,assess treatment effects,improve experimental designs,reduce the use of experimental animals,enhance animal facility management,and improve animal welfare.This article reviews the advancements in AI biomedical research over the past decade and discusses its contributions to disease phenotype identification,the selection and design of experimental animal models,animal behavior analysis,and animal facility management.It also points out the challenges related to data standardization,AI model selection and interpretability,the extrapolation process from AI models to animal experiments and clinical practice,as well as ethical considerations in using AI in sensitive areas involving human genetics and personalized medicine.This review aims to help researchers and practitioners in relevant fields understand the current state and opportunities of AI development,thus providing support for its broader application.

OBJECTIVE To establish a determination method for the related substances of LPM3480392, a novel Gi protein-biased opioid receptor(MOR) agonist. METHODS The separation was carried out with Waters Symmetry Shield RP18 (150 mm×4.6 mm, 3.5 μm) by gradient elution method, using a mixture of 0.002 5 mol·L–1 sodium 1-octanesulfonate monohydrate in 0.01 mol·L–1 potassium dihydrogen phosphate-water solution(containing 0.1% triethylamine, adjusted pH to 2.50 with phosphate acid) and acetonitrile as the mobile phase at a flow rate of 1.0 mL·min–1 and the UV detection wavelength was set at 210 nm. RESULTS The chromatographic peaks of LPM3480392 and impurity A, impurity B, impurity C, impurity E and impurity F could be completely separated, the linear relationship of LPM3480392 was good in 0.064 9−5.191 2 μg·mL–1, while impurity A, impurity B, impurity C, impurity E and impurity F showed good linear relationship within 0.066 6−7.610 4 μg·mL–1, 0.166 0−3.794 0 μg·mL–1, 0.209 2−4.463 2 μg·mL–1, 0.167 9−7.672 6 μg·mL–1 and 0.016 4−7.505 7 μg·mL–1, respectively. The recovery rate was within 93.0%−103.2%. CONCLUSION The method is suitable for the determination of related substances in LPM3480392, and can provide valuable reference for the follow-up research and development of LPM3480392.

Objective: This study’s objective is to assess the efficacy and safety of Pulsed Magnetic Therapy System (PMTS) in improving insomnia disorder. Methods: Participants with insomnia disorder were randomly assigned to receive either PMTS or sham treatment for four weeks (n= 153; PMTS: 76, sham: 77). Primary outcomes are the Insomnia Severity Index (ISI) scores at week 0 (baseline), 1, 2, 3, 4 (treatment), and 5 (follow-up). Secondary outcomes are the Pittsburgh Sleep Quality Index at baseline and week 4, and weekly sleep diary-derived values for sleep latency, sleep efficiency, real sleep time, waking after sleep onset, and sleep duration. Results: The ISI scores of the PMTS group and the sham group were 7.13±0.50, 11.07±0.51 at week 4, respectively. There was a significant group×time interaction for ISI (F3.214, 485.271=24.25, p<0.001, ηp 2=0.138). Only the PMTS group experienced continuous improvement throughout the study; in contrast, the sham group only experienced a modest improvement after the first week of therapy. At the end of the treatment and one week after it, the response of the PMTS group were 69.7% (95% confidence interval [CI]: 58.6%–79.0%), 75.0% (95% CI: 64.1%–83.4%), respectively, which were higher than the response of the sham group (p<0.001). For each of the secondary outcomes, similar group×time interactions were discovered. The effects of the treatment persisted for at least a week. Conclusion PMTS is safe and effective in improving insomnia disorders.

Brachial plexus avulsion (BPA) is a combined injury involving the central and peripheral nervous systems. Patients with BPA often experience severe neuropathic pain (NP) in the affected limb. NP is insensitive to the existing treatments, which makes it a challenge to researchers and clinicians. Accumulated evidence shows that a BPA-induced pain state is often accompanied by sympathetic nervous dysfunction, which suggests that the excitation state of the sympathetic nervous system is correlated with the existence of NP. However, the mechanism of how somatosensory neural crosstalk with the sympathetic nerve at the peripheral level remains unclear. In this study, through using a novel BPA C7 root avulsion mouse model, we found that the expression of BDNF and its receptor TrκB in the DRGs of the BPA mice increased, and the markers of sympathetic nervous system activity including α1 and α2 adrenergic receptors (α1-AR and α2-AR) also increased after BPA. The phenomenon of superexcitation of the sympathetic nervous system, including hypothermia and edema of the affected extremity, was also observed in BPA mice by using CatWalk gait analysis, an infrared thermometer, and an edema evaluation. Genetic knockdown of BDNF in DRGs not only reversed the mechanical allodynia but also alleviated the hypothermia and edema of the affected extremity in BPA mice. Further, intraperitoneal injection of adrenergic receptor inhibitors decreased neuronal excitability in patch clamp recording and reversed the mechanical allodynia of BPA mice. In another branch experiment, we also found the elevated expression of BDNF, TrκB, TH, α1-AR, and α2-AR in DRG tissues from BPA patients compared with normal human DRGs through western blot and immunohistochemistry. Our results revealed that peripheral BDNF is a key molecule in the regulation of somatosensory-sympathetic coupling in BPA-induced NP. This study also opens a novel analgesic target (BDNF) in the treatment of this pain with fewer complications, which has great potential for clinical transformation.
Humans ; Mice ; Animals ; Hyperalgesia/metabolism* ; Brain-Derived Neurotrophic Factor/metabolism* ; Hypothermia/metabolism* ; Neuralgia ; Brachial Plexus/injuries* ; Edema/metabolism*

Objective:To investigate the effects of ursolic acid (UA) on oxidative stress and inflammatory factors in a rat model of AR after PM2.5 exposure.Methods:Sixty healthy female SD rats were randomly divided into five groups: normal control group (NC group), PM2.5 unexposed AR group (AR group), PM2.5 exposed AR group (ARE group), UA intervention AR group (AR+UA group), and UA intervention PM2.5 exposed AR group (ARE+UA group), with 12 rats in each group. AR model was performed by a basal sensitization with intraperitoneal injection of ovalbumin (OVA) and followed by nasal instillation. PM2.5 exposure was carried out by inhalation exposure system at a concentration of 200 μg/m 3 for 3 h/d for 30 days. UA intervention group was given UA intragastric administration at 20 mg/(kg·d). AR symptoms including sneezing, nasal scratching and nasal secretion of rats in each group were observed. The activities of superoxide dismutase (SOD) and the level of malondialdehyde (MDA) in nasal mucosa were tested. The pathological changes of nasal mucosa were observed by HE staining. The levels of OVA-sIgE, IL-6 and IL-17 in serum were measured by enzyme-linked immunosorbent assay (ELISA). Protein microarray was used to measure the expression of multiple inflammation cell factors in nasal mucosa. Statistical analysis was performed with SPSS 20.0. Results:After UA intervention, the frequency of nasal sneezing, scratching and nasal secretion in ARE+UA group were lower than those of ARE group ( P<0.05). Pathological examination of nasal mucosa showed that ARE+UA group had less inflammatory granulocyte infiltration and less pathological damage to the epithelial layer than ARE group. The activities of SOD in nasal mucosa of ARE+UA group were higher than those of ARE group ((50.10±3.09) U/mg vs (20.13±1.30) U/mg, F value was 597.54, P<0.01). The contents of MDA in nasal mucosa of ARE+UA group were lower than those of ARE group ((57.78±12.36) nmol/g vs (124.12±9.40) nmol/g, F value was 115.51, P<0.01). The expression levels of OVA-sIgE, IL-6 and IL-17 proteins were lower in the ARE+UA group than those in ARE group ((11.61±0.27) ng/ml vs (20.30±0.67) ng/ml, (47.59±15.49) pg/ml vs (98.83±10.98) pg/ml, (623.30±8.75) pg/ml vs (913.32±9.06) pg/ml, F value was 283.42, 80.45, 683.73, respectively, all P<0.01). After UA intervention, protein microarray analysis showed that the expression of IL-4, IL-6, IL-13, chemokine CXCL7, IL-1α, IL-1β, MMP-8 and MCP-1 in ARE+UA group was decreased compared with ARE group while IFN-γ and IL-10 increased (all P<0.01). Conclusion:UA can reduce the aggravated AR symptoms and pathological damage of nasal mucosa, inhibit oxidative stress and release of inflammatory factors after PM2.5 exposure, and thus plays a protective role in the pathological damage of AR induced by PM2.5 exposure.

Leptin, an adipocyte-derived peptide hormone, has been shown to facilitate breathing. However, the central sites and circuit mechanisms underlying the respiratory effects of leptin remain incompletely understood. The present study aimed to address whether neurons expressing leptin receptor b (LepRb) in the nucleus tractus solitarii (NTS) contribute to respiratory control. Both chemogenetic and optogenetic stimulation of LepRb-expressing NTS (NTS^LepRb) neurons notably activated breathing. Moreover, stimulation of NTS^LepRb neurons projecting to the lateral parabrachial nucleus (LPBN) not only remarkably increased basal ventilation to a level similar to that of the stimulation of all NTS^LepRb neurons, but also activated LPBN neurons projecting to the preBötzinger complex (preBötC). By contrast, ablation of NTS^LepRb neurons projecting to the LPBN notably eliminated the enhanced respiratory effect induced by NTS^LepRb neuron stimulation. In brainstem slices, bath application of leptin rapidly depolarized the membrane potential, increased the spontaneous firing rate, and accelerated the Ca²⁺ transients in most NTS^LepRb neurons. Therefore, leptin potentiates breathing in the NTS most likely via an NTS-LPBN-preBötC circuit.
Leptin/pharmacology* ; Membrane Potentials ; Neurons/metabolism* ; Solitary Nucleus/metabolism*

Objective: To explore the role of autophagy in PM2.5-induced inflammation in human nasal epithelial cells and related mechanism. Methods: Human nasal epithelial cells were exposed to different concentration of PM2.5 for different times, and the expression levels of microtubule-associated protein-1 light chain-3 Ⅱ (LC3 Ⅱ) and Beclin1 proteins were measured by Western blot. The typical autophagosome and autolysosome were observed by using transmission electron microscopy (TEM). To observe autophagic flux, mRFP-GFP-LC3 plasmid was transfected to nasal epithelial cells and the punctate staining of mRFP-GFP-LC3 were determined by confocal laser scanning microscope. The expression of inflammatory cytokines interleukin 6 (IL-6) and tumor necrosis factor-α (TNF-α) in cell culture supernatant were assessed by enzyme-linked immunosorbent assay (ELISA). To assess the role of autophagy in PM2.5-mediated inflammation, autophagy related gene Atg5 and Beclin-1 were silenced by siRNA knockdown, and inflammatory cytokines were analyzed.GraphPad Prism 6.0 was used for statistical analysis. Results: PM2.5 exposure increased the expression of LC3 Ⅱ and Beclin-1 proteins in a dose- (in PM2.5 group with concentration of 0, 15, 30, 60, 120 μg/ml, the expression of LC3 Ⅱ was 0.021±0.001(±s), 0.037±0.002, 0.058±0.005, 0.075±0.006, 0.085±0.004, respectively, F=126.8, P<0.05; the expression of Beclin-1 was 0.002±0.000, 0.003±0.000, 0.005±0.000, 0.007±0.001, 0.008±0.001, respectively, F=137.3, P<0.05) and time-dependent manner (in PM2.5 group with exposure time of 0, 3, 6, 12, 24 h, the expression of LC3Ⅱ was 0.160±0.007, 0.222±0.003, 0.251±0.015, 0.483±0.029, 0.585±0.035, respectively, F=215.3, P<0.05; the expression of Beclin-1 was 0.059±0.002, 0.080±0.002, 0.087±0.002, 0.183±0.007, 0.228±0.005, respectively, F=137.3, P<0.05) in human nasal epithelial cells. TEM analysis showed typical autophagosome and autolysosome in cells after PM2.5 exposure for 24 h. PM2.5 significantly increased the number of yellow and red dots representing autophagosomes and autolysosomes respectively, indicating autophagic flux was elevated. Moreover, PM2.5 enhanced the secretion of inflammatory cytokines such as IL-6 and TNF-α, which was dramatically prevented by Atg5-siRNA and Beclin-1-siRNA. Conclusion Autophagy plays an important role in PM2.5-caused inflammation response in nasal epithelial cells, which can induce release of inflammatory factors such as IL-6 and TNF-α and advance the inflammatory reaction.