The " liver disease affecting to the spleen" theory first appeared in Nanjing and was further elaborated in Jingui Yaolue. This theory encapsulates the traditional Chinese medicine principles of the " unity of the five viscera" and the " preventive treatment of disease" . The theory emphasizes that the spleen is the pivotal point where depression may progress from a functional disorder to an organic disease. The liver governs the emotions and qi flow, whereas the spleen is responsible for qi, blood, and body. In the early stages of the disease, emotional disorders and qi flow disorders primarily affect the liver, manifesting as depression or low mood. As the condition progresses, the liver (Wood) overacts on the spleen (Earth), disrupting liver and spleen functions and causing qi and blood disharmony. This stage is marked by fatigue and psychomotor retardation. Prolonged illness depletes qi and blood, eventually involving all five viscera, disrupting the harmony of the five spirits, and affecting both body and spirit. At this advanced phase, intense emotional distress or agitation often arises, accompanied by a heightened risk of suicide. The disease progression follows a dynamic " qi-blood-spirit" pattern, in which depression begins in the liver, characterized by qi stagnation, then affects the spleen, involving blood disharmony. In later stages, the disease eventually affects all viscera, with profound effects on both physical and mental health. Treatment strategies should align with the disease stage. Early intervention should focus on regulating the flow of qi, treating the liver, and strengthening the spleen. In the middle stages, qi and blood should be harmonized while promoting the harmonized functions of the liver and spleen. In the later stages, treatment should harmonize the five viscera to restore balance between body and spirit. Guided by this theory, integrating modern medical understanding of the progression of depression from emotional to somatic symptoms and adopting a stage-based approach to treatment in clinical practice can yield effective therapeutic outcomes for managing depression and related disorders.

ObjectivePrimary liver cancer, predominantly hepatocellular carcinoma (HCC), is a significant global health issue, ranking as the sixth most diagnosed cancer and the third leading cause of cancer-related mortality. Accurate and early diagnosis of HCC is crucial for effective treatment, as HCC and non-HCC malignancies like intrahepatic cholangiocarcinoma (ICC) exhibit different prognoses and treatment responses. Traditional diagnostic methods, including liver biopsy and contrast-enhanced ultrasound (CEUS), face limitations in applicability and objectivity. The primary objective of this study was to develop an advanced, light-weighted classification network capable of distinguishing HCC from other non-HCC malignancies by leveraging the automatic analysis of brightness changes in CEUS images. The ultimate goal was to create a user-friendly and cost-efficient computer-aided diagnostic tool that could assist radiologists in making more accurate and efficient clinical decisions. MethodsThis retrospective study encompassed a total of 161 patients, comprising 131 diagnosed with HCC and 30 with non-HCC malignancies. To achieve accurate tumor detection, the YOLOX network was employed to identify the region of interest (ROI) on both B-mode ultrasound and CEUS images. A custom-developed algorithm was then utilized to extract brightness change curves from the tumor and adjacent liver parenchyma regions within the CEUS images. These curves provided critical data for the subsequent analysis and classification process. To analyze the extracted brightness change curves and classify the malignancies, we developed and compared several models. These included one-dimensional convolutional neural networks (1D-ResNet, 1D-ConvNeXt, and 1D-CNN), as well as traditional machine-learning methods such as support vector machine (SVM), ensemble learning (EL), k-nearest neighbor (KNN), and decision tree (DT). The diagnostic performance of each method in distinguishing HCC from non-HCC malignancies was rigorously evaluated using four key metrics: area under the receiver operating characteristic (AUC), accuracy (ACC), sensitivity (SE), and specificity (SP). ResultsThe evaluation of the machine-learning methods revealed AUC values of 0.70 for SVM, 0.56 for ensemble learning, 0.63 for KNN, and 0.72 for the decision tree. These results indicated moderate to fair performance in classifying the malignancies based on the brightness change curves. In contrast, the deep learning models demonstrated significantly higher AUCs, with 1D-ResNet achieving an AUC of 0.72, 1D-ConvNeXt reaching 0.82, and 1D-CNN obtaining the highest AUC of 0.84. Moreover, under the five-fold cross-validation scheme, the 1D-CNN model outperformed other models in both accuracy and specificity. Specifically, it achieved accuracy improvements of 3.8% to 10.0% and specificity enhancements of 6.6% to 43.3% over competing approaches. The superior performance of the 1D-CNN model highlighted its potential as a powerful tool for accurate classification. ConclusionThe 1D-CNN model proved to be the most effective in differentiating HCC from non-HCC malignancies, surpassing both traditional machine-learning methods and other deep learning models. This study successfully developed a user-friendly and cost-efficient computer-aided diagnostic solution that would significantly enhances radiologists’ diagnostic capabilities. By improving the accuracy and efficiency of clinical decision-making, this tool has the potential to positively impact patient care and outcomes. Future work may focus on further refining the model and exploring its integration with multimodal ultrasound data to maximize its accuracy and applicability.

Objective     To construct a radiomics model for identifying clinical high-risk carotid plaques. Methods     A retrospective analysis was conducted on patients with carotid artery stenosis in China-Japan Friendship Hospital from December 2016 to June 2022. The patients were classified as a clinical high-risk carotid plaque group and a clinical low-risk carotid plaque group according to the occurrence of stroke, transient ischemic attack and other cerebrovascular clinical symptoms within six months. Six machine learning models including eXtreme Gradient Boosting, support vector machine, Gaussian Naive Bayesian, logical regression, K-nearest neighbors and artificial neural network were established. We also constructed a joint predictive model combined with logistic regression analysis of clinical risk factors. Results    Finally 652 patients were collected, including 427 males and 225 females, with an average age of 68.2 years. The results showed that the prediction ability of eXtreme Gradient Boosting was the best among the six machine learning models, and the area under the curve (AUC) in validation dataset was 0.751. At the same time, the AUC of eXtreme Gradient Boosting joint prediction model established by clinical data and carotid artery imaging data validation dataset was 0.823. Conclusion     Radiomics features combined with clinical feature model can effectively identify clinical high-risk carotid plaques.

Social behavior is extremely important for the physical and mental health of individuals, their growth and development, and for social development. Social behavioral disorders have become a typical clinical representation of a variety of psychiatric disorders and have serious adverse effects on the development of individuals. The prefrontal cortex, as one of the key areas responsible for social behavior, involves in many advanced brain functions such as social behavior, emotion, and decision-making. The neural activity of prefrontal cortex has a major impact on the performance of social behavior. Numerous studies demonstrate that neurons and glial cells can regulate certain social behaviors by themselves or the interaction which we called neural microcircuits; and the collaboration with other brain regions also regulates different types of social behaviors. The prefrontal cortex (PFC)-thalamus projections mainly influence social dominance and social preference; the PFC-amygdala projections play a key role in fear behavior, emotional behavior, social exploration, and social identification; and the PFC-nucleus accumbens projections mainly involve social preference, social memory, social cognition, and spatial-social associative learning. Based on the above neural mechanism, many studies have focused on applying the non-invasive neurostimulation to social deficit-related symptoms, including transcranial magnetic stimulation (TMS), transcranial electrical stimulation (TES) and focused ultrasound stimulation (FUS). Our previous study also investigated that repetitive transcranial magnetic stimulation can improve the social behavior of mice and low-intensity focused ultrasound ameliorated the social avoidance behavior of mice by enhancing neuronal activity in the prefrontal cortex. In this review, we summarize the relationship between neurons, glial cells, brain projection and social behavior in the prefrontal cortex, and systematically show the role of the prefrontal cortex in the regulation of social behavior. We hope our summarization will provide a reference for the neural mechanism and effective treatment of social disorders.

Hypoxia-inducible factor (HIF-1α), a core transcription factor responding to changes in cellular oxygen levels, is closely associated with a wide range of physiological and pathological conditions. However, its differential impacts on vascular cell types and molecular programs modulating human vascular homeostasis and regeneration remain largely elusive. Here, we applied CRISPR/Cas9-mediated gene editing of human embryonic stem cells and directed differentiation to generate HIF-1α-deficient human vascular cells including vascular endothelial cells, vascular smooth muscle cells, and mesenchymal stem cells (MSCs), as a platform for discovering cell type-specific hypoxia-induced response mechanisms. Through comparative molecular profiling across cell types under normoxic and hypoxic conditions, we provide insight into the indispensable role of HIF-1α in the promotion of ischemic vascular regeneration. We found human MSCs to be the vascular cell type most susceptible to HIF-1α deficiency, and that transcriptional inactivation of ANKZF1, an effector of HIF-1α, impaired pro-angiogenic processes. Altogether, our findings deepen the understanding of HIF-1α in human angiogenesis and support further explorations of novel therapeutic strategies of vascular regeneration against ischemic damage.
Humans ; Vascular Endothelial Growth Factor A/metabolism* ; Endothelial Cells/metabolism* ; Transcription Factors/metabolism* ; Gene Expression Regulation ; Hypoxia/metabolism* ; Cell Hypoxia/physiology*

ObjectiveTriple-negative breast cancer (TNBC) is the breast cancer subtype with the worst prognosis, and lacks effective therapeutic targets. Colony stimulating factors (CSFs) are cytokines that can regulate the production of blood cells and stimulate the growth and development of immune cells, playing an important role in the malignant progression of TNBC. This article aims to construct a novel prognostic model based on the expression of colony stimulating factors-related genes (CRGs), and analyze the sensitivity of TNBC patients to immunotherapy and drug therapy. MethodsWe downloaded CRGs from public databases and screened for differentially expressed CRGs between normal and TNBC tissues in the TCGA-BRCA database. Through LASSO Cox regression analysis, we constructed a prognostic model and stratified TNBC patients into high-risk and low-risk groups based on the colony stimulating factors-related genes risk score (CRRS). We further analyzed the correlation between CRRS and patient prognosis, clinical features, tumor microenvironment (TME) in both high-risk and low-risk groups, and evaluated the relationship between CRRS and sensitivity to immunotherapy and drug therapy. ResultsWe identified 842 differentially expressed CRGs in breast cancer tissues of TNBC patients and selected 13 CRGs for constructing the prognostic model. Kaplan-Meier survival curves, time-dependent receiver operating characteristic curves, and other analyses confirmed that TNBC patients with high CRRS had shorter overall survival, and the predictive ability of CRRS prognostic model was further validated using the GEO dataset. Nomogram combining clinical features confirmed that CRRS was an independent factor for the prognosis of TNBC patients. Moreover, patients in the high-risk group had lower levels of immune infiltration in the TME and were sensitive to chemotherapeutic drugs such as 5-fluorouracil, ipatasertib, and paclitaxel. ConclusionWe have developed a CRRS-based prognostic model composed of 13 differentially expressed CRGs, which may serve as a useful tool for predicting the prognosis of TNBC patients and guiding clinical treatment. Moreover, the key genes within this model may represent potential molecular targets for future therapies of TNBC.

italic>Lamiophlomis rotata is an important medicinal plant species endemic to the Tibetan Plateau, which is prone to strong climate change impacts on its habitable range due to the high sensitivity of the Tibetan Plateau to climate change. Accurate quantification of species vulnerability to climate change is essential for assessing species extinction risk and developing effective conservation strategies. Therefore, we carried out the α-shape analysis to determine the habitat of L. rotata. We then carried out the climate-niche factor analysis (CNFA) to assess the vulnerability of L. rotata to climate change based on five climate variables (i.e., mean diurnal range, temperature seasonality, mean temperature of warmest quarter, precipitation of driest month and precipitation of warmest quarter) in the context of two shared socioeconomic pathways (i.e., SSP126 and SSP585) and three global climate models (CMCC-ESM2: Centro Euro-Mediterraneo sui Cambiamenti Climatici-Earth System Model version 2; HadGEM3-GC31-LL: Hadley Global Environment Model version 3-Global Coupled configuration 3.1; IPSL-CM6A-LR: Institut Pierre Simon Laplace-Climate Model version 6) during two different periods (2041-2060 and 2081-2100). The vulnerability of L. rotata to climate change was calculated by integrating the sensitivity and exposure indices of L. rotata to five climate variables. The results showed that L. rotata had the highest vulnerability to the precipitation of warmest quarter. Its vulnerability within its habitat range generally showed a spatial pattern of high value in the southern region and low in the northern region, high in the western region and low in the eastern region. In general, the vulnerability of L. rotata under the SSP585 scenario was higher than that under the SSP126 scenario. The climate data of different global climate models have some influence on the results, while the resulted uncertainty can be reduced by data integration methods. As a result of climate change, the pressure on the survival of L. rotata in the future will be intensified in the low-altitude areas such as the Yarlung Zangbo River, Yigongzangbu River, Zayu River, and Jiaomuzu River, etc., while the highly weathered scree flats or stony alpine meadows in the high-altitude zones, such as the eastern Tanggula Mountain Range, the northern part of Hengduan Mountain Range, and the western part of the Qinling Mountains, may become its refuge. It is necessary to focus on and strengthen the protection and management of L. rotata resources in these vulnerble and critical areas.

Objective To investigate the toxicokinetic differences of 3,4-methylenedioxy-N-methylamphetamine(MDMA)and its metabolite 4,5-methylene dioxy amphetamine(MDA)in rats af-ter single and continuous administration of MDMA,providing reference data for the forensic identifica-tion of MDMA.Methods A total of 24 rats in the single administration group were randomly divided into 5,10 and 20 mg/kg experimental groups and the control group,with 6 rats in each group.The ex-perimental group was given intraperitoneal injection of MDMA,and the control group was given intraperi-toneal injection of the same volume of normal saline as the experimental group.The amount of 0.5 mL blood was collected from the medial canthus 5 min,30 min,1 h,1.5 h,2 h,4 h,6 h,8 h,10 h,12 h after administration.In the continuous administration group,24 rats were randomly divided into the experi-mental group(18 rats)and the control group(6 rats).The experimental group was given MDMA 7 d by continuous intraperitoneal injection in increments of 5,7,9,11,13,15,17 mg/kg per day,respectively,while the control group was given the same volume of normal saline as the experimental group by in-traperitoneal injection.On the eighth day,the experimental rats were randomly divided into 5,10 and 20 mg/kg dose groups,with 6 rats in each group.MDMA was injected intraperitoneally,and the con-trol group was injected intraperitoneally with the same volume of normal saline as the experimental group.On the eighth day,0.5 mL of blood was taken from the medial canthus 5 min,30 min,1 h,1.5 h,2 h,4 h,6 h,8 h,10 h,12 h after administration.Liquid chromatography-triple quadrupole tandem mass spectrometry was used to detect MDMA and MDA levels,and statistical software was employed for data analysis.Results In the single-administration group,peak concentrations of MDMA and MDA were reached at 5 min and 1 h after administration,respectively,with the largest detection time limit of 12 h.In the continuous administration group,peak concentrations were reached at 30 min and 1.5 h af-ter administration,respectively,with the largest detection time limit of 10 h.Nonlinear fitting equations for the concentration ratio of MDMA and MDA in plasma and administration time in the single-administration group and continuous administration group were as follows:T=10.362C-1.183,R2=0.974 6;T=7.397 3C-0.694,R2=0.961 5(T:injection time;C:concentration ratio of MDMA to MDA in plasma).Conclusions The toxicokinetic data of MDMA and its metabolite MDA in rats,obtained through single and continuous administration,including peak concentration,peak time,detection time limit,and the relationship between concentration ratio and administration time,provide a theoretical and data foundation for relevant forensic identification.

Important forensic diagnostic indicators of sudden death in coronary atherosclerotic heart dis-ease,such as acute or chronic myocardial ischemic changes,sometimes make it difficult to locate the ischemic site due to the short death process,the lack of tissue reaction time.In some cases,the de-ceased died of sudden death on the first-episode,resulting in difficulty for medical examiners to make an accurate diagnosis.However,clinical studies on coronary instability plaque revealed the key role of coronary spasm and thrombosis caused by their lesions in sudden coronary death process.This paper mainly summarizes the pathological characteristics of unstable coronary plaque based on clinical medi-cal research,including plaque rupture,plaque erosion and calcified nodules,as well as the influencing factors leading to plaque instability,and briefly describes the research progress and technique of the atherosclerotic plaques,in order to improve the study on the mechanism of sudden coronary death and improve the accuracy of the forensic diagnosis of sudden coronary death by diagnosing different patho-logic states of coronary atherosclerotic plaques.

Bacterial biofilms gave rise to persistent infections and multi-organ failure, thereby posing a serious threat to human health. Biofilms were formed by cross-linking of hydrophobic extracellular polymeric substances (EPS), such as proteins, polysaccharides, and eDNA, which were synthesized by bacteria themselves after adhesion and colonization on biological surfaces. They had the characteristics of dense structure, high adhesiveness and low drug permeability, and had been found in many human organs or tissues, such as the brain, heart, liver, spleen, lungs, kidneys, gastrointestinal tract, and skeleton. By releasing pro-inflammatory bacterial metabolites including endotoxins, exotoxins and interleukin, biofilms stimulated the body’s immune system to secrete inflammatory factors. These factors triggered local inflammation and chronic infections. Those were the key reason for the failure of traditional clinical drug therapy for infectious diseases.In order to cope with the increasingly severe drug-resistant infections, it was urgent to develop new therapeutic strategies for bacterial-biofilm eradication and anti-bacterial infections. Based on the nanoscale structure and biocompatible activity, nanobiomaterials had the advantages of specific targeting, intelligent delivery, high drug loading and low toxicity, which could realize efficient intervention and precise treatment of drug-resistant bacterial biofilms. This paper highlighted multiple strategies of biofilms eradication based on nanobiomaterials. For example, nanobiomaterials combined with EPS degrading enzymes could be used for targeted hydrolysis of bacterial biofilms, and effectively increased the drug enrichment within biofilms. By loading quorum sensing inhibitors, nanotechnology was also an effective strategy for eradicating bacterial biofilms and recovering the infectious symptoms. Nanobiomaterials could intervene the bacterial metabolism and break the bacterial survival homeostasis by blocking the uptake of nutrients. Moreover, energy-driven micro-nano robotics had shown excellent performance in active delivery and biofilm eradication. Micro-nano robots could penetrate physiological barriers by exogenous or endogenous driving modes such as by biological or chemical methods, ultrasound, and magnetic field, and deliver drugs to the infection sites accurately. Achieving this using conventional drugs was difficult. Overall, the paper described the biological properties and drug-resistant molecular mechanisms of bacterial biofilms, and highlighted therapeutic strategies from different perspectives by nanobiomaterials, such as dispersing bacterial mature biofilms, blocking quorum sensing, inhibiting bacterial metabolism, and energy driving penetration. In addition, we presented the key challenges still faced by nanobiomaterials in combating bacterial biofilm infections. Firstly, the dense structure of EPS caused biofilms spatial heterogeneity and metabolic heterogeneity, which created exacting requirements for the design, construction and preparation process of nanobiomaterials. Secondly, biofilm disruption carried the risk of spread and infection the pathogenic bacteria, which might lead to other infections. Finally, we emphasized the role of nanobiomaterials in the development trends and translational prospects in biofilm treatment.