This study aims to explore the mechanism of Jiaotai Pills in treating depression based on metabolomics and network pharmacology. The chemical constituents of Jiaotai Pills were identified by UHPLC-Orbitrap Exploris 480, and the targets of Jiaotai Pills and depression were retrieved from online databases. STRING and Cytoscape 3.7.2 were used to construct the protein-protein interaction network of core targets of Jiaotai Pills in treating depression and the "compound-target-pathway" network. DAVID was used for Gene Ontology(GO) function and Kyoto Encyclopedia of Genes and Genomes(KEGG) pathway enrichment analyses of the core targets. The mouse model of depression was established with chronic unpredictable mild stress(CUMS) and treated with different doses of Jiaotai Pills. The behavioral changes and pathological changes in the hippocampus were observed. UHPLC-Orbitrap Exploris 120 was used for metabolic profiling of the serum, from which the differential metabolites and related metabolic pathways were screened. A "metabolite-reaction-enzyme-gene" network was constructed for the integrated analysis of metabolomics and network pharmacology. A total of 34 chemical components of Jiaotai Pills were identified, and 143 core targets of Jiaotai Pills in treating depression were predicted, which were mainly involved in the arginine and proline, sphingolipid, and neurotrophin metabolism signaling pathways. The results of animal experiments showed that Jiaotai Pills alleviated the depression behaviors and pathological changes in the hippocampus of the mouse model of CUMS-induced depression. In addition, Jiaotai Pills reversed the levels of 32 metabolites involved in various pathways such as arginine and proline metabolism, sphingolipid metabolism, and porphyrin metabolism in the serum of model mice. The integrated analysis showed that arginine and proline metabolism, cysteine and methionine metabolism, and porphyrin metabolism might be the key pathways in the treatment of depression with Jiaotai Pills. In conclusion, metabolomics combined with network pharmacology clarifies the antidepressant mechanism of Jiaotai Pills, which may provide a basis for the clinical application of Jiaotai Pills in treating depression.
Animals ; Drugs, Chinese Herbal/chemistry* ; Depression/genetics* ; Mice ; Network Pharmacology ; Metabolomics ; Male ; Disease Models, Animal ; Humans ; Protein Interaction Maps/drug effects* ; Antidepressive Agents

Based on serum metabolomics and gut microbiota technology, this study explores the effects and mechanisms of the water extract of Ziziphi Spinosae Semen(SZRW) and the petroleum ether extract of Ziziphi Spinosae Semen(SZRO) in improving depressive-like behaviors induced by sleep deprivation. A modified multi-platform water environment method was employed to establish a rat model of sleep deprivation. Depressive-like behaviors in rats were assessed through the sucrose preference test and forced swim test. The expression of barrier proteins, such as Occludin, in the colon was determined by immunofluorescence. UPLC-Q-Orbitrap MS was utilized to analyze the serum metabolic profiles of sleep-deprived rats, screen for differential metabolites, and analyze metabolic pathways. The diversity of the gut microbiota was detected using 16S rRNA gene sequencing. Spearman correlation coefficient analysis was conducted to assess the correlation between differential metabolites and gut microbiota. The results indicated that SZRO significantly increased the sucrose preference index and decreased the immobility time in the forced swim test in rats. A total of 34 differential metabolites were identified through serum metabolomics. SZRW and SZRO shared five metabolic pathways, including phenylalanine metabolism. SZRW uniquely featured taurine and hypotaurine metabolism, while SZRO uniquely featured linoleic acid metabolism and tyrosine metabolism. Correlation analysis revealed that SZRW could upregulate the abundance of Bilophila, promoting the production of indole-3-propionic acid and subsequently upregulating the expression levels of intestinal tight junction proteins such as ZO-1, Occludin, and Claudin-1. SZRO could indirectly influence metabolic pathways such as arginine metabolism and linoleic acid metabolism by upregulating the abundance of gut microbiota such as Coprococcus and Eubacterium species. Both SZRW and SZRO can regulate endogenous metabolism, including amino acids, energy, and lipids, alter the gut microbiota microecology, and improve depressive-like behaviors. SZRO demonstrated superior effects in regulating metabolic pathways and gut microbiota structure compared to SZRW. The findings of this study provide a scientific basis for elucidating the pharmacodynamic material basis of Ziziphi Spinosae Semen.
Animals ; Rats ; Gastrointestinal Microbiome/drug effects* ; Male ; Metabolomics ; Drugs, Chinese Herbal/administration & dosage* ; Depression/blood* ; Rats, Sprague-Dawley ; Sleep Deprivation/complications* ; Ziziphus/chemistry* ; Antidepressive Agents/administration & dosage* ; Behavior, Animal/drug effects* ; Humans

OBJECTIVE: To investigate the variability of bone metabolism levels among different populations and its association with knee osteoarthritis (KOA) pain. METHODS: A total of 50 people (control group) who participated in physical examination from January 2023 to June 2023 were selected, including 26 males and 24 females, wtih a mean aged of (52.14±9.04) years old ranging 41 to 65 years old. The other 50 patients with knee osteoarthritis(case group) who attended the outpatient clinic of the Orthopedics and Traumatology Department in the same time period, including 19 males and 31 females, with a mean age of (53.60±7.76) years old ranging 40 to 65 years. The two groups of Western Ontario and McMaster Universities Osteoarthritis Index(WOMAC) and bone metabolism markers, such as 25-hydroxy-cholecalciferol[25(OH)D], β-isomerized typeⅠcollagen C-telopeptide breakdown products (β-CTX), total typeⅠprocollagen N-terminal propeptide (t-PINP), osteocalcin (OC), parathormone (PTH) levels were compared. Pearson correlation analysis was used to compare the correlation between two groups of bone metabolism related markers and WOMAC. RESULTS: The WOMAC score of the case group (39.90±2.34) was higher than that of the control group (3.60±0.57), with significant difference (P<0.05). There was no significant difference between the two groups of 25 (OH)D, β-CTX and PTH (P>0.05). The t-PINP and OC of the case group were (62.90±52.40) and (19.88±10.15) ng·ml-1, respectively, and those of the control group were (38.86±10.82) and (14.90±3.62) ng·ml^-1, respectively;the t-PINP and OC of the case group were higher than those of the control group, with significant difference (P<0.05). Pearson correlation analysis showed that t-PINP was positively correlated with WOMAC pain score in the case group (r²=0.045, P<0.01). CONCLUSION Bone metabolism levels in the serum of patients with knee osteoarthritis are different from those of healthy people, and the difference between OC and t-PINP is the most obvious, and the concentration of t-PINP levels is positively correlated with pain symptoms in patients with KOA. However, the specific mechanism of correlation between the bone metabolism levels of patients with KOA and their pain symptoms needs to be further elucidated by basic experimental research as well as by enlarging the samples.
Humans ; Female ; Male ; Middle Aged ; Osteoarthritis, Knee/metabolism* ; Aged ; Adult ; Bone and Bones/metabolism* ; Pain/etiology* ; Biomarkers/metabolism*

BACKGROUND: Early control of low-density lipoprotein cholesterol (LDL-C) is crucial for reducing the progress of cardiovascular disease. However, its additional role to the risk of primary osteoporosis in men with coronary heart disease was inconclusive. Our study aims to determine the association of LDL-C and its trajectories for osteoporosis risk in the middle-aged and aged men of China. METHODS: The retrospective cohort study of 1546 men aged 69.74 ± 11.30 years conducted in Beijing, China from 2015 to 2022. And the incidence of primary osteoporosis was annually recorded. LDL-C trajectories were further identified by latent class growth model using repeated measurements of LDL-C. The association of baseline LDL-C for osteoporosis was estimated using hazard ratio (HR) with 95% CI in Cox proportional hazard model, while mean level and trajectories of LDL-C for osteoporosis were evaluated using odds ratio (OR) with 95% CI in logistic regression model. RESULTS: During the median 6.2-year follow-up period, 70 men developed primary osteoporosis. The higher level of baseline LDL-C (HR = 1.539, 95% CI: 1.012-2.342) and mean LDL-C (OR = 2.190, 95% CI: 1.443-3.324) were associated with higher risk of osteoporosis in men with coronary heart disease after adjusted for covariates. Compared with those in the LDL-C trajectory of low-stable decrease, participants with medium-fluctuant trajectory, whose longitudinal LDL-C started with a medium LDL-C level and appeared an increase and then decrease, were negatively associated with osteoporosis risk (OR = 2.451, 95% CI: 1.152-5.216). And participants with initially high LDL-C level and then a rapid decrease demonstrated a tendency towards reduced risk (OR = 0.718, 95% CI: 0.212-2.437). CONCLUSIONS Elevated LDL-C level and its long-term fluctuation may increase the risk of primary osteoporosis in men. Early controlling a stable level of LDL-C is also essential for bone health.

OBJECTIVE: To investigate the effects of acupuncture on the neurotransmitter levels and gut microbiota in a mouse model of Tourette syndrome (TS). METHODS: Thirty-six male C57/BL6 mice were randomly divided into 4 groups using a random number table method: 3,3'-iminodipropionitrile (IDPN) group, control group, acupuncture group, and tiapride group, with 9 mice in each group. In the IDPN group, acupuncture group, and tiapride group, mice received daily intraperitoneal injections of IDPN (300 mg/kg body weight) for 7 consecutive days to induce stereotyped behaviors. Subsequently, in the acupuncture intervention group, standardized acupuncture treatment was administered for 14 consecutive days to IDPN-induced TS model mice. The selected acupoints included Baihui (DU 20), Yintang (DU 29), Waiguan (SJ 5), and Zulinqi (GB 41). In the tiapride group, mice were administered tiapride (50 mg/kg body weight) via oral gavage daily for 14 consecutive days. The control group, IDPN group, and acupuncture group received the same volume of saline orally for 14 consecutive days. Stereotypic behaviors were quantified through behavioral assessments. Neurotransmitter levels, including dopamine (DA), glutamate (Glu), and aspartate (ASP) in striatal tissue were measured using enzyme-linked immunosorbent assay. Dopamine transporter (DAT) expression levels were additionally quantified through quantitative polymerase chain reaction (qPCR). Gut microbial composition was analyzed through 16S ribosomal RNA gene sequencing, while metabolic profiling was conducted using liquid chromatography-mass spectrometry (LC-MS). RESULTS: Acupuncture administration significantly attenuated stereotypic behaviors, concurrently reducing striatal levels of DA, Glu and ASP concentrations while upregulating DAT expression compared with untreated TS controls (P<0.05 or P<0.01). Comparative analysis identified significant differences in Muribaculaceae (P=0.001), Oscillospiraceae (P=0.049), Desulfovibrionaceae (P=0.001), and Marinifilaceae (P=0.014) following acupuncture intervention. Metabolomic profiling revealed alterations in 7 metabolites and 18 metabolic pathways when compared to the TS mice, which involved various amino acid metabolisms associated with DA, Glu, and ASP. CONCLUSIONS Acupuncture demonstrates significant modulatory effects on both central neurotransmitter systems and gut microbial ecology, thereby highlighting its dual therapeutic potential for TS management through gut-brain axis regulation.
Animals ; Tourette Syndrome/metabolism* ; Gastrointestinal Microbiome ; Neurotransmitter Agents/metabolism* ; Acupuncture Therapy ; Male ; Mice, Inbred C57BL ; Mice

The objective assessment of hearing loss is one of the critical components in forensic clini-cal research.Auditory brainstem response(ABR)is an important method for objectively assessing hearing levels.It is divided into various types based on different stimulus signals,each with its own characteris-tics and applications.Among them,narrow-band Chirp ABR,due to its frequency specificity,fulfills the basic requirements for objective assessment of forensic audiology,promising to be an important method of objective hearing assessment in forensic medicine.This article reviews the development history,charac-teristics and clinical applications of Chirp ABR,and envisions its application prospects in forensic audi-tory identification.

Objective To initially explore the possibility of applying the fluorescence micro-optical sectioning tomography(fMOST)high-resolution 3D reconstruction system to the morphological study of the intestinal nervous system and to preliminarily establish a method for studying the morphology of the intestinal nervous system using this system.Methods fMOST high-resolution 3D reconstruction system was used to study the intestinal nervous system of C57BL/6 mice in detail.Based on this method,a new morphological method of the visceral nervous system of small animal models was explored at the single-cell level.Results Compared with the large intestine,the small intestine lacked the typical myenteric plexus(Auerbach),deep mucosal plexus(Henley),and submucosal superficial plexus(Meissner).Conclusion The result of this paper provide a clearer and systematic display of the anatomical structure of the enteric nervous system in C57BL/6 mice,and further clarify the similarities and differences between the enteric nervous system of mice and human,and provide a theoretical basis for its rational application in the study of digestive system diseases.The morphological study of fMOST high-resolution 3D reconstruction system is not limited to the central nervous system,but can be extended to the morphological study of multiple visceral nervous systems.

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.

ObjectiveDirect continuous monitoring of arterial blood pressure is invasive and continuous monitoring cannot be achieved by traditional cuffed indirect blood pressure measurement methods. Previously, continuous non-invasive arterial blood pressure monitoring was achieved by using photoplethysmography (PPG), but it is discrete values of systolic and diastolic blood pressures rather than continuous values constructing arterial blood pressure waves. This study aimed to reconstruct arterial blood pressure wave signal based on CNN-LSTM using PPG to achieve continuous non-invasive arterial blood pressure monitoring. MethodsA CNN-LSTM hybrid neural network model was constructed, and the PPG and arterial blood pressure wave synchronized recorded signal data from the Medical Information Mart for Intensive Care (MIMIC) were used. The PPG signals were input to this model after noise reduction, normalization, and sliding window segmentation. The corresponding arterial blood pressure waves were reconstructed from PPG by using the CNN-LSTM hybrid model. ResultsWhen using the CNN-LSTM neural network with a window length of 312, the error between the reconstructed arterial blood pressure values and the actual arterial blood pressure values was minimal: the values of mean absolute error (MAE) and root mean square error (RMSE) were 2.79 mmHg and 4.24 mmHg, respectively, and the cosine similarity is the optimal. The reconstructed arterial blood pressure values were highly correlated with the actual arterial blood pressure values, which met the Association for the Advancement of Medical Instrumentation (AAMI) standards. ConclusionCNN-LSTM hybrid neural network can reconstruct arterial blood pressure wave signal using PPG to achieve continuous non-invasive arterial blood pressure monitoring.

ObjectiveBased on real-world clinical data of traditional Chinese medicine （TCM）， a Cox proportional hazards model was built to predict the risk factors of complications in patients with Corona Virus Disease 2019 （COVID-19） or influenza A/B， and the cumulative occurrence function graph was used to present the prediction output. MethodThe medical records of the patients with respiratory infectious diseases， including COVID-19 and influenza A/B， treated in the First Affiliated Hospital of Heilongjiang University of Chinese Medicine from November 2022 to October 2023 were collected. The data from the electronic medical record system were integrated into a data warehouse. The information of the patients with respiratory diseases caused by influenza A and B viruses and SARS-CoV-2 from November 2022 to October 2023 was retrospectively collected. The information involved age， gender， disease course， past medical history， laboratory test results， tongue manifestation， pulse manifestation， TCM syndrome， and main therapeutic drugs. The outcome indicators of whether complications occurred were obtained by telephone follow-up and review of readmission records. The data was divided into a training set and a validation set in a ratio of 70% and 30%， respectively. In the training set， the Cox proportional hazards model was used to identify the key factors affecting patient complications. Then， the combination of variables was optimized by stepwise elimination method， and an efficient complication risk assessment model was constructed， which was visualized in the form of histogram. The C-index， receiver operating characteristic （ROC） curve， calibration error graph， and decision curve analysis were employed to comprehensively measure the prediction performance of the model. ResultThe history of chronic lung diseases ［hazard ratio （HR） 4.46， 95% confidence interval （95%CI） 1.79-11.12］， Qi deficiency （HR 5.74， 95%CI 2.14-15.39）， thready and weak pulse （HR 4.45， 95%CI 1.88-10.50）， hormone use history （HR 4.57， 95%CI 2.04-10.23）， procalcitonin （PCT>10 μg·L^-1） （HR 1.23， 95%CI 0.06-0.86）， serum amyloid A （SAA）>100 mg·L^-1 （HR 9.80， 95%CI 7.24-59.75）， and platelet （PLT）>303×10⁹ /L （HR 5.66， 95%CI 2.01-16.00） were the risk factors for complications. Chinese medicine intervention （HR 0.20， 95%CI 0.06-0.70） was the protective factor for complications. Based on the above risk factors， the prediction model was constructed. In the training set， the C-index was estimated to be 0.765， and the CI was within the range of 0.667 to 0.859. In the validation set， the C-index was 0.804， and the CI varied within the range of 0.773 to 0.855. The temporal variation graph of C-index was then described. The area under the ROC curve （AUC） at 5， 10， 15 months was 0.61， 0.72， and 0.79 in the training set and 0.60， 0.67， and 0.62 in the validation set， respectively. In addition， calibration and decision curves were drawn for 5， 10， 15 months for both training and validation sets， which showed that the model had good calibration performance and was effective in clinical practice. ConclusionThe history of chronic lung diseases， Qi deficiency， thready and weak pulse， hormone use history， PCT>10 μg·L^-1， SAA>100 mg·L^-1， and PLT>303×10⁹ /L were risk factors for complications in patients with COVID-19 or influenza A/B， while Chinese medicine intervention was a protective factor. The prediction model was established based on the indicators above. The model showcased excellent distinguishing performance， calibration performance， and clinical practicability， providing scientific support for the prevention and control of complications caused by respiratory viral infections.