Aim To investigate the protective effect of dimethyl fumarate on spleen injury induced by gamma radiation in mice and the related mechanism. Methods C57BL/6 mice were randomly divided into the blank control group, radiation model group and DMF administration group, which were administered once at 12 h before irradiation and once at 0. 5 h, 12 h, 24 h and 48 h after irradiation. The 30-day survival rate, body weight and pathological injury of spleen were measured after a one-time total body irradiation of Co 7 rays (8 Gy). TUNEL staining was used to detect apoptosis of spleen cells. Enzyme-linked immunoassay ( ELISA) was applied to detect the contents of TNF-a, IL-1 p, IL-6, IL-18, NLRP3 and AIM2 in spleen. Western blot test and immunofluorescence staining test was employed to verify the changes of NLRP3 and AIM2 contents in spleen tissue after irradiation. Results DMF could obviously improve the survival rate of irradiated mice, improve the weight loss of irradiated mice, re-duce the pathological injury of spleen, and inhibit the apoptosis of spleen cells after irradiation. ELISA results showed that DMF could significantly inhibit the increase of spleen inflammatory cytokines TNF-a, IL-lp, IL-6, IL-18 and inflammasome components NL-RP3 and AIM2 induced by irradiation. Western blot and tissue immunofluorescence staining also confirmed that DMF could inhibit the increase of NLRP3 and AIM2 inflammasome protein levels caused by irradiation. Meanwhile, NLRP3 agonist and AIM2 agonist could antagonize the radiation protection effect of DMF on spleen cells. Conclusion DMF can ameliorate spleen injury of Co 7-ray injured mice, and its mechanism is closely related to NLRP3/AIM2 inflamma-somes, which can be used as a potential protective drug for radiation injury.

AIM To investigate the variation rules of main secondary metabolites in Hedysari Radix before and after rubbing strip.METHODS UPLC-MS/MS was adopted in the content determination of formononetin,ononin,calycosin,calycosin-7-glucoside,medicarpin,genistein,luteolin,liquiritigenin,isoliquiritigenin,vanillic acid,ferulic acid,γ-aminobutyric acid,adenosine and betaine,after which cluster analysis,principal component analysis and orthogonal partial least squares discriminant analysis were used for chemical pattern recognition to explore differential components.RESULTS After rubbing strip,formononetin,calycosin,liquiritigenin and γ-aminobutynic acid demonstrated increased contents,along with decreased contents of ononin,calycosin-7-glucoside and vanillic acid.The samples with and without rubbing strip were clustered into two types,calycosin-7-glucoside,formononetin,γ-aminobutynic acid,vanillic acid,calycosin-7-glucoside and formononetin were differential components.CONCLUSION This experiment clarifies the differences of chemical constituents in Hedysari Radix before and after rubbing strip,which can provide a reference for the research on rubbing strip mechanism of other medicinal materials.

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.

Tuberous sclerosis complex(TSC)is a rare genetic disease that can lead to benign dysplasia in multiple organs such as the skin, brain, eyes, oral cavity, heart, lungs, kidneys, liver, and bones. Its main symptoms include epilepsy, intellectual disabilities, skin depigmentation, and facial angiofibromas, whilst incidence is approximately 1 in 10 000 to 1 in 6000 newborns. This case presents a middle-aged woman who initially manifested with epilepsy and nodular depigmentation. Later, she developed a lower abdominal mass, elevated creatinine, and severe anemia. Based on clinical features and whole exome sequencing, the primary diagnosis was confirmed as TSC. Laboratory and imaging examinations revealed that the lower abdominal mass originated from the uterus. CT-guided biopsy pathology and surgical pathology suggested a combination of leiomyoma and abscess. With the involvement of multiple organs and various complications beyond the main diagnosis, the diagnostic and therapeutic process for this patient highlights the importance of rigorous clinical thinking and multidisciplinary collaboration in the diagnosis and treatment of rare and challenging diseases.

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.

The lingnan liver-soothing and spirit-regulating acupuncture and moxibustion technique,developed by Professor FU Wen-Bin,a renowned traditional Chinese medicine expert in Guangdong Province,represents an innovative achievement in acupuncture therapy for depression-related disorders.Drawing upon the rich legacy of master scholars,meticulous study of medical literature,and over three decades of continuous research and innovation,Professor FU has formulated this technique with profound influence and widespread application.By tracing the developmental trajectory of the Lingnan liver-soothing and spirit-regulating technique,this paper sheds light on its significant guiding principles and reference value for the development of other distinctive acupuncture techniques.Furthermore,it offers insights and inspiration for advancement in various fields of traditional Chinese medicine.

Traditional Chinese medicine has a long history of treating epidemic diseases.Numerous references are available.As early as the Yellow Emperor's Classic of Internal Medicine-Su Wen Chapter mentioned:"the five epidemics are all similar,regardless of their size,and the symptoms are similar".In the prevention and treatment of the coronavirus disease 2019(COVID-19),Chinese medicine has shown its unique advantages.Pulmonary infection is one of the most common diseases associated with sudden epidemic diseases,which is mostly induced by various infectious factors such as epidemic bacteria or viruses.If the patient has a history of chronic lung disease,it will further aggravate the clinical symptoms,and is easy to develop into severe pneumonia,with high mortality,rapid progress,long course of treatment,poor prognosis,and many complications,and the general antibiotic application is not as effective as desired."Jue heat overcomes the complex,Yin and Yang are complicated"comes from the chapter of Jueyin disease in Shanghan Lun(Treatise on Febrile Diseases),which is one of the typical features of fever occurring from illness to Jueyin.To explore the correlation between Jueyin disease and the pathogenesis of severe pneumonia from multiple angles,analyze the mechanism of Qingfei Paidu decoction in the treatment of Jueyin fever,better understand the essence of Zhang Zhongjing's prescription,and provide new ideas for the syndrome differentiation process of patients with pulmonary infection caused by epidemic diseases.At present,the prevention and control mechanism of COVID-19 in China has been gradually improved,the number of patients with COVID-19 has been gradually reduced,and the epidemic situation is easing.However,even so,it is still impossible to predict whether sudden and major epidemic diseases similar to COVID-19 will break out again in the future.

The update of multi-omics technology is a key means to promote the rapid development of accurate health model in the whole life cycle.It can formulate dynamic and accurate nursing measures and provide massive data information from the perspective of nursing biology of health and disease.At present,clinical nursing research faces many challenges such as insufficient application and transformation ability of multi-omics technology.This paper introduces the multi-omics technology,reviews the application status of multi-omics technology in cancer nursing,maternal and child nursing,chronic metabolic disease nursing and symptom management,and puts forward the cross integration and prospect of multi-omics technology and nursing research,so as to strengthen the information mining ability of nurses at different levels of health and disease,and provide an important basis for accelerating the clinical transformation of precision nursing.

Objective:To investigate the factors influencing fall risk scores in elderly individuals.Methods:A total of 4 419 individuals were randomly selected using the cluster sampling method from Beijing, Nanning(Guangxi), and Yinchuan(Ningxia).Data on demographic characteristics and fall-related incidents were gathered and analyzed for their correlation with fall risk scores.Results:The fall risk score showed significant associations with various factors, such as the history of falls within one year( β=-3.607, 95% CI: -3.881 to -3.332), care methods( β=2.442, 95% CI: 2.226 to 2.658), exercise( β=0.714, 95% CI: 0.443 to 0.986), retirement( β=-0.585, 95% CI: -0.819 to -0.351), age( β=0.173, 95% CI: 0.159 to 0.187), and use of walking aids( β=-3.737, 95% CI: -4.054 to -3.421). Conclusions:Fall risk scores in older adults are influenced by a variety of factors.Factors such as no history of falls within the past year, living independently, engaging in physical activity, and being employed may contribute to lower fall risk scores in older adults.