The innate immune system can be boosted in response to subsequent triggers by pre-exposure to microbes or microbial products, known as “trained immunity”. Compared to classical immune memory, innate trained immunity has several different features. Firstly, the molecules involved in trained immunity differ from those involved in classical immune memory. Innate trained immunity mainly involves innate immune cells (e.g., myeloid immune cells, natural killer cells, innate lymphoid cells) and their effector molecules (e.g., pattern recognition receptor (PRR), various cytokines), as well as some kinds of non-immune cells (e.g., microglial cells). Secondly, the increased responsiveness to secondary stimuli during innate trained immunity is not specific to a particular pathogen, but influences epigenetic reprogramming in the cell through signaling pathways, leading to the sustained changes in genes transcriptional process, which ultimately affects cellular physiology without permanent genetic changes (e.g., mutations or recombination). Finally, innate trained immunity relies on an altered functional state of innate immune cells that could persist for weeks to months after initial stimulus removal. An appropriate inducer could induce trained immunity in innate lymphocytes, such as exogenous stimulants (including vaccines) and endogenous stimulants, which was firstly discovered in bone marrow derived immune cells. However, mature bone marrow derived immune cells are short-lived cells, that may not be able to transmit memory phenotypes to their offspring and provide long-term protection. Therefore, trained immunity is more likely to be relied on long-lived cells, such as epithelial stem cells, mesenchymal stromal cells and non-immune cells such as fibroblasts. Epigenetic reprogramming is one of the key molecular mechanisms that induces trained immunity, including DNA modifications, non-coding RNAs, histone modifications and chromatin remodeling. In addition to epigenetic reprogramming, different cellular metabolic pathways are involved in the regulation of innate trained immunity, including aerobic glycolysis, glutamine catabolism, cholesterol metabolism and fatty acid synthesis, through a series of intracellular cascade responses triggered by the recognition of PRR specific ligands. In the view of evolutionary, trained immunity is beneficial in enhancing protection against secondary infections with an induction in the evolutionary protective process against infections. Therefore, innate trained immunity plays an important role in therapy against diseases such as tumors and infections, which has signature therapeutic effects in these diseases. In organ transplantation, trained immunity has been associated with acute rejection, which prolongs the survival of allografts. However, trained immunity is not always protective but pathological in some cases, and dysregulated trained immunity contributes to the development of inflammatory and autoimmune diseases. Trained immunity provides a novel form of immune memory, but when inappropriately activated, may lead to an attack on tissues, causing autoinflammation. In autoimmune diseases such as rheumatoid arthritis and atherosclerosis, trained immunity may lead to enhance inflammation and tissue lesion in diseased regions. In Alzheimer’s disease and Parkinson’s disease, trained immunity may lead to over-activation of microglial cells, triggering neuroinflammation even nerve injury. This paper summarizes the basis and mechanisms of innate trained immunity, including the different cell types involved, the impacts on diseases and the effects as a therapeutic strategy to provide novel ideas for different diseases.

Aim To study the neuroprotective effects of Herba siegesbeckiae extract on cerebral ischemia/ reperfusion rats and its mechanism. Methods Sixty SD rats were randomly divided into model group, low, middle and high dose groups of Herba siegesbeckiae, and Sham operation group, and the drug was given continuously for seven days. The degree of neurologic impairment was evaluated by mNSS, and the infarct volume was measured by MRI. The number of Nissl-posi- tive cells was detected by Nissl staining, and the apop- tosis was accessed by Tunel staining. Furthermore, the expression of Bax, Bcl-2 and NeuN was observed by Western blot, and the expression of NeuN was detected by immunofluorescence staining. The expression of IL- 1β, TNF-α and IL-6 mRNA was performed by RT- qPCR. Results The mNSS score and the volume of ischemic cerebral infarction in the model group were significantly increased, and Herba siegesbeckiae extract treatment significantly decreased the mNSS score and infarct volume (P<0.05, P<0.01). Herba siegesbeckiae extract could increase the number of Nissl-pos- itive cells and the expression of NeuN (P<0.01), and reduce the number of Tunel-positive cells (P<0.01). Western blot showed that Herba siegesbeckiae extract inhibited the expression of Bax, increased Bcl-2 and NeuN in ischemic brain tissue (P<0.01). RT-qPCR showed that Herba siegesbeckiae extract inhibited the expression of IL-1 β, TNF-α and IL-6 mRNA in the is-chemic brain tissue (P<0.01). Conclusions Herba siegesbeckiae extract can reduce the cerebral infarction volume, improve the neurological function damage, inhibit the apoptosis of nerve cells and the expression of inflammatory factors and promote the expression of NeuN, there by exerting protective effects on MCAO rats.

The coronavirus disease 2019 (COVID-19) is an acute infectious disease caused by the new severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection, which has led to serious worldwide economic burden. Due to the continuous emergence of variants, vaccines and monoclonal antibodies are only partial effective against infections caused by distinct strains of SARS-CoV-2. Therefore, it is still of great importance to call for the development of broad-spectrum and effective small molecule drugs to combat both current and future outbreaks triggered by SARS-CoV-2. Cathepsin L (CatL) cleaves the spike glycoprotein (S) of SARS-CoV-2, playing an indispensable role in enhancing virus entry into host cells. Therefore CatL is one of the ideal targets for the development of pan-coronavirus inhibitor-based drugs. In this study, a CatL enzyme inhibitor screening model was established based on fluorescein labeled substrate. Two CatL inhibitors IMB 6290 and IMB 8014 with low cytotoxicity were obtained through high-throughput screening, the half inhibition concentrations (IC₅₀) of which were 11.53 ± 0.68 and 1.56 ± 1.10 μmol·L^-1, respectively. SDS-PAGE and cell-cell fusion experiments confirmed that the compounds inhibited the hydrolysis of S protein by CatL in a concentration-dependent manner. Surface plasmon resonance (SPR) detection showed that both compounds exhibited moderate binding affinity with CatL. Molecular docking revealed the binding mode between the compound and the CatL active pocket. The pseudovirus experiment further confirmed the inhibitory effects of IMB 8014 on the S protein mediated entry process. In vitro pharmacokinetic evaluation indicated that the compounds had relatively good drug-likeness properties. Our research suggested that these two compounds have the potential to be further developed as antiviral drugs for COVID-19 treatment.

There are huge differences between tumor cells and normal cells in material metabolism, and tumor cells mainly show increased anabolism, decreased catabolism, and imbalance in substance metabolism. These differences provide the necessary material basis for the growth and reproduction of tumor cells, and also provide important targets for the treatment of tumors. Ferroptosis is an iron-dependent form of cell death characterized by an imbalance of iron-dependent lipid peroxidation and lipid membrane antioxidant systems in cells, resulting in excessive accumulation of lipid peroxide, causing damage to lipid membrane structure and loss of function, and ultimately cell death. The regulation of ferroptosis involves a variety of metabolic pathways, including glucose metabolism, lipid metabolism, amino acid metabolism, nucleotide metabolism and iron metabolism. In order for tumor cells to grow rapidly, their metabolic needs are more vigorous than those of normal cells. Tumor cells are metabolically reprogrammed to meet their rapidly proliferating material and energy needs. Metabolic reprogramming is mainly manifested in glycolysis and enhancement of pentose phosphate pathway, enhanced glutamine metabolism, increased nucleic acid synthesis, and iron metabolism tends to retain more intracellular iron. Metabolic reprogramming is accompanied by the production of reactive oxygen species and the activation of the antioxidant system. The state of high oxidative stress makes tumor cells more susceptible to redox imbalances, causing intracellular lipid peroxidation, which ultimately leads to ferroptosis. Therefore, in-depth study of the molecular mechanism and metabolic basis of ferroptosis is conducive to the development of new therapies to induce ferroptosis in cancer treatment. Ferroptosis, as a regulated form of cell death, can induce ferroptosis in tumor cells by pharmacologically or genetically targeting the metabolism of substances in tumor cells, which has great potential value in tumor treatment. This article summarizes the effects of cellular metabolism on ferroptosis in order to find new targets for tumor treatment and provide new ideas for clinical treatment.

Objective To establish a high performance liquid chromatography-tandem mass spectrometry(HPLC-MS/MS)for the simultaneous determination of gefitinib,erlotinib,nillotinib and imatinib plasma concentrations and analyze the results.Methods The plasma samples were treated with acetonitrile precipitation and separated by Diamonsil C18 column(150 mm ×4.6 mm,3.5 μm)with mobile phase of 0.1％formic acid water(A)-0.1％formic acid acetonitrile(B).The flow rate of gradient elution was 0.7 mL·min-1,and the column temperature was 40 ℃ and the injection volume was 3 μL.Using arotinib as the internal standard,the scanning was carried out by using electrospray ionization source in positive ionization mode with multi-reaction monitoring.The specificity,standard curve,lower limit of quantitation,precision,accuracy,recovery rate,matrix effect and stability of the method were investigated.The concentrations of imatinib and erlotinib in 20 patients with chronic myelogenous leukemia(CML)and gefitinib and erlotinib in 3 patients with non-small cell lung cancer were measured.Results The standard curves of the four drugs were as follows,gefitinib:y=2.536 × 10-3x+9.362 × 10-3(linear range 20-2 000 ng·mL-1,R2=0.996 6);erlotinib:y=3.575× 10-3x+7.406 × 10-3(linear range 50-5 000 ng·mL-1,R2=0.994 9);nilotinib:y=1.945 x 10-3x+0.015 643(linear range 50-5 000 ng·mL-1,R2=0.990 6);imatinib:y=4.56 x 10-3x+0.010 451(linear range 100～104 ng·mL-1,R2=0.9963).RSD of intra-day and inter-day were less than 10％,and the accuracy ranged from 90％to 110％,and the recovery rates were 91.35％to 98.93％(RSD＜10％);the matrix effect ranged from 91.64％to 107.50％(RSD＜10％).Determination of 23 patients showed that the blood concentration of nilotinib ranged from 623.76 to 2 934.13 ng·mL-1,and the blood concentration of imatinib ranged from 757.77 to 2 637.71 ng·mL-1,and the blood concentration of gefitinib ranged from 214.76 to 387.40 ng·mL-1.The serum concentration of erlotinib was 569.57 ng·mL-1.Conclusion The method of this research is simple,fast,sensitive and dedicated,which can be monitored by the concentration of clinical blood.

Temporomandibular joint (TMJ) diseases are common clinical conditions. The number of patients with TMJ diseases is large, and the etiology, epidemiology, disease spectrum, and treatment of the disease remain controversial and unknown. To understand and master the current situation of the occurrence, development and prevention of TMJ diseases, as well as to identify the patterns in etiology, incidence, drug sensitivity, and prognosis is crucial for alleviating patients′suffering.This will facilitate in-depth medical research, effective disease prevention measures, and the formulation of corresponding health policies. Cohort construction and research has an irreplaceable role in precise disease prevention and significant improvement in diagnosis and treatment levels. Large-scale cohort studies are needed to explore the relationship between potential risk factors and outcomes of TMJ diseases, and to observe disease prognoses through long-term follw-ups. The consensus aims to establish a standard conceptual frame work for a cohort study on patients with TMJ disease while providing ideas for cohort data standards to this condition. TMJ disease cohort data consists of both common data standards applicable to all specific disease cohorts as well as disease-specific data standards. Common data were available for each specific disease cohort. By integrating different cohort research resources, standard problems or study variables can be unified. Long-term follow-up can be performed using consistent definitions and criteria across different projects for better core data collection. It is hoped that this consensus will be facilitate the development cohort studies of TMJ diseases.

AIM To study the chemical constituents from the leaves of Cyanocarya paliurus(Batalin)Iljinskaja and their α-glucosidase inhibitory activities.METHODS The 95%ethanol extract from the leaves of C.paliurus was isolated and purified by macroporous resin,silica gel,Sephadex LH-20,polyamide,C18 reversed-phase silica gel and semi-preparative HPLC,then the structures of obtained compounds were identified by physicochemical properties and spectral data.Their α-glucosidase inhibitory activities were evaluated by PNPG.RESULTS Fifteen compounds were isolated and identified as cyclopaloside C(1),cyclopaloside A(2),juglanosides E(3),vaccinin A(4),ent-murin A(5),kaempferol 3-O-α-L-rhamnopyranoside(6),kaempferol-3-O-β-D-glucopyranoside(7),kaempferol-3-O-β-D-glucuronide methyl ester(8),kaempferol-3-O-β-D-glucuronide ethyl ester(9),kaempferol-3-O-β-D-glucuronide butyl ester(10),quercetin-3-O-α-L-rhamnopyranoside(11)quercetin-3-O-β-D-glucopyranoside(12),quercetin-3-O-β-D-galactopyranoside(13),quercetin-3-O-β-D-glucuronide butyl ester(14),dihydrokaempferol(15).The IC50 value of total extracts ihibited α-glucosidase was(1.83±0.04)μg/mL,and the IC50 values of compounds 1,4-5 were(29.48±1.86),(0.50±0.07),(0.71±0.07)μmol/L,respectively.CONCLUSION Compound 1 is a new tetrahydronaphthalene glycoside.Compounds 4-5,8-10 and 14 are isolated from the leaves of C.paliurus for the first time.Compounds 4-5 are relatively rare flavonoid lignans with potential inhibitory activities against α-glucosidase.

Objective To explore the predictive value of gait and balance on the frailty in community-dwelling older adults aged 65 years and older in Shanghai. Methods A total of 414 community-dwelling older adults aged 65 years or older were recruited from Shanghai,from De-cember,2022 to April,2023.They were investigated with Fried's Frailty Phenotype Scale,Timed Up and Go Test(TUGT),one leg standing test(OLST)and self-designed questionnaire.The factors related to frailty were ana-lyzed using multivariate Logistic regression. Results The prevalence of pre-frailty and frailty were 62.8%and 10.9%,respectively.Living in rural residence,older age,low income,smoking,sedentary lifestyle and no regular exercise were the risk factors for frailty among com-munity-dwelling older adults.Time of TUGT increased,and time of closed eyes OLST descreased respectively in the frail older adults(P<0.05).The area under curve was 0.846(95%CI 0.808 to 0.884,P<0.001)as prediction for frailty using the combination of TUGT and closed eye OLST,with a sensitivity of 0.726 and a specificity of 0.817 at the optimal threshold. Conclusion Gait and balance may be a valuable predictor of frailty in community-dwelling older adults in Shanghai.

Objective:To analyze the release and distribution characteristics of Chinese medical insurance payment policies,and to provide reference for future policy formulation in the field of medical insurance payment construction.Methods: Content analysis method was used to construct a two-dimensional framework of "policy goals-policy tools",and text analysis was carried out according to 63 policy documents.Results: A total of 493 policy codes were completed.From the perspective of policy goals,the policy objectives of Chinese medical insurance payment mainly focused on three aspects: improving the payment level,optimizing the medical insurance environment,and standardizing the supervision regulations.From the perspective of policy tools,environmental policy tools are the most used policy tools,followed by supply and demand tools.There is a shortage of financial input and talent training in all policy objectives,so more attention should be paid to demonstration and Category of payment.Conclusion: Our country puts forth effort to build a perfect medical insurance payment system,but should further strengthen policy content supplement,optimize the structure of policy tools,and give full play to the payment ability of medical insurance when pulling the demand of medical insurance payment and driving the supply of medical insurance payment.

Near infrared spectroscopy(NIRS)technology combined with chemometrics algorithms has been widely used in quantitative and qualitative analysis of food and medicine.However,traditional chemometrics methods,especially linear classification methods,often yield unsatisfactory results when addressing multi-class classification problems.Convolutional neural network(CNN)is adept at extracting deep-level features from data and suitable for handling non-linear relationships.The modeling performance of CNN depends on the size and diversity of sample,while the collection and preprocessing of NIRS sample data is often time-consuming and labor-intensive.This study proposed a NIRS qualitative analysis method based on data augmentation strategies and CNN.The data augmentation strategy included two steps.Firstly,applying Bootstrap resampling and generative adversarial network(GAN)methods to augment three NIRS datasets(Medicine,coffee and grape).Secondly,combining the original samples(Y)with the Bootstrap augmented samples(B)and GAN augmented samples(G)to obtain three augmented datasets(Y-B,Y-G and Y-B-G).Based on this,a CNN model structure suitable for these datasets was designed,consisting of 2 one-dimensional convolutional layers,1 max-pooling layer,and 1 fully connected layer.The results showed that compared to the optimal models of partial least squares discriminant analysis(PLS-DA),support vector machine(SVM),and back propagation neural network(BP),the CNN model based on Y-B dataset achieved average accuracy improvements of 3.998%,9.364%,and 4.689%for medicine(Binary classification);the CNN model based on the Y-B-G dataset achieved average accuracy improvements of 6.001%,2.004%,and 7.523%for coffee(7-class classification);and the CNN model based on the Y-B dataset achieved average accuracy improvements of 33.408%,51.994%,and 34.378%for grapes(20-class classification).It was evident that the models established based on data augmentation strategies and CNN demonstrated better classification accuracy and generalization performance with different datasets and classification categories.