Lung cancer is the most common malignant tumor worldwide, ranking first in both incidence and mortality rates. According to the latest statistics from the International Agency for Research on Cancer (IARC), approximately 2.5 million new cases and around 1.8 million deaths from lung cancer occurred in 2022, placing a tremendous burden on global healthcare systems. The high mortality rate of lung cancer is closely linked to its subtle early symptoms, which often lead to diagnosis at advanced stages. This not only complicates treatment but also results in substantial economic losses. Current treatment options for lung cancer include surgery, radiotherapy, chemotherapy, targeted drug therapy, and immunotherapy. Among these, immunotherapy has emerged as the most groundbreaking advancement in recent years, owing to its unique antitumor mechanisms and impressive clinical benefits. Unlike traditional therapies such as radiotherapy and chemotherapy, immunotherapy activates or enhances the patient’s immune system to recognize and eliminate tumor cells. It offers advantages such as more durable therapeutic effects and relatively fewer toxic side effects. The main approaches to lung cancer immunotherapy include immune checkpoint inhibitors, tumor-specific antigen-targeted therapies, adoptive cell therapies, cancer vaccines, and oncolytic virus therapies. Among these, immune checkpoint inhibitors and tumor-specific antigen-targeted therapies have received approval from the U.S. Food and Drug Administration (FDA) for clinical use in lung cancer, significantly improving outcomes for patients with advanced non-small cell lung cancer. Although other immunotherapy strategies are still in clinical trials, they show great potential in improving treatment precision and efficacy. This article systematically reviews the latest research progress in lung cancer immunotherapy, including the development of novel immune checkpoint molecules, optimization of treatment strategies, identification of predictive biomarkers, and findings from recent clinical trials. It also discusses the current challenges in the field and outlines future directions, such as the development of next-generation immunotherapeutic agents, exploration of more effective combination regimens, and the establishment of precise efficacy prediction systems. The aim is to provide a valuable reference for the continued advancement of lung cancer immunotherapy.

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.

Alzheimer’s disease (AD) is a prevalent neurodegenerative condition characterized by progressive cognitive decline and memory loss. As the incidence of AD continues to rise annually, researchers have shown keen interest in the active components found in natural plants and their neuroprotective effects against AD. Quercetin, a flavonol widely present in fruits and vegetables, has multiple biological effects including anticancer, anti-inflammatory, and antioxidant. Oxidative stress plays a central role in the pathogenesis of AD, and the antioxidant properties of quercetin are essential for its neuroprotective function. Quercetin can modulate multiple signaling pathways related to AD, such as Nrf2-ARE, JNK, p38 MAPK, PON2, PI3K/Akt, and PKC, all of which are closely related to oxidative stress. Furthermore, quercetin is capable of inhibiting the aggregation of β‑amyloid protein (Aβ) and the phosphorylation of tau protein, as well as the activity of β‑secretase 1 and acetylcholinesterase, thus slowing down the progression of the disease.The review also provides insights into the pharmacokinetic properties of quercetin, including its absorption, metabolism, and excretion, as well as its bioavailability challenges and clinical applications. To improve the bioavailability and enhance the targeting of quercetin, the potential of quercetin nanomedicine delivery systems in the treatment of AD is also discussed. In summary, the multifaceted mechanisms of quercetin against AD provide a new perspective for drug development. However, translating these findings into clinical practice requires overcoming current limitations and ongoing research. In this way, its therapeutic potential in the treatment of AD can be fully utilized.

Aim To express and purify recombinant hCGH-CTP fusion protein in high-density suspension culture of Chinese hamster ovary cells (CHO-S), and to verify the lipid accumulation effect of rhCGH-CTP on 3T3-L1 mature adipocytes. Methods The recombinant protein expression vector (pcDNA3. 1-rhCGH-CTP) was constructed, achieved by fusing the human glycoprotein hormone beta 5/alpha 2 cDNA with CTP Linker. The expression plasmid was transiently transfected into the suspended CHO-S to express rhCGH-CTP protein and then purified, and the protein biological activity was verified. Intervention with 3T3-L1 mature adipocyte cells for 24 h was performed to detect the changes of intracellular triglyceride (TG) level. Results Western blot results showed that rhCGH-CTP protein was successfully expressed in CHO-S cells, and the yield was up to 715. 4 mg • L~ . The secreted protein was purified by AKTA pure system with higher purity that was up to 90% as identified by SDS-PAGE. In addition, the intracellular cAMP content of mature adipocytes with high expression of TSHR gene significantly increased after intervention with different concentrations of rhCGH-CTP protein by ELISA kit, indicating that rhCGH-CTP protein had biological activity. Oil red 0 staining showed that compared with the control group, the lipid content of mature adipocytes in the intervention groups with different concentrations of rhCGH-CTP protein significantly decreased (P < 0. 05) . Conclusions The rhCGH-CTP protein has been successfully expressed and purified with biological activity, and effectively reduce TG. This research provides an important theoretical basis for further revealing the physiological role of CGH protein and its potential application in clinical practice.

Umbilical cord mesenchymal stem cells (UC-MSCs) have been widely used in regenerative medicine, but there is limited research on the stability of UC-MSCs formulation during production. This study aims to assess the stability of the cell stock solution and intermediate product throughout the production process, as well as the final product following reconstitution, in order to offer guidance for the manufacturing process and serve as a reference for formulation reconstitution methods. Three batches of cell formulation were produced and stored under low temperature (2-8 ℃) and room temperature (20-26 ℃) during cell stock solution and intermediate product stages. The storage time intervals for cell stock solution were 0, 2, 4, and 6 h, while for intermediate products, the intervals were 0, 1, 2, and 3 h. The evaluation items included visual inspection, viable cell concentration, cell viability, cell surface markers, lymphocyte proliferation inhibition rate, and sterility. Additionally, dilution and culture stability studies were performed after reconstitution of the cell product. The reconstitution diluents included 0.9% sodium chloride injection, 0.9% sodium chloride injection + 1% human serum albumin, and 0.9% sodium chloride injection + 2% human serum albumin, with dilution ratios of 10-fold and 40-fold. The storage time intervals after dilution were 0, 1, 2, 3, and 4 h. The reconstitution culture media included DMEM medium, DMEM + 2% platelet lysate, 0.9% sodium chloride injection, and 0.9% sodium chloride injection + 1% human serum albumin, and the culture duration was 24 h. The evaluation items were viable cell concentration and cell viability. The results showed that the cell stock solution remained stable for up to 6 h under both low temperature (2-8 ℃) and room temperature (20-26 ℃) conditions, while the intermediate product remained stable for up to 3 h under the same conditions. After formulation reconstitution, using sodium chloride injection diluted with 1% or 2% human serum albumin maintained a viability of over 80% within 4 h. It was observed that different dilution factors had an impact on cell viability. After formulation reconstitution, cultivation in medium with 2% platelet lysate resulted in a cell viability of over 80% after 24 h. In conclusion, the stability of cell stock solution within 6 h and intermediate product within 3 h meets the requirements. The addition of 1% or 2% human serum albumin in the reconstitution diluent can better protect the post-reconstitution cell viability.

Objective:To explore the expression of long non-coding RNA(lncRNA)ZIM2-AS1 in hepatocellular carcinoma(HCC)and its clinical significance as well as diagnostic value using the data obtained from the Cancer Genome Atlas(TCGA).Meth-ods:The transcriptome sequencing(RNA-seq)data and clinical information of 374 HCC tissues and 50 paired paracancerous tissues were gathered from the TCGA database,then the expression trends of ZIM2-AS1 in HCC and its correlation with clinicopathological features,prognosis,immune cell infiltration,as well as diagnostic value was inspected by bioinformatics analysis using relevant R packages.The expression of ZIM2-AS1 in human normal liver cell line and HCC cell lines was examined by qRT-PCR.Results:The ex-pression of ZIM2-AS1 was highly expressed in HCC tissues(P<0.001),and its expression level was significantly correlated with age,gender,N stage,histologic grade and AFP level(P all<0.05).The overall survival(OS)and disease specific survival(DSS)of patients with high ZIM2-AS1 expression were significantly shorter than those of patients with low expression(P<0.05),and ZIM2-AS1 was an in-dependent risk factor affecting OS.Immune cell infiltration analysis showed that ZIM2-AS1 was closely related to the infiltration of Th2 cells,CD56brightNK cells,follicular helper T cells(Tfh),neutrophils and plasmacytoid dendritic cells(pDC)(|Spearman's r|>0.1,P<0.05)in HCC.ROC curve analysis revealed that the expression level of ZIM2-AS1 possesse potential diagnostic value in HCC,N0 stage,histologic grade G1 and G2,OS and DSS(AUC all>0.50).qRT-PCR results showed that the expression level of ZIM2-AS1 in HCC cell lines was significantly higher than that in human normal liver cells(P all<0.05).Conclusion:The elevated expression of lncRNA ZIM2-AS1 is an independent risk factor for poor prognosis of HCC patient and has potential application value as a biomarker for HCC diagnosis,prognosis as well as tumor immune microenvironment assessment.

AIM To analyze the component composition of Xeriga-4 Powder,and to determine the contents of phellodendrine,chlorogenic acid,gardenoside,berberine,rutin and curcumin.METHODS The high performance liquid chromatography-Q-exactive orbitrap mass spectrometry(HPLC-Q-Exactive-MS)qualitative analysis was performed on a 35℃thermostatic Agilent ZORBAX SB-Aq column(4.6 mm×150 mm,5 &mu;m),with the mobile phase comprising of methanol-0.1%formic acid flowing at 0.35 mL/min in a gradient elution manner,and electron spray ionization source was adopted in positive and negative ion scanning.High performance liquid chromatography tandem mass spectrometry(HPLC-MS/MS)quantitative analysis was performed on a 35℃thermostatic Shim-pack GIST-HP C18 column(2.1 mm×100 mm,3 &mu;m),with the mobile phase comprising of methanol-0.1%formic acid flowing at 0.25 mL/min in a gradient elution manner,and electron spray ionization source was adopted in positive and negative ion scanning with multiple reaction monitoring mode.RESULTS Total 65 constituents were identified,containing 19 alkaloids,13 organic acids,13 flavonoids,7 curcumins,6 iridoids,4 fatty acids,2 aldehydes,and 1 amino acid.Six constituents showed good linear relationships within their own ranges(r≥0.999 1),whose average recoveries were 96.44%-102.37%with the RSDs of 2.05%-3.74%.CONCLUSION This study can provide a reference for the quality control for Xieriga-4 Powder.

Objective:To explore the effectiveness of a hysteroscopic endometrial lesion diagnosis model de-veloped based on deep learning(DL)algorithm combined with gradient-weighted class activation mapping(Grad-CAM)visualization technology.Methods:303 hysteroscopy videos(4781 images)of 291 patients who un-derwent hysteroscopy examination in the Department of Gynecology,Renmin Hospital of Wuhan University from June 1,2021 to December 31,2022 were selected.The dataset was divided into a training set(3703 images)and a test set(1078 images)by weight sampling method.After the training set was used for model learning and train-ing,two model architectures,residual neural network(ResNet18)and efficient neural network(EfficientNet-B0),were selected to verify the model in the test set by five-class and two-class classification tasks,respectively.Tak-ing histopathology as the gold standard,the diagnostic efficacy was evaluated to select the optimal model,and the Grad-CAM layer was embedded in the optimal model to output hysteroscopy images of Grad-CAM.Results:①In the five-class classification tasks,the accuracy of EfficientNet-B0 model(93.23％)was higher than that of Res-Net18 model(84.23％);the area under the curve(AUC)of EfficientNet-B0 model in the diagnosis of five disea-ses,including atypical endometrial hyperplasia,endometrial polyps,endometrial cancer,endometrial atypical hy-perplasia,and submucous myoma,was slightly higher than that of ResNet18 model,and the AUC of both models was almost above 0.980.②In the binary classification task of accuracy and the evaluation of specificity,the two models were similar,both above 93.00％,and the sensitivity of EfficientNet-B0 model(91.14％)was significantly better than that of ResNet18 model(77.22％).③EfficientNet-B0 model combined with Grad-CAM algorithm could identify the abnormal areas in the image.After biopsy and pathological examination,it was confirmed that about 95％of the marked areas in the model's output heatmap were lesion areas.Conclusions:The hysteroscopy di-agnostic model developed by EfficientNet-B0 model combined with Grad-CAM has high diagnostic accuracy,sen-sitivity,and specificity,and has application value in the diagnosis of endometrial lesions.

As the global population continues to age, the incidence of Alzheimer’s disease (AD), one of the most common neurodegenerative diseases, continues to rise significantly. As the disease progresses, the patient’s daily living abilities gradually decline, potentially leading to a complete loss of self-care abilities. According to estimates by the Alzheimer’s Association and the World Health Organization, AD accounts for 60%-70% of all other dementia cases, affecting over 55 million people worldwide. The case number is estimated to double by 2050. Despite extensive research, the precise etiology and pathogenesis of AD remain elusive. Researchers have a profound understanding of the disease’s pathological hallmarks, which include amyloid plaques and neurofibrillary tangles resulting from the abnormal phosphorylation of Tau protein. However, the exact causes and mechanisms of the disease are still not fully understood, leaving a vital gap in our knowledge and understanding of this debilitating disease. A crucial player that has recently emerged in the field of AD research is the α7 nicotinic acetylcholine receptor (α7nAChR). α7nAChR is composed of five identical α7 subunits that form a homopentamer. This receptor is a significant subtype of acetylcholine receptor in the central nervous system and is widely distributed in various regions of the brain. It is particularly prevalent in the hippocampus and cortical areas, which are regions associated with learning and memory. α7nAChR plays a pivotal role in several neurological processes, including neurotransmitter release, neuronal plasticity, cell signal transduction, and inflammatory response, suggesting its potential involvement in numerous neurodegenerative diseases, including AD. In recent years, the role of α7nAChR in AD has been the focus of extensive research. Emerging evidence suggests that α7nAChR is involved in several critical steps in the disease progression of AD. These include involvement in the metabolism of amyloid β-protein (Aβ), the phosphorylation of Tau protein, neuroinflammatory response, and oxidative stress. Each of these processes contributes to the development and progression of AD, and the involvement of α7nAChR in these processes suggests that it may play a crucial role in the disease’s pathogenesis. The potential significance of α7nAChR in AD is further reinforced by the observation that alterations in its function or expression can have significant effects on cognitive abilities. These findings suggest that α7nAChR could be a promising target for therapeutic intervention in AD. At present, the results of drug clinical studies targeting α7nAChR show that these compounds have improvement and therapeutic effects in AD patients, but they have not reached the degree of being widely used in clinical practice, and their drug development still faces many challenges. Therefore, more research is needed to fully understand its role and to develop effective treatments based on this understanding. This review aims to summarize the current understanding of the association between α7nAChR and AD pathogenesis. We provide an overview of the latest research developments and insights, and highlight potential avenues for future research. As we deepen our understanding of the role of α7nAChR in AD, it is hoped that this will pave the way for the development of novel therapeutic strategies for this devastating disease. By targeting α7nAChR, we may be able to develop more effective treatments for AD, ultimately improving the quality of life for patients and their families.

Objective:To explore the effectiveness of a hysteroscopic endometrial lesion diagnosis model de-veloped based on deep learning(DL)algorithm combined with gradient-weighted class activation mapping(Grad-CAM)visualization technology.Methods:303 hysteroscopy videos(4781 images)of 291 patients who un-derwent hysteroscopy examination in the Department of Gynecology,Renmin Hospital of Wuhan University from June 1,2021 to December 31,2022 were selected.The dataset was divided into a training set(3703 images)and a test set(1078 images)by weight sampling method.After the training set was used for model learning and train-ing,two model architectures,residual neural network(ResNet18)and efficient neural network(EfficientNet-B0),were selected to verify the model in the test set by five-class and two-class classification tasks,respectively.Tak-ing histopathology as the gold standard,the diagnostic efficacy was evaluated to select the optimal model,and the Grad-CAM layer was embedded in the optimal model to output hysteroscopy images of Grad-CAM.Results:①In the five-class classification tasks,the accuracy of EfficientNet-B0 model(93.23％)was higher than that of Res-Net18 model(84.23％);the area under the curve(AUC)of EfficientNet-B0 model in the diagnosis of five disea-ses,including atypical endometrial hyperplasia,endometrial polyps,endometrial cancer,endometrial atypical hy-perplasia,and submucous myoma,was slightly higher than that of ResNet18 model,and the AUC of both models was almost above 0.980.②In the binary classification task of accuracy and the evaluation of specificity,the two models were similar,both above 93.00％,and the sensitivity of EfficientNet-B0 model(91.14％)was significantly better than that of ResNet18 model(77.22％).③EfficientNet-B0 model combined with Grad-CAM algorithm could identify the abnormal areas in the image.After biopsy and pathological examination,it was confirmed that about 95％of the marked areas in the model's output heatmap were lesion areas.Conclusions:The hysteroscopy di-agnostic model developed by EfficientNet-B0 model combined with Grad-CAM has high diagnostic accuracy,sen-sitivity,and specificity,and has application value in the diagnosis of endometrial lesions.