Immune checkpoints include a series of receptor-ligand pairs that play a key role in the proliferation, activation, and immune regulatory responses of immune cells. Although immune checkpoint inhibitors (ICIs), such as programmed death protein 1 (PD-1) and cytotoxic T-lymphocyte-associated protein 4 (CTLA-4) have achieved good therapeutic effects in clinical practice, some patients still experience ineffective treatment and immune resistance. A large amount of evidence has shown that immune checkpoint proteins are related to cell metabolism during immune regulation. On the one hand, immune checkpoints connect to alter the metabolic reprogramming of tumor cells to compete for nutrients required by immune cells. On the other hand, immune checkpoints regulate the metabolic pathways of immune cells, such as phosphatidylinositol 3-kinase/protein kinase B/mammalian target of rapamycin (PI3K/AKT/mTOR) to affect the activation of immune cells. Based on a review of the literature, this article reviews the mechanisms by which PD-1, CTLA-4, T cell immunoreceptor with Ig and ITIM domains (TIGIT), T cell immunoglobulin and mucin domain-containing protein 3 (TIM-3), cluster of differentiation 47 (CD47), and indoleamine 2,3-dioxygenase 1 (IDO1) regulate cell metabolic reprogramming, and looks forward to whether targeting the ligand-receptor pairs of immune checkpoints in a "dual regulation" manner and inhibiting metabolic pathways can effectively solve the problem of tumor immune resistance.
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Humans ; Neoplasms/genetics* ; Metabolic Networks and Pathways/immunology* ; Animals ; Immune Checkpoint Inhibitors/pharmacology*

Immune checkpoint blockade therapy has demonstrated remarkable efficacy in treating various malignancies; however, its clinical application remains challenged by low response rates and immune-related adverse events. Immunoglobulin superfamily member 11 (IGSF11), an inhibitory immune checkpoint molecule, serves as a specific ligand for the V-domain immunoglobulin suppressor of T cell activation (VISTA). Through the IGSF11/VISTA axis, it suppresses T cell function and represents a promising novel target for cancer immunotherapy. IGSF11 is widely expressed across multiple tumor types, though its regulatory mechanisms vary depending on the malignancy. Studies have confirmed that blocking the IGSF11-VISTA interaction or specifically inhibiting IGSF11 exerts antitumor effects. While IGSF11 is closely associated with patient prognosis, its prognostic significance differs among cancer types. This review systematically summarizes the structural characteristics of IGSF11, its regulatory mechanisms, interaction with VISTA, and functional role within the tumor microenvironment.
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Humans ; Immunotherapy ; Neoplasms/metabolism* ; B7 Antigens/chemistry* ; Animals ; Molecular Targeted Therapy ; Tumor Microenvironment

Osteoporosis is a prevalent skeletal condition characterized by reduced bone mass and strength, leading to increased fragility. Buqi-Tongluo (BQTL) decoction, a traditional Chinese medicine (TCM) prescription, has yet to be fully evaluated for its potential in treating bone diseases such as osteoporosis. To investigate the mechanism by which BQTL decoction inhibits osteoclast differentiation in vitro and validate these findings through in vivo experiments. We employed MTS assays to assess the potential proliferative or toxic effects of BQTL on bone marrow macrophages (BMMs) at various concentrations. TRAcP experiments were conducted to examine BQTL's impact on osteoclast differentiation. RT-PCR and Western blot analyses were utilized to evaluate the relative expression levels of osteoclast-specific genes and proteins under BQTL stimulation. Finally, in vivo experiments were performed using an osteoporosis model to further validate the in vitro findings. This study revealed that BQTL suppressed receptor activator of NF-κB ligand (RANKL)-induced osteoclastogenesis and osteoclast resorption activity in vitro in a dose-dependent manner without observable cytotoxicity. The inhibitory effects of BQTL on osteoclast formation and function were attributed to the downregulation of NFATc1 and c-fos activity, primarily through attenuation of the MAPK, NF-κB, and Calcineurin signaling pathways. BQTL's inhibitory capacity was further examined in vivo using an ovariectomized (OVX) rat model, demonstrating a strong protective effect against bone loss. BQTL may serve as an effective therapeutic TCM for the treatment of postmenopausal osteoporosis and the alleviation of bone loss induced by estrogen deficiency and related conditions.
Animals ; NFATC Transcription Factors/genetics* ; Drugs, Chinese Herbal/pharmacology* ; Ovariectomy ; Osteoclasts/metabolism* ; Female ; Osteogenesis/drug effects* ; Rats, Sprague-Dawley ; Rats ; NF-kappa B/genetics* ; Osteoporosis/genetics* ; Signal Transduction/drug effects* ; Bone Resorption/genetics* ; Cell Differentiation/drug effects* ; Humans ; RANK Ligand/metabolism* ; Mitogen-Activated Protein Kinases/genetics* ; Transcription Factors

Objective To investigate the diversity and composition of gut fungi microbiota in patients with polycystic kidney disease(PKD)and its correlation with clinical indicators.Methods A total of 44 PKD patients,44 patients with non-polycystic chronic kidney disease(NPCKD)and 22 healthy controls(HC)admitted to our hospital from February 2023 to February 2024 were recruited.ITS1 DNA sequencing was applied to analyze the gut fungal composition.Bioinformatics analysis was used to compare the diversity and structural differences of fungi among the 3 groups.Pearson correlation analysis was performed to analyze the relationship between gut fungi and clinical indicators.Results There were no significant differences in baseline characteristics(gender,age,body mass index,etc.)among the 3 groups,but statistical differences were seen in terms of serum indicators(such as serum creatinine,blood urea nitrogen,uric acid,estimated glomerular filtration rate,etc.)(P<0.01).Alpha diversity analysis showed no significant difference was seen between the PKD and HC groups,but the PKD group had significant differences to the NPCKD group(P<0.01).Beta diversity analysis revealed significant differences among the 3 groups and in pairwise comparisons(P=0.001).Fungi composition analysis found that the abundance of Candida was significantly higher in the PKD group than the other 2 groups(P<0.01),while the abundances of Aspergillus and Cladosporium were significantly lower in the PKD group than the HC group(P<0.05).Linear discriminant analysis(LEFSe)indicated that Candida was significantly enriched,while Aspergillus and Cladosporium were significantly reduced in the PKD group.Correlation analysis revealed that the abundance of Cladosporium was negatively correlated with cyst diameter and immunoglobulin light chain Kappa/Lambda ratio in the PKD group(P<0.05),while the abundance of Candida was positively correlated with liver/kidney cyst diameter(P<0.01).Conclusion PKD patients exhibit characteristic changes in gut fungi diversity and composition.The abundances of Cladosporium and Candida are closely associated with clinical indicators of PKD patients.

Objective To compare and find an optimal model for predicting the risk of DKD occurrence in patients with type 2 diabetes mellitus(T2DM).Methods A total of 2005 patients with T2DM were enrolled in this study from The Second Hospital of Shijiazhuang City during December 2017 to December 2022.All the subjects were divided into a training set(n=1403)and a validation set(n=602)according to the ratio of 3∶1 by simple random sampling.With the occurrence of DKD as the outcome variablein the training set,important feature variables were screened by LASSO regression.Six different machine learning models were established according to the feature variables,thenthe optimal model was determined by comparison,and anonlinerisk predictor for DKD occurrence was constructed in patients with T2DM.Results Taking the occurrence of DKD as the outcome variable in the training set,the results of LASSO regression analysis showed that the optimal value of the model was 10-fold cross validation lambda.1se=0.01662473,and 15 characteristic variables with nonzero coefficient were screened out to be related to the occurrence of DKD.The data included sex,age,family history of DM,DM duration,LDL-C,HbA1c,WBC,PDW,Scr,urine α1-microglobulin,urine β2-microglobulin,urine microalbumin,hypertension,hypokalemia,and DR.In the training set and validation set,the prediction performance of XGBoost model was better than that of other models(AUC=0.872,0.893,95%CI 0.853～0.891,0.865～0.921),the sensitivity was 0.779,0.863,and the specificity was 0.721,0.758,respectively.The F1 scores were 0.774 and 0.787.DCA analysis showed that the XGBoost model had a greater net benefit and threshold probability.According to the XGBoost model,the online predictor of DKD risk in T2DM patients was laid out,and two patients were selected for application,the results showed that the predictive value of the model was 0.185 in non-DKD patients,and the predictive value was 0.510 in DKD patients.Conclusions The XGBoost model is the best model for predicting the occurrence of DKD in T2DM patients,and an online predictor was successfully built.

Objective To compare and find an optimal model for predicting the risk of DKD occurrence in patients with type 2 diabetes mellitus(T2DM).Methods A total of 2005 patients with T2DM were enrolled in this study from The Second Hospital of Shijiazhuang City during December 2017 to December 2022.All the subjects were divided into a training set(n=1403)and a validation set(n=602)according to the ratio of 3∶1 by simple random sampling.With the occurrence of DKD as the outcome variablein the training set,important feature variables were screened by LASSO regression.Six different machine learning models were established according to the feature variables,thenthe optimal model was determined by comparison,and anonlinerisk predictor for DKD occurrence was constructed in patients with T2DM.Results Taking the occurrence of DKD as the outcome variable in the training set,the results of LASSO regression analysis showed that the optimal value of the model was 10-fold cross validation lambda.1se=0.01662473,and 15 characteristic variables with nonzero coefficient were screened out to be related to the occurrence of DKD.The data included sex,age,family history of DM,DM duration,LDL-C,HbA1c,WBC,PDW,Scr,urine α1-microglobulin,urine β2-microglobulin,urine microalbumin,hypertension,hypokalemia,and DR.In the training set and validation set,the prediction performance of XGBoost model was better than that of other models(AUC=0.872,0.893,95%CI 0.853～0.891,0.865～0.921),the sensitivity was 0.779,0.863,and the specificity was 0.721,0.758,respectively.The F1 scores were 0.774 and 0.787.DCA analysis showed that the XGBoost model had a greater net benefit and threshold probability.According to the XGBoost model,the online predictor of DKD risk in T2DM patients was laid out,and two patients were selected for application,the results showed that the predictive value of the model was 0.185 in non-DKD patients,and the predictive value was 0.510 in DKD patients.Conclusions The XGBoost model is the best model for predicting the occurrence of DKD in T2DM patients,and an online predictor was successfully built.

BACKGROUND: Electromagnetic navigation bronchoscopy is a tool that can accurately navigation peripheral lung lesions. Because of electromagnetic navigation bronchoscopy (ENB) is too expensive, it has not been widely used in China. It is urgent for us to summarize experience in clinical application, especially in the diagnosis of pulmonary nodules. METHODS: The clinical data of patients with pulmonary peripheral lesions (PPLs) in our department undergoing ENB biopsy between July 2017 and December 2018 were retrospectively analyzed. RESULTS: There were 18 patients with 21 PPLs (10 males and 8 females). Among them, 11 patients got the final pathological diagnosis, 8 cases were diagnosed with adenocarcinoma lung cancer, 1 case was diagnosed with tuberculosis and 2 cases were diagnosed with small cell lung cancer. The positive rate of diagnosis was 61.1%. The sensitivity was 73.3%. The positive diagnosis rate is related to the size of the lesion, the positive diagnosis rate for lesions >2 cm is 100.0% (P=0.04). CONCLUSIONS Electromagnetic navigation bronchoscope is safe and effective in clinic. It has a high positive rate for the diagnosis of peripheral lung lesions larger than 2 cm, ENB has broad clinical application prospects.

N-methyladenosine (mA), a ubiquitous RNA modification, is installed by METTL3-METTL14 complex. The structure of the heterodimeric complex between the methyltransferase domains (MTDs) of METTL3 and METTL14 has been previously determined. However, the MTDs alone possess no enzymatic activity. Here we present the solution structure for the zinc finger domain (ZFD) of METTL3, the inclusion of which fulfills the methyltransferase activity of METTL3-METTL14. We show that the ZFD specifically binds to an RNA containing 5'-GGACU-3' consensus sequence, but does not to one without. The ZFD thus serves as the target recognition domain, a structural feature previously shown for DNA methyltransferases, and cooperates with the MTDs of METTL3-METTL14 for catalysis. However, the interaction between the ZFD and the specific RNA is extremely weak, with the binding affinity at several hundred micromolar under physiological conditions. The ZFD contains two CCCH-type zinc fingers connected by an anti-parallel β-sheet. Mutational analysis and NMR titrations have mapped the functional interface to a contiguous surface. As a division of labor, the RNA-binding interface comprises basic residues from zinc finger 1 and hydrophobic residues from β-sheet and zinc finger 2. Further we show that the linker between the ZFD and MTD of METTL3 is flexible but partially folded, which may permit the cooperation between the two domains during catalysis. Together, the structural characterization of METTL3 ZFD paves the way to elucidate the atomic details of the entire process of RNA mA modification.

Objective: To analyze the clinical characteristics and long-term outcomes with multicenter study for acute lymphoblastic leukemia (ALL) in children over 10 years old and adolescents. Method: Newly diagnosed ALL patients aged from 10 to 18 years old in three hospitals were included in the study from May 1^st 2005 to April 30^th 2015. They were received ALL-2005/2009 protocol following up to December 31^st 2016. The clinical characteristics, outcomes and the prognostic analysis were evaluated between the two protocols. Results: Totally, 237 patients were involved in the study, 76 cases for ALL-2005 and 161 cases for ALL-2009 protocol. Complete remission (CR) after induction therapy was 94.5%. 64 (28.6%) patients relapsed with a median time of 14.5 months and 70 (29.5%) patients passed away during the following time. In long-term follow-up, the 5-year event-free survival (EFS) and 5-year overall survival (OS) of ALL patients were (63.1±3.3)% and (68.4±3.2)%. The 7-year EFS and OS were (61.0±3.5)% and (67.6±3.3)%.The 5-year EFS of intermediate risk group in ALL-2005 and ALL-2009 protocol were (73.6±6.1)% and (71.7±4.3)% with no difference (χ²=0.064, P=0.801). The 5-year EFS of high risk group in two protocols were (27.6±9.6)% and (33.9±9.3)%, showing no significant difference (χ²=0.296, P=0.586). Five years relapsed rate of two protocols were (33.8±5.7)% and (32.6±4.1)% with no difference (χ²=0.055, P=0.815). The mortalities were 36.8% and 29.8% separately (χ²=2.869, P=0.090). Univariate analysis indicated that age, male, risk, BCR/ABL translocation/t(9;22) and resistant to induction were risk prognostic factors in long-term survival (χ²=4.764, 4.796, 46.410, 9.560, 25.450; P=0.029, 0.029, <0.001, 0.049, <0.001). Cox multivariate analysis showed male, risk and resistant to induction were independent risk prognostic factors (RR=1.790, 2.727, 2.719; P=0.021, 0.000, 0.012). Conclusion Protocol ALL-2009 enhanced the chemotherapy intensity in intermediate risk group with no benefit of survival. BCR-ABL fusion or t(9;22) translocation was still the risk factor of prognosis. TKI inhibitor used in these patients could improve survival. EFS rate was increased a little and death rate was decreased in ALL-2009 protocol with no significant lower relapsed rate comparing with ALL-2005 protocol.

OBJECTIVETo explore the efficacy and safety of deferasirox in aplastic anemia (AA)patients with iron overload.

METHODSA single arm, multi- center, prospective, open- label study was conducted to evaluate absolute change in serum ferritin (SF)from baseline to 12 months of deferasirox administration, initially at a dose of 20 mg·kg(-1)·d(-1), and the safety in 64 AA patients with iron overload.

RESULTSAll patients started their deferasirox treatment with a daily dose of 20 mg · kg(-1) ·d(-1). The mean actual dose was (18.6±3.60) mg · kg(-1)·d(-1). The median SF decreased from 4 924 (2 718- 6 765)μg/L at baseline (n=64) to 3 036 (1 474- 5 551)μg/L at 12 months (n=23) with the percentage change from baseline as 38%. A median SF decrease of 651 (126-2 125)μg/L was observed at the end of study in 23 patients who completed 12 months' treatment, the median SF level decreased by 1 167(580-4 806)μg/L [5 271(3 420-8 278)μg/L at baseline; 3 036(1 474-5 551)μg/L after 12 months' treatment; the percentage change from baseline as 42% ] after 12 months of deferasirox treatment. The most common adverse events (AEs) were increased serum creatinine levels (40.98%), gastrointestinal discomfort (40.98%), elevated liver transaminase (ALT: 21.31%; AST: 13.11%)and proteinuria (24.59%). The increased serum creatinine levels were reversible and non-progressive. Of 38 patients with concomitant cyclosporine use, 12(31.8%)patients had two consecutive values >ULN, 10(26.3%)patients had two consecutive values >1.33 baseline values, but only 1(2.6%)patient's serum creatinine increased more than 1.33 baseline values and exceeded ULN. For both AST and ALT, no patients experienced two post- baseline values >5 ×ULN or >10 × ULN during the whole study. In AA patients with low baseline PLT count (less than 50 × 10(9)/L), there was no decrease for median PLT level during 12 months' treatment period.

CONCLUSIONSAA patients with iron overload could achieve satisfactory efficacy of iron chelation by deferasirox treatment. The drug was well tolerated with a clinically manageable safety profile and no major adverse events.

Anemia, Aplastic ; drug therapy ; Benzoates ; therapeutic use ; Blood Transfusion ; China ; Ferritins ; blood ; Humans ; Iron ; blood ; Iron Chelating Agents ; therapeutic use ; Iron Overload ; drug therapy ; Liver ; Prospective Studies ; Triazoles ; therapeutic use