AIM: To analyze the factors affecting non-contact intraocular pressure(IOPNCT)measurements after femtosecond laser-assisted small incision lenticule extraction(SMILE), explore the correlation of IOPNCT with central corneal thickness(CCT)and corneal curvature after SMILE, and construct the corresponding regression model which will provide scientific basis for clinical evaluation of the true IOP of patients after SMILE.METHODS: Data from a retrospective analysis of 107 myopic patients(206 eyes)who underwent SMILE and 107 myopic patients(201 eyes)received femtosecond laser-assisted in situ keratomileusis(FS-LASIK)surgery from June 2023 to May 2024 were examined. IOPNCT, CCT, and corneal curvature before surgery and at 1 and 3 mo were collected. The preoperative and postoperative IOPNCT, CCT and corneal curvature were analyzed by ANOVA and Pearson correlation analysis, and multiple linear regression models were constructed to evaluate the association of postoperative changes of IOPNCT, CCT and corneal curvature.RESULTS: There were significant differences in IOPNCT, CCT, and corneal curvature of both SMILE and FS-LASIK patients(all P<0.001), there was no significant difference between two groups and interaction effects(all P>0.05), and the IOPNCT, CCT and corneal curvature at 1 and 3 mo post-surgery were significantly lower than preoperative(all P<0.05). Pearson correlation analysis showed a positive correlation between IOPNCT and CCT at 1 and 3 mo after SMILE(r=0.261, 0.267, all P<0.001), but no significant correlation with corneal curvature(all P>0.05). Multiple linear regression analysis of IOPNCT with CCT and corneal curvature at 1 mo after SMILE indicated that the regression equation was: Y=3.426+0.019X1-0.058X2(Y represents IOPNCT, X1 represents the CCT, and X2 represents the corneal curvature), with statistical significant difference in the equation(F=7.654, P=0.001); the regression equation for 3 mo after surgery was: Y=2.056+0.020X1-0.038 X2(Y represents IOPNCT, X1 represents the CCT, and X2 represents the corneal curvature), with statistically significance in the equation(F=7.903, P<0.001). The regression equation of postoperative IOPNCT change(△IOPNCT)and intraoperative cutting corneal thickness(△CCT)and corneal curvature at 1 mo was Y=-2.252+0.008X1+0.587X2(Y represents △IOPNCT, X1 stands for the △CCT, X2 represents the corneal curvature change value), with statistical significant difference in the equation(F=17.550, P<0.001); the regression equation for 3 mo after surgery was: Y=-2.168+0.024X1+0.281X2(Y represents △IOPNCT, X1 represents △CCT, X2 indicates the corneal curvature change values), with statistical significant difference in the equation(F=16.030, P<0.001).CONCLUSION: After SMILE and FS-LASIK surgery, the IOPNCT value of patients was mainly affected by CCT compared with preoperative surgery, and the short-term use of hormone eye drops, fluorometholone, did not cause a significant increase in IOP; both the IOP correction formula at 1 and 3 mo postoperatively can be used clinically to evaluate and correct actual IOP in patients after SMILE.

AIM: To analyze the factors affecting non-contact intraocular pressure(IOPNCT)measurements after femtosecond laser-assisted small incision lenticule extraction(SMILE), explore the correlation of IOPNCT with central corneal thickness(CCT)and corneal curvature after SMILE, and construct the corresponding regression model which will provide scientific basis for clinical evaluation of the true IOP of patients after SMILE.METHODS: Data from a retrospective analysis of 107 myopic patients(206 eyes)who underwent SMILE and 107 myopic patients(201 eyes)received femtosecond laser-assisted in situ keratomileusis(FS-LASIK)surgery from June 2023 to May 2024 were examined. IOPNCT, CCT, and corneal curvature before surgery and at 1 and 3 mo were collected. The preoperative and postoperative IOPNCT, CCT and corneal curvature were analyzed by ANOVA and Pearson correlation analysis, and multiple linear regression models were constructed to evaluate the association of postoperative changes of IOPNCT, CCT and corneal curvature.RESULTS: There were significant differences in IOPNCT, CCT, and corneal curvature of both SMILE and FS-LASIK patients(all P<0.001), there was no significant difference between two groups and interaction effects(all P>0.05), and the IOPNCT, CCT and corneal curvature at 1 and 3 mo post-surgery were significantly lower than preoperative(all P<0.05). Pearson correlation analysis showed a positive correlation between IOPNCT and CCT at 1 and 3 mo after SMILE(r=0.261, 0.267, all P<0.001), but no significant correlation with corneal curvature(all P>0.05). Multiple linear regression analysis of IOPNCT with CCT and corneal curvature at 1 mo after SMILE indicated that the regression equation was: Y=3.426+0.019X1-0.058X2(Y represents IOPNCT, X1 represents the CCT, and X2 represents the corneal curvature), with statistical significant difference in the equation(F=7.654, P=0.001); the regression equation for 3 mo after surgery was: Y=2.056+0.020X1-0.038 X2(Y represents IOPNCT, X1 represents the CCT, and X2 represents the corneal curvature), with statistically significance in the equation(F=7.903, P<0.001). The regression equation of postoperative IOPNCT change(△IOPNCT)and intraoperative cutting corneal thickness(△CCT)and corneal curvature at 1 mo was Y=-2.252+0.008X1+0.587X2(Y represents △IOPNCT, X1 stands for the △CCT, X2 represents the corneal curvature change value), with statistical significant difference in the equation(F=17.550, P<0.001); the regression equation for 3 mo after surgery was: Y=-2.168+0.024X1+0.281X2(Y represents △IOPNCT, X1 represents △CCT, X2 indicates the corneal curvature change values), with statistical significant difference in the equation(F=16.030, P<0.001).CONCLUSION: After SMILE and FS-LASIK surgery, the IOPNCT value of patients was mainly affected by CCT compared with preoperative surgery, and the short-term use of hormone eye drops, fluorometholone, did not cause a significant increase in IOP; both the IOP correction formula at 1 and 3 mo postoperatively can be used clinically to evaluate and correct actual IOP in patients after SMILE.

[摘 要] 目的：构建程序性死亡受体1（PD-1）高表达的细菌质膜纳米囊泡（BMV）BMV-PD-1，评估其对小鼠肺癌移植瘤组织的靶向性。方法：通过质粒转化将PD-1与膜孔蛋白细胞溶素A（ClyA）融合质粒ClyA-PD-1-EGFP转入大肠杆菌BL21-Codonplus，使用激光共聚焦显微镜、SDS-PAGE和WB法检测融合蛋白ClyA-PD-1-EGFP的表达。提取质膜并采用挤出法，利用挤出器制备BMV-PD-1。采用透射电子显微镜（TEM）、纳米粒子跟踪分析（NTA）技术分别对BMV-PD-1的形态、粒径和膜电位进行检测，用WB鉴定PD-1蛋白的携带情况。采用激光共聚焦成像检测Lewis肺癌LLC细胞对BMV-PD-1的摄取。建立肺癌LLC细胞C57BL/6J小鼠皮下移植瘤模型，采用小动物活体成像系统评估BMV-PD-1的肿瘤靶向性。结果：激光共聚焦显微成像结果显示，质粒ClyA-PD-1-EGFP被转入BL21-Codonplus并成功表达蛋白。SDS-PAGE结果表明，ClyA-PD-1-EGFP在BL21-Codonplus中过表达。WB分析表明，PD-1在细菌中表达且在BMV-PD-1上呈高表达（P < 0.001）状态。NTA和TEM分析表明，BMV-PD-1是一种粒径为（145 ± 14） nm、表面呈负电性的球状囊泡。激光共聚焦成像显示，PD-1高表达能显著提升肺癌细胞对BMV-PD-1的摄取（P < 0.01），小动物活体成像也进一步证实PD-1高表达能有效提升BMV-PD-1的肿瘤靶向性（P < 0.01）。结论：本研究成功构建了PD-1高表达的细菌纳米囊泡BMV-PD-1，发现PD-1高表达可显著提高BMV-PD-1的肺癌LLC细胞移植瘤组织的靶向性，为进一步开发以BMV-PD-1为载体的肿瘤靶向药物递送系统奠定基础。

OBJECTIVE To comparatively analyze tumor staging versus clinical staging in reimbursement scope restrictions under medical insurance for antineoplastic agents in order to better implement the medicare drug payment policy. METHODS Antineoplastic agents included in the National Basic Medical Insurance， Workers’ Compensation Insurance and Maternity Insurance Drug Catalogue （2024） （hereinafter referred to as the “Medical Insurance Catalog”） were used as research subject to compile and analyze reimbursement scope restrictions regarding tumor staging. By consulting clinical diagnosis and treatment guidelines and relevant literature， the tumor staging in reimbursement scope restrictions of the Medical Insurance Catalog was mapped and compared with clinical staging. RESULTS & CONCLUSIONS A total of 89 antineoplastic agents’ medical insurance payments had tumor staging. Among these， there were 86 western drugs （including 17 ordinary western drugs， 68 negotiated drugs， and 1 competitive drug） and 3 Chinese patent medicines （including 1 ordinary Chinese patent medicine and 2 negotiated drugs）. Non-small cell lung cancer involved the most restricted payment drugs， with 36 drugs. The tumor staging in reimbursement scope restrictions was mostly “metastatic” and “locally advanced”， involving 67 and 48 drugs respectively. Tumor staging in most reimbursement scope restrictions could correspond to the clinical staging of the tumor. However， mid-advanced esophageal cancer， unresectable gastrointestinal stromal tumors， unresectable locally advanced neuroendocrine tumors， locally advanced basal cell carcinoma， and unresectable neurofibromatosis type Ⅰ did not have a corresponding clinical staging mentioned in authoritative guidelines or high-quality clinical studies and need to be determined by the clinic according to the actual situation of the patient. Therefore， it is recommended that the interpretation of tumor staging in reimbursement scope restrictions should be accurately defined and standardized， so as to improve the accuracy of the drug payment policy in the actual implementation process.

Background: s/Aims: Sarcomatoid hepatocellular carcinoma (HCC) is a rare histological subtype of HCC characterized by extremely poor prognosis; however, its molecular characterization has not been elucidated. Methods: In this study, we conducted an integrated multiomics study of whole-exome sequencing, RNA-seq, spatial transcriptome, and immunohistochemical analyses of 28 paired sarcomatoid tumor components and conventional HCC components from 10 patients with sarcomatoid HCC, in order to identify frequently altered genes, infer the tumor subclonal architectures, track the genomic evolution, and delineate the transcriptional characteristics of sarcomatoid HCCs. Results: Our results showed that the sarcomatoid HCCs had poor prognosis. The sarcomatoid tumor components and the conventional HCC components were derived from common ancestors, mostly accessing similar mutational processes. Clonal phylogenies demonstrated branched tumor evolution during sarcomatoid HCC development and progression. TP53 mutation commonly occurred at tumor initiation, whereas ARID2 mutation often occurred later. Transcriptome analyses revealed the epithelial–mesenchymal transition (EMT) and hypoxic phenotype in sarcomatoid tumor components, which were confirmed by immunohistochemical staining. Moreover, we identified ARID2 mutations in 70% (7/10) of patients with sarcomatoid HCC but only 1–5% of patients with non-sarcomatoid HCC. Biofunctional investigations revealed that inactivating mutation of ARID2 contributes to HCC growth and metastasis and induces EMT in a hypoxic microenvironment. Conclusions We offer a comprehensive description of the molecular basis for sarcomatoid HCC, and identify genomic alteration (ARID2 mutation) together with the tumor microenvironment (hypoxic microenvironment), that may contribute to the formation of the sarcomatoid tumor component through EMT, leading to sarcomatoid HCC development and progression.

Background: s/Aims: Sarcomatoid hepatocellular carcinoma (HCC) is a rare histological subtype of HCC characterized by extremely poor prognosis; however, its molecular characterization has not been elucidated. Methods: In this study, we conducted an integrated multiomics study of whole-exome sequencing, RNA-seq, spatial transcriptome, and immunohistochemical analyses of 28 paired sarcomatoid tumor components and conventional HCC components from 10 patients with sarcomatoid HCC, in order to identify frequently altered genes, infer the tumor subclonal architectures, track the genomic evolution, and delineate the transcriptional characteristics of sarcomatoid HCCs. Results: Our results showed that the sarcomatoid HCCs had poor prognosis. The sarcomatoid tumor components and the conventional HCC components were derived from common ancestors, mostly accessing similar mutational processes. Clonal phylogenies demonstrated branched tumor evolution during sarcomatoid HCC development and progression. TP53 mutation commonly occurred at tumor initiation, whereas ARID2 mutation often occurred later. Transcriptome analyses revealed the epithelial–mesenchymal transition (EMT) and hypoxic phenotype in sarcomatoid tumor components, which were confirmed by immunohistochemical staining. Moreover, we identified ARID2 mutations in 70% (7/10) of patients with sarcomatoid HCC but only 1–5% of patients with non-sarcomatoid HCC. Biofunctional investigations revealed that inactivating mutation of ARID2 contributes to HCC growth and metastasis and induces EMT in a hypoxic microenvironment. Conclusions We offer a comprehensive description of the molecular basis for sarcomatoid HCC, and identify genomic alteration (ARID2 mutation) together with the tumor microenvironment (hypoxic microenvironment), that may contribute to the formation of the sarcomatoid tumor component through EMT, leading to sarcomatoid HCC development and progression.

Background: s/Aims: Sarcomatoid hepatocellular carcinoma (HCC) is a rare histological subtype of HCC characterized by extremely poor prognosis; however, its molecular characterization has not been elucidated. Methods: In this study, we conducted an integrated multiomics study of whole-exome sequencing, RNA-seq, spatial transcriptome, and immunohistochemical analyses of 28 paired sarcomatoid tumor components and conventional HCC components from 10 patients with sarcomatoid HCC, in order to identify frequently altered genes, infer the tumor subclonal architectures, track the genomic evolution, and delineate the transcriptional characteristics of sarcomatoid HCCs. Results: Our results showed that the sarcomatoid HCCs had poor prognosis. The sarcomatoid tumor components and the conventional HCC components were derived from common ancestors, mostly accessing similar mutational processes. Clonal phylogenies demonstrated branched tumor evolution during sarcomatoid HCC development and progression. TP53 mutation commonly occurred at tumor initiation, whereas ARID2 mutation often occurred later. Transcriptome analyses revealed the epithelial–mesenchymal transition (EMT) and hypoxic phenotype in sarcomatoid tumor components, which were confirmed by immunohistochemical staining. Moreover, we identified ARID2 mutations in 70% (7/10) of patients with sarcomatoid HCC but only 1–5% of patients with non-sarcomatoid HCC. Biofunctional investigations revealed that inactivating mutation of ARID2 contributes to HCC growth and metastasis and induces EMT in a hypoxic microenvironment. Conclusions We offer a comprehensive description of the molecular basis for sarcomatoid HCC, and identify genomic alteration (ARID2 mutation) together with the tumor microenvironment (hypoxic microenvironment), that may contribute to the formation of the sarcomatoid tumor component through EMT, leading to sarcomatoid HCC development and progression.

In recent years, the application of artificial intelligence (AI) in the field of biology has witnessed remarkable advancements. Among these, the most notable achievements have emerged in the domain of protein structure prediction and design, with AlphaFold and related innovations earning the 2024 Nobel Prize in Chemistry. These breakthroughs have transformed our ability to understand protein folding and molecular interactions, marking a pivotal milestone in computational biology. Looking ahead, it is foreseeable that the accurate prediction of various physicochemical properties of proteins—beyond static structure—will become the next critical frontier in this rapidly evolving field. One of the most important protein properties is thermodynamic stability, which refers to a protein’s ability to maintain its native conformation under physiological or stress conditions. Accurate prediction of protein stability, especially upon single-point mutations, plays a vital role in numerous scientific and industrial domains. These include understanding the molecular basis of disease, rational drug design, development of therapeutic proteins, design of more robust industrial enzymes, and engineering of biosensors. Consequently, the ability to reliably forecast the stability changes caused by mutations has broad and transformative implications across biomedical and biotechnological applications. Historically, protein stability was assessed via experimental methods such as differential scanning calorimetry (DSC) and circular dichroism (CD), which, while precise, are time-consuming and resource-intensive. This prompted the development of computational approaches, including empirical energy functions and physics-based simulations. However, these traditional models often fall short in capturing the complex, high-dimensional nature of protein conformational landscapes and mutational effects. Recent advances in machine learning (ML) have significantly improved predictive performance in this area. Early ML models used handcrafted features derived from sequence and structure, whereas modern deep learning models leverage massive datasets and learn representations directly from data. Deep neural networks (DNNs), graph neural networks (GNNs), and attention-based architectures such as transformers have shown particular promise. GNNs, in particular, excel at modeling spatial and topological relationships in molecular structures, making them well-suited for protein modeling tasks. Furthermore, attention mechanisms enable models to dynamically weigh the contribution of specific residues or regions, capturing long-range interactions and allosteric effects. Nevertheless, several key challenges remain. These include the imbalance and scarcity of high-quality experimental datasets, particularly for rare or functionally significant mutations, which can lead to biased or overfitted models. Additionally, the inherently dynamic nature of proteins—their conformational flexibility and context-dependent behavior—is difficult to encode in static structural representations. Current models often rely on a single structure or average conformation, which may overlook important aspects of stability modulation. Efforts are ongoing to incorporate multi-conformational ensembles, molecular dynamics simulations, and physics-informed learning frameworks into predictive models. This paper presents a comprehensive review of the evolution of protein thermodynamic stability prediction techniques, with emphasis on the recent progress enabled by machine learning. It highlights representative datasets, modeling strategies, evaluation benchmarks, and the integration of structural and biochemical features. The aim is to provide researchers with a structured and up-to-date reference, guiding the development of more robust, generalizable, and interpretable models for predicting protein stability changes upon mutation. As the field moves forward, the synergy between data-driven AI methods and domain-specific biological knowledge will be key to unlocking deeper understanding and broader applications of protein engineering.

Objective To analyze the clinical risk factors for chronic complications in patients with type 2 diabetes and their correlation with bone mineral density and 1,25-dihydroxyvitamin D3. Methods A total of 163 patients with type 2 diabetes mellitus were selected as research subjects and were divided into complication group and non-complication group according to the presence or absence of chronic complications. The independent related factors for chronic complications in patients with type 2 diabetes mellitus were analyzed. Spearman rank correlation analysis was used to evaluate the correlation between bone mineral density, 1,25-dihydroxyvitamin D3 and chronic complications. Results Among the 326 patients with type 2 diabetes mellitus, 202 developed chronic complications (61.96%), including 71 cases of cardiovascular disease, 59 cases of neuropathy, 33 cases of renal lesion, and 28 cases of retinopathy. There were statistically significant differences in the duration of diabetes mellitus, fasting blood glucose, systolic blood pressure, glycosylated hemoglobin, triglyceride, low density lipoprotein cholesterol, serum creatinine, bone mineral density, and 1,25-dihydroxyvitamin D3 between the complication group and the non-complication group (P<0.05). Logistic multivariate regression analysis showed that the duration of diabetes mellitus, systolic blood pressure, glycosylated hemoglobin, ow density lipoprotein cholesterol, serum creatinine, bone mineral density, and 1,25-dihydroxyvitamin D3 were all independent related factors for the occurrence of chronic complications in patients with type 2 diabetes mellitus (P<0.05). Spearman correlation analysis showed that bone mineral density and 1,25-dihydroxyvitamin D3 were negatively correlated with chronic complications (P<0.05). Conclusion Bone mineral density and 1,25-dihydroxyvitamin D3 in patients with type 2 diabetes mellitus are closely related to chronic complications.

Objective To evaluate the application value of hydrogel in image guided radiotherapy(IGRT)for prostate cancer(PCa).Methods Eighty PCa patients in the First Affiliated Hospital of Hebei North University from October 2022 to February 2023 were collected.The patients were divided into experiment group injected with hydrogel(n=33)and control group without hydrogel(n=47)by central random system allocation.The acute and advanced radiation proctitis(RP)incidence rate of the two groups were compared.Patients in experimental group were divided into RP group(n=5)and non-RP group(n=28).The independent risk factors leading to RP were analyzed by using logistic regression for PCa patients.Results In experiment group,12.1％(4/33)of the patients developed acute grade Ⅰ RP,and 3.0％(1/33)developed advanced grade Ⅰ RP.In control group,31.9％(15/47)of the patients developed acute grade Ⅰ RP,and 12.8％(6/47)developed acute grade Ⅱ RP;19.1％(9/47)of patients developed advanced grade Ⅰ RP,4.3％(2/47)developed advanced grade Ⅱ RP,and 2.1％(1/47)developed advanced grade Ⅲ RP.The incidence of acute and advanced RP in experiment group was lower than that in control group(P<0.05).The application of hydrogel effectively reduced rectal toxicity.Age,rectal volume,V70 and V78 were independent risk factors for the incidence of RP in Pca patients(P<0.05).The characteristics of hydrogel injection were not related to the incidence of RP for Pca patients.Conclusions Hydrogel can effectively reduce the rectal toxicity for Pca patients in IGRT and has little impact on the overall treatment.Hydrogel has certain clinical application and promotion value.