The interest in covalent drugs has resurged in recent decades, spurring the development of numerous specialized computational docking tools to facilitate covalent ligand design and screening. Herein, we present CarsiDock-Cov, a new paradigm distinguishing itself as the first deep learning (DL)-guided approach for covalent docking. CarsiDock-Cov retains the core components of its non-covalent predecessor, leveraging a DL model pretrained on millions of docking complexes to predict protein-ligand distance matrices, along with a dedicated-designed geometric optimization procedure to convert these distances into refined binding poses. Additionally, it incorporates several key enhancements specifically tailored to optimize the protocol for covalent docking applications. Our approach has been extensively validated on multiple public datasets regarding the docking and screening of covalent ligands, and the results indicate that our approach not only achieves comparably improved applicability compared to its non-covalent predecessor, but also exhibits competitive performance against various state-of-the-art covalent docking tools. Collectively, our approach represents a significant advance in covalent docking methodology, offering an automated and efficient solution that shows considerable promise for accelerating covalent drug discovery and design.

Objective:To explore the feasibility of radiomics-based quantitative analysis for molecular pathological subtyping in renal computed tomography angiography（CTA）and to establish a predictive model for renal mass subgroups.Methods:We retrospectively enrolled 535 patients with renal masses，including malignant lesions［223 clear cell renal cell carcinomas（ccRCC），84 papillary renal cell carcinomas（pRCC），113 chromophobe renal cell carcinomas（chrRCC）］and benign lesions［62 fat-poor angiomyolipomas（fpAML），53 oncocytomas］. There were 195 males and 340 females，with a median age of 52（range 49 to 80）years old. All patients underwent standard renal CTA prior to surgery. Radiomics features were extracted from CTA images. Data were categorized into six subgroups（malignant vs. benign，ccRCC vs. other renal masses，pRCC vs. other renal masses，chrRCC vs. other renal masses，fpAML vs. other renal masses，oncocytomas vs. other renal masses）. The dataset was randomised into training and validation cohorts by dividing the patients in a 2∶1 ratio. A machine learning-based predictive model（Radiomics-CTA）was developed using selected radiomic features in the training cohort. The model efficacy was assessed in the training cohort and validation cohort separately by plotting subject operating characteristic（ROC）curves，calculating area under the curve（AUC），and plotting clinical decision curves for model efficacy assessment.Results:For the malignant subgroup，Radiomics-CTA achieved area under the receiver operating characteristic curve（AUC）values of 0.823（95% CI 0.751?0.894）and 0.833（95% CI 0.783?0.883）in the training and validation cohorts，respectively. For ccRCC identification，the model showed AUCs of 0.928（95% CI 0.89?0.955）and 0.925（95% CI 0.881?0.968）in the two cohorts. For the other subtypes identification，such as pRCC，chrRCC，fpAML，and oncocytomas，the model showed AUCs of 0.862（95% CI 0.826?0.898），0.882（95% CI 0.849?0.915），0.921（95% CI 0.898? 0.943），and 0.865（95% CI 0.787?0.944）in the training cohort，and the AUC of 0.823（95% CI 0.776?0.870），0.842（95% CI 0.754?0.929），0.930（95% CI 0.892?0.968）and 0.876（95% CI 0.847? 0.906）in the validation cohort . Radiomics-CTA outperformed senior radiologists in diagnosing ccRCC［87.1%（466/535）vs. 83.2%（445/535）， P=0.03）］and chrRCC［82.1%（439/535）vs. 80.0（428/535）， P<0.01］. Conclusions:The Radiomics-CTA model can extract deep pathological information from CTA images through radiomics methods，and has the ability to distinguish pathological subtypes of renal tumors. It can also provide assistance for accurate diagnosis by radiologists to a certain extent.

Objective:To explore the feasibility of radiomics-based quantitative analysis for molecular pathological subtyping in renal computed tomography angiography（CTA）and to establish a predictive model for renal mass subgroups.Methods:We retrospectively enrolled 535 patients with renal masses，including malignant lesions［223 clear cell renal cell carcinomas（ccRCC），84 papillary renal cell carcinomas（pRCC），113 chromophobe renal cell carcinomas（chrRCC）］and benign lesions［62 fat-poor angiomyolipomas（fpAML），53 oncocytomas］. There were 195 males and 340 females，with a median age of 52（range 49 to 80）years old. All patients underwent standard renal CTA prior to surgery. Radiomics features were extracted from CTA images. Data were categorized into six subgroups（malignant vs. benign，ccRCC vs. other renal masses，pRCC vs. other renal masses，chrRCC vs. other renal masses，fpAML vs. other renal masses，oncocytomas vs. other renal masses）. The dataset was randomised into training and validation cohorts by dividing the patients in a 2∶1 ratio. A machine learning-based predictive model（Radiomics-CTA）was developed using selected radiomic features in the training cohort. The model efficacy was assessed in the training cohort and validation cohort separately by plotting subject operating characteristic（ROC）curves，calculating area under the curve（AUC），and plotting clinical decision curves for model efficacy assessment.Results:For the malignant subgroup，Radiomics-CTA achieved area under the receiver operating characteristic curve（AUC）values of 0.823（95% CI 0.751?0.894）and 0.833（95% CI 0.783?0.883）in the training and validation cohorts，respectively. For ccRCC identification，the model showed AUCs of 0.928（95% CI 0.89?0.955）and 0.925（95% CI 0.881?0.968）in the two cohorts. For the other subtypes identification，such as pRCC，chrRCC，fpAML，and oncocytomas，the model showed AUCs of 0.862（95% CI 0.826?0.898），0.882（95% CI 0.849?0.915），0.921（95% CI 0.898? 0.943），and 0.865（95% CI 0.787?0.944）in the training cohort，and the AUC of 0.823（95% CI 0.776?0.870），0.842（95% CI 0.754?0.929），0.930（95% CI 0.892?0.968）and 0.876（95% CI 0.847? 0.906）in the validation cohort . Radiomics-CTA outperformed senior radiologists in diagnosing ccRCC［87.1%（466/535）vs. 83.2%（445/535）， P=0.03）］and chrRCC［82.1%（439/535）vs. 80.0（428/535）， P<0.01］. Conclusions:The Radiomics-CTA model can extract deep pathological information from CTA images through radiomics methods，and has the ability to distinguish pathological subtypes of renal tumors. It can also provide assistance for accurate diagnosis by radiologists to a certain extent.

This article briefly describes the imaging performance standards of the kilovolt X-ray image guidance system used in radiotherapy, analyzes the main aspects that should be considered in the image quality of X-IGRT system, and focuses on parameters that should be considered in the imaging performance evaluation criteria of the CBCT X-IGRT. The purpose is to sort out the imaging performance evaluation standards of kilovolt X-IGRT system, clarify the image quality requirements of X-IGRT equipment, and reach a consensus when evaluating the imaging performance of X-IGRT system.
Radiotherapy Planning, Computer-Assisted/methods* ; Cone-Beam Computed Tomography/methods* ; Spiral Cone-Beam Computed Tomography ; Radiotherapy, Intensity-Modulated/methods* ; Radiotherapy, Image-Guided/methods*

Background: Esophageal mucosal injury induced by gastroesophageal reflux is a key link to the development of Barrett's esophagus-associated adenocarcinoma. However, the molecular mechanism is still not elucidated. Aims: To investigate the role of differentially expressed genes (DEGs) after stimulating esophageal cells with acid and bile acid in the development of esophageal adenocarcinoma (EAC). Methods: The DEGs were obtained through bioinformatics methods after stimulating esophageal cells with low pH and deoxycholic acid, and GO, KEGG enrichment analysis were performed. Protein-protein interaction (PPI) network was performed to screen the hub genes, and their relationships with prognosis and tumor stage of EAC patients were analyzed. The role of co-expressed genes of GADD45B in EAC was also analyzed. Results: Thirty-one overlapping DEGs were obtained after stimulating esophageal cells with low pH and deoxycholic acid, which mainly enriched in the cytokine-cytokine receptor interaction, transcription factors activity, and regulation of cell proliferation and apoptotic process. High expression of GADD45B was correlated with the survival prognosis and tumor stage of EAC patients. GADD45B and its co-expressed genes were involved in the production of tumor necrosis factor. Conclusions: The high expression of GADD45B induced by acid and bile acid is correlated with the prognosis and tumor stage of EAC patients, and is a potential diagnosis and treatment target for Barrett's esophagus-associated adenocarcinoma.

Objective To investigate the risk factors of pancreatic cancer in Wenzhou area.Methods A case control study was conducted on 220 cases with pancreatic cancer and 220 matched controls using conditional logistic regression analysis.Results Univariate analysis showed BMI ＞ 30 kg/m2,smoking,reproductive history,diabetes,cholecystitis,chronic pancreatitis,history of appendectomy,history of partial gastrectomy,history of cancer and familial history of cancer were associated with pancreatic cancer,while multivariate conditional logistic regression analysis showed that the risk factors of pancreatic cancer included smoking (OR =3.624,95％ CI i.474 ～ 8.907),BMI ＞ 30 kg/m2 (OR =1.789,95％ CI 1.030 ～ 3.108),diabetes (OR =3.191,95％ CI 1.094 ～ 9.309),chronic pancreatitis (OR =4.972,95％ CI 1.593 ～ 14.898),and cholecystitis (OR =2.289,95 ％ CI 1.024 ～5.116).Conclusions BMI ＞ 30 kg/m2,smoking and diabetes are risk factors for pancreatic cancer in Wenzhou area.

Objective To investigate the relationship between pancreatic cancer and diabetes mellitus (DM). Methods Two hundreds and twenty patients with pancreatic cancer and 300 controls, who suffered from non-digestive tract, non-neoplastic or non-hormone-related disorders, were enrolled from 1997 to 2007. The incidence of diabetes between the two groups and the relationship between pancreatic cancer and diabetes were compared, pancreatic cancer patients with DM were compared with patients without DM for their gender, age, location and differentiate degree of the cancer. Results The incidence of DM in the two groups were 33.1% and 9.67%, respectively, and the difference was significant (P < 0.05). In the pancreatic cancer group, the proportion of patients with DM diagnosed within 2 years or for more than 10 years were 25.91% (57/73) and 3.18% (7/73), which were significant higher than those in the control group 6.0% (18/29) and 0.67% (2/29)) (χ2=46.15, P<0.01, 0R=6.07; χ2 =4.72, P<0.01, OR=4.90). In the pancreatic cancer group, the proportion of patients with DM diagnosed within 2～5 years or 5～10 years was not significant different when compared with that of the control group, and there was no significant difference in terms of gender, age and cancer location between the pancreatic cancer patients with DM and without DM. The majority of pancreatic cancer patients with DM had corpora mammillaria or well differentiated adenocarcinoma, and the majority of pancreatic cancer patients without DM had differentiated adenocarcinoma. Conclusions DM was closely related with pancreatic cancer and DM may be one of the presentations of pancreatic cancer, as well as a possible risk factor for the tumor.