Accurate prediction of drug responses in cancer cell lines (CCLs) and transferable prediction of clinical drug responses using CCLs are two major tasks in personalized medicine. Despite the rapid advancements in existing computational methods for preclinical and clinical cancer drug response (CDR) prediction, challenges remain regarding the generalization of new drugs that are unseen in the training set. Herein, we propose a multimodal fusion deep learning (DL) model called drug-target and single-cell language based CDR (DTLCDR) to predict preclinical and clinical CDRs. The model integrates chemical descriptors, molecular graph representations, predicted protein target profiles of drugs, and cell line expression profiles with general knowledge from single cells. Among these features, a well-trained drug-target interaction (DTI) prediction model is used to generate target profiles of drugs, and a pretrained single-cell language model is integrated to provide general genomic knowledge. Comparison experiments on the cell line drug sensitivity dataset demonstrated that DTLCDR exhibited improved generalizability and robustness in predicting unseen drugs compared with previous state-of-the-art baseline methods. Further ablation studies verified the effectiveness of each component of our model, highlighting the significant contribution of target information to generalizability. Subsequently, the ability of DTLCDR to predict novel molecules was validated through in vitro cell experiments, demonstrating its potential for real-world applications. Moreover, DTLCDR was transferred to the clinical datasets, demonstrating satisfactory performance in the clinical data, regardless of whether the drugs were included in the cell line dataset. Overall, our results suggest that the DTLCDR is a promising tool for personalized drug discovery.

Accurate prediction of drug responses in cancer cell lines(CCLs)and transferable prediction of clinical drug responses using CCLs are two major tasks in personalized medicine.Despite the rapid advancements in existing computational methods for preclinical and clinical cancer drug response(CDR)prediction,chal-lenges remain regarding the generalization of new drugs that are unseen in the training set.Herein,we propose a multimodal fusion deep learning(DL)model called drug-target and single-cell language based CDR(DTLCDR)to predict preclinical and clinical CDRs.The model integrates chemical descriptors,mo-lecular graph representations,predicted protein target profiles of drugs,and cell line expression profiles with general knowledge from single cells.Among these features,a well-trained drug-target interaction(DTI)prediction model is used to generate target profiles of drugs,and a pretrained single-cell language model is integrated to provide general genomic knowledge.Comparison experiments on the cell line drug sensitivity dataset demonstrated that DTLCDR exhibited improved generalizability and robustness in predicting unseen drugs compared with previous state-of-the-art baseline methods.Further ablation studies verified the effectiveness of each component of our model,highlighting the significant contribution of target information to generalizability.Subsequently,the ability of DTLCDR to predict novel molecules was validated through in vitro cell experiments,demonstrating its potential for real-world applications.Moreover,DTLCDR was transferred to the clinical datasets,demonstrating satisfactory performance in the clinical data,regardless of whether the drugs were included in the cell line dataset.Overall,our results suggest that the DTLCDR is a promising tool for personalized drug discovery.

Focal therapy has emerged as an alternative to surgical treatment for selected renal tumors.The core principle of this approach lies in the local destruction of tumor tissue through physical or chemical means to achieve tumor control.Focal therapy is particularly applicable to patients with tumors ≤4 cm in diameter who are unfit for surgery or anesthesia due to advanced age,comorbidities,solitary kidney,or renal insufficiency,as well as those with recurrent tumors after prior partial nephrectomy.This review summarizes the mechanisms,advantages,limitations,and clinical efficacy of six focal therapy modalities for renal tumors,including radiofrequency ablation,cryoablation,microwave ablation,irreversible electroporation,high-intensity focused ultrasound,and stereotactic ablative radiotherapy.The aim is to provide reference for achieving individualized and precise clinical management.

Focal therapy has emerged as an alternative to surgical treatment for selected renal tumors.The core principle of this approach lies in the local destruction of tumor tissue through physical or chemical means to achieve tumor control.Focal therapy is particularly applicable to patients with tumors ≤4 cm in diameter who are unfit for surgery or anesthesia due to advanced age,comorbidities,solitary kidney,or renal insufficiency,as well as those with recurrent tumors after prior partial nephrectomy.This review summarizes the mechanisms,advantages,limitations,and clinical efficacy of six focal therapy modalities for renal tumors,including radiofrequency ablation,cryoablation,microwave ablation,irreversible electroporation,high-intensity focused ultrasound,and stereotactic ablative radiotherapy.The aim is to provide reference for achieving individualized and precise clinical management.

Objectives:To examine the functional outcomes of robot-assisted radical prostatectomy (RARP) with preservation of pelvic floor stabilized structure and early elevated retrograde liberation of the neurovascular bundle (PEEL).Methods:This study was a retrospective cohort study. Between June 1, 2022, and March 20, 2023, 27 cases of RARP with PEEL and 153 cases of RARP with preservation of pelvic floor stabilized structure (PPSS) were included in this study. All patients were males, aged (62.5±5.2) years (range: 50 to 73 years). There were 18 cases of ≤T2b stage and 9 cases of T2c stage. After 1∶1 propensity score matching, the postoperative functional outcomes of 27 cases of RARP with PEEL and 27 cases of RARP with PPSS were compared. All surgeries were performed by a single surgeon and included patients were clinically staged as cT1-2N0M0 without preoperative urinary incontinence or erectile dysfunction. In RARP with PEEL, the prostate was cut near the midline at the front when dissecting the neurovascular bundle, dissection was performed between the visceral layer of the pelvic fascia and the prostatic fascia, preserving the parietal layer and the visceral layer of the pelvic fascia, and the neurovascular bundle was retrogradely released from the apex. The cumulative probability curve was plotted using the Kaplan-Meier method and the Log-rank test was used to compare the differences in functional outcomes between the two groups. Univariate and multivariate analysis with the Cox proportional hazards model was used to compare postoperative urinary continence and sexual function.Results:The recovery time of continence and potency was significantly longer in the PPSS group than in the PEEL group (all P<0.05). The continence rate of the PEEL group was significantly higher than that of the PPSS group (92.59% vs. 68.10%, P=0.026) at 3 months after surgery. The potency rate of the PEEL group was also significantly higher than that of the PPSS group (40.70% vs. 15.10%, P=0.037) at 3 months after surgery. In the univariate analysis, compared to the PPSS technique, the PEEL technique was associated with a shorter recovery time of continence ( HR=1.94, 95% CI: 1.08 to 3.48, P=0.027) and a shorter recovery time of potency ( HR=2.06, 95% CI: 1.03 to 4.13, P=0.042). In the multivariate analysis, the PEEL technique was an independent prognosis factor for postoperative recovery of continence ( HR=2.05, 95% CI: 1.01 to 4.17, P=0.047) and potency ( HR=3.57, 95% CI: 1.43 to 8.92, P=0.007). All the cases of the PPSS group and the PEEL group were performed successfully with negative surgical margins. Conclusion:Compared with PPSS, PEEL may be more conducive to the recovery of urinary continence and sexual function after RARP.