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X-ray diffraction crystallography has been most widely used for protein three-dimensional (3D) structure determination for which whether proteins are crystallizable is a central prerequisite. Yet, there are a number of procedures during protein crystallization, including protein material production, purification, and crystal production, which take turns affecting the crystallization outcome. Due to the expensive and laborious nature of this multi-stage process, various computational tools have been developed to predict protein crystallization propensity, which is then used to guide the experimental determination. In this study, we presented a novel deep learning framework, PLMC, to improve multi-stage protein crystallization propensity prediction by leveraging a pre-trained protein language model. To effectively train PLMC, two groups of features of each protein were integrated into a more comprehensive representation, including protein language embeddings from the large-scale protein sequence database and a handcrafted feature set consisting of physicochemical, sequence-based and disordered-related information. These features were further separately embedded for refinement, and then concatenated for the final prediction. Notably, our extensive benchmarking tests demonstrate that PLMC greatly outperforms other state-of-the-art methods by achieving AUC scores of 0.773, 0.893, and 0.913, respectively, at the aforementioned individual stages, and 0.982 at the final crystallization stage. Furthermore, PLMC is shown to be superior for predicting the crystallization of both globular and membrane proteins, as demonstrated by an AUC score of 0.991 for the latter. These results suggest the significant potential of PLMC in assisting researchers with the experimental design of crystallizable protein variants.
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Background: Preoperative percutaneous computed tomography (CT)-guided localization of pulmonary nodules plays a pivotal role in the diagnosis and treatment of early-stage lung cancer. However, conventional manual localization techniques have inherent limitations in achieving a high degree of accuracy. Consequently, a novel robotic-assisted navigation system was developed to attain precise localization of small lung nodules. This study aims to investigate the accuracy and safety of this system in clinical applications. Methods: Patients with peripheral solitary pulmonary nodules measuring less than 20 mm were enrolled. The robotic-assisted navigation system generated a three-dimensional (3D) model based on the patient's CT images, determining the optimal puncture path. The robotic arm then accurately located the nodule and, following percutaneous puncture, indocyanine green (ICG) was injected. The primary outcome measure was the accuracy of pulmonary nodule localization, while secondary outcomes included the complication rate, procedural duration, and total radiation exposure. Results: A total of 33 nodules were successfully localized using the robotic-assisted navigation system and resected through video-assisted thoracoscopic surgery (VATS). The first-pass success rate was 100%, with a median deviation of 6.1 mm [interquartile range (IQR), 2.5-7.2 mm] between the localizer and the nodule. The median localization time was 25.0 minutes, and the single and cumulative exam dose-length products (DLP) were 534.0 and 1491.0 mGy·cm, respectively. Notably, no observable complications were reported during the procedures. Conclusions: The innovative robotic-assisted navigation system demonstrated satisfactory accuracy and holds promise for improving the percutaneous localization of lung nodules. This method represents a safe and viable alternative to traditional CT-guided manual localization techniques.
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Background: Robot-assisted surgery (RAS) systems have been developed but rarely applied to lung nodule localization. This study aimed to assess the feasibility and safety of using a robot-assisted navigation system in percutaneous lung nodule localization. Methods: A computed tomography (CT)-guided robot-assisted navigation system was used to localize the simulated peripheral nodule in the swine lung through fluorescent agent injection. After the localization, fluorescent thoracoscopic wedge resection was performed. The deviation between the target point and the needle tip was measured using a professional 3-dimensional (3D) distance measurement software. The primary outcome was the localization accuracy (deviation) of the localization. The secondary outcomes were the localization-related complication rate, the localization duration, and the success rate. Results: A total of 4 pigs were enrolled, and 20 peripheral lung nodules were created and localized successfully. All nodules underwent subsequent wedge resection for verification. The mean deviation by measuring the 3D distance was 3.81 mm [standard deviation (SD): 1.29 mm, 95% confidence interval (CI): 2.936-4.536 mm]. The technical success rate for localization was 100%, and the mean localization time was 14.69 minutes (SD: 4.67 minutes). The complication rate was 5% (1/20), with 1 pneumothorax after localization, and no mortality occurred. Conclusions: This pilot animal study demonstrated the promising potential of the robot-assisted navigation technique in peripheral lung nodule localization, with high accuracy and feasibility. Further clinical trials are needed to validate its safety compared to traditional manual localization.
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Thiopurines are commonly used in the treatment of acute lymphoblastic leukaemia and autoimmune conditions, can be limited by myelosuppression. The NUDT15 c.415C>T variant is strongly associated with thiopurine-induced myelosuppression, especially in Asians. The purpose of this study was to develop a fast and reliable genotyping method for NUDT15 c.415C>T and investigate the polymorphic distribution among different races in China. A single-tube multiplex real-time PCR assay for NUDT15 c.415C>T genotyping was established using allele-specific TaqMan probes. In 229 samples, the genotyping results obtained through the established method were completely concordant with those obtained by Sanger sequencing. The distributions of NUDT15 c.415C>T among 173 Han Chinese, 48 Miaos, 40 Kazakhs, and 40 Kirghiz were different, with allelic frequencies of 0.06, 0.02, 0.07, and 0, respectively. This method will provide a powerful tool for the implementation of the genotyping-based personalized prescription of thiopurines in clinical settings.
