Robotic-assisted navigation system for preoperative lung nodule localization: a pilot study.

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.

A weakly supervised deep learning model integrating noncontrasted computed tomography images and clinical factors facilitates haemorrhagic transformation prediction after intravenous thrombolysis in acute ischaemic stroke patients.

BACKGROUND: Haemorrhage transformation (HT) is a serious complication of intravenous thrombolysis (IVT) in acute ischaemic stroke (AIS). Accurate and timely prediction of the risk of HT before IVT may change the treatment decision and improve clinical prognosis. We aimed to develop a deep learning method for predicting HT after IVT for AIS using noncontrast computed tomography (NCCT) images. METHODS: We retrospectively collected data from 828 AIS patients undergoing recombinant tissue plasminogen activator (rt-PA) treatment within a 4.5-h time window (n = 665) or of undergoing urokinase treatment within a 6-h time window (n = 163) and divided them into the HT group (n = 69) and non-HT group (n = 759). HT was defined based on the criteria of the European Cooperative Acute Stroke Study-II trial. To address the problems of indiscernible features and imbalanced data, a weakly supervised deep learning (WSDL) model for HT prediction was constructed based on multiple instance learning and active learning using admission NCCT images and clinical information in addition to conventional deep learning models. Threefold cross-validation and transfer learning were performed to confirm the robustness of the network. Of note, the predictive value of the commonly used scales in clinics associated with NCCT images (i.e., the HAT and SEDAN score) was also analysed and compared to measure the feasibility of our proposed DL algorithms. RESULTS: Compared to the conventional DL and ML models, the WSDL model had the highest AUC of 0.799 (95% CI 0.712-0.883). Significant differences were observed between the WSDL model and five ML models (P < 0.05). The prediction performance of the WSDL model outperforms the HAT and SEDAN scores at the optimal operating point (threshold = 1.5). Further subgroup analysis showed that the WSDL model performed better for symptomatic intracranial haemorrhage (AUC = 0.833, F1 score = 0.909). CONCLUSIONS: Our WSDL model based on NCCT images had relatively good performance for predicting HT in AIS and may be suitable for assisting in clinical treatment decision-making.

Robot-assisted navigation for percutaneous localization of peripheral pulmonary nodule: an in vivo swine study.

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.

Using Machine Learning Techniques to Develop Risk Prediction Models for the Risk of Incident Diabetic Retinopathy Among Patients With Type 2 Diabetes Mellitus: A Cohort Study.

Objective: To construct and validate prediction models for the risk of diabetic retinopathy (DR) in patients with type 2 diabetes mellitus. Methods: Patients with type 2 diabetes mellitus hospitalized over the period between January 2010 and September 2018 were retrospectively collected. Eighteen baseline demographic and clinical characteristics were used as predictors to train five machine-learning models. The model that showed favorable predictive efficacy was evaluated at annual follow-ups. Multi-point data of the patients in the test set were utilized to further evaluate the model's performance. We also assessed the relative prognostic importance of the selected risk factors for DR outcomes. Results: Of 7943 collected patients, 1692 (21.30%) developed DR during follow-up. Among the five models, the XGBoost model achieved the highest predictive performance with an AUC, accuracy, sensitivity, and specificity of 0.803, 88.9%, 74.0%, and 81.1%, respectively. The XGBoost model's AUCs in the different follow-up periods were 0.834 to 0.966. In addition to the classical risk factors of DR, serum uric acid (SUA), low-density lipoprotein cholesterol (LDL-C), total cholesterol (TC), estimated glomerular filtration rate (eGFR), and triglyceride (TG) were also identified to be important and strong predictors for the disease. Compared with the clinical diagnosis method of DR, the XGBoost model achieved an average of 2.895 years prior to the first diagnosis. Conclusion: The proposed model achieved high performance in predicting the risk of DR among patients with type 2 diabetes mellitus at each time point. This study established the potential of the XGBoost model to facilitate clinicians in identifying high-risk patients and making type 2 diabetes management-related decisions.

A deep-learning model for identifying fresh vertebral compression fractures on digital radiography.

OBJECTIVES: To develop a deep-learning (DL) model for identifying fresh VCFs from digital radiography (DR), with magnetic resonance imaging (MRI) as the reference standard. METHODS: Patients with lumbar VCFs were retrospectively enrolled from January 2011 to May 2020. All patients underwent DR and MRI scanning. VCFs were categorized as fresh or old according to MRI results, and the VCF grade and type were assessed. The raw DR data were sent to InferScholar Center for annotation. A DL-based prediction model was built, and its diagnostic performance was evaluated. The DeLong test was applied to assess differences in ROC curves between different models. RESULTS: A total of 1877 VCFs in 1099 patients were included in our study and randomly divided into development (n = 824 patients) and test (n = 275 patients) datasets. The ensemble model identified fresh and old VCFs, reaching an AUC of 0.80 (95% confidence interval [CI], 0.77-0.83), an accuracy of 74% (95% CI, 72-77%), a sensitivity of 80% (95% CI, 77-83%), and a specificity of 68% (95% CI, 63-72%). Lateral (AUC, 0.83) views exhibited better performance than anteroposterior views (AUC, 0.77), and the best performance among respective subgroupings was obtained for grade 3 (AUC, 0.89) and crush-type (AUC, 0.87) subgroups. CONCLUSION: The proposed DL model achieved adequate performance in identifying fresh VCFs from DR. KEY POINTS: • The ensemble deep-learning model identified fresh VCFs from DR, reaching an AUC of 0.80, an accuracy of 74%, a sensitivity of 80%, and a specificity of 68% with the reference standard of MRI. • The lateral views (AUC, 0.83) exhibited better performance than anteroposterior views (AUC, 0.77). • The grade 3 (AUC, 0.89) and crush-type (AUC, 0.87) subgroups showed the best performance among their respective subgroupings.

Diagnosing autism spectrum disorder in children using conventional MRI and apparent diffusion coefficient based deep learning algorithms.

OBJECTIVE: To develop and validate deep learning (DL) methods for diagnosing autism spectrum disorder (ASD) based on conventional MRI (cMRI) and apparent diffusion coefficient (ADC) images. METHODS: A total of 151 ASD children and 151 age-matched typically developing (TD) controls were included in this study. The data from these subjects were assigned to training and validation datasets. An additional 20 ASD children and 25 TD controls were acquired, whose data were utilized in an independent test set. All subjects underwent cMRI and diffusion-weighted imaging examination of the brain. We developed a series of DL models to separate ASD from TD based on the cMRI and ADC data. The seven models used include five single-sequence models (SSMs), one dominant-sequence model (DSM), and one all-sequence model (ASM). To enhance the feature detection of the models, we embed an attention mechanism module. RESULTS: The highest AUC (0.824 ~ 0.850) was achieved when applying the SSM based on either FLAIR or ADC to the validation and independent test sets. A DSM using the combination of FLAIR and ADC showed an improved AUC in the validation (0.873) and independent test sets (0.876). The ASM also showed better diagnostic value in the validation (AUC = 0.838) and independent test sets (AUC = 0.836) compared to the SSMs. Among the models with attention mechanism, the DSM achieved the highest diagnostic performance with an AUC, accuracy, sensitivity, and specificity of 0.898, 84.4%, 85.0%, and 84.0% respectively. CONCLUSIONS: This study established the potential of DL models to distinguish ASD cases from TD controls based on cMRI and ADC images. KEY POINTS: • Deep learning models based on conventional MRI and ADC can be used to diagnose ASD. • The model (DSM) based on the FLAIR and ADC sequence achieved the best diagnostic performance with an AUC of 0.836 in the independent test sets. • The attention mechanism further improved the diagnostic performance of the models.

Bioinformatics Analysis and Characterization of Highly Efficient Polyvinyl Alcohol (PVA)-Degrading Enzymes from the Novel PVA Degrader Stenotrophomonas rhizophila QL-P4.

Polyvinyl alcohol (PVA) is used widely in industry, and associated environmental pollution is a serious problem. Herein, we report a novel, efficient PVA degrader, Stenotrophomonas rhizophila QL-P4, isolated from fallen leaves from a virgin forest in the Qinling Mountains. The complete genome was obtained using single-molecule real-time (SMRT) technology and corrected using Illumina sequencing. Bioinformatics analysis revealed eight PVA/vinyl alcohol oligomer (OVA)-degrading genes. Of these, seven genes were predicted to be involved in the classic intracellular PVA/OVA degradation pathway, and one (BAY15_3292) was identified as a novel PVA oxidase. Five PVA/OVA-degrading enzymes were purified and characterized. One of these, BAY15_1712, a PVA dehydrogenase (PVADH), displayed high catalytic efficiency toward PVA and OVA substrate. All reported PVADHs only have PVA-degrading ability. Most importantly, we discovered a novel PVA oxidase (BAY15_3292) that exhibited higher PVA-degrading efficiency than the reported PVADHs. Further investigation indicated that BAY15_3292 plays a crucial role in PVA degradation in S. rhizophila QL-P4. Knocking out BAY15_3292 resulted in a significant decline in PVA-degrading activity in S. rhizophila QL-P4. Interestingly, we found that BAY15_3292 possesses exocrine activity, which distinguishes it from classic PVADHs. Transparent circle experiments further proved that BAY15_3292 greatly affects extracellular PVA degradation in S. rhizophila QL-P4. The exocrine characteristics of BAY15_3292 facilitate its potential application to PVA bioremediation. In addition, we report three new efficient secondary alcohol dehydrogenases (SADHs) with OVA-degrading ability in S. rhizophila QL-P4; in contrast, only one OVA-degrading SADH was reported previously.IMPORTANCE With the widespread application of PVA in industry, PVA-related environmental pollution is an increasingly serious issue. Because PVA is difficult to degrade, it accumulates in aquatic environments and causes chronic toxicity to aquatic organisms. Biodegradation of PVA, as an economical and environment-friendly method, has attracted much interest. To date, effective and applicable PVA-degrading bacteria/enzymes have not been reported. Herein, we report a new efficient PVA degrader (S. rhizophila QL-P4) that has five PVA/OVA-degrading enzymes with high catalytic efficiency, among which BAY15_1712 is the only reported PVADH with both PVA- and OVA-degrading abilities. Importantly, we discovered a novel PVA oxidase (BAY15_3292) that is not only more efficient than other reported PVA-degrading PVADHs but also has exocrine activity. Overall, our findings provide new insight into PVA-degrading pathways in microorganisms and suggest S. rhizophila QL-P4 and its enzymes have the potential for application to PVA bioremediation to reduce or eliminate PVA-related environmental pollution.

The Bioinformatics Analysis of Comparative Genomics of Mycobacterium tuberculosis Complex (MTBC) Provides Insight into Dissimilarities between Intraspecific Groups Differing in Host Association, Virulence, and Epitope Diversity.

Tuberculosis now exceeds HIV as the top infectious disease cause of mortality, and is caused by the Mycobacterium tuberculosis complex (MTBC). MTBC strains have highly conserved genome sequences (similarity >99%) but dramatically different phenotypes. To analyze the relationship between genotype and phenotype, we conducted the comparative genomic analysis on 12 MTBC strains representing different lineages (i.e., Mycobacterium bovis; M. bovis BCG; M. microti; M. africanum; M. tuberculosis H37Rv; M. tuberculosis H37Ra, and six M. tuberculosis clinical isolates). The analysis focused on the three aspects of pathogenicity: host association, virulence, and epitope variations. Host association analysis indicated that eight mce3 genes, two enoyl-CoA hydratases, and five PE/PPE family genes were present only in human isolates; these may have roles in host-pathogen interactions. There were 15 SNPs found on virulence factors (including five SNPs in three ESX secretion proteins) only in the Beijing strains, which might be related to their more virulent phenotype. A comparison between the virulent H37Rv and non-virulent H37Ra strains revealed three SNPs that were likely associated with the virulence attenuation of H37Ra: S219L (PhoP), A219E (MazG) and a newly identified I228M (EspK). Additionally, a comparison of animal-associated MTBC strains showed that the deletion of the first four genes (i.e., pe35, ppe68, esxB, esxA), rather than all eight genes of RD1, might play a central role in the virulence attenuation of animal isolates. Finally, by comparing epitopes among MTBC strains, we found that four epitopes were lost only in the Beijing strains; this may render them better capable of evading the human immune system, leading to enhanced virulence. Overall, our comparative genomic analysis of MTBC strains reveals the relationship between the highly conserved genotypes and the diverse phenotypes of MTBC, provides insight into pathogenic mechanisms, and facilitates the development of potential molecular targets for the prevention and treatment of tuberculosis.

Biological phosphorylation of an Unnatural Base Pair (UBP) using a Drosophila melanogaster deoxynucleoside kinase (DmdNK) mutant.

One research goal for unnatural base pair (UBP) is to replicate, transcribe and translate them in vivo. Accordingly, the corresponding unnatural nucleoside triphosphates must be available at sufficient concentrations within the cell. To achieve this goal, the unnatural nucleoside analogues must be phosphorylated to the corresponding nucleoside triphosphates by a cascade of three kinases. The first step is the monophosphorylation of unnatural deoxynucleoside catalyzed by deoxynucleoside kinases (dNK), which is generally considered the rate limiting step because of the high specificity of dNKs. Here, we applied a Drosophila melanogaster deoxyribonucleoside kinase (DmdNK) to the phosphorylation of an UBP (a pyrimidine analogue (6-amino-5-nitro-3-(1'-b-d-2'-deoxyribofuranosyl)-2(1H)-pyridone, Z) and its complementary purine analogue (2-amino-8-(1'-b-d-2'-deoxyribofuranosyl)-imidazo[1,2-a]-1,3,5-triazin-4(8H)-one, P). The results showed that DmdNK could efficiently phosphorylate only the dP nucleoside. To improve the catalytic efficiency, a DmdNK-Q81E mutant was created based on rational design and structural analyses. This mutant could efficiently phosphorylate both dZ and dP nucleoside. Structural modeling indicated that the increased efficiency of dZ phosphorylation by the DmdNK-Q81E mutant might be related to the three additional hydrogen bonds formed between E81 and the dZ base. Overall, this study provides a groundwork for the biological phosphorylation and synthesis of unnatural base pair in vivo.

Precision methylome characterization of Mycobacterium tuberculosis complex (MTBC) using PacBio single-molecule real-time (SMRT) technology.

Tuberculosis (TB) remains one of the most common infectious diseases caused by Mycobacterium tuberculosis complex (MTBC). To panoramically analyze MTBC's genomic methylation, we completed the genomes of 12 MTBC strains (Mycobacterium bovis; M. bovis BCG; M. microti; M. africanum; M. tuberculosis H37Rv; H37Ra; and 6 M. tuberculosis clinical isolates) belonging to different lineages and characterized their methylomes using single-molecule real-time (SMRT) technology. We identified three (m6)A sequence motifs and their corresponding methyltransferase (MTase) genes, including the reported mamA, hsdM and a newly discovered mamB. We also experimentally verified the methylated motifs and functions of HsdM and MamB. Our analysis indicated the MTase activities varied between 12 strains due to mutations/deletions. Furthermore, through measuring 'the methylated-motif-site ratio' and 'the methylated-read ratio', we explored the methylation status of each modified site and sequence-read to obtain the 'precision methylome' of the MTBC strains, which enabled intricate analysis of MTase activity at whole-genome scale. Most unmodified sites overlapped with transcription-factor binding-regions, which might protect these sites from methylation. Overall, our findings show enormous potential for the SMRT platform to investigate the precise character of methylome, and significantly enhance our understanding of the function of DNA MTase.

The history and advances of reversible terminators used in new generations of sequencing technology.

DNA sequencing using reversible terminators, as one sequencing by synthesis strategy, has garnered a great deal of interest due to its popular application in the second-generation high-throughput DNA sequencing technology. In this review, we provided its history of development, classification, and working mechanism of this technology. We also outlined the screening strategies for DNA polymerases to accommodate the reversible terminators as substrates during polymerization; particularly, we introduced the "REAP" method developed by us. At the end of this review, we discussed current limitations of this approach and provided potential solutions to extend its application.

Combining the physical adsorption approach and the covalent attachment method to prepare a bifunctional bioreactor.

Aminopropyl-functionalized SBA-15 mesoporous silica was used as a support to adsorb myoglobin. Then, in order to avoid the leakage of adsorbed myoglobin, lysozyme was covalently tethered to the internal and external surface of the mesoporous silica with glutaraldehyde as the coupling agent. The property of amino-functionalized mesoporous silica was characterized by N(2) adsorption-desorption and thermogravimetric (TG) analysis. The feature of the silica-based matrix before and after myoglobin adsorption was identified by fourier transform infrared (FTIR) and UV/VIS measurement. With o-dianisidine and H(2)O(2) as the substrate, the peroxidase activity of adsorbed myoglobin was determined. With Micrococus lysodeilicus as the substrate, the antibacterial activity of covalently tethered lysozyme was measured. Results demonstrated that the final product not only presented peroxidase activity of the myoglobin but yielded antibacterial activity of the lysozyme.