Theoretical Analysis of Selectivity Differences in Ketoreductases toward Aldehyde and Ketone Carbonyl Groups.

Lactobacillus kefir alcohol dehydrogenase (LkADH) and ketoreductase from Chryseobacterium sp. CA49 (ChKRED12) exhibit different chemoselectivity and stereoselectivity toward a substrate with both keto and aldehyde carbonyl groups. LkADH selectively reduces the keto carbonyl group while retaining the aldehyde carbonyl group, producing optically pure R-alcohols. In contrast, ChKRED12 selectively reduces the aldehyde group and exhibits low reactivity toward ketone carbonyls. This study investigated the structural basis for these differences and the role of specific residues in the active site. Molecular dynamics (MD) simulations and quantum chemical calculations were used to investigate the interactions between the substrate and the enzymes and the essential cause of this phenomenon. The present study has revealed that LkADH and ChKRED12 exhibit significant differences in the structure of their respective active pockets, which is a crucial determinant of their distinct chemoselectivity toward the same substrate. Moreover, residues N89, N113, and E144 within LkADH as well as Q151 and D190 within ChKRED12 have been identified as key contributors to substrate stabilization within the active pocket through electrostatic interactions and van der Waals forces, followed by hydride transfer utilizing the coenzyme NADPH. Furthermore, the enantioselectivity mechanism of LkADH has been elucidated using quantum chemical methods. Overall, these findings not only provide fundamental insights into the underlying reasons for the observed differences in selectivity but also offer a detailed mechanistic understanding of the catalytic reaction.

A radiomics nomogram prediction for survival of patients with "driver gene-negative" lung adenocarcinomas (LUAD).

BACKGROUND: To study the role of computed tomography (CT)-derived radiomics features and clinical characteristics on the prognosis of "driver gene-negative" lung adenocarcinoma (LUAD) and to explore the potential molecular biological which may be helpful for patients' individual postoperative care. METHODS: A total of 180 patients with stage I-III "driver gene-negative" LUAD in the First Affiliated Hospital of Sun Yat-Sen University from September 2003 to June 2015 were retrospectively collected. The Least Absolute Shrinkage and Selection Operator (LASSO) Cox regression model was used to screen radiomics features and calculated the Rad-score. The prediction performance of the nomogram model based on radiomics features and clinical characteristics was validated and then assessed with respect to calibration. Gene set enrichment analysis (GSEA) was used to explore the relevant biological pathways. RESULTS: The radiomics and the clinicopathological characteristics were combined to construct a nomogram resulted in better performance for the estimation of OS (C-index: 0.815; 95% confidence interval [CI]: 0.756-0.874) than the clinicopathological nomogram (C-index: 0.765; 95% CI: 0.692-0.837). Decision curve analysis demonstrated that in terms of clinical usefulness, the radiomics nomogram outperformed the traditional staging system and the clinicopathological nomogram. The clinical prognostic risk score of each patient was calculated based on the radiomics nomogram and divided by X-tile into high-risk (> 65.28) and low-risk (≤ 65.28) groups. GSEA results showed that the low-risk score group was directly related to amino acid metabolism, and the high-risk score group was related to immune and metabolism pathways. CONCLUSIONS: The radiomics nomogram was promising to predict the prognosis of patients with "driver gene-negative" LUAD. The metabolism and immune-related pathways may provide new treatment orientation for this genetically unique subset of patients, which may serve as a potential tool to guide individual postoperative care for those patients.

Predicting response to immunotherapy plus chemotherapy in patients with esophageal squamous cell carcinoma using non-invasive Radiomic biomarkers.

OBJECTIVES: To develop and validate a radiomics model for evaluating treatment response to immune-checkpoint inhibitor plus chemotherapy (ICI + CT) in patients with advanced esophageal squamous cell carcinoma (ESCC). METHODS: A total of 64 patients with advance ESCC receiving first-line ICI + CT at two centers between January 2019 and June 2020 were enrolled in this study. Both 2D ROIs and 3D ROIs were segmented. ComBat correction was applied to minimize the potential bias on the results due to different scan protocols. A total of 788 features were extracted and radiomics models were built on corrected/uncorrected 2D and 3D features by using 5-fold cross-validation. The performance of the radiomics models was assessed by its discrimination, calibration and clinical usefulness with independent validation. RESULTS: Five features and support vector machine algorithm were selected to build the 2D uncorrected, 2D corrected, 3D uncorrected and 3D corrected radiomics models. The 2D radiomics models significantly outperformed the 3D radiomics models in both primary and validation cohorts. When ComBat correction was used, the performance of 2D models was better (p = 0.0059) in the training cohort, and significantly better (p < 0.0001) in the validation cohort. The 2D corrected radiomics model yielded the optimal performance and was used to build the nomogram. The calibration curve of the radiomics model demonstrated good agreement between prediction and observation and the decision curve analysis confirmed the clinical utility. CONCLUSIONS: The easy-to-use 2D corrected radiomics model could facilitate noninvasive preselection of ESCC patients who would benefit from ICI + CT.

A computed tomography (CT)-derived radiomics approach for predicting primary co-mutations involving TP53 and epidermal growth factor receptor (EGFR) in patients with advanced lung adenocarcinomas (LUAD).

BACKGROUND: Epidermal growth factor receptor (EGFR) co-mutated with TP53 could reduce responsiveness to tyrosine kinase inhibitors (TKIs) and worsen patients' prognosis compared to TP53 wild type patients in. EGFR: mutated lung adenocarcinomas (LUAD). To identify this genetically unique subset prior to treatment through computed tomography (CT) images had not been reported yet. METHODS: Stage III and IV LUAD with known mutation status of EGFR and TP53 from The First Affiliated Hospital of Sun Yat-sen University (May 1, 2017 to June 1, 2020) were collected. Characteristics of pretreatment enhanced-CT images were analyzed. One-versus-one was used as the multiclass classification strategy to distinguish the three subtypes of co-mutations: EGFR + & TP53 +, EGFR + & TP53 -, EGFR -. The clinical model, semantic model, radiomics model and integrated model were built. Area under the receiver-operating characteristic curves (AUCs) were used to evaluate the prediction efficacy. RESULTS: A total of 199 patients were enrolled, including 83 (42%) cases of EGFR -, 55 (28%) cases of EGFR + & TP53 +, 61 (31%) cases of EGFR + & TP53 -. Among the four different models, the integrated model displayed the best performance for all the three subtypes of co-mutations: EGFR - (AUC, 0.857; accuracy, 0.817; sensitivity, 0.998; specificity, 0.663), EGFR + & TP53 + (AUC, 0.791; accuracy, 0.758; sensitivity, 0.762; specificity, 0.783), EGFR + & TP53 - (AUC, 0.761; accuracy, 0.813; sensitivity, 0.594; specificity, 0.977). The radiomics model was slightly inferior to the integrated model. The results for the clinical and the semantic models were dissatisfactory, with AUCs less than 0.700 for all the three subtypes. CONCLUSIONS: CT imaging based artificial intelligence (AI) is expected to distinguish co-mutation status involving TP53 and EGFR. The proposed integrated model may serve as an important alternative marker for preselecting patients who will be adaptable to and sensitive to TKIs.

A CT-derived deep neural network predicts for programmed death ligand-1 expression status in advanced lung adenocarcinomas.

BACKGROUND: Programmed death ligand-1 (PD-L1) expression remains a crucial predictor in selecting patients for immunotherapy. The current study aimed to non-invasively predict PD-L1 expression based on chest computed tomography (CT) images in advanced lung adenocarcinomas (LUAD), thus help select optimal patients who can potentially benefit from immunotherapy. METHODS: A total of 127 patients with stage III and IV LUAD were enrolled into this study. Pretreatment enhanced thin-section CT images were available for all patients and were analyzed in terms of both morphologic characteristics by radiologists and deep learning (DL), so to further determine the association between CT features and PD-L1 expression status. Univariate analysis and multivariate logical regression analysis were applied to evaluate significant variables. For DL, the 3D DenseNet model was built and validated. The study cohort were grouped by PD-L1 Tumor Proportion Scores (TPS) cutoff value of 1% (positive/negative expression) and 50% respectively. RESULTS: Among 127 LUAD patients, 46 (36.2%) patients were PD-L1-positive and 38 (29.9%) patients expressed PD-L1-TPS ≥50%. For morphologic characteristics, univariate and multivariate analysis revealed that only lung metastasis was significantly associated with PD-L1 expression status despite of different PD-L1 TPS cutoff values, and its Area under the receiver operating characteristic curve (AUC) for predicting PD-L1 expression were less than 0.700. On the other hand, the predictive value of DL-3D DenseNet model was higher than that of the morphologic characteristics, with AUC more than 0.750. CONCLUSIONS: The traditional morphologic CT characteristics analyzed by radiologists show limited prediction efficacy for PD-L1 expression. By contrast, CT-derived deep neural network improves the prediction efficacy, it may serve as an important alternative marker for clinical PD-L1 detection.

Effect of Serum Vitamin D Levels on Cellular Immunity and Antiviral Effects in Chronic Hepatitis B Patients.

BACKGROUND: To study the effect of 25-hydroxyl vitamin D3 on peripheral blood T lymphocyte immune function and antiviral effects in chronic hepatitis B patients. METHODS: The clinical data for 70 patients with chronic hepatitis B were analyzed. Serum 25-hydroxyl vitamin D was determined by electrochemical luminescence, and hepatitis B virus serological markers were determined by fluorescence quantitative polymerase chain reaction. Subsets of T lymphocytes were determined by immune fluorescence labeling method. These patients were divided into three groups based on serum 25-hydroxyl vitamin D level. After six months of pegylated interferon treatment, three groups have their number of T lymphocyte, liver functions, and virological indexes examined at the corresponding time. RESULTS: The years and ratio of gender have no statistical differences in these three groups. The proportion of CD3+, CD4+ T lymphocytes and the ratio of CD4+/CD8+ significantly increased (p < 0.05) as the level of 25-hydroxyl vitamin D increased, but the proportion of CD8+ decreased. Interferon treatment can improve the T cells subgroup, and the high level group of serum 25-hydroxyl vitamin D improved more obviously. The positive ratio of HBeAg, HBsAg and the titer of HBV DNA decreased with the increase of serum vitamin D, and the difference between the high and low level 25-hydroxyl vitamin D groups was significant (p < 0.05). The treatment of interferon can obviously improve the hepatitis B virus serological markers; the high level group of serum 25-hydroxyl vitamin D can obtain better virological response. However, there was no significant difference between the three groups of serological markers of liver function. CONCLUSIONS: Vitamin D may play a part in the immunologic function adjustment and immune tolerance in the natural course of chronic HBV infection, and high levels of vitamin D may be able to achieve sustained virological response. These findings may shed light on the research and treatment of chronic hepatitis B pathogenesis.

[Clinical application and value of 3 Tesla contrast enhanced whole-heart coronary magnetic resonance angiography].

OBJECTIVE: To investigate the clinical application, feasibility and value of 3 T whole-heart contrast enhanced free-breathing navigator-gated three-dimensional coronary magnetic resonance angiography (CE-CMRA). METHODS: 3 T CE-CMRA was used to examine patients with suspected coronary heart disease (CAD). Gd-BOPTA (0.2 mmol/kg) was injected intravenously with slow infusion rate (0.3 ml/s) to perform enhancement. Data were post-processed to obtain principal branches of coronary artery and picture quality was evaluated. According to results of selective coronary arteriography (SCAG), the diagnostic accuracy of CE-CMRA for diagnosing CAD was judged by means of detecting significant stenosis (> 50%) of the principal branches based on the 9 segments of coronary artery. RESULTS: Twenty-three out of 26 patients successfully completed the examination. The mean scanning time was (10.4 ± 2.1) minutes, 178 out of 202 (88.1%) SCAG demonstrated segments could be evaluated by CE-CMRA. The imaging quality was superior in proximal and middle segments of coronary artery principal branches than in distal segments. Based on patient-level, there were 9 positive cases and 14 negative cases examined by CE-CMRA compared with 11 positive cases and 12 negative cases examined by SCAG, respectively. The whole diagnose accordance rate of CE-CMRA was 91.3% (21/23) compared with SCAG. The sensitivity, specificity and negative predictive values were 81.8% (9/11), 88.5% (169/191) and 98.8% (9/31) respectively. CONCLUSIONS: 3 T CE-CMRA is a feasible non-invasive imaging modality for diagnosing CAD, especially to detect significant stenosis in proximal and middle segments of coronary artery principal branches. However, the detecting efficacy is limited in assessing stenosis of distal segment and small branches of coronary artery.