Fenton-like activity and pathway modulation via single-atom sites and pollutants comediates the electron transfer process.

The studies on the origin of versatile oxidation pathways toward targeted pollutants in the single-atom catalysts (SACs)/peroxymonosulfate (PMS) systems were always associated with the coordination structures rather than the perspective of pollutant characteristics, and the analysis of mechanism commonality is lacking. In this work, a variety of single-atom catalysts (M-SACs, M: Fe, Co, and Cu) were fabricated via a pyrolysis process using lignin as the complexation agent and substrate precursor. Sixteen kinds of commonly detected pollutants in various references were selected, and their lnkobs values in M-SACs/PMS systems correlated well (R2 = 0.832 to 0.883) with their electrophilic indexes (reflecting the electron accepting/donating ability of the pollutants) as well as the energy gap (R2 = 0.801 to 0.840) between the pollutants and M-SACs/PMS complexes. Both the electron transfer process (ETP) and radical pathways can be significantly enhanced in the M-SACs/PMS systems, while radical oxidation was overwhelmed by the ETP oxidation toward the pollutants with lower electrophilic indexes. In contrast, pollutants with higher electrophilic indexes represented the weaker electron-donating capacity to the M-SACs/PMS complexes, which resulted in the weaker ETP oxidation accompanied with noticeable radical oxidation. In addition, the ETP oxidation in different M-SACs/PMS systems can be regulated via the energy gaps between the M-SACs/PMS complexes and pollutants. As a result, the Fenton-like activities in the M-SACs/PMS systems could be well modulated by the reaction pathways, which were determined by both electrophilic indexes of pollutants and single-atom sites. This work provided a strategy to establish PMS-based AOP systems with tunable oxidation capacities and pathways for high-efficiency organic decontamination.

Development and Validation of a Post-Radiotherapy Prediction Model for Bowel Dysfunction After Rectal Cancer Resection.

BACKGROUND & AIMS: The benefit of radiotherapy for rectal cancer is based largely on a balance between a decrease in local recurrence and an increase in bowel dysfunction. Predicting postoperative disability is helpful for recovery plans and early intervention. We aimed to develop and validate a risk model to improve the prediction of major bowel dysfunction after restorative rectal cancer resection with neoadjuvant radiotherapy using perioperative features. METHODS: Eligible patients more than 1 year after restorative resection following radiotherapy were invited to complete the low anterior resection syndrome (LARS) score at 3 national hospitals in China. Clinical characteristics and imaging parameters were assessed with machine learning algorithms. The post-radiotherapy LARS prediction model (PORTLARS) was constructed by means of logistic regression on the basis of key factors with proportional weighs. The accuracy of the model for major LARS prediction was internally and externally validated. RESULTS: A total of 868 patients reported a mean LARS score of 28.4 after an average time of 4.7 years since surgery. Key predictors for major LARS included the length of distal rectum, anastomotic leakage, proximal colon of neorectum, and pathologic nodal stage. PORTLARS had a favorable area under the curve for predicting major LARS in the internal dataset (0.835; 95% CI, 0.800-0.870, n = 521) and external dataset (0.884; 95% CI, 0.848-0.921, n = 347). The model achieved both sensitivity and specificity >0.83 in the external validation. In addition, PORTLARS outperformed the preoperative LARS score for prediction of major events. CONCLUSIONS: PORTLARS could predict major bowel dysfunction after rectal cancer resection following radiotherapy with high accuracy and robustness. It may serve as a useful tool to identify patients who need additional support for long-term dysfunction in the early stage. CLINICALTRIALS: gov, number NCT05129215.

CT-based deep learning model for the prediction of DNA mismatch repair deficient colorectal cancer: a diagnostic study.

BACKGROUND: Stratification of DNA mismatch repair (MMR) status in patients with colorectal cancer (CRC) enables individual clinical treatment decision making. The present study aimed to develop and validate a deep learning (DL) model based on the pre-treatment CT images for predicting MMR status in CRC. METHODS: 1812 eligible participants (training cohort: n = 1124; internal validation cohort: n = 482; external validation cohort: n = 206) with CRC were enrolled from two institutions. All pretherapeutic CT images from three dimensions were trained by the ResNet101, then integrated by Gaussian process regression (GPR) to develop a full-automatic DL model for MMR status prediction. The predictive performance of the DL model was evaluated using the area under the receiver operating characteristic curve (AUC) and then tested in the internal and external validation cohorts. Additionally, the participants from institution 1 were sub-grouped by various clinical factors for subgroup analysis, then the predictive performance of the DL model for identifying MMR status between participants in different groups were compared. RESULTS: The full-automatic DL model was established in the training cohort to stratify the MMR status, which presented promising discriminative ability with the AUCs of 0.986 (95% CI 0.971-1.000) in the internal validation cohort and 0.915 (95% CI 0.870-0.960) in the external validation cohort. In addition, the subgroup analysis based on the thickness of CT images, clinical T and N stages, gender, the longest diameter, and the location of tumors revealed that the DL model showed similar satisfying prediction performance. CONCLUSIONS: The DL model may potentially serve as a noninvasive tool to facilitate the pre-treatment individualized prediction of MMR status in patients with CRC, which could promote the personalized clinical-making decision.

Size-Dependent Catalysis in Fenton-like Chemistry: From Nanoparticles to Single Atoms.

State-of-the-art Fenton-like reactions are crucial in advanced oxidation processes (AOPs) for water purification. This review explores the latest advancements in heterogeneous metal-based catalysts within AOPs, covering nanoparticles (NPs), single-atom catalysts (SACs), and ultra-small atom clusters. A distinct connection between the physical properties of these catalysts, such as size, degree of unsaturation, electronic structure, and oxidation state, and their impacts on catalytic behavior and efficacy in Fenton-like reactions. In-depth comparative analysis of metal NPs and SACs is conducted focusing on how particle size variations and metal-support interactions affect oxidation species and pathways. The review highlights the cutting-edge characterization techniques and theoretical calculations, indispensable for deciphering the complex electronic and structural characteristics of active sites in downsized metal particles. Additionally, the review underscores innovative strategies for immobilizing these catalysts onto membrane surfaces, offering a solution to the inherent challenges of powdered catalysts. Recent advances in pilot-scale or engineering applications of Fenton-like-based devices are also summarized for the first time. The paper concludes by charting new research directions, emphasizing advanced catalyst design, precise identification of reactive oxygen species, and in-depth mechanistic studies. These efforts aim to enhance the application potential of nanotechnology-based AOPs in real-world wastewater treatment.

METnet: A novel deep learning model predicting MET dysregulation in non-small-cell lung cancer on computed tomography images.

BACKGROUND: Mesenchymal epithelial transformation (MET) is a key molecular target for diagnosis and treatment of non-small cell lung cancer (NSCLC). The corresponding molecularly targeted therapeutics have been approved by Food and Drug Administration (FDA), achieving promising results. However, current detection of MET dysregulation requires biopsy and gene sequencing, which is invasive, time-consuming and difficult to obtain tumor samples. METHODS: To address the above problems, we developed a noninvasive and convenient deep learning (DL) model based on Computed tomography (CT) imaging data for prediction of MET dysregulation. We introduced the unsupervised algorithm RK-net for automated image processing and utilized the MedSAM large model to achieve automated tissue segmentation. Based on the processed CT images, we developed a DL model (METnet). The model based on the grouped convolutional block. We evaluated the performance of the model over the internal test dataset using the area under the receiver operating characteristic curve (AUROC) and accuracy. We conducted subgroup analysis on the basis of clinical data of the lung cancer patients and compared the performance of the model in different subgroups. RESULTS: The model demonstrated a good discriminative ability over the internal test dataset. The accuracy of METnet was 0.746 with an AUC value of 0.793 (95% CI 0.714-0.871). The subgroup analysis revealed that the model exhibited similar performance across different subgroups. CONCLUSIONS: METnet realizes prediction of MET dysregulation in NSCLC, holding promise for guiding precise tumor diagnosis and treatment at the molecular level.

Unsupervised Learning Composite Network to Reduce Training Cost of Deep Learning Model for Colorectal Cancer Diagnosis.

Deep learning facilitates complex medical data analysis and is increasingly being explored in colorectal cancer diagnostics. However, the training cost of the deep learning model limits its real-world medical utility. In this study, we present a composite network that combines deep learning and unsupervised K-means clustering algorithm (RK-net) for automatic processing of medical images. RK-net was more efficient in image refinement compared with manual screening and annotation. The training of a deep learning model for colorectal cancer diagnosis was accelerated by two times with utilization of RK-net-processed images. Better performance was observed in training loss and accuracy achievement as well. RK-net could be useful to refine medical images of the ever-expanding quantity and assist in subsequent construction of the artificial intelligence model.

To unravel the connection between the non-equilibrium and equilibrium solvation dynamics of tryptophan: success and failure of the linear response theory of fluorescence Stokes shift.

The connections between the non-equilibrium solvation dynamics upon optical transitions and the system's equilibrium fluctuations are explored in aqueous liquid. Linear response theory correlates time-dependent fluorescence with the equilibrium time correlation functions. In the previous work [T. Li, J. Chem. Theory Comput., 2017, 13, 1867], Stokes shift was explicitly decomposed into the contributions of various order time correlation functions on the excited state surface. Gaussian fluctuations of the solute-solvent interactions validate linear response theory. Correspondingly, the deviation of the Gaussian statistics causes the inefficiency of linear response evaluation. The above mechanism is thoroughly tested in this study. By employing molecular simulations, multiple non-equilibrium processes, not necessarily initiated from the ground state equilibrium minimum, were examined for tryptophan. Both the success and failure of linear response theory are found for this simple system and the mechanism is analyzed. These observations, assisted by the width dynamics, the initial state linear response approach, and the variation of the solvation structures, integrally verify the virtue of the excited state Gaussian statistics on the dynamics of Stokes shift.

Linear Response Theory for Decomposition Energies of Stokes Shift in Proteins.

In aqueous solution, fluorescence Stokes shift experiments monitor the relaxation of the solute-solvent interactions upon photon excitation of the solute chromophore. Linear response (LR) theory expects the identical dynamics between the Stokes shift and the system's spontaneous fluctuations. Whether this identity guarantees similar dynamics between the nonequilibrated and equilibrium processes for the decomposition energy of the Stokes shift is the main focus of this study. In our previous work [Li, T. J. Chem. Theory Comput. 2017, 13, 1867-1873], Stokes shift is properly correlated with various order time-correlation functions. As a continuation, its decomposition energy from the subsystem is further represented as the full summation of all of the cross-time correlation functions between the decomposition energy and the total solute-solvent interactions. Gaussian statistics of the total solute-solvent interactions ensure the same decay rates among the odd orders not only for the time-correlation functions but also for the cross-time correlation functions, validating the LR of the Stokes shift and the decompositions, respectively. The above mechanism is verified by molecular dynamics simulations in the protein Staphylococcus nuclease and is robust even as the decomposed energy associated with an individual residue exhibits typical non-Gaussian properties. Further examinations reveal the consistent molecular motions for a specific residue over the nonequilibrium and equilibrium processes, which are responsible for the nonequilibrium dynamics of the associated decomposed energy. Our results show the appropriateness of LR on finer molecular scales.