Fine-Tuning Electron Transfer for Nanozyme Design.

Nanozymes, with their versatile composition and structural adaptability, present distinct advantages over natural enzymes including heightened stability, customizable catalytic activity, cost-effectiveness, and simplified synthesis process, making them as promising alternatives in various applications. Recent advancements in nanozyme research have shifted focus from serendipitous discovery toward a more systematic approach, leveraging machine learning, theoretical calculations, and mechanistic explorations to engineer nanomaterial structures with tailored catalytic functions. Despite its pivotal role, electron transfer, a fundamental process in catalysis, has often been overlooked in previous reviews. This review comprehensively summarizes recent strategies for modulating electron transfer processes to fine-tune the catalytic activity and specificity of nanozymes, including electron-hole separation and carrier transfer. Furthermore, the bioapplications of these engineered nanozymes, including antimicrobial treatments, cancer therapy, and biosensing are also introduced. Ultimately, this review aims to offer invaluable insights for the design and synthesis of nanozymes with enhanced performance, thereby advancing the field of nanozyme research.

[Effect of Integrated Landscape Characteristics Around Chaohu Lake on River Water Quality Based on Watershed Units].

Considering the impact of differences in watershed characteristics on river water quality, with the Chaohu Lake Basin as the research object, based on the data of water quality, meteorology, topography, soil, and remote sensing images of the river monitoring points from October 2019 to September 2020, the watershed unit at each monitoring point was divided through digital terrain analysis, and the comprehensive landscape characteristics based on the watershed unit were explored through the comprehensive use of correlation analysis, redundancy analysis, and multiple regression analysis to investigate the influence of comprehensive landscape characteristics based on watershed units (including land use, climate, topography, soil, etc.) on the water quality of rivers around Chaohu Lake. The results showed that:① the water quality of rivers around Chaohu Lake had large spatial differences, with the main pollutants being total nitrogen and ammonia nitrogen. Most of the rivers had total nitrogen concentrations exceeding the Class V water quality standards, and the areas with serious nitrogen and phosphorus pollution were concentrated in the urban area of Hefei and the surrounding rivers, as well as in the middle and lower reaches of the Fengle and Hangbu Rivers. ② The comprehensive landscape characteristics of the watershed unit had a significant impact on the river water quality. Among them, the proportion of built-up land, the density of patches, the dispersion and juxtaposition index, and the Shannon diversity index were positively correlated with the water quality indicators, whereas the proportion of forest and grassland and the spreading index were negatively correlated with the water quality indicators. ③ In different seasons, the effect of the integrated landscape characteristics of the watershed unit on river water quality was stronger in the wet season than in the dry season, which was mainly caused by the difference in precipitation in the dry and wet seasons.

Long-term outcomes of newly diagnosed POEMS syndrome patients who received first-line lenalidomide-based therapy.

Not available.

Interictal EEG features as computational biomarkers of West syndrome.

Background: West syndrome (WS) is a devastating epileptic encephalopathy with onset in infancy and early childhood. It is characterized by clustered epileptic spasms, developmental arrest, and interictal hypsarrhythmia on electroencephalogram (EEG). Hypsarrhythmia is considered the hallmark of WS, but its visual assessment is challenging due to its wide variability and lack of a quantifiable definition. This study aims to analyze the EEG patterns in WS and identify computational diagnostic biomarkers of the disease. Method: Linear and non-linear features derived from EEG recordings of 31 WS patients and 20 age-matched controls were compared. Subsequently, the correlation of the identified features with structural and genetic abnormalities was investigated. Results: WS patients showed significantly elevated alpha-band activity (0.2516 vs. 0.1914, p < 0.001) and decreased delta-band activity (0.5117 vs. 0.5479, p < 0.001), particularly in the occipital region, as well as globally strengthened theta-band activity (0.2145 vs. 0.1655, p < 0.001) in power spectrum analysis. Moreover, wavelet-bicoherence analysis revealed significantly attenuated cross-frequency coupling in WS patients. Additionally, bi-channel coherence analysis indicated minor connectivity alterations in WS patients. Among the four non-linear characteristics of the EEG data (i.e., approximate entropy, sample entropy, permutation entropy, and wavelet entropy), permutation entropy showed the most prominent global reduction in the EEG of WS patients compared to controls (1.4411 vs. 1.5544, p < 0.001). Multivariate regression results suggested that genetic etiologies could influence the EEG profiles of WS, whereas structural factors could not. Significance: A combined global strengthening of theta activity and global reduction of permutation entropy can serve as computational EEG biomarkers for WS. Implementing these biomarkers in clinical practice may expedite diagnosis and treatment in WS, thereby improving long-term outcomes.

Multihierarchical Regulation To Achieve Quantum Dot Nanospheres with a Photoluminescence Quantum Yield Close to 100.

Quantum dots (QDs) exhibit superior brightness and photochemical stability, making them the preferred option for highly sensitive single-molecule detection compared with fluorescent dyes or proteins. Nevertheless, their high surface energy leads to nonspecific adsorption and poor colloidal stability. In the past decades, we have found that QD-based fluorescent nanoparticles (FNs) can not only address these limitations but also enhance detection sensitivity. However, the photoluminescence quantum yield (PLQY) of FNs is significantly lower compared with that of original QDs. It is urgent to develop a strategy to solve the issue, aiming to further enhance detection sensitivity. In this study, we found that the decrease of PLQY of FNs prepared by free radical polymerization was attributed to two factors: (1) generation of defects that can cause nonradiative transitions resulting from QD-ligands desorption and QD-shell oxidation induced by free radicals; (2) self-absorption resulting from aggregation caused by incompatibility of QDs with polymers. Based on these, we proposed a multihierarchical regulation strategy that includes: (1) regulating QD-ligands; (2) precisely controlling free radical concentration; and (3) constructing cross-linked structures of polymer to improve compatibility and to reduce the formation of surface defects. It is crucial to emphasize that the simultaneous coordination of multiple factors is essential. Consequently, a world-record PLQY of 97.6% for FNs was achieved, breaking through the current bottleneck at 65%. The flexible application of this regulatory concept paves the way for the large-scale production of high-brightness QD-polymer complexes, enhancing their potential applications in sensitive biomedical detection.

Effect of navigation endoscopy combined with three-dimensional printing technology in the treatment of orbital blowout fractures.

AIM: To explore the combined application of surgical navigation nasal endoscopy (NNE) and three-dimensional printing technology (3DPT) for the adjunctive treatment of orbital blowout fractures (OBF). METHODS: Retrospective analysis was conducted on the data of patients with OBF who underwent surgical treatment at the Affiliated Eye Hospital of Nanchang University between July 2012 and November 2022. The control group consisted of patients who received traditional surgical treatment (n=43), while the new surgical group (n=52) consisted of patients who received NNE with 3DPT. The difference in therapeutic effects between the two groups was evaluated by comparing the duration of the operation, best corrected visual acuity (BCVA), enophthalmos difference, recovery rate of eye movement disorder, recovery rate of diplopia, and incidence of postoperative complications. RESULTS: The study included 95 cases (95 eyes), with 63 men and 32 women. The patients' age ranged from 5 to 67y (35.21±15.75y). The new surgical group and the control group exhibited no statistically significant differences in the duration of the operation, BCVA and enophthalmos difference. The recovery rates of diplopia in the new surgical group were significantly higher than those in the control group at 1mo [OR=0.03, 95%CI (0.01-0.15), P<0.0000] and 3mo [OR=0.11, 95%CI (0.03-0.36), P<0.0000] post-operation. Additionally, the recovery rates of eye movement disorders at 1 and 3mo after surgery were OR=0.08, 95%CI (0.03-0.24), P<0.0000; and OR=0.01, 95%CI (0.00-0.18), P<0.0000. The incidence of postoperative complications was lower in the new surgical group compared to the control group [OR=4.86, 95%CI (0.95-24.78), P<0.05]. CONCLUSION: The combination of NNE and 3DPT can shorten the recovery time of diplopia and eye movement disorder in patients with OBF.

Trichothecenes with cytotoxic activity and benzene derivatives with hypolipidemic activity from trichothecium sp.

Five trichothecenes including a new one, together with two previously undescribed benzene derivatives were isolated from the solid culture of Trichothecium sp. Their structures were established by 1D and 2D NMR data in conjunction with HR-ESI-MS analysis. Compounds 1-5 exhibited cytotoxicity against MCF-7 cell lines at various levels ranging from IC50 of 7.23 to 16.95 µM. Compound 6 decreased the concentration of blood lipids in zebra fish at the concentration of 20 µM.

Haplotype-resolved 3D chromatin architecture of the hybrid pig.

In diploid mammals, allele-specific three-dimensional (3D) genome architecture may lead to imbalanced gene expression. Through ultradeep in situ Hi-C sequencing of three representative somatic tissues (liver, skeletal muscle, and brain) from hybrid pigs generated by reciprocal crosses of phenotypically and physiologically divergent Berkshire and Tibetan pigs, we uncover extensive chromatin reorganization between homologous chromosomes across multiple scales. Haplotype-based interrogation of multi-omic data revealed the tissue dependence of 3D chromatin conformation, suggesting that parent-of-origin-specific conformation may drive gene imprinting. We quantify the effects of genetic variations and histone modifications on allelic differences of long-range promoter-enhancer contacts, which likely contribute to the phenotypic differences between the parental pig breeds. We also observe the fine structure of somatically paired homologous chromosomes in the pig genome, which has a functional implication genome-wide. This work illustrates how allele-specific chromatin architecture facilitates concomitant shifts in allele-biased gene expression, as well as the possible consequential phenotypic changes in mammals.

A Strategy based on Bioinformatics and Machine Learning Algorithms Reveals Potential Mechanisms of Shelian Capsule against Hepatocellular Carcinoma.

BACKGROUND: Hepatocellular carcinoma (HCC) is a prevalent and life-threatening form of cancer, with Shelian Capsule (SLC), a traditional Chinese medicine (TCM) formulation, being recommended for clinical treatment. However, the mechanisms underlying its efficacy remain elusive. This study sought to uncover the potential mechanisms of SLC in HCC treatment using bioinformatics methods. METHODS: Bioactive components of SLC were obtained from the Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform (TCMSP), and HCC-related microarray chip data were sourced from the Gene Expression Omnibus (GEO) database. The selection criteria for components included OB ≧ 30% and DL ≧ 0.18. By integrating the results of differential expression analysis and weighted gene co-expression network analysis (WGCNA), disease-related genes were identified. Therapeutic targets were determined as shared items between candidate targets and disease genes. Protein-protein interaction (PPI) network analysis was conducted for concatenated genes, with core protein clusters identified using the MCODE plugin. Machine learning algorithms were applied to identify signature genes within therapeutic targets. Subsequently, immune cell infiltration analysis, single-cell RNA sequencing (sc-RNA seq) analysis, molecular docking, and ADME analysis were performed for the screened genes. RESULTS: A total of 153 SLC ingredients and 170 candidate targets were identified, along with 494 HCCrelated disease genes. Overlapping items between disease genes and drug candidates represented therapeutic genes, and PPI network analysis was conducted using concatenated genes. MCODE1 and MCODE2 cluster genes underwent Disease Ontology (DO), Gene Ontology (GO), and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses. Four signature genes (TOP2A, CYP1A2, CYP2B6, and IGFBP3) were identified from 28 therapeutic genes using 3 machine learning algorithms, with ROC curves plotted. Molecular docking validated the interaction modes and binding abilities between signature genes and corresponding compounds, with free binding energy all <-7 kcal/mol. Finally, ADME analysis revealed similarities between certain SLC components and the clinical drugs Sorafenib and Lenvatinib. CONCLUSION: In summary, our study revealed that the mechanism underlying the anti-HCC effects of SLC involves interactions at three levels: components (quercetin, beta-sitosterol, kaempferol, baicalein, stigmasterol, and luteolin), pathways (PI3K-Akt signaling pathway, TNF signaling pathway, and IL-17 signaling pathway), and targets (TOP2A, CYP1A2, CYP2B6, and IGFBP3). This study provides preliminary insights into the potential pharmacological mechanisms of SLC in HCC treatment, aiming to support its clinical application and serve as a reference for future laboratory investigations.

Machine learning prediction models for in-hospital postoperative functional outcome after moderate-to-severe traumatic brain injury.

AIM: This study aims to utilize machine learning (ML) and logistic regression (LR) models to predict surgical outcomes among patients with traumatic brain injury (TBI) based on admission examination, assisting in making optimal surgical treatment decision for these patients. METHOD: We conducted a retrospective review of patients hospitalized in our department for moderate-to-severe TBI. Patients admitted between October 2011 and October 2022 were assigned to the training set, while patients admitted between November 2022 and May 2023 were designated as the external validation set. Five ML algorithms and LR model were employed to predict the postoperative Glasgow Outcome Scale (GOS) status at discharge using clinical and routine blood data collected upon admission. The Shapley (SHAP) plot was utilized for interpreting the models. RESULTS: A total of 416 patients were included in this study, and they were divided into the training set (n = 396) and the external validation set (n = 47). The ML models, using both clinical and routine blood data, were able to predict postoperative GOS outcomes with area under the curve (AUC) values ranging from 0.860 to 0.900 during the internal cross-validation and from 0.801 to 0.890 during the external validation. In contrast, the LR model had the lowest AUC values during the internal and external validation (0.844 and 0.567, respectively). When blood data was not available, the ML models achieved AUCs of 0.849 to 0.870 during the internal cross-validation and 0.714 to 0.861 during the external validation. Similarly, the LR model had the lowest AUC values (0.821 and 0.638, respectively). Through repeated cross-validation analysis, we found that routine blood data had a significant association with higher mean AUC values in all ML and LR models. The SHAP plot was used to visualize the contributions of all predictors and highlighted the significance of blood data in the lightGBM model. CONCLUSION: The study concluded that ML models could provide rapid and accurate predictions for postoperative GOS outcomes at discharge following moderate-to-severe TBI. The study also highlighted the crucial role of routine blood tests in improving such predictions, and may contribute to the optimization of surgical treatment decision-making for patients with TBI.

Machine learning models for predicting in-hospital outcomes after non-surgical treatment among patients with moderate-to-severe traumatic brain injury.

AIM: This study aims to develop prediction models for in-hospital outcomes after non-surgical treatment among patients with moderate-to-severe traumatic brain injury (TBI). METHOD: We conducted a retrospective review of patients hospitalized for moderate-to-severe TBI in our department from 2011 to 2020. Five machine learning (ML) algorithms and the conventional logistic regression (LR) model were employed to predict in-hospital mortality and the Glasgow Outcome Scale (GOS) functional outcomes. These models utilized clinical and routine blood data collected upon admission. RESULTS: This study included a total of 196 patients who received only non-surgical treatment after moderate-to-severe TBI. When predicting mortality, ML models achieved area under the curve (AUC) values of 0.921 to 0.994 using clinical and routine blood data, and 0.877 to 0.982 using only clinical data. In comparison, LR models yielded AUCs of 0.762 and 0.730 respectively. When predicting the GOS outcome, ML models achieved AUCs of 0.870 to 0.915 using clinical and routine blood data, and 0.858 to 0.927 using only clinical data. In comparison, the LR model yielded AUCs of 0.798 and 0.787 respectively. Repeated internal validation showed that the contributions of routine blood data for prediction models may depend on different prediction algorithms and different outcome measurements. CONCLUSION: The study reported ML-based prediction models that provided rapid and accurate predictions on short-term outcomes after non-surgical treatment among patients with moderate-to-severe TBI. The study also highlighted the superiority of ML models over conventional LR models and proposed the complex contributions of routine blood data in such predictions.

Effect of the ripening stage on the pulsed vacuum drying behavior of goji berry (Lycium barbarum L.): Ultrastructure, drying characteristics, and browning mechanism.

In current work, the effect of ripening stages (I, II, and III) on pulsed vacuum drying (PVD) behavior of goji berry was explored. The shortest drying time of goji berry was observed at stage I (6.99 h) which was 13.95 %, and 28.85 % shorter than those at stages II, and III, respectively. This phenomenon was closely associated with the ripening stage, as contributed by the initial physiochemical differences, ultrastructure alterations, and moisture distribution. In addition, lower maturity suffered more severe browning, primarily due to the enzymatic and non-enzymatic reactions of phenolics, followed by pigment degradation and the Maillard reaction. Additionally, the PVD process promoted the rupture and transformation of the pectin fractions, also causing browning either directly or indirectly through participation in other chemical reactions. These findings suggest that the appropriate ripening stage of goji berry should be considered as having a significant impact on drying behaviors and quality attributes.

Vacuum-steam pulsed blanching: An emerging method to enhance texture softening, drying behavior and physicochemical properties of Cornus officinalis.

Vacuum steam pulsed blanching (VSPB) was employed as a novel blanching technology on Cornus officinalis to soften the tissue for subsequent coring and dehydration. The current work aims to explore its effect on mass transfer behavior, PPO inactivation, drying characteristics, physicochemical properties, antioxidant capacity, and microstructure of C. officinalis. Results showed that VSPB increased water loss, decreased solid gain, and increased weight reduction with increased blanching cycles. Besides, VSPB significantly changed physical properties and extensively reduced drying time which was attributed to the cell wall components dissolving and cell turgor pressure decreasing, also verified by observing microstructure alteration. PPO was completely denatured after blanching in 6 cycles, but phenolic compounds were still diffused or degraded. Notably, the content of flavonoids and antioxidant capacity significantly increased compared to fresh samples probably due to increased extractability caused by the disrupting cell structure. Besides, the carotenoids and ascorbic acid could be well preserved.

Long-Term Outcomes of Autologous Stem Cell Transplantation in Patients with Newly Diagnosed POEMS Syndrome.

POEMS (polyneuropathy, organomegaly, endocrinopathy, monoclonal gammopathy, and skin changes) syndrome is a rare form of plasma cell dyscrasia often treated with high-dose chemotherapy and autologous stem cell transplantation (ASCT). ASCT has resulted in satisfactory and sustained therapeutic outcomes. However, a substantial number of patients eventually experience disease progression, requiring second-line treatment. Therefore, it would be of further benefit to identify patients who will acquire the best long-term survival after ASCT. The aim of this study was to fully reveal the outcomes of patients undergoing ASCT in a large series with long-term follow-up. Long-term outcomes of 239 patients with newly diagnosed POEMS syndrome undergoing ASCT at a single center were evaluated retrospectively. Rates of hematologic complete response (CRH) and vascular endothelial growth factor (VEGF) complete response (CRV) were 57.3% and 68.6%, respectively, with 90.5% of patients achieving an overall clinical response. At a median follow-up of 94 months, the 5-year overall survival (OS) rate was 92.8%, and the 5-year time to next-line treatment (TTNT) rate was 72.2% (median TTNT, 96 months). Patients achieving CRH (5-year TTNT rate, 82.5% versus 60.7%; P < .0001) or CRV (5-year TTNT rate 83.7% versus 54.2%; P < .0001) had better survival outcomes compared to non-CR group patients. Dual hematologic and VEGF complete responses carry further benefit for survival (median TTNT, 129 months versus 68 months; P < .0001). Seven cases of second primary malignancy were recorded, all of which were solid tumors. Front-line ASCT resulted in excellent long-term survival in patients with POEMS syndrome, with the best survival observed in those achieving dual hematologic and VEGF CRs.

Correlation between intraosseous thermal change and drilling impulse data during osteotomy within autonomous dental implant robotic system: An in vitro study.

OBJECTIVES: This study aims at examining the correlation of intraosseous temperature change with drilling impulse data during osteotomy and establishing real-time temperature prediction models. MATERIALS AND METHODS: A combination of in vitro bovine rib model and Autonomous Dental Implant Robotic System (ADIR) was set up, in which intraosseous temperature and drilling impulse data were measured using an infrared camera and a six-axis force/torque sensor respectively. A total of 800 drills with different parameters (e.g., drill diameter, drill wear, drilling speed, and thickness of cortical bone) were experimented, along with an independent test set of 200 drills. Pearson correlation analysis was done for linear relationship. Four machining learning (ML) algorithms (e.g., support vector regression [SVR], ridge regression [RR], extreme gradient boosting [XGboost], and artificial neural network [ANN]) were run for building prediction models. RESULTS: By incorporating different parameters, it was found that lower drilling speed, smaller drill diameter, more severe wear, and thicker cortical bone were associated with higher intraosseous temperature changes and longer time exposure and were accompanied with alterations in drilling impulse data. Pearson correlation analysis further identified highly linear correlation between drilling impulse data and thermal changes. Finally, four ML prediction models were established, among which XGboost model showed the best performance with the minimum error measurements in test set. CONCLUSION: The proof-of-concept study highlighted close correlation of drilling impulse data with intraosseous temperature change during osteotomy. The ML prediction models may inspire future improvement on prevention of thermal bone injury and intelligent design of robot-assisted implant surgery.

Genome-wide DNA methylation analysis identifies potent CpG signature for temzolomide response in non-G-CIMP glioblastomas with unmethylated MGMT promoter: MGMT-dependent roles of GPR81.

PURPOSES: To identify potent DNA methylation candidates that could predict response to temozolomide (TMZ) in glioblastomas (GBMs) that do not have glioma-CpGs island methylator phenotype (G-CIMP) but have an unmethylated promoter of O-6-methylguanine-DNA methyltransferase (unMGMT). METHODS: The discovery-validation approach was planned incorporating a series of G-CIMP-/unMGMT GBM cohorts with DNA methylation microarray data and clinical information, to construct multi-CpG prediction models. Different bioinformatic and experimental analyses were performed for biological exploration. RESULTS: By analyzing discovery sets with radiotherapy (RT) plus TMZ versus RT alone, we identified a panel of 64 TMZ efficacy-related CpGs, from which a 10-CpG risk signature was further constructed. Both the 64-CpG panel and the 10-CpG risk signature were validated showing significant correlations with overall survival of G-CIMP-/unMGMT GBMs when treated with RT/TMZ, rather than RT alone. The 10-CpG risk signature was further observed for aiding TMZ choice by distinguishing differential outcomes to RT/TMZ versus RT within each risk subgroup. Functional studies on GPR81, the gene harboring one of the 10 CpGs, indicated its distinct impacts on TMZ resistance in GBM cells, which may be dependent on the status of MGMT expression. CONCLUSIONS: The 64 TMZ efficacy-related CpGs and in particular the 10-CpG risk signature may serve as promising predictive biomarker candidates for guiding optimal usage of TMZ in G-CIMP-/unMGMT GBMs.

[Review of Research Advances in Medical Service Robotics].

With the progress of science and technology and the increase of clinical demand, medical robots have developed rapidly and played a important role in promoting the medical cause. Service robot is a branch of medical robot, which is mainly oriented to medical service and assistance needs, and has been applied in many medical scenarios and achieved demonstration effects. This research first describes the development of medical service robots, and then summarizes the key technologies and clinical applications of robots. Finally, it points out the challenges and directions that medical service robots face at present, and puts forward prospects for their further development in the medical field.