BACKGROUND:How to repair bone defect has been a clinical problem for a long time.The effective ingredients of traditional Chinese medicine have good biological activity and therapeutic effect,and the combination of effective ingredients of traditional Chinese medicine and tissue engineering materials has a broad prospect in the field of bone repair.The combination of different effective ingredients of traditional Chinese medicine and scaffolds has similarities in their functional relationships. OBJECTIVE:To collect the cases of the combinations of effective ingredients of traditional Chinese medicine and scaffolds,then analogize tissue engineering scaffolds and effective ingredients of traditional Chinese medicine into two types of traditional Chinese medicine that generate compatibility relationships based on the inspiration of the compatibility of seven emotions and summarize the relationship between the two based on their functional relationships. METHODS:Relevant articles from January 1998 to January 2024 were searched in PubMed and China National Knowledge Infrastructure(CNKI),using English search terms"traditional Chinese medicine,Chinese medicine,traditional Chinese medicine monomers,bone defect,bone repair,bone tissue engineering,tissue engineering,scaffold"and Chinese search terms"traditional Chinese medicine,effective ingredients of traditional Chinese medicine,traditional Chinese medicine monomers,bone tissue engineering,bone tissue engineering scaffold,scaffold,tissue engineering,bone defect,bone repair."A total of 88 articles were included for review and analysis. RESULTS AND CONCLUSION:(1)Both tissue engineering scaffold materials and active ingredients of traditional Chinese medicine have been widely used in the field of bone repair.Although they have obvious advantages in osteogenesis,there are still many shortcomings.Many studies are dedicated to preparing composite materials from the two,hoping to exert a detoxification and synergism through the interaction between the two.(2)Some drugs and materials can promote each other in osteogenesis,antibacterial,and promoting angiogenesis,enhancing their original effects.Inspired by the traditional concept of prescription compatibility,this article summarized it as a"Mutual promotion"relationship and provided examples to support it.(3)Some drugs can enhance the strength of materials,while some materials can achieve sustained release and controlled release effects,increase drug loading and stability,or achieve targeted delivery of drugs loaded on them.The article summarized this unilateral enhancement effect as a"Mutual assistance"relationship.(4)The combination of some traditional Chinese medicine and materials can reduce the toxic side effects of the other party.The article summarizes this detoxification relationship as"Mutual restraint and detoxification."(5)The article provided a new perspective on traditional Chinese medicine composite scaffolds,inspired by the seven emotions compatibility relationship and based on the classification of action relationships.It introduced traditional Chinese medicine concepts into the field of tissue engineering,providing new research ideas for subsequent researchers of composite scaffolds,and providing certain convenience in material selection and matching.

Purpose: The genomic characteristics of uterine sarcomas have not been fully elucidated. This study aimed to explore the genomic landscape of the uterine sarcomas (USs). Materials and Methods: Comprehensive genomic analysis through RNA-sequencing was conducted. Gene fusion, differentially expressed genes (DEGs), signaling pathway enrichment, immune cell infiltration, and prognosis were analyzed. A deep learning model was constructed to predict the survival of US patients. Results: A total of 71 US samples were examined, including 47 endometrial stromal sarcomas (ESS), 18 uterine leiomyosarcomas (uLMS), three adenosarcomas, two carcinosarcomas, and one uterine tumor resembling an ovarian sex-cord tumor. ESS (including high-grade ESS [HGESS] and low-grade ESS [LGESS]) and uLMS showed distinct gene fusion signatures; a novel gene fusion site, MRPS18A–PDC-AS1 could be a potential diagnostic marker for the pathology differential diagnosis of uLMS and ESS; 797 and 477 uterine sarcoma DEGs (uDEGs) were identified in the ESS vs. uLMS and HGESS vs. LGESS groups, respectively. The uDEGs were enriched in multiple pathways. Fifteen genes including LAMB4 were confirmed with prognostic value in USs; immune infiltration analysis revealed the prognositic value of myeloid dendritic cells, plasmacytoid dendritic cells, natural killer cells, macrophage M1, monocytes and hematopoietic stem cells in USs; the deep learning model named Max-Mean Non-Local multi-instance learning (MMN-MIL) showed satisfactory performance in predicting the survival of US patients, with the area under the receiver operating curve curve reached 0.909 and accuracy achieved 0.804. Conclusion USs harbored distinct gene fusion characteristics and gene expression features between HGESS, LGESS, and uLMS. The MMN-MIL model could effectively predict the survival of US patients.

Purpose: The genomic characteristics of uterine sarcomas have not been fully elucidated. This study aimed to explore the genomic landscape of the uterine sarcomas (USs). Materials and Methods: Comprehensive genomic analysis through RNA-sequencing was conducted. Gene fusion, differentially expressed genes (DEGs), signaling pathway enrichment, immune cell infiltration, and prognosis were analyzed. A deep learning model was constructed to predict the survival of US patients. Results: A total of 71 US samples were examined, including 47 endometrial stromal sarcomas (ESS), 18 uterine leiomyosarcomas (uLMS), three adenosarcomas, two carcinosarcomas, and one uterine tumor resembling an ovarian sex-cord tumor. ESS (including high-grade ESS [HGESS] and low-grade ESS [LGESS]) and uLMS showed distinct gene fusion signatures; a novel gene fusion site, MRPS18A–PDC-AS1 could be a potential diagnostic marker for the pathology differential diagnosis of uLMS and ESS; 797 and 477 uterine sarcoma DEGs (uDEGs) were identified in the ESS vs. uLMS and HGESS vs. LGESS groups, respectively. The uDEGs were enriched in multiple pathways. Fifteen genes including LAMB4 were confirmed with prognostic value in USs; immune infiltration analysis revealed the prognositic value of myeloid dendritic cells, plasmacytoid dendritic cells, natural killer cells, macrophage M1, monocytes and hematopoietic stem cells in USs; the deep learning model named Max-Mean Non-Local multi-instance learning (MMN-MIL) showed satisfactory performance in predicting the survival of US patients, with the area under the receiver operating curve curve reached 0.909 and accuracy achieved 0.804. Conclusion USs harbored distinct gene fusion characteristics and gene expression features between HGESS, LGESS, and uLMS. The MMN-MIL model could effectively predict the survival of US patients.

In protein engineering, while computational models are increasingly used to predict mutation effects, their evaluations primarily rely on high-throughput deep mutational scanning (DMS) experiments that use surrogate readouts, which may not adequately capture the complex biochemical properties of interest. Many proteins and their functions cannot be assessed through high-throughput methods due to technical limitations or the nature of the desired properties, and this is particularly true for the real industrial application scenario. Therefore, the desired testing datasets, will be small-size (∼10-100) experimental data for each protein, and involve as many proteins as possible and as many properties as possible, which is, however, lacking. Here, we present VenusMutHub, a comprehensive benchmark study using 905 small-scale experimental datasets curated from published literature and public databases, spanning 527 proteins across diverse functional properties including stability, activity, binding affinity, and selectivity. These datasets feature direct biochemical measurements rather than surrogate readouts, providing a more rigorous assessment of model performance in predicting mutations that affect specific molecular functions. We evaluate 23 computational models across various methodological paradigms, such as sequence-based, structure-informed and evolutionary approaches. This benchmark provides practical guidance for selecting appropriate prediction methods in protein engineering applications where accurate prediction of specific functional properties is crucial.

Bacterial biofilms can make traditional antibiotics impenetrable and even promote the development of antibiotic-resistant strains. Therefore, non-antibiotic strategies to effectively penetrate and eradicate the formed biofilms are urgently needed. Here, we demonstrate the development of self-propelled biohybrid microrobots that can enhance the degradation and penetration effects for Pseudomonas aeruginosa biofilms in minimally invasive strategy. The biohybrid microrobots (CR@Alg) are constructed by surface modification of Chlamydomonas reinhardtii (CR) microalgae with alginate lyase (Alg) via biological orthogonal reaction. By degrading the biofilm components, the number of CR@Alg microrobots with fast-moving capability penetrating the biofilm increases by around 2.4-fold compared to that of microalgae. Massive reactive oxygen species are subsequently generated under laser irradiation due to the presence of chlorophyll, inherent photosensitizers of microalgae, thus triggering photodynamic therapy (PDT) to combat bacteria. Our algae-based microrobots with superior biocompatibility eliminate biofilm-infections efficiently and tend to suppress the inflammatory response in vivo, showing huge promise for the active treatment of biofilm-associated infections.

Schizophrenia (SZ) stands as a severe psychiatric disorder. This study applied diffusion tensor imaging (DTI) data in conjunction with graph neural networks to distinguish SZ patients from normal controls (NCs) and showcases the superior performance of a graph neural network integrating combined fractional anisotropy and fiber number brain network features, achieving an accuracy of 73.79% in distinguishing SZ patients from NCs. Beyond mere discrimination, our study delved deeper into the advantages of utilizing white matter brain network features for identifying SZ patients through interpretable model analysis and gene expression analysis. These analyses uncovered intricate interrelationships between brain imaging markers and genetic biomarkers, providing novel insights into the neuropathological basis of SZ. In summary, our findings underscore the potential of graph neural networks applied to multimodal DTI data for enhancing SZ detection through an integrated analysis of neuroimaging and genetic features.
Humans ; Schizophrenia/pathology* ; Diffusion Tensor Imaging/methods* ; Male ; Female ; Adult ; Brain/metabolism* ; Young Adult ; Middle Aged ; White Matter/pathology* ; Gene Expression ; Nerve Net/diagnostic imaging* ; Graph Neural Networks

OBJECTIVE: To investigate the relationship between physical activity and genetic risk and their combined effects on the risk of developing chronic obstructive pulmonary disease. METHODS: This prospective cohort study included 318,085 biobank participants from the UK. Physical activity was assessed using the short form of the International Physical Activity Questionnaire. The participants were stratified into low-, intermediate-, and high-genetic-risk groups based on their polygenic risk scores. Multivariate Cox regression models and multiplicative interaction analyses were used. RESULTS: During a median follow-up period of 13 years, 9,209 participants were diagnosed with chronic obstructive pulmonary disease. For low genetic risk, compared to low physical activity, the hazard ratios ( HRs) for moderate and high physical activity were 0.853 (95% confidence interval [ CI]: 0.748-0.972) and 0.831 (95% CI: 0.727-0.950), respectively. For intermediate genetic risk, the HRs were 0.829 (95% CI: 0.758-0.905) and 0.835 (95% CI: 0.764-0.914), respectively. For participants with high genetic risk, the HRs were 0.809 (95% CI: 0.746-0.877) and 0.818 (95% CI: 0.754-0.888), respectively. A significant interaction was observed between genetic risk and physical activity. CONCLUSION Moderate or high levels of physical activity were associated with a lower risk of developing chronic obstructive pulmonary disease across all genetic risk groups, highlighting the need to tailor activity interventions for genetically susceptible individuals.
Humans ; Pulmonary Disease, Chronic Obstructive/epidemiology* ; Exercise ; Male ; Female ; Middle Aged ; Prospective Studies ; Aged ; Genetic Predisposition to Disease ; Risk Factors ; United Kingdom/epidemiology* ; Incidence ; Adult

Endotoxins are common exogenous pyrogens. Excessive endotoxins in medical devices and injections can lead to serious consequences such as sepsis, septic shock, and even death. Therefore, endotoxin detection plays a crucial role in medical, pharmaceutical, and food sectors. The wide application of Limulus amebocyte lysate (LAL) has led to a sharp decline in the number of horseshoe crabs. Moreover, the LAL assay has limitations such as interbatch variations and difficulty in quantification. The recombinant factor C (rFC) assay is stable between batches, highly sensitive, and capable of quantitation, and thus it can be used as an alternative for the LAL assay. However, the high cost and complex procedures involved in producing recombinant factor C have limited the widespread application of this method. In order to simplify the preparation and reduce the production cost of recombinant factor C, this study focuses on the production of recombinant factor C based on the baculovirus expression system. Multiple measures such as a high-yield and anti-apoptotic vector qBac-IIIG, the optimal signal peptide, and the optimized codon were used to reach the goal of endotoxin detection with cell supernatant. This method simplifies the steps of protein purification. The sensitivity of the supernatant reached 0.05 EU/mL in a 1-L fermentation system, and 500 000 detecting reactions can be supported per liter of fermentation broth. This study increases the yield and activity of recombinant factor C, simplifies the procedures of protein purification, and reduces the cost, laying a foundation for the promotion and application of recombinant factor C in endotoxin detection.
Animals ; Recombinant Proteins/genetics* ; Horseshoe Crabs/chemistry* ; Baculoviridae/metabolism* ; Endotoxins/analysis* ; Protein C/biosynthesis* ; Genetic Vectors/genetics* ; Arthropod Proteins/genetics* ; Enzyme Precursors ; Serine Endopeptidases

Objective To investigate the effects of a 100 mT static magnetic field(SMF)on emotional behavior and brain damage-related molecules in mice.Methods Fifty-eight C57BL/6N mice were randomly divided into control group(n=25)and observation group(n=33).Mice in observation group were exposed to a 100 mT SMF for 0.5 h/d over 14 consecutive days,while mice in control group underwent pseudo-exposure.On the 7 and 14 days of exposure,anxiety-like behavior was assessed using open field and elevated plus maze tests.Cerebral blood flow was monitored using laser speckle imaging,and the levels of tumor necrosis factor-α(TNF-α),interleukin(IL)-1β,IL-4,central nervous system specific protein β(S100β),neuron-specific enolase(NSE),and brain-derived neurotrophic factor(BDNF)were measured by radioimmunoassay.BDNF expression in the brain was detected by immunofluorescence.Results On the 7 and 14 days of SMF exposure,the open field and elevated plus maze tests showed no statistically significant differences between observation and control groups in the frequencies,durations,and distance entering the central area of the open field and the open arm of the elevated plus maze(P>0.05).Laser speckle imaging revealed no significant difference in cerebral cortical perfusion compared with pre-exposure period(P>0.05).The results of radioimmunoassay showed that compared with control group,on the 7 d of SMF exposure,the serum IL-1β,NSE and S100β levels were significantly increased(P<0.05),the serum BDNF level was significantly decreased(P<0.05),and the IL-1β and TNF-α contents in brain tissues were significantly increased in observation group(P<0.01).On the 14 d of SMF exposure,serum IL-1β,TNF-α,NSE,and S100β levels were significantly increased(P<0.05,P<0.0001),and the brain IL-1β and TNF-α levels were significantly increased(P<0.01)in observation group.No statistically significant differences were found in anti-inflammatory cytokine IL-4 level of serum and brain tissue or BDNF content of brain tissue between the two groups(P>0.05).Conclusion Continuous exposure to a 100 mT SMF for 14 d at 0.5 h/d induces neuroinflammation and brain damage in mice,without inducing anxiety-like behavior.