ObjectiveTo address the challenges of unstructured classical Chinese expressions， nested entity relationships， and limited annotated data in famous traditional Chinese medicine（TCM） case records， this study proposes a joint relation extraction framework that integrates data augmentation and entity mapping， aiming to support the construction of TCM diagnostic knowledge graphs and clinical pattern mining. MethodsWe developed an annotation structure for entities and their relationships in TCM case texts and applied a data augmentation strategy by incorporating multiple ancient texts to expand the relation extraction dataset. A cascade binary tagging framework for relation triple extraction（CasRel） model for TCM semantics was designed， integrating a pre-trained bidirectional encoder representations from transformers（BERT） layer for classical TCM texts to enhance semantic representation， and using a head entity-relation-tail entity mapping mechanism to address entity nesting and relation overlapping issues. ResultsExperimental results showed that the CasRel model， combining data augmentation and entity mapping， outperformed the pipeline-based Bert-Radical-Lexicon（BRL）-bidirectional long short-term memory（BiLSTM）-Attention model. The overall precision， recall， and F₁-score across 12 relation types reached 65.73%， 64.03%， and 64.87%， which represent improvements of 14.26%， 7.98%， and 11.21% compared to the BRL-BiLSTM-Attention model， respectively. Notably， the F₁-score for tongue syndrome relations increased by 22.68%（69.32%）， and the prescription-syndrome relations performed the best with the F₁-score of 70.10%. ConclusionThe proposed framework significantly improves the semantic representation and complex dependencies in TCM texts， offering a reusable technical framework for structured mining of TCM case records. The constructed knowledge graph can support clinical syndrome differentiation， prescription optimization， and drug compatibility， providing a methodological reference for TCM artificial intelligence research.

Serine protease inhibitor Kazal-type (SPINK) is a skin keratinizing protease inhibitor, which was initially found in animal serum and is widely present in plants, animals, bacteria, and viruses, and they act as key regulators of skin keratinizing proteases and are involved in the regulation of keratinocyte proliferation and inflammation, primarily through the inhibition of deregulated tissue kinin-releasing enzymes (KLKs) in skin response. This process plays a crucial role in alleviating various skin problems caused by hyperkeratinization and inflammation, and can greatly improve the overall condition of the skin. Specifically, the different members of the SPINK family, such as SPINK5, SPINK6, SPINK7, and SPINK9, each have unique biological functions and mechanisms of action. The existence of these members demonstrates the diversity and complexity of skin health and disease. First, SPINK5 mutations are closely associated with the development of various skin diseases, such as Netherton’s syndrome and atopic dermatitis, and SPINK5 is able to inhibit the activation of the STAT3 signaling pathway, thereby effectively preventing the metastasis of melanoma cells, which is important in preventing the invasion and migration of malignant tumors. Secondly, SPINK6 is mainly distributed in the epidermis and contains lysine and glutamate residues, which can act as a substrate for epidermal transglutaminase to maintain the normal structure and function of the skin. In addition, SPINK6 can activate the intracellular ERK1/2 and AKT signaling pathways through the activation of epidermal growth factor receptor and protease receptor-2 (EphA2), which can promote the migration of melanoma cells, and SPINK6 further deepens its role in stimulating the migration of malignant tumor cells by inhibiting the activation of STAT3 signaling pathway. This process further deepens its potential impact in stimulating tumor invasive migration. Furthermore, SPINK7 plays a role in the pathology of some inflammatory skin diseases, and is likely to be an important factor contributing to the exacerbation of skin diseases by promoting aberrant proliferation of keratinocytes and local inflammatory responses. Finally, SPINK9 can induce cell migration and promote skin wound healing by activating purinergic receptor 2 (P2R) to induce phosphorylation of epidermal growth factor and further activating the downstream ERK1/2 signaling pathway. In addition, SPINK9 also plays an antimicrobial role, preventing the interference of some pathogenic microorganisms. Taken as a whole, some members of the SPINK family may be potential targets for the treatment of dermatological disorders by regulating multiple biological processes such as keratinization metabolism and immuno-inflammatory processes in the skin. The development of drugs such as small molecule inhibitors and monoclonal antibodies has great potential for the treatment of dermatologic diseases, and future research on SPINK will help to gain a deeper understanding of the physiopathologic processes of the skin. Through its functions and regulatory mechanisms, the formation and maintenance of the skin barrier and the occurrence and development of inflammatory responses can be better understood, which will provide novel ideas and methods for the prevention and treatment of skin diseases.

Electromagnetic fields can regulate the fundamental biological processes involved in bone remodeling. As a non-invasive physical therapy, electromagnetic fields with specific parameters have demonstrated therapeutic effects on bone remodeling diseases, such as fractures and osteoporosis. Electromagnetic fields can be generated by the movement of charged particles or induced by varying currents. Based on whether the strength and direction of the electric field change over time, electromagnetic fields can be classified into static and time-varying fields. The treatment of bone remodeling diseases with static magnetic fields primarily focuses on fractures, often using magnetic splints to immobilize the fracture site while studying the effects of static magnetic fields on bone healing. However, there has been relatively little research on the prevention and treatment of osteoporosis using static magnetic fields. Pulsed electromagnetic fields, a type of time-varying field, have been widely used in clinical studies for treating fractures, osteoporosis, and non-union. However, current clinical applications are limited to low-frequency, and research on the relationship between frequency and biological effects remains insufficient. We believe that different types of electromagnetic fields acting on bone can induce various “secondary physical quantities”, such as magnetism, force, electricity, acoustics, and thermal energy, which can stimulate bone cells either individually or simultaneously. Bone cells possess specific electromagnetic properties, and in a static magnetic field, the presence of a magnetic field gradient can exert a certain magnetism on the bone tissue, leading to observable effects. In a time-varying magnetic field, the charged particles within the bone experience varying Lorentz forces, causing vibrations and generating acoustic effects. Additionally, as the frequency of the time-varying field increases, induced currents or potentials can be generated within the bone, leading to electrical effects. When the frequency and power exceed a certain threshold, electromagnetic energy can be converted into thermal energy, producing thermal effects. In summary, external electromagnetic fields with different characteristics can generate multiple physical quantities within biological tissues, such as magnetic, electric, mechanical, acoustic, and thermal effects. These physical quantities may also interact and couple with each other, stimulating the biological tissues in a combined or composite manner, thereby producing biological effects. This understanding is key to elucidating the electromagnetic mechanisms of how electromagnetic fields influence biological tissues. In the study of electromagnetic fields for bone remodeling diseases, attention should be paid to the biological effects of bone remodeling under different electromagnetic wave characteristics. This includes exploring innovative electromagnetic source technologies applicable to bone remodeling, identifying safe and effective electromagnetic field parameters, and combining basic research with technological invention to develop scientifically grounded, advanced key technologies for innovative electromagnetic treatment devices targeting bone remodeling diseases. In conclusion, electromagnetic fields and multiple physical factors have the potential to prevent and treat bone remodeling diseases, and have significant application prospects.

ObjectiveTo combine metabolomics， proteomics， and transcriptomics to analyze the biological characteristics of damp-heat toxin accumulation syndrome and damp-heat accumulation syndrome in acute gout. MethodsBlood samples were collected from 15 patients with damp-heat toxin accumulation syndrome and 15 patients with damp-heat accumulation syndrome in acute gout in clinical practice. Metabolomics technology was applied to detect serum metabolites， and an orthogonal partial sample least squares discriminant analysis model was constructed to screen for metabolites with significant intergroup changes， and enrichment pathway analysis and receiver operating characteristic （ROC） curve analysis were performed. Astral data independence acquisition （DIA） was used to detect serum proteins， perform principal component analysis and screen differential proteins， demonstrate differential ploidy by radargram， apply subcellular localisation to analyse protein sources， and finally apply weighted gene co-expression network analysis （WGCNA） to find key proteins. Transcriptome sequencing technology was also applied to detect whole blood mRNA， screen differential genes and perform WGCNA， and construct machine learning models to screen key genes. ResultsMetabolome differential analysis revealed 62 differential metabolites in positive ion mode and 26 in negative ion mode. These differential metabolites were mainly enriched in the mTOR signaling pathway and FoxO signaling pathway， with trans-3,5-dimethoxy-4-hydroxycinnamaldehyde， guanabenz， 4-aminophenyl-1-thio-beta-d-galactopyranoside showing the highest diagnostic efficacy. The proteome differential analysis found that 55 proteins up-regulated and 20 proteins down-regulated in the samples of damp-heat toxin accumulation syndrome. Notably， myelin basic protein （MBP）， transferrin （TF）， DKFZp686N02209， and apolipoprotein B （APOB） showed the most significant differences in expression. Differential proteins were mainly enriched in pathways related to fat digestion and absorption， lipid and atherosclerosis， and cholesterol metabolism. WGCNA showed the highest correlation between damp-heat toxin accumulation syndrome and the brown module， with proteins in this module primarily enriched in the hypoxia-inducible factor 1 （HIF-1） signaling pathway and lipid and atherosclerosis. Transcriptomic differential analysis identified 252 differentially expressed genes， with WGCNA indicating the highest correlation between damp-heat toxin accumulation syndrome and the midnight blue module. The random forest （RF） model was identified as the optimal machine learning model， predicting apolipoprotein B receptor （APOBR）， far upstream element-binding protein 2 （KHSRP）， POU domain class 2 transcription factor 2 （POU2F2）， EH domain-containing protein 1 （EHD1）， and family with sequence similarity 110A （FAM110A） as key genes. Integrated multi-omics analysis suggested that damp-heat toxin accumulation syndrome in the acute phase of gout is closely associated with lipid metabolism， particularly APOB. ConclusionCompared to damp-heat accumulation syndrome in the acute phase of gout， damp-heat toxin accumulation syndrome is more closely associated with lipid metabolism， particularly APOB， and lipid metabolism disorders contribute to the development of damp-heat toxin accumulation syndrome in patients with acute gout.

Objective To compare the performance and efficacy of prognostic models constructed by different machine learning algorithms in predicting the survival period of lung transplantation (LTx) recipients. Methods Data from 483 recipients who underwent LTx were retrospectively collected. All recipients were divided into a training set and a validation set at a ratio of 7:3. The 24 collected variables were screened based on variable importance (VIMP). Prognostic models were constructed using random survival forest (RSF) and extreme gradient boosting tree (XGBoost). The performance of the models was evaluated using the integrated area under the curve (iAUC) and time-dependent area under the curve (tAUC). Results There were no significant statistical differences in the variables between the training set and the validation set. The top 15 variables ranked by VIMP were used for modeling and the length of stay in the intensive care unit (ICU) was determined as the most important factor. Compared with the XGBoost model, the RSF model demonstrated better performance in predicting the survival period of recipients (iAUC 0.773 vs. 0.723). The RSF model also showed better performance in predicting the 6-month survival period (tAUC _{6 months} 0.884 vs. 0.809, P = 0.009) and 1-year survival period (tAUC _{1 year} 0.896 vs. 0.825, P = 0.013) of recipients. Based on the prediction cut-off values of the two algorithms, LTx recipients were divided into high-risk and low-risk groups. The survival analysis results of both models showed that the survival rate of recipients in the high-risk group was significantly lower than that in the low-risk group (P<0.001). Conclusions Compared with XGBoost, the machine learning prognostic model developed based on the RSF algorithm may preferably predict the survival period of LTx recipients.

Objective To investigate the new-onset conduction block after transcatheter aortic valve replacement (TAVR) and summarize the relevant experience. Methods The perioperative data of TAVR patients in the Second Hospital of Hebei Medical University from January 2016 to February 2023 were collected, and the new-onset incidence of conduction block after TAVR was analyzed retrospectively. Results Finally 352 patients were included, including 225 males and 127 females, with an average age of (67.2±5.1) years, among whom 256 patients were treated with Venus-A valves, 69 patients with Vita-Flow valves, and 27 patients with J-Valve valves. There were 38 (10.8%) patients of new-onset postoperative block. There were 6 (1.7%) patients of new-onset postoperative grade Ⅲ atrioventricular block, including 5 (2.0%) patients of Venus-A and 1 (1.4%) patient of Vita-Flow. Conduction function was restored in 2 patients within 14 days after surgery, and failed to be restored in 4 patients, who then received permanent pacemaker implantation in the Department of Cardiology. There were 27 (7.7%) patients of new left bundle branch block after surgery, including 22 (8.6%) patients of Venus-A, 4 (5.8%) patients of Vita-Flow and 1 (3.7%) patient of J-Valve; and conduction function was restored within 7 days after surgery in 23 patients, and 5 (1.4%) patients developed new right bundle branch blocks after surgery including 4 (1.5%) patients of Venus-A and 1 (1.4%) patient of Vita-Flow. Conclusion New-onset conduction block is a common complication after TAVR, and the new-onset rate of left bundle branch block is the highest, followed by the grade Ⅲ atrioventricular block. Mastering reasonable methods and applying appropriate strategies can effectively reduce the new-onset rate of postoperative conduction block and improve the overall success rate of TAVR surgery.

ObjectiveThe rapid advancement of bioanalytical technologies has heightened the demand for high-throughput, label-free, and real-time cellular analysis. Electrochemical impedance spectroscopy (EIS) operating in the GHz frequency range (GHz-EIS) has emerged as a promising tool for characterizing cell suspensions due to its ability to rapidly and non-invasively capture the dielectric properties of cells and their microenvironment. Although GHz-EIS enables rapid and label-free detection of cell suspensions, significant challenges remain in interpreting GHz impedance data for complex samples, limiting the broader application of this technique in cellular research. To address these challenges, this study presents a novel method that integrates GHz-EIS with deep learning algorithms, aiming to improve the precision of cell suspension concentration identification and quantification. This method provides a more efficient and accurate solution for the analysis of GHz impedance data. MethodsThe proposed method comprises two key components: dielectric property dataset construction and backpropagation (BP) neural network modeling. Yeast cell suspensions at varying concentrations were prepared and separately introduced into a coaxial sensor for impedance measurement. The dielectric properties of these suspensions were extracted using a GHz-EIS dielectric property extraction method applied to the measured impedance data. A dielectric properties dataset incorporating concentration labels was subsequently established and divided into training and testing subsets. A BP neural network model employing specific activation functions (ReLU and Leaky ReLU) was then designed. The model was trained and tested using the constructed dataset, and optimal model parameters were obtained through this process. This BP neural network enables automated extraction and analytical processing of dielectric properties, facilitating precise recognition of cell suspension concentrations through data-driven training. ResultsThrough comparative analysis with conventional centrifugal methods, the recognized concentration values of cell suspensions showed high consistency, with relative errors consistently below 5%. Notably, high-concentration samples exhibited even smaller deviations, further validating the precision and reliability of the proposed methodology. To benchmark the recognition performance against different algorithms, two typical approaches—support vector machines (SVM) and K-nearest neighbor (KNN)—were selected for comparison. The proposed method demonstrated superior performance in quantifying cell concentrations. Specifically, the BP neural network achieved a mean absolute percentage error (MAPE) of 2.06% and an R² value of 0.997 across the entire concentration range, demonstrating both high predictive accuracy and excellent model fit. ConclusionThis study demonstrates that the proposed method enables accurate and rapid determination of unknown sample concentrations. By combining GHz-EIS with BP neural network algorithms, efficient identification of cell concentrations is achieved, laying the foundation for the development of a convenient online cell analysis platform and showing significant application prospects. Compared to typical recognition approaches, the proposed method exhibits superior capabilities in recognizing cell suspension concentrations. Furthermore, this methodology not only accelerates research in cell biology and precision medicine but also paves the way for future EIS biosensors capable of intelligent, adaptive analysis in dynamic biological research.

Purpose: Erectile dysfunction (ED) is a common male sexual dysfunction. Gut microbiota plays an important role in various diseases. To investigate the effects and mechanisms of intestinal flora dysregulation induced by high-fat diet (HFD) on erectile function. Materials and Methods: Male Sprague–Dawley rats aged 8 weeks were randomly divided into the normal diet (ND) and HFD groups. After 24 weeks, a measurement of erectile function was performed. We performed 16S rRNA sequencing of stool samples. Then, we established fecal microbiota transplantation (FMT) rat models by transplanting fecal microbiota from rats of ND group and HFD group to two new groups of rats respectively. After 24 weeks, erectile function of the rats was evaluated and 16S rRNA sequencing was performed, and serum samples were collected for the untargeted metabolomics detection. Results: The erectile function of rats and the species diversity of intestinal microbiota in the HFD group was significantly lower, and the characteristics of the intestinal microbiota community structure were also significantly different between the two groups. The erectile function of rats in the HFD-FMT group was significantly lower than that of rats in the ND-FMT group. The characteristics of the intestinal microbiota community structure were significantly different. In the HFD-FMT group, 27 metabolites were significantly different and they were mainly involved in the several inflammation-related pathways. Conclusions Intestinal microbiota disorders induced by HFD can damage the intestinal barrier of rats, change the serum metabolic profile, induce low-grade inflammation and apoptosis in the corpus cavernosum of the penis, and lead to ED.

Purpose: Erectile dysfunction (ED) is a common male sexual dysfunction. Gut microbiota plays an important role in various diseases. To investigate the effects and mechanisms of intestinal flora dysregulation induced by high-fat diet (HFD) on erectile function. Materials and Methods: Male Sprague–Dawley rats aged 8 weeks were randomly divided into the normal diet (ND) and HFD groups. After 24 weeks, a measurement of erectile function was performed. We performed 16S rRNA sequencing of stool samples. Then, we established fecal microbiota transplantation (FMT) rat models by transplanting fecal microbiota from rats of ND group and HFD group to two new groups of rats respectively. After 24 weeks, erectile function of the rats was evaluated and 16S rRNA sequencing was performed, and serum samples were collected for the untargeted metabolomics detection. Results: The erectile function of rats and the species diversity of intestinal microbiota in the HFD group was significantly lower, and the characteristics of the intestinal microbiota community structure were also significantly different between the two groups. The erectile function of rats in the HFD-FMT group was significantly lower than that of rats in the ND-FMT group. The characteristics of the intestinal microbiota community structure were significantly different. In the HFD-FMT group, 27 metabolites were significantly different and they were mainly involved in the several inflammation-related pathways. Conclusions Intestinal microbiota disorders induced by HFD can damage the intestinal barrier of rats, change the serum metabolic profile, induce low-grade inflammation and apoptosis in the corpus cavernosum of the penis, and lead to ED.

Purpose: Erectile dysfunction (ED) is a common male sexual dysfunction. Gut microbiota plays an important role in various diseases. To investigate the effects and mechanisms of intestinal flora dysregulation induced by high-fat diet (HFD) on erectile function. Materials and Methods: Male Sprague–Dawley rats aged 8 weeks were randomly divided into the normal diet (ND) and HFD groups. After 24 weeks, a measurement of erectile function was performed. We performed 16S rRNA sequencing of stool samples. Then, we established fecal microbiota transplantation (FMT) rat models by transplanting fecal microbiota from rats of ND group and HFD group to two new groups of rats respectively. After 24 weeks, erectile function of the rats was evaluated and 16S rRNA sequencing was performed, and serum samples were collected for the untargeted metabolomics detection. Results: The erectile function of rats and the species diversity of intestinal microbiota in the HFD group was significantly lower, and the characteristics of the intestinal microbiota community structure were also significantly different between the two groups. The erectile function of rats in the HFD-FMT group was significantly lower than that of rats in the ND-FMT group. The characteristics of the intestinal microbiota community structure were significantly different. In the HFD-FMT group, 27 metabolites were significantly different and they were mainly involved in the several inflammation-related pathways. Conclusions Intestinal microbiota disorders induced by HFD can damage the intestinal barrier of rats, change the serum metabolic profile, induce low-grade inflammation and apoptosis in the corpus cavernosum of the penis, and lead to ED.