“Bio-narrative integration” refers to the process in which a life subject with narrative consciousness actively reviews and reintegrates their life stories， or a subject lacking life and health narrative awareness， with the intervention of healthcare professionals， tells their own life stories and integrates them into a coherent and constantly evolving life narrative process. Starting from the keyword of bio-narrative integration， this paper proposed a classification model of narrative integration. From the perspective of life stages， it was divided into “phasic narrative integration” and “holistic narrative integration.” In terms of integrated narrative style， it was categorized as “positive narrative integration style” and “negative narrative integration style.” Regarding subjective initiative， it was classified as “active narrative integration regulation” and “passive narrative integration regulation.” Then it elaborated the significant value of narrative integration for every life subject， especially in pain relief， the improvement of life resilience， the healthy aging of the elderly， and the ultimate peace of the dying. It was advocated that healthcare practitioners should enhance their professional narrative competence， effectively guide patients to engage in bio-narrative integration regulation， and help them overcome narrative closure， thereby improving the quality of medical care.

Objective To explore whether there is a causal relationship between intestinal flora and esophageal cancer. Methods Summary statistics of intestinal flora and esophageal cancer were obtained from the Genome-wide Association Studies (GWAS) database. Five methods, including inverse variance weighted (IVW), weighted median estimation, Mendelian randomization (MR)-Egger regression, single mode, and weighted mode, were used for analysis, with IVW as the main analysis method. Sensitivity analysis was used to evaluate the reliability of MR results. Results In the IVW method, Oxalobacteraceae [OR=1.001, 95%CI (1.000, 1.002), P=0.023], Faecalibacterium [OR=1.001, 95%CI (1.000, 1.002), P=0.028], Senegalimassilia [OR=1.002, 95%CI (1.000, 1.003), P=0.006] and Veillonella [OR=1.001, 95%CI (1.000, 1.002), P=0.018] were positively correlated with esophageal cancer, while Burkholderiales [OR=0.999, 95%CI (0.998, 1.000), P=0.002], Eubacterium oxidoreducens [OR=0.998, 95%CI (0.997, 0.999), P=0.038], Romboutsia [OR=0.999, 95%CI (0.998, 1.000), P=0.048] and Turicibacter [OR=0.998, 95%CI (0.997, 0.999), P=0.013] were negatively correlated with esophageal cancer. Sensitivity analysis showed no evidence of heterogeneity, horizontal pleiotropy and reverse causality. Conclusion Oxalobacteraceae, Faecalibacterium, Senegalimassilia and Veillonella increase the risk of esophageal cancer, while Burkholderiales, Eubacterium oxidoreducens, Romboutsia and Turicibacter decrease the risk of esophageal cancer. Further studies are needed to explore how these bacteria affect the progression of esophageal cancer.

ObjectiveTo investigate the distribution of traditional Chinese medicine （TCM） syndrome elements in type 2 diabetes mellitus （T2DM） patients based on maximum body mass index （maxBMI） and explore their association with complication risks. MethodsA retrospective cross-sectional study was used to collect clinical data from hospitalized T2DM patients， extracting age， gender， smoking history， alcohol consumption history， duration of disease， HbA1c level， complications， and TCM syndromes， and extracting the syndrome elements of disease location and disease nature based on their TCM syndromes. MaxBMI was calculated by telephone survey of patients' self-reported maximum body weight； patients with maxBMI ≥24 kg/m² were classified into spleen-heat syndrome group， and those with maxBMI ＜24 kg/m² were classified into consumptive-heat syndrome group. The distribution of TCM syndrome types and syndrome elements of patients in the two groups were analysed. Then the propensity score matching method was used to balance the baseline characteristics between the two groups and compare the differences in the distribution of syndrome types and syndrome elements and the risk of macrovascular and microvascular complications between the two groups. ResultsAmong the 1178 T2DM patients， syndrome elements in spleen-heat patients （1034 cases） were primarily located in the spleen （351 cases， 33.95%）， liver （240 cases， 23.21%）， and stomach （139 cases， 13.44%）， while in consumptive-heat patients （144 cases）， they were concentrated in the spleen （57 cases， 39.58%）， liver （34 cases， 23.61%）， and kidneys （17 cases， 11.81%）； regarding syndrome elements of disease nature， spleen-heat patients were predominantly characterized by qi deficiency （481 cases， 46.52%）， phlegm （353 cases， 22.73%）， and dampness （241 cases， 23.31%）， whereas consumptive-heat patients showed more qi deficiency （84 cases， 58.33%） and yin deficiency （44 cases， 30.56%）. After propensity score matching， 132 cases were included in each group， and no statistically significant differences were observed in the distribution of syndrome elements of disease location between the two groups （P＞0.05）， but the phlegm element was significantly more prevalent in spleen-heat patients than in consumptive-heat patients （P = 0.006）. Regarding the risk of complications， spleen-heat patients had a significantly higher risk of developing macrovascular complications compared to consumptive-heat patients （OR=2.04， P=0.010）， while no significant differences were found between groups in the occurrence of microvascular complications （P＞0.05）. ConclusionThe spleen-heat T2DM patients show a more frequent syndrome element of disease nature of phlegm， and a higher risk of developing macrovascular complications compared to consumptive-heat patients.

Objective To identify causal effects and potential mechanisms of oxidative stress (OS)-related genes in lung cancer. Methods OS-related genes were extracted from the GeneCards database. Integration analysis of genome-wide association study (GWAS) data for lung cancer with gene expression and DNA methylation quantitative trait locus (QTL), including eQTL and mQTL in blood was performed using the summary data-based Mendelian randomization (SMR) approach to determine the causal relationship between OS-related genes and lung cancer risk. Colocalization analysis of OS-related gene QTL and lung cancer risk locus was performed to gain insight into the potential regulatory mechanisms of lung cancer risk. Results A total of 1 188 OS-related genes were obtained from the GeneCards database. A potential causal relationship between OS-related genes and lung cancer was identified by SMR analysis. AGER expression level [OR=1.944, 95%CI (1.431, 2.640), P<0.001], and ATF6B expression level [OR=1.508, 95%CI (1.287, 1.767), P<0.001] were associated with lung cancer risk. Meanwhile, ATF6B methylation level was also associated with lung cancer risk. Conclusion OS-related genes are associated with lung cancer, which may be a potential target of anti-cancer drugs.

Accurate prediction of drug responses in cancer cell lines(CCLs)and transferable prediction of clinical drug responses using CCLs are two major tasks in personalized medicine.Despite the rapid advancements in existing computational methods for preclinical and clinical cancer drug response(CDR)prediction,chal-lenges remain regarding the generalization of new drugs that are unseen in the training set.Herein,we propose a multimodal fusion deep learning(DL)model called drug-target and single-cell language based CDR(DTLCDR)to predict preclinical and clinical CDRs.The model integrates chemical descriptors,mo-lecular graph representations,predicted protein target profiles of drugs,and cell line expression profiles with general knowledge from single cells.Among these features,a well-trained drug-target interaction(DTI)prediction model is used to generate target profiles of drugs,and a pretrained single-cell language model is integrated to provide general genomic knowledge.Comparison experiments on the cell line drug sensitivity dataset demonstrated that DTLCDR exhibited improved generalizability and robustness in predicting unseen drugs compared with previous state-of-the-art baseline methods.Further ablation studies verified the effectiveness of each component of our model,highlighting the significant contribution of target information to generalizability.Subsequently,the ability of DTLCDR to predict novel molecules was validated through in vitro cell experiments,demonstrating its potential for real-world applications.Moreover,DTLCDR was transferred to the clinical datasets,demonstrating satisfactory performance in the clinical data,regardless of whether the drugs were included in the cell line dataset.Overall,our results suggest that the DTLCDR is a promising tool for personalized drug discovery.

Human taste is an important function of chemical perception. In recent years, brain taste evoked potentials have received more and more attention as a feasible tool for objective assessment of taste dysfunction. This paper reviews the main characteristics of gustatory evoked potential signals, the most widely used recording and processing techniques, and the scientific advances and relevance of gustatory evoked potentials in many important applications. In particular, taste evoked potentials are used to study the central effects of food intake and taste disorders, which may affect cognition and personality, or may be potential indicators of the onset or progression of neurological disorders. For these reasons, this paper presents and analyzes the latest scientific results and future challenges of using gustatory evoked potentials as an attractive solution to objective monitoring techniques for taste disorders. Human taste is an important function of chemical perception. In recent years, brain gustatory evoked potentials have received more and more attention as a feasible tool for objective assessment of taste dysfunction. This paper reviews the main characteristics of gustatory evoked potential signals, the most widely used recording and processing techniques, and the scientific advances and relevance of gustatory evoked potentials in many important applications. In particular, gustatory evoked potentials are used to study the central effects of food intake and taste disorders, which may affect cognition and personality, or may be potential indicators of the onset or progression of neurological disorders. For these reasons, this paper presents and analyzes the latest scientific results and future challenges of using gustatory evoked potentials as an attractive solution to objective monitoring techniques for taste disorders.
Humans ; Evoked Potentials ; Taste/physiology* ; Taste Perception/physiology*

Mitochondrial dysfunction in chondrocytes is a key pathogenic factor in osteoarthritis (OA), but directly modulating mitochondria in vivo remains a significant challenge. This study is the first to verify a correlation between mitochondrial dysfunction and the downregulation of the FOXO3 gene in the cartilage of OA patients, highlighting the potential for regulating mitophagy via FOXO3 gene modulation to alleviate OA. Consequently, we developed a chondrocyte-targeting CRISPR/Cas9-based FOXO3 gene-editing tool (FoxO3) and integrated it within a nanoengineered 'truck' (NETT, FoxO3-NETT). This was further encapsulated in injectable hydrogel microspheres (FoxO3-NETT@SMs) to harness the antioxidant properties of sodium alginate and the enhanced lubrication of hybrid exosomes. Collectively, these FoxO3-NETT@SMs successfully activate mitophagy and rebalance mitochondrial function in OA chondrocytes through the Foxo3 gene-modulated PINK1/Parkin pathway. As a result, FoxO3-NETT@SMs stimulate chondrocytes proliferation, migration, and ECM production in vitro, and effectively alleviate OA progression in vivo, demonstrating significant potential for clinical applications.

The cultivation of top innovative nursing talents with Chinese characteristics in the new era lends critical support to the accomplishment of the strategic goal of the Healthy China Initiative.Herein,we reviewed the historical development of nursing science in China,clarified the conceptual framework of nursing science with Chinese characteristics in the new era,and identified the essential qualities and competencies required for top innovative nursing talents.Furthermore,we analyzed the mission and challenges in cultivating these nursing talents,and put forward new approaches,including formulating new ethics and political education theories specific to nursing science with Chinese characteristics,establishing a cross-disciplinary educational model of Nursing+X,and creating a new nursing talent cultivation ecosystem adapted to the era of human-machine symbiosis.This study provides theoretical insights into the cultivation of top innovative nursing talents who align their development well with national strategic needs,embody patriotism,and possess a strong sense of contemporary responsibility.

This study investigated the current status of depressive symptoms among Chinese college students and analyzed the association between being an only child and depressive symptoms. A cross-sectional survey was conducted with 771 college students from five universities in Jilin Province. Depressive symptoms were assessed using the Center for Epidemiological Studies Depression Scale (CES-D). The results indicated that 28.4% of college students exhibited depressive symptoms. Only children reported significantly higher depressive symptom scores compared to non-only children, and female students had higher scores than their male counterparts. These findings highlight the importance of targeted mental health interventions for only children.

Accurate prediction of drug responses in cancer cell lines (CCLs) and transferable prediction of clinical drug responses using CCLs are two major tasks in personalized medicine. Despite the rapid advancements in existing computational methods for preclinical and clinical cancer drug response (CDR) prediction, challenges remain regarding the generalization of new drugs that are unseen in the training set. Herein, we propose a multimodal fusion deep learning (DL) model called drug-target and single-cell language based CDR (DTLCDR) to predict preclinical and clinical CDRs. The model integrates chemical descriptors, molecular graph representations, predicted protein target profiles of drugs, and cell line expression profiles with general knowledge from single cells. Among these features, a well-trained drug-target interaction (DTI) prediction model is used to generate target profiles of drugs, and a pretrained single-cell language model is integrated to provide general genomic knowledge. Comparison experiments on the cell line drug sensitivity dataset demonstrated that DTLCDR exhibited improved generalizability and robustness in predicting unseen drugs compared with previous state-of-the-art baseline methods. Further ablation studies verified the effectiveness of each component of our model, highlighting the significant contribution of target information to generalizability. Subsequently, the ability of DTLCDR to predict novel molecules was validated through in vitro cell experiments, demonstrating its potential for real-world applications. Moreover, DTLCDR was transferred to the clinical datasets, demonstrating satisfactory performance in the clinical data, regardless of whether the drugs were included in the cell line dataset. Overall, our results suggest that the DTLCDR is a promising tool for personalized drug discovery.