Liver cancer (LC) remains one of the most prevalent tumors globally. Despite considerable advancements in its clinical treatment in recent years, the global burden of this disease is projected to increase. According to World Health Organization statistics, the number of LC deaths worldwide in 2022 was 760,000, ranking third among all cancers. Therefore, there is an urgent need to delve deeper into the realms of LC diagnosis, prognosis, and treatment. Proteomics, as an emerging technology leveraging mass spectrometry (MS)-based protein identification, liquid chromatography separation, and bioinformatics-driven data integration, enables the systematic characterization of proteome dynamics to address critical challenges in LC research. These challenges include the discovery of diagnostic and prognostic biomarkers as well as therapeutic targets. By scrutinizing the expression of specific proteins in both normal and cancerous tissues through high-throughput protein microarrays, researchers can uncover novel biomarkers. The use of these biomarkers alone or in combination with established ones holds the potential to significantly advance the fields of LC diagnosis, prognosis, and treatment. In general, the application of proteomics has significantly promoted the development of LC research. In this review, we summarized the latest advancements in the application of proteomics in LC, with a particular focus on diagnosis, prognosis, and treatment. We also concluded selected protein markers, aiming to offer guidance for further applications and present additional therapeutic targets for LC.

As a new type of model organism， zebrafish is gradually gaining prominence in the field of scientific research. The unique biological characteristics and advantages of zebrafish make them play an increasingly important role in the quality evaluation of traditional Chinese medicine. Compared with other common experimental animals， zebrafish have a fast reproductive and growth speed and high embryo transparency， making them an ideal model for evaluating the quality of traditional Chinese medicine. This provides a new perspective and method for research on traditional Chinese medicine. With the growing global interest in traditional Chinese medicine， it has become crucial to find scientific and accurate methods to evaluate the quality and effectiveness of traditional Chinese medicine. The introduction of the zebrafish model has brought new breakthroughs in the quality evaluation of traditional Chinese medicine. To further promote the application of zebrafish in evaluating the quality of traditional Chinese medicine， this article systematically searched and sorted out the previous studies related to the application of zebrafish for this purpose since 2023. The commonly used disease models and indicators of zebrafish in evaluating the effectiveness of traditional Chinese medicine， as well as the mechanism of zebrafish in exploring the active ingredients of traditional Chinese medicine， were primarily reviewed. The application of zebrafish in evaluating the safety of traditional Chinese medicine and the typical examples in ensuring the quality of traditional Chinese medicine were demonstrated. The limitations encountered by zebrafish models in evaluating the quality of traditional Chinese medicine were highlighted. The resolution of these problems will help further improve the accuracy and reliability of zebrafish in evaluating the quality of traditional Chinese medicine. The article discussed the evaluation of effectiveness， safety， and quality control of zebrafish applied in traditional Chinese medicine， so as to provide a reference for establishing standards for traditional Chinese medicine and promoting its modernization in the future.

ObjectiveBased on the zebrafish model， the hepatotoxicity and anti-osteoporotic activity of evodiamine （EVO） were studied. The mechanism of EVO in treating osteoporosis was explored by using network pharmacology and real-time polymerase chain reaction（Real-time PCR）. MethodsThree days after fertilization （3 dpf）， zebrafish were randomly selected and exposed to different concentrations of EVO solution for 96 hours. The mortality rate of zebrafish at different concentrations was calculated at the exposure endpoint， and a "dose-toxicity" curve was drawn. The 10% lethal concentration （LC₁₀） was calculated. Liver phenotype， acridine orange staining， and pathological tissue sections of liver-transgenic zebrafish ［CZ16 （gz15Tg.Tg （fabp 10a： ds Red； ela31： EGFP））］ were used to confirm hepatotoxicity of EVO. On this basis， prednisolone was used to create a model of osteoporosis in zebrafish. The skull development， area of the skull stained by alizarin red， and cumulative optical density were used as indicators to evaluate the anti-osteoporotic activity of EVO in a safe dose. Based on network pharmacology， the mechanism of action of EVO in the treatment of osteoporosis was predicted and verified through Real-time PCR. ResultsThe LC₁₀ of EVO on zebrafish （7 dpf） was determined to be 0.4 mg·L^-1. Compared with the control group， sublethal concentrations （10=0.36 mg·L^-1 and 1/2LC₁₀=0.18 mg·L^-1） significantly decreased the fluorescence area of zebrafish liver （P<0.05，P<0.01） and increased the number of liver cell apoptosis. The arrangement of liver tissue was disordered and loose， and the vacuole was obvious， especially in the 0.36 mg·L^-1 group. Compared with the control group， under safe dose conditions， 0.08 and 0.02 mg·L^-1 of EVO had no significant effect on the liver. Prednisolone administration significantly reduced the mineralization area and cumulative optical density of zebrafish skulls （P<0.01） compared with the control group. The mineralization area and cumulative optical density of zebrafish skulls in the 0.08 and 0.02 mg·L^-1 groups of EVO were significantly increased compared with the model group （P<0.01）. Network pharmacology prediction suggested that EVO may regulate key targets such as avian sarcoma viral oncogene homolog （SRC）， signal transducer and activator of transcription 3 （STAT3）， interleukin-8 （CXCL8） and tyrosine kinase receptor 2 （ERBB2） to regulate signaling pathways related to lipid and atherosclerosis in diabetic complications， as well as the regulation of inflammatory mediators of tryptophan （TRP） channels. Real-time PCR experiment results indicated that EVO could regulate the above-mentioned targets， as well as genes related to osteoblasts and osteoclasts. ConclusionExcessive doses of EVO can lead to hepatotoxicity in zebrafish. Under safe dosage conditions， EVO can regulate relevant targets to exert anti-osteoporotic activity， providing a reference for the clinical safe use of EVO and ideas for the development of new drugs for osteoporosis.

Background: The immune system plays a critical role in the development and progression of osteoporosis and fractures. However, the causal relationships between antibody immune responses, immune cells, and these bone conditions remain unclear. This study aimed to explore these relationships using Mendelian randomization (MR) analysis. Methods: We collected complete blood count data from patients with fractures and healthy individuals and analyzed their differences. Then, we conducted a 2-sample, 2-step MR analysis to investigate the causal effects of antibody immune responses on osteoporosis and fractures, using inverse-variance weighted (IVW) as the primary method. We also explored whether immune cells mediate the pathway between antibodies and osteoporosis or fractures. Finally, we analyzed the functions and expression levels of key genes involved. Results: Overall, the fracture group exhibited increased white blood cell count, absolute neutrophil count, absolute monocyte count, platelet count, and their respective proportions, while absolute lymphocyte count, absolute eosinophil count, absolute basophil count, red blood cell count, and their proportions were decreased. We identified 44 causal relationships between antibodies and osteoporosis or fractures, with 7 supported by multiple MR methods, and 5 showing odds ratios significantly deviating from 1 in the IVW analysis. Epstein-Barr virus-related antibodies had a notable impact on osteoporosis and fractures. The human leukocyte antigen (HLA) gene family, particularly HLA-DPB1, emerged as a significant risk factor. However, immune cells were not found to mediate these effects. Conclusions This study elucidated the causal relationships between antibody immune responses, immune cells, and osteoporosis or fractures. The HLA gene family plays a crucial role in the interaction between antibodies and these bone conditions, with HLA-DPB1 identified as a key risk gene. Immune cells do not serve as mediators in this process. These findings provide valuable insights for future research.

Background Kurtosis reflecting noise's temporal structure is an effective metric for evaluating noise-induced hearing loss (NIHL), and its threshold is still unclear. Objective To explore the energy range of kurtosis and the threshold of NIHL induced by kurtosis in this energy rangeMethods Using cross-sectional design, 4631 noise-exposed workers in manufacturing industry were selected. The hearing loss and noise exposure data of each worker were collected. Logistic regression model was used to establish the dose-response relationship between noise exposure and hearing loss and estimate the range of energy effects. Restricted cubic spline model was utilized to analyze the dose-response relationship curves of kurtosis to noise-induced hearing loss (NIHI) and high-frequency noise-induced hearing loss (HFNIHL) under different 8 h equivalent sound levels (L_{EX,8 h}), as well as to estimate the kurtosis effect threshold. Results The logistic regression model analysis showed that the prevalence of NIHI and HFNIHL increased significantly with increase of L_{EX,8 h} for steady noise; when L_{EX,8 h} of non-steady noise was 80-100 dB(A), the risk of hearing loss increased with an increase in kurtosis (P<0.05). The restricted cubic spline model showed that when L_{EX,8 h} of non-steady noise was 0-80 dB(A) or >100 dB(A), there was no significant relationship between kurtosis and NIHI or HFNIHL. When L_{EX,8 h} was 80-100 dB(A), there was a significant nonlinear dose-response relationship between kurtosis and NIHL or HFNIHL (P <0.001), and kurtosis > 28.08 exerted effect in elevating NIHI or HFNIHL. Conclusion The effect threshold of kurtosis on NIHL is 28.08 when non-steady noise levels are in the range of 80-100 dB(A). The effect threshold of kurtosis needs further verification.

Background Noise-induced hearing loss (NIHL) is a prevalent occupational health problem in workplace settings, with non-steady noise exposure being particularly widespread. Although kurtosis-adjusted equivalent sound level (\begin{document}$ {{L'}}_{\text{EX,}\text{8}\text{ h}} $\end{document}) methods based on linear regression are available to assess hearing damage, the complexity of kurtosis effects necessitates introducing new metrics through nonlinear regression to improve predictive accuracy for hearing loss from non-steady noise exposure. Objective To examine the adjustment terms [\begin{document}$ \mathrm{lg}(\beta _{N}/{\beta }_{G}) $\end{document}] and coefficients (λ) of \begin{document}$ {{L'}}_{\text{EX,}\text{8}\text{ h}} $\end{document} using nonlinear regression and evaluate the method’s effectiveness in assessing occupational hearing loss associated with non-steady noise exposure. Methods A cross-sectional study design was employed, enrolling 1034 manufacturing workers and evaluating noise exposure to estimate hearing loss metrics. Quantile regression analysis quantified the proportional contributions of influencing factors for noise-induced permanent threshold shift at 3, 4, and 6 kHz frequencies (NIPTS₃₄₆). \begin{document}$ {{L'}}_{\text{EX,}\text{8}\text{ h}} $\end{document} was computed using multiple linear regression and nonlinear least squares optimization. Paired t-tests analyzed predictive efficacy before and after kurtosis adjustment to evaluate its impact on ISO 1999 NIPTS₃₄₆ predictions. Chow tests compared the similarity of logistic regression curves for high-frequency noise-induced hearing loss (HFNIHL) incidence between non-steady noise (\begin{document}$ {{L'}}_{\text{EX,}\text{8}\text{ h}} $\end{document}) and steady noise (\begin{document}$ {{L}}_{\text{EX,}\text{8}\text{ h}} $\end{document}), thereby validating the effectiveness of \begin{document}$ {{L'}}_{\text{EX,}\text{8}\text{ h}} $\end{document}. Results The quantile regression analysis showed that kurtosis was a significant indicator of occupational hearing loss risk from non-steady noise (contribution rate was 27.5%, P<0.05). The linear regression and nonlinear least squares methods obtained six different combinations of coefficients (λ) and adjustment terms [\begin{document}$ \mathrm{lg}(\beta _{N}/{\beta }_{G}) $\end{document}]. Incorporating these \begin{document}$ {{L'}}_{\text{EX,}\text{8}\text{ h}} $\end{document} adjustments into the ISO 1999 predictive model significantly reduced NIPTS underestimation (from 14.2 dB HL with \begin{document}$ {{L}}_{\text{EX,}\text{8}\text{ h}} $\end{document} to 11.5–5.6 dB HL with \begin{document}$ {{L'}}_{\text{EX,}\text{8}\text{ h}} $\end{document}, P < 0.001). The model \begin{document}$ {{{L'}}_{\text{EX,}\text{8}\text{ h}\text{,6}}=L}_{\mathrm{E}\mathrm{X},8\;\mathrm{h}}+7.9\mathrm{lg}({{\beta }_{N}}/{10}) $\end{document} achieved the most significant improvement, reducing underestimation by 8.7 dB HL. The logistic curve analysis further demonstrated that all \begin{document}$ {{L'}}_{\text{EX,}\text{8}\text{ h}} $\end{document} dose-response relationships for non-steady noise aligned more closely with the steady noise group than \begin{document}$ {{L}}_{\text{EX,}\text{8}\text{ h}} $\end{document}, with \begin{document}$ {{L'}}_{\text{EX,}\text{8}\text{ h}\text{,6}} $\end{document} showing the smallest divergence (difference: 4.1%). Conclusions \begin{document}$ {{L'}}_{\text{EX,}\text{8}\text{ h}} $\end{document} demonstrates superior efficacy in assessing the risk of occupational hearing loss induced by non-steady noise exposure. The \begin{document}$ {{L'}}_{\text{EX,}\text{8}\text{ h}} $\end{document} metric, constructed by calculating adjustment terms and coefficients through nonlinear regression analysis, exhibits greater effectiveness than linear regression methods in evaluating occupational hearing loss associated with non-steady noise exposure.

Background Temporal kurtosis (without frequency weighting, i.e., Z-weighted kurtosis) can evaluate noise-induced hearing loss (NIHL). However, few studies have considered the function of frequency weighting (A- or C-weighted) kurtosis on NIHL. Objective To study the significance of A- and C-weighted kurtosis adjustment for equivalent sound level (L'_{EX,8 h}) in evaluating occupational hearing loss. Methods A cross-sectional survey was used to select 973 noise-exposed workers in seven industries as the subjects. The noise exposure of all workers was assessed by distributions of A-, C-, and Z-weighted kurtosis (e.g., K_A, K_C, and K_Z) and respective adjusted equivalent sound level (e.g., L'_{EX,8 h}-K_A, L'_{EX,8 h}-K_C, and L'_{EX,8 h}-K_Z). The significance of A- and C-weighted kurtosis in evaluating NIHL was evaluated by correlations between three types of L'_{EX,8 h} and NIHL, and improvement of noise-induced permanent threshold shift (NIPTS) underestimation predicted by the ISO prediction model (Acoustics—Estimation of noise-induced hearing loss, ISO 1999-2013). Results The median K_A, K_C, and K_Z were 68.33, 28.22, and 19.82, respectively. The binary logistic regression showed that L_{EX, 8 h}-K_A, L_{EX, 8 h}-K_C, and L'_{EX, 8 h}-K_Z were risk factors for NIHL (OR>1, P<0.001). The receiver operating characteristic (ROC) curve showed that when the outcome variable was noise-induced hearing impairment (NIHI), the areas under the curves corresponding to L'_{EX,8 h}-K_A, L'_{EX,8 h}-K_C, and L'_{EX,8 h}-K_Z were 0.625, 0.628, and 0.625, respectively. When the outcome variable was high-frequency noise-induced hearing loss (HFNIHL), the areas under the curves corresponding to L'_{EX,8 h}-K_A, L'_{EX, 8 h}-K_C, and L'_{EX,8 h}-K_Z were 0.624, 0.623, and 0.622, respectively (P<0.05). The order of underestimation improvement values predicted by L'_{EX,8 h} for NIPTS₁₂₃₄ was: L'_{EX,8 h}-K_A (4.68 dB HL)>L'_{EX,8 h}-K_C (4.38 dB HL)>L'_{EX,8 h}-K_Z (4.28 dB HL) (P<0.001). The order of underestimation improvement values predicted by L'_{EX,8 h}-K for NIPTS₃₄₆ was: L'_{EX,8 h}-K_A (7.20 dB HL)>L'_{EX,8 h}-K_C (6.83 dB HL)>L'_{EX,8 h}-K_Z (6.71 dB HL) (P<0.001). Conclusion The adjustment of A- and C-weighted kurtosis to equivalent sound level L_{EX,8 h} can effectively improve the accuracy of the ISO 1999 prediction model in NIPTS prediction, and compared with the C-weighted, the A-weighted kurtosis can improve the result of the ISO 1999 prediction model in terms of underestimating NIPTS.

Social robots play an important role in the field of medical companionship， providing services such as companion communication， drug monitoring， and rehabilitation guidance for the elderly and other subjects. However， social robots also pose the risk of deceiving medical users. Although certain forms of social robots’ deception can be used for therapeutic purposes， unethical deception can have adverse consequences for patients， doctors， and even society. These risks include causing patients to develop attachment disorders， violating their privacy， endangering their health， and even undermining the credibility of the healthcare system. Faced with the deception problem of social robots， starting from relational theory， medical artificial intelligence developers can conduct ethical regulation from the following two paths. First， social robots should be ethically programmed， including embedding programs for limiting benevolent lies， implementing informed consent principles， and ensuring information accuracy. Second， the deceptive behaviors of social robots should be controlled， requiring developers to take full-process supervision responsibility， design medical social robots that can supervise each other， and participate in formulating quality standards and evidence mechanisms for deception issues.

Objective: To explore the prevalence of screening myopia and depressive symptom among primary and secondary school students in Xuzhou, and to explore the association of sleep and screen time on the coexistence of screening myopia and depressive symptom, so as to provide scientific references for developing intervention strategies to address the development of myopia and promote mental health in children and adolescents. Methods: From September to October 2024, a stratified cluster random sampling method was used to select 6 605 students in grade 4 to 12 in 2 urban and 2 suburban districts in Xuzhou. The students health condition and influencing factors questionnaire were used to assess students basic information, sleep time, and screen time. The Center for Epidemiological Studies Depression Scale (CES-D) was used to assess primary and secondary school students depressive symptom.Unaided distance visual acuity examination was conducted, and refractive assessment was performed using an automated refractometer without cycloplegic agents. The Chi-square test and multiple Logistic regression analysis were used to evaluate the association of sleep and screen time with the coexistence of screening myopia and depressive symptom. Results: The detection rates of screening myopia, depressive symptom, and screening myopia and depressive symptoms co morbidity among primary and secondary school students in Xuzhou were 60.35%, 4.45% and 18.61% respectively. Results from the multinomial Logistic regression analysis, using the healthy group as the reference and after adjusting for confounding factors, showed that students with insufficient sleep duration were more likely to have depressive symptom ( OR=1.57, 95%CI =1.08-2.27) and the coexistence of screening myopia and depressive symptom ( OR=1.85, 95%CI =1.45-2.36). Students with daily screen time≥2 h were more likely to have depressive symptom only ( OR=1.41, 95%CI =1.04-1.93) and the coexistence of screening myopia and depressive symptom ( OR=1.31, 95%CI =1.06-1.61). Further stratified analysis based on sufficient and insufficient sleep duration revealed that only in the insufficient sleep duration group, students with daily screen time≥2 h had an increased risk of depressive symptom only ( OR=1.49, 95%CI =1.07-2.07) and the coexistence of screening positive myopia and depressive symptom ( OR=1.40, 95%CI =1.11- 1.77 ) (all P <0.05). Conclusions Primary and secondary school students with insufficient sleep duration and daily screen time≥2 h have higher risks of depressive symptoms and the coexistence of screening myopia and depressive symptoms. It is recommended to ensure adequate sleep duration and limit screen time for children and adolescents.

Background: The immune system plays a critical role in the development and progression of osteoporosis and fractures. However, the causal relationships between antibody immune responses, immune cells, and these bone conditions remain unclear. This study aimed to explore these relationships using Mendelian randomization (MR) analysis. Methods: We collected complete blood count data from patients with fractures and healthy individuals and analyzed their differences. Then, we conducted a 2-sample, 2-step MR analysis to investigate the causal effects of antibody immune responses on osteoporosis and fractures, using inverse-variance weighted (IVW) as the primary method. We also explored whether immune cells mediate the pathway between antibodies and osteoporosis or fractures. Finally, we analyzed the functions and expression levels of key genes involved. Results: Overall, the fracture group exhibited increased white blood cell count, absolute neutrophil count, absolute monocyte count, platelet count, and their respective proportions, while absolute lymphocyte count, absolute eosinophil count, absolute basophil count, red blood cell count, and their proportions were decreased. We identified 44 causal relationships between antibodies and osteoporosis or fractures, with 7 supported by multiple MR methods, and 5 showing odds ratios significantly deviating from 1 in the IVW analysis. Epstein-Barr virus-related antibodies had a notable impact on osteoporosis and fractures. The human leukocyte antigen (HLA) gene family, particularly HLA-DPB1, emerged as a significant risk factor. However, immune cells were not found to mediate these effects. Conclusions This study elucidated the causal relationships between antibody immune responses, immune cells, and osteoporosis or fractures. The HLA gene family plays a crucial role in the interaction between antibodies and these bone conditions, with HLA-DPB1 identified as a key risk gene. Immune cells do not serve as mediators in this process. These findings provide valuable insights for future research.