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: 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: 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: 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.

Oroxylin A (OA), a natural compound extracted from Scutellaria baicalensis, demonstrates preventive potential against ultraviolet B (UVB)-induced non-melanoma skin cancer (NMSC), the most prevalent cancer worldwide with increasing incidence. Utilizing SKH-1 hairless mice exposed to UVB, this study showed that OA delayed NMSC onset and alleviated acute skin damage. Mechanistic investigations revealed its dual action: inhibiting inflammation and enhancing nucleotide excision repair (NER) by stabilizing XPA, a crucial deoxyribonucleic acid (DNA) repair protein. This stabilization occurred through OA's interaction with glucose-regulated protein 94 (GRP94), which disrupted murine double minute 2 (MDM2)-mediated XPA ubiquitination and proteasomal degradation. By maintaining XPA levels, OA expedited photoproduct clearance and diminished genomic instability, ultimately impeding NMSC development. These findings suggest OA as a promising chemopreventive agent targeting the GRP94/MDM2-XPA axis to counteract UVB-induced carcinogenesis.
Animals ; Ultraviolet Rays/adverse effects* ; Skin Neoplasms/prevention & control* ; Flavonoids/pharmacology* ; Mice ; Xeroderma Pigmentosum Group A Protein/genetics* ; Humans ; Proto-Oncogene Proteins c-mdm2/genetics* ; DNA Repair/drug effects* ; Scutellaria baicalensis/chemistry* ; Mice, Hairless ; Skin/radiation effects*

Objective To find out the willingness to choose old-age care mode and the influencing factors of the elderly in the younger age group.Methods A multi-stage stratified whole cluster random sampling method was used to conduct a questionnaire survey on the willingness of the younger elderly in Zhangye City.Results The proportion of low-aged elderly in Zhangye City who chose family old-age care was the highest,and factors such as who they relied on to solve their old-age problems,opinions on moving into old-age care institutions,knowledge of the combination of medical care and nursing care,loneliness,and the number of chronic illnesses affected the willingness of low-aged elderly to live in old-age care.Conclusion In Zhangye City,family care is the main mode of old-age care for low-aged elderly.In the future,the relevant government departments should take into account the diversified needs of the elderly and the characteristics of the influencing factors,in order to realise the precise supply of the demand for elderly care services for the elderly.

Objective:To analyze predictive risk indicators associated with the development of Kümmell's disease (KD) in patients with osteoporotic vertebral compression fractures (OVCFs).Methods:A 1∶1 frequency-matched case-control study design was employed, selecting patients who visited the Department of Spine Surgery at Liuzhou Workers' Hospital from January 2021 to June 2023. Patients were divided into case and control groups based on whether they progressed to Kümmell's disease (KD). Detailed demographic information, comorbidities, and laboratory data were collected, and baseline characteristics of the two groups were compared. Initial predictive variables significantly associated with the target variable were preliminarily screened through univariate analysis. A correlation heatmap was then constructed to assess collinearity among these variables, followed by further selection of potential predictors using the Lasso regression model. Finally, a multivariable logistic regression model was used for the prediction and analysis of KD-related risk indicators.Results:Univariate analysis identified significant predictors of Kümmell's disease, including patient age, bone mineral density, kyphotic Cobb angle, and multiple vertebral fractures. These were included in the subsequent Lasso regression analysis, which identified key predictors with non-zero coefficients: age, bone density, Cobb angle, multiple vertebral fractures, platelet count (PLT), aspartate aminotransferase/alanine aminotransferase (AST/ALT), albumin (Alb), albumin/globulin ratio (Alb/Glb), alkaline phosphatase (ALP), urea (UREA), serum uric acid (SUA), fibrinogen (Fn), blood glucose (BG), and C-reactive protein (CRP). The correlation heatmap revealed the correlation and collinearity risks between these variables, with ALT and AST/ALT showing a high correlation ( r=0.750) and PLT and Alb showing a low correlation ( r=-0.110). Multivariable logistic regression indicated that the presence of multiple vertebral fractures [ OR=2.078, 95% CI (1.072, 4.025), P=0.030], increased Cobb angle [ OR=1.033, 95% CI (1.008, 1.058), P=0.009], elevated levels of ALP [ OR=1.013, 95% CI(1.004, 1.023), P=0.006], and SUA [ OR=1.004, 95% CI (1.000, 1.007), P=0.043] were associated with an increased risk of KD in patients with OVCFs. Conversely, decreased levels of Fn [ OR=0.996, 95% CI (0.992, 0.999), P=0.008] were linked to an increased risk of KD. Conclusion:Multiple vertebral fractures, increased Cobb angle, elevated levels of ALP and SUA, along with decreased levels of Fn, can be used as early-warning indicators to predict whether patients with OVCFs will develop KD. Monitoring these indicators is crucial for the early detection and intervention in these patients.