ObjectiveTo investigate the value of predictive models established based on machine learning methods in predicting the risk of metabolic associated fatty liver disease （MAFLD）， and to analyze its key risk factors. MethodsA retrospective analysis was performed for the 50 variables of 2 168 healthy individuals who underwent physical examination in Department of Health Assessment， Xiyuan Hospital， China Academy of Chinese Medical Sciences， from January 2021 to December 2024， including body composition， past history， and laboratory tests， and according to whether they were diagnosed with MAFLD or not， they were divided into MAFLD group with 265 individuals and non-MAFLD group with 1 903 individuals. The Mann-Whitney U test was used for comparison of continuous data between two groups， and the chi-square test was used for comparison of categorical data between two groups. Randomly split the research data into a training set and a validation set in a 70% to 30% ratio. Predictive factors were screened from the training set data using univariate analysis， LASSO regression， and multivariate Logistic regression analysis. Predictive models were then constructed using seven machine learning methods： Logistic regression， decision tree， random forest （RF）， eXtreme gradient boosting， light gradient boosting machine， support vector machine， and artificial neural network. Model performance was evaluated by plotting receiver operating characteristic curve for the validation set and calculating the area under the curve （AUC）， sensitivity， specificity， and Youden index for each model. Furthermore， the SHapley Additive exPlanation （SHAP） method was used to analyze the contribution of variables in the optimal model. ResultsThe prevalence rate of MAFLD among the 2 168 subjects was 12.22% （265/2 168）. Smoking， diastolic blood pressure， phase angle， visceral fat area， muscle fat ratio， waist-to-hip ratio， aspartate aminotransferase， non-HDL-C/HDL-C ratio， triglyceride-glucose index， and gallstones were independent risk factors for MAFLD （all P<0.05）. The seven predictive models of support vector machine， eXtreme gradient boosting， decision tree， light gradient boosting machine， artificial neural network， RF， and Logistic regression had an AUC of 0.738， 0.754， 0.757， 0.786， 0.795， 0.796， and 0.815， respectively， in the validation set， among which the RF model had the best discriminatory ability （AUC=0.796， 95% confidence interval： 0.754 — 0.839）， with a sensitivity of 81.01%， a specificity of 63.16%， and a Youden index of 44.17%. The SHAP analysis showed that visceral fat area， waist-to-hip ratio， and diastolic blood pressure were the top three predictive factors in terms of importance. ConclusionThe RF model， constructed based on body composition and clinical indicators， has a good performance in predicting the risk of MAFLD， and its interpretability can help to identify high-risk individuals in the early stage in clinical practice.

Obesity is a chronic low-grade inflammation and a risk factor for diseases such as diabetes， hypertension， dyslipidemia， and malignant tumors， demonstrating an increasingly grim development situation. The nuclear factor-kappa B （NF-κB） signaling pathway is a key signaling pathway involved in the immune response and inflammatory response. In obese individuals， the expression of NF-κB is overactivated， which leads to abnormal inflammatory responses in the body. Therefore， it is expected to alleviate inflammation and treat obesity by regulating the NF-κB signaling pathway， which has been proven effective by a large number of studies. The available studies on the NF-κB signaling pathway mostly focus on tumors， and there is no systematic review of the mechanism of this pathway in mediating obesity and the traditional Chinese medicine （TCM） treatment. We reviewed the research progress in the pathological and physiological processes of obesity mediated by NF-κB signaling pathway and TCM treatment， aiming to give insights into the clinical treatment of obesity with TCM and provide reference targets and research directions for exploring the biological foundations and the development of new TCM preparations.

[Objective] To compare the diagnostic accuracy of three imaging modalities, inlducing CT urography (CTU), contrast-enhanced MRI (CE-MRI), and contrast-enhanced ultrasound (CEUS) in the qualitative diagnosis of renal space-occupying lesions. [Methods] A retrospective analysis was performed on 542 patients with renal lesions confirmed by surgical pathology in our hospital during Jan.2019 and May 2024.The diagnostic results of CTU, CE-MRI and CEUS were compared and analyzed based on the patients' clinical and pathological data. [Results] The diagnostic accuracy rate of CTU, CE-MRI and CEUS were 84.50%, 83.14% and 86.14%, respectively.For the 161 patients who underwent all three examinations, CEUS was significantly more accurate than CTU (84.16% vs. 77.02%, P=0.018), while there was no significant difference between CTU or CEUS and CE-MRI (79.81%) (P>0.05). Further analysis found that for lesions ≤4 cm, the accuracy of the three examinations was as follows: CEUS=CTU 79.55%, CE-MRI 76.14%, with no significant difference (P>0.05). However, for lesions >4 cm, CEUS ranked the first, followed by CE-MRI and CTU (89.73% vs. 84.25% vs. 73.97%), and CEUS and CE-MRI were better than CTU (P<0.05). Additionally, for the diagnosis of clear cell renal carcinoma and benign renal space-occupying lesions, there was no statistically significant difference among the three imaging modalities (P>0.05), while for the qualitative diagnosis of non-clear cell renal carcinoma, CEUS ranked the first, followed by CE-MRI and CTU (83.87% vs. 74.19% vs. 56.45%), and CE-MRI and CEUS were better than CTU (P<0.05). [Conclusion] All of them have important diagnostic value, and the appropriate selection should be based on patients' specifc conditions.CEUS and CE-MRI are more accurate in the qualitative diagnosis of renal space-occupying lesions than CTU, especially for large lesions and non-clear cell carcinoma.

Objective: To compare the value of a body shape index (ABSI) and body roundness index (BRI) in predicting non-alcoholic fatty liver disease (NAFLD) among non-obese population, so as to provide a reference for the early identification of populations at high risk of NAFLD. Methods: Adults with a body mass index (BMI) of less than 28 kg/m2 who underwent health check-ups in Xiyuan Hospital of China Academy of Chinese Medical Sciences from 2022 to 2024 were selected as the study subjects. Demographic information, disease history, height, weight, waist circumference, blood pressure, and blood lipid indicators were collected, and ABSI and BRI were calculated. NAFLD was diagnosed using abdominal ultrasound. A multivariable logistic regression model was employed to analyze the relationships between ABSI, BRI and NAFLD among non-obese population. A generalized additive model combined with the penalized spline method was used to fit smooth curves to identify nonlinear relationships, and threshold effects were utilized to determine inflection points. The values of ABSI and BRI in predicting NAFLD among non-obese population were used the receiver operating characteristic (ROC) curve. Results: A total of 1 195 individuals were surveyed, including 345 males (28.87%) and 850 females (71.13%). A total of 348 cases of NAFLD were detected among the non-obese population, with a detection rate of 29.12%. The adjusted ABSI (sABSI) in the NAFLD group and non-NAFLD group were 7.95±0.33 and 8.08±0.34, while the BRI were 3.35±0.79 and 4.15±0.64, respectively, with statistically significant differences between the two groups (both P<0.05). Multivariable logistic regression analysis showed that, after adjusting for demographic information, disease history, blood pressure, and blood lipid indicators, both sABSI (OR=1.932, 95%CI: 1.184-3.158) and BRI (OR=1.594, 95%CI: 1.071-2.360) were significantly associated with NAFLD among non-obese population. Nonlinear positive correlations were observed between sABSI, BRI, and NAFLD among non-obese population. When sABSI≤8.46 and BRI≥2.72, both indices were positively associated with NAFLD. The area under the ROC curve for ABSI and BRI in predicting NAFLD risk among non-obese population were 0.619 and 0.782, respectively, with optimal cut-off values of 0.082 and 3.656, respectively. Conclusions ABSI and BRI show a non-linear relationship with NAFLD among non-obese population. BRI demonstrates relatively better performance in predicting NAFLD risk among this population and can serve as an auxiliary indicator for the early identification of NAFLD among non-obese population.

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.

background Current assessment of noise-induced hearing loss relies on the hearing threshold statistical distribution table of ISO 7029-2017 standard (ISO 7029), which is based on foreign population data and lacks a hearing threshold distribution table derived from pure-tone audiometry data of the Chinese population, hindering accurate evaluation of hearing loss in this group. Objective To establish a statistical distribution table of hearing threshold level (HTL) for otologically normal Chinese adults and to provide a scientific basis for revising the diagnostic criteria of occupational noise-induced deafness in China. Methods A total of 1271 otologically normal Chinese adults aged 18-60 years were divided into four groups with 10-year age intervals. Based on the pure tone audiometry data and power function operation formula, the relevant parameters of the median and percentile calculation formula of ISO 7029 were adjusted. Based on the adjusted parameters, a statistical distribution table of HTL of normal Chinese adults in otology was preliminarily constructed according to the calculation formula of ISO 7029. A nonparametric rank sum test was used to compare the difference between the adjusted median deviation value and the ISO 7029 calculation. Results Except for a few frequencies (2000, 3000, and 8000 Hz), the value of parameter α adjusted for the auditory threshold deviation of the Chinese population (α_CHN-md) increased with the increase of frequency, while the value of parameter β adjusted for the auditory threshold deviation of the Chinese population (β_CHN-md) decreased with the increase of frequency. The preliminary distribution table of otologically normal Chinese adults' median hearing threshold deviation (ΔH_CHN-md) showed that the hearing threshold deviation increased with age and frequency. At 8000 Hz, the increase in ΔH_CHN-md with age was the greatest for both men and women, from 0 dB to 23 dB and 21 dB respectively. The minimal threshold elevation was noted at 500, 1000, and 2000 Hz, which increased from 0 dB to 2 dB. In otologically normal Chinese adults, the maximum increase of ΔH_CHN-md with frequency was observed at age of 50 years, which increased from 2 dB at 500 Hz to 23 dB at 8000 Hz in men and from 2 dB at 500 Hz to 21 dB at 8000 Hz in women. The comparison results with the ISO 7029 calculation showed that the trend was consistent. The median hearing threshold deviation value of the adjusted Chinese population was lower than that of the ISO 7029 calculation (ΔH_md). However, they had no significant statistical difference (P>0.05). For otologically normal normal Chinese men and women, the maximum difference of ΔH_md between the 50-year-old group and the ISO calculation at 8000 Hz was 7 dB and 9 dB, respectively. Conclusion This preliminary analysis of hearing threshold deviations in otologically normal Chinese adults suggests that current standards may potentially underestimate hearing loss at frequencies below 8000 Hz while possibly overestimating it at 8000 Hz. These findings require further validation through expanded sample sizes to more accurately assess the characteristics of hearing thresholds in the Chinese population and their discrepancies with existing standards.

HDAC7, a member of class IIa HDACs, plays a pivotal regulatory role in tumor, immune, fibrosis, and angiogenesis, rendering it a potential therapeutic target. Nevertheless, due to the high similarity in the enzyme active sites of class IIa HDACs, inhibitors encounter challenges in discerning differences among them. Furthermore, the substitution of key residue in the active pocket of class IIa HDACs renders them pseudo-enzymes, leading to a limited impact of enzymatic inhibitors on their function. In this study, proteolysis targeting chimera (PROTAC) technology was employed to develop HDAC7 drugs. We developed an exceedingly selective HDAC7 PROTAC degrader B14 which showcased superior inhibitory effects on cell proliferation compared to TMP269 in various diffuse large B cell lymphoma (DLBCL) and acute myeloid leukemia (AML) cells. Subsequent investigations unveiled that B14 disrupts BCL6 forming a transcriptional inhibition complex by degrading HDAC7, thereby exerting proliferative inhibition in DLBCL. Our study broadened the understanding of the non-enzymatic functions of HDAC7 and underscored the importance of HDAC7 in the treatment of hematologic malignancies, particularly in DLBCL and AML.

Objective:To evaluate the efficacy and safety of hypomethylating agents (HMA) in patients with myelodysplastic syndromes (MDS) .Methods:A total of 409 MDS patients from 45 hospitals in Zhejiang province who received at least four consecutive cycles of HMA monotherapy as initial therapy were enrolled to evaluate the efficacy and safety of HMA. Mann-Whitney U or Chi-square tests were used to compare the differences in the clinical data. Logistic regression and Cox regression were used to analyze the factors affecting efficacy and survival. Kaplan-Meier was used for survival analysis. Results:Patients received HMA treatment for a median of 6 cycles (range, 4-25 cycles) . The complete remission (CR) rate was 33.98% and the overall response rate (ORR) was 77.02%. Multivariate analysis revealed that complex karyotype ( P=0.02, OR=0.39, 95% CI 0.18-0.84) was an independent favorable factor for CR rate. TP53 mutation ( P=0.02, OR=0.22, 95% CI 0.06-0.77) was a predictive factor for a higher ORR. The median OS for the HMA-treated patients was 25.67 (95% CI 21.14-30.19) months. HMA response ( P=0.036, HR=0.47, 95% CI 0.23-0.95) was an independent favorable prognostic factor, whereas complex karyotype ( P=0.024, HR=2.14, 95% CI 1.10-4.15) , leukemia transformation ( P<0.001, HR=2.839, 95% CI 1.64-4.92) , and TP53 mutation ( P=0.012, HR=2.19, 95% CI 1.19-4.07) were independent adverse prognostic factors. There was no significant difference in efficacy and survival between the reduced and standard doses of HMA. The CR rate and ORR of MDS patients treated with decitabine and azacitidine were not significantly different. The median OS of patients treated with decitabine was longer compared with that of patients treated with azacitidine (29.53 months vs 20.17 months, P=0.007) . The incidence of bone marrow suppression and pneumonia in the decitabine group was higher compared with that in the azacitidine group. Conclusion:Continuous and regular use of appropriate doses of hypomethylating agents may benefit MDS patients to the greatest extent if it is tolerated.