In recent years, due to the development of radiotherapy technology and nuclear energy, people have paid more and more attention to the various effects of ionizing radiation on organisms. Ionizing radiation can induce protein, DNA and other biological macromolecules to damage, resulting in apoptosis, senescence, cancer and a series of changes. For a long time, it has been believed that the main target of ionizing radiation damage is DNA in the nucleus. However, it has been reported in recent years that ionizing radiation has both direct and indirect effects, and the theory of ROS damage in the indirect effects believes that ionizing radiation has target uncertainty, so it is not comprehensive enough to evaluate only the DNA damage in the nucleus. It has been reported that ionizing radiation can cause damage to organelles as well as damage to cells. Mitochondria are important damaged organelles because mitochondria occupy as much as 30% of the entire cell volume in the cytoplasm, which contains DNA and related enzymes that are closely related to cellular ATP synthesis, aerobic respiration and other life activities. What is more noteworthy is that mitochondria are the only organelles in which DNA exists in the human body, which makes researchers pay attention to various damage to mitochondrial DNA caused by ionizing radiation (such as double-strand breaks, base mismatching, and fragment loss). Although these damages also occur in the nucleus, mitochondrial DNA is more severely damaged than nuclear DNA due to its lack of histone protection, so mitochondria are important targets of ionizing radiation damage in addition to the nucleus. Mitochondrial DNA is not protected by histones and has little repair ability. When exposed to ionizing radiation, common deletions occur at an increased frequency and are passed on to offspring. For large-scale mitochondrial DNA damage, mitochondria indirectly compensate for the amount of damaged DNA by increasing the number of DNA copies and maintaining the normal function of mitochondrial DNA. Mitochondria are in a state of oxidative stress after exposure to ionizing radiation, and this oxidative stress will promote the change in mitochondrial function. When mitochondria are damaged, the activity of proteins related to aerobic respiration decreases, and oxidative respiration is inhibited to a certain extent. At the same time, a large amount of active superoxide anions are continuously produced to stimulate mitochondrial oxidative stress, and the signal of such damage is transmitted to the surrounding mitochondria, resulting in a cascade of damage reaction, which further activates the signalling pathway between mitochondria and nucleus. The cell nucleus is also in a state of oxidative stress, and finally, the level of free radicals is high, causing secondary damage to the genetic material DNA of mitochondria and nucleus. In this paper, the damage effects of ionizing radiation on mitochondria are reviewed, to provide a new idea for radiation protection.

ObjectiveTo investigate the influence of histone deacetylase 3 (HDAC3) on the occurrence, development of psoriasis-like inflammation in mice, and the relative immune mechanisms. MethodsHealthy C57BL/6 mice aged 6-8 weeks were selected and randomly divided into 3 groups: control group (Control), psoriasis model group (IMQ), and HDAC3 inhibitor RGFP966-treated psoriasis model group (IMQ+RGFP966). One day prior to the experiment, the back hair of the mice was shaved. After a one-day stabilization period, the mice in Control group was treated with an equal amount of vaseline, while the mice in IMQ group was treated with imiquimod (62.5 mg/d) applied topically on the back to establish a psoriasis-like inflammation model. The mice in IMQ+RGFP966 group received intervention with a high dose of the HDAC3-selective inhibitor RGFP966 (30 mg/kg) based on the psoriasis-like model. All groups were treated continuously for 5 d, during which psoriasis-like inflammation symptoms (scaling, erythema, skin thickness), body weight, and mental status were observed and recorded, with photographs taken for documentation. After euthanasia, hematoxylin-eosin (HE) staining was used to assess the effect of RGFP966 on the skin tissue structure of the mice, and skin thickness was measured. The mRNA and protein expression levels of HDAC3 in skin tissues were detected using reverse transcription real-time quantitative polymerase chain reaction (RT-qPCR) and Western blot (WB), respectively. Flow cytometry was employed to analyze neutrophils in peripheral blood and lymph nodes, CD4⁺ T lymphocytes, CD8⁺ T lymphocytes in peripheral blood, and IL-17A secretion by peripheral blood CD4⁺ T lymphocytes. Additionally, spleen CD4⁺ T lymphocyte expression of HDAC3, CCR6, CCR8, and IL-17A secretion levels were analyzed. Immunohistochemistry was used to detect the localization and expression levels of HDAC3, IL-17A, and IL-10 in skin tissues. ResultsCompared with the Control group, the IMQ group exhibited significant psoriasis-like inflammation, characterized by erythema, scaling, and skin wrinkling. Compared with the IMQ group, RGFP966 exacerbated psoriasis-like inflammatory symptoms, leading to increased hyperkeratosis. The psoriasis area and severity index (PASI) skin symptom scores were higher in the IMQ group than those in the Control group, and the scores were further elevated in the IMQ+RGFP966 group compared to the IMQ group. Skin thickness measurements showed a trend of IMQ+RGFP966>IMQ>Control. The numbers of neutrophils in the blood and lymph nodes increased sequentially in the Control, IMQ, and IMQ+RGFP966 groups, with a similar trend observed for CD4⁺ and CD8⁺ T lymphocytes in the blood. In skin tissues, compared with the Control group, the mRNA and protein levels of HDAC3 decreased in the IMQ group, but RGFP966 did not further reduce these expressions. HDAC3 was primarily located in the nucleus. Compared with the Control group, the nuclear HDAC3 content decreased in the skin tissues of the IMQ group, and RGFP966 further reduced nuclear HDAC3. Compared with the Control and IMQ groups, RGFP966 treatment decreased HDAC3 expression in splenic CD4⁺ and CD8⁺ T cells. RGFP966 treatment increased the expression of CCR6 and CCR8 in splenic CD4⁺ T cells and enhanced IL-17A secretion by peripheral blood and splenic CD4⁺ T lymphocytes. Additionally, compared with the IMQ group, RGFP966 reduced IL-10 protein levels and upregulated IL-17A expression in skin tissues. ConclusionRGFP966 exacerbates psoriatic-like inflammatory responses by inhibiting HDAC3, increasing the secretion of the cytokine IL-17A, and upregulating the expression of chemokines CCR8 and CCR6.

Objective: Machine learning (ML) has been reported to have better predictive capability than traditional statistical techniques. The aim of this study was to assess the efficacy of ML algorithms and logistic regression (LR) for predicting depressive symptoms during the COVID-19 pandemic. Methods: Analyses were carried out in a national cross-sectional study involving 21,916 participants. The ML algorithms in this study included random forest (RF), support vector machine (SVM), neural network (NN), and gradient boosting machine (GBM) methods. The performance indices were sensitivity, specificity, accuracy, precision, F1-score, and area under the receiver operating characteristic curve (AUC). Results: LR and NN had the best performance in terms of AUCs. The risk of overfitting was found to be negligible for most ML models except for RF, and GBM obtained the highest sensitivity, specificity, accuracy, precision, and F1-score. Therefore, LR, NN, and GBM models ranked among the best models. Conclusion Compared with ML models, LR model performed comparably to ML models in predicting depressive symptoms and identifying potential risk factors while also exhibiting a lower risk of overfitting.

Objective: Machine learning (ML) has been reported to have better predictive capability than traditional statistical techniques. The aim of this study was to assess the efficacy of ML algorithms and logistic regression (LR) for predicting depressive symptoms during the COVID-19 pandemic. Methods: Analyses were carried out in a national cross-sectional study involving 21,916 participants. The ML algorithms in this study included random forest (RF), support vector machine (SVM), neural network (NN), and gradient boosting machine (GBM) methods. The performance indices were sensitivity, specificity, accuracy, precision, F1-score, and area under the receiver operating characteristic curve (AUC). Results: LR and NN had the best performance in terms of AUCs. The risk of overfitting was found to be negligible for most ML models except for RF, and GBM obtained the highest sensitivity, specificity, accuracy, precision, and F1-score. Therefore, LR, NN, and GBM models ranked among the best models. Conclusion Compared with ML models, LR model performed comparably to ML models in predicting depressive symptoms and identifying potential risk factors while also exhibiting a lower risk of overfitting.

Objective: Machine learning (ML) has been reported to have better predictive capability than traditional statistical techniques. The aim of this study was to assess the efficacy of ML algorithms and logistic regression (LR) for predicting depressive symptoms during the COVID-19 pandemic. Methods: Analyses were carried out in a national cross-sectional study involving 21,916 participants. The ML algorithms in this study included random forest (RF), support vector machine (SVM), neural network (NN), and gradient boosting machine (GBM) methods. The performance indices were sensitivity, specificity, accuracy, precision, F1-score, and area under the receiver operating characteristic curve (AUC). Results: LR and NN had the best performance in terms of AUCs. The risk of overfitting was found to be negligible for most ML models except for RF, and GBM obtained the highest sensitivity, specificity, accuracy, precision, and F1-score. Therefore, LR, NN, and GBM models ranked among the best models. Conclusion Compared with ML models, LR model performed comparably to ML models in predicting depressive symptoms and identifying potential risk factors while also exhibiting a lower risk of overfitting.

Objective: To investigate the characteristics of the breakthrough cases of mumps in Ningbo City, Zhejiang Province from 2018 to 2023, so as to provide insights into improving prevention and control measures for mumps. Methods: Data of mumps cases and mumps containing vaccine (MuCV) vaccination in Ningbo City from 2018 to 2023 were collected through the Infectious Disease Reporting Information System of Chinese Disease Prevention and Control Information System and Zhejiang Immunization Planning Information System. The population distribution characteristics and MuCV immunization history of mumps breakthrough cases were described. The impacts of the final immunization age and immunization interval on the age of onset were analyzed. Results: A total of 6 643 mumps cases were reported in Ningbo City from 2018 to 2023, with an average incidence rate of 11.72/105. There were 5 142 breakthrough cases (77.40%), including 3 173 males (61.71%) and 1 969 females (38.29%). The median age of onset was 6.00 (interquartile range, 4.00) years. There were 2 487 cases in preschool children (48.37%) and 2 232 cases in students (43.41%). There were 4 736 one-dose breakthrough cases (92.10%) and 406 two-dose breakthrough cases (7.90%). The proportion of two-dose breakthrough cases among all mumps cases increased from 1.00% in 2018 to 25.32% in 2023. Among individuals born after December 2018, the median age of onset of two-dose breakthrough cases was 3.00 (interquartile range, 1.00) years, which was older than that of one-dose breakthrough cases at 1.00 (interquartile range, 2.00) year (P<0.05). The ages of onset of mumps breakthrough cases differed significantly with varying final immunization ages and immunization intervals (both P<0.05). Conclusions The breakthrough cases of mumps in Ningbo City from 2018 to 2023 were mainly males, preschool children and students. The proportion of two-dose breakthrough cases increased, and the age of onset delayed.

Objective: Machine learning (ML) has been reported to have better predictive capability than traditional statistical techniques. The aim of this study was to assess the efficacy of ML algorithms and logistic regression (LR) for predicting depressive symptoms during the COVID-19 pandemic. Methods: Analyses were carried out in a national cross-sectional study involving 21,916 participants. The ML algorithms in this study included random forest (RF), support vector machine (SVM), neural network (NN), and gradient boosting machine (GBM) methods. The performance indices were sensitivity, specificity, accuracy, precision, F1-score, and area under the receiver operating characteristic curve (AUC). Results: LR and NN had the best performance in terms of AUCs. The risk of overfitting was found to be negligible for most ML models except for RF, and GBM obtained the highest sensitivity, specificity, accuracy, precision, and F1-score. Therefore, LR, NN, and GBM models ranked among the best models. Conclusion Compared with ML models, LR model performed comparably to ML models in predicting depressive symptoms and identifying potential risk factors while also exhibiting a lower risk of overfitting.

Objective: Machine learning (ML) has been reported to have better predictive capability than traditional statistical techniques. The aim of this study was to assess the efficacy of ML algorithms and logistic regression (LR) for predicting depressive symptoms during the COVID-19 pandemic. Methods: Analyses were carried out in a national cross-sectional study involving 21,916 participants. The ML algorithms in this study included random forest (RF), support vector machine (SVM), neural network (NN), and gradient boosting machine (GBM) methods. The performance indices were sensitivity, specificity, accuracy, precision, F1-score, and area under the receiver operating characteristic curve (AUC). Results: LR and NN had the best performance in terms of AUCs. The risk of overfitting was found to be negligible for most ML models except for RF, and GBM obtained the highest sensitivity, specificity, accuracy, precision, and F1-score. Therefore, LR, NN, and GBM models ranked among the best models. Conclusion Compared with ML models, LR model performed comparably to ML models in predicting depressive symptoms and identifying potential risk factors while also exhibiting a lower risk of overfitting.

OBJECTIVE To construct three-class （insufficient， normal， excessive） and two-class （insufficient， normal） models for predicting plasma concentration of valproic acid （VPA）， and compare the performance of these two models， with the aim of providing a reference for formulating clinical medication strategies. METHODS The clinical data of 480 patients who received VPA treatment and underwent blood concentration test at the Xi’an International Medical Center Hospital were collected from November 2022 to September 2024 （a total of 695 sets of data）. In this study， predictive models were constructed for target variables of three-class and two-class models. Feature ranking and selection were carried out using XGBoost scores. Twelve different machine learning algorithms were used for training and validation， and the performance of the models was evaluated using three indexes: accuracy， F1 score， and the area under the working characteristic curve of the subject （AUC）. RESULTS XGBoost feature importance scores revealed that in the three-class model， the importance ranking of kidney disease and electrolyte disorders was higher. However， in the two-class model， the importance ranking of these features significantly decreased， suggesting a close association with the excessive blood concentration of VPA. In the three-class model， Random Forest method performed best， with F1 score of 0.704 0 and AUC of 0.519 3 on the test set； while in the two-class model， CatBoost method performed optimally， with F1 score of 0.785 7 and AUC of 0.819 5 on the test set. CONCLUSIONS The constructed three-class model has the ability to predict excessive VPA blood concentration， but its prediction and model generalization abilities are poor； the constructed two-class model can only perform classification prediction for insufficient and normal blood concentration cases， but its model performance is stronger.

Background::There is a need for effective and safe therapies for psoriasis that provide sustained benefits. The aim of this study was to assess the efficacy and safety of tildrakizumab, an anti-interleukin-23p19 monoclonal antibody, for treating moderate-to-severe plaque psoriasis in Chinese patients.Methods::In this multi-center, double-blind, phase III trial, patients with moderate-to-severe plaque psoriasis were enrolled and randomly assigned (1:1) to receive subcutaneous tildrakizumab 100 mg or placebo at weeks 0 and 4. Patients initially assigned to placebo were switched to receive tildrakizumab at weeks 12, 16, and every 12 weeks thereafter. Patients in the tildrakizumab group continued with tildrakizumab at week 16, and every 12 weeks until week 52. The primary endpoint was the Psoriasis Area and Severity Index (PASI 75) response rate at week 12.Results::At week 12, tildrakizumab demonstrated significantly higher PASI 75 response rates (66.4% [73/110] vs. 12.7% [14/110]; difference, 51.4% [95% confidence interval (CI), 40.72, 62.13]; P <0.001) and Physician’s Global Assessment (60.9% [67/110] vs. 10.0% [11/110]; difference, 49.1% [95% CI, 38.64, 59.62]; P <0.001) compared to placebo. PASI 75 response continued to improve over time in both tildrakizumab and placebo-switching to tildrakizumab groups, reaching maximal efficacy after 28 weeks (86.8% [92/106] vs. 82.4% [89/108]) and maintained up to 52 weeks (91.3% [95/104] vs. 87.4% [90/103]). Most treatment-emergent adverse events were mild and not related to tildrakizumab. Conclusion::Tildrakizumab demonstrated durable efficacy through week 52 and was well tolerated in Chinese patients with moderate-to-severe plaque psoriasis.Trial registration::ClinicalTrials.gov, NCT05108766.