OBJECTIVE To systematically evaluate risk prediction models for chemotherapy-induced myelosuppression in breast cancer， and provide a scientific reference for clinical healthcare workers in selecting or developing effective predictive models. METHODS A systematic search was conducted for studies on predictive models of the risk of chemotherapy-induced myelosuppression in breast cancer across the CNKI， VIP， Wanfang， PubMed， Web of Science， Cochrane Library， Embase， and Scopus databases， with a time frame of the establishment of the database to May 7， 2024. Literature was independently screened by 2 investigators， data were extracted according to critical appraisal and data extraction for systematic reviews of predictive model studies， and the risk of bias evaluation tool for predictive model studies was used to analyze the risk of bias and applicability of the included studies. RESULTS There were totally 7 studies， comprising 12 models. Among them， 11 models indicated an area under the subject operating characteristic curve of 0.600-0.908； 2 models indicated calibration. The common predictor variables of the included models were age， pre-chemotherapy neutrophil count， pre-chemotherapy lymphocyte count， and pre-chemotherapy albumin. The overall risk of bias of the 7 studies was high， which was mainly attributed to the flaws in the study design， insufficient sample sizes， inappropriate treatment of variables， non-reporting of missing data， and the lack of indicators for the assessment of the models， but the applicability was good. CONCLUSIONS The predictive performance of risk predictive models for chemotherapy-induced myelosuppression in breast cancer remains to be further enhanced， and the overall risk of model bias is high. Future studies should follow the specifications of model development and reporting， then combine machine learning algorithms to develop risk predictive models with good predictive performance， high stability， and low risk of bias， so as to provide a decision-making basis for the clinic.

In order to clarify the name,plant species and clinical use of the traditional Uyghur medicine"Heliyun",this article combed Uyghur medical classics such as Ehtiyarat bedey,Aksaray,Jimi elaj and Mehzenul edwiye,as well as modern literature.Herbal textual research was performed on its name,origin,properties,functional indications,etc.At the same time,on-site inspections were conducted to verify the clinical use of"Heliyun"in Xinjiang Uyghur Medical Hospital,medicinal samples were collected,and original plant identification was conducted.After herbal textual research,confirmation of clinical use of medicinal materials in Uyghur medicine,and botanical identification,the Uyghur medicinal material"Heliyun"is actually the dried fruit of Withania somnifera(L.)Dunal,and the Chinese name for this medicinal plant should be"Shuiqieguo",and the Uyghur name remains"Heliyun".This could provide a basis for standardizing the name,confirming the plant species,and promoting the clinical application of"Heliyun"in Uyghur medicine.

Real-time acquisition of pulmonary ventilation and perfusion information through thoracic electrical impedance tomography (EIT) holds significant clinical value. This study proposes a novel method based on the rime (RIME) algorithm-optimized variational mode decomposition (VMD) to separate lung ventilation and perfusion signals directly from raw voltage data prior to EIT image reconstruction, enabling independent imaging of both parameters. To validate this approach, EIT data were collected from 16 healthy volunteers under normal breathing and inspiratory breath-holding conditions. The RIME algorithm was employed to optimize VMD parameters by minimizing envelope entropy as the fitness function. The optimized VMD was then applied to separate raw data across all measurement channels in EIT, with spectral analysis identifying relevant components to reconstruct ventilation and perfusion signals. Results demonstrated that the structural similarity index (SSIM) between perfusion images derived from normal breathing and breath-holding states averaged approximately 84% across all 16 subjects, significantly outperforming traditional frequency-domain filtering methods in perfusion imaging accuracy. This method offers a promising technical advancement for real-time monitoring of pulmonary ventilation and perfusion, holding significant value for advancing the clinical application of EIT in the diagnosis and treatment of respiratory diseases.
Humans ; Electric Impedance ; Algorithms ; Tomography/methods* ; Pulmonary Ventilation/physiology* ; Lung/diagnostic imaging* ; Image Processing, Computer-Assisted/methods* ; Adult

Objective:To investigate human factors underlying radiotherapy planning safety incidents and quantitatively explore their interrelationships.Methods:A total of 1 619 safety incidents recorded in the automated plan checking system developed by West China Hospital of Sichuan University were utilized. Human factors were identified and statistically analyzed using the Human Factors Analysis and Classification System (HFACS). A Bayesian network model was developed and combined with sensitivity analysis for quantitative assessment.Results:Key contributing factors included organizational processes (12.89%), inadequate supervision (11.85%), and personnel factors (13.50%). Utilizing the established HFACS Bayesian network hybrid model in conjunction with sensitivity analysis, it has been found that the most significant influences on skill‐based errors and decision errors were condition of operators and environmental factors, with corresponding indices of 0.96 and 0.76. Additionally, personnel factors had the greatest impact on routine, with an index of 3.51.Conclusions:Key contributing factors span all HFACS levels, with organizational processes, supervision, personnel, and condition of operators each playing a significant role. Upstream factors — such as organizational climate, environment factors, and personnel factors — strongly influence downstream risks. These offer actionable insights for developing targeted safety protocols.

Objective To explore the applicative value of spectral CT in increasing positive rates of lung cancer puncture and reducing complications during CT guided percutaneous transthoracic needle biopsy(PTNB).Methods The pathological results and complica-tion incidences of 260 PTNB patients were analyzed retrospectively.All patients were divided into three groups:group A(conventional CT group,103 cases)used a scheme based on conventional enhanced CT;group B(PET/CT group,84 cases)used a scheme combining the maximum standardized uptake value(SUVmax)with conventional enhanced CT;group C(spectral CT group,73 cases)used a scheme of quantitative spectral CT parameters and images.Results Group A included 103 cases in total,of which 87 were positive(84.47％),41 pneumothorax(39.81％),and 31 hemorrhage(30.10％).Group B totaled 84 cases,including 82 positive cases(97.62％),19 cases of pneumothorax(22.62％),and 11 cases of hemorrhage(13.10％).Group C was of 73 cases,including 70 positive cases(95.89％),16 cases of pneumothorax(21.92％),and 10 cases of hemorrhage(13.70％).There were statistically significant differ-ences in biopsy positive rates,pneumothorax incidences,and hemorrhage incidences among groups A,B,and C(P＜0.05).There were also statistically significant differences in biopsy positive rates,pneumothorax incidences,and hemorrhage incidences between groups A and B or groups A and C(P＜0.016 7),respectively.However,no statistically significant differences were found between groups B and C in biopsy positive rates,pneumothorax incidences,and hemorrhage incidences(P＞0.016 7).Conclusion Spectral CT can improve the positive rate of lung cancer and reduce the risk of pneumothorax and hemorrhage with PTNB.

OBJECTIVE To construct a prediction model for the efficacy of serotonin-norepinephrine reuptake inhibitor(SNRI)in inpatients with moderate and severe depression by using a machine learning method.METHODS The case records of inpatients with moderate and severe depression treated with SNRI antidepressants were collected from a third-grade class-A hospital in Xinjiang from January 2022 to October 2024;those patients were divided into effective group and ineffective group based on the Hamilton depression scale-24 score reduction rate.After screening the characteristic variables related to the therapeutic efficacy of SNRI drugs through LASSO regression,five prediction models including support vector machine,k-nearest neighbor,random forest,lightweight gradient boosting machine and extreme gradient boosting were constructed using the training set.Bayesian optimization was used to adjust the hyperparameters of these models.The performance of the models was evaluated in the validation set to select the optimal model.The Shapley additive explanations method was used to perform explainable analysis on the best model.RESULTS The medical records from 355 hospitalized patients with moderate and severe depression were collected,comprising 285 cases in the effective group and 70 cases in the ineffective group,resulting in an overall therapeutic response rate of 80.28%.After feature variable screening,five characteristic variables for therapeutic efficacy were obtained,including Hamilton anxiety scale,blood urea nitrogen,combination of anti-anxiety drugs,drinking history,and first onset of the disease.Compared with other models,the random forest model performed the best.The area under the receiver operating characteristic curve was 0.85,the area under the precision-recall curve was 0.87,the accuracy was 0.74,and the recall rate value was 0.75.CONCLUSIONS The random forest model constructed based on five characteristic variables demonstrates potential for predicting the therapeutic efficacy of SNRI antidepressants in hospitalized patients with moderate and severe depression.

Objective To explore the applicative value of spectral CT in increasing positive rates of lung cancer puncture and reducing complications during CT guided percutaneous transthoracic needle biopsy(PTNB).Methods The pathological results and complica-tion incidences of 260 PTNB patients were analyzed retrospectively.All patients were divided into three groups:group A(conventional CT group,103 cases)used a scheme based on conventional enhanced CT;group B(PET/CT group,84 cases)used a scheme combining the maximum standardized uptake value(SUVmax)with conventional enhanced CT;group C(spectral CT group,73 cases)used a scheme of quantitative spectral CT parameters and images.Results Group A included 103 cases in total,of which 87 were positive(84.47％),41 pneumothorax(39.81％),and 31 hemorrhage(30.10％).Group B totaled 84 cases,including 82 positive cases(97.62％),19 cases of pneumothorax(22.62％),and 11 cases of hemorrhage(13.10％).Group C was of 73 cases,including 70 positive cases(95.89％),16 cases of pneumothorax(21.92％),and 10 cases of hemorrhage(13.70％).There were statistically significant differ-ences in biopsy positive rates,pneumothorax incidences,and hemorrhage incidences among groups A,B,and C(P＜0.05).There were also statistically significant differences in biopsy positive rates,pneumothorax incidences,and hemorrhage incidences between groups A and B or groups A and C(P＜0.016 7),respectively.However,no statistically significant differences were found between groups B and C in biopsy positive rates,pneumothorax incidences,and hemorrhage incidences(P＞0.016 7).Conclusion Spectral CT can improve the positive rate of lung cancer and reduce the risk of pneumothorax and hemorrhage with PTNB.

In order to clarify the name,plant species and clinical use of the traditional Uyghur medicine"Heliyun",this article combed Uyghur medical classics such as Ehtiyarat bedey,Aksaray,Jimi elaj and Mehzenul edwiye,as well as modern literature.Herbal textual research was performed on its name,origin,properties,functional indications,etc.At the same time,on-site inspections were conducted to verify the clinical use of"Heliyun"in Xinjiang Uyghur Medical Hospital,medicinal samples were collected,and original plant identification was conducted.After herbal textual research,confirmation of clinical use of medicinal materials in Uyghur medicine,and botanical identification,the Uyghur medicinal material"Heliyun"is actually the dried fruit of Withania somnifera(L.)Dunal,and the Chinese name for this medicinal plant should be"Shuiqieguo",and the Uyghur name remains"Heliyun".This could provide a basis for standardizing the name,confirming the plant species,and promoting the clinical application of"Heliyun"in Uyghur medicine.

Objective:To investigate human factors underlying radiotherapy planning safety incidents and quantitatively explore their interrelationships.Methods:A total of 1 619 safety incidents recorded in the automated plan checking system developed by West China Hospital of Sichuan University were utilized. Human factors were identified and statistically analyzed using the Human Factors Analysis and Classification System (HFACS). A Bayesian network model was developed and combined with sensitivity analysis for quantitative assessment.Results:Key contributing factors included organizational processes (12.89%), inadequate supervision (11.85%), and personnel factors (13.50%). Utilizing the established HFACS Bayesian network hybrid model in conjunction with sensitivity analysis, it has been found that the most significant influences on skill‐based errors and decision errors were condition of operators and environmental factors, with corresponding indices of 0.96 and 0.76. Additionally, personnel factors had the greatest impact on routine, with an index of 3.51.Conclusions:Key contributing factors span all HFACS levels, with organizational processes, supervision, personnel, and condition of operators each playing a significant role. Upstream factors — such as organizational climate, environment factors, and personnel factors — strongly influence downstream risks. These offer actionable insights for developing targeted safety protocols.

Objective To investigate the role of hydrogen therapy in reducing radiation-induced lung injury and the specific mechanism. Methods Forty C57BL/6 mice were randomly divided into four groups: normal control group, model group, hydrogen therapy group I, and hydrogen therapy group II. A mouse model of radiation-induced lung injury was established. The pathological changes in the lung tissue of the mice were examined with HE staining. Immunofluorescence staining was used to detect the expression of surface markers of M1 and M2 macrophages to observe macrophage polarization. The expression of interleukin (IL)-6, tumor necrosis factor-α (TNF-α), and IL-10 in the lung tissue was measured by immunohistochemistry. The expression of nuclear factor-kappa B (NF-κB) p65 and phosphorylated NF-κB (P-NF-κB) p65 was measured by Western blot. Results HE staining showed that compared with the control group, the model group exhibited alveolar septal swelling and thickening, vascular dilatation and congestion, and inflammatory cell infiltration in the lung tissue; the hydrogen groups had significantly reduced pathological damage and inflammatory response than the model group, with more improvements in hydrogen group II than in hydrogen group I. Immunohistochemical results showed that compared with those in the control group, the levels of the inflammatory cytokines IL-6 and TNF-α were significantly increased in the model group; the hydrogen groups showed significantly decreased IL-6 and TNF-α levels and a significantly increased level of the anti-inflammatory factor IL-10 than the model group, which were more marked in hydrogen group II than in hydrogen group I. Immunofluorescence results showed that compared with the control group, the expression of the surface marker of M1 macrophages in the model group was significantly upregulated; the hydrogen groups showed significantly downregulated M1 marker and significantly upregulated M2 marker, and hydrogen group II showed significantly increased M2 marker compared with hydrogen group I. Western blot results showed that compared with that in the control group, the ratio of P-NF-κB p65/NF-κB p65 in the model group was significantly increased; the P-NF-κB p65/NF-κB p65 ratio was significantly reduced in the hydrogen groups than in the model group, and was significantly lower in hydrogen group II than in hydrogen group I. Conclusion Hydrogen inhalation therapy may reduce the inflammatory response of radiation-induced lung injury by inhibiting the NF-κB signaling pathway to promote the polarization of the macrophage M1 subtype to the M2 subtype.