Ovarian cancer （OC） is one of the most common malignant tumors in women， with the mortality rate being the highest among gynaecological malignant tumors. As the atypical symptoms of OC are difficult to be detected in the early stage， most patients are already in the advanced stage when being diagnosed. As a result， the clinical treatment has limited effects. Currently， the main therapies for OC are surgery and chemotherapy， while their drug resistance and adverse reactions seriously reduce the quality of life of patients. In recent years， traditional Chinese medicine （TCM） has attracted the attention of clinicians and researchers because of its high efficacy， low toxicity， and mild side effects. According to the TCM philosophy of treatment based on syndrome differentiation， the Chinese medicines with multiple targets， wide range， and mild side effects can be screened based on the molecular targets involved in the occurrence and development of OC， which can bring out the unique advantages of TCM in the treatment of OC. Modern studies have shown that the occurrence and development of OC are closely related to the abnormal expression of multiple signaling pathways. The continued abnormal activation of the signal transducer and activator of transcription 3 （STAT3） signaling pathway can lead to abnormal proliferation and malignancy of OC. cause abnormal proliferation and malignant transformation of OC， which is closely related to the development of OC. In addition， studies have shown that Chinese medicine can inhibit the proliferation， angiogenesis， invasion， and metastasis and promote the autophagy and apoptosis of OC cells by regulating the Janus kinase 2 （JAK2）/STAT3 signaling pathway， providing new therapeutic strategies and ideas for the prevention and treatment of OC. This paper summarizes the role of JAK2/STAT3 signaling pathway in OC development by reviewing the relevant articles and reviews the mechanism and research progress of active components and compound prescriptions of Chinese medicine intervening in OC development by regulating the JAK2/STAT3 signaling pathway. This review is expected to provide a systematic reference for clinical research and drug development of OC.

ObjectiveTo explore the effectiveness and safety of Shenlong Dingji Formula （参龙定悸方） on the quality of life in patients with paroxysmal atrial fibrillation （PAF） of qi-yin deficiency and phlegm-stasis obstructing collaterals syndrome. MethodsA total of 60 patients with PAF of qi-yin deficiency and phlegm-stasis obstructing collaterals syndrome were recruited and randomly divided into a treatment group and a control group， with 30 patients in each group. The control group received standard western medicine treatment， while the treatment group was additionally given Shenlong Dingji Formula orally， one dose per day. Both groups were treated for 4 weeks. The primary outcome measure is the Atrial Fibrillation Effect on Quality of Life （AFEQT） score including scores of four dimensions，i.e. atrial fibrillation-related symptoms， treatment concerns， daily activities， and treatment satisfaction. The secondary outcome measures included the frequency and duration of symptomatic atrial fibrillation episodes and traditional Chinese medicine （TCM） syndrome scores covering symptoms such as palpitations， chest tightness， fatigue， shortness of breath， reluctance to speak， spontaneous sweating， stabbing pain， and insomnia. These indicators were assessed at baseline （before treatment）， after 2-week of treatment， after 4-week of treatment， and 4 weeks after the end of treatment （follow-up）. Additionally， safety indicators before and after treatment and adverse events occurring during the trial were recorded to evaluate safety. ResultsA total of 56 patients completed the study， with 28 in each group. Primary outcome indicators： 1） the treatment group showed significant improvement in the total score of the AFEQT scale， with significantly higher total scores after 2-week treatment， 4-week treatment， and follow-up compared to the previous time point （P＜0.05）. In the control group， the AFEQT score significantly increased only after 4-week treatment compared to baseline （P＜0.05）. In the treatment group， the AFEQT scores after 2-week， 4-week treatment， and during follow-up were all higher than those of the control group at the corresponding time points （P＜0.01）. 2） In the treatment group， there was no statistically significant difference in the AFEQT treatment satisfaction dimension score during follow-up compared to that after 4-week treatment （P＞0.05）. However， the scores for all other dimensions at each time point were higher than those at the previous time point （P＜0.05）. In the control group， the scores for the atrial fibrillation-related symptom dimension were higher after 2-week and 4-week treatment than those of the previous time points （P＜0.05）. For the treatment satisfaction dimension， significant increases were observed only after 2-week and 4-week treatment compared to baseline （P＜0.05）. Secondary outcome indicators： 1） In the treatment group， the frequency and duration of symptomatic atrial fibrillation episodes decreased significantly at each time point compared to the previous time point （P＜0.05）， except for the duration of trial fibrillation at follow-up. In the control group， the frequency of episodes decreased significantly at all time points compared to baseline （P＜0.05）， while the duration of trial fibrillation showed a significant reduction at follow-up compared to those after 2-week treatment （P＜0.05）. 2） In the treatment group， TCM syndrome scores significantly reduced after 2-week treatment， 4-week treatment， and during follow-up compared to the previous time point and baseline （P＜0.05）. In the control group， significant reductions were observed only after 4-week after treatment and during follow-up （P＜0.05）. The TCM syndrome scores in the treatment group were lower than those in the control group at the same time points （P＜0.01）. No adverse events occurred during the trial in either group， and safety indicators showed no significant changes after treatment. ConclusionShenlong Dingji Formula effectively improves the quality of life， alleviates TCM syndromes， and reduces the frequency and duration of symptomatic atrial fibrillation in patients with PAF of qi-yin deficiency and phlegm-stasis obstructing collaterals syndrome， and demonstrates good safety.

ObjectiveThe controllability changes of structural brain network were explored based on the control and brain network theory in young smokers, this may reveal that the controllability indicators can serve as a powerful factor to predict the sleep status in young smokers. MethodsFifty young smokers and 51 healthy controls from Inner Mongolia University of Science and Technology were enrolled. Diffusion tensor imaging (DTI) was used to construct structural brain network based on fractional anisotropy (FA) weight matrix. According to the control and brain network theory, the average controllability and the modal controllability were calculated. Two-sample t-test was used to compare the differences between the groups and Pearson correlation analysis to examine the correlation between significant average controllability and modal controllability with Fagerström Test of Nicotine Dependence (FTND) in young smokers. The nodes with the controllability score in the top 10% were selected as the super-controllers. Finally, we used BP neural network to predict the Pittsburgh Sleep Quality Index (PSQI) in young smokers. ResultsThe average controllability of dorsolateral superior frontal gyrus, supplementary motor area, lenticular nucleus putamen, and lenticular nucleus pallidum, and the modal controllability of orbital inferior frontal gyrus, supplementary motor area, gyrus rectus, and posterior cingulate gyrus in the young smokers’ group, were all significantly different from those of the healthy controls group (P<0.05). The average controllability of the right supplementary motor area (SMA.R) in the young smokers group was positively correlated with FTND (r=0.393 0, P=0.004 8), while modal controllability was negatively correlated with FTND (r=-0.330 1, P=0.019 2). ConclusionThe controllability of structural brain network in young smokers is abnormal. which may serve as an indicator to predict sleep condition. It may provide the imaging evidence for evaluating the cognitive function impairment in young smokers.

To investigate the awareness of cognitive impairment disorders among residents of the Xizang Autonomous Region and its influencing factors, thereby providing a basis for targeted prevention and treatment efforts.

Objective: To evaluate the feasibility of dynamic contrast-enhanced MRI (DCE-MRI) in differentiating tumor deposits (TDs) from metastatic lymph nodes (MLNs) in rectal cancer. Materials and Methods: A retrospective analysis was conducted on 70 patients with rectal cancer, including 168 lesions (70 TDs and 98 MLNs confirmed by histopathology), who underwent pretreatment MRI and subsequent surgery between March 2019 and December 2022. The morphological characteristics of TDs and MLNs, along with quantitative parameters derived from DCE-MRI (K trans , kep, and v e) and DWI (ADCmin, ADCmax, and ADCmean), were analyzed and compared between the two groups.Multivariable binary logistic regression and receiver operating characteristic (ROC) curve analyses were performed to assess the diagnostic performance of significant individual quantitative parameters and combined parameters in distinguishing TDs from MLNs. Results: All morphological features, including size, shape, border, and signal intensity, as well as all DCE-MRI parameters showed significant differences between TDs and MLNs (all P < 0.05). However, ADC values did not demonstrate significant differences (all P > 0.05). Among the single quantitative parameters, v e had the highest diagnostic accuracy, with an area under the ROC curve (AUC) of 0.772 for distinguishing TDs from MLNs. A multivariable logistic regression model incorporating short axis, border, v e, and ADC mean improved diagnostic performance, achieving an AUC of 0.833 (P = 0.027). Conclusion The combination of morphological features, DCE-MRI parameters, and ADC values can effectively aid in the preoperative differentiation of TDs from MLNs in rectal cancer.

Purpose: The genomic characteristics of uterine sarcomas have not been fully elucidated. This study aimed to explore the genomic landscape of the uterine sarcomas (USs). Materials and Methods: Comprehensive genomic analysis through RNA-sequencing was conducted. Gene fusion, differentially expressed genes (DEGs), signaling pathway enrichment, immune cell infiltration, and prognosis were analyzed. A deep learning model was constructed to predict the survival of US patients. Results: A total of 71 US samples were examined, including 47 endometrial stromal sarcomas (ESS), 18 uterine leiomyosarcomas (uLMS), three adenosarcomas, two carcinosarcomas, and one uterine tumor resembling an ovarian sex-cord tumor. ESS (including high-grade ESS [HGESS] and low-grade ESS [LGESS]) and uLMS showed distinct gene fusion signatures; a novel gene fusion site, MRPS18A–PDC-AS1 could be a potential diagnostic marker for the pathology differential diagnosis of uLMS and ESS; 797 and 477 uterine sarcoma DEGs (uDEGs) were identified in the ESS vs. uLMS and HGESS vs. LGESS groups, respectively. The uDEGs were enriched in multiple pathways. Fifteen genes including LAMB4 were confirmed with prognostic value in USs; immune infiltration analysis revealed the prognositic value of myeloid dendritic cells, plasmacytoid dendritic cells, natural killer cells, macrophage M1, monocytes and hematopoietic stem cells in USs; the deep learning model named Max-Mean Non-Local multi-instance learning (MMN-MIL) showed satisfactory performance in predicting the survival of US patients, with the area under the receiver operating curve curve reached 0.909 and accuracy achieved 0.804. Conclusion USs harbored distinct gene fusion characteristics and gene expression features between HGESS, LGESS, and uLMS. The MMN-MIL model could effectively predict the survival of US patients.

Objective: To evaluate the feasibility of dynamic contrast-enhanced MRI (DCE-MRI) in differentiating tumor deposits (TDs) from metastatic lymph nodes (MLNs) in rectal cancer. Materials and Methods: A retrospective analysis was conducted on 70 patients with rectal cancer, including 168 lesions (70 TDs and 98 MLNs confirmed by histopathology), who underwent pretreatment MRI and subsequent surgery between March 2019 and December 2022. The morphological characteristics of TDs and MLNs, along with quantitative parameters derived from DCE-MRI (K trans , kep, and v e) and DWI (ADCmin, ADCmax, and ADCmean), were analyzed and compared between the two groups.Multivariable binary logistic regression and receiver operating characteristic (ROC) curve analyses were performed to assess the diagnostic performance of significant individual quantitative parameters and combined parameters in distinguishing TDs from MLNs. Results: All morphological features, including size, shape, border, and signal intensity, as well as all DCE-MRI parameters showed significant differences between TDs and MLNs (all P < 0.05). However, ADC values did not demonstrate significant differences (all P > 0.05). Among the single quantitative parameters, v e had the highest diagnostic accuracy, with an area under the ROC curve (AUC) of 0.772 for distinguishing TDs from MLNs. A multivariable logistic regression model incorporating short axis, border, v e, and ADC mean improved diagnostic performance, achieving an AUC of 0.833 (P = 0.027). Conclusion The combination of morphological features, DCE-MRI parameters, and ADC values can effectively aid in the preoperative differentiation of TDs from MLNs in rectal cancer.

Objective: To evaluate the feasibility of dynamic contrast-enhanced MRI (DCE-MRI) in differentiating tumor deposits (TDs) from metastatic lymph nodes (MLNs) in rectal cancer. Materials and Methods: A retrospective analysis was conducted on 70 patients with rectal cancer, including 168 lesions (70 TDs and 98 MLNs confirmed by histopathology), who underwent pretreatment MRI and subsequent surgery between March 2019 and December 2022. The morphological characteristics of TDs and MLNs, along with quantitative parameters derived from DCE-MRI (K trans , kep, and v e) and DWI (ADCmin, ADCmax, and ADCmean), were analyzed and compared between the two groups.Multivariable binary logistic regression and receiver operating characteristic (ROC) curve analyses were performed to assess the diagnostic performance of significant individual quantitative parameters and combined parameters in distinguishing TDs from MLNs. Results: All morphological features, including size, shape, border, and signal intensity, as well as all DCE-MRI parameters showed significant differences between TDs and MLNs (all P < 0.05). However, ADC values did not demonstrate significant differences (all P > 0.05). Among the single quantitative parameters, v e had the highest diagnostic accuracy, with an area under the ROC curve (AUC) of 0.772 for distinguishing TDs from MLNs. A multivariable logistic regression model incorporating short axis, border, v e, and ADC mean improved diagnostic performance, achieving an AUC of 0.833 (P = 0.027). Conclusion The combination of morphological features, DCE-MRI parameters, and ADC values can effectively aid in the preoperative differentiation of TDs from MLNs in rectal cancer.

Purpose: The genomic characteristics of uterine sarcomas have not been fully elucidated. This study aimed to explore the genomic landscape of the uterine sarcomas (USs). Materials and Methods: Comprehensive genomic analysis through RNA-sequencing was conducted. Gene fusion, differentially expressed genes (DEGs), signaling pathway enrichment, immune cell infiltration, and prognosis were analyzed. A deep learning model was constructed to predict the survival of US patients. Results: A total of 71 US samples were examined, including 47 endometrial stromal sarcomas (ESS), 18 uterine leiomyosarcomas (uLMS), three adenosarcomas, two carcinosarcomas, and one uterine tumor resembling an ovarian sex-cord tumor. ESS (including high-grade ESS [HGESS] and low-grade ESS [LGESS]) and uLMS showed distinct gene fusion signatures; a novel gene fusion site, MRPS18A–PDC-AS1 could be a potential diagnostic marker for the pathology differential diagnosis of uLMS and ESS; 797 and 477 uterine sarcoma DEGs (uDEGs) were identified in the ESS vs. uLMS and HGESS vs. LGESS groups, respectively. The uDEGs were enriched in multiple pathways. Fifteen genes including LAMB4 were confirmed with prognostic value in USs; immune infiltration analysis revealed the prognositic value of myeloid dendritic cells, plasmacytoid dendritic cells, natural killer cells, macrophage M1, monocytes and hematopoietic stem cells in USs; the deep learning model named Max-Mean Non-Local multi-instance learning (MMN-MIL) showed satisfactory performance in predicting the survival of US patients, with the area under the receiver operating curve curve reached 0.909 and accuracy achieved 0.804. Conclusion USs harbored distinct gene fusion characteristics and gene expression features between HGESS, LGESS, and uLMS. The MMN-MIL model could effectively predict the survival of US patients.

Objective: To evaluate the feasibility of dynamic contrast-enhanced MRI (DCE-MRI) in differentiating tumor deposits (TDs) from metastatic lymph nodes (MLNs) in rectal cancer. Materials and Methods: A retrospective analysis was conducted on 70 patients with rectal cancer, including 168 lesions (70 TDs and 98 MLNs confirmed by histopathology), who underwent pretreatment MRI and subsequent surgery between March 2019 and December 2022. The morphological characteristics of TDs and MLNs, along with quantitative parameters derived from DCE-MRI (K trans , kep, and v e) and DWI (ADCmin, ADCmax, and ADCmean), were analyzed and compared between the two groups.Multivariable binary logistic regression and receiver operating characteristic (ROC) curve analyses were performed to assess the diagnostic performance of significant individual quantitative parameters and combined parameters in distinguishing TDs from MLNs. Results: All morphological features, including size, shape, border, and signal intensity, as well as all DCE-MRI parameters showed significant differences between TDs and MLNs (all P < 0.05). However, ADC values did not demonstrate significant differences (all P > 0.05). Among the single quantitative parameters, v e had the highest diagnostic accuracy, with an area under the ROC curve (AUC) of 0.772 for distinguishing TDs from MLNs. A multivariable logistic regression model incorporating short axis, border, v e, and ADC mean improved diagnostic performance, achieving an AUC of 0.833 (P = 0.027). Conclusion The combination of morphological features, DCE-MRI parameters, and ADC values can effectively aid in the preoperative differentiation of TDs from MLNs in rectal cancer.