Objective:To investigate the alterations of individual metabolic brain network properties in patients with mild cognitive impairment (MCI) and their correlations with cognitive function.Methods:One hundred and five participants from Alzheimer's Disease Neuroimaging Initiative (ADNI) database enrolled from March 2012 to February 2016 were chosen, including 61 MCI patients and 44 normal controls (NC). Cognitive assessments, including mini-mental state examination (MMSE), auditory verbal learning test (AVLT), trail making test (TMT), and semantic verbal fluency (SVF) score, were performed in both groups; differences of above scores and clinical data between the participants from the two groups were compared. T1-weighted imaging and fluorodeoxyglucose positron emission tomography (FDG-PET) images were collected in both groups; individual metabolic brain networks were constructed based on differences in effect sizes between brain regions and network properties were calculated. Spatial correlation analysis was used to compare the correlations of metabolic brain networks at the individual and group levels. General linear model was employed to compare the differences in network properties between the two groups. Partial correlation analysis was used to examine the correlations of differential network properties with cognitive function in MCI patients. A support vector machine (SVM) classification model was constructed based on individual metabolic brain network properties, and receiver operating characteristic (ROC) curve was used to explore the diagnostic value of this SVM classification model in MCI.Results:(1) Compared with the NC group, the MCI group had significantly lower MMSE and AVLT-immediate recall scores, and longer TMT-A completion time ( P<0.05). (2) Spatial correlation analysis revealed a positive correlation between individual metabolic brain networks and group-level metabolic brain networks in patients of the MCI group ( r=0.825, P<0.001). No significant differences in global network properties were noted between the two groups ( P>0.05). Compared with the NC group, the MCI group significantly decreased degree centrality in the left A8vl, right A39c, and right V5/MT+ regions, increased degree centrality in the left anterior cuneus, decreased nodal efficiency in the left A8vl, right V5/MT+, and right caudal hippocampus regions, increased nodal shortest path length and nodal clustering coefficient in the left A8vl region ( P<0.05). (3) The degree centrality at the A8vl of ventral part of the left middle frontal gyrus and nodal efficiency in right caudal hippocampus region were positively correlated with AVLT-immediate recall scores ( r=0.331, P=0.010; r=0.282, P=0.030), nodal efficiency in the left A8vl region was negatively correlated with TMT-A completion time ( r=-0.470, P<0.001), and nodal efficiency in the left A8vl region was positively correlated with SVF score ( r=0.263, P=0.044). (4) Area under the curve of SVM classification model in diagnosing MCI was 0.880 (95% CI: 0.813-0.945, P<0.001), with an accuracy rate of 0.790. Conclusions:Patients with MCI have alterations in individual metabolic brain network properties, among which the degree centrality and nodal efficiency of some nodes are closely related to cognitive function changes. Models constructed based on individual metabolic brain network properties can help to effectively diagnose MCI.

Objective:To investigate the alterations of individual metabolic brain network properties in patients with mild cognitive impairment (MCI) and their correlations with cognitive function.Methods:One hundred and five participants from Alzheimer's Disease Neuroimaging Initiative (ADNI) database enrolled from March 2012 to February 2016 were chosen, including 61 MCI patients and 44 normal controls (NC). Cognitive assessments, including mini-mental state examination (MMSE), auditory verbal learning test (AVLT), trail making test (TMT), and semantic verbal fluency (SVF) score, were performed in both groups; differences of above scores and clinical data between the participants from the two groups were compared. T1-weighted imaging and fluorodeoxyglucose positron emission tomography (FDG-PET) images were collected in both groups; individual metabolic brain networks were constructed based on differences in effect sizes between brain regions and network properties were calculated. Spatial correlation analysis was used to compare the correlations of metabolic brain networks at the individual and group levels. General linear model was employed to compare the differences in network properties between the two groups. Partial correlation analysis was used to examine the correlations of differential network properties with cognitive function in MCI patients. A support vector machine (SVM) classification model was constructed based on individual metabolic brain network properties, and receiver operating characteristic (ROC) curve was used to explore the diagnostic value of this SVM classification model in MCI.Results:(1) Compared with the NC group, the MCI group had significantly lower MMSE and AVLT-immediate recall scores, and longer TMT-A completion time ( P<0.05). (2) Spatial correlation analysis revealed a positive correlation between individual metabolic brain networks and group-level metabolic brain networks in patients of the MCI group ( r=0.825, P<0.001). No significant differences in global network properties were noted between the two groups ( P>0.05). Compared with the NC group, the MCI group significantly decreased degree centrality in the left A8vl, right A39c, and right V5/MT+ regions, increased degree centrality in the left anterior cuneus, decreased nodal efficiency in the left A8vl, right V5/MT+, and right caudal hippocampus regions, increased nodal shortest path length and nodal clustering coefficient in the left A8vl region ( P<0.05). (3) The degree centrality at the A8vl of ventral part of the left middle frontal gyrus and nodal efficiency in right caudal hippocampus region were positively correlated with AVLT-immediate recall scores ( r=0.331, P=0.010; r=0.282, P=0.030), nodal efficiency in the left A8vl region was negatively correlated with TMT-A completion time ( r=-0.470, P<0.001), and nodal efficiency in the left A8vl region was positively correlated with SVF score ( r=0.263, P=0.044). (4) Area under the curve of SVM classification model in diagnosing MCI was 0.880 (95% CI: 0.813-0.945, P<0.001), with an accuracy rate of 0.790. Conclusions:Patients with MCI have alterations in individual metabolic brain network properties, among which the degree centrality and nodal efficiency of some nodes are closely related to cognitive function changes. Models constructed based on individual metabolic brain network properties can help to effectively diagnose MCI.

Objective:To systematically integrate qualitative researches on the financial toxicity experience of cancer patients, so as to provide reference for formulating intervention strategies for financial toxicity in cancer patients.Methods:Qualitative studies on financial toxicity experience of cancer patients were searched in Web of Science, PubMed, Embase, Cochrane Library, China National Knowledge Infrastructure, VIP, Wanfang data, and China Biology Medicine disc, with a search period from database establishment to August 31, 2023. The quality of the included literature was evaluated using the quality evaluation criteria for qualitative research of the Joanna Briggs Institute Evidence Based Health Care Center in Australia. The aggregation integration method was used to integrate the results.Results:A total of 12 articles were included, and 68 research results were extracted and categorized into 15 new categories. Four integrated results were obtained, including anxiety and stress under financial difficulties, relying on family members to start a new in adversity, and dancing together with treatment and life to write a new article, and great expectations for the future.Conclusions:Pay attention to the financial toxicity of cancer patients, actively seek response strategies, and bring benefits to cancer patients.

Objective To develop and validate a nomogram model for predicting the risk of kinesiophobia in patients after lung cancer surgery.Methods A total of 164 lung cancer patients who underwent surgery in a Grade ⅢA hospital in Xiamen were recruited from October 2022 to May 2023 in this study.Logistic regression was conducted to identify independent risk factors of kinesiophobia in patients who recieved lung cancer surgery.A Nomogram model was developed using R software for predicting the risk of kinesiophobia.The predictive performance of the model was assessed by calculating the receiver-operating-characteristics curve(ROC)and the area under curve(AUC).Results The incidence of postoperative kinesiophobia in lung cancer patients was at 44.51％.Logistic regression analysis showed that pain and fatigue were the risk factors for the occurrence of postoperative kinesiophobia in the patients(P<0.05)and self-efficacy was a protective factor(P<0.05).Validation of the Nomogram model showed that the ROC curve indicated an AUC of 0.888(95％CI 0.836-0.940)for predicting kenesiophobia in patients after lung cancer surgery and the calibration curve presented as a line with a slope close to 1.The Hosmer-Lemeshow goodnee-of-fit test showed that the model could accurately predict the risk of postoperative kinesiophobia in lung cancer patients(χ 2=1.931,P=0.983).Conclusion Self-efficacy,pain and fatigue are the influencing factors for the occurrence of postoperative kinesiophobia in the patients who received lung cancer surgery.The nomogram prediction model has good accuracy and discrimination,and it may assist the healthcare professionals to predict the occurrence of postoperative kinesiophobia in the patients after lung cancer surgery and take pertinent measures to minimise the incidence.

Objective To develop and validate a nomogram model for predicting the risk of kinesiophobia in patients after lung cancer surgery.Methods A total of 164 lung cancer patients who underwent surgery in a Grade ⅢA hospital in Xiamen were recruited from October 2022 to May 2023 in this study.Logistic regression was conducted to identify independent risk factors of kinesiophobia in patients who recieved lung cancer surgery.A Nomogram model was developed using R software for predicting the risk of kinesiophobia.The predictive performance of the model was assessed by calculating the receiver-operating-characteristics curve(ROC)and the area under curve(AUC).Results The incidence of postoperative kinesiophobia in lung cancer patients was at 44.51％.Logistic regression analysis showed that pain and fatigue were the risk factors for the occurrence of postoperative kinesiophobia in the patients(P<0.05)and self-efficacy was a protective factor(P<0.05).Validation of the Nomogram model showed that the ROC curve indicated an AUC of 0.888(95％CI 0.836-0.940)for predicting kenesiophobia in patients after lung cancer surgery and the calibration curve presented as a line with a slope close to 1.The Hosmer-Lemeshow goodnee-of-fit test showed that the model could accurately predict the risk of postoperative kinesiophobia in lung cancer patients(χ 2=1.931,P=0.983).Conclusion Self-efficacy,pain and fatigue are the influencing factors for the occurrence of postoperative kinesiophobia in the patients who received lung cancer surgery.The nomogram prediction model has good accuracy and discrimination,and it may assist the healthcare professionals to predict the occurrence of postoperative kinesiophobia in the patients after lung cancer surgery and take pertinent measures to minimise the incidence.

BACKGROUND: The mortality rate of lung cancer meningeal metastasis is extremely high. Circulating tumor DNA (ctDNA) has been confirmed to be contain the genomic alterations present in tumors and has been used to monitor tumor progression and response to treatments. Due to the presence of blood-brain barrier and other factors, peripheral blood ctDNA cannot reflect the information of brain lesions for patients with meningeal metastases. However, cerebrospinal fluid ctDNA as a test sample can better reflect the genetic status of intracranial tumors and guide clinical targeted treatment of intracranial lesions. This study explored the feasibility of cerebrospinal fluid ctNDA for evaluating non-small cell lung cancer (NSCLC) meningeal metastasis and the potential clinical value of cerebrospinal fluid ctDNA detection in NSCLC meningeal metastasis. METHODS: A total of 21 patients with NSCLC meningeal metastasis were included. Tumor genomic variation was performed on the cerebrospinal fluid and peripheral blood samples of patients by second-generation gene sequencing technology. The situation was examined, and pathological evaluation of cerebrospinal fluid cytology and head magnetic resonance imaging (MRI) enhanced examination were performed. RESULTS: ctDNA was detected in the cerebrospinal fluid of 21 patients. The sensitivity of cerebrospinal fluid ctDNA detection was superior to cytology in the diagnosis of meningeal metastasis (P<0.001). The detection rate and gene mutation abundance of cerebrospinal fluid were higher than plasma (P<0.001). Cerebro-spinal fluid had a unique genetic profile. In 6 patients with dynamic detection, changes of ctDNA allele fraction occurred at the same time or earlier than clinical disease changes, which could timely monitor drug resistance mechanism and relapse trend. CONCLUSIONS The detection rate of ctDNA in cerebrospinal fluid is higher than that in cytology and imaging. The detection of ctDNA in cerebrospinal fluid can reveal the specific mutation map of meningeal metastasis lesions. The dynamic monitoring of ctDNA in cerebrospinal fluid has hint significance for clinical response of lung cancer patients.