Impact of age on central lymph nodes involvement in papillary thyroid cancer.

BACKGROUND: Total thyroidectomy is the main line of treatment for papillary thyroid cancer. Central lymph node dissection (CLND) is still debatable. In this study, we aimed to correlate the central lymph node status with the age of patients. METHODS: This is a retrospective study including patients with papillary thyroid cancer (PTC) who underwent total thyroidectomy and CLND at a tertiary cancer center during the period from January 2012 to September 2022. Patients were subdivided into 3groups: patients younger than 20 years old, patients between 20 and 40 years old, and patients older than 40 years old. Correlation between central lymph node status, lateral lymph node status, and harvest count with each other and between age groups was done. RESULTS: 315 patients were included. The younger the age group the higher the possibility of harboring positive central nodes, however, the positivity of lateral nodes was similar. Neither central nodal harvest nor positive central node count significantly differed between groups. The lateral nodal harvest was significantly higher in the < 20 years group with no affection to the number of positive nodes retrieved. The younger the age group the longer the disease-free survival (DFS). CONCLUSION: We can conclude that patients younger than twenty years had a higher probability of harboring malignancy in central nodes and higher lateral node harvest on dissection. In contrast, they do have a lower incidence of recurrence.

Network analysis of acute stress reaction in a sample of Chinese male military college students.

Background: Acute stress reaction (ASR) following a stressful event is associated with stress-related mental disorders. However, no studies have investigated the relationships between ASR symptom clusters. The present study aimed to provide a fine-grained understanding of the complex relationships among symptom clusters and identify the central symptom clusters of ASR using network analysis. Methods: The Acute Stress Reaction Scale (ASRS) was used to investigate the network structure of ASR in 1792 Chinese male military college students who were about to participate in an important physical fitness test. We calculated the weights of the edges connecting different symptom clusters and the central indices of 25 symptom clusters in the final network. Results: There were five strongest edges with significantly higher weights than most other edge weights, including the edges between "Less communication" and "Isolated from others." The symptom clusters of "Somatic symptoms," "Hypoprosexia," and "Anxiety" were found to be the central nodes with the highest expected influences (primary centrality index). Conclusion: The present study explored the network structure of ASR, revealed complex connections between symptom clusters, and identified central clusters. These findings have important clinical implications, and it is suggested that the three central symptom clusters may be potential targets for effective interventions for ASR.

The relations between different components of intolerance of uncertainty and symptoms of depression during the COVID-19 pandemic: A network analysis.

Background: The relations between depression and intolerance of uncertainty (IU) have been extensively investigated during the COVID-19 pandemic. However, there is a lack of understanding on how each component of IU may differentially affect depression symptoms and vice versa. The current study used a network approach to reveal the component-to-symptom interplay between IU and depression and identify intervention targets for depression during the COVID-19 pandemic. Methods: A total of 624 college students participated in the current study. An IU-Depression network was estimated using items from the 12-item Intolerance of Uncertainty Scale and the Patient Health Questionnaire-9. We examined the network structure, node centrality, and node bridge centrality to identify component-to-symptom pathways, central nodes, and bridge nodes within the IU-Depression network. Results: Several distinct pathways (e.g., "Frustration when facing uncertainty" and "Feelings of worthlessness") emerged between IU and Depression. "Fatigue" and "Frustration when facing uncertainty" were identified as the central nodes in the estimated network. "Frustration when facing uncertainty," "Psychomotor agitation/retardation," and "Depressed or sad mood" were identified as bridging nodes between the IU and Depression communities. Conclusion: By delineating specific pathways between IU and depression and highlighting the influential role of "Frustration when facing uncertainty" in maintaining the IU-Depression co-occurrence, current findings may inform targeted prevention and interventions for depression during the COVID-19 pandemic.

Interaction network of differentially expressed genes between Kashin-Beck disease and osteoarthritis / 中华地方病学杂志

Objective To investigate the data of gene expression microarray by protein interaction network analysis,establish an interaction network of differentially expressed genes between Kashin-Beck disease (KBD) and osteoarthritis (OA) and choose the central nodes of the network.Methods The articular cartilage samples of degrees Ⅱ ° and Ⅲ ° KBD and OA patients were selected according to the national diagnosis criteria for KBD and the Western Ontario and McMaster Universities (WOMAC) for OA.Chondrocytes of 8 patients with KBD and 7 with OA were selected.About 1 000 different genes detected by gene expression microarray were inputted into STRING 9.1 database online for analysis and establishment of the interaction network.The interaction data were imported into Cytoscape 3.2.1 software for screening the central nodes of the network.KEGG database was exploited for pathway analysis and functional study of the central node genes,Real-time PCR (RT-PCR) was used for verification.Results The protein products of 334 differentially expressed genes between KBD and OA had interrelation,forming a complicated interaction network.About 150 central nodes were selected by Cytoscape 3.2.1 that involved in more than ten signal pathways involved in mitochondria,bone metabolism and inflammatory cytokine.Conclusion The interaction network of the differentially expressed genes between KBD and OA,especially the central nodes of this network,can provide clues to the mechanism and early diagnosis and molecular targeted therapy of KBD and OA.

Interaction network analysis of differentially expressed genes and screening of cancer marker in the urine of patients with invasive bladder cancer.

OBJECTIVE: To detect the expression profile of bladder cancer and to delineate the interaction network of these genes in invasive bladder cancer. METHODS: A total of 126 differentially expressed genes were identified, and input into STRING online database to delineate interaction network. The network data were screened with central nodes. The expression of genes with the most evident change in the exfoliated cells of urine was detected. RNA markers with over-expression in stage Ta tumor and/or T1 to T4 tumors but low expression in blood or inflammatory cells were characterized. RESULTS: On the basis of assay of 21,639 whole-genome oligonucleotide microarray, a total of 126 differentially expressed genes were identified, of which 69 had up-regulated expression and 57 had down-regulated expression. STRING screening showed there were interactions among 103 genes in the bladder cancer which formed a complex network. A total of 23 central nodes were screened with Cytoscape and are involved in multiple signaling pathways related to tumorigenesis. The test specificity was 80% for the 30 control patients with urinary tract infections. The combination of BLCA-4 and HOXA13 could distinguish between low and high grade tumors, with specificity and sensitivity of 80%. CONCLUSION: The interaction network of differentially expressed genes, especially the central nodes of this network, can provide evidence for the early diagnosis and molecular targeted therapy of invasive bladder cancer, and combined detection of IGF-1, hTERT, BLCA-4 and HOXA13 genes is helpful to evaluate BTCC at different stages.