BackgroundWe examined the clinical value of two serum markers of low-grade inflammation, C-reactive protein (CRP) and receptor of advanced glycation products (RAGE), as prognostic indices for cognitive decline.MethodsPatients with cognitive impairment (n = 377) and controls (n = 66) were examined by blood biochemistry tests, including ELISAs of serum CRP and RAGE, the Mini-mental State Examination (MMSE) and Montreal Cognitive Assessment (MoCA), and STEAM 1H-MRS of the left hippocampus and thalamus.ResultsCompared to the control group, the cognitive impairment group was older (63.10 ± 9.70 years vs. 55.09 ± 10.77 years, P = 0.000) and had fewer years of formal education (9.01 ± 4.01 vs. 12.94 ± 3.0, P = 0.000). There were no significant differences in the frequencies of type 2 diabetes, hypertension, or hyperlipidemia between groups. Serum CRP and RAGE were higher in the cognitive impairment group (CRP: 2.08 mg/L, range 1.07 − 3.36 mg/L vs. 0.21 mg/L, range 0.18 − 0.42 mg/L; RAGE: 4.01, range 2.49 − 5.71, vs. 2.28, range 1.84 − 3.03; P < 0.05 for both). In patients with cognitive impairment, there were negative correlations between cognitive function (as measured by MMSE and MoCA) and both CRP and RAGE levels (P < 0.05). Patients over 55 years exhibited a positive correlation between CRP and myo-inositol peak area in the left hippocampus (P < 0.05), while there was no relationship between RAGE and any metabolite (P > 0.05). Multiple linear regression revealed that CRP was influenced by hypertension (P = 0.026) and cognitive impairment (P = 0.042).ConclusionsChronic low-grade inflammation is present in patients with cognitive impairment. Serum CRP, RAGE, and left hippocampal myo-inositol may provide prognostic information on cognitive decline.

Long non-coding RNAs (lncRNAs) are known to regulate tumorigenesis. Although breast cancer tissues show a high expression of LINC00894, its specific biological role in breast cancer progression is still unknown. In this study, lncRNA microarray was used to analyze the lncRNA expression in breast cancer tissues, and LINC00894 was selected for further analysis. Materials and Methods: Expression of LINC00894 in 45 pairs of breast cancer tissues and normal tissues obtained from patients with breast cancer was assessed by quantitative reverse transcription-PCR, while proliferation and invasion of breast cancer cells were assessed using a Cell Counting Kit-8 (CCK-8), EdU assay, colony formation experiment, and transwell assays. A dual-luciferase reporter gene assay and bioinformatics analysis were employed to detect potential targets of LINC00894. Additionally, RNA Binding Protein Immunoprecipitation (RIP) and Western blot assays were utilized to clarify its interaction and roles in the regulation of breast cancer progression. Results: High expression of LINC00894 was observed in breast cancer cells, and its overexpression significantly expedited cell proliferation and invasion. Moreover, LINC00894 positively regulated the expression of ZEB1 by competitively binding to miR-429. Conclusion: Taken together, these results suggest that LINC00894 competitively binds to miR-429 to mediate ZEB1 expression; consequently, it is implicated to play a role in the progression of breast cancer.

In females, higher parity may be associated with an increased risk of gallbladder cancer. In the future, high-quality cohort studies with larger sample sizes and randomized controlled trials are needed to fully scrutinize this association.

Background and objective: Combined evaluation of lumbosacral structures (e.g. nerves, bone) on multimodal radiographic images is routinely conducted prior to spinal surgery and interventional procedures. Generally, magnetic resonance imaging is conducted to differentiate nerves, while computed tomography (CT) is used to observe bony structures. The aim of this study is to investigate the feasibility of automatically segmenting lumbosacral structures (e.g. nerves & bone) on non-contrast CT with deep learning. Methods: a total of 50 cases with spinal CT were manually labeled for lumbosacral nerves and bone with Slicer 4.8. The ratio of training: validation: testing is 32:8:10. A 3D-Unet is adopted to build the model SPINECT for automatically segmenting lumbosacral structures. Pixel accuracy, IoU, and Dice score are used to assess the segmentation performance of lumbosacral structures. Results: the testing results reveals successful segmentation of lumbosacral bone and nerve on CT. The average pixel accuracy is 0.940 for bone and 0.918 for nerve. The average IoU is 0.897 for bone and 0.827 for nerve. The dice score is 0.945 for bone and 0.905 for nerve. Conclusions: this pilot study indicated that automatic segmenting lumbosacral structures (nerves and bone) on non-contrast CT is feasible and may have utility for planning and navigating spinal interventions and surgery.