Serum Fatty Acid Profiles and Neurofilament Light Chain Levels in the General Population.

BACKGROUND: Previous studies have demonstrated associations between fatty acids and neurological disorders. However, no studies have examined the relationship between serum fatty acid levels and serum neurofilament light chain (NfL), a biomarker of neurological disorders. OBJECTIVE: This study aimed to comprehensively investigate the intricate relationship between 30 serum fatty acids and serum NfL levels in a nationally representative sample of U.S. adults, utilizing data from the 2013-2014 National Health and Nutrition Examination Survey. METHODS: Employing a cross-sectional analysis, multivariable linear regression models were utilized to explore the associations between 30 serum fatty acids and serum NfL levels. This analysis involved adjustment for potential confounding variables, including age, sex, race, body-mass index (BMI), smoking status, hyperlipidemia, and diabetes, to clarify the association between serum fatty acids and serum NfL levels. RESULTS: The analysis revealed that certain fatty acids exhibited distinct associations with serum NfL levels. Notably, Docosanoic acid (22:0) and Tricosanoic acid (C23:0) were found to be inversely associated with serum NfL levels (ß = -0.280, 95% CI: -0.525, -0.035; ß = -0.292, 95% CI: -0.511, -0.072). Conversely, Palmitoleic acid (16:1n-7) demonstrated a positive association with serum NfL levels (ß = 0.125, 95% CI: 0.027, 0.222). Notably, these associations remained significant even after adjustment for potential confounders. CONCLUSIONS: Individuals with high relative concentrations of certain saturated fatty acids exhibited decreased serum NfL, whereas those with high relative concentrations of certain monounsaturated fatty acids showed increased serum NfL. These findings contribute to a deeper understanding of the potential impact of serum fatty acids on NfL levels, shedding light on novel avenues for further investigation and potential interventions in the context of neurological health.

Trends of mortality from epilepsy in the United States, 1979-2021.

PURPOSE: The analysis of long-term trends of mortality from epilepsy has not been conducted, which is crucial for estimating the future burden of epilepsy. We therefore aimed to investigate the long-term trends of mortality from epilepsy in the United States from 1979 to 2021. METHODS: The cause-of-death and demographic data were from the National Center for Health Statistics (1979-2021) and population estimates were from the US Census Bureau. We used the joinpoint regression model to analyze secular trends in the mortality of epilepsy spanning from 1979 to 2021. Age-adjusted mortality from epilepsy was assessed based on the year 2000 U.S. population data, stratified by age, sex, and race. RESULTS: The age-adjusted mortality from epilepsy increased from 0.78 per 100,000 population in 1979 to 1.01 per 100,000 population in 2021, with an average annual percent change (AAPC) of 0.58% (95% confidence interval [CI]: 0.45% - 0.72%). The overall age-adjusted mortality of epilepsy had been on the rise between 2011 and 2021. The mortality rate generally increases with age. The mortality of epilepsy was higher in the Afro-American people and men. The mortality of epilepsy in both sexes declined first and then increased, with AAPC 1.02% (95% CI: 0.88%, 1.23%) in women and 0.10% (95% CI: -0.002%, 0.21%) in men. Mortality in all races including White, Afro-American people, and other races individuals fell first and then rose. The AAPC of mortality in White, other races, and Afro-American people were 0.89% (95% CI: 0.79%, 1.02%), -0.87% (95% CI: -1.84%, 0.88%), and -0.31% (95% CI: -0.48%, -0.13%), respectively. CONCLUSION: Although the mortality rate from epilepsy has experienced a period of decline, it is worth noting that the last decade has seen a rapid increase. A comprehensive assessment of long-term trends in mortality from epilepsy holds significance for healthcare prioritization.

Cervical extracellular matrix hydrogel optimizes tumor heterogeneity of cervical squamous cell carcinoma organoids.

Cervical cancer, primarily squamous cell carcinoma, is the most prevalent gynecologic malignancy. Organoids can mimic tumor development in vitro, but current Matrigel inaccurately replicates the tissue-specific microenvironment. This limitation compromises the accurate representation of tumor heterogeneity. We collected para-cancerous cervical tissues from patients diagnosed with cervical squamous cell carcinoma (CSCC) and prepared uterine cervix extracellular matrix (UCEM) hydrogels. Proteomic analysis of UCEM identified several tissue-specific signaling pathways including human papillomavirus, phosphatidylinositol 3-kinase-AKT, and extracellular matrix receptor. Secreted proteins like FLNA, MYH9, HSPA8, and EEF1A1 were present, indicating UCEM successfully maintained cervical proteins. UCEM provided a tailored microenvironment for CSCC organoids, enabling formation and growth while preserving tumorigenic potential. RNA sequencing showed UCEM-organoids exhibited greater similarity to native CSCC and reflected tumor heterogeneity by exhibiting CSCC-associated signaling pathways including virus protein-cytokine, nuclear factor κB, tumor necrosis factor, and oncogenes EGR1, FPR1, and IFI6. Moreover, UCEM-organoids developed chemotherapy resistance. Our research provides insights into advanced organoid technology through native matrix hydrogels.

Dietary Diversity Changes and Cognitive Frailty in Chinese Older Adults: A Prospective Community-Based Cohort Study.

Evidence for the effects of dietary diversity changes and cognitive frailty (CF) in the older adults is not clear. This study aimed to investigate the relationship between dietary diversity changes and CF in older adults Chinese. A total of 14,382 participants (mean age: 82.3 years) were enrolled. Dietary diversity scores (DDSs) were collected and calculated using a food frequency questionnaire. DDS changes between baseline and first follow-up were categorized into nine patterns. The associations between DDS changes and the incidence of CF were estimated using Cox proportional hazards models. During an 80,860 person-year follow-up, 3023 CF cases were identified. Groups with a decrease in DDS had increased CF risk compared with the high-to-high DDS group, with adjusted hazard ratios (HRs; 95% confidence intervals (Cis)) of 1.30 (1.06, 1.59), 2.04 (1.51, 2.74), and 1.81 (1.47, 2.22) for high-to-medium, high-to-low, and medium-to-low groups, respectively. Lower overall DDS groups were associated with greater CF risks, with HRs (95% CIs) of 1.49 (1.19, 1.86) for the low-to-medium group and 1.96 (1.53, 2.52) for the low-to-low group. Compared with the high-to-high group, significant associations with CF were found in other DDS change groups; HRs ranged from 1.38 to 3.12 for the plant-based DDS group and from 1.24 to 1.32 for the animal-based DDS group. Additionally, extreme and moderate declines in overall DDS increased CF risk compared with stable DDS, with HRs (95% CIs) of 1.67 (1.50, 1.86) and 1.13 (1.03, 1.24), respectively. In conclusion, among older adults, a declining or persistently low DDS and a moderately or extremely declining DDS were linked to higher incident CF. Plant-based DDS changes correlated more strongly with CF than animal-based DDS changes.

SDResU-Net: Separable and Dilated Residual U-Net for MRI Brain Tumor Segmentation.

BACKGROUND: Glioma is one of the most common and aggressive primary brain tumors that endanger human health. Tumors segmentation is a key step in assisting the diagnosis and treatment of cancer disease. However, it is a relatively challenging task to precisely segment tumors considering characteristics of brain tumors and the device noise. Recently, with the breakthrough development of deep learning, brain tumor segmentation methods based on fully convolutional neural network (FCN) have illuminated brilliant performance and attracted more and more attention. METHODS: In this work, we propose a novel FCN based network called SDResU-Net for brain tumor segmentation, which simultaneously embeds dilated convolution and separable convolution into residual U-Net architecture. SDResU-Net introduces dilated block into a residual U-Net architecture, which largely expends the receptive field and gains better local and global feature descriptions capacity. Meanwhile, to fully utilize the channel and region information of MRI brain images, we separate the internal and inter-slice structures of the improved residual U-Net by employing separable convolution operator. The proposed SDResU-Net captures more pixel-level details and spatial information, which provides a considerable alternative for the automatic and accurate segmentation of brain tumors. RESULTS AND CONCLUSION: The proposed SDResU-Net is extensively evaluated on two public MRI brain image datasets, i.e., BraTS 2017 and BraTS 2018. Compared with its counterparts and stateof- the-arts, SDResU-Net gains superior performance on both datasets, showing its effectiveness. In addition, cross-validation results on two datasets illuminate its satisfying generalization ability.