Search

Abstract Background Schizophrenia is a debilitating mental disorder affecting about 1% of the global population, characterized by significant cognitive impairments and a strong hereditary component. Carnitine, particularly Levo-carnitine and its derivatives, plays a crucial role in cellular metabolism and mitochondrial function, with evidence suggesting a link between levo-carnitine deficiency and schizophrenia pathology. This study aims to investigate the causal relationship between different subtypes of levo-carnitine and the susceptibility to schizophrenia using Mendelian randomization analysis. Methods Forward Mendelian randomization analysis was conducted using levo-carnitine and its derivatives as exposure and schizophrenia as the outcome. Candidate data were obtained from the Open-GWAS database. Instrumental variables were identified as single nucleotide polymorphisms closely associated with exposure and harmonized with the outcome data after removing confounders and outliers. Mendelian randomization analysis was performed using inverse variance weighting as the primary approach, and sensitivity analysis was conducted to assess the reliability and robustness of the results. Finally, a reverse Mendelian randomization analysis was carried out using the same analytical procedures. Results The Mendelian randomization results indicate a significant negative causal relationship between isovaleryl-levo-carnitine and schizophrenia (P 0.05). Additionally, the reverse Mendelian randomization analysis did not identify any causal relationship between schizophrenia and levo-carnitine related exposures (P > 0.05). Sensitivity analyses, including pleiotropy and heterogeneity analysis, did not reveal any potential bias in the Mendelian randomization results (P > 0.05). Conclusion The results suggest that elevated levels of isovaleryl-levo-carnitine may potentially mitigate the risk of developing schizophrenia, highlighting the prospective therapeutic and preventive implications of isovaleryl-levo-carnitine in the clinical management of schizophrenia.

This study employed a two-step Mendelian randomization analysis to explore the causal relationship between telomere length, as a marker of aging, and anorexia nervosa and to evaluate the mediating role of changes in the white matter microstructure across different brain regions. We selected genetic variants associated with 675 diffusion magnetic resonance imaging phenotypes representing changes in brain white matter. F-statistics confirmed the validity of the instruments, ensuring robust causal inference. Sensitivity analyses, including heterogeneity tests, horizontal pleiotropy tests, and leave-one-out tests, validated the results. The results show that telomere length is significantly negatively correlated with anorexia nervosa in a unidirectional manner (p = 0.017). Additionally, changes in specific white matter structures, such as the internal capsule, corona radiata, posterior thalamic radiation, left cingulate gyrus, left longitudinal fasciculus, and left forceps minor (p < 0.05), were identified as mediators. These findings enhance our understanding of the neural mechanisms, underlying the exacerbation of anorexia nervosa with aging; emphasize the role of brain functional networks in disease progression; and provide potential biological targets for future therapeutic interventions.

Fourier light-field microscopy (FLFM) uses a microlens array (MLA) to segment the Fourier plane of the microscopic objective lens to generate multiple two-dimensional perspective views, thereby reconstructing the three-dimensional (3D) structure of the sample using 3D deconvolution calculation without scanning. However, the resolution of FLFM is still limited by diffraction, and furthermore, it is dependent on the aperture division. In order to improve its resolution, a super-resolution optical fluctuation Fourier light-field microscopy (SOFFLFM) was proposed here, in which the super-resolution optical fluctuation imaging (SOFI) with the ability of super-resolution was introduced into FLFM. SOFFLFM uses higher-order cumulants statistical analysis on an image sequence collected by FLFM, and then carries out 3D deconvolution calculation to reconstruct the 3D structure of the sample. The theoretical basis of SOFFLFM on improving resolution was explained and then verified with the simulations. Simulation results demonstrated that SOFFLFM improved the lateral and axial resolution by more than [Formula: see text] and 2 times in the second- and fourth-order accumulations, compared with that of FLFM.