Explore the vast range of titles available.

Background: The microbiota–gut–brain axis plays a key role in regulating mental health, including anxiety, stress, and sleep quality. Vitamins B1 and B2 may influence these outcomes by modulating gut microbiota. This study aimed to examine the relationships among mental health indicators, gut microbiota, and levels of vitamins B1 and B2. Methods: This study conducted a cross-sectional analysis to explore associations between mental health status, gut microbiota composition and function, and circulating vitamin B1/B2 levels. Ten representative microbes were selected for analysis. Mediation models were used to assess whether gut microbiota mediate the effects of vitamins on mental health. Results: Vitamin B1 and B2 levels were significantly associated with stress, sleep quality, and sleepiness (p < 0.05). The abundance of specific gut microbiota also showed significant inter-correlations (p < 0.05). Specific gut microbiota abundances are correlated with host anxiety, stress, sleep, and sleepiness levels (p < 0.05). We did not observe significant differences in the abundance of specific gut microbiota associated with different vitamin B1 and B2 nutritional levels in the host (p > 0.05). Gut microbial diversity and composition varied notably between different vitamin level groups and anxiety, stress, sleep quality, and sleepiness groups. Although both vitamin B2 and Bacteroides had significant direct effects on sleep quality (p < 0.05), no mediating effect of Bacteroides was observed (p > 0.05). Conclusions: These findings suggest potential associations between vitamins B1 and B2 and mental health, as well as between gut microbiota and host psychological outcomes; no significant mediating effect of the microbiota was observed. These exploratory results offer preliminary insights for future research on microbiota-targeted interventions and precision nutrition strategies.

Background: Anxiety, stress, and sleep disturbances significantly affect overall health. Research suggests that vitamins B1 and B2 may play a role in mood regulation and neuroprotection. This study aimed to investigate the effects of vitamin B1 and B2 supplementation in alleviating anxiety and stress and improving sleep quality. Methods: This study was a parallel randomized, double-blind, placebo-controlled clinical trial. Participants (n = 43) were randomized to receive one of the following two interventions: 100 mg of vitamin B1 and 100 mg of vitamin B2 or placebo. Intervention outcomes were assessed at baseline and week four, including SAS (Self-Rating Anxiety Scale), PSS (Perceived Stress Scale), PSQI (Sleep Quality Index), ESS (Sleepiness Scale), and measurement of urinary vitamin B1 and B2 levels. Results: After four weeks, urinary vitamin B1 levels increased from 158 ± 108.9 ng to 1333.1 ± 1204.5 ng (p < 0.01), and urinary vitamin B2 levels increased from 308.0 ± 198.3 ng to 6123.2 ± 4847.2 ng in the supplement group (p < 0.01). The PSS scores decreased significantly in the supplement group from 21.5 ± 4.1 to 15.5 ± 4.5 (p < 0.05), while the placebo group showed a change from 20.3 ± 4.3 to 19.8 ± 5.5. Vitamins B1 and B2 did not have a significant effect on anxiety improvement (p > 0.05). The PSQI scores decreased in the supplement group from 8.0 ± 3.12 to 6.3 ± 2.0 (p < 0.05), while the placebo group worsened from 5.7 ± 2.7 to 7.4 ± 2.9. Meanwhile, the ESS scores in the supplement group decreased from 13.0 ± 3.4 to 9.1 ± 3.9 (p < 0.05), demonstrating a significant improvement compared to the placebo group. Conclusions: The clinical trial findings demonstrated that while vitamin B1 and B2 supplements helped reduce stress, enhance sleep, and reduce sleepiness, they had no discernible impact on reducing anxiety. Future studies should focus on the long-term effects of vitamin B1 and B2 supplements, exploring the combined effects of combined vitamin B1 and B2 medications for the treatment of stress and sleep disorders.

Clouds significantly affects the power output of solar energy systems, and it also decrease the life of modules in photovoltaic system and receivers in the concentrating solar power system, resulting in costs of operation and maintenance. In the present study, the MRR-YOLO model was proposed, which was based on deep learning and instance segmentation technique. The MSDA, the RCS-OSA, and the RFAConv modules were used, and their functions to the cloud segmentation were investigated. Results found that the MSDA module helped maintain the model’s lightweight, the RFAConv module had a better feature extraction of the clouds. The instance segmentation method was better than semantic segmentation in clouds detection, especially for clouds of varying shapes. The PB, the RB, and the mAP50B of the MRR-YOLO model were 79.2%, 66%, and 74.7%, respectively. For the segmentation task, the PM, the RM, and the mAP50M were 79.3%, 64.8%, and 73%, respectively. The MRR-YOLO model was validated by the SWIMSEG, the CCSN, the all-sky datasets, and the real cloud images in Zhengzhou city, it had a better detection performance and applicability than other models. A heatmap comparison was also conducted, showing that the model accurately detected all features of the clouds.

Background N-methyl-D-aspartate receptor (NMDAR) is an ionotropic glutamate (Glu) receptor that is widely expressed in the central nervous system (CNS), mainly in the hippocampus. We present a case in which the patient had atypical clinical manifestations and was positive for anti-NMDAR antibodies. Case presentation A 40-year-old male was admitted to the hospital with “dizziness and double vision for 2 months”. At admission, the patient was lethargic, had short-term memory loss, exhibited loss of orientation (time, place, and person) and calculation ability, and had limited left eye abduction. After admission, serum anti- NMDAR antibody was 1:32, and cerebrospinal fluid was 1:1. Magnetic resonance imaging (MRI) revealed diffuse abnormal signals in the bilateral basal ganglia, thalamus, brainstem, hippocampus, and temporal lobe, with patchy and heterogeneous enhancement. A stereotactic brain biopsy was performed, and the pathological results indicated normal brain tissue. Preliminary diagnosis suggested anti-NMDAR antibody encephalitis. The patient was treated with methylprednisolone combined with intravenous gamma globulin; the symptoms were alleviated, and the patient was discharged. Two months later, the patient’s symptoms worsened, and a second stereotactic brain biopsy was performed. The pathological results showed that the patient had primary diffuse large B-cell lymphoma of the CNS, and the patient was transferred to the Department of Hematology and received chemotherapy combined with rituximab. The patient was in stable condition. Conclusions When the primary CNS diffuses large B-cell lymphoma is associated with autoimmune encephalitis, it is very easy to be misdiagnosed. The diagnosis should not be based on the pathological examination that was performed in the early stage of the disease. Therefore, in the diagnosis of immune diseases caused by nervous system infections, it is necessary to dynamically observe the evolution of the disease, perform differential diagnoses when necessary, and ultimately improve our understanding of the disease.