Physical activity is an adjunctive therapy that improves symptoms in people living with systemic lupus erythematosus (SLE), yet the mechanisms underlying this benefit remain unclear. We carried out a cohort study of 123 patients with SLE enrolled in the California Lupus Epidemiology Study (CLUES). The primary predictor variable was self-reported physical activity, which was measured using a previously validated instrument. We analyzed peripheral blood mononuclear cell (PBMC) single-cell RNA sequencing (scRNA-seq) data available from the cohort. From the scRNA-seq data, we compared immune cell frequencies, cell-specific gene expression, biological signalling pathways, and upstream cytokine activation states between physically active and inactive patients, adjusting for age, sex and race. We found that physical activity influenced immune cell frequencies, with sedentary patients most notably demonstrating greater CD4+ T cell lymphopenia (Padj=0.028). Differential gene expression analysis identified a transcriptional signature of physical inactivity across five cell types. In CD4+ and CD8+ T cells, this signature was characterized by 686 and 445 differentially expressed genes (Padj<0.1). Gene set enrichment analysis demonstrated enrichment of proinflammatory genes in the TNF-α signalling through NF-kB, interferon-γ (IFN-γ), IL2/STAT5, and IL6/JAK/STAT3 signalling pathways. Computational prediction of upstream cytokine activation states suggested CD4+ T cells from physically inactive patients exhibited increased activation of TNF-α, IFN-γ, IL1Β, and other proinflammatory cytokines. Network analysis demonstrated interconnectivity of genes driving the proinflammatory state of sedentary patients. Findings were consistent in sensitivity analyses adjusting for corticosteroid treatment and physical function. Taken together, our findings suggest a mechanistic explanation for the observed benefits of physical activity in patients with SLE. Specifically, we find that physical inactivity is associated with altered frequencies and transcriptional profiles of immune cell populations and may exacerbate pathologic inflammatory signalling via CD4+ and CD8+ T cells. This work was supported by the US National Institutes of Health (NIH) (R01 AR069616, K23HL138461-01A1, K23AT011768) the US CDC (U01DP0670), and the CZ Biohub.

The ecological impact of microplastics (MPs) pollution to aquatic ecosystems have garnered significant attention. However, further research is required to fully comprehend the accumulation of MPs by biofilms and their impact on mature freshwater biofilms. In this study, mature freshwater biofilms were exposed to polyethylene microplastics (PE-MPs) at concentrations of 0 mg/L, 10 mg/L, 50 mg/L, and 100 mg/L for a duration of 7 days in order to investigate the enrichment mechanism of biofilms onto MPs as well as the influence exerted by MPs on biofilms. At the conclusion of the experiment, the biofilms in the low, medium, and high concentration PE-MPs treatment groups contained 5060 ± 1316 mg/kg, 5712 ± 1513 mg/kg, and 8881 ± 1126 mg/kg of MPs respectively, showing positive correlations between concentration of MPs and their enrichment effects on biofilms. High-throughput sequencing analysis revealed significant alterations in biofilm microbial community structure caused by MPs exposure. Following 7 days of experimentation, high concentrations of PE-MPs resulted in a noticeable decrease in Planctomycetes and increases in Cyanobacteria and Acidobacteria. Additionally, there were increased abundances of Pseudomonas and Flavobacterium, enhancing the nitrogen and phosphorus cycling functions of biofilms. Conversely, the abundance of Leptothrix decreased, resulting in a suppression of the iron cycling function. Moreover, the enrichment of PE-MPs resulted in an augmented prevalence of metabolic pathways associated with sulfur metabolism, thereby enhancing their capacity for sulfur cycling. This study offers valuable insights into the toxicological implications of PE-MPs pollution.

BackgroundDistraction enterogenesis lengthens the intestine through applied mechanical stress. The Hedgehog pathway (Hh) is responsible for intestinal tract development and directing the multi-layer patterning of the intestinal lumen. This study investigates the alteration in the principal components of this pathway in spring-mediated colonic lengthening. MethodsSamples from the murine cecal lengthening model were used to study Hh alteration during the cecal lengthening process. Primary components of this pathway were analyzed using RT-qPCR and immunostaining after 7 and 14 days of force application. The spring-mediated lengthened segments were compared to untreated control segments within each animal. ResultsThe spring-treated segments showed a 50% increase in length. There was a significant increase in the expression of the Desert Hedgehog ligand as opposed to the Sonic Hedgehog and Indian Hedgehog ligands. Additionally, the downstream targets of the pathway, Gli1, Gli2, and Gli3, were significantly overexpressed. The highest alterations in these components occurred at the earlier time point, after 7 days. ConclusionsThese findings highlight the contribution of the conserved Hedgehog developmental pathway during mechanical force-induced cecal lengthening, primarily through the Desert Hedgehog ligand. These data suggest that the Desert Hedgehog pathway may serve as therapeutic targets for intestinal regeneration.

A method for simultaneous quantitation of lidocaine orphenadrine, chlorpheniramine and chloroquine on GC-MS using the modified QuEChERS extraction technique was developed and validated. Brucine was used as an internal standard. Separation was achieved on GC-MS (Agilent-7890A/5975, capillary column DB-5, 15m x 250µm x 0.25 µm, splitless, run time 14.75 min) using helium as carrier gas. Selected Ion Monitoring (SIM) mode was used for the quantitation. LOQ for all the analytes was 100 µg/L. Method demonstrated a good linearity range (r2>0.995) for all analytes from 100 to 1000 µg/L. No interference from the matrices was observed. Method depicted a recovery of above 90 % for all the analytes. Validated method was successfully applied for quantitative analysis of these drugs in real case samples (i.e. liver, blood and urine). Quantitated concentrations of chloroquine and orphenadrine fell in fatal range while those for lidocaine and chlorpheniramine fell in non-fatal range in real case samples. Current method is quick, easy, cheap, robust and reproducible. Current method is found to be an excellent tool for simultaneous monitoring of these therapeutic drugs commonly encountered in cases received in forensic toxicology labs.

Upon irradiation with high-frequency circularly polarized light, the Heisenberg spin system on a honeycomb lattice develops a next-nearest neighbor Dzyaloshinskii-Moriya interaction (DMI) term, transforming it into a magnonic Floquet topological insulator with intriguing physical properties. In this context, we investigate the many-body interaction effects of Floquet magnons in a laser-irradiated Heisenberg honeycomb ferromagnet featuring DMI under circularly polarized off-resonant light illumination. Our analysis employs the magnon Floquet-Bloch theory and Green's function method. We demonstrate that quantum ferromagnet systems driven periodically by lasers exhibit temperature-driven topological phase transitions due to Floquet magnon-magnon interactions, transitions that are absent when such interactions are neglected. Furthermore, we observe that the critical temperature necessary for reversing the sign of the topological phase gradually increases with elevated light intensity. This study introduces a novel approach to constructing Floquet topological phases in periodically driven quantum magnet systems.

N-methyl-d-aspartate (NMDA) receptors, activated by glutamate, play a crucial role in learning and memory. Memantine (MEM), a non-competitive NMDA receptor antagonist, is currently prescribed for the treatment of Alzheimer's disease or dementia, which meanwhile simultaneously promotes a need to clarify its potential pro-cognitive effects that exist in normal healthy individuals. However, the neurobehavioral mechanisms underlying the cognitive improvement by MEM in normal individuals remain to be elucidated. This study aimed to assess the effects of MEM on working memory, measured by a discrete paired-trial delay alternation task in a T-maze in normal male rats. The impacts of MEM were hypothesized to vary depending on different baseline levels of working memory performance. Neurochemical examination of the levels of calcium/calmodulin-dependent kinase 2 (CaMKII) and NMDA receptor subunits within five targeted brain regions was conducted after behavioral tests. The results showed that acute administration of MEM enhanced working memory performance, with 2.5, 5.0, and 10 mg/kg doses increasing task accuracy compared to the vehicle, particularly in low performers. Neurochemically, the protein expression of CaMKII in the amygdala and that of the glutamate (Glu) N2A subunit in the dorsal striatum were greater in the low-performance group than in the high-performance group. Additionally, the protein expression of the GluN2A subunit in the dorsal striatum was negatively associated with task performance at baseline. The expression of GluN1 and GluN2B in the nucleus accumbens was negatively associated with task performance in the retest three weeks after drug treatment. These findings underscore the baseline-dependent improvement of working memory resulting from MEM administration, with observed drug effects associated with alterations in the levels of NMDA receptor subunits in striatal subareas and CaMKII in the amygdala.

The prediction of battery state of health (SOH) plays a vital role in battery management systems. A fusion model framework was proposed by integrating an improved single-particle model (SPM) with data-driven deep learning algorithms to enhance predictive accuracy and further elucidate the intrinsic mechanisms of battery aging. First, seven electrochemical features were extracted by the improved SPM, which exhibits a significant reduction in computational complexity compared to conventional electrochemical models. The validity of the extracted features was further verified through the utilization of differential voltage analysis (DVA). Second, a hybrid model was constructed which combines temporal convolutional network (TCN) and bidirectional long short-term memory network (BiLSTM). The effectiveness and superiority of the proposed model was demonstrated, with the full electrochemical features, on Oxford University dataset. Finally, experimental measurements were conducted on five different batteries with two different electrode materials combinations to further study SOH estimation across battery types. To address the forecasting challenges arising from data scarcity for a new type of battery, transfer learning was introduced. The results highlight the potential of this fusion framework to achieve more efficient and accurate SOH prediction.