Experimental Biology and Medicine最新文献

This research aims to investigate the potential of using intrinsic optical signal (IOS) optoretinography (ORG) to objectively detect dark adaptation (DA) abnormalities related to rod photoreceptor degeneration. Functional optical coherence tomography (OCT) was employed in both wild-type (WT) and retinal degeneration 10 (rd10) mice to conduct this assessment. Dynamic OCT measurements captured the changes in retinal thickness and reflectance from light-to-dark transition. Comparative analysis revealed significant IOS alterations within the outer retina. Specifically, a reduction in thickness from external limiting membrane (ELM) peak to retinal pigment epithelium (RPE) peak was observed (WT: 1.13 ± 0.69 µm, 30 min DA; rd10: 2.64 ± 0.86 µm, 30 min DA), as well as a decrease in the intensity of the inner segment ellipsoid zone (EZ) in 30 min DA compared to light adaptation (LA). The reduction of relative EZ intensity was notable in rd10 after 5 min DA and in WT after 15 min DA, with a distinguishable difference between rd10 and WT after 10 min DA. Furthermore, our findings indicated a significant decrease in the relative intensity of the hypo-reflective band between EZ and RPE in rd10 retinas during DA, which primarily corresponds to the outer segment (OS) region. In conclusion, the observed DA-IOS abnormalities, including changes in ELM-RPE thickness, EZ, and OS intensity, hold promise as differentiators between WT and rd10 mice before noticeable morphological abnormalities occur. These findings suggest the potential of this non-invasive imaging technique for the early detection of dysfunction in retinal photoreceptors.

The presence of inhibitory immune cells and difficulty in generating activated effector T cells remain obstacles to development of effective cancer vaccines. We designed a vaccine regimen combining human telomerase reverse transcriptase (hTERT) peptides with concomitant therapies targeting regulatory T cells (Tregs) and cyclooxygenase-2 (COX2)-mediated immunosuppression. This Phase 1 trial combined an hTERT-derived 7-peptide library, selected to ensure presentation by both HLA class-I and class-II in 90% of patients, with oral low-dose cyclophosphamide (to modulate Tregs) and the COX2 inhibitor celecoxib. Adjuvants were Montanide and topical TLR-7 agonist, to optimise antigen presentation. The primary objective was determination of the safety and tolerability of this combination therapy, with anti-cancer activity, immune response and detection of antigen-specific T cells as additional endpoints. Twenty-nine patients with advanced solid tumours were treated. All were multiply-pretreated, and the majority had either colorectal or prostate cancer. The most common adverse events were injection-site reactions, fatigue and nausea. Median progression-free survival was 9 weeks, with no complete or partial responses, but 24% remained progression-free for ≥6 months. Immunophenotyping showed post-vaccination expansion of CD4⁺ and CD8⁺ T cells with effector phenotypes. The in vitro re-challenge of T cells with hTERT peptides, TCR sequencing, and TCR similarity index analysis demonstrated the expansion following vaccination of oligoclonal T cells with specificity for hTERT. However, a population of exhausted PD-1⁺ cytotoxic T cells was also expanded in vaccinated patients. This vaccine combination regimen was safe and associated with antigen-specific immunological responses. Clinical activity could be improved in future by combination with anti-PD1 checkpoint inhibition to address the emergence of an exhausted T cell population.

We present a pipeline in which machine learning techniques are used to automatically identify and evaluate subtypes of hospital patients admitted between 2017 and 2021 in a large UK teaching hospital. Patient clusters are determined using routinely collected hospital data, such as those used in the UK's National Early Warning Score 2 (NEWS2). An iterative, hierarchical clustering process was used to identify the minimum set of relevant features for cluster separation. With the use of state-of-the-art explainability techniques, the identified subtypes are interpreted and assigned clinical meaning, illustrating their robustness. In parallel, clinicians assessed intracluster similarities and intercluster differences of the identified patient subtypes within the context of their clinical knowledge. For each cluster, outcome prediction models were trained and their forecasting ability was illustrated against the NEWS2 of the unclustered patient cohort. These preliminary results suggest that subtype models can outperform the established NEWS2 method, providing improved prediction of patient deterioration. By considering both the computational outputs and clinician-based explanations in patient subtyping, we aim to highlight the mutual benefit of combining machine learning techniques with clinical expertise.

Mesenchymal stem cells (MSCs) have been widely used in the treatment of ischemic stroke. However, factors such as high glucose, oxidative stress, and aging can lead to the reduced function of donor MSCs. The p38 mitogen-activated protein kinase (MAPK) signaling pathway is associated with various functions, such as cell proliferation, apoptosis, senescence, differentiation, and paracrine secretion. This study examined the hypothesis that the downregulation of p38 MAPK expression in MSCs improves the prognosis of mice with ischemic stroke. Lentiviral vector-mediated short hairpin RNA (shRNA) was constructed to downregulate the expression level of p38 MAPK in mouse bone marrow-derived MSCs. The growth cycle, apoptosis, and senescence of MSCs after infection were examined. A mouse model of ischemic stroke was constructed. After MSC transplantation, the recovery of neurological function in the mice was evaluated. Lentivirus-mediated shRNA significantly downregulated the mRNA and protein expression levels of p38 MAPK. The senescence of MSCs in the p38 MAPK downregulation group was significantly reduced, but the growth cycle and apoptosis did not significantly change. Compared with the control group, the infarct volume was reduced, and the neurological function and the axonal remodeling were improved in mice with ischemic stroke after transplantation of MSCs with downregulated p38 MAPK. Immunohistochemistry confirmed that in the p38 MAPK downregulation group, apoptotic cells were reduced, and the number of neuronal precursors and the formation of white matter myelin were increased. In conclusion, downregulation of p38 MAPK expression in MSCs improves the therapeutic effect in mice with ischemic stroke, an effect that may be related to a reduction in MSC senescence. This method is expected to improve the efficacy of MSCs in patients, especially in patients with underlying diseases such as diabetes, thus providing a basis for clinical individualized treatment for cerebral infarction.

Amyotrophic lateral sclerosis (ALS) is a fatal neurodegenerative disorder and the most common form of motor neurone disease (MND) which is characterized by the damage and death of motor neurons in the brain and spinal cord of affected individuals. Due to the heterogeneity of the disease, a better understanding of the interaction between genetics and biochemistry with the identification of biomarkers is crucial for therapy development. In this study, we used cerebrospinal fluid (CSF) RNA-sequencing data from the New York Genome Center (NYGC) ALS Consortium and analyzed differential gene expression between 47 MND individuals and 29 healthy controls. Pathway analysis showed that the affected genes are enriched in many pathways associated with ALS, including nucleocytoplasmic transport, autophagy, and apoptosis. Moreover, we assessed differential expression on both gene- and transcript-based levels and demonstrate that the expression of previously identified potential biomarkers, including CAPG, CCL3, and MAP2, was significantly higher in MND individuals. Ultimately, this study highlights the transcriptomic composition of CSF which enables insights into changes in the brain in ALS and therefore increases the confidence in the use of CSF for biomarker development.

Curcumin, an antitumor agent, has been shown to inhibit cell growth and metastasis in osteosarcoma. However, there is no evidence of curcumin and its regulation of cell ferroptosis and nuclear factor E2-related factor 2 (Nrf2)/glutathione peroxidase 4 (GPX4) signaling pathways in osteosarcoma. This study aimed to investigate the effects of curcumin on osteosarcoma both in vitro and in vivo. To explore the effects and mechanisms of curcumin on osteosarcoma, cells (MNNG/HOS and MG-63) and xenograft mice models were established. Cell viability, cell apoptosis rate, cycle distribution, cell migration, cell invasion, reactive oxygen species, malonaldehyde and glutathione abilities, and protein levels were detected by cell counting kit-8, flow cytometry, wound healing, transwell assay, respectively. Nrf2 and GPX4 expressions were detected using an immunofluorescence assay. Nrf2/GPX4-related protein levels were detected using western blotting. The results showed that curcumin effectively decreased cell viability and increased apoptosis rate. Meanwhile, curcumin inhibited tumor volume in the xenograft model, and Nrf2/GPX4-related protein levels were also altered. Interestingly, the effects of curcumin were reversed by liproxstatin-1 (an effective inhibitor of ferroptosis) and bardoxolone-methyl (an effective activator of Nrf2). Our results indicate that curcumin has therapeutic effects on osteosarcoma cells and a xenograft model by regulating the expression of the Nrf2/GPX4 signaling pathway.

In our previous study, we demonstrated the feasibility of producing a proactive statin prescription strategy - a personalized statin treatment plan (PSTP) - using neural networks with big data. However, its non-transparency limited result interpretations and clinical usability. To improve the transparency of our previous approach with minimal compromise to the maximal statin treatment benefit-to-risk ratio, this study proposed a five-step pipeline approach called the decision rules for statin treatment (DRST). Steps 1-3 of our proposed pipeline improved our previous PSTP model in optimizing individual benefit-to-risk ratio; Step 4 used a decision tree model (DRST) to provide straightforward rules in the initial statin treatment plan; Step 5 aimed to evaluate the efficacy of these decision rules by conducting a clinical trial simulation. We included 107,739 de-identified patient data from Optum Labs Database Warehouse in this study. The final decision rules were compact and efficient, resulting from a decision tree with only a maximum depth of 3 and 11 nodes. The DRST identified three factors that are easily obtainable at the point of care: age, low-density lipoprotein cholesterol (LDL-C) level, and age-adjusted Charlson score. Moreover, it also identified six subpopulations that can benefit most from these decision rules. In our clinical trial simulations, DRST was found to improve statin benefit in LDL-C reduction by 4.15 percentage points (pp) and reduce risks of statin-associated symptoms (SAS) and statin discontinuation by 11.71 and 3.96 pp, respectively, when compared to the standard of care. Moreover, these DRST results were only less than 0.6 pp suboptimal to PSTP, demonstrating that building DRST that provide transparency with minimal compromise to the maximal benefit-to-risk ratio of statin treatments is feasible.