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Synthetic biology is a powerful tool to create therapeutics which can be rationally designed to enable unique and combinatorial functionalities. Here we utilize non-pathogenic E coli Nissle as a versatile platform for the development of a living biotherapeutic for the treatment of cancer. The engineered bacterial strain, referred to as SYNB1891, targets STING-activation to phagocytic antigen-presenting cells (APCs) in the tumor and activates complementary innate immune pathways. SYNB1891 treatment results in efficacious antitumor immunity with the formation of immunological memory in murine tumor models and robust activation of human APCs. SYNB1891 is designed to meet manufacturability and regulatory requirements with built in biocontainment features which do not compromise its efficacy. This work provides a roadmap for the development of future therapeutics and demonstrates the transformative potential of synthetic biology for the treatment of human disease when drug development criteria are incorporated into the design process for a living medicine. Synthetic biology can be used to create rationally designed living therapeutics. Here the authors engineer E. coli Nissle to target STING activation in antigen presenting cells for the treatment of solid tumors and demonstrate preclinical activity in murine models.

Due to the overlapping emission spectra of fluorophores, fluorescence microscopy images often have bleed-through problems, leading to a false positive detection. This problem is almost unavoidable when the samples are labeled with three or more fluorophores, and the situation is complicated even further when imaged under a multiphoton microscope. Several methods have been developed and commonly used by biologists for fluorescence microscopy spectral unmixing, such as linear unmixing, non-negative matrix factorization, deconvolution, and principal component analysis. However, they either require pre-knowledge of emission spectra or restrict the number of fluorophores to be the same as detection channels, which highly limits the real-world applications of those spectral unmixing methods. In this paper, we developed a robust and flexible spectral unmixing method: Learning Unsupervised Means of Spectra (LUMoS), which uses an unsupervised machine learning clustering method to learn individual fluorophores' spectral signatures from mixed images, and blindly separate channels without restrictions on the number of fluorophores that can be imaged. This method highly expands the hardware capability of two-photon microscopy to simultaneously image more fluorophores than is possible with instrumentation alone. Experimental and simulated results demonstrated the robustness of LUMoS in multi-channel separations of two-photon microscopy images. We also extended the application of this method to background/autofluorescence removal and colocalization analysis. Lastly, we integrated this tool into ImageJ to offer an easy to use spectral unmixing tool for fluorescence imaging. LUMoS allows us to gain a higher spectral resolution and obtain a cleaner image without the need to upgrade the imaging hardware capabilities.

Functional magnetic resonance imaging (fMRI) has allowed the noninvasive study of task-based and resting-state brain dynamics in humans by inferring neural activity from blood-oxygenation-level dependent (BOLD) signal changes. An accurate interpretation of the hemodynamic changes that underlie fMRI signals depends on the understanding of the quantitative relationship between changes in neural activity and changes in cerebral blood flow, oxygenation and volume. While there has been extensive study of neurovascular coupling in anesthetized animal models, anesthesia causes large disruptions of brain metabolism, neural responsiveness and cardiovascular function. Here, we review work showing that neurovascular coupling and brain circuit function in the awake animal are profoundly different from those in the anesthetized state. We argue that the time is right to study neurovascular coupling and brain circuit function in the awake animal to bridge the physiological mechanisms that underlie animal and human neuroimaging signals, and to interpret them in light of underlying neural mechanisms. Lastly, we discuss recent experimental innovations that have enabled the study of neurovascular coupling and brain-wide circuit function in un-anesthetized and behaving animal models. •We review the effects of anesthesia on neurovascular coupling and brain circuit function.•Anesthesia decreases brain metabolism and perturbs other physiological processes.•Neural excitability and neurovascular coupling are altered by anesthesia.•Brain circuit function are changed by anesthesia relative to the awake animal.•Techniques for imaging in awake rodents are presented.

As artificial intelligence becomes increasingly prevalent in education, ensuring educational fairness has emerged as a critical concern. Algorithmic bias can lead to inequitable predictions, resulting in the unfair allocation of educational resources and posing significant social and ethical challenges. Therefore, designing and evaluating fair educational algorithms is a pressing challenge. This paper introduces FairEduNet, a novel framework for fairness optimization that combines a Mixture of Experts (MoE) architecture with adversarial training. The MoE architecture enhances prediction accuracy, while the adversarial network systematically reduces the model’s dependence on sensitive attributes. This dual-component design allows FairEduNet to significantly mitigate implicit bias while maintaining high predictive accuracy for student dropout prediction. We evaluate FairEduNet on three educational datasets–the Duolingo dataset, the Portuguese Student Performance dataset, and the Open University Learning Analytics Dataset–comparing its performance against four debiasing algorithms. Experimental results demonstrate that FairEduNet achieves a significant improvement in fairness metrics across these datasets without compromising predictive accuracy.

Multi-dimensional uncertain differential equation is a tool to model an uncertain multi-dimensional dynamic system. Furthermore, stability has a significant role in the field of differential equations because it can be describe the effect of the initial value on the solution of the differential equation. Hence, the concept of almost sure stability is presented concerning multi-dimensional uncertain differential equation in this paper. Moreover, a stability theorem, that is a condition, is derived to judge whether a multi-dimensional uncertain differential equation is almost surely stable or not. Additionally, the paper takes a counterexample to show that the given condition is not necessary for a multi-dimensional uncertain differential equation being almost surely stable.

Background The very-long-chain fatty acid elongase (ELOVL) family plays essential roles in lipid metabolism and cellular functions. This comprehensive review explores the structural characteristics, functional properties, and physiological significance of individual ELOVL isoforms, providing insights into lipid biosynthesis, cell membrane dynamics, and signaling pathways. Aim of review This review aims to highlight the significance of the ELOVL family in normal physiology and disease development. By synthesizing current knowledge, we underscore the relevance of ELOVLs as potential therapeutic targets. Key scientific concepts of review We emphasize the association between dysregulated ELOVL expression and diseases, including metabolic disorders, skin diseases, neurodegenerative conditions, and cancer. The intricate involvement of ELOVLs in cancer biology, from tumor initiation to metastasis, highlights their potential as targets for anticancer therapies. Additionally, we discuss the prospects of using isoform-specific inhibitors and activators for metabolic disorders and cancer treatment. The identification of ELOVL-based biomarkers may advance diagnostics and personalized medicine. Conclusion The ELOVL family's multifaceted roles in lipid metabolism and cellular physiology underscore its importance in health and disease. Understanding their functions offers potential therapeutic avenues and personalized treatments.

Nod-like receptor protein 3 (NLRP3)-associated neuroinflammation mediated by activated microglia is involved in the pathogenesis of depression. The role of the pore-forming protein gasdermin D (GSDMD), a newly identified pyroptosis executioner downstream of NLRP3 inflammasome mediating inflammatory programmed cell death, in depression has not been well defined. Here, we provide evidence that paeoniflorin (PF), a monoterpene glycoside compound derived from Paeonia lactiflora , ameliorated reserpine-induced mouse depression-like behaviors, characterized as increased mobility time in tail suspension test and forced swimming test, as well as the abnormal alteration of synaptic plasticity in the depressive hippocampus. The molecular docking simulation predicted that PF would interact with C-terminus of GSDMD. We further demonstrated that PF administration inhibited the enhanced expression of GSDMD which mainly distributed in microglia, along with the proteins involved in pyroptosis signaling transduction including caspase (CASP)-11, CASP-1, NLRP3, and interleukin (IL)-1β in the hippocampus of mice treated with reserpine. And also, PF prevented lipopolysaccharide (LPS) and adenosine triphosphate (ATP)-induced pyroptosis in murine N9 microglia in vitro, evidenced by inhibiting the expression of CASP-11, NLRP3, CASP-1 cleavage, as well as IL-1β. Furthermore, VX-765, an effective and selective inhibitor for CASP-1 activation, reduced the expression of inflammasome and pyroptosis-associated proteins in over-activated N9 and also facilitated PF-mediated inhibition of pyroptosis synergistically. Collectively, the data indicated that PF exerted antidepressant effects, alleviating neuroinflammation through inhibiting CASP-11-dependent pyroptosis signaling transduction induced by over-activated microglia in the hippocampus of mice treated with reserpine. Thus, GSDMD-mediated pyroptosis in activated microglia is a previously unrecognized inflammatory mechanism of depression and represents a unique therapeutic opportunity for mitigating depression given PF administration.

Serum neurofilament light chain (sNfL), a protein released into the bloodstream post-neuronal axonal damage, has been validated as a robust biomarker for a range of neurological and systemic diseases. Concurrently, Life's Essential 8 (LE8) comprises a holistic suite of health behaviors and metabolic markers that are essential for assessing and enhancing cardiovascular health. Nevertheless, the interrelation between LE8 and sNfL is not yet fully elucidated. This investigation seeks to evaluate the association between LE8 and sNfL within the framework of the National Health and Nutrition Examination Survey (NHANES). According to data from the 2013-2014 NHANES, the study enrolled a total of 5262 participants aged between 20 and 75 years. We excluded 3035 individuals lacking sNfL measurements, included 2071 subjects for analysis, and further excluded cases from LE8 due to missing data. Ultimately, 1691 valid datasets were obtained. Hierarchical and multiple regression analyses were conducted, supplemented by smooth curve fitting and saturation effect analysis to investigate the relationship between LE8 and sNfL. An inverse correlation was observed between LE8 scores and sNfL levels. For each SD change increase in LE8, log-transformed sNfL levels decreased by 0.14 (-0.17, -0.11 in the non-adjusted model), 0.08 (-0.10, -0.05 in the minimally adjusted model), and 0.08 (-0.12, -0.05 in the fully adjusted model). The multi-factor adjusted β coefficients and 95% confidence intervals (CIs) for LE8 categories (<50, 50 ~ 80, and ≥80) were as follows: reference, -0.20 (-0.34, -0.06), and -0.26 (-0.42, -0.10). The inflection point was determined to be 58.12, identified using a two-piece linear regression model. The analysis indicated a non-linear relationship between LE8 scores and sNfL levels. Associations were noted a positive association between LE8 and sNfL. These results suggest that lifestyle modifications and optimization of metabolic markers could potentially correlate with reduced sNfL levels; further investigation is necessary to confirm a causal relationship.

With rapid advancements in transformers, the reconstruction of hyperspectral images from RGB images, also known as spectral super-resolution (SSR), has made significant breakthroughs. However, existing transformer-based methods often struggle to balance computational efficiency with long-range receptive fields. Recently, Mamba has demonstrated linear complexity in modeling long-range dependencies and shown broad applicability in vision tasks. This paper proposes a multi-scale spectral–spatial sequence learning method, named MSS-Mamba, for reconstructing hyperspectral images from RGB images. First, we introduce a continuous spectral–spatial scan (CS3) mechanism to improve cross-dimensional feature extraction of the foundational Mamba model. Second, we propose a sequence tokenization strategy that generates multi-scale-aware sequences to overcome Mamba’s limitations in hierarchically learning multi-scale information. Specifically, we design the multi-scale information fusion (MIF) module, which tokenizes input sequences before feeding them into Mamba. The MIF employs a dual-branch architecture to process global and local information separately, dynamically fusing features through an adaptive router that generates weighting coefficients. This produces feature maps that contain both global contextual information and local details, ultimately reconstructing a high-fidelity hyperspectral image. Experimental results on the ARAD_1k, CAVE and grss_dfc_2018 dataset demonstrate the performance of MSS-Mamba.

Epidemiologic studies exploring causal associations between serum lipids and breast cancer risk have reported contradictory results. We conducted a meta-analysis of prospective cohort studies to evaluate these associations. Relevant studies were identified by searching PubMed and EMBASE through April 2015. We included prospective cohort studies that reported relative risk (RR) estimates with 95% confidence intervals (CIs) for the associations of specific lipid components (i.e., total cholesterol [TC], high-density lipoprotein cholesterol [HDL-C], low-density lipoprotein cholesterol [LDL-C], and triglycerides [TG]) with breast cancer risk. Either a fixed- or a random-effects model was used to calculate pooled RRs. Fifteen prospective cohort studies involving 1,189,635 participants and 23,369 breast cancer cases were included in the meta-analysis. The pooled RRs of breast cancer for the highest versus lowest categories were 0.96 (95% CI: 0.86-1.07) for TC, 0.92 (95% CI: 0.73-1.16) for HDL-C, 0.90 (95% CI: 0.77-1.06) for LDL-C, and 0.93 (95% CI: 0.86-1.00) for TG. Notably, for HDL-C, a significant reduction of breast cancer risk was observed among postmenopausal women (RR = 0.77, 95% CI: 0.64-0.93) but not among premenopausal women. Similar trends of the associations were observed in the dose-response analysis. Our findings suggest that serum levels of TG but not TC and LDL-C may be inversely associated with breast cancer risk. Serum HDL-C may also protect against breast carcinogenesis among postmenopausal women.