Machine learning-based evaluation of spontaneous pain and analgesics from cellular calcium signals in the mouse primary somatosensory cortex using explainable features.

Introduction: Pain that arises spontaneously is considered more clinically relevant than pain evoked by external stimuli. However, measuring spontaneous pain in animal models in preclinical studies is challenging due to methodological limitations. To address this issue, recently we developed a deep learning (DL) model to assess spontaneous pain using cellular calcium signals of the primary somatosensory cortex (S1) in awake head-fixed mice. However, DL operate like a "black box", where their decision-making process is not transparent and is difficult to understand, which is especially evident when our DL model classifies different states of pain based on cellular calcium signals. In this study, we introduce a novel machine learning (ML) model that utilizes features that were manually extracted from S1 calcium signals, including the dynamic changes in calcium levels and the cell-to-cell activity correlations. Method: We focused on observing neural activity patterns in the primary somatosensory cortex (S1) of mice using two-photon calcium imaging after injecting a calcium indicator (GCaMP6s) into the S1 cortex neurons. We extracted features related to the ratio of up and down-regulated cells in calcium activity and the correlation level of activity between cells as input data for the ML model. The ML model was validated using a Leave-One-Subject-Out Cross-Validation approach to distinguish between non-pain, pain, and drug-induced analgesic states. Results and discussion: The ML model was designed to classify data into three distinct categories: non-pain, pain, and drug-induced analgesic states. Its versatility was demonstrated by successfully classifying different states across various pain models, including inflammatory and neuropathic pain, as well as confirming its utility in identifying the analgesic effects of drugs like ketoprofen, morphine, and the efficacy of magnolin, a candidate analgesic compound. In conclusion, our ML model surpasses the limitations of previous DL approaches by leveraging manually extracted features. This not only clarifies the decision-making process of the ML model but also yields insights into neuronal activity patterns associated with pain, facilitating preclinical studies of analgesics with higher potential for clinical translation.

Syringaresinol Alleviates Oxaliplatin-Induced Neuropathic Pain Symptoms by Inhibiting the Inflammatory Responses of Spinal Microglia.

Oxaliplatin-induced peripheral neuropathy (OIPN) is a serious side effect that impairs the quality of life of patients treated with the chemotherapeutic agent, oxaliplatin. The underlying pathophysiology of OIPN remains unclear, and there are no effective therapeutics. This study aimed to investigate the causal relationship between spinal microglial activation and OIPN and explore the analgesic effects of syringaresinol, a phytochemical from the bark of Cinnamomum cassia, on OIPN symptoms. The causality between microglial activation and OIPN was investigated by assessing cold and mechanical allodynia in mice after intrathecal injection of the serum supernatant from a BV-2 microglial cell line treated with oxaliplatin. The microglial inflammatory response was measured based on inducible nitric oxide synthase (iNOS), phosphorylated extracellular signal-regulated kinase (p-ERK), and phosphorylated nuclear factor-kappa B (p-NF-κB) expression in the spinal dorsal horn. The effects of syringaresinol were tested using behavioral and immunohistochemical assays. We found that oxaliplatin treatment activated the microglia to increase inflammatory responses, leading to the induction of pain. Syringaresinol treatment significantly ameliorated oxaliplatin-induced pain and suppressed microglial expression of inflammatory signaling molecules. Thus, we concluded that the analgesic effects of syringaresinol on OIPN were achieved via the modulation of spinal microglial inflammatory responses.

Accurate and efficient numerical solutions for elliptic obstacle problems.

Elliptic obstacle problems are formulated to find either superharmonic solutions or minimal surfaces that lie on or over the obstacles, by incorporating inequality constraints. In order to solve such problems effectively using finite difference (FD) methods, the article investigates simple iterative algorithms based on the successive over-relaxation (SOR) method. It introduces subgrid FD methods to reduce the accuracy deterioration occurring near the free boundary when the mesh grid does not match with the free boundary. For nonlinear obstacle problems, a method of gradient-weighting is introduced to solve the problem more conveniently and efficiently. The iterative algorithm is analyzed for convergence for both linear and nonlinear obstacle problems. An effective strategy is also suggested to find the optimal relaxation parameter. It has been numerically verified that the resulting obstacle SOR iteration with the optimal parameter converges about one order faster than state-of-the-art methods and the subgrid FD methods reduce numerical errors by one order of magnitude, for most cases. Various numerical examples are given to verify the claim.

PLGA nanofiber membranes loaded with epigallocatechin-3-O-gallate are beneficial to prevention of postsurgical adhesions.

This study concentrates on the development of biodegradable nanofiber membranes with controlled drug release to ensure reduced tissue adhesion and accelerated healing. Nanofibers of poly(lactic-co-glycolic acid) (PLGA) loaded with epigallocatechin-3-O-gallate (EGCG), the most bioactive polyphenolic compound in green tea, were electrospun. The physicochemical and biomechanical properties of EGCG-releasing PLGA (E-PLGA) nanofiber membranes were characterized by atomic force microscopy, EGCG release and degradation profiles, and tensile testing. In vitro antioxidant activity and hemocompatibility were evaluated by measuring scavenged reactive oxygen species levels and activated partial thromboplastin time, respectively. In vivo antiadhesion efficacy was examined on the rat peritonea with a surgical incision. The average fiber diameter of E-PLGA membranes was approximately 300-500 nm, which was almost similar to that of pure PLGA equivalents. E-PLGA membranes showed sustained EGCG release mediated by controlled diffusion and PLGA degradation over 28 days. EGCG did not adversely affect the tensile strength of PLGA membranes, whereas it significantly decreased the elastic modulus and increased the strain at break. E-PLGA membranes were significantly effective in both scavenging reactive oxygen species and extending activated partial thromboplastin time. Macroscopic observation after 1 week of surgical treatment revealed that the antiadhesion efficacy of E-PLGA nanofiber membranes was significantly superior to those of untreated controls and pure PLGA equivalents, which was comparable to that of a commercial tissue-adhesion barrier. In conclusion, the E-PLGA hybrid nanofiber can be exploited to craft strategies for the prevention of postsurgical adhesions.

Cell-adhesive RGD peptide-displaying M13 bacteriophage/PLGA nanofiber matrices for growth of fibroblasts.

BACKGROUND: M13 bacteriophages can be readily fabricated as nanofibers due to non-toxic bacterial virus with a nanofiber-like shape. In the present study, we prepared hybrid nanofiber matrices composed of poly(lactic-co-glycolic acid, PLGA) and M13 bacteriophages which were genetically modified to display the RGD peptide on their surface (RGD-M13 phage). RESULTS: The surface morphology and chemical composition of hybrid nanofiber matrices were characterized by scanning electron microscopy (SEM) and Raman spectroscopy, respectively. Immunofluorescence staining was conducted to investigate the existence of M13 bacteriophages in RGD-M13 phage/PLGA hybrid nanofibers. In addition, the attachment and proliferation of three different types of fibroblasts on RGD-M13 phage/PLGA nanofiber matrices were evaluated to explore how fibroblasts interact with these matrices. SEM images showed that RGD-M13 phage/PLGA hybrid matrices had the non-woven porous structure, quite similar to that of natural extracellular matrices, having an average fiber diameter of about 190 nm. Immunofluorescence images and Raman spectra revealed that RGD-M13 phages were homogeneously distributed in entire matrices. Moreover, the attachment and proliferation of fibroblasts cultured on RGD-M13 phage/PLGA matrices were significantly enhanced due to enriched RGD moieties on hybrid matrices. CONCLUSIONS: These results suggest that RGD-M13 phage/PLGA matrices can be efficiently used as biomimetic scaffolds for tissue engineering applications.