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

IntroductionPain 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.MethodWe 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 discussionThe 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

Neuroprotective Effects of Cuscutae Semen in a Mouse Model of Parkinson’s Disease

Parkinson’s disease (PD) is a neurodegenerative movement disorder that is characterized by the progressive degeneration of the dopaminergic (DA) pathway. 1-Methyl-4-phenyl-1,2,3,6-tetrahydropyridine (MPTP) causes damage to the DA neurons, and 1-4-methyl-4-phenylpyridinium (MPP+) causes cell death in differentiated PC12 cells that is similar to the degeneration that occurs in PD. Moreover, MPTP treatment increases the activity of the brain’s immune cells, reactive oxygen species- (ROS-) generating processes, and glutathione peroxidase. We recently reported that Cuscutae Semen (CS), a widely used traditional herbal medicine, increases cell viability in a yeast model of PD. In the present study, we examined the inhibitory effect of CS on the neurotoxicity of MPTP in mice and on the MPP+-induced cell death in differentiated PC12 cells. The MPTP-induced loss of nigral DA neurons was partly inhibited by CS-mediated decreases in ROS generation. The activation of microglia was slightly inhibited by CS, although this effect did not reach statistical significance. Furthermore, CS may reduce the MPP+ toxicity in PC12 cells by suppressing glutathione peroxidase activation. These results suggest that CS may be beneficial for the treatment of neurodegenerative diseases such as PD

Prognostic value of tumor regression grade on MR in rectal cancer: A large-scale, single-center experience

Objective: To determine the prognostic value of MRI-based tumor regression grading (mrTRG) in rectal cancer compared with pathological tumor regression grading (pTRG), and to assess the effect of diffusion-weighted imaging (DWI) on interobserver agreement for evaluating mrTRG. Materials and Methods: Between 2007 and 2016, we retrospectively enrolled 321 patients (male:female = 208:113; mean age, 60.2 years) with rectal cancer who underwent both pre-chemoradiotherapy (CRT) and post-CRT MRI. Two radiologists independently determined mrTRG using a 5-point grading system with and without DWI in a one-month interval. Two pathologists graded pTRG using a 5-point grading system in consensus. Kaplan-Meier estimation and Cox-proportional hazard models were used for survival analysis. Cohen's kappa analysis was used to determine interobserver agreement. Results: According to mrTRG on MRI with DWI, there were 6 mrTRG 1, 48 mrTRG 2, 109 mrTRG 3, 152 mrTRG 4, and 6 mrTRG 5. By pTRG, there were 7 pTRG 1, 59 pTRG 2, 180 pTRG 3, 73 pTRG 4, and 2 pTRG 5. A 5-year overall survival (OS) was significantly different according to the 5-point grading mrTRG (p = 0.024) and pTRG (p = 0.038). The 5-year disease-free survival (DFS) was significantly different among the five mrTRG groups (p = 0.039), but not among the five pTRG groups (p = 0.072). OS and DFS were significantly different according to post-CRT MR variables: extramural venous invasion after CRT (hazard ratio = 2.259 for OS, hazard ratio = 5.011 for DFS) and extramesorectal lymph node (hazard ratio = 2.610 for DFS). For mrTRG, k value between the two radiologists was 0.309 (fair agreement) without DWI and slightly improved to 0.376 with DWI. Conclusion: mrTRG may predict OS and DFS comparably or even better compared to pTRG. The addition of DWI on T2-weighted MRI may improve interobserver agreement on mrTRG.N