The causal effects of immune cells on pancreatic cancer: A 2­sample Mendelian randomization study.

Leveraging publicly available genetic datasets, we conducted a comprehensive 2-sample Mendelian randomization (MR) analysis to explore the causal links between 731 immunophenotypes and the risk of pancreatic cancer (PC). To ensure the robustness of our findings, extensive sensitivity analyses were performed, evaluating stability, heterogeneity, and potential horizontal pleiotropy. Our analysis pinpointed 24 immunophenotypes significantly associated with the risk of PC. Notably, phenotypes such as CD4+ CD8dim %leukocyte (OR = 0.852, 95% CI = 0.729-0.995, P = .0430) and HLA DR+ CD4+ AC (OR = 0.933, 95% CI = 0.883-0.986) in TBNK were inversely correlated with PC risk. Conversely, phenotypes like CD28 on CD45RA- CD4 non-Treg (OR = 1.155, 95% CI = 1.028-1.297, P = .016) and CD25 on activated Treg (OR = 1.180, 95% CI = 1.014-1.374, P = .032) in Treg cells, among others, exhibited a positive correlation. These insights offer a valuable genetic perspective that could guide future clinical research in this area.

Emodin ameliorates myocardial fibrosis in mice by inactivating the ROS/PI3K/Akt/mTOR axis.

BACKGROUND: Emodin is a traditional medicine that has been shown to exert anti-inflammatory and anti-oxidative effects. Previous research has indicated that emodin can alleviate myocardial remodeling and inhibit myocardial hypertrophy and fibrosis. However, the mechanism by which emodin affects myocardial fibrosis (MF) has not yet been elucidated. METHODS: Fibroblasts were treated with ANGII, and a mouse model of MF was established by ligation of the left anterior descending coronary artery. Cell proliferation was examined by a Cell Counting Kit-8 (CCK8) assay. Dihydroethidium (DHE) was used to measure reactive oxygen species (ROS) levels, and Masson and Sirius red staining were used to examine changes in collagen fiber levels. PI3K was over-expressed by lentiviral transfection to verify the effect of emodin on the PI3K/AKT/mTOR signaling axis. Changes in cardiac function in each group were examined by echocardiography. RESULTS: Emodin significantly inhibited fibroblast proliferation, decreased intracellular ROS levels, significantly upregulated collagen II expression, downregulated α-SMA expression, and inhibited PI3K/AKT/mTOR pathway activation in vitro. Moreover, the in vivo results were consistent with the in vitro. Emodin significantly decreased ROS levels in heart tissue and reduced collagen fibrillogenesis. Emodin could regulate the activity of PI3K to increase the expression of collagen II and downregulate α-SMA expression in part through the PI3K/AKT/mTOR pathway, and emodin significantly improved cardiac structure and function in mice. CONCLUSIONS: This study revealed that emodin targeted the PI3K/AKT/mTOR pathway to inhibit the development of myocardial fibrosis and may be an antifibrotic agent for the treatment of cardiac fibrosis.

Achieving efficient interpretability of reinforcement learning via policy distillation and selective input gradient regularization.

Although deep Reinforcement Learning (RL) has proven successful in a wide range of tasks, one challenge it faces is interpretability when applied to real-world problems. Saliency maps are frequently used to provide interpretability for deep neural networks. However, in the RL domain, existing saliency map approaches are either computationally expensive and thus cannot satisfy the real-time requirement of real-world scenarios or cannot produce interpretable saliency maps for RL policies. In this work, we propose an approach of Distillation with selective Input Gradient Regularization (DIGR) which uses policy distillation and input gradient regularization to produce new policies that achieve both high interpretability and computation efficiency in generating saliency maps. Our approach is also found to improve the robustness of RL policies to multiple adversarial attacks. We conduct experiments on three tasks, MiniGrid (Fetch Object), Atari (Breakout) and CARLA Autonomous Driving, to demonstrate the importance and effectiveness of our approach.

Radiomics in Nasopharyngeal Carcinoma.

Nasopharyngeal carcinoma (NPC) is one of the most common head and neck malignancies, and the primary treatment methods are radiotherapy and chemotherapy. Radiotherapy alone, concurrent chemoradiotherapy, and induction chemotherapy combined with concurrent chemoradiotherapy can be used according to different grades. Treatment options and prognoses vary greatly depending on the grade of disease in the patients. Accurate grading and risk assessment are required. Recently, radiomics has combined a large amount of invisible high-dimensional information extracted from computed tomography, magnetic resonance imaging, or positron emission tomography with powerful computing capabilities of machine-learning algorithms, providing the possibility to achieve an accurate diagnosis and individualized treatment for cancer patients. As an effective tumor biomarker of NPC, the radiomic signature has been widely used in grading, differential diagnosis, prediction of prognosis, evaluation of treatment response, and early identification of therapeutic complications. The process of radiomic research includes image segmentation, feature extraction, feature selection, model establishment, and evaluation. Many open-source or commercial tools can be used to achieve these procedures. The development of machine-learning algorithms provides more possibilities for radiomics research. This review aimed to summarize the application of radiomics in NPC and introduce the basic process of radiomics research.

Neurorobots as a Means Toward Neuroethology and Explainable AI.

Understanding why deep neural networks and machine learning algorithms act as they do is a difficult endeavor. Neuroscientists are faced with similar problems. One way biologists address this issue is by closely observing behavior while recording neurons or manipulating brain circuits. This has been called neuroethology. In a similar way, neurorobotics can be used to explain how neural network activity leads to behavior. In real world settings, neurorobots have been shown to perform behaviors analogous to animals. Moreover, a neuroroboticist has total control over the network, and by analyzing different neural groups or studying the effect of network perturbations (e.g., simulated lesions), they may be able to explain how the robot's behavior arises from artificial brain activity. In this paper, we review neurorobot experiments by focusing on how the robot's behavior leads to a qualitative and quantitative explanation of neural activity, and vice versa, that is, how neural activity leads to behavior. We suggest that using neurorobots as a form of computational neuroethology can be a powerful methodology for understanding neuroscience, as well as for artificial intelligence and machine learning.

Neuromodulated attention and goal-driven perception in uncertain domains.

In uncertain domains, the goals are often unknown and need to be predicted by the organism or system. In this paper, contrastive Excitation Backprop (c-EB) was used in two goal-driven perception tasks - one with pairs of noisy MNIST digits and the other with a robot in an action-based attention scenario. The first task included attending to even, odd, low, and high digits, whereas the second task included action goals, such as "eat", "work-on-computer", "read", and "say-hi" that led to attention to objects associated with those actions. The system needed to increase attention to target items and decrease attention to distractor items and background noise. Because the valid goal was unknown, an online learning model based on the cholinergic and noradrenergic neuromodulatory systems was used to predict a noisy goal (expected uncertainty) and re-adapt when the goal changed (unexpected uncertainty). This neurobiologically plausible model demonstrates how neuromodulatory systems can predict goals in uncertain domains and how attentional mechanisms can enhance the perception for that goal.