Computationally guided AAV engineering for enhanced gene delivery.

Gene delivery vehicles based on adeno-associated viruses (AAVs) are enabling increasing success in human clinical trials, and they offer the promise of treating a broad spectrum of both genetic and non-genetic disorders. However, delivery efficiency and targeting must be improved to enable safe and effective therapies. In recent years, considerable effort has been invested in creating AAV variants with improved delivery, and computational approaches have been increasingly harnessed for AAV engineering. In this review, we discuss how computationally designed AAV libraries are enabling directed evolution. Specifically, we highlight approaches that harness sequences outputted by next-generation sequencing (NGS) coupled with machine learning (ML) to generate new functional AAV capsids and related regulatory elements, pushing the frontier of what vector engineering and gene therapy may achieve.

Cancer Pain Management with Traditional Chinese Medicine: Current Status and Future Perspectives.

Cancer pain, especially the moderate-to-severe pain experienced by patients with advanced cancer, is still one of the most challenging clinical problems. The current mainstream pharmacological treatment for cancer pain involves applying opioid medications and other pain-killing drugs. However, analgesic drugs have many adverse effects such as addiction, tolerance, and other formidable clinical and social issues. Thus, finding a new therapeutic approach to treat cancer pain is essential. Traditional Chinese medicine (TCM) has been increasingly applied in clinical practice because of its good efficacy and few side effects. However, its mechanisms of action in treating pain are still under investigation. The most important mechanism of cancer pain is that a large amount of pain-causing substances are secreted from cancer cells and promote their growth and invasion. The physical and chemical stimulations of these substances exist along with the cancer growth, leading to constantly increased pain sensation. Whether cancer pain can be alleviated by inhibiting cancer cells from releasing the substances and changing the microenvironment around the cancer mass, or even by eliminating pain-causing substances, is largely unknown. Based on TCM theory, this study reported that the aforementioned approach could effectively manage different cancer pains by tonifying qi, clearing and activating channels and meridians, and strengthening body resistance. The TCM therapies activate blood circulation, remove blood stasis, and nourish the heart. Commonly used Chinese herbal drugs include Corydalis yanhusuo, Angelica dahurica, and Ligusticum chuanxiong. Instead of using conventional analgesics to reduce pain, we should focus on using TCM modalities to alleviate cancer pain and increase the quality of life in patients suffering from cancer pain. TCM should provide us with a new strategy for managing cancer pain.

Macrophage heterogeneity in bone metastasis.

Previous studies illustrated that macrophage, a type of innate immune cell, plays critical roles in tumour progression and metastasis. Bone is the most frequent site of metastasis for several cancer types including breast, prostate, and lung. In bone metastasis, osteoclast, a macrophage subset specialized in bone resorption, was heavily investigated in the past. Recent studies illustrated that other macrophage subsets, e.g. monocyte-derived macrophages, and bone resident macrophages, promoted bone metastasis independent of osteoclast function. These novel mechanisms further improved our understanding of macrophage heterogeneity in the context of bone metastasis and illustrated new opportunities for future studies.

Protocol for near-infrared optogenetics manipulation of neurons and motor behavior in C. elegans using emissive upconversion nanoparticles.

In deep tissue, optogenetics faces limitations with visible light. Here, we present a protocol for near-infrared (NIR) optogenetics manipulation of neurons and motor behavior in Caenorhabditis elegans using emissive upconversion nanoparticles (UCNPs). We describe steps for synthesizing and modifying UCNPs. We then detail procedures for regulating neurons using these UCNPs in the model organism C. elegans. Using NIR light allows for superior tissue penetration to manipulate neuronal activities and locomotion behavior. For complete details on the use and execution of this protocol, please refer to Guo et al.,1 Ao et al.,2 and Zhang et al.3.

Substrate mechanics unveil early structural and functional pathology in iPSC micro-tissue models of hypertrophic cardiomyopathy.

Hypertension is a major cause of morbidity and mortality in patients with hypertrophic cardiomyopathy (HCM), suggesting a potential role for mechanics in HCM pathogenesis. Here, we developed an in vitro physiological model to investigate how mechanics acts together with HCM-linked myosin binding protein C (MYBPC3) mutations to trigger disease. Micro-heart muscles (µHM) were engineered from induced pluripotent stem cell (iPSC)-derived cardiomyocytes bearing MYBPC3+/- mutations and challenged to contract against substrates of different elasticity. µHMs that worked against substrates with stiffness at or exceeding the stiffness of healthy adult heart muscle exhibited several hallmarks of HCM, including cellular hypertrophy, impaired contractile energetics, and maladaptive calcium handling. Remarkably, we discovered changes in troponin C and T localization in MYBPC3+/- µHM that were entirely absent in 2D culture. Pharmacologic studies suggested that excessive Ca2+ intake through membrane-embedded channels underlie the observed electrophysiological abnormalities. These results illustrate the power of physiologically relevant engineered tissue models to study inherited disease with iPSC technology.

Engineered tissue geometry and Plakophilin-2 regulate electrophysiology of human iPSC-derived cardiomyocytes.

Engineered heart tissues have been created to study cardiac biology and disease in a setting that more closely mimics in vivo heart muscle than 2D monolayer culture. Previously published studies suggest that geometrically anisotropic micro-environments are crucial for inducing "in vivo like" physiology from immature cardiomyocytes. We hypothesized that the degree of cardiomyocyte alignment and prestress within engineered tissues is regulated by tissue geometry and, subsequently, drives electrophysiological development. Thus, we studied the effects of tissue geometry on electrophysiology of micro-heart muscle arrays (µHM) engineered from human induced pluripotent stem cells (iPSCs). Elongated tissue geometries elicited cardiomyocyte shape and electrophysiology changes led to adaptations that yielded increased calcium intake during each contraction cycle. Strikingly, pharmacologic studies revealed that a threshold of prestress and/or cellular alignment is required for sodium channel function, whereas L-type calcium and rapidly rectifying potassium channels were largely insensitive to these changes. Concurrently, tissue elongation upregulated sodium channel (NaV1.5) and gap junction (Connexin 43, Cx43) protein expression. Based on these observations, we leveraged elongated µHM to study the impact of loss-of-function mutation in Plakophilin 2 (PKP2), a desmosome protein implicated in arrhythmogenic disease. Within µHM, PKP2 knockout cardiomyocytes had cellular morphology similar to what was observed in isogenic controls. However, PKP2-/- tissues exhibited lower conduction velocity and no functional sodium current. PKP2 knockout µHM exhibited geometrically linked upregulation of sodium channel but not Cx43, suggesting that post-translational mechanisms, including a lack of ion channel-gap junction communication, may underlie the lower conduction velocity observed in tissues harboring this genetic defect. Altogether, these observations demonstrate that simple, scalable micro-tissue systems can provide the physiologic stresses necessary to induce electrical remodeling of iPS-CM to enable studies on the electrophysiologic consequences of disease-associated genomic variants.

Fully Automatic Deep Learning Model for Spine Refracture in Patients with OVCF: A Multi-Center Study.

BACKGROUND: The reaserch of artificial intelligence (AI) model for predicting spinal refracture is limited to bone mineral density, X-ray and some conventional laboratory indicators, which has its own limitations. Besides, it lacks specific indicators related to osteoporosis and imaging factors that can better reflect bone quality, such as computed tomography (CT). OBJECTIVE: To construct a novel predicting model based on bone turn-over markers and CT to identify patients who were more inclined to suffer spine refracture. METHODS: CT images and clinical information of 383 patients (training set = 240 cases of osteoporotic vertebral compression fractures (OVCF), validation set = 63, test set = 80) were retrospectively collected from January 2015 to October 2022 at three medical centers. The U-net model was adopted to automatically segment ROI. Three-dimensional (3D) cropping of all spine regions was used to achieve the final ROI regions including 3D_Full and 3D_RoiOnly. We used the Densenet 121-3D model to model the cropped region and simultaneously build a T-NIPT prediction model. Diagnostics of deep learning models were assessed by constructing ROC curves. We generated calibration curves to assess the calibration performance. Additionally, decision curve analysis (DCA) was used to assess the clinical utility of the predictive models. RESULTS: The performance of the test model is comparable to its performance on the training set (dice coefficients of 0.798, an mIOU of 0.755, an SA of 0.767, and an OS of 0.017). Univariable and multivariable analysis indicate that T_P1NT was an independent risk factor for refracture. The performance of predicting refractures in different ROI regions showed that 3D_Full model exhibits the highest calibration performance, with a Hosmer-Lemeshow goodness-of-fit (HL) test statistic exceeding 0.05. The analysis of the training and test sets showed that the 3D_Full model, which integrates clinical and deep learning results, demonstrated superior performance with significant improvement (p-value < 0.05) compared to using clinical features independently or using only 3D_RoiOnly. CONCLUSION: T_P1NT was an independent risk factor of refracture. Our 3D-FULL model showed better performance in predicting high-risk population of spine refracture than other models and junior doctors do. This model can be applicable to real-world translation due to its automatic segmentation and detection.