Functional Neural Networks in Human Brain Organoids.

Human brain organoids are 3-dimensional brain-like tissues derived from human pluripotent stem cells and hold promising potential for modeling neurological, psychiatric, and developmental disorders. While the molecular and cellular aspects of human brain organoids have been intensively studied, their functional properties such as organoid neural networks (ONNs) are largely understudied. Here, we summarize recent research advances in understanding, characterization, and application of functional ONNs in human brain organoids. We first discuss the formation of ONNs and follow up with characterization strategies including microelectrode array (MEA) technology and calcium imaging. Moreover, we highlight recent studies utilizing ONNs to investigate neurological diseases such as Rett syndrome and Alzheimer's disease. Finally, we provide our perspectives on the future challenges and opportunities for using ONNs in basic research and translational applications.

RBNE-CMI: An Efficient Method for Predicting circRNA-miRNA Interactions via Multiattribute Incomplete Heterogeneous Network Embedding.

Circular RNA (circRNA)-microRNA (miRNA) interaction (CMI) plays crucial roles in cellular regulation, offering promising perspectives for disease diagnosis and therapy. Therefore, it is necessary to employ computational methods for the rapid and cost-effective prediction of potential circRNA-miRNA interactions. However, the existing methods are limited by incomplete data; therefore, it is difficult to model molecules with different attributes on a large scale, which greatly hinders the efficiency and performance of prediction. In this study, we propose an effective method for predicting circRNA-miRNA interactions, called RBNE-CMI, and introduce a framework that can embed incomplete multiattribute CMI heterogeneous networks. By combining the proposed method, we integrate different data sets in the CMI prediction field into one incomplete network for modeling, achieving superior performance in 5-fold cross-validation. Moreover, in the prediction task based on complete data, the proposed method still achieves better performance than the known model. In addition, in the case study, we successfully predicted 18 of the 20 potential cancer biomarkers. The data and source code can be found at https://github.com/1axin/RBNE-CMI.

Carrier-Free Nanoagent Interfering with Cancer-Associated Fibroblasts' Metabolism to Promote Tumor Penetration for Boosted Chemotherapy.

Elevated production of extracellular matrix (ECM) in tumor stroma is a critical obstacle for drug penetration. Here we demonstrate that ATP-citrate lyase (ACLY) is significantly upregulated in cancer-associated fibroblasts (CAFs) to produce tumor ECM. Using a self-assembling nanoparticle-design approach, a carrier-free nanoagent (CFNA) is fabricated by simply assembling NDI-091143, a specific ACLY inhibitor, and doxorubicin (DOX) or paclitaxel (PTX), the first-line chemotherapeutic drug, via multiple noncovalent interactions. After arriving at the CAFs-rich tumor site, NDI-091143-mediated ACLY inhibition in CAFs can block the de novo synthesis of fatty acid, thereby dampening the fatty acid-involved energy metabolic process. As the lack of enough energy, the energetic CAFs will be in a dispirited state that is unable to produce abundant ECM, thereby significantly improving drug perfusion in tumors and enhancing the efficacy of chemotherapy. Such a simple drug assembling strategy aimed at CAFs' ACLY-mediated metabolism pathway presents the feasibility of stromal matrix reduction to potentiate chemotherapy.

Ranking the attribution of high-risk genotypes among women with cervical precancers and cancers: a cross-sectional study in Ningbo, China.

BACKGROUND: The region-specific importance of carcinogenic HPV genotypes is required for optimizing HPV-based screening and promoting appropriate multivalent HPV prophylactic vaccines. This information is lacking for Ningbo, one of the first cities of China's Healthy City Innovation Pilot Program for Cervical Cancer Elimination. Here, we investigated high-risk HPV (HR-HPV) genotype-specific distribution and attribution to biopsy-confirmed cervical intraepithelial neoplasia grade 2 or worse (CIN2+) before mass vaccination in Ningbo, China. METHODS: A total of 1393 eligible CIN2+ archived blocks (including 161 CIN2, 1107 CIN3, and 125 invasive cervical cancers [ICC]) were collected from 2017 to 2020 in Ningbo. HR-HPV DNA was genotyped using the SPF10-DEIA-LiPA25 version 1 detection system and the SureX HPV 25X Genotyping Kit. Genotype-specific attribution to CIN2+ was estimated using a fractional contribution approach. RESULTS: Ranking by the attributable proportions, HPV16 remained the most important genotype in both cervical precancers and cancers, accounting for 36.8% of CIN2, 53.2% of CIN3, and 73.3% of ICC cases. Among cervical precancers, HPV52 (17.3% in CIN2, 12.7% in CIN3) and HPV58 (13.9%, 14.9%) ranked second and third, while HPV33 (8.3%, 7.9%) and HPV31 (6.5%, 4.1%) ranked fourth and fifth, respectively. However, among ICCs, HPV18 (5.7%) accounted for the second highest proportion, followed by HPV33 (5.4%), HPV58 (4.0%), and HPV45 (3.2%). HPV18/45 together accounted for 46.8% of adenocarcinomas, which was slightly lower than that of HPV16 (47.7%). The remaining HR-HPV genotypes (HPV35/39/51/56/59/66/68) combined accounted for only 6.7% of CIN2, 2.9% of CIN3, and 4.2% of ICC. CONCLUSIONS: With Ningbo's strong medical resources, it will be important to continue HPV16/18 control efforts, and could broaden to HPV31/33/45/52/58 for maximum health benefits. However, different strategies should be proposed for other HR-HPV genotypes based on their lower carcinogenic risks.

Monitoring of trace oxytetracycline using a porphyrin-MOF layer-based electrochemical aptasensor.

A two-dimensional porphyrin-MOF nanolayer was developed to construct an electrochemical aptasensor for monitoring oxytetracycline from 0.01 pg mL-1 to 0.1 ng mL-1. This aptasensor exhibited high sensitivity, outstanding selectivity, good stability, fine reproducibility, and quantitative detection ability in real samples.

Sensors and Devices Guided by Artificial Intelligence for Personalized Pain Medicine.

Personalized pain medicine aims to tailor pain treatment strategies for the specific needs and characteristics of an individual patient, holding the potential for improving treatment outcomes, reducing side effects, and enhancing patient satisfaction. Despite existing pain markers and treatments, challenges remain in understanding, detecting, and treating complex pain conditions. Here, we review recent engineering efforts in developing various sensors and devices for addressing challenges in the personalized treatment of pain. We summarize the basics of pain pathology and introduce various sensors and devices for pain monitoring, assessment, and relief. We also discuss advancements taking advantage of rapidly developing medical artificial intelligence (AI), such as AI-based analgesia devices, wearable sensors, and healthcare systems. We believe that these innovative technologies may lead to more precise and responsive personalized medicine, greatly improved patient quality of life, increased efficiency of medical systems, and reducing the incidence of addiction and substance use disorders.

Object Motion Detection Enabled by Reconfigurable Neuromorphic Vision Sensor under Ferroelectric Modulation.

Increasing the demand for object motion detection (OMD) requires shifts of reducing redundancy, heightened power efficiency, and precise programming capabilities to ensure consistency and accuracy. Drawing inspiration from object motion-sensitive ganglion cells, we propose an OMD vision sensor with a simple device structure of a WSe2 homojunction modulated by a ferroelectric copolymer. Under optical mode and intermediate ferroelectric modulation, the vision sensor can generate progressive and bidirectional photocurrents with discrete multistates under zero power consumption. This design enables reconfigurable devices to emulate long-term potentiation and depression for synaptic weights updating, which exhibit 82 states (more than 6 bits) with a uniform step of 6 pA. Such OMD devices also demonstrate nonvolatility, reversibility, symmetry, and ultrahigh linearity, achieving a fitted R2 of 0.999 and nonlinearity values of 0.01/-0.01. Thus, a vision sensor could implement motion detection by sensing only dynamic information based on the brightness difference between frames, while eliminating redundant data from static scenes. Additionally, the neural network utilizing a linear result can recognize the essential moving information with a high recognition accuracy of 96.8%. We also present the scalable potential via a uniform 3 × 3 neuromorphic vision sensor array. Our work offers a platform to achieve motion detection based on controllable and energy-efficient ferroelectric programmability.

Effects of slurry ice treatment on the physicochemical changes and proteome of large yellow croaker (Larimichthys crocea).

Large yellow croaker (Larimichthys crocea) is susceptible to oxidative denaturation during storage. This work is to investigate the quality alterations by analyzing its physicochemical changes and proteomics throughout preservation under refrigeration, frozen, and slurry ice (SI) conditions. Results revealed that the freshness of large yellow croaker, as evaluated by indicators such as total volatile basic nitrogen, total viable count, and thiobarbituric acid reactive substances, was well maintained while stored in the SI group. Meanwhile, the water distribution in the muscle tissue of group SI exhibited slower fluctuations, thereby preserving the integrity of fish muscle cells. Based on label-free proteomic analysis, a considerable downregulation was observed in the mitogen-activated protein kinase (MAPK) signaling pathway, indicating that SI decelerated this metabolic pathway and effectively delayed the deterioration of muscle. Therefore, the application of SI provides potential for maintaining the quality stability of large yellow croaker.

Vascular network-inspired diffusible scaffolds for engineering functional neural organoids.

Organoids, three-dimensional in vitro organ-like tissue cultures derived from stem cells, show promising potential for developmental biology, drug discovery, and regenerative medicine. However, the function and phenotype of current organoids, especially neural organoids, are still limited by insufficient diffusion of oxygen, nutrients, metabolites, signaling molecules, and drugs. Herein, we present Vascular network-Inspired Diffusible (VID) scaffolds to fully recapture the benefits of physiological diffusion physics for generating functional organoids and phenotyping their drug response. In a proof-of-concept application, the VID scaffolds, 3D-printed meshed tubular channel networks, support the successful generation of engineered human midbrain organoids almost without necrosis and hypoxia in commonly used well-plates. Compared to conventional organoids, these engineered organoids develop with more physiologically relevant features and functions including midbrain-specific identity, oxygen metabolism, neuronal maturation, and network activity. Moreover, these engineered organoids also better recapitulate pharmacological responses, such as neural activity changes to fentanyl exposure, compared to conventional organoids with significant diffusion limits. Combining these unique scaffolds and engineered organoids may provide insights for organoid development and therapeutic innovation.

Evaluating the Efficacy of Wrist-Ankle Acupuncture Combined With Patient-Controlled Intravenous Analgesia for Managing Post Uvulopalatopharyngoplasty.

OBJECTIVE: The aim of this study is to investigate the impact of combining wrist-ankle acupuncture with patient-controlled intravenous analgesia (PCIA) on active pain and food intake in patients with obstructive sleep apnea-hypopnea syndrome (OSAHS) after undergoing uvulopalatopharyngoplasty (UPPP). METHODS: Sixty patients with OSAHS who underwent UPPP at our hospital's Department of Otorhinolaryngology from January 2020 to October 2023 were selected and randomly divided into 2 groups of 30 each: an observation group and a control group. The control group received general anesthesia administered by an anesthesiologist and used a PCIA pump. In addition to this treatment, the observation group received the combined intervention of wrist-ankle acupuncture. Active pain levels were monitored at 0, 6, 12, 24, 36, and 48 hours after UPPP, and food intake was observed at 24, 36, and 48 hours postoperation. The results were compared and recorded for both groups. RESULTS: The analgesic effect on active pain in the observation group was significantly greater than in the control group at 6, 12, 24, 36, and 48 hours postoperation, and the differences were statistically significant (P<0.05). In addition, when comparing food intake scores at 24, 36, and 48 hours postoperation, the observation group had significantly higher food intake than the control group, and the differences were statistically significant (P<0.05). CONCLUSIONS: The combined intervention of wrist-ankle acupuncture and PCIA provides effective pain relief for OSAHS patients after UPPP, enhances their food intake, improves their quality of life, and supports early recovery.

Colonoscopy Training on Virtual-Reality Simulators or Physical Model Simulators: A Randomized Controlled Trial.

OBJECTIVE: This study employed a randomized controlled trial to assess the efficacy of virtual-reality (VR) simulators and physical model simulators on colonoscopy training to explore the optimal and evidence-based simulation training. DESIGN: Forty participants were divided into 2 groups and randomized as dyads: the VR simulator group and the physical model simulator group. All the participants performed a baseline test through porcine colonoscopy. After a 6 h simulation training, each participant underwent a post-test on a pig after bowel preparation, and the procedures were video-recorded. Both the baseline test and the post-test were blindly assessed by 2 experienced assistant director physicians based on the GAGES-C scoring system. SETTING: Simulation center, Renji Hospital, Shanghai Jiaotong University School of Medicine, Shanghai. PARTICIPANTS: Forty surgical residents without colonoscopy experience. RESULTS: Both the VR simulator group and the physical model simulator group improved significantly over the baseline test. The VR simulator group performed significantly better than the physical model simulator group, p=0.042. The participants in both groups expressed a high level of simulator satisfaction. CONCLUSIONS: Novice residents can benefit from both VR simulators and physical model simulators. The VR simulator was shown to be more effective for colonoscopy training. VR simulators were more recommended for novices conducting basic colonoscopy training.

Facile synthesis of hierarchical core-shell carbon@ZnIn2S4 composite for boosted photothermal-assisted photocatalytic H2 production.

Against the backdrop of energy shortage, hydrogen energy has attracted much attention as a green and clean energy source. In order to explore efficient hydrogen production pathways, we designed a composite photocatalyst with carbon-based core-shell photothermal-assisted photocatalytic system (Carbon@ZnIn2S4, denoted as C@ZIS). The well-designed catalyst C@ZIS composites demonstrated a photocatalytic hydrogen precipitation rate of 2.97 mmol g-1 h-1 even in the absence of the noble metal Pt co-catalyst. The incorporation of carbon-based core-shell photocatalysts into a photocatalytic reaction significantly affects the activity of the reaction by triggering a photothermal effect in the reaction solution. The results of the physicochemical experiments demonstrated that the carbon spheres in C@ZIS composite system could provide a greater number of active sites, thereby accelerating the electron transfer and separation efficiency, and thus enhancing the photocatalytic activity. The study presents an efficacious design concept for the development of efficacious carbon-based core-shell photothermal-assisted photocatalysts, which is anticipated to facilitate the efficient conversion of solar energy to hydrogen energy.

Human brain organoids for understanding substance use disorders.

Substance use disorders (SUDs) are complex mental health conditions involving a problematic pattern of substance use. Challenges remain in understanding their neural mechanisms, which are likely to lead to improved SUD treatments. Human brain organoids, brain-like 3D in vitro cultures derived from human stem cells, show unique potential in recapitulating the response of a developing human brain to substances. Here, we review the recent progress in understanding SUDs using human brain organoid models focusing on neurodevelopmental perspectives. We first summarize the background of SUDs in humans. Moreover, we introduce the development of various human brain organoid models and then discuss current progress and findings underlying the abuse of substances like nicotine, alcohol, and other addictive drugs using organoid models. Furthermore, we review efforts to develop organ chips and microphysiological systems to engineer better human brain organoids for advancing SUD studies. Lastly, we conclude by elaborating on the current challenges and future directions of SUD studies using human brain organoids.

Successful treatment of hemophagocytic intravascular large B-cell lymphoma with CNS involvement with BTK inhibitor combined with rituximab and high-dose methotrexate.

This is a case of hemophagocytic intravascular large B-cell lymphoma (IVLBCL) with central nervous system (CNS) involvement. Although R-CHOP chemotherapy regimen has been shown significant improvement in survival rate. The prognosis and outcomes remain unsatisfactory, which is identified as outstanding challenges and need solutions. Gene and molecular profiling studies may provide new therapeutic strategies, especially the BCR/TLR/IL-1R/NF-κB signaling pathway in IVLBCL. Here, we treated the hemophagocytic IVLBCL CNS-involved patient with the Bruton tyrosine kinase inhibitor (BTKi) to block NF-κB pathway, and indicated that the second-generation BTKi zanubrutinib-based treatment was feasible and efficient.

Multiscale dynamically parallel shrinkage network for fault diagnosis of aviation hydraulic pump and its generalizable applications.

Aiming to address the multiscale characteristics and noise corruption problems in the vibration signals of aviation hydraulic pumps, this article develops a novel Multiscale Dynamically Parallel Shrinkage Network (MDPSN) to learn complementary and rich fault-related multiscale features, with the ultimate goal of yielding higher diagnostic accuracy. One significant property is the development of a novel dynamically parallel shrinking module (DPSM) that adaptively generates independent soft thresholds for different scales, effectively shrinking noise-related features to zeros. On one hand, DPSM aggregates and interacts with features at all scales to construct a global feature representation containing richer fault-related information, which is served as the foundation for soft thresholding generation, significantly improving the accuracy and rationality of the generated thresholds. On the other hand, DPSM can adaptively generate individual soft threshold for each scale, allowing each scale to use an independent threshold tailored to its own characteristic to eliminate noise-related information. This avoids the issues of over-denoising or under-denoising caused by the uniform application of thresholds across all scales. Finally, the effectiveness of MDPSN is validated by a series of experiment comparisons on an aviation hydraulic pump dataset and two bearing datasets with various types of noise. The experimental results demonstrate that MDPSN achieves superior diagnostic accuracy compared to five other comparison methods.

Multi-Objectives Optimization of Plastic Injection Molding Process Parameters Based on Numerical DNN-GA-MCS Strategy.

An intelligent optimization technique has been presented to enhance the multiple structural performance of PA6-20CF carbon fiber-reinforced polymer (CFRP) plastic injection molding (PIM) products. This approach integrates a deep neural network (DNN), Non-dominated Sorting Genetic Algorithm II (NSGA-II), and Monte Carlo simulation (MCS), collectively referred to as the DNN-GA-MCS strategy. The main objective is to ascertain complex process parameters while elucidating the intrinsic relationships between processing methods and material properties. To realize this, a numerical study on the PIM structural performance of an automotive front engine hood panel was conducted, considering fiber orientation tensor (FOT), warpage, and equivalent plastic strain (PEEQ). The mold temperature, melt temperature, packing pressure, packing time, injection time, cooling temperature, and cooling time were employed as design variables. Subsequently, multiple objective optimizations of the molding process parameters were employed by GA. The utilization of Z-score normalization metrics provided a robust framework for evaluating the comprehensive objective function. The numerical target response in PIM is extremely intricate, but the stability offered by the DNN-GA-MCS strategy ensures precision for accurate results. The enhancement effect of global and local multi-objectives on the molded polymer-metal hybrid (PMH) front hood panel was verified, and the numerical results showed that this strategy can quickly and accurately select the optimal process parameter settings. Compared with the training set mean value, the objectives were increased by 8.63%, 6.61%, and 9.75%, respectively. Compared to the full AA 5083 hood panel scenario, our design reduces weight by 16.67%, and achievements of 92.54%, 93.75%, and 106.85% were obtained in lateral, longitudinal, and torsional strain energy, respectively. In summary, our proposed methodology demonstrates considerable potential in improving the, highlighting its significant impact on the optimization of structural performance.

The role of metabolic memory in diabetic kidney disease: identification of key genes and therapeutic targets.

Diabetic kidney disease (DKD) is characterized by complex pathogenesis and poor prognosis; therefore, an exploration of novel etiological factors may be beneficial. Despite glycemic control, the persistence of transient hyperglycemia still induces vascular complications due to metabolic memory. However, its contribution to DKD remains unclear. Using single-cell RNA sequencing data from the Gene Expression Omnibus (GEO) database, we clustered 12 cell types and employed enrichment analysis and a cell‒cell communication network. Fibrosis, a characteristic of DKD, was found to be associated with metabolic memory. To further identify genes related to metabolic memory and fibrosis in DKD, we combined the above datasets from humans with a rat renal fibrosis model and mouse models of metabolic memory. After overlapping, NDRG1, NR4A1, KCNC4 and ZFP36 were selected. Pharmacology analysis and molecular docking revealed that pioglitazone and resveratrol were possible agents affecting these hub genes. Based on the ex vivo results, NDRG1 was selected for further study. Knockdown of NDRG1 reduced TGF-ß expression in human kidney-2 cells (HK-2 cells). Compared to that in patients who had diabetes for more than 10 years but not DKD, NDRG1 expression in blood samples was upregulated in DKD patients. In summary, NDRG1 is a key gene involved in regulating fibrosis in DKD from a metabolic memory perspective. Bioinformatics analysis combined with experimental validation provided reliable evidence for identifying metabolic memory in DKD patients.

Early Radiographic and Clinical Outcomes of Robotic-arm-assisted versus Conventional Total Knee Arthroplasty: A Multicenter Randomized Controlled Trial.

OBJECTIVE: A robotic system was recently introduced to improve prosthetic alignment during total knee arthroplasty (TKA). The purpose of this multicenter, prospective, randomized controlled trial (RCT) was to determine whether robotic-arm-assisted TKA improves clinical and radiological outcomes when compared to conventional TKA. METHODS: One hundred and thirty patients who underwent primary TKA were enrolled in this prospective, randomized controlled trial, which was conducted at three hospitals. Five patients were lost to follow-up 6 weeks after surgery. Therefore, 125 participants (63 in the intervention group and 62 in the control group) remained in the final analysis. The primary outcome was the rate at which the mechanical axis of the femur deviated by less than 3° from the mechanical axis of the tibia. This was evaluated by full-length weight-bearing X-rays of the lower limb 6 weeks postoperatively. Secondary outcomes included operation times, 6-week postoperative functional outcomes evaluated by the American Knee Society score (KSS) and the Western Ontario and McMaster Universities osteoarthritis index (WOMAC), short form-36 (SF-36) health survey results, and the occurrence of adverse events (AEs) and serious adverse events (SAEs). RESULTS: At 6 weeks postoperatively, we found that the rate of radiographic inliers was significantly higher in the intervention group (78.7% vs 51.6%; p = 0.00; 95% confidence interval, 10.9% to 43.2%). The operation was significantly longer in the intervention group than in the control group (119.5 vs 85.0 min; p = 0.00). There were no significant differences in the 6-week postoperative functional outcomes, SF-36, AEs, and SAEs between the two groups. There were no AEs or SAEs that were determined to be "positively related" to the robotic system. CONCLUSION: Robotic-arm-assisted TKA is safe and effective, as demonstrated in this trial.