A biodegradable and flexible neural interface for transdermal optoelectronic modulation and regeneration of peripheral nerves.

Optoelectronic neural interfaces can leverage the photovoltaic effect to convert light into electrical current, inducing charge redistribution and enabling nerve stimulation. This method offers a non-genetic and remote approach for neuromodulation. Developing biodegradable and efficient optoelectronic neural interfaces is important for achieving transdermal stimulation while minimizing infection risks associated with device retrieval, thereby maximizing therapeutic outcomes. We propose a biodegradable, flexible, and miniaturized silicon-based neural interface capable of transdermal optoelectronic stimulation for neural modulation and nerve regeneration. Enhancing the device interface with thin-film molybdenum significantly improves the efficacy of neural stimulation. Our study demonstrates successful activation of the sciatic nerve in rodents and the facial nerve in rabbits. Moreover, transdermal optoelectronic stimulation accelerates the functional recovery of injured facial nerves.

MRI- and DWI-Based Radiomics Features for Preoperatively Predicting Meningioma Sinus Invasion.

The aim of this study was to use multimodal imaging (contrast-enhanced T1-weighted (T1C), T2-weighted (T2), and diffusion-weighted imaging (DWI)) to develop a radiomics model for preoperatively predicting venous sinus invasion in meningiomas. This prediction would assist in selecting the appropriate surgical approach and forecasting the prognosis of meningiomas. A retrospective analysis was conducted on 331 participants who had been pathologically diagnosed with meningiomas. For each participant, 3948 radiomics features were acquired from the T1C, T2, and DWI images. Minimum redundancy maximum correlation, rank sum test, and multi-factor recursive elimination were used to extract the most significant features of different models. Then, multivariate logistic regression was used to build classification models to predict meningioma venous sinus invasion. The diagnostic capabilities were assessed using receiver operating characteristic (ROC) analysis. In addition, a nomogram was constructed by incorporating clinical and radiological characteristics and a radiomics signature. To assess the clinical usefulness of the nomogram, a decision curve analysis (DCA) was performed. Tumor shape, boundary, and enhancement features were independent predictors of meningioma venous sinus invasion (p = 0.013, p = 0.013, p = 0.005, respectively). Eleven (T2:1, T1C:4, DWI:6) of the 3948 radiomics features were screened for strong association with meningioma sinus invasion. The areas under the ROC curves for the training and external test sets were 0.946 and 0.874, respectively. The clinicoradiomic model showed excellent predictive performance for invasive meningioma, which may help to guide surgical approaches and predict prognosis.

Complete Restoration of Hearing Loss and Cochlear Synaptopathy via Minimally Invasive, Single-Dose, and Controllable Middle Ear Delivery of Brain-Derived Neurotrophic Factor-Poly(dl-lactic acid-co-glycolic acid)-Loaded Hydrogel.

Noise-induced hearing loss (NIHL) often accompanies cochlear synaptopathy, which can be potentially reversed to restore hearing. However, there has been little success in achieving complete recovery of sensorineural deafness using nearly noninvasive middle ear drug delivery before. Here, we present a study demonstrating the efficacy of a middle ear delivery system employing brain-derived neurotrophic factor (BDNF)-poly-(dl-lactic acid-co-glycolic acid) (PLGA)-loaded hydrogel in reversing synaptopathy and restoring hearing function in a mouse model with NIHL. The mouse model achieved using the single noise exposure (NE, 115 dBL, 4 h) exhibited an average 20 dBL elevation of hearing thresholds with intact cochlear hair cells but a loss of ribbon synapses as the primary cause of hearing impairment. We developed a BDNF-PLGA-loaded thermosensitive hydrogel, which was administered via a single controllable injection into the tympanic cavity of noise-exposed mice, allowing its presence in the middle ear for a duration of 2 weeks. This intervention resulted in complete restoration of NIHL at frequencies of click, 4, 8, 16, and 32 kHz. Moreover, the cochlear ribbon synapses exhibited significant recovery, whereas other cochlear components (hair cells and auditory nerves) remained unchanged. Additionally, the cochlea of NE treated mice revealed activation of tropomyosin receptor kinase B (TRKB) signaling upon exposure to BDNF. These findings demonstrate a controllable and minimally invasive therapeutic approach that utilizes a BDNF-PLGA-loaded hydrogel to restore NIHL by specifically repairing cochlear synaptopathy. This tailored middle ear delivery system holds great promise for achieving ideal clinical outcomes in the treatment of NIHL and cochlear synaptopathy.

Graph Convolution and Self-attention Enhanced CNN with Domain Adaptation for Multi-site COVID-19 Diagnosis.

In 2019, coronavirus disease (COVID-19) is an acute disease that can rapidly develop into a very serious state. Therefore, it is of great significance to realize automatic COVID-19 diagnosis. However, due to the small difference in the characteristics of computed tomography (CT) between community acquire pneumonia (CP) and COVID-19, the existing model is unsuitable for the three-class classifications of healthy control, CP and COVID-19. The current model rarely optimizes the data from multiple centers. Therefore, we propose a diagnosis model for COVID-19 patients based on graph enhanced 3D convolution neural network (CNN) and cross-center domain feature adaptation. Specifically, we first design a 3D CNN with graph convolution module to enhance the global feature extraction capability of the CNN. Meanwhile, we use the domain adaptive feature alignment method to optimize the feature distance between different centers, which can effectively realize multi-center COVID-19 diagnosis. Our experimental results achieve quite promising COVID-19 diagnosis results, which show that the accuracy in the mixed dataset is 98.05%, and the accuracy in cross-center tasks are 85.29% and 87.53%.

Nodal properties of the resting-state brain functional network in childhood and adolescence.

BACKGROUND AND PURPOSE: Changes in the topological properties of brain functional network nodes during childhood and adolescence can provide more detailed and intuitive information on the rules of brain development. This study aims to explore the characteristics of nodal attributes in child and adolescent brain functional networks and analyze the correlation between nodal attributes in different brain regions and age. METHODS: Forty-two healthy volunteers aged 6-18 years who were right-handed primary and middle school students were recruited, and the subgroup analysis included children (6-12 years, n = 19) and adolescents (13-18 years, n = 23). Resting-state functional magnetic resonance imaging data were collected using a 3.0 Tesla MRI scanner. The topological properties of the functional brain network were analyzed using graph theory. RESULTS: Compared with the children group, the degree centrality and nodal efficiency of multiple brain regions in the adolescent group were significantly increased, and the nodal shortest path was reduced (q<0.05, false discovery rate corrected). These brain regions were widely distributed in the whole brain and significantly correlated with age. Compared with the children group, reduced degree centralities were observed in the left dorsolateral fusiform gyrus, left rostral cuneus gyrus, and right medial superior occipital gyrus. CONCLUSION: The transmission efficiency of the brain's core network gradually increased, and the subnetwork function gradually improved in children and adolescents with age. The functional development of each brain area in the occipital visual cortex was uneven and there was functional differentiation within the occipital visual cortex.

A Biodegradable, Adhesive, and Stretchable Hydrogel and Potential Applications for Allergic Rhinitis and Epistaxis.

Bioadhesive hydrogels have attracted considerable attention as innovative materials in medical interventions and human-machine interface engineering. Despite significant advances in their application, it remains critical to develop adhesive hydrogels that meet the requirements for biocompatibility, biodegradability, long-term strong adhesion, and efficient drug delivery vehicles in moist conditions. A biocompatible, biodegradable, soft, and stretchable hydrogel made from a combination of a biopolymer (unmodified natural gelatin) and stretchable biodegradable poly(ethylene glycol) diacrylate is proposed to achieve durable and tough adhesion and explore its use for convenient and effective intranasal hemostasis and drug administration. Desirable hemostasis efficacy and enhanced therapeutic outcomes for allergic rhinitis are accomplished. Biodegradation enables the spontaneous removal of materials without causing secondary damage and minimizes medical waste. Preliminary trials on human subjects provide an essential foundation for practical applications. This work elucidates material strategies for biodegradable adhesive hydrogels, which are critical to achieving robust material interfaces and advanced drug delivery platforms for novel clinical treatments.

A radiomics model enables prediction venous sinus invasion in meningioma.

OBJECTIVE: Preoperative prediction of meningioma venous sinus invasion would facilitate the selection of surgical approaches and predicting the prognosis. To predict venous sinus invasion in meningiomas, we used radiomic signatures to construct a model based on preoperative contrast-enhanced T1-weighted (T1C) and T2-weighted (T2) magnetic resonance imaging. METHODS: In total, 599 patients with pathologically confirmed meningioma were retrospectively enrolled. For each patient enrolled in this study, 1595 radiomic signatures were extracted from T1C and T2 image sequences. Pearson correlation analysis and recursive feature elimination were used to select the most relevant signatures extracted from different image sequences, and logistic regression algorithms were used to build a radiomic model for risk prediction of meningioma sinus invasion. Furthermore, a nomogram was built by incorporating clinical characteristics and radiomic signatures, and a decision curve analysis was used to evaluate the clinical utility of the nomogram. RESULTS: Twenty radiomic signatures that were significantly related to venous sinus invasion were screened from 3190 radiomic signatures. Venous sinus invasion was associated with tumor position, and the clinicoradiomic model that incorporated the above characteristics (20 radiomic signatures and tumor position) had the best discriminating ability. The areas under the curve for the training and validation cohorts were 0.857 (95% confidence interval [CI], 0.824-0.890) and 0.824 (95% CI, 0.752-0.8976), respectively. INTERPRETATION: The clinicoradiomic model had good predictive performance for venous sinus invasion in meningioma, which can aid in devising surgical strategies and predicting prognosis.

GIONet: Global information optimized network for multi-center COVID-19 diagnosis via COVID-GAN and domain adversarial strategy.

The outbreak of coronavirus disease (COVID-19) in 2019 has highlighted the need for automatic diagnosis of the disease, which can develop rapidly into a severe condition. Nevertheless, distinguishing between COVID-19 pneumonia and community-acquired pneumonia (CAP) through computed tomography scans can be challenging due to their similar characteristics. The existing methods often perform poorly in the 3-class classification task of healthy, CAP, and COVID-19 pneumonia, and they have poor ability to handle the heterogeneity of multi-centers data. To address these challenges, we design a COVID-19 classification model using global information optimized network (GIONet) and cross-centers domain adversarial learning strategy. Our approach includes proposing a 3D convolutional neural network with graph enhanced aggregation unit and multi-scale self-attention fusion unit to improve the global feature extraction capability. We also verified that domain adversarial training can effectively reduce feature distance between different centers to address the heterogeneity of multi-center data, and used specialized generative adversarial networks to balance data distribution and improve diagnostic performance. Our experiments demonstrate satisfying diagnosis results, with a mixed dataset accuracy of 99.17% and cross-centers task accuracies of 86.73% and 89.61%.

Fully Biodegradable and Long-Term Operational Primary Zinc Batteries as Power Sources for Electronic Medicine.

Given the advantages of high energy density and easy deployment, biodegradable primary battery systems remain as a promising power source to achieve bioresorbable electronic medicine, eliminating secondary surgeries for device retrieval. However, currently available biobatteries are constrained by operational lifetime, biocompatibility, and biodegradability, limiting potential therapeutic outcomes as temporary implants. Herein, we propose a fully biodegradable primary zinc-molybdenum (Zn-Mo) battery with a prolonged functional lifetime of up to 19 days and desirable energy capacity and output voltage compared with reported primary Zn biobatteries. The Zn-Mo battery system is shown to have excellent biocompatibility and biodegradability and can significantly promote Schwann cell proliferation and the axonal growth of dorsal root ganglia. The biodegradable battery module with 4 Zn-Mo cells in series using gelatin electrolyte accomplishes electrochemical generation of signaling molecules (nitric oxide, NO) that can modulate the behavior of the cellular network, with efficacy comparable with that of conventional power sources. This work sheds light on materials strategies and fabrication schemes to develop high-performance biodegradable primary batteries to achieve a fully bioresorbable electronic platform for innovative medical treatments that could be beneficial for health care.

Cryptococcal Osteomyelitis of the Left Acetabulum: A Case Report.

BACKGROUND: Cryptococcus, as a classical "opportunistic" fungal pathogen, is capable of disseminating an invasive infection in immunocompromised hosts. The primary sites of infection include the respiratory and central nervous systems, and skeletal infection was rarely reported. In this case, we describe a case of cryptococcal osteomyelitis involving the left side of the acetabulum in a Chinese patient with chronic hepatitis B. CASE PRESENTATION: We retrospectively reviewed the case of a female (with chronic hepatitis B) with left acetabulum pain and limited mobility, with fever occurring during the infection who presented to the Fifth Affiliated Hospital of Zunyi Medical University. Upon imaging, we found osteolytic bone destruction in the left acetabulum with inflammatory changes in the surrounding bone and soft tissue, accompanied by abscess formation. Following an 11-month of antifungal therapy, the clinical symptoms improved and the lesion area reduced in size. In addition, there was no sign of recurrence. CONCLUSION: Cryptococcus infections should be considered in the differential diagnosis of infectious osteolytic bone lesions, particularly when patients with immune insufficiency. Pathological examinations and fungal cultures are essential to provide a differential diagnosis.

Focal Lipogranuloma Inflammation: A Case Report of the Lung Origin.

INTRODUCTION: Limited experience exists regarding the clinical features of focal lipogranuloma inflammation of the lung, and there are no supporting guidelines for the diagnostic approach. CASE DESCRIPTION: We present the case of a 20-year-old man who was found to have a pulmonary nodule with a smooth margin during a health examination. He underwent wedge resection of the left lower lung to achieve a tissue diagnosis; this showed focal lipogranuloma inflammation of the lung. CONCLUSION: In this report, we combined imaging and pathological findings to reach the diagnosis, with a view to assisting clinicians in navigating this challenging clinical situation.

Nomogram based on MRI can preoperatively predict brain invasion in meningioma.

Predicting brain invasion preoperatively should help to guide surgical decision-making and aid the prediction of meningioma grading and prognosis. However, only a few imaging features have been identified to aid prediction. This study aimed to develop and validate an MRI-based nomogram to predict brain invasion by meningioma. In this retrospective study, 658 patients were examined via routine MRI before undergoing surgery and were diagnosed with meningioma by histopathology. Least absolute shrinkage and selection operator (LASSO) regularization was used to determine the optimal combination of clinical characteristics and MRI features for predicting brain invasion by meningiomas. Logistic regression and receiver operating characteristic (ROC) curve analyses were used to determine the discriminatory ability. Furthermore, a nomogram was constructed using the optimal MRI features, and decision curve analysis was used to validate the clinical usefulness of the nomogram. Eighty-one patients with brain invasion and 577 patients without invasion were enrolled. According to LASSO regularization, tumour shape, tumour boundary, peritumoral oedema, and maximum diameter were independent predictors of brain invasion. The model showed good discriminatory ability for predicting brain invasion in meningiomas, with an AUC of 0.905 (95% CI, 0.871-0.940) vs 0.898 (95% CI, 0.849-0.947) and sensitivity of 93.0% vs 92.6% in the training vs validation cohorts. Our predictive model based on MRI features showed good performance and high sensitivity for predicting the risk of brain invasion in meningiomas and can be applied in the clinical setting.

A flexible and highly sensitive organic electrochemical transistor-based biosensor for continuous and wireless nitric oxide detection.

As nitric oxide (NO) plays significant roles in a variety of physiological processes, the capability for real-time and accurate detection of NO in live organisms is in great demand. Traditional assessments of NO rely on indirect colorimetric techniques or electrochemical sensors that often comprise rigid constituent materials and can hardly satisfy sensitivity and spatial resolution simultaneously. Here, we report a flexible and highly sensitive biosensor based on organic electrochemical transistors (OECTs) capable of continuous and wireless detection of NO in biological systems. By modifying the geometry of the active channel and the gate electrodes of OECTs, devices achieve optimum signal amplification of NO. The sensor exhibits a low response limit, a wide linear range, high sensitivity, and excellent selectivity, with a miniaturized active sensing region compared with a conventional electrochemical sensor. The device demonstrates continuous detection of the nanomolar range of NO in cultured cells for hours without significant signal drift. Real-time and wireless measurement of NO is accomplished for 8 d in the articular cavity of New Zealand White rabbits with anterior cruciate ligament (ACL) rupture injuries. The observed high level of NO is associated with the onset of osteoarthritis (OA) at the later stage. The proposed device platform could provide critical information for the early diagnosis of chronic diseases and timely medical intervention to optimize therapeutic efficacy.

A Magnetic Resonance Imaging-Based Radiomic Model for the Noninvasive Preoperative Differentiation Between Transitional and Atypical Meningiomas.

Preoperative distinction between transitional meningioma and atypical meningioma would aid the selection of appropriate surgical techniques, as well as the prognosis prediction. Here, we aimed to differentiate between these two tumors using radiomic signatures based on preoperative, contrast-enhanced T1-weighted and T2-weighted magnetic resonance imaging. A total of 141 transitional meningioma and 101 atypical meningioma cases between January 2014 and December 2018 with a histopathologically confirmed diagnosis were retrospectively reviewed. All patients underwent magnetic resonance imaging before surgery. For each patient, 1227 radiomic features were extracted from contrast-enhanced T1-weighted and T2-weighted images each. Least absolute shrinkage and selection operator regression analysis was performed to select the most informative features of different modalities. Subsequently, stepwise multivariate logistic regression was chosen to further select strongly correlated features and build classification models that can distinguish transitional from atypical meningioma. The diagnostic abilities were evaluated by receiver operating characteristic analysis. Furthermore, a nomogram was built by incorporating clinical characteristics, radiological features, and radiomic signatures, and decision curve analysis was used to validate the clinical usefulness of the nomogram. Sex, tumor shape, brain invasion, and four radiomic features differed significantly between transitional meningioma and atypical meningioma. The clinicoradiomic model derived by fusing the above features resulted in the best discrimination ability, with areas under the curves of 0.809 (95% confidence interval, 0.743-0.874) and 0.795 (95% confidence interval, 0.692-0.899) and sensitivity values of 74.0% and 71.4% in the training and validation cohorts, respectively. The clinicoradiomic model demonstrated good performance for the differentiation between transitional and atypical meningioma. It is a quantitative tool that can potentially aid the selection of surgical techniques and the prognosis prediction and can thus be applied in patients with these two meningioma subtypes.

A deep learning radiomics analysis for identifying sinus invasion in patients with meningioma before operation using tumor and peritumoral regions.

BACKGROUND: For patients with meningioma, surgical procedures are different because of the status of sinus invasion. However, there is still no suitable technique to identify the status of sinus invasion in patients with meningiomas. We aimed to build a deep learning radiomics model to identify sinus invasion before surgery. METHODS: A total of 1048 patients with meningiomas were retrospectively enrolled from two hospitals. T1 enhanced-weighted (T1c) and T2-weighted MRI data for each patient were collected. Tumors and their corresponding peritumors were analyzed. Four ResNet50 models were built with different types of regions of interest (ROIs) (tumor and peritumor) and different modal images (T1c and T2) to predict the status of sinus invasion. Several data enhancement methods were applied before ResNet50 model building. The final model was generated by combining four ResNet50 models. RESULTS: The models with a combination of tumors and peritumors using multimodal images achieved the highest predictive performance (AUC = 0.884, ACC = 78.1%) in the independent test cohort. The Delong test proved that the model built with combination ROIs achieved significantly higher performance than the model built only with tumors. The net reclassification improvement and integrated discrimination improvement tests both proved that including peritumor ROIs in the tumor ROIs could significantly improve the prediction ability. CONCLUSION: In the current study, the deep learning model showed potential for identifying sinus invasion before surgery in patients with meningioma. Including peritumors could significantly improve predictive performance.

Association of Sleep Duration and Screen Time With Anxiety of Pregnant Women During the COVID-19 Pandemic.

The COVID-19 pandemic has dramatically changed the patterns of lifestyle and posed psychological stress on pregnant women. However, the association of sleep duration and screen time with anxiety among pregnant women under the backdrop of the COVID-19 pandemic scenario has been poorly addressed. We conducted one large-scale, multicenter cross-sectional study which recruited 1794 pregnant women across middle and west China. Self-reported demographic characteristics, lifestyle, and mental health status were collected from 6th February to 8th May 2020. We investigated the association of sleep duration and screen time with the risk of anxiety by multivariable logistic regression analysis and linear regression analysis after adjusting potential confounders. The dose-response relationship of sleep duration and screen time with anxiety was visualized using a cubic spline plot. Our data revealed that almost 35% of pregnant women suffered from anxiety during the COVID-19 pandemic. Sleep duration was dose-dependently associated with a lower risk of anxiety among pregnant women (OR = 0.41, 95% CI: 0.27-0.63), while screen time exhibited a conversed effect (OR = 2.01, 95% CI:1.00-4.39). Notably, sleep duration (≥8 h/day) synergistically combined with screen time (3-7 h/day) to diminish the risk of anxiety (OR = 0.70, 95% CI: 0.50-0.99). Taken together, sleep duration and screen time were independently and jointly associated with anxiety (P < 0.05). Therefore, promoting a more active lifestyle and maintaining higher sleep quality could improve the mental health of pregnant women, especially under public health emergency.

Kinetic Control in Assembly of Plasmid DNA/Polycation Complex Nanoparticles.

Polyelectrolyte complex (PEC) nanoparticles assembled from plasmid DNA (pDNA) and polycations such as linear polyethylenimine (lPEI) represent a major nonviral delivery vehicle for gene therapy tested thus far. Efforts to control the size, shape, and surface properties of pDNA/polycation nanoparticles have been primarily focused on fine-tuning the molecular structures of the polycationic carriers and on assembly conditions such as medium polarity, pH, and temperature. However, reproducible production of these nanoparticles hinges on the ability to control the assembly kinetics, given the nonequilibrium nature of the assembly process and nanoparticle composition. Here we adopt a kinetically controlled mixing process, termed flash nanocomplexation (FNC), that accelerates the mixing of pDNA solution with polycation lPEI solution to match the PEC assembly kinetics through turbulent mixing in a microchamber. This achieves explicit control of the kinetic conditions for pDNA/lPEI nanoparticle assembly, as demonstrated by the tunability of nanoparticle size, composition, and pDNA payload. Through a combined experimental and simulation approach, we prepared pDNA/lPEI nanoparticles having an average of 1.3 to 21.8 copies of pDNA per nanoparticle and average size of 35 to 130 nm in a more uniform and scalable manner than bulk mixing methods. Using these nanoparticles with defined compositions and sizes, we showed the correlation of pDNA payload and nanoparticle formulation composition with the transfection efficiencies and toxicity in vivo. These nanoparticles exhibited long-term stability at -20 °C for at least 9 months in a lyophilized formulation, validating scalable manufacture of an off-the-shelf nanoparticle product with well-defined characteristics as a gene medicine.

Surface Coating Approach to Overcome Mucosal Entrapment of DNA Nanoparticles for Oral Gene Delivery of Glucagon-like Peptide 1.

Oral delivery of nucleic acid therapy is a promising strategy in treating various diseases because of its higher patient compliance and therapeutic efficiency compared to parenteral routes of administration. However, its success has been limited by the low transfection efficiency resulting from nucleic acid entrapment in the mucus layer and epithelial barrier of the gastrointestinal (GI) tract. Herein, we describe an approach to overcome this phenomenon and improve oral DNA delivery in the context of treating type II diabetes (T2D). Linear PEI (lPEI) was used as a carrier to form complexes with plasmid DNA encoding glucagon-like peptide 1 (GLP-1), a common target in T2D treatments. These nanoparticles were then coated with a mixture of 1,2-dipalmitoyl-sn-glycero-3-phosphocholine (DPPC) and 1,2-dimyristoyl-rac-glycero-3-methoxy poly(ethylene glycol)-2000 (DMG-PEG) to render the nanoparticle surface hydrophilic and electrostatically neutral. The surface-modified lPEI/DNA nanoparticles showed higher diffusivity and transport in the mucus layer of the GI tract and mediated high levels of transfection efficiency in vitro and in vivo. Moreover, these modified nanoparticles demonstrated high levels of GLP-1 expression for more than 24 h in the liver, lungs, and intestine in a T2D murine model after a single dose, as well as controlled blood glucose levels within a normal range for at least 18 h with repeatable therapeutic effects upon multiple dosages. Taken together, this work demonstrates the feasibility of an oral plasmid DNA delivery approach in the treatment of T2D through a facile surface modification to improve the mucus permeability and delivery efficiency of the nanoparticles.

Sustained release of exendin-4 from tannic acid/Fe (III) nanoparticles prolongs blood glycemic control in a mouse model of type II diabetes.

Exendin-4 has been clinically adopted as an effective drug for treating type 2 diabetes (T2D), but its short circulation half-life in the blood requires two injections per day to maintain effective glycemic control. This significantly limits its clinical application. In this study, we developed a tannic acid/exendin-4/Fe3+ ternary nanoparticle system to provide sustained release of exendin-4 in vivo. The formation of these nanoparticles relies on TA/exendin-4 complexation and stabilization through TA-Fe3+ coordination, where the rapid reaction kinetics can benefit from efficient mixing of all three components. Adapting our recently developed flash nanocomplexation (FNC) method, we formulated nanoparticles with high encapsulation efficiency (~ 100%) of exendin-4, high payload capacity, and high degrees of uniformity and stability because the rapid turbulent mixing facilitated a homogeneous distribution of all three components in the complexation process. Intraperitoneal injection in mice showed that exendin-4 released from the nanoparticles had an AUC 7.2-fold higher than the free exendin-4 injection. Efficacy study in a T2D mouse model showed that the optimized formulation achieved a rapid reduction of the blood glucose level to the normal range within <12 h and maintained the same level for 72 h following a single intraperitoneal dose. The blood glucose level was maintained to below the therapeutic level (< 15 mmol/L) for 6 days, and the treatment led to reduced body weight with pathological and functional improvements in the kidney and liver. This tannic acid/exendin-4/Fe3+ ternary nanoparticle system holds translational potential in treating T2D, due to its improved treatment outcomes in terms of extended release of exendin-4, prolonged control of blood glucose level, reduced dosing frequency, and improved pathological indicators.

Size-controlled lipid nanoparticle production using turbulent mixing to enhance oral DNA delivery.

Lipid-based nanoparticles (LNPs) have been developed to address the transport and uptake barriers to enhance the delivery efficiency of plasmid DNA therapeutics. In these systems, plasmid DNA can be encapsulated through condensation by a cationic lipid to form lipo-complexes, or polycation following complexation into cationic liposomes to form lipo-polyplexes. Conventional methods for achieving these two DNA-delivering LNP vehicles suffer from significant batch-to-batch variation, poor scalability and complicated multi-step preparation procedures. Resultant nanoparticles often have uncontrollable size and surface charge with wide distribution, and poor stability when exposed to physiological media. Here we report a single-step flash nanocomplexation (FNC) process using turbulent mixing to prepare uniform lipo-complex or lipo-polyplex LNPs in a scalable manner, demonstrating excellent control over the nanoparticle size (from 40 to several hundred nm) and surface charge, with narrow size distribution. The FNC-produced LNPs could be purified and concentrated using a tangential flow filtration (TFF) process in a scalable manner. An optimized formulation of purified lipo-complex LNPs (DOTAP/Chol/DNA, 45 nm) showed significantly higher (5-fold in the lungs and 4-fold in the liver) transgene expression activity upon oral dosage than lipo-polyplex LNPs (DPPC/Chol/lPEI/DNA, 75 nm) or lPEI/DNA nanoparticles (43 nm). Repeated dosing (4 days, 150 µg/day) of the lipo-complex LNPs sustained the transgene activity over a period of one week without detectable toxicity in major organs, suggesting its potential for clinical translation. STATEMENT OF SIGNIFICANCE: We report a new method to prepare uniform size-controlled lipid-based DNA-loaded nanoparticles by turbulent mixing delivered by a multi-inlet vortex mixer. Two distinct compositions were successfully prepared: (1) lipo-complexes, through condensation of the plasmid DNA by cationic lipids; (2) lipo-polyplexes, by encapsulation of DNA/PEI together with neutral lipids. Comparing with conventional methods, which use multi-step processes with high batch-to-batch variations and poor control over nanoparticle characteristics, this method offers a single-step, continuous and reproducible assembly methodology that would promote the translation of such gene medicine products. Effective purification and concentration of nanoparticles were achieved by adopted tangential flow filtration method. Following oral gavage in mice, the lipo-complex nanoparticles showed the highest level of transgene expression in the lung and liver.