Irregular Shape Research Articles

Can the Bladder Itself "Measure" Volume, and Thereby Help to Determine When Initiation of Voiding Should Occur? ICI-RS 2024.

To answer the question of whether the bladder itself can to any extent control or modulate the initiation of voiding. This subject was discussed at the International Consultation on Incontinence-Research Society (ICI-RS) 2024 conference in Bristol, UK in a proposal session. Cells in the bladder wall sense the local environment via a diverse array of ion channels and receptors which together provide input to motor-sensory and signal transduction mechanisms. A purinergic signal transduction system provides a high-gain mucosal chemosensitive transduction pathway between bladder wall stretch during filling and graded afferent activation. Recent studies established cross-species similarities in the regulation of urine storage which include the upregulation of aquaporin (water) channels during bladder filling/wall stretch, in the bladder. In addition to the endocrine hypothalamus/pituitary axis production, urothelial production of arginine vasopressin acts on urothelial vasopressin receptors in a paracrine manner causing aquaporin channel upregulation, reducing the bladder volume and delaying sensation of fullness. Bladder shape influences the sensory systems involved in the perception of bladder volume; moreover irregular bladder shapes may correlate with overactive bladder. Volume measuring and signaling threshold-determining mechanisms in the bladder along with shape and permeability act to influence the timing and type of signaling to the CNS; although this is not always followed by a consecutive action. The hierarchical grading of the signals originating from the bladder among other peripheral bodily or central signals are crucial factors that determine whether the bladder is "allowed" to initiate voiding.

Autonomous particle-shape-based classification and identification of calcareous soils through machine learning

Recent geotechnical and geological applications have highlighted numerous challenges associated with conventional particle-size based soil classification, particularly for special calcareous soils widely used in marine and offshore constructions. Calcareous soils are always characterized by complex microstructures in terms of loose fabric, irregular particle shapes and abundant intra-particle pores. Therefore, this study aims to establish an autonomous particle-shape categorization criteria for calcareous soils, and further facilitate their intelligent particle identification and classification using image-based machine learning (ML) techniques. First, an extensive collection of images of individual calcareous sand particles were acquired through microscopic photography to establish the datasets for ML training. Four key shape parameters including circularity, roundness, aspect ratio and convexity were measured based on image processing and analysis. Subsequently, an unsupervised ML algorithm, K-means clustering, was applied to these shape parameters to autonomously determine optimal particle-shape categories for calcareous soils. Finally, based on the obtained particle shape classes, two supervised CNN algorithms, AlexNet and YOLO-v3, were trained and applied to automatically identify and classify individual particles or particle assemblies of calcareous soils from various raw image types. The predictive results demonstrate the high accuracy and efficiency of ML models in identifying and classifying the particle shapes of calcareous soils.

Cutaneous Wound-Healing Activity of Quercetin-Functionalized Bimetallic Nanoparticles.

Quercetin, a natural flavonol, is reported to have significant antioxidant and anti-inflammatory activity, which further aids in its good wound-healing properties via acting on acute as well as chronic inflammatory phases. The current study is focused on understanding the potential of the green-synthesized iron and zinc oxide bimetallic (i.e., zinc ferrite) nanoparticles of quercetin (ZFQNP) on wound healing by an in vivo study model. Bimetallic quercetin nanoparticles were prepared by the co-precipitation method and characterized by UV-visible spectroscopy, scanning electron microscopy (SEM), and dynamic light scattering (DLS) analyses. Synthesized ZFQNP was utilized to prepare the ointment for topical application, and wound-healing activity was evaluated by using the excisional wound method in Wistar rats. The binding affinity of quercetin was ascertained against various wound-healing protein targets by molecular docking. Characterization data confirmed the synthesis of bimetallic ZFQNP of an irregular shape. Molecular docking studies showed satisfactory binding potential of quercetin with selected molecular targets. The study results of various parameters corroborated the significant wound-healing properties of ZFQNP, possibly attributed to the promising binding potential of quercetin with vital wound-healing targets. The study demonstrated that the quercetin bimetallic nanoparticles could provide a promising wound-healing effect.

Fractal Dimensional Analysis of Building Facades: The Case of Office Buildings in Erbil City

Fractal dimension is a characteristic parameter used to measure the complexity and irregularity of geometric shapes and patterns. It is applied in architecture to explore complexity and irregularity and to assess the aesthetic preferences in architectural design. Office building facade design pattern, as an observation unit, has a positive connection with the aesthetic value. This study aims to evaluate facade design styles in terms of two aesthetic qualities, visual complexity and visual diversity, via applying fractal dimension to three design styles of office building facades in Erbil City. The study uses a combination of qualitative and quantitative evaluations to achieve this goal. It employs box-counting analysis through the ImageJ plugin to FracLac and the mathematical perplexity equation to evaluate visual complexity and diversity. The results indicate that the neo-classical office facade style, with a visual complexity value of 1.7008 and visual diversity of 21.27, presents an elevated level of aesthetics similar to the saccadic pattern facade. This study concluded that a neo-classical architectural style for office building facades is the most aesthetically preferable. Modern facade design is considered a secondary architectural style aimed at achieving aesthetic value. Ultimately, the high-tech style is the least attractive facade style. This study contributes to avoiding designs of unattractive office building facades due to a lack of architectural design vocabulary while avoiding overly complex designs that prove visually upsetting for viewers.

Cascaded Feature Fusion Grasping Network for Real-Time Robotic Systems

Grasping objects of irregular shapes and various sizes remains a key challenge in the field of robotic grasping. This paper proposes a novel RGB-D data-based grasping pose prediction network, termed Cascaded Feature Fusion Grasping Network (CFFGN), designed for high-efficiency, lightweight, and rapid grasping pose estimation. The network employs innovative structural designs, including depth-wise separable convolutions to reduce parameters and enhance computational efficiency; convolutional block attention modules to augment the model’s ability to focus on key features; multi-scale dilated convolution to expand the receptive field and capture multi-scale information; and bidirectional feature pyramid modules to achieve effective fusion and information flow of features at different levels. In tests on the Cornell dataset, our network achieved grasping pose prediction at a speed of 66.7 frames per second, with accuracy rates of 98.6% and 96.9% for image-wise and object-wise splits, respectively. The experimental results show that our method achieves high-speed processing while maintaining high accuracy. In real-world robotic grasping experiments, our method also proved to be effective, achieving an average grasping success rate of 95.6% on a robot equipped with parallel grippers.

A binary-tree subdivision method for evaluation of singular integrals with discontinuous kernel in 3D BEM

PurposeA binary-tree subdivision method (BTSM) for numerical evaluation of weakly singular integrals with discontinuous kernel in the three-dimensional (3D) boundary element method (BEM) is presented in this paper.Design/methodology/approachIn this method, the singular boundary element is split into two sub-elements and subdivided recursively until the termination criterion is met and the subdivision is stopped. Then, the source point is surrounded by one or more spherical cavities determined by the discontinuous kernel function. The sub-elements located in spherical cavities will be eliminated, and the regular triangular or rectangle elements are employed to fill the spherical cavities.FindingsWith the proposed method, the obtained sub-elements are automatically refined as they approach the source point, and they are “good” in shape and size for standard Gaussian quadrature. Thus, the proposed method can be used to evaluate singular integrals owing discontinuous kernel function accurately for cases of different element shapes and various source point locations.Originality/valueNumerical examples show that the BTSM is suitable for planar and curved elements of arbitrary regular or irregular shape at various source point locations, and the results have much better accuracy and robustness than conventional subdivision method (CSM) when the kernel function is discontinuous.

Endothermal pupilloplasty : Aminimally traumatic technique for centering the pupil in patients with corectopia. Video article

The aim of endothermal pupilloplasty (EP) is to optimize centering of the pupil, contour, or size. The EP is performed in patients with congenital or acquired corectopia or an irregular pupil shape. In individual cases the technique has also been used to tighten the iris in floppy iris syndrome and to adapt iris tissue edges in patients with sectoral iris defects or iridodialysis. In patients with pronounced iris stromal atrophy or major iris tissue defects, e.g., congenital or posttraumatic, EP could be indicated after careful consideration. Viscoelastic substances are injected into the anterior chamber via a23-gauge paracentesis. Using the blunt tip of abipolar endodiathermy probe, the iris tissue is selectively cauterized at several points, stretching the pupil in the direction to which cauterization is applied. The higher the energy level applied and the closer the cauterization is to the pupil margin, the more effect is achieved. The viscoelastic agents are suctioned off and the paracentesis is sealed by hydration (and additionally sewn in children). Avideo of the operation, which is available online, shows the surgical technique in detail. Control examinations are recommended after 1 day, 1 week and 1-3 months post-EP. Acombination of topical steroids and antibiotics, e.g., dexamethasone and gentamicin eye drops, should be applied 5 times daily for 1 week and 3 times daily for another week. To date, isolated positive retrospective case reports on EP have been published. There is alack of prospective studies, reviews or meta-analyses.

Clustering explanation based on multi-hyperrectangle

Clustering plays a crucial role in data mining and pattern recognition, but the interpretation of clustering results is often challenging. Existing interpretation methods usually lack an intuitive and accurate description of irregular shapes and high dimensional datas. This paper proposes a novel clustering explanation method based on a Multi-HyperRectangle(MHR), for extracting post hoc explanations of clustering results. MHR first generates initial hyperrectangles to cover each cluster, and then these hyper-rectangles are gradually merged until the optimal shape is obtained to fit the cluster. The advantage of this method is that it recognizes the shape of irregular clusters and finds the optimal number of hyper-rectangles based on the hierarchical tree structure, which discovers structural relationships between rectangles. Furthermore, we propose a refinement method to improve the tightness of the hyperrectangles, resulting in more precise and comprehensible explanations. Experimental results demonstrate that MHR significantly outperforms existing methods in both the tightness and accuracy of cluster interpretation, highlighting its effectiveness and innovation in addressing the challenges of clustering interpretation.

Prognostic value of nomogram model based on clinical risk factors and CT radiohistological features in hypertensive intracerebral hemorrhage.

To construct a nomogram model based on clinical risk factors and CT radiohistological features to predict the prognosis of hypertensive intracerebral hemorrhage (HICH). A total of 148 patients with HICH from April 2022 to July 2024 were retrospectively selected as the research subjects. According to the modified Rankin scale at the time of discharge, they were divided into good group (Rankin scale score 0-2) and bad group (Rankin scale score 3-6). To compare the clinical data and the changes of CT radiographic characteristics in patients with different prognosis. Relevant factors affecting the prognosis were analyzed, and nomogram model was established based on the influencing factors. The fitting degree, prediction efficiency and clinical net benefit of the nomogram model were evaluated by calibration curve, ROC curve and clinical decision curve (DCA). Compared with the good group, the hematoma volume in the poor group was significantly increased, the serum thromboxane 2(TXB2) and lysophosphatidic acid receptor 1(LPAR1) levels were significantly increased, and the energy balance related protein (Adropin) level was significantly decreased. The proportions of irregular shape, promiscuous sign, midline displacement, island sign and uneven density were all significantly increased (p < 0.05). In Logistic multivariate analysis, hematoma volume, Adropin, TXB2, LPAR1 and CT radiological features were all independent factors influencing the poor prognosis of HICH (p < 0.05). A nomogram prediction model was established based on the influencing factors. The calibration curve showed that the C-index was 0.820 (95% CI: 0.799-0.861), the goodness of fit test χ2 = 5.479, and p = 0.391 > 0.05, indicating a high degree of fitting. The ROC curve showed that the AUC was 0.896 (95% CI: 0.817-0.923), indicating that this model had high prediction ability. The DCA curve shows that the net benefit of the nomogram model is higher when the threshold probability is 0.1-0.9. The nomogram prediction model established based on hematoma volume, Adropin, TXB2, LPAR1 and other clinical risk factors as well as CT radiographic characteristics has high accuracy and prediction value in the diagnosis of poor prognosis in patients with HICH.

Novel Pathway and Recent Advances for Targeting Sickle Cell Anemia through Novel Drug Delivery System.

Red blood cells with sickle cell anemia (SCA) have an irregular shape, and it is a genetic blood condition that can cause several problems and shorten life expectancy. Traditional treatments have focused on symptom management, but recent advancements in drug delivery systems offer promising pathways for targeted therapies. This abstract explores novel approaches to combat SCA through innovative drug delivery systems, gene therapy, and new pharmaceutical interventions. One novel pathway for targeting SCA involves utilizing advanced drug delivery systems to enhance the effectiveness of therapeutic agents. Nanotechnology-based delivery systems, such as nanoparticles and liposomes, offer precise drug targeting, controlled release, and improved bioavailability. These systems can encapsulate anti-sickling agents, like hydroxyurea, and enable their specific delivery to affected cells, reducing side effects and enhancing therapeutic outcomes. Additionally, therapy has become a ground-breaking method of treating SCA. CRISPR/Cas9 technology presents a groundbreaking opportunity to correct the genetic mutation responsible for sickle hemoglobin production. By precisely editing the HBB gene, which encodes the abnormal hemoglobin, researchers aim to restore normal hemoglobin expression, potentially offering a curative treatment for SCA. Furthermore, recent advancements in drug development have led to the discovery of promising candidates targeting specific pathways involved in SCA pathophysiology. Experimental drugs, such as voxelotor and crizanlizumab focus on modifying hemoglobin properties or inhibiting cell adhesion, respectively, thereby preventing sickle cell-related complications and reducing vaso-occlusive crisis frequency.

Liver tumor segmentation method combining multi-axis attention and conditional generative adversarial networks.

In modern medical imaging-assisted therapies, manual annotation is commonly employed for liver and tumor segmentation in abdominal CT images. However, this approach suffers from low efficiency and poor accuracy. With the development of deep learning, automatic liver tumor segmentation algorithms based on neural networks have emerged, for the improvement of the work efficiency. However, existing liver tumor segmentation algorithms still have several limitations: (1) they often encounter the common issue of class imbalance in liver tumor segmentation tasks, where the tumor region is significantly smaller than the normal tissue region, causing models to predict more negative samples and neglect the tumor region; (2) they fail to adequately consider feature fusion between global contexts, leading to the loss of crucial information; (3) they exhibit weak perception of local details such as fuzzy boundaries, irregular shapes, and small lesions, thereby failing to capture important features. To address these issues, we propose a Multi-Axis Attention Conditional Generative Adversarial Network, referred to as MA-cGAN. Firstly, we propose the Multi-Axis attention mechanism (MA) that projects three-dimensional CT images along different axes to extract two-dimensional features. The features from different axes are then fused by using learnable factors to capture key information from different directions. Secondly, the MA is incorporated into a U-shaped segmentation network as the generator to enhance its ability to extract detailed features. Thirdly, a conditional generative adversarial network is built by combining a discriminator and a generator to enhance the stability and accuracy of the generator's segmentation results. The MA-cGAN was trained and tested on the LiTS public dataset for the liver and tumor segmentation challenge. Experimental results show that MA-cGAN improves the Dice coefficient, Hausdorff distance, average surface distance, and other metrics compared to the state-of-the-art segmentation models. The segmented liver and tumor models have clear edges, fewer false positive regions, and are closer to the true labels, which plays an active role in medical adjuvant therapy. The source code with our proposed model are available at https://github.com/jhliao0525/MA-cGAN.git.

Vibrational Analysis of Building Structures with Irregularities

This paper presents a mathematical model for predicting vibrations in lightweight, timber-based floor/ceiling structures, enhanced to account for irregularities in joist shape and stiffness, as well as floor stiffness. Building on a prior model that assumed precise geometry and homogeneous material properties, the study now incorporates real timber measurements via the power spectral density of irregularities to incorporate their impact on the system’s vibrational response, including mid-frequency vibrations. Existing results underscore the critical role of component connections in shaping vibration behavior, while the present paper gives new principles for building a model to assess how uncertainties in these connections or material properties affect the overall structural response. This new model maintains the property of efficient computation so that irregularities in the components may be included in the design stage to improve the mid-frequency performance of lightweight, timber-framed structures.

Ultrasonic cavitation finishing of 316 L stainless steel using different abrasive particles: A combined SPH simulation and experimental study

Ultrasonic cavitation and its driving effect on abrasive particles have been studied and applied to the surface finishing of 316 L stainless steel. To explore the influence of abrasive characteristic parameters on the surface finishing process, a cavitation-driven single abrasive impact model was established by coupling the smoothed particle hydrodynamics (SPH) method with the finite element method (FEM). The changes in the workpiece surface and the wear of abrasive particles after the impact were analysed, the effects of the abrasive particle size, material and shape were discussed, and the effectiveness of the model was verified through ultrasonic finishing experiments. SiC abrasive particles possessing irregular shape and Al2O3 abrasive particles possessing irregular and spherical shapes were used for comparison. Both simulation and experimental results indicate that the abrasive particle with a high hardness and a spherical shape has better material removal capability. However, the spherical abrasive particles tended to occur large cracks and split into small pieces, which accelerated the wear of the abrasives. In this work, the best finishing result was obtained by using irregularly shaped SiC abrasive particles, and the surface roughness Ra was improved by 12.87 %. This study provides a comprehensive understanding of the material removal process using cavitation-driven abrasives and examines the influence of key characteristics of abrasive particles. These insights are significant for advancing the application and enhancement of this technology in metal surface treatment.

Simulating waves induced by landslide using coupled smoothed particle hydrodynamics and discrete element method: Evaluating the impact of irregular rock shapes

The morphology of rock plays an important role in the process of landslide-induced wave, yet it is often neglected in current studies. This work aims to fill this gap by investigating the impact of irregular rock shapes on landslide-induced wave generation and propagation via coupling smooth particle fluid dynamics and discrete element method from a multi-scale perspective. Initially, the wave induced by particle column collapse is reproduced and validated against existing results. Subsequently, the influence of rock shapes, particularly the aspect ratio of particles on landslide-induced waves, is analyzed. The findings indicate that spherical particles, due to their low self-locking tendency and simple force chain structure, exhibit higher average velocities and more stable velocity changes during the landslide process. Spherical particles generate larger free surface waves with smoother and more regular waveforms when entering the water. In contrast, irregular polyhedral particles produce multiple secondary wave peaks alongside the main wave. The wave height induced by these particles is negatively correlated with aspect ratio. Specifically, the maximum run-up height of waves generated by elliptical particles with the highest aspect ratio is 11.7% lower than that of spherical particles. This research highlights the influence mechanism of particle morphology on landslide and tsunami dynamics, which provides a theoretical foundation for disaster prediction and assessment.

Yttria-stabilized zirconia and lanthanum cerate granules with YSZ whiskers prepared by spray drying for thermal barrier coatings

The aim of this study was to explore the effect of different solid phases and varied amounts of polyvinyl alcohol (PVA) on granule size of plasma-sprayable yttria-stabilized zirconia (YSZ) with YSZ whiskers (YSZ/W) and lanthanum cerate (La2Ce2O7, LC) with YSZ whiskers (LC/W) as composite powders. The initial phase of this study involved the preparation and optimization of a YSZ suspension using varying solid loadings of YSZ powder (ranging from 30-75 wt.%) and PVA serving as both a binder and dispersant. The suspension was subjected to rigorous optimization procedures to meet the required standards. The suspensions were spray dried, and the resulting granulates were examined using SEM to determine their shape and size. The particle size of YSZ granules increased with increasing solid loading of YSZ in the suspension. The optimum amount of dispersant was found to be 1 wt.% related to the weight of solid, while the solid loading was 75 wt.% of YSZ. In some cases, excessive YSZ solid loading and dispersant impaired the formation of spherical granules. Composite YSZ/W and LC/W granules were also prepared with spherical, lemon, or irregular shapes, with the whiskers embedded in the YSZ or LC powder.

A Data-Driven Approach for Stylolite Detection

Summary Stylolite is a specific geopattern that can occur in both sedimentary rocks and deformed zones, which could change the porosity of the reservoir, modify the permeability, and even result in horizontal permeability barriers. Though there are many related studies to characterize this geopattern, most of them focus on experimental methods. In this work, we investigated a new approach for recovering geometrical information of the stylolite zone (including its size and location) based on neural network architectures including convolutional neural network (CNN), long short-term memory (LSTM), and transformer encoder, which could serve as a data-driven solution to the problem. To our knowledge, this paper is the first to exclusively use well testing data for deducing field geopatterns, whereas other studies have relied on additional data sources such as well logging data. To simplify the problem, we first conducted simulation by building 3D multilayer reservoir models with one stylolite zone. We considered both simplified cases with only a few homogeneous layers and cases with heterogeneous layers to generalize our work. For the heterogeneous case, we extracted the permeability from SPE10 Model 2, a commonly used public resource (SPE 10 Benchmark, Model 2 2008). Producing and observing wells in the model are at different locations and provide pressure and production rate data as inputs for the deep learning models, in the form of multivariant time series data. For homogeneous cases, after zero-padding and standardizing our inputs to tackle different-length data and features with different scales, we applied a CNN-LSTM model to our data set, leveraging the CNN’s ability to capture short-term local features and the LSTM’s capacity to extract long-term dependencies. This combination improves the extraction of important information from time series data related to stylolites. The two subnetworks are connected in parallel to combine the advantages of CNN in extracting local temporal features and the strengths of LSTM in capturing long-time dependency via self-loops. Our work also separately covers the two subnetworks of the CNN-LSTM model as baseline models. For heterogeneous cases, a CNN-based model U-Net and an attention-based model set function for time series (SeFT) were introduced to make the predictions. In a more realistic scenario featuring fluid pathways with irregular shapes within a heterogeneous reservoir, we employed a transformer encoder model to predict both the shape and location of the fluid pathways. On the homogeneous data set, our CNN-LSTM model achieved a satisfactory performance and could predict the locations and sizes of the stylolite zone and outperformed the two baseline models. On the more challenging heterogeneous data set, our baseline and CNN-LSTM models failed to deliver meaningful results. In contrast, SeFT and U-Net performed better in the sense that we could successfully predict the locations of the stylolite zones. In a more realistic scenario involving irregularly-shaped fluid pathways, our transformer encoder model achieved accurate predictions of their locations.

Crocin-1 laden thermosensitive chitosan-based hydrogel with smart anti-inflammatory performance for severe full-thickness burn wound therapeutics

Burns are the fourth most common type of civilian trauma worldwide, and the management of severe irregular scald wounds remains a significant challenge. Herein, crocin-1 laden hydroxybutyl chitosan (CRO-HBC) thermosensitive hydrogel with smart anti-inflammatory performance was developed for accelerating full-thickness burn healing. The injectable and shape adaptability of the CRO-HBC gel make it a promising candidate for effectively filling scald wounds with irregular shapes, while simultaneously providing protection against external pathogens. The CRO-HBC gel network formed by hydrophobic interactions exhibited an initial burst release of crocin-1, followed by a gradual and sustained release over time. The excessive release of ROS and pro-inflammatory cytokines should be effectively regulated in the early stage of wound healing. The controlled release of crocin-1 from the CRO-HBC gel adequately addresses this requirement for wound healing. The CRO-HBC hydrogel also exhibited an excellent biocompatibility, an appropriate biodegradability, keratinocyte migration facilitation properties, and a reactive oxygen species scavenging capability. The composite CRO-HBC hydrogel intelligently mitigated inflammatory responses, promoted angiogenesis, and exhibited a commendable efficacy for tissue regeneration in a full-thickness scalding model. Overall, this innovative temperature-sensitive CRO-HBC injectable hydrogel dressing with smart anti-inflammatory performance has enormous potential for managing severe scald wounds.

Polar coordinate-based background removal algorithm for 2D x-ray scattering data.

During the data collection of x-ray diffraction experiments with various detectors, background signals are often unavoidable along with the sample signal. Addressing the background during post-data analysis is not a straightforward task. In this work, we introduced an algorithm specifically designed to handle centrally symmetric two-dimensional x-ray diffraction data and processed the data using the Python programming language. The two-dimensional data are first transformed from Cartesian coordinates to polar coordinates. Second, utilizing existing background processing algorithms, one-dimensional background curves are identified for each azimuth angle. These background data are then merged to generate two-dimensional background data. Finally, by subtracting the background from the original data, we obtain the clear diffraction signal. The algorithm can effectively remove the background from x-ray diffraction data and exhibits the ability to handle backgrounds with high intensity and irregular shapes, and the discernibility of the weak signal is significantly enhanced. Moreover, researchers have the flexibility to choose whether to preserve or eliminate the signals from additional amorphous components based on their needs. This algorithm will provide researchers with the possibility for further data analysis.

In vitro and in vivo characterization of Invega Sustenna® (paliperidone palmitate long-acting injectable suspension).

The aim of this study was to comprehensively characterize paliperidone palmitate (PP) long-acting suspension (Invega Sustenna®) through reverse engineering. We developed a series of analytical methods to assess critical quality attributes of four batches of Invega Sustenna®. The size distributions of the four batches of suspensions were measured using laser diffraction, and variations in the D50 and D90 parameters were observed. The morphology of suspension was determined through scanning electron microscope (SEM), which exhibited irregular granular shape across all batches. The size distributions determined by SEM images were similar to the laser diffraction results. Thermal characteristics were detected using differential scanning calorimetry (DSC) and crystalline properties were assessed by powder X-ray diffraction (PXRD), displaying consistency among the four batches in these two aspects. In vitro dissolution methods (sample separation and dialysis bag methods) were developed to evaluate the release behaviors of Invega Sustenna® and four lots showed a similar dissolution pattern. Furthermore, following a single-dose intramuscular administration to rats, two batches of Invega Sustenna® with the largest size differences demonstrated comparable plasma concentration-time profiles and pharmacokinetics parameters, indicative of one month long-acting release. In summary, we established a systematic quality characteristics assessment for Invega Sustenna®, including particle size distribution, particle morphology, thermal characteristics, crystalline properties, in vitro dissolution kinetics and in vivo pharmacokinetics. Our work will assist pharmaceutical companies and regulatory agencies in the development and regulatory assessment of novel or generic products of long-acting injectable suspension.

Effects of high hydrostatic pressure treatment on in vitro digestibility and in vivo glycemic response of sweet potato flour: Effects of HHP on digestibility and glycemic response of sweet potato starch

Sweet potato is a rich source of resistant starch, fibre, vitamins and minerals, which is associated with healthy benefits. In this study, the effects of high hydrostatic pressure (HHP) treatment at 200–600 MPa on the digestibility of sweet potato flour (SPF) through starch composition analysis, in vitro digestion assays, and glycemic index (GI) and response in diabetic mice. Changes in the morphology of starch granules post-treatment were observed using an scanning electron microscope (SEM). Treatments at 400 and 600 MPa increased amylose in sweet potato starch and significantly increased the proportion of slowly digestible starch (SDS), as indicated by in vitro digestion assays. Resistant starch content decreased, with no significant change in rapidly digestible starch. Peak glucose levels in diabetic mice post-consuming SPF treated at 400 and 600 MPa were 476 and 514, respectively, significantly lower than 608 in the control group. The area under the curve (AUC) of 400 MPa treated-SPF is 14% lower than control SPF. The GI of SPF in the control group was 78.6, reducing to 66.2 and 68.3 post-HHP treatment at 400 and 600 MPa, respectively, with no significant difference in the 200 MPa group from that of the control group. SEM observations showed that damage to starch granules increased with increasing processing pressure. HHP caused the starch granules to gelatinize and recoagulate, forming an irregular shape. The HHP treatment changed the composition of SPF, significantly increasing SDS content and it stabilized postprandial blood glucose levels and reduced GI, rendering it a potential method to develop processed products of sweet potatoes with a low GI, providing a novel technical option for developing health-promoting products.