Leveraging ANFIS with Adam and PSO optimizers for Parkinson's disease.

Parkinson's disease (PD) is an age-related neurodegenerative disorder characterized by motor deficits, including tremor, rigidity, bradykinesia, and postural instability. According to the World Health Organization, about 1 % of the global population has been diagnosed with PD, and this figure is expected to double by 2040. Early and accurate diagnosis of PD is critical to slowing down the progression of the disease and reducing long-term disability. Due to the complexity of the disease, it is difficult to accurately diagnose it using traditional clinical tests. Therefore, it has become necessary to develop intelligent diagnostic models that can accurately detect PD. This article introduces a novel hybrid approach for accurate prediction of PD using an ANFIS with two optimizers, namely Adam and PSO. ANFIS is a type of fuzzy logic system used for nonlinear function approximation and classification, while Adam optimizer has the ability to adaptively adjust the learning rate of each individual parameter in an ANFIS at each training step, which helps the model find a better solution more quickly. PSO is a metaheuristic approach inspired by the behavior of social animals such as birds. Combining these two methods has potential to provide improved accuracy and robustness in PD diagnosis compared to existing methods. The proposed method utilized the advantages of both optimization techniques and applied them on the developed ANFIS model to maximize its prediction accuracy. This system was developed by using an open access clinical and demographic data. The chosen parameters for the ANFIS were selected through a comparative experimental analysis to optimize the model considering the number of fuzzy membership functions, number of epochs of ANFIS, and number of particles of PSO. The performance of the two ANFIS models: ANFIS (Adam) and ANFIS (PSO) focusing at ANFIS parameters and various evaluation metrics are further analyzed in detail and presented, The experimental results showed that the proposed ANFIS (PSO) shows better results in terms of loss and precision, whereas, the ANFIS (Adam) showed the better results in terms of accuracy, f1-score and recall. Thus, this adaptive neural-fuzzy algorithm provides a promising strategy for the diagnosis of PD, and show that the proposed models show their suitability for many other practical applications.

Weighted Bayesian Belief Network for diabetics: a predictive model.

Diabetes is an enduring metabolic condition identified by heightened blood sugar levels stemming from insufficient production of insulin or ineffective utilization of insulin within the body. India is commonly labeled as the "diabetes capital of the world" owing to the widespread prevalence of this condition. To the best of the authors' last knowledge updated on September 2021, approximately 77 million adults in India were reported to be affected by diabetes, reported by the International Diabetes Federation. Owing to the concealed early symptoms, numerous diabetic patients go undiagnosed, leading to delayed treatment. While Computational Intelligence approaches have been utilized to improve the prediction rate, a significant portion of these methods lacks interpretability, primarily due to their inherent black box nature. Rule extraction is frequently utilized to elucidate the opaque nature inherent in machine learning algorithms. Moreover, to resolve the black box nature, a method for extracting strong rules based on Weighted Bayesian Association Rule Mining is used so that the extracted rules to diagnose any disease such as diabetes can be very transparent and easily analyzed by the clinical experts, enhancing the interpretability. The WBBN model is constructed utilizing the UCI machine learning repository, demonstrating a performance accuracy of 95.8%.

BT-CNN: a balanced binary tree architecture for classification of brain tumour using MRI imaging.

Deep learning is a very important technique in clinical diagnosis and therapy in the present world. Convolutional Neural Network (CNN) is a recent development in deep learning that is used in computer vision. Our medical investigation focuses on the identification of brain tumour. To improve the brain tumour classification performance a Balanced binary Tree CNN (BT-CNN) which is framed in a binary tree-like structure is proposed. It has a two distinct modules-the convolution and the depthwise separable convolution group. The usage of convolution group achieves lower time and higher memory, while the opposite is true for the depthwise separable convolution group. This balanced binarty tree inspired CNN balances both the groups to achieve maximum performance in terms of time and space. The proposed model along with state-of-the-art models like CNN-KNN and models proposed by Musallam et al., Saikat et al., and Amin et al. are experimented on public datasets. Before we feed the data into model the images are pre-processed using CLAHE, denoising, cropping, and scaling. The pre-processed dataset is partitioned into training and testing datasets as per 5 fold cross validation. The proposed model is trained and compared its perforarmance with state-of-the-art models like CNN-KNN and models proposed by Musallam et al., Saikat et al., and Amin et al. The proposed model reported average training accuracy of 99.61% compared to other models. The proposed model achieved 96.06% test accuracy where as other models achieved 68.86%, 85.8%, 86.88%, and 90.41% respectively. Further, the proposed model obtained lowest standard deviation on training and test accuracies across all folds, making it invariable to dataset.

Oxymatrine combined with rapamycin to attenuate acute cardiac allograft rejection.

Background and aim: Solid organ transplantation remains a life-saving therapeutic option for patients with end-stage organ dysfunction. Acute cellular rejection (ACR), dominated by dendritic cells (DCs) and CD4+ T cells, is a major cause of post-transplant mortality. Inhibiting DC maturation and directing the differentiation of CD4+ T cells toward immunosuppression are keys to inhibiting ACR. We propose that oxymatrine (OMT), a quinolizidine alkaloid, either alone or in combination with rapamycin (RAPA), attenuates ACR by inhibiting the mTOR-HIF-1α pathway. Methods: Graft damage was assessed using haematoxylin and eosin staining. Intragraft CD11c+ and CD4+ cell infiltrations were detected using immunohistochemical staining. The proportions of mature DCs, T helper (Th) 1, Th17, and Treg cells in the spleen; donor-specific antibody (DSA) secretion in the serum; mTOR-HIF-1α expression in the grafts; and CD4+ cells and bone marrow-derived DCs (BMDCs) were evaluated using flow cytometry. Results: OMT, either alone or in combination with RAPA, significantly alleviated pathological damage; decreased CD4+ and CD11c+ cell infiltration in cardiac allografts; reduced the proportion of mature DCs, Th1 and Th17 cells; increased the proportion of Tregs in recipient spleens; downregulated DSA production; and inhibited mTOR and HIF-1α expression in the grafts. OMT suppresses mTOR and HIF-1α expression in BMDCs and CD4+ T cells in vitro. Conclusions: Our study suggests that OMT-based therapy can significantly attenuate acute cardiac allograft rejection by inhibiting DC maturation and CD4+ T cell responses. This process may be related to the inhibition of the mTOR-HIF-1α signaling pathway by OMT.

Examining the Pathogenesis of MAFLD and the Medicinal Properties of Natural Products from a Metabolic Perspective.

Metabolic-associated fatty liver disease (MAFLD), characterized primarily by hepatic steatosis, has become the most prevalent liver disease worldwide, affecting approximately two-fifths of the global population. The pathogenesis of MAFLD is extremely complex, and to date, there are no approved therapeutic drugs for clinical use. Considerable evidence indicates that various metabolic disorders play a pivotal role in the progression of MAFLD, including lipids, carbohydrates, amino acids, and micronutrients. In recent years, the medicinal properties of natural products have attracted widespread attention, and numerous studies have reported their efficacy in ameliorating metabolic disorders and subsequently alleviating MAFLD. This review aims to summarize the metabolic-associated pathological mechanisms of MAFLD, as well as the natural products that regulate metabolic pathways to alleviate MAFLD.

A Primary Report 166 Cases of Abdominal or Pelvic Neuroblastoma Surgery Utilizing the International Neuroblastoma Surgical Report Form (INSRF).

BACKGROUND AND AIMS: Surgery is pivotal in the management of neuroblastoma (NB), particularly in patients with Image-Defined Risk Factors (IDRFs). The International Neuroblastoma Surgical Report Form (INSRF) was introduced to enhance surgical reporting quality and analyze the defining role of extensive surgery in NB. This study reports our experience with INSRF and explores new criteria for evaluating the extent of surgical resection. METHODS: INSRF was deployed to critically analyze 166 patients with abdominal or pelvic NB who underwent surgery at our department between October 2021 and June 2023. Patient demographics, clinical characteristics, surgical datasets, and postoperative complications were described in detail. Receiver operating characteristic (ROC) curves were used to explore a new method to evaluate the extent of resection. A questionnaire was formulated to obtain attitudes/feedback and commentary from surgical oncologists with INSRF. RESULTS: 166 neuroblastoma patients with a median disease age 36.50 months. This study collated 320 INSRF reports. Among the 166 index cases, 137 were documented by two surgeons, with a concordance rate of 16.78%. Items with high inconsistency were (i) the extent of tumor resection (29.20%), (ii) renal vein involvement (25.55%), (iii) abdominal aorta encasement (16.79%), and (iv) mesenteric infiltration (17.52%). According to INSRF, the extent of resection was complete excision in 86 (51.81%) patients, minimal residual tumor < 5 cm3 in 67 (40.36%) patients, and incomplete excision > 5 cm3 in 13 (7.83%) patients. In ROC curve analysis, the number of vessels encased by tumors > 3 had a high predictive value in determining that a tumor could not be completely resected (AUC 0.916, sensitivity 0.838, specificity 0.826) using INSRF as the gold standard reference. The questionnaires showed that surgeons agreed that the extent of resection and tumor involvement of organ/vascular structures were important, while the definition and intervention(s) of intraoperative complications were less operational and understandable. CONCLUSIONS: INSRF has significant clinical application in neuroblastoma surgery. The extent of resection can be predicted based on the number of tumor-encased blood vessels. Supplementary information should be considered with the INSRF to aid practitioner reporting. Multicenter studies are needed to explore the defining role of INSRF in NB surgical management.

Phenotypic comparison and the potential antitumor function of immortalized bone marrow-derived macrophages (iBMDMs).

Introduction: Macrophages are an important component of innate immunity and involved in the immune regulation of multiple diseases. The functional diversity and plasticity make macrophages to exhibit different polarization phenotypes after different stimuli. During tumor progression, the M2-like polarized tumor-associated macrophages (TAMs) promote tumor progression by assisting immune escape, facilitating tumor cell metastasis, and switching tumor angiogenesis. Our previous studies demonstrated that functional remodeling of TAMs through engineered-modifying or gene-editing provides the potential immunotherapy for tumor. However, lack of proliferation capacity and maintained immune memory of infused macrophages restricts the application of macrophage-based therapeutic strategies in the repressive tumor immune microenvironment (TIME). Although J2 retrovirus infection enabled immortalization of bone marrow-derived macrophages (iBMDMs) and facilitated the mechanisms exploration and application, little is known about the phenotypic and functional differences among multi kinds of macrophages. Methods: HE staining was used to detect the biosafety of iBMDMs, and real-time quantitative PCR, immunofluorescence staining, and ELISA were used to detect the polarization response and expression of chemokines in iBMDMs. Flow cytometry, scratch assay, real-time quantitative PCR, and crystal violet staining were used to analyze its phagocytic function, as well as its impact on tumor cell migration, proliferation, and apoptosis. Not only that, the inhibitory effect of iBMDMs on tumor growth was detected through subcutaneous tumor loading, while the tumor tissue was paraffin sectioned and flow cytometry was used to detect its impact on the tumor microenvironment. Results: In this study, we demonstrated iBMDMs exhibited the features of rapid proliferation and long-term survival. We also compared iBMDMs with RAW264.7 cell line and mouse primary BMDMs with in vitro and in vivo experiments, indicating that the iBMDMs could undergo the same polarization response as normal macrophages with no obvious cellular morphology changes after polarization. What's more, iBMDMs owned stronger phagocytosis and pro-apoptosis functions on tumor cells. In addition, M1-polarized iBMDMs could maintain the anti-tumor phenotypes and domesticated the recruited macrophages of receptor mice, which further improved the TIME and repressed tumor growth. Discussion: iBMDMs can serve as a good object for the function and mechanism study of macrophages and the optional source of macrophage immunotherapy.

Recombinant human IL-37 attenuates acute cardiac allograft rejection in mice.

BACKGROUND: Allograft rejection remains a major obstacle to long-term graft survival. Although previous studies have demonstrated that IL-37 exhibited significant immunomodulatory effects in various diseases, research on its role in solid organ transplantation has not been fully elucidated. In this study, the therapeutic effect of recombinant human IL-37 (rhIL-37) was evaluated in a mouse cardiac allotransplantation model. METHODS: The C57BL/6 recipients mouse receiving BALB/c donor hearts were treated with rhIL-37. Graft pathological and immunohistology changes, immune cell populations, and cytokine profiles were analyzed on postoperative day (POD) 7. The proliferative capacities of Th1, Th17, and Treg subpopulations were assessed in vitro. Furthermore, the role of the p-mTOR pathway in rhIL-37-induced CD4+ cell inhibition was also elucidated. RESULTS: Compared to untreated groups, treatment of rhIL-37 achieved long-term cardiac allograft survival and effectively alleviated allograft rejection indicated by markedly reduced infiltration of CD4+ and CD11c+ cells and ameliorated graft pathological changes. rhIL-37 displayed significantly less splenic populations of Th1 and Th17 cells, as well as matured dendritic cells. The percentages of Tregs in splenocytes were significantly increased in the therapy group. Furthermore, rhIL-37 markedly decreased the levels of TNF-α and IFN-γ, but increased the level of IL-10 in the recipients. In addition, rhIL-37 inhibited the expression of p-mTOR in CD4+ cells of splenocytes. In vitro, similar to the in vivo experiments, rhIL-37 caused a decrease in the proportion of Th1 and Th17, as well as an increase in the proportion of Treg and a reduction in p-mTOR expression in CD4+ cells. CONCLUSIONS: We demonstrated that rhIL-37 effectively suppress acute rejection and induce long-term allograft acceptance. The results highlight that IL-37 could be novel and promising candidate for prevention of allograft rejection.

Melatonin pretreatment improves endometrial regenerative cell-mediated therapeutic effects in experimental colitis.

BACKGROUND: Endometrial regenerative cells (ERCs) have been proven to be an effective strategy for attenuating experimental colitis, but the complex in vivo microenvironment such as oxidative stress may largely limit and weaken ERC efficacy. Melatonin (MT) works as an anti-oxidative agent in a variety of preclinical diseases, and has been identified to promote mesenchymal stem cell-mediated therapeutic effects in different diseases. However, the ability of MT to enhance ERC-mediated effects in colitis is currently poorly understood. METHODS: Menstrual blood was collected from healthy female volunteers to obtain ERCs and identified. In vitro, H2O2-induced oxidative stress was introduced to test if MT could prevent ERCs from damage through detection of intracellular reactive oxidative species (ROS) and apoptosis assay. In vivo, dextran sodium sulfate (DSS)-induced acute colitis was treated by ERCs and MT-primed ERCs, therapeutic effects were assayed by the disease activity index (DAI), histological features, and macrophage and CD4+ T cell in the spleen and colon, and cytokine profiles in the sera and colon were also measured. RESULTS: In vitro, ERCs that underwent MT-precondition were found to possess more anti-oxidative potency in comparison to naïve ERCs, which were characterized by decreased apoptosis rate and intracellular ROS under H2O2 stimulation. In vivo, MT pretreatment can significantly enhance the therapeutic effects of ERCs in the attenuation of experimental colitis, including decreased DAI index and damage score. In addition, MT pretreatment was found to promote ERC-mediated inhibition of Th1, Th17, and M1 macrophage and pro-inflammatory cytokines, increase of Treg, and immunomodulation of cytokines in the spleen and colon. CONCLUSIONS: MT pretreatment facilitates the promotion of cell viability under oxidative stress in vitro, while also enhancing ERC-mediated therapeutic effects in experimental colitis.

Multimodal Physiological Analysis of Impact of Emotion on Cognitive Control in VR.

Cognitive control is often perplexing to elucidate and can be easily influenced by emotions. Understanding the individual cognitive control level is crucial for enhancing VR interaction and designing adaptive and self-correcting VR/AR applications. Emotions can reallocate processing resources and influence cognitive control performance. However, current research has primarily emphasized the impact of emotional valence on cognitive control tasks, neglecting emotional arousal. In this study, we comprehensively investigate the influence of emotions on cognitive control based on the arousal-valence model. A total of 26 participants are recruited, inducing emotions through VR videos with high ecological validity and then performing related cognitive control tasks. Leveraging physiological data including EEG, HRV, and EDA, we employ classification techniques such as SVM, KNN, and deep learning to categorize cognitive control levels. The experiment results demonstrate that high-arousal emotions significantly enhance users' cognitive control abilities. Utilizing complementary information among multi-modal physiological signal features, we achieve an accuracy of 84.52% in distinguishing between high and low cognitive control. Additionally, time-frequency analysis results confirm the existence of neural patterns related to cognitive control, contributing to a better understanding of the neural mechanisms underlying cognitive control in VR. Our research indicates that physiological signals measured from both the central and autonomic nervous systems can be employed for cognitive control classification, paving the way for novel approaches to improve VR/AR interactions.

EvoAI enables extreme compression and reconstruction of the protein sequence space.

Designing proteins with improved functions requires a deep understanding of how sequence and function are related, a vast space that is hard to explore. The ability to efficiently compress this space by identifying functionally important features is extremely valuable. Here, we first establish a method called EvoScan to comprehensively segment and scan the high-fitness sequence space to obtain anchor points that capture its essential features, especially in high dimensions. Our approach is compatible with any biomolecular function that can be coupled to a transcriptional output. We then develop deep learning and large language models to accurately reconstruct the space from these anchors, allowing computational prediction of novel, highly fit sequences without prior homology-derived or structural information. We apply this hybrid experimental-computational method, which we call EvoAI, to a repressor protein and find that only 82 anchors are sufficient to compress the high-fitness sequence space with a compression ratio of 1048. The extreme compressibility of the space informs both applied biomolecular design and understanding of natural evolution.

Predictive healthcare modeling for early pandemic assessment leveraging deep auto regressor neural prophet.

In this paper, NeuralProphet (NP), an explainable hybrid modular framework, enhances the forecasting performance of pandemics by adding two neural network modules; auto-regressor (AR) and lagged-regressor (LR). An advanced deep auto-regressor neural network (Deep-AR-Net) model is employed to implement these two modules. The enhanced NP is optimized via AdamW and Huber loss function to perform multivariate multi-step forecasting contrast to Prophet. The models are validated with COVID-19 time-series datasets. The NP's efficiency is studied component-wise for a long-term forecast for India and an overall reduction of 60.36% and individually 34.7% by AR-module, 53.4% by LR-module in MASE compared to Prophet. The Deep-AR-Net model reduces the forecasting error of NP for all five countries, on average, by 49.21% and 46.07% for short-and-long-term, respectively. The visualizations confirm that forecasting curves are closer to the actual cases but significantly different from Prophet. Hence, it can develop a real-time decision-making system for highly infectious diseases.

Dynamic Motion Transition: A Hybrid Data-driven and Model-driven Method for Human Pose Transitions.

The rapid, accurate, and robust computation of virtual human figures' "in-between" pose transitions from available and sometimes sparse inputs is of fundamental significance to 3D interactive graphics and computer animation. Various methods have been proposed to produce natural lifelike transitions of human pose automatically in recent decades. Nevertheless, conventional pure model-driven methods require heuristic knowledge (e.g., least motion guided by physics laws) and ad-hoc clues (e.g., splines with non-uniform time warp) that are difficult to obtain, learn, and infer. With the fast emergence of large-scale datasets readily available to animators in the most recent years, deep models afford a powerful alternative to tackle the aforementioned challenges. However, pure data-driven methods still suffer from the remaining challenges such as unseen data in practice and less generative power in model/domain/data transfer, and the measurement of the generative power has always been omitted in these works. In essence, data-driven methods solely rely on the qualities and quantities of training datasets. In this paper, we propose a hybrid approach built upon the seamless integration of data-driven and model-driven methods, called Dynamic Motion Transition (DMT), with the following salient modeling advantages: (1) The data augmentation capability based on the limited human locomotion data capture and the concept of force-derived directly from physical laws; (2) Force learning by which skeleton joints are driven to move, and the Conditional Temporal Transformer (CTT) being trained to learn the force change in the local range, both at the fine level; and (3) At the coarse level, the effective and flexible creation of the subsequent step motion using Dynamic Movement Primitives (DMP) until the target is reached. Our extensive experiments have confirmed that our model can outperform the state-of-the-art methods under the newly devised metric by virtue of the least action loss function. In addition, our novel method and system are of immediate benefit to many other animation tasks such as motion synthesis and control, and motion tracking and prediction in this bigdata graphics era.

Identified S100A9 as a target for diagnosis and treatment of ulcerative colitis by bioinformatics analysis.

Ulcerative colitis (UC) is a chronic, recurrent inflammatory bowel disease. UC confronts with severe challenges including the unclear pathogenesis and lack of specific diagnostic markers, demanding for identifying predictive biomarkers for UC diagnosis and treatment. We perform immune infiltration and weighted gene co-expression network analysis on gene expression profiles of active UC, inactive UC, and normal controls to identify UC related immune cell and hub genes. Neutrophils, M1 macrophages, activated dendritic cells, and activated mast cells are significantly enriched in active UC. MMP-9, CHI3L1, CXCL9, CXCL10, CXCR2 and S100A9 are identified as hub genes in active UC. Specifically, S100A9 is significantly overexpressed in mice with colitis. The receiver operating characteristic curve demonstrates the excellent performance of S100A9 expression in diagnosing active UC. Inhibition of S100A9 expression reduces DSS-induced colonic inflammation. These identified biomarkers associated with activity in UC patients enlighten the new insights of UC diagnosis and treatment.

Clinical features, treatment strategies, and prognosis of epithelioid inflammatory myofibroblastic sarcoma in children: a multicenter experience.

Background: Inflammatory myofibroblastic tumors (IMTs) are a spectrum of tumors that range in morphology and biological behavior from benign, intermediate, to apparently malignant and epithelioid inflammatory myofibroblastic sarcoma (EIMS) is one of the malignant subtypes. This study tried to provide experience and new ideas for treating this rare disease. Methods: This study retrospectively analyzed and followed up 12 children with EIMS admitted to Beijing Children's Hospital, Baoding Children's Hospital, and Children's Hospital of Chongqing Medical University from August 2016 to May 2022. Results: Of the 12 children, 7 were male and 5 were female, with a median age of 74.50 [interquartile range (IQR), 61.50-90.00] months. Of these patients, eight had a single lesion and four had multiple lesions. The maximum diameter of the single tumor foci was 19.30 cm, the full meridian of the multiple tumor foci target lesions was 32.67 cm, and the median maximum tumor size was 11.99 (IQR, 7.80-15.70) cm. The site of disease was the abdominopelvic cavity in eight cases, the thoracic cavity in two cases, the maxillofacial region in one case, and the larynx in one case. The clinical manifestations were predominantly elevated body temperature (n=8). There was one case of ROS1 fusion mutation and nine cases of ALK fusion mutation. Of the 12 children, 6 were biopsied at the initial diagnosis and 6 were surgically treated. Follow-up treatment included preoperative neoadjuvant chemotherapy (n=4), peritoneal thermal perfusion therapy (n=2), targeted therapy (n=3), postoperative chemotherapy (n=5), and radiotherapy (n=3). The follow-up time was 14.50 (IQR, 10.50-31.50) months, with eight cases of tumor-free survival, two cases of death, and two cases of loss of follow-up. Conclusions: EIMS in children is extremely rare and clinically aggressive. The clinical presentation is nonspecific, and the initial diagnosis of the tumor is often large. Mutations in the ALK gene are common in EIMS. Surgery is the mainstay of EIMS treatment, and patients benefit from a multidisciplinary combination that includes targeted therapies, with long-term prognosis remaining subject to ongoing follow-up.

Nutritional, Textural, and Sensory Attributes of Protein Bars Formulated with Mycoproteins.

Research accumulated over the past decades has shown that mycoprotein could serve as a healthy and safe alternative protein source, offering a viable substitute for animal- and plant-derived proteins. This study evaluated the impact of substituting whey protein with fungal-derived mycoprotein at different levels (10%, 20%, and 30%) on the quality of high-protein nutrition bars (HPNBs). It focused on nutritional content, textural changes over storage, and sensory properties. Initially, all bars displayed similar hardness, but storage time significantly affected textural properties. In the early storage period (0-5 days), hardness increased at a modest rate of 0.206 N/day to 0.403 N/day. This rate dramatically escalated from 1.13 N/day to 1.36 N/day after 5 days, indicating a substantial textural deterioration over time. Bars with lower mycoprotein levels (10%) exhibited slower hardening rates compared with those with higher substitution levels (20% and 30%), pointing to a correlation between mycoprotein content and increased bar hardness during storage. Protein digestibility was assessed through in vitro gastric and intestinal phases. Bars with no or low-to-medium levels of mycoprotein substitution (PB00, PB10, and PB20) showed significantly higher digestibility (40.3~43.8%) compared with those with the highest mycoprotein content (PB30, 32.9%). However, digestibility rates for all mycoprotein-enriched bars were lower than those observed for whey-protein-only bars (PB00, 84.5%), especially by the end of the intestinal digestion phase. The introduction of mycoprotein enriched the bars' dietary fiber content and improved their odor, attributing a fresh mushroom-like smell. These findings suggest that modest levels of mycoprotein can enhance nutritional value and maintain sensory quality, although higher substitution levels adversely affect texture and protein digestibility. This study underscores the potential of mycoprotein as a functional ingredient in HPNBs, balancing nutritional enhancement with sensory acceptability, while also highlighting the challenges of textural deterioration and reduced protein digestibility at higher substitution levels.

A case series of clinical characteristics and prognosis of congenital hepatoblastoma in a single center.

INTRODUCTION AND IMPORTANCE: Congenital hepatoblastoma is an exceedingly rare neoplasm, predominantly documented as isolated instances, with contentious aspects surrounding its therapeutic approaches and prognostic implications. This study aims to comprehensively summarize and evaluate the management experience of congenital hepatoblastoma (CHB). CASE PRESENTATION: This cohort comprised five infants diagnosed with hepatoblastoma, confirmed through pathological examination, and with an onset of symptoms before 28 days of age. They were enrolled between November 2019 and May 2022. The treatment course they underwent has been summarized, and their prognosis has been subject to analysis. CLINICAL DISCUSSION: Distinguishing congenital hepatoblastoma from other medical conditions is typically necessary. Given the patient's tender age, the approach to treatment demands comprehensive assessment, particularly in cases involving unique tumor locations or substantial tumor sizes. The selection of treatment modalities, encompassing preoperative neoadjuvant chemotherapy and surgical techniques, becomes of paramount importance. Furthermore, determining the treatment's endpoint poses a notable challenge and often necessitates a comprehensive evaluation. CONCLUSION: For pediatric patients afflicted with CHB, the application of preoperative neoadjuvant chemotherapy mitigates surgical risks, while the incorporation of surgical procedures followed by postoperative chemotherapy significantly enhances the overall prognosis. Additionally, AFP-L3% levels may serve as a valuable adjunctive marker signifying the conclusion of treatment.

Fluid Inverse Volumetric Modeling and Applications from Surface Motion.

In this study, we devise a framework for volumetrically reconstructing fluid from observable, measurable free surface motion. Our innovative method amalgamates the benefits of deep learning and conventional simulation to preserve the guiding motion and temporal coherence of the reproduced fluid. We infer surface velocities by encoding and decoding spatiotemporal features of surface sequences, and a 3D CNN is used to generate the volumetric velocity field, which is then combined with 3D labels of obstacles and boundaries. Concurrently, we employ a network to estimate the fluid's physical properties. To progressively evolve the flow field over time, we input the reconstructed velocity field and estimated parameters into the physical simulator as the initial state. Our approach yields promising results for both synthetic fluid generated by different fluid solvers and captured real fluid. The developed framework naturally lends itself to a variety of graphics applications, such as 1) effective reproductions of fluid behaviors visually congruent with the observed surface motion, and 2) physics-guided re-editing of fluid scenes. Extensive experiments affirm that our novel method surpasses state-of-the-art approaches for 3D fluid inverse modeling and animation in graphics.