AVaTER: Fusing Audio, Visual, and Textual Modalities Using Cross-Modal Attention for Emotion Recognition.

Multimodal emotion classification (MEC) involves analyzing and identifying human emotions by integrating data from multiple sources, such as audio, video, and text. This approach leverages the complementary strengths of each modality to enhance the accuracy and robustness of emotion recognition systems. However, one significant challenge is effectively integrating these diverse data sources, each with unique characteristics and levels of noise. Additionally, the scarcity of large, annotated multimodal datasets in Bangla limits the training and evaluation of models. In this work, we unveiled a pioneering multimodal Bangla dataset, MAViT-Bangla (Multimodal Audio Video Text Bangla dataset). This dataset, comprising 1002 samples across audio, video, and text modalities, is a unique resource for emotion recognition studies in the Bangla language. It features emotional categories such as anger, fear, joy, and sadness, providing a comprehensive platform for research. Additionally, we developed a framework for audio, video and textual emotion recognition (i.e., AVaTER) that employs a cross-modal attention mechanism among unimodal features. This mechanism fosters the interaction and fusion of features from different modalities, enhancing the model's ability to capture nuanced emotional cues. The effectiveness of this approach was demonstrated by achieving an F1-score of 0.64, a significant improvement over unimodal methods.

Enhancing image caption generation through context-aware attention mechanism.

Image captioning, the process of generating natural language descriptions based on image content, has garnered attention in AI research for its implications in scene understanding and human-computer interaction. While much prior research has focused on caption generation for English, addressing low-resource languages like Bengali presents challenges, particularly in producing coherent captions linking visual objects with corresponding words. This paper proposes a context-aware attention mechanism over semantic attention to accurately diagnose objects for image captioning in Bengali. The proposed architecture consists of an encoder and a decoder block. We chose ResNet-50 over the other pre-trained models for encoding the image features due to its ability to solve the vanishing gradient problem and recognize complex object features. For decoding generated captions, a bidirectional Gated Recurrent Unit (GRU) architecture combined with an attention mechanism captures contextual dependencies in both directions, resulting in more accurate captions. The paper also highlights the challenge of transferring knowledge between domains, especially with culturally specific images. Evaluation of three Bengali benchmark datasets, namely BAN-Cap, BanglaLekhaImageCaption, and Bornon, demonstrates significant performance improvement in METEOR score over existing methods by approximately 30%, 18%, and 45%, respectively. The proposed context-aware, attention-based image captioning system significantly outperforms current state-of-the-art models in Bengali caption generation despite limitations in reference captions on certain datasets.

A systematic review on EEG-based neuromarketing: recent trends and analyzing techniques.

Neuromarketing is an emerging research field that aims to understand consumers' decision-making processes when choosing which product to buy. This information is highly sought after by businesses looking to improve their marketing strategies by understanding what leaves a positive or negative impression on consumers. It has the potential to revolutionize the marketing industry by enabling companies to offer engaging experiences, create more effective advertisements, avoid the wrong marketing strategies, and ultimately save millions of dollars for businesses. Therefore, good documentation is necessary to capture the current research situation in this vital sector. In this article, we present a systematic review of EEG-based Neuromarketing. We aim to shed light on the research trends, technical scopes, and potential opportunities in this field. We reviewed recent publications from valid databases and divided the popular research topics in Neuromarketing into five clusters to present the current research trend in this field. We also discuss the brain regions that are activated when making purchase decisions and their relevance to Neuromarketing applications. The article provides appropriate illustrations of marketing stimuli that can elicit authentic impressions from consumers' minds, the techniques used to process and analyze recorded brain data, and the current strategies employed to interpret the data. Finally, we offer recommendations to upcoming researchers to help them investigate the possibilities in this area more efficiently in the future.

A Case Report on an Unusual Presentation of Pernicious Anemia.

Pernicious anemia is an autoimmune disease leading to impaired absorption of dietary cobalamin. Patients with pernicious anemia can present with multiple hematological, neurological and gastrointestinal complaints. Herein, we have a case of pernicious anemia presenting with alternating bowel habit. This was challenging and unique as the patient didn't have any usual condition responsible for alternating bowel habit and it is not reported in cases of pernicious anemia either. The case is a 46-year-old male who was admitted with alternating bowel habit, paresthesia and fever for the last 6 months. Patient was found to be severely anemic. After full workup, he was diagnosed with pernicious anemia. The patient was treated with IM Injections of Vitamin B12. After 3 months of discharge, the patient was free of all the symptoms. This case emphasizes the importance of investigating anemic patients with alternating bowel habit for pernicious anemia and also the need to exclude other causes of this symptom before labeling it as pernicious anemia only.

ViTab Transformer Framework for Predicting Induced Electric Field and Focality in Transcranial Magnetic Stimulation.

Transcranial magnetic stimulation is an electromagnetic induction-based non-invasive therapeutic technique for neurological diseases. For finding new clinical applications and enhancing the efficacy of TMS in existing neurological disorders, the current study focuses on a deep learning-based prediction model as an alternative to time-consuming electromagnetic (EM) simulation software. The main bottleneck of the existing prediction models is to consider very few input parameters of a standard coil such as coil type and coil position for predicting an output of electric field value. To overcome this limitation, a transformer-based prediction model titled as ViTab transformer is developed in this work to predict electric field (E-max), focality or area of stmulation (S-half), and volume of stimulation (V-half) by considering several input parameters such as sources of MRI images, types of coils, coil position, rate of change of current, brain tissues conductivity, and coil distance from the scalp. The proposed framework consists of a vision and a tab transformer to handle both image and tabular-type data. The prediction performance of the offered model is evaluated in terms of coefficient determination, R2 score, for E-max, V-half, and S-half in the testing phase. The obtained result in terms of R2 score for E-max, V-half, and S-half are found 0.97, 0.87, and 0.90 respectively. The results indicate that the suggested ViTab transformer model can predict electric field as well as focality more accurately than the current state-of-the-art methods. The reduced computational time, as well as efficient prediction accuracy, resembles that ViTab transformer can assist the neuroscientist and neurosurgeon prior to providing superior TMS treatment in near future.

MAVS signaling is required for preventing persistent chikungunya heart infection and chronic vascular tissue inflammation.

Chikungunya virus (CHIKV) infection has been associated with severe cardiac manifestations, yet, how CHIKV infection leads to heart disease remains unknown. Here, we leveraged both mouse models and human primary cardiac cells to define the mechanisms of CHIKV heart infection. Using an immunocompetent mouse model of CHIKV infection as well as human primary cardiac cells, we demonstrate that CHIKV directly infects and actively replicates in cardiac fibroblasts. In immunocompetent mice, CHIKV is cleared from cardiac tissue without significant damage through the induction of a local type I interferon response from both infected and non-infected cardiac cells. Using mice deficient in major innate immunity signaling components, we found that signaling through the mitochondrial antiviral-signaling protein (MAVS) is required for viral clearance from the heart. In the absence of MAVS signaling, persistent infection leads to focal myocarditis and vasculitis of the large vessels attached to the base of the heart. Large vessel vasculitis was observed for up to 60 days post infection, suggesting CHIKV can lead to vascular inflammation and potential long-lasting cardiovascular complications. This study provides a model of CHIKV cardiac infection and mechanistic insight into CHIKV-induced heart disease, underscoring the importance of monitoring cardiac function in patients with CHIKV infections.

Conv-ViT: A Convolution and Vision Transformer-Based Hybrid Feature Extraction Method for Retinal Disease Detection.

The current advancement towards retinal disease detection mainly focused on distinct feature extraction using either a convolutional neural network (CNN) or a transformer-based end-to-end deep learning (DL) model. The individual end-to-end DL models are capable of only processing texture or shape-based information for performing detection tasks. However, extraction of only texture- or shape-based features does not provide the model robustness needed to classify different types of retinal diseases. Therefore, concerning these two features, this paper developed a fusion model called 'Conv-ViT' to detect retinal diseases from foveal cut optical coherence tomography (OCT) images. The transfer learning-based CNN models, such as Inception-V3 and ResNet-50, are utilized to process texture information by calculating the correlation of the nearby pixel. Additionally, the vision transformer model is fused to process shape-based features by determining the correlation between long-distance pixels. The hybridization of these three models results in shape-based texture feature learning during the classification of retinal diseases into its four classes, including choroidal neovascularization (CNV), diabetic macular edema (DME), DRUSEN, and NORMAL. The weighted average classification accuracy, precision, recall, and F1 score of the model are found to be approximately 94%. The results indicate that the fusion of both texture and shape features assisted the proposed Conv-ViT model to outperform the state-of-the-art retinal disease classification models.

The γδ IEL effector API5 masks genetic susceptibility to Paneth cell death.

Loss of Paneth cells and their antimicrobial granules compromises the intestinal epithelial barrier and is associated with Crohn's disease, a major type of inflammatory bowel disease1-7. Non-classical lymphoid cells, broadly referred to as intraepithelial lymphocytes (IELs), intercalate the intestinal epithelium8,9. This anatomical position has implicated them as first-line defenders in resistance to infections, but their role in inflammatory disease pathogenesis requires clarification. The identification of mediators that coordinate crosstalk between specific IEL and epithelial subsets could provide insight into intestinal barrier mechanisms in health and disease. Here we show that the subset of IELs that express γ and δ T cell receptor subunits (γδ IELs) promotes the viability of Paneth cells deficient in the Crohn's disease susceptibility gene ATG16L1. Using an ex vivo lymphocyte-epithelium co-culture system, we identified apoptosis inhibitor 5 (API5) as a Paneth cell-protective factor secreted by γδ IELs. In the Atg16l1-mutant mouse model, viral infection induced a loss of Paneth cells and enhanced susceptibility to intestinal injury by inhibiting the secretion of API5 from γδ IELs. Therapeutic administration of recombinant API5 protected Paneth cells in vivo in mice and ex vivo in human organoids with the ATG16L1 risk allele. Thus, we identify API5 as a protective γδ IEL effector that masks genetic susceptibility to Paneth cell death.

Shapley-Additive-Explanations-Based Factor Analysis for Dengue Severity Prediction using Machine Learning.

Dengue is a viral disease that primarily affects tropical and subtropical regions and is especially prevalent in South-East Asia. This mosquito-borne disease sometimes triggers nationwide epidemics, which results in a large number of fatalities. The development of Dengue Haemorrhagic Fever (DHF) is where most cases occur, and a large portion of them are detected among children under the age of ten, with severe conditions often progressing to a critical state known as Dengue Shock Syndrome (DSS). In this study, we analysed two separate datasets from two different countries- Vietnam and Bangladesh, which we referred as VDengu and BDengue, respectively. For the VDengu dataset, as it was structured, supervised learning models were effective for predictive analysis, among which, the decision tree classifier XGBoost in particular produced the best outcome. Furthermore, Shapley Additive Explanation (SHAP) was used over the XGBoost model to assess the significance of individual attributes of the dataset. Among the significant attributes, we applied the SHAP dependence plot to identify the range for each attribute against the number of DHF or DSS cases. In parallel, the dataset from Bangladesh was unstructured; therefore, we applied an unsupervised learning technique, i.e., hierarchical clustering, to find clusters of vital blood components of the patients according to their complete blood count reports. The clusters were further analysed to find the attributes in the dataset that led to DSS or DHF.

CNN-XGBoost fusion-based affective state recognition using EEG spectrogram image analysis.

Recognizing emotional state of human using brain signal is an active research domain with several open challenges. In this research, we propose a signal spectrogram image based CNN-XGBoost fusion method for recognising three dimensions of emotion, namely arousal (calm or excitement), valence (positive or negative feeling) and dominance (without control or empowered). We used a benchmark dataset called DREAMER where the EEG signals were collected from multiple stimulus along with self-evaluation ratings. In our proposed method, we first calculate the Short-Time Fourier Transform (STFT) of the EEG signals and convert them into RGB images to obtain the spectrograms. Then we use a two dimensional Convolutional Neural Network (CNN) in order to train the model on the spectrogram images and retrieve the features from the trained layer of the CNN using a dense layer of the neural network. We apply Extreme Gradient Boosting (XGBoost) classifier on extracted CNN features to classify the signals into arousal, valence and dominance of human emotion. We compare our results with the feature fusion-based state-of-the-art approaches of emotion recognition. To do this, we applied various feature extraction techniques on the signals which include Fast Fourier Transformation, Discrete Cosine Transformation, Poincare, Power Spectral Density, Hjorth parameters and some statistical features. Additionally, we use Chi-square and Recursive Feature Elimination techniques to select the discriminative features. We form the feature vectors by applying feature level fusion, and apply Support Vector Machine (SVM) and Extreme Gradient Boosting (XGBoost) classifiers on the fused features to classify different emotion levels. The performance study shows that the proposed spectrogram image based CNN-XGBoost fusion method outperforms the feature fusion-based SVM and XGBoost methods. The proposed method obtained the accuracy of 99.712% for arousal, 99.770% for valence and 99.770% for dominance in human emotion detection.

Driving maneuver classification from time series data: a rule based machine learning approach.

Drivers' improper driving behavior plays a vital role in road accidents. Different approaches have been proposed to classify and evaluate driving performance to ensure road safety. However, most of the techniques are based on neural networks which work like a black box and make the logical reasoning behind the classification decision unclear. In this paper, we propose a rule-based machine learning technique using a sequential covering algorithm to classify the driving maneuvers from time-series data. In the sequential covering algorithm, the impact of each rule is measured as the metrics of coverage and accuracy, where the coverage and accuracy indicate the amount of covered and correctly identified instances in a maneuver class, respectively. The final ruleset for each maneuver class is formed with only the significant rules. In this way, the rules are learned in an unsupervised manner and only the best performance of the rules are included in the ruleset. The set of rules is also optimized by pruning based on the performance of the test data. Application of the proposed system is beneficial compared to the traditional machine learning and deep learning approaches which typically require a larger dataset and higher computational time and complexity.

NLIMED: Natural Language Interface for Model Entity Discovery in Biosimulation Model Repositories.

Semantic annotation is a crucial step to assure reusability and reproducibility of biosimulation models in biology and physiology. For this purpose, the COmputational Modeling in BIology NEtwork (COMBINE) community recommends the use of the Resource Description Framework (RDF). This grounding in RDF provides the flexibility to enable searching for entities within models (e.g., variables, equations, or entire models) by utilizing the RDF query language SPARQL. However, the rigidity and complexity of the SPARQL syntax and the nature of the tree-like structure of semantic annotations, are challenging for users. Therefore, we propose NLIMED, an interface that converts natural language queries into SPARQL. We use this interface to query and discover model entities from repositories of biosimulation models. NLIMED works with the Physiome Model Repository (PMR) and the BioModels database and potentially other repositories annotated using RDF. Natural language queries are first "chunked" into phrases and annotated against ontology classes and predicates utilizing different natural language processing tools. Then, the ontology classes and predicates are composed as SPARQL and finally ranked using our SPARQL Composer and our indexing system. We demonstrate that NLIMED's approach for chunking and annotating queries is more effective than the NCBO Annotator for identifying relevant ontology classes in natural language queries.Comparison of NLIMED's behavior against historical query records in the PMR shows that it can adapt appropriately to queries associated with well-annotated models.

Machine learning in modelling land-use and land cover-change (LULCC): Current status, challenges and prospects.

Land-use and land-cover change (LULCC) are of importance in natural resource management, environmental modelling and assessment, and agricultural production management. However, LULCC detection and modelling is a complex, data-driven process in the remote sensing field due to the processing of massive historical and current data, real-time interaction of scenario data, and spatial environmental data. In this paper, we review principles and methods of LULCC modelling, using machine learning and beyond, such as traditional cellular automata (CA). Then, we examine the characteristics, capabilities, limitations, and perspectives of machine learning. Machine learning has not yet been dramatic in modelling LULCC, such as urbanization prediction and crop yield prediction because competition and transition between land cover types are dynamic at a local scale under varying natural drivers and human activities. Upcoming challenges of machine learning in modelling LULCC remain in the detection and prediction of LULC evolutionary processes if considering their applicability and feasibility, such as the spatio-temporal transition mechanisms to describe occurrence, transition, spreading, and spatial patterns of changes, availability of training data of all the change drivers, particularly sequence data, and identification and inclusion of local ecological, hydrological, and social-economic drivers in addressing the spectral feature change. This review points out the need for multidisciplinary research beyond image processing and pattern recognition of machine learning in accelerating and advancing studies of LULCC modelling. Despite this, we believe that machine learning has strong potentials to incorporate new exploratory variables in modelling LULCC through expanding remote sensing big data and advancing transient algorithms.

Are childbirth location and mode of delivery associated with favorable early breastfeeding practices in hard to reach areas of Bangladesh?

BACKGROUND: Early initiation of breastfeeding within one hour of birth (EIBF) and no prelacteal feeding are WHO recommended practices for improving maternal and newborn health outcomes. Globally, EIBF can avert around 22% of newborn death. In recent years, Bangladesh has experienced increasing facility delivery coverage and cesarean section rates. However, the impact of these changes on early breastfeeding initiation in hard to reach areas (HtR) of the country is still poorly understood. Therefore, this study aimed to examine the independent associations between childbirth locations and mode of delivery with favorable early breastfeeding practices in four hard to reach areas of Bangladesh. METHOD: We extracted data from a cross-sectional study conducted in four HtR areas of Bangladesh in 2017. A total of 2768 women, having birth outcomes in the past 12 months of the survey, were interviewed using structured questionnaires. EIBF and no prelacteal feeding were considered as favorable early breastfeeding practices. The categories of childbirth locations were defined by the place of birth (home vs. facility) and the delivery sector (public/NGO vs. private). The mode of delivery was categorized into vaginal delivery and cesarean section. Generalized linear models were used to test the independent associations while adjusting for potential confounders. RESULTS: The prevalence of EIBF practices were 69.6%(95% CI:67.8-71.3); 72.2%(95% CI:67.8-71.3) among home births Vs 63.0%(95% CI:59.5%-66.4%) among facility births. Around 73.9% (95% CI:72.3-75.6) mother's in the study areas reported no-prelacteal feeding. Compared to home births, women delivering in the facilities had lower adjusted odds of EIBF (aOR = 0.51; 95%CI:0.35-0.75). Cesarean section was found to be negatively associated with EIBF (aOR = 0.20; 95%CI:0.12-0.35), after adjusting for potential confounders. We could not find any significant associations between the place of birth and mode of delivery with no prelacteal feeding. DISCUSSIONS: This study found that facility births and cesarean deliveries were negatively associated with EIBF. Although the implementation of "Baby-Friendly Hospital Initiatives" could be a potential solution for improving EIBF and no prelacteal feeding practices, the challenges of reduced service availability and accessibility in HtR areas must be considered while devising effective intervention strategies. Future studies can explore potential interventions to promote early breastfeeding for facility births and cesarean deliveries in HtR areas.

RAGE impairs murine diabetic atherosclerosis regression and implicates IRF7 in macrophage inflammation and cholesterol metabolism.

Despite advances in lipid-lowering therapies, people with diabetes continue to experience more limited cardiovascular benefits. In diabetes, hyperglycemia sustains inflammation and preempts vascular repair. We tested the hypothesis that the receptor for advanced glycation end-products (RAGE) contributes to these maladaptive processes. We report that transplantation of aortic arches from diabetic, Western diet-fed Ldlr-/- mice into diabetic Ager-/- (Ager, the gene encoding RAGE) versus WT diabetic recipient mice accelerated regression of atherosclerosis. RNA-sequencing experiments traced RAGE-dependent mechanisms principally to the recipient macrophages and linked RAGE to interferon signaling. Specifically, deletion of Ager in the regressing diabetic plaques downregulated interferon regulatory factor 7 (Irf7) in macrophages. Immunohistochemistry studies colocalized IRF7 and macrophages in both murine and human atherosclerotic plaques. In bone marrow-derived macrophages (BMDMs), RAGE ligands upregulated expression of Irf7, and in BMDMs immersed in a cholesterol-rich environment, knockdown of Irf7 triggered a switch from pro- to antiinflammatory gene expression and regulated a host of genes linked to cholesterol efflux and homeostasis. Collectively, this work adds a new dimension to the immunometabolic sphere of perturbations that impair regression of established diabetic atherosclerosis and suggests that targeting RAGE and IRF7 may facilitate vascular repair in diabetes.

An intestinal organoid-based platform that recreates susceptibility to T-cell-mediated tissue injury.

A goal in precision medicine is to use patient-derived material to predict disease course and intervention outcomes. Here, we use mechanistic observations in a preclinical animal model to design an ex vivo platform that recreates genetic susceptibility to T-cell-mediated damage. Intestinal graft-versus-host disease (GVHD) is a life-threatening complication of allogeneic hematopoietic cell transplantation. We found that intestinal GVHD in mice deficient in Atg16L1, an autophagy gene that is polymorphic in humans, is reversed by inhibiting necroptosis. We further show that cocultured allogeneic T cells kill Atg16L1-mutant intestinal organoids from mice, which was associated with an aberrant epithelial interferon signature. Using this information, we demonstrate that pharmacologically inhibiting necroptosis or interferon signaling protects human organoids derived from individuals harboring a common ATG16L1 variant from allogeneic T-cell attack. Our study provides a roadmap for applying findings in animal models to individualized therapy that targets affected tissues.

Prevalence of hypercholesterolemia in Nigeria: a systematic review and meta-analysis.

OBJECTIVES: Elevated blood cholesterol (hypercholesterolemia) is a significant cause of cardiovascular disease. We aimed to estimate national and zonal prevalence of hypercholesterolemia in Nigeria to help guide targeted public health programs. STUDY DESIGN: This is a systematic review and synthesis of publicly available epidemiologic data on hypercholesterolemia in Nigeria. METHODS: We systematically searched MEDLINE, EMBASE, Global Health, and Africa Journals Online for studies on the prevalence of hypercholesterolemia in Nigeria published between 1990 and 2018. We used a random-effects meta-analysis (Freeman-Tukey double arcsine transformation) and meta-regression model to estimate the prevalence of hypercholesterolemia in Nigeria in 1995 and 2015. RESULTS: In total, 13 studies (n = 16,981) were retrieved. The pooled crude prevalence of hypercholesterolemia in Nigeria was 38% (95% confidence interval: 26-51), with prevalence in women slightly higher (42%, 23-63) compared with men (38%, 20-58). The prevalence was highest in the South-south (53%, 38-68) and lowest in the South-west (3%, 2-4) and North-east (4%, 2-7). Urban dwellers had a significantly higher rate (52%, 24-79) compared with rural dwellers (10%, 6-15). We estimated over 8.2 million persons (age-adjusted prevalence 16.5%) aged 20 years or more had hypercholesterolemia in Nigeria in 1995, increasing to 21.9 million persons (age-adjusted prevalence 25.9%) in 2015. CONCLUSIONS: Our findings suggest a high prevalence of hypercholesterolemia in Nigeria. Urbanization, lifestyles, diets, and culture appear to be driving an increasing prevalence, especially among women. Population-wide awareness and education on reducing elevated cholesterol levels and associated risks should be prioritized.

Model annotation and discovery with the Physiome Model Repository.

BACKGROUND: Mathematics and Phy sics-based simulation models have the potential to help interpret and encapsulate biological phenomena in a computable and reproducible form. Similarly, comprehensive descriptions of such models help to ensure that such models are accessible, discoverable, and reusable. To this end, researchers have developed tools and standards to encode mathematical models of biological systems enabling reproducibility and reuse, tools and guidelines to facilitate semantic description of mathematical models, and repositories in which to archive, share, and discover models. Scientists can leverage these resources to investigate specific questions and hypotheses in a more efficient manner. RESULTS: We have comprehensively annotated a cohort of models with biological semantics. These annotated models are freely available in the Physiome Model Repository (PMR). To demonstrate the benefits of this approach, we have developed a web-based tool which enables users to discover models relevant to their work, with a particular focus on epithelial transport. Based on a semantic query, this tool will help users discover relevant models, suggesting similar or alternative models that the user may wish to explore or use. CONCLUSION: The semantic annotation and the web tool we have developed is a new contribution enabling scientists to discover relevant models in the PMR as candidates for reuse in their own scientific endeavours. This approach demonstrates how semantic web technologies and methodologies can contribute to biomedical and clinical research. The source code and links to the web tool are available at https://github.com/dewancse/model-discovery-tool.

Autophagy protein ATG16L1 prevents necroptosis in the intestinal epithelium.

A variant of the autophagy gene ATG16L1 is associated with Crohn's disease, an inflammatory bowel disease (IBD), and poor survival in allogeneic hematopoietic stem cell transplant recipients. We demonstrate that ATG16L1 in the intestinal epithelium is essential for preventing loss of Paneth cells and exaggerated cell death in animal models of virally triggered IBD and allogeneic hematopoietic stem cell transplantation. Intestinal organoids lacking ATG16L1 reproduced this loss in Paneth cells and displayed TNFα-mediated necroptosis, a form of programmed necrosis. This cytoprotective function of ATG16L1 was associated with the role of autophagy in promoting mitochondrial homeostasis. Finally, therapeutic blockade of necroptosis through TNFα or RIPK1 inhibition ameliorated disease in the virally triggered IBD model. These findings indicate that, in contrast to tumor cells in which autophagy promotes caspase-independent cell death, ATG16L1 maintains the intestinal barrier by inhibiting necroptosis in the epithelium.

The TGF-ß/Smad4 Signaling Pathway in Pancreatic Carcinogenesis and Its Clinical Significance.

Pancreatic ductal adenocarcinoma (PDAC) is one of the most fatal human cancers due to its complicated genomic instability. PDAC frequently presents at an advanced stage with extensive metastasis, which portends a poor prognosis. The known risk factors associated with PDAC include advanced age, smoking, long-standing chronic pancreatitis, obesity, and diabetes. Its association with genomic and somatic mutations is the most important factor for its aggressiveness. The most common gene mutations associated with PDAC include KRas2, p16, TP53, and Smad4. Among these, Smad4 mutation is relatively specific and its inactivation is found in more than 50% of invasive pancreatic adenocarcinomas. Smad4 is a member of the Smad family of signal transducers and acts as a central mediator of transforming growth factor beta (TGF-ß) signaling pathways. The TGF-ß signaling pathway promotes many physiological processes, including cell growth, differentiation, proliferation, fibrosis, and scar formation. It also plays a major role in the development of tumors through induction of angiogenesis and immune suppression. In this review, we will discuss the molecular mechanism of TGF-ß/Smad4 signaling in the pathogenesis of pancreatic adenocarcinoma and its clinical implication, particularly potential as a prognostic factor and a therapeutic target.