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Gastroenteropancreatic neuroendocrine neoplasm (GEP-NEN) is a rare, heterogeneous tumor that can be divided into neuroendocrine tumor (NET) and neuroendocrine carcinoma (NEC) due to its degree of differentiation. With the gradual deepening of the exploration of its pathogenesis and treatment methods, the importance of preclinical models has become increasingly prominent. Here we reviewed the published preclinical models such as cell lines, organoid models, patient-derived xenograft models and genetically engineered mouse models. We also discussed the current practical applications of these models and their strengths and weaknesses, to provide threads and directions for future research.

Depression is a prevalent mental condition, and it is essential to diagnose and treat patients as soon as possible to maximize their chances of rehabilitation and recovery. An intelligent detection model based on multimodal fusion technology is proposed based on the findings of this study to address the difficulties associated with depression detection. Text data and electroencephalogram (EEG) data are used in the model as representatives of subjective and objective nature, respectively. These data are processed by the BERT–TextCNN model and the CNN–LSTM model, which are responsible for processing them. While the CNN–LSTM model is able to handle time-series data in an effective manner, the BERT–TextCNN model is able to adequately capture the semantic information that is included in text data. This enables the model to consider the various features that are associated with the various types of data. In this research, a weighted fusion technique is utilized to combine the information contained within the two modal datasets. This strategy involves assigning a weight to the outcomes of each modal data processing in accordance with the degree of contribution that each modal data will make to produce the ultimate depression detection results. In regard to the task of depression identification, the suggested model demonstrates great validity and robustness, as demonstrated by the results of the experimental validation that we carried out on a dataset that we manufactured ourselves. A viable and intelligent solution for the early identification of depression is provided by the proposed model. This solution will likely be widely utilized in clinical practice and will provide new ideas and approaches for the growth of the field of precision medicine. [ABSTRACT FROM AUTHOR]

Mental health is critical to an individual’s life and social functioning and affects emotions, cognition and behavior. Mental health status assessments can help individuals understand their own psychological status, identify potential problems in real-time and implement effective interventions to promote favorable mental health. In this study, a deep learning approach was used to construct a simple-minded and flexible model for electroencephalogram (EEG)-based mental health status assessment to construct the corresponding M4EEG model. This model is suitable not only for supervised learning tasks containing a large amount of labeled data but also for few-shot classification tasks in special cases. During execution, certain components of a pretrained transformer model are utilized as the model’s foundation. After deriving feature values from different inputs, these features are decoupled by cross-connecting them into the relation module. Finally, the correlation between the outputs and the classification results are determined by a relation score. In experiments, the Database for Emotion Analysis using Physiological Signals (DEAP) and Affective Mood and Interpersonal Goals in the School Environment (AMIGOS) datasets were partitioned into K-Shot files as the input information, and the classification results were derived from the M4EEG model. These results showed that the M4EEG model is capable of assessing mental health status through EEG, and the model can obtain results that cannot be achieved by existing models that do not apply comparable data labeling. [ABSTRACT FROM AUTHOR]

Simple Summary: Thymic neuroendocrine tumors (Th-NETs) are rare, with limited research on prognostic markers and treatments. Our retrospective study evaluated clinicopathological characteristics and therapeutic strategies for Th-NETs, filling gaps in existing studies. We highlighted the importance of biomarkers such as serum neuron-specific enolase, inflammatory factors, and oxygen 6-methylguanine-DNA methyltransferase, as well as the roles of surgical interventions, postoperative therapies, and temozolomide-based chemotherapy in improving patient survival. Our findings confirmed the crucial role of surgical treatment and the potential benefits of postoperative adjuvant radiotherapy. Furthermore, temozolomide-based chemotherapy demonstrated efficacy in Th-NETs treatment, with the neutrophil-to-lymphocyte ratio and serum neuron-specific enolase proving to be valuable prognostic markers necessitating further research. Background: Thymic neuroendocrine tumors (Th-NETs) are rare and aggressive, with a scarcity of research on predicting patient prognosis. Our study aimed to assess the impact of prognostic markers and temozolomide (TMZ)-based chemotherapy on survival in Th-NETs. Methods: We retrospectively reviewed the medical records of patients diagnosed with Th-NETs between 2013 and 2023 at our institution. We collected clinicopathological data, including tumor pathological grading, staging, serum concentrations of neuron-specific enolase (NSE) and pro-gastrin-releasing peptide, levels of inflammatory factors, and expression of oxygen 6-methylguanine-DNA methyltransferase (MGMT). Treatment details (such as surgery and chemotherapy) and survival outcomes were also documented. Results: A total of 32 patients were included in our study after excluding those without complete available information. The median progression-free survival (PFS) was 12.5 months (95%CI, 8–16 months) for 19 patients who received TMZ-based chemotherapy. Twenty-one patients underwent surgery as the primary treatment, demonstrating a median disease-free survival (DFS) of 51.0 months. The inflammatory factor neutrophil-to-lymphocyte ratio (NLR) was an independent prognostic indicator of DFS in postoperative patients and PFS in TMZ-treated patients. The overall 3-, 5-, and 10-year survival rates were 86.6%, 69.5%, and 33.8%, respectively. Ki67 level exceeding 10% (p = 0.048) and absence of surgical resection (p = 0.003) were significantly associated with worse overall survival (OS). Conclusion: Surgical treatment was currently the primary method for treating Th-NETs, and postoperative adjuvant therapy was an essential consideration for specific patient cohorts. Despite widespread positive MGMT expression, TMZ-based chemotherapy showed promise. Some potential prognostic biomarkers such as NLR and NSE need more attention. [ABSTRACT FROM AUTHOR]