Sentinel and networked symptoms in patients with breast cancer undergoing chemotherapy.

PURPOSE: It was designed to identify the symptom clusters and sentinel symptoms among patients with breast cancer receiving chemotherapy at the community level, and to explore core and bridge symptoms at the global level. METHODS: A cross-sectional survey was conducted using the MD Anderson Symptom Inventory. Patients with breast cancer receiving chemotherapy, recruited from the "Be Resilient to Breast Cancer" project between January 2023 and December 2023, were included in the study. Symptom clusters and their sentinel symptoms were identified using exploratory factor analysis and Apriori algorithm. Core and bridge symptoms were identified using network analysis. RESULTS: A total of 468 patients with breast cancer participated in the current study. At the community level, three symptom clusters and their corresponding sentinel symptoms were identified: a gastrointestinal symptom cluster (with nausea as the sentinel symptom), a psycho-sleep-related symptom cluster (with distress as the sentinel symptom), and a neurocognition symptom cluster (with dry mouth as the sentinel symptom). At the global level, fatigue emerged as the core symptom, while disturbed sleep and lack of appetite as bridge symptoms. CONCLUSION: Addressing nausea, distress, and dry mouth are imperative for alleviating specific symptom clusters at the community level. Furthermore, targeting fatigue, disturbed sleep, and lack of appetite are crucial to break the interactions among diverse symptoms at the global level.

Domain Generalization for Named Entity Boundary Detection via Metalearning.

Named entity recognition (NER) aims to recognize mentions of rigid designators from text belonging to predefined semantic types, such as person, location, and organization. In this article, we focus on a fundamental subtask of NER, named entity boundary detection, which aims at detecting the start and end boundaries of an entity mention in the text, without predicting its semantic type. The entity boundary detection is essentially a sequence labeling problem. Existing sequence labeling methods either suffer from sparse boundary tags (i.e., entities are rare and nonentities are common) or they cannot well handle the issue of variable size output vocabulary (i.e., need to retrain models with respect to different vocabularies). To address these two issues, we propose a novel entity boundary labeling model that leverages pointer networks to effectively infer boundaries depending on the input sequence. On the other hand, training models on source domains that generalize to new target domains at the test time are a challenging problem because of the performance degradation. To alleviate this issue, we propose Metabdry, a novel domain generalization approach for entity boundary detection without requiring any access to target domain information. Especially, adversarial learning is adopted to encourage domain-invariant representations. Meanwhile, metalearning is used to explicitly simulate a domain shift during training so that metaknowledge from multiple resource domains can be effectively aggregated. As such, Metabdry explicitly optimizes the capability of "learning to generalize," resulting in a more general and robust model to reduce the domain discrepancy. We first conduct experiments to demonstrate the effectiveness of our novel boundary labeling model. We then extensively evaluate Metabdry on eight data sets under domain generalization settings. The experimental results show that Metabdry achieves state-of-the-art results against the recent seven baselines.

Establishment of BV2 microglia polarization model and its effect on Toxoplasma gondii proliferation.

Toxoplasma gondii is an intracellular opportunistic, parasitic protozoan. Microglia have been classified into two main types: M1 (classically activated macrophages) and M2 (alternatively activated macrophages). BV2 cells were used in this study, together with lipopolysaccharide (LPS) and interferon (IFN)-γ or interleukin (IL)-4, which were used to induce resting microglia. Expression levels of M1/M2 markers were determined at both mRNA and protein levels, using PCR, western blot, and flow cytometry. Furthermore, cells were infected with T. gondii PLK strain, and the dynamic changes in M1/M2 marker expression levels were determined. An in vitro polarization model was successfully established. Expression of Nos2 and M1-associated markers was significantly upregulated at 12 h post-infection in BV2 cells. Further, the JAK/STAT1 and NF-κB signaling pathways were also activated following T. gondii infection. This demonstrated that T. gondii infection induces M1-type microglial polarization in vitro. The present study demonstrated that T. gondii infection affects microglial activation in vitro and elucidated the effects of activated microglia on T. gondii proliferation. This data may serve as a useful reference for more detailed elucidation of interactions between T. gondii and the innate immune system.

[Study on quantitative methods of cleistocalycis operculati cortex].

Cleistocalycis Operculati Cortex is the dry bark of Cleistocalyx operculatus. It is the raw material of Compound Hibiscuse which is external sterilization antipruritic drugs. The quality standard of Cleistocalycis Operculati Cortex in Guangdong Province "standard for the traditional Chinese medicine" (second volumes) only contains TLC identification. It is unable to effectively monitor and control the quality of Cleistocalycis Operculati Cortex. A reversed-phase HPLC method was established for the determination of 3, 3'-O-dimethylellagic acid from Cleistocalycis Operculati Cortex and the content was calculated by external standard method for the first time. Under the selected chromatographic conditions, the target components between peaks to achieve effective separation. 3,3'-O- dimethylellagic acid standard solution at the concentration of 1.00 - 25.0 mg x L(-1) showed a good linear relationship. The standard curve was Y = 77.33X + 7.904, r = 0.999 5. The average recovery was 101.0%, RSD was 1.3%. The HPLC method for the determination of 3,3'-O-dimethylellagic acid in Cleistocalycis Operculati Cortex is accurate and reliable. It can provide a strong technical support for monitoring the quality of Cleistocalycis Operculati Cortex.