Against the backdrop of an increasingly large stock market and a growing number of investors, many individuals have faced bankruptcy due to blindly following trends in stock investment. This situation has created a demand for methods to predict stock prices in advance. To address this issue, researchers have previously proposed various approaches, including traditional analytical methods based on historical data, statistical analysis methods, machine learning and deep learning methods, etc. The research focus of this paper is to investigate the specific conditions under which the Backpropagation Neural Network model delivers superior performance in stock price prediction. This study utilizes 44 years of historical stock price data from Apple Inc. (AAPL), encompassing key features such as Date, Opening Price, Highest Price of the Day, Lowest Price of the Day, Closing Price, Adjusted Closing Price, and Trading Volume. Specifically, the study focuses on selecting the Opening Price, Highest Price of the Day, Lowest Price of the Day, Closing Price, and Trading Volume as input features. The research ultimately achieved relatively accurate results. These findings are then leveraged to extend insights into effective configuration strategies for a wider range of other prediction methods.

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This study aims to investigate the multi-target and multi-pathway mechanisms of Artemisia annua in the prevention and treatment of Alzheimer's disease (AD) using a network pharmacology approach. Active compounds of Artemisia annua were identified from the Traditional Chinese Medicine Systems Pharmacology Database (TCMSP) based on ADME criteria (oral bioavailability ≥30%, drug-likeness ≥0.18). AD-related targets were retrieved from the GeneCards and DisGeNet databases. The intersection of compound targets and disease genes was analyzed to construct a protein-protein interaction (PPI) network. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were performed, followed by the development of a compound-target-pathway network. Through database analysis, 23 key active ingredients of Artemisia annua and 64 key targets were identified. Core targets, including PSEN2, NOS3, PSEN1, MPO, APP, may play critical roles in regulating β-amyloid (Aβ) dysmetabolism and modulating immuno-inflammatory responses. GO and KEGG enrichment analyses suggest that Artemisia annua is involved Aβ formation and calcium ion regulation pathways. Additionally, it may exert immunomodulatory effects through various signaling pathways, including the Notch and phosphatidylinositol signaling systems. This study provides valuable insights into the therapeutic mechanisms of Artemisia annua in AD, demonstrating its potential to regulate key pathological features through multi-target and multi-pathway interactions. These findings offer a theoretical basis for integrating Artemisia annua into innovative AD treatment strategies and warrant further experimental validation for clinical application.

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Categorized as a subtype of endometrial cancer, uterine serous carcinoma (USC) is the most destructive among other subtypes, where surgery, chemotherapy, and radiotherapy were historically the only curative modalities. Although these modalities may appear significantly effective, the first 5-year survival rate after first-phase treatments remains low due to the high recurrence rate of USC tumors. To address these challenges, many tests are done to identify mutated genes, and corresponding targeted therapies are developed, such as TP53-targeting drugs, HER-2-targeted therapies, and immune checkpoint inhibitors (ICIs), which will be elaborated on in the subsequent sections. The main reason for developing targeted therapies is that the side effects are significantly reduced compared to conventional therapies; they offer greater precision, which leads to better outcomes, and a possible cancer-free future for patients. This paper provides an overview of mechanisms and emerging targeted therapies for USC, along with a brief analysis of USC and its conventional therapies, which offers new ideas and directions for pathological research and clinical treatment of USC and other complex cancers.

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Huntington's disease (HD) is a currently incurable neurodegenerative disorder caused by an autosomal dominant mutation in the HTT gene, leading to the production of mutant huntingtin protein (mHTT) with an expanded polyglutamine (polyQ) tract. This aberrant protein aggregation results in progressive neuronal dysfunction, particularly in the striatum and cortex, manifesting as involuntary choreiform movements (resembling dance-like behaviors), cognitive decline, and psychiatric disturbances. Despite advances in symptomatic management—such as antidepressants, dopamine-modulating agents, and physical therapy—existing treatments fail to halt disease progression or reverse neuronal damage.In recent years, novel therapeutic strategies have emerged, offering hope for disease modification rather than mere symptom alleviation. One promising approach involves mini-intrabodies, engineered antibody fragments designed to selectively bind and neutralize mHTT. These intrabodies facilitate the degradation of toxic protein aggregates via lysosomal pathways, effectively reducing neuronal toxicity. Other cutting-edge interventions include antisense oligonucleotides (ASOs) to suppress mHTT expression, CRISPR-based gene editing to correct the HTT mutation, and stem cell therapy to replace damaged neurons.This article evaluates these innovative strategies, with a focus on lysosome-targeted mini-intrabodies as a potential curative approach. By analyzing preclinical and clinical advancements, we aim to highlight future research directions that could transform HD treatment from palliative care to definitive therapy.

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