Jingzhou Central Hospital

Daily equity return forecasting is challenging due to low signal-to-noise ratios, heavy-tailed innovations, and persistent distribution drift. We study one-step-ahead log-return prediction using daily market variables and return-based transformations. We propose a CNN–LightGBM hybrid that transfers a last-step CNN embedding to a gradient-boosted tree regressor through explicit embedding standardization, which stabilizes the representation interface for tree learning. To reduce train-to-evaluation mismatch under drift, we adopt split-wise, training-only standardization with a recency-aware fit-latest-W rule. Return-related predictors are anchored on a one-sided wavelet-denoised close series, while other market channels are retained in their original form to preserve episodic extremes. Experiments on NIFTY50 with walk-forward model selection show statistically reliable accuracy gains over Naive0 and competitive performance against representative deep sequence baselines, and the supplementary evaluations on HDFC and INDA provide additional out-of-sample evidence on these two assets under the same strictly chronological protocol. A long-or-cash decision rule based on the return forecasts yields positive risk-adjusted performance under realistic transaction-cost assumptions, supporting the practical relevance of the predictive signal.

As a multiparameter, high-throughput single-cell analysis technology, flow cytometry has become a core tool for biomedical research and clinical diagnosis because of its ability to detect the physical properties (such as size and complexity) and biochemical characteristics (such as surface markers and intracellular proteins) of cells. This article reviews its technical development history, application fields and future challenges. At the technical level, flow cytometry has gradually developed from early single-parameter detection to innovative technologies such as multicolor fluorescence, mass spectrometry flow and imaging flow, realizing the simultaneous analysis and dynamic visualization of over 40 parameters of single cells. In clinical applications, flow cytometry is widely used in the fields of immune diseases and blood tumors, significantly improving diagnostic accuracy and treatment monitoring efficiency. In addition, it also plays a key role in cell biology and drug development. Although flow cytometry faces technical challenges such as high cost, complex data processing and multicolor fluorescence interference, future development directions should focus on intelligence (AI-assisted analysis), portability and multiomics integration, which is expected to further promote the development of precision medicine and personalized treatment. This article systematically reviews the technical evolution and application value of flow cytometry, providing a reference for understanding its core position and future potential in modern medicine.

ABSTRACTBackgroundThis study investigates the impact of glycemic variability on sudomotor dysfunction in type 2 diabetes mellitus.MethodsA total of 206 type 2 diabetes mellitus patients and 34 healthy controls were included. Sweat function (SF) was assessed using electrochemical conductance of hands (HESC) and feet (FESC). Type 2 diabetes mellitus patients underwent continuous glucose monitoring (CGM), and blood glucose variability was analyzed using various metrics. Type 2 diabetes mellitus patients were classified into normal, abnormal, reversible, and persistent abnormal SF groups.ResultsIn healthy controls, SF showed no circadian rhythm. Type 2 diabetes mellitus patients with abnormal SF had longer diabetes duration, higher glucose variability, and greater SF impairment (P < 0.05). The reversible group exhibited the largest SF fluctuations (~26 μS) and a significant correlation between glucose variability and SF (P < 0.05). Blood glucose levels of 5–10 mmol/L were associated with improved SF in this group.ConclusionsThe findings suggest that greater glucose variability correlates with more severe peripheral nerve damage, and controlling blood glucose within 5–10 mmol/L may improve SF, offering insights for personalized treatment strategies in diabetic peripheral neuropathy (DPN) prevention.

Radiotherapy (RT) is widely used for malignant tumors ablation in the clinic. However, redundant doses of X-rays might cause irreversible side effects in the periphery of tumor sites. To address this, the convergence of low-dose RT with a novel therapeutic method is promising. Here, a hydrogel system was created that combines nanozymes with NIR-II photothermal therapy and RT sensitization. Meteor hammer-like nanozyme Au-MnOx and agarose hydrogel were mixed to form multifunctional nanozyme hydrogel (MNH). The system experienced a reduction in MNH hardness when exposed to 1064nm laser irradiation, allowing the release of Au-MnOx to react with H2O2 in the tumor microenvironment, resulting in the production of ·OH, which destroys bioactive substances and increases oxidative stress. This process also improves the effectiveness of hyperthermia in photothermal therapy (PTT). In addition, MNH-baed PTT enhances the RT sensitivity. Additionally, Au, with a high atomic number emits X-rays, leading to the generation of reactive oxygen species (ROS) through preexisting processes, which enhances radiation-induced DNA damage. Notably, the in vivo results indicated that MNH + NIR + RT could achieve a potent tumor inhibition rate with negligible side effects to normal tissues. This work paves a new avenue for novel nanozyme-based radiosensitization.

Aspartame, an artificial sweetener, has been implicated in the formation of kidney stones, although the underlying mechanisms remain poorly understood. This study aims to analyze the toxicity of aspartame on kidney stone development using network toxicology and molecular docking strategies. We used ChEMBL, SwissTargetPrediction, and STITCH databases to identify 145 unique targets of aspartame action, and GeneCards and OMIM databases to identify 776 unique targets related to kidney stones. Integration of these datasets revealed 19 common targets for aspartame-induced kidney stones. A Protein-Protein Interaction (PPI) network was constructed utilizing the STRING database, and the topological properties of this network were evaluated using Cytoscape software. This analysis revealed five key targets: ACE, IL1B, REN, CASP3, and NOS3. To further understand the biological significance of these targets, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were conducted using the DAVID database. The results indicated that core targets are primarily involved in endocrine processes, endopeptidase activity, and aspartic-type endopeptidase activity. Notably, signaling pathways such as the renin-angiotensin system (RAS) demonstrated significant associations with aspartame-related toxicity. Molecular docking analysis using Autodock Vina revealed a positive affinity between the proteins and aspartame. Molecular Dynamics (MD) simulations revealed stable interactions and optimized binding conformations for all targets, suggesting significant roles for specific residues like GLU384 in ACE, ARG341 in CASP3, ARG183 in NOS3, and ARG98 in IL1B. This study successfully identifies and characterizes the key molecular targets and pathways associated with aspartame-induced kidney stones, providing a foundational understanding of the underlying biological mechanisms. Additional experiments are necessary to verify these findings and explore therapeutic approaches targeting these pathways.

Despite advances in treatment, the prognosis of colon cancer is still unsatisfactory, and there is an urgent need for reliable biomarkers and mechanistic studies. The role of long non-coding RNAs (lncRNAs) in tumor progression is well established, but the prognostic value and mechanism of LINC01063 in colon cancer are unclear. We analyzed samples of colon cancer tissues and paracancerous normal tissues from 116 patients and performed functional experiments in the RKO/HCT116 cell line. The expressions of LINC01063 and miR-134-5p were measured using qRT-PCR. Clinical significance was assessed by statistical methods, and their interactions were verified by dual luciferase assay. The biological effects were evaluated by CCK-8, Transwell, and apoptosis assays. LINC01063 was upregulated in cancer tissues, associated with the TNM staging system and lymph node dissemination, and an independent predictor of poor prognosis. LINC01063 acts as a "molecular sponge" to adsorb miR-134-5p and deregulate its inhibitory effect on KRAS. Knockdown of LINC01063 inhibited cell proliferation/invasion and promoted apoptosis, partially reversed by miR-134-5p inhibition. LINC01063 sponged miR-134-5p to relieve inhibition of KRAS, with KRAS mediating its pro-tumor effects. In this study, we demonstrated that LINC01063 can be used as an independent prognostic marker for colon cancer and explored its promotion of colon cancer cancer progression through the miR-134-5p/KRAS axis. Future studies will focus on in vivo validation and elucidation of downstream signaling cascades, while clinically, LINC01063 holds potential as a prognostic indicator and a therapeutic target for KRAS-mutant colon cancer, aiding in personalized treatment strategies.

BackgroundHepatocellular carcinoma (HCC) rupture is a life-threatening complication associated with poor prognosis. This study comprehensively analysed risk factors for HCC rupture and developed a predictive model supplemented by machine learning models for early risk identification and clinical decision-making.MethodsThis retrospective study analysed patients with and without HCC rupture from tertiary centres in China between January 2016 and June 2019. Propensity score matching (PSM) was used to reduce baseline differences between the rupture and non-rupture groups. Random forest and deep learning models were developed to enhance predictive accuracy and interpret variable importance. Model performance was evaluated using metrics such as precision, recall, and the F1 score across training, validation, and test cohorts.ResultsAmong the 5952 HCC patients, the median follow-up duration was 48.6 months. Key risk factors for HCC rupture identified in this study include cirrhosis, protrusion ratio, and tumour maximum length. The CAPTure nomogram, constructed based on these predictors, yielded area under the curve (AUC) values of 0.857, 0.824, and 0.840 in the training, validation, and test cohorts, respectively. In the test cohort, the random forest and deep learning models achieved AUCs of 0.870 and 0.872, respectively.ConclusionThis study provides a comprehensive analysis of risk factors for HCC rupture and introduces the CAPTure model as a practical and accurate tool for clinical use. By integrating traditional and machine learning approaches, the findings of this study offer robust methods for early risk assessment, resource optimization, and improved management of HCC rupture.