Efficacy of c isplatin in combination with paclitaxel for oral cancer and its effect on cellular immunity

Purpose: To study the efficacy of cisplatin in combination with paclitaxel in the treatment of oral cancer and its effect on cellular immunity. Methods: A total of 100 patients with oral cancer, treated in the First Affiliated Hospital of Dalian Medical University from May 2018 to April 2020 were included and evenly allocated to study and control groups. The patients in the study group received cisplatin plus paclitaxel, while the patients in the control group received only cisplatin. The serum levels of T lymphocytes, interleukin (IL) -4, IL-2, and interferon gamma (IFN-γ) were determined. Results: After treatment, the study group showed significantly higher levels of CD3+, CD4+ and CD4/CD8, but a lower CD8+ level (26.17 ± 2.14 μL). than those before treatment (p < 0.05). The control group was associated with higher post-treatment CD3+, CD4+, CD8+, and CD4/CD8 levels and lower CD8+ levels versus patients in the study group (p < 0.05). The study group showed higher levels of IL-2 and INF-γ, (246.77 ± 13.68 and 1194.62 ± 123.15 pg/mL), respectively, but lower IL-4 levels (392.48 ± 13.25 pg/mL) after treatment than before treatment. Control group was associated with higher post-treatment IL-2 and INF-γ levels and lower IL-4 levels compared to patients in the study group (p < 0.05). Conclusion: Cisplatin and paclitaxel combination offers a viable treatment alternative for oral cancer, as it enhances patients’ immune function and disease prognosis, regulates inflammatory responses, and promotes patients’ recovery. Further investigations in larger population settings are, however, recommended

Intertemporal Relation Between The Expected Return And Risk: An Evaluation Of Emerging Market

This paper explores the intertemporal relationship between the expected return and risk in Chinese exchange market. We investigate the characterization of time-series variation in conditional variance and capture the cross-sectional correlation among equity portfolios by incorporating multivariate GARCH-M model with dynamic conditional covariance (DCC). Restricting the slope to be the same across risky assets, the risk-return coefficient is estimated to be positive and highly significant. In addition, the estimates of portfolio-specific slopes provide evidence to support the robustness across different portfolio formations. Our findings, in the Intertemporal Capital Asset Pricing Model (ICAPM) framework, reveal that the risk premium induced by the conditional covariation of equity portfolio with the market portfolio remain positive after controlling for risk premia induced by conditional covariation with Fama French benchmark factors (HML and SMB). The SMB factor might provide a significant predictive power to hedge against market risk. However, four indices of alternative investments are not consistently priced in the ICAPM framework

Output Feedback Control for Couple-Group Consensus of Multiagent Systems

This paper deals with the couple-group consensus problem for multiagent systems via output feedback control. Both continuous- and discrete-time cases are considered. The consensus problems are converted into the stability problem of the error systems by the system transformation. We obtain two necessary and sufficient conditions of couple-group consensus in different forms for each case. Two different algorithms are used to design the control gains for continuous- and discrete-time case, respectively. Finally, simulation examples are given to show the effectiveness of the proposed results

Winner versus Loser: Time-Varying Performance And Dynamic Conditional Correlation

Using multi-factor models in OLS and GARCH-M methodology, this paper provides a cross-sectional and time-series investigation of conditional and unconditional expected returns of real REITs index momentum portfolios against real estate property, large-cap stock small-cap stock, and bond index in USA. The expected returns and dynamic conditional correlations between REITs and those of other financial and tangible assets vary in period 1989-2010. REITs returns exhibit a higher correlation with up move of financial market, but a lower correlation in market downturns. REITs may possibly provide diversification benefits to multi-asset investment portfolio. We find that the performances of momentum returns are different from the NAREIT index, and display asymmetric volatility as well. Additionally, we find evidence that REITs momentum returns are varying between winner and loser by Wald test. The results of regressions also indicate that REITs return exhibits the greater sensitivity to large- and small-cap stock index, and less closely with those of bond and real estate index. The results also suggest that REITs not be viewed as a complete substitute for investment in tangible property of real estate

Cloning and characterization of maize ZmSPK1, a homologue to nonfermenting1-related protein kinase2

SnRK2s play important roles in plant stresses responses. One full-length cDNA encoding a SnRK2b homologue was isolated from maize by RT-PCR and named as ZmSPK1 (for stress-induced protein kinase). The ZmSPK1 protein has 364 amino acids with an estimated molecular mass of 41.8 KD and an isoelectric point of 5.8. The deduced protein sequence has the closest identities to the members of SnRK2b group. RT-PCR analysis showed that the ZmSPK1 expression was induced by mannitol, salt and abscisic acid (ABA). Furthermore, in different tissues the ZmSPK1 showed different expression patterns and was most abundant in reproductive organs. These results suggested that ZmSPK1 might play multiple roles in abiotic stress resistance pathways, as well as in plant reproductive development.Key words: Zea mays L., SnRK2b, expression pattern, abiotic stres

PathAsst: Redefining Pathology through Generative Foundation AI Assistant for Pathology

As advances in large language models (LLMs) and multimodal techniques continue to mature, the development of general-purpose multimodal large language models (MLLMs) has surged, with significant applications in natural image interpretation. However, the field of pathology has largely remained untapped in this regard, despite the growing need for accurate, timely, and personalized diagnostics. To bridge the gap in pathology MLLMs, we present the PathAsst in this study, which is a generative foundation AI assistant to revolutionize diagnostic and predictive analytics in pathology. To develop PathAsst, we collect over 142K high-quality pathology image-text pairs from a variety of reliable sources, including PubMed, comprehensive pathology textbooks, reputable pathology websites, and private data annotated by pathologists. Leveraging the advanced capabilities of ChatGPT/GPT-4, we generate over 180K instruction-following samples. Furthermore, we devise additional instruction-following data, specifically tailored for the invocation of the pathology-specific models, allowing the PathAsst to effectively interact with these models based on the input image and user intent, consequently enhancing the model's diagnostic capabilities. Subsequently, our PathAsst is trained based on Vicuna-13B language model in coordination with the CLIP vision encoder. The results of PathAsst show the potential of harnessing the AI-powered generative foundation model to improve pathology diagnosis and treatment processes. We are committed to open-sourcing our meticulously curated dataset, as well as a comprehensive toolkit designed to aid researchers in the extensive collection and preprocessing of their own datasets. Resources can be obtained at https://github.com/superjamessyx/Generative-Foundation-AI-Assistant-for-Pathology.Comment: 13 pages, 5 figures, conferenc

Fully Automated Deep Learning-enabled Detection for Hepatic Steatosis on Computed Tomography: A Multicenter International Validation Study

Despite high global prevalence of hepatic steatosis, no automated diagnostics demonstrated generalizability in detecting steatosis on multiple international datasets. Traditionally, hepatic steatosis detection relies on clinicians selecting the region of interest (ROI) on computed tomography (CT) to measure liver attenuation. ROI selection demands time and expertise, and therefore is not routinely performed in populations. To automate the process, we validated an existing artificial intelligence (AI) system for 3D liver segmentation and used it to purpose a novel method: AI-ROI, which could automatically select the ROI for attenuation measurements. AI segmentation and AI-ROI method were evaluated on 1,014 non-contrast enhanced chest CT images from eight international datasets: LIDC-IDRI, NSCLC-Lung1, RIDER, VESSEL12, RICORD-1A, RICORD-1B, COVID-19-Italy, and COVID-19-China. AI segmentation achieved a mean dice coefficient of 0.957. Attenuations measured by AI-ROI showed no significant differences (p = 0.545) and a reduction of 71% time compared to expert measurements. The area under the curve (AUC) of the steatosis classification of AI-ROI is 0.921 (95% CI: 0.883 - 0.959). If performed as a routine screening method, our AI protocol could potentially allow early non-invasive, non-pharmacological preventative interventions for hepatic steatosis. 1,014 expert-annotated liver segmentations of patients with hepatic steatosis annotations can be downloaded here: https://drive.google.com/drive/folders/1-g_zJeAaZXYXGqL1OeF6pUjr6KB0igJX