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Developing a privacy-preserving data publishing algorithm that stops individuals from disclosing their identities while not ignoring data utility remains an important goal to achieve. Because finding the trade-off between data privacy and data utility is an NP-hard problem and also a current research area. When existing approaches are investigated, one of the most significant difficulties discovered is the presence of outlier data in the datasets. Outlier data has a negative impact on data utility. Furthermore, k-anonymity algorithms, which are commonly used in the literature, do not provide adequate protection against outlier data. In this study, a new data anonymization algorithm is devised and tested for boosting data utility by incorporating an outlier data detection mechanism into the Mondrian algorithm. The connectivity-based outlier factor (COF) algorithm is used to detect outliers. Mondrian is selected because of its capacity to anonymize multidimensional data while meeting the needs of real-world data. COF, on the other hand, is used to discover outliers in high-dimensional datasets with complicated structures. The proposed algorithm generates more equivalence classes than the Mondrian algorithm and provides greater data utility than previous algorithms based on k-anonymization. In addition, it outperforms other algorithms in the discernibility metric (DM), normalized average equivalence class size (Cavg), global certainty penalty (GCP), query error rate, classification accuracy (CA), and F-measure metrics. Moreover, the increase in the values of the GCP and error rate metrics demonstrates that the proposed algorithm facilitates obtaining higher data utility by grouping closer data points when compared to other algorithms.

This survey provides an in-depth review of large language models (LLMs), highlighting the significant paradigm shift they represent in artificial intelligence. Our purpose is to consolidate state-of-the-art advances in LLM design, training, adaptation, evaluation, and application for both researchers and practitioners. To accomplish this, we trace the evolution of language models and describe core approaches, including parameter-efficient fine-tuning (PEFT). The methodology involves a thorough survey of real-world LLM applications across the scientific, engineering, healthcare, and creative sectors, coupled with a review of current benchmarks. Our findings indicate that high training and inference costs are shaping market structures, raising economic and labor concerns, while also underscoring a persistent need for human oversight in assessment. Key trends include the development of unified multimodal architectures capable of processing varied data inputs and the emergence of agentic systems that exhibit complex behaviors such as tool use and planning. We identify critical open problems, such as detectability, data contamination, generalization, and benchmark diversity. Ultimately, we conclude that overcoming these complex technical, economic, and social challenges necessitates collaborative advancements in adaptation, evaluation, infrastructure, and governance.

Developing a privacy-preserving data publishing algorithm that prevents individuals’ identity information from being disclosed without ignoring the utility of the data is still an important goal to be achieved. Finding the optimal balance between data utility and data privacy is an NP-hard problem. In this study, a new k-anonymization-based data anonymization algorithm is proposed. The proposed algorithm partitions the data space with a new efficient partitioning strategy based on the k-dimensional tree (KD-tree), resolves the boundary problem while maintaining the trade-off balance, and introduces a new mechanism to address the problems caused by outliers. Moreover, it can be applied to both numerical and categorical data. The experimental results indicate that the proposed algorithm achieves competitive or improved performance compared with the baseline algorithms across seven commonly used evaluation metrics. Overall, the findings suggest that the proposed framework can improve the utility–privacy trade-off in multidimensional k-anonymization.

Trabectedin (Yondelis, ET-743) is a marine-derived tetrahydroisoquinoline alkaloid. It is originally derived from the Caribbean marine tunicate Ecteinascidia turbinata and currently produced synthetically. Trabectedin is active against a variety of tumor cell lines growing in culture. The present study focused on the effect of trabectedin in cell proliferation, cell cycle progression, apoptosis and spheroid formation in prostate cancer stem cells (CSCs). Cluster of differentiation (CD) 133+high/CD44+high prostate CSCs were isolated from the DU145 and PC-3 human prostate cancer cell line through flow cytometry. We studied the growth-inhibitory effects of trabectedin and its molecular mechanisms on human prostate CSCs and non-CSCs. DU-145 and PC-3 CSCs were treated with 0.1, 1, 10 and 100 nM trabectedin for 24, 48 and 72 h and the growth inhibition rates were examined using the sphere-forming assay. Annexin-V assay and immunofluorescence analyses were performed for the detection of the cell death. Concentration-dependent effects of trabectedin on the cell cycle were also evaluated. The cells were exposed to the different doses of trabectedin for 24, 48 and 72 h to evaluate the effect of trabectedin on the number and diameter of spheroids. According to the results, trabectedin induced cytotoxicity and apoptosis at the IC50 dose, resulting in a significant increase expression of caspase-3, caspase-8, caspase-9, p53 and decrease expression of bcl-2 in dose-dependent manner. Cell cycle analyses revealed that trabectedin induces dose-dependent G2/M-phase cell cycle arrest, particularly at high-dose treatments. Three-dimensional culture studies showed that trabectedin reduced the number and diameter of spheroids of DU145 and PC3 CSCs. Furthermore, we have found that trabectedin disrupted cell-cell interactions via E-cadherin in prostasphere of DU-145 and PC-3 CSCs. Our results showed that trabectedin inhibits cellular proliferation and accelerates apoptotic events in prostate CSCs; and may be a potential effective therapeutic agent against prostate cancer.

Trabectedin (Yondelis, ET-743) is a marine-derived tetrahydroisoquinoline alkaloid. It is originally derived from the Caribbean marine tunicate Ecteinascidia turbinata and currently produced synthetically. Trabectedin is active against a variety of tumor cell lines growing in culture. The present study focused on the effect of trabectedin in cell proliferation, cell cycle progression, apoptosis and spheroid formation in prostate cancer stem cells (CSCs). Cluster of differentiation (CD) 133+high/CD44+high prostate CSCs were isolated from the DU145 and PC-3 human prostate cancer cell line through flow cytometry. We studied the growth-inhibitory effects of trabectedin and its molecular mechanisms on human prostate CSCs and non-CSCs. DU-145 and PC-3 CSCs were treated with 0.1, 1, 10 and 100 nM trabectedin for 24, 48 and 72 h and the growth inhibition rates were examined using the sphere-forming assay. Annexin-V assay and immunofluorescence analyses were performed for the detection of the cell death. Concentration-dependent effects of trabectedin on the cell cycle were also evaluated. The cells were exposed to the different doses of trabectedin for 24, 48 and 72 h to evaluate the effect of trabectedin on the number and diameter of spheroids. According to the results, trabectedin induced cytotoxicity and apoptosis at the IC50 dose, resulting in a significant increase expression of caspase-3, caspase-8, caspase-9, p53 and decrease expression of bcl-2 in dose-dependent manner. Cell cycle analyses revealed that trabectedin induces dose-dependent G2/M-phase cell cycle arrest, particularly at high-dose treatments. Three-dimensional culture studies showed that trabectedin reduced the number and diameter of spheroids of DU145 and PC3 CSCs. Furthermore, we have found that trabectedin disrupted cell-cell interactions via E-cadherin in prostasphere of DU-145 and PC-3 CSCs. Our results showed that trabectedin inhibits cellular proliferation and accelerates apoptotic events in prostate CSCs; and may be a potential effective therapeutic agent against prostate cancer.