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As more and more researchers are turning to big data for new opportunities of biomedical discoveries, machine learning models, as the backbone of big data analysis, are mentioned more often in biomedical journals. However, owing to the inherent complexity of machine learning methods, they are prone to misuse. Because of the flexibility in specifying machine learning models, the results are often insufficiently reported in research articles, hindering reliable assessment of model validity and consistent interpretation of model outputs. To attain a set of guidelines on the use of machine learning predictive models within clinical settings to make sure the models are correctly applied and sufficiently reported so that true discoveries can be distinguished from random coincidence. A multidisciplinary panel of machine learning experts, clinicians, and traditional statisticians were interviewed, using an iterative process in accordance with the Delphi method. The process produced a set of guidelines that consists of (1) a list of reporting items to be included in a research article and (2) a set of practical sequential steps for developing predictive models. A set of guidelines was generated to enable correct application of machine learning models and consistent reporting of model specifications and results in biomedical research. We believe that such guidelines will accelerate the adoption of big data analysis, particularly with machine learning methods, in the biomedical research community.

The bottlenecks in photocatalytic materials primarily center on light absorption capacities and rapid charge recombination. Thus, many gigantic effects have been undertaken by worldwide scientists to address the issues. In this concept, carbon‐based photocatalysts, such as graphene or graphitic carbon nitrides (g‐C3N4), would frequently capture scientific fascination due to their distinct properties in catalytic applications. However, traditional materials would possess the drawbacks mentioned above. In the current era, nitrogen‐rich graphitic carbon nitrides (g‐C3N5) have emerged as a promising star for photocatalytic applications due to the significant enhancements in light absorption properties, which can activate in ultraviolet, visible, and even under near‐infrared irradiations. This review will summarize the recent progress in the fabrication of g‐C3N5 and the photocatalytic application of these based materials by thoroughly investigating current literature studies. Thus, updating the current trend in state‐of‐the‐art materials would motivate researchers to explore the field further. This study summarized the recent development of g‐C3N5‐based photocatalysts with five main applications: water reduction, oxygen reduction, nitrogen fixation, CO2 reduction, and water and air purification. This research would give insights into the new low‐bandgap carbon materials in the field, which would be promising for practical uses owing to the significant improvements in catalytic outcomes.

Corporate social responsibility (CSR) is becoming one of the most critical challenges that firms must address to survive in the competitive market. This study investigates the impact of customers’ CSR perceptions on their purchase intentions as mediated by brand equity, brand credibility, and brand reputation in order to identify the benefits of CSR integration for business development. The study employs a quantitative approach to collect data from customers who purchase cosmetics through an online survey. PLS-SEM software is used to analyze the data from the 380 responses. The results indicate that customers’ perceptions of the CSR of a firm affect their intention to purchase its brands in the future. Brand equity, brand credibility, and brand reputation mediate the impact of CSR perceptions on purchase intentions. Since previous studies have not employed a comprehensive approach to verifying the influence that CSR exerts through brand credibility, brand reputation, and brand equity, the results provide an essential reference for academics who conduct empirical research on the subject. This paper is also particularly beneficial for marketers and managers who wish to develop marketing strategies and brand management techniques that boost business efficiency.

We show that by combining deterministic lateral displacement (DLD) with electrokinetics, it is possible to sort cells based on differences in their membrane and/or internal structures. Using heat to deactivate cells, which change their viability and structure, we then demonstrate sorting of a mixture of viable and non-viable cells for two different cell types. For Escherichia coli, the size change due to deactivation is insufficient to allow size-based DLD separation. Our method instead leverages the considerable change in zeta potential to achieve separation at low frequency. Conversely, for Saccharomyces cerevisiae (Baker’s yeast) the heat treatment does not result in any significant change of zeta potential. Instead, we perform the sorting at higher frequency and utilize what we believe is a change in dielectrophoretic mobility for the separation. We expect our work to form a basis for the development of simple, low-cost, continuous label-free methods that can separate cells and bioparticles based on their intrinsic properties.

Deterministic Lateral Displacement (DLD) is a label-free particle sorting method that separates by size continuously and with high resolution. By combining DLD with electric fields (eDLD), we show separation of a variety of nano and micro-sized particles primarily by their zeta potential. Zeta potential is an indicator of electrokinetic charge—the charge corresponding to the electric field at the shear plane—an important property of micro- and nanoparticles in colloidal or separation science. We also demonstrate proof of principle of separation of nanoscale liposomes of different lipid compositions, with strong relevance for biomedicine. We perform careful characterization of relevant experimental conditions necessary to obtain adequate sorting of different particle types. By choosing a combination of frequency and amplitude, sorting can be made sensitive to the particle subgroup of interest. The enhanced displacement effect due to electrokinetics is found to be significant at low frequency and for particles with high zeta potential. The effect appears to scale with the square of the voltage, suggesting that it is associated with either non-linear electrokinetics or dielectrophoresis (DEP). However, since we observe large changes in separation behavior over the frequency range at which DEP forces are expected to remain constant, DEP can be ruled out.

Adversarial attacks pose serious challenges to the robustness of deep Convolutional Neural Networks (CNNs) in image classification. In this study, we evaluated the vulnerability of popular CNN modelsResNet50, ResNet101, AlexNet, MobileNetV2, DenseNet121, and InceptionNetV3-under white-box attacks, including FGSM, PGD, BIM, and C&W. Experiments are conducted on standard datasets such as MNIST, CIFAR-10, CIFAR-100, and ImageNet. To enhance model robustness, we propose three regularized adversarial training methods: ATWR (Adversarial Training with Weight Regularization), ATGR (Adversarial Training with Gradient Regularization), and EATR (Ensemble Adversarial Training with Regularization). Our results show that ATWR reduces the accuracy drop under the PGD attack on CIFAR-10 from 65.93% to 0.00%, and under C&W attack on MNIST from 100% to 0.31%. EATR achieves consistent robustness across all attacks and models, reducing the accuracy drop in CIFAR-10 (PGD) from 65.93% to 0%, while maintaining the classification accuracy within 10% of the original. ATGR, while reducing classification accuracy, enhances adversarial detection by amplifying the difference in output behavior under attack. The proposed methods strike varying trade-offs between robustness, generalization, and detectability. These findings offer practical guidance for securing deep CNNs against strong white-box adversarial threats. The source codes are available at: https://github.com/AdversarialAttack/DefenseAndAttack.

The gut microbiota influences immune checkpoint inhibitors (ICIs) efficacy. Microbiome-modulating strategies (MMSs), including probiotics, synbiotics, and faecal microbiota transplantation (FMT), have emerged as promising adjuncts, but their clinical impact remains uncertain. We systematically reviewed PubMed, Embase, and CENTRAL to February 2025 for clinical cohorts evaluating MMS in cancer patients receiving ICIs. Thirty-six studies (25 trials/cohorts; n  = 2,746) were included. Meta-analyses, and subgroup analyses were performed for efficacy along with microbiome shifts and safety. MMS plus ICIs achieved a pooled objective response rate (ORR) of 40% (95% CI: 31%–49%; I² = 63.4%; p  = 0.0003; 95% PI: 15%–72%). Descriptive proportions showed ORR of 45% (95% CI: 32%–58%; I² = 72.5%; p  = 0.0058) for probiotics and 33% (95% CI: 22%–48%; I² = 60.7%; p  = 0.0064) for FMT; however, these findings are non-comparative and confounded by study differences. Exploratory subgroup signals were noted for probiotics in NSCLC (ORR 55%; 95%CI: 45%–64%; I² = 0%; p  = 0.3683) and FMT in melanoma (ORR 39%; 95% CI: 15%–69%; I² = 72.5%; p  = 0.0262). Dual ICI regimens showed the highest point estimate for ORR (43%; 95% CI: 17%–73%; I² = 68.5%; p  = 0.0747) but increased toxicity. Microbiome analyses revealed enrichment of short-chain fatty acid-producing taxa and Bifidobacterium spp. among responders. Based on a limited pooled sample size ( n  = 143), MMS-related adverse events were mostly grade 1–2 (42%; 95% CI: 14%–77%, I ² = 53.8%, p  = 0.0210), with rare severe events (1%). Overall, MMS show promising, though preliminary, hypothesis-generating signals for modulating ICI response. Given high heterogeneity and reliance on early-phase, single-arm trials, the findings underscore urgent need for large, biomarker-driven randomized controlled trials to define optimal interventions and cautiously integrate microbiome modulation into immuno-oncology care.

Episodes of economic and political instability in the United States have frequently been considered sources of disruption for emerging economies that are closely integrated with global markets. This research investigates how shocks are transmitted from US economic and political volatility to corporate equity performance in Vietnam, emphasizing the moderating influence of firm size and government ownership. Applying the System Generalized Method of Moments (SGMM) approach, our results reveal a positive causal linkage between US uncertainty and the stock performance of Vietnamese firms. Such a surprising outcome may be explained by a \"flight-to-safety\" mechanism, in which investors redirect capital toward emerging markets like Vietnam during times of turbulence in the US. The strength of this effect is not uniform across firms; smaller entities and those without state participation display greater positive spillovers. These findings provide meaningful guidance for investors, corporate leaders, and policymakers in developing economies.

The existence of massive quantity of clinical text in electronic medical records (EMRs) has created significant demand for clinical text processing and information extraction in the field of health care and medical research. Detailed clinical observations of patients are typically recorded chronologically. Temporal information in such clinical texts consist of three elements: temporal expressions, temporal events, and temporal relations. Due to the implicit expression of temporal information, lack of writing quality, and domain-specific nature in the clinical text, extraction of temporal information is much more complex than for newswire texts. In spite of these difficulties, to extract temporal information using the annotated corpora, few research works reported rule-based, machine-learning, and hybrid methods. On the other hand, creating the annotated corpora is expensive, time-consuming, and demands significant human effort; the processing quality is inevitably affected by the small size of corpora. Motivated by this issue, in this research work, we present a novel method to effectively extract the temporal information from EMR clinical texts. The essential idea of this method is first to build a feature set appropriately for clinical expressions, followed by the development of a semi-supervised framework for temporal event extraction, and finally detection of temporal relations among events with a newly formulated hypothesis. Comparative experimental evaluation on the I2B2 data set has clearly shown improved performance of the proposed methods. Specifically, temporal event and relation extraction is possible with an F -measure 89.98 and 67.1% respectively.

In particular, the link between green foreign direct investments (GFDI) and environmental performance (EP) is the focus of this study’s empirical analysis of the effects of GFDI on environmental sustainability. According to measurements like the environmental performance index (EPI) and indicators like health and ecosystem preservation (HLT and ECO), the results show that bigger GFDI sizes benefit environmental performance. Using a variety of econometric approaches, this result is derived using a worldwide sample that includes European nations from 2001 to 2023. Even after adding more explanatory factors and using a variety of econometric techniques, these results hold up well. Furthermore, the research explores the immediate and long-term impacts of GFDI on EP, emphasizing that the relationship between GFDI and EP becomes increasingly evident with time. Additionally, research will investigate how different transmission mechanisms allow green FDI to influence environmental sustainability. These results highlight how GFDI may be used to support industry environmental sustainability.