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Patients with non-small-cell lung cancer (NSCLC) that is resistant to PD-1 and PD-L1 (PD[L]-1)-targeted therapy have poor outcomes. Studies suggest that radiotherapy could enhance antitumour immunity. Therefore, we investigated the potential benefit of PD-L1 (durvalumab) and CTLA-4 (tremelimumab) inhibition alone or combined with radiotherapy. This open-label, multicentre, randomised, phase 2 trial was done by the National Cancer Institute Experimental Therapeutics Clinical Trials Network at 18 US sites. Patients aged 18 years or older with metastatic NSCLC, an Eastern Cooperative Oncology Group performance status of 0 or 1, and progression during previous PD(L)-1 therapy were eligible. They were randomly assigned (1:1:1) in a web-based system by the study statistician using a permuted block scheme (block sizes of three or six) without stratification to receive either durvalumab (1500 mg intravenously every 4 weeks for a maximum of 13 cycles) plus tremelimumab (75 mg intravenously every 4 weeks for a maximum of four cycles) alone or with low-dose (0·5 Gy delivered twice per day, repeated for 2 days during each of the first four cycles of therapy) or hypofractionated radiotherapy (24 Gy total delivered over three 8-Gy fractions during the first cycle only), 1 week after initial durvalumab–tremelimumab administration. Study treatment was continued until 1 year or until progression. The primary endpoint was overall response rate (best locally assessed confirmed response of a partial or complete response) and, along with safety, was analysed in patients who received at least one dose of study therapy. The trial is registered with ClinicalTrials.gov, NCT02888743, and is now complete. Between Aug 24, 2017, and March 29, 2019, 90 patients were enrolled and randomly assigned, of whom 78 (26 per group) were treated. This trial was stopped due to futility assessed in an interim analysis. At a median follow-up of 12·4 months (IQR 7·8–15·1), there were no differences in overall response rates between the durvalumab–tremelimumab alone group (three [11·5%, 90% CI 1·2–21·8] of 26 patients) and the low-dose radiotherapy group (two [7·7%, 0·0–16·3] of 26 patients; p=0·64) or the hypofractionated radiotherapy group (three [11·5%, 1·2–21·8] of 26 patients; p=0·99). The most common grade 3–4 adverse events were dyspnoea (two [8%] in the durvalumab–tremelimumab alone group; three [12%] in the low-dose radiotherapy group; and three [12%] in the hypofractionated radiotherapy group) and hyponatraemia (one [4%] in the durvalumab–tremelimumab alone group vs two [8%] in the low-dose radiotherapy group vs three [12%] in the hypofractionated radiotherapy group). Treatment-related serious adverse events occurred in one (4%) patient in the durvalumab–tremelimumab alone group (maculopapular rash), five (19%) patients in the low-dose radiotherapy group (abdominal pain, diarrhoea, dyspnoea, hypokalemia, and respiratory failure), and four (15%) patients in the hypofractionated group (adrenal insufficiency, colitis, diarrhoea, and hyponatremia). In the low-dose radiotherapy group, there was one death from respiratory failure potentially related to study therapy. Radiotherapy did not increase responses to combined PD-L1 plus CTLA-4 inhibition in patients with NSCLC resistant to PD(L)-1 therapy. However, PD-L1 plus CTLA-4 therapy could be a treatment option for some patients. Future studies should refine predictive biomarkers in this setting. The US National Institutes of Health and the Dana-Farber Cancer Institute.

Dynamic consent management allows a data subject to dynamically govern her consent to access her data. Clearly, security and privacy guarantees are vital for the adoption of dynamic consent management systems. In particular, specific data protection guarantees can be required to comply with rules and laws (e.g., the General Data Protection Regulation (GDPR)). Since the primary instantiation of the dynamic consent management systems in the existing literature is towards developing sustainable e-healthcare services, in this paper, we study data protection issues in dynamic consent management systems, identifying crucial security and privacy properties and discussing severe limitations of systems described in the state of the art. We have presented the precise definitions of security and privacy properties that are essential to confirm the robustness of the dynamic consent management systems against diverse adversaries. Finally, under those precise formal definitions of security and privacy, we have proposed the implications of state-of-the-art tools and technologies such as differential privacy, blockchain technologies, zero-knowledge proofs, and cryptographic procedures that can be used to build dynamic consent management systems that are secure and private by design.

Recent evidence indicates that ionizing radiation can enhance immune responses to tumors. Advances in radiation delivery techniques allow hypofractionated delivery of conformal radiotherapy. Hypofractionation or other modifications of standard fractionation may improve radiation’s ability to promote immune responses to tumors. Other novel delivery options may also affect immune responses, including T-cell activation and tumor-antigen presentation changes. However, there is limited understanding of the immunological impact of hypofractionated and unique multifractionated radiotherapy regimens, as these observations are relatively recent. Hence, these differences in radiotherapy fractionation result in distinct immune-modulatory effects. Radiation oncologists and immunologists convened a virtual consensus discussion to identify current deficiencies, challenges, pitfalls and critical gaps when combining radiotherapy with immunotherapy and making recommendations to the field and advise National Cancer Institute on new directions and initiatives that will help further development of these two fields.This commentary aims to raise the awareness of this complexity so that the need to study radiation dose, fractionation, type and volume is understood and valued by the immuno-oncology research community. Divergence of approaches and findings between preclinical studies and clinical trials highlights the need for evaluating the design of future clinical studies with particular emphasis on radiation dose and fractionation, immune biomarkers and selecting appropriate end points for combination radiation/immune modulator trials, recognizing that direct effect on the tumor and potential abscopal effect may well be different. Similarly, preclinical studies should be designed as much as possible to model the intended clinical setting. This article describes a conceptual framework for testing different radiation therapy regimens as separate models of how radiation itself functions as an immunomodulatory ‘drug’ to provide alternatives to the widely adopted ‘one-size-fits-all’ strategy of frequently used 8 Gy×3 regimens immunomodulation.

It is critical for organizations to self-assess their Industry 4.0 readiness to survive and thrive in the age of the Fourth Industrial Revolution. Thereon, conceptualization or development of an Industry 4.0 readiness model with the fundamental model dimensions is needed. This paper used a systematic literature review (SLR) methodology with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines and content analysis strategy to review 97 papers in peer-reviewed academic journals and industry reports published from 2000 to 2019. The review identifies 30 Industry 4.0 readiness models with 158 unique model dimensions. Based on this review, there are two theoretical contributions. First, this paper proposes six dimensions (Technology, People, Strategy, Leadership, Process and Innovation) that can be considered as the most important dimensions for organizations. Second, this review reveals that 70 (44%) out of total 158 total unique dimensions on Industry 4.0 pertain to the assessment of technology alone. This establishes that organizations need to largely improve on their technology readiness, to strengthen their Industry 4.0 readiness. In summary, these six most common dimensions, and in particular, the dominance of the technology dimension provides a research agenda for future research on Industry 4.0 readiness.

Societies today require research that delves into social issues while integrating economic and business perspectives to achieve sustainable growth and development. Since its inception, the journal has made significant achievements, including high readership and citation rates. Additionally, a study examines the impact of effective enterprise resource planning (ERP) system utilization on economic sustainability, emphasizing product life cycle cost control as a key factor in financial stability. [...]the issue features research on financial inclusion and the sustainability of indigenous microenterprises in Africa, emphasizing the role of financial literacy in supporting these enterprises. Collectively, these papers contribute valuable insights into the broader discourse on socio-economic development, sustainability and the achievement of Sustainable Development Goals (SDGs).

Drug–radiation combination therapy is a practical approach to improving clinical outcomes for many tumours. Unfortunately, most clinical combination studies combine drugs with radiotherapy empirically and do not exploit mechanistic synergy in cell death and the interconnectivity of molecular pathways of tumours or rationale for selecting the dose, fractionation, and schedule, which can result in suboptimal efficacy and exacerbation of toxic effects. However, opportunities exist to generate compelling preclinical evidence for combination therapies from fit-for-purpose translational studies for simulating the intended clinical study use scenarios with standardised preclinical assays and algorithms to evaluate complex molecular interactions and analysis of synergy before clinical research. Here, we analyse and discuss the core issues in the translation of preclinical data to enhance the relevance of preclinical assays, in vitro clonogenic survival along with apoptosis, in vivo tumour regression and growth delay assays, and toxicology of organs at risk without creating barriers to innovation and provide a synopsis of emerging areas in preclinical radiobiology.

Background: Isofraxidin is a hydroxylcoumarin derived from herbal Fraxinus and Eleutherococcus. It has been shown that isofraxidin has antioxidant, anti-inflammatory, anti-diabetic, and anti-lipidemic effects. The study aimed to examine the therapeutic effects of isofraxidin with and without methylprednisolone to ameliorate lipopolysaccharide (LPS)-induced cytokine-releasing syndrome. Methods: The study comprised two phases: preventive and therapeutic. In all the experiments that involved LPS induction, a single dose of LPS (5 mg/kg) was used. The preventive phase involved the administration of the agents before LPS induction, in which 50 mg/kg of methylprednisolone, 15 mg/kg of isofraxidin, or a combination of 7.5 mg/kg of isofraxidin plus 25 mg/kg methylprednisolone were given daily for 3 days before induction. The therapeutic phase involved the administration of the following agents after LPS induction: 50 mg/kg methylprednisolone, 15 mg/kg of isofraxidin, or a combination of 7.5 mg/kg of isofraxidin plus 25 mg/kg methylprednisolone were given once daily was given for 7 days. Results: Isofraxidin treatment with or without methylprednisolone ameliorates LPS-induced inflammatory and oxidative stress damage in mice; it reduces the inflammatory (IL-6, TNF-α, IL-1β, IL-8, Malondialdehyde, and IFN-γ) and oxidative stress markers. Additionally, isofraxidin treatment with or without methylprednisolone prevented liver and lung tissue damage induced by LPS. Conclusions: Isofraxidin exhibited preventive and therapeutic properties against lipopolysaccharide-induced cytokine storms in mice via anti-inflammatory and antioxidant pathways, and its combination with methylprednisolone demonstrated synergistic outcomes.

This study aims to enhance the accuracy of predicting transposon-derived piRNAs through the development of a novel computational method namely TranspoPred. TranspoPred leverages positional, frequency, and moments-based features extracted from RNA sequences. By integrating multiple deep learning networks, the objective is to create a robust tool for forecasting transposon-derived piRNAs, thereby contributing to a deeper understanding of their biological functions and regulatory mechanisms. Piwi-interacting RNAs (piRNAs) are currently considered the most diverse and abundant class of small, non-coding RNA molecules. Such accurate instrumentation of transposon-associated piRNA tags can considerably involve the study of small ncRNAs and support the understanding of the gametogenesis process. First, a number of moments were adopted for the conversion of the primary sequences into feature vectors. Bagging, boosting, and stacking based ensemble classification approaches were employed during the study. Classifiers such as Random Forest (RF), Extra Trees (ET), and Decision Tree were utilized in the Bagging approach. The Boosting approach involved the use of XGBoost (XGB), AdaBoost, and Gradient Boost. For the Stacking method, base learners such as k-Nearest Neighbor (KNN), Support Vector Machine (SVM), Artificial Neural Network (ANN), and Decision Trees were employed, with a Neural Network (NN) serving as the meta-learner. The computational models underwent rigorous evaluation through 2 5-fold cross-validation, 10-fold cross-validation, and independent testing across datasets from three species: human, mouse, and Drosophila. The evaluation metrics used were Accuracy (ACC), Specificity (SP), Sensitivity (SN), and Matthew’s Correlation Coefficient (MCC) along with F-1 measure. The ensemble methods consistently outperformed others in almost all testing scenarios. Notably, stacking achieved perfect scores for accuracy, specificity, sensitivity, and MCC in independent set testing for human and Drosophila datasets, and nearly perfect scores for the mouse dataset. Use of independent set testing accross species evaluates the generalizability and adaptability of the model for diverse data samples. The proposed method TranspoRed achieved exquisite results on diverse datasets for humans, mouse and Drosophila. Our methods exhibited superior performance compared to other state-of-the-art techniques for predicting transposon-derived piRNA. The proposed approaches show great potential for enhancing the accuracy of piRNA prediction, significantly aiding future research and the scientific community in the in-silico identification of piRNA. The source codes and datasets utilized in this study are accessible at https://github.com/MansoorAhmadRasheed/piRNA-codes-and-result .

Myeloid cells are recruited to damaged tissues where they can resolve infections and tumor growth or stimulate wound healing and tumor progression. Recruitment of these cells is regulated by integrins, a family of adhesion receptors that includes integrin CD11b. Here we report that, unexpectedly, integrin CD11b does not regulate myeloid cell recruitment to tumors but instead controls myeloid cell polarization and tumor growth. CD11b activation promotes pro-inflammatory macrophage polarization by stimulating expression of microRNA Let7a . In contrast, inhibition of CD11b prevents Let7a expression and induces cMyc expression, leading to immune suppressive macrophage polarization, vascular maturation, and accelerated tumor growth. Pharmacological activation of CD11b with a small molecule agonist, Leukadherin 1 (LA1), promotes pro-inflammatory macrophage polarization and suppresses tumor growth in animal models of murine and human cancer. These studies identify CD11b as negative regulator of immune suppression and a target for cancer immune therapy. Recruitment of myeloid cells can be regulated by integrin CD11b. Here the authors show that in the tumor microenvironment, CD11b is not essential for recruitment of myeloid cells but rather induces macrophage anti-tumorigenic polarization via stimulating let7a and NFκB signaling and that pharmacological activation of CD11b enhances survival in mouse models of cancer.

In this study, the Land Use/Land Cover (LULC) change has been observed in wetlands comprises of Manchar Lake, Keenjhar Lake, and Chotiari Reservoir in Pakistan over the last four decades from 1972 to 2020. Each wetland has been categorized into four LULC classes; water, natural vegetation, agriculture land, and dry land. Multitemporal Landsat satellite data including; Multi-Spectral Scanner (MSS), Thematic Mapper (TM), and Operational Land Imager (OLI) images were used for LULC changes evaluation. The Supervised Maximum-likelihood classifier method is used to acquire satellite imagery for detecting the LULC changes during the whole study period. Soil adjusted vegetation index technique (SAVI) was also used to reduce the effects of soil brightness values for estimating the actual vegetation cover of each study site. Results have shown the significant impact of human activities on freshwater resources by changing the natural ecosystem of wetlands. Change detection analysis showed that the impacts on the land cover affect the landscape of the study area by about 40% from 1972 to 2020. The vegetation cover of Manchar Lake and Keenjhar Lake has been decreased by 6,337.17 and 558.18 ha, respectively. SAVI analysis showed that soil profile is continuously degrading which vigorously affects vegetation cover within the study area. The overall classification accuracy and Kappa statistics showed an accuracy of >90% for all LULC mapping studies. This work demonstrates the LULC changes as a critical monitoring basis for ongoing analyses of changes in land management to enable decision-makers to establish strategies for effectively using land resources.