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Tumor histology is an important predictor of therapeutic response and outcomes in lung cancer. Tissue sampling for pathologist review is the most reliable method for histology classification, however, recent advances in deep learning for medical image analysis allude to the utility of radiologic data in further describing disease characteristics and for risk stratification. In this study, we propose a radiomics approach to predicting non-small cell lung cancer (NSCLC) tumor histology from non-invasive standard-of-care computed tomography (CT) data. We trained and validated convolutional neural networks (CNNs) on a dataset comprising 311 early-stage NSCLC patients receiving surgical treatment at Massachusetts General Hospital (MGH), with a focus on the two most common histological types: adenocarcinoma (ADC) and Squamous Cell Carcinoma (SCC). The CNNs were able to predict tumor histology with an AUC of 0.71(p = 0.018). We also found that using machine learning classifiers such as k-nearest neighbors (kNN) and support vector machine (SVM) on CNN-derived quantitative radiomics features yielded comparable discriminative performance, with AUC of up to 0.71 (p = 0.017). Our best performing CNN functioned as a robust probabilistic classifier in heterogeneous test sets, with qualitatively interpretable visual explanations to its predictions. Deep learning based radiomics can identify histological phenotypes in lung cancer. It has the potential to augment existing approaches and serve as a corrective aid for diagnosticians.

Judgement, as one of the core tenets of medicine, relies upon the integration of multilayered data with nuanced decision making. Cancer offers a unique context for medical decisions given not only its variegated forms with evolution of disease but also the need to take into account the individual condition of patients, their ability to receive treatment, and their responses to treatment. Challenges remain in the accurate detection, characterization, and monitoring of cancers despite improved technologies. Radiographic assessment of disease most commonly relies upon visual evaluations, the interpretations of which may be augmented by advanced computational analyses. In particular, artificial intelligence (AI) promises to make great strides in the qualitative interpretation of cancer imaging by expert clinicians, including volumetric delineation of tumors over time, extrapolation of the tumor genotype and biological course from its radiographic phenotype, prediction of clinical outcome, and assessment of the impact of disease and treatment on adjacent organs. AI may automate processes in the initial interpretation of images and shift the clinical workflow of radiographic detection, management decisions on whether or not to administer an intervention, and subsequent observation to a yet to be envisioned paradigm. Here, the authors review the current state of AI as applied to medical imaging of cancer and describe advances in 4 tumor types (lung, brain, breast, and prostate) to illustrate how common clinical problems are being addressed. Although most studies evaluating AI applications in oncology to date have not been vigorously validated for reproducibility and generalizability, the results do highlight increasingly concerted efforts in pushing AI technology to clinical use and to impact future directions in cancer care.

Non-small-cell lung cancer (NSCLC) patients often demonstrate varying clinical courses and outcomes, even within the same tumor stage. This study explores deep learning applications in medical imaging allowing for the automated quantification of radiographic characteristics and potentially improving patient stratification. We performed an integrative analysis on 7 independent datasets across 5 institutions totaling 1,194 NSCLC patients (age median = 68.3 years [range 32.5-93.3], survival median = 1.7 years [range 0.0-11.7]). Using external validation in computed tomography (CT) data, we identified prognostic signatures using a 3D convolutional neural network (CNN) for patients treated with radiotherapy (n = 771, age median = 68.0 years [range 32.5-93.3], survival median = 1.3 years [range 0.0-11.7]). We then employed a transfer learning approach to achieve the same for surgery patients (n = 391, age median = 69.1 years [range 37.2-88.0], survival median = 3.1 years [range 0.0-8.8]). We found that the CNN predictions were significantly associated with 2-year overall survival from the start of respective treatment for radiotherapy (area under the receiver operating characteristic curve [AUC] = 0.70 [95% CI 0.63-0.78], p < 0.001) and surgery (AUC = 0.71 [95% CI 0.60-0.82], p < 0.001) patients. The CNN was also able to significantly stratify patients into low and high mortality risk groups in both the radiotherapy (p < 0.001) and surgery (p = 0.03) datasets. Additionally, the CNN was found to significantly outperform random forest models built on clinical parameters-including age, sex, and tumor node metastasis stage-as well as demonstrate high robustness against test-retest (intraclass correlation coefficient = 0.91) and inter-reader (Spearman's rank-order correlation = 0.88) variations. To gain a better understanding of the characteristics captured by the CNN, we identified regions with the most contribution towards predictions and highlighted the importance of tumor-surrounding tissue in patient stratification. We also present preliminary findings on the biological basis of the captured phenotypes as being linked to cell cycle and transcriptional processes. Limitations include the retrospective nature of this study as well as the opaque black box nature of deep learning networks. Our results provide evidence that deep learning networks may be used for mortality risk stratification based on standard-of-care CT images from NSCLC patients. This evidence motivates future research into better deciphering the clinical and biological basis of deep learning networks as well as validation in prospective data.

The exploration and development of hydrocarbon resources in the Western Desert require more continuous activities. The Silah is a newly discovered field in this region. Therefore, this study emphasizes the application of petrophysical evaluation to sandstone and carbonate reservoirs from the late and early Cretaceous. These formations are the most potential hydrocarbon reservoirs in the studied area as a part of the western desert. Additionally, this study involves a comparative evaluation of the Abu Roash, Bahariya, and Kharita reservoirs using well-logging data by applying different cross-plots that are used for determining different petrophysical parameters such as shale volume, porosity, fluid saturation, permeability, and net-to-gross ratio. These logs are gamma-ray (GR), calliper, resistivity (RLA5, RLA3, and RXOZ), photoelectric effect (PEFZ), neutron (APLC), and density (RHOZ). These plots agree with the results deduced from the interpretation of lithologic logs. Fourteen hydrocarbon-bearing zones are identified in the Silah field. Only two zones, namely, Zone 2 in Silah-15 and Zone 1 in South Silah-1X, are considered the best for hydrocarbon generation. These zones are characterized by low to moderate shale volume, moderate to high total porosity, good effective porosity, low water saturation, and high net-to-gross ratio. These zones lie in the Abu Roash/F member. These deduced points prove that the Abu Roash/F member can be an abundant hydrocarbon reservoir. This member in the Silah field appears to be a promising hydrocarbon reservoir because it matches the petrophysical parameters of the investigated zones and others in the northwestern Desert. This suggests that there may be reservoir continuity and similarity.

The Metaverse has been the centre of attraction for educationists for quite some time. This field got renewed interest with the announcement of social media giant Facebook as it rebranding and positioning it as Meta. While several studies conducted literature reviews to summarize the findings related to the Metaverse in general, no study to the best of our knowledge focused on systematically summarizing the finding related to the Metaverse in education. To cover this gap, this study conducts a systematic literature review of the Metaverse in education. It then applies both content and bibliometric analysis to reveal the research trends, focus, and limitations of this research topic. The obtained findings reveal the research gap in lifelogging applications in educational Metaverse. The findings also show that the design of Metaverse in education has evolved over generations, where generation Z is more targeted with artificial intelligence technologies compared to generation X or Y. In terms of learning scenarios, there have been very few studies focusing on mobile learning, hybrid learning, and micro learning. Additionally, no study focused on using the Metaverse in education for students with disabilities. The findings of this study provide a roadmap of future research directions to be taken into consideration and investigated to enhance the adoption of the Metaverse in education worldwide, as well as to enhance the learning and teaching experiences in the Metaverse.

Unplugged activities as a low-cost solution to foster computational thinking (CT) skills seem to be a trend in recent years. However, current evidence of the effectiveness of unplugged activities in promoting students’ CT skills has been inconsistent. To understand the potential of unplugged activities on computational thinking skills, a systematic review and meta-analysis were conducted. Our review of 49 studies examined the influence of unplugged activities to improve students’ CT skills in K–12 education between 2006 and 2022. The literature review showed that studies on CT skills were mainly (81.64%) conducted in computer science and STEM education, with board and card games being the most common unplugged activities for fostering CT skills in K–12 education. CT diagnostic tools (36.37%) were frequently used as assessment tools. A follow-up meta-analysis of 13 studies with 16 effect sizes showed a generally large overall effect size (Hedges’s g = 1.028, 95% CI [0.641, 1.415], p < 0.001) for the use of unplugged activities in promoting students’ CT skills. The analysis of several moderator variables (i.e., grade level, class size, intervention duration, and learning tools) and their possible effects on CT skills indicated that unplugged activities are a promising instructional strategy for enhancing students’ CT skills. Taken together, the results highlight the affordances of unplugged pedagogy for promoting CT skills in K–12 education. Recommendations for policies, practice, and research are provided accordingly.

Man-made armors often rely on rigid structures for mechanical protection, which typically results in a trade-off with flexibility and maneuverability. Chitons, a group of marine mollusks, evolved scaled armors that address similar challenges. Many chiton species possess hundreds of small, mineralized scales arrayed on the soft girdle that surrounds their overlapping shell plates. Ensuring both flexibility for locomotion and protection of the underlying soft body, the scaled girdle is an excellent model for multifunctional armor design. Here we conduct a systematic study of the material composition, nanomechanical properties, three-dimensional geometry, and interspecific structural diversity of chiton girdle scales. Moreover, inspired by the tessellated organization of chiton scales, we fabricate a synthetic flexible scaled armor analogue using parametric computational modeling and multi-material 3D printing. This approach allows us to conduct a quantitative evaluation of our chiton-inspired armor to assess its orientation-dependent flexibility and protection capabilities. Biology has often served as the inspiration for the design of body armor; one common limitation is the flexibility of the resultant armor. Here, the authors examine the armour of chiton and use the observed design principles to 3D print flexible armor.

Research on student engagement has recently gained popularity as it can address problems such as early dropout and poor achievement. The growing interest in investigating student engagement during the Covid-19 pandemic is reflected in increased publications addressing this topic. However, no review provided research evidence and an overview of existing literature on student engagement during emergency remote teaching (ERT). We reviewed how student engagement studies were undertaken during ERT from three perspectives: (1) the landscape of studies, (2) methodologies issues, and (3) the strategies used to facilitate student engagement. 42 articles were analysed from an initial pool of 436 search results. The findings illustrate that current studies were predominately undertaken in the United States (36%) and China (22%) with focusing on STEM subjects as a dominant discipline. The literature was largely inconsistent in defining and measuring student engagement. In addition, the majority of studies (57%) investigated students’ engagement from the perspective of students, unlike other stakeholders. The most prominent finding is that ERT promoted several important engagement strategies, including motivational factors, teachers’ facilitation, a hybrid learning model, and using learning technologies to boost students’ engagement.

Background/Objectives: The widespread phenomenon of “brain rot”, named the Oxford Word of the Year 2024, refers to the cognitive decline and mental exhaustion experienced by individuals, particularly adolescents and young adults, due to excessive exposure to low-quality online materials, especially on social media. The present study is exploratory and interpretative in nature, aiming to investigate the phenomenon of “brain rot”, with a focus on its key pillars, psychological factors, digital behaviors, and the cognitive impact resulting from the overconsumption of low-quality digital content. Methods: This study employs a rapid review approach, examining research published between 2023 and 2024 across PubMed, Google Scholar, PsycINFO, Scopus, and Web of Science. It explores the causes and effects of brain rot, focusing on the overuse of social media, video games, and other digital platforms. Results: The findings reveal that brain rot leads to emotional desensitization, cognitive overload, and a negative self-concept. It is associated with negative behaviors, such as doomscrolling, zombie scrolling, and social media addiction, all linked to psychological distress, anxiety, and depression. These factors impair executive functioning skills, including memory, planning, and decision-making. The pervasive nature of digital media, driven by dopamine-driven feedback loops, exacerbates these effects. Conclusions: The study concludes by offering strategies to prevent brain rot, such as controlling screen time, curating digital content, and engaging in non-digital activities. Given the increasing prevalence of digital engagement, it is essential to explore a variety of strategies, including mindful technology use, to support cognitive health and emotional well-being. The results can guide various stakeholders—policymakers, practitioners, researchers, educators, and parents or caregivers—in addressing the pervasive impact of brain rot and promoting a balanced approach to technology use that fosters cognitive resilience among adolescents and young adults.

Brucellosis in dromedary camel bulls leads to either temporary or permanent loss of fertility. Camel brucellosis is associated with both orchitis and epididymitis. However, the clinical signs of camel brucellosis are not clear as those in cattle. Therefore, this study aimed to diagnose camel brucellosis based on a serological screening using Rose Bengal plate test (RBPT) followed by competitive ELISA. To understand the impact of brucellosis on camel bull fertility, this study aimed to examine the semen characteristics, evaluate the testicular histopathology, examine hormonal profile, antioxidants and acute phase proteins (APP). A total of 150 mature bulls were used in this study. Blood samples were collected for serological, hormonal, and biochemical analysis. This study revealed that 6.6% and 7.3% of the examined bulls were Brucella -seropositive using RBPT and competitive ELISA, respectively. The Brucella -seropositive dromedary bulls showed poor semen quality, pathological changes orchitis, and lower testosterone. Moreover, our findings showed a higher cortisol level, and significant impairments in the measured APP and antioxidants in Brucella -seropositive bulls. In conclusion, the Brucella -seropositive dromedary bulls showed lower fertility due to poor semen quality and lower testosterone levels. Such lower fertility is likely mediated by high cortisol levels, and impaired APP and antioxidants’ defense response.