Browse Articles

Machine learning-based identification of diagnostic and prognostic mitotic cell cycle genes in hepatocellular carcinoma

Mitotic cell cycle (MCC) is a critical process in cell growth and division, and dysregulation of MCC genes may contribute to tumorigenesis. In this study, to identify diagnostic and prognostic value of MCC genes, differentially expressed MCC genes between HCC and normal tissues were identified and subjected to machine learning methods. SVM-RFE and RF-RFE were employed to select the most informative diagnostic genes. The SVM-RFE model demonstrated high performance in TCGA (AUC = 1.0), and generalizability across GSE77509 (AUC = 0.95) and GSE144269 (AUC = 0.879), outperforming RF-RFE. Permutation testing confirmed that these AUCs were outside the null distribution for all datasets. Nine genes, CDKN3, TRIP13, RACGAP1, FBXO43, EZH2, SPDL1, E2F1, TUBE1 and CDC6, were common in SVM-RFE and RF-RFE and showed robust individual diagnostic performance across datasets (AUCs > 0.81). Univariate Cox regression followed by LASSO Cox regression was used for identification of prognostic gene signature consisted of eight MCC genes, BCAT1, DPF1, CDKN2B, CDKN2C, TUBA3C, IGF1, CDC14B and SMARCA2, that predicted overall survival of HCC patients. The risk score was shown to be an independent prognostic factor for HCC and its combination with AJCC stage improved prognostic value. Kaplan–Meier analysis showed that high-risk score was associated to poorer survival across clinical subgroups; stage, grade, age, and gender. Additionally, risk score was significantly higher in patients with advanced-stage and high-grade tumors. In conclusion, diagnostic biomarker candidates classifying HCC patients and healthy controls, and a novel prognostic gene signature predicting overall survival of HCC patients were identified by using machine learning approaches.

Generative AI and academic scientists in US universities: Perception, experience, and adoption intentions

The integration of generative Artificial Intelligence (AI) into academia has sparked interest and debate among academic scientists. This paper explores the early adoption and perceptions of US academic scientists regarding the use of generative AI in teaching and research activities. To do so, this analysis focuses exclusively on STEM fields due to their high exposure to rapid technological advancements. Drawing from a nationally representative survey of 232 respondents, we examine academic scientists’ attitudes, experiences, and intentions regarding AI adoption. Results indicate that 65% of respondents have utilized generative AI in teaching or research activities, with 20% applying it in both areas. Among those currently using AI, 84% intend to continue its application, indicating a high level of confidence in its perceived benefits. AI is most frequently used in teaching to develop pedagogical materials (51%) and in research for writing, reviewing, and editing tasks (40%). Despite concerns about misinformation, with 78% of respondents indicating it as their top concern regarding AI, there is broad recognition of AI’s potential impact on society. Most academic scientists have already integrated AI into their academic activities, demonstrating cautious yet optimistic adoption due to perceived risks. Furthermore, there is strong support for academic-led regulation of AI, highlighting the need for responsible governance to maximize benefits while minimizing risks in educational and research settings.

Ongoing genome doubling shapes evolvability and immunity in ovarian cancer

Correction: The role of supplier-induced demand on the occurrence of information overload in managerial reporting environments

Optical generative models

Abstract Generative models cover various application areas, including image and video synthesis, natural language processing and molecular design, among many others1–11. As digital generative models become larger, scalable inference in a fast and energy-efficient manner becomes a challenge12–14. Here we present optical generative models inspired by diffusion models4, where a shallow and fast digital encoder first maps random noise into phase patterns that serve as optical generative seeds for a desired data distribution; a jointly trained free-space-based reconfigurable decoder all-optically processes these generative seeds to create images never seen before following the target data distribution. Except for the illumination power and the random seed generation through a shallow encoder, these optical generative models do not consume computing power during the synthesis of the images. We report the optical generation of monochrome and multicolour images of handwritten digits, fashion products, butterflies, human faces and artworks, following the data distributions of MNIST15, Fashion-MNIST16, Butterflies-10017, Celeb-A datasets18, and Van Gogh’s paintings and drawings19, respectively, achieving an overall performance comparable to digital neural-network-based generative models. To experimentally demonstrate optical generative models, we used visible light to generate images of handwritten digits and fashion products. In addition, we generated Van Gogh-style artworks using both monochrome and multiwavelength illumination. These optical generative models might pave the way for energy-efficient and scalable inference tasks, further exploiting the potentials of optics and photonics for artificial-intelligence-generated content.

Fluid-derived lattices for unbiased modeling of bacterial colony growth

Bacterial colonies can form a wide variety of shapes and structures based on ambient and internal conditions. To help understand the mechanisms that determine the structure of and the diversity within these colonies, various numerical modeling techniques have been applied. The most commonly used ones are continuum models, agent-based models, and lattice models. Continuum models are usually computationally fast, but disregard information at the level of the individual, which can be crucial to understanding diversity in a colony. Agent-based models resolve local details to a greater level, but are computationally costly. Lattice-based approaches strike a balance between these two limiting cases. However, this is known to come at the price of introducing undesirable artifacts into the structure of the colonies. For instance, square lattices tend to produce square colonies even where an isotropic shape is expected. Here, we aim to overcome these limitations and we therefore study lattice-induced orientational symmetry in a class of hybrid numerical methods that combine aspects of lattice-based and continuum descriptions. We characterize these artifacts and show that they can be circumvented through the use of a disordered lattice which derives from an unstructured fluid. The main advantage of this approach is that the lattice itself does not imbue the colony with a preferential directionality. We demonstrate that our implementation enables the study of colony growth involving millions of individuals within hours of computation time on an ordinary desktop computer, while retaining many of the desirable features of agent-based models. Furthermore, our method can be readily adapted for a wide range of applications, opening up new avenues for studying the formation of colonies with diverse shapes and complex internal interactions.

Thioester-mediated RNA aminoacylation and peptidyl-RNA synthesis in water

Abstract To orchestrate ribosomal peptide synthesis, transfer RNAs (tRNAs) must be aminoacylated, with activated amino acids, at their 2′,3′-diol moiety1,2, and so the selective aminoacylation of RNA in water is a key challenge that must be resolved to explain the origin of protein biosynthesis. So far, there have been no chemical methods to effectively and selectively aminoacylate RNA-2′,3′-diols with the breadth of proteinogenic amino acids in water3–5. Here we demonstrate that (biological) aminoacyl-thiols (1) react selectively with RNA diols over amine nucleophiles, promoting aminoacylation over adventitious (non-coded) peptide bond formation. Broad side-chain scope is demonstrated, including Ala, Arg, Asp, Glu, Gln, Gly, His, Leu, Lys, Met, Phe, Pro, Ser and Val, and Arg aminoacylation is enhanced by unprecedented side-chain nucleophilic catalysis. Duplex formation directs chemoselective 2′,3′-aminoacylation of RNA. We demonstrate that prebiotic nitriles, N-carboxyanhydrides and amino acid anhydrides, as well as biological aminoacyl-adenylates, all react with thiols (including coenzymes A and M) to selectively yield aminoacyl-thiols (1) in water. Finally, we demonstrate that the switch from thioester to thioacid activation inverts diol/amine selectivity, promoting peptide synthesis in excellent yield. Two-step, one-pot, chemically controlled formation of peptidyl-RNA is observed in water at neutral pH. Our results indicate an important role for thiol cofactors in RNA aminoacylation before the evolution of proteinaceous synthetase enzymes.

Best practices of judicial governance: A scoping review protocol

Background Enhancing performance in the public sector is closely tied to institutional structures, governance models, and the behavior of public officials. In the Judiciary, these factors significantly affect the effectiveness of court administration and justice delivery. Judicial governance is a complex and evolving concept encompassing standards and practices related to accountability, independence, resource management, and institutional performance, progressively integrating principles from public management reforms. Despite its growing relevance, the field remains fragmented, with limited evidence connecting international standards to best governance practices in judicial administration. Objective This protocol outlines a scoping review designed to identify, map, and synthesize evidence on best practices in judicial governance, examining their relationship with the effective administration of justice and identifying research gaps to propose a future research agenda. Method This review will follow the JBI methodology and the PRISMA-ScR guidelines. A comprehensive search will be conducted in databases such as Scopus, Web of Science, DOAJ, and JSTOR, as well as additional searches in grey literature. The PCC (Population, Concept, and Context) framework guided the eligibility criteria, and the PRESS 2015 checklist was used to validate the search strategy. The PRISMA-S checklist will inform the reporting of the search process. Studies of all designs and publication statuses will be considered, with no restrictions on language or publication date. Two reviewers will independently screen using Rayyan software, with a third reviewer resolving any disagreements. Data extraction will occur at two levels: general source information and specific content related to the review scope. Qualitative data will be analyzed using NVivo software, enabling categorization, descriptive synthesis, gap identification, and the development of a research agenda. Discussion This scoping review aims to generate key evidence that can inform institutional standards and best governance practices to support evidence-based policymaking; while it does not assess the risk of bias, its systematic methodology and inclusion of grey literature enhance its relevance for future research and innovations in the justice sector. Through this scoping review, key evidence will generate insights that can enhance institutional standards and best practices in governance, enabling evidence-based policymaking. Although the review does not assess the risk of bias, its systematic approach and inclusion of grey literature strengthen its potential to support future research and governance innovations in the justice sector. Trial registration OSF Registries, Jan 21, 2024: https://doi.org/10.17605/osf.io/agv3b.

Supportive care needs and quality of life among cancer patients in China: A cross-sectional study

Introduction In this study, the factors associated with unmet supportive care needs (SCNs) were examined, and their correlation with quality of life (QoL) among cancer patients was explored. Methods This cross-sectional study included 137 cancer patients who were recruited via convenience sampling from an oncology department at a tertiary cancer hospital in China. Three assessment instruments were employed herein: a general information questionnaire, the Chinese version of the Supportive Care Needs Survey short form (SCNS-SF34-C), and the functional subscale of the European Organization for Research and Treatment of Cancer Quality of Life Questionnaire-Core 30 (EORTC QLQ-C30). Results Among the five domains of the SCNS-SF34-C, the health system and information domain had the highest mean score (M = 2.74; SD = 0.75), followed by the psychological domain (M = 2.36; SD = 0.85). Five factors were associated with the unmet SCNs of cancer patients: sex, educational background, disease awareness, smoking status, and drinking habits (all p < 0.05). Moreover, participants with unmet psychological, physical and daily living needs had significantly lower QoL scores in all functional and symptom domains (all p < 0.05). Conclusions Chinese cancer patients face notable unmet needs in the health system and information domain. Future studies should focus on designing individualized interventions to improve supportive care among cancer patients and to enhance their QoL.

Single nucleus RNA sequencing of the cerebral cortex from genetically diverse inbred mouse strains reveals differences in pericyte and endothelial cell composition

Genetic background influences animal behavior and susceptibility to brain diseases. To evaluate how genetic background influences the relative number and/or types of cells in the brain, we performed single nucleus RNA sequencing (snRNAseq) on fourteen genetically distinct collaborative cross (CC) inbred mouse strains. These data comprise over 287,000 nuclei derived from 92 samples across the strains. We identified 12 principal cell types and 60 refined cell types in each of the strains. Pericytes and endothelial cells were the two principal cell types to show a statistically significant difference in cell proportion between strains. We validated these findings histologically by staining for pericyte (CD13) and endothelial cell (PECAM-1) markers. Consistent with our snRNAseq analyses, we histologically observed differences in pericyte and endothelial cell counts between strains. In addition, we found that the proportion of certain subsets of excitatory and inhibitory neurons varied in some strains. Overall, our study suggests that genetic background can influence brain cell type composition, with a notable influence on cells that make up the neurovascular unit.

MiR-146a participates in regulating the progression of periodontitis through the Wnt/β-catenin signaling pathway

Objectives This study aims to investigate the potential role of miR-146a/Wnt/β-catenin signaling axis in the pathogenesis of periodontitis, using LPS-stimulated hPDLCs as a cell model. Methods Saliva samples were collected from the subjects and qRT-PCR was used to detect the expression of miR-146a and β-catenin in saliva. Clinical parameters, including probing depth (PD) and attachment loss (AL), were measured and their correlation with miR- 146a and β-catenin levels was determined. Cell proliferation capacity was assessed through CCK-8 assay and the production of inflammatory cytokines was evaluated through ELISA kits. Cell cycle distribution was detected by flow cytometry, and gene expression was detected by qRT-PCR and Western blot. Results Our research indicates that compared with the control group, the CP group shows a higher miR-146a expression and a lower β-catenin expression in saliva (P < 0.0001). The expression of miR-146a is positively correlated with AL and PD (P < 0.001), and the expression of β-catenin is negatively correlated with AL and PD (P < 0.001). Inhibiting the expression of miR-146a can promote cell proliferation by regulating cell cycle distribution, reduce the production of inflammatory cytokines, inhibit the expression of p21 and promote the expression of CDK2 and CyclinD1. However, overexpression of miR-146a can result in the opposite effect. In LPS-stimulated hPDLCs, miR-146a expression is up-regulated while β-catenin expression is down-regulated. In addition, overexpression of miR-146a can inhibit β-catenin expression in cells. Simultaneously inhibiting the expression of miR-146a and β-catenin can reverse the effect of inhibiting miR-146a alone on alleviating LPS-induced cell damage. Conclusion miR-146a can inhibit LPS-induced damage to hPDLCs by regulating the Wnt/β-catenin signaling pathway.

Correction: Leadership development as a novel strategy to mitigate burnout among female physicians

Gene therapy marks a turning point for rare skin diseases

Energy-efficient communication between IoMT devices and emergency vehicles for improved patient care

The rising integration of emergency healthcare services with the Internet of Medical Things (IoMT) creates a significant opportunity to improve real-time communication between patients and emergency vehicles like ambulances. Fast and reliable data interchange is crucial in an emergency, especially for those with chronic conditions who rely on wearable IoMT devices to monitor vital health signs. However, establishing consistent communication in real-world conditions such as restricted signal strength, changing distances, and power constraints remains a major difficulty. This paper provides an intelligent communication framework that uses a one-dimensional deep convolutional neural network (1D-CNN) and Lagrange optimization techniques to improve energy efficiency and data transmission speeds. Unlike many earlier models, our technique takes into consideration real-world characteristics such as signal-to-interference-plus-noise ratio (SINR), transmission power, and the distance between the ambulance and the patient’s device. The primary goal is to identify the ideal communication distance for dependable, energy-efficient data transfer during urgent emergency situations. The findings show that the suggested system enhances communication reliability, consumes less energy, and increases the possible data rate. This framework accelerates, smartens, and strengthens emergency healthcare communication systems by combining deep learning and mathematical optimization. These findings contribute to the progress of intelligent healthcare infrastructure, opening the way for responsive and dependable emergency services that can adapt to changing conditions while maintaining high performance and patient safety.

Hazardous science that helps to save and improve lives needs more support

Unraveling the after-hours dilemma: Consequences of overworking among teleworkers—A scoping review protocol

Background Telework, also referred to as telecommuting, remote work, flexible work, and virtual work, involves working from a location different from the traditional office and often uses online communication technologies. Despite the numerous advantages associated with teleworking, it also raises concerns about work-life balance and health implications due to working after hours (WAH). Objective This proposed study aims to understand the health consequences of teleworkers working beyond their scheduled hours. Methods This review will search seven online databases (APA PsycINFO, Medline, Embase, Scopus, Business Source Premier, CINAHL, and Sociological Abstracts) to gather relevant articles. The inclusion criteria will encompass peer-reviewed studies published from 2010 onwards, focusing on WAH among teleworkers and reporting mental and physical health consequences. The exclusion criteria will include non-peer-reviewed articles, grey literature, and studies involving patients with pre-existing conditions. Discussion This review will provide valuable insights into the mental and physical health consequences of WAH among teleworkers, underscoring the urgent need for strategies to mitigate these risks and promote overall well-being. Future efforts, including collaborations between researchers, industry leaders, and policymakers, can guide the development of targeted interventions and evidence-based policies that improve telework environments and support long-term worker health and productivity.

Long-term neurological consequences following benzodiazepine exposure: A scoping review

Benzodiazepine acute withdrawal syndrome is well known, but the long-term neurological consequences of benzodiazepine exposure are much less familiar. A scoping review was conducted of electronic databases for studies that reported on patient outcomes four or more weeks after complete cessation of benzodiazepine use. Forty-six results were retrieved in total, some of which provided signals for protracted symptoms, often reported as incidental findings, and others that showed benzodiazepine discontinuation was beneficial. Some overlap occurred in the outcomes, but these two groups of studies suggest that the benefits of benzodiazepine discontinuation for many patients tended to obscure the more prolonged, severe, and sometimes debilitating symptoms that persisted for months and years in a subpopulation of patients. The prevalence or trajectory of these enduring symptoms could not be determined from these studies. Further elucidation of the potential neurotoxicity of benzodiazepines is needed to better understand protracted symptoms and their treatment. Clinicians, patients, and the healthcare system must be cognizant of the risks of benzodiazepine exposure beyond two to four weeks.

Determinant factors of Chief Data Officer adoption in government: A topic model and structural equation modelling approach

With the generation of massive amounts of data, the Chief Data Officer (CDO) has been introduced in governments worldwide. Existing research on CDO is quite limited and primarily focuses on general descriptions of CDO. However, there is little research exploring the underlying reasons for the establishment of the CDO in government. To address this gap, this paper employs topic modeling to analyze government documents, identify factors influencing the adoption of CDO, and construct a research model. Data were collected from 277 employees within Chinese government organizations through a questionnaire survey and a quantitative analysis was performed to evaluate five hypotheses using structural equation modeling (SEM). The findings suggest that (1) data exploitation, data sharing and data management significantly influence data dividends, and (2) both data dividends and institutional pressures are key predictors of the intention to adopt the CDO, with digital dividends exerting a greater effect than institutional pressures.

Immigrant–native pay gap driven by lack of access to high-paying jobs

Eradication of Mycoplasma pneumoniae biofilm towers by treatment with hydrogen peroxide or antibiotic combinations acting synergistically

Mycoplasma pneumoniae is an important chronic, asthma-associated pathogen that is increasingly antibiotic-resistant. These bacteria have highly reduced genomes and lack a cell wall and numerous other antibiotic targets. They form biofilm towers after prolonged growth both axenically and on tissue culture cells. The biofilm towers have features associated with chronic infection: they are highly resistant to erythromycin and have substantially increased resistance to complement, although they are sensitive to a combination of the two. This work sought to characterize the profile of agents that could eradicate M. pneumoniae biofilm towers. Biofilm towers were found to provide no defense against H2O2, an M. pneumoniae virulence factor whose production is severely attenuated during biofilm tower growth. Checkerboard assays revealed that dual combinations of erythromycin, moxifloxacin, and doxycycline acted synergistically against two strains of M. pneumoniae. Crystal violet assays suggested that pairs of these agents, when used at clinically relevant concentrations, had substantial efficacy against pre-formed biofilm towers, but scanning electron microscopy revealed that the eradication of biofilm towers was even more complete than crystal violet assays indicated. Although the use of fluoroquinolones and tetracyclines in children, who are the most frequently infected population, is not preferred over macrolides due to potential side effects, this work shows that synergistic interactions among therapeutic agents provide potential clinical paths to substantially reducing or eradicating M. pneumoniae biofilms, thereby decreasing morbidity. Furthermore, the sensitivity to H2O2 suggests that small-molecule therapeutics may also be suitable for biofilm clearance.