Browse Articles
Discover research articles across all indexed journals
Ribosomal modifications are associated with mesenchymal fate selection in the neural crest lineage
Abstract Neural crest cells contribute to craniofacial formation by differentiating into skeletogenic mesenchyme and neuro-glial lineages. Using Smart-seq2 single-cell transcriptomics, we show that mesenchymal fate commitment correlates specifically with the expression of rRNA-modifying and ribosome assembly factors, rather than structural ribosomal proteins. Notably, EMG1 and NHP2 introduce key post-transcriptional modifications into 18S rRNA, including m¹acp³ψ at U1248, which requires TSR3 for final maturation. Disrupting NHP2 or TSR3 in vitro and in vivo perturbs cranial neural crest differentiation; post-migratory temporal knockout of Polr1a or Polr1c also causes craniofacial malformations. These findings align with cell type-specific m¹acp³ψ levels during neural crest differentiation. Given the neural crest contribution to neuroblastoma, we analyze patient data to find that elevated ribosomal control and rRNA-modifying proteins predict poorer outcomes. Complementary experiments in neuroblastoma cell lines reveal functional roles for TSR3 and WDR74 in mesenchymal-like tumor states. Together, our results link rRNA modifications and ribosome assembly to fate decisions, suggesting ribosomal heterogeneity shapes both normal development and tumor progression.
Dietary astragalus stems polysaccharides improve antioxidant capacity and lactation performance in dairy goats through integrated metabolomic and mechanistic analyses
Getting robots back on track by reconstituting control in unexpected situations with online learning
Abstract Robotic systems are increasingly common in strictly controlled environments (e.g. warehouses), but they have yet to fully integrate into our everyday lives. Everyday scenarios are a challenge for robot controllers due to disturbances that can be caused by unforeseen conditions (e.g., damage, flat tires, or wind gusts) or unexpected usage. In these cases, operators may lose control of their systems, leading to potentially catastrophic outcomes, such as crashes or failed operations. Our method, “Fast Learning-based Adaptation for Immediate Recovery”, uses machine learning to counter loss of control by enhancing existing controllers. It rapidly diagnoses and compensates for unseen perturbations by updating an onboard model every 225 milliseconds. Even in a setting with only onboard compute, we show that operators equipped with our method regain control and operate as effectively as in unperturbed conditions across a wide range of perturbations. Other state-of-the-art methods, such as an optimal control and an adaptive control baseline, were found to be half as effective at recovering from perturbations, while an online Deep Reinforcement Learning baseline proved entirely ineffective. In this work, we demonstrate that our online learning method enhances robotic resilience by mitigating the impact of perturbations on system operability.
The relationship between perceived stress, psychological resilience, and depressive symptoms in college students, and the moderating role of gender
Long-read sequencing of families reveals increased germline and postzygotic mutation rates in repetitive DNA
Abstract Long-read sequencing improves sensitivity to discover variation in complex repetitive regions, assign parent-of-origin, and distinguish germline from postzygotic mutations. We applied Illumina, Oxford Nanopore Technologies, and PacBio sequencing to discover and validate de novo mutations in 73 children from 42 autism families (157 individuals). We assay 2.77 Gbp of the human genome, yielding on average 95 de novo mutations per transmission (87.5 single-nucleotide substitutions, 7.8 indels), with no significant difference in mutation rate or profile between probands and their unaffected siblings. Long reads increase de novo mutation discovery by 20-40% and double the mutations classified as early embryonic. The germline mutation rate is 1.30×10 −8 substitutions/base pair/generation; the postzygotic rate is 0.23×10 −8 . These rates are significantly increased in repetitive DNA, where segmental duplication mutability is dependent on length and percent identity. Here, we show that enrichment in repeats occurs predominantly postzygotically, likely resulting from faulty DNA repair and interlocus gene conversion.
Blood-based circulating microRNAs as diagnostic biomarkers in cutaneous melanoma: a systematic review and meta-analysis
DPEP2 suppresses hyperinflammation via metabolic reprogramming of macrophages in sepsis
Experiences and opinion of medical professionals regarding the use of telemedicine tools in management of patients with chronic diseases: a cross-sectional survey
Distinct genomic trajectory among invasive Salmonella Typhimurium ST313 infections
Measurement of the d31 piezoelectric coefficient of compliant materials by non-contact polarization and resonant signal enhancement
Bioengineered ferritin-based lysosome-targeting chimera platform for tumor-targeted therapy
General-practitioner-centered health care: current results from the implementation of the German model
Abstract Primary care-centered healthcare models, particularly those led by general practitioners (GPs), are increasingly adopted to address global healthcare challenges including rising costs, fragmented services, and chronic disease burdens. In Germany, the “Hausarztzentrierte Versorgung” (English: General Practitioner-Centered Health Care, GPCHC) program aims to reinforce the role of GPs as care coordinators. Within this study we evaluated data from the implemented German GPCHC model in Baden-Württemberg, a German federal State with about 11 Mio inhabitants and compared outcomes with international benchmarks for strong primary care. The analysis is based on administrative health insurance data of almost two million individuals. We compared patients enrolled in GPCHC with patients receiving regular primary care in 2022 in terms of key indicators of healthcare utilization (GP contacts, uncoordinated consultations with a non-GP-specialist, all-cause hospitalizations, potentially avoidable hospitalizations (PAHs), and prescription of me-too drugs. For patients enrolled in the GPCHC program, consistently favorable outcomes were observed with respect to these key indicators. These findings align with international evidence from strong primary care systems in the Netherlands, the United Kingdom, and Nordic countries. The proposed model presents a scalable framework for strengthening primary care delivery in complex healthcare systems.
Amphotericin B promotes respiratory viral entry by enhancing late endosomal maturation and fusion via glucocerebrosidase-mediated ceramide remodeling
Abstract Respiratory viral infections, such as influenza and COVID-19, pose significant global health challenges. For patients with invasive pulmonary aspergillosis, a subsequent viral infection can lead to markedly worse clinical outcomes. Although amphotericin B (AmB) remains a cornerstone antifungal therapy, our investigation demonstrates that it paradoxically enhances the entry of influenza A virus and SARS-CoV-2. Mechanistically, AmB directly binds to and activates glucocerebrosidase, leading to ceramide accumulation and RAB7 upregulation in the late endosomes, thereby enhancing late endosomal maturation and fusion with viruses. In animal models, AmB treatment enhances viral infection in both influenza A virus–infected mice and SARS-CoV-2–challenged hamsters, resulting in accelerated weight loss, higher viral loads, and aggravated tissue damage. Consistently, in our propensity score-matched cohort of patients with culture-confirmed invasive pulmonary aspergillosis (2016–2025, n = 1,072), systemic use of AmB is associated with a significantly higher incidence of subsequent viral infection compared to other antifungals (21.55% vs. 7.76%, P = 0.003), which is further supported by multivariable analysis confirming AmB as an independent risk factor (adjusted OR = 3.45, 95% CI 2.20–5.41, P = 7.174 × 10 -8 ). In summary, our findings provide crucial clinical evidence to guide antifungal therapy and reveal glucocerebrosidase as a potential target for developing novel antiviral strategies.
Hypergraph-based contrastive embedding and attention fusion for detection of skin cancer
Abstract Skin diseases involve a spectrum of problems including infections, and malignancies. Melanoma, the deadliest kind of skin cancer, starts in melanocytes, which make melanin. Early detection is really important, but it’s hard since the visual indications are often quite little and there is a big class imbalance in diagnostic datasets. The proposed C2G-HFMTA framework consists of three hierarchical levels: (a) an overall contrastive learning (CL) framework, (b)two major feature learning branches, namely the Graph Contrastive Embedding Framework (GCEF) and the High-dimensional Feature with Multimodal Transformer Attention (HFMTA), and (c) attention and fusion sub-modules including Hypergraph Bi-Convolutional Attention and Multiscale Transformer Attention, which operate within these branches to enhance discriminative representation learning. The proposed method demonstrates strong performance on benchmark dermoscopic datasets and has the potential to support computer-aided diagnosis systems, subject to further may support future computer-aided diagnosis systems validation and real-world testing. We have used Clustered Class-Based Segmentation (CCBS) for changing the training distributions. Our Class-Based Contrastive Loss (CBCL) works directly on original dermoscopic pictures, that preserves the semantic integrity of the images while making it easier to tell the difference between classes. Our framework outperforms several recent CNN- and transformer-based baselines in controlled experimental settings. It gets 93.2% accuracy and a 92.9% F1-score, and it does well on minority classes. Experiments were conducted on the HAM10000 dataset containing 10,015 dermoscopic images across seven diagnostic categories, using a stratified train–validation–test split of 70%–10%–20%. Performance was evaluated using accuracy, precision, recall, and F1-score, using five-fold stratified cross-validation to ensure robust performance estimation. Ablation experiments show that grouping, cross-branch fusion, and semantic-guided attention are important.
Perovskite solar cells with enhanced thermal fatigue resistance under extreme temperature cycling
Abstract Metal halide perovskite solar cells combine high power density with low-cost manufacturing, but durability under repeated extreme temperature cycling remains insufficiently understood. We investigate thermal fatigue under cycling between −80 °C and +80 °C as an accelerated stress protocol. Mismatched thermal expansion between the perovskite absorber and glass substrate induces biaxial tensile strain, leading to degradation at the substrate–perovskite interface and within grain boundaries. To mitigate these failure modes, we introduce a co-additive molecular strategy based on lipoic acid, dihydrolipoic acid, and a sulfonium-based derivative to enhance interfacial adhesion, while in situ polymerization during annealing reinforces grain-boundary cohesion. This dual reinforcement improves robustness and performance, achieving stabilized efficiencies of 26% under standard solar illumination. Devices retain 84% of initial efficiency after 16 extreme temperature cycles. Our experiments reveal that thermal exposure duration is more critical than cycle number, with most degradation occurring during initial cycles.
Sustainable synthesis and characterization of high-surface-area activated carbons from walnut and pistachio shell wastes via chemical activation
Abstract Valorization of agricultural residues into high-performance porous carbons is an effective route to sustainable adsorbents. Here, walnut green outer shell and pistachio pink outer shell were converted into activated carbons via chemical activation using KOH (1:1, 1:2, 1:3) and ZnCl₂ (1:1). Without any pretreatment, precursors were carbonized at 500 °C (1 h, nitrogen atmosphere), followed by activation at 800 °C (KOH, 1 h) or 500 °C (ZnCl₂, 1 h). Textural analyses revealed a strong dependence on both precursor type and activating agent. Walnut-shell carbons reached exceptionally high surface areas of 1028–2347 m² g⁻¹, with the maximum obtained for KOH (1:3), whereas pistachio-shell carbons achieved 788–1324 m² g⁻¹ under the same KOH series. In contrast, ZnCl₂ activation produced markedly lower areas (445–750 m² g⁻¹) and grinding caused only minor changes. FTIR/XRD/SEM/Elemental analyses collectively supported the formation of defect-rich, turbostratic carbon frameworks with well-developed porous morphologies, highlighting walnut shell as a particularly promising precursor for sustainable, high-surface-area activated carbons suitable for adsorption-driven environmental applications.