Browse Articles

Dose-dependent effects of zinc oxide nanoparticles on pre-osteoblast cellular response

Driven by climate change, sudden swings between wet and dry create “hydrologic whiplash”

Correction: Consistent MRI pattern in ADSS1 myopathy with variable clinical presentations: A Korean cohort study

Self-medication with herbal remedies and pharmaceuticals for COVID-19 among healthcare students in Maputo

Abstract The COVID-19 pandemic triggered widespread self-medication, posing substantial public health risks. Healthcare students represent a critical population to study, as their self-care practices may influence their future professional behavior and patient guidance. This study assessed the prevalence and patterns of self-medication with herbal remedies and conventional medicines for COVID-19 among healthcare students in Maputo, Mozambique. A cross-sectional online survey was conducted in November 2022 among students from Eduardo Mondlane University (UEM), the Higher Institute of Science and Technology of Mozambique (ISCTEM) and the Higher Institute of Health Sciences (ISCISA). Among 390 eligible respondents, 83.6% reported self-medicating for COVID-19: A total of 78.7% used herbal remedies, with 23 plant species and one commercial herbal mixture identified. Eucalyptus sp. was the most prevalent (68.2%) used mainly for steam inhalation, followed by lemon (60.5%), ginger (41.3%), garlic (22.1%), and onion (16.2%). Conventional medicines were used by 34.9%, with azithromycin being the most frequent (20.3%). Paracetamol, various antibiotics, and anti-inflammatories were also commonly reported. Notably, 30% of students used both herbal and conventional remedies. The findings suggest the need for targeted educational interventions within healthcare curricula to address potential misinformation, highlight the risks of antimicrobial resistance, and promote evidence-based practices among future health professionals.

Correction for Forrest et al., Early evidence for a stable and flexible foraging niche in the evolution of <i>Homo</i>

Editorial Note: On the utilization of the induced pluripotent stem cell (iPSC) model to study substance use disorders: A scoping review protocol

Theory-based flood disaster education enhances disaster preparedness among adolescents in Selangor, Malaysia: a cluster randomized controlled study

High-throughput screening identifies a critical role of the SPOP–PABPC1 axis in lung adenocarcinoma progression

Lung adenocarcinoma (LUAD) is the most common and deadly subtype of lung cancer, with limited therapeutic options and poor prognosis. Ubiquitination is an important posttranslational modification (PTM) that controls protein turnover, localization and interaction. The dysregulated ubiquitination machinery is a hallmark of cancer, contributing to tumor development. Identifying key E3 ubiquitin ligases and deubiquitinases in LUAD could lead to new biomarkers and treatments. Through RNA sequencing, bioinformatics, and clinical studies, SPOP has been identified as a promising E3 ligase target in LUAD. SPOP is downregulated in LUAD samples and associated with poorer patient outcomes. Functional analyses demonstrate that SPOP suppresses LUAD cell migration, proliferation, and in vivo tumor growth. Mechanistically, SPOP targets poly(A)-binding protein cytoplasmic 1 (PABPC1) for nonproteolytic ubiquitination, leading to its nuclear retention and inhibition of global protein synthesis, thereby suppressing the oncogenic effects of PABPC1. Furthermore, the deubiquitinase HAUSP is able to counteract SPOP-mediated ubiquitination of PABPC1, enhancing its oncogenic role in LUAD cells. Collectively, our findings reveal a SPOP/HAUSP-PABPC1 regulatory axis in LUAD, where SPOP and HAUSP oppositely modulate PABPC1 ubiquitination to control LUAD cell proliferation and migration. This axis represents a promising therapeutic target in LUAD.

Addressing the disposal of unused and expired medications in Saudi households: Time to bridge the regulatory gaps

Introduction This study aimed to investigate the awareness, and practice towards disposal of unused, leftover, and expired medications among the residents of Qassim, Saudi Arabia. Materials and methods A cross-sectional survey was conducted among 877 residents through an online-based questionnaire Participants have to be adults aged 18 years or older of either gender, psychologically stable, able to provide informed consent and understand the Arabic language otherwise, were excluded. A pre-validated, structured questionnaire was used for data collection. Data was analyzed using R software. For all analyses, p &lt; 0.05 was considered significant. Results Most of the [70%] respondents had unused medications, [62%] reported to store their unused medications in the refrigerator followed by bedroom cabinet [36%]. The majority [88%] dispose of unused medicines by throwing away in the household dustbins. The vast majority throw away near expiry [73%], and expired medicines [92%], in the household dustbins. Majority [86%] of the participants denied receiving formal education/awareness regarding safe medications disposal where [60%] agrees that the responsible sector for educating the public about safe medications disposal should be pharmacies. A significant but weak association [p &lt; 0.05; φc = 0.1], between the storage of unused medicines at home and demographic factors such as age, marital status, education, and income was found. Conclusions In conclusion our study highlighted the concern that study respondents frequently store medications in refrigerator [62%] and discard unused and expired medications improperly in the dustbin [88%]. Study results call upon concerned authorities to raise awareness and provide regulatory guidelines for the safe disposal of medications by the residents.

SpatialCell AI achieves reference-free single-cell resolution from spot-based spatial transcriptomics through morphology-guided enhancement

Abstract Spatial transcriptomics platforms such as 10x Visium capture whole-transcriptome expression but at spot-level resolution where each measurement aggregates multiple cells. Existing computational deconvolution methods require external scRNA-seq references, and the growing set of morphology-guided methods (iStar, SpaHDmap, GHIST, Thor, PRTS) each requires per-dataset model training or paired subcellular spatial data. Here we present SpatialCell AI, the first framework to combine training-free operation, reference-free expression integration, and per-cell output granularity for spatial transcriptomics. We validated SpatialCell AI on a matched colorectal cancer sample analyzed across Visium (55 μm), Visium HD (8 μm and 16 μm), and Xenium (single-cell ground truth). On Visium HD 8 μm input, SpatialCell AI achieves an expression correlation of r  = 0.791 against the Xenium reference — the highest of any method tested on this sample. Across six distribution-based validation metrics on the matched 408-gene panel, the SpatialCell AI HD variants lead on the majority of metrics, with no competing method winning on any. A strict matched-region head-to-head comparison reveals a clear architectural distinction: training-free integration improves monotonically as input bins approach single-cell scale, while the trained morphology-guided methods iStar and SpaHDmap track the raw baseline on every platform. SpatialCell AI achieves its strongest performance on Visium HD inputs, where it substantially outperforms both raw baselines and competing morphology-guided methods while transforming spot-level data into individual cell records with spatial coordinates and per-cell expression — an output no raw spot measurement can provide.

Loss of erythrocyte sialic acid in sepsis disrupts inhibitory Siglec interactions, driving neutrophil hyperactivation and NET outspread

Systemic inflammation is accompanied by overwhelming neutrophil activation, resulting in extensive formation of neutrophil extracellular traps (NETs). Although NETs are beneficial for pathogen entrapment, they become detrimental; therefore, their endogenous regulation is pivotal for survival. In this context, the observation that healthy individuals’ red blood cells (RBCs) can inhibit NET formation induced by lipopolysaccharide (LPS)-primed neutrophils is noteworthy. However, during systemic inflammation, NET formation in the vasculature is robust despite constant neutrophil exposure to RBCs. Herein, we reveal that for RBCs and neutrophils from septic individuals, but not from those with local inflammation, interactions cease and NET inhibition is abolished. This is observed even if only one cell type originates from an inflamed individual, and occurs in both human and murine cells. Moreover, 6 to 10 d post–sepsis induction (mice) or diagnosis (humans), during the resolution of inflammation, interactions are restored and hence fail during the critical stages of inflammation. Mechanistically, this is not due to passive LPS absorption by RBCs, which would limit its availability for neutrophils. In contrast, the cessation of NET inhibition is due to inflammation-induced RBC desialylation (removal of α2,3-linked sialic acids) and decreased expression of inhibitory Siglec-E/9 and -G, but not Siglec-F/5, on neutrophils. Accordingly, concurrent addition of exogenous polysialic acid (PolySia) and pharmacologically controlled accumulation of Siglec molecules on neutrophils preserves the ability of erythrocytes to inhibit PAD4-dependent NET formation upon interaction with neutrophils of endotoxemic mice. These findings open avenues for clinically controlling NET formation during systemic inflammation, given the abundance of erythrocytes in the blood.

From words to action? Linking ESG reports to environmental performance

Companies increasingly use ESG reports to communicate their environmental priorities, yet it remains unclear whether the issues they emphasise correspond to measurable improvements in environmental performance. Using computational linguistics, we analyse the full texts of 1,477 ESG reports from companies listed in the STOXX Europe 600 index, identifying 34 main topics—six of which are related to environment. We describe how these topics evolve over time and differ across sectors, cluster companies based on their thematic focus, and test whether specific topics are associated with tangible low-carbon transition efforts. Our findings show that the alignment between environmental communication and environmental performance is topic-dependent. Topics such as sustainable value chains and renewable energy are associated with improvements in environmental KPIs, whereas others, including emissions or electric vehicles, show no significant association. Methodologically, our study advances prior work by combining structural topic modelling with firm-level KPI analysis to evaluate ESG disclosure at the level of specific environmental themes rather than aggregate reporting measures. Our study demonstrates how topic-specific reporting patterns relate to environmental outcomes and offers a more nuanced understanding of when ESG reporting reflects substantive environmental action.

A donor-recipient ranking model to optimize long-term survival post liver transplant

Abstract Liver transplant (LT) is a life-saving treatment for patients with cirrhosis and/or hepatocellular carcinoma (HCC), but organ shortages and suboptimal donor-recipient matching remain major challenges and existing donor-recipient matching risk models offer limited predictive accuracy. Our study aims to develop a machine-learning-based model to predict and rank long-term post-transplant survival. We included adult deceased donor liver transplant recipients from the Scientific Registry of Transplant Recipients (2009–2019). We developed a first of its kind XGBoost-based survival model to predict graft survival at important discrete timepoints and built a corresponding ranking score. The reliability of the score was compared to existing scores. We included 60,649 waitlist registrants, of which 41,058 (67.7%) were males. We found that our predictive model, DisCScore , was the strongest at predicting donor-recipient pair survival times, with a C-index of 0.858 (95%CI: 0.8525–0.8643). We achieved the highest mean time-dependent AUROC at 6-months, 1- and 3- years post-LT using DisCScore (mean AUC (95% CI): 0.797 (0.783–0.812), 0.821 (0.809–0.832) and 0.865 (0.857–0.874) respectively and DisCScore outperformed CoxPH and DeepSurv models in all three timepoints. We found that our model performed best in the high MELD group (MELD &gt; 30) – where the C-index was the highest: 0.873 (95% CI 0.863–0.891), as compared to the low and medium MELD groups. Our proposed model can accurately predict donor-recipient pair survival probabilities, especially in those recipients with high MELD scores. This has the potential to be integrated into the complex decision-making pathway of organ allocation.

General inverse-cube thickness scaling of projectile penetration energy in ultrathin films

Ultrathin films of widely different materials exhibit a dramatic enhancement of projectile penetration resistance under high–velocity impact. Despite extensive simulations and experiments, a unifying physical explanation has remained elusive. Here we show that the specific penetration energy follows a general inverse-cube scaling law, E p ∗ ( h ) = E p , ∞ ∗ + B h − 3 , across chemically and structurally distinct systems. The inverse–cube scaling is traced to a finite–size correction to the effective shear modulus caused by suppression of long–wavelength nonaffine modes. The scaling describes impact data for multilayer graphene, graphene oxide, and polymer thin films, revealing a common elastic contribution to nanoscale impact resistance.

Depositional architecture and post-depositional alteration of the Toutunhe Formation (J2t) in the Louzhuangzi area, Southern Junggar Basin: Implications for uranium mineralization

In recent years, economically significant sandstone-hosted uranium mineralization has been identified in the Louzhuangzi area along the southern margin of the Junggar Basin. However, the controls on uranium enrichment and their links to depositional architecture and post-depositional fluid processes remain insufficiently constrained. This study integrates field geological investigations, drill-core lithofacies logging, and systematic sampling with petrographic and micro-analytical techniques, including optical microscopy, scanning electron microscopy (SEM), electron probe microanalysis (EPMA), and transmission electron microscopy (TEM). The objective is to elucidate the depositional characteristics, alteration processes, and uranium occurrence mechanisms of the ore-bearing sandstones within the Toutunhe Formation (J 2 t ). Results show that the upper member (J 2 t 2 ) represents meandering-river deposits, whereas economically significant uranium mineralization is hosted in the lower member (J 2 t 1 ), characterized by braided-river sandstone units with high permeability. The sandstones of the Toutunhe Formation (J 2 t ) exhibit intense oxidation by surficial fluids, overprinted by post-mineralization hydrothermal alteration and sulfide-forming alteration associated with reducing fluids. Uranium is closely associated with pyrite, organic matter, and clay minerals. Uranium minerals are dominated by coffinite and pitchblende (~68%), with UO₂ contents of 52.89–86.07%. Minor Ti-bearing uranium phases (~16%), interpreted as possible brannerite, contain 38.01–41.46% UO₂ and 31.67–36.09% TiO₂, while nanoscale uranium minerals (~16%) show UO₂ contents of 9.15–60.28%. These results indicate that uranium mineralization was controlled by the coupling of braided-channel architecture and multi-stage fluid processes. Uranium was initially precipitated from oxidized fluids and subsequently modified and preserved by later thermal and reducing fluids, highlighting the importance of multi-fluid interactions in sandstone-hosted uranium systems.

Interocular difference in refraction and ocular biometry in children with anisometropia

Quantum algorithms for viscosity solutions to nonlinear Hamilton–Jacobi equations based on an entropy penalization method

We present a framework for efficient extraction of the viscosity solutions of nonlinear Hamilton–Jacobi equations with convex Hamiltonians. These viscosity solutions play a central role in areas such as front propagation, mean-field games, optimal control, machine learning, and a direct application to the forced Burgers’ equation. Our method is based on an entropy penalization method which generalizes the Cole–Hopf transform from quadratic to general convex Hamiltonians, allowing an approximation of viscous Hamilton–Jacobi dynamics by a discrete-time linear dynamics which approximates a linear heat-like parabolic equation, and thus can also extend to continuous-time dynamics. This makes the method suitable for quantum simulation. The validity of these results hold for arbitrary nonlinearity that correspond to convex Hamiltonians, and for arbitrarily long times, thus obviating a chief obstacle in most quantum algorithms for nonlinear partial differential equations. We provide quantum algorithms—both analog and digital—for extracting pointwise values, gradients, minima, and function evaluations at the minimizer of the viscosity solution, without requiring nonlinear updates or full state reconstruction.

Spatio-temporal changes in clusters of gastric cancer incidence: The impact of nationwide cancer control programs in South Korea

Background Gastric cancer (GC) is a global health concern, with incidence and mortality varying significantly across populations. The spatial heterogeneity of GC may reflect differences in prevention strategies and their effectiveness, highlighting the need to identify high-risk areas and their possible contributing factors. Objective Leveraging the decline of GC incidence since 2012 affected by nationwide cancer prevention programs in South Korea, this study aimed to identify high- and low-risk clusters and explore the changes in spatial clusters of GC incidence between 2009–2013 and 2014–2018 before and after the decline, respectively. In addition, we identified geographic characteristics associated with cluster. Method Using national cancer registry and geographic data across 243 districts, we performed spatial clustering analyses in each period. We identified the high- and low-risk areas as the overlapping clusters by two common clustering approaches of local Moran’s I and Getis Ord Gi*. Then, we compared 23 geographic characteristics between high- and low-risk areas and two periods. Results The average GC incidence rate declined in both high- (96–79 per 100,000 people) and low-risk districts (73–61) between 2009–2013 and 2014–2018. While cluster locations remained stable, low-risk districts expanded notably (26–37) and high-risk districts slightly reduced (31–28). As opposed to little geographic characteristics that showed the significant difference between high- and low-risk areas in the early period, the later period gave large green space, frequent regular walking, and reduced self-related obesity in the low-risk area, in addition to sociodemographic advantages, compared to those in the high-risk area (p-value &lt;0.002). Conclusion Our findings suggest the potential effectiveness of lifestyle- and/or environment-focused prevention for GC incidence and the need of locally-tailored strategies to reduce cancer burden.

Assessing the reliability and quality of avascular necrosis of the femoral head content on social media: a cross-sectional content analysis

Ultrarapid MC1R protein and associated plumage color evolution in the domestic chicken

The domestic chicken exhibits extraordinarily high plumage diversity in sharp contrast to the uniform plumage phenotype in its wild ancestor, the red junglefowl. The melanocortin-1 receptor ( MC1R ) locus is the most variable pigmentation locus in domestic chicken. Here we have analyzed whole-genome sequence data from 10,026 birds, including red junglefowls and 393 populations of domestic chicken. We document ultrarapid evolution of 18 MC1R alleles, due to the accumulation of 9 missense mutations combined with reshuffling of sequence variants within the single MC1R exon. The lack of linkage disequilibrium at the MC1R locus strongly suggests that this combinatorial allele diversity is generated by interallelic gene conversion, possibly promoted by its high guanine–cytosine content. Functional analyses of MC1R variants expressed in transfected cells, using second-messenger and bioluminescence resonance energy transfer assays together with homology modeling, demonstrate that most missense mutations have significant effects on MC1R signaling and include both activating and inhibitory changes. One common MC1R allele differs by as many as three functionally relevant missense mutations from the most closely related wild-type allele, and its phenotype reflects their combined effects. Genome-wide analyses further show that MC1R acts as a modifier of dominant white and recessive white phenotypes caused by mutations in premelanosome protein and tyrosinase , respectively. In both cases, MC1R alleles promote a pure white phenotype, either by enhancing eumelanin pigmentation (dominant white) or inhibiting eumelanin pigmentation (recessive white).