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Cadmium exposure during adolescence and young adulthood induces signatures of metabolic dysfunction-associated steatotic liver disease

Molecular diagnosis of Trichuris trichiura: Prevalence and associated risk factors in children under five living in a malaria-endemic area in Papua, Indonesia

Background Trichuris trichiura , a soil-transmitted helminth (STH), infection is highly prevalent in children and, if not treated, can cause adverse health consequences. Microscopy using the Kato-Katz method is the mainstay of STH diagnosis in most settings but has low sensitivity. This study aims to quantify prevalence and examine risk factors of trichuriasis among children in a malaria-endemic area. Methods The study recruited 181 children aged <5 years old from a household survey conducted in 16 villages in Timika, Papua, Indonesia, from April to July 2013. Clinical and laboratory data were collected, and stool samples were analysed later using quantitative PCR (qPCR). Results The prevalence of T. trichiura infections was 13.8% (25/181; 95%CI, 9.1–19.7) by Kato-Katz microscopy examination and 31.5% (57/181; 95%CI, 24.8–38.8) by qPCR analysis, increasing the prevalence by 17.7% (p = 0.0001). Children aged >3 years old had a 3 times increased risk of having trichuriasis compared to younger ones (aOR 3.29 [95%CI, 1.42–7.63], p = 0.006). Children with Hb ≤ 10 g/dL were 2 times more likely to have trichuriasis compared to those with Hb > 10 g/dL, (OR 2.46 [95%CI, 1.14–5.31], p = 0.020). Malaria prevalence was 9.9% (18/181; 95%CI, 6–15.3). Coinfections with malaria increased the risk of anaemia (OR 11.7 [95%CI, 2.0–67.0], p = 0.004. No apparent association was found between trichuriasis and undernutrition (wasting and stunting). Conclusions The prevalence of trichuriasis in under-five children is underestimated and together with malaria, the infections are associated with anaemia. Public health strategy should include STH prevention targeted to young children living in malaria-endemic areas.

Hierarchical assembly of biomass fiber–lamella–macromolecule networks for biocomposites with high strength and water-resistant sealing

Enhancing mechanical strength and water resistance in cellulose fiber-based materials is crucial for their adoption as sustainable alternatives to petroleum plastics. However, achieving these improvements through a simple, economical, and ecofriendly approach remains a major challenge. Here, we present a chitosan (CS)-driven multiscale assembly and rearrangement strategy that produces fiber–lamella biocomposites with outstanding mechanical strength and water resistance, achieved without any chemical modification, thermal treatments, or mechanical pressing. This method leverages synergetic electrostatic interactions, hydrogen-bonding, and hydrophobic association where negatively charged microscale pliable pollen lamella and positively charged macromolecular CS sequentially assemble within pulp fibers to form dense, water-resistant networks. Relying solely on the spontaneous organization of the three components, the resulting fiber/pollen-CS (FP-CS) biocomposites exhibit superior mechanical strength (~80 MPa) and maintain water stability for up to 100 d. Remarkably, they also enable seamless water-resistant sealing through simple CS application, facilitating ecofriendly production of straws, packaging, and water-resistant patches. This green, scalable, and energy-efficient process uses only biomass feedstocks to produce high-performance biocomposites, offering a promising sustainable alternative to conventional plastics.

Perceived restorativeness of urban green spaces in southern China during summer

Construction and validation of an anoikis-related prognostic model for lung adenocarcinoma based on bulk and single-cell transcriptomic data

Lung adenocarcinoma (LUAD) is a highly aggressive lung cancer with poor prognosis due to lack of reliable biomarkers. Resistance to anoikis drives tumor progression and metastasis. This study aims to develop and validate an anoikis-related prognostic model for LUAD. We employed univariate Cox regression analysis, LASSO regression, and random forest algorithms to identify anoikis-related genes (ARG) from bulk transcriptomic datasets, and establish a 7-gene prognostic signature, validated in two LUAD cohorts from GEO database. We evaluated immune infiltration, molecular functions, and genomic alterations between risk groups and analyzed single-cell RNA sequencing data. IHC and mIF validated TIMP1 expression and its interaction with Treg cells. We developed a 7-gene prognostic model (LDHA, PLK1, TRAF2, ITGB4, SLCO1B3, TIMP1, ZEB2) using machine learning to predict survival in LUAD patients. The model accurately predicted 1-year survival rates (GSE31210: AUC = 0.805; GSE30219: AUC = 0.787), 2-year survival rates (GSE31210: AUC = 0.769; GSE30219: AUC = 0.681), and 3-year survival rates (GSE31210: AUC = 0.695; GSE30219: AUC = 0.735) and correlated with clinical features, immune infiltration, and tumor microenvironment (TME) remodeling. Single-cell sequencing data showed that LUAD patients exhibited an immunosuppressive TME phenotype, which was exacerbated by high TIMP1 expression in epithelial cells, promoting Treg cell activity. The 7-gene ARG prognostic model established in this study shows promising potential as a clinically applicable tool for decision-making.

Parallel systems for social and spatial cognition reaching the cortical apex

How are systems supporting high-level cognition organized in the human brain? We hypothesize that cognitive processes involved in understanding people and places are implemented by distinct neural systems with parallel anatomical organization. We test this hypothesis using precision neuroimaging of individual human brains on diverse tasks involving perception and cognition in the domains of familiar people, places, and objects. We find that thinking about people and places elicits responses in distinct areas of high-level association cortex within the default mode network, spanning the frontal, parietal, and temporal lobes. Person- and place-preferring brain regions are systematically spatially adjacent across cortical zones. These areas have strongly domain-specific response profiles across visual, semantic, and episodic tasks and are specifically functionally connected to other parts of association cortex with like domain preference. Social and spatial networks remain anatomically separated at the apex of a unimodal-to-transmodal gradient across cortex and include regions with anatomical connections to the hippocampal formation. These results demonstrate the existence of parallel, domain-specific networks reaching the cortical apex.

Utilization of expired sulphadimidine in the fabrication of a metal free graphitic carbon nitride to photodegrade doxycycline in water: an experimental and statistical study

Which demographic characteristics are associated with willingness to take part in recontact studies? A cross-sectional study

Background Lower rates of participation in research by ethnic minority groups and socioeconomically deprived populations has led to efforts to develop recruitment strategies which aim to address this imbalance. Little is known about participation in recontact studies (where existing research participants are recruited to further studies). Identifying factors which predict rates of participation and retention is crucial to ensure the benefits of diversified recruitment are realised upon study completion. Methods This secondary data analysis utilised data from the Multi-Ethnic Lifestyle Study (MELS) which was a multi-centre study. Modified Poisson regression models were applied in Stata version 18.0 to examine differences in demographic characteristics (age, gender, ethnicity, migration status, education, employment) between existing MELS participants who did and did not consent to be recontacted for future health research, (2) consent to link study data to their health records, (3) and consent to share research data with universities and the NHS. Results A total of 6147 participants (mean age: 51.9 years, ± 17.12) were included in the analysis. Older age, higher education level (A levels or above compared to GCSEs) participants were more likely to consent to be recontacted, and to agree to link study data to their health records. South Asian participants were less likely to consent to recontact (PR = 0.79, 95% CI [0.71–0.88], p < 0.001) and data linkage (0.72, 95% CI [0.64–0.82], p < 0.001) compared to White participants. As the number of long-term conditions increased, so did the likelihood of consenting to re-contact (PR = 1.14, 95% CI [1.04–1.23], p = 0.002) and data linkage (PR = 1.13, 95% CI [1.04–1.22], p = 0.003). For data linkage, participants who were not born in the UK, compared to UK born participants, were less likely to consent to having their data linked to health records, although the difference was not statistically significant (PR = 1.06, 95% CI [0.94–1.20], p = 0.316). Conclusions Our findings highlight that willingness to participate in health research studies, consent to data linkage, and data sharing varies across demographic groups. Inclusive recruitment and retention strategies must be developed to encourage participation and retention in follow-up studies, especially among historically underserved groups.

Accurate site-specific folding via conditional diffusion based on AlphaFold3

Accurate structure prediction of biomolecular complexes is crucial for understanding biological processes and enabling drug discovery. While AlphaFold3 represents a significant advancement, enhancing its accuracy for specific binding sites remains a challenge. We present SiteAF3, a method for accurate site-specific folding via conditional diffusion, built upon the AlphaFold3 framework. SiteAF3 refines the diffusion process by fixing the receptor structure and optionally incorporating binding pocket and hotspot residue information. Comprehensive evaluations on protein–small molecule, protein–peptide, and protein–nucleic acid datasets demonstrate that SiteAF3 consistently outperforms AlphaFold3, achieving higher accuracy in complex structure prediction especially for orphan proteins and allosteric ligands, with reduced computational cost. SiteAF3 offers a user-friendly plug-in compatible with AlphaFold3, providing a valuable tool for more accurate modeling of biomolecular interactions.

Synthesis and structural characterization of MAX phase thin films by radio frequency sputtering and annealing

Standardized surgical technique for single-port totally extraperitoneal inguinal hernia repair using the glove method with an intraumbilical incision

Background The safety of single-port totally extraperitoneal (STEP) inguinal hernia repair compared to conventional totally extraperitoneal (CTEP) has been supported by various randomized controlled trials (RCTs). However, the optimal method remains unclear because of variations in the location and length of the incision as well as different uses of the single-port device. We standardized STEP using the glove method with a straight umbilical incision that maintained the wound profile within the confines of the umbilicus for better cosmetic satisfaction and port operative pain reduction. Methods The incision length was limited to 1–1.5 cm, extending no further than the umbilicus. The STEP is performed utilizing the glove method using Alexis of XXS size. To minimize forceps interference, the surgeon dissected the spermatic cord into two distinct phases. As the mesh was inserted within the pneumoperitoneum, it was deployed safely and securely. In addition, we present the surgical outcomes at our institution for an early career surgeon. Results A total of 25 unilateral inguinal hernia STEP procedures were performed between April and October 2023. The median operative time was 68 minutes, and the procedure was performed safely with no complications requiring treatment. Conclusion In conclusion, STEP using the glove method with a total intraumbilical incision can be safely performed by an early career surgeon.

WrtF from <i>Rhizobium tropici</i> CIAT 899 is a GT-A fold fucosyltransferase that binds its donor nonproductively

l -Fucose, a 6-deoxy-monosaccharide, is often incorporated into O-antigens in Rhizobia where it serves as an important marker for host recognition. Here, we biochemically and structurally characterize WrtF, an O-antigen polysaccharide l -fucosyltransferase from the plant endosymbiont Rhizobium tropici CIAT 899. We show that WrtF transfers l -fucose from its donor, guanosine 5′-diphospho-β- l -fucose (GDP-Fuc), to a nonreducing end glucose, creating an α-(1→4) linkage. We determined structures of WrtF at resolutions between 1.45 and 2.3 Å in the apo state, and in complex with GDP, GDP-Fuc, nonacetylated and acetylated trisaccharide acceptors, and with a tetrasaccharide product. WrtF has an N-terminal glycosyltransferase A (GT-A) fold and a unique C-terminal α-helical bundle domain. The structures, combined with activity assays, and sequence analysis show that WrtF is unusual in using neither divalent cations nor basic residues to coordinate and stabilize the donor pyrophosphate. In contrast to almost all previously characterized GT-A glycosyltransferases that use α-linked donors that present the donor monosaccharide axially, GDP-Fuc presents its monosaccharide equatorially. GDP-Fuc binds in a compact conformation with the l -fucose endocyclic oxygen and C6 methyl packed against ribose C5 and C4, respectively. This binding mode paradoxically shields the anomeric carbon from attack; superposition of the donor and acceptor complexes suggests that these substrates would sterically hinder one another and do not appropriately orient to form a Michaelis complex. We therefore propose that the donor l -fucose must reorient in the active site prior to turnover. The preferred occluded donor binding mode may serve to “prescreen” GDP-Fuc from structurally similar candidate donors, while also minimizing counterproductive donor hydrolysis.

Biogenic synthesis of magnetic nanozymes with peroxidase-like activity for water remediation

Expression of Concern: Ubiquitination and Degradation of Ribonucleotide Reductase M1 by the Polycomb Group Proteins RNF2 and Bmi1 and Cellular Response to Gemcitabine

In vivo parcellation of the human superior colliculus from brain-wide probabilistic connectivity

The Superior Colliculus (SC) is a multimodal integration midbrain structure receiving input from various sensory systems (visual, auditory, and somatosensory) and using this information to guide rapid, reflexive responses. Research using invasive animal model techniques has extensively characterized the SC connectional fingerprint and subcomponent differentiation. However, the extent to which these findings can be generalized to humans remains largely unknown. Here, we developed a fully data-driven approach to examine the wiring diagram of each voxel in the human SC in a cohort of 200 participants. Using diffusion MRI to infer probabilistic tractography, we identified three subregions within the human SC with distinct topography and unique brain-wide connectivity patterns. A superficial division is primarily linked with visual cortical areas, the amygdala, and the posterior thalamus. An intermediate-lateral division is connected predominantly with the auditory and somatosensory cortices and with sectors of the posterior parietal cortex. A deep subregion exhibits preferential connectivity with the brainstem and cerebellum. These in vivo features highlight the heterogeneity in the extrinsic connectivity of the human SC. Probabilistic coupling partly reflects conserved SC interaction motifs reported in other mammals with direct tracing methods, while also showing potential adaptations of cortical–midbrain interactions compatible with human neocortical expansion.

Deep segmentation of 3+1D radar point cloud for real-time roadside traffic user detection

Abstract Smart cities rely on intelligent infrastructure to enhance road safety, optimize traffic flow, and enable vehicle-to-infrastructure (V2I) communication. A key component of such infrastructure is an efficient and real-time perception system that accurately detects diverse traffic participants. Among various sensing modalities, automotive radar is one of the best choices due to its robust performance in adverse weather and low-light conditions. However, due to low spatial resolution, traditional clustering-based approaches for radar object detection often struggle with vulnerable road user detection and nearby object separation. Hence, this paper proposes a deep learning-based $$3+1$$ D radar point cloud clustering methodology tailored for smart infrastructure-based perception applications. This approach first performs semantic segmentation of the radar point cloud, followed by instance segmentation to generate well-formed clusters with class labels using a deep neural network. It also detects single-point objects that conventional methods often miss. The described approach is developed and experimented using a smart infrastructure-based sensor setup and it performs segmentation of the point cloud in real-time. Experimental results demonstrate 95.35% F1-macro score for semantic segmentation and 91.03% mean average precision (mAP) at an intersection over union (IoU) threshold of 0.5 for instance segmentation. Further, the complete pipeline operates at 43.61 frames per second with a memory requirement of less than 0.7 MB on the edge device (Nvidia Jetson AGX Orin). We will release the RoadsideRadar dataset along with the code implementation of this work at https://github.com/bhanderisavan/roadside-radar-seg .

Studies in rats of a target specific and reversible general anesthetic with a favorable safety profile

Delirium and cognitive decline are linked to clinically relevant anesthetics in the vulnerable elderly population, prompting the need for new and safer anesthetic strategies. Most general anesthetics potentiate the activity of GABA A receptors. However, these drugs act on myriad other targets, causing unwanted effects. Dexmedetomidine (Dex), a selective α 2 adrenergic receptor agonist, is associated with reduced incidences of delirium and cognitive decline in the elderly. Unfortunately, despite its sedative effect, Dex is not suitable for general anesthesia when used alone. We previously demonstrated that enhancing Dex with low doses of either sevoflurane or propofol resulted in a potent general anesthetic that was rapidly reversible. In this study we assessed whether Dex enhanced by magnesium (Mg 2+ ) infusion could produce a general anesthetic. Mg 2+ is an essential ion in the body, possessing sedative effects attributable to antagonizing NMDA receptors and voltage-gated Ca 2+ channels and it may indirectly potentiate GABAergic signaling. Mg 2+ has been shown to be neuroprotective and safe to use even in pregnant women. Mg 2+ is a safer adjunct agent than either sevoflurane or propofol. For this study, rats of both sexes were anesthetized with a combination of Dex and Mg 2+ and then underwent procedures to determine the efficacy of the anesthetic. Dex with Mg 2+ produced an effective general anesthetic that was reversed by a combination of low dose atipamezole, an α 2 competitive antagonist, and caffeine. We compared Dex supplemented with Mg 2+ to Dex supplemented with midazolam, a selective positive GABA A modulator and found that immobility, antinociception, EEG signatures, and hemodynamic profiles were comparable. Our findings showed that activation of α 2 receptors by Dex, with blockade of NMDA receptors/ Ca 2+ channels by Mg 2+ produce an effective and reversible general anesthetic with possible neuroprotective properties that may be appropriate for cognitively vulnerable patients like the elderly.

Divergent patterns of engagement with partisan and low-quality news across seven social media platforms

In recent years, social media has become increasingly fragmented, as platforms evolve and new alternatives emerge. Yet most research studies a single platform—typically X/Twitter, or occasionally Facebook—leaving little known about the broader social media landscape. Here, we shed light on patterns of cross-platform variation in the high-stakes context of news sharing. We examine the relationship between user engagement and news domains’ political orientation and quality across seven platforms: X/Twitter, BlueSky, TruthSocial, Gab, GETTR, Mastodon, and LinkedIn. Using an exhaustive sample, we analyze all (over 10 million) posts containing links to news domains shared on these platforms during January 2024. We find that news shared on platforms with more conservative user bases is significantly lower quality on average. Turning to engagement, we find—contrary to hypotheses of a consistent “right-wing advantage” on social media—that the relationship between political lean and engagement is strongly heterogeneous across platforms. Conservative news posts receive more engagement on platforms where most content is conservative, and vice versa for liberal news posts, consistent with an “echo platform” perspective. In contrast, the relationship between news quality and engagement is strikingly consistent: Across all platforms examined, a given user’s lower-quality news posts received higher average engagement, even though higher-quality news is substantially more prevalent and garners far more total engagement across posts. This pattern holds when accounting for poster-level variation and is observed even in the absence of ranking algorithms, suggesting that user preferences—not algorithmic bias—may underlie the underperformance of higher-quality news.

Structural insights into natural compound inhibitors of the human metapneumovirus nucleocapsid protein via molecular dynamics and free energy landscape analyses

Delineating SARS-CoV-2 spike protein and antibodies interaction interfaces via siamese neural networks: A geometric and image-based analysis

The analysis of molecular interactions between antigens and antibodies is crucial for understanding the immunological mechanisms underlying the immune response and for developing effective therapies against various diseases. In this context, the ability to distinguish between protein interfaces that form stable and unstable complexes is a key step in the design of therapeutic antibodies and vaccines. In recent years, deep learning models have provided advanced tools for biomedical research. This work introduces a novel approach to analyzing antibody-antigen interactions, and in particular SARS-CoV-2 spike protein-targeting antibodies, using a Siamese Neural Network specifically designed to integrate depth maps with geometric descriptors of molecular surfaces. By combining these representations, the model captures geometrical shape complementarity to differentiate between stable and unstable protein complexes. The network was trained using image-based representations of antigens and antibodies interfaces enriched with geometric descriptors, using data that include binders and non-binders of the SARS-CoV-2 spike protein receptor-binding domain. The deep learning network operates by comparing feature vectors representing these molecular surfaces; pairs with closer vectors in feature space are associated with stable interactions, while those with more distant vectors suggest instability. Extensive testing with different configurations achieved an accuracy of 90%, demonstrating the robustness of this approach to predict interactions. This innovative integration of artificial intelligence, depth maps and geometric descriptors offers promising applications for designing novel antibodies and vaccines.