Browse Articles

Precision targeting of β-catenin induces tumor reprogramming and immunity in hepatocellular cancers

Abstract First-line immune checkpoint inhibitor (ICI) combinations show responses in subsets of hepatocellular carcinoma (HCC) patients. Nearly half of HCCs are Wnt-active with mutations in CTNNB1 (encoding for β-catenin), AXIN1/2 , or APC , and demonstrate heterogeneous and limited benefit to ICI due to an immune excluded tumor microenvironment. We show significant tumor responses in multiple β-catenin-mutated immunocompetent HCC models to a novel siRNA encapsulated in lipid nanoparticle targeting CTNNB1 (LNP-CTNNB1). Both single-cell and spatial transcriptomics reveal cellular and zonal reprogramming, along with activation of immune regulatory transcription factors IRF2 and POU2F1, re-engaged type I/II interferon signaling, and alterations in both innate and adaptive immunity upon β-catenin suppression with LNP-CTNNB1 at early- and advanced-stage disease. Moreover, ICI enhances response to LNP-CTNNB1 in advanced-stage disease by preventing T cell exhaustion and through formation of lymphoid aggregates (LA). In fact, expression of an LA-like gene signature prognosticates survival for patients receiving atezolizumab plus bevacizumab in the IMbrave150 phase III trial and inversely correlates with CTNNB1 -mutatational status in this patient cohort. In conclusion, LNP-CTNNB1 is efficacious as monotherapy and in combination with ICI in CTNNB1 -mutated HCCs through impacting tumor cell-intrinsic signaling and remodeling global immune surveillance, providing rationale for clinical investigations.

Behavioral and emotional difficulties in maltreated children: Associations with epigenetic clock changes and visual attention to social cues

Research indicates that childhood maltreatment leads to adverse outcomes later in life and accelerated aging. However, few studies have investigated how age acceleration manifests during childhood. This study aimed to investigate the impact of child maltreatment on DNA methylation age (mAge) acceleration using a case-control study design and its association with visual attention and behavioral and emotional outcomes in maltreated children (CM). We hypothesized that CM experience atypical aging, which adversely affects their behavioral and emotional outcomes by disrupting the cognitive development necessary for forming interpersonal relationships. The study included 36 CM and 60 typically developing (TD) children with an average age of 4–5 years. We compared their DNA mAge acceleration, measured through buccal DNA samples. Additionally, we conducted a behavioral assessment of their cognitive functions related to interpersonal interactions, using an eye-tracking system to measure their gaze points at various social stimuli. Behavioral and emotional outcomes were evaluated using the Strength and Difficulties Questionnaire (SDQ). The results showed that CM exhibited significantly higher mAge acceleration and spent significantly less time gazing at the eye region during facial expression presentations. While a significant association between these attributes was observed, a comprehensive path analysis revealed that each attribute independently correlated with higher SDQ scores, suggesting that child maltreatment leads to these difficulties through accelerated aging and decreased eye contact. This study provides significant insights into how child maltreatment impacts children’s development. It demonstrates that mAge acceleration and reduced attention to the eye region are critical factors associated with the adverse behavioral and emotional outcomes observed in maltreated children. These findings highlight the importance of early intervention and support for maltreated children to mitigate the long-term effects of accelerated aging and social cognitive deficits.

Single-cell and spatial transcriptome analyses reveal tumor heterogeneity and immune remodeling involved in pituitary neuroendocrine tumor progression

ArsenicNet: An efficient way of arsenic skin disease detection using enriched fusion Xception model

Arsenic contamination of drinking water is a significant health risk. Countries such as Bangladesh’s rural areas and regions are in the red alert zone because groundwater is the only primary source of drinking. Early detection of arsenic disease is critical for mitigating long-term health issues. However, these approaches are not widely accepted. In this study, we proposed a fusion approach for the detection of arsenic skin disease. The proposed model is a combination of the Xception model with the Inception module in a deep learning architecture named “ArsenicNet." The model was trained and tested on a publicly available image dataset named “ArsenicSkinImageBD" which contains only 1287 samples and is based on Bangladeshi people. The proposed model achieved the best accuracy through proper experimentation compared to several state-of-the-art deep learning models, including InceptionV3, VGG19, EfficientNetV2B0, ResNet152V2, ViT, and Xception. The proposed model achieved an accuracy of 97.69% and an F1 score of 97.63%, demonstrating superior performance. This research indicates that our proposed model can detect complex patterns in which arsenic skin disease is present, leading to a superior detection performance. Moreover, data augmentation techniques and earlystoping function were used to prevent models overfitting. This study highlights the potential of sophisticated deep learning methodologies to enhance the accuracy of arsenic detection and prevent premature interventions in the diagnosis of arsenic-related illnesses in people. This research contributes to ongoing efforts to develop robust and scalable solutions to monitor and manage arsenic contamination-related health issues.

Ultrapotent SARS coronavirus-neutralizing single-domain antibodies that clamp the spike at its base

Impact of living kidney donation on blood pressure and arterial stiffness: Systematic review and meta-analysis

Background We aimed to conduct a systematic review and meta-analysis (PROSPERO CRD42023480478) regarding the impact of kidney donation on arterial stiffness indices such as pulse wave velocity (PWV) and augmentation index (AIx), along with its effect on blood pressure. Methods We searched for publications related to kidney/renal donors, arterial stiffness, blood pressure, and cardiovascular risk, and included every study employing those terms. A p-value < 0.05 was considered statistically significant. Results Twelve studies and 2059 individuals, with a mean age of 46.53 ± 11.27 years, were included in the analysis. Male donors constituted 40.6% of the participants, and the mean follow-up was 2.62 ± 3.2 years. Eleven studies indicated that systolic (SBP) and diastolic blood pressure remained stable within the first year after nephrectomy. However, as the follow-up period extended, especially beyond one year, both were increased (median difference (MD) of SBP was 2.09 [0.06, 4.12] over the first year and 7.7 [6.96, 8.44] over the 5 years of follow-up). Regarding PWV and AIx, assessed in 6 studies, no fluctuations were observed post-donation (MD of PWV was 0.1620 [−0.0423; 0.3662], and MD of AIx was 8.2265 [3.6450; 12.8080]). In addition, 11 studies revealed a decline in the estimated glomerular filtration rate (eGFR) after nephrectomy (MD −27.4960, p-value < 0.0001), though no albuminuria was observed. Lastly, BMI demonstrated negligible changes throughout the follow-up. Conclusion Kidney donation is a relatively safe procedure, and despite the observed decline in eGFR, it does not per se impose further cardiovascular burden on the donors. However, the heterogeneity and the lack of data underscores the need for high-quality studies so as to elucidate the connection between arterial stiffness, blood pressure, and GFR level.

Bacterial microcompartments and energy metabolism drive gut colonization by Bilophila wadsworthia

Abstract High-fat diets reshape gut microbiota composition and promote the expansion of Bilophila wadsworthia , a sulfidogenic bacterium linked to inflammation and gut barrier dysfunction. The genetic basis for its colonisation and physiological effects remain poorly understood. Here, we show that B. wadsworthia colonises the gut of germ-free male mice fed a high-fat diet by relying on genes involved in microcompartment formation and anaerobic energy metabolism. Using genome-wide transposon mutagenesis, metatranscriptomics and metabolomics, we identify 34 genes essential for gut colonisation, including two clusters encoding a bacterial microcompartment (BMC), and a NADH dehydrogenase ( hdrABC-flxABCD ) complex. These systems enable B. wadsworthia to metabolise taurine and isethionate, producing H 2 S, acetate, and ethanol. We further demonstrate that B. wadsworthia can produce and consume ethanol depending on the available electron donors. While B. wadsworthia reached higher abundance and H₂S production in the absence of the simplified microbiota, its co-colonisation with the defined microbial consortium exacerbated host effects, including increased gut permeability, slightly elevated liver ethanol concentrations, and hepatic macrophage infiltration. Our findings reveal how microbial interactions and metabolic flexibility -including using alternative energy sources such as formate- rather than H₂S alone, shape B. wadsworthia ’s impact on host physiology, with implications for understanding diet-driven microbiome–host interactions.

Exploring the feasibility, effectiveness, and acceptability of telehealth for delivering a pain management group program: A retrospective study

Objective The COVID-19 pandemic caused significant changes in healthcare, particularly in pain management. To maintain care, the Multidisciplinary Activity Improvement Program (MAiP), at the Department of Pain Medicine, Liverpool Hospital, was adapted for delivery through telehealth. Although MAiP’s effectiveness is well-documented, its telehealth adaptation has not been studied. This study retrospectively assesses the feasibility, effectiveness trends, and acceptability of a telehealth-based pain program. Methods Using a single-group retrospective cohort design, participants were patients who completed the telehealth-based MAiP between 2020 and 2022. Primary outcomes: pain severity, pain interference, anxiety, stress, depression, pain self-efficacy, and pain catastrophising, were evaluated pre- and post-treatment. Participants’ satisfaction with the program was assessed through a post-program survey. Data analysis employed a generalised estimation equation modelling technique. Results 33 patients were enrolled in a telehealth MAIP during the study period, with outcomes available for 22 patients (68% female, mean age 51.45 ± 10.41, 72.7% with pain duration >5 years). Significant improvements were observed in the primary outcome measures, indicating the effectiveness of the telehealth-based MAiP. Standardised effect sizes (ES) for all outcomes ranged from small to large. Of the 22 participants, 14 completed the satisfaction survey, with the majority expressing satisfaction and finding the telehealth-based program beneficial. Conclusion The study’s findings present initial evidence for the effectiveness and acceptability of delivering pain management group programs via telehealth, expanding the range of services available to patients. These promising results advocate for continued exploration of telehealth as a vital avenue for pain management service delivery, warranting further investigation and advancement in this evolving field.

Bayesian deep-learning structured illumination microscopy enables reliable super-resolution imaging with uncertainty quantification

Computational investigation and experimental validation of the molecular mechanism of Solanecio mannii aqueous roots extract against cervical cancer

Cervical cancer remains one of the leading causes of cancer-related mortality among women worldwide, particularly in low- and middle-income countries, highlighting the need for improved strategies in treatment and management. This study aimed to investigate the anti-cervical cancer potential and molecular mechanisms of Solanecio mannii (S. mannii) aqueous extract using a “multi-compound, multi-target, multi-pathway” approach, integrating both computational and experimental methods. The metabolomics profile of the extract was analysed, and its selective cytotoxicity was assessed against human cervical cancer cell lines (HeLa cells) using the CCK8 assay. A network pharmacology approach identified potential molecular targets and pathways, which was complemented by molecular docking and dynamic simulation. The expression levels of key targets were validated experimentally using quantitative real-time polymerase chain reaction. Additionally, the extract’s effects on apoptosis, autophagy, and cell cycle progression were studied experimentally. The aqueous roots extract exhibited selective cytotoxicity against HeLa cells with an IC50 of 12.53 ± 4.983 μg/ml. The network pharmacology analysis identified 25 drug-like compounds targeting 493 unique cervical cancer-associated proteins, forming a protein-protein interaction network of 465 nodes and 2230 edges, and implicated in 178 enriched KEGG pathways. Key targets, including NFΚB1, PIK3CA, HIF1A, STAT3, HSP90AA1, HSP90AB1, PPARG, and ESR1 were experimentally downregulated. Furthermore, S. mannii aqueous roots extract triggered apoptosis through endoplasmic reticulum stress, DNA damage, and activation of the non-transcriptional, P53-mediated mitochondrial apoptotic pathway. Additionally, the extract inhibited hypoxia and autophagy, and induced cell cycle arrest at the G2/M phase, even in the presence of oncogenic HPV proteins (E6 and E7). In conclusion, Solanecio mannii aqueous roots extract demonstrates a “multi-compound, multi-target, multi-pathway” molecular mechanism against cervical cancer.

Multi-tissue expression and splicing data prioritise anatomical subsite- and sex-specific colorectal cancer susceptibility genes

Abstract Genome-wide association studies have suggested numerous colorectal cancer (CRC) susceptibility genes, but their causality and therapeutic potential remain unclear. To prioritise causal associations between gene expression/splicing and CRC risk (52,775 cases; 45,940 controls), we perform a transcriptome-wide association study (TWAS) across six tissues with Mendelian randomisation and colocalisation, integrating sex- and anatomical subsite-specific analyses. Here we reveal 37 genes with robust causal links to CRC risk, ten of which have not previously been reported by TWAS. Most likely causal genes with evidence of cancer cell dependency show elevated expression linked to risk, suggesting therapeutic potential. Notably, SEMA4D, encoding a protein targeted by an investigational CRC therapy, emerges as a key risk gene. We also identify a female-specific association with CRC risk for CCM2 expression and subsite-specific associations, including LAMC1 with rectal cancer risk. These findings offer valuable insights into CRC molecular mechanisms and support promising therapeutic avenues.

Prediction of drug-target interactions based on substructure subsequences and cross-public attention mechanism

Drug-target interactions (DTIs) play a critical role in drug discovery and repurposing. Deep learning-based methods for predicting drug-target interactions are more efficient than wet-lab experiments. The extraction of original and substructural features from drugs and proteins plays a key role in enhancing the accuracy of DTI predictions, while the integration of multi-feature information and effective representation of interaction data also impact the precision of DTI forecasts. Consequently, we propose a drug-target interaction prediction model, SSCPA-DTI, based on substructural subsequences and a cross co-attention mechanism. We use drug SMILES sequences and protein sequences as inputs for the model, employing a Multi-feature information mining module (MIMM) to extract original and substructural features of DTIs. Substructural information provides detailed insights into molecular local structures, while original features enhance the model’s understanding of the overall molecular architecture. Subsequently, a Cross-public attention module (CPA) is utilized to first integrate the extracted original and substructural features, then to extract interaction information between the protein and drug, addressing issues such as insufficient accuracy and weak interpretability arising from mere concatenation without interactive integration of feature information. We conducted experiments on three public datasets and demonstrated superior performance compared to baseline models.

Human endothelial cells promote a human neural stem cell type B phenotype via Notch signaling

Abstract Neural stem and progenitor cell (NSPC) and vessel-forming endothelial cell (EC) communication throughout development and adulthood is vital for normal brain function. However, much remains unclear regarding coordinated regulation of these cells, particularly in humans. We find that contact with hECs increases hNSPC type B cells, which are GFAP-expressing adult NSPCs in the subventricular zone (SVZ), leading to generation of a human type B single-cell RNA sequencing (scRNAseq) dataset. Differential gene expression demonstrates an increase in Notch downstream mediators in type B hNSPCs after hEC contact. Blocking hNSPC Notch signaling, and reducing hEC expression of the Notch ligand DLL4, abrogates the effect of hECs on type B hNSPCs. We identify S100A6 and LeX as human type B cell markers, and analysis of the postnatal human SVZ confirms co-expression of GFAP, SOX2, S100A6, LeX and PROM1 in type B cells. Sites of contact are identified between type B hNSPCs and vasculature in the SVZ, providing evidence of human type B cell contact with hECs in the postnatal human brain. Thus, hEC contact promotes human type B cells via Notch signaling and these cells are in contact in stem cell niches in the human brain.

Enhancing the dataset of CycleGAN-M and YOLOv8s-KEF for identifying apple leaf diseases

Accurate diagnosis of apple diseases is vital for tree health, yield improvement, and minimizing economic losses. This study introduces a deep learning-based model to tackle issues like limited datasets, small sample sizes, and low recognition accuracy in detecting apple leaf diseases. The approach begins with enhancing the CycleGAN-M network using a multi-scale attention mechanism to generate synthetic samples, improving model robustness and generalization by mitigating imbalances in disease-type representation. Next, an improved YOLOv8s-KEF model is introduced to overcome limitations in feature extraction, particularly for small lesions and complex textures in natural environments. The model’s backbone replaces the standard C2f structure with C2f-KanConv, significantly enhancing disease recognition capabilities. Additionally, we optimize the detection head with Efficient Multi-Scale Convolution (EMS-Conv), improving the model’s ability to detect small targets while maintaining robustness and generalization across diverse disease types and conditions. Incorporating Focal-EIoU further reduces missed and false detections, enhancing overall accuracy. The experiment results demonstrate that the YOLOv8s-KEF model achieves 95.0% in accuracy, 93.1% in recall, 95.8% in precision, and an F1-score of 94.5%. Compared to the original YOLOv8s model, the proposed model improves accuracy by 7.2%, precision by 6.5%, and F1-score by 5.0%, with only a modest 6MB increase in model size. Furthermore, compared to Faster RCNN, ResNet50, SSD, YOLOv3-tiny, YOLOv6, YOLOv9s, and YOLOv10m, our model demonstrates substantial improvements, with up to 30.2% higher precision and 18.0% greater accuracy. This study used CycleGAN-M and YOLOv8s-KEF methods to enhance the detection capability of apple leaf diseases.

Latent EBV enhances the efficacy of anti-CD3 mAb in Type 1 diabetes

Measuring alignment of structural proteins in engineered tissue constructs using polarized Raman spectroscopy

Measures of structural protein alignment within biological and engineered tissues are needed for improved understanding of their mechanical behavior and functionality. We advance our method of measuring protein alignment using polarized Raman spectroscopy (PRS). It provides a promising alternative to conventional microscopy-based methods as it is non-destructive and allows analysis of extracellular components without additional protein labeling. Previously, we used a machine learning-based alignment metric to compare the extent of alignment between various soft tissues. This study demonstrates that PRS can be successfully used to provide a sensitive measure of alignment in engineered tissues despite the challenges of water-dominated spectra, which have limited prior efforts. A framework for capturing spatial variation of the amplitude and angle of bulk protein alignment was developed. Engineered tissue constructs were generated using collagen type-I solutions seeded with mouse myoblast (C2C12) cells. Tissue alignment was introduced as samples contracted over 12 days of culture. PRS measures of alignment within three selected regions captured a 32% change in extent of alignment and a 30° change in angle between center and corner regions. A computational model was used to bridge between discrete fiber measures of alignment determined with standard immunofluorescence microscopy and our PRS technique. The model applied contraction strains within a hyperelastic continuum to model cell contraction, and model-derived alignment measures showed good agreement between microscopy and PRS measures. Overall, our study provides additional analysis tools for quantifying alignment with PRS and showed the high potential of this PRS technique to non-invasively measure spatial variation within engineered tissues. Such measurement tools are needed to engineer regional alignments aimed at capturing specific mechanical and functional capabilities.

Assessing and improving reliability of neighbor embedding methods: a map-continuity perspective

3-Deoxysappanchalcone attenuates LPS-induced neuroinflammation in microglia cell culture and ameliorates cognitive impairment in traumatic brain injury

Background As one of the major public health security problems, traumatic brain injury (TBI) is characterized by cerebral dysfunction. The following neuroinflammation is considered as the main secondary injury factor. Targeting the expression of inflammatory cytokines could be effective in alleviating TBI-induced neuroinflammation. The anti-inflammatory role of natural products is increasingly receiving attention. 3-Deoxysappanchalcone (3-DSC) is a bioactive compound from Caesalpinia sappan L. Methods The present study was designed to investigate the impact of 3-DSC on neuroinflammation in primary microglia and TBI models. To assess cytotoxicity, cell viability tests were conducted with varying concentrations of 3-DSC ranging from 5 to 20 μM. Quantitative PCR (qPCR) and Enzyme-Linked Immunosorbent Assay (ELISA) were utilized to measure the production of inflammatory cytokines in LPS-activated primary microglia treated with or without 3-DSC (at 10 μM). Immune blotting arrays were used to examine the activation of canonical inflammation signaling pathways. To further elucidate the anti-inflammation effect of 3-DSC, RNA-seq was carried out between LPS and LPS + 3-DSC group. In vitro co-culture experiments were carried out to evaluate the protective effect of 3-DSC on neurons against inflammation-mediated apoptosis. Additionally, in vivo experiments were performed to observe the impact of 3-DSC on TBI-induced microglia activation and spatial memory impairment. 3-DSC (160 μg/kg, 320 μg/kg) were administered via the tail vein at day 1 after TBI (n = 6). Behavioral tests were conducted 7 days after traumatic brain injury (TBI) to detect the spatial memory ability of rats. Results The cell viability results revealed that within the concentration range of 5–20 μM, 3-DSC did not cause significant cytotoxicity. In the qPCR and ELISA assays, it was found that 3-DSC at 10 μM led to a reduction in the production of inflammatory cytokines. The immune blotting arrays demonstrated that 3-DSC inhibited the activation of NF-kB and MAPK signaling pathways. The results of RNA sequencing revealed the altered signaling pathways and key hub genes. The in vitro co-culture outcomes indicated that 3-DSC could safeguard neurons from apoptosis caused by neuroinflammation. Finally, the in vivo experiments showed that 3-DSC was effective in alleviating TBI-induced microglia activation and spatial memory impairment. Discussion Collectively, these findings suggest that 3-DSC holds promise as a potential compound for the development of therapeutic and preventive agents aimed at treating neuroinflammation-related disorders. It offers a new avenue for further research and potential clinical applications in the context of TBI and neuroinflammation related disorders.

Allelic variations in GA20ox3 regulate fruit length and seed germination timing for high-altitude adaptation in Arabidopsis thaliana

Predictive modeling of hemoglobin refractive index using Gaussian process regression with interpretability through partial dependence plots

Accurately predicting the refractive index of hemoglobin across various wavelengths and concentrations is critical for advancing optical diagnostic techniques in biological and clinical applications. This study introduces a predictive model based on Gaussian Process Regression (GPR) to estimate the refractive index of hemoglobin in both oxygenated and deoxygenated states, covering wavelengths from 400 to 700 nm and concentrations ranging from 0 to 140 g/L. The GPR model effectively captures non-linear relationships, achieving high prediction accuracy with R2 values of 99.4% for the training dataset and 99.3% for the testing dataset. An independent external dataset was used to validate the model’s robustness further, yielding an R2 value of 92.80%, RMSE of 0.0042, and MSE of 1.77 × 10 ⁻ ⁵, demonstrating the model’s strong generalizability. To enhance interpretability, Partial Dependence Plots (PDPs) were employed to visualize the influence of wavelength and concentration on refractive index predictions, offering clear insights into hemoglobin’s optical behavior. The model’s ability to provide accurate and interpretable predictions has significant implications for improving the reliability of biophotonic diagnostic tools, such as optical coherence tomography and reflectance spectroscopy, in clinical settings. By combining machine learning with interpretability techniques, this study advances the understanding of hemoglobin’s optical properties and sets a benchmark for predictive modeling in biomedical optics, paving the way for more precise and dependable diagnostic applications.