Browse Articles

Plasma-based Raman spectroscopy for early detection of acute myocardial infarction in murine models

Abstract Acute myocardial infarction (AMI) is a leading cause of cardiovascular mortality. Current diagnostic dilemma suffers from limited sensitivity, insufficiently timely and effective specificity. To address this dilemma, Raman spectroscopy, a rapid and non-invasive technique with significant potential for plasma metabolic profiling, although AMI involves rapid metabolic alterations, diagnostic approaches based on plasma metabolites remain underexplored. In murine AMI model induced by coronary artery ligation, we acquired Raman spectra from ultrafiltered plasma samples 8 h post-surgery. We identified eight distinct Raman peaks, assigned to amino acids, lipids, and nucleic acids, which collectively differentiated AMI group ( n  = 27) from the Sham group ( n  = 27). To ensure optimal accuracy, we employed five different algorithms including SVM, LR, RF, LDA and PLS-DA to analyze the Raman spectra, both RF and LDA achieved the highest accuracy of 79.6%, specificity of 85.2%, and sensitivity of 74.1%. Furthermore, metabolomic analysis revealed significant down-regulation of most lipid classes, aligning with the downregulation observed in the Raman peaks at 2885 cm⁻¹ and 2940 cm⁻¹. These results demonstrate a high concordance between plasma metabolic profiling via Raman spectroscopy and MS analysis. The integration of Raman spectroscopy with machine learning has remarkable potential for the early and accurate diagnosis of AMI, offering a promising approach for clinical translation.

Molecular detection of bee pathogens in honey from various botanical origins

Honey bees play a crucial role in pollination and global food security, yet their populations are declining due to various environmental stressors, including pathogenic infections. Recently, molecular research in honey has been proposed as a powerful, non-invasive tool for detecting and monitoring honey bee pathogens and parasites. This study analysed 679 honey samples from all Italian regions to detect the presence of 8 pathogens (DWV, CBPV, ABPV, BQCV, KBV, Nosema ceranae , Crithidia mellificae , Lotmaria passim ) using qPCR assays. Overall, 97.5% of the honey samples tested positive for at least one pathogen, with the most prevalent being DWV (81.7%), N. ceranae (56.1%), and CBPV (56.0%). None of the samples tested positive for KBV or C. mellificae . Statistical analyses revealed significant variations in pathogen prevalence and copy number depending on the honey type, geographic origin and correlations among different pathogens. Additionally, co-presence was common, with over 77% of honey samples containing multiple pathogens. These findings support honey analysis as an effective and valid method for pathogen surveillance in honey bee populations. By providing valuable insights into disease dynamics, this approach could enhance epidemiological monitoring and contribute to improved honey bee health management strategies.

A hardware demonstration of a universal programmable RRAM-based probabilistic computer for molecular docking

Effect of different transverse reinforcement confinements on circular columns cast with recycled concrete aggregates and treated wastewater

Abstract The use of recycled concrete aggregate (RCA) and treated wastewater (TWW) in the concrete industry promotes sustainability, conserves potable water, reduces environmental impact, and minimizes waste, offering an eco-friendly alternative to conventional concrete production. However, RCA and TWW can negatively impact concrete performance, as RCA’s high variability, porosity, adhered mortar, and crushing-induced defects weaken the microstructure, while TWW’s dissolved salts and organic compounds interfere with cement hydration. Therefore, it is essential to fully understand their effects on concrete performance at both the material and structural levels and develop strategies to mitigate these drawbacks. Contributing to this, the present study investigated the effect of using RCA and/or TWW on the strength of concrete and the behavior of reinforced concrete (RC) circular columns. A total of 10 mixes were developed with RCA replacement levels of 0%, 30%, 50%, and 100%, and TWW replacement levels of 0%, 50%, and 100%. These concrete mixes were used to cast 24 RC columns, which were tested under monotonic concentric load. The columns were reinforced with different types of transverse reinforcement—stirrups at various spacings (200 mm, 150 mm, and 100 mm) and spiral reinforcement at different pitches (60 mm and 40 mm)—to provide varying levels of confinement, aiming to mitigate the negative effects of RCA and TWW on column capacity. The results indicated that the capacity of columns negatively affected by incorporating RCA and/or TWW. In columns with 200 mm spaced stirrups, as the RCA replacement increased from 0 to 30% and 50%, the failure loads decreased by 16.1% and 29.8%, respectively. The use of closely spaced stirrups or spiral reinforcement provided higher confinement, effectively alleviating the reduction in capacity caused by the use of RCA. Reducing the stirrups space to 100 mm allowed for up to 30% RCA replacement with no reduction in capacity, and up to 100% RCA replacement with a maximum capacity reduction of around 14.9%. Similarly, the use of spiral reinforcement at pitch of 40 mm enabled the use of up to 100% RCA with a reduction of 8.6%, compared to corresponding columns cast with no RCA. Higher reductions in capacity were observed when TWW was used, which further limits its application in concrete.

Multi-dimensional analysis of the global burden of colorectal cancer disease from 1990 to 2021 and prediction of future trends: A comprehensive study based on the GBD database

Background Colorectal cancer (CRC) is one of the malignancies with high morbidity and mortality rates worldwide, and its disease burden continues to increase with population aging and changes in lifestyle and dietary habits. Based on the Global Burden of Disease database (GBD), this study analyzed trends in global CRC incidence, deaths, and disability-adjusted life years (DALYs) from 1990 to 2021, and explored health inequalities across countries and regions. Methods This study used data from the GBD Database 2021 to quantify the contribution of population aging, population growth, and epidemiological changes to the burden of CRC. Health inequalities were assessed on a global scale through the Slope index (SII) and concentration index (CI), and the potential room for improvement in the control of DALYs burden in CRC was assessed by countries using frontier analysis. The future disease burden of CRC was predicted based on a Bayesian Age-Period-cohort model (BAPC). Results Worldwide, the incidence, death and DALYs burden of CRC increased significantly, mainly driven by population aging and population growth. Areas with high socio-demographic index (SDI) have significantly reduced the burden of disease through epidemiological changes, while the burden remains higher in areas with low SDI. Health inequalities have improved, but areas with low SDI still face a higher burden of disease. Frontier analysis shows that there is still much room for improvement in CRC prevention and control in countries with high SDI. Projections show that despite the decline in CRC deaths, the number of cases of CRC is expected to continue to increase due to the impact of population aging and population growth. Conclusions Population aging and growth drive the global CRC burden increase. Low – SDI regions’ epidemiological changes have limited impact. Future policies should focus on low – SDI areas’ early prevention and screening and optimize resource allocation.

Editing DNA methylation in vivo

Energy efficient UAV-assisted two layer hierarchical bit-mapping access based MAC protocol

Abstract Reliable and efficient medium access control (MAC) protocols are crucial for energy-efficient data communication and effective resource utilization in Wireless Sensor Networks (WSNs). In this work, we propose an Energy-Efficient UAV-assisted Two-Layer Hierarchical (EE-UAV-TLH) MAC protocol, designed for mission-critical applications with energy constraints and limited bandwidth. The protocol employs a two-level hierarchical clustering structure, where sensor nodes communicate with their Cluster Head (CH), and the CH coordinates with an Unmanned Aerial Vehicle (UAV) for data collection and transmission scheduling. By adopting a bit-mapping access scheme, EE-UAV-TLH significantly reduces idle listening and control overheads, while the UAV acts as a mobile sink and synchronization controller, enhancing scalability and minimizing energy usage. Simulation results across diverse scenarios demonstrate that the TLH-ETDMA variant consistently outperforms its counterparts (TLH-TDMA and TLH-BMA), yielding average energy savings of about 20% over TLH-TDMA and 34% over TLH-BMA. In peak cases, EE-UAV-TLH achieves up to 31% improvement against TLH-TDMA and up to 52% against TLH-BMA. Even under large-scale deployment with 110 nodes, it sustains 17% and 39% savings, respectively, highlighting its superior efficiency and scalability for UAV-assisted WSNs.

Hybrid quantum neural network models for fruit quality assessment

This study investigates hybrid quantum neural networks for fruit quality assessment, with a focus on the impact of the entangling gate choice. Two architectures were developed: NNQEv1, utilizing controlled-NOT (CNOT) gates, and NNQEv2, employing controlled-phase (CZ) gates. A theoretical justification is provided, based on gate decomposition and hardware-aware noise considerations, suggesting the CZ-based architecture is likely to be more stable. The performance of the models was evaluated through the computational execution of their quantum circuits on classical hardware and compared against classical and state-of-the-art deep learning models. The proposed models demonstrated competitive performance, achieving test accuracies of 98.7% on MNIST, 98.6% on the FruitQ dataset, and 96.7% on a custom, data-scarce Apple dataset. The experimental results align with the theoretical analysis: the CZ-based NNQEv2 model, when compared to the CNOT-based NNQEv1, consistently showed more stable training dynamics and yielded tighter confidence intervals in cross-validation. This work presents a foundational, computational study on the role of gate-level design choices, intended to inform the development of future quantum machine learning algorithms.

SLFN11 counteracts the RFWD3-PRIMPOL DNA damage tolerance axis to restrain gapped DNA synthesis in response to replication stress

Abstract Schlafen family member 11 (SLFN11) expression sensitizes cells to a spectrum of DNA-damaging chemotherapies. Previous studies have shown that SLFN11 is recruited to stalled replication forks in response to replication stress; however, the role of SLFN11 at stressed replication forks remains unclear. Using single-molecule DNA fiber analysis and super-resolution microscopy to interrogate the dynamics of individual replication forks, we show that SLFN11 acts upon stalled replication forks to suppress efficient fork restart. In the absence of SLFN11 expression, fork restart proceeds through a pathway involving the ubiquitin ligase RFWD3 and the DNA primase-polymerase PRIMPOL to facilitate gapped DNA synthesis, thereby ensuring that cells do not accumulate replication-associated DNA damage. SLFN11 antagonizes this pathway by disrupting recruitment of RFWD3 and PRIMPOL to stalled forks in a manner dependent on a functional ATPase domain and persistent fork localization, but not on tRNA hydrolysis or ssDNA binding. Collectively, our results provide a mechanistic basis for how SLFN11 can counteract DNA damage tolerance by suppressing the RFWD3-PRIMPOL fork restart pathway.

Reduced life cycle climate impact from manure through catalytic methane conversion and carbon dioxide removal

Abstract Agri-food systems constitute around one-third of global greenhouse gas (GHG) emissions, with roughly half consisting of non-CO 2 GHGs, mainly methane (CH 4 ) and nitrous oxide (N 2 O). Methods and technologies to mitigate non-CO 2 GHGs are currently limited, which is a reason for agriculture being categorised as a hard-to-abate sector. This study examines mitigation of GHG emissions from manure storage headspace through oxidisation of CH 4 emissions at low concentrations, using a thermal catalytic process with and without subsequent CO 2 capture and storage (CCS). The technology is studied using a combination of process modelling and life cycle assessment at four CH 4 concentrations: 300, 1000, 3000 and 10,000 ppmv. The primary energy demand and net climate effect were evaluated, reaching a net climate effect of +0.10, -0.77, -0.91 and -0.97 g CO 2 -eq emitted/g CO 2 mitigated, respectively. The wide range of results is mainly influenced by the process energy demand being strongly correlated to the CH 4 concentration. The sensitivity analysis shows that a net negative climate effect can also be achieved at 300 ppmv with access to low emission energy sources. Coupling CCS worsens the net climate effect of the system at all studied CH 4 concentrations, mainly due to the additional energy demand for CO 2 separation.

STFANet: A spatial and temporal feature aggregation network for fake face detection in videos

The verification of video authenticity has become progressively more challenging with the rapid advancements in video synthesis technologies. However, current detection approaches predominantly depend on intra-frame spatial artifacts and temporal inconsistencies, restricting their capacity to fully exploit the spatio-temporal characteristics of manipulated videos. To address this problem, we propose the S patial and T emporal F eature A ggregation N etwork (STFANet), which employs a two-path structure to extract spatial and temporal features independently. These extracted features are subsequently integrated to construct high-fidelity spatio-temporal representations. Additionally, we incorporate a Vision Transformer module to capture global dependencies within the feature maps, enhancing the overall feature representation. Extensive experiments validate the efficacy of the proposed approach in detecting facial forgery in videos. Performance evaluations on benchmark datasets, including FaceForensics++ and Celeb-DF, confirm the effectiveness of our method, yielding AUC scores of 0.9933 and 0.9829, respectively. Furthermore, we investigate the impact of feature aggregation at different stages on the generated feature maps, revealing significant improvements in the quality of spatio-temporal representations.

Molecular architecture of the human TRPC1/C5 heteromeric channel

Comparative evaluation and simulation of blockchain consensus mechanisms for secure and scalable peer to peer energy trading in microgrids

Spatial clustering of zero dose children aged 12 to 59 months across 33 countries in sub-Saharan Africa: A multiscale geographically weighted regression analysis

While prior studies have identified sociodemographic correlates of zero-dose status within populations in sub-Saharan Africa (SSA), few have applied spatial regression techniques to explore geographic variability in these relationships. We aimed to address this gap using data from Demographic and Health Surveys conducted in SSA between 2010 and 2020. Our sample comprised children aged 12–59 months in 33 countries and 329 survey regions. Data were aggregated to the first-level administrative unit prior to analysis. First, using ordinary least squares regression, we documented global relationships between theoretically important sociodemographic characteristics and zero-dose prevalence. Next, we identified patterns, i.e., geographic clustering, of zero-dose prevalence. Finally, using multiscale geographically weighted regression, we described spatial variability in relationships between sociodemographic characteristics and zero-dose prevalence. We detected 27 regions with higher than expected concentrations of zero-dose children. All but one of these hot spots were observed in 7 Western and Central African countries; only 1 was located in an Eastern African country. Regions with higher proportions of mothers with no antenatal care visits were consistently found to have higher rates of zero-dose children. In contrast, relationships between zero-dose prevalence and indicators of religious affiliation, delivery site, maternal age, maternal education, and maternal employment were found to vary locally in terms of their strength and/or direction. Study findings underscore spatial disparities in zero-dose prevalence within SSA and, further, highlight the importance of geographically informed strategies to effectively address immunization gaps. Implementing targeted interventions based on regional sociodemographic dynamics is crucial for achieving comprehensive vaccination coverage in SSA.

Structural basis of calcium-dependent C1ql1/BAI3 assemblies in synaptic connectivity

Targeting fibroblast activation protein in solid tumors via LNP-mediated CAR-mRNA delivery promotes durable regression in murine models

Prevalence and determinants of precancerous cervical lesions among women screened for cervical cancer in Africa: A systematic review and meta-analysis

Background Precancerous cervical lesions, or cervical intraepithelial neoplasia (CIN), represent a significant precursor to cervical cancer, posing a considerable threat to women’s health globally, particularly in developing countries. In Africa, the burden of premalignant cervical lesions is not well studied. Therefore, the main purpose of this systematic review and meta-analysis was to determine the overall prevalence of precancerous cervical lesions and identifying determinants among women who underwent cervical cancer screening in Africa. Methods This study followed the Preferred Reporting Item Review and Meta-analysis (PRISMA) guidelines. The protocol for this systematic review and meta-analysis was registered on the International Prospective Register of Systematic Reviews (PROSPERO) (ID: CRD42025645427). We carried out a systematic and comprehensive search on electronic databases such as PubMed and Hinari. In addition, Google Scholar and ScienceDirect were utilized to find relevant studies related to precancerous cervical lesions. Data from the included studies were extracted using an Excel spreadsheet and analyzed using STATA version 17. The methodological quality of the eligible studies was examined using the Joanna Briggs Institute (JBI) assessment tool. Publication bias was checked by using the funnel plot and Egger’s tests. A random-effects model using the Der Simonian Laird method was used to estimate the pooled prevalence of pre-cancerous cervical lesions in Africa. The I-squared and Cochrane Q statistics were used to assess the level of statistical heterogeneity among the included studies. Results A total of 112 eligible articles conducted in Africa, encompassing 212,984 study participants, were included in the quantitative meta-analysis. Thus, the pooled prevalence of pre-cancerous cervical lesions in Africa was 17.06% (95% confidence interval: 15.47%−18.68%). In this review, having no formal education (AOR = 4.07, 95% CI: 1.74, 9.53), being rural dweller(AOR = 2.38, 95% CI: 1.64, 3.46), history of STIs (AOR = 3.94, 95% CI: 2.97, 5.23), history of having multiple partners (AOR = 2.73, 95% CI: 2.28, 3.28), early initiation of coitus (AOR = 2.77, 95% CI: 2.11, 3.62), being HIV-seropositive women (AOR = 3.33, 95% CI: 2.32, 4.78), a CD4 count <200 cells/mm³ (AOR = 5.17, 95% CI: 1.70, 15.71), not being on ART (AOR = 2.58, 95% CI: 1.45, 4.58), smoking (AOR = 3.91, 95% CI: 1.43, 10.67) and prolonged use of oral contraceptive pills (AOR = 4.39, 95% CI: 2.77, 6.96) were significantly associated with precancerous cervical lesions. Conclusions In Africa, the overall prevalence of pre-cancerous cervical lesions is high (17%). The findings of this review highlight that health professionals, health administrators, and all other concerned bodies need to work in collaboration to expand comprehensive cervical cancer screening methods in healthcare facilities for early detection and treatment of cervical lesions. In addition, increasing community awareness and health education, expanding visual inspection of the cervix with acetic acid in rural areas, offering special attention to high-risk groups (HIV-positive women), encouraging adherence to antiretroviral therapy for HIV-positive women, overcoming risky sexual behaviors and practices, and advocating early detection and treatment of precancerous cervical lesions.

Potent neutralization by antibodies targeting the MPXV A28 protein

Abstract Monkeypox virus (MPXV) is the most pathogenic Poxvirus in circulation, yet key viral antigens remain immunologically unexplored. We isolate and characterize a panel of monoclonal antibodies (mAbs) targeting MPXV A28 (OPG153), an important membranal protein present on mature MPXV virions. From male convalescent individuals, we isolate anti-A28 mAbs alongside additional mAbs targeting the A35 and H3 proteins. Anti-A28 mAbs potently neutralize MPXV and Vaccinia virus (VACV) through complement-dependent mechanisms involving C1q and C3 deposition. High-resolution crystal structures of two anti-A28 mAbs, 10M2146 and 8M2110, in complex with VACV A26 reveal two distinct and highly conserved proximal epitopes within the N-terminal domain. Passive transfer of 8M2110 modestly attenuates disease in infected female mice. Moreover, immunization with A28 elicits antigen-specific B cells and robust neutralizing antibody responses and provides protection against lethal VACV challenge. These findings identify MPXV A28 as a promising central target for the induction of neutralizing antibodies and antiviral interventions.

ParkMAE: a cross-linguistic masked autoencoder framework for robust Parkinson’s disease detection from speech

Identifying and characterizing ideologically homogeneous clusters on Twitter and Parler during the 2020 election

During the 2020 U.S. presidential election cycle, a combination of public statements and social media posts cast doubt on the legitimacy of the election. These sentiments flowed through various social networks and eventually sparked the January 6th insurrection at the Capitol. Here, we analyze both the network-level and content-level data that made the #StopTheSteal movement so effective online. We use Louvain clustering and a novel homogeneity metric to identify the most ideologically homogeneous groups within the discussion on the mainstream social network Twitter and alternative social network Parler. We show that these ideologically homogeneous groups spread messages further than their ideologically diverse counterparts. Our results also differentiate between ideologically homogeneous left- and right-leaning groups by measuring the characteristics of their texts, finding that right-leaning texts are stylistically similar to worldbuilding language that can be found in conspiracy theory texts.