This article presents an in-depth analysis of selected CASP16 targets, with a focus on their biological and functional significance. The authors highlight the most relevant features of the target proteins and discuss how well these were reproduced in the submitted predictions. While the overall performance of structure prediction methods remains impressive, challenges persist, particularly in modeling rare structural motifs, flexible regions, small molecule interactions, post-translational modifications, and biologically important interfaces. Addressing these limitations can strengthen the role of structure prediction in complementing experimental efforts and advancing both basic research and biomedical applications.

In this paper, we propose a coexistence scheme based on orthogonal frequency division multiplexing (OFDM) and affine frequency division multiplexing (AFDM) to enable efficient joint sensing and communication (JSAC) under different channel conditions. Specifically, the distinct signal representations of both waveforms in the frequency and affine domains are exploited to ensure the orthogonality and flexibility of the proposed mechanism. Furthermore, a novel pilot design is introduced to enable joint channel estimation for both communication and sensing tasks. To further enhance the scheme's adaptability, the proposed approach dynamically responds to channel mobility, ensuring seamless operation in JSAC networks. Numerical results validate the effectiveness of the design, demonstrating accurate low-complexity radar parameter estimation while maintaining high data rates, as well as reducing the peak-to-average-power ratio (PAPR) compared with the conventional OFDM and AFDM.

In this update to Transdimensional Number Theory (TNT), we propose a revised model for tunnel operators, which are functions responsible for transporting mathematical objects, particles, and values across dimensional layers. Building on the concept of dimensional density, we redefine tunnel operators as probabilistic, decay-sensitive functions governed by the permissibility and entropy differential between dimensions. This paper formalizes a new expression for tunnel behavior using decay coefficients and survival probability functions. By integrating concepts from topology, quantum field theory, and chronotopology, the new tunnel operator model introduces more accurate predictions of object existence during traversal, supports entangled dimensional interactions, and improves our understanding of the mathematical limits of interdimensional transport. Real-world parallels are discussed in terms of information theory, quantum teleportation, and relativistic tunneling.

Understanding spatial variation in grassland productivity is critical for ecological modeling and management. The Great Plains of North America, a vast grassland region east of the Rocky Mountains, exhibits strong gradients in above-ground net primary production (ANPP) linked to precipitation and finer-scale topoedaphic variation. Swales typically produce 40–80% more ANPP than adjacent ridges due to deeper, nutrient-rich soils with higher water-holding capacity. We used the DayCent-UV model to simulate interannual ANPP differences between ridges and swales at three grassland sites across a west-to-east precipitation gradient. This DayCent-UV version includes higher N fixation by plants in the swales. We evaluated the performance of remote sensing-derived vegetation indices (NDVI, EVI, NIRv) in capturing interannual biomass variability across topoedaphic positions. To overcome saturation and cloud limitations in the most humid sites, we incorporated Sentinel-1 radar backscatter as an alternative proxy for vegetation biomass, which successfully detected both interannual and spatial differences and showed potential for identifying woody encroachment. We also compared model performance with RAP and RCTM, two satellite-driven models that use NDVI to estimate productivity. These models better captured swale dynamics than DayCent but overestimated ridge ANPP. Our results demonstrate a methodological framework that integrates process-based modeling with multi-source remote sensing to improve detection of fine-scale productivity variation in grassland ecosystems.

RationalePre-eclampsia, a severe hypertensive disorder of pregnancy, poses significant maternal and perinatal risks. Artificial intelligence (AI) offers the potential for improved prediction, risk stratification, and personalized management. This umbrella review aims to synthesize existing systematic reviews to evaluate AI’s current applications, benefits, limitations, and ethical considerations in pre-eclampsia care. MethodsThis umbrella review will follow the Joanna Briggs Institute (JBI) methodology and the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. We will systematically search major databases for relevant systematic reviews that examine the application of AI in pre-eclampsia. Data extraction will include information on AI algorithm performance, clinical applications, predictive variables, population diversity, ethical considerations, and limitations. Quantitative and qualitative synthesis of the extracted data will be performed to address the specific aims. Discussion This review’s findings will critically examine AI’s translational potential in pre-eclampsia care. We will discuss the balance between the promise of enhanced predictive accuracy and the practical challenges of clinical implementation, including data quality, model interpretability, and the need for rigorous validation across diverse populations. Ultimately, this review will contribute to a nuanced understanding of how AI can be responsibly leveraged to improve maternal and perinatal outcomes in pre-eclampsia.

We explore The density of dimensions is a fundamental concept that examines how spatial, temporal, and abstract dimensional structures are distributed, scaled, and interwoven across various frameworks of mathematics and physics. This paper introduces a formal approach to defining and analyzing dimensional density, considering its implications in fields such as topology, tensor calculus, and quantum mechanics. By exploring the scaling properties of high-dimensional manifolds, the compactness of mathematical spaces, and the fractal-like nature of dimension transitions, we aim to establish a unified theory that quantifies the density of dimensions in both discrete and continuous systems. Furthermore, connections to Transdimensional Number Theory and Chronotopology are examined, providing a deeper understanding of dimensional interactions beyond classical Euclidean constraints. Applications in quantum computing, relativity, and geometric algebra are discussed, demonstrating how the density of dimensions influences computational complexity, physical laws, and the emergence of novel mathematical structures.

Limited knowledge of the geographical distribution of brocket deer persists due to their elusive behavior and the morphological similarity among species. A population of red brocket deer from the genus Mazama was recently found in a protected area of the Brazilian Atlantic Forest, the Rio Doce State Park (Brazilian acronym PERD), but identification to species level was not available. Since the Atlantic Forest harbors two endemic and threatened red brocket deer, this study aimed to identify which species occur in PERD through fecal DNA analysis. Fecal samples collected in PERD and throughout the Atlantic Forest were sequenced for six mitochondrial DNA region fragments (1450bp) and analyzed towards a group of reference specimens. We performed phylogenetic analyses and implemented coalescent methods of molecular species delimitation (GMYC and bPTP) to identify molecular operational taxonomic units (MOTUs). We also constructed a haplotype network and a genetic distance matrix. The phylogenetic hypothesis recovered the samples from PERD linked to the Small Red Brocket deer ( Mazama jucunda), but in a reciprocally monophyletic topology, corresponding to two exclusive MOTUs. PERD has unique haplotypes and its genetic distance to M. jucunda is similar to that between M. jucunda and its most closely related species, the Brazilian Dwarf Brocket ( Mazama nana). Herein we report an evolutionary significant unit (ESU) of M. jucunda 700 km apart from the species current distribution. The observed genetic structure and the isolation context of PERD raise the hypothesis of a new red brocket species that should be further tested, including cytogenetic data. This study represents an important advance in the knowledge of the geographical distribution and genetic structure of a threatened Neotropical forest deer endemic to a devasted hotspot with direct and short-term implications for its conservation.

The current decline in pollinator abundance and diversity poses a significant threat to the natural world and the food and economic security of human societies. A major challenge faced by the scientific community in pollinator conservation is the lack of sufficient data. Citizen science has emerged as a promising avenue for addressing this issue. In this article, we present the global perspective of citizen science projects focused on pollinator monitoring. Our analysis reveals a notable underrepresentation of developing and tropical countries in citizen science-driven data generation efforts. More than 70% of the listed studies are conducted in North America (n:64), followed by Europe (n:22). Together, Europe and North America account for ~ 97% (n:86) of all the projects listed. Thirty-three percent of the projects are hosted on iNaturalist. The majority of projects focus on insects as pollinators, with only 52% recording the pollinated species. We classified these projects into structured, semi-structured, and unstructured categories based on their methodologies. Linear regression analysis was performed to evaluate the influence of various factors on the potential for generating outputs. The regression model explained 82% of the variance in document production (Adjusted R-squared = 0.766, F(10, 33) = 15.06, p < 0.001). Structured projects significantly contributed to document output (Estimate = 12.44, p < 0.001), as did the inclusion of training (Estimate = 6.89, p < 0.001). Fisher’s Exact Test for Count Data also revealed a significant association for outputs generated with the structured methodology (p < 0.001). Additionally, we discuss the merits and drawbacks of different approaches and propose recommendations for subsequent research.

Introduction: Healthcare professionals face numerous stress-inducing daily stimuli, which can negatively impact their well-being, individual performance, and the dynamics of hospital teams. Studies have shown that self-knowledge is a mental health tool that benefits quality of life and stress. However, there are still no studies on healthcare professionals in hospital services. Thus, the present study aims to understand the effect of self-knowledge training, based on the Enneagram, on the well-being of healthcare professionals and hospital teams. Methods: This is a controlled longitudinal exploratory study with intervention. With a sample of 24 health professionals from Coimbra’s University Hospital Center (CHUC), 12 constituted the intervention group, which was given a self-knowledge course based on the Enneagram of Personalities, and 12 formed the control group. Both groups answered an online questionnaire before and after the intervention, consisting of seven scales – Perceived Quality of Life (EQ-VAS), Job-related Affective Well-being (JAWS), Self-Reflection and Insight (SRIS), Self-Awareness (SAOQ), Perceived Stress (PSS-10), Rumination and Reflection (RRQ), and Psychological Flexibility (Psy-Flex). The data obtained were analyzed descriptively and inferentially with the parametric Student’s t-test. The contents of the six open questions were subjected to a qualitative analysis. Results: The qualitative assessment revealed individual, interpersonal, and professional gains, with significant improvements in self-awareness, self-reflection, and insight, as well as enhanced relationships, empathy, and compassion. It also introduced new dimensions to the study, specifically teamwork, relationship improvement, empathy, difficulty with change, and difficulty in self-understanding. In the quantitative assessment, the intervention group started with lower proactivity (p=0.033) and a greater need for self-reflection (p=0.03), maintaining the latter after the intervention (p=0.023). By the end, the control group showed decreased acceptance (p=0.01) and, compared to the intervention group, showed a higher insight (p=0.01). Discussion: Despite the limitations of the study sample, particularly the small size and high heterogeneity, the intervention did not lead to any worsening but rather an improvement in the well-being of healthcare professionals, as demonstrated in the qualitative analysis. Considering the high burnout levels in the intervention group, the self-knowledge training appeared to play an essential role in maintaining the participants’ well-being. A long-term qualitative reassessment is suggested for further investigation. Conclusion: It was concluded that training in self-knowledge can be an effective tool in stress prevention and well-being preservation of hospital healthcare professionals with high levels of exhaustion.

Klebsiella pneumoniae is an opportunistic pathogen of great medical relevance due to its high virulence and resistance to antimicrobials. Carbapenem resistance is mediated by carbapenemases and represents a global clinical challenge due to the limited therapeutic arsenal. Transcriptomic assays can elucidate antimicrobial resistance mechanisms and enables the identification of pharmacological targets in extensively drug-resistant (XDR) Klebsiella pneumoniae. This study evaluated the transcriptomic profile of carbapenemase-producing K. pneumoniae after exposure to meropenem and assessed its morpho-functional and metabolic pathway modifications in response to antibiotic treatment. The sensitivity of carbapenemase-producing K. pneumoniae, isolated from the ICU of the University Hospital Walter Cantídio in Fortaleza, CE, was determined by disk diffusion, broth microdilution, and the automated VITEK-2® method. The strain was exposed to subinhibitory concentrations of meropenem and their bacterial growth curve was determined. Total RNA was extracted from bacteria with and without antibiotic treatment at their growth curve log phase. RNA sequencing (RNA-Seq) was performed on a high-throughput MiSeq platform, and differential expression analysis identified both upregulated and downregulated transcripts. Following exposure to meropenem, K. pneumoniae showed increased expression of genes related to the plasma membrane, efflux pumps, and cell wall modifications, as well as genes involved in resistance to polymyxins and changes in energy metabolism, such as glycolysis and fermentation. Furthermore, genes associated with antioxidant systems and oxidative stress response were expressed at elevated levels, indicating a survival mechanism under selective pressure. On the other hand, transcripts associated with protein metabolism and DNA synthesis were downregulated, suggesting a global metabolic reprogramming aimed at cell maintenance under hostile conditions. Thus, exposure to meropenem induced complex adaptations in K. pneumoniae. The results provide valuable insights into the resistance and adaptation mechanisms of this bacterium, highlighting potential targets for new therapeutic strategies.

Objective: To evaluate the predictive value of first-trimester ultrasound and biochemical markers in determining pregnancy outcomes among women with symptoms of threatened miscarriage (TM). Design: Prospective cohort study Setting: Accident and Emergency Department of the national public hospital in Malta Sample: In total, 118 singleton pregnancies between 5 +0 and 12 +6 weeks’ gestation, confirmed as viable via transvaginal ultrasound, were recruited between January 2023 and June 2024, using convenience sampling. Methods: Ultrasound and biochemical markers were measured and other clinical and sociodemographic parameters were collected via questionnaire. Univariate logistic regression identified individual predictors of pregnancy loss. Multivariate logistic regression (MLR) and random forest (RF) modelling were applied to assess combined predictive performance. Main outcome measures: Comparison of MLR and RF in predicting pregnancy outcome in TM. Results: Among 118 women with TM, 77% progressed to live birth and 23% experienced pregnancy loss. Multivariate logistic regression identified progesterone, cervical length, mean gestational sac diameter (MGSD), trophoblast thickness, sFlt-1:PlGF ratio, and maternal age as significant predictors of outcome. Higher progesterone, cervical length, MGSD and sFlt-1:PlGF ratio were associated with reduced risk, while maternal age over 35 raised the likelihood of loss. The model achieved 82.7% accuracy (AUC = 0.89). Random forest modelling further improved accuracy to 93.1% (AUC = 0.97), confirming the combined predictive value of ultrasound and biochemical markers. Conclusion: Ultrasound and biochemical markers offer predictive value in TM. Machine learning models, particularly random forest, may enhance early risk stratification in clinical settings.

The purinergic receptor family is primarily activated by nucleotides and contains members of both G protein coupled-receptor (GPCR) superfamily (P1, P2Y) and ligand-gated ion channel (P2X). The P2Y receptors are widely expressed in the human body, and given the ubiquitous nature of nucleotides, purinergic signalling is involved with a plethora of molecular physiological functions. The widespread nature of P2Y receptors make them a viable therapeutic target, but with the negative risk of on-target side effects. Some of the side effects associated with P1 and P2Y receptors arise due to the pleotropic nature of many GPCRs, as a singular GPCR can activate several G proteins, as well as recruit β-arrestins. The utilization of ligands that exhibit downstream pathway-specific activation, also known as biased signalling, at the P2Y receptors could circumvent these issues. This review will cover the signalling nature and impact of the P2Y family, with an emphasis on individual activity patterns of the P2Y receptors. This review will also discuss current literature on the development of biased ligands for these receptors, with an aim to highlight the most beneficial targets and outcomes.

Coastal zones worldwide increasingly face conflicts as societal demands, economic development, including tourism, and nature conservation all compete for limited coastal space. Although lagoon coasts are ecologically valuable and morphologically stable, these remain especially vulnerable to small-scale, tourism-driven alterations. Fragmented monitoring means that many of these changes escape detection, limiting opportunities for effective enforcement. Focusing on eight independent campgrounds along a Natura 2000-designated low-energy lagoon coast (Baltic Sea), we analysed long-term shoreline and land-use changes from 1976 to 2024.This study presents first scientific documentation of unpermitted artificial beach nourishment by private leaseholders, resulting in significant habitat loss, shoreline progradation of up to 60 m, and hectares-gained land reclamation prone to erosion. Our monitoring method accurately identified both natural morphodynamics and unpermitted human interventions, demonstrating its effectiveness for resource-efficient coastal management. The study introduces a six-stage model of campground-driven shoreline transformation and fills a key gap in Integrated Coastal Zone Management practice. By demonstrating how subtle, dispersed coastal interventions can trigger large-scale degradation, we highlight the urgent need for scalable monitoring frameworks to support timely enforcement and long-term protection of ecologically vulnerable coastal systems globally.

As global land degradation becomes increasingly serious, comprehensive land consolidation (CLC) has become a critical policy tool for effective land resource management worldwide. Effective multi-subject collaboration is essential to enhancing sustainable development of CLC. However, What conditions of multi-subject collaboration can achieve efficient collaboration is a key question in research multi-subject collaboration in CLC. Based on collaborative governance theory, this study builds a theoretical model of ”policy environment - collaborative foundation - collaborative motivation - collaborative dependence”, selects typical Chinese CLC project areas as samples, and by integrating fuzzy-set qualitative comparative analysis (fsQCA) and necessary condition analysis (NCA) methods, we investigated the complex causal relationships between various factors of multi-subject collaboration and the collaboration effect from the configuration perspective and revealed the configurational effects that produce efficient collaboration. The study revealed four configuration pathways lead to efficient collaboration: collaborative foundation-driven configuration, policy-dynamics-driven configuration, policy-structure dependence-driven configuration, and multi-factor-driven configuration. No single factor is found to be a necessary condition for achieving efficient collaboration. Our findings help enhance the research on multi-subject collaboration in CLC, providing theoretical and practical insights for developing and adjusting targeted collaboration policies to promote the sustainable development of CLC.

Soil resistance and load-bearing capacity are mainly influenced by the soil’s clay content, climatic conditions, and the type of machinery used. The risk of soil compaction is therefore dynamic, varying across time and space. This study mapped the spatial-temporal dynamics of compaction risk in the state of Bahia, northeastern Brazil, with the aim of identifying areas susceptible to compaction and improving forest harvest planning. The analysis used data on clay content for the 0 to 0.40 m layer, from 671 pedological profiles, maps with scale of 1:20,000, and average bi-weekly rainfall from 2010 to 2021, obtained from 18 weather stations, covering an area of 10,078 km 2. Six distinct periods of compaction risk were identified. Forest harvesting restriction, due to soil high risk of compaction, should affecting between 11.9% and 31.6% of the area, over one third of the year, from the second half of April to July. During this period, up to 27.4% of the study area was at moderate risk, while at list 56.9% showed low risk. The most favorable period for harvesting is between the second half of September and the first half of March, when the risk of soil compaction is low across the entire study area. Two transition periods were also identified between seasons (March to April and August to September), with 37.2% of the area at moderate risk. The developed maps provide essential knowledge for a sustainable forest harvesting planning, promoting a better soil management.

Soil organic carbon (SOC) as an indicator of soil quality, plays a dual role in stabilizing oasis ecosystems and regulating carbon sequestration in arid, lakeside environments. However, the accurate estimation of SOC using visible-near-infrared (VNIR) spectral data is limited by spectral redundancy and high dimensionality. This research enhances SOC estimation accuracy by combining wavelet analysis and machine learning in the lakeside oasis of Bosten Lake in Xinjiang. SOC content was measured for each sample (82 samples from the 0–20 cm depth), and their corresponding VNIR spectral data were obtained. The hyperspectral reflectance data were processed using continuous wavelet transform (CWT) and discrete wavelet transform (DWT). The successive projections algorithm (SPA), Boruta, and competitive adaptive reweighted sampling (CARS) algorithm identified relevant spectral bands to develop SOC estimation models based on partial least squares regression (PLSR), backpropagation neural networks (BPNN), and random forest (RF) algorithms. The results revealed that CWT offered superior noise reduction performance, particularly at low decomposition scales (1–5), achieving a 19.21% improvement in noise suppression compared to DWT. The optimal CWT-based model showed 23.20% improvement in residual prediction deviation (RPD) compared to the DWT-based counterpart. Feature selection algorithms significantly improved estimation accuracy, with enhancements of up to 49.04% in the determination coefficient (R 2) and 58.23% in RPD. Among the algorithms, CARS provided the highest improvement, followed by SPA and Boruta. Thus, the combination of CWT-1-CARS and the RF algorithm showed the strongest nonlinear modeling performance. This configuration achieved calibration metrics of R 2 = 0.79, root mean square error (RMSE) = 2.57, and RPD = 2.23 to outperform the original spectral models, with improvements of 63.3% over PLSR (RPD = 1.84) and BPNN (RPD = 1.91). The spatial interpolation analysis showed 91.3% consistency with field-measured SOC values, validating the model’s practical reliability. The most sensitive spectral response bands for SOC were primarily located in the visible range (401–504 nm) and the near-infrared range (1,638–2,369 nm). This study establishes a robust technical foundation for accurate estimation of SOC, for precise ecological monitoring, and sustainable management of arid, lakeside oases.

Soil aggregate structure is pivotal in regulating essential physical and biological processes within soil plant-microbe systems, thereby influencing various soil mechanisms. The alterations in aggregate pore structure during the transition from bare soil or weed-dominated degraded grasslands to artificial grasslands dominated by gramineous species remain unclear. To elucidate the impact of artificial grassland establishment on the pore structure of soil aggregates in degraded grasslands, this study selected natural alpine marsh meadows, degraded alpine marsh meadows, and artificially established meadows in Maqin County, Qinghai Province, as research subjects. Utilizing micro –CT technology, 3 –5 mm soil aggregates were scanned to obtain slice images with a resolution of 5.652 μm. These images were subjected to three-dimensional reconstruction and quantitative analysis using Avizo software and its plugins. The results demonstrated that, compared to degraded swamp meadows, soil organic carbon (SOC) in artificially established meadows increased by approximately 45.08%, yet remained lower than that of native alpine swamp meadows by about 26.37%. Porosity and macropores (>100 μm) increased by 22.03% and 2.81%, respectively. Regarding aggregate pore morphology, the establishment of artificial meadows led to increases in fractal dimension (FD), connectivity, and elongated pores by 2.66%, 1.18%, and 7.25%, respectively. During the degradation and restoration of alpine swamp meadows, SOC exhibited significant positive correlations with porosity, elongated pores, connectivity, and FD. In summary, the degradation of alpine swamp meadows severely disrupts the microstructure of soil aggregates, whereas the establishment of artificial meadows improves soil structure, rendering soil pore architecture more complex and thereby promoting the stabilization of soil ecosystems.

Animals may interact incidentally, by sharing space, or intentionally, by seeking out interactions. Understanding which elements of social interactions can be explained by spatial behavior and which cannot may uncover drivers of individual fitness and population functioning. In an avian scavenger, we tested how space use covaried with social position while flying, feeding, and roosting. We also identified the deviation of observed social centrality from chance and examined how this non-incidental social centrality covaried with space use. In flight, space use was positively associated only with observed social centrality, suggesting that interactions while flying emerge primarily from co-movement. In contrast, space use covaried with non-incidental social centrality while roosting, suggesting a stronger importance of social preferences when interacting at roost sites. Our work demonstrates that the role of animal movements in shaping social interactions differs across social situations. Such an understanding of the spatial-social interface is essential for predicting population responses to environmental changes and conserving threatened species. 

Tropical forest degradation, particularly within protected areas (PAs), represents an insidious form of land degradation that undermines ecosystem resilience, biodiversity, and carbon storage. Yet, degradation dynamics along PA boundaries remain poorly quantified at fine spatial scales. This study provides a spatially explicit assessment of edge-driven forest degradation in three ecologically significant PAs in Bangladesh—Bhawal National Park (BNP), Teknaf Wildlife Sanctuary (TWS), and Rema-Kalenga Wildlife Sanctuary (RKWS)—over a 23-year period (2001–2023). Leveraging the Hansen Global Forest Change dataset at 30 m resolution, we quantified tree cover loss across concentric buffer zones (0–500 m, 500–1000 m, and 1000–1500 m) and stratified results by canopy density to identify structurally vulnerable forest patches. Results reveal that degradation is spatially concentrated along PA edges: RKWS and TWS experienced the highest losses in the 0–500 m zone (12.4 ha/ha and 11.7 ha/ha, respectively). High-canopy forests (>75% cover) were disproportionately affected, contributing to 85% of total loss in RKWS. BNP exhibited a more diffuse degradation pattern, while TWS and RKWS showed intensified edge fragmentation. These findings expose a critical governance blind spot: legal protection does not ensure ecological integrity without spatially informed management. By integrating fine-scale remote sensing with landscape metrics, this study introduces a transferable methodology for identifying degradation-prone zones and prioritizing spatially targeted restoration. The approach offers broader utility for sustainable land management, particularly in tropical nations balancing conservation mandates with socio-demographic pressures. Insights are directly relevant to achieving ecosystem restoration targets under the Bonn Challenge and the UN Decade on Ecosystem Restoration.

Aims Fruquintinib, a highly selective small-molecule tyrosine kinase inhibitor targeting the vascular endothelial growth factors (VEGF)-1, -2, and -3, has demonstrated significant efficacy in prolonging progression-free survival and overall survival in patients with refractory metastatic colorectal cancer. This study aims to generate robust scientific evidence for clinical practice and regulatory decision-making by elucidating pharmacovigilance signals associated with fruquintinib through systematic characterization of adverse event (AE) profiles derived from the WHO-VigiAccess and FAERS databases. Method AE reports associated with fruquintinib were extracted from the FAERS and VigiAccess databases for retrospective quantitative analysis. Signal detection was performed using the Proportional Reporting Ratio (PRR), Reporting Odds Ratio (ROR), with cross-database consistency evaluated. Results The investigation revealed 4641 reports from FAERS and 3,909 reports from WHO-VigiAccess related to fruquintinib. AEs not documented in the drug label included dysphonia, nausea, pain, and others. In the FAERS database, signals for fruquintinib were detected across 25 System Organ Classes (SOCs). Systems not covered in the drug label included blood and lymphatic system disorders, psychiatric disorders, immune system disorders, reproductive system and breast disorders, ear and labyrinth disorders, and various musculoskeletal and connective tissue disorders. Unmentioned signals encompassed pain, myelosuppression, headache, dizziness, dyspnea, etc. Conclusions This study confirms the known adverse reactions of fruquintinib and reveals potential AEs. It also underscores the importance of drug safety monitoring to maximize therapeutic efficacy and mitigate the risk of adverse reactions. These findings provide critical safety information for clinicians using fruquintinib in the treatment of mCRC patients.

Helicobacter pylori is a significant human pathogen associated with gastric diseases, yet the contribution of plasmids in its pathogenicity remains largely unexplored. In this study, we combined plasmid network analysis, dereplication, functional annotation and phylogenetic approaches to provide a comprehensive genomic and functional characterization of the H. pylori plasmidome using publicly available plasmid sequences. Of 322 plasmids analyzed, we identified 158 high-confidence plasmid sequences, representing 76 non-redundant plasmids. Notably, several sequences previously annotated as plasmids were reclassified as Integrative and Conjugative Elements (ICEs). NR-plasmids were enriched in genes encoding Filamentation induced by cAMP (Fic) family proteins, which clustered into two distinct phylogenetic groups. Conserved motif analysis suggests that these two Fic proteins types may form a novel toxin-antitoxin (TA) system, with Type-2 proteins potentially suppressing Type-1 activity, analogous to the TA mechanism described in Campylobacter fetus subsp. venerealis. Additionally, we identified genes encoding ABC and MFS efflux pumps, as well as the virulence-associated protein VapD, which may contribute to antimicrobial resistance and host colonization, respectively. Our findings reveal the genomic and functional diversity of the H. pylori plasmidome and highlight the need for experimental validation to clarify its role in pathogenicity, antimicrobial resistance, and bacterial adaptation.

Background: Blinatumomab has demonstrated its efficacy and safety in pediatric patients with B-cell acute lymphoblastic leukemia (B-ALL). Methods: We enrolled patients aged 0-18 years who were diagnosed with CD19-positive B-ALL and treated with blinatumomab between January 2021 and May 2023 from 14 centers in China. Results: A total of 304 patients were enrolled in this analysis. In the patients with ＞5% blasts before blinatumomab (non-complete remission, NCR group), 75.9% achieved complete remission (CR) and 69.0% achieved minimal residual disease (MRD) negativity. Among the patients with ≤5% blasts but multiparametric flow cytometry MRD (MFC-MRD) positive (MRD+ group), 98.9% achieved MRD negativity. Of the MFC-MRD negative patients (MRD- group), the quantitative polymerase chain reaction MRD (qPCR-MRD) and next-generation sequencing MRD (NGS-MRD) clearance rate was 60.0% and 65.5%, respectively. Additionally, Patients in the MRD+ and MRD- groups had significantly better outcomes than those in the NCR group, with 30-month overall survival (OS) rates of 95.3% (95% CI: 91.4-99.3%), 91.2% (95% CI: 85.0-97.8%), and 77.6% (95% CI: 67.4-89.4%), respectively (P<0.001), and 30-month event-free survival (EFS) rates of 93.9% (95% CI: 89.6-98.3%), 90.8% (95% CI: 85.0-97.1%), and 56.7% (95% CI: 41.0-78.6%), respectively (P<0.001). In this study, 41.4% of patients experienced grade ≥3 adverse events (AEs), with hematological toxicity being the most common (33.2%). The severe adverse events, such as cytokine release syndrome (CRS) and neurotoxicity, occurred at a low rate, particularly grade ≥3, at 3.6% and 2.6%, respectively. Conclusions: Overall, these results indicate that blinatumomab is effective and well-tolerated. Patients with a lower leukemia burden before blinatumomab administration tend to have better OS and EFS with fewer AEs.

A case of a novel Perforin gene variant in severe

Radioembolization with yttrium-90 (Y-90) microspheres is primarily used in liver tumor patients with suboptimal response to chemotherapy. Radioembolization-induced liver disease (REILD) is a potentially fatal complication of Y-90 radioembolization (Y-90 RE). The clinical presentation of REILD mimics sinusoidal obstructive syndrome (SOS), also known as hepatic veno-occlusive disease (VOD), sharing features including hyperbilirubinemia, coagulopathy, and ascites. While SOS/VOD in pediatric bone marrow transplant recipients is commonly treated with defibrotide, no standardized guidelines exist for the treatment of REILD. We present a case of a pediatric liver transplant recipient who developed REILD after Y-90 RE and was successfully treated with defibrotide.