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Prolonged preoperative wait time associated with elevated postoperative thirty-day mortality following intracranial tumor craniotomy in adult patients: A retrospective cohort study

Objective Prior studies have established preoperative wait time as a potential risk factor for postoperative outcomes across various clinical conditions. However, associations between wait time and short-term prognosis following intracranial tumor surgery are still largely unknown. Our study sought to investigate associations between preoperative wait time and postoperative thirty-day mortality following intracranial tumor craniotomy in adult patients. Methods This retrospective cohort study utilized data extracted from the ACS NSQIP database, comprising 18,298 adult patients who underwent intracranial tumor craniotomy between 2012 and 2015. The primary exposure and outcome were preoperative wait time and postoperative thirty-day mortality, respectively. Smooth curve fitting evaluated the linear or nonlinear association between them. The effects of exposure on outcome were evaluated using multivariate Cox proportional hazard regression models and Kaplan-Meier curves. Subgroup analyses and interaction testing were conducted to evaluate the effect modification of confounding factors. The robustness of the main results was assessed through propensity score matching and sensitivity analyses. Results Prolonged preoperative wait time was independently and linearly related to elevated thirty-day mortality (HR = 1.075, 95%CI: 1.040–1.110). The ventilator-dependent status significantly modify the relationship between wait time and mortality. The linear wait time-mortality association was observed solely in non-ventilator-dependent patients, showing an 8.3% increase in thirty-day mortality risk for each additional day of waiting (HR = 1.083, 95%CI: 1.049–1.119). Patients who waited ≥1 day had a 0.74% higher absolute risk and a 31.3% higher relative risk of thirty-day mortality compared to those who waited <1 day. The sensitivity analyses corroborated the robustness of these results. Conclusions Prolonged preoperative wait time has an independent linear association with elevated postoperative thirty-day mortality in non-ventilator-dependent adult patients undergoing intracranial tumor craniotomy. Clinicians should minimize preoperative wait time to mitigate the risk of thirty-day mortality. Nonetheless, further research is warranted to validate the results and establish causality.

Development of a small compound that regulates the function of a maltodextrin-binding protein of Streptococcus pyogenes by multifaceted screenings

Abstract Group A Streptococcus (GAS) are gram-positive bacteria that cause various symptoms. The treatment of GAS infections currently relies on antibiotics, but new treatment options are needed due to the spread of antibiotic resistance. To develop novel treatment methods that circumvent the generation of antibiotic resistance, we used virtual screening followed by several biophysical-based screening methods to identify antibacterial compounds that target SPs0871, which is a maltodextrin-binding protein that is involved in carbohydrate catabolism in GAS. We narrowed down the list of compounds in the library via multi-step screening and finally isolated a compound that bacteriostatically inhibited the growth of GAS. Together with our previous study showing that an anti-SPs0871 variable heavy domain of heavy chain antibody, which completely blocked ligand binding, did not suppress bacterial growth, our results provide guidelines for designing an antistreptococcal therapeutic.

High‐Fidelity In Vitro Packaging of Diverse Synthetic Cargo into Encapsulin Protein Cages

AbstractCargo‐filled protein cages are powerful tools in biotechnology with demonstrated potential as catalytic nanoreactors and vehicles for targeted drug delivery. While endogenous biomolecules can be packaged into protein cages during their expression and self‐assembly inside cells, synthetic cargo molecules are typically incompatible with live cells and must be packaged in vitro. Here, we report a fusion‐based in vitro assembly method for packaging diverse synthetic cargo into encapsulin protein cages that outperforms standard in cellulo assembly, producing cages with superior uniformity and thermal stability. Fluorescent dyes, proteins and cytotoxic drug molecules can all be selectively packaged with high efficiency via a peptide‐mediated targeting process. The exceptional fidelity and broad compatibility of our in vitro assembly platform enables generalisable access to cargo‐filled protein cages that host novel synthetic functionality for diverse biotechnological applications.

Engineering Heteronuclear Dual‐Metal Active Sites in Ordered Macroporous Architectures for Enhanced C₂H₄ Production from CO₂ Photoreduction

AbstractPhotocatalytic C2H4 synthesis from CO2 and H2O by utilizing solar energy represents a promising sustainable process, yet its efficiency remains significantly limited. Herein, we proposed a dual‐engineered strategy integrating 3D ordered macroporous (3DOM) architectures with heteronuclear dual‐metal active sites to synergistically promote the photocatalytic C2H4 production. As an example, the Cu/3DOM‐In2O3 photocatalyst was synthesized by in situ incorporating Cu single atoms (Cu SAs) into 3DOM In2O3 through a template‐assisted pyrolysis process. The strong interaction between Cu SAs and In2O3 resulted in the formation of charge‐polarized Cu─In active sites along with abundant oxygen vacancies (OVs). 3DOM architectures serving as special nanoreactors displayed significant advantages in promoting CO2 enrichment and confining key intermediates, thereby increasing *CO coverage. Meanwhile, the charge‐polarized Cu─In active sites effectively mitigated electrostatic repulsion and promoted the formation of *CO + *CHO intermediates, resulting in a thermodynamically spontaneous C─C coupling step. Therefore, the Cu/3DOM‐In2O3 photocatalyst exhibited robust CO2 reduction to C2H4, achieving high C2H4 evolution rates under various CO2 concentrations, including pure CO2, 10% CO2 in Ar (simulated flue gas), and 0.04% CO2 in Ar (simulated air). This work offers a novel strategy for the construction of photocatalysts with tailored microstructures and specific active sites to promote the conversion of CO2 and H2O into multicarbon products.

Correction: Power analysis for personal light exposure measurements and interventions

Exploring syphilis transmission dynamics with congenital infection and disability compartments

Biomimetic Adhesion/Detachment Using Layered Polymers with Light‐Induced Rapid Shape Changes

AbstractGeckos achieve rapid and efficient adhesion and detachment on various surfaces within milliseconds due to the hierarchically structured fibrillar architecture of their toe pads. Extensive research has focused on developing adhesive materials that mimic these micro‐nano structures. However, no conventional adhesives have matched the performance of their natural counterparts, which are both non‐degrading and self‐cleaning. Here, we develop a chemically crosslinked polymer film with a nanoscale layered structure that exhibits high‐speed switching of mechanical motions (expansion and contraction) in equation both forward and reverse directions through controlled ultraviolet (UV) irradiation. Under UV light on/off switching, the film shows reversable shape changes in the direction perpendicular to the molecular alignment. These photoresponsive molecular movements in the film is demonstrated for remote control in a smart adhesion system with rapid responsiveness and high reproducibility. The films respond to UV light to release picked‐up objects and immediately regain their adhesion when the UV light ceases. Additionally, we propose the working principles and mechanisms of these reusable adhesive films, providing new insights into the development of smart soft materials. The material's rapid deformation, high responsiveness, and flexibility make it a promising candidate for light‐controlled object transport and remote‐controlled robotics.

Development and validation of nomograms including individual- and area-level variables to predict risk of fatal and non-fatal cardiovascular diseases among Russian population

Introduction Cardiovascular diseases (CVD) are the greatest threat to health worldwide and in Russia. Our study aimed to use Cox proportional hazards models to develop cardio-vascular risk scores and nomograms based on prospective data from studies conducted in Russia. Methods All materials used in this study were obtained from the epidemiological study “Epidemiology of Cardiovascular Diseases in the Regions of the Russian Federation” (ESSE-RF): ESSE-RF (2012-2014) and ESSE-RF2 (2017). A total of 18,454 individuals without CVD aged 25–64 years were included in our study. The participants were randomly divided into a training and testing set at a ratio of 7:3. The Russian deprivation index and its components (social, economic and environmental) were used as area-level predictors. To select the best potential predictive variables for our models, the random forests variable selection algorithm based on minimal depth was used. To predict three- and five-year CVD-free survival, four prognostic nomograms were developed from the results of multivariate analysis. Results The nomograms had considerable discriminative power, calibrating abilities and clinical effectiveness. The time dependent AUC was > 0.7 for the prediction of CVD-free survival in both the training and testing sets. Conclusion For the first time, the nomograms have been created that include area-level predictors (socio-economic and environmental) and lipid spectrum indicators (triglycerides, high-density lipoprotein cholesterol and low-density lipoprotein cholesterol) and assess the probability of fatal and non-fatal cardiovascular events among the Russian population.

Marginal adaptation and porosity of calcium silicate-based cements in furcation perforations: a micro-CT comparative study

Perovskite Homojunction Solar Cells by Buried Interface Engineering

AbstractConstructing a strong p–n junction is an effective strategy to drive the separation of photogenerated charge carriers and boost the photovoltaic performance of solar cells. However, forming p‐type and n‐type semiconductors in perovskites is not as straightforward as in archetypal Si by doping electron‐accepting and electron‐donating elements. Here, we observe the transition of p‐type to n‐type characteristics in a perovskite layer via buried interface engineering. The perfluorinated copper phthalocyanine (F16CuPc) molecules with strong electronegativity are employed to modify the NiOx/Me‐2PACz substrate, which not only facilitates the crystallization of the perovskite, but also induces the formation of p‐type perovskite at its buried interface. We observe a gradual shift of the Fermi level from near valence band at the perovskite buried interface to near conduction band at the perovskite top surface, manifesting the transition from p‐type to n‐type within the monolithic perovskite layer. Such a p–n homojunction provides an extra electric field for accelerating charge carrier transportation, and thus enhances the device photovoltaic performance. The F16CuPc induced perovskite homojunction solar cells achieved a champion efficiency of 25.0% and it retained over 80% of its initial efficiency for more than 1100 h. We believe that the perovskite homojunction strategy will also pave the way for other perovskite‐based optoelectronic devices.

Correction: The rhizosphere of Phaseolus vulgaris L. cultivars hosts a similar bacterial community in local agricultural soils

Design and evaluation of substituted cinnamoyl piperidinyl acetate derivatives as potent cholinesterase inhibitors

Early detection of occupational stress: Enhancing workplace safety with machine learning and large language models

Occupational stress is a major concern for employers and organizations as it compromises decision-making and overall safety of workers. Studies indicate that work-stress contributes to severe mental strain, increased accident rates, and in extreme cases, even suicides. This study aims to enhance early detection of occupational stress through machine learning (ML) methods, providing stakeholders with better insights into the underlying causes of stress to improve occupational safety. Utilizing a newly published workplace survey dataset, we developed a novel feature selection pipeline identifying 39 key indicators of work-stress. An ensemble of three ML models achieved a state-of-the-art accuracy of 90.32%, surpassing existing studies. The framework’s generalizability was confirmed through a three-step validation technique: holdout-validation, 10-fold cross-validation, and external-validation with synthetic data generation, achieving an accuracy of 89% on unseen data. We also introduced a 1D-CNN to enable hierarchical and temporal learning from the data. Additionally, we created an algorithm to convert tabular data into texts with 100% information retention, facilitating domain analysis with large language models, revealing that occupational stress is more closely related to the biomedical domain than clinical or generalist domains. Ablation studies reinforced our feature selection pipeline, and revealed sociodemographic features as the most important. Explainable AI techniques identified excessive workload and ambiguity (27%), poor communication (17%), and a positive work environment (16%) as key stress factors. Unlike previous studies relying on clinical settings or biomarkers, our approach streamlines stress detection from simple survey questions, offering a real-time, deployable tool for periodic stress assessment in workplaces.

Ensemble-based eye disease detection system utilizing fundus and vascular structures

Probing Inside the Catalyst Layer on Gas Diffusion Electrodes in Electrochemical Reduction of CO and CO₂

AbstractGas diffusion electrodes (GDEs) are widely used in electrochemical CO and CO2 reduction reactions (CO(2)RR) in flow cells due to their ability to alleviate mass transport limitations of gaseous reactants. The flow cell configuration makes uniform distribution of reactants, intermediates, products, and speciation within the catalyst layer (CL) unlikely. In this work, a first‐of‐its‐kind in situ characterization technique capable of probing the cross section of the CL with confocal Raman spectroscopy was developed to investigate the speciation distribution across the Cu CL in CO(2)RR with a spatial resolution of ∼4 µm. In both CORR in alkaline medium and CO2RR in acidic electrolyte, the active region of CL was identified as that with the presence of the Raman band for adsorbed CO (COad). The strong correlation of COad and CO32− bands provides the first spectroscopic evidence that CO2RR only occurs in an alkaline microenvironment.

Bulk and single-cell RNA-sequencing analyses revealed potential key genes and the role of CCL19/CCL21-CCR7 axis in hidradenitis suppurativa

Hidradenitis suppurativa (HS) is a chronic inflammatory skin disorder, affecting the pilosebaceous unit in apocrine gland-rich areas, characterized by painful nodules, abscesses and draining tunnels. The underlying molecular and immunological mechanisms remain poorly understood. This study aimed to identify key gene expression patterns, hub genes, and analyze the potential role of the CCL19/CCL21-CCR7 axis in HS lesions and peripheral blood using bulk and single-cell RNA sequencing analyses. By employing an integrative approach that included three machine learning methods and subsequent validation on an independent dataset, we successfully identified AKR1B10, IGFL2, WNK2, SLAMF7, and CCR7 as potential hub genes and therapeutic targets for HS treatment. Furthermore, our study found that CCL19 and CCL21 may originate from various cells such as fibroblasts and dendritic cells, playing a crucial role in recruiting CCR7-associated immune cells, particularly Treg cells. The involvement of the CCL19/CCL21-CCR7 axis in HS pathogenesis suggests that other CCR7-expressing cells may also be recruited, contributing to disease progression. These findings significantly advance our understanding of HS pathogenesis offer promising avenues for future CCR7-targeted therapeutic interventions.

Computational approaches in drug chemistry leveraging python powered QSPR study of antimalaria compounds by using artificial neural networks

Inside Front Cover: Avoiding the Kauzmann Paradox via Interface‐Driven Divergence in States (Angew. Chem. Int. Ed. 23/2025)

Tubular Nanostructures from Large‐Pore 2D Covalent Organic Frameworks

AbstractThe synthesis of a wavy mesoporous 2D covalent organic framework (COF) with a 6‐nm hexagonal pore lattice (Joa‐COF‐1) is reported. This has been achieved by the synthesis of a terpyrenyl linker of approximately 2.7 nm in length and its subsequent condensation with a 3‐connected non‐planar cata‐hexabenzocoronene. Joa‐COF‐1 exists as non‐covalent tubular domains composed of π‐stacked 4 to 5 pores in 2D COF sections that can be separated by mild sonication, resulting in a family of tubular COF nanostructures that combine a 1D morphology with accessible mesopores.

Development of a Pediatric Vascular Catheterization Complication Score (Ped-VCCScore) for predicting post-cardiac catheterization complications

Cardiac catheterization, which is essential for clinical diagnosis and treatment, carries certain risks in pediatric patients, including complications such as loss of pulse, internal bleeding, vessel rupture, and subcutaneous hematoma. We investigated vascular complications in pediatric cardiac patients undergoing catheterization and developed a scoring system to predict these risks. We investigated pediatric patients aged <15 years who underwent cardiac catheterization at a tertiary hospital between January 2017 and December 2019 and developed a statistical model identifying the key factors influencing the risk of complications based on complication frequency, patient demographics, and treatment types. The identified key factors were body weight, procedure type, and maximum sheath size in the arterial-side-to-body-weight ratio. Using the scores determined by the model, participants were categorized into low-, intermediate-, and high-risk groups. The effectiveness of the model was assessed based on accuracy, alignment with real outcomes, and the ability to distinguish between cases with and without complications. Of the 390 patients, 6.2% experienced complications after cardiac catheterization. Transient pulse loss was the predominant complication (72%), followed by subcutaneous hematoma (12%) and bleeding (16%). In the development dataset, the vascular complication rates were 1.8%, 6.8%, and 26.1% in the low-, intermediate-, and high-risk groups, respectively. The likelihood ratios for vascular complications in the low-, intermediate-, and high-risk groups were 0.27 (95% confidence interval [CI]: 0.07, 0.81; P = 0.014), 1.12 (95% CI: 0.42, 2.67; P = 0.828), and 5.38 (95% CI: 2.23, 12.33; P < 0.001), respectively. Our model based on body weight, procedure type, and sheath size-to-body weight ratio accurately predicted vascular complications in pediatric cardiac catheterization. As one of the first studies to identify these risk factors, this study highlights the model’s potential applicability to support risk stratification-based clinical decision-making. Further validation in diverse clinical settings is needed to confirm its generalizability and predictive performance.