Autonomous enzymatic synthesis of functional nucleic acids for sensitive measurement of long noncoding RNA in human lung tissues.

Aberrant long noncoding RNA (lncRNA) expression is linked to varied pathological processes and malignant tumors, and lncRNA can serve as potential disease biomarkers. Herein, we demonstrate the autonomous enzymatic synthesis of functional nucleic acids for sensitive measurement of lncRNA in human lung tissues on the basis of multiple primer generation-mediated rolling circle amplification (mPG-RCA). This assay involves two padlock probes that act as both a detection probe for recognizing target lncRNA and a domain for producing complementary DNAzyme. Two padlock probes can hybridize with target lncRNA at different sites, followed by ligation to form a circular template with the aid of RNA ligase. The circular template can initiate mPG-RCA to generate abundant Mg2+-dependent DNAzymes that can specifically cleave signal probes to induce the recovery of Cy3 fluorescence. The inherent characteristics of ligase-based ligation reaction and DNAzymes endow this assay with excellent specificity, and the introduction of multiple padlock probes endows this assay with high sensitivity. This strategy can rapidly and sensitively measure lncRNA with a wide linear range of 1 fM - 1 nM and a detection limit of 678 aM within 1.5 h, and it shows distinct advantages of simplicity and immobilization-free without the need of precise temperature control and tedious procedures of nanomaterial preparation. Moreover, it enables accurate measurement of lncRNA level in normal cells and malignant tumor cells as well as differentiation of lncRNA expressions in tissues of non-small cell lung cancer (NSCLC) patients and normal individuals, with promising applications in biomedical studies and disease diagnosis.

Birch WRKY transcription factor, BpWRKY32, confers salt tolerance by mediating stomatal closing, proline accumulation, and reactive oxygen species scavenging.

Plant WRKY transcription factors (TFs) play important roles in abiotic stress responses. However, how WRKY facilitate physiological changes to confer salt tolerance still needs to be studied. Here, we identified a WRKY TF from birch (Betula platyphylla Suk), BpWRKY32, which is significantly (P < 0.05) induced by salt stress. BpWRKY32 binds to W-box motif and is located in the nucleus. Under salt stress conditions, fresh weights (FW) of OE lines (BpWRKY32 overexpression lines) are increased by 66.36% than that of WT, while FW of knockout of BpWRKY32 (bpwrky32) lines are reduced by 39.49% compared with WT. BpWRKY32 regulates the expression of BpRHC1, BpNRT1, and BpMYB61 to reduce stomatal, and width-length ratio of the stomatal aperture in OE lines are reduced by 46.23% and 64.72% compared with in WT and bpwrky32 lines. BpWRKY32 induces P5CS expression, but inhibits P5CDH expression, leading to the proline content in OE lines are increased by 33.41% and 97.58% compared with WT and bpwrky32 lines. Additionally, BpWRKY32 regulates genes encoding SOD and POD family members, which correspondingly increases the activities of SOD and POD. These results suggested that BpWRKY32 regulates target genes to reduce the water loss rate, enhance the osmotic potential, and reduce the ROS accumulation, leading to improved salt tolerance.

Large-Area Flexible Carbon Nanofilms with Synergistically Enhanced Transmittance and Conductivity Prepared by Reorganizing Single-Walled Carbon Nanotube Networks.

Large-area flexible transparent conductive films (TCFs) are highly desired for future electronic devices. Nanocarbon TCFs are one of the most promising candidates, but some of their properties are mutually restricted. Here, a novel carbon nanotube network reorganization (CNNR) strategy, that is, the facet-driven CNNR (FD-CNNR) technique, is presented to overcome this intractable contradiction. The FD-CNNR technique introduces an interaction between single-walled carbon nanotube (SWNT) and Cu─-O. Based on the unique FD-CNNR mechanism, large-area flexible reorganized carbon nanofilms (RNC-TCFs) are designed and fabricated with A3-size and even meter-length, including reorganized SWNT (RSWNT) films and graphene and RSWNT (G-RSWNT) hybrid films. Synergistic improvement in strength, transmittance, and conductivity of flexible RNC-TCFs is achieved. The G-RSWNT TCF shows sheet resistance as low as 69 Ω sq-1 at 86% transmittance, FOM value of 35, and Young's modulus of ≈45 MPa. The high strength enables RNC-TCFs to be freestanding on water and easily transferred to any target substrate without contamination. A4-size flexible smart window is fabricated, which manifests controllable dimming and fog removal. The FD-CNNR technique can be extended to large-area or even large-scale fabrication of TCFs and can provide new insights into the design of TCFs and other functional films.

Low-carbon energy transition multi-agent network evolutionary under carbon trading scheme.

Transitioning to low-carbon energy is key for reaching carbon neutrality and modernizing our energy systems, but it presents significant cost-related challenges for energy businesses. To foster optimal outcomes, this paper develops a game model including power generators, high-energy businesses, and consumers in the carbon trading framework. The model explores how different entities evolve their low-carbon strategies under social learning influence to optimize utility. Stability analysis of strategy and simulation experiments reveal the following findings: (1) Greater carbon quotas reduce power generators' low-carbon transition willingness while high-energy-consuming enterprises and consumers remain unchanged. (2) Higher prices for low-carbon products offered by high-energy-consuming enterprises boost low-carbon transition motivation across all parties. (3) Increased green premiums enhance revenue for all parties but are constrained by policy and carbon pricing. (4) Both direct and indirect increases in carbon emissions negatively impact the revenue and utility for all stakeholders. (5) Increasing social learning effect fosters a shift towards low-carbon strategies, accelerating the attainment of game equilibrium, and enhancing market stability and sustainability. This research provides decision support for carbon trading policy design and low-carbon transition of energy enterprises.

Revisiting the complex time-varying effect of non-pharmaceutical interventions on COVID-19 transmission in the United States.

Introduction: Although the global COVID-19 emergency ended, the real-world effects of multiple non-pharmaceutical interventions (NPIs) and the relative contribution of individual NPIs over time were poorly understood, limiting the mitigation of future potential epidemics. Methods: Based on four large-scale datasets including epidemic parameters, virus variants, vaccines, and meteorological factors across 51 states in the United States from August 2020 to July 2022, we established a Bayesian hierarchical model with a spike-and-slab prior to assessing the time-varying effect of NPIs and vaccination on mitigating COVID-19 transmission and identifying important NPIs in the context of different variants pandemic. Results: We found that (i) the empirical reduction in reproduction number attributable to integrated NPIs was 52.0% (95%CI: 44.4, 58.5%) by August and September 2020, whereas the reduction continuously decreased due to the relaxation of NPIs in following months; (ii) international travel restrictions, stay-at-home requirements, and restrictions on gathering size were important NPIs with the relative contribution higher than 12.5%; (iii) vaccination alone could not mitigate transmission when the fully vaccination coverage was less than 60%, but it could effectively synergize with NPIs; (iv) even with fully vaccination coverage >60%, combined use of NPIs and vaccination failed to reduce the reproduction number below 1 in many states by February 2022 because of elimination of above NPIs, following with a resurgence of COVID-19 after March 2022. Conclusion: Our results suggest that NPIs and vaccination had a high synergy effect and eliminating NPIs should consider their relative effectiveness, vaccination coverage, and emerging variants.

MHD-Net: Memory-aware Hetero-modal Distillation Network for Thymic Epithelial Tumor Typing with Missing Pathology Modality.

Fusing multi-modal radiology and pathology data with complementary information can improve the accuracy of tumor typing. However, collecting pathology data is difficult since it is high-cost and sometimes only obtainable after the surgery, which limits the application of multi-modal methods in diagnosis. To address this problem, we propose comprehensively learning multi-modal radiology-pathology data in training, and only using uni-modal radiology data in testing. Concretely, a Memory-aware Hetero-modal Distillation Network (MHD-Net) is proposed, which can distill well-learned multi-modal knowledge with the assistance of memory from the teacher to the student. In the teacher, to tackle the challenge in hetero-modal feature fusion, we propose a novel spatial-differentiated hetero-modal fusion module (SHFM) that models spatial-specific tumor information correlations across modalities. As only radiology data is accessible to the student, we store pathology features in the proposed contrast-boosted typing memory module (CTMM) that achieves type-wise memory updating and stage-wise contrastive memory boosting to ensure the effectiveness and generalization of memory items. In the student, to improve the cross-modal distillation, we propose a multi-stage memory-aware distillation (MMD) scheme that reads memory-aware pathology features from CTMM to remedy missing modal-specific information. Furthermore, we construct a Radiology-Pathology Thymic Epithelial Tumor (RPTET) dataset containing paired CT and WSI images with annotations. Experiments on the RPTET and CPTAC-LUAD datasets demonstrate that MHD-Net significantly improves tumor typing and outperforms existing multi-modal methods on missing modality situations.

ZFHX3 variants cause childhood partial epilepsy and infantile spasms with favourable outcomes.

BACKGROUND: The ZFHX3 gene plays vital roles in embryonic development, cell proliferation, neuronal differentiation and neuronal death. This study aims to explore the relationship between ZFHX3 variants and epilepsy. METHODS: Whole-exome sequencing was performed in a cohort of 378 patients with partial (focal) epilepsy. A Drosophila Zfh2 knockdown model was used to validate the association between ZFHX3 and epilepsy. RESULTS: Compound heterozygous ZFHX3 variants were identified in eight unrelated cases. The burden of ZFHX3 variants was significantly higher in the case cohort, shown by multiple/specific statistical analyses. In Zfh2 knockdown flies, the incidence and duration of seizure-like behaviour were significantly greater than those in the controls. The Zfh2 knockdown flies exhibited more firing in excitatory neurons. All patients presented partial seizures. The five patients with variants in the C-terminus/N-terminus presented mild partial epilepsy. The other three patients included one who experienced frequent non-convulsive status epilepticus and two who had early spasms. These three patients had also neurodevelopmental abnormalities and were diagnosed as developmental epileptic encephalopathy (DEE), but achieved seizure-free after antiepileptic-drug treatment without adrenocorticotropic-hormone/steroids. The analyses of temporal expression (genetic dependent stages) indicated that ZFHX3 orthologous were highly expressed in the embryonic stage and decreased dramatically after birth. CONCLUSION: ZFHX3 is a novel causative gene of childhood partial epilepsy and DEE. The patients of infantile spasms achieved seizure-free after treatment without adrenocorticotropic-hormone/steroids implies a significance of genetic diagnosis in precise treatment. The genetic dependent stage provided an insight into the underlying mechanism of the evolutional course of illness.

A novel therapy for fracture healing by increasing lymphatic drainage.

Background: The musculoskeletal system contains an extensive network of lymphatic vessels. Decreased lymph flow of the draining collecting lymphatics usually occurs in clinic after traumatic fractures. However, whether defects in lymphatic drainage can affect fracture healing is unclear. Methods: To investigate the effect of lymphatic dysfunction on fracture healing, we used a selective VEGFR3 tyrosine kinase inhibitor to treat tibial fractured mice for 5 weeks versus a vehicle-treated control. To ensure successfully establishing deceased lymphatic drainage model for fractured mice, we measured lymphatic clearance by near infrared indocyanine green lymphatic imaging (NIR-ICG) and the volume of the draining popliteal lymph nodes (PLNs) by ultrasound at the whole phases of fracture healing. In addition, hindlimb edema from day 0 to day 7 post-fracture, pain sensation by Hargreaves test at day 1 post-fracture, bone histomorphometry by micro-CT and callus composition by Alcian Blue-Hematoxylin/Orange G staining at day 14 post-fracture, and bone quality by biomechanical testing at day 35 post-fracture were applied to evaluate fracture healing. To promote fracture healing via increasing lymphatic drainage, we then treated fractured mice with anti-mouse podoplanin (PDPN) neutralizing antibody or isotype IgG antibody for 1 week to observe lymphatic drainage function and assess bone repair as methods described above. Results: Compared to vehicle-treated group, SAR-treatment group significantly decreased lymphatic clearance and the volume of draining PLNs. SAR-treatment group significantly increased soft tissue swelling, and reduced bone volume (BV)/tissue volume (TV), trabecular number (Tb.N), woven bone and biomechanical properties of fracture callus. In addition, anti-PDPN treated group significantly reduced the number of CD41+ platelets in PLNs and increased the number of pulsatile lymphatic vessels, lymphatic clearance and the volume of PLNs. Moreover, anti-PDPN treated group significantly reduced hindlimb edema and pain sensation and increased BV/TV, trabecular number (Tb.Th), woven bone and biomechanical properties of fracture callus. Conclusions: Inhibition of proper lymphatic drainage function delayed fracture healing. Use of a anti-PDPN neutralizing antibody reduced lymphatic platelet thrombosis (LPT), increased lymphatic drainage and improved fracture healing. The translational potential of this article: (1) We demonstrated lymphatic drainage function is crucial for fracture healing. (2) To unblock the lymphatic drainage and prevent the risk of bleeding and mortality by blood thinner, we demonstrated PDPN neutralizing antibody is a novel and safe way forward in the treatment of bone fracture healing by eliminating LPT and increasing lymphatic drainage.

Efficacy and toxicity of immune checkpoint inhibitors combination therapy for advanced renal cell carcinoma: a systematic review and network meta-analysis.

Background: Although immune checkpoint inhibitors (ICIs) show a significant overall survival advantage over standard advanced renal cell carcinoma (aRCC) therapies, tumor response to these agents remains poor. Some studies have shown that combination therapy including an ICI appears to be the best treatment; however, the overall benefit in terms of efficacy and toxicity still needs to be assessed. Thus, we performed a network meta-analysis to evaluate the differences in the efficacy of several combinations that include an ICI to provide a basis for clinical treatment selection. Methods: We conducted a thorough search of PubMed, EMBASE, and the Cochrane Library for articles from January 2010 to June 2023. R 4.4.2 and STATA 16.0 were used to analyze data; hazard ratio (HR) and odds ratio (OR) with 95% confidence intervals (CI) were used to assess the results. Results: An indirect comparison showed that nivolumab plus cabozantinib and pembrolizumab plus lenvatinib were the most effective treatments for progression-free survival (PFS), with no significant differences between the two interventions (HR, 1.31; 95% CI, 0.96-1.78; P=0.08); rank probability showed that pembrolizumab plus lenvatinib had a 57.1% chance of being the preferred treatment. In the absence of indirect comparisons between pembrolizumab plus axitinib, nivolumab plus ipilimumab, avelumab plus axitinib, nivolumab plus cabozantinib, and pembrolizumab plus lenvatinib, pembrolizumab plus axitinib (40.2%) was the best treatment option for overall survival (OS). Compared to pembrolizumab plus lenvatinib, nivolumab plus ipilimumab (OR, 0.07; 95% CI, 0.01-0.65; P=0.02) and pembrolizumab plus axitinib (OR, 0.05; 95% CI, 0.00-0.78; P<0.001) had a lower incidence of overall adverse events (AEs). Conclusion: Pembrolizumab plus lenvatinib and pembrolizumab plus axitinib resulted in the highest PFS and OS rates, respectively. Pembrolizumab plus axitinib may be the best option when AEs are a concern. Systematic review registration: https://inplasy.com/, identifier INPLASY202410078.

Method for Estimating Road Impulse Ahead of Vehicles in Urban Environment with Microelectromechanical System Three-Dimensional Sensor.

Most automated vehicles (AVs) are equipped with abundant sensors, which enable AVs to improve ride comfort by sensing road elevation, such as speed bumps. This paper proposes a method for estimating the road impulse features ahead of vehicles in urban environments with microelectromechanical system (MEMS) light detection and ranging (LiDAR). The proposed method deploys a real-time estimation of the vehicle pose to solve the problem of sparse sampling of the LiDAR. Considering the LiDAR error model, the proposed method builds the grid height measurement model by maximum likelihood estimation. Moreover, it incorporates height measurements with the LiDAR error model by the Kalman filter and introduces motion uncertainty to form an elevation weight method by confidence eclipse. In addition, a gate strategy based on the Mahalanobis distance is integrated to handle the sharp changes in elevation. The proposed method is tested in the urban environment. The results demonstrate the effectiveness of our method.

Advanced Fog Harvesting Method by Coupling Plasma and Micro/Nano Materials.

Harvesting fog is a potential and effective way to alleviate the crisis of water resource shortage. A highly efficient and economical fog harvesting method has always been a global and common goal. Here, a promising fog harvesting method by coupling plasma and micro/nano materials is proposed, which can achieve 93% fog collection efficiency with consuming power of only 0.76 W/0.04 m2. The basic method is to utilize nanoparticles to decorate both the discharge electrode and the collecting electrode of the micro/nano electrostatic fog collector. For the discharge electrode, the nanoparticles can achieve an order of magnitude higher electric field strength and a 28.6% decrease in the operating voltage (14 kV decreases to 10 kV). For the collecting electrode, a novel composite structure of hydrophobic/hydrophilic (HB/HL) is proposed. The core advantage is the directional droplet transport at the junction of HB and HL caused by surface tension can adjust the accumulated droplets on the two sides, which avoids the droplet residue and mesh blockage in the general structure. This technology provides an innovative approach for the collection of microdroplets and a new design idea for the fog collector to deal with the water crisis.

Predicting the incidence of infectious diarrhea with symptom surveillance data using a stacking-based ensembled model.

BACKGROUND: Infectious diarrhea remains a major public health problem worldwide. This study used stacking ensemble to developed a predictive model for the incidence of infectious diarrhea, aiming to achieve better prediction performance. METHODS: Based on the surveillance data of infectious diarrhea cases, relevant symptoms and meteorological factors of Guangzhou from 2016 to 2021, we developed four base prediction models using artificial neural networks (ANN), Long Short-Term Memory networks (LSTM), support vector regression (SVR) and extreme gradient boosting regression trees (XGBoost), which were then ensembled using stacking to obtain the final prediction model. All the models were evaluated with three metrics: mean absolute percentage error (MAPE), root mean square error (RMSE), and mean absolute error (MAE). RESULTS: Base models that incorporated symptom surveillance data and weekly number of infectious diarrhea cases were able to achieve lower RMSEs, MAEs, and MAPEs than models that added meteorological data and weekly number of infectious diarrhea cases. The LSTM had the best prediction performance among the four base models, and its RMSE, MAE, and MAPE were: 84.85, 57.50 and 15.92%, respectively. The stacking ensembled model outperformed the four base models, whose RMSE, MAE, and MAPE were 75.82, 55.93, and 15.70%, respectively. CONCLUSIONS: The incorporation of symptom surveillance data could improve the predictive accuracy of infectious diarrhea prediction models, and symptom surveillance data was more effective than meteorological data in enhancing model performance. Using stacking to combine multiple prediction models were able to alleviate the difficulty in selecting the optimal model, and could obtain a model with better performance than base models.

Assessing the effectiveness of the expanded hepatitis A vaccination program in China: an interrupted time series design.

INTRODUCTION: China initialised the expanded hepatitis A vaccination programme (EHAP) in 2008. However, the effectiveness of the programme remains unclear. We aimed to comprehensively evaluate the effectiveness of EHAP in the country. METHODS: Based on the provincial data on the incidence of hepatitis A (HepA), the population and meteorological variables in China, we developed interrupted time series (ITS) models to estimate the effectiveness of EHAP with the autocorrelation, seasonality and the meteorological confounders being controlled. Results were also stratified by economic zones, age groups and provinces. RESULTS: We found a 0.9% reduction (RR=0.991, 95% CI: 0.990 to 0.991) in monthly HepA incidence after EHAP, which was 0.3% greater than the reduction rate before EHAP in China. Across the three economic regions, we found a 1.1% reduction in HepA incidence in both central and western regions after EHAP, which were 0.3% and 1.2% greater than the reduction rates before EHAP, respectively. We found a decreased reduction rate for the eastern region. In addition, we found generally increased reduction rate after EHAP for age groups of 0-4, 5-14 and 15-24 years. However, we found decreased reduction rate among the 25-64 and ≥65 years groups. We found a slight increased rate after EHAP in Shanxi Province but not elsewhere. CONCLUSION: Our finding provides comprehensive evidence on the effectiveness of EHAP in China, particularly in the central and western regions, and among the population aged 0-24 years old. This study has important implications for the adjustment of vaccination strategies for other regions and populations.

MGIML: Cancer Grading with Incomplete Radiology-Pathology Data via Memory Learning and Gradient Homogenization.

Taking advantage of multi-modal radiology-pathology data with complementary clinical information for cancer grading is helpful for doctors to improve diagnosis efficiency and accuracy. However, radiology and pathology data have distinct acquisition difficulties and costs, which leads to incomplete-modality data being common in applications. In this work, we propose a Memory-and Gradient-guided Incomplete Modal-modal Learning (MGIML) framework for cancer grading with incomplete radiology-pathology data. Firstly, to remedy missing-modality information, we propose a Memory-driven Hetero-modality Complement (MH-Complete) scheme, which constructs modal-specific memory banks constrained by a coarse-grained memory boosting (CMB) loss to record generic radiology and pathology feature patterns, and develops a cross-modal memory reading strategy enhanced by a fine-grained memory consistency (FMC) loss to take missing-modality information from well-stored memories. Secondly, as gradient conflicts exist between missing-modality situations, we propose a Rotation-driven Gradient Homogenization (RG-Homogenize) scheme, which estimates instance-specific rotation matrices to smoothly change the feature-level gradient directions, and computes confidence-guided homogenization weights to dynamically balance gradient magnitudes. By simultaneously mitigating gradient direction and magnitude conflicts, this scheme well avoids the negative transfer and optimization imbalance problems. Extensive experiments on CPTAC-UCEC and CPTAC-PDA datasets show that the proposed MGIML framework performs favorably against state-of-the-art multi-modal methods on missing-modality situations.

Boron nitride nanoplate-based strand exchange amplification with enhanced sensitivity and rapidity for quantitative detection of Staphylococcus aureus in food samples.

Staphylococcus aureus is one of the most common foodborne pathogens that can cause serious food poisoning and infectious diseases in humans. Standard identification approaches include nucleic acid amplification, but current amplification tools suffer from low amplification efficiency, resulting in the risk of low sensitivity and long detection time. Herein, boron nitride nanoplates (BNNPs) were chosen as an additive for enhancing the sensitivity and rapidity of strand exchange amplification (SEA), thereby successfully expanding the application of nucleic acid detection for detecting Staphylococcus aureus in food samples. As a result, SEA based on boron nitride nanoplates (BNNP-SEA) was employed for sensitive and rapid detection of foodborne pathogen Staphylococcus aureus. Compared with classical SEA, the BNNP-based SEA assay was more than 10-fold sensitive, and the detection time was reduced by 15 minutes. The optimized BNNP-based SEA shows a wide linear range from 40 pg to 50 ng in a diluted solution of the target DNA with a low detection limit of 40 pg. Moreover, the BNNP-based SEA achieves the quantitative detection of Staphylococcus aureus in different food samples (pork, beef, mutton, duck, milk and shrimp). In contrast to the classical SEA, the BNNP-based SEA method enabled sensitive and rapid detection of Staphylococcus aureus in the above food samples at concentrations as low as 5 × 103 CFU mL-1. The BNNP-based SEA assay is specific, sensitive and reliable, offering a valuable diagnostic technology for routine analysis in food safety research.

Prevalence and genetic characteristics of Salmonella enterica serovar Meleagridis from animals and humans.

Salmonella enterica serovar Meleagridis (S. Meleagridis) is a non-typhoidal Salmonella serotype commonly found in food and humans. In this study, we investigated 61 Chinese S. Meleagridis isolates from various sources, predominantly from pigs and pig products. Additionally, the serotype was also identified in samples from human infections. Whole-genome sequencing analysis of these isolates, combined with 10 isolates from other countries, demonstrated that the Chinese isolates formed a distinct Cluster C, further divided into two subclusters (Cluster C-1 and Cluster C-2) based on cgMLST analysis. CRISPR typing divided the 61 isolates into three CRISPR types (MCT1, MCT2, MCT3), belonging to Cluster I (96.7%, 59/61) and Cluster II (3.3%, 2/61), which corresponded to Cluster C-2 and Cluster C-1, respectively. Among the 48 identified spacers, the spacer SoeB5 was the only target differentiating MCT1 and MCT2 isolates of Cluster I. MelB12 and MelB13, identified in US and Denmark isolates, were not found among the 61 Chinese isolates. Examination of antimicrobial resistance gene profiles and their genetic contexts uncovered the presence of IncR plasmids in 43 (70.5%, 43/61) isolates within Cluster C, conferring resistance to tetracycline and trimethoprim/sulfamethoxazole. Homology analysis of spacers showed that 12 spacers exhibited similarity to sequences in phages or plasmids. Additionally, five spacers showed homology to sequences in plasmids from other Salmonella serotypes, suggesting their potential role in helping S. Meleagridis resist against Salmonella isolates carrying similar plasmids. The comprehensive analysis of CRISPR, cgMLST, and antimicrobial resistance in S. Meleagridis highlights the pig reservoir as a crucial factor in the evolution and transmission of this serotype to humans.

Photosensitizer-free visible-light-promoted glycosylation enabled by 2-glycosyloxy tropone donors.

Photochemical glycosylation has attracted considerable attention in carbohydrate chemistry. However, to the best of our knowledge, visible-light-promoted glycosylation via photoactive glycosyl donor has not been reported. In the study, we report a photosensitizer-free visible-light-mediated glycosylation approach using a photoactive 2-glycosyloxy tropone as the donor. This glycosylation reaction proceeds at ambient temperature to give a wide range of O-glycosides or oligosaccharides with yields up to 99%. This method is further applied in the stereoselective preparation of various functional glycosyl phosphates/phosphosaccharides, the construction of N-glycosides/nucleosides, and the late-stage glycosylation of natural products or pharmaceuticals on gram scales, and the iterative synthesis of hexasaccharide. The protocol features uncomplicated conditions, operational simplicity, wide substrate scope (58 examples), excellent compatibility with functional groups, scalability of products (7 examples), and high yields. It provides an efficient glycosylation method for accessing O/N-glycosides and glycans.

A 3D-Printed Cuttlefish Bone Elastomeric Sponge Rapidly Controlling Noncompressible Hemorrhage.

Developing a self-expanding hemostatic sponge with high blood absorption and rapid shape recovery for noncompressible hemorrhage remains a challenge. In this study, a 3D-printed cuttlefish bone elastomeric sponge (CBES) is fabricated, which combined ordered channels and porous structures, presented tunable mechanical strength, and shape memory potentials. The incorporation of cuttlefish bone powder (CBp) plays key roles in concentrating blood components, promoting aggregation of red blood cells and platelets, and activating platelets, which makes CBES show enhanced hemostatic performance compared with commercial gelatin sponges in vivo. Moreover, CBES promotes more histiocytic infiltration and neovascularization in the early stage of degradation than gelatin sponges, which is conducive to the regeneration and repair of injured tissue. To conclude, CBp loaded 3D-printed elastomeric sponges can promote coagulation, present the potential to guide tissue healing, and broaden the hemostatic application of traditional Chinese medicine.

Converging bioprinting and organoids to better recapitulate the tumor microenvironment.

Bioprinting shows excellent potential for preclinical tumor modeling, with significant advantages over 2D cell cultures in replicating the tumor microenvironment (TME). Recently, the use of tumor organoids in bioprinting models has emerged as a groundbreaking approach to simulate volumetric tumor tissues. This synergetic fabrication method leverages the advantages of the spatial and geometric control of bioprinting to assemble heterogeneous TME components, while tumor organoids maintain collective cell behaviors. In this review, we provide a landscape of the latest progress on the convergence of 3D bioprinting and tumor organoids. Furthermore, we discuss the potential to incorporate organ-on-a-chip with bioprinting tumor organoids to improve the biomimicry and predictability of therapeutic performance. Lastly, we address the challenges to personalized medicine and predictive clinical integration.

Development and validation of a parsimonious prediction model for positive urine cultures in outpatient visits.

Urine culture is often considered the gold standard for detecting the presence of bacteria in the urine. Since culture is expensive and often requires 24-48 hours, clinicians often rely on urine dipstick test, which is considerably cheaper than culture and provides instant results. Despite its ease of use, urine dipstick test may lack sensitivity and specificity. In this paper, we use a real-world dataset consisting of 17,572 outpatient encounters who underwent urine cultures, collected between 2015 and 2021 at a large multi-specialty hospital in Abu Dhabi, United Arab Emirates. We develop and evaluate a simple parsimonious prediction model for positive urine cultures based on a minimal input set of ten features selected from the patient's presenting vital signs, history, and dipstick results. In a test set of 5,339 encounters, the parsimonious model achieves an area under the receiver operating characteristic curve (AUROC) of 0.828 (95% CI: 0.810-0.844) for predicting a bacterial count ≥ 105 CFU/ml, outperforming a model that uses dipstick features only that achieves an AUROC of 0.786 (95% CI: 0.769-0.806). Our proposed model can be easily deployed at point-of-care, highlighting its value in improving the efficiency of clinical workflows, especially in low-resource settings.