Cu,Ce-containing phosphotungstates as laccase-like nanozyme for colorimetric detection of Cr(VI) and Fe(â ¢).

Herein, a nanocomposite of Cu,Ce-containing phosphotungstates (Cu,Ce-PTs) with outstanding laccase-like activity was fabricated via a one-pot microwave-assisted hydrothermal method. Notably, it was discovered that both Fe3+ and Cr6+ could significantly enhance the electron transfer rates of Ce3+ and Ce4+, along with generous Cu2+ with high catalytic activity, thereby promoting the laccase-like activity of Cu,Ce-PTs. The proposed system can be used for the detection of Fe3+ and Cr6+ in a range of 0.667-333.33 µg/mL and 0.033-33.33 µg/mL with a low detection limit of 0.135 µg/mL and 0.0288 µg/mL, respectively. The proposed assay exhibits excellent reusability and selectivity and can be used in traditional Chinese medicine samples analysis.

Robo-Matter towards reconfigurable multifunctional smart materials.

Maximizing materials utilization efficiency via enhancing their reconfigurability and multifunctionality offers a promising avenue in addressing the global challenges in sustainability. To this end, significant efforts have been made in developing reconfigurable multifunctional smart materials, which can exhibit remarkable behaviors such as morphing and self-healing. However, the difficulty in efficiently manipulating and controlling matter at the building block level with manageable cost and complexity, which is crucial to achieving superior responsiveness to environmental clues and stimuli, has significantly hindered the further development of such smart materials. Here we introduce a concept of Robo-Matter, which can be activated and controlled through external information exchange at the building block level, to enable a high-level of controllability, mutability and versatility for reconfigurable multifunctional smart materials. Using specially designed micro-robot building blocks with symmetry-breaking active motion modes, tunable anisotropic interactions, and interactive coupling with a programmable spatial-temporal dynamic light field, we demonstrate an emergent Robot-Matter duality, which enables a spectrum of desirable behaviors spanning from matter-like properties such as ultra-fast self-assembly and adaptivity, to robot-like properties including active force output, smart healing, smart morphing and infiltration. Our work demonstrates a promising direction for designing next-generation smart materials and large-scale robotic swarms.

Should I stay or should I go again: Multiple switching between fee-for-service Medicare and Medicare advantage among older beneficiaries.

OBJECTIVE: To evaluate whether having previously disenrolled from Medicare Advantage (MA) is associated with lower hazards of future MA enrollment. DATA SOURCES AND STUDY SETTING: Secondary data from Medicare. STUDY DESIGN: We examined beneficiaries with baseline FFS enrollment from 2017-2019 using a 20% sample of Medicare claims. Cox proportional hazard models were used to examine the association of prior MA enrollment (in the three years prior to baseline FFS enrollment) with MA re-enrollment, and whether this association is modified by Alzheimer's Disease and Related Dementias (ADRD), prior nursing home use, chronic illness, dual eligible status, and availability of MA plans and quality. DATA COLLECTION: Not applicable. PRINCIPAL FINDINGS: Overall, 3.3% of beneficiaries switched to MA annually. Of those with prior MA enrollment, MA switching percentages were 9.0%, 4.6%, and 6.8% for those whose most recent MA enrollments were 1, 2, and 3 years prior to their baseline FFS year. Comparatively, the switching percentages was 3.2% for those with no prior MA enrollment. The hazards of switching to MA were 2.73 (p < 0.001), 1.29 (p < 0.001), and 1.97 (p < 0.001) times greater than remaining in FFS for beneficiaries whose most recent MA enrollments were one, two, and three years prior to their baseline FFS year. Hazards of switching were generally similar between those with and without ADRD, stratified by recency in prior MA experience, except those with dual eligibility. Among those with ADRD, switching hazards were greatest for 3 years prior MA enrollees in counties with the fewest available (HR: 3.84, p < 0.001) and lowest-rated plans (HR: 4.02, p < 0.001). CONCLUSIONS: Recency of switching from MA to FFS was the strongest predictor of a FFS-to-MA switch, identifying a population of beneficiaries who multiply switch regardless of health status or MA access. Future health policy considerations should more closely examine the vulnerabilities and long-term outcomes of this population.

Neutrophil extracellular traps and cardiovascular disease: Associations and potential therapeutic approaches.

Cardiovascular disease (CVD) is a significant global health concern, ranking among the top five causes of disability-adjusted life-years (DALY) in 190 countries and territories. Neutrophils, key players in the innate immune system, combat infections by releasing neutrophil extracellular traps (NETs) composed of DNA, histones, elastase, myeloperoxidase, and antimicrobial peptides. This paper explores the relationship between NETs and cardiovascular diseases, focusing on conditions such as heart failure, pulmonary hypertension, atrial fibrillation, and ischemia-reperfusion injury. Particularly, it delves into the impact of NETs on atrial fibrillation and pulmonary hypertension, as well as the role of myeloperoxidase (MPO) and neutrophil elastase (NE) in these diseases. Furthermore, the potential of targeting NETs for the treatment of cardiovascular diseases is discussed.

Machine Learning Model to Predict Early Recurrence in Patients with Perihilar Cholangiocarcinoma Planned Treatment with Curative Resection: A Multicenter Study.

BACKGROUND & AIMS: Early recurrence is the leading cause of death for patients with perihilar cholangiocarcinoma (pCCA) after surgery. Identifying high-risk patients preoperatively is important. This study aimed to construct a preoperative prediction model for the early recurrence of pCCA patients planned treatment with curative resection. METHODS: This study ultimately enrolled 400 pCCA patients after curative resection in five hospitals between 2013 and 2019. They were randomly divided into training (n=300) and testing groups (n=100) at a ratio of 3:1. Associated variables were identified via LASSO regression. Four machine learning models were constructed: support vector machine (SVM), random forest (RF), logistic regression, and K-nearest neighbors (KNN). The predictive ability of the models was evaluated via receiver operating characteristic (ROC) curves, precision-recall curve (PRC) curves, and decision curve analysis (DCA). KaplanMeier survival curves were drawn for the high/low-risk population. RESULTS: Five factors, CA19-9, tumor size, total bilirubin, hepatic artery invasion, and portal vein invasion, were selected by LASSO regression. In both the training and testing groups, the ROC curve (AUC: 0.983 vs 0.952) and the PRC (0.981 vs 0.939) showed that RF was the best. The cutoff value for distinguishing high- and low-risk patients was 0.51. KM survival curves revealed that in both groups, there was a significant difference in RFS between high- and low-risk patients (P<0.001). CONCLUSION: This study used preoperative variables from a large, multicenter database to construct a machine learning model that could effectively predict the early recurrence of pCCA patients planned treatment with curative resection and help clinicians make better treatment decisions.

Voluntarily stopping eating and drinking (VSED): A systematic mixed-methods review focusing on the carers' experiences.

BACKGROUND: Voluntary stopping of eating and drinking (VSED) is a way to end one's life prematurely. We synthesized the empirical data on VSED. METHODS: In this systematic mixed-methods review, we searched MEDLINE, CINAHL, PsycINFO, Google Scholar, and BELIT for English and German articles published between January 1, 2013 and November 12, 2021. We included quantitative and qualitative research examining the experiences, attitudes, and knowledge of people confronted with VSED. We inductively analyzed the data after quantitative data transformation. We assessed quality and confidence using the Mixed-Methods Appraisal Tool and GRADE-CERQual approach, respectively. This study was registered in PROSPERO (CRD42022283743). FINDINGS: We identified 22 eligible articles, comprising 16 studies. The participants were healthcare professionals and relatives, but not individuals undertaking VSED. We present here our findings on the challenges of accompanying VSED, positive experiences with VSED, and the identified needs. Support during VSED is needed at multiple levels (medical care, family relief, course planning), and the willingness to accompany VSED is very high among healthcare professionals. However, there are several problems, the most obvious being the lack of knowledge and expertise regarding VSED, placing a great burden on families and professionals. The confidence in the review findings ranged from moderate to low. INTERPRETATION: Evidence-based guidance, in-depth knowledge, and training of healthcare professionals can greatly reduce the burden and fear among individuals accompanying VSED.

Vinylene-Linking of Polycyclic Aromatic Hydrocarbons to π-Extended Two-Dimensional Covalent Organic Framework Photocatalyst for H2O2 Synthesis.

Polycyclic aromatic hydrocarbons (PAHs) hold the predominant role either as individual molecules or building blocks in the field of organic semiconductors or nanocarbons. Connecting PAHs via sp2-carbon bridges to form high-crystalline p-extended structures is highly desired not only for enlarging the regimes of two-dimensional materials but also for achieving exceptional properties/functions. In this work, we developed 5,10-dimethyl-4,9-diazapyrene as a key monomer, whose two methyl groups at the positions adjacent to nitrogen atoms, can helpfully increase the solubility, and serve as the active connection sites. In the presence of organic acids, this monomer enables smoothly conducting Knoevenagel condensation to form two vinylene-linked PAH-cored COFs, which show high-crystalline honeycomb structures with large surface areas up to 1238 m2 g-1. Owing to the direct connection mode of PAH building blocks with vinylene, the as-prepared COFs possess spatially extended p-conjugation and substantial semiconducting properties. Consequently, their visible-light photocatalysis with exceptional activity and durability was manifested to generate H2O2 up to 3820 µmol g-1 h-1 in pure water, and even 17080 µmol g-1 h-1 using benzyl alcohol as a hole sacrificial agent.

Development and validation of a deep learning-based survival prediction model for pediatric glioma patients: A retrospective study using the SEER database and Chinese data.

OBJECTIVE: Develop a time-dependent deep learning model to accurately predict the prognosis of pediatric glioma patients, which can assist clinicians in making precise treatment decisions and reducing patient risk. STUDY DESIGN: The study involved pediatric glioma patients from the Surveillance, Epidemiology, and End Results (SEER) Registry (2000-2018) and Tangdu Hospital in China (2010-2018) within specific time frames. For training, we selected two neural network-based algorithms (DeepSurv, neural multi-task logistic regression [N-MTLR]) and one ensemble learning-based algorithm (random survival forest [RSF]). Additionally, a multivariable Cox proportional hazard (CoxPH) model was developed for comparison purposes. The SEER dataset was randomly divided into 80 % for training and 20 % for testing, while the Tangdu Hospital dataset served as an external validation cohort. Super-parameters were fine-tuned through 1000 repeated random searches and 5-fold cross-validation on the training cohort. Model performance was assessed using the concordance index (C-index), Brier score, and Integrated Brier Score (IBS). Furthermore, the accuracy of predicting survival at 1, 3, and 5 years was evaluated using receiver operating characteristic (ROC) curves, calibration curves, and the area under the ROC curves (AUC). The generalization ability of the model was assessed using the C-index of the Tangdu Hospital data, ROC curves for 1, 3, and 5 years, and AUC values. Lastly, decision curve analysis (DCA) curves for 1, 3, and 5-year time frames are provided to assess the net benefits across different models. RESULTS: A total of 9532 patients with pediatric glioma were included in this study, comprising 9274 patients from the SEER database and 258 patients from Tangdu Hospital in China. The average age at diagnosis was 9.4 ± 6.2 years, and the average survival time was 96 ± 66 months. Through comprehensive performance comparison, the DeepSurv model demonstrated the highest effectiveness, with a C-index of 0.881 on the training cohort. Furthermore, it exhibited excellent accuracy in predicting the 1-year, 3-year, and 5-year survival rates (AUC: 0.903-0.939). Notably, the DeepSurv model also achieved remarkable performance and accuracy on the Chinese dataset (C-index: 0.782, AUC: 0.761-0.852). Comprehensive analysis of DeepSurv, N-MTLR, and RSF revealed that tumor stage, radiotherapy, histological type, tumor size, chemotherapy, age, and surgical method are all significant factors influencing the prognosis of pediatric glioma. Finally, an online version of the pediatric glioma survival predictor based on the DeepSurv model has been established and can be accessed through https://pediatricglioma-tangdu.streamlit.app. CONCLUSIONS: The DeepSurv model exhibits exceptional efficacy in predicting the survival of pediatric glioma patients, demonstrating strong performance in discrimination, calibration, stability, and generalization. By utilizing the online version of the pediatric glioma survival predictor, which is based on the DeepSurv model, clinicians can accurately predict patient survival and offer personalized treatment options.

Divergent syntheses of complex Veratrum alkaloids.

The Veratrum alkaloids are a class of highly intricate natural products renowned for their complex structural and stereochemical characteristics, which underlie a diverse array of pharmacological activities ranging from anti-hypertensive properties to antimicrobial effects. These properties have generated substantial interest among both synthetic chemists and biologists. While numerous advancements have been made in the synthesis of jervanine and veratramine subtypes over the past 50 years, the total synthesis of highly oxidized cevanine subtypes has remained relatively scarce. Building on the efficiency of our previously developed strategy for constructing the hexacyclic carbon skeleton of the Veratrum alkaloid family via a stereoselective intramolecular Diels-Alder reaction and radical cyclization, here we show the development of a unified synthetic approach to access highly oxidized Veratrum alkaloids. This includes the total synthesis of (-)-zygadenine, (-)-germine, (-)-protoverine and the alkamine of veramadine A, by capitalizing on a meticulously designed sequence of redox manipulations and a late-stage neighboring-group participation strategy.

Pan-genome analysis of GT64 gene family and expression response to Verticillium wilt in cotton.

BACKGROUND: The GT64 subfamily, belonging to the glycosyltransferase family, plays a critical function in plant adaptation to stress conditions and the modulation of plant growth, development, and organogenesis processes. However, a comprehensive identification and systematic analysis of GT64 in cotton are still lacking. RESULTS: This study used bioinformatics techniques to conduct a detailed investigation on the GT64 gene family members of eight cotton species for the first time. A total of 39 GT64 genes were detected, which could be classified into five subfamilies according to the phylogenetic tree. Among them, six genes were found in upland cotton. Furthermore, investigated the precise chromosomal positions of these genes and visually represented their gene structure details. Moreover, forecasted cis-regulatory elements in GhGT64s and ascertained the duplication type of the GT64 in the eight cotton species. Evaluation of the Ka/Ks ratio for similar gene pairs among the eight cotton species provided insights into the selective pressures acting on these homologous genes. Additionally, analyzed the expression profiles of the GT64 gene family. Overexpressing GhGT64_4 in tobacco improved its disease resistance. Subsequently, VIGS experiments conducted in cotton demonstrated reduced disease resistance upon silencing of the GhGT64_4, may indicate its involvement in affecting lignin and jasmonic acid biosynthesis pathways, thus impacting cotton resistance. Weighted Gene Co-expression Network Analysis (WGCNA) revealed an early immune response against Verticillium dahliae in G. barbadense compared to G. hirsutum. Quantitative Reverse Transcription Polymerase Chain Reaction (qRT-PCR) analysis indicated that some GT64 genes might play a role under various biotic and abiotic stress conditions. CONCLUSIONS: These discoveries enhance our knowledge of GT64 family members and lay the groundwork for future investigations into the disease resistance mechanisms of this gene in cotton.

Network Pharmacology and Experimental Validation of Jinwei Decoction for Enhancement of Glucocorticoid Anti-Inflammatory Effect in COPD through miR-155-5p.

BACKGROUND: Jinwei decoction can enhance the anti-inflammatory effect of glucocorticoid (GC) on chronic obstructive pulmonary disease (COPD) by restoring the activity of human histone deacetylase-2 (HDAC2). However the upstream mechanism of Jinwei decoction on HDAC2 expression is not clear. OBJECTIVE: To explore the target of Jinwei decoction to enhance the anti-inflammatory effect of GC on COPD through microRNA155-5p (miR-155-5p) by network pharmacology and experimental verification. METHODS: The TCMSP database was used to screen active ingredients and target genes of Jinwei decoction, and miRWalk2.0 was used to predict downstream target genes of miR-155-5p. COPD-related genes were identified by searching GeneCards, Grugbank and OMIM databases; Venny 2.1 was used to screen intersection genes; Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways of intersection genes were analyzed by R software. Protein-protein interactions (PPIs) were analyzed by Cytoscape 3.7.2 software to identify core genes. Finally, interactions between main compounds and potential targets were verified by molecular docking. A COPD cell model was established by 5% cigarette smoke extract (CSE)- induced bronchial epithelial cell (BEAS-2B), and the results of network pharmacology were verified by in vitro experiments. RESULTS: Two hundred thirty-one active ingredients, 352 Jinwei decoction drug targets, 5949 miR-155-5p target genes, 8286 COPD target genes, and 127 intersection genes were identified. Twelve core proteins of PPI networks may be involved. GO enrichment analysis showed that regulation of membrane potential， response to steroid hormone, and histone modification were involved; KEGG pathway enrichment analysis concentrated in the PI3K-Akt, mitogen-activated protein kinase (MAPK), HIF-1, and other signaling pathways. The molecular docking results showed that quercetin, luteolin and stigmasterol have higher affinity with PTGS2, HIF1A and AKT1. The results of cell experiments revealed that Jinwei decoction not only enhances the anti- inflammatory effect of GC in the COPD cell model but also reverses the high expression of miR-155-5p、PI3k、Akt, and low expression of HDAC2, thereby inhibiting the inflammatory response of COPD. CONCLUSION: Jinwei decoction can regulate HDAC2 activity and enhance the anti-inflammatory effect of GC on COPD by modulating miR-155-5p. Its mechanism of action may be related to its effect on the PI3K-Akt through miR-155-5p.

Synergistic Effect of Two Peptaibols from Biocontrol Fungus Trichoderma longibrachiatum Strain 40418 on CO-Induced Plant Resistance.

Trichoderma longibrachiatum is a filamentous fungus used as a biological control agent against different plant diseases. The multifunctional secondary metabolites synthesized by Trichoderma, called peptaibols, have emerged as key elicitors in plant innate immunity. This study obtained a high-quality genome sequence for the T. longibrachiatum strain 40418 and identified two peptaibol biosynthetic gene clusters using knockout techniques. The two gene cluster products were confirmed as trilongin AIV a (11-residue) and trilongin BI (20-residue) using liquid chromatography coupled with tandem mass spectrometry. Further investigations revealed that these peptaibols induce plant resistance to Pseudomonas syringae pv tomato (Pst) DC3000 infection while triggering plant immunity and cell death. Notably, the two peptaibols exhibit synergistic effects in plant-microbe signaling interactions, with trilongin BI having a predominant role. Moreover, the induction of tomato resistance against Meloidogyne incognita showed similarly promising results.

Carbon dots based AuAg@AuNPs with oxidase-like activity for SERS dual-readouts detection of D-penicillamine.

An oxidase (OXD) -like AuAg@AuNPs nanozyme was prepared by Au seeds growth using dopamine carbon dots as reducing and capping agents. The AuAg@AuNPs show excellent OXD-like and surface-enhanced Raman spectroscopy (SERS) activities and can oxidize the non-Raman-active leucomalachite green (LMG) into the Raman-active malachite green (MG). The research displays that D-penicillamine (D-PA) can effectively inhibit the OXD-like activity of Au@AgNPs and enhance the SERS signals as substrate. It is attributed to the formation of S-Au bond due to thiol (-SH) in D-PA. Therefore, a highly sensitive and specific SERS dual-readout sensing platform was proposed to assay D-PA with a limit of detection of 0.1 µg/mL (direct SERS mode) and 6.64 µg/L (indirect SERS mode). This approach was successfully used to determine D-PA in actual pharmaceutical formulations.

Pixel-based small-window parametric ultrasound imaging for liver tumor characterization.

Purpose: Characterizing liver tumors remains a challenge in clinical practice. Ultrasound parametric imaging based on statistical distribution can enhance image contrast compared with B-mode imaging, requiring scatterers following specific distributions. This study proposes a pixel-based small-window parametric ultrasound imaging method using weighted horizontally normalized Shannon entropy (WhNSE) and fuzzy entropy (FE) to improve detectability liver tumor. Methods: Pixel-based parametric imaging requires a sliding window to traverse across the B-mode image with the step of one pixel, while calculating the entropy by the pixel values in the window. The entropy is assigned to the center pixel of the sliding window. The entropy image is obtained after getting the entropy values of all pixels. FE and WhNSE are two novel entropies first applied to parametric imaging. The detection abilities of regions of interest (ROI) and the contrast-to-noise ratio (CNR) were evaluated through simulations and clinical explorations. Results: In simulations, FE imaging showed the highest improvement in detecting hyperechoic ROIs, with a CNR gain up to 457.31% (p < 0.01) in simulations. WhNSE imaging demonstrated the best performance in hyperechoic ROI detection, with a CNR of 1.607 ± 0.816 (p = 0.05), significantly higher than B-mode images. Conclusions: The proposed pixel-based parametric imaging method based on fuzzy entropy and weighted horizontally normalized Shannon entropy both effectively enhance the contrast and detectability of ultrasound images. The imaging enhancement method of the pixel-based fuzzy entropy imaging with proper parameters got better detection performance, due to the consideration of the relationship of neighboring pixels.

Deciphering the role of rhizosphere microbiota in modulating disease resistance in cabbage varieties.

BACKGROUND: Cabbage Fusarium wilt (CFW) is a devastating disease caused by the soil-borne fungus Fusarium oxysporum f. sp. conglutinans (Foc). One of the optimal measures for managing CFW is the employment of tolerant/resistant cabbage varieties. However, the interplay between plant genotypes and the pathogen Foc in shaping the rhizosphere microbial community, and the consequent influence of these microbial assemblages on biological resistance, remains inadequately understood. RESULTS: Based on amplicon metabarcoding data, we observed distinct differences in the fungal alpha diversity index (Shannon index) and beta diversity index (unweighted Bray-Curtis dissimilarity) within the rhizosphere of the YR (resistant to Foc) and ZG (susceptible to Foc) cabbage varieties, irrespective of Foc inoculation. Notably, the Shannon diversity shifts in the resistant YR variety were more pronounced following Foc inoculation. Disease-resistant plant variety demonstrate a higher propensity for harboring beneficial microorganisms, such as Pseudomonas, and exhibit superior capabilities in evading harmful microorganisms, in contrast to their disease-susceptible counterparts. Furthermore, the network analysis was performed on rhizosphere-associated microorganisms, including both bacteria and fungi. The networks of association recovered from YR exhibited greater complexity, robustness, and density, regardless of Foc inoculation. Following Foc infection in the YR rhizosphere, there was a notable increase in the dominant bacterium NA13, which is also a hub taxon in the microbial network. Reintroducing NA13 into the soil significantly improved disease resistance in the susceptible ZG variety, by directly inhibiting Foc and triggering defense mechanisms in the roots. CONCLUSIONS: The rhizosphere microbial communities of these two cabbage varieties are markedly distinct, with the introduction of the pathogen eliciting significant alterations in their microbial networks which is correlated with susceptibility or resistance to soil-borne pathogens. Furthermore, we identified a rhizobacteria species that significantly boosts disease resistance in susceptible cabbages. Our results indicated that the induction of resistance genes leading to varied responses in microbial communities to pathogens may partly explain the differing susceptibilities of the cabbage varieties tested to CFW. Video Abstract.

A pentavalent peptide vaccine elicits Aß and tau antibodies with prophylactic activity in an Alzheimer's disease mouse model.

Amyloid-ß (Aß) and hyperphosphorylated tau protein are targets for Alzheimer's Disease (AD) immunotherapies, which are generally focused on single epitopes within Aß or tau. However, due to the complexity of both Aß and tau in AD pathogenesis, a multipronged approach simultaneously targeting multiple epitopes of both proteins could overcome limitations of monotherapies. Herein, we propose an active AD immunotherapy based on a nanoparticle vaccine comprising two Aß peptides (1-14 and pyroglutamate pE3-14) and three tau peptides (centered on phosphorylated pT181, pT217 and pS396/404). These correspond to both soluble and aggregated targets and are displayed on the surface of immunogenic liposomes in an orientation that maintains reactivity with epitope-specific monoclonal antibodies. Intramuscular immunization of mice with individual epitopes resulted in minimally cross-reactive antibody induction, while simultaneous co-display of 5 antigens ("5-plex") induced antibodies against all epitopes without immune interference. Post-immune sera recognized plaques and neurofibrillary tangles from human AD brain tissue. Vaccine administration to 3xTg-AD mice using a prophylactic dosing schedule inhibited tau and amyloid pathologies and resulted in improved cognitive function. Immunization was well tolerated and did not induce antigen-specific cellular responses or persistent inflammatory responses in the peripheral or central nervous system. Antibody levels could be reversed by halting monthly vaccinations. Altogether, these results indicate that active immune therapies based on nanoparticle formulations of multiple Aß and tau epitopes warrant further study for treating early-stage AD.

Evaluating approach uncertainties of quantitative detection of SARS-CoV-2 in wastewater: Concentration, extraction and amplification.

Substantial uncertainties pose challenges to the accuracy of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) quantification in wastewater. We conducted a comprehensive evaluation of two concentration methods, three nucleic acid extraction methods, and the amplification performance of eight primer-probe sets. Our results showed that the two concentration methods exhibited similar recovery rates. Specifically, using a 30 kDa cut-off ultrafilter and a centrifugal force of 2500 g achieved the highest virus recovery rates (27.32 ± 8.06 % and 26.37 ± 7.77 %, respectively), with lower corresponding quantification uncertainties of 29.51 % and 29.47 % in ultrafiltration methods. Similarly, a 15 % PEG concentration with 1.5 M NaCl markedly improved virus recovery (26.76 ± 5.92 % and 28.47 ± 6.74 %, respectively), and reducing variation to 22.16 % and 23.66 % in the PEG precipitation method. Additionally, employing a vigorous bead-beating approach at 6 m/s during viral RNA extraction significantly increased RNA yield, with an efficiency reaching up to 82.18 %. Among the evaluated eight primer-probe sets, the E_Sarbeco primer-probe set provided the most stable and consistent quantitative results across various sample matrices. These findings are crucial for establishing robust viral quantification protocols and enhancing methodological precision for effective wastewater surveillance, enabling sensitive and precise detection of SARS-CoV-2.

Predicting the Risk of In-Hospital Mortality in Traumatic Brain Injury Patients on Invasive Mechanical Ventilation in the Intensive Care Unit: Construction and Validation of an Online Nomogram.

OBJECTIVE: To explore mortality risk factors and to construct an online nomogram for predicting in-hospital mortality in traumatic brain injury (TBI) patients receiving invasive mechanical ventilation (IMV) in intensive care unit (ICU). METHODS: We retrospectively analyzed TBI patients on IMV in ICU from Medical Information Mart for Intensive Care IV database and 2 hospitals. Least absolute shrinkage and selection operation regression and multiple logistic regression were used to detect predictors of in-hospital mortality and to construct an online nomogram. The predictive performance of nomogram was evaluated using area under the receiver operating characteristic curves (AUC), calibration curves, decision curve analysis, and clinical impact curves. RESULTS: Five hundred ten from Medical Information Mart for Intensive Care IV database were enrolled for nomogram construction (80%, n = 408) and internal validation (20%, n = 102). One hundred eighty-five from 2 hospitals were enrolled for external validation. Least absolute shrinkage and selection operation-logistic regression revealed predictors of in-hospital mortality among TBI patients on IMV in ICU included Glasgow Coma Scale (GCS) after ICU admission, Acute Physiology Score III (APS III) after ICU admission, neutrophil and lymphocyte ratio after IMV, blood urea nitrogen after IMV, arterial serum lactate after IMV, and in-hospital tracheotomy. The AUC, calibration curves, decision curve analysis, and clinical impact curves indicated the nomogram had good discrimination, calibration, clinical benefit, and applicability. The multimodel comparisons revealed the nomogram had higher AUC than GCS, APS III, and Simplified Acute Physiology Score II. CONCLUSIONS: We constructed and validated an online nomogram based on routinely recorded factors at admission to ICU and at the beginning of IMV to target prediction of in-hospital mortality among TBI patients on IMV in ICU.

Soft Crawling Microrobot Based on Flexible Optoelectronics Enabling Autonomous Phototaxis in Terrestrial and Aquatic Environments.

Many organisms move directly toward light for prey hunting or navigation, which is called phototaxis. Mimicking this behavior in robots is crucially important in the energy industry and environmental exploration. However, the phototaxis robots with rigid bodies and sensors still face challenges in adapting to unstructured environments, and the soft phototaxis robots often have high requirements for light sources with limited locomotion performance. Here, we report a 3.5 g soft microrobot that can perceive the azimuth angle of light sources and exhibit rapid phototaxis locomotion autonomously enabled by three-dimensional flexible optoelectronics and compliant shape memory alloy (SMA) actuators. The optoelectronics is assembled from a planar patterned flexible circuit with miniature photodetectors, introducing the self-occlusion to light, resulting in high sensing ability (error < 3.5°) compared with the planar counterpart. The actuator produces a straightening motion driven by an SMA wire and is then returned to a curled shape by a prestretched elastomer layer. The actuator exhibits rapid actuation within 0.1 s, a significant degree of deformation (curvature change of ∼87 m-1) and a blocking force of ∼0.4 N, which is 68 times its own weight. Finally, we demonstrated the robot is capable of autonomously crawling toward a moving light source in a hybrid aquatic-terrestrial environment without human intervention. We envision that our microrobot could be widely used in autonomous light tracking applications.

Trends in diabetic ketoacidosis- and hyperosmolar hyperglycemic state-related mortality during the COVID-19 pandemic in the United States: A population-based study.

BACKGROUND: During the pandemic, a notable increase in diabetic ketoacidosis (DKA) and hyperosmolar hyperglycemic state (HHS), conditions that warrant emergent management, was reported. We aimed to investigate the trend of DKA- and HHS-related mortality and excess deaths during the pandemic. METHODS: Annual age-standardized mortality rates related to DKA and HHS between 2006 and 2021 were estimated using a nationwide database. Forecast analyses based on prepandemic data were conducted to predict the mortality rates during the pandemic. Excess mortality rates were calculated by comparing the observed versus predicted mortality rates. Subgroup analyses of demographic factors were performed. RESULTS: There were 71 575 DKA-related deaths and 8618 HHS-related deaths documented during 2006-2021. DKA, which showed a steady increase before the pandemic, demonstrated a pronounced excess mortality during the pandemic (36.91% in 2020 and 46.58% in 2021) with an annual percentage change (APC) of 29.4% (95% CI: 16.0%-44.0%). Although HHS incurred a downward trend during 2006-2019, the excess deaths in 2020 (40.60%) and 2021 (56.64%) were profound. Pediatric decedents exhibited the highest excess mortality. More than half of the excess deaths due to DKA were coronavirus disease 2019 (COVID-19) related (51.3% in 2020 and 63.4% in 2021), whereas only less than a quarter of excess deaths due to HHS were COVID-19 related. A widened racial/ethnic disparity was observed, and females exhibited higher excess mortality than males. CONCLUSIONS: The DKA- and HHS-related excess mortality during the pandemic and relevant disparities emphasize the urgent need for targeted strategies to mitigate the escalated risk in these populations during public health crises.