Epidermal secretion-purified biosensing patch with hydrogel sebum filtering membrane and unidirectional flow microfluidic channels.

The development of biosensing electronics for real-time sweat analysis has attracted increasing research interest due to their promising applications for non-invasive health monitoring. However, one of the critical challenges lies in the sebum interference that largely limits the sensing reliability in practical scenarios. Herein, we report a flexible epidermal secretion-purified biosensing patch with a hydrogel filtering membrane that can effectively eliminate the impact of sebum and sebum-soluble substances. The as-prepared sebum filtering membranes feature a dual-layer sebum-resistant structure based on the poly(hydroxyethyl methacrylate) hydrogel functionalized with nano-brush structured poly(sulfobetaine) to eliminate interferences and provide self-cleaning capability. Furthermore, the unidirectional flow microfluidic channels design based on the Tesla valve was incorporated into the biosensing patch to prevent external sebum contamination and allow effective sweat refreshing for reliable sensing. By seamlessly combining these components, the epidermal secretion-purified biosensing patch enables continuous monitoring of sweat uric acid, pH, and sodium ions with significantly improved accuracy of up to 12 %. The proposed strategy for enhanced sweat sensing reliability without sebum interference shows desirable compatibility for different types of biosensors and would inspire the advances of flexible and wearable devices for non-invasive healthcare.

Pan-Immune-Inflammatory Value is Superior to Other Inflammatory Indicators in Predicting Inpatient Major Adverse Cardiovascular Events and Severe Coronary Artery Stenosis after Percutaneous Coronary Intervention in STEMI Patients.

Background: The inflammatory response to atherosclerosis is a process that leads to coronary artery disease. Pan-immune-inflammation value (PIV) has emerged as a new and simple biomarker of inflammation. However, studies on the predictive power of PIV for major adverse cardiovascular events (MACE) or the degree of coronary artery stenosis are scarce. We aimed to explore the predictive ability of PIV for MACE and the degree of coronary artery stenosis in patients with ST-segment elevation myocardial infarction (STEMI) after percutaneous coronary intervention (PCI) during hospitalization. Methods: This study included 542 patients who were diagnosed with STEMI and who underwent PCI between 2016 and 2023 and whose PIV and other inflammatory markers were measured. Using univariate and multivariate logistic regression analysis, risk variables for MACE following PCI and severe coronary stenosis during hospitalization were assessed to create receiver operating characteristic (ROC) curves and determine the best thresholds for inflammatory markers. Spearman correlation analysis was used to evaluate the correlation of PIV and other inflammatory markers with the Gensini score (GS). Results: Compared with the systemic inflammatory index (SII), platelet-to-lymphocyte ratio (PLR), and neutrophil-to-lymphocyte ratio (NLR), the PIV may have greater predictive value in terms of the occurrence of MACE and the degree of coronary stenosis after PCI in hospitalized STEMI patients. The correlation between the PIV and GS was strong. Conclusions: PIV was superior to the SII, PLR, and NLR in predicting inpatient prognosis and severe coronary stenosis after PCI for STEMI patients.

Self-flickering bioinspired actuator with autonomous motion and structural color switching.

Color-tunable actuators with motion and color-changing functions have attracted considerable attention in recent years, yet it remains a challenge to achieve the autonomous regulation of motion and color. Inspired by Apatura ilia butterfly with dynamic structural color and Pelargonium carnosum plant with moisture responsive bilayer structure, an automatic color-tunable actuator is developed by integrating photonic crystals layer and hygroscopic layer. Taking advantage of the asymmetric hygroscopicity between two layers and the angle-dependent structural color of photonic crystals, this actuator can continuously self-flicker in humid environment by visual switching in structural color due to automated cyclic motion. The actuator is assembled into the self-flapping biomimetic butterfly with switchable color and the self-reporting information array with dynamic visual display, demonstrating its autoregulatory motion and color. This work provides a new strategy for developing automatic color-tunable actuator and suggests its potential in the intelligent robot and optical display.

Integrating machine learning and artificial intelligence in life-course epidemiology: pathways to innovative public health solutions.

The integration of machine learning (ML) and artificial intelligence (AI) techniques in life-course epidemiology offers remarkable opportunities to advance our understanding of the complex interplay between biological, social, and environmental factors that shape health trajectories across the lifespan. This perspective summarizes the current applications, discusses future potential and challenges, and provides recommendations for harnessing ML and AI technologies to develop innovative public health solutions. ML and AI have been increasingly applied in epidemiological studies, demonstrating their ability to handle large, complex datasets, identify intricate patterns and associations, integrate multiple and multimodal data types, improve predictive accuracy, and enhance causal inference methods. In life-course epidemiology, these techniques can help identify sensitive periods and critical windows for intervention, model complex interactions between risk factors, predict individual and population-level disease risk trajectories, and strengthen causal inference in observational studies. By leveraging the five principles of life-course research proposed by Elder and Shanahan-lifespan development, agency, time and place, timing, and linked lives-we discuss a framework for applying ML and AI to uncover novel insights and inform targeted interventions. However, the successful integration of these technologies faces challenges related to data quality, model interpretability, bias, privacy, and equity. To fully realize the potential of ML and AI in life-course epidemiology, fostering interdisciplinary collaborations, developing standardized guidelines, advocating for their integration in public health decision-making, prioritizing fairness, and investing in training and capacity building are essential. By responsibly harnessing the power of ML and AI, we can take significant steps towards creating healthier and more equitable futures across the life course.

Deficiency of geranylgeranyl biphosphate synthase in kidney tubules causes cystic kidney disease.

Polycystic kidney disease (PKD) is a common hereditary kidney disease. Although PKD occurrence is associated with certain gene mutations, its onset regulatory mechanisms are still not well understood. Here, we first report that the key enzyme geranylgeranyl diphosphate synthase (GGPPS) is specifically expressed in renal tubular epithelial cells of mouse kidneys. We aimed to explore the role of GGPPS in PKD. In this study, we established a Ggppsfl/fl:Cdh16cre mouse model and compared its phenotype with that of wild-type mice. A Ggpps-downregulation HK2 cell model was also used to further determine the role of GGPPS. We found that GGPPS was specifically expressed in renal tubular epithelial cells of mouse kidneys. Its expression also increased with age. Low GGPPS expression was observed in human ADPKD tissues. In the Ggppsfl/fl:Cdh16cre mouse model, Ggpps deletion in renal tubular epithelial cells induced the occurrence and development of renal tubule cystic dilation and caused the death of mice after birth due to abnormal renal function. Enhanced proliferation of cyst-lining epithelial cells was also observed after the knockout of Ggpps. These processes were related to the increased rate of Rheb on membrane/cytoplasm and hyperactivation of mTORC1 signaling. In conclusion, the deficiency of GGPPS in kidney tubules induced the formation of renal cysts. It may play a critical role in PKD pathophysiology. A novel therapeutic strategy could be designed according to this work.

Investigation and Regulation of DNA Nanostructures on Activating cGAS-STING Signaling.

DNA nanostructures have shown great potential in biomedical fields. However, the immune responses, especially the activation of the cGAS-STING signaling (A-cGSs), induced by DNA nanostructures, remain incompletely understood. Here, the ability of various DNA nanostructures from double-stranded DNA (dsDNA), single-stranded tiles (SSTs) to DNA origami is investigated on A-cGSs. Unlike natural dsDNA which triggers potent A-cGSs, the structural interconnectivity of various DNA configurations can substantially reduce the occurrence of A-cGSs, irrespective of their form, dimensions, and conformation. However, wireframe DNA nanostructures can activate the cGAS-STING signaling, suggesting that decreasing A-cGSs is dsDNA compactness-dependent. Based on this, a reconfigurable DNA Origami Domino Array (DODA) is used to systematically interrogate how dsDNA influences the A-cGSs and demonstrates that the length, number, and space of dsDNA array coordinately influence the activation level of cGAS-STING signaling, realizing a regulation of innate immune response. The above data and findings enhance the understanding of how DNA nanostructures affect cellular innate immune responses and new insights into the modulation of innate immune responses by DNA nanomedicine.

Developing Interdisciplinary Competency in Voice Performance: A Phenomenological Study of Higher Education in China.

This phenomenological study investigates the experiences and perceptions of interdisciplinary competencies for voice performance in higher education in China through semistructured interviews with four vocal education experts. Participants were selected via selective sampling based on their teaching experience, theoretical research, location, and professional development contributions. Coding and thematic analyses identify key interdisciplinary domains crucial for voice performers. Physiological and anatomical principles, informed by life sciences, are fundamental for vocal health and technique. Incorporating historical and cultural knowledge enriches performers' interpretive depth and emotional expression. Digital technologies further modernize vocal training and prepare students for contemporary performance environments. The findings illuminate that Chinese interdisciplinary competency in vocal performance has unique characteristics, emphasizing cultural literacy and the fusion of Italian bel canto with Chinese Indigenous vocalization methods, but it has its limitations. This study contributes to the global discourse on higher education voice performance studies by presenting the lived experiences of Chinese voice professors in higher education, which can inspire and inform educational initiatives worldwide.

Improving microbial activity in high-salt wastewater: A review of innovative approaches.

The Zero discharge technology has become an important pathroute for sustainable development of high salt wastewater treatment. However, the cohabitation of organic and inorganic debris can cause serious problems such membrane clogging and the formation of hazardous impurity salts that further restrict the recovery of all salt varieties by evaporating and crystallizing. In highly salinized wastewater, biological treatments offer advantages in terms of cost and sustainability when used as a pre-treatment step to eliminate organic debris. On the other hand, high salinity is always a major obstacle to microbial diversity, abundance, and activity, which can result in low organic matter removal effectiveness or the failure of the microbial treatment system. Biofortification techniques can attenuate the negative effects of salt stress and other unfavourable conditions on microorganisms, while the regulation mechanisms of microbial and community collaboration by fortification methods have been an open question. Therefore, a comprehensive summary of the types, mechanisms, and effects of the major biofortification techniques is proposed. This review dialyzes the characteristics and sources of hypersaline wastewater and the main treatment methods. Then, the mechanisms of microbial salt tolerance are summarized and discussed based on microbial characteristics and the protective effects provided by the processes. Finally, the research and application of the main bioaugmentation methods are developed in detail, describing the characteristics, advantages and disadvantages of the different enhancement methods in their implementation. This review provides a more comprehensive perspective on the future engineering applications of bioaugmentation technology, and explores in depth the possibilities of applying biological methods to high-salinity wastewater treatment.

Molecular Evidence for the Axial Coordination Effect of Atomic Iodine on Fe-N4 Sites in Oxygen Reduction Reaction.

We present a molecular-scale investigation of the axial coordination effect of atomic iodine on Fe-N4 sites in the oxygen reduction reaction (ORR) by electrochemical scanning tunneling microscopy (ECSTM). A well-defined model catalytic system with explicit and uniform iodine-coordinated Fe-N4 sites was constructed facilely by the self-assembly of iron(II) phthalocyanine (FePc) on an I-modified Au(111) surface. The electrocatalytic activity of FePc for the ORR shows a tremendous enhancement with axial iodine ligands. The ingenious modulation of the electronic structure of Fe sites to evoke a higher spin configuration by axial iodine was evidenced. In addition, the interaction strength between reactive oxygen species and active centers becomes weaker due to the presence of iodine ligands, and the reaction is thermodynamically preferable. Moreover, the facilitated reaction dynamics of FePc on I/Au(111) were explicitly determined via in-situ ECSTM potential pulse experiments. Noteworthily, axial atomic iodine was found inefficacious for improving the activity of Co-N4 sites, and electron rearrangement was not detected, demonstrating that adequate interactions between axial ligands and metal sites for optimizing electronic structures and catalytic behaviors are prerequisites for the impactful role of axial ligands.

Effects of Different Types of Starch on Physicochemical Properties and Microstructure of Beef during Cold Storage.

The purpose of this study was to identify the most effective method for enhancing the quality of beef gel during refrigeration. To achieve this objective, the effects of various types of starch on the physicochemical properties and microstructure of beef gel during refrigeration were investigated. In this study, ground beef gel was chosen as the research subject, and six different types of starch were added: 6% tapioca starch, cassava-modified starch (acetylated distarch phosphate, ADSP), potato starch (PSP), modified potato starch (acetate starch, SA), corn starch (CSP), and modified corn starch (hydroxypropyl distarch phosphate, HPDSP). The quality indicators of ground beef were measured and analyzed throughout the cold storage at 4 °C on days 1, 3, 5, 7, and 9. The results demonstrated that the water capacity of beef mince supplemented with PSP and HPDSP was significantly greater (p < 0.05). Additionally, the gel strength was found to be the highest, while the mesh structure formed in the ADSP group was the greatest. Furthermore, HPDSP, PSP, and SA effectively inhibited the oxidation of meat fat, with SA showing a relatively good effect on delaying the oxidation of meat mince protein. The addition of starch can, to a certain extent, inhibit lipid and protein oxidation in meat mince. In conclusion, starch significantly enhances the quality of beef mince by improving water retention, gel strength, and microstructure during refrigeration.

Emerging role of liver-bone axis in osteoporosis.

Background: Increasing attention to liver-bone crosstalk has spurred interest in targeted interventions for various forms of osteoporosis. Liver injury induced by different liver diseases can cause an imbalance in bone metabolism, indicating a novel regulatory paradigm between the liver and bone. However, the role of the liver-bone axis in both primary and secondary osteoporosis remains inadequately elucidated. Therefore, exploring the exact regulatory mechanisms of the liver-bone axis may offer innovative clinical approaches for treating diseases associated with the liver and bone. Methods: Here, we summarize the latest research on the liver-bone axis by searching the PubMed and Web of Science databases and discuss the possible mechanism of the liver-bone axis in different types of osteoporosis. The literature directly reporting the regulatory role of the liver-bone axis in different types of osteoporosis from the PubMed and Web of Science databases has been included in the discussion of this review (including but not limited to the definition of the liver-bone axis, clinical studies, and basic research). In addition, articles discussing changes in bone metabolism caused by different etiologies of liver injury have also been included in the discussion of this review (including but not limited to clinical studies and basic research). Results: Several endocrine factors (IGF-1, FGF21, hepcidin, vitamin D, osteocalcin, OPN, LCAT, Fetuin-A, PGs, BMP2/9, IL-1/6/17, and TNF-α) and key genes (SIRT2, ABCB4, ALDH2, TFR2, SPTBN1, ZNF687 and SREBP2) might be involved in the regulation of the liver-bone axis. In addition to the classic metabolic pathways involved in inflammation and oxidative stress, iron metabolism, cholesterol metabolism, lipid metabolism and immunometabolism mediated by the liver-bone axis require more research to elucidate the regulatory mechanisms involved in osteoporosis. Conclusion: During primary and secondary osteoporosis, the liver-bone axis is responsible for liver and bone homeostasis via several hepatokines and osteokines as well as biochemical signaling. Combining multiomics technology and data mining technology could further advance our understanding of the liver-bone axis, providing new clinical strategies for managing liver and bone-related diseases.The translational potential of this article is as follows: Abnormal metabolism in the liver could seriously affect the metabolic imbalance of bone. This review summarizes the indispensable role of several endocrine factors and biochemical signaling pathways involved in the liver-bone axis and emphasizes the important role of liver metabolic homeostasis in the pathogenesis of osteoporosis, which provides novel potential directions for the prevention, diagnosis, and treatment of liver and bone-related diseases.

Multi-scale Hierarchical Organic Photocatalytic Platform for Self-Suspending Sacrificial Hydrogen Production from Seawater.

The widespread application of photocatalysis for converting solar energy and seawater into hydrogen is generally hindered by limited catalyst activity and the lack of sustainable large-scale platforms. Here, a multi-scale hierarchical organic photocatalytic platform was developed, combining a photosensitive molecular heterojunction with a molecular-scale gradient energy level alignment and micro-nanoscale hierarchical pore structures. The ternary system facilitates efficient charge transfer and enhances photocatalytic activity compared to conventional donor-acceptor pairs. Meanwhile, the super-wetted hierarchical interfaces of the platform endow it with the ability to repeatedly capture light and self-suspend below the water surface, which simultaneously improves the light utilization efficiency, and reduces the adverse effects of salt deposition. Under a Xe lamp illumination, the hydrogen evolution rate of this organic platform utilizing a sacrificial agent can reach 165.8 mmol h-1 m-2, exceeding that of mostly inorganic systems as reported. And upon constructing a scalable system, the platform produced 80.6 ml m-2 of hydrogen from seawater within five hours at noon. More importantly, the outcomes suggest an innovative multi-scale approach that bridges disciplines, advancing the frontier of sustainable seawater hydrogen production driven by solar energy.

Size-dependent catalytic activity for CO oxidation over sub-nano-Au clusters.

Gold (Au) nanocatalysts present outstanding activity for many reactions and have long attracted much attention, but the size effect of sub-nano-clusters on catalytic activity lacks systematic research. Using CO oxidation as a probe reaction, the size-dependent catalytic capability of sub-nano-Au clusters was explored. The global-minimum (GM) structures of AuN (N = 2-300, <2.5 nm) were obtained utilizing revised particle swarm optimization (RPSO) combined with density functional theory (DFT) calculations and the Gupta empirical potential. Geometric structural descriptors built a bridge among geometric features, adsorption energy, and the CO oxidation rate of each site of any given sub-nano-Au clusters, making it possible for high-throughput evaluation of the adsorption energy and catalytic activity of the whole sub-nano-Au cluster. The activity per unit mass of sub-nano-Au clusters shows a volcano-shaped relationship with the cluster size, where the sub-nano-Au clusters with a 0.75 nm diameter possess the highest CO2 formation rate per unit mass. The Edge and Kink sites have a higher turnover frequency (approximately 106) than the Face sites (approximately 102), which contribute the most to CO2 formation. The weak adsorption of CO and O2 was found to be a crucial factor determining the inferior activity of the Face site to the Kink and Edge sites. The adsorption process rather than the surface reaction step becomes the rate-determining step on the Face site, attributed to the decreased activity per unit mass of sub-nano-Au clusters. This work provides an in-depth mechanistic understanding of size-dependent catalytic activity for Au clusters at the sub-nano level.

Genome-wide discovery and integrative genomic characterization of insulin resistance loci using serum triglycerides to HDL-cholesterol ratio as a proxy.

Insulin resistance causes multiple epidemic metabolic diseases, including type 2 diabetes, cardiovascular disease, and fatty liver, but is not routinely measured in epidemiological studies. To discover novel insulin resistance genes in the general population, we conducted genome-wide association studies in 382,129 individuals for triglyceride to HDL-cholesterol ratio (TG/HDL), a surrogate marker of insulin resistance calculable from commonly measured serum lipid profiles. We identified 251 independent loci, of which 62 were more strongly associated with TG/HDL compared to TG or HDL alone, suggesting them as insulin resistance loci. Candidate causal genes at these loci were prioritized by fine mapping with directions-of-effect and tissue specificity annotated through analysis of protein coding and expression quantitative trait variation. Directions-of-effect were corroborated in an independent cohort of individuals with directly measured insulin resistance. We highlight two phospholipase encoding genes, PLA2G12A and PLA2G6, which liberate arachidonic acid and improve insulin sensitivity, and VGLL3, a transcriptional co-factor that increases insulin resistance partially through enhanced adiposity. Finally, we implicate the anti-apoptotic gene TNFAIP8 as a sex-dimorphic insulin resistance factor, which acts by increasing visceral adiposity, specifically in females. In summary, our study identifies several candidate modulators of insulin resistance that have the potential to serve as biomarkers and pharmacological targets.

[Effect of total saponins from Panacis Majoris Rhizoma on proliferation, apoptosis and autophagy of human cervical carcinoma HeLa cells].

This paper investigated the effect of total saponins from Rhizoma Panacis Majoris on the proliferation, apoptosis, and autophagy of human cervical carcinoma HeLa cells. The saponin content was detected by ultraviolet-visible spectrophotometry. Cell coun-ting kit-8(CCK-8) assay, 4,6-diamidino-2-phenylindole(DAPI) staining, and flow cytometry were used to detect the effects of total saponins of Panacis Majoris Rhizoma on cell viability, morphology, cell cycle and apoptosis of HeLa cells. Western blot was used to detect the expression of apoptosis-related proteins B cell lymphoma-2(Bcl-2), Bcl-2-associated X protein(Bax), cleaved caspase-9, and cleaved caspase-3, autophagy-related proteins Beclin-1 and SQSTM1(p62), and the proteins related to the phosphatidylinositol 3-kinase/protein kinase B/mammalian target of rapamycin(PI3K/Akt/mTOR) and mitogen-activated protein kinase(MAPK) signaling pathways. It was found that the yield and saponin content of total saponins from Rhizoma Panacis Majoris were 6.3% and 78.3%, respectively. Total saponins from Rhizoma Panacis Majoris could significantly inhibit the proliferation(P<0.001), effect the nuclear morphology, block the G_0/G_1 cycle, and induce cell apoptosis in HeLa cells with a concentration-dependent manner. In addition, total saponins from Rhizoma Panacis Majoris up-regulated the expression of pro-apoptotic proteins Bax, cleaved caspase-9, and cleaved caspase-3, and autophagy-related protein p62(P<0.05), while down-regulated the expression of anti-apoptotic protein Bcl-2 and autophagy-related protein Beclin-1(P<0.01). Total saponins from Rhizoma Panacis Majoris could promote the expression of p-p38/p38, p-Jun N-terminal kinase(JNK)/JNK, p-PI3K/PI3K, p-Akt/Akt, p-mTOR/mTOR proteins in PI3K/Akt/mTOR and MAPK signaling pathways(P<0.05). In contrast, the effect on p-ERK/ERK expression was not obvious. Therefore, total saponins from Rhizoma Panacis Majoris may inhibit autophagy and promote apoptosis of HeLa cells through the activation of the PI3K/Akt/mTOR, c-JNK, and p38 MAPK signaling pathways, which indicates that total saponins from Rhizoma Panacis Majoris may have a potential role in cervical cancer treatment.

A novel scoring system based on sIL-2R for predicting IVIG resistance in Chinese children with KD.

OBJECTIVE: This study aimed to develop a novel scoring system utilizing circulating interleukin (IL) levels to predict resistance to intravenous immunoglobulin (IVIG) in Chinese patients with Kawasaki disease (KD). We further compared this scoring system against six previously established scoring methods to evaluate its predictive performance. METHODS: A retrospective analysis was conducted on KD patients who were treated at the cardiovascular medical ward of our institution from January 2020 to December 2022. Six scoring systems (Egami, Formosa, Harada, Kobayashi, Lan and Yang) were analyzed, and a new scoring system was developed based on our data. RESULTS: In our study, 521 KD patients were recruited, 42 of whom (8.06%) were identified as resistant to IVIG. Our study indicated that IVIG-resistant KD patients were at an increased risk for the development of coronary arterial lesions (CALs) (P = 0.001). The evaluation of IVIG resistance using various scoring systems revealed differing levels of sensitivity and specificity, as follows: Egami (38.10% and 88.52%), Formosa (95.24% and 41.13%), Harada (78.57% and 43.22%), Kobayashi (66.67% and 74.95%), Lan (66.67% and 73.49%), and Yang (69.05% and 77.24%). Our novel scoring system utilizing sIL-2R demonstrated the highest sensitivity and specificity of 69.29% and 83.91%, respectively, and calibration curves indicated a favorable predictive accuracy of the model. CONCLUSION: Our newly developed scoring system utilizing sIL-2R demonstrated superior predictive performance in identifying IVIG resistance among Chinese patients with KD.

Exploring the Potential of Microbial Coalbed Methane for Sustainable Energy Development.

By allowing coal to be converted by microorganisms into products like methane, hydrogen, methanol, ethanol, and other products, current coal deposits can be used effectively, cleanly, and sustainably. The intricacies of in situ microbial coal degradation must be understood in order to develop innovative energy production strategies and economically viable industrial microbial mining. This review covers various forms of conversion (such as the use of MECoM, which converts coal into hydrogen), stresses, and in situ use. There is ongoing discussion regarding the effectiveness of field-scale pilot testing when translated to commercial production. Assessing the applicability and long-term viability of MECoM technology will require addressing these knowledge gaps. Developing suitable nutrition plans and utilizing lab-generated data in the field are examples of this. Also, we recommend directions for future study to maximize methane production from coal. Microbial coal conversion technology needs to be successful in order to be resolved and to be a viable, sustainable energy source.

Thermodynamic and Kinetic Behaviors of Electrolytes Mediated by Intermolecular Interactions Enabling High-Performance Lithium-Ion Batteries.

Electrolyte solvation chemistry regulated by lithium salts, solvents, and additives has garnered significant attention since it is the most effective strategy for designing high-performance electrolytes in lithium-ion batteries (LIBs). However, achieving a delicate balance is a persistent challenge, given that excessively strong or weak Li+-solvent coordination markedly undermines electrolyte properties, including thermodynamic redox stability and Li+-desolvation kinetics, limiting the practical applications. Herein, we elucidate the crucial influence of solvent-solvent interactions in modulating the Li+-solvation structure to enhance electrolyte thermodynamic and kinetic properties. As a paradigm, by combining strongly coordinated propylene carbonate (PC) with weakly coordinated cyclopentylmethyl ether (CPME), we identified intermolecular interactions between PC and CPME using 1H-1H correlation spectroscopy. Experimental and computational findings underscore the crucial role of solvent-solvent interactions in regulating Li+-solvent/anion interactions, which can enhance both the thermodynamic (i.e., antireduction capability) and kinetic (i.e., Li+-desolvation process) aspects of electrolytes. Additionally, we introduced an interfacial model to reveal the intricate relationship between solvent-solvent interactions, electrolyte properties, and electrode interfacial behaviors at a molecular scale. This study provides valuable insights into the critical impact of solvent-solvent interactions on electrolyte properties, which are pivotal for guiding future efforts in functionalized electrolyte engineering for metal-ion batteries.

The first-in-human study to assess the safety, tolerability, pharmacokinetics, and pharmacodynamics of the factor XI monoclonal antibody SHR-2004 in healthy subjects.

BACKGROUND: Inhibiting the coagulation factor XI (FXI) is a novel strategy for prevention and treatment of thromboembolism without affecting extrinsic coagulation pathways. SHR-2004 is a humanized monoclonal antibody that selectively binds to FXI and factor XIa (FXIa). RESEARCH DESIGN & METHODS: This randomized, double-blind, dose-escalation, placebo-controlled study evaluated SHR-2004 administered either intravenously (i.v.; Part A) or subcutaneously (s.c.; Part B). In Part A, 24 subjects received a single i.v. dose of SHR-2004 (0.1, 0.3, or 1.0 mg/kg) or placebo. In Part B, 40 subjects received a single s.c. dose of SHR-2004 (0.5, 1.0, 3.0, or 4.5 mg/kg) or placebo. RESULTS: SHR-2004 was well tolerated. Plasma exposure to SHR-2004 increased in a dose-dependent manner. The geometric mean half-time ranged from 11.6 to 13.0 days. FXI activity decreased, and the activated partial thromboplastin time (APTT) was prolonged after i.v. and s.c. administration in a dose- and time-dependent manner. FXI activity was nearly completely abolished immediately after administering the highest i.v. dose, with the average APTT prolonged to nearly three times of baseline. CONCLUSION: SHR-2004 is a promising candidate for further development as an anticoagulant drug that exerts effective anticoagulation with minimal risk of bleeding. CLINICAL TRIAL REGISTRATION: www.clinicaltrials.gov identifier is NCT05369767.

ICDXML: enhancing ICD coding with probabilistic label trees and dynamic semantic representations.

Accurately assigning standardized diagnosis and procedure codes from clinical text is crucial for healthcare applications. However, this remains challenging due to the complexity of medical language. This paper proposes a novel model that incorporates extreme multi-label classification tasks to enhance International Classification of Diseases (ICD) coding. The model utilizes deformable convolutional neural networks to fuse representations from hidden layer outputs of pre-trained language models and external medical knowledge embeddings fused using a multimodal approach to provide rich semantic encodings for each code. A probabilistic label tree is constructed based on the hierarchical structure existing in ICD labels to incorporate ontological relationships between ICD codes and enable structured output prediction. Experiments on medical code prediction on the MIMIC-III database demonstrate competitive performance, highlighting the benefits of this technique for robust clinical code assignment.