Simultaneous Manipulation of Membrane Enthalpy and Entropy Barriers towards Superior Ion Separations.

Sub-nanoporous membranes with ion selective transport functions are important for energy utilization, environmental remediation, and fundamental bioinspired engineering. Although mono/multivalent ions can be separated by monovalent ion selective membranes (MISMs), the current theory fails to inspire rapid advances in MISMs. Here, we apply transition state theory (TST) by regulating the enthalpy barrier (△H) and entropy barrier (△S) for designing next-generation monovalent cation exchange membranes (MCEMs) with great improvement in ion selective separation. Using a molecule-absorbed porous material as an interlayer to construct a denser selective layer can achieve a greater absolute value of △S for Li+ and Mg2+ transport, greater △H for Mg2+ transport and lower △H for Li+ transport. This recorded performance with a Li+/Mg2+ perm-selectivity of 25.50 and a Li+ flux of 1.86 mol·m-2·h-1 surpasses the contemporary "upper bound" plot for Li+/Mg2+ separations. Most importantly, our synthesized MCEM also demonstrates excellent operational stability during the selective electrodialysis (S-ED) processes for realizing scalability in practical applications.

Development and External Validation of a Machine Learning-based Fall Prediction Model for Nursing Home Residents: A Prospective Cohort Study.

OBJECTIVES: To develop and externally validate a machine learning-based fall prediction model for ambulatory nursing home residents. The focus is on predicting fall occurrences within 6 months after baseline assessment through a binary classification task, aiming to provide staff with an effective and user-friendly fall-risk assessment tool. DESIGN: Prospective cohort study. SETTING AND PARTICIPANTS: A total of 864 older residents living in 4 nursing homes between May 2022 and March 2023 in China. METHODS: Potential fall-risk predictors were collected through in-person interviews and assessments of anthropometric and physical function. Participants were followed for 6 months, with falls recorded by trained nurses. Seven machine learning algorithms, including Logistic Regression (LR), Gradient Boosting Machine (GBM), eXtreme Gradient Boosting (XGBoost), Random Forest (RF), Support Vector Machine (SVM), Neural Networks (NN), and Decision Tree (DT), were used to develop prediction models. Performance was evaluated using the area under the receiver operating characteristic curve (ROC-AUC) and Precision-Recall curve (PR-AUC), with calibration assessed via a calibration curve. Feature importance was visualized using SHapley Additive exPlanations (SHAP). RESULTS: The 6 selected predictors were balance, grip strength, fatigue, fall history, age, and comorbidity. The ROC-AUC for the models ranged from 0.710 to 0.750, PR-AUC from 0.415 to 0.473, sensitivity from 0.704 to 0.914, and specificity from 0.511 to 0.687 in the validation cohort. The LR model was converted into a nomogram. CONCLUSIONS AND IMPLICATIONS: The machine learning-based fall-prediction models effectively identified nursing home residents at high risk of falls. The developed nomogram can be integrated into clinical practice to enhance fall risk assessment protocols, ultimately improving patient safety and care in nursing homes.

Prevalence of physical inactivity and its determinants among older adults living in nursing homes: A cross-sectional study based on COM-B model.

AIMS: To investigate the prevalence of physical inactivity in older adults living in nursing homes and explore the determinants of physical inactivity by using the Capability, Opportunity, Motivation-Behaviour model. DESIGN: A multisite, cross-sectional study was performed by convenience sampling and questionnaire survey. METHODS: A total of 390 nursing home residents were recruited from three nursing homes in Southern China from May 2022 to April 2023. The participants completed a self-designed general information questionnaire, Physical Activity Scale for the Elderly, Self-Efficacy for Exercise Scale, Exercise Benefits Scale, Patient Health Questionnaire-9 and the Short Physical Performance Battery test. Descriptive statistics, univariate analysis, Spearman correlation analysis, and ordinal logistic regression were applied for data analysis. RESULTS: The prevalence of physical inactivity among the nursing home residents reached 88.46%. Ordinal logistic regression results showed that exercise self-efficacy, perceived exercise benefits, physical function, availability of physical activity instruction, having depression, number of chronic diseases and living with spouse were the main influencing determinants of physical inactivity and explained 63.7% of the variance. CONCLUSIONS: Physical inactivity was considerable in nursing home residents in China and influenced by complex factors. Tailored measures should be designed and implemented based on these factors to enhance physical activity while considering the uniqueness of Chinese culture. IMPLICATIONS FOR THE PROFESSION AND PATIENT CARE: Healthcare professionals should enhance physical activity of residents by increasing benefits understanding, boosting self-efficacy, improving physical function, alleviating depression and integrating personalized physical activity guidance into routine care services. And more attention should be paid to the residents who had more chronic diseases or did not live with spouse. IMPACT: Physical inactivity is a significant problem in nursing home residents. Understanding physical inactivity and its determinants enables the development of tailored interventions to enhance their physical activity level. REPORTING METHOD: This study was reported conforming to the STROBE statement. PATIENTS OR PUBLIC CONTRIBUTION: Nursing home residents who met the inclusion criteria were recruited.

Adverse event reporting attitude and its individual and organizational predictors among nursing staff: A multisite study in Chinese nursing homes.

This study explored the status of adverse event reporting attitudes and its predictors among nursing staff in Chinese nursing homes. A cross-sectional study was conducted with 475 nursing staff, and they completed sociodemographic and facility-related questionnaire, Incident Reporting Attitude Scale, Adverse Event Reporting Awareness Scale, and Nursing Home Survey on Patient Safety Culture. Univariate analysis and multiple linear regression models were performed. The mean score for adverse event reporting attitude was 125.87 (SD=15.35). The predictors included individual variables, such as education level (ß=0.129, p = 0.001) and working years (ß=-0.102, p = 0.007), and organizational variables, such as patient safety culture (ß=0.503, p < 0.001) and adverse event reporting awareness (ß=0.261, p < 0.001). These factors explained 35.3 % of total variance. Managers in nursing homes should strengthen team-targeted education and training for nursing staff with longer working years and lower educational backgrounds. Meanwhile, a simplified and non-punitive reporting system should be established to create positive safety management climate.

Chelation-directed interface engineering of in-place self-cleaning membranes.

Water-energy sustainability will depend upon the rapid development of advanced pressure-driven separation membranes. Although energy-efficient, water-treatment membranes are constrained by ubiquitous fouling, which may be alleviated by engineering self-cleaning membrane interfaces. In this study, a metal-polyphenol network was designed to direct the armorization of catalytic nanofilms (ca. 18 nm) on inert polymeric membranes. The chelation-directed mineralized coating exhibits high polarity, superhydrophilicity, and ultralow adhesion to crude oil, enabling cyclable crude oil-in-water emulsion separation. The in-place flux recovery rate exceeded 99.9%, alleviating the need for traditional ex situ cleaning. The chelation-directed nanoarmored membrane exhibited 48-fold and 6.8-fold figures of merit for in-place self-cleaning regeneration compared to the control membrane and simple hydraulic cleaning, respectively. Precursor interaction mechanisms were identified by density functional theory calculations. Chelation-directed armorization offers promise for sustainable applications in catalysis, biomedicine, environmental remediation, and beyond.

Comparison of Postoperative Outcomes Between Near-Infrared Fluorescent Imaging-Guided Mediastinal Lymphadenectomy and Conventional Surgery for Esophageal Cancer.

BACKGROUND: The study aimed to evaluate the efficacy of using near-infrared fluorescent imaging (NIRF) imaging with indocyanine green as an intraoperative tool for achieving complete mediastinal lymph node (LN) resection. PATIENTS AND METHODS: Between September 2019 and July 2021, patients with potential for esophagectomy due to middle and lower thoracic esophageal cancer were enrolled in this study. All patients were scheduled for NIRF-guided mediastinal lymphadenectomy during esophageal cancer surgery and were appropriately assigned to the NIRF group. Patients who underwent esophagectomy between September 2017 and September 2019 were assigned to the historical control group upon satisfying the inclusion/exclusion criteria. Surgical outcomes and the number of removed LNs were compared between the two groups using 1:1 propensity score matching. RESULTS: Of 67 eligible patients, 59 patients were included in the NIRF group after postsurgical exclusions. The operative time was significantly shorter in the NIRF group than in the historical control group [180 (140-420) min versus 202 (137-338) min; P < 0.001]. The incidence of postoperative chylothorax and hoarseness were significantly lower in the NIRF group than in the historical control group (0% versus 10.2 %; P = 0.036, 3.4% versus 13.6%; P = 0.047). The number of dissected total LNs, mediastinal LNs, and negative LNs was significantly larger in the NIRF group than in the historical control group. The number of overall metastatic LNs and abdominal LNs was comparable between the two groups. CONCLUSIONS: NIRF imaging can assist in the thorough and complete mediastinal LNs dissections without increasing complications in patients undergoing esophagectomy.

Ultrapermeable Gel Membranes Enabling Superior Carbon Capture.

Membrane technology is rapidly gaining broad attraction as a viable alternative for carbon capture to mitigate increasingly severe global warming. Emerging CO2 -philic membranes have become crucial players in efficiently separating CO2 from light gases, leveraging their exceptional solubility-selectivity characteristics. However, economic and widespread deployment is greatly dependent on the boosted performance of advanced membrane materials for carbon capture. Here, we design a unique gel membrane composed of CO2 -philic molecules for accelerating CO2 transportation over other gases for ultrapermeable carbon capture. The molecular design of such soft membranes amalgamates the advantageous traits of augmented permeation akin to liquid membranes and operational stability akin to solid membranes, effectively altering the membrane's free volume characteristics validated by both experiments and molecular dynamics simulation. Surprisingly, gas diffusion through the free-volume-tuned gel membrane undergoes a 9-fold improvement without compromising the separation factor for the superior solubility selectivity of CO2 -philic materials, and CO2 permeability achieves a groundbreaking record of 5608 Barrer surpassing the capabilities of nonfacilitated CO2 separation materials and exceeding the upper bound line established in 2019 even by leading-edge porous polymer materials. Our designed gel membrane can maintain exceptional separation performance during prolonged operation, enabling the unparalleled potential of solubility-selective next-generation materials towards sustainable carbon capture.

Protein-activated atomic layer deposition for robust crude-oil-repellent hierarchical nano-armored membranes.

Atomic layer deposition (ALD) offers unique capabilities to fabricate atomically engineered porous materials with precise pore tuning and multi-functionalization for diverse applications like advanced membrane separations towards sustainable energy-water systems. However, current ALD technique is inhibited on most non-polar polymeric membranes due to lack of accessible nucleation sites. Here, we report a facile method to efficiently promote ALD coating on hydrophobic surface of polymeric membranes via novel protein activation/sensitization. As a proof of concept, TiO2 ALD-coated membranes activated by bovine serum albumin exhibit remarkable superhydrophilicity, ultralow underwater crude oil adhesion, and robust tolerance to rigorous environments including acid, alkali, saline, and ethanol. Most importantly, excellent cyclable crude oil-in-water emulsion separation performance can be achieved. The mechanism for activation/sensitization is rooted in reactivity for a particular set of amino acids. Furthermore, the universality of protein-sensitized ALD is demonstrated using common egg white, promising numerous potential usages in biomedical engineering, environmental remediation, low-carbon manufacturing, catalysis, and beyond.

Improving astaxanthin-loaded chitosan/polyvinyl alcohol/graphene oxide nanofiber membranes and their application in periodontitis.

Periodontitis is a chronic inflammatory disease primarily driven by host inflammation and plaque-induced immune responses. Controlling the host inflammatory response and improving the periodontal inflammatory microenvironment are crucial to promoting periodontal tissue regeneration. In this study, the blended nanofiber membranes previously prepared by our research group were improved, and we developed multifunctional chitosan/polyvinyl alcohol/graphene oxide/astaxanthin coaxial nanofiber membranes. Scanning electron microscopy showed that the prepared nanofibers had a smooth surface and a uniform diameter distribution. The mechanical property test results showed that the coaxial nanofiber membranes exhibited higher tensile strength compared to the blended nanofiber membranes, which increased from 4.50 ± 0.32 and 3.70 ± 0.45 MPa to 7.12 ± 0.22 and 5.62 ± 0.79 MPa respectively. Drug release studies indicated that the "shell-core" structure of coaxial nanofibers significantly reduced the initial burst release of astaxanthin (ASTA), with only 13.49 % and 10.71 % release in the first 24 h, and drug release lasted for over a week. Animal experiments confirmed that the coaxial nanofiber membranes loaded with ASTA promoted periodontal bone defect repair while inhibiting periodontal inflammation. In conclusion, the prepared coaxial nanofiber membranes are a promising sustained-release drug system for treating periodontitis.

Engineering In Situ Catalytic Cleaning Membrane Via Prebiotic-Chemistry-Inspired Mineralization.

Pressure-driven membrane separation promises a sustainable energy-water nexus but is hindered by ubiquitous fouling. Natural systems evolved from prebiotic chemistry offer a glimpse of creative solutions. Herein, a prebiotic-chemistry-inspired aminomalononitrile (AMN)/Mn2+ -mediated mineralization method is reported for universally engineering a superhydrophilic hierarchical MnO2 nanocoating to endow hydrophobic polymeric membranes with exceptional catalytic cleaning ability. Green hydrogen peroxide catalytically triggered in-situ cleaning of the mineralized membrane and enabled operando flux recovery to reach 99.8%. The mineralized membrane exhibited a 9-fold higher recovery compared to the unmineralized membrane, which is attributed to active catalytic antifouling coupled with passive hydration antifouling. Electron density differences derived from the precursor interaction during mediated mineralization unveiled an electron-rich bell-like structure with an inner electron-deficient Mn core. This work paves the way to construct multifunctional engineered materials for energy-efficient water treatment as well as for diverse promising applications in catalysis, solar steam generation, biomedicine, and beyond.

Ice-confined synthesis of highly ionized 3D-quasilayered polyamide nanofiltration membranes.

Existing polyamide (PA) membrane synthesis protocols are underpinned by controlling diffusion-dominant liquid-phase reactions that yield subpar spatial architectures and ionization behavior. We report an ice-confined interfacial polymerization strategy to enable the effective kinetic control of the interfacial reaction and thermodynamic manipulation of the hexagonal polytype (Ih) ice phase containing monomers to rationally synthesize a three-dimensional quasilayered PA membrane for nanofiltration. Experiments and molecular simulations confirmed the underlying membrane formation mechanism. Our ice-confined PA nanofiltration membrane features high-density ionized structure and exceptional transport channels, realizing superior water permeance and excellent ion selectivity.

Biodegradable electrospinning superhydrophilic nanofiber membranes for ultrafast oil-water separation.

Although membrane technology has attracted considerable attention for oily wastewater treatment, the plastic waste generated from discarded membranes presents an immediate challenge for achieving eco-friendly separation. We designed on-demand biodegradable superhydrophilic membranes composed of polylactic acid nanofibers in conjunction with polyethylene oxide hydrogels using electrospinning technology for ultrafast purification of oily water. Our results showed that the use of the polyethylene oxide hydrogels increased the number of hydrogen bonds formed between the membrane surface and water molecules by 357.6%. This converted hydrophobic membranes into superhydrophilic ones, which prevented membrane fouling and accelerated emulsion penetration through the membranes. The oil-in-water emulsion permeance of our newly designed nanofiber membranes increased by 61.9 times (2.1 × 104 liters per square meter per hour per bar) with separation efficiency >99.6%, which was superior to state-of-the-art membranes. Moreover, the formation of hydrogen bonds was found to accelerate polylactic acid biodegradation into lactic acid by over 30%, offering a promising approach for waste membrane treatment.

Incidence and Risk Factors of Falls Among Older People in Nursing Homes: Systematic Review and Meta-Analysis.

OBJECTIVES: Falls are common among older people in nursing homes, and the assessment of fall risk factors is critical for the success of fall prevention interventions. This study aimed to systematically assess the incidence and risk factors of falls in older people living in nursing homes. DESIGN: Systematic review and meta-analysis. SETTING AND PARTICIPANTS: Older people living in nursing homes. METHODS: Literature searches were conducted independently by 2 researchers in 8 databases. Qualities of included studies were assessed using the Newcastle-Ottawa Scale. The prevalence and risk factors of falls were analyzed with a random effects model. All analyses were performed by R software, x64 4.2.2. RESULTS: In 18 prospective studies addressing older adults living in nursing homes, the pooled incidence of falls was 43% (95% CI 38%-49%), and the meta-regression analysis indicated that the incidence generally decreased from 1998 to 2021. The following risk factors had a strong association with all falls: fall history, impaired ADL performance, insomnia, and depression. Risk factors with low to moderate correlation were vertigo, walking aids, poor balance, use of antidepressants, use of benzodiazepines, use of antipsychotics, use of anxiolytics, polypharmacy, dementia, unsteady gait, hearing problems, and gender (being male). Having bed rails was identified as a protective environmental factor. CONCLUSIONS AND IMPLICATIONS: The results from our meta-analysis suggest that the incidence of falls of older adults living in nursing homes is high, and the risk factors for falls are various. Assessments of balance and mobility, medical condition, and use of medications should be included as key elements in the fall risk assessments of older people in nursing homes. Environmental risk factors still need to be explored in future studies. Tailored fall prevention strategies should be implemented by addressing the modifiable risk factors.

Evaluating the ecological vulnerability of Chongqing using deep learning.

This study used deep learning to evaluate the ecological vulnerability of Chongqing, China, discuss the deep learning evaluations of ecological vulnerability, and generate vulnerability maps that support local ecological environment protection and governance decisions and provide reference for future studies. The information gain ratio was used to screen the influencing factors, selecting 16 factors that influence ecological vulnerability. Deep neural network (DNN) and convolutional neural network (CNN) methods were used for modeling, and two ecological vulnerability maps of the study area were generated. The results showed that the mean absolute error and root mean square error of the DNN and CNN models were relatively small, and the fitting accuracy was high. The area under the receiver operating characteristic curve of the CNN model was 0.926, which was better than that of the DNN model (0.888). Random forest was applied to calculate the importance of the influencing factors in the two models. Because the main factor was geological features, the relative ecological vulnerability was mainly affected by karst topography. Through the analysis of the ecological vulnerability map, the areas with higher vulnerability are the karst mountains of Dabashan, Wushan, and Qiyaoshan in the northeast and southeast, as well as the valley between mountains and cities in the center and west of the study area. According to the investigation of these areas, the primary ecological problems are low forest quality, structural irregularities caused by self-geological factors, severe desertification, and soil erosion. Human activity is also an important factor that causes ecological vulnerability in the study area. In conclusion, deep learning, particularly CNN models, can be used for ecological vulnerability assessments. The ecological vulnerability maps conformed to the basic cognition of field surveys and can provide references for other deep learning vulnerability studies. While the overall vulnerability of the study area is not high, ecological problems that lead to its vulnerability should be addressed by future ecological protection and management measures.

Integrating Enrichment, Reduction, and Oxidation Sites in One System for Artificial Photosynthetic Diluted CO2 Reduction.

Artificial photosynthetic diluted CO2 reduction directly driven by natural sunlight is a challenging, but promising way to realize carbon-resources recycling utilization. Herein, a three-in-one photocatalytic system of CO2 enrichment, CO2 reduction and H2 O oxidation sites is designed for diluted CO2 reduction. A Zn-Salen-based covalent organic framework (Zn-S-COF) with oxidation and reductive sites is synthesized; then, ionic liquids (ILs) are loaded into the pores. As a result, [Emim]BF4 @Zn-S-COF shows a visible-light-driven CO2 -to-CO conversion rate of 105.88 µmol g-1 h-1 under diluted CO2 (15%) atmosphere, even superior than most photocatalysts in high concentrations CO2 . Moreover, natural sunlight driven diluted CO2 reduction rate also reaches 126.51 µmol g-1 in 5 h. Further experiments and theoretical calculations reveal that the triazine ring in the Zn-S-COF promotes the activity of H2 O oxidation and CO2 reduction sites, and the loaded ILs provide an enriched CO2 atmosphere, realizing the efficient photocatalytic activity in diluted CO2 reduction.

Surface manipulation for prevention of migratory viscous crude oil fouling in superhydrophilic membranes.

Here, we present a proactive fouling prevention mechanism that endows superhydrophilic membranes with antifouling capability against migratory viscous crude oil fouling. By simulating the hierarchical architecture/chemical composition of a dahlia leaf, a membrane surface is decorated with wrinkled-pattern microparticles, exhibiting a unique proactive fouling prevention mechanism based on a synergistic hydration layer/steric hindrance. The density functional theory and physicochemical characterizations demonstrate that the main chains of the microparticles are bent towards Fe3+ through coordination interactions to create nanoscale wrinkled patterns on smooth microparticle surfaces. Nanoscale wrinkled patterns reduce the surface roughness and increase the contact area between the membrane surface and water molecules, expanding the steric hindrance between the oil molecules and membrane surface. Molecular dynamic simulations reveal that the water-molecule densities and strengths of the hydrogen bonds are higher near the resultant membrane surface. With this concept, we can successfully inhibit the initial adhesion, migration, and deposition of oil, regardless of the viscosity, on the membrane surface and achieve migratory viscous crude oil antifouling. This research on the PFP mechanism opens pathways to realize superwettable materials for diverse applications in fields related to the environment, energy, health, and beyond.

Ultra-Permeable Dual-Mechanism-Driven Graphene Oxide Framework Membranes for Precision Ion Separations.

Two-dimensional graphene oxide (GO) membranes are gaining popularity as a promising means to address global water scarcity. However, current GO membranes fail to sufficiently exclude angstrom-sized ions from solution. Herein, a de novo "posterior" interfacial polymerization (p-IP) strategy is reported to construct a tailor-made polyamide (PA) network in situ in an ultrathin GO membrane to strengthen size exclusion while imparting a positively charged membrane surface to repel metal ions. The electrostatic repulsion toward metal ions, coupled with the reinforced size exclusion, synergistically drives the high-efficiency metal ion separation through the synthesized positively charged GO framework (PC-GOF) membrane. This dual-mechanism-driven PC-GOF membrane exhibits superior metal ion rejection, anti-fouling ability, good operational stability, and ultra-high permeance (five times that of pristine GO membranes), enabling a sound step towards a sustainable water-energy-food nexus.

[Extracellular Signal-regulated Kinase 1/2 Signaling Regulates Cell Invasion:a Review].

Extracellular signal-regulated kinase 1/2 (ERK1/2) is a serine/threoninekinase involved in the signal transduction cascade of Ras-Raf-mitogen-activated protein kinase (MEK)-ERK.It participates in the cell growth,proliferation and even invasion by regulating gene transcription and expression.The occurrence of a variety of diseases such as lung cancer,liver cancer,ovarian cancer,cervical cancer,endometriosis,and preeclampsia,as well the metastasis and disease progression,is closely associated with the regulation of cell invasion by ERK1/2 signaling pathway.Therefore,exploring the regulation of ERK1/2 signaling on cell invasion and its role in pathogenesis of diseases may help to develop more effective treatment schemes.This article introduces recent progress in the regulation of ERK1/2 signaling on cell invasion and the role of such regulation in diseases,with a view to give new insights into the clinical treatment of ERK 1/2-related diseases.

Boosting membrane carbon capture via multifaceted polyphenol-mediated soldering.

Advances in membrane technologies are significant for mitigating global climate change because of their low cost and easy operation. Although mixed-matrix membranes (MMMs) obtained via the combination of metal-organic frameworks (MOFs) and a polymer matrix are promising for energy-efficient gas separation, the achievement of a desirable match between polymers and MOFs for the development of advanced MMMs is challenging, especially when emerging highly permeable materials such as polymers of intrinsic microporosity (PIMs) are deployed. Here, we report a molecular soldering strategy featuring multifunctional polyphenols in tailored polymer chains, well-designed hollow MOF structures, and defect-free interfaces. The exceptional adhesion nature of polyphenols results in dense packing and visible stiffness of PIM-1 chains with strengthened selectivity. The architecture of the hollow MOFs leads to free mass transfer and substantially improves permeability. These structural advantages act synergistically to break the permeability-selectivity trade-off limit in MMMs and surpass the conventional upper bound. This polyphenol molecular soldering method has been validated for various polymers, providing a universal pathway to prepare advanced MMMs with desirable performance for diverse applications beyond carbon capture.