The Dame Barbara Windsor Dementia Goals Programme was launched by the UK Government to accelerate the development and delivery of new treatments for dementia. We present the recommendations from the Scientific Advisory Board, to enable timely access to therapies for the wider population, reducing health system burden while improving patient outcomes. The recommendations focus on three areas: (i) establishing a new dynamic national patient registry for clinical trial recruitment; (ii) the use of biomarkers to improve early and accurate diagnosis; and (iii) a framework for end-to-end implementation across the landscape of healthcare, research and regulators. A Brain Aging Registry for Biomarkers, Access to trials, Research and Adoption would support recruitment, monitoring, and personalized care. Embedding digital and biomarker innovations into routine care would improve personalized and equitable dementia services, with earlier diagnosis and more effective prevention. Robust patient and public involvement is required, to ensure transparency, trustworthiness, and meaningful participation.
Antibody-drug conjugates (ADCs) are rapidly evolving from conventional cytotoxic delivery systems into multifunctional, immune-integrated therapeutic platforms. Highlights from the 2026 American Association for Cancer Research (AACR) Annual Meeting demonstrate significant advances in ADC design, including dual- and multi-payload constructs, multispecific targeting strategies, and immunostimulatory payloads. These innovations aim to overcome key limitations such as tumor heterogeneity, resistance, and systemic toxicity. Novel approaches targeting the tumor microenvironment, including depletion of regulatory T cells and tumor-associated macrophages, further expand the therapeutic scope of ADCs beyond direct tumor cell killing. In parallel, integration with emerging modalities such as engineered CD16-enhanced natural killer T (NKT) cells underscores the potential for synergy between ADCs and cellular immunotherapies. Advances in AI-guided target discovery and antibody engineering are also enhancing tumor selectivity and internalization. Collectively, these developments highlight a paradigm shift toward precision, multi-mechanistic ADCs with the potential to improve clinical outcomes across diverse cancer types.
Biomolecular interactions involving proteins, nucleic acids, and small molecules constitute the molecular foundation of cellular regulation, signaling, and therapeutic intervention. Advances in mass spectrometry-based proteomics have enabled the systematic characterization of these interactions at unprecedented depth, sensitivity, and structural resolution. This chapter provides a comprehensive overview of state-of-the-art proteomics methodologies developed to investigate protein-protein, protein-nucleic acid, and protein-drug interactions, with particular emphasis on experimental design, sample preparation, and data quality control. Targeted and untargeted strategies are discussed, including affinity purification-mass spectrometry, proximity-dependent labeling, cross-linking mass spectrometry, blue native electrophoresis, and size-exclusion chromatography-mass spectrometry for protein-protein interactions; affinity capture, EMSA-MS, chromatin immunoprecipitation-mass spectrometry, CRISPR-based locus-specific enrichment, and CLIP-based approaches for protein-nucleic acid complexes; and chemoproteomics, thermal proteome profiling, and label-free structural proteomics for protein-drug interaction analysis. The chapter further highlights recent technological innovations, computational tools, and integrative multi-omics strategies that enhance interaction mapping across biological scales. By critically evaluating the strengths, limitations, and appropriate applications of each methodology, this work aims to provide practical guidance for researchers seeking to design robust interactomics experiments and to interpret complex molecular networks in both basic and translational research contexts.
Phosphorylation is one of the most prevalent and dynamic post-translational modifications. It regulates aspects of cellular signaling, metabolism, and disease progression. Comprehensive characterization of phosphoproteins and their phosphorylation remain analytically challenging due to their low abundance, the dynamic nature of the phosphorylation, substoichiometric modification levels, and the complexity of biological matrices. However, recent advancements in enrichment strategies have substantially increased the depth and precision of phosphoproteomics analyses using mass spectrometry. Strategies such as immobilized metal ion affinity chromatography and metal oxide affinity chromatography refine the selective isolation of phosphorylated peptides from complex mixtures. Emerging materials, such as advanced metal nanoparticles, MXenes, and carbon-based nanostructures, are increasingly being used in phosphoproteomics enrichment due to their inherent features such as high surface areas, easily tunable surface chemistry, and strong structural stability, which provide enhanced enrichment efficiency and selectivity. Here, we outline strategies and innovations in phosphoprotein enrichment materials in quantitative proteomics MS platforms.
The aberrant activation of the NOTCH1 signaling pathway underlies the aggressive malignancy and poor prognosis of T-cell acute lymphoblastic leukemia (T-ALL). T-ALL cell lines (Jurkat and Molt4) were treated with chiglitazar to evaluate viability, proliferation, apoptosis, and cell cycle. RNA-seq, qRT-PCR, and Western blotting were used to examine NOTCH1 signaling. Mechanistic assays included luciferase reporter, DNA affinity precipitation, co-immunoprecipitation, and ChIP. In vivo, cell line-derived xenograft (CDX) and patient-derived xenograft (PDX) models were generated by intravenous engraftment of leukemic cells into sublethally irradiated mice, followed by treatment with chiglitazar alone or combined with venetoclax. Therapeutic efficacy was assessed by survival, flow cytometric tumor burden, and histopathology (HE and IHC). We report that therapeutic activation of peroxisome proliferator-activated receptor α (PPARα) significantly represses the leukemogenesis of T-ALL in vitro and in vivo by blocking the NOTCH1 signaling pathway. Mechanistically, PPARα directly binds to the promoter region of the NOTCH1 gene and inhibits its transcriptional activity. Furthermore, PPARα interacts with signal transducer and activator of transcription 3 (STAT3) and attenuates the transcriptional activation effect of STAT3 on the NOTCH1 gene promoter. Importantly, we also found that therapeutic activation of PPARα using chiglitazar synergizes with venetoclax to suppress T-ALL progression in PDX models. We conclude that targeting PPARα to suppress T-ALL progression by blocking the NOTCH1 pathway represents a potential novel therapeutic strategy for the treatment of T-ALL.
Simultaneous and sensitive detection of multiple nucleic acids in complex matrices has always been a significant challenge. Herein, we reported a novel nanopore biosensing strategy that achieved label-free, high-discrimination detection of multiplex nucleic acid targets by ingeniously integrating programmable DNA probe with click chemistry amplified strategy. In this platform, probe DNAs with alkynyl modification at different positions were clicked with azidoadamantane, which were then reacted with cucurbit[6]uril to produce host-guest complexes as signal tags. These reporters showed highly distinguishable electrical signals when analyzed by α-hemolysin (α-HL) nanopore. Coupled with target-specific asymmetric PCR-triggered strand displacement reaction, probe DNAs were released in the presence of target DNA and generated signal tags, which were analyzed with α-HL nanopore to achieve multiplex signal output. Through this strategy, multiple target DNAs could be simultaneously detected with a satisfactory limit of detection (LOD) as low as 18.78 pM. We further applied this method to complex dairy matrices for goat milk authenticity detection, successfully achieving simultaneous detection of adulterated DNA from cow milk and soy milk in goat milk samples. This demonstrated its good applicability in complicated food matrices, and it was expected to be a universal platform for detecting various nucleic acid markers in other matrices.
Canine distemper virus (CDV) can cause fatal viral infection in domestic and wild animals globally. Several lineages are known, originating from distinct geographical regions and hosts, and can spread naturally or through human intervention into new geographic areas. The Arctic lineage was first described in carnivores of the Arctic ecosystems and subsequently reported in several European and Asian countries, yet its origin, evolution, and ecology remain partially unresolved. In this study, we generated genome sequence data of (n = 16) CDV strains of Arctic lineage collected from dogs in Italy over a nearly 15-year period, providing an extensive dataset to investigate the evolution of this particular lineage. We also generated genome data of seven Europe strains of another major lineage collected during the same period from red foxes (n = 3) and dogs (n = 4). Inter-lineage recombination events were identified in two CDV sequences. Sequence 2008 of the European lineage acquired a fragment from an Arctic lineage virus between the N and P genes. Sequence 2015 of the Arctic lineage displayed a more complex recombination pattern with fragments from Europe, America-2, and Rockborn lineages across multiple genes and hosts. Phylogenetic tree showed that the oldest Italian Arctic lineage from 2006 was more similar to the oldest Arctic CDV isolates, whilst a well-defined sub-cluster circulated from 2009 onwards in domestic and wild carnivores. These results provide novel insights into CDV evolution in Europe and emphasize the importance of ongoing genomic monitoring.
In gas chromatography (GC) analysis, the gas flow control performance of the Electronic Pressure Control (EPC) system that is responsible for signal analysis and processing, critically determines the reliability and accuracy of analytical results. However, during the gas flow control, unknown hysteresis characteristics, voltage saturation, and state constraints significantly impact control performance. This study addresses the fuzzy adaptive practically finite-time output feedback and signal processing problem for the EPC system in GC, incorporating system state constraints and unknown hysteresis characteristics. First, a modified Prandtl-Ishlinskii model is employed to accurately describe the valve's unknown asymmetric hysteresis. A dynamic model reflecting the actual system, incorporating gas resistance characteristics, is then established. Second, a fuzzy state observer based on a fuzzy logic system (FLS) is designed to estimate unmeasurable system states. Third, considering state constraints and potential computational complexity, a fuzzy adaptive practically finite-time controller is proposed. This controller, built upon the observer, integrates backstepping, dynamic surface control (DSC), and barrier Lyapunov functions (BLF), utilizing filters for smooth processing of virtual signals. System stability is then proven via Lyapunov theory. Finally, experimental verification is performed using both step and dynamic gas flow targets with a newly constructed EPC system. The results demonstrate that the proposed controller achieves precise and stable tracking control of gas flow, even in the presence of unknown hysteresis, state constraints, and voltage saturation.
Glucagon-like peptide-1 receptor agonists (GLP-1RAs) have transformed the management of obesity in adults and are now gaining attention in pediatric populations facing a dramatic rise of obesity prevalence and related comorbidities. In addition to weight loss, their role extends to cardiometabolic effects and improvements of kidney function. Liraglutide and semaglutide have demonstrated clinically meaningful efficacy in adolescents, leading to FDA and EMA approvals for patients ≥12 years. Ongoing trials are being conducted to combine GLP-1 analogues with other effective molecules or with bariatric surgery. Current evidence on safety most frequently highlights gastrointestinal adverse events, with no consistent impact on growth or pubertal development reported to date. Psychosocial dimensions, including stigma, mental health risks, and potential disordered eating, together with economic barriers and disparities in access, require careful consideration and efforts to be overcome. Implementing intensive lifestyle interventions is mandatory, including nutritional education, physical activity promotion, and family-based behavioral strategies, to support long-term weight management and address the broader determinants of health. Preliminary studies suggest complementary roles for GLP-1RAs alongside metabolic bariatric surgery in selected high-risk patients. Long-term data on safety and multidisciplinary approaches are required to define the optimal integration of pharmacotherapy into comprehensive, family-centered pediatric obesity care models.
Gut microbiota may modulate pulmonary inflammation through the gut-lung axis. This study investigated the association between washed microbiota transplantation (WMT) and short-term changes in pulmonary function, inflammatory markers, and gut microbiota in patients with abnormal spirometric patterns. A total of 110 patients who underwent fecal microbiota transplantation, also referred to as WMT, were consecutively screened between March 2023 and January 2025. Of these, 47 patients with paired baseline and post-WMT spirometric data were included in the primary spirometric analysis. According to baseline spirometric patterns, WMT recipients were classified into an abnormal spirometric-pattern group (DG, n = 19) and a normal spirometric-pattern WMT-recipient group (HC, n = 28; HC denotes WMT recipients with normal spirometry rather than healthy community controls). In addition, 43 patients receiving conventional treatment without WMT were included as a non-WMT comparison group (CON). The WMT group underwent multi-course interventions with longitudinal monitoring of pulmonary function parameters, inflammatory markers, breath-holding time (BHT), and 36-Item Short Form Health Survey scores (SF-36). Gut microbiota composition and predicted functional profiles were analyzed using 16S rRNA gene sequencing. After one WMT course, DG patients showed increases in forced vital capacity (FVC) and forced expiratory volume in 1 s (FEV1). Compared with the non-WMT comparison group, the change in FVC was greater in WMT recipients, whereas the between-group difference in FEV1 change was not statistically significant. Other spirometric indices, BHT, inflammatory markers, SF-36 scores, and microbiome-related findings were considered exploratory. Exploratory 16S rRNA gene sequencing identified differences in selected gut microbial taxa between WMT recipients with abnormal and normal spirometric patterns, including differences in Firmicutes, Faecalibacterium, and Alistipes. Predicted functional profiling suggested changes in glycerolipid metabolism-, Nod-like receptor signaling-, and bacterial chemotaxis-related functional potentials. WMT was associated with short-term changes in selected spirometric parameters, particularly FVC and FEV1, in patients with abnormal spirometric patterns. Changes in inflammatory markers, BHT, SF-36 scores, and microbiome-related findings were exploratory and hypothesis-generating. Further randomized, disease-specific studies with standardized pulmonary function testing and mechanistic validation are needed.
The remediation of cadmium (Cd)-contaminated agricultural soils poses great challenges. Electrokinetic technology can effectively remediate Cd-contaminated soils, but the electrode polarization effect restricts its remediation efficiency. Therefore, in this study, Cd-contaminated paddy soil samples from northern Guangxi were used as the research object. An L₉(3⁴) orthogonal experimental design was employed to investigate the effects of power supply duration, voltage gradient, power supply mode, and electrolyte type on the remediation efficiency of Cd-contaminated soil via electrogeochemical survey technology, and to determine the optimal electrokinetic remediation parameters. The results indicate that the optimal electrokinetic remediation parameters were a voltage gradient of 0.6 V/cm, a duration of 144 h, continuous power supply, and EDTA-2Na as the electrolyte. Among all experimental runs, the highest measured removal efficiency (49.14%) was achieved in the EK6 group. Statistical analysis revealed that the priority of influence of each factor is electrolyte type > voltage gradient > power supply duration, whereas the effect of the power supply mode was not significant. Mechanistic analysis reveals that EDTA-2Na forms EDTA4- at the cathode, which coordinates with Cd2+ to generate the stable [Cd-EDTA]2- complex. This process effectively mobilizes the recalcitrant Cd fractions, and increases the proportion of the water-soluble fraction of Cd in soil from less than 0.1% to over 35%. Concurrently, the electrogeochemical survey configuration suppressed electrode polarization, and no white film deposition was observed on any of the electrodes. These combined effects resulted in an average Cd removal efficiency of 46.6% with EDTA-2Na, substantially outperforming both citric acid (39.4%) and double deionized water (41.2%). The combined application of electrogeochemical survey with EDTA-2Na forms a synergistic multiphase electrochemical reaction mechanism, significantly improving the overall remediation efficiency of Cd-contaminated soil.
Light regulates the biosynthesis of plant volatile flavor metabolites, yet how supplemental blue light modulates the accumulation of volatiles in distinct celery (Apium graveolens L.) tissues and the associated molecular mechanisms remain largely unclear. In the present study, volatile metabolomics and transcriptomics approaches were employed to analyze volatile organic compounds (VOCs) and gene expression levels in celery leaves and petioles under supplementary blue light. The results revealed that supplementary blue light induced 251 and 318 differentially abundant metabolites in celery leaves and petioles, respectively. The content of 16 compounds (4 upregulated and 12 downregulated) in celery leaves significantly changed, including those with fruity, sweet, green, popcorn, and vegetable notes. Furthermore, the content of 42 compounds (31 upregulated and 11 downregulated) associated with sweet, green, fruity, cucumber, and citrus notes in celery petioles significantly changed. The combined transcriptomic and metabolomic analysis results revealed significant enrichment in phenylpropanoid metabolic pathway in celery petioles. Specifically, supplementary blue light significantly reduced CAD and CCR gene expression levels in celery, potentially reducing the contents of methyl chavicol and anethole. In conclusion, supplemental blue light significantly altered the accumulation profiles of flavor volatiles across different celery tissues, with metabolic changes concentrated in petioles and showing prominent tissue specificity. Moreover, blue light reprogrammed phenylpropanoid metabolism in petioles, driving the alterations of flavor-related volatiles. Our research clarifies the physiological basis of light-mediated flavor formation in vegetables and offers a foundation for elucidating the underlying metabolic pathways involved in plant secondary metabolism.
Multifunctional perovskite nanostructures capable of addressing sustainable energy and biomedical challenges are of great interest to researchers. In this study, BiFeO3 (BFO), Ag-modified BFO (BFO-Ag), fuel-assisted α-BFO, and biofuel-assisted Ag-modified α-BFO (α-BFO-Ag) nanostructures were synthesized via solution combustion using Ravenia spectabilis leaf extract as fuel to investigate the effects of compositional and synthetic modifications on structural, magnetic, anticancer, and electrochemical properties. The evaluated band gap (3.09 and 3.15 eV) suggests effective charge-transport. The formation of rhombohedral BiFeO3 (JCPDS #01-074-2016) was confirmed by PXRD analysis in all samples with distinct diffraction planes. Conversely, the weak secondary reflections of Bi2O3 observed in pristine BFO were significantly diminished in the Ag-modified and fuel-assisted samples, indicating enhanced phase purity. Also, the same was affirmed by conducting Rietveld refinements for the obtained pattern. Morphological analysis revealed densely packed, agglomerated, and polyhedral nanostructures with distinct grain boundaries and compact surface features, characteristic of combustion-derived materials. BET studies revealed the mesoporous feature of the synthesized α-BFO-Ag nanostructure. Magnetic measurements indicated an enhanced magnetic response for α-BFO-Ag compared to the other synthesized samples, which may be associated with Ag incorporation and defect-induced modifications in the magnetic ordering. The nanostructures also exhibited dose-dependent cytotoxicity against MDA-MB-231 triple-negative breast cancer cells, with α-BFO-Ag demonstrating comparatively higher anticancer activity among the investigated samples. Based on electrochemical investigations, the BiFeO3@Ag + Fuel electrode demonstrated low overpotentials of 82 mV at 10 mA cm- 2 and low charge-transfer resistance (11.97 Ω) along with enhanced hydrogen evolution reaction (HER) efficiency. Ag incorporation and fuel-assisted synthesis led to enhanced charge transport, surface activity, and biological response, making these nanostructures promising dual-functional materials for sustainable hydrogen production and anticancer applications.
Developing effective antiviral strategies is urgently needed during global viral pandemics. Traditional approaches, including small-molecule inhibitors, neutralizing antibodies, and RNA interference (RNAi), often face challenges such as drug resistance, limited specificity, and inefficient delivery. These limitations highlight the pressing need for innovative strategies focused on the targeted degradation of viral proteins. We developed an optimized Trim-Away system employing a receptor-Fc fusion protein strategy. This system integrates the E3 ubiquitin ligase TRIM21 with engineered receptor-Fc proteins to ensure highly specific recognition and intracellular degradation. A key innovation is the use of the Semliki Forest virus (SFV) self-amplifying replicon (pSFV). This platform enables sustained and robust expression of the Trim-Away components. Furthermore, this plasmid-based delivery eliminates the need for protein purification, thereby streamlining the process and improving delivery efficiency. The system effectively degrades diverse viral targets. Specifically, it successfully degraded the spike proteins of both wild-type SARS-CoV-2 and its Omicron variant. It also targeted adeno-associated virus (AAV) capsid proteins. In vivo assays further confirmed that the self-amplifying replicon markedly reduces AAV-encoded luciferase expression. These data demonstrate that the system maintains high potency even at low dosages. Our findings demonstrate that the pSFV-driven Trim-Away system is a powerful tool for viral protein degradation. The receptor-Fc strategy provides a significant advantage against rapidly mutating viruses. This study establishes a versatile and adaptable platform for future antiviral intervention.
In recent years, the analysis of intact peptides (peptidomics) has remained a relatively under-explored field, where its potential as a bridge between proteomics and metabolomics has only recently been recognized. Currently, immunoassay techniques are the primary method for quantifying endogenous peptides in biological samples, mainly due to their high-throughput and minimal sample manipulation. However, antibody-based strategies face important limitations, particularly in terms of specificity.Liquid chromatography-tandem mass spectrometry has emerged as a powerful and reliable alternative for the analysis of native peptides in biological fluids. While conventional sample preparation approaches, such as protein precipitation, enable rapid and broad peptide extraction, they are often inadequate for quantifying low-abundance peptides, leading to matrix effects and ion suppression.This chapter explores the critical aspects of antibody-free sample preparation in peptidomics, emphasizing advanced and innovative approaches. We also discuss methods for peptide isolation and purification, spanning from traditional approaches such as protein precipitation and solid-phase extraction to more recent microextraction techniques. Special attention is given to microextraction by packed sorbent, ultrafiltration, and electromembrane extraction, which offer significant advantages in efficiency, sensitivity, and scalability. By minimizing both solvent and sample requirements, these approaches provide new opportunities for improving analytical performance in peptidomics.
Renewable energy will reduce the strain on the energy supply to some degree; however, many challenges exist in its organic integration with the current energy system, thus prompting a new round of transformation of the existing energy system. Inspired by the Internet concepts, methods, and technologies, the Energy Internet, an open and equal facility for convenient access and intelligent use of energy throughout the chain from production and transmission to consumption, has become a significant development trend. Energy, big data has enormous potential value in facilitating the demand-driven allocation of energy resources and the optimization and transition of the energy structure. The green quality evaluation of metropolis energy big data belongs to the MAGDM category. Recently, ExpTODIM and PROMETHEE techniques have been applied to solve MAGDM problems. In the green quality evaluation of metropolis' energy big data, probabilistic linguistic term sets (PLTSs) characterize uncertain information. In this paper, the probabilistic linguistic ExpTODIM-MABAC (PL-ExpTODIM-MABAC) technique is constructed and proposed to solve MAGDM problems with PLTSs. The MEREC technique obtains weights under PLTSs. Finally, an example of the green quality evaluation of metropolis' energy big data is provided to demonstrate the ExpTODIM-MABAC approach.
Although the safety and effectiveness of robot-assisted surgery for rectal cancer have been demonstrated, its peri-operative safety and long-term survival benefit in patients who have received neoadjuvant chemoradiotherapy (NCRT) remain unclear. A retrospective cohort of 463 consecutive rectal-cancer patients who underwent either robot-assisted anterior resection or laparoscopic anterior resection after NCRT at Sun Yat-sen University Cancer Center from June 2016 to August 2023 was analyzed. Propensity-score matching (PSM) was applied to balance baseline variables that could affect surgical outcomes and survival. Peri-operative parameters, complication rates, and pathological findings were compared, and 5-year overall survival (OS) and disease-free survival (DFS) were calculated. A total of 121 patients undergoing robotic-assisted surgery and 342 patients undergoing laparoscopic surgery were included in the study. Operative time was longer in the robot-assisted group both before and after matching. Before matching, the robot-assisted group showed a lower rate of ileostomy (65.3% vs 76.6%, P = 0.021), an advantage that disappeared after PSM (65.3% vs 75.2%, P = 0.063). In the matched cohort, the 5-year DFS was 89.8% for robot-assisted versus 82.3% for laparoscopic (P = 0.130), whereas the 5-year OS was higher in the robot-assisted group than in the laparoscopic group (96.8% vs 86.8%; P = 0.019). In the primary preoperative covariate-adjusted Cox model after PSM, the association between robot-assisted surgery and OS was attenuated and did not reach conventional statistical significance (HR = 0.231, 95% CI 0.052-1.015; P = 0.052). Robot-assisted and laparoscopic surgery showed comparable perioperative outcomes and DFS after NCRT for rectal cancer. Although an apparent OS difference was observed in the matched cohort, this finding was attenuated in the primary preoperative covariate-adjusted Cox model and should be interpreted cautiously given the potential for residual confounding.
Unmineable coal seams and residual coal resources in closed mines provide ideal carriers for CO₂ sequestration. However, the evolution mechanisms of permeability and displacement efficiency during CO₂-ECBM in low-permeability coal seams remain unclear, which restricts the engineering application efficiency of this technology. In this study, the No. 15 coal seam of the Lutaishan Coal Mine was taken as the research object. A fully coupled thermal-hydraulic-mechanical model of CO₂-ECBM was constructed and validated using COMSOL Multiphysics software, and a quantitative evaluation method for key influencing factors was proposed based on coal deformation effects. The results indicate that injection pressure is the dominant factor affecting permeability, and its influence range expands with increasing pressure and time. In the near-injection well region, coal matrix swelling induced by CO₂ adsorption is stronger than matrix shrinkage caused by CH₄ desorption, leading to a decrease in coal reservoir permeability; the higher the injection pressure, the more significant the permeability reduction. In the near-production well region, CH₄ desorption causes matrix shrinkage, resulting in increased permeability. The intermediate zone remains relatively stable. The influence of injection temperature is weak: in the near-injection well area, thermal expansion reduces permeability; in the adjacent zone, reduced gas adsorption capacity leads to a recovery in permeability; the peripheral zone is stable, and the near-production well area remains dominated by CH₄ desorption. Increasing injection pressure accelerates displacement, promoting CH₄ recovery and CO₂ storage, while increasing temperature slightly reduces recovery efficiency but has a minor impact. Under all scenarios, CO₂ storage exceeds CH₄ production. This study aims to provide guidance for injection strategies of CO₂-ECBM in low-permeability coal seams, and its findings help optimize displacement parameters, thereby improving engineering implementation efficiency.
Health economics and implementation science play a critical role in the uptake of evidence-based practice but have largely sat siloed. This paper summarises findings from a 3-day workshop on health economics and implementation science. Workshop attendees included 30 health economists, implementation scientists, patient contributors, and patient and public involvement and engagement researchers from Australia and the United Kingdom. A shared vision for moving from siloed to synergistic disciplinary approaches was derived through consensus. This article outlines to researchers and methodologists what synergistic disciplinary approaches could look like. We highlight opportunities for health economics and implementation science to integrate along the innovation pathway, from the development and evaluation of innovation to eventual uptake and spread. Greater collaboration between implementation scientists and health economists has the potential to optimise implementation strategies, provide robust evidence for value for money and ultimately improve care delivery. Stronger integration of health economics and implementation science may also shed more light on the equity impacts of implementation strategies and guide their further design to promote more equitable care and outcomes.
Hospital-at-Home (HaH) has been shown to be clinically and cost effective, but many programs struggle to scale. We conducted a systematic review of HaH studies reporting implementation contexts, strategies and outcomes with the primary aim to synthesise the implementation evidence on HaH. Our secondary aim was to develop a framework of HaH implementation outcome indicators for future studies to apply. We searched the literature PubMed, Embase and Scopus, for publications from 2012 to 2022. We included studies related to HaH that included at least one implementation context and at least one implementation outcome variable, excluding clinical outcomes measures. At least two reviewers independently selected studies, abstracted data and assessed quality. We coded the implementation determinants to the Consolidated Framework for Implementation Research (CFIR); implementation strategies to the Expert Recommendations for Implementing Change (ERIC); and implementation outcomes to the RE-AIM (Reach, Effectiveness, Adoption, Implementation, Maintenance) frameworks. We included 27 studies in the final review, which reported 24 CFIR contextual factors, 25 ERIC strategies, and all domains of the RE-AIM evaluation framework. The most commonly reported implementation determinant, identified in six studies, was information technology infrastructure, which included streamlining documentation processes and improving access to patient records. Five studies described the following implementation factors: (i) work infrastructure, focusing on organising manpower effectively; (ii) capability of innovation deliverers, referring to staff posessing the necessary skills to perform their roles; and (iii) local conditions, such as the geographical challenges associated with HaH. The most frequently referenced implementation strategies were conducting ongoing training (nine studies), creating new clinical teams (eight studies), and promoting adaptability (seven studies). All assessed implementation outcomes were aligned to the RE-AIM framework, with volume of admissions, patient-reported experiences or outcome measures, and home visit frequency being most commonly reported (four studies each). The majority of implementation measures lacked a common denominator, and use of validated tools across studies was lacking. We identified a list of implementation determinants, strategies and outcomes that can be used to inform scale-up of HaH, and have developed a framework for consistent reporting of HaH implementation studies grounded onto RE-AIM.