Low Cost Research Articles

High-resolution high-throughput spatiotemporal strain imaging reveals loss mechanisms in a surface acoustic wave device

Surface acoustic wave devices are key components for processing radio frequency signals in wireless communication because these devices offer simultaneously high performance, compact size and low cost. The optimization of the device structure requires a quantitative understanding of energy conversion and loss mechanisms. Here we use stroboscopic full-field diffraction x-ray microscopy to reveal an unanticipated acoustic loss in a prototypical one-port resonator device. A non-uniform acoustic excitation in the active area was responsible for the substantial end and side leakages observed at the design frequency. Quantitative analysis of the strain amplitude using a wave decomposition method allowed the determination of several key device parameters. This high-resolution high-throughput spatiotemporal strain imaging technique is more generally applicable to the study of dynamic strain modulation in nanoscale acoustic, electronic, optical and quantum devices. The high sensitivity allows precise measurement of the strain modulation with picometer-scale amplitude.

Enhanced electrochemical performance of zinc-ion batteries using functionalized nano-chitin separators

The development and evaluation of a novel separator material for aqueous zinc-ion batteries (ZIBs) are discussed, which are promising for energy storage due to their use of abundant zinc metal, eco-friendly electrolytes, and high safety. The main challenges in ZIBs are the formation of zinc dendrites and the hydrogen evolution reaction (HER), which can lead to short circuits and reduced battery life. To address these issues, the authors have functionalized chitin separators through a process involving alkaline treatment and mechanical grinding, resulting in nano-chitin fibers with varying degrees of deacetylation (D-x-ChNF). The D-4-ChNF separator, in particular, has been shown to have uniformly distributed nanochannels, high mechanical strength, and excellent electrochemical stability. It also exhibits a strong affinity for aqueous ZnSO4 electrolytes and enhances the electrochemical reversibility of zinc through increased coordination with Zn2+ ions. This leads to a significant improvement in the battery’s cycle life, with the D-4-ChNF separator outperforming traditional glass fiber (GF) separators by more than six times in cycle life at 5 mA cm−2 and 5 mAh cm−2. The D-4-ChNF separator also demonstrates superior rate performance and long-term cycling stability, with capacity retention rates of 90.46% and 96.68% after 1000 cycles at 5 A g−1 and 10 A g−1, respectively. The separator’s ability to suppress dendrite growth and improve the uniformity of zinc deposition is attributed to its ability to reduce nucleation overpotential and promote uniform zinc deposition along the (002) crystal plane. The D-4-ChNF separator, with its low cost and high stability, offers a promising solution for enhancing the performance and practical application of ZIBs, providing new insights into the development of efficient and sustainable energy storage technologies.

Physics-based forward model for near-real-time quantitative imaging of spent nuclear fuel assemblies

The Passive Gamma Emission Tomography (PGET) instrument, authorized by the International Atomic Energy Agency (IAEA) for verification of spent nuclear fuel, aims to reconstruct 2D cross-sectional images of spent fuel assemblies (SFAs), identify missing or present fuel pins, and quantify fuel pin activities. Although the first two objectives are reliably achieved, accurate determination of fuel pin activities remains a challenge due to intense self-shielding and scattering effects. We have developed a linear inverse approach that addresses these effects and demonstrated superior image quality and identification accuracy in simulation studies. This approach frames the image reconstruction process as an inverse problem, relying on a physics-based forward model of the PGET system. We improved our forward model by incorporating collimator septal penetration and detector scattering effects. The enhanced forward model enables near-real-time sinogram simulation and system matrix calculation, which is> 100,000 times faster than 3D Monte Carlo simulations. The model was validated through simulations of VVER-1000 and VVER-440 SFA, and a relative difference of 3.7% in counts was achieved between MCNP and our forward model. Based on this enhanced model, we successfully reconstructed images from the simulated data, identified 100% of the fuel pins, and achieved an average uncertainty of 2.3% in activity quantification. We applied the reconstruction method to measured data of VVER-440 SFAs, successfully imaging all the pins, including the innermost ones, and identifying the water channel within the SFA. The high accuracy and low computational cost of our forward model demonstrate its potential for real-world inspection scenarios and enable future algorithm development.

The Healthy Hearts program to improve primary care for hypertension in seven rural health units of Iloilo Province, Philippines: a comparative cost study

BackgroundIn 2021, the Philippines launched the Healthy Hearts demonstration project for delivering hypertension (HTN) services in seven Rural Health Units (RHUs) in District 1 of Iloilo Province, West Visayas Region. This study evaluates the provider time cost and medication cost of delivering these services under three medication procurement scenarios, projecting them to the district and province levels to inform scaling-up efforts.MethodsA mixed-methods design was used for cost data collection, including key informant interviews (KII), focus group discussions (FGD), and secondary data sources. The HEARTS costing tool was adapted to analyze program costs per patient from the health system perspective. Three scenarios were assessed, depending on the procurement scheme of HTN medications: baseline local government procurement, pooled procurement through the Philippine Pharma Procurement Inc. (PPPI) national pooling mechanism, and private pharmacy outsourcing. We assessed annual provider labor costs and medication costs per patient for each scenario.ResultsThe average provider cost per patient was considerably lower for patients with controlled HTN than for patients with uncontrolled HTN: USD 5 (range USD 3.4–6.1 across RHUs) vs. USD 32.9 (range USD 28.8–38.4)) due to the need for more frequent follow-up visits for the latter. Average medication costs per patient were estimated at USD 9.1 (range USD 7.2–11.5) using local procurement prices, USD 2.9 (range USD 2.3–3.7) using PPPI pooled procurement prices, and USD 23 (range USD 17.9–30.5) using private pharmacy outsourced prices. The higher medicine costs in the pharmacy outsourcing scenario were partially offset by lower provider costs (an average reduction of USD 1.5 per patient per year) due to reduced on-site dispensing time in this scenario.ConclusionsThe findings from this study indicate two key opportunities for cost savings in HTN management in the Philippines' rural health units system: 1) enhancing the control of HTN, thereby reducing the need for follow-up visits and cutting down on provider time costs, and 2) utilizing pooled medication procurement mechanisms such as through the Philippine Pharma Procurement Inc. Provider time costs can also be partially reduced through outsourcing the dispensing of medications to private pharmacies, although doing so is currently associated with higher medication costs, further underscoring the utility of pooled procurement mechanisms for essential hypertension medicines.

Ion-Docking Effect Enabling Rechargeable High-Voltage Magnesium-Iodine/Chlorine Battery.

Rechargeable magnesium (Mg) batteries represent a promising energy storage system by offering low cost and dendrite-less propensity. However, the limited selection of cathode materials, and often with low voltage and capacity, constrain Mg batteries. Herein, by exploiting the ion-docking effect between two halogen species - iodine cations (I+) and chlorine anions (Cl-) - we activate the cathodic activity of halogens and develop a magnesium-iodine/chlorine (Mg-I/Cl) battery prototype with high energy and power density. The ion-docking effect enables I+ and Cl- to mutually balance and disperse their charges, weakens the coordination strength between Cl- and Mg2+ while enhances the stability of I+, thus facilitating the multi-electron (2+1/3) redox reactions of halogens. We also find the solvation state of iodine species determine the reaction process of the I0/I3-/I- redox couples. The here-developed magnesium-iodine/chlorine battery features an impressively high discharge plateau of up to 3.0 V with a high capacity exceeding 400 mAh g-1, and demonstrates a stable lifespan for 500 cycles, with the ability of ultra-fast charging at 20C and low-temperature cycling under -30 °C. These findings may provide new insights for developing high-energy-density Mg battery systems.

LESS IS MORE: classified management of hypertriglyceridemia-induced acute pancreatitis on the basis of a propensity score matching cohort study

BackgroundEffective management of hypertriglyceridemia is crucial in the treatment of hypertriglyceridemia-induced acute pancreatitis (HTG-AP). The prognosis of HTG-AP may vary with different serum triglyceride levels, suggesting the need for stratified treatment approaches. In this study, we investigated hypertriglyceridemia management in HTG-AP patients and the optimal strategy.MethodsPatients with HTG-AP from October 2020 to October 2022 were included in the study. Propensity score matching was used to balance the bias and confounding variables. A mixed-effects model was used to analyse the decreasing tendency of triglycerides.ResultsA total of 171 patients who were diagnosed with HTG-AP were enrolled in this cohort. Patients with very severe serum triglycerides (> 22.6mmol/L) had a higher proportion of severe acute pancreatitis (p < 0.05) than patients with severe hypertriglyceridemia (11.3–22.6 mmol/L). For the very severe hypertriglyceridemia group, no significant differences in prognosis were noted between the insulin and heparin group and the plasma exchange group. The cost of the insulin and heparin group was significantly lower than that of the plasma exchange group (p < 0.01). In patients with severe hypertriglyceridemia, no significant differences in prognosis were noted between the nothing-by-mouth (NPO) group and the insulin and heparin group. Compared with the insulin and heparin group, the NPO group had lower hospital costs (p < 0.05).ConclusionHTG-AP patients with very severe hypertriglyceridemia may be treated safely and effectively with insulin and heparin, potentially offering a more cost-effective treatment approach. Similarly, patients with severe hypertriglyceridemia might benefit from treatment involving NPO, which may be associated with lower costs. Further studies are needed to validate these findings in diverse populations and through long-term follow-up.

Conductive Carbon Fibrous Interlayer Embedded with MoS2@CNT Composites for Mitigating Polysulfide Shuttling by Absorption and Catalysis in Lithium-Sulfur Batteries.

Lithium-sulfur batteries (LSBs) have emerged as promising energy storage systems due to their high energy density, low cost, and environmental friendliness. However, the "shuttle effect" of lithium polysulfides (LiPSs) leads to rapid capacity decay and poor cycle stability in LSBs, hindering further development and application of LSBs. In addition, it is difficult for existing strategies to provide effective adsorption and catalytic properties for polysulfides while simultaneously ensuring rapid ion transport. To address this issue, a composite film made of carbon fibers embedded with MoS2@CNT is proposed as an interlayer between the separator and the cathode. Results show that such a conductive interlayer can effectively capture LiPSs and catalyze their transformation. The as-assembled LSBs deliver an initial discharge-specific capacity of 1179.03 mAh/g at 0.5 C, and a capacity of 1086.33 mAh/g remains after 100 cycles. During long-term cycling tests, the LSBs show discharge capacities of 463.13 mAh/g with a decay rate per cycle of 0.07% after 500 cycles at 3 C, representing a reduction of 0.06% compared to that of commercial batteries (0.13%). This work demonstrates the potential of the independent conductive interlayer design of carbon fibrous films embedded with MoS2@CNT composites for enhancing battery performance. The film can not only provide an efficient conductive network for accelerating ion transport but also suppress the shuttle effect and catalyze the transformation of LiPSs, boosting electrochemical reaction kinetics.

Aqueous-S vs Organic-S Battery: Volmer-Step Involved Sulfur Reaction.

Aqueous-S batteries (ASBs) are emerging as promising energy storage technologies due to their high safety, low cost, and high theoretical energy density. However, the present understanding of sulfur evolution in water relies on experience derived from conventional organic electrolyte-based sulfur batteries (OSBs). The gap between ASB and OSB has impeded progress in advancing the rational design of sulfur catalysts in the aqueous phase. Herein, we reveal the unique interaction between H2O and S species, which is fundamentally distinguishable from the organic counterparts. A series of spectroscopy analyses discloses that elemental sulfur is initially reduced to polysulfides (mainly S42-), which subsequently react with H2O to generate HS-, involving both polysulfide conversion and the Volmer step of water dissociation. Combined electrochemical and computational analysis further proposes an aqueous-S catalyst selection metric based on simultaneous polysulfide adsorption and Volmer-step catalysis. As a proof of concept, we have successfully prioritized the Mo2C-catalyzed ASBs with a superior rate capability of 1040 mAh g-1 than the Fe3C (693 mAh g-1) and pure C (510 mAh g-1) at a high current density of 5 A g-1. This work provides insights into the aqueous-S charge storage mechanism and establishes a foundational catalyst research paradigm for advancing the following ASBs.

A Deep-Learning Empowered, Real-Time Processing Platform of fNIRS/DOT for Brain Computer Interfaces and Neurofeedback.

Brain-Computer Interfaces (BCI) and Neurofeedback (NFB) approaches, which both rely on real-time monitoring of brain activity, are increasingly being applied in rehabilitation, assistive technology, neurological diseases and behavioral disorders. Functional near-infrared spectroscopy (fNIRS) and diffuse optical tomography (DOT) are promising techniques for these applications due to their non-invasiveness, portability, low cost, and relatively high spatial resolution. However, real-time processing of fNIRS/DOT data remains a significant challenge as it requires establishing a baseline of the measurement, simultaneously performing real-time motion artifact (MA) correction across all channels, and (in the case of DOT) addressing the time-consuming process of image reconstruction. This study proposes a real-time processing system for fNIRS/DOT that integrates baseline calibration, denoising autoencoder (DAE) based MA correction model with a sliding window strategy, and a pre-calculated inverse Jacobian matrix to streamline the reconstructed 3D brain hemodynamics. The DAE model was trained on an extensive whole-head high-density DOT (HD-DOT) dataset and tested on separate motor imagery dataset augmented with artificial MA. The system demonstrated the capability to simultaneously process approximately 750 channels in real-time. Our results show that the DAE-based MA correction method outperformed traditional MA correction in terms of mean squared error and correlation to the known MA-free data while maintaining low latency, which is critical for effective BCI and NFB applications. The system's high-channel, real-time processing capability provides channel-wise oxygenation information and functional 3D imaging, making it well-suited for fNIRS/DOT applications in BCI and NFB, particularly in movement-intensive scenarios such as motor rehabilitation and assistive technology for mobility support.

Arthroscopic Bankart repair using trans-glenoid double-loaded grand knots versus double-loaded suture anchors; is there a difference? a randomized controlled study

BackgroundAnatomical repair of Bankart lesions and restoring the tension of the antero-inferior capsulo-labral complex is the optimum method of surgical treatment with a variety of fixation methods including suture anchors and trans-glenoid sutures. Grand knot technique is a modification of the trans-glenoid sutures technique that can be an alternative to double-loaded suture anchors with a lower cost. We aimed to compare the outcomes and complications of both techniques.MethodsThis is a randomized controlled study that was conducted on 200 patients with recurrent anterior glenohumeral dislocation, of whom 170 patients completed at least a three-year follow-up period. Arthroscopic Bankart repair using two double-loaded knotted suture anchors was performed in 78 cases (Group A) while repair was done using two trans-glenoid grand knots in other 92 cases (Group B). Patients were evaluated in terms of range of motion, functional scores (Constant, Rowe, and ASES), and complication rate.ResultsThe mean operative time was significantly longer in Group B (87.7 ± 24) minutes compared to Group A (61.2 ± 28.1) minutes (P = 0.002). No statistically significant difference was found between both groups regarding postoperative external rotation range of adducted arm, functional scores, and rate of recurrence. Only forward flexion and external rotation of abducted arm were significantly better in Group A (P = 0.005 and < 0.001 respectively).ConclusionTrans-glenoid double-loaded grand knot technique is an alternative surgical option for the treatment of Bankart lesions with comparable results to double-loaded anchors regarding the functional outcomes and failure rates.Clinical Trial Registration (Retrospectively registered)Registration number: NCT06394609 28-4-2024.

A Facile Method for Gel Electrophoresis with Intrinsic Fluorescence Imaging for Self-Aggregation and Stability Assay of Monoclonal Antibody.

Monoclonal antibodies (mAbs) are widely used in tumor and autoimmune disease therapy and clinical diagnosis, but they suffer from self-aggregation, particularly dimer formation, impacting their stability, efficacy, and potentially causing severe allergies. Traditional methods for detecting mAb dimers, such as TEM, AUC, DLS, SEC, and CE, are limited by low throughput, high costs, and qualitative data, making them unsuitable for large-scale sample assays in biopharmaceuticals industry as well as antibody research. To address these issues, we developed a facile method of protein cross-linking gel electrophoresis with online intrinsic fluorescence imaging (PC-GE-IFI) for self-aggregation and stability assays of mAbs. This method enables the real-time quantification of monoclonal antibody (mAb) monomers and dimers with exceptional sensitivity, characterized by low detection limits (monomer: 0.9 nM; dimer: 0.45 nM) and a broad dynamic range (monomer: 2.50-2500 nM; dimer: 1.25-1250 nM). Furthermore, the sample wells can be used as windows for the assay of precipitates formed by mAb aggregation. The method supports accurate assessment of mAb dimerization and monomer purity under various stress conditions, including thermal stress, mechanical agitation, and freeze-thaw cycles. Moreover, the method allows concurrent analysis of dimers and precipitates across multiple samples at different concentrations, while nonlinear fitting provides the dissociation constant (Kd) for monomer-dimer interactions, a critical parameter that aids in assessing aggregation propensity, and informs the design and development of mAb products The PC-GE-IFI method has great potential for development, quality control, and safety assessment of mAbs, bispecific antibodies, ADCs, and protein drugs.

A Framework for Quantifying the Size and Fractal Dimension of Compacting Soot Particles.

Black carbon (BC) is a strongly absorbing component of atmospheric aerosols that has a significant warming effect. BC particles are emitted from combustion sources as open-structured fractal aggregates. After emission, BC is often compacted due to capillary condensation of semivolatile vapors to form coatings. The addition of coatings influences the size and radiative properties of BC, but representing these details in radiative transfer models is computationally difficult and often neglected. Laboratory studies have measured BC restructuring during coating but rarely provide information on changes in particle shape. Here, we combine laboratory measurements of BC compaction with detailed restructuring models to develop a framework for predicting the size and shape of BC as a function of coating volume ratio, a property already tracked in large-scale atmospheric models. The framework predicts the mobility diameter and fractal dimension of BC particles as a function of coating volume throughout compaction with root-mean-squared error (RMSE) values less than 6.8 and 4.3%, respectively. These properties are predicted for both the coated particle and the BC core. Our proposed framework will enable a more complete representation of the evolving size and shape of BC throughout its atmospheric lifetime, thereby improving model accuracy at a low computational cost.

Drivers of Intraoperative Costs for Transsphenoidal Endoscopic Surgery for Sellar Lesions: A Time-Driven Activity-Based Cost Analysis.

Neurosurgeons lack precise insights into the true costs of transsphenoidal endoscopic surgery for sellar and suprasellar lesions (TESS), including pituitary adenomas, craniopharyngiomas, and apoplexy. To address this critical knowledge gap, we employ time-driven activity-based costing (TDABC) for TESS. We analyzed 221 TESS procedures performed between 2017 and 2022 at a large academic medical center. Costs were calculated using TDABC. Software was developed to extract information regarding all resources utilized intraoperatively. Supply cost was calculated as the aggregate of expenses related to implants, consumables, medications, and surgical tray sterilization. Personnel cost was determined by multiplying the per-minute wages of all intraoperative personnel by the amount of time they spent in the operating room. Patient and disease-specific variables were collected. Multivariable regression models were performed to assess predictors of cost. The average total cost of a TESS procedure was $7557 ± $2,365, with primary cost drivers being supplies ($2,811, 37%) and personnel ($4,426, 59%). On multivariable regression, factors independently associated with higher total cost were hospital site (β-coefficient: $1,028, P < 0.001), intraoperative blood loss (β-coefficient: $12, P < 0.001), length of stay (β-coefficient: $23, P= 0.015), and the use of a nasoseptal flap (β-coefficient: $731, P= 0.012). Conversely, apoplexy was associated with lower total cost (β-coefficient: $-1,149, P= 0.001), which was explained by faster operating room times and lower personnel cost (β-coefficient: $-702, P= 0.003). This study represents the first application of intraoperative TDABC for transsphenoidal endoscopic surgery. Such efforts can promote value-based healthcare by identifying areas for cost reduction and surgical resource management.

Imaging-Guided Microscale Photothermal Stereolithography Bioprinting.

Stereolithography bioprinting relies heavily on costly photoinitiators for polymerization, limiting its potential for further technical advancement to meet growing needs in tissue engineering and regenerative medicine. Thermal initiators, in contrast, are low cost, and rapid growth of the photothermal conversion field offers a wide range of materials and tools to convert light into heat. However, high-resolution photothermal stereolithography bioprinting remains unattainable due to the difficulty of confining heat in an aqueous environment. Here, this challenge has been fully addressed by establishing imaging-guided microscale photothermal stereolithography bioprinting (ImPSB). This technique is achieved through building a novel imaging-guided stereolithography system that provides depth-resolved visualization of the printing dynamics, creating a unique photothermal initiator in the second near-infrared window, and developing a new bioink by seeing and controlling the photothermal gelation process. ImPSB achieves a printing resolution of ≈47 µm and generates smooth lines of arbitrarily designed shapes with a cross-sectional diameter as small as ≈104 µm, representing an unprecedented scale from photothermal aqueous stereolithography. Its cellular biocompatibility in printing both bioscaffold and cell-laden hydrogel is demonstrated, and its feasibility of transdermal printing is also shown. This work sets a new path for high-resolution stereolithography bioprinting where the vast photothermal resources can be utilized.

Double-Chain Copolymer Network via In Situ Polymerization Enables High-Stability and Lead-Safe Perovskite Solar Cells.

Lead halide perovskite solar cells (PSCs) have made significant progress due to their low cost and high efficiency. However, the long-term stability and lead toxicity of PSCs remain a huge challenge for further commercialization. Herein, we designed a multifunctional additive PAE with photosensitive properties to overcome these difficulties. A unique double-chain copolymer network can be constructed via in situ polymerization induced by ultraviolet (UV) light. The multiple active sites in the polymer chains can passivate various defects and enhance charge transfer. Meanwhile, the vast network provides an effective defense for perovskite to stabilize the internal structure and resist harsh external environments, thus delaying the degradation of devices and maximizing the suppression of toxic lead leakage. Remarkably, the devices protected by the network achieved a champion power conversion efficiency (PCE) of 26.20% (certified as 25.69%), the unencapsulated devices suppressed 80% of lead leakage and the encapsulated devices maintained 93% of the initial PCE after 1000 h in a humid and thermal environment (65 °C and 85% RH).

High-Rate Quinone Cathodes and Nafion Conditioning for Improved Stability in Aqueous Zinc-Ion Batteries.

The growing need for fast and reliable energy delivery in various applications ranging from electric vehicles and portable electronics to grid-scale storage demands high-performance energy storage systems capable of operating at high charge/discharge rates (C-rates). Aqueous zinc-ion batteries (AZIBs) offer a promising alternative to conventional lithium-ion batteries primarily due to their inherent safety, environmental friendliness, low cost, and high theoretical capacity. Quinone-based cathodes, with their fast redox kinetics and high theoretical capacities, are particularly suitable for high-rate applications. However, their practical application in AZIBs is limited by their high solubility in aqueous electrolytes, leading to significant capacity fading and poor long-term cycling stability, especially at elevated C-rates. To address these challenges, this study investigates the use of Nafion membranes as ion-selective barriers to stabilize quinone cathodes and prevent the dissolution of active materials. The study evaluates four quinone-based cathodes─2,3,5,6-tetrachloro-1,4-benzoquinone (TCBQ), 1,4-naphthoquinone (NQ), anthraquinone (AQ), and poly(2-chloro-3,5,6-trisulfide-1,4-benzoquinone) (PCTBQ)─in AZIBs, focusing on the effect of Nafion membrane conditioning in 1 M ZnSO4 electrolyte. The results demonstrate that optimized Nafion conditioning significantly enhances the stability and performance of quinone cathodes, reducing dissolution, improving cyclability, and maintaining stable capacity retention under high-rate conditions, i.e., 35C. These findings emphasize the importance of membrane conditioning and demonstrate its potential to advance the development of durable, high-rate AZIBs for rapid energy storage applications.

Multitrack Boosted Hard Carbon Anodes: Innovative Paths and Advanced Performances in Sodium-Ion Batteries.

Sodium-ion batteries (SIBs) are emerging as a potential alternative to traditional lithium-ion batteries due to the abundant sodium resources. Carbon anodes, with their stable structure, wide availability, low cost, excellent conductivity, and tunable morphology and pore structure, exhibit outstanding performance in SIBs. This review summarizes the research progress of hard carbon anodes in SIBs, emphasizing the innovative paths and advanced performances achieved through multitrack optimization, including dimensional engineering, heteroatom doping, and microstructural tailoring. Each dimension of carbon material-0D, 1D, 2D, and 3D-offers unique advantages: 0D materials ensure uniform dispersion, 1D materials have short Na+ diffusion paths, 2D materials possess large specific surface areas, and 3D materials provide e-/Na+ conductive networks. Heteroatom doping with elements such as N, S, and P can tune electronic distribution, expand interlayer spacing of carbon, and induce Fermi level shifts, thereby enhancing sodium storage capability. In addition, defect engineering improves electrochemical performance by modifying graphitic crystal structure. Furthermore, suitable pore structure design, particularly closed pore structures, can increase capacity, minimizes side reactions, and suppress degradation. In future studies, optimizing morphology design, exploring heteroatom co-doping, and developing environmentally friendly, low-cost carbon anode methods will drive the application of high-performance and long cycle life SIBs.

Cost-effectiveness of diagnosing and treating patients with early Alzheimer's disease with anti-amyloid treatment in a clinical setting.

BackgroundThe introduction of anti-amyloid treatments (AAT) for Alzheimer's disease (AD) has put the cost-effectiveness into focus.ObjectiveEstimate the potential cost-effectiveness of diagnostic pathways combined with AAT for early AD.MethodsDiagnostic accuracy of blood-based (BBM) and cerebrospinal fluid (CSF) biomarkers was obtained from Norwegian memory clinics using positron emission tomography (PET) as reference standard. In a health-economic model, the cost-effectiveness of three diagnostic strategies was estimated relying either on BBM (p-tau 217), CSF (Aβ42/40 ratio), and BBM with CSF confirmatory testing and compared with standard of care (SoC) and compared with CSF-AAT. The model consisted of a decision tree reflecting the diagnostic process and a subsequent Markov cohort model starting at mild cognitive impairment due to AD. All strategies except SoC were combined with AAT including costs of treatment (assumed €5000/year), infusions and monitoring.ResultsCompared with SoC all three strategies (CSF-AAT, BBM-AAT, and BBM-CSF-AAT) resulted in QALY gains at higher costs, with an incremental cost-effectiveness ratio (ICER) of 110k€, 141k€ and 110k€ respectively. Compared with CSF-AAT both BBM-AAT and BBM-CSF-AAT strategies resulted in QALYs lost at lower costs, with an ICER of 27k€ and 109k€ respectively. Results were particularly sensitive to the price of AAT and possible subcutaneous administration.ConclusionsCompared with SoC all three strategies are potentially not cost-effective as they exceeded the Swedish maximum willingness to pay threshold of €94,800 per QALY gained. BBM-CSF-AAT versus CSF-AAT is potentially cost-effective if willing to accept its QALY loss. Discussions on budget impact on different payers are needed after introducing AAT.

A Nonparametric Approach for Estimating the Effective Sample Size in Gaussian Approximation of Expected Value of Sample Information.

The effective sample size (ESS) measures the informational value of a probability distribution in terms of an equivalent number of study participants. The ESS plays a crucial role in estimating the expected value of sample information (EVSI) through the Gaussian approximation approach. Despite the significance of ESS, except for a limited number of scenarios, existing ESS estimation methods within the Gaussian approximation framework are either computationally expensive or potentially inaccurate. To address these limitations, we propose a novel approach that estimates the ESS using the summary statistics of generated datasets and nonparametric regression methods. The simulation experiments suggest that the proposed method provides accurate ESS estimates at a low computational cost, making it an efficient and practical way to quantify the information contained in the probability distribution of a parameter. Overall, determining the ESS can help analysts understand the uncertainty levels in complex prior distributions in the probability analyses of decision models and perform efficient EVSI calculations.HighlightsEffective sample size (ESS) quantifies the informational value of probability distributions, essential for calculating the expected value of sample information (EVSI) using the Gaussian approximation approach. However, current ESS estimation methods are limited by high computational demands and potential inaccuracies.We propose a novel ESS estimation method that uses summary statistics and nonparametric regression models to efficiently and accurately estimate ESS.The effectiveness and accuracy of our method are validated through simulations, demonstrating significant improvements in computational efficiency and estimation accuracy.

Interpretable Identification of Single-Molecule Charge Transport via Fusion Attention-Based Deep Learning.

Interpretability is fundamental in the precise identification of single-molecule charge transport, and its absence in deep learning models is currently the major barrier to the usage of such powerful algorithms in the field. Here, we have pioneered a novel identification method employing fusion attention-based deep learning technologies. Central to our approach is the innovative neural network architecture, SingleFACNN, which integrates convolutional neural networks with a fusion of multihead self-attention and spatial attention mechanisms. Our findings demonstrate that SingleFACNN accurately classifies the three-type and four-type STM-BJ data sets, leveraging the convolutional layers' robust feature extraction and the attention layers' capacity to capture long-range interactions. Through comprehensive gradient-weighted class activation mapping and ablation studies, we identified and analyzed the critical features impacting classification outcomes with remarkable accuracy, thus enhancing the interpretability of our deep learning model. Furthermore, SingleFACNN's application was extended to mixed samples with varying proportions, achieving commendable prediction performance at low computational cost. Our study underscores the potential of SingleFACNN in advancing the interpretability and credibility of deep learning applications in single-molecule charge transport, opening new avenues for single-molecule detection in complex systems.