Gas Evolution Kinetics in Overlithiated Positive Electrodes and its Impact on Electrode Design.

Increasing lithium contents within the lattice of positive electrode materials is projected in pursuit of high-energy-density batteries. However, it intensifies the release of lattice oxygen and subsequent gas evolution during operations. This poses significant challenges for managing internal pressure of batteries, particularly in terms of the management of gas evolution in composite electrodes-an area that remains largely unexplored. Conventional assumptions postulate that the total gas evolution is estimated by multiplying the total particle count by the quantities of gas products from an individual particle. Contrarily, this investigation on overlithiated materials-a system known to release the lattice oxygen-demonstrates that loading densities and inter-particle spacing in electrodes significantly govern gas evolution rates, leading to distinct extents of gas formation despite of an equivalent quantity of released lattice oxygen. Remarkably, this study discoveres that O2 and CO2 evolution rates are proportional to 1O2 concentration by the factor of second and first-order, respectively. This indicates an exceptionally greater change in the evolution rate of O2 compared to CO2 depending on local 1O2 concentration. These insights pave new routes for more sophisticated approaches to manage gas evolution within high-energy-density batteries.

Structurally robust lithium-rich layered oxides for high-energy and long-lasting cathodes.

O2-type lithium-rich layered oxides, known for mitigating irreversible transition metal migration and voltage decay, provide suitable framework for exploring the inherent properties of oxygen redox. Here, we present a series of O2-type lithium-rich layered oxides exhibiting minimal structural disordering and stable voltage retention even with high anionic redox participation based on the nominal composition. Notably, we observe a distinct asymmetric lattice breathing phenomenon within the layered framework driven by excessive oxygen redox, which includes substantial particle-level mechanical stress and the microcracks formation during cycling. This chemo-mechanical degradation can be effectively mitigated by balancing the anionic and cationic redox capabilities, securing both high discharge voltage (~ 3.43 V vs. Li/Li+) and capacity (~ 200 mAh g-1) over extended cycles. The observed correlation between the oxygen redox capability and the structural evolution of the layered framework suggests the distinct intrinsic capacity fading mechanism that differs from the previously proposed voltage fading mode.

Self-Assembled Nanostructures Presenting Repetitive Arrays of Subunit Antigens for Enhanced Immune Response.

Infectious diseases pose persistent threats to public health, demanding advanced vaccine technologies. Nanomaterial-based delivery systems offer promising solutions to enhance immunogenicity while minimizing reactogenicity. We introduce a self-assembled vaccine (SAV) platform employing antigen-polymer conjugates designed to facilitate robust immune responses. The SAVs exhibit efficient cellular uptake by dendritic cells (DCs) and macrophages, which are crucial players in the innate immune system. The high-density antigen presentation of this SAV platform enhances the affinity for DCs through multivalent recognition, significantly augmenting humoral immunity. SAV induced high levels of immunoglobulin G (IgG), IgG1, and IgG2a, suggesting that mature DCs efficiently induced B cell activation through multivalent antigen recognition. Universality was confirmed by applying it to respiratory viruses, showcasing its potential as a versatile vaccine platform. Furthermore, we have also demonstrated strong protection against influenza A virus infection with SAV containing hemagglutinin, which is used in influenza A virus subunit vaccines. The efficacy and adaptability of this nanostructured vaccine present potential utility in combating infectious diseases.

Label-Free and Colorimetric Detection of Influenza A Virus via Receptor-Mediated Viral Fusion with Plasmonic Vesicles.

The rapid transmission and numerous re-emerging human influenza virus variants that spread via the respiratory system have led to severe global damage, emphasizing the need for detection tools that can recognize active and intact virions with infectivity. Here, this work presents a plasmonic vesicle-mediated fusogenic immunoassay (PVFIA) comprising gold nanoparticle (GNP) encapsulating fusogenic polymeric vesicles (plasmonic vesicles; PVs) for the label-free and colorimetric detection of influenza A virus (IAV). The PVFIA combines two sequential assays: a biochip-based immunoassay for target-specific capture and a PV-induced fusion assay for color change upon the IAV-PV fusion complex formation. The PVFIA demonstrates excellent specificity in capturing the target IAV, while the fusion conditions and GNP induce a significant color change, enabling visual detection. The integration of two consecutive assays results in a low detection limit (100.7919 EID50 mL-1 ) and good reliability (0.9901), indicating sensitivity that is 104.208 times higher than conventional immunoassay. Leveraging the PV viral membrane fusion activity renders the PVFIA promising for point-of-care diagnostics through colorimetric detection. The innovative approach addresses the critical need for detecting active and intact virions with infectivity, providing a valuable tool with which to combat the spread of the virus.

Amplified fluorogenic immunoassay for early diagnosis and monitoring of Alzheimer's disease from tear fluid.

Accurate diagnosis of Alzheimer's disease (AD) in its earliest stage can prevent the disease and delay the symptoms. Therefore, more sensitive, non-invasive, and simple screening tools are required for the early diagnosis and monitoring of AD. Here, we design a self-assembled nanoparticle-mediated amplified fluorogenic immunoassay (SNAFIA) consisting of magnetic and fluorophore-loaded polymeric nanoparticles. Using a discovery cohort of 21 subjects, proteomic analysis identifies adenylyl cyclase-associated protein 1 (CAP1) as a potential tear biomarker. The SNAFIA demonstrates a low detection limit (236 aM), good reliability (R2 = 0.991), and a wide analytical range (0.320-1000 fM) for CAP1 in tear fluid. Crucially, in the verification phase with 39 subjects, SNAFIA discriminates AD patients from healthy controls with 90% sensitivity and 100% specificity in under an hour. Utilizing tear fluid as a liquid biopsy, SNAFIA could potentially aid in long-term care planning, improve clinical trial efficiency, and accelerate therapeutic development for AD.

A Robust Quadruple Protein-Based Indirect ELISA for Detection of Antibodies to African Swine Fever Virus in Pigs.

African swine fever (ASF) emerged in domestic pigs and wild boars in China in 2018 and rapidly spread to neighboring Asian countries. Currently, no effective vaccine or diagnostic tests are available to prevent its spread. We developed a robust quadruple recombinant-protein-based indirect enzyme-linked immunosorbent assay (QrP-iELISA) using four antigenic proteins (CD2v, CAP80, p54, and p22) to detect ASF virus (ASFV) antibodies and compared it with a commercial kit (IDvet) using ASFV-positive and -negative serum samples. The maximum positive/negative value was 24.033 at a single antigen concentration of 0.25 µg/mL and quadruple ASFV antigen combination of 1 µg/mL at a 1:100 serum dilution. Among 70 ASFV-positive samples, 65, 67, 65, 70, 70, and 14 were positive above the cut-offs of 0.121, 0.121, 0.183, 0.065, 0.201, and 0.122, for CD2v, CAP80, p54, p22-iELISA, QrP-iELISA, and IDvet, respectively, with sensitivities of 92.9%, 95.7%, 92.9%, 100%, 100%, and 20%, respectively, all with 100% specificity. The antibody responses in QrP-iELISA and IDvet were similar in pigs infected with ASFV I. QrP-iELISA was more sensitive than IDvet for early antibody detection in pigs infected with ASFV II. These data provide a foundation for developing advanced ASF antibody detection kits critical for ASF surveillance and control.

Advanced Strategies for Developing Vaccines and Diagnostic Tools for African Swine Fever.

African swine fever (ASF) is one of the most lethal infectious diseases affecting domestic pigs and wild boars of all ages. Over a span of 100 years, ASF has continued to spread over continents and adversely affects the global pig industry. To date, no vaccine or treatment has been approved. The complex genome structure and diverse variants facilitate the immune evasion of the ASF virus (ASFV). Recently, advanced technologies have been used to design various potential vaccine candidates and effective diagnostic tools. This review updates vaccine platforms that are currently being used worldwide, with a focus on genetically modified live attenuated vaccines, including an understanding of their potential efficacy and limitations of safety and stability. Furthermore, advanced ASFV detection technologies are presented that discuss and incorporate the challenges that remain to be addressed for conventional detection methods. We also highlight a nano-bio-based system that enhances sensitivity and specificity. A combination of prophylactic vaccines and point-of-care diagnostics can help effectively control the spread of ASFV.

A Whole-Genome Analysis of the African Swine Fever Virus That Circulated during the First Outbreak in Vietnam in 2019 and Subsequently in 2022.

Since its initial report in Vietnam in early 2019, the African swine fever (ASF), a highly lethal and severe viral swine disease worldwide, continues to cause outbreaks in other Southeast Asian countries. This study analyzed and compared the genomic sequences of ASF viruses (ASFVs) during the first outbreak in Hung Yen (VN/HY/2019-ASFV1) and Quynh Phu provinces (VN/QP/2019-ASFV1) in Vietnam in 2019, and the subsequent outbreak in Hung Yen (VN/HY/2022-ASFV2) in 2022, to those of other ASFV strains. VN/HY/2019-ASFV1, VN/QP/2019-ASFV1, and VN/HY/2022-ASFV2 genomes were 189,113, 189,081, and 189,607 bp in length, encoding 196, 196, and 203 open reading frames (ORFs), respectively. VN/HY/2019-ASFV1 and VN/QP/2019-ASFV1 shared a 99.91-99.99% average nucleotide identity with genotype II strains. Variations were identified in 28 ORFs in VN/HY/2019-ASFV1 and VN/QP/2019-ASFV1 compared to 20 ASFV strains, and 16 ORFs in VN/HY/2022-ASFV2 compared to VN/HY/2019-ASFV1 and VN/QP/2019-ASFV1. Vietnamese ASFV genomes were classified as IGR II variants between the I73R and I329L genes, with two copy tandem repeats between the A179L and A137R genes. A phylogenetic analysis based on the whole genomes of 27 ASFV strains indicated that the Vietnamese ASFV strains are genetically related to Estonia 2014, ASFV-SY18, and Russia/Odintsovo_02/14. These results reveal the complete genome sequences of ASFV circulating during the first outbreak in 2019, providing important insights into understanding the evolution, transmission, and genetic variation of ASFV in Vietnam.

Mucosal and cellular immune responses elicited by nasal and intramuscular inoculation with ASFV candidate immunogens.

African swine fever (ASF) is an infectious disease caused by African swine fever virus (ASFV) that is highly contagious and has an extremely high mortality rate (infected by virulent strains) among domestic and wild pigs, causing huge economic losses to the pig industry globally. In this study, SDS-PAGE gel bands hybridized with ASFV whole virus protein combined with ASFV-convalescent and ASFV-positive pig serum were identified by mass spectrometry. Six antigens were detected by positive serum reaction bands, and eight antigens were detected in ASFV-convalescent serum. In combination with previous literature reports and proteins corresponding to MHC-II presenting peptides screened from ASFV-positive pig urine conducted in our lab, seven candidate antigens, including KP177R (p22), K78R (p10), CP204L (p30), E183L (p54), B602L (B602L), EP402R-N (CD2V-N) and F317L (F317L), were selected. Subunit-Group 1 was prepared by mixing above-mentioned seven ASFV recombinant proteins with MONTANIDETM1313 VG N mucosal adjuvant and immunizing pigs intranasally and intramuscularly. Subunit-Group 2 was prepared by mixing four ASFV recombinant proteins (p22, p54, CD2V-N1, B602L) with Montanide ISA 51 VG adjuvant and immunizing pigs by intramuscular injection. Anticoagulated whole blood, serum, and oral fluid were collected during immunization for flow cytometry, serum IgG as well as secretory sIgA antibody secretion, and cytokine expression testing to conduct a comprehensive immunogenicity assessment. Both immunogen groups can effectively stimulate the host to produce ideal humoral, mucosal, and cellular immune responses, providing a theoretical basis for subsequent functional studies, such as immunogens challenge protection and elucidation of the pathogenic mechanism of ASFV.

Learning heterogeneous reaction kinetics from X-ray videos pixel by pixel.

Reaction rates at spatially heterogeneous, unstable interfaces are notoriously difficult to quantify, yet are essential in engineering many chemical systems, such as batteries1 and electrocatalysts2. Experimental characterizations of such materials by operando microscopy produce rich image datasets3-6, but data-driven methods to learn physics from these images are still lacking because of the complex coupling of reaction kinetics, surface chemistry and phase separation7. Here we show that heterogeneous reaction kinetics can be learned from in situ scanning transmission X-ray microscopy (STXM) images of carbon-coated lithium iron phosphate (LFP) nanoparticles. Combining a large dataset of STXM images with a thermodynamically consistent electrochemical phase-field model, partial differential equation (PDE)-constrained optimization and uncertainty quantification, we extract the free-energy landscape and reaction kinetics and verify their consistency with theoretical models. We also simultaneously learn the spatial heterogeneity of the reaction rate, which closely matches the carbon-coating thickness profiles obtained through Auger electron microscopy (AEM). Across 180,000 image pixels, the mean discrepancy with the learned model is remarkably small (<7%) and comparable with experimental noise. Our results open the possibility of learning nonequilibrium material properties beyond the reach of traditional experimental methods and offer a new non-destructive technique for characterizing and optimizing heterogeneous reactive surfaces.

Effect of closing material on hearing rehabilitation in stapedectomy and stapedotomy: A finite element analysis.

Stapedotomy or stapedectomy operations are often performed to treat otosclerosis. During the operation, the space created by bone removal is usually filled with a closing material such as fat or fascia. In this study, the effect of the Young's modulus of the closing material on the hearing level was investigated through the 3D finite element model of a human head including auditory periphery. The Young's moduli of the closing material used to implement stapedotomy and stapedectomy conditions in the model were varied from 1 kPa to 24 MPa. The results showed that the hearing level improved when the closing material was more compliant after stapedotomy. Therefore, when the stapedotomy was performed using fat whose Young's modulus is lowest among the potential closing materials, the hearing level recovered the best among all simulated cases. On the other hand, in stapedectomy, the Young's modulus did not have the linear relationship between the hearing level and the compliance of the closing material. Hence, the Young's modulus causing the best hearing rehabilitation in stapedectomy was found not at the end of the investigated range of Young's modulus but somewhere in the middle of the given range.

Rapid Visible Detection of African Swine Fever Virus Using Hybridization Chain Reaction-Sensitized Magnetic Nanoclusters and Affinity Chromatography.

African swine fever virus (ASFV) is a severe and persistent threat to the global swine industry. As there are no vaccines against ASFV, there is an immense need to develop easy-to-use, cost-effective, and rapid point-of-care (POC) diagnostic platforms to detect and prevent ASFV outbreaks. Here, a novel POC diagnostic system based on affinity column chromatography for the optical detection of ASFV is presented. This system employs an on-particle hairpin chain reaction to sensitize magnetic nanoclusters with long DNA strands in a target-selective manner, which is subsequently fed into a column chromatography device to produce quantitatively readable and colorimetric signals. The detection approach does not require expensive analytical apparatus or immobile instrumentation. The system can detect five genes constituting the ASFV whole genome with a detection limit of ≈19.8 pm in swine serum within 30 min at laboratory room temperature. With an additional pre-amplification step using polymerase chain reaction (PCR), the assay is successfully applied to detect the presence of ASFV in 30 suspected swine samples with 100% sensitivity and specificity, similar to quantitative PCR. Thus, this simple, inexpensive, portable, robust, and customizable platform for the early detection of ASFV can facilitate the timely surveillance and implementation of control measures.

Reference velocity of a human head in bone conduction hearing: Finite element study.

A whole head or temporal bone has been used in experiments to understand the mechanism of bone conduction (BC) hearing. In these experiments, two assumptions are generally accepted: (1) a promontory can be a representative point to show the motion of a specimen in BC hearing, and (2) the promontory velocity is proportional to a cochlear response so that the higher the promontory velocity, the better the BC hearing. To confirm the two assumptions, we investigated the velocities of various points corresponding to different BC input types and directions in the head. In this investigation, we used the three-dimensional finite element model of a human head, including an auditory periphery. Results showed that a single promontory was insufficient to be a representative point to show the motion of a specimen because the specimen could have rotational motion at frequencies below 0.5 kHz and the localized deformation at frequencies above 3 kHz. The promontory velocity had the same pattern as the basilar membrane velocity at low and high frequencies. However, at mid-frequencies between 0.5 and 3 kHz, the promontory did not exhibit the same pattern of velocity as the basilar membrane. Therefore, one's BC hearing ability must be carefully determined on the basis of promontory velocity.

Effect of Contrast Level and Image Format on a Deep Learning Algorithm for the Detection of Pneumothorax with Chest Radiography.

Under the black-box nature in the deep learning model, it is uncertain how the change in contrast level and format affects the performance. We aimed to investigate the effect of contrast level and image format on the effectiveness of deep learning for diagnosing pneumothorax on chest radiographs. We collected 3316 images (1016 pneumothorax and 2300 normal images), and all images were set to the standard contrast level (100%) and stored in the Digital Imaging and Communication in Medicine and Joint Photographic Experts Group (JPEG) formats. Data were randomly separated into 80% of training and 20% of test sets, and the contrast of images in the test set was changed to 5 levels (50%, 75%, 100%, 125%, and 150%). We trained the model to detect pneumothorax using ResNet-50 with 100% level images and tested with 5-level images in the two formats. While comparing the overall performance between each contrast level in the two formats, the area under the receiver-operating characteristic curve (AUC) was significantly different (all p < 0.001) except between 125 and 150% in JPEG format (p = 0.382). When comparing the two formats at same contrast levels, AUC was significantly different (all p < 0.001) except 50% and 100% (p = 0.079 and p = 0.082, respectively). The contrast level and format of medical images could influence the performance of the deep learning model. It is required to train with various contrast levels and formats of image, and further image processing for improvement and maintenance of the performance.

Solid-State Reaction Heterogeneity During Calcination of Lithium-Ion Battery Cathode.

During solid-state calcination, with increasing temperature, materials undergo complex phase transitions with heterogeneous solid-state reactions and mass transport. Precise control of the calcination chemistry is therefore crucial for synthesizing state-of-the-art Ni-rich layered oxides (LiNi1-x-y Cox Mny O2 , NRNCM) as cathode materials for lithium-ion batteries. Although the battery performance depends on the chemical heterogeneity during NRNCM calcination, it has not yet been elucidated. Herein, through synchrotron-based X-ray, mass spectrometry microscopy, and structural analyses, it is revealed that the temperature-dependent reaction kinetics, the diffusivity of solid-state lithium sources, and the ambient oxygen control the local chemical compositions of the reaction intermediates within a calcined particle. Additionally, it is found that the variations in the reducing power of the transition metals (i.e., Ni, Co, and Mn) determine the local structures at the nanoscale. The investigation of the reaction mechanism via imaging analysis provides valuable information for tuning the calcination chemistry and developing high-energy/power density lithium-ion batteries.

A flap endonuclease 1-assisted universal viral nucleic acid sensing system using surface-enhanced Raman scattering.

The continued uncertainty of emerging infectious viral diseases has led to an extraordinary urgency to develop advanced molecular diagnostic tests that are faster, more reliable, simpler to use, and readily available than traditional methods. This study presents a system that can accurately and rapidly trace viral nucleic acids by employing flap endonuclease 1 (FEN1)-assisted specific DNA cleavage reactions and surface-enhanced Raman scattering (SERS)-based analysis. The designed Raman tag-labeled 5'- and 3'-flap provider DNA yielded structurally defined DNA substrates on magnetic nanoparticle surfaces when a target was present. The FEN1 enzyme subsequently processes the substrates formed via an invasive cleavage reaction, producing 5'-flap DNA products. Magnetic separation allows efficient purification of flap products from reaction mixtures. The isolated solution was directly applied onto high aspect-ratio plasmonic silver nanopillars serving as SERS-active substrates to induce amplified SERS signals. We verified the developed SERS-based sensing system using a synthetic target complementary to an avian influenza A (H9N2) virus gene and examined the detection performance of the system using complementary DNA (cDNA) derived from H9N2 viral RNA. As a result, we could detect a synthetic target with a detection limit of 41.1 fM with a single base-pair discrimination ability and achieved multiplexed detection capability for two targets. Using cDNA samples from H9N2 viruses, we observed a high concordance of R2 = 0.917 between the data obtained from SERS and the quantitative polymerase chain reaction. We anticipate that this enzyme-assisted SERS sensor may provide insights into the development of high-performance molecular diagnostic tools that can respond rapidly to viral pathogens.

Non-fluorinated non-solvating cosolvent enabling superior performance of lithium metal negative electrode battery.

The growth of dendrites on lithium metal electrodes is problematic because it causes irreversible capacity loss and safety hazards. Localised high-concentration electrolytes (LHCEs) can form a mechanically stable solid-electrolyte interphase and prevent uneven growth of lithium metal. However, the optimal physicochemical properties of LHCEs have not been clearly determined which limits the choice to fluorinated non-solvating cosolvents (FNSCs). Also, FNSCs in LHCEs raise environmental concerns, are costly, and may cause low cathodic stability owing to their low lowest unoccupied molecular orbital level, leading to unsatisfactory cycle life. Here, we spectroscopically measured the Li+ solvation ability and miscibility of candidate non-fluorinated non-solvating cosolvents (NFNSCs) and identified the suitable physicochemical properties for non-solvating cosolvents. Using our design principle, we proposed NFNSCs that deliver a coulombic efficiency up to 99.0% over 1400 cycles. NMR spectra revealed that the designed NFNSCs were highly stable in electrolytes during extended cycles. In addition, solvation structure analysis by Raman spectroscopy and theoretical calculation of Li+ binding energy suggested that the low ability of these NFNSCs to solvate Li+ originates from the aromatic ring that allows delocalisation of electron pairs on the oxygen atom.

Cationic poly(amino acid) surface functionalized manganese nanoparticles for nitric oxide-based immunotherapy and magnetic resonance imaging.

The low therapeutic efficacy of conventional cancer chemotherapy has been associated with an immunosuppressive tumor microenvironment (TME). Tumor-associated macrophages (TAMs), which display an M2-like phenotype, are abundant in many tumors and facilitate tumor growth and resistance to therapy. Here, we show that poly(L-arginine) (PLR), a cationic poly(amino acid) can induce the polarization of macrophages into the tumor-suppressive M1 phenotype, in vitro. Further, we demonstrate that hyaluronic acid (HA) and PLR-coated manganese dioxide (MnO2) nanoparticles (hpMNPs) display efficient anti-cancer effects by upregulating nitric oxide (NO) production. Surface modification with biocompatible HA reduced the cytotoxicity of the cationic PLR. Additionally, manganese ions released from these nanoparticles by the high concentrations of glutathione (GSH) in the TME increased iNOS expression level in macrophages and enhanced the performance of T1 weighted magnetic resonance imaging. Particularly, our results illustrate the therapeutic effects, such as growth inhibition and apoptosis of tumor cells, of hpMNP treated macrophages. Therefore, the newly designed multifunctional PLR-assisted MNPs may facilitate the polarization of M2 macrophages into the M1 phenotype, which can mediate NO-dependent anticancer immunotherapy.

Cell-mimetic biosensors to detect avian influenza virus via viral fusion.

Avian influenza virus (AIV) causes acute infectious diseases in poultry, critically impacting food supply. Highly pathogenic avian influenza viruses (HPAIVs), in particular, cause morbidity and mortality, resulting in significant economic losses in the poultry industry. To prevent the spread of HPAIVs, detection at early stages is critical to implement effective countermeasures such as quarantine and isolation. Through a viral fusion mechanism, cell-mimetic nanoparticles (CMPs), developed in the current study, can rapidly detect HPAIV and low pathogenic AIV (LPAIV). The CMPs comprise polymeric nanoparticles, which are constructed using sialic acid and fluorescence resonance energy transfer (FRET) dye pairs that expose the FRET off signal in response to LPAIV and HPAIV, after activation by enzymatic cleavage in the endosomal environment. The CMPs detect a wide variety of LPAIVs and HPAIVs in biological environments. Additionally, the cross-reactivity of CMPs is determined by testing their function with different viral species. Therefore, these findings demonstrate the significant potential of the proposed strategy for mimicking viral infection in vitro and using them as a highly effective diagnostic assay to rapidly detect LPAIV and HPAIV, preventing economic losses associated with viral outbreaks.

Automatic Segmentation for Favourable Delineation of Ten Wrist Bones on Wrist Radiographs Using Convolutional Neural Network.

Purpose: This study aimed to develop and validate an automatic segmentation algorithm for the boundary delineation of ten wrist bones, consisting of eight carpal and two distal forearm bones, using a convolutional neural network (CNN). Methods: We performed a retrospective study using adult wrist radiographs. We labeled the ground truth masking of wrist bones, and propose that the Fine Mask R-CNN consisted of wrist regions of interest (ROI) using a Single-Shot Multibox Detector (SSD) and segmentation via Mask R-CNN, plus the extended mask head. The primary outcome was an improvement in the prediction of delineation via the network combined with ground truth masking, and this was compared between two networks through five-fold validations. Results: In total, 702 images were labeled for the segmentation of ten wrist bones. The overall performance (mean (SD] of Dice coefficient) of the auto-segmentation of the ten wrist bones improved from 0.93 (0.01) using Mask R-CNN to 0.95 (0.01) using Fine Mask R-CNN (p < 0.001). The values of each wrist bone were higher when using the Fine Mask R-CNN than when using the alternative (all p < 0.001). The value derived for the distal radius was the highest, and that for the trapezoid was the lowest in both networks. Conclusion: Our proposed Fine Mask R-CNN model achieved good performance in the automatic segmentation of ten overlapping wrist bones derived from adult wrist radiographs.