Search

Summary: This article aims to develop and validate a pathological prognostic model for predicting prognosis in patients with isocitrate dehydrogenase (IDH)-mutant gliomas and reveal the biological underpinning of the prognostic pathological features. The pathomic model was constructed based on whole slide images (WSIs) from a training set (N = 486) and evaluated on internal validation set (N = 209), HPPH validation set (N = 54), and TCGA validation set (N = 352). Biological implications of PathScore and individual pathomic features were identified by pathogenomics set (N = 100). The WSI-based pathological signature was an independent prognostic factor. Incorporating the pathological features into a clinical model resulted in a pathological-clinical model that predicted survival better than either the pathological model or clinical model alone. Ten categories of pathways (metabolism, proliferation, immunity, DNA damage response, disease, migrate, protein modification, synapse, transcription and translation, and complex cellular functions) were significantly correlated with the WSI-based pathological features.

Tiger nuts are rich in both oil and starch. In this paper, an immiscible binary solvent consisted of water and hexane was used to separate oil and starch from tiger nut with the assistance of ultrasound. The effects of various factors including ultrasonic power, ultrasonic time, extraction temperature, water-hexane ratio and solid–liquid ratio on the oil yield were examined. The oil and starch obtained were characterized and compared with those obtained through conventional methods, such as mechanical pressing (MP) and solvent extraction (SE). The results indicated that all examined factors, especially water-hexane ratio significantly affected the oil yield. Under the optimum conditions, ultrasound-assisted immiscible binary solvent extraction (UASE) yielded more oil than SE and MP. The quality analysis revealed that the acid value and iodine value of UASE oil were comparable to those extracted using the MP and SE methods, but the peroxide value was lower, and the vitamin E content was higher. In addition, the starch from tiger nut after UASE exhibited lower viscosity, greater solubility and swelling power, and better thermal and freeze–thaw stability. This study demonstrated that the UASE method is an efficient and environmentally friendly technique for simultaneously extracting oil and starch from tiger nuts.

Chemodynamic therapy, which relies on the generation of cytotoxic radicals, can be amplified by a nanoplatform that produces hydroxyl radicals while also compromising natural radical scavenging mechanisms. For this purpose, a well-defined amphiphilic terpolymer, poly(oligo(ethylene glycol) monomethyl ether methacrylate)-block-poly(N,N-dimethyl aminoethyl methacrylate-statistical-monomer bearing ferrocene graft via azobenzene linker) (POEGMA-b-P(DMAEMA-st-(M-Azo-Fc), denoted as PAzo-Fc) is prepared by a consecutive reversible addition-fragmentation chain transfer (RAFT) polymerization technique, and is further used for doxorubicin (DOX) encapsulation to afford DOX-loaded stabilized nanomicelles, DOX@PAzo-Fc with an average hydrodynamic diameter of 86.0 nm. DOX@PAzo-Fc shows a self-cascade property for amplified CDT. That is, Azo cleavage-induced glutathione (GSH) depletion alleviates reactive oxygen species (ROS) scavenging. Together with the DOX-enhanced hydrogen peroxide generation, the Fc-mediated Fenton reaction is boosted for enhanced CDT. More importantly, the resulting amplified cascade chemo-chemodynamic therapy exerts a synergistic immunogenic cell death (ICD) enhancement effect for effective cancer immunotherapy, which further resulted in a high tumor inhibition rate of 87.8 % in murine tumor models. The uniqueness of this study is the construction of a minimalist nanoplatform based on M-Azo-Fc units for amplified CDT via simultaneously producing hydroxyl radicals and compromising natural radical scavenging mechanisms. Overall, this self-cascade terpolymer platform fabricated herein offers a facile yet robust approach for advanced combinatory cancer therapy with great potential for clinical translations.

Abstract Background Optimizing patient selection for neoadjuvant chemotherapy in patients with breast cancer remains an unmet clinical need. Quantitative features from medical imaging were reported to be predictive of treatment responses. However, the biologic meaning of these latent features is poorly understood, preventing the clinical use of such noninvasive imaging markers. The study aimed to develop a deep learning signature (DLS) from pretreatment magnetic resonance imaging (MRI) for predicting responses to neoadjuvant chemotherapy in patients with breast cancer and to further investigate the biologic meaning of the DLS by identifying its underlying pathways using paired MRI and proteomic sequencing data. Methods MRI‐based DLS was constructed (radiogenomic training dataset, n = 105) and validated (radiogenomic validation dataset, n = 26) for the prediction of pathologic complete response (pCR) to neoadjuvant chemotherapy. Proteomic sequencing revealed biological functions facilitating pCR (n = 139). Their associations with DLS were uncovered by radiogenomic analysis. Results The DLS achieved a prediction accuracy of 0.923 with an AUC of 0.958, higher than the performance of the model trained by transfer learning. Cellular membrane formation, endocytosis, insulin‐like growth factor binding, protein localization to membranes, and cytoskeleton‐dependent trafficking were differentially regulated in patients showing pCR. Oncogenic signaling pathways, features correlated with human phenotypes, and features correlated with general biological processes were significantly correlated with DLS in both training and validation dataset (p.adj

Abstract Current diagnosis of glioma types requires combining both histological features and molecular characteristics, which is an expensive and time-consuming procedure. Determining the tumor types directly from whole-slide images (WSIs) is of great value for glioma diagnosis. This study presents an integrated diagnosis model for automatic classification of diffuse gliomas from annotation-free standard WSIs. Our model is developed on a training cohort (n = 1362) and a validation cohort (n = 340), and tested on an internal testing cohort (n = 289) and two external cohorts (n = 305 and 328, respectively). The model can learn imaging features containing both pathological morphology and underlying biological clues to achieve the integrated diagnosis. Our model achieves high performance with area under receiver operator curve all above 0.90 in classifying major tumor types, in identifying tumor grades within type, and especially in distinguishing tumor genotypes with shared histological features. This integrated diagnosis model has the potential to be used in clinical scenarios for automated and unbiased classification of adult-type diffuse gliomas.

Abstract Background We aimed to develop machine learning models for prediction of molecular subgroups (low-risk group and intermediate/high-risk group) and molecular marker (KIAA1549-BRAF fusion) of pediatric low-grade gliomas (PLGGs) based on radiomic features extracted from multiparametric MRI. Methods 61 patients with PLGGs were included in this retrospective study, which were divided into a training set and an internal validation set at a ratio of 2:1 based on the molecular subgroups or the molecular marker. The patients were classified into low-risk and intermediate/high-risk groups, BRAF fusion positive and negative groups, respectively. We extracted 5929 radiomic features from multiparametric MRI. Thereafter, we removed redundant features, trained random forest models on the training set for predicting the molecular subgroups or the molecular marker, and validated their performance on the internal validation set. The performance of the prediction model was verified by 3-fold cross-validation. Results We constructed the classification model differentiating low-risk PLGGs from intermediate/high-risk PLGGs using 4 relevant features, with an AUC of 0.833 and an accuracy of 76.2% in the internal validation set. In the prediction model for predicting KIAA1549-BRAF fusion using 4 relevant features, an AUC of 0.818 and an accuracy of 81.0% were achieved in the internal validation set. Conclusions The current study demonstrates that MRI radiomics is able to predict molecular subgroups of PLGGs and KIAA1549-BRAF fusion with satisfying sensitivity. Trial registration This study was retrospectively registered at clinicaltrials.gov (NCT04217018).

Tumor subtyping based on its immune landscape may guide precision immunotherapy. The aims of this study were to identify immune subtypes of adult diffuse gliomas with RNA sequencing data, and to noninvasively predict this subtype using a biologically interpretable radiomic signature from MRI. A subtype discovery dataset (n = 210) from a public database and two radiogenomic datasets (n = 130 and 55, respectively) from two local hospitals were included. Brain tumor microenvironment‐specific signatures were constructed from RNA sequencing to identify the immune types. A radiomic signature was built from MRI to predict the identified immune subtypes. The pathways underlying the radiomic signature were identified to annotate their biological meanings. The reproducibility of the findings was verified externally in multicenter datasets. Three distinctive immune subtypes were identified, including an inflamed subtype marked by elevated hypoxia‐induced immunosuppression, a “cold” subtype that exhibited scarce immune infiltration with downregulated antigen presentation, and an intermediate subtype that showed medium immune infiltration. A 10‐feature radiomic signature was developed to predict immune subtypes, achieving an AUC of 0.924 in the validation dataset. The radiomic features correlated with biological functions underpinning immune suppression, which substantiated the hypothesis that molecular changes can be reflected by radiomic features. The immune subtypes, predictive radiomic signature, and radiomics‐correlated biological pathways were validated externally. Our data suggest that adult‐type diffuse gliomas harbor three distinctive immune subtypes that can be predicted by MRI radiomic features with clear biological significance. The immune subtypes, radiomic signature, and radiogenomic links can be replicated externally.

Abstract Background Cryptocaryon irritans is a fatal parasite for marine teleosts and causes severe economic loss for aquaculture. Galvanized materials have shown efficacy in controlling this parasite infestation through the release of zinc ions to induce oxidative stress. Methods In this study, the resistance mechanism in C. irritans against oxidative stress induced by zinc ions was investigated. Untargeted metabolomics analysis was used to determine metabolic regulation in C. irritans in response to zinc ion treatment by the immersion of protomonts in ZnSO4 solution at a sublethal dose (20 μmol). Eight differential metabolites were selected to assess the efficacy of defense against zinc ion stimulation in protomonts of C. irritans. Furthermore, the mRNA relative levels of glutathione metabolism-associated enzymes were measured in protomonts following treatment with ZnSO4 solution at sublethal dose. Results The results showed that zinc ion exposure disrupted amino acid metabolism, carbohydrate metabolism, lipid metabolism, and nucleotide metabolism in C. irritans. Four antioxidants, namely ascorbate, S-hexyl-glutathione, syringic acid, and ubiquinone-1, were significantly increased in the Zn group (P

Multi-contrast magnetic resonance imaging (MRI) is wildly applied to identify tuberous sclerosis complex (TSC) children in a clinic. In this work, a deep convolutional neural network with multi-contrast MRI is proposed to diagnose pediatric TSC. Firstly, by combining T2W and FLAIR images, a new synthesis modality named FLAIR3 was created to enhance the contrast between TSC lesions and normal brain tissues. After that, a deep weighted fusion network (DWF-net) using a late fusion strategy is proposed to diagnose TSC children. In experiments, a total of 680 children were enrolled, including 331 healthy children and 349 TSC children. The experimental results indicate that FLAIR3 successfully enhances the visibility of TSC lesions and improves the classification performance. Additionally, the proposed DWF-net delivers a superior classification performance compared to previous methods, achieving an AUC of 0.998 and an accuracy of 0.985. The proposed method has the potential to be a reliable computer-aided diagnostic tool for assisting radiologists in diagnosing TSC children.

ObjectivesPhosphatase and tensin homolog (PTEN) mutation is an indicator of poor prognosis of low-grade and high-grade glioma. This study built a reliable model from multi-parametric magnetic resonance imaging (MRI) for predicting the PTEN mutation status in patients with glioma.MethodsIn this study, a total of 244 patients with glioma were retrospectively collected from our center (n = 77) and The Cancer Imaging Archive (n = 167). All patients were randomly divided into a training set (n = 170) and a validation set (n = 74). Three models were built from preoperative MRI for predicting PTEN status, including a radiomics model, a convolutional neural network (CNN) model, and an integrated model based on both radiomics and CNN features. The performance of each model was evaluated by accuracy and the area under the receiver operating characteristic curve (AUC).ResultsThe CNN model achieved an AUC of 0.84 and an accuracy of 0.81, which performed better than did the radiomics model, with an AUC of 0.83 and an accuracy of 0.66. Combining radiomics with CNN will further benefit the predictive performance (accuracy = 0.86, AUC = 0.91).ConclusionsThe combination of both the CNN and radiomics features achieved significantly higher performance in predicting the mutation status of PTEN in patients with glioma than did the radiomics or the CNN model alone.

Background: To develop and validate a deep learning signature (DLS) from diffusion tensor imaging (DTI) for predicting overall survival in patients with infiltrative gliomas, and to investigate the biological pathways underlying the developed DLS. Methods: The DLS was developed based on a deep learning cohort (n = 688). The key pathways underlying the DLS were identified on a radiogenomics cohort with paired DTI and RNA-seq data (n=78), where the prognostic value of the pathway genes was validated in public databases (TCGA, n = 663; CGGA, n = 657). Findings: The DLS was associated with survival (log-rank P < 0.001) and was an independent predictor (P < 0.001). Incorporating the DLS into existing risk system resulted in a deep learning nomogram predicting survival better than either the DLS or the clinicomolecular nomogram alone, with a better calibration and classification accuracy (net reclassification improvement 0.646, P < 0.001). Five kinds of pathways (synaptic transmission, calcium signaling, glutamate secretion, axon guidance, and glioma pathways) were significantly correlated with the DLS. Average expression value of pathway genes showed prognostic significance in our radiogenomics cohort and TCGA/CGGA cohorts (log-rank P < 0.05). Interpretation: DTI-derived DLS can improve glioma stratification by identifying risk groups with dysregulated biological pathways that contributed to survival outcomes. Therapies inhibiting neuron-to-brain tumor synaptic communication may be more effective in high-risk glioma defined by DTI-derived DLS. Funding: A full list of funding bodies that contributed to this study can be found in the Acknowledgements section.

Objective: Axillary lymph node (ALN) metastasis status is important in guiding treatment in breast cancer. The aims were to assess how deep convolutional neural network (CNN) performed compared with radiomics analysis in predicting ALN metastasis using breast ultrasound, and to investigate the value of both intratumoral and peritumoral regions in ALN metastasis prediction.Methods: We retrospectively enrolled 479 breast cancer patients with 2,395 breast ultrasound images. Based on the intratumoral, peritumoral, and combined intra- and peritumoral regions, three CNNs were built using DenseNet, and three radiomics models were built using random forest, respectively. By combining the molecular subtype, another three CNNs and three radiomics models were built. All models were built on training cohort (343 patients 1,715 images) and evaluated on testing cohort (136 patients 680 images) with ROC analysis. Another prospective cohort of 16 patients was enrolled to further test the models.Results: AUCs of image-only CNNs in both training/testing cohorts were 0.957/0.912 for combined region, 0.944/0.775 for peritumoral region, and 0.937/0.748 for intratumoral region, which were numerically higher than their corresponding radiomics models with AUCs of 0.940/0.886, 0.920/0.724, and 0.913/0.693. The overall performance of image-molecular CNNs in terms of AUCs on training/testing cohorts slightly increased to 0.962/0.933, 0.951/0.813, and 0.931/0.794, respectively. AUCs of both CNNs and radiomics models built on combined region were significantly better than those on either intratumoral or peritumoral region on the testing cohort (p < 0.05). In the prospective study, the CNN model built on combined region achieved the highest AUC of 0.95 among all image-only models.Conclusions: CNNs showed numerically better overall performance compared with radiomics models in predicting ALN metastasis in breast cancer. For both CNNs and radiomics models, combining intratumoral, and peritumoral regions achieved significantly better performance.

ObjectiveAn abnormal state of the central auditory system (CAS) likely plays a large role in the occurrence of phantom sound of tinnitus. Various tinnitus studies using resting-state functional MRI (RS-fMRI) have reported aberrant spontaneous brain activity in the non-auditory system and altered functional connectivity between the CAS and non-auditory system. This study aimed to investigate abnormal functional connections between the aberrant spontaneous activity in the CAS and the whole brain in tinnitus patients, compared to healthy controls (HC) using RS-fMRI.Materials and MethodsRS-fMRI from 16 right-ear tinnitus patients with normal hearing (TNHs) and 15 HC individuals was collected, and the time series were extracted from different clusters of a CAS template, supplied by the Anatomy Toolbox of the Statistical Parametric Mapping software. These data were used to derive the smoothed mean amplitude of low-frequency fluctuation (smALFF) values and calculate the relationship between such values and the corresponding clinical data. In addition, clusters in the CAS identified by the smALFF maps were set as seed regions for calculating and comparing the brain-wide connectivity between TNH and HC.ResultsWe identified the different clusters located in the left higher auditory cortex (HAC) and the right inferior colliculus (IC) from the smALFF maps that contained increased (HAC) and decreased (IC) activity when the TNH group was compared to the HC group, respectively. The value of increased smALFF cluster in the HAC was positively correlated with the tinnitus score, but the decreased smALFF cluster in the IC was not correlated with any clinical characters of tinnitus. The TNH group displayed increased connectivity, compared to the HC group, in brain regions that encompassed the left IC, bilateral Heschl gyrus, bilateral supplementary motor area, right insula, bilateral superior temporal gyrus, right middle temporal gyrus, left hippocampus, left amygdala, and right supramarginal gyrus.ConclusionTinnitus may be linked to abnormal spontaneous activity in the HAC, which can arise from the neural plasticity induced from the increased functional connectivity between the auditory network, cerebellum, and limbic system.

Background. Panax notoginseng (Burk.) F. H. Chen is one of the most common herbs in China. Because of its good efficacy and little adverse reaction, Panax notoginseng has been used widely to treat cardiovascular diseases (CVDs). Objective. To investigate effects of Panax notoginseng flower (PN-F) on rats with myocardial infarction (MI). Methods. The proximal left anterior descending coronary artery in rats was ligated to induce acute myocardial infarction. Then, animals were randomly assigned to four experimental groups: MI control group, Betaloc control group (with Betaloc 10 mg/kg/d), FD500 (low-dose) group (Panax notoginseng flower decoction 500 mg/kg, n=10), and FD1000 (high-dose) group (Panax notoginseng flower decoction 1000 mg/kg, n=10). Panax notoginseng flower decoction or Betaloc was orally administrated for two to four weeks before and after operation. Sham-operated group was used as a normal untreated group, in which animals were treated with double distilled water, once daily. HE (hematoxylin and eosin) staining, immunofluorescent assay, TUNEL assay, quantitative real-time PCR, and western blot analysis were, respectively, performed to observe morphology, count mean minimal vessels, investigate apoptotic cells, and record gene (HIF-1, VEGFA, and KDR) and protein (Bcl-2 and Bax) expressions. Results. Two weeks after MI, PN-F significantly enhanced capillary density in the border area of MI, decreased infarct size, improved minimal vessels, suppressed cell apoptosis, and enhanced expressions of genes (HIF-1, VEGFA, and KDR) and proteins (Bcl-2 and Bax). Conclusions. PN-F demonstrated a potential herb to treat rats with myocardial infarction through promoting angiogenesis and inhibition of apoptosis in the infarct area.

Using Dachengqi Tang (DCQT) as a model, high performance liquid chromatography (HPLC) fingerprints were applied to optimize machine extracting process with the BoxâBehnken experimental design. HPLC fingerprints were carried out to investigate the chemical ingredients of DCQT; synthetic weighing method based on analytic hierarchy process (AHP) and criteria importance through intercriteria correlation (CRITIC) was performed to calculate synthetic scores of fingerprints; using the mark ingredients contents and synthetic scores as indicators, the BoxâBehnken design was carried out to optimize the process parameters of machine decocting process under high pressure for DCQT. Results of optimal process showed that the herb materials were soaked for 45 min and extracted with 9 folds volume of water in the decocting machine under the temperature of 140 °C till the pressure arrived at 0.25 MPa; then hot decoction was excreted to soak Dahuang and Mangxiao for 5 min. Finally, obtained solutions were mixed, filtrated and packed. It concluded that HPLC fingerprints combined with the BoxâBehnken experimental design could be used to optimize extracting process of traditional Chinese medicine (TCM). Keywords: Dachengqi Tang, HPLC fingerprints, BoxâBehnken design, Synthetic weighing method

Infection with hepatitis C virus (HCV) is etiologically involved in liver cirrhosis, hepatocellular carcinoma and B-cell lymphomas. It has been demonstrated previously that HCV non-structural protein 3 (NS3) is involved in cell transformation. In this study, a yeast two-hybrid screening experiment was conducted to identify cellular proteins interacting with HCV NS3 protein. Cytosolic 5'(3')-deoxyribonucleotidase (cdN, dNT-1) was found to interact with HCV NS3 protein. Binding domains of HCV NS3 and cellular cdN proteins were also determined using the yeast two-hybrid system. Interactions between HCV NS3 and cdN proteins were further demonstrated by co-immunoprecipitation and confocal analysis in cultured cells. The cellular cdN activity was partially repressed by NS3 protein in both the transiently-transfected and the stably-transfected systems. Furthermore, HCV partially repressed the cdN activity while had no effect on its protein expression in the systems of HCV sub-genomic replicons and infectious HCV virions. Deoxyribonucleotidases are present in most mammalian cells and involve in the regulation of intracellular deoxyribonucleotides pools by substrate cycles. Control of DNA precursor concentration is essential for the maintenance of genetic stability. Reduction of cdN activity would result in the imbalance of DNA precursor concentrations. Thus, our results suggested that HCV partially reduced the cdN activity via its NS3 protein and this may in turn cause diseases.

The dinuclear title compound, [Cd2(C7H5O3)4(C12H8N2)2], is located on a crystallographic rotation twofold axis. The two CdII ions are connected by two tridentate bridging 2-hydroxybenzoate anions. Each CdII ion is seven-coordinated by five O atoms from three 2-hydroxybenzoate ligands and two N atoms from 1,10-phenanthroline. The 2-hydroxybenzoate molecules adopt two kinds of coordination mode, bidentate chelating and tridentate bridging–chelating. Intramolecular hydrogen bonds between hydroxy and carboxylate groups from 2-hydroxybenzoate groups and π–π stacking interactions between parallel 1,10-phenanthroline ligands [centroid–centroid distances = 3.707 (3) and 3.842 (3) Å] are observed. Furthermore, adjacent benzene rings from 2-hydroxybenzoate ligands are involved in π–π interactions with interplanar distances of 3.642 (3) Å, thereby forming a chain along the a axis direction.

Abstract Objectives This study aimed to investigate the intra- and inter-observer consistency of the Visually Accessible Rembrandt Images (VASARI) feature set before and after dichotomization, and the association between dichotomous VASARI features and the overall survival (OS) in glioblastoma (GBM) patients. Methods This retrospective study included 351 patients with pathologically confirmed IDH1 wild-type GBM between January 2016 and June 2022. Firstly, VASARI features were assessed by four radiologists with varying levels of experience before and after dichotomization. Cohen’s kappa coefficient (κ) was calculated to measure the intra- and inter-observer consistency. Then, after adjustment for confounders using propensity score matching, Kaplan-Meier curves were used to compare OS differences for each dichotomous VASARI feature. Next, patients were randomly stratified into a training set (n = 211) and a test set (n = 140) in a 3:2 ratio. Based on the training set, Cox proportional hazards regression analysis was adopted to develop combined and clinical models to predict OS, and the performance of the models was evaluated with the test set. Results Eleven VASARI features with κ value of 0.61–0.8 demonstrated almost perfect agreement after dichotomization, with the range of κ values across all readers being 0.874–1.000. Seven VASARI features were correlated with GBM patient OS. For OS prediction, the combined model outperformed the clinical model in both training set (C-index, 0.762 vs. 0.723) and test set (C-index, 0.812 vs. 0.702). Conclusion The dichotomous VASARI features exhibited excellent inter- and intra-observer consistency. The combined model outperformed the clinical model for OS prediction.

Purpose: This study aimed to investigate the biomechanical and electromyographic factors underlying shooting consistency among archers of different skill levels, providing insights into performance optimization and training strategies. Methods: The study analyzed 28 left-handed archers, including elite, mid-level, and novice athletes. Using an eight-camera infrared motion capture system and a wireless surface EMG system, kinematic and muscle activity data were collected for key upper limb muscles during two action phases: drawing and aiming. Coefficients of variation (CV) were calculated for both kinematic and EMG parameters, and statistical analysis was conducted to compare the groups. Results: Elite and mid-level archers exhibited significantly lower CVs in key kinematic parameters (e.g., shoulder abduction) and muscle activation consistency during the drawing phase compared to novices. Differences in muscle activation were most pronounced in the flexor muscle groups. These findings highlight the critical role of movement consistency in archery performance. Conclusion: Greater consistency in kinematic and muscle activation patterns distinguishes higher-skilled archers, particularly during the drawing phase. These results emphasize the importance of targeting movement consistency and coordination in training programs to enhance performance in precision sports like archery.

Purpose: Joint coordination is a critical factor influencing high-quality technical movements. By analyzing the changes between joints through functional data analysis, characteristic parameters can be derived. This study employs functional data analysis to explore the differences in joint coordination characteristics among archery athletes at various skill levels, offering a new perspective for coaches and athletes to analyze specific movement techniques. Methods: Twenty-eight archery athletes were selected as subjects, including eight international master athletes, twelve national first-level athletes, and eight national second-level athletes from Chinese national and province team. An infrared light spot motion capture system was utilized to gather kinematic parameters during the archery shooting process. Functional data analysis was employed to determine the upper limb joint coordination characteristic parameters of athletes at different skill levels. Results: Archery athletes at different levels exhibit three types of joint coordination characteristics between the shoulder and elbow joints during the drawing and aiming phases. The first type of joint coordination characteristic shows statistical differences across levels (P=0.03). These differences are primarily due to the following: 1) For international master athletes, the movement speeds of the shoulder and elbow joints both peak in the early phase of drawing; 2) The range of coupled motion between the shoulder and elbow joints is higher in the early phase of drawing and lower in the latter half, and consistently lower during the aiming phase; 3) The duration of the drawing phase is shorter. Conclusion: Functional data analysis effectively captures the differences in upper limb joint coordination characteristics among archery athletes at various levels. Superior athletes typically exhibit synchronous shoulder-elbow joint movements, enhanced feedforward capabilities, and a quicker rhythm of movement.

Research on fine motor skills in children has evolved considerably over the last 30 years. However, a scientometric analysis of these changes over time, along with an assessment of researchers’ networks and scientific productivity, is currently lacking. We conducted a scientometric analysis of the scientific literature on fine motor skills in preschool and school-aged children to evaluate key themes and trends over the past decades, thereby informing future research directions. We conducted a search in the Web of Science Core Collection from 1994 to 2024 utilizing pertinent keywords. The evolution of co-reference clusters and the performance of research networks across countries, institutions, and authors were analyzed using the Bibliometrix R package (v4.3.0) and CiteSpace (v6.4.R3). There has been an exponential growth in publications, showing a 7.5% annual increase from 2006 to 2021, and a positive correlation (R² = 0.89) between the publication year and output, indicating a promising trend in the field. The co-cited reference network analysis revealed 10 clusters in a well-structured network (Q = 0.835) with strong clustering reliability (S = 0.94). The primary research trends identifiedare: 1) the link between fine motor skills and academic achievement; and 2) the traits of fine motor impairment in children with neurodevelopmental disorders. The latest research trends in fine motor skills for children include developing assessment tools and interventions for both typically developing children and those with neurodevelopmental disorders.These findings can inform future directions for funding agencies and research groups.