A Yb:CaGd_(0.33)Y_(0.625)AlO_(4)(Yb:CGYA)laser crystal of high optical quality has been successfully synthesized via the Czochralski method.The introduction of Gd^(3+)ions preserves the original structure and efficien...A Yb:CaGd_(0.33)Y_(0.625)AlO_(4)(Yb:CGYA)laser crystal of high optical quality has been successfully synthesized via the Czochralski method.The introduction of Gd^(3+)ions preserves the original structure and efficiently generates inhomogeneous broadening of the Yb^(3+)ion emission spectra.The fluorescence emission peak wavelength of the Yb:CGYA crystal is 1053 nm,and the corresponding measured full width at halfmaximum is 93 nm.A tunable laser output ranging from 1017 nm to 1073 nm is achieved by using a birefringent filter,which represents the broadest tuning range reported in a short cavity to date.The compact laser offers significant advantages for its applications around the 1μm wavelength band.展开更多
Large language models(LLMs)and natural language processing(NLP)have significant promise to improve efficiency and refine healthcare decision-making and clinical results.Numerous domains,including healthcare,are rapidl...Large language models(LLMs)and natural language processing(NLP)have significant promise to improve efficiency and refine healthcare decision-making and clinical results.Numerous domains,including healthcare,are rapidly adopting LLMs for the classification of biomedical textual data in medical research.The LLM can derive insights from intricate,extensive,unstructured training data.Variants need to be accurately identified and classified to advance genetic research,provide individualized treatment,and assist physicians in making better choices.However,the sophisticated and perplexing language of medical reports is often beyond the capabilities of the devices we now utilize.Such an approach may result in incorrect diagnoses,which could affect a patient’s prognosis and course of therapy.This study evaluated the efficacy of the proposed model by looking at publicly accessible textual clinical data.We have cleaned the clinical textual data using various text preprocessing methods,including stemming,tokenization,and stop word removal.The important features are extracted using Bag of Words(BoW)and Term Frequency-Inverse Document Frequency(TFIDF)feature engineering methods.The important motive of this study is to predict the genetic variants based on the clinical evidence using a novel method with minimal error.According to the experimental results,the random forest model achieved 61%accuracy with 67%precision for class 9 using TFIDF features and 63%accuracy and a 73%F1 score for class 9 using Bag of Words features.The accuracy of the proposed BERT(Bidirectional Encoder Representations from Transformers)model was 70%with 5-fold cross-validation and 71%with 10-fold cross-validation.The research results provide a comprehensive overview of current LLM methods in healthcare,benefiting academics as well as professionals in the discipline.展开更多
With the advancement of deep learning in the automotive domain,more and more researchers are focusing on autonomous driving.Among these tasks,free space detection is particularly crucial.Currently,many model-based app...With the advancement of deep learning in the automotive domain,more and more researchers are focusing on autonomous driving.Among these tasks,free space detection is particularly crucial.Currently,many model-based approaches have achieved autonomous driving on well-structured urban roads,but these efforts primarily focus on urban road environments.In contrast,there are fewer deep learningmethods specifically designed for off-road traversable area detection,and their effectiveness is not yet satisfactory.This is because detecting traversable areas in complex outdoor environments poses significant challenges,and current methods often rely on single-image inputs,which do not align with contemporary multimodal approaches.Therefore,in this study,we propose a CFH-Net model for off-road traversable area detection.This model employs a Transformer architecture to enhance its capability of capturing global information.For multimodal feature extraction and fusion,we integrate the CM-FRM module for feature extraction and introduce the novel FFX module for feature fusion,thereby improving the perception capability of autonomous vehicles on unstructured roads.To address upsampling,we propose a new convolution precorrection method to reduce model parameters and computational complexity while enhancing the model’s ability to capture complex features.Finally,we conducted experiments on the ORFD off-road dataset and achieved outstanding results.展开更多
Tensegrity structures,embodying the principles of continuous tensioning and discrete compression,have emerged as fundamental frameworks in locomotive soft robotics for navigating uneven and unpredictable environments,...Tensegrity structures,embodying the principles of continuous tensioning and discrete compression,have emerged as fundamental frameworks in locomotive soft robotics for navigating uneven and unpredictable environments,owing to their flexible and resilient traits.By means of a straightforward and cost-effective method to achieve structure-driven,vibration-driven tensegrity shows great potential,particularly in tasks demanding random exploration.However,the design guidance for vibration-driven tensegrity and their performance evaluation in unstructured terrain remain unrevealed due to the complex dynamics of the structure.This paper presents a small six-bar tensegrity robot,driven by wireless vibration motors,designed for deployment in disaster rescue and search scenarios.Finite element simulation is used to investigate how structural characteristics,excitation parameters,and the arrangement of motors affect the kinematic performance of this tensegrity system.A prototype of the six-bar tensegrity robot with three motors located on the lower ends of the three lower struts is designed and manufactured after the numerical simulations.A simple control policy which adjusts the motion of the tensegrity robot by turning on or off the motors on different locations is proposed.The prototype with and without the control policy is tested in man-made environments of various complexity.It shows that the ability and efficiency of the tensegrity robot in exploring unstructured environments is significantly enhanced by the proposed control policy.It is believed that the potential of the vibration-driven tensegrity robot could be further exploited by integrating multi-source sensors and more intelligent control policies.展开更多
Traditional Computational Fluid Dynamics(CFD)simulations are computationally expensive when applied to complex fluid–structure interaction problems and often struggle to capture the essential flow features governing ...Traditional Computational Fluid Dynamics(CFD)simulations are computationally expensive when applied to complex fluid–structure interaction problems and often struggle to capture the essential flow features governing vortex-induced vibrations(VIV)of floating structures.To overcome these limitations,this study develops a hybrid framework that integrates high-fidelity CFD modeling with deep learning techniques to enhance the accuracy and efficiency of VIV response prediction.First,an unstructured finite-volume fluid–structure coupling model is established to generate high-resolution flow field data and extract multi-component time-series feature tensors.These tensors serve as inputs to a Squeeze-and-Excitation Convolutional Neural Network(SE-CNN),which models the nonlinear coupling between flow disturbances and structural responses.The SE-CNN architecture incorporates an attention-based weighting mechanism through an embedded Squeeze-and-Excitation module,dynamically optimizing channel feature importance and improving sensitivity to critical flow characteristics.During training,multidimensional inputs,including pressure,velocity gradient,and displacement sequences,are used to capture the full complexity of fluid–structure interactions.Results demonstrate that the proposed method achieves a maximum amplitude prediction error of only 2.9%and a main frequency deviation below 0.03 Hz,outperforming conventional CNN models by reducing amplitude prediction error from 3.2%to 1.9%.The approach is validated using a representative semi-submersible platform,confirming its robustness across varying damping conditions and flow velocities.展开更多
Catalytic doping is one of the economic and efficient strategies to optimize the operating temperature and kinetic behavior of magnesium hydride(MgH_(2)).Herein,efficient regulation of electronic and structural rearra...Catalytic doping is one of the economic and efficient strategies to optimize the operating temperature and kinetic behavior of magnesium hydride(MgH_(2)).Herein,efficient regulation of electronic and structural rearrangements in niobium-rich nickel oxides was achieved through precise compositional design and niobium cation functionalized doping,thereby greatly enhancing its intrinsic catalytic activity in hydrogen storage systems.As the niobium concentration increased,the Ni-Nb catalysts transformed into a mixed state of multi-phase nanoparticles(composed of nickel and niobium-rich nickel oxides)with smaller particle size and uniform distribution,thus exposing more nucleation sites and diffusion channels at the MgH_(2)/Mg interface.In addition,the additional generation of active Ni-Nb-O mixed phase induced numerous highly topical disordered and distorted crystalline,promoting the transfer and reorganization of H atoms.As a result,a stable and continuous multi-phase/component synergistic catalytic microenvironment could be constructed,exerting remarkable enhancement on MgH_(2)’s hydrogen storage performance.After comparative tests,Ni_(0.7)Nb_(0.3)-doped MgH_(2) presented the optimal low-temperature kinetics with a dehydrogenation activation energy of 78.8 kJ·mol^(−1).The onset dehydrogenation temperature of MgH_(2)+10 wt%Ni_(0.7)Nb_(0.3) was reduced to 198℃ and 6.18 wt%H_(2) could be released at 250℃ within 10 min.In addition,the dehydrogenated MgH_(2)–NiNb composites absorbed 4.87 wt%H_(2) in 10 min at 125℃ and a capacity retention rate was maintained at 6.18 wt%even after 50 reaction cycles.In a word,our work supplies fresh insights for designing novel defective-state multiphase catalysts for hydrogen storage and other energy related field.展开更多
Bimetallic surfaces play a pivotal role in heterogeneous catalysis,yet their theoretical modeling has long been hindered by the computational chal-lenges of capturing configurational disorder,a critical feature govern...Bimetallic surfaces play a pivotal role in heterogeneous catalysis,yet their theoretical modeling has long been hindered by the computational chal-lenges of capturing configurational disorder,a critical feature governing their catalytic properties.Tradition-al approaches rely on oversimplified ordered surface models or restrict dis-order to a few atomic layers,limiting their predictive power.Here,we Cu_(1-x)Zn_(x)Cu_(1-x)Zn_(x)present an accurate and efficient computational framework that integrates machine learning force fields(MLFFs)with the cluster expansion(CE)method to study configurationally dis-ordered bimetallic surfaces at finite temperatures.We have developed an efficient workflow in which the MLFF is first trained iteratively via an active learning protocol,and then used to generate accurate energetic data for thousands of configurations that enable robust CE model construction.By treating bulk and surface clusters separately,we can build CE models for surface slabs with an arbitrary number of layers.Using as a case study,our CE-based Monte Carlo simulations reveal key structural insights that are relevant to the under-standing of catalytic properties of surfaces.This work demonstrates how MLFF-aided CE can overcome traditional limitations in theoretical modeling of bimetallic surfaces and highlights pathways toward more realistic modeling of heterogeneous catalysts.展开更多
Intrinsically disordered proteins(IDPs)and their regions(IDRs)play crucial roles in cellular func-tions despite their lack of stable three-dimensional structures.In this study,we investigate the interac-tions between ...Intrinsically disordered proteins(IDPs)and their regions(IDRs)play crucial roles in cellular func-tions despite their lack of stable three-dimensional structures.In this study,we investigate the interac-tions between the C-terminal do-main of protein 4.1G(4.1G CTD)and the nuclear mitotic apparatus protein(NuMA)under varying pH and salt ion conditions to under-stand the regulatory mechanisms affecting their binding.4.1G CTD and NuMA bind effec-tively under neutral and alkaline conditions,but their interaction is disrupted under acidic conditions(pH 3.6).The protonation of positively charged residues at the C-terminal of 4.1G CTD under acidic conditions leads to increased electrostatic repulsion,weakening the overall binding free energy.Secondary structure analysis shows that specific regions of 4.1G CTD re-main stable under both pH conditions,but the C-terminal region(aa 990−1000)and the N-terminal region of NuMA(aa 1800−1810)exhibit significant reductions in secondary struc-ture probability under acidic conditions.Contact map analysis and solvent-accessible surface area analysis further support these findings by showing a reduced contact probability be-tween these regions under pH 3.6.These results provide a comprehensive understanding of how pH and ionic strength regulate the binding dynamics of 4.1G CTD and NuMA,emphasiz-ing the regulatory role of electrostatic interactions.展开更多
We investigate the mixed-state entanglement between two spins embedded in the XXZ Heisenberg chain under thermal equilibrium.By deriving an analytical expression for the entanglement of two-spin thermal states and ext...We investigate the mixed-state entanglement between two spins embedded in the XXZ Heisenberg chain under thermal equilibrium.By deriving an analytical expression for the entanglement of two-spin thermal states and extending this analysis to larger spin chains,we demonstrate that mixed-state entanglement is profoundly shaped by both disorder and temperature.Our results reveal a sharp distinction between many-body localized and ergodic phases,with entanglement vanishing above diferent fnite temperature thresholds.Furthermore,by analyzing non-adjacent spins,we uncover an approximate exponential decay of entanglement with separation.This work advances the understanding of the quantum-to-classical transition by linking the entanglement properties of small subsystems to the broader thermal environment,ofering an explanation for the absence of entanglement in macroscopic systems.These fndings provide critical insights into quantum many-body physics,bridging concepts from thermalization,localization,and quantum information theory.展开更多
Lithium-rich layered oxides (LLOs) are increasingly recognized as promising cathode materials for nextgeneration high-energy-density lithium-ion batteries (LIBs).However,they suffer from voltage decay and low initial ...Lithium-rich layered oxides (LLOs) are increasingly recognized as promising cathode materials for nextgeneration high-energy-density lithium-ion batteries (LIBs).However,they suffer from voltage decay and low initial Coulombic efficiency (ICE) due to severe structural degradation caused by irreversible O release.Herein,we introduce a three-in-one strategy of increasing Ni and Mn content,along with Li/Ni disordering and TM–O covalency regulation to boost cationic and anionic redox activity simultaneously and thus enhance the electrochemical activity of LLOs.The target material,Li_(1.2)Ni_(0.168)Mn_(0.558)Co_(0.074)O_(2)(L1),exhibits an improved ICE of 87.2%and specific capacity of 293.2 mA h g^(-1)and minimal voltage decay of less than 0.53 m V cycle-1over 300 cycles at 1C,compared to Li_(1.2)Ni_(0.13)Mn_(0.54)Co_(0.13)O_(2)(Ls)(274.4 mA h g^(-1)for initial capacity,73.8%for ICE and voltage decay of 0.84 mV/cycle over 300 cycles at 1C).Theoretical calculations reveal that the density of states (DOS) area near the Fermi energy level for L1 is larger than that of Ls,indicating higher anionic and cationic redox reactivity than Ls.Moreover,L1 exhibits increased O-vacancy formation energy due to higher Li/Ni disordering of 4.76%(quantified by X-ray diffraction Rietveld refinement) and enhanced TM–O covalency,making lattice O release more difficult and thus improving electrochemical stability.The increased Li/Ni disordering also leads to more Ni^(2+)presence in the Li layer,which acts as a pillar during Li+de-embedding,improving structural stability.This research not only presents a viable approach to designing low-Co LLOs with enhanced capacity and ICE but also contributes significantly to the fundamental understanding of structural regulation in high-performance LIB cathodes.展开更多
Clinically differentiating bipolarⅡdisorder(BD-Ⅱ)from major depressive disorder(MDD)remains a significant challenge in modern psychiatry.These two conditions share substantial clinical symptomatology,making accurate...Clinically differentiating bipolarⅡdisorder(BD-Ⅱ)from major depressive disorder(MDD)remains a significant challenge in modern psychiatry.These two conditions share substantial clinical symptomatology,making accurate diagnosis difficult in routine clinical practice.Misdiagnosis may lead to inappropriate treatment strategies,increased psychological and physical burdens,reduced quality of life,and impaired social functioning.Genetic overlap may partially explain the clinical similarities between MDD and BD-Ⅱ,and biomarkers along with neuroimaging techniques are receiving increasing attention as tools to aid in diagnosis.For example,electroencephalography has been shown to effectively distinguish between unipolar depression and bipolar depression;serum levels of glycogen synthase kinase-3 have also been investigated as a potential tool for differentiating between the two disorders.A comprehensive assessment integrating clinical characteristics,genetic basis research,and multimodal evaluations using neuroimaging and biomarkers through a multidisciplinary approach will help enhance clinicians'ability to distinguish between MDD and BD-Ⅱ.By improving diagnostic accuracy,more personalized and effective treatment strategies can be developed,ultimately improving patients'health outcomes and quality of life.展开更多
As a novel lightweight metallic material with excellent heat and corrosion resistance,elastic disordered microporous metal rubber(EDMMR)functions as an effective damping and support element in high-temperature environ...As a novel lightweight metallic material with excellent heat and corrosion resistance,elastic disordered microporous metal rubber(EDMMR)functions as an effective damping and support element in high-temperature environments where traditional polymer rubber fails.In this paper,a multi-scale finite element model for EDMMR is constructed using virtual manufacturing technology(VMT).Thermo-mechanical coupling analysis reveals a distinct inward expansion and dissipation phenomenon in EDMMR under high-temperature conditions,distinguishing it from porous materials.This phenomenon has the potential to impact the overall dimensions of EDMMR through transmission and accumulation processes.The experimental results demonstrate a random distribution of internal micro springs in EDMMR,considering the contact composition of spring microelements and the pore structure.By incorporating material elasticity,a predictive method for the thermal expansion coefficient of EDMMR based on the Schapery model is proposed.Additionally,standardized processes are employed to manufacture multiple sets of cylindrical EDMMR samples with similar dimensions but varying porosities.Thermal expansion tests are conducted on these samples,and the accuracy of the predicted thermal expansion coefficient is quantitatively validated through residual analysis.This research indicates that EDMMR maintains good structural stability in high-temperature environments.The thermal expansion rate of the material exhibits an opposite trend to the variation of elastic modulus with temperature,as the porosity rate changes.展开更多
Non-right-handedness(NRH),encompassing left-handedness and mixed-handedness,has been frequently reported at elevated rates in individuals with various psychiatric disorders.The consistency of this association across m...Non-right-handedness(NRH),encompassing left-handedness and mixed-handedness,has been frequently reported at elevated rates in individuals with various psychiatric disorders.The consistency of this association across multiple conditions and its underlying mechanisms is the subject of ongoing investigation.This review synthesized current evidence to explore the association between NRH and psychiatric disorders from epidemiological,genetic,and neurobiological perspectives.We systematically identified and appraised relevant literature investigating NRH prevalence in psychiatric populations and potential explanatory mechanisms.Epidemiological evidence indicates an elevated prevalence of NRH,particularly within neurodevelopmental disorders.Potential contributing mechanisms identified include early developmental disruptions,shared genetic predispositions,and atypical patterns of brain lateralization.While the association between NRH and psychiatric conditions,especially neurodevelopmental disorders,is evident,the causal pathways and relative contributions of identified mechanisms are complex and debated.This review highlighted key areas requiring further research to elucidate these relationships.展开更多
Mitochondrial dysfunction has emerged as a critical factor in the etiology of various neurodevelopmental disorders, including autism spectrum disorders, attention-deficit/hyperactivity disorder, and Rett syndrome. Alt...Mitochondrial dysfunction has emerged as a critical factor in the etiology of various neurodevelopmental disorders, including autism spectrum disorders, attention-deficit/hyperactivity disorder, and Rett syndrome. Although these conditions differ in clinical presentation, they share fundamental pathological features that may stem from abnormal mitochondrial dynamics and impaired autophagic clearance, which contribute to redox imbalance and oxidative stress in neurons. This review aimed to elucidate the relationship between mitochondrial dynamics dysfunction and neurodevelopmental disorders. Mitochondria are highly dynamic organelles that undergo continuous fusion and fission to meet the substantial energy demands of neural cells. Dysregulation of these processes, as observed in certain neurodevelopmental disorders, causes accumulation of damaged mitochondria, exacerbating oxidative damage and impairing neuronal function. The phosphatase and tensin homolog-induced putative kinase 1/E3 ubiquitin-protein ligase pathway is crucial for mitophagy, the process of selectively removing malfunctioning mitochondria. Mutations in genes encoding mitochondrial fusion proteins have been identified in autism spectrum disorders, linking disruptions in the fusion-fission equilibrium to neurodevelopmental impairments. Additionally, animal models of Rett syndrome have shown pronounced defects in mitophagy, reinforcing the notion that mitochondrial quality control is indispensable for neuronal health. Clinical studies have highlighted the importance of mitochondrial disturbances in neurodevelopmental disorders. In autism spectrum disorders, elevated oxidative stress markers and mitochondrial DNA deletions indicate compromised mitochondrial function. Attention-deficit/hyperactivity disorder has also been associated with cognitive deficits linked to mitochondrial dysfunction and oxidative stress. Moreover, induced pluripotent stem cell models derived from patients with Rett syndrome have shown impaired mitochondrial dynamics and heightened vulnerability to oxidative injury, suggesting the role of defective mitochondrial homeostasis in these disorders. From a translational standpoint, multiple therapeutic approaches targeting mitochondrial pathways show promise. Interventions aimed at preserving normal fusion-fission cycles or enhancing mitophagy can reduce oxidative damage by limiting the accumulation of defective mitochondria. Pharmacological modulation of mitochondrial permeability and upregulation of peroxisome proliferator-activated receptor gamma coactivator 1-alpha, an essential regulator of mitochondrial biogenesis, may also ameliorate cellular energy deficits. Identifying early biomarkers of mitochondrial impairment is crucial for precision medicine, since it can help clinicians tailor interventions to individual patient profiles and improve prognoses. Furthermore, integrating mitochondria-focused strategies with established therapies, such as antioxidants or behavioral interventions, may enhance treatment efficacy and yield better clinical outcomes. Leveraging these pathways could open avenues for regenerative strategies, given the influence of mitochondria on neuronal repair and plasticity. In conclusion, this review indicates mitochondrial homeostasis as a unifying therapeutic axis within neurodevelopmental pathophysiology. Disruptions in mitochondrial dynamics and autophagic clearance converge on oxidative stress, and researchers should prioritize validating these interventions in clinical settings to advance precision medicine and enhance outcomes for individuals affected by neurodevelopmental disorders.展开更多
BACKGROUND Non-suicidal self-injury(NSSI)is common among adolescents with depressive disorders and poses a major public health challenge.Rumination,a key cognitive feature of depression,includes different subtypes tha...BACKGROUND Non-suicidal self-injury(NSSI)is common among adolescents with depressive disorders and poses a major public health challenge.Rumination,a key cognitive feature of depression,includes different subtypes that may relate to NSSI through distinct psychological mechanisms.However,how these subtypes interact with specific NSSI behaviors remains unclear.AIM To examine associations between rumination subtypes and specific NSSI behaviors in adolescents.METHODS We conducted a cross-sectional study with 305 hospitalized adolescents diagnosed with depressive disorders.The subjects ranged from 12-18 years in age.Rumi-nation subtypes were assessed using the Ruminative Response Scale,and 12 NSSI behaviors were evaluated using a validated questionnaire.Network analysis was applied to explore symptom-level associations and identify central symptoms.RESULTS The network analysis revealed close connections between rumination subtypes and NSSI behaviors.Brooding was linked to behaviors such as hitting objects and burning.Scratching emerged as the most influential NSSI symptom.Symptomfocused rumination served as a key bridge connecting rumination and NSSI.CONCLUSION Symptom-focused rumination and scratching were identified as potential intervention targets.These findings highlight the psychological significance of specific cognitive-behavioral links in adolescent depression and suggest directions for tailored prevention and treatment.However,the cross-sectional,single-site design limits causal inference and generalizability.Future longitudinal and multi-center studies are needed to confirm causal pathways and verify the generalizability of the findings to broader adolescent populations.展开更多
Musculoskeletal injuries are among the most common causes of disability worldwide,with early detection and appropriate intervention critical to minimizing long-term complications.Infrared thermography(IRT)has emerged ...Musculoskeletal injuries are among the most common causes of disability worldwide,with early detection and appropriate intervention critical to minimizing long-term complications.Infrared thermography(IRT)has emerged as a noninvasive,real-time imaging modality that captures superficial temperature changes reflecting underlying physiological processes such as inflammation and vascular alterations.This review explores the fundamental principles of medical thermography,differentiates between passive and active approaches,and outlines key technological advancements including artificial intelligence integration.The clinical utility of IRT is discussed in various contexts–ranging from acute soft tissue injuries and overuse syndromes to chronic pain and rehabilitation monitoring.Comparative insights with conventional imaging techniques such as ultrasound and magnetic resonance imaging are also presented.While IRT offers functional imaging capabilities with advantages in portability,safety,and speed,its limitations–such as lack of deep-tissue penetration and protocol standardization–remain significant barriers to broader adoption.Future directions include the integration of IRT with other imaging modalities and digital health platforms to enhance musculoskeletal assessment and injury prevention strategies.展开更多
Objective Previous studies link lower body mass index(BMI)with increased obsessive-compulsive disorder(OCD)risk,yet other body mass indicators may be more etioloically relevant.We dissected the causal association betw...Objective Previous studies link lower body mass index(BMI)with increased obsessive-compulsive disorder(OCD)risk,yet other body mass indicators may be more etioloically relevant.We dissected the causal association between body fat mass(FM)and OCD.Methods Summary statistics from genome-wide association studies of European ancestry were utilized to conduct two-sample Mendelian randomization analysis.Heterogeneity,horizontal pleiotropy,and sensitivity analyses were performed to assess the robustness.Results The inverse variance weighting method demonstrated that a genetically predicted decrease in FM was causally associated with an increased OCD risk[odds ratio(OR)=0.680,95%confidence interval(CI):0.528–0.875,P=0.003].Similar estimates were obtained using the weighted median approach(OR=0.633,95%CI:0.438–0.915,P=0.015).Each standard deviation increases in genetically predicted body fat percentage corresponded to a reduced OCD risk(OR=0.638,95%CI:0.455–0.896,P=0.009).The sensitivity analysis confirmed the robustness of these findings with no outlier instrument variables identified.Conclusion The negative causal association between FM and the risk of OCD suggests that the prevention or treatment of mental disorders should include not only the control of BMI but also fat distribution and body composition.展开更多
BACKGROUND Timely and accurate evaluation of mental disorders in adolescents using appropriate mental health literacy assessment tools is essential for improving their mental health literacy levels.AIM To develop an e...BACKGROUND Timely and accurate evaluation of mental disorders in adolescents using appropriate mental health literacy assessment tools is essential for improving their mental health literacy levels.AIM To develop an evaluation index system for the mental health literacy of adolescent patients with mental disorders,providing a scientific,comprehensive,and reliable tool for the monitoring and intervention of mental health literacy of such patients.METHODS From December 2022 to June 2023,the evaluation index system for mental health literacy of adolescents with mental disorders was developed through literature reviews,semi-structured interviews,expert letter consultations,and the analytic hierarchy process.Based on this index system,a self-assessment questionnaire was compiled and administered to 305 adolescents with mental disorders to test the reliability and validity of the index system.RESULTS The final evaluation index system for mental health literacy of adolescents with mental disorders included 4 first-level indicators,10 second-level indicators,and 52 third-level indicators.The overall Cronbach’sαcoefficient of the index system was 0.957,with a partial reliability of 0.826 and a content validity index of 0.975.The cumulative variance contribution rate of 10 common factors was 66.491%.The correlation coefficients between each dimension and the total questionnaire ranged from 0.672 to 0.724,while the correlation coefficients in each dimension ranged from 0.389 to 0.705.CONCLUSION The evaluation index system for mental health literacy of adolescents with mental disorders,developed in this study,demonstrated notable reliability and validity,making it a valuable tool for evaluating mental health literacy in this population.展开更多
Functional gastrointestinal disorders(FGIDs),including irritable bowel syndrome(IBS),functional dyspepsia(FD),and gastroesophageal reflux disease(GERD),present persistent diagnostic and therapeutic challenges due to s...Functional gastrointestinal disorders(FGIDs),including irritable bowel syndrome(IBS),functional dyspepsia(FD),and gastroesophageal reflux disease(GERD),present persistent diagnostic and therapeutic challenges due to symptom heterogeneity and the absence of reliable biomarkers.Artificial intelligence(AI)enables the integration of multimodal data to enhance FGID management through precision diagnostics and preventive healthcare.This minireview summarizes recent advancements in AI applications for FGIDs,highlighting progress in diagnostic accuracy,subtype classification,personalized interventions,and preventive strategies inspired by the traditional Chinese medicine concept of“treating the undiseased”.Machine learning and deep learning algorithms have demonstrated value in improving IBS diagnosis,refining FD neuro-gastrointestinal subtyping,and screening for GERD-related complications.Moreover,AI supports dietary,psychological,and integrative medicine-based interventions to improve patient adherence and quality of life.Nonetheless,key challenges remain,including data heterogeneity,limited model interpretability,and the need for robust clinical validation.Future directions emphasize interdisciplinary collaboration,the development of multimodal and explainable AI models,and the creation of patientcentered platforms to facilitate a shift from reactive treatment to proactive prevention.This review provides a systematic framework to guide the clinical application and theoretical innovation of AI in FGIDs.展开更多
Obesity is widely recognized as a global epidemic,primarily driven by an imbalance between energy expenditure and caloric intake associated with a sedentary lifestyle.Diets high in carbohydrates and saturated fats,par...Obesity is widely recognized as a global epidemic,primarily driven by an imbalance between energy expenditure and caloric intake associated with a sedentary lifestyle.Diets high in carbohydrates and saturated fats,particularly palmitic acid,are potent inducers of chronic low-grade inflammation,largely due to disruptions in glucose metabolism and the onset of insulin resistance(Qiu et al.,2022).While many organs are affected,the brain,specifically the hypothalamus,is among the first to exhibit inflammation in response to an unhealthy diet,suggesting that obesity may,in fact,be a brain-centered disease with neuroinflammation as a central factor(Thaler et al., 2012).展开更多
文摘A Yb:CaGd_(0.33)Y_(0.625)AlO_(4)(Yb:CGYA)laser crystal of high optical quality has been successfully synthesized via the Czochralski method.The introduction of Gd^(3+)ions preserves the original structure and efficiently generates inhomogeneous broadening of the Yb^(3+)ion emission spectra.The fluorescence emission peak wavelength of the Yb:CGYA crystal is 1053 nm,and the corresponding measured full width at halfmaximum is 93 nm.A tunable laser output ranging from 1017 nm to 1073 nm is achieved by using a birefringent filter,which represents the broadest tuning range reported in a short cavity to date.The compact laser offers significant advantages for its applications around the 1μm wavelength band.
基金funded by Princess Nourah bint Abdulrahman University and Researchers Supporting Project number(PNURSP2025R346),Princess Nourah bint Abdulrahman University,Riyadh,Saudi Arabia.
文摘Large language models(LLMs)and natural language processing(NLP)have significant promise to improve efficiency and refine healthcare decision-making and clinical results.Numerous domains,including healthcare,are rapidly adopting LLMs for the classification of biomedical textual data in medical research.The LLM can derive insights from intricate,extensive,unstructured training data.Variants need to be accurately identified and classified to advance genetic research,provide individualized treatment,and assist physicians in making better choices.However,the sophisticated and perplexing language of medical reports is often beyond the capabilities of the devices we now utilize.Such an approach may result in incorrect diagnoses,which could affect a patient’s prognosis and course of therapy.This study evaluated the efficacy of the proposed model by looking at publicly accessible textual clinical data.We have cleaned the clinical textual data using various text preprocessing methods,including stemming,tokenization,and stop word removal.The important features are extracted using Bag of Words(BoW)and Term Frequency-Inverse Document Frequency(TFIDF)feature engineering methods.The important motive of this study is to predict the genetic variants based on the clinical evidence using a novel method with minimal error.According to the experimental results,the random forest model achieved 61%accuracy with 67%precision for class 9 using TFIDF features and 63%accuracy and a 73%F1 score for class 9 using Bag of Words features.The accuracy of the proposed BERT(Bidirectional Encoder Representations from Transformers)model was 70%with 5-fold cross-validation and 71%with 10-fold cross-validation.The research results provide a comprehensive overview of current LLM methods in healthcare,benefiting academics as well as professionals in the discipline.
文摘With the advancement of deep learning in the automotive domain,more and more researchers are focusing on autonomous driving.Among these tasks,free space detection is particularly crucial.Currently,many model-based approaches have achieved autonomous driving on well-structured urban roads,but these efforts primarily focus on urban road environments.In contrast,there are fewer deep learningmethods specifically designed for off-road traversable area detection,and their effectiveness is not yet satisfactory.This is because detecting traversable areas in complex outdoor environments poses significant challenges,and current methods often rely on single-image inputs,which do not align with contemporary multimodal approaches.Therefore,in this study,we propose a CFH-Net model for off-road traversable area detection.This model employs a Transformer architecture to enhance its capability of capturing global information.For multimodal feature extraction and fusion,we integrate the CM-FRM module for feature extraction and introduce the novel FFX module for feature fusion,thereby improving the perception capability of autonomous vehicles on unstructured roads.To address upsampling,we propose a new convolution precorrection method to reduce model parameters and computational complexity while enhancing the model’s ability to capture complex features.Finally,we conducted experiments on the ORFD off-road dataset and achieved outstanding results.
基金supported by the National Natural Science Foundation of China(Grant Nos.52178175 and 52108182)the National Natural Science Foundation of Zhejiang Province(Grant No.LZ23E080003).
文摘Tensegrity structures,embodying the principles of continuous tensioning and discrete compression,have emerged as fundamental frameworks in locomotive soft robotics for navigating uneven and unpredictable environments,owing to their flexible and resilient traits.By means of a straightforward and cost-effective method to achieve structure-driven,vibration-driven tensegrity shows great potential,particularly in tasks demanding random exploration.However,the design guidance for vibration-driven tensegrity and their performance evaluation in unstructured terrain remain unrevealed due to the complex dynamics of the structure.This paper presents a small six-bar tensegrity robot,driven by wireless vibration motors,designed for deployment in disaster rescue and search scenarios.Finite element simulation is used to investigate how structural characteristics,excitation parameters,and the arrangement of motors affect the kinematic performance of this tensegrity system.A prototype of the six-bar tensegrity robot with three motors located on the lower ends of the three lower struts is designed and manufactured after the numerical simulations.A simple control policy which adjusts the motion of the tensegrity robot by turning on or off the motors on different locations is proposed.The prototype with and without the control policy is tested in man-made environments of various complexity.It shows that the ability and efficiency of the tensegrity robot in exploring unstructured environments is significantly enhanced by the proposed control policy.It is believed that the potential of the vibration-driven tensegrity robot could be further exploited by integrating multi-source sensors and more intelligent control policies.
基金sponsored by the National Natural Science Foundation of China(Grant No.52301320)the Natural Science Founds of Fujian Province(No.2023J01790).
文摘Traditional Computational Fluid Dynamics(CFD)simulations are computationally expensive when applied to complex fluid–structure interaction problems and often struggle to capture the essential flow features governing vortex-induced vibrations(VIV)of floating structures.To overcome these limitations,this study develops a hybrid framework that integrates high-fidelity CFD modeling with deep learning techniques to enhance the accuracy and efficiency of VIV response prediction.First,an unstructured finite-volume fluid–structure coupling model is established to generate high-resolution flow field data and extract multi-component time-series feature tensors.These tensors serve as inputs to a Squeeze-and-Excitation Convolutional Neural Network(SE-CNN),which models the nonlinear coupling between flow disturbances and structural responses.The SE-CNN architecture incorporates an attention-based weighting mechanism through an embedded Squeeze-and-Excitation module,dynamically optimizing channel feature importance and improving sensitivity to critical flow characteristics.During training,multidimensional inputs,including pressure,velocity gradient,and displacement sequences,are used to capture the full complexity of fluid–structure interactions.Results demonstrate that the proposed method achieves a maximum amplitude prediction error of only 2.9%and a main frequency deviation below 0.03 Hz,outperforming conventional CNN models by reducing amplitude prediction error from 3.2%to 1.9%.The approach is validated using a representative semi-submersible platform,confirming its robustness across varying damping conditions and flow velocities.
基金financial supports from the National Key R&D Program of China(2023YFB3809103)the National Natural Science Foundation of China(U23A20128).
文摘Catalytic doping is one of the economic and efficient strategies to optimize the operating temperature and kinetic behavior of magnesium hydride(MgH_(2)).Herein,efficient regulation of electronic and structural rearrangements in niobium-rich nickel oxides was achieved through precise compositional design and niobium cation functionalized doping,thereby greatly enhancing its intrinsic catalytic activity in hydrogen storage systems.As the niobium concentration increased,the Ni-Nb catalysts transformed into a mixed state of multi-phase nanoparticles(composed of nickel and niobium-rich nickel oxides)with smaller particle size and uniform distribution,thus exposing more nucleation sites and diffusion channels at the MgH_(2)/Mg interface.In addition,the additional generation of active Ni-Nb-O mixed phase induced numerous highly topical disordered and distorted crystalline,promoting the transfer and reorganization of H atoms.As a result,a stable and continuous multi-phase/component synergistic catalytic microenvironment could be constructed,exerting remarkable enhancement on MgH_(2)’s hydrogen storage performance.After comparative tests,Ni_(0.7)Nb_(0.3)-doped MgH_(2) presented the optimal low-temperature kinetics with a dehydrogenation activation energy of 78.8 kJ·mol^(−1).The onset dehydrogenation temperature of MgH_(2)+10 wt%Ni_(0.7)Nb_(0.3) was reduced to 198℃ and 6.18 wt%H_(2) could be released at 250℃ within 10 min.In addition,the dehydrogenated MgH_(2)–NiNb composites absorbed 4.87 wt%H_(2) in 10 min at 125℃ and a capacity retention rate was maintained at 6.18 wt%even after 50 reaction cycles.In a word,our work supplies fresh insights for designing novel defective-state multiphase catalysts for hydrogen storage and other energy related field.
基金supported by the National Natural Science Foundation of China(No.22273002)the National Key Research and Development Program of China(No.2022YFB4101401).We acknowledge the High-performance Computing Platform of Peking University for providing the computational facility.
文摘Bimetallic surfaces play a pivotal role in heterogeneous catalysis,yet their theoretical modeling has long been hindered by the computational chal-lenges of capturing configurational disorder,a critical feature governing their catalytic properties.Tradition-al approaches rely on oversimplified ordered surface models or restrict dis-order to a few atomic layers,limiting their predictive power.Here,we Cu_(1-x)Zn_(x)Cu_(1-x)Zn_(x)present an accurate and efficient computational framework that integrates machine learning force fields(MLFFs)with the cluster expansion(CE)method to study configurationally dis-ordered bimetallic surfaces at finite temperatures.We have developed an efficient workflow in which the MLFF is first trained iteratively via an active learning protocol,and then used to generate accurate energetic data for thousands of configurations that enable robust CE model construction.By treating bulk and surface clusters separately,we can build CE models for surface slabs with an arbitrary number of layers.Using as a case study,our CE-based Monte Carlo simulations reveal key structural insights that are relevant to the under-standing of catalytic properties of surfaces.This work demonstrates how MLFF-aided CE can overcome traditional limitations in theoretical modeling of bimetallic surfaces and highlights pathways toward more realistic modeling of heterogeneous catalysts.
基金supported by the National Natural Science Foundation of China(No.22073018,No.22377015).
文摘Intrinsically disordered proteins(IDPs)and their regions(IDRs)play crucial roles in cellular func-tions despite their lack of stable three-dimensional structures.In this study,we investigate the interac-tions between the C-terminal do-main of protein 4.1G(4.1G CTD)and the nuclear mitotic apparatus protein(NuMA)under varying pH and salt ion conditions to under-stand the regulatory mechanisms affecting their binding.4.1G CTD and NuMA bind effec-tively under neutral and alkaline conditions,but their interaction is disrupted under acidic conditions(pH 3.6).The protonation of positively charged residues at the C-terminal of 4.1G CTD under acidic conditions leads to increased electrostatic repulsion,weakening the overall binding free energy.Secondary structure analysis shows that specific regions of 4.1G CTD re-main stable under both pH conditions,but the C-terminal region(aa 990−1000)and the N-terminal region of NuMA(aa 1800−1810)exhibit significant reductions in secondary struc-ture probability under acidic conditions.Contact map analysis and solvent-accessible surface area analysis further support these findings by showing a reduced contact probability be-tween these regions under pH 3.6.These results provide a comprehensive understanding of how pH and ionic strength regulate the binding dynamics of 4.1G CTD and NuMA,emphasiz-ing the regulatory role of electrostatic interactions.
基金supported by the National Natural Science Foundation of China(Grant Nos.92365202,12475011,and 11921005)the National Key R&D Program of China(Grant No.2024YFA1409002)Shanghai Municipal Science and Technology Major Project(Grant No.2019SHZDZX01)。
文摘We investigate the mixed-state entanglement between two spins embedded in the XXZ Heisenberg chain under thermal equilibrium.By deriving an analytical expression for the entanglement of two-spin thermal states and extending this analysis to larger spin chains,we demonstrate that mixed-state entanglement is profoundly shaped by both disorder and temperature.Our results reveal a sharp distinction between many-body localized and ergodic phases,with entanglement vanishing above diferent fnite temperature thresholds.Furthermore,by analyzing non-adjacent spins,we uncover an approximate exponential decay of entanglement with separation.This work advances the understanding of the quantum-to-classical transition by linking the entanglement properties of small subsystems to the broader thermal environment,ofering an explanation for the absence of entanglement in macroscopic systems.These fndings provide critical insights into quantum many-body physics,bridging concepts from thermalization,localization,and quantum information theory.
基金National Natural Science Foundation of China (No.52202046)Natural Science Foundation of Shaanxi Province (No.2021JQ-034)。
文摘Lithium-rich layered oxides (LLOs) are increasingly recognized as promising cathode materials for nextgeneration high-energy-density lithium-ion batteries (LIBs).However,they suffer from voltage decay and low initial Coulombic efficiency (ICE) due to severe structural degradation caused by irreversible O release.Herein,we introduce a three-in-one strategy of increasing Ni and Mn content,along with Li/Ni disordering and TM–O covalency regulation to boost cationic and anionic redox activity simultaneously and thus enhance the electrochemical activity of LLOs.The target material,Li_(1.2)Ni_(0.168)Mn_(0.558)Co_(0.074)O_(2)(L1),exhibits an improved ICE of 87.2%and specific capacity of 293.2 mA h g^(-1)and minimal voltage decay of less than 0.53 m V cycle-1over 300 cycles at 1C,compared to Li_(1.2)Ni_(0.13)Mn_(0.54)Co_(0.13)O_(2)(Ls)(274.4 mA h g^(-1)for initial capacity,73.8%for ICE and voltage decay of 0.84 mV/cycle over 300 cycles at 1C).Theoretical calculations reveal that the density of states (DOS) area near the Fermi energy level for L1 is larger than that of Ls,indicating higher anionic and cationic redox reactivity than Ls.Moreover,L1 exhibits increased O-vacancy formation energy due to higher Li/Ni disordering of 4.76%(quantified by X-ray diffraction Rietveld refinement) and enhanced TM–O covalency,making lattice O release more difficult and thus improving electrochemical stability.The increased Li/Ni disordering also leads to more Ni^(2+)presence in the Li layer,which acts as a pillar during Li+de-embedding,improving structural stability.This research not only presents a viable approach to designing low-Co LLOs with enhanced capacity and ICE but also contributes significantly to the fundamental understanding of structural regulation in high-performance LIB cathodes.
文摘Clinically differentiating bipolarⅡdisorder(BD-Ⅱ)from major depressive disorder(MDD)remains a significant challenge in modern psychiatry.These two conditions share substantial clinical symptomatology,making accurate diagnosis difficult in routine clinical practice.Misdiagnosis may lead to inappropriate treatment strategies,increased psychological and physical burdens,reduced quality of life,and impaired social functioning.Genetic overlap may partially explain the clinical similarities between MDD and BD-Ⅱ,and biomarkers along with neuroimaging techniques are receiving increasing attention as tools to aid in diagnosis.For example,electroencephalography has been shown to effectively distinguish between unipolar depression and bipolar depression;serum levels of glycogen synthase kinase-3 have also been investigated as a potential tool for differentiating between the two disorders.A comprehensive assessment integrating clinical characteristics,genetic basis research,and multimodal evaluations using neuroimaging and biomarkers through a multidisciplinary approach will help enhance clinicians'ability to distinguish between MDD and BD-Ⅱ.By improving diagnostic accuracy,more personalized and effective treatment strategies can be developed,ultimately improving patients'health outcomes and quality of life.
基金Supported by National Natural Science Foundation of China(Grant Nos.U2330202,52175162,51805086,51975123)Fujian Provincial Technological Innovation Key Research and Industrialization Projects(Grant Nos.2023XQ005,2024XQ010)Project of Guangdong Provincial Science and Technology Bureau of Jiangmen City(Grant No.2023780200030009506)。
文摘As a novel lightweight metallic material with excellent heat and corrosion resistance,elastic disordered microporous metal rubber(EDMMR)functions as an effective damping and support element in high-temperature environments where traditional polymer rubber fails.In this paper,a multi-scale finite element model for EDMMR is constructed using virtual manufacturing technology(VMT).Thermo-mechanical coupling analysis reveals a distinct inward expansion and dissipation phenomenon in EDMMR under high-temperature conditions,distinguishing it from porous materials.This phenomenon has the potential to impact the overall dimensions of EDMMR through transmission and accumulation processes.The experimental results demonstrate a random distribution of internal micro springs in EDMMR,considering the contact composition of spring microelements and the pore structure.By incorporating material elasticity,a predictive method for the thermal expansion coefficient of EDMMR based on the Schapery model is proposed.Additionally,standardized processes are employed to manufacture multiple sets of cylindrical EDMMR samples with similar dimensions but varying porosities.Thermal expansion tests are conducted on these samples,and the accuracy of the predicted thermal expansion coefficient is quantitatively validated through residual analysis.This research indicates that EDMMR maintains good structural stability in high-temperature environments.The thermal expansion rate of the material exhibits an opposite trend to the variation of elastic modulus with temperature,as the porosity rate changes.
文摘Non-right-handedness(NRH),encompassing left-handedness and mixed-handedness,has been frequently reported at elevated rates in individuals with various psychiatric disorders.The consistency of this association across multiple conditions and its underlying mechanisms is the subject of ongoing investigation.This review synthesized current evidence to explore the association between NRH and psychiatric disorders from epidemiological,genetic,and neurobiological perspectives.We systematically identified and appraised relevant literature investigating NRH prevalence in psychiatric populations and potential explanatory mechanisms.Epidemiological evidence indicates an elevated prevalence of NRH,particularly within neurodevelopmental disorders.Potential contributing mechanisms identified include early developmental disruptions,shared genetic predispositions,and atypical patterns of brain lateralization.While the association between NRH and psychiatric conditions,especially neurodevelopmental disorders,is evident,the causal pathways and relative contributions of identified mechanisms are complex and debated.This review highlighted key areas requiring further research to elucidate these relationships.
文摘Mitochondrial dysfunction has emerged as a critical factor in the etiology of various neurodevelopmental disorders, including autism spectrum disorders, attention-deficit/hyperactivity disorder, and Rett syndrome. Although these conditions differ in clinical presentation, they share fundamental pathological features that may stem from abnormal mitochondrial dynamics and impaired autophagic clearance, which contribute to redox imbalance and oxidative stress in neurons. This review aimed to elucidate the relationship between mitochondrial dynamics dysfunction and neurodevelopmental disorders. Mitochondria are highly dynamic organelles that undergo continuous fusion and fission to meet the substantial energy demands of neural cells. Dysregulation of these processes, as observed in certain neurodevelopmental disorders, causes accumulation of damaged mitochondria, exacerbating oxidative damage and impairing neuronal function. The phosphatase and tensin homolog-induced putative kinase 1/E3 ubiquitin-protein ligase pathway is crucial for mitophagy, the process of selectively removing malfunctioning mitochondria. Mutations in genes encoding mitochondrial fusion proteins have been identified in autism spectrum disorders, linking disruptions in the fusion-fission equilibrium to neurodevelopmental impairments. Additionally, animal models of Rett syndrome have shown pronounced defects in mitophagy, reinforcing the notion that mitochondrial quality control is indispensable for neuronal health. Clinical studies have highlighted the importance of mitochondrial disturbances in neurodevelopmental disorders. In autism spectrum disorders, elevated oxidative stress markers and mitochondrial DNA deletions indicate compromised mitochondrial function. Attention-deficit/hyperactivity disorder has also been associated with cognitive deficits linked to mitochondrial dysfunction and oxidative stress. Moreover, induced pluripotent stem cell models derived from patients with Rett syndrome have shown impaired mitochondrial dynamics and heightened vulnerability to oxidative injury, suggesting the role of defective mitochondrial homeostasis in these disorders. From a translational standpoint, multiple therapeutic approaches targeting mitochondrial pathways show promise. Interventions aimed at preserving normal fusion-fission cycles or enhancing mitophagy can reduce oxidative damage by limiting the accumulation of defective mitochondria. Pharmacological modulation of mitochondrial permeability and upregulation of peroxisome proliferator-activated receptor gamma coactivator 1-alpha, an essential regulator of mitochondrial biogenesis, may also ameliorate cellular energy deficits. Identifying early biomarkers of mitochondrial impairment is crucial for precision medicine, since it can help clinicians tailor interventions to individual patient profiles and improve prognoses. Furthermore, integrating mitochondria-focused strategies with established therapies, such as antioxidants or behavioral interventions, may enhance treatment efficacy and yield better clinical outcomes. Leveraging these pathways could open avenues for regenerative strategies, given the influence of mitochondria on neuronal repair and plasticity. In conclusion, this review indicates mitochondrial homeostasis as a unifying therapeutic axis within neurodevelopmental pathophysiology. Disruptions in mitochondrial dynamics and autophagic clearance converge on oxidative stress, and researchers should prioritize validating these interventions in clinical settings to advance precision medicine and enhance outcomes for individuals affected by neurodevelopmental disorders.
基金Supported by Key Research and Development Program of Shaanxi Province,China,No.2024SF-YBXM-078.
文摘BACKGROUND Non-suicidal self-injury(NSSI)is common among adolescents with depressive disorders and poses a major public health challenge.Rumination,a key cognitive feature of depression,includes different subtypes that may relate to NSSI through distinct psychological mechanisms.However,how these subtypes interact with specific NSSI behaviors remains unclear.AIM To examine associations between rumination subtypes and specific NSSI behaviors in adolescents.METHODS We conducted a cross-sectional study with 305 hospitalized adolescents diagnosed with depressive disorders.The subjects ranged from 12-18 years in age.Rumi-nation subtypes were assessed using the Ruminative Response Scale,and 12 NSSI behaviors were evaluated using a validated questionnaire.Network analysis was applied to explore symptom-level associations and identify central symptoms.RESULTS The network analysis revealed close connections between rumination subtypes and NSSI behaviors.Brooding was linked to behaviors such as hitting objects and burning.Scratching emerged as the most influential NSSI symptom.Symptomfocused rumination served as a key bridge connecting rumination and NSSI.CONCLUSION Symptom-focused rumination and scratching were identified as potential intervention targets.These findings highlight the psychological significance of specific cognitive-behavioral links in adolescent depression and suggest directions for tailored prevention and treatment.However,the cross-sectional,single-site design limits causal inference and generalizability.Future longitudinal and multi-center studies are needed to confirm causal pathways and verify the generalizability of the findings to broader adolescent populations.
文摘Musculoskeletal injuries are among the most common causes of disability worldwide,with early detection and appropriate intervention critical to minimizing long-term complications.Infrared thermography(IRT)has emerged as a noninvasive,real-time imaging modality that captures superficial temperature changes reflecting underlying physiological processes such as inflammation and vascular alterations.This review explores the fundamental principles of medical thermography,differentiates between passive and active approaches,and outlines key technological advancements including artificial intelligence integration.The clinical utility of IRT is discussed in various contexts–ranging from acute soft tissue injuries and overuse syndromes to chronic pain and rehabilitation monitoring.Comparative insights with conventional imaging techniques such as ultrasound and magnetic resonance imaging are also presented.While IRT offers functional imaging capabilities with advantages in portability,safety,and speed,its limitations–such as lack of deep-tissue penetration and protocol standardization–remain significant barriers to broader adoption.Future directions include the integration of IRT with other imaging modalities and digital health platforms to enhance musculoskeletal assessment and injury prevention strategies.
基金supported by the Yanzhao Gold Talent Project of Hebei Province(NO.HJZD202506)。
文摘Objective Previous studies link lower body mass index(BMI)with increased obsessive-compulsive disorder(OCD)risk,yet other body mass indicators may be more etioloically relevant.We dissected the causal association between body fat mass(FM)and OCD.Methods Summary statistics from genome-wide association studies of European ancestry were utilized to conduct two-sample Mendelian randomization analysis.Heterogeneity,horizontal pleiotropy,and sensitivity analyses were performed to assess the robustness.Results The inverse variance weighting method demonstrated that a genetically predicted decrease in FM was causally associated with an increased OCD risk[odds ratio(OR)=0.680,95%confidence interval(CI):0.528–0.875,P=0.003].Similar estimates were obtained using the weighted median approach(OR=0.633,95%CI:0.438–0.915,P=0.015).Each standard deviation increases in genetically predicted body fat percentage corresponded to a reduced OCD risk(OR=0.638,95%CI:0.455–0.896,P=0.009).The sensitivity analysis confirmed the robustness of these findings with no outlier instrument variables identified.Conclusion The negative causal association between FM and the risk of OCD suggests that the prevention or treatment of mental disorders should include not only the control of BMI but also fat distribution and body composition.
基金Supported by Inter Disciplinary Direction Cultivation Project of Hunan University of Chinese Medicine,No.2025JC01032025 Hunan Province Science and Technology Innovation Plan Project,No.2025RC9012+2 种基金2022"Unveiling and Leading"Project of Discipline Construction at Hunan University of Chinese Medicine,No.22JBZ044Changsha Municipal Natural Science Foundation,No.kq2402174Hunan Provincial Science Popularization Fund Project,No.2025ZK4223.
文摘BACKGROUND Timely and accurate evaluation of mental disorders in adolescents using appropriate mental health literacy assessment tools is essential for improving their mental health literacy levels.AIM To develop an evaluation index system for the mental health literacy of adolescent patients with mental disorders,providing a scientific,comprehensive,and reliable tool for the monitoring and intervention of mental health literacy of such patients.METHODS From December 2022 to June 2023,the evaluation index system for mental health literacy of adolescents with mental disorders was developed through literature reviews,semi-structured interviews,expert letter consultations,and the analytic hierarchy process.Based on this index system,a self-assessment questionnaire was compiled and administered to 305 adolescents with mental disorders to test the reliability and validity of the index system.RESULTS The final evaluation index system for mental health literacy of adolescents with mental disorders included 4 first-level indicators,10 second-level indicators,and 52 third-level indicators.The overall Cronbach’sαcoefficient of the index system was 0.957,with a partial reliability of 0.826 and a content validity index of 0.975.The cumulative variance contribution rate of 10 common factors was 66.491%.The correlation coefficients between each dimension and the total questionnaire ranged from 0.672 to 0.724,while the correlation coefficients in each dimension ranged from 0.389 to 0.705.CONCLUSION The evaluation index system for mental health literacy of adolescents with mental disorders,developed in this study,demonstrated notable reliability and validity,making it a valuable tool for evaluating mental health literacy in this population.
基金Supported by The Natural Science Foundation of China,No.82374292the Plans for Major Provincial Science and Technology Projects of Anhui Province,No.202303a07020003the Innovation Team and Talents Cultivation Program of the National Administration of Traditional Chinese Medicine,No.ZYYCXTD-C-202401.
文摘Functional gastrointestinal disorders(FGIDs),including irritable bowel syndrome(IBS),functional dyspepsia(FD),and gastroesophageal reflux disease(GERD),present persistent diagnostic and therapeutic challenges due to symptom heterogeneity and the absence of reliable biomarkers.Artificial intelligence(AI)enables the integration of multimodal data to enhance FGID management through precision diagnostics and preventive healthcare.This minireview summarizes recent advancements in AI applications for FGIDs,highlighting progress in diagnostic accuracy,subtype classification,personalized interventions,and preventive strategies inspired by the traditional Chinese medicine concept of“treating the undiseased”.Machine learning and deep learning algorithms have demonstrated value in improving IBS diagnosis,refining FD neuro-gastrointestinal subtyping,and screening for GERD-related complications.Moreover,AI supports dietary,psychological,and integrative medicine-based interventions to improve patient adherence and quality of life.Nonetheless,key challenges remain,including data heterogeneity,limited model interpretability,and the need for robust clinical validation.Future directions emphasize interdisciplinary collaboration,the development of multimodal and explainable AI models,and the creation of patientcentered platforms to facilitate a shift from reactive treatment to proactive prevention.This review provides a systematic framework to guide the clinical application and theoretical innovation of AI in FGIDs.
文摘Obesity is widely recognized as a global epidemic,primarily driven by an imbalance between energy expenditure and caloric intake associated with a sedentary lifestyle.Diets high in carbohydrates and saturated fats,particularly palmitic acid,are potent inducers of chronic low-grade inflammation,largely due to disruptions in glucose metabolism and the onset of insulin resistance(Qiu et al.,2022).While many organs are affected,the brain,specifically the hypothalamus,is among the first to exhibit inflammation in response to an unhealthy diet,suggesting that obesity may,in fact,be a brain-centered disease with neuroinflammation as a central factor(Thaler et al., 2012).
