Diffusion Transformer Model With Compact Prior for Low-dose PET Reconstruction

Positron emission tomography (PET) is an advanced medical imaging technique that plays a crucial role in non-invasive clinical diagnosis. However, while reducing radiation exposure through low-dose PET scans is beneficial for patient safety, it often results in insufficient statistical data. This scarcity of data poses significant challenges for accurately reconstructing high-quality images, which are essential for reliable diagnostic outcomes. In this research, we propose a diffusion transformer model (DTM) guided by joint compact prior (JCP) to enhance the reconstruction quality of low-dose PET imaging. In light of current research findings, we present a pioneering PET reconstruction model that integrates diffusion and transformer models for joint optimization. This model combines the powerful distribution mapping abilities of diffusion models with the capacity of transformers to capture long-range dependencies, offering significant advantages for low-dose PET reconstruction. Additionally, the incorporation of the lesion refining block and penalized weighted least squares (PWLS) enhance the recovery capability of lesion regions and preserves detail information, solving blurring problems in lesion areas and texture details of most deep learning frameworks. Experimental results demonstrate the effectiveness of DTM in enhancing image quality and preserving critical clinical information for low-dose PET scans. Our approach not only reduces radiation exposure risks but also provides a more reliable PET imaging tool for early disease detection and patient management

DYNAMIC MODELING OF TUNNEL SURVEY SPATIOTEMPORAL DATA

Currently, for tunnels, the design centerline and design cross-section with timestamps are used for dynamic three-dimensional (3D) modeling. However, thisapproach cannot correctly reflect some qualities of tunneling or some special cases,such as landslips. Therefore, a dynamic 3D model of a tunnel based onspatiotemporal data from survey cross-sections is proposed in this paper. Thismodel can not only playback the excavation process but also reflect qualities of aproject typically missed. In this paper, a new conceptual model for dynamic 3Dmodeling of tunneling survey data is introduced. Some specific solutions areproposed using key corresponding technologies for coordinate transformation of cross-sections from linear engineering coordinates to global projection coordinates,data structure of files and database, and dynamic 3D modeling. A 3D tunnel TINmodel was proposed using the optimized minimum direction angle algorithm. Thelast section implements the construction of a survey data collection, acquisition, anddynamic simulation system, which verifies the feasibility and practicality of thismodeling method

PET Tracer Conversion among Brain PET via Variable Augmented Invertible Network

Positron emission tomography (PET) serves as an essential tool for diagnosis of encephalopathy and brain science research. However, it suffers from the limited choice of tracers. Nowadays, with the wide application of PET imaging in neuropsychiatric treatment, 6-18F-fluoro-3, 4-dihydroxy-L-phenylalanine (DOPA) has been found to be more effective than 18F-labeled fluorine-2-deoxyglucose (FDG) in the field. Nevertheless, due to the complexity of its preparation and other limitations, DOPA is far less widely used than FDG. To address this issue, a tracer conversion invertible neural network (TC-INN) for image projection is developed to map FDG images to DOPA images through deep learning. More diagnostic information is obtained by generating PET images from FDG to DOPA. Specifically, the proposed TC-INN consists of two separate phases, one for training traceable data, the other for rebuilding new data. The reference DOPA PET image is used as a learning target for the corresponding network during the training process of tracer conversion. Meanwhile, the invertible network iteratively estimates the resultant DOPA PET data and compares it to the reference DOPA PET data. Notably, the reversible model employs variable enhancement technique to achieve better power generation. Moreover, image registration needs to be performed before training due to the angular deviation of the acquired FDG and DOPA data information. Experimental results exhibited excellent generation capability in mapping between FDG and DOPA, suggesting that PET tracer conversion has great potential in the case of limited tracer applications

DYNAMIC MODELING OF TUNNEL SURVEY SPATIOTEMPORAL DATA

Currently, for tunnels, the design centerline and design cross-section with timestamps are used for dynamic three-dimensional (3D) modeling. However, thisapproach cannot correctly reflect some qualities of tunneling or some special cases,such as landslips. Therefore, a dynamic 3D model of a tunnel based onspatiotemporal data from survey cross-sections is proposed in this paper. Thismodel can not only playback the excavation process but also reflect qualities of aproject typically missed. In this paper, a new conceptual model for dynamic 3Dmodeling of tunneling survey data is introduced. Some specific solutions areproposed using key corresponding technologies for coordinate transformation of cross-sections from linear engineering coordinates to global projection coordinates,data structure of files and database, and dynamic 3D modeling. A 3D tunnel TINmodel was proposed using the optimized minimum direction angle algorithm. Thelast section implements the construction of a survey data collection, acquisition, anddynamic simulation system, which verifies the feasibility and practicality of thismodeling method

Case Report: Whole exome sequencing identifies compound heterozygous variants in the TRAPPC9 gene in a child with developmental delay

BackgroundDevelopmental delay in children under 5 years old, which occurs globally with an incidence of 10%–15%, is caused by multiple factors including genetics, prenatal conditions, perinatal complications, postnatal influences, social factors, and nutritional deficiencies. Gene variants such as EFNB1, MECP2 and TRAPPC9 play a significant role in protein deformation and downregulation of nuclear factor κB (NF-κB) activity.MethodsA 3-year-old girl, who exhibits poor gross motor skills, personal-social development, auditory language, hand-eye coordination, and visual performance, was diagnosed with global developmental delay. Trio whole exome sequencing was conducted to identify the genetic etiology of her condition. The identified genetic etiology was then validated through Sanger sequencing and quantitative polymerase chain reaction (qPCR).ResultsGenetic analysis revealed that the patient had compound heterozygous variants in the TRAPPC9 gene. These include a c.1928del frameshift variant inherited from the unaffected father and a deletion in exon 12 inherited from the unaffected mother. According to the American College of Medical Genetics (ACMG) guidelines, these variants were classified as “likely pathogenic”.ConclusionThe study revealed that compound heterozygous TRAPPC9 gene variants cause developmental delay in a Chinese girl. These variants have been classified as having significant pathogenic effect according to the ACMG criteria, suggesting a recessive genetic pattern and highlighting the importance of prenatal testing for future offspring. Furthermore, our findings expand the genotype spectrum of the TRAPPC9 gene, and provide more comprehensive information regarding genetic counseling for children experiencing developmental delay

DISC-LawLLM: Fine-tuning Large Language Models for Intelligent Legal Services

We propose DISC-LawLLM, an intelligent legal system utilizing large language models (LLMs) to provide a wide range of legal services. We adopt legal syllogism prompting strategies to construct supervised fine-tuning datasets in the Chinese Judicial domain and fine-tune LLMs with legal reasoning capability. We augment LLMs with a retrieval module to enhance models' ability to access and utilize external legal knowledge. A comprehensive legal benchmark, DISC-Law-Eval, is presented to evaluate intelligent legal systems from both objective and subjective dimensions. Quantitative and qualitative results on DISC-Law-Eval demonstrate the effectiveness of our system in serving various users across diverse legal scenarios. The detailed resources are available at https://github.com/FudanDISC/DISC-LawLLM

DeepSeek-VL: Towards Real-World Vision-Language Understanding

We present DeepSeek-VL, an open-source Vision-Language (VL) Model designed for real-world vision and language understanding applications. Our approach is structured around three key dimensions: We strive to ensure our data is diverse, scalable, and extensively covers real-world scenarios including web screenshots, PDFs, OCR, charts, and knowledge-based content, aiming for a comprehensive representation of practical contexts. Further, we create a use case taxonomy from real user scenarios and construct an instruction tuning dataset accordingly. The fine-tuning with this dataset substantially improves the model's user experience in practical applications. Considering efficiency and the demands of most real-world scenarios, DeepSeek-VL incorporates a hybrid vision encoder that efficiently processes high-resolution images (1024 x 1024), while maintaining a relatively low computational overhead. This design choice ensures the model's ability to capture critical semantic and detailed information across various visual tasks. We posit that a proficient Vision-Language Model should, foremost, possess strong language abilities. To ensure the preservation of LLM capabilities during pretraining, we investigate an effective VL pretraining strategy by integrating LLM training from the beginning and carefully managing the competitive dynamics observed between vision and language modalities. The DeepSeek-VL family (both 1.3B and 7B models) showcases superior user experiences as a vision-language chatbot in real-world applications, achieving state-of-the-art or competitive performance across a wide range of visual-language benchmarks at the same model size while maintaining robust performance on language-centric benchmarks. We have made both 1.3B and 7B models publicly accessible to foster innovations based on this foundation model.Comment: https://github.com/deepseek-ai/DeepSeek-V

Depression and anxiety in cervical degenerative disc disease: Who are susceptible?

BackgroundPre-operative depression and anxiety are associated with poorer patient-reported outcomes following cervical spine surgery. Identification of and interventions for these disorders are key to preventing related negative effects. However, most spine surgeons do not routinely evaluate mental health disorders. Few studies have investigated which patients with cervical degenerative disc diseases (CDDD) are susceptible to depression and anxiety.ObjectiveTo determine the factors associated with depression and anxiety in patients with CDDD.MethodsThree hundred twelve patients with CDDD were recruited in this cross-sectional case-control study. Patients underwent a structured interview to acquire demographic and clinical characteristic information, which included the Neck Disability Index (NDI), modified Japanese Orthopedic Association (mJOA), and Visual Analog Scale (VAS) for neck/arm pain. Depression and anxiety were evaluated using the Zung Self-Rating Depression and Anxiety Scales. Univariate and multivariate logistic regression analyses were used to identify factors associated with depression and anxiety.ResultsOf all patients, 102 (32.7%) had depression and 92 (29.5%) had anxiety. Two hundred six (66.0%) patients with neither depression nor anxiety were defined as the control group. Univariate analysis indicated that gender, educational level, occupation type, Charlson comorbidity index, symptom duration, symptomatology, surgery history, NDI, mJOA, VAS-neck, and VAS-arm scores were associated with depression and anxiety (except for symptom duration for anxiety). Multivariate logistic regression analysis indicated that females [odds ratio (OR) 1.81, 95% confidence interval (CI) 1.01–3.23], physical work (OR 2.06, 95% CI 1.16–3.65), poor mJOA score (ORmoderate 2.67, 95% CI 1.40–5.07; ORsevere 7.63, 95% CI 3.85–15.11), and high VAS-neck score (OR 1.24, 95% CI 1.11–1.39) were independent risk factors for depression. Physical work (OR 1.84, 95% CI 1.01–3.35), poor mJOA score (ORmoderate 2.66, 95% CI 1.33–5.33; ORsevere 9.26, 95% CI 4.52–18.99), and high VAS-neck score (OR 1.34, 95% CI 1.19–1.51) were independent risk factors for anxiety.ConclusionApproximately one-third of patients with CDDD had depression or anxiety. Patients who engaged in heavy work and had severe symptoms (poor mJOA and high VAS-neck scores) are susceptible to depression and anxiety. Additionally, female patients are susceptible to depression. Our findings may help identify CDDD patients with depression and anxiety in clinical practice

Association of systemic inflammatory markers with clinical adverse prognosis and outcomes in HFpEF: a systematic review and meta-analysis of cohort studies

ObjectiveTo evaluate the association between systemic inflammatory markers and clinical outcomes (all-cause mortality, cardiovascular mortality, and rehospitalization) in patients with heart failure with preserved ejection fraction (HFpEF).MethodsWe conducted a comprehensive literature search in PubMed, Embase, and Ovid Medline databases from inception to June 27, 2024. Studies were included if they were observational clinical studies involving HFpEF patients over 18 years old, with exposure to systemic inflammatory markers and reporting on adverse prognosis outcomes. The Newcastle-Ottawa Scale (NOS) was used to assess study quality.ResultsEight studies ultimately included in the meta-analysis which involved 9,744 participants from six countries. The meta-analysis showed that systemic inflammatory markers were significantly associated with all-cause mortality (HR 1.43, 95% CI 1.19–1.72, p < 0.05), cardiovascular mortality (HR 2.04, 95% CI 1.33–3.12, p < 0.05), and cardiovascular rehospitalization (HR 2.83, 95% CI 0.92–8.67, p < 0.05) in HFpEF patients. Low heterogeneity was observed across studies (I2 = 0.00%). Sensitivity and publication bias analyses indicated that the results were robust.ConclusionSystemic inflammatory markers demonstrate significant predictive value for adverse clinical outcomes in HFpEF patients. The findings suggest that monitoring systemic inflammation may provide valuable prognostic information for clinicians managing HFpEF patients.Systematic Review Registrationhttps://www.crd.york.ac.uk/PROSPERO/display_record.php?RecordID=562698, identifier (CRD42024562698)

Non-contrast computed tomography-based radiomics for staging of connective tissue disease-associated interstitial lung disease

Rationale and introductionIt is of significance to assess the severity and predict the mortality of patients with connective tissue disease-associated interstitial lung disease (CTD-ILD). In this double-center retrospective study, we developed and validated a radiomics nomogram for clinical management by using the ILD-GAP (gender, age, and pulmonary physiology) index system.Materials and methodsPatients with CTD-ILD were staged using the ILD-GAP index system. A clinical factor model was built by demographics and CT features, and a radiomics signature was developed using radiomics features extracted from CT images. Combined with the radiomics signature and independent clinical factors, a radiomics nomogram was constructed and evaluated by the area under the curve (AUC) from receiver operating characteristic (ROC) analyses. The models were externally validated in dataset 2 to evaluate the model generalization ability using ROC analysis.ResultsA total of 245 patients from two clinical centers (dataset 1, n = 202; dataset 2, n = 43) were screened. Pack-years of smoking, traction bronchiectasis, and nine radiomics features were used to build the radiomics nomogram, which showed favorable calibration and discrimination in the training cohort {AUC, 0.887 [95% confidence interval (CI): 0.827–0.940]}, the internal validation cohort [AUC, 0.885 (95% CI: 0.816–0.922)], and the external validation cohort [AUC, 0.85 (95% CI: 0.720–0.919)]. Decision curve analysis demonstrated that the nomogram outperformed the clinical factor model and radiomics signature in terms of clinical usefulness.ConclusionThe CT-based radiomics nomogram showed favorable efficacy in predicting individual ILD-GAP stages