Performance of large language models on benign prostatic hyperplasia frequently asked questions.

BACKGROUND: Benign prostatic hyperplasia (BPH) is a common condition, yet it is challenging for the average BPH patient to find credible and accurate information about BPH. Our goal is to evaluate and compare the accuracy and reproducibility of large language models (LLMs), including ChatGPT-3.5, ChatGPT-4, and the New Bing Chat in responding to a BPH frequently asked questions (FAQs) questionnaire. METHODS: A total of 45 questions related to BPH were categorized into basic and professional knowledge. Three LLM-ChatGPT-3.5, ChatGPT-4, and New Bing Chat-were utilized to generate responses to these questions. Responses were graded as comprehensive, correct but inadequate, mixed with incorrect/outdated data, or completely incorrect. Reproducibility was assessed by generating two responses for each question. All responses were reviewed and judged by experienced urologists. RESULTS: All three LLMs exhibited high accuracy in generating responses to questions, with accuracy rates ranging from 86.7% to 100%. However, there was no statistically significant difference in response accuracy among the three (p > 0.017 for all comparisons). Additionally, the accuracy of the LLMs' responses to the basic knowledge questions was roughly equivalent to that of the specialized knowledge questions, showing a difference of less than 3.5% (GPT-3.5: 90% vs. 86.7%; GPT-4: 96.7% vs. 95.6%; New Bing: 96.7% vs. 93.3%). Furthermore, all three LLMs demonstrated high reproducibility, with rates ranging from 93.3% to 97.8%. CONCLUSIONS: ChatGPT-3.5, ChatGPT-4, and New Bing Chat offer accurate and reproducible responses to BPH-related questions, establishing them as valuable resources for enhancing health literacy and supporting BPH patients in conjunction with healthcare professionals.

Full Stokes polarimetry based on an inverse-designed multi-foci metalens.

In the realm of metasurface-based polarimetry, well-known for its remarkable compactness and integration capabilities, previous attempts have been hindered by limitations such as the restricted choices of target polarization states and the inefficient focusing of light. To address these problems, this study introduces and harnesses a novel, to our knowledge, forward-solving model, grounded in the equivalence principle and dyadic Green's function, to inversely optimize the vectorial focusing patterns of metalenses. Leveraging this methodology, we develop and experimentally validate a single multi-foci metalens-based polarimeter, capable of simultaneously separating and concentrating four distinct elliptical polarization states at a wavelength of 10.6 µm. Rigorous experimental evaluations, involving the assessment of 18 scalar polarized beams, reveal an average error of 5.92% and a high contrast ratio of 0.92, which demonstrates the efficacy of the polarimeter. The results underscore the potential of our system in diverse sectors, including military defense, healthcare, and autonomous vehicle technology.

Generalizability and Diagnostic Performance of AI Models for Thyroid US.

Background Artificial intelligence (AI) models have improved US assessment of thyroid nodules; however, the lack of generalizability limits the application of these models. Purpose To develop AI models for segmentation and classification of thyroid nodules in US using diverse data sets from nationwide hospitals and multiple vendors, and to measure the impact of the AI models on diagnostic performance. Materials and Methods This retrospective study included consecutive patients with pathologically confirmed thyroid nodules who underwent US using equipment from 12 vendors at 208 hospitals across China from November 2017 to January 2019. The detection, segmentation, and classification models were developed based on the subset or complete set of images. Model performance was evaluated by precision and recall, Dice coefficient, and area under the receiver operating characteristic curve (AUC) analyses. Three scenarios (diagnosis without AI assistance, with freestyle AI assistance, and with rule-based AI assistance) were compared with three senior and three junior radiologists to optimize incorporation of AI into clinical practice. Results A total of 10 023 patients (median age, 46 years [IQR 37-55 years]; 7669 female) were included. The detection, segmentation, and classification models had an average precision, Dice coefficient, and AUC of 0.98 (95% CI: 0.96, 0.99), 0.86 (95% CI: 0.86, 0.87), and 0.90 (95% CI: 0.88, 0.92), respectively. The segmentation model trained on the nationwide data and classification model trained on the mixed vendor data exhibited the best performance, with a Dice coefficient of 0.91 (95% CI: 0.90, 0.91) and AUC of 0.98 (95% CI: 0.97, 1.00), respectively. The AI model outperformed all senior and junior radiologists (P < .05 for all comparisons), and the diagnostic accuracies of all radiologists were improved (P < .05 for all comparisons) with rule-based AI assistance. Conclusion Thyroid US AI models developed from diverse data sets had high diagnostic performance among the Chinese population. Rule-based AI assistance improved the performance of radiologists in thyroid cancer diagnosis. © RSNA, 2023 Supplemental material is available for this article.

Enhanced Biogenic Sulfidation of Zero-Valent Iron in Columns: Implications for Promoting Dechlorination in Permeable Reactive Barriers.

Biogenic sulfidation of zero-valent iron (ZVI) using sulfate reducing bacteria (SRB) has shown enhanced dechlorination rates comparable to those produced by chemical sulfidation. However, controlling and sustaining biogenic sulfidation to enhance in situ dechlorination are poorly understood. Detailed interactions between SRB and ZVI were examined for 4 months in column experiments under enhanced biogenic sulfidation conditions. SRB proliferation and changes in ZVI surface properties were characterized along the flow paths. The results show that ZVI can stimulate SRB activity by removing excessive free sulfide (S2-), in addition to lowering reduction potential. ZVI also hinders downgradient movement of SRB via electrostatic repulsion, restricting SRB presence near the upgradient interface. Dissolved organic carbon (e.g., >2.2 mM) was essential for intense biogenic sulfidation in ZVI columns. The presence of SRB in the upgradient zone appeared to promote the formation of iron polysulfides. Biogenic FeSx deposition increased the S content on ZVI surfaces ∼3-fold, corresponding to 3-fold and 2-fold improvements in the trichloroethylene degradation rate and electron efficiency in batch tests. Elucidation of SRB and ZVI interactions enhances sustained sulfidation in ZVI permeable reactive barrier.

The uptake of metal-organic frameworks: a journey into the cell.

The application of metal-organic frameworks (MOFs) in drug delivery has advanced rapidly over the past decade, showing huge progress in the development of novel systems. Although a large number of versatile MOFs that can carry and release multiple compounds have been designed and tested, one of the main limitations to their translation to the clinic is the limited biological understanding of their interaction with cells and the way they penetrate them. This is a crucial aspect of drug delivery, as MOFs need to be able not only to enter into cells but also to release their cargo in the correct intracellular location. While small molecules can enter cells by passive diffusion, nanoparticles (NPs) usually require an energy-dependent process known as endocytosis. Importantly, the fate of NPs after being taken up by cells is dependent on the endocytic pathways they enter through. However, no general guidelines for MOF particle internalization have been established due to the inherent complexity of endocytosis as a mechanism, with several factors affecting cellular uptake, namely NP size and surface chemistry. In this review, we cover recent advances regarding the understanding of the mechanisms of uptake of nano-sized MOFs (nanoMOFs)s, their journey inside the cell, and the importance of biological context in their final fate. We examine critically the impact of MOF physicochemical properties on intracellular trafficking and successful cargo delivery. Finally, we highlight key unanswered questions on the topic and discuss the future of the field and the next steps for nanoMOFs as drug delivery systems.

Chlorogenic acid inhibits forming of diabetes mellitus in rats induced by high-fat high-sucrose and streptozotocin.

To evaluate the inhibitory effect of chlorogenic acid on the forming of type 2 diabetes mellitus (T2DM), using Sprague Dawley (SD) rats, a recognized T2DM model induced by high-fat high-sucrose diet (HFSD) and streptozotocin (STZ). Thirty female SD rats were assigned equally to three groups randomly: normal control with standard commercial (NC), chlorogenic acid treatment with HFSD and chlorogenic acid (90mg/kg, CA), and diabetes model with HFSD (DM). Upon treatment with chlorogenic acid, suppression of the onset of diabetes, reduced serum glucose and insulin concentrations, improved glucose tolerance and increased body weight and visceral fat weight were observed. Serum triglyceride, total cholesterol, low density lipoprotein levels, and kidney and pancreas morphology were significantly ameliorated. Chlorogenic acid also inhibited the mRNA levels of hepatic G-6-Pase and up-regulated the mRNA levels of skeletal muscle GLUT4. Our results indicated that before the onset of diabetes, chlorogenic acid had an inhibitory effect against the forming of T2DM induced by HFSD and STZ through regulating the glucose and lipid metabolism.

Mathematical models and analysis tools for risk assessment of unnatural epidemics: a scoping review.

Predicting, issuing early warnings, and assessing risks associated with unnatural epidemics (UEs) present significant challenges. These tasks also represent key areas of focus within the field of prevention and control research for UEs. A scoping review was conducted using databases such as PubMed, Web of Science, Scopus, and Embase, from inception to 31 December 2023. Sixty-six studies met the inclusion criteria. Two types of models (data-driven and mechanistic-based models) and a class of analysis tools for risk assessment of UEs were identified. The validation part of models involved calibration, improvement, and comparison. Three surveillance systems (event-based, indicator-based, and hybrid) were reported for monitoring UEs. In the current study, mathematical models and analysis tools suggest a distinction between natural epidemics and UEs in selecting model parameters and warning thresholds. Future research should consider combining a mechanistic-based model with a data-driven model and learning to pursue time-varying, high-precision risk assessment capabilities.

Risk Prediction for Sudden Cardiac Death in the General Population: A Systematic Review and Meta-Analysis.

Objective: Identification of SCD risk is important in the general population from a public health perspective. The objective is to summarize and appraise the available prediction models for the risk of SCD among the general population. Methods: Data were obtained searching six electronic databases and reporting prediction models of SCD risk in the general population. Studies with duplicate cohorts and missing information were excluded from the meta-analysis. Results: Out of 8,407 studies identified, fifteen studies were included in the systematic review, while five studies were included in the meta-analysis. The Cox proportional hazards model was used in thirteen studies (96.67%). Study locations were limited to Europe and the United States. Our pooled meta-analyses included four predictors: diabetes mellitus (ES = 2.69, 95%CI: 1.93, 3.76), QRS duration (ES = 1.16, 95%CI: 1.06, 1.26), spatial QRS-T angle (ES = 1.46, 95%CI: 1.27, 1.69) and factional shortening (ES = 1.37, 95%CI: 1.15, 1.64). Conclusion: Risk prediction model may be useful as an adjunct for risk stratification strategies for SCD in the general population. Further studies among people except for white participants and more accessible factors are necessary to explore.

An efficient framework for lesion segmentation in ultrasound images using global adversarial learning and region-invariant loss.

Lesion segmentation in ultrasound images is an essential yet challenging step for early evaluation and diagnosis of cancers. In recent years, many automatic CNN-based methods have been proposed to assist this task. However, most modern approaches often lack capturing long-range dependencies and prior information making it difficult to identify the lesions with unfixed shapes, sizes, locations, and textures. To address this, we present a novel lesion segmentation framework that guides the model to learn the global information about lesion characteristics and invariant features (e.g., morphological features) of lesions to improve the segmentation in ultrasound images. Specifically, the segmentation model is guided to learn the characteristics of lesions from the global maps using an adversarial learning scheme with a self-attention-based discriminator. We argue that under such a lesion characteristics-based guidance mechanism, the segmentation model gets more clues about the boundaries, shapes, sizes, and positions of lesions and can produce reliable predictions. In addition, as ultrasound lesions have different textures, we embed this prior knowledge into a novel region-invariant loss to constrain the model to focus on invariant features for robust segmentation. We demonstrate our method on one in-house breast ultrasound (BUS) dataset and two public datasets (i.e., breast lesion (BUS B) and thyroid nodule from TNSCUI2020). Experimental results show that our method is specifically suitable for lesion segmentation in ultrasound images and can outperform the state-of-the-art approaches with Dice of 0.931, 0.906, and 0.876, respectively. The proposed method demonstrates that it can provide more important information about the characteristics of lesions for lesion segmentation in ultrasound images, especially for lesions with irregular shapes and small sizes. It can assist the current lesion segmentation models to better suit clinical needs.

Transforming free-text radiology reports into structured reports using ChatGPT: A study on thyroid ultrasonography.

PURPOSE: The importance of structured radiology reports has been fully recognized, as they facilitate efficient data extraction and promote collaboration among healthcare professionals. Our purpose is to assess the accuracy and reproducibility of ChatGPT, a large language model, in generating structured thyroid ultrasound reports. METHODS: This is a retrospective study that includes 184 nodules in 136 thyroid ultrasound reports from 136 patients. ChatGPT-3.5 and ChatGPT-4.0 were used to structure the reports based on ACR-TIRADS guidelines. Two radiologists evaluated the responses for quality, nodule categorization accuracy, and management recommendations. Each text was submitted twice to assess the consistency of the nodule classification and management recommendations. RESULTS: On 136 ultrasound reports from 136 patients (mean age, 52 years ± 12 [SD]; 61 male), ChatGPT-3.5 generated 202 satisfactory structured reports, while ChatGPT-4.0 only produced 69 satisfactory structured reports (74.3 % vs. 25.4 %, odds ratio (OR) = 8.490, 95 %CI: 5.775-12.481, p < 0.001). ChatGPT-4.0 outperformed ChatGPT-3.5 in categorizing thyroid nodules, with an accuracy of 69.3 % compared to 34.5 % (OR = 4.282, 95 %CI: 3.145-5.831, p < 0.001). ChatGPT-4.0 also provided more comprehensive or correct management recommendations than ChatGPT-3.5 (OR = 1.791, 95 %CI: 1.297-2.473, p < 0.001). Finally, ChatGPT-4.0 exhibits higher consistency in categorizing nodules compared to ChatGPT-3.5 (ICC = 0.732 vs. ICC = 0.429), and both exhibited moderate consistency in management recommendations (ICC = 0.549 vs ICC = 0.575). CONCLUSIONS: Our study demonstrates the potential of ChatGPT in transforming free-text thyroid ultrasound reports into structured formats. ChatGPT-3.5 excels in generating structured reports, while ChatGPT-4.0 shows superior accuracy in nodule categorization and management recommendations.

Clinical application potential of large language model: a study based on thyroid nodules.

BACKGROUND: Limited data indicated the performance of large language model (LLM) taking on the role of doctors. We aimed to investigate the potential for ChatGPT-3.5 and New Bing Chat acting as doctors using thyroid nodules as an example. METHODS: A total of 145 patients with thyroid nodules were included for generating questions. Each question was entered into chatbot of ChatGPT-3.5 and New Bing Chat five times and five responses were acquired respectively. These responses were compared with answers given by five junior doctors. Responses from five senior doctors were regarded as gold standard. Accuracy and reproducibility of responses from ChatGPT-3.5 and New Bing Chat were evaluated. RESULTS: The accuracy of ChatGPT-3.5 and New Bing Chat in answering Q2, Q3, Q5 were lower than that of junior doctors (all P < 0.05), while both LLMs were comparable to junior doctors when answering Q4 and Q6. In terms of "high reproducibility and accuracy", ChatGPT-3.5 outperformed New Bing Chat in Q1 and Q5 (P < 0.001 and P = 0.008, respectively), but showed no significant difference in Q2, Q3, Q4, and Q6 (P > 0.05 for all). New Bing Chat generated higher accuracy than ChatGPT-3.5 (72.41% vs 58.62%) (P = 0.003) in decision making of thyroid nodules, and both were less accurate than junior doctors (89.66%, P < 0.001 for both). CONCLUSIONS: The exploration of ChatGPT-3.5 and New Bing Chat in the diagnosis and management of thyroid nodules illustrates that LLMs currently demonstrate the potential for medical applications, but do not yet reach the clinical decision-making capacity of doctors.

AMES: An Automated Self-Administered Scale to Detect Incipient Cognitive Decline in Primary Care Settings.

Early identification and intervention of cognitive decline could be effective to prevent progression to dementia. We developed a self-delivered cognitive screening tool, Automated Memory and Executive Screening (AMES), to assess cognitive domains including memory, language, and executive function. 189 participants with diagnoses of mild cognitive impairment (MCI, n = 43), subjective cognitive decline (SCD, n = 29), objectively-defined subtle cognitive decline (obj-SCD, n = 18), and normal controls (NC, n = 99) completed the study. AMES had good convergent validity with conventional scales, and was good to discriminate patients with MCI (area under the curve [AUC] = 0.88; sensitivity = 86%; specificity = 80%) and obj-SCD (AUC = 0.78; sensitivity = 89%; specificity = 63%) from NC. These findings support that AMES is an easy to administer and effective instrument to screen for early cognitive impairment in community-based settings.

Oxygen-Independent Sulfate Radical for Stimuli-Responsive Tumor Nanotherapy.

Variant modalities are quested and merged into the tumor nanotherapy by leveraging the excitation from external or intratumoral incentives. However, the ubiquitous hypoxia and the insufficient content of hydrogen peroxide (H2 O2 ) in tumor microenvironments inevitably hinder the effective production of reactive oxygen species (ROS). To radically extricate from the shackles, peroxymonosulfate (PMS: HSO5- )-loaded hollow mesoporous copper sulfide (CuS) nanoparticles (NPs) are prepared as the distinct ROS donors for sulfate radical (•SO4- )-mediated and stimuli-responsive tumor nanotherapy in an oxygen-independent manner. In this therapeutic modality, the second near-infrared laser irradiation, together with the released copper ions as well as the heat produced by CuS after illumination, work together to activate PMS thus triply ensuring the copious production of •SO4- . Different from conventional ROS, the emergence of •SO4- , possessing a longer half-life and more rapid reaction, is independent of the oxygen (O2 ) and H2 O2 content within the tumor. In addition, this engineered nanosystem also exerts the function of photoacoustic imaging and skin restoration on the corresponding animal models. This study reveals the enormous potential of sulfate radical in oncotherapy and broadens pave for exploring the application of multifunctional and stimuli-responsive nanosystems in biomedicine.

Multi-Attribute Attention Network for Interpretable Diagnosis of Thyroid Nodules in Ultrasound Images.

Ultrasound (US) is the primary imaging technique for the diagnosis of thyroid cancer. However, accurate identification of nodule malignancy is a challenging task that can elude less-experienced clinicians. Recently, many computer-aided diagnosis (CAD) systems have been proposed to assist this process. However, most of them do not provide the reasoning of their classification process, which may jeopardize their credibility in practical use. To overcome this, we propose a novel deep learning (DL) framework called multi-attribute attention network (MAA-Net) that is designed to mimic the clinical diagnosis process. The proposed model learns to predict nodular attributes and infer their malignancy based on these clinically-relevant features. A multi-attention scheme is adopted to generate customized attention to improve each task and malignancy diagnosis. Furthermore, MAA-Net utilizes nodule delineations as nodules spatial prior guidance for the training rather than cropping the nodules with additional models or human interventions to prevent losing the context information. Validation experiments were performed on a large and challenging dataset containing 4554 patients. Results show that the proposed method outperformed other state-of-the-art methods and provides interpretable predictions that may better suit clinical needs.

Boundary-rendering network for breast lesion segmentation in ultrasound images.

Breast Ultrasound (BUS) has proven to be an effective tool for the early detection of cancer in the breast. A lesion segmentation provides identification of the boundary, shape, and location of the target, and serves as a crucial step toward accurate diagnosis. Despite recent efforts in developing machine learning algorithms to automate this process, problems remain due to the blurry or occluded edges and highly irregular nodule shapes. Existing methods often produce over-smooth or inaccurate results, failing the need of identifying detailed boundary structures which are of clinical interest. To overcome these challenges, we propose a novel boundary-rendering framework that explicitly highlights the importance of boundary for automated nodule segmentation in BUS images. It utilizes a boundary selection module to automatically focuses on the ambiguous boundary region and a graph convolutional-based boundary rendering module to exploit global contour information. Furthermore, the proposed framework embeds nodule classification via semantic segmentation and encourages co-learning across tasks. Validation experiments were performed on different BUS datasets to verify the robustness of the proposed method. Results show that the proposed method outperforms states-of-art segmentation approaches (Dice=0.854, IOU=0.919, HD=17.8) in nodule delineation, as well as obtains a higher classification accuracy than classical classification models.

AW3M: An auto-weighting and recovery framework for breast cancer diagnosis using multi-modal ultrasound.

Recently, more clinicians have realized the diagnostic value of multi-modal ultrasound in breast cancer identification and began to incorporate Doppler imaging and Elastography in the routine examination. However, accurately recognizing patterns of malignancy in different types of sonography requires expertise. Furthermore, an accurate and robust diagnosis requires proper weights of multi-modal information as well as the ability to process missing data in practice. These two aspects are often overlooked by existing computer-aided diagnosis (CAD) approaches. To overcome these challenges, we propose a novel framework (called AW3M) that utilizes four types of sonography (i.e. B-mode, Doppler, Shear-wave Elastography, and Strain Elastography) jointly to assist breast cancer diagnosis. It can extract both modality-specific and modality-invariant features using a multi-stream CNN model equipped with self-supervised consistency loss. Instead of assigning the weights of different streams empirically, AW3M automatically learns the optimal weights using reinforcement learning techniques. Furthermore, we design a light-weight recovery block that can be inserted to a trained model to handle different modality-missing scenarios. Experimental results on a large multi-modal dataset demonstrate that our method can achieve promising performance compared with state-of-the-art methods. The AW3M framework is also tested on another independent B-mode dataset to prove its efficacy in general settings. Results show that the proposed recovery block can learn from the joint distribution of multi-modal features to further boost the classification accuracy given single modality input during the test.

Analytical method development and comparability study for AmBisome® and generic Amphotericin B liposomal products.

Liposomal Amphotericin B, known as AmBisome®, is a life-saving antifungal product that sold $407 million in 2019. AmBisome® has a rather complex physical structure in that Amphotericin B (AmpB) forms a stable ionic complex with the lipid bilayer to maintain AmBisome®'s low toxicity and high stability in systemic circulation. Failed attempts to reproduce AmBisome®'s precise structure has resulted in faster drug release and higher toxicity both in vitro and in vivo. In this study, we established several analytical methodologies to quantify liposomal AmpB components, characterize thermal properties of the liposome, and determine particle size distribution, AmpB aggregation state, and drug release kinetics. We applied these methodologies together with in vitro hemolytic potential and antifungal activity tests to characterize multiple lots of AmBisome® and two generic products approved in India, Phosome® and Amphonex®. We also used Fungizone®, a micellar AmpB formulation, and "leaky" AmpB liposomes as negative controls. Our results showed that Phosome® and Amphonex® were both similar to AmBisome®, while Fungizone® and 'leaky" liposomes exhibited differences in both thermal properties and AmpB aggregation state, leading to faster drug release and higher toxicity. Due to the increased interest of the pharmaceutical industry in making generic AmBisome® and the lack of standard analytical methods to characterize liposomal AmpB products, the methodologies described here are valuable for the development of generic liposomal AmpB products.

Activatable NIR-II photoacoustic imaging and photochemical synergistic therapy of MRSA infections using miniature Au/Ag nanorods.

Multidrug-resistant Staphylococcus aureus (MRSA) seriously endanger human health. The development of efficient methods to eliminate the infections and monitor the treatment process are of great significance. Near-infrared-II (NIR-II) photoacoustic (PA) imaging and photothermal therapy (PTT) are highly integrated theranostic platforms with superior performance including a low imaging background, increased tissue penetration depth, and high photothermal threshold. Herein, we report an activatable near-infrared II (NIR-II) phototheranostic strategy using miniature Au/Ag nanorods (NRs) for the photochemical synergistic therapy of MRSA infections and in situ monitoring of the treatment progress. Au/Ag NRs were efficiently activated by ferricyanide solution and allowed to continuously release free Ag+ to eliminate MRSA, triggering NIR-II photothermal and PA performance enhancement. The activated NIR-II photothermal effect in turn accelerated the release of free Ag+ from Au/Ag NRs for the synergistic elimination of gram-positive Staphylococcus aureus and promoted wound healing. No photothermal damages or free Ag+-induced side effects were observed in treated mice. After synergistic treatment, a 20-fold NIR-II PA signal increase with a maximum signal-to-noise measurement of 9.5 was observed between the implanted site and normal tissue, enabling sensitive monitoring of Ag+ release process. The prepared Au/Ag NRs were stable and biocompatible, showing great potential for activatable NIR-II phototheranostic application.

Abdominal adipose tissue thickness measured using magnetic resonance imaging is associated with lumbar disc degeneration in a Chinese patient population.

The relationship between abdominal adiposity and disc degeneration remains largely uninvestigated. Here, we investigated the association between abdominal adipose tissue thickness and lumbar disc degeneration in a cross-sectional study of 2415 participants from The Second Affiliated Hospital of Wenzhou Medical University. All subjects were scanned with a 3T Magnetic Resonance Imaging system to evaluate the degree of lumbar disc degeneration. Multiple logistic regression analysis revealed that men in the highest quartiles for abdominal diameter (AD), sagittal diameter (SAD), and ventral subcutaneous thickness (VST) were at higher odds ratio for severe lumbar disc degeneration than men in the lowest quartiles. The adjusted model revealed that women in the highest quartiles for AD and SAD were also at higher odds ratio for severe lumbar disc degeneration than women in the lowest quartiles. Our results suggest that abdominal obesity might be one of underlying mechanisms of lumbar disc degeneration, and preventive strategies including weight control could be useful to reduce the incidence of lumbar disc degeneration. Prospective studies are needed to this confirm these results and to identify more deeper underlying mechanisms.