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Intermittent fasting boosts sexual behavior by limiting the central availability of tryptophan and serotonin

Cell Metabolism 2025年第5期37卷 1189-1205.e7页

作者： Xie, Kan Wang, Chengfeng Scifo, Enzo Pearson, Brandon Ryan, Devon Henzel, Kristin Markert, Astrid Schaaf, Kristina Mi, Xue Tian, Xin Jia, Jiajia Wang, Meiqin Bonn, Stefan Schölling, Manuel Möhl, Christoph Bano, Daniele Zhou, Yu Ehninger, Dan Translational Biogerontology Lab German Center for Neurodegenerative Diseases (DZNE) Venusberg-Campus 1/99 Bonn 53127 Germany Department of Intensive Care Unit University of Health and Rehabilitation Sciences Qingdao Hospital (Qingdao Municipal Hospital) School of Life Sciences and Health University of Health and Rehabilitation Sciences Shandong Qingdao 266000 China Institute of Brain Sciences and Related Disorders Medical College of Qingdao University Shandong Qingdao 266071 China Department of Physiology & Pathophysiology School of Basic Medical Sciences Qingdao University Shandong Qingdao 266071 China Institute of Medical Systems Biology Hamburg Center for Biomedical AI (bAIome) Molecular Neurobiology Hamburg (ZMNH) and Translational Immunology (HCTI) University Medical Center Hamburg-Eppendorf Falkenried 94 Hamburg 20251 Germany Image and Data Analysis Facility German Center for Neurodegenerative Diseases (DZNE) Venusberg-Campus 1/99 Bonn 53127 Germany Aging and Neurodegeneration Lab German Center for Neurodegenerative Diseases (DZNE) Venusberg-Campus 1/99 Bonn 53127 Germany

Aging affects reproductive capabilities in males through physiological and behavioral alterations, including endocrine changes and decreased libido. In this study, we investigated the influence of intermittent fasting (IF) on these aging-related declines, using male C57BL/6J mice. Our findings revealed that IF significantly preserved reproductive success in aged mice, not by improving traditional reproductive metrics such as sperm quality or endocrine functions but by enhancing mating behavior. This behavioral improvement was attributed to IF's ability to counter age-dependent increases in serotonergic inhibition, primarily through the decreased supply of the serotonin precursor tryptophan from the periphery to the brain. Our research underscores the potential of dietary interventions like IF in mitigating age-associated declines in male reproductive health and suggests a novel approach to managing conditions related to reduced sexual desire, highlighting the complex interplay between diet, metabolism, and reproductive behavior. © 2025 The Author(s)

关键词： aging amino acid metabolism brain fertility intermittent fasting male reproduction serotonin sexual behavior tryptophan

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Tumor Detection, Segmentation and Classification Challenge on Automated 3D Breast Ultrasound: The TDSC-ABUS Challenge

arXiv

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arXiv 2025年

作者： Luo, Gongning Xu, Mingwang Chen, Hongyu Liang, Xinjie Tao, Xing Ni, Dong Jeong, Hyunsu Kim, Chulhong Stock, Raphael Baumgartner, Michael Kirchhoff, Yannick Rokuss, Maximilian Maier-Hein, Klaus Yang, Zhikai Fan, Tianyu Boutry, Nicolas Tereshchenko, Dmitry Moine, Arthur Charmetant, Maximilien Sauer, Jan Du, Hao Bai, Xiang-Hui Raikar, Vipul Pai Montoya-Del-Angel, Ricardo Martí, Robert Luna, Miguel Lee, Dongmin Qayyum, Abdul Mazher, Moona Guo, Qihui Wang, Changyan Awasthi, Navchetan Zhao, Qiaochu Wang, Wei Wang, Kuanquan Wang, Qiucheng Dong, Suyu School of Computer Science and Technology Harbin Institute of Technology Harbin150001 China Department of Mathematics Faculty of Science National University of Singapore Singapore National-Regional Key Technology Engineering Laboratory for Medical Ultrasound School of Biomedical Engineering Shenzhen University Medical School Shenzhen University Shenzhen China Laboratory Shenzhen University Shenzhen China School of Biomedical Engineering and Informatics Nanjing Medical University Nanjing China Pohang Korea Republic of Heidelberg Division of Medical Image Computing Heidelberg Germany Faculty of Mathematics and Computer Science Heidelberg University Germany Heidelberg Germany HIDSS4Health - Helmholtz Information and Data Science School for Health Karlsruhe Heidelberg Germany Pattern Analysis and Learning Group Department of Radiation Oncology Heidelberg University Hospital Germany Department of Biomedical Engineering and Health KTH Royal Institute of Technology Stockholm Sweden France FathomX Singapore Saw Swee Hock School of Public Health National University of Singapore Singapore Philips Research University of Girona Spain Department of Robotics and Mechatronics Engineering DGIST Korea Republic of Department of Interdisciplinary Studies of Artificial Intelligence DGIST Korea Republic of National Heart and Lung Institute Faculty of Medicine Imperial College London London United Kingdom Centre for Medical Image Computing Department of Computer Science University College London London United Kingdom Lab School of Communication and Information Engineering Shanghai University Shanghai China Faculty of Science Mathematics and Computer Science Informatics Institute University of Amsterdam Amsterdam1090 GH Netherlands Department of Biomedical Engineering and Physics Amsterdam UMC Amsterdam1081 HV Netherlands Xi’an Jiaotong-Liverpool University China Department of Ultrasound Harbin Medical University Cancer Hospital No. 150 Haping Road Nangang

Breast cancer is one of the most common causes of death among women worldwide. Early detection helps in reducing the number of deaths. Automated 3D Breast Ultrasound (ABUS) is a newer approach for breast screening, which has many advantages over handheld mammography such as safety, speed, and higher detection rate of breast cancer. Tumor detection, segmentation, and classification are key components in the analysis of medical images, especially challenging in the context of 3D ABUS due to the significant variability in tumor size and shape, unclear tumor boundaries, and a low signal-to-noise ratio. The lack of publicly accessible, well-labeled ABUS datasets further hinders the advancement of systems for breast tumor analysis. Addressing this gap, we have organized the inaugural Tumor Detection, Segmentation, and Classification Challenge on Automated 3D Breast Ultrasound 2023 (TDSC-ABUS2023). This initiative aims to spearhead research in this field and create a definitive benchmark for tasks associated with 3D ABUS image analysis. In this paper, we summarize the top-performing algorithms from the challenge and provide critical analysis for ABUS image examination. We offer the TDSC-ABUS challenge as an open-access platform at https://***/ to benchmark and inspire future developments in1algorithmic research. © 2025, CC BY.

关键词： image segmentation

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medical history predicts phenome-wide disease onset and enables the rapid response to emerging health threats (vol 16, 585, 2025)

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NATURE COMMUNICATIONS 2025年第1期16卷 1-15页

作者： Steinfeldt, Jakob Wild, Benjamin Buergel, Thore Pietzner, Maik Upmeier zu Belzen, Julius Vauvelle, Andre Hegselmann, Stefan Denaxas, Spiros Hemingway, Harry Langenberg, Claudia Landmesser, Ulf Deanfield, John Eils, Roland Department of Cardiology Angiology and Intensive Care Medicine Deutsches Herzzentrum der Charité (DHZC) Berlin Germany Charité – Universitätsmedizin Berlin corporate member of Freie Universität Berlin and Humboldt Universität zu Berlin Klinik/Centrum Berlin Germany Computational Medicine Berlin Institute of Health (BIH) Charite - University Medicine Berlin Berlin Germany Friede Springer Cardiovascular Prevention Center@Charite Charite - University Medicine Berlin Berlin Germany Institute of Cardiovascular Sciences University College London London UK Berlin Institute of Health (BIH) Charite - University Medicine Berlin Berlin Germany DZHK (German Centre for Cardiovascular Research) Partner Site Berlin Berlin Berlin Germany MRC Epidemiology Unit Institute of Metabolic Science University of Cambridge Cambridge UK Precision Health University Research Institute Queen Mary University of London and Barts NHS Trust London UK Center for Digital Health Berlin Institute of Health (BIH) Charite - University Medicine Berlin Berlin Germany Health Data Science Unit Heidelberg University Hospital and BioQuant Heidelberg Germany Institute of Health Informatics University College London London UK British Heart Foundation Data Science Centre London UK Health Data Research UK London UK National Institute for Health Research Biomedical Research Centre at University College London Hospitals London UK Institute for Medical Engineering and Science Massachusetts Institute of Technology Massachusetts USA Pattern Recognition and Image Analysis Lab University of Münster Münster Germany

The COVID-19 pandemic exposed a global deficiency of systematic, data-driven guidance to identify high-risk individuals. Here, we illustrate the utility of routinely recorded medical history to predict the risk for 1741 diseases across clinical specialties and support the rapid response to emerging health threats such as COVID-19. We developed a neural network to learn from health records of 502,489 UK Biobank participants. Importantly, we observed discriminative improvements over basic demographic predictors for 1546 (88.8%) endpoints. After transferring the unmodified risk models to the All of US cohort, we replicated these improvements for 1115 (78.9%) of 1414 investigated endpoints, demonstrating generalizability across healthcare systems and historically underrepresented groups. Ultimately, we showed how this approach could have been used to identify individuals vulnerable to severe COVID-19. Our study demonstrates the potential of medical history to support guidance for emerging pandemics by systematically estimating risk for thousands of diseases at once at minimal cost.

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Sensor networked mobile robotics

Sensor networked mobile robotics

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Conference on Computer Vision and Pattern Recognition (CVPR)

作者： T. Christensen M. Noergaard C. Madsen A. Hoover Department of Medical Inf & image Analysis Laboratory of Image Analysis University of Aalborg Denmark Aalborg Universitet Aalborg DK Electrical and Computer Engineering Department Data Fusion Lab Clemson University Clemson SC USA

The dominant architecture for mobile robot perception uses sensors on-board the robot, providing a first-person perspective on the environment. We demonstrate a novel mobile robot architecture that uses an environment-based sensor network, which provides third-person perception. The idea is that a mobile robot working in the area tunes in to broadcasts from the video camera network (or in this case from an environment-based computer processing the video frames) to receive sensor data. This distributed sensing configuration offers several advantages over on-board sensing.

关键词： Mobile robots Robot sensing systems

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Uncertainty Estimation and Propagation in Accelerated MRI Reconstruction

arXiv

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arXiv 2023年

作者： Fischer, Paul Küstner, Thomas Baumgartner, Christian F. Cluster of Excellence – Machine Learning for Science University of Tübingen Germany Medical Image and Data Analysis Lab University Hospital of Tübingen Germany

MRI reconstruction techniques based on deep learning have led to unprecedented reconstruction quality especially in highly accelerated settings. However, deep learning techniques are also known to fail unexpectedly and hallucinate structures. This is particularly problematic if reconstructions are directly used for downstream tasks such as real-time treatment guidance or automated extraction of clinical paramters (e.g. via segmentation). Well-calibrated uncertainty quantification will be a key ingredient for safe use of this technology in clinical practice. In this paper we propose a novel probabilistic reconstruction technique (PHiRec) building on the idea of conditional hierarchical variational autoencoders. We demonstrate that our proposed method produces high-quality reconstructions as well as uncertainty quantification that is substantially better calibrated than several strong baselines. We furthermore demonstrate how uncertainties arising in the MR reconstruction can be propagated to a downstream segmentation task, and show that PHiRec also allows well-calibrated estimation of segmentation uncertainties that originated in the MR reconstruction process. Copyright © 2023, The Authors. All rights reserved.

关键词： Uncertainty analysis

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One-shot black-blood late gadolinium enhancement imaging for rapid, motion-free, and diagnostically accurate scar imaging

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Journal of Cardiovascular Magnetic Resonance 2025年 27卷 101602-101602页

作者： De Villedon de Naide, Victor, MSc Narceau, Kalvin, MSc Durand, Baptiste, MD, PhD Kuestner, Thomas, PhD, MSc Gut, Pauline, MSc Villegas-Martinez, Manuel, PhD, MSc Génisson, Thais, MSc Richard, Théo, MSc Jaïs, Pierre, MD, PhD Stuber, Mathias, PhD Cochet, Hubert, MD, PhD Bustin, Aurélien, PhD, MSc IHU LIRYC Heart Rhythm Disease Institute Université de Bordeaux – INSERM U1045 Avenue du Haut Lévêque 33604 Pessac France Bordeaux Aquitaine France Department of Cardiovascular Imaging Hôpital Cardiologique du Haut-Lévêque CHU de Bordeaux Avenue de Magellan 33604 Pessac France Bordeaux Aquitaine France Medical Image and Data Analysis (MIDAS.lab) Department of Diagnostic and Interventional Radiology University Hospital of Tübingen 72076 Tübingen Germany Tübingen Baden-Wurttemberg Germany Department of Diagnostic and Interventional Radiology Lausanne University Hospital and University of Lausanne Lausanne Switzerland Lausanne Vaud Switzerland Lausanne University Hospital (CHUV) and University of Lausanne (UNIL) Lausanne Vaud Switzerland

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Motion-Guided Physics-Based Learning for Cardiac MRI Reconstruction

Motion-Guided Physics-Based Learning for Cardiac MRI Reconst...

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Asilomar Conference on Signals, Systems & Computers

作者： Kerstin Hammernik Jiazhen Pan Daniel Rueckert Thomas Küstner Technical University of Munich Munich Germany Imperial College London London United Kingdom Klinikum Rechts der Isar Technical University of Munich Munich Germany Medical Image And Data Analysis (MIDAS.Lab) University Hospital of Tübingen Tübingen Germany

In this work, we propose a robust learning-based cardiac motion estimation framework, to estimate non-rigid cardiac motion fields from undersampled cardiac data. Our proposed frameworks leverages the advantages of a lightweight motion estimation network and a combination of photometric and smoothness losses. This framework enables the prediction of cardiac motion fields to further improve on the downstream task of motion-compensated image reconstruction. We evaluate our motion estimation framework qualitatively and quantitatively on 41 in-house acquired 2D cardiac CINE MRIs. Our proposed method provides quantitatively competitive results to state-of-the art methods in motion estimation, and superior results in image reconstruction in terms of structural similarity metric and peak-signal-to-noise ratio. Furthermore, our frameworks allows for ~3500x faster motion estimation compared to state-of-the-art approaches, opening up the practical application potential for motion-guided physics-based image reconstruction.

关键词： Training Measurement Motion estimation Magnetic resonance imaging Life estimation Peak to average power ratio Myocardium

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RecycleNet: Latent Feature Recycling Leads to Iterative Decision Refinement

arXiv

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arXiv 2023年

作者： Koehler, Gregor Wald, Tassilo Ulrich, Constantin Zimmerer, David Jaeger, Paul F. Franke, Jörg K.H. Kohl, Simon Isensee, Fabian Maier-Hein, Klaus H. Heidelberg Division of Medical Image Computing Germany Helmholtz Information and Data Science School for Health Karlsruhe Heidelberg Germany Helmholtz Imaging DKFZ Germany NCT Heidelberg a partnership between DKFZ University Medical Center Heidelberg Germany Interactive Machine Learning Group DKFZ Applied Computer Vision Lab DKFZ Machine Learning Lab University of Freiburg Freiburg Germany London United Kingdom Pattern Analysis and Learning Group Heidelberg University Hospital Heidelberg Germany

Despite the remarkable success of deep learning systems over the last decade, a key difference still remains between neural network and human decision-making: As humans, we can not only form a decision on the spot, but also ponder, revisiting an initial guess from different angles, distilling relevant information, arriving at a better decision. Here, we propose RecycleNet, a latent feature recycling method, instilling the pondering capability for neural networks to refine initial decisions over a number of recycling steps, where outputs are fed back into earlier network layers in an iterative fashion. This approach makes minimal assumptions about the neural network architecture and thus can be implemented in a wide variety of contexts. Using medical image segmentation as the evaluation environment, we show that latent feature recycling enables the network to iteratively refine initial predictions even beyond the iterations seen during training, converging towards an improved decision. We evaluate this across a variety of segmentation benchmarks and show consistent improvements even compared with top-performing segmentation methods. This allows trading increased computation time for improved performance, which can be beneficial, especially for safety-critical applications. © 2023, CC BY.

关键词： Network layers

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RecycleNet: Latent Feature Recycling Leads to Iterative Decision Refinement

RecycleNet: Latent Feature Recycling Leads to Iterative Deci...

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IEEE Workshop on Applications of Computer Vision (WACV)

作者： Gregor Koehler Tassilo Wald Constantin Ulrich David Zimmerer Paul F. Jaeger Jörg K. H. Franke Simon Kohl Fabian Isensee Klaus H. Maier-Hein Division of Medical Image Computing German Cancer Research Center (DKFZ) Heidelberg Germany Helmholtz Information and Data Science School for Health Karlsruhe/Heidelberg Germany Helmholtz Imaging DKFZ National Center for Tumor Diseases (NCT) NCT Heidelberg a Partnership Between DKFZ University Medical Center Heidelberg Interactive Machine Learning Group DKFZ Machine Learning Lab University of Freiburg Freiburg Germany Latent Labs (***) London UK Applied Computer Vision Lab DKFZ Pattern Analysis and Learning Group Heidelberg University Hospital Heidelberg Germany

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Author Correction: medical history predicts phenome-wide disease onset and enables the rapid response to emerging health threats

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Nature communications 2025年第1期16卷 1507页

作者： Jakob Steinfeldt Benjamin Wild Thore Buergel Maik Pietzner Julius Upmeier Zu Belzen Andre Vauvelle Stefan Hegselmann Spiros Denaxas Harry Hemingway Claudia Langenberg Ulf Landmesser John Deanfield Roland Eils Department of Cardiology Angiology and Intensive Care Medicine Deutsches Herzzentrum der Charité (DHZC) Berlin Germany. Charité - Universitätsmedizin Berlin corporate member of Freie Universität Berlin and Humboldt Universität zu Berlin Klinik/Centrum Berlin Germany. Computational Medicine Berlin Institute of Health (BIH) Charite - University Medicine Berlin Berlin Germany. Friede Springer Cardiovascular Prevention Center@Charite Charite - University Medicine Berlin Berlin Germany. Institute of Cardiovascular Sciences University College London London UK. Center for Digital Health Berlin Institute of Health (BIH) Charite - University Medicine Berlin Berlin Germany. MRC Epidemiology Unit Institute of Metabolic Science University of Cambridge Cambridge UK. Precision Health University Research Institute Queen Mary University of London and Barts NHS Trust London UK. Institute of Health Informatics University College London London UK. Institute for Medical Engineering and Science Massachusetts Institute of Technology Massachusetts USA. Pattern Recognition and Image Analysis Lab University of Münster Münster Germany. British Heart Foundation Data Science Centre London UK. Health Data Research UK London UK. National Institute for Health Research Biomedical Research Centre at University College London Hospitals London UK. Berlin Institute of Health (BIH) Charite - University Medicine Berlin Berlin Germany. DZHK (German Centre for Cardiovascular Research) Partner Site Berlin Berlin Berlin Germany. Center for Digital Health Berlin Institute of Health (BIH) Charite - University Medicine Berlin Berlin Germany. roland.eils@bih-charite.de. Health Data Science Unit Heidelberg University Hospital and BioQuant Heidelberg Germany. roland.eils@bih-charite.de.

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