Jiaming Li, Mengmeng Jiang, Qiaoran Wang, Zikai Zheng, Jianghua Shen, Jingyi Li, Muzhao Xiong, Yandong Zheng, Xiaoyong Lu, Yusheng Cai, Yanling Fan, Lingling Geng, Qianzhao Ji, Qianqian Peng, Shuhui Sun, Yuanyuan Wang, Zijuan Xin, Kaowen Yan, Yuanhan Yang, Jun Yu, Haoteng Yan, Ding Ai, Yongping Bai, Yan Bi, Xiu-Wu Bian, Pengcheng Bu, Jian-Ping Cai, Chun-Mei Cao, Feng Cao, Zhongwei Cao, Renjie Chai, Piu Chan, Chang Chen, Cheng-Shui Chen, Chunying Chen, Di Chen, Hou-Zao Chen, Lin Chen, Quan Chen, Xiao Chen, Xiaochun Chen, Yu Chen, Zi-Jiang Chen, Weimin Ci, Zhe Dai, Qiurong Ding, Birong Dong, Jiahong Dong, Jian-Gao Fan, Shiqing Feng, Xin Feng, Yun Feng, Xiaobing Fu, Xiaolong Fu, Feng Gao, Jiangang Gao, Qiang Gao, Shaorong Gao, Yonghao Gu, Youfei Guan, Feifan Guo, Jing-Dong J. Han, Haiping Hao, Jihui Hao, Fuchu He, Jinhan He, Ming He, Mingguang He, Qiyang He, Zhiying He, Zuhong He, Huashan Hong, Jiaxu Hong, Shengping Hou, Cheng Hu, Ping Hu, Zhibin Hu, Canhua Huang, Jun Huang, Kai Huang, Pengyu Huang, Xunming Ji, Yong Ji, Shunji Jia, Hong Jiang, Wenjian Jiang, Lingjing Jin, Zi-Bing Jin, Shenghong Ju, Zhenyu Ju, Qing-Peng Kong, Wei Kong, Wei-Jia Kong, Xiangqing Kong, Guanghua Lei, Geng-Lin Li, Ji Li, Jian Li, Mengfeng Li, Rong Li, Wei Li, Wei Li, Xiao-Jun Li, Xin Li, Qingfeng Liang, Zhen Liang, Haotian Lin, Baohua Liu, Cai-Yue Liu, Changsheng Liu, Feng Liu, Jianfeng Liu, Jun-Ping Liu, Ke Liu, Lin Liu, Pingsheng Liu, Qiang Liu, Qiang Liu, Tiemin Liu, Wenwen Liu, Xingguo Liu, Yajun Liu, Yong Liu, Youhua Liu, Youshuo Liu, Zhili Liu, Xiao Long, Yao Lu, Jian Luo, Xianghang Luo, Chunhong Ma, Shuai Ma, Xinran Ma, Jianhua Mao, Zhiyong Mao, Shyh-Chang Ng, Guangjun Nie, Yuyu Niu, Yaojin Peng, Jun Pu, Jieyu Qi, Li Qiang, Jie Qiao, Yingying Qin, Aijuan Qu, Jing Qu, Jie Ren, Ruibao Ren, Xiong Z. Ruan, Anbing Shi, Haibo Shi, Jie Shi, Kwok-Fai So, Moshi Song, Weihong Song, Zhou Songyang, Jiacan Su, Aijun Sun, Liang Sun, Qiang Sun, Yi Eve Sun, Yu Sun, Peifu Tang, Qi-Qun Tang, Yi Tang, Jun Tao, Ling Tao, Mei Tian, Xiao-Li Tian, Ye Tian, Xiaolin Tong, Cong-Yi Wang, Haibo Wang, Hongmei Wang, Huating Wang, Jianan Wang, Jianwei Wang, Jianwei Wang, Jiqiu Wang, Liheng Wang, Lin Wang, Miao Wang, Qiang Wang, Si Wang, Sijia Wang, Songlin Wang, Wengong Wang, Xiaoming Wang, Xiaoning Wang, Yan Wang, Yan-Jiang Wang, Yuan Wang, Yunfang Wang, Zhenning Wang, Xiawei Wei, Jianping Weng, Haitao Wu, Jihong Wu, Xiaohuan Xia, Yang Xia, Andy Peng Xiang, Guozhi Xiao, Junjie Xiao, Yichuan Xiao, Zhi-Xiong Jim Xiao, Zhengwei Xie, Wei Xiong, Aimin Xu, Hua Xu, Lingyan Xu, Ming Xu, Liying Yan, Jiayin Yang, Jichun Yang, Liu Yang, Yun-Gui Yang, Ze Yang, Zhenglin Yang, Hongjie Yao, Jing Ye, Chengqi Yi, Fan Yi, Honghua Yu, Yang Yu, Zhengrong Yu, Ti-Fei Yuan, Jirong Yue, Rui Yue, Chen Zhang, Chunxiang Zhang, Cuntai Zhang, Feng Zhang, Hongbo Zhang, Hongjia Zhang, Huijie Zhang, Jie Zhang, Jingjing Zhang, Licheng Zhang, Lingqiang Zhang, Luoying Zhang, Qingjiong Zhang, Wei Zhang, Weiping J. Zhang, Xin Zhang, Xuan Zhang, Yong Zhang, Yun-Wu Zhang, Zhanjun Zhang, Zhuohua Zhang, Bing Zhao, Guoguang Zhao, Jiajun Zhao, Meng Zhao, Tongbiao Zhao, Jialin C. Zheng, Junke Zheng, Zhuozhao Zheng, Huixia Zhou, Lili Zhou, Xiangtian Zhou, Yongsheng Zhou, Zhongjun Zhou, Lan Zhu, Yizhun Zhu, Zhiming Zhu, Wenjuan Zhuang, Weiguo Zou, Weiqi Zhang, Gang Pei, Guang-Hui Liu <<<

Variational quantum algorithms (VQAs) are leading strategies to reachpractical utilities of near-term quantum devices. However, the no-cloningtheorem in quantum mechanics precludes standard backpropagation, leading toprohibitive quantum resource costs when applying VQAs to large-scale tasks. Toaddress this challenge, we reformulate the training dynamics of VQAs as anonlinear partial differential equation and propose a novel protocol thatleverages physics-informed neural networks (PINNs) to model this dynamicalsystem efficiently. Given a small amount of training trajectory data collectedfrom quantum devices, our protocol predicts the parameter updates of VQAs overmultiple iterations on the classical side, dramatically reducing quantumresource costs. Through systematic numerical experiments, we demonstrate thatour method achieves up to a 30x speedup compared to conventional methods andreduces quantum resource costs by as much as 90\% for tasks involving up to 40qubits, including ground state preparation of different quantum systems, whilemaintaining competitive accuracy. Our approach complements existing techniquesaimed at improving the efficiency of VQAs and further strengthens theirpotential for practical applications. <<<

Shuanghao Bai, Wenxuan Song, Jiayi Chen, Yuheng Ji, Zhide Zhong, Jin Yang, Han Zhao, Wanqi Zhou, Wei Zhao, Zhe Li, Pengxiang Ding, Cheng Chi, Haoang Li, Chang Xu, Xiaolong Zheng, Donglin Wang, Shanghang Zhang, Badong Chen <<<

Embodied intelligence has witnessed remarkable progress in recent years,driven by advances in computer vision, natural language processing, and therise of large-scale multimodal models. Among its core challenges, robotmanipulation stands out as a fundamental yet intricate problem, requiring theseamless integration of perception, planning, and control to enable interactionwithin diverse and unstructured environments. This survey presents acomprehensive overview of robotic manipulation, encompassing foundationalbackground, task-organized benchmarks and datasets, and a unified taxonomy ofexisting methods. We extend the classical division between high-level planningand low-level control by broadening high-level planning to include language,code, motion, affordance, and 3D representations, while introducing a newtaxonomy of low-level learning-based control grounded in training paradigmssuch as input modeling, latent learning, and policy learning. Furthermore, weprovide the first dedicated taxonomy of key bottlenecks, focusing on datacollection, utilization, and generalization, and conclude with an extensivereview of real-world applications. Compared with prior surveys, our work offersboth a broader scope and deeper insight, serving as an accessible roadmap fornewcomers and a structured reference for experienced researchers. All relatedresources, including research papers, open-source datasets, and projects, arecurated for the community athttps://github.com/BaiShuanghao/Awesome-Robotics-Manipulation. <<<

Daniel Dukes, Kathryn Abrams, Ralph Adolphs, Mohammed E. Ahmed, Andrew Beatty, Kent C. Berridge, Susan Broomhall, Tobias Brosch, Joseph J. Campos, Zanna Clay, Fabrice Clément, William A. Cunningham, Antonio Damasio, Hanna Damasio, Justin D’Arms, Jane W. Davidson, Beatrice de Gelder, Julien Deonna, Ronnie de Sousa, Paul Ekman, Phoebe C. Ellsworth, Ernst Fehr, Agneta Fischer, Ad Foolen, Ute Frevert, Didier Grandjean, Jonathan Gratch, Leslie Greenberg, Patricia Greenspan, James J. Gross, Eran Halperin, Arvid Kappas, Dacher Keltner, Brian Knutson, David Konstan, Mariska E. Kret, Joseph E. LeDoux, Jennifer S. Lerner, Robert W. Levenson, George Loewenstein, Antony S. R. Manstead, Terry A. Maroney, Agnes Moors, Paula Niedenthal, Brian Parkinson, Ioannis Pavlidis, Catherine Pelachaud, Seth D. Pollak, Gilles Pourtois, Birgitt Roettger-Roessler, James A. Russell, Disa Sauter, Andrea Scarantino, Klaus R. Scherer, Peter Stearns, Jan E. Stets, Christine Tappolet, Fabrice Teroni, Jeanne Tsai, Jonathan Turner, Carien Van Reekum, Patrik Vuilleumier, Tim Wharton, David Sander <<<

Shiping Yang, Yijie Wang, Qin Huang, Mengyuan Wang, Shuhui Wang, Xiaomeng Fu, Chaohui Zhu, Jinkui Cheng, Shengxue Liu, Zhirui Yang, Ning Yang, Jianbing Yan, Xiaohong Yang, Feng Qin <<<

Tina M. Schnoeder, Adrian Schwarzer, Ashok Kumar Jayavelu, Chen-Jen Hsu, Joanna Kirkpatrick, Konstanze Döhner, Florian Perner, Theresa Eifert, Nicolas Huber, Patricia Arreba-Tutusaus, Anna Dolnik, Salam A. Assi, Monica Nafria, Lu Jiang, Yu-Ting Dai, Zhu Chen, Sai-Juan Chen, Sophie G. Kellaway, Anetta Ptasinska, Elizabeth S. Ng, Edouard G. Stanley, Andrew G. Elefanty, Marcus Buschbeck, Holger Bierhoff, Steffen Brodt, Georg Matziolis, Klaus-Dieter Fischer, Andreas Hochhaus, Chun-Wei Chen, Olaf Heidenreich, Matthias Mann, Steven W. Lane, Lars Bullinger, Alessandro Ori, Björn von Eyss, Constanze Bonifer, Florian H. Heidel <<<

Abstract In an effort to identify novel drugs targeting fusion-oncogene–induced acute myeloid leukemia (AML), we performed high-resolution proteomic analysis. In AML1-ETO (AE)-driven AML, we uncovered a deregulation of phospholipase C (PLC) signaling. We identified PLCgamma 1 (PLCG1) as a specific target of the AE fusion protein that is induced after AE binding to intergenic regulatory DNA elements. Genetic inactivation of PLCG1 in murine and human AML inhibited AML1-ETO dependent self-renewal programs, leukemic proliferation, and leukemia maintenance in vivo. In contrast, PLCG1 was dispensable for normal hematopoietic stem and progenitor cell function. These findings are extended to and confirmed by pharmacologic perturbation of Ca++-signaling in AML1-ETO AML cells, indicating that the PLCG1 pathway poses an important therapeutic target for AML1-ETO+ leukemic stem cells. <<<

Polymer-based simulations and experimental studies indicate the existence of a spatial dependency between the adjacent DNA fibers involved in the formation of chromatin loops. However, the existing strategies for detecting differential chromatin interactions assume that the interacting segments are spatially independent from the other segments nearby. To resolve this issue, we developed a new computational method, FIND, which considers the local spatial dependency between interacting loci. FIND uses a spatial Poisson process to detect differential chromatin interactions that show a significant difference in their interaction frequency and the interaction frequency of their neighbors. Simulation and biological data analysis show that FIND outperforms the widely used count-based methods and has a better signal-to-noise ratio. <<<

Frontier reasoning models have exhibited incredible capabilities across awide array of disciplines, driven by posttraining large language models (LLMs)with reinforcement learning (RL). However, despite the widespread success ofthis paradigm, much of the literature has been devoted to disentangling trulynovel behaviors that emerge during RL but are not present in the base models.In our work, we approach this question from a different angle, instead askingwhether comparable reasoning capabilites can be elicited from base models atinference time by pure sampling, without any additional training. Inspired byMarkov chain Monte Carlo (MCMC) techniques for sampling from sharpeneddistributions, we propose a simple iterative sampling algorithm leveraging thebase models' own likelihoods. Over different base models, we show that ouralgorithm offers substantial boosts in reasoning that nearly match and evenoutperform those from RL on a wide variety of single-shot tasks, includingMATH500, HumanEval, and GPQA. Moreover, our sampler avoids the collapse indiversity over multiple samples that is characteristic of RL-posttraining.Crucially, our method does not require training, curated datasets, or averifier, suggesting broad applicability beyond easily verifiable domains. <<<

For many years, the gold standard in the study of malignant tumors has been the in vitro culture of tumor cells, in vivo xenografts or genetically modified animal models. Meanwhile, three-dimensional cell models (3D cultures) have been added to the arsenal of modern biomedical research. 3D cultures reproduce tissue-specific features of tissue topology. This makes them relevant tissue models in terms of cell differentiation, metabolism and the development of drug resistance. Such models are already being used by many research groups for both basic and translational research, and may substantially reduce the number of animal studies, for example in the field of oncological research. In the current literature, 3D cultures are classified according to the technique of their formation (with or without a scaffold), cultivation conditions (static or dynamic), as well as their cellular organization and function. In terms of cellular organization, 3D cultures are divided into “spheroid models”, “organoids”, “organs-ona-chip” and “microtissues”. Each of these models has its own unique features, which should be taken into account when using a particular model in an experiment. The simplest 3D cultures are spheroid models which are floating spherical cell aggregates. An organoid is a more complex 3D model, in which a self-organizing 3D structure is formed from stem cells (SCs) capable of self-renewal and differentiation within the model. Organ-on-a-chip models are chips of microfluidic systems that simulate dynamic physical and biological processes found in organs and tissues in vitro. By combining different cell types into a single structure, spheroids and organoids can act as a basis for the formation of a microtissue – a hybrid 3D model imitating a specific tissue phenotype and containing tissuespecific extracellular matrix (ECM) components. This review presents a brief history of 3D cell culture. It describes the main characteristics and perspectives of the use of “spheroid models”, “organoids”, “organ-on-a-chip” models and “microtissues” in immune oncology research of solid tumors. <<<

Abstract Background Subcellular RNA localization is crucial for the spatio-temporal control of protein synthesis and underlies key processes during development, homeostasis, and disease. In epithelial cells, RNA can localize asymmetrically along the apico-basal axis. Yet, the localization of most transcripts as well as the diversity of patterns that they adopt remains unexplored. Results Here, we use APEX-seq for proximity labeling and MERFISH for spatial transcriptomics to map subcellular transcript localization in intestinal organoids and tissue from adult mice. Many transcripts present localization bias, often localizing in granular structures. We uncover intrinsic and environmental factors that influence the formation of these patterns. Additionally, we identify translation-dependent and -independent localization patterns and pinpoint the role of 3′ untranslated regions and RNA-binding proteins. Conclusions This subcellular RNA atlas presents a detailed resource for understanding intestinal physiology. <<<

In this paper, we investigate whether better schools can compensate for the effects of children’s genetic differences. To this end, we combine data from the Norwegian Mother, Father, and Child Cohort Study (MoBa) with Norwegian register data to estimate the interaction between measures of children’s predisposition to education and school quality. We use MoBa’s genetic data to compute polygenic indices for educational attainment ( PGI EA ). Importantly, MoBa includes genetic data on mother-father-child trios, allowing us to identify causal genetic effects using within-family variation. We calculate school value-added measures from Norwegian register data, allowing us to causally estimate school quality effects. Leveraging the advantages of both data sources, we provide a causally identified study of gene–environment interactions in the school context. We find evidence for substitutability of PGI EA and school quality in reading but not numeracy: A 1 SD increase of school quality decreases the impact of a 1 SD increase of PGI EA on reading test scores by 6%. The substitutability arises through gains of students at the lower end of the PGI EA distribution. This suggests that investments in school quality may help reduce educational inequalities arising from genetic differences between students. <<<

2048 is a stochastic single-player game involving 16 cells on a 4 by 4 grid,where a player chooses a direction among up, down, left, and right to obtain ascore by merging two tiles with the same number located in neighboring cellsalong the chosen direction. This paper presents that a variant 2048-4x3 12cells on a 4 by 3 board, one row smaller than the original, has been stronglysolved. In this variant, the expected score achieved by an optimal strategy isabout $50724.26$ for the most common initial states: ones with two tiles ofnumber 2. The numbers of reachable states and afterstates are identified to be$1,152,817,492,752$ and $739,648,886,170$, respectively. The key technique isto partition state space by the sum of tile numbers on a board, which we callthe age of a state. An age is invariant between a state and its successiveafterstate after any valid action and is increased two or four by stochasticresponse from the environment. Therefore, we can partition state space by agesand enumerate all (after)states of an age depending only on states with therecent ages. Similarly, we can identify (after)state values by going along withages in decreasing order. <<<

Aya Ludin, Georgia L. Stirtz, Asaf Tal, Ajit J. Nirmal, Kathleen L. Pfaff, Michael Manos, Naomi Besson, Nebiyat Eskndir, Billie Porter, Stephanie M. Jones, Hannah M. Faulkner, Qiyu Gong, Sophia Liu, Irving Barrera, Lijian Wu, Cecilia Pessoa Rodrigues, Aditi Sahu, Elizabeth Jerison, Joao V. Alessi, Biagio Ricciuti, Douglas S. Richardson, Jodi D. Weiss, Hadley M. Moreau, Meredith E. Stanhope, Alexander B. Afeyan, James Sefton, Wyatt D. McCall, Emily Formato, Song Yang, Yi Zhou, David P. Hoytema van Konijnenburg, Hannah L. Cole, Miguel Cordova, Liang Deng, Milind Rajadhyaksha, Stephen R. Quake, Mark M. Awad, Fei Chen, Kai W. Wucherpfennig, Peter K. Sorger, F. Stephen Hodi, Scott J. Rodig, George F. Murphy, Leonard I. Zon <<<

T cell-mediated tumor killing underlies immunotherapy success. Here, we used long-term in vivo imaging and high-resolution spatial transcriptomics of zebrafish endogenous melanoma, as well as multiplex imaging of human melanoma, to identify domains facilitating the immune response during immunotherapy. We identified cancer regions of antigen presentation and T cell engagement and retention (CRATERs) as pockets at the stroma-melanocyte boundaries of zebrafish and human melanoma. CRATERs are rich in antigen-recognition molecules, harboring the highest density of CD8 T cells in tumors. In zebrafish, CD8 T cells formed prolonged interactions with melanoma cells within CRATERs, characteristic of antigen recognition. Following immunostimulatory treatment, CRATERs expanded, becoming the major sites of activated CD8 T cell accumulation and tumor killing. In humans, elevation in CRATER density in biopsies following immune checkpoint blockade (ICB) therapy correlated with a clinical response to therapy. CRATERs are structures that show active tumor killing and may be useful as a diagnostic indicator for immunotherapy success. <<<

Ren Ji, Kelvin K. Ng, Wenqi Chen, Weihong Yang, Hongtao Zhu, Tan-To Cheung, Chi-Leung Chiang, Tiffany C.L. Wong, Feng-Ming Kong, G. Wu, Chung-Mau Lo <<<

Abstract Stereotactic body radiotherapy (SBRT) is a novel noninvasive treatment for unresectable hepatocellular carcinoma (HCC). Whether its efficacy is comparable to radiofrequency ablation (RFA), a recommended therapy for unresectable HCC, is unknown. The present study aims to compare the clinical outcome between SBRT and RFA for patients with unresectable HCC. The clinical data of 60 patients with unresectable HCC from January 2018 to January 2021 were retrospectively reviewed. There were 22 cases treated by SBRT and 38 cases by RFA. The short-term and long-term clinical outcomes were compared. There was no significant difference in the baseline demographic characteristics between two groups. The complete remission rate at 3 months was comparable between SBRT group (81.8%) and RFA group (89.4%). Local tumor control rate was also similar between two groups (90.9% vs. 94.7%). There was no severe complication (grade IIIa or above) in both groups. The 1-year and 2-year overall survival rates were 88.2% and 85.7% in SBRT group and 100% and 75% in RFA group, respectively. There was no statistical significant difference between groups (P = .576). SBRT can achieve similar short and long-term clinical outcome as RFA for unresectable HCC. Future prospective clinical study is needed to justify its role in patients with HCC. <<<

Yasuaki Mohri, Jialiang Nie, Hironobu Morinaga, Tomoki Kato, Takahiro Aoto, Takashi Yamanashi, Daisuke Nanba, Hiroyuki Matsumura, Sakura Kirino, Kouji Kobiyama, Ken J. Ishii, Masahiro Hayashi, Tamio Suzuki, Takeshi Namiki, Jun Seita, Emi K. Nishimura <<<

María Bueno Álvez, Sofia Bergström, Josefin Kenrick, Emil Johansson, Mikael Åberg, Murat Akyildiz, Ozlem Altay, Hilda Sköld, Konstantinos Antonopoulos, Emmanouil Apostolakis, Yasin Hasan Balcioglu, Anna Bergström, Göran Bergström, Sophia Björkander, Suzanne Egyhazi Brage, Petter Brodin, Lynn Butler, Sara Cajander, Hanna Danielsson, Murat Dayangac, Gizem Dinler-Doganay, Levent Doğanay, Gunilla Enblad, Malin Enblad, Linn Fagerberg, Sara Falck-Jones, Anna Färnert, Mattias Forsberg, Laura Gonzalez, Anders Gummesson, Karin Gunnarsson, Iva Gunnarsson, Ulf Gyllensten, Göran Hesselager, Andreas Hober, Martin Höglund, Marie Holmqvist, Begum Horuluoglu, Rebecka Hultgren, Maria Jesus Iglesias, Helena Janols, Fredric Johansson, Anette Johnsson, Lars Klareskog, David Kotol, Inger Kull, Marika Kvarnström, Maximilian Julius Lautenbach, Ulrika Liljedahl, Henrik Lindman, Cecilia Lindskog, Miklos Lipcsey, Ingrid E Lundberg, Adil Mardinoglu, Erik Melén, Lingqi Meng, Anne-Sophie Merritt, Jan Mulder, Mai Thi-Huyen Nguyen, Jessica Nordlund, Anna Norrby-Teglund, Antonella Notarnicola, Piotr Nowak, Jacob Odeberg, Per Oksvold, Tomas Olsson, Leonid Padyukov, Karlis Pauksens, Fredrik Piehl, Elisa Pin, Fredrik Pontén, Natallia Rameika, Anton Reepalu, Joy Roy, Jochen M. Schwenk, Meltem Sen, Antti Siika, Oscar E. Simonson, Åsa Sivertsson, Tobias Sjöblom, Evelina Sjöstedt, Lovisa Skoglund, Anna Smed-Sörensen, Klara Sondén, Anders Sönnerborg, Karin Stålberg, Kristoffer Strålin, Jonas Sundén-Cullberg, Christopher Sundling, Thanadol Sutantiwanichkul, Fernanda Costa Svedman, Mattias Svensson, Elisabet Svenungsson, Tadepally Lakshmikanth, Khue Hua Tran-Minh, Hasan Türkez, Christian Unge, Per Venge, Marie Wahren-Herlenius, Jakob Woessmann, Hong Yang, Umit Haluk Yeşilkaya, Meng Yuan, Mujdat Zeybel, Cheng Zhang, Wen Zhong, Martin Zwahlen, Kalle von Feilitzen, Peter Nilsson, Fredrik Edfors, Mathias Uhlén <<<

The human blood proteome provides a holistic readout of health states through the assessment of thousands of circulating proteins. Here, we present a pan-disease resource to enable the study of diverse disease phenotypes within a harmonized proteomics dataset. By profiling protein concentrations across 59 diseases and healthy cohorts, we identified proteins associated with age, sex, and BMI, as well as disease-specific signatures. This study highlights shared and distinct protein patterns across conditions, demonstrating the power of a unified proteomics approach to uncover biological insights. The dataset, covering 8,262 individuals and up to 5,416 proteins, serves as an online resource for exploring disease-specific protein profiles and advancing precision medicine research. <<<