Beyond Clicking:A Step Towards Generalist GUI Grounding via Text Dragging

By: Zeyi Liao · Yadong Lu · Boyu Gou · Huan Sun · Ahmed Awadallah

13 January 2026 at 13:00

arXiv:2601.06031v1 Announce Type: cross Abstract: Graphical user interface (GUI) grounding, the process of mapping human instructions to GUI actions, serves as a fundamental basis to autonomous GUI agents. While existing grounding models achieve promising performance to simulate the mouse click action on various click-based benchmarks, another essential mode of mouse interaction, namely dragging, remains largely underexplored. Yet, dragging the mouse to select and manipulate textual content represents a prevalent and important usage in practical GUI scenarios. To narrow this gap, we first introduce GUI-Drag, a diverse dataset of 161K text dragging examples synthesized through a scalable pipeline. To support systematic and robust evaluation, we further construct ScreenDrag, a benchmark with 5,333 examples spanning three levels of interface context, together with three dedicated metrics designed for assessing text dragging capability. Models trained on GUI-Drag with an efficient continual training strategy achieve substantial improvements on ScreenDrag, while preserving the original click-based performance on ScreenSpot, ScreenSpot-v2, and OSWorld-G. Our work encourages further research on broader GUI grounding beyond just clicking and paves way toward a truly generalist GUI grounding model. All benchmark, data, checkpoints, and code are open-sourced and available at https://osu-nlp-group.github.io/GUI-Drag.

Decoding the enigma of multiple primary lung cancers: from mechanism to bedside-a narrative review

(Multiomics OR Omics) AND (Lung OR gastric OR Hepatocellular)

By: Pan Yang · Siyu Liu · Yuansi Chen · Yihan Guo · Yi Li · Zhen Wang · Weimin Li · Zhoufeng Wang

10 December 2025 at 19:00

Transl Lung Cancer Res. 2025 Nov 30;14(11):5181-5197. doi: 10.21037/tlcr-2025-957. Epub 2025 Nov 27.

ABSTRACT

BACKGROUND AND OBJECTIVE: Lung cancer is the leading cause of global cancer mortality. Multiple primary lung cancer (MPLC) represents a clinically challenging subtype characterized by independent tumor foci. Distinguishing MPLC from intrapulmonary metastases is crucial for prognosis and treatment. This review integrates current evidence on MPLC's etiology, molecular mechanisms, diagnosis, and management, aiming to provide a clinical reference and highlight future precision medicine directions.

METHODS: We searched PubMed/MEDLINE, Web of Science, and Google Scholar for articles published between January 2000 and September 2024. Search terms included "multiple primary lung cancer", "diagnosis", "molecular characteristics", and "treatment". The selection focused on English-language research and reviews addressing MPLC pathogenesis, diagnosis, or management.

KEY CONTENT AND FINDINGS: The review delineates the multifactorial pathogenesis of MPLC, encompassing genetic susceptibility, somatic heterogeneity, clonal evolution, and epigenetic dysregulation. It frames these mechanisms against a backdrop of "field cancerization" and dynamic tumor microenvironment interactions. The evolution of diagnosis from histology to integrated molecular-artificial intelligence (AI) models is detailed, alongside treatment strategies that must overcome the challenge of inter-lesional heterogeneity.

CONCLUSIONS: MPLC is a distinct entity arising from genetic, epigenetic, and microenvironmental interplay. Advancing its management requires multi-omics integration to decipher pathology and identify biomarkers. Future work should develop AI-enhanced diagnostics and lesion-specific treatment strategies. This review synthesizes current evidence to inform and direct future research and clinical innovation in MPLC.

PMID:41367572 | PMC:PMC12683420 | DOI:10.21037/tlcr-2025-957

The Alignment Paradox of Medical Large Language Models in Infertility Care: Decoupling Algorithmic Improvement from Clinical Decision-making Quality

cs.AI, q-bio.NC updates on arXiv.org

By: Dou Liu · Ying Long · Sophia Zuoqiu · Kaipeng Xie · Runze Yang · Di Liu · Kang Li · Yiting Lin · Hanyi Liu · Rong Yin · Tian Tang

25 November 2025 at 13:00

arXiv:2511.18084v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly adopted in clinical decision support, yet aligning them with the multifaceted reasoning pathways of real-world medicine remains a major challenge. Using more than 8,000 infertility treatment records, we systematically evaluate four alignment strategies: Supervised Fine-Tuning (SFT), Direct Preference Optimization (DPO), Group Relative Policy Optimization (GRPO), and In-Context Learning (ICL) through a dual-layer framework combining automatic benchmarks with blinded doctor-in-the-loop assessments. GRPO achieves the highest algorithmic accuracy across multiple decision layers, confirming the value of reinforcement-based optimization for structured prediction tasks. However, clinicians consistently prefer the SFT model, citing clearer reasoning processes (p = 0.035) and higher therapeutic feasibility (p = 0.019). In blinded pairwise comparisons, SFT attains the highest winning rate (51.2%), outperforming both GRPO (26.2%) and even physicians' original decisions (22.7%). These results reveal an alignment paradox: algorithmic improvements do not necessarily translate into higher clinical trust, and may diverge from human-centered preferences. Our findings highlight the need for alignment strategies that prioritize clinically interpretable and practically feasible reasoning, rather than solely optimizing decision-level accuracy.

Considerations for Patient Privacy of Large Language Models in Health Care: Scoping Review

Journal of Medical Internet Research

By: Xiaoying Zhong · Siyi Li · Zhao Chen · Long Ge · Dongdong Yu · Shijia Wang · Liangzhen You · Hongcai Shang

21 November 2025 at 21:15

Background: The application of large language models (LLMs) in health care holds significant potential for enhancing patient care and advancing medical research. However, the protection of patient privacy remains a critical issue, especially when handling patient health information (PHI). Objective: This scoping review aims to evaluate the adequacy of current approaches and identify areas in need of improvement to ensure robust patient privacy protection in the existing studies about PHI-LLMs within the health care domain. Methods: A search of the literature published from January 1, 2022, to July 20, 2025, was performed on July 20, 2025, using 2 databases (PubMed and Embase). This scoping review focused on the following three research questions: (1) What studies on the development and application of LLMs using PHI currently exist within the health care domain? (2) What patient privacy considerations are addressed in existing PHI-LLMs research, and are these measures sufficient? (3) How can future research on the development and application of LLMs using PHI better protect patient privacy? Studies were included if they focused on the development and application of LLMs within health care using PHI, encompassing activities such as model construction, fine-tuning, optimization, testing, and performance comparison. Eligible literature comprised original research articles written in English. Conversely, studies were excluded if they used publicly available datasets, under the assumption that such data have been adequately deidentified. Additionally, non-English publications, reviews, abstracts, incomplete reports, and preprints were excluded from the review due to the lack of rigorous peer review. Results: This study systematically identified 9823 studies on PHI-LLM and included 464 studies published between 2022 and 2025. Among the 464 studies, (1) a small number of studies neglected ethical review (n=45, 9.7%) and patient informed consent (n=148, 31.9%) during the research process, (2) more than a third of the studies (n=178, 38.4%) failed to report whether to implement effective measures to protect PHI, and (3) there was a significant lack of transparency and comprehensive detail in anonymization and deidentification methods. Conclusions: We propose comprehensive recommendations across 3 phases—study design, implementation, and reporting—to strengthen patient privacy protection and transparency in PHI-LLM. This study emphasizes the urgent need for the development of stricter regulatory frameworks and the adoption of advanced privacy protection technologies to effectively safeguard PHI. It is anticipated that future applications of LLMs in the health care field will achieve a balance between innovation and robust patient privacy protection, thereby enhancing ethical standards and scientific credibility.

Stereo-seq V2: Spatial mapping of total RNA on FFPE sections with high resolution

Cell

By: Yu Zhao · Young Li · Ying He · Junqi Wu · Yi Liu · Xinxing Li · Zhaoxun Li · Qiaomei Yuan · Jialuo Li · Xinya Zhang · Xiaole Hu · Guifang Wu · Ying Qiu · Jie Yuan · Xin Huang · Mengru Xu · Mei Yang · Jing Guo · Tingting Cao · Qiuling Chen · Dandan Li · Jiajun Zhang · Xiaoxi Zhou · Xia Jiang · Fan Zhu · Xuan Dong · Rong Xiang · Hailin Pan · Lei Han · Ziqing Deng · Haohao Deng · Yan Zhang · Maoyuan Liu · Qin Wu · Gaoyang Wang · Jie Zhai · Weihong Tan · Xiawei Liu · Zehao Wang · Shanshan Li · Tianbi Duan · Longqi Liu · Ao Chen · Haiying Liu · Chang Chen · Sha Liao · Xun Xu

28 August 2025 at 08:00

Stereo-seq V2 facilitates single-cell-resolution spatial RNA mapping in FFPE samples through random primer capture, uncovering ncRNAs, host-pathogen transcriptome profiling, and spatial immune repertoires in situ.

Prevalence of Dropout and Influencing Factors in Digital Psychosocial Intervention Trials for Adult Illicit Substance Users: Systematic Review and Meta-Analysis

Journal of Medical Internet Research

By: Jiayi Li · Xinyi Liu · Xiayu Du · Tingni Mi · Zhihong Ren

11 October 2025 at 05:45

Background: Globally, the number of illegal drug users is rising, posing mental and physical health challenges and increasing societal burdens. Despite a significant need for treatment, only about 10% of these individuals receive it worldwide, often with poor adherence. Traditional treatments, while effective, suffer from high dropout rates due to limitations. The COVID-19 pandemic has spurred the growth of digital interventions like apps and online platforms, offering flexibility and cost-effectiveness that better meet patient needs and improve engagement. However, addressing the persistently high dropout rates in these online treatments is crucial and necessitates further research. Objective: This study aimed to estimate dropout rates among adults with illicit drug use participating in digital psychosocial intervention trials, and to identify factors associated with attrition. Methods: We conducted a systematic search of five major databases for English-language randomized trials published up to January 27, 2025. A total of 40 studies (80 arms; 9,563 participants) reporting 46 dropout rate estimates were included. A random-effects model was used to calculate pooled dropout rates, with meta-regression and subgroup analyses exploring potential moderators. The study was registered on PROSPERO (CRD42024534389). Results: At post-test, the pooled dropout rate in the intervention group across 17 studies was 22.4% (95% CI: 12.4%–37.2%). Dropout was significantly associated with education level, employment status, baseline clinical diagnosis, intervention frequency, and initial medication use. During the longest follow-up (29 studies), the dropout rate was 27.9% (95% CI: 18.8%–39.3%), with marital status, recruitment source, medication frequency, and intervention modality as significant predictors. Control group dropout rates were 25.9% and 28.3%, both higher than those in the intervention group. Conclusions: This meta-analysis revealed substantial dropout among adults with illicit drug use receiving digital psychosocial interventions. Targeted modifications to intervention design may improve engagement and long-term retention. Clinical Trial: The study was registered on PROSPERO (CRD42024534389).

Navigating the Boundaries of Teleconsultation—Capabilities, Limitations, and Pathways for Improvement: Qualitative Study of the Experiences of Patients With Stroke

Journal of Medical Internet Research

By: Arkers Kwan Ching Wong · Xiaomin Lu · Joyce Xiaoli Wang · Rose Sin Yi Lin · Jing Jing Su · Robbie Mian Wang · Vivian Wai Yan Kwok

19 September 2025 at 04:00

Background: Survivors of stroke often face persistent challenges accessing postdischarge care due to mobility limitations, transportation burdens, and inflexible scheduling. Teleconsultation has emerged as a potential solution to improve continuity of care, but its perceived strengths and limitations from the patient perspective remain insufficiently understood. Objective: This study aimed to explore the experiences of survivors of stroke with a nurse-led teleconsultation program to (1) identify perceived capabilities; (2) understand limitations in usability, accessibility, and clinical function; and (3) generate patient-informed recommendations for improvement. Methods: A qualitative study was embedded within a 3-month nurse-led teleconsultation intervention delivered by advanced practice nurses. A total of 21 survivors of ischemic stroke (aged 45-76 y; female: n=11, 52%) who had preserved cognitive function (Montreal Cognitive Assessment score ≥22) and smartphone access participated in 6 focus groups conducted via Zoom. Data were analyzed thematically using an established framework. Data saturation was achieved. Results: Participants widely valued teleconsultation for reducing logistical burdens; enhancing access; and offering a more comfortable, emotionally supportive setting for follow-up care. Many reported increased awareness and motivation for self-monitoring. However, limitations included an inability to perform physical assessments or respond to emergencies; digital and usability barriers, especially among older users; and scheduling inflexibility. Participants emphasized the need for patient-initiated follow-up mechanisms, physician collaboration for medication management, and greater support for users considered digitally marginalized. They also highlighted the potential of teleconsultation to serve as a triage tool, reserving in-person care for complex cases. Conclusions: Nurse-led teleconsultation was perceived as a convenient and supportive modality for poststroke care, particularly for stable follow-ups and psychosocial support. However, its long-term viability depends on addressing clinical and technical limitations, enhancing user autonomy, and integrating interdisciplinary input. By centering the lived experiences of survivors of stroke, this study offers concrete recommendations to guide the development of more inclusive, responsive, and patient-centered teleconsultation models.

Stereo-seq V2: Spatial mapping of total RNA on FFPE sections with high resolution

(Multiomics OR Omics) AND (Lung OR gastric OR Hepatocellular)

By: Yu Zhao · Young Li · Ying He · Junqi Wu · Yi Liu · Xinxing Li · Zhaoxun Li · Qiaomei Yuan · Jialuo Li · Xinya Zhang · Xiaole Hu · Guifang Wu · Ying Qiu · Jie Yuan · Xin Huang · Mengru Xu · Mei Yang · Jing Guo · Tingting Cao · Qiuling Chen · Dandan Li · Jiajun Zhang · Xiaoxi Zhou · Xia Jiang · Fan Zhu · Xuan Dong · Rong Xiang · Hailin Pan · Lei Han · Ziqing Deng · Haohao Deng · Yan Zhang · Maoyuan Liu · Qin Wu · Gaoyang Wang · Jie Zhai · Weihong Tan · Xiawei Liu · Zehao Wang · Shanshan Li · Tianbi Duan · Longqi Liu · Ao Chen · Haiying Liu · Chang Chen · Sha Liao · Xun Xu

29 August 2025 at 18:00

Cell. 2025 Aug 22:S0092-8674(25)00922-5. doi: 10.1016/j.cell.2025.08.008. Online ahead of print.

ABSTRACT

Performing total RNA profiling on formalin-fixed, paraffin-embedded (FFPE) samples, the predominant sample conservation method in clinical practice, remains challenging for current spatial transcriptomics techniques. Here, we introduce Stereo-seq V2, which employs random primers to capture and sequence RNAs in situ on FFPE sections and provides single-cell resolution. The random-priming-based strategy offers unbiased transcript capturing and uniform gene body coverage, which increase the sensitivity to marker genes, the efficiency of non-polyadenylation (poly(A)) RNA profiling, and immune repertoire coverage. We demonstrated the robust performance of Stereo-seq V2 on clinical FFPE samples using triple-negative breast cancer (TNBC) sections and identified tumor-specific alternative splicing events. In a Mycobacterium tuberculosis (Mtb)-infected mouse model, we monitored gene expression dynamics of host and pathogen transcriptomes simultaneously by utilizing Stereo-seq V2. We also assembled immune repertoires and identified Mtb-specific BCR clones, which could also be observed in human tuberculous lung samples. These results highlight Stereo-seq V2's potential in biomedical research and personalized medicine.

PMID:40882628 | DOI:10.1016/j.cell.2025.08.008

Integrative single-cell multi-omics profiling of human pancreatic islets identifies T1D-associated genes and regulatory signals

(Multiomics OR Omics) AND (Pancreatic)

By: Ricardo D'Oliveira Albanus · Xiaoshan Zhang · Zeping Zhao · Henry J Taylor · Xuming Tang · Yuling Han · Peter Orchard · Arushi Varshney · Tuo Zhang · Nandini Manickam · Michael R Erdos · Narisu Narisu · Leland Taylor · Xiaxia Saavedra · Xinyi Liu · Aaron Zhong · Bo Li · Ting Zhou · Ali Naji · Chengyang Liu · Francis S Collins · Stephen C J Parker · Shuibing Chen

30 July 2025 at 18:00

Cell Rep. 2025 Jul 29;44(8):116065. doi: 10.1016/j.celrep.2025.116065. Online ahead of print.

ABSTRACT

Genome-wide association studies (GWASs) have identified over 100 signals associated with type 1 diabetes (T1D). However, it has been challenging to translate any given T1D GWAS signal into mechanistic insights, such as causal variants, their target genes, and the specific cell types involved. Here, we present a comprehensive multi-omic integrative analysis of single-cell/nucleus resolution profiles of gene expression and chromatin accessibility in human pancreatic islets under baseline and T1D-stimulating conditions. We nominate effector cell types for all T1D GWAS signals and the regulatory elements and genes for three independent T1D signals acting through β cells at the DLK1/MEG3, RASGRP1, and TOX loci. Subsequently, we validated the functional impact of these genes and regulatory regions using isogenic human embryonic stem cells (hESCs). We found that loss of RASGRP1 or DLK1, as well as disruption of their corresponding regulatory regions, led to increased β cell apoptosis. Furthermore, β cells derived from isogenic hESCs carrying the T1D risk allele of rs3783355 associated with DLK1 showed elevated β cell death. Through additional RNA sequencing (RNA-seq) and assay for transposase-accessible chromatin using sequencing (ATAC-seq) analyses, we identified five genes upregulated in both RASGRP1^-/- and DLK1^-/- β-like cells, four of which are near T1D GWAS signals. This integrative approach combining single-cell multi-omics, GWASs, and isogenic human pluripotent stem cell (hPSC)-derived β-like cells illuminates cell type context, genes, single nucleotide polymorphisms (SNPs), and regulatory elements underlying T1D-associated signals, providing insights into the biological functions and molecular mechanisms involved.

PMID:40737125 | DOI:10.1016/j.celrep.2025.116065

An organoid co-culture model for probing systemic anti-tumor immunity in lung cancer

(Multiomics OR Omics) AND (Lung OR gastric OR Hepatocellular)

By: Kaiyi Li · Chang Liu · Xizhao Sui · Chao Li · Ting Zhang · Tian Zhao · Dong Zhang · Hainan Wu · Yuhan Liu · Shuai Wang · Yingshun Yang · Baobao Lin · Wenyan Wang · Fan Yang · Xiaofang Chen · Peng Liu

13 June 2025 at 18:00

Cell Stem Cell. 2025 Jun 6:S1934-5909(25)00191-2. doi: 10.1016/j.stem.2025.05.011. Online ahead of print.

ABSTRACT

Deciphering interactions between tumor micro- and systemic immune macroenvironments is essential for developing more effective cancer diagnosis and therapeutic strategies. Here, we established a gel-liquid interface (GLI) co-culture model of lung cancer organoids (LCOs) and paired peripheral-blood mononuclear cells (PBMCs), featuring enhanced interactions between immune cells and tumor organoids for optimized simulation of in vivo systemic anti-tumor immunity. By constructing a cohort of lung cancer patients, we demonstrated that the responses of GLI models under αPD1 treatment reflected the immunotherapy outcomes of the corresponding patients precisely. Furthermore, we dissected the various tumor immune processes mediated by PBMC-derived T cells within GLI models through functional multi-omics analyses, along with the characterization of circulating tumor-reactive T cells (GNLY⁺CD44⁺CD9⁺) with effector memory-like phenotypes as a potential indicator of immunotherapy efficacy. Our findings indicate that the GLI co-culture model can be used to develop diagnostic strategies for precision immunotherapies, as well as understanding the underlying mechanisms.

PMID:40513558 | DOI:10.1016/j.stem.2025.05.011

Reading view