Acute gastric dilation (AGD) is a rare but critical medical condition characterized by a rapid and massive expansion of the stomach. While AGD secondary to binge-eating has been documented in literature, cases involving rapid consumption of carbonated beverages leading to acute gastric distention with subsequent pneumomediastinum are rarely reported. We present a case of a 17-year-old male who developed AGD following competitive ingestion of hamburgers and carbonated beverages, subsequently complicated by subcutaneous and mediastinal emphysema. Despite immediate gastric decompression via a nasogastric tube, the patient developed hemodynamic instability and oliguria. An emergency exploratory laparotomy was performed, during which a total of 3.6 liters of gastric contents were aspirated. The patient recuperated gradually under supportive care. This case highlights the necessity of early gastric decompression in binge-eating patients with significant carbonated beverage consumption who develop abdominal symptoms. The onset of hemodynamic instability or oliguria mandates urgent surgical intervention.

Cholangiocarcinoma is a heterogeneous biliary malignancy characterized by late diagnosis, stromal desmoplasia, immune exclusion, and frequent resistance to chemotherapy, immunotherapy, and molecularly targeted therapy. Although genomic stratification has enabled biomarker-directed treatment for selected patients, cancer cell-intrinsic alterations alone do not fully explain disease progression or therapeutic failure. Recent advances in single-cell RNA sequencing, single-cell multi-omics, spatial transcriptomics, spatial proteomics, multiplex imaging, and pathomics have redefined the CCA tumor microenvironment as an active ecosystem composed of malignant cholangiocytes, cancer-associated fibroblasts, tumor-associated macrophages, regulatory T cells, exhausted CD8 + T cells, natural killer cells, B cells/plasma cells, endothelial cells, extracellular matrix, and tertiary lymphoid structures. These approaches have identified clinically relevant cellular states and spatial niches, including CD146 + vascular cancer-associated fibroblasts, LGALS1 + fibroblasts, SPP1 + macrophages, MEOX1 + regulatory T cells, POSTN+ fibroblast-rich invasive fronts, and CAF-TAM-vascular interaction units. Major communication axes such as IL-6/IL-6R, TGF-β/TGFBR, SPP1/CD44, CXCL12/CXCR4, PD-1/PD-L1, VEGF/VEGFR, and POSTN/integrins connect cellular heterogeneity with immune escape, invasion, angiogenesis, and therapy resistance. This review synthesizes current single-cell and spatial atlases of CCA and proposes a conceptual translational framework in which TME-derived cellular states, spatial biomarkers, and druggable communication axes may inform patient stratification and rational combination therapy. Because many proposed TME phenotypes and composite biomarkers remain atlas-derived, correlative, or preclinical, this framework should be viewed as a roadmap for prospective validation rather than as a clinically established classification.

OBJECTIVES:This is a protocol for a Cochrane Review (intervention). The objectives are as follows: To assess the benefits and harms of clarithromycin for treating sepsis in adults versus standard care with or without placebo or with an alternative active intervention, or clarithromycin in combination with antibiotic therapy versus standard care.

BACKGROUND:Sepsis is a highly heterogeneous organ dysfunction syndrome. There is limited evidence regarding phenotypes and clinical outcomes in sepsis patients with initial normal lactate levels. We sought to identify the lactate-based clinical phenotypes and outcomes of sepsis patients. METHODS:The Medical Information Mart for Intensive Care IV (MIMIC-IV) and eICU databases were used to conduct a retrospective cohort study. Adult sepsis patients were included. Lactate was measured via blood gas, and the same assay type was used across both databases. Serial lactate measurements were analyzed via a two-point classification system based on the highest values recorded during two consecutive 24-hour periods following ICU admission. The first measurement window (T1) comprised the initial 24 h post-admission, whereas the second window (T2) covered 24-48 h post-admission. The lactate difference was defined as the numerical change between the highest lactate level at T2 and the highest level at T1. The time interval between these two measurements was fixed, with T2 commencing immediately after T1, together encompassing the first 48 h post-ICU admission. A normal lactate level was defined as ≤2 mmol/L, and an elevated level was defined as >2 mmol/L. Sepsis patients were stratified into four trajectory phenotypes: (1) normal‒normal (N‒N); (2) normal-elevated (N‒E); (3) elevated-normal (E‒N); and (4) elevated-elevated (E‒E). The primary outcome was in-hospital mortality. RESULTS:This study enrolled 6,926 sepsis patients. The clinical phenotypes of the sepsis patients were as follows: N‒N (24.4%), N‒E (3.8%), E‒N (36.4%), and E‒E (35.3%). The in-hospital mortality rates of sepsis patients with the four phenotypes from the MIMIC-IV and eICU databases were as follows (N‒N: 18.9% vs. 17.6%, P=0.66; N‒E: 35.3% vs. 29.2%, P=0.45; E‒N: 16.6% vs. 14.2%, P=0.14; E‒E: 43.6% vs. 37.8%, P=0.01). After adjusting for age, sex, Sequential Organ Failure Assessment (SOFA) score, vasopressor therapy, and infection sites, the N‒E phenotype was associated with a higher risk of in-hospital mortality (odds ratio [OR] 1.44; 95% confidence intervals [95% CI] 1.11-1.86; P=0.006; adjusted OR 1.61; 95% CI 1.23-2.11; P<0.001). The E‒N phenotype was associated with the most favorable outcomes for in-hospital mortality in the multivariable analysis (adjusted OR 0.41; 95% CI 0.36-0.46; P<0.001). The E‒E phenotype was associated with the highest risk of in-hospital mortality in the overall cohort (adjusted OR 3.00; 95% CI2.67-3.37; P<0.001). CONCLUSION:In sepsis patients with normal initial lactate levels, serial lactate measurements could be valuable for prognostic assessment.

Recent studies highlight the critical role of gut microbiota in sepsis pathogenesis and its potential link to neurological disorders, particularly sepsis-associated encephalopathy (SAE). However, the exact relationship between gut microbiota, their metabolites, and SAE’s etiology and progression remains enigmatic. We aimed to elucidate how gut bacteria, fungi, and their metabolites contribute to the development and progression of SAE. This study was a prospective cohort study. Patients who met the criteria for sepsis 3.0 were included and were divided into SAE and non-SAE groups according to the presence or absence of SAE. Baseline characteristics were collected and mortality was followed up for 28 days. We conducted 16 S and ITS rRNA sequencing of rectal swabs, fecal and plasma metabolomic analysis in septic patients with and without SAE to identify differential bacteria, fungi and microbiota-related metabolites. And we identified potential biomarkers of bacteria and fungi through LEfSe analysis. Differential metabolites were screened and their sources were identified using MetOrigin, followed by identification of KEGG pathways related to gut microbiota, host, and co-metabolism that might play important roles in SAE. Lastly, correlation analysis was performed among differential bacteria, fungi, gut metabolites, plasma metabolites and clinical indicators and we revealed vital flora and metabolites. The study included 42 SAE patients, 129 non-SAE patients, and 35 age-matched healthy volunteers. The 28-day mortality rate of SAE patients was higher than that of non-SAE patients (28.57

BACKGROUND:Osteoarthritis (OA) in working-age individuals (aged 30-64 years) adversely affects health and reduces productivity. In this study, the disease burden and economic impact of OA on this demographic are examined, and the trends from 1990 to 2021 are analyzed. METHODS:Using data from the 2021 Global Burden of Disease Study, incident cases, prevalent cases, years lived with disability (YLDs), and the corresponding age-standardized rates of OA in the working-age population from 1990 to 2021 were examined. To evaluate the changes in trends, the average annual percentage change (AAPC) of these age-standardized rates was calculated. Subgroup analyses based on sex, age, sociodemographic index (SDI) level, and joint site were conducted. The economic burden was assessed by integrating data from the World Health Organization (WHO), the World Bank, and the International Labour Organization (ILO). RESULTS:By 2021, 329 million working-age individuals had OA, a 123% increase over 1990. Age-standardized rates of incidence, prevalence, and YLDs increased globally by 116%, 123%, and 125%, respectively, with the most rapid growth occurring in low-middle-SDI regions. The total economic burden in 2021 was $350 billion, with $165 billion in direct medical costs and $185 billion in productivity losses, representing 0.32% of the global GDP. High-SDI regions bore a greater economic burden, representing approximately 50% of the total. CONCLUSION:The increasing prevalence of OA and its significant economic impact on the working-age population highlight the need for targeted policies and preventive strategies. The growing burden, especially in low-middle-SDI countries, underscores the importance of sustainable health development.

BACKGROUND: Although the Confusion Assessment Methods for the Intensive Care Unit (CAM-ICU) is a recommended tool for diagnosing sepsis-associated encephalopathy (SAE), it has several limitations. Mismatch-negativity (MMN) and P3a are components of event-related potentials (ERPs) used with electroencephalography (EEG) and are associated with cerebral function changes in critically ill patients. This study aimed to provide a quantitative, non-invasive method to guide SAE diagnosis in non-sedated patients. METHODS: From January 2022 to March 2023, sepsis patients without sedation were enrolled and assessed via the CAM-ICU, Glasgow Coma Scale (GCS), and ERP under standard procedures. Both MMN and P3a data were collected. The diagnostic value of MMN and P3a was assessed with processed ERP data. RESULTS: Thirty-six patients were included in this study, comprising 19 patients with SAE and 17 patients without SAE (NSAE). MMN and P3a amplitudes decreased, and only FzMMN amplitude significantly decreased in SAE patients (2.03 [1.08, 2.93] mV vs. 3.21 [1.92, 4.34] mV, P=0.040). After median dichotomization, low F3P3a and FzP3a amplitudes were associated with higher CAM-ICU positivity rates and APACHE II scores. Both amplitude in F3P3a (AUC=0.710, 95%CI: 0.527-0.893, P=0.034) and FzP3a (AUC=0.700, 95%CI: 0.519-0.881, P=0.041) exhibited moderate diagnostic efficacy for SAE, while FzMMN amplitude lacks effective diagnostic value. CONCLUSION: In this pilot study, ERP components F3P3a and FzP3a amplitudes demonstrated moderate diagnostic value for SAE. These exploratory findings require confirmation in larger and powered cohorts.

Objective The integration of Large Language Models (LLMs) into cancer research has progressed rapidly, but a comprehensive understanding of global trends, key contributors, and emerging research areas remains lacking. This gap hinders a comprehensive understanding of the development landscape for LLM applications in clinical oncology. Methods A bibliometric analysis was conducted using publications retrieved from the Web of Science Core Collection on March 15, 2026. Eligible studies were limited to English-language articles and reviews published till 2025. Records unrelated to LLMs or cancer, duplicates, retracted publications, and those missing complete metadata were excluded. A total of 896 publications were analyzed using VOSviewer, CiteSpace, and R. ClinicalTrials.gov was searched with the same term, obtaining 29 eligible trials. Results Publication output increased sharply from 2022 to 2025. The USA and China dominated global output, with Germany demonstrating disproportionate citation efficiency relative to volume, and Heidelberg University and Harvard University leading institutionally. Research hotspots converged on LLM benchmarking, domain-specific fine-tuning, multi-omics integration, and perioperative applications. Among 29 registered trials, application areas spanned patient communication, shared decision-making, and care equity outcomes, reflecting a transition from proof-of-concept toward randomized evaluation. Conclusions LLM-driven oncology research has expanded rapidly but remains geographically and institutionally concentrated, with prospective multicenter validation still scarce. Research is transitioning from foundational benchmarking toward fine-tuning, multimodal integration, and clinical deployment. Strengthening cross-institutional collaboration, diversifying trial populations, and developing standardized safety evaluation frameworks are essential for translating bibliometric growth into meaningful advances in cancer diagnosis, treatment, and patient outcomes.

Research linking circadian dysregulation to cancer development has received increasing attention recently. However, a comprehensive understanding of research hotspots and trends in this area remains limited. International studies on the circadian rhythms in cancer were retrieved and downloaded from the Web of Science database. Bibliometric analysis and visualization were performed using VOSviewer, CiteSpace, and HistCite. Three thousand three hundred and eighteen English articles from 2004 to 2024 were screened and evaluated. The increase in publications and citations reflected the rapid expansion of the field. Scholars and institutions in the United States have relatively high academic productivity and impact. Chronobiology International is the most popular journal. Key clustering analysis identified six themes: biochemistry and molecular biology, physiology and immunomodulation, night shift work and health effects, physiological and mental health, tumor therapy research, and oxidative stress and cancer-related mechanisms. Keyword burst analysis identified the regulation of circadian rhythms on cells and tumor microenvironment as the research frontiers. The role of circadian rhythms in tumor immunotherapy was a current research hotspot identified by reference co-citation clustering analysis. This study reveals the current status of research on the circadian rhythms in cancer and predicts future trends. These findings provide new ideas for developing novel cancer prevention and treatment strategies.

BACKGROUND:Sepsis, a common acute and critical disease, leads to 11 million deaths annually worldwide. Probiotics are living microorganisms that are beneficial to the host and may benefit sepsis outcomes, but their effects are still inconclusive. This study aimed to evaluate the overall effect of probiotics on the prognosis of patients with sepsis. DATA RESOURCES:We searched several sources for published/presented studies, including PubMed, EMBASE, Web of Science, the Cochrane Library and the US National Library of Medicine Clinical Trials Register (www.clinicaltrials.gov) updated through July 30, 2023, to identify all relevant randomized controlled trials (RCTs) or observational studies that assessed the effectiveness of probiotics or synbiotics in patients with sepsis and reported mortality. We focused primarily on mortality during the study period and analyzed secondary outcomes, including 28-day mortality, in-intensive care unit (ICU) mortality and other outcomes. RESULTS:Data from 405 patients in five RCTs and 108 patients in one cohort study were included in the analysis. The overall quality of the studies was satisfactory, but clinical heterogeneity existed. All adult studies reported a tendency for probiotics to reduce the mortality of patients with sepsis, and most studies reported a decreasing trend in the incidence of infectious complications, length of ICU stay and duration of antibiotic use. There was only one RCT involving children. CONCLUSION:Probiotics show promise for improving the prognosis of patients with sepsis, including reducing mortality and the incidence of infectious complications, particularly in adult patients. Despite the limited number of studies, especially in children, these findings will be encouraging for clinical practice in the treatment of sepsis and suggest that gut microbiota-targeted therapy may improve the prognosis of patients with sepsis.

Background:Sepsis is a life-threatening organ dysfunction syndrome, with an overall mortality rate of 32.8%. Platelets have been shown to have a central role in the pathogenesis of a diverse array of immune-mediated and infectious diseases, and both thrombocytopenia and platelet hyperreactivity independently correlate with elevated sepsis-related morbidity and mortality. Methods:From February 2021 to June 2022, patients diagnosed with sepsis (according to the Sepsis 3.0 criteria) from the emergency department were screened and enrolled in a prospective observational cytokine analysis cohort. "Bio-Plex Pro Human Cytokine Grp I Panel 17-plex" was used for cytokines analysis. Patients were stratified into high- and low-platelet groups using a discharge platelet threshold of 150 × 109/L. Data were analyzed with R 4.4.0 and GraphPad Prism 9, calculating medians and frequencies for variables. Results:Fifty-seven patients were enrolled and were classified into high- and low-platelet groups (31 vs 26). IL-6 had a significant difference between the two groups after adjusting for admission platelet level and length of stay in hospital (2072 vs 107.1, q < 0.05). Receiver operating characteristic curve also showed IL-6 had high degrees of sensitivity and specificity for predicting higher platelet levels. Conclusion:Higher IL-6 levels at admission for sepsis were associated with higher platelet levels at discharge.

Background: Septic shock is a life-threatening disease with high mortality rates, and the relevant hub genes and biomarkers are poorly understood. We aimed to identify hub genes and prognostic biomarkers of mRNAs/lncRNAs in septic shock to rapidly and accurately diagnose infection, identify patients at a high risk of developing septic shock, and predict prognosis. Methods: Gene expression profiles of 279 patients with septic shock and 100 healthy controls were analyzed using bioinformatics methods. We screened for differentially expressed genes (DEGs), identified hub genes, and investigated the correlations between mRNA/lncRNA expression and disease severity/prognosis. Protein level validation was performed using blood proteomic data from an independent cohort study. Results: The protein-protein interaction network constructed using upregulated DEGs contained 102 nodes and 222 edges, with LTF, MMP8, MMP9, CEACAM8, CTSG, LCN2, and PRTN3 identified as hub genes. There was a possible association between LCN2 mRNA upregulation and increased severity of septic shock (odds ratio: 1.518; 95% confidence interval: 0.999-2.305; P = 0.050), approaching statistical significance, and BCL2A1 mRNA upregulation correlated with higher mortality risk (odds ratio: 1.178; 95% confidence interval: 1.035-1.341; P = 0.013). No significant prognostic correlation was observed for lncRNAs. The validation cohort confirmed significant upregulation of MMP9, CTSG, LCN2, LTF, and MMP8 proteins in patients with septic shock, with MMP9, LCN2, CTSG, and LTF exhibiting strong diagnostic performance (area under the curve >0.8). Conclusion: Seven hub genes related to septic shock were identified, including MMP9, LCN2, CTSG, and LTF, which could potentially function as candidate biotargets and biomarkers for the diagnosis and prognostic prediction of septic shock, though further validation is needed. Notably, LCN2 showed a trend toward association with disease severity, while BCL2A1 correlated with mortality risk.

Fat embolism syndrome (FES) is a clinical syndrome in which the obstruction of small blood vessels by fat emboli triggers a systemic inflammatory response, leading to organ dysfunction. Due to a lack of specific laboratory tests and physical examination, FES is clinically underdiagnosed. We report a case of a 39-year-old woman who presented with dyspnea that had developed after augmentation mammaplasty and vaginal tightening with autologous fat. Bedside transthoracic echocardiography (TTE) carried out in our emergency department evidently revealed right heart embolic material presumed to be fat. Based on echocardiography findings, combined with medical history and computed tomography pulmonary angiography images, a diagnosis of pulmonary fat embolism was made. This case presents valuable echocardiographic images and emphasizes the availability of bedside TTE in the diagnosis of fat embolism in a patient with dyspnea after plastic surgery, highlighting the value of bedside TTE in rapidly identifying pulmonary fat embolism.

Sepsis-associated encephalopathy (SAE) is a severe complication of sepsis, often leading to poor neurological outcomes. Lipid molecules are increasingly recognized for their potential involvement in both sepsis and cognitive impairment. However, the relationship between lipidomic alterations and SAE remains incompletely understood. This study aims to investigate lipidomic changes in patients with SAE and explore potential associations between lipid metabolism and the development of SAE. Sepsis patients without pre-existing central nervous system disorders were prospectively enrolled. SAE was defined as a positive result on the Confusion Assessment Method for the ICU (CAM-ICU) or a Glasgow Coma Scale (GCS) score < 15. Blood samples were collected at enrollment and upon any change in cognitive status. Cerebrospinal fluid (CSF) samples were collected based on the physician assessment. Lipid metabolites were analyzed using high-performance liquid chromatography coupled with mass spectrometry. A total of 98 sepsis patients were enrolled, with 39 classified into the SAE group and 59 into the non-SAE group. Plasma levels of phosphatidylethanolamines (PE) and phosphatidylcholines (PC) were significantly decreased in SAE patients. Among these, LPC (18:2), LPC (16:0), LPE (22:6), and LPE (20:4) showed the most notable reductions. Additionally, 3-hydroxy-3-methylglutaryl coenzyme A(HMG-CoA) levels were decreased in SAE patients, while proinflammatory cytokines such as IFN-γ, IL-1ra, MCP-1, and IP-10 were elevated. Reduced levels of PC and PE lipids in SAE patients may reflect underlying inflammatory processes. The observed downregulation of HMG-CoA and upregulation of IP-10 and MCP-1 suggest a potential therapeutic role for statins in the management of SAE. Clinical Trial Registry number and website where it was obtained: Clinical Trial NCT04230447 (Registration Date: 01/02/2021; https://www.clinicaltrials.gov/study/NCT04230447?cond=Sepsis ).

Background: This study aims to evaluate the prognostic value of baseline nutritional-inflammatory markers in patients with unresectable hepatocellular carcinoma (HCC) receiving immune checkpoint inhibitor (ICI)-based therapy. Methods: Following the PRISMA statement, a systematic review was conducted. Results: A total of 47 studies were included, involving 8483 patients. Elevated NLR (HR: 2.46, 95% CI: 1.99–3.04), PLR (HR: 2.21, 95% CI: 1.48–3.31), SII (HR: 1.73, 95% CI: 1.09–1.25), mGPS (1 vs 0, HR: 2.18, 95% CI: 1.14–4.18; 2 vs 0, HR: 2.82, 95% CI: 1.33–5.96), and SIRI (HR: 2.69, 95% CI: 1.7–4.25), as well as reduced PNI (HR: 0.55, 95% CI: 0.41–0.73) and GNRI (low vs high, HR: 1.77, 95% CI: 1.31–2.38), were significantly associated with poorer overall survival. Similarly, higher NLR (HR: 1.85, 95% CI: 1.54–2.23), PLR (HR: 1.8, 95% CI: 1.39–2.32), SII (HR: 1.61, 95% CI: 1.23–2.10), SIRI (HR: 2.61, 95% CI: 1.27–5.39), and lower PNI (HR: 0.71, 95% CI: 0.57–0.87) were linked to poorer progression-free survival. Subgroup analysis indicated that lower cutoffs for NLR (2.165–3) may enhance prognostic stratification of overall survival. The certainty of evidence for key outcomes ranged from low to moderate, which is attributed to the presence of bias and heterogeneity. Conclusion: Nutritional-inflammatory biomarkers, especially NLR for OS, show significant prognostic value in unresectable HCC patients receiving ICI therapy and may help guide personalized treatment.

Hepatic ischemia-reperfusion injury (HIRI) is a major complication following liver transplantation. Bioinformatic analysis was performed to elucidate the PANoptosis-related molecular mechanisms underlying HIRI. Comprehensive analysis of bulk and single-cell RNA sequencing data from human liver tissue before and after HIRI was performed. Differential expression analysis, weighted gene coexpression analysis, and protein interaction network analysis were used to identify candidate biomarkers. Multiple machine learning methods were utilized to screen for core biomarkers and construct a diagnostic predictive model. Functional and interaction analyses of the genes were also performed. Cellular clustering and annotation, pseudotemporal trajectory, and intercellular communication analyses of HIRI were conducted. Six PANoptosis-associated genes (CEBPB, HSPA1A, HSPA1B, IRF1, SERPINE1, and TNFAIP3) were identified as HIRI-related biomarkers. These biomarkers are regulated by NF-κB and miRNA-155. A nomogram for HIRI prediction based on these biomarkers was constructed and validated. In addition, the heterogeneity and dynamic changes in macrophage subpopulations during HIRI were revealed, highlighting the roles of Kupffer cells and monocyte-derived macrophages in modulating the hepatic microenvironment. The MIF and VISFATIN signaling pathways play important roles in the interaction between macrophages and other cells. These findings enhance our understanding of the mechanisms of PANoptosis in HIRI and provide a new basis and potential targets for prevention and treatment strategies for HIRI.

Background and Objective: The rapid advancement of artificial intelligence (AI) has ushered in a new era in natural language processing (NLP), with large language models (LLMs) like ChatGPT leading the way. This paper explores the profound impact of AI, particularly LLMs, in the field of medical image processing. The objective is to provide insights into the transformative potential of AI in improving healthcare by addressing historical challenges associated with manual image interpretation. Methods: A comprehensive literature search was conducted on the Web of Science and PubMed databases from 2013 to 2023, focusing on the transformations of LLMs in Medical Imaging Processing. Recent publications on the arXiv database were also reviewed. Our search criteria included all types of articles, including abstracts, review articles, letters, and editorials. The language of publications was restricted to English to facilitate further content analysis. Key Content and Findings: The review reveals that AI, driven by LLMs, has revolutionized medical image processing by streamlining the interpretation process, traditionally characterized by time-intensive manual efforts. AI's impact on medical care quality and patient well-being is substantial. With their robust interactivity and multimodal learning capabilities, LLMs offer immense potential for enhancing various aspects of medical image processing. Additionally, the Transformer architecture, foundational to LLMs, is gaining prominence in this domain. Conclusions: In conclusion, this review underscores the pivotal role of AI, especially LLMs, in advancing medical image processing. These technologies have the capacity to enhance transfer learning efficiency, integrate multimodal data, facilitate clinical interactivity, and optimize cost-efficiency in healthcare. The potential applications of LLMs in clinical settings are promising, with far-reaching implications for future research, clinical practice, and healthcare policy. The transformative impact of AI in medical image processing is undeniable, and its continued development and implementation are poised to reshape the healthcare landscape for the better.

Background:Age-related macular degeneration (AMD) represents a significant clinical concern, particularly in aging populations, and recent advancements in artificial intelligence (AI) have catalyzed substantial research interest in this domain. Despite the growing body of literature, there remains a need for a comprehensive, quantitative analysis to delineate key trends and emerging areas in the field of AI applications in AMD. This bibliometric analysis sought to systematically evaluate the landscape of AI-focused research on AMD to illuminate publication patterns, influential contributors, and focal research trends. Methods:Using the Web of Science Core Collection (WoSCC), a search was conducted to retrieve relevant publications from 1992 to 2023. This analysis involved an array of bibliometric indicators to map the evolution of AI research in AMD, assessing parameters such as publication volume, national/regional and institutional contributions, journal impact, author influence, and emerging research hotspots. Visualization tools, including Bibliometrix, CiteSpace and VOSviewer, were employed to generate comprehensive assessments of the data. Results:A total of 1,721 publications were identified, with the USA leading in publication output and the University of Melbourne as the most prolific institution. The journal Investigative Ophthalmology & Visual Science published the highest number of articles, and Schmidt-Eerfurth emerged as the most active author. Keyword and clustering analyses, along with citation burst detection, revealed three distinct research stages within the field from 1992 to 2023. Presently, research efforts are concentrated on developing deep learning (DL) models for AMD diagnosis and progression prediction. Prominent emerging themes include early detection, risk stratification, and treatment efficacy prediction. The integration of large language models (LLMs) and vision-language models (VLMs) for enhanced image processing also represents a novel research frontier. Conclusions:This bibliometric analysis provides a structured overview of prevailing research trends and emerging directions in AI applications for AMD. These findings furnish valuable insights to guide future research and foster collaborative advancements in this evolving field.

Acoustic holography (AH), a promising approach for cell patterning, emerges as a powerful tool for constructing novel invitro 3D models that mimic organs and cancers features. However, understanding changes in cell function post-AH remains limited. Furthermore, replicating complex physiological and pathological processes solely with cell lines proves challenging. Here, we employed acoustical holographic lattice to assemble primary hepatocytes directly isolated from mice into a cell cluster matrix to construct a liver-shaped tissue sample. For the first time, we evaluated the liver functions of AH-patterned primary hepatocytes. The patterned model exhibited large numbers of self-assembled spheroids and superior multifarious core hepatocyte functions compared to cells in 2D and traditional 3D culture models. AH offers a robust protocol for long-term in vitro culture of primary cells, underscoring its potential for future applications in disease pathogenesis research, drug testing, and organ replacement therapy.