Efficient Large-Area (81 cm2) Ternary Copper Halides Light-Emitting Diodes with External Quantum Efficiency Exceeding 13% via Host-Guest Strategy.

Ternary copper (Cu) halides are promising candidates for replacing toxic lead halides in the field of perovskite light-emitting diodes (LEDs) toward practical applications. However, the electroluminescent performance of Cu halide-based LEDs remains a great challenge due to the presence of serious nonradiative recombination and inefficient charge transport in Cu halide emitters. Here, the rational design of host-guest [dppb]2Cu2I2 (dppb denotes 1,2-bis[diphenylphosphino]benzene) emitters and its utility in fabricating efficient Cu halide-based green LEDs that show a high external quantum efficiency (EQE) of 13.39% are reported. The host-guest [dppb]2Cu2I2 emitters with mCP (1,3-bis(N-carbazolyl)benzene) host demonstrate a significant improvement of carrier radiative recombination efficiency, with the photoluminescence quantum yield increased by nearly ten times, which is rooted in the efficient energy transfer and type-I energy level alignment between [dppb]2Cu2I2 and mCP. Moreover, the charge-transporting mCP host can raise the carrier mobility of [dppb]2Cu2I2 films, thereby enhancing the charge transport and recombination. More importantly, this strategy enables a large-area prototype LED with a record-breaking area up to 81 cm2, along with a decent EQE of 10.02% and uniform luminance. It is believed these results represent an encouraging stepping stone to bring Cu halide-based LEDs from the laboratory toward commercial lighting and display panels.

Correlating Aggregation Ability of Polymer Donors with Film Formation Kinetics for Organic Solar Cells with Improved Efficiency and Processability.

Film formation kinetics significantly impact molecular processability and power conversion efficiency (PCE) of organic solar cells. Here, two ternary random copolymerization polymers are reported, D18─N-p and D18─N-m, to modulate the aggregation ability of D18 by introducing trifluoromethyl-substituted pyridine unit at para- and meta-positions, respectively. The introduction of pyridine unit significantly reduces material aggregation ability and adjusts the interactions with acceptor L8-BO, thereby leading to largely changed film formation kinetics with earlier phase separation and longer film formation times, which enlarge fiber sizes in blend films and improve carrier generation and transport. As a result, D18─N-p with moderate aggregation ability delivers a high PCE of 18.82% with L8-BO, which is further improved to 19.45% via interface engineering. Despite the slightly inferior small area device performances, D18─N-m shows improved solubility, which inspires to adjust the ratio of meta-trifluoromethyl pyridine carefully and obtain a polymer donor D18─N-m-10 with good solubility in nonhalogenated solvent o-xylene. High PCEs of 13.07% and 12.43% in 1 cm2 device and 43 cm2 module fabricated with slot-die coating method are achieved based on D18─N-m-10:L8-BO blends. This work emphasizes film formation kinetics optimization in device fabrication via aggregation ability modulation of polymer donors for efficient devices.

A novel anoikis-related signature predicts prognosis risk and treatment responsiveness in diffuse large B-cell lymphoma.

BACKGROUND: Although anoikis plays a role in cancer metastasis and aggressiveness, it has rarely been reported in diffuse large B cell lymphoma (DLBCL). METHODS: We obtained RNA sequencing data and matched clinical data from the GEO database. An anoikis-related genes (ARGs)-based risk signature was developed in GSE10846 training cohort and validated in three other cohorts. Additionally, we predicted half-maximal inhibitory concentration (IC50) of drugs based on bioinformatics method and obtained the actual IC50 to some chemotherapy drugs via cytotoxicity assay. RESULTS: The high-risk group, as determined by our signature, was associated with worse prognosis and an immunosuppressive environment in DLBCL. Meanwhile, the nomogram based on eight variables had more accurate ability in forecasting the prognosis than the international prognostic index in DLBCL. The prediction of IC50 indicated that DLBCL patients in the high-risk group were more sensitive to doxorubicin, IPA-3, lenalidomide, gemcitabine, and CEP.701, while patients in the low-risk group were sensitive to cisplatin and dasatinib. Consistent with the prediction, cytotoxicity assay suggested the higher sensitivity to doxorubicin and gemcitabine and the lower sensitivity to dasatinib in the high-risk group in DLBCL. CONCLUSION: The ARG-based signature may provide a promising direction for prognosis prediction and treatment optimization for DLBCL patients.

Roles of RNA m5C modification patterns in prognosis and tumor microenvironment infiltration of diffuse large B-cell lymphoma.

Genetic and epigenetic aberrations display an essential role in the initiation and progression of diffuse large B-cell lymphoma (DLBCL). 5-methylcytosine (m5C), a common RNA modification, regulates various cellular processes and contributes to tumorigenesis and cancer progression. However, m5C alterations in DLBCL remain unclear. Our research constructed an m5C prognostic model utilizing GEO data sets, which can efficiently predict the prognosis of patients with DLBCL, and verified the m5C prognostic model genes by immunohistochemistry analysis. This model was constructed using unsupervised consensus clustering analyses, Least Absolute Shrinkage and Selection Operator (LASSO), and multivariate Cox regression analyses. Based on the expression of m5C genes in the model, patients with DLBCL could be effectively divided into groups with significant survival time differences. The m5C risk-score signature demonstrated a highly significant independent prognostic value. Results from tumor microenvironment analyses revealed that m5C genes altered the infiltration of eosinophils, Tregs, and M2 macrophages. Additionally, they regulated T cell activation by modulating the expression of CTLA4, PDL1, B2M, CD8A, ICOS, and other relevant immune checkpoint expressions. In conclusion, our study presents a robust m5C prognostic model that effectively predicts prognosis in DLBCL. This model may offer a new approach for prognostic stratification and potential therapeutic interventions for patients with DLBCL.

Assessing the Responses of Large Language Models (ChatGPT-4, Gemini, and Microsoft Copilot) to Frequently Asked Questions in Breast Imaging: A Study on Readability and Accuracy.

Background Large language models (LLMs), such as ChatGPT-4, Gemini, and Microsoft Copilot, have been instrumental in various domains, including healthcare, where they enhance health literacy and aid in patient decision-making. Given the complexities involved in breast imaging procedures, accurate and comprehensible information is vital for patient engagement and compliance. This study aims to evaluate the readability and accuracy of the information provided by three prominent LLMs, ChatGPT-4, Gemini, and Microsoft Copilot, in response to frequently asked questions in breast imaging, assessing their potential to improve patient understanding and facilitate healthcare communication. Methodology We collected the most common questions on breast imaging from clinical practice and posed them to LLMs. We then evaluated the responses in terms of readability and accuracy. Responses from LLMs were analyzed for readability using the Flesch Reading Ease and Flesch-Kincaid Grade Level tests and for accuracy through a radiologist-developed Likert-type scale. Results The study found significant variations among LLMs. Gemini and Microsoft Copilot scored higher on readability scales (p < 0.001), indicating their responses were easier to understand. In contrast, ChatGPT-4 demonstrated greater accuracy in its responses (p < 0.001). Conclusions While LLMs such as ChatGPT-4 show promise in providing accurate responses, readability issues may limit their utility in patient education. Conversely, Gemini and Microsoft Copilot, despite being less accurate, are more accessible to a broader patient audience. Ongoing adjustments and evaluations of these models are essential to ensure they meet the diverse needs of patients, emphasizing the need for continuous improvement and oversight in the deployment of artificial intelligence technologies in healthcare.

Text summarization with ChatGPT for drug labeling documents.

Text summarization is crucial in scientific research, drug discovery and development, regulatory review, and more. This task demands domain expertise, language proficiency, semantic prowess, and conceptual skill. The recent advent of large language models (LLMs), such as ChatGPT, offers unprecedented opportunities to automate this process. We compared ChatGPT-generated summaries with those produced by human experts using FDA drug labeling documents. The labeling contains summaries of key labeling sections, making them an ideal human benchmark to evaluate ChatGPT's summarization capabilities. Analyzing >14000 summaries, we observed that ChatGPT-generated summaries closely resembled those generated by human experts. Importantly, ChatGPT exhibited even greater similarity when summarizing drug safety information. These findings highlight ChatGPT's potential to accelerate work in critical areas, including drug safety.

Adjoint Algorithm Design of Selective Mode Reflecting Metastructure for BAL Applications.

Broad-area lasers (BALs) have found applications in a variety of crucial fields on account of their high output power and high energy transfer efficiency. However, they suffer from poor spatial beam quality due to multi-mode behavior along the waveguide transverse direction. In this paper, we propose a novel metasurface waveguide structure acting as a transverse mode selective back-reflector for BALs. In order to effectively inverse design such a structure, a digital adjoint algorithm is introduced to adapt the considerably large design area and the high degree of freedom. As a proof of the concept, a device structure with a design area of 40 × 20 µm2 is investigated. The simulation results exhibit high fundamental mode reflection (above 90%), while higher-order transverse mode reflections are suppressed below 0.2%. This is, to our knowledge, the largest device structure designed based on the inverse method. We exploited such a device and the method and further investigated the device's robustness and feasibility of the inverse method. The results are elaborately discussed.

Advances in Two-Dimensional Ion-Selective Membranes: Bridging Nanoscale Insights to Industrial-Scale Salinity Gradient Energy Harvesting.

Salinity gradient energy, often referred to as the Gibbs free energy difference between saltwater and freshwater, is recognized as "blue energy" due to its inherent cleanliness, renewability, and continuous availability. Reverse electrodialysis (RED), relying on ion-selective membranes, stands as one of the most prevalent and promising methods for harnessing salinity gradient energy to generate electricity. Nevertheless, conventional RED membranes face challenges such as insufficient ion selectivity and transport rates and the difficulty of achieving the minimum commercial energy density threshold of 5 W/m2. In contrast, two-dimensional nanostructured materials, featuring nanoscale channels and abundant functional groups, offer a breakthrough by facilitating rapid ion transport and heightened selectivity. This comprehensive review delves into the mechanisms of osmotic power generation within a single nanopore and nanochannel, exploring optimal nanopore dimensions and nanochannel lengths. We subsequently examine the current landscape of power generation using two-dimensional nanostructured materials in laboratory-scale settings across various test areas. Furthermore, we address the notable decline in power density observed as test areas expand and propose essential criteria for the industrialization of two-dimensional ion-selective membranes. The review concludes with a forward-looking perspective, outlining future research directions, including scalable membrane fabrication, enhanced environmental adaptability, and integration into multiple industries. This review aims to bridge the gap between previous laboratory-scale investigations of two-dimensional ion-selective membranes in salinity gradient energy conversion and their potential large-scale industrial applications.

Identification of CD5/SOX11 double-negative pleomorphic mantle cell lymphoma.

Cyclin D1 protein-positive diffuse large B cell lymphoma (DLBCL) has an immunophenotype of CD5(-) cyclin D1(+) SOX11(-), and most cases lack a CCND1 rearrangement and have a gene expression profile of DLBCL. Rarely, cyclin D1 protein-positive DLBCL harbors a CCND1 rearrangement, and some genetic copy number features typical of mantle cell lymphoma (MCL) have been detected. Since gene expression studies have not been performed, whether such CCND1-rearranged cases represent cyclin D1 protein-positive DLBCL or CD5/SOX11 double-negative pleomorphic MCL remains unclear. To date, no cases of CD5/SOX11 double-negative MCL have been reported. In this study, we collected eight cases initially diagnosed as cyclin D1 protein-positive DLBCL, including four with a CCND1 rearrangement and four without. Immunohistochemically, all four CCND1-rearranged cases had >50% of tumor cells positive for cyclin D1 protein, whereas only one (25%) non-rearranged case had >50% positive tumor cells. Analysis of genome-wide copy number, mutational, and gene expression profiles revealed that CCND1-rearranged cases were similar to MCL, whereas CCND1-non-rearranged cases resembled DLBCL. Despite the SOX11 negativity by immunohistochemistry, CCND1-rearranged cases had a notable trend (P = 0.064) of higher SOX11 mRNA levels compared to non-rearranged cases. Here, we show for the first time that CCND1 rearrangement could be useful for identifying CD5/SOX11 double-negative pleomorphic MCL in cases diagnosed as cyclin D1 protein-positive DLBCL. Cases with >50% cyclin D1 protein-positive tumor cells immunohistochemically and higher SOX11 mRNA levels are more likely to have a CCND1 rearrangement, and fluorescence in situ hybridization can be used to detect the rearrangement.

GPT-4 Turbo with Vision fails to outperform text-only GPT-4 Turbo in the Japan Diagnostic Radiology Board Examination.

PURPOSE: To assess the performance of GPT-4 Turbo with Vision (GPT-4TV), OpenAI's latest multimodal large language model, by comparing its ability to process both text and image inputs with that of the text-only GPT-4 Turbo (GPT-4 T) in the context of the Japan Diagnostic Radiology Board Examination (JDRBE). MATERIALS AND METHODS: The dataset comprised questions from JDRBE 2021 and 2023. A total of six board-certified diagnostic radiologists discussed the questions and provided ground-truth answers by consulting relevant literature as necessary. The following questions were excluded: those lacking associated images, those with no unanimous agreement on answers, and those including images rejected by the OpenAI application programming interface. The inputs for GPT-4TV included both text and images, whereas those for GPT-4 T were entirely text. Both models were deployed on the dataset, and their performance was compared using McNemar's exact test. The radiological credibility of the responses was assessed by two diagnostic radiologists through the assignment of legitimacy scores on a five-point Likert scale. These scores were subsequently used to compare model performance using Wilcoxon's signed-rank test. RESULTS: The dataset comprised 139 questions. GPT-4TV correctly answered 62 questions (45%), whereas GPT-4 T correctly answered 57 questions (41%). A statistical analysis found no significant performance difference between the two models (P = 0.44). The GPT-4TV responses received significantly lower legitimacy scores from both radiologists than the GPT-4 T responses. CONCLUSION: No significant enhancement in accuracy was observed when using GPT-4TV with image input compared with that of using text-only GPT-4 T for JDRBE questions.

GestaltMML: Enhancing Rare Genetic Disease Diagnosis through Multimodal Machine Learning Combining Facial Images and Clinical Texts.

Individuals with suspected rare genetic disorders often undergo multiple clinical evaluations, imaging studies, laboratory tests and genetic tests, to find a possible answer over a prolonged period of time. Addressing this "diagnostic odyssey" thus has substantial clinical, psychosocial, and economic benefits. Many rare genetic diseases have distinctive facial features, which can be used by artificial intelligence algorithms to facilitate clinical diagnosis, in prioritizing candidate diseases to be further examined by lab tests or genetic assays, or in helping the phenotype-driven reinterpretation of genome/exome sequencing data. Existing methods using frontal facial photos were built on conventional Convolutional Neural Networks (CNNs), rely exclusively on facial images, and cannot capture non-facial phenotypic traits and demographic information essential for guiding accurate diagnoses. Here we introduce GestaltMML, a multimodal machine learning (MML) approach solely based on the Transformer architecture. It integrates facial images, demographic information (age, sex, ethnicity), and clinical notes (optionally, a list of Human Phenotype Ontology terms) to improve prediction accuracy. Furthermore, we also evaluated GestaltMML on a diverse range of datasets, including 528 diseases from the GestaltMatcher Database, several in-house datasets of Beckwith-Wiedemann syndrome (BWS, over-growth syndrome with distinct facial features), Sotos syndrome (overgrowth syndrome with overlapping features with BWS), NAA10-related neurodevelopmental syndrome, Cornelia de Lange syndrome (multiple malformation syndrome), and KBG syndrome (multiple malformation syndrome). Our results suggest that GestaltMML effectively incorporates multiple modalities of data, greatly narrowing candidate genetic diagnoses of rare diseases and may facilitate the reinterpretation of genome/exome sequencing data.

Emerging opportunities of using large language models for translation between drug molecules and indications.

A drug molecule is a substance that changes an organism's mental or physical state. Every approved drug has an indication, which refers to the therapeutic use of that drug for treating a particular medical condition. While the Large Language Model (LLM), a generative Artificial Intelligence (AI) technique, has recently demonstrated effectiveness in translating between molecules and their textual descriptions, there remains a gap in research regarding their application in facilitating the translation between drug molecules and indications (which describes the disease, condition or symptoms for which the drug is used), or vice versa. Addressing this challenge could greatly benefit the drug discovery process. The capability of generating a drug from a given indication would allow for the discovery of drugs targeting specific diseases or targets and ultimately provide patients with better treatments. In this paper, we first propose a new task, the translation between drug molecules and corresponding indications, and then test existing LLMs on this new task. Specifically, we consider nine variations of the T5 LLM and evaluate them on two public datasets obtained from ChEMBL and DrugBank. Our experiments show the early results of using LLMs for this task and provide a perspective on the state-of-the-art. We also emphasize the current limitations and discuss future work that has the potential to improve the performance on this task. The creation of molecules from indications, or vice versa, will allow for more efficient targeting of diseases and significantly reduce the cost of drug discovery, with the potential to revolutionize the field of drug discovery in the era of generative AI.

Impact of pandemic mobility restrictions on kidney transplant patients' experience: lessons on quality of care.

BACKGROUND: Patient perspectives on their post-operative health are acknowledged as valuable healthcare outcomes and should be scrupulously considered when designing interventions for patient-centered healthcare. Yet, following the COVID-19 lockdown and in the absence of standardized guidelines on how to best provide virtual chronic care to kidney transplant recipients, little is known about how this unique population coped and managed to comply with public health indications during confinement. METHODS: This study addresses this shortcoming by examining the experiences of patients from a tertiary hospital in Spain during the initial weeks of the lockdown decreed by the national government. Specifically, we focus our attention on the perceptions and experiences of these patients by retrieving robust qualitative and quantitative data: the former based on a thematic analysis of focus group transcripts, the latter obtained from a large-scale survey. RESULTS: Our findings identify opportunities for improvement in the quality of care and point to the provisions that might be made when facing future pandemics or lockdown-requiring situations. CONCLUSIONS: As healthcare services navigate evolving landscapes, our findings on the experience of kidney transplant recipients should enable hospital services to improve the quality of care they are able to provide to such patients during periods of restricted mobility, especially those associated with future disease emergencies, and considering that home confinement is often part of the natural course of post-operative care of these patients.

Correlations of dynamic changes in lipid and protein of salted large yellow croaker during storage.

Protein and lipid are two major components that undergo significant changes during processing of aquatic products. This study focused on the protein oxidation, protein conformational states, lipid oxidation and lipid molecule profiling of salted large yellow croaker during storage, and their correlations were investigated. The degree of oxidation of protein and lipid was time-dependent, leading to an increase in carbonyl content and surface hydrophobicity, a decrease in sulfhydryl groups, and an increase in conjugated diene, peroxide value and thiobarbituric acid reactive substances value. Oxidation caused protein structure denaturation and aggregation during storage. Lipid composition and content changed dynamically, with polyunsaturated phosphatidylcholine (PC) was preferentially oxidized compared to polyunsaturated triacylglycerol. Correlation analysis showed that the degradation of polyunsaturated key differential lipids (PC 18:2_20:5, PC 16:0_22:6, PC 16:0_20:5, etc.) was closely related to the oxidation of protein and lipid. The changes in protein conformation and the peroxidation of polyunsaturated lipids mutually promote each other's oxidation process.

Hydrogel/microcarrier cell scaffolds for rapid expansion of satellite cells from large yellow croakers: Differential analysis between 2D and 3D cell culture.

Cell culture meat is based on the scaled-up expansion of seed cells. The biological differences between seed cells from large yellow croakers in the two-dimensional (2D) and three-dimensional (3D) culture systems have not been explored. Here, satellite cells (SCs) from large yellow croakers (Larimichthys crocea) were grown on cell climbing slices, hydrogels, and microcarriers for five days to analyze the biological differences of SCs on different cell scaffolds. The results exhibited that SCs had different cell morphologies in 2D and 3D cultures. Cell adhesion receptors (Itgb1andsdc4) and adhesion spot markervclof the 3D cultures were markedly expressed. Furthermore, myogenic decision markers (Pax7andmyod) were significantly enhanced. However, the expression of myogenic differentiation marker (desmin) was significantly increased in the microcarrier group. Combined with the transcriptome data, this suggests that cell adhesion of SCs in 3D culture was related to the integrin signaling pathway. In contrast, the slight spontaneous differentiation of SCs on microcarriers was associated with rapid cell proliferation. This study is the first to report the biological differences between SCs in 2D and 3D cultures, providing new perspectives for the rapid expansion of cell culture meat-seeded cells and the development of customized scaffolds.

Primary central nervous system lymphoma: A mirror type presentation in an immunocompetent patient.

Background: Primary central nervous system (CNS) lymphoma is a very rare extranodal non-Hodgkin lymphoma. The bilateral pattern, as we call it "mirror type", has been identified in other CNS lesions such as gliomas, metastases, and demyelinating lesions, so the differential diagnosis includes imaging studies such as magnetic resonance imaging contrasted with spectroscopy, ruling out immunodeficiency or metastatic disease. Case Description: A 65-year-old female presented progressing headache, loss of memory and language alterations, as well as sensory alterations. Neuroimaging showed the presence of two equidistant periventricular lesions at the level of both ventricular atria, a spectroscopy study suggestive of malignancy. Serological studies showed no evidence of immunodeficiency or the presence of positive tumor markers; however, a biopsy was performed, which revealed a histopathological result of primary lymphoma of the CNS. Conclusion: In neuro-oncology, primary CNS tumors with multiple lesions are rare, even more, the "mirror type" lesions. Lymphomas are lesions that can present in different ways on imaging and clinical presentation. These tumors that present a vector effect due to their size, perilesional edema, or that lead to loss of neurological function are highly discussed in diagnostic and surgical treatment. Due to their prognosis, action on diagnosis and treatment must be taken as quickly as hospital resources allow.

Thrombectomy and stenting of pseudoaneurysm from transcarotid artery revascularization.

Background: Transcarotid artery revascularization (TCAR) is becoming an increasingly popular treatment of carotid stenosis. Despite this rapid adoption, little in the literature describes the associated complications of this procedure. Case Description: We report a case of a left M1 large-vessel occlusion following treatment of symptomatic, high-grade carotid stenosis with a TCAR procedure approximately three weeks earlier. The initial angiography demonstrated a pseudoaneurysm in the left common carotid artery at the site of TCAR access with a distal clot in the carotid stent. The clot within the stent was aspirated, and a mechanical thrombectomy was performed with a combination of a stent-retriever and aspiration catheter for thrombolysis in cerebral infarction 2B revascularization. Conclusion: The TCAR procedure offers a novel method for revascularization of carotid lesions; it does include its complications. While generally safe, access site complications such as pseudoaneurysms can always occur. Knowledge of this risk allows for appropriate surveillance and management should it occur.

[Optimization of phacoemulsification technique for Morgagnian cataract]. / Optimizatsiya tekhnologii fakoemul'sifikatsii morganievoi katarakty.

PURPOSE: This study was conducted to develop a new optimized phacoemulsification technique for Morgagnian cataract taking into account the anatomical and topographic parameters of the lens nucleus. MATERIAL AND METHODS: A working classification of Morgagnian cataract was developed based on the size of the nucleus: if the edge of the nucleus is visualized at the upper edge of the pupil or between the upper edge and the middle of the pupil, it was classified as an initial stage of Morgagnian cataract with a large nucleus; if the upper edge of the nucleus is visualized in the middle of the pupil and below, it was classified as an advanced stage of Morgagnian cataract with a small nucleus. The first group included six patients who underwent surgery using the scaffold technique with removal of the whole small nucleus into the anterior chamber. The second group included 11 patients who underwent surgery using the scaffold technique with removal of the last fragment of the nucleus into the anterior chamber. RESULTS: The use of the scaffold technique with removal of the nucleus into the anterior chamber helped reduce the number of intraoperative complications to 16.7% in the first group, compared to 27.3% in the second group, and the percentage of endothelial cell loss to 10.1% in the first group, compared to 10.7% in the second group. CONCLUSIONS: The anatomical and topographic features of the lens and the anterior segment of the eye in Morgagnian cataract with a small nucleus allow for preliminary implantation of an intraocular lens into the capsular bag to protect the posterior capsule during phacoemulsification of the nucleus with minimal mechanical, hydrodynamic and acoustic damage to the surrounding structures of the eye.

The association of the hepatitis B virus infection and diffuse large B-cell lymphoma.

OBJECTIVES: To investigate the basic characteristics of patients with diffuse large B-cell lymphoma (DLBCL) and whether hepatitis B surface antigen positive (HBsAg [+]) affects the survival of patients with DLBCL. METHODS: The study was carried out at Affiliated Hospital of Hebei University, Baoding, China, including 602 DLBCL cases from January 2011 to December 2021. We analyzed patients' general clinical data and applied multivariate and univariate Cox analyses to assess the factors influencing their survival times. RESULTS: The HBsAg(+) and HBsAg(-) groups comprised 154 (25.6%) and 448 (74.4%) of the 602 cases, respectively. HBsAg(+) cases tended to be later-stage (III-IV) with higher international prognostic index (IPI) points (3-5) and a greater tendency toward B symptoms, impaired liver function, and recurrence than HBsAg(-) cases (all p<0.05). After follow-up, 194 (32.2%) patients died. The median overall survival (OS) and 5-year OS rates in the HBsAg(+) and HBsAg(-) groups were 16.5 months (42%) and 35 months (63%), respectively. Cox analyses indicated that HBsAg(+) affected the prognosis of DLBCL cases (HR=1.46, 95%CI=1.07-1.99, p=0.017). CONCLUSION: The HBsAg(+) seems to be an independent hazard factor for the worse prognosis of DLBCL patients; hence, a focus on these patients in clinic is required.