Pulmonary mucosa-associated lymphoid tissue (MALT) lymphoma, a distinctive subtype of non-Hodgkin’s lymphoma (1), exemplifies primary extranodal lymphomas originating in the lung (2). Renowned for its indolent nature and infrequent occurrence, the clinical presentation of pulmonary MALT lymphoma is subtle (3), and its radiological manifestations are diverse, posing considerable diagnostic challenges (4). In contrast to more aggressive lymphomas, MALT lymphoma often lacks the hallmark symptoms of high-grade malignancies (5), making early detection elusive and potentially causing delays in therapeutic intervention.

Clinical manifestations of MALT lymphoma may vary but often include non-specific symptoms such as cough, chest pain, or shortness of breath (6,7). Systemic symptoms, such as fever and weight loss, are less common but can occur (8). The overall incidence of pulmonary MALT lymphoma is relatively low compared to other lymphomas (9,10). Risk factors for developing pulmonary MALT lymphoma may include a history of autoimmune diseases, chronic infections, or exposure to environmental factors that trigger chronic inflammation (11).

The treatment landscape for pulmonary MALT lymphoma primarily revolves around surgical resection, radiotherapy, and chemotherapy, with surgery being the preferred modality for localized disease (12,13). The indolent course of MALT lymphoma, coupled with its relative insensitivity to chemotherapy and radiotherapy, underscores the importance of accurate diagnosis and appropriate selection of treatment modality (14,15). Moreover, the prognosis of MALT lymphoma is generally favorable, with surgical interventions yielding better outcomes compared to cases where complete resection is not feasible (16).

The clinical significance of pulmonary MALT lymphoma transcends its rarity, delving into the domain of differential diagnosis (17). This is particularly critical when distinguishing it from prevalent pulmonary pathologies like adenocarcinomas, focal invasive mucinous adenocarcinoma of the lung, focal organizing pneumonia or infectious granulomas (18,19). The management and prognosis of these conditions vary significantly, underscoring the importance of accurate differentiation. The complexity of pulmonary MALT lymphoma is further complicated by its etiology, commonly associated with chronic inflammatory stimuli (20). This association is notable, especially in patients with autoimmune diseases or a history of chronic infections, adding layers of intricacy to the understanding of the disease (21,22).

Radiologically, pulmonary MALT lymphoma displays a spectrum of patterns on high-resolution computed tomography (HRCT), ranging from solitary or multiple nodules, areas of consolidation, to ground-glass opacities (23,24). These imaging features, although valuable, overlap significantly with those of other pulmonary conditions, thereby necessitating a more nuanced approach to interpretation (25,26). The role of imaging in MALT lymphoma extends to not only diagnosis but also to treatment planning and monitoring response to therapy. Pathologically, MALT lymphoma is characterized by the proliferation of marginal zone B-cells, which may manifest in a variety of cytological appearances (27). Immunohistochemistry plays a pivotal role in diagnosis, with markers such as CD20 and CD79a often showing positivity (28,29). The Ki67 proliferation index is another valuable tool, providing insights into the tumor’s growth dynamics (30). Ki67 indicates the level of cellular proliferation activity, representing the proliferation rate of MALT tumor cells. It reflects the degree of malignancy of the cells and is related to the prognosis of the patients. However, the lack of a histological grading system in MALT lymphoma contrasts with other lymphomas, where such grading significantly influences treatment decisions (31).

This study aimed to elucidate the imaging and pathological characteristics of pulmonary MALT lymphoma based on a comprehensive analysis of 20 cases from a thoracic specialty hospital. Our focus is to assist radiologists in understanding the disease’s unique imaging features from a pathological perspective, thereby improving differential diagnosis during initial chest imaging assessments. This understanding is critical in guiding further biopsy for definitive diagnosis and timely surgical intervention when feasible, or alternatively, opting for radiotherapy or chemotherapy.

Researchers have explored the evolution of the genetic change that causes chronic myeloid leukaemia and show its ability to drive the disease.

A research team from Helmholtz Munich and the Technical University of Munich has developed an advanced delivery system that transports gene-editing tools based on the CRISPR/Cas9 gene-editing system into living cells with significantly greater efficiency than before. Their technology, ENVLPE, uses engineered non-infectious virus-like particles to precisely correct defective genes – demonstrated successfully in living mouse models that are blind due to a mutation. This system also holds promise for advancing cancer therapy by enabling precise genetic manipulation of engineered immune cells making them more universally compatible and thus more accessible for a larger group of cancer patients.

Researchers at ETH Zurich have unravelled the complex network that cells use to repair their genetic material. By examining thousands upon thousands of genetic interactions, the team has discovered new vulnerabilities in cancer cells that could be exploited therapeutically in the future.

On January 27, 2025, the release of the new open-source large language model (LLM), DeepSeek, caused a global sensation. Humans have been working on developing artificial intelligence (AI) capable of natural language processing (NLP) and LLM is the biggest breakthrough to date. Even before the emergence of DeepSeek, LLMs had already demonstrated their vast potential in the medical field. The advantage of DeepSeek lies in its ability to achieve performance comparable to (or perhaps even superior to) top-tier closed-source LLMs like OpenAI at an extremely low cost—a level of performance once considered exclusive to proprietary LLMs. Given the long-standing advantages of open-source LLMs in terms of flexibility, cost-effectiveness, and transparency, the success of DeepSeek seems to signal that the medical community is one step closer to the “AI era”. Thoracic surgery is a discipline that has long been intertwined with AI. Twenty years ago, computer-aided diagnosis (CAD) was already being used in the diagnosis and treatment of pulmonary nodules (1). In this article, we will discuss the opportunities and challenges that thoracic surgery will face in the “DeepSeek era”.

In the “pre-DeepSeek era”, AI had already permeated the entire process of diagnosis and treatment in thoracic surgery, spanning preoperative, intraoperative, and postoperative stages. With the advancement of machine learning (ML), deep learning (DL), and convolutional neural networks (CNNs), AI has become deeply involved in the interpretation of imaging results in thoracic surgery (2). In cases where historical imaging data is insufficient, AI has even demonstrated greater accuracy than human doctors (3). AI has also gradually been applied to the interpretation of pathological results. While insufficient tissue samples often limit the practical application of immunohistochemistry and genetic testing, AI can make precise judgments even with limited samples (4). Furthermore, AI appears to outperform intraoperative frozen sections in identifying spread through air spaces, which directly impacts the surgical approach for early-stage non-small cell lung cancer (NSCLC) (5).

Meanwhile, AI has gradually become involved in the surgical procedures of thoracic surgery. By preoperatively delineating tumor boundaries, AI has reduced the time required to locate tumors during surgery while ensuring complete resection margins (R0 resection). Recently, the results of JCOG0802 and CALGB140503 have significantly elevated the status of segmentectomy as a curative surgery for early-stage NSCLC. Accurately identifying intersegmental planes during segmentectomy is a challenging task, but augmented reality (AR) and virtual reality (VR) technologies can clearly expose anatomical structures, greatly simplifying this process (6,7).

These achievements, however, do not mean that thoracic surgery has entered the “AI era”. First, the high economic burden has long been a concern for the adoption of AI. Second, many AI models are trained on small-scale, highly specialized datasets, which can lead to “overfitting” and diminish their performance in real-world applications. Most importantly, AI still cannot directly participate in medical decision-making. Medical decision-making is a complex reasoning task, where the reasoning process is as important as the outcome. For doctors, relying on an AI that only provides answers without transparency is unimaginable. Some closed-source LLMs (e.g., OpenAI) can provide reasoning processes, but the “black box” nature of these models still lacks transparency and persuasiveness at a technical level.

DeepSeek is set to change this. DeepSeek can construct a complete chain of thought for any response, demonstrating powerful reasoning capabilities. As an open-source LLM, DeepSeek’s reasoning abilities can be widely validated at a technical level. Therefore, doctors can confidently refer to DeepSeek’s recommendations during decision-making without worrying about the transparency or safety of the advice’s origin. Additionally, DeepSeek can extensively access large medical public databases and stay updated with the latest medical advancements, significantly enhancing the reliability of its recommendations. While DeepSeek cannot replace human doctors in decision-making, it can make decisions more accurate and faster. This is particularly valuable in critical situations, such as clinical decision-making for patients with severe conditions like heart valve rupture or aortic dissection, where time is of the essence.

DeepSeek can also assist in thoracic surgical procedures. By accurately interpreting imaging results and performing preoperative 3D reconstruction of the surgical field’s anatomical structures, AI can help surgeons plan surgeries more precisely before the operation (8). Beyond preoperative planning, DeepSeek can effectively assist in managing postoperative complications. Thoracic surgeries, especially complex cardiac surgeries, still have a high incidence of postoperative complications. DeepSeek can comprehensively assess a patient’s overall condition and various test indicators, providing early warnings of complication risks even before surgery (9). Moreover, DeepSeek’s NLP capabilities enable it to answer frequently asked questions in real time, greatly aiding patient education before and after surgery (10).

Using AI to process experimental data in medical research is no longer news (11). However, DeepSeek can do much more. DeepSeek can efficiently read literature, helping researchers overcome language barriers when reading non-native language publications. Novelty is a critical metric in evaluating medical research. By extensively reviewing literature, DeepSeek can quickly identify potential research hotspots and present them to researchers. Similarly, DeepSeek can shorten the lengthy preliminary processes required for writing meta-analyses and systematic reviews, such as literature collection and screening, generating forest plots, and conducting heterogeneity analyses. In summary, DeepSeek allows researchers to focus on the “research” itself rather than being overwhelmed by massive amounts of data and text. Multidisciplinary collaboration is the future trend in medical research, and achieving this requires a robust public platform. The success of the BioChatter platform (12) demonstrates that the open, diverse, and inclusive community environment of open-source LLMs has significantly contributed to the advancement of medical research. We believe DeepSeek will perform even better in the future.

Despite significant advances in Parkinson’s disease (PD) treatment, it remains incurable, with limited therapeutic options. Currently, repurposing already tested, safe drugs has emerged as an effective therapeutic strategy against various neurodegenerative diseases, including PD. Using a drug-repurposing approach, the current study investigated the neuroregenerative potential of polysialic acid mimicking compounds, 5-nonyloxytryptamine oxalate (5-NOT) and Epirubicin (Epi), an anti-cancer drug, in 1-methyl-4-phenylpyridinium (MPP⁺)-treated human neuroblastoma SH-SY5Y cells as a PD model.