The gut microbiota plays a critical role in maintaining gastrointestinal homeostasis, immune regulation, and metabolic processes. Recent evidence has highlighted its significant influence on gastric carcinogenesis. Helicobacter pylori, a well-established class I carcinogen, remains the most prominent microbial risk factor for gastric cancer. However, emerging studies indicate that alterations in the broader gastric and intestinal microbial communities, referred to as dysbiosis, may also contribute to tumor initiation, progression, and immune evasion. These microbial shifts can lead to chronic inflammation, genotoxic metabolite production, and modulation of signaling pathways such as NF-κB and Wnt/β-catenin. This review explores the current understanding of the gut microbiome’s contribution to gastric cancer pathogenesis, including microbial signatures associated with precancerous lesions and the tumor microenvironment. Furthermore, the potential of microbiota-based biomarkers and therapeutic interventions, including probiotics, prebiotics, and fecal microbiota transplantation, is discussed as part of emerging precision medicine strategies.

Gastrointestinal (GI) lymphomas are a diverse group of extranodal non-Hodgkin lymphomas primarily affecting the stomach, small intestine, and colon. They present with non-specific symptoms such as abdominal pain, weight loss, or GI bleeding, making early diagnosis challenging. Histologic subtypes vary, with mucosa-associated lymphoid tissue (MALT) lymphoma and diffuse large B-cell lymphoma (DLBCL) being the most common. Diagnosis involves endoscopic evaluation with biopsy, cross-sectional imaging, and often PET-CT. Management is subtype-dependent, including antibiotics for H. pylori-associated MALT lymphoma, chemotherapy, immunotherapy, and occasionally surgery. A multidisciplinary approach is essential for optimal outcomes. Core Tip: Gastrointestinal lymphomas are rare but clinically significant malignancies with variable presentations. Accurate diagnosis and tailored treatment based on the histologic subtype and site are critical, requiring close collaboration among gastroenterologists, pathologists, oncologists, and radiologists.

This case report presents a rare occurrence of adrenal insufficiency induced by Zytiga (abiraterone acetate) in a patient with high-risk localized prostatic adenocarcinoma. Abiraterone acetate is a potent, selective and irreversible CYP17A1 inhibitor and is commonly used in the treatment of prostate cancer, but it can cause various endocrine side effects, especially if not used concurrently with the appropriate treatment. The clinical implications of this adverse event and management strategies are discussed here in this case report to raise awareness about this potential risk in patients with prostate cancer undergoing treatment with abiraterone acetate, especially when used in an erroneous manner without monitoring.

Hematologic malignancies, including acute myeloid leukemia, myelodysplastic syndromes, lymphoma, and multiple myeloma, are characterized by profound biological heterogeneity and highly dynamic treatment trajectories that conventional, static prognostic systems incompletely capture. Cellular therapies, such as chimeric antigen receptor T-cell therapy and hematopoietic stem cell transplantation, offer potentially curative options for relapsed or refractory disease, yet outcomes remain highly variable, and management decisions regarding conditioning intensity, lymphodepletion, immunosuppression, and toxicity surveillance continue to be largely protocol-driven rather than individually adapted. Artificial intelligence (AI) and machine learning (ML) have demonstrated substantial promise in diagnostic support, prognostic stratification, and multimodal data integration across hematologic malignancies, but existing models remain predominantly static and related to pre-treatment in orientation, limiting their utility for real-time clinical guidance. This review summarizes current AI applications in hematologic oncology; critically compares the strengths, limitations, and clinical applicability of major AI model classes, including traditional machine learning, deep learning, multimodal integrative frameworks, reinforcement learning, digital twins, and emerging foundation models and large language models; and proposes an adaptive, multimodal paradigm. We examine key enabling technologies and address the clinical, regulatory, ethical, and implementation challenges that must be resolved before these systems can be deployed at the bedside. We argue that the central challenge facing the field is no longer whether AI can predict outcomes, but whether it can actively guide real-time therapeutic decisions, and that achieving this transition will require interdisciplinary collaboration, prospective validation, and governance frameworks capable of ensuring interpretability, equity, and clinical trustworthiness.

Acute myeloid leukemia (AML) is predominantly a disease of older adults, with a median age at diagnosis of 68–72 years. Management in this population is complicated by adverse cytogenetic and molecular features, higher rates of secondary disease, and patient-specific factors such as comorbidity and frailty. This narrative review synthesizes the current evidence on risk stratification and treatment of AML in adults aged 60 years and older, with dedicated attention to patients aged 60–75 (“fit” older adults) and those older than 75, including octogenarians. The VIALE-A trial established venetoclax plus azacitidine as standard of care for patients ineligible for intensive chemotherapy (composite complete remission 66.4%; median overall survival 14.7 vs. 9.6 months with azacitidine alone). For fit adults aged 60–75, the choice between intensive chemotherapy and venetoclax-based regimens remains under active investigation, with emerging randomized data suggesting comparable outcomes and a possible advantage for venetoclax-based approaches in adverse-risk disease. Risk stratification has evolved beyond the 2022 European LeukemiaNet (ELN) classification, which was developed in intensively treated cohorts; the 2024 ELN genetic risk classification for less-intensive therapy now provides a framework specific to this population. TP53-mutated AML, including multi-hit disease, remains the dominant unmet need across all treatment modalities. Advances in reduced-intensity conditioning have expanded transplant eligibility into the seventh and eighth decades, and molecularly targeted approaches, including FLT3, IDH1/2, and menin inhibitors, are reshaping frontline and relapsed/refractory therapy. This review covers epidemiology, risk stratification, treatment stratified by age, fitness, and genetic subgroup, the role of transplantation and maintenance therapy, and the limitations of the current evidence base for this heterogeneous population.

This case report presents a rare occurrence of adrenal insufficiency induced by Zytiga (abiraterone acetate) in a patient with high risk localized prostatic adenocarcinoma. Abiraterone acetate is a potent, selective and irreversible CYP17A1 inhibitor, and is commonly used in the treatment of prostate cancer, but it can cause various endocrine side effects, especially if not used concurrently with the appropriate treatment. The clinical implications of this adverse event and the management strategies are discussed here in this case report to raise awareness about this potential risk in patients with prostate cancer undergoing treatment with abiraterone acetate especially when used in an erroneous manner without monitoring.