Precision medicine represents a paradigm shift in healthcare, emphasizing individualized approaches to disease prevention, diagnosis, and treatment based on a patient’s genetic, proteomic, and immunologic profile. In the field of oncology, this paradigm has gained traction, particularly with the integration of immunotherapeutic modalities. Among the most promising advancements are therapeutic cancer vaccines, which harness the body’s immune system to fight tumors more effectively. This mini-review highlights recent developments in therapeutic vaccine engineering. It also discusses key barriers to clinical translation and summarizes findings from contemporary human clinical trials evaluating personalized cancer vaccines. In addition, it evaluates the growing potential of these therapies to redefine cancer treatment.

Mitochondrial respiratory complexes (Complexes I–V) and their assembly into respiratory supercomplexes (SCs) are fundamental to liver bioenergetics, redox homeostasis, and metabolic adaptability. Disruption of these systems contributes to major liver diseases, including non-alcoholic fatty liver disease, alcoholic liver disease, drug-induced liver injury, viral hepatitis, and hepatocellular carcinoma, by impairing adenosine triphosphate synthesis, increasing oxidative stress, and altering metabolic pathways. Recent advances have clarified the structural-functional interdependence of individual complexes within SCs, revealing their dynamic remodeling in response to physiological stress and pathological injury. These insights open opportunities for clinical translation, such as targeting SC stability with pharmacological agents, nutritional strategies, or gene therapy, and employing mitochondrial transplantation in cases of severe mitochondrial failure. Precision medicine approaches, incorporating multi-omics profiling and patient-derived models, may enable individualized interventions and early detection using SC integrity as a biomarker. By linking molecular mechanisms to therapeutic strategies, this review underscores the potential of mitochondrial-targeted interventions to improve outcomes in patients with liver disease.

Potential celiac disease (PCD) is defined as elevated celiac serology with a preserved small intestinal mucosa. This study aimed to identify baseline characteristics and the outcomes of children with PCD consuming a gluten-containing diet.

This was a retrospective cohort study of pediatric PCD patients diagnosed between 12/2018 and 10/2024. Baseline data included demographics, anthropometrics, clinical symptoms and signs, celiac serology, and biopsy results. Follow-up data included repeat serology and biopsy results when performed.

PCD was diagnosed in 75/517 (14.5%) children undergoing upper endoscopy for suspected celiac disease (CeD). Baseline anti-transglutaminase IgA (TTG) was above 10× the upper limit of normal (ULN) in 18 (24%), between 3–10× ULN in 52 (69.3%), and <3× ULN in five (6.6%). Anti-endomysial antibody was positive in 57 (76%). Among 48 children (64%) with at least one year of follow-up, TTG normalized in 26 (54.1%), decreased to <3× ULN in 13 (27.1%), was between 3–10× ULN in six (12.5%), and was above 10× ULN in three (6.3%). Nine children had a repeat endoscopy, and six (66.7%) were diagnosed with CeD, while three remained PCD. Among the 11 children with TTG >10× ULN and at least one year of follow-up, TTG normalized in three, declined in five, and increased or remained above 10× ULN in three.

PCD is common and may be found in children with TTG above 10× ULN; approximately half will normalize TTG. The omission of biopsies may result in an erroneous diagnosis of CeD.

Lung cancer remains the leading cause of cancer-related mortality worldwide, with marked phenotypic differences observed among its major histological subtypes, adenocarcinoma (ADC), squamous cell carcinoma (SCC), and small cell lung cancer (SCLC), in both clinical presentation and therapeutic response. In recent years, metabolomics has emerged as a powerful tool for studying cancer metabolic reprogramming, providing new insights into the metabolic distinctions among lung cancer subtypes. This review summarizes recent research advances in the metabolomics of ADC, SCC, and SCLC. Studies have revealed that ADC and SCC display distinct metabolic profiles in lipid metabolism, amino acid metabolism, and cell membrane synthesis, while SCLC demonstrates a unique metabolic pattern. Through metabolomic technologies, particularly mass spectrometry and liquid chromatography, it is possible to effectively differentiate lung cancer subtypes and identify potential biomarkers for early diagnosis and personalized treatment. This review also explores the clinical potential of metabolomics in lung cancer, emphasizing its critical role in early diagnosis and subtype stratification. These methodological advances establish a robust foundation for precision oncology paradigms in thoracic malignancies.

Systemic light chain (AL) amyloidosis is a rare and potentially fatal disease characterized by the abnormal deposition of homogeneous, amorphous amyloid proteins in tissues and organs. This deposition leads to varying degrees of structural and functional abnormalities, ultimately causing organ dysfunction and failure. The disease often involves multiple systems and organs, including the heart, kidneys, gastrointestinal tract, liver, and nervous system, with cardiac and renal involvement being the most common. Due to its rarity, multisystem involvement, and rapid progression, a comprehensive summary of the diagnosis and treatment of AL amyloidosis is crucial for guiding clinical practice and advancing research in this field. This article reviews the progress in diagnosis and discusses future treatment of AL amyloidosis, aiming to provide expanded options for clinical practice.

Artificial intelligence (AI) is transforming the diagnosis, treatment, monitoring, and research of soft tissue disorders, which include muscles, tendons, ligaments, fascia, nerves, and blood vessels. Traditional diagnostic methods often rely on imaging, histopathology, and clinical evaluation, which can be time-consuming and prone to human error. This review aims to explore the impact of AI on enhancing soft tissue care. The review examines the application of deep learning algorithms in medical imaging, pathology, predictive analytics, and treatment planning. It also evaluates AI’s role in monitoring and rehabilitation, as well as its contributions to research in soft tissue disorders. AI significantly improves the accuracy of medical imaging analysis, facilitating the detection of abnormalities such as tumors and tears. AI-powered pathology tools automate slide analysis, enhancing diagnostic consistency and efficiency. Predictive analytics enable early risk assessment and personalized patient management. In surgical contexts, AI supports preoperative simulations and robotic-assisted procedures, leading to improved outcomes. Additionally, AI enhances patient monitoring through wearable devices and telemedicine. The integration of AI into soft tissue diagnostics and therapeutics presents transformative potential for personalized and efficient healthcare. However, challenges related to data security, algorithm bias, interpretability, and ethical considerations must be addressed. Overall, AI holds promise for improving patient outcomes and advancing medical science in the field of soft tissue disorders.

Colorectal cancer (CRC), like all other cancers, results from genetic and epigenetic alterations of the genome. The mechanisms leading to epigenetic alterations include DNA methylation, histone modifications, and small non-coding RNAs. As shown in many studies, some histone modifications such as acetylation, methylation, and phosphorylation are reported to be altered in CRC. Since these epigenetic alterations are reversible, they can be targeted as a strategy for CRC treatment. Numerous studies demonstrate the effects of molecules (both natural and synthetic) as inhibitors of enzymes responsible for histone acetylation, methylation, and phosphorylation in CRC cell lines. Some of these molecules have reached clinical trial stages. Vorinostat and belinostat, as histone deacetylase inhibitors; pinometostat and ribavirin, as histone methyltransferase inhibitors; and staurosporine and barasertib, which target histone phosphorylation, are among the promising epigenetic modifiers targeting histone alterations. Some of these modifiers can be used alone or in combination with other anticancer drugs or radiotherapy to increase efficacy. This review aims to identify molecules that target enzymes responsible for altering acetylation, methylation, and phosphorylation of histones in CRC.

The association between chronic inflammation and cancer has reshaped our understanding of tumorigenesis and cancer therapy. Inflammatory responses can both promote and suppress cancer, depending on the context and timing. Key molecular players, such as nuclear factor kappa-light-chain-enhancer of activated B cells, interleukin-6, signal transducer and activator of transcription 3, and a variety of immune cell types, including tumor-associated macrophages, myeloid-derived suppressor cells, and regulatory T cells, orchestrate an environment conducive to tumor survival, angiogenesis, metastasis, and immune evasion. In recent years, immunotherapy, particularly immune checkpoint inhibitors, has revolutionized cancer treatment. However, its success varies across tumor types and patients, underscoring the need to understand the tumor microenvironment and inflammatory context. This review examines the mechanistic underpinnings of inflammation-driven cancer, discusses translational research efforts targeting inflammatory pathways, and explores clinical applications, including the integration of immunotherapy with anti-inflammatory agents and biomarkers for personalized treatment. Future directions in the field include the application of artificial intelligence, microbiome research, single-cell technologies, and gene editing tools to further tailor therapies and overcome resistance mechanisms.