Hepatocellular carcinoma (HCC) exhibits profound metabolic reprogramming, with enhanced aerobic glycolysis and excess lactate production as defining features. Lactate, once regarded simply as a metabolic byproduct, is now recognized as a powerful epigenetic driver through lysine lactylation, while tumor cells co-opt autophagy to endure metabolic stress. Although traditionally investigated as separate hallmarks of cancer, growing evidence points to their functional intersection. Here, we propose the concept of a coordinated “lactylation-autophagy axis” and clarify its role in remodeling the tumor immune microenvironment. Within this integrated metabolic-epigenetic-proteostatic network, lactate-induced histone lactylation, such as H3K18la, exerts transcriptional control over tumor-associated macrophages, shifting them toward an immunosuppressive M2-like state. Excess extracellular lactate and robust autophagic adaptation cooperate to impair the effector functions of CD8+ T cells and natural killer cells, while providing regulatory T cells with metabolic advantages. We further explore the bidirectional crosstalk within this axis: lactate modulates autophagic flux, potentially through mTOR signaling and non-histone lactylation of autophagy-related proteins (e.g., VPS34, TFEB), whereas autophagy in turn regulates intracellular metabolite pools and lactylation substrates. Notably, several nodes, particularly non-histone lactylation of VPS34, TFEB, and mTOR (K921), have been demonstrated mainly in non-HCC systems; we therefore grade each mechanism as confirmed in HCC, extrapolated from other systems, or hypothetical. This proposed self-reinforcing axis may sustain tumor plasticity, contribute to immune evasion and immunotherapy resistance, and we discuss rational combinatorial strategies—combined targeting of lactate metabolism, lactylation machinery, and autophagic pathways—to sensitize advanced HCC to immune checkpoint blockade.

The prevalence of metabolic dysfunction-associated fatty liver disease (MAFLD) continues to increase worldwide, partly because of rising rates of obesity and metabolic syndrome. However, a substantial proportion of patients with MAFLD are neither overweight nor obese and are classified as having lean MAFLD. The increasingly recognized phenotype of lean MAFLD challenges the traditional paradigm linking obesity to metabolic risk. Despite a normal body mass index (BMI), lean MAFLD is associated with substantial metabolic dysfunction and a range of extrahepatic manifestations. Evidence from systematic reviews and meta-analyses including more than 10 million individuals indicates that patients with lean MAFLD have metabolic profiles comparable to those of overweight or obese patients with MAFLD, including insulin resistance, dyslipidemia, and hypertension. Importantly, although cardiovascular and cancer-related mortality appear broadly similar across BMI categories, liver-related mortality is consistently higher in lean individuals, suggesting distinct hepatocentric vulnerability. Variation in all-cause mortality estimates is influenced by disease severity, study design, and ethnicity. Important mechanistic factors include genetic susceptibility, sarcopenia, and visceral adiposity. These findings support comprehensive metabolic and hepatic assessment in lean MAFLD beyond BMI-based risk classification. This review examines lean MAFLD as a hepatocentric phenotype with systemic effects beyond BMI.

Epithelial-myoepithelial neoplasms of the breast are biphasic tumors composed of both epithelial and myoepithelial cell populations. Their rarity, broad morphologic spectrum, and evolving molecular characterization pose substantial diagnostic challenges, underscoring the need for an integrated review of their diagnostic and molecular features. This review aims to provide a comprehensive overview of epithelial-myoepithelial neoplasms of the breast, emphasizing their morphologic spectrum, diagnostic challenges, and emerging molecular features.

This narrative review is based on a review of the pertinent English-language literature and the author’s firsthand experience as a practicing breast pathologist.

Benign epithelial-myoepithelial tumors include pleomorphic adenoma and classic adenomyoepithelioma. Pleomorphic adenoma of the breast closely resembles its salivary gland counterpart as well as the benign mixed tumor of the skin. In contrast, adenomyoepithelioma is unique to the breast and exhibits a broad spectrum of histomorphologic patterns. Most adenomyoepitheliomas follow a benign clinical course; however, atypical adenomyoepitheliomas may demonstrate more aggressive behavior, including local recurrence, malignant transformation, and, rarely, metastasis. Molecular studies have identified PLAG1/HMGA2 rearrangements in a subset of pleomorphic adenomas and recurrent HRAS Q61 and PI3K-AKT pathway alterations in adenomyoepitheliomas. In malignant adenomyoepithelioma, the malignant component may arise from the ductal epithelial cells, the myoepithelial cells, or both.

The rarity of epithelial-myoepithelial tumors, together with their marked histologic heterogeneity, makes them diagnostically challenging and underscores the potential for diagnostic pitfalls.

Large-scale critical care databases have expanded opportunities for clinical research. Previous reviews have summarized open-access intensive care unit databases and specific analytical methods, but integrated, practice-oriented guidance on commonly used and emerging methods and their selection remains limited. The objective of this narrative review is to provide a practice-oriented overview of commonly used and emerging statistical and modeling methods in critical care database research, with emphasis on their appropriate applications, strengths, limitations, and considerations for method selection. This narrative review summarizes commonly used and emerging methods in critical care database research, focusing on their applications, strengths, limitations, and practical considerations. The methods reviewed range from descriptive and regression analyses to prediction modeling, longitudinal analysis, causal inference, and artificial intelligence-based approaches. Common problems include missing data, temporal bias, confounding, overfitting, and limited external validation. Careful method selection and appropriate validation are therefore important when working with complex critical care data. Closer coordination of clinical questions, data structures, analytical methods, and reporting standards can strengthen research using intensive care unit databases. Paying closer attention to data quality, causal hypotheses, validation, reproducibility, and generalizability may improve the methodological rigor and translational value of these studies.

Therapeutic hypothermia (TH) is widely used as a neuroprotective strategy, but variability in bedside implementation may limit its optimal application. This study aimed to develop a standardized TH management protocol for patients with neurocritical illness and evaluate the clinical outcomes associated with its implementation.

An evidence-based TH protocol was developed through a two-round Delphi consensus process. A subsequent observational before-and-after study included 244 patients with neurocritical illness receiving TH. The historical control group (n = 120) received routine care, whereas the TH protocol group (n = 124) was managed according to the standardized protocol. No single primary endpoint was prespecified for the final comparative analysis. Outcomes were interpreted as exploratory and included temperature-management process indicators, individual TH-related complications requiring intervention, and neurological outcomes assessed using the Glasgow Outcome Scale.

Protocol implementation was associated with more favorable temperature-management process indicators, including earlier TH initiation, a shorter recorded time to target temperature, fewer unplanned treatment interruptions, a longer controlled rewarming phase, and greater adherence to rewarming criteria. In exploratory analyses, the TH protocol group had lower incidences of selected TH-related complications and more favorable Glasgow Outcome Scale outcomes at discharge than the historical control group.

Implementation of a consensus-derived TH management protocol was associated with more consistent temperature-management processes, fewer selected TH-related complications, and more favorable short-term neurological outcomes. These findings support prospective evaluation of protocolized TH management in larger multicenter cohorts.

Neurodevelopmental, psychiatric, and neurodegenerative disorders are traditionally studied as distinct disease categories, yet they show overlapping disturbances in systems that regulate neural function and resilience. This article proposes maladaptive brain-state locking (MBSL) as a lifespan framework for examining whether otherwise distinct disorders may converge, in some contexts, on impaired biological-state reversibility. MBSL is defined as a failure of adaptive state-transition or recovery dynamics in which a maladaptive state becomes insufficiently reversible and the nervous system loses its normal capacity to terminate, recalibrate, or transition away from that state. Locking is therefore distinguished from persistence alone: an abnormal state maintained solely by a continuing pathological driver does not, by itself, constitute MBSL. Candidate operational features include abnormal persistence, impaired transition toward an adaptive state, delayed or incomplete recovery after perturbation reduction or removal, and history-dependent loss of flexibility. Lifespan timing, regional context, disease stage, severity, duration, and resilience are proposed to modify phenotypic consequences rather than assign disorder classes exclusively to specific age periods. Existing evidence is compatible with components of the hypothesis, but direct demonstration of MBSL will require longitudinal or perturbational evidence of impaired recovery or state-transition dynamics. Major limitations include etiological and pathological heterogeneity, nonspecific biomarkers, unresolved causality, and challenges in operationalizing multiscale state dynamics. If validated, the framework could support research focused on measurable reversibility, longitudinal monitoring, and interventions designed to restore adaptive state regulation rather than symptom improvement alone.