Background/Objectives: Large language models (LLMs) have shown significant potential to transform various aspects of healthcare. This review aims to explore the current applications, challenges, and future prospects of LLMs in medical education, clinical decision support, and healthcare administration. Methods: A comprehensive literature review was conducted, examining the applications of LLMs across the three key domains. The analysis included their performance, challenges, and advancements, with a focus on techniques like retrieval-augmented generation (RAG). Results: In medical education, LLMs show promise as virtual patients, personalized tutors, and tools for generating study materials. Some models have outperformed junior trainees in specific medical knowledge assessments. Concerning clinical decision support, LLMs exhibit potential in diagnostic assistance, treatment recommendations, and medical knowledge retrieval, though performance varies across specialties and tasks. In healthcare administration, LLMs effectively automate tasks like clinical note summarization, data extraction, and report generation, potentially reducing administrative burdens on healthcare professionals. Despite their promise, challenges persist, including hallucination mitigation, addressing biases, and ensuring patient privacy and data security. Conclusions: LLMs have transformative potential in medicine but require careful integration into healthcare settings. Ethical considerations, regulatory challenges, and interdisciplinary collaboration between AI developers and healthcare professionals are essential. Future advancements in LLM performance and reliability through techniques such as RAG, fine-tuning, and reinforcement learning will be critical to ensuring patient safety and improving healthcare delivery.

Mitosis relies on forces generated in the spindle, a micro-machine composed of microtubules and associated proteins. Forces are required for the congression of chromosomes to the metaphase plate and their separation in anaphase. However, besides forces, torques may exist in the spindle, yet they have not been investigated. Here we show that the spindle is chiral. Chirality is evident from the finding that microtubule bundles in human spindles follow a left-handed helical path, which cannot be explained by forces but rather by torques. Kinesin-5 (Kif11/Eg5) inactivation abolishes spindle chirality. Our theoretical model predicts that bending and twisting moments may generate curved shapes of bundles. We found that bundles turn by about −2 deg µm⁻¹ around the spindle axis, which we explain by a twisting moment of roughly −10 pNµm. We conclude that torques, in addition to forces, exist in the spindle and determine its chiral architecture.

Since its inception more than thirty years ago, electroformation has become the most commonly used method for growing giant unilamellar vesicles (GUVs). Although the method seems quite straightforward at first, researchers must consider the interplay of a large number of parameters, different lipid compositions, and internal solutions in order to avoid artifactual results or reproducibility problems. These issues motivated us to write a short review of the most recent methodological developments and possible pitfalls. Additionally, since traditional manual analysis can lead to biased results, we have included a discussion on methods for automatic analysis of GUVs. Finally, we discuss possible improvements in the preparation of GUVs containing high cholesterol contents in order to avoid the formation of artifactual cholesterol crystals. We intend this review to be a reference for those trying to decide what parameters to use as well as an overview providing insight into problems not yet addressed or solved.

Background: Due to recent changes in breast cancer treatment strategy, significantly more patients are treated with neoadjuvant systemic therapy (NST). Radiological methods do not precisely determine axillary lymph node status, with up to 30% of patients being misdiagnosed. Hence, supplementary methods for lymph node status assessment are needed. This study aimed to apply and evaluate machine learning models on clinicopathological data, with a focus on patients meeting NST criteria, for lymph node metastasis prediction. Methods: From the total breast cancer patient data (n = 8381), 719 patients were identified as eligible for NST. Machine learning models were applied for the NST-criteria group and the total study population. Model explainability was obtained by calculating Shapley values. Results: In the NST-criteria group, random forest achieved the highest performance (AUC: 0.793 [0.713, 0.865]), while in the total study population, XGBoost performed the best (AUC: 0.762 [0.726, 0.795]). Shapley values identified tumor size, Ki-67, and patient age as the most important predictors. Conclusion: Tree-based models achieve a good performance in assessing lymph node status. Such models can lead to more accurate disease stage prediction and consecutively better treatment selection, especially for NST patients where radiological and clinical findings are often the only way of lymph node assessment.

Cholesterol (Chol) is an essential component of animal cell membranes and is most abundant in plasma membranes (PMs) where its concentration typically ranges from 10 to 30 mol%. However, in red blood cells and Schwann cells, PMs Chol content is as high as 50 mol%, and in the PMs of the eye lens fiber cells, it can reach up to 66 mol%. Being amphiphilic, Chol molecules are easily incorporated into the lipid bilayer where they affect the membrane lateral organization and transmembrane physical properties. In the aqueous phase, Chol cannot form free bilayers by itself. However, pure Chol bilayer domains (CBDs) can form in lipid bilayer membranes with the Chol content exceeding 50 mol%. The range of Chol concentrations surpassing 50 mol% is less frequent in biological membranes and is consequently less investigated. Nevertheless, it is significant for the normal functioning of the eye lens and understanding how Chol plaques form in atherosclerosis. The most commonly used membrane models are unilamellar and multilamellar vesicles (MLVs) and supported lipid bilayers (SLBs). CBDs have been observed directly using confocal microscopy, X-ray reflectometry and saturation recovery electron paramagnetic resonance (SR EPR). Indirect evidence of CBDs has also been reported by using atomic force microscopy (AFM) and fluorescence recovery after photobleaching (FRAP) experiments. The overall goal of this review is to demonstrate the advantages and limitations of the various membrane models and experimental techniques suitable for the detection and investigation of the lateral organization, function and physical properties of CBDs.

Iron overload is often associated with type 2 diabetes (T2D), indicating that hepcidin, the master regulator of iron homeostasis, might be involved in diabetes pathogenesis. Alcohol consumption may also result in increased body iron stores. However, the moderate consumption of wine with meals might be beneficial in T2D. This effect has been mainly attributed to both the ethanol and the polyphenolic compounds in wine. Therefore, we examined the effects of red wine on hepcidin in T2D patients and non-diabetic controls. The diabetic patients (n = 18) and age- and BMI-matched apparently healthy controls (n = 13) were men, aged 40–65 years, non-smoking, with BMI < 35 kg/m². Following a 2-week alcohol-free period, both groups consumed 300 mL of red wine for 3 weeks. The blood samples for the iron status analysis were taken at the end of each period. The red wine intake resulted in a decrease in serum hepcidin in both the diabetic subjects (p = 0.045) and controls (p = 0.001). The levels of serum ferritin also decreased after wine in both groups, reaching statistical significance only in the control subjects (p = 0.017). No significant alterations in serum iron, transferrin saturation, or soluble transferrin receptors were found. The suppression of hepcidin, a crucial iron-regulatory hormone and acute-phase protein, in T2D patients and healthy controls, is a novel biological effect of red wine. This may deepen our understanding of the mechanisms of the cardiometabolic effects of wine in T2D.