Objective: Stroke is a severe complication of type 2 diabetes mellitus. Glucagon-like peptide-1 receptor agonists have been shown to have a neuroprotective effect in experimental diabetes. The aim of this study was to determine if their neuroprotective effect is an independent property of the drug independent of glycaemic control. Methods: This two-phase study used male Wistar rats. In the first phase, experimental animals were pretreated with liraglutide, while controls received only vehicle. After transient focal brain ischaemia modelling, neurological deficit and brain infarct volume were measured. In the second phase, the first and the second groups of experimental animals with type 2 diabetes mellitus received liraglutide and metformin, respectively, while control animals with diabetes received only vehicle. After transient focal brain ischaemia modelling, neurological deficit and brain infarct volume were evaluated. Results: Pretreatment with liraglutide in diabetic and non-diabetic animals reduced infarct size as compared to controls, while only non-diabetic liraglutide-treated rats presented neurologic deficit decreases. Despite glycaemia normalization, metformin-treated diabetic rats had no differences in stroke outcome when compared to the control group. Conclusion: The neuroprotective effect of liraglutide is not associated with glycaemic control amelioration in experimental type 2 diabetes mellitus.

Medical article медицинская статья

ЖАЛОБЫ НА НАРУШЕНИЯ ДЫХАНИЯ ВО СНЕ И ФАКТОРЫ РИСКА СЕРДЕЧНО-СОСУДИСТЫХ ЗАБОЛЕВАНИЙ В РЕГИОНАХ РОССИИ: ДАННЫЕ ИССЛЕДОВАНИЯ ЭССЕ-РФ

Background: Large language models (LLMs) hold promise for supporting clinical tasks, particularly in data-driven and technical disciplines such as radiation oncology. While prior evaluation studies have focused on examination-style settings for evaluating LLMs, their performance in real-life clinical scenarios remains unclear. In the future, LLMs might be used as general AI assistants to answer questions arising in clinical practice. It is unclear how well a modern LLM, locally executed within the infrastructure of a hospital, would answer such questions compared with clinical experts. Objective: This study aimed to assess the performance of a locally deployed, state-of-the-art medical LLM in answering real-world clinical questions in radiation oncology compared with clinical experts. The aim was to evaluate the overall quality of answers, as well as the potential harmfulness of the answers if used for clinical decision-making. Methods: Physicians from 10 departments of European hospitals collected questions arising in the clinical practice of radiation oncology. Fifty of these questions were answered by 3 senior radiation oncology experts with at least 10 years of work experience, as well as the LLM Llama3-OpenBioLLM-70B (Ankit Pal and Malaikannan Sankarasubbu). In a blinded review, physicians rated the overall answer quality on a 5-point Likert scale (quality), assessed whether an answer might be potentially harmful if used for clinical decision-making (harmfulness), and determined if responses were from an expert or the LLM (recognizability). Comparisons between clinical experts and LLMs were then made for quality, harmfulness, and recognizability. Results: There were no significant differences between the quality of the answers between LLM and clinical experts (mean scores of 3.38 vs 3.63; median 4.00, IQR 3.00-4.00 vs median 3.67, IQR 3.33-4.00; P=.26; Wilcoxon signed rank test). The answers were deemed potentially harmful in 13% of cases for the clinical experts compared with 16% of cases for the LLM (P=.63; Fisher exact test). Physicians correctly identified whether an answer was given by a clinical expert or an LLM in 78% and 72% of cases, respectively. Conclusions: A state-of-the-art medical LLM can answer real-life questions from the clinical practice of radiation oncology similarly well as clinical experts regarding overall quality and potential harmfulness. Such LLMs can already be deployed within the local hospital environment at an affordable cost. While LLMs may not yet be ready for clinical implementation as general AI assistants, the technology continues to improve at a rapid pace. Evaluation studies based on real-life situations are important to better understand the weaknesses and limitations of LLMs in clinical practice. Such studies are also crucial to define when the technology is ready for clinical implementation. Furthermore, education for health care professionals on generative AI is needed to ensure responsible clinical implementation of this transforming technology.

BACKGROUND: Thalamic stroke (TS) often presents with complex clinical manifestations, including sleep-wake disturbances, cognitive deficits, and autonomic dysregulation, yet the interaction between these functional alterations remains poorly understood. We aimed to investigate these interactions in a case-control lesion study. METHODS: Patients with acute TS and no-stroke controls were included prospectively in this study. The data were collected from June 2020 to September 2022 at the stroke unit or sleep laboratory of the Inselspital (Bern). Sleep-wake variables (questionnaires, actigraphy, polysomnography including electroencephalography-based sleep macroarchitecture and microarchitecture, and analysis of electroencephalography spectral power), nocturnal heart rate variability, and cognition (5 tests: processing speed, attention, working memory, visual memory, and verbal memory) were assessed at study inclusion (within 5 days poststroke for patients with stroke). RESULTS: Data from 16 patients with TS and 32 control volunteers were analyzed. All patients with stroke had lesions of the ventral nuclei, while 9 of 16 patients with stroke also had lesions in the mediodorsal nucleus (1 bilateral). TS was characterized by long sleep duration and high nocturnal heart rate variability with parasympathetic dominance. The alterations in sleep electroencephalography included a decrease in cyclic alternating pattern index, slow spindle density, the quantity of isolated sawtooth wave segments, and electroencephalography spectral power predominantly affecting the alpha band. The mediodorsal lesions were associated with a decrease in sleep spindle amplitude and slow wave amplitude and with an increase in phasic rapid eye movement sleep. Furthermore, patients with TS had deficits in processing speed, working memory, and verbal memory, mostly pronounced in patients with mediodorsal lesions. In a combined data set, multiple correlations were observed between sleep-wake, autonomic, and cognitive parameters, many of which depended on the presence of a TS. CONCLUSIONS: These findings emphasize the role of the thalamus in the regulation of sleep-wake, autonomic, and cognitive functions and their interactions and provide the theoretical basis for the therapies targeting the thalamus.

The thalamus, and its projections to the cerebral cortex, are crucial for regulating sleep rhythms, such as sleep spindles, and for maintaining arousal and sleep homeostasis. Moreover, they play a significant role in memory, executive functioning, and attention. Altered thalamocortical circuitry caused by vascular lesions affects sleep–wake architecture and may contribute to cognitive deficits observed in thalamic stroke patients. This review summarizes the biology of the thalamus and current knowledge regarding the impact of thalamic circuitry on sleep regulation and cognition, drawing from clinical and pre-clinical studies. Furthermore, deep brain stimulation and transcranial magnetic stimulation are discussed as possible therapeutic approaches targeting thalamic circuits. Understanding the role of the thalamus in sleep and cognition opens new avenues for developing novel therapeutic strategies to improve sleep and cognitive functions in affected individuals.