Natural Language Processing (NLP) is a subset of artificial intelligence that enables machines to understand and respond to human language through Large Language Models (LLMs)‥ These models have diverse applications in fields such as medical research, scientific writing, and publishing, but concerns such as hallucination, ethical issues, bias, and cybersecurity need to be addressed. To understand the scientific community’s understanding and perspective on the role of Artificial Intelligence (AI) in research and authorship, a survey was designed for corresponding authors in top medical journals. An online survey was conducted from July 13^th, 2023, to September 1^st, 2023, using the SurveyMonkey web instrument, and the population of interest were corresponding authors who published in 2022 in the 15 highest-impact medical journals, as ranked by the Journal Citation Report. The survey link has been sent to all the identified corresponding authors by mail. A total of 266 authors answered, and 236 entered the final analysis. Most of the researchers (40.6%) reported having moderate familiarity with artificial intelligence, while a minority (4.4%) had no associated knowledge. Furthermore, the vast majority (79.0%) believe that artificial intelligence will play a major role in the future of research. Of note, no correlation between academic metrics and artificial intelligence knowledge or confidence was found. The results indicate that although researchers have varying degrees of familiarity with artificial intelligence, its use in scientific research is still in its early phases. Despite lacking formal AI training, many scholars publishing in high-impact journals have started integrating such technologies into their projects, including rephrasing, translation, and proofreading tasks. Efforts should focus on providing training for their effective use, establishing guidelines by journal editors, and creating software applications that bundle multiple integrated tools into a single platform.

Muscle motor-evoked potentials are commonly monitored during brain tumour surgery in motor areas, as these are assumed to reflect the integrity of descending motor pathways, including the corticospinal tract. However, while the loss of muscle motor-evoked potentials at the end of surgery is associated with long-term motor deficits (muscle motor-evoked potential-related deficits), there is increasing evidence that motor deficit can occur despite no change in muscle motor-evoked potentials (muscle motor-evoked potential-unrelated deficits), particularly after surgery of non-primary regions involved in motor control. In this study, we aimed to investigate the incidence of muscle motor-evoked potential-unrelated deficits and to identify the associated brain regions. We retrospectively reviewed 125 consecutive patients who underwent surgery for peri-Rolandic lesions using intra-operative neurophysiological monitoring. Intraoperative changes in muscle motor-evoked potentials were correlated with motor outcome, assessed by the Medical Research Council scale. We performed voxel–lesion–symptom mapping to identify which resected regions were associated with short- and long-term muscle motor-evoked potential-associated motor deficits. Muscle motor-evoked potentials reductions significantly predicted long-term motor deficits. However, in more than half of the patients who experienced long-term deficits (12/22 patients), no muscle motor-evoked potential reduction was reported during surgery. Lesion analysis showed that muscle motor-evoked potential-related long-term motor deficits were associated with direct or ischaemic damage to the corticospinal tract, whereas muscle motor-evoked potential-unrelated deficits occurred when supplementary motor areas were resected in conjunction with dorsal premotor regions and the anterior cingulate. Our results indicate that long-term motor deficits unrelated to the corticospinal tract can occur more often than currently reported. As these deficits cannot be predicted by muscle motor-evoked potentials, a combination of awake and/or novel asleep techniques other than muscle motor-evoked potentials monitoring should be implemented.

Ventricular–arterial coupling (VAC) describes the dynamic interaction between left ventricular (LV) systolic elastance and the time-varying elastance/impedance of the arterial tree, a relationship that governs the instantaneous generation of aortic flow and ultimately cardiac output. VAC, typically expressed as the ratio of effective arterial elastance (Ea) to LV end-systolic elastance (Ees), has provided valuable mechanistic and prognostic insights, but is limited by its lumped, largely steady-state nature and by the need for pressure–volume modeling or complex surrogate formulas. Contemporary time-domain and wave-intensity approaches have underscored that the shape of proximal aortic pressure–flow waveforms encodes rich beat-by-beat information about ventricular–arterial interaction and energy transfer. Doppler echocardiography of the ascending aorta provides a readily available, high-temporal resolution measure of proximal aortic flow that is already used to quantify stroke volume, cardiac output and valvular lesions. We propose that proximal aortic flow, as recorded by Doppler echocardiography, may serve as a clinically practical proxy for beat-by-beat VAC, reflecting the instantaneous matching of ventricular and aortic elastances, which regulates the ejected flow towards peripheral tissues according to metabolic needs.

Article: Awake tracheostomy intubation and subsequent endobronchial blockade for one-lung ventilation in tracheostomized patients: the safest strategy - Minerva Surgery 2026 Jan 27 - Minerva Medica - Journals