Surgical knot-tying requires careful selection of the appropriate technique for optimal outcomes; however, evidence is lacking about the common ligation techniques combining square and slip knots (combination knots). This study investigates the strength of a combination knot, compared with other techniques. In this observational cohort study, we assessed the strengths of square, slip-granny, slip-reef, and combination knots using absorbable monofilament sutures, and compared them using ANOVA and a multilevel linear regression analysis. We also assessed the correlation between the number of throws and knot strength using Spearman's correlation analysis. Among 80 ligatures, ANOVA indicated significant differences in knot strength among the techniques (p < 0.01). Multilevel linear regression showed that a combination knot was stronger than a slip knot (p < 0.01) and comparable to a square knot (p = 0.0721). Secondary analysis indicated that for combination knots, five throws could achieve sufficient knot strength. Combination knots are as strong as square knots and stronger than slip knots, and thus present a reasonable option for surgical ligation.

We present a publicly-detectable watermarking scheme for LMs: the detection algorithm contains no secret information, and it is executable by anyone. We embed a publicly-verifiable cryptographic signature into LM output using rejection sampling and prove that this produces unforgeable and distortion-free (i.e., undetectable without access to the public key) text output. We make use of error-correction to overcome periods of low entropy, a barrier for all prior watermarking schemes. We implement our scheme and find that our formal claims are met in practice.

Artificial Intelligence (AI) deployment in healthcare is accelerating, yet comprehensive governance frameworks remain fragmented and often assume extensive resources. Through a systematic review of 22 frameworks published between 2019-2024, we identified seven critical domains of healthcare AI governance: organizational structure, problem formulation, external product evaluation, algorithm development, model evaluation, deployment integration, and monitoring maintenance. While existing frameworks provide valuable guidance, they frequently target only large academic medical centers, creating barriers for smaller healthcare organizations. To address this gap, we propose the Healthcare AI governance Readiness Assessment (HAIRA), a five-level maturity model that provides actionable governance pathways based on organizational resources and capabilities. HAIRA spans from Level 1 (Initial / Ad Hoc) suitable for small practices to Level 5 (Leading) for major academic centers, with specific benchmarks across all seven governance domains. This tiered approach enables healthcare organizations to assess their current AI governance capabilities and establish appropriate advancement targets. Our framework addresses a critical need for adaptive governance strategies that can support AI-enabled healthcare value across diverse settings and ensures that AI implementation delivers tangible benefits to healthcare systems of varying sizes and resource levels.

Background: Peripheral arterial disease (PAD) is known to be strongly linked to major adverse limb events, ultimately leading to an increased risk of limb-threatening conditions. We developed a predictive model using five identified biomarkers to predict major adverse limb events, limb loss, diabetic (DM) foot ulcers, and vascular intervention in patients with underlying PAD and DM over 2 years. Methods: A single-center prospective case control study with was conducted with 2 years’ follow up. In the discovery phase the cohort was randomly split into a 70:30 ratio, and proteins with a higher mean level of expression in the DM PAD group compared to the DM non-PAD group were identified. Next, a random forest model was trained using (1) clinical characteristics, (2) a five-protein panel, and (3) clinical characteristics combined with the five-protein panel. Demographic data were analyzed by independent t-test and chi-square test. The importance of predictive features was calculated using the variable importance (gain) score. The model was used and assessed for its ability to diagnose PAD, predict limb loss, predict major adverse limb events (MALEs), predict diabetic foot ulcers, and predict the need for vascular surgery. The model was evaluated using area under the receiver operating characteristic curve and net reclassification index. Results: The cohort of 392 patients was matched for age, sex, and comorbidities. Five proteins were identified (TNFa: tumor necrosis factor alpha, BMP-10: bone morphogenic protein 10, CCL15/MIP1 delta: chemokine (c-c motif) ligand 15/macrophage inflammatory protein 1 delta, MMP-10: matrix metalloprotease 10, and HTRA2/Omi: HTRA2, also known as Omi) as having a significantly higher level of expression in the DM PAD group. HTRA/Omi had the highest contribution to the model’s ability to diagnose PAD in diabetic patients. Model performance was best when combined with clinical characteristics to predict limb loss (AUROC 0.86, 0.76, 0.80), foot ulcer (AUROC 0.87, 0.82, 0.67), MALE (AUROC 0.81, 0.78, 0.67), and the need for vascular surgery (AUROC 0.82, 0.81, 0.61). Conclusions: In this study, we describe a biomarker panel that can be used in combination with clinical characteristics to create an accurate prediction model for diagnosis and prognostication of PAD in the setting of DM.

La ricerca indaga il ruolo dell'intelligenza artificiale (AI) nelle moderne pratiche compositive, con particolare attenzione a due casi di studio: Convergence (2020/21) di Alexander Schubert, per Ensemble d'archi e sistema AI, e In Human Memories (2024) di Francesco Vitucci, per quattro violoncelli ed elettronica, la cui installazione è stata realizzata dalla Scuola di Musica Elettronica del Conservatorio “N. Piccinni” di Bari. L'indagine storica iniziale traccia l'evoluzione dell'uso della tecnologia nella composizione, rivelando come la ricerca di nuove possibilità espressive abbia guidato lo sviluppo di strumenti tecnologici attraverso la sperimentazione. La comparsa dell'IA segna un cambiamento fondamentale: non più solo uno strumento, l'IA pone ora domande e sfide al compositore. Impegnarsi con l'IA diventa un'esplorazione di un'entità sconosciuta, le cui capacità sono ancora in gran parte da scoprire. Questa nuova interazione porta i compositori a confrontarsi con l'IA non solo come risorsa passiva ma come partecipante al processo creativo, delineando un percorso etico per recuperare la tecnologia come strumento creativo. In Human Memories riflette questa dinamica, dimostrando come l'IA possa servire sia come ispirazione che come forza trainante nel plasmare la musica contemporanea.

Service-Learning (SL) projects, analysing its use at three levels: as a central object, as a resource for project development and as a tool for planning and evaluation. It also reviews different educational approaches: educating with AI, educating about AI and educating to AI, highlighting the pedagogical implications in each of them. The text also addresses the ethical challenges related to the application of AI to SL, highlighting the need to ensure responsible use that promotes well-being without perpetuating inequalities or social exclusions.