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The surface plasmon resonance (SPR) sensor is an important tool widely used for studying binding kinetics between biomolecular species. The SPR approach offers unique advantages in light of its real-time and label-free sensing capabilities. Until now, nearly all established SPR instrumentation schemes are based on single- or several-channel configurations. With the emergence of drug screening and investigation of biomolecular interactions on a massive scale these days for finding more effective treatments of diseases, there is a growing demand for the development of high-throughput 2-D SPR sensor arrays based on imaging. The so-called SPR imaging (SPRi) approach has been explored intensively in recent years. This review aims to provide an up-to-date and concise summary of recent advances in SPRi. The specific focuses are on practical instrumentation designs and their respective biosensing applications in relation to molecular sensing, healthcare testing, and environmental screening.

Recent advancements in artificial intelligence have witnessed human-level performance; however, AI-enabled cognitive assistance for therapeutic procedures has not been fully explored nor pre-clinically validated. Here we propose AI-Endo, an intelligent surgical workflow recognition suit, for endoscopic submucosal dissection (ESD). Our AI-Endo is trained on high-quality ESD cases from an expert endoscopist, covering a decade time expansion and consisting of 201,026 labeled frames. The learned model demonstrates outstanding performance on validation data, including cases from relatively junior endoscopists with various skill levels, procedures conducted with different endoscopy systems and therapeutic skills, and cohorts from international multi-centers. Furthermore, we integrate our AI-Endo with the Olympus endoscopic system and validate the AI-enabled cognitive assistance system with animal studies in live ESD training sessions. Dedicated data analysis from surgical phase recognition results is summarized in an automatically generated report for skill assessment. AI-enabled cognitive assistance for therapeutic procedures has rarely been pre-clinically validated. Here, the authors propose an intelligent surgical workflow recognition suit AI-Endo for endoscopic submucosal dissection, extensively validated on external and animal trial datasets.

Continuous monitoring of cardiovascular risk factors in daily life is crucial for disease prevention and management. Current wearable systems, such as photoplethysmography (PPG), ultrasound, and pressure sensors, can capture some of these parameters but require precise sensor alignment over arteries. This alignment dependency complicates daily use and makes the signals highly susceptible to motion artifacts. In this work, we present a textile-based alignment-free electrophysiological sensing sleeve (TAESS) that can be comfortably worn on the upper arm. The TAESS integrates impedance plethysmography (IPG) and electrocardiography (ECG) to enable synchronized cardiovascular haemodynamic monitoring, including blood pressure (BP), cardiac output (CO), systemic vascular resistance (SVR), heart rate (HR), and other metrics. The sleeve is fabricated using silver-based conductive yarns, forming flexible, breathable, and stretchable electrodes that are produced via an automated, low-cost knitting process. Compared to commercial electrodes, TAESS demonstrates superior permeability (37.5 mg·cm −2 ·h −1 ), stretchability (exceeding 45% in wale direction), and thermal regulation (remaining within 0.4 °C after exercise). Most importantly, it maintains high signal fidelity and is minimally affected by radial movements, outperforming commercial PPG sensors in blood volume detection. The TAESS achieved systolic and diastolic BP prediction root-mean-squared errors of 7.05 mmHg and 5.93 mmHg, respectively, even under respiratory interference and after re-wearing. This scalable, low-cost sensing sleeve offers a robust and alignment-free solution for continuous cardiovascular monitoring, paving the way for personalized healthcare in daily life.

BackgroundPrognostic value of positron emission tomography (PET) myocardial perfusion imaging (MPI) is well established. There is paucity of data on how the prognostic value of PET relates to the hemodynamic response to vasodilator stress. We hypothesize that inadequate hemodynamic response will affect the prognostic value of PET MPI.Methods and resultsUsing a multicenter rubidium (Rb)-82 PET registry, 3406 patients who underwent a clinically indicated rest/stress PET MPI with a vasodilator agent were analyzed. Patients were categorized as, “responders” [increase in heart rate ≥ 10 beats per minute (bpm) and decrease in systolic blood pressure (SBP) ≥10 mmHg], “partial responders” (either a change in HR or SBP), and “non-responders” (no change in HR or SBP). Primary outcome was all-cause death (ACD), and secondary outcome was cardiac death (CD). Ischemic burden was measured using summed stress score (SSS) and % left ventricular (LV) ischemia. After a median follow-up of 1.68 years (interquartile range = 1.17- 2.55), there were 7.9% (n = 270) ACD and 2.6% (n = 54) CD. Responders with a normal PET MPI had an annualized event rate (AER) of 1.22% (SSS of 0–3) and 1.58% (% LV ischemia = 0). Partial and non-responders had higher AER with worsening levels of ischemic burden. In the presence of severe SSS ≥12 and LV ischemia of ≥10%, partial responders had an AER of 10.79% and 10.36%, compared to non-responders with an AER of 19.4% and 12.43%, respectively. Patient classification was improved when SSS was added to a model containing clinical variables (NRI: 42%, p < 0.001) and responder category was added (NRI: 61%, p < 0.001). The model including clinical variables, SSS and hemodynamic response has good discrimination ability (Harrell C statistics: 0.77 [0.74–0.80]).ConclusionHemodynamic response during a vasodilator Rb-82 PET MPI is predictive of ACD. Partial and non-responders may require additional risk stratification leading to altered patient management.

Extent of coronary atherosclerotic disease (CAD) burden on coronary computed tomography angiography (CCTA) as measured by segment involvement score (SIS) has a prognostic value. We sought to investigate the incremental prognostic value of ‘age adjusted SIS’ (aSIS), which may be a marker of premature atherosclerosis and vascular age. Consecutive patients were prospectively enrolled into the CONFIRM (Coronary CT Angiography EvaluatioN For Clinical Outcomes: An InteRnational Multicentre) multinational observational study. Patients were followed for the outcome of all-cause death. aSIS was calculated on CCTA for each patient, and its incremental prognostic value was evaluated. A total of 22,211 patients [mean age 58.5 ± 12.7 years, 55.8% male) with a median follow-up of 27.3 months (IQR 17.8, 35.4)] were identified. After adjustment for clinical factors and presence of obstructive CAD, higher aSIS was associated with increased death on multivariable analysis, with hazard ratio (HR) 2.40 (1.83–3.16, p < 0.001), C-statistic 0.723 (0.700–0.756), net reclassification improvement (NRI) 0.36 (0.26–0.47, p < 0.001), and relative integrated discrimination improvement (IDI) 0.33 (p = 0.009). aSIS had HR 3.48 (2.33–5.18, p < 0.001) for mortality in those without obstructive CAD, compared to HR 1.79 (1.25–2.58, p = 0.02) in those with obstructive CAD. In conclusion, aSIS has an incremental prognostic value to traditional risk factors and obstructive CAD, and may enhance CCTA risk stratification.

Artificial intelligence (AI) education for K-12 students is an emerging necessity, owing to the rapid advancement and deployment of AI technologies. It is essential to take teachers’ perspectives into account when creating ecologically valid AI education programmes for K-12 settings. However, very few studies investigated teacher perception of AI education. Phenomenography is an empirical research method that was widely used to understand teacher’s interpretive understanding of new phenomenon, in this study, the teaching of AI in secondary school. Therefore, the present study investigated teachers’ conceptions of teaching AI using a phenomenographic approach. Twenty-eight in-service teachers from 17 secondary schools in Hong Kong were invited to participate in an interview after implementing an AI curriculum. Six categories of teacher conceptions were identified: (1) technology bridging, (2) knowledge delivery, (3) interest stimulation, (4) ethics establishment, (5) capability cultivation, and (6) intellectual development. The hierarchical relationships of the six concepts were organised as an outcome space. The space shows a range of surface to deep conceptions and offers an understanding of how teachers perceive AI education through their teaching experience. Two learning paths have been suggested for cultivating technical and non-technical teachers for teaching AI. These learning paths provide insights for teacher educators and policymakers to enhance teachers’ competence in teaching AI and promote general AI education for K-12 students.

Contributions: The Chinese University of Hong Kong (CUHK)-Jockey Club AI for the Future Project (AI4Future) co-created an AI curriculum for pre-tertiary education and evaluated its efficacy. While AI is conventionally taught in tertiary level education, our co-creation process successfully developed the curriculum that has been used in secondary school teaching in Hong Kong and received positive feedback. Background: AI4Future is a cross-sector project that engages five major partners - CUHK Faculty of Engineering and Faculty of Education, Hong Kong secondary schools, the government and the AI industry. A team of 14 professors with expertise in engineering and education collaborated with 17 principals and teachers from 6 secondary schools to co-create the curriculum. This team formation bridges the gap between researchers in engineering and education, together with practitioners in education context. Research Questions: What are the main features of the curriculum content developed through the co-creation process? Would the curriculum significantly improve the students perceived competence in, as well as attitude and motivation towards AI? What are the teachers perceptions of the co-creation process that aims to accommodate and foster teacher autonomy? Methodology: This study adopted a mix of quantitative and qualitative methods and involved 335 student participants. Findings: 1) two main features of learning resources, 2) the students perceived greater competence, and developed more positive attitude to learn AI, and 3) the co-creation process generated a variety of resources which enhanced the teachers knowledge in AI, as well as fostered teachers autonomy in bringing the subject matter into their classrooms.

Thermal discomfort is one of the main triggers for occupants’ interactions with components of the built environment such as adjustments of thermostats and/or opening windows and strongly related to the energy use in buildings. Understanding causes for thermal (dis-)comfort is crucial for design and operation of any type of building. The assessment of human thermal perception through rating scales, for example in post-occupancy studies, has been applied for several decades; however, long-existing assumptions related to these rating scales had been questioned by several researchers. The aim of this study was to gain deeper knowledge on contextual influences on the interpretation of thermal perception scales and their verbal anchors by survey participants. A questionnaire was designed and consequently applied in 21 language versions. These surveys were conducted in 57 cities in 30 countries resulting in a dataset containing responses from 8225 participants. The database offers potential for further analysis in the areas of building design and operation, psycho-physical relationships between human perception and the built environment, and linguistic analyses.

Cardiac CT is a non-invasive modality with the ability to estimate LVEF. However, given its limited temporal resolution and radiation, there has been initial resistance to use CT to measure LVEF. Developing an accurate, fast, low radiation dose protocol is desirable. The objective of this study is to demonstrate that a ‘low radiation dose’ 64 slice cardiac computed tomography (CT) protocol is feasible and can accurately measure left ventricular ejection fraction (LVEF) while delivering a radiation dose lower than radionuclide angiography (RNA). Patients undergoing RNA were prospectively screened and enrolled to undergo a ‘low-dose’ 64 slice CT LVEF protocol. LVEF measures, duration of each study and radiation dose between CT and RNA were compared. A total of 77 patients (mean age = 61.8 ± 12.2 years and 58 men) were analyzed. The mean LVEF measured by CT and RNA were 41.9 ± 15.2% and 39.4 ± 13.9%, respectively, (P = 0.154) with a good correlation (r = 0.863). Bland-Altman plot revealed a good agreement between the CT and RNA LVEF (mean difference of −2.4). There was good agreement between CT LVEF and RNA for identifying patients with LVEF ≤30% (kappa = 0.693) and LVEF ≥50% (kappa = 0.749). The mean dose estimated effective dose for CT and RNA were 4.7 ± 1.6 and 9.5 ± 1.0 mSv, respectively. The mean CT LVEF imaging duration (4:32 ± 3:05 minutes) was significantly shorter than the RNA image acquisition time (9:05 ± 2:36 minutes; p < 0.001). The results of our study suggest that low-dose CT LVEF protocol is feasible, accurate, and fast while delivering a lower radiation dose than traditional RNA.