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Tumor localization during pulmonary surgery has become increasingly challenging with the earlier detection of smaller and smaller lung nodules. Concomitantly, minimally invasive surgical (MIS) techniques have been increasingly adopted within the field of thoracic surgical oncology. Surgeons face growing challenges not only with locating these small tumors, but also with immediate margin assessment, reduced tactile feedback, and nodal assessment. Intraoperative molecular imaging (IMI) has emerged as a promising adjunct to address these challenges by enabling real-time visualization of malignant tissue during pulmonary resection. In its current form, IMI integrates systemically administered, tumor-targeting near-infrared fluorophores with fluorescence-capable imaging platforms to enhance intraoperative decision-making. Early clinical experiences in thoracic surgery suggest particular utility in the localization of small or nonpalpable pulmonary nodules and for improved margin assessment during MIS. Despite encouraging preliminary data, widespread adoption of IMI remains limited by biologic variability in target expression, optical depth constraints, false-positive fluorescence in inflammatory tissue, and challenges in workflow integration. Applications for nodal evaluation, staging, and longer-term oncologic outcome improvement remain investigational. Addressing these multifaceted barriers will be essential for the translation of IMI from a promising, experimental adjunct to a more broadly implementable surgical technology. This work summarizes the current state of IMI in thoracic surgical oncology, highlighting key translational studies, established and emerging clinical applications, and critical limitations within the current landscape. The authors also outline future directions for the field, including quantitative fluorescence interpretation, standardized reporting, and outcomes-driven clinical trials evaluating margin adequacy, recurrence, staging impact, and cost-effectiveness to support widespread evidence-based implementation.

The rapid integration of generative artificial intelligence (AI) into medical education presents both opportunities and continuing challenges for faculty and learners alike. This study provides an update on ongoing efforts to integrate AI into the curriculum at Quillen College of Medicine, East Tennessee State University in the United States, focusing on evolving student perceptions and a structured faculty development initiative. Using our previously published satisfaction instrument, we tracked shifting attitudes toward AI among our pre-clerkship and clerkship students. Incoming students demonstrated increasing awareness of AI's impact on education and the profession, alongside a stronger commitment to developing core clinical competencies independent of technology. The response rate for AI use was higher in 2026 compared to 2025 (97%, n=68 vs. 89%, n=62) for rising third-year students (class sizes of 80). Those students showed a meaningful shift toward using AI for active learning, particularly practice question generation and self-testing (Mean 2.30, SD 1.10 for 2025; Mean 2.77, SD 1.14 for 2026), while upper-level clerkship students consistently prioritized the ability to function as competent clinicians without AI assistance. To address faculty readiness, we developed a multi-dimensional framework for responsible AI integration and implemented a series of faculty development workshops. The first workshop produced an increase in confidence (+0.75) and practical engagement (+0.43). A second workshop focused on ethical and policy considerations yielded greater critical awareness, though with a more cautious outlook. Feedback across both workshops consistently highlighted the need for hands-on training, prompt engineering instruction, and role-specific, tool-agnostic development pathways. Our findings underscore that integration of AI into medical education requires sustained, flexible, and mission-aligned faculty development. As AI becomes standard in clinical practice, equipping both educators and learners with the skills for responsible, thoughtful AI use is not optional; it is essential.

Radiotherapy is an essential component of multidisciplinary cancer treatment. Its role has expanded from conventional local tumor eradication to active regulation of the tumor immune microenvironment. In recent years, emerging radiotherapy strategies, including FLASH radiotherapy, boron neutron capture therapy, lattice radiotherapy, spatially fractionated radiation therapy, precision particle therapy, immunomodulatory stereotactic body radiotherapy, and immune-optimized carbon ion therapy, have provided new opportunities to improve tumor control, reduce normal tissue toxicity, and overcome radioresistance. Radiotherapy can induce DNA damage and immunogenic cell death, promote tumor antigen release, enhance dendritic cell maturation and antigen cross-presentation, and increase CD8+ T-cell infiltration and antitumor immunity through the cGAS-STING type I interferon pathway. However, radiotherapy may also trigger immunosuppressive feedback, including the accumulation of myeloid-derived suppressor cells, tumor-associated macrophages, and regulatory T cells, as well as the upregulation of immune checkpoint molecules such as programmed death-ligand 1 (PD-L1). These changes may limit antitumor immune responses and contribute to radioresistance. Combining radiotherapy with immune checkpoint inhibitors can amplify antitumor immunity, but therapeutic efficacy is influenced by dose fractionation, treatment timing, tumor type, and baseline immune status. This mini review summarizes emerging radiotherapy strategies and their regulatory effects on the tumor immune microenvironment, and discusses the mechanistic basis, current challenges, and future directions of radiotherapy combined with immunotherapy.

High-throughput sequencing and multi-omics are transforming Traditional Chinese Medicine (TCM) research from empirical descriptions toward data-driven mechanistic analyses. Unlike earlier systems pharmacology frameworks that relied primarily on static network topology and docking-based target prediction, current multi-omics approaches integrate genomic, transcriptomic, proteomic, and metabolomic data to capture dynamic, multi-scale biological responses. This review summarizes recent progress in four related areas: (i) genomic and epigenomic dissection of geo-authentic (Daodi) medicinal materials; (ii) biosynthetic pathway elucidation for major bioactive compound classes; (iii) synthetic biology platforms for heterologous production; and (iv) systems pharmacology integration for mechanism-of-action studies. We identify a central, recurrent gap: most published multi-omics analyses remain at the level of statistical association, and the biosynthetic and pharmacological pathways inferred from such data have not been validated at the causal level. To address this, we propose a tiered experimental validation framework-from biochemical target engagement through genetic perturbation to in vivo functional confirmation-and an iterative computational-experimental feedback loop. We further outline practical priorities for future work, including standardized data formats, community-endorsed metadata checklists, and coordinated DBTL pilot projects. By connecting descriptive multi-omics patterns to experimentally testable mechanistic models, TCM research can move toward precision-oriented medicine while preserving the multi-component character of traditional formulations.

Rapid detection of foodborne pathogenic and spoilage microorganisms is critical for ensuring food safety and quality in liquid matrices. While Raman tweezers spectroscopy (RTS) enables label-free single-cell analysis, its application in high-throughput inline inspection faces a fundamental bottleneck: high flow rates required for efficiency induce severe motion blur and low signal-to-noise ratios (SNR), which blind automated control systems and destabilize optical trapping. To overcome this, we present a Spatiotemporal Video-Enhanced Raman Tweezers (SVERT) system integrating a deceleration-optimized microfluidic chip with a deep learning-based visual feedback loop. We propose a Local-Global Unified Denoising Network (LGU-Net) tailored to recover high-fidelity bacterial structures from low-SNR video streams, achieving a deterministic processing latency of ~0.49 ms. Experimental results demonstrate that SVERT improves the optical trapping success rate from 21.27% ± 2% to 91.47% ± 1.8% compared to raw video input, enabling a four-fold increase in spectral acquisition efficiency. Leveraging the acquired high-quality dataset, we achieved a classification accuracy of 96.74% across four bacterial species of relevance to food safety and quality. Crucially, we validated the system's practical robustness by successfully isolating and tracking trace E. coli in an unpurified commercial beverage. This capability to effectively mitigate natural background interference demonstrates the system's promising potential to be expanded for broader applications in liquid food safety screening.

The integration of olfactory feedback into Virtual Reality (VR) applications remains significantly underexplored compared with other sensory modalities, particularly within room-scale Cave Automatic Virtual Environments (CAVEs), where related research is even more limited. To address this gap, this paper presents Scentree, a custom olfactory system capable of delivering scents in real time based on user interactions, along with Smelling Ancient Greece, an olfactory-enhanced VR experience developed for integration within our CAVE system. Central to the proposed approach is the concept of the Diegetic Olfactory Feedback Loop, which reframes olfaction from a passive ambient effect into an active, interaction-driven feedback mechanism embedded within the narrative context of the virtual environment. To evaluate the system, we conducted a technical performance assessment and an exploratory user study (N=51) examining participant perceptions of immersion, presence, perceived realism, usability, and overall user experience. The findings support the feasibility of interaction-driven olfactory feedback as a multisensory design approach for CAVE environments and provide a foundation for future controlled investigations of olfactory feedback in immersive VR.

Abiotic stresses, including drought, salinity, alkalinity, temperature extremes, flooding, heavy metals, and emerging pollutants, challenge plant growth and productivity by disturbing water relations, ion balance, redox homeostasis, membrane stability, energy metabolism, and developmental progression. Although substantial progress has been made in the identification of stress-responsive hormones, second messengers, kinases, transcription factors, transporters, and metabolic regulators, plant stress adaptation cannot be fully explained by linear signaling cascades or single tolerance genes. A major unresolved question is how early molecular events are reorganized into coordinated physiological and developmental outputs that support survival, recovery, and productivity. In this review, we propose an intermolecular interaction-driven adaptive remodeling framework for plant abiotic stress responses. This framework emphasizes that stress tolerance emerges from dynamic changes in receptor-ligand recognition, protein-protein interactions, calcium decoding, redox-sensitive modification, phosphorylation networks, transcriptional regulation, chromatin-associated control, and metabolite-mediated feedback. We further emphasize ROS as integrative redox switches that connect stress sensing, defense activation, senescence-related transitions, and recovery, and chromatin-associated mechanisms as regulators that may stabilize primed or memory-like adaptive states. We discuss how these interaction networks converge on core signaling hubs, including abscisic acid, reactive oxygen species, Ca2+, and kinase/phosphatase systems, and how they remodel stomatal behavior, root architecture, ion and pH homeostasis, redox buffering, metabolism, development, and reproductive resilience. We further highlight how natural variation, multi-omics, genome editing, high-throughput phenotyping, and field validation can translate interaction-centered stress biology into crop resilience. This perspective provides a conceptual bridge between molecular stress perception, network behavior, physiological adaptation, and climate-resilient agriculture.

Spinal Charcot arthropathy (SCA) is a rare, progressive, and destructive neurogenic joint disorder that develops in the setting of impaired sensory and autonomic innervation. Although once associated primarily with tabes dorsalis, spinal cord injury (SCI) is now the leading cause. The condition arises through a combination of autonomic dysregulation, bone resorption, and loss of proprioceptive feedback, resulting in progressive joint destruction and spinal instability. Diagnosis is often delayed, as symptoms such as deformity, instability, or autonomic dysreflexia (AD) may develop insidiously over the years. We report the case of a 28-year-old female patient with paraplegia secondary to a chronic T5 SCI who presented with progressive thoracic deformity due to a severe Charcot joint at T12, associated with fracture-dislocation and a large pseudomeningocele. Despite complete thecal sac transection, her neurological function remained stable. Surgical management involved T5-pelvis posterior fusion with vertebrectomy, expandable cage placement, and a four-rod construct for maximal biomechanical stability. Intraoperative neuromonitoring was utilized throughout the procedure. The thecal sac was ligated to control cerebrospinal fluid (CSF) leakage. Postoperatively, the patient experienced transient hypotension consistent with autonomic dysfunction, but otherwise recovered well, achieving stable spinal alignment and resolution of her spinal deformity at three-month follow-up. This case highlights several critical considerations in managing SCA, including the need for long-segment fixation extending to the pelvis to reduce recurrence, the advantages of multirod constructs for enhanced mechanical durability, and the importance of addressing CSF leaks from thecal sac injury. AD and hemodynamic instability must also be anticipated in this patient population. SCA should be suspected in paraplegic patients presenting with new deformity or instability, even at a young age. Early recognition and individualized surgical planning, including long-segment fusion, anterior column support, and definitive dural management, are essential to achieve durable stabilization and prevent recurrence. This case also highlights the importance of considering SCA among the differential diagnoses for progressive deformity in chronically paraplegic patients, while also recognizing alternative etiologies such as prior destabilizing surgery. This case contributes to the limited literature on SCA in young patients and offers practical insights for complex spinal reconstruction in neurologically impaired patients.

Portable biofeedback technologies are increasingly used in rehabilitation; however, the validity of surface electromyography (sEMG) as a surrogate indicator of deep abdominal muscle function remains unclear. This study aimed to validate a portable sEMG-based visual biofeedback system by examining its relationship with ultrasound-derived measures of deep abdominal muscle activation. Twenty-nine healthy adults were randomly assigned to a Visual Biofeedback group (n = 14) or a Verbal Feedback group (n = 15). Both groups performed a standardized 2-week core stabilization program. Muscle activation of the deep abdominal muscle complex (transversus abdominis-internal oblique; TrA-IO) and external oblique (EO) was measured using sEMG (%MVIC), while ultrasound imaging was used to assess transversus abdominis thickness and contractile activity (ADIM-Rest index). Between-group differences and correlations between EMG and ultrasound variables were analyzed. The Visual Biofeedback group demonstrated significantly greater improvements in TrA-IO activation and in the preferential activation ratio (TrA-IO/EO) compared to the Verbal group (p = 0.004). Ultrasound analysis revealed significantly greater increases in TrA thickness and contractile activity in the Visual group (p < 0.001). A significant positive correlation was observed between changes in TrA-IO activation and TrA thickness (&#x3c1; = 0.51, p < 0.001). Portable sEMG-based visual biofeedback demonstrated physiological relevance by reflecting ultrasound-derived changes in deep abdominal muscle function. These findings support the use of sEMG as a practical surrogate tool for monitoring deep core muscle activation and highlight the potential of portable biofeedback systems in scalable and accessible rehabilitation.

Medical education in conflict-affected settings faces severe barriers, including collapsed infrastructure, faculty shortages, and sex-based exclusion. This study evaluated first-term learning outcomes among Afghan female medical trainees whose university education was interrupted following restrictions on women's access to higher education. Most participants resided in Afghanistan, with a minority in neighbouring countries. The Canadian Virtual Medical University Initiative (CVMUI) delivers a fully online, competency-based curriculum aligned with Entrustable Professional Activities (EPAs). We implemented a multi-system, pre-internship digital curriculum spanning five EPAs (cardiovascular, respiratory, gastrointestinal, musculoskeletal, and central nervous systems). Lecturio provided theoretical instruction, and CyberPatient enabled simulation-based clinical training. The study was conducted from November 2024 to July 2025 at a single virtual site (CVMUI). Ninety-two Level I-II female students who had previously been enrolled in medical school in Afghanistan were recruited; inclusion criteria were English proficiency (TOEFL iBT &#x2265;70), reliable internet access, and commitment &#x2265;15 h/week. The main outcomes were changes in knowledge (multiple-choice questions) and clinical skills (virtual Objective Structured Clinical Examinations) measured pre- and post-training. Secondary outcomes included student satisfaction and curriculum quality. Analyses used Shapiro-Wilk tests for normality, paired t-tests or Wilcoxon tests, and effect sizes. Level I students demonstrated significant knowledge gains across all systems (mean increases +52 to +58 points; p < 0.0001). Level II students showed substantial improvements in knowledge, clinical performance, and total scores (mean differences 57-62 points; large effect sizes). More than 90% of participants rated learning objectives, feedback, and educational climate as very good or excellent. Convergent findings across knowledge, skills, and satisfaction supported strong construct validity and alignment with prior pilot data. A fully digital, simulation-enabled, competency-based model can deliver high-quality pre-internship medical education in conflict-affected settings, offering a scalable and equitable pathway for women excluded from traditional training. Future work should examine integration of this model into national and international credentialing frameworks. CanHealth International and philanthropic contributions.

Early identification of speech, language, hearing, social communication, and feeding difficulties depends substantially on parental recognition of developmental red flags. In Saudi Arabia, limited parental awareness may contribute to delayed referral and intervention. This pilot study evaluated the preliminary effectiveness of a 25-item Arabic gamified quiz in improving parental recognition of early developmental red flags in children from birth to 3 years of age. A single-session pre-post interventional design was used. Parents completed a pre-test, engaged with the Arabic gamified quiz, and then completed a post-test during the same online session. The quiz was designed as a multiple-choice, scenario-based educational tool with immediate explanatory feedback. Seventy-nine participants completed both assessments and were included in the matched analysis. Mean knowledge scores increased significantly from 78.6% to 92.5% (p < 0.001), with a large effect size (Cohen's d = 1.04). Most participants (87.3%) demonstrated improved post-test scores. These findings suggest that a brief Arabic gamified quiz may be a promising caregiver-facing educational tool for improving recognition of early developmental red flags in Arabic-speaking populations. Further research is needed to assess long-term knowledge retention, behavioral impact, and broader implementation.

Despite the prevalence of applications which offer remote patient monitoring (RPM) in chronic illness management, the use of RPM in healthcare systems remains low. There is limited use of data generated in an RPM, to describe the work of clinicians. The study's aim was the use a novel evaluation method to characterise the nurse work in an RPM intervention. A convergent mixed methods design was used to perform secondary analysis on feasibility trial data. Data sets: (1) RPM data set: data generated by usage, and (2) Interview data set: nurse user interviews. In the RPM data set, patient entered data, software data labelling, and nurse responses were defined as data strings. Quantitative variables were summarized using descriptive statistics. Both data sets were analysed using systematic text condensation. In the RPM data set themes were translated into quantitative data enabling reportable links to RPM functionality. Themes converged and narrative integration triangulated the data. Seven patients (heart failure (n=4); colorectal cancer (n=3)) and eight nurses were selected. Their RPM intervention engagement generated a mean of 97 data strings. Converging themes included: (1) intervention technical and operational work, (2) digitally enabled care management and coordination, and (3) educational and relational work. The evaluation method provided a detailed characterization of the digital nurse work performed in the RPM intervention. While data presented is foundational, it highlights the potential of how evaluation of nurse engagement with RPM application may provide essential feedback to refine digital interventions to ensure efficient integration in healthcare systems. Our study looked at how nurses use remote patient monitoring (RPM) app to support people with long-term health conditions. Our goal was to better understand the types of work nurses do in digital health interventions to improve the design of future RPM systems. There are lots of new RPM apps being created, but the use of them in healthcare systems outside of research studies is limited. Rarely is the data generated using apps used to describe the work of nurses around such digital health tools. We re-analysed data from a feasibility study of an RPM intervention. We combined two types of information: data automatically recorded by the RPM app when patients and nurses used it and interviews from the nurses. We called each occurrence of patient-entered information, app labels and nurse responses as a &#x201c;data string&#x201d;. We used both simple counts and detailed text analysis to describe the work nurses did around the app. Seven patients and eight nurses took part and on average generated 97 data strings per patient. By combing the data, we observe three types of digital nursing work: technical and operational tasks to keep the intervention running; digitally enabled care management and coordination; and educational and relational work to support patients. Our new way of evaluating digital health applications provided clearer pictures of the work nurses do around an RPM app. These findings are early stage, but they show that analysing detailed usage data together with nurse interviews can give important feedback which will improve digital health tools. These improvements will better support nurses and increase adoption in healthcare services.

This study aimed to explore nurses' experiences with the implementation of infection control measures, with a particular focus on perceived barriers, compliance practices, and organizational support mechanisms. While infection control is a critical component of patient safety, the practical implementation of infection control protocols often encounters systemic and environmental barriers, particularly in inpatient care settings. A qualitative study was conducted using the interpretative phenomenological analysis (IPA) approach. Semistructured interviews were conducted online via Zoom with 15 nurses working in internal medicine units at a university hospital in T&#xfc;rkiye between September and December 2024. Each participant had at least 2&#x2009;years of professional experience. Data were analyzed inductively in line with IPA principles to identify recurring themes and meaning structures. Four main themes emerged: (1) Awareness and Compliance, reflecting nurses' knowledge and routine practices related to infection control; (2) Challenges in Implementation, including workload pressures, staffing shortages, and physical environment limitations; (3) The Role of Training, emphasizing the perceived value of interactive and practice-based education; and (4) Monitoring and Continuous Improvement, highlighting the need for regular audits and constructive feedback mechanisms. Notably, the barriers identified were predominantly organizational and systemic rather than individual, underscoring the importance of institutional support. Although nurses demonstrated strong awareness and commitment to infection prevention, structural and managerial challenges appeared to limit the consistent implementation of infection control measures. Strengthening institutional support, revising audit mechanisms, and enhancing staff engagement may help support sustainable infection control practices. Nurse leaders and healthcare administrators may consider addressing workforce capacity, supporting practice-oriented training approaches, and implementing constructive supervision and feedback systems to enhance adherence to infection control standards and promote a culture of safety in clinical practice.

In the context of population aging and the growing burden of chronic conditions, promoting exercise participation has become an important strategy for supporting healthy aging. However, older adults with chronic conditions often face multiple constraints related to symptom burden, risk perception, and everyday life. A theory-informed understanding of the determinants of exercise participation in this population is therefore needed. This study adopted a theory-informed qualitative descriptive design and conducted face-to-face semi-structured interviews with 30 community-dwelling older adults with chronic conditions. Purposive sampling was used to ensure variation in age, sex, chronic condition type, and exercise participation. Data were analyzed using the framework method guided by the Theoretical Domains Framework (TDF), and the resulting themes were subsequently mapped onto the Capability, Opportunity, Motivation-Behavior (COM-B) model. Participants were aged 60-86 years, and most were women, had low educational attainment, came from rural backgrounds, and lived with multimorbidity. Participants described exercise participation as a day-to-day process of negotiating symptoms, risk, functional boundaries, and everyday responsibilities rather than as a simple matter of willingness. Although most participants recognized the value of exercise, many lacked disease-specific knowledge about suitable exercise types, safe intensity, progression, and warning signs. Symptom burden and functional limitations constrained exercise, but many participants used symptom-based self-regulation strategies, such as resting, slowing down, or modifying activity when discomfort occurred. Family members, peers, health professionals, and community resources could either facilitate exercise or restrict it, depending on their accessibility, continuity, specificity, and practical relevance. Continued participation was closely linked to perceived benefits, controllable risk, self-efficacy, positive emotional experience, and immediate bodily feedback. Exercise promotion for older adults with chronic conditions should move beyond general advice and provide disease-adapted exercise education, symptom-based self-regulation strategies, family and peer support, professional guidance, age-friendly community resources, and feedback mechanisms that support long-term maintenance.

Autonomous mobile robots are used in optimizing warehouse logistics, yet achieving precise positioning during docking maneuvers and autonomous planning remains a technical challenge. This study presents a custom vision-based control system designed for an autonomous omnidirectional wheeled robot. The proposed methodology acquires visual feedback using a stereo camera integrated within the Robot Operating System framework. Two visual feedback control laws are formulated and rigorously evaluated: a Classic Position-Based Visual Servoing algorithm, which minimizes pose error using a quaternion-based approach, and a second solution that utilizes Dual Lie Algebra to compute the 3D visual sensor's velocities, ensuring convergence towards the desired point-feature configuration. Experimental validation reveals that while both methods achieve docking, the dual pose-free approach enables more robust, effortless movement of the robot platform than Classic Position-Based Visual Servoing. Consequently, these findings indicate that integrating depth-based feature recovery with advanced algebraic strategies offers a stable control strategy for automated industrial scenarios.

Background/Objectives: Parenting interventions are an effective way to support child development, and brief screening tools can support equitable implementation of parenting interventions by reducing program costs, increasing accessibility, and engaging populations who have traditionally been underserved. However, brief assessments are frequently overlooked and underutilized. The Family Check-Up (FCU) Online is a digital parenting intervention that integrates a brief FCU Online Assessment, feedback, and parenting skills via an app along with optional provider support. To date, no prior work has validated the FCU Online Assessment. Method: The current study combined two samples of parents participating in FCU Online studies and assessed: (1) reliability, (2) construct validity, (3) convergent validity by comparing FCU Online Assessment subscales to similar parenting and child behavior measures, and (4) predictive validity by using FCU Online Assessment at pretest to predict posttest scores as well as parenting and child behaviors at time 2 and time 3. Results: Strong reliability was found among all five subscales, including Low Conflict (7 items, &#x3b1; = .81), Positive Parenting Practices (11 items, &#x3b1; = .80), Positive School Behaviors (5 items, &#x3b1; = .83), Consistent Rules and Routines (11 items, &#x3b1; = .81), and Child Mental Health (5 items, &#x3b1; = .80). The FCU Online Assessment demonstrated construct and convergent validity, as well as predictive validity in that the FCU Online Assessment at pretest predicted posttest scores. Conclusions: The FCU Online Assessment is a brief, reliable, and valid measure of parenting and child wellbeing. It can be used by parents and providers alike to evaluate parenting skills and child mental health, develop targeted goals and intervention approaches, and assess family wellbeing over time.

Antimicrobial stewardship programs (ASPs) are critical for promoting rational antibiotic use. While early implementation outcomes have been reported, extended follow-up sustainability and the impact on high-priority broad-spectrum antibiotics in South Korean secondary/tertiary hospitals require further validation. This study aimed to evaluate the extended outcomes and sustainability of an ASP over a 14-month period. This retrospective, single-center study analyzed ASP activities from January 2025 to February 2026 at a tertiary hospital in South Korea. Interventions included prospective audit and feedback (PAF) for restricted antibiotics and recommendations for prolonged prescriptions (&#x2265;14 days). Primary outcomes were the monthly rejection rate of restricted antibiotics and the acceptance rate of ASP interventions. Secondary outcomes included the days of therapy (DOT) per 1000 patient-days for meropenem and piperacillin/tazobactam (Pip/Taz). During the 14-month period, the ASP intervention acceptance rate increased significantly from a mean of 72.0% in the implementation phase (January-April 2025) to 81.2% in the stabilization phase (May 2025-February 2026) (p = 0.035). The DOT for Pip/Taz decreased significantly from 169.4 to 151.8 per 1000 patient-days (p = 0.002), with a significant negative correlation identified between the intervention acceptance rate and Pip/Taz consumption (r = -0.625, p = 0.017). Although overall meropenem DOT showed seasonal fluctuations without reaching statistical significance across phases, a year-over-year comparison revealed a 7.5% reduction in meropenem DOT (January-February 2025: 54.8 vs. January-February 2026: 50.7 per 1000 patient-days). The rejection rate for restricted antibiotics declined from 3.8% to 2.6%, suggesting that clinicians increasingly self-regulated inappropriate prescribing attempts. The ASP demonstrated extended follow-up sustainability with a significant reduction in the consumption of key broad-spectrum antibiotics. A progressive increase in clinician acceptance of ASP interventions from 72.0% to 81.2%, combined with a concurrent decline in the restricted antibiotic rejection rate, reflected a measurable shift in institutional prescribing culture and confirmed the successful transition to a stabilized program. These findings support the necessity of sustained multidisciplinary ASPs, even in resource-limited settings, to combat antimicrobial resistance effectively.

Tumor metastasis constitutes a frequent contributor to high mortality rates, with EMT intimately implicated in this disseminative process. Accumulating evidence in recent years indicates that neoplastic cells undergoing EMT frequently exhibit concurrent metabolic reprogramming. Multiple modalities-including glycolysis, mitochondrial oxidative phosphorylation, lipid metabolism, as well as amino acid metabolism-cooperatively supply energy, facilitate membrane remodeling, and sustain redox homeostasis. Specifically, glycolytic flux, oxidative phosphorylation, lipid turnover, and amino acid catabolism/anabolism function in a concerted manner to meet the bioenergetic demands, support biogenesis of cellular membranes, and preserve the intracellular redox equilibrium during phenotypic conversion. Notably, intermediate metabolites can in turn modulate the trajectory of EMT through signal transduction cascades or epigenetic modifications. This review systematically delineates the bidirectional regulatory circuitry interconnecting EMT and metabolic reprogramming; furthermore, it examines the implications of this crosstalk for neoplastic disease progression. Finally, therapeutic strategies targeting the nexus of metabolic reprogramming and EMT are summarized.

This paper introduces an electrocardiogram (ECG) noise removal front-end amplifier circuit based on a current-feedback operational amplifier (CFOA) that uses the current feedback to detect error signals and control the output. This ECG circuit focuses on denoising the ECG noise to accentuate the ECG electrical signals from the heart. Noises in ECG refer to baseline wander (BW), powerline interference (PLI) and motion artifacts. We proposed a CFOA-based ECG pre-amplifier using the AD844 commercial operational amplifier built inside with a positive second-generation current conveyor (CCII+) and a voltage follower circuit. This work introduces an ECG noise removal front-end amplifier based on a CFOA. The primary innovation lies in the balancing instrumentation amplifier architecture that utilizes the high-speed and robust properties of the AD844 commercial operational amplifier to achieve superior noise rejection. To protect against high-frequency interference, we introduce a novel cascaded low-pass filter (LPF) stage that ensures a sharper cut-off compared to traditional single-stage designs. Experimental results validate the design's effectiveness, achieving a high common-mode rejection ratio (CMRR) of 75.4 dB and a mid-band gain of 46.5 dB. These performance metrics, combined with the circuit's ability to eliminate BW and PLI, confirm its robust suitability for high-fidelity wearable ECG monitoring.