Abstract: Background/Objectives: Type 2 diabetes (T2D) affects more than 38 million Americans and remains a leading public health challenge. Behavioral self-management is central to glycemic control but is often undermined by dysregulated and addictive-like eating. Continuous glucose monitoring (CGM) offers immediate feedback that may strengthen self-regulation, yet the psychological processes linking CGM use, food addiction (FA), and behavior change are poorly understood. This secondary mixed-methods study examined how CGM-supported group medical visits (GMVs) influence glycemic outcomes and FA symptoms in adults with diabetes. Methods: Adults with T2D participated in a 14-week GMV program integrating CGM review with education on nutrition, physical activity, sleep, stress, and intermittent fasting. Thirteen participants had paired CGM summaries and psychosocial data. Quantitative outcomes included mean glucose, glycemic variability, time-in-range (TIR) and symptoms of food addiction using the modified Yale Food Addiction Scale 2.0 (mYFAS 2.0). Qualitative data came from open-ended surveys analyzed using reflexive thematic analysis. Integration followed a convergent design, merging individual change trajectories with thematic interpretations and case vignettes. Results: Mean glucose decreased by 21 mg/dL and TIR improved by 9 percentage points. Among six participants with baseline FA symptoms, all showed improvement. Four moved from mild to no symptoms, one from moderate to no symptoms, and one from severe to no symptoms. Across the full sample, the mean change was a reduction of 1.2 in the mYFAS 2.0 symptom counts per participant. Thematic analysis identified four interrelated psychological mechanisms: enhanced awareness of food–glucose relationships, increased accountability through shared tracking, motivation via gamified self-monitoring, and relief from cognitive burden associated with dietary uncertainty. Conclusions: Integrating CGM feedback into GMVs may reduce addictive-like eating and promote glycemic improvement by enhancing awareness, accountability, and self-regulatory engagement. These findings position CGM as a behavioral intervention tool that complements its traditional monitoring role and highlight the value of combining real-time biofeedback with group-based support in diabetes care.

Abstract: Background: In triple-negative breast cancer (TNBC), the addition of immunotherapy has significantly improved outcomes. Immune-related adverse events (irAEs) can be accelerated in patients with pre-existing autoimmune (AI) conditions. The treatment-response standardised protocol used in clinical care raises concerns about the need for right-sizing strategies. As the use of immunotherapy expands, recognising toxicity from recurrence and optimising response-adapted approaches are essential to balance cure with quality of survival. Case Presentation: A 38-year-old pregnant woman with a distant history of uveitis and psoriasis was discovered to have pregnancy-associated TNBC. Postnatally, she was treated with neoadjuvant chemotherapy and pembrolizumab, followed by wire-guided left breast wide local excision and sentinel lymph node biopsy of left axilla. After surgery, residual cancer was noted. She continued adjuvant pembrolizumab and adjuvant radiotherapy 40.05Gy/15fr to the breast and nodes, followed by 13.35Gy/5fr. Despite a persistent residual tumour, pembrolizumab was continued as per protocol in a response-agnostic manner. At the end of one year of adjuvant pembrolizumab, she developed progressive numbness and weakness in the ipsilateral arm, initially raising suspicion for local recurrence. Comprehensive MRI and PET-CT imaging did not identify recurrent tumour or new metastatic disease. Electromyography confirmed a lower-trunk brachial plexopathy without a structural cause. An immune-mediated process was diagnosed by a process of elimination. Despite treatment with 1st-line high-dose corticosteroids and 2nd-line intravenous immunoglobulin (IVIG), improvement was limited. Therapeutic plasmapheresis, theoretically removing circulating immune complexes, cytokines, or checkpoint inhibitors, led to marked functional recovery and symptom resolution 20 months later. Discussion: Four main challenges are identified: (1) the diagnostic difficulty in identifying local recurrence or radiation injury from immune-related neuropathy; (2) the emerging therapeutic role of plasmapheresis in steroid-refractory irAEs; (3) the possible inconsistencies between rare toxicities observed in clinical trials vs clinical practice; and (4) the limitations in response in adjuvant therapy, particularly in patients with coexisting AI conditions. Conclusion: Although N-irAEs are rare, survival in early TNBC declines markedly when they occur during treatment. Early recognition and accurate distinction from tumour recurrence, as well as support for plasmapheresis as a potential option in steroid-refractory presentations, have been shown to improve patient survival and symptom reduction. With increasing use of immunotherapy, real-world toxicity data, predictive biomarkers, and personalised treatment strategies are urgently needed to balance cure with long-term functional outcomes.

Abstract: The advancement of autonomous driving technologies necessitates the development of sophisticated object detection systems capable of integrating heterogeneous sensor data to overcome the inherent limitations of unimodal approaches. While multi-modal fusion strategies offer promising solutions, they confront significant challenges including data alignment complexities in early fusion and computational burdens coupled with overfitting risks in deep fusion methodologies. We propose a Multi-modal Multi-class Late Fusion (MMLF) framework that operates at the decision level. This design preserves the architectural integrity of individual detectors and facilitates the flexible integration of diverse modalities. A key innovation of our approach is the incorporation of an evidence-theoretic uncertainty quantification mechanism, built upon Dempster-Shafer theory, which provides a mathematically grounded measure of confidence and significantly enhances the reliability and interpretability of the detection outcomes. Comprehensive experimental evaluation on the KITTI benchmark dataset demonstrates that our method achieves substantial performance improvements across multiple metrics, including 2D detection, 3D localization, and bird’s-eye view tasks. The framework reduces uncertainty estimates across different object categories. This work provides a versatile and scalable solution for multi-modal object detection that effectively addresses critical challenges in autonomous driving applications.

Abstract: To ensure the security and confidentiality of various data types (including text, images, audio, and video), this paper proposes a multi-wavelet figure-and-text hiding algorithm (MWFTHA) and its corresponding multi-wavelet figure-and-text restoration algorithm (MWFTRA). These algorithms facilitate the encoding and embedding of text and color images into a one-dimensional signal through multi-wavelet transforms. Text data is encoded using a character dataset, while color images are processed via a linear transformation before being integrated into the signal. Subsequently, the original text and image can be precisely retrieved from the synthesized signal using MWFTRA. An illustrative case demonstrates the efficacy of this approach. The efficiency of MWFTHA and MWFTRA is verified through 1,000 simulations. The results indicate rapid data hiding and recovery, as indicated by the mean execution time and standard deviation. The method's performance is evaluated using the structural similarity index measure (SSIM) and peak signal-to-noise ratio (PSNR), which indicate slight improvements in quality relative to traditional wavelet and integer wavelet transforms. Additionally, the system's security is analyzed, with a focus on private-key mechanisms and resistance to data tampering. This steganography technology provides a robust solution for the secure transmission and storage of sensitive data, thereby reducing the risk of information leakage.

Abstract: Safety management in the chemical process industry remains a critical challenge due to recurring high impact industrial accidents and the limited predictive capability of conventional threshold based safety systems. Traditional PLC–SCADA frameworks rely on static alarm limits and reactive shutdown logic, which often fail to detect early stage nonlinear deviations in complex, multivariate processes. This study presents ChemSafeAI+, a machine learning driven dynamic safety and optimization framework designed to augment existing industrial control architectures. The system integrates real-time anomaly detection using gradient-boosting models, predictive analytics, safety action processing, operator aware visualization dashboards, and traceable console logging within a unified, modular architecture. The framework is evaluated using a validated synthetic dataset derived from the Haber–Bosch ammonia synthesis process, capturing realistic thermodynamic, kinetic, and operational variability across 5000 operating scenarios. Experimental results demonstrate strong anomaly detection capability and consistent early warning behavior across multiple abnormal operating conditions. SHAP-based explainability provides both global and local interpretability, aligning model decisions with domain relevant process variables. By combining predictive intelligence with safety oriented decision logic and operator traceability, ChemSafeAI+ demonstrates the feasibility of ML driven supervisory safety systems for proactive risk mitigation and improved operational resilience in industrial chemical environments.

Abstract: As the threat landscape advances and pressure to reduce the energy footprint grows, it is crucial to understand how security mechanisms affect the power consumption of cloud-native platforms. Although several studies in this domain have investigated the performance impact of security practices or the energy characteristics of containerized applications, their combined effect remains largely underexplored. This study examines how common Kubernetes (K8s) safeguards influence cluster energy use across varying security configurations and workload conditions. By employing runtime and network monitoring, encryption, and vulnerability-scanning tools under diverse workloads (idle, stressed, realistic application), we compare the baseline system behavior against the energy consumption introduced by each security configuration. Our findings reveal that always-on security mechanisms impose a persistent baseline energy cost—occasionally making an idle protected cluster comparable to a heavily loaded unprotected one, while security under load results in substantial incremental overhead. In particular, service meshes and full-tunnel encryption show the largest sustained overhead, while eBPF telemetry, network security monitoring, and vulnerability scans add modest or short-lived costs. These findings provide useful security-energy insights and trade-offs for configuring K8s in resource-constrained settings, including IoT/smart city deployments.

Abstract: Shapley values are the most widely used point-valued solution concept for cooperative games and have recently garnered attention for their applicability in explainable machine learning. Due to the complexity of Shapley value computation, users mostly resort to Monte Carlo approximations for large problems. We take a detailed look at an approximation method grounded in multilinear extensions proposed by Okhrati and Lipani (2021) under the name Owen sampling. We point out why Owen sampling is biased and propose unbiased alternatives based on combining multilinear extensions with stratified sampling and importance sampling. Finally, we discuss empirical results of the presented algorithms for various cooperative games including real-world explainability scenarios.

Abstract: This study was designed to explore the effects of Qi Ling Gui Fu Prescription on tissue fibrosis in broilers with ascites syndrome (AS). A total of 120 8-day-old Ross broilers were randomly divided into six groups: the Blank group (B group), the Model group (M group), the High and Low dose of Qi Ling Gui Fu Prescription groups (H and L groups), the Positive Chinese medicine group (P group), and the L-arginine group (L-arg group). The broilers at 35 days old were dissected to record the ascites heart index (AHI). The collagen fibers in the lung were observed using Masson’s trichrome stain, and the indexes of serum oxidative stress were measured. Enzyme-linked immunosorbent assay was used to detect the contents of tumor necrosis factor alpha, interleukin (IL)-4, IL-10, and IL-1β in different tissues. Compared to the B group, the ascites heart index in the M group was significantly increased (P < 0.01); the results of Masson’s trichrome stain showed an accumulation of collagen fiber in lung tissue; there was no significant difference in serum concentration of glutathione peroxidase (P > 0.05); the malondialdehyde concentration was notably elevated (P < 0.01), while the superoxide dismutase concentration was markedly decreased (P < 0.05); moreover, the contents of tumor necrosis factor alpha, IL-4, and IL-1β proteins were considerably raised in all tissues (P < 0.01), while the content of IL-10 protein was significantly reduced (P < 0.01, P < 0.05). Relative to the M group, the above indexes in all treatment groups were improved to different degrees (P < 0.01, P < 0.05), with the H group showing the most significant effect. Qi Ling Gui Fu Prescription and L-arg can improve the fibrosis of broilers with AS by mediating oxidative stress, and moderating inflammatory responses. Notably, the effect of a high dose of Qi Ling Gui Fu Prescription was better, which can prevent and treat AS in broilers more effectively.

Abstract:

Extreme precipitation poses a major natural hazard in the western Mediterranean, particularly along the Valencia coast, where torrential events recur with significant societal impacts. This study evaluates the feasibility and added value of an explicitly spatial approach for estimating return periods of extreme precipitation in the Júcar and Turia basins, moving beyond traditional point-based or micro-catchment analyses. Our methodology consists of progressive spatial aggregation of time series within a basin to better estimate return periods of exceeding specific catastrophic rainfall thresholds. This technique allows us to compare 10-min rainfall data of a reference station (e.g. Turis, València, 29 October 2024 catastrophe) with long-term annual maxima from 98 stations. Temporal structure is characterized using the fractal--intermittency \( n \)-index, while tail behavior is modeled using several extreme-value distributions (Gumbel, GEV, Weibull, Gamma, and Pareto) and guided by empirical errors. Results show that return periods systematically decrease and stabilize as stations are added, forming a plateau with about 15-20 stations, once the relevant spatial heterogeneity is sampled. The analysis of the precipitation in the 2024 catastrophe highlights the role of time concentration of large amounts over short effective durations. Overall, the results demonstrate that spatially-integrated return-period estimation is operational, physically consistent, and better suited for basin-scale risk assessment than purely point-based approaches.

Abstract: Airway management in head/neck cancer patients with post-radiotherapy is challenging in the real world. In this case report, we described our practice with StyleTubation (using video intubating stylet technique) in combination of awake intubation strategy (local anesthetics topicalization, nerve blocks, deep sedation, and nasal high-flow oxygenation) in a patient with nasopharyngeal carcinoma, neck radiation fibrosis, and instrumented with halo-vest stabilization. The trans-oral endotracheal intubation was smooth (with first-pass success) and swift (28 s) without any complications.

Abstract: This study aimed to investigate whether the consumption of low-molecular weight polyphenols (LMWP, Oligonol) affects metabolic status related to fatigue and oxidative stress responses during a maximal exercise test in healthy young men. A blinded, crossover design was employed, con-sisting of a placebo condition, single consumption of LMWP (S-LMWP), and 5-day consumption of LMWP (5-LMWP), with washout intervals of at least two weeks between interventions. Among the volunteers, ten participants who met the criteria were finally enrolled in the study. Exercise performance, fatigue-related metabolic parameters, and oxidative stress markers were measured before and immediately after the maximal exercise test, as well as after a 30-min recovery period. Heart rate and lactate, as key fatigue-related parameters, were additionally assessed for 5 min immediately following the exercise. Exercise performance, and anthropometric parameters were not significantly different among the groups. However, both LMWP groups showed significantly lower blood lactate levels at the 30-min recovery period compared with placebo group. Addi-tionally, malondialdehyde levels which increased immediately after exercise, significantly re-covered toward baseline levels at 30 min in the LMWP groups, particularly in the S-LMWP group. In conclusion, short-term consumption of Oligonol may attenuate exercise-induced fatigue and oxidative stress responses during a maximal exercise test.

Abstract:

Background: High-voltage electrical injuries, though less common than domestic electrocution in developing countries, can be devastating, particularly when involving bilateral lower limbs. These injuries pose significant challenges due to prolonged immobilisation and complications such as deep vein thrombosis, pressure ulcers, and potential limb loss. Case Presentation: We report the case of a 60-year-old male who sustained bilateral ankle high-voltage electrical burns following accidental contact with an overhead transmission line. The injuries resulted in extensive soft-tissue loss and exposed joints. Management: The patient was managed through a multidisciplinary approach involving the burns team, orthopaedic surgeons, rehabilitation specialists, and social counsellors. The treatment strategy included serial debridements, negative pressure wound therapy (NPWT), skeletal stabilisation using hybrid Ilizarov external fixators, and staged soft-tissue reconstruction with a reverse sural artery flap and split-thickness skin grafting. Outcome: The limb salvage outcome was successful, with progressive wound healing and functional recovery. The coordinated involvement of multiple specialties played a pivotal role in managing this complex case. Conclusion: This case highlights the importance of early multidisciplinary collaboration in the successful management of high-voltage electrical injuries, particularly those involving bilateral lower extremities with joint exposure.

Abstract: Background/Objectives: A comprehensive geriatric assessment (CGA) was demon-strated to reduce treatment-related toxicities CTCAE III-V° in older adults with cancer undergoing systemic cancer treatments. However, practical implementation of this important procedure is insufficient. To evaluate the feasibility of implementing CGA into routine care and multidisciplinary tumor boards (MDTs) in Germany, we per-formed this bicentric feasibility trial. Methods: Patients ≥65 years with positive geriatric screening (G8< 15 points) and all patients ≥70 years received CGA as part of their routine care. Results were presented during MDT discussions to derive treatment recommendations. After CGA, patients were asked for trial participation which included data analysis and a telephone fol-low-up after 3 months. Physicians participating in the MDT were asked about the added value of CGA presentation. Primary endpoint was the estimation of patient’s willing-ness to participate with an accuracy of ± 7.5% to inform design for a later effectiveness trial. Results: 75 patients received CGA. Of those, 72 (96%) agreed to participate (95% confi-dence interval, [0.8875; 0.9917]). With an accuracy of estimating the willingness to par-ticipate of < |7.5%|, the primary endpoint was reached. The median age was 76.6 years (range: 69-92 years). A member of the geriatric team attended 2/3 of the MDT meetings. Physicians rated the integration of CGA results predominantly as useful. Conclusions: Integration of CGA into routine care of older cancer patients is feasible but will likely require adequate geriatric staffing per center. A larger implementation study, evaluating efficacy and cost effectiveness in the German healthcare system, is necessary.

Abstract: Background/Objectives: Digital health encompasses telemedicine, mobile health (mHealth), wearable technologies, big data analytics, artificial intelligence (AI), machine learning (ML), and immersive technologies. In oncology, where care is complex, multidisciplinary, and longitudinal, these tools offer opportunities to enhance prevention, early detection, treatment planning, patient–clinician communication, survivorship, and palliative care. However, inconsistent definitions and ongoing ethical, regulatory, and implementation challenges hinder optimal integration. This review aims to synthesize current evidence on digital health in oncology and examine its applications across the cancer care continuum. Methods: A comprehensive narrative review of peer-reviewed literature was conducted, including clinical studies, trials, and systematic reviews evaluating digital health technologies in oncology. Evidence was organized according to key phases of the cancer care continuum, from prevention and diagnosis to treatment delivery, survivorship, and end-of-life care. Results: Digital health applications extend beyond virtual consultations. AI- and ML-driven systems support diagnostics, medical imaging, genomics, and treatment planning, while mHealth applications and wearable devices enable real-time symptom monitoring, toxicity reporting, and long-term follow-up. Digital education and communication platforms improve shared decision-making and patient engagement. Across diverse oncology settings, these tools demonstrate feasibility, high patient and clinician satisfaction, and potential improvements in care coordination and efficiency. Nevertheless, challenges related to data quality, interoperability, privacy, algorithmic bias, equity of access, and regulatory oversight persist. Conclusions: Digital health is increasingly embedded across the oncology care continuum and holds substantial promise for advancing personalized, patient-centred cancer care. Continued multidisciplinary collaboration, robust clinical validation, and responsible governance are essential to ensure safe, equitable, and clinically meaningful global implementation.

Abstract:

Based on mode crosstalk theory, this paper develops a spontaneous Raman scattering (SpRS) model for the quantum-classical coexistence system using few-mode fiber (FMF) integrated with wavelength-division multiplexing (WDM) and spatial-division multiplexing (SDM). Through numerical calculations, the influence degrees of three factors (mode coupling, the number of modes and wavelengths) on SpRS have been analyzed. The investigation identifies the dominant contributors to SpRS and reveals their relative impact magnitudes. Based on these results, a ring-assisted FMF is proposed to mitigate noise impacts on quantum signals. Numerical results show that the optimized FMF enhances quantum signal transmission distance by up to 41.5%.

Abstract:

This paper introduces GISMOL (General Intelligent Systems Modelling Language), a Python library under active development for modeling and prototyping general intelligent systems based on the Constrained Object Hierarchies (COH) theoretical framework. COH provides a neuroscience-inspired 9-tuple model that integrates symbolic constraints with neural computation, addressing limitations in current AI paradigms that often separate statistical learning from symbolic reasoning. GISMOL aims to operationalize COH through modular components supporting hierarchical object composition, constraint-aware neural networks, multi-domain reasoning engines, and natural language understanding with constraint validation. To illustrate its potential, we present six conceptual case studies spanning healthcare, smart manufacturing, autonomous drone delivery, finance, governance, and education. These examples demonstrate how GISMOL can translate COH theory into executable prototypes that prioritize safety, compliance, and adaptability in solving complex real-world problems. Preliminary comparative analysis suggests GISMOL’s promise in explainability, modularity, and cross-domain applicability relative to existing frameworks. This work contributes both a theoretical foundation for neuro-symbolic integration and an evolving practical toolkit that seeks to bridge the gap between AGI theory and deployable intelligent systems.

Abstract: Earth observation data has significant potential to support sustainable finance. However, despite the interest and rapidly growing availability of earth observation data, uptake and integration is low within the sector. We explore the barriers experienced by the UK financial sector in using earth observation data for sustainable investment decision-making. We take a reflexive approach to explore the intersection of earth observation technology development, sustainable finance and responsible innovation with the intention of identifying opportunities to share understanding and increase responsible uptake within the sector. From our insights, we set out the stakeholders of earth observation data, their data needs and five challenges to the uptake of earth observation data for sustainable financial decision-making. We develop a baseline of needs across stakeholders and propose the inclusion of responsible innovation principles to support the development of earth observation applications for the sector.

Abstract:

Background: Diabetes among adults is becoming a major public health crisis in the United States. Numerous authors have documented the rising prevalence of diabetes, with notable variations found within the United States at the census level, state, and county levels. Yet, there is a need to understand whether diabetes prevalence varies between urban centers within a particular state. Methods: This ecological study provides a longitudinal investigation of the prevalence of adult diabetes across five major metropolitan urban areas in Texas (Austin, Dallas, Fort Worth, Houston, and San Antonio) from 2011 to 2023. By utilizing data from the Behavioral Risk Factor Surveillance System (BRFSS) and statistical testing, we evaluate both the temporal trajectory and city-level geographic disparities of diabetes prevalence. Findings: Upon aggregating all five urban centers, the findings demonstrate a significant statewide increase (β=0.12, P=0.026) in diabetes prevalence over the thirteen-year study period. Furthermore, profound regional variations were observed, with San Antonio having a significantly (P<0.05) higher prevalence than Austin, Dallas, and Fort Worth, with the San Antonio area exhibiting the highest mean prevalence at 12.1% and the Austin area maintaining the lowest at 9%. Interpretation: This research emphasizes the necessity for synchronized public health policies that account for localized contexts while addressing the broader metabolic crisis facing the Texas urban corridor.

Abstract: Rapid technological progress in the 21st century has transformed neuroradiology from primarily a diagnostic tool into a vital component of modern neuroscience. This abstract summarizes innovations that have marked a paradigm shift in neurological and neurosurgical patient care. From symptom onset through hospitalization and recovery, these advancements have introduced novel techniques and redefined existing models to improve short-term and long-term outcomes.Artificial intelligence (AI) integration has significant implications in stroke triage, brain tumor segmentation, and treatment planning. Wearable AI-enabled devices detect abnormal changes in movement and speech, prompting patients to seek care immediately to ensure they remain within the therapeutic window for reperfusion therapy. AI models can also identify tumors that may be undetectable to the naked eye and provide precise lesion margins, reducing overestimation and underestimation during surgical planning. Image-guided minimally invasive procedures have reduced reliance on open surgery while increasing precision, minimizing perioperative risk, shortening postoperative hospital stays, improving functional outcomes, and lowering recurrence rates. Refinements in stent and catheter technology have further enhanced the safety and efficiency of these procedures in treating neurological disorders. Advanced imaging enables neurosurgeons to target surgically inaccessible lesions while preserving surrounding tissue and to analyze tumor behavior to objectively predict treatment response. Innovations in neuroradiology address disparities in access to care, as minimally invasive approaches offer therapeutic options for patients ineligible for open surgery, and remote interventions aim to expand timely access to specialized treatment in underserved regions. Continued advancements in neuroradiology will further optimize interventions, enhancing patient care.

Abstract: Background: Cryptococcal meningitis (CM) remains a leading cause of mortality among people with advanced HIV disease (AHD) in sub-Saharan Africa. Current guidelines recommend induction therapy with amphotericin B and flucytosine, typically administered in an inpatient setting due to concerns over severe clinical presentation and drug-related toxicities. This requirement poses a significant burden on resource-limited health systems. We evaluated the real-world outcomes of a fully outpatient model for CM therapy in Maputo, Mozambique. Methods: A longitudinal retrospective cohort study was conducted at the Centro de Referência de Alto-Maé (CRAM), a specialized AHD outpatient clinic. We included 83 PLWH with laboratory-confirmed CM treated between October 2020 and December 2024. The primary outcome was hospitalization-free survival (HFS) within the first 10 weeks of treatment. Secondary outcomes included the frequency and severity of adverse drug reactions (ADRs), analysed by tracking haemoglobin (Hgb), potassium (K+), and creatinine (Creat) levels on days 1, 3, and 7 of induction therapy, and retention in care (RIC) at 6, 12, and 24 months. Statistical analyses included Kaplan-Meier survival estimates and paired t-tests. Results: The median age was 37 years (IQR: 27-42), 63.9% were male, and the median CD4 count was 62 cells/µL (IQR: 27-105). Most patients (95.2%) were symptomatic at presentation, and 56.6% had concurrent tuberculosis. For the 52 patients who completed the full induction protocol at CRAM, the HFS rate at 10 weeks was 84.6% (44/52), with an overall survival of 90.4% (47/52). ADR analysis (n=52) showed a predictable pattern of mild, manageable toxicity: a significant decline in Hgb (11.2 ± 1.8 to 10.6 ± 2.0 g/dL, p&lt;0.001) and K+ (4.27 ± 0.66 to 3.86 ± 0.78 mmol/L, p=0.008), and a transient increase in Creat (0.83 ± 0.42 to 1.13 ± 0.64 mg/dL, p=0.001) from day 1 to day 3, with stabilization or trend toward recovery by day 7. No significant differences in ADRs were found between single-dose (47%) and multiple-dose (53%) L-AmB regimens. RIC for the entire cohort (n=83) was high, at 81.9% at 6 months, declining to 74.0% at 12 months and 70.4% at 24 months. Conclusion: An ambulatory model for CM therapy is feasible and effective in a resource-limited setting, demonstrating high hospitalization-free survival, manageable and reversible adverse drug reactions, and excellent medium-term retention in care. These findings provide compelling evidence to reconsider the standard of inpatient care and support the integration of outpatient CM management into AHD care packages to alleviate health system burdens and improve patient outcomesAn integrated care approach is essential to improving survival in resource-limited settings.