Frontiers in medical technology最新文献

Conventional protocols for cancer treatment are usually accompanied by serious limitations, which can be addressed using innovative, minimally invasive methods, such as photothermal therapy (PTT). But, using this method as a standalone therapy is usually accompanied by uneven heat distribution within tumor tissues, and the risk of cancer recurrence. Integrating PTT with complementary therapeutic modalities can help prevent these issues. The development of nanophotothermal systems (NPTSs) as multipurpose, multimodal platforms for smart drug delivery has greatly broadened the therapeutic landscape of cancer treatment. Additionally, recent developments in bio-based and biocompatible nanomaterials have further expanded the potential of NPTSs in clinical applications. Furthermore, NPTSs combine therapeutic functionalities and diagnostic imaging, i.e., tracking modalities, such as photoacoustic, magnetic resonance, or fluorescence imaging, to provide real-time monitoring of drug delivery and treatment response. Translation of preclinical trials into clinical applications for these systems remains challenging due to issues such as toxicity, biodegradability, scalability, standardization, and regulatory approval. This narrative review analyzes two of the most versatile natural polysaccharides, chitosan (CS) and hyaluronic acid (HA), which have complementary physicochemical and biological properties. Covering the advantages of CS and HA for the application in multifuntional and multimodal nanophotothermal systems, their complementary effects, as well as their functional implications in therapeutic settings, this work aims to create a framework that will serve as a basis for further research in optimizing the design of multifunctional drug-delivery nanosystems, and at the same time, facilitate the transition of photothermal therapy from the bench to clinical use.

The loss of homeostasis of the local microenvironment is one of the processes encountered in chronic wound repair and is therefore a big challenge in clinical practice. Endogenous electric fields, being important biophysical cues in the wound bed, guide the direction of movement of the reparative cells, control the inflammatory process and help in the regeneration of the tissues. The classical external electrical stimulation equipment is strongly limited in the portability that can be offered and in the biocompatibility that can be ensured, which limits the wider clinical application of electroceutical approaches for wound care. Some recent developments in the field of electroactive materials give new methodologies for wound therapy by using electrical stimulation. In this article, first, the physiological process of wound closure and the origin and the properties of the endogenous electric fields are summarized. Then, it concentrates on three types of electroactive materials: conductive hydrogels that allow the effective conduction and enhancement of electrical signals through flexible interfaces; self-powered materials that generate their own power, e.g., by means of piezoelectricity or triboelectricity, transforming ambient mechanical energy into therapeutic electrical fields; and multifunctional composite materials that combine electrical stimulation together with antibacterial, exudate control, regulated drug delivery and immunomodulatory functions in a single system. Then the review explains in an ordered fashion the different mechanisms how the electric field can help wound repair, from the generation of directional signaling cues to the modulation of the inflammatory, proliferative and remodeling stages and also to the antimicrobial actions. Finally, it discusses existing barriers, i.e., the conflict between standard and personalized electrical stimulation parameters, the lack of long-term durability assessments and comprehensive biosafety evaluations. The review ends with some outlooks on the progress of future integrated closed-loop intelligent repair systems with sensing, decision-making and responsive functionalities.

Segmentation of ultrasound images is complex, and although deep learning has improved automation, trust remains limited, restricting adoption. Interpretability methods may help reveal model behaviours and failure modes, supporting more trustworthy AI systems. A U-Net with a ResNet-18 backbone was trained to segment pipes in images of the Edinburgh Pipe Phantom acquired using ten ultrasound probes (N = 162). Performance was assessed using five-fold cross-validation. Predictive entropy and variance across 20 Monte Carlo dropout forward passes were used as approximations of data-related and model-related uncertainty. Calibration was assessed using the Brier score and expected calibration error (ECE). Input-gradient saliency and Grad-CAM maps from four encoder stages and the final decoder block were evaluated. For exploratory prospective quality control, complete empty-mask failures were assessed directly from model output, while non-empty predictions were classified retrospectively using reference-based performance criteria and reference-free uncertainty and morphological features were evaluated as candidate indicators. The model achieved a Dice score of 0.81 ± 0.03 and an HD95 of 11.98 ± 3.39. Seven segmentation errors were identified: three complete failures in which no foreground mask was predicted and four non-empty predictions in which the incorrect pipe was segmented. Dice score was associated with the Brier score (Spearman ρ = -0.96) and ECE (ρ = -0.72), whereas predictive entropy and Monte Carlo dropout variance showed weak associations with segmentation performance (|ρ| < 0.4). In selected failure examples, uncertainty and activation sometimes overlapped the reference pipe region despite incorrect segmentation. Complete empty-mask failures were deterministically identifiable from the model output, whereas the evaluated reference-free indicators showed limited ability to identify poor-quality non-empty segmentations. Within this phantom dataset and model configuration, calibration metrics were more closely associated with segmentation quality than the evaluated uncertainty approximations. Attention and uncertainty maps provided complementary descriptive information about selected errors, but indicated spatial sensitivity rather than causal model reasoning. Simple output checks enabled deterministic identification of complete segmentation failures; however, the evaluated reference-free indicators were insufficient for validated prospective identification of subtler non-empty errors. These findings support multimodal model evaluation for EPP segmentation while emphasising the need for validation on independent datasets, devices and architectures in future studies.

Background: People living with rare diseases (PLWRD) often face significant challenges in receiving timely and accurate diagnoses, leading to what is known as a diagnostic odyssey. Digital phenotyping (DP) offers a promising solution by leveraging advanced technology to capture unique digital signatures associated with various rare diseases. This innovative approach not only aids in the identification of these conditions but also facilitates the detection of digital biomarkers (DBM). By utilizing DP and DBMs, we can improve both the diagnosis and management of RDs, ultimately leading to better health outcomes for affected participants.

Objective: To identify whether DBMs can be identified by DP utilizing 3D facial imaging techniques in outpatient settings in participants with RDs.

Design setting and participants: This multicentre observational study was conducted in 3 hospitals in Singapore. The eligible participants were male and female RD participants of various age groups. The study duration was 4 years and 8 months.

Interventions: Twenty participants of Chinese genetic ancestry were photographed using a 3D camera. Additionally, two participants with hereditary angioedema (HAE) were photographed during acute stages of disease flare-ups.

Main outcomes and measures: The obtained facial scans of participants (that included participants with HAE in non-acute phase) were plotted using Artificial Intelligence-powered software - Cliniface. The growth curves and facial landmarks obtained were compared against the growth curves of normal RD-unaffected individuals of Chinese genetic ancestry. The two participants with HAE were photographed qualitatively over a longer period of time, and their scans were plotted, yielding growth curves.

Results: Distinct facial markers such as periorbital swelling were identified in two qualitatively assessed HAE participants during flare-up stages. This provides an opportunity to explore and validate whether these facial signatures in a disease condition can be assigned as DBM for HAE.

Conclusions: and relevance: This study explores the utility of 3D facial analysis as a DBM in rare diseases such as HAE. These findings offer preliminary objective evidence that supports existing subjective reports of facial features in the literature. Additionally, while DP's diagnostic capabilities may be limited, it successfully identified DBM, which could facilitate disease monitoring in conditions such as HAE.

Sepsis remains a diagnostic challenge because early clinical manifestations are non-specific and microbiological confirmation often arrives too late to guide first-hour decisions. The 2026 Surviving Sepsis Campaign guidelines reinforce early clinical recognition using validated clinical scores, appropriate diagnostic strategies, and responsible antimicrobial use and de-escalation. Yet, initial management still frequently occurs under diagnostic uncertainty. Hundreds of biomarkers have been proposed to improve sepsis diagnosis, risk stratification and treatment guidance; however, only a limited subset has progressed from discovery to regulated in vitro diagnostic use. This Mini Review examines selected sepsis biomarkers and diagnostic platforms with emphasis on technologies that have reached certified or cleared IVD implementation, or that illustrate a credible route toward regulated use. We discuss conventional soluble biomarkers such as procalcitonin, C-reactive protein, interleukin-6 and lactate; emerging severity and host-response markers including presepsin, pancreatic stone protein, monocyte distribution width and mid-regional proadrenomedullin; transcriptomic and cellular host-response tests; and functional immune technologies such as the LPS-induced TNF-alpha release assay, also referred to as TARA. Rather than ranking biomarkers solely by diagnostic performance, we emphasize intended clinical use, biological rationale, sample type, turnaround time, regulatory status and workflow compatibility. The review highlights the persistent gap between biomarker discovery and clinical deployment, set against an evolving regulatory landscape shaped by the EU IVDR and the phased rollout of EUDAMED. Future sepsis diagnostics will likely move beyond isolated inflammatory analytes toward integrated host-response, near-patient and functional immune assays able to support early triage, risk stratification, antimicrobial stewardship and precision immune monitoring.

Introduction: The market for drug delivery devices (DDDs), including combination products that integrate pharmaceuticals and medical devices, has expanded rapidly with the increasing use of biopharmaceuticals and growing adoption of self-administration. However, the extent and causes of approval lag and approval loss for device presentations, defined as presentations approved in one country but unavailable in the other, have not been systematically evaluated between Japan and the United States.

Methods: We analyzed approvals of biopharmaceuticals in various device presentations, including prefilled syringes (PFS), autoinjectors, multidose pen injectors, on-body injectors (OBIs), and cartridge formulations, using the Pharmaceuticals and Medical Devices Agency (PMDA) and U.S. Food and Drug Administration (FDA) databases from 2000 to 2025.

Results: Among 176 biopharmaceutical products approved in the United States, 16% were not approved in Japan. However, most cases were attributable to approval loss by drug, reflecting products whose active substances were not approved for the Japanese market, rather than approval loss by device presentation. Although median approval lag tended to vary across device presentations, with multidose pen injectors and cartridge presentations showing median delays exceeding approximately two years and autoinjectors showing the shortest lag at approximately 10 months, these differences were not statistically significant. Statistical analyses using Pearson's chi-square, Fisher's exact, and Kruskal-Wallis tests showed no significant differences in device presentation approval between Japan and the United States after excluding products lacking active substance approval in either country (p = 0.961 and 0.963), or in approval lag among device presentations in Japan-first and U.S.-first products (p = 0.088 and 0.299).

Discussion: In the United States, multiple device presentations were often approved simultaneously, whereas Japan more frequently adopted a stepwise approval approach. Differences in the regulatory scope of combination products and co-packaging requirements also contributed to country- specific packaging, potentially prolonging pre-approval preparation and increasing supply chain complexity. Overall, regulatory approval discrepancies between Japan and the United States were driven primarily by drug loss rather than device presentation loss. Accelerating drug approval, promoting simultaneous development of multiple device presentations, and enhancing regulatory harmonization for combination products may reduce approval lag by device presentation and improve patient access.

As biomedical research moves toward greater use of human-based experimental systems, the practical problem is no longer simply how to develop new models, but how to determine when they are valid and predictable for a particular use. Independent testing infrastructure can help make that determination. The UC Davis NAMs Testing Center is one model for bringing comparative biology, engineering, clinical and computational expertise together to evaluate NAMs for defined biomedical research purposes.

Introduction: Clear role definitions in performing and interpreting neurophysiological and neuroimaging procedures are essential for patient safety and diagnostic accuracy. There is currently no comprehensive overview of the international landscape of health professionals responsible for these tasks. In the present review, we aimed to summarize local practices for electroencephalography (EEG), brain magnetic resonance imaging (MRI), transcranial magnetic stimulation (TMS), transcranial Doppler (TCD), and focused ultrasound stimulation (FUS).

Methods: We systematically searched scientific literature, national health organizations websites, and contacted local health authorities from 26 countries.

Results: EEG and brain MRI are typically performed by technicians and interpreted by physicians (neurologists or radiologists). TCD is performed and interpreted predominantly by physicians, since it requires real-time evaluation. Single-pulse TMS is either performed by physicians or requires the presence of physicians when the procedure is performed by a technician. The interpretation is done by physicians. Despite existing international recommendations, there is a significant absence of local guidelines for responsibilities in performing and interpreting TMS and FUS. In countries with information available, therapeutic use of rTMS, and FUS is performed by physicians (neurologists, psychiatrists, or surgeons).

Discussion: The development of standardized national protocols is needed to ensure patients' safety and diagnostic accuracy in clinical settings for TMS and FUS.

Problem: While voice-based analysis has emerged as a potential non-invasive approach for exploring acoustic alterations associated with Type 2 Diabetes Mellitus (T2DM), current approaches often fail to fully capture disease-relevant vocal patterns due to simplified recording protocols and insufficient feature modeling.

Aim: This study aims to investigate the feasibility of voice-based classification of T2DM using a multi-feature fusion framework, leveraging sustained vowel phonations and integrating multiple acoustic feature types to enhance diagnostic performance.

Methods: Voice recordings were collected from 378 participants, including T2DM patients and healthy controls. Each participant pronounced six Mandarin vowels, from which acoustic features-including Mel-frequency cepstral coefficients (MFCCs), glottal parameters, and eGeMAPS descriptors-were extracted. Neural networks, including convolutional and deep architectures, were applied to capture pathology-relevant segments. Vowel-level fusion modules aggregated features across vowels, and an attention-based hierarchical fusion mechanism combined the three feature types, adaptively emphasizing features most relevant to T2DM.

Results: The proposed dual-branch hierarchical fusion model achieved 80.48% detection accuracy.

Conclusion: These results suggest that sustained vowel phonations contain potentially informative acoustic patterns associated with T2DM status. However, further validation using independent cohorts is required before clinical application.