Search Results (5,791)

Background: This study examined the incidence of acute complications within the first year following combat-related lower extremity injuries in United States (U.S.) Service members (SMs). The research compared outcomes between primary amputation (PA), limb salvage (LS), and non-threatening limb trauma (NTLT) groups, and conducted a subgroup analysis within the LS group, differentiating between SM who underwent a secondary amputation (LS-SA) and those who did not (LS-NA). Methods: A retrospective analysis of combat-related lower extremity injuries sustained between January 2001 and October 2015 was performed using data from the Military Health System Medical Data Repository. Chi-square tests and adjusted logistic regression analysis were used to compare complication frequencies by injury severity. Results: The analysis of the 4275 SM revealed that 21% had undergone PA, 47% LS (with 13% experiencing LS-SA and 87% LS-NA), and NTLT was observed in 32% of cases. The PA group exhibited higher rates of most acute complications compared to other groups, with three exceptions—i.e., non-union fractures, compartment syndrome, and orthopedic device complications were more prevalent in the LS group than the PA group. The LS-SA group had higher complication rates than the LS-NA group for most complications. Notably, the PA group was associated with the highest rates of post-hemorrhagic anemia and heterotopic ossification, while the LS-SA group exhibited the highest rates of osteomyelitis, non-union fractures, non-healing wounds, and compartment syndrome. Conclusions: Individuals with amputation (PA or LS-SA) were more likely to experience acute complications compared to their counterparts (PA vs. LS and NTLT; LS-SA vs. LS-NA), with the exception of non-union fractures, which were more frequent in the LS group than the PA group. These findings highlight the need for close monitoring and targeted interventions to address post-surgical complications in Service members with limb salvage. Full article

In Maxillofacial Reconstruction Applications (MRA), nonunion is one of the critical complications after the reconstruction process and fracture treatment, including bone grafts and vascularized flap. Nonunion describes the failure of a fractured bone to heal and mend after an extended period. Different systems and methods have been developed to monitor bone regeneration and fracture healing during and after the treatment. However, the developed systems have limitations and are yet to be used in MRA. The proposed smart reconstruction plate is a microdevice that could be used in MRA for wireless monitoring of fracture healing by measuring the forces applied to the reconstruction plate. The device is wireless and can transmit the acquired data to a human–machine interface or an application. The designed system is small and suitable for use in MRA. The results of finite element analysis, as well as experimental verification, showed the functionality of the proposed system in measuring small changes on the surface strain of the reconstruction plate and determining the corresponding load. By using the proposed system, continuous monitoring of bone regeneration and fracture healing in oral and maxillofacial areas is possible. Full article

Identifying parathyroid glands during surgery is challenging and time-consuming due to their small size (3–5 mm) and camouflaged appearance in the background of the thyroid, lymph nodes, fat, and other neck structures. For the gland itself, it is also important to differentiate abnormal ones from normal ones. Accidental damage or removal of the normal glands can result in complications like hypocalcemia, which may necessitate lifelong medication dependence, and, in extreme cases, lead to death. The study of autofluorescence optical properties of normal and abnormal parathyroid glands and the surrounding tissue will be helpful for developing non-invasive detection devices. The near-infrared (NIR) autofluorescence characteristics of parathyroid and thyroid tissues have been studied extensively and are now used for parathyroid gland detection during surgery. Additionally, there have been a few reports on the UV-visible light-excited autofluorescence characteristics of these tissues with a focus on spectroscopy. However, there is a lack of high-resolution, side-by-side autofluorescence imaging comparisons of both tissue types under various excitation wavelengths, ranging from visible to NIR. We developed a standalone tabletop autofluorescence imaging system to acquire images of ex vivo specimens in the operating room under different excitation wavelengths: visible 405 nm, 454 nm, 520 nm, 628 nm, and NIR 780 nm. Autofluorescence imaging features of parathyroid adenomas for each excitation wavelength were described and compared. It was found that visible light excites much stronger autofluorescence from parathyroid adenoma tissue compared to NIR light. However, NIR excitation provides the best intensity difference/contrast between parathyroid adenoma and thyroid tissue, making it optimal for differentiating these two tissue types, and detecting parathyroid adenoma during surgery. The high fluorescent site under the NIR 780 nm excitation also generates high fluorescence under visible excitation wavelengths. Heterogeneous fluorescence patterns were observed in most of the parathyroid adenoma cases across all the excitation wavelengths. Full article

An elastic bandage or tensor bandage is widely known as a flexible medical device made of polymer materials. It is usually made of cotton and reinforced with elastic yarns. Depending on the therapy and clinical picture, elastic bandages are used for compression treatment and as support bandages. The aim of this work was to carry out a washing process and investigate its influence on the physical–mechanical properties of elastic bandages. The washing process was carried out at 40 °C with 25, 50, 60, and 70 wash cycles using Autowash 311L. The elastic bandages were subjected to a tensile load. The tensile strength, elongation, elasticity, and structural properties of the elastic bandages were determined. The results of the elongation show that the values increase with the wash cycles (in standard soap) and decrease after 70 cycles (in standard soap without phosphate). The tensile strength values are highest after 25 wash cycles. The results of the cyclic elasticity measurements show that the elastic bandages do not lose their elongation properties after the first cycle. After the second to fifth cycle, the samples do not return to their original or almost original dimensions when the tensile load is stopped. In addition, an analysis of the data from the 3D leg scanning and an approximation of the compression of the bandages was performed. The circularity and compression values after 60 washes remain within the limits in which the compression bandage can fulfill the compression function. Full article

This study presents an advanced model of thermal Resistances and heat Capacities model approach (RC model), applied to a custom-built skin calorimeter for the in vivo characterization of localized thermal behavior of the skin. The device integrates a heat flux sensor and a programmable thermostat, and is capable of measuring the heat flux, heat capacity, internal thermal resistance, and subcutaneous temperature of the skin, under both resting and exercising conditions. The model, refined through extensive experimental validation, incorporates the skin as part of the system and is adapted to three modes of operation: calibration base, ambient air, and direct skin contact. Simulations are used to analyze heat flux dynamics, optimize control parameters, and validate analytical expressions. Under resting conditions, the model enables the estimation of the skin’s heat capacity and thermal resistance. During exercise, it allows the determination of heat flux and internal temperature variations using simplified expressions. The system demonstrates high sensitivity (195.5 mV/W) and provides a robust, non-invasive method for extracting medically relevant thermal parameters from a 2 × 2 cm² skin area. Full article

This study examines how the effective dielectric characteristics of the human torso affect the carrier-link-margin (CLM) and data-link-margin (DLM) of a biocompatible gelatin-encapsulated implantable medical device (IMD) that consists of a small implantable antenna, battery, printed circuit board (PCB), camera, and sensor operating at 2.5 GHz. The specific absorption rate (SAR) and the radio frequency (RF) link performances of the IMD are tested for ±20% changes in reference to the mean values of the effective relative permittivity, ɛ_eff, and the effective conductivity, σ_eff, of the human body model. An artificial neural network (ANN) with two inputs (ɛ_eff, σ_eff) and five outputs (SAR_1g, SAR_10g, fractional bandwidth, CLM, and DLM) is trained by 80% of the total scenarios and tested by 20% of them in order to provide reliable dependent analyses. The highest changes in 1g SAR value, 10g SAR value, fractional bandwidth, CLM, and DLM at a 4 m distance for 100 Kbps are 63%, 41.6%, 17.97%, 26.79%, and 5.89%, respectively, when compared to the reference effective electrical properties of the homogeneous human body model. This work is the first to accurately depend on the electrical analyses of the human body for the link margins of an implantable antenna system. Furthermore, the work’s uniqueness is distinguished by the application of the CLM and DLM principles in the sphere of IMD communication. Full article

Ultra-reliable low-latency communication (URLLC) is a cornerstone of beyond 5G and future 6G networks, particularly for mission-critical applications such as the healthcare Internet of Things. In applications such as remote surgery, emergency services, and real-time health monitoring, it is imperative to ensure stringent latency and reliability requirements. However, the energy constraints of wearable and implantable medical devices pose stringent challenges to conventional URLLC methods. This paper proposes an energy-aware URLLC framework that dynamically prioritizes healthcare traffic to optimize transmission energy and reliability. The framework integrates a priority-aware packet scheduler, adaptive transmission control, and edge-enabled reliability management. Extensive Monte Carlo simulations are carried out on various network loads and varying edge computing delays to evaluate performance metrics, like latency, throughput, reliability score, energy consumption, delay violation rate, and Jain’s fairness index. Results illustrate that the suggested technique achieves lower latency, energy consumption, and delay violation rates and higher throughput and reliability scores, sacrificing Jain’s fairness index graciously at peak network overload. This study is a potential research lead for green URLLC in healthcare IoT systems to come. Full article

Failure Mode and Effect Analysis (FMEA) is crucial for identifying risk reduction opportunities in design. This study aims to aid in the design of sophisticated medical devices by setting guidelines and addressing weaknesses in data collection and risk priority numbers (RPNs). This is achieved by developing an FMEA framework with potential efficiency and efficacy benefits for design engineers, surgeons and patients. The FMEA framework covered risk analysis and risk evaluation by integrating a cascading Delphi method to address data collection and Multi-Criteria Decision-Making (MCDM) technique to address RPN calculations. This study involved the design of a flexible fetoscope for minimally invasive fetal intervention, analyzing and evaluating risks. The cascading FMEA framework had two stages for data collection, namely risk identification by individual interview and risk evaluation by individual email. The cascading Delphi FMEA framework with MCDM identified the potential risks for the mother at the tip (risk score = 0.927) and subsequent risks such as debris loss (risk score = 0.896), material degradation (risk score = 0.896), and glue dislodging (risk score = 0.896) as critical issues. By identifying failure modes early, medical device designers can better mitigate risks during the initial design stages. Full article

Printed electronics employ established printing methods to create low-cost, mechanically flexible devices including batteries, supercapacitors, sensors, antennas and RFID tags on plastic, paper and textile substrates. This review focuses on the specific contribution of screen printing to that landscape, examining how ink viscosity, mesh selection and squeegee dynamics govern film uniformity, pattern resolution and ultimately device performance. Recent progress in advanced ink systems is surveyed, highlighting carbon allotropes (graphene, carbon nano-onions, carbon nanotubes, graphite), silver and copper nanostructures, MXene and functional oxides that collectively enhance mechanical robustness, electrical conductivity and radio-frequency behavior. Parallel improvements in substrate engineering such as polyimide, PET, TPU, cellulose and elastomers demonstrate the technique’s capacity to accommodate complex geometries for wearable, medical and industrial applications while supporting environmentally responsible material choices such as water-borne binders and bio-based solvents. By mapping two decades of developments across energy-storage layers and functional electronics, the article identifies the key process elements, recurring challenges and emerging sustainable practices that will guide future optimization of screen-printing materials and protocols for high-performance, customizable and eco-friendly flexible devices. Full article

Progressive global aging has increased the number of elderly individuals living alone. The consequent rise in fall accidents has worsened physical injuries, reduced the quality of life, and increased medical expenses. Existing wearable fall-detection devices may cause discomfort, and camera-based systems raise privacy concerns. Here, we propose a non-contact fall-detection system that integrates 4D imaging radar sensors with artificial intelligence (AI) technology to detect falls through real-time monitoring and visualization using a web-based dashboard and Unity engine-based avatar, along with immediate alerts. The system eliminates the need for uncomfortable wearable devices and mitigates the privacy issues associated with cameras. The radar sensors generate Point Cloud data (the spatial coordinates, velocity, Doppler power, and time), which allow analysis of the body position and movement. A CNN model classifies postures into standing, sitting, and lying, while changes in the speed and position distinguish falling actions from lying-down actions. The Point Cloud data were normalized and organized using zero padding and k-means clustering to improve the learning efficiency. The model achieved 98.66% accuracy in posture classification and 95% in fall detection. This study demonstrates the effectiveness of the proposed fall detection approach and suggests future directions in multi-sensor integration for indoor applications. Full article

Many kinds of silicones are a wide family of hybrid inorganic–organic polymers which have valuable physical and chemical properties and find plenty of practical applications, not only industrial, but also numerous medical and pharmaceutical ones, mainly due to their good thermal and chemical stability, hydrophobicity, low surface tension, biocompatibility, and bio-durability. The important biomedical applications of silicones include drains, shunts, and catheters, used for medical treatment and short-term implants; inserts and implants to replace various body parts; treatment, assembly, and coating of various medical devices; breast and aesthetic implants; specialty contact lenses; and components of cosmetics, drugs, and drug delivery systems. The most important achievements concerning the biomedical and pharmaceutical applications of silicones, their copolymers and blends, and also silanes and low-molecular-weight siloxanes have been summarized and updated. The main physiological properties of organosilicon compounds and silicones, and the methods of antimicrobial protection of silicone implants, have also been described and discussed. The toxicity of silicones, the negative effects of breast implants, and the environmental effects of silicone-containing personal care and cosmetic products have been reported and analyzed. Important examples of the 3D printing of silicone elastomers for biomedical applications have been presented as well. Full article

Background: Right ventricular primary graft dysfunction (RV-PGD) is a rare but serious complication following heart transplantation, associated with a high morbidity and mortality. Temporary mechanical circulatory support is indicated when patients fail to respond to pharmacological therapy. This study aimed to evaluate the outcomes of patients with RV-PGD who received RV mechanical support with the Impella RP Flex device at our institution. Methods: Medical records of patients with RV-PGD supported by the Impella RP Flex device between December 2022 and March 2024 were reviewed retrospectively to assess survival, procedural complications, duration of support, and end organ dysfunction. Results: Of the 20 patients reviewed, 5 met the inclusion criteria. All five patients demonstrated recovery of RV function after a mean support duration of 8.6 ± 3.05 days. One pump showed transient evidence of biologic material ingestion during a weaning trial. No cases of tricuspid valve injury were observed. The most common complications were hemolysis, bleeding, and acute kidney dysfunction, with all patients requiring hemodialysis. Conclusions: Impella RP Flex support is safe and effective for managing primary and isolated RV-PGD without the need for additional blood oxygenation. However, bleeding complications requiring intervention remain a significant concern, and further evaluation of renal recovery is warranted. Full article

Background/Objectives: The clinical forms of coronavirus disease 2019 (COVID-19) vary widely in severity, ranging from asymptomatic or moderate cases to severe pneumonia that can lead to acute respiratory failure, acute respiratory distress syndrome, multiple organ dysfunction syndrome, and death. Our main objective was to determine the prevalence of bacterial and fungal secondary infections in an intensive care unit (ICU). Secondary objectives included analyzing the impact of these infections on mortality and medical resource utilization, as well as assessing antimicrobial resistance in this context. Methods: We conducted a retrospective cohort study that included critically ill severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) patients treated in an ICU and analyzed the prevalence of co-infections and superinfections. Results: A multivariate analysis of mortality found that the presence of superinfections increased the odds of death by more than 15-fold, while the Sequential Organ Failure Assessment (SOFA) score and C-reactive protein (adjusted for confounders) increased the odds of mortality by 51% and 13%, respectively. The antibiotic resistance profile of microorganisms indicated a high prevalence of resistant strains. Carbapenems, glycopeptides, and oxazolidinones were the most frequently used classes of antibiotics. Among patients, 27.9% received a single antibiotic, 47.5% received two from different classes, and 24.4% were treated with three or more. Conclusions: The incidence and spectrum of bacterial and fungal superinfections are higher in critically ill ICU patients, leading to worse outcomes in COVID-19 cases. Multidrug-resistant pathogens present significant challenges for ICU and public health settings. Early screening, accurate diagnosis, and minimal use of invasive devices are essential to reduce risks and improve patient outcomes. Full article

This work presents a current-controlled CMOS ring oscillator (CCRO) optimized for ultra-low-voltage applications in next-generation energy-constrained systems. Leveraging bulk voltage tuning in 22 nm FDSOI differential inverter stages, the topology enables frequency adjustment while operating MOSFETs in the subthreshold region—critical for minimizing power in sub-1 V environments. Simulations at 0.4 V supply demonstrate robust performance: a three-stage oscillator achieves a 537–800 MHz tuning range with bias current (I_BIAS) modulation from 30–130 nA, while a four-stage configuration spans 388–587 MHz. At 70 nA I_BIAS, the three-stage design delivers a nominal frequency of 666.8 MHz with just 10.23 µW power dissipation, underscoring its suitability for ultra-low-power IoT and biomedical applications. The oscillator’s linear frequency sensitivity (2.63 MHz/nA) allows precise, dynamic control over performance–power tradeoffs. To address diverse application needs, the design integrates three tunability mechanisms: programmable capacitor arrays for coarse frequency adjustments, configurable stage counts (three- or four-stage topologies), and supply voltage scaling. This multi-modal approach extends the operational range to 1 MHz–1 GHz, ensuring compatibility with low-speed sensor interfaces and high-speed edge-computing tasks. The CCRO’s subthreshold operation at 0.4 V—coupled with nanoampere-level current consumption—makes it uniquely suited for battery-less systems, wearable health monitors, and implantable medical devices where energy efficiency and adaptive clocking are paramount. By eliminating traditional voltage-controlled oscillators’ complexity, this topology offers a compact, scalable solution for emerging ultra-low-power technologies. Full article

The growing adoption of the Internet of Things (IoT) in healthcare has led to a surge in real-time data from wearable devices, medical sensors, and patient monitoring systems. This latency-sensitive environment poses significant challenges to traditional cloud-centric infrastructures, which often struggle with unpredictable service demands, network congestion, and end-to-end delay constraints. Consistently meeting the stringent QoS requirements of smart healthcare, particularly for life-critical applications, requires new adaptive architectures. We propose ML-RASPF, a machine learning-based framework for efficient service delivery in smart healthcare systems. Unlike existing methods, ML-RASPF jointly optimizes latency and service delivery rate through predictive analytics and adaptive control across a modular mist–edge–cloud architecture. The framework formulates task provisioning as a joint optimization problem that aims to minimize service latency and maximize delivery throughput. We evaluate ML-RASPF using a realistic smart hospital scenario involving IoT-enabled kiosks and wearable devices that generate both latency-sensitive and latency-tolerant service requests. Experimental results demonstrate that ML-RASPF achieves up to 20% lower latency, 18% higher service delivery rate, and 19% reduced energy consumption compared to leading baselines. Full article