Frontiers in Medical Technology最新文献

An innovative nasal filter was tested, based on aerodynamic air filtration and not on conventional air filtration by means of mesh filters. A custom testing system was designed and three sizes of the filter have been tested vs. monodispersed SiO₂ particles sized 5 μm, 1 μm, and 0.5 μm under cycling flow of 6 liters per minute, provided by an artificial lung breather simulating spontaneous breathing. Accelerated testing was implemented, challenging filters with a maximum load of 200 mg per cubic meter. All three filters' sizes showed initial filtration efficiencies above 90% vs. all particles' sizes, decreased to not less than 80% after 30 min of accelerated testing, corresponding to 4.5 days of continuous use at 2 mg challenge, this value being associated with hazardous air conditions in the PSI scale. Results in this study indicate that nasal filters based on aerodynamic air filtration can provide fine and ultrafine filtration, offering protection in day-to-day life from risks associated with pollens, mites, PM, pollutants, and respiratory infectious agents, introducing acceptable respiratory resistance.

Anastomotic leaks (AL) and staple line leaks are a serious post-operative complication that can develop following bariatric surgery. The delay in the onset of symptoms following a leak usually results in reactive diagnostics and treatment, leading to increased patient morbidity and mortality, and a clinical and economic burden on both the patient and the hospital. Despite support in literature for pH as a biomarker for early detection of AL, the current methods of pH detection require significant clinician involvement and resources. Presented here is a polyaniline (PANI)-based pH sensor that can be connected inline to surgical drains to continuously monitor peritoneal secretion in real time for homeostatic changes in pH. During this study, the baseline peritoneal fluid pH was measured in two pigs using the PANI sensor and verified using a benchtop pH probe. The PANI sensor was then utilized to continuously monitor the changes in the pH of peritoneal effluent, as a gastric leak was simulated. The inline sensors were able to detect the resulting local changes in drainage pH within 10 min of leak induction. The successful implementation of this sensor in clinical practice can both enable high efficiency continuous monitoring of patient status and drastically decrease the time required to detect AL, thus potentially decreasing the clinical and economic burden incurred by gastric leaks.

3D printing enables the rapid manufacture of patient-specific anatomical models that substantially improve patient consultation and offer unprecedented opportunities for surgical planning and training. However, the multistep preparation process may inadvertently lead to inaccurate anatomical representations which may impact clinical decision making detrimentally. Here, we investigated the dimensional accuracy of patient-specific vascular anatomical models manufactured via digital anatomical segmentation and Fused-Deposition Modelling (FDM), Stereolithography (SLA), Selective Laser Sintering (SLS), and PolyJet 3D printing, respectively. All printing modalities reliably produced hand-held patient-specific models of high quality. Quantitative assessment revealed an overall dimensional error of 0.20 ± 3.23%, 0.53 ± 3.16%, -0.11 ± 2.81% and -0.72 ± 2.72% for FDM, SLA, PolyJet and SLS printed models, respectively, compared to unmodified Computed Tomography Angiograms (CTAs) data. Comparison of digital 3D models to CTA data revealed an average relative dimensional error of -0.83 ± 2.13% resulting from digital anatomical segmentation and processing. Therefore, dimensional error resulting from the print modality alone were 0.76 ± 2.88%, + 0.90 ± 2.26%, + 1.62 ± 2.20% and +0.88 ± 1.97%, for FDM, SLA, PolyJet and SLS printed models, respectively. Impact on absolute measurements of feature size were minimal and assessment of relative error showed a propensity for models to be marginally underestimated. This study revealed a high level of dimensional accuracy of 3D-printed patient-specific vascular anatomical models, suggesting they meet the requirements to be used as medical devices for clinical applications.

Liver transplantation is a well-established treatment for many with end-stage liver disease. Unfortunately, the increasing organ demand has surpassed the donor supply, and approximately 30% of patients die while waiting for a suitable liver. Clinicians are often forced to consider livers of inferior quality to increase organ donation rates, but ultimately, many of those organs end up being discarded. Extensive testing in experimental animals and humans has shown that ex-vivo machine preservation allows for a more objective characterization of the graft outside the body, with particular benefit for suboptimal organs. This review focuses on the history of the implementation of ex-vivo liver machine preservation and how its enactment may modify our current concept of organ acceptability. We provide a brief overview of the major drivers of organ discard (age, ischemia time, steatosis, etc.) and how this technology may ultimately revert such a trend. We also discuss future directions for this technology, including the identification of new markers of injury and repair and the opportunity for other ex-vivo regenerative therapies. Finally, we discuss the value of this technology, considering current and future donor characteristics in the North American population that may result in a significant organ discard.

Introduction: Surgical Site Infection (SSI) is a common healthcare-associated infection that imposes a considerable clinical and economic burden on healthcare systems. Advances in wearable sensors and digital technologies have unlocked the potential for the early detection and diagnosis of SSI, which can help reduce this healthcare burden and lower SSI-associated mortality rates.

Methods: In this study, we evaluated the ability of a multi-modal bio-signal system to predict current and developing superficial incisional infection in a porcine model infected with Methicillin Susceptible Staphylococcus Aureus (MSSA) using a bagged, stacked, and balanced ensemble logistic regression machine learning model.

Results: Results demonstrated that the expression levels of individual biomarkers (i.e., peri-wound tissue oxygen saturation, temperature, and bioimpedance) differed between non-infected and infected wounds across the study period, with cross-correlation analysis indicating that a change in bio-signal expression occurred 24 to 31 hours before this change was reflected by clinical wound scoring methods employed by trained veterinarians. Moreover, the multi-modal ensemble model indicated acceptable discriminability to detect the presence of a current superficial incisional SSI (AUC = 0.77), to predict an SSI 24 hours in advance of veterinarian-based SSI diagnosis (AUC = 0.80), and to predict an SSI 48 hours in advance of veterinarian-based SSI diagnosis (AUC = 0.74).

Discussion: In sum, the results of the current study indicate that non-invasive multi-modal sensor and signal analysis systems have the potential to detect and predict superficial incisional SSIs in porcine subjects under experimental conditions.

Introduction: X-ray Velocimetry (XV) ventilation analysis is a 4-dimensional imaging-based method for quantifying regional ventilation, aiding in the assessment of lung function. We examined the performance characteristics of XV ventilation analysis by examining correlation to spirometry and measurement repeatability.

Methods: XV analysis was assessed in 27 patients receiving thoracic radiotherapy for non-lung cancer malignancies. Measurements were obtained pre-treatment and at 4 and 12-months post-treatment. XV metrics such as ventilation defect percent (VDP) and regional ventilation heterogeneity (VH) were compared to spirometry at each time point, using correlation analysis. Repeatability was assessed between multiple runs of the analysis algorithm, as well as between multiple breaths in the same patient. Change in VH and VDP in a case series over 12 months was used to determine effect size and estimate sample sizes for future studies.

Results: VDP and VH were found to significantly correlate with FEV₁ and FEV₁/FVC (range: -0.36 to -0.57; p < 0.05). Repeatability tests demonstrated that VDP and VH had less than 2% variability within runs and less than 8% change in metrics between breaths. Three cases were used to illustrate the advantage of XV over spirometry, where XV indicated a change in lung function that was either undetectable or delayed in detection by spirometry. Case A demonstrated an improvement in XV metrics over time despite stable spirometric values. Case B demonstrated a decline in XV metrics as early as 4-months, although spirometric values did not change until 12-months. Case C demonstrated a decline in XV metrics at 12 months post-treatment while spirometric values remained normal throughout the study. Based on the effect sizes in each case, sample sizes ranging from 10 to 38 patients would provide 90% power for future studies aiming to detect similar changes.

Conclusions: The performance and safety of XV analysis make it ideal for both clinical and research applications across most lung indications. Our results support continued research and provide a basis for powering future studies using XV as an endpoint to examine lung health and determine therapeutic efficacy.