Predicting Obstructive Sleep Apnea Based on Computed Tomography Scan Using Deep Learning Models.

RATIONALE: The incidence of clinically undiagnosed obstructive sleep apnea (OSA) is high among the general population due to limited access to polysomnography. Computed tomography (CT) of craniofacial regions obtained for other purposes can be beneficial in predicting OSA and its severity. OBJECTIVES: To predict OSA and its severity based on paranasal CT using a 3-dimensional deep learning algorithm. METHODS: One internal dataset (n=798) and two external datasets (n=135 and 85) were used in this study. In the internal dataset, 92 normal, 159 mild, 201 moderate, and 346 severe OSA participants were enrolled to derive the deep learning model. A multimodal deep learning model was elicited from the connection between a 3-dimensional convolutional neural network (CNN)-based part treating unstructured data (CT images) and a multi-layer perceptron (MLP)-based part treating structured data (age, sex, and body mass index) to predict OSA and its severity. MEASUREMENTS AND MAIN RESULTS: In four-class classification for predicting the severity of OSA, the AirwayNet-MM-H model (multimodal model with airway-highlighting preprocessing algorithm) showed an average accuracy of 87.6% (95% confidence interval [CI] 86.8-88.6) in the internal dataset and 84.0% (95% CI 83.0-85.1) and 86.3% (95% CI 85.3-87.3) in the two external datasets, respectively. In the two-class classification for predicting significant OSA (moderate to severe OSA), The area under the receiver operating characteristics (AUROC), accuracy, sensitivity, specificity, and F1 score were 0.910 (95% CI 0.899-0.922), 91.0% (95% CI 90.1-91.9), 89.9% (95% CI 88.8-90.9), 93.5% (95% CI 92.7-94.3), and 93.2% (95% CI 92.5-93.9), respectively, in the internal dataset. Furthermore, the diagnostic performance of the Airway Net-MM-H model outperformed that of the other six state-of-the-art deep learning models in terms of accuracy for both four- and two-class classifications and AUROC for two-class classification (p<0.001). CONCLUSIONS: A novel deep learning model, including a multimodal deep learning model and an airway-highlighting preprocessing algorithm from CT images obtained for other purposes, can provide significantly precise outcomes for OSA diagnosis.

Deep Learning-Based Diagnostic System for Velopharyngeal Insufficiency Based on Videofluoroscopy in Patients With Repaired Cleft Palates.

Velopharyngeal insufficiency (VPI), which is the incomplete closure of the velopharyngeal valve during speech, is a typical poor outcome that should be evaluated after cleft palate repair. The interpretation of VPI considering both imaging analysis and perceptual evaluation is essential for further management. The authors retrospectively reviewed patients with repaired cleft palates who underwent assessment for velopharyngeal function, including both videofluoroscopic imaging and perceptual speech evaluation. The final diagnosis of VPI was made by plastic surgeons based on both assessment modalities. Deep learning techniques were applied for the diagnosis of VPI and compared with the human experts' diagnostic results of videofluoroscopic imaging. In addition, the results of the deep learning techniques were compared with a speech pathologist's diagnosis of perceptual evaluation to assess consistency with clinical symptoms. A total of 714 cases from January 2010 to June 2019 were reviewed. Six deep learning algorithms (VGGNet, ResNet, Xception, ResNext, DenseNet, and SENet) were trained using the obtained dataset. The area under the receiver operating characteristic curve of the algorithms ranged between 0.8758 and 0.9468 in the hold-out method and between 0.7992 and 0.8574 in the 5-fold cross-validation. Our findings demonstrated the deep learning algorithms performed comparable to experienced plastic surgeons in the diagnosis of VPI based on videofluoroscopic velopharyngeal imaging.

Ex Vivo Evaluation of Mechanical Anisotropic Tissues with High-Frequency Ultrasound Shear Wave Elastography.

The use of imaging devices to assess directional mechanics of tissues is highly desirable. This is because the directional mechanics depend on fiber orientation, and altered directional mechanics are closely related to the pathological status of tissues. However, measuring directional mechanics in tissues with high-stiffness is challenging due to the difficulty of generating localized displacement in these tissues using acoustic radiation force, a general method for generating displacement in ultrasound-based elastography. In addition, common ultrasound probes do not provide rotational function, which makes the measurement of directional mechanics inaccurate and unreliable. Therefore, we developed a high-frequency ultrasound mechanical wave elastography system that can accommodate a wide range of tissue stiffness and is also equipped with a motorized rotation stage for precise imaging of directional mechanics. A mechanical shaker was applied to the elastography system to measure tissues with high-stiffness. Phantom and ex vivo experiments were performed. In the phantom experiments, the lateral and axial resolution of the system were determined to be 144 µm and 168 µm, respectively. In the ex vivo experiments, we used swine heart and cartilage, both of which are considered stiff. The elastography system allows us to acquire the directional mechanics with high angular resolution in the heart and cartilage. The results demonstrate that the developed elastography system is capable of imaging a wide range of tissues and has high angular resolution. Therefore, this system might be useful for the diagnostics of mechanically anisotropic tissues via ex vivo tests.

HER3-targeted protein chimera forms endosomolytic capsomeres and self-assembles into stealth nucleocapsids for systemic tumor homing of RNA interference in vivo.

RNA interference represents a potent intervention for cancer treatment but requires a robust delivery agent for transporting gene-modulating molecules, such as small interfering RNAs (siRNAs). Although numerous molecular approaches for siRNA delivery are adequate in vitro, delivery to therapeutic targets in vivo is limited by payload integrity, cell targeting, efficient cell uptake, and membrane penetration. We constructed nonviral biomaterials to transport small nucleic acids to cell targets, including tumor cells, on the basis of the self-assembling and cell-penetrating activities of the adenovirus capsid penton base. Our recombinant penton base chimera contains polypeptide domains designed for noncovalent assembly with anionic molecules and tumor homing. Here, structural modeling, molecular dynamics simulations, and functional assays suggest that it forms pentameric units resembling viral capsomeres that assemble into larger capsid-like structures when combined with siRNA cargo. Pentamerization forms a barrel lined with charged residues mediating pH-responsive dissociation and exposing masked domains, providing insight on the endosomolytic mechanism. The therapeutic impact was examined on tumors expressing high levels of HER3/ErbB3 that are resistant to clinical inhibitors. Our findings suggest that our construct may utilize ligand mimicry to avoid host attack and target the siRNA to HER3+ tumors by forming multivalent capsid-like structures.

Imbalanced Loss-Integrated Deep-Learning-Based Ultrasound Image Analysis for Diagnosis of Rotator-Cuff Tear.

A rotator cuff tear (RCT) is an injury in adults that causes difficulty in moving, weakness, and pain. Only limited diagnostic tools such as magnetic resonance imaging (MRI) and ultrasound Imaging (UI) systems can be utilized for an RCT diagnosis. Although UI offers comparable performance at a lower cost to other diagnostic instruments such as MRI, speckle noise can occur the degradation of the image resolution. Conventional vision-based algorithms exhibit inferior performance for the segmentation of diseased regions in UI. In order to achieve a better segmentation for diseased regions in UI, deep-learning-based diagnostic algorithms have been developed. However, it has not yet reached an acceptable level of performance for application in orthopedic surgeries. In this study, we developed a novel end-to-end fully convolutional neural network, denoted as Segmentation Model Adopting a pRe-trained Classification Architecture (SMART-CA), with a novel integrated on positive loss function (IPLF) to accurately diagnose the locations of RCT during an orthopedic examination using UI. Using the pre-trained network, SMART-CA can extract remarkably distinct features that cannot be extracted with a normal encoder. Therefore, it can improve the accuracy of segmentation. In addition, unlike other conventional loss functions, which are not suited for the optimization of deep learning models with an imbalanced dataset such as the RCT dataset, IPLF can efficiently optimize the SMART-CA. Experimental results have shown that SMART-CA offers an improved precision, recall, and dice coefficient of 0.604% (+38.4%), 0.942% (+14.0%) and 0.736% (+38.6%) respectively. The RCT segmentation from a normal ultrasound image offers the improved precision, recall, and dice coefficient of 0.337% (+22.5%), 0.860% (+15.8%) and 0.484% (+28.5%), respectively, in the RCT segmentation from an ultrasound image with severe speckle noise. The experimental results demonstrated the IPLF outperforms other conventional loss functions, and the proposed SMART-CA optimized with the IPLF showed better performance than other state-of-the-art networks for the RCT segmentation with high robustness to speckle noise.

Non-invasive measurement of hemodynamic change during 8 MHz transcranial focused ultrasound stimulation using near-infrared spectroscopy.

BACKGROUND: Transcranial focused ultrasound (tFUS) attracts wide attention in neuroscience as an effective noninvasive approach to modulate brain circuits. In spite of this, the effects of tFUS on the brain is still unclear, and further investigation is needed. The present study proposes to use near-infrared spectroscopy (NIRS) to observe cerebral hemodynamic change caused by tFUS in a noninvasive manner. RESULTS: The results show a transient increase of oxyhemoglobin and decrease of deoxyhemoglobin concentration in the mouse model induced by ultrasound stimulation of the somatosensory cortex with a frequency of 8 MHz but not in sham. In addition, the amplitude of hemodynamics change can be related to the peak intensity of the acoustic wave. CONCLUSION: High frequency 8 MHz ultrasound was shown to induce hemodynamic changes measured using NIRS through the intact mouse head. The implementation of NIRS offers the possibility of investigating brain response noninvasively for different tFUS parameters through cerebral hemodynamic change.

Fighting Cancer with Corroles.

Corroles are exceptionally promising platforms for the development of agents for simultaneous cancer-targeting imaging and therapy. Depending on the element chelated by the corrole, these theranostic agents may be tuned primarily for diagnostic or therapeutic function. Versatile synthetic methodologies allow for the preparation of amphipolar derivatives, which form stable noncovalent conjugates with targeting biomolecules. These conjugates can be engineered for imaging and targeting as well as therapeutic function within one theranostic assembly. In this review, we begin with a brief outline of corrole chemistry that has been uniquely useful in designing corrole-based anticancer agents. Then we turn attention to the early literature regarding corrole anticancer activity, which commenced one year after the first scalable synthesis was reported (1999-2000). In 2001, a major advance was made with the introduction of negatively charged corroles, as these molecules, being amphipolar, form stable conjugates with many proteins. More recently, both cellular uptake and intracellular trafficking of metallocorroles have been documented in experimental investigations employing advanced optical spectroscopic as well as magnetic resonance imaging techniques. Key results from work on both cellular and animal models are reviewed, with emphasis on those that have shed new light on the mechanisms associated with anticancer activity. In closing, we predict a very bright future for corrole anticancer research, as it is experiencing exponential growth, taking full advantage of recently developed imaging and therapeutic modalities.

Jitter reduction technique for acoustic radiation force impulse microscopy via photoacoustic detection.

We demonstrate a jitter noise reduction technique for acoustic radiation force impulse microscopy via photoacoustic detection (PA-ARFI), which promises to be capable of measuring cell mechanics. To reduce the jitter noise induced by Q-switched pulsed laser operated at high repetition frequency, photoacoustic signals from the surface of an ultrasound transducer are aligned by cross-correlation and peak-to-peak detection, respectively. Each method is then employed to measure the displacements of a target sample in an agar phantom and a breast cancer cell due to ARFI application, followed by the quantitative comparison between their performances. The suggested methods for PA-ARFI significantly reduce jitter noises, thus allowing us to measure displacements of a target cell due to ARFI application by less than 3 µm.

Power MOSFET-diode-based limiter for high-frequency ultrasound systems.

The purpose of the limiter circuits used in the ultrasound imaging systems is to pass low-voltage echo signals generated by ultrasonic transducers while preventing high-voltage short pulses transmitted by pulsers from damaging front-end circuits. Resistor-diode-based limiters (a 50 Ω resistor with a single cross-coupled diode pair) have been widely used in pulse-echo measurement and imaging system applications due to their low cost and simple architecture. However, resistor-diode-based limiters may not be suited for high-frequency ultrasound transducer applications since they produce large signal conduction losses at higher frequencies. Therefore, we propose a new limiter architecture utilizing power MOSFETs, which we call a power MOSFET-diode-based limiter. The performance of a power MOSFET-diode-based limiter was evaluated with respect to insertion loss (IL), total harmonic distortion (THD), and response time (RT). We compared these results with those of three other conventional limiter designs and showed that the power MOSFET-diode-based limiter offers the lowest IL (-1.33 dB) and fastest RT (0.10 µs) with the lowest suppressed output voltage (3.47 Vp-p) among all the limiters at 70 MHz. A pulse-echo test was performed to determine how the new limiter affected the sensitivity and bandwidth of the transducer. We found that the sensitivity and bandwidth of the transducer were 130% and 129% greater, respectively, when combined with the new power MOSFET-diode-based limiter versus the resistor-diode-based limiter. Therefore, these results demonstrate that the power MOSFET-diode-based limiter is capable of producing lower signal attenuation than the three conventional limiter designs at higher frequency operation.

Intelligent Bladder Volume Monitoring for Wearable Ultrasound Devices: Enhancing Accuracy through Deep Learning-based Coarse-to-Fine Shape Estimation.

Accurate and continuous bladder volume monitoring is crucial for managing urinary dysfunctions. Wearable ultrasound devices offer a solution by enabling non-invasive and real-time monitoring. Previous studies have limitations in power consumption and computation cost or quantitative volume estimation capability. To alleviate this, we present a novel pipeline that effectively integrates conventional feature extraction and deep learning to achieve continuous quantitative bladder volume monitoring efficiently. Particularly, in the proposed pipeline, bladder shape is coarsely estimated by a simple bladder wall detection algorithm in wearable devices, and the bladder wall coordinates are wirelessly transferred to an external server. Subsequently, a roughly estimated bladder shape from the wall coordinates is refined in an external server with a diffusion-based model. With this approach, power consumption and computation costs on wearable devices remained low, while fully harnessing the potential of deep learning for accurate shape estimation. To evaluate the proposed pipeline, we collected a dataset of bladder ultrasound images and RF signals from 250 patients. By simulating data acquisition from wearable devices using the dataset, we replicated real-world scenarios and validated the proposed method within these scenarios. Experimental results exhibit superior improvements, including +9.32% of IoU value in 2D segmentation and -22.06 of RMSE in bladder volume regression compared to state-of-the-art performance from alternative methods, emphasizing the potential of this approach in continuous bladder volume monitoring in clinical settings. Therefore, this study effectively bridges the gap between accurate bladder volume estimation and the practical deployment of wearable ultrasound devices, promising improved patient care and quality of life.

Investigating contactless high frequency ultrasound microbeam stimulation for determination of invasion potential of breast cancer cells.

In this article, we investigate the application of contactless high frequency ultrasound microbeam stimulation (HFUMS) for determining the invasion potential of breast cancer cells. In breast cancer patients, the finding of tumor metastasis significantly worsens the clinical prognosis. Thus, early determination of the potential of a tumor for invasion and metastasis would significantly impact decisions about aggressiveness of cancer treatment. Recent work suggests that invasive breast cancer cells (MDA-MB-231), but not weakly invasive breast cancer cells (MCF-7, SKBR3, and BT-474), display a number of neuronal characteristics, including expression of voltage-gated sodium channels. Since sodium channels are often co-expressed with calcium channels, this prompted us to test whether single-cell stimulation by a highly focused ultrasound microbeam would trigger Ca(2+) elevation, especially in highly invasive breast cancer cells. To calibrate the diameter of the microbeam ultrasound produced by a 200-MHz single element LiNbO3 transducer, we focused the beam on a wire target and performed a pulse-echo test. The width of the beam was ∼17 µm, appropriate for single cell stimulation. Membrane-permeant fluorescent Ca(2+) indicators were utilized to monitor Ca(2+) changes in the cells due to HFUMS. The cell response index (CRI), which is a composite parameter reflecting both Ca(2+) elevation and the fraction of responding cells elicited by HFUMS, was much greater in highly invasive breast cancer cells than in the weakly invasive breast cancer cells. The CRI of MDA-MB-231 cells depended on peak-to-peak amplitude of the voltage driving the transducer. These results suggest that HFUMS may serve as a novel tool to determine the invasion potential of breast cancer cells, and with further refinement may offer a rapid test for invasiveness of tumor biopsies in situ.

New Metric to Evaluate Cardiac Anisotropic Mechanics by Directional High-Frequency Ultrasound-Based Transverse Wave Elastography.

The evaluation of cardiac anisotropic mechanics is important in the diagnosis of heart disease. However, other representative ultrasound imaging-based metrics, which are capable of quantitatively evaluating anisotropic cardiac mechanics, are insufficient for accurately diagnosing heart disease due to the influence of viscosity and geometry of cardiac tissues. In this study, we propose a new ultrasound imaging-based metric, maximum cosine similarity (MaxCosim), for quantifying anisotropic mechanics of cardiac tissues by evaluating the periodicity of the transverse wave speeds depending on the measurement directions using ultrasound imaging. We developed a high-frequency ultrasound-based directional transverse wave imaging system to measure the transverse wave speed in multiple directions. The ultrasound imaging-based metric was validated by performing experiments on 40 rats randomly assigned to four groups; three doxorubicin (DOX) treatment groups received 10, 15, or 20 mg/kg DOX, while the control group received 0.2 mL/kg saline. In each heart sample, the developed ultrasound imaging system allowed measuring transverse wave speeds in multiple directions, and the new metric was then calculated from 3-D ultrasound transverse wave images to evaluate the degree of anisotropic mechanics of the heart sample. The results of the metric were compared with histopathological changes for validation. A decrease in the MaxCosim value was observed in the DOX treatment groups, with the degree of decrease depending on the dose. These results are consistent with the histopathological features, suggesting that our ultrasound imaging-based metric can quantify the anisotropic mechanics of cardiac tissues and potentially be used for the early diagnosis of heart disease.

Multi-Task and Few-Shot Learning-Based Fully Automatic Deep Learning Platform for Mobile Diagnosis of Skin Diseases.

Fluorescence imaging-based diagnostic systems have been widely used to diagnose skin diseases due to their ability to provide detailed information related to the molecular composition of the skin compared to conventional RGB imaging. In addition, recent advances in smartphones have made them suitable for application in biomedical imaging, and therefore various smartphone-based optical imaging systems have been developed for mobile healthcare. However, an advanced analysis algorithm is required to improve the diagnosis of skin diseases. Various deep learning-based algorithms have recently been developed for this purpose. However, deep learning-based algorithms using only white-light reflectance RGB images have exhibited limited diagnostic performance. In this study, we developed an auxiliary deep learning network called fluorescence-aided amplifying network (FAA-Net) to diagnose skin diseases using a developed multi-modal smartphone imaging system that offers RGB and fluorescence images. FAA-Net is equipped with a meta-learning-based algorithm to solve problems that may occur due to the insufficient number of images acquired by the developed system. In addition, we devised a new attention-based module that can learn the location of skin diseases by itself and emphasize potential disease regions, and incorporated it into FAA-Net. We conducted a clinical trial in a hospital to evaluate the performance of FAA-Net and to compare various evaluation metrics of our developed model and other state-of-the-art models for the diagnosis of skin diseases using our multi-modal system. Experimental results demonstrated that our developed model exhibited an 8.61% and 9.83% improvement in mean accuracy and area under the curve in classifying skin diseases, respectively, compared with other advanced models.

Tumor detection and elimination by a targeted gallium corrole.

Sulfonated gallium(III) corroles are intensely fluorescent macrocyclic compounds that spontaneously assemble with carrier proteins to undergo cell entry. We report in vivo imaging and therapeutic efficacy of a tumor-targeted corrole noncovalently assembled with a heregulin-modified protein directed at the human epidermal growth factor receptor (HER). Systemic delivery of this protein-corrole complex results in tumor accumulation, which can be visualized in vivo owing to intensely red corrole fluorescence. Targeted delivery in vivo leads to tumor cell death while normal tissue is spared. These findings contrast with the effects of doxorubicin, which can elicit cardiac damage during therapy and required direct intratumoral injection to yield similar levels of tumor shrinkage compared with the systemically delivered corrole. The targeted complex ablated tumors at >5 times a lower dose than untargeted systemic doxorubicin, and the corrole did not damage heart tissue. Complexes remained intact in serum and the carrier protein elicited no detectable immunogenicity. The sulfonated gallium(III) corrole functions both for tumor detection and intervention with safety and targeting advantages over standard chemotherapeutic agents.

Explainable deep learning algorithm for distinguishing incomplete Kawasaki disease by coronary artery lesions on echocardiographic imaging.

BACKGROUND AND OBJECTIVE: Incomplete Kawasaki disease (KD) has often been misdiagnosed due to a lack of the clinical manifestations of classic KD. However, it is associated with a markedly higher prevalence of coronary artery lesions. Identifying coronary artery lesions by echocardiography is important for the timely diagnosis of and favorable outcomes in KD. Moreover, similar to KD, coronavirus disease 2019, currently causing a worldwide pandemic, also manifests with fever; therefore, it is crucial at this moment that KD should be distinguished clearly among the febrile diseases in children. In this study, we aimed to validate a deep learning algorithm for classification of KD and other acute febrile diseases. METHODS: We obtained coronary artery images by echocardiography of children (n = 138 for KD; n = 65 for pneumonia). We trained six deep learning networks (VGG19, Xception, ResNet50, ResNext50, SE-ResNet50, and SE-ResNext50) using the collected data. RESULTS: SE-ResNext50 showed the best performance in terms of accuracy, specificity, and precision in the classification. SE-ResNext50 offered a precision of 81.12%, a sensitivity of 84.06%, and a specificity of 58.46%. CONCLUSIONS: The results of our study suggested that deep learning algorithms have similar performance to an experienced cardiologist in detecting coronary artery lesions to facilitate the diagnosis of KD.

Speckle Reduction via Deep Content-Aware Image Prior for Precise Breast Tumor Segmentation in an Ultrasound Image.

The performance of computer-aided diagnosis (CAD) systems that are based on ultrasound imaging has been enhanced owing to the advancement in deep learning. However, because of the inherent speckle noise in ultrasound images, the ambiguous boundaries of lesions deteriorate and are difficult to distinguish, resulting in the performance degradation of CAD. Although several methods have been proposed to reduce speckle noise over decades, this task remains a challenge that must be improved to enhance the performance of CAD. In this article, we propose a deep content-aware image prior (DCAIP) with a content-aware attention module (CAAM) for superior despeckling of ultrasound images without clean images. For the image prior, we developed a CAAM to deal with the content information in an input image. In this module, super-pixel pooling (SPP) is used to give attention to salient regions in an ultrasound image. Therefore, it can provide more content information regarding the input image when compared to other attention modules. The DCAIP consists of deep learning networks based on this attention module. The DCAIP is validated by applying it as a preprocessing step for breast tumor segmentation in ultrasound images, which is one of the tasks in CAD. Our method improved the segmentation performance by 15.89% in terms of the area under the precision-recall (PR) curve (AUPRC). The results demonstrate that our method enhances the quality of ultrasound images by effectively reducing speckle noise while preserving important information in the image, promising for the design of superior CAD systems.

Deep Learning-Based Framework for Fast and Accurate Acoustic Hologram Generation.

Acoustic holography has been gaining attention for various applications, such as noncontact particle manipulation, noninvasive neuromodulation, and medical imaging. However, only a few studies on how to generate acoustic holograms have been conducted, and even conventional acoustic hologram algorithms show limited performance in the fast and accurate generation of acoustic holograms, thus hindering the development of novel applications. We here propose a deep learning-based framework to achieve fast and accurate acoustic hologram generation. The framework has an autoencoder-like architecture; thus, the unsupervised training is realized without any ground truth. For the framework, we demonstrate a newly developed hologram generator network, the holographic ultrasound generation network (HU-Net), which is suitable for unsupervised learning of hologram generation, and a novel loss function that is devised for energy-efficient holograms. Furthermore, for considering various hologram devices (i.e., ultrasound transducers), we propose a physical constraint (PC) layer. Simulation and experimental studies were carried out for two different hologram devices, such as a 3-D printed lens, attached to a single element transducer, and a 2-D ultrasound array. The proposed framework was compared with the iterative angular spectrum approach (IASA) and the state-of-the-art (SOTA) iterative optimization method, Diff-PAT. In the simulation study, our framework showed a few hundred times faster generation speed, along with comparable or even better reconstruction quality, than those of IASA and Diff-PAT. In the experimental study, the framework was validated with 3-D printed lenses fabricated based on different methods, and the physical effect of the lenses on the reconstruction quality was discussed. The outcomes of the proposed framework in various cases (i.e., hologram generator networks, loss functions, and hologram devices) suggest that our framework may become a very useful alternative tool for other existing acoustic hologram applications, and it can expand novel medical applications.

A mechanistic study of tumor-targeted corrole toxicity.

HerGa is a self-assembled tumor-targeted particle that bears both tumor detection and elimination activities in a single, two-component complex (Agadjanian et al. Proc. Natl. Acad. Sci. U.S.A.2009, 106, 6105-6110). Given its multifunctionality, HerGa (composed of the fluorescent cytotoxic corrole macrocycle, S2Ga, noncovalently bound to the tumor-targeted cell penetration protein, HerPBK10) has the potential for high clinical impact, but its mechanism of cell killing remains to be elucidated, and hence is the focus of the present study. Here we show that HerGa requires HerPBK10-mediated cell entry to induce toxicity. HerGa (but not HerPBK10 or S2Ga alone) induced mitochondrial membrane potential disruption and superoxide elevation, which were both prevented by endosomolytic-deficient mutants, indicating that cytosolic exposure is necessary for corrole-mediated cell death. A novel property discovered here is that corrole fluorescence lifetime acts as a pH indicator, broadcasting the intracellular microenvironmental pH during uptake in live cells. This feature in combination with two-photon imaging shows that HerGa undergoes early endosome escape during uptake, avoiding compartments of pH < 6.5. Cytoskeletal disruption accompanied HerGa-mediated mitochondrial changes whereas oxygen scavenging reduced both events. Paclitaxel treatment indicated that HerGa uptake requires dynamic microtubules. Unexpectedly, low pH is insufficient to induce release of the corrole from HerPBK10. Altogether, these studies identify a mechanistic pathway in which early endosomal escape enables HerGa-induced superoxide generation leading to cytoskeletal and mitochondrial damage, thus triggering downstream cell death.

Intelligent smartphone-based multimode imaging otoscope for the mobile diagnosis of otitis media.

Otitis media (OM) is one of the most common ear diseases in children and a common reason for outpatient visits to medical doctors in primary care practices. Adhesive OM (AdOM) is recognized as a sequela of OM with effusion (OME) and often requires surgical intervention. OME and AdOM exhibit similar symptoms, and it is difficult to distinguish between them using a conventional otoscope in a primary care unit. The accuracy of the diagnosis is highly dependent on the experience of the examiner. The development of an advanced otoscope with less variation in diagnostic accuracy by the examiner is crucial for a more accurate diagnosis. Thus, we developed an intelligent smartphone-based multimode imaging otoscope for better diagnosis of OM, even in mobile environments. The system offers spectral and autofluorescence imaging of the tympanic membrane using a smartphone attached to the developed multimode imaging module. Moreover, it is capable of intelligent analysis for distinguishing between normal, OME, and AdOM ears using a machine learning algorithm. Using the developed system, we examined the ears of 69 patients to assess their performance for distinguishing between normal, OME, and AdOM ears. In the classification of ear diseases, the multimode system based on machine learning analysis performed better in terms of accuracy and F1 scores than single RGB image analysis, RGB/fluorescence image analysis, and the analysis of spectral image cubes only, respectively. These results demonstrate that the intelligent multimode diagnostic capability of an otoscope would be beneficial for better diagnosis and management of OM.

Ultrasonic blood flowmeter with a novel Xero algorithm for a mechanical circulatory support system.

Mechanical circulatory support systems (MCSSs) are crucial devices for transplants in patients with heart failure. The blood flowing through the MCSS can be recirculated or even stagnated in the event of critical blood flow issues. To avoid emergencies due to abnormal changes in the flow, continuous changes of the flowrate should be measured with high accuracy and robustness. For better flowrate measurements, a more advanced ultrasonic blood flowmeter (UFM), which is a noninvasive measurement tool, is needed. In this paper, we propose a novel UFM sensor module using a novel algorithm (Xero) that can exploit the advantages of both conventional cross-correlation (Xcorr) and zero-crossing (Zero) algorithms, using only the zero-crossing-based algorithm. To ensure the capability of our own developed and optimized ultrasonic sensor module for MCSSs, the accuracy, robustness, and continuous monitoring performance of the proposed algorithm were compared to those of conventional algorithms after application to the developed sensor module. The results show that Xero is superior to other algorithms for flowrate measurements under different environments and offers an error rate of at least 0.92%, higher robustness for changing fluid temperatures than conventional algorithms, and sensitive responses to sudden changes in flowrates. Thus, the proposed UFM system with Xero has a great potential for flowrate measurements in MCSSs.