Pathological Insights: Enhanced Vision Transformers for the Early Detection of Colorectal Cancer.

Endoscopic pathological findings of the gastrointestinal tract are crucial for the early diagnosis of colorectal cancer (CRC). Previous deep learning works, aimed at improving CRC detection performance and reducing subjective analysis errors, are limited to polyp segmentation. Pathological findings were not considered and only convolutional neural networks (CNNs), which are not able to handle global image feature information, were utilized. This work introduces a novel vision transformer (ViT)-based approach for early CRC detection. The core components of the proposed approach are ViTCol, a boosted vision transformer for classifying endoscopic pathological findings, and PUTS, a vision transformer-based model for polyp segmentation. Results demonstrate the superiority of this vision transformer-based CRC detection method over existing CNN and vision transformer models. ViTCol exhibited an outstanding performance in classifying pathological findings, with an area under the receiver operating curve (AUC) value of 0.9999 ± 0.001 on the Kvasir dataset. PUTS provided outstanding results in segmenting polyp images, with mean intersection over union (mIoU) of 0.8673 and 0.9092 on the Kvasir-SEG and CVC-Clinic datasets, respectively. This work underscores the value of spatial transformers in localizing input images, which can seamlessly integrate into the main vision transformer network, enhancing the automated identification of critical image features for early CRC detection.

Vision Transformers for Breast Cancer Human Epidermal Growth Factor Receptor 2 Expression Staging without Immunohistochemical Staining.

Accurate staging of human epidermal growth factor receptor 2 (HER2) expression is vital for evaluating breast cancer treatment efficacy. However, it typically involves costly and complex immunohistochemical staining, along with hematoxylin and eosin staining. This work presents customized vision transformers for staging HER2 expression in breast cancer using only hematoxylin and eosin-stained images. The proposed algorithm comprised three modules: a localization module for weakly localizing critical image features using spatial transformers, an attention module for global learning via vision transformers, and a loss module to determine proximity to a HER2 expression level based on input images by calculating ordinal loss. Results, reported with 95% CIs, reveal the proposed approach's success in HER2 expression staging: area under the receiver operating characteristic curve, 0.9202 ± 0.01; precision, 0.922 ± 0.01; sensitivity, 0.876 ± 0.01; and specificity, 0.959 ± 0.02 over fivefold cross-validation. Comparatively, this approach significantly outperformed conventional vision transformer models and state-of-the-art convolutional neural network models (P < 0.001). Furthermore, it surpassed existing methods when evaluated on an independent test data set. This work holds great importance, aiding HER2 expression staging in breast cancer treatment while circumventing the costly and time-consuming immunohistochemical staining procedure, thereby addressing diagnostic disparities in low-resource settings and low-income countries.

Domain and Histopathology Adaptations-Based Classification for Malignancy Grading System.

Accurate proliferation rate quantification can be used to devise an appropriate treatment for breast cancer. Pathologists use breast tissue biopsy glass slides stained with hematoxylin and eosin to obtain grading information. However, this manual evaluation may lead to high costs and be ineffective because diagnosis depends on the facility and the pathologists' insights and experiences. Convolutional neural network acts as a computer-based observer to improve clinicians' capacity in grading breast cancer. Therefore, this study proposes a novel scheme for automatic breast cancer malignancy grading from invasive ductal carcinoma. The proposed classifiers implement multistage transfer learning incorporating domain and histopathologic transformations. Domain adaptation using pretrained models, such as InceptionResNetV2, InceptionV3, NASNet-Large, ResNet50, ResNet101, VGG19, and Xception, was applied to classify the ×40 magnification BreaKHis data set into eight classes. Subsequently, InceptionV3 and Xception, which contain the domain and histopathology pretrained weights, were determined to be the best for this study and used to categorize the Databiox database into grades 1, 2, or 3. To provide a comprehensive report, this study offered a patchless automated grading system for magnification-dependent and magnification-independent classifications. With an overall accuracy (means ± SD) of 90.17% ± 3.08% to 97.67% ± 1.09% and an F1 score of 0.9013 to 0.9760 for magnification-dependent classification, the classifiers in this work achieved outstanding performance. The proposed approach could be used for breast cancer grading systems in clinical settings.

Vision-Transformer-Based Transfer Learning for Mammogram Classification.

Breast mass identification is a crucial procedure during mammogram-based early breast cancer diagnosis. However, it is difficult to determine whether a breast lump is benign or cancerous at early stages. Convolutional neural networks (CNNs) have been used to solve this problem and have provided useful advancements. However, CNNs focus only on a certain portion of the mammogram while ignoring the remaining and present computational complexity because of multiple convolutions. Recently, vision transformers have been developed as a technique to overcome such limitations of CNNs, ensuring better or comparable performance in natural image classification. However, the utility of this technique has not been thoroughly investigated in the medical image domain. In this study, we developed a transfer learning technique based on vision transformers to classify breast mass mammograms. The area under the receiver operating curve of the new model was estimated as 1 ± 0, thus outperforming the CNN-based transfer-learning models and vision transformer models trained from scratch. The technique can, hence, be applied in a clinical setting, to improve the early diagnosis of breast cancer.

BUViTNet: Breast Ultrasound Detection via Vision Transformers.

Convolutional neural networks (CNNs) have enhanced ultrasound image-based early breast cancer detection. Vision transformers (ViTs) have recently surpassed CNNs as the most effective method for natural image analysis. ViTs have proven their capability of incorporating more global information than CNNs at lower layers, and their skip connections are more powerful than those of CNNs, which endows ViTs with superior performance. However, the effectiveness of ViTs in breast ultrasound imaging has not yet been investigated. Here, we present BUViTNet breast ultrasound detection via ViTs, where ViT-based multistage transfer learning is performed using ImageNet and cancer cell image datasets prior to transfer learning for classifying breast ultrasound images. We utilized two publicly available ultrasound breast image datasets, Mendeley and breast ultrasound images (BUSI), to train and evaluate our algorithm. The proposed method achieved the highest area under the receiver operating characteristics curve (AUC) of 1 ± 0, Matthew's correlation coefficient (MCC) of 1 ± 0, and kappa score of 1 ± 0 on the Mendeley dataset. Furthermore, BUViTNet achieved the highest AUC of 0.968 ± 0.02, MCC of 0.961 ± 0.01, and kappa score of 0.959 ± 0.02 on the BUSI dataset. BUViTNet outperformed ViT trained from scratch, ViT-based conventional transfer learning, and CNN-based transfer learning in classifying breast ultrasound images (p < 0.01 in all cases). Our findings indicate that improved transformers are effective in analyzing breast images and can provide an improved diagnosis if used in clinical settings. Future work will consider the use of a wide range of datasets and parameters for optimized performance.

Ultrasound-Responsive Nanocarriers for Breast Cancer Chemotherapy.

Breast cancer is the most common type of cancer and it is treated with surgical intervention, radiotherapy, chemotherapy, or a combination of these regimens. Despite chemotherapy's ample use, it has limitations such as bioavailability, adverse side effects, high-dose requirements, low therapeutic indices, multiple drug resistance development, and non-specific targeting. Drug delivery vehicles or carriers, of which nanocarriers are prominent, have been introduced to overcome chemotherapy limitations. Nanocarriers have been preferentially used in breast cancer chemotherapy because of their role in protecting therapeutic agents from degradation, enabling efficient drug concentration in target cells or tissues, overcoming drug resistance, and their relatively small size. However, nanocarriers are affected by physiological barriers, bioavailability of transported drugs, and other factors. To resolve these issues, the use of external stimuli has been introduced, such as ultrasound, infrared light, thermal stimulation, microwaves, and X-rays. Recently, ultrasound-responsive nanocarriers have become popular because they are cost-effective, non-invasive, specific, tissue-penetrating, and deliver high drug concentrations to their target. In this paper, we review recent developments in ultrasound-guided nanocarriers for breast cancer chemotherapy, discuss the relevant challenges, and provide insights into future directions.

Automatic Cancer Cell Taxonomy Using an Ensemble of Deep Neural Networks.

Microscopic image-based analysis has been intensively performed for pathological studies and diagnosis of diseases. However, mis-authentication of cell lines due to misjudgments by pathologists has been recognized as a serious problem. To address this problem, we propose a deep-learning-based approach for the automatic taxonomy of cancer cell types. A total of 889 bright-field microscopic images of four cancer cell lines were acquired using a benchtop microscope. Individual cells were further segmented and augmented to increase the image dataset. Afterward, deep transfer learning was adopted to accelerate the classification of cancer types. Experiments revealed that the deep-learning-based methods outperformed traditional machine-learning-based methods. Moreover, the Wilcoxon signed-rank test showed that deep ensemble approaches outperformed individual deep-learning-based models (p < 0.001) and were in effect to achieve the classification accuracy up to 97.735%. Additional investigation with the Wilcoxon signed-rank test was conducted to consider various network design choices, such as the type of optimizer, type of learning rate scheduler, degree of fine-tuning, and use of data augmentation. Finally, it was found that the using data augmentation and updating all the weights of a network during fine-tuning improve the overall performance of individual convolutional neural network models.

Patchless Multi-Stage Transfer Learning for Improved Mammographic Breast Mass Classification.

Despite great achievements in classifying mammographic breast-mass images via deep-learning (DL), obtaining large amounts of training data and ensuring generalizations across different datasets with robust and well-optimized algorithms remain a challenge. ImageNet-based transfer learning (TL) and patch classifiers have been utilized to address these challenges. However, researchers have been unable to achieve the desired performance for DL to be used as a standalone tool. In this study, we propose a novel multi-stage TL from ImageNet and cancer cell line image pre-trained models to classify mammographic breast masses as either benign or malignant. We trained our model on three public datasets: Digital Database for Screening Mammography (DDSM), INbreast, and Mammographic Image Analysis Society (MIAS). In addition, a mixed dataset of the images from these three datasets was used to train the model. We obtained an average five-fold cross validation AUC of 1, 0.9994, 0.9993, and 0.9998 for DDSM, INbreast, MIAS, and mixed datasets, respectively. Moreover, the observed performance improvement using our method against the patch-based method was statistically significant, with a p-value of 0.0029. Furthermore, our patchless approach performed better than patch- and whole image-based methods, improving test accuracy by 8% (91.41% vs. 99.34%), tested on the INbreast dataset. The proposed method is of significant importance in solving the need for a large training dataset as well as reducing the computational burden in training and implementing the mammography-based deep-learning models for early diagnosis of breast cancer.

A Novel Multistage Transfer Learning for Ultrasound Breast Cancer Image Classification.

Breast cancer diagnosis is one of the many areas that has taken advantage of artificial intelligence to achieve better performance, despite the fact that the availability of a large medical image dataset remains a challenge. Transfer learning (TL) is a phenomenon that enables deep learning algorithms to overcome the issue of shortage of training data in constructing an efficient model by transferring knowledge from a given source task to a target task. However, in most cases, ImageNet (natural images) pre-trained models that do not include medical images, are utilized for transfer learning to medical images. Considering the utilization of microscopic cancer cell line images that can be acquired in large amount, we argue that learning from both natural and medical datasets improves performance in ultrasound breast cancer image classification. The proposed multistage transfer learning (MSTL) algorithm was implemented using three pre-trained models: EfficientNetB2, InceptionV3, and ResNet50 with three optimizers: Adam, Adagrad, and stochastic gradient de-scent (SGD). Dataset sizes of 20,400 cancer cell images, 200 ultrasound images from Mendeley and 400 ultrasound images from the MT-Small-Dataset were used. ResNet50-Adagrad-based MSTL achieved a test accuracy of 99 ± 0.612% on the Mendeley dataset and 98.7 ± 1.1% on the MT-Small-Dataset, averaging over 5-fold cross validation. A p-value of 0.01191 was achieved when comparing MSTL against ImageNet based TL for the Mendeley dataset. The result is a significant improvement in the performance of artificial intelligence methods for ultrasound breast cancer classification compared to state-of-the-art methods and could remarkably improve the early diagnosis of breast cancer in young women.

An Alternative Athlete Monitoring System Using Cost-Effective Inertial Sensing Instrumentation.

An examination of the human gait is feasible using inertial sensing. The embedded accelerometer and gyroscope in an inertial measurement unit can evaluate physical activity-based sports and this unit is relatively affordable compared to global positioning systems or video recording quantification. This study developed a cost-effective sports monitoring investigation method with an inertial sensor attached to the right leg of the athletes. In total, four parameters were simultaneously tracked to assess the entire sensor performance in real-time. The accelerometer measured the typical leg angle when walking and running, whereas the gyroscope processed the raw data to obtain the stride frequency from the time-domain data. Moreover, a comparison between the accelerometer and gyroscope was presented while simultaneously attaining the signal to convert the time-domain data to frequency results. Also, the number of strides and linear velocity was expressed as results in this study. To confirm the results, a statistical hypothesis test was implemented for all obtained results. The results indicated that the inertial sensing instrumentation used in this study is promising and could be an affordable alternative option for a sports monitoring system.

Objective Numerical Evaluation of Diffuse, Optically Reconstructed Images Using Structural Similarity Index.

Diffuse optical tomography is emerging as a non-invasive optical modality used to evaluate tissue information by obtaining the optical properties' distribution. Two procedures are performed to produce reconstructed absorption and reduced scattering images, which provide structural information that can be used to locate inclusions within tissues with the assistance of a known light intensity around the boundary. These methods are referred to as a forward problem and an inverse solution. Once the reconstructed image is obtained, a subjective measurement is used as the conventional way to assess the image. Hence, in this study, we developed an algorithm designed to numerically assess reconstructed images to identify inclusions using the structural similarity (SSIM) index. We compared four SSIM algorithms with 168 simulated reconstructed images involving the same inclusion position with different contrast ratios and inclusion sizes. A multiscale, improved SSIM containing a sharpness parameter (MS-ISSIM-S) was proposed to represent the potential evaluation compared with the human visible perception. The results indicated that the proposed MS-ISSIM-S is suitable for human visual perception by demonstrating a reduction of similarity score related to various contrasts with a similar size of inclusion; thus, this metric is promising for the objective numerical assessment of diffuse, optically reconstructed images.

Progress of Microfluidic Continuous Separation Techniques for Micro-/Nanoscale Bioparticles.

Separation of micro- and nano-sized biological particles, such as cells, proteins, and nucleotides, is at the heart of most biochemical sensing/analysis, including in vitro biosensing, diagnostics, drug development, proteomics, and genomics. However, most of the conventional particle separation techniques are based on membrane filtration techniques, whose efficiency is limited by membrane characteristics, such as pore size, porosity, surface charge density, or biocompatibility, which results in a reduction in the separation efficiency of bioparticles of various sizes and types. In addition, since other conventional separation methods, such as centrifugation, chromatography, and precipitation, are difficult to perform in a continuous manner, requiring multiple preparation steps with a relatively large minimum sample volume is necessary for stable bioprocessing. Recently, microfluidic engineering enables more efficient separation in a continuous flow with rapid processing of small volumes of rare biological samples, such as DNA, proteins, viruses, exosomes, and even cells. In this paper, we present a comprehensive review of the recent advances in microfluidic separation of micro-/nano-sized bioparticles by summarizing the physical principles behind the separation system and practical examples of biomedical applications.

Transfer Learning in Breast Cancer Diagnoses via Ultrasound Imaging.

Transfer learning is a machine learning approach that reuses a learning method developed for a task as the starting point for a model on a target task. The goal of transfer learning is to improve performance of target learners by transferring the knowledge contained in other (but related) source domains. As a result, the need for large numbers of target-domain data is lowered for constructing target learners. Due to this immense property, transfer learning techniques are frequently used in ultrasound breast cancer image analyses. In this review, we focus on transfer learning methods applied on ultrasound breast image classification and detection from the perspective of transfer learning approaches, pre-processing, pre-training models, and convolutional neural network (CNN) models. Finally, comparison of different works is carried out, and challenges-as well as outlooks-are discussed.

Overexpression of Activin Receptor-Like Kinase 1 in Endothelial Cells Suppresses Development of Arteriovenous Malformations in Mouse Models of Hereditary Hemorrhagic Telangiectasia.

RATIONALE: Hereditary hemorrhagic telangiectasia (HHT) is a genetic disease caused by mutations in ENG, ALK1, or SMAD4. Since proteins from all 3 HHT genes are components of signal transduction of TGF-ß (transforming growth factor ß) family members, it has been hypothesized that HHT is a disease caused by defects in the ENG-ALK1-SMAD4 linear signaling. However, in vivo evidence supporting this hypothesis is scarce. OBJECTIVE: We tested this hypothesis and investigated the therapeutic effects and potential risks of induced-ALK1 or -ENG overexpression (OE) for HHT. METHODS AND RESULTS: We generated a novel mouse allele (ROSA26Alk1) in which HA (human influenza hemagglutinin)-tagged ALK1 and bicistronic eGFP expression are induced by Cre activity. We examined whether ALK1-OE using the ROSA26Alk1 allele could suppress the development of arteriovenous malformations (AVMs) in wounded adult skin and developing retinas of Alk1- and Eng-inducible knockout (iKO) mice. We also used a similar approach to investigate whether ENG-OE could rescue AVMs. Biochemical and immunofluorescence analyses confirmed the Cre-dependent OE of the ALK1-HA transgene. We could not detect any pathological signs in ALK1-OE mice up to 3 months after induction. ALK1-OE prevented the development of retinal AVMs and wound-induced skin AVMs in Eng-iKO as well as Alk1-iKO mice. ALK1-OE normalized expression of SMAD and NOTCH target genes in ENG-deficient endothelial cells (ECs) and restored the effect of BMP9 (bone morphogenetic protein 9) on suppression of phosphor-AKT levels in these endothelial cells. On the other hand, ENG-OE could not inhibit the AVM development in Alk1-iKO models. CONCLUSIONS: These data support the notion that ENG and ALK1 form a linear signaling pathway for the formation of a proper arteriovenous network during angiogenesis. We suggest that ALK1 OE or activation can be an effective therapeutic strategy for HHT. Further research is required to study whether this therapy could be translated into treatment for humans.

A Macro Lens-Based Optical System Design for Phototherapeutic Instrumentation.

Light emitting diode (LED) and ultrasound have been powerful treatment stimuli for tumor cell growth due to non-radiation effects. This research is the first preliminary study of tumor cell suppression using a macro-lens-supported 460-nm LED combined with high-frequency ultrasound. The cell density, when exposed to the LED combined with ultrasound, was gradually reduced after 30 min of induction for up to three consecutive days when 48-W DC, 20-cycle, and 50 Vp-p sinusoidal pulses were applied to the LEDs through a designed macro lens and to the ultrasound transducer, respectively. Using a developed macro lens, the non-directional light beam emitted from the LED could be localized to a certain spot, likewise with ultrasound, to avoid additional undesirable thermal effects on the small sized tumor cells. In the experimental results, compared to LED-only induction (14.49 ± 2.73%) and ultrasound-only induction (13.27 ± 2.33%), LED combined with ultrasound induction exhibited the lowest cell density (6.25 ± 1.25%). Therefore, our measurement data demonstrated that a macro-lens-supported 460-nm LED combined with an ultrasound transducer could possibly suppress early stage tumor cells effectively.

An Alternative Approach to Detecting Cancer Cells by Multi-Directional Fluorescence Detection System Using Cost-Effective LED and Photodiode.

The enumeration of cellular proliferation by covering from hemocytometer to flow cytometer is an important procedure in the study of cancer development. For example, hemocytometer has been popularly employed to perform manual cell counting. It is easily achieved at a low-cost, however, manual cell counting is labor-intensive and prone to error for a large number of cells. On the other hand, flow cytometer is a highly sophisticated instrument in biomedical and clinical research fields. It provides detailed physical parameters of fluorescently labeled single cells or micro-sized particles depending on the fluorescence characteristics of the target sample. Generally, optical setup to detect fluorescence uses a laser, dichroic filter, and photomultiplier tube as a light source, optical filter, and photodetector, respectively. These components are assembled to set up an instrument to measure the amount of scattering light from the target particle; however, these components are costly, bulky, and have limitations in selecting diverse fluorescence dyes. Moreover, they require multiple refined and expensive modules such as cooling or pumping systems. Thus, alternative cost-effective components have been intensively developed. In this study, a low-cost and miniaturized fluorescence detection system is proposed, i.e., costing less than 100 US dollars, which is customizable by a 3D printer and light source/filter/sensor operating at a specific wavelength using a light-emitting diode with a photodiode, which can be freely replaceable. The fluorescence detection system can quantify multi-directional scattering lights simultaneously from the fluorescently labeled cervical cancer cells. Linear regression was applied to the acquired fluorescence intensities, and excellent linear correlations (R2 > 0.9) were observed. In addition, the enumeration of the cells using hemocytometer to determine its performance accuracy was analyzed by Student's t-test, and no statistically significant difference was found. Therefore, different cell concentrations are reversely calculated, and the system can provide a rapid and cost-effective alternative to commercial hemocytometer for live cell or microparticle counting.

Acoustic Stimulation by Shunt-Diode Pre-Linearizer using Very High Frequency Piezoelectric Transducer for Cancer Therapeutics.

In this paper, we proposed cancer cell acoustic stimulation by shunt-diode pre-linearizer scheme using a very high frequency (≥100 MHz) piezoelectric transducer. To verify the concept of our proposed scheme, we performed pulse-echo detection, and accessed therapeutic effects of human cervical cancer cells exposed to acoustic stimulation experiments using 100 MHz focused piezoelectric transducer triggered by PA with and without the proposed shunt-diode pre-linearizer scheme. In the pulse-echo detection responses, the peak-to-peak voltage of the echo signal when using the PA with shunt-diode pre-linearizer (49.79 mV) was higher than that when using the PA alone (29.87 mV). In the experimental results, the cell densities of cancer cells on Day 4 when using no acoustic stimulation (control group), the very high-frequency piezoelectric transducer triggered by PA only and PA combined with proposed pre-linearizer schemes (1 V and 5 V DC bias voltages) showed 100%, 92.8 ± 4.2%, 84.2 ± 4.6%, and 78 ± 2.9%, respectively. Therefore, we confirmed that the shunt-diode pre-linearizer could improve the performances of the pulse signals of the PA, thus, enabling better therapeutic stimulation performances for cancer cell suppression.

SMAD4 Deficiency Leads to Development of Arteriovenous Malformations in Neonatal and Adult Mice.

Background Hereditary hemorrhagic telangiectasia ( HHT ) is a rare genetic vascular disorder caused by mutations in endoglin ( ENG ), activin receptor-like kinase 1 ( ACVRL 1; ALK 1), or SMAD 4. Major clinical symptoms of HHT are arteriovenous malformations ( AVM s) found in the brain, lungs, visceral organs, and mucosal surface. Animal models harboring mutations in Eng or Alk1 recapitulate all of these HHT clinical symptoms and have been useful resources for studying mechanisms and testing potential drugs. However, animal models representing SMAD 4 mutations have been lacking. The goal of this study is to evaluate Smad4-inducible knockout ( iKO ) mice as an animal model of HHT and compare the phenotypes with other established HHT animal models. Methods and Results Global Smad4 deletion was induced at neonatal and adult stages, and hemoglobin levels, gastrointestinal hemorrhage, and presence of aberrant arteriovenous connections were examined. Neonatal Smad4- iKO mice exhibited signs of gastrointestinal bleeding and AVM s in the brain, intestine, nose, and retina. The radial expansion was decreased, and AVM s were detected on both distal and proximal retinal vasculature of Smad4- iKO s. Aberrant smooth muscle actin staining was observed in the initial stage AVM s and their connecting veins, indicating abnormal arterial flow to veins. In adult mice, Smad4 deficiency caused gastrointestinal bleeding and AVM s along the gastrointestinal tract and wounded skin. HHT -related phenotypes of Smad4- iKO s appeared to be comparable with those found in Alk1- iKO and Eng- iKO mice. Conclusions These data further confirm that SMAD signaling is crucial for normal arteriovenous network formation, and Smad4- iKO will be an alternative animal model of AVM research associated with HHT .

Therapeutic Effect Enhancement by Dual-bias High-voltage Circuit of Transmit Amplifier for Immersion Ultrasound Transducer Applications.

The dual-bias high-voltage circuit of a transmit amplifier for immersion ultrasound transducer applications is proposed to enhance the therapeutic effect of human HeLa cells. Highvoltage output signals generated from a transmit amplifier are typically preferable for immersion ultrasound transducers owing to their high sensitivity at the desired frequency. However, highvoltage output signals typically produce high-order harmonic distortions, thus triggering several unwanted high-order spectral signals in the ultrasound transducers. By reducing high-order harmonic distortions, we expect that improving the signal quality of excited pulses for immersion ultrasound transducers would be beneficial for the therapeutic effect on human cervical cancer HeLa cell suppression. Therefore, an additional bias circuit is developed to merge with the original bias circuit for transmit amplifier to control the harmonic distortions of the immersion ultrasonic transducer. To properly select the components of dual-bias high-voltage circuit, we need to calculate and measure the DC bias voltages of the transmit amplifier with and without dual-bias high-voltage circuit for different period of the time for therapeutic applications. To evaluate the performances of the developed circuit, pulse-echo measurements using a transmit amplifier with or without dualbias high-voltage circuit were obtained. The measured second, third, and fourth harmonic distortions of the echo signals when using the transmit amplifier with dual-bias high-voltage circuit at 10 V DC bias voltage are lower than those when using the transmit amplifier only. Subsequently, the therapeutic effects using the enhanced performances of the transmit amplifier with dual-bias high-voltage circuit were verified and compared with those using the performances of the transmit amplifier by comparison of quantitative changes in HeLa cell concentrations. The control group without any ultrasonic induction increased the cell density up to about 100% on Day4, however the experimental groups with ultrasonic induction (TA = 91.2 ± 0.8%, TA+Dual-bias high-voltage circuit (0.8V) = 78.8±1.7% and TA+Dual-bias high-voltage circuit (10 V) = 66.3 ± 1.1%) showed statistically significant cell density changes compared to the control group. We confirmed that the therapeutic effect from using the dual-bias high-voltage circuit is improved. Therefore, it can be a potential candidate to improve the therapeutic effect of HeLa cells.

Suppression Technique of HeLa Cell Proliferation Using Ultrasonic Power Amplifiers Integrated with a Series-Diode Linearizer.

A series-diode linearizer scheme is developed, which can possibly generate higher voltage signals. To verify our proposed concept, ultrasonic power amplifiers with and without the linearizer were tested for HeLa cells proliferation in vitro. In general, ultrasonic stimulus initiates the process of cavitation which can cause cell lysis and disruption of cell attachment. The cavitation can also induce formation of free radicals so that a rigid membrane of malignant cancer cells have increased sensitivity to ultrasonic stimulus. The cell density of the control group increased up to almost 100% on Day 3. However, cell densities of the experimental group when using an isolated ultrasonic power amplifier, and ultrasonic power amplifiers integrated with the linearizer at 1 V and 5 V DC (direct current) bias could be suppressed more than that when using an ultrasonic power amplifier (90.7 ± 1.2%, 75.8 ± 3.5%, and 68.1 ± 1.1%, respectively). Additionally, the proliferation suppressing ratios of each experimental group confirmed that the cell density decrements of the experimental groups exhibited statistical significance compared to the control group (ultrasonic power amplifier = 8.87%, ultrasonic power amplifier with 1 V biased linearizer = 23.87%, and ultrasonic power amplifier with 5 V biased linearizer = 31.56%).