Dark-field optical fault inspection of ∼10 nm scale room-temperature silicon single-electron transistors.

Dark-field (DF) optical microscopy, combined with optical simulation based on modal diffraction theory for transverse electric polarized white light, is shown to provide non-invasive, sub-wavelength geometrical information for nanoscale etched device structures. Room temperature (RT) single electron transistors (SETs) in silicon, defined using etched ∼10 nm point-contacts (PCs) and in-plane side gates, are investigated to enable fabrication fault detection. Devices are inspected using scanning electron microscopy, bright-field (BF) and DF imaging. Compared to BF, DF imaging enhances contrast from edge diffraction by ×3.5. Sub-wavelength features in the RT SET structure lead to diffraction peaks in the DF intensity patterns, creating signatures for device geometry. These features are investigated using a DF line scan optical simulation approximation of the experimental results. Dark field imaging and simulation are applied to three types of structures, comprising successfully-fabricated, over-etched and interconnected PC/gate devices. Each structure can be identified via DF signatures, providing a non-invasive fault detection method to investigate etched nanodevice morphology.

Efficient Convolution Network to Assist Breast Cancer Diagnosis and Target Therapy.

Breast cancer is the leading cause of cancer-related deaths among women worldwide, and early detection and treatment has been shown to significantly reduce fatality rates from severe illness. Moreover, determination of the human epidermal growth factor receptor-2 (HER2) gene amplification by Fluorescence in situ hybridization (FISH) and Dual in situ hybridization (DISH) is critical for the selection of appropriate breast cancer patients for HER2-targeted therapy. However, visual examination of microscopy is time-consuming, subjective and poorly reproducible due to high inter-observer variability among pathologists and cytopathologists. The lack of consistency in identifying carcinoma-like nuclei has led to divergences in the calculation of sensitivity and specificity. This manuscript introduces a highly efficient deep learning method with low computing cost. The experimental results demonstrate that the proposed framework achieves high precision and recall on three essential clinical applications, including breast cancer diagnosis and human epidermal receptor factor 2 (HER2) amplification detection on FISH and DISH slides for HER2 target therapy. Furthermore, the proposed method outperforms the majority of the benchmark methods in terms of IoU by a significant margin (p<0.001) on three essential clinical applications. Importantly, run time analysis shows that the proposed method obtains excellent segmentation results with notably reduced time for Artificial intelligence (AI) training (16.93%), AI inference (17.25%) and memory usage (18.52%), making the proposed framework feasible for practical clinical usage.

A Soft Label Deep Learning to Assist Breast Cancer Target Therapy and Thyroid Cancer Diagnosis.

According to the World Health Organization Report 2022, cancer is the most common cause of death contributing to nearly one out of six deaths worldwide. Early cancer diagnosis and prognosis have become essential in reducing the mortality rate. On the other hand, cancer detection is a challenging task in cancer pathology. Trained pathologists can detect cancer, but their decisions are subjective to high intra- and inter-observer variability, which can lead to poor patient care owing to false-positive and false-negative results. In this study, we present a soft label fully convolutional network (SL-FCN) to assist in breast cancer target therapy and thyroid cancer diagnosis, using four datasets. To aid in breast cancer target therapy, the proposed method automatically segments human epidermal growth factor receptor 2 (HER2) amplification in fluorescence in situ hybridization (FISH) and dual in situ hybridization (DISH) images. To help in thyroid cancer diagnosis, the proposed method automatically segments papillary thyroid carcinoma (PTC) on Papanicolaou-stained fine needle aspiration and thin prep whole slide images (WSIs). In the evaluation of segmentation of HER2 amplification in FISH and DISH images, we compare the proposed method with thirteen deep learning approaches, including U-Net, U-Net with InceptionV5, Ensemble of U-Net with Inception-v4, Inception-Resnet-v2 encoder, and ResNet-34 encoder, SegNet, FCN, modified FCN, YOLOv5, CPN, SOLOv2, BCNet, and DeepLabv3+ with three different backbones, including MobileNet, ResNet, and Xception, on three clinical datasets, including two DISH datasets on two different magnification levels and a FISH dataset. The result on DISH breast dataset 1 shows that the proposed method achieves high accuracy of 87.77 ± 14.97%, recall of 91.20 ± 7.72%, and F1-score of 81.67 ± 17.76%, while, on DISH breast dataset 2, the proposed method achieves high accuracy of 94.64 ± 2.23%, recall of 83.78 ± 6.42%, and F1-score of 85.14 ± 6.61% and, on the FISH breast dataset, the proposed method achieves high accuracy of 93.54 ± 5.24%, recall of 83.52 ± 13.15%, and F1-score of 86.98 ± 9.85%, respectively. Furthermore, the proposed method outperforms most of the benchmark approaches by a significant margin (p <0.001). In evaluation of segmentation of PTC on Papanicolaou-stained WSIs, the proposed method is compared with three deep learning methods, including Modified FCN, U-Net, and SegNet. The experimental result demonstrates that the proposed method achieves high accuracy of 99.99 ± 0.01%, precision of 92.02 ± 16.6%, recall of 90.90 ± 14.25%, and F1-score of 89.82 ± 14.92% and significantly outperforms the baseline methods, including U-Net and FCN (p <0.001). With the high degree of accuracy, precision, and recall, the results show that the proposed method could be used in assisting breast cancer target therapy and thyroid cancer diagnosis with faster evaluation and minimizing human judgment errors.

Tandem mass neonatal screening in Taiwan--report from one center.

BACKGROUND/PURPOSE: Neonatal screening using tandem mass spectrometry (MS/MS) started in Taiwan in 2000. We evaluated the efficacy of this system by analyzing the frequency of diseases and the outcome of the patients identified. METHODS: Between August 2001 and July 2004, 199, 922 neonates were screened for 10 amino acids and acylcarnitines using MS/MS in a single center. RESULTS: In total, 29 cases of inborn errors of metabolism were detected. The overall prevalence was one per 6894 births. The most common inborn error found was 3-methylcrotonyl CoA carboxylase deficiency (10 cases, 34.5%), but none of the cases needed aggressive treatment. There were two cases of type I glutaric aciduria, two cases of maple syrup urine disease, and one case of type II citrullinemia, and early therapeutic intervention was effective for all of them. CONCLUSION: We found that MS/MS neonatal screening was valuable in the early diagnosis of severe and treatable inborn errors of metabolism such as organic acidemias and urea cycle disorders. It also detected less severe disorders that required only observation.

Carbohydrate deficient glycoprotein syndrome type Ia.

Carbohydrate deficient glycoprotein syndromes (CDG) are inherited multisystem disorders characterized by the abnormal glycosylation of a number of serum glycoproteins. CDG-Ia results from deficiency of phosphomannomutase that catalyzes the conversion of mannose-6-phosphate to mannose-1-phosphate in the cytosol. We report a case of CDG-Ia in an 11-month-old girl with developmental delay, failure to thrive, inverted nipples and abnormal fat pads. The abnormal pattern of transferrin glycosylation and phosphomannomutase activity assay confirmed the diagnosis of CDG type Ia. Unfortunately, an efficient treatment is still not available for CDG type Ia patients. This is the first report of a Taiwanese patient with this syndrome.