COVID-CLNet; COVID-19 Detection with Compressive Deep Learning Approaches

One of the most serious global health threats is COVID-19 pandemic. The emphasis on increasing the diagnostic capability helps stopping its spread significantly. Therefore, to assist the radiologist or other medical professional to detect and identify the COVID-19 cases in the shortest possible time, we propose a computer-aided detection (CADe) system that uses the computed tomography (CT) scan images. This proposed boosted deep learning network (CLNet) is based on the implementation of Deep Learning (DL) networks as a complementary to the Compressive Learning (CL). We utilize our inception feature extraction technique in the measurement domain using CL to represent the data features into a new space with less dimensionality before accessing the Convolutional Neural Network. All original features have been contributed equally to the new space using a sensing matrix. Experiments performed on different compressed methods show promising results for COVID-19 detection.

DIAROP: Automated Deep Learning-Based Diagnostic Tool for Retinopathy of Prematurity

Retinopathy of Prematurity (ROP) affects preterm neonates and could cause blindness. Deep learning (DL) can assist ophthalmologists in the diagnosis of ROP. This paper proposes an automated and reliable diagnostic tool based on DL techniques called DIAROP to support the ophthalmologic diagnosis of ROP. It extracts significant features by first obtaining spatial features from the four convolution neural networks (CNNs) DL techniques using transfer learning and then applying Fast Walsh Hadamard Transform (FWHT) to integrate these features. Moreover, DIAROP explores the best-integrated features extracted from the CNNs that influence its diagnostic capability. The results of DIAROP indicate that DIAROP achieved an accuracy of 93.2% and an area under receiving operating characteristic curve (AUC) of 0.98. Furthermore, DIAROP performance is compared with recent ROP diagnostic tools. Its promising performance shows that DIAROP may assist the ophthalmologic diagnosis of ROP.

Event Monitoring of Transformer Discharge Sounds based on Voiceprint

This paper investigates the operation inspection and anomaly diagnosis of transformers in substations, and carries out an application study of artificial intelligence-based sound recognition technology in transformer discharge diagnosis to improve the timeliness and diagnostic capability of intelligent monitoring of substation equipment operation. In this study, a sound parameterization technology in the field of sound recognition is used to implement automatic discharge sound detections. The sound samples are pre-processed and then Mel-frequency cepstrum coefficients (MFCCs) are extracted as features, which are used to train Gaussian mixture models (GMMs). Finally, the trained GMMs are used to detect discharge sounds in the place of transformers in substations. The test results demonstrate that the audio anomaly detection based on MFCCs and GMMs can be used to effectively recognize anomalous discharge in the high scenario of transformers.

Targeted Bisulfite Sequencing Reveals DNA Methylation Changes in Zinc Finger Family Genes Associated With KRAS Mutated Colorectal Cancer

Background: Colorectal cancer (CRC) is a leading cause of cancer death, and early diagnosis of CRC could significantly reduce its mortality rate. Previous studies suggest that the DNA methylation status of zinc finger genes (ZFGs) could be of potential in CRC early diagnosis. However, the comprehensive evaluation of ZFGs in CRC is still lacking.Methods: We first collected 1,426 public samples on genome-wide DNA methylation, including 1,104 cases of CRC tumors, 54 adenomas, and 268 para-tumors. Next, the most differentially methylated ZFGs were identified and validated in two replication cohorts comprising 218 CRC patients. Finally, we compared the prediction capabilities between the ZFGs and the SEPT9 in all CRC patients and the KRAS + and KRAS- subgroup.Results: Five candidate ZFGs were selected: ESR1, ZNF132, ZNF229, ZNF542, and ZNF677. In particular, ESR1 [area under the curve (AUC) = 0.91] and ZNF132 (AUC = 0.93) showed equivalent or better diagnostic capability for CRC than SEPT9 (AUC = 0.91) in the validation dataset, suggesting that these two ZFGs might be of potential for CRC diagnosis in the future. Furthermore, we performed subgroup analysis and found a significantly higher diagnostic capability in KRAS + (AUC ranged from 0.97 to 1) than that in KRAS- patients (AUC ranged from 0.74 to 0.86) for all these five ZFGs, suggesting that these ZFGs could be ideal diagnostic markers for KRAS mutated CRC patients.Conclusion: The methylation profiles of the candidate ZFGs could be potential biomarkers for the early diagnosis of CRC, especially for patients carrying KRAS mutations.

Immunoglobulin E Detection Method Based on Cascade Enzymatic Reaction Utilizing Portable Personal Glucose Meter

We herein describe a cascade enzymatic reaction (CER)-based IgE detection method utilizing a personal glucose meter (PGM), which relies on alkaline phosphatase (ALP) activity that regulates the amount of adenosine triphosphate (ATP). The amount of sandwich assay complex is determined according to the presence or absence of the target IgE. Additionally, the ALP in the sandwich assay catalyzes the dephosphorylation of ATP, a substrate of CER, which results in the changes in glucose level. By employing this principle, IgE was reliably detected at a concentration as low as ca. 29.6 ng/mL with high specificity toward various proteins. Importantly, the limit of detection (LOD) of this portable PGM-based approach was comparable to currently commercialized ELISA kit without expensive and bulky analysis equipment as well as complexed washing step. Finally, the diagnostic capability of this method was also successfully verified by reliably detecting IgE present in a real human serum sample with an excellent recovery ratio within 100 ± 6%.

The Current Status and Path Forward on Clinicians’ Assisted Decision Making by Artificial Intelligence-Enabled Technology: Mixed Method Approach (Preprint)

BACKGROUND With the potential and rapid development of artificial intelligence and related technologies, AI algorithms are being embedded into various health information technologies to assist clinicians’ decision making in clinician-patient encounters. OBJECTIVE The objective of this study is to explore how clinicians perceive AI assistance in their diagnosis decision making and suggest paths forward as to what necessitates to achieve AI-human teaming in healthcare decision making. METHODS This study uses a mixed methods approach utilizing hierarchical linear modeling (HLM) and sentiment analysis through natural language understanding (NLU) techniques. RESULTS A total of 114 clinicians who practice in family medicine and interact with AI algorithm to make patient diagnosis participated in online simulation surveys during 2020- 2021. Our qualitative results show a promise that clinicians’ overall sentiment toward AI-assisted patient diagnosis was positive and comparable to those of live patient encounters. However, it also showed that the process of diagnosis decision making by the given AI physiology algorithms did not align with the way clinicians make diagnosis decision. In the follow-up quantitative survey, clinicians perceive that current AI assistance was not likely to enhance their diagnostic capability and rather negatively affect their overall task performance (β=-0.421, p=0.016). Interestingly, clinician’s level of clinical diagnosis capability is rather associated with clinicians’ ex ante quality such as education (β=1.880, p=0.072) and age (β=2.428, p=0.071) on diagnostic capability as well as existing technology habit on both dependent variables (β=0.232, p=0.009 and β=0.244, p=0.003, respectively). CONCLUSIONS This paper sheds light on clinicians’ current perception and sentiment toward AI-enabled diagnosis technology in healthcare decision makings. We showed here that while overall sentiment toward the AI assistance was positive, current form of AI assistance is not linked to efficient decision-making in that AI algorithms are not aligned with humans’ subjective clinical reasoning. We suggest that health policy makers and HIT developers need to gather behavioral data from clinicians in various disciplines and specialties to make clinical AI algorithms to be aligned with humans’ subjective and unique clinical reasoning patterns.

An Efficient Magnetic Nanocatalyst Induced Chemo- and Ferroptosis Synergistic Cancer Therapy in Combination with T1-T2 Dual-Mode Magnetic Resonance Imaging through Doxorubicin Delivery

Excessive iron ions in cancer cells can catalyze H2O2 into highly toxic ·OH and then promote the generation of reactive oxygen species (ROS), inducing cancer ferroptosis. However, the efficacy of ferroptosis catalyst is still insufficient because of low Fe(II) release, which severely limited its application in clinics. Herein, we developed a novel magnetic nanocatalyst for MRI-guided chemo- and ferroptosis synergistic cancer therapies through iRGD-PEG-ss-PEG modified gadolinium engineering magnetic iron oxide loaded Dox (ipGdIO-Dox). The introduction of gadolinium compound disturbed the structure of ipGdIO-Dox, making magnetic nanocatalyst be more sensitive to weak acid. When the ipGdIO-Dox entered into cancer cells, abundance of Fe(II) ions were released and then catalyzed H2O2 into highly toxic OH·, which would elevate cellular oxidative-stress to damage mitochondria and cell membranes and induced cancer ferroptosis. In addition, the iRGD-PEG-ss-PEG chain coated onto nanoplatform were also broken by high expression of GSH, and then the Dox was released. This process not only effectively inhibited DNA replication, but further activated cellular ROS, making nanoplatform achieve stronger anticancer ability. Besides, the systemic delivery ipGdIO-Dox significantly enhanced T1- and T2-weighted MRI signal of tumor, endowing accurate diagnostic capability for tumor recognition. Therefore, the ipGdIO-Dox might be a promising candidate for developing MRI guided chemo- and chemdynamic synergistic theranostic system.