RT-RPA-PfAgo detection platform for one-tube simultaneous typing diagnosis of human respiratory syncytial virus.

Human respiratory syncytial virus (HRSV) is the most prevalent pathogen contributing to acute respiratory tract infections (ARTI) in infants and young children and can lead to significant financial and medical costs. Here, we developed a simultaneous, dual-gene and ultrasensitive detection system for typing HRSV within 60 minutes that needs only minimum laboratory support. Briefly, multiplex integrating reverse transcription-recombinase polymerase amplification (RT-RPA) was performed with viral RNA extracted from nasopharyngeal swabs as a template for the amplification of the specific regions of subtypes A (HRSVA) and B (HRSVB) of HRSV. Next, the Pyrococcus furiosus Argonaute (PfAgo) protein utilizes small 5'-phosphorylated DNA guides to cleave target sequences and produce fluorophore signals (FAM and ROX). Compared with the traditional gold standard (RT-qPCR) and direct immunofluorescence assay (DFA), this method has the additional advantages of easy operation, efficiency and sensitivity, with a limit of detection (LOD) of 1 copy/µL. In terms of clinical sample validation, the diagnostic accuracy of the method for determining the HRSVA and HRSVB infection was greater than 95%. This technique provides a reliable point-of-care (POC) testing for the diagnosis of HRSV-induced ARTI in children and for outbreak management, especially in resource-limited settings.

Evaluation of an Active Disturbance Rejection Controller for Ophthalmic Robots with Piezo-Driven Injector.

Retinal vein cannulation involves puncturing an occluded vessel on the micron scale. Even single millinewton force can cause permanent damage. An ophthalmic robot with a piezo-driven injector is precise enough to perform this delicate procedure, but the uncertain viscoelastic characteristics of the vessel make it difficult to achieve the desired contact force without harming the retina. The paper utilizes a viscoelastic contact model to explain the mechanical characteristics of retinal blood vessels to address this issue. The uncertainty in the viscoelastic properties is considered an internal disturbance of the contact model, and an active disturbance rejection controller is then proposed to precisely control the contact force. The experimental results show that this method can precisely adjust the contact force at the millinewton level even when the viscoelastic parameters vary significantly (up to 403.8%). The root mean square (RMS) and maximum value of steady-state error are 0.32 mN and 0.41 mN. The response time is below 2.51 s with no obvious overshoot.

Robust gesture recognition based on attention-deep fast convolutional neural network and surface electromyographic signals.

The surface electromyographic (sEMG) signals reflect human motor intention and can be utilized for human-machine interfaces (HMI). Comparing to the sparse multi-channel (SMC) electrodes, the high-density (HD) electrodes have a large number of electrodes and compact space between electrodes, which can achieve more sEMG information and have the potential to achieve higher performance in myocontrol. However, when the HD electrodes grid shift or damage, it will affect gesture recognition and reduce recognition accuracy. To minimize the impact resulting from the electrodes shift and damage, we proposed an attention deep fast convolutional neural network (attention-DFCNN) model by utilizing the temporary and spatial characteristics of high-density surface electromyography (HD-sEMG) signals. Contrary to the previous methods, which are mostly base on sEMG temporal features, the attention-DFCNN model can improve the robustness and stability by combining the spatial and temporary features. The performance of the proposed model was compared with other classical method and deep learning methods. We used the dataset provided by The University Medical Center Göttingen. Seven able-bodied subjects and one amputee involved in this work. Each subject executed nine gestures under the electrodes shift (10 mm) and damage (6 channels). As for the electrodes shift 10 mm in four directions (inwards; onwards; upwards; downwards) on seven able-bodied subjects, without any pre-training, the average accuracy of attention-DFCNN (0.942 ± 0.04) is significantly higher than LSDA (0.910 ± 0.04, p < 0.01), CNN (0.920 ± 0.05, p < 0.01), TCN (0.840 ± 0.07, p < 0.01), LSTM (0.864 ± 0.08, p < 0.01), attention-BiLSTM (0.852 ± 0.07, p < 0.01), Transformer (0.903 ± 0.07, p < 0.01) and Swin-Transformer (0.908 ± 0.09, p < 0.01). The proposed attention-DFCNN algorithm and the way of combining the spatial and temporary features of sEMG signals can significantly improve the recognition rate when the HD electrodes grid shift or damage during wear.

Fusion inception and transformer network for continuous estimation of finger kinematics from surface electromyography.

Decoding surface electromyography (sEMG) to recognize human movement intentions enables us to achieve stable, natural and consistent control in the field of human computer interaction (HCI). In this paper, we present a novel deep learning (DL) model, named fusion inception and transformer network (FIT), which effectively models both local and global information on sequence data by fully leveraging the capabilities of Inception and Transformer networks. In the publicly available Ninapro dataset, we selected surface EMG signals from six typical hand grasping maneuvers in 10 subjects for predicting the values of the 10 most important joint angles in the hand. Our model's performance, assessed through Pearson's correlation coefficient (PCC), root mean square error (RMSE), and R-squared (R2) metrics, was compared with temporal convolutional network (TCN), long short-term memory network (LSTM), and bidirectional encoder representation from transformers model (BERT). Additionally, we also calculate the training time and the inference time of the models. The results show that FIT is the most performant, with excellent estimation accuracy and low computational cost. Our model contributes to the development of HCI technology and has significant practical value.

Cross-Subject Lifelong Learning for Continuous Estimation From Surface Electromyographic Signal.

The employment of surface electromyographic (sEMG) signals in the estimation of hand kinematics represents a promising non-invasive methodology for the advancement of human-machine interfaces. However, the limitations of existing subject-specific methods are obvious as they confine the application to individual models that are custom-tailored for specific subjects, thereby reducing the potential for broader applicability. In addition, current cross-subject methods are challenged in their ability to simultaneously cater to the needs of both new and existing users effectively. To overcome these challenges, we propose the Cross-Subject Lifelong Network (CSLN). CSLN incorporates a novel lifelong learning approach, maintaining the patterns of sEMG signals across a varied user population and across different temporal scales. Our method enhances the generalization of acquired patterns, making it applicable to various individuals and temporal contexts. Our experimental investigations, encompassing both joint and sequential training approaches, demonstrate that the CSLN model not only attains enhanced performance in cross-subject scenarios but also effectively addresses the issue of catastrophic forgetting, thereby augmenting training efficacy.

A rotary transformer cross-subject model for continuous estimation of finger joints kinematics and a transfer learning approach for new subjects.

Introduction: Surface Electromyographic (sEMG) signals are widely utilized for estimating finger kinematics continuously in human-machine interfaces (HMI), and deep learning approaches are crucial in constructing the models. At present, most models are extracted on specific subjects and do not have cross-subject generalizability. Considering the erratic nature of sEMG signals, a model trained on a specific subject cannot be directly applied to other subjects. Therefore, in this study, we proposed a cross-subject model based on the Rotary Transformer (RoFormer) to extract features of multiple subjects for continuous estimation kinematics and extend it to new subjects by adversarial transfer learning (ATL) approach. Methods: We utilized the new subject's training data and an ATL approach to calibrate the cross-subject model. To improve the performance of the classic transformer network, we compare the impact of different position embeddings on model performance, including learnable absolute position embedding, Sinusoidal absolute position embedding, and Rotary Position Embedding (RoPE), and eventually selected RoPE. We conducted experiments on 10 randomly selected subjects from the NinaproDB2 dataset, using Pearson correlation coefficient (CC), normalized root mean square error (NRMSE), and coefficient of determination (R2) as performance metrics. Results: The proposed model was compared with four other models including LSTM, TCN, Transformer, and CNN-Attention. The results demonstrated that both in cross-subject and subject-specific cases the performance of RoFormer was significantly better than the other four models. Additionally, the ATL approach improves the generalization performance of the cross-subject model better than the fine-tuning (FT) transfer learning approach. Discussion: The findings indicate that the proposed RoFormer-based method with an ATL approach has the potential for practical applications in robot hand control and other HMI settings. The model's superior performance suggests its suitability for continuous estimation of finger kinematics across different subjects, addressing the limitations of subject-specific models.

A fast gradient convolution kernel compensation method for surface electromyogram decomposition.

Decomposition of EMG signals provides the decoding of motor unit (MU) discharge timings. In this study, we propose a fast gradient convolution kernel compensation (fgCKC) decomposition algorithm for high-density surface EMG decomposition and apply it to an offline and real-time estimation of MU spike trains. We modified the calculation of the cross-correlation vectors to improve the calculation efficiency of the gradient convolution kernel compensation (gCKC) algorithm. Specifically, the new fgCKC algorithm considers the past gradient in addition to the current gradient. Furthermore, the EMG signals are divided by sliding windows to simulate real-time decomposition, and the proposed algorithm was validated on simulated and experimental signals. In the offline decomposition, fgCKC has the same robustness as gCKC, with sensitivity differences of 2.6 ± 1.3 % averaged across all trials and subjects. Nevertheless, depending on the number of MUs and the signal-to-noise ratio of signals, fgCKC is approximately 3 times faster than gCKC. In the real-time part, the processing only needed 240 ms average per window of EMG signals on a regular personal computer (IIntel(R) Core(TM) i5-12490F 3 GHz, 16 GB memory). These results indicate that fgCKC achieves real-time decomposition by significantly reducing processing time, providing more possibilities for non-invasive neuronal behavior research.

A Bi-GRU-attention neural network to identify motor units from high-density surface electromyographic signals in real time.

To utilize surface electromyographics (sEMG) for control purposes, it is necessary to perform real-time estimation of the neural drive to the muscles, which involves real-time decomposition of the EMG signals. In this paper, we propose a Bidirectional Gate Recurrent Unit (Bi-GRU) network with attention to perform online decomposition of high-density sEMG signals. The model can give different levels of attention to different parts of the sEMG signal according to their importance using the attention mechanism. The output of gradient convolutional kernel compensation (gCKC) algorithm was used as the training label, and simulated and experimental sEMG data were divided into windows with 120 sample points for model training, the sampling rate of sEMG signal is 2048 Hz. We test different attention mechanisms and find out the ones that could bring the highest F1-score of the model. The simulated sEMG signal is synthesized from Fuglevand method (J. Neurophysiol., 1993). For the decomposition of 10 Motor Units (MUs), the network trained on simulated data achieved an average F1-score of 0.974 (range from 0.96 to 0.98), and the network trained on experimental data achieved an average F1-score of 0.876 (range from 0.82 to 0.97). The average decomposition time for each window was 28 ms (range from 25.6 ms to 30.5 ms), which falls within the lower bound of the human electromechanical delay. The experimental results show the feasibility of using Bi-GRU-Attention network for the real-time decomposition of Motor Units. Compared to the gCKC algorithm, which is considered the gold standard in the medical field, this model sacrifices a small amount of accuracy but significantly improves computational speed by eliminating the need for calculating the cross-correlation matrix and performing iterative computations.

From Forearm to Wrist: Deep Learning for Surface Electromyography-Based Gesture Recognition.

Though the forearm is the focus of the prostheses, myoelectric control with the electrodes on the wrist is more comfortable for general consumers because of its unobtrusiveness and incorporation with the existing wrist-based wearables. Recently, deep learning methods have gained attention for myoelectric control but their performance is unclear on wrist myoelectric signals. This study compared the gesture recognition performance of myoelectric signals from the wrist and forearm between a state-of-the-art method, TDLDA, and four deep learning models, including convolutional neural network (CNN), temporal convolutional network (TCN), gate recurrent unit (GRU) and Transformer. It was shown that with forearm myoelectric signals, the performance between deep learning models and TDLDA was comparable, but with wrist myoelectric signals, the deep learning models outperformed TDLDA significantly with a difference of at least 9%, while the performance of TDLDA was close between the two signal modalities. This work demonstrated the potential of deep learning for wrist-based myoelectric control and would facilitate its application into more sections.

Milletdb: a multi-omics database to accelerate the research of functional genomics and molecular breeding of millets.

Millets are a class of nutrient-rich coarse cereals with high resistance to abiotic stress; thus, they guarantee food security for people living in areas with extreme climatic conditions and provide stress-related genetic resources for other crops. However, no platform is available to provide a comprehensive and systematic multi-omics analysis for millets, which seriously hinders the mining of stress-related genes and the molecular breeding of millets. Here, a free, web-accessible, user-friendly millets multi-omics database platform (Milletdb, http://milletdb.novogene.com) has been developed. The Milletdb contains six millets and their one related species genomes, graph-based pan-genomics of pearl millet, and stress-related multi-omics data, which enable Milletdb to be the most complete millets multi-omics database available. We stored GWAS (genome-wide association study) results of 20 yield-related trait data obtained under three environmental conditions [field (no stress), early drought and late drought] for 2 years in the database, allowing users to identify stress-related genes that support yield improvement. Milletdb can simplify the functional genomics analysis of millets by providing users with 20 different tools (e.g., 'Gene mapping', 'Co-expression', 'KEGG/GO Enrichment' analysis, etc.). On the Milletdb platform, a gene PMA1G03779.1 was identified through 'GWAS', which has the potential to modulate yield and respond to different environmental stresses. Using the tools provided by Milletdb, we found that the stress-related PLATZs TFs (transcription factors) family expands in 87.5% of millet accessions and contributes to vegetative growth and abiotic stress responses. Milletdb can effectively serve researchers in the mining of key genes, genome editing and molecular breeding of millets.

Evaluation of continuous curvilinear capsulorhexis based on a neural-network.

PURPOSE: Continuous curvilinear capsulorhexis (CCC), as a prerequisite for successful cataract surgery, is one of the most important and difficult steps in phacoemulsification. In clinical practice, the size and circularity of the capsular tear and eccentricity with the lens are often employed as indicators to evaluate the effect of CCC. METHODS: We present a neural network-based model to improve the efficiency and accuracy of evaluation for capsulorhexis results. The capsulorhexis results evaluation model consists of the detection network based on U-Net and the nonlinear fitter built from fully connected layers. The detection network is responsible for detecting the positions of the round capsular tear and lens margin, and the nonlinear fitter is utilized to fit the outputs of the detection network and to compute the capsulorhexis results evaluation indicators. We evaluate the proposed model on an artificial eye phantom and compare its performance with the medical evaluation method. RESULTS: The experimental results show that the average detection error of the proposed evaluation model is within 0.04 mm. Compared with the medical method (the average detection error is 0.28 mm), the detection accuracy of the proposed evaluation model is more accurate and stable. CONCLUSION: We propose a neural network-based capsulorhexis results evaluation model to improve the accuracy of evaluation for capsulorhexis results. The results of the evaluation experiments show that the proposed results evaluation model evaluates of the effect of capsulorhexis better than the medical evaluation method.

Analysis of Trapping Effect on Large-Signal Characteristics of GaN HEMTs Using X-Parameters and UV Illumination.

GaN high-electron-mobility transistors (HEMTs) have attracted widespread attention for high-power microwave applications, owing to their superior properties. However, the charge trapping effect has limitations to its performance. To study the trapping effect on the device large-signal behavior, AlGaN/GaN HEMTs and metal-insulator-semiconductor HEMTs (MIS-HEMTs) were characterized through X-parameter measurements under ultraviolet (UV) illumination. For HEMTs without passivation, the magnitude of the large-signal output wave (X21FB) and small-signal forward gain (X2111S) at fundamental frequency increased, whereas the large-signal second harmonic output wave (X22FB) decreased when the device was exposed to UV light, resulting from the photoconductive effect and suppression of buffer-related trapping. For MIS-HEMTs with SiN passivation, much higher X21FB and X2111S have been obtained compared with HEMTs. It suggests that better RF power performance can be achieved by removing the surface state. Moreover, the X-parameters of the MIS-HEMT are less dependent on UV light, since the light-induced performance enhancement is offset by excess traps in the SiN layer excited by UV light. The radio frequency (RF) power parameters and signal waveforms were further obtained based on the X-parameter model. The variation of RF current gain and distortion with light was consistent with the measurement results of X-parameters. Therefore, the trap number in the AlGaN surface, GaN buffer, and SiN layer must be minimized for a good large-signal performance of AlGaN/GaN transistors.

Comparison of 14 respiratory pathogens among hospitalized children during and after the COVID-19 outbreak in Chaoshan area.

BACKGROUND: Since January 2020, measures has been adopted in the Chaoshan area to limit the spread of COVID-19. Restrictions were removed after August 2020. At the same time, children returned to school. We previously reported the changes of 14 main respiratory pathogens in hospitalized children before and during the COVID-19 outbreak in Chaoshan area. However, the changes of respiratory pathogen spectrum in hospitalized children after the epidemic are still unknown, which will be elucidated in this study. METHODS: There are 6201 children hospitalized with respiratory tract infection were enrolled in the study, which were divided into two groups: 2533 from outbreak group (1 January 2020-31 December 2020), and 3668 from post-outbreak group (1 January 2021-31 December 2021). Pharyngeal swab samples were collected. 14 respiratory tract pathogens were detected by liquid chip technology. RESULTS: The positive rate of pathogen detection is significantly lower in the outbreak group (65.42%, 1657/2533) than that in the post-outbreak group (70.39%, 2582/3668; χ2 = 17.15, P < 0.05). The Influenza A virus (FluA) detection rate was 1.9% (49) in 2020, but 0% (0) in 2021. The detection rates of Bordetella pertussis (BP) decreased from 1.4% (35) in 2020 to 0.5% (17) in 2021. In contrast, the detection rates of  Influenza B virus (FluB), Cytomegalovirus (CMV), Haemophilus influenzae (HI), Streptococcus pneumoniae (SP) increased from 0.3% (8), 24.7% (626), 2.0% (50) and 19.4% (491) in 2020 to 3.3% (121), 27.9% (1025), 4.6% (169), 22.8% (836) in 2021, respectively (P < 0.01). CONCLUSIONS: The detection rates of pathogens such as FluA, FluB, CMV, HI, SP, BP were statistically different between 2020 and 2021. From 2020 to 2021, the positive rates of Flu, CMV, HI and SP increased, while the positive rates of FluA and BP decreased. After the COVID-19 prevention and control measures are gradually relaxed, the positive rate of respiratory pathogens in children aged from 6 months to 6 years will increase.

Pangenomic analysis identifies structural variation associated with heat tolerance in pearl millet.

Pearl millet is an important cereal crop worldwide and shows superior heat tolerance. Here, we developed a graph-based pan-genome by assembling ten chromosomal genomes with one existing assembly adapted to different climates worldwide and captured 424,085 genomic structural variations (SVs). Comparative genomics and transcriptomics analyses revealed the expansion of the RWP-RK transcription factor family and the involvement of endoplasmic reticulum (ER)-related genes in heat tolerance. The overexpression of one RWP-RK gene led to enhanced plant heat tolerance and transactivated ER-related genes quickly, supporting the important roles of RWP-RK transcription factors and ER system in heat tolerance. Furthermore, we found that some SVs affected the gene expression associated with heat tolerance and SVs surrounding ER-related genes shaped adaptation to heat tolerance during domestication in the population. Our study provides a comprehensive genomic resource revealing insights into heat tolerance and laying a foundation for generating more robust crops under the changing climate.

Evaluation of the Hand Motion and Peeling Force in Inner Limiting Membrane Peeling.

Purpose: Robot assistance in membrane peeling may improve precision and dexterity or prevent complications by task automation. To design robotic devices, surgical instruments' velocity, acceptable position/pose error, and load ability need to be precisely quantified. Methods: A fiber Bragg grating and inertial sensors are attached to forceps. Data collected from forceps and microscope images are used to quantify a surgeon's hand motion (tremor, velocity, posture perturbation) and operation force (voluntary and involuntary) in inner limiting membrane peeling. All peeling attempts are performed on rabbit eyes in vivo by expert surgeons. Results: The root mean square (RMS) of the tremor amplitude is 20.14 µm (transverse, X), 23.99 µm (transverse, Y), and 11.68 µm (axial, Z). The RMS posture perturbation is 0.43° (around X), 0.74° (around Y), and 0.46° (around Z). The RMS angular velocities are 1.74°/s (around X), 1.66°/s (around Y), and 1.46°/s (around Z), whereas the RMS velocities are 1.05 mm/s (transverse) and 1.44 mm/s (axial). The RMS force is 7.39 mN (voluntary force), 7.41 mN (operation force), and 0.5 mN (involuntary force). Conclusions: Hand motion and operation force are measured in membrane peeling. These parameters provide a potential baseline for determining a surgical robot's accuracy, velocity, and load capacity. Translational Relevance: Baseline data are obtained that can be used to guide ophthalmic robot design/evaluation.

An optimized machine learning technology scheme and its application in fault detection in wireless sensor networks.

Aiming at the problem of fault detection in data collection in wireless sensor networks, this paper combines evolutionary computing and machine learning to propose a productive technical solution. We choose the classical particle swarm optimization (PSO) and improve it, including the introduction of a biological population model to control the population size, and the addition of a parallel mechanism for further tuning. The proposed RS-PPSO algorithm was successfully used to optimize the initial weights and biases of back propagation neural network (BPNN), shortening the training time and raising the prediction accuracy. Wireless sensor networks (WSN) has become the key supporting platform of Internet of Things (IoT). The correctness of the data collected by the sensor nodes has a great influence on the reliability, real-time performance and energy saving of the entire network. The optimized machine learning technology scheme given in this paper can effectively identify the fault data, so as to ensure the effective operation of WSN.

Effects of Organic Elicitors on the Recycled Production of Ginkgolide B in Immobilized Cell Cultures of Ginkgo biloba.

Ginkgo biloba is a medicinal plant used in complementary and alternative medicines. Ginkgo biloba extracts contain many compounds with medical functions, of which the most critical is ginkgolide B (GB). The major role that GB plays is to function as an antagonist to the platelet-activating factor, which is one of the causes of thrombosis and cardiovascular diseases. Currently, GB is obtained mainly through extraction and purification from the leaves of Ginkgo biloba; however, the yield of GB is low. Alternatively, the immobilized cultivation of ginkgo calluses with biomaterial scaffolds and the addition of organic elicitors to activate the cell defense mechanisms were found to stimulate increases in GB production. The aim of this study was to use Ginkgo biloba calluses for immobilized cultures with different elicitors to find a more suitable method of ginkgolide B production via a recycling process.

SMN promotes mitochondrial metabolic maturation during myogenesis by regulating the MYOD-miRNA axis.

Spinal muscular atrophy (SMA) is a congenital neuromuscular disease caused by the mutation or deletion of the survival motor neuron 1 (SMN1) gene. Although the primary cause of progressive muscle atrophy in SMA has classically been considered the degeneration of motor neurons, recent studies have indicated a skeletal muscle-specific pathological phenotype such as impaired mitochondrial function and enhanced cell death. Here, we found that the down-regulation of SMN causes mitochondrial dysfunction and subsequent cell death in in vitro models of skeletal myogenesis with both a murine C2C12 cell line and human induced pluripotent stem cells. During myogenesis, SMN binds to the upstream genomic regions of MYOD1 and microRNA (miR)-1 and miR-206. Accordingly, the loss of SMN down-regulates these miRs, whereas supplementation of the miRs recovers the mitochondrial function, cell survival, and myotube formation of SMN-deficient C2C12, indicating the SMN-miR axis is essential for myogenic metabolic maturation. In addition, the introduction of the miRs into ex vivo muscle stem cells derived from Δ7-SMA mice caused myotube formation and muscle contraction. In conclusion, our data revealed novel transcriptional roles of SMN during myogenesis, providing an alternative muscle-oriented therapeutic strategy for SMA patients.

A BERT Based Method for Continuous Estimation of Cross-Subject Hand Kinematics From Surface Electromyographic Signals.

Estimation of hand kinematics from surface electromyographic (sEMG) signals provides a non-invasive human-machine interface. This approach is usually subject-specific, so that the training on one individual does not generalise to different subjects. In this paper, we propose a method based on Bidirectional Encoder Representation from Transformers (BERT) structure to predict the movement of hands from the root mean square (RMS) feature of the sEMG signal following µ -law normalization. The method was tested for within-subject and cross-subject conditions. We trained the model with two hard sample mining methods, Gradient Harmonizing Mechanism (GHM) and Online Hard Sample Mining (OHEM). The proposed method was compared with classic approaches, including long short-term memory (LSTM) and Temporal Convolutional Network (TCN) as well as a recent method called Long Exposure Convolutional Memory Network (LE-ConvMN). Correlation coefficient (CC), normalized root mean square error (NRMSE) and time costs were used as performance metrics. Our method (sBERT-OHEM) achieved state-of-the-art performance in cross-subject evaluation as well as high performance in subject-specific tests on the Ninapro dataset. The above tests are based on the same randomly selected 10 subjects. Generally, in the cross-subject situation, with the increasing of the subjects' number, it unavoidably leads to the decline of the performance. While the performance of our method on 38 subjects was significantly higher than the other methods on 10 subjects in cross-subject conditions, which further verified the advantage of our methods.

Opportunities and Challenges of Human IPSC Technology in Kidney Disease Research.

Human induced pluripotent stem cells (iPSCs), since their discovery in 2007, open a broad array of opportunities for research and potential therapeutic uses. The substantial progress in iPSC reprogramming, maintenance, differentiation, and characterization technologies since then has supported their applications from disease modeling and preclinical experimental platforms to the initiation of cell therapies. In this review, we started with a background introduction about stem cells and the discovery of iPSCs, examined the developing technologies in reprogramming and characterization, and provided the updated list of stem cell biobanks. We highlighted several important iPSC-based research including that on autosomal dominant kidney disease and SARS-CoV-2 kidney involvement and discussed challenges and future perspectives.