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Metal-insulator transitions (MITs) in resistive switching materials can be triggered by an electric stimulus that produces significant changes in the electrical response. When these phases have distinct magnetic characteristics, dramatic changes in the spin excitations are also expected. The transition metal oxide La0.7Sr0.3MnO3 (LSMO) is a ferromagnetic metal at low temperatures and a paramagnetic insulator above room temperature. When LSMO is in its metallic phase, a critical electrical bias has been shown to lead to an MIT that results in the formation of a paramagnetic resistive barrier transverse to the applied electric field. Using spin-transfer ferromagnetic resonance spectroscopy, we show that even for electrical biases less than the critical value that triggers the MIT, there is magnetic phase separation, with the spin-excitation resonances varying systematically with applied bias. Therefore, voltage-triggered MITs in LSMO can alter magnetic resonance characteristics, offering an effective method for tuning synaptic weights in neuromorphic circuits.

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Dosimetry modeling and point of departure (POD) estimation using in vitro data are essential for mechanism-based hazard identification and risk assessment. This study aimed to develop a putative adverse outcome pathway (AOP) for humidifier disinfectant (HD) substances used in South Korea through a systematic review and benchmark dose (BMD) modeling. We collected in vitro toxicological studies on HD substances, including polyhexamethylene guanidine hydrochloride (PHMG-HCl), PHMG phosphate (PHMG-p), a mixture of 5-chloro-2-methyl-4-isothiazolin-3-one and 2-methyl-4-isothiazolin-3-one (CMIT/MIT), CMIT, and MIT from scientific databases. A total of 193 sets of dose-response data were extracted from 34 articles reporting in vitro experimental results of HD toxicity. The risk of bias (RoB) in each study was assessed following the office of health assessment and translation (OHAT) guideline. The BMD of each HD substance at different toxicity endpoints was estimated using the US Environmental Protection Agency (EPA) BMD software (BMDS). Interspecies- or interorgan differences or most critical effects in the toxicity of the HD substances were analyzed using a 95% lower confidence limit of the BMD (BMDL). We found a critical molecular event and cells susceptible to each HD substance and constructed an AOP of PHMG-p- or CMIT/MIT-induced damage. Notably, PHMG-p induced ATP depletion at the lowest in vitro concentration, endoplasmic reticulum (ER) stress, epithelial-to-mesenchymal transition (EMT), inflammation, leading to fibrosis. CMIT/MIT enhanced mitochondrial reactive oxygen species (ROS) production, oxidative stress, mitochondrial dysfunction, resulting in cell death. Our approach will increase the current understanding of the effects of HD substances on human health and contribute to evidence-based risk assessment of these compounds.

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INTRODUCTION: Introduction: Renal cell carcinoma (RCC) is a very rare pediatric renal tumor. Robust evidence to guide treatment is lacking and knowledge on targeted therapies and immunotherapy is mainly based on adult studies. Currently, the International Society of Pediatric Oncology-Renal Tumor Study Group (SIOP-RTSG) 2016 UMBRELLA protocol recommends sunitinib for metastatic or unresectable RCC. METHODS: This retrospective study describes the effects of tyrosine kinase inhibitors (TKI), anti-programmed cell death 1 (PD-(L)1) monoclonal antibodies, and immunotherapeutic regimens in advanced-stage and relapsed pediatric RCC. RESULTS: Of the 31 identified patients (0-18 years) with histologically proven RCC, 3/31 presented with TNM stage I/II, 8/31 with TNM stage III, and 20/31 with TNM stage IV at diagnosis. The majority were diagnosed with translocation type RCC (MiT-RCC) (21/31) and the remaining patients mainly presented with papillary or clear-cell RCC. Treatment in a neoadjuvant or adjuvant setting, or upon relapse or progression, included mono- or combination therapy with a large variety of drugs, illustrating center specific choices in most patients. Sunitinib was often administered as first choice and predominantly resulted in stable disease (53%). Other frequently used drugs included axitinib, cabozantinib, sorafenib, and nivolumab; however, no treatment seemed more promising than sunitinib. Overall, 15/31 patients died of disease, 12/31 are alive with active disease, and only four patients had a complete response. The sample size and heterogeneity of this cohort only allowed descriptive statistical analysis. CONCLUSION: This study provides an overview of a unique series of clinical and treatment characteristics of pediatric patients with RCC treated with targeted therapies.

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Individuals diagnosed with Complex PTSD (C-PTSD) have experienced repeated and often prolonged traumatic events. From a therapeutic perspective this can lead to difficulties in emotion regulation within-session, challenges with patient-therapist attunement, and impaired coregulation of emotions during therapeutic interactions. As a result, frequent therapeutic alliance ruptures can emerge, which in turn pose challenges for symptom-focused work. We describe a case study involving a 38-year-old woman presenting with C-PTSD, dissociation, anxiety and borderline and dependent personality disorder traits. We explore how difficulties in attunement and emotion regulation during therapy were mostly attributable to (i) maladaptive ideas regarding the self and others; and (ii) difficulties in recognizing both her own mental states and those of her therapist. For instance, the patient believed that the therapist was distant and critical; which she held to be fact rather than reflective of a mental state. We show how the therapist addressed these difficulties, incorporating repair of the therapeutic alliance, which enabled a return to symptom focused work. The case description offers guidance on how to maintain a dual focus on therapeutic alliance alongside symptoms when treating C-PTSD (with or without comorbidity).

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In clinical datasets, missing data often occur due to various reasons including non-response, data corruption, and errors in data collection or processing. Such missing values can lead to biased statistical analyses, reduced statistical power, and potentially misleading findings, making effective imputation critical. Traditional imputation methods, such as Zero Imputation, Mean Imputation, and k-Nearest Neighbors (KNN) Imputation, attempt to address these gaps. However, these methods often fall short of accurately capturing the underlying data complexity, leading to oversimplified assumptions and errors in prediction. This study introduces a novel Imputation model employing transformer-based architectures to address these challenges. Notably, the model distinguishes between complete EEG signal amplitude data and incomplete data in two datasets: PhysioNet and CHB-MIT. By training exclusively on complete amplitude data, the TabTransformer accurately learns and predicts missing values, capturing intricate patterns and relationships inherent in EEG amplitude data. Evaluation using various error metrics and R2 score demonstrates significant enhancements over traditional methods such as Zero, Mean, and KNN imputation. The Proposed Model achieves impressive R2 scores of 0.993 for PhysioNet and 0.97 for CHB-MIT, highlighting its efficacy in handling complex clinical data patterns and improving dataset integrity. This underscores the transformative potential of transformer models in advancing the utility and reliability of clinical datasets.

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Applying machine-learning techniques for imbalanced data sets presents a significant challenge in materials science since the underrepresented characteristics of minority classes are often buried by the abundance of unrelated characteristics in majority of classes. Existing approaches to address this focus on balancing the counts of each class using oversampling or synthetic data generation techniques. However, these methods can lead to loss of valuable information or overfitting. Here, we introduce a deep learning framework to predict minority-class materials, specifically within the realm of metal-insulator transition (MIT) materials. The proposed approach, termed boosting-CGCNN, combines the crystal graph convolutional neural network (CGCNN) model with a gradient-boosting algorithm. The model effectively handled extreme class imbalances in MIT material data by sequentially building a deeper neural network. The comparative evaluations demonstrated the superior performance of the proposed model compared to other approaches. Our approach is a promising solution for handling imbalanced data sets in materials science.

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The classification of inter-patient ECG data for arrhythmia detection using electrocardiogram (ECG) signals presents a significant challenge. Despite the recent surge in deep learning approaches, there remains a noticeable gap in the performance of inter-patient ECG classification. In this study, we introduce an innovative approach for ECG classification in arrhythmia detection by employing a 1D convolutional neural network (CNN) to leverage both morphological and temporal characteristics of cardiac cycles. Through the utilization of 1D-CNN layers, we automatically capture the morphological attributes of ECG data, allowing us to represent the shape of the ECG waveform around the R peaks. Additionally, we incorporate four RR interval features to provide temporal context, and we explore the potential application of entropy rate as a feature extraction technique for ECG signal classification. Consequently, the classification layers benefit from the combination of both temporal and learned features, leading to the achievement of the final arrhythmia classification. We validate our approach using the MIT-BIH arrhythmia dataset, employing both intra-patient and inter-patient paradigms for model training and testing. The model's generalization ability is assessed by evaluating it on the INCART dataset. The model attains average accuracy rates of 99.13% and 99.17% for 2-fold and 5-fold cross-validation, respectively, in intra-patient classification with five classes. In inter-patient classification with three and five classes, the model achieves average accuracies of 98.73% and 97.91%, respectively. For the INCART dataset, the model achieves an average accuracy of 98.20% for three classes. The experimental outcomes demonstrate the superiority of the proposed model compared to state-of-the-art models in recognizing arrhythmias. Thus, the proposed model exhibits enhanced generalization and the potential to serve as an effective solution for recognizing arrhythmias in real-world datasets characterized by class imbalances in practical applications.

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For a family of self-adjoint Dirac operators - i c ( α · ∇ ) + c 2 2 subject to generalized MIT bag boundary conditions on domains in R 3 , it is shown that the nonrelativistic limit in the norm resolvent sense is the Dirichlet Laplacian. This allows to transfer spectral geometry results for Dirichlet Laplacians to Dirac operators for large c.

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MiT family translocation renal cell carcinomas (tRCCs) primarily include Xp11.2/transcription factor E3 (TFE3) gene fusion-associated renal cell carcinoma (Xp11.2 tRCC) and t(6;11)/TFEB gene fusion-associated RCC. Clinical cases of these carcinomas are rare. Fluorescence in situ hybridization can be used to identify the type, but there are no standard diagnostic and treatment methods available, and the prognosis remains controversial. Herein, we present a case of a patient with Xp11.2 tRCC at 29 weeks of gestation. The baby was successfully delivered, and radical surgery was performed for renal cancer at the same time. This is a unique and extremely rare case. We have described the case and performed a literature review to report the progress of current research on the treatment and prognosis of pregnant patients with Xp11.2/TFE3 translocation renal cell carcinoma. This study aims to contribute to improving the diagnosis and treatment of Xp11.2 tRCC in pregnant patients.

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The global burden of atrial fibrillation (AFIB) is constantly increasing, and its early detection is still a challenge for public health and motivates researchers to improve methods for automatic AFIB prediction and management. This work proposes higher-order spectra analysis, especially the bispectrum of electrocardiogram (ECG) signals combined with the convolution neural network (CNN) for AFIB detection. Like other biomedical signals, ECG is non-stationary, non-linear, and non-Gaussian in nature, so the spectra of higher-order cumulants, in this case, bispectra, preserve valuable features. The two-dimensional (2D) bispectrum images were applied as input for the two CNN architectures with the output AFIB vs. no-AFIB: the pre-trained modified GoogLeNet and the proposed CNN called AFIB-NET. The MIT-BIH Atrial Fibrillation Database (AFDB) was used to evaluate the performance of the proposed methodology. AFIB-NET detected atrial fibrillation with a sensitivity of 95.3%, a specificity of 93.7%, and an area under the receiver operating characteristic (ROC) of 98.3%, while for GoogLeNet results for sensitivity and specificity were equal to 96.7%, 82%, respectively, and the area under ROC was equal to 96.7%. According to preliminary studies, bispectrum images as input to 2D CNN can be successfully used for AFIB rhythm detection.

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Given continuous improvements in industrial production and living standards, the analysis and detection of complex biological sample systems has become increasingly important. Common complex biological samples include blood, serum, saliva, and urine. At present, the main methods used to separate and recognize target analytes in complex biological systems are electrophoresis, spectroscopy, and chromatography. However, because biological samples consist of complex components, they suffer from the matrix effect, which seriously affects the accuracy, sensitivity, and reliability of the selected separation analysis technique. In addition to the matrix effect, the detection of trace components is challenging because the content of the analyte in the sample is usually very low. Moreover, reasonable strategies for sample enrichment and signal amplification for easy analysis are lacking. In response to the various issues described above, researchers have focused their attention on immuno-affinity technology with the aim of achieving efficient sample separation based on the specific recognition effect between antigens and antibodies. Following a long period of development, this technology is now widely used in fields such as disease diagnosis, bioimaging, food testing, and recombinant protein purification. Common immuno-affinity technologies include solid-phase extraction (SPE) magnetic beads, affinity chromatography columns, and enzyme linked immunosorbent assay (ELISA) kits. Immuno-affinity techniques can successfully reduce or eliminate the matrix effect; however, their applications are limited by a number of disadvantages, such as high costs, tedious fabrication procedures, harsh operating conditions, and ligand leakage. Thus, developing an effective and reliable method that can address the matrix effect remains a challenging endeavor. Similar to the interactions between antigens and antibodies as well as enzymes and substrates, biomimetic molecularly imprinted polymers (MIPs) exhibit high specificity and affinity. Furthermore, compared with many other biomacromolecules such as antigens and aptamers, MIPs demonstrate higher stability, lower cost, and easier fabrication strategies, all of which are advantageous to their application. Therefore, molecular imprinting technology (MIT) is frequently used in SPE, chromatographic separation, and many other fields. With the development of MIT, researchers have engineered different types of imprinting strategies that can specifically extract the target analyte in complex biological samples while simultaneously avoiding the matrix effect. Some traditional separation technologies based on MIP technology have also been studied in depth; the most common of these technologies include stationary phases used for chromatography and adsorbents for SPE. Analytical methods that combine MIT with highly sensitive detection technologies have received wide interest in fields such as disease diagnosis and bioimaging. In this review, we highlight the new MIP strategies developed in recent years, and describe the applications of MIT-based separation analysis methods in fields including chromatographic separation, SPE, diagnosis, bioimaging, and proteomics. The drawbacks of these techniques as well as their future development prospects are also discussed.

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Objective.This study aims to address the challenges of imbalanced heartbeat classification using electrocardiogram (ECG). In this proposed novel deep-learning method, the focus is on accurately identifying minority classes in conditions characterized by significant imbalances in ECG data.Approach.We propose a feature fusion neural network enhanced by a dynamic minority-biased batch weighting loss function. This network comprises three specialized branches: the complete ECG data branch for a comprehensive view of ECG signals, the local QRS wave branch for detailed features of the QRS complex, and theRwave information branch to analyzeRwave characteristics. This structure is designed to extract diverse aspects of ECG data. The dynamic loss function prioritizes minority classes while maintaining the recognition of majority classes, adjusting the network's learning focus without altering the original data distribution. Together, this fusion structure and adaptive loss function significantly improve the network's ability to distinguish between various heartbeat classes, enhancing the accuracy of minority class identification.Main results.The proposed method demonstrated balanced performance within the MIT-BIH dataset, especially for minority classes. Under the intra-patient paradigm, the accuracy, sensitivity, specificity, and positive predictive value for Supraventricular ectopic beat were 99.63%, 93.62%, 99.81%, and 92.98%, respectively, and for Fusion beat were 99.76%, 85.56%, 99.87%, and 84.16%, respectively. Under the inter-patient paradigm, these metrics were 96.56%, 89.16%, 96.84%, and 51.99%for Supraventricular ectopic beat, and 96.10%, 77.06%, 96.25%, and 13.92%for Fusion beat, respectively.Significance.This method effectively addresses the class imbalance in ECG datasets. By leveraging diverse ECG signal information and a novel loss function, this approach offers a promising tool for aiding in the diagnosis and treatment of cardiac conditions.

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The coccidian parasite Cyclospora cayetanensis is the causative agent for foodborne outbreaks of cyclosporiasis disease and multiple annual fresh produce recalls. The aim of this study was to identify potential cross-reacting species for the C. cayetanensis 18S rRNA and MIT1C gene target real-time quantitative polymerase chain reaction (qPCR) assays. The environmental samples evaluated were irrigation pond water, produce wash water, and wastewater treatment sludge from a previous study with qPCR detections of C. cayetanensis by the 18S rRNA gene target qPCR. From these samples, longer regions of the 18S rRNA gene and the mitochondrial cytochrome c oxidase subunit III gene (cox3) were sequenced. Of 65 irrigation pond water samples with positive test results using the C. cayetanensis 18S rRNA gene qPCR assay, none had MIT1C qPCR assay detections or sequences that clustered with C. cayetanensis based on sequencing of the cox3 and 18S rRNA gene. Sequences from these samples clustered around coccidia sequences found in bird, fish, reptile, and amphibian hosts. Of 26 sludge samples showing detections by either qPCR assay, 14 (54%) could be confirmed as containing C. cayetanensis by sequencing of cox3 and 18S rRNA gene regions. In three of the remaining sludge samples, sequenced reads clustered with coccidia from rodents. This study demonstrated that caution should be taken when interpreting qPCR C. cayetanensis detection data in environmental samples and sequencing steps will likely be needed for confirmation. IMPORTANCE: Fresh produce is a leading transmission source in cyclosporiasis outbreaks. It is therefore essential to understand the role that produce-growing environments play in the spread of this disease. To accomplish this, sensitive and specific tests for environmental and irrigation waters must be developed. Potential cross-reactions of Cyclospora cayetanensis real-time quantitative polymerase chain reaction (qPCR) assays have been identified, hindering the ability to accurately identify this parasite in the environment. Amplicon sequencing of the cox3 and 18S rRNA genes revealed that all irrigation pond water and two sludge samples that initially detected C. cayetanensis by qPCR were most likely cross-reactions with related coccidian organisms shed from birds, fish, reptiles, amphibians, and rodents. These results support that a single testing method for environmental samples is likely not adequate for sensitive and specific detection of C. cayetanensis.

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OBJECTIVES: Electrocardiogram (ECG) signals are extensively utilized in the identification and assessment of diverse cardiac conditions, including congestive heart failure (CHF) and cardiac arrhythmias (ARR), which present potential hazards to human health. With the aim of facilitating disease diagnosis and assessment, advanced computer-aided systems are being developed to analyze ECG signals. METHODS: This study proposes a state-of-the-art ECG data pattern recognition algorithm based on Continuous Wavelet Transform (CWT) as a novel signal preprocessing model. The Motif Transformation (MT) method was devised to diminish the drawbacks and limitations inherent in the CWT, such as the issue of boundary effects, limited localization in time and frequency, and overfitting conditions. This transformation technique facilitates the formation of diverse patterns (motifs) within the signals. The patterns (motifs) are constructed by comparing the amplitudes of each individual sample value in the ECG signals in terms of their largeness and smallness. In the subsequent stage, the obtained one-dimensional signals from the MT transformation were subjected to CWT to obtain scalogram images. In the last stage, the obtained scalogram images were subjected to classification using DenseNET deep transfer learning techniques. RESULTS AND CONCLUSIONS: The combined approach of MT + CWT + DenseNET yielded an impressive success rate of 99.31 %.

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Polyhexamethylene guanidine (PHMG) is a guanidine-based chemical that has long been used as an antimicrobial agent. However, recently raised concerns regarding the pulmonary toxicity of PHMG in humans and aquatic organisms have led to research in this area. Along with PHMG, there are concerns about the safety of non-guanidine 5-chloro-2-methylisothiazol-3(2H)-one/2-methylisothiazol-3(2H)-one (CMIT/MIT) in human lungs; however, the safety of such chemicals can be affected by many factors, and it is difficult to rationalize their toxicity. In this study, we investigated the adsorption characteristics of CMIT/ MIT on a model pulmonary surfactant (lung surfactant, LS) using a Langmuir trough attached to a fluorescence microscope. Analysis of the π-A isotherms and lipid raft morphology revealed that CMIT/MIT exhibited minimal adsorption onto the LS monolayer deposited at the air/water interface. Meanwhile, PHMG showed clear signs of adsorption to LS, as manifested by the acceleration of the L o phase growth with increasing surface pressure. Consequently, in the presence of CMIT/MIT, the interfacial properties of the model LS monolayer exhibited significantly fewer changes than PHMG.

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Algorithms for QRS detection are fundamental in the ECG interpretive processing chain. They must meet several challenges, such as high reliability, high temporal accuracy, high immunity to noise, and low computational complexity. Unfortunately, the accuracy expressed by missed or redundant events statistics is often the only parameter used to evaluate the detector's performance. In this paper, we first notice that statistics of true positive detections rely on researchers' arbitrary selection of time tolerance between QRS detector output and the database reference. Next, we propose a multidimensional algorithm evaluation method and present its use on four example QRS detectors. The dimensions are (a) influence of detection temporal tolerance, tested for values between 8.33 and 164 ms; (b) noise immunity, tested with an ECG signal with an added muscular noise pattern and signal-to-noise ratio to the effect of "no added noise", 15, 7, 3 dB; and (c) influence of QRS morphology, tested on the six most frequently represented morphology types in the MIT-BIH Arrhythmia Database. The multidimensional evaluation, as proposed in this paper, allows an in-depth comparison of QRS detection algorithms removing the limitations of existing one-dimensional methods. The method enables the assessment of the QRS detection algorithms according to the medical device application area and corresponding requirements of temporal accuracy, immunity to noise, and QRS morphology types. The analysis shows also that, for some algorithms, adding muscular noise to the ECG signal improves algorithm accuracy results.

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Lysosomes are central to metabolic homeostasis. The microphthalmia bHLH-LZ transcription factors (MiT/TFEs) family members MITF, TFEB, and TFE3 promote the transcription of lysosomal and autophagic genes and are often deregulated in cancer. Here, we show that the GATOR2 complex, an activator of the metabolic regulator TORC1, maintains lysosomal function by protecting MiT/TFEs from proteasomal degradation independent of TORC1, GATOR1, and the RAG GTPase. We determine that in GATOR2 knockout HeLa cells, members of the MiT/TFEs family are ubiquitylated by a trio of E3 ligases and are degraded, resulting in lysosome dysfunction. Additionally, we demonstrate that GATOR2 protects MiT/TFE proteins in pancreatic ductal adenocarcinoma and Xp11 translocation renal cell carcinoma, two cancers that are driven by MiT/TFE hyperactivation. In summary, we find that the GATOR2 complex has independent roles in TORC1 regulation and MiT/TFE protein protection and thus is central to coordinating cellular metabolism with control of the lysosomal-autophagic system.

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The COVID-19 pandemic caused by SARS-CoV-2 becomes a serious threat to global health and requires the development of effective antiviral therapies. Current therapies that target viral proteins have limited efficacy with side effects. In this study, we investigated the antiviral activity of MIT-001, a small molecule reactive oxygen species (ROS) scavenger targeting mitochondria, against SARS-CoV-2 and other zoonotic viruses in vitro. The antiviral activity of MIT-001 was quantified by RT-qPCR and plaque assay. We also evaluated the functional analysis of MIT-001 by JC-1 staining to measure mitochondrial depolarization, total RNA sequencing to investigate gene expression changes, and immunoblot to quantify protein expression levels. The results showed that MIT-001 effectively inhibited the replication of B.1.617.2 and BA.1 strains, Zika virus, Seoul virus, and Vaccinia virus. Treatment with MIT-001 restored the expression of heme oxygenase-1 (HMOX1) and NAD(P)H: quinone oxidoreductase 1 (NqO1) genes, anti-oxidant enzymes reduced by SARS-CoV-2, to normal levels. The presence of MIT-001 also alleviated mitochondrial depolarization caused by SARS-CoV-2 infection. These findings highlight the potential of MIT-001 as a broad-spectrum antiviral compound that targets for zoonotic RNA and DNA viruses, providing a promising therapeutic approach to combat viral infection.

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Use of support and relief services for parents of children in need of care: Results of the FamBer observational study Abstract: Background: Parents of children in need of care in Germany can fall back on a variety of relief and support services. So far, however, there has been a lack of systematic studies and quantitative data on the use of such offers at the individual level of parents and other legal guardians. Aim: The study on the compatibility of care and work for parents with a child in need of care (FamBer; funding: Federal Ministry for Family Affairs, Senior Citizens, Women and Youth, Germany) examines the knowledge of relief and support services, their use and the perceived benefits of these offers. Methods: 1070 parents answered a multidimensional online questionnaire in the cross-sectional study that was developed based on the Kindernetzwerk Study 2 from 2013 and the German socio-economic panel (SOEP). In addition to descriptive analyses, group comparisons were carried out using Chi2, Mann-Whitney U or Kruskal-Wallis H tests. Results: 43 to 58% of parents are aware of the respective legal options for taking time off work, but only very few families make use of them. The other support offers differ significantly in terms of the level of knowledge and utilization; these vary primarily with the education of the parents and the care needs of the child. They assessed the used services for consultation and advice as only little helpful. Conclusions: Due to the study design, we cannot rule out that the findings are also based on personal characteristics of the parents and their living conditions. Nevertheless, a large number of problems (e.g. a lack of information, low using, ineffectiveness of support services) can be identified that need to be overcome.

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Metal-insulator transition (MIT) coupled with an ultrafast, significant, and reversible resistive change in Mott insulators has attracted tremendous interest for investigation into next-generation electronic and optoelectronic devices, as well as a fundamental understanding of condensed matter systems. Although the mechanism of MIT in Mott insulators is still controversial, great efforts have been made to understand and modulate MIT behavior for various electronic and optoelectronic applications. In this review, recent progress in the field of nanoelectronics utilizing MIT is highlighted. A brief introduction to the physics of MIT and its underlying mechanisms is begun. After discussing the MIT behaviors of various Mott insulators, recent advances in the design and fabrication of nanoelectronics devices based on MIT, including memories, gas sensors, photodetectors, logic circuits, and artificial neural networks are described. Finally, an outlook on the development and future applications of nanoelectronics utilizing MIT is provided. This review can serve as an overview and a comprehensive understanding of the design of MIT-based nanoelectronics for future electronic and optoelectronic devices.