Deciphering the Prognostic Efficacy of MRI Radiomics in Nasopharyngeal Carcinoma: A Comprehensive Meta-Analysis.

This meta-analysis investigates the prognostic value of MRI-based radiomics in nasopharyngeal carcinoma treatment outcomes, specifically focusing on overall survival (OS) variability. The study protocol was registered with INPLASY (INPLASY202420101). Initially, a systematic review identified 15 relevant studies involving 6243 patients through a comprehensive search across PubMed, Embase, and Web of Science, adhering to PRISMA guidelines. The methodological quality was assessed using the Quality in Prognosis Studies (QUIPS) tool and the Radiomics Quality Score (RQS), highlighting a low risk of bias in most domains. Our analysis revealed a significant average concordance index (c-index) of 72% across studies, indicating the potential of radiomics in clinical prognostication. However, moderate heterogeneity was observed, particularly in OS predictions. Subgroup analyses and meta-regression identified validation methods and radiomics software as significant heterogeneity moderators. Notably, the number of features in the prognosis model correlated positively with its performance. These findings suggest radiomics' promising role in enhancing cancer treatment strategies, though the observed heterogeneity and potential biases call for cautious interpretation and standardization in future research.

Standalone deep learning versus experts for diagnosis lung cancer on chest computed tomography: a systematic review.

PURPOSE: To compare the diagnostic performance of standalone deep learning (DL) algorithms and human experts in lung cancer detection on chest computed tomography (CT) scans. MATERIALS AND METHODS: This study searched for studies on PubMed, Embase, and Web of Science from their inception until November 2023. We focused on adult lung cancer patients and compared the efficacy of DL algorithms and expert radiologists in disease diagnosis on CT scans. Quality assessment was performed using QUADAS-2, QUADAS-C, and CLAIM. Bivariate random-effects and subgroup analyses were performed for tasks (malignancy classification vs invasiveness classification), imaging modalities (CT vs low-dose CT [LDCT] vs high-resolution CT), study region, software used, and publication year. RESULTS: We included 20 studies on various aspects of lung cancer diagnosis on CT scans. Quantitatively, DL algorithms exhibited superior sensitivity (82%) and specificity (75%) compared to human experts (sensitivity 81%, specificity 69%). However, the difference in specificity was statistically significant, whereas the difference in sensitivity was not statistically significant. The DL algorithms' performance varied across different imaging modalities and tasks, demonstrating the need for tailored optimization of DL algorithms. Notably, DL algorithms matched experts in sensitivity on standard CT, surpassing them in specificity, but showed higher sensitivity with lower specificity on LDCT scans. CONCLUSION: DL algorithms demonstrated improved accuracy over human readers in malignancy and invasiveness classification on CT scans. However, their performance varies by imaging modality, underlining the importance of continued research to fully assess DL algorithms' diagnostic effectiveness in lung cancer. CLINICAL RELEVANCE STATEMENT: DL algorithms have the potential to refine lung cancer diagnosis on CT, matching human sensitivity and surpassing in specificity. These findings call for further DL optimization across imaging modalities, aiming to advance clinical diagnostics and patient outcomes. KEY POINTS: Lung cancer diagnosis by CT is challenging and can be improved with AI integration. DL shows higher accuracy in lung cancer detection on CT than human experts. Enhanced DL accuracy could lead to improved lung cancer diagnosis and outcomes.

Deep Learning for Nasopharyngeal Carcinoma Segmentation in Magnetic Resonance Imaging: A Systematic Review and Meta-Analysis.

Nasopharyngeal carcinoma is a significant health challenge that is particularly prevalent in Southeast Asia and North Africa. MRI is the preferred diagnostic tool for NPC due to its superior soft tissue contrast. The accurate segmentation of NPC in MRI is crucial for effective treatment planning and prognosis. We conducted a search across PubMed, Embase, and Web of Science from inception up to 20 March 2024, adhering to the PRISMA 2020 guidelines. Eligibility criteria focused on studies utilizing DL for NPC segmentation in adults via MRI. Data extraction and meta-analysis were conducted to evaluate the performance of DL models, primarily measured by Dice scores. We assessed methodological quality using the CLAIM and QUADAS-2 tools, and statistical analysis was performed using random effects models. The analysis incorporated 17 studies, demonstrating a pooled Dice score of 78% for DL models (95% confidence interval: 74% to 83%), indicating a moderate to high segmentation accuracy by DL models. Significant heterogeneity and publication bias were observed among the included studies. Our findings reveal that DL models, particularly convolutional neural networks, offer moderately accurate NPC segmentation in MRI. This advancement holds the potential for enhancing NPC management, necessitating further research toward integration into clinical practice.

Systematic review and meta-analysis of deep learning applications in computed tomography lung cancer segmentation.

BACKGROUND: Accurate segmentation of lung tumors on chest computed tomography (CT) scans is crucial for effective diagnosis and treatment planning. Deep Learning (DL) has emerged as a promising tool in medical imaging, particularly for lung cancer segmentation. However, its efficacy across different clinical settings and tumor stages remains variable. METHODS: We conducted a comprehensive search of PubMed, Embase, and Web of Science until November 7, 2023. We assessed the quality of these studies by using the Checklist for Artificial Intelligence in Medical Imaging and the Quality Assessment of Diagnostic Accuracy Studies-2 tools. This analysis included data from various clinical settings and stages of lung cancer. Key performance metrics, such as the Dice similarity coefficient, were pooled, and factors affecting algorithm performance, such as clinical setting, algorithm type, and image processing techniques, were examined. RESULTS: Our analysis of 37 studies revealed a pooled Dice score of 79 % (95 % CI: 76 %-83 %), indicating moderate accuracy. Radiotherapy studies had a slightly lower score of 78 % (95 % CI: 74 %-82 %). A temporal increase was noted, with recent studies (post-2022) showing improvement from 75 % (95 % CI: 70 %-81 %). to 82 % (95 % CI: 81 %-84 %). Key factors affecting performance included algorithm type, resolution adjustment, and image cropping. QUADAS-2 assessments identified ambiguous risks in 78 % of studies due to data interval omissions and concerns about generalizability in 8 % due to nodule size exclusions, and CLAIM criteria highlighted areas for improvement, with an average score of 27.24 out of 42. CONCLUSION: This meta-analysis demonstrates DL algorithms' promising but varied efficacy in lung cancer segmentation, particularly higher efficacy noted in early stages. The results highlight the critical need for continued development of tailored DL models to improve segmentation accuracy across diverse clinical settings, especially in advanced cancer stages with greater challenges. As recent studies demonstrate, ongoing advancements in algorithmic approaches are crucial for future applications.

Progress in Serial Imaging for Prognostic Stratification of Lung Cancer Patients Receiving Immunotherapy: A Systematic Review and Meta-Analysis.

Immunotherapy, particularly with checkpoint inhibitors, has revolutionized non-small cell lung cancer treatment. Enhancing the selection of potential responders is crucial, and researchers are exploring predictive biomarkers. Delta radiomics, a derivative of radiomics, holds promise in this regard. For this study, a meta-analysis was conducted that adhered to PRISMA guidelines, searching PubMed, Embase, Web of Science, and the Cochrane Library for studies on the use of delta radiomics in stratifying lung cancer patients receiving immunotherapy. Out of 223 initially collected studies, 10 were included for qualitative synthesis. Stratifying patients using radiomic models, the pooled analysis reveals a predictive power with an area under the curve of 0.81 (95% CI 0.76-0.86, p < 0.001) for 6-month response, a pooled hazard ratio of 4.77 (95% CI 2.70-8.43, p < 0.001) for progression-free survival, and 2.15 (95% CI 1.73-2.66, p < 0.001) for overall survival at 6 months. Radiomics emerges as a potential prognostic predictor for lung cancer, but further research is needed to compare traditional radiomics and deep-learning radiomics.

Brain metastasis tumor segmentation and detection using deep learning algorithms: A systematic review and meta-analysis.

BACKGROUND: Manual detection of brain metastases is both laborious and inconsistent, driving the need for more efficient solutions. Accordingly, our systematic review and meta-analysis assessed the efficacy of deep learning algorithms in detecting and segmenting brain metastases from various primary origins in MRI images. METHODS: We conducted a comprehensive search of PubMed, Embase, and Web of Science up to May 24, 2023, which yielded 42 relevant studies for our analysis. We assessed the quality of these studies using the QUADAS-2 and CLAIM tools. Using a random-effect model, we calculated the pooled lesion-wise dice score as well as patient-wise and lesion-wise sensitivity. We performed subgroup analyses to investigate the influence of factors such as publication year, study design, training center of the model, validation methods, slice thickness, model input dimensions, MRI sequences fed to the model, and the specific deep learning algorithms employed. Additionally, meta-regression analyses were carried out considering the number of patients in the studies, count of MRI manufacturers, count of MRI models, training sample size, and lesion number. RESULTS: Our analysis highlighted that deep learning models, particularly the U-Net and its variants, demonstrated superior segmentation accuracy. Enhanced detection sensitivity was observed with an increased diversity in MRI hardware, both in terms of manufacturer and model variety. Furthermore, slice thickness was identified as a significant factor influencing lesion-wise detection sensitivity. Overall, the pooled results indicated a lesion-wise dice score of 79%, with patient-wise and lesion-wise sensitivities at 86% and 87%, respectively. CONCLUSIONS: The study underscores the potential of deep learning in improving brain metastasis diagnostics and treatment planning. Still, more extensive cohorts and larger meta-analysis are needed for more practical and generalizable algorithms. Future research should prioritize these areas to advance the field. This study was funded by the Gen. & Mrs. M.C. Peng Fellowship and registered under PROSPERO (CRD42023427776).

Evaluating the Potential of Delta Radiomics for Assessing Tyrosine Kinase Inhibitor Treatment Response in Non-Small Cell Lung Cancer Patients.

Our study aimed to harness the power of CT scans, observed over time, in predicting how lung adenocarcinoma patients might respond to a treatment known as EGFR-TKI. Analyzing scans from 322 advanced stage lung cancer patients, we identified distinct image-based patterns. By integrating these patterns with comprehensive clinical information, such as gene mutations and treatment regimens, our predictive capabilities were significantly enhanced. Interestingly, the precision of these predictions, particularly related to radiomics features, diminished when data from various centers were combined, suggesting that the approach requires standardization across facilities. This novel method offers a potential pathway to anticipate disease progression in lung adenocarcinoma patients treated with EGFR-TKI, laying the groundwork for more personalized treatments. To further validate this approach, extensive studies involving a larger cohort are pivotal.

Optimization of performance and sensitivity: preparation of two Ag(I)-based ECPs by using isomeric ligands.

For energetic compounds, their structure determines their performance, and even minor variations in their structure can have a significant impact on their performance. The application scenarios for energetic materials are diverse, and their performance requirements vary as well. To investigate the influence of different substituent positions on the performance of primary explosives, we prepared two Ag(I)-based complexes, [Ag(2-IZCA)ClO4]n (ECPs-1) and [Ag(4-IZCA)ClO4]n (ECPs-2), using structurally isomeric ligands, 1H-imidazole-2-carbohydrazide (2-IZCA) and 1H-imidazole-4-carbohydrazide (4-IZCA). The structures were confirmed using infrared, elemental analysis, and single-crystal X-ray diffraction. Experimental results demonstrate that both ECPs exhibit good thermal stability. However, compared to ECPs-1, ECPs-2 exhibits a lower thermal initial decomposition temperature (Td = 210 °C), lower mechanical sensitivity (IS = 27 J, FS = 84 N), and more concentrated energy output. Although theoretical predictions suggest similar detonation velocities and pressures for both compounds, actual detonation performance tests indicate that ECPs-2 has stronger explosive power and initiating capability, with potential for use as a laser initiator (E = 126 mJ). The simple preparation method and inexpensive starting materials enrich the research on primary explosives.

Differences in Non-suicidal Self-injury Behaviors between Unipolar Depression and Bipolar Depression in Adolescent Outpatients.

OBJECTIVE: Non-suicidal self-injury (NSSI) has a higher prevalence in adolescents with depressive disorders than in community adolescents. This study examined the differences in NSSI behaviors between adolescents with unipolar depression (UD) and those with bipolar depression (BD). METHODS: Adolescents with UD or BD were recruited from 20 general or psychiatric hospitals across China. The methods, frequency, and function of NSSI were assessed by Functional Assessment of Self-Mutilation. The Beck Suicide Ideation Scale was used to evaluate adolescents' suicidal ideation, and the 10-item Kessler Psychological Distress Scale to estimate the anxiety and depression symptoms. RESULTS: The UD group had higher levels of depression (19.16 vs.17.37, F=15.23, P<0.001) and anxiety symptoms (17.73 vs.16.70, F=5.00, P=0.026) than the BD group. Adolescents with BD had a longer course of NSSI than those with UD (2.00 vs.1.00 year, Z=-3.39, P=0.001). There were no statistical differences in the frequency and the number of methods of NSSI between the UD and BD groups. Depression (r=0.408, P<0.01) and anxiety (r=0.391, P<0.01) were significantly and positively related to NSSI frequency. CONCLUSION: Adolescents with BD had a longer course of NSSI than those with UD. More importantly, NSSI frequency were positively and strongly correlated with depression and anxiety symptoms, indicating the importance of adequate treatment of depression and anxiety in preventing and intervening adolescents' NSSI behaviors.

Structural, vibrational and electronic properties of nitrogen-rich 2,4,6-triazide-1,3,5-triazine under high pressure.

CONTEXT AND RESULTS: 2,4,6-triazide-1,3,5-triazine (TAT) has received widespread attention for its great potential to synthesize or convert to nitrogen-rich high energy density materials (HEDMs). The TAT structure alteration in the compression process up to 30 GPa has characteristics as follows: (a) [N3] groups straighten; (b) [N3] groups gather toward the six-membered C-N heterocycles. At about 5 GPa, Raman peak split at 700 cm-1 was observed both in calculation and in-situ Raman experiment, which is caused by pressure-induced intramolecular stress. Besides, the broad band of the amorphous two-dimensional C=N network (centered at 1630 cm-1) occurred at about 12 GPa. Meantime, the study on electronic features suggests the pressure-induced deformation in TAT molecular structure cause the discontinuous change of band gap at about 4.5 GPa and 8.0 GPa, respectively. COMPUTATIONAL AND THEORETICAL TECHNIQUES: The static compression process of TAT was explored in the range of 0-30 GPa by using dispersion corrected density functional theory (DFT-D) calculations combined with in-situ Raman experiment. The GGA/PBE+G06 method that has less errors than other calculation methods was used to predict the geometry structure, vibrational properties and electronic structure of TAT under pressure.

Application of Radiomics in Prognosing Lung Cancer Treated with Epidermal Growth Factor Receptor Tyrosine Kinase Inhibitors: A Systematic Review and Meta-Analysis.

In the context of non-small cell lung cancer (NSCLC) patients treated with EGFR tyrosine kinase inhibitors (TKIs), this research evaluated the prognostic value of CT-based radiomics. A comprehensive systematic review and meta-analysis of studies up to April 2023, which included 3111 patients, was conducted. We utilized the Quality in Prognosis Studies (QUIPS) tool and radiomics quality scoring (RQS) system to assess the quality of the included studies. Our analysis revealed a pooled hazard ratio for progression-free survival of 2.80 (95% confidence interval: 1.87-4.19), suggesting that patients with certain radiomics features had a significantly higher risk of disease progression. Additionally, we calculated the pooled Harrell's concordance index and area under the curve (AUC) values of 0.71 and 0.73, respectively, indicating good predictive performance of radiomics. Despite these promising results, further studies with consistent and robust protocols are needed to confirm the prognostic role of radiomics in NSCLC.

Preparation of Primary Explosive by Self-assembly of Combustible and Oxidizing Agents under Acid.

In order to further explore the effect of ligands on the performance of primary explosives and gain a deeper understanding of the coordination mechanism, we designed furan-2-carbohydrazide (FRCA), a ligand, by using oxygen-containing heterocycles and carbohydrazide. Then, FRCA and Cu(ClO4)2 were used to synthesize coordination compounds [Cu(FRCA)2(H2O)(ClO4)2]·CH3OH (ECCs-1·CH3OH) and Cu(FRCA)2(H2O)(ClO4)2 (ECCs-1). The structure of the ECCs-1 was confirmed by single-crystal X-ray diffraction, IR and EA characterization. Further experiments on ECCs-1 show that ECCs-1 has good thermal stability, but is sensitive to mechanical stimuli (impact sensitivity = IS = 8 J, friction sensitivity = FS = 20 N). The predicted value of the detonation parameter is DEXPLO 5 = 6.6 km s-1, PEXPLO 5 = 18.8 GPa, but the ignition test, laser test, and lead plate detonation experiment show that ECCs-1 has excellent detonation performance, which is very worthy of attention.

Preparation of High-Energy ECPs by Retaining the Coordination Ability of Carbohydrazide Groups.

In order to preserve the coordinating ability of the hydrazide group, we used retrosynthetic analysis to design and synthesize ligand furan-2,5-dicarbohydrazide and its complex [Cu(FDCA)(H2O)ClO4]n(ClO4)n·nH2O (ECPs-1·H2O). The structure of the product was confirmed by single-crystal X-ray diffraction, infrared spectroscopy, and elemental analysis. The solvent-free target material ECPs-1 exhibited good thermal stability, sensitivity to mechanical stimuli, and excellent explosive properties. Furthermore, it had good potential for laser ignition and comparable detonation power to LA. The simple preparation method and inexpensive starting materials enriched the research on primary explosives.

A radiomics-based deep learning approach to predict progression free-survival after tyrosine kinase inhibitor therapy in non-small cell lung cancer.

BACKGROUND: The epidermal growth factor receptor (EGFR) tyrosine kinase inhibitors (TKIs) are a first-line therapy for non-small cell lung cancer (NSCLC) with EGFR mutations. Approximately half of the patients with EGFR-mutated NSCLC are treated with EGFR-TKIs and develop disease progression within 1 year. Therefore, the early prediction of tumor progression in patients who receive EGFR-TKIs can facilitate patient management and development of treatment strategies. We proposed a deep learning approach based on both quantitative computed tomography (CT) characteristics and clinical data to predict progression-free survival (PFS) in patients with advanced NSCLC after EGFR-TKI treatment. METHODS: A total of 593 radiomic features were extracted from pretreatment chest CT images. The DeepSurv models for the progression risk stratification of EGFR-TKI treatment were proposed based on CT radiomic and clinical features from 270 stage IIIB-IV EGFR-mutant NSCLC patients. Time-dependent PFS predictions at 3, 12, 18, and 24 months and estimated personalized PFS curves were calculated using the DeepSurv models. RESULTS: The model combining clinical and radiomic features demonstrated better prediction performance than the clinical model. The model achieving areas under the curve of 0.76, 0.77, 0.76, and 0.86 can predict PFS at 3, 12, 18, and 24 months, respectively. The personalized PFS curves showed significant differences (p < 0.003) between groups with good (PFS > median) and poor (PFS < median) tumor control. CONCLUSIONS: The DeepSurv models provided reliable multi-time-point PFS predictions for EGFR-TKI treatment. The personalized PFS curves can help make accurate and individualized predictions of tumor progression. The proposed deep learning approach holds promise for improving the pre-TKI personalized management of patients with EGFR-mutated NSCLC.

Artificial Intelligence for Early Detection of Chest Nodules in X-ray Images.

Early detection increases overall survival among patients with lung cancer. This study formulated a machine learning method that processes chest X-rays (CXRs) to detect lung cancer early. After we preprocessed our dataset using monochrome and brightness correction, we used different kinds of preprocessing methods to enhance image contrast and then used U-net to perform lung segmentation. We used 559 CXRs with a single lung nodule labeled by experts to train a You Only Look Once version 4 (YOLOv4) deep-learning architecture to detect lung nodules. In a testing dataset of 100 CXRs from patients at Taipei Veterans General Hospital and 154 CXRs from the Japanese Society of Radiological Technology dataset, the sensitivity of the AI model using a combination of different preprocessing methods performed the best at 79%, with 3.04 false positives per image. We then tested the AI by using 383 sets of CXRs obtained in the past 5 years prior to lung cancer diagnoses. The median time from detection to diagnosis for radiologists assisted with AI was 46 (3-523) days, longer than that for radiologists (8 (0-263) days). The AI model can assist radiologists in the early detection of lung nodules.

Intelligent Bio-Impedance System for Personalized Continuous Blood Pressure Measurement.

Continuous blood pressure (BP) measurement is crucial for long-term cardiovascular monitoring, especially for prompt hypertension detection. However, most of the continuous BP measurements rely on the pulse transit time (PTT) from multiple-channel physiological acquisition systems that impede wearable applications. Recently, wearable and smart health electronics have become significant for next-generation personalized healthcare progress. This study proposes an intelligent single-channel bio-impedance system for personalized BP monitoring. Compared to the PTT-based methods, the proposed sensing configuration greatly reduces the hardware complexity, which is beneficial for wearable applications. Most of all, the proposed system can extract the significant BP features hidden from the measured bio-impedance signals by an ultra-lightweight AI algorithm, implemented to further establish a tailored BP model for personalized healthcare. In the human trial, the proposed system demonstrates the BP accuracy in terms of the mean error (ME) and the mean absolute error (MAE) within 1.7 ± 3.4 mmHg and 2.7 ± 2.6 mmHg, respectively, which agrees with the criteria of the Association for the Advancement of Medical Instrumentation (AAMI). In conclusion, this work presents a proof-of-concept for an AI-based single-channel bio-impedance BP system. The new wearable smart system is expected to accelerate the artificial intelligence of things (AIoT) technology for personalized BP healthcare in the future.

A High-Voltage TENG-Based Droplet Energy Generator With Ultralow Liquid Consumption.

A solid-liquid triboelectric nanogenerator (TENG) has attracted increasing research interest in relation to the development of regeneration energy based on water resources. The output of solid-liquid TENG remains unsolved, however, because of the low voltage output that impedes wide applications. To this end, in this work we developed a miniaturized microfluidic channel-based TENG device for highly efficient conversion of energy from the transport of a water droplet to an electrical output. We investigated an optimized design in a triboelectric material, the droplet transport and the electrostatic induction layer to provide a high voltage output and stable energy harvesting. The optimized device demonstrated maximum voltage amplitude 102 mV with an ultralow liquid consumption, 0.36 [Formula: see text], resulting in sample-energy conversion 283.33 mV/ [Formula: see text]. This novel device is expected potentially to address the limitations imposed by sample consumption in energy harvesting in the future.

Nickel clusters accelerating hierarchical zinc indium sulfide nanoflowers for unprecedented visible-light hydrogen production.

As a typical two-dimensional (2D) metal chalcogenides and visible-light responsive semiconductor, zinc indium sulfide (ZnIn2S4) has attracted much attention in photocatalysis. However, the high recombination rate of photogenerated electrons and holes seriously limits its performance for hydrogen production. In this work, we report in-situ photodeposition of Ni clusters in hierarchical ZnIn2S4 nanoflowers (Ni/ZnIn2S4) to achieve unprecedented photocatalytic hydrogen production. The Ni clusters not only provide plenty of active sites for reactions as evidenced by in-situ photoluminescence measurement, but also effectively accelerate the separation and migration of the photogenerated electrons and holes in ZnIn2S4. Consequently, the Ni/ZnIn2S4 composites exhibit good stability and reusability with highly enhanced visible-light hydrogen production. In particular, the best Ni/ZnIn2S4 photocatalyst exhibits an unprecedented hydrogen production rate of 22.2 mmol·h-1·g-1, 10.6 times that of the pure ZnIn2S4 (2.1 mmol·h-1·g-1). And its apparent quantum yield (AQY) is as high as 56.14% under 450 nm monochromatic light. Our work here suggests that depositing non-precious Ni clusters in ZnIn2S4 is quite promising for the potential practical photocatalysis in solar energy conversion.

Bio-Impedance Measurement Optimization for High-Resolution Carotid Pulse Sensing.

Continuous hemodynamic monitoring is important for long-term cardiovascular healthcare, especially in hypertension. The impedance plethysmography (IPG) based carotid pulse sensing is a non-invasive diagnosis technique for measuring pulse signals and further evaluating the arterial conditions of the patient such as continuous blood pressure (BP) monitoring. To reach the high-resolution IPG-based carotid pulse detection for cardiovascular applications, this study provides an optimized measurement parameter in response to obvious pulsation from the carotid artery. The influence of the frequency of excitation current, electrode cross-sectional area, electrode arrangements, and physiological site of carotid arteries on IPG measurement resolution was thoroughly investigated for optimized parameters. In this study, the IPG system was implemented and installed on the subject's neck above the carotid artery to evaluate the measurement parameters. The measurement results within 6 subjects obtained the arterial impedance variation of 2137 mΩ using the optimized measurement conditions, including excitation frequency of 50 kHz, a smaller area of 2 cm2, electrode spacing of 4 cm and 1.7 cm for excitation and sensing functions, and location on the left side of the neck. The significance of this study demonstrates an optimized measurement methodology of IPG-based carotid pulse sensing that greatly improves the measurement quality in cardiovascular monitoring.

IPG-based field potential measurement of cultured cardiomyocytes for optogenetic applications.

BACKGROUND: Electrophysiological sensing of cardiomyocytes (CMs) in optogenetic preparations applies various techniques, such as patch-clamp, microelectrode array, and optical mapping. However, challenges remain in decreasing the cost, system dimensions, and operating skills required for these technologies. OBJECTIVE: This study developed a low-cost, portable impedance plethysmography (IPG)-based electrophysiological measurement of cultured CMs for optogenetic applications. METHODS: To validate the efficacy of the proposed sensor, optogenetic stimulation with different pacing cycle lengths (PCL) was performed to evaluate whether the channelrhodopsin-2 (ChR2)-expressing CM beating rhythm measured by the IPG sensor was consistent with biological responses. RESULTS: The experimental results show that the CM field potential was synchronized with external optical pacing with PCLs ranging from 250 ms to 1000 ms. Moreover, irregular fibrillating waveforms induced by CM arrhythmia were detected after overdrive optical pacing. Through the combined evidence of the theoretical model and experimental results, this study confirmed the feasibility of long-term electrophysiological sensing for optogenetic CMs. CONCLUSION: This study proposes an IPG-based sensor that is low-cost, portable, and requires low-operating skills to perform real-time CM field potential measurement in response to optogenetic stimulation. SIGNIFICANCE: This study demonstrates a new methodology for convenient electrophysiological sensing of CMs in optogenetic applications.