Prognostic Role of Pre-Treatment Body Composition Parameters in Patients Undergoing First-Line Immunotherapy for Metastatic Renal Cell Carcinoma.

Purpose: We investigated the relationship between body mass index (BMI), radiological body composition, and survival outcomes in patients with metastatic renal cell carcinoma (mRCC) underwent first-line immune checkpoint inhibitor (ICI)-based therapy. Methods: Analyzing data from 102 patients treated between November 2019 and March 2023, pre-treatment computed tomography (CT) scans assessed fat and muscle areas. BMI and body composition indices were examined, including skeletal muscle index, subcutaneous fat index (SFI), visceral fat index, and total fat index. Kaplan-Meier curves and Log rank tests compared progression-free survival (PFS) and overall survival (OS), while multivariable Cox proportional regression analysis was performed to identify the variables significantly associated with survival outcomes. Results: 54 patients (52.9%) experienced disease progression, and 26 (25.5%) died during a median follow-up of 17.4 months. High SFI was significantly associated with improved OS (p = 0.018) but not PFS (p = 0.090). Multivariable analysis confirmed the positive impact of high SFI on OS (adjusted HR: 0.37, p = 0.029) and suggested a trend towards improved PFS (adjusted HR: 0.61, p = 0.088). Notably, in the ipilimumab + nivolumab subgroup, high SFI significantly correlated with both PFS and OS (p = 0.047 and p = 0.012, respectively). Conclusion: High SFI predicts favorable OS in patients with mRCC receiving first-line ICI-based therapy, especially patients treated with ipilimumab + nivolumab displayed a significant association between high SFI and favorable PFS and OS.

Efficient data labeling strategies for automated muscle segmentation in lower leg MRIs of Charcot-Marie-Tooth disease patients.

We aimed to develop efficient data labeling strategies for ground truth segmentation in lower-leg magnetic resonance imaging (MRI) of patients with Charcot-Marie-Tooth disease (CMT) and to develop an automated muscle segmentation model using different labeling approaches. The impact of using unlabeled data on model performance was further examined. Using axial T1-weighted MRIs of 120 patients with CMT (60 each with mild and severe intramuscular fat infiltration), we compared the performance of segmentation models obtained using several different labeling strategies. The effect of leveraging unlabeled data on segmentation performance was evaluated by comparing the performances of few-supervised, semi-supervised (mean teacher model), and fully-supervised learning models. We employed a 2D U-Net architecture and assessed its performance by comparing the average Dice coefficients (ADC) using paired t-tests with Bonferroni correction. Among few-supervised models utilizing 10% labeled data, labeling three slices (the uppermost, central, and lowermost slices) per subject exhibited a significantly higher ADC (90.84±3.46%) compared with other strategies using a single image slice per subject (uppermost, 87.79±4.41%; central, 89.42±4.07%; lowermost, 89.29±4.71%, p < 0.0001) or all slices per subject (85.97±9.82%, p < 0.0001). Moreover, semi-supervised learning significantly enhanced the segmentation performance. The semi-supervised model using the three-slices strategy showed the highest segmentation performance (91.03±3.67%) among 10% labeled set models. Fully-supervised model showed an ADC of 91.39±3.76. A three-slice-based labeling strategy for ground truth segmentation is the most efficient method for developing automated muscle segmentation models of CMT lower leg MRI. Additionally, semi-supervised learning with unlabeled data significantly enhances segmentation performance.

The Fibrotic Phenotype of Human Precision-Cut Lung Slices Is Maintained after Cryopreservation.

Human precision-cut lung slices (hPCLS) prepared from fibrotic lungs recapitulate the pathophysiological hallmarks of fibrosis. These hallmark features can also be induced by treating non-fibrotic hPCLS with a fibrotic cocktail (FC). As a result, the fibrotic and fibrosis-induced hPCLS are rapidly emerging as preferred models for disease modeling and drug discovery. However, current hPCLS models are limited by tissue viability in culture, as they are usually only viable for one week after harvesting. Here, we demonstrate that the fibrotic hPCLS can be cryopreserved, stored for months, and then thawed on demand without loss of hPCLS viability or protein content for 14 days post-thawing. Cryopreservation also preserves the pro-fibrotic potential of non-fibrotic hPCLS. Specifically, when we treated the thawed non-fibrotic hPCLS with an FC, we observed significant pro-fibrotic cytokine secretion and elevated tissue stiffness. These pro-fibrotic changes were inhibited by the small-molecule tyrosine kinase inhibitor, Nintedanib. Taken together, our work indicates that a feasible solution to prolong the pre-clinical utility of fibrotic and fibrosis-induced hPCLS is cryopreservation. We anticipate that cryopreserved hPCLS will serve as an advantageous predictive model for the evaluation of pro-fibrotic pathways during acute and chronic toxicity testing.

Deep Cascade of Convolutional Neural Networks for Quantification of Enlarged Perivascular Spaces in the Basal Ganglia in Magnetic Resonance Imaging.

In this paper, we present a cascaded deep convolution neural network (CNN) for assessing enlarged perivascular space (ePVS) within the basal ganglia region using T2-weighted MRI. Enlarged perivascular spaces (ePVSs) are potential biomarkers for various neurodegenerative disorders, including dementia and Parkinson's disease. Accurate assessment of ePVS is crucial for early diagnosis and monitoring disease progression. Our approach first utilizes an ePVS enhancement CNN to improve ePVS visibility and then employs a quantification CNN to predict the number of ePVSs. The ePVS enhancement CNN selectively enhances the ePVS areas without the need for additional heuristic parameters, achieving a higher contrast-to-noise ratio (CNR) of 113.77 compared to Tophat, Clahe, and Laplacian-based enhancement algorithms. The subsequent ePVS quantification CNN was trained and validated using fourfold cross-validation on a dataset of 76 participants. The quantification CNN attained 88% accuracy at the image level and 94% accuracy at the subject level. These results demonstrate significant improvements over traditional algorithm-based methods, highlighting the robustness and reliability of our deep learning approach. The proposed cascaded deep CNN model not only enhances the visibility of ePVS but also provides accurate quantification, making it a promising tool for evaluating neurodegenerative disorders. This method offers a novel and significant advancement in the non-invasive assessment of ePVS, potentially aiding in early diagnosis and targeted treatment strategies.

Comprehensive deep learning-based assessment of living liver donor CT angiography: from vascular segmentation to volumetric analysis.

BACKGROUND: Precise preoperative assessment of liver vasculature and volume in living donor liver transplantation is essential for donor safety and recipient surgery. Traditional manual segmentation methods are being supplemented by deep learning (DL) models, which may offer more consistent and efficient volumetric evaluations. METHODS: This study analyzed living liver donors from Samsung Medical Center using preoperative CT angiography data between April 2022 and February 2023. A DL-based 3D residual U-Net model was developed and trained on segmented CT images to calculate the liver volume and segment vasculature, with its performance compared to traditional manual segmentation by surgeons and actual graft weight. RESULTS: The DL model achieved high concordance with manual methods, exhibiting Dice Similarity Coefficients of 0.94±0.01 for the right lobe and 0.91±0.02 for the left lobe. The liver volume estimates by DL model closely matched those of surgeons, with a mean discrepancy of 9.18 mL, and correlated more strongly with actual graft weights (R-squared value of 0.76 compared to 0.68 for surgeons). CONCLUSION: The DL model demonstrates potential as a reliable tool for enhancing preoperative planning in liver transplantation, offering consistency and efficiency in volumetric assessment. Further validation is required to establish its generalizability across various clinical settings and imaging protocols.

Elucidating the interaction between stretch and stiffness using an agent-based spring network model of progressive pulmonary fibrosis.

Pulmonary fibrosis is a deadly disease that involves the dysregulation of fibroblasts and myofibroblasts, which are mechanosensitive. Previous computational models have succeeded in modeling stiffness-mediated fibroblasts behaviors; however, these models have neglected to consider stretch-mediated behaviors, especially stretch-sensitive channels and the stretch-mediated release of latent TGF-ß. Here, we develop and explore an agent-based model and spring network model hybrid that is capable of recapitulating both stiffness and stretch. Using the model, we evaluate the role of mechanical signaling in homeostasis and disease progression during self-healing and fibrosis, respectively. We develop the model such that there is a fibrotic threshold near which the network tends towards instability and fibrosis or below which the network tends to heal. The healing response is due to the stretch signal, whereas the fibrotic response occurs when the stiffness signal overpowers the stretch signal, creating a positive feedback loop. We also find that by changing the proportional weights of the stretch and stiffness signals, we observe heterogeneity in pathological network structure similar to that seen in human IPF tissue. The system also shows emergent behavior and bifurcations: whether the network will heal or turn fibrotic depends on the initial network organization of the damage, clearly demonstrating structure's pivotal role in healing or fibrosis of the overall network. In summary, these results strongly suggest that the mechanical signaling present in the lungs combined with network effects contribute to both homeostasis and disease progression.

3D auto-segmentation of biliary structure of living liver donors using magnetic resonance cholangiopancreatography for enhanced preoperative planning.

BACKGROUND: This study aimed to develop an automated segmentation system for biliary structures using a deep learning model, based on data from magnetic resonance cholangiopancreatography (MRCP). MATERIALS AND METHODS: Living liver donors who underwent MRCP using the gradient and spin echo technique followed by three-dimensional modeling were eligible for this study. A three-dimensional residual U-Net model was implemented for the deep learning process. Data were divided into training and test sets at a 9:1 ratio. Performance was assessed using the dice similarity coefficient to compare the model's segmentation with the manually labeled ground truth. RESULTS: The study incorporated 250 cases. There was no difference in the baseline characteristics between the train set (n=225) and test set (n=25). The overall mean Dice Similarity Coefficient was 0.80±0.20 between the ground truth and inference result. The qualitative assessment of the model showed relatively high accuracy especially for the common bile duct (88%), common hepatic duct (92%), hilum (96%), right hepatic duct (100%), and left hepatic duct (96%), while the third-order branch of the right hepatic duct (18.2%) showed low accuracy. CONCLUSION: The developed automated segmentation model for biliary structures, utilizing MRCP data and deep learning techniques, demonstrated robust performance and holds potential for further advancements in automation.

CoFe2O4 on Mica Substrate as Flexible Ethanol Gas Sensor in Self-Heating Mode.

In this study, a novel flexible ethanol gas sensor was created by the deposition of a CoFe2O4 (CFO) thin film on a thin mica substrate using the pulsed laser deposition technique. Transition electron microscopy (TEM) investigations clearly demonstrated the successful growth of CFO on the mica, where a well-defined interface was observed. Ethanol gas-sensing studies showed optimal performance at 200 °C, with the highest response of 19.2 to 100 ppm ethanol. Operating the sensor in self-heating mode under 7 V applied voltage, which corresponds to a temperature of approximately 200 °C, produced a maximal response of 19.2 to 100 ppm ethanol. This aligned with the highest responses observed during testing at 200 °C, confirming the sensor's accuracy and sensitivity to ethanol under self-heating conditions. In addition, the sensor exhibited good selectivity to ethanol and excellent flexibility, maintaining its high performance after bending and tilting up to 5000 times. As this is the first report on flexible self-heated CFO gas sensors, we believe that this research holds great promise for the future development of high-quality sensors based on this approach.

Selective Spin Dewetting for Perovskite Solar Modules Fabricated on Engineered Au/ITO Substrates.

We introduce a novel method for fabricating perovskite solar modules using selective spin-coating on various Au/ITO patterned substrates. These patterns were engineered for two purposes: (1) to enhance selectivity of monolayers primarily self-assembling on the Au electrode, and (2) to enable seamless interconnection between cells through direct contact of the top electrode and the hydrophobic Au connection electrode. Utilizing SAMs-treated Au/ITO, we achieved sequential selective deposition of the electron transport layer (ETL) and the perovskite layer on the hydrophilic amino-terminated ITO, while the hole transport layer (HTL) was deposited on the hydrophobic CH3-terminated Au connection electrodes. Importantly, our approach had a negligible impact on the series resistance of the solar cells, as evidenced by the measured specific contact resistivity of the multilayers. A significant outcome was the production of a six-cell series-connected solar module with a notable average PCE of 8.32%, providing a viable alternative to the conventional laser scribing technique.

nnU-Net-Based Pancreas Segmentation and Volume Measurement on CT Imaging in Patients with Pancreatic Cancer.

RATIONALE AND OBJECTIVES: To develop and validate a deep learning (DL)-based method for pancreas segmentation on CT and automatic measurement of pancreatic volume in pancreatic cancer. MATERIALS AND METHODS: This retrospective study used 3D nnU-net architecture for fully automated pancreatic segmentation in patients with pancreatic cancer. The study used 851 portal venous phase CT images (499 pancreatic cancer and 352 normal pancreas). This dataset was divided into training (n = 506), internal validation (n = 126), and external test set (n = 219). For the external test set, the pancreas was manually segmented by two abdominal radiologists (R1 and R2) to obtain the ground truth. In addition, the consensus segmentation was obtained using Simultaneous Truth and Performance Level Estimation (STAPLE) algorithm. Segmentation performance was assessed using the Dice similarity coefficient (DSC). Next, the pancreatic volumes determined by automatic segmentation were compared to those determined by manual segmentation by two radiologists. RESULTS: The DL-based model for pancreatic segmentation showed a mean DSC of 0.764 in the internal validation dataset and DSC of 0.807, 0.805, and 0.803 using R1, R2, and STAPLE as references in the external test dataset. The pancreas parenchymal volume measured by automatic and manual segmentations were similar (DL-based model: 65.5 ± 19.3 cm3 and STAPLE: 65.1 ± 21.4 cm3; p = 0.486). The pancreatic parenchymal volume difference between the DL-based model predictions and the manual segmentation by STAPLE was 0.5 cm3, with correlation coefficients of 0.88. CONCLUSION: The DL-based model efficiently generates automatic segmentation of the pancreas and measures the pancreatic volume in patients with pancreatic cancer.

Radiation shielding effects of lead equivalent thickness of a radiation protective apron and distance during C-arm fluoroscopy-guided pain interventions: A randomized trial.

BACKGROUND: The present study aimed to evaluate the degree of radiation shielding effects according to lead equivalent thickness and distance during C-arm fluoroscopy-guided lumbar interventions. METHODS: The exposure time and air kerma were recorded using a fluoroscope. The effective dose (ED) was measured with and without the shielding material of the lead apron using 2 dosimeters at 2 positions. According to the lead equivalent thickness of the shielding material and distance from the side of the table, the groups were divided into 4 groups: group 1 (lead equivalent thickness 0.6 mm, distance 0 cm), group 2 (lead equivalent thickness 0.6 mm, distance 5 cm), group 3 (lead equivalent thickness 0.3 mm, distance 0 cm), and group 4 (lead equivalent thickness 0.3 mm, distance 5 cm). Mean differences such as air kerma, exposure time, ED, and ratio of EDs (ED with protector/ED without protector) were analyzed. RESULTS: A total of 400 cases (100 cases in each group) were collected. The ratio of ED was significantly lower in groups 1 and 2 (9.18 ±â€…2.78% and 9.56 ±â€…3.29%, respectively) when compared to that of groups 3 and 4 (21.93 ±â€…4.19% and 21.53 ±â€…4.30%, respectively). The reductive effect of a 5-cm distance was 33.3% to 36.1% when comparing the ED between groups 1 and 2 and groups 3 and 4. CONCLUSIONS: The 0.3- and 0.6-mm lead equivalent thickness protectors have a radiation attenuation effect of 78.1% to 78.5% and 90.4% to 90.8%, respectively. The 5-cm distance from the side of the table reduces radiation exposure by 33.3% to 36.1%.

Blunt Trauma in Children: Efficacy and Safety of Transarterial Embolization, 10-Year Experiences in a Single Trauma Center.

BACKGROUND: Transcatheter arterial embolization (TAE) is an established approach for controlling hemorrhage in adults with acute abdominal and pelvic trauma. However, its application in pediatric trauma is not well established. This study aimed to evaluate the safety and effectiveness of TAE in a population of pediatric patients with blunt trauma. METHODS: This retrospective study was conducted in pediatric patients (<18 years) who underwent TAE for blunt trauma between February 2014 and July 2022. The patients were categorized into subgroups based on age and body weight. Patient demographics, injury severity, transfusion requirements, and clinical outcomes were analyzed. RESULTS: Exactly 73 patients underwent TAE. Technical success was achieved in all patients (100%), and clinical success was achieved in 83.6%. The mortality and complication rates were 4.1% and 1.4%, respectively. The mean duration of hospitalization was 19.3 days. Subgroup analysis showed that age, body weight, and sex did not significantly affect clinical success. The injury severity score and transfusion requirement were predictors of clinical success, with lower values associated with better outcomes. CONCLUSIONS: TAE is effective and safe for managing blunt pediatric trauma in younger and lighter patients. Injury severity and transfusion requirement are predictors of clinical success.

Automated 3D liver segmentation from hepatobiliary phase MRI for enhanced preoperative planning.

Recent advancements in deep learning have facilitated significant progress in medical image analysis. However, there is lack of studies specifically addressing the needs of surgeons in terms of practicality and precision for surgical planning. Accurate understanding of anatomical structures, such as the liver and its intrahepatic structures, is crucial for preoperative planning from a surgeon's standpoint. This study proposes a deep learning model for automatic segmentation of liver parenchyma, vascular and biliary structures, and tumor mass in hepatobiliary phase liver MRI to improve preoperative planning and enhance patient outcomes. A total of 120 adult patients who underwent liver resection due to hepatic mass and had preoperative gadoxetic acid-enhanced MRI were included in the study. A 3D residual U-Net model was developed for automatic segmentation of liver parenchyma, tumor mass, hepatic vein (HV), portal vein (PV), and bile duct (BD). The model's performance was assessed using Dice similarity coefficient (DSC) by comparing the results with manually delineated structures. The model achieved high accuracy in segmenting liver parenchyma (DSC 0.92 ± 0.03), tumor mass (DSC 0.77 ± 0.21), hepatic vein (DSC 0.70 ± 0.05), portal vein (DSC 0.61 ± 0.03), and bile duct (DSC 0.58 ± 0.15). The study demonstrated the potential of the 3D residual U-Net model to provide a comprehensive understanding of liver anatomy and tumors for preoperative planning, potentially leading to improved surgical outcomes and increased patient safety.

Peptide Nucleic Acids Containing Cationic/Amino-Alkyl Modified Bases Promote Enhanced Hybridization Kinetics and Thermodynamics with Single-Strand DNA.

Peptide nucleic acids (PNAs) are antisense molecules with excellent polynucleotide hybridization properties; they are resistant to nuclease degradation but often have poor cell permeability leading to moderate cellular activity and limited clinical results. The addition of cationic substitutions (positive charges) to PNA molecules greatly increases cell permeability. In this report, we describe the synthesis and polynucleotide hybridization properties of a novel cationic/amino-alkyl nucleotide base-modified PNA (OPNA). This study was designed to quantitate the effect the cationic/amino-alkyl nucleotide base modification had on the kinetic and thermodynamic properties of OPNA-DNA hybridization using surface plasmon resonance and UV thermal melt studies. Kinetic studies reveal a favorable 10-30 fold increase in affinity for a single cationic modification on the base of an adenine, cytosine, or guanidine OPNA sequence compared to the nonmodified PNA strand. The increase in affinity is correlated directly with a favorable decrease in the dissociation rate constant and increase in the association rate constant. Introducing additional amino-alkyl base modifications further favors a decrease in the dissociation rate (3-10-fold per amino-alkyl). The thermodynamics driving the OPNA hybridization is promoted by an additional favorable -80 kJ/mol enthalpy of binding for a single amino-alkyl modification compared to the PNA strand. This increase in enthalpy is consistent with an ion-ion interaction with the DNA strand. These kinetic and thermodynamic hybridization studies reveal for the first time that this type of cationic/amino-alkyl base-modified PNA has favorable hybridization properties suitable for development as an antisense oligomer.

Predicting Non-Small-Cell Lung Cancer Survival after Curative Surgery via Deep Learning of Diffusion MRI.

BACKGROUND: the objective of this study is to evaluate the predictive power of the survival model using deep learning of diffusion-weighted images (DWI) in patients with non-small-cell lung cancer (NSCLC). METHODS: DWI at b-values of 0, 100, and 700 sec/mm2 (DWI0, DWI100, DWI700) were preoperatively obtained for 100 NSCLC patients who underwent curative surgery (57 men, 43 women; mean age, 62 years). The ADC0-100 (perfusion-sensitive ADC), ADC100-700 (perfusion-insensitive ADC), ADC0-100-700, and demographic features were collected as input data and 5-year survival was collected as output data. Our survival model adopted transfer learning from a pre-trained VGG-16 network, whereby the softmax layer was replaced with the binary classification layer for the prediction of 5-year survival. Three channels of input data were selected in combination out of DWIs and ADC images and their accuracies and AUCs were compared for the best performance during 10-fold cross validation. RESULTS: 66 patients survived, and 34 patients died. The predictive performance was the best in the following combination: DWI0-ADC0-100-ADC0-100-700 (accuracy: 92%; AUC: 0.904). This was followed by DWI0-DWI700-ADC0-100-700, DWI0-DWI100-DWI700, and DWI0-DWI0-DWI0 (accuracy: 91%, 81%, 76%; AUC: 0.889, 0.763, 0.711, respectively). Survival prediction models trained with ADC performed significantly better than the one trained with DWI only (p-values < 0.05). The survival prediction was improved when demographic features were added to the model with only DWIs, but the benefit of clinical information was not prominent when added to the best performing model using both DWI and ADC. CONCLUSIONS: Deep learning may play a role in the survival prediction of lung cancer. The performance of learning can be enhanced by inputting precedented, proven functional parameters of the ADC instead of the original data of DWIs only.

Real-time morphological detection of label-free submicron-sized plastics using flow-channeled differential interference contrast microscopy.

Owing to the surge in plastic waste generated during the COVID-19 pandemic, concerns regarding microplastic pollution in aqueous environments are increasing. Since microplastics (MPs) are broken down into submicron (< 1 µm) and nanoscale plastics, their real-time morphological detection in water is necessary. However, the decrease in the scattering cross-section of MPs in aqueous media precludes morphological detection by bright-field microscopy. To address this problem, we propose and demonstrate a differential interference contrast (DIC) system that incorporates a magnification-enhancing system to detect MPs in aqueous samples. To detect MPs in both the stationary and mobile phases, a microfluidic chip was designed, taking into consideration the imaging depth of focus and flow resistance. MPs of various sizes flowing in deionized, tap, and pond water at varying speeds were observed under Static and Flow conditions. Successful real-time morphological detection and quantification of polystyrene beads down to 200 nm at a constant flow rate in water were achieved. Thus, the proposed novel method can significantly reduce analysis time and improve the size-detection limit. The proposed DIC microscopy system can be coupled with Raman or infrared spectroscopy in future studies for chemical composition analysis. ENVIRONMENTAL IMPLICATION: Microplastics (MPs), particularly submicron plastics < 1-µm, can pose a risk to human health and aquatic ecosystems. Existing methods for detecting MPs in the aqueous phase have several limitations, including the use of expensive instruments and prolonged and labor-intensive procedures. Our results clearly demonstrated that a new low-cost flow-channeled differential interference contrast microscopy enables the real-time morphological detection and quantification of MPs down to 200 nm under flowing conditions without sample labeling. Consequently, our proposed rapid method for accurate quantitative measurements can serve as a valuable reference for detecting submicron plastics in water samples.

Multi-scale vascularization strategy for 3D-bioprinted tissue using coaxial core-shell pre-set extrusion bioprinting and biochemical factors.

Three-dimensional bioprinting is a key technology in bioartificial organ production. However, production of bioartificial organs has significant limitations because it is hard to build vascular structures, especially capillaries, in printed tissue owing to its low resolution. As the vascular structure plays a critical role in delivering oxygen and nutrients to cells and removing metabolic waste, building vascular channels in bioprinted tissue is essential for bioartificial organ production. In this study, we demonstrated an advanced strategy for fabricating multi-scale vascularized tissue using a pre-set extrusion bioprinting technique and endothelial sprouting. Using a coaxial precursor cartridge, mid-scale vasculature-embedded tissue was successfully fabricated. Furthermore, upon generating a biochemical gradient environment in the bioprinted tissue, capillaries were formed in this tissue. In conclusion, this strategy for multi-scale vascularization in bioprinted tissue is a promising technology for bioartificial organ production.

Eye-Mimicked Neural Network Composed of Photosensitive Neural Spheroids with Human Opsin Proteins.

An in vitro model, composed of the short-wavelength human opsins and rhodopsins, is created. Two types of photosensitive neural spheroids are transfected for selective reaction under bluish-purple and green lights. These are employed to two devices with intact neuron and neural-spheroid to study the interaction. By photostimulation, the photosensitive spheroid initiated photoactivation, and the signal generated from its body is transmitted to adjacent neural networks. Specifically, the signal traveled through the axon bundle in narrow gap from photosensitive spheroid to intact spheroid as an eye-to-brain model including optic nerve. The whole process with photosensitive spheroid is monitored by calcium ion detecting fluorescence images. The results of this study can be applied to examine vision restoration and novel photosensitive biological systems with spectral sensitivity.