Imaging Methods Research Articles

Detection of heat transfer mechanism in biological systems and HIF-1 α inducing invasion of liver cancer cells in hypoxic environments by activating STAT3

HIF-1α, as a hypoxia-inducing factor, can promote the invasion and metastasis of cancer cells by activating downstream signaling pathways. However, different heat transfer mechanisms in biological systems have important effects on the survival and function of tumor cells. The aim of this study was to investigate how the mechanisms of heat transfer in biological systems affect HIF-1α-mediated STAT3 activation and further induce the invasion ability of liver cancer cells in anoxic environment. The expression and activation of HIF-1α and STAT3 in hepatocellular carcinoma cell lines were detected by establishing anoxic model. At the same time, thermal imaging and heat transfer analysis methods were used to evaluate the heat transfer characteristics of cells under different temperature and hypoxia conditions. The effects of HIF-1α and STAT3 on invasion of hepatocellular carcinoma cells were analyzed in combination with cell migration and invasion experiments. The results showed that the expression of HIF-1α was significantly increased and the STAT3 signaling pathway was activated under hypoxia. The abnormal change of the heat transfer mechanism of biological system accelerates the invasion ability of hepatocellular carcinoma cells. Inhibition of HIF-1α expression can significantly reduce the activation level of STAT3, thereby inhibiting cell migration and invasion. This study reveals the interaction between the biological heat transfer mechanism and HIF-1α-STAT3 signaling pathway under hypoxia environment, which provides a new potential target for the treatment of malignant tumors such as liver cancer, and emphasizes the importance of regulating the heat transfer mechanism.

Diagnosis and management of multiple primary lung cancer.

Multiple primary lung cancer (MPLC), can be categorized as synchronous multiple primary lung cancer (sMPLC) and metachronous multiple primary lung cancer (mMPLC), which are becoming increasingly common in clinical practice. A precise differential diagnosis between MPLC and intrapulmonary metastases (IPM) is essential for determining the appropriate management strategy. MPLC is primarily diagnosed through histology, imaging, and molecular methods. Imaging serves as an essential foundation for preoperative diagnosis, while histology is a critical tool for establishing a definitive diagnosis. As molecular biology advances, the diagnosis of MPLC has stepped into the era of molecular precision. Surgery is the preferred treatment approach, with stereotactic radiotherapy and ablation being viable options for unresectable lesions. Targeted therapy and immunotherapy can be considered for specific patients. A multidisciplinary team approach to evaluation and the application of combination therapy can benefit more patients. Looking ahead, the development of more authoritative guidelines will be instrumental in streamlining the diagnosis and management of MPLC.

Magnetic Resonance Assessment of Bone Quality in Metabolic Bone Diseases.

Metabolic bone diseases (MBDs) are a diverse group of diseases, affecting the mass or structure of bones and leading to reduced bone quality. Parameters representing different aspects of bone health can be obtained from various magnetic resonance imaging (MRI) methods such as proton MR spectroscopy, as well as chemical shift encoding-based water-fat imaging, that have been frequently applied to study bone marrow in particular. Furthermore, T2* mapping and high-resolution trabecular bone imaging have been implemented to study bone microstructure. In addition, quantitative susceptibility mapping and ultrashort echo time imaging are used for trabecular and cortical bone assessment. This review offers an overview of technical aspects, as well as major clinical applications and derived main findings, for MRI-based assessment of bone quality in MBDs. It focuses on osteoporosis as the most common MBD.

Modeling Rayleigh wave in viscoelastic media with constant Q model using fractional time derivatives

The propagation of seismic waves within the near-surface weathering layers, characterized by their low-quality factors (Q), is often accompanied by strong attenuation and dispersion phenomena. Among these, the Rayleigh wave, with its sensitivity to dispersion, has proven to be a powerful tool for near-surface exploration. We propose a novel approach for simulating Rayleigh wave propagation in such low-Q media. Our method uses the time-domain fractional wave equation with memory effect, based on Kjartansson's constant-Q (CQ) model, for accurate characterization of the propagation process. To solve numerically the wave equation with the fractional derivatives, we employ a finite-difference method combined with the auxiliary differential equation-perfectly matched layer (ADE-PML) and the acoustic-elastic boundary approach (AEA). The algorithm's high computational accuracy is verified through comparison with the conventional integer-order wave equation based on the nearly constant-Q (NCQ) models in strong attenuation media. The research in this paper deepens our understanding of the propagation characteristics of Rayleigh waves in strongly weathering layers. This new method strongly supports those seismic imaging and inversion methods depending on seismic modeling, including the reverse time migration and the full waveform inversion of the internal structure of low-Q media.

Hybrid Approach of Multiclassification for Lung Cancer Types Identification

As a widespread health concern, lung cancer requires sophisticated detection techniques for a precise classification. This research proposes an integrated framework for improving how well lung cancer is detected by combining processing methods for images and machine learning. The dataset, which includes normal cases, large-cell carcinoma, squamous cell carcinoma, and adenocarcinoma, allows for a detailed investigation of various forms of lung cancer. K-means clustering is the initial stage of the approach of segmenting complex images. Utilizing the VGG16 model, features are extracted that are important for classification. Several classification models, such as SVM, Logistic Regression, and Feedforward Neural Network, are trained using the combined features from clustering and VGG16. This research goes beyond the dichotomy of benign and malignant cases, investigating in more detail the subtypes of large-cell carcinoma, squamous cell carcinoma, and adenocarcinoma. Including different classes of lung cancer increases the granularity of the classification process, improving diagnostic accuracy and clinical insights. The results highlight how well the suggested strategy works, which is a major advancement in the accurate identification of distinct kinds of lung cancer.

Quantitative spatial visualization of X-ray irradiation via redox reaction by dynamic nuclear polarization magnetic resonance imaging

The dose of X-ray irradiation is commonly measured by point assessment with an ionization chamber dosimeter. However, to achieve spatially accurate delivery of X-ray to avoid the exposure to normal tissues, an accurate imaging method for spatially and quantitatively detecting exposure is required. Herein, we present a novel method to visualize X-ray exposure using low-field dynamic nuclear polarization magnetic resonance imaging (DNP-MRI) with nitroxyl radical tempol as the chemical dosimeter. In this system, gel phantoms containing glutathione (GSH) and the paramagnetic tempol radical were used to monitor the deposited X-ray-irradiation via the redox reaction. The tempol radical level was evaluated by DNP-MRI whose signal intensity was linearly correlated with the radical concentration. The radical level in the presence of GSH decreased in proportion to the dose of X-irradiation deposited. In an imaging experiment simulating clinical radiotherapy, we used a clinical linear accelerator with a radiotherapy planning software to confirm the utility of the exposure imaging. The X-ray exposure and its distribution were clearly visualized on the gel phantom image acquired by DNP-MRI. The results were consistent with those specified in the radiotherapy plan where the intensity of the radiation beam was modulated. This exposure estimation will be useful for determining an accurate irradiation field and reducing off-target exposure in clinical settings.

Entangled Optical Quantum Imaging Method Assisted by AUVs Based on Underwater Light Transmission Characteristics

Entangled Optical Quantum Imaging Method Assisted by AUVs Based on Underwater Light Transmission Characteristics

Gas flows generated by pulse-periodic optical breakdown and quiet optical discharge

Quiet optical discharge in high-pressure xenon is visually stable pre-breakdown stage of optical discharge supported by pulse-periodic short-wavelength infrared laser radiation with intensity of the order of 109 W/cm2. Increasing the laser intensity leads to the transition of the quiet discharge into a pulse-periodic optical breakdown. In this study, quasi-stationary directional flows generated by both the quiet discharge and the optical breakdown are observed. The flows and their initial stages under limited number of discharge pulses are visualized by shadow imaging method using a point-like laser-plasma radiation source. It is shown that the gas streams outflow points in a quiet discharge correspond to the positions of the laser radiation intensity local maxima in the focal beam waist with astigmatism complicated by defocusing effect of thermal lens arising in the discharge zone. The pulse-periodic optical breakdown emits a turbulent gas flow toward the laser beam. The beam refraction on density gradients in the turbulent flow causes the breakdown instability from pulse to pulse. It was found that time-average absorption of laser radiation in a quiet discharge is about 3% (also in a pulse), while that in optical breakdown is 15% and higher (from 70% to 100% in a pulse). Laser beam intensity for quiet discharge at pulse repetition rate νp = 20 kHz ranges from 1.3 × 109 to 1.5 × 109 W/cm2. At intensities above 4 × 109 W/cm2 and repetition rates νp > 50–55 kHz, laser breakdowns are observed in each laser pulse with the focal ratio f/d ≤ 7. At f/d = 10.6 and νp > 28 kHz, the quiet discharge does not transit to laser breakdown due to defocusing by the thermal lens induced.

Detection of damage caused by Nezara viridula on soybean using novel imaging approaches based on computed tomography and image color analysis.

Soybean (Glycine max L.) is an important leguminous plant, in which pests trigger significant damage every year. Important members of this community are insects with piercing-sucking mouthpart, especially the southern green stinkbug, Nezara viridula L.. This insect with its extraoral digestion causes visible alterations (morphological and color changes) in the seeds. We aimed to obtain precise information about the extent and nature of damage in soybeans caused by N. viridula using nondestructive imaging methods. Two infestation conditions were applied: one with controlled numbers of pests (six insects/15 pods) and another with naturally occurring pests (samples collected from the apical part of the plant and samples from whole plants). An intact control group was also included, resulting in four treatment groups. Seed samples were analyzed by computed tomography (CT) and image color analysis under laboratory conditions. According to our CT findings, the damage caused by N. viridula changed the radiodensity, volume, and shape (Solidity) of the soybean seeds during the pod-filling and maturing period. Radiodensity was significantly reduced in all three damaged categories compared to the intact sample; the mean radiodensity reduction range was 49-412 HU. The seed volume also decreased significantly (25%-80% decrease), with a threefold reduction for samples exposed to regulated damage compared to natural ones. The samples exposed to natural damage showed significant but minor reduction in solidity, while samples exposed to regulated damage showed a prominent decrease (~12%). Image color analysis showed that the damaged samples were well distinguishable, and the differences were statistically verifiable. The achieved data derived from our external and internal imaging approaches contribute to a better understanding of the internal chemical processes, and CT analysis helps to understand the alteration trends of the hidden structure of seeds caused by a pest. Our results can contribute to the development of a practically applicable system based on image analysis, which can identify lots damaged by insects.

Early-stage cardiomegaly detection and classification from X-ray images using convolutional neural networks and transfer learning

Cardiomyopathy is a serious condition that can result in heart failure, sudden cardiac death, malignant arrhythmias, and thromboembolism. It is a significant contributor to morbidity and mortality globally. The initial finding of cardiomegaly on radiological imaging may signal a deterioration of a known heart condition, an unknown heart disease, or a heart complication related to another illness. Further cardiological evaluation is needed to confirm the diagnosis and determine appropriate treatment. A chest radiograph (X-ray) is the main imaging method used to identify cardiomegaly when the heart is enlarged. A prompt and accurate diagnosis is essential to help healthcare providers determine the most appropriate treatment options before the condition worsens. This study aims to utilize convolutional neural networks and transfer learning techniques, specifically Inception, DenseNet-169, and ResNet-50, to classify cardiomegaly from chest X-ray images automatically. The utilization of block-matching and 3D filtering (BM3D) techniques aimed at enhancing image edge retention, decreasing noise, and utilizing contrast limited adaptive histogram equalization (CLAHE) to enhance contrast in low-intensity images. Gradient-weighted Class Activation Mapping (GradCAM) was used to visualize the significant activation regions contributing to the model's decision. After evaluating all the models, the ResNet-50 model showed outstanding performance. It achieved perfect accuracy of 100 % in both training, and validation, and an impressive 99.8 % accuracy in testing. Additionally, it displayed complete 100 % precision, recall, and F1-score. These findings demonstrate that ResNet-50 surpassed all other models in the study. As a result, the impressive performance of the ResNet-50 model suggests that it could be a valuable tool in improving the efficiency and accuracy of cardiomyopathy diagnosis, ultimately leading to better patient outcomes.

Cerebral venous thrombosis in children an 18-year review of a Portuguese hospital

IntroductionCerebral venous thrombosis (CVT) is an uncommon and clinically heterogeneous cerebrovascular particularly in children, only a few published case series focused in the pediatric population. Patients and methodsRetrospective single-center observational and analytical study of consecutive pediatric patients admitted in a level II Portuguese hospital with a confirmed diagnosis of CVT, from 2003 to 2021. Clinical presentation, neuroimaging findings, prothrombotic factors, treatment strategies, outcome and recanalization were documented. ResultsTwelve children were included (58% female). Mean age was 7.3 years. The most frequent symptoms were vomiting, headache and behavioral alterations. Infection was the triggering factor in 50% of the cases. The diagnosis of CVT was made based on imaging evidence of thrombosis through magnetic imaging resonance (MRI) with venography and/or computed tomography (CT) with venography. In 67% of cases there were multiples sinuses involved; the transverse sinus was the most affected, followed by the sigmoid sinus. In 83% of cases anticoagulant therapy was initiated with low molecular weight heparin (LMWH) and associated prothrombotic factors were investigated, with no major prothrombotic factors identified. No deaths occurred, but 30% had long-term neurological sequelae. One patient recurred 18 years later. ConclusionThe results of this study are consistent with data from other published studies. MRI is the preferred imaging method for diagnosis in children by avoiding ionizing radiation and allowing identification of subjacent causes. Anticoagulation with LMWH is recommended and important to reduce mortality and sequelae. Infectious diseases are the most common trigger for CVT and can also be the cause for high morbidity and poor outcomes.

ATR-FTIR spectroscopic imaging with variable angles of incidence of crude oil deposits formed by flocculant flow

Attenuated total reflection Fourier transform infrared (ATR-FTIR) spectroscopic imaging is a method for spatially resolved analysis of materials that combines the capabilities of ATR-FTIR spectroscopy with the use of a focal plane array detector. This paper presents the methodological aspects of adapting the ATR accessory with variable single reflection angle to the FTIR spectroscopic imaging method. The use of a variable reflection angle allows the image to be studied at different sample depths. Using examples of BMIMPF6 ionic liquid and crude oil droplets placed on the working surface of an internal reflection element, the characteristics of image acquisition as the angle of reflection is varied are discussed. The possibility of obtaining crude oil deposits directly on the working surface of the internal reflection element under the influence of a flocculant flow (n-heptane, acetone) and their study by ATR-FTIR spectroscopic image was demonstrated. Crude oil deposits were obtained under different formation conditions (flow rates of flocculant) and their spectroscopic images were also obtained at different single reflection angles. This information gives an indication of the composition of the deposit’s functional groups not only at spatial resolution but also at depth.

Dynamics of single cavitation bubble collapse jet under particle-wall synergy

The interaction between a particle and a cavitation bubble significantly influences the erosive effect on the wall surface of flow passage components in fluid machinery. This paper investigates the dynamics of a single bubble collapse jet under the synergetic effects of a particle and a wall, using Kelvin impulse theory and high-speed photographic experiments. A theoretical model to predict the intensity and direction of the collapse jet at arbitrary locations near the particle and the wall is constructed on the basis of the image method and Weiss's theorem. The accuracy of the model is verified by comparison with a large number of experimental results. The mechanisms underlying the relative contributions of the particle and wall to the behavior of jet intensity and direction are explored. The effects of key parameters on jet intensity and direction are also quantitatively analyzed, including the relative positions of the particle, wall, and the bubble and the dimensionless particle radius. The main conclusions are as follows: (1) the particle will cause a deflection in the direction of the collapse jet near the wall, leading to the formation of a jet attraction zone. The proposed theoretical model effectively predicts the spatial location of this zone. (2) There exists a region in which the jet is weak, and there is a jet equilibrium point with zero impulse between the particle and the wall. The position of this equilibrium point gradually approaches the wall in a nonlinear manner with increasing particle size and in a quasi-linear manner with decreasing particle–wall distance. (3) When the particle and the bubble are the same distance from the wall, the jet direction gradually changes from toward the particle to vertical to the wall in a nonlinear manner as the bubble–particle distance increases. Moreover, the effective range of the particle's influence on the jet direction decreases as the particle–wall distance decreases.

Endogenous enzyme-activated AND-gate DNA nanomachines for intracellular miRNA detection and cell-selective imaging

The occurrence and development of tumors are accompanied by the abnormal expression of specific microRNAs (miRNAs). Therefore, miRNAs are considered as an important biomarker. The establishment of efficient, simple and sensitive miRNA imaging methods in living cells will contribute to the early diagnosis, treatment and drug development of diseases. In this study, we developed an endogenous enzyme-initiated AND logic circuit using gold nanocubes (AuNCs) as carriers for simultaneous detection of miRNA-21 and miRNA-210 in cells. Apurinic/apyrimidinic endonuclease 1 (APE1) and telomerase (TE), which are overexpressed in cancer cells, act as control switches in a logic circuit that enables sensitive in situ analysis of intracellular miRNAs without additional external intervention. At the same time, due to the lack of necessary enzymes as activation switches, the DNA circuit in normal cells remains in an inactive state. This strategy effectively reduces the risk of false positive signal generation. Our research results show that the logic circuit can not only distinguish between cancer cells and normal cells, and able to distinguish between different types of cancer cells. This finding provides a promising approach to accurately identify cell types.

Long-term three-dimensional high-resolution imaging of live unlabeled small intestinal organoids via low-coherence holotomography.

Organoids, which are miniature in vitro versions of organs, possess significant potential for studying human diseases and elucidating their underlying mechanisms. Live imaging techniques play a crucial role in organoid research and contribute to elucidating the complex structure and dynamic biological phenomena of organoids. However, live, unlabeled high-resolution imaging of native organoids is challenging, primarily owing to the complexities of sample handling and optical scattering inherent in three-dimensional (3D) structures. Additionally, conventional imaging methods fail to capture the real-time dynamic processes of growing organoids. In this study, we introduce low-coherence holotomography as an advanced, label-free, quantitative imaging modality designed to overcome several technical obstacles for long-term live imaging of 3D organoids. We demonstrate the efficacy of low-coherence holotomography by capturing high-resolution morphological details and dynamic activities within mouse small intestinal organoids at subcellular resolution. Moreover, our approach facilitates the distinction between viable and nonviable organoids, significantly enhancing its utility in organoid-based research. This advancement underscores the critical role of live imaging in organoid studies, offering a more comprehensive understanding of these complex systems.

Incidence of Ipsilateral Femoral Neck and Shaft Fractures in Pediatric and Adolescent Patients

OBJECTIVES: To identify the incidence, patient characteristics, and effectiveness of radiographic screening methods for detecting ipsilateral femoral neck and shaft fractures in pediatric and adolescent trauma patients. METHODS: Design: Retrospective cohort study. Setting: This study was conducted at a tertiary pediatric trauma hospital. Patient Selection Criteria: Patients younger than 18 years treated for a femoral shaft fracture between 2004 and 2018 were reviewed. Pathologic (metabolic bone disease or bone lesion), periprosthetic, and penetrating traumatic femoral shaft fractures were excluded. Outcome Measurements and Comparisons: Patient demographics, mechanisms of injury, treatment methods, and associated injuries were analyzed. Pretreatment x-rays and computed tomography (CT) scans were reviewed for the identification of ipsilateral femoral neck and shaft fractures. RESULTS: Among the 840 pediatric patients included in this study, 4 patients (0.5%) sustained ipsilateral femoral neck and shaft fractures. All the femoral neck fractures were observed in adolescents (aged 13–17 years) and involved in high-energy traumas. In adolescents involved in high-energy trauma, the incidence increased to 1.7%. Pretreatment sensitivity of both x-rays and CT scans was only 50% for the detection of femoral neck fractures. CONCLUSIONS: This study reveals that ipsilateral femoral neck and shaft fractures in pediatric patients are rare, occurring in adolescents involved in high-energy trauma. The findings suggest the need for a selective, rather than routine, use of CT scans based on the patient's age and the mechanism of injury. The use of alternative imaging methods such as magnetic resonance imaging should be considered to balance diagnostic accuracy while minimizing radiation exposure. LEVEL OF EVIDENCE: Prognostic Level IV. See Instructions for Authors for a complete description of levels of evidence.

A dual-signal biosensor based on surface-enhanced Raman spectroscopy for high-sensitivity quantitative detection and imaging of circRNA in living cells

Circular RNAs (circRNAs) are non-coding RNAs that play key roles in the development and progression of cancer through various mechanisms of action, making them promising biomarkers for cancer diagnosis, prognosis, and treatment. In the present study, a biosensor based on surface-enhanced Raman spectroscopy (SERS) was developed for rapid, simple, and sensitive quantitative detection of intracellular circRNAs for the first time. A dual-signal SERS nanoprobe with a 4MBN and ROX signal molecule was fabricated, and the ROX signal intensity was used to determine the concentration of target circSATB2. 4MBN was used as an internal standard to calibrate the ROX signal, thereby achieving highly sensitive and reliable detection of the target circRNA with a limit of detection of 0.043 pM. Furthermore, the relatively high expression of circSATB2 in lung cancer cells compared to that in normal lung epithelial cells was successfully characterized by the proposed SERS imaging method, which is consistent with the results of standard reverse transcription-polymerase chain reaction (RT-PCR). Monitoring of specific circRNAs using this SERS-based biosensor is a promising method for cancer diagnosis and gene therapy.

Underwater image restoration via spatially adaptive polarization imaging and color correction

Polarization imaging is extensively employed in underwater image restoration owing to its effectiveness in eliminating backscattered light. However, the existing polarization-based imaging methods rely on the strong assumption that the degrees of polarization (DoPs) of the target and backscattered light remain constant. Additionally, these methods ignore wavelength-specific absorption phenomena. To address these challenges, we propose an underwater image restoration method via spatially adaptive polarization imaging and color correction. First, based on optimal polarization difference images, we propose a method for calculating the spatial DoP of the target. In this method, the polarization characteristics of the target and background are fully exploited, and a scoring function is designed to achieve an optimal differential image. Second, we propose an adaptive local optimization method to estimate the spatial DoP of backscattering, which involves dividing the image into multiple local regions, and then employing improved particle swarm optimization (IPSO) algorithms to obtain the optimal value of DoP for each region. Finally, we introduce an adaptive compensation method based on the channel with minimal color absorption to correct color shifts during underwater polarization imaging. Extensive experiments demonstrate that our method exhibits superior generalization capability and outstanding restoration performance compared to existing methods.

Evaluation of canalis sinuosus and accessory canal morphology by cone-beam computed tomography.

To evaluate canalis sinuosus (CS) and accessory canalis sinuosus (AC) morphology and their relationship with the impacted canine on cone-beam computed tomography (CBCT) images. The diameter and location of the CS, its distance from the nasal cavity (NC-CS), its distance from the buccal cortical plate (BC-CS), and its distance from the alveolar ridge crest (AR-CS) were evaluated on 1000 CBCT scans. The prevalence and termination of AC and the presence of impacted canines were also evaluated. CS was detected in 89 (8.9%) of 1000 CBCTs. The mean CS diameter was found as 1.34 ± 0.53mm. No statistically significant difference was found between gender, age, direction, and CS presence and diameter. CS was most frequently seen in regions 11 (23.6%) and 13 (23.6%). The average NC-CS, BC-CS, and AR-CS length was 6.14, 6.06 and 4.35mm, respectively. AC was detected in 22 patients (24.71%). There was no statistically significant difference between the presence of AC and gender, age, CS diameter, NC-CS, BC-CS, and AR-CS distance. BC-CS length and AR-CS length were statistically significantly higher in patients with impacted canines. It should be kept in mind that the CS diameter, NC-CS, BC-CS, and AR-CS distance may increase in the presence of an impacted canine and the integrity of the neurovascular structure should be preserved. The fact that the CS is often localized in the palatial region requires a detailed evaluation of the anterior maxillary region with three-dimensional imaging methods.

Leukocyte detection based on convolutional neural network fusion with color Fourier ptychographic microscopy

Abstract In the field of optical microscopic imaging, color Fourier Ptychographic Microscopy (FPM) technology has attracted much attention due to its advantages of large field of view, high resolution, and quantitative phase imaging. In this paper, a color FPM fusion algorithm based on deep learning is proposed in combination with Convolutional Neural Networks (CNN) and applied to leukocyte detection. Firstly, this paper introduces a fusion model of a convolutional neural network based on the traditional color FPM imaging method and fuses low-resolution color images and high-resolution grayscale images through a multilayer convolutional network. This method improves the quality of reconstructed images while reducing the reconstruction time. Secondly, this paper constructs a leukocyte detection dataset by using an improved color FPM reconstruction algorithm and builds a leukocyte detection system based on the YOLOv7 architecture. This paper shows that combining convolutional neural networks with color FPM technology can provide higher-quality reconstructed images in medical imaging and cell detection, which provides strong technical support for digital pathology and medical diagnosis.