Index Measurements Research Articles

Generalized 3D registration algorithm for enhancing retinal optical coherence tomography images.

Optical coherence tomography (OCT) has emerged as the standard of care for diagnosing and monitoring the treatment of various ocular disorders due to its noninvasive nature and in vivo volumetric acquisition capability. Despite its widespread applications in ophthalmology, motion artifacts remain a challenge in OCT imaging, adversely impacting image quality. While several multivolume registration algorithms have been developed to address this issue, they are often designed to cater to one specific OCT system or acquisition protocol. We aim to generate an OCT volume free of motion artifacts using a system-agnostic registration algorithm that is independent of system specifications or protocol. We developed a B-scan registration algorithm that removes motion and corrects for both translational eye movements and rotational angle differences between volumes. Tests were carried out on various datasets obtained from two different types of custom-built OCT systems and one commercially available system to determine the reliability of the proposed algorithm. Additionally, different system specifications were used, with variations in axial resolution, lateral resolution, signal-to-noise ratio, and real-time motion tracking. The accuracy of this method has further been evaluated through mean squared error (MSE) and multiscale structural similarity index measure (MS-SSIM). The results demonstrate improvements in the overall contrast of the images, facilitating detailed visualization of retinal vasculatures in both superficial and deep vasculature plexus. Finer features of the inner and outer retina, such as photoreceptors and other pathology-specific features, are discernible after multivolume registration and averaging. Quantitative analyses affirm that increasing the number of averaged registered volumes will decrease MSE and increase MS-SSIM as compared to the reference volume. The multivolume registered data obtained from this algorithm offers significantly improved visualization of the retinal microvascular network as well as retinal morphological features. Furthermore, we have validated that the versatility of our methodology extends beyond specific OCT modalities, thereby enhancing the clinical utility of OCT for the diagnosis and monitoring of ocular pathologies.

Measurement of Load Index and Labor Needs Using FTE Method in RHI PT-PKC Department

Measurement of Load Index and Labor Needs Using FTE Method in RHI PT-PKC Department

The Effects of Natural Window Views in Classrooms on College Students’ Mood and Learning Efficiency

Observing peaceful natural environments has been shown to restore cognitive abilities and reduce stress. As a result, visual access to natural environments is becoming increasingly common in educational settings. However, most current research on classroom window views has examined classroom environments in elementary and secondary schools, and only some university classrooms have been used as study sites. This study investigated the relationship between the naturalness of university classroom window views and physiological and emotional responses and standardized tests of attentional focus (learning efficiency) in university students. Thirty participants (undergraduates 21.16 ± 1.01 years old) viewed architectural window views and natural window views for 3 min each, and physiological measures of EEG, HRV index, and psychometric measures of Semantic Differences Questionnaire and Emotional State Questionnaire generated data. Measurements were generated. The results indicated that the natural window view significantly enhanced theta, alpha, and beta waves of brain activity, provided a sense of comfort, relaxation, and pleasure, and increased learning efficiency compared to the architectural window view. The findings support the beneficial associations between window views on university campuses and students’ mood and learning efficiency, emphasizing the importance of considering natural window views in the planning and designing of university classrooms.

Contrast enhancement algorithm for infrared images based on atmospheric scattering model

With the rapid development of infrared technology, infrared cameras are widely used in the military, medical, and industrial fields. However, the thermal radiation information received by infrared cameras often undergoes degradation due to random factors such as scattering or reflection during transmission. Consequently, the final infrared image suffers from poor contrast and blurred vision. To address these problems, this paper proposes an infrared image contrast enhancement algorithm based on the atmospheric scattering model. Firstly, we modify the hypothesis of the dark channel prior theory, and the pseudo dark channel prior theory is deduced to meet the processing requirements of single-channel images. Secondly, we propose anti-target interference and filtering threshold method to accurately estimate the atmospheric illumination value and transmittance of the atmospheric scattering model. These methods effectively mitigate the influence of infrared targets on the atmospheric illumination value and eliminate the blocking artifact effect caused by pixel value jumps in the transmittance images. Furthermore, we employ adaptive histogram equalization with contrast limitation, highlighting the global contrast. This paper conducts experiments on the M3FD dataset and employs objective evaluations using Visible Edge (E), Figure Definition (FD), Peak Signal-to-Noise Ratio (PSNR), Information Entropy (IE), Structure Similarity Index Measure (SSIM), and Visual Information Fidelity (VIF). The results indicate that the proposed algorithm enhances the overall contrast of images while highlighting fine texture details, exhibiting good visual effects. Both subjective and objective evaluations outperform seven other contrast algorithms, demonstrating the effectiveness of the proposed method.

CycleGAN-Driven MR-Based Pseudo-CT Synthesis for Knee Imaging Studies

In the field of knee imaging, the incorporation of MR-based pseudo-CT synthesis holds the potential to mitigate the need for separate CT scans, simplifying workflows, enhancing patient comfort, and reducing radiation exposure. In this work, we present a novel DL framework, grounded in the development of the Cycle-Consistent Generative Adversarial Network (CycleGAN) method, tailored specifically for the synthesis of pseudo-CT images in knee imaging to surmount the limitations of current methods. Upon visually examining the outcomes, it is evident that the synthesized pseudo-CTs show an excellent quality and high robustness. Despite the limited dataset employed, the method is able to capture the particularities of the bone contours in the resulting image. The experimental Mean Absolute Error (MAE), Peak Signal-to-Noise Ratio (PSNR), Structural Similarity Index Measure (SSIM), Zero-Normalized Cross Correlation (ZNCC), Mutual Information (MI), Relative Change (RC), and absolute Relative Change (|RC|) report values of 30.4638 ± 7.4770, 28.1168 ± 1.5245, 0.9230 ± 0.0217, 0.9807 ± 0.0071, 0.8548 ± 0.1019, 0.0055 ± 0.0265, and 0.0302 ± 0.0218 (median ± median absolute deviation), respectively. The voxel-by-voxel correlation plot shows an excellent correlation between pseudo-CT and ground-truth CT Hounsfield units (m = 0.9785; adjusted R2 = 0.9988; ρ = 0.9849; p < 0.001). The Bland–Altman plot shows that the average of the differences is low ((HUCT−HUpseudo−CT = 0.7199 ± 35.2490; 95% confidence interval [−68.3681, 69.8079]). This study represents the first reported effort in the field of MR-based knee pseudo-CT synthesis, shedding light to significantly advance the field of knee imaging.

Real-Time Motion Blur Using Multi-Layer Motion Vectors

Traditional methods for motion blur, often relying on a single layer, deviate from the correct colors. We propose a multilayer rendering method that closely approximates the motion blur effect. Our approach stores motion vectors for each pixel, divides these vectors into multiple sample points, and performs a backward search from the current pixel. The color at a sample point is sampled if it shares the same motion vector as its origin. This procedure repeats across layers, with only the nearest color values sampled for depth testing. The average color sampled at each point becomes that of the motion blur. Our experimental results indicate that our method significantly reduces the color deviation commonly found in traditional approaches, achieving structural similarity index measures (SSIM) of 0.8 and 0.92, which represent substantial improvements over the accumulation method.

High circulating MIF levels indicate the association with atypical antipsychotic-induced adverse metabolic effects.

Atypical antipsychotics (AAPs) are primary medications for schizophrenia (SZ). However, their use is frequently associated with the development of adverse metabolic effects, and the mechanisms behind these negative effects remain inadequately elucidated. To investigate the role of macrophage migration inhibitory factor (MIF) in regulating antipsychotic-induced metabolic abnormalities, between 2017 and 2020, a cross-sectional study was conducted, involving 142 healthy individuals and 388 SZ patients undergoing treatment with either typical antipsychotic (TAP) or AAP medications. Symptoms of SZ patients were evaluated using the Positive and Negative Syndrome Scale (PANSS), and measurements of metabolic indices and plasma MIF levels were performed on all individuals. A significant increase in plasma MIF levels was observed in groups receiving five major AAP monotherapies in comparison to healthy controls (all p < 0.0001). There was no such increase shown in the group receiving TAP treatment (p > 0.05). Elevated plasma MIF levels displayed a notable correlation with insulin resistance (β = 0.024, p = 0.020), as well as with the levels of triglycerides (β = 0.019, p = 0.001) and total cholesterol (β = 0.012, p = 0.038) in the groups receiving AAPs. However, while the TAP group also displayed certain metabolic dysfunction compared to healthy controls, no significant association was evident with plasma MIF levels (all p > 0.05). In conclusion, plasma MIF levels exhibit a distinctive correlation with metabolic abnormalities triggered by AAPs. Hence, there is potential for further development of MIF as a distinctive marker for monitoring adverse metabolic effects induced by AAPs in clinical settings.

Correlation between the radial artery resistance index and the systemic vascular resistance index: a cross-sectional study.

Ultrasound measurement of the radial resistance index (RRI) in the anatomical snuffbox has been proposed as a useful method for assessing the systemic vascular resistance index (SVRI). This study aims to establish the correlation between SVRI measured by pulmonary artery catheter (PAC) and RRI. A cross-sectional study included all consecutive patients undergoing postoperative (POP) cardiac surgery with hemodynamic monitoring using PAC. Hemodynamic assessment was performed using PAC, and RRI was measured with ultrasound in the anatomical snuffbox. The Pearson correlation test was used to establish the correlation between RRI and SVRI measured using PAC. Hemodynamic behavior concerning RRI with a cutoff point of 1.1 (described to estimate under SVRI) was examined. Additionally, consistency between two evaluators was assessed for RRI using the intraclass correlation coefficient and Bland-Altman analysis. A total of 35 measurements were obtained. The average cardiac index (CI) was 2.73 ± 0.64L/min/m², and the average SVRI was 1967.47 ± 478.33 dyn·s·m²/cm5. The correlation between RRI and SVRI measured using PAC was 0.37 [95% CI 0.045-0.62]. The average RRI was 0.94 ± 0.11. RRI measurements > 1.1 had a mean SVRI of 2120.79 ± 673.48 dyn·s·m²/cm5, while RRI measurements ≤ 1.1 had a mean SVRI of 1953.1 ± 468.17 dyn·s·m²/cm5 (p = 0.62). The consistency between evaluators showed an intraclass correlation coefficient of 0.88 [95% CI 0.78-0.93], and Bland-Altman analysis illustrated adequate agreement of RRI evaluators. For patients in cardiac surgery POP, the correlation between the SVRI measured using PAC and the RRI measured in the anatomical snuffbox is low. Using the RRI as a SVRI estimator for patients is not recommended in this clinical scenario.

TIST-Net: style transfer in dynamic contrast enhanced MRI using spatial and temporal information

Objective. Training deep learning models for image registration or segmentation of dynamic contrast enhanced (DCE) MRI data is challenging. This is mainly due to the wide variations in contrast enhancement within and between patients. To train a model effectively, a large dataset is needed, but acquiring it is expensive and time consuming. Instead, style transfer can be used to generate new images from existing images. In this study, our objective is to develop a style transfer method that incorporates spatio-temporal information to either add or remove contrast enhancement from an existing image. Approach. We propose a temporal image-to-image style transfer network (TIST-Net), consisting of an auto-encoder combined with convolutional long short-term memory networks. This enables disentanglement of the content and style latent spaces of the time series data, using spatio-temporal information to learn and predict key structures. To generate new images, we use deformable and adaptive convolutions which allow fine grained control over the combination of the content and style latent spaces. We evaluate our method, using popular metrics and a previously proposed contrast weighted structural similarity index measure. We also perform a clinical evaluation, where experts are asked to rank images generated by multiple methods. Main Results. Our model achieves state-of-the-art performance on three datasets (kidney, prostate and uterus) achieving an SSIM of 0.91 ± 0.03, 0.73 ± 0.04, 0.88 ± 0.04 respectively when performing style transfer between a non-enhanced image and a contrast-enhanced image. Similarly, SSIM results for style transfer from a contrast-enhanced image to a non-enhanced image were 0.89 ± 0.03, 0.82 ± 0.03, 0.87 ± 0.03. In the clinical evaluation, our method was ranked consistently higher than other approaches. Significance. TIST-Net can be used to generate new DCE-MRI data from existing images. In future, this may improve models for tasks such as image registration or segmentation by allowing small training datasets to be expanded.

Application of Multi-Level Thresholding Fuzzy Entropy and Differential Evolution Techniques to Medical Image Compression

Each storage medium possesses a capacity that is vital to technological progress in supporting internet speed for downloading extensive data. Effective storage media management includes compressing image data to optimize available space utilization for efficient usage. Digital processing of image data for each patient over a specific duration is crucial. This study proposes medical image compression utilizing multi-level thresholding employing the fuzzy entropy and differential evolution (FE-DE) algorithms. The compression results are subsequently assessed using the Peak Signal to Ratio (PSNR), Structural Similarity Index Measure (SSIM), and Feature Similarity Index Measure (FSIM) to appraise the algorithm's performance and the quality of the compressed images. Employing Fuzzy Compression yields increasing PSNR, SSIM, and FSIM values as the threshold level rises, with the highest values achieved at threshold level 40. The increase in value is in line with the visual quality and compressed image produced.

Refractive Index and Dispersion Measurement Principle with Polarization Change in Total Internal Reflection

Refractive Index and Dispersion Measurement Principle with Polarization Change in Total Internal Reflection

An inversion method for calculating formation and borehole parameters in X-ray lithology density logging

The use of X-ray sources in place of the 137Cs sources used in traditional lithology density logging methods has become a new trend in the development of nuclear logging techniques. How to eliminate the effects of drilling fluids or mudcake in the measurement process is a key question that determines the accuracy of measurement. In order to reduce the effects of mudcake and improve the accuracy of measurement of formation parameters, this paper presents an inversion method that can accurately calculate formation and borehole parameters and is suitable for X-ray lithology density logging. The general process of this inversion method is described below. First, a response model for broad-beam attenuation during X-ray lithology density logging is derived. Subsequently, the responses of four detectors under various formation and borehole conditions are studied by means of Monte Carlo simulation, and the energy spectra measured by each detector are divided into four energy windows (ranges) depending on the correlation with formation parameters. Finally, accurate values of formation and borehole parameters are obtained through iterative inversion using the Levenberg-Marquardt (LM) algorithm. The results of this study show that compared with previously established analysis methods, the inversion method based on forward modeling can effectively improve the accuracy of measurement of formation density and lithology index during X-ray lithology density logging, reduce the influence of the borehole environment, and overcome the deficiencies of data processing techniques based on the spine and ribs plot.

Adiposity trajectories and cardiovascular disease risk in women: a population-based cohort study with a focus on menopausal status

ObjectivesA single measurement of adiposity indices could predict the incidence of cardiovascular disease (CVD); nonetheless their long-term pattern and its association with incident CVD are rarely studied. This study aimed to determine distinct trajectories of adiposity indices among participants of Tehran Lipid and Glucose Study (TLGS) and their association with incident CVD. Furthermore, this study aimed to investigate whether this association differed among individuals according to their menopausal status.MethodA total of 6840 women participated in TLGS, aged 20 years and older were included in this study; they were followed for a median of 16 years. Body mass index (BMI), waist circumference (WC), conicity index (CI) and body roundness index (BRI) were included in the analysis as adiposity indices. The cohort outcome panel of medical specialists identified the CVD outcomes. Trajectory analyses were used to identify homogeneous distinct clusters of adiposity indices trajectories. The association between the trajectory group membership and incident CVD were explored by Cox proportional hazard models, with unadjusted and adjusted model for baseline age, physical activity, smoking status, menopause and family history of CVD.ResultsThree BMI trajectory groups of low, medium, and high and two trajectories for WC, BRI and CI were identified. Adjusted cox proportional hazard models revealed significant associations between the hazard of CVD experience and the high trajectory group of the BMI (HR: 2.06, 95% CI: 1.38-3.07), WC (HR: 2.71, 95% CI: 1.98-3.70), CI (HR: 1.87, 95% CI: 1.26-2.77) and BRI (HR: 1.55-95% CI: 1.12-2.15), compared to the low trajectory group. Subgroup analysis based on the menopausal status of participants showed that the HR of CVD incidences for all of trajectories adiposity indices, except BMI, was statistically significant. Adjusted cox proportional hazard models, in those women not reached menopause during study, revealed that the HR (95% CI) of CVD incidences for high trajectory of BMI, WC, CI and BRI were 2.80 (1.86-7.05); 2.09 (1.40-6.16); 1.72 (1.42-5.61), and 3.09 (1.06-9.01), respectively. These values for those were menopause at the initiation of the study were 1.40 (1.11, 2.53); 1.65 (1.04-2.75); 1.69 (1.01-2.87), and 1.61 (0.98-2.65), respectively.ConclusionOur findings suggest that adiposity trajectories, particularly central adiposity index of CI, could precisely predict the CVD risk. Consequently, preventive strategies should be tailored accordingly.

Transcriptome and metabolome analyses reveal the regulatory role of MdPYL9 in drought resistance in apple

BackgroundThe mechanisms by which the apple MdPYL9 gene mediates the response to drought stress remain unclear. Here, transcriptome and metabolome analyses of apple plants under drought were used to investigate the mechanisms by which MdPYL9 regulates the response to drought stress in apple. MdPYL9-overexpressed transgenic and non-transgenic apple histoculture seedlings were rooted, transplanted, and subjected to drought treatments to clarify the mechanisms underlying the responses of apples to drought stress through phenotypic observations, physiological and biochemical index measurements, and transcriptomic and metabolomic analyses.ResultsUnder drought stress treatment, transgenic plants were less affected by drought stress than non-transgenic plants. Decreases in the net photosynthetic rate, stomatal conductance, and transpiration rate of transgenic apple plants were less pronounced in transgenic plants than in non-transgenic plants, and increases in the intercellular CO2 concentration were less pronounced in transgenic plants than in non-transgenic plants. The relative electrical conductivity and content of malondialdehyde, superoxide anion, and hydrogen peroxide were significantly lower in transgenic plants than in non-transgenic plants, and the chlorophyll content and activities of antioxidant enzymes (superoxide dismutase, peroxidase, and catalase) were significantly higher in transgenic plants than in non-transgenic plants. The number of differentially expressed genes (DEGs) involved in the response to drought stress was lower in transgenic plants than in non-transgenic plants, and the most significant and highly annotated DEGs in the transgenic plants were involved in the flavonoid biosynthesis pathway, and the most significant and highly annotated DEGs in control plants were involved in the phytohormone signal transduction pathway. The number of differentially accumulated metabolites involved in the response to drought stress was lower in transgenic plants than in non-transgenic plants, and up-regulated metabolites were significantly enriched in apigenin-7-O-glucoside in transgenic plants and in abscisic acid in non-transgenic plants. In the flavonoid biosynthetic pathway, the expression of genes encoding chalcone synthase (CHS) and chalcone isomerase (CHI) was more significantly down-regulated in non-transgenic plants than in transgenic plants, and the expression of the gene encoding 4-coumarate-CoA ligase (4CL) was more significantly up-regulated in transgenic plants than in non-transgenic plants, which resulted in the significant up-regulation of apigenin-7-O-glucoside in transgenic plants.ConclusionsThe above results indicated that the over-expression of MdPYL9 increased the drought resistance of plants under drought stress by attenuating the down-regulation of the expression of genes encoding CHS and CHI and enhancing the up-regulated expression of the gene encoding 4CL, which enhanced the content of apigenin-7-O-glucoside.

Opening the dialogue: A preliminary exploration of hair color, hair cleanliness, light, and motion effects on fNIRS signal quality.

Functional near-infrared spectroscopy (fNIRS) is a promising tool for studying brain activity, offering advantages such as portability and affordability. However, challenges in data collection persist due to factors like participant physiology, environmental light, and gross-motor movements, with limited literature on their impact on fNIRS signal quality. This study addresses four potentially influential factors-hair color, hair cleanliness, environmental light, and gross-motor movements-on fNIRS signal quality. Our aim is to raise awareness and offer insights for future fNIRS research. Six participants (4 Females, 2 Males) took part in four different experiments investigating the effects of hair color, hair cleanliness, environmental light, and gross-motor movements on fNIRS signal quality. Participants in Experiment 1, categorized by hair color, completed a finger-tapping task in a between-subjects block design. Signal quality was compared between each hair color. Participants in Experiments 2 and 3 completed a finger-tapping task in a within-subjects block design, with signal quality being compared across hair cleanliness (i.e., five consecutive days without washing the hair) and environmental light (i.e., sunlight, artificial light, no light, etc.), respectively. Experiment 4 assessed three gross-motor movements (i.e., walking, turning and nodding the head) in a within-subjects block design. Motor movements were then compared to resting blocks. Signal quality was evaluated using Scalp Coupling Index (SCI) measurements. Lighter hair produced better signals than dark hair, while the impact of environmental light remains uncertain. Hair cleanliness showed no significant effects, but gross motor movements notably reduced signal quality. Our results suggest that hair color, environmental light, and gross-motor movements affect fNIRS signal quality while hair cleanliness does not. Nevertheless, future studies with larger sample sizes are warranted to fully understand these effects. To advance future research, comprehensive documentation of participant demographics and lab conditions, along with signal quality analyses, is essential.

Proposal for a New Differential High-Sensitivity Refractometer for the Simultaneous Measurement of Two Refractive Indices and Their Differences

The refractive index of a liquid serves as a fundamental parameter reflecting its composition, thereby enabling the determination of component concentrations in various fields such as chemical research, the food industry, and environmental monitoring. Traditional methods for refractive index (RI) measurement rely on light deflection angles at interfaces between the liquid and a material with a known refractive index. In this paper, the authors present a new differential refractometer for the highly sensitive measurement of RI differences between two liquid samples. Using a configuration with two cells equipped with flat parallel plates as measuring elements, the instrument facilitates accurate analysis. Namely, the sensor signals from both the solution and the solvent cuvette are generated simultaneously with one laser pulse, reducing the possible fluctuations in the laser radiation intensity. Our evaluation shows the high sensitivity of RI measurements &lt; 7 × 10−6, so this differential refractometer can be proposed not only as a high-sensitivity sensing tool that can be used for mobile detection of nanoparticles in solution samples but also to determine the level of environmental nano-pollution using water (including rain, snow) samples from various natural as well as industrial sources, thus helping to solve some important environmental problems.

#540 Investigation of vascular sound visualization system using electronic stethoscope

Abstract Background and Aims The routine evaluation of VA involves visual, palpatory, and auscultatory examinations, as well as monitoring of venous pressure and other parameters. Surveillance indices include Flow Volume (FV) and Resistance Index (RI) obtained by Doppler ultrasound (DU) and Access Blood Flow (Qa) obtained by the Ultrasound Dilution Method (UDM) are well known. On the other hand, the MSS-U11C electronic stethoscope (Fig. 1) and dedicated application (Fig. 2) developed by Pioneer Inc. converts vascular sound into digital data and calculates the INDEX value obtained from the sound intensity (dB) analyzed by the vascular sound visualization system. In this study, we examined the correlation between INDEX values and conventional surveillance indices. Method 1. Eighty-three cases in which INDEX, FV, RI, and Qa were simultaneously measured in hemodialysis patients with AVF at the Artificial Kidney Department of Osaka Metropolitan University Hospital were included. 2. The measurement site for INDEX was just above the anastomosis of the AVF. 3. FV and RI were measured using a KONICA MINOLTA SONIMAGE HS2 diagnostic ultrasound system with the brachial artery as the measurement site. 4. Measurements were expressed in Median [IQR], and for statistical examination, Pearson's correlation coefficient was used for the correlation of each measurement, transformed to the natural logarithm to satisfy the assumption of normality of the residuals. The significance level for all hypothesis tests was 5%. For statistical analysis, R version 4.2.1 (https://www.r-project.org/foundation/) with the “rms” and “RcmdrPlugin.EZR ” packages were used. 5. This study protocol was conducted in accordance with the Principles of the Declaration of Helsinki. The ethics review boards of Osaka Metropolitan University approved the study (No. 2021-243). Opt-out consent was obtained instead of written informed consent. Results INDEX showed a significant positive correlation with Qa and FV (R = 0.449, P = 0.002 / R = 0.484, P &amp;lt; 0.001, respectively), INDEX showed a significant negative correlation with RI (R = −0.336, P = 0.002). Conclusion INDEX measurement using an electronic stethoscope is a monitoring index and showed a positive correlation with FV and Qa, which are established surveillance indices, and a weak negative correlation with RI. In Japan, FV and RI are commonly used as surveillance indices, but it has been reported that these indices can be underestimated or overestimated depending on the skill of the examiner, and that Qa requires specialized equipment and cannot be measured depending on the puncture site. We believe that monitoring by stethoscope is influenced by subjective factors of staff members, which may lead to differences in evaluation, but INDEX could become a more quantitative and standardized indicator. In addition, the electronic stethoscope could be used without any difference compared to the stethoscope currently in clinical use.

#2623 Homeostatic Model Assessment (HOMA) in pediatric patients with nephrotic syndrome receiving tacrolimus

Abstract Background and Aims Children with nephrotic syndrome (NS) receive diabetogenic drugs like tacrolimus (TAC) and steroids. TAC is toxic to pancreatic β-cells and suppresses insulin production in a time and dose dependent manner. This is well documented in renal transplant patients who develop post-transplant diabetes mellitus (PTDM). Although overt diabetes is uncommon in children receiving TAC for NS, the subclinical effect on β cell function has not been studied. Our aim was to study pancreatic beta cell function as assessed by homeostasis model assessment (HOMA) indices in children with NS who received TAC therapy for at least 1 year duration. Method We performed a prospective descriptive study where we followed children (1-18 years) with NS who received TAC therapy for at least 1 year. We noted patient demographics, clinical and histologic pattern of NS, cumulative steroid dose (mg/kg/day) received 90 days prior and TAC C0 levels. Fasting insulin, blood sugar to calculate HOMA indices for IR (insulin resistance), HOMA % β for beta cell function, HOMA-IS (insulin sensitivity), C-peptide and HbA1C were done once at study inclusion and yearly intervals when required. Statistical analysis included Pearson's correlation, paired t test and regression analysis. Results We performed HOMA indices in 37 children (19 males, 18 females) between September 2021-February 2023. Six patients had 2 measurements during the study period. The mean age of onset of NS was 28 months (95% CI [20; 36]) and mean age at HOMA indices measurement was 69 months (95% CI [59; 79]). Of the 37 patients, 54% (20/37) had late steroid resistance, 32% (12/37) had early steroid resistance, 8% (3/37) had steroid dependent NS, 6% (2/37) had frequently relapsing NS. Kidney biopsy performed in 36 children showed minimal change disease in 55.5% (20/36) and focal segmental glomerulosclerosis in 44.5% (16/36). The mean TAC duration, TAC Co level and mean cumulative steroid dose received in last 90 days were 27 months (95% CI [22;32]), 4.54 ng/ml (95% CI [4.12;4.97]) and 0.36 mg/kg/d (95% CI [0.28;0.44]) respectively. Eleven (30%) patients had cutaneous signs of insulin resistance like acanthosis nigricans and 9 (24%) patients had fasting blood sugar of &amp;gt;100 mg/dl. In Pearson's correlation analysis we found positive correlation between TAC Co level, HOMA IR (r = 0.4, P = 0.008) and TAC Co, HOMA %β (r = 0.44, P = 0.003) and cumulative steroid dose, fasting blood glucose (r = 0.3, P = 0.46). There was negative correlation between TAC Co level, HOMA IS (r = −0.4, P = 0.007). In regression analysis, only TAC Co level significantly influenced all HOMA indices including insulin resistance, insulin sensitivity and beta cell function. A HbA1C of &amp;gt; 5.7 (pre-diabetes range) was found in 32.4% (12/37) patients which warrants further monitoring. C-peptide levels were significantly higher after 12 months of TAC therapy in 6 patients who had two measurements (p = 0.032). Conclusion Tacrolimus therapy for nephrotic syndrome in children can affect pancreatic beta cell function as assessed by HOMA indices. Long term monitoring of pancreatic beta cell function in children with NS on TAC is needed to assess the full extent of these subclinical alterations.

Non-systematic noise reduction framework for ToF camera

Time-of-flight (ToF) cameras enable a diverse range of applications due to their high frame rate, high resolution, and low cost. However, these cameras suffer from non-systematic noise during the acquisition of high-quality depth images, severely affecting their range accuracy. In this paper, we propose a non-systematic noise reduction framework named “DCS2Noise” to address this issue. This framework comprises a three-stage denoising strategy, involving noise standardization, deep learning-based differential correlation sampling (DCS) denoising and further enhancement with pairwise noise suppression. This framework directly captures and denoises DCS images during the ToF imaging process, making it more suitable for non-systematic noise reduction in ToF cameras. Compared to traditional methods, our approach significantly reduces the root mean squared error (RMSE) and improves the noise reduction ratio, peak signal to noise ratio (PSNR), and structural similarity index measure (SSIM). We believe that this study provides new insights into understanding noise in ToF cameras and offers effective references for reducing non-systematic noise in three-dimensional measuring instruments.

Multimodality Image Segmentation Embedded with Metasurface Encoder

AbstractFeature dimensionality plays an important role for modern medical diagnosis and image processing. In this work, the study introduces an optoelectronic neural network for multimodal image segmentation, which dramatically improves computing speed and decreases imaging acquisition cost in brain tumor diagnostics. Multi‐layer metasurfaces are utilized as an image preprocessor that reduces image dimensionality at the physical layer. The low‐dimensional image is then processed to a U‐Net semantic segmentation network, to handle the complex and heterogeneous nature of brain image data. By using diffractive neural network, the metasurface encoder is optimized and physically constructed with high‐efficiency transmission metasurfaces. The entire optoelectronic network attains a structural similarity index measure (SSIM) of 96%, demonstrating its potential to revolutionize on‐site medical image processing with its high precision in segmenting brain imaging data.