Index Measurements Research Articles

MiR-21, miR-221, miR-29 and miR-34 are distinguishable molecular features of a metabolically unhealthy phenotype in young adults.

Discrepancies between the measurement of body mass index (BMI) and metabolic health status have been described for the onset of metabolic diseases. Studying novel biomarkers, some of which are associated with metabolic syndrome, can help us to understand the differences between metabolic health (MetH) and BMI. A group of 1469 young adults with pre-specified anthropometric and blood biochemical parameters were selected. Of these, 80 subjects were included in the downstream analysis that considered their BMI and MetH parameters for selection as follows: norm weight metabolically healthy (MHNW) or metabolically unhealthy (MUNW); overweight/obese metabolically healthy (MHOW) or metabolically unhealthy (MUOW). Our results showed for the first time the differences when the MetH status and the BMI are considered as global MetH statures. First, all the evaluated miRNAs presented a higher expression in the metabolically unhealthy group than the metabolically healthy group. The higher levels of leptin, IL-1b, IL-8, IL-17A, miR-221, miR-21, and miR-29 are directly associated with metabolic unhealthy and OW/OB phenotypes (MUOW group). In contrast, high levels of miR34 were detected only in the MUNW group. We found differences in the SIRT1-PGC1α pathway with increased levels of SIRT1+ cells and diminished mRNA levels of PGCa in the metabolically unhealthy compared to metabolically healthy subjects. Our results demonstrate that even when metabolic diseases are not apparent in young adult populations, MetH and BMI have a distinguishable phenotype print that signals the potential to develop major metabolic diseases.

Unoccupied aerial vehicles as a tool to map lizard operative temperature in tropical environments

AbstractTo understand how ectotherms will respond to warming temperatures, we require information on thermal habitat quality at spatial resolutions and extents relevant to the organism. Measuring thermal habitat quality is either limited to small spatial extents, such as with ground‐based 3D operative temperature (Te) replicas, representing the temperature of the animal at equilibrium with its environment, or is based on microclimate derived from physical models that use land cover variables and downscale coarse climate data. We draw on aspects of both these approaches and test the ability of unoccupied aerial vehicle (UAV) data (optical RGB) to predict fine‐scale heterogeneity in sub‐canopy lizard (Anolis bicaorum) Te in tropical forest using random forest models. Anolis bicaorum is an endemic, critically endangered, species, facing significant threats of habitat loss and degradation, and work was conducted as part of a larger project. Our findings indicate that a model incorporating solely air temperature, measured at the centre of the 20 × 20 m plot, and ground‐based leaf area index (LAI) measurements, measured at directly above the 3D replica, predicted Te well. However, a model with air temperature and UAV‐derived canopy metrics performed slightly better with the added advantage of enabling the mapping of Te with continuous spatial extent at high spatial resolutions, across the whole of the UAV orthomosaic, allowing us to capture and map Te across the whole of the survey plot, rather than purely at 3D replica locations. Our work provides a feasible workflow to map sub‐canopy lizard Te in tropical environments at spatial scales relevant to the organism, and across continuous areas. This can be applied to other species and can represent species within the same community that have evolved a similar thermal niche. Such methods will be imperative in risk modelling of such species to anthropogenic land cover and climate change.

Responses of Broiler Chickens Under Dexamethasone-Induced Stress with or Without Seleno Methionine and Vitamin E Supplementation

Heat stress poses a significant threat to the health and productivity of both humans and animals, with modern commercial broilers being particularly susceptible. This study explored the physiological responses of broiler chickens under chronic heat stress conditions with antioxidant supplementation. A total of 135 unsexed Arbor acre broiler chicks were randomly assigned to three experimental groups: control, dexamethasone (DEX) only, and dexamethasone with selenomethionine and vitamin E supplementation (DEX+SeMet+Vit.E) and replicated three times in a completely randomized design. The birds were exposed to daily doses of dexamethasone for 14 days. Temperature-Humidity Index (THI) measurements revealed severe heat stress in the afternoons, impacting (p<0.05) respiratory rates and body temperatures. Supplementation with selenomethionine and vitamin E improved (p<0.05) body temperature of the broiler chickens. DEX significantly (p<0.05) affected all growth performance indices (feed intake, weight gain and feed conversion ratio), despite antioxidant supplementation. SeMet and Vitamin E showed significantly (p<0.05) positive effect on leukocytes in spite of DEX administration. Intestinal morphometry revealed negative effects on villi area, perimeter, and width due to dexamethasone. It was concluded that dexamethasone influenced thermoregulation, growth, blood and intestinal indices in broiler chickens, with antioxidant supplementation with SeMet and Vitamin E partially ameliorating these effects.

Role of inferior vena cava assessment in volume management in acute kidney injury patients

BackgroundIntravascular volume assessment is critical to guiding volume management in patients with acute kidney injury (AKI). This study aimed to compare the impact of using inferior vena cava (IVC) diameter and collapsibility index (IVC-CI) measurements versus clinical assessment on the management of volume status and improvement of renal function in patients with AKI.MethodsThis prospective comparative study included 88 patients with AKI or AKI on top of chronic kidney disease (CKD) who were randomly allocated into two groups: In group 1, volume status was managed according to IVC assessment, while in group 2, volume status was managed according to clinical assessment. In addition, group 1 patients were assessed clinically and compared with IVC measurements in the same group.ResultsThere was moderate agreement between the IVC and clinical methods in diagnosing 86.4% of patients (P < 0.001) within group 1. The percentage of patients with edema increased post-treatment in group 2. There was no statistically significant difference between the two groups regarding renal recovery and survival.ConclusionBedside ultrasonographic IVC assessment is a non-invasive method that can facilitate volume management in AKI patients, helping to administer fluids more wisely without unintended excess fluid administration.

An interactive method based on multi-objective optimization for limited-angle CT reconstruction

Objective. Limited-angle x-ray computed tomography (CT) is a typical ill-posed inverse problem, leading to artifacts in the reconstructed image due to the incomplete projection data. Most iteration CT reconstruction methods involve optimization for a single object. This paper explores a multi-objective optimization model and an interactive method based on multi-objective optimization to suppress the artifacts of limited-angle CT. Approach. The model includes two objective functions on the dual domain within the data consistency constraint. In the interactive method, the structural similarity index measure (SSIM) is regarded as the value function of the decision maker (DM) firstly. Secondly, the DM arranges the objective functions of the multi-objective optimization model to be optimized according to their absolute importance. Finally, the SSIM and the simulated annealing (SA) method help the DM choose the desirable reconstruction image by improving the SSIM value during the iteration process. Main results. Simulation and real data experiments demonstrate that the artifacts can be suppressed by the proposed method, and the results were superior to those reconstructed by the other three reconstruction methods in preserving the edge structure of the image. Significance. The proposed interactive method based on multi-objective optimization shows some potential advantages over classical single object optimization methods.

Estimation of the mass density of biological matter from refractive index measurements

The quantification of physical properties of biological matter gives rise to novel ways of understanding functional mechanisms. One of the basic biophysical properties is the mass density (MD). It affects the dynamics in sub-cellular compartments and plays a major role in defining the opto-acoustical properties of cells and tissues. As such, the MD can be connected to the refractive index (RI) via the well known Lorentz-Lorenz relation, which takes into account the polarizability of matter. However, computing the MD based on RI measurements poses a challenge, as it requires detailed knowledge of the biochemical composition of the sample. Here we propose a methodology on how to account for assumptions about the biochemical composition of the sample and respective RI measurements. To this aim, we employ the Biot mixing rule of RIs alongside the assumption of volume additivity to find an approximate relation of MD and RI. We use Monte-Carlo simulations and Gaussian propagation of uncertainty to obtain approximate analytical solutions for the respective uncertainties of MD and RI. We validate this approach by applying it to a set of well-characterized complex mixtures given by bovine milk and intralipid emulsion and employ it to estimate the MD of living zebrafish (Danio rerio) larvae trunk tissue. Our results illustrate the importance of implementing this methodology not only for MD estimations but for many other related biophysical problems, such as mechanical measurements using Brillouin microscopy and transient optical coherence elastography.

Chaff jamming recognition and suppression based on semi‐realistic dataset and Pre‐Decluttering Dual‐Stage UNet

AbstractCountermeasures for chaff jamming have drawn great attention in the field of radar target detection and tracking. Current approaches for chaff jamming recognition and suppression exhibit limitations in practical effect, generalisation ability, and hybrid jamming handling. To address the above problems, the authors first transform the traditional 1D signal processing problem into a 2D semantic segmentation task and then solve it from the perspective of the dataset construction and algorithm design. For the dataset construction, the authors use both measured and simulated data to synthesise a more realistic labelled dataset (semi‐realistic dataset), which is also with good diversity due to its adjustable chaff interference background. For the algorithm design, the authors propose a Pre‐Decluttering Dual‐Stage UNet (D2UNet) to recognise and suppress chaff jamming in two stages successively, where the former provides prior attention masks for the latter. To further improve the performance of D2UNet, the authors also design a multi‐stage loss function to achieve progressive training. Extensive experimental results demonstrate that D2UNet delivers remarkable recognition accuracy (99.305%) and suppression performance (41.326 dB peak signal‐to‐jamming ratio, 0.9952 structure similarity index measure) on the semi‐realistic dataset. Its practical effect is further verified on measured data.

SM-CycleGAN: crop image data enhancement method based on self-attention mechanism CycleGAN

Crop disease detection and crop baking stage judgement require large image data to improve accuracy. However, the existing crop disease image datasets have high asymmetry, and the poor baking environment leads to image acquisition difficulties and colour distortion. Therefore, we explore the potential of the self-attention mechanism on crop image datasets and propose an innovative crop image data-enhancement method for recurrent generative adversarial networks (GANs) fused with the self-attention mechanism to significantly enhance the perception and information capture capabilities of recurrent GANs. By introducing the self-attention mechanism module, the cycle-consistent GAN (CycleGAN) is more adept at capturing the internal correlations and dependencies of image data, thus more effectively capturing the critical information among image data. Furthermore, we propose a new enhanced loss function for crop image data to optimise the model performance and meet specific task requirements. We further investigate crop image data enhancement in different contexts to validate the performance and stability of the model. The experimental results show that, the peak signal-to-noise ratio of the SM-CycleGAN for tobacco images and tea leaf disease images are improved by 2.13% and 3.55%, and the structural similarity index measure is improved by 1.16% and 2.48% compared to CycleGAN, respectively.

Fetal Echocardiographic Evaluation of Tricuspid Valve and Right Ventricular Function Including Global Longitudinal Strain in Hypoplastic Left Heart Syndrome and Association with Postnatal Outcomes.

Despite significant advancements in the care of patients with hypoplastic left heart syndrome (HLHS) morbidity and mortality remain high. Postnatal right ventricular dysfunction and tricuspid regurgitation (TR) are associated with worse outcomes in HLHS. We aim to determine if right ventricle functional parameters and TR on fetal echocardiogram are associated with postnatal outcomes in HLHS patients. Retrospective review was performed on all fetuses with HLHS from 2014 to 2022 at our institution. Initial and follow up fetal echocardiogram measurements of right ventricular myocardial performance index (MPI), fractional area change (FAC) and global longitudinal strain (GLS) were retrospectively measured. The presence and severity of TR was recorded from the fetal echocardiogram reports. Postnatal outcomes including transplant-free survival, hospital length of stay > 30days after initial palliation and need for bidirectional Glenn at < 4months were reviewed. Forty-three subjects met inclusion criteria. Mean gestational age at presentation was 26.1 ± 5.9weeks. Nine subjects died and 3 required heart transplantation. Initial fetal echocardiogram MPI was significantly lower (better) (0.36 ± 0.06 vs 0.44 ± 0.11; p = < 0.001) and FAC was significantly higher (better) (45 ± 6% vs 40 ± 8%; p = 0.035) in transplant-free survivors. Fetal right ventricular GLS and presence of mild TR were not associated with postnatal outcome. In fetuses with HLHS, abnormal MPI and right ventricular FAC are associated with decreased transplant-free survival. There was no observed association between GLS and postnatal outcomes. To our knowledge this is the first study examining fetal right ventricular function and GLS in HLHS patients and its link to postnatal outcomes.

Diffusion Generative Model-Based Learning for Smart Layer-Wise Monitoring of Additive Manufacturing

Abstract Despite the rapid adoption of deep learning models in additive manufacturing (AM), significant quality assurance challenges continue to persist. This is further emphasized by the limited availability of sample objects for monitoring AM-fabricated builds. Thus, this study advances an emerging diffusion generative model, i.e., the denoising diffusion implicit model (DDIM), for layer-wise image augmentation and monitoring in AM. The generative model can be used to generate potential layer-wise variations, which can be further studied to understand their causation and prevent their occurrence. The proposed models integrate two proposed kernel-based distance metrics into the DDIM framework for effective layer-wise AM image augmentation. These newly proposed metrics include a modified version of the kernel inception distance (m-KID) as well as an integration of m-KID and the inception score (IS) termed KID-IS. These novel integrations demonstrate great potential for maintaining both similarity and consistency in AM layer-wise image augmentation, while simultaneously exploring possible unobserved process variations. In the case study, six different cases based on both metal-based and polymer-based fused filament fabrication (FFF) are examined. The results indicate that both the proposed DDIM/m-KID and DDIM/KID-IS models outperform the four benchmark methods, including the popular denoising diffusion probabilistic models (DDPMs), and three other generative adversarial networks (GANs). Overall, DDIM/KID-IS emerges as the best-performing model with an average KID score of 0.840, m-KID score of 0.1185, peak signal-to-noise ratio (PSNR) of 18.150, and structural similarity index measure (SSIM) of 0.173, which demonstrated strong capabilities in generating potential AM process variations in terms of layer-wise images.

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

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.&#xD; &#xD;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.&#xD; &#xD;We propose a Temporal Image-to-Image Style Transfer Network (TIST-Net), consisting of an auto-encoder combined with convolutional long short-term memory (LSTM) 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 (CW-SSIM). We also perform a clinical evaluation, where experts are asked to rank images generated by multiple methods. &#xD; &#xD;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.&#xD; &#xD;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.

Orbital doppler ultrasound as an ancillary test for diagnosing brain death: A prospective, single blind comparative study

ObjectiveTranscranial Doppler ultrasound (TDUS), computed tomography angiography (CTA), and transcranial Doppler ultrasound to detect cerebral blood flow are among the adjunctive tests in diagnosing brain death. This study aimed to investigate the effectiveness of orbital doppler ultrasound (ODUS). MethodsThis prospective, single-blind study included 66 patients for whom brain death was to be diagnosed. Primary outcome measures were ODUS measurements, Ophthalmic artery peak systolic velocity (PSV), end-diastolic velocity (EDV), and resistive indices (RI) measurements recorded during the brain death determination process. Secondary outcome measures were computed tomography angio (CTA), transcranial Doppler ultrasound (TDUS), and demographic data. ResultsThis study investigating the effectiveness of ODUS in diagnosing brain death provided diagnostic success with 100% sensitivity and 93% specificity compared to CT angiography. It was noted that anatomical variations may limit its use. ConclusionODUS was found to have high sensitivity and specificity in the diagnosis of clinical brain death. It may assist in early prognostic assessment and shorten patient follow-up and diagnostic processes.

MRI super-resolution using similarity distance and multi-scale receptive field based feature fusion GAN and pre-trained slice interpolation network

Challenges arise in achieving high-resolution Magnetic Resonance Imaging (MRI) to improve disease diagnosis accuracy due to limitations in hardware, patient discomfort, long acquisition times, and high costs. While Convolutional Neural Networks (CNNs) have shown promising results in MRI super-resolution, they often don't look into the structural similarity and prior information available in consecutive MRI slices. By leveraging information from sequential slices, more robust features can be obtained, potentially leading to higher-quality MRI slices. We propose a multi-slice two-dimensional (2D) MRI super-resolution network that combines a Generative Adversarial Network (GAN) with feature fusion and a pre-trained slice interpolation network to achieve three-dimensional (3D) super-resolution. The proposed model requires consecutively acquired three low-resolution (LR) MRI slices along a specific axis, and achieves the reconstruction of the MRI slices in the remaining two axes. The network effectively enhances both in-plane and out-of-plane resolution along the sagittal axis while addressing computational and memory constraints in 3D super-resolution. The proposed generator has a in-plane and out-of-plane Attention (IOA) network that fuses both in-plane and out-plane features of MRI dynamically. In terms of out-of-plane attention, the network merges features by considering the similarity distance between features and for in-plane attention, the network employs a two-level pyramid structure with varying receptive fields to extract features at different scales, ensuring the inclusion of both global and local features. Subsequently, to achieve 3D MRI super-resolution, a pre-trained slice interpolation network is used that takes two consecutive super-resolved MRI slices to generate a new intermediate slice. To further enhance the network performance and perceptual quality, we introduce a feature up-sampling layer and a feature extraction block with Scaled Exponential Linear Unit (SeLU). Moreover, our super-resolution network incorporates VGG loss from a fine-tuned VGG-19 network to provide additional enhancement. Through experimental evaluations on the IXI dataset and BRATS dataset, using the peak signal-to-noise ratio (PSNR), structural similarity index measure (SSIM) and the number of training parameters, we demonstrate the superior performance of our method compared to the existing techniques. Also, the proposed model can be adapted or modified to achieve super-resolution for both 2D and 3D MRI data.

Construction and characterization of a modA gene mutant strain of Klebsiella pneumoniae

To construct a mutant strain of Klebsiella pneumoniae NTUH- K2044 with modA gene deletion and its complementary strain and explore the role of modA gene in modulating anaerobic nitrate respiratory growth and phenotypes of K. pneumoniae. The modA deletion mutant K. pneumoniae strain was constructed by homologous recombination using the suicide vector pKO3-Km. To obtain the complementary strain C-modA, the whole sequence fragment containing the promoter, open reading frame and terminator regions of modA was cloned into pGEM-T-easy and electrically transformed into the modA deletion mutant. The NTUH-K2044 wild-type strain, modA gene deletion mutant and complementary strain were compared by measuring in vitro anaerobic nitrate respiration growth, competitiveness index, biofilm quantification, mucoviscosity assay and morphological measurement using Image J. The modA deletion mutant strain ΔmodA and the complementary strain C-modA were successfully constructed. The modA gene knockout strain showed inhibited anaerobic nitrate respiratory growth compared with the wild- type and C-modA strains with significantly weakened competitiveness, reduced capacity of biofilm synthesis during anaerobiosis, and lowered mucoviscosity under anaerobic conditions. The ΔmodA strain showed a spherical morphology in anaerobic conditions as compared with the normal short rod-like morphology of K. pneumoniae, with also distinctly shorter length than the wild-type and C-modA strains. The molybdate transport system encoding gene modA is associated with the pathogenic capacity of K. pneumoniae by modulating its anaerobic nitrate respiration, competitiveness, biofilm formation, hypermucoviscous phenotype and morphology.

Experimental and Biological Approaches for Genotype X Environment Interactions Estimation for Wheat Genotypes Evaluated Under Multi Locational Trials

Genotypes VL907, HS562, HPW484 were ranked as topped three in comparison to the other during the evaluation of nine wheat genotypes at major locations of the north hills zone of the country under rain fed conditions. The least values of AMMI stability measure (ASV) had expressed the desirability of HPW484, HS562, VL2041 genotypes whereas the genotypes HS562, HPW484, VL2041 had been identified by least values of Modified Ammi Stability Value (MASV). The minimum value of simultaneous selection index measure based on the MASV (ssiMASV) had selected HS562, HPW484, VL2041 wheat genotypes while values of ssiWAASB measure found the suitability of HPW484, HS562, HS691 wheat genotypes. The composite non parametric measure NPi (2) had favoured the VL892, HS562 genotypes and values of NPi (3) measure had settled for VL892, HS562 genotypes while VL892, HPW349 wheat genotypes had been pointed by the last composite measure NPi (4). The Ward’s method of Hierarchical Clustering had placed the VL907 genotype in a separate group as compared to others. The shorter rays of measures IPC2, IPC5, IPC3, SD had reflected the less contribution of the joint effects of genotypes and measures in the biplot analysis. Non parametric composite measure NPi (1) had expressed tight direct relation with Si1,Si3,Si4,Si5,Si6,Si7 values. The values of IPC6 &amp; IPC4 had maintained the direct association with BLUP based analytic measures HMGV, RPGV, HMPRVG*Meanb, GAI, Meanb, RPGV*Meanb values. Moreover the values of CV measure had clustered with Si2, Si3, Si4,Si5, Si7 measures of this study.

Assessment of Serum Atherogenic Indices and Insulin Resistance in Retinal Vein Occlusion.

This study aimed to investigate serum atherogenic indices as novel cardiovascular risk factors associated with retinal vein occlusion (RVO). This retrospective case-control study included 57 patients with newly diagnosed RVO whose plasma lipid profile (low-density lipoprotein cholesterol [LDL-C], high-density lipoprotein cholesterol [HDL-C], total cholesterol [TC], and triglycerides [TG]) and insulin resistance were examined. Serum atherogenic indices (LDL-C/HDL-C, TC/HDL-C, TG/HDL-C, and non-HDL-C/HDL-C ratios) and presence of insulin resistance were compared between the patients and 63 healthy subjects. Cut-off values were determined by receiver operating characteristic curve analysis. The mean age of the RVO patients was 63.7±9.4 years. Plasma levels of LDL-C, HDL-C, TC, and TG showed no significant difference between the patient and control groups (p>0.05). However, LDL-C/HDL-C, non-HDL-C/HDL-C, and TC/HDL-C ratios were higher in the RVO group compared to healthy subjects (p=0.015, p=0.036, and p=0.015, respectively). Fasting insulin concentrations, plasma insulin, and homeostasis model assessment of insulin resistance (HOMA-IR) index were higher in the RVO patients compared to controls (p=0.003, p=0.001, and p=0.001, respectively). LDL-C/HDL-C, TC/HDL-C, and non-HDL-C/HDL-C ratios were found to be increased in RVO. Compared to the traditional plasma lipid profile, serum atherogenic indices were found to be superior predictors of RVO development. Measurement of HOMA-IR index should be taken into consideration in the evaluation of insulin resistance. High serum atherogenic indexes in RVO patients reveal the need to take precautions against the risk of cardiovascular disease and stroke.

Evaluation of angular, linear, and body index measures of pêga donkeys (Equus Asinus) from southeast of Brazil

The objective of this study was to perform body evaluation of Pêga donkeys (40 females and 10 males) from the Southeast Region of Brazil. Hippometer, measuring tape, and arthrogoniometer were used, considering the left antimere of the animals in a forced quadrupedal position. The analysis of variance showed a difference (p&lt;0.05) between males and females for chest width, CPr, withers height, croup height, body length, back-loin length, posterior pastern length, and the angle of the tibiometatarsal joint. The values for body length, anterior pastern length, scapulohumeral angle, and metacarpophalangeal joint differed (p&lt;0.05) between farms. Compactness index 1 and conformation index classified the animals as suited for saddle activities, and compactness index 2 classified them as suited for light drafting. The dactylothoracic index classified the animals into eumetric. Cannon load index was similar between sexes and between states. The load on the back by step index was 167.9±12.6 kg, and the LGI was 95.9±6.0 kg, equal between sexes and higher for the animals from RJ. It is concluded that the Pêga breed has sexual dimorphism and regional selection, interfering with morphometry and aptitude for saddle and light draft.

Effect of dolutegravir-based regimen on the body mass index of adolescents living with HIV in an African population: a pre-post design

ABSTRACT Dolutegravir, an integrase strand inhibitor, has been linked to an increase in body mass index among adults. However, little is known about its effect on adolescents. The study assessed the effect of a dolutegravir-based regimen on adolescents’ body weight and emphasized the gender-specific differences in weight gain, if any. This prospective cohort study was conducted among 182 treatment-experienced adolescents living with HIV at a tertiary hospital in Southeast Nigeria. The body mass index measurements were calculated before transitioning to dolutegravir and after six months of being on the dolutegravir regimen. A comparison of the difference in mean body mass index before and after dolutegravir-based therapy was determined using a paired t-test. At the end of the six-month follow-up, there was a significant increase in the respondents’ overall body mass index z-score (t = −10.00, p < 0.001). There was a significant increase in the body mass index z-score of both genders (male; t = −7.49, p < 0.001, female; t = −6.91, p < 0.001). The dolutegravir-based therapy (β-coefficient = −0.17, t = − 2.09, p < 0.001) and baseline body mass index z-score (β-coefficient = 0.76, t = 15.86, p < 0.001) were significant predictors of body mass index z-score at six months. In adolescents living with HIV, the transition to dolutegravir is associated with increased body weight. Female adolescents tend to gain more weight compared to males. Therefore, there is a need for regular monitoring of body weight for early detection of obesity and prompt intervention.

Hybrid image encryption using advanced least significant bit algorithm, chaotic maps and DNA encoding for digital healthcare

Security and privacy are major concerns in this modern world. Medical documentation of patient data needs to be transmitted between hospitals for medical experts opinions on critical cases which may cause threats to the data. Nowadays most of the hospitals use electronic methods to store and transmit data with basic security measures, but these methods are still vulnerable. There is no perfect solution that solves the security problems in any industry, especially healthcare. So, to cope with the arising need to increase the security of the data from being manipulated the proposed method uses a hybrid image encryption technique to hide the data in an image so it becomes difficult to sense the presence of data in the image while transmission. It combines Least Significant Bit (LSB) Algorithm using Arithmetic Division Operation along with Canny edge detection to embed the patient data in medical images. The image is subsequently encrypted using keys of six different chaotic maps sequentially to increase the integrity and robustness of the system. Finally, an encrypted image is converted into DNA sequence using DNA encoding rule to improve reliability. The experimentation is done on the Chest XRay image, Knee Magnetic Resonance Imaging (MRI) image, Neck MRI image, Lungs Computed Tomography (CT) Scan image datasets and patient medical data with 500 characters, 1000 characters and 1500 characters. And, it is evaluated based on time coefficient of encryption and decryption, histogram, entropy, similarity score (Mean Square Error), quality score (peak signal-to-noise ratio), motion activity index (number of changing pixel rate), unified average changing intensity, image similarity score (structure similarity index measurement) between original and encrypted images. Also, the proposed technique is compared with other recent state of arts methods for 500 characters embedding and performed better than those techniques. The proposed method is more stable and embeds comparatively more data than other recent works with lower Mean Square Error value of 4748.12 which is the main factor used to determine how well the data is hidden and cannot be interpreted easily. Also, it achieved a Peak Signal-Noise Ratio (PSNR) value of 71.34 dB, which is superior than other recent works, verifying that the image quality remains uncompromising even after being encrypted.

Design of floating point multiplier using approximate hybrid Radix-4/ Radix-8 booth encoder for image analysis

This paper presents a novel power efficient Hybrid floating point multiplier (HFPM) with approximate hybrid radix-4/radix-8 booth encoder. This approach efficiently increases the speed of operation and optimises hardware. Compared to existing HFPMs, the proposed HFPM utilises a novel hybrid radix-4/radix-8 booth encoder. The hybrid architecture reduces the computations by an average of k/3 for a k-bit 2’s complement representation by reducing the number of partial products. In addition, to achieve an area-efficient and power-efficient design, approximate computational units are added to the radix-8 booth encoder in the hybrid architecture with a slight reduction in accuracy. The efficiency of the approximate architecture is measured in scales of Error Rate (ER) and Normalized Error Distance (NED). The proposed design is implemented on Kintex 7 FPGA and realized on 45nm ASIC platforms. The experimental study shows that the proposed multiplier has utilised hardware (831 slice LUT, 828 LUT as logic) and consumed 6110 μm2 of area and 83 μW of power which is less as compared to existing floating point multipliers. The experimental results in terms of average Structural Similarity Index Measure (SSIM) is 0.98 which is better than the existing floating point multipliers are compared.