Visual Evaluation Research Articles

Barriers and Facilitators to Shared Decision-Making for HPV Self-Sampling in LMICs: A Thematic Analysis

Cervical cancer is the second most common cancer among women in lower-middle-income countries (LMICs), contributing to the disproportionately high number of disability-adjusted life years in these regions.1,2 Shared decision-making (SDM) is a healthcare model that promotes patient-centered care by involving patients and family caregivers in collaborative treatment decision-making.3 Despite relatively high SDM awareness in high-income countries (HICs), seven studies have highlighted low awareness among healthcare professionals in LMICs, with cultural and operational barriers impeding its practice.4 The HPV-Automated Visual Evaluation (PAVE) Study is a multinational initiative focused on improving cervical cancer prevention in resource-constrained regions.5 This study aims to investigate stakeholders' understanding, views, and attitudes towards cervical cancer prevention and screening, and their preferences for receiving information and participating in decision-making.5 This will be achieved by analyzing the qualitative interviews with scientific experts and healthcare providers from the four participating sites (Brazil, El Salvador, Nigeria, and Tanzania).5 Thematic analysis is being applied to the interview transcripts to identify common themes and patterns, and enhance our understanding of stakeholder perspectives. Due to structural and cultural differences between HICs and LMICs, we expect that tailoring SDM to address specific barriers in LMICs will enhance its acceptance and utilization, leading to improved patient outcomes and satisfaction in cervical cancer prevention and treatment. If validated, the findings from the PAVE study may inform the widespread adoption and enhancement of cervical cancer prevention programs on a global scale. References Bray F, Ferlay J, Soerjomataram I, Siegel RL, Torre LA, Jemal A. Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries [published correction appears in CA Cancer J Clin. 2020 Jul;70(4):313. doi: 10.3322/caac.21609]. CA Cancer J Clin. 2018;68(6):394-424. doi:10.3322/caac.21492 Momenimovahed, Z., Mazidimoradi, A., Maroofi, P., Allahqoli, L., Salehiniya, H., & Alkatout, I. (2023). Global, regional and national burden, incidence, and mortality of cervical cancer. Cancer reports (Hoboken, N.J.), 6(3), e1756. https://doi.org/10.1002/cnr2.1756 Faiman B, Tariman JD. Shared Decision Making: Improving Patient Outcomes by Understanding the Benefits of and Barriers to Effective Communication. Clin J Oncol Nurs. 2019;23(5):540-542. doi:10.1188/19.CJON.540-542 Sam S, Sharma R, Corp N, Igwesi-Chidobe C, Babatunde OO. Shared decision making in musculoskeletal pain consultations in low- and middle-income countries: a systematic review. Int Health. 2020;12(5):455-471. doi:10.1093/inthealth/ihz077 de Sanjosé S, Perkins RB, Campos NG, et al. Design of the HPV-Automated Visual Evaluation (PAVE) Study: Validating a Novel Cervical Screening Strategy. Preprint. medRxiv. 2023;2023.08.30.23294826. Published 2023 Oct 23. doi:10.1101/2023.08.30.23294826

131I]6ß-Iodomethyl-19-norcholesterol SPECT/CT for the Lateralization of Mineralocorticoid Overproduction in Primary Aldosteronism.

In 42 consecutive patients with confirmed primary aldosteronism, molecular imaging has been performed. After dexamethasone suppression of the non-affected adrenal tissue, 37 MBq [131I]6ß-iodomethyl-19-norcholesterol was injected i.v., and SPECT/CT images were taken 7 days later. Based on the visual evaluation of the images by two nuclear medicine specialists, patients with one-sided tracer accumulation underwent adrenalectomy. To identify a SPECT/CT parameter that best characterizes the side difference, the maximum counts and the mean counts of spherical VOIs were analyzed. Of the 42 patients, 24 had one-sided aldosterone overproduction by SPECT/CT. After surgical removal of the involved adrenal, all 24 patients with SPECT/CT-identified unilateral aldosteronism achieved biochemical cure, defined as a normalized potassium level combined with an aldosterone-to-renin ratio ≤ 30. To identify the best measurable parameter of SPECT/CT side difference, the mean counts and maximum counts of a series of spherical VOIs of different diameters were analyzed. The ratio of the mean counts of 3 cm spherical VOIs of the right and left adrenal regions (lateralization index) was the best discriminator; a ratio of ≥1.29 was characteristic of one-sided disease, without overlap between the one-sided and two-sided patient groups. [131I]6ß-iodomethyl-19-norcholesterol SPECT/CT with a count-based image interpretation and side-ratio calculation may be an equipollent non-invasive substitute for adrenal venous sampling in the lateralization of mineralocorticoid overproduction. It reliably identifies unilateral disease and facilitates patients' selection for surgical intervention. If confirmed by others, this functional imaging may replace AVS when lateralization is required for management decisions in primary aldosteronism.

Impact of Display Sub-Pixel Arrays on Perceived Gloss and Transparency.

In recent years, improvements in display image quality have made it easier to perceive rich object information, such as gloss and transparency, from images, known as shitsukan. Do the different display specifications in the world affect their appearance? Clarifying the effects of differences in pixel structure on shitsukan perception is necessary to realize shitsukan management for displays with different hardware structures, which has not been fully clarified before. In this study, we experimentally investigated the effects of display pixel arrays on the perception of glossiness and transparency. In a visual evaluation experiment, we investigated the effects of three types of sub-pixel arrays (RGB, RGBW, and PenTile) on the perception of glossiness and transparency using natural images. The results confirmed that sub-pixel arrays affect the appearance of glossiness and transparency. A general relationship of RGB > PenTile > RGBW for glossiness and RGB > RGBW > PenTile for transparency was found; however, detailed analysis, such as cluster analysis, confirmed that the relative superiority of these sub-pixel arrays may vary depending on the observer and image content.

Lung disease prediction based on CT images using REInf-net and world cup optimization based BI-LSTM classification

ABSTRACT A major global source of disability as well as mortality is respiratory illness. Though visual evaluation of computed tomography (CT) images and chest radiographs are a primary diagnostic for respiratory illnesses, it is limited in its ability to assess severity and predict patient outcomes due to low specificity and fundamental infectious organisms. In order to address these problems, world cup optimization-based Bi-LSTM classification and lung disease prediction on CT images using REINF-net were employed. To enhance the image quality, the gathered lung CT images are pre-processed using Lucy Richardson and CLAHE algorithms. For the purpose of lung infection segmentation, the pre-processed images are segmented using the REInf-net. The GLRLM method is used to extract features from the segmented images. In order to predict lung disease in CT images, the extracted features are trained using the Bi-LSTM based on world cup optimization. Accuracy, Precision, recall, Error and Specificity for the proposed model are 97.8%, 96.7%, 96.7%, 2.2% and 98.3%. These evaluated values are contrasted with the results of existing methods like WCO-BiLSTM, MLP, CNN and LSTM. Finally, the Lung disease prediction based on CT images using REINF-Net and world cup optimization based BI-LSTM classification performs better than the existing model.

Skin cancer classification leveraging multi-directional compact convolutional neural network ensembles and gabor wavelets

Skin cancer (SC) is an important medical condition that necessitates prompt identification to ensure timely treatment. Although visual evaluation by dermatologists is considered the most reliable method, its efficacy is subjective and laborious. Deep learning-based computer-aided diagnostic (CAD) platforms have become valuable tools for supporting dermatologists. Nevertheless, current CAD tools frequently depend on Convolutional Neural Networks (CNNs) with huge amounts of deep layers and hyperparameters, single CNN model methodologies, large feature space, and exclusively utilise spatial image information, which restricts their effectiveness. This study presents SCaLiNG, an innovative CAD tool specifically developed to address and surpass these constraints. SCaLiNG leverages a collection of three compact CNNs and Gabor Wavelets (GW) to acquire a comprehensive feature vector consisting of spatial–textural–frequency attributes. SCaLiNG gathers a wide range of image details by breaking down these photos into multiple directional sub-bands using GW, and then learning several CNNs using those sub-bands and the original picture. SCaLiNG also combines attributes taken from various CNNs trained with the actual images and subbands derived from GW. This fusion process correspondingly improves diagnostic accuracy due to the thorough representation of attributes. Furthermore, SCaLiNG applies a feature selection approach which further enhances the model’s performance by choosing the most distinguishing features. Experimental findings indicate that SCaLiNG maintains a classification accuracy of 0.9170 in categorising SC subcategories, surpassing conventional single-CNN models. The outstanding performance of SCaLiNG underlines its ability to aid dermatologists in swiftly and precisely recognising and classifying SC, thereby enhancing patient outcomes.

An adaptive low‐rank group sparse model based on edge‐preserving for eliminating mixed noise in SRTM

AbstractThe Shuttle Radar Topography Mission (SRTM) is a digital representation of the terrain surface morphology that contains rich terrain information and is widely used in environmental analyses. However, SRTM is adversely affected by mixed noise, which typically include random and stripe noise. Mixed noise results in the significant loss of topographic information, which reduce the validity of related research. To eliminate mixed noise in SRTM data, we propose an adaptive low‐rank group sparse model based on edge preservation (ALGS_EP) to remove mixed noise from datasets. The method relies on a low‐rank group sparse model that considers the gradient features of the terrain. It calculates a terrain factor to adapt the noise elimination model to terrain changes. Additionally, it integrates with the edge structure of elevation data and applies a double‐gradient constraint to preserve the structural details of the elevation data. The proposed model, built upon the alternating direction multiplier method framework, enhances the traditional weighted kernel paradigm minimization algorithm by introducing variable weights that adjust according to the gradient of elevation data during iterations. Additionally, it incorporates the correlation between strip noise and residual data blocks when computing the iteration count, ensuring an iterative solution approach that converges to the optimal solution. We used ALGS_EP to process global SRTM 1 data and published a higher‐quality and higher‐precision elevation dataset. The elevation data noise before and after noise elimination were statistically analyzed. Simulated and empirical results show that the model is highly robust and more effective than existing methods in both visual and quantitative evaluations. The noise elimination rate was 97.6%, compared to the original data. Therefore, this research was valuable for applications that use digital elevation model as an important data layer.

Выявление патогенов картофеля с использованием различных методов диагностики

Abstract. Potato diseases have far reaching implications for the agricultural industry as they not only reduce productivity and quality of potato crop but also negatively affect the livelihood of farmers. The purpose of the studies detection and identification of potato phytopathogens by visual assessment, real time polymerase chain reaction (PCR) and molecular genetic screening for phytopathogen resistance genes. Methods. Visual evaluation of each potato variety for major fungal, viral and bacterial diseases was carried out during the period of full sprouting, at plant height of 15–20 cm and in the phase of full flowering. The percentage of plants affected by diseases in relation to the total number of inspected plants was calculated. Diagnosis of resistance of potato samples to diseases was carried out on the basis of PCR. DNA markers of phytopathogen resistance genes YES3-3A, RYSC3, Ry186, TG 689, Gro1-4-1, Gpa2-2, PVX were used for molecular screening of potato. Infection of samples by PCR-RV (real-time) was determined using reagent kits of Syntol LLC Potato Virus X and Potato Virus Y, Potato spindle tuber viroid-PB, Clavibacter michiganensis subsp. sepedonicus-PB, Dickeya spp-PB. Results. According to the results of visual evaluation, the absence of symptoms of wrinkle mosaic in 20% of hybrids, mottle mosaic in 40%, mosaic leaf curl in 96 %, leaf curl in 72 hybrids, black leg in 100%, phytophthorosis in 88% of hybrids, alternaria in 84 %, rhizoctoniosis in 96 % of hybrids were observed. According to the results of PCR-RV analysis in 21 samples detected virus Y. The scientific novelty consists in the diagnosis of potato viruses using traditional visual assessment with the use of molecular markers that allow to identify correlation relationship between trhis methods.

Greenhouse Screening of Cotton Varieties Against Fusarium Wilt Complex

Fusarium oxysporum f. sp. vasinfectum race 1 and 2 (FOV) and the Meloidogyne incognita (southern root-knot nematode, RKN) complex can cause severe losses in cotton, and management recommendations include using varieties that are resistant to RKN. The objective of this project was to test the response of different varieties to the Fusarium wilt complex with and without RKN resistance. Four greenhouse trials were conducted, where cultivars infested with RKN and FOV were evaluated on (i) leaf wilt symptoms rated every 3 to 4 days to calculate the area under disease progress curve (AUDPC) and (ii) visual ratings of root and stem necrosis at the end of each trial. RKN-resistant cultivars PHY 480 W3FE and DP 1747NR B2XF had statistically similar AUDPC, root necrosis, and stem necrosis as RKN-susceptible cultivars DP 1522 B2XF and DP 1646 B2XF in all trials. This demonstrated that RKN resistance does not correlate to FOV resistance, and RKN resistance alone is not sufficient to manage Fusarium wilt. Cultivar ST 4946GLB2, which carries both RKN and FOV resistance, statistically had the lowest AUDPC, root necrosis, and stem necrosis in all greenhouse trials.

Association of quantitative histopathology measurements with antemortem medial temporal lobe cortical thickness in the Alzheimer’s disease continuum

The medial temporal lobe (MTL) is a hotspot for neuropathology, and measurements of MTL atrophy are often used as a biomarker for cognitive decline associated with neurodegenerative disease. Due to the aggregation of multiple proteinopathies in this region, the specific relationship of MTL atrophy to distinct neuropathologies is not well understood. Here, we develop two quantitative algorithms using deep learning to measure phosphorylated tau (p-tau) and TDP-43 (pTDP-43) pathology, which are both known to accumulate in the MTL and are associated with MTL neurodegeneration. We focus on these pathologies in the context of Alzheimer’s disease (AD) and limbic predominant age-related TDP-43 encephalopathy (LATE) and apply our deep learning algorithms to distinct histology sections, on which MTL subregions were digitally annotated. We demonstrate that both quantitative pathology measures show high agreement with expert visual ratings of pathology and discriminate well between pathology stages. In 140 cases with antemortem MR imaging, we compare the association of semi-quantitative and quantitative postmortem measures of these pathologies in the hippocampus with in vivo structural measures of the MTL and its subregions. We find widespread associations of p-tau pathology with MTL subregional structural measures, whereas pTDP-43 pathology had more limited associations with the hippocampus and entorhinal cortex. Quantitative measurements of p-tau pathology resulted in a significantly better model of antemortem structural measures than semi-quantitative ratings and showed strong associations with cortical thickness and volume. By providing a more granular measure of pathology, the quantitative p-tau measures also showed a significant negative association with structure in a severe AD subgroup where semi-quantitative ratings displayed a ceiling effect. Our findings demonstrate the advantages of using quantitative neuropathology to understand the relationship of pathology to structure, particularly for p-tau, and motivate the use of quantitative pathology measurements in future studies.

The impact of visual field defects following anterior temporal lobectomy

Objective: This prospective study aimed to quantify visual field defects (VFDs) along with other defects in ophthalmic functions after anterior temporal lobectomy (ATL) in patients with mesial temporal lobe epilepsy (MTLE) and their impact on quality of life using a VFQ-25 questionnaire. Methods: Ophthalmic evaluation was done during pre-operative evaluation as well as 3 months after surgery. It included the Best Corrected Visual Acuity (BCVA), visual field evaluation, colour vision examination, fundoscopy, extraocular movements and diplopia charting. Visual field evaluation was done using a Humphrey field analyser. The foveal threshold (FT), mean deviation (MD) and pattern standard deviation (PSD) were calculated for each patient. Quality of life assessment was done using the Visual Functioning Questionnaire-25 (VFQ-25). Two-tailed independent T-tests and Chi-square tests were performed for comparison of results. Results: Thirty-four patients were included in this study (23 men and 11 women). The mean age was 26.6 years (range 9-44 years). Post-operatively, none complained of a visual field disturbance. Thirty-one (91.17%) of 34 patients had a new quantifiable superior quadrantanopia. FT, MD and PSD of patients ranged between 29db to 40db, -1.23 to -14.28 and 1.75 to 15.34, respectively. There was no detectable abnormality in visual acuity, colour vision, fundoscopy, diplopia charting or extraocular movements. VFQ-25 scores ranged from 84 to 100. The difference between VFQ scores of patients with and without quadrantanopia was not significant. Conclusion: Standard ATL produces asymptomatic VFDs that do not affect quality of life. A combined effort of the neurologist and ophthalmologist is recommended to counsel the patient before surgery.

Multi-Dimensional Medical Image Fusion With Complex Sparse Representation.

In the field of medical imaging, the fusion of data from diverse modalities plays a pivotal role in advancing our understanding of pathological conditions. Sparse representation (SR), a robust signal modeling technique, has demonstrated noteworthy success in multi-dimensional (MD) medical image fusion. However, a fundamental limitation appearing in existing SR models is their lack of directionality, restricting their efficacy in extracting anatomical details from different imaging modalities. To tackle this issue, we propose a novel directional SR model, termed complex sparse representation (ComSR), specifically designed for medical image fusion. ComSR independently represents MD signals over directional dictionaries along specific directions, allowing precise analysis of intricate details of MD signals. Besides, current studies in medical image fusion mostly concentrate on addressing either 2D or 3D fusion problems. This work bridges this gap by proposing a MD medical image fusion method based on ComSR, presenting a unified framework for both 2D and 3D fusion tasks. Experimental results across six multi-modal medical image fusion tasks, involving 93 pairs of 2D source images and 20 pairs of 3D source images, substantiate the superiority of our proposed method over 11 state-of-the-art 2D fusion methods and 4 representative 3D fusion methods, in terms of both visual quality and objective evaluation. The source code of our fusion method is available at https://github.com/Imagefusions/imagefusions/tree/main.

Intrapapillary vessel density using optical coherence tomography angiography in primary open-angle glaucoma and normal eyes

Background: Optical coherence tomography angiography (OCTA) is used to quantify optic nerve blood flow in patients with primary open-angle glaucoma (POAG). Intrapapillary vessel density (iVD) has a high diagnostic accuracy for differentiating healthy from glaucomatous eyes. We compared the iVD of patients with POAG with that of healthy controls in an Egyptian tertiary referral center. Methods: This cross-sectional study consecutively recruited patients with medically controlled POAG and age- and sex-matched healthy individuals. All study participants underwent a detailed medical history evaluation and comprehensive ophthalmic examination, with recording of the cup-to-disc ratio (C/D ratio) and intraocular pressure (IOP). Humphrey visual field evaluation using the standard 24-2 program was performed and global indices, including mean deviation (MD) and pattern standard deviation (PSD), were extracted. OCTA and spectral-domain (SD) OCT images were obtained. Average thickness of the retinal nerve fiber layer (RNFL) and thicknesses in the superior, inferior, nasal, and temporal quadrants were recorded. OCTA imaging was used to measure vessel density, and the automatically processed data for iVD were extracted. Results: We included 86 eyes, 43 in the POAG and 43 in the healthy control group, with male predominance in both groups and mean (standard deviation [SD]) ages of 42.1 (9.4) and 39.3 (9.6) years, respectively. The two groups were comparable in terms of mean age, sex ratio, laterality of the included eyes, and mean IOP (all P > 0.05). The mean (SD) C/D ratio, MD, and PSD were significantly higher in the POAG group than in the control group (all P < 0.01). The mean (SD) average RNFL thickness and RNFL thicknesses in the four quadrants were significantly less in glaucomatous eyes than in healthy control eyes (all P < 0.05). Eyes with POAG had a significantly lower mean (SD) iVD than healthy control eyes (P < 0.01). Linear regression analysis revealed a significant positive correlation between iVD and average RNFL thickness (r = + 0.52; P < 0.001) and a significant negative correlation between iVD and PSD (r = - 0.31; P = 0.042) in eyes with POAG. Conclusions: The structural, vascular, and functional parameters measured in this study deteriorated in eyes with POAG compared to controls. Significant circumpapillary RNFL thinning correlated well with reduced iVD in eyes with POAG. Similarly, a lower iVD detected using OCTA had a significant inverse correlation with PSD in the perimetry of eyes with POAG. Further studies with additional parameters and longer follow-up periods are required to verify our preliminary findings.

Fluoxetine as a possible treatment for adult amblyopia: results of a double-blind, randomized, placebo-controlled trial

PurposeTo evaluate the effects of oral fluoxetine on visual acuity and visual-evoked potential (VEP) parameters in adults with amblyopia. MethodsIn this randomized clinical trial, adults (>18 years of age) with anisometropic or strabismic amblyopia were assigned randomly to a treatment (fluoxetine) group or a placebo group. Standard treatments for amblyopia (glasses prescription and patching) were prescribed for 4 months for all patients. The first group received fluoxetine (20 mg per day) and the second group received a placebo for 3 months. Visual acuity evaluation and VEP were performed before and after treatment. ResultsA total of 55 participants were included: 29 in the fluoxetine group and 26 in the placebo group. Mean age was 27.2 ± 8.6 years (18-54). The mean logMAR visual acuity of the amblyopic eye improved by 0.20 ± 0.24 (0-0.8) in the fluoxetine group (P < 0.001) and by 0.08 ± 0.15 (0-0.7) in the placebo group (P = 0.01); mean logMAR improvement was significantly higher in the fluoxetine group than in the placebo group (P = 0.04). At the end of the study, mean visual acuity of the fluoxetine group (0.36 ± 0.21 log MAR) was better than the placebo group (0.43 ± 0.35 log MAR). Among the VEP parameters, N75 amplitude did not change significantly in either group relative to baseline, but the changes were statistically significantly different between the two groups (P = 0.05); N135 latency improved from baseline in the fluoxetine group (P = 0.03). ConclusionsIn our study cohort, fluoxetine treatment for adult amblyopia resulted in greater improvement in visual acuity than placebo.

Visualization and quantitative evaluation of aerosol deposition using 3D-printed adult nose cavities

Local steroid medication is one of the most important treatment options for chronic rhinosinusitis. Regional deposition has a higher clinical value compared with total deposition in predicting treatment outcomes or evaluating adverse reactions. The goal of this project is to propose an effective technique for visualizing and quantifying aerosol deposition in a three-dimensional adult nasal cavity, and to verify the practicality of this method. Three-dimensional (3D) nasal cavity models were constructed from computed tomography (CT) scans of one post-operative rhinosinusitis subject using imaging software. The nasal cast was coated with a water-indicating paste and deposited with saline; a liquid dressing was added to visualize the progress. The quantity of liquid dressing was evaluated via HPLC and the liquid deposition was analyzed within the nasal cast cavity. Herein, 98.77% of the particles generated by the nebulizer were over 5 μm, suggesting that most of the aerosol could effectively enter the nasal cavity instead of the lower respiratory system. The liquid dressing was mainly deposited in the nasal cavity, ethmoid sinus, and frontal sinus according to the visualization tests. HPLC results suggested that the main deposits were the frontal sinus (up to 41.80%) as well as in the sphenoid sinus and ethmoid sinus (14.00%). The large particle nebulizer (BM-TCA) generally led to better deposition in sinus areas when compared to the smaller particle nebulizer (PARI). This technology allows for in vitro testing of various types of nasal preparations and equipment under various test methods.

Extending Cucumber Shelf Life: A Comparative Study of Low-Cost Drip Cooling Systems Using Different Fabric Coverings

Aims: Postharvest losses of cucumbers pose significant challenges for small-scale farmers in developing regions due to limited access to modern cold storage. This study investigated the effectiveness of low-cost indigenous drip coolers with different fabric coverings as an alternative preservation method. Study Design: Completely Randomized Design (CRD) with five treatments and three replications. Place and Duration of Study: Department of Crop Science, Surigao del Norte State University – Mainit Campus, Mainit, Surigao del Norte, Philippines on March 1-20, 2024. Methodology: Cucumber fruits were stored in indigenous drip coolers constructed with various recycled fabric coverings. Treatment combinations were: T1 - Control ambient temperature, T2 - Alpha Gina fabric, T3 - Silk satin bedsheets, T4 – Cotton comforter, and T5 - Cotton Blanket. The study evaluated color change, firmness, shriveling index, visual quality rating, weight loss, and damage rate over 20 days storage period. Results: Significant differences in cucumber fruit quality emerged at different time points across various parameters. Differences in color change and firmness were observed on day 15 (p = 0.023), while shriveling and visual quality showed differences by day 20 of storage (p = 0.042 and p = 0.042, respectively). However, in terms of weight loss, the Alpha gina fabric significantly had the highest loss among fabric covers used (p = 0.038). Consistently, the control ambient group exhibited the poorest quality, while treatments like the cotton comforter and silk satin bedsheet were most effective in preserving cucumber quality. Conclusion: This study demonstrates the effectiveness of indigenous drip coolers in preserving cucumber quality, with silk satin bedsheets and cotton comforter coverings consistently outperforming other materials and ambient conditions. These coolers were effective in slowing color change, maintaining firmness, reducing shriveling, and preserving overall visual quality. The study highlights the importance of fabric selection in drip cooler design, with cotton comforter and silk satin bedsheet coverings likely offering better air circulation, moisture retention, and insulation.

Radiogenomics: bridging the gap between imaging and genomics for precision oncology.

Genomics allows the tracing of origin and evolution of cancer at molecular scale and underpin modern cancer diagnosis and treatment systems. Yet, molecular biomarker-guided clinical decision-making encounters major challenges in the realm of individualized medicine, consisting of the invasiveness of procedures and the sampling errors due to high tumor heterogeneity. By contrast, medical imaging enables noninvasive and global characterization of tumors at a low cost. In recent years, radiomics has overcomes the limitations of human visual evaluation by high-throughput quantitative analysis, enabling the comprehensive utilization of the vast amount of information underlying radiological images. The cross-scale integration of radiomics and genomics (hereafter radiogenomics) has the enormous potential to enhance cancer decoding and act as a catalyst for digital precision medicine. Herein, we provide a comprehensive overview of the current framework and potential clinical applications of radiogenomics in patient care. We also highlight recent research advances to illustrate how radiogenomics can address common clinical problems in solid tumors such as breast cancer, lung cancer, and glioma. Finally, we analyze existing literature to outline challenges and propose solutions, while also identifying future research pathways. We believe that the perspectives shared in this survey will provide a valuable guide for researchers in the realm of radiogenomics aiming to advance precision oncology.

Patterns of glucose hypometabolism can help differentiate FTLD-FET from other types of FTLD.

FTLD-FET is a newly described subtype of frontotemporal lobar degeneration (FTLD characterized by pathologic inclusions of FET proteins: fused in sarcoma (FUS), Ewing sarcoma, and TATA-binding protein-associated factor 2N (TAF15)). Severe caudate volume loss on MRI has been linked to FTLD-FUS, yet glucose hypometabolism in FTLD-FET has not been studied. We assessed [18F] fluorodeoxyglucose PET (FDG-PET) hypometabolism in FTLD-FET subtypes and compared metabolism to FTLD-tau and FTLD-TDP. We retrospectively reviewed medical records of 26 autopsied FTLD patients (six FTLD-FET, ten FTLD-Tau, and ten FTLD-TDP) who had completed antemortem FDG-PET. We evaluated five regions, caudate nucleus, medial frontal cortex, lateral frontal cortex, and medial temporal using a 0-3 visual rating scale and validated our findings quantitatively using CORTEX-ID suite Z scores. Of the six FTLD-FET cases (three females) with median age at onset = 36, three were atypical FTLD-U (aFTLD-U) and three were neuronal intermediate filament inclusion disease (NIFID). bvFTD was the most common presentation. Four of the six FTLD cases (3 aFTLD-U + 1 NIFID) showed prominent caudate hypometabolism relatively early in the disease course. FTLD-tau and FTLD-TDP did not show early prominent caudate hypometabolism. Hypometabolism in medial and lateral temporal cortex was associated with FTLD-TDP, while FTLD-tau had normal-minimal regional metabolism. Prominent caudate hypometabolism, especially early in the disease course, appears to be a hallmark feature of the aFTLD-U subtype of FTLD-FET. Assessing caudate and temporal hypometabolism on FDG-PET will help to differentiate FTLD-FET from FTLD-tau and FTLD-TDP.

Nonrigid registration method for longitudinal chest CT images in COVID-19

Rationale and objectivesTo analyze morphological changes in patients with COVID-19-associated pneumonia over time, a nonrigid registration technique is required that reduces differences in respiratory phase and imaging position and does not excessively deform the lesion region. A nonrigid registration method using deep learning was applied for lung field alignment, and its practicality was verified through quantitative evaluation, such as image similarity of whole lung region and image similarity of lesion region, as well as visual evaluation by a physician. Materials and methodsFirst, the lung field positions and sizes of the first and second CT images were roughly matched using a classical registration method based on iterative calculations as a preprocessing step. Then, voxel-by-voxel transformation was performed using VoxelMorph, a nonrigid deep learning registration method. As an objective evaluation, the similarity of the images was calculated. To evaluate the invariance of image features in the lesion site, primary statistics and 3D shape features were calculated and statistically analyzed. Furthermore, as a subjective evaluation, the similarity of images and whether nonrigid transformation caused unnatural changes in the shape and size of the lesion region were visually evaluated by a pulmonologist. ResultsThe proposed method was applied to 509 patient data points with high image similarity. The variances in histogram characteristics before and after image deformation were confirmed. Visual evaluation confirmed the agreement between the shape and internal structure of the lung field and the natural deformation of the lesion region. ConclusionThe developed nonrigid registration method was shown to be effective for quantitative time series analysis of the lungs.

Visual evaluation of graph representation learning based on the presentation of community structures

Various graph representation learning models convert graph nodes into vectors using techniques like matrix factorization, random walk, and deep learning. However, choosing the right method for different tasks can be challenging. Communities within networks help reveal underlying structures and correlations. Investigating how different models preserve community properties is crucial for identifying the best graph representation for data analysis. This paper defines indicators to explore the perceptual quality of community properties in representation learning spaces, including the consistency of community structure, node distribution within and between communities, and central node distribution. A visualization system presents these indicators, allowing users to evaluate models based on community structures. Case studies demonstrate the effectiveness of the indicators for the visual evaluation of graph representation learning models.

Resting-state EEG predicts cognitive decline in a neuropathologically diagnosed longitudinal community autopsied cohort

ObjectiveTo assess correlative strengths of quantitative electroencephalography (qEEG) and visual rating scale EEG features on cognitive outcomes in only autopsied cases from the Arizona Study of Neurodegenerative Disorders (AZSAND). We hypothesized that autopsy proven Parkinson Disease will show distinct EEG features from Alzheimer's Disease prior to dementia (mild cognitive impairment). BackgroundCognitive decline is debilitating across neurodegenerative diseases. Resting-state EEG analysis, including spectral power across frequency bins (qEEG), has shown significant associations with neurodegenerative disease classification and cognitive status, with autopsy confirmed diagnosis relatively lacking. MethodsBiannual EEG was analyzed from autopsied cases in AZSAND who had at least one rsEEG (>1 min eyes closed±eyes open). Analysis included global relative spectral power and a previously described visual rating scale (VRS). Linear mixed regression was performed for neuropsychological assessment and testing within 2 years of death (n = 236, 594 EEG exams) in a mixed linear regression model. ResultsThe cohort included cases with final clinicopathologic diagnoses of Parkinson's disease (n = 73), Alzheimer disease (n = 65), and tauopathy not otherwise specified (n = 56). A VRS score of 3 diffuse or frequent generalized slowing) over the study duration was associated with an increase in consensus diagnosis cognitive worsening at 4.9 (3.1) years (HR 2.02, CI 1.05–3.87). Increases in global theta power% and VRS were the most consistently associated with large regression coefficients inversely with cognitive performance measures. ConclusionResting-state EEG analysis was meaningfully related to cognitive performance measures in a community-based autopsy cohort. EEG deserves further study and use as a cognitive biomarker.