Smart and emerging point of care electrochemical sensors based on nanomaterials for SARS-CoV-2 virus detection: Towards designing a future rapid diagnostic tool.

In the recent years, researchers from all over the world have become interested in the fabrication of advanced and innovative electrochemical and/or biosensors for respiratory virus detection with the use of nanotechnology. These fabricated sensors demonstrated a number of benefits, including precision, affordability, accessibility, and miniaturization which makes them a promising test method for point-of-care (PoC) screening for SARS-CoV-2 viral infection. In order to comprehend the principles of electrochemical sensing and the role of various types of sensing interfaces, we comprehensively explored the underlying principles of electroanalytical methods and terminologies related to it in this review. In addition, it is addressed how to fabricate electrochemical sensing devices incorporating nanomaterials as graphene, metal/metal oxides, metal organic frameworks (MOFs), MXenes, quantum dots, and polymers. We took an effort to carefully compile current developments, advantages, drawbacks, possible solutions in nanomaterials based electrochemical sensors.

Transparent and Flexible Actuator Based on a Hybrid Dielectric Layer of Wavy Polymer and Dielectric Fluid Mixture.

Transparent and flexible vibrotactile actuators play an essential role in human-machine interaction applications by providing mechanical stimulations that can effectively convey haptic sensations. In the present study, we fabricated an electroactive, flexible, and transparent vibrotactile actuator with a dielectric layer including a dielectric elastomer and dielectric fluid mixture. The dielectric fluid mixture of propylene carbonate (PC) and acetyl tributyl citrate (ATBC) was injected to obtain a transparent dielectric layer. To further improve the haptic performance, different weight ratios of dielectric fluid (PC: ATBC) were injected. The fabricated vibrotactile actuators based on a transparent dielectric layer were investigated for their electrical and electromechanical behavior. The proposed actuators generate a large vibrational intensity (~2.5 g) in the range of 200-250 Hz. Hence, the proposed actuators open up a new class of vibrotactile actuators for possible use in various domains, including robotics, smart textiles, teleoperation, and the metaverse.

MR imaging findings of ovarian lymphoma: differentiation from other solid ovarian tumors.

PURPOSE: To evaluate magnetic resonance imaging (MRI) findings for distinguishing ovarian lymphomas from other solid ovarian tumors. METHODS: This retrospective multicenter study included 14 women (median age, 46.5 years; range, 26-81 years) with surgically proven ovarian lymphoma and 28 women with solid ovarian tumors other than lymphoma. We conducted a subjective image analysis of factors including laterality, shape, composition, T2 signal intensity (SI), heterogeneity, diffusion restriction, enhancement, and presence of peripheral follicles. A generalized estimating equation was used to identify MRI findings that could be used to distinguish ovarian lymphomas from other solid ovarian tumors. Diagnostic performance of the identified MRI findings was assessed using the area under the receiver-operating characteristic curve (AUC). RESULTS: Ovarian lymphoma more frequently showed homogeneous high SI on T2-weighted imaging (81.8% vs. 19.4%, P < 0.001) and peripheral ovarian follicles (40.9% vs. 8.3%, P = 0.01) than other ovarian solid tumors did. Bilaterality, shape, size, diffusion restriction, and enhancement did not differ between the two groups (P > 0.05 for all). Homogeneous high SI on T2-weighted imaging was the only independent MRI finding (OR = 15.19; 95% CI 3.15-73.33; P = 0.001) in the multivariable analysis. Homogeneous high SI on T2-weighted imaging yielded an AUC of 0.82 with a sensitivity of 81.8% and specificity of 80.6% in distinguishing ovarian lymphomas from other solid ovarian tumors. CONCLUSION: Homogeneous high signal intensity on T2-weighted imaging was helpful in distinguishing ovarian lymphomas from other solid ovarian tumors. Peripheral ovarian follicles might be an additional clue that suggests a diagnosis of ovarian lymphoma.

Prediction of acute rejection in renal allografts using shear-wave dispersion slope.

OBJECTIVES: To evaluate the role of shear-wave dispersion slope for predicting renal allograft dysfunction. METHODS: We retrospectively reviewed 128 kidney transplant recipients (median age, 55 years [interquartile range, 43-62 years]; male, 68) who underwent biopsy for allograft evaluation from November 2022 to February 2023. Cortex and renal sinus fat stiffness and shear-wave dispersion slope were obtained at shear-wave elastography (SWE). Cortex-to-sinus stiffness ratio (SR) and dispersion slope ratio (DSR)-related clinical and pathologic factors were evaluated using multivariable linear regression analysis. We conducted univariate and multivariate analyses for multiparametric ultrasound (US) parameters for identifying acute rejection and calculated the area under the receiver operating curve (AUC) values. RESULTS: Of 128 patients, 31 (24.2%) demonstrated acute rejection. The SR value did not differ between patient groups (1.21 vs. 1.20, p = 0.47). Patients with acute rejection demonstrated a higher DSR than those without rejection (1.4 vs. 1.21, p < 0.01). Interstitial fibrosis and tubular atrophy grade (IFTA; coefficient, 0.11/grade; p = 0.04) and renal transplant and biopsy interval (coefficient, 0.00007/day; p = 0.03) were SR determinant factors, whereas only IFTA grade (coefficient, 0.10/grade; p = 0.01) for DSR. Multivariate analysis revealed mean resistive index (odds ratio [OR] 1.08, 95% confidence interval [CI] 1.02-1.14, p = 0.01) and DSR value (OR 16.0, 95% CI 3.0-85.8, p = 0.001) as independent factors for predicting acute rejection. An AUC of 0.74 for detecting acute rejection was achieved by combining the resistive index and DSR value. CONCLUSION: Shear-wave dispersion slope obtained at SWE may help identify renal allograft dysfunction. CLINICAL RELEVANCE STATEMENT: Acute rejection in renal allografts is a major cause of allograft failure, but noninvasive diagnosis is a challenge. Shear-wave dispersion slope can identify acute rejection non-invasively. KEY POINTS: • The interstitial fibrosis and tubular atrophy grade was a determinant factor for stiffness ratio and shear-wave dispersion slope ratio between cortex and renal sinus fat. • Shear-wave dispersion slope ratio between cortex and renal sinus fat could identify acute rejection in renal allografts. • A shear-wave dispersion slope has a potential to reduce unnecessary renal biopsy for evaluating renal allografts.

[Renal Biopsy]. / ì‹ ìž¥ì˜ 조직 검사.

The extent of renal biopsy indication is being widened because of the increasing incidence of incidental renal masses; the increasing treatment options for renal cell carcinoma, including ablation therapy and novel targeted treatment; and the increasing incidence of kidney transplantation. However, percutaneous renal biopsy is technically difficult, particularly for beginners, because the skin-to-organ distance is relatively longer than those associated with other organs. In the present review, we will discuss the indications, technical considerations, efficacy, and complications of renal biopsy. Furthermore, we share practical tips of renal biopsy through many examples to help radiologists perform renal biopsy safely and effectively in various situations.

Cellulose graphitic carbon directed iron oxide interfaced polypyrrole electrode materials for high performance supercapacitors.

The rising demand for green and clean energy urges the enlargement of economical and proficient electrode materials for supercapacitors. Herein, we designed a novel electrode material by porous cellulose graphitic carbon (CC) derived from bio-waste cornhusk via the pyrolysis route, and α-Fe2O3 decorated nanostructure with CC (CCIO) was achieved in situ pyrolysis of corn-husk and Fe(NO3)3·9H2O metal salt followed by a coating of polypyrrole (CCIOP). The CC, CCIO, and CCIOP nanocomposite electrodes were characterized by XRD, Raman, FTIR, FE-SEM/EDX, FE-TEM, XPS, and BET analysis. The CCIOP nanocomposite electrode exhibits an enhanced specific capacitance (Csp) of 290.9 F/g, which is substantial to its pristine CC (128.3 F/g), PPy (140.3 F/g), and CCIO (190.7 F/g). The Csp of CCIOP in a three-electrode system, using 1 M Na2SO4 electrolyte exhibits excellent capacity retention of 79.1 % even at a high current density of 10 A/g. The as-fabricated asymmetric supercapacitor (ASC) delivered a remarkable capacity retention of 88.7 % with a coulombic efficiency of 98.8 % even after 3000 cycles. The study shows successful utilization of cellulose from bio-waste cornhusk into a substantial template applicable in future alternative energy storage devices.

Diagnostic performance of MRI for prediction of recurrent prostate cancer after high-intensity focused ultrasound: a systematic review and meta-analysis.

Purpose: This article aims to evaluate the pooled diagnostic performance control MRI for prediction of recurrent prostate cancer (PCa) after high-intensity focused ultrasound (HIFU). Materials and methods: MEDLINE, EMBASE, and Cochrane library databases up to December 31, 2021, were searched. We included studies providing 2×2 contingency table for diagnostic performance of MRI in predicting recurrent PCa after HIFU, using control biopsy as reference standard. The quality of the included studies was assessed using Quality Assessment of Diagnostic Accuracy Studies-2 (QUADAS-2). Sensitivity and specificity were pooled and displayed in a summary receiver operating characteristics (SROC) plot. Meta-regression analysis using clinically relevant covariates was performed for the causes of heterogeneity. Results: Nineteen studies (703 patients) were included. All included studies satisfied at least four of the seven QUADAS-2 domains. Pooled sensitivity was 0.81 (95% CI 0.72-0.90) with specificity of 0.91 (95% CI 0.86-0.96), with area under the SROC curve of 0.81. Larger studies including more than 50 patients showed relatively poor sensitivity (0.68 vs. 0.84) and specificity (0.75 vs. 0.93). The diagnostic performance of studies reporting higher nadir serum prostate-specific antigen levels (>1 ng/mL) after HIFU was inferior, and differed significantly in sensitivity (0.54 vs. 0.78) rather than specificity (0.85 vs. 0.91). Conclusions: Although MRI showed adequate diagnostic performance in predicting PCa recurrence after HIFU, these results may have been exaggerated.

Synergetic effects of Mo2C sphere/SCN nanocatalysts interface for nanomolar detection of uric acid and folic acid in presence of interferences.

Till to date, the application of sulfur-doped graphitic carbon nitride supported transition metal carbide interface for electrochemical sensor fabrication was less explored. In this work, we designed a simple synthesis of molybdenum carbide sphere embedded sulfur doped graphitic carbon nitride (Mo2C/SCN) catalyst for the nanomolar electrochemical sensor application. The synthesized Mo2C/SCN nanocatalyst was systematically characterized using X-ray diffraction (XRD) and scanning electron microscopy (SEM) with elemental mapping. The SEM images show that the porous SCN network adhered uniformly on Mo2C, causing a loss of crystallinity in the diffractogram. The corresponding elemental mapping of Mo2C/SCN shows distinct peaks for carbon (41.47%), nitrogen (32.54%), sulfur (1.37%), and molybdenum (24.62%) with no additional impurity peaks, reflecting the successful synthesis. Later, the glassy carbon electrode (GCE) was modified by Mo2C/SCN nanocatalyst for simultaneous sensing of uric acid (UA) and folic acid (FA). The fabricated Mo2C/SCN/GCE is capable of simultaneous and interference free electrochemical detection of UA and FA in a binary mixture. The limit of detection (LOD) calculated at Mo2C/SCN/GCE for UA and FA was 21.5 nM (0.09 - 47.0 µM) and 14.7 nM (0.09 - 167.25 µM) respectively by differential pulse voltammetric (DPV) technique. The presence of interferons has no significant effect on the sensor's performance, making it suitable for real sample analysis. The present method can be extended to fabricate an electrochemical sensor for various molecules.

Characteristics of recurrent acute urinary retention in BPH patients in the United States: Retrospective analysis of US-based insurance claims database.

PURPOSE: The objective of this study is to analyze characteristics of recurrent acute urinary retention (AUR) in patients with benign prostatic hyperplasia (BPH), utilizing a population based data set. Also, we sought to report on how AUR was treated, specifically regarding the need and length of catheterization and types of procedures utilized for mitigation. MATERIALS & METHODS: A retrospective observational cohort study was performed using Optum's deidentified Clinformatics® Data Mart Database. We compared two groups, BPH patients with AUR (n = 180,737) and BPH patients without AUR (n = 1,139,760) from January 1, 2003 to December 31, 2017. Also, we analyzed the factors affecting the development of multiple episodes of AUR through age-adjusted multivariate analysis. RESULTS: In contrast to the 47.7% of patients who had a single AUR episode, 33.5% of AUR patients developed 3 or more subsequent episodes of retention. For age matched patients, the risks of additional episodes of retention increase significantly with older age, Caucasian race, diabetes, neurologic conditions, or low income. Overall, the rate of BPH surgery in AUR patients over the study period decreased and the most common procedure was transurethral resection of the prostate. CONCLUSIONS: Risk factors for multiple episodes of AUR included age (60 and older), Caucasian race, lower income socioeconomic status, diabetes, and neurological disorders. Patients with a high probability of developing recurrent episodes of AUR are recommended to receive preemptive BPH medication before such AUR occurrences. Also, more expeditious surgical treatment should be considered rather than temporary catheterization when AUR occurs.

Accuracy of actual stage prediction using Vesical Imaging Reporting and Data System (VI-RADS) before radical cystectomy for urothelial carcinoma in SUPER-UC-Cx.

Background: Previous studies using the Vesical Imaging Reporting and Data System (VI-RADS) to predict muscle-invasive bladder cancer (MIBC) had some limitations. Most studies were performed with transurethral resection of bladder tumor (TUR-BT) specimens with few samples. This study was conducted to address these shortcomings and confirm the accuracy of VI-RADS for bladder cancer. Methods: This study used data from the Seoul National University Prospectively Enrolled Registry for Urothelial Cancer-Radical Cystectomy (SUPER-UC-Cx). Patients who underwent multiparametric magnetic resonance imaging (mp-MRI) before radical cystectomy (RC) were included in this study between March 2020 and March 2022. All images were reported by radiologists and reviewed by two urologists. The patient characteristics and clinical information were blinded during the review. The performance of qualitative and quantitative variables in predicting muscle layer invasion or perivesical fat infiltration was verified by receiver operating characteristic (ROC) curve analysis. Results: Of 208 patients, 182 (87.5%) underwent mp-MRI before RC. Twenty-three patients with non-urothelial carcinoma, inappropriate MRI scans, and bladder filling were excluded. Cut-off for muscle invasion, VI-RADS score of 4 had the highest area under the curve (AUC) (sensitivity 0.84; specificity 0.93; accuracy 0.90; positive predictive value (PPV) 0.84; negative predictive value (NPV) 0.93, and AUC 0.88). Cut-off for perivesical fat invasion and VI-RADS score of 5 had the highest AUC (sensitivity, 0.78; specificity, 0.99; accuracy, 0.95; PPV, 0.96; NPV, 0.95; and AUC, 0.89). Conclusions: VI-RADS is a good predictor of bladder cancer staging before RC and is especially helpful in predicting muscle invasion and perivesical fat infiltration.

Fully automatic volume measurement of the adrenal gland on CT using deep learning to classify adrenal hyperplasia.

OBJECTIVES: To develop a fully automated deep learning model for adrenal segmentation and to evaluate its performance in classifying adrenal hyperplasia. METHODS: This retrospective study evaluated automated adrenal segmentation in 308 abdominal CT scans from 48 patients with adrenal hyperplasia and 260 patients with normal glands from 2010 to 2021 (mean age, 42 years; 156 women). The dataset was split into training, validation, and test sets at a ratio of 6:2:2. Contrast-enhanced CT images and manually drawn adrenal gland masks were used to develop a U-Net-based segmentation model. Predicted adrenal volumes were obtained by fivefold splitting of the dataset without overlapping the test set. Adrenal volumes and anthropometric parameters (height, weight, and sex) were utilized to develop an algorithm to classify adrenal hyperplasia, using multilayer perceptron, support vector classification, a random forest classifier, and a decision tree classifier. To measure the performance of the developed model, the dice coefficient and intraclass correlation coefficient (ICC) were used for segmentation, and area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, and specificity were used for classification. RESULTS: The model for segmenting adrenal glands achieved a Dice coefficient of 0.7009 for 308 cases and an ICC of 0.91 (95% CI, 0.90-0.93) for adrenal volume. The models for classifying hyperplasia had the following results: AUC, 0.98-0.99; accuracy, 0.948-0.961; sensitivity, 0.750-0.813; and specificity, 0.973-1.000. CONCLUSION: The proposed segmentation algorithm can accurately segment the adrenal glands on CT scans and may help clinicians identify possible cases of adrenal hyperplasia. KEY POINTS: • A deep learning segmentation method can accurately segment the adrenal gland, which is a small organ, on CT scans. • The machine learning algorithm to classify adrenal hyperplasia using adrenal volume and anthropometric parameters (height, weight, and sex) showed good performance. • The proposed segmentation algorithm may help clinicians identify possible cases of adrenal hyperplasia.

CT-based preoperative risk assessment of prolonged urine leak in patients undergoing partial nephrectomy.

OBJECTIVES: This study aims to evaluate risk factors of prolonged urine leak following partial nephrectomy (PN) to identify objective imaging characteristics on preoperative CT. METHODS: A total of 865 patients who underwent PN and had preoperative CT and postoperative imaging were included. We set a twofold size-matched control group without urine leak, with all tumors located ≤ 4 mm to the collecting system to identify imaging parameters that increase the risk of urine leak other than tumor size and location. Four CT parameters that show the relationship of the tumor and collecting system, namely, curvilinear border length, protruding distance, margin at the interface, and pelvicalyceal contact, were analyzed. Multivariate logistic regression analyses were performed to identify significant predictors of urine leak. The diagnostic performance of the significant parameters was evaluated using the area under the receiver operating characteristic curve (AUC). RESULTS: Fifty-three of 865 patients (6.1%) demonstrated urine leak. Compared with the control group, urine leak group showed longer curvilinear border length, longer protruding distance, frequent non-smooth contact interface, and frequent direct pelvicalyceal contact (p < 0.05 for all). In the multivariate analysis, pelvicalyceal contact was the independent predictor of urine leak (OR = 2.62; 95% C.I 1.02-6.63). Combining four CT parameters, an AUC of 0.70 with a sensitivity of 58.5% and a specificity of 79.2% for identifying urine leak after PN could be obtained. CONCLUSIONS: The four CT features that describe the relationship between the tumor and collecting system might be useful for evaluating the risk of urine leak before PN. KEY POINTS: • Four CT parameters (curvilinear border length, protruding distance, margin at the interface, and pelvicalyceal contact) were significantly associated with postoperative urine leak after partial nephrectomy. • A comprehensive preoperative imaging evaluation of the relationship between the tumor and renal sinus may help in selecting the optimal surgical options and afford better patient counseling of complication risk.

Quantitative magnetic resonance imaging of chronic kidney disease: an experimental in vivo study using rat chronic kidney disease models.

BACKGROUND: Recent advances in magnetic resonance imaging (MRI) may allow it to be an alternative emerging tool for the non-invasive evaluation of renal parenchymal disease. PURPOSE: To validate the usefulness of quantitative multiparametric MRI protocols and suggest the suitable quantitative MR sequence protocol to evaluate parenchymal fibrosis using an animal model of chronic kidney disease (CKD) by long-term adenine intake. MATERIAL AND METHODS: In this prospective animal study, 16 male Wistar rats were analyzed and categorized into three groups. Rats in the CKD groups underwent 0.25% adenine administration for three or six weeks. Quantitative MRI protocols, including diffusion-weighted imaging (DWI), T1ρ (T1 rho), and T2* mapping were performed using a 9.4-T animal MR scanner. A semi-quantitative histopathologic analysis for renal fibrosis was conducted. Quantitative MR values measured from anatomic regions of kidneys underwent intergroup comparative analyses. RESULTS: The apparent diffusion coefficient (ADC) and T1 (T1 rho) values were significantly increased in all CKD groups. Values measured from the cortex and outer medulla showed significant intergroup differences. Total ADC values tended to increase according to periods, and T1ρ values increased in three weeks and decreased in six weeks. CONCLUSION: Quantitative MRI protocols could be a non-invasive assessment modality in the diagnosis and evaluation of CKD. Particularly, T1ρ may be a suitable MR sequence to quantitatively assess renal parenchymal fibrosis.

Development of early prediction model for pregnancy-associated hypertension with graph-based semi-supervised learning.

Clinical guidelines recommend several risk factors to identify women in early pregnancy at high risk of developing pregnancy-associated hypertension. However, these variables result in low predictive accuracy. Here, we developed a prediction model for pregnancy-associated hypertension using graph-based semi-supervised learning. This is a secondary analysis of a prospective study of healthy pregnant women. To develop the prediction model, we compared the prediction performances across five machine learning methods (semi-supervised learning with both labeled and unlabeled data, semi-supervised learning with labeled data only, logistic regression, support vector machine, and random forest) using three different variable sets: [a] variables from clinical guidelines, [b] selected important variables from the feature selection, and [c] all routine variables. Additionally, the proposed prediction model was compared with placental growth factor, a predictive biomarker for pregnancy-associated hypertension. The study population consisted of 1404 women, including 1347 women with complete follow-up (labeled data) and 57 women with incomplete follow-up (unlabeled data). Among the 1347 with complete follow-up, 2.4% (33/1347) developed pregnancy-associated HTN. Graph-based semi-supervised learning using top 11 variables achieved the best average prediction performance (mean area under the curve (AUC) of 0.89 in training set and 0.81 in test set), with higher sensitivity (72.7% vs 45.5% in test set) and similar specificity (80.0% vs 80.5% in test set) compared to risk factors from clinical guidelines. In addition, our proposed model with graph-based SSL had a higher performance than that of placental growth factor for total study population (AUC, 0.71 vs. 0.80, p < 0.001). In conclusion, we could accurately predict the development pregnancy-associated hypertension in early pregnancy through the use of routine clinical variables with the help of graph-based SSL.

Prediction of treatment response following ethanol sclerotherapy of renal cysts by computed tomography.

OBJECTIVES: To investigate predictive factors of treatment response following ethanol sclerotherapy of large renal cysts via computed tomography (CT). METHODS: Retrospective study reviewed 71 patients (61.0 ± 13.2 years; M:F = 32:39) who underwent pretreatment CT and were treated with sclerotherapy of a large (> 5 cm) renal cyst (mean volume: 279.8 cc) using 99% ethanol from January 2010 to February 2019. Patients were followed up at least two times, short-term (defined as < 6 months, median 2.1 months) and long-term (defined as > 1 year, median 15.5 months), via ultrasound or CT. Suboptimal response was defined as the volume of residual cyst > 20 mL in each follow-up. Predictive variables of radiologic findings and radiomics features were analyzed using logistic regression analysis. RESULTS: Suboptimal response rates were 33.8% and 18.3% at short-term and long-term follow-ups, respectively. In radiologic findings, patients with suboptimal response in the short-term follow-up showed a more frequent estimated cyst volume ≥ 270 mL (OR 14.8, 95% CI 3.9-55.9, p < 0.001) and sinus protrusion (OR 7.0, 95% CI 1.7-28.5, p = 0.007). Cyst volume ≥ 270 mL was also associated with suboptimal response in the long-term follow-up (OR 4.6, 95% CI 1.3-16.9, p = 0.021). When radiomics features were combined, the area under the curve increased from 0.83 to 0.86 and from 0.68 to 0.82 to predict suboptimal response in short-term and long-term follow-ups, respectively. CONCLUSION: Greater estimated volume, sinus protrusion, and radiomics features of the cysts in pretreatment CT can help predict suboptimal response of renal cyst after sclerotherapy.

A comparison of conventional and advanced electroanalytical methods to detect SARS-CoV-2 virus: A concise review.

Respiratory viruses are a serious threat to human wellbeing that can cause pandemic disease. As a result, it is critical to identify virus in a timely, sensitive, and precise manner. The present novel coronavirus-2019 (COVID-19) disease outbreak has increased these concerns. The research of developing various methods for COVID-19 virus identification is one of the most rapidly growing research areas. This review article compares and addresses recent improvements in conventional and advanced electroanalytical approaches for detecting COVID-19 virus. The popular conventional methods such as polymerase chain reaction (PCR), loop mediated isothermal amplification (LAMP), serology test, and computed tomography (CT) scan with artificial intelligence require specialized equipment, hours of processing, and specially trained staff. Many researchers, on the other hand, focused on the invention and expansion of electrochemical and/or bio sensors to detect SARS-CoV-2, demonstrating that they could show a significant role in COVID-19 disease control. We attempted to meticulously summarize recent advancements, compare conventional and electroanalytical approaches, and ultimately discuss future prospective in the field. We hope that this review will be helpful to researchers who are interested in this interdisciplinary field and desire to develop more innovative virus detection methods.

Differentiating renal epithelioid angiomyolipoma from clear cell carcinoma: using a radiomics model combined with CT imaging characteristics.

PURPOSE: This study aims to assess the computed tomography (CT) findings of renal epithelioid angiomyolipoma (EAML) and develop a radiomics-based model for differentiating EAMLs and clear cell renal cell carcinomas (RCCs). METHOD: This two-center retrospective study included 28 histologically confirmed EAMLs and 56 size-matched clear cell RCCs with preoperative three-phase kidney CTs. We conducted subjective image analysis to determine the CT parameters that can distinguish EAMLs from clear cell RCCs. Training and test sets were divided by chronological order of CT scans, and radiomics model was built using ten selected features among radiomics and CT features. The diagnostic performance of the radiomics model was compared with that of the three radiologists using the area under the receiver-operating characteristic curve (AUC). RESULTS: The mean size of the EAMLs was 6.2 ± 5.0 cm. On multivariate analysis, a snowman or ice cream cone tumor shape (OR 16.3; 95% CI 1.7-156.9, P = 0.02) and lower tumor-to-cortex (TOC) enhancement ratio in the corticomedullary phase (OR 33.4; 95% CI 5.7-197, P < 0.001) were significant independent factors for identifying EAMLs. The diagnostic performance of the radiomics model (AUC 0.89) was similar to those of genitourinary radiologists (AUC 0.78 and 0.81, P > 0.05) and superior to that of a third-year resident (AUC 0.63, P = 0.04). CONCLUSIONS: A snowman or ice cream cone shape and lower TOC ratio were more closely associated with EAMLs than with clear cell RCCs. A CT radiomics model was useful for differentiating EAMLs from clear cell RCCs with better diagnostic performance than an inexperienced radiologist.

Impact of no residual disease on postoperative computed tomography on survival in patients with optimally debulked advanced high-grade serous ovarian cancer during upfront surgery.

OBJECTIVE: We sought to investigate the impact of size of residual tumors as determined by postoperative computed tomography (CT) on survival of patients with advanced, high-grade serous ovarian carcinoma (HGSC) who achieved residual disease less than 1 cm after primary debulking surgery (PDS). METHODS: We collected data of patients with stage III HGSC who had residual tumor less than 1 cm after PDS between 2013 and 2018. Surgeon-assessed residual disease during surgery was defined as sR0 (no gross residual) or sR1 (gross residual <1 cm), and radiologist-assessed residual disease on postoperative CT was defined as rR0 (no evidence of disease) or rRany (existing residual disease). All patients were classified into the following groups: sR0/rR0, sR1/rR0, sR0/rRany, and sR1/rRany. RESULTS: A total of 436 patients was placed into the sR0/rR0 (n = 187, 42.9%), sR1/rR0 (n = 59, 13.5%), sR0/rRany (n = 79, 18.1%), or sR1/rRany group (n = 111, 25.5%). Discrepancies between surgical and radiological assessments were recorded for 176 patients (40.4%) including 38 cases of sR1/rRany group with discordant residual tumor location indicated between two methods. During multivariate analysis, patients with ascites on preoperative CT, sR0/rRany group inclusion, and sR1/rRany group inclusion showed unfavorable progression-free and overall survival. CONCLUSIONS: The incorporation of surgical and radiological evaluations for determining the size of residual tumors was more accurate than surgical evaluation only for predicting survival among patients with advanced ovarian cancer who underwent PDS to residual disease less than 1 cm.

Impact of Digital Device, Exercise, and Music Intervention Programs on the Cognition and Depression of the Elderly in South Korea: A Meta-Regression Analysis.

BACKGROUND: This study examined the effect of digital devices, exercise, and music intervention programs for the elderly in Korea on their cognition and depression. METHODS: This study selected 70 cognition programs and 46 depression programs for the elderly in Korea. This study controlled the characteristics of the programs and participants, and conducted a meta-regression analysis to estimate the intervention effect size of digital devices, exercise, and music on cognition and depression. RESULTS: The meta-regression analysis revealed that digital device programs had a smaller effect size with respect to the improvement of cognitive functions than programs that did not use digital devices. The exercise programs had a small effect size on depression, but their effect size on cognition was not significantly different. DISCUSSION: These findings provide implications for developing a program that combines music therapy with digital devices and exercise interventions, which can be effective in addressing both cognition and depression.

CRISPR/Cas9 and Nanotechnology Pertinence in Agricultural Crop Refinement.

The CRISPR/Cas9 (Clustered Regularly Interspaced Short Palindromic Repeats/CRISPR-associated protein 9) method is a versatile technique that can be applied in crop refinement. Currently, the main reasons for declining agricultural yield are global warming, low rainfall, biotic and abiotic stresses, in addition to soil fertility issues caused by the use of harmful chemicals as fertilizers/additives. The declining yields can lead to inadequate supply of nutritional food as per global demand. Grains and horticultural crops including fruits, vegetables, and ornamental plants are crucial in sustaining human life. Genomic editing using CRISPR/Cas9 and nanotechnology has numerous advantages in crop development. Improving crop production using transgenic-free CRISPR/Cas9 technology and produced fertilizers, pesticides, and boosters for plants by adopting nanotechnology-based protocols can essentially overcome the universal food scarcity. This review briefly gives an overview on the potential applications of CRISPR/Cas9 and nanotechnology-based methods in developing the cultivation of major agricultural crops. In addition, the limitations and major challenges of genome editing in grains, vegetables, and fruits have been discussed in detail by emphasizing its applications in crop refinement strategy.