Xin-Yi-Qing-Fei-Tang and its critical components reduce asthma symptoms by suppressing GM-CSF and COX-2 expression in RBL-2H3 cells.

ETHNOPHARMACOLOGICAL RELEVANCE: The traditional Chinese medicine (TCM) XYQFT is composed of 10 herbs. According to the NHIRD, XYQFT is one of the top ten most commonly used TCM prescriptions for asthma treatment. AIM OF THE STUDY: The aim of this study was to explore whether XYQFT reduces asthma symptoms in a mouse model of chronic asthma and determine the immunomodulatory mechanism of mast cells. MATERIALS AND METHODS: BALB/c mice were intratracheally (it) stimulated with 40 µL (2.5 µg/µL) of Dermatophagoides pteronyssinus (Der p) once a week for 6 consecutive weeks and orally administered XYQFT at 1 g/kg 30 min before Der p stimulation. Airway hypersensitivity, inflammatory cells in the BALF and total IgE in the blood were assessed in mice. In addition, RBL-2H3 cells (mast cells) were stimulated with DNP-IgE, after which different concentrations of XYQFT were added for 30 min to evaluate the effect of XYQFT on the gene expression and degranulation of DNP-stimulated RBL-2H3 cells. After the compounds in XYQFT were identified using LC‒MS/MS, the PBD method was used to identify the chemical components that inhibited the expression of the GM-CSF and COX-2 genes in mast cells. RESULTS: The airway hypersensitivity assay demonstrated that XYQFT significantly alleviated Der p-induced airway hypersensitivity. Moreover, cell counting and typing of bronchoalveolar lavage fluid revealed a significant reduction in Der p-induced inflammatory cell infiltration with XYQFT treatment. ELISA examination further indicated a significant decrease in Der p-induced total IgE levels in serum following XYQFT administration. In addition, XYQFT inhibited the degranulation and expression of genes (IL-3, IL-4, ALOX-5, IL-13, GM-CSF, COX-2, TNF-α, and MCP-1) in RBL-2H3 cells after DNP stimulation. The compounds timosaponin AIII and genkwanin in XYQFT were found to be key factors in the inhibition of COX-2 and GM-CSF gene expression in mast cells. CONCLUSION: By regulating mast cells, XYQFT inhibited inflammatory cell infiltration, airway hypersensitivity and specific immunity in a mouse model of asthma. In addition, XYQFT synergistically inhibited the expression of the GM-CSF and COX-2 genes in mast cells through timosaponin AIII and genkwanin.

Development of the Low-Temperature Inducible System for Recombinant Protein Production in Escherichia coli Nissle 1917.

The pET system is commonly used for producing foreign proteins in Escherichia coli, but its reliance on the costly and metabolically demanding inducer IPTG limits its industrial use. This study engineered a low-temperature inducible system (LTIS) in E. coli Nissle 1917 (EcN) by combining the T7 expression system with the thermal inducible mechanism CI857-λPRPL to generate the new LTIS strain, ENL7P. The strain ENL7P-sfGFP-Km underwent overnight culture at 37 °C for 14-16 h, followed by subculturing at 30 °C for 24 h. This resulted in a notable 5.53-fold increase in the sfGFP induction rate when the strain was cultivated under 37-30 °C conditions. Moreover, gene expression was induced using a two-stage strategy. Initially, the strain was cultured overnight at 39 °C for 14-16 h, followed by a subculture at 30 °C for 6 h, and finally, another subculture at 30 °C for 24 h. This cultivation strategy led to an impressive 158.37-fold induction rate for sfGFP. Similar effects could be achieved through utilization of the LTIS system for inducing the production of thermophilic trehalose synthase from Thermus antranikianii (TaTS). The results of this study proved that the LTIS system has the potential for industrial applications.

Validation of the Machine Learning-Based Stroke Impact Scale With a Cross-Cultural Sample.

IMPORTANCE: The machine learning-based Stroke Impact Scale (ML-SIS) is an efficient short-form measure that uses 28 items to provide domain scores comparable to those of the original 59-item Stroke Impact Scale-Third Edition (SIS 3.0). However, its utility is largely unknown because it has not been cross-validated with an independent sample. OBJECTIVE: To examine the ML-SIS's comparability and test-retest reliability with that of the original SIS 3.0 in an independent sample of people with stroke. DESIGN: Comparability was examined with the coefficient of determination (R2), mean absolute error, and root-mean-square error (RMSE). Test-retest reliability was examined using the intraclass correlation coefficient (ICC). SETTING: Five hospitals in Taiwan. PARTICIPANTS: Data of 263 persons with stroke were extracted from a previous study; 144 completed repeated assessments after a 2-wk interval. RESULTS: High R2 (.87-.95) and low mean absolute error or RMSE (about 2.4 and 3.3) of the domain scores, except for the Emotion scores (R2 = .08), supported the comparability of the two measures. Similar ICC values (.39-.87 vs. .46-.87) were found between the two measures, suggesting that the ML-SIS is as reliable as the SIS 3.0. CONCLUSIONS AND RELEVANCE: The ML-SIS provides scores mostly identical to those of the original measure, with similar test-retest reliability, except for the Emotion domain. Thus, it is a promising alternative that can be used to lessen the burden of routine assessments and provide scores comparable to those of the original SIS 3.0. Plain-Language Summary: The machine learning-based Stroke Impact Scale (ML-SIS) is as reliable as the original Stroke Impact Scale-Third Edition, except for the Emotion domain. Thus, the ML-SIS can be used to improve the efficiency of clinical assessments and also relieve the burden on people with stroke who are completing the assessments.

Machine Learning Model with Computed Tomography Radiomics and Clinicobiochemical Characteristics Predict the Subtypes of Patients with Primary Aldosteronism.

RATIONALE AND OBJECTIVES: Adrenal venous sampling (AVS) is the primary method for differentiating between primary aldosterone (PA) subtypes. The aim of study is to develop prediction models for subtyping of patients with PA using computed tomography (CT) radiomics and clinicobiochemical characteristics associated with PA. MATERIALS AND METHODS: This study retrospectively enrolled 158 patients with PA who underwent AVS between January 2014 and March 2021. Neural network machine learning models were developed using a two-stage analysis of triple-phase abdominal CT and clinicobiochemical characteristics. In the first stage, the models were constructed to classify unilateral or bilateral PA; in the second stage, they were designed to determine the predominant side in patients with unilateral PA. The final proposed model combined the best-performing models from both stages. The model's performance was evaluated using repeated stratified five-fold cross-validation. We employed paired t-tests to compare its performance with the conventional imaging evaluations made by radiologists, which categorize patients as either having bilateral PA or unilateral PA on one side. RESULTS: In the first stage, the integrated model that combines CT radiomic and clinicobiochemical characteristics exhibited the highest performance, surpassing both the radiomic-alone and clinicobiochemical-alone models. It achieved an accuracy and F1 score of 80.6% ± 3.0% and 74.8% ± 5.2% (area under the receiver operating curve [AUC] = 0.778 ± 0.050). In the second stage, the accuracy and F1 score of the radiomic-based model were 88% ± 4.9% and 81.9% ± 6.2% (AUC=0.831 ± 0.087). The proposed model achieved an accuracy and F1 score of 77.5% ± 3.9% and 70.5% ± 7.1% (AUC=0.771 ± 0.046) in subtype diagnosis and lateralization, surpassing the accuracy and F1 score achieved by radiologists' evaluation (p < .05). CONCLUSION: The proposed machine learning model can predict the subtypes and lateralization of PA. It yields superior results compared to conventional imaging evaluation and has potential to supplement the diagnostic process in PA.

Discrimination of invasive lung adenocarcinoma from Lung-RADS category 2 nonsolid nodules through visual assessment: a retrospective study.

OBJECTIVES: Invasive adenocarcinomas (IADs) have been identified among nonsolid nodules (NSNs) assigned as Lung Imaging Reporting and Data System (Lung-RADS) category 2. This study used visual assessment for differentiating IADs from noninvasive lesions (NILs) in this category. METHODS: This retrospective study included 222 patients with 242 NSNs, which were resected after preoperative computed tomography (CT)-guided dye localization. Visual assessment was performed by using the lung and bone window (BW) settings to classify NSNs into BW-visible (BWV) and BW-invisible (BWI) NSNs. In addition, nodule size, shape, border, CT attenuation, and location were evaluated and correlated with histopathological results. Logistic regression was performed for multivariate analysis. A p value of < 0.05 was considered statistically significant. RESULTS: A total of 242 NSNs (mean diameter, 7.6 ± 2.8 mm), including 166 (68.6%) BWV and 76 (31.4%) BWI NSNs, were included. IADs accounted for 31% (75) of the nodules. Only 4 (5.3%) IADs were identified in the BWI group and belonged to the lepidic-predominant (n = 3) and acinar-predominant (n = 1) subtypes. In univariate analysis for differentiating IADs from NILs, the nodule size, shape, CT attenuation, and visual classification exhibited statistical significance. Nodule size and visual classification were the significant predictors for IAD in multivariate analysis with logistic regression (p < 0.05). The sensitivity, specificity, positive predictive value, and negative predictive value of visual classification in IAD prediction were 94.7%, 43.1%, 42.8%, and 94.7%, respectively. CONCLUSIONS: The window-based visual classification of NSNs is a simple and objective method to discriminate IADs from NILs. CLINICAL RELEVANCE STATEMENT: The present study shows that using the bone window to classify nonsolid nodules helps discriminate invasive adenocarcinoma from noninvasive lesions. KEY POINTS: • Evidence has shown the presence of lung adenocarcinoma in Lung-RADS category 2 nonsolid nodules. • Nonsolid nodules are classified into the bone window-visible and the bone window-invisible nonsolid nodules, and this classification differentiates invasive adenocarcinoma from noninvasive lesions. • The Lung-RADS category 2 nonsolid nodules are unlikely invasive adenocarcinoma if they show nonvisualization in the bone window.

Influence of autonomous cortisol secretion in patients with primary aldosteronism: subtype analysis and postoperative outcome.

Context: Autonomous cortisol secretion (ACS) has a relatively high prevalence in patients with primary aldosteronism (PA). There is still a lack of relevant studies to analyze the influence of ACS on diagnosing and managing PA. Objective: To evaluate the influence of ACS on image-adrenal venous sampling (AVS) correlation and the postoperative results. Methods: This was a retrospective study using the Taiwan Primary Aldosteronism Investigation database from July 2017 to April 2020, with 327 PA patients enrolled. A total of 246 patients were included in the image-AVS analysis. Patients who had undergone unilateral adrenalectomy and a 12-month follow-up were included in the postoperative analysis. Results: Sixty-five patients (26.4%) had ACS. The image-AVS discordance rate was higher in the ACS group compared to the non-ACS group (75.4% (n = 49) vs 56.4% (n = 102); odds ratio (OR) = 2.37 (CI: 1.26-4.48); P = 0.007). The complete biochemical success rate was higher in the non-ACS group than that in the ACS group (98.1% (n = 51) vs 64.3% (n = 9); OR = 28.333 (CI: 2.954-271.779); P = 0.001). In logistic regression analysis, ACS was the only factor associated with lower biochemical success (OR = 0.035 (CI: 0.004-0.339), P = 0.004). Conclusion: PA patients with ACS have higher image-AVS discordance rate and worse biochemical outcomes after surgery. ACS was the only negative predictor of postoperative biochemical outcomes. Further studies and novel biomarkers for AVS are crucial for obtaining better postoperative outcomes in PA patients with ACS.

Test-Retest Reliability and Responsiveness of the Computerized Adaptive Testing System of the Functional Assessment of Stroke.

OBJECTIVE: To examine the test-retest reliability, responsiveness, and clinical utility of the Computerized Adaptive Testing System of the Functional Assessment of Stroke (CAT-FAS) in persons with stroke. DESIGN: Repeated measurements design. SETTING: A department of rehabilitation in a medical center. PARTICIPANTS: 30 persons with chronic stroke (for test-retest reliability) and 65 persons with subacute stroke (for responsiveness) were recruited. To examine the test-retest reliability, the participants received measurements twice at 1-month intervals. To examine the responsiveness, the data were collected at admission and discharge from hospital. INTERVENTIONS: Not applicable. MAIN OUTCOME MEASUREMENT TOOL: CAT-FAS. RESULTS: The intra-class correlation coefficients of the CAT-FAS were ≥0.82, indicating good to excellent test-retest reliability. The Kazis' effect size and standardized response mean of the CAT-FAS were ≥0.96, indicating good group-level responsiveness. For individual-level responsiveness, approximately two-thirds of the participants exceeded the conditional minimal detectable change. On average, the CAT-FAS was completed within 9 items and 3 minutes per administration. CONCLUSIONS: Our results suggest the CAT-FAS is an efficient measurement tool with good to excellent test-retest reliability and responsiveness. In addition, the CAT-FAS can be used routinely in clinical settings to monitor progress of the crucial 4 domains for persons with stroke.

Optimizing adrenal vein sampling in primary aldosteronism subtyping through LC-MS/MS and secretion ratios of aldosterone, 18-oxocortisol, and 18-hydroxycortisol.

Adrenal venous sampling (AVS) is the gold standard for identifying curable unilateral aldosterone excess in primary aldosteronism (PA). Studies have demonstrated the value of steroid profiling through liquid chromatography-tandem mass spectrometry (LC-MS/MS) in AVS interpretation. First, the performance of LC-MS/MS and immunoassay in assessing selectivity and lateralization was compared. Second, the utility of the proportion of individual steroids in adrenal veins in subtyping PA was analyzed. We enrolled 75 consecutive patients with PA who underwent AVS between 2020 and 2021. Fifteen adrenal steroids were analyzed in peripheral and adrenal veins through LC-MS/MS before and after adrenocorticotropic hormone (ACTH) stimulation. Through selectivity index that was based on cortisol and alternative steroids, LC-MS/MS rescued 45% and 66% of failed cases judged by immunoassay in unstimulated and stimulated AVS, respectively. LC-MS/MS identified more unilateral diseases than did immunoassay (76% vs. 45%, P < 0.05) and provided adrenalectomy opportunities to 69% of patients judged through immunoassay to have bilateral disease. The secretion ratios (individual steroid concentration/total steroid concentration) of aldosterone, 18-oxocortisol, and 18-hydroxycortisol were novel indicators for identifying unilateral PA. The 18-oxocortisol secretion ratio of ≥0.785‰ (sensitivity/specificity: 0.90/0.77) at pre-ACTH and aldosterone secretion ratio of ≤0.637‰ (sensitivity/specificity: 0.88/0.85) at post-ACTH enabled optimal accuracy for predicting ipsilateral and contralateral disease, respectively, in robust unilateral PA. LC-MS/MS improved the success rate of AVS and identified more unilateral diseases than immunoassay. The secretion ratios of steroids can be used to discriminate the broad PA spectrum.

Test-Retest Reliability and Responsiveness of the Computerized Adaptive Test of Social Functioning in Persons With Stroke.

OBJECTIVE: To examine the test-retest reliability, minimal detectable change (MDC), responsiveness, and efficiency of the Computerized Adaptive Test of Social Functioning (Social-CAT) in patients with stroke. DESIGN: Repeated-assessments design. SETTING: A department of rehabilitation of a medical center. PARTICIPANTS: In total, 31 patients with chronic stroke and 65 patients with subacute stroke were recruited. INTERVENTION: Not available. MAIN OUTCOME MEASURE: Social-CAT. RESULTS: The Social-CAT showed acceptable test-retest reliability (intraclass correlation coefficient, 0.80) and small random measurement error (MDC%: 18.0%). However, heteroscedasticity was found (r between the means and the absolute change scores: 0.32), so the MDC% adjusted cut-off score is recommended for determining real improvement. Regarding responsiveness, the Social-CAT showed large differences (Kazis' effect size and standardized mean response: 1.15 and 1.09, respectively) in subacute patients. Regarding efficiency, the Social-CAT required an average of 5 items and less than 2 minutes for completion. CONCLUSIONS: Our findings indicate that the Social-CAT is a reliable and efficient measure with good test-retest reliability, small random measurement error, and good responsiveness. Thus, the Social-CAT is a useful outcome measure for routine monitoring of the changes in social function of patients with stroke.

Detection of pancreatic cancer with two- and three-dimensional radiomic analysis in a nationwide population-based real-world dataset.

BACKGROUND: CT is the major detection tool for pancreatic cancer (PC). However, approximately 40% of PCs < 2 cm are missed on CT, underscoring a pressing need for tools to supplement radiologist interpretation. METHODS: Contrast-enhanced CT studies of 546 patients with pancreatic adenocarcinoma diagnosed by histology/cytology between January 2005 and December 2019 and 733 CT studies of controls with normal pancreas obtained between the same period in a tertiary referral center were retrospectively collected for developing an automatic end-to-end computer-aided detection (CAD) tool for PC using two-dimensional (2D) and three-dimensional (3D) radiomic analysis with machine learning. The CAD tool was tested in a nationwide dataset comprising 1,477 CT studies (671 PCs, 806 controls) obtained from institutions throughout Taiwan. RESULTS: The CAD tool achieved 0.918 (95% CI, 0.895-0.938) sensitivity and 0.822 (95% CI, 0.794-0.848) specificity in differentiating between studies with and without PC (area under curve 0.947, 95% CI, 0.936-0.958), with 0.707 (95% CI, 0.602-0.797) sensitivity for tumors < 2 cm. The positive and negative likelihood ratios of PC were 5.17 (95% CI, 4.45-6.01) and 0.10 (95% CI, 0.08-0.13), respectively. Where high specificity is needed, using 2D and 3D analyses in series yielded 0.952 (95% CI, 0.934-0.965) specificity with a sensitivity of 0.742 (95% CI, 0.707-0.775), whereas using 2D and 3D analyses in parallel to maximize sensitivity yielded 0.915 (95% CI, 0.891-0.935) sensitivity at a specificity of 0.791 (95% CI, 0.762-0.819). CONCLUSIONS: The high accuracy and robustness of the CAD tool supported its potential for enhancing the detection of PC.

Evaluation of You-Gui-Wan critical compounds inhibiting ALOX-5 and HDC gene expression in RBL-2H3 cells using a fractional factorial design.

ETHNOPHARMACOLOGICAL RELEVANCE: The traditional Chinese medicine (TCM) You-Gui-Wan (YGW) has been used to treat asthma for hundreds of years. AIM OF THE STUDY: YGW is composed of 10 types of medicinal materials. However, the immune mechanism of YGW in asthma treatment has not been elucidated. Therefore, this study investigated asthma symptoms attenuated by YGW and the underlying immune regulatory mechanism. MATERIALS AND METHODS: Intratracheal (i.t.) stimulation of BALB/c mice with Dermatophagoides pteronyssinus (Der p) was performed once per week (40 µL, 2.5 µg/µL). For six consecutive weeks, different doses of YGW (0.2 g/kg and 0.5 g/kg) were orally administered 30 min before stimulation with Der p. After the last stimulation, airway hyperreactivity, lung gene expression, and total immunoglobulin E (IgE) in blood were evaluated using a whole-body plethysmograph system, real-time PCR, and ELISA, respectively. In addition, DNP-IgE/DNP-BSA was added to stimulate mast cells (RBL-2H3), and YGW or various compound compositions (Trial) were added to RBL-2H3 cells for 30 min to evaluate the effects of the drug on mast cell degranulation and on gene expression. JMP 5.1 software was used to design and analyze YGW's critical compounds by which it inhibited ALOX-5 and HDC gene expression in RBL-2H3 cells. RESULTS: YGW significantly decreased serum total IgE levels and airway hyperresponsiveness in asthmatic mice. YGW also reduced the gene expression of IL-6, TNF-α, IL-4, IL-13, and COX-2 in the lungs of asthmatic mice and RBL-2H3 cells. YGW and the compound (Trial 21) present in YGW inhibited the gene expression of ALOX-5 and HDC in RBL-2H3 cells. CONCLUSION: The experimental results indicate that YGW exhibits anti-airway hyperresponsiveness and specific immunomodulatory effects. In addition, YGW synergistically inhibits ALOX-5 and HDC gene expression in mast cells through a combination of 21 compounds, including luteolin, quercetin, and ß-carotene.

Pancreatic Cancer Detection on CT Scans with Deep Learning: A Nationwide Population-based Study.

Background Approximately 40% of pancreatic tumors smaller than 2 cm are missed at abdominal CT. Purpose To develop and to validate a deep learning (DL)-based tool able to detect pancreatic cancer at CT. Materials and Methods Retrospectively collected contrast-enhanced CT studies in patients diagnosed with pancreatic cancer between January 2006 and July 2018 were compared with CT studies of individuals with a normal pancreas (control group) obtained between January 2004 and December 2019. An end-to-end tool comprising a segmentation convolutional neural network (CNN) and a classifier ensembling five CNNs was developed and validated in the internal test set and a nationwide real-world validation set. The sensitivities of the computer-aided detection (CAD) tool and radiologist interpretation were compared using the McNemar test. Results A total of 546 patients with pancreatic cancer (mean age, 65 years ± 12 [SD], 297 men) and 733 control subjects were randomly divided into training, validation, and test sets. In the internal test set, the DL tool achieved 89.9% (98 of 109; 95% CI: 82.7, 94.9) sensitivity and 95.9% (141 of 147; 95% CI: 91.3, 98.5) specificity (area under the receiver operating characteristic curve [AUC], 0.96; 95% CI: 0.94, 0.99), without a significant difference (P = .11) in sensitivity compared with the original radiologist report (96.1% [98 of 102]; 95% CI: 90.3, 98.9). In a test set of 1473 real-world CT studies (669 malignant, 804 control) from institutions throughout Taiwan, the DL tool distinguished between CT malignant and control studies with 89.7% (600 of 669; 95% CI: 87.1, 91.9) sensitivity and 92.8% specificity (746 of 804; 95% CI: 90.8, 94.5) (AUC, 0.95; 95% CI: 0.94, 0.96), with 74.7% (68 of 91; 95% CI: 64.5, 83.3) sensitivity for malignancies smaller than 2 cm. Conclusion The deep learning-based tool enabled accurate detection of pancreatic cancer on CT scans, with reasonable sensitivity for tumors smaller than 2 cm. © RSNA, 2022 Online supplemental material is available for this article. See also the editorial by Aisen and Rodrigues in this issue.

Bioethanol production from microalgae biomass at high-solids loadings.

Industrial adoption of microalgae biofuel technology has always been hindered by its economic viability. To increase the feasibility of bioethanol production from microalgae, fermentation was applied to Chlorella vulgaris FSP-E biomass at high-solids loading conditions. First, Chlorella vulgaris FSP-E was cultivated to produce microalgae biomass with high carbohydrate content. Next, different ethanol-producing microorganisms were screened. Saccharomyces cerevisiae FAY-1 showed no inhibition when fermenting high initial glucose concentrations and was selected for the fermentation experiments at high-solids loadings. Optimization of acid hydrolysis at high biomass loading was also performed. The fermentation of microalgal biomass hydrolysate produced a final ethanol concentration and yield higher than most reported literature using microalgae feedstock. In addition, the kinetics of bioethanol fermentation of microalgae hydrolysate under high-solids loading were evaluated. These results showed the potential of fermenting microalgae biomass at high-solids loading in improving the viability of microalgae bioethanol production.

Integration of Multiple Phage Attachment Sites System to Create the Chromosomal T7 System for Protein Production in Escherichia coli Nissle 1917.

Escherichia coli Nissle 1917 (EcN) is a probiotic used to treat gastrointestinal diseases. The probiotic and endotoxin-free characteristics of EcN support its potential to be developed into a microbial expression system. With this aim, in this study, the powerful T7 expression system was constructed in the cryptic plasmid-free EcN (EcNP) to generate the T7 expression host ENL6P. The concept of multiple copies of gene expression cassettes regulated by the chromosomal T7 promoter was promoted due to plasmid instability issues with protein production in ENL6P. The integration of multiple phage attachment sites (IMPACT) system, which combined Cre-lox72, CRIM, and lambda red recombinase systems, was designed to simplify the manipulation and achieve the multiple φ80 bacterial attachment sites (attB) in ENL6P to generate the new strain ENL6PP4 with four φ80 attB sites. The strain can simultaneously integrate four copies of gene expression cassettes in the chromosome to produce recombinant proteins. The IMPACT systems incorporated several tools in gene editing to rapidly achieve more robust and stable microbial strains for research and various industrial applications.

Highly Substituted Acephenanthrylenes and Their π-Extended Derivatives: Syntheses, Structural Analyses, and Properties.

This investigation demonstrates that a series of (highly) substituted acephenanthrylenes (APs) 2, benzo[l]acephenanthrylene 3, and dicyclopenta[cd,mn]pyrene 4 are easily prepared by palladium-catalyzed cycloaromatization of 2,3-diethynylbiphenyls 1. The first nonpyrolysis synthesis of 4 by two-fold cycloaromatization was achieved in a high yield. This synthetic protocol has two crucial advantages: facile introduction of various substituents and efficient extension of the AP backbone.

Radiomics utilization to differentiate nonfunctional adenoma in essential hypertension and functional adenoma in primary aldosteronism.

We performed the present study to investigate the role of computed tomography (CT) radiomics in differentiating nonfunctional adenoma and aldosterone-producing adenoma (APA) and outcome prediction in patients with clinically suspected primary aldosteronism (PA). This study included 60 patients diagnosed with essential hypertension (EH) with nonfunctional adenoma on CT and 91 patients with unilateral surgically proven APA. Each whole nodule on unenhanced and venous phase CT images was segmented manually and randomly split into training and test sets at a ratio of 8:2. Radiomic models for nodule discrimination and outcome prediction of APA after adrenalectomy were established separately using the training set by least absolute shrinkage and selection operator (LASSO) logistic regression, and the performance was evaluated on test sets. The model can differentiate adrenal nodules in EH and PA with a sensitivity, specificity, and accuracy of 83.3%, 78.9% and 80.6% (AUC = 0.91 [0.72, 0.97]) in unenhanced CT and 81.2%, 100% and 87.5% (AUC = 0.98 [0.77, 1.00]) in venous phase CT, respectively. In the outcome after adrenalectomy, the models showed a favorable ability to predict biochemical success (Unenhanced/venous CT: AUC = 0.67 [0.52, 0.79]/0.62 [0.46, 0.76]) and clinical success (Unenhanced/venous CT: AUC = 0.59 [0.47, 0.70]/0.64 [0.51, 0.74]). The results showed that CT-based radiomic models hold promise to discriminate APA and nonfunctional adenoma when an adrenal incidentaloma was detected on CT images of hypertensive patients in clinical practice, while the role of radiomic analysis in outcome prediction after adrenalectomy needs further investigation.

Fermentative lactic acid production from seaweed hydrolysate using Lactobacillus sp. And Weissella sp.

Lactic acid (LA) is an essential commodity chemical, with bio-based LA ruling the market share. Macroalgae are a desirable feedstock for LA fermentation due to their high carbohydrate and low lignin content. Ulva sp., Gracilaria sp., and Sargassum cristaefolium were evaluated as a feedstock for LA fermentation. Mild acid-thermal hydrolysis (sulfuric acid concentrations < 5%) resulted in superior reducing sugar recovery. Gracilaria sp. attained maximum reducing sugar recovery (0.39 g/g biomass) and lactate yield (0.94 g/g). LA fermentation of fucose-rich hydrolysate of Sargassum cristaefolium is demonstrated for the first time, with 0.81 g/g LA yield and 0.36 g/g reducing sugars. Ulva sp. attained 0.21 g/g reducing sugars and 0.85 g/g LA yield. The efficiency of macroalgae for lactate bioconversion was in the order: red macroalgae > green macroalgae > brown macroalgae. L. rhamnosus and L. plantarum could efficaciously utilize seaweed sugars for LA production. Macroalgae can potentially replace lignocellulosic biomass as a feedstock in LA fermentation.

Evaluation of Abdominal Computed Tomography Scans for Differentiating the Discrepancies in Abdominal Adipose Tissue Between Two Major Subtypes of Primary Aldosteronism.

The aim of this study was to analyze the differences in the distribution of abdominal adipose tissue between the two subtypes of primary aldosteronism (PA) using abdominal computed tomography. We retrospectively analyzed patients diagnosed as having essential hypertension (EH) or PA from the prospectively collected Taiwan Primary Aldosteronism Investigation (TAIPAI) database. Patients with PA were divided into the subgroups of idiopathic hyperaldosteronism (IHA) and unilateral aldosterone-producing adenoma (APA). Patients' basic clinicodemographic data were collected, and a self-developed CT-based software program was used to quantify the abdominal adiposity indexes, including visceral adipose tissue (VAT) area, VAT ratio, waist circumference (WC), subcutaneous adipose tissue (SAT) area, and SAT ratio. We included 190 patients with EH and 436 patients with PA (238 with IHA and 198 with APA). The APA group had significantly lower abdominal adiposity indexes than the other groups. We also found negative correlations of aldosterone-to-renin ratio (ARR) with VAT area, VAT ratio, WC, and body mass index (BMI) in the APA group. After propensity score matching (which left 184 patients each in the IHA and APA groups), patients in the APA group still had significantly lower WC, SAT area, SAT ratio, and VAT ratio than those in the IHA group. Furthermore, logistic regression analysis indicated that lower probability of abdominal obesity was significantly related to patients with APA. Our data revealed that the distribution of abdominal adipose tissue was similar in patients with IHA and those with EH, but the abdominal adiposity indexes were significantly lower in patients with APA than in those with IHA and EH.

Radiomic Features at CT Can Distinguish Pancreatic Cancer from Noncancerous Pancreas.

Purpose To identify distinguishing CT radiomic features of pancreatic ductal adenocarcinoma (PDAC) and to investigate whether radiomic analysis with machine learning can distinguish between patients who have PDAC and those who do not. Materials and Methods This retrospective study included contrast material-enhanced CT images in 436 patients with PDAC and 479 healthy controls from 2012 to 2018 from Taiwan that were randomly divided for training and testing. Another 100 patients with PDAC (enriched for small PDACs) and 100 controls from Taiwan were identified for testing (from 2004 to 2011). An additional 182 patients with PDAC and 82 healthy controls from the United States were randomly divided for training and testing. Images were processed into patches. An XGBoost (https://xgboost.ai/) model was trained to classify patches as cancerous or noncancerous. Patients were classified as either having or not having PDAC on the basis of the proportion of patches classified as cancerous. For both patch-based and patient-based classification, the models were characterized as either a local model (trained on Taiwanese data only) or a generalized model (trained on both Taiwanese and U.S. data). Sensitivity, specificity, and accuracy were calculated for patch- and patient-based analysis for the models. Results The median tumor size was 2.8 cm (interquartile range, 2.0-4.0 cm) in the 536 Taiwanese patients with PDAC (mean age, 65 years ± 12 [standard deviation]; 289 men). Compared with normal pancreas, PDACs had lower values for radiomic features reflecting intensity and higher values for radiomic features reflecting heterogeneity. The performance metrics for the developed generalized model when tested on the Taiwanese and U.S. test data sets, respectively, were as follows: sensitivity, 94.7% (177 of 187) and 80.6% (29 of 36); specificity, 95.4% (187 of 196) and 100% (16 of 16); accuracy, 95.0% (364 of 383) and 86.5% (45 of 52); and area under the curve, 0.98 and 0.91. Conclusion Radiomic analysis with machine learning enabled accurate detection of PDAC at CT and could identify patients with PDAC. Keywords: CT, Computer Aided Diagnosis (CAD), Pancreas, Computer Applications-Detection/Diagnosis Supplemental material is available for this article. © RSNA, 2021.