Biogas upgrading and membrane anti-fouling mechanisms in electrochemical anaerobic membrane bioreactor (EC-AnMBR): Focusing on spatio-temporal distribution of metabolic functionality of microorganisms.

Electrochemical anaerobic membrane bioreactor (EC-AnMBR) by integrating a composite anodic membrane (CAM), represents an effective method for promoting methanogenic performance and mitigating membrane fouling. However, the development and formation of electroactive biofilm on CAM, and the spatio-temporal distribution of key functional microorganisms, especially the degradation mechanism of organic pollutants in metabolic pathways were not well documented. In this work, two AnMBR systems (EC-AnMBR and traditional AnMBR) were constructed and operated to identify the role of CAM in metabolic pathway on biogas upgrading and mitigation of membrane fouling. The methane yield of EC-AnMBR at HRT of 20 days was 217.1 ± 25.6 mL-CH4/g COD, about 32.1 % higher compared to the traditional AnMBR. The 16S rRNA analysis revealed that the EC-AnMBR significantly promoted the growth of hydrolysis bacteria (Lactobacillus and SJA-15) and methanogenic archaea (Methanosaeta and Methanobacterium). Metagenomic analysis revealed that the EC-AnMBR promotes the upregulation of functional genes involved in carbohydrate metabolism (gap and kor) and methane metabolism (mtr, mcr, and hdr), improving the degradation of soluble microbial products (SMPs)/extracellular polymeric substances (EPS) on the CAM and enhancing the methanogens activity on the cathode. Moreover, CAM biofilm exhibits heterogeneity in the degradation of organic pollutants along its vertical depth. The bacteria with high hydrolyzing ability accumulated in the upper part, driving the feedstock degradation for higher starch, sucrose and galactose metabolism. A three-dimensional mesh-like cake structure with larger pores was formed as a biofilter in the middle and lower part of CAM, where the electroactive Geobacter sulfurreducens had high capabilities to directly store and transfer electrons for the degradation of organic pollutants. This outcome will further contribute to the comprehension of the metabolic mechanisms of CAM module on membrane fouling control and organic solid waste treatment and disposal.

Exploring the Low-Dose Limit for Focal Hepatic Lesion Detection with a Deep Learning-Based CT Reconstruction Algorithm: A Simulation Study on Patient Images.

This study aims to investigate the maximum achievable dose reduction for applying a new deep learning-based reconstruction algorithm, namely the artificial intelligence iterative reconstruction (AIIR), in computed tomography (CT) for hepatic lesion detection. A total of 40 patients with 98 clinically confirmed hepatic lesions were retrospectively included. The mean volume CT dose index was 13.66 ± 1.73 mGy in routine-dose portal venous CT examinations, where the images were originally obtained with hybrid iterative reconstruction (HIR). Low-dose simulations were performed in projection domain for 40%-, 20%-, and 10%-dose levels, followed by reconstruction using both HIR and AIIR. Two radiologists were asked to detect hepatic lesion on each set of low-dose image in separate sessions. Qualitative metrics including lesion conspicuity, diagnostic confidence, and overall image quality were evaluated using a 5-point scale. The contrast-to-noise ratio (CNR) for lesion was also calculated for quantitative assessment. The lesion CNR on AIIR at reduced doses were significantly higher than that on routine-dose HIR (all p < 0.05). Lower qualitative image quality was observed as the radiation dose reduced, while there were no significant differences between 40%-dose AIIR and routine-dose HIR images. The lesion detection rate was 100%, 98% (96/98), and 73.5% (72/98) on 40%-, 20%-, and 10%-dose AIIR, respectively, whereas it was 98% (96/98), 73.5% (72/98), and 40% (39/98) on the corresponding low-dose HIR, respectively. AIIR outperformed HIR in simulated low-dose CT examinations of the liver. The use of AIIR allows up to 60% dose reduction for lesion detection while maintaining comparable image quality to routine-dose HIR.

Application of deep learning image reconstruction-high algorithm in one-stop coronary and carotid-cerebrovascular CT angiography with low radiation and contrast doses.

Background: For patients with suspected simultaneous coronary and cerebrovascular atherosclerosis, conventional single-site computed tomography angiography (CTA) for both sites can result in nonnegligible radiation and contrast agent dose. The purpose of this study was to validate the feasibility of one-stop coronary and carotid-cerebrovascular CTA (C&CC-CTA) with a "double-low" (low radiation and contrast) dose protocol reconstructed with deep learning image reconstruction with high setting (DLIR-H) algorithm. Methods: From February 2018 to January 2019, 60 patients referred to C&CC-CTA simultaneously in West China Hospital were recruited in this prospective cohort study. By random assignment, patients were divided into two groups: double-low dose group (n=30) used 80 kVp and 24 mgI/kg/s contrast dose with images reconstructed using DLIR-H; and routine-dose group (n=30) used 100 kVp and 32 mgI/kg/s contrast dose with images reconstructed using 50% adaptive statistical iterative reconstruction-V (ASIR-V50%). Radiation and contrast doses, subjective image quality score, CT attenuation values, noise, signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) were measured and compared between the groups. Results: The DLIR-H group used 30% less contrast dose (35.80±4.85 vs. 51.13±6.91 mL) and 48% less overall radiation dose (1.00±0.09 vs. 1.91±0.42 mSv) than the ASIR-V50% group (both P<0.001). There was no statistically significant difference on subjective quality score between the two groups (C-CTA: 4.38±0.67 vs. 4.17±0.81, P=0.337 and CC-CTA: 4.18±0.87 vs. 4.08±0.79, P=0.604). For coronary CTA, lower background noise (18.93±1.43 vs. 22.86±3.75 HU) was reached in DLIR-H group, and SNR and CNR at all assessed branches were significantly increased compared to ASIR-V50% group (all P<0.05), except SNR of left anterior descending (P>0.05). For carotid-cerebrovascular CTA, DLIR-H group was comparable in background noise (19.25±1.42 vs. 20.23±2.40 HU), SNR and CNR at all assessed branches with ASIR-V50% group (all P>0.05). Conclusions: The "double-low" dose one-stop C&CC-CTA with DLIR-H obtained higher image quality compared with the routine-dose protocol with ASIR-V50% while achieving 48% and 30% reduction in radiation and contrast dose, respectively.

Ivy Sign: Usefulness in Diagnosis and Prognosis Prediction of Moyamoya Disease.

BACKGROUND: Moyamoya disease (MMD) cannot be found commonly as a rare type compared with other vascular disease, such as aneurysm. However, it cannot be ignored for its high fatality and disability rates. In addition, exact pathogenesis study of this disease is still on the way. The ivy sign is always observed in MMD, but the clinical importance of this sign in MMD isn't clearly known. The main purpose of this research was to specifically investigate the clinical significance. METHODS: In this retrospective cohort study to gather the baseline clinical and imaging study, the patients with MMD were hospitalized from January 2016 to 2020. In the analysis, univariate and multivariate logistic regression was used to testify whether ivy sign was independently associated with MMD characteristics including cerebrovascular morphology, cerebral hemodynamics, cerebrovascular events, and postoperative collateral formation (PCF). RESULTS: We included 156 patients with 312 hemispheres. As for the result of multivariate logistic regression analysis, we could discover a fact that ivy sign was tightly connected to the Suzuki stage ≥IV (odds ratio [OR], 1.386; 95% confidence interval [CI], 1.055-1.822; P = 0.019), cerebral blood flow (CBF) decreased type (OR, 2.330; 95% CI, 1.733-3.133; P = 0.000), age acted as a protective factor for CBF (OR, 0.966; 95% CI, 0.946-0.986; P = 0.001), the elder was more likely associated with decreased CBF. Ivy sign also played a significant role in ischemic cerebrovascular events (OR, 5.653; 95% CI, 3.092-10.336; P = 0.003), their remarkable connection could be seen on the study. We could also find that ivy sign was closely connected to the good PCF (OR, 2.830; 95% CI, 1.329-6.027; P = 0.007), and we couldn't ignore the fact that age was associated with good PCF as well (OR, 0.933; 95% CI, 0.882-0.987; P = 0.015). DISCUSSION: We could be more aware of the connection between ivy sign and Moyamoya disease from this study in order to implement diagnosis, treatment, and prognosis more efficiently.

Dual-layer spectral computed tomography aortography using a seventy-five-percent-reduced iodine dose protocol and multiparameter spectral imaging: comparison with conventional computed tomography imaging.

Background: Computed tomography angiography (CTA) is the recommended diagnostic and follow-up imaging modality for acute aortic dissection (AD). However, the high-contrast medium burden associated with repeated CT aortography follow-ups remains a significant concern. This prospective study aimed to assess whether an ultra-low contrast dose (75% cutoff) aortic CTA protocol on dual-layer spectral CT could achieve comparable image quality with the full dose protocol. We also investigated the image quality of the virtual noncontrast (VNC) images derived from the ultra-low dose protocol. Methods: This study included 37 consecutive patients who were referred to aortic CTA from May 2022 to August 2022. The enrolled patients underwent full-dose contrast CTA and ultra-low dose (reduced to 25% of conventional) contrast CTA on dual-layer spectral CT in 1 day. Virtual monochromatic images (VMIs) were reconstructed with 40 and 70 keV. The VNC images were reconstructed for both protocols. Objective image quality evaluation, recorded as signal-to-noise ratios (SNRs) and contrast-to-noise ratios (CNRs), was compared between the groups using 1-way analysis of variance and post hoc analysis with Bonferroni correction. Subjective image quality was also compared between the groups. Finally, VNC images derived from the low-dose (VNClow) and full-dose (VNCfull) protocols were compared to the true noncontrast (TNC) images. Results: Neither CNR nor SNR was lower for the 40-keV images reconstructed from the ultra-low dose group compared to the conventional images. Both were significantly higher than those of the 70-keV images. Regarding subjective image quality, vessel enhancement was not significantly different between the 40-keV VMI and full-dose images [ascending aorta (AAO): 4.37±0.46 vs. 4.57±0.48, P=0.096; brachiocephalic arteries: 4.34±0.45 vs. 4.51±0.49, P=0.152; abdominal aortic side branch: 4.42±0.48 vs. 4.51±0.49, P=0.480]. The VNClow images were similar to the TNC images but significantly different from the VNCfull images (P<0.001). Conclusions: Ultra-low contrast aortic CTA with a 75%-reduced iodine dose using dual-layer spectral CT and the derived VNC achieved image quality comparable to that of conventional CTA and TNC images.

Self-assembled electrochemically active biofilms doped with carbon nanotubes: Electron exchange efficiency and cytotoxicity evaluation.

Thick electrochemically active biofilms (EABs) will lead to insufficient extracellular electron transfer (EET) rate because of the limitation of both substrate diffusion and electron exchange. Herein, carbon nanotubes (CNTs)-doped EABs are developed through self-assembly. The highly conductive biofilms (internal resistance of ∼211 Ω) are efficiently enriched at CNTs dosage of 1 g L-1, with the stable power output of 0.568 W m-2 over three months. The embedded CNTs can act as electron tunnel to accelerate the EET rate in thick biofilm. Self-charging/discharging experiments and Nernst-Monod model stimulation demonstrate a higher net charge storage capacity (0.15 C m-2) and more negative half-saturation potential (-0.401 V) for the hybrid biofilms than that of the control (0.09 C m-2, and -0.378 V). Enzyme activity tests and the observation of confocal laser scanning microscopy by live/dead staining show a nearly negligible cytotoxicity of CNTs, and non-targeted metabonomics analysis reveals fourteen differential metabolites that do not play key roles in microbial central metabolic pathways according to KEGG compound database. The abundance of typical exoelectrogens Geobacter sp. is 2-fold of the control, resulting in a better bioelectrocatalytic activity. These finding provide a possible approach to prolong electron exchange and power output by developing a hybrid EABs doped with conductive material.

Patching sulfur vacancies: A versatile approach for achieving ultrasensitive gas sensors based on transition metal dichalcogenides.

Transition metal dichalcogenides (TMDCs) garner significant attention for their potential to create high-performance gas sensors. Despite their favorable properties such as tunable bandgap, high carrier mobility, and large surface-to-volume ratio, the performance of TMDCs devices is compromised by sulfur vacancies, which reduce carrier mobility. To mitigate this issue, we propose a simple and universal approach for patching sulfur vacancies, wherein thiol groups are inserted to repair sulfur vacancies. The sulfur vacancy patching (SVP) approach is applied to fabricate a MoS2-based gas sensor using mechanical exfoliation and all-dry transfer methods, and the resulting 4-nitrothiophenol (4NTP) repaired molybdenum disulfide (4NTP-MoS2) is prepared via a sample solution process. Our results show that 4NTP-MoS2 exhibits higher response (increased by 200 %) to ppb-level NO2 with shorter response/recovery times (61/82 s) and better selectivity at 25 °C compared to pristine MoS2. Notably, the limit of detection (LOD) toward NO2 of 4NTP-MoS2 is 10 ppb. Kelvin probe force microscopy (KPFM) and density functional theory (DFT) reveal that the improved gas sensing performance is mainly attributed to the 4NTP-induced n-doping effect on MoS2 and the corresponding increment of surface absorption energy to NO2. Additionally, our 4NTP-induced SVP approach is universal for enhancing gas sensing properties of other TMDCs, such as MoSe2, WS2, and WSe2.

Characteristic CT angiography findings and clinical value of catheter-related central venous thrombosis in hemodialysis patients with end-stage renal disease.

BACKGROUND: Central catheter-related thrombosis (CRT) is the most common catheter-related complication in patients with end-stage renal disease (ESRD) but is often underappreciated and misdiagnosed by radiologist. PURPOSE: To find the computed tomography angiography (CTA) characteristics of central CRT, then raise the diagnosis of this disorder. MATERIAL AND METHODS: A total of 301 eligible patients with ESRD who experienced both chest multi-phase multidetector CTA (MDCTA) and digital subtraction angiography were enrolled in the final analysis. The location, shape, and related signs of the central CRT in MDCTA images were evaluated. Independent-samples T test, chi-square test, and binary logistic regression were analyzed using SPSS software. RESULTS: In total, 166 patients were found to have CRT using MDCTA, and this was verified by DSA. Central CRT was usually irregular in the superior vena cava segment, and the angle of the contact area between central CRT and catheter was <180° (all P < 0.05). Age, collateral circulation, and venous stenosis were shown to have significant differences when compared to patients without CRT (all P < 0.05), but there were no significant differences about the sex or catheter insertion site. In addition, age and collateral circulation were the factors found to be significantly associated with thrombosis (P < 0.05). In particular, the thrombosis was 2.213 times more likely to be found in those patients with collateral circulation (odds ratio = 2.213, 95% confidence interval = 1.236-3.961). CONCLUSION: Chest multi-phase MDCTA can effectively reduce the missed diagnosis and misdiagnosis of central CRT. It is worth paying more attention to the central CRT especially when the collateral circulation is observed.

Impact of sandwich-type composite anodic membrane on membrane fouling and methane recovery from sewage sludge and food waste via electrochemical anaerobic membrane bioreactor.

Membrane fouling presents a big challenge for the real-world implementation of anaerobic membrane bioreactors (AnMBRs) in digesting high-solid biowastes. In this study, an electrochemical anaerobic membrane bioreactor (EC-AnMBR) with a novel sandwich-type composite anodic membrane was designed and constructed for controlling membrane fouling whilst improving the energy recovery. The results showed that EC-AnMBR produced a higher methane yield of 358.5 ± 74.8 mL/d, rising by 12.8% compared to the AnMBR without applied voltage. Integration of composite anodic membrane induced a stable membrane flux and low transmembrane pressure through forming an anodic biofilm while total coliforms removal reached 97.9%. The microbial community analysis further provided compelling evidence that EC-AnMBR enriched the relative abundance of hydrolyzing (Chryseobacterium 2.6%) bacteria and methane-producing (Methanobacterium 32.8%) archaea. These findings offered new insights into anti-biofouling performance and provided significant implications for municipal organic waste treatment and energy recovery in the new EC-AnMBR.

Feasibility of accelerated non-contrast-enhanced whole-heart bSSFP coronary MR angiography by deep learning-constrained compressed sensing.

OBJECTIVES: To examine a compressed sensing artificial intelligence (CSAI) framework to accelerate image acquisition in non-contrast-enhanced whole-heart bSSFP coronary magnetic resonance (MR) angiography. METHODS: Thirty healthy volunteers and 20 patients with suspected coronary artery disease (CAD) scheduled for coronary computed tomography angiography (CCTA) were enrolled. Non-contrast-enhanced coronary MR angiography was performed with CSAI, compressed sensing (CS), and sensitivity encoding (SENSE) methods in healthy participants and with CSAI in patients. Acquisition time, subjective image quality score, and objective image quality measurement (blood pool homogeneity, signal-to-noise ratio [SNR], and contrast-to-noise ratio [CNR]) were compared among the three protocols. The diagnostic performance of CASI coronary MR angiography for predicting significant stenosis (≥ 50% diameter stenosis) on CCTA was evaluated. The Friedman test was performed to compare the three protocols. RESULTS: Acquisition time was significantly shorter in the CSAI and CS groups than in the SENSE group (10.2 ± 3.2 min vs. 10.9 ± 2.9 min vs. 13.0 ± 4.1 min, p < 0.001). However, the CSAI approach had the highest image quality scores, blood pool homogeneity, mean SNR value, and mean CNR value (all p < 0.001) compared with the CS and SENSE approaches. The sensitivity, specificity, and accuracy of CSAI coronary MR angiography per patient were 87.5% (7/8), 91.7% (11/12), and 90.0% (18/20); those per vessel were 81.8% (9/11), 93.9% (46/49), and 91.7% (55/60); and those per segment were 84.6% (11/13), 98.0% (244/249), and 97.3% (255/262), respectively. CONCLUSIONS: CSAI yielded superior image quality within a clinically feasible acquisition time in healthy participants and patients with suspected CAD. CLINICAL RELEVANCE STATEMENT: The non-invasive and radiation-free CSAI framework could be a promising tool for rapid screening and comprehensive examination of the coronary vasculature in patients with suspected CAD. KEY POINTS: • This prospective study showed that CSAI enables a reduction in acquisition time by 22% with superior diagnostic image quality compared with the SENSE protocol. • CSAI replaces the wavelet transform with a CNN as a sparsifying transform in the CS algorithm, achieving high coronary MR image quality with reduced noise. • CSAI achieved per-patient sensitivity of 87.5% (7/8) and specificity of 91.7% (11/12) respectively for detecting significant coronary stenosis.

Reducing both radiation and contrast doses for overweight patients in coronary CT angiography with 80-kVp and deep learning image reconstruction.

PURPOSE: To investigate the use of an 80-kVp tube voltage combined with a deep learning image reconstruction (DLIR) algorithm in coronary CT angiography (CCTA) for overweight patients to reduce radiation and contrast doses in comparison with the 120-kVp protocol and adaptive statistical iterative reconstruction (ASIR-V). METHODS: One hundred consecutive CCTA patients were prospectively enrolled and randomly divided into a low-dose group (n = 50) with 80-kVp, smart mA for noise index (NI) of 36 HU, contrast dose rate of 18 mgI/kg/s and DLIR and 60 % ASIR-V and a standard-dose group (n = 50) with 120-kVp, smart mA for NI of 25 HU, contrast dose rate of 32 mgI/kg/s and 60 % ASIR-V. The radiation and contrast dose, subjective image quality score, attenuation values, noise, signal-to-noise ratio (SNR), and contrast-to-noise ratio (CNR) were compared. RESULTS: The low-dose group achieved a significant reduction in the effective radiation dose (1.01 ± 0.45 mSv vs 1.85 ± 0.40 mSv, P < 0.001) and contrast dose (33.69 ± 3.87 mL vs 59.11 ± 5.60 mL, P < 0.001) compared to the standard-dose group. The low-dose group with DLIR presented similar enhancement but lower noise, higher SNR and CNR and higher subjective quality scores than the standard-dose group. Moreover, the same patient comparison in the low-dose group between different reconstructions showed that DLIR images had slightly and consistently higher CT values in small vessels, indicating better defined vessels, much lower image noise, higher SNR and CNR and higher subjective quality scores than ASIR-V images (all P < 0.001). CONCLUSIONS: The application of 80-kVp and DLIR allows for significant radiation and dose reduction while further improving image quality in CCTA for overweight patients.

Nomogram to Predict Good Collateral Formation After the STA-MCA Bypass Surgery in Adult Patients With Moyamoya Disease.

BACKGROUND: Collateral formation from the extracranial carotid artery to ischemic brain tissue determines the clinical success of superficial temporal artery (STA) to middle cerebral artery (MCA) bypass surgery in adult patients with moyamoya disease, but postoperative collateral formation (PCF) after STA-MCA bypass surgery is unpredictable. Accurate preoperative prediction of acceptable PCF could improve patient selection. This study aims to develop a prediction nomogram model for PCF in this patient population. METHODS: Adult patients with moyamoya disease undergoing the STA-MCA bypass surgery between January 2013 and December 2020 at a single institution were retrospectively or prospectively enrolled in this observational study. Data including potential clinical and radiological predictors were obtained from hospital records. A nomogram was generated based on a multivariate logistic regression analysis, to identify potential predictors associated with good PCF. The performance of the nomogram was evaluated for discrimination, calibration, and clinical utility. RESULTS: Data from 243 patients with moyamoya disease who underwent the STA-MCA bypass surgery were analyzed to build the nomogram. After 1-year follow-up, 162 (66.7%) hemispheres had good PCF and 81 (33.3%) had poor PCF. Good PCF is associated with 3 preoperative factors: age at operation, a diameter of donor branch of STA, and the preinfarction period stage. Incorporating these 3 factors, the model achieved a concordance index of 0.88 (95% CI, 0.84-0.92) and had a well-fitted calibration curve and good clinical application value. A cutoff value of 100 was determined to predict good PCF via this nomogram. CONCLUSIONS: The nomogram exhibits high accuracy in predicting good PCF after the STA-MCA bypass surgery in adult patients with moyamoya disease and may allow surgeons to better evaluate preoperatively candidacy for successful bypass surgery.

Deep Learning-Based Acceleration of Compressed Sensing for Noncontrast-Enhanced Coronary Magnetic Resonance Angiography in Patients With Suspected Coronary Artery Disease.

BACKGROUND: The clinical application of coronary MR angiography (MRA) remains limited due to its long acquisition time and often unsatisfactory image quality. A compressed sensing artificial intelligence (CSAI) framework was recently introduced to overcome these limitations, but its feasibility in coronary MRA is unknown. PURPOSE: To evaluate the diagnostic performance of noncontrast-enhanced coronary MRA with CSAI in patients with suspected coronary artery disease (CAD). STUDY TYPE: Prospective observational study. POPULATION: A total of 64 consecutive patients (mean age ± standard deviation [SD]: 59 ± 10 years, 48.4% females) with suspected CAD. FIELD STRENGTH/SEQUENCE: A 3.0-T, balanced steady-state free precession sequence. ASSESSMENT: Three observers evaluated the image quality for 15 coronary segments of the right and left coronary arteries using a 5-point scoring system (1 = not visible; 5 = excellent). Image scores ≥3 were considered diagnostic. Furthermore, the detection of CAD with ≥50% stenosis was evaluated in comparison to reference standard coronary computed tomography angiography (CTA). Mean acquisition times for CSAI-based coronary MRA were measured. STATISTICAL TESTS: For each patient, vessel and segment, sensitivity, specificity, and diagnostic accuracy of CSAI-based coronary MRA for detecting CAD with ≥50% stenosis according to coronary CTA were calculated. Intraclass correlation coefficients (ICCs) were used to assess the interobserver agreement. RESULTS: The mean MR acquisition time ± SD was 8.1 ± 2.4 minutes. Twenty-five (39.1%) patients had CAD with ≥50% stenosis on coronary CTA and 29 (45.3%) patients on MRA. A total of 885 segments on the CTA images and 818/885 (92.4%) coronary MRA segments were diagnostic (image score ≥3). The sensitivity, specificity, and diagnostic accuracy were as follows: per patient (92.0%, 84.6%, and 87.5%), per vessel (82.9%, 93.4%, and 91.1%), and per segment (77.6%, 98.2%, and 96.6%), respectively. The ICCs for image quality and stenosis assessment were 0.76-0.99 and 0.66-1.00, respectively. DATA CONCLUSION: The image quality and diagnostic performance of coronary MRA with CSAI may show good results in comparison to coronary CTA in patients with suspected CAD. EVIDENCE LEVEL: 1. TECHNICAL EFFICACY: 2.

Mutator-Derived lncRNA Landscape: A Novel Insight Into the Genomic Instability of Prostate Cancer.

Background: Increasing evidence has emerged to reveal the correlation between genomic instability and long non-coding RNAs (lncRNAs). The genomic instability-derived lncRNA landscape of prostate cancer (PCa) and its critical clinical implications remain to be understood. Methods: Patients diagnosed with PCa were recruited from The Cancer Genome Atlas (TCGA) program. Genomic instability-associated lncRNAs were identified by a mutator hypothesis-originated calculative approach. A signature (GILncSig) was derived from genomic instability-associated lncRNAs to classify PCa patients into high-risk and low-risk groups. The biochemical recurrence (BCR) model of a genomic instability-derived lncRNA signature (GILncSig) was established by Cox regression and stratified analysis in the train set. Then its prognostic value and association with clinical features were verified by Kaplan-Meier (K-M) analysis and receiver operating characteristic (ROC) curve in the test set and the total patient set. The regulatory network of transcription factors (TFs) and lncRNAs was established to evaluate TF-lncRNA interactions. Results: A total of 95 genomic instability-associated lncRNAs of PCa were identified. We constructed the GILncSig based on 10 lncRNAs with independent prognostic value. GILncSig separated patients into the high-risk (n = 121) group and the low-risk (n = 121) group in the train set. Patients with high GILncSig score suffered from more frequent BCR than those with low GILncSig score. The results were further validated in the test set, the whole TCGA cohort, and different subgroups stratified by age and Gleason score (GS). A high GILncSig risk score was significantly associated with a high mutation burden and a low critical gene expression (PTEN and CDK12) in PCa. The predictive performance of our BCR model based on GILncSig outperformed other existing BCR models of PCa based on lncRNAs. The GILncSig also showed a remarkable ability to predict BCR in the subgroup of patients with TP53 mutation or wild type. Transcription factors, such as FOXA1, JUND, and SRF, were found to participate in the regulation of lncRNAs with prognostic value. Conclusion: In summary, we developed a prognostic signature of BCR based on genomic instability-associated lncRNAs for PCa, which may provide new insights into the epigenetic mechanism of BCR.

Diagnostic study on clinical feasibility of an AI-based diagnostic system as a second reader on mobile CT images: a preliminary result.

Background: Artificial intelligence (AI) has breathed new life into the lung nodules detection and diagnosis. However, whether the output information from AI will translate into benefits for clinical workflow or patient outcomes in a real-world setting remains unknown. This study was to demonstrate the feasibility of an AI-based diagnostic system deployed as a second reader in imaging interpretation for patients screened for pulmonary abnormalities in a clinical setting. Methods: The study included patients from a lung cancer screening program conducted in Sichuan Province, China using a mobile computed tomography (CT) scanner which traveled to medium-size cities between July 10th, 2020 and September 10th, 2020. Cases that were suspected to have malignant nodules by junior radiologists, senior radiologists or AI were labeled a high risk (HR) tag as HR-junior, HR-senior and HR-AI, respectively, and included into final analysis. The diagnosis efficacy of the AI was evaluated by calculating negative predictive value and positive predictive value when referring to the senior readers' final results as the gold standard. Besides, characteristics of the lesions were compared among cases with different HR labels. Results: In total, 251/3,872 patients (6.48%, male/female: 91/160, median age, 66 years) with HR lung nodules were included. The AI algorithm achieved a negative predictive value of 88.2% [95% confidence interval (CI): 62.2-98.0%] and a positive predictive value of 55.6% (95% CI: 49.0-62.0%). The diagnostic duration was significantly reduced when AI was used as a second reader (223±145.6 vs. 270±143.17 s, P<0.001). The information yielded by AI affected the radiologist's decision-making in 35/145 cases. Lesions of HR cases had a higher volume [309.9 (214.9-732.5) vs. 141.3 (79.3-380.8) mm3, P<0.001], lower average CT number [-511.0 (-576.5 to -100.5) vs. -191.5 (-487.3 to 22.5), P=0.010], and pure ground glass opacity rather than solid. Conclusions: The AI algorithm had high negative predictive value but low positive predictive value in diagnosing HR lung lesions in a clinical setting. Deploying AI as a second reader could help avoid missed diagnoses, reduce diagnostic duration, and strengthen diagnostic confidence for radiologists.

[Clinical Application of "Three-Low" Technique Combined with Artificial Intelligence Iterative Reconstruction Algorithm in Aortic CT Angiography].

Objective: To explore the application value of the "three-low" technique (low radiation dose, low contrast agent dosage and low contrast agent flow rate) combined with artificial intelligence iterative reconstruction (AIIR) in aortic CT angiography (CTA). Methods: A total of 33 patients who underwent aortic CTA were prospectively enrolled. Based on the time of their follow-up examinations, the imaging data were divided into Group A and Group B, with Group A being the control group (100 kV, 0.8 mL/kg, 5 mL/s) and Group B being the "three-low" technique group (70 kV, 0.5 mL/kg, 3 mL/s). In group A, the images were reconstructed by Karl iterative algorithm. Group B was divided into B1 and B2 subgroups, with their images being reconstructed by Karl iterative algorithm and AIIR, respectively. The CT and SD values of the ascending aorta, descending aorta, abdominal aorta, left common iliac artery and right common iliac artery were measured, and the signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) were calculated. The subjective scoring of image quality was performed. The radiation dose parameters were documented. Results: Differences in the CT value, SD value, SNR and CNR of the three groups were statistically significant ( P<0.001). The CT value, SNR and CNR of group B2 were significantly higher than those of group B1, while the SD value of group B2 was significantly lower than that of group B1 ( P<0.017). There was no significant difference between the CT values of group A and those of group B2 ( P>0.017). The SD values, SNR and CNR in group B2 were better than those in group A ( P>0.017). There was significant difference in the subjective evaluation of image quality among the three groups ( P<0.05), but there was no significant difference between group A and group B2 ( P>0.017). The radiation dose and contrast medium dosage in group B decreased 84.14% and 37.08%, respectively, compared with those of group A. Conclusion: With the "three-low" technique combined with AIIR algorithm, the image quality of aortic CTA obtained is comparable to that of conventional dose scanning, while the radiation dose, contrast agent dosage and contrast agent flow rate of patients are significantly reduced.

Quantitative evaluation of disease severity in connective tissue disease-associated interstitial lung disease by dual-energy computed tomography.

BACKGROUND: High-resolution computed tomography (HRCT) is recommended diagnosing and monitoring connective tissue disease-associated interstitial lung disease (CTD-ILD). Quantitative computed tomography has the potential to precisely assess the radiological severity of CTD-ILD, but has still been under study. OBJECTIVE: To investigate whether dual-energy computed tomography (DECT), a novel quantitative technique, can be used for quantitative severity assessment in CTD-ILD. METHODS: This cross sectional study recruited adult CTD-ILD patients who underwent DECT scans from the ICE study between October 2019 and November 2021. DECT parameters, including effective atomic number (Zeff), lung (lobe) volume, and monochromatic CT number (MCTN) of each lung lobe, were evaluated. CTD-ILD was classified into extensive CTD-ILD and limited CTD-ILD by staging algorithm using combined forced vital capacity (FVC)%predicted and total extent of ILD (TEI) on CT. Dyspnea, cough, and life quality were scored by Borg dyspnea score, Leicester cough questionnaire (LCQ), and short-form 36 health survey questionnaire (SF-36), respectively. RESULTS: There was a total of 147 patients with DECT scans enrolled. Higher Zeff value (3.104 vs 2.256, p < 0.001), higher MCTN (- 722.87 HU vs - 802.20 HU, p < 0.001), and lower lung volume (2309.51cm3 vs 3475.21cm3, p < 0.001) were found in extensive CTD-ILD compared with limited CTD-ILD. DECT parameters had significant moderate correlations with FVC%predicted (|r|= 0.542-0.667, p < 0.01), DLCO%predicted (|r|= 0.371-0.427, p < 0.01), and TEI (|r|= 0.485-0.742, p < 0.01). Receiver operating characteristic (ROC) analysis indicated MCTN averaged over the whole lung had the best performance for extensive CTD-ILD discrimination (AUC = 0.901, cut-off: - 762.30 HU, p < 0.001), with a sensitivity of 82.1% and a specificity of 85.4%. The Zeff value was the independent risk factor for dyspnea (OR = 3.644, 95% CI: 1.846-7.192, p < 0.001) and cough (OR = 3.101, 95% CI: 1.528-6.294, p = 0.002), and lung volume significantly contributed to the mental component summary (MCS) in SF-36 (standardized ß = 0.198, p < 0.05). CONCLUSIONS: DECT can be applied to evaluate the severity of CTD-ILD.

Image quality assessment of artificial intelligence iterative reconstruction for low dose aortic CTA: A feasibility study of 70 kVp and reduced contrast medium volume.

PURPOSE: To investigate the image quality and feasibility of a novel artificial intelligence iterative reconstruction (AIIR) algorithm for aortic computer tomography angiography (CTA) with a low radiation dose and contrast material (CM) dosage protocol in comparison with hybrid iterative reconstruction (HIR) algorithm for standard-of-care aortic CTA. METHODS: Fifty consecutive patients (mean age 58 ± 14 years, mean BMI 24.5 ± 4.7 kg/m2) with aortic diseases were prospectively enrolled. All patients underwent at least twice follow-up aortic CTA examinations. Standard dose CT (SDCT) was applied in the initial follow-up examination (100 kVp, auto mAs, contrast dose 0.8 mgL/kg), images were reconstructed with HIR (SDCT-HIR). In the second follow-up examination, patients underwent scanning with low dose CT (LDCT) (70 kVp, auto mAs, contrast dose 0.5 mgL/kg), images were reconstructed with HIR (LDCT-HIR) as well as AIIR (LDCT-AIIR). Attenuation values, noise, signal-to-noise ratio (SNR), and contrast-to-noise ratio (CNR) were measured for objective analysis. Subjective image quality was rated by two blinded radiologists using a 5-point scale. The effective radiation dose and CM dosage were also recorded. RESULTS: The effective radiation dose (1.58 ± 0.17 mSv vs. 9.96 ± 1.05 mSv, P < 0.001) and CM dosage (34.38 ± 5.43 ml vs. 54.64 ± 8.63 ml, P < 0.001) achieved a remarkable reduction of 84.14% and 37.08% in the LDCT compared to the SDCT. The attenuation was similar among the three reconstructed images (P > 0.05). Compared to LDCT-HIR images, LDCT-AIIR showed a lower noise and higher SNR and CNR. For qualitative analysis, there were no significant differences between the LDCT-AIIR and the SDCT-HIR images among four metrics (P > 0.05). CONCLUSIONS: Compared to standard-of-care aortic CTA with HIR, the application of the AIIR algorithm allows for radiation dose and CM dosage reduction while preserving image quality on low dose aortic CTA.

High-strength deep learning image reconstruction in coronary CT angiography at 70-kVp tube voltage significantly improves image quality and reduces both radiation and contrast doses.

OBJECTIVES: To explore the use of 70-kVp tube voltage combined with high-strength deep learning image reconstruction (DLIR-H) in reducing radiation and contrast doses in coronary CT angiography (CCTA) in patients with body mass index (BMI) < 26 kg/m2, in comparison with the conventional scan protocol using 120 kVp and adaptive statistical iterative reconstruction (ASIR-V). METHODS: A total of 100 patients referred to CCTA were prospectively enrolled and randomly divided into two groups: low-dose group (n = 50) with 70 kVp, Smart mA for noise index (NI) of 36HU, contrast dose rate of 16mgI/kg/s, and DLIR-H, and conventional group (n = 50) with 120 kV, Smart mA for NI of 25HU, contrast dose rate of 32mgI/kg/s, and 60%ASIR-V. Radiation and contrast dose, subjective image quality score, and objective image quality measurement (image noise, contrast-noise-ratio (CNR), and signal-noise-ratio (SNR) for vessel) were compared between the two groups. RESULTS: Low-dose group used significantly reduced contrast dose (23.82 ± 3.69 mL, 50.6% reduction) and radiation dose (0.75 ± 0.14 mSv, 54.5% reduction) compared to the conventional group (48.23 ± 6.38 mL and 1.65 ± 0.66 mSv, respectively) (all p < 0.001). Both groups had similar enhancement in vessels. However, the low-dose group had lower background noise (23.57 ± 4.74 HU vs. 35.04 ± 8.41 HU), higher CNR in RCA (48.63 ± 10.76 vs. 29.32 ± 5.52), LAD (47.33 ± 10.20 vs. 29.27 ± 5.12), and LCX (46.74 ± 9.76 vs. 28.58 ± 5.12) (all p < 0.001) compared to the conventional group. CONCLUSIONS: The use of 70-kVp tube voltage combined with DLIR-H for CCTA in normal size patients significantly reduces radiation dose and contrast dose while further improving image quality compared with the conventional 120-kVp tube voltage with 60%ASIR-V. KEY POINTS: • The combination of 70-kVp tube voltage and high-strength deep learning image reconstruction (DLIR-H) algorithm protocol reduces approximately 50% of radiation and contrast doses in coronary computed tomography angiography (CCTA) compared with the conventional scan protocol. • CCTA of normal size (BMI < 26 kg/m2) patients acquired at sub-mSv radiation dose and 24 mL contrast dose through the combination of 70-kVp tube voltage and DLIR-H algorithm achieves excellent diagnostic image quality with a good inter-rater agreement. • DLIR-H algorithm shows a higher capacity of significantly reducing image noise than adaptive statistical iterative reconstruction algorithm in CCTA examination.