Dark papillary muscles sign is a new prognostic indicator in patients with dilated cardiomyopathy: A multi-center study.

OBJECTIVES: The prognostic value of left ventricular (LV) papillary muscle anomalies in dilated cardiomyopathy (DCM) patients is unclear. The objective of this study was to evaluate the prognostic significance of LV papillary muscle anomalies in DCM patients using cardiac magnetic resonance (CMR). METHODS: 369 DCM patients who underwent CMR at two Chinese medical facilities from January 2019 to June 2023 were retrospectively and consecutively included in total. The various features of the LV papillary muscles were taken into consideration: thickness, attachment, supernumerary papillary muscles, angles, and signal intensity. The end-systolic signal hypointensity of both papillary muscles in early post-contrast cine CMR images was identified as Dark-Paps. Major adverse cardiac events (MACEs) were assessed, and all patients were followed up. RESULTS: 119 patients (32.2 %) had Dark-Paps and 141 patients (38.2 %) experienced MACE during a median follow-up of 22 months. According to Kaplan-Meier curve analysis, patients who had Dark-Paps had a lower survival rate free from MACE (log-rank, p < 0.001). Dark-Paps maintained an independent predictor of MACE in a multivariate model that included left ventricular ejection fraction (LVEF) and late gadolinium enhancement (LGE) extent (HR: 3.49; p < 0.001). Furthermore, adding Dark-Paps to the multivariate model greatly enhanced the prognostic role of endpoint events (C-statistic improvement: 0.652-0.777, Delong test: p < 0.001). CONCLUSION: Dark-Paps is a potent independent indicator of major adverse cardiac events in dilated cardiomyopathy patients. In addition, Dark-Paps can provide additional prognostic value over the multivariable baseline clinical model.

An ensemble machine learning model assists in the diagnosis of gastric ectopic pancreas and gastric stromal tumors.

OBJECTIVE: To develop an ensemble machine learning (eML) model using multiphase computed tomography (MPCT) for distinguishing between gastric ectopic pancreas (GEP) and gastric stromal tumors (GIST) in lesions < 3 cm. METHODS: In this study, we retrospectively collected MPCT images from 138 patients between April 2017 and June 2023 across two centers. Cohort 1 comprised 94 patients divided into a training cohort and an internal validation cohort, while the 44 patients from Cohort 2 constituted the external validation cohort. Deep learning (DL) models were constructed based on the lesion region, and radiomics features were extracted to develop radiomics models, which were later integrated into the fusion model. Model performance was assessed through the analysis of the area under the receiver operating characteristic curve (AUROC). The diagnostic efficacy of the optimal model was compared with that of a radiologist. Additionally, the radiologist with the assistance of the eML model provides a secondary diagnosis, to assess the potential clinical value of the model. RESULTS: After evaluation using an external validation cohort, the radiomics model demonstrated the highest performance in the venous phase, achieving AUROC of 0.87. The DL model showed optimal performance in the non-contrast phase, with AUROC of 0.81. The eML achieved the best performance across all models, with AUROC of 0.90. The use of eML-assisted analysis resulted in a significant improvement in the junior radiologist's accuracy, rising from 0.77 to 0.93 (p < 0.05). However, the senior radiologist's accuracy, while improving from 0.86 to 0.95, did not exhibit a statistically significant difference. CONCLUSION: eML model based on MPCT can effectively distinguish between GEPs and GISTs < 3 cm. CRITICAL RELEVANCE STATEMENT: The multiphase CT-based fusion model, incorporating radiomics and DL technology, proves effective in distinguishing between GEP and gastric stromal tumors, serving as a valuable tool to enhance diagnoses and offering references for clinical decision-making. KEY POINTS: No studies yet differentiated these tumors via radiomics or DL. Radiomics and DL methodologies unveil potentially distinct phenotypes within lesions. Quantitative analysis on CT for GIST and ectopic pancreas. Ensemble learning aids accurate diagnoses, assisting treatment decisions.

Multimodal MRI-based radiomics models for the preoperative prediction of lymphovascular space invasion of endometrial carcinoma.

PURPOSE: To evaluate the predictive capabilities of MRI-based radiomics for detecting lymphovascular space invasion (LVSI) in patients diagnosed with endometrial carcinoma (EC). MATERIALS AND METHODS: A retrospective analysis was conducted on 160 female patients diagnosed with EC. The radiomics model including T2-weighted and dynamic contrast-enhanced MRI (DCE-MRI) images was established. Additionally, a conventional MRI model, which incorporated MRI-reported FIGO stage, deep myometrial infiltration (DMI), adnexal involvement, and vaginal/parametrial involvement, was established. Finally, a combined model was created by integrating the radiomics signature and conventional MRI characteristics. The predictive performance was validated by the area under the curve (AUC) of the receiver operating characteristic (ROC) curves. A stratified analysis was conducted to compare the differences between the three models by Delong test. RESULTS: In predicting LVSI, the radiomics model outperformed the clinical model in the training cohort (AUC: 0.899 vs. 0.8862) but not in the test cohort (AUC: 0.812 vs. 0.8758). The combined model demonstrated superior performance in both the training and test cohorts (training cohort: AUC = 0.934, 95% CI: 0.8807-0.9873; testing cohort: AUC = 0.905, 95% CI: 0.7679-1). CONCLUSIONS: The combined model exhibited utility in preoperatively predicting LVSI in patients with EC, offering potential benefits for clinical decision-making.

The application value of support vector machine model based on multimodal MRI in predicting IDH-1mutation and Ki-67 expression in glioma.

PURPOSE: To investigate the application value of support vector machine (SVM) model based on diffusion-weighted imaging (DWI), dynamic contrast-enhanced (DCE) and amide proton transfer- weighted (APTW) imaging in predicting isocitrate dehydrogenase 1(IDH-1) mutation and Ki-67 expression in glioma. METHODS: The DWI, DCE and APTW images of 309 patients with glioma confirmed by pathology were retrospectively analyzed and divided into the IDH-1 group (IDH-1(+) group and IDH-1(-) group) and Ki-67 group (low expression group (Ki-67 ≤ 10%) and high expression group (Ki-67 > 10%)). All cases were divided into the training set, and validation set according to the ratio of 7:3. The training set was used to select features and establish machine learning models. The SVM model was established with the data after feature selection. Four single sequence models and one combined model were established in IDH-1 group and Ki-67 group. The receiver operator characteristic (ROC) curve was used to evaluate the diagnostic performance of the model. Validation set data was used for further validation. RESULTS: Both in the IDH-1 group and Ki-67 group, the combined model had better predictive efficiency than single sequence model, although the single sequence model had a better predictive efficiency. In the Ki-67 group, the combined model was built from six selected radiomics features, and the AUC were 0.965 and 0.931 in the training and validation sets, respectively. In the IDH-1 group, the combined model was built from four selected radiomics features, and the AUC were 0.997 and 0.967 in the training and validation sets, respectively. CONCLUSION: The radiomics model established by DWI, DCE and APTW images could be used to detect IDH-1 mutation and Ki-67 expression in glioma patients before surgery. The prediction performance of the radiomics model based on the combination sequence was better than that of the single sequence model.

CT-based radiomics nomogram to predict proliferative hepatocellular carcinoma and explore the tumor microenvironment.

BACKGROUND: Proliferative hepatocellular carcinomas (HCCs) is a class of aggressive tumors with poor prognosis. We aimed to construct a computed tomography (CT)-based radiomics nomogram to predict proliferative HCC, stratify clinical outcomes and explore the tumor microenvironment. METHODS: Patients with pathologically diagnosed HCC following a hepatectomy were retrospectively collected from two medical centers. A CT-based radiomics nomogram incorporating radiomics model and clinicoradiological features to predict proliferative HCC was constructed using the training cohort (n = 184), and validated using an internal test cohort (n = 80) and an external test cohort (n = 89). The predictive performance of the nomogram for clinical outcomes was evaluated for HCC patients who underwent surgery (n = 201) or received transarterial chemoembolization (TACE, n = 104). RNA sequencing data and histological tissue slides from The Cancer Imaging Archive database were used to perform transcriptomics and pathomics analysis. RESULTS: The areas under the receiver operating characteristic curve of the radiomics nomogram to predict proliferative HCC were 0.84, 0.87, and 0.85 in the training, internal test, and external test cohorts, respectively. The radiomics nomogram could stratify early recurrence-free survivals in the surgery outcome cohort (hazard ratio [HR] = 2.25; P < 0.001) and progression-free survivals in the TACE outcome cohort (HR = 2.21; P = 0.03). Transcriptomics and pathomics analysis indicated that the radiomics nomogram was associated with carbon metabolism, immune cells infiltration, TP53 mutation, and heterogeneity of tumor cells. CONCLUSION: The CT-based radiomics nomogram could predict proliferative HCC, stratify clinical outcomes, and measure a pro-tumor microenvironment.

Migraine versus tension-type headache in automatic emotional processing: A visual mismatch negativity study.

OBJECTIVE: It is important to discriminate different headaches in clinical practice, and neurocognitive biomarkers may serve as objective tools. Several reports have suggested potential cognitive impairment for primary headaches, whereas cognitions within specific domains remain elusive, e.g., emotional processing. In this study, we aimed to characterize processing of facial expressions in migraine and tension-type headache (TTH) by analyzing expression-related visual mismatch negativity (EMMN) and explored whether their processing patterns were distinct. METHODS: Altogether, 73 headache patients (20 migraine with aura (MA), 28 migraine without aura (MwoA), 25 TTH) and 27 age-matched healthy controls were recruited. After a battery of mood/neuropsychological evaluations, an expression-related oddball paradigm containing multiple models of neutral, happy and sad faces was used to investigate automatic emotional processing. RESULTS: We observed cognitive impairment in all headache patients, especially in attention/execution subdomains, but no discrepancy existed among different headaches. Although analyses of P1/N170 did not reach significant levels, amplitude of early and late EMMN was markedly diminished in MA and MwoA compared with controls and TTH, regardless of happy or sad expression. Moreover, sad EMMN was larger (more negative) than happy EMMN only in controls, while not in all headache groups. CONCLUSIONS: Our findings implied that migraine, rather than TTH, might lead to more severe impairment of automatic emotional processing, which was manifested as no observable EMMN elicitation and disappearance of negative bias effect. The EMMN component could assist in discrimination of migraine from TTH and diagnosis of undefined headaches, and its availability needed further validations.

Co3O4/CuO@C catalyst based on cobalt-doped HKUST-1 as an efficient peroxymonosulfate activator for pendimethalin degradation: Catalysis and mechanism.

Pendimethalin (PM) is an organic pollutant (herbicide), and systematic studies on PM degradation are scarce. The efficient degradation of PM in water remains a challenge that requires to be addressed. Herein, for the first time, elemental Co was doped into HKUST-1 using a solvothermal method to generate Co3O4/CuO@C via pyrolysis. The as-prepared catalyst was used to activate peroxymonosulfate (PMS) for PM degradation, obtaining a PM degradation efficiency of 98.2 % after 30 min. The assessment of the effects of various factors on the degradation efficiency revealed that 1O2 dominated PM degradation, whereas the contribution of SO4•- was negligible. Although 3Co3O4/CuO@C exhibited a good degradation performance against other organic pollutants, its degradation performance in real water was poor. The carbon layer reduced metal-ion leaching (Co and Cu), and the synergistic interactions between Co3O4 and CuO promoted PMS activation. The roles of the components of 3Co3O4/CuO@C in PM degradation by activated PMS were investigated in the presence of CoIV and Co-OOSO3-. Two possible PM degradation pathways were systematically proposed, and the toxicity of the intermediates was analyzed. Finally, a mechanism for PM degradation by 3Co3O4/CuO@C-activated PMS was proposed.

Gd-EOB-DTPA enhanced MRI nomogram model to differentiate hepatocellular carcinoma and focal nodular hyperplasia both showing iso- or hyperintensity in the hepatobiliary phase.

BACKGROUND: To develop and validate a nomogram model based on Gd-EOB-DTPA enhanced MRI for differentiation between hepatocellular carcinoma (HCC) and focal nodular hyperplasia (FNH) showing iso- or hyperintensity in the hepatobiliary phase (HBP). METHODS: A total of 75 patients with 49 HCCs and 26 FNHs randomly divided into a training cohort (n = 52: 34 HCC; 18 FNH) and an internal validation cohort (n = 23: 15 HCC; 8 FNH). A total of 37 patients (n = 37: 25 HCC; 12 FNH) acted as an external test cohort. The clinical and imaging characteristics between HCC and FNH groups in the training cohort were compared. The statistically significant parameters were included into the FAE software, and a multivariate logistic regression classifier was used to identify independent predictors and establish a nomogram model. Receiver operating characteristic (ROC) curves were used to evaluate the prediction ability of the model, while the calibration and decision curves were used for model validation. Subanalysis was used to compare qualitative and quantitative characteristics of patients with chronic hepatitis and cirrhosis between the HCC and FNH groups. RESULTS: In the training cohort, gender, age, enhancement rate in the arterial phase (AP), focal defects in uptake were significant predictors for HCC showing iso- or hyperintensity in the HBP. In the training cohort, area under the curve (AUC), sensitivity and specificity of the nomogram model were 0.989(95%CI: 0.967-1.000), 97.1% and 94.4%. In the internal validation cohort, the above three indicators were 0.917(95%CI: 0.782-1.000), 93.3% and 87.5%. In the external test cohort, the above three indicators were 0.960(95%CI: 0.905-1.000), 84.0% and 100.0%. The results of subanalysis showed that age was the independent predictor in the patients with chronic hepatitis and cirrhosis between HCC and FNH groups. CONCLUSIONS: Gd-EOB-DTPA enhanced MRI nomogram model may be useful for discriminating HCC and FNH showing iso- or hyperintensity in the HBP before surgery.

Sex-related patterns of functional brain networks in children and adolescents with autism spectrum disorder.

Although numerous studies have emphasized the male predominance in autism spectrum disorder (ASD), how sex differences are related to the topological organization of functional networks remains unclear. This study utilized imaging data from 86 ASD (43 females, aged 7-18 years) and 86 typically developing controls (TCs) (43 females, aged 7-18 years) obtained from Autism Brain Imaging Data Exchange databases, constructed individual whole-brain functional networks, used a graph theory analysis to compute topological metrics, and assessed sex-related differences in topological metrics using a 2 × 2 factorial design. At the global level, females with ASD exhibited significantly higher cluster coefficient and local efficiency than female TCs, while no significant difference was observed between males with ASD and male TCs. Meanwhile, the neurotypical sex differences in cluster coefficient and local efficiency observed in TCs were not present in ASD. At the nodal level, ASD exhibited abnormal nodal centrality in the left middle temporal gyrus.

The impact of Hashimoto's thyroiditis on the clinical outcome of papillary thyroid cancer after radioactive iodine therapy: a propensity score matching study.

OBJECTIVE: The potential association between Hashimoto's thyroiditis (HT) and papillary thyroid carcinoma (PTC) has been studied from different perspectives for many years. This study was aimed to evaluate the impact of HT on the clinical outcomes of PTC patients after radioactive iodine (RAI) therapy. MATERIAL AND METHODS: We conducted a retrospective study on consecutive patients with PTC who underwent RAI therapy from April 2017 to May 2020. The diagnosis of HT was based on pathological examination, and patients were divided into the HT and non-HT group. Distributions of age, gender, ultrasound features, papillary variants, extrathyroidal extension, and other histopathological characteristics were observed. Propensity score matching (PSM) was used to compare the clinical features and outcomes between the two groups at 1 and 3-year follow-up. RESULTS: In total, 782 patients with PTC were enrolled (570 women, 212 men). HT was presented in 130 (16.6%) patients, and was associated with younger age, smaller primary tumors, less extrathyroidal extension, and less lymph node metastasis at presentation. On review of the images, only calcification and blood flow distribution were significantly different among the US features (P < 0.05). At the end of follow-up (three years), the responses to RAI therapy were significantly different between the two groups (ER: 76.9% vs 64.9%; IDR:11.5% vs 17.2%; BIR: 4.6% vs 10.7%; SIR: 6.9% vs 7.2%, P = 0.03). Patients with HT had less frequently evidence of disease (11.6% vs 17.9%). When compared with the matched groups, 123 pairs of patients were successfully matched, PTC patients with HT were found to have a better response to RAI therapy. CONCLUSIONS: PTC patients with HT had less aggressive characteristics at presentation. Importantly, the presence of HT not only had a significant association with the outcome, but was also protective from the risk of recurrence.

Biparametric MRI of the prostate radiomics model for prediction of pelvic lymph node metastasis in prostate cancers : a two-centre study.

OBJECTIVES: Exploring the value of adding correlation analysis (radiomic features (RFs) of pelvic metastatic lymph nodes and primary lesions) to screen RFs of primary lesions in the feature selection process of establishing prediction model. METHODS: A total of 394 prostate cancer (PCa) patients (263 in the training group, 74 in the internal validation group and 57 in the external validation group) from two tertiary hospitals were included in the study. The cases with pelvic lymph node metastasis (PLNM) positive in the training group were diagnosed by biopsy or MRI with a short-axis diameter ≥ 1.5 cm, PLNM-negative cases in the training group and all cases in validation group were underwent both radical prostatectomy (RP) and extended pelvic lymph node dissection (ePLND). The RFs of PLNM-negative lesion and PLNM-positive tissues including primary lesions and their metastatic lymph nodes (MLNs) in the training group were extracted from T2WI and apparent diffusion coefficient (ADC) map to build the following two models by fivefold cross-validation: the lesion model, established according to the primary lesion RFs selected by t tests and absolute shrinkage and selection operator (LASSO); the lesion-correlation model, established according to the primary lesion RFs selected by Pearson correlation analysis (RFs of primary lesions and their MLNs, correlation coefficient > 0.9), t test and LASSO. Finally, we compared the performance of these two models in predicting PLNM. RESULTS: The AUC and the DeLong test of AUC in the lesion model and lesion-correlation model were as follows: training groups (0.8053, 0.8466, p = 0.0002), internal validation group (0.7321, 0.8268, p = 0.0429), and external validation group (0.6445, 0.7874, p = 0.0431), respectively. CONCLUSION: The lesion-correlation model established by features of primary tumors correlated with MLNs has more advantages than the lesion model in predicting PLNM.

The Effect of Transverse Sinus Stenosis Caused by Arachnoid Granulation on Patients with Venous Pulsatile Tinnitus: A Multiphysics Interaction Simulation Investigation.

This study aimed to investigate the effect of the transverse sinus (TS) stenosis (TSS) position caused by arachnoid granulation on patients with venous pulsatile tinnitus (VPT) and to further identify the types of TSS that are of therapeutic significance for patients. Multiphysics interaction models of six patients with moderate TSS caused by arachnoid granulation and virtual stent placement in TSS were reconstructed, including three patients with TSS located in the middle segment of the TS (group 1) and three patients with TTS in the middle and proximal involvement segment of the TS (group 2). The transient multiphysics interaction simulation method was applied to elucidate the differences in biomechanical and acoustic parameters between the two groups. The results revealed that the blood flow pattern at the TS and sigmoid sinus junction was significantly changed depending on the stenosis position. Preoperative patients had increased blood flow in the TSS region and TSS downstream where the blood flow impacted the vessel wall. In group 1, the postoperative blood flow pattern, average wall pressure, vessel wall vibration, and sound pressure level of the three patients were comparable to the preoperative state. However, the postoperative blood flow velocity decreased in group 2. The postoperative average wall pressure, vessel wall vibration, and sound pressure level of the three patients were significantly improved compared with the preoperative state. Intravascular intervention therapy should be considered for patients with moderate TSS caused by arachnoid granulations in the middle and proximal involvement segment of the TS. TSS might not be considered the cause of VPT symptoms in patients with moderate TSS caused by arachnoid granulation in the middle segment of the TS.

The relationship of peripheral blood cell inflammatory biomarkers and psychological stress in unmedicated major depressive disorder.

BACKGROUND: Recent research has explored the linkage between major depressive disorder (MDD) and inflammation, especially via altered peripheral blood immune markers. However, the relationship between several novel leukocyte-derived ratios (LDR) and psychological stress in MDD remains uncertain. This study aimed to explore the relationship between LDR, clinical characteristics, recent life events, and childhood maltreatment in MDD patients. METHODS: A cross-sectional case-control study was conducted involving 59 healthy controls (HC) and 50 unmedicated MDD patients. Subjects underwent psychological assessments and peripheral blood measurements. LDR assessed in this study included neutrophil-to-lymphocyte ratio (NLR), derived NLR (dNLR), monocyte-to-lymphocyte ratio (MLR), platelet-to-lymphocyte ratio (PLR), white blood cell-to-mean platelet volume ratio (WMR), systemic immune inflammation index (SII), multiplication of neutrophil and monocyte counts (MNM), and systemic inflammation response index (SIRI). RESULTS: MDD patients displayed significant alterations in WMR, PLR, and MNM compared to HC, as well as correlations between several LDR and various clinical features (duration of untreated psychosis and dNLR, the nine-item Patient Health Questionnaire and PLR, the 7-item Generalized Anxiety Disorder Questionnaire and SIRI (NLR and dNLR). There was a significant difference in the comparison of WMR in first-episode patients than in recurrent patients. Analyses further revealed an association between Life Event Scale total scores and NLR (dNLR). No correlation was found between Childhood Trauma Questionnaire total (or subscale) scores and LDR. Additionally, WMR and dNLR presented potential predictive value for distinguishing between MDD and HC. CONCLUSION: The study concludes that MDD and some clinical features are associated with alterations in some peripheral blood LDR. These findings emphasize the potential role of peripheral blood LDR in the pathogenesis and clinical heterogeneity of MDD.

Development and Validation of a Multimodality Model Based on Whole-Slide Imaging and Biparametric MRI for Predicting Postoperative Biochemical Recurrence in Prostate Cancer.

Purpose To develop and validate a machine learning multimodality model based on preoperative MRI, surgical whole-slide imaging (WSI), and clinical variables for predicting prostate cancer (PCa) biochemical recurrence (BCR) following radical prostatectomy (RP). Materials and Methods In this retrospective study (September 2015 to April 2021), 363 male patients with PCa who underwent RP were divided into training (n = 254; median age, 69 years [IQR, 64-74 years]) and testing (n = 109; median age, 70 years [IQR, 65-75 years]) sets at a ratio of 7:3. The primary end point was biochemical recurrence-free survival. The least absolute shrinkage and selection operator Cox algorithm was applied to select independent clinical variables and construct the clinical signature. The radiomics signature and pathomics signature were constructed using preoperative MRI and surgical WSI data, respectively. A multimodality model was constructed by combining the radiomics signature, pathomics signature, and clinical signature. Using Harrell concordance index (C index), the predictive performance of the multimodality model for BCR was assessed and compared with all single-modality models, including the radiomics signature, pathomics signature, and clinical signature. Results Both radiomics and pathomics signatures achieved good performance for BCR prediction (C index: 0.742 and 0.730, respectively) on the testing cohort. The multimodality model exhibited the best predictive performance, with a C index of 0.860 on the testing set, which was significantly higher than all single-modality models (all P ≤ .01). Conclusion The multimodality model effectively predicted BCR following RP in patients with PCa and may therefore provide an emerging and accurate tool to assist postoperative individualized treatment. Keywords: MR Imaging, Urinary, Pelvis, Comparative Studies Supplemental material is available for this article. © RSNA, 2024.

Association between severity of nonalcoholic fatty liver disease and major adverse cardiovascular events in patients assessed by coronary computed tomography angiography.

BACKGROUND: The effect of nonalcoholic fatty liver disease (NAFLD) on major adverse cardiovascular events (MACEs) can be influenced by the degree of coronary artery stenosis. However, the association between the severity of NAFLD and MACEs in patients who underwent coronary computed tomography angiography (CCTA) is unclear. METHODS: A total of 341 NAFLD patients who underwent CCTA were enrolled. The severity of NAFLD was divided into mild NAFLD and moderate-severe NAFLD by abdominal CT results. The degree of coronary artery stenosis was evaluated by using Coronary Artery Disease Reporting and Data System (CAD-RADS) category. Cox regression analysis and Kaplan-Meier analysis were used to assess poor prognosis. RESULTS: During the follow-up period, 45 of 341 NAFLD patients (13.20%) who underwent CCTA occurred MACEs. The severity of NAFLD (hazard ratio [HR] = 2.95[1.54-5.66]; p = 0.001) and CAD-RADS categories 3-5 (HR = 16.31[6.34-41.92]; p < 0.001) were independent risk factors for MACEs. The Kaplan-Meier analysis showed that moderate to severe NAFLD patients had a worsen prognosis than mild NAFLD patients (log-rank p < 0.001). Moreover, the combined receiver operating characteristic curve of the severity of NAFLD and CAD-RADS category showed a good predicting performance for the risk of MACEs, with an area under the curve of 0.849 (95% CI = 0.786-0.911). CONCLUSION: The severity of NAFLD was independent risk factor for MACEs in patients with obstructive CAD, having CAD-RADS 3-5 categories on CCTA.

Impact of the Alberta Stroke Program CT Score subregions on long-term functional outcomes in acute ischemic stroke: Results from two multicenter studies in China.

Background and Objectives: The Alberta Stroke Program CT Score (ASPECTS) is a widely used rating system for assessing infarct extent and location. We aimed to investigate the prognostic value of ASPECTS subregions' involvement in the long-term functional outcomes of acute ischemic stroke (AIS). Materials and Methods: Consecutive patients with AIS and anterior circulation large-vessel stenosis and occlusion between January 2019 and December 2020 were included. The ASPECTS score and subregion involvement for each patient was assessed using posttreatment magnetic resonance diffusion-weighted imaging. Univariate and multivariable regression analyses were conducted to identify subregions related to 3-month poor functional outcome (modified Rankin Scale scores, 3-6) in the reperfusion and medical therapy cohorts, respectively. In addition, prognostic efficiency between the region-based ASPECTS and ASPECTS score methods were compared using receiver operating characteristic curves and DeLong's test. Results: A total of 365 patients (median age, 64 years; 70% men) were included, of whom 169 had poor outcomes. In the reperfusion therapy cohort, multivariable regression analyses revealed that the involvement of the left M4 cortical region in left-hemisphere stroke (adjusted odds ratio [aOR] 5.39, 95% confidence interval [CI] 1.53-19.02) and the involvement of the right M3 cortical region in right-hemisphere stroke (aOR 4.21, 95% CI 1.05-16.78) were independently associated with poor functional outcomes. In the medical therapy cohort, left-hemisphere stroke with left M5 cortical region (aOR 2.87, 95% CI 1.08-7.59) and caudate nucleus (aOR 3.14, 95% CI 1.00-9.85) involved and right-hemisphere stroke with right M3 cortical region (aOR 4.15, 95% CI 1.29-8.18) and internal capsule (aOR 3.94, 95% CI 1.22-12.78) affected were related to the increased risks of poststroke disability. In addition, region-based ASPECTS significantly improved the prognostic efficiency compared with the conventional ASPECTS score method. Conclusion: The involvement of specific ASPECTS subregions depending on the affected hemisphere was associated with worse functional outcomes 3 months after stroke, and the critical subregion distribution varied by clinical management. Therefore, region-based ASPECTS could provide additional value in guiding individual decision making and neurological recovery in patients with AIS.

In Situ Construction of CdS/g-C3N4 Heterojunctions in Spent Thiolation@Wood-Aerogel for Efficient Excitation Peroxymonosulfate to Degradation Tetracycline.

Pollutant treatment, hazardous solid waste conversion, and biomass resource utilization are significant topics in environmental pollution control, and simultaneously achieving them is challenging. Herein, we developed a "from waste absorbent to effective photocatalyst" upcycle strategy for nontoxic conversion of Cd(II) adsorbed on thiolation@wood-aerogel (TWA) into CdS/g-C3N4 heterojunctions through the in situ chemical deposition high-temperature carbonization combined conversion method to overcome the above problems simultaneously. We used Schiff base reaction to graft l-cysteine into dialdehyde@wood-aerogel to prepare TWA with a high Cd(II) adsorption capacity (600 mg/L, 294.66 mg/g). Subsequently, the spent Cd(II)-loaded-TWA was used as a substrate for in situ construction of Cd(II) into CdS/g-C3N4 heterojunction for activating peroxymonosulfate (PMS) under simulated sunlight [simulated solar light (SSL)], achieving efficient tetracycline (TC) degradation (20 mg/L, 95.32%). The Langmuir and pseudo-second-order models indicate single-layer chemical adsorption of Cd(II) on the TWA adsorption process. In the PMS/SSL system, CdS/g-C3N4@TWA efficiently and rapidly degraded TC via an adsorption-photocatalytic synergistic degradation mechanism. The used CdS/g-C3N4@TWA has a good biocompatibility. This study proposed design and preparation of a new type of wood aerogel absorbent and provided a novel upcycling strategy for innovative use of the spent waste adsorbent.

Causal relationship between lipid-lowering drugs and ovarian cancer, cervical cancer: a drug target mendelian randomization study.

BACKGROUND: The causal impact of lipid-lowering drugs on ovarian cancer (OC) and cervical cancer (CC) has received considerable attention, but its causal relationship is still a subject of debate. Hence, the objective of this study is to evaluate the impact of lipid-lowering medications on the occurrence risk of OC and CC through Mendelian randomization (MR) analysis of drug targets. METHODS: This investigation concentrated on the primary targets of lipid-lowering medications, specifically, 3-hydroxy-3-methylglutaryl-coenzyme A reductase (HMGCR) and proprotein convertase kexin 9 (PCSK9). Genetic variations associated with HMGCR and PCSK9 were derived from published genome-wide association study (GWAS) findings to serve as substitutes for HMGCR and PCSK9 inhibitors. Employing a MR approach, an analysis was conducted to scrutinize the impact of inhibitors targeting HMGCR and PCSK9 on the occurrence of OC and CC. Coronary heart disease (CHD) risk was utilized as a positive control, and the primary outcomes encompassed OC and CC. RESULTS: The findings of the study suggest a notable elevation in the risk of OC among patients treated with HMGCR inhibitors (OR [95%CI] = 1.815 [1.316, 2.315], p = 0.019). In contrast, no significant correlation was observed between PCSK9 inhibitors and the occurrence of OC. Additionally, the analysis did not reveal any noteworthy connection between HMGCR inhibitors, PCSK9 inhibitors, and CC. CONCLUSION: HMGCR inhibitors significantly elevate the risk of OC in patients, but their mechanism needs further investigation, and no influence of PCSK9 inhibitors on OC has been observed. There is no significant relationship between HMGCR inhibitors, PCSK9 inhibitors, and CC.

Risk factors for suspected pulmonary embolism in children: Complication of Mycoplasma pneumoniae pneumonia.

PURPOSE: Pulmonary embolism (PE) is not a rare complication of Mycoplasma pneumoniae pneumonia (MPP) in children. We sought to determine the incidence of PE in children with MPP who underwent clinically indicated CT pulmonary angiography (CTPA) and to evaluate the risk factors for PE. METHODS: All 106 children with MPP who were clinically suspected of having PE and who underwent CTPA were retrospectively enrolled from June 2018 to December 2021. The clinical features, laboratory data, and radiological parameters were recorded (e.g., lung consolidation involved and the Qanadli score). A Cox proportional hazards model and area under the receiver operating characteristic (ROC) curve were used to evaluate the risk factors and prognostic discriminatory capacity for PE. RESULTS: PE was detected in 26 of 106 (24.5 %) children (mean age, 6.2 years ± 3.3 years; 53 boys). Sixteen of the 26 (61.5 %) children with PE were boys. The mean age of the children with PE was 8.1 ± 2.9 years, and the mean Qanadli score was 15.3 ± 10.2. Children with PE had higher D-dimer levels (9.3 ± 7.1 mg/Lvs. 3.6 ± 3.8 mg/L) and a greater frequency of lung lobe consolidation (25 (96.2 %) vs. 64 (80.0 %)) (all P < 0.05). For children with MPP, age (hazard ratio (HR) = 1.96 (95 % CI1.04, 3.71; P = 0.037), D-dimer level (HR = 1.52, 95 % CI: 1.03, 2.24; P = 0.029), and bilateral lung consolidation (HR = 2.41, 95 % CI: 1.03, 5.58; P = 0.043) were found to be independent predictors of PE. CONCLUSION: Clinical and CT radiological predictors could be used to predict PE in children with MPP. The use of risk factor assessment as a tool has the potential to guide more appropriate use of CTPA in children.

Deep learning-based platform performs high detection sensitivity of intracranial aneurysms in 3D brain TOF-MRA: An external clinical validation study.

PURPOSE: To evaluate the diagnostic efficacy of a developed artificial intelligence (AI) platform incorporating deep learning algorithms for the automated detection of intracranial aneurysms in time-of-flight (TOF) magnetic resonance angiography (MRA). METHOD: This retrospective study encompassed 3D TOF MRA images acquired between January 2023 and June 2023, aiming to validate the presence of intracranial aneurysms via our developed AI platform. The manual segmentation results by experienced neuroradiologists served as the "gold standard". Following annotation of MRA images by neuroradiologists using InferScholar software, the AI platform conducted automatic segmentation of intracranial aneurysms. Various metrics including accuracy (ACC), balanced ACC, area under the curve (AUC), sensitivity (SE), specificity (SP), F1 score, Brier Score, and Net Benefit were utilized to evaluate the generalization of AI platform. Comparison of intracranial aneurysm identification performance was conducted between the AI platform and six radiologists with experience ranging from 3 to 12 years in interpreting MR images. Additionally, a comparative analysis was carried out between radiologists' detection performance based on independent visual diagnosis and AI-assisted diagnosis. Subgroup analyses were also performed based on the size and location of the aneurysms to explore factors impacting aneurysm detectability. RESULTS: 510 patients were enrolled including 215 patients (42.16 %) with intracranial aneurysms and 295 patients (57.84 %) without aneurysms. Compared with six radiologists, the AI platform showed competitive discrimination power (AUC, 0.96), acceptable calibration (Brier Score loss, 0.08), and clinical utility (Net Benefit, 86.96 %). The AI platform demonstrated superior performance in detecting aneurysms with an overall SE, SP, ACC, balanced ACC, and F1 score of 91.63 %, 92.20 %, 91.96 %, 91.92 %, and 90.57 % respectively, outperforming the detectability of the two resident radiologists. For subgroup analysis based on aneurysm size and location, we observed that the SE of the AI platform for identifying tiny (diameter＜3mm), small (3 mm ≤ diameter＜5mm), medium (5 mm ≤ diameter＜7mm) and large aneurysms (diameter ≥ 7 mm) was 87.80 %, 93.14 %, 95.45 %, and 100 %, respectively. Furthermore, the SE for detecting aneurysms in the anterior circulation was higher than that in the posterior circulation. Utilizing the AI assistance, six radiologists (i.e., two residents, two attendings and two professors) achieved statistically significant improvements in mean SE (residents: 71.40 % vs. 88.37 %; attendings: 82.79 % vs. 93.26 %; professors: 90.07 % vs. 97.44 %; P < 0.05) and ACC (residents: 85.29 % vs. 94.12 %; attendings: 91.76 % vs. 97.06 %; professors: 95.29 % vs. 98.82 %; P < 0.05) while no statistically significant change was observed in SP. Overall, radiologists' mean SE increased by 11.40 %, mean SP increased by 1.86 %, and mean ACC increased by 5.88 %, mean balanced ACC promoted by 6.63 %, mean F1 score grew by 7.89 %, and Net Benefit rose by 12.52 %, with a concurrent decrease in mean Brier score declined by 0.06. CONCLUSIONS: The deep learning algorithms implemented in the AI platform effectively detected intracranial aneurysms on TOF-MRA and notably enhanced the diagnostic capabilities of radiologists. This indicates that the AI-based auxiliary diagnosis model can provide dependable and precise prediction to improve the diagnostic capacity of radiologists.