Prognostic prediction of left ventricular myocardial noncompaction using machine learning and cardiac magnetic resonance radiomics.

Background: Although there are many studies on the prognostic factors of left ventricular myocardial noncompaction (LVNC), the determinants are varied and not entirely consistent. This study aimed to build predictive models using radiomics features and machine learning to predict major adverse cardiovascular events (MACEs) in patients with LVNC. Methods: In total, 96 patients with LVNC were included and randomly divided into training and test cohorts. A total of 105 cine cardiac magnetic resonance (CMR)-derived radiomics features and 35 clinical characteristics were extracted. Five different oversampling algorithms were compared for selection of the optimal imbalanced processing. Feature importance was assessed with extreme gradient boosting (XGBoost). We compared the performance of 5 machine learning classification methods with different sample:feature ratios to determine the optimal hybrid classification strategy. Subsequently, radiomics, clinical, and combined radiomics-clinical models were developed and compared. Results: The machine learning pipeline included an adaptive synthetic (ADASYN) algorithm for imbalanced processing, XGBoost feature selection with a sample:feature ratio of 10, and support vector machine (SVM) modeling. The areas under the receiver operating characteristic curves (AUCs) of the radiomics model, clinical model, and combined model in the validation cohort were 0.87 (sensitivity 83.33%, specificity 64.29%), 0.65 (sensitivity 16.67%, specificity 78.57%), and 0.92 (specificity 33.33%, sensitivity 100.00%), respectively. The radiomics model performed similarly to the clinical and combined models (P=0.124 and P=0.621, respectively). The performance of the combined model was significantly better than that of the clinical model (P=0.003). Conclusions: The machine learning-based cine CMR radiomics model performed well at predicting MACEs in patients with LVNC. Adding radiomics features offered incremental prognostic value over clinical factors alone.

Experimental Boson Sampling Enabling Cryptographic One-Way Function.

Boson sampling is a computational problem, which is commonly believed to be a representative paradigm for attaining the milestone of quantum advantage. So far, massive efforts have been made to the experimental large-scale boson sampling for demonstrating this milestone, while further applications of the machines remain a largely unexplored area. Here, we investigate experimentally the efficiency and security of a cryptographic one-way function that relies on coarse-grained boson sampling, in the framework of a photonic boson-sampling machine fabricated by a femtosecond laser direct writing technique. Our findings demonstrate that the implementation of the function requires moderate sample sizes, which can be over 4 orders of magnitude smaller than the ones predicted by the Chernoff bound; whereas for numbers of photons n≥3 and bins d∼poly(m,n), the same output of the function cannot be generated by nonboson samplers. Our Letter is the first experimental study that deals with the potential applications of boson sampling in the field of cryptography and paves the way toward additional studies in this direction.

Prognostic Value of Left Atrial Reservoir Strain in Left Ventricular Myocardial Noncompaction: A 3.0 T Cardiac Magnetic Resonance Feature Tracking Study.

BACKGROUND: The relationship of left atrial (LA) strain to high-risk heart failure (HF) events in patients with left ventricular myocardial noncompaction (LVNC) remains to be thoroughly investigated. PURPOSE: To evaluate the LA performance in patients with LVNC, and to investigate the prognostic value of LA phasic strain on high-risk HF events, and its influencing factors. STUDY TYPE: Retrospective. POPULATION: A total of 95 LVNC patients (74 with LA enlargement [LAE] and 21 without LAE) and 50 healthy controls. FIELD STRENGTH/SEQUENCE: A 3.0 T, balanced steady-state free-precession cine imaging. ASSESSMENT: LA longitudinal strains were measured by cardiac MRI feature tracking technique. LA volume index (LAVI) and LA ejection fraction (LAEF) were calculated. Their intraobserver and interobserver reproducibility were evaluated. The primary outcome was high-risk HF events, a composite of first HF hospitalization, hospitalization for worsening HF and death from HF. STATISTICAL TESTS: Student's t/Mann-Whitney U, one-way analysis of variance/Kruskal-Wallis, Chi-squared, receiver operating characteristic, Kaplan-Meier, log-rank, Cox regression, Pearson and Spearman correlation and linear regression analyses were performed. The significance threshold was set at P < 0 .05. RESULTS: LAEF and LA longitudinal strains decreased in LVNC patients irrespective of the presence of LAE. During a median follow-up of 32.17 months, high-risk HF occurred in 13 (13.68%) patients. Patients with increased LAVI, decreased LAEF and decreased LA longitudinal strain had significantly higher risks of high-risk HF events. In patients with LVNC, LA reservoir strain (εs) was independently associated with high-risk HF (hazard ratio = 23.208 [95% CI: 2.993-179.967]). LV global longitudinal strain (LV GLS) (ß = -1.783 [95% CI: -2.493 to -1.073]) was significantly and independently associated with εs. Intraobserver and interobserver reproducibility was excellent for LAVI, LAEF, and LA strain. CONCLUSION: In patients with LVNC, εs was an independent predictor for high-risk HF events. LV GLS was an independent determinant of εs in LVNC. EVIDENCE LEVEL: 4 TECHNICAL EFFICACY: Stage 4.

Direct Observation of Dynamically Localized Quantum Optical States.

Quantum-correlated biphoton states play an important role in quantum communication and processing, especially considering the recent advances in integrated photonics. However, it remains a challenge to flexibly transport quantum states on a chip, when dealing with large-scale sophisticated photonic designs. The equivalence between certain aspects of quantum optics and solid-state physics makes it possible to utilize a range of powerful approaches in photonics, including topologically protected boundary states, graphene edge states, and dynamic localization. Optical dynamic localization allows efficient protection of classical signals in photonic systems by implementing an analogue of an external alternating electric field. Here, we report on the observation of dynamic localization for quantum-correlated biphotons, including both the generation and the propagation aspects. As a platform, we use sinusoidal waveguide arrays with cubic nonlinearity. We record biphoton coincidence count rates as evidence of robust generation of biphotons and demonstrate the dynamic localization features in both spatial and temporal space by analyzing the quantum correlation of biphotons at the output of the waveguide array. Experimental results demonstrate that various dynamic modulation parameters are effective in protecting quantum states without introducing complex topologies. Our Letter opens new avenues for studying complex physical processes using photonic chips and provides an alternative mechanism of protecting communication channels and nonclassical quantum sources in large-scale integrated quantum optics.

Diagnostic accuracy and potential covariates of artificial intelligence for diagnosing orthopedic fractures: a systematic literature review and meta-analysis.

OBJECTIVES: To systematically quantify the diagnostic accuracy and identify potential covariates affecting the performance of artificial intelligence (AI) in diagnosing orthopedic fractures. METHODS: PubMed, Embase, Web of Science, and Cochrane Library were systematically searched for studies on AI applications in diagnosing orthopedic fractures from inception to September 29, 2021. Pooled sensitivity and specificity and the area under the receiver operating characteristic curves (AUC) were obtained. This study was registered in the PROSPERO database prior to initiation (CRD 42021254618). RESULTS: Thirty-nine were eligible for quantitative analysis. The overall pooled AUC, sensitivity, and specificity were 0.96 (95% CI 0.94-0.98), 90% (95% CI 87-92%), and 92% (95% CI 90-94%), respectively. In subgroup analyses, multicenter designed studies yielded higher sensitivity (92% vs. 88%) and specificity (94% vs. 91%) than single-center studies. AI demonstrated higher sensitivity with transfer learning (with vs. without: 92% vs. 87%) or data augmentation (with vs. without: 92% vs. 87%), compared to those without. Utilizing plain X-rays as input images for AI achieved results comparable to CT (AUC 0.96 vs. 0.96). Moreover, AI achieved comparable results to humans (AUC 0.97 vs. 0.97) and better results than non-expert human readers (AUC 0.98 vs. 0.96; sensitivity 95% vs. 88%). CONCLUSIONS: AI demonstrated high accuracy in diagnosing orthopedic fractures from medical images. Larger-scale studies with higher design quality are needed to validate our findings. KEY POINTS: • Multicenter study design, application of transfer learning, and data augmentation are closely related to improving the performance of artificial intelligence models in diagnosing orthopedic fractures. • Utilizing plain X-rays as input images for AI to diagnose fractures achieved results comparable to CT (AUC 0.96 vs. 0.96). • AI achieved comparable results to humans (AUC 0.97 vs. 0.97) but was superior to non-expert human readers (AUC 0.98 vs. 0.96, sensitivity 95% vs. 88%) in diagnosing fractures.

Aggressive hydration compared to standard hydration with lactated ringer's solution for prevention of post endoscopic retrograde cholangiopancreatography pancreatitis.

BACKGROUND AND AIMS: Previous studies have suggested that aggressive hydration with lactated ringer solution are one of the protective factors in preventing post endoscopic retrograde cholangiopancreatography (post-ERCP). We conducted a systematic review and meta-analysis to examine the efficacy aggressive hydration with lactated Ringer solution in preventing PEP. METHODS: All published and unpublished articles on aggressive hydration with lactated ringer solution in those underwent ERCP procedure for any reasons were screened for eligibility. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines were followed. This paper doesn't need the IRB approval. RESULTS: Seven RCTs met the inclusion criteria. Meta-analysis indicates that aggressive hydration with lactated Ringer solution were associated with lower PEP rate.[odds ratio (OR) 0.29; 95% confidence interval (CI) 0.18-0.48]; lower incidence of hyperamylasemia (OR 0.49; 95% CI 0.35, 0.69) and lower risk of pain (OR 0.28; 95% CI 0.10-0.81). The association between aggressive hydration with lactated Ringer solution and incidence of moderate severity PEP were unclear (OR 0.57; 95% CI 0.22, 1.45). Sensitivity analyses also showed that omitting 1 study from analysis of PEP rate could reduce the heterogeneity but did not change the conclusion of this meta-analysis. A cumulating meta-analysis was performed statistically which showed a stable result of overall incidence of PEP. CONCLUSIONS: Aggressive hydration with lactated Ringer solution was a protective factor in reducing the overall incidence of PEP, hyperamylasemia and risk of abdominal pain.

Non-coding RNAS and colorectal cancer liver metastasis.

More than 50% of colorectal cancer (CRC) deaths are attributed to metastasis, and the liver is the most common distant metastatic site of CRC. The molecular mechanisms underlying CRC liver metastasis are very complicated and remain largely unknown. Accumulated evidence has shown that non-coding RNAs (NcRNAs) play critical roles in tumor development and progression. Here we reviewed the roles and underlying mechanisms of NcRNAs in CRC liver metastasis.

Pre-treatment ADC image-based random forest classifier for identifying resistant rectal adenocarcinoma to neoadjuvant chemoradiotherapy.

OBJECTIVE: To develop a predicting model for tumor resistance to neoadjuvant chemoradiotherapy (NCRT) in locally advanced rectal cancer (LARC) by using pre-treatment apparent diffusion coefficient (ADC) image-derived radiomics features. METHOD: A total of 89 patients with LARC were randomly assigned into training (N = 66) and testing cohorts (N = 23) at the ratio of 3:1. Radiomics features were derived from manually determined tumor region of pre-treatment ADC images. Random forest algorithm was used to determine the most relevant features and then to construct a predicting model for identifying resistant tumor. Stability and diagnostic performance of the random forest model was evaluated with the testing cohort. RESULTS: The top 10 most relevant features (entropymean, inverse variance, energymean, small area emphasis, ADCmin, ADCmean, sdGa02, small gradient emphasis, age, and size) were determined from clinical characteristics and 133 radiomics features. In the prediction of resistant tumor of the testing cohort, the random forest model constructed based on these most relevant features achieved an area under the receiver operating characteristic curve of 0.83, with the highest accuracy of 91.3%, a sensitivity of 88.9%, and a specificity of 92.8%. CONCLUSION: The random forest classifier based on radiomics features derived from pre-treatment ADC images have the potential to predict tumor resistance to NCRT in patients with LARC, and the use of predicting model may facilitate individualized management of rectal cancer.

PIK3CD induces cell growth and invasion by activating AKT/GSK-3ß/ß-catenin signaling in colorectal cancer.

The catalytic subunit p110δ of phosphoinositide 3-kinase (PI3K) encoded by PIK3CD has been implicated in some human solid tumors. However, its roles in colorectal cancer (CRC) remain largely unknown. Here we found that PIK3CD was overexpressed in colon cancer tissues and CRC cell lines and was an independent predictor for overall survival (OS) of patients with colon cancer. The ectopic overexpression of PIK3CD significantly promoted CRC cell growth, migration and invasion in vitro and tumor growth in vivo. In contrast, inhibition of PIK3CD by specific small-interfering RNA or idelalisib dramatically suppressed CRC cell growth, migration and invasion in vitro and tumor growth in vivo. Moreover, PIK3CD overexpression increased AKT activity, nuclear translocation of ß-catenin and T-cell factor/lymphoid enhancer factor (TCF/LEF) transcriptional activity and decreased glycogen synthase kinase 3ß (GSK-3ß) activity, whereas PIK3CD inhibition exhibited the opposite effects. Furthermore, PIK3CD-mediated cell growth, migration and invasion were reversed by blockade of AKT signaling or depletion of ß-catenin. In addition, PIK3CD expression in colon cancer tissues positively correlated with ß-catenin abnormal expression, which was an independent predictor for OS of colon cancer patients. Taken together, our findings demonstrate that PIK3CD is an independent prognostic factor in CRC and that PIK3CD induces CRC cell growth, migration and invasion by activating AKT/GSK-3ß/ß-catenin signaling, suggesting that PIK3CD might be a novel prognostic biomarker and a potential therapeutic target for CRC.