Head-to-head comparison of prostate-specific membrane antigen positron emission tomography/computed tomography and multiparametric magnetic resonance imaging in the detection of biochemical recurrence of prostate cancer: a systematic review and meta-analysis

AIM

Our main goal of this meta-analytical analysis was to evaluate the diagnostic effectiveness of prostate-specific membrane antigen (PSMA) positron emission tomography (PET)/computed tomography (CT) against multiparametric magnetic resonance imaging (mpMRI) in the context of identifying biochemical recurrence in patients with prostate cancer (PCa).

MATERIALS AND METHODS

A thorough search covering articles published until March 2023 was carried out across major databases such as PubMed, Embase, and Web of Science. Studies examining the direct comparison of PSMA PET/CT and mpMRI in patients with PCa suffering biochemical recurrence were included in the inclusion criteria. Using the renowned Quality Assessment of Diagnostic Performance Studies-2 technique, each study's methodological rigor was assessed.

RESULTS

We analyzed data from six eligible studies involving 290 patients in total. The combined data showed that for PSMA PET/CT and mpMRI, respectively, the pooled overall detection rates for recurrent PCa after definitive treatment were 0.69 (95% confidence interval [CI]: 0.45-0.89) and 0.70 (95% CI: 0.44-0.91). The detection rates for local recurrence were specifically 0.52 (95% CI: 0.39-0.65) and 0.62 (95% CI: 0.31-0.89), while they were 0.50 (95% CI: 0.26-0.74) and 0.32 (95% CI: 0.18-0.48) for lymph node metastasis. Notably, there was no discernible difference between the two imaging modalities in terms of the overall detection rate (P = 0.95). The detection rates for local recurrence and lymph node metastasis did not differ statistically significantly (P = 0.55, 0.23).

CONCLUSION

The performance of PSMA PET/CT and mpMRI in identifying biochemical recurrence in PCa appears to be comparable. However, the meta-analysis' findings came from research with modest sample sizes. In this context, more extensive research should be conducted in the future.

Ultrasound deep learning radiomics and clinical machine learning models to predict low nuclear grade, ER, PR, and HER2 receptor status in pure ductal carcinoma in situ

Background

Low nuclear grade ductal carcinoma in situ (DCIS) patients can adopt proactive management strategies to avoid unnecessary surgical resection. Different personalized treatment modalities may be selected based on the expression status of molecular markers, which is also predictive of different outcomes and risks of recurrence. DCIS ultrasound findings are mostly non mass lesions, making it difficult to determine boundaries. Currently, studies have shown that models based on deep learning radiomics (DLR) have advantages in automatic recognition of tumor contours. Machine learning models based on clinical imaging features can explain the importance of imaging features.

Methods

The available ultrasound data of 349 patients with pure DCIS confirmed by surgical pathology [54 low nuclear grade, 175 positive estrogen receptor (ER+), 163 positive progesterone receptor (PR+), and 81 positive human epidermal growth factor receptor 2 (HER2+)] were collected. Radiologists extracted ultrasonographic features of DCIS lesions based on the 5th Edition of Breast Imaging Reporting and Data System (BI-RADS). Patient age and BI-RADS characteristics were used to construct clinical machine learning (CML) models. The RadImageNet pretrained network was used for extracting radiomics features and as an input for DLR modeling. For training and validation datasets, 80% and 20% of the data, respectively, were used. Logistic regression (LR), support vector machine (SVM), random forest (RF), and eXtreme Gradient Boosting (XGBoost) algorithms were performed and compared for the final classification modeling. Each task used the area under the receiver operating characteristic curve (AUC) to evaluate the effectiveness of DLR and CML models.

Results

In the training dataset, low nuclear grade, ER+, PR+, and HER2+ DCIS lesions accounted for 19.20%, 65.12%, 61.21%, and 30.19%, respectively; the validation set, they consisted of 19.30%, 62.50%, 57.14%, and 30.91%, respectively. In the DLR models we developed, the best AUC values for identifying features were 0.633 for identifying low nuclear grade, completed by the XGBoost Classifier of ResNet50; 0.618 for identifying ER, completed by the RF Classifier of InceptionV3; 0.755 for identifying PR, completed by the XGBoost Classifier of InceptionV3; and 0.713 for identifying HER2, completed by the LR Classifier of ResNet50. The CML models had better performance than DLR in predicting low nuclear grade, ER+, PR+, and HER2+ DCIS lesions. The best AUC values by classification were as follows: for low nuclear grade by RF classification, AUC: 0.719; for ER+ by XGBoost classification, AUC: 0.761; for PR+ by XGBoost classification, AUC: 0.780; and for HER2+ by RF classification, AUC: 0.723.

Conclusions

Based on small-scale datasets, our study showed that the DLR models developed using RadImageNet pretrained network and CML models may help predict low nuclear grade, ER+, PR+, and HER2+ DCIS lesions so that patients benefit from hierarchical and personalized treatment.

Development of a polarity-sensitive ratiometric fluorescent probe based on the intramolecular reaction of spiro-oxazolidine and its applications for in situ visualizing the fluctuations of polarity during ER stress

Polarity is a vital element in endoplasmic reticulum (ER) microenvironment, and its variation is closely related to many physiological and pathological activities of ER, so it is necessary to trace fluctuations of polarity in ER. However, most of fluorescent probes for detecting polarity dependent on the changes of single emission, which could be affected by many factors and cause false signals. Ratiometric fluorescent probe with "built-in calibration" can effectively avoid detection errors. Here, we have designed a ratiometric fluorescent probe HM for monitoring the ER polarity based on the intramolecular reaction of spiro-oxazolidine. It forms ring open/closed isomers driven by polarity to afford ratiometric sensing. Probe HM have manifested its ratiometric responses to polarity in spectroscopic results, which could offer much more precise information for the changes of polarity in living cells with the internal built-in correction. It also showed large emission shift ( 133 nm), high selectivity and photo-stability. In biological imaging, HM could selectively accumulate in ER with high photo-stability. Importantly, HM has ability for in situ tracing the changes of ER polarity with ratiometric behavior during the ER stress process with the stimulation of tunicamycin, dithiothreitol and hypoxia, suggesting that HM is an effective molecule tool for monitoring the variations of ER polarity.

A pH-response-based fluorescent probe for detecting the mitophagy process by tracing changes in colocalization coefficients

Mitochondria are not only the center of energy metabolism but also involved in regulating cellular activities. Quality and quantity control of mitochondria is therefore essential. Mitophagy is a lysosome-dependent process to clear dysfunctional mitochondria, and abnormal mitophagy can cause metabolic disorders. Therefore, it is necessary to monitor the mitophagy in living cells on a real-time basis. Herein, we developed a pH-responsive fluorescent probe MP for the detection of the mitophagy process using real-time tracing colocalization coefficients. Probe MP showed good pH responses with high selectivity and sensitivity in spectral testing. Probe MP is of positive charge, which is beneficial for accumulating into mitochondrial in living cells. Cells exhibited pH-dependent fluorescence when they were treated with different pH media. Importantly, the changes in the colocalization coefficient between probe MP and Lyso Tracker® Deep Red from 0.4 to 0.8 were achieved in a real-time manner during the mitophagy stimulated by CCCP, starvation and rapamycin. Therefore, combined with the parameter of the colocalization coefficient, probe MP is a potential molecular tool for the real-time tracing of mitophagy to further explore the details of mitophagy.

Dynamics of magnetization at infinite temperature in a Heisenberg spin chain

Understanding universal aspects of quantum dynamics is an unresolved problem in statistical mechanics. In particular, the spin dynamics of the one-dimensional Heisenberg model were conjectured as to belong to the Kardar-Parisi-Zhang (KPZ) universality class based on the scaling of the infinite-temperature spin-spin correlation function. In a chain of 46 superconducting qubits, we studied the probability distribution of the magnetization transferred across the chain's center, [Formula: see text]. The first two moments of [Formula: see text] show superdiffusive behavior, a hallmark of KPZ universality. However, the third and fourth moments ruled out the KPZ conjecture and allow for evaluating other theories. Our results highlight the importance of studying higher moments in determining dynamic universality classes and provide insights into universal behavior in quantum systems.

Sparse-view cone-beam computed tomography iterative reconstruction based on new multi-gradient direction total variation

AIM

The accurate reconstruction of cone-beam computed tomography (CBCT) from sparse projections is one of the most important areas for study. The compressed sensing theory has been widely employed in the sparse reconstruction of CBCT. However, the total variation (TV) approach solely uses information from the i-coordinate, j-coordinate, and k-coordinate gradients to reconstruct the CBCT image.

MATERIALS AND METHODS

It is well recognized that the CBCT image can be reconstructed more accurately with more gradient information from different directions. Thus, this study introduces a novel approach, named the new multi-gradient direction total variation minimization method. The method uses gradient information from the ij-coordinate, ik-coordinate, and jk-coordinate directions to reconstruct CBCT images, which incorporates nine different types of gradient information from nine directions.

RESULTS

This study assessed the efficacy of the proposed methodology using under-sampled projections from four different experiments, including two digital phantoms, one patient's head dataset, and one physical phantom dataset. The results indicated that the proposed method achieved the lowest RMSE index and the highest SSIM index. Meanwhile, we compared the voxel intensity curves of the reconstructed images to assess the edge structure preservation. Among the various methods compared, the curves generated by the proposed method exhibited the highest level of consistency with the gold standard image curves.

CONCLUSION

In summary, the proposed method showed significant potential in enhancing the quality and accuracy of CBCT image reconstruction.

Efficacy and safety of intrathecal pemetrexed for TKI-failed leptomeningeal metastases from EGFR+ NSCLC: an expanded, single-arm, phase II clinical trial

BACKGROUND

This study aimed to evaluate the efficacy and safety of intrathecal pemetrexed (IP) for treating patients with leptomeningeal metastases (LM) from non-small-cell lung cancer (NSCLC) who progressed from epidermal growth factor receptor (EGFR)-tyrosine kinase inhibitor (TKI) treatment in an expanded, prospective, single-arm, phase II clinical study (ChiCTR1800016615).

PATIENTS AND METHODS

Patients with confirmed NSCLC-LM who progressed from TKI received IP (50 mg, day 1/day 5 for 1 week, then every 3 weeks for four cycles, and then once monthly) until disease progression or intolerance. Objectives were to assess overall survival (OS), response rate, and safety. Measurable lesions were assessed by investigator according to RECIST version 1.1. LM were assessed according to the Response Assessment in Neuro-Oncology (RANO) criteria.

RESULTS

The study included 132 patients; 68% were female and median age was 52 years (31-74 years). The median OS was 12 months (95% confidence interval 10.4-13.6 months), RANO-assessed response rate was 80.3% (106/132), and the most common adverse event was myelosuppression (n = 42; 31.8%), which reversed after symptomatic treatment. The results of subgroup analysis showed that absence of brain parenchymal metastasis, good Eastern Cooperative Oncology Group score, good response to IP treatment, negative cytology after treatment, and patients without neck/back pain/difficult defecation had longer survival. Gender, age, previous intrathecal methotrexate/cytarabine, and whole-brain radiotherapy had no significant influence on OS.

CONCLUSIONS

This study further showed that IP is an effective and safe treatment method for the EGFR-TKI-failed NSCLC-LM, and should be recommended for these patients in clinical practice and guidelines.

Stable quantum-correlated many-body states through engineered dissipation

Engineered dissipative reservoirs have the potential to steer many-body quantum systems toward correlated steady states useful for quantum simulation of high-temperature superconductivity or quantum magnetism. Using up to 49 superconducting qubits, we prepared low-energy states of the transverse-field Ising model through coupling to dissipative auxiliary qubits. In one dimension, we observed long-range quantum correlations and a ground-state fidelity of 0.86 for 18 qubits at the critical point. In two dimensions, we found mutual information that extends beyond nearest neighbors. Lastly, by coupling the system to auxiliaries emulating reservoirs with different chemical potentials, we explored transport in the quantum Heisenberg model. Our results establish engineered dissipation as a scalable alternative to unitary evolution for preparing entangled many-body states on noisy quantum processors.

[The clinical significance of lateral pelvic sentinel lymph node biopsy using indocyanine green fluorescence navigation in laparoscopic lateral pelvic lymph node dissection]

Objectives: This study aims to explore the clinical significance of lateral pelvic sentinel lymph node biopsy (SLNB) using indocyanine green (ICG) fluorescence navigation in laparoscopic lateral pelvic lymph node dissection (LLND) and evaluate the accuracy and feasibility of this technique to predict the status of lateral pelvic lymph nodes (LPLNs). Methods: The clinical and pathological characteristics, surgical outcomes, lymph node findings and perioperative complications of 16 rectal cancer patients who underwent SLNB using ICG fluorescence navigation in laparoscopic LLND in the Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College during April 2017 and October 2022 were retrospectively collected and analyzed. The patients did not receive preoperative neoadjuvant radiotherapy and presented with LPLNs but without LPLN enlargement (MRI showed the maximum short axes of the LPLNs were ≥5 mm and <10 mm at first visit). Results: All 16 patients were successfully performed SLNB using ICG fluorescence navigation in laparoscopic LLND. Three patients underwent bilateral LLND and 13 patients underwent unilateral LLND. The lateral pelvic sentinel lymph nodes (SLNs) were clearly fluorescent before dissection in 14 patients and the detection rate of SLNs for these patients was 87.5%. Lateral pelvic SLN metastasis was diagnosed in 2 patients and negative results were found in 12 patients by frozen pathological examinations. Among the 14 patients in whom lateral pelvic SLNs were detected, the dissected lateral pelvic non-SLNs were all negative. All dissected LPLNs were negative in two patients without fluorescent lateral pelvic SLNs. The specificity, sensitivity, negative predictive value, and accuracy was 85.7%, 100%, 100%, and 100%, respectively. Conclusions: This study indicates that lateral pelvic SLNB using ICG fluorescence navigation shows promise as a safe and feasible procedure with good accuracy. This technique may replace preventive LLND for locally advanced lower rectal cancer.

[Association between CD4+T lymphocyte and body composition with physical frailty among elderly HIV-infected patients in Chongqing City]

Objective: To identify the association between CD4+T lymphocyte (CD4) counts and physical frailty among HIV-infected people aged 65 years and older, and evaluate whether this association will be modified by the indicators of body composition. Methods: From May to October 2022, 485 elderly HIV-infected patients receiving antiretroviral therapy (ART) were recruited from 7 antiviral treatment sites in Jiangjin District Center for Disease Control and Prevention, Chongqing. The data of basic characteristics (age and gender), living habits (smoking and drinking) and disease history (metabolic diseases, cardiovascular and cerebrovascular diseases, respiratory disease and malignant tumors) were collected through the face-to-face investigation with self-made questionnaires. Fried Frailty Scale was used to evaluate the status of physical frailty. Physical fitness (walking speed, grip strength, height, and weight) and body composition (skeletal muscle mass, body fat mass, and basal metabolic rate) were measured. The antiretroviral treatment data were obtained from the China AIDS Integrated Prevention and Treatment Data information management system. The prevalence of physical frailty was calculated among the HIV-infected patients. The potential effects of CD4 counts on physical frailty were explored by using multivariate logistic regression. Subgroup analyses were repeated in the logistic regression with muscle mass, body fat mass, and other indicators of body composition as subgroup variables to determine whether the association might be modified by body composition. Results: The age of 485 patients were (72±5) years old, of which 48.2% (234 cases) were>70 years old and 70.9% (344 cases) were male, and all of whom had initiated the ART treatment. The prevalence of physical frailty among these patients was 7.4% (36/485). Multivariate logistic regression showed that after adjusting for age, sex, smoking, drinking, body composition index, ART duration, viral load and the number of comorbidities, increased CD4 cell level was associated with decreased prevalent risk of physical frailty among elderly HIV-infected patients. For every increase of 5.0×107 CD4 cells/L, the prevalent risk of physical frailty decreased by 12% [OR (95%CI): 0.88 (0.76-1.01)]. Compared with the low CD4 cell level group, the risk of physical frailty in those with normal CD4 cell level decreased by 69% [OR (95%CI): 0.31 (0.10-0.92)]. Subgroup analysis of body composition indicators showed that the protective effect of normal CD4 cell level on physical frailty was more pronounced in the high skeletal muscle mass and high basal metabolic rate group (Pinteraction<0.05). Conclusion: The prevalence of physical frailty among elderly HIV-infected patients is relatively lower in Chongqing, and the CD4 cell level, skeletal muscle mass and basal metabolic rate are related to physical frailty.

MRI radiomics-based decision support tool for a personalized classification of cervical disc degeneration: a two-center study

Objectives: To develop and validate an MRI radiomics-based decision support tool for the automated grading of cervical disc degeneration. Methods: The retrospective study included 2,610 cervical disc samples of 435 patients from two hospitals. The cervical magnetic resonance imaging (MRI) analysis of patients confirmed cervical disc degeneration grades using the Pfirrmann grading system. A training set (1,830 samples of 305 patients) and an independent test set (780 samples of 130 patients) were divided for the construction and validation of the machine learning model, respectively. We provided a fine-tuned MedSAM model for automated cervical disc segmentation. Then, we extracted 924 radiomic features from each segmented disc in T1 and T2 MRI modalities. All features were processed and selected using minimum redundancy maximum relevance (mRMR) and multiple machine learning algorithms. Meanwhile, the radiomics models of various machine learning algorithms and MRI images were constructed and compared. Finally, the combined radiomics model was constructed in the training set and validated in the test set. Radiomic feature mapping was provided for auxiliary diagnosis. Results: Of the 2,610 cervical disc samples, 794 (30.4%) were classified as low grade and 1,816 (69.6%) were classified as high grade. The fine-tuned MedSAM model achieved good segmentation performance, with the mean Dice coefficient of 0.93. Higher-order texture features contributed to the dominant force in the diagnostic task (80%). Among various machine learning models, random forest performed better than the other algorithms (p < 0.01), and the T2 MRI radiomics model showed better results than T1 MRI in the diagnostic performance (p < 0.05). The final combined radiomics model had an area under the receiver operating characteristic curve (AUC) of 0.95, an accuracy of 89.51%, a precision of 87.07%, a recall of 98.83%, and an F1 score of 0.93 in the test set, which were all better than those of other models (p < 0.05). Conclusion: The radiomics-based decision support tool using T1 and T2 MRI modalities can be used for cervical disc degeneration grading, facilitating individualized management.

A difficulty-aware and task-augmentation method based on meta-learning model for few-shot diabetic retinopathy classification

Background

Accurate classification techniques are essential for the early diagnosis and treatment of patients with diabetic retinopathy (DR). However, the limited amount of annotated DR data poses a challenge for existing deep-learning models. This article proposes a difficulty-aware and task-augmentation method based on meta-learning (DaTa-ML) model for few-shot DR classification with fundus images.

Methods

The difficulty-aware (Da) method operates by dynamically modifying the cross-entropy loss function applied to learning tasks. This methodology has the ability to intelligently down-weight simpler tasks, while simultaneously prioritizing more challenging tasks. These adjustments occur automatically and aim to optimize the learning process. Additionally, the task-augmentation (Ta) method is used to enhance the meta-training process by augmenting the number of tasks through image rotation and improving the feature-extraction capability. To implement the expansion of the meta-training tasks, various task instances can be sampled during the meta-training stage. Ultimately, the proposed Ta method was introduced to optimize the initialization parameters and enhance the meta-generalization performance of the model. The DaTa-ML model showed promising results by effectively addressing the challenges associated with few-shot DR classification.

Results

The Asia Pacific Tele-Ophthalmology Society (APTOS) 2019 blindness detection data set was used to evaluate the DaTa-ML model. The results showed that with only 1% of the training data (5-way, 20-shot) and a single update step (training time reduced by 90%), the DaTa-ML model had an accuracy rate of 89.6% on the test data, which is a 1.7% improvement over the transfer-learning method [i.e., residual neural network (ResNet)50 pre-trained on ImageNet], and a 16.8% improvement over scratch-built models (i.e., ResNet50 without pre-trained weights), despite having fewer trainable parameters (the parameters used by the DaTa-ML model are only 0.47% of the ResNet50 parameters).

Conclusions

The DaTa-ML model provides a more efficient DR classification solution with little annotated data and has significant advantages over state-of-the-art methods. Thus, it could be used to guide and assist ophthalmologists to determine the severity of DR.

Hierarchical-Instance Contrastive Learning for Minority Detection on Imbalanced Medical Datasets

Deep learning methods are often hampered by issues such as data imbalance and data-hungry. In medical imaging, malignant or rare diseases are frequently of minority classes in the dataset, featured by diversified distribution. Besides that, insufficient labels and unseen cases also present conundrums for training on the minority classes. To confront the stated problems, we propose a novel Hierarchical-instance Contrastive Learning (HCLe) method for minority detection by only involving data from the majority class in the training stage. To tackle inconsistent intra-class distribution in majority classes, our method introduces two branches, where the first branch employs an auto-encoder network augmented with three constraint functions to effectively extract image-level features, and the second branch designs a novel contrastive learning network by taking into account the consistency of features among hierarchical samples from majority classes. The proposed method is further refined with a diverse mini-batch strategy, enabling the identification of minority classes under multiple conditions. Extensive experiments have been conducted to evaluate the proposed method on three datasets of different diseases and modalities. The experimental results show that the proposed method outperforms the state-of-the-art methods.

Home-based tele-rehabilitation versus hospital-based outpatient rehabilitation for pain and function after initial total knee arthroplasty: A systematic review and meta-analysis

BACKGROUND

This systematic review and meta-analysis aims to compare the effectiveness of home-based tele-rehabilitation programs with hospital-based rehabilitation programs in improving pain and function at various time points (≤6 weeks, ≤14 weeks, and ≤ 52 weeks) following the initial total knee arthroplasty.

METHODS

This study used PRISMA and AMSTAR reporting guidelines. We systematically searched 5 databases (PubMed, Embase, Web of Science, Cochrane Library, and Medline) to identify randomized controlled trials published from January 1, 2019, to January 1, 2023. The primary outcomes were pain, knee injury and osteoarthritis outcome score, and mobility (knee range of motion).

RESULTS

We included 9 studies involving 1944 patients. Low-quality evidence showed hospital-based rehabilitation was better than home-based tele-rehabilitation in knee injury and osteoarthritis outcome score (mean difference [MD], -2.62; 95% confidence interval [CI], -4.65 to -0.58; P = .01) at ≤ 14 weeks after total knee arthroplasty. Based on low-quality evidence, home-based tele-rehabilitation was better than hospital-based rehabilitation in knee range of motion (MD, 2.00; 95% CI, 0.60 to 3.40; P = .005). There was no significant difference between hospital-based rehabilitation and home-based tele-rehabilitation in knee pain at ≤ 6 weeks (MD, 0.18; 95% CI, -0.07 to 0.42; P = .16), 14 weeks (MD, 0.12; 95% CI, -0.26 to 0.49; P = .54), and ≤ 52 weeks (MD, 0.16; 95% CI, -0.11 to 0.43; P = .24).

CONCLUSION

Home-based tele-rehabilitation and hospital-based rehabilitation programs showed comparable long-term outcomes in pain, mobility, physical function, and patient-reported health status after primary total knee arthroplasty. Considering the economic costs, home-based tele-rehabilitation programs are recommended as a viable alternative to hospital-based rehabilitation programs.

Risk factors in ICU patients with initial acquisition of carbapenemase-resistant Klebsiella Pneumoniae

OBJECTIVE: To identify the risk factors associated with antimicrobial use on the initial acquisition of carbapenem-resistant Klebsiella pneumoniae (CRKP) in elderly intensive care unit (ICU) patients.METHODS: Respiratory secretion, blood, urine, anal swab and peritoneal drainage samples from all elderly patients with non-colonised CRKP who had been hospitalised from January 2021 to December 2022 were collected, and screened for CRKP colonisation using surveillance culture at the time of the first ICU admission and weekly thereafter in Zhejiang Provincial Hospital of Chinese Medicine, Zhejiang, China. Cumulative antibiotic variables included duration of antibiotic use, total amount of antimicrobials received in grams, total antibiotic consumption (defined daily dose) and the types of antimicrobial exposure. A time-dependent model based on Cox regression analysis was used to investigate the effect of each variable on the initial acquisition of CRKP infection or colonisation.RESULTS: Of 214 patients, 44 were infected or had CRKP colonies and death rate was 34.1%. males were the risk factor for acquiring CRKP in culture (HR 2.12, 95% CI 1.06-4.21; P = 0.033). It is notable that the hazard of acquiring CRKP increased by 9% with every single-point increase in the APACHE II score (HR 1.09, 95% CI 1.01-1.18; P = 0.025). The hazard of acquiring CRKP doubled when carbapenems were administered (HR 1.81, 95% CI 1.42-2.30; P < 0.001), In contrast, exposure to quinolone antimicrobials had a smaller effect on acquiring CRKP (HR 1.07; 95% CI 1.01-1.14; P = 0.024).CONCLUSION: This study found that male sex, APACHE II score and exposure to quinolones and carbapenems were independent risk factors for acquiring CRKP.

[Comparison of diagnostic criteria for acute kidney injury in critically ill children]

Objective: The kidney disease: improving global outcome (KDIGO) and pediatric reference change value optimized for acute kidney injury (pROCK) criteria were used to evaluate the incidence, stages and mortality of acute kidney injury (AKI). The differences between the 2 criteria were compared for exploring the value of pROCK criteria in diagnosing pediatric AKI and predicting adverse outcomes. Methods: In the multicenter prospective clinical cohort study, we collected general data and clinical data such as serum creatinine values from 1 120 children admitted to 4 PICUs of Children's Hospital of Soochow University, Children's Hospital of Fudan University, Anhui Provincial Children's Hospital, and Xuzhou Children's Hospital from September 2019 to February 2021. AKI was defined and staged according to the KDIGO and pROCK criteria. The incidence of AKI, the consistency of AKI definite diagnosis and stages, and the mortality in PICU were compared between the 2 groups. The chi-square test or Fisher's exact test was applied for comparison between 2 groups. The Cohen's Kappa and Weighted Kappa analyses were used for evaluating diagnostic consistency. The Cox regression analysis was used to evaluate the correlation between AKI and mortality. Results: A total of 1 120 critically ill children were included, with an age of 33 (10, 84) months. There are 668 boys and 452 girls. The incidence of AKI defined by the KDIGO guideline was higher than that defined by pROCK criteria (27.2%(305/1 120), 14.7%(165/1 120), χ2=52.78, P<0.001). The concordance rates of the 2 criteria for the diagnosis of AKI and AKI staging were 87.0% (κ=0.62) and 79.7% (κ=0.58), respectively. Totally 63 infants with AKI stage 1 defined by the KDIGO guideline were redefined as non-AKI by following the pROCK criteria. The PICU mortality rate of these infants was similar to patients without AKI defined by KDIGO guideline(P=0.761). After adjusting for confounders, AKI defined by KDIGO or pROCK criteria was an independent risk factor of death in PICU (AHR=2.04, 2.73,95%CI 1.27-3.29, 1.74-4.28, both P<0.01), and the risk of death was higher when using the pROCK compared with the KDIGO criteria. As for the KDIGO criteria, mild AKI was not associated with the mortality in PICU (P=0.702), while severe AKI was associated with increased mortality (P<0.001). As for the pROCK criteria, both mild and severe AKI were risk factors of PICU death in children (HR=3.51, 6.70, 95%CI 1.94-6.34, 4.30-10.44, both P<0.001). In addition, The AKI severity was positively associated with the mortality. Conclusions: The AKI incidence and staging varied depending on the used diagnostic criteria. The KDIGO definition is more sensitive, while the pROCK-defined AKI is more strongly associated with high mortality rate.

Robust deep learning-based PET prognostic imaging biomarker for DLBCL patients: a multicenter study

OBJECTIVE

To develop and independently externally validate robust prognostic imaging biomarkers distilled from PET images using deep learning techniques for precise survival prediction in patients with diffuse large B cell lymphoma (DLBCL).

METHODS

A total of 684 DLBCL patients from three independent medical centers were included in this retrospective study. Deep learning scores (DLS) were generated from PET images using deep convolutional neural network architecture known as VGG19 and DenseNet121. These DLSs were utilized to predict progression-free survival (PFS) and overall survival (OS). Furthermore, multiparametric models were designed based on results from the Cox proportional hazards model and assessed through calibration curves, concordance index (C-index), and decision curve analysis (DCA) in the training and validation cohorts.

RESULTS

The DLSPFS and DLSOS exhibited significant associations with PFS and OS, respectively (P<0.05) in the training and validation cohorts. The multiparametric models that incorporated DLSs demonstrated superior efficacy in predicting PFS (C-index: 0.866) and OS (C-index: 0.835) compared to competing models in training cohorts. In external validation cohorts, the C-indices for PFS and OS were 0.760 and. 0.770 and 0.748 and 0.766, respectively, indicating the reliable validity of the multiparametric models. The calibration curves displayed good consistency, and the decision curve analysis (DCA) confirmed that the multiparametric models offered more net clinical benefits.

CONCLUSIONS

The DLSs were identified as robust prognostic imaging biomarkers for survival in DLBCL patients. Moreover, the multiparametric models developed in this study exhibited promising potential in accurately stratifying patients based on their survival risk.

Measurement-induced entanglement and teleportation on a noisy quantum processor

Measurement has a special role in quantum theory1: by collapsing the wavefunction, it can enable phenomena such as teleportation2 and thereby alter the 'arrow of time' that constrains unitary evolution. When integrated in many-body dynamics, measurements can lead to emergent patterns of quantum information in space-time3-10 that go beyond the established paradigms for characterizing phases, either in or out of equilibrium11-13. For present-day noisy intermediate-scale quantum (NISQ) processors14, the experimental realization of such physics can be problematic because of hardware limitations and the stochastic nature of quantum measurement. Here we address these experimental challenges and study measurement-induced quantum information phases on up to 70 superconducting qubits. By leveraging the interchangeability of space and time, we use a duality mapping9,15-17 to avoid mid-circuit measurement and access different manifestations of the underlying phases, from entanglement scaling3,4 to measurement-induced teleportation18. We obtain finite-sized signatures of a phase transition with a decoding protocol that correlates the experimental measurement with classical simulation data. The phases display remarkably different sensitivity to noise, and we use this disparity to turn an inherent hardware limitation into a useful diagnostic. Our work demonstrates an approach to realizing measurement-induced physics at scales that are at the limits of current NISQ processors.

CREB3L2 Regulates Hemidesmosome Formation during Epithelial Sealing

The long-term success rate of dental implants can be improved by establishing a favorable biological sealing with a high-quality epithelial attachment. The application of mesenchymal stem cells (MSCs) holds promise for facilitating the soft tissue integration around implants, but the molecular mechanism is still unclear and the general application of MSC sheet for soft tissue integration is also relatively unexplored. We found that gingival tissue-derived MSC (GMSC) sheet treatment significantly promoted the expression of hemidesmosome (HD)-related genes and proteins in gingival epithelial cells (GECs). The formation of HDs played a key role in strengthening peri-implant epithelium (PIE) sealing. Further, high-throughput transcriptome sequencing showed that GMSC sheet significantly upregulated the PI3K/AKT pathway, confirming that cell adhesion and HD expression in GECs were regulated by GMSC sheet. We observed that the expression of transcription factor CREB3L2 in GECs was downregulated. After treatment with PI3K pathway inhibitor LY294002, CREB3L2 messenger RNA and protein expression levels were upregulated. Further experiments showed that overexpression or knockdown of CREB3L2 could significantly inhibit or promote HD-related genes and proteins, respectively. We confirmed that CREB3L2 was a transcription factor downstream of the PI3K/AKT pathway and participated in the formation of HDs regulated by GMSC sheet. Finally, through the establishment of early implant placement model in rats, we clarified the molecular function of CREB3L2 in PIE sealing as a mechanical transmission molecule in GECs. The application of GMSC sheet-implant complex could enhance the formation of HDs at the implant-PIE interface and decrease the penetration distance of horseradish peroxidase between the implant and PIE. Meanwhile, GMSC sheet reduced the length of CREB3L2 protein expression on PIE. These findings elucidate the potential function and molecular mechanism of MSC sheet regulating the epithelial sealing around implants, providing new insights and ideas for the application of stem cell therapy in regenerative medicine.

Anatomically Guided Cross-Domain Repair and Screening for Ultrasound Fetal Biometry

Ultrasound based estimation of fetal biometry is extensively used to diagnose prenatal abnormalities and to monitor fetal growth, for which accurate segmentation of the fetal anatomy is a crucial prerequisite. Although deep neural network-based models have achieved encouraging results on this task, inevitable distribution shifts in ultrasound images can still result in severe performance drop in real world deployment scenarios. In this article, we propose a complete ultrasound fetal examination system to deal with this troublesome problem by repairing and screening the anatomically implausible results. Our system consists of three main components: A routine segmentation network, a fetal anatomical key points guided repair network, and a shape-coding based selective screener. Guided by the anatomical key points, our repair network has stronger cross-domain repair capabilities, which can substantially improve the outputs of the segmentation network. By quantifying the distance between an arbitrary segmentation mask to its corresponding anatomical shape class, the proposed shape-coding based selective screener can then effectively reject the entire implausible results that cannot be fully repaired. Extensive experiments demonstrate that our proposed framework has strong anatomical guarantee and outperforms other methods in three different cross-domain scenarios.

Hybrid-Supervised Deep Learning for Proton-Acoustic Reconstruction for 3D In Vivo Proton Dose Verification

PURPOSE/OBJECTIVE(S)

Proton-acoustic (PA) image has shown great potential to provide real-time 3D dose verification of proton therapy. However, the PA image quality suffers from severe limited view artifacts, which significantly impairs its accuracy for dose verification. In this study, we developed a hybrid-supervised deep learning method for PA reconstruction to address the limited-view issues.

MATERIALS/METHODS

Our method consists of two stages. In the first stage, a transformer-based network was proposed to reconstruct initial pressure maps from protoacoustic signals. The network was first trained using supervision by the iteratively reconstructed pressure map and then fine-tuned using transfer learning and self-supervision based on the data fidelity constraint. In the second stage, the PA image was further enhanced by a 3D U-net. The final PA images were converted to dose maps using conversion coefficients derived from CT images. Data from 126 prostate cancer patients treated by proton therapy were collected under an IRB protocol and were split into 86 and 40 patients for model training and testing, respectively. Data of each patient contains the planning CT scan, the corresponding clinical treatment plan, and the dose map calculated by commercial software. The radiofrequency signals were generated by performing proton acoustic simulation based on CT images and the ground truth pressure map derived from the treatment plan. An ultrasound detector matrix with 64 × 64 size and 500kHz central frequency was simulated under the perineum to acquire the signals in the prostate area. In the testing results, the method's accuracy was evaluated using Root-mean-squared-error (RMSE) and structural-similarity-index-measure (SSIM) between the reconstructed and ground truth pressure map and dose distribution.

RESULTS

Testing results showed that the reconstructed pressure map achieved an average RMSE/SSIM of 0.0292/0.96, demonstrating excellent 3D information with details. Dose maps derived from the pressure map achieved an average RMSE/SSIM of 0.018/0.99 with a gamma index of 94.7% and 95.7% for 1%/3 mm and 1%/5 mm criteria compared to the ground truth dose maps. The reconstruction time was 6s, which can be further reduced using GPU.

CONCLUSION

Our study achieves start-of-the-art performance in the challenging task of direct reconstruction from limited-view radiofrequency signals, demonstrating the great promise of PA imaging as a highly efficient and accurate tool for in-vivo 3D proton dose verification. Such high-precision 3D online dose verification can substantially reduce the range uncertainties of proton therapy to significantly improve its precision and outcomes.

A Radiotherapy Positioning Method for Both Coarse Guidance and Precise Verification Based on Integration of AR and Optical Surface Imaging

PURPOSE/OBJECTIVE(S)

Traditional methods of radiotherapy positioning have shortcomings such as fragile skin-markers, additional doses and lack of information integration. Emerging technologies may provide alternatives for the relevant clinical practice. We proposed a noninvasive radiotherapy positioning method integrating augmented reality (AR) and optical surface, and evaluated its feasibility in clinical workflow.

MATERIALS/METHODS

AR and structured light-based surface were integrated to implement the coarse-to-precise positioning through two coherent steps, i) the AR-based coarse guidance. To implement quality assurance, recognition of face and pattern was used for patient authentication, case association and accessory validation in AR scenes. The holographic images reconstructed from simulation computed tomography (CT) images, guided the initial posture correction by virtual-real alignment. ii) optical surface-based precise verification. The point clouds were fused, with the calibration and pose estimation of structured light cameras, and segmented according to the preset regions of interest (ROIs). The global-to-local registration for cross-source point clouds was achieved to calculate couch shifts in 6 degrees-of-freedom (DoF), which were ultimately transmitted to AR scenes. The evaluation based on phantom and human-body (4 volunteers) included, i) quality assurance workflow, ii) errors of both steps and correlation analysis, and iii) receiver operating characteristic (ROC).

RESULTS

The maximum errors in phantom evaluation were 3.4±2.5 mm in Vrt and 1.4±1.0° in Pitch for the coarse guidance step, while 1.6±0.9 mm in Vrt and 0.6±0.4° in Pitch for the precise verification step. The Pearson correlation coefficients between precise verification and cone beam CT (CBCT) results were distributed in the interval [0.81, 0.85]. In ROC analysis, the areas under the curve (AUC) were 0.87 and 0.89 for translation and rotation respectively. In human body-based evaluation, the errors of thorax and abdomen (T&A) were significantly greater than those of head and neck (H&N) in Vrt (2.6±1.3 vs. 1.7±1.1, p<0.01), Lng (2.4±1.3 vs. 1.4±0.1, p<0.01) and Rtn (0.8±0.5 vs. 0.6±0.4, p = 0.03) while relatively similar in Lat (1.7±1.0 vs. 1.9±1.1, p = 0.13).

CONCLUSION

The combination of AR and optical surface has utility and feasibility for patient positioning, in terms of both safety and accuracy.

Computational fluid dynamics can detect changes in airway resistance for patients after COVID-19 infection

Infection with COVID-19 can cause severe complication in the respiratory system, which may be related to increased respiratory resistance. Computational fluid dynamics(CFD) was used in this study to calculate the airway resistance based on the airway anatomy and a common air flowrate. The correlation between airway resistance and COVID-19 prognosis was then investigated. A total of 23 COVID-19 patients with 54 CT scans were grouped into the good prognosis and bad prognosis group based on whether the CT scan shows significant decrease in the pneumonia volume after one week treatment and retrospectively analyzed. A baseline group of 8 healthy people with the same age and gender ratio is enrolled for comparison. Results show that the airway resistance at admission is significantly higher for COVID-19 patients with poor prognosis than those with good prognosis and the baseline(0.063 ± 0.055 vs 0.029 ± 0.011 vs 0.017 ± 0.006 Pa/(ml/s),p = 0.01). In the left superior lobe (r = 0.3974,p = 0.01),left inferior lobe (r = 0.4843,p < 0.01), the right inferior lobe (r = 0.5298,p < 0.0001), the airway resistance was significantly correlated with the degree of pneumonia infection. It is concluded that for COVID-19 patients', airway resistance at admission is closely associated with their prognosis, and has the clinical potential to be used as an index for patients' diagnosis.

[Targeting microRNA-125b inhibited the metastasis of Alisertib resistance cells through mediating p53 pathway]

Objective: To clarify the mechanisms involvement in Alisertib-resistant colorectal cells and explore a potential target to overcome Alisertib-resistance. Methods: Drug-resistant colon cancer cell line (named as HCT-8-7T cells) was established and transplanted into immunodeficient mice. The metastasis in vivo were observed. Proliferation and migration of HCT-8-7T cells and their parental cells were assessed by colony formation and Transwell assay, respectively. Glycolytic capacity and glutamine metabolism of cells were analyzed by metabolism assays. The protein and mRNA levels of critical factors which are involved in mediating glycolysis and epithelial-mesenchymal transition (EMT) were examined by western blot and reverse transcription-quantitative real-time polymerase chain reaction(RT-qPCR), respectively. Results: In comparison with the mice transplanted with HCT-8 cells, which were survival with limited metastatic tumor cells in organs, aggressive metastases were observed in liver, lung, kidney and ovary of HCT-8-7T transplanted mice (P<0.05). The levels of ATP [(0.10±0.01) mmol/L], glycolysis [(81.77±8.21) mpH/min] and the capacity of glycolysis [(55.50±3.48) mpH/min] in HCT-8-7T cells were higher than those of HCT-8 cells [(0.04±0.01) mmol/L, (27.77±2.55) mpH/min and(14.00±1.19) mpH/min, respectively, P<0.05]. Meanwhile, the levels of p53 protein and mRNA in HCT-8-7T cells were potently decreased as compared to that in HCT-8 cells (P<0.05). However, the level of miRNA-125b (2.21±0.12) in HCT-8-7T cells was significantly elevated as compared to that in HCT-8 cells (1.00±0.00, P<0.001). In HCT-8-7T cells, forced-expression of p53 reduced the colon number (162.00±24.00) and the migration [(18.53±5.67)%] as compared with those in cells transfected with control vector [274.70±40.50 and (100.00±29.06)%, P<0.05, respectively]. Similarly, miR-125b mimic decreased the glycolysis [(25.28±9.51) mpH/min] in HCT-8-7T cells as compared with that [(54.38±12.70)mpH/min, P=0.003] in HCT-8-7T cells transfected with control. Meanwhile, in comparison with control transfected HCT-8-7T cells, miR-125b mimic also significantly led to an increase in the levels of p53 and β-catenin, in parallel with a decrease in the levels of PFK1 and HK1 in HCT-8-7T cells (P<0.05). Conclusions: Silencing of p53 by miR-125b could be one of the mechanisms that contributes to Alisertib resistance. Targeting miR-125b could be a strategy to overcome Alisertib resistance.

Non-Abelian braiding of graph vertices in a superconducting processor

Indistinguishability of particles is a fundamental principle of quantum mechanics¹. For all elementary and quasiparticles observed to date-including fermions, bosons and Abelian anyons-this principle guarantees that the braiding of identical particles leaves the system unchanged^2,3. However, in two spatial dimensions, an intriguing possibility exists: braiding of non-Abelian anyons causes rotations in a space of topologically degenerate wavefunctions^4-8. Hence, it can change the observables of the system without violating the principle of indistinguishability. Despite the well-developed mathematical description of non-Abelian anyons and numerous theoretical proposals^9-22, the experimental observation of their exchange statistics has remained elusive for decades. Controllable many-body quantum states generated on quantum processors offer another path for exploring these fundamental phenomena. Whereas efforts on conventional solid-state platforms typically involve Hamiltonian dynamics of quasiparticles, superconducting quantum processors allow for directly manipulating the many-body wavefunction by means of unitary gates. Building on predictions that stabilizer codes can host projective non-Abelian Ising anyons^9,10, we implement a generalized stabilizer code and unitary protocol²³ to create and braid them. This allows us to experimentally verify the fusion rules of the anyons and braid them to realize their statistics. We then study the prospect of using the anyons for quantum computation and use braiding to create an entangled state of anyons encoding three logical qubits. Our work provides new insights about non-Abelian braiding and, through the future inclusion of error correction to achieve topological protection, could open a path towards fault-tolerant quantum computing.

[Molecular pathological mechanism of liver metabolic disorder in mice with severe spinal muscular atrophy]

OBJECTIVE

To explore the molecular pathological mechanism of liver metabolic disorder in severe spinal muscular atrophy (SMA).

METHODS

The transgenic mice with type Ⅰ SMA (Smn^-/- SMN2^0tg/2tg) and littermate control mice (Smn^+/- SMN2^0tg/2tg) were observed for milk suckling behavior and body weight changes after birth. The mice with type Ⅰ SMA mice were given an intraperitoneal injection of 20% glucose solution or saline (15 μL/12 h), and their survival time was recorded. GO enrichment analysis was performed using the RNA-Seq data of the liver of type Ⅰ SMA and littermate control mice, and the results were verified using quantitative real-time PCR. Bisulfite sequencing was performed to examine CpG island methylation level in Fasn gene promoter region in the liver of the neonatal mice.

RESULTS

The neonatal mice with type Ⅰ SMA showed normal milk suckling behavior but had lower body weight than the littermate control mice on the second day after birth. Intraperitoneal injection of glucose solution every 12 h significantly improved the median survival time of type Ⅰ SMA mice from 9±1.3 to 11± 1.5 days (P < 0.05). Analysis of the RNA-Seq data of the liver showed that the expression of the target genes of PPARα related to lipid metabolism and mitochondrial β oxidation were down-regulated in the liver of type Ⅰ SMA mice. Type Ⅰ SMA mice had higher methylation level of the Fasn promoter region in the liver than the littermate control mice (76.44% vs 58.67%). In primary cultures of hepatocytes from type Ⅰ SMA mice, treatment with 5-AzaC significantly up-regulated the expressions of the genes related to lipid metabolism by over 1 fold (P < 0.01).

CONCLUSION

Type Ⅰ SMA mice have liver metabolic disorder, and the down-regulation of the target genes of PPARα related to lipid and glucose metabolism due to persistent DNA methylation contributes to the progression of SMA.

Self-supervised anomaly detection, staging and segmentation for retinal images

Unsupervised anomaly detection (UAD) is to detect anomalies through learning the distribution of normal data without labels and therefore has a wide application in medical images by alleviating the burden of collecting annotated medical data. Current UAD methods mostly learn the normal data by the reconstruction of the original input, but often lack the consideration of any prior information that has semantic meanings. In this paper, we first propose a universal unsupervised anomaly detection framework SSL-AnoVAE, which utilizes a self-supervised learning (SSL) module for providing more fine-grained semantics depending on the to-be detected anomalies in the retinal images. We also explore the relationship between the data transformation adopted in the SSL module and the quality of anomaly detection for retinal images. Moreover, to take full advantage of the proposed SSL-AnoVAE and apply towards clinical usages for computer-aided diagnosis of retinal-related diseases, we further propose to stage and segment the anomalies in retinal images detected by SSL-AnoVAE in an unsupervised manner. Experimental results demonstrate the effectiveness of our proposed method for unsupervised anomaly detection, staging and segmentation on both retinal optical coherence tomography images and color fundus photograph images.

Musculoskeletal Ultrasound Image-Based Radiomics for the Diagnosis of Achilles Tendinopathy in Skiers

OBJECTIVES

Our study aimed to develop and validate an efficient ultrasound image-based radiomic model for determining the Achilles tendinopathy in skiers.

METHODS

A total of 88 feet of skiers clinically diagnosed with unilateral chronic Achilles tendinopathy and 51 healthy feet were included in our study. According to the time order of enrollment, the data were divided into a training set (n = 89) and a test set (n = 50). The regions of interest (ROIs) were segmented manually, and 833 radiomic features were extracted from red, green, blue color channels and grayscale of ROIs using Pyradiomics, respectively. Three feature selection and three machine learning modeling algorithms were implemented respectively, for determining the optimal radiomics pipeline. Finally, the area under the receiver operating characteristic curve (AUC), consistency analysis, and decision analysis were used to evaluate the diagnostic performance.

RESULTS

By comparing nine radiomics analysis strategies of three color channels and grayscale, the radiomic model under the green channel obtained the best diagnostic performance, using the Random Forest selection and Support Vector Machine modeling, which was selected as the final machine learning model. All the selected radiomic features were significantly associated with the Achilles tendinopathy (P < .05). The radiomic model had a training AUC of 0.98, a test AUC of 0.99, a sensitivity of 0.90, and a specificity of 1, which could bring sufficient clinical net benefits.

CONCLUSIONS

Ultrasound image-based radiomics achieved high diagnostic performance, which could be used as an intelligent auxiliary tool for the diagnosis of Achilles tendinopathy.

MRI-based pre-Radiomics and delta-Radiomics models accurately predict the post-treatment response of rectal adenocarcinoma to neoadjuvant chemoradiotherapy

Objectives

To develop and validate magnetic resonance imaging (MRI)-based pre-Radiomics and delta-Radiomics models for predicting the treatment response of local advanced rectal cancer (LARC) to neoadjuvant chemoradiotherapy (NCRT).

Methods

Between October 2017 and August 2022, 105 LARC NCRT-naïve patients were enrolled in this study. After careful evaluation, data for 84 patients that met the inclusion criteria were used to develop and validate the NCRT response models. All patients received NCRT, and the post-treatment response was evaluated by pathological assessment. We manual segmented the volume of tumors and 105 radiomics features were extracted from three-dimensional MRIs. Then, the eXtreme Gradient Boosting algorithm was implemented for evaluating and incorporating important tumor features. The predictive performance of MRI sequences and Synthetic Minority Oversampling Technique (SMOTE) for NCRT response were compared. Finally, the optimal pre-Radiomics and delta-Radiomics models were established respectively. The predictive performance of the radionics model was confirmed using 5-fold cross-validation, 10-fold cross-validation, leave-one-out validation, and independent validation. The predictive accuracy of the model was based on the area under the receiver operator characteristic (ROC) curve (AUC).

Results

There was no significant difference in clinical factors between patients with good and poor reactions. Integrating different MRI modes and the SMOTE method improved the performance of the radiomics model. The pre-Radiomics model (train AUC: 0.93 ± 0.06; test AUC: 0.79) and delta-Radiomcis model (train AUC: 0.96 ± 0.03; test AUC: 0.83) all have high NCRT response prediction performance by LARC. Overall, the delta-Radiomics model was superior to the pre-Radiomics model.

Conclusion

MRI-based pre-Radiomics model and delta-Radiomics model all have good potential to predict the post-treatment response of LARC to NCRT. Delta-Radiomics analysis has a huge potential for clinical application in facilitating the provision of personalized therapy.

[Cost-effectiveness prediction of AIDS interventions among men who have sex with men in Ningbo]

Objective: To provide information reference for resource allocation and decision-making in related fields, the cost-effectiveness of HIV input among men who have sex with men (MSM) in Ningbo. Different intervention coverages were compared. Methods: Taking MSM as the target population, data were collected and modeled by Optima HIV for the corresponding HIV health output and the budget under different intervention coverages. Results: According to the estimated size of the MSM population, which was 19 584 in Ningbo in 2020, if the coverage of 2020 baseline intervention is maintained in the next ten years, the number of HIV cases, new HIV infections, and HIV-related deaths among this population will show an upward trend. It is estimated that from 2021 to 2030, 7.9% of new infections and 1.7% of deaths can be avoided and the relevant funding investment comed to 2.4 time the baseline if the intervention coverage rate expanded to 3.0 times the 2020 baseline. After the coverage rate of intervention expanded to 3 times the baseline, it continued to grow, the health effect did not increase. Conclusions: At present, expanding the baseline coverage of HIV-related intervention projects among MSM in Ningbo and increasing capital investment will still reverse HIV-related death and reduce new infections. Moreover, there is a saturation point of the intervention effect. Researchers and policymakers must explore more effective interventions/combinations to obtain more significant health outcomes.

A fast fluorescent probe for tracing endoplasmic reticulum-located carboxylesterase in living cells

Carboxylesterase (CEs), mainly localized in endoplasmic reticulum (ER), are responsible for hydrolyzing compounds containing various ester bonds. They have been closely associated with drug metabolism and cellular homeostasis. Although some CE fluorescent probes have been developed, there are still a lack of probes that could target to the ER. Here, we developed a novel fluorescent probe CR with a specific ER anchor for monitoring CEs. In CR, p-toluenesulfonamide was chosen for precise ER targeting. A simple acetyl moiety was used as the CE response site and fluorescence modulation unit. During the spectral tests, CR displayed a fast response speed (within 10 s) towards CEs. In addition, it showed high sensitivity [limit of detection (LOD) = 5.1 × 10^-3 U/ml] and high selectivity with CEs. In biological imaging, probe CR could especially locate in the ER in HepG2 cells. After cells were treated with orilistat, CR succeeded in monitoring the changes in the CEs. Importantly, CR also had the ability to trace the changes in CEs in a tunicamycin-induced ER stress model. Therefore, probe CR could be a powerful molecular tool for further investigating the functions of CEs in the ER.

Formation of robust bound states of interacting microwave photons

Systems of correlated particles appear in many fields of modern science and represent some of the most intractable computational problems in nature. The computational challenge in these systems arises when interactions become comparable to other energy scales, which makes the state of each particle depend on all other particles1. The lack of general solutions for the three-body problem and acceptable theory for strongly correlated electrons shows that our understanding of correlated systems fades when the particle number or the interaction strength increases. One of the hallmarks of interacting systems is the formation of multiparticle bound states2-9. Here we develop a high-fidelity parameterizable fSim gate and implement the periodic quantum circuit of the spin-½ XXZ model in a ring of 24 superconducting qubits. We study the propagation of these excitations and observe their bound nature for up to five photons. We devise a phase-sensitive method for constructing the few-body spectrum of the bound states and extract their pseudo-charge by introducing a synthetic flux. By introducing interactions between the ring and additional qubits, we observe an unexpected resilience of the bound states to integrability breaking. This finding goes against the idea that bound states in non-integrable systems are unstable when their energies overlap with the continuum spectrum. Our work provides experimental evidence for bound states of interacting photons and discovers their stability beyond the integrability limit.

Noise-resilient edge modes on a chain of superconducting qubits

Inherent symmetry of a quantum system may protect its otherwise fragile states. Leveraging such protection requires testing its robustness against uncontrolled environmental interactions. Using 47 superconducting qubits, we implement the one-dimensional kicked Ising model, which exhibits nonlocal Majorana edge modes (MEMs) with ℤ 2 parity symmetry. We find that any multiqubit Pauli operator overlapping with the MEMs exhibits a uniform late-time decay rate comparable to single-qubit relaxation rates, irrespective of its size or composition. This characteristic allows us to accurately reconstruct the exponentially localized spatial profiles of the MEMs. Furthermore, the MEMs are found to be resilient against certain symmetry-breaking noise owing to a prethermalization mechanism. Our work elucidates the complex interplay between noise and symmetry-protected edge modes in a solid-state environment.

Deep learning in bladder cancer imaging: A review

Deep learning (DL) is a rapidly developing field in machine learning (ML). The concept of deep learning originates from research on artificial neural networks and is an upgrade of traditional neural networks. It has achieved great success in various domains and has shown potential in solving medical problems, particularly when using medical images. Bladder cancer (BCa) is the tenth most common cancer in the world. Imaging, as a safe, noninvasive, and relatively inexpensive technique, is a powerful tool to aid in the diagnosis and treatment of bladder cancer. In this review, we provide an overview of the latest progress in the application of deep learning to the imaging assessment of bladder cancer. First, we review the current deep learning approaches used for bladder segmentation. We then provide examples of how deep learning helps in the diagnosis, staging, and treatment management of bladder cancer using medical images. Finally, we summarize the current limitations of deep learning and provide suggestions for future improvements.

Predicting the probability of malignant pathological type of kidney cancer based on mass size: A retrospective study

BACKGROUND

Different degrees of malignancy of renal cell carcinoma (RCC) correspond to dissimilar therapies, and the prediction of malignancy of kidney cancer based on tumor size is still not fully studied.

METHODS

We evaluated a total of 50,776 patients with T1-T2, N0, M0 RCC diagnosed between 2004 to 2015 based on the Surveillance, Epidemiology, and End Results database. Three and four fuhrman grade clear cell RCC, three and four fuhrman grade papillary RCC, collecting duct RCC, sarcomatoid differentiation RCC and unclassified RCC were classified as aggressive RCC. The other RCC was classified as indolent RCC. The probability of aggressive and indolent was estimated according to tumor size using a logistic regression model. Differences in survival between subgroups were assessed using the Kaplan-Meier method.

RESULTS

There were 38,003 cases of indolent tumor and 12,773 cases of aggressive tumor totally. As tumor size increases, the predicted probability of an aggressive tumor also increases. Concretely, kidney cancers of 2cm, 3cm and 4cm were estimated to be 19.6%, 21.6% and 23.7% more likely to be aggressive. And for the same tumor size, clear cell RCC in men is more likely to be invasive relative to women and other kidney cancer pathology types. In addition, both the overall and tumor-specific survival are longer for indolent tumors than for aggressive tumors.

CONCLUSION

We evaluated the degree of malignancy of different sizes RCC in a retrospective study. This result may be helpful in the choice of initial therapy strategies for kidney cancer patients.

Wavelet transformation can enhance computed tomography texture features: a multicenter radiomics study for grade assessment of COVID-19 pulmonary lesions

Background

This study set out to develop a computed tomography (CT)-based wavelet transforming radiomics approach for grading pulmonary lesions caused by COVID-19 and to validate it using real-world data.

Methods

This retrospective study analyzed 111 patients with 187 pulmonary lesions from 16 hospitals; all patients had confirmed COVID-19 and underwent non-contrast chest CT. Data were divided into a training cohort (72 patients with 127 lesions from nine hospitals) and an independent test cohort (39 patients with 60 lesions from seven hospitals) according to the hospital in which the CT was performed. In all, 73 texture features were extracted from manually delineated lesion volumes, and 23 three-dimensional (3D) wavelets with eight decomposition modes were implemented to compare and validate the value of wavelet transformation for grade assessment. Finally, the optimal machine learning pipeline, valuable radiomic features, and final radiomic models were determined. The area under the receiver operating characteristic (ROC) curve (AUC), calibration curve, and decision curve were used to determine the diagnostic performance and clinical utility of the models.

Results

Of the 187 lesions, 108 (57.75%) were diagnosed as mild lesions and 79 (42.25%) as moderate/severe lesions. All selected radiomic features showed significant correlations with the grade of COVID-19 pulmonary lesions (P<0.05). Biorthogonal 1.1 (bior1.1) LLL was determined as the optimal wavelet transform mode. The wavelet transforming radiomic model had an AUC of 0.910 in the test cohort, outperforming the original radiomic model (AUC =0.880; P<0.05). Decision analysis showed the radiomic model could add a net benefit at any given threshold of probability.

Conclusions

Wavelet transformation can enhance CT texture features. Wavelet transforming radiomics based on CT images can be used to effectively assess the grade of pulmonary lesions caused by COVID-19, which may facilitate individualized management of patients with this disease.

Application of deep learning to identify ductal carcinoma in situ and microinvasion of the breast using ultrasound imaging

Background

The treatment and prognosis of breast ductal carcinoma in situ (DCIS) with and without microinvasion (MIC) are different. Ultrasound imaging shows that DCIS is a heterogeneous breast tumor with diverse manifestations. DCIS means that the cancer cells are confined in the duct without penetrating the basement membrane, MIC means that the cancer cells penetrate the basement membrane and the maximum diameter of any largest invasive lesion is less than or equal to 1 mm. This study was designed to evaluate how deep learning can be used to identify DCIS with MIC on ultrasound images.

Methods

The clinical and ultrasound data of 467 consecutive inpatients diagnosed with DCIS (213 with MIC) in West China Hospital of Sichuan University were collected from January 2013 to April 2019 and randomly apportioned to training and internal validation sets. An external validation set comprised data from Sichuan Provincial People's Hospital with 101 patients (33 with MIC) collected between January 2017 and December 2019. There were 2,492 original images; 66% of these were used to establish a model, and the remaining 34% were used to evaluate the model. Three experienced breast ultrasound clinicians analyzed the ultrasound images to establish a logistic regression model. Finally, the logistic regression model and five deep learning models (ResNet-50, ResNet-101, DenseNet-161, DenseNet-169, and Inception-v3) were compared and evaluated to assess their diagnostic efficiency when identifying MIC based on ultrasound image data.

Results

The characteristics of high nuclear grade (P<0.001), necrosis (P=0.006), estrogen receptor negative (ER⁻; P=0.003), progesterone receptor negative (PR⁻; P=0.001), human epidermal growth factor receptor 2 positive (HER2+; P=0.034), lymphatic metastasis (P=0.008), and calcification (P<0.001) all showed significant correlations with MIC. The Inception-v3 model achieved the best performance (P<0.05) in MIC identification. The area under the receiver operating curve (AUC) of the Inception-v3 model was 0.803 [95% confidence interval (CI): 0.709 to 0.878], with a classification accuracy of 0.766, a sensitivity of 0.767, and a specificity of 0.765.

Conclusions

Deep learning can be used to identify MIC of breast DCIS from ultrasound images. Models based on Inception-v3 can provide automated detection of DCIS with MIC from ultrasound images.

Semi-supervised classification of fundus images combined with CNN and GCN

PURPOSE

Diabetic retinopathy (DR) is one of the most serious complications of diabetes, which is a kind of fundus lesion with specific changes. Early diagnosis of DR can effectively reduce the visual damage caused by DR. Due to the variety and different morphology of DR lesions, automatic classification of fundus images in mass screening can greatly save clinicians' diagnosis time. To alleviate these problems, in this paper, we propose a novel framework-graph attentional convolutional neural network (GACNN).

METHODS AND MATERIALS

The network consists of convolutional neural network (CNN) and graph convolutional network (GCN). The global and spatial features of fundus images are extracted by using CNN and GCN, and attention mechanism is introduced to enhance the adaptability of GCN to topology map. We adopt semi-supervised method for classification, which greatly improves the generalization ability of the network.

RESULTS

In order to verify the effectiveness of the network, we conducted comparative experiments and ablation experiments. We use confusion matrix, precision, recall, kappa score, and accuracy as evaluation indexes. With the increase of the labeling rates, the classification accuracy is higher. Particularly, when the labeling rate is set to 100%, the classification accuracy of GACNN reaches 93.35%. Compared with DenseNet121, the accuracy rate is improved by 6.24%.

CONCLUSIONS

Semi-supervised classification based on attention mechanism can effectively improve the classification performance of the model, and attain preferable results in classification indexes such as accuracy and recall. GACNN provides a feasible classification scheme for fundus images, which effectively reduces the screening human resources.

[The surgical management of benign tumors of the lateral skull base with intracranial invasion: experience from a single centre over ten years]

Objective: To investigate the clinical features, pathological types, imaging features, and surgical strategies of lateral skull base benign tumors with intracranial invasion. Methods: From January 2011 to March 2021, 36 patients of lateral skull base benign tumors with intracranial invasion were included in this retrospective study. Among the 36 patients, 14 cases were male, 22 cases were female, the aged range from 20-67, with the median age of 48. The clinical manifestations, characteristic imaging findings, pathological types, surgical approach selection, and prognosis were analyzed. Results: 36 cases of lateral skull base tumors with intracranial invasion were all accepted surgeries. 23 cases were neurogenic tumors, facial nerve tumors (n=8), neurogenic tumors in jugular foramen with unknown origin(n=6), hypoglossal schwannoma (n=3), transotic intralabyrinthine schwannoma (n=3), vestibular schwannoma involving the middle ear(n=2), vagal nerve schwannoma(n=1). Other types of tumors included meningioma (n=10) and paraganglioma (Di 1 or 2,n=3). Different pathological types of tumors had different clinical manifestations and imaging manifestations. Sixteen cases were subjected to primary resection, while, other 20 cases underwent staged operation. Among the patients with staged operation, 10 patients had completed the second stage operation, five patients were waiting for the second stage operation, the other five patient's residual intracranial tumor were significantly reduced and the space between tumor and brain tissues widened after the first stage operation, so, the following up with "wait and scan"policy was suggested. The total resection rate of tumors was related to the pathological nature, in which neurogenic tumors were 15/17, and meningiomas were 5/8. The main postoperative complications were cerebrospinal fluid leakage and infection in the operation area. There were two cases of postoperative intracranial infection, and three cases of cerebrospinal fluid leakage occurred in non staged operation cases. Conclusions: Lateral skull base tumors with intracranial invasion are rare. The most common pathological type is schwannoma, followed by meningioma and paraganglioma. For this type of tumor, if there is infection in the operation area and neck invasion is large, it is suggested to choose staged surgery, which can reduce the risk of intracranial infection and the incidence of cerebrospinal fluid leakage. Staged surgery strategy can also reduce the difficulty of second stage surgery, so the operation is much safer than non staged surgery.

SuperSonic shear imaging for the differentiation between benign and malignant thyroid nodules: a meta-analysis

PURPOSE

To assess the diagnostic value of SuperSonic shear imaging (SSI) for the differentiation between benign and malignant thyroid nodules through meta-analysis.

METHODS

Online database searches were performed on PubMed, EMBASE, the Cochrane Library, and the Web of Science until 31 July 2021. The Quality Assessment of Diagnostic Accuracy Studies-2 tool was used to assess the quality of the included studies. Three measures of diagnostic test performance were used to examine the value of SSI, including the summary area under the receiver operating characteristic curve (AUROC), the summary diagnostic odds ratio (DOR), and the summary sensitivity and specificity. Heterogeneity was explored using meta-regression and subgroup analyses.

RESULTS

Finally, 21 studies with 3376 patients were included in this study. There were a total of 4296 thyroid nodules, in which 1806 malignant nodules and 2490 benign ones were involved. Thyroid nodules exhibited a malignancy rate of 42.0% (range 5.6-79.8%), 95.1% of which were of papillary variant. SSI showed a summary sensitivity of 74% [95% confidence interval (CI) 67-79%], specificity of 82% (95% CI 77-87%) and AUROC of 0.85 (95% CI 0.82-0.88) for the differentiation between benign and malignant thyroid nodules. The summary positive likelihood ratio (LR), negative LR, and DOR were 4.2 (95% CI 3.3-5.3), 0.32 (95% CI 0.26-0.40), and 13 (95% CI 9-18), respectively.

CONCLUSIONS

SSI showed high accuracy in the diagnostic differentiation between benign and malignant thyroid nodules and can be served as a noninvasive and important adjunct for thyroid nodule evaluation.