Correlation between oxygenation status of extra-synovial tissue of wrist and disease activity in rheumatoid arthritis: a photoacoustic imaging study.

OBJECTIVES: Rheumatoid arthritis (RA) is characterized by hypoxia in the synovial tissue. While photoacoustic imaging (PA) offers a method to evaluate tissue oxygenation in RA patients, studies exploring the link between extra-synovial tissue of wrist oxygenation and disease activity remain scarce. We aimed to assess synovial oxygenation in RA patients using a multimodal photoacoustic-ultrasound (PA/US) imaging system and establish its correlation with disease activity. METHODS: A retrospective study was conducted on 111 patients with RA and 72 healthy controls from 2022 to 2023. Dual-wavelength PA imaging quantified oxygen saturation (So2) levels in the synovial membrane and peri-wrist region. Oxygenation states were categorised as hyperoxia, intermediate oxygenation, and hypoxia based on So2 values. The association between oxygenation levels and the clinical disease activity index was evaluated using a one-way analysis of variance, complemented by the Kruskal-Wallis test with Bonferroni adjustment. RESULTS: Of the patients with RA, 39 exhibited hyperoxia, 24 had intermediate oxygenation, and 48 had hypoxia in the wrist extra-synovial tissue. All of the control participants exhibited the hyperoxia status. Oxygenation levels in patients with RA correlated with clinical metrics. Patients with intermediate oxygenation had a lower disease activity index compared with those with hypoxia and hyperoxia. CONCLUSION: A significant correlation exists between wrist extra-synovial tissue oxygenation and disease activity in patients with RA.

Development and validation of nomograms using photoacoustic imaging and 2D ultrasound to predict breast nodule benignity and malignancy.

BACKGROUND: The application of photoacoustic imaging (PAI), utilizing laser-induced ultrasound, shows potential in assessing blood oxygenation in breast nodules. However, its effectiveness in distinguishing between malignant and benign nodules remains insufficiently explored. PURPOSE: This study aims to develop nomogram models for predicting the benign or malignant nature of breast nodules using PAI. METHOD: A prospective cohort study enrolled 369 breast nodules, subjecting them to PAI and ultrasound examination. The training and testing cohorts were randomly divided into two cohorts in a ratio of 3:1. Based on the source of the variables, three models were developed, Model 1: photoacoustic-BIRADS+BMI + blood oxygenation, Model 2: BIRADS+Shape+Intranodal blood (Doppler) + BMI, Model 3: photoacoustic-BIRADS+BIRADS+ Shape+Intranodal blood (Doppler) + BMI + blood oxygenation. Risk factors were identified through logistic regression, resulting in the creation of three predictive models. These models were evaluated using calibration curves, subject receiver operating characteristic (ROC), and decision curve analysis. RESULTS: The area under the ROC curve for the training cohort was 0.91 (95% confidence interval, 95% CI: 0.88-0.95), 0.92 (95% CI: 0.89-0.95), and 0.97 (95% CI: 0.96-0.99) for Models 1-3, and the ROC curve for the testing cohort was 0.95 (95% CI: 0.91-0.98), 0.89 (95% CI: 0.83-0.96), and 0.97 (95% CI: 0.95-0.99) for Models 1-3. CONCLUSIONS: The calibration curves demonstrate that the model's predictions agree with the actual values. Decision curve analysis suggests a good clinical application.

A validation of an entropy-based artificial intelligence for ultrasound data in breast tumors.

BACKGROUND: The application of artificial intelligence (AI) in the ultrasound (US) diagnosis of breast cancer (BCa) is increasingly prevalent. However, the impact of US-probe frequencies on the diagnostic efficacy of AI models has not been clearly established. OBJECTIVES: To explore the impact of using US-video of variable frequencies on the diagnostic efficacy of AI in breast US screening. METHODS: This study utilized different frequency US-probes (L14: frequency range: 3.0-14.0 MHz, central frequency 9 MHz, L9: frequency range: 2.5-9.0 MHz, central frequency 6.5 MHz and L13: frequency range: 3.6-13.5 MHz, central frequency 8 MHz, L7: frequency range: 3-7 MHz, central frequency 4.0 MHz, linear arrays) to collect breast-video and applied an entropy-based deep learning approach for evaluation. We analyzed the average two-dimensional image entropy (2-DIE) of these videos and the performance of AI models in processing videos from these different frequencies to assess how probe frequency affects AI diagnostic performance. RESULTS: The study found that in testing set 1, L9 was higher than L14 in average 2-DIE; in testing set 2, L13 was higher in average 2-DIE than L7. The diagnostic efficacy of US-data, utilized in AI model analysis, varied across different frequencies (AUC: L9 > L14: 0.849 vs. 0.784; L13 > L7: 0.920 vs. 0.887). CONCLUSION: This study indicate that US-data acquired using probes with varying frequencies exhibit diverse average 2-DIE values, and datasets characterized by higher average 2-DIE demonstrate enhanced diagnostic outcomes in AI-driven BCa diagnosis. Unlike other studies, our research emphasizes the importance of US-probe frequency selection on AI model diagnostic performance, rather than focusing solely on the AI algorithms themselves. These insights offer a new perspective for early BCa screening and diagnosis and are of significant for future choices of US equipment and optimization of AI algorithms.

The research on artificial intelligence-assisted breast diagnosis often relies on static images or dynamic videos obtained from ultrasound probes with different frequencies. However, the effect of frequency-induced image variations on the diagnostic performance of artificial intelligence models remains unclear. In this study, we aimed to explore the impact of using ultrasound images with variable frequencies on AI's diagnostic efficacy in breast ultrasound screening. Our approach involved employing a video and entropy-based feature breast network to compare the diagnostic efficiency and average two-dimensional image entropy of the L14 (frequency range: 3.0-14.0 MHz, central frequency 9 MHz), L9 (frequency range: 2.5-9.0 MHz, central frequency 6.5 MHz) linear array probe and L13 (frequency range: 3.6-13.5 MHz, central frequency 8 MHz), and L7 (frequency range: 3-7 MHz, central frequency 4.0 MHz) linear array probes. The results revealed that the diagnostic efficiency of AI models differed based on the frequency of the ultrasound probe. It is noteworthy that ultrasound images acquired with different frequency probes exhibit different average two-dimensional image entropy, while higher average two-dimensional image entropy positively affect the diagnostic performance of the AI model. We concluded that a dataset with higher average two-dimensional image entropy is associated with superior diagnostic efficacy for AI-based breast diagnosis. These findings contribute to a better understanding of how ultrasound image variations impact AI-assisted breast diagnosis, potentially leading to improved breast cancer screening outcomes.

Design of a Robust Synchronization-Based Topology Observer for Complex Delayed Networks with Fixed and Adaptive Coupling Strength.

Network topology plays a key role in determining the characteristics and dynamical behaviors of a network. But in practice, network topology is sometimes hidden or uncertain ahead of time because of network complexity. In this paper, a robust-synchronization-based topology observer (STO) is proposed and applied to solve the problem of identifying the topology of complex delayed networks (TICDNs). In comparison to the existing literature, the proposed STO does not require any prior knowledge about the range of topological parameters and does not have strict limits on topology type. Furthermore, the proposed STO is suitable not only for networks with fixed coupling strength, but also for networks with adaptive coupling strength. Finally, a few comparison examples for TICDNs are used to verify the feasibility and efficiency of the proposed STO, and the results show that the proposed STO outperforms the other methods.

The use of histotripsy as intratumoral immunotherapy beyond tissue ablation-the rationale for exploring the immune effects of histotripsy.

Mechanical high-intensity focused ultrasound (M-HIFU), which includes histotripsy, is a non-ionizing, non-thermal ablation technology that can be delivered by noninvasive methods. Because acoustic cavitation is the primary mechanism of tissue disruption, histotripsy is distinct from the conventional HIFU techniques resulting in hyperthermia and thermal injury. Phase I human trials have shown the initial safety and efficacy of histotripsy in treating patients with malignant liver tumors. In addition to tissue ablation, a promising benefit of M-HIFU has been stimulating a local and systemic antitumor immune response in preclinical models and potentially in the Phase I trial. Preclinical studies combining systemic immune therapies appear promising, but clinical studies of combinations have been complicated by systemic toxicities. Consequently, combining M-HIFU with systemic immunotherapy has been demonstrated in preclinical models and may be testing in future clinical studies. An additional alternative is to combine intratumoral M-HIFU and immunotherapy using microcatheter-placed devices to deliver both M-HIFU and immunotherapy intratumorally. The promise of M-HIFU as a component of anti-cancer therapy is promising, but as forms of HIFU are tested in preclinical and clinical studies, investigators should report not only the parameters of the energy delivered but also details of the preclinical models to enable analysis of the immune responses. Ultimately, as clinical trials continue, clinical responses and immune analysis of patients undergoing M-HIFU including forms of histotripsy will provide opportunities to optimize clinical responses and to optimize application and scheduling of M-HIFU in the context of the multi-modality care of the cancer patient.

Artificial intelligence for non-mass breast lesions detection and classification on ultrasound images: a comparative study.

BACKGROUND: This retrospective study aims to validate the effectiveness of artificial intelligence (AI) to detect and classify non-mass breast lesions (NMLs) on ultrasound (US) images. METHODS: A total of 228 patients with NMLs and 596 volunteers without breast lesions on US images were enrolled in the study from January 2020 to December 2022. The pathological results served as the gold standard for NMLs. Two AI models were developed to accurately detect and classify NMLs on US images, including DenseNet121_448 and MobileNet_448. To evaluate and compare the diagnostic performance of AI models, the area under the curve (AUC), accuracy, specificity and sensitivity was employed. RESULTS: A total of 228 NMLs patients confirmed by postoperative pathology with 870 US images and 596 volunteers with 1003 US images were enrolled. In the detection experiment, the MobileNet_448 achieved the good performance in the testing set, with the AUC, accuracy, sensitivity, and specificity were 0.999 (95%CI: 0.997-1.000),96.5%,96.9% and 96.1%, respectively. It was no statistically significant compared to DenseNet121_448. In the classification experiment, the MobileNet_448 model achieved the highest diagnostic performance in the testing set, with the AUC, accuracy, sensitivity, and specificity were 0.837 (95%CI: 0.990-1.000), 70.5%, 80.3% and 74.6%, respectively. CONCLUSIONS: This study suggests that the AI models, particularly MobileNet_448, can effectively detect and classify NMLs in US images. This technique has the potential to improve early diagnostic accuracy for NMLs.

Appendicitis in pregnant women: A systematic review and meta-analysis of the diagnostic performance of ultrasonography.

OBJECTIVES: The accuracy of ultrasound in the detection of appendicitis in pregnant women was examined in a meta-analysis. METHODS: Pregnant women with suspected acute appendicitis were evaluated using ultrasound in a systematic search of PubMed, EMBASE, and Cochrane Library databases from January 1, 2011 to August 10, 2023. The sensitivity and specificity values and diagnostic odds ratios were obtained using the pooled data. RESULTS: A total of 239 patients were studied in four relevant investigations. Ultrasonography has a sensitivity of 56% and a specificity of 88% for the diagnosis of acute appendicitis, with an area under the receiver operating characteristic curve of 0.66%. Ultrasonography had a positive likelihood ratio of 4.65 (95% confidence interval, 1.42-15.23) and a negative likelihood ratio of 0.50 (95% confidence interval, 0.41-0.62). There was no evidence of publication bias (p = 0.93). CONCLUSIONS: Ultrasound has moderate sensitivity for identifying appendicitis in pregnant women and may be utilized as an alternative diagnostic method.

One step further into the blackbox: a pilot study of how to build more confidence around an AI-based decision system of breast nodule assessment in 2D ultrasound.

OBJECTIVES: To investigate how a DL model makes decisions in lesion classification with a newly defined region of evidence (ROE) by incorporating "explainable AI" (xAI) techniques. METHODS: A data set of 785 2D breast ultrasound images acquired from 367 females. The DenseNet-121 was used to classify whether the lesion is benign or malignant. For performance assessment, classification results are evaluated by calculating accuracy, sensitivity, specificity, and receiver operating characteristic for experiments of both coarse and fine regions of interest (ROIs). The area under the curve (AUC) was evaluated, and the true-positive, false-positive, true-negative, and false-negative results with breakdown in high, medium, and low resemblance on test sets were also reported. RESULTS: The two models with coarse and fine ROIs of ultrasound images as input achieve an AUC of 0.899 and 0.869, respectively. The accuracy, sensitivity, and specificity of the model with coarse ROIs are 88.4%, 87.9%, and 89.2%, and with fine ROIs are 86.1%, 87.9%, and 83.8%, respectively. The DL model captures ROE with high resemblance of physicians' consideration as they assess the image. CONCLUSIONS: We have demonstrated the effectiveness of using DenseNet to classify breast lesions with limited quantity of 2D grayscale ultrasound image data. We have also proposed a new ROE-based metric system that can help physicians and patients better understand how AI makes decisions in reading images, which can potentially be integrated as a part of evidence in early screening or triaging of patients undergoing breast ultrasound examinations. KEY POINTS: • The two models with coarse and fine ROIs of ultrasound images as input achieve an AUC of 0.899 and 0.869, respectively. The accuracy, sensitivity, and specificity of the model with coarse ROIs are 88.4%, 87.9%, and 89.2%, and with fine ROIs are 86.1%, 87.9%, and 83.8%, respectively. • The first model with coarse ROIs is slightly better than the second model with fine ROIs according to these evaluation metrics. • The results from coarse ROI and fine ROI are consistent and the peripheral tissue is also an impact factor in breast lesion classification.

A Fishery Water Quality Monitoring and Prediction Evaluation System for Floating UAV Based on Time Series.

In recent years, fishery has developed rapidly. For the vital interests of the majority of fishermen, this paper makes full use of Internet of Things and air-water amphibious UAV technology to provide an integrated system that can meet the requirements of fishery water quality monitoring and prediction evaluation. To monitor target water quality in real time, the water quality monitoring of the system is mainly completed by a six-rotor floating UAV that carries water quality sensors. The GPRS module is then used to realize remote data transmission. The prediction of water quality transmission data is mainly realized by the algorithm of time series comprehensive analysis. The evaluation rules are determined according to the water quality evaluation standards to evaluate the predicted water quality data. Finally, the feasibility of the system is proved through experiments. The results show that the system can effectively evaluate fishery water quality under different weather conditions. The prediction accuracy of the pH, dissolved oxygen content, and ammonia nitrogen content of fishery water quality can reach 99%, 98%, and 99% on sunny days, and reach 92%, 98%, and 91% on rainy days.

Recent Advances in Transducers for Intravascular Ultrasound (IVUS) Imaging.

As a well-known medical imaging methodology, intravascular ultrasound (IVUS) imaging plays a critical role in diagnosis, treatment guidance and post-treatment assessment of coronary artery diseases. By cannulating a miniature ultrasound transducer mounted catheter into an artery, the vessel lumen opening, vessel wall morphology and other associated blood and vessel properties can be precisely assessed in IVUS imaging. Ultrasound transducer, as the key component of an IVUS system, is critical in determining the IVUS imaging performance. In recent years, a wide range of achievements in ultrasound transducers have been reported for IVUS imaging applications. Herein, a comprehensive review is given on recent advances in ultrasound transducers for IVUS imaging. Firstly, a fundamental understanding of IVUS imaging principle, evaluation parameters and IVUS catheter are summarized. Secondly, three different types of ultrasound transducers (piezoelectric ultrasound transducer, piezoelectric micromachined ultrasound transducer and capacitive micromachined ultrasound transducer) for IVUS imaging are presented. Particularly, the recent advances in piezoelectric ultrasound transducer for IVUS imaging are extensively examined according to their different working mechanisms, configurations and materials adopted. Thirdly, IVUS-based multimodality intravascular imaging of atherosclerotic plaque is discussed. Finally, summary and perspectives on the future studies are highlighted for IVUS imaging applications.

Clinical Value of the Elastographic Q-Analysis Score in Assisting Real-Time Elastography-Guided Prostate Biopsy: A Retrospective Study of 125 Patients.

OBJECTIVES: This study aimed to evaluate the clinical value of the elastographic Q-analysis score (EQS) in assisting real-time elastography- and transrectal US-guided prostate biopsy. METHODS: A total of 125 patients with 301 lesions were enrolled in this study; all were confirmed by pathologic results. The patients underwent transrectal US and elastographic examinations before biopsy. Elastographic Q-analysis score analysis software was used for measuring the mean EQS of the elastic images. First, the suspicious regions on elastography underwent biopsy. Then 12-core systematic prostate biopsy was performed. An EQS curve was used to calculate the mean EQS, and a receiver operating characteristic curve was drawn to find the cutoff point for the EQS to predict prostate cancer. RESULTS: Of the 301 lesions in this study, 125 were malignant, and 176 were benign. The mean EQS values of benign and malignant lesions ± SD were 1.47 ± 0.75 and 2.98 ± 1.06, respectively. The difference was statistically significant (P < .05). The area under the receiver operating characteristic curve was 0.87. When the cutoff point was 1.95 for diagnosing malignant and benign lesions, the sensitivity, specificity, positive predictive value, negative predictive value, positive likelihood ratio, and negative likelihood ratio were 83.5%, 84.4%, 76.8%, 89.2%, 5.35, and 0.20. CONCLUSIONS: The EQS could be used as a way to predict benign and malignant lesions and thus could serve as guidance for adding targeted biopsy.

A Unified Fourth-Order Tensor-Based Smart Community System.

Empowered by the ubiquitous sensing capabilities of Internet of Things (IoT) technologies, smart communities could benefit our daily life in many aspects. Various smart community studies and practices have been conducted, especially in China thanks to the government's support. However, most intelligent systems are designed and built individually by different manufacturers in diverging platforms with different functionalities. Therefore, multiple individual systems must be deployed in a smart community to have a set of functions, which could lead to hardware waste, high energy consumption and high deployment cost. More importantly, current smart community systems mainly focus on the technologies involved, while the effects of human activity are neglected. In this paper, a fourth-order tensor model representing object, time, location and human activity is proposed for human-centered smart communities, based on which a unified smart community system is designed. Thanks to the powerful data management abilities of a high-order tensor, multiple functions can be integrated into our system. In addition, since the tensor model embeds human activity information, complex functions could be implemented by exploring the effects of human activity. Two exemplary applications are presented to demonstrate the flexibility of the proposed unified fourth-order tensor-based smart community system.

Diagnostic Performance of Multimodal Sound Touch Elastography for Differentiating Benign and Malignant Breast Masses.

OBJECTIVES: Evaluating the value of screening breast masses by separate or combined use of multimodal Sound Touch Elastography. METHODS: Women with 159 masses (mean size, 14.86 ± 6.57 mm; range, 5.30-30.00 mm) were enrolled in the study. The pathology results were adopted as diagnostic standards. The abilities of Young's modulus (E), shear modulus (G), and shear wave (C) to differentiate malignant and benign breast masses based on receiver operating characteristic curves were evaluated, and the optimal cutoff values were obtained. Sensitivity, specificity, positive likelihood ratio, and negative likelihood ratio were calculated. Then, the values were combined to perform an overall analysis of Sound Touch Elastography using evidence-based medicine, construct forest plots, and calculate areas under the summary receiver operating characteristic curves, pooled sensitivity, specificity, positive likelihood ratio, negative likelihood ratio, and diagnostic scores. RESULTS: A total of 159 masses with a mean size of 14.86 ± 6.57 mm (range, 5.30-30.00 mm) were included. For the various parameters, the diagnostic values were as follows: Gmax > Emax > Cmax > Csd > Esd > Gsd > Emean > Gmean > Cmean . There were no significant differences in Emin , Gmin , or Cmin . When the 9 parameters were combined, the pooled sensitivity, specificity, positive likelihood ratio, negative likelihood ratio, diagnostic scores and areas under the summary receiver operating characteristic curves were 84% (95% confidence interval [CI], 79%-88%), 82% (95% CI, 80%-84%), 4.75 (95% CI, 4.15-5.43), 0.20 (95% CI, 0.15-0.25), 3.19 (95% CI, 2.84-3.54) and 90.2% (95% CI, 87%-92%), respectively. CONCLUSIONS: Sound Touch Elastography can be recognized as a new ultrasound-based diagnostic method for differentiation between benign and malignant breast masses.

Photoacoustic Imaging Radiomics to Identify Breast Cancer in BI-RADS 4 or 5 Lesions.

OBJECTIVES: To develop a nomogram based on photoacoustic imaging (PAI) radiomics and BI-RADs to identify breast cancer (BC) in BI-RADS 4 or 5 lesions detected by ultrasound (US). METHODS: In this retrospective study, 119 females with 119 breast lesions at US and PAI examination were included (January 2022 to December 2022). Patients were divided into the training set (n = 83) or testing set (n = 36) to develop a nomogram to identify BC in BI-RADS 4 or 5 lesions. Relevant factors at clinic, BI-RADS category, and PAI were reviewed. Univariate and multivariate regression was used to evaluate factors for associations with BC. To evaluate the diagnostic performance of nomogram, the area under the curve (AUC) of receiver operating characteristic curve, accuracy, specificity and sensitivity was employed. RESULTS: The nomogram that included BI-RADS category and PAI radiomics score demonstrated a high AUC of 0.925 (95%CI: 0.8467-0.9712) in the training set and 0.926 (95%CI: 0.846-1.000) in the test set. The nomogram also showed significantly better discrimination than the radiomics score (P = .048) or BI-RADS category (P = .009) in the training set. These significant differences were demonstrated in the testing set, outperform the radiomics score (P = .038) and BI-RADS category (P = .013). CONCLUSIONS: The nomogram developed with BI-RADS and PAI radiomics score can effectively identify BC in BI-RADS 4 or 5 lesions. This technique has the potential to further improve early diagnostic accuracy for BC.

Ba2Ga2F6(IO3)(PO4): the first fluoride-containing iodate-phosphate with a 1D [Ga2F6(IO3)(PO4)]4- helix chain.

Herein, the first F-containing iodate-phosphate, namely Ba2Ga2F6(IO3)(PO4), was prepared via a hydrothermal reaction, in which HPF6 (70 wt% solution in water) was used as the source of both fluoride and phosphate anions for the first time. Ba2Ga2F6(IO3)(PO4) features an unprecedented 1D [Ga2F6(IO3)(PO4)]4- helix chain, composed of a 1D Ga(1)(IO3)O4F chain via the bridging of 0D Ga(2)(PO4)F5. The UV-Vis spectrum shows that Ba2Ga2F6(IO3)(PO4) has a wide bandgap with a short-UV absorption edge (4.35 eV; 253 nm). Birefringence measurement under a polarizing microscope shows that Ba2Ga2F6(IO3)(PO4) displays a moderate birefringence of 0.072@550 nm, which is consistent with the value (0.070@550 nm) obtained by DFT calculations, indicating that Ba2Ga2F6(IO3)(PO4) has potential applications as a short-UV birefringent material. This study highlights the crucial role played by the incorporation of specific functional groups into compounds, shedding light on their contribution to promising inorganic functional materials.

Multimodal PA/US imaging in Rheumatoid Arthritis: Enhanced correlation with clinical scores.

Background: Accurate assessment of Rheumatoid Arthritis (RA) activity remains a challenge. Multimodal photoacoustic/ultrasound (PA/US) joint imaging emerges as a novel imaging modality capable of depicting microvascularization and oxygenation levels in inflamed joints associated with RA. However, the scarcity of large-scale studies limits the exploration of correlating joint oxygenation status with disease activity. Objective: This study aimed to explore the correlation between multimodal PA/US imaging scores and RA disease activity, assessing its clinical applicability in managing RA. Methods: In this study, we recruited 111 patients diagnosed with RA and conducted examinations of seven small joints on their clinically dominant side using a PA/US imaging system. The PA and power Doppler ultrasound (PDUS) signals were semi-quantitatively assessed using a 0-3 grading system. The cumulative scores for PA and PDUS across these seven joints (PA-sum and PDUS-sum) were calculated. Relative oxygen saturation (So2) values of inflamed joints on the clinically dominant side were measured, and categorized into four distinct PA+So2 patterns. The correlation between PA/US imaging scores and disease activity indices was systematically evaluated. Results: Analysis of 777 small joints in 111 patients revealed that the PA-sum scores exhibited a strong positive correlation with standard clinical scores for RA, including DAS28 [ESR] (ρ = 0.682), DAS28 [CRP] (ρ = 0.683), CDAI (ρ = 0.738), and SDAI (ρ = 0.739), all with p < 0.001. These correlations were superior to those of the PDUS-sum scores (DAS28 [ESR] ρ = 0.559, DAS28 [CRP] ρ = 0.555, CDAI ρ = 0.575, SDAI ρ = 0.581, p < 0.001). Significantly, in patients with higher PA-sum scores, notable differences were observed in the erythrocyte sedimentation rate (ESR) (p < 0.01) and swollen joint count 28 (SJC28) (p < 0.01) between hypoxia and intermediate groups. Notably, RA patients in the hypoxia group exhibited higher clinical scores in certain clinical indices. Conclusion: Multi-modal PA/US imaging introduces potential advancements in RA assessment, especially regarding So2 evaluations in synovial tissues and associated PA scores. However, further studies are warranted, particularly with more substantial sample sizes and in multi-center settings. Summary: This study utilized multi-modal PA/US imaging to analyze Rheumatoid Arthritis (RA) patients' synovial tissues and affected joints. When juxtaposed with traditional PDUS imaging, the PA approach demonstrated enhanced sensitivity, especially concerning detecting small vessels in thickened synovium and inflamed tendon sheaths. Furthermore, correlations between the derived PA scores, PA+So2 patterns, and standard clinical RA scores were observed. These findings suggest that multi-modal PA/US imaging could be a valuable tool in the comprehensive assessment of RA, offering insights not only into disease activity but also into the oxygenation status of synovial tissues. However, as promising as these results are, further investigations, especially in larger and diverse patient populations, are imperative. Key points: ⸸ Multi-modal PA/US Imaging in RA: This novel technique was used to assess the So2 values in synovial tissues and determine PA scores of affected RA joints.⸸ Correlation significantly with Clinical RA Scores: Correlations significantly were noted between PA scores, PA+So2 patterns, and standard clinical RA metrics, hinting at the potential clinical applicability of the technique.

Nonalcoholic Fatty Liver Disease: Changes in Gut Microbiota and Blood Lipids.

Background and Aims: The global prevalence of nonalcoholic fatty liver disease (NAFLD) is 25%. This study aimed to explore differences in the gut microbial community and blood lipids between normal livers and those affected by NAFLD using 16S ribosomal deoxyribonucleic acid sequencing. Methods: Gut microbiome profiles of 40 NAFLD and 20 non-NAFLD controls were analyzed. Information about four blood lipids and 13 other clinical features was collected. Patients were divided into three groups by ultrasound and FibroScan, those with a normal liver, mild FL (FL1), and moderate-to-severe FL (FL2). FL1 and FL2 patients were divided into two groups, those with either hyperlipidemia or non-hyperlipidemia based on their blood lipids. Potential keystone species within the groups were identified using univariate analysis and a specificity-occupancy plot. Significant difference in biochemical parameters ion NAFLD patients and healthy individuals were identified by detrended correspondence analysis and canonical correspondence analysis. Results: Decreased gut bacterial diversity was found in patients with NAFLD. Firmicutes/Bacteroidetes decreased as NAFLD progressed. Faecalibacterium and Ruminococcus 2 were the most representative fatty-related bacteria. Glutamate pyruvic transaminase, aspartate aminotransferase, and white blood cell count were selected as the most significant biochemical indexes. Calculation of areas under the curve identified two microbiomes combined with the three biochemical indexes that identified normal liver and FL2 very well but performed poorly in diagnosing FL1. Conclusions: Faecalibacterium and Ruminococcus 2, combined with glutamate pyruvic transaminase, aspartate aminotransferase, and white blood cell count distinguished NAFLD. We speculate that regulating the health of gut microbiota may release NAFLD, in addition to providing new targets for clinicians to treat NAFLD.

Assessment of Oxygen Saturation in Breast Lesions Using Photoacoustic Imaging: Correlation With Benign and Malignant Disease.

BACKGROUND: Hypoxia is a hallmark of breast cancer (BC). Photoacoustic (PA) imaging, based on the use of laser-generated ultrasound (US), can detect oxygen saturation (So2) in the tissues of breast lesion patients. PURPOSE: To measure the oxygenation status of tissue in and on both sides of the lesion in breast lesion participants using a multimodal Photoacoustic/ultrasound (PA/US) imaging system and to determine the correlation between So2 measured by PA imaging and benign or malignant disease. MATERIALS AND METHODS: Multimodal PA/US imaging and gray-scale US (GSUS) of breast lesion was performed in consecutive breast lesion participants imaged in the US Outpatient Clinic between 2022 and 2023. Dual-wavelength PA imaging was used to measure the So2 value inside the lesion and on both sides of the tissue, and to distinguish benign from malignant lesions based on the So2 value. The ability of So2 to distinguish benign from malignant breast lesions was evaluated by the receiver operating characteristic curve (ROC) and the De-Long test. RESULTS: A total of 120 breast lesion participants (median age, 42.5 years) were included in the study. The malignant lesions exhibited lower So2 levels compared to benign lesions (malignant: 71.30%; benign: 83.81%; P < .01). Moreover, PA/US imaging demonstrates superior diagnostic results compared to GSUS, with an area under the curve (AUC) of 0.89 versus 0.70, sensitivity of 89.58% versus 85.42%, and specificity of 86.11% versus 55.56% at the So2 cut-off value of 78.85 (P < .001). The false positive rate in GSUS reduced by 30.75%, and the false negative rate diminished by 4.16% with PA /US diagnosis. Finally, the So2 on both sides tissues of malignant lesions are lower than that of benign lesions (P < .01). CONCLUSION: PA imaging allows for the assessment of So2 within the lesions of breast lesion patients, thereby facilitating a superior distinction between benign and malignant lesions.

Diagnostic Performance of Deep Learning in Video-Based Ultrasonography for Breast Cancer: A Retrospective Multicentre Study.

OBJECTIVE: Although ultrasound is a common tool for breast cancer screening, its accuracy is often operator-dependent. In this study, we proposed a new automated deep-learning framework that extracts video-based ultrasound data for breast cancer screening. METHODS: Our framework incorporates DenseNet121, MobileNet, and Xception as backbones for both video- and image-based models. We used data from 3907 patients to train and evaluate the models, which were tested using video- and image-based methods, as well as reader studies with human experts. RESULTS: This study evaluated 3907 female patients aged 22 to 86 years. The results indicated that the MobileNet video model achieved an AUROC of 0.961 in prospective data testing, surpassing the DenseNet121 video model. In real-world data testing, it demonstrated an accuracy of 92.59%, outperforming both the DenseNet121 and Xception video models, and exceeding the 76.00% to 85.60% accuracy range of human experts. Additionally, the MobileNet video model exceeded the performance of image models and other video models across all evaluation metrics, including accuracy, sensitivity, specificity, F1 score, and AUC. Its exceptional performance, particularly suitable for resource-limited clinical settings, demonstrates its potential for clinical application in breast cancer screening. CONCLUSIONS: The level of expertise reached by the video models was greater than that achieved by image-based models. We have developed an artificial intelligence framework based on videos that may be able to aid breast cancer diagnosis and alleviate the shortage of experienced experts.

High-Performance PMN-PT Single-Crystal-Based 1-3 Composite Transducer Integrated with a Biopsy Needle.

To address the need for high-resolution imaging in lung nodule detection and overcome the limitations of the shallow imaging depth associated with high-frequency ultrasound and the complex structure of lung tissue, we successfully integrated 50 MHz ultrasound transducers with 18-gauge biopsy needles. Featuring a miniaturized size of 0.6 × 0.5 × 0.5 mm3, the 50 MHz micromachined 1-3 composite transducer was tested to perform mechanical scanning of a nodule within a lung-tissue-mimicking phantom in vitro. The high-frequency transducer demonstrated the ability to achieve imaging with an axial resolution of 30 µm for measuring nodule edges. Moreover, the integrated biopsy needle prototype exhibited high accuracy (1.74% discrepancy) in estimating nodule area compared to actual dimensions in vitro. These results underscore the promising potential of biopsy-needle-integrated transducers in enhancing the accuracy of endoscopic ultrasound-guided fine needle aspiration biopsy (EUS-FNA) for clinical applications.