Zinc ions regulate mitochondrial quality control in neurons under oxidative stress and reduce PANoptosis in spinal cord injury models via the Lgals3-Bax pathway.

Spinal cord injury is a serious traumatic nervous system disorder characterized by extensive neuronal apoptosis. Oxidative stress, a key factor in neuronal apoptosis, leads to the accumulation of reactive oxygen species, making mitochondrial quality control within cells crucial. Previous studies have demonstrated zinc's anti-inflammatory and anti-apoptotic properties in protecting mitochondria during spinal cord injury treatment, yet the precise mechanisms remain elusive. Single-cell sequencing analysis has identified Lgals3 and Bax as core genes in apoptosis. This study aimed to investigate whether zinc ions protect intracellular mitochondria by inhibiting the apoptotic proteins Lgals3 and Bax. We elucidated zinc ions' key role in mitigating mitochondrial quality control dysfunction triggered by oxidative stress and confirmed this was achieved by targeting the Lgals3-Bax pathway. Zinc's inhibitory effect on this pathway not only preserved mitochondrial integrity but also significantly reduced PANoptosis after spinal cord injury. Under oxidative stress, zinc ion regulation of mitochondrial quality control reveals an organelle-targeted therapeutic strategy, offering a novel approach for more precise treatment of spinal cord injury.

Inhibition of UHRF1 Improves Motor Function in Mice with Spinal Cord Injury.

Spinal-cord injury (SCI) is a severe condition that can lead to limb paralysis and motor dysfunction, and its pathogenesis is not fully understood. The objective of this study was to characterize the differential gene expression and molecular mechanisms in the spinal cord of mice three days after spinal cord injury. By analyzing RNA sequencing data, we identified differentially expressed genes and discovered that the immune system and various metabolic processes play crucial roles in SCI. Additionally, we identified UHRF1 as a key gene that plays a significant role in SCI and found that SCI can be improved by suppressing UHRF1. These findings provide important insights into the molecular mechanisms of SCI and identify potential therapeutic targets that could greatly contribute to the development of new treatment strategies for SCI.

Application of computer-aided diagnosis to predict malignancy in BI-RADS 3 breast lesions.

Purpose: To evaluate the ability of computer-aided diagnosis (CAD) system (S-Detect) to identify malignancy in ultrasound (US) -detected BI-RADS 3 breast lesions. Materials and methods: 148 patients with 148 breast lesions categorized as BI-RADS 3 were included in the study between January 2021 and September 2022. The malignancy rate, accuracy, sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and area under the curve (AUC) were calculated. Results: In this study, 143 breast lesions were found to be benign, and 5 breast lesions were malignant (malignancy rate, 3.4 %, 95 % confidence interval (CI): 0.5-6.3). The malignancy rate rose significantly to 18.2 % (4/22, 95 % CI: 2.1-34.3) in the high-risk group with a "possibly malignant" CAD result (p = 0.017). With a "possibly benign" CAD result, the malignancy rate decreased to 0.8 % (1/126, 95 % CI: 0-2.2) in the low-risk group (p = 0.297). The AUC, sensitivity, specificity, accuracy, PPV, and NPV of the CAD system in BI-RADS 3 breast lesions were 0.837 (95 % CI: 77.7-89.6), 80.0 % (95 % CI: 73.6-86.4), 87.4 % (95 % CI: 82.0-92.7), 87.2 % (95 % CI: 81.8-92.6), 18.2 % (95 % CI: 2.1-34.3) and 99.2 % (95 % CI: 97.8-100.0), respectively. Conclusions: CAD system (S-Detect) enables radiologists to distinguish a high-risk group and a low-risk group among US-detected BI-RADS 3 breast lesions, so that patients in the low-risk group can receive follow-up without anxiety, while those in the high-risk group with a significantly increased malignancy rate should actively receive biopsy to avoid delayed diagnosis of breast cancer.

Clinical Application of Computer-Aided Diagnosis System in Breast Ultrasound: A Prospective Multicenter Study.

OBJECTIVES: Ultrasound tends to present very high sensitivity but relatively low specificity and positive predictive value (PPV), which would result in unnecessary breast biopsies. The purpose of this study is to analyze the diagnostic performance of computer-aided diagnosis (CAD) (S-Detect) system in differentiating breast lesions and reducing unnecessary biopsies in non-university hospitals in less-developed regions of China. METHODS: The study was a prospective multicenter study from 8 hospitals. The ultrasound images, and cine, CAD analysis, and BI-RADS were recorded. The accuracy, sensitivity, specificity, PPV, negative predictive value (NPV), and area under the curve (AUC) were analyzed and compared between CAD and radiologists. The Youden Index (YI) was used to determine optimal cut-off for the number of planes to downgrade. RESULTS: A total of 491 breast lesions were included in the study. Less-experienced radiologists combined CAD was superior to less-experienced radiologists alone in AUC (0.878 vs 0.712, p < 0.001), and specificity (81.3% vs 44.6%, p < 0.001). There was no statistical difference in AUC (0.891 vs 0.878, p = 0.346), and specificity (82.3% vs 81.3%, p = 0.791) between experienced radiologists and less-experienced radiologists combined CAD. With CAD assistance, the biopsy rate of less-experienced radiologists was significantly decreased (100.0% vs 25.6%, p < 0.001), and malignant rate of biopsy was significantly increased (15.0% vs 43.9%, p < 0.001). CONCLUSIONS: CAD system can be an effective auxiliary tool in differentiating breast lesions and reducing unnecessary biopsies for radiologists from non-university hospitals in less-developed regions of China.

Deep Learning-Based Computer-Aided Diagnosis for Breast Lesion Classification on Ultrasound: A Prospective Multicenter Study of Radiologists Without Breast Ultrasound Expertise.

BACKGROUND. Computer-aided diagnosis (CAD) systems for breast ultrasound interpretation have been primarily evaluated at tertiary and/or urban medical centers by radiologists with breast ultrasound expertise. OBJECTIVE. The purpose of this study was to evaluate the usefulness of deep learning-based CAD software on the diagnostic performance of radiologists without breast ultrasound expertise at secondary or rural hospitals in the differentiation of benign and malignant breast lesions measuring up to 2.0 cm on ultrasound. METHODS. This prospective study included patients scheduled to undergo biopsy or surgical resection at any of eight participating secondary or rural hospitals in China of a breast lesion classified as BI-RADS category 3-5 on prior breast ultrasound from November 2021 to September 2022. Patients underwent an additional investigational breast ultrasound, performed and interpreted by a radiologist without breast ultrasound expertise (hybrid body/breast radiologists, either who lacked breast imaging subspecialty training or for whom the number of breast ultrasounds performed annually accounted for less than 10% of all ultrasounds performed annually by the radiologist), who assigned a BI-RADS category. CAD results were used to upgrade reader-assigned BI-RADS category 3 lesions to category 4A and to downgrade reader-assigned BI-RADS category 4A lesions to category 3. Histologic results of biopsy or resection served as the reference standard. RESULTS. The study included 313 patients (mean age, 47.0 ± 14.0 years) with 313 breast lesions (102 malignant, 211 benign). Of BI-RADS category 3 lesions, 6.0% (6/100) were upgraded by CAD to category 4A, of which 16.7% (1/6) were malignant. Of category 4A lesions, 79.1% (87/110) were downgraded by CAD to category 3, of which 4.6% (4/87) were malignant. Diagnostic performance was significantly better after application of CAD, in comparison with before application of CAD, in terms of accuracy (86.6% vs 62.6%, p < .001), specificity (82.9% vs 46.0%, p < .001), and PPV (72.7% vs 46.5%, p < .001) but not significantly different in terms of sensitivity (94.1% vs 97.1%, p = .38) or NPV (96.7% vs 97.0%, p > .99). CONCLUSION. CAD significantly improved radiologists' diagnostic performance, showing particular potential to reduce the frequency of benign breast biopsies. CLINICAL IMPACT. The findings indicate the ability of CAD to improve patient care in settings with incomplete access to breast imaging expertise.

Ultrasound findings of subcutaneous soft tissue metastasis of renal cell carcinoma: A case report.

In renal cell carcinoma (RCC), metastasis to subcutaneous soft tissues is rare, with only a small number of cases reported to date, especially those based on ultrasound findings. This case report presents the ultrasound findings of an 81-year-old Chinese man who developed RCC presenting as subcutaneous soft-tissue metastasis 15 years after curative nephrectomy. We suggest that ultrasound-guided puncture biopsy should be considered when such suspicious lesions are found.

Thermal shielding performance of self-healing hydrogel in tumor thermal ablation.

Thermal ablation therapy is widely used in the surgical treatment of tumors. Clinically, normal saline is generally used as an insulator to protect adjacent tissues from local high-temperature burns caused by thermal ablation. However, the flow of saline causes fluid loss, requiring frequent injections and complex operation, which is easy to lead to complications such as secondary injury and hematoma. Here, a self-healing chitosan-PEG (CP) hydrogel was proposed as a protective medium to challenge the clinical preparations. Compared with saline and non-self-healing hydrogel F127, CP hydrogel exhibited outstanding thermal shielding performance in the thermal ablation of thyroid nodule in a Beagle dog model. The transient plane source (TPS) method is used to measure thermal properties, including thermal conductivity, thermal diffusivity and specific heat capacity. The thermal shielding mechanism and clinical advantages including operability, biodegradability, and biological safety of self-healing hydrogel are then revealed in-depth. Therefore, self-healing hydrogel can achieve much better thermal management in tumor thermal ablation.