Expression profiles of urine exosomal tRNA-derived small RNAs and their potential roles in calcium oxalate stone disease.

Background and objective: Exosomes have been confirmed to be implicated in the pathogenesis of calcium oxalate (CaOx) stones. tRNA-derived small RNAs (tsRNAs) are among the oldest small RNAs involved in exosome-mediated intercellular communication, yet their role in kidney stones remains unexplored. This pilot study aimed to identify differentially expressed tsRNAs (DEtsRNAs) in urine exosomes between CaOx stone patients and healthy controls and explore their potential roles in nephrolithiasis. Method: First-morning urine samples were collected from three CaOx stone patients and three healthy controls. Urinary exosomes were isolated and analyzed by high-throughput sequencing to generate the expression profiles of tsRNAs and detect DEtsRNAs. Predicted target genes of DEtsRNAs were subjected to functional enrichment analysis. The authors also combined the public dataset GSE73680 to investigate how DEtsRNAs were related to stone formation. Results: Four DEtsRNAs were significantly upregulated in CaOx stone patients compared to healthy controls. tRF-Lys-TTT-5005c was the most elevated, followed by tRF-Lys-CTT-5006c, tRF-Ala-AGC-5017b, and tRF-Gly-CCC-5004b. Bioinformatics analysis indicated that these four types of DEtsRNAs might serve distinct biological functions. Combined with data mining from the public dataset GSE73680, the authors assumed that exosomes carrying tRF-Lys-TTT-5005c and tRF-Lys-CTT-5006c could inhibit the expression of SMAD6, FBN1, and FZD1, thereby activating the BMP signaling pathway, which might induce an osteogenic-like transformation in target cells, resulting in the formation of Randall's plaques and CaOx stones. Conclusion: The authors' findings shed light on the potential roles of tsRNAs in the pathogenesis of CaOx stone disease, highlighting exosomal DEtsRNAs as promising diagnostic biomarkers and therapeutic targets in nephrolithiasis.

Multicenter investigation of preoperative distinction between primary central nervous system lymphomas and glioblastomas through interpretable artificial intelligence models.

OBJECTIVE: Research into the effectiveness and applicability of deep learning, radiomics, and their integrated models based on Magnetic Resonance Imaging (MRI) for preoperative differentiation between Primary Central Nervous System Lymphoma (PCNSL) and Glioblastoma (GBM), along with an exploration of the interpretability of these models. MATERIALS AND METHODS: A retrospective analysis was performed on MRI images and clinical data from 261 patients across two medical centers. The data were split into a training set (n = 153, medical center 1) and an external test set (n = 108, medical center 2). Radiomic features were extracted using Pyradiomics to build the Radiomics Model. Deep learning networks, including the transformer-based MobileVIT Model and Convolutional Neural Networks (CNN) based ConvNeXt Model, were trained separately. By applying the "late fusion" theory, the radiomics model and deep learning model were fused to produce the optimal Max-Fusion Model. Additionally, Shapley Additive exPlanations (SHAP) and Grad-CAM were employed for interpretability analysis. RESULTS: In the external test set, the Radiomics Model achieved an Area under the receiver operating characteristic curve (AUC) of 0.86, the MobileVIT Model had an AUC of 0.91, the ConvNeXt Model demonstrated an AUC of 0.89, and the Max-Fusion Model showed an AUC of 0.92. The Delong test revealed a significant difference in AUC between the Max-Fusion Model and the Radiomics Model (P = 0.02). CONCLUSION: The Max-Fusion Model, combining different models, presents superior performance in distinguishing PCNSL and GBM, highlighting the effectiveness of model fusion for enhanced decision-making in medical applications. CLINICAL RELEVANCE STATEMENT: The preoperative non-invasive differentiation between PCNSL and GBM assists clinicians in selecting appropriate treatment regimens and clinical management strategies.

Complete mitochondrial genomes of Dactylogyrus crucifer and Dactylogyrus zandti reveal distinct patterns of codon usage within Dactylogyrus.

Monogeneans of the genus Dactylogyrus Diesing, 1850, the largest genus in the family Dactylogyridae, mostly parasitize the gills of cyprinoid hosts; however, only 3 Dactylogyrus' mitochondrial genomes (mitogenomes) are studied so far. The aim of this research is to extend our understanding of the mitogenomes of Dactylogyrus. We sequenced the mitogenomes of D. crucifer and D. zandti isolated from Rutilus rutilus and Abramis brama orientalis in northwest China, and then we compared these mitogenomes with other monogeneans. We used Illumina NovaSeq to sequence the entire mitochondrial genomes of D. crucifer and D. zandti and characterized the mitogenomes to understand the gene structure, gene identity, the secondary structures of the 22 tRNA genes, and relative synonymous codon usage. We used the analytic Bayesian Information and Maximum Likelihood methods to determine their associated phylogenetic trees. The mitogenomes of D. crucifer and D. zandti were 14,403 and 18,584 bp, respectively. Organization and positioning of these genes were in accordance with Dactylogyrus lamellatus and Dactylogyrus tuba. The nucleotide composition of Dactylogyridae was different from other families of Monogenea, and the A+T count of genus Dactylogyrus (54 - 58.4 %) was lower than other genus species of the family Dactylogyridea (63.9 - 78.4 %) in protein-coding genes. Dactylogyrus members displayed a codon usage bias. The relative synonymous codon used by Dactylogyrus was not conserved and was lower than other monogeneans. The codon use patterns of closely-related species isolated from closely-related hosts were identical. Phylogenetic analyses using mitogenomic dataset produced Dactylogyrus isolated from host subfamily Leuciscinae formed a sister-group. Our results contributed significantly to an increased database of mitogenomes, more than 50 %, for Dactylogyrus that may help future studies of mitochondrial genes and codon uses for the analysis of monogenean phylogenetics.

MRI radiomics-based interpretable model and nomogram for preoperative prediction of Ki-67 expression status in primary central nervous system lymphoma.

Objectives: To investigate the value of interpretable machine learning model and nomogram based on clinical factors, MRI imaging features, and radiomic features to predict Ki-67 expression in primary central nervous system lymphomas (PCNSL). Materials and methods: MRI images and clinical information of 92 PCNSL patients were retrospectively collected, which were divided into 53 cases in the training set and 39 cases in the external validation set according to different medical centers. A 3D brain tumor segmentation model was trained based on nnU-NetV2, and two prediction models, interpretable Random Forest (RF) incorporating the SHapley Additive exPlanations (SHAP) method and nomogram based on multivariate logistic regression, were proposed for the task of Ki-67 expression status prediction. Results: The mean dice Similarity Coefficient (DSC) score of the 3D segmentation model on the validation set was 0.85. On the Ki-67 expression prediction task, the AUC of the interpretable RF model on the validation set was 0.84 (95% CI:0.81, 0.86; p < 0.001), which was a 3% improvement compared to the AUC of the nomogram. The Delong test showed that the z statistic for the difference between the two models was 1.901, corresponding to a p value of 0.057. In addition, SHAP analysis showed that the Rad-Score made a significant contribution to the model decision. Conclusion: In this study, we developed a 3D brain tumor segmentation model and used an interpretable machine learning model and nomogram for preoperative prediction of Ki-67 expression status in PCNSL patients, which improved the prediction of this medical task. Clinical relevance statement: Ki-67 represents the degree of active cell proliferation and is an important prognostic parameter associated with clinical outcomes. Non-invasive and accurate prediction of Ki-67 expression level preoperatively plays an important role in targeting treatment selection and patient stratification management for PCNSL thereby improving prognosis.

An interpretable artificial intelligence model based on CT for prognosis of intracerebral hemorrhage: a multicenter study.

OBJECTIVES: To develop and validate a novel interpretable artificial intelligence (AI) model that integrates radiomic features, deep learning features, and imaging features at multiple semantic levels to predict the prognosis of intracerebral hemorrhage (ICH) patients at 6 months post-onset. MATERIALS AND METHODS: Retrospectively enrolled 222 patients with ICH for Non-contrast Computed Tomography (NCCT) images and clinical data, who were divided into a training cohort (n = 186, medical center 1) and an external testing cohort (n = 36, medical center 2). Following image preprocessing, the entire hematoma region was segmented by two radiologists as the volume of interest (VOI). Pyradiomics algorithm library was utilized to extract 1762 radiomics features, while a deep convolutional neural network (EfficientnetV2-L) was employed to extract 1000 deep learning features. Additionally, radiologists evaluated imaging features. Based on the three different modalities of features mentioned above, the Random Forest (RF) model was trained, resulting in three models (Radiomics Model, Radiomics-Clinical Model, and DL-Radiomics-Clinical Model). The performance and clinical utility of the models were assessed using the Area Under the Receiver Operating Characteristic Curve (AUC), calibration curve, and Decision Curve Analysis (DCA), with AUC compared using the DeLong test. Furthermore, this study employs three methods, Shapley Additive Explanations (SHAP), Grad-CAM, and Guided Grad-CAM, to conduct a multidimensional interpretability analysis of model decisions. RESULTS: The Radiomics-Clinical Model and DL-Radiomics-Clinical Model exhibited relatively good predictive performance, with an AUC of 0.86 [95% Confidence Intervals (CI): 0.71, 0.95; P < 0.01] and 0.89 (95% CI: 0.74, 0.97; P < 0.01), respectively, in the external testing cohort. CONCLUSION: The multimodal explainable AI model proposed in this study can accurately predict the prognosis of ICH. Interpretability methods such as SHAP, Grad-CAM, and Guided Grad-Cam partially address the interpretability limitations of AI models. Integrating multimodal imaging features can effectively improve the performance of the model. CLINICAL RELEVANCE STATEMENT: Predicting the prognosis of patients with ICH is a key objective in emergency care. Accurate and efficient prognostic tools can effectively prevent, manage, and monitor adverse events in ICH patients, maximizing treatment outcomes.

Non-target discovery and risk prediction of per- and polyfluoroalkyl substances (PFAS) and transformation products in wastewater treatment systems.

Wastewater treatment plants (WWTPs) serve as the main destination of many wastes containing per- and polyfluoroalkyl substances (PFAS). Here, we investigated the occurrence and transformation of PFAS and their transformation products (TPs) in wastewater treatment systems using high-resolution mass spectrometry-based target, suspect, and non-target screening approaches. The results revealed the presence of 896 PFAS and TPs in aqueous and sludge phases, of which 687 were assigned confidence levels 1-3 (46 PFAS and 641 TPs). Cyp450 metabolism and environmental microbial degradation were found to be the primary metabolic transformation pathways for PFAS within WWTPs. An estimated 52.3 %, 89.5 %, and 13.6 % of TPs were believed to exhibit persistence, bioaccumulation, and toxicity effects, respectively, with a substantial number of TPs posing potential health risks. Notably, the length of the fluorinated carbon chain in PFAS and TPs was likely associated with increased hazard, primarily due to the influence of biodegradability. Ultimately, two high riskcompounds were identified in the effluent, including one PFAS (Perfluorobutane sulfonic acid) and one enzymatically metabolized TP (23-(Perfluorobutyl)tricosanoic acid@BTM0024_cyp450). It is noteworthy that the toxicity of some TPs exceeded that of their parent compounds. The results from this study underscores the importance of PFAS TPs and associated environmental risks.

Predicting Outcome of Patients With Cerebral Hemorrhage Using a Computed Tomography-Based Interpretable Radiomics Model: A Multicenter Study.

OBJECTIVE: The aim of this study was to develop and validate an interpretable and highly generalizable multimodal radiomics model for predicting the prognosis of patients with cerebral hemorrhage. METHODS: This retrospective study involved 237 patients with cerebral hemorrhage from 3 medical centers, of which a training cohort of 186 patients (medical center 1) was selected and 51 patients from medical center 2 and medical center 3 were used as an external testing cohort. A total of 1762 radiomics features were extracted from nonenhanced computed tomography using Pyradiomics, and the relevant macroscopic imaging features and clinical factors were evaluated by 2 experienced radiologists. A radiomics model was established based on radiomics features using the random forest algorithm, and a radiomics-clinical model was further trained by combining radiomics features, clinical factors, and macroscopic imaging features. The performance of the models was evaluated using area under the curve (AUC), sensitivity, specificity, and calibration curves. Additionally, a novel SHAP (SHAPley Additive exPlanations) method was used to provide quantitative interpretability analysis for the optimal model. RESULTS: The radiomics-clinical model demonstrated superior predictive performance overall, with an AUC of 0.88 (95% confidence interval, 0.76-0.95; P < 0.01). Compared with the radiomics model (AUC, 0.85; 95% confidence interval, 0.72-0.94; P < 0.01), there was a 0.03 improvement in AUC. Furthermore, SHAP analysis revealed that the fusion features, rad score and clinical rad score, made significant contributions to the model's decision-making process. CONCLUSION: Both proposed prognostic models for cerebral hemorrhage demonstrated high predictive levels, and the addition of macroscopic imaging features effectively improved the prognostic ability of the radiomics-clinical model. The radiomics-clinical model provides a higher level of predictive performance and model decision-making basis for the risk prognosis of cerebral hemorrhage.

Defective natriuresis contributes to hyperkalemia in db/db mice during potassium supplementation.

OBJECTIVES: Potassium supplementation reduces blood pressure and the occurrence of cardiovascular diseases, with K + -induced natriuresis playing a potential key role in this process. However, whether these beneficial effects occur in diabetes remains unknown. METHODS: In this study, we examined the impact of high-K + intake on renal Na + /K + transport by determining the expression of major apical Na + transporters, diuretics responses (as a proxy for specific Na + transporter function), urinary Na + /K + excretion, and plasma Na + /K + concentrations in db/db mice, a model of type 2 diabetes mellitus. RESULTS: Although db/m mice exhibited increased fractional excretion of sodium (FE Na ) and fractional excretion of potassium (FE K ) under high-K + intake, these responses were largely blunted in db/db mice, suggesting impaired K + -induced natriuresis and kaliuresis in diabetes. Consequently, high-K + intake increased plasma K + levels in db/db mice, which could be attributed to the abnormal activity of sodium-hydrogen exchanger 3 (NHE3), sodium-chloride cotransporter (NCC), and epithelial Na + channel (ENaC), as high-K + intake could not effectively decrease NHE3 and NCC and increase ENaC expression and activity in the diabetic group. Inhibition of NCC by hydrochlorothiazide could correct the hyperkalemia in db/db mice fed a high-K + diet, indicating a key role for NCC in K + -loaded diabetic mice. Treatment with metformin enhanced urinary Na + /K + excretion and normalized plasma K + levels in db/db mice with a high-K + diet, at least partially, by suppressing NCC activity. CONCLUSION: Collectively, the impaired K + -induced natriuresis in diabetic mice under high-K + intake may be primarily attributed to impaired NCC-mediated renal K + excretion, despite the role of NHE3.

Preliminary efficacy and safety of YSCH-01 in patients with advanced solid tumors: an investigator-initiated trial.

OBJECTIVE: To evaluate the safety and preliminary efficacy of YSCH-01 (Recombinant L-IFN adenovirus) in subjects with advanced solid tumors. METHODS: In this single-center, open-label, investigator-initiated trial of YSCH-01, 14 patients with advanced solid tumors were enrolled. The study consisted of two distinct phases: (1) the dose escalation phase and (2) the dose expansion phase; with three dose groups in the dose escalation phase based on dose levels (5.0×109 viral particles (VP)/subject, 5.0×1010 VP/subject, and 5.0×1011 VP/subject). Subjects were administered YSCH-01 injection via intratumoral injections. The safety was assessed using National Cancer Institute Common Terminology Criteria for Adverse Events V.5.0, and the efficacy evaluation was performed using Response Evaluation Criteria in Solid Tumor V.1.1. RESULTS: 14 subjects were enrolled in the study, including 9 subjects in the dose escalation phase and 5 subjects in the dose expansion phase. Of the 13 subjects included in the full analysis set, 4 (30.8%) were men and 9 (69.2%) were women. The most common tumor type was lung cancer (38.5%, 5 subjects), followed by breast cancer (23.1%, 3 subjects) and melanoma (23.1%, 3 subjects). During the dose escalation phase, no subject experienced dose-limiting toxicities. The content of recombinant L-IFN adenovirus genome and recombinant L-IFN protein in blood showed no trend of significant intergroup changes. No significant change was observed in interleukin-6 and interferon-gamma. For 11 subjects evaluated for efficacy, the overall response rate with its 95% CI was 27.3% (6.02% to 60.97%) and the disease control rate with its 95% CI was 81.8% (48.22% to 97.72%). The median progression-free survival was 4.97 months, and the median overall survival was 8.62 months. In addition, a tendency of decrease in the sum of the diameters of target lesions was observed. For 13 subjects evaluated for safety, the overall incidence of adverse events (AEs) was 92.3%, the overall incidence of adverse drug reactions (ADRs) was 84.6%, and the overall incidence of >Grade 3 AEs was 7.7%, while no AEs/ADRs leading to death occurred. The most common AEs were fever (69.2%), nausea (30.8%), vomiting (30.8%), and hypophagia (23.1%). CONCLUSIONS: The study shows that YSCH-01 injections were safe and well tolerated and exhibited preliminary efficacy in patients with advanced solid tumors, supporting further investigation to evaluate its efficacy and safety. TRIAL REGISTRATION NUMBER: NCT05180851.

[A comparative study of antioxidant active components in different parts of Artemisia argyi by UPLC-ABTS-Q-TOF-MS].

In order to characterize and identify the chemical components in different parts of Artemisia argyi(roots, stems, leaves, and seeds), compounds with antioxidant activity were screened. In this study, ultra-performance liquid chromatography-2,2'-azino-bis(3-ethylbenzothiazoline-6-sulfonic acid) diammonium salt-quadrupole time-of-flight-tandem mass spectrometry(UPLC-ABTS-Q-TOF-MS) was used as an online combination technique. Poroshell 120 SB-Aq(3.0 mm×150 mm, 2.7 µm) was used as the column, and acetonitrile(A)-0.2% formic acid water(B) was adopted as the mobile phase to perform gradient elution and was scanned in positive and negative ion modes. MassLynx software was utilized, and combined with reference substances and related literature, the chemical components of different parts of A. argyi were identified and compared. The antioxidant active components were detected by using the online detection system, and the antioxidant activities of active components of different parts of A. argyi were compared and evaluated by scavenging efficiency. As a result, a total of 87 compounds were identified from extracts of different parts of A. argyi, and 38, 72, 85, and 33 components were identified from roots, stems, leaves, and seeds. 22 compounds with antioxidant activity were screened, and 14, 17, 20, and 11 compounds with antioxidant activity were identified from roots, stems, leaves, and seeds. The results show that there are certain differences in chemical components and antioxidant components of different parts of A. argyi, which provides data support for the resource utilization and further research and development of A. argyi.

[A fluorescence method based on malachite green/aptamer for detection of ochratoxin A in traditional Chinese medicines].

A label-free fluorescence method based on malachite green/aptamer was developed for the detection of ochratoxin A(OTA) in traditional Chinese medicines. Malachite green itself exhibits weak fluorescence. Upon interaction with the aptamer specific to OTA, the G-quadruplex structure of the aptamer provides a protective microenvironment for malachite green, which significantly enhances its fluorescence signal. After OTA is added, preferential binding occurs between the aptamer and OTA, and malachite green will be released from the aptamer, which weakens the fluorescence signal. According to this principle, this paper established a fluorescence method with the aptamer of OTA as the recognition element and malachite green as the fluorescent probe for the detection of OTA in traditional Chinese medicines. The key experimental factors such as the concentrations of metal ions, aptamer, and malachite green were optimized to improve the performance of the method. OTA was detected under the optimal experimental conditions, and the results showed that with the increase in OTA concentration, the fluorescence signal gradually weakened. Within the range of 20-1 000 nmol·L~(-1), the OTA concentration was linearly correlated with the fluorescence signal ratio ΔF/F(ΔF=F_0-F, where F_0 is the fluorescence signal of aptamer/malachite green, and F is the fluorescence signal of OTA/aptamer/malachite green), with R~2 of 0.995. The limit of detection of the established method was 7.1 nmol·L~(-1). Furthermore, three substances structurally similar to OTA and two mycotoxins that may coexist with OTA were selected for experiments, which aimed to examine the cross-reactivity and specificity of the established method. The cross-reactivity experiments demonstrated that the interferers did not significantly affect the fluorescence signal of the detection system. The specificity experiments revealed that when mycotoxins were mixed with OTA, the fluorescence signal generated by the mixture closely resembled that of OTA itself. The results indicated that even in the presence of interferents, the established method remained unaffected and demonstrated excellent specificity. Additionally, this method exhibited remarkable reproducibility and stability. In the case of simple centrifugation and dilution of traditional Chinese medicine samples(Puerariae Lobatae Radix, Sophorae Flavescentis Radix, and Periplocae Cortex), the OTA detection method was applicable, with recovery rates ranging from 91.5% to 121.3%. Notably, this approach does not need complex pretreatment of traditional Chinese medicines while offering simple operation, low detection costs, and short detection time. Furthermore, by incorporating aptamers into the quality evaluation of traditional Chinese medicines, this method expands the application scope of aptamers.

Impaired distal renal potassium handling in streptozotocin-induced diabetic mice.

Diabetes is closely associated with K+ disturbances during disease progression and treatment. However, it remains unclear whether K+ imbalance occurs in diabetes with normal kidney function. In this study, we examined the effects of dietary K+ intake on systemic K+ balance and renal K+ handling in streptozotocin (STZ)-induced diabetic mice. The control and STZ mice were fed low or high K+ diet for 7 days to investigate the role of dietary K+ intake in renal K+ excretion and K+ homeostasis and to explore the underlying mechanism by evaluating K+ secretion-related transport proteins in distal nephrons. K+-deficient diet caused excessive urinary K+ loss, decreased daily K+ balance, and led to severe hypokalemia in STZ mice compared with control mice. In contrast, STZ mice showed an increased daily K+ balance and elevated plasma K+ level under K+-loading conditions. Dysregulation of the NaCl cotransporter (NCC), epithelial Na+ channel (ENaC), and renal outer medullary K+ channel (ROMK) was observed in diabetic mice fed either low or high K+ diet. Moreover, amiloride treatment reduced urinary K+ excretion and corrected hypokalemia in K+-restricted STZ mice. On the other hand, inhibition of SGLT2 by dapagliflozin promoted urinary K+ excretion and normalized plasma K+ levels in K+-supplemented STZ mice, at least partly by increasing ENaC activity. We conclude that STZ mice exhibited abnormal K+ balance and impaired renal K+ handling under either low or high K+ diet, which could be primarily attributed to the dysfunction of ENaC-dependent renal K+ excretion pathway, despite the possible role of NCC.NEW & NOTEWORTHY Neither low dietary K+ intake nor high dietary K+ intake effectively modulates renal K+ excretion and K+ homeostasis in STZ mice, which is closely related to the abnormality of ENaC expression and activity. SGLT2 inhibitor increases urinary K+ excretion and reduces plasma K+ level in STZ mice under high dietary K+ intake, an effect that may be partly due to the upregulation of ENaC activity.

A Novel Interpretable Radiomics Model to Distinguish Nodular Goiter From Malignant Thyroid Nodules.

OBJECTIVES: The purpose of this study is to inquire about the potential association between radiomics features and the pathological nature of thyroid nodules (TNs), and to propose an interpretable radiomics-based model for predicting the risk of malignant TN. METHODS: In this retrospective study, computed tomography (CT) imaging and pathological data from 141 patients with TN were collected. The data were randomly stratified into a training group (n = 112) and a validation group (n = 29) at a ratio of 4:1. A total of 1316 radiomics features were extracted by using the pyradiomics tool. The redundant features were removed through correlation testing, and the least absolute shrinkage and selection operator (LASSO) or the minimum redundancy maximum relevance standard was used to select features. Finally, 4 different machine learning models (RF Hybrid Feature, SVM Hybrid Feature, RF, and LASSO) were constructed. The performance of the 4 models was evaluated using the receiver operating characteristic curve. The calibration curve, decision curve analysis, and SHapley Additive exPlanations method were used to evaluate or explain the best radiomics machine learning model. RESULTS: The optimal radiomics model (RF Hybrid Feature model) demonstrated a relatively high degree of discrimination with an area under the receiver operating characteristic curve (AUC) of 0.87 (95% CI, 0.70-0.97; P < 0.001) for the validation cohort. Compared with the commonly used LASSO model (AUC, 0.78; 95% CI, 0.60-0.91; P < 0.01), there is a significant improvement in AUC in the validation set, net reclassification improvement, 0.79 (95% CI, 0.13-1.46; P < 0.05), and integrated discrimination improvement, 0. 20 (95% CI, 0.10-0.30; P < 0.001). CONCLUSION: The interpretable radiomics model based on CT performs well in predicting benign and malignant TNs by using quantitative radiomics features of the unilateral total thyroid. In addition, the data preprocessing method incorporating different layers of features has achieved excellent experimental results. CLINICAL RELEVANCE STATEMENT: As the detection rate of TNs continues to increase, so does the diagnostic burden on radiologists. This study establishes a noninvasive, interpretable and accurate machine learning model to rapidly identify the nature of TN found in CT.

Diagnostic performance of 18F-labeled PSMA PET/CT in patients with biochemical recurrence of prostate cancer: a systematic review and meta-analysis.

BACKGROUND: After initial treatment of prostate cancer, increases in prostate-specific antigen (PSA) levels commonly signify potential relapse or metastasis. 18F-labeled prostate-specific membrane antigen (PSMA) is considered a promising treatment due to its favorable physical properties. PURPOSE: To investigate the diagnostic value of 18F-PSMA PET/CT for the recurrence and/or metastasis of biochemical recurrence of prostate cancer (BRPca). MATERIAL AND METHODS: A comprehensive literature search was conducted in PubMed, EMBASE, Web of Science, and the Cochrane Library databases. Combined sensitivity and specificity values for the use of 18F-PSMA PET/CT in patients with BRPca were obtained. The quality of the studies was tested using the Diagnostic Accuracy Research Quality Assessment tool. Meta-analysis was performed using STATA 15 software, and heterogeneity was subsequently tested. RESULTS: A total of 16 studies (1162 patients) were enrolled and had significant heterogeneity. The pooled sensitivity, specificity, and AUC values for 18F-PSMA PET/CT in the diagnosis of prostate recurrence and/or metastasis were 0.93 (0.89-0.95), 0.94 (0.85-0.98), and 0.96 (0,94-0.98), respectively. Meta-regression analyses showed that the sources of heterogeneity did not relate to ligands, study designs, or participants. The pooled sensitivity and specificity values of 18F-DCFPyL PET/CT were 0.90 (0.85-0.94) and 0.89 (0.85-0.93), respectively. The pooled sensitivity and specificity values of 18F-PSMA-1007 PET/CT were 0.89 (0.85-0.93) and 0.93 (0.70-0.99), respectively. The per-patient pooled sensitivity and specificity values were 0.92 (0.86-0.96) and 0.83 (0.41-0.97), respectively. The per-lesion pooled sensitivity and specificity values were 0.91 (0.86-0.94) and 0.91 (0.86-0.94), respectively. CONCLUSION: According to our meta-analysis, 18F-PSMA PET/CT has the potential to be critical for the diagnosis of recurrence and/or metastasis in patients with BRPca.

Inhibition of AT1R/IP3/IP3R-mediated Ca2+ release protects against calcium oxalate crystals-induced renal oxidative stress.

Calcium oxalate (CaOx) stones are the most prevalent type of kidney stones. CaOx crystals can stimulate reactive oxygen species (ROS) generation and induce renal oxidative stress to promote stone formation. Intracellular Ca2+ is an important signaling molecule, and an elevation of cytoplasmic Ca2+ levels could trigger oxidative stress. Our previous study has revealed that upregulation of Ang II/AT1R promoted renal oxidative stress during CaOx exposure. IP3/IP3R/Ca2+ signaling pathway activated via Ang II/AT1R is involved in several diseases, but its role in stone formation has not been reported. Herein, we focus on the role of AT1R/IP3/IP3R-mediated Ca2+ release in CaOx crystals-induced oxidative stress and explore whether inhibition of this pathway could alleviate renal oxidative stress. NRK-52E cells were exposed to CaOx crystals pretreated with AT1R inhibitor losartan or IP3R inhibitor 2-APB, and glyoxylic acid monohydrate-induced CaOx stone-forming rats were treated with losartan or 2-APB. The intracellular Ca2+ levels, ROS levels, oxidative stress indexes, and the gene expression of this pathway were detected. Our results showed that CaOx crystals activated AT1R to promote IP3/IP3R-mediated Ca2+ release, leading to increased cytoplasmic Ca2+ levels. The Ca2+ elevation was able to stimulate NOX2 and NOX4 to generate ROS, induce oxidative stress, and upregulate the expression of stone-related proteins. 2-APB and losartan reversed the referred effects, reduced CaOx crystals deposition and alleviated tissue injury in the rat kidneys. In summary, our results indicated that CaOx crystals promoted renal oxidative stress by activating the AT1R/IP3/IP3R/Ca2+ pathway. Inhibition of AT1R/IP3/IP3R-mediated Ca2+ release protected against CaOx crystals-induced renal oxidative stress. 2-APB and losartan might be promising preventive and therapeutic agents for the treatment of kidney stone disease.

A comparative study of the ameliorative effects of hyaluronic acid oligosaccharides and hyaluronic acid on DSS-induced colitis in mice and research on relevant mechanisms.

As a dietary supplement, hyaluronic acid (HA) has exhibited appreciable immunomodulatory activity and an ameliorative effect on rodent colitis. However, its high viscosity is not only refractory to absorb through the gut, but also causes flatulence. In contrast to HA, hyaluronic acid oligosaccharides (o-HAs) can overcome the above-mentioned constraints, but their treatment effect still remains ill-defined contemporarily. Herein, the current study intends to compare the modulatory effects of HA and o-HA on colitis and assess the underlying molecular mechanism. We first showed that o-HA had a better preventive effect than HA in alleviating colitis symptoms, as evidenced by lower body weight loss, lower disease activity index scores, a lower inflammatory response (TNF-α, IL-6, IL-1ß, p-NF-κB), and more intact colon epithelial integrity in vivo. The best efficiency was observed in the o-HA treated group with a dosage of 30 mg kg-1. In an in vitro barrier function assay, o-HA exerted a better protective effect on the transepithelial electrical resistance (TEER), FITC permeability, and wound healing and modulated the expression of tight junction (TJ) proteins (ZO-1, occludin) in lipopolysaccharide (LPS)-stimulated Caco-2 cells. In summary, both HA and o-HA showed the potential to reduce inflammation and ameliorate intestinal damage in DSS-induced colitis and LPS-induced inflammation, but o-HA had improved outcomes. The results also provided a glimpse of the latent mechanism by which HA and o-HA enhanced intestinal barrier function via MLCK/p-MLC signaling pathway suppression.

Anomanolide C suppresses tumor progression and metastasis by ubiquitinating GPX4-driven autophagy-dependent ferroptosis in triple negative breast cancer.

Anomanolide C (AC), a natural withanolide isolated from Tubocapsicum anomalum, has been reported to have exhibits remarkable anti-tumour activities in several types of human cancers, particularly triple-negative breast cancer (TNBC). However, its intricate mechanisms still remain need to be clarified. Here, we evaluated whether AC could inhibit cell proliferation and the role of AC in ferroptosis induction and autophagy activation. Subsequently, the anti-migration potential of AC was found via autophagy-dependent ferroptosis. Additionally, we found that AC reduced the expression of GPX4 by ubiquitination and inhibited TNBC proliferation and metastasis in vitro and in vivo. Moreover, we demonstrated that AC induced autophagy-dependent ferroptosis, and led to Fe2+ accumulation via ubiquitinating GPX4. Moreover, AC was shown to induce autophagy-dependent ferroptosis as well as to inhibit TNBC proliferation and migration via GPX4 ubiquitination. Together, these results demonstrated that AC inhibited the progression and metastasis of TNBC by inducing autophagy-dependent ferroptosis via ubiquitinating GPX4, which might shed light on exploiting AC as a new drug candidate for the future TNBC therapy.