Automated segmentation and volume prediction in pediatric Wilms' tumor CT using nnu-net.

BACKGROUND: Radiologic volumetric evaluation of Wilms' tumor (WT) is an important indicator to guide treatment decisions. However, due to the heterogeneity of the tumors, radiologists have main-guard differences in diagnosis that can lead to misdiagnosis and poor treatment. The aim of this study was to explore whether CT-based outlining of WT foci can be automated using deep learning. METHODS: We included CT intravenous phase images of 105 patients with WT and double-blind outlining of lesions by two radiologists. Then, we trained an automatic segmentation model using nnUnet. The Dice similarity coefficient (DSC) and 95th percentile Hausdorff distance (HD95) were used to assess the performance. Next, we optimized the automatic segmentation results based on the ratio of the three-dimensional diameter of the lesion to improve the performance of volumetric assessment. RESULTS: The DSC and HD95 was 0.83 ± 0.22 and 10.50 ± 8.98 mm. The absolute difference and percentage difference in tumor size was 72.27 ± 134.84 cm3 and 21.08% ± 30.46%. After optimization according to our method, it decreased to 40.22 ± 96.06 cm3 and 10.16% ± 9.70%. CONCLUSION: We introduce a novel method that enhances the accuracy of predicting WT volume by integrating AI automated outlining and 3D tumor diameters. This approach surpasses the accuracy of using AI outcomes alone and has the potential to enhance the clinical evaluation of pediatric patients with WT. By intertwining AI outcomes with clinical data, this method becomes more interpretive and offers promising applications beyond Wilms tumor, extending to other pediatric diseases.

Efficacy of levetiracetam combined with oxcarbazepine in the treatment of adults with temporal lobe epilepsy and its impact on memory and cognitive function.

OBJECTIVE: To explore the effect of levetiracetam combined with oxcarbazepine on the memory and cognitive function of adult patients with temporal lobe epilepsy. METHODS: This retrospective analysis included 91 adult patients with temporal lobe epilepsy treated at Xianyang Hospital from June 2020 to December 2022. Based on their medication regimen, patients were categorized into an observation group (n=51) receiving levetiracetam plus oxcarbazepine and a control group (n=40) receiving only levetiracetam. Both groups underwent 3 months of continuous treatment. Therapeutic efficacy, pre- and post-treatment memory function (assessed using the Clinical Memory Scale, CMS), cognitive function (evaluated with the Wechsler Adult Intelligence Scale-Revised in China, WAISRC), anxiety and depression levels (measured by the Hamilton Anxiety Scale, HAMA, and Hamilton Depression Scale, HAMD), as well as adverse reactions, were compared between the two groups. Independent factors influencing treatment efficacy were also analyzed. RESULTS: CMS and WAISRC scores significantly increased in both groups after treatment (both P=0.001), with the observation group showing more significant improvements than the control group (P=0.001). The improvements in HAMA and HAMD scores in the observation group were significantly better than the control group (all P<0.001). Adverse reaction occurrence showed no significant difference between the two groups (P>0.05). Prognostic analysis identified seizure frequency and treatment regimen as independent factors influencing efficacy. CONCLUSION: Levetiracetam combined with oxcarbazepine effectively improves cognitive dysfunction in adults with temporal lobe epilepsy, with superior efficacy to levetiracetam alone, and good safety.

Multifocal fluorescence video-rate imaging of centimetre-wide arbitrarily shaped brain surfaces at micrometric resolution.

Fluorescence microscopy allows for the high-throughput imaging of cellular activity across brain areas in mammals. However, capturing rapid cellular dynamics across the curved cortical surface is challenging, owing to trade-offs in image resolution, speed, field of view and depth of field. Here we report a technique for wide-field fluorescence imaging that leverages selective illumination and the integration of focal areas at different depths via a spinning disc with varying thickness to enable video-rate imaging of previously reconstructed centimetre-scale arbitrarily shaped surfaces at micrometre-scale resolution and at a depth of field of millimetres. By implementing the technique in a microscope capable of acquiring images at 1.68 billion pixels per second and resolving 16.8 billion voxels per second, we recorded neural activities and the trajectories of neutrophils in real time on curved cortical surfaces in live mice. The technique can be integrated into many microscopes and macroscopes, in both reflective and fluorescence modes, for the study of multiscale cellular interactions on arbitrarily shaped surfaces.

Bio-friendly long-term subcellular dynamic recording by self-supervised image enhancement microscopy.

Fluorescence microscopy has become an indispensable tool for revealing the dynamic regulation of cells and organelles. However, stochastic noise inherently restricts optical interrogation quality and exacerbates observation fidelity when balancing the joint demands of high frame rate, long-term recording and low phototoxicity. Here we propose DeepSeMi, a self-supervised-learning-based denoising framework capable of increasing signal-to-noise ratio by over 12 dB across various conditions. With the introduction of newly designed eccentric blind-spot convolution filters, DeepSeMi effectively denoises images with no loss of spatiotemporal resolution. In combination with confocal microscopy, DeepSeMi allows for recording organelle interactions in four colors at high frame rates across tens of thousands of frames, monitoring migrasomes and retractosomes over a half day, and imaging ultra-phototoxicity-sensitive Dictyostelium cells over thousands of frames. Through comprehensive validations across various samples and instruments, we prove DeepSeMi to be a versatile and biocompatible tool for breaking the shot-noise limit.

Short-term safety of inactivated SARS-Cov-2 vaccines in Chinese patients with central nervous system inflammatory demyelinating diseases.

Objective: This study aims to evaluate the short-term safety of inactivated severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) vaccines in Chinese patients with central nervous system inflammatory demyelinating diseases (CNS IDDs). Methods: A web-based survey was conducted among patients with CNS IDDs from April 15 to 19, 2022 in China. In total, 645 patients with CNS IDDs were identified, including 425 patients with multiple sclerosis (MS), 194 with neuromyelitis optica spectrum disorder (NMOSD), and 26 with other CNS IDDs. The questionnaire consisted of demographic data, clinical records, history of SARS-CoV-2 vaccination, and vaccination-related symptoms within one month after vaccination. The demographic data, clinical information, and relapse rates between vaccinated and non-vaccinated patients were compared. Results: Among 645 patients with CNS IDDs, 78 were vaccinated and 567 were non-vaccinated with the vaccination rate of 12.1 %. Compared to non-vaccinated group, a lower percentage of patients on DMDs therapy (41.0 % vs. 71.8 %, P < 0.001) and an increased proportion of patients with other vaccination in past 3 years (17.9 % vs. 4.8 %, P < 0.001) were observed in vaccinated group. Six patients experienced a relapse within 30 days of a vaccination. Additionally, vaccine-associated relapse rates in vaccinated patients did not significantly differ from these in non-vaccinated patients among 2020, 2021, and from January 1 to October 1, 2022. Conclusions: No increased risk of vaccination-associated relapses among Chinese patients with CNS IDDs indicated that inactivated SARS-CoV-2 vaccines appear to be safe for this population.

Diagnostic value of circulating microRNAs for esophageal cancer: a meta-analysis based on Asian data

Background and objective: esophageal cancer (EC) is one of the most common gastrointestinal malignant diseases. We conducted a comprehensive meta-analysis to explore the clinical applicability of circulating microRNA for the diagnosis of EC. Methods: as of September 10, 2021, a comprehensive literature search was conducted on PubMed, Embase, Web of Science, Cochrane Library, Wanfang Database, and China National Knowledge Infrastructure (CNKI) to identify eligible studies. The sensitivity, specificity, positive likelihood ratio (PLR), negative likelihood ratio (NLR), diagnostic odds ratio (DOR), and area under the curve (AUC) were pooled to evaluate the test performance. The potential sources of heterogeneity were analyzed by subgroup analysis. Deeks' funnel plot was used to assess publication bias. Results: 85 studies from 50 articles were included in the current meta-analysis. The overall pooled sensitivity was 0.82 (95 % CI, 0.79-0.84), specificity was 0.84 (95 % CI, 0.81-0.86), PLR was 4.9 (95 % CI, 4.2-5.9), NLR was 0.22 (95 % CI, 0.19-0.25), DOR was 22 (95 % CI, 17-29) and AUC was 0.89 (95 % CI, 0.86-0.92), respectively. Subgroup analysis suggested that miRNA clusters with a large sample size showed better diagnostic accuracy. Publication bias was not found. Conclusions: circulating miRNAs can be used as a potential non-invasive biomarker for the diagnosis of EC in Asian populations. (AU)

Rapid detection of neurons in widefield calcium imaging datasets after training with synthetic data.

Widefield microscopy can provide optical access to multi-millimeter fields of view and thousands of neurons in mammalian brains at video rate. However, tissue scattering and background contamination results in signal deterioration, making the extraction of neuronal activity challenging, laborious and time consuming. Here we present our deep-learning-based widefield neuron finder (DeepWonder), which is trained by simulated functional recordings and effectively works on experimental data to achieve high-fidelity neuronal extraction. Equipped with systematic background contribution priors, DeepWonder conducts neuronal inference with an order-of-magnitude-faster speed and improved accuracy compared with alternative approaches. DeepWonder removes background contaminations and is computationally efficient. Specifically, DeepWonder accomplishes 50-fold signal-to-background ratio enhancement when processing terabytes-scale cortex-wide functional recordings, with over 14,000 neurons extracted in 17 h.

Diagnostic value of circulating microRNAs for esophageal cancer: a meta-analysis based on Asian data.

BACKGROUND AND OBJECTIVE: esophageal cancer (EC) is one of the most common gastrointestinal malignant diseases. We conducted a comprehensive meta-analysis to explore the clinical applicability of circulating microRNA for the diagnosis of EC. METHODS: as of September 10, 2021, a comprehensive literature search was conducted on PubMed, Embase, Web of Science, Cochrane Library, Wanfang Database, and China National Knowledge Infrastructure (CNKI) to identify eligible studies. The sensitivity, specificity, positive likelihood ratio (PLR), negative likelihood ratio (NLR), diagnostic odds ratio (DOR), and area under the curve (AUC) were pooled to evaluate the test performance. The potential sources of heterogeneity were analyzed by subgroup analysis. Deeks' funnel plot was used to assess publication bias. RESULTS: 85 studies from 50 articles were included in the current meta-analysis. The overall pooled sensitivity was 0.82 (95 % CI, 0.79-0.84), specificity was 0.84 (95 % CI, 0.81-0.86), PLR was 4.9 (95 % CI, 4.2-5.9), NLR was 0.22 (95 % CI, 0.19-0.25), DOR was 22 (95 % CI, 17-29) and AUC was 0.89 (95 % CI, 0.86-0.92), respectively. Subgroup analysis suggested that miRNA clusters with a large sample size showed better diagnostic accuracy. Publication bias was not found. CONCLUSIONS: circulating miRNAs can be used as a potential non-invasive biomarker for the diagnosis of EC in Asian populations.

Real-time denoising enables high-sensitivity fluorescence time-lapse imaging beyond the shot-noise limit.

A fundamental challenge in fluorescence microscopy is the photon shot noise arising from the inevitable stochasticity of photon detection. Noise increases measurement uncertainty and limits imaging resolution, speed and sensitivity. To achieve high-sensitivity fluorescence imaging beyond the shot-noise limit, we present DeepCAD-RT, a self-supervised deep learning method for real-time noise suppression. Based on our previous framework DeepCAD, we reduced the number of network parameters by 94%, memory consumption by 27-fold and processing time by a factor of 20, allowing real-time processing on a two-photon microscope. A high imaging signal-to-noise ratio can be acquired with tenfold fewer photons than in standard imaging approaches. We demonstrate the utility of DeepCAD-RT in a series of photon-limited experiments, including in vivo calcium imaging of mice, zebrafish larva and fruit flies, recording of three-dimensional (3D) migration of neutrophils after acute brain injury and imaging of 3D dynamics of cortical ATP release. DeepCAD-RT will facilitate the morphological and functional interrogation of biological dynamics with a minimal photon budget.

Deep Learning-Based Classification of Cancer Cell in Leptomeningeal Metastasis on Cytomorphologic Features of Cerebrospinal Fluid.

Background: It is a critical challenge to diagnose leptomeningeal metastasis (LM), given its technical difficulty and the lack of typical symptoms. The existing gold standard of diagnosing LM is to use positive cerebrospinal fluid (CSF) cytology, which consumes significantly more time to classify cells under a microscope. Objective: This study aims to establish a deep learning model to classify cancer cells in CSF, thus facilitating doctors to achieve an accurate and fast diagnosis of LM in an early stage. Method: The cerebrospinal fluid laboratory of Xijing Hospital provides 53,255 cells from 90 LM patients in the research. We used two deep convolutional neural networks (CNN) models to classify cells in the CSF. A five-way cell classification model (CNN1) consists of lymphocytes, monocytes, neutrophils, erythrocytes, and cancer cells. A four-way cancer cell classification model (CNN2) consists of lung cancer cells, gastric cancer cells, breast cancer cells, and pancreatic cancer cells. Here, the CNN models were constructed by Resnet-inception-V2. We evaluated the performance of the proposed models on two external datasets and compared them with the results from 42 doctors of various levels of experience in the human-machine tests. Furthermore, we develop a computer-aided diagnosis (CAD) software to generate cytology diagnosis reports in the research rapidly. Results: With respect to the validation set, the mean average precision (mAP) of CNN1 is over 95% and that of CNN2 is close to 80%. Hence, the proposed deep learning model effectively classifies cells in CSF to facilitate the screening of cancer cells. In the human-machine tests, the accuracy of CNN1 is similar to the results from experts, with higher accuracy than doctors in other levels. Moreover, the overall accuracy of CNN2 is 10% higher than that of experts, with a time consumption of only one-third of that consumed by an expert. Using the CAD software saves 90% working time of cytologists. Conclusion: A deep learning method has been developed to assist the LM diagnosis with high accuracy and low time consumption effectively. Thanks to labeled data and step-by-step training, our proposed method can successfully classify cancer cells in the CSF to assist LM diagnosis early. In addition, this unique research can predict cancer's primary source of LM, which relies on cytomorphologic features without immunohistochemistry. Our results show that deep learning can be widely used in medical images to classify cerebrospinal fluid cells. For complex cancer classification tasks, the accuracy of the proposed method is significantly higher than that of specialist doctors, and its performance is better than that of junior doctors and interns. The application of CNNs and CAD software may ultimately aid in expediting the diagnosis and overcoming the shortage of experienced cytologists, thereby facilitating earlier treatment and improving the prognosis of LM.

Ocular manifestations of multiple sclerosis in patients from three countries: A Web-based survey.

OBJECTIVE: This study evaluates the epidemiological characteristics, ophthalmological manifestations, and different therapeutic options available for patients with multiple sclerosis (MS) in China, Spain, and Cuba. METHODS: A self-designed questionnaire was used to conduct a comparable descriptive cross-sectional study on patients with MS. The survey included patients' demographic data, ocular manifestations related to MS, and treatment methodology followed in the three countries. The online survey was designed using the Wenjuanxing survey platform, and a survey link was circulated through WhatsApp, WeChat, and emails. Quantitative data were expressed as mean and standard deviation, the Kruskal-Wallis test was used for non-parametric variables. Qualitative data were expressed as numerical and percentage. The chi-square test (χ2) was used to compare the group's response categories. The statistical difference was considered significant when p < 0.05. RESULTS: The female-to-male ratio in all the three countries was 2-3:1, and relapsing-remitting MS (RRMS) was the most frequent in all three countries. Vision loss was slow and progressive in half of the patients from the three countries, with no significant differences (p = 0.524). A higher percentage of steroid treatment was observed in Chinese patients in comparison with the patients from other two countries (p < 0.001), and a similar trend was seen in the use of traditional medicines. Almost one-third of patients who did not receive any treatment recovered spontaneously in all the three countries (p = 0.097). CONCLUSIONS: MS occurs more frequently in the relapsing-remitting clinical form and there is a clear female predominance. The first ocular crisis or clinical debut of MS is characterized by slow and progressive visual impairment, increasing and adding to other ocular manifestations during its evolutionary course. Spontaneous recovery of vision after an attack of optic neuritis in the course of MS is possible.

Reinforcing neuron extraction and spike inference in calcium imaging using deep self-supervised denoising.

Calcium imaging has transformed neuroscience research by providing a methodology for monitoring the activity of neural circuits with single-cell resolution. However, calcium imaging is inherently susceptible to detection noise, especially when imaging with high frame rate or under low excitation dosage. Here we developed DeepCAD, a self-supervised deep-learning method for spatiotemporal enhancement of calcium imaging data that does not require any high signal-to-noise ratio (SNR) observations. DeepCAD suppresses detection noise and improves the SNR more than tenfold, which reinforces the accuracy of neuron extraction and spike inference and facilitates the functional analysis of neural circuits.

The Potential Diagnostic Accuracy of Let-7 Family for Cancer: A Meta-Analysis.

BACKGROUND: Cancer is a global public health problem affecting human health. Early stage of cancer diagnosis, when it is not too large and has not spread is important for successful treatment. Many researchers have proposed that the let-7 microRNA family can be used as a biomarker for cancer diagnosis. The aim of this meta-analysis is to evaluate whether let-7 family can be used as a diagnostic tool for cancer patients. METHODS: We conducted a comprehensive literature search on PubMed, EMBASE, Web of Science, Cochrane Library, Google Scholar, China National Knowledge Infrastructure (CNKI) and Wanfang database, updated to October 23, 2020. A random effects model was used to pool the sensitivity and specificity. Besides, we measured the diagnostic value using positive likelihood ratio (PLR), negative likelihood ratio (NLR), diagnostic odds ratio (DOR) and area under the curve (AUC) were pooled. In addition, meta-regression and subgroup analysis were performed to explore the possible sources of heterogeneity, and Deeks' funnel chart was used to assess whether there was publication bias. RESULTS: 31 studies from 15 articles were included in the current meta-analysis. The overall sensitivity, specificity, PLR, NLR, DOR and AUC were 0.80 (95% CI: 0.75-0.85), 0.81 (95% CI: 0.74-0.86), 4.2 (95% CI: 2.9-5.9), 0.24 (95% CI: 0.19-0.32), 17 (95% CI: 10-29) and 0.87 (95% CI: 0.84-0.90), respectively. Subgroup analysis shows that the let-7 family cluster of serum type showed a better diagnostic accuracy of cancer, especially the breast cancer. Although there is no publication bias, it still has some limitations. CONCLUSIONS: let-7 family can be considered as a promising non-invasive diagnostic biomarker for cancer.

Iterative tomography with digital adaptive optics permits hour-long intravital observation of 3D subcellular dynamics at millisecond scale.

Long-term subcellular intravital imaging in mammals is vital to study diverse intercellular behaviors and organelle functions during native physiological processes. However, optical heterogeneity, tissue opacity, and phototoxicity pose great challenges. Here, we propose a computational imaging framework, termed digital adaptive optics scanning light-field mutual iterative tomography (DAOSLIMIT), featuring high-speed, high-resolution 3D imaging, tiled wavefront correction, and low phototoxicity with a compact system. By tomographic imaging of the entire volume simultaneously, we obtained volumetric imaging across 225 × 225 × 16 µm3, with a resolution of up to 220 nm laterally and 400 nm axially, at the millisecond scale, over hundreds of thousands of time points. To establish the capabilities, we investigated large-scale cell migration and neural activities in different species and observed various subcellular dynamics in mammals during neutrophil migration and tumor cell circulation.

Rehabilitation guidance for hip fracture patients during the COVID-19 pandemic using chat software: A new model.

BACKGROUND: Hip fractures are serious fractures for the elderly. The rehabilitation of patients with hip fractures has been greatly affected by the coronavirus disease 2019 (COVID-19) pandemic. OBJECTIVE: We have piloted a new model for tracking patients and providing rehabilitation guidance that uses WeChat. The purpose of this study is to explore the role of chat software in rehabilitation guidance for hip fracture patients during COVID-19. METHODS: Patients treated for hip fractures from February 1 to April 30, 2020 were randomly divided into a control group and an observation group. The control group was given conventional discharge guidance, while the observation group also followed up the patients using WeChat to guide the exercise. Satisfaction, the Harris Hip Score, complications and the mortality of the two groups after discharge were compared. RESULTS: The incidence of complications and mortality in the observation group were significantly lower than in the control group: p= 0.022 and p= 0.048, respectively. The Harris Hip Score and satisfaction were significantly better than the control group's: p= 0.000 and p= 0.007, respectively. CONCLUSION: During the COVID-19 pandemic, it is very helpful to use WeChat software or other social software with similar functions (such as WhatsApp and Facebook) to guide the rehabilitation of hip fractures.

The Potential Diagnostic Accuracy of Circulating MicroRNAs for Leukemia: A Meta-Analysis.

BACKGROUND: Leukemia is a common malignant disease in the human blood system. Many researchers have proposed circulating microRNAs as biomarkers for the diagnosis of leukemia. We conducted a meta-analysis to evaluate the diagnostic accuracy of circulating miRNAs in the diagnosis of leukemia. METHODS: A comprehensive literature search (updated to October 13, 2020) in PubMed, EMBASE, Web of Science, Cochrane Library, Wanfang database and China National Knowledge Infrastructure (CNKI) was performed to identify eligible studies. The sensitivity, specificity, positive likelihood ratio (PLR), negative likelihood ratio (NLR), diagnostic odds ratio (DOR), and area under the curve (AUC) for diagnosing leukemia were pooled for both overall and subgroup analysis. The meta-regression and subgroup analysis were performed to explore heterogeneity and Deeks' funnel plot was used to assess publication bias. RESULTS: 49 studies from 22 publications with a total of 3,489 leukemia patients and 2,756 healthy controls were included in this meta-analysis. The overall sensitivity, specificity, positive likelihood ratio, negative likelihood ratio, diagnostic odds ratio and area under the curve were 0.83, 0.92, 10.8, 0.18, 59 and 0.94, respectively. Subgroup analysis shows that the microRNA clusters of plasma type could carry out a better diagnostic accuracy of leukemia patients. In addition, publication bias was not found. CONCLUSIONS: Circulating microRNAs can be used as a promising noninvasive biomarker in the early diagnosis of leukemia.

Unsupervised content-preserving transformation for optical microscopy.

The development of deep learning and open access to a substantial collection of imaging data together provide a potential solution for computational image transformation, which is gradually changing the landscape of optical imaging and biomedical research. However, current implementations of deep learning usually operate in a supervised manner, and their reliance on laborious and error-prone data annotation procedures remains a barrier to more general applicability. Here, we propose an unsupervised image transformation to facilitate the utilization of deep learning for optical microscopy, even in some cases in which supervised models cannot be applied. Through the introduction of a saliency constraint, the unsupervised model, named Unsupervised content-preserving Transformation for Optical Microscopy (UTOM), can learn the mapping between two image domains without requiring paired training data while avoiding distortions of the image content. UTOM shows promising performance in a wide range of biomedical image transformation tasks, including in silico histological staining, fluorescence image restoration, and virtual fluorescence labeling. Quantitative evaluations reveal that UTOM achieves stable and high-fidelity image transformations across different imaging conditions and modalities. We anticipate that our framework will encourage a paradigm shift in training neural networks and enable more applications of artificial intelligence in biomedical imaging.

Systematically characterization of in vivo substances of Ziziphi Spinosae Semen in rats by ultra-high-performance liquid chromatography coupled with quadrupole time-of-flight tandem mass spectrometry analysis.

Ziziphi Spinosae Semen (ZSS), the seeds of Ziziphus jujuba var. spinosa, is widely used in China or other Asian countries for the treatment of insomnia and palpitation. In our previous work, chemical constituents in ZSS were profiled by ultra-high-performance liquid chromatography coupled with quadrupole time-of-flight tandem mass spectrometry (UHPLC/Q-TOF MS). Notably, characterization of substances in vivo was of great importance to reveal the therapy basis or mechanism in further work. Till now, there were few reports about in vivo substances' investigation of ZSS. In the present study, an integrated strategy contained represented compounds and diagnostic ions extraction was applied to characterize metabolism feature of ZSS in rats based on UHPLC/Q-TOF MS method. First, the metabolic information of four compounds (spinosin, isovitexin, jujuboside B, betulinic acid) featuring three representative chemical structures (flavonoids, saponins, terpenes) in ZSS was conducted, and their metabolism features were summarized, especially for flavonoid C-glycosides. Second, the absorbed compounds and representative compounds-related metabolites were quickly screened out; during this time, the diagnostic ions were sorted out. Last, with the help of diagnostic ions and summarized metabolic reactions, other metabolites were characterized. As a result, a total of 151 xenobiotics (58 prototypes and 93 metabolites) were identified or tentatively characterized in rats after ingestion of ZSS. Among them, 16 substances were presented in plasma, 114 in urine, 51 in bile, and 120 in feces, respectively. Hydrogenation, hydrolysis, and glucuronidation were the major metabolic reactions of ZSS in rats. The present study provided meaningful data for further pharmacological mechanism research or pharmacokinetics evaluation of ZSS.

Potential diagnostic value of circulating miRNA for multiple myeloma: A meta-analysis.

BACKGROUND: Multiple myeloma (MM) is the second incurable hematological malignancy. In recent years, due to the rise of microRNA (miRNA), many scholars have participated in the study of its value in the diagnosis of MM, and have obtained good but inconsistent results. Therefore, in order to determine the role of miRNA in the early diagnosis of MM, we performed this meta-analysis. METHODS: We searched for related studies including PubMed, Web of Science, EMBASE, Cochrane Library, China National Knowledge Infrastructure (CNKI) and Wanfang Database as of July 20, 2020 to conduct this meta-analysis. To improve the accuracy, the quality assessment of Diagnostic Accuracy Study 2 (QUADAS-2) was used. We also applied random effects models to summarize sensitivity and specificity, positive likelihood ratio (PLR), negative likelihood ratio (NLR), diagnostic odds ratio (DOR) and area under the curve (AUC) to measure diagnostic values, and subgroup analysis used to discover potential sources of heterogeneity. RESULTS: We finally collected 32 studies from 15 articles that included a total of 2053 MM patients and 1118 healthy controls in this meta-analysis. The overall sensitivity, specificity, PLR, NLR, DOR and AUC were 0.81, 0.85, 5.5, 0.22, 25 and 0.90, respectively. Subgroup analysis shows that the down-regulation of microRNA clusters with larger samples size of plasma type could carry out a better diagnostic accuracy of MM patients. In addition, publication bias was not found. CONCLUSIONS: Circulating miRNA could be a potential non-invasive biomarker for early diagnosis of MM. However, multi-center, more rigorous, and larger-scale studies are needed to verify our conclusions.

Potential diagnostic value of miRNAs in peripheral blood for osteosarcoma: A meta-analysis.

Osteosarcoma (OS) is one of the most common primary malignant tumors in adolescents. In recent years, multiple studies have reported the value of miRNAs in the diagnosis of OS, but the results were very different from each other. Therefore, we conducted this meta-analysis to determine the accuracy of miRNAs in the diagnosis of OS. The meta-analysis searched for relevant researches including PubMed, EMBASE, Web of Science, Wanfang database and China National Knowledge Infrastructure (CNKI) as of June 1, 2020. We used the quality assessment of Diagnostic Accuracy Study 2 (QUADAS-2) to score the quality of each study. A random effects model was used to pool the sensitivity and specificity. We measured the diagnostic value using positive likelihood ratio (PLR), negative likelihood ratio (NLR), diagnostic odds ratio (DOR) and area under the curve (AUC). Subgroup and meta-regression analysis were used to find potential sources of heterogeneity. The meta-analysis finally included 31 articles about 2634 OS patients and 1715 healthy controls. The pooled estimations showed that the circulating miRNAs has a high accuracy in diagnosing OS, with a sensitivity of 0.79, specificity of 0.89, PLR of 7.3, NLR of 0.23, DOR of 31, and AUC of 0.90. In addition, subgroup and meta-regression analysis showed that miRNA clusters have higher diagnostic accuracy than single miRNA, and miRNAs in plasma were more reliable than those in serum. In conclusion, peripheral blood miRNA is a potential noninvasive biomarker to assist in the early diagnosis of OS, especially young patients with bone pain and/or indeterminate radiology findings.