Phase 2 Study of Preoperative Tislelizumab in Combination with Low-dose Nab-Paclitaxel in Patients with Muscle-invasive Bladder Cancer.

BACKGROUND AND OBJECTIVE: Combinations of immune checkpoint inhibitors and nab-paclitaxel have achieved significant therapeutic effects in the treatment of advanced urothelial carcinoma. Our aim was to assess the efficacy and safety of tislelizumab combined with low-dose nab-paclitaxel in patients with muscle-invasive bladder cancer (MIBC). METHODS: TRUCE-01 was a single-arm phase 2 study that included 62 patients with T2-4a N0/X M0 MIBC tumors with predominant urothelial carcinoma histology. Eligible patients received three 21-d cycles of intravenous 200 mg tislelizumab on day 1 plus intravenous 200 mg nab-paclitaxel on day 2, followed by surgical assessment. The primary study endpoint was a clinical complete response (cCR). Treatment-related adverse event (TRAE) profiles were recorded according to Common Terminology Criteria for Adverse Events version 5.0. KEY FINDINGS AND LIMITATIONS: The safety analysis included all 62 patients and the efficacy analysis included 48 patients. The primary efficacy endpoint (cCR) was met by 25 patients (52%) patients. Among the 62 patients in the safety analysis, six (9.7%) had grade ≥3 TRAEs. CONCLUSIONS: Tislelizumab combined with low-dose nab-paclitaxel showed promising antitumor effectiveness and was generally well tolerated, which makes it an excellent preoperative therapy option for MIBC. PATIENT SUMMARY: We found that a combination of the drugs tislelizumab and low-dose nab-paclitaxel had satisfactory efficacy and safety for preoperative treatment of muscle-invasive bladder cancer.

Prediction of Prostate Cancer Risk Stratification Based on A Nonlinear Transformation Stacking Learning Strategy.

PURPOSE: Prostate cancer (PCa) is an epithelial malignancy that originates in the prostate gland and is generally categorized into low, intermediate, and high-risk groups. The primary diagnostic indicator for PCa is the measurement of serum prostate-specific antigen (PSA) values. However, reliance on PSA levels can result in false positives, leading to unnecessary biopsies and an increased risk of invasive injuries. Therefore, it is imperative to develop an efficient and accurate method for PCa risk stratification. Many recent studies on PCa risk stratification based on clinical data have employed a binary classification, distinguishing between low to intermediate and high risk. In this paper, we propose a novel machine learning (ML) approach utilizing a stacking learning strategy for predicting the tripartite risk stratification of PCa. METHODS: Clinical records, featuring attributes selected using the lasso method, were utilized with 5 ML classifiers. The outputs of these classifiers underwent transformation by various nonlinear transformers and were then concatenated with the lasso-selected features, resulting in a set of new features. A stacking learning strategy, integrating different ML classifiers, was developed based on these new features. RESULTS: Our proposed approach demonstrated superior performance, achieving an accuracy of 0.83 and an area under the receiver operating characteristic curve value of 0.88 in a dataset comprising 197 PCa patients with 42 clinical characteristics. CONCLUSION: This study aimed to improve clinicians' ability to rapidly assess PCa risk stratification while reducing the burden on patients. This was achieved by using artificial intelligence-related technologies as an auxiliary method for diagnosing PCa.

Ureteral reimplantation for the management of pelvic lipomatosis.

Introduction: Pelvic lipomatosis is a rare benign disease characterized by urethral elongation, bladder deformity, and/or hydronephrosis. Conservative management is not effective, and urinary diversion is the most effective treatment option but is usually unacceptable for relatively young patients. Ureteral reimplantation seemed to be an appropriate modality under these conditions. We present one case in which pelvic lipomatosis was managed with ureteral reimplantation. Patient presentation: A 45-year-old, previously healthy man presented with right flank pain. Pelvic CT and CT urography showed excessive pelvic fat, bilateral hydronephrosis, tortuous ureters, and a pear-shaped bladder, all of which indicated a diagnosis of pelvic lipomatosis. We performed laparoscopic bilateral urinary tract infection on this patient. At follow-up, bilateral hydronephrosis and flank pain were greatly relieved. Conclusion: Pelvic lipomatosis can be managed safely and effectively by urinary tract infection, but longer follow-up periods are needed to evaluate the long-term efficacy of this approach.

Predicting survival of patients with bone metastasis of unknown origin.

Purpose: Bone metastasis of unknown origin is a rare and challenging situation, which is infrequently reported. Therefore, the current study was performed to analyze the clinicopathologic features and risk factors of survival among patients with bone metastasis of unknown origin. Patients and methods: We retrospectively analyzed the clinical data for patients with bone metastasis of unknown origin between 2010 and 2016 based on the Surveillance, Epidemiology, and End Results (SEER) database. Overall survival (OS) and cancer-specific survival (CSS) were first analyzed by applying univariable Cox regression analysis. Then, we performed multivariable analysis to confirm independent survival predictors. Results: In total, we identified 1224 patients with bone metastasis of unknown origin for survival analysis, of which 704 males (57.5%) and 520 females (42.5%). Patients with bone metastasis of unknown origin had a 1-year OS rate of 14.50% and CSS rate of 15.90%, respectively. Race, brain metastasis, liver metastasis, radiotherapy, and chemotherapy were significant risk factors of OS on both univariable and multivariable analyses (p <0.05). As for CSS, both univariable and multivariable analyses revealed that no brain metastasis, no liver metastasis, radiotherapy, and chemotherapy were associated with increased survival (p <0.05). Conclusion: Patients with bone metastasis of unknown origin experienced an extremely poor prognosis. Radiotherapy and chemotherapy were beneficial for prolonging the survival of those patients.

Preclinical Evaluation of a Protein-Based Nanoscale Contrast Agent for MR Angiography at an Ultralow Dose.

Purpose: BSA-biomineralized Gd nanoparticles (Gd@BSA NPs) have been recognized as promising nanoscale MR contrast agents. The aim of this study was to carry out a preclinical evaluation of these NPs in a middle-sized animal model (rabbits). Methods: New Zealand white rabbits were treated intravenously with Gd@BSA NPs (0.02 mmol Gd/kg) via a clinically-used high-pressure injector, with commercial Gd-diethylene triamine pentaacetate (Gd-DTPA)-injected group as control. Then MR angiography was performed according to the standard clinical protocol with a 3.0-T MR scanner. The SNR and CNR of the main arteries and branches were monitored. Pharmacokinetics and bioclearance were continuously evaluated in blood, urine, and feces. Gd deposition in vital organs was measured by ICP‒MS. Weight monitoring, HE staining, and blood biochemical analysis were also performed to comprehensively estimate systemic toxicity. Results: The ultrasmall Gd@BSA NPs (<6 nm) exhibited high stability and T1 relaxivity. Compared to Gd-DTPA, Gd@BSA NPs demonstrated superior vascular system imaging performance at ultralow doses, especially of the cardiac artery and other main branches, and exhibited a significantly higher SNR and CNR. Notably, the Gd@BSA NPs showed a shorter half-life in blood, less retention in organs, and improved biocompatibility. Conclusion: The preclinical evaluations here demonstrated that Gd@BSA NPs are promising and advantageous MR CA candidates that can be used at a low dose with excellent MR imaging performance, thus suggesting its further clinical trials and applications.

Development and validation of a clinic machine-learning nomogram for the prediction of risk stratifications of prostate cancer based on functional subsets of peripheral lymphocyte.

BACKGROUND: Non-invasive risk stratification contributes to the precise treatment of prostate cancer (PCa). In previous studies, lymphocyte subsets were used to differentiate between low-/intermediate-risk and high-risk PCa, with limited clinical value and poor interpretability. Based on functional subsets of peripheral lymphocyte with the largest sample size to date, this study aims to construct an easy-to-use and robust nomogram to guide the tripartite risk stratifications for PCa. METHODS: We retrospectively collected data from 2039 PCa and benign prostate disease (BPD) patients with 42 clinical characteristics on functional subsets of peripheral lymphocyte. After quality control and feature selection, clinical data with the optimal feature subset were utilized for the 10-fold cross-validation of five Machine Learning (ML) models for the task of predicting low-, intermediate- and high-risk stratification of PCa. Then, a novel clinic-ML nomogram was constructed using probabilistic predictions of the trained ML models via the combination of a multivariable Ordinal Logistic Regression analysis and the proposed feature mapping algorithm. RESULTS: 197 PCa patients, including 56 BPD, were enrolled in the study. An optimal subset with nine clinical features was selected. Compared with the best ML model and the clinic nomogram, the clinic-ML nomogram achieved the superior performance with a sensitivity of 0.713 (95% CI 0.573-0.853), specificity of 0.869 (95% CI 0.764-0.974), F1 of 0.699 (95% CI 0.557-0.841), and AUC of 0.864 (95% CI 0.794-0.935). The calibration curve and Decision Curve Analysis (DCA) indicated the predictive capacity and net benefits of the clinic-ML nomogram were improved. CONCLUSION: Combining the interpretability and simplicity of a nomogram with the efficacy and robustness of ML models, the proposed clinic-ML nomogram can serve as an insight tool for preoperative assessment of PCa risk stratifications, and could provide essential information for the individual diagnosis and treatment in PCa patients.

Multimodality deep learning radiomics nomogram for preoperative prediction of malignancy of breast cancer: a multicenter study.

Background. Breast cancer is the most prevalent cancer diagnosed in women worldwide. Accurately and efficiently stratifying the risk is an essential step in achieving precision medicine prior to treatment. This study aimed to construct and validate a nomogram based on radiomics and deep learning for preoperative prediction of the malignancy of breast cancer (MBC).Methods. The clinical and ultrasound imaging data, including brightness mode (B-mode) and color Doppler flow imaging, of 611 breast cancer patients from multiple hospitals in China were retrospectively analyzed. Patients were divided into one primary cohort (PC), one validation cohort (VC) and two test cohorts (TC1 and TC2). A multimodality deep learning radiomics nomogram (DLRN) was constructed for predicting the MBC. The performance of the proposed DLRN was comprehensively assessed and compared with three unimodal models via the calibration curve, the area under the curve (AUC) of receiver operating characteristics and the decision curve analysis.Results. The DLRN discriminated well between the MBC in all cohorts [overall AUC (95% confidence interval): 0.983 (0.973-0.993), 0.972 (0.952-0.993), 0.897 (0.823-0.971), and 0.993 (0.977-1.000) on the PC, VC, test cohorts1 (TC1) and test cohorts2 TC2 respectively]. In addition, the DLRN performed significantly better than three unimodal models and had good clinical utility.Conclusion. The DLRN demonstrates good discriminatory ability in the preoperative prediction of MBC, can better reveal the potential associations between clinical characteristics, ultrasound imaging features and disease pathology, and can facilitate the development of computer-aided diagnosis systems for breast cancer patients. Our code is available publicly in the repository athttps://github.com/wupeiyan/MDLRN.

ULS4US: universal lesion segmentation framework for 2D ultrasound images.

Objective. Deep learning (DL) methods have been widely utilized in ultrasound (US) image segmentation tasks. However, current DL segmentation methods for US images are typically developed only for lesion segmentation of specific organs; e.g. breast or thyroid US. So far, there is currently no general-purpose lesion segmentation framework for US images that can be implemented across various organs in computer aided diagnosis scenarios. Considering that most lesion locations in US images have abnormal ultrasonic echo intensities or patterns that may be visually distinct from surrounding normal tissues or organs, it is thus possible to develop a universal lesion segmentation framework for US images (named as ULS4US), focusing on effectively identifying and segmenting lesions of various sizes in different organs.Approach. The proposed ULS4US framework comprises three components: (1) a multiple-in multi-out (MIMO) UNet that incorporates multiscale features extracted from the US image and lesion, (2) a novel two-stage lesion-aware learning algorithm that recursively locates and segments the lesions in a reinforced manner, and (3) a lesion-adaptive loss function for the MIMO-UNet that integrates two weighted components and one self-supervised component designed for intra- and inter-branches of network outputs, respectively.Main Results. Compared to six state-of-the-art segmentation models, ULS4US has achieved superior performance (accuracy of 0.956, DSC of 0.836, HD of 7.849, and mIoU of 0.731) in a unified dataset consisting of two public and three private US image datasets, which include over 2200 images of three specific types of organs. Comparative experiments on both individual and unified datasets suggest that ULS4US is likely scalable with additional data.Significance. The study demonstrates the potential of DL-based universal lesion segmentation approaches in clinical US, which would substantially reduce clinician workload and enhance diagnostic accuracy.

Deep multimodal learning for lymph node metastasis prediction of primary thyroid cancer.

Objective. The incidence of primary thyroid cancer has risen steadily over the past decades because of overdiagnosis and overtreatment through the improvement in imaging techniques for screening, especially in ultrasound examination. Metastatic status of lymph nodes is important for staging the type of primary thyroid cancer. Deep learning algorithms based on ultrasound images were thus developed to assist radiologists on the diagnosis of lymph node metastasis. The objective of this study is to integrate more clinical context (e.g., health records and various image modalities) into, and explore more interpretable patterns discovered by, deep learning algorithms for the prediction of lymph node metastasis in primary thyroid cancer patients.Approach. A deep multimodal learning network was developed in this study with a novel index proposed to compare the contribution of different modalities when making the predictions.Main results. The proposed multimodal network achieved an average F1 score of 0.888 and an average area under the receiver operating characteristic curve (AUC) value of 0.973 in two independent validation sets, and the performance was significantly better than that of three single-modality deep learning networks. Moreover, among three modalities used in this study, the deep multimodal learning network relied generally more on image modalities than the data modality of clinic records when making the predictions.Significance. Our work is beneficial to prospective clinic trials of radiologists on the diagnosis of lymph node metastasis in primary thyroid cancer, and will better help them understand how the predictions are made in deep multimodal learning algorithms.

Effect of N6-methyladenosine (m6A) regulator-related immunogenes on the prognosis and immune microenvironment of breast cancer.

Background: Breast cancer is one of the most common malignant tumor and the prognosis remains unsatisfying. Various studies demonstrate that m6A modulators are new predictors of prognosis in immune microenvironment. We aimed to identify several m6A regulator-related immunogenes and explore the relationship between m6A regulator-related immunogenes and breast cancer prognosis as well as the tumor immune microenvironment (TIME). Methods: RNA sequencing data and clinical information on 21 m6A regulators in 1,047 breast cancer samples were downloaded from The Cancer Genome Atlas (TCGA), and immune gene data were downloaded from InnateDB. Kaplan-Meier survival analysis was conducted with log-rank test using the survival package. An m6A-related immunogene-prognostic signature was then constructed, followed by immune infiltration and checkpoint analyses. Results: A risk prognostic signature of m6A regulator-related immunogenes, including TOX, PSME2, MCTS1, NFKBIE, SH3BP4, RSPH1, JAK1, MLLT4, and PTGES3, was constructed. Furthermore, univariate and multivariate Cox regression analyses suggested that the tumor stage and risk score could be independent prognostic factors for patients with breast cancer. Immune infiltration analysis showed that the infiltration levels of T cells, memory B cells, activated NK cells, and macrophages between the high- and low-risk groups were significantly different. In addition, checkpoint analyses demonstrated that the levels of immune checkpoint genes, such as those of LAG3, PDCD1, CTLA4, and HAVCR2, were downregulated in the high-risk group compared to those in the low-risk group. Conclusions: Our findings suggest that the m6A regulator-related risk prognostic signature can predict the prognosis of breast cancer and that it is related to the immune microenvironment.

lncRNA MIR4435­2HG promotes the progression of liver cancer by upregulating B3GNT5 expression.

Several studies have indicated that dysregulation of long non­coding RNAs (lncRNAs) participates in the initiation and progression of cancer. The lncRNA MIR4435­2HG was previously reported to act as an oncogene in human cancer, including liver cancer. However, its role in the pathogenesis in liver cancer is largely unclear. The present study aimed to reveal the molecular mechanism by which MIR4435­2HG regulates liver cancer. The expression levels of MIR4435­2HG in liver cancer and adjacent normal tissues were analyzed using The Cancer Genome Atlas database. MIR4435­2HG expression was validated by reverse transcription­quantitative polymerase chain reaction (RT­qPCR) in cancer cells in vitro. The target genes of MIR4435­2HG were predicted using bioinformatics analysis. Interactions between miR­136­5p, MIR4435­2HG and B3GNT5 were detected using luciferase reporter assays, and their effects on cell viability, migration and invasion were assessed using Cell Counting Kit­8, wound healing and Transwell assays. The effects of miR­136­5p and MIR4435­2HG on B3GNT5 expression were confirmed by western blot analysis. The results revealed that MIR4435­2HG expression was upregulated in primary liver cancer and liver cancer cell lines, and was positively associated with advanced tumor stage, metastasis and poor prognosis in patients with liver cancer. Knockdown of MIR4435­2HG significantly inhibited the proliferation, migration and invasion of liver cancer cells. Furthermore, miR­136­5p was determined to be a direct target of MIR4435­2HG and suppressed MIR4435­2HG expression by binding with the seed region of the 3'­UTR of MIR4435­2HG in liver cancer cells. Functional studies showed that the inhibitory effects of MIR4435­2HG knockdown on cell proliferation, migration and invasion were significantly rescued by inhibiting miR­136­5p. Furthermore, the target gene, B3GNT5, of miR­136­5p was confirmed by bioinformatics analysis and RT­qPCR. In addition, B3GNT5 expression was regulated by the MIR4435­2HG/miR­136­5p axis. In conclusion, the present study indicated that MIR4435­2HG facilitated the progression of liver cancer via the MIR4435­2HG/miR­136­5p/B3GNT5 axis, which demonstrated that MIR4435­2HG may be a potential biomarker for the prognosis and treatment of liver cancer.

Effect of Narrative Nursing Intervention Based on Targeted Nursing Intervention on Anxiety and Nursing Satisfaction of Patients with Malignant Tumors Undergoing Chemotherapy.

Objective: To explore the effect of narrative nursing intervention based on targeted nursing intervention on anxiety and nursing satisfaction of patients with malignant tumors undergoing chemotherapy. Methods: 120 malignant tumor patients treated with chemotherapy in our hospital from January 2019 to January 2020 were selected as the research objects and randomly divided into group A and group B, with 60 cases in each group. The targeted nursing intervention was performed to group B, and the targeted nursing intervention centering on narrative nursing was performed to group A, so as to compare their distress thermometer (DT) scale scores, depression and anxiety scale scores, Medical Coping Modes Questionnaire (MCMQ) scores, Functional Assessment of Cancer Therapy-General (FACT-G) scores for quality of life, and nursing satisfaction. Results: After nursing intervention, group A obtained 5.00 ± 1.20 points in the DT score, which were significantly lower than group B (P < 0.05); and group A achieved significantly lower depression and anxiety scale scores (P < 0.001), better MCMQ scores (P < 0.05), and higher FACT-G scores (P < 0.05) and nursing satisfaction (P < 0.05) than group B. Conclusion: The targeted nursing intervention based primarily on narrative nursing can greatly reduce negative emotions, alleviate anxiety, and improve confidence in treatment and quality of life for malignant tumor patients undergoing chemotherapy, with higher nursing satisfaction, which should be promoted and applied in the practice.

Acupuncture versus Lornoxicam in the Treatment of Acute Renal Colic: A Randomized Controlled Trial.

OBJECTIVE: To compare the analgesic efficacy and safety of acupuncture and lornoxicam in acute renal colic (ARC). DESIGN SETTING PARTICIPANT: A randomized, double-blind, parallel-controlled, single-centered trial was conducted at Susong County People's Hospital from October 2019 to November 2020. Eighty-four patients with ARC were randomly divided into lornoxicam group (Group L) and acupuncture group (Group A). Group A was treated with acupuncture at Sanyinjiao (SP6), Yinlingquan (SP9) and normal saline, and Group L was treated with sham acupuncture at SP6, SP9 and lornoxicam. MAIN OUTCOME MEASURES: Visual analogue scale (VAS) scores and adverse reactions such as nausea and dizziness were recorded within 5, 10, 15, 20 and 40 minutes after treatment. The main outcome of this study was the short-term effective (STE) rate, the secondary outcome was the onset time, and the safety index was incidence of adverse reactions. RESULTS: A total of 80 patients completed this study, including 41 patients (21 males and 20 females) in Group L and 39 patients (21 males and 18 females) in Group A. Group A exhibited lower scores versus group L after treatment (P < 0.05). The overall STE of group L was 61.00% (25/41), significantly lower than group A [84.62% (33/39)] (P < 0.001). There was no difference in the incidence of adverse reactions between group A [2.6% (1/39)] and group L [7.3% (3/41)] (P = 0.616). The ordered logistic regression analysis showed patients receiving acupuncture therapy are more likely to be cured [OR = 2.887, 95% CI: (1.190, 7.000), P = 0.019]. CONCLUSION: Acupuncture at SP6, SP9 and intramuscular injection of lornoxicam can effectively and safely relieve ARC, but the former has faster and better analgesic effect. Moreover, the incidence of adverse reactions was similar between the two treatments. This acupuncture therapy is recommended as a complementary therapy for ARC.

Effect of Amoxicillin and Clavulanate Potassium Combined with Bazhengsan on Pediatric Urinary Tract Infection.

OBJECTIVE: To explore the therapeutic effect of amoxicillin and clavulanate potassium combined with Bazhengsan on pediatric urinary tract infection (UTI). METHODS: The data of 120 UTI children treated in Wuhan Xinzhou District People's Hospital from February 2019 to February 2020 were retrospectively analyzed. They were equally split into experimental group (EG) and control group (CG) according to the order of admission. All children were treated with amoxicillin and clavulanate potassium for suspension (twice a day), and EG was additionally treated with one dose of Bazhengsan daily. Both groups were treated for 10 days. After treatment, the immune function indexes, inflammatory factor levels, and clinical efficacy were compared before and after treatment. RESULTS: No remarkable differences in the general data such as blood routine and urine routine results were observed between the two groups before treatment (P > 0.05). After treatment, EG achieved obviously better immune function indexes (P < 0.001) and lower levels of inflammatory factors (P < 0.05) compared with CG. Besides, the treatment effective rate in EG (96.7%) was higher than that in CG (P < 0.05). CONCLUSION: Amoxicillin and clavulanate potassium combined with Bazhengsan can improve the immune function of UTI children and reduce the levels of inflammatory factors, with remarkable effects, which should be popularized in practice.

Knockdown of long noncoding RNA colorectal neoplasia differentially expressed inhibits hepatocellular carcinoma progression by mediating the expression of nuclear autoantigenic sperm protein.

Long noncoding RNAs (lncRNAs) play critical roles in the tumorigenesis and progression of hepatocellular carcinoma (HCC). As the most common malignant cancer type in humans, HCC poses a great threat to human health. However, the function of lncRNA colorectal neoplasia differentially expressed (CRNDE) in HCC has not been extensively studied. The chief aim of the present study was to reveal the potential role of CRNDE in HCC. Expression levels of CRNDE in HCC tissues and cell lines were detected by reverse transcription­quantitative (RT­q) PCR, and Cell Counting kit 8, wound­healing and Transwell assays were used to evaluate the influences of CRNDE on in vitro cellular proliferation, migration and invasiveness, respectively. The interaction between CRNDE and microRNA (miR)­29c­3p was determined by dual­luciferase reporter assay, and rescue experiments were conducted to evaluate the interactive relationships between CRNDE and miR­29c­3p or nuclear autoantigenic sperm protein (NASP). CRNDE was found to be upregulated in HCC tissues and cells, and to be positively associated with the poor prognosis of patients with HCC. Furthermore, CRNDE­knockdown suppressed cell proliferation, migration and invasion abilities. Bioinformatics and RT­qPCR analysis indicated miR­29c­3p as a potential target of CRNDE. In line with previous reports, as a tumor suppressor, downregulated expression of miR­29c­3p was observed in HCC. In addition, the present study revealed that miR­29c­3p directly targeted NASP. NASP expression was markedly elevated following transfection with an miR­29c­3p inhibitor, while knocking down CRNDE inhibited NASP expression. Moreover, the effects of CRNDE and NASP on HCC cells were reversed by miR­29c­3p. Collectively, the results of the present study revealed that CRNDE was upregulated and exerted an oncogenic role in HCC by targeting miR­29c­3p, and that the upregulation of CRNDE also promoted NASP expression. These findings indicate a novel role for CRNDE in the progression of HCC.

The emerging landscape of long non-coding RNAs in hepatocellular carcinoma.

Hepatocellular carcinoma (HCC) is one of the most common and aggressive cancers. HCC shows high prevalence and lethality caused by a variety of etiologic factors. However, the underlying mechanisms and the diagnostic markers identifying patients at risk in advance has not been entirely elucidated. Long non-coding RNAs (lncRNAs) are a subgroup of non-coding RNAs greater than 200 nucleotides in length with no protein-coding capability. With the progress in sequencing technologies and bioinformatic tools, the landscape of lncRNAs is being revealed. Numerous discoveries point out that lncRNAs participate in HCC carcinogenesis and metastasis through altering cell proliferation and invasion ability, apoptosis, and chemo- or radio-sensitivity. Moreover, lncRNA is easy to detect compared to the traditional diagnostic methods. This review summarizes the mechanisms of major lncRNAs in HCC discovered in recent years and lncRNAs as early diagnostic markers for HCC.

Efficacy of Concurrent Chemoradiotherapy With S-1 vs Radiotherapy Alone for Older Patients With Esophageal Cancer: A Multicenter Randomized Phase 3 Clinical Trial.

IMPORTANCE: Most older patients with esophageal cancer cannot complete the standard concurrent chemoradiotherapy (CCRT). An effective and tolerable chemoradiotherapy regimen for older patients is needed. OBJECTIVE: To evaluate the efficacy and toxic effects of CCRT with S-1 vs radiotherapy (RT) alone in older patients with esophageal cancer. DESIGN, SETTING, AND PARTICIPANTS: A randomized, open-label, phase 3 clinical trial was conducted at 23 Chinese centers between June 1, 2016, and August 31, 2018. The study enrolled 298 patients aged 70 to 85 years. Eligible participants had histologically confirmed esophageal cancer, stage IB to IVB disease based on the 6th edition of the American Joint Committee on Cancer (stage IVB: only metastasis to the supraclavicular/celiac lymph nodes) and an Eastern Cooperative Oncology Group performance status of 0 to 1. Data analysis was performed from August 1, 2020, to March 10, 2021. INTERVENTIONS: Patients were stratified according to age (<80 vs ≥80 years) and tumor length (<5 vs ≥5 cm) and randomly assigned (1:1) to receive either CCRT with S-1 or RT alone. MAIN OUTCOMES AND MEASURES: The primary end point was the 2-year overall survival rate using intention-to-treat analysis. RESULTS: Of the 298 patients enrolled, 180 (60.4%) were men. The median age was 77 (interquartile range, 74-79) years in the CCRT group and 77 (interquartile range, 74-80) years in the RT alone group. A total of 151 patients (50.7%) had stage III or IV disease. The CCRT group had a significantly higher complete response rate than the RT group (41.6% vs 26.8%; P = .007). Surviving patients had a median follow-up of 33.9 months (interquartile range: 28.5-38.2 months), and the CCRT group had a significantly higher 2-year overall survival rate (53.2% vs 35.8%; hazard ratio, 0.63; 95% CI, 0.47-0.85; P = .002). There were no significant differences in the incidence of grade 3 or higher toxic effects between the CCRT and RT groups except that grade 3 or higher leukopenia occurred in more patients in the CCRT group (9.5% vs 2.7%; P = .01). Treatment-related deaths were observed in 3 patients (2.0%) in the CCRT group and 4 patients (2.7%) in the RT group. CONCLUSIONS AND RELEVANCE: In this phase 3 randomized clinical trial, CCRT with S-1 was tolerable and provided significant benefits over RT alone in older patients with esophageal cancer. TRIAL REGISTRATION: ClinicalTrials.gov Identifier: NCT02813967.

Rapid whole-brain electric field mapping in transcranial magnetic stimulation using deep learning.

Transcranial magnetic stimulation (TMS) is a non-invasive neurostimulation technique that is increasingly used in the treatment of neuropsychiatric disorders and neuroscience research. Due to the complex structure of the brain and the electrical conductivity variation across subjects, identification of subject-specific brain regions for TMS is important to improve the treatment efficacy and understand the mechanism of treatment response. Numerical computations have been used to estimate the stimulated electric field (E-field) by TMS in brain tissue. But the relative long computation time limits the application of this approach. In this paper, we propose a deep-neural-network based approach to expedite the estimation of whole-brain E-field by using a neural network architecture, named 3D-MSResUnet and multimodal imaging data. The 3D-MSResUnet network integrates the 3D U-net architecture, residual modules and a mechanism to combine multi-scale feature maps. It is trained using a large dataset with finite element method (FEM) based E-field and diffusion magnetic resonance imaging (MRI) based anisotropic volume conductivity or anatomical images. The performance of 3D-MSResUnet is evaluated using several evaluation metrics and different combinations of imaging modalities and coils. The experimental results show that the output E-field of 3D-MSResUnet provides reliable estimation of the E-field estimated by the state-of-the-art FEM method with significant reduction in prediction time to about 0.24 second. Thus, this study demonstrates that neural networks are potentially useful tools to accelerate the prediction of E-field for TMS targeting.

DiSegNet: A deep dilated convolutional encoder-decoder architecture for lymph node segmentation on PET/CT images.

PURPOSE: Automated lymph node (LN) recognition and segmentation from cross-sectional medical images is an important step for the automated diagnostic assessment of patients with cancer. Yet, it is still a difficult task owing to the low contrast of LNs and surrounding soft tissues as well as due to the variation in nodal size and shape. In this paper, we present a novel LN segmentation method based on a newly designed neural network for positron emission tomography/computed tomography (PET/CT) images. METHODS: This work communicates two problems involved in LN segmentation task. Firstly, an efficient loss function named cosine-sine (CS) is proposed for the voxel class imbalance problem in the convolution network training process. Second, a multi-stage and multi-scale Atrous (Dilated) spatial pyramid pooling sub-module, named MS-ASPP, is introduced to the encoder-decoder architecture (SegNet), which aims to make use of multi-scale information to improve the performance of LN segmentation. The new architecture is named DiSegNet (Dilated SegNet). RESULTS: Four-fold cross-validation is performed on 63 PET/CT data sets. In each experiment, 10 data sets are selected randomly for testing and the other 53 for training. The results show that we reach an average 77 % Dice similarity coefficient score with CS loss function by trained DiSegNet, compared to a baseline method SegNet by cross-entropy (CE) with 71 % Dice similarity coefficient. CONCLUSIONS: The performance of the proposed DiSegNet with CS loss function suggests its potential clinical value for disease quantification.

CircNEIL3 promotes cervical cancer cell proliferation by adsorbing miR-137 and upregulating KLF12.

BACKGROUND: CircRNAs play crucial roles in multiple tumours. However, the functions of most circRNAs in cervical cancer remain unclear. METHODS: This study collected GSE113696 data from the GEO database to search for differentially expressed circRNAs in cervical cancer. Quantitative reverse transcription PCR was used to detect the expression level of circNEIL3 in cervical cancer cells and tissues. Then, functional experiments in vitro and in vivo were performed to evaluate the effects of circNEIL3 in cervical cancer. RESULTS: CircNEIL3 was highly expressed in cervical cancer. In vivo and in vitro experiments verified that circNEIL3 enhanced the proliferation capacity of cervical cancer cells. RNA immunoprecipitation, luciferase reporter assay, pull-down assay, and fluorescent in situ hybridization confirmed the interaction between circNEIL3 and miR-137 in cervical cancer. A luciferase reporter assay showed that circNEIL3 adsorbed miR-137 and upregulated KLF12 to regulate the proliferation of cervical cancer cells. CONCLUSIONS: CircNEIL3 is an oncogene in cervical cancer and might serve as a ceRNA that competitively binds to miR-137, thereby indirectly upregulating the expression of KLF12 and promoting the proliferation of cervical cancer cells.