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.

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.

Magnetic mesoporous silica nanoparticles-aided dual MR/NIRF imaging to identify macrophage enrichment in atherosclerotic plaques.

Active foamy macrophage enrichment drives atherosclerotic plaque initiation and evolution, and is the prominent target for precisely identifying vulnerable plaque. Precise imaging of high-risk plaque allows promotion of treatment and prevention of vascular pathema. However, current iron oxide (IO) nanoparticles-based magnetic resonance (MR) imaging of plaque is often limited by insufficient perfusion and nonspecific accumulation of peri-aortic lymph nodes. Besides that, intrinsic defects of MR also impede its use for accurately identifying plaque details. Herein, by conjugating with PP1 peptide, a novel magnetic mesoporous silica nanoparticle (PIMI) loaded with near-infrared fluorescence (NIRF) dye (IR820) was fabricated to specifically target and quantify macrophage enrichment of atherosclerotic plaque in ApoE-/- mice using dual MR/NIRF imaging. Biocompatibility experiments ulteriorly confirmed the high safety of PIMI nanoparticles in vivo, which lays the foundation of next-generation contrast agent for recognizing macrophage-rich plaque in the near future.

An injectable recombinant human milk fat globule-epidermal growth factor 8-loaded copolymer system for spinal cord injury reduces inflammation through NF-κB and neuronal cell death.

Spinal cord injury (SCI) is a common disease and a major cause of paralysis, carrying much burden around the world. Despite the progress made with growth factors therapy, the response rate of acute SCI treatment still remains unsatisfactory, due largely to complex and severe inflammatory reactions. Herein, we prepare a MFG-E8-loaded copolymer system-based anti-inflammation therapy for SCI treatment. It is shown that the MFG-E8-loaded copolymer system can decrease pro-inflammatory cytokine expression and neuron death. In a rat model of crush-caused SCI, the copolymer system shows significant therapeutic efficacy by ameliorating inflammation, decreasing fibrotic scar, promoting myelin regeneration and suppressing overall SCI severity.

Microporous acellular extracellular matrix combined with adipose-derived stem cell sheets as a promising tissue patch promoting articular cartilage regeneration and interface integration.

BACKGROUND: Acute or chronic injury of articular cartilage leads to localized destruction. Difficulties with interface integration between the implant and native cartilage tissue can lead to an undesirable outcome. To improve cartilage repair and interface integration, we explored the therapeutic efficacy of microporous acellular extracellular matrix (ECM) combined with adipose-derived stem cell (ASC) sheets. METHODS: Methods for fabricating ASC sheets and microporous acellular ECM were explored before transplanting the constructed ASC sheet/matrix in vivo and in vitro, respectively. After the operation, distal femur samples were collected at 6 and 12 weeks for further analysis. RESULTS: The decellularization process removed 90% of the DNA but retained 82.4% of glycosaminoglycans (GAGs) and 82.8% of collagen, which are the primary components of cartilage matrix. The acellular matrix/ASC sheet construct treatment in vivo showed better interface integration, cartilage regeneration, and collagenous fiber arrangement, which resembles the native structure. There was a significant increase in GAG and collagen accumulation at the zone of regeneration and integration compared to other groups. Gene expression analysis showed that the mRNA level associated with cartilage formation significantly increased in the acellular matrix/ASC sheet group (p<0.05), which is consistent with the histological analysis. DISCUSSION: ASC sheets promote interface integration between the implant and native tissue. This effect, together with the acellular matrix as a graft, is beneficial for cartilage defect repair, which suggests that acellular matrix/ASC sheet bioengineered cartilage implants may be a better approach for cartilage repair due to their enhanced integration.

Endovascular embolization is applicable for large and giant intracranial aneurysms: experience in one center with long-term angiographic follow-up.

BACKGROUND: Endovascular treatment of large and giant intracranial aneurysms and long-term results of angiographic follow-up of these aneurysms treated endovascularly are not known currently. PURPOSE: To investigate the outcome of endovascular treatment of large and giant aneurysms and the long-term angiographic follow-up results. MATERIAL AND METHODS: A retrospective analysis of all patients with endovascular treatment of large and giant aneurysms between 1998 and 2009 was performed. There were 90 large or giant aneurysms treated with coiling alone, stent-assisted coiling, covered-stent deployment, or parent artery occlusion (PAO) in 88 patients (female/male, 54/34; age range, 23-92 years; mean age, 56 years). RESULTS: Immediately after the initial endovascular embolization procedure, complete occlusion was achieved in 56.7%, near complete occlusion in 37.8%, and incomplete occlusion in 5.5%. The total periprocedural complication rate excluding subarachnoid hemorrhage (SAH)-induced vasospasm was 10.2% with a mortality rate of 2.3%. Follow-up angiography was performed in all of the aneurysms with the longest follow-up duration of 131 months. Among 38 aneurysms initially treated with coiling alone and 17 initially treated with stent-assisted coiling, 22 (57.9%) and four (23.5%) recurred, respectively, during follow-up. No recurrence occurred in aneurysms initially treated with covered-stent deployment or PAO. Aneurysm recurrence was predominantly seen in older and female patients, in larger aneurysms, and in aneurysms treated with coiling alone. Twenty-three aneurysms were successfully retreated endovascularly. CONCLUSION: Endovascular intervention with coiling alone or stent-assisted coiling for large and giant cerebral aneurysms is not very effective, while covered stents are more promising. Better endovascular devices are needed to obtain more secure closure.

Images of deep neck space infection and the clinical significance.

BACKGROUND: Deep neck infection is not difficult to diagnose clinically, but correct localization of the involved space for timely incision and drainage is not easy without assistance of imaging. PURPOSE: To investigate the images of deep neck space infection of phlegmon and abscess and the role of imaging examination in correct localization and treatment. MATERIAL AND METHODS: Between June 2004 and June 2010, 28 patients were diagnosed with deep neck infection (14 men, 14 women; age range, 17-72 years; mean age, 46 years). Clinical presentations included neck swelling, pain, dysphagia, fever, and elevated white blood cell count. Of the 28 cases, 20 had computed tomography (CT) scans, 18 had magnetic resonance imaging (MRI) examinations, and 10 had both CT and MRI. RESULTS: All 28 patients were confirmed by CT and/or MRI to have deep neck infection, with 11 cases in the retropharyngeal space, five in the parapharyngeal space, four in the masseteric space, and eight in multiple spaces. Thirteen cases had abscesses that were successfully treated with incision and drainage under CT guidance in combination with large doses of antibiotics, and 15 had phlegmon managed with large doses of antibiotics. Followed up for 5-20 months, all patients recovered completely. Two patients were confirmed by imaging examination to have retropharyngeal infection spreading to the superior mediastinum with abscess formation and another two patients had multiple space infection because inappropriate puncture or incision for drainage without imaging guidance in these patients caused the spread of infection. Clinical diagnosis was not accurate with only 12 patients (42.9%) being correctly diagnosed of the exact deep neck space involved before imaging confirmation. CT and/or MRI made the correct diagnosis in all 28 patients. CT and/or MRI also directly changed the treatment plan in seven patients and contributed to the recovery of these patients. CONCLUSION: CT and MRI play a crucial role in both the diagnosis and correct puncture and incision for drainage of the deep neck space infection.

Structural tensor and frequency guided semi-supervised segmentation for medical images.

BACKGROUND: The method of semi-supervised semantic segmentation entails training with a limited number of labeled samples alongside many unlabeled samples, aiming to reduce dependence on pixel-level annotations. Most semi-supervised semantic segmentation methods primarily focus on sample augmentation in spatial dimensions to reduce the shortage of labeled samples. These methods tend to ignore the structural information of objects. In addition, frequency-domain information also supplies another perspective to evaluate information from images, which includes different properties compared to the spatial domain. PURPOSE: In this study, we attempt to answer these two questions: (1) is it helpful to provide structural information of objects in semi-supervised semantic segmentation tasks for medical images? (2) is it more effective to evaluate the segmentation performance in the frequency domain compared to the spatial domain for semi-supervised medical image segmentation? Therefore, we seek to introduce structural and frequency information to improve the performance of semi-supervised semantic segmentation for medical images. METHODS: We present a novel structural tensor loss (STL) to guide feature learning on the spatial domain for semi-supervised semantic segmentation. Specifically, STL utilizes the structural information encoded in the tensors to enforce the consistency of objects across spatial regions, thereby promoting more robust and accurate feature extraction. Additionally, we proposed a frequency-domain alignment loss (FAL) to enable the neural networks to learn frequency-domain information across different augmented samples. It leverages the inherent patterns present in frequency-domain representations to guide the network in capturing and aligning features across diverse augmentation variations, thereby enhancing the model's robustness for the inputting variations. RESULTS: We conduct our experiments on three benchmark datasets, which include MRI (ACDC) for cardiac, CT (Synapse) for abdomen organs, and ultrasound image (BUSI) for breast lesion segmentation. The experimental results demonstrate that our method outperforms state-of-the-art semi-supervised approaches regarding the Dice similarity coefficient. CONCLUSIONS: We find the proposed approach could improve the final performance of the semi-supervised medical image segmentation task. It will help reduce the need for medical image labels. Our code will are available at https://github.com/apple1986/STLFAL.

The Effect of a Cervical Brace on Postoperative Axial Symptoms Following Single-level Anterior Cervical Discectomy and Fusion.

STUDY DESIGN: Retrospective study. OBJECTIVE: This study aims to investigate the effect of cervical brace utilization on postoperative axial symptoms in patients undergoing single-segment anterior cervical discectomy and fusion (ACDF). SUMMARY OF BACKGROUND DATA: Anterior cervical discectomy and fusion (ACDF) is the most commonly used surgical method in the treatment of cervical spondylosis. For patients with single-segment ACDF. The absence of a neck brace after surgery is safe and does not affect the outcome of surgery. However, the effect on the incidence of AS is unclear. METHODS: Patients who underwent anterior cervical single-segment ACDF between May 2020 and August 2021 were retrospectively analyzed. Participants were divided into brace group and nonbraced groups. The incidence of axial symptoms, cervical mobility, and postoperative quality of life were then compared between the 2 groups. RESULTS: A total of 121 patients were included in this study: 62 in the brace group and 59 in the nonbraced group. There were no statistically significant variations observed in the overall demographic characteristics, including age, sex, body mass index, smoking status, and disease duration. The study findings showed that there was a significant decrease in the occurrence of axial symptoms among patients in nonbraced group, in addition to a considerable increase in cervical mobility 1 month following the surgery. CONCLUSIONS: The omission of a cervical brace following surgery in patients undergoing single-segment ACDF reduced the incidence of early postoperative axial symptoms, improved their overall quality of life, and facilitated the recovery of postoperative cervical mobility.

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.

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.

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.

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.

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.

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.

[Effects of zhizhu tongbian decoction on the colon ink propelling rate, GDNF, and NOS mRNA expression in rats with slow transit constipation].

OBJECTIVE: To observe the effects of Zhizhu Tongbian Decoction (ZTD) on the enteric nervous system, mRNA expressions of glial cell line derived neurotrophic factor (GDNF) and nitric oxide synthase (NOS) in the slow transit constipation (STC) rats. METHODS: Thirty STC rat model was established by gastric irrigation of rhubarb. After the model building, they were randomly divided into three groups, i. e., the model group, the high dose ZTD group, and the low dose ZTD group, 10 in each. Another 10 rats were selected as the blank control group. Rats in the high dose ZTD group and the low dose ZTD group were administered with ZTD (at the daily dose of crude drug 4.8 g/kg and 2.4 g/kg respectively) by gastrogavage. Normal saline was given to rats in the blank control group and the model group. The ink propelling rate was determined using ink propelling test. Meantime, mRNA expressions of GDNF and NOS in the rat colon were measured using reverse transcriptional polymerase chain reaction (RT-PCR). RESULTS: Compared with the blank control group, the ink propelling rate and GDNF mRNA expression decreased, and NOS mRNA increased in the model group, showing statistical difference (P<0.01, P<0.05). Compared with the model group, the ink propelling rate increased in the high and low dose ZTD groups (P<0.01, P<0.05). The mRNA expressions of GDNF increased and the mRNA expressions of NOS decreased in the high dose ZTD group with statistical difference (P<0.01, P<0.05). But there was no difference in any index between the high and low dose ZTD groups. CONCLUSION: High dose ZTD could obviously improve the intestinal transmission function possibly through up-regulating the mRNA expressions of GDNF and down-regulating the mRNA expressions of NOS in STC rats.

[Treatment of functional dyspepsia by Chinese medical syndrome typing: a randomized control research].

OBJECTIVE: To assess the short- and long-term efficacy and safety of treating functional dyspepsia (FD) by Chinese medical syndrome typing (CMST). METHODS: A randomized, positive-drug parallel controlled study was conducted. Recruited were 170 FD patients who were randomly assigned to the test group (13 cases, treated by Chinese herbs) and the control group (34 cases, treated by Western medicine) in the ratio of 4:1. Different recipes were administered to patients in the test group according to CMST at the 1st, 2nd, and 4th week, respectively, while those in the control group took Domperidone or Esomeprazole Magnesium Enteric-coated Tablet according to Roma III Criteria. The therapeutic efficacy was observed at the 1st, 2nd, and 4th week of the treatment, including (1) clinical symptom score; (2) the score of SF-36 quality of life scale; (3) safety (4) compliance; (5) satisfaction; (6) the relapse rate; (7) cost-effectiveness ratio (C/E). The follow-up were performed at the 1st, 3rd, and 6th month. RESULTS: Sixteen patients fell off in the test group and 4 fell off i the control group, and the expulsion rate being 11.76% in the two groups, showing no statistical difference ( P > 0.05). The clinical symptom scores in the test group decreased from 5.62 +/- 2.30 before treatment to 1.41 +/- 1.22 after 4-week treatment, showing statistical difference (P < 0.01), but with no statistical difference when compared with the control group at the same time point (P>0.05). The healing rate and the total effective rate at week 4 were 38.24% and 86.76% respectively in the test group, and they were 60.00% and 65.00% at 6-month withdrawal. They were 41.18%, 79.41%, 46.67%, and 50.00%, respectively, in the control group. There was no statistical difference between the two groups (P>0.05). The scores of physical component-summary (PCS) and mental component-summary (MCS) both increased after 4-week treatment in the two groups, showing no statistical difference when compared with before treatment (P>0.05). There was statistical difference in the scores of PCS and MCS between at 6-month withdrawal and before treatment (P<0.05), but there was no statistical difference between the two groups (P>0.05). No obvious adverse reaction occurred in the two groups. The compliance and satisfaction after 4-week treatment were 95.59% and 91.91% in the test group, and 94.12% and 91.18% in the control group, showing no statistical difference between the two groups (P>0.05). The relapse rate in the test group was 10.29%, 19.12%, and 29.41%, respectively, after 1, 3, 6-month withdrawal, lower than that of the control group (17.65%, 23.53%, and 35.29%, respectively) at the same time point, but with no statistical difference. The C/E ratio of the test group/the control group was 15.59: 16. 53 at 4-week treatment and 22.27:28.28 after 6-month withdrawal respectively. The further analysis of incremental cost/incremental effectiveness showed that the ratio in the long-term decreased from 5.44 to 2.35 in the test group. CONCLUSIONS: The 4-week treatment of CMST had definite short- and long-term efficacy on FD patients, and improved their quality of life. It had better safety, compliance, and satisfaction. It was dominant in lower relapse rate and the cost/effectiveness. Therefore, it was worth spreading.

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.