Graph Convolutional Network With Connectivity Uncertainty for EEG-Based Emotion Recognition.

Automatic emotion recognition based on multichannel Electroencephalography (EEG) holds great potential in advancing human-computer interaction. However, several significant challenges persist in existing research on algorithmic emotion recognition. These challenges include the need for a robust model to effectively learn discriminative node attributes over long paths, the exploration of ambiguous topological information in EEG channels and effective frequency bands, and the mapping between intrinsic data qualities and provided labels. To address these challenges, this study introduces the distribution-based uncertainty method to represent spatial dependencies and temporal-spectral relativeness in EEG signals based on Graph Convolutional Network (GCN) architecture that adaptively assigns weights to functional aggregate node features, enabling effective long-path capturing while mitigating over-smoothing phenomena. Moreover, the graph mixup technique is employed to enhance latent connected edges and mitigate noisy label issues. Furthermore, we integrate the uncertainty learning method with deep GCN weights in a one-way learning fashion, termed Connectivity Uncertainty GCN (CU-GCN). We evaluate our approach on two widely used datasets, namely SEED and SEEDIV, for emotion recognition tasks. The experimental results demonstrate the superiority of our methodology over previous methods, yielding positive and significant improvements. Ablation studies confirm the substantial contributions of each component to the overall performance.

PIVKA-II combined with alpha-fetoprotein for the diagnostic value of hepatic tumors in children: a multicenter, prospective observational study.

BACKGROUND: To investigate whether protein induced by vitamin K antagonist-II (PIVKA-II) combined with alpha-fetoprotein (AFP) can improve the diagnostic and differential diagnostic accuracy of childhood hepatic tumors. METHODS: A multi-center prospective observational study was performed at nine regional institutions around China. Children with hepatic mass (Group T) were divided into hepatoblastoma group (Group THB) and hemangioendothelioma group (Group THE), children with extrahepatic abdominal mass (Group C). Peripheral blood was collected from each patient prior to surgery or chemotherapy. The area under the curve (AUROC) was used to evaluate the diagnostic efficiency of PIVKA-II and the combined tumor markers with AFP. RESULTS: The mean levels of PIVKA-II and AFP were both significantly higher in Group T than Group C (p = 0.001, p < 0.001), in Group THB than Group THE (p = 0.018, p = 0.013) and in advanced HB than non-advanced HB (p = 0.001, p = 0.021). For the diagnosis of childhood hepatic tumors, AUROC of PIVKA-II (cut-off value 32.6 mAU/mL) and AFP (cut-off value 120 ng/mL) was 0.867 and 0.857. The differential diagnostic value of PIVKA-II and AFP in hepatoblastoma from hemangioendothelioma was further assessed, AUROC of PIVKA-II (cut-off value 47.1mAU/mL) and AFP (cut-off value 560 ng/mL) was 0.876 and 0.743. The combined markers showed higher AUROC (0.891, 0.895 respectively) than PIVKA-II or AFP alone. CONCLUSIONS: The serum level of PIVKA-II was significantly higher in children with hepatic tumors, especially those with malignant tumors. The combination of PIVKA-II with AFP further increased the diagnostic performance. TRIAL REGISTRATION: Clinical Trials, NCT03645655. Registered 20 August 2018, https://www. CLINICALTRIALS: gov/ct2/show/NCT03645655 .

Whole-genome sequencing and RNA sequencing analysis reveals novel risk genes and differential expression patterns in hepatoblastoma.

Hepatoblastoma (HB) is an uncommon malignant liver cancer primarily affecting infants and children, characterized by the presence of tissue that resembling fetal hepatocytes, mature liver cells or bile duct cells. The primary symptom in affected children is abdominal lumps. HB constitutes approximately 28% of all liver tumors and two-thirds of liver malignancies in the pediatric and adolescent population. Despite its high prevalence, the underlying mechanism of HB pathogenesis remain largely unknown. To reveal the genetic alternations associated with HB, we conducted a comprehensive genomic study using whole-genome sequencing (WGS) and RNA sequencing (RNA-seq) techniques on five HB patients. We aimed to use WGS to identify somatic variant loci associated with HB, including single nucleotide polymorphisms (SNPs), insertions and deletions (Indels), and copy number variations (CNVs). Notably, we found deleterious mutation in CTNNB1, AXIN2 and PARP1, previously implicated in HB. In addition, we discovered multiple novel genes potentially associated with HB, including BRCA2 and GPC3 which require further functional validation to reveal their contributions to HB development. Furthermore, the American College of Medical Genetics and Genomics (ACMG) analysis identified the ABCC2 gene was the pathogenic gene as a potential risk gene linked with HB. To study the gene expression patterns in HB, we performed RNA-seq analysis and qPCR validation to reveal differential expression of four candidate genes (IGF1R, METTL1, AXIN2 and TP53) in tumors compared to nonneoplastic liver tissue in HB patients (P-Val < 0.01). These findings shed lights on the molecular mechanisms underlying HB development and facilitate to advance future personalized diagnosis and therapeutic interventions of HB.

ECG-CL: A Comprehensive Electrocardiogram Interpretation Method Based on Continual Learning.

The value of Electrocardiogram (ECG) monitoring in early cardiovascular disease (CVD) detection is undeniable, especially with the aid of intelligent wearable devices. Despite this, the requirement for expert interpretation significantly limits public accessibility, underscoring the need for advanced diagnosis algorithms. Deep learning-based methods represent a leap beyond traditional rule-based algorithms, but they are not without challenges such as small databases, inefficient use of local and global ECG information, high memory requirements for deploying multiple models, and the absence of task-to-task knowledge transfer. In response to these challenges, we propose a multi-resolution model adept at integrating local morphological characteristics and global rhythm patterns seamlessly. We also introduce an innovative ECG continual learning (ECG-CL) approach based on parameter isolation, designed to enhance data usage effectiveness and facilitate inter-task knowledge transfer. Our experiments, conducted on four publicly available databases, provide evidence of our proposed continual learning method's ability to perform incremental learning across domains, classes, and tasks. The outcome showcases our method's capability in extracting pertinent morphological and rhythmic features from ECG segmentation, resulting in a substantial enhancement of classification accuracy. This research not only confirms the potential for developing comprehensive ECG interpretation algorithms based on single-lead ECGs but also fosters progress in intelligent wearable applications. By leveraging advanced diagnosis algorithms, we aspire to increase the accessibility of ECG monitoring, thereby contributing to early CVD detection and ultimately improving healthcare outcomes.

A Causal Intervention Scheme for Semantic Segmentation of Quasi-Periodic Cardiovascular Signals.

Precise segmentation is a vital first step to analyze semantic information of cardiac cycle and capture anomaly with cardiovascular signals. However, in the field of deep semantic segmentation, inference is often unilaterally confounded by the individual attribute of data. Towards cardiovascular signals, quasi-periodicity is the essential characteristic to be learned, regarded as the synthesize of the attributes of morphology ( Am) and rhythm ( Ar). Our key insight is to suppress the over-dependence on Am or Ar while the generation process of deep representations. To address this issue, we establish a structural causal model as the foundation to customize the intervention approaches on Am and Ar, respectively. In this article, we propose contrastive causal intervention (CCI) to form a novel training paradigm under a frame-level contrastive framework. The intervention can eliminate the implicit statistical bias brought by the single attribute and lead to more objective representations. We conduct comprehensive experiments with the controlled condition for QRS location and heart sound segmentation. The final results indicate that our approach can evidently improve the performance by up to 0.41% for QRS location and 2.73% for heart sound segmentation. The efficiency of the proposed method is generalized to multiple databases and noisy signals.

ACLY-ß-catenin axis modulates hepatoblastoma cell proliferation.

HB (hepatoblastoma) is most common in children with liver cancer and few options for treating HB. Thus, it is of great significance to investigate the regulatory mechanism of HB and/or identify new therapeutic targets for clinical treatment of HB. Here, we showed that ACLY (ATP citrate lyase), an important lipometabolic enzyme for de novo biosynthesis of fatty acids and steroids, has a higher expression in HB tissues than noncancerous tissues, and is required for HB cell proliferation. Moreover, knocking down ACLY in HB cells caused severe S-phase arrest and apoptosis. Mechanistically, ACLY knockdown significantly silenced the Wnt signaling pathway and reduced ß-catenin expression in HB cells. Conversely, the apoptotic alleviation of HB cells by overexpressing ACLY was blocked by silencing ß-catenin, suggesting the modulation of HB cells by ACLY-ß-catenin axis. Our results uncovered the role of ACLY in HB cells and presented a theoretical approach for HB targeted therapy in the future.

Extracranial metastasis sites correlate to the incidence risk of brain metastasis in stage IV non-small cell lung cancer: a population-based study.

PURPOSE: The aim of this study was to analyse the correlation of extracranial metastasis sites (ECMs) to the incidence risk of brain metastasis (BMs) in stage IV non-small cell lung cancer (NSCLC). METHODS: 18349 newly diagnosed patients were retrospectively analysed, and 4919 pairs of cases were matched by propensity score matching in a 1:1 ratio. Alternative factors were analysed by multivariable logistic regression analysis. And the interaction analysis and subgroup analysis were carried out. RESULTS: The incidences of Brain, Lung, Liver, Bone, Multiple and Other metastasis were 26.9%, 20.2%, 4.6%, 19.9%, 16.9% and 38.3%, respectively. Results suggested that Age, Race, Histological type, Grade, T stage, N stage and Organ metastasis site were risk factors (p < 0.05). The interaction analysis suggested interaction effects between the Primary site, T stage, N stage and Organ metastasis site. The subgroup analysis showed that the Organ metastasis site and the risk of BMs were statistically significant except that the Overlapping subgroup (p = 0.267) of the Primary site. And the incidence risk of BMs in Lung metastasis, Liver metastasis and Bone metastasis groups was lower than that in other metastasis group (OR 1, p < 0.05). There was no significant difference between the Multiple metastasis group and the other metastasis group (OR 1.091, p = 0.169). CONCLUSION: Advanced age, non-Asian/Pacific Islander, non-squamous cell carcinoma, poorly differentiated grade, and higher T/N stage were risk factors for increased BMs in stage IV NSCLC, and the ECMs were associated with the risk of BMs.

SSA-ICL: Multi-domain adaptive attention with intra-dataset continual learning for Facial expression recognition.

Facial expression recognition (FER) is a kind of affective computing that identifies the emotional state represented in facial photographs. Various methods have been developed for completing this critical task. In spite of this progress, three significant obstacles, the interaction between spatial action units, the inadequacy of semantic information about spectral expressions and the unbalanced data distribution, are not well addressed. In this work, we propose SSA-ICL, a novel approach for FER, and solve these three difficulties inside a coherent framework. To address the first two challenges, we develop a Spectral and Spatial Attention (SSA) module that integrates spectral semantics with spatial locations to improve the performance of the model. We provide an Intra-dataset Continual Learning (ICL) module to combat the issue of long-tail distribution in FER datasets. By subdividing a single long-tail dataset into multiple sub-datasets, ICL repeatedly trains well-balanced representations from each subset and finally develop a independent classifier. We performed extensive experiments on two publicly available datasets, AffectNet and RAFDB. In comparison to existing attention modules, our SSA achieves an accuracy improvement of 3.8%∼6.7%, as evidenced by testing results. In the meanwhile, our proposed SSA-ICL can achieve superior or comparable performance to state-of-the-art FER methods (65.78% on AffectNet and 89.44% on RAFDB).

Comparison of factors influencing the willingness to donate biospecimens among guardians of children with cancer and adult cancer patients.

BACKGROUND: This study examined and compared the attitudes and willingness of guardians of children with cancer and adult cancer patients toward donating biospecimens and clinical data for cancer research. METHODS: We conducted a cross-sectional study among guardians of children with cancer (Guardian group) from Shanghai Children's Medical Center and adult cancer patients (Adult group) from Shanghai Ninth People's Hospital between February 1, 2019, and January 31, 2020. Participants' demographic data, willingness, and motivations for biospecimen donation were collected and analyzed. RESULTS: Of 670 participants, 90.8% (318/350) in the Guardian group and 88.1% (282/320) in the Adult group completed the questionnaire. Most participants were willing to donate residual tissue samples (92.8% in the Guardian group vs. 79.4% in the Adult group, pψ  = 0.032) and clinical data (94.0% vs. 72.3%, pψ  < 0.001) for medical research. Logistic regression analysis indicated that only child status (odds ratio [OR] = 0.140, p = 0.02), history of blood donation (OR = 4.467, p = 0.019) in the Guardian group, education (OR = 0.387, p = 0.037), and history of blood donation (OR = 2.556, p = 0.016) in the Adult group were significantly associated with participants' willingness to donate biospecimens. The primary motivation for donation was helping other patients with cancer (65.4% vs. 24.5%, pψ  < 0.001). The major barriers to donation were the potential to cause physical discomfort (61.0% vs. 64.9%, pψ  = 0.032). CONCLUSIONS: Guardians of children with cancer were more willing to donate biospecimens than adult cancer patients in China. It is essential to promote awareness of biospecimens donation, especially in adult cancer patients.

Identifying Tumor-Associated Genes from Bilayer Networks of DNA Methylation Sites and RNAs.

Network theory has attracted much attention from the biological community because of its high efficacy in identifying tumor-associated genes. However, most researchers have focused on single networks of single omics, which have less predictive power. With the available multiomics data, multilayer networks can now be used in molecular research. In this study, we achieved this with the construction of a bilayer network of DNA methylation sites and RNAs. We applied the network model to five types of tumor data to identify key genes associated with tumors. Compared with the single network, the proposed bilayer network resulted in more tumor-associated DNA methylation sites and genes, which we verified with prognostic and KEGG enrichment analyses.

An atrial fibrillation detection system based on machine learning algorithm with mix-domain features and hardware acceleration.

This paper presents a real-time electrocardiogram (ECG) analysis system that can detect atrial fibrillation (AF) using machine learning algorithms without a cloud server. The system takes advantage of the heterogeneous structure of the Zynq system-on-chip (SoC) to optimize the tasks of local implementation of AF detection. The features extraction is based on multi-domain features including entropy features and RR interval features, which is conducted using the embedded micro controller to generate significant features for AF detection. An AF classifier based on artificial neural network (ANN) algorithm is then implemented in the programmable logic of the SoC for acceleration. The validation of the proposed system is performed by using the real-world ECG data from MIT-BIH database and CPSC 2018 database. The experimental results show an accuracy 93.60% and 97.78% when tested on these two databases respectively. The AF detection performance of the embedded algorithm is majorly identical to that of the PC-based algorithm, indicating a robust performance of hardware implementation of the AF detection.

Inflammatory myofibroblastic tumor successfully treated with metformin: A case report and review of literature.

BACKGROUND: Inflammatory myofibroblastic tumor (IMT) is a distinct tumor with a low incidence rate, which can be diagnosed at any age with a predilection for children and adolescents. Although IMT is visible in any tissues and organs, it is more commonly found in the lungs. The clinical and radiological manifestations of IMT lack specificity, hence resulting in frequent misdiagnosis. Surgical resection is currently the main therapeutic approach for IMT. Only scarce cases of IMT treated with metformin have been reported. Here we report the case of an IMT patient with partial penile resection treated with metformin. CASE SUMMARY: A 1-year-old boy was born with a shorter penis, and his foreskin could not be completely turned over. When he was 6 month old, a well-circumscribed mass on the glans was found, while it did not attract the attention of his parents. The mass gradually increased in size over time before he was admitted to the hospital, where physical examination was performed. It was revealed that the glans hidden behind the foreskin had a mass with a diameter of about 4 cm surrounding the penis. The mass appeared to be hard with a smooth surface and poor mobility. The two testicles examined at the bottom of the scrotum were revealed to have a normal size. Magnetic resonance imaging showed a tumor with rich blood supply encircling the cavernosum with a size of 3.5 cm × 2.1 cm × 2.0 cm. A thick urinary line was found without urine dripping, urgency, and urodynia. Surgical treatment was performed. During the operation, it was observed that the mass had surrounded and invaded the cavernosum without obvious boundaries, and that the tumor occupied about one-half of the penis cross-section as well as infiltrated more than one-half of the glans. In addition, the tumor had caused urethral invasion and anterior urethrostenosis. With the intention of keeping the glans and cavernosum, the tumor at the anterior urethra was partially removed, leaving about 30% of the tumor mass. Pathology analysis demonstrated that the tumor was rich in spindle cells with infiltration of inflammatory cells. Immuno-histochemistry analysis indicated that the cells were positive for CD4, CD99, Ki67, BCL2, and CD68, and negative for ALK, MyoG, S100, SOX10, PR, and EMA. Hence, the tumor was diagnosed as IMT. Metformin was prescribed for the patient after the operation, following which an oral dose of 7 mg/kg was given three times a day after meals. Three months later, it was observed that the remaining tumor had completely disappeared and that the urination process from the urethra opening had resumed normal. In addition, there were no side effects observed. There was also no tumor recurrence. The growth and development of the boy were unaffected as a result of the treatment. CONCLUSION: The tumor was observed to have completely disappeared after treatment with metformin. Our finding is of great significance to facilitate future clinical treatment with IMT.

Towards Magnetic Field Gradient-Based Imaging and Control of In-Body Devices.

This papers reports a magnetic field gradient-based imaging system for in-body devices which takes inspiration from the localization principles of magnetic resonance imaging. By applying three orthogonal magnetic field gradients, the location of a device inside the body can be determined by measuring the magnetic fields in the device and transmitting this information to an external reader. The proposed system consists of one pair of Helmholtz coils and two pairs of saddle coils and is capable of generating the three orthogonal gradient fields. To emulate an implantable device, a miniature sensor module was designed using off-the-shelf components and semi-passive UHF RFID. The proposed localization system produces magnetic field gradients up to 187.4 G/m while consuming 1 A and achieves an average localization error of 80 µm.

Melanotic neuroectodermal tumor of infancy successfully treated with metformin: A case report.

RATIONALE: Melanotic neuroectodermal tumor of infancy (MNTI) is a rare tumor originated from neural crest cells with the potential for recurrence and metastasis. The peak age for the disease is during the first year after birth. The current therapy is primarily surgery. The patient reported here is the first case of MNTI treated with metformin. PATIENT CONCERNS: A case of a 4-month-old infant with a history of swelling in the mouth for 1 month. DIAGNOSIS: The tumor was diagnosed using radiology, pathology, and immunohistochemistry, and it was performed with complete surgical resection. Unfortunately, the tumor recurred 3 months after surgery. INTERVENTIONS: We prescribed metformin for the infant. OUTCOMES: Currently, after 9 months of treatment, the tumor is well controlled without apparent side effects. LESSONS: The case presented suggested that metformin may be an underlying therapy for MNTI.

Mining prognostic factors of extensive-stage small-cell lung cancer patients using nomogram model.

This study is to establish the nomogram model and provide clinical therapy decision-making for extensive-stage small-cell lung cancer (ES-SCLC) patients with different metastatic sites using the Surveillance, Epidemiology, and End Results (SEER) Program.A total of 10,025 patients of ES-SCLC with metastasis from January 2010 to December 2016 were enrolled from the SEER database. All samples were randomly divided into a derivation cohort and a validation cohort, and the derivation cohort was divided into 6 groups by different metastatic sites: bone, liver, lung, brain, multiple organs, and other organs. Using Cox proportional hazards models to analyze candidate prognostic factors, screening out the independent prognostic factors to establish the nomogram. Compare the different models by Net reclassification improvement and integrated discrimination improvement. Concordance index (C-index) and the calibration curve were used to verify the prediction efficiency of the nomogram in the derivation cohort and validation cohort.In the derivation cohort, the median overall survival was 7 months. The overall survival rates at 6-month, 1-year, and 2-year were 55.07%, 24.61%, and 7.56%, respectively. The median survival time was 10, 8, 7, 9, 7, and 6 months for the 6 groups of different metastatic sites: other, bone, liver, lung, brain, and multiple organs, respectively. Age, sex, race, T, N, distant metastatic site, and chemotherapy were contained in the final nomogram prognostic model. The C-index was 0.6569777 in the derivation cohort and 0.8386301 in the validation cohort.The survival time of ES-SCLC patients with different metastatic sites was significantly different. The nomogram can effectively predict the prognosis of individuals and provide a basis for clinical decision-making.

An Explainable Artificial Intelligence Predictor for Early Detection of Sepsis.

OBJECTIVES: Early detection of sepsis is critical in clinical practice since each hour of delayed treatment has been associated with an increase in mortality due to irreversible organ damage. This study aimed to develop an explainable artificial intelligence model for early predicting sepsis by analyzing the electronic health record data from ICU provided by the PhysioNet/Computing in Cardiology Challenge 2019. DESIGN: Retrospective observational study. SETTING: We developed our model on the shared ICUs publicly data and verified on the full hidden populations for challenge scoring. PATIENTS: Public database included 40,336 patients' electronic health records sourced from Beth Israel Deaconess Medical Center (hospital system A) and Emory University Hospital (hospital system B). A total of 24,819 patients from hospital systems A, B, and C (an unidentified hospital system) were sequestered as full hidden test sets. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: A total of 168 features were extracted on hourly basis. Explainable artificial intelligence sepsis predictor model was trained to predict sepsis in real time. Impact of each feature on hourly sepsis prediction was explored in-depth to show the interpretability. The algorithm demonstrated the final clinical utility score of 0.364 in this challenge when tested on the full hidden test sets, and the scores on three separate test sets were 0.430, 0.422, and -0.048, respectively. CONCLUSIONS: Explainable artificial intelligence sepsis predictor model achieves superior performance for predicting sepsis risk in a real-time way and provides interpretable information for understanding sepsis risk in ICU.

Gorham-Stout disease successfully treated with sirolimus (rapamycin): a case report and review of the literature.

BACKGROUND: Gorham-Stout disease (GSD) is a rare disease characterized by bone lesions and osteolysis. Therapy usually involves surgical resection. Sirolimus (Rapamycin) is used in some patients with GSD but the efficacy and safety of Sirolimus remains unclear. We propose that Sirolimus may be a novel therapeutic for GSD and present a case and review of literature that supports this. CASE PRESENTATION: We presented a 1-year-old boy with GSD involving osteolysis of the right humerus with fracture of the left femur complicated by an effusion in the right pleural cavity. X-rays showed osteolysis in the right clavicle. A large pleural effusion was observed on the right-side, and the left lung was significantly compressed. X-rays also showed a fracture of the left femur. A femoral biopsy was performed that showed necrotic tissue in the cortical bone and a large number of irregularly shaped capillaries that proliferated within the necrotic tissue. Dilated lymphatic vessels were seen adjacent to the cortex, with fibrous tissue hyperplasia. We prescribed sirolimus, which is an oral mTOR inhibitor, for two consecutive years. The boy recovered well without other progressive bone lesions and participates in normal daily activities. His growth and development are the same as that of his peers. DISCUSSION AND CONCLUSION: Gorham-Stout disease is a rare and enigmatic disease characterized by the presentation of an intraosseous lymphatic anomaly (LM), which results in progressive bone resorption. Based on this case report and a literature review, we conclude that sirolimus may be an effective alternative medication for GSD.

Pharyngeal perforation following laryngoscopy in a patient with dysphagia secondary to diffuse idiopathic skeletal hyperostosis: A case report.

RATIONALE: Dysphagia is a common presenting symptom in elderly people. Nevertheless, dysphagia resulting from diffuse idiopathic skeletal hyperostosis (DISH) of patients' cervical spine may be due to several factors. Despite computed tomography scan showing the size and shape of osteophytes, endoscopy may be necessary to exclude other intrinsic causes of dysphagia. Due to the anatomic variation of the pharynx secondary to DISH, patients undergoing endoscopy are at risk of perforation. Once perforation occurs, inappropriate treatments may finally lead to an irretrievable outcome. PATIENT CONCERNS: A 58-year-old male patient with a 20-day history of dysphagia initially visited an ear-nose-throat (ENT) doctor. He had no neck pain and no other history of cervical disease. DIAGNOSIS: This patient with dysphagia due to DISH of the cervical spine underwent laryngoscopy to exclude other causes. Pharyngeal perforation resulted as a complication of the procedure. INTERVENTIONS: The patient underwent laryngoscopy and biopsy by an ENT doctor to exclude intrinsic causes. After the procedure, a perforation was formed on the posterior wall of the pharynx. Conservative management, that is, 1 week of nothing per oral, and 1 month of antibiotics, was adopted. On the 30th day after the examination, the patient was voluntarily discharged from the hospital and recommended to take antibiotics orally. OUTCOMES: On the 56th day, the patient experienced fever and neck pain. Magnetic resonance imaging showed that the cervical vertebral bodies and spinal cord were infected. On the midday of the 60th day, the patient had a failed resuscitation and died. LESSONS: DISH involving the cervical spine is a complicated cause of dysphagia. Due to the anatomic variation of the pharynx secondary to DISH, patients undergoing endoscopy are at risk of perforation. If other intrinsic causes of dysphagia have to be excluded with the aid of endoscopy, plain films and computed tomography images should be read carefully first. To minimize the risk of perforation, it is necessary to perform endoscopy extremely carefully, especially biopsy. Once perforation occurs, operative treatment may be more appropriate and effective.

Cervus and cucumis peptides combined umbilical cord mesenchymal stem cells therapy for rheumatoid arthritis.

Cervus and cucumis peptides (Lugua polypeptides, LG) are traditional Chinese medicine, which are active components of polypeptide extracted from Sika deer bone and melon seed, and they contain bone induced polypeptide biological factors. Umbilical cord mesenchymal stem cell, (UC-MSC) have tissue repair multiple effects, anti-inflammatory, and immune regulation function, which become a very promising start in rheumatoid arthritis (RA) treatment. Hence, LG combined UC-MSC can significantly enhance the UC-MSC treatment of rheumatoid arthritis (RA).To explore the clinical curative effect and therapeutic mechanism of LG combined UC-MSC for treating RA.119 patients were divided into control and treatment groups, and both groups were treated with methotrexate tablets, leflunomide, and UC-MSC. But, LG were added to the treatment group. In vitro, the effects of LG on UC-MSC cell secretion of anti-inflammatory factors were also performed.The Health Assessment Questionnaire; the 28 joint disease activity score; C reactive protein; the erythrocyte sedimentation rate; rheumatoid factor; and anti-cyclic citrullinated peptide antibody were significantly reduced in treatment group 1 year after treatment (P < .05). In vitro, compared with the control group, the number of hepatocyte growth factor (HGF), the secretion of prostaglandin E2 (PGE2) and tumor necrosis factor-inducible gene 6 protein (TSG6) increased significantly (P < .05).LG combined UC-MSCs can significantly improve the curative effect of RA patients, while LG may reduce inflammatory cytokines, regulate immunity, improve microcirculation, and are conducive to UC-MSCs migration and the repair of damaged tissue.

Clinical complete regression after local radiotherapy combined with chemotherapy for stage IV rectal cancer: A case report.

Colorectal cancer is the fourth most common type of cancer worldwide with about 0.8 million new cases annually. Improving patient survival remains a challenge for clinicians. Observation waiting method provides improved quality of life compared with direct surgery. This case report suggested that colorectal cancer patients could choose active observation waiting method for treatment. A 59-year-old male patient, with rectal bleeding and an Eastern Cooperative Oncology Group (ECOG) performance status score of 0, was admitted to the hospital due to increased fecal blood volume. The electronic colonoscopy revealed multiple polyps in colon and rectum, whereas the pathological biopsy indicated poorly differentiated rectal adenocarcinoma. The clinical stage was defined as T3N2M1a according to the TNM classification of the American Joint Committee on Cancer (AJCC) staging manual (version 8). In addition, positron emission tomography/computed tomography (PET/CT) examination showed non-regional lymph node metastasis (subclavian). Subsequently, the expression of PD-L1 (-), NRAS (-), KRAS (-), HRAS (-), BRAF (-) (-, negative) and the microsatellite stability (MSS) were detected in the rectal cancer lesion using molecular pathological examination. Patients with primary rectal cancer and pelvic lymph node metastasis were treated with three-dimensional conformal radiotherapy (3D-CRT; dose, 60 Gy/30 Fr) and XELOX chemotherapy (200 mg oxaliplatin at day 1 plus 1.5 g capecitabine twice a day from day 1-14 for a total of 5 cycles). PET/CT scan revealed that the metabolism levels of the lesion returned to normal. In addition, the routine re-examination showed progressive improvement of tumor lesions. Until recently, the carcinoembryonic antigen (CEA) level of the male patient has been within normal range. The observation waiting method rather than the direct sequential surgical resection of the primary lesion in patients with advanced rectal cancer who achieved complete clinical remission (CCR) may provide a novel treatment method for rectal cancer. Thus, overall survival (OS) and quality of survival (QoS) differences between the two strategies need to be further verified by multicenter clinical trials.