Clinical and Molecular Characteristics of Megakaryocytes in Myelodysplastic Syndrome.

Objective Myelodysplastic syndrome (MDS) is a malignant clonal disorder of hematopoietic stem cells which is characterized by morphologic dysplasia. However, the pathological characteristics of megakaryocytes (MKs) in MDS patients with gene mutation are not well established. Methods Bone marrow MK specimens from 104 patients with primary MDS were evaluated, and all patients were distributed into two groups according to gene mutation associated with functional MKs. The morphologic and cellular characteristics of MKs and platelets were recorded and compared. Results The more frequently mutated genes in MDS patients were TUBB1 (11.54%), VWF (8.65%), NBEAL2 (5.77%), and the most common point mutation was TUBB1 p.(R307H) and p.(Q43P). Patients with MK mutation showed a decrease in adenosine diphosphate-induced platelet aggregation, high proportion of CD34 + CD61 + MKs (10.00 vs. 4.00%, p = 0.012), and short overall survival (33.15 vs. 40.50 months, p = 0.013). Further, patients with a higher percent of CD34 + CD61 + MKs (≧20.00%) had lower platelet counts (36.00 × 10 9 /L vs. 88.50 × 10 9 /L, p = 0.015) and more profound emperipolesis ( p = 0.001). By analyzing RNA-sequencing of MKs, differentially expressed mRNA was involved in physiological processes including platelet function and platelet activation, especially for MDS patients with high percent of CD34 + CD61 + MKs. The high levels of expression of CD62P, CXCL10, and S100A9 mRNA, shown by RNA sequencing, were validated by PCR assay. Conclusion High proportion of CD34 + CD61 + MKs was a poor prognostic factor in MDS patients with MK mutation. CD62P, CXCL10, and S100A9 may be the potential targets to evaluate the molecular link between gene defects and platelet function.

GCY-20 signaling controls suppression of Caenorhabditis elegans egg laying by moderate cold.

Organisms sensing environmental cues and internal states and integrating the sensory information to control fecundity are essential for survival and proliferation. The present study finds that a moderate cold temperature of 11°C reduces egg laying in Caenorhabditis elegans. ASEL and AWC neurons sense the cold via GCY-20 signaling and act antagonistically on egg laying through the ASEL and AWC/AIA/HSN circuits. Upon cold stimulation, ASEL and AWC release glutamate to activate and inhibit AIA interneurons by acting on highly and lowly sensitive ionotropic GLR-2 and GLC-3 receptors, respectively. AIA inhibits HSN motor neuron activity via acetylcholinergic ACR-14 receptor signaling and suppresses egg laying. Thus, ASEL and AWC initiate and reduce the cold suppression of egg laying. ASEL's action on AIA and egg laying dominates AWC's action. The biased opposite actions of these neurons on egg laying provide animals with a precise adaptation of reproductive behavior to environmental temperatures.

ChatGPT Performs on the Chinese National Medical Licensing Examination.

ChatGPT, a language model developed by OpenAI, uses a 175 billion parameter Transformer architecture for natural language processing tasks. This study aimed to compare the knowledge and interpretation ability of ChatGPT with those of medical students in China by administering the Chinese National Medical Licensing Examination (NMLE) to both ChatGPT and medical students. We evaluated the performance of ChatGPT in three years' worth of the NMLE, which consists of four units. At the same time, the exam results were compared to those of medical students who had studied for five years at medical colleges. ChatGPT's performance was lower than that of the medical students, and ChatGPT's correct answer rate was related to the year in which the exam questions were released. ChatGPT's knowledge and interpretation ability for the NMLE were not yet comparable to those of medical students in China. It is probable that these abilities will improve through deep learning.

Neglected Mendelian causes of stroke in adult Chinese patients who had an ischaemic stroke or transient ischaemic attack.

BACKGROUND AND PURPOSE: Multiple factors play important roles in the occurrence and prognosis of stroke. However, the roles of monogenic variants in all-cause ischaemic stroke have not been systematically investigated. We aim to identify underdiagnosed monogenic stroke in an adult ischaemic stroke/transient ischaemic attack (TIA) cohort (the Third China National Stroke Registry, CNSR-III). METHODS: Targeted next-generation sequencing for 181 genes associated with stroke was conducted on DNA samples from 10 428 patients recruited through CNSR-III. The genetic and clinical data from electronic health records (EHRs) were reviewed for completion of the diagnostic process. We assessed the percentages of individuals with pathogenic or likely pathogenic (P/LP) variants, and the diagnostic yield of pathogenic variants in known monogenic disease genes with associated phenotypes. RESULTS: In total, 1953 individuals harboured at least one P/LP variant out of 10 428 patients. Then, 792 (7.6%) individuals (comprising 759 individuals harbouring one P/LP variant in one gene, 29 individuals harbouring two or more P/LP variants in different genes and 4 individuals with two P/LP variants in ABCC6) were predicted to be at risk for one or more monogenic diseases based on the inheritance pattern. Finally, 230 of 792 individuals manifested a clinical phenotype in the EHR data to support the diagnosis of stroke with a monogenic cause. The most diagnosed Mendelian cause of stroke in the cohort was cerebral autosomal dominant arteriopathy with subcortical infarcts and leukoencephalopathy. There were no relationships between age or family history and the incidence of first symptomatic monogenic stroke in patients. CONCLUSION: The rate of monogenic cause of stroke was 2.2% after reviewing the clinical phenotype. Possible reasons that Mendelian causes of stroke may be missed in adult patients who had an ischaemic stroke/TIA include a late onset of stroke symptoms, combination with common vascular risks and the absence of a prominent family history.

A chatbot for mental health support: exploring the impact of Emohaa on reducing mental distress in China.

Introduction: The growing demand for mental health support has highlighted the importance of conversational agents as human supporters worldwide and in China. These agents could increase availability and reduce the relative costs of mental health support. The provided support can be divided into two main types: cognitive and emotional. Existing work on this topic mainly focuses on constructing agents that adopt Cognitive Behavioral Therapy (CBT) principles. Such agents operate based on pre-defined templates and exercises to provide cognitive support. However, research on emotional support using such agents is limited. In addition, most of the constructed agents operate in English, highlighting the importance of conducting such studies in China. To this end, we introduce Emohaa, a conversational agent that provides cognitive support through CBT-Bot exercises and guided conversations. It also emotionally supports users through ES-Bot, enabling them to vent their emotional problems. In this study, we analyze the effectiveness of Emohaa in reducing symptoms of mental distress. Methods and Results: Following the RCT design, the current study randomly assigned participants into three groups: Emohaa (CBT-Bot), Emohaa (Full), and control. With both Intention-To-Treat (N=247) and PerProtocol (N=134) analyses, the results demonstrated that compared to the control group, participants who used two types of Emohaa experienced considerably more significant improvements in symptoms of mental distress, including depression (F[2,244]=6.26, p=0.002), negative affect (F[2,244]=6.09, p=0.003), and insomnia (F[2,244]=3.69, p=0.026). Discussion: Based on the obtained results and participants' satisfaction with the platform, we concluded that Emohaa is a practical and effective tool for reducing mental distress.

Disexcitation in the ASH/RIM/ADL negative feedback circuit fine-tunes hyperosmotic sensation and avoidance in Caenorhabditis elegans.

Sensations, especially nociception, are tightly controlled and regulated by the central and peripheral nervous systems. Osmotic sensation and related physiological and behavioral reactions are essential for animal well-being and survival. In this study, we find that interaction between secondary nociceptive ADL and primary nociceptive ASH neurons upregulates Caenorhabditis elegans avoidance of the mild and medium hyperosmolality of 0.41 and 0.88 Osm but does not affect avoidance of high osmolality of 1.37 and 2.29 Osm. The interaction between ASH and ADL is actualized through a negative feedback circuit consisting of ASH, ADL, and RIM interneurons. In this circuit, hyperosmolality-sensitive ADL augments the ASH hyperosmotic response and animal hyperosmotic avoidance; RIM inhibits ADL and is excited by ASH; thus, ASH exciting RIM reduces ADL augmenting ASH. The neuronal signal integration modality in the circuit is disexcitation. In addition, ASH promotes hyperosmotic avoidance through ASH/RIC/AIY feedforward circuit. Finally, we find that in addition to ASH and ADL, multiple sensory neurons are involved in hyperosmotic sensation and avoidance behavior.

Positive interaction between ASH and ASK sensory neurons accelerates nociception and inhibits behavioral adaptation.

Central and peripheral sensory neurons tightly regulate nociception and avoidance behavior. The peripheral modulation of nociception provides more veridical and instantaneous information for animals to achieve rapid, more fine-tuned and concentrated behavioral responses. In this study, we find that positive interaction between ASH and ASK sensory neurons is essential for the fast-rising phase of ASH Ca2+ responses to noxious copper ions and inhibits the adaption of avoiding Cu2+. We reveal the underlying neuronal circuit mechanism. ASK accelerates the ASH Ca2+ responses by transferring cGMP through gap junctions. ASH excites ASK via a disinhibitory neuronal circuit composed of ASH, AIA, and ASK. Avoidance adaptation depends on the slope rate of the rising phase of ASH Ca2+ responses. Thus, in addition to amplitude, sensory kinetics is significant for sensations and behaviors, especially for sensory and behavioral adaptations.

3D FRN-ResNet: An Automated Major Depressive Disorder Structural Magnetic Resonance Imaging Data Identification Framework.

Major Depressive Disorder (MDD) is the most prevalent psychiatric disorder, seriously affecting people's quality of life. Manually identifying MDD from structural magnetic resonance imaging (sMRI) images is laborious and time-consuming due to the lack of clear physiological indicators. With the development of deep learning, many automated identification methods have been developed, but most of them stay in 2D images, resulting in poor performance. In addition, the heterogeneity of MDD also results in slightly different changes reflected in patients' brain imaging, which constitutes a barrier to the study of MDD identification based on brain sMRI images. We propose an automated MDD identification framework in sMRI data (3D FRN-ResNet) to comprehensively address these challenges, which uses 3D-ResNet to extract features and reconstruct them based on feature maps. Notably, the 3D FRN-ResNet fully exploits the interlayer structure information in 3D sMRI data and preserves most of the spatial details as well as the location information when converting the extracted features into vectors. Furthermore, our model solves the feature map reconstruction problem in closed form to produce a straightforward and efficient classifier and dramatically improves model performance. We evaluate our framework on a private brain sMRI dataset of MDD patients. Experimental results show that the proposed model exhibits promising performance and outperforms the typical other methods, achieving the accuracy, recall, precision, and F1 values of 0.86776, 0.84237, 0.85333, and 0.84781, respectively.

scSE-NL V-Net: A Brain Tumor Automatic Segmentation Method Based on Spatial and Channel "Squeeze-and-Excitation" Network With Non-local Block.

Intracranial tumors are commonly known as brain tumors, which can be life-threatening in severe cases. Magnetic resonance imaging (MRI) is widely used in diagnosing brain tumors because of its harmless to the human body and high image resolution. Due to the heterogeneity of brain tumor height, MRI imaging is exceptionally irregular. How to accurately and quickly segment brain tumor MRI images is still one of the hottest topics in the medical image analysis community. However, according to the brain tumor segmentation algorithms, we could find now, most segmentation algorithms still stay in two-dimensional (2D) image segmentation, which could not obtain the spatial dependence between features effectively. In this study, we propose a brain tumor automatic segmentation method called scSE-NL V-Net. We try to use three-dimensional (3D) data as the model input and process the data by 3D convolution to get some relevance between dimensions. Meanwhile, we adopt non-local block as the self-attention block, which can reduce inherent image noise interference and make up for the lack of spatial dependence due to convolution. To improve the accuracy of convolutional neural network (CNN) image recognition, we add the "Spatial and Channel Squeeze-and-Excitation" Network (scSE-Net) to V-Net. The dataset used in this paper is from the brain tumor segmentation challenge 2020 database. In the test of the official BraTS2020 verification set, the Dice similarity coefficient is 0.65, 0.82, and 0.76 for the enhanced tumor (ET), whole tumor (WT), and tumor core (TC), respectively. Thereby, our model can make an auxiliary effect on the diagnosis of brain tumors established.

OTO-Net: An Automated MRA Image Segmentation Network for Intracranial Aneurysms.

Intracranial aneurysms are local dilations of the cerebral blood vessels; people with intracranial aneurysms have a high risk to cause bleeding in the brain, which is related to high mortality and morbidity rates. Accurate detection and segmentation of intracranial aneurysms from Magnetic Resonance Angiography (MRA) images are essential in the clinical routine. Manual annotations used to assess the intracranial aneurysms on MRA images are substantial interobserver variability for both aneurysm detection and assessment of aneurysm size and growth. Many prior automated segmentation works have focused their efforts on tackling the problem, but there is still room for performance improvement due to the significant variability of lesions in the location, size, structure, and morphological appearance. To address these challenges, we propose a novel One-Two-One Fully Convolutional Networks (OTO-Net) for intracranial aneurysms automated segmentation in MRA images. The OTO-Net uses full convolution to achieve intracranial aneurysms automated segmentation through the combination of downsampling, upsampling, and skip connection. In addition, loss ensemble is used as the objective function to steadily improve the backpropagation efficiency of the network structure during the training process. We evaluated the proposed OTO-Net on one public benchmark dataset and one private dataset. Our proposed model can achieve the automated segmentation accuracy with 98.37% and 97.86%, average surface distances with 1.081 and 0.753, dice similarity coefficients with 0.9721 and 0.9813, and Hausdorff distance with 0.578 and 0.642 on these two datasets, respectively.

A comparison of transcriptome analysis methods with reference genome.

BACKGROUND: The application of RNA-seq technology has become more extensive and the number of analysis procedures available has increased over the past years. Selecting an appropriate workflow has become an important issue for researchers in the field. METHODS: In our study, six popular analytical procedures/pipeline were compared using four RNA-seq datasets from mouse, human, rat, and macaque, respectively. The gene expression value, fold change of gene expression, and statistical significance were evaluated to compare the similarities and differences among the six procedures. qRT-PCR was performed to validate the differentially expressed genes (DEGs) from all six procedures. RESULTS: Cufflinks-Cuffdiff demands the highest computing resources and Kallisto-Sleuth demands the least. Gene expression values, fold change, p and q values of differential expression (DE) analysis are highly correlated among procedures using HTseq for quantification. For genes with medium expression abundance, the expression values determined using the different procedures were similar. Major differences in expression values come from genes with particularly high or low expression levels. HISAT2-StringTie-Ballgown is more sensitive to genes with low expression levels, while Kallisto-Sleuth may only be useful to evaluate genes with medium to high abundance. When the same thresholds for fold change and p value are chosen in DE analysis, StringTie-Ballgown produce the least number of DEGs, while HTseq-DESeq2, -edgeR or -limma generally produces more DEGs. The performance of Cufflinks-Cuffdiff and Kallisto-Sleuth varies in different datasets. For DEGs with medium expression levels, the biological verification rates were similar among all procedures. CONCLUSION: Results are highly correlated among RNA-seq analysis procedures using HTseq for quantification. Difference in gene expression values mainly come from genes with particularly high or low expression levels. Moreover, biological validation rates of DEGs from all six procedures were similar for genes with medium expression levels. Investigators can choose analytical procedures according to their available computer resources, or whether genes of high or low expression levels are of interest. If computer resources are abundant, one can utilize multiple procedures to obtain the intersection of results to get the most reliable DEGs, or to obtain a combination of results to get a more comprehensive DE profile for transcriptomes.

The Development and Validation of the Doctoral Student Identity Scale.

Objective: Based on how the identity of doctoral students is recognized and understood in the context of Chinese culture, we developed a doctoral identity scale using both qualitative and quantitative analyses. Methods: The initial project of the Scale was formed through qualitative analyses and expert consultation. Nine hundred and ninety-one doctoral students were officially tested, and 982 valid questionnaires were obtained. They were randomly divided into two parts, and 491 of which were assessed for item Response Theory (IRT) and exploratory factor analysis (EFA) and 491 of which were assessed for confirmatory factor analysis (CFA). The Subjective Well-Being Scale (SWB), the Rosenberg Self-Esteem Scale (RSE), and the Psychological Sense of School Membership Scale (PSSM) were used to test its the criterion-related validity. One hundred and forty-one students were selected for retesting after 8 weeks. Results: The doctoral student identity questionnaire consisted of two factors identity exploration and identity commitment, explaining 57% of the total variance. The results of CFA showed that the two-factor model fitted the data well. The two dimensions of the Doctoral Student Identity Scale were significantly and positively correlated with the two dimensions of the SWB scale (0.32-0.66), the latent factor of the RSE scale (0.42-0.55), and the latent factor of the PSSM scale (0.52-0.62). Composite reliability values for exploration and commitment were 0.79 and 0.83 respectively, and the values of McDonald's omega for exploration and commitment were 0.81 and 0.85 respectively. The test-retest reliability of the total questionnaire was 0.842. Conclusion: The Doctoral Student Identity Scale was developed with good reliability and validity, and can be used as a reliable tool for measuring the doctoral student identity. In addition, the questionnaire will provide corresponding ideas and methods for studying the identity issues of specific groups.

Ni(OH)2 nanodot-decorated Co-Co LDH/C hollow nanocages for a high performance supercapacitor.

A novel Co-Co LDH/C/Ni(OH)2 nanostructure was constructed by loading Ni(OH)2 nanodots on hollow Co-Co LDH/C nanocages derived from MOFs. The Co-Co LDH/C/Ni(OH)2 nanostructure revealed a high specific capacitance of up to 1426 F g-1 at 1 A g-1 and an outstanding rate capability with 90.2% retention at 10 A g-1 owing to the cooperative effect of the Ni(OH)2 nanodots and hollow Co-Co LDH/C nanocages. The electrochemical kinetic analysis showed that the Co-Co LDH/C/Ni(OH)2 electrode was dominated by surface capacitance control, demonstrating the origins of performance improvement. This work may provide an effective strategy by combining nanodots with hollow porous structures for low-cost and efficient energy storage materials.

Measurement of Chest Physiological Signals using Wirelessly Coupled Bio-Impedance Patches.

Continuous monitoring of respiration and heart pressure pulse waves generated by lung and heart movements is essential in the diagnosis and management of cardiovascular and lung diseases. Traditional methods in measuring these physiological signals are not convenient for long-term monitoring during daily activities and sleeping due to their use of long wires and/or face masks, and leading to patients having to spend long duration in clinics while undergoing supervised monitoring. In this paper, we present a new method to measure global chest physiological signals using small-sized smart band-age like patches placed at different locations of the chest. The introduced patches communicate with each other using human body and detect small variations in the bio-Impedance (Bio-Z) depending on the mechanical heart and lung movements, where one patch injects an AC current from one side of the chest and several Bio-Z sensors measure the voltage difference across different locations of the chest. In order to prevent usage of long wires and increase convenience for wearable applications, electrical connection between current injection and Bio-Z sensing patches are eliminated. Independent component analysis is used to separate sources of physiological observations and improve accuracy of the system. We show that the presented system can successfully detect respiration rate and heart rate with an average error of 4.86% (less than 1 breath per minute) and 1.86% (less than 2 beats per minute) respectively tested on 6 healthy subjects over 6 minutes of data collection for each subject.

Comprehensive evaluation on low-carbon development of coal enterprise groups.

Scientifically evaluating the level of low-carbon development in terms of theoretical and practical significance is extremely important to coal enterprise groups for implementing national energy-related systems. This assessment can assist in building institutional mechanisms that are conducive for the economic development of coal business cycle and energy conservation as well as promoting the healthy development of coal enterprises to realize coal scientific development and resource utilization. First, by adopting systematic analysis method, this study builds low-carbon development evaluation index system for coal enterprise groups. Second, to determine the weight serving as guideline and criteria of the index, analytic hierarchy process (AHP) is applied using integrated linear weighted sum method to evaluate the level of low-carbon development of coal enterprise groups. Evaluation is also performed by coal enterprise groups, and the process comprises field analysis and evaluation. Finally, industrial policies are proposed regarding the development of low-carbon coal conglomerate strategies and measures. This study aims mainly to guide the low-carbon development of coal enterprise groups, solve the problem of coal mining and the destruction of ecological environment, support the conservation of raw materials and various resources, and achieve the sustainable development of the coal industry.

Allocating on coal consumption and CO2 emission from fair and efficient perspective: empirical analysis on provincial panel data of China.

This paper considers a problem of how to allocate resource effectively and equitably among provinces. To address the problem, a total factor resource input-oriented data envelopment analysis (DEA) model is used to evaluate the energy and environmental efficiency for 30 provinces in China during 2009-2013 in this paper. Based on the evaluation results, from efficient and fair perspective, a revised DEA-based resource allocation model is established. It is worth pointing out that the model takes the input orientation and output orientation into account at the same time and can be used to allocate coal consumption and carbon emission by 2020 for 30 provinces in China. Results indicate that if the Chinese government wants to fulfill the CO2 emission reduction targets of 40-45% by 2020, and coal consumption intensity reduction target during 13th Five-Year Plan, inefficient provinces will undertake more coal consumption and carbon emission intensity reduction obligation share. And provinces with historical high coal consumption and high CO2 emission intensity will have greater potential of coal consumption and carbon emission intensity reduction. In addition, this paper set several scenarios of gross domestic product (GDP) growth rate, under the scenarios analysis, finds the growth rate of GDP has negative effect on reduction of coal consumption and carbon dioxide emissions intensity. This research provides more realistic practical significance for achieving sustainable economic development.

Abnormalities in Prefrontal Cortical Gene Expression Profiles Relevant to Schizophrenia in MK-801-Exposed C57BL/6 Mice.

MK-801, a non-competitive NMDA receptor (NMDAR) antagonist, disturbs NMDAR function in rodents and induces psychological and behavioral changes similar to schizophrenia (SCZ). However, the effects of MK-801 treatment on gene expression are largely unknown. Here we performed RNA-sequencing on the prefrontal cortex of MK-801-exposed male mice in order to analyze gene expression and co-expression patterns related to SCZ and to identify mechanisms that underlie the molecular etiology of this disorder. Transcriptome analysis revealed that the differentially expressed genes were more often associated with biological processes that included postsynaptic transmission, immune system process, response to external stimulus and hemostasis. In order to extract comprehensive biological information, we used an approach for biclustering, called FABIA, to simultaneously cluster transcriptomic data across genes and conditions. When combined with analyses using DAVID and STRING databases, we found that co-expression patterns were altered in synapse-related genes and genes central to the mitochondrial network. Abnormal co-expression of genes mediating synaptic vesicle cycling could disturb release, uptake and reuptake of glutamate, and the perturbation in co-expression patterns for mitochondrial respiratory chain complexes was extensive. Our study supports the hypothesis that research using MK-801-exposed male mice as an animal model of SCZ offers important insights into the pathogenesis of SCZ.

Abnormalities in behaviour, histology and prefrontal cortical gene expression profiles relevant to schizophrenia in embryonic day 17 MAM-Exposed C57BL/6 mice.

Gestational and perinatal disruption of neural development increases the risk of developing schizophrenia (SCZ) later in life. Embryonic day 17 (E17) methylazoxymethanol (MAM) treatment leads to histological, physiological and behavioural abnormalities in post-puberty rats that model the neuropathological and cognitive deficits reported in SCZ patients. However, the validity of E17 MAM-exposed mice to model SCZ has not been explored. Here we treated E17 C57BL/6 mouse dams with various dosages of MAM. We found that this mouse strain was more vulnerable to MAM treatment than rats and there were gender differences in behavioural abnormalities, histological changes and prefrontal cortical gene expression profiles in MAM (7.5 mg/kg)-exposed mice. Both male and female MAM-exposed mice had deficits in prepulse inhibition. Female MAM-exposed mice exhibited mildly increased spontaneous locomotion activity and social recognition deficits, while male mice were normal. Consistently, only female MAM-exposed mice exhibited reduced brain weight, decreased size of prefrontal cortex (PFC) and enlarged lateral ventricles. Transcriptome analysis of the PFC revealed that there were more differentially expressed genes in female MAM-exposed mice than those in male mice. Moreover, expression of Pvalb, Arc and genes in their association networks were downregulated in the PFC of female MAM-exposed mice. These results indicate that E17 MAM-exposure in C57BL/6 mice leads to behavioural changes that model certain deficits reported in SCZ patients. MAM-exposed female mice may be used to study gene expression changes, inhibitory neural circuit dysfunction and glutamatergic synaptic plasticity deficits with a possible relation to those in the brains of SCZ patients.

Insulin-like growth factor binding protein-4 inhibits cell growth, migration and invasion, and downregulates COX-2 expression in A549 lung cancer cells.

Insulin-like growth factor binding protein 4 (IGFBP-4) and cyclooxygenase2 (COX-2) are associated with tumor inflammatory microenvironment which is involved in the progression of tumor. However, it is unclear that the roles of IGFBP-4 in lung cancer and the effects of IGFBP-4 on COX-2 expression. In this study, we showed that IGFBP-4 could decrease COX-2 production in lung cancer A549 cells. IGFBP-4 expression was significantly lower but COX-2 expression was higher in lung cancer tissues compared to matched adjacent normal tissues. In addition, IGFBP-4 could inhibit lung cancer cell proliferation, migration and invasion, and suppress the phosphorylation of PI3 K/AKT, ERK, and CREB. These results indicate that IGFBP-4 has potent antitumor effects in non-small cell lung cancer cells.

Independent Tumor Origin in Two Cases of Synchronous Bilateral Clear Cell Renal Cell Carcinoma.

Bilateral renal cell carcinomas (RCCs) pose a challenge for clinical treatment and management. Most bilateral RCCs are sporadic, and do not show a hereditary pattern indicative of VHL syndrome or other inherited cancers. The origin and evolution of these sporadic bilateral RCCs remains elusive. We obtained normal and tumor samples from two male patients suffering from early stage synchronous bilateral clear cell RCC (ccRCC), and analyzed genomic DNA using whole exome sequencing and bisulfite pyrosequencing. We detected distinct 3p loss of heterozygosity (LOH) in both tumors in each patient. Two tumors within the same patient harbored distinct driver mutations and different CpG hypermethylation sites in the VHL promoter. Moreover, tumors exhibit independent evolutionary trajectories. Therefore, distinct 3p LOH, combined with contingent driver gene mutations and independent VHL hypermethylation, led to independent tumor origin and parallel evolution of bilateral ccRCC in these two patients. Our results indicate that tumors in these two cases were not due to common germline oncogenic mutations. They were results of multiple de novo mutations in each kidney, rather than primary ccRCC with contralateral renal metastasis. Therefore, histopathologic and genetic profiling from single tumor specimen may underestimate the mutational burden and somatic heterogeneity of bilateral ccRCCs.