Alterations in Serum Lipids and Lipoproteins Induced by Neoadjuvant Chemotherapy in Patients with Osteosarcoma around the Knee Joint: A Retrospective Analysis.

OBJECTIVE: To investigate the serum lipid profiles of patients with localized osteosarcoma around the knee joint before and after neoadjuvant chemotherapy. METHODS: After retrospectively screening the data of 742 patients between January 2007 and July 2020, 50 patients aged 13 to 39 years with Enneking stage II disease were included in the study. Serum lipid levels, including total cholesterol (TC), triglycerides (TG), high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C), lipoprotein-α [Lp(a)], and apolipoprotein A1, B, and E (ApoA1, ApoB, and ApoE), and clinicopathological characteristics were collected before and after neoadjuvant chemotherapy. RESULTS: The mean levels of TC, TG, and ApoB were significantly increased following neoadjuvant chemotherapy (16%, 38%, and 20%, respectively, vs. pretreatment values; P<0.01). The mean levels of LDL-C and ApoE were also 19% and 16% higher, respectively (P<0.05). No correlation was found between the pretreatment lipid profile and the histologic response to chemotherapy. An increase in Lp(a) was strongly correlated with the Ki-67 index (R=0.31, P=0.023). Moreover, a trend toward longer disease-free survival (DFS) was observed in patients with decreased TG and increased LDL-C following chemotherapy, although this difference was not statistically significant (P=0.23 and P=0.24, respectively). CONCLUSION: Significant elevations in serum lipids were observed after neoadjuvant chemotherapy in patients with localized osteosarcoma. There was no prognostic significance of pretreatment serum lipid levels on histologic response to neoadjuvant chemotherapy. The scale of increase in serum Lp(a) might have a potential prognostic role in osteosarcoma. Patients with increased LDL-C or reduced TG after chemotherapy seem to exhibit a trend toward favorable DFS.

EfficientTrain++: Generalized Curriculum Learning for Efficient Visual Backbone Training.

The superior performance of modern computer vision backbones (e.g., vision Transformers learned on ImageNet-1K/22K) usually comes with a costly training procedure. This study contributes to this issue by generalizing the idea of curriculum learning beyond its original formulation, i.e., training models using easier-to-harder data. Specifically, we reformulate the training curriculum as a soft-selection function, which uncovers progressively more difficult patterns within each example during training, instead of performing easier-to-harder sample selection. Our work is inspired by an intriguing observation on the learning dynamics of visual backbones: during the earlier stages of training, the model predominantly learns to recognize some 'easier-to-learn' discriminative patterns in the data. These patterns, when observed through frequency and spatial domains, incorporate lower-frequency components, and the natural image contents without distortion or data augmentation. Motivated by these findings, we propose a curriculum where the model always leverages all the training data at every learning stage, yet the exposure to the 'easier-to-learn' patterns of each example is initiated first, with harder patterns gradually introduced as training progresses. To implement this idea in a computationally efficient way, we introduce a cropping operation in the Fourier spectrum of the inputs, enabling the model to learn from only the lower-frequency components. Then we show that exposing the contents of natural images can be readily achieved by modulating the intensity of data augmentation. Finally, we integrate these two aspects and design curriculum learning schedules by proposing tailored searching algorithms. Moreover, we present useful techniques for deploying our approach efficiently in challenging practical scenarios, such as large-scale parallel training, and limited input/output or data pre-processing speed. The resulting method, EfficientTrain++, is simple, general, yet surprisingly effective. As an off-the-shelf approach, it reduces the training time of various popular models (e.g., ResNet, ConvNeXt, DeiT, PVT, Swin, CSWin, and CAFormer) by [Formula: see text] on ImageNet-1K/22K without sacrificing accuracy. It also demonstrates efficacy in self-supervised learning (e.g., MAE). Code is available at: https://github.com/LeapLabTHU/EfficientTrain.

Latency-aware Unified Dynamic Networks for Efficient Image Recognition.

Dynamic computation has emerged as a promising strategy to improve the inference efficiency of deep networks. It allows selective activation of various computing units, such as layers or convolution channels, or adaptive allocation of computation to highly informative spatial regions in image features, thus significantly reducing unnecessary computations conditioned on each input sample. However, the practical efficiency of dynamic models does not always correspond to theoretical outcomes. This discrepancy stems from three key challenges: 1) The absence of a unified formulation for various dynamic inference paradigms, owing to the fragmented research landscape; 2) The undue emphasis on algorithm design while neglecting scheduling strategies, which are critical for optimizing computational performance and resource utilization in CUDA-enabled GPU settings; and 3) The cumbersome process of evaluating practical latency, as most existing libraries are tailored for static operators. To address these issues, we introduce Latency-Aware Unified Dynamic Networks (LAUDNet), a comprehensive framework that amalgamates three cornerstone dynamic paradigms-spatially-adaptive computation, dynamic layer skipping, and dynamic channel skipping-under a unified formulation. To reconcile theoretical and practical efficiency, LAUDNet integrates algorithmic design with scheduling optimization, assisted by a latency predictor that accurately and efficiently gauges the inference latency of dynamic operators. This latency predictor harmonizes considerations of algorithms, scheduling strategies, and hardware attributes. We empirically validate various dynamic paradigms within the LAUDNet framework across a range of vision tasks, including image classification, object detection, and instance segmentation. Our experiments confirm that LAUDNet effectively narrows the gap between theoretical and real-world efficiency. For example, LAUDNet can reduce the practical latency of its static counterpart, ResNet-101, by over 50% on hardware platforms such as V100, RTX3090, and TX2 GPUs. Furthermore, LAUDNet surpasses competing methods in the trade-off between accuracy and efficiency. Code is available at: https://www.github.com/LeapLabTHU/LAUDNet.

Fine-Grained Recognition With Learnable Semantic Data Augmentation.

Fine-grained image recognition is a longstanding computer vision challenge that focuses on differentiating objects belonging to multiple subordinate categories within the same meta-category. Since images belonging to the same meta-category usually share similar visual appearances, mining discriminative visual cues is the key to distinguishing fine-grained categories. Although commonly used image-level data augmentation techniques have achieved great success in generic image classification problems, they are rarely applied in fine-grained scenarios, because their random editing-region behavior is prone to destroy the discriminative visual cues residing in the subtle regions. In this paper, we propose diversifying the training data at the feature-level to alleviate the discriminative region loss problem. Specifically, we produce diversified augmented samples by translating image features along semantically meaningful directions. The semantic directions are estimated with a covariance prediction network, which predicts a sample-wise covariance matrix to adapt to the large intra-class variation inherent in fine-grained images. Furthermore, the covariance prediction network is jointly optimized with the classification network in a meta-learning manner to alleviate the degenerate solution problem. Experiments on four competitive fine-grained recognition benchmarks (CUB-200-2011, Stanford Cars, FGVC Aircrafts, NABirds) demonstrate that our method significantly improves the generalization performance on several popular classification networks (e.g., ResNets, DenseNets, EfficientNets, RegNets and ViT). Combined with a recently proposed method, our semantic data augmentation approach achieves state-of-the-art performance on the CUB-200-2011 dataset. Source code is available at https://github.com/LeapLabTHU/LearnableISDA.

Multidimensional Refinement Graph Convolutional Network With Robust Decouple Loss for Fine-Grained Skeleton-Based Action Recognition.

Graph convolutional networks (GCNs) have been widely used in skeleton-based action recognition. However, existing approaches are limited in fine-grained action recognition due to the similarity of interclass data. Moreover, the noisy data from pose extraction increase the challenge of fine-grained recognition. In this work, we propose a flexible attention block called channel-variable spatial-temporal attention (CVSTA) to enhance the discriminative power of spatial-temporal joints and obtain a more compact intraclass feature distribution. Based on CVSTA, we construct a multidimensional refinement GCN (MDR-GCN) that can improve the discrimination among channel-, joint-, and frame-level features for fine-grained actions. Furthermore, we propose a robust decouple loss (RDL) that significantly boosts the effect of the CVSTA and reduces the impact of noise. The proposed method combining MDR-GCN with RDL outperforms the known state-of-the-art skeleton-based approaches on fine-grained datasets, FineGym99 and FSD-10, and also on the coarse NTU-RGB + D 120 dataset and NTU-RGB + D X-view version. Our code is publicly available at https://github.com/dingyn-Reno/MDR-GCN.

Probabilistic Contrastive Learning for Long-Tailed Visual Recognition.

Long-tailed distributions frequently emerge in real-world data, where a large number of minority categories contain a limited number of samples. Such imbalance issue considerably impairs the performance of standard supervised learning algorithms, which are mainly designed for balanced training sets. Recent investigations have revealed that supervised contrastive learning exhibits promising potential in alleviating the data imbalance. However, the performance of supervised contrastive learning is plagued by an inherent challenge: it necessitates sufficiently large batches of training data to construct contrastive pairs that cover all categories, yet this requirement is difficult to meet in the context of class-imbalanced data. To overcome this obstacle, we propose a novel probabilistic contrastive (ProCo) learning algorithm that estimates the data distribution of the samples from each class in the feature space, and samples contrastive pairs accordingly. In fact, estimating the distributions of all classes using features in a small batch, particularly for imbalanced data, is not feasible. Our key idea is to introduce a reasonable and simple assumption that the normalized features in contrastive learning follow a mixture of von Mises-Fisher (vMF) distributions on unit space, which brings two-fold benefits. First, the distribution parameters can be estimated using only the first sample moment, which can be efficiently computed in an online manner across different batches. Second, based on the estimated distribution, the vMF distribution allows us to sample an infinite number of contrastive pairs and derive a closed form of the expected contrastive loss for efficient optimization. Other than long-tailed problems, ProCo can be directly applied to semi-supervised learning by generating pseudo-labels for unlabeled data, which can subsequently be utilized to estimate the distribution of the samples inversely. Theoretically, we analyze the error bound of ProCo. Empirically, extensive experimental results on supervised/semi-supervised visual recognition and object detection tasks demonstrate that ProCo consistently outperforms existing methods across various datasets.

An Energy Matching Vessel Segmentation Framework in 3-D Medical Images.

Accurate vascular segmentation from High Resolution 3-Dimensional (HR3D) medical scans is crucial for clinicians to visualize complex vasculature and diagnose related vascular diseases. However, a reliable and scalable vessel segmentation framework for HR3D scans remains a challenge. In this work, we propose a High-resolution Energy-matching Segmentation (HrEmS) framework that utilizes deep learning to directly process the entire HR3D scan and segment the vasculature to the finest level. The HrEmS framework introduces two novel components. Firstly, it uses the real-order total variation operator to construct a new loss function that guides the segmentation network to obtain the correct topology structure by matching the energy of the predicted segment to the energy of the manual label. This is different from traditional loss functions such as dice loss, which matches the pixels between predicted segment and manual label. Secondly, a curvature-based weight-correction module is developed, which directs the network to focus on crucial and complex structural parts of the vasculature instead of the easy parts. The proposed HrEmS framework was tested on three in-house multi-center datasets and three public datasets, and demonstrated improved results in comparison with the state-of-the-art methods using both topology-relevant and volumetric-relevant metrics. Furthermore, a double-blind assessment by three experienced radiologists on the critical points of the clinical diagnostic processes provided additional evidence of the superiority of the HrEmS framework.

[Influence of Land Use Structure and Spatial Pattern on Water Quality of Small and Medium-sized Rivers in Poyang Lake Basin].

To reveal the influence mechanism of land use structure and spatial pattern on water quality of small and medium-sized rivers, water samples were collected from 25 sampling points in three small and medium-sized rivers of the Poyang Lake Basin in January 2022 and July 2022. Bioenv analysis, the Mantel test, and variance partitioning analysis were used to quantify the effects of land use structure and spatial patterns on water quality at different spatial scales; generalized additive models were used to fit the relationship between water quality and different land use structures and spatial patterns; and a generalized linear model was used to construct segmented regression models and calculate the thresholds based on the stepwise recursive method. The results showed that:① the average interpretation rate of land use structure and spatial pattern on river water quality was 59.72% during the wet period and 48.95% during the dry period. The sub-basin and riparian 100 m scales were the key scales of land use structure and spatial pattern affecting water quality in small and medium-sized rivers, with an average explanation rate of 54.70% and 64.88%, respectively. The joint explanation of land use structure and spatial pattern was an important factor driving the change in river water quality, accounting for 66.90% of the total explanation. ② The impact of land use structure on the water quality of small and medium-sized rivers had a significant threshold effect. When the proportion of construction land was less than 2%, farmland was less than 8%, or forest land was more than 82% at the sub-basin scale and the proportion of construction land was less than 12%, farmland was less than 41%, or forest land was more than 49% at the riparian buffer scale, all could significantly improve water quality. ③ The effect of spatial pattern on water quality in small and medium-sized rivers also had a threshold effect but was weaker than that of land use structure. A patch shape value more than 28.77 or patch diversity more than 0.69 at the sub-basin scale and a patch shape value more than 2.99 or patch diversity more than 1.02 at the riparian buffer scale could improve water quality. The above results showed that strengthening the management of land use at the sub-basin and riparian 100 m scales and setting a reasonable threshold of land use structure and spatial pattern can effectively prevent water quality from deteriorating.

Domain Adaptation via Prompt Learning.

Unsupervised domain adaptation (UDA) aims to adapt models learned from a well-annotated source domain to a target domain, where only unlabeled samples are given. Current UDA approaches learn domain-invariant features by aligning source and target feature spaces through statistical discrepancy minimization or adversarial training. However, these constraints could lead to the distortion of semantic feature structures and loss of class discriminability. In this article, we introduce a novel prompt learning paradigm for UDA, named domain adaptation via prompt learning (DAPrompt). In contrast to prior works, our approach learns the underlying label distribution for target domain rather than aligning domains. The main idea is to embed domain information into prompts, a form of representation generated from natural language, which is then used to perform classification. This domain information is shared only by images from the same domain, thereby dynamically adapting the classifier according to each domain. By adopting this paradigm, we show that our model not only outperforms previous methods on several cross-domain benchmarks but also is very efficient to train and easy to implement.

Hundreds Guide Millions: Adaptive Offline Reinforcement Learning With Expert Guidance.

Offline reinforcement learning (RL) optimizes the policy on a previously collected dataset without any interactions with the environment, yet usually suffers from the distributional shift problem. To mitigate this issue, a typical solution is to impose a policy constraint on a policy improvement objective. However, existing methods generally adopt a "one-size-fits-all" practice, i.e., keeping only a single improvement-constraint balance for all the samples in a mini-batch or even the entire offline dataset. In this work, we argue that different samples should be treated with different policy constraint intensities. Based on this idea, a novel plug-in approach named guided offline RL (GORL) is proposed. GORL employs a guiding network, along with only a few expert demonstrations, to adaptively determine the relative importance of the policy improvement and policy constraint for every sample. We theoretically prove that the guidance provided by our method is rational and near-optimal. Extensive experiments on various environments suggest that GORL can be easily installed on most offline RL algorithms with statistically significant performance improvements.

A review on nondiabetic hypoglycemia from various causes: Case series report.

RATIONALE: Hypoglycemia is common in patients with glucose regulation disorders and related diabetic treatments but is rare in nondiabetic patients. Severe hypoglycemia can cause harm to patients' cognition, consciousness, central nervous system, cardiovascular and cerebrovascular system, and even death. However, the most fundamental way to control hypoglycemia is to identify the cause and deal with the primary disease. This article introduces 3 cases of nondiabetic hypoglycemia with different causes, aiming to improve our understanding of nondiabetic hypoglycemia and improve the ability of early diagnosis and differential diagnosis. PATIENT CONCERNS: Case 1 is a 19-year-old female with a history of recurrent coma, and magnetic resonance imaging and endoscopic ultrasound of the pancreas suggest insulinoma. Case 2 is a 74-year-old male with a history of viral hepatitis, and computerized tomography shows multiple nodules in the liver, which is diagnosed as liver cancer. Case 3 is a 39-year-old female with a history of taking methimazole, who tested positive for insulin antibodies, and was diagnosed with insulin autoimmune syndrome. DIAGNOSIS: All 3 patients were diagnosed with nondiabetic hypoglycemia, but the causes varied, and included insulinoma, non-islet cell tumor-induced hypoglycemia, and insulin autoimmune syndrome. INTERVENTIONS: Case 1 underwent pancreatic tail resection; case 2 refused anti-tumor treatment and received glucose injections for palliative treatment only; and case 3 stopped taking methimazole. OUTCOMES: After surgery, the blood sugar in case 1 returned to normal, and the blood sugar in case 2 was maintained at about 6.0 mmol/L. The symptoms of hypoglycemia gradually improved in case 3 after stopping the medication. LESSONS: Non-diabetic hypoglycemia requires further examination to clarify the cause, and the correct differential diagnosis can provide timely and effective treatment, improving the patient's prognosis.

Application of negative pressure wound therapy after skin grafting in the treatment of skin cancer: A case report.

BACKGROUND: Skin cancer is a common malignant tumor in dermatology. A large area must be excised to ensure a negative incisal margin on huge frontotemporal skin cancer, and it is difficult to treat the wound. In the past, treatment with skin grafting and pressure dressing was easy to cause complications such as wound infections, subcutaneous effusion, skin necrosis, and contracture. Negative pressure wound therapy (NPWT) has been applied to treat huge frontotemporal skin cancer. CASE SUMMARY: Herein, we report the case of a 92-year-old woman with huge frontotemporal skin cancer. The patient presented to the surgery department complaining of ruptured bleeding and pain in a right frontal mass. The tumor was pathologically diagnosed as highly differentiated squamous cell carcinoma. The patient underwent skin cancer surgery and skin grafting, after which NPWT was used. She did not experience a relapse during the three-year follow-up period. CONCLUSION: NPWT is of great clinical value in the postoperative treatment of skin cancer. It is not only inexpensive but also can effectively reduce the risk of surgical effusion, infection, and flap necrosis.

[Community Composition, Symbiotic Patterns, and Influencing Factors of Planktonic Fungi in Urban Lakes of Nanchang].

To investigate the characteristics of planktonic fungal communities in Nanchang lakes and the mechanism of environmental stress on planktonic fungal communities, surface water samples were collected from seven major urban lakes evenly distributed in different county-level districts of Nanchang in the dry (February and December), normal (April and October), and wet (June and August) seasons, respectively. The environmental stressors such as WT, DO, NH4+-N, and NO3--N were measured; the characteristics of planktonic fungal communities were studied using high-throughput sequencing; the symbiotic patterns of planktonic fungal communities were elucidated using network analysis and other methods; and the environmental stressors affecting the structure and symbiotic patterns of planktonic fungal communities were revealed. The results showed that ① the planktonic fungal community composition in lakes of Nanchang varied significantly among seasons but not significantly among the lakes. WT, DO, pH, and NH4+-N were the significant environmental stressors affecting the planktonic fungal community composition. ② The dominant phyla of the planktonic fungal community were Chytridiomycota (9.55%-33.14%), Basidiomycota (0.48%-4.25%), and Ascomycota (1.29%-3.19%), and the sizes of the dominant phyla were in the following order:wet season>normal season>dry season. The relative abundance of Chytridiomycota was significantly higher in the wet season than that in the normal season and the dry season, the relative abundance of Basidiomycota was significantly lower in the dry season than that in the normal and wet seasons, and the difference in Ascomycota among seasons was not significant. ③ The stability size of the planktonic fungal community symbiosis network in lakes of Nanchang was in the following order:wet season>normal season>dry season. WT was the best environmental stressor affecting the planktonic fungal community symbiosis pattern. The study can provide theoretical basis for the comprehensive evaluation and management study of the lake and provide guidance for protecting the lake ecosystem in the middle and lower reaches of the Yangtze River.

SePiCo: Semantic-Guided Pixel Contrast for Domain Adaptive Semantic Segmentation.

Domain adaptive semantic segmentation attempts to make satisfactory dense predictions on an unlabeled target domain by utilizing the supervised model trained on a labeled source domain. One popular solution is self-training, which retrains the model with pseudo labels on target instances. Plenty of approaches tend to alleviate noisy pseudo labels, however, they ignore the intrinsic connection of the training data, i.e., intra-class compactness and inter-class dispersion between pixel representations across and within domains. In consequence, they struggle to handle cross-domain semantic variations and fail to build a well-structured embedding space, leading to less discrimination and poor generalization. In this work, we propose emantic-Guided Pixel Contrast (SePiCo), a novel one-stage adaptation framework that highlights the semantic concepts of individual pixels to promote learning of class-discriminative and class-balanced pixel representations across domains, eventually boosting the performance of self-training methods. Specifically, to explore proper semantic concepts, we first investigate a centroid-aware pixel contrast that employs the category centroids of the entire source domain or a single source image to guide the learning of discriminative features. Considering the possible lack of category diversity in semantic concepts, we then blaze a trail of distributional perspective to involve a sufficient quantity of instances, namely distribution-aware pixel contrast, in which we approximate the true distribution of each semantic category from the statistics of labeled source data. Moreover, such an optimization objective can derive a closed-form upper bound by implicitly involving an infinite number of (dis)similar pairs, making it computationally efficient. Extensive experiments show that SePiCo not only helps stabilize training but also yields discriminative representations, making significant progress on both synthetic-to-real and daytime-to-nighttime adaptation scenarios. The code and models are available at https://github.com/BIT-DA/SePiCo.

Nomogram predicts risk of perineural invasion based on serum biomarkers for pancreatic cancer.

BACKGROUND: Pancreatic cancer is a fatal tumor, and the status of perineural invasion (PNI) of pancreatic cancer was positively related to poor prognosis including overall survival and recurrence-free survival. This study aims to develop and validate a predictive model based on serum biomarkers to accurately predict the perineural invasion. MATERIALS AND METHODS: The patients from No.924 Hospital of PLA Joint Logistic Support Force were included. The predictive model was developed in the training cohort using logistic regression analysis, and then tested in the validation cohort. The area under curve (AUC), calibration curves and decision curve analysis were used to validate the predictive accuracy and clinical benefits of nomogram. RESULTS: A nomogram was developed using preoperative total bilirubin, preoperative blood glucose, preoperative CA19-9. It achieved good AUC values of 0.753 and 0.737 in predicting PNI in training and validation cohorts, respectively. Calibration curves showed nomogram had good uniformity of the practical probability of PNI. Decision curve analyses revealed that the nomogram provided higher diagnostic accuracy and superior net benefit compared to single indicators. CONCLUSION: The present study constructed and validate a novel nomogram predicted the PNI of resectable PHAC patients with high stability and accuracy. Besides, it could better screen high-risk probability of PNI in these patients, and optimize treatment decision-making.

Ethnicity-specific association between TERT rs2736100 (A > C) polymorphism and lung cancer risk: a comprehensive meta-analysis.

The rs2736100 (A > C) polymorphism of the second intron of Telomerase reverse transcriptase (TERT) has been confirmed to be closely associated with the risk of Lung cancer (LC), but there is still no unified conclusion on the results of its association with LC. This study included Genome-wide association studies (GWAS) and case-control studies reported so far on this association between TERT rs2736100 polymorphism and LC to clarify such a correlation with LC and the differences in it between different ethnicities and different types of LC. Relevant literatures published before May 7, 2022 on 'TERT rs2736100 polymorphism and LC susceptibility' in PubMed, EMbase, CENTRAL, MEDLINE databases were searched through the Internet, and data were extracted. Statistical analysis of data was performed in Revman5.3 software, including drawing forest diagrams, drawing funnel diagrams and so on. Sensitivity and publication bias analysis were performed in Stata 12.0 software. The C allele of TERT rs2736100 was associated with the risk of LC (Overall population: [OR] = 1.21, 95%CI [1.17, 1.25]; Caucasians: [OR] = 1.11, 95%CI [1.06, 1.17]; Asians: [OR] = 1.26, 95%CI [1.21, 1.30]), and Asians had a higher risk of LC than Caucasians (C vs. A: Caucasians: [OR] = 1.11 /Asians: [OR]) = 1.26). The other gene models also showed similar results. The results of stratified analysis of LC patients showed that the C allele was associated with the risk of Non-small-cell lung carcinoma (NSCLC) and Lung adenocarcinoma (LUAD), and the risk of NSCLC and LUAD in Asians was higher than that in Caucasians. The C allele was associated with the risk of Lung squamous cell carcinoma (LUSC) and Small cell lung carcinoma(SCLC) in Asians but not in Caucasians. NSCLC patients ([OR] = 1.27) had a stronger correlation than SCLC patients ([OR] = 1.03), and LUAD patients ([OR] = 1.32) had a stronger correlation than LUSC patients ([OR] = 1.09).In addition, the C allele of TERT rs2736100 was associated with the risk of LC, NSCLC and LUAD in both smoking groups and non-smoking groups, and the risk of LC in non-smokers of different ethnic groups was higher than that in smokers. In the Asians, non-smoking women were more at risk of developing LUAD. The C allele of TERT rs2736100 is a risk factor for LC, NSCLC, and LUAD in different ethnic groups, and the Asian population is at a more common risk. The C allele is a risk factor for LUSC and SCLC in Asians but not in Caucasians. And smoking is not the most critical factor that causes variation in TERT rs2736100 to increase the risk of most LC (NSCLC, LUAD). Therefore, LC is a multi-etiological disease caused by a combination of genetic, environmental and lifestyle factors.

Detection of Cd2+ based on Nano-Fe3O4/MoS2/Nafion/GCE sensor.

It is necessary to detect cadmium ions in seawater with high sensitivity because the pollution of cadmium ions seriously endangers the health and life of human beings. Nano-Fe3O4/MoS2/Nafion modified glassy carbon electrode was prepared by a drop coating method. The electrocatalytic properties of Nano-Fe3O4/MoS2/Nafion were measured by Cyclic Voltammetry (CV). Differential Pulse Voltammetry (DPV) was used to study the stripping Voltammetry response of the modified electrode to Cd2+. The optimal conditions were determined: In 0.1 mol/L HAc-NaAc solution, the solution pH was 4.2, the deposition potential was - 1.0 V, and the deposition time was 720 s, the membrane thickness was 8 µL. Under the optimum condition, the linear relation of Cd2+ concentration was found in the range of 5-300 µg/L, and the detection limit was 0.053 µg/L. The recovery of Cd2+ in seawater ranged from 99.2 to 102.9%. A composite material with simple operation, rapid response and high sensitivity was constructed for the determination of Cd2+ in seawater.

Glucocorticoids promote lung metastasis of pancreatic cancer cells through enhancing cell adhesion, migration and invasion.

Glucocorticoids (GCs) are the important stress hormones and widely prescribed as drugs. Although stress has been suggested as a promoter of tumor progression, the direct influence of GCs on metastasis of tumor is not fully understood. Metastasis is a major cause of death in pancreatic cancer patients. In the present study, we investigated the effect of GCs on progression of pancreatic cancer and elucidated the underlying mechanism. It was found that GCs significantly promote cell adhesion, migration, and invasion of pancreatic cancer cells in vitro and their lung metastasis in vivo. Further mechanistic studies showed that GCs notably up-regulate the expression of a trans-membrane glycoprotein, mucin 1 (MUC1) and increase the activation of AKT. Inhibiting MUC1 expression not only attenuates the activation of AKT, but also significantly reduces the promoting effects of GCs on cell adhesion, migration, invasion, and lung metastasis of pancreatic cancer cells. Moreover, GCs not only significantly up-regulate expression of Rho-associated kinase 1/2 (ROCK1/2) and matrix metalloproteinase 3 and 7 (MMP3/7), but also activate ROCK2, which are also involved in the pro-migratory and pro-invasive effects of GCs in pancreatic cancer cells. Taken together, our findings reveal that GCs promote metastasis of pancreatic cancer cells through complex mechanism. MUC1-PI3K/AKT pathway, ROCK1/2 and MMP3/7 are involved in the promoting effect of GCs on cell migration, invasion and metastasis in pancreatic cancer cells. These results suggest the importance of reducing stress and GCs administration in patients with pancreatic cancer to avoid an increased risk of cancer metastasis.

Novel characteristics for immunophenotype, FISH pattern and molecular cytogenetics in synovial sarcoma.

As a rare and highly aggressive soft tissue sarcoma, the new immunophenotype, atypical FISH pattern and relevant molecular cytogenetics of synovial sarcoma (SS) remain less known, although it is characteristically represented by a pathognomonic chromosomal translocation t (X; 18) (p11.2; q11.2). Methodologically, the morphology was retrospectively analysed by using H&E staining, and immunohistochemical features were investigated by using markers that have been recently applied in other soft tissue tumors. Moreover, FISH signals for SS18 and EWSR-1 break-apart probes were examined. Finally, cytogenetic characteristics were analysed via RT-PCR and Sanger sequencing. Consequently, nine out of thirteen cases that were histologically highly suspected as SS were finally identified as SS via molecular analysis. Histologically, nine SS cases were divided into monophasic fibrous SS (4/9), biphasic SS (4/9) and poorly differentiated SS (1/9). Immunohistochemically, SOX-2 immunostaining was positive in eight cases (8/9) and PAX-7 immunostaining was diffusely positive in the epithelial component of biphasic SS (4/4). Nine cases showed negative immunostaining for NKX3.1 and reduced or absent immunostaining for INI-1. Eight cases showed typically positive FISH signalling for the SS18 break-apart probe, whereas one case exhibited an atypical FISH pattern (complete loss of green signalling, case 2). Furthermore, the SS18-SSX1 and SS18-SSX2 fusion genes were identified in seven cases and two cases, respectively. The fusion site in 8 out of 9 cases was common in the literature, whereas the fusion site in case 2 was involved in exon 10 codon 404 in SS18 and exon 7 codon 119 in SSX1 (which has not been previously reported), which notably corresponded to the complete loss of green signalling in the FISH pattern. Additionally, FISH analysis of the EWSR-1 gene in nine SS cases demonstrated aberrant signalling in three cases that were recognized as a monoallelic loss of EWSR-1 (1/9), an amplification of EWSR-1 (1/9) and a translocation of EWSR-1 (1/9). In conclusion, SS18-SSX fusion gene sequencing is obligatory for a precise diagnosis of SS when dealing with a confusing immunophenotype and atypical or aberrant FISH signalling for SS18 and EWSR-1 detection.

ncRNA-mediated upregulation of FAM83A is associated with poor prognosis and immune infiltration in pancreatic cancer.

Introduction: Malignant pancreatic cancer has poor long-term survival. Increasing evidence shows that FAM83A (family with sequence similarity 83 member A) plays a vital role in tumorigenesis and malignant progression in some human cancer types. The present study explored the potential mechanism of FAM83A in improving the prognosis of pancreatic cancer patients. Methods: Transcriptomic and clinical data from patients were obtained from The Cancer Genome Atlas while FAM83A expression was measured in tumorous pancreatic tissue compared with normal controls by quantitative real-time PCR and immunohistochemistry. Results: FAM83A is a vital prognostic indicator and potential oncogene in pancreatic cancer via pan-cancer analysis. In silico analysis revealed that AL049555.1/hsa-miR-129-5p axis was the pivotal upstream ncRNA- mediated pathway of FAM83A in pancreatic cancer. Furthermore, FAM83A expression was related to immune cell infiltration through vital immune-related genes including programmed cell death 1 (PDCD1), and tumorigenesis through common mutation genes including KRAS protooncogene GTPase (KRAS), and SMAD family member 4 (SMAD4). In summary, ncRNA-mediated upregulation of FAM83A is associated with poor long-term survival and immune cell infiltration in pancreatic cancer. Discussion: FAM83A may be used as a novel survival-related and immune-related biomarker. This information suggests that FAM83A may be a novel therapeutic target for combined or individual treatment for patients with pancreatic cancer.