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With the aggressive and amazing scaling of the feature size of semiconductors, hotspot detection has become a crucial and challenging problem in the generation of optimized mask design for better printability. Machine learning techniques, especially deep learning, have attained notable success on hotspot detection tasks. However, most existing hotspot detectors suffer from suboptimal performance due to two-stage flow and less efficient representations of layout features. What is more, most works can only solve simple benchmarks with apparent hotspot patterns like ICCAD 2012 Contest benchmarks. In this paper, we firstly develop a new end-to-end hotspot detection flow where layout feature embedding and hotspot detection are jointly performed. An attention mechanism-based deep convolutional neural network is exploited as the backbone to learn embeddings for layout features and classify the hotspots simultaneously. Experimental results demonstrate that our framework achieves accuracy improvement over prior arts with fewer false alarms and faster inference speed on much more challenging benchmarks.

As the circuit feature size continuously shrinks down, hotspot detection has become a more challenging problem in modern DFM flows. Developed deep learning techniques have recently shown their superiorities on hotspot detection tasks. However, existing hotspot detectors can only handle defect detection from one small layout clip each time, thus may be very time-consuming when dealing with a large full-chip layout. In this paper, we develop a new end-to-end framework that can detect multiple hotspots in a large region at a time and promise a better hotspot detection performance. We design a joint auto-encoder and inception module for efficient feature extraction. A two-stage classification and regression framework is designed to detect hotspot with progressive accurate localization, which provides a promising performance improvement. Experimental results show that our framework enables a significant speed improvement over existing methods with higher accuracy and fewer false alarms.

Layout hotspot detection is of great importance in the physical verification flow. Deep neural network models have been applied to hotspot detection and achieved great successes. The layouts can be viewed as binary images. The binarized neural network (BNN) can thus be suitable for the hotspot detection problem. In this article, we propose a new deep learning architecture based on BNNs to speed up the neural networks in hotspot detection. A new binarized residual neural network is carefully designed for hotspot detection. Experimental results on ICCAD 2012 and 2019 benchmarks show that our architecture outperforms previous hotspot detectors in detecting accuracy and has an $8\times $ speedup over the best deep learning-based solution. Since the BNN-based model is quite computationally efficient, a good tradeoff can be achieved between the efficiency and performance of the hotspot detector by applying ensemble learning approaches. Experimental results show that the ensemble models achieve better hotspot detection performance than the original with acceptable speed loss.

Chronic pain is considered an economic burden on society as it often results in disability, job loss, and early retirement. Opioids are the most common analgesics prescribed for the management of moderate to severe pain. However, chronic exposure to these drugs can result in opioid tolerance and opioid-induced hyperalgesia. On pain modulation strategies, exploiting the multitarget drugs with the ability of the superadditive or synergistic interactions attracts more attention. In the present report, we have reviewed the analgesic effects of different dopamine receptors, particularly D1 and D2 receptors, in different regions of the central nervous system, including the spinal cord, striatum, nucleus accumbens (NAc), and periaqueductal gray (PAG). According to the evidence, these regions are not only involved in pain modulation but also express a high density of DA receptors. The findings can be categorized as follows: (1) D2-like receptors may exert a higher analgesic potency, but D1-like receptors act in different manners across several mechanisms in the mentioned regions; (2) in the spinal cord and striatum, antinociception of DA is mainly mediated by D2-like receptors, while in the NAc and PAG, both D1- and D2-like receptors are involved as analgesic targets; and (3) D2-like receptor agonists can act as adjuvants of μ-opioid receptor agonists to potentiate analgesic effects and provide a better approach to pain relief.

Cracking is a common form of damage in reinforced-concrete (RC) beams. Cracks affect the frequency and deflection of RC beams. Although a model for the natural frequencies of RC beams with one or two flexural cracks has been proposed, a study of the effect of numerous initial flexural cracks on RC beams is still lacking. In the present study, the frequency and deflection of a 60-year-old RC box beam with four initial flexural cracks were tested, and the natural frequency of the RC box beam was theoretically analysed using Rayleigh's method. Theoretical and test results showed that: (a) the natural frequency and stiffness of the RC box beam with four initial flexural cracks decreased by 39·0 and 62·8%, respectively; (b) the model for the natural frequency of the RC box beam with N initial flexural cracks was proposed based on the theoretical and test results; (c) the deflections of the RC box beam with four initial flexural cracks obtained from Branson's method and Eurocode 2 were both close to those from the test; and (d) Branson's method for calculating the deflection of beams with four initial flexural cracks was conservative.

The thermally activated delayed fluorescence (TADF) emitters based on donor-acceptor (D-A) configuration were continuously developed in the past few years, whereas an unsymmetrical TADF emitter with A-D-A' configuration has never been reported. Herein, an A-D-A' type TADF emitter of TRZ-SBA-NAI was firstly developed by simultaneously integrating 2-phenyl-1H-benzo[de]isoquinoline-1,3(2H)-dione and 2,4,6-triphenyl-1,3,5-triazine acceptors into a spirobiacridine donor core. Due to the coexistence of double charge-transfer excited states, TRZ-SBA-NAI displayed dual emission containing a dominant orange-red emission and an anti-Kasha's rule sky-blue emission shoulder in solution. As doped into the host matrix, TRZ-SBA-NAI only exhibited an orange-red emission, together with a high photoluminescence quantum yield of 87%. The linear molecular shape imparted TRZ-SBA-NAI with a high horizontal dipole ratio of 88%. As a result, the TRZ-SBA-NAI based devices achieved a record-high external quantum efficiency of 31.7% with an electroluminescence peak at 593 nm. This finding not only enriches the diversity in TADF molecular design, but also unlocks the huge potential of A-D-A' type TADF emitters for excellent device performance.

Scanning electron microscope (SEM) metrology is critical in semiconductor manufacturing for patterning process quality assessment and monitoring. Besides feature width and feature-feature space dimension measurements from critical dimension SEM (CDSEM) images, visual inspection of SEM image also offers rich information on the quality of patterning. However, visual inspection alone leaves considerable room of ambiguity regarding patterning quality. To narrow the room of ambiguity and to obtain more statistically quantitative information on patterning quality, SEM-image contours are often extracted to serve such purposes. From contours, important information such as critical dimension and resist sidewall angle at any location can be estimated. Those geometrical information can be used for optical proximity correction (OPC) model verification and lithography hotspot detection, etc. Classical contour extraction algorithms based on local information have insufficient capability in dealing with noisy and low contrast images. To achieve reliable contours from noisy and low contrast images, information beyond local should be made use of as much as possible. In this regard, deep convolutional neural network (DCNN) has proven its great capability, as manifested in various computer vision tasks. Taking the full advantages of this maturing technology, we have designed a DCNN network and applied it to the task of extracting contours from noisy and low contrast SEM images. It turns out that the model is capable of separating the resist top and bottom contours reliably. In addition, the model does not generate false contours, it also can suppress the generation of broken contours when ambiguous area for contour extraction is small and non-detrimental. With advanced image alignment algorithm with sub-pixel accuracy, contours from different exposure fields of same process condition can be superposed to estimate process variation band, furthermore, stochastic effect induced edge placement variation statistics can easily be inferred from the extracted contours.

How to control the dipole orientation of organic emitters is a challenge in the field of organic light-emitting diodes (OLEDs). Herein, a linear thermally activated delayed fluorescence (TADF) molecule, PhNAI-PMSBA, bearing a 1,8-naphthalimide-acridine framework was designed by a double-site long-axis extension strategy to actively control the dipole orientation. The horizontal ratio of emitting dipole orientation of PhNAI-PMSBA reaches 95%, substantially higher than that of isotropic emitters (67%). This unique feature is associated with the intrinsically horizontal molecular orientation of PhNAI-PMSBA and the good agreement between its transition dipole moment direction and molecular long axis. The PhNAI-PMSBA-based OLED achieves an ultrahigh optical outcoupling efficiency of 43.2% and thus affords one of the highest red electroluminescence with an external quantum efficiency of 22.3% and the Commission International de l’Eclairage 1931 coordinates at around (0.60, 0.40).

BACKGROUND Accurate detection of anaplastic lymphoma kinase (ALK) rearrangement is the prerequisite for anti-ALK therapy for the patient with non-small cell lung cancer (NSCLC). Fusion-induced asymmetric transcription assay (FIATA)-based reverse transcription droplet digital PCR (RT-ddPCR) was developed and performed for ALK status survey in NSCLC samples. METHODS A total of 269 cases of formalin-fixed paraffin-embedded (FFPE) specimens from NSCLC, in which ALK status was confirmed by both fluorescence in situ hybridization (FISH) and immunohistochemistry (IHC), were analyzed by FIATA-based RT-ddPCR. RESULTS In the ALK-positive group, the 3' ALK transcript copies range was 336.6-107 955.4, and the R3 [(the ratio of the 3' ALK transcript copy numbers to the internal reference gene transcript copy numbers) × 100] was 17.23-672.77. In the ALK-negative group, the 3' ALK transcript copies range was 3.7-1370.6, and the R3 range was 0.10-15.57. The lowest R3 level in the ALK-positive group was significantly higher than the highest R3 level in the ALK-negative group. A positive correlation between the proportion of cancer cells in the tissue section and ALK RNA expression level (R3) was found (P

Flavonoids are an important class of phytopharmaceuticals in plants. Naringin (naringenin- 7-O-rhamnoglucoside) is a flavanone glycoside isolated from folk herbal medicine Exocarpium Citri grandis (called Huajuhong in Chinese). Massive experimental works have been performed on naringin describing its phytochemical, pharmacokinetic, and bioactive properties. Naringin was found to possess multiple pharmacological activities in relieving inflammation, diabetes, neurodegeneration, cardiovascular disorders, and metabolic syndrome. Recently, it has been approved as a potential antitussive and expectorant for clinical trials. However, the pharmacokinetic aspects of naringin and its therapeutic potentials in respiratory diseases have not been comprehensively reviewed. The present review provides highlights of naringin with respect to its absorption, distribution, metabolism, excretion and its therapeutic effects on cough, phlegm, and pulmonary inflammation. This review would be helpful for the interpretation of pharmacokinetics and pharmacodynamics of naringin in clinical trials.

Objectives Non-Hodgkin’s lymphoma (NHL) primarily derived from the base of the tongue, is rare. Human papillomavirus (HPV) and Epstein-Barr virus (EBV) are important aetiological risk factors for tumours of the head and neck. This study describes the clinicopathological features of NHL in the tongue base and the status of HPV and EBV in these cases. Methods Seven cases were identified from the Pathological Registry Database at Peking Union Medical College Hospital (PUMCH). The study utilized immunochemistry, in situ hybridization (ISH), and gene rearrangement to confirm the disease and and performed a clinical follow up for each case. Results All 7 lymphomas were localized at the base of the tongue. Six of the cases exhibited tongue base masses with smooth surface membranes. One case presented as multiple deep ulcers. The most common histologic subtype was diffuse large B-cell lymphoma (DLBCL), which occurred in five cases. The other two cases were mantle cell lymphoma (MCL) and peripheral T cell lymphoma, not otherwise specified (PTCL, NOS). One of the DLBCL cases was positive for HPV DNA and diffusely expressed P16 protein. During the follow up period, the MCL patient and an elderly DLBCL patient died. The remaining five patients were alive through the end of follow up. Conclusions Most lymphomas of the tongue base manifest as an endogenous mass without membranous change. The most common subtype of NHLs of the tongue base is DLBCL, and the occurrence at this site may have a good prognosis. With proper therapy, even late stage tongue base lymphomas can be suppressed and remain in remission.

The unsatisfactory response rate of immune checkpoint blockade (ICB) immunotherapy severely limits its clinical application as a tumor therapy. Here, we generate a vaccine-based nanosystem by integrating siRNA for Cd274 into the commercial human papillomavirus (HPV) L1 (HPV16 L1) protein. This nanosystem has good biosafety and enhances the therapeutic response rate of anti-tumor immunotherapy. The HPV16 L1 protein activates innate immunity through the type I interferon pathway and exhibits an efficient anti-cancer effect when cooperating with ICB therapy. For both resectable and unresectable breast tumors, the nanosystem decreases 71% tumor recurrence and extends progression-free survival by 67%. Most importantly, the nanosystem successfully induces high response rates in various genetically modified breast cancer models with different antigen loads. The strong immune stimulation elicited by this vaccine-based nanosystem might constitute an approach to significantly improve current ICB immunotherapy., Immune checkpoint blockade (ICB) immunotherapy efficacy has still limitations. Here, the authors generate a vaccine that integrates CD274 siRNA into the L1 protein of human papillomavirus, which cooperates with ICB by activating innate immunity in breast cancer models.

Naoxintong Capsule (NXT), a Chinese medicine, has been widely used for the treatment of cardiovascular diseases in clinic. The present study was designed to clarify the effects of NXT on haemorheology, blood coagulation, hepatic function, antioxidant capacity and inflammatory responses in a rat model of blood stasis syndrome. Rats were placed in ice-cold water for 5 min during the time interval of two adrenaline injections to induce blood circulation disorders after intragastric administration of 10 consecutive days. Blood was collected from abdominal aorta to determine whole blood viscosity (WBV), plasma viscosity (PV), activated partial thromboplastin time (APTT), prothrombin time (PT), fibrinogen (FIB), total antioxidant capacity (T-AOC), superoxide dismutase (SOD), malonaldehyde (MDA), alanine transaminase (ALT), aspartate transaminase (AST), interleukin-1β (IL-1β), interleukin-6 (IL-6), platelet activating factor (PAF). The results showed that NXT could significantly decrease the WBV, PV, FIB and prolong the APTT and PT. Meanwhile, NXT significantly decreased the serum contents of MDA, IL-1β, IL-6, ALT, AST and PAF, while elevating T-AOC and SOD levels. These results indicate that NXT could improve coagulation function, enhance antioxidant capacity and suppress inflammation, which might partly account for its mechanisms of action in preventing and treating cardiovascular diseases.

Introduction LncRNA CCAT1 promotes inflammatory responses, which contribute to tuberculosis. Therefore, CCAT1 may participate in tuberculosis. Therefore, we analyzed the involvement of CCAT1 in tuberculosis. Methods Plasma samples were donated by a total of 200 patients with newly developed tuberculosis (N‐TB), 102 patients with recurrent tuberculosis (R‐TB), and 102 healthy controls on the day of admission. Plasma samples were also collected from N‐TB and R‐TB patients every month after the initiation of treatment for a total of 6 months. CCAT1 expression in these samples was detected by quantitative reverse transcription polymerase chain reaction. Levels of IFN‐γ, IL‐1β, iNOS, TNF‐α, and IL‐10 in plasma were determined by enzyme‐linked immunosorbent assay. N‐TB and R‐TB patients were monitored for 2 months to analyze their survival. Results On the day of admission, the highest levels of CCAT1, IFN‐γ, IL‐1β, iNOS, and TNF‐α were detected in N‐TB patients, followed by R‐TB patients and controls, while the lowest levels of plasma IL‐10 were detected in N‐TB patients, followed by R‐TB patients and controls. Across R‐TB and N‐TB patients, CCAT1 was inversely correlated with IL‐10 but not closely correlated with other inflammatory factors. During the treatment, plasma CCAT1 levels decreased in both N‐TB and R‐TB patients. High CCAT1 levels were closely correlated with high mortality rates of both N‐TB and R‐TB patients. Conclusion CCAT1 is overexpressed in tuberculosis patients and predicts their survival. Its function in tuberculosis may be related to IL‐10.

In the past decade, the sparsity prior of image is investigated and utilized widely as the development of compressed sensing theory. The dictionary learning combined with the convex optimization methods promotes the sparse representation to be one of the state-of-the-art techniques in image processing, such as denoising, super-resolution, deblurring, and inpainting. Empirically, the sparser of image representation, the better of image restoration. In this work, the non-local clustering sparse representation is applied with optimized matching strategies of self-similar patches, which break through the bottleneck of search window (localization) and improve the estimation effect of the sparse coefficient. The experimental results show that the proposed method provides an effective suppression on noise, preserves more details of image and presents more comfortable visual experience.

Knee osteoarthritis (OA) is a chronic disease that considerably reduces patients' life quality. Preventive therapies require early detection and lifetime monitor of OA progression. In the clinical environment, the severity of OA is classified by Kellgren and Lawrence (KL) grading system, ranging from KL-0 to KL-4. Recently, deep learning methods were applied to OA severity assessment to improve the accuracy and efficiency. Researchers fine-tuned convolution neural networks (CNN) on the OA dataset and built end-to-end approaches. However, this task is still challenging due to the ambiguity between adjacent grading, especially in early-stage OA. Low confident samples, which are less representative than the typical ones, undermine the training process. Targeting the uncertainty in the OA dataset, we propose a novel learning scheme that dynamically separates the data into two sets according to their reliability. Besides, we design a hybrid loss function to help CNN learn from the two sets accordingly. With the proposed approach, we emphasize the typical samples and control the impacts of low confident cases. Experiments are conducted in a five-fold manner. Our method achieves a mean accuracy of 70.13\% on the five-class OA assessment task, which outperforms all other start-of-art methods. Despite that early-stage OA detection still benefits from the human intervention of lesion region selection, our approach achieves superior performance on the KL-0 vs. KL-2 task. Moreover, we design an experiment to validate large-scale automatic data refining during training. The result verifies the ability of characterizing low confidence samples by our approach. Dataset used in this paper was obtained from the osteoarthritis Initiative.

Typical engineering design of rock weirs rely on simplified one-dimensional equations dependent on empirical coefficients. However, most simplified methods fail to accurately predict the hy...

Thermally activated delayed fluorescence (TADF) emitters featuring preferential horizontal emitting dipole orientation (EDO) are in urgent demand for enhanced optical outcoupling efficiency in organic light-emitting diodes (OLEDs). However, simultaneously manipulating EDO and optoelectronic properties remains a formidable challenge. Here, an extended linear D-A-D structure with both enlarged donor (D) and acceptor (A) π-systems is established, not only elaborately manipulating parallel horizontal molecular orientation and EDO along its long axis by multi-driving-forces for a high horizontal dipole ratio (Θ// ), but also delocalizing distribution of frontier energy levels for optimized electronic properties. The proof-of-the-concept emitter simultaneously affords a high Θ// of 92%, a high photoluminescence quantum yield of 95%, and a fast reverse intersystem crossing rate of 1.16 × 106 s-1 . The corresponding OLED achieves a champion maximum external quantum efficiency of 39.1% among all green TADF devices without any external light-extraction techniques, together with a maximum power efficiency of 112.0 lm W-1 and alleviated efficiency roll-off. These findings may inspire even better full-color TADF emitters that push the device efficiency toward the theoretical limits.

Purpose Pancreatic cancer(PC) is a common cancer with high lethality and low survival rate. Autophagy is involved in the biological process of PC. Thus, we intended to explore the function of autophagy-related long noncoding RNA signature for survival assessment in PC. Methods Based on 10 autophagy-related lncRNAs, the prognostic model was attained through univariate and multivariate Cox regression analysis. Subsequently, the relationship network of 10 lncRNAs was crystallized in co-expression network and Sankey diagram. Survival analysis and ROC curve were used to evaluate the signature. GSEA was utilized to screen enriched gene sets. Result The OS has significant deference in low-risk group and high-risk group(P

The brain and the spinal cord together make up the central nervous system (CNS). The functions of the human brain have been the focus of neuroscience research for a long time. However, the spinal cord is largely ignored, and the functional interaction of these two parts of the CNS is only partly understood. This study developed a novel method to simultaneously record spinal cord electrophysiology (SCE) and electroencephalography (EEG) signals and validated its performance using a classical resting-state study design with two experimental conditions: eyes-closed (EC) and eyes-open (EO). We recruited nine postherpetic neuralgia patients implanted with a spinal cord stimulator, which was modified to record SCE signals simultaneously with EEG signals. For both EEG and SCE, similar differences were found in delta- and alpha-band oscillations between the EC and EO conditions, and the spectral power of these frequency bands was able to predict EC/EO behaviors. Moreover, causal connectivity analysis suggested a top-down regulation in delta-band oscillations from the brain to the spinal cord. Altogether, this study demonstrates the validity of simultaneous SCE-EEG recording and shows that the novel method is a valuable tool to investigate the brain-spinal interaction. With this method, we can better unite knowledge about the brain and the spinal cord for a deeper understanding of the functions of the whole CNS.

Purpose Human epidermal growth factor receptor 2 (HER2) status is both an independent prognostic factor and a predictive factor for the efficacy of targeted therapy for breast cancer, so it is critical to accurately detect HER2 protein expression and/or gene amplification. According to the recommendations of the 2013 American Society of Clinical Oncology and College of American Pathologists (ASCO/CAP) guidelines for HER2 breast cancer testing, an additional test should be pursued on a different block from the same tumor as one of the options for patients with immunohistochemistry (IHC) 2+ and a HER2/CEP17 ratio of

Background Hearing loss is one of the most common modifiable factors associated with cognitive and functional decline in geriatric populations. An accurate, easy-to-apply, and inexpensive hearing screening method is needed to detect hearing loss in community-dwelling elderly people, intervene early and reduce the negative consequences and burden of untreated hearing loss on individuals, families and society. However, available hearing screening tools do not adequately meet the need for large-scale geriatric hearing detection due to several barriers, including time, personnel training and equipment costs. This study aimed to propose an efficient method that could potentially satisfy this need. Methods In total, 1793 participants (≥60 years) were recruited to undertake a standard audiometric air conduction pure tone test at 4 frequencies (0.5–4 kHz). Audiometric data from one community were used to train the decision tree model and generate a pure tone screening rule to classify people with or without moderate or more serious hearing impairment. Audiometric data from another community were used to validate the tree model. Results In the decision tree analysis, 2 kHz and 0.5 kHz were found to be the most important frequencies for hearing severity classification. The tree model suggested a simple two-step screening procedure in which a 42 dB HL tone at 2 kHz is presented first, followed by a 47 dB HL tone at 0.5 kHz, depending on the individual’s response to the first tone. This approach achieved an accuracy of 91.20% (91.92%), a sensitivity of 95.35% (93.50%) and a specificity of 86.85% (90.56%) in the training dataset (testing dataset). Conclusions A simple two-step screening procedure using the two tones (2 kHz and 0.5 kHz) selected by the decision tree analysis can be applied to screen moderate-to-profound hearing loss in a community-based geriatric population in Shanghai. The decision tree analysis is useful in determining the optimal hearing screening criteria for local elderly populations. Implanting the pair of tones into a well-calibrated sound generator may create a simple, practical and time-efficient screening tool with high accuracy that is readily available at healthcare centers of all levels, thereby facilitating the initiation of extensive nationwide hearing screening in older adults.

Background Following drug resistance in patients with lung cancer treated by EGFR TKIs, a biopsy is required to obtain sufficient cancer tissue for T790M detection in order to select potential beneficiaries suitable for third-generation EGFR TKIs, such as osimertinib. The purpose of this study was to explore the feasibility of using a new in situ analysis technique based on RNA target sequences to detect EGFR T790M in lung cancer. Methods A total of 28 formalin-fixed paraffin-embedded (FFPE) samples from 24 lung adenocarcinoma patients archived in Peking Union Medical College Hospital from 2015 to 2016 were collected. The BaseScope T790M detection technique by in situ hybridization on FFPE slides was used to analyze the mutation of EGFR T790M, and then the results were compared with the data acquired by Scorpions ARMS assay, which is the so-called gold standard for EGFR gene mutation testing. The sensitivity and specificity of the BaseScope T790M detection technique were preliminarily evaluated. Results Of the 28 FFPE specimens, the average proportion of T790M-positive cells was 35.78% ± 17.68% in 18 samples with EGFR T790M, confirmed by Scorpions ARMS assay, Compared with real-time PCR assay, the sensitivity and specificity of BaseScope T790M were all 100% in our cohort. Conclusion BaseScope T790M assay could be completed on only one FFPE slide and the visualized molecular result overplayed with histomorphological information perfectly, so it may be the alternative method for EGFR T790M evaluation. BaseScope assay has potential clinical utility, and it will be necessary to carry out validation with a large number of cases.

Using a photothrombotic mouse model of single stroke, we show that a single stroke onset increases the nuclear factor-κB (NF-κB), NLR family CARD domain containing protein 4 (NLRC4), and absent in melanoma 2 (AIM2) inflammasomes, as well as the mRNA levels of NLRP3. Next, using a photothrombotic mouse model of recurrent stroke, we found that recurrent strokes increased the activation of NLRP3, exacerbated the brain damage and the pro-inflammatory response in wild type (WT) mice, but not in NLRP3 knockout ( NLRP3 KO) mice. Additionally, we found that apoptosis-associated speck-like protein containing a CARD (ASC) protein level surrounding the infarct area was comparatively increased, but that ASC specks outside of microglia in both the ipsilateral and contralateral of stroke site were decreased in NLRP3 KO mice relative to wild-type (WT) controls, and the number of ASC specks surrounding the second infarct area was positively correlated to the damage scores. Mechanistically, we found that recombinant ASC (RecASC) activated NLRP3 and induced pro-inflammatory responses, exacerbating the outcome of ischemic stroke, in WT mice, but not in NLRP3 KO mice. We therefore conclude that the NLRP3 inflammasome is activated by two attacks of stroke, which act together with ASC to exacerbate recurrent strokes.

Two low cost hexa-substituted benzene derivatives, namely HFB-OMeDPA and HPB-OMeDPA, have been successfully utilized as hole transporting materials (HTMs) for efficient perovskite solar cells (PSCs). The relationships between molecular structure, electronic properties of the semiconductor and eventually the photovoltaic performance are investigated. The planar PSCs employing HPB-OMeDPA as HTM exhibit excellent power conversion efficiencies exceeding 16% under AM 1.5G illumination conditions, which are comparable to the reference cells based on spiro-OMeTAD.

Theranostic systems are able to detect and treat diseases with only one procedure, thus greatly lessening the pain of patients. Since each patient's disease can be considered as a new clinical subtype, it is essential to develop theranostic nanomaterials with changeable functions for personal treatment. In this work, a novel modular theranostic platform was designed to control the stimuli-responsive drug release. As a patch board, mesoporous silica nanoparticles (MSNs) were functionalized with a linear pH-responsive benzimidazole (Bz)-polyethylene glycol (PEG) chain containing a redox-responsive ferrocene (Fc) oxide stopper at the end. As the plug, the β-CD ring was initially located at the Bz position. In an acidic tumor microenvironment, the pH sensitive Bz was protonated and the complex formation constant between Bz and β-CD decreased. At the same time, the complex formation constant between Fc and β-CD increased remarkably. As a result, the β-CD ring would depart from the nanoparticle surface to the Fc position at pH 6.2 & 10 mM GSH, physically causing an "And" logic gate type drug release. Herein, a "plug and play" method was used to achieve changeable functions with only one platform. By plugging modified β-CD into the patch board, theranostic systems with changeable functions can be achieved easily.

Abnormal cell metabolism with vigorous nutrition consumption is one of the major physiological characteristics of cancers. As such, the strategy of cancer starvation therapy through blocking the blood supply, depleting glucose/oxygen and other critical nutrients of tumors has been widely studied to be an attractive way for cancer treatment. However, several undesirable properties of these agents, such as low targeting efficacy, undesired systemic side effects, elevated tumor hypoxia, induced drug resistance, and increased tumor metastasis risk, limit their future applications. The recent development of starving-nanotherapeutics combined with other therapeutic methods displayed the promising potential for overcoming the above drawbacks. This review highlights the recent advances of nanotherapeutic-based cancer starvation therapy and discusses the challenges and future prospects of these anticancer strategies.

Objective: Dry Weight (DW) is a typical hemodialysis (HD) prescription for End-Stage Renal Disease (ESRD) patients. However, an accurate DW assessment is difficult due to the complication of body components and individual variations. Our objective is to model a clinically practicable DW estimator. Method: We proposed a time series-based regression method to evaluate the weight fluctuation of HD patients according to Electronic Health Record (EHR). A total of 34 patients with 5100 HD sessions data were selected and partitioned into three groups; in HD-stabilized, HD-intolerant, and near-death. Each group’s most recent 150 HD sessions data were adopted to evaluate the proposed model. Results: Within a 0.5 kg absolute error margin, our model achieved 95.44%, 91.95%, and 83.12% post-dialysis weight prediction accuracies for the HD-stabilized, HD-intolerant, and near-death groups, respectively. Within a 1%relative error margin, the proposed method achieved 97.99%, 95.36%, and 66.38% accuracies. For HD-stabilized patients, the Mean Absolute Error (MAE) of the proposed method was 0.17 kg ± 0.04 kg. In the model comparison experiment, the performance test showed that the quality of the proposed model was superior to those of the state-of-the-art models. Conclusion: The outcome of this research indicates that the proposed model could potentially automate the clinical weight management for HD patients. Clinical Impact: This work can aid physicians to monitor and estimate DW. It can also be a health risk indicator for HD patients., This paper presents a clinically practicable dry weight estimator for hemodialysis (HD). The proposed time series-based regression method to evaluate the weight fluctuation of HD patients achieved achieved 95.44%, 91.95%, and 83.12% post-dialysis weight prediction accuracies for HD-stabilized, HD-intolerant, and near-death groups, respectively (0.5 kg absolute error margin). The proposed model could potentially automate clinical weight management for HD patients.

Blue as one of the three primary colors is of great significance for lighting and full-color displays, and the development of blue emitters with satisfactory color purity and high efficiency is still a formidable challenge for organic light-emitting diodes (OLEDs). In this study, a spiro-linked double donor–acceptor (D–A) molecular architecture is introduced to selectively improve the horizontal emitting dipole orientation of blue emitters and thereby boost the electroluminescence (EL) performance of blue OLEDs. To testify the validity, a thermally activated delayed fluorescence (TADF) emitter, namely SBA-2DPS, is designed by connecting two D–A pairs with a sp3 hybrid carbon atom. Compared to the prototypical emitter DMAC-1DPS, the shape of SBA-2DPS is elongated dramatically without expanding the degree of π-conjugation owing to the σ-spacer linkage. Consequently, a high horizontal dipole ratio (Θ‖ = 87%) is achieved for SBA-2DPS. Besides, SBA-2DPS exhibits high thermal stability, pure blue emission and distinct TADF characteristics. OLEDs based on SBA-2DPS exhibit an external quantum efficiency (ηext) of 25.5% and favorable Commission International de l’Eclairage (CIE) coordinates of (0.15, 0.20). Such an inspiring performance is rationalized by their high out-coupling efficiency (Φout = 38.2%) and the optical microcavity effect. Unambiguously, this finding demonstrates the validity of the spiro-linked double D–A molecular architecture in constructing good blue TADF emitters with a preferentially horizontal emitting dipole orientation for high-efficiency blue OLEDs.

The development of highly efficient orange-red organic thermally activated delayed fluorescence (TADF) emitters is currently challenging. Herein, we reported two new emitters, namely, BTDMAc-NAI and BFDMAc-NAI with benzofuran and benzothiophene fused to the acridine frame. Inspiringly, the target emitters showed very different emissions from orange to pure red, with the emission peak of BTDMAc-NAI red-shifting to 650 nm. Meanwhile, these two emitters showed distinct TADF characteristics and high photoluminescence quantum yields (PLQYs). As a result, the orange-red OLED based on BFDMAc-NAI demonstrated an excellent performance with external quantum efficiency (EQE) beyond 20%. This work unveils the great potential of the fused polycycle molecular design in exploiting new orange-red TADF emitters for high-performance OLEDs.

High-performance amorphous oxide semiconductor thin film transistors (AOS-TFT) with copper (Cu) electrodes are of great significance for next-generation large-size, high-refresh rate and high-resolution panel display technology. In this work, using rare earth dopant, neodymium-doped indium-zinc-oxide (NdIZO) film was optimized as the active layer of TFT with Cu source and drain (S/D) electrodes. Under the guidance of the Taguchi orthogonal design method from Minitab software, the semiconductor characteristics were evaluated by microwave photoconductivity decay (μ-PCD) measurement. The results show that moderate oxygen concentration (~5%), low sputtering pressure (≤5 mTorr) and annealing temperature (≤300 °C) are conducive to reducing the shallow localized states of NdIZO film. The optimized annealing temperature of this device configuration is as low as 250 °C, and the contact resistance (RC) is modulated by gate voltage (VG) instead of a constant value when annealed at 300 °C. It is believed that the adjustable RC with VG is the key to keeping both high mobility and compensation of the threshold voltage (Vth). The optimal device performance was obtained at 250 °C with an Ion/Ioff ratio of 2.89 × 107, a saturation mobility (μsat) of 24.48 cm2/(V·s) and Vth of 2.32 V.

Ethnopharmacological relevance Fufang Danshen Tablet (FDT) is a traditional Chinese medicine (TCM) formula composed of three Chinese medicinal materials comprising Salviae Miltiorrhizar Radix et Rhizoma (Dan-Shen in Chinese), Notoginseng Radix et Rhizoma (San-Qi), and Borneolum Syntheticum (Bing-Pian). It has been documented to exert significant effects in promoting blood circulation and removing blood stasis, and become a frequently used formula in the treatment of cardiovascular and cerebrovascular diseases. Aim of the review To systematically analyze and summarize the research findings concerning the chemical composition, quality control, pharmacokinetics, pharmacological properties, clinical applications, and toxicity of FDT, so as to point out some typical problems and provides opinions for future study. Materials and methods Literatures involving FDT were collected from online scientific databases including China National Knowledge Infrastructure, WanFang Data, PubMed, Science Direct, Scopus, Web of Science, Springer Link, SciFinder, and Google Scholar up to March 2021. All eligible studies are analyzed and summarized in this review. Results This review summarizes reported results concerning the post-marketing quality and efficacy of FDT. Some problems are pointed out for FDT. Hereon we propose several directions for future study: (a) improvement of quality control based on exact overall chemical profiles, entire production process monitoring, and biopotency-associated multi-index content determination method; (b) clarification of functional mechanisms focused on pharmacokinetic profiles in human, interplay with gut microbiota, and integration of multi-omics technologies; (c) reconfirmation of clinical effectiveness and safety from large-scale clinical studies based on evidence-based medicine. Conclusions FDT is a typical TCM formula in treating cardiovascular and cerebrovascular diseases, but there are also some troubles. Future studies should focus on the improvement of quality control, the clarification of functional mechanisms, as well as the reconfirmation of clinical effectiveness and safety.

An effect method based on machine learning is developed for hotspots mining in lithography. A series of models are trained independently and combined to achieve high accuracy. Innovatively, contour information is firstly adopted to assist the hotspot detection, which eases the very challenging task of detection for topology aberration caused by complicated process effects. More importantly, the proposed method based on new methodology provides high efficiency which is competent for high volume manufacturing.

Background and Objective Personality disorders are frequently associated with insomnia and depression, but little is known about the inter-relationships among these variables. Therefore, this study examined these inter-relationships and the possible mediating effect of insomnia on the association between specific personality pathologies and depression severity. Methods There were 138 study participants, including 69 individuals with depression and 69 healthy controls. The main variables were measured by the Hamilton Depression Rating Scale-24 (HAMD-24), Athens Sleep Insomnia Scale (AIS), and the Personality Diagnostic Questionnaire (PDQ-4+). Multivariate linear regression and mediation analysis were conducted. Results With the exception of the antisocial personality score, all the PDQ-4+ scores and AIS scores were significantly higher in the depression group than in the healthy control group (p < 0.001). In the total sample, all personality pathology scores (p < 0.001), except the antisocial personality score, had significant positive correlations with the AIS scores and HAMD-24 scores, and the AIS scores and HAMD-24 scores were positively correlated (r = 0.620, p < 0.001). Regression analysis revealed that borderline personality, passive-aggressive personality, and insomnia positively predicted the severity of depression, after adjusting for sociodemographic covariates, and that insomnia partially mediated the associations of borderline personality and passive-aggressive personality with depression severity. Conclusions Borderline personality, passive-aggressive personality, and insomnia tend to increase the severity of depression, and the effect of borderline and passive-aggressive personality on depression severity may be partially mediated by insomnia. This is the first study to report these findings in a Chinese sample, and they may help researchers to understand the pathways from specific personality pathologies to the psychopathology of depression better, which should be useful for designing interventions to relieve depression severity, as the impact of specific personality pathology and insomnia should be considered.

Background and AimPoor response to immune checkpoint inhibitors (ICIs) has been observed in most triple-negative breast cancer (TNBC) cases (around 80%). Our aim was to investigate the status of mismatch repair (MMR), microsatellite instability (MSI), programmed death-ligand 1 (PD-L1), and lymphocyte-activation gene 3 (LAG-3) in TNBC.MethodsA total of 74 TNBC samples were retrospectively analyzed. MMR and MSI were evaluated by immunohistochemistry (IHC) and polymerase chain reaction (PCR) using Promega 1.2 and NCI panels, respectively. PD-L1, LAG-3, and CD8 expression was assessed by IHC.ResultsNone of the cases demonstrated deficient MMR (dMMR) or MSI. In total, 43/74 cases (58.1%) were PD-L1+, including 1 tumor PD-L1+, 25 tumor-infiltrating lymphocytes (TILs) PD-L1+, and 17 cases involving concurrence of tumor and TIL PD-L1+. The rate of TIL PD-L1+ was remarkably higher than that of tumor PD-L1+ (PConclusiondMMR/MSI characteristics may not be a practical predictive marker for ICIs in TNBC. PD-L1+ is more common in TILs than in tumors. In the PD-L1+ population, approximately half of the cases showed LAG-3 co-expression. For patients with a poor response to PD-1(L1) mono ICI, dual blockade of PD-1(L1) and LAG-3 may be a viable option for the management of TNBC.

Tea is a popular daily beverage worldwide, especially in China. Tea white scab disease affects tea quality is poorly understood. In the current study, we aimed determine whether tea quality will variation. we collected green tea samples from five samples (with varying morbidity from CK to Ⅳ on Baiyun Mountain in Hunan Province. Results showed that tea quality are obviously decreased as infected by tea white scab disease . Results showed that an increase in incidence rate decreased total tea polyphenols (TP) , water extract and caffeine but increased amino acids (AA) , Nonetheless, the constituents of polyphenolic compounds were differentially altered. Additionally, the percentage of (−)-epicatechin (EC) and (−)-epicatechin-3-gallate (ECG) increased with increasing morbidity. (−)-epigallocatechin (EGC) and (−)-epigallocatechin-3-gallate(EGCG) just the opposite. The constituents of AA, especially Alanine, γ-aminobutyric acid, serine and phenylalanine increased with increasing elevational gradients. Proline, and theanine were reduce along with morbidity level. As a whole. This observation demands development of effective measures for sustaining green tea quality in the face of tea white scab disease.

This paper introduces a real-time processing and classification of raw sensor data using a convolutional neural network (CNN). The established system is a microcontroller-unit (MCU) implementation of an intelligent gripper for shape identification of grasped objects. The pneumatic gripper has two embedded accelerometers to sense acceleration (in the form of vibration signals) on the jaws for identification. The raw data is firstly transferred into images by short-time Fourier transform (STFT), and then the CNN algorithm is adopted to extract features for classifying objects. In addition, the hyperparameters of the CNN are optimized to ensure hardware implementation. Finally, the proposed artificial intelligent model is implemented on a MCU (Renesas RX65N) from raw data to classification. Experimental results and discussions are introduced to show the performance and effectiveness of our proposed approach.

SEM-image contours provide valuable information about patterning quality and capability. Geometrical properties such as critical dimension and resist sidewall angle could be extracted or estimated from SEM image contours. Those geometrical properties can be used for OPC model calibration, OPC model verification and lithography hotspot detection. This work presents a machine learning based method for contour extraction of SEM image. A designed DCNN network and self-made high quality dataset are combined for contour model training. Based on the high capability of image/feature representation and remarkable advantage of parallel computing with hardware acceleration, the model achieves high accuracy and real-time operation for contour extraction, more importantly, it provides the ability to distinguish and separate the top and bottom contours of SEM images. Additionally, the model not only removes the abundant edges but also repairs the local discontinuity caused by imperfect process and measuring technique.

The present study focuses on evaluating the effects of reformate gas (RG) (i.e. 10% CH4, 20% H2, 5% CO, 13% CO2 and 52% N2 by volume) addition on the laminar burning velocity (LBV) and Markstein length of natural gas (NG) fuel. The experimental measurements were performed using a 5.86-L cylindrical constant volume combustion chamber and a schlieren photography system equipped with a high-speed camera was used to record the spherical flame propagation. The numerical simulations were obtained using CHEMKIN-PRO software and six kinetic mechanisms. Measurements were compared with simulations and the FFCM-1 model shows a better prediction with the present experimental data. The experimental measurements and numerical simulations were conducted for NG/RG-air mixture at an initial temperature of 298 K, a pressure of 0.1 MPa and equivalence ratios ranging from 0.7 to 1.3 with varying the RG proportions from 10 to 90%. Results show that the LBV of NG-air flame decreases with the increase of the RG content due to the decrease of adiabatic flame temperature. Moreover, for equivalence ratios ranging from 0.7 to 1.1 and increasing the RG content, the instability of laminar NG/RG-air flames increases due to the increase of CO2 and N2 concentrations. The sensitivity analysis implies that R1 (H + O2 = O + OH) and R15 (H + O2 (+M) = HO2 (+M)) are the most important reactions affecting the LBV as the RG content increases. The analysis of radical concentration shows that the active radical production is deteriorated by 38.38% for OH, 24.47% for H and 34.94% for O when the RG content increased from 10% to 90% in the mixture while the reaction pathway has little changes. The present study demonstrates that the addition of a small percentage of RG is a viable approach to maintain flame stability and reactivity.

Acceleration and wide deployability in deeper recurrent neural network is hindered by high demand for computation and memory storage on devices with memory and latency constraints. In this work, we propose a novel regularization method to learn hardware-friendly sparse structures for deep recurrent neural networks. Considering the consistency of dimension in continuous time units in recurrent neural networks, low-rank structured sparse approximations of the weight matrices are learned through the regularization without dimension distortion. Our method is architecture agnostic and can learn compact models with higher degree of sparsity than the state-of-the-art structured sparsity learning method. The structured sparsity rather than random sparsity also facilitates the hardware implementation. Experiments on language modeling of Penn TreeBank dataset show that our approach can reduce the parameters of stacked recurrent neural network model by over 90% with less than 1% perplexity loss. It is also successfully evaluated on larger highway neural network model with word2vec dataset like enwik8 and text8 using only 20M weights.

Background Pharmacokinetics provides a scientific basis for drug product design, dosage regimen planning, understanding the body's action on the drug, and relating the time course of the drug in the body to its pharmacodynamics and/or toxic effects. Recently, naringin, a natural flavonoid, was approved for clinical trials as a first-class new drug product by the China Food and Drug Administration, owing to its nonclinical efficacy in relieving cough, reducing sputum, and low toxicity. Previous reports focused on the pharmacokinetic studies of naringin or its active metabolite naringenin in rats, which were scattered and insufficient because naringin was coadministered with mixtures such as herbs, fruits, and other traditional medicines. The purpose of this study was to evaluate the pharmacokinetics and metabolism of naringin and naringenin, following oral and intravenous administration of naringin in rats, dogs, and humans, which can be beneficial for new drug development. Methods Separate bioanalytical methods were developed and validated to determine the concentrations of naringin and its active metabolite naringenin in biological samples obtained from rats, dogs, and humans. Comprehensive nonclinical and clinical data were used to estimate the pharmacokinetic parameters of naringin and naringenin. Experiments included single-dose studies (oral and intravenous administration), multiple-dose studies, and an assessment of food-effects. Furthermore, the metabolism of naringin and naringenin was studied in rat and human liver and kidney microsomes. All biological samples were analyzed using liquid chromatography-tandem mass spectrometry. Results The pharmacokinetic parameters of naringin and naringenin were calculated and the results show an insignificant influence of high-fat diet and insignificant accumulation of the drugs after multiple dosing. Twelve metabolites were detected in the liver and kidney microsomes of rats and humans; naringin metabolism was a complex process simultaneously catalyzed by multiple human enzymes. All evaluated species demonstrated differences in the pharmacokinetics and metabolism of naringin and naringenin. Conclusion The results can be used to design a dosage regimen, deepen understanding of mechanisms, and accelerate new drug development. Clinical trial registration http://www.chinadrugtrials.org.cn/eap/main, identifiers CTR20130704 and CTR20190127.

CD19+CD24hiCD38hi B cells are immature transitional B cells that, in normal individuals, exert suppressive effects by IL-10 production but are quantitatively altered and/or functionally impaired in individuals with various autoimmune diseases. Primary biliary cholangitis (PBC), an autoimmune disease, clinically presents as chronic cholestasis and nonsuppurative destructive cholangitis. A role for CD19+CD24hiCD38hi B cells in PBC is unknown. This study investigated the frequency and functional variation of circulating CD19+CD24hiCD38hi B cells in PBC patients. Flow cytometry was employed to quantify the percentage of CD19+CD24hiCD38hi B cells in peripheral blood samples. Correlations between CD19+CD24hiCD38hi B cells and routine laboratory parameters were assessed. Levels of IL-10, TNF-α, IL-6 and IL-12, and Tim-1 in CD19+CD24hiCD38hi B cells from PBC patients were analyzed. The effect of CD19+CD24hiCD38hi B cells on CD4+T cell differentiation was evaluated. The percentage of CD19+CD24hiCD38hi B cells in PBC patients was significantly higher than in healthy controls and was positively correlated with liver cholestasis. After activation by anti-B cell receptor and CpG, the production of IL-10 was decreased and the production of IL-6 and IL-12 was increased in CD19+CD24hiCD38hi B cells from PBC patients. Moreover, Tim-1 levels were significantly downregulated in CD19+CD24hiCD38hi B cells from PBC patients. Coculture showed that PBC-derived CD19+CD24hiCD38hi B cells were less capable of CD4+T cell inhibition, but promoted Th1 cell differentiation. In conclusion, PBC patients have expanded percentages, but impaired CD19+CD24hiCD38hi B cells, which correlate with disease damage. In PBC patients, this B cell subset has a skewed proinflammatory cytokine profile and a decreased capacity to suppress immune function, which may contribute to the pathogenesis of PBC.

Accurate and rapid defect identification based on pulsed eddy current testing (PECT) plays an important role in the structural integrity and health monitoring (SIHM) of in-service equipment in the renewable energy system. However, in conventional data-driven defect identification methods, the signal feature extraction is time consuming and requires expert experience. To avoid the difficulty of manual feature extraction and overcome the shortcomings of the classic deep convolutional network (DCNN), such as large memory and high computational cost, an intelligent defect recognition pipeline based on the general Warblet transform (GWT) method and optimized two-dimensional (2-D) DCNN is proposed. The GWT method is used to convert the one-dimensional (1-D) PECT signal to a 2D grayscale image used as the input of 2D DCNN. A compound method is proposed to optimize the baseline VGG16, a well-known DCNN, from four aspects including reducing the input size, adding batch normalization layer (BN) after every convolutional layer(Conv) and fully connection layer (FC), simplifying the FCs, and removing unimportant filters in Convs so as to reduce memory and computational costs while improving accuracy. Through a pulsed eddy current testing (PECT) experiment considering interference factors including liftoff and noise, the following conclusion can be obtained. The time-frequency representation (TFR) obtained by the GWT method not only has excellent ability in terms of the transient component analysis but also is less affected by the reduction of image size; the proposed optimized DCNN can accurately identify defect types without manual feature extraction. And compared to the baseline VGG16, the accuracy obtained by the optimized DCNN is improved by 7%, to about 99.58%, and the memory and computational cost are reduced by 98%. Moreover, compared with other well-known DCNNs, such as GoogLeNet, Inception V3, ResNet50, and AlexNet, the optimized network has significant advantages in terms of accuracy and computational cost, too.

To investigate whether there is an association between gene polymorphisms, genetic and environmental interactions, and psychological stress reactivity in Chinese subjects living in the Guangxi minority region. This cross-sectional study enrolled subjects older than 18 years, living in Nandan county, Guangxi minority region, China for at least 1 year. All participants were healthy, without any mental diseases, and were able to communicate. Eligible participants were randomly selected. The Life Event Scale Questionnaire, Simplified Coping Style Questionnaire, and Social Support Rating Scale were used to measure the physiological stress, coping style, and social support, respectively, in individuals. A total of 600 participants were recruited. A decreased risk of psychological stress was only found in TT of NPSR1 (rs324981): A allele carriers vs. TT genotype (OR 1.64, 95% CI 1.11, 2.42), and AT genotype vs. TT genotype (OR 1.76, 95% CI 1.17, 2.65). The overall coping style was positively associated with psychological stress, and no significant interactions between genetics and environment were found. We found that the NPSR1 (rs324981) T/T genotype decreased the risk of psychological stress, while the overall coping style was a risk factor for psychological stress. However, there was no interactive effects of genes and environment on psychological stress. Our findings will improve understanding of the biological basis underlying psychological stress if the results can be replicated in further research.

Cervical lymph node metastasis is a prognostic factor of papillary thyroid carcinoma (PTC). Skip metastasis (central lymph node negative and lateral lymph node positive) of PTC is not uncommon. This study aimed to retrospectively investigate the risk factors for skip metastasis in PTC and develop a prediction model for skip metastasis. A total of 745 PTC patients underwent total thyroidectomy and central plus lateral lymph node dissection at the First Affiliated Hospital of Chongqing Medical University from January 2012 to December 2017. Clinicopathological characteristics were collected and analyzed. Univariate and multivariate analyses were performed to detect the risk factors for skip metastasis. A prediction model was established based on the results of multivariate analyses. The skip metastasis rate was 9.7% (72/745). Age > 55 years (OR 2.63, 95% CI 1.34–5.04, p = 0.004), tumor located in the upper portion (OR 4.15, 95% CI 2.30–7.63, p = 0.001), and unilaterality (OR 2.76, 95% CI 1.14–8.23, p = 0.040) were independent risk factors for skip metastasis. Clinically lymph node-negative (cN0) patients with tumor in the upper portion (24.6%, 43/175) had higher possibility of skip metastasis than those of clinically lateral lymph node-positive (cN1b) patients (5.9%, 10/169) (p = 0.001). The area under the receiver operating characteristic curve of prediction model was 0.734 and 0.740 in derivation group and validation group, respectively. However, skip metastasis was not associated with tumor-free survival rate of PTC patients (p = 0.274). Age > 55 years, tumor located in the upper portion, and unilaterality may increase the risk of skip metastasis. We developed the first prediction model for skip metastasis based on clinicopathological parameters in PTC patients.

Background This study was conducted to evaluate the efficacy and safety of nimotuzumab combined with chemotherapy as first-line therapy in advanced lung squamous cell carcinoma (LSCC), and to explore predictive biomarkers of the efficacy of nimotuzumab. Methods A retrospective study was conducted of patients with advanced LSCC administered nimotuzumab combined with chemotherapy as first-line therapy from June 2012 to December 2016 at the Department of Respiratory Medicine, Peking Union Medical College Hospital. The associations between EGFR expression, EGFR gene copy numbers, and clinical efficacy were detected by immunohistochemistry and fluorescence in situ hybridization (FISH). Results Twenty-six patients were enrolled, including 22 men and 4 women. The objective response rate was 50% and the disease control rate was 100%. The median progression-free survival (PFS) and overall survival were 6.7 and 16.3 months, respectively. Patients whose samples were tested via FISH and showed positive EGFR expression had a trend of longer median PFS (10.0 months; P = 0.10). Adverse effects included 15 cases (57.7%) of bone marrow suppression, 15 (57.7%) of sensory neuropathy, 14 (53.8%) of alopecia, nine (34.6%) of nausea/vomiting and one case (3.8%) of elevated creatinine level. All adverse effects were attributed to chemotherapy. Conclusion Nimotuzumab combined with chemotherapy might be a possible option as first-line therapy in patients with advanced LSCC. EGFR gene copy number examined by FISH might be a possible predictive biomarker.

Primary adrenal lymphoma is a rare entity that, in most cases, is derived from B cells. The most commonly seen primary adrenal lymphoma is diffuse large B cell lymphoma (DLBCL). To better understand the clinicopathological and molecular features of these tumors, we studied 14 Chinese patients with DLBCL who initially presented with an adrenal tumor. The clinicopathological features of the 14 primary adrenal DLBCL cases were retrospectively reviewed using immunohistochemistry, immunoglobulin gene rearrangement analysis, evaluation of Epstein-Barr virus status, and fluorescence in situ hybridization. Patient age ranged from 43 to 69 years, with a mean age of 58 years. The patients most commonly presented with abdominal pain and adrenal mass. Ten patients exhibited bilateral adrenal masses, and four had unilateral adrenal masses (three left, one right). Thirteen of 14 DLBCLs were DLBCL not otherwise specified, and one was an intravascular large B cell lymphoma. According to the algorithm of Hans et al. (Blood 103:275-282, 2004), 13 and 1 cases were classified as the non-germinal center B-cell-like subtype and the germinal center B-cell-like subtype, respectively. The Ki-67 index ranged from 35 to 80%. Epstein-Barr virus-encoded RNA was detected by in situ hybridization in 6 of the 12 available cases. Two patients showed BCL-6 rearrangements. The follow-up period ranged from 1 to 87 months. During the follow-up period, four patients died of the disease, five were alive with the disease, four were alive without disease, and one was lost during the follow-up period. In summary, most primary adrenal lymphomas are non-germinal-center B-cell-like subtype DLBCLs, which have high proliferative activity and a poor prognosis.