Generative emulation of weather forecast ensembles with diffusion models.

Uncertainty quantification is crucial to decision-making. A prominent example is probabilistic forecasting in numerical weather prediction. The dominant approach to representing uncertainty in weather forecasting is to generate an ensemble of forecasts by running physics-based simulations under different conditions, which is a computationally costly process. We propose to amortize the computational cost by emulating these forecasts with deep generative diffusion models learned from historical data. The learned models are highly scalable with respect to high-performance computing accelerators and can sample thousands of realistic weather forecasts at low cost. When designed to emulate operational ensemble forecasts, the generated ones are similar to physics-based ensembles in statistical properties and predictive skill. When designed to correct biases present in the operational forecasting system, the generated ensembles show improved probabilistic forecast metrics. They are more reliable and forecast probabilities of extreme weather events more accurately. While we focus on weather forecasting, this methodology may enable creating large climate projection ensembles for climate risk assessment.

Comprehensive HRMS Screening and Risk Assessments of Aristolochic Acid Analogues in Asari Radix et Rhizoma and Related Commercial Health Products.

Aristolochic acid analogues (AAAs) are well-known toxins. We performed the first comprehensive screening on AAAs in Asari Radix et Rhizoma (underground part of Asarum heterotropoides Schmidt), the only Aristolochiaceae plant widely used in clinical practice. LC-HRMS revealed 70 trace AAAs using polygonal mass defect filtering and precursor ion list strategies, 38 of which were newly discovered in A. heterotropoides. UHPLC-QTrap-MS/MS was then utilized for quantitative/semiquantitative analysis of 26 abundant compounds. Seventeen AAAs were detected from 91 batches of A. heterotropoides and 20 AAAs from 166 consumable products. For 141 Asari-containing proprietary products, aristolactam I and aristolactam II-glucoside exhibited the widest distribution, present in 98% products. AA IVa was the most abundant, detected in 91%. Notably, 60% of the products contained AA I (0.03-0.79 ppm). The safety was assessed using linear extrapolation, permitted daily exposure, cumulative amount, and the margin of exposure. It is recommended that AA I content be limited to 3 ppm.

Systematical targeted multicomponent characterization and comparison of Arnebiae Radix and its three confusing species by offline two-dimensional liquid chromatography/LTQ-Orbitrap mass spectrometry.

Arnebiae Radix, commonly known as "Zicao," can be easily confused with other compounding species, posing challenges for its clinical use. Here, we developed a comprehensive strategy to systematically characterize the diverse components across Arnebiae Radix and its three confusing species. First, an offline two-dimensional liquid chromatography (2D-LC) system integrating hydrophilic interaction chromatography (HILIC) and reverse phase (RP) separations was established, enabling effective separation and detection of more trace constituents. Second, a polygonal mass defect filtering (MDF) workflow was implemented to screen target ions and generate a precursor ion list (PIL) to guide multistage mass (MSn) data acquisition. Third, a three-step characterization strategy utilizing diagnostic ions and neutral losses was developed for rapid determination of molecular formulas, structure classes, and compound identification. This approach enabled systematic characterization of Arnebiae Radix and its three confusing species, with 437 components characterized including 112 shikonins, 22 shikonfurans, 144 phenolic acids, 131 glycosides, 18 flavonoids, and 10 other compounds. Additionally, 361, 230, 340, and 328 components were identified from RZC, YZC, DZC, and ZZC, respectively, with 142 common components and 30 characteristic components that may serve as potential markers for distinguishing the four species. In summary, this is the first comprehensive characterization and comparison of the phytochemical profiles of Arnebiae Radix and its three confusing species, advancing our understanding of this herbal medicine for quality control.

MATLAB language assisted data acquisition and processing in liquid chromatography Orbitrap mass spectrometry: Application to the identification and differentiation of Radix Bupleuri from its adulterants.

Comprehensive and rapid analysis of secondary metabolites like saponins remains challenging. This study aimed to establish a semi-automated workflow for filtration, identification, and characterization of saikosaponins in six Bupleurum species. Radix Bupleuri, a high-sales herbal medicine, is often adulterated, restricting its quality control and applications. Two authentic Radix Bupleuri species and four major adulterants were analyzed through UHPLC-LTQ-Orbitrap-MS for targeted saikosaponin analysis. To reveal trace saikosaponins and obtain quality fragment data, a MATLAB-based process automatically enumerating "sugar chain + aglycone + side chain" combinations and deduplicating generated a predicted saikosaponin database covering all possible saikosaponins as a precursor ion list for comprehensive targeted acquisition. To focus on informative ions and reduce MS analysis workload, we utilized MATLAB to automatically filtrate the false positive ions by MS1 and MS2 spectrometry. The newly established MATLAB-assisted data acquisition approach exhibited 50 % improvement in characterization of targeted saikosaponins. Furthermore, positive and negative ionization workflows were designed for accurate saikosaponins characterization based on fragmentation rules. In total, 707 saikosaponins were characterized, including over 500 potential new compounds and previously unreported C29 aglycones. We identified 25 saikosaponins present in both authentic species but absent in adulterants as potential markers. This unprecedented comprehensive multi-origin species differentiation demonstrates the promise of MATLAB-assisted acquisition and processing to advance saponin identification and standardize the Radix Bupleuri market.

A novel nomogram for the preoperative prediction of sentinel lymph node metastasis in breast cancer.

BACKGROUND OR PURPOSE: A practical noninvasive method to identify sentinel lymph node (SLN) status in breast cancer patients, who had a suspicious axillary lymph node (ALN) at ultrasound (US), but a negative clinical physical examination is needed. To predict SLN metastasis using a nomogram based on US and biopsy-based pathological features, this retrospective study investigated associations between clinicopathological features and SLN status. METHODS: Patients treated with SLN dissection at four centers were apportioned to training, internal, or external validation sets (n = 472, 175, and 81). Lymph node ultrasound and pathological characteristics were compared using chi-squared and t-tests. A nomogram predicting SLN metastasis was constructed using multivariate logistic regression models. RESULTS: In the training set, statistically significant factors associated with SLN+ were as follows: histology type (p < 0.001); progesterone receptor (PR: p = 0.003); Her-2 status (p = 0.049); and ALN-US shape (p = 0.034), corticomedullary demarcation (CMD: p < 0.001), and blood flow (p = 0.001). With multivariate analysis, five independent variables (histological type, PR status, ALN-US shape, CMD, and blood flow) were integrated into the nomogram (C-statistic 0.714 [95% CI: 0.688-0.740]) and validated internally (0.816 [95% CI: 0.784-0.849]) and externally (0.942 [95% CI: 0.918-0.966]), with good predictive accuracy and clinical applicability. CONCLUSION: This nomogram could be a direct and reliable tool for individual preoperative evaluation of SLN status, and therefore aids decisions concerning ALN dissection and adjuvant treatment.

Drinking From a Firehose: Continual Learning With Web-Scale Natural Language.

Continual learning systems will interact with humans, with each other, and with the physical world through time - and continue to learn and adapt as they do. An important open problem for continual learning is a large-scale benchmark which enables realistic evaluation of algorithms. In this paper, we study a natural setting for continual learning on a massive scale. We introduce the problem of personalized online language learning (POLL), which involves fitting personalized language models to a population of users that evolves over time. To facilitate research on POLL, we collect massive datasets of Twitter posts. These datasets, Firehose10 M and Firehose100 M, comprise 100 million tweets, posted by one million users over six years. Enabled by the Firehose datasets, we present a rigorous evaluation of continual learning algorithms on an unprecedented scale. Based on this analysis, we develop a simple algorithm for continual gradient descent (ConGraD) that outperforms prior continual learning methods on the Firehose datasets as well as earlier benchmarks. Collectively, the POLL problem setting, the Firehose datasets, and the ConGraD algorithm enable a complete benchmark for reproducible research on web-scale continual learning.

Effects of Type and Content of Fibers, Water-to-Cement Ratio, and Cementitious Materials on the Shrinkage and Creep of Ultra-High Performance Concrete.

The effects of the type and content of fibers, water to cement ratio (W/C), and content of cementitious materials on the shrinkage and creep of ultra-high performance concrete (UHPC) were investigated. The relationships between curing age, shrinkage, and unit creep of the UHPC were also discussed. The results showed that the shrinkage of the UHPC decreased with the increase in W/C, where there existed a quadratic function between shrinkage and W/C. However, the unit creep of the UHPC increased with W/C. The shrinkage and unit creep of the UHPC increased with the increase in the content of the cementitious materials. The type and content of fibers had different effects on the shrinkage and unit creep of the UHPC, that is, the shrinkage of the UHPC first increased and then decreased with the increase in the content of steel fibers, where there existed a quadratic function between them. There was a linear function between the shrinkage of the UHPC and the content of carbon fibers, but the shrinkage of the UHCP first increased and then decreased with the increase in PVA content. The shrinkage and unit creep of the UHPC at the initial curing age were significant, which tended to be constant with the increase in curing age. Although the steel fibers had a significant inhibiting effect on the unit creep of the UHPC, the carbon fibers and PVA had positive and negative effects on the unit creep of the UHPC. The effects of the type and content of fibers on the shrinkage and unit creep of the UHPC were caused by the slenderness ratio, shape, surface roughness, and elasticity modulus of the fibers. The shrinkage and creep of the UHPC were caused by the chemical autogenous shrinkage and free water evaporation of the UHPC.

Workability and Mechanical Properties of Superplasticized Microfine Cement Grouts.

Superplasticizer (SP) is essential to enhance the groutability of microfine cement (MC) in civil engineering, however, combined effects of cement type, SP type, amount of SP and water-solid ratio (W/S) on engineering performance of MC are not clear currently. In this research, workability and mechanical properties of superplasticized microfine cement grouts (SMCG) with various SPs are evaluated systematically. Three different MCs (CEM I, CEM II/B-M and CEM III/B based on EN 197-1) and four SPs (one naphthalene-based (N), one melamine-based (M) and two polycarboxylate-based (PCE)) were used to study the effect of grout formulation. The properties investigated included rheological behavior (mini-slump, flowability, time-dependent viscosity and initial viscosity), fresh-state property (bleeding, effective W/S and final setting time), mechanical performance (shrinkage, flexural strength (FS), unconfined compressive strength (UCS), and FS/UCS) and microstructure. The new method of static viscosity was adopted and viscoelasticity was evaluated. The ranges of W/S and SP content were 1.0-2.0 and 0-2.5%, respectively. The results show that the dispersion effects of SP on rheological behavior were followed by PCE, M and N in order of the influence degree. The instability, long-setting and oversaturation were easily caused by excessive SP. SP could be helpful for improving FS or bending toughness. Considering workability and mechanical performance of SMCG, the W/S is suggested to be within 1.5, the optimal amounts of N, M and PCE are recommended as 1.5-2.0%, 1.2-1.5% and 0.9-1.2%, respectively.

Synthesis and anti-inflammatory activity of novel steroidal chalcones with 3ß-pregnenolone ester derivatives in RAW 264.7 cells in vitro.

To identify new potential anti-inflammatory agents, we herein report the synthesis of novel steroidal chalcones with 3ß-pregnenolone esters of cinnamic acid derivatives using pregnenolone as the starting material. The structures of the newly synthesised compounds were confirmed by 1H NMR, 13C NMR, HRMS and infrared imaging. All the derivatives were examined to determine their in vitro anti-inflammatory profiles against LPS-induced inflammation in RAW 264.7 cells; the derivates were evaluated by the quantification of the pro-inflammatory mediator nitric oxide (NO) in the cell culture supernatant based on the Griess reaction, which measures nitrite levels, followed by an in vitro cytotoxicity study. Among these novel derivatives, compound 11e [3ß-3-phenyl acrylate-pregn-5-en-17ß-yl-3' -(p-fluoro)-phenylprop-2'-en-1'-one] was identified as the most potent anti-inflammatory agent, which showed significant anti-inflammatory activity by inhibiting the LPS-induced pro-inflammatory mediator NO in a dose-dependent manner without any cytotoxicity. Moreover, compound 11e markedly inhibited the expression of pro-inflammatory cytokines, including inducible nitric oxide synthase (iNOS), interleukin-6 (IL-6), tumour necrosis factor-α (TNF-α), and cyclooxygenase-2 (COX-2), in LPS-induced RAW 264.7 cells. Further studies confirmed that compound 11e significantly suppressed the transcriptional activity of NF-κB in activated RAW 264.7 cells. Molecular docking study revealed the strong binding affinity of compound 11e to the active site of the pro-inflammatory proteins, which confirmed that compound 11e acted as an anti-inflammatory mediator. These results indicated that steroidal chalcones with 3ß-pregnenolone esters of cinnamic acid derivatives might be considered for further research in the design of anti-inflammatory drugs, and compound 11e might be a promising therapeutic anti-inflammatory drug candidate.

Deep learned tissue "fingerprints" classify breast cancers by ER/PR/Her2 status from H&E images.

Because histologic types are subjective and difficult to reproduce between pathologists, tissue morphology often takes a back seat to molecular testing for the selection of breast cancer treatments. This work explores whether a deep-learning algorithm can learn objective histologic H&E features that predict the clinical subtypes of breast cancer, as assessed by immunostaining for estrogen, progesterone, and Her2 receptors (ER/PR/Her2). Translating deep learning to this and related problems in histopathology presents a challenge due to the lack of large, well-annotated data sets, which are typically required for the algorithms to learn statistically significant discriminatory patterns. To overcome this limitation, we introduce the concept of "tissue fingerprints," which leverages large, unannotated datasets in a label-free manner to learn H&E features that can distinguish one patient from another. The hypothesis is that training the algorithm to learn the morphological differences between patients will implicitly teach it about the biologic variation between them. Following this training internship, we used the features the network learned, which we call "fingerprints," to predict ER, PR, and Her2 status in two datasets. Despite the discovery dataset being relatively small by the standards of the machine learning community (n = 939), fingerprints enabled the determination of ER, PR, and Her2 status from whole slide H&E images with 0.89 AUC (ER), 0.81 AUC (PR), and 0.79 AUC (Her2) on a large, independent test set (n = 2531). Tissue fingerprints are concise but meaningful histopathologic image representations that capture biological information and may enable machine learning algorithms that go beyond the traditional ER/PR/Her2 clinical groupings by directly predicting theragnosis.

Insights into the formation of secondary organic carbon in the summertime in urban Shanghai.

To investigate formation mechanisms of secondary organic carbon (SOC) in Eastern China, measurements were conducted in an urban site in Shanghai in the summer of 2015. A period of high O3 concentrations (daily peak > 120 ppb) was observed, during which daily maximum SOC concentrations exceeding 9.0 µg/(C·m3). Diurnal variations of SOC concentration and SOC/organic carbon (OC) ratio exhibited both daytime and nighttime peaks. The SOC concentrations correlated well with Ox (= O3 + NO2) and relative humidity in the daytime and nighttime, respectively, suggesting that secondary organic aerosol formation in Shanghai is driven by both photochemical production and aqueous phase reactions. Single particle mass spectrometry was used to examine the formation pathways of SOC. Along with the daytime increase of SOC, the number fraction of elemental carbon (EC) particles coated with OC quickly increased from 38.1% to 61.9% in the size range of 250-2000 nm, which was likely due to gas-to-particle partitioning of photochemically generated semi-volatile organic compounds onto EC particles. In the nighttime, particles rich in OC components were highly hygroscopic, and number fraction of these particles correlated well with relative humidity and SOC/OC nocturnal peaks. Meanwhile, as an aqueous-phase SOC tracer, particles that contained oxalate-Fe(III) complex also peaked at night. These observations suggested that aqueous-phase processes had an important contribution to the SOC nighttime formation. The influence of aerosol acidity on SOC formation was studied by both bulk and single particle level measurements, suggesting that the aqueous-phase formation of SOC was enhanced by particle acidity.

Hormone receptors analysis in Chinese patients with dermatofibrosarcoma protuberans.

BACKGROUND AND OBJECTIVES: Dermatofibrosarcoma protuberans (DFSP) is a relatively rare skin tumor. Clinical observations indicated that DFSP has a more aggressive behavior during pregnancy, which suggest there might be a hormonal influence on this tumor. We evaluated the expression of estrogen receptor (ER) and progesterone receptor (PR) in DFSP patients. METHODS: In our present case series, patients with histopathological-confirmed DFSP at a single institution were identified. The clinical, pathological, and immunohistochemical data were gathered for each patient. Expression of ER and PR were determined on formalin-fixed, paraffin-embedded tissue sections using immunohistochemistry. Some objective clinical and pathological indicators were then selected to compare between ER and PR status. RESULTS: Immunoreactivity revealed none of these tumors stained positively for ER, while eight tumors (28.6%) stained positively for PR. There was a statistically significant difference in the distribution of tumor location between the PR-positive/negative groups. CONCLUSIONS: This finding suggests that progesterone may have potential effects in growth of DFSPs. Further studies are needed to fully address this question.

Aligning Where to See and What to Tell: Image Captioning with Region-Based Attention and Scene-Specific Contexts.

Recent progress on automatic generation of image captions has shown that it is possible to describe the most salient information conveyed by images with accurate and meaningful sentences. In this paper, we propose an image captioning system that exploits the parallel structures between images and sentences. In our model, the process of generating the next word, given the previously generated ones, is aligned with the visual perception experience where the attention shifts among the visual regions-such transitions impose a thread of ordering in visual perception. This alignment characterizes the flow of latent meaning, which encodes what is semantically shared by both the visual scene and the text description. Our system also makes another novel modeling contribution by introducing scene-specific contexts that capture higher-level semantic information encoded in an image. The contexts adapt language models for word generation to specific scene types. We benchmark our system and contrast to published results on several popular datasets, using both automatic evaluation metrics and human evaluation. We show that either region-based attention or scene-specific contexts improves systems without those components. Furthermore, combining these two modeling ingredients attains the state-of-the-art performance.

Rac1-mediated cytoskeleton rearrangements induced by intersectin-1s deficiency promotes lung cancer cell proliferation, migration and metastasis.

BACKGROUND: The mechanisms involved in lung cancer (LC) progression are poorly understood making discovery of successful therapies difficult. Adaptor proteins play a crucial role in cancer as they link cell surface receptors to specific intracellular pathways. Intersectin-1s (ITSN-1s) is an important multidomain adaptor protein implicated in the pathophysiology of numerous pulmonary diseases. To date, the role of ITSN-1s in LC has not been studied. METHODS: Human LC cells, human LC tissue and A549 LC cells stable transfected with myc-ITSN-1s construct (A549 + ITSN-1s) were used in correlation with biochemical, molecular biology and morphological studies. In addition scratch assay with time lapse microscopy and in vivo xenograft tumor and mouse metastasis assays were performed. RESULTS: ITSN-1s, a prevalent protein of lung tissue, is significantly downregulated in human LC cells and LC tissue. Restoring ITSN-1s protein level decreases LC cell proliferation and clonogenic potential. In vivo studies indicate that immunodeficient mice injected with A549 + ITSN-1s cells develop less and smaller metastatic tumors compared to mice injected with A549 cells. Our studies also show that restoring ITSN-1s protein level increases the interaction between Cbl E3 ubiquitin ligase and Eps8 resulting in enhanced ubiquitination of the Eps8 oncoprotein. Subsequently, downstream unproductive assembly of the Eps8-mSos1 complex leads to impaired activation of the small GTPase Rac1. Impaired Rac1 activation mediated by ITSN-1s reorganizes the cytoskeleton (increased thick actin bundles and focal adhesion (FA) complexes as well as collapse of the vimentin filament network) in favor of decreased LC cell migration and metastasis. CONCLUSION: ITSN-1s induced Eps8 ubiquitination and impaired Eps8-mSos1 complex formation, leading to impaired activation of Rac1, is a novel signaling mechanism crucial for abolishing the progression and metastatic potential of LC cells.

[Review on biological treatment of cyanobacterial toxin in natural waters]. / çŽ¯å¢ƒæ°´ä½“è—»æ¯’ç´ ç”Ÿç‰©å¤„ç†æŠ€æœ¯ç ”ç©¶è¿›å±•.

In recent years, eutrophication has not been effectively controlled and algal pollution still breaks out frequently. At the same time, the cyanobacteria and release of algal toxins have posed a serious threat to the environment and human health. In this review, the classification and main physicochemical properties of algal toxins were summarized, and the biological removal strategies of algal toxins including biological contact oxidation process, biological filter process and biological ecological coupling process and their biodegradation mechanisms were reviewed. The influence factors on biological treatment process, including operation conditions (temperature, pH and hydraulic retention time), water property and nutrient limitation factors, were analyzed. The research on the removalmechanisms of other algae toxins and the development and optimization of new biological technology were prospected to develop and improve algal toxin removal technology and ensure drin-king water safety.

Potential risk and control strategy of biofilm pretreatment process treating raw water.

An enhanced lab-scale biofilm pretreatment process treating raw water obtained from eutrophicated water bodies was established and started up with a novel strategy of low-level nutrients addition and effluent recirculation. Results showed that the startup strategy was useful for biofilm formation and pollutants removal, but it had the risks of increasing substrate affinity constant (Ks) and biofilm decay in treating raw water. Fortunately, the increased Ks value did not affected the NH4(+)-N removal performance via keeping the NH4(+)-N loading rate larger than 6.29 mg L(-1)d(-1). In addition, lower hydraulic retention time (HRT) favored the removal of organic matters, and the maximum TOC removal rate of 76.5 mg L(-1)d(-1) were achieved at HRT of 2h. After long-term acclimatization at oligotrophic niche, the decrease of Ks value and increase of biomass, extracellular polymeric substances, bioactivity were achieved. Finally, the stable operation of biofilm pretreatment process was realized in treating polluted raw water.

Performance and enhanced mechanism of a novel bio-diatomite biofilm pretreatment process treating polluted raw water.

A lab-scale novel bio-diatomite biofilm process (BDBP) was established for the polluted raw water pretreatment in this study. Results showed that a shorter startup period of BDBP system was achieved under the completely circulated operation mode, and the removal efficiencies of nitrogen and disinfection by-product precursor were effective at low hydraulic retention time of 2-4 h due to high biomass attached to the carrier and diatomite. A maximum NH4(+)-N oxidation potential predicted by modified Stover-Kincannon model was 333.3 mg L(-1) d(-1) in the BDBP system, which was 4.7 times of that in the control reactor. Results demonstrated that the present of bio-diatomite favors the accumulation of functional microbes in the oligotrophic niche, and the pollutants removal performance of this novel process was enhanced for polluted raw water pretreatment.

Endocytic deficiency induced by ITSN-1s knockdown alters the Smad2/3-Erk1/2 signaling balance downstream of Alk5.

Recently, we demonstrated in cultured endothelial cells and in vivo that deficiency of an isoform of intersectin-1, ITSN-1s, impairs caveolae and clathrin-mediated endocytosis and functionally upregulates compensatory pathways and their morphological carriers (i.e. enlarged endocytic structures, membranous rings or tubules) that are normally underrepresented. We now show that these endocytic structures internalize the broadly expressed transforming growth factor ß receptor I (TGFß-RI or TGFBR1), also known as Alk5, leading to its ubiquitylation and degradation. Moreover, the apoptotic or activated vascular cells of the ITSN-1s-knockdown mice release Alk5-bearing microparticles to the systemic circulation. These interact with and transfer Alk5 to endocytosis-deficient endothelial cells, resulting in lung endothelial cell survival and phenotypic alteration towards proliferation through activation of Erk1 and Erk2 (also known as MAPK3 and MAPK1, respectively). We also show that non-productive assembly of the Alk5-Smad-SARA (Smad anchor for receptor activation, also known as ZFYVE9) signaling complex and preferential formation of the Alk5-mSos-Grb2 complex account for Erk1/2 activation downstream of Alk5 and proliferation of pulmonary endothelial cells. Taken together, our studies demonstrate a functional relationship between the intercellular transfer of Alk5 by microparticles and endothelial cell survival and proliferation, and define a novel molecular mechanism for TGFß and Alk5-dependent Erk1/2(MAPK) signaling that is significant for proliferative signaling and abnormal growth.

Biomarkers for cancer-related fatigue and adverse reactions to chemotherapy in lung cancer patients.

This study was conducted to investigate the biomarkers that appear to be correlated with cancer-related fatigue (CRF) and the adverse reactions (ADRs) to chemotherapy. A total of 100 lung cancer patients were selected and CRF prior to and following chemotherapy was evaluated. The plasma levels of tumor necrosis factor (TNF)-α and interleukin (IL)-1 and the level of 17-hydroxycorticosteroid (17-OHCS) in the urine were analyzed and correlated with CRF and the ADRs associated with chemotherapy. The incidence of CRF was found to be 88.0% and ADRs following chemotherapy occurred in 15.0% of the patients. An increase in the TNF-α and IL-1 levels was detected in patients with CRF. The level of 17-OHCS in the urine was found to be elevated in cases that experienced ADRs following chemotherapy. In conclusion, CRF is closely correlated with increased plasma levels of TNF-α and IL-1. Furthermore, an abnormally elevated 17-OHCS level in the urine may be an important indicator predicting ADR development following chemotherapy.

[Clinicopathologic features and expression of epidermal growth factor receptor and vascular endothelial growth factor in adrenocortical tumors].

OBJECTIVE: To study the clinicopathologic features and expression of epidermal growth factor receptor (EGFR) and vascular endothelial growth factor (VEGF) in adrenocortical tumors. METHODS: Forty-two cases of adrenocortical tumors operated at the Beijing Union Medical College Hospital during the period from July, 2001 to July, 2010 were retrospectively reviewed. Immunohistochemical study for EGFR and VEGF was carried out. The clinical information and follow-up data were analyzed. RESULTS: The cases included 21 adrenocortical carcinomas (ACC) and 21 adrenocortical adenomas (ACA). Nine patients suffered from primary aldosterone syndrome, including 8 cases with ACA and 1 case with ACC. The average tumor size, tumor weight, and duration between disease onset and diagnosis in the 21 cases of ACC were 11.7 cm, 542 g and 8.5 months, respectively. This was in contrast to 3 cm, 9.8 g and 45.6 months, respectively in cases of ACA. Histologically, the WEISS score in all the 21 cases of ACA was ≤ 2 (average = 0.9). None of the ACC cases had score less than 4 (average = 6.6). The presence of sinus invasion correlated with tumor metastasis (P < 0.01). Immunohistochemical study showed that EGFR was expressed in 61.9% of ACC patients (13/21), whereas EGFR staining was mostly negative in ACA (except for weak staining in 5 cases and moderate staining in 1 case). The difference of EGFR expression between ACC and ACA was statistically significant (P = 0.030). On the other hand, the positive rate of VEGF in ACC was 71.4% (15/21), including 28.6% (6/21) with strong expression and 28.6% (6/21) with moderate expression. In contrast, the expression rate of VEGF in ACA was 30.0% (7/21), including 14.3% (3/21) with moderate expression. The difference of VEGF expression between ACC and ACA was statistically significant (P = 0.013). There was correlation between VEGF expression and venous invasion (P = 0.028). The average duration of survival in patients with ACC was shorter than that in ACA. The tumor weight in ACC also correlated with prognosis. CONCLUSIONS: Tumor size, weight and presence of endocrine symptoms may help in the differential diagnosis between ACC and ACA. A WEISS score of ≥ 3 highly suggests ACC. The presence of sinus invasion is associated with metastasis. EGFR or VEGF expression may also be important in differentiating ACC from ACA.