Image_3_v1_A modified survival model for patients with esophageal squamous cell carcinoma based on lymph nodes: A study based on SEER database and external validation.tif

BackgroundThe counts of examined lymph nodes (ELNs) in predicting the prognosis of patients with esophageal squamous cell carcinoma (ESCC) is a controversial issue. We conducted a retrospective study to develop an ELNs-based model to individualize ESCC prognosis.MethodsPatients with ESCC from the SEER database and our center were strictly screened. The optimal threshold value was determine by the X-tile software. A prognostic model for ESCC patients was developed and validated with R. The model’s efficacy was evaluated by C-index, ROC curve, and decision curve analysis (DCA).Results3,629 cases and 286 cases were screened from the SEER database and our center, respectively. The optimal cut-off value of ELNs was 10. Based on this, we constructed a model with a favorable C-index (training group: 0.708; external group 1: 0.687; external group 2: 0.652). The model performance evaluated with ROC curve is still reliable among the groups. 1-year AUC for nomogram in three groups (i.e., 0.753, 0.761, and 0.686) were superior to that of the TNM stage (P ConclusionMore than 10 ELNs are helpful to evaluate the survival of ESCC patients. Based on this, an improved model for predicting the prognosis of ESCC patients was proposed.</p

Image_2_v1_A modified survival model for patients with esophageal squamous cell carcinoma based on lymph nodes: A study based on SEER database and external validation.tif

BackgroundThe counts of examined lymph nodes (ELNs) in predicting the prognosis of patients with esophageal squamous cell carcinoma (ESCC) is a controversial issue. We conducted a retrospective study to develop an ELNs-based model to individualize ESCC prognosis.MethodsPatients with ESCC from the SEER database and our center were strictly screened. The optimal threshold value was determine by the X-tile software. A prognostic model for ESCC patients was developed and validated with R. The model’s efficacy was evaluated by C-index, ROC curve, and decision curve analysis (DCA).Results3,629 cases and 286 cases were screened from the SEER database and our center, respectively. The optimal cut-off value of ELNs was 10. Based on this, we constructed a model with a favorable C-index (training group: 0.708; external group 1: 0.687; external group 2: 0.652). The model performance evaluated with ROC curve is still reliable among the groups. 1-year AUC for nomogram in three groups (i.e., 0.753, 0.761, and 0.686) were superior to that of the TNM stage (P ConclusionMore than 10 ELNs are helpful to evaluate the survival of ESCC patients. Based on this, an improved model for predicting the prognosis of ESCC patients was proposed.</p

Additional file 1:Figure S1. of Calcium/calmodulin alleviates substrate inhibition in a strawberry UDP-glucosyltransferase involved in fruit anthocyanin biosynthesis

Amino acid sequence alignment of FvUGT1 and six other plant UGTs. Color and intensity change indicates the differences in the level of conservation. Dark red and dark blue represent the highest and lowest conserved levels, respectively. α-helices and β-strands are marked by black lines. The putative secondary plant glycosyltransferase (PSPG) motif is underlined by a red line and 10 conserved sugar donor interacting residues of the PSPG motif are marked with black solid triangles. The putative calmodulin-binding region in FvUGT1 is indicated by a black open box. The GenBank accession numbers or sources of proteins are FvUGT1 (KP165417; F. vesca), VtGT1 (AAB81682; grape), FaGT1 (AAU09442; F. × ananassa), Ct3GT (BAF49297; C. ternatea), MtUGT78G1 (A6XNC6.1; M. truncatula), AtUGT72B1 (Q9M156.1; A. thialiana), MtUGT71G1 (AAW56092.1; M. truncatula), and MtUGT85H2 (2PQ6_A; M. truncatula). (PDF 787 kb

Image_1_v1_A modified survival model for patients with esophageal squamous cell carcinoma based on lymph nodes: A study based on SEER database and external validation.tif

BackgroundThe counts of examined lymph nodes (ELNs) in predicting the prognosis of patients with esophageal squamous cell carcinoma (ESCC) is a controversial issue. We conducted a retrospective study to develop an ELNs-based model to individualize ESCC prognosis.MethodsPatients with ESCC from the SEER database and our center were strictly screened. The optimal threshold value was determine by the X-tile software. A prognostic model for ESCC patients was developed and validated with R. The model’s efficacy was evaluated by C-index, ROC curve, and decision curve analysis (DCA).Results3,629 cases and 286 cases were screened from the SEER database and our center, respectively. The optimal cut-off value of ELNs was 10. Based on this, we constructed a model with a favorable C-index (training group: 0.708; external group 1: 0.687; external group 2: 0.652). The model performance evaluated with ROC curve is still reliable among the groups. 1-year AUC for nomogram in three groups (i.e., 0.753, 0.761, and 0.686) were superior to that of the TNM stage (P ConclusionMore than 10 ELNs are helpful to evaluate the survival of ESCC patients. Based on this, an improved model for predicting the prognosis of ESCC patients was proposed.</p

DataSheet_2_Discovery of potent STAT3 inhibitors using structure-based virtual screening, molecular dynamic simulation, and biological evaluation.csv

IntroductionSignal transducer and activator of transcription 3 (STAT3) is ubiquitously hyper-activated in numerous cancers, rendering it an appealing target for therapeutic intervention.Methods and resultsIn this study, using structure-based virtual screening complemented by molecular dynamics simulations, we identified ten potential STAT3 inhibitors. The simulations pinpointed compounds 8, 9, and 10 as forming distinct hydrogen bonds with the SH2 domain of STAT3. In vitro cytotoxicity assays highlighted compound 4 as a potent inhibitor of gastric cancer cell proliferation across MGC803, KATO III, and NCI-N87 cell lines. Further cellular assays substantiated the ability of compound 4 to attenuate IL-6-mediated STAT3 phosphorylation at Tyr475. Additionally, oxygen consumption rate assays corroborated compound 4's deleterious effects on mitochondrial function.DiscussionCollectively, our findings position compound 4 as a promising lead candidate warranting further exploration in the development of anti-gastric cancer therapeutics.</p

DataSheet_1_Discovery of potent STAT3 inhibitors using structure-based virtual screening, molecular dynamic simulation, and biological evaluation.pdf

IntroductionSignal transducer and activator of transcription 3 (STAT3) is ubiquitously hyper-activated in numerous cancers, rendering it an appealing target for therapeutic intervention.Methods and resultsIn this study, using structure-based virtual screening complemented by molecular dynamics simulations, we identified ten potential STAT3 inhibitors. The simulations pinpointed compounds 8, 9, and 10 as forming distinct hydrogen bonds with the SH2 domain of STAT3. In vitro cytotoxicity assays highlighted compound 4 as a potent inhibitor of gastric cancer cell proliferation across MGC803, KATO III, and NCI-N87 cell lines. Further cellular assays substantiated the ability of compound 4 to attenuate IL-6-mediated STAT3 phosphorylation at Tyr475. Additionally, oxygen consumption rate assays corroborated compound 4's deleterious effects on mitochondrial function.DiscussionCollectively, our findings position compound 4 as a promising lead candidate warranting further exploration in the development of anti-gastric cancer therapeutics.</p

Data_Sheet_1_Nutrition by Design: Boosting Selenium Content and Fresh Matter Yields of Salad Greens With Preharvest Light Intensity and Selenium Applications.PDF

Selenium (Se) is an essential mineral in multiple human metabolic pathways with immune modulatory effects on viral diseases including the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and HIV. Plant-based foods contain Se metabolites with unique functionalities for the human metabolism. In order to assess the value of common salad greens as Se source, we conducted a survey of lettuce commercially grown in 15 locations across the USA and Canada and found a tendency for Se to accumulate higher (up to 10 times) in lettuce grown along the Colorado river basin region, where the highest amount of annual solar radiation of the country is recorded. In the same area, we evaluated the effect of sunlight reduction on the Se content of two species of arugula [Eruca sativa (E. sativa) cv. “Astro” and Diplotaxis tenuifolia (D. tenuifolia) cv. “Sylvetta”]. A 90% light reduction during the 7 days before harvest resulted in over one-third Se decline in D. tenuifolia. The effect of light intensity on yield and Se uptake of arugula microgreens was also examined under indoor controlled conditions. This included high intensity (HI) (160 μ mol−2 s−1 for 12 h/12 h light/dark); low intensity (LI) (70 μ mol m−2 s−1 for 12 h/12 h light/dark); and HI-UVA (12 h light of 160 μ mol m−2 s−1, 2 h UVA of 40 μ mol m−2 s−1, and 10 h dark) treatments in a factorial design with 0, 1, 5, and 10 ppm Se in the growing medium. HI and HI-UVA produced D. tenuifolia plants with 25–100% higher Se content than LI, particularly with the two higher Se doses. The addition of Se produced a marked increase in fresh matter (>35% in E. sativa and >45% in D. tenuifolia). This study (i) identifies evidence to suggest the revision of food composition databases to account for large Se variability, (ii) demonstrates the potential of introducing preharvest Se to optimize microgreen yields, and (iii) provides the controlled environment industry with key information to deliver salad greens with targeted Se contents.</p

Metabolomic Assessment Reveals an Elevated Level of Glucosinolate Content in CaCl₂ Treated Broccoli Microgreens

Preharvest calcium application has been shown to increase broccoli microgreen yield and extend shelf life. In this study, we investigated the effect of calcium application on its metabolome using ultra-high-performance liquid chromatography with mass spectrometry. The data collected were analyzed using principal component analysis and orthogonal projection to latent structural discriminate analysis. Chemical composition comparison shows that glucosinolates, a very important group of phytochemicals, are the major compounds enhanced by preharvest treatment with 10 mM calcium chloride (CaCl₂). Aliphatic glucosinolates (glucoerucin, glucoiberin, glucoiberverin, glucoraphanin, pentyl glucosinolate, and hexyl glucosinolate) and indolic glucosinolates (glucobrassicin, neoglucobrassicin, and 4-hydroxyglucobrassicin) were increased significantly in the CaCl₂ treated microgreens using metabolomic approaches. Targeted glucosinolate analysis using the ISO 9167-1 method was further employed to confirm the findings. Results indicate that glucosinolates can be considered as a class of compounds that are responsible for the difference between two groups and a higher glucosinolate level was found in CaCl₂ treated groups at each time point after harvest in comparison with the control group

DataSheet1_Contrastive learning and subtyping of post-COVID-19 lung computed tomography images.docx

Patients who recovered from the novel coronavirus disease 2019 (COVID-19) may experience a range of long-term symptoms. Since the lung is the most common site of the infection, pulmonary sequelae may present persistently in COVID-19 survivors. To better understand the symptoms associated with impaired lung function in patients with post-COVID-19, we aimed to build a deep learning model which conducts two tasks: to differentiate post-COVID-19 from healthy subjects and to identify post-COVID-19 subtypes, based on the latent representations of lung computed tomography (CT) scans. CT scans of 140 post-COVID-19 subjects and 105 healthy controls were analyzed. A novel contrastive learning model was developed by introducing a lung volume transform to learn latent features of disease phenotypes from CT scans at inspiration and expiration of the same subjects. The model achieved 90% accuracy for the differentiation of the post-COVID-19 subjects from the healthy controls. Two clusters (C1 and C2) with distinct characteristics were identified among the post-COVID-19 subjects. C1 exhibited increased air-trapping caused by small airways disease (4.10%, p = 0.008) and diffusing capacity for carbon monoxide %predicted (DLCO %predicted, 101.95%, p < 0.001), while C2 had decreased lung volume (4.40L, p < 0.001) and increased ground glass opacity (GGO%, 15.85%, p < 0.001). The contrastive learning model is able to capture the latent features of two post-COVID-19 subtypes characterized by air-trapping due to small airways disease and airway-associated interstitial fibrotic-like patterns, respectively. The discovery of post-COVID-19 subtypes suggests the need for different managements and treatments of long-term sequelae of patients with post-COVID-19.</p

Development of a Portable Aerosol Collector and Spectrometer (PACS)

This article presents the development of a Portable Aerosol Collector and Spectrometer (PACS), an instrument designed to measure particle number, surface area, and mass concentrations continuously and time-weighted mass concentration by composition from 10 nm to 10 µm. The PACS consists of a six-stage particle size selector, a valve system, a water condensation particle counter to detect number concentrations, and a photometer to detect mass concentrations. The stages of the selector include three impactor and two diffusion stages, which resolve particles by size and collect particles for later chemical analysis. Particle penetration by size was measured through each stage to determine actual collection performance and account for particle losses. The data inversion algorithm uses an adaptive grid-search process with a constrained linear least-square solver to fit a tri-modal (ultrafine, fine, and coarse), log-normal distribution to the input data (number and mass concentration exiting each stage). The measured 50% cutoff diameter of each stage was similar to the design. The pressure drop of each stage was sufficiently low to permit its operation with portable air pumps. Sensitivity studies were conducted to explore the influence of unknown particle density (range from 500 to 3,000 kg/m3) and shape factor (range from 1.0 to 3.0) on algorithm output. Assuming standard density spheres, the aerosol size distributions fit well with a normalized mean bias of −4.9% to 3.5%, normalized mean error of 3.3% to 27.6%, and R2 values of 0.90 to 1.00. The fitted number and mass concentration biases were within ±10% regardless of uncertainties in density and shape. However, fitted surface area concentrations were more likely to be underestimated/overestimated due to the variation in particle density and shape. The PACS represents a novel way to simultaneously assess airborne aerosol composition and concentration by number, surface area, and mass over a wide size range. Copyright © 2018 American Association for Aerosol Research</p