Flexible/Regenerative Nanosensor with Automatic Sweat Collection for Cytokine Storm Biomarker Detection.

The real-time monitoring of low-concentration cytokines such as TNF-α in sweat can aid clinical physicians in assessing the severity of inflammation. The challenges associated with the collection and the presence of impurities can significantly impede the detection of proteins in sweat. This issue is addressed by incorporating a nanosphere array designed for automatic sweat transportation, coupled with a reusable sensor that employs a Nafion/aptamer-modified MoS2 field-effect transistor. The nanosphere array with stepwise wettability enables automatic collection of sweat and blocks impurities from contaminating the detection zone. This device enables direct detection of TNF-α proteins in undiluted sweat, within a detection range of 10 fM to 1 nM. The use of an ultrathin, ultraflexible substrate ensures stable electrical performance, even after up to 30 extreme deformations. The findings indicate that in clinical scenarios, this device could potentially provide real-time evaluation and management of patients' immune status via sweat testing.

Yeast9: a consensus genome-scale metabolic model for S. cerevisiae curated by the community.

Genome-scale metabolic models (GEMs) can facilitate metabolism-focused multi-omics integrative analysis. Since Yeast8, the yeast-GEM of Saccharomyces cerevisiae, published in 2019, has been continuously updated by the community. This has increased the quality and scope of the model, culminating now in Yeast9. To evaluate its predictive performance, we generated 163 condition-specific GEMs constrained by single-cell transcriptomics from osmotic pressure or reference conditions. Comparative flux analysis showed that yeast adapting to high osmotic pressure benefits from upregulating fluxes through central carbon metabolism. Furthermore, combining Yeast9 with proteomics revealed metabolic rewiring underlying its preference for nitrogen sources. Lastly, we created strain-specific GEMs (ssGEMs) constrained by transcriptomics for 1229 mutant strains. Well able to predict the strains' growth rates, fluxomics from those large-scale ssGEMs outperformed transcriptomics in predicting functional categories for all studied genes in machine learning models. Based on those findings we anticipate that Yeast9 will continue to empower systems biology studies of yeast metabolism.

Pediococcus pentosaceus PP34 Ameliorates 5-Fluorouracil-Induced Intestinal Mucositis via Inhibiting Oxidative Stress and Restoring the Gut Microbiota.

Chemotherapy-induced intestinal mucositis based on 5-fluorouracil (5-FU) slows down the progress of cancer treatment and causes significant suffering to patients. Pediococcus pentosaceus (P. pentosaceus), as a type of LAB, has a range of probiotic properties, including antioxidant, immune benefits, and cholesterol-lowering effects, which are attracting increasing attention. However, studies on the protective effect of P. pentosaceus against chemotherapeutic-induced intestinal mucositis caused by 5-FU remain unclear. Therefore, this study aimed to investigate the potential relieving effects of P. pentosaceus PP34 on 5-FU-induced intestinal mucositis and its mechanism. In the present study, a P. pentosaceus PP34 solution (2 × 109 CFU/mL) was administered daily by gavage followed by intraperitoneal injection of 5-FU to model intestinal mucositis. The body weight, serum biochemical indices, jejunal pathological organization, and expression levels of inflammatory cytokines in the jejunum were examined. The results indicated that the mice induced with 5-FU developed typical intestinal mucositis symptoms and histopathological changes with intense inflammatory and oxidative responses. Moreover, the gut microbiota was disturbed, while PP34 effectively decreased the oxidative reactions and the expression levels of inflammatory mediators and regulated the gut microbiota in 5-FU-exposed mice. Taken together, the study indicated that P. pentosaceus PP34 ameliorates 5-Fluorouracil-induced intestinal mucositis via inhibiting oxidative stress and restoring the gut microbiota.

Sustainable Oily Liquid-Proof Passive Cooling (SOC) Textile for Personal Thermal Management.

Sweat passive-cooling textiles with asymmetric wettabilities on different sides offer an effective and low-energy consumption solution to personal thermal management in extreme thermal environments. However, the sweat-wicking and the cooling abilities decrease when the textile is contaminated by low-surface tension oily liquid fouling. The integration of anti-oily liquid fouling and sweat-wicking abilities on textile involves resolving the contradiction between hydrophilic and oleophobic properties and seeking eco-friendly short-chain fluorides to reduce the surface energy. Herein, a sustainable oily liquid-proof passive cooling (SOC) textile for personal thermal management is proposed. The SOC textile is obtained by applying a fluoride-free hydrophobic coating layer to one side of the high thermal conductive superoleophobic/superhydrophilic basal textile, which is fabricated using eco-friendly short-chain fluoride. The SOC textile preserves the anti-oily liquid fouling property even after 2000 abrasion cycles. Experimental test revealed that the SOC textile exhibits a cooling effect of ≈5 °C compared with the cotton textile, and the up to 70% reduction in sweating rate under the constant metabolic heat production rates. The configuration of the SOC textile would inspire the future design of intelligent textiles for personal thermal management, and the proposed strategy have implications for fabrication of eco-friendly oil-water separation materials.

Seaweed-inspired underwater anti-oil-fouling and anti-fogging coating with mechanical durability.

Ecofriendly fabrication of anti-oil-fouling materials is of interest. Surfaces with underwater superoleophobicity have been fabricated which exhibit limited mechanical durability and water resistance. In this study, we report on a bioinspired bilayer design of a transparent anti-oil-fouling coating. Seaweed surfaces show anti-oil-fouling in the sea due to its high surface hydration ability. Mussels can adhere tightly onto a surface with good stability in the sea by virtue of its levodopa-containing secretions. The surface layer was fabricated using a crosslinked combination of carboxymethyl cellulose (CMC) and sodium alginate (AlgS) inspired by seaweed, with the addition of calcium ions. Polydopamine (PDA), a derivative of levodopa, was used as the underlayer to enhance bonding strength and water resistance. Oil that adhered to the coated surface was spontaneously detached upon immersion in water. The mechanism underlying this anti-oil-fouling effect was elucidated using Gibbs free energy theory. The coating exhibited mechanical durability and water resistance. The coating is transparent and preserves the original color of the substrate. The coated glass showed stable anti-fogging and anti-frost performance. These coatings hold promise for a wide range of anti-oil-fouling applications.

Metagenomic shotgun sequencing reveals the enrichment of Salmonella and Mycobacterium in larynx due to prolonged ethanol exposure.

The exposure of ethanol increases the risk of head and neck inflammation and tumor progression. However, limited studies have investigated the composition and functionality of laryngeal microbiota under ethanol exposure. We established an ethanol-exposed mouse model to investigate the changes in composition and function of laryngeal microbiota using Metagenomic shotgun sequencing. In the middle and late stages of the experiment, the laryngeal microbiota of mice exposed to ethanol exhibited obvious distinguished from that of the control group on principal-coordinate analysis (PCoA) plots. Among the highly abundant species, Salmonella enterica and Mycobacterium marinum were likely to be most impacted. Our findings indicated that the exposure to ethanol significantly increased their abundance in larynxes in mice of the same age, which has been confirmed through FISH experiments. Among the species-related functions and genes, metabolism is most severely affected by ethanol. The difference was most obvious in the second month of the experiment, which may be alleviated later because the animal established tolerance. Notable enrichments concerning energy, amino acid, and carbohydrate metabolic pathways occurred during the second month under ethanol exposure. Finally, based on the correlation between species and functional variations, a network was established to investigate relationships among microbiota, functional pathways, and related genes affected by ethanol. Our data first demonstrated the continuous changes of abundance, function and their interrelationship of laryngeal microbiota under ethanol exposure by Metagenomic shotgun sequencing. Importance: Ethanol may participate in the inflammation and tumor progression by affecting the composition of the laryngeal microbiota. Here, we applied the metagenomic shotgun sequencing instead of 16 S rRNA sequencing method to identify the laryngeal microbiota under ethanol exposure. Salmonella enterica and Mycobacterium marinum are two dominant species that may play a role in the reconstruction of the laryngeal microenvironment, as their local abundance increases following exposure to ethanol. The metabolic function is most evidently impacted, and several potential metabolic pathways could be associated with alterations in microbiota composition. These findings could help us better understand the impact of prolonged ethanol exposure on the microbial composition and functionality in the larynx.

Reconstruction, simulation and analysis of enzyme-constrained metabolic models using GECKO Toolbox 3.0.

Genome-scale metabolic models (GEMs) are computational representations that enable mathematical exploration of metabolic behaviors within cellular and environmental constraints. Despite their wide usage in biotechnology, biomedicine and fundamental studies, there are many phenotypes that GEMs are unable to correctly predict. GECKO is a method to improve the predictive power of a GEM by incorporating enzymatic constraints using kinetic and omics data. GECKO has enabled reconstruction of enzyme-constrained metabolic models (ecModels) for diverse organisms, which show better predictive performance than conventional GEMs. In this protocol, we describe how to use the latest version GECKO 3.0; the procedure has five stages: (1) expansion from a starting metabolic model to an ecModel structure, (2) integration of enzyme turnover numbers into the ecModel structure, (3) model tuning, (4) integration of proteomics data into the ecModel and (5) simulation and analysis of ecModels. GECKO 3.0 incorporates deep learning-predicted enzyme kinetics, paving the way for improved metabolic models for virtually any organism and cell line in the absence of experimental data. The time of running the whole protocol is organism dependent, e.g., ~5 h for yeast.

Reconstruction and metabolic profiling of the genome-scale metabolic network model of Pseudomonas stutzeri A1501.

Pseudomonas stutzeri A1501 is a non-fluorescent denitrifying bacteria that belongs to the gram-negative bacterial group. As a prominent strain in the fields of agriculture and bioengineering, there is still a lack of comprehensive understanding regarding its metabolic capabilities, specifically in terms of central metabolism and substrate utilization. Therefore, further exploration and extensive studies are required to gain a detailed insight into these aspects. This study reconstructed a genome-scale metabolic network model for P. stutzeri A1501 and conducted extensive curations, including correcting energy generation cycles, respiratory chains, and biomass composition. The final model, iQY1018, was successfully developed, covering more genes and reactions and having higher prediction accuracy compared with the previously published model iPB890. The substrate utilization ability of 71 carbon sources was investigated by BIOLOG experiment and was utilized to validate the model quality. The model prediction accuracy of substrate utilization for P. stutzeri A1501 reached 90 %. The model analysis revealed its new ability in central metabolism and predicted that the strain is a suitable chassis for the production of Acetyl CoA-derived products. This work provides an updated, high-quality model of P. stutzeri A1501for further research and will further enhance our understanding of the metabolic capabilities.

Fusobacterium nucleatum-triggered purine metabolic reprogramming drives tumorigenesis in head and neck carcinoma.

BACKGROUND: Fusobacterium nucleatum (F. nucleatum) is a vital pro-oncogenic bacterium. Our previous study revealed that a high abundance of F. nucleatum in head and neck squamous cell carcinoma (HNSCC) is correlated with poor patient prognosis. However, the impact of F. nucleatum on metabolic reprogramming and tumor progression in HNSCC awaits more exploration. METHODS: Liquid chromatography‒mass spectrometry (LC‒MS) was applied to analyze the altered metabolites in a head and neck carcinoma cell line (AMC-HN-8) after coculture with F. nucleatum for 24 hrs and 48 hrs. Both univariate and multivariate analyses were used to screen for differential metabolites. Kyoto Encyclopedia of Genes and Genomes (KEGG) metabolic pathway enrichment analysis was further used to explore the metabolic changes. RESULTS: We observed a significantly altered metabolic profile in AMC-HN-8 cells over time after coculture with F. nucleatum. Among the several enriched pathways, the purine metabolic pathway was the most significantly enriched (P = 0.0005), with downregulation of purine degradation. Furthermore, uric acid, the end product of purine metabolism, significantly reversed F. nucleatum-triggered tumor progression and altered the intracellular reactive oxygen species (ROS) level. Moreover, the negative correlation between the serum uric acid level and the abundance of F. nucleatum was verified in 113 HNSCC patients (P = 0.0412, R = - 0.1924). CONCLUSIONS: Our study revealed obviously aberrant purine metabolism driven by F. nucleatum in HNSCC, which was closely related to tumor progression and patient prognosis. These findings indicate the possibility of targeting F. nucleatum-induced purine metabolism reprogramming in the future treatment of HNSCC.

Evaluating the prognostic significance of p53 and TP53 mutations in HPV-negative hypopharyngeal carcinoma patients: a 5-year follow-up retrospective study.

PURPOSE: To evaluate prognostic significance of human papillomavirus (HPV) in hypopharyngeal squamous cell carcinoma patients, and to investigate the effect of p53 and TP53 mutations on the prognosis of patients. METHODS: A total of 111 patients were enrolled in our retrospective study. HPV infection status was detected in formalin-fixed paraffin-embedded tissue by real-time multiplex PCR test. p53 expression was evaluate by immunohistochemical staining. TP53 exon mutations were analyzed by PCR amplification and Sanger sequencing. HPV infection status, p53 expression and TP53 mutation were compared with clinical outcome including overall survival and recurrence-free survival by Kaplan-Meier method and Log-rank test. RESULTS: Of the 111 investigated patients, 18 (16.22%) were positive for HPV infection. HPV(-) patients have a worse clinical outcome than HPV(+) patients. TP53 mutations have similar mutation rates in patients with and without HPV (55.56% vs. 41.94%). p53 and TP53 mutation were not associated with prognosis of patients in HPV(-) patients. TP53 disruptive mutations were found both in patients with or without HPV infection. Furthermore, TP53 non-disruptive mutation had a significantly better clinical outcome than those with disruptive mutation in HPV(-) patients. CONCLUSION: Our results showed that HPV infection status is a strong prognostic indicator of survival. p53 and TP53 mutations do not appear to significantly impact survival in HPV(-) patients. TP53 disruptive mutation is associated with reduced survival in HPV(-)/TP53 mutation patients.

Extrahepatic transplantation of 3D cultured stem cell-derived islet organoids on microporous scaffolds.

Stem cell differentiation methods have been developed to produce cells capable of insulin secretion which are showing promise in clinical trials for treatment of type-1 diabetes. Nevertheless, opportunities remain to improve cell maturation and function. Three-dimensional (3D) culture has demonstrated improved differentiation and metabolic function in organoid systems, with biomaterial scaffolds employed to direct cell assembly and facilitate cell-cell contacts. Herein, we investigate 3D culture of human stem cell-derived islet organoids, with 3D culture initiated at the pancreatic progenitor, endocrine progenitor, or immature ß-cell stage. Clusters formed by reaggregation of immature ß-cells could be readily seeded into the microporous poly(lactide-co-glycolide) scaffold, with control over cell number. Culture of islet organoids on scaffolds at the early to mid-stage beta cell progenitors had improved in vitro glucose stimulated insulin secretion relative to organoids formed at the pancreatic progenitor stage. Reaggregated islet organoids were transplanted into the peritoneal fat of streptozotocin-induced diabetic mice, which resulted in reduced blood glucose levels and the presence of systemic human C-peptide. In conclusion, 3D cell culture supports development of islet organoids as indicated by insulin secretion in vitro and supports transplantation to extrahepatic sites that leads to a reduction of hyperglycemia in vivo.

A Survival Nomogram Containing Lymph Node Ratio for Hypopharyngeal Carcinoma Patients After Neck Dissection.

OBJECTIVE: The purpose of our study is to establish a survival nomogram based on lymph node ratio (LNR) in hypopharyngeal carcinoma. STUDY DESIGN: Retrospective cohort study. SETTING: Hypopharyngeal squamous cell carcinoma (HPSCC) is prone to regional metastasis. Emerging evidence has shown that LNR is a promising prognostic factor in HPSCC. METHODS: From January 2004 to January 2018, 411 HPSCC patients who underwent neck dissection at our institution were retrospectively studied. The enrolled patients were divided into training and validation cohorts at a ratio of 7:3. A survival nomogram was finally built based on factors screened from multivariate analysis using the bidirectional stepwise method. RESULTS: LNR was superior to other nodal classifications for survival prediction and was used to establish the R classification. A nomogram was developed using R classification (p < .001), pT classification (p < .001), tumor invasive depth (p < .001), and internal jugular vein adhesion (p = .001). The C-indexes were 0.712 and 0.703 in the training and validation dataset. The 36- and 60-month AUCs were 0.767 and 0.766 in the training dataset and 0.713 and 0.757 in the validation dataset, respectively. The calibration curves showed relatively good agreement between the predicted and actual probability. CONCLUSION: Based on the LNR, we developed a survival nomogram for HPSCC after neck dissection, which will be a practical tool to discriminate patients with different survival risks.

Monitoring Cd2+ in oily wastewater using an aptamer-graphene field-effect transistor with a selective wetting surface.

The discharge of oily industrial wastewater containing heavy metal ions with the development of industry severely threatens the environment and human health. Therefore, it is of great significance to monitor the concentration of heavy metal ions in oily wastewater quickly and effectively. Here, an integrated Cd2+ monitoring system consisting of an aptamer-graphene field-effect transistor (A-GFET), oleophobic/hydrophilic surface and monitoring-alarm circuits was presented for monitoring the Cd2+ concentration in oily wastewater. In the system, oil and other impurities in wastewater are isolated by an oleophobic/hydrophilic membrane before detection. The concentration of Cd2+ is then detected by a graphene field-effect transistor with a Cd2+ aptamer modifying the graphene channel. Finally, the detected signal is collected and processed by signal processing circuits to judge whether the Cd2+ concentration exceeds the standard. Experimental results demonstrated that the separation efficiency of the oleophobic/hydrophilic membrane to an oil/water mixture was up to 99.9%, exhibiting a high oil/water separation ability. The A-GFET detecting platform could respond to changes in the Cd2+ concentration within 10 min with a limit of detection (LOD) of 0.125 pM. The sensitivity of this detection platform to Cd2+ near 1 nM was 7.643 × 10-2 nM-1. Compared with control ions (Cr3+, Pb2+, Mg2+, Fe3+), this detection platform exhibited a high specificity to Cd2+. Moreover, the system could send out a photoacoustic alarm signal when the Cd2+ concentration in the monitoring solution exceeds the preset value. Therefore, the system is practical for monitoring the concentration of heavy metal ions in oily wastewater.

Characteristics of key microorganisms and metabolites in irradiated marbled beef.

Insights into changes in microorganisms and metabolites in irradiated marbled beef may help elucidate the beneficial effects of irradiation on prolonging the shelf life of meat. In this study, 16S rRNA gene sequencing, ultra-high-performance liquid chromatography-tandem mass spectrometry, and Pearson's correlation analyses were conducted to detect key microorganisms, core metabolites, and potential correlation between the microbiome and metabolome in marbling beef. Microbiome analysis showed that irradiation effectively eradicated the spoilage bacterium Leuconostoc and reduced the proportions of Carnobacterium and Lactobacillus in marbled beef. Additionally, results of metabolomic analysis involving irradiated marbled beef revealed that metabolites with significant differences were mainly organic acids and their derivatives, lipids, and lipid-like molecules, including six core metabolites. Furthermore, a significant correlation between key bacteria and metabolites was observed. Carnobacterium, Lactobacillus, and Leuconostoc affected the accumulation of core metabolites in irradiated marbled beef by influencing amino acid and lipid metabolism. Characterization of the microbiota and metabolites, as well as clarification of their correlation, can contribute to a better understanding of the mechanisms whereby irradiation helps maintain meat quality.

HGTphyloDetect: facilitating the identification and phylogenetic analysis of horizontal gene transfer.

BACKGROUND: Horizontal gene transfer (HGT) is an important driver in genome evolution, gain-of-function, and metabolic adaptation to environmental niches. Genome-wide identification of putative HGT events has become increasingly practical, given the rapid growth of genomic data. However, existing HGT analysis toolboxes are not widely used, limited by their inability to perform phylogenetic reconstruction to explore potential donors, and the detection of HGT from both evolutionarily distant and closely related species. RESULTS: In this study, we have developed HGTphyloDetect, which is a versatile computational toolbox that combines high-throughput analysis with phylogenetic inference, to facilitate comprehensive investigation of HGT events. Two case studies with Saccharomyces cerevisiae and Candida versatilis demonstrate the ability of HGTphyloDetect to identify horizontally acquired genes with high accuracy. In addition, HGTphyloDetect enables phylogenetic analysis to illustrate a likely path of gene transmission among the evolutionarily distant or closely related species. CONCLUSIONS: The HGTphyloDetect computational toolbox is designed for ease of use and can accurately find HGT events with a very low false discovery rate in a high-throughput manner. The HGTphyloDetect toolbox and its related user tutorial are freely available at https://github.com/SysBioChalmers/HGTphyloDetect.

Diagnostic and prognostic value of plasma cell-free DNA combined with VEGF-C in laryngeal squamous cell carcinoma.

BACKGROUND: Circulating cell-free DNA (cfDNA) and vascular endothelial growth factor-C (VEGF-C) can be utilized to detect cancer and predict its prognosis. However, their potential application in laryngeal squamous cell carcinoma (LSCC) is unclear. PURPOSE: This study aimed to identify the diagnostic and prognostic value of cfDNA and VEGF-C in LSCC patients. METHODS: The plasma cfDNA of 148 LSCC patients and 43 non-tumor patients were isolated. Quantitative real-time PCR (qRT-PCR) was performed to assess long and short DNA fragments in plasma by amplifying the ALU repeats. ALU-qPCR results (ALU247/ALU115) were used to calculate cfDNA integrity index. Vascular endothelial growth factor-C (VEGF-C) level was detected by ELISA assay. Correlation between cfDNA and clinical features was analyzed. For detecting the sensitivity and specificity of cfDNA and VEGF-C alone or in combination for diagnosing LSCC, receiver operator characteristic (ROC) was established. For evaluating the overall survival (OS) of LSCC, Kaplan-Meier curves were established. RESULTS: LSCC patients had significantly higher levels of plasma cfDNA (ALU115, ALU247, and cfDNA integrity index) and VEGF-C than those without cancer (p < 0.05), showing area under the curve (AUC) values of 0.79, 0.74, 0.62 and 0.80, when cutoff value was correspondingly defined at 2.14 ng/mL, 1.39 ng/mL, 0.73 and 412.90 pg/mL, respectively. The AUC for distinguishing LSCC patients from non-tumor patients by plasma cfDNA combined with VEGF-C was 0.89 (95% CI: 0.83-0.94). A significant correlation was found between plasma cfDNA levels and Ki-67, tumor size, pT stage, and smoking history (p < 0.05). Based on survival analysis, low VEGF-C concentration groups had longer OS than those with high VEGF-C concentration (p = 0.02). CONCLUSION: Indicators such as plasma cfDNA and VEGF-C may be used to diagnose and monitor LSCC for its noninvasiveness and rapid accessibility.

Membrane-coated nanoparticles for direct recognition by T cells.

The direct modulation of T cell responses is an emerging therapeutic strategy with the potential to modulate undesired immune responses including, autoimmune disease, and allogeneic cells transplantation. We have previously demonstrated that poly(lactide-co-glycolide) particles were able to modulate T cell responses indirectly through antigen-presenting cells (APCs). In this report, we investigated the design of nanoparticles that can directly interact and modulate T cells by coating the membranes from APCs onto nanoparticles to form membrane-coated nanoparticles (MCNPs). Proteins within the membranes of the APCs, such as Major Histocompatibility Complex class II and co-stimulatory factors, were effectively transferred to the MCNP. Using alloreactive T cell models, MCNP derived from allogeneic dendritic cells were able to stimulate proliferation, which was not observed with membranes from syngeneic dendritic cells and influenced cytokine secretion. Furthermore, we investigated the engineering of the membranes either on the dendritic cells or postfabrication of MCNP. Engineered membranes could be to promote antigen-specific responses, to differentially activate T cells, or to directly induce apoptosis. Collectively, MCNPs represent a tunable platform that can directly interact with and modulate T cell responses.

A Nomogram to Predict Nodal Response after Induction Chemotherapy for Hypopharyngeal Carcinoma.

BACKGROUND: For hypopharyngeal carcinoma, metastatic neck nodes with a low response to induction chemotherapy (ICT) should not be managed with concomitant chemoradiotherapy (CCRT), and the prediction of chemosensitivity before ICT could prevent adverse events from occurring during chemotherapy. In this study, we developed a nomogram to predict the regional response to ICT. METHODS: A total of 153 hypopharyngeal carcinoma patients with regional metastasis treated with ICT in our institution from January 2010 to September 2020 were retrospectively studied. According to ICT response evaluated by RECIST 1.1, patients were divided into chemo-insensitive (PR < 70%/SD/PD) (group 1) and chemosensitive (CR/PR ≥ 70%) (group 2) groups. Patients' clinical, image, and hematologic data before ICT were collected. The nomogram was built based on multivariate analysis and stepwise logistic regression and was evaluated from the aspects of discrimination and calibration. RESULTS: A nomogram based on five critical predictors, namely, tumor differentiation degree, T classification, metastatic lymph node size, number of lymph node metastases, and cervical nodal necrosis, was developed. The areas under the curve (AUC) values were 0.76 and 0.70 after adjusting the results using bootstrap methods. The calibration curve showed relatively good agreement between the predicted and observed probabilities. CONCLUSIONS: Our nomogram yielded good accuracy in predicting the regional ICT response and will be a useful tool to assist clinicians in decision making. LEVEL OF EVIDENCE: 4 Laryngoscope, 133:849-855, 2023.

GotEnzymes: an extensive database of enzyme parameter predictions.

Enzyme parameters are essential for quantitatively understanding, modelling, and engineering cells. However, experimental measurements cover only a small fraction of known enzyme-compound pairs in model organisms, much less in other organisms. Artificial intelligence (AI) techniques have accelerated the pace of exploring enzyme properties by predicting these in a high-throughput manner. Here, we present GotEnzymes, an extensive database with enzyme parameter predictions by AI approaches, which is publicly available at https://metabolicatlas.org/gotenzymes for interactive web exploration and programmatic access. The first release of this data resource contains predicted turnover numbers of over 25.7 million enzyme-compound pairs across 8099 organisms. We believe that GotEnzymes, with the readily-predicted enzyme parameters, would bring a speed boost to biological research covering both experimental and computational fields that involve working with candidate enzymes.