CT-derived Radiomics Predicts the Efficacy of Tyrosine Kinase Inhibitors in Osteosarcoma Patients with Pulmonary Metastasis.

BACKGROUND: To construct and validate the CT-based radiomics model for predicting the tyrosine kinase inhibitors (TKIs) effects in osteosarcoma (OS) patients with pulmonary metastasis. METHODS: OS patients with pulmonary metastasis treated with TKIs were randomly separated into training and testing cohorts (2:1 ratio). Radiomic features were extracted from the baseline unenhanced chest CT images. The random survival forest (RSF) and Kaplan-Meier survival analyses were performed to construct and evaluate radiomics signatures (R-model-derived). The univariant and multivariant Cox regression analyses were conducted to establish clinical (C-model) and combined models (RC-model). The discrimination abilities, goodness of fit and clinical benefits of the three models were assessed and validated in both training and testing cohorts. RESULTS: A total of 90 patients, 57 men and 33 women, with a mean age of 18 years and median progression-free survival (PFS) of 7.2 months, were enrolled. The R-model was developed with nine radiomic features and demonstrated significant predictive and prognostic values. In both training and testing cohorts, the time-dependent area under the receiver operating characteristic curves (AUC) of the R-model and RC-model exhibited obvious superiority over C-model. The calibration and decision curve analysis (DCA) curves indicated that the accuracy of the R-model was comparable to RC-model, which exhibited significantly better performance than C-model. CONCLUSIONS: The R-model showed promising potential as a predictor for TKI responses in OS patients with pulmonary metastasis. It can potentially identify pulmonary metastatic OS patients most likely to benefit from TKIs treatment and help guide optimized clinical decisions.

Revealing Na+-coordination induced Failure Mechanism of Metal Sulfide Anode for Sodium Ion Batteries.

Metal sulfide (MS) is regarded as a promising candidate of the anode materials for sodium-ion battery (SIB) with ideal capacity and low cost, yet still suffers from the inferior cycling stability and voltage degradation. Herein, the coordination relationship between the discharge product Na2S with the Na+ (NaPF6) in the electrolyte, is revealed as the root cause for the cycling failure of MS. Na+-coordination effect assistants the dissolution of Na2S, further delocalizing Na2S from the reaction interface under the function of electric field, which leads to the solo oxidation of the discharge product element metal without the participation of Na2S. Besides, the higher highest occupied molecular orbital of Na2S suggest the facilitated Na2S solo oxidation to produce sodium polysulfides (NaPSs). Based on these, lowering the Na+ concentration of the electrolyte is proposed as a potential improvement strategy to change the coordination environment of Na2S, suppressing the side reactions of the solo-oxidation of element metal and Na2S. Consequently, the enhanced conversion reaction reversibility and prolonged cycle life are achieved. This work renders in-depth perception of failure mechanism and inspiration for realizing advanced conversion-type anode.

Discovering covalent inhibitors of protein-protein interactions from trillions of sulfur(VI) fluoride exchange-modified oligonucleotides.

Molecules that covalently engage target proteins are widely used as activity-based probes and covalent drugs. The performance of these covalent inhibitors is, however, often compromised by the paradox of efficacy and risk, which demands a balance between reactivity and selectivity. The challenge is more evident when targeting protein-protein interactions owing to their low ligandability and undefined reactivity. Here we report sulfur(VI) fluoride exchange (SuFEx) in vitro selection, a general platform for high-throughput discovery of covalent inhibitors from trillions of SuFEx-modified oligonucleotides. With SuFEx in vitro selection, we identified covalent inhibitors that cross-link distinct residues of the SARS-CoV-2 spike protein at its protein-protein interaction interface with the human angiotensin-converting enzyme 2. A separate suite of covalent inhibitors was isolated for the human complement C5 protein. In both cases, we observed a clear disconnection between binding affinity and cross-linking reactivity, indicating that direct search for the aimed reactivity-as enabled by SuFEx in vitro selection-is vital for discovering covalent inhibitors of high selectivity and potency.

Corrigendum: Progress and perspectives of perioperative immunotherapy in non-small cell lung cancer.

[This corrects the article DOI: 10.3389/fonc.2023.1011810.].

Progress and perspectives of perioperative immunotherapy in non-small cell lung cancer.

Lung cancer is one of the leading causes of cancer-related death. Lung cancer mortality has decreased over the past decade, which is partly attributed to improved treatments. Curative surgery for patients with early-stage lung cancer is the standard of care, but not all surgical treatments have a good prognosis. Adjuvant and neoadjuvant chemotherapy are used to improve the prognosis of patients with resectable lung cancer. Immunotherapy, an epoch-defining treatment, has improved curative effects, prognosis, and tolerability compared with traditional and ordinary cytotoxic chemotherapy, providing new hope for patients with non-small cell lung cancer (NSCLC). Immunotherapy-related clinical trials have reported encouraging clinical outcomes in their exploration of different types of perioperative immunotherapy, from neoadjuvant immune checkpoint inhibitor (ICI) monotherapy, neoadjuvant immune-combination therapy (chemoimmunotherapy, immunotherapy plus antiangiogenic therapy, immunotherapy plus radiotherapy, or concurrent chemoradiotherapy), adjuvant immunotherapy, and neoadjuvant combined adjuvant immunotherapy. Phase 3 studies such as IMpower 010 and CheckMate 816 reported survival benefits of perioperative immunotherapy for operable patients. This review summarizes up-to-date clinical studies and analyzes the efficiency and feasibility of different neoadjuvant therapies and biomarkers to identify optimal types of perioperative immunotherapy for NSCLC.

Vessel state and immune infiltration of the angiogenesis subgroup and construction of a prediction model in osteosarcoma.

Angiogenesis has been recognized as a pivotal contributor to tumorigenesis and progression. However, the role of angiogenesis-related genes (ARGs) in vessel state, immune infiltration, and prognosis remains unknown in osteosarcoma (OS). Bulk RNA sequencing data of osteosarcoma patients were obtained from the Therapeutically Applicable Research to Generate Effective Treatments (TARGET) database, and patients were divided into two angiogenesis subgroups according to the expression of ARGs. We compared their vessel state and used two independent algorithms to evaluate the tumor microenvironment (TME) in the two subgroups. Furthermore, hub genes of differentially expressed genes (DEGs) in the two subgroups were selected to perform LASSO regression and multivariate Cox stepwise regression, and two prognostic hub genes were found. An ARG_score based on prognostic hub genes was calculated and proved to be reliable in the overall survival prediction in OS patients. Furthermore, the ARG_score was significantly associated with ARGs, immune infiltration, response to immunotherapy, and drug sensitivity. To make our prediction model perform well, clinical features were added and a highly accurate interactive nomogram was constructed. Immunohistochemistry and qRT-PCR were utilized to verify the expression of prognostic hub genes. GSE21257 from the Gene Expression Omnibus (GEO) database was used as a validation dataset to verify its robustness. In conclusion, our comprehensive analysis of angiogenesis subgroups in OS illustrated that angiogenesis may lead to different vessel states and further affect immune infiltration and prognosis of OS patients. Our findings may bring a novel perspective for the immunotherapy strategies for OS patients.

DNA Base Pairing-Inspired Supramolecular Nanodrug Camouflaged by Cancer-Cell Membrane for Osteosarcoma Treatment.

Osteosarcoma (OS) is one of the most common bone malignant tumors which mainly develops in adolescents. Although neoadjuvant chemotherapy has improved the prognosis of patients, numerous chemotherapeutic challenges still limit their use. Here, inspired by the Watson-Crick base pairing in nucleic acids, hydrophobic (methotrexate) and hydrophilic (floxuridine) chemo-drugs are mixed and self-assembled into M:F nanoparticles (M:F NPs) through molecular recognition. Then, the obtained NPs are co-extruded with membranes derived from OS cells to form cancer-cell membrane-coated NPs (CCNPs). With protected membranes at the outer layer, CCNPs are highly stable in both physiological and weak acid tumor conditions and possess homologous tumor targeted capability. Furthermore, the proteomic analysis first identifies over 400 proteins reserved in CCNPs, most of them participating in tumor cell targeting and adhesion processes. In vitro studies reveal that CCNPs significantly inhibit the PI3K/AKT/mTOR pathway, which promotes cell apoptosis and cell cycle arrest. More importantly, cell membrane camouflage significantly prolongs the circulation half-life of CCNPs, elevates the drug accumulation at tumor sites, and promotes anti-tumor efficacy in vivo. As a convenient and effective strategy to construct a biomimetic NP with high drug loading ratio, the CCNPs provide new potentials for precise and synergistic antitumor treatment.

Prediction of grain structure after thermomechanical processing of U-10Mo alloy using sensitivity analysis and machine learning surrogate model.

Hot rolling and annealing are critical intermediate steps for controlling microstructures and thickness variations when fabricating uranium alloyed with 10% molybdenum (U-10Mo), which is highly relevant to worldwide nuclear non-proliferation efforts. This work proposes a machine-learning surrogate model combined with sensitivity analysis to identify and predict U-10Mo microstructure development during thermomechanical processing. Over 200 simulations were collected using physics-based microstructure models covering a wide range of thermomechanical processing routes and initial alloy grain features. Based on the sensitivity analysis, we determined that an increase in rolling reduction percentage at each processing pass has the strongest effect in reducing the grain size. Multi-pass rolling and annealing can significantly improve recrystallization regardless of the reduction percentage. With a volume fraction below 2%, uranium carbide particles were found to have marginal effects on the average grain size and distribution. The proposed stratified stacking ensemble surrogate predicts the U-10Mo grain size with a mean square error four times smaller than a standard single deep neural network. At the same time, with a significant speedup (1000×) compared to the physics-based model, the machine learning surrogate shows good potential for U-10Mo fabrication process optimization.

Construction and validation of a novel apoptosis-associated prognostic signature related to osteosarcoma metastasis and immune infiltration.

BACKGROUND: Apoptosis played vital roles in the formation and progression of osteosarcoma. However, no studies elucidated the prognostic relationships between apoptosis-associated genes (AAGs) and osteosarcoma. METHODS: The differentially expressed genes associated with osteosarcoma metastasis and apoptosis were identified from GEO and MSigDB databases. The apoptosis-associated prognostic signature was established through univariate and multivariate cox regression analyses. The Kaplan-Meier (KM) survival curve, ROC curve and nomogram were constructed to investigate the predictive value of this signature. CIBERSORT algorithm and ssGSEA were used to explore the relationships between immune infiltration and AAG signature. The above results were validated in another GEO dataset and the expression of AAGs was also validated in osteosarcoma patient samples by immunohistochemistry. RESULTS: HSPB1 and IER3 were involved in AAG signature. In training and validation datasets, apoptosis-associated risk scores were negatively related to patient survival rates and the AAG signature was regarded as the independent prognostic factor. ROC and calibration curves demonstrated the signature and nomogram were reliable. GSEA revealed the signature related to immune-associated pathways. ssGSEA indicated that one immune cell and three immune functions were significantly dysregulated. The immunohistochemistry analyses of patients' samples revealed that AAGs were significantly differently expressed between metastasis and non-metastasis osteosarcomas. CONCLUSIONS: The present study identified and validated a novel apoptosis-associated prognostic signature related to osteosarcoma metastasis. It could serve as the potential biomarker and therapeutic targets for osteosarcoma in the future.

The key to immunotherapy: how to choose better therapeutic biomarkers for patients with non-small cell lung cancer.

Immunotherapy has become the standard of care for non-small cell lung cancer (NSCLC), either in combination or monotherapy. However, there are still some patients who cannot benefit from it. Immunization strategies for NSCLC are based on the expression of PD-L1 on tumor cells and TMB, and although these indicators have a certain predictive effect, their predictive performance is not good. Therefore, clinicians must make adjustments to recognize markers. This is a review article that summarized immunotherapeutic biomarkers according to the "seed-soil-environment", generalizes primary resistance to immunotherapy, and summarizes the integration of markers.

Complexing the Pre-assembled Brush-like siRNA with Poly(ß-amino ester) for Efficient Gene Silencing.

Small interfering RNA (siRNA) has been emerging as a highly selective and effective pharmaceutics for treating broad classes of diseases. However, the practical application of siRNA agent is often hampered by its poor crossing of the cellular membrane barrier and ineffective releasing from endosome to cytoplasm, leading to low gene silencing efficacy for clinical purposes. Thus far, cationic lipid and polymer-based vectors have been extensively explored for gene delivery. Yet condensing the rigid and highly negatively charged siRNA duplex to form a stable complex vehicle usually requires a large load of cationic carriers, prone to raising the toxicity issue for delivery. Herein, we develop a simple strategy that can efficiently condense the siRNAs into nanoparticle vehicles for target gene regulation. In this approach, we first employ a DNA-grafted polycaprolactone (DNA-g-PCL) brush as template to organize the small rigid siRNAs into a large brush-like structure (siRNA-brush) through nucleic acid hybridization. Then, the siRNA-brush assembly is condensed by an ionizable and biodegradable polymer (poly(ß-amino ester), PBAE) under acidic buffer condition to form a stable nanoparticle for siRNA delivery. Compared to the free siRNAs with poor complexing capability with PBAE, the large brush-like siRNA assemblies with more complicated topological architecture significantly promotes their electrostatic interaction with PBAE, enabling the formation of complexed nanoparticles at low weight ratio of polymer to siRNA. Additionally, PBAE/siRNA-brush complexes exhibit good biocompatibility and stability under physiological condition, as well as enhanced cellular internalization. When equipped with functional siRNAs, the obtained delivery system demonstrates excellent downregulation of target genes both in vitro and in vivo, through which the progression of hypertrophic scars can be retarded with negligible adverse effects in an xenografted mouse model.

The Outcome of Discontinuing Tyrosine Kinase Inhibitors in Advanced Sarcoma Following a Favorable Tumor Response to Antiangiogenics Therapy.

(1) Background: The use of antiangiogenic TKIs (AA-TKIs) has recently emerged as a major paradigm shift in the treatment of advanced sarcoma. However, the feasibility of drug holidays for patients demonstrating a very favorable response remains unknown. (2) Methods: We aim to explore the outcomes of patients with advanced sarcoma who discontinued AA-TKIs after a (near-) complete remission or were long-term responders. Patients with advanced disease were included if they had bilateral or multiple lung metastases, extrapulmonary recurrence, a short disease-free interval, etc., at the initiation of AA-TKIs. (3) Results: A total of 22 patients with AA-TKI discontinuation were analyzed, with a median follow-up of 22.3 months post-discontinuation. Prior to discontinuation, there were four drug-induced complete remissions (CRs), twelve surgical CRs, and six long-term responders. Disease progression was observed in 17/22 (77.3%) patients, with a median of 4.2 months. However, since the majority were still sensitive to the original AA-TKIs and amenable to a second surgical remission, 7 out of these 17 patients achieved a second CR after disease progression and were thus considered as relapse-free post-discontinuation (pd-RFS). Therefore, the pd-RFS and post-discontinuation overall survival (pd-OS) in the last follow-up were 12/22 (54.5%) and 16/22 (72.7%), respectively. Remarkably, surgical CR and drug tapering off (versus abrupt stopping) were associated with a greater pd-RFS and pd-OS (p < 0.05). Furthermore, higher necrosis rates (p = 0.040) and lower neutrophil-to-lymphocyte ratios (NLR) (p = 0.060) before discontinuation tend to have a better pd-RFS. (4) Conclusions: Our results suggest that AA-TKI discontinuation with a taper-off strategy might be safe and feasible in highly selected patients with advanced sarcoma. Surgical CR, NLR, and tumor necrosis rates before discontinuation were potential biomarkers for AA-TKI withdrawal.

Exploration and Validation of a Novel Inflammatory Response-Associated Gene Signature to Predict Osteosarcoma Prognosis and Immune Infiltration.

BACKGROUND: Inflammatory response took part in the progression of tumor and was regarded as the hallmark of cancer. However, the prognostic relationship between osteosarcoma and inflammatory response-associated genes (IRGs) was unclear. This research aimed to explore the correlations between osteosarcoma prognosis and IRG signature. METHODS: The inflammatory response-associated differentially expressed messenger RNAs (DEmRNAs) were screened out through Gene Expression Omnibus (GEO) and Molecular Signature Database (MSigDB) databases. Univariate and multivariate cox regression analyses were utilized to construct the IRG signature. The prognostic value of signature was investigated through Kaplan-Meier (KM) survival curve and nomogram. DEmRNAs among high and low inflammatory response-associated risks were identified and functional enrichment analyses were conducted. ESTIMATE, CIBERSORT and single-sample gene set enrichment analyses (ssGSEA) were implied to reveal the alterations in immune infiltration. All the above results were validated in Target database. The expression of IRGs was also validated in different cell lines by quantitative real-time PCR (qRT-PCR) and osteosarcoma patient samples by immunohistochemistry. RESULTS: The IRG signature that consisted of two genes (MYC, CLEC5A) was established. In training and validation datasets, patients with lower risk scores survived longer and the IRG signature was confirmed as the independent prognostic factor in osteosarcoma. The nomogram was constructed and the calibration curves demonstrated the reliability of this model. Functional analysis of risk score-associated DEmRNAs indicated that immune-related pathways and functions were significantly enriched. ssGSEA revealed that 14 immune cells and 11 immune functions were significantly dysregulated. The qRT-PCR results indicated IRGs were significantly differently expressed in osteosarcoma and osteoblast cell lines. The immunohistochemistry analyses of patients' samples revealed the same result. CONCLUSION: The novel osteosarcoma inflammatory response-associated prognostic signature was established and validated in this study. This model could serve as the biomarker and therapeutic target for osteosarcoma in the future.

MCM4 Is a Novel Biomarker Associated With Genomic Instability, BRCAness Phenotype, and Therapeutic Potentials in Soft-Tissue Sarcoma.

Soft-tissue sarcoma (STS) is represented by a heterogeneous group of rare malignancies with various molecular oncogenesis. Therapies targeting DNA repair pathways in STS have achieved minimal progress, potentially due to the lack of molecular biomarker(s) beyond the histology subtype. In this report, we comprehensively analyzed the expression profiles of 100 liposarcomas (LPSs), the most common STS subtype, in comparison with 21 adipose tissues from multiple GEO datasets to identify the potential prognostic and therapeutic biomarker for LPS. Furthermore, we investigated TCGA database, our archived tumor samples, and patient-derived tumor cell cultures (PTCCs) as a validation. We identified a total of 69 common differentially expressed genes (DEGs) among public datasets, with mini-chromosome maintenance protein 4 (MCM4) identified as a novel biomarker correlated with patients' clinical staging and survival outcome. MCM4-high expression LPS was characterized by MCM4 copy number increase, genomic instability, and BRCAness phenotype compared with the MCM4-low expression counterpart. In contrast, the mutational and the immune landscape were minimally different between the two groups. Interestingly, the association of MCM4-high expression with genomic instability and BRCAness were not only validated in LPS samples from our institution (n = 66) but also could be expanded to the pan-sarcoma cohort from TCGA database (n = 263). Surprisingly, based on four sarcoma cell lines and eight PTCCs (three LPS and five other sarcoma), we demonstrated that MCM4 overexpression tumors were therapeutically sensitive to PARP inhibitor (PARPi) and platinum chemotherapy, independent of the histology subtypes. Our study, for the first time, suggested that MCM4 might be a novel prognostic biomarker, associated with dysregulated DNA repair pathways and potential therapeutic vulnerability in STS.

Development and Validation of a Hypoxia-Associated Prognostic Signature Related to Osteosarcoma Metastasis and Immune Infiltration.

BACKGROUND: Increasing evidence has shown that hypoxia microenvironment relates to tumor initiation and progression. However, no studies focus on the application of hypoxia-associated genes in predicting osteosarcoma patients' prognosis. This research aims to identify the hypoxia-associated genes related to osteosarcoma metastasis and construct a gene signature to predict osteosarcoma prognosis. METHODS: The differentially expressed messenger RNAs (DEmRNAs) related to osteosarcoma metastasis were identified from Therapeutically Applicable Research to Generate Effective Treatments (Target) database. Univariate and multivariate cox regression analyses were performed to develop the hypoxia-associated prognostic signature. The Kaplan-Meier (KM) survival analyses of patients with high and low hypoxia risk scores were conducted. The nomogram was constructed and the gene signature was validated in the external Gene Expression Omnibus (GEO) cohort. Single-sample gene set enrichment analysis (ssGSEA) was conducted to investigate the relationships between immune infiltration and gene signature. RESULTS: Two genes, including decorin (DCN) and prolyl 4-hydroxylase subunit alpha 1 (P4HA1), were involved in the hypoxia-associated gene signature. In training and testing datasets, patients with high-risk scores showed lower survival rates and the gene signature was identified as the independent prognostic factor. Receiver operating characteristic (ROC) curves demonstrated the robustness of signature. Functional analyses of DEmRNAs among high- and low-risk groups revealed that immune-associated functions and pathways were significantly enriched. Furthermore, ssGSEA showed that five immune cells (DCs, macrophages, neutrophils, pDCs, and TIL) and three immune features (CCR, APC co inhibition, and Check-point) were down-regulated in the high-risk group. CONCLUSION: The current study established and validated a novel hypoxia-associated gene signature in osteosarcoma. It could act as a prognostic biomarker and serve as therapeutic guidance in clinical applications.

Development and analysis of long non-coding RNA-associated competing endogenous RNA network for osteosarcoma metastasis.

BACKGROUND: Osteosarcoma is the primary bone malignant neoplasm that often develops metastasis. Increasing evidences have shown that non-coding RNAs (ncRNAs) relate to the progression of osteosarcoma. However, the ncRNAs' roles in osteosarcoma metastasis are still unknown. METHODS: Differentially expressed (DE) RNAs were identified from Gene Expression Omnibus (GEO) database. Protein-protein interaction (PPI) of DE messenger RNAs (DEmRNAs) was built through STRING database. The target mRNAs and long ncRNAs (lncRNAs) of microRNAs (miRNA) were predicted through miRDB, Targetscan and Genecode databases, which then cross-checked with previously obtained DERNAs to construct competing endogenous RNA (ceRNA) network. All networks were visualized via Cytoscape and the hub RNAs were screened out through Cytoscape plug-in Cytohubba. The gene functional and pathway analyses were performed through DAVID and MirPath databases. The survival analyses of hub RNAs were obtained through Kaplan-Meier (KM) survival curves. RESULTS: Five hundred sixty-four DEmRNAs, 16 DElncRNAs and 22 DEmiRNAs were screened out. GO functional and KEGG pathway analyses showed that DERNAs were significantly associated with tumor metastasis. The ceRNA network including 6 lncRNAs, 55 mRNAs and 20 miRNAs were constructed and the top 10 hub RNAs were obtained. Above all, PI3K/AKT signaling pathway was identified as the most important osteosarcoma metastasis-associated pathway and its hub ceRNA module was constructed. The survival analyses showed that the RNAs in hub ceRNA module closely related to osteosarcoma patients' prognosis. CONCLUSIONS: The current study provided a new perspective on osteosarcoma metastasis. More importantly, the RNAs in hub ceRNA module might act as the novel therapeutic targets and prognostic factors for osteosarcoma patients.

Targeting autophagy enhances atezolizumab-induced mitochondria-related apoptosis in osteosarcoma.

In this study, we identified the multifaceted effects of atezolizumab, a specific monoclonal antibody against PD-L1, in tumor suppression except for restoring antitumor immunity, and investigated the promising ways to improve its efficacy. Atezolizumab could inhibit the proliferation and induce immune-independent apoptosis of osteosarcoma cells. With further exploration, we found that atezolizumab could impair mitochondria of osteosarcoma cells, resulting in increased release of reactive oxygen species and cytochrome-c, eventually leading to mitochondrial-related apoptosis via activating JNK pathway. Nevertheless, the excessive release of reactive oxygen species also activated the protective autophagy of osteosarcoma cells. Therefore, when we combined atezolizumab with autophagy inhibitors, the cytotoxic effect of atezolizumab on osteosarcoma cells was significantly enhanced in vitro. Further in vivo experiments also confirmed that atezolizumab combined with chloroquine achieved the most significant antitumor effect. Taken together, our study indicates that atezolizumab can induce mitochondrial-related apoptosis and protective autophagy independently of the immune system, and targeting autophagy is a promising combinatorial approach to amplify its cytotoxicity.

Review of Analytical Methods to Detect Adulteration in Coffee.

As one of the most consumed beverages in the world, coffee plays many major socioeconomical roles in various regions. Because of the wide coffee varieties available in the marketplaces, and the substantial price gaps between them (e.g., Arabica versus Robusta; speciality versus commodity coffees), coffees are susceptible to intentional or accidental adulteration. Therefore, there is a sustaining interest from the producers and regulatory agents to develop protocols to detect fraudulent practices. In general, strategies to authenticate coffee are based on targeted chemical profile analyses to determine specific markers of adulterants, or nontargeted analyses based on the "fingerprinting" concept. This paper reviews the literature related to chemometric approaches to discriminate coffees based on nuclear magnetic resonance spectroscopy, chromatography, infrared/Raman spectroscopy, and array sensors/indicators. In terms of chemical profiling, the paper focuses on the detection of diterpenes, homostachydrine, phenolic acids, carbohydrates, fatty acids, triacylglycerols, and deoxyribonucleic acid. Finally, the prospects of coffee authentication are discussed.

Analysis of key genes and pathways associated with the pathogenesis of intervertebral disc degeneration.

BACKGROUND: Intervertebral disc degeneration (IDD) is widely known as the main contributor to low back pain which has a negative socioeconomic impact worldwide. However, the underlying mechanism remains unclear. This study aims to analyze the dataset GSE23130 using bioinformatics methods to identify the pivotal genes and pathways associated with IDD. MATERIAL/METHODS: The gene expression data of GSE23130 was downloaded, and differentially expressed genes (DEGs) were extracted from 8 samples and 15 controls. GO and KEGG pathway enrichment analyses were performed. Also, protein-protein interaction (PPI) network was constructed and visualized, followed by identification of hub genes and key module. RESULTS: A total of 30 downregulated and 79 upregulated genes were identified. The DEGs were mainly enriched in the regulation of protein catabolic process, extracellular matrix organization, collagen fibril organization, and extracellular structure organization. Meanwhile, we found that most DEGs were primarily enriched in the PI3K-Akt signaling pathway. The top 10 hub genes were FN1, COL1A2, SPARC, COL3A1, CTGF, LUM, TIMP1, THBS2, COL5A2, and TGFB1. CONCLUSIONS: In summary, key candidate genes and pathways were identified by using integrated bioinformatics analysis, which may provide insights into the underlying mechanisms and offer potential target genes for the treatment of IDD.

Investigation of the factors that affect the volume and stability of espresso crema.

This study investigated the effects of key brewing parameters (basket size, particle size, temperature, brewing ratio, coffee freshness, bean variety, and degree of roast) on the crema volume and stability of espresso coffees. Moreover, the brewing time and pressure, as well as brew total solid and extraction yield were characterized. The results show that the crema volume was mainly affected by the coffee variety and freshness. A larger crema volume was generated from Robusta than Arabica beans. Moreover, the fresher the coffee, the greater the crema volume. Crema stability was strongly affected by basket size, with double- and triple-shot baskets producing more stable crema than that from the single-shot basket. Arabica beans resulted in brews with a more stable crema than Robusta, attributable to the higher lipids content in the former. In comparison, crema volume and stability were less affected by the particle size, brewing ratio, and brewing temperature. Besides elucidating the parameters that affect the crema volume and stability of espresso coffee, findings from this study are expected to be useful for café barista and coffee connoisseurs in preparing espresso coffee with consistent crema quality.