Ligand recognition and G-protein coupling of trace amine receptor TAAR1.

Trace-amine-associated receptors (TAARs), a group of biogenic amine receptors, have essential roles in neurological and metabolic homeostasis1. They recognize diverse endogenous trace amines and subsequently activate a range of G-protein-subtype signalling pathways2,3. Notably, TAAR1 has emerged as a promising therapeutic target for treating psychiatric disorders4,5. However, the molecular mechanisms underlying its ability to recognize different ligands remain largely unclear. Here we present nine cryo-electron microscopy structures, with eight showing human and mouse TAAR1 in a complex with an array of ligands, including the endogenous 3-iodothyronamine, two antipsychotic agents, the psychoactive drug amphetamine and two identified catecholamine agonists, and one showing 5-HT1AR in a complex with an antipsychotic agent. These structures reveal a rigid consensus binding motif in TAAR1 that binds to endogenous trace amine stimuli and two extended binding pockets that accommodate diverse chemotypes. Combined with mutational analysis, functional assays and molecular dynamic simulations, we elucidate the structural basis of drug polypharmacology and identify the species-specific differences between human and mouse TAAR1. Our study provides insights into the mechanism of ligand recognition and G-protein selectivity by TAAR1, which may help in the discovery of ligands or therapeutic strategies for neurological and metabolic disorders.

Structure-based identification of a G protein-biased allosteric modulator of cannabinoid receptor CB1.

Cannabis sativa is known for its therapeutic benefit in various diseases including pain relief by targeting cannabinoid receptors. The primary component of cannabis, Δ9-tetrahydrocannabinol (THC), and other agonists engage the orthosteric site of CB1, activating both Gi and ß-arrestin signaling pathways. The activation of diverse pathways could result in on-target side effects and cannabis addiction, which may hinder therapeutic potential. A significant challenge in pharmacology is the design of a ligand that can modulate specific signaling of CB1. By leveraging insights from the structure-function selectivity relationship (SFSR), we have identified Gi signaling-biased agonist-allosteric modulators (ago-BAMs). Further, two cryoelectron microscopy (cryo-EM) structures reveal the binding mode of ago-BAM at the extrahelical allosteric site of CB1. Combining mutagenesis and pharmacological studies, we elucidated the detailed mechanism of ago-BAM-mediated biased signaling. Notably, ago-BAM CB-05 demonstrated analgesic efficacy with fewer side effects, minimal drug toxicity and no cannabis addiction in mouse pain models. In summary, our finding not only suggests that ago-BAMs of CB1 provide a potential nonopioid strategy for pain management but also sheds light on BAM identification for GPCRs.

Structural insights into the recognition of telomeric variant repeat TTGGGG by broad-complex, tramtrack and bric-à-brac - zinc finger protein ZBTB10.

Multiple proteins bind to telomeric DNA and are important for the role of telomeres in genome stability. A recent study established a broad-complex, tramtrack and bric-à-brac - zinc finger (BTB-ZF) protein, ZBTB10 (zinc finger and BTB domain-containing protein 10), as a telomeric variant repeat-binding protein at telomeres that use an alternative method for lengthening telomeres). ZBTB10 specifically interacts with the double-stranded telomeric variant repeat sequence TTGGGG by employing its tandem C2H2 zinc fingers (ZF1-2). Here, we solved the crystal structure of human ZBTB10 ZF1-2 in complex with a double-stranded DNA duplex containing the sequence TTGGGG to assess the molecular details of this interaction. Combined with calorimetric analysis, we identified the vital residues in TTGGGG recognition and determined the specific recognition mechanisms that are different from those of TZAP (telomere zinc finger-associated protein), a recently defined telomeric DNA-binding protein. Following these studies, we further identified a single amino-acid mutant (Arg767Gln) of ZBTB10 ZF1-2 that shows a preference for the telomeric DNA repeat TTAGGG sequence. We solved the cocrystal structure, providing a structural basis for telomeric DNA recognition by C2H2 ZF proteins.

DeepDDS: deep graph neural network with attention mechanism to predict synergistic drug combinations.

MOTIVATION: Drug combination therapy has become an increasingly promising method in the treatment of cancer. However, the number of possible drug combinations is so huge that it is hard to screen synergistic drug combinations through wet-lab experiments. Therefore, computational screening has become an important way to prioritize drug combinations. Graph neural network has recently shown remarkable performance in the prediction of compound-protein interactions, but it has not been applied to the screening of drug combinations. RESULTS: In this paper, we proposed a deep learning model based on graph neural network and attention mechanism to identify drug combinations that can effectively inhibit the viability of specific cancer cells. The feature embeddings of drug molecule structure and gene expression profiles were taken as input to multilayer feedforward neural network to identify the synergistic drug combinations. We compared DeepDDS (Deep Learning for Drug-Drug Synergy prediction) with classical machine learning methods and other deep learning-based methods on benchmark data set, and the leave-one-out experimental results showed that DeepDDS achieved better performance than competitive methods. Also, on an independent test set released by well-known pharmaceutical enterprise AstraZeneca, DeepDDS was superior to competitive methods by more than 16% predictive precision. Furthermore, we explored the interpretability of the graph attention network and found the correlation matrix of atomic features revealed important chemical substructures of drugs. We believed that DeepDDS is an effective tool that prioritized synergistic drug combinations for further wet-lab experiment validation. AVAILABILITY AND IMPLEMENTATION: Source code and data are available at https://github.com/Sinwang404/DeepDDS/tree/master.

Incorporating clinician insight and care plans into an audit and feedback initiative for antipsychotic prescribing to Medicaid-enrolled youth in Philadelphia.

BACKGROUND: Audit and feedback (A/F), which include initiatives like report cards, have an inconsistent impact on clinicians' prescribing behavior. This may be attributable to their focus on aggregate prescribing measures, a one-size-fits-all approach, and the fact that A/F initiatives rarely engage with the clinicians they target. METHODS: In this study, we describe the development and delivery of a report card that summarized antipsychotic prescribing to publicly-insured youth in Philadelphia, which was introduced by a Medicaid managed care organization in 2020. In addition to measuring aggregate prescribing behavior, the report card included different elements of care plans, including whether youth were receiving polypharmacy, proper medication management, and the concurrent use of behavioral health outpatient services. The A/F initiative elicited feedback from clinicians, which we refer to as an "audit and feedback loop." We also evaluate the impact of the report card by comparing pre-post differences in prescribing measures for clinicians who received the report card with a group of clinicians who did not receive the report card. RESULTS: Report cards indicated that many youth who were prescribed antipsychotics were not receiving proper medication management or using behavioral health outpatient services alongside the antipsychotic prescription, but that polypharmacy was rare. In their feedback, clinicians who received report cards cited several challenges related to antipsychotic prescribing, such as the logistical difficulties of entering lab orders and family members' hesitancy to change care plans. The impact of the report card was mixed: there was a modest reduction in the share of youth receiving polypharmacy following the receipt of the report card, while other measures did not change. However, we documented a large reduction in the number of youth with one or more antipsychotic prescription fill among clinicians who received a report card. CONCLUSIONS: A/F initiatives are a common approach to improving the quality of care, and often target specific practices such as antipsychotic prescribing. Report cards are a low-cost and feasible intervention but there is room for quality improvement, such as adding measures that track medication management or eliciting feedback from clinicians who receive report cards. To ensure that the benefits of antipsychotic prescribing outweigh its risks, it is important to promote quality and safety of antipsychotic prescribing within a broader care plan.

High-efficiency broadband achromatic metalenses for visible full-stokes polarization imaging.

Polarization-imaging technology has important applications in target detection, communication, biomedicine, and other fields. A polarization imaging system based on metalenses, which provides new possibilities for the realization of highly integrated full-Stokes polarization imaging systems, can solve the problems of traditional polarization imaging systems, such as complex structures, large volumes, and the inability to simultaneously obtain linear and circular polarization states. However, currently designed metalens arrays that can achieve real-time full-Stokes polarization imaging can generally only be used for monochromatic detection, which significantly limits the amount of measured information of the object. Broad-spectrum polarization color imaging allows more image degrees of freedom, enabling more accurate characterization of polarization for multi-target object scenes in complex environments. To achieve broad-spectrum polarization imaging, we propose and design a metalens array that can achieve full-Stokes polarization imaging in the broadband visible range, in which the design process of metalenses for splitting and focusing broadband orthogonal circularly polarized light is emphasized. To design metalenses that can achieve polarization splitting and efficient focusing, we simulate and optimize the height and period of the nano-units and show that smaller periods and larger heights do not always result in higher-performance devices when designing multifunctional metalenses. The designed metalens array can split and diffraction-limited focus the orthogonal polarized incident light to the designated position with average focusing efficiencies of 59.2% under 460-680 nm TM linearly polarized light, 53.1% under TE linearly polarized light, 58.8% under left-handed circularly polarized light, and 52.7% under right-handed circularly polarized light. The designed metalenses can be applied to imaging systems, such as polarization imaging and polarization light-field imaging systems.

Construction of a hypoxia-immune-related prognostic model and targeted therapeutic strategies for cervical cancer.

Emerging evidence indicates that hypoxia and immunity play important roles in tumorigenesis and development. However, the hypoxia-immune-related prognostic risk model has not been established in cervical cancer (CC). We aimed to construct a hypoxia-immune-related prognostic risk model, which has potential application in predicting the prognosis of CC patients and the response to targeted therapy. The RNA-seq data and corresponding clinical information were retrieved from The Cancer Genome Atlas (TCGA) database. The hypoxia status and immune status of CC patients were evaluated using the Consensus Clustering method and single-sample gene set enrichment analysis (ssGSEA), respectively. The univariate Cox regression, least absolute shrinkage and selection operator (LASSO) and multivariate Cox regression were applied to establish the prognostic risk model of CC. The chemotherapy response for six chemotherapeutic agents of each CC patient was calculated according to the Genomics of Drug Sensitivity in Cancer (GDSC). And the Connectivity Map (CMap) database was performed to screen candidate small-molecule drugs. In this study, we identified seven gene signatures (P4HA2, MSMO1, EGLN1, ZNF316, IKZF3, ISCU and MYO1B) with prognostic values. And the survival time of patients with low risk was significantly longer than those with high risk. Meanwhile, CC patients in the high-risk group yielded higher sensitivity to five chemotherapeutic agents. And we listed 10 candidate small-molecule drugs that exhibited a high correlation with the prognosis of CC. Thus, the prognostic model can accurately predict the prognosis of patients with CC and may be helpful for the development of new hypoxia-immune prognostic markers and therapeutic strategies for CC.

Immune-Related Genes' Prognostic, Therapeutic and Diagnostic Value in Ovarian Cancer Immune-Related Gene Biomarker in Ovarian Cancer.

OBJECTIVES: The abnormal expression of immune-related genes (IRGs) plays an important role in the occurrence and progression of ovarian cancer (OC), which is the main cause of mortality among gynecological cancer patients. This study aims to establish a prognostic risk model and comprehensively analyze the relationship between OC risk score and prognosis, immune cell infiltration (ICI) and therapeutic sensitivity in OC. METHODS: We retrospectively evaluated the clinicopathological characteristics of consecutive OC patients in the Cancer Genome Atlas (TCGA) database. First, the prognostic risk model was constructed by bioinformatics methods. And then, we systematically assessed model robustness, and correlations between risk score and prognosis, and immune cell infiltration. The ICGC cohort was used to verify the prognostic risk model. Finally, we evaluated their value in the treatment of OC immunotherapy and chemotherapy. RESULTS: A total of 10 IRGs were identified to construct the prognostic risk model. Survival analysis revealed that patients in the low-risk group had a better prognosis (P < .01), and the risk score might be considered an independent predictor for predicting the prognosis. In addition, risk scores and patient clinical information were used to construct clinical nomograms, improving the prediction's precision. We also explored the relationship between the risk score and ICI, immunotherapy and drug sensitivity. CONCLUSIONS: Collectively, we identified a novel ten IRGs signature that may be applied as a prognostic predictor of OC, thereby benefiting clinical decision-making and personalized treatment of patients.

Costunolide Protects Myocardium From Ischemia Reperfusion Injury by Inhibiting Oxidative Stress Through Nrf2/Keap1 Pathway Activation.

ABSTRACT: Costunolide (Cos) is a naturally occurring sesquiterpene lactone that exhibits antioxidative properties. In this study, we demonstrate the protective mechanism of Cos against ischemia/reperfusion (I/R)-induced myocardial injury. Cos significantly decreased levels of reactive oxygen species and ameliorated apoptosis of I/R cardiomyocytes both in vitro and in vivo. Further investigation revealed that Cos increased expression of the antioxidant proteins HO-1 and NQO-1 and decreased the Bax/Bcl-2 ratio, thus protecting cardiac cells. NF-E2-related factor 2 (Nrf2) silencing significantly attenuated the protective effects of Cos in tert-butyl hydroperoxide (TBHP)-treated H9C2 cells. Additionally, Cos significantly intensified the I/R- or TBHP-induced dissociation of the Kelch-like ECH-associated protein 1 (Keap1)/Nrf2 complex both in vitro and in vivo. These results suggest that activation of Nrf2/Keap1 using Cos may be a therapeutic strategy for myocardial I/R injury.

Building an Integrated Data Infrastructure to Examine the Spectrum of Suicide Risk Factors in Philadelphia Medicaid.

While there are many data-driven approaches to identifying individuals at risk of suicide, they tend to focus on clinical risk factors, such as previous psychiatric hospitalizations, and rarely include risk factors that occur in nonclinical settings, such as jails or emergency shelters. A better understanding of system-level encounters by individuals at risk of suicide could help inform suicide prevention efforts. In Philadelphia, we built a community-level data infrastructure that encompassed suicide death records, behavioral health claims, incarceration episodes, emergency housing episodes, and involuntary commitment petitions to examine a broader spectrum of suicide risk factors. Here, we describe the development of the data infrastructure, present key trends in suicide deaths in Philadelphia, and, for the Medicaid-eligible population, determine whether suicide decedents were more likely to interact with the behavioral health, carceral, and housing service systems compared to Medicaid-eligible Philadelphians who did not die by suicide. Between 2003 and 2018, there was an increase in the number of annual suicide deaths among Medicaid-eligible individuals, in part due to changes in Medicaid eligibility. There were disproportionately more suicide deaths among Black and Hispanic individuals who were Medicaid-eligible, who were younger on average, compared to suicide decedents who were never Medicaid-eligible. However, when we accounted for the racial and ethnic composition of the Medicaid population at large, we found that White individuals were four times as likely to die by suicide, while Asian, Black, Hispanic, and individuals of other races were less likely to die by suicide. Overall, 58% of individuals who were Medicaid-eligible and died by suicide had at least one Medicaid-funded behavioral health claim, 10% had at least one emergency housing episode, 25% had at least one incarceration episode, and 22% had at least one involuntary commitment. By developing a data infrastructure that can incorporate a broader spectrum of risk factors for suicide, we demonstrate how communities can harness administrative data to inform suicide prevention efforts. Our findings point to the need for suicide prevention in nonclinical settings such as jails and emergency shelters, and demonstrate important trends in suicide deaths in the Medicaid population.

Overexpression of SHARPIN promotes tumor progression in ovarian cancer.

SHARPIN (Shank-associated RH domain interacting protein) plays an important role in tumorigenesis. However, its role in ovarian cancer remains largely unknown. To investigate this issue, we systematically analyzed the amplification and expression of the SHARPIN in the TCGA database. From the database, we found that SHARPIN was amplified in ovarian cancer compared to normal ovarian tissue, and the mRNA level of SHARPIN was significantly elevated in ovarian cancer compared to non-tumorigenic ovarian tissue. In addition, we observed similar results from ovarian cancer cell lines and clinical samples from ovarian cancer patients, which indicated that increased SHARPIN expression is associated with tumorigenesis in ovarian cancer. SHARPIN knockdown inhibited the migration and invasion of ovarian cancer cells, also inhibited cell cycle and promoted apoptosis, thereby suppressing cell proliferation. RNA-seq results showed that SHARPIN significantly increased the expression of P53 and P21 and decreased the expression of Cyclin D1 and c-Myc, all of which are involved in the regulation of cell proliferation. Subsequent mechanistic exploration revealed that SHARPIN knockdown increased the expression of caspases 3 and 9, leading to apoptosis of ovarian cancer cells. We also found that high expression of SHARPIN was associated with poor prognosis of ovarian cancer patients. Collectively, we demonstrated a positive correlation between SHARPIN and ovarian cancer progression and provide a basis for combined targeted therapy strategies for future ovarian cancer treatment.

Comprehensive analysis of prognosis-related alternative splicing events in ovarian cancer.

Ovarian cancer (OV) is characterized by high incidence and poor prognosis. Increasing evidence indicates that aberrant alternative splicing (AS) events are associated with the pathogenesis of cancer. We examined prognosis-related alternative splicing events and constructed a clinically applicable model to predict patients' outcomes. Public database including The Cancer Genome Atlas (TCGA), TCGA SpliceSeq, and the Genomics of Drug Sensitivity in Cancer databases were used to detect the AS expression, immune cell infiltration and IC50. The prognosis-related AS model was constructed and validated by using Cox regression, LASSO regression, C-index, calibration plots, and ROC curves. A total of eight AS events (including FLT3LG|50942|AP) were selected to establish the prognosis-related AS model. Compared with high-risk group, low-risk group had a better outcome (P = 1.794e-06), was more sensitive to paclitaxel (P = 0.022), and higher proportions of plasma cells. We explored the upstream regulatory mechanisms of prognosis-related AS and found that two splicing factor and 156 tag single nucleotide polymorphisms may be involved in the regulation of prognosis-related AS. In order to assess patient prognosis more comprehensively, we constructed a clinically applicable model combining risk score and clinicopathological features, and the 1 -, and 3-year AUCs of the clinically applicable model were 0.812, and 0.726, which were 7.5% and 3.3% higher than that of the risk score. We constructed a prognostic signature for OV patients and comprehensively analysed the regulatory characteristics of the prognostic AS events in OV.

ERH facilitates microRNA maturation through the interaction with the N-terminus of DGCR8.

The microprocessor complex cleaves the primary transcript of microRNA (pri-miRNA) to initiate miRNA maturation. Microprocessor is known to consist of RNase III DROSHA and dsRNA-binding DGCR8. Here, we identify Enhancer of Rudimentary Homolog (ERH) as a new component of Microprocessor. Through a crystal structure and biochemical experiments, we reveal that ERH uses its hydrophobic groove to bind to a conserved region in the N-terminus of DGCR8, in a 2:2 stoichiometry. Knock-down of ERH or deletion of the DGCR8 N-terminus results in a reduced processing of suboptimal pri-miRNAs in polycistronic miRNA clusters. ERH increases the processing of suboptimal pri-miR-451 in a manner dependent on its neighboring pri-miR-144. Thus, the ERH dimer may mediate 'cluster assistance' in which Microprocessor is loaded onto a poor substrate with help from a high-affinity substrate in the same cluster. Our study reveals a role of ERH in the miRNA biogenesis pathway.

LGCCT: A Light Gated and Crossed Complementation Transformer for Multimodal Speech Emotion Recognition.

Semantic-rich speech emotion recognition has a high degree of popularity in a range of areas. Speech emotion recognition aims to recognize human emotional states from utterances containing both acoustic and linguistic information. Since both textual and audio patterns play essential roles in speech emotion recognition (SER) tasks, various works have proposed novel modality fusing methods to exploit text and audio signals effectively. However, most of the high performance of existing models is dependent on a great number of learnable parameters, and they can only work well on data with fixed length. Therefore, minimizing computational overhead and improving generalization to unseen data with various lengths while maintaining a certain level of recognition accuracy is an urgent application problem. In this paper, we propose LGCCT, a light gated and crossed complementation transformer for multimodal speech emotion recognition. First, our model is capable of fusing modality information efficiently. Specifically, the acoustic features are extracted by CNN-BiLSTM while the textual features are extracted by BiLSTM. The modality-fused representation is then generated by the cross-attention module. We apply the gate-control mechanism to achieve the balanced integration of the original modality representation and the modality-fused representation. Second, the degree of attention focus can be considered, as the uncertainty and the entropy of the same token should converge to the same value independent of the length. To improve the generalization of the model to various testing-sequence lengths, we adopt the length-scaled dot product to calculate the attention score, which can be interpreted from a theoretical view of entropy. The operation of the length-scaled dot product is cheap but effective. Experiments are conducted on the benchmark dataset CMU-MOSEI. Compared to the baseline models, our model achieves an 81.0% F1 score with only 0.432 M parameters, showing an improvement in the balance between performance and the number of parameters. Moreover, the ablation study signifies the effectiveness of our model and its scalability to various input-sequence lengths, wherein the relative improvement is almost 20% of the baseline without a length-scaled dot product.

Identification of autophagy-related risk signatures for the prognosis, diagnosis, and targeted therapy in cervical cancer.

BACKGROUND: To rummage autophagy-related prognostic, diagnostic, and therapeutic biomarkers in cervical cancer (CC). METHODS: The RNA-sequence and clinical information were from the TCGA and GTEx databases. We operated Cox regression to determine signatures related to overall survival (OS) and recurrence-free survival (RFS) respectively. The diagnostic and therapeutic effectiveness of prognostic biomarkers were further explored. RESULTS: We identified nine (VAMP7, MTMR14, ATG4D, KLHL24, TP73, NAMPT, CD46, HGS, ATG4C) and three risk signatures (SERPINA1, HSPB8, SUPT20H) with prognostic values for OS and RFS respectively. Six risk signatures (ATG4C, ATG4D, CD46, TP73, SERPINA1, HSPB8) were selected for qPCR. We screened five prognostic signatures(ATG4C, CD46, HSPB8, MTMR14, NAMPT) with diagnostic function through the GEO database. Correlation between our models and treatment targets certificated the prognostic score provided a reference for precision medicine. CONCLUSIONS: We constructed OS and RFS prognostic models in CC. Autophagy-related risk signatures might serve as diagnostic and therapeutic biomarkers.

Trends in antipsychotic prescribing for approved and unapproved indications to Medicaid-enrolled youth in Philadelphia, Pennsylvania between 2014 and 2018.

BACKGROUND: Antipsychotic prescribing to Medicaid-enrolled youth has been the target of numerous policy initiatives, including prior authorization and quality monitoring programs, which often target specific populations. Whether these efforts have changed the level or composition of antipsychotic prescribing is unclear. METHODS: Using 2014-2018 administrative claims data for Medicaid enrollees aged 21 years and under in Philadelphia, Pennsylvania, we measured antipsychotic prescription fills overall and for youth without an approved indication (autism, bipolar disorder, or psychosis). We then assessed whether trends differed for populations that have been targeted by policy initiatives, including younger children and foster care-enrolled youth. We also identified the most common approved and unapproved indications and examined whether the treatment duration of antipsychotic prescriptions differed based on whether the youth had an approved or unapproved indication. RESULTS: Overall, the number of Medicaid youth with an antipsychotic prescription fill halved between 2014 and 2018. Youth aged 17 years and under and foster care-enrolled youth, who were targeted by prior authorization and quality improvement efforts, experienced larger declines. Roughly half of prescriptions were for unapproved indications in both 2014 and 2018; the most common unapproved indication was ADHD, and the treatment duration was shorter for unapproved indications compared to approved indications. CONCLUSIONS: Antipsychotic prescribing to Medicaid-enrolled youth is declining, particularly among populations that have been targeted by policy initiatives like prior authorization and quality monitoring programs. Despite the fact that these initiatives often assess diagnostic criteria, half of antipsychotic prescriptions were for unapproved indications in both 2014 and 2018. More research is needed to gauge whether this prescribing is appropriate.

Changes of the flavor substances and protein degradation of black carp (Mylopharyngodon piceus) pickled products during steaming.

BACKGROUND: Among various cooking methods, steaming is favored by many because it can cause less damage to nutrient components in muscle, retain the inherent food flavor, and reduce the generation of harmful substances. Steaming conditions are closely related to fish flavor, fat and protein oxidation, and digestibility. RESULTS: The black carp steamed for 4 to 14 min was studied in this article based on sensory assessment, electronic tongue, free amino acids, adenosine triphosphate (ATP)-related compounds, total nitrogen and non-protein nitrogen to explore the effect of steaming time on the taste substances and protein degradation of pickled black carp. The experimental result showed that the meat steamed within 8 min tasted better, showing high tastiness. The sensory assessment score increased significantly to the maximum value of 82.33 at 6 min. The content of umami and sweet amino acids increased significantly to the maximum value of 1.6801 g kg-1 at 6 min. In the meantime, the IMP (inosine monophosphate) content was 1.9128 g kg-1 , with its taste activity value (TAV) reaching 7.65, which proved that IMP affected the taste most. Furthermore, the total nitrogen content was 30.77 g kg-1 , which meant protein degraded a great deal. Based on equivalent umami concentration (EUC) and its TAV, the meat tasted best at 6-8 min. The longer the steaming time, the faster the protein degradation and the more the flavor precursors. CONCLUSION: The black carp pickled products (with a weight of 20 g, with the size of 3 cm × 3 cm × 2 cm) is suggested to be steamed for 6 to 8 min. This conclusion provides a theoretical basis for its better taste quality. © 2020 Society of Chemical Industry.

Development of a Nomogram Model Based on Lactate-To-Albumin Ratio for Prognostic Prediction in Hospitalized Patients with Intracerebral Hemorrhage.

OBJECTIVE: This study aims to develop a nomogram model incorporating lactate-to-albumin ratio (LAR) to predict the prognosis of hospitalized patients with intracerebral hemorrhage (ICH) and demonstrate its excellent predictive performance. METHODS: A total of 226 patients with ICH from the Medical information mart for intensive care III (MIMIC Ⅲ) database were randomly split into 8:2 ratio training and experimental groups, and 38 patients from the eICU-CRD for external validation. Univariate and multivariate Cox proportional hazards regression analysis was performed to identify independent factors associated with ICH, and multivariate Cox regression was used to construct nomograms for 7-day and 14-day overall survival (OS). The performance of nomogram was verified by the calibration curves, decision curves, and receiver operating characteristic (ROC) curves. RESULTS: Our study identified LAR, glucose, mean blood pressure, sodium, and ethnicity as independent factors influencing in-hospital prognosis. The predictive performance of our nomogram model for predicting 7-day and 14 -day OS (AUCs: 0.845 and 0.830 respectively) are both superior to Oxford Acute Severity of Illness Score, Simplified acute physiology score II, and SIRS (AUCs: 0.617, 0.620 and 0.591 and AUCs: 0.709, 0.715 and 0.640, respectively) in internal validation, and also demonstrate favorable predictive performance in external validation (AUCs: 0.778 and 0.778 respectively). CONCLUSIONS: LAR as a novel biomarker is closely associated with an increased risk of in-hospital mortality of patients with ICH. The nomogram model incorporating LAR along with glucose, mean blood pressure, sodium, and ethnicity demonstrate excellent predictive performance for predicting the prognosis of 7- and 14-day OS of hospitalized patients with ICH.

Bovine FRAS1: mRNA Expression Profile, Genetic Variations, and Significant Correlations with Ovarian Morphological Traits, Mature Follicle, and Corpus Luteum.

The amelioration of bovine fertility caused by a multi-factorial problem has always been a hot topic, among which the detection of available target genes is the most crucial. It was hypothesized that the Fraser extracellular matrix complex subunit 1 (FRAS1) gene detected by GWAS is involved in physiological activities such as ovarian development. Herein, unilateral ovaries from 2111 cows were used to examine the mRNA expression profile and polymorphisms of bovine FRAS1 and their associations with fertility-related characteristics. Firstly, it was confirmed that FRAS1 gene transcripts are expressed in various bovine tissues. Then, among five potential insertion-deletion (indel) loci, the 20 bp (named P3-D20-bp) and 15 bp (P4-D15-bp) deletion mutations were confirmed to be polymorphic with linkage equilibrium. Secondly, the P3-D20-bp polymorphism was significantly associated with ovarian weight and corpus luteum diameter in the metaestrus phase and ovarian length in the dioestrum stage. Additionally, both ovarian length and mature follicle diameter in metaestrus are significantly correlated with different genotypes of P4-D15-bp. Thirdly, the transcriptional expression of the FRAS1 gene in groups with a minimum value of ovarian weight or volume was significantly higher than the expression in groups with a maximum value. Instead of that, the more corpus luteum and mature follicles there are, the higher the transcription expression of the FRAS1 gene is. Furthermore, FRAS1 expression in cows with a heterozygous genotype (ID) of P3-D20-bp was significantly higher than others. Eventually, P3-D20-bp deletion could disturb the binding efficiency of WT1-I and Sox2 to FRAS1 sequence according to binding prediction, indicating that mutation may affect gene expression and traits by influencing the binding of transcription factors. Overall, the polymorphisms of P3-D20-bp and P4-D15-bp of the bovine FRAS1 gene significantly correlated to follicle or ovarian traits that could be applied in optimizing female fertility in cow MAS breeding programs.

Enhancing Global Estimation of Fine Particulate Matter Concentrations by Including Geophysical a Priori Information in Deep Learning.

Global fine particulate matter (PM2.5) assessment is impeded by a paucity of monitors. We improve estimation of the global distribution of PM2.5 concentrations by developing, optimizing, and applying a convolutional neural network with information from satellite-, simulation-, and monitor-based sources to predict the local bias in monthly geophysical a priori PM2.5 concentrations over 1998-2019. We develop a loss function that incorporates geophysical a priori estimates and apply it in model training to address the unrealistic results produced by mean-square-error loss functions in regions with few monitors. We introduce novel spatial cross-validation for air quality to examine the importance of considering spatial properties. We address the sharp decline in deep learning model performance in regions distant from monitors by incorporating the geophysical a priori PM2.5. The resultant monthly PM2.5 estimates are highly consistent with spatial cross-validation PM2.5 concentrations from monitors globally and regionally. We withheld 10% to 99% of monitors for testing to evaluate the sensitivity and robustness of model performance to the density of ground-based monitors. The model incorporating the geophysical a priori PM2.5 concentrations remains highly consistent with observations globally even under extreme conditions (e.g., 1% for training, R2 = 0.73), while the model without exhibits weaker performance (1% for training, R2 = 0.51).