iNP_ESM: Neuropeptide Identification Based on Evolutionary Scale Modeling and Unified Representation Embedding Features.

Neuropeptides are biomolecules with crucial physiological functions. Accurate identification of neuropeptides is essential for understanding nervous system regulatory mechanisms. However, traditional analysis methods are expensive and laborious, and the development of effective machine learning models continues to be a subject of current research. Hence, in this research, we constructed an SVM-based machine learning neuropeptide predictor, iNP_ESM, by integrating protein language models Evolutionary Scale Modeling (ESM) and Unified Representation (UniRep) for the first time. Our model utilized feature fusion and feature selection strategies to improve prediction accuracy during optimization. In addition, we validated the effectiveness of the optimization strategy with UMAP (Uniform Manifold Approximation and Projection) visualization. iNP_ESM outperforms existing models on a variety of machine learning evaluation metrics, with an accuracy of up to 0.937 in cross-validation and 0.928 in independent testing, demonstrating optimal neuropeptide recognition capabilities. We anticipate improved neuropeptide data in the future, and we believe that the iNP_ESM model will have broader applications in the research and clinical treatment of neurological diseases.

Discovery of Novel Oxathiapiprolin Derivatives as Potent Fungicide Candidates.

Oxathiapiprolin (OXA), which targets the oxysterol-binding protein (OSBP), is an outstanding piperidinyl thiazole isoxazoline (PTI) fungicide that can be used to control oomycetes diseases. In this study, starting from the structure of OXA, a series of novel OSBP inhibitors were designed and synthesized by introducing an indole moiety to replace the pyrazole in OXA. Finally, compound b24 was found to exhibit the highest control effect (82%) against cucumber downy mildew (CDM) in the greenhouse at a very low dosage of 0.069 mg/L, which was comparable to that of OXA (88%). Furthermore, it showed better activity against potato late blight (PLB) than other derivatives of indole. The computational results showed that the R-conformation of b24 should be the dominant conformation binding to PcOSBP. The results of the present work indicate that the 3-fluorine-indole ring is a favorable fragment to increasing the electronic energy when binding with PcOSBP. Furthermore, compound b24 could be used as a lead compound for the discovery of new OSBP inhibitors.

Machine learning-based prediction of diabetic patients using blood routine data.

Diabetes stands as one of the most prevalent chronic diseases globally. The conventional methods for diagnosing diabetes are frequently overlooked until individuals manifest noticeable symptoms of the condition. This study aimed to address this gap by collecting comprehensive datasets, including 1000 instances of blood routine data from diabetes patients and an equivalent dataset from healthy individuals. To differentiate diabetes patients from their healthy counterparts, a computational framework was established, encompassing eXtreme Gradient Boosting (XGBoost), random forest, support vector machine, and elastic net algorithms. Notably, the XGBoost model emerged as the most effective, exhibiting superior predictive results with an area under the receiver operating characteristic curve (AUC) of 99.90% in the training set and 98.51% in the testing set. Moreover, the model showcased commendable performance during external validation, achieving an overall accuracy of 81.54%. The probability generated by the model serves as a risk score for diabetes susceptibility. Further interpretability was achieved through the utilization of the Shapley additive explanations (SHAP) algorithm, identifying pivotal indicators such as mean corpuscular hemoglobin concentration (MCHC), lymphocyte ratio (LY%), standard deviation of red blood cell distribution width (RDW-SD), and mean corpuscular hemoglobin (MCH). This enhances our understanding of the predictive mechanisms underlying diabetes. To facilitate the application in clinical and real-life settings, a nomogram was created based on the logistic regression algorithm, which can provide a preliminary assessment of the likelihood of an individual having diabetes. Overall, this research contributes valuable insights into the predictive modeling of diabetes, offering potential applications in clinical practice for more effective and timely diagnoses.

Destabilized reporters for background-subtracted, chemically-gated, and multiplexed deep-tissue imaging.

Tracking gene expression in deep tissues requires genetic reporters that can be unambiguously detected using tissue penetrant techniques. Magnetic resonance imaging (MRI) is uniquely suited for this purpose; however, there is a dearth of reporters that can be reliably linked to gene expression with minimal interference from background tissue signals. Here, we present a conceptually new method for generating background-subtracted, drug-gated, multiplex images of gene expression using MRI. Specifically, we engineered chemically erasable reporters consisting of a water channel, aquaporin-1, fused to destabilizing domains, which are stabilized by binding to cell-permeable small-molecule ligands. We showed that this approach allows for highly specific detection of gene expression through differential imaging. In addition, by engineering destabilized aquaporin-1 variants with orthogonal ligand requirements, it is possible to distinguish distinct subpopulations of cells in mixed cultures. Finally, we demonstrated this approach in a mouse tumor model through differential imaging of gene expression with minimal background.

Effect of modified opioid sparing anaesthesia on postoperative quality of recovery in patients undergoing laparoscopic bariatric surgery: protocol for a monocentre, double-blind randomised controlled trial - the MOSA study.

INTRODUCTION: Obesity patients undergoing laparoscopic bariatric surgery (LBS) are frequently encountered perioperative adverse events related to opioids-based anaesthesia (OBA) or opioids-free anaesthesia (OFA). While modified opioid-sparing anaesthesia (MOSA) has been shown to lower the occurrence of adverse events related to OBA and OFA. This study is to assess the efficacy of MOSA in enhancing the recovery quality among individuals undergoing LBS. METHODS AND ANALYSIS: A single-centre, prospective, double-blind, randomised controlled trial is conducted at a tertiary hospital. A total of 74 eligible participants undergoing elective LBS will be recruited and randomly allocated. Patients in the MOSA group will receive a combination of low-dose opioids, minimal dexmedetomidine, esketamine and lidocaine, while in the OBA group will receive standard general anaesthesia with opioids. Patients in both groups will receive standard perioperative care. The primary outcome is the quality of recovery-15 score assessed at 24 hours after surgery. Secondary outcomes include pain levels, anxiety and depression assessments, gastrointestinal function recovery, perioperative complication rates, opioid consumption and length of hospital stay. ETHICS AND DISSEMINATION: Ethical approval has been provided by the Ethical Committee of Yan'an Hospital of Kunming City (approval No. 2023-240-01). Eligible patients will provide written informed consent to the investigator. The outcomes of this trial will be disseminated in a peer-reviewed scholarly journal. TRIAL REGISTRATION NUMBER: The study protocol is registered at https://www.chictr.org.cn/ on 19 December 2023. (identifier: ChiCTR2300078806). The trial was conducted using V.1.0.

The protective effects of Kefir extract (KE) on intestinal damage in larval zebrafish induced by Oxytetracycline: Insights into intestinal function, morphology, and molecular mechanisms.

The antibiotic oxytetracycline (OTC) can be detected in contemporary natural aquatic environments and has been implicated in causing intestinal damage in humans exposed to OTC-contaminated food or water. The irreversible damage caused by high concentrations of OTC to the intestine suggests that treatment through dietary means could still be necessary. This study proved the effectiveness of kefir extract (KE) in reversing intestinal damage caused by oxytetracycline (OTC) exposure. Following a 24-hour KE treatment subsequent to OTC exposure from 3 to 8 days post-fertilization of zebrafish larvae, molecular-level and microbiomic assessments revealed significant improvements. These included reduced expression of proinflammatory factors (IL-8 and IL-1ß), increased antioxidant levels, and reversed unhealthy distribution of intestinal microbiota. Furthermore, KE supplementation showed potential in enhancing intestinal motility in the experiment of Nile red staining and fluorescent microbead transit. However, histological analysis showed that this short-term treatment with KE only partially reversed the intestinal morphological changes induced by OTC, suggesting that a longer treatment period might be necessary for complete restoration.

Learning interpretable causal networks from very large datasets, application to 400,000 medical records of breast cancer patients.

Discovering causal effects is at the core of scientific investigation but remains challenging when only observational data are available. In practice, causal networks are difficult to learn and interpret, and limited to relatively small datasets. We report a more reliable and scalable causal discovery method (iMIIC), based on a general mutual information supremum principle, which greatly improves the precision of inferred causal relations while distinguishing genuine causes from putative and latent causal effects. We showcase iMIIC on synthetic and real-world healthcare data from 396,179 breast cancer patients from the US Surveillance, Epidemiology, and End Results program. More than 90% of predicted causal effects appear correct, while the remaining unexpected direct and indirect causal effects can be interpreted in terms of diagnostic procedures, therapeutic timing, patient preference or socio-economic disparity. iMIIC's unique capabilities open up new avenues to discover reliable and interpretable causal networks across a range of research fields.

Fano resonance in molecular junctions of spin crossover complexes.

In this paper, we introduce a novel molecular switch paradigm that integrates spin crossover complexes with the Fano resonance effect. Specifically, by performing density-functional theory calculations, the feasibility of achieving Fano resonance using spin crossover complexes is demonstrated in our designed molecular junctions using the complex {Fe[H2B(pz)2]2[Bp(bipy)]} [pz = 1-pyrazolyl, Bp(bipy) = bis(phenylethynyl)(2,2'-bipyridine)]. It is further revealed that the Fano resonance, particularly the Fano dip, is most prominent in the junction with cobalt tips among all the schemes, together with the spin-filtering effect. Most importantly, this junction of cobalt tips is able to exhibit three distinct conductance states, which are controlled by the modulation of Fano resonance due to the spin-state transition of the complex and the applied gate voltage. Such a molecular switch paradigm holds potential for applications in logic gates, memory units, sensors, thermoelectrics, and beyond.

A novel curdlan/methyl cellulose/walnut green husk polyphenol edible composite film for walnut packaging.

In this study, polyphenols were extracted from walnut green husk, an agricultural waste, and were incorporated into curdlan (CD) and methyl cellulose (MC) to create a novel edible composite film. For structural character, the film matrix was tightly bound primarily by non-covalent bonds and the addition of walnut green husk polyphenols (WGHP) significantly reduced the surface roughness of the composite film. For mechanical properties, the addition of WGHP improve the flexibility of films, and it significantly improved the barrier ability of ultraviolet rays and water-vapor. Furthermore, the incorporation of WGHP to the CD-MC film resulted in enhanced antioxidant and antibacterial effects, which effectively retards lipid oxidation in fried walnuts. Consequently, the fabricated CD-MC-WGHP composite film bears immense potential for use in food preservation applications, particularly in extending the shelf life of fried walnuts.

Unlocking the link: how hippocampal glutathione-glutamate coupling predicts cognitive impairment in multiple sclerosis patients.

Cognitive impairment is a common symptom of multiple sclerosis and profoundly impacts quality of life. Glutathione (GSH) and glutamate (Glu) are tightly linked in the brain, participating in cognitive function. However, GSH-Glu couplings in cognitive brain regions and their relationship with cognitive impairment in relapsing-remitting multiple sclerosis (RRMS) remains unclear. Forty-one RRMS patients and 43 healthy controls underwent magnetic resonance spectroscopy to measure GSH and Glu levels in the posterior cingulate cortex, medial prefrontal cortex and left hippocampus. Neuropsychological tests were used to evaluate the cognitive function. The Glu/GSH ratio was used to indicate the coupling between GSH and Glu and was tested as a predictor of cognitive performance. The results show that RRMS patients exhibited reduced hippocampal GSH and Glu levels, which were found to be significant predictors of worse verbal and visuospatial memory, respectively. Moreover, GSH levels were dissociated from Glu levels in the left hippocampus of RRMS patients. Hippocampal Glu/GSH ratio is significantly correlated with processing speed and has a greater predictive effect. Here we show the hippocampal Glu/GSH ratio could serve as a new potential marker for characterizing cognitive impairment in RRMS, providing a new direction for clinical detection of cognitive impairment.

Integrating somatic mutation profiles with structural deep clustering network for metabolic stratification in pancreatic cancer: a comprehensive analysis of prognostic and genomic landscapes.

Pancreatic cancer is a globally recognized highly aggressive malignancy, posing a significant threat to human health and characterized by pronounced heterogeneity. In recent years, researchers have uncovered that the development and progression of cancer are often attributed to the accumulation of somatic mutations within cells. However, cancer somatic mutation data exhibit characteristics such as high dimensionality and sparsity, which pose new challenges in utilizing these data effectively. In this study, we propagated the discrete somatic mutation data of pancreatic cancer through a network propagation model based on protein-protein interaction networks. This resulted in smoothed somatic mutation profile data that incorporate protein network information. Based on this smoothed mutation profile data, we obtained the activity levels of different metabolic pathways in pancreatic cancer patients. Subsequently, using the activity levels of various metabolic pathways in cancer patients, we employed a deep clustering algorithm to establish biologically and clinically relevant metabolic subtypes of pancreatic cancer. Our study holds scientific significance in classifying pancreatic cancer based on somatic mutation data and may provide a crucial theoretical basis for the diagnosis and immunotherapy of pancreatic cancer patients.

MYL3 protects chondrocytes from senescence by inhibiting clathrin-mediated endocytosis and activating of Notch signaling.

As the unique cell type in articular cartilage, chondrocyte senescence is a crucial cellular event contributing to osteoarthritis development. Here we show that clathrin-mediated endocytosis and activation of Notch signaling promotes chondrocyte senescence and osteoarthritis development, which is negatively regulated by myosin light chain 3. Myosin light chain 3 (MYL3) protein levels decline sharply in senescent chondrocytes of cartilages from model mice and osteoarthritis (OA) patients. Conditional deletion of Myl3 in chondrocytes significantly promoted, whereas intra-articular injection of adeno-associated virus overexpressing MYL3 delayed, OA progression in male mice. MYL3 deficiency led to enhanced clathrin-mediated endocytosis by promoting the interaction between myosin VI and clathrin, further inducing the internalization of Notch and resulting in activation of Notch signaling in chondrocytes. Pharmacologic blockade of clathrin-mediated endocytosis-Notch signaling prevented MYL3 loss-induced chondrocyte senescence and alleviated OA progression in male mice. Our results establish a previously unknown mechanism essential for cellular senescence and provide a potential therapeutic direction for OA.

Recent Advances in the Biosynthesis of Natural Sugar Substitutes in Yeast.

Natural sugar substitutes are safe, stable, and nearly calorie-free. Thus, they are gradually replacing the traditional high-calorie and artificial sweeteners in the food industry. Currently, the majority of natural sugar substitutes are extracted from plants, which often requires high levels of energy and causes environmental pollution. Recently, biosynthesis via engineered microbial cell factories has emerged as a green alternative for producing natural sugar substitutes. In this review, recent advances in the biosynthesis of natural sugar substitutes in yeasts are summarized. The metabolic engineering approaches reported for the biosynthesis of oligosaccharides, sugar alcohols, glycosides, and rare monosaccharides in various yeast strains are described. Meanwhile, some unresolved challenges in the bioproduction of natural sugar substitutes in yeast are discussed to offer guidance for future engineering.

Structural deep clustering network for stratification of breast cancer patients through integration of somatic mutation profiles.

BACKGROUND AND OBJECTIVE: Breast cancer is among of the most malignant tumor that occurs in women and is one of the leading causes of death from gynecologic malignancy worldwide. The high degree of heterogeneity that characterizes breast cancer makes it challenging to devise effective therapeutic strategies. Accumulating evidence highlights the crucial role of stratifying breast cancer patients into clinically significant subtypes to achieve better prognoses and treatments. The structural deep clustering network is a graph convolutional network-based clustering algorithm that integrates structural information and has achieved state-of-the-art performance in various applications. METHODS: In this study, we employed structural deep clustering network to integrate somatic mutation profiles for stratifying 2526 breast cancer patients from the Memorial Sloan Kettering Cancer Center into two clinically differentiable subtypes. RESULTS: Breast cancer patients in cluster 1 exhibited better prognosis than breast cancer patients in cluster 2, and the difference between them was statistically significant. The immunogenomic landscape further demonstrated that cluster 1 was associated with remarkable infiltration of the tumor infiltrating lymphocytes. The clustering subtype could be used to evaluate the therapeutic benefit of immunotherapy and chemotherapy in breast cancer patients. Furthermore, our approach effectively classified patients from eight different cancer types, demonstrating its generalizability. CONCLUSIONS: Our study represents a step towards a generic methodology for classifying cancer patients using only somatic mutation data and structural deep clustering network approaches. Employing structural deep clustering network to identify breast cancer subtypes is promising and can inform the development of more accurate and personalized therapies.

Highly efficient and selective CO2 capture of Li2CO3- and (Li-K)2CO3-based porous carbon composites.

In this study, Li2CO3- and (Li-K)2CO3-based porous carbon composites were synthesized from terephthalic acid, lithium hydroxide and sodium hydroxide through calcination at different temperatures. These materials were fully characterized through X-ray diffraction, Raman spectroscopy, and nitrogen adsorption and desorption. Results showed that the excellent CO2 capture capacities of LiC-700 °C and LiKC-600 °C were 140 and 82 mg CO2 g-1 at 0 °C and 25 °C, respectively. Additionally, it is calculated that the selectivity of LiC-600 °C and LiKC-700 °C with a CO2/N2 (15 : 85) mixture was about 27.41 and 15.04, respectively. Therefore, Li2CO3- and (Li-K)2CO3-based porous carbon materials could be used to effectively capture CO2 with high capacity and high selectivity.

Engineering ligand stabilized aquaporin reporters for magnetic resonance imaging.

Imaging transgene expression in live tissues requires reporters that are detectable with deeply penetrant modalities, such as magnetic resonance imaging (MRI). Here, we show that LSAqp1, a water channel engineered from aquaporin-1, can be used to create background-free, drug-gated, and multiplex images of gene expression using MRI. LSAqp1 is a fusion protein composed of aquaporin-1 and a degradation tag that is sensitive to a cell-permeable ligand, which allows for dynamic small molecule modulation of MRI signals. LSAqp1 improves specificity for imaging gene expression by allowing reporter signals to be conditionally activated and distinguished from the tissue background by difference imaging. In addition, by engineering destabilized aquaporin-1 variants with different ligand requirements, it is possible to image distinct cell types simultaneously. Finally, we expressed LSAqp1 in a tumor model and showed successful in vivo imaging of gene expression without background activity. LSAqp1 provides a conceptually unique approach to accurately measure gene expression in living organisms by combining the physics of water diffusion and biotechnology tools to control protein stability.

Metatranscriptomic analysis revealed Prevotella as a potential biomarker of oropharyngeal microbiomes in SARS-CoV-2 infection.

Background and objectives: Disease severity and prognosis of coronavirus disease 2019 (COVID-19) disease with other viral infections can be affected by the oropharyngeal microbiome. However, limited research had been carried out to uncover how these diseases are differentially affected by the oropharyngeal microbiome of the patient. Here, we aimed to explore the characteristics of the oropharyngeal microbiota of COVID-19 patients and compare them with those of patients with similar symptoms. Methods: COVID-19 was diagnosed in patients through the detection of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) by quantitative reverse transcription polymerase chain reaction (RT-qPCR). Characterization of the oropharyngeal microbiome was performed by metatranscriptomic sequencing analyses of oropharyngeal swab specimens from 144 COVID-19 patients, 100 patients infected with other viruses, and 40 healthy volunteers. Results: The oropharyngeal microbiome diversity in patients with SARS-CoV-2 infection was different from that of patients with other infections. Prevotella and Aspergillus could play a role in the differentiation between patients with SARS-CoV-2 infection and patients with other infections. Prevotella could also influence the prognosis of COVID-19 through a mechanism that potentially involved the sphingolipid metabolism regulation pathway. Conclusion: The oropharyngeal microbiome characterization was different between SARS-CoV-2 infection and infections caused by other viruses. Prevotella could act as a biomarker for COVID-19 diagnosis and of host immune response evaluation in SARS-CoV-2 infection. In addition, the cross-talk among Prevotella, SARS-CoV-2, and sphingolipid metabolism pathways could provide a basis for the precise diagnosis, prevention, control, and treatment of COVID-19.

More efficient and inclusive time-to-event trials with covariate adjustment: a simulation study.

Adjustment for prognostic covariates increases the statistical power of randomized trials. The factors influencing the increase of power are well-known for trials with continuous outcomes. Here, we study which factors influence power and sample size requirements in time-to-event trials. We consider both parametric simulations and simulations derived from the Cancer Genome Atlas (TCGA) cohort of hepatocellular carcinoma (HCC) patients to assess how sample size requirements are reduced with covariate adjustment. Simulations demonstrate that the benefit of covariate adjustment increases with the prognostic performance of the adjustment covariate (C-index) and with the cumulative incidence of the event in the trial. For a covariate that has an intermediate prognostic performance (C-index=0.65), the reduction of sample size varies from 3.1% when cumulative incidence is of 10% to 29.1% when the cumulative incidence is of 90%. Broadening eligibility criteria usually reduces statistical power while our simulations show that it can be maintained with adequate covariate adjustment. In a simulation of adjuvant trials in HCC, we find that the number of patients screened for eligibility can be divided by 2.4 when broadening eligibility criteria. Last, we find that the Cox-Snell [Formula: see text] is a conservative estimation of the reduction in sample size requirements provided by covariate adjustment. Overall, more systematic adjustment for prognostic covariates leads to more efficient and inclusive clinical trials especially when cumulative incidence is large as in metastatic and advanced cancers. Code and results are available at https://github.com/owkin/CovadjustSim .

Novel antiosteoporotic peptides purified from protein hydrolysates of taihe black-boned silky fowl: By larval zebrafish model and molecular docking.

The black-boned silky fowl (BSF) muscle protein hydrolysate was gained by alcalase. The hydrolysate could stimulate MC3T3-E1 cell proliferation, as well as enhance alkaline phosphatas (ALP) activity and deposits of minerals. After isolation and purification, 55 peptide sequences with Mascot score over 40 were identified. Combined with molecular docking simulation and molecular dynamics analysis, two novel peptides (PASTGAAK and PGPPGTPF) were identified with the lowest binding energy of -4.99 kcal/mol and -3.07 kcal/mol with receptor BMPR1A of BMP-2/Smad pathway, showing the ability to increase BMPR1A stability. Moreover, both PASTGAAK and PGPPGTPF revealed strong anti-osteoporosis activities in the zebrafish model induced by dexamethasone. Additionally, the identified peptides could be beneficial for the differentiation of MC3T3-E1 cell for upregulating the expression of some osteoblast-related genes and proteins by stimulating BMP-2/Smad pathway. Overall, the two newly identified peptides could be the potential candidate to prevent osteoporosis.

Stratification of ovarian cancer patients from the prospect of drug target-related transcription factor protein activity: the prognostic and genomic landscape analyses.

The expression and activity of transcription factors, which directly mediate gene transcription, are strictly regulated to control numerous normal cellular processes. In cancer, transcription factor activity is often dysregulated, resulting in abnormal expression of genes related to tumorigenesis and development. The carcinogenicity of transcription factors can be reduced through targeted therapy. However, most studies on the pathogenic and drug-resistant mechanisms of ovarian cancer have focused on the expression and signaling pathways of individual transcription factors. To improve the prognosis and treatment of patients with ovarian cancer, multiple transcription factors should be evaluated simultaneously to determine the effects of their protein activity on drug therapies. In this study, the transcription factor activity of ovarian cancer samples was inferred from virtual inference of protein activity by enriched regulon algorithm using mRNA expression data. Patients were clustered according to their transcription factor protein activities to investigate the association of transcription factor activities of different subtypes with prognosis and drug sensitivity for filtering subtype-specific drugs. Meanwhile, master regulator analysis was utilized to identify master regulators of differential protein activity between clustering subtypes, thereby identifying transcription factors associated with prognosis and assessing their potential as therapeutic targets. Master regulator risk scores were then constructed for guiding patients' clinical treatment, providing new insights into the treatment of ovarian cancer at the level of transcriptional regulation.