Generating Artificial Ribozymes Using Sparse Coevolutionary Models.

RNA ribozyme (Walter Engelke, Biologist (London, England) 49:199-203, 2002) datasets typically contain from a few hundred to a few thousand naturally occurring sequences. However, the potential sequence space of RNA is huge. For example, the number of possible RNA sequences of length 150 nucleotides is approximately 1 0 90 , a figure that far surpasses the estimated number of atoms in the known universe, which is around 1 0 80 . This disparity highlights a vast realm of sequence variability that remains unexplored by natural evolution. In this context, generative models emerge as a powerful tool. Learning from existing natural instances, these models can create artificial variants that extend beyond the currently known sequences. In this chapter, we will go through the use of a generative model based on direct coupling analysis (DCA) (Russ et al., Science 369:440-445, 2020; Trinquier et al., Nat Commun 12:5800, 2021; Calvanese et al., Nucleic Acids Res 52(10):5465-5477, 2024) applied to the twister ribozyme RNA family with three key applications: generating artificial twister ribozymes, designing potentially functional mutations of a natural wild type, and predicting mutational effects.

AI-enhanced diagnostic model for pulmonary nodule classification.

Background: The identification of benign and malignant pulmonary nodules (BPN and MPN) can significantly reduce mortality. However, a reliable and validated diagnostic model for clinical decision-making is still lacking. Methods: Enzyme-linked immunosorbent assay and electro chemiluminescent immunoassay were utilized to determine the serum concentrations of 7AABs (p53, GAGE7, PGP9.5, CAGE, MAGEA1, SOX2, GBU4-5), and 4TTMs (CYFR21, CEA, NSE and SCC) in 260 participants (72 BPNs and 188 early-stage MPNs), respectively. The malignancy probability was calculated using Artificial intelligence pulmonary nodule auxiliary diagnosis system, or Mayo model. Along with age, sex, smoking history and nodule size, 18 variables were enrolled for model development. Baseline comparison, univariate ROC analysis, variable correlation analysis, lasso regression, univariate and stepwise logistic regression, and decision curve analysis (DCA) was used to reduce and screen variables. A nomogram and DCA were built for model construction and clinical use. Training (60%) and validation (40%) cohorts were used to for model validation. Results: Age, CYFRA21_1, AI, PGP9.5, GAGE7, and GBU4_5 was screened out from 18 variables and utilized to establish the regression model for identifying BPN and early-stage MPN, as well as nomogram and DCA for clinical practical use. The AUC of the nomogram in the training and validation cohorts were 0.884 and 0.820, respectively. Moreover, the calibration curve showed high coherence between the predicted and actual probability. Conclusion: This diagnostic model and DCA could provide evidence for upgrading or maintaining the current clinical decision based on malignancy probability stratification. It enables low and moderate risk or ambiguous patients to benefit from more precise clinical decision stratification, more timely detection of malignant nodules, and early treatment.

Uncoupling as initiating event in mitochondrial dysfunction after diuron exposure.

Diuron, a herbicide derived from urea, has been shown to induce urinary bladder urothelial tumors in rodents, leading the U.S. Environmental Protection Agency (USEPA) to designate it as a 'known/likely' human carcinogen. In our laboratory, a series of studies investigating the carcinogenic mode of action (MoA) of Diuron have consistently revealed its cytotoxic effects on the urinary bladder urothelium. Prolonged exposure to relatively high doses of Diuron results in urothelial necrosis, regenerative hyperplasia, and eventually, the development of tumors. The hypothesis posited is that Diuron and its metabolites exert toxicity by causing damage to mitochondria, a phenomenon referred to as mitotoxicity. Our research focuses on evaluating how Diuron and its metabolites affect mitochondria isolated from both the urothelium and the liver, the primary organ for Diuron biotransformation. In this context, we present and discuss data pertaining to mitochondria isolated from the liver of Wistar rats exposed to Diuron or its metabolites 3-(3,4-diclorofenil)-1-metilureia (DCPMU) or 3,4-dichloroaniline (DCA) at concentrations ranging from 0.5 to 500 µM in vitro. The findings indicate that, at concentrations of 100 and 500 µM, the tested chemicals induce uncoupling of oxidative phosphorylation, as evidenced by the dissipation of mitochondrial membrane potential and basal oxygen consumption. Notably, at 500 µM, DCA causes mitochondrial swelling, a morphofunctional indicator of severe organelle damage. These outcomes underscore the classification of Diuron and its metabolites, DCA and DCPMU, as mitotoxic to liver cells, given the pronounced mitochondrial dysfunction they induce.

Diuron, DCA, and DCPMU uncouple the electron transport chain in to hepatic mitochondria.Diuron, DCA, and DCPMU are mitotoxic agents.The metabolite DCA is the most toxic to hepatic mitochondria and can induce mitochondrial swelling.

Deep-learning-based design of synthetic orthologs of SH3 signaling domains.

Evolution-based deep generative models represent an exciting direction in understanding and designing proteins. An open question is whether such models can learn specialized functional constraints that control fitness in specific biological contexts. Here, we examine the ability of generative models to produce synthetic versions of Src-homology 3 (SH3) domains that mediate signaling in the Sho1 osmotic stress response pathway of yeast. We show that a variational autoencoder (VAE) model produces artificial sequences that experimentally recapitulate the function of natural SH3 domains. More generally, the model organizes all fungal SH3 domains such that locality in the model latent space (but not simply locality in sequence space) enriches the design of synthetic orthologs and exposes non-obvious amino acid constraints distributed near and far from the SH3 ligand-binding site. The ability of generative models to design ortholog-like functions in vivo opens new avenues for engineering protein function in specific cellular contexts and environments.

A m6A regulators-related classifier for prognosis and tumor microenvironment characterization in hepatocellular carcinoma.

Background: Increasing evidence have highlighted the biological significance of mRNA N6-methyladenosine (m6A) modification in regulating tumorigenicity and progression. However, the potential roles of m6A regulators in tumor microenvironment (TME) formation and immune cell infiltration in liver hepatocellular carcinoma (LIHC or HCC) requires further clarification. Method: RNA sequencing data were obtained from TCGA-LIHC databases and ICGC-LIRI-JP databases. Consensus clustering algorithm was used to identify m6A regulators cluster subtypes. Weighted gene co-expression network analysis (WGCNA), LASSO regression, Random Forest (RF), and Support Vector Machine-Recursive Feature Elimination (SVM-RFE) were applied to identify candidate biomarkers, and then a m6Arisk score model was constructed. The correlations of m6Arisk score with immunological characteristics (immunomodulators, cancer immunity cycles, tumor-infiltrating immune cells (TIICs), and immune checkpoints) were systematically evaluated. The effective performance of nomogram was evaluated using concordance index (C-index), calibration plots, decision curve analysis (DCA), and receiver operating characteristic curve (ROC). Results: Two distinct m6A modification patterns were identified based on 23 m6A regulators, which were correlated with different clinical outcomes and biological functions. Based on the constructed m6Arisk score model, HCC patients can be divided into two distinct risk score subgroups. Further analysis indicated that the m6Arisk score showed excellent prognostic performance. Patients with a high m6Arisk score was significantly associated with poorer clinical outcome, lower drug sensitivity, and higher immune infiltration. Moreover, we developed a nomogram model by incorporating the m6Arisk score and clinicopathological features. The application of the m6Arisk score for the prognostic stratification of HCC has good clinical applicability and clinical net benefit. Conclusion: Our findings reveal the crucial role of m6A modification patterns for predicting HCC TME status and prognosis, and highlight the good clinical applicability and net benefit of m6Arisk score in terms of prognosis, immunophenotype, and drug therapy in HCC patients.

Dichloroacetate Prevents Sepsis Associated Encephalopathy by Inhibiting Microglia Pyroptosis through PDK4/NLRP3.

Dichloroacetate (DCA), a pyruvate dehydrogenase kinase inhibitor, is often used to treat lactic acidosis and malignant tumors. Increasing studies have shown that DCA has neuroprotective effects. Here, we explored the role and mechanism of DCA in Sepsis associated encephalopathy (SAE). Single-cell analysis was used to determine the important role of PDK4 in SAE and identify the cell type. GO and GSEA analysis were used to determine the correlation between DCA and pyroptosis. Through LPS + ATP stimulation, a microglia pyroptosis model was established to observe the expression level of intracellular pyroptosis-related proteins under DCA intervention, and further detect the changes in intracellular ROS and JC-1. Additionally, a co-culture environment of microglia and neuron was simply constructed to evaluate the effect of DCA on activated microglia-mediated neuronal apoptosis. Finally, Novel object recognition test and the Morris water maze were used to explore the effect of DCA on cognitive function in mice from different groups after intervention. Based on the above experiments, this study concludes that DCA can improve the ratio of peripheral and central M1 macrophages, inhibit NLRP3-mediated pyroptosis through ROS and mitochondrial membrane potential (MMP). DCA can reduce neuron death caused by SAE and improve cognitive function in LPS mice. In SAE, DCA may be a potential candidate drug for the treatment of microglia-mediated neuroinflammation.

Strain-dependent induction of primary bile acid 7-dehydroxylation by cholic acid.

BACKGROUND: Bile acids (BAs) are steroid-derived molecules with important roles in digestion, the maintenance of host metabolism, and immunomodulation. Primary BAs are synthesized by the host, while secondary BAs are produced by the gut microbiome through transformation of the former. The regulation of microbial production of secondary BAs is not well understood, particularly the production of 7-dehydroxylated BAs, which are the most potent agonists for host BA receptors. The 7-dehydroxylation of cholic acid (CA) is well established and is linked to the expression of a bile acid-inducible (bai) operon responsible for this process. However, little to no 7-dehydroxylation has been reported for other host-derived BAs (e.g., chenodeoxycholic acid, CDCA or ursodeoxycholic acid, UDCA). RESULTS: Here, we demonstrate that the 7-dehydroxylation of CDCA and UDCA by the human isolate Clostridium scindens is induced when CA is present, suggesting that CA-dependent transcriptional regulation is required for substantial 7-dehydroxylation of these primary BAs. This is supported by the finding that UDCA alone does not promote expression of bai genes. CDCA upregulates expression of the bai genes but the expression is greater when CA is present. In contrast, the murine isolate Extibacter muris exhibits a distinct response; CA did not induce significant 7-dehydroxylation of primary BAs, whereas BA 7-dehydroxylation was promoted upon addition of germ-free mouse cecal content in vitro. However, E. muris was found to 7-dehydroxylate in vivo. CONCLUSIONS: The distinct expression responses amongst strains indicate that bai genes are regulated differently. CA promoted bai operon gene expression and the 7-dehydroxylating activity in C. scindens strains. Conversely, the in vitro activity of E. muris was promoted only after the addition of cecal content and the isolate did not alter bai gene expression in response to CA. The accessory gene baiJ was only upregulated in the C. scindens ATCC 35704 strain, implying mechanistic differences amongst isolates. Interestingly, the human-derived C. scindens strains were also capable of 7-dehydroxylating murine bile acids (muricholic acids) to a limited extent. This study shows novel 7-dehydroxylation activity in vitro resulting from the presence of CA and suggests distinct bai gene expression across bacterial species.

A novel temporal-frequency combination pattern optimization approach based on information fusion for motor imagery BCIs.

Motor imagery (MI) stands as a powerful paradigm within Brain-Computer Interface (BCI) research due to its ability to induce changes in brain rhythms detectable through common spatial patterns (CSP). However, the raw feature sets captured often contain redundant and invalid information, potentially hindering CSP performance. Methodology-wise, we propose the Information Fusion for Optimizing Temporal-Frequency Combination Pattern (IFTFCP) algorithm to enhance raw feature optimization. Initially, preprocessed data undergoes simultaneous processing in both time and frequency domains via sliding overlapping time windows and filter banks. Subsequently, we introduce the Pearson-Fisher combinational method along with Discriminant Correlation Analysis (DCA) for joint feature selection and fusion. These steps aim to refine raw electroencephalogram (EEG) features. For precise classification of binary MI problems, an Radial Basis Function (RBF)-kernel Support Vector Machine classifier is trained. To validate the efficacy of IFTFCP and evaluate it against other techniques, we conducted experimental investigations using two EEG datasets. Results indicate a notably superior classification performance, boasting an average accuracy of 78.14% and 85.98% on dataset 1 and dataset 2, which is better than other methods outlined in this article. The study's findings suggest potential benefits for the advancement of MI-based BCI strategies, particularly in the domain of feature fusion.

Isolation and characterisation of two epithelial-like cell lines from the gills of Chrysophrys auratus (Australasian snapper) and Oncorhynchus tshawytscha (Chinook salmon) and their use in aquatic toxicology.

In vitro gill models are becoming increasingly important in aquatic toxicology, yet the fish gill invitrome is underrepresented, encompassing approximately 0.1% of extant species. Here, we describe the establishment and characterisation of two gill-derived, epithelial-like cell lines isolated from fish species of significant importance to New Zealand: Chrysophrys auratus (Australasian snapper) and Oncorhynchus tshawytscha (Chinook salmon). Designated CAgill1PFR (Chrysophrys auratus, gill 1, Plant & Food Research) and OTgill1PFR (Oncorhynchus tshawytscha, gill 1, Plant & Food Research), these cell lines have each been passaged greater than each 70 times over several years and are considered spontaneously immortalised. Both cell lines required serum for growth and exhibited differential responses to basal media formulations. CAgill1PFR was sensitive to low temperatures (4 °C) but replicated at high temperatures (30 °C), whereas OTgill1PFR was sensitive to high temperatures but remained viable at low temperatures, mirroring the natural environment of their host species. Immunostaining revealed expression of epithelial cell markers cytokeratin and E-cadherin, alongside positivity for the mesenchymal cell marker, vimentin. CAgill1PFR was more sensitive to the environmental toxin 3,4 dichloroaniline than OTgill1PFR through measurements of metabolic activity, membrane integrity, and lysosomal function. Furthermore, CAgill1PFR produced less CYP1A activity, indicative of ongoing biotransformation processes, in response to beta-naphthoflavone than OTgill1PFR. These cell lines expand the toolbox of resources and emphasise the need for species-specific aquatic toxicology research.

Development and validation of a lung metastases-predicting nomogram for intermediate- to high-risk differentiated thyroid carcinoma patients.

Aim: This research aimed to construct a clinical model for forecasting the likelihood of lung metastases in differentiated thyroid carcinoma (DTC) with intermediate- to high-risk. Methods: In this study, 375 DTC patients at intermediate to high risk were included. They were randomly divided into a training set (70%) and a validation set (30%). A nomogram was created using the training group and then validated in the validation set using calibration, decision curve analysis (DCA) and receiver operating characteristic (ROC) curve. Results: The calibration curves demonstrated excellent consistency between the predicted and the actual probability. ROC analysis showed that the area under the curve in the training cohort was 0.865 and 0.845 in the validation cohort. Also, the DCA curve indicated that this nomogram had good clinical utility. Conclusion: A user-friendly nomogram was constructed to predict the lung metastases probability with a high net benefit.

[Box: see text].

Development and internal validation of a nomogram to predict temporary acute agitated delirium after surgery for chronic subdural hematoma in elderly patients: an analysis of the clinical database.

Background: This study aimed to develop a nomogram for predicting temporary acute agitated delirium after surgery in patients with chronic subdural hematoma (CSH) without neurological compromise and hospitalized in the neurosurgery. Methods: We included 289 patients with chronic subdural hematoma (CSH) from the medical information system of Yuebei People's Hospital of Shaoguan City, Guangdong Province, and collected 16 clinical indicators within 24 h of admission. We used the least absolute shrinkage and selection operator (LASSO) regression to identify risk factors. We established a multivariate logistic regression model and constructed a nomogram. We performed internal validation by 1,000 bootstrap samples; we plotted a receiver operating curve (ROC) and calculated the area under the curve (AUC), sensitivity, and specificity. We also evaluated the calibration of our model by the calibration curve and the Hosmer-Lemeshow goodness-of-fit test (HL test). We performed a decision curve analysis (DCA) and a clinical impact curve (CIC) to assess the net clinical benefit of our model. Results: The nomogram included alcoholism history, hepatic insufficiency, verbal rating scale for postoperative pain (VRS), pre-hospital modified Rankin Scale (mRS), and preoperative hematoma thickness as predictors. Our model showed satisfactory diagnostic performance with an AUC value of 0.8474 in the validation set. The calibration curve and the HL test showed good agreement between predicted and observed outcomes (p = 0.9288). The DCA and CIC showed that our model had a high predictive ability for the occurrence of postoperative delirium in patients with CSDH. Conclusion: We identified alcoholism, liver dysfunction, pre-hospital mRS, preoperative hematoma thickness, and postoperative VRS pain as predictors of postoperative delirium in chronic subdural hematoma patients. We developed and validated a multivariate logistic regression model and a nomogram.

Extending culture time to improve Mitotic Index for cytogenetic dosimetry.

PURPOSE: To analyze the effects of extending lymphocyte cultivation time on the Mitotic Index, frequency of first-division cells, and dose estimation after irradiating blood samples with different doses of radiation. MATERIALS AND METHODS: Blood samples from two healthy male volunteers were separately irradiated with three doses (3, 5, and 6 Gy) using a 60Co gamma source (average dose rate: 1.48 kGy.h-1) and cultivated in vitro for conventional (48 h) and extended (56, 68, and 72 h) amounts of time. Colcemid (0.01 µg.mL-1) was added at the beginning of the culture period. Cells were fixed, stained with fluorescence plus Giemsa (FPG), and analyzed under a light microscope. The effects of prolonged culture duration on the Mitotic Index (MI), frequency of first-division cells (M1 cells), and the First-Division Mitotic Index (FDMI) were investigated. The estimation of delivered doses was conducted using a conventional 48h-culture calibration curve. RESULTS: Overall, cells presented higher MI (up to 12-fold) with the extension of culture, while higher radiation doses led to lower MI values (up to 80% reduction at 48 h). Cells irradiated with higher doses (5 and 6 Gy) had the most significant increase (5- to 12-fold) of MI as the cultivation was prolonged. The frequency of M1 cells decreased with the prolongation of culture for all doses (up to 75% reduction), while irradiated cells presented higher frequencies of M1 cells than non-irradiated ones. FDMI increased for all irradiated cultures but most markedly in those irradiated with higher doses (up to 10-fold). The conventional 48h-culture calibration curve proved adequate for assessing the delivered dose based on dicentric frequency following a 72-hour culture. CONCLUSION: Compared to the conventional 48-hour protocol, extending the culture length to 72 hours significantly increased the Mitotic Index and the number of first-division metaphases of irradiated lymphocytes, providing slides with a better scorable metaphase density. Extending the culture time to 72 hours, combined with FPG staining to score exclusively first-division metaphases, improved the counting of dicentric chromosomes. The methodology presented and discussed in this study can be a powerful tool for dicentric-based biodosimetry, especially when exposure to high radiation doses is involved.

Dichloroacetate and Quercetin Prevent Cell Proliferation, Induce Cell Death and Slow Tumor Growth in a Mouse Model of HPV-Positive Head and Neck Cancer.

Elevated glucose uptake and production of lactate are common features of cancer cells. Among many tumor-promoting effects, lactate inhibits immune responses and is positively correlated with radioresistance. Dichloroacetate (DCA) is an inhibitor of pyruvate dehydrogenase kinase that decreases lactate production. Quercetin is a flavonoid compound found in fruits and vegetables that inhibits glucose uptake and lactate export. We investigated the potential role and mechanisms of DCA, quercetin, and their combination, in the treatment of HPV-positive head and neck squamous cell carcinoma, an antigenic cancer subtype in need of efficacious adjuvant therapies. C57Bl/6-derived mouse oropharyngeal epithelial cells, a previously developed mouse model that was retrovirally transduced with HPV type-16 E6/E7 and activated Ras, were used to assess these compounds. Both DCA and quercetin inhibited colony formation and reduced cell viability, which were associated with mTOR inhibition and increased apoptosis through enhanced ROS production. DCA and quercetin reduced tumor growth and enhanced survival in immune-competent mice, correlating with decreased proliferation as well as decreased acidification of the tumor microenvironment and reduction of Foxp (+) Treg lymphocytes. Collectively, these data support the possible clinical application of DCA and quercetin as adjuvant therapies for head and neck cancer patients.

Dorsal CA1 lesions of the hippocampus impact mating tactics in prairie voles by shifting non-monogamous males' use of space to resemble monogamous males.

Alternative mating tactics within mating systems are characterized by discrete patterns of spatio-temporal overlap with same-and opposite-sex conspecifics and mating-relevant outcomes. Socially monogamous "residents" maintain relatively small home range sizes, have territories that almost exclusively overlap with their mating partners, and are more likely to produce offspring than non-bonded "wandering" conspecifics. Because mating tactics appear to be so closely tied to patterns of space use, differences in spatial cognitive abilities might differentially impact individual males' decisions to adopt a particular mating tactic and/or how efficient they are within their chosen mating tactic. Yet few studies have considered how the hippocampus, a brain region important for encoding cognitive maps and for processing contextual information, might impact how individuals adopt mating tactics or the spatio-temporal behaviors closely associated with them. We assessed the impact of lesions to the dorsal CA1 (dCA1) region of the hippocampus on male prairie vole space use, reproductive success, and mating tactics in semi-natural outdoor field conditions. Interestingly, dCA1 lesions did not impact the proportion of males that adopted resident or wandering mating tactics, and dCA1 lesions did not impact a male's ability to form a pair bond in the lab. In contrast, we found that lesioning the dCA1 shifted the home range size of reproductively successful and unsuccessful males. Furthermore, we found that patterns of space use among residents were unaffected by dCA1 lesions, whereas wanderers with dCA1 lesions showed pronounced reductions of their space use habits and resembled non-lesioned residents. Collectively, our study supports the hypothesis that wanderer male prairie voles rely on dCA1-mediated spatial cognition to navigate their world in a way that resident males do not. Such differences might have implications for how individuals efficiently attract and defend mates, obtain resources, defend territories, and outcompete rivals.

Development and validation of a prognostic model for predicting post-discharge mortality risk in patients with ST-segment elevation myocardial infarction (STEMI) undergoing primary percutaneous coronary intervention (PPCI).

BACKGROUND: Accurately predicting post-discharge mortality risk in patients with ST-segment elevation myocardial infarction (STEMI) undergoing primary percutaneous coronary intervention (PPCI) remains a complex and critical challenge. The primary objective of this study was to develop and validate a robust risk prediction model to assess the 12-month and 24-month mortality risk in STEMI patients after hospital discharge. METHODS: A retrospective study was conducted on 664 STEMI patients who underwent PPCI at Xiangtan Central Hospital Chest Pain Center between 2020 and 2022. The dataset was randomly divided into a training cohort (n = 464) and a validation cohort (n = 200) using a 7:3 ratio. The primary outcome was all-cause mortality following hospital discharge. The least absolute shrinkage and selection operator (LASSO) regression model was employed to identify the optimal predictive variables. Based on these variables, a regression model was constructed to determine the significant predictors of mortality. The performance of the model was evaluated using receiver operating characteristic (ROC) curve analysis and decision curve analysis (DCA). RESULTS: The prognostic model was developed based on the LASSO regression results and further validated using the independent validation cohort. LASSO regression identified five important predictors: age, Killip classification, B-type natriuretic peptide precursor (NTpro-BNP), left ventricular ejection fraction (LVEF), and the usage of angiotensin-converting enzyme inhibitors/angiotensin receptor blockers/angiotensin receptor-neprilysin inhibitors (ACEI/ARB/ARNI). The Harrell's concordance index (C-index) for the training and validation cohorts were 0.863 (95% CI: 0.792-0.934) and 0.888 (95% CI: 0.821-0.955), respectively. The area under the curve (AUC) for the training cohort at 12 months and 24 months was 0.785 (95% CI: 0.771-0.948) and 0.812 (95% CI: 0.772-0.940), respectively, while the corresponding values for the validation cohort were 0.864 (95% CI: 0.604-0.965) and 0.845 (95% CI: 0.705-0.951). These results confirm the stability and predictive accuracy of our model, demonstrating its reliable discriminative ability for post-discharge all-cause mortality risk. DCA analysis exhibited favorable net benefit of the nomogram. CONCLUSION: The developed nomogram shows potential as a tool for predicting post-discharge mortality in STEMI patients undergoing PPCI. However, its full utility awaits confirmation through broader external and temporal validation.

Dietary resistant starch supplementation increases gut luminal deoxycholic acid abundance in mice.

Bile acids (BA) are among the most abundant metabolites produced by the gut microbiome. Primary BAs produced in the liver are converted by gut bacterial 7-α-dehydroxylation into secondary BAs, which can differentially regulate host health via signaling based on their varying affinity for BA receptors. Despite the importance of secondary BAs in host health, the regulation of 7-α-dehydroxylation and the role of diet in modulating this process is incompletely defined. Understanding this process could lead to dietary guidelines that beneficially shift BA metabolism. Dietary fiber regulates gut microbial composition and metabolite production. We tested the hypothesis that feeding mice a diet rich in a fermentable dietary fiber, resistant starch (RS), would alter gut bacterial BA metabolism. Male and female wild-type mice were fed a diet supplemented with RS or an isocaloric control diet (IC). Metabolic parameters were similar between groups. RS supplementation increased gut luminal deoxycholic acid (DCA) abundance. However, gut luminal cholic acid (CA) abundance, the substrate for 7-α-dehydroxylation in DCA production, was unaltered by RS. Further, RS supplementation did not change the mRNA expression of hepatic BA producing enzymes or ileal BA transporters. Metagenomic assessment of gut bacterial composition revealed no change in the relative abundance of bacteria known to perform 7-α-dehydroxylation. P. ginsenosidimutans and P. multiformis were positively correlated with gut luminal DCA abundance and increased in response to RS supplementation. These data demonstrate that RS supplementation enriches gut luminal DCA abundance without increasing the relative abundance of bacteria known to perform 7-α-dehydroxylation.

Development of a nomogram for prediction of central lymph node metastasis of papillary thyroid microcarcinoma.

BACKGROUND: Papillary thyroid carcinoma (PTC) is the most frequent malignant tumor in thyroid carcinoma. The aim of this study was to explore the risk factors associated with central lymph node metastasis in papillary thyroid microcarcinoma (PTMC) and establish a nomogram model that can assess the probability of central lymph node metastasis (CLNM). METHODS: The clinicopathological data of 377 patients with cN0 PTMC were collected and analyzed from The Second Affiliated Hospital of Fujian Medical University from July 1st, 2019 to December 30th, 2021. All patients were examined by underwent ultrasound (US), found without metastasis to central lymph nodes, and diagnosed with PTMC through pathologic examination. All patients received thyroid lobectomy or total thyroidectomy with therapeutic or prophylactic central lymph node dissection (CLND). R software (Version 4.1.0) was employed to conduct a series of statistical analyses and establish the nomogram. RESULTS: A total of 119 patients with PTMC had central lymph node metastases (31.56%). After that, age (P < 0.05), gender (P < 0.05), tumor size (P < 0.05), tumor multifocality (P < 0.05), and ultrasound imaging-suggested tumor boundaries (P < 0.05) were identified as the risk factors associated with CLNM. Subsequently, multivariate logistic regression analysis indicated that the area under the receiver operating characteristic (ROC) curve (AUC) of the training cohort was 0.703 and that of the validation cohort was 0.656, demonstrating that the prediction ability of this model is relatively good compared to existing models. The calibration curves indicated a good fit for the nomogram model. Finally, the decision curve analysis (DCA) showed that a probability threshold of 0.15-0.50 could benefit patients clinically. The probability threshold used in DCA captures the relative value the patient places on receiving treatment for the disease, if present, compared to the value of avoiding treatment if the disease is not present. CONCLUSION: CLNM is associated with many risk factors, including age, gender, tumor size, tumor multifocality, and ultrasound imaging-suggested tumor boundaries. The nomogram established in our study has moderate predictive ability for CLNM and can be applied to the clinical management of patients with PTMC. Our findings will provide a better preoperative assessment and treatment strategies for patients with PTMC whether to undergo central lymph node dissection.

The locus coeruleus-dorsal hippocampal CA1 pathway is involved in depression-induced perioperative neurocognitive disorders in adult mice.

BACKGROUND: Patients undergoing surgical anesthesia increasingly suffer from preoperative depression. Clinical studies have shown that depression is a risk factor for perioperative neurocognitive disorders (PNDs) in elder patients. However, the underlying mechanism, especially at the neural circuit level, remains poorly understood. METHODS: Right carotid artery separation under sevoflurane and chronic social defeat stress (CSDS) in adult mice were used to establish surgical anesthesia and chronic depression models. Cognitive function was assessed by the Y maze and novel object recognition tests. A chemogenetic approach was used to modulate the locus coeruleus-dorsal hippocampal CA1 (LC-dCA1) circuit. Hippocampal synaptic alterations were evaluated by Golgi staining and whole-cell patch clamp recording. RESULTS: We found that CSDS induced synaptic impairments in dorsal hippocampal CA1 pyramidal neurons and cognitive deficits in adult mice after surgery under sevoflurane. Chemogenetic activation of the LC-dCA1 pathway significantly alleviated the CSDS-induced synaptic impairments and cognitive dysfunction. On the contrary, inhibition of this pathway could mimic CSDS-induced deficits. Furthermore, we showed that dopamine played an important role in CSDS-induced PNDs in adult mice after surgery/sevoflurane. CONCLUSION: Overall, our results have demonstrated a vital role for the LC-dCA1 pathway in CSDS-induced PNDs in adult mice undergoing surgery with sevoflurane anesthesia.

Altered bile acid metabolism in skin tissues in response to ionizing radiation: deoxycholic acid (DCA) as a novel treatment for radiogenic skin injury.

OBJECTIVE: Radiogenic skin injury (RSI) is a common complication during cancer radiotherapy or accidental exposure to radiation. The aim of this study is to investigate the metabolism of bile acids (BAs) and their derivatives during RSI. METHODS: Rat skin tissues were irradiated by an X-ray linear accelerator. The quantification of BAs and their derivatives were performed by liquid chromatography-mass spectrometry (LC-MS)-based quantitative analysis. Key enzymes in BA biosynthesis were analyzed from single-cell RNA sequencing (scRNA-Seq) data of RSI in the human patient and animal models. The in vivo radioprotective effect of deoxycholic acid (DCA) was detected in irradiated SD rats. RESULTS: Twelve BA metabolites showed significant differences during the progression of RSI. Among them, the levels of cholic acid (CA), DCA, muricholic acid (MCA), chenodeoxycholic acid (CDCA), glycocholic acid (GCA), glycohyodeoxycholic acid (GHCA), 12-ketolithocholic acid (12-ketoLCA) and ursodeoxycholic acid (UDCA) were significantly elevated in irradiated skin, whereas lithocholic acid (LCA), tauro-ß-muricholic acid (Tß-MCA) and taurocholic acid (TCA) were significantly decreased. Additionally, the results of scRNA-Seq indicated that genes involved in 7a-hydroxylation process, the first step in BA synthesis, showed pronounced alterations in skin fibroblasts or keratinocytes. The alternative pathway of BA synthesis is more actively altered than the classical pathway after ionizing radiation. In the model of rat radiogenic skin damage, DCA promoted wound healing and attenuated epidermal hyperplasia. CONCLUSIONS: Ionizing radiation modulates the metabolism of BAs. DCA is a prospective therapeutic agent for the treatment of RSI.

Markov chain stochastic DCA and applications in deep learning with PDEs regularization.

This paper addresses a large class of nonsmooth nonconvex stochastic DC (difference-of-convex functions) programs where endogenous uncertainty is involved and i.i.d. (independent and identically distributed) samples are not available. Instead, we assume that it is only possible to access Markov chains whose sequences of distributions converge to the target distributions. This setting is legitimate as Markovian noise arises in many contexts including Bayesian inference, reinforcement learning, and stochastic optimization in high-dimensional or combinatorial spaces. We then design a stochastic algorithm named Markov chain stochastic DCA (MCSDCA) based on DCA (DC algorithm) - a well-known method for nonconvex optimization. We establish the convergence analysis in both asymptotic and nonasymptotic senses. The MCSDCA is then applied to deep learning via PDEs (partial differential equations) regularization, where two realizations of MCSDCA are constructed, namely MCSDCA-odLD and MCSDCA-udLD, based on overdamped and underdamped Langevin dynamics, respectively. Numerical experiments on time series prediction and image classification problems with a variety of neural network topologies show the merits of the proposed methods.