Catalytic mechanism study of ATP-citrate lyase during citryl-CoA synthesis process.

ATP-citrate lyase (ACLY) is a critical metabolic enzyme and promising target for drug development. The structure determinations of ACLY have revealed its homotetramer states with various subunit symmetries, but catalytic mechanism of ACLY tetramer and the importance of subunit symmetry have not been clarified. Here, we constructed the free energy landscape of ACLY tetramer with arbitrary subunit symmetries and investigated energetic and conformational coupling of subunits during citryl-CoA synthesis process. The optimal conformational pathway indicates that ACLY tetramer encounters three critical conformational barriers and undergoes a loss of rigid-D2 symmetry to gain an energetic advantage. Energetic coupling of conformational changes and biochemical reactions suggests that these biological events are not independent but rather coupled with each other, showing a comparable energy barrier to the experimental data for the rate-limiting step. These findings could contribute to further research on catalytic mechanism, functional modulation, and inhibitor design of ACLY.

Exploring the Activation Process of the Glycine Receptor.

Glycine receptors (GlyR) conduct inhibitory glycinergic neurotransmission in the spinal cord and the brainstem. They play an important role in muscle tone, motor coordination, respiration, and pain perception. However, the mechanism underlying GlyR activation remains unclear. There are five potential glycine binding sites in α1 GlyR, and different binding patterns may cause distinct activation or desensitization behaviors. In this study, we investigated the coupling of protein conformational changes and glycine binding events to elucidate the influence of binding patterns on the activation and desensitization processes of α1 GlyRs. Subsequently, we explored the energetic distinctions between the apical and lateral pathways during α1 GlyR conduction to identify the pivotal factors in the ion conduction pathway preference. Moreover, we predicted the mutational effects of the key residues and verified our predictions using electrophysiological experiments. For the mutants that can be activated by glycine, the predictions of the mutational directions were all correct. The strength of the mutational effects was assessed using Pearson's correlation coefficient, yielding a value of -0.77 between the calculated highest energy barriers and experimental maximum current amplitudes. These findings contribute to our understanding of GlyR activation, identify the key residues of GlyRs, and provide guidance for mechanistic studies on other pLGICs.

Identifying Potential Factors Associated With Racial Disparities in COVID-19 Outcomes: Retrospective Cohort Study Using Machine Learning on Real-World Data.

BACKGROUND: Racial disparities in COVID-19 incidence and outcomes have been widely reported. Non-Hispanic Black patients endured worse outcomes disproportionately compared with non-Hispanic White patients, but the epidemiological basis for these observations was complex and multifaceted. OBJECTIVE: This study aimed to elucidate the potential reasons behind the worse outcomes of COVID-19 experienced by non-Hispanic Black patients compared with non-Hispanic White patients and how these variables interact using an explainable machine learning approach. METHODS: In this retrospective cohort study, we examined 28,943 laboratory-confirmed COVID-19 cases from the OneFlorida Research Consortium's data trust of health care recipients in Florida through April 28, 2021. We assessed the prevalence of pre-existing comorbid conditions, geo-socioeconomic factors, and health outcomes in the structured electronic health records of COVID-19 cases. The primary outcome was a composite of hospitalization, intensive care unit admission, and mortality at index admission. We developed and validated a machine learning model using Extreme Gradient Boosting to evaluate predictors of worse outcomes of COVID-19 and rank them by importance. RESULTS: Compared to non-Hispanic White patients, non-Hispanic Blacks patients were younger, more likely to be uninsured, had a higher prevalence of emergency department and inpatient visits, and were in regions with higher area deprivation index rankings and pollutant concentrations. Non-Hispanic Black patients had the highest burden of comorbidities and rates of the primary outcome. Age was a key predictor in all models, ranking highest in non-Hispanic White patients. However, for non-Hispanic Black patients, congestive heart failure was a primary predictor. Other variables, such as food environment measures and air pollution indicators, also ranked high. By consolidating comorbidities into the Elixhauser Comorbidity Index, this became the top predictor, providing a comprehensive risk measure. CONCLUSIONS: The study reveals that individual and geo-socioeconomic factors significantly influence the outcomes of COVID-19. It also highlights varying risk profiles among different racial groups. While these findings suggest potential disparities, further causal inference and statistical testing are needed to fully substantiate these observations. Recognizing these relationships is vital for creating effective, tailored interventions that reduce disparities and enhance health outcomes across all racial and socioeconomic groups.

An ensemble deep learning model for predicting minimum inhibitory concentrations of antimicrobial peptides against pathogenic bacteria.

The rise of antibiotic resistance necessitates effective alternative therapies. Antimicrobial peptides (AMPs) are promising due to their broad inhibitory effects. This study focuses on predicting the minimum inhibitory concentration (MIC) of AMPs against whom-priority pathogens: Staphylococcus aureus ATCC 25923, Escherichia coli ATCC 25922, and Pseudomonas aeruginosa ATCC 27853. We developed a comprehensive regression model integrating AMP sequence-based and genomic features. Using eight AI-based architectures, including deep learning with protein language model embeddings, we created an ensemble model combining bi-directional long short-term memory (BiLSTM), convolutional neural network (CNN), and multi-branch model (MBM). The ensemble model showed superior performance with Pearson correlation coefficients of 0.756, 0.781, and 0.802 for the bacterial strains, demonstrating its accuracy in predicting MIC values. This work sets a foundation for future studies to enhance model performance and advance AMP applications in combating antibiotic resistance.

Pharmacological mechanisms of Ma Xing Shi Gan Decoction in treating influenza virus-induced pneumonia: intestinal microbiota and pulmonary glycolysis.

Background: Influenza virus is one of the most common pathogens that cause viral pneumonia. During pneumonia, host immune inflammation regulation involves microbiota in the intestine and glycolysis in the lung tissues. In the clinical guidelines for pneumonia treatment in China, Ma Xing Shi Gan Decoction (MXSG) is a commonly prescribed traditional Chinese medicine formulation with significant efficacy, however, it remains unclear whether its specific mechanism of action is related to the regulation of intestinal microbiota structure and lung tissue glycolysis. Objective: This study aimed to investigate the mechanism of action of MXSG in an animal model of influenza virus-induced pneumonia. Specifically, we aimed to elucidate how MXSG modulates intestinal microbiota structure and lung tissue glycolysis to exert its therapeutic effects on pneumonia. Methods: We established a mouse model of influenza virus-induced pneumoni, and treated with MXSG. We observed changes in inflammatory cytokine levels and conducted 16S rRNA gene sequencing to assess the intestinal microbiota structure and function. Additionally, targeted metabolomics was performed to analyze lung tissue glycolytic metabolites, and Western blot and enzyme-linked immunosorbent assays were performed to assess glycolysis-related enzymes, lipopolysaccharides (LPSs), HIF-1a, and macrophage surface markers. Correlation analysis was conducted between the LPS and omics results to elucidate the relationship between intestinal microbiota and lung tissue glycolysis in pneumonia animals under the intervention of Ma Xing Shi Gan Decoction. Results: MXSG reduced the abundance of Gram-negative bacteria in the intestines, such as Proteobacteria and Helicobacter, leading to reduced LPS content in the serum and lungs. This intervention also suppressed HIF-1a activity and lung tissue glycolysis metabolism, decreased the number of M1-type macrophages, and increased the number of M2-type macrophages, effectively alleviating lung damage caused by influenza virus-induced pneumonia. Conclusion: MXSG can alleviate glycolysis in lung tissue, suppress M1-type macrophage activation, promote M2-type macrophage activation, and mitigate inflammation in lung tissue. This therapeutic effect appears to be mediated by modulating gut microbiota and reducing endogenous LPS production in the intestines. This study demonstrates the therapeutic effects of MXSG on pneumonia and explores its potential mechanism, thus providing data support for the use of traditional Chinese medicine in the treatment of respiratory infectious diseases.

Overexpression of the transcription factor ANAC017 results in a genomes uncoupled phenotype under lincomycin.

Over-expression (OE) lines for the ER-tethered NAC transcription factor ANAC017 displayed de-repression of gun marker genes when grown on lincomycin (lin). RNA-seq revealed that ANAC017OE2 plants constitutively expressed greater than 40% of the genes induced in wild-type with lin treatment, including plastid encoded genes ycf1.2 and the gene cluster ndhH-ndhA-ndhI-ndhG-ndhE-psaC-ndhD, documented as direct RNA targets of GUN1. Genes encoding components involved in organelle translation were enriched in constitutively expressed genes in ANAC017OE2. ANAC017OE resulted in constitutive location in the nucleus and significant constitutive binding of ANAC017 was detected by ChIP-Seq to target genes. ANAC017OE2 lines maintained the ability to green on lin, were more ABA sensitive, did not show photo-oxidative damage after exposure of de-etiolated seedlings to continuous light and the transcriptome response to lin were as much as 80% unique compared to gun1-1. Both double mutants, gun1-1:ANAC017OE and bzip60:ANAC017OE (but not single bzip60), have a gun molecular gene expression pattern and result in variegated and green plants, suggesting that ANAC017OE may act through an independent pathway compared to gun1. Over-expression of ANAC013 or rcd1 did not produce a GUN phenotype or green plants on lin. Thus, constitutive ANAC017OE2 establishes an alternative transcriptional program that likely acts through a number of pathways, that is, maintains plastid gene expression, and induction of a variety of transcription factors involved in reactive oxygen species metabolism, priming plants for lin tolerance to give a gun phenotype.

Polysaccharides from bile Arisaema exert an antipyretic effect on yeast-induced fever rats through regulating gut microbiota and metabolic profiling.

In our previous study, bile Arisaema was elucidated to have a significant anti-febrile effect, but the pharmacodynamic material basis of this effect remains uncertain. Herein, we found that the soluble polysaccharide fraction from bile Arisaema presents a remarkable antipyretic effect through balancing the gut microbiota and regulating metabolic profiling. Bile Arisaema polysaccharide (BAP) was characterized for its monosaccharide composition with arabinose, galactose, glucose, mannose and xylose (0.028:0.072:0.821:0.05:0.029, molar ratios) and amino acid composition with arginine, threonine, alanine, glycine, serine, proline and tyrosine (109.33, 135.78, 7.22, 8.86, 21.07, 4.96, 12.31 µg/mg). A total of 50 peptides were identified from BAP using Ltq-Orbitrap MS/MS. The oral administration of 100 mg/kg BAP significantly increased the antipyretic effect in yeast-induced fever rats by comparing the rectal temperature. Mechanistically, the inflammation and disorders of neurotransmitters caused by fever were improved by treatment with BAP. The western blotting results suggested that BAP could suppress fever-induced inflammation by down-regulating the NF-κB/TLR4/MyD88 signaling pathway. We also demonstrated that BAP affects lipid metabolism, amino acid metabolism and carbohydrate metabolism and balances the gut microbiota. In summary, the present study provides a crucial foundation for determining polysaccharide activity in bile Arisaema and further investigating the underlying mechanism of action.

Dapagliflozin in heart failure and type 2 diabetes: Efficacy, cardiac and renal effects, safety.

BACKGROUND: Heart failure (HF), especially HF with reduced ejection fraction (HFrEF), presents complex challenges, particularly in the aging population where it often coexists with type 2 diabetes mellitus (T2DM). AIM: To analyze the effect of dapagliflozin treatment on cardiac, renal function, and safety in patients with HFrEF combined with T2DM. METHODS: Patients with T2DM complicated with HFrEF who underwent treatment in our hospital from February 2018 to March 2023 were retrospectively analyzed as the subjects of this study. The propensity score matching method was used, and a total of 102 eligible samples were scaled. The clinical efficacy of the two groups was evaluated at the end of the treatment, comparing the results of blood glucose, insulin, cardiac function, markers of myocardial injury, renal function indexes, and 6-min walk test (6MWT) before and after the treatment. We compared the occurrence of adverse effects on the treatment process of the two groups of patients. The incidence of adverse outcomes in patients within six months of treatment was counted. RESULTS: The overall clinical efficacy rate of patients in the study group was significantly higher than that of patients in the control group (P = 0.013). After treatment, the pancreatic beta-cell function index, left ventricular ejection fraction, and glomerular filtration rate of patients in the study group were significantly higher than control group (P < 0.001), while their fasting plasma glucose, 2-h postprandial glucose, glycosylated hemoglobin, insulin resistance index, left ventricular end-systolic diameter, left ventricular end-diastolic diameter, cardiac troponin I, creatine kinase-MB, N-terminal pro b-type natriuretic peptide, serum creatinine, and blood urea nitrogen were significantly lower than those of the control group. After treatment, patients in the study group had a significantly higher 6MWT than those in the control group (P < 0.001). Hypoglycemic reaction (P = 0.647), urinary tract infection (P = 0.558), gastrointestinal adverse effect (P = 0.307), respiratory disturbance (P = 0.558), and angioedema (P = 0.647) were not statistically different. There was no significant difference between the incidence of adverse outcomes between the two groups (P = 0.250). CONCLUSION: Dapagliflozin significantly enhances clinical efficacy, cardiac and renal function, and ambulatory capacity in patients with HFrEF and T2DM without an increased risk of adverse effects or outcomes.

Parameter-free super-resolution structured illumination microscopy via a physics-enhanced neural network.

With full-field imaging and high photon efficiency advantages, structured illumination microscopy (SIM) is one of the most potent super-resolution (SR) modalities in bioscience. Regarding SR reconstruction for SIM, spatial domain reconstruction (SDR) has been proven to be faster than traditional frequency domain reconstruction (FDR), facilitating real-time imaging of live cells. Nevertheless, SDR relies on high-precision parameter estimation for reconstruction, which tends to suffer from low signal-to-noise ratio (SNR) conditions and inevitably leads to artifacts that seriously affect the accuracy of SR reconstruction. In this Letter, a physics-enhanced neural network-based parameter-free SDR (PNNP-SDR) is proposed, which can achieve SR reconstruction directly in the spatial domain. As a result, the peak-SNR (PSNR) of PNNP-SDR is improved by about 4 dB compared to the cross-correlation (COR) SR reconstruction; meanwhile, the reconstruction speed of PNNP-SDR is even about five times faster than the fast approach based on principal component analysis (PCA). Given its capability of achieving parameter-free imaging, noise robustness, and high-fidelity and high-speed SR reconstruction over conventional SIM microscope hardware, the proposed PNNP-SDR is expected to be widely adopted in biomedical SR imaging scenarios.

Clinical management of children with tic disorder: insights from therapeutic visits in China-a real-world study.

Objective: This retrospective study aims to investigate the treatment of tic disorder (TD) in Dongfang Hospital affiliated with Beijing University of Chinese Medicine, explore its underlying mechanism, and provide valuable insights for future research and clinical management of TD. Methods: The electronic medical records of children with TD, from 2015 to 2021, were extracted from the information system of Dongfang Hospital affiliated with Beijing University of Chinese Medicine. The clinical characteristics of TD, utilization patterns of Chinese herbal medicine and synthetic drugs in prescriptions, as well as their pharmacological effects, were statistically described and categorized. In addition, association rules and network pharmacology were employed to identify core prescriptions (CPs) and elucidate their microscopic molecular mechanisms in treating TD. Results: The age range of the children was from 6 to 11 years, with a higher proportion of male participants than female ones. The average duration of treatment was 6 weeks. Regimen Z for the treatment of TD can be summarized as follows: Chinese herbal medicine [Saposhnikoviae Radix (FangFeng), Puerariae Lobatae Radix (GeGen), Uncariae Ramulus cum Uncis (GouTeng), Acori Tatarinowii Rhizoma (ShiChangPu), Chuanxiong Rhizoma (ChuanXiong)] and vitamins [lysine, inosite, and vitamin B12 oral solution] form the basic treatment, combined with immunomodulators, antibiotics, electrolyte-balancing agents, and antiallergic agents. CPs primarily exerted their effects through the modulation of gene expression (transcription), the immune system, and signal transduction pathways, with interleukin-4 and interleukin-13 pathways being particularly crucial. Among the lysine synthetic drugs used, inosite and vitamin B12 oral solution were the most frequently prescribed. Conclusion: The regimen Z drug treatment holds significant importance in the field, as it exerts its therapeutic effects through a multitude of pathways and intricate interventions. Chinese herbal medicine primarily regulates immune system-related pathways, while synthetic drugs predominantly consist of vitamins.

ChatGPT's Role in Gerontology Research.

BACKGROUND: Chat Generative Pre-trained Transformer (ChatGPT) and other ChatBots have emerged as tools for interacting with information in manners resembling natural human speech. Consequently, the technology is used across various disciplines, including business, education, and even in biomedical sciences. There is a need to better understand how ChatGPT can be used to advance gerontology research. Therefore, we evaluated ChatGPT responses to questions on specific topics in gerontology research, and brainstormed recommendations for its use in the field. METHODS: We conducted semistructured brainstorming sessions to identify uses of ChatGPT in gerontology research. We divided a team of multidisciplinary researchers into 4 topical groups: (a) gero-clinical science, (b) basic geroscience, (c) informatics as it relates to electronic health records, and (d) gero-technology. Each group prompted ChatGPT on a theory-, methods-, and interpretation-based question and rated responses for accuracy and completeness based on standardized scales. RESULTS: ChatGPT responses were rated by all groups as generally accurate. However, the completeness of responses was rated lower, except by members of the informatics group, who rated responses as highly comprehensive. CONCLUSIONS: ChatGPT accurately depicts some major concepts in gerontological research. However, researchers have an important role in critically appraising the completeness of its responses. Having a single generalized resource like ChatGPT may help summarize the preponderance of evidence in the field to identify gaps in knowledge and promote cross-disciplinary collaboration.

Cannabis use and acute postoperative pain outcomes in older adults: a propensity matched retrospective cohort study.

INTRODUCTION: Cannabis use is increasing among older adults, but its impact on postoperative pain outcomes remains unclear in this population. We examined the association between cannabis use and postoperative pain levels and opioid doses within 24 hours of surgery. METHODS: We conducted a propensity score-matched retrospective cohort study using electronic health records data of 22 476 older surgical patients with at least 24-hour hospital stays at University of Florida Health between 2018 and 2020. Of the original cohort, 2577 patients were eligible for propensity-score matching (1:3 cannabis user: non-user). Cannabis use status was determined via natural language processing of clinical notes within 60 days of surgery and structured data. The primary outcomes were average Defense and Veterans Pain Rating Scale (DVPRS) score and total oral morphine equivalents (OME) within 24 hours of surgery. RESULTS: 504 patients were included (126 cannabis users and 378 non-users). The median (IQR) age was 69 (65-72) years; 295 (58.53%) were male, and 442 (87.70%) were non-Hispanic white. Baseline characteristics were well balanced. Cannabis users had significantly higher average DVPRS scores (median (IQR): 4.68 (2.71-5.96) vs 3.88 (2.33, 5.17); difference=0.80; 95% confidence limit (CL), 0.19 to 1.36; p=0.01) and total OME (median (IQR): 42.50 (15.00-60.00) mg vs 30.00 (7.50-60.00) mg; difference=12.5 mg; 95% CL, 3.80 mg to 21.20 mg; p=0.02) than non-users within 24 hours of surgery. DISCUSSION: This study showed that cannabis use in older adults was associated with increased postoperative pain levels and opioid doses.

Predictors of left atrial appendage thrombus in atrial fibrillation patients undergoing cardioversion.

BACKGROUND: Atrial fibrillation and atrial flutter represent the most prevalent clinically significant cardiac arrhythmias. While the CHA2DS2-VASc score is commonly used to inform anticoagulation therapy decisions for patients with these conditions, its predictive power is limited. Therefore, we sought to improve risk prediction for left atrial appendage thrombus (LAAT), a known risk factor for stroke in these patients. METHODS: We developed and validated an explainable machine learning model using the eXtreme Gradient Boosting algorithm with 5 × 5 nested cross-validation. The primary outcome was to predict the probability of LAAT in patients with atrial fibrillation and atrial flutter who underwent transesophageal echocardiogram prior to cardioversion. Our algorithm used 37 demographic, comorbid, and transthoracic echocardiographic variables. RESULTS: A total of 795 patients were included in our analysis. LAAT was present in 11.3% of the patients. The average age of patients was 63.3 years and 34.7% were women. Patients with LAAT had significantly lower left ventricular ejection fraction (29.9% vs 43.5%; p < 0.001), lower E' lateral velocity (5.7 cm vs. 7.9 cm; p < 0.001) and higher E/A ratio (2.6 vs 1.8; p = 0.002). Our machine learning model achieved a high AUC of 0.79, with a high specificity of 0.82, and modest sensitivity of 0.57. Left ventricular ejection fraction was the most important variable in predicting LAAT. Patients were split into 10 buckets based on the percentile of their predicted probability of having thrombus. The lower the percentile (e.g., 10%), the lower the probability of having thrombus. Using a cutoff point of 0.16 which includes 10.0% of the patients, we can rule out thrombus with 100% confidence. CONCLUSION: Using machine learning, we refined the predictive power of predicting LAAT and explained the model. These results show promise in providing better guidance for anticoagulation therapy and cardioversion in AF and AFL patients.

Outcomes of Mitral Valve Repair for Posterior Leaflet Prolapse, Anterior Leaflet Prolapse, and Bileaflet Prolapse.

Background: Mitral valve repair (MVr) is an effective treatment for degenerative mitral regurgitation (DMR).And the outcomes and repair rates for posterior leaflet prolapse (PLP), anterior leaflet prolapse (ALP), and bileaflet prolapse (BLP) vary. This study aimed to compare the outcomes of mitral valve repair for patients with PLP, ALP, and BLP. Methods: From 2010 to 2019, 1192 patients with degenerative mitral valve regurgitation underwent surgery at our hospital. And 1069 patients were identified. The average age of all patients was (54.74 ± 12.17) years old for all patients. 273 patients (25.5%) had ALP, 148 patients (13.8%) had BLP, and 648 patients (60.6%) had PLP. All patients were followed up for an average duration of 5.1 years. We compared the outcomes of patients with ALP, PLP, and BLP. Results: Patients with ALP were the youngest of the 3 groups and had the highest prevalence of atrial fibrillation. Patients with PLP had the highest prevalence of hypertension, whereas patients with BLP and ALP had larger left ventricular end-diastolic and left ventricular end-systolic diameters. ALP and BLP repairs had a longer cardiopulmonary bypass and aortic cross-clamp time.10 patients dead in-hospital, 5 patients had PLP, 3 had ALP, and 2 had BLP. The 10-year survival cumulative incidences of reoperation among ALP, BLP, and PLP repairs were not significantly different. ALP repair still had higher cumulative incidences of recurrent mitral regurgitation (MR) compared to PLP. Conclusions: The rates of long-term survival and freedom from reoperation were not significantly different among patients with ALP, BLP, and PLP. ALP repair has higher cumulative incidences of recurrent MR compared to PLP.

Cannabis Use and Inhalational Anesthesia Administration in Older Adults: A Propensity Matched Retrospective Cohort Study.

BACKGROUND: Cannabis use is associated with higher intravenous anesthetic administration. Similar data regarding inhalational anesthetics are limited. With rising cannabis use prevalence, understanding any potential relationship with inhalational anesthetic dosing is crucial. We compared average intraoperative isoflurane/sevoflurane minimum alveolar concentration equivalents between older adults with and without cannabis use. METHODS: The electronic health records of 22,476 surgical patients ≥65 years old at the University of Florida Health System between 2018-2020 were reviewed. The primary exposure was cannabis use within 60 days of surgery, determined via i) a previously published natural language processing algorithm applied to unstructured notes and ii) structured data, including International Classification of Disease codes for cannabis use disorders and poisoning by cannabis, laboratory cannabinoids screening results, and RxNorm codes. The primary outcome was the intraoperative time-weighted average of isoflurane/sevoflurane minimum alveolar concentration equivalents at one-minute resolution. No a priori minimally clinically important difference was established. Patients demonstrating cannabis use were matched 4:1 to non-cannabis use controls using a propensity score. RESULTS: Among 5,118 meeting inclusion criteria, 1,340 patients (268 cannabis users and 1,072 nonusers) remained after propensity score matching. The median and interquartile range (IQR) age was 69 (67, 73) years; 872 (65.0%) were male, and 1,143 (85.3%) were non-Hispanic White. The median (IQR) anesthesia duration was 175 (118, 268) minutes. After matching, all baseline characteristics were well-balanced by exposure. Cannabis users had statistically significantly higher average minimum alveolar concentrations than nonusers [mean±SD: 0.58±0.23 versus 0.54±0.22, respectively; mean difference=0.04; 95% confidence limits, 0.01 to 0.06; p=0.020]. CONCLUSION: Cannabis use was associated with administering statistically significantly higher inhalational anesthetic minimum alveolar concentration equivalents in older adults, but the clinical significance of this difference is unclear. These data do not support the hypothesis that cannabis users require clinically meaningfully higher inhalational anesthetics doses.

An explainable machine learning approach using contemporary UNOS data to identify patients who fail to bridge to heart transplantation.

Background: The use of Intra-aortic Balloon Pump (IABP) and Impella devices as a bridge to heart transplantation (HTx) has increased significantly in recent times. This study aimed to create and validate an explainable machine learning (ML) model that can predict the failure of status two listings and identify the clinical features that significantly impact this outcome. Methods: We used the UNOS registry database to identify HTx candidates listed as UNOS Status 2 between 2018 and 2022 and supported with either Impella (5.0 or 5.5) or IABP. We used the eXtreme Gradient Boosting (XGBoost) algorithm to build and validate ML models. We developed two models: (1) a comprehensive model that included all patients in our cohort and (2) separate models designed for each of the 11 UNOS regions. Results: We analyzed data from 4,178 patients listed as Status 2. Out of them, 12% had primary outcomes indicating Status 2 failure. Our ML models were based on 19 variables from the UNOS data. The comprehensive model had an area under the curve (AUC) of 0.71 (±0.03), with a range between 0.44 (±0.08) and 0.74 (±0.01) across different regions. The models' specificity ranged from 0.75 to 0.96. The top five most important predictors were the number of inotropes, creatinine, sodium, BMI, and blood group. Conclusion: Using ML is clinically valuable for highlighting patients at risk, enabling healthcare providers to offer intensified monitoring, optimization, and care escalation selectively.

Regulation of gut microbiota and serum neurotransmitters in mice by Streptococcus thermophilus GA8- and Lacticaseibacillus rhamnosus HAO9-fermented milk containing high levels of gamma-aminobutyric acid.

BACKGROUND: Gamma-aminobutyric acid (GABA) is an important neurotransmitter in the human body, with several negative emotions reported as being associated with GABA dysregulation. This study investigates the safety and modulatory effects of GABA-enriched milk, fermented by Streptococcus thermophilus GA8 and Lacticasebacillus rhamnosus HAO9, on the gut microbiota and neurotransmitter profiles in mice. RESULTS: Through rigorous culturing and fermentation processes, we achieved consistent GABA production in milk, with concentrations reaching 4.6 and 8.5 g L-1 for GA8-fermented and co-fermented milk, respectively, after 48 h. Using SPF male C57BL/6J mice, we administered either mono-culture or combined-culture milk treatments and monitored physiological impacts. The treatments did not affect mouse body weight but induced significant changes in gut microbiota composition. Beta diversity analysis revealed distinct microbial profiles between treatment groups, highlighting fermentation-specific microbial shifts, such as an increase in Verrucomicrobia for the GA8 group and a modulation in Saccharibacteria_genera_incertae_sedis for the GA8 + HAO9 group. Serum neurotransmitter levels were elevated in both treatment groups, with significant increases in l-glutamine, l-tryptophan and, notably, serotonin hydrochloride in the GA8 + HAO9 group. Correlation analysis identified a positive association between specific bacterial genera and neurotransmitter levels, suggesting a probiotic effect on neuroactive substances. CONCLUSION: These findings suggest that fermented milk has potential as a probiotic supplement for mood improvement and stress relief, highlighting its role in modulating the gut-brain axis. © 2024 Society of Chemical Industry.

Exploring the Activation Mechanism of the GPR183 Receptor.

The G protein-coupled receptors (GPCRs) play a pivotal role in numerous biological processes as crucial cell membrane receptors. However, the dynamic mechanisms underlying the activation of GPR183, a specific GPCR, remain largely elusive. To address this, we employed computational simulation techniques to elucidate the activation process and key events associated with GPR183, including conformational changes from inactive to active state, binding interactions with the Gi protein complex, and GDP release. Our findings demonstrate that the association between GPR183 and the Gi protein involves the formation of receptor-specific conformations, the gradual proximity of the Gi protein to the binding pocket, and fine adjustments of the protein conformation, ultimately leading to a stable GPR183-Gi complex characterized by a high energy barrier. The presence of Gi protein partially promotes GPR183 activation, which is consistent with the observation of GPCR constitutive activity test experiments, thus illustrating the reliability of our calculations. Moreover, our study suggests the existence of a stable partially activated state preceding complete activation, providing novel avenues for future investigations. In addition, the relevance of GPR183 for various diseases, such as colitis, the response of eosinophils to Mycobacterium tuberculosis infection, antiviral properties, and pulmonary inflammation, has been emphasized, underscoring its therapeutic potential. Consequently, understanding the activation process of GPR183 through molecular dynamic simulations offers valuable kinetic insights that can aid in the development of targeted therapies.

Exploration of the mechanism of Traditional Chinese Medicine for anxiety and depression in patients with diarrheal irritable bowel syndrome based on network pharmacology and meta-analysis.

Background: The efficacy of Chinese herbal medicine (CHM) in managing irritable bowel syndrome with diarrhea (IBS-D) accompanied by anxiety and depression remains uncertain. Thus, a systematic review was carried out employing meta-analysis and network pharmacology to ascertain the efficacy and underlying mechanisms of CHM therapy. Methods: By conducting a systematic review, including literature search, screening, and data extraction, we identified 25 randomized controlled trials to assess CHM's effectiveness in treating irritable bowel syndrome alongside anxiety and depression. Network pharmacology was utilized to scrutinize the metabolite utility of CHM in addressing this condition. Potential primary mechanisms were synthesized using information sourced from the PubMed database. Results: Twenty-five studies, including 2055 patients, were analyzed, revealing significant treatment efficacy for IBS-D in the trial group compared to controls [OR = 4.01, 95% CI (2.99, 5.36), I2 = 0%] Additionally, treatment for depression [SMD = -1.08, 95% CI (-1.30, -0.86), p < 0.00001, I2 = 68%; SDS: SMD = -1.69, 95% CI (-2.48, -0.90), p < 0.0001, I2 = 96%] and anxiety [HAMA: SMD = -1.29, 95% CI (-1.68, -0.91), p < 0.00001, I2 = 89%; SAS: SMD = -1.75, 95% CI (-2.55, -0.95), p < 0.00001, I2 = 96%] significantly improved in the trial group. Furthermore, the trial group exhibited a significantly lower disease relapse rate [OR = 0.30, 95% CI (0.20, 0.44), p < 0.00001, I2 = 0%]. CHM treatment consistently improved IBS severity (IBS-SSS) and symptom scores. Network pharmacology analysis identified key chemical metabolites in traditional Chinese medicine formulations, including Beta-sitosterol, Stigmasterol, Quercetin, Naringenin, Luteolin, Kaempferol, Nobiletin, Wogonin, Formononetin, and Isorhamnetin. Utilizing the STRING database and Cytoscape v3.9.0 software, a protein-protein interaction (PPI) network revealed the top eight key targets: IL-6, TNF, PPARG, PTGS2, ESR1, NOS3, MAPK8, and AKT1, implicated in anti-inflammatory responses, antioxidant stress modulation, and neurotransmitter homeostasis maintenance. Conclusion: Chinese Herbal Medicine (CHM) offers a promising and safe treatment approach for patients dealing with Diarrheal Irritable Bowel Syndrome (IBS-D) accompanied by anxiety and depression; thus, indicating its potential for practical implementation. The most active metabolites of CHM could simultaneously act on the pathological targets of IBS-D, anxiety, and depression.The diverse scope of CHM's therapeutic role includes various aspects and objectives, underscoring its potential for broad utilization.

Multi-Responsive Peptide-Based Ultrathin Nanosheets Prepared by a Horizontal Monolayer Assembly.

In this study, peptide-based self-assembled nanosheets with a thickness of approximately 1 nm were prepared using a hierarchical covalent physical fabrication strategy. The covalent alternating polymerization of helical peptide E3 with an azobenzene (AZO) structure yielded copolymers CoP(E3-AZO), which physically self-assembled into ultrathin nanosheets in an unanticipated two-dimensional horizontal monolayer arrangement. This special monolayer arrangement enabled the thickness of the nanosheets to be equal to the cross-sectional diameter of a single linear copolymer, which is a rare phenomenon. Molecular dynamics simulations suggested that the synergistic effect of multiple molecular interactions drives the self-assembly of CoP(E3-AZO) into nanosheets and that various methods, including phototreatment, pH adjustment, the addition of additives, and introduction of cosolvents, can alter the molecular interactions and modulate the self-assembly of CoP(E3-AZO), yielding diverse nanostructures. Remarkably, the ultrathin nanosheets selectively inhibited cancer cells at certain concentrations.