Guangjian Liu

Background: Sepsis and septic shock impose severe cardiovascular stress characterized by sympathetic overactivation and detrimental tachycardia, which is strongly linked to poor outcomes. While ultra-short-acting β ₁ -blockers have shown promise in controlled trials for heart rate (HR) control, a critical gap remains regarding the optimal β ₁ -selective regimen for complex, multimorbid, vasopressor-dependent patients with septic shock complicated by tachyarrhythmia in real-world settings. Objective: To compare survival and hemodynamic trajectories associated with two intravenous β ₁ -selective blockers—esmolol and metoprolol, when initiated during septic shock complicated by tachyarrhythmia in a real-world setting. Methods: In this retrospective cohort study, we examined the MIMIC-IV database for vasopressor-dependent patients with septic shock and incident tachyarrhythmia. Employing propensity score matching and restricted cubic splines analysis, we compared the mortality of intravenous esmolol and metoprolol in septic shock patients with tachyarrhythmia. Results: The primary outcome was 28-day ICU mortality; the secondary outcomes were longitudinal hemodynamic indices (heart rate, blood pressure, lactate). Overall, 31.82% (1,494/4,695) cases died by day 28. Metoprolol was used in 44.6% and esmolol in 1.9%. In multivariable Cox models with spline-informed covariate handling, both esmolol and metoprolol were associated with lower 28-day mortality versus no β ₁ -blocker (esmolol HR, 0.69; 95% CI, 0.51–0.95; metoprolol HR, 0.28; 95% CI, 0.25–0.32). Results remained directionally consistent after the concurrent exclusion of sinus tachycardia and ventricular fibrillation and in propensity-score–matched comparisons. Longitudinal analyses showed that survivors across all regimens maintained relatively stable SBP/DBP through day 8, whereas esmolol non-survivors exhibited marked SBP/DBP fluctuations with a concurrent lactate surge around day 2; metoprolol was associated with more stable trajectories. Conclusion: In real-world septic shock with arrhythmia, intravenous β ₁ -blockade was associated with a more favorable 28-day survival trajectory, with metoprolol showing a more prominent survival association and more stable blood-pressure/lactate dynamics compared to esmolol.

This retrospective study aimed to derive the clinical phenotypes of ventilated ICU patients to predict the outcomes on the first day of ventilation. Clinical phenotypes were derived from the eICU Collaborative Research Database (eICU) cohort via cluster analysis and were validated in the Medical Information Mart for Intensive Care (MIMIC-IV) cohort. Four clinical phenotypes were identified and compared in the eICU cohort (n = 15,256). Phenotype A (n = 3112) was associated with respiratory disease, had the lowest 28-day mortality (16%), and had a high extubation success rate (~80%). Phenotype B (n = 3335) was correlated with cardiovascular disease, had the second-highest 28-day mortality (28%), and had the lowest extubation success rate (69%). Phenotype C (n = 3868) was correlated with renal dysfunction, had the highest 28-day mortality (28%), and had the second-lowest extubation success rate (74%). Phenotype D (n = 4941) was associated with neurological and traumatic diseases, had the second-lowest 28-day mortality (22%), and had the highest extubation success rate (>80%). These findings were validated in the validation cohort (n = 10,813). Additionally, these phenotypes responded differently to ventilation strategies in terms of duration of treatment, but had no difference in mortality. The four clinical phenotypes unveiled the heterogeneity of ICU patients and helped to predict the 28-day mortality and the extubation success rate.

ADAMTS13 (A Disintegrin and Metalloprotease with Thrombospondin type 1 repeats, member 13) cleaves von Willebrand Factor (VWF) multimers to limit the prothrombotic function of VWF. The deficiency of ADAMTS13 causes a lethal thrombotic microvascular disease, thrombotic thrombocytopenic purpura (TTP). ADAMTS13 circulates in a “closed” conformation with the distal domain associating the Spacer domain to avoid off-target proteolysis or recognition by auto-antibodies. However, the interactions of the distal TSP8 domain and the Spacer domain remain elusive. Here, we constructed the TSP8-Spacer complex by a combination of homology modelling and flexible docking. Molecular dynamics simulation was applied to map the binding sites on the TSP8 or Spacer domain. The results predicted that R1075, D1090, R1095, and C1130 on the TSP8 domain were key residues that interacted with the Spacer domain. R1075 and R1095 bound exosite-4 tightly, D1090 formed multiple hydrogen bonds and salt bridges with exosite-3, and C1130 interacted with both exosite-3 and exosite-4. Specific mutations of exosite-3 (R568K/F592Y/R660K/Y661F/Y665F) or the four key residues (R1075A/D1090A/R1095A/C1130A) impaired the binding of the TSP8 domain to the Spacer domain. These results shed new light on the understanding of the auto-inhibition of ADAMTS13.

Background: Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is a virus that causes severe respiratory illness in humans, which results in global outbreak of novel coronavirus disease (COVID-19) currently. This study aimed to evaluate the characteristics of publications involving coronaviruses as well as COVID-19 by using topic modeling. Methods: We extracted all abstracts and retained the most informative words from the COVID-19 Open Research Dataset, which contains 35,092 pieces of coronavirus related literature published up to March 20, 2020. Using Latent Dirichlet Allocation modeling, we trained a topic model from the corpus, analyzed the semantic relationships between topics and compared the topic distribution between COVID-19 and other CoV infections. Results: Eight topics emerged overall: clinical characterization, pathogenesis research, therapeutics research, epidemiological study, virus transmission, vaccines research, virus diagnostics, and viral genomics. It was observed that current COVID-19 research puts more emphasis on clinical characterization, epidemiological study, and virus transmission. In contrast, topics about diagnostics, therapeutics, vaccines, genomics and pathogenesis only account for less than 10% or even 4% of all the COVID-19 publications, much lower than those of other CoV infections. Conclusions: These results identified knowledge gaps in the area of COVID-19 and offered directions for future research.

Journal of Medical Internet Research - International Scientific Journal for Medical Research, Information and Communication on the Internet #Preprint #PeerReviewMe: Warning: This is a unreviewed preprint. Readers are warned that the document has not been peer-reviewed by expert/patient reviewers or an academic editor, may contain misleading claims, and is likely to undergo changes before final publication, if accepted, or may have been rejected/withdrawn. Readers with interest and expertise are encouraged to sign up as peer-reviewer, if the paper is within an open peer-review period. Please cite this preprint only for review purposes or for grant applications and CVs (if you are the author). Background: Central precocious puberty (CPP) in girls seriously affects the physical and mental development in childhood. The diagnosis method, Gonadotropin releasing hormone (GnRH) or GnRH analogue (GnRHa) stimulation test is expensive and makes patients uncomfortable with repeated blood sampling. Objective: We combined multiple CPP-related features and constructed machine learning models to predict response to the GnRHa stimulation test. Methods: We leveraged clinical and laboratory data of 1,757 girls performed with GnRHa test, to develop XGBoost and Random Forest classifiers for prediction of response to GnRHa test. Meanwhile, the local interpretable model-agnostic explanations (LIME) algorithm was used to the black-box classifiers to increase their interpretability. Results: Both of the XGBoost and Random Forest models achieved good performance in distinguishing positive and negative responses, with the AUC ranging from 0.88 to 0.90, the sensitivity from 77.91% to 77.94% and the specificity from 84.32% to 87.66%. Basal serum luteinizing hormone, follicle-stimulating hormone, and insulin-like growth factor-I are the three most important factors. In the interpretable models of LIME, above variables are demonstrated to have high contributions to the prediction probability. Conclusions: The prediction models we developed can help diagnose CPP and may be used as a pre-screening tool before GnRHa stimulation test.

BACKGROUND Central precocious puberty (CPP) in girls seriously affects their physical and mental development in childhood. The method of diagnosis—gonadotropin-releasing hormone (GnRH)–stimulation test or GnRH analogue (GnRHa)–stimulation test—is expensive and makes patients uncomfortable due to the need for repeated blood sampling. OBJECTIVE We aimed to combine multiple CPP–related features and construct machine learning models to predict response to the GnRHa-stimulation test. METHODS In this retrospective study, we analyzed clinical and laboratory data of 1757 girls who underwent a GnRHa test in order to develop XGBoost and random forest classifiers for prediction of response to the GnRHa test. The local interpretable model-agnostic explanations (LIME) algorithm was used with the black-box classifiers to increase their interpretability. We measured sensitivity, specificity, and area under receiver operating characteristic (AUC) of the models. RESULTS Both the XGBoost and random forest models achieved good performance in distinguishing between positive and negative responses, with the AUC ranging from 0.88 to 0.90, sensitivity ranging from 77.91% to 77.94%, and specificity ranging from 84.32% to 87.66%. Basal serum luteinizing hormone, follicle-stimulating hormone, and insulin-like growth factor-I levels were found to be the three most important factors. In the interpretable models of LIME, the abovementioned variables made high contributions to the prediction probability. CONCLUSIONS The prediction models we developed can help diagnose CPP and may be used as a prescreening tool before the GnRHa-stimulation test.

Blockage of the interactions between immunologic checkpoint protein PD-1 and its ligand PD-L1 showed efficacy for cancer treatment. X-ray structures have captured static conformational snapshots of PD-1 and revealed that the CC′ loop adopts an open conformation in the apo-protein but turns into a closed form and interacts with PD-L1 in the complex. This structural heterogeneity brings difficulties for structure-based drug discovery targeting PD-1. To gain insights into the role of the CC′ loop in molecular recognition, we have undertaken a comparative study between the open and closed conformations in apo-PD-1 and the PD-1/PD-L1 complex using molecular dynamics simulations. Results show that the moderate stability of intramolecular hydrogen bonds between SER71 and THR120 allows the CC′ loop to sample both the open and closed states in apo-PD-1. Binding of PD-L1 accelerates the open-to-closed switch and locks the loop in the closed state through four newly formed intermolecular hydrogen bonds. Thus, we suggest a complex binding mechanism between PD-1 and PD-L1 where both the conformational selection and induced fit theories play a role.