Quantification of the Role of Chemical Desorption in Molecular Clouds

Predictors of breakthrough invasive fungal infections (BIFI) in pediatric acute leukemia: a retrospective analysis and predictive model development

Objective

This study aims to identify key risk factors associated with the development of breakthrough invasive fungal infections (BIFI) in pediatric acute leukemia patients to improve early detection and intervention strategies.

Method

A retrospective analysis was conducted on 160 pediatric patients with acute leukemia admitted to Anhui Provincial Children's Hospital between October 2018 and June 2022. The study evaluated the impact of various clinical parameters on BIFI risk using univariate and multivariable analyses, with data including patient demographics, treatment regimens, and infection outcomes. The predictive model was assessed using receiver operating characteristic (ROC) curve analysis, calibration plots, and decision curve analysis (DCA).

Result

Among the 160 pediatric acute leukemia patients, 34 (22.22%) developed BIFI. Univariate analysis identified longer durations of neutrophil deficiency (P < 0.001), broad-spectrum antibiotic use (P < 0.001), higher volumes of red blood cell transfusions (P = 0.001), and elevated C-reactive protein (CRP) levels (P < 0.001) as significant factors associated with BIFI. Multivariable analysis confirmed these as significant predictors, with odds ratios for neutrophil deficiency (OR = 1.38, 95% CI [1.15, 1.69]), antibiotic use (OR = 1.41, 95% CI [1.10, 1.84]), transfusions (OR = 2.54, 95% CI [1.39, 5.13]), and CRP levels (OR = 1.10, 95% CI [1.04, 1.17]). The model validation showed strong predictive performance with an AUC of 0.890 (95% CI: 0.828-0.952), good calibration (Brier score = 0.099), and demonstrated clinical utility across a range of risk thresholds.

Conclusion

The study highlights the importance of considering these key predictors in the management of pediatric acute leukemia patients to mitigate the risk of BIFI. Incorporating these factors into personalized treatment strategies could enhance early intervention, reduce infection rates, and improve overall patient outcomes.

Advancing Emergency Department Triage Prediction With Machine Learning to Optimize Triage for Abdominal Pain Surgery Patients

BACKGROUND

The development of emergency department (ED) triage systems remains challenging in accurately differentiating patients with acute abdominal pain (AAP) who are critical and urgent for surgery due to subjectivity and limitations. We use machine learning models to predict emergency surgical abdominal pain patients in triage, and then compare their performance with conventional Logistic regression models.

METHODS

Using 38 214 patients presenting with acute abdominal pain at Zhongnan Hospital of Wuhan University between March 1, 2014, and March 1, 2022, we identified all adult patients (aged ≥18 years). We utilized routinely available triage data in electronic medical records as predictors, including structured data (eg, triage vital signs, gender, and age) and unstructured data (chief complaints and physical examinations in free-text format). The primary outcome measure was whether emergency surgery was performed. The dataset was randomly sampled, with 80% assigned to the training set and 20% to the test set. We developed 5 machine learning models: Light Gradient Boosting Machine (Light GBM), eXtreme Gradient Boosting (XGBoost), Deep Neural Network (DNN), and Random Forest (RF). Logistic regression (LR) served as the reference model. Model performance was calculated for each model, including the area under the receiver-work characteristic curve (AUC) and net benefit (decision curve), as well as the confusion matrix.

RESULTS

Of all the 38 214 acute abdominal pain patients, 4208 underwent emergency abdominal surgery while 34 006 received non-surgical treatment. In the surgery outcome prediction, all 4 machine learning models outperformed the reference model (eg, AUC, 0.899 [95%CI 0.891-0.903] in the Light GBM vs. 0.885 [95%CI 0.876-0.891] in the reference model), Similarly, most machine learning models exhibited significant improvements in net reclassification compared to the reference model (eg, NRIs of 0.0812[95%CI, 0.055-0.1105] in the XGBoost), with the exception of the RF model. Decision curve analysis shows that across the entire range of thresholds, the net benefits of the XGBoost and the Light GBM models were higher than the reference model. In particular, the Light GBM model performed well in predicting the need for emergency abdominal surgery with higher sensitivity, specificity, and accuracy.

CONCLUSIONS

Machine learning models have demonstrated superior performance in predicting emergency abdominal pain surgery compared to traditional models. Modern machine learning improves clinical triage decisions and ensures that critically needy patients receive priority for emergency resources and timely, effective treatment.

Development and validation of a prediction model for rehospitalization among people with schizophrenia discharged from acute inpatient care

Objective

Relapses and rehospitalization prevent the recovery of individuals with schizophrenia or related psychoses. We aimed to build a model to predict the risk of rehospitalization among people with schizophrenia or related psychoses, including those with multiple episodes.

Methods

This retrospective cohort study included individuals aged 18 years or older, with schizophrenia or related psychoses, and discharged between January 2014 and December 2018 from one of three Japanese psychiatric hospital acute inpatient care ward. We collected nine predictors at the time of recruitment, followed up with the participants for 12 months, and observed whether psychotic relapse had occurred. Next, we applied the Cox regression model and used an elastic net to avoid overfitting. Then, we examined discrimination using bootstrapping, Steyerberg's method, and "leave-one-hospital-out" cross-validation. We also constructed a bias-corrected calibration plot.

Results

Data from a total of 805 individuals were analyzed. The significant predictors were the number of previous hospitalizations (HR 1.42, 95% CI 1.22-1.64) and the current length of stay in days (HR 1.31, 95% CI 1.04-1.64). In model development for relapse, Harrell's c-index was 0.59 (95% CI 0.55-0.63). The internal and internal-external validation for rehospitalization showed Harrell's c-index to be 0.64 (95% CI 0.59-0.69) and 0.66 (95% CI 0.57-0.74), respectively. The calibration plot was found to be adequate.

Conclusion

The model showed moderate discrimination of readmission after discharge. Carefully defining a research question by seeking needs among the population with chronic schizophrenia with multiple episodes may be key to building a useful model.

Early clinical outcome prediction based on the initial National Early Warning Score + Lactate (News+L) Score among adult emergency department patients

BACKGROUND

The National Early Warning Score + Lactate (NEWS+L) Score has been previously shown to outperform NEWS alone in prediction of mortality and need for critical care in a small adult ED study. We validated the score in a large patient data set and constructed a model that allows early prediction of the probability of clinical outcomes based on the individual's NEWS+L Score.

METHODS

In this retrospective study, we included all adult patients who visited the ED of a single urban academic tertiary-care university hospital in South Korea for five consecutive years (1 January 2015 to 31 December 2019). The initial (<1 hour) NEWS+L Score is routinely recorded electronically at our ED and was abstracted for each visit. The outcomes were hospital death or a composite of hospital death and intensive care unit admission at 24 hours, 48 hours and 72 hours. The data set was randomly split into train and test sets (1:1) for internal validation. The area under the receiver operating characteristic curve (AUROC) value and area under the precision and recall curve (AUPRC) value were evaluated and logistic regression models were used to develop an equation to calculate the predicted probabilities for each of these outcomes according to the NEWS+L Score.

RESULTS

After excluding 808 patients (0.5%) from 149 007 patients in total, the study cohort consisted of 148 199 patients. The mean NEWS+L Score was 3.3±3.8. The AUROC value was 0.789~0.813 for the NEWS+L Score with good calibration (calibration-in-the-large=-0.082~0.001, slope=0.964~0.987, Brier Score=0.011~0.065). The AUPRC values of the NEWS+L Score for outcomes were 0.331~0.415. The AUROC and AUPRC values of the NEWS+L Score were greater than those of NEWS alone (AUROC 0.744~0.806 and AUPRC 0.316~0.380 for NEWS). Using the equation, 48 hours hospital mortality rates for NEWS+L Score of 5, 10 and 15 were found to be 1.1%, 3.1% and 8.8%, and for the composite outcome 9.2%, 27.5% and 58.5%, respectively.

CONCLUSION

The NEWS+L Score has acceptable to excellent performance for risk estimation among undifferentiated adult ED patients, and outperforms NEWS alone.

Radical reactions on interstellar icy dust grains: Experimental investigations of elementary processes

Molecular clouds (MCs) in space are the birthplace of various molecular species. Chemical reactions occurring on the cryogenic surfaces of cosmic icy dust grains have been considered to play important roles in the formation of these species. Radical reactions are crucial because they often have low barriers and thus proceed even at low temperatures such as ∼10 K. Since the 2000s, laboratory experiments conducted under low-temperature, high-vacuum conditions that mimic MC environments have revealed the elementary physicochemical processes on icy dust grains. In this review, experiments conducted by our group in this context are explored, with a focus on radical reactions on the surface of icy dust analogues, leading to the formation of astronomically abundant molecules such as H₂, H₂O, H₂CO, and CH₃OH and deuterium fractionation processes. The development of highly sensitive, non-destructive methods for detecting adsorbates and their utilization for clarifying the behavior of free radicals on ice, which contribute to the formation of complex organic molecules, are also described.

Insights into the characteristics, adsorption and desorption behaviors of microplastics aged with or without fulvic acid

Many aging experiments on microplastics (MPs) have been carried out using UV radiation or strong oxidants. Little attention has been paid to the role of water environmental factors such as dissolved organic matter (DOM). In this study, the role of fulvic acid (FA), the main component of DOM, in the UV-aging process of MPs was explored. MPs aged under UV, and UV along with 0.5 mg/L and 2 mg/L FA, were selected as subjects. The results showed that (1) FA accelerated the aging process of polyethylene (PE). PE aged with FA had a larger specific area (S_BET), with more holes and cracks on the surface. (2) FA enhanced the adsorption capacity of PE. The TC adsorption quantities of 0, 0.5, and 2 mg/L FA-aged PE were 1.100, 1.447, and 1.812 mg/L, respectively. (3) The quantity of TC desorbed by PE increased, whereas the desorption rate decreased as the FA concentration increased. The desorption rates of TC at 0, 0.5, and 2 mg/L FA-aged PE were 25.16%, 22.05%, and 19.52% in water, and 72.10%, 70.36%, and 59.51% in simulated intestinal fluid. This study explored the role of FA in the aging process of MPs. Moreover, research on the aging mechanism of MPs is enriched.

Energy Redistribution Following CO2 Formation on Cold Amorphous Solid Water

The formation of molecules in and on amorphous solid water (ASW) as it occurs in interstellar space releases appreciable amounts of energy that need to be dissipated to the environment. Here, energy transfer between CO₂ formed within and on the surface of amorphous solid water (ASW) and the surrounding water is studied. Following CO(¹Σ⁺) + O(¹D) recombination the average translational and internal energy of the water molecules increases on the ∼10 ps time scale by 15–25% depending on whether the reaction takes place on the surface or in an internal cavity of ASW. Due to tight coupling between CO₂ and the surrounding water molecules the internal energy exhibits a peak at early times which is present for recombination on the surface but absent for the process inside ASW. Energy transfer to the water molecules is characterized by a rapid ∼10 ps and a considerably slower ∼1 ns component. Within 50 ps a mostly uniform temperature increase of the ASW across the entire surface is found. The results suggest that energy transfer between a molecule formed on and within ASW is efficient and helps to stabilize the reaction products generated.

International Validation of the SORG Machine-learning Algorithm for Predicting the Survival of Patients with Extremity Metastases Undergoing Surgical Treatment

BACKGROUND

The Skeletal Oncology Research Group machine-learning algorithms (SORG-MLAs) estimate 90-day and 1-year survival in patients with long-bone metastases undergoing surgical treatment and have demonstrated good discriminatory ability on internal validation. However, the performance of a prediction model could potentially vary by race or region, and the SORG-MLA must be externally validated in an Asian cohort. Furthermore, the authors of the original developmental study did not consider the Eastern Cooperative Oncology Group (ECOG) performance status, a survival prognosticator repeatedly validated in other studies, in their algorithms because of missing data.

QUESTIONS/PURPOSES

(1) Is the SORG-MLA generalizable to Taiwanese patients for predicting 90-day and 1-year mortality? (2) Is the ECOG score an independent factor associated with 90-day and 1-year mortality while controlling for SORG-MLA predictions?

METHODS

All 356 patients who underwent surgery for long-bone metastases between 2014 and 2019 at one tertiary care center in Taiwan were included. Ninety-eight percent (349 of 356) of patients were of Han Chinese descent. The median (range) patient age was 61 years (25 to 95), 52% (184 of 356) were women, and the median BMI was 23 kg/m2 (13 to 39 kg/m2). The most common primary tumors were lung cancer (33% [116 of 356]) and breast cancer (16% [58 of 356]). Fifty-five percent (195 of 356) of patients presented with a complete pathologic fracture. Intramedullary nailing was the most commonly performed type of surgery (59% [210 of 356]), followed by plate screw fixation (23% [81 of 356]) and endoprosthetic reconstruction (18% [65 of 356]). Six patients were lost to follow-up within 90 days; 30 were lost to follow-up within 1 year. Eighty-five percent (301 of 356) of patients were followed until death or for at least 2 years. Survival was 82% (287 of 350) at 90 days and 49% (159 of 326) at 1 year. The model's performance metrics included discrimination (concordance index [c-index]), calibration (intercept and slope), and Brier score. In general, a c-index of 0.5 indicates random guess and a c-index of 0.8 denotes excellent discrimination. Calibration refers to the agreement between the predicted outcomes and the actual outcomes, with a perfect calibration having an intercept of 0 and a slope of 1. The Brier score of a prediction model must be compared with and ideally should be smaller than the score of the null model. A decision curve analysis was then performed for the 90-day and 1-year prediction models to evaluate their net benefit across a range of different threshold probabilities. A multivariate logistic regression analysis was used to evaluate whether the ECOG score was an independent prognosticator while controlling for the SORG-MLA's predictions. We did not perform retraining/recalibration because we were not trying to update the SORG-MLA algorithm in this study.

RESULTS

The SORG-MLA had good discriminatory ability at both timepoints, with a c-index of 0.80 (95% confidence interval 0.74 to 0.86) for 90-day survival prediction and a c-index of 0.84 (95% CI 0.80 to 0.89) for 1-year survival prediction. However, the calibration analysis showed that the SORG-MLAs tended to underestimate Taiwanese patients' survival (90-day survival prediction: calibration intercept 0.78 [95% CI 0.46 to 1.10], calibration slope 0.74 [95% CI 0.53 to 0.96]; 1-year survival prediction: calibration intercept 0.75 [95% CI 0.49 to 1.00], calibration slope 1.22 [95% CI 0.95 to 1.49]). The Brier score of the 90-day and 1-year SORG-MLA prediction models was lower than their respective null model (0.12 versus 0.16 for 90-day prediction; 0.16 versus 0.25 for 1-year prediction), indicating good overall performance of SORG-MLAs at these two timepoints. Decision curve analysis showed SORG-MLAs provided net benefits when threshold probabilities ranged from 0.40 to 0.95 for 90-day survival prediction and from 0.15 to 1.0 for 1-year prediction. The ECOG score was an independent factor associated with 90-day mortality (odds ratio 1.94 [95% CI 1.01 to 3.73]) but not 1-year mortality (OR 1.07 [95% CI 0.53 to 2.17]) after controlling for SORG-MLA predictions for 90-day and 1-year survival, respectively.

CONCLUSION

SORG-MLAs retained good discriminatory ability in Taiwanese patients with long-bone metastases, although their actual survival time was slightly underestimated. More international validation and incremental value studies that address factors such as the ECOG score are warranted to refine the algorithms, which can be freely accessed online at https://sorg-apps.shinyapps.io/extremitymetssurvival/.

LEVEL OF EVIDENCE

Level III, therapeutic study.

Molecular Dynamics Simulations of Energy Dissipation on Amorphous Solid Water: Testing the Validity of Equipartition

Many different molecular species have been observed in the interstellar medium. These range from simple diatomic species to saturated organic molecules with several carbon atoms. The latter molecules are assumed to be formed predominantely on the surface of interstellar dust grains. All surface reactions that can proceed under the low interstellar temperatures are exothermic. Their exothermicity can be as high as a few electron volts, which is considerable compared to the thermal energy of the molecules at 10 K. It is postulated that this exothermicity can be used for the desorption of reaction products from the grain. In previous studies, we have shown that translational excitation can lead to desorption, whereas vibrational and rotational excitations are much less efficient in the desorption of surface products. Vibrational excitation is however much more likely upon bond formation than translational excitation. The present study follows energy dissipation upon translational, vibrational, or rotational excitation of admolecules on a surface and its conversion, or lack thereof, to different energy contributions. To this end, thousands of molecular dynamics simulations were performed with an admolecule on top of a surface that received a fixed amount of energy, vibrational, rotational, or translational. Three different surface species have been considered, CO₂, H₂O, and CH₄, spanning a range in binding energies, the number of internal degrees of freedom, and molecular weights. A fast exchange of energy between vibrational stretches is observed, but only very limited exchange to rotational or translation excitation has been found. For the dissipation of energy to the surface, excitation of the surface-admolecule bond is critical. Astrochemical models often assume instantaneous equipartition of energy after a reaction process to estimate the amount of available energy for chemical desorption. Based on the present study, we conclude that this assumption is not justified.