Incorporating preoperative frailty to assist in early prediction of postoperative pneumonia in elderly patients with hip fractures: an externally validated online interpretable machine learning model.

BACKGROUND: This study aims to implement a validated prediction model and application medium for postoperative pneumonia (POP) in elderly patients with hip fractures in order to facilitate individualized intervention by clinicians. METHODS: Employing clinical data from elderly patients with hip fractures, we derived and externally validated machine learning models for predicting POP. Model derivation utilized a registry from Nanjing First Hospital, and external validation was performed using data from patients at the Fourth Affiliated Hospital of Nanjing Medical University. The derivation cohort was divided into the training set and the testing set. The least absolute shrinkage and selection operator (LASSO) and multivariable logistic regression were used for feature screening. We compared the performance of models to select the optimized model and introduced SHapley Additive exPlanations (SHAP) to interpret the model. RESULTS: The derivation and validation cohorts comprised 498 and 124 patients, with 14.3% and 10.5% POP rates, respectively. Among these models, Categorical boosting (Catboost) demonstrated superior discrimination ability. AUROC was 0.895 (95%CI: 0.841-0.949) and 0.835 (95%CI: 0.740-0.930) on the training and testing sets, respectively. At external validation, the AUROC amounted to 0.894 (95% CI: 0.821-0.966). The SHAP method showed that CRP, the modified five-item frailty index (mFI-5), and ASA body status were among the top three important predicators of POP. CONCLUSION: Our model's good early prediction ability, combined with the implementation of a network risk calculator based on the Catboost model, was anticipated to effectively distinguish high-risk POP groups, facilitating timely intervention.

Resistomycin Inhibits Wnt/ß-Catenin Signaling to Induce the Apoptotic Death of Human Colorectal Cancer Cells.

Resistomycin is a natural antibiotic related to quinone that has been shown to exhibit robust antitumor activity. To further characterize the mechanistic basis for such activity, human colorectal cancer (CRC) cells were selected as a model to explore the role of Wnt/ß-catenin signaling in the ability of resistomycin to induce apoptotic cell death. These analyses revealed that resistomycin was able to suppress ß-catenin, TCF4, and GSK-3ß expression, together with that of the downstream targets c-Myc and survivin. This coincided with elevated cleaved caspase-3 and Bax protein levels and a decline in Bcl-2 content. When ß-catenin was silenced, this further enhanced the ability of resistomycin to induce apoptotic CRC cell death, whereas this apoptotic process was partially ablated when cells were treated using lithium chloride to activate Wnt/ß-catenin signaling. Overall, these results support a model wherein resistomycin inhibits Wnt/ß-catenin signaling within CRC cells, thereby inducing apoptotic death. Further research may be warranted to better clarify the potential utility of this compound as a candidate drug for use in the treatment of patients suffering from this form of cancer.

Fabrication of diamond microlenses by chemical reflow method.

We introduce a chemical reflow method to fabricate diamond microlenses. First, photoresist pillars developed by photolithography are reflowed in organic solvent vapor atmosphere at 20 °C to form spherical segment patterns on diamond substrate. The effects of chemical solvent type and reflow time on photoresist pattern profiles are investigated. Second, via dry etching, diamond microlenses are fabricated by transferring the spherical segment pattern into substrate. Furthermore, these diamond microlenses demonstrate low numerical aperture, well-controllable curvature, and good imaging performance with projecting experiment.

A machine learning stacking model accurately estimating gastric fluid volume in patients undergoing elective sedated gastrointestinal endoscopy.

BACKGROUND: The current point-of-care ultrasound (POCUS) assessment of gastric fluid volume primarily relies on the traditional linear approach, which often suffers from moderate accuracy. This study aimed to develop an advanced machine learning (ML) model to estimate gastric fluid volume more accurately. METHODS: We retrospectively analyzed the clinical data and POCUS data (D1: craniocaudal diameter, D2: anteroposterior diameter) of 1386 patients undergoing elective sedated gastrointestinal endoscopy (GIE) at Nanjing First Hospital to predict gastric fluid volume using ML techniques, including six different ML models and a stacking model. We evaluated the models using the adjusted Coefficient of Determination (R2), mean absolute error (MAE) and root mean square error (RMSE). The SHapley Additive exPlanations (SHAP) method was used to interpret the importance of the variables. Finally, a web calculator was constructed to facilitate its clinical application. RESULTS: The stacking model (Linear regression + Multilayer perceptron) performed best, with the highest adjusted R2 of 0.718 (0.632 to 0.804). The mean prediction bias was 4 ml (MAE: 4.008 (3.68 to 4.336)), which is better than that of the linear model. D1 and D2 ranked high in the SHAP plot and performed better in the right lateral decubitus (RLD) than in the supine position. The web calculator can be accessed at https://cheason.shinyapps.io/Stacking_regressor/. CONCLUSION: The stacking model and its web calculator can serve as practical tools for accurately estimating gastric fluid volume in patients undergoing elective sedated GIE. It is recommended that anesthesiologists measure D1 and D2 in the patient's RLD position.

Inhibition mechanism of fusarium graminearum growth by g-C3N4 homojunction and its application in barley malting.

The increase of deoxynivalenol (DON) caused by Fusarium graminearum (F. graminearum) during the malting process is a serious safety problem. In our work, the inhibition mechanism of F. graminearum growth by g-C3N4 homojunction and its application in barley malting were studied. The reason why the growth activity of F. graminearum decreased after photocatalysis by g-C3N4 homojunction was that under visible light irradiation, a large amount of •O2- elicited by g-C3N4 homojunction destroyed the cell structure of F. graminearum, leading to the deficiency of cell membrane selective permeability and serious disorder of intracellular metabolism. The application of photocatalysis technology in malting can effectively inhibit the growth of F. graminearum and the accumulation of ergosterol was reduced by 30.55 %, thus reducing the DON content in finished malt by 31.82 %. Meanwhile, the physicochemical indexes of barley malt after photocatalytic treatment still met the requirements of second class barley malt in Chinese light industry standard QB/T 1686-2008. Our work provides a new idea for the control of fungal contamination in barley malt.

Polyguluronic acid alleviates doxorubicin-induced cardiotoxicity by suppressing Peli1-NLRP3 inflammasome-mediated pyroptosis.

Polyguluronic acid (PG), a polysaccharide from alginate, possesses excellent bioactivities. We prepared high-purity PG with 10.41 kDa molecular weight (Mw) and a 59 average degree of polymerization (DP) by acid hydrolysis, three pH grades, Q-Sepharose column elution, and Sephadex G-25 column desalination. Then, we evaluated the PG protective effects on doxorubicin-induced cardiotoxicity (DIC) in vitro and in vivo. The nontoxic PG enhanced cellular viability, reduced cell pyroptosis morphology, diminished the LDH and IL-1ß release, and downregulated expressions of ASC oligomerization, NLRP3, cl-CASP1, and GSDMD, by which PG protected the cardiomyocytes from NLRP3 inflammasome-mediated pyroptosis in doxorubicin-stimulated HL-1 cells and C57BL/6J mice. The probable underlying mechanism may be that PG downregulated doxorubicin -induced Peli1, the deficiency of which could inhibit doxorubicin-induced NLRP3 inflammasome-mediated pyroptosis. These results suggested that polysaccharide PG from alginate could prevent DIC and may be a potential therapeutic agent or bioactive material for preventing DIC.

A convenient machine learning model to predict full stomach and evaluate the safety and comfort improvements of preoperative oral carbohydrate in patients undergoing elective painless gastrointestinal endoscopy.

BACKGROUND AND AIMS: Assessment of the patient's gastric contents is the key to avoiding aspiration incidents, however, there is no effective method to determine whether elective painless gastrointestinal endoscopy (GIE) patients have a full stomach or an empty stomach. And previous studies have shown that preoperative oral carbohydrates (POCs) can improve the discomfort induced by fasting, but there are different perspectives on their safety. This study aimed to develop a convenient, accurate machine learning (ML) model to predict full stomach. And based on the model outcomes, evaluate the safety and comfort improvements of POCs in empty- and full stomach groups. METHODS: We enrolled 1386 painless GIE patients between October 2022 and January 2023 in Nanjing First Hospital, and 1090 patients without POCs were used to construct five different ML models to identify full stomach. The metrics of discrimination and calibration validated the robustness of the models. For the best-performance model, we further interpreted it through SHapley Additive exPlanations (SHAP) and constructed a web calculator to facilitate clinical use. We evaluated the safety and comfort improvements of POCs by propensity score matching (PSM) in the two groups, respectively. RESULTS: Random Forest (RF) model showed the greatest discrimination with the area under the receiver operating characteristic curve (AUROC) 0.837 [95% confidence interval (CI): 79.1-88.2], F1 71.5%, and best calibration with a Brier score of 15.2%. The web calculator can be visited at https://medication.shinyapps.io/RF_model/. PSM results demonstrated that POCs significantly reduced the full stomach incident in empty stomach group (p < 0.05), but no differences in full stomach group (p > 0.05). Comfort improved in both groups and was more significant in empty stomach group. CONCLUSIONS: The developed convenient RF model predicted full stomach with high accuracy and interpretability. POCs were safe and comfortably improved in both groups, with more benefit in empty stomach group. These findings may guide the patients' gastrointestinal preparation.

This study is the first model utilizing advanced ML techniques based on multiple clinical variables to identify full stomach. The model is suitable for patient-rich outpatient clinics, primary hospitals, remote regions, and specific clinical settings where POCUS is not available.The developed convenient RF model predicted full stomach with high accuracy and interpretability. The test cohort AUROC was 0.837. We further established an online accessible individualized risk calculator and provided waterfall plots to increase the interpretability of each prediction.The propensity score matching (PSM) showed that preoperative oral carbohydrate (POCs) were safe and comfortably improved in both groups, with more benefit in empty stomach group. These findings may provide information for anesthesiologists to guide patients on POCs.