Association of magnesium sulfate use with mortality in critically ill patients with sepsis: a retrospective propensity score-matched cohort study.

BACKGROUND: Trials have demonstrated lower rates of acute kidney injury in critically ill patients receiving magnesium supplementation, but they have yielded conflicting results regarding mortality. METHODS: This is a retrospective cohort study based on the MIMIC-IV (Medical Information Mart in Intensive Care-IV) database. Adult critically ill patients with sepsis were included in the analysis. The exposure was magnesium sulfate use during ICU stay. The primary outcome was 28-day all-cause mortality. Propensity score matching (PSM) was conducted at a 1:1 ratio. Multivariable analyses were used to adjust for confounders. RESULTS: The pre-matched and propensity score-matched cohorts included 10 999 and 6052 patients, respectively. In the PSM analysis, 28-day all-cause mortality rate was 20.2% (611/3026) in the magnesium sulfate use group and 25.0% (757/3026) in the no use group. Magnesium sulfate use was associated with lower 28-day all-cause mortality (hazard ratio [HR], 0.70; 95% CI, 0.61-0.79; P<0.001). Lower mortality was observed regardless of baseline serum magnesium status: for hypomagnesaemia, HR, 0.64; 95% confidence interval (CI), 0.45-0.93; P=0.020; for normomagnesaemia, HR, 0.70; 95% CI, 0.61-0.80; P<0.001. Magnesium sulfate use was also associated with lower ICU mortality (odds ratio [OR], 0.52; 95% CI, 0.42-0.64; P<0.001), lower in-hospital mortality (OR, 0.65; 95% CI, 0.55-0.77; P<0.001), and renal replacement therapy (OR, 0.67; 95% CI, 0.52-0.87; P=0.002). A sensitivity analysis using the entire cohort also demonstrated lower 28-day all-cause mortality (HR, 0.62; 95% CI, 0.56-0.69; P<0.001). CONCLUSIONS: Magnesium sulfate use was associated with lower mortality in critically ill patients with sepsis. Prospective studies are needed to verify this finding.

Predicting diabetic kidney disease for type 2 diabetes mellitus by machine learning in the real world: a multicenter retrospective study.

Objective: Diabetic kidney disease (DKD) has been reported as a main microvascular complication of diabetes mellitus. Although renal biopsy is capable of distinguishing DKD from Non Diabetic kidney disease(NDKD), no gold standard has been validated to assess the development of DKD.This study aimed to build an auxiliary diagnosis model for type 2 Diabetic kidney disease (T2DKD) based on machine learning algorithms. Methods: Clinical data on 3624 individuals with type 2 diabetes (T2DM) was gathered from January 1, 2019 to December 31, 2019 using a multi-center retrospective database. The data fell into a training set and a validation set at random at a ratio of 8:2. To identify critical clinical variables, the absolute shrinkage and selection operator with the lowest number was employed. Fifteen machine learning models were built to support the diagnosis of T2DKD, and the optimal model was selected in accordance with the area under the receiver operating characteristic curve (AUC) and accuracy. The model was improved with the use of Bayesian Optimization methods. The Shapley Additive explanations (SHAP) approach was used to illustrate prediction findings. Results: DKD was diagnosed in 1856 (51.2 percent) of the 3624 individuals within the final cohort. As revealed by the SHAP findings, the Categorical Boosting (CatBoost) model achieved the optimal performance 1in the prediction of the risk of T2DKD, with an AUC of 0.86 based on the top 38 characteristics. The SHAP findings suggested that a simplified CatBoost model with an AUC of 0.84 was built in accordance with the top 12 characteristics. The more basic model features consisted of systolic blood pressure (SBP), creatinine (CREA), length of stay (LOS), thrombin time (TT), Age, prothrombin time (PT), platelet large cell ratio (P-LCR), albumin (ALB), glucose (GLU), fibrinogen (FIB-C), red blood cell distribution width-standard deviation (RDW-SD), as well as hemoglobin A1C(HbA1C). Conclusion: A machine learning-based model for the prediction of the risk of developing T2DKD was built, and its effectiveness was verified. The CatBoost model can contribute to the diagnosis of T2DKD. Clinicians could gain more insights into the outcomes if the ML model is made interpretable.

A visualized dynamic prediction model for survival of patients with geriatric thyroid cancer: A population-based study.

Objective: Thyroid cancer (TC) is a common malignancy with a poor prognosis with aging. However, no accurate predictive survival model exists for patients with geriatric TC.We aimed to establish prediction models of prognosis in elderly TC. Methods: We retrospectively reviewed the clinicopathology characteristics of patients with geriatric TC in the Surveillance, Epidemiology, and End Results database (SEER) from 2004 to 2018. The risk predictors used to build the nomograms were derived from the Cox proportional risk regression. These nomograms were used to predict 1-, 3-, and 5-year overall survival and cancer-specific survival in elderly patients with TC. The accuracy and discriminability of the new model were evaluated by the consistency index (C-index) and calibration curve. The clinical applicability value of the model was assessed using the decision curve analysis. Results: We used the SEER database to include 16475 patients with geriatric TC diagnosed from 2004 to 2018. The patients from 2004 to 2015 were randomly sorted out on a scale of 7:3. They were classified into a training group (n = 8623) and a validation group (n = 3669). Patients with TC diagnosed in 2016-2018 were classified into external validation groups (n = 4183). The overall survival nomogram consisted of 10 variables (age, gender, marital status, histologic type, grade, TNM stage, surgery status, and tumor size). A cancer-specific survival nomogram consisted of eight factors (age, tumor size, grade, histologic type, surgery, and TNM stage). The C-index values for the training, validation, and external validation groups were 0.775 (95% confidence interval [CI] 0.785-0.765), 0.776 (95% CI 0.792-0.760), and 0.895(95% CI 0.873-0.917), respectively. The overall survival was consistent with a nomogram based on the calibration curve. Besides, the decision curve analysis showed excellent clinical application value of the nomogram. Additionally, we found that surgery could improve the prognosis of patients with geriatric at high-risk (P < 0.001) but not those at low-risk (P = 0.069). Conclusion: This was the first study to construct predictive survival nomograms for patients with geriatric TC. The well-established nomograms and the actual results could guide follow-up management strategies.

Clinical Predictive Models for Delayed Cerebral Infarction After Ruptured Intracranial Aneurysm Clipping for Patients: A Retrospective Study.

Objective: A nomogram was developed in this work to predict the probability of delayed cerebral infarction (DCI) after ruptured intracranial aneurysms (RIA) clipping. Methods: Clinical data of patients with intracranial aneurysm were obtained from the neurosurgery department of the First Affiliated Hospital of Chongqing Medical University from January 2016 to December 2020. A total of 419 patients receiving surgery of ruptured intracranial aneurysm clipping were included and a total of 37 patients with DCI were set as the observation group. The control group consisted of 382 patients without DCI. Risk factors of DCI were screened by univariate and multivariate logistic regression analysis and included in the nomogram. Results: Univariate analysis showed that female (P = 0.009), small aneurysm (P = 0.031), intraoperative aneurysm rupture (P = 0.007) and cerebral vasospasm (P < 0.001) were risk factors for postoperative DCI while smoking history (P = 0.044) were protective factors for postoperative DCI. Multivariate Logistic regression analysis showed that small aneurysm (P = 0.002, OR = 3.332, 95%-7.104), intraoperative aneurysm rupture (P = 0.004, OR = 0.122, 95%-CI, 0.029-0.504)and cerebral vasospasm (P < 0.001, OR = 0.153, 95%-CI, 0.070-0.333) were independent risk factors of postoperative DCI. The calibration curve of the probability of occurrence showed that the nomogram was in good correspondence with the observed results with a C-index of 0.766 (95% CI, 0.684-0.848). Meanwhile, the Decision curve analysis (DCA) showed that the established predictive model had a good clinical net benefit. Conclusion: The well-established nomogram is expected to be an effective tool to predict the occurrence of DCI after intracranial ruptured aneurysm and can be used to assist clinicians to develop more effective treatment strategies and improve the prognosis of patients.

Winemaking Characteristics of Red-Fleshed Dragon Fruit from Three Locations in Guizhou Province, China.

The aim of this study was to identify the locations and harvest months in Guizhou province, China, producing the most suitable red dragon fruit (Hylocereus polyrhizus) for winemaking. Fruit from Guanling, Luodian and Zhenfeng counties was harvested separately from successive fruit cycles in August, September and October, respectively. The key traits measured were fruit weight, pulp yield, soluble solids content, and titratable acid. Wine characteristics measured were alcohol content, total carbohydrates, titratable acidity, volatile acidity, and betacyanin content. The overall suitability of fruit from each location for winemaking was evaluated using a multi-factor, unweighted, scorecard. On that basis, fruit from Guanling county harvested in August was the most suitable. Fruit from Luodian, and Zhenfeng was most suitable when harvested in August and September, and September, respectively. These results provide a preliminary guide for the sourcing of red dragon fruit from Guizhou for wine production.

The development of dual-label time-resolved fluorescence immunoassay (TRFIA) for screening of ovarian cancer based on simultaneous detection of human epididymis protein-4 and cancer antigen 125.

A two-step dual-label TRFIA was developed for the simultaneous detection of human epididymis protein-4 and cancer antigen 125 in a single run. The performance of this assay was first evaluated using clinical serum samples, and then compared with commercialized kits. The sensitivity of this assay for cancer antigen 125 detection was 0.5 U/mL (dynamic range, 0-1400 U/L), and the sensitivity for human epididymis protein-4 detection was 1 pM (dynamic range, 1-900 pM). High correlation coefficients (R) were obtained between the present dual-label TRFIA and commercially available kits (R = 0.99). The present dual-label TRFIA has high sensitivity, specificity, and accuracy in clinical sample analysis. It is a good alternative to the single-label diagnostic methods.

Crystal structure of a novel antifungal protein distinct with five disulfide bridges from Eucommia ulmoides Oliver at an atomic resolution.

EAFP2 is a novel antifungal protein isolated from the bark of the tree Eucommia ulmoides Oliver. It consists of 41 residues and is characterized with a five-disulfide motif and the inhibitory effects on the growth of both cell wall chitin-containing and chitin-free fungi. The crystal structure of EAFP2 at an atomic resolution of 0.84 A has been determined by using Shake-and-Bake direct methods with the program SnB. The phases obtained were of sufficient quality to permit the initial model built automatically and the structural refinement carried out using anisotropic displacement parameters resulted in a final crystallographic R factor of 6.8%. In the resulting structural model, all non-hydrogen protein atoms including an unusual pyroglutamyl acid residue at the N-terminal can fit to the articulated electron densities with one centre and more than 65% of the hydrogen atoms in the protein can be observed as individual peaks in the difference map. The general fold of EAFP2 is composed of a 3(10) helix (Cys3-Arg6), an alpha-helix (Ala27-Cys31) and a three-stranded antiparallel beta-sheet (Cys16-Ser18, Cys23-Ser25, and Cys35-Cys37) and cross-linked by five disulfide bridges. The tertiary structure of EAFP2 can be divided into two structural sectors, A and B. Sector A composed of residues 11-30 adopts a conformation similar to the chitin-binding domain in the hevein-like proteins and features a hydrophobic surface embraced a chitin-binding site (Tyr20, 22, 29, and Ser18). The distinct disulfide bridge Cys7-Cys37 connects the N-terminal ten residues with the C-terminal segment 35-41 to form the sector B, which features a cationic surface distributing all four positively charged residues, Arg6, 9, 36, and 40. Based on these structural features, the possible structural basis of the functional properties of EAFP2 is discussed.

Crystallization and preliminary crystallographic studies of a novel antifungal protein with five disulfide bridges from Eucommia ulmoides Oliver.

Two antifungal proteins, named Eucommia antifungal peptides 1 and 2 (EAFP1 and EAFP2), have been purified from the bark of the tree E. ulmoides Oliver and show a relatively broad spectrum of antifungal activity against several agriculturally important plant pathogens. One of these small proteins (EAFP2) has been crystallized. The crystal belongs to space group P2(1), with unit-cell parameters a = 19.01, b = 23.16, c = 30.69 A, beta = 98.54 degrees. 1.0 A resolution data were collected from an EAFP2 crystal and have been used to obtain phase information directly by an ab initio method.

Two novel antifungal peptides distinct with a five-disulfide motif from the bark of Eucommia ulmoides Oliv.

Two antifungal peptides, named EAFP1 and EAFP2, have been purified from the bark of Eucommia ulmoides Oliv. Each of the sequences consists of 41 residues with a N-terminal blockage by pyroglutamic acid determined by automated Edman degradation in combination with the tandem mass spectroscopy and the C-terminal ladder sequencing analysis. The primary structurs all contain 10 cysteines, which are cross-linked to form five disulfide bridges with a pairing pattern (C1-C5, C2-C9, C3-C6, C4-C7, C8-C10). This is the first finding of a plant antifungal peptide with a five-disulfide motif. EAFP1 and EAFP2 show characteristics of hevein domain and exhibit chitin-binding properties similar to the previously identified hevein-like peptides. They exhibit relatively broad spectra of antifungal activities against eight pathogenic fungi from cotton, wheat, potato, tomato and tobacco. The inhibition activity of EAFP1 and EAFP2 can be effective on both chitin-containing and chitin-free fungi. The values of IC(50) range from 35 to 155 microg/ml for EAFP1 and 18 to 109 microg/ml for EAFP2. Their antifungal effects are strongly antagonized by calcium ions.