UNASSIGNED: Prolonged wound drainage (PWD) is one of the most important reasons that increase the risk of early periprosthetic joint infection after arthroplasty. It is very important to evaluate the risk factors for PWD in the surgical field after arthroplasty surgery. This can be accomplished using machine learning or artificial intelligence methods. Our aim in this study was to compare machine learning methods in predicting possible PWD.
UNASSIGNED: The study was carried out on clinical, laboratory, and radiological data of 313 patients who underwent hemiarthroplasty (HA) for proximal femur fractures. We preprocessed the dataset and trained and tested machine learning methods using cross validation. We compared various machine learning algorithms (linear discriminant analysis, decision tree, k-nearest neighbors, gradient boosting machine, and logistic regression [LR]) based on performance measures. We also combined the most successful algorithms with a metaclassifier. To help understand the relationship between risk factors, we provided a risk factor severity ranking.
UNASSIGNED: To estimate the risk of PWD, classification was performed with first-level classifiers and then integrated as a LR-based meta-learner stacking method. More performance improvements were achieved with the stacking method.
UNASSIGNED: We found that the stacking method was superior to other methods in PWD classification. We determined that the volume of fluid collected from the drain, morbid obesity class, blood transfusion, and body mass index score were the four most important risk factors according to stacking.

Prolonged hyperglycemia during diabetes mellitus (DM) is associated with severe complications that may affect both the anterior and posterior ocular segments, leading to impaired vision or blindness. The cornea is a vital part of the eye that has a dual role as a protective transparent barrier and as a major refractive structure and is likewise negatively affected by hyperglycemia in DM. Understanding the cellular and molecular mechanisms underlying the phenotypic changes associated with DM is critical to developing targeted therapies to promote tissue integrity. In this proof-of-concept study, we applied a cell sheet-based approach to generate stacked constructs of physiological corneal thickness using primary human corneal fibroblasts isolated from cadaveric control (healthy), Type 1 DM and Type 2 DM corneal tissues. Self-assembled corneal stromal sheets were generated after 2 weeks in culture, isolated, and subsequently assembled to create stacked constructs, which were evaluated using transmission electron microscopy. Analysis of gene expression patterns revealed significant downregulation of fibrotic markers, α-smooth muscle actin, and collagen type 3, with stacking in Type 2 DM constructs when compared to controls. IGF1 expression was significantly upregulated in Type 2 DM constructs compared to controls with a significant reduction induced by stacking. This study describes the development of a thicker, self-assembled corneal stromal construct as a platform to evaluate phenotypic differences associated with DM-derived corneal fibroblasts and enable the development of targeted therapeutics to promote corneal integrity.

UNASSIGNED: Hypertension is a major public health problem, and its resulting other cardiovascular diseases are the leading cause of death worldwide. In this study, we constructed a convenient and high-performance hypertension risk prediction model to assist in clinical diagnosis and explore other important influencing factors.
UNASSIGNED: We included 8,073 people from NHANES (2017-March 2020), using their 120 features to form the original dataset. After data pre-processing, we removed several redundant features through LASSO regression and correlation analysis. Thirteen commonly used machine learning methods were used to construct prediction models, and then, the methods with better performance were coupled with recursive feature elimination to determine the optimal feature subset. After data balancing through SMOTE, we integrated these better-performing learners to construct a fusion model based for predicting hypertension risk on stacking strategy. In addition, to explore the relationship between serum ferritin and the risk of hypertension, we performed a univariate analysis and divided it into four level groups (Q1 to Q4) by quartiles, with the lowest level group (Q1) as the reference, and performed multiple logistic regression analysis and trend analysis.
UNASSIGNED: The optimal feature subsets were: age, BMI, waist, SBP, DBP, Cre, UACR, serum ferritin, HbA1C, and doctors recommend reducing salt intake. Compared to other machine learning models, the constructed fusion model showed better predictive performance with precision, accuracy, recall, F1 value and AUC of 0.871, 0.873, 0.871, 0.869 and 0.966, respectively. For the analysis of the relationship between serum ferritin and hypertension, after controlling for all co-variates, OR and 95% CI from Q2 to Q4, compared to Q1, were 1.396 (1.176-1.658), 1.499 (1.254-1.791), and 1.645 (1.360-1.989), respectively, with P < 0.01 and P for trend <0.001.
UNASSIGNED: The hypertension risk prediction model developed in this study is efficient in predicting hypertension with only 10 low-cost and easily accessible features, which is cost-effective in assisting clinical diagnosis. We also found a trend correlation between serum ferritin levels and the risk of hypertension.

UNASSIGNED: We attempted to establish a model for predicting the mortality risk of sepsis patients during hospitalization.
UNASSIGNED: Data on patients with sepsis were collected from a clinical record mining database, who were hospitalized at the Affiliated Dongyang Hospital of Wenzhou Medical University between January 2013 and August 2022. These included patients were divided into modeling and validation groups. In the modeling group, the independent risk factors of death during hospitalization were determined using univariate and multi-variate regression analyses. After stepwise regression analysis (both directions), a nomogram was drawn. The discrimination ability of the model was evaluated using the area under the curve (AUC) of the receiver operating characteristic (ROC) curve, and the GiViTI calibration chart assessed the model calibration. The Decline Curve Analysis (DCA) was performed to evaluate the clinical effectiveness of the prediction model. Among the validation group, the logistic regression model was compared to the models established by the SOFA scoring system, random forest method, and stacking method.
UNASSIGNED: A total of 1740 subjects were included in this study, 1218 in the modeling population and 522 in the validation population. The results revealed that serum cholinesterase, total bilirubin, respiratory failure, lactic acid, creatinine, and pro-brain natriuretic peptide were the independent risk factors of death. The AUC values in the modeling group and validation group were 0.847 and 0.826. The P values of calibration charts in the two population sets were 0.838 and 0.771. The DCA curves were above the two extreme curves. Moreover, the AUC values of the models established by the SOFA scoring system, random forest method, and stacking method in the validation group were 0.777, 0.827, and 0.832, respectively.
UNASSIGNED: The nomogram model established by combining multiple risk factors could effectively predict the mortality risk of sepsis patients during hospitalization.

Tetrathiafulvalene (TTF) and its derivatives are very well known as electron donors with widespread use in the field of organic conductors and superconductors. Stacking interactions between two neutral TTF fragments were studied by analysing data from Cambridge Structural Database crystal structures and by quantum chemical calculations. Analysis of the contacts found in crystal structures shows high occurrence of parallel displaced orientations of TTF molecules. In the majority of the contacts, two TTF molecules are displaced along their longer C2 axis. The most frequent geometry has the strongest TTF-TTF stacking interaction, with CCSD(T)/CBS energy of -9.96 kcal mol-1. All the other frequent geometries in crystal structures are similar to geometries of the minima on the calculated potential energy surface.

Extracellular plaques, the hallmark of Alzheimer\'s disease brains, consist of insoluble amyloid fibrils that result from the aggregation of amyloid beta peptides. None of the few therapeutic options currently adopted, address the cause of the disease. Instead, they reduce symptom of the disease. Inhibition of aggregation or destabilization of aggregates therefore, emerges as a preferable therapeutic approach. Designing inhibitors or destabilizers demands comprehensive knowledge of the residues of amyloid beta responsible for the phenomenal structural stability of the aggregate. For the purpose, we have compared the effect on structural destabilization of 13 in silico mutations (single and double) with the wild type counterpart of beta-strand-turn-beta-strand motif of the amyloid beta protofibrils by molecular dynamics simulation. Besides the already known salt bridge interaction between K28 and D23, our analyses expose more significant role of K28 as the only positive charge present in the vicinity. Amongst the two consecutive aromatic residues, F19 is involved in stacking interaction; although effect of F20 mutation is more pronounced. Face to face arrangement of A21 and V36 acts as a pillar maintaining the necessary optimum distance between consecutive chains to promote stabilizing interactions. In addition to providing stability to the first beta-strand, large sized negatively charged E22 facilitates salt bridge formation by ensuring fixed relative position of D23 and in turn K28. Likewise, the hydrophobic residues I32 and L34 pack the protofibril core, once again fostering salt bridge interaction. Prospectively, these findings may be compiled for efficient identification or design of scaffolds accountable for protofibril destabilization.Communicated by Ramaswamy H. Sarma.

We present the results of an experimental and computational study of structural changes in two polymorphs of tolazamide {systematic name: 1-[(azepan-1-ylamino)carbonyl]-4-methylbenzenesulfonamide}, C14H21N3O3S, on cooling to 100 K and reverse heating. No phase transitions occurred in this temperature range. The anisotropy of the thermal expansion was different for the two polymorphs and differed from that reported previously for the hydrostatic compression. The changes in different intermolecular contacts responsible for the strain anisotropy were analysed. Relative shortening of the contacts was related directly to their initial length and reversely to the steric density around them. Increasing steric density is likely to be the driving force for the conformational ordering of the azepane ring under compression.

The reaction pathway of the cyclization of 2-phenoxybenzophenone into 9-phenyl-9H-xanthen-9-ol in the presence of acid and an excess of AlCl33 was studied using density functional theory. This type of reaction is known to occur during the Friedel-Crafts polycondensation of poly(aryl ether ketones) following the undesired benzoylation of nucleophilic positions ortho- to the growing polymer\'s ether groups. The formed defect acts as an undesired terminator of the polymer chain, causing severe problems in the polymer\'s melt state. A branched, multistep mechanism reminiscent of the Friedel-Crafts acylation reaction is discovered; the reaction starts with the protonation of the carbonyl oxygen, followed by intramolecular electrophilic attack on the carbonyl carbon that determines the turnover frequency of the catalytic cycle and ends by deprotonation of the Wheland intermediate.

During the last years, several authors have focused on the characterization of the size and charge of the nanoparticles by capillary electrophoresis. However, considering that nanoparticles are generally suspended in a solvent different from those commonly used as background electrolytes (BGE), an appropriate characterization of the behavior of the nanoparticles in the sample-BGE interface is required, as this might affect the overall electrophoretic behavior of the nanoparticles. In the present work, we address the evaluation of the behavior of COOH-coated maghemite nanoparticles in the vicinity of a pH boundary. To do so, different suspensions of nanoparticles prepared in acid media were injected into a borate/NaOH pH 9.5 BGE. The formation and evolution of boundaries in the sample-BGE interface in such systems was modeled by computer simulation. A systematic evaluation of the effect that parameters such as the co-ion, the sample pH or the injection time have on the electrophoretic behavior of the nanoparticles was carried out.