Synthetic minority over-sampling technique-enhanced machine learning models for predicting recurrence of postoperative chronic subdural hematoma.

Objective: Chronic subdural hematoma (CSDH) is a neurological condition with high recurrence rates, primarily observed in the elderly population. Although several risk factors have been identified, predicting CSDH recurrence remains a challenge. Given the potential of machine learning (ML) to extract meaningful insights from complex data sets, our study aims to develop and validate ML models capable of accurately predicting postoperative CSDH recurrence. Methods: Data from 447 CSDH patients treated with consecutive burr-hole irrigations at Wenzhou Medical University's First Affiliated Hospital (December 2014-April 2019) were studied. 312 patients formed the development cohort, while 135 comprised the test cohort. The Least Absolute Shrinkage and Selection Operator (LASSO) method was employed to select crucial features associated with recurrence. Eight machine learning algorithms were used to construct prediction models for hematoma recurrence, using demographic, laboratory, and radiological features. The Border-line Synthetic Minority Over-sampling Technique (SMOTE) was applied to address data imbalance, and Shapley Additive Explanation (SHAP) analysis was utilized to improve model visualization and interpretability. Model performance was assessed using metrics such as AUROC, sensitivity, specificity, F1 score, calibration plots, and decision curve analysis (DCA). Results: Our optimized ML models exhibited prediction accuracies ranging from 61.0% to 86.2% for hematoma recurrence in the validation set. Notably, the Random Forest (RF) model surpassed other algorithms, achieving an accuracy of 86.2%. SHAP analysis confirmed these results, highlighting key clinical predictors for CSDH recurrence risk, including age, alanine aminotransferase level, fibrinogen level, thrombin time, and maximum hematoma diameter. The RF model yielded an accuracy of 92.6% with an AUC value of 0.834 in the test dataset. Conclusion: Our findings underscore the efficacy of machine learning algorithms, notably the integration of the RF model with SMOTE, in forecasting the recurrence of postoperative chronic subdural hematoma. Leveraging the RF model, we devised an online calculator that may serve as a pivotal instrument in tailoring therapeutic strategies and implementing timely preventive interventions for high-risk patients.

An Unexpected Decrease in Vibrational Entropy of Multicomponent Rutile Oxides.

High-entropy oxides (HEOs), featuring infinite chemical composition and exceptional physicochemical properties, are attracting much attention. The configurational entropy caused by a component disorder of HEOs is popularly believed to be the main driving force for thermal stability, while the role of vibrational entropy in the thermodynamic landscape has been neglected. In this study, we systematically investigated the vibrational entropy of multicomponent rutile oxides (including Fe0.5Ta0.5O2, Fe0.333Ti0.333Ta0.333O2, Fe0.25Ti0.25Ta0.25Sn0.25O2, and Fe0.21Ti0.21Ta0.21Sn0.21Ge0.16O2) by precise heat capacity measurements. It is found that vibrational entropy gradually decreases with increasing component disorder, beyond what one could expect from an equilibrium thermodynamics perspective. Moreover, all multicomponent rutile oxides exhibit a positive excess vibrational entropy at 298.15 K. Upon examinations of configuration disorder, size mismatch, phase transition, and polyhedral distortions, we demonstrate that the excess vibrational entropy plays a pivotal role in lowering the crystallization temperature of multicomponent rutile oxides. These findings represent the first experimental confirmation of the role of lattice vibrations in the thermodynamic landscape of rutile HEOs. In particular, vibrational entropy could serve as a novel descriptor to guide the predictive design of multicomponent oxide materials.

Development and performance assessment of novel machine learning models for predicting postoperative pneumonia in aneurysmal subarachnoid hemorrhage patients: external validation in MIMIC-IV.

Background: Postoperative pneumonia (POP) is one of the primary complications after aneurysmal subarachnoid hemorrhage (aSAH) and is associated with postoperative mortality, extended hospital stay, and increased medical fee. Early identification of pneumonia and more aggressive treatment can improve patient outcomes. We aimed to develop a model to predict POP in aSAH patients using machine learning (ML) methods. Methods: This internal cohort study included 706 patients with aSAH undergoing intracranial aneurysm embolization or aneurysm clipping. The cohort was randomly split into a train set (80%) and a testing set (20%). Perioperative information was collected from participants to establish 6 machine learning models for predicting POP after surgical treatment. The area under the receiver operating characteristic curve (AUC), precision-recall curve were used to assess the accuracy, discriminative power, and clinical validity of the predictions. The final model was validated using an external validation set of 97 samples from the Medical Information Mart for Intensive Care IV (MIMIC-IV) database. Results: In this study, 15.01% of patients in the training set and 12.06% in the testing set with POP after underwent surgery. Multivariate logistic regression analysis showed that mechanical ventilation time (MVT), Glasgow Coma Scale (GCS), Smoking history, albumin level, neutrophil-to-albumin Ratio (NAR), c-reactive protein (CRP)-to-albumin ratio (CAR) were independent predictors of POP. The logistic regression (LR) model presented significantly better predictive performance (AUC: 0.91) than other models and also performed well in the external validation set (AUC: 0.89). Conclusion: A machine learning model for predicting POP in aSAH patients was successfully developed using a machine learning algorithm based on six perioperative variables, which could guide high-risk POP patients to take appropriate preventive measures.

Role of connexin 32 in the directional differentiation of induced pluripotent stem cells into hepatocytes.

This study aimed to explore the role of connexin 32 (Cx32) in the directional differentiation of induced pluripotent stem cells (iPSCs) into hepatocytes. Urine-derived epithelial cells were collected from the fresh urine of a healthy donor and transducted with reprogramming plasmid mixture to generate iPSCs. The iPSCs were then directionally differentiated into hepatocytes. During the differentiation, the upregulated and downregulated groups were treated with vitamin K2 (VK2) and 2-aminoethoxyboronate diphenylester (2-APB) to increase and inhibit Cx32 expression, respectively. The control group was not treated with the regulatory factor. Expression of Cx32 and hepatocyte-specific markers, including AFP, hepatocyte nuclear factor 4α (HNF-4α), albumin (ALB) and cytokeratin 18 (CK18) were detected. It indicated that Cx32 expression was not observed in iPSCs, but gradually increased during the process of hepatic differentiation from iPSCs. Upregulation of Cx32 expression by VK2 treatment promoted hepatocyte maturation and enhanced the expression of the aforementioned hepatic specific markers, whereas downregulation of Cx32 expression by 2-APB treatment had the opposite effects. In conclusion, urine-derived iPSCs could be directionally differentiated into hepatocytes. Up-regulation of Cx32 improves the efficiency and maturity of differentiation of iPSCs into hepatocytes, and Cx32 may be a promoting factor during the process of hepatic differentiation from iPSCs.

Efficient and heritable A-to-K base editing in rice and tomato.

Cytosine and adenosine base editors (CBE and ABE) have been widely used in plants, greatly accelerating gene function research and crop breeding. Current base editors can achieve efficient A-to-G and C-to-T/G/A editing. However, efficient and heritable A-to-Y (A-to-T/C) editing remains to be developed in plants. In this study, a series of A-to-K base editor (AKBE) systems were constructed for monocot and dicot plants. Furthermore, nSpCas9 was replaced with the PAM-less Cas9 variant (nSpRY) to expand the target range of the AKBEs. Analysis of 228 T0 rice plants and 121 T0 tomato plants edited using AKBEs at 18 endogenous loci revealed that, in addition to highly efficient A-to-G substitution (41.0% on average), the plant AKBEs can achieve A-to-T conversion with efficiencies of up to 25.9 and 10.5% in rice and tomato, respectively. Moreover, the rice-optimized AKBE generates A-to-C conversion in rice, with an average efficiency of 1.8%, revealing the significant value of plant-optimized AKBE in creating genetic diversity. Although most of the A-to-T and A-to-C edits were chimeric, desired editing types could be transmitted to the T1 offspring, similar to the edits generated by the traditional ABE8e. Besides, using AKBEs to target tyrosine (Y, TAT) or cysteine (C, TGT) achieved the introduction of an early stop codon (TAG/TAA/TGA) of target genes, demonstrating its potential use in gene disruption.

Development of a reverse transcription loop-mediated isothermal amplification based clustered regularly interspaced short palindromic repeats Cas12a assay for duck Tembusu virus.

Duck Tembusu virus (DTMUV) is an emerging pathogen that poses a serious threat to the duck industry in China. Currently, polymerase chain reaction (PCR), quantitative PCR (qPCR) and reverse transcription loop-mediated isothermal amplification (RT-LAMP) are commonly used for DTMUV detection. However, these methods require complex steps and special equipment and easily cause false-positive results. Therefore, we urgently need to establish a simple, sensitive and specific method for the clinical field detection of DTMUV. In this study, we developed an RT-LAMP-based CRISPR-Cas12a assay targeting the C gene to detect DTMUV with a limited detection of 3 copies/µL. This assay was specific for DTMUV without cross-reaction with other common avian viruses and only required some simple pieces of equipment, such as a thermostat water bath and blue/UV light transilluminator. Furthermore, this assay showed 100% positive predictive agreement (PPA) and negative predictive agreement (NPA) relative to SYBR Green qPCR for DTMUV detection in 32 cloacal swabs and 22 tissue samples, supporting its application for clinical field detection.

STOP1 and STOP1-like proteins, key transcription factors to cope with acid soil syndrome.

Acid soil syndrome leads to severe yield reductions in various crops worldwide. In addition to low pH and proton stress, this syndrome includes deficiencies of essential salt-based ions, enrichment of toxic metals such as manganese (Mn) and aluminum (Al), and consequent phosphorus (P) fixation. Plants have evolved mechanisms to cope with soil acidity. In particular, STOP1 (Sensitive to proton rhizotoxicity 1) and its homologs are master transcription factors that have been intensively studied in low pH and Al resistance. Recent studies have identified additional functions of STOP1 in coping with other acid soil barriers: STOP1 regulates plant growth under phosphate (Pi) or potassium (K) limitation, promotes nitrate (NO3 -) uptake, confers anoxic tolerance during flooding, and inhibits drought tolerance, suggesting that STOP1 functions as a node for multiple signaling pathways. STOP1 is evolutionarily conserved in a wide range of plant species. This review summarizes the central role of STOP1 and STOP1-like proteins in regulating coexisting stresses in acid soils, outlines the advances in the regulation of STOP1, and highlights the potential of STOP1 and STOP1-like proteins to improve crop production on acid soils.

Cancer-associated fibroblasts suppress ferroptosis and induce gemcitabine resistance in pancreatic cancer cells by secreting exosome-derived ACSL4-targeting miRNAs.

BACKGROUND: Pancreatic cancer continues to be one of the world's most lethal cancers. Chemotherapy resistance in patients with advanced pancreatic cancer often accompany with dismal prognosis, highlighting the need to investigate mechanisms of drug resistance and develop therapies to overcome chemoresistance. METHODS: This research was filed with the Chinese Clinical Trial Registry (ChiCTR2200061320). In order to isolate primary normal fibroblasts (NFs) and cancer-associated fibroblasts (CAFs) samples of pancreatic ductal adenocarcinoma (PDAC) and paracancerous pancreatic tissue from individuals diagnosed with PDAC were obtained. The exosomes were obtained using ultracentrifugation, and their characteristics were determined by Western blotting, nanoparticle tracking analysis, and transmission electron microscopy. CAF-derived miRNAs were analyzed by RT-qPCR and high-throughput sequencing. Gemcitabine (GEM) was employed to promote ferroptosis, and ferroptosis levels were determined by monitoring lipid reactive oxygen species (ROS), cell survival, and intracellular Fe2+ concentrations. To assess in vivo tumor response to GEM therapy, a xenograft tumor mouse model was utilized. RESULTS: Exosomes derived from CAFs in PDAC did not exhibit innate GEM resistance. CAFs promoted chemoresistance in PDAC cells following GEM treatment by secreting exosomes, and maintaining signaling communication with cancer cells. Mechanistically, miR-3173-5p derived from CAF exosomes sponged ACSL4 and inhibited ferroptosis after uptake by cancer cells. CONCLUSION: This work demonstrates a novel mode of acquired chemoresistance in PDAC and identifies the miR-3173-5p/ACSL4 pathway as a promising treatment target for GEM-resistant pancreatic cancer.

Targeted insertion of regulatory elements enables translational enhancement in rice.

In-locus editing of agronomically-important genes to optimize their spatiotemporal expression is becoming an important breeding approach. Compared to intensive studies on mRNA transcription, manipulating protein translation by genome editing has not been well exploited. Here, we found that precise knock-in of a regulating element into the 5'UTR of a target gene could efficiently increase its protein abundance in rice. We firstly screened a translational enhancer (AMVE) from alfalfa mosaic virus using protoplast-based luciferase assays with an 8.5-folds enhancement. Then the chemically modified donor of AMVE was synthesized and targeted inserted into the 5'UTRs of two genes (WRKY71 and SKC1) using CRISPR/Cas9. Following the in-locus AMVE knock-in, we observed up to a 2.8-fold increase in the amount of WRKY71 protein. Notably, editing of SKC1, a sodium transporter, significantly increased salt tolerance in T2 seedlings, indicating the expected regulation of AMVE knock-in. These data demonstrated the feasibility of such in-locus editing to enhance protein expression, providing a new approach to manipulating protein translation for crop breeding.

Wind speed and direction measurement method based on intelligent optimization algorithm.

Accurate and rapid measurement of wind speed and direction is an important research topic. However, the current measurement algorithms based on ultrasonic arrays are constrained by the large computational effort caused by the spectrum peak search, which hinders the development and application of ultrasonic array wind parameter measurement technology. To overcome this problem, this study applies an intelligent optimization algorithm for measuring wind speed and direction based on a co-prime arc ultrasonic array, which avoids the problem of a large number of calculations in the spectrum peak search. First, the spatial-spectral function of the propagator method algorithm is employed as the fitness function of the particle swarm optimization algorithm. Then, the wind parameter estimation problem is formulated as a function optimization problem, which realizes the fast and accurate measurement of wind speed and direction. Then, the artificial bee colony algorithm is used to measure wind speed and direction, further reducing the calculation amount of the wind parameter measurement. The performance and speed of the proposed method are verified by the design simulation and comparison experiments, reducing the time complexity by up to 90%. In addition, the feasibility of the proposed method is validated in hardware experiments.

High-throughput genome editing in rice with a virus-based surrogate system.

With the widespread use of clustered regularly interspaced palindromic repeats (CRISPR)/CRISPR-associated nuclease (Cas) technologies in plants, large-scale genome editing is increasingly needed. Here, we developed a geminivirus-mediated surrogate system, called Wheat Dwarf Virus-Gate (WDV-surrogate), to facilitate high-throughput genome editing. WDV-Gate has two parts: one is the recipient callus from a transgenic rice line expressing Cas9 and a mutated hygromycin-resistant gene (HygM) for surrogate selection; the other is a WDV-based construct expressing two single guide RNAs (sgRNAs) targeting HygM and a gene of interest, respectively. We evaluated WDV-Gate on six rice loci by producing a total of 874 T0 plants. Compared with the conventional method, the WDV-Gate system, which was characterized by a transient and high level of sgRNA expression, significantly increased editing frequency (66.8% vs. 90.1%), plantlet regeneration efficiency (2.31-fold increase), and numbers of homozygous-edited plants (36.3% vs. 70.7%). Large-scale editing using pooled sgRNAs targeting the SLR1 gene resulted in a high editing frequency of 94.4%, further demonstrating its feasibility. We also tested WDV-Gate on sequence knock-in for protein tagging. By co-delivering a chemically modified donor DNA with the WDV-Gate plasmid, 3xFLAG peptides were successfully fused to three loci with an efficiency of up to 13%. Thus, by combining transiently expressed sgRNAs and a surrogate selection system, WDV-Gate could be useful for high-throughput gene knock-out and sequence knock-in.

Transcriptomic and metabolomic investigation of molecular inactivation mechanisms in Escherichia coli triggered by graphene quantum dots.

Graphene quantum dots (GQDs), a novel broad-spectrum antibacterial agent, are considered potential candidates in the field of biomedical and food safety due to their outstanding antimicrobial properties and excellent biocompatibility. To uncover the molecular regulatory mechanisms underlying the phenotypes, the overall regulation of genes and metabolites in Escherichia coli (E. coli) after GQDs stimulation was investigated by RNA-sequencing and LC-MS. Gene transcription and metabolite expression related to a series of crucial biomolecular processes were influenced by the GQDs stimulation, including biofilm formation, bacterial secretion system, sulfur metabolism and nitrogen metabolism, etc. This study could provide profound insights into the GQDs stress response in E. coli, which would be useful for the development and application of GQDs in food safety.

Specifically functionalized MTT-Ag NP/SA film sensor for the ultrasensitive detection of Hg2+ in lettuce samples.

In the present study, highly efficient 5-Methyl-1,3,4-thiadiazole-2-thiol-modified silver nanoparticles (MTT-Ag NPs) were successfully synthesized and could be used for convenient and sensitive detection of Hg2+. MTT acts as a protective agent by forming Ag-S bonds with Ag NPs, meantime, MTT can also be captured Hg2+ through NN bonds. Furthermore, to improve the sustainability and stability of MTT-Ag NPs, sodium alginate (SA) was used as a substrate material for the formation of SA-MTT-Ag NPs films. As expected, SA-MTT-Ag NPs could be stored for more than 180 days at room temperature. When used SA-MTT-Ag NPs thin films as colourimetric sensors for detection of Hg2+ in lettuce, the low detection limit could be down to 0.22 µM (44 ppb) with wide linear range (0-1 µM and 1-150 µM) and good recovery (96.25 % - 98.75 %). Therefore, the method enables highly selective and efficient monitoring of Hg2+ in food samples.

Governance mechanisms for chronic disease diagnosis and treatment systems in the post-pandemic era.

"Re-visits and drug renewal" is difficult for chronic disease patients during COVID-19 and will continue in the post-pandemic era. To overcome this dilemma, the scenario of chronic disease diagnosis and treatment systems was set, and an evolutionary game model participated by four stakeholder groups including physical medical institutions, medical service platforms, intelligent medical device providers, and chronic disease patients, was established. Ten possible evolutionary stabilization strategies (ESSs) with their mandatory conditions were found based on Lyapunov's first method. Taking cardiovascular and cerebrovascular diseases, the top 1 prevalent chronic disease, as a specific case context, and resorting to the MATLAB simulation, it is confirmed that several dual ESSs and four unique ESS circumstances exist, respectively, and the evolution direction is determined by initial conditions, while the evolution speed is determined by the values of the conditions based on the quantitative relations of benefits, costs, etc. Accordingly, four governance mechanisms were proposed. By their adjustment, the conditions along with their values can be interfered, and then the chronic disease diagnosis and treatment systems can be guided toward the desired direction, that is, toward the direction of countermeasure against the pandemic, government guidance, global trends of medical industry development, social welfare, and lifestyle innovation. The dilemma of "Re-visits and drug renewal" actually reflects the uneven distribution problem of qualified medical resources and the poor impact resistance capability of social medical service systems under mass public emergency. Human lifestyle even the way of working all over the world will get a spiral upgrade after experiencing COVID-19, such as consumption, and meeting, while medical habits react not so rapidly, especially for mid or aged chronic disease patients. We believe that telemedicine empowered by intelligent medical devices can benefit them and will be a global trend, governments and the four key stakeholders should act according to the governance mechanisms suggested here simultaneously toward novel social medical ecosystems for the post-pandemic era.

Effects of alfalfa meal on quality and function of pork meatballs.

Alfalfa (Medicago sativa L.) is abundant in dietary fiber, alfalfa saponins, and other active ingredients. However, the application of alfalfa is scarce in food. Meatball is one of the most popular meat products in daily life, but eating too many meatballs could result in obesity, hyperlipidemia, and other diseases. With increasing attention to healthy diet, how to keep the original color, aroma, taste, and shape of food with low fat and nutrition has become an urgent problem to be solved. In this study, different amounts of alfalfa meal or extruded alfalfa meal were added to pork meatballs to explore the optimal adding ratio of two kinds of alfalfa meal in pork meatballs. Further animal experiments were conducted for two weeks to prove the efficacy of two kinds of alfalfa balls in lowering blood lipid and body weight. The results showed that 0.5% alfalfa meal and 1% extruded alfalfa meal could improve the quality of prepared pork meatballs. Animal experiments demonstrated that two kinds of alfalfa meal pork meatballs had a good effect of reducing blood lipid, and the alfalfa meal pork meatballs had a better effect on reducing serum cholesterol and average daily weight gain of mice. This study provided a theoretical basis for making healthy and nutritious pork meatballs, which could provide more delicious food for people, especially people who are obese and the elderly.

Construction of Electrochemical Sensors for Antibiotic Detection Based on Carbon Nanocomposites.

Excessive antibiotic residues in food can cause detrimental effects on human health. The establishment of rapid, sensitive, selective, and reliable methods for the detection of antibiotics is highly in demand. With the inherent advantages of high sensitivity, rapid analysis time, and facile miniaturization, the electrochemical sensors have great potential in the detection of antibiotics. The electrochemical platforms comprising carbon nanomaterials (CNMs) have been proposed to detect antibiotic residues. Notably, with the introduction of functional CNMs, the performance of electrochemical sensors can be bolstered. This review first presents the significance of functional CNMs in the detection of antibiotics. Subsequently, we provide an overview of the applications for detection by enhancing the electrochemical behaviour of the antibiotic, as well as a brief overview of the application of recognition elements to detect antibiotics. Finally, the trend and the current challenges of electrochemical sensors based on CNMs in the detection of antibiotics is outlined.

Engineering surface segregation of perovskite oxide through wet exsolution for CO catalytic oxidation.

Cation segregation occurring near the surface or interfaces of solid catalysts plays an important role in catalytic reactions. Unfortunately, the native surface of perovskite oxides is dominated by passivated A-site segregation, which severely hampers the catalytic activity and durability of the system. To address this issue, herein, we present a wet exsolution method to reconstruct surface segregation in perovskite cobalt oxide. Under reduction etching treatment of glycol solution, inert surface Sr segregation was transformed into active Co3O4 segregation. By varying the reaction time, we achieved differing coverage of the active Co3O4 segregation on the La0.5Sr0.5CoO3-δ (LSCO) perovskite oxide surface. This study reveals that CO oxidation activity exhibits a volcano-shaped dependence on the coverage of Co3O4 segregation at the surface of a perovskite cobalt oxide. Furthermore, we find that a suitable coverage of Co3O4 segregation can dramatically improve the catalytic activity of the perovskite catalyst by enhancing interface interactions. Co K-edge, Co L-edge, and O K-edge X-ray absorption spectra confirm that the synergistic effect optimizes the covalence of the metal-oxygen bond at the surface and interface. This work not only contributes to the design and development of perovskite-type catalysts, but also provides important insight into the relationship between surface segregation and catalytic activity.

Confinement chemistry of FeOx centers for activating molecular oxygen under ambient conditions.

Activating molecular oxygen under mild conditions is highly important for developing advanced green technologies and for understanding the origin and running of life as well, which still remains a challenge. In this work, we report on the confinement chemistry for activating molecular oxygen over oxides under mild conditions by presenting the synthesis and characterization of FeOx species confined to the pores of support CeO2 nanospheres. Active catalytic materials are obtained by a controllable three-step method via the formation of porous CeO2 nanospheres that have an average diameter of 120 nm and exhibit a large surface area of 168 m2 g-1 and a pore size of 18.7 nm, confining FeOx in intimate contact with ultra-small Pt particles in pores. The optimized PtOy-FeOx/CeO2-H catalyst showed an excellent performance in the preferential oxidation of CO reactions, as featured by 100% CO conversion at room temperature with almost no attenuation in a prolonged operation, which could not be accessible without pore-confined FeOx centers. Mechanical studies prove that the reaction progresses via abnormal non-competitive adsorption associated with synergistic roles from uniform loading, stabilization of divalent Fe species, surface oxygen activation on CeO2 supports, and the reduced H2 spillover effect on Pt0, making the CO species adsorbed on Ptδ+ easier to be desorbed. The methodology demonstrated here may inspire one to explore more advanced catalysts with high activity at room temperature essential for a wide range of applications.

Synthesis, modification and application of titanium dioxide nanoparticles: a review.

Titanium dioxide (TiO2) has been heavily investigated owing to its low cost, benign nature and strong photocatalytic ability. Thus, TiO2 has broad applications including photocatalysts, Li-ion batteries, solar cells, medical research and so on. However, the performance of TiO2 is not satisfactory due to many factors such as the broad band gap (3.01 to 3.2 eV) and fast recombination of electron-hole pairs (10-12 to 10-11 s). Plenty of work has been undertaken to improve the properties, such as structural and dopant modifications, which broaden the applications of TiO2. This review mainly discusses the aspects of TiO2-modified nanoparticles including synthetic methods, modifications and applications.