Distinct characteristics of the gut virome in patients with osteoarthritis and gouty arthritis.

BACKGROUND/PURPOSE(S): The gut microbiota and its metabolites play crucial roles in pathogenesis of arthritis, highlighting gut microbiota as a promising avenue for modulating autoimmunity. However, the characterization of the gut virome in arthritis patients, including osteoarthritis (OA) and gouty arthritis (GA), requires further investigation. METHODS: We employed virus-like particle (VLP)-based metagenomic sequencing to analyze gut viral community in 20 OA patients, 26 GA patients, and 31 healthy controls, encompassing a total of 77 fecal samples. RESULTS: Our analysis generated 6819 vOTUs, with a considerable proportion of viral genomes differing from existing catalogs. The gut virome in OA and GA patients differed significantly from healthy controls, showing variations in diversity and viral family abundances. We identified 157 OA-associated and 94 GA-associated vOTUs, achieving high accuracy in patient-control discrimination with random forest models. OA-associated viruses were predicted to infect pro-inflammatory bacteria or bacteria associated with immunoglobulin A production, while GA-associated viruses were linked to Bacteroidaceae or Lachnospiraceae phages. Furthermore, several viral functional orthologs displayed significant differences in frequency between OA-enriched and GA-enriched vOTUs, suggesting potential functional roles of these viruses. Additionally, we trained classification models based on gut viral signatures to effectively discriminate OA or GA patients from healthy controls, yielding AUC values up to 0.97, indicating the clinical utility of the gut virome in diagnosing OA or GA. CONCLUSION: Our study highlights distinctive alterations in viral diversity and taxonomy within gut virome of OA and GA patients, offering insights into arthritis etiology and potential treatment and prevention strategies.

Graphene-based Two-Stage Enhancement Pressure Sensor for Subtle Mechanical Force Monitoring.

The development of pressure sensors with high sensitivity and a low detection limit for subtle mechanical force monitoring and the understanding of the sensing mechanism behind subtle mechanical force monitoring are of great significance for intelligent technology. Here, we proposed a graphene-based two-stage enhancement pressure sensor (GTEPS), and we analyzed the difference between subtle mechanical force monitoring and conventional mechanical force monitoring. The GTEPS exhibited a high sensitivity of 62.2 kPa-1 and a low detection limit of 0.1 Pa. Leveraging its excellent performance, the GTEPS was successfully applied in various subtle mechanical force monitoring applications, including acoustic wave detection, voice-print recognition, and pulse wave monitoring. In acoustic wave detection, the GTEPS achieved a 100% recognition accuracy for six words. In voiceprint recognition, the sensor exhibited accurate identification of distinct voiceprints among individuals. Furthermore, in pulse wave monitoring, GTEPS demonstrated effective detection of pulse waves. By combination of the pulse wave signals with electrocardiogram (ECG) signals, it enabled the assessment of blood pressure. These results demonstrate the excellent performance of GTEPS and highlight its great potential for subtle mechanical force monitoring and its various applications. The current results indicate that GTEPS shows great potential for applications in subtle mechanical force monitoring.

Ochrobactrum chromiisoli sp. nov., Isolated from Chromium-Contaminated Soil.

A Gram-stain-negative, non-spore-forming, flagellated, motile, aerobic, rod-shaped bacteria strain, designated YY2XT, was isolated from chromium-contaminated soil. Phylogenetic analysis based on 16S rRNA gene, recA gene, and whole genome indicated that the strain represented a new member of the genus Ochrobactrum, family Brucellaceae, class Alphaproteobacteria. The phylogenetic trees based on 16 s rRNA gene, revealed that Falsochrobactrum ovis DSM26720T (96.7%), Ochrobactrum gallinifaecis DSM15295T (96.2%), and Pseudochrobactrum asaccharolyticum DSM25619T (96.2%) are the most closely related phylogenetic neighbors of strain YY2XT. The draft genome of YY2XT was approximately 4,650,646 bp in size with a G + C content of 53.0 mol%. Average nucleotide identity and digital DNA-DNA hybridization values among strain YY2XT and the selected Brucellaceae species were 71.4-83.1% and 13.5-42.7%, which are below the recommended cut-off values for species delineation. Growth of strain YY2XT occurred within pH 5-10 (optimum, pH 7-8), 4 â„ƒ-42 °C (optimum, 30 °C), and NaCl concentrations of 0.0-6.0% (optimum, 1.0%). Major quinone system was ubiquinone 10, the major fatty acids were C16:0, C18:1ω7c, and C16:1ω7c and the major polyamines were spermidine and putrescine. Major polar lipids were diphosphatidylglycerol, phosphatidylglycerol, phosphatidylcholine, phosphatidylmonomethylethanolamine, phosphatidylethanolamine, and four undefined lipids. On the basis of the phenotypic, genotypic and chemotaxonomic traits, strain YY2XT was considered to represent a novel species of the genus Ochrobactrum, for which the name Ochrobactrum chromiisoli sp. nov. is proposed. The type strain is YY2XT (= CCTCC AB 2023035T = JCM 36000T).

Investigating Anisotropic Magnetoresistance in Epitaxially Strained CoFe Thin Films on a Flexible Mica.

This study investigates the crystal structure, epitaxial relation, and magnetic properties in CoFe thin films deposited on a flexible mica substrate. The epitaxial growth of CoFe thin films was successfully achieved by DC magnetron sputtering, forming three CoFe(002) domains exhibiting four-fold symmetry on the mica substrate. A notable achievement of this work was the attainment of the highest anisotropic magnetoresistance (AMR) value reported to date on a flexible substrate. Additionally, it was observed that the magnetic characteristics of the CoFe films on the flexible mica substrate display reversibility upon strain release. More importantly, the AMR effect of epitaxial CoFe films on flexible mica shows lesser dependence on the crystalline orientation and remains the same under different bending states. These findings demonstrate the potential of utilizing CoFe films on flexible substrates to develop wearable magnetoresistance sensors with diverse applications.

A model for predicting lymph node metastasis of thyroid carcinoma: a multimodality convolutional neural network study.

Background: Early preoperative evaluation of cervical lymph node metastasis (LNM) in papillary thyroid carcinoma (PTC) is critical for further surgical treatment. However, insufficient accuracy in predicting LNM status for PTC based on ultrasound images is a problem that needs to be urgently resolved. This study aimed to clarify the role of convolutional neural networks (CNNs) in predicting LNM for PTC based on multimodality ultrasound. Methods: In this study, the data of 308 patients who were clinically diagnosed with PTC and had confirmed LNM status via postoperative pathology at Beijing Tiantan Hospital, Capital Medical University, from August 2018 to April 2022 were incorporated into CNN algorithm development and evaluation. Of these patients, 80% were randomly included into the training set and 20% into the test set. The ultrasound examination of cervical LNM was performed to assess possible metastasis. Residual network 50 (Resnet50) was employed for feature extraction from the B-mode and contrast-enhanced ultrasound (CEUS) images. For each case, all of features were extracted from B-mode ultrasound images and CEUS images separately, and the ultrasound examination data of cervical LNM information were concatenated together to produce a final multimodality LNM prediction. Sensitivity, specificity, accuracy, and area under the receiver operating characteristic curve (AUC) were used to evaluate the performance of the predictive model. Heatmaps were further developed for visualizing the attention region of the images of the best-working model. Results: Of the 308 patients with PTC included in the analysis, 158 (51.3%) were diagnosed as LNM and 150 (48.7%) as non-LNM. In the test set, when a triple-modality method (i.e., B-mode image, CEUS image, and ultrasound examination of cervical LNM) was used, accuracy was maximized at 80.65% (AUC =0.831; sensitivity =80.65%; specificity =82.26%), which showed an expected increased performance over B-mode alone (accuracy =69.00%; AUC =0.720; sensitivity =70.00%; specificity =73.00%) and a dual-modality method (B-mode image plus CEUS image: accuracy =75.81%; AUC =0.742; sensitivity =74.19%; specificity =77.42%). The heatmaps of our triple-modality model demonstrated a possible focus area and revealed the model's flaws. Conclusions: The PTC lymph node prediction model based on the triple-modality features significantly outperformed all the other feature configurations. This deep learning model mimics the workflow of a human expert and leverages multimodal data from patients with PTC, thus further supporting clinical decision-making.

[Efficacy of Venetoclax Plus Azacitidine in Relapsed/Refractory Acute Myeloid Leukemia Patients with FLT3-ITD Mutation].

OBJECTIVE: To explore the efficacy of venetoclax (VEN) plus azacitidine (AZA) in patients with FLT3-ITD mutated relapsed/refractory acute myeloid leukemia (FLT3-ITDmut R/R AML) and analyze the molecular genetic characteristics of the patients. METHODS: Clinical baseline characteristics and follow-up data of 16 R/R AML patients treatd with VEN plus AZA in the hematology department of Shenzhen Second People's Hospital from November 2018 to April 2021 were collected. Leukemia related genes were detected by next-generation sequencing(NGS) or PCR. The relationship between the efficacy of VEN plus AZA and molecular genetics characteristics of patients with FLT3-ITDmut R/R AML were analyzed. RESULTS: 14.3% (1/7) of the patients in FLT3-ITDmut group and 22.2% (2/9) of the patients in FLT3-ITDwt group achieved complete remission (CR)/CR with incomplete blood count recovery (CRi), respectively, with no significant difference (P=0.69). There was no significant difference in overall response rate (ORR) (CR/CRi+PR) between FLT3-ITDmut group and FLT3-ITDwt group [42.9%(3/7) vs 44.4%(4/9), P=0.95], too. The median overall survival (OS) time of FLT3-ITDmut patients was significantly shorter than that of FLT3-ITDwt patients (130 vs 300 days, respectively) (P =0.02). Co-existing mutations of FLT3-ITD and IDH1 were detected in one patient who achieved CR. Co-existing mutations of FLT3-ITD and SF3B1 were found in one patient who achieved PR. Three FLT3-ITDmut R/R AML patients accompanied with NPM1 mutation had no response to VEN plus AZA. CONCLUSION: VEN plus AZA showed a certain effect on patients with FLT3-ITDmut R/R AML. To improve OS of the patients, bridging transplantation is need. IDH1 and SF3B1 mutations might predict that patients with FLT3-ITDmut R/R AML have treatment response to VEN plus AZA, while the combination of NPM1 mutation may indicate poor response.

Design strategy and research progress of multifunctional nanoparticles in lung cancer therapy.

INTRODUCTION: Lung cancer is one of the cancer types with the highest mortality rate, exploring a more effective treatment modality that improves therapeutic efficacy while mitigating side effects is now an urgent requirement. Designing multifunctional nanoparticles can be used to overcome the limitations of drugs and conventional drug delivery systems. Nanotechnology has been widely researched, and through different needs, suitable nanocarriers can be selected to load anti-cancer drugs to improve the therapeutic effect. It is foreseeable that with the rapid development of nanotechnology, more and more lung cancer patients will benefit from nanotechnology. This paper reviews the merits of various multifunctional nanoparticles in the treatment of lung cancer to provide novel ideas for lung cancer treatment. AREAS COVERED: This review focuses on summarizing various nanoparticles for targeted lung cancer therapy and their advantages and disadvantages, using nanoparticles loaded with anti-cancer drugs, delivered to lung cancer sites, enhancing drug half-life, improving anti-cancer drug efficacy and reducing side effects. EXPERT OPINION: The delivery mode of nanoparticles with superior pharmacokinetic properties in the in vivo circulation enhances the half-life of the drug, and provides tissue-targeted selectivity and the ability to overcome biological barriers, bringing a revolution in the field of oncology.

Preoperative prediction of extrathyroidal extension: radiomics signature based on multimodal ultrasound to papillary thyroid carcinoma.

BACKGROUND: There is a recognized need for additional approaches to improve the accuracy of extrathyroidal extension (ETE) diagnosis in papillary thyroid carcinoma (PTC) before surgery. Up to now, multimodal ultrasound has been widely applied in disease diagnosis. We investigated the value of radiomic features extracted from multimodal ultrasound in the preoperative prediction of ETE. METHODS: We retrospectively pathologically confirmed PTC lesions in 235 patients from January 2019 to April 2022 in our hospital, including 45 ETE lesions and 205 non-ETE lesions. MaZda software was employed to obtain radiomics parameters in multimodal sonography. The most valuable radiomics features were selected by the Fisher coefficient, mutual information, probability of classification error and average correlation coefficient methods (F + MI + PA) in combination with the least absolute shrinkage and selection operator (LASSO) method. Finally, the multimodal model was developed by incorporating the clinical records and radiomics features through fivefold cross-validation with a linear support vector machine algorithm. The predictive performance was evaluated by sensitivity, specificity, accuracy, F1 scores and the area under the receiver operating characteristic curve (AUC) in the training and test sets. RESULTS: A total of 5972 radiomics features were extracted from multimodal sonography, and the 13 most valuable radiomics features were selected from the training set using the F + MI + PA method combined with LASSO regression. The multimodal prediction model yielded AUCs of 0.911 (95% CI 0.866-0.957) and 0.716 (95% CI 0.522-0.910) in the cross-validation and test sets, respectively. The multimodal model and radiomics model showed good discrimination between ETE and non-ETE lesions. CONCLUSION: Radiomics features based on multimodal ultrasonography could play a promising role in detecting ETE before surgery.

Determining half-life of SARS-CoV-2 antigen in respiratory secretion.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is primarily transmitted from person to person through respiratory droplets and aerosols. It is also possible for the virus to be transmitted indirectly through environmental contamination. The likelihood of environmental transmission depends on several factors, including the survival time of the virus in respiratory secretions. However, the stability of SARS-CoV-2 in respiratory secretions has not been investigated. In this study, we compared the half-life of the SARS-CoV-2 antigen in respiratory secretion under different conditions. We applied respiratory secretion (5 µL) to glass slides, air-dried the slides for 1 h, and kept them at 24 °C or 4 °C for 10 days. Respiratory secretions were also placed in test tubes (sealed to preserve moisture) and in normal saline for 10 days. The concentration of SARS-CoV-2 antigen in all samples was simultaneously measured using colloidal gold immunochromatography, and the half-life of the antigen was calculated. The half-life of the antigen in the wet (sealed tube) and saline samples at room temperature was 5.0 and 2.92 days, respectively. The half-life of the antigen in the air-dried sample at room temperature and at 4 °C was 2.93 and 11.4 days, respectively. The half-life was longer in respiratory secretions than that in normal saline. The half-life was also longer in respiratory secretions, at a lower temperature, and under wet conditions. Therefore, environmental transmission can also play a significant role in the spread of the virus. Robust prevention and control strategies could be developed based on the half-life of the antigen in respiratory secretions.

Application of decision tree algorithms to predict central lymph node metastasis in well-differentiated papillary thyroid carcinoma based on multimodal ultrasound parameters: a retrospective study.

Background: Prophylactic central neck dissection (pCND) in patients with well-differentiated primary papillary thyroid carcinoma (PTC) has become controversial. Several attempts have been made to predict central compartment lymph node metastasis (CLNM) based on clinical and conventional ultrasonic parameters. This study aimed to develop a decision tree (DT) model for predicting the risk of CLNM in patients with PTC based on clinical and preoperative multimodal ultrasound (US) characteristics. Methods: A total of 148 PTC nodules confirmed by surgical pathology at Beijing Tiantan Hospital were retrospectively analyzed. All nodules underwent multimodal US examinations preoperatively from January 2020 to September 2021. Correlation analysis of CLNM with clinical characteristics as well as multimodal US parameters of PTC lesions based on gray-scale US, color Doppler flow imaging (CDFI), superb microvascular imaging (SMI), contrast-enhanced ultrasound (CEUS), and shear wave elastography (SWE) technology was carried out. Finally, the chi-squared automatic interaction detector (CHAID) with a 10-fold cross-validation was used to establish DTs for CLNM prediction. The area under the curve was calculated to compare the predictive performance. Results: Univariate analysis indicated that CLNM was positively correlated with thyroglobulin level, maximum size, taller-than-wide, the number of microcalcifications greater than or equal to 5, contact capsule, abnormal cervical lymph node on conventional US, noncentripetal perfusion, delayed clearance, the average shear wave velocity (SWV mean), and the SWV ratio (P<0.05). The multimodal US DT based on taller-than-wide, contact capsule, abnormal cervical lymph node on conventional US, and centripetal enhancement as independent variables showed good discrimination: the sensitivity, specificity, accuracy, and area under the receiver operating characteristic curve were 80.0%, 76.7%, 78.4%, and 0.837 [95% confidence interval (CI): 0.771-0.902]. There was a significant difference between the multimodal and conventional US DTs (P=0.009). Conclusions: Our results indicated that the DT based on the preoperative multimodal US characteristics of PTCs has a reasonable predictive ability for CLNM and can be conveniently used for clinical decision-making of individualized treatment in patients with well-differentiated PTC.

Recent Advancements in Physiological, Biochemical, and Multimodal Sensors Based on Flexible Substrates: Strategies, Technologies, and Integrations.

Flexible wearable devices have been widely used in biomedical applications, the Internet of Things, and other fields, attracting the attention of many researchers. The physiological and biochemical information on the human body reflects various health states, providing essential data for human health examination and personalized medical treatment. Meanwhile, physiological and biochemical information reveals the moving state and position of the human body, and it is the data basis for realizing human-computer interactions. Flexible wearable physiological and biochemical sensors provide real-time, human-friendly monitoring because of their light weight, wearability, and high flexibility. This paper reviews the latest advancements, strategies, and technologies of flexibly wearable physiological and biochemical sensors (pressure, strain, humidity, saliva, sweat, and tears). Next, we systematically summarize the integration principles of flexible physiological and biochemical sensors with the current research progress. Finally, important directions and challenges of physiological, biochemical, and multimodal sensors are proposed to realize their potential applications for human movement, health monitoring, and personalized medicine.

Contrast-enhanced spectral mammography: are kinetic patterns useful for differential diagnoses of enhanced lesions?

PURPOSE: To investigate the diagnostic efficiency of the kinetic curves of enhanced lesions on contrast-en-hanced spectral mammography (CESM) and whether they were similar to those of magnetic resonance imaging (MRI). METHODS: Two hundred and twelve patients with 222 enhanced lesions were included in this prospective study. Single-view craniocaudal of an affected breast was acquired at 3, 5, and 7 min after contrast media injection. The kinetic patterns of each lesion were evaluated and classified as elevated (type I), steady (type II), and depressed (type III). Statistical comparison used the chi-squared test, the receiver operating characteristic (ROC) curve, and Cohen's kappa. RESULTS: Of 222 enhanced lesions, 140 were breast cancers, and 82 were benign lesions. The distribution of the kinetic curves for breast cancer was type I, 3.57%, type II, 35.71%, and type III, 60.72%. As for benign lesions, the distribution was type I, 43.90%, type II, 45.12%, and type III, 10.98%. The difference in the enhancement patterns between benign lesions and breast cancers was significant (P < 0.001). The likelihood of breast cancer related to a type I, II, and III curve was 12.20%, 57.47%, and 90.43%, respectively. For the enhancement intensity, the area under curve (AUC) of the ROC curves was 0.702 ± 0.036; for enhancement patterns, the AUC increased to 0.819 ± 0.030. Cohen's kappa coefficient was 0.752 (P < 0.001) regarding the kinetic curves for CESM and MRI. CONCLUSION: The kinetic patterns on CESM show promise in differentiating between benign lesions and breast cancers, with good agreement, when compared with MRI.

Biomarker identification of immune-related genes in pheochromocytoma and paraganglioma.

Background: Although we have a good understanding of the diagnosis and treatment of pheochromocytoma and paraganglioma (PPGL), the underlying pathogenesis and molecular pathways of PPGL need to be further studied. This study aimed to use bioinformatics to analyze the role of immune-related genes (IRGs) in the pathogenesis of PPGL. Methods: GSE19422 and GSE60459 microarray data were obtained from the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) were identified using the "limma" package in R, and genes overlapping with IRGs were screened using the "VennDiagram" package. A protein-protein interaction (PPI) network was constructed in the Search Tool for the Retrieval of Interacting Genes/Proteins (STRING) database, and the core genes were identified by Cytoscape, followed by enrichment analysis and receiver operating characteristic (ROC) curve analysis to evaluate the diagnostic efficacy of the core genes. In addition, the level of immune cell infiltration of PPGL was analyzed and the target drug of the core gene was predicted. Results: A total of 1,105 DEGs were identified from the 2 datasets, of which 94 were IRGs, suggesting that the occurrence of PPGL involved immune-related pathways. Through PPI and Cytoscape, a total of 2 core genes: fibroblast growth factor 2 (FGF2), FYN proto-oncogene (FYN), and vascular cell adhesion molecule 1 (VCAM1) were identified, and the ROC curve showed that these 3 core genes had good efficacy in the diagnosis of PPGL, and more than 50 potential therapeutic drugs could be predicted based on these 3 core genes. Subsequent immunoinfiltration analysis showed that mast cells activated were significantly elevated in patients with PPGL, negatively correlated with macrophages M2, and positively correlated with the level of dendritic cells activated. Conclusions: This study found that immunity is closely related to the occurrence of PPGL, and that FGF2, FYN, and VCAM1 may be potential biomarkers and therapeutic targets of PPGL.

Transcranial sonography with clinical and demographic characteristics to predict cognitive impairment in PD: a longitudinal study.

BACKGROUND: Parkinson's disease (PD) is a neurodegenerative disease and is clinically characterized by a series of motor symptoms (MS) and nonmotor symptoms (NMS). NMS often appear before MS, while cognitive impairment mostly occurs within a few years after the diagnosis of PD. Therefore, we aimed to predict the risk factors for cognitive impairment (CI) in PD patients based on transcranial sonography, clinical symptoms, and demographic characteristics. METHODS: Based on the occurrence time of CI, a total of 172 PD patients were divided into non-CI (N-CI, n = 48), CI at the first treatment (F-CI, n = 58), and CI at the last treatment (L-CI, n = 66) groups. Clinical data (including MS and NMS) and ultrasonic data of all patients at the first treatment and the last treatment were collected retrospectively. Independent samples t tests were used to compare continuous data, and chi-square tests were used to compare categorical data. The risk factors for CI and Parkinson's disease dementia were identified by logistic regression analysis, and an ROC curve was established to explore the diagnostic efficacy. RESULTS: 1) The age of onset, first treatment and smoking history of CI patients were significantly different from those of N-CI patients. When age of first treatment ≥61 years was considered the boundary value to diagnose CI, the sensitivity and specificity were 77.40 and 66.70%, respectively. 2) The severity of depression was significantly different between F-CI and N-CI patients at the first treatment, while the cumulative and new or aggravated memory deficit was significantly different between the L-CI and N-CI patients at the last treatment. 3) There was a significant difference in TCS grading between the first and last treatment in L-CI patients. 4) Depression, sexual dysfunction, and olfactory dysfunction in NMS were independent risk factors for CI during the last treatment. 5) The sensitivity and specificity of predicting CI in PD patients were 81.80 and 64.60%, respectively. CONCLUSIONS: PD patients with CI were older, and most of them had a history of smoking. Furthermore, there was good diagnostic efficiency for predicting CI in PD via TCS combined with clinical characteristics (especially NMS).

Multichannel Flexible Pulse Perception Array for Intelligent Disease Diagnosis System.

Pressure sensors with high sensitivity, a wide linear range, and a quick response time are critical for building an intelligent disease diagnosis system that directly detects and recognizes pulse signals for medical and health applications. However, conventional pressure sensors have limited sensitivity and nonideal response ranges. We proposed a multichannel flexible pulse perception array based on polyimide/multiwalled carbon nanotube-polydimethylsiloxane nanocomposite/polyimide (PI/MPN/PI) sandwich-structure pressure sensor that can be applied for remote disease diagnosis. Furthermore, we established a mechanical model at the molecular level and guided the preparation of MPN. At the structural level, we achieved high sensitivity (35.02 kPa-1) and a broad response range (0-18 kPa) based on a pyramid-like bilayer microstructure with different upper and lower surfaces. A 27-channel (3 × 9) high-density sensor array was integrated at the device level, which can extract the spatial and temporal distribution information on a pulse. Furthermore, two intelligent algorithms were developed for extracting six-dimensional pulse information and automatic pulse recognition (the recognition rate reaches 97.8%). The results indicate that intelligent disease diagnosis systems have great potential applications in wearable healthcare devices.

Climate extremes drive the phenology of a dominant species in meadow steppe under gradual warming.

Plant phenology in terrestrial ecosystems, especially in the Northern Hemisphere, is expected to change owing to the projected increasing frequency and intensity of climate extremes in the context of global warming. Although such changes under mean climate change have been extensively reported in the literature, little is known about the impacts of climate extremes. In this study, climatic changes and their effects on plant phenology were characterized using long-term climatic and phenological data from the start and end of the growing season (SOS and EOS, respectively) from 2005 to 2020 for Stipa baicalensis, a dominant species in a temperate meadow steppe. The results showed that the temperature, including the mean and minimum temperatures, and extreme warm indices significantly increased; however, annual precipitation, and the frequency of extreme cold and precipitation events decreased. The SOS of S. baicalensis was initially earlier and later, whereas the EOS trended to be delayed. However, the growing season (LOS) was slightly prolonged. Compared with the indices under mean temperature, the pre-season (before SOS or EOS) minimum temperature dominantly affected SOS and EOS, whereas the mean and extreme precipitation slightly affected them. Furthermore, the findings showed that plant phenology responded to extreme temperatures quicker and stronger than mean temperatures. This study provides insight into how key extreme climatic factors could affect plant phenophases and improve and refine the phenological model. This could also be useful in enhancing grassland ecosystem management and sustainable development.

Deep metagenomic characterization of gut microbial community and function in preeclampsia.

Preeclampsia (PE) is a pregnancy complication characterized by severe hypertension and multiple organ damage. Gut microbiota has been linked to PE by previous amplicon sequencing studies. To resolve the PE gut microbiota in a higher taxonomy resolution, we performed shotgun metagenomic sequencing on the fecal samples from 40 early-onset PE and 37 healthy pregnant women. We recovered 1,750 metagenome-assembled genomes (representing 406 species) from the metagenomic dataset and profiled their abundances. We found that PE gut microbiota had enriched in some species belonging to Blautia, Pauljensenia, Ruminococcus, and Collinsella and microbial functions such as the bacitracin/lantibiotics transport system, maltooligosaccharide transport system, multidrug efflux pump, and rhamnose transport system. Conversely, the gut microbiome of healthy pregnant women was enriched in species of Bacteroides and Phocaeicola and microbial functions including the porphyrin and chlorophyll metabolism, pyridoxal-P biosynthesis, riboflavin metabolism, and folate biosynthesis pathway. PE diagnostic potential of gut microbial biomarkers was developed using both species and function profile data. These results will help to explore the relationships between gut bacteria and PE and provide new insights into PE early warning.

Negative pressure wound therapy reduces the incidence of postoperative wound dehiscence and surgical site infections after total knee arthroplasty in patients with obesity.

Obesity is a risk factor for total knee arthroplasty (TKA). Wound dehiscence and surgical site infections (SSIs) are the main complications of TKA in patients with obesity. They can profoundly affect patients because they often require readmission, additional surgical interventions, lengthy intravenous antibiotic administration, and delayed rehabilitation. Negative pressure wound therapy (NPWT) exposes the wound site to negative pressure, resulting in the improvement of blood supply, removal of excess fluid, and stimulation of cellular proliferation of granulation tissue. This study aims to assess the incidence of wound dehiscence and SSIs in patients with obesity undergoing TKA after the routine use of NPWT. This sduty enrolled adult patients with obesity who underwent TKA within 8 years. A total of 360 adult patients with obesity (NPWT: 150, non-NPWT: 210) underwent TKA, and the baseline characteristics were similar between the 2 groups. Compared with the non-NPWT group, the NPWT group had a 50% lower incidence of wound dehiscence (3.33% vs 9.52%; P < .05) and a significantly lower incidence of SSIs (11.33% vs 25.24%; P < .05), including prosthetic joint infection (4.0% vs 10.0%; P < .05) and superficial wound infection (7.33% vs 15.24%; P < .05). In addition, the NPWT group had a lower need to return to the operating room for new interventions for any reason (2.67% vs 9.05%; P = .0107) than the non-NPWT group. Conventional incision NPWT can significantly reduce the incidence of wound dehiscence and SSIs in patients with obesity after TKA.

Supercritical Fluid-Assisted Fabrication of Pd Nanoparticles/Graphene Using a Choline Chloride-Oxalic Acid Deep Eutectic Solvent for Enhancing the Electrochemical Oxidation of Glycerol.

A green method for synthesizing Pd nanoparticles/graphene composites from a choline chloride-oxalic acid deep eutectic solvent (DES) without a reducing agent or a surfactant is reported. Deep eutectic solvents are usually composed of halide salts and hydrogen-bond donors, and many are biocompatible and biodegradable. The merits of deep eutectic solvents include that they serve as reducing agents and dispersants, and Pd nanoparticles are tightly anchored to graphene. The size and dispersion of Pd particles are improved when supercritical carbon dioxide (scCO2) is used because it has gaslike diffusivity and near-zero surface tension, which results in excellent wettability between the scCO2 and the carbon surface. The prepared sc-Pd NPs/GR/SPCE shows excellent activity toward glycerol oxidation compared to composites not fabricated by scCO2 processes. This study demonstrates the potential of using this scCO2-assisted protocol combined with deep eutectic solvents to further construct nanoparticles/graphene composites.

QSPR study on Hydrophobicity of Pt(II) complexes with surface electrostatic potential-based descriptors.

Pt(II) complexes play an important role in bioinorganic chemistry due to their antitumor activities. In the present study, we focused on building predictive models for the hydrophobicity of Pt(II) complexes. A five-parameter model, integrating frontier orbital energies (EHOMO, ELUMO) and descriptors derived from electrostatic potentials on molecular surface, was firstly constructed by using multiple linear regression (MLR) method. Mechanistic interpretations of the introduced descriptors were elucidated in terms of intermolecular interactions in the n-octanol/water partition system. Then, four up-to-date modeling methods, including support vector machine (SVM), least-squares support vector machine (LSSVM), random forest (RF) and Gaussian process (GP), were utilized to build the nonlinear models. Systematical validations including leave-one-out cross-validation, the validation for test set, as well as a very rigorous Monte Carlo cross-validation (MCCV) were performed to verify the reliability of the constructed models. The peak, median and integralRext2 values of the best GP model are 0.88, 0.86 and 0.84, respectively. The root mean squared errors for the test set (RMSEP) of the MLR, SVM, LSSVM and GP models fall in the range of 0.62-0.71. Although they are not superior to prior models built with the use of a number of descriptors, the results are satisfactory. Applicability domain of the model was evaluated.