Arbuscular Mycorrhizal Fungi Diversity in Sophora japonica Rhizosphere at Different Altitudes and Lithologies.

Arbuscular mycorrhizal fungi play a key role in mediating soil-plant relationships within karst ecosystems. Sophora japonica, a medicinal plant with anti-inflammatory and antitumor properties, is widely cultivated in karst areas of Guangxi, China. We considered limestone, dolomite, and sandstone at altitudes ranging from 100 to 800 m and employed Illumina sequencing to evaluate AMF diversity and identify the factors driving S. japonica rhizosphere AMF community changes. We showed that the increase in altitude increased S. japonica AMF colonization and the Shannon index. The colonization of limestone plots was higher than that of other lithology. In total, 3,096,236 sequences and 5767 OTUs were identified in S. japonica rhizosphere soil. Among these, 270 OTUs were defined at the genus level and divided into 7 genera and 35 species. Moreover, available nitrogen, soil organic matter, and available calcium content had a coupling effect and positive influence on AMF colonization and Shannon and Chao1 indices. Conversely, available phosphorus, available potassium, and available magnesium negatively affected AMF Shannon and Chao1 indices. Lithology, altitude, pH, and available phosphorus are important factors that affect the dynamics of AMF in the S. japonica rhizosphere.

Scoring PD-L1 Expression in Urothelial Carcinoma: An International Multi-Institutional Study on Comparison of Manual and Artificial Intelligence Measurement Model (AIM-PD-L1) Pathology Assessments.

Assessing programmed death ligand 1 (PD-L1) expression on tumor cells (TCs) using Food and Drug Administration-approved, validated immunoassays can guide the use of immune checkpoint inhibitor (ICI) therapy in cancer treatment. However, substantial interobserver variability has been reported using these immunoassays. Artificial intelligence (AI) has the potential to accurately measure biomarker expression in tissue samples, but its reliability and comparability to standard manual scoring remain to be evaluated. This multinational study sought to compare the %TC scoring of PD-L1 expression in advanced urothelial carcinoma, assessed by either an AI Measurement Model (AIM-PD-L1) or expert pathologists. The concordance among pathologists and between pathologists and AIM-PD-L1 was determined. The positivity rate of ≥ 1%TC PD-L1 was between 20-30% for 8/10 pathologists, and the degree of agreement and scoring distribution for among pathologists and between pathologists and AIM-PD-L1 was similar both scored as a continuous variable or using the pre-defined cutoff. Numerically higher score variation was observed with the 22C3 assay than with the 28-8 assay. A 2-h training module on the 28-8 assay did not significantly impact manual assessment. Cases exhibiting significantly higher variability in the assessment of PD-L1 expression (mean absolute deviation > 10) were found to have patterns of PD-L1 staining that were more challenging to interpret. An improved understanding of sources of manual scoring variability can be applied to PD-L1 expression analysis in the clinical setting. In the future, the application of AI algorithms could serve as a valuable reference guide for pathologists while scoring PD-L1.

Risk assessment of arrhythmias related to three antiseizure medications: a systematic review and single-arm meta-analysis.

Objective: Antiseizure medications (ASMs) are first line therapy for seizure disorders. Their effects on arrhythmias, especially the risk of arrhythmias associated with lacosamide (LCM), levetiracetam (LEV), and perampanel (PER), have been intensely investigated. Methods: We searched four databases (PubMed, EMBASE, Cochrane Library, and Web of Science) until August 6, 2023. We used a common effects model and reported data as pooled incidence with 95% CIs. Meta-analyses were conducted to elucidate the risk of arrhythmias with different drugs, and Egger's regression was performed to detect publication bias analysis. Results: We included 11 clinical trials with 1,031 participants. The pooled incidence of arrhythmias in the LEV group was 0.005 (95% CI: 0.001-0.013), while it was 0.014 in the LCM group (95% CI: 0.003-0.030). Publication bias analyses indicated no significant bias in the LEV group (t = 0.02, df = 4, p-value = 0.9852) but a significant bias in the LCM group (t = 5.94, df = 3, p-value = 0.0095). We corrected for this bias in the LCM group using the trim-and-fill method, which yielded a similar pooled incidence of 0.0137 (95% CI: 0.0036-0.0280), indicating good reliability. Due to insufficient studies, we could not conduct a meta-analysis for PER, and we analyzed them in our systematic review. Conclusion: The use of LCM significantly elevated the risk of arrhythmias, while LEV had non-significant arrhythmogenic effects. As for the arrhythmogenic effects of PER, more clinical trials are needed in the future.

Modular Synthesis of 1,2-Benzothiazines and 1,2-Benzothiazine 1-Imines via Palladium-Catalyzed C-H/C-C Activation Reactions.

In this study, a modular approach toward cyclic sulfoximines and sulfondiimines via palladium-catalyzed intramolecular C-H/C-C activation reactions was reported. Various 1,2-benzothiazines including bicyclic, tricyclic, highly fused ones, ones of the seven-membered ring, along with 1,2-benzothiazine 1-imines were accessed in good yields. KIE experiment demonstrated that the C-H bond cleavage at the position ortho to the sulfoximine group is not the rate-determining step in the coupling reaction.

Deep Learning for Microfluidic-Assisted Caenorhabditis elegans Multi-Parameter Identification Using YOLOv7.

The Caenorhabditis elegans (C. elegans) is an ideal model organism for studying human diseases and genetics due to its transparency and suitability for optical imaging. However, manually sorting a large population of C. elegans for experiments is tedious and inefficient. The microfluidic-assisted C. elegans sorting chip is considered a promising platform to address this issue due to its automation and ease of operation. Nevertheless, automated C. elegans sorting with multiple parameters requires efficient identification technology due to the different research demands for worm phenotypes. To improve the efficiency and accuracy of multi-parameter sorting, we developed a deep learning model using You Only Look Once (YOLO)v7 to detect and recognize C. elegans automatically. We used a dataset of 3931 annotated worms in microfluidic chips from various studies. Our model showed higher precision in automated C. elegans identification than YOLOv5 and Faster R-CNN, achieving a mean average precision (mAP) at a 0.5 intersection over a union (mAP@0.5) threshold of 99.56%. Additionally, our model demonstrated good generalization ability, achieving an mAP@0.5 of 94.21% on an external validation set. Our model can efficiently and accurately identify and calculate multiple phenotypes of worms, including size, movement speed, and fluorescence. The multi-parameter identification model can improve sorting efficiency and potentially promote the development of automated and integrated microfluidic platforms.

Using a 3D Navigation Template to Increase the Accuracy of Thoracic Pedicle Screws in Patients with Scoliosis.

This study compares the accuracy and safety of pedicle screw placement using a 3D navigation template with the free-hand fluoroscopy technique in scoliotic patients. Fifteen scoliotic patients were recruited and divided into a template group (eight cases) and a free-hand group (seven cases). All patients received posterior corrective surgeries, and the pedicle screw was placed using a 3D navigation template or a free-hand technique. After surgery, the positions of the pedicle screws were evaluated using CT. A total of 264 pedicle screws were implanted in 15 patients. Both the two techniques were found to achieve satisfactory safety of screw insertion in scoliotic patients (89.9% vs. 90.5%). In the thoracic region, the 3D navigation template was able to achieve a much higher accuracy of screw than the free-hand technique (75.3% vs. 60.4%). In the two groups, the accuracy rates on the convex side were slightly higher than on the concave side, while no significance was seen. In terms of rotational vertebrae, no significant differences were seen in Grades I or II vertebrae between the two groups. In conclusion, the 3D navigation template technique significantly increased the accuracy of thoracic pedicle screw placement, which held great potential for extensively clinical application.

Static Respiratory System Compliance as a Predictor of Extubation Failure in Patients with Acute Respiratory Failure.

PURPOSE: Ventilator weaning protocols rely in part on objective indices to best predict extubation failure in the critically ill. We investigated static respiratory system compliance (RC) as a predictor of extubation failure, in comparison to extubation readiness using rapid shallow breathing index (RSBI). MATERIAL AND METHODS: This was a cross-sectional, multi-institutional study of mechanically ventilated patients admitted between 12/01/2017 and 12/01/2019. All patients older than 18 years with a documented spontaneous breathing trial and extubation trial were included. RC and RSBI were calculated prior to the extubation trial. The primary outcome was extubation failure-defined as need for reintubation within 72 h from time of extubation. RESULTS: Of the 2263 patients, 55.8% were males with a mean age of 68 years. The population consisted mostly of Caucasians (73%) and African Americans (20.4%). 274 (12.1%) patients required reintubation within 72 h. On multivariate logistic regression after adjusting for age, sex, body mass index (BMI), admission Sequential Organ Failure Assessment (SOFA) score, number of ventilator days, and the P/F ratio on the day of extubation, RC remained the strongest predictor for extubation failure at 24 h (aOR 1.45; 95% CI 1.00-2.10) and 72 h (aOR 1.58; 95% CI 1.15-2.17). There was no significant association between RSBI and extubation failure at 24 (aOR 1.00; 95% CI 0.99-1.01) or at 72 h (aOR 1.00; 95% CI 0.99-1.01). CONCLUSION: RC measured on the day of extubation is a promising physiological discriminant to potentially risk stratify patients with acute respiratory failure for extubation readiness. We recommend further validation studies in prospective cohorts.

Correlation of Serum IGF-1 and sFlt-1 Levels with Adverse Pregnancy Outcomes in Patients with Severe Preeclampsia.

Objective: Our aim is to analyze the association of serum insulin-like growth factor 1 (IGF-1) and soluble fms-like tyrosine kinase 1 (sFlt-1) levels with adverse pregnancy outcomes in patients with severe preeclampsia (SPE). Methods: A total of 108 patients with SPE who received treatment in Tianjin Medical University General Hospital in China from January 2017 to December 2019 were selected for the study. According to the presence or lack of presence of adverse pregnancy outcomes, the patients were divided into the occurrence group (n = 34) and the nonoccurrence group (n = 74). Before treatment, patient serum vitamin A (VA), vitamin E (VE), IGF-1 and sFlt-1 levels were measured. Logistic regression analysis was performed for the correlation of serum IGF-1 and sFlt-1 with adverse pregnancy outcomes in patients with severe SPE. In addition, a Receiver Operator Characteristic (ROC) curve was plotted to test the prediction value of patient serum VA, VE, IGF-1 and sFlt-1 levels. Results: Compared with the nonoccurrence group, patients in the occurrence group had much lower serum VA, VE and IGF-1 levels and significantly higher sFlt-1 levels. Logistic regression analysis revealed that serum levels of VA, VE, IGF-1 and sFlt-1 before treatment were associated with adverse pregnancy outcomes in patients with SPE, and the ROC curve proved the accuracy of serum VA, VE, IGF-1 and sFlt-1 levels in predicting adverse pregnancy outcomes in patients with SPE. Conclusion: Abnormal expression of serum IGF-1 and sFlt-1 before treatment in patients with SPE is correlated with adverse pregnancy outcomes. Clinically, the risk for adverse pregnancy outcomes can be predicted and intervention instituted by detecting pretreatment serum IGF-1 and sFlt-1 expression in patients with SPE.

Recent Advances in Ferroelectric Materials-Based Photoelectrochemical Reaction.

Inorganic perovskite ferroelectric-based nanomaterials as sustainable new energy materials, due to their intrinsic ferroelectricity and environmental compatibility, are intended to play a crucial role in photoelectrochemical field as major functional materials. Because of versatile physical properties and excellent optoelectronic properties, ferroelectric-based nanomaterials attract much attention in the field of photocatalysis, photoelectrochemical water splitting and photovoltaic. The aim of this review is to cover the recent advances by stating the different kinds of ferroelectrics separately in the photoelectrochemical field as well as discussing how ferroelectric polarization will impact functioning of photo-induced carrier separation and transportation in the interface of the compounded semiconductors. In addition, the future prospects of ferroelectric-based nanomaterials are also discussed.

[Comparison of short-term effectiveness of Prodisc-C Vivo artificial disc replacement and Zero-P fusion for treatment of single-segment cervical spondylosis].

Objective: To compare the short-term effectiveness and the impact on cervical segmental range of motion using Prodisc-C Vivo artificial disc replacement and Zero-P fusion for the treatment of single-segment cervical spondylosis. Methods: The clinical data of 56 patients with single-segment cervical spondylosis who met the selection criteria between January 2015 and December 2018 were retrospectively analyzed, and they were divided into study group (27 cases, using Prodisc-C Vivo artificial disc replacement) and control group (29 cases, using Zero-P fusion) according to different surgical methods. There was no significant difference between the two groups in terms of gender, age, type of cervical spondylosis, disease duration, involved segments and preoperative pain visual analogue scale (VAS) score, Japanese Orthopaedic Association (JOA) score, neck disability index (NDI), surgical segments range of motion, upper and lower adjacent segments range of motion, overall cervical spine range of motion, and cervical curvature (P>0.05). The operation time, intraoperative blood loss, postoperative hospitalization stay, time of returning to work, clinical effectiveness indicators (VAS score, JOA score, NDI, and improvement rate of each score), and imaging indicators (surgical segments range of motion, upper and lower adjacent segments range of motion, overall cervical spine range of motion, and cervical curvature, prosthesis position, bone absorption, heterotopic ossification, etc.) were recorded and compared between the two groups. Results: There was no significant difference in operation time and intraoperative blood loss between the two groups (P>0.05); the postoperative hospitalization stay and time of returning to work in the study group were significantly shorter than those in the control group (P<0.05). Both groups were followed up 12-64 months, with an average of 26 months. There was no complication such as limb or organ damage, implant failure, and severe degeneration of adjacent segments requiring reoperation. The VAS score, JOA score, and NDI of the two groups at each time point after operation significantly improved when compared with those before operation (P<0.05); there was no significant difference in the above scores at each time point after operation between the two groups (P>0.05); there was no significant difference in the improvement rate of each score between the two groups at last follow-up (P>0.05). The surgical segments range of motion in the study group maintained to varying degrees after operation, while it in the control group basically disappeared after operation, showing significant differences between the two groups (P<0.05). At last follow-up, there was no significant difference in the upper and lower adjacent segments range of motion in the study group when compared with preoperative ones (P>0.05), while the upper adjacent segments range of motion in the control group increased significantly (P<0.05). The overall cervical spine range of motion and cervical curvature of the two groups decreased at 3 months after operation, and increased to varying degrees at last follow-up, but there was no significant difference between groups and within groups (P>0.05). At last follow-up, X-ray films and CT examinations showed that no prosthesis loosening, subsidence, or displacement was found in all patients; there were 2 cases (7.4%) of periprosthetic bone resorption and 3 cases (11.1%) of heterotopic ossification which did not affect the surgical segments range of motion. Conclusion: Both the Prodisc-C Vivo artificial disc replacement and Zero-P fusion have satisfactory short-term effectiveness in treatment of single-segment cervical spondylosis. Prodisc-C Vivo artificial disc replacement can also maintain the cervical spine range of motion to a certain extent, while reducing the occurrence of excessive motion of adjacent segments after fusion.

Towards Deep Radar Perception for Autonomous Driving: Datasets, Methods, and Challenges.

With recent developments, the performance of automotive radar has improved significantly. The next generation of 4D radar can achieve imaging capability in the form of high-resolution point clouds. In this context, we believe that the era of deep learning for radar perception has arrived. However, studies on radar deep learning are spread across different tasks, and a holistic overview is lacking. This review paper attempts to provide a big picture of the deep radar perception stack, including signal processing, datasets, labelling, data augmentation, and downstream tasks such as depth and velocity estimation, object detection, and sensor fusion. For these tasks, we focus on explaining how the network structure is adapted to radar domain knowledge. In particular, we summarise three overlooked challenges in deep radar perception, including multi-path effects, uncertainty problems, and adverse weather effects, and present some attempts to solve them.

MiR-212-3p promotes proliferation and migration of trophoblast in fetal growth restriction by targeting placental growth factor.

The purpose of this study was to evaluate the function and possible mechanism of miR-212-3p in fetal growth restriction (FGR) and to demonstrate the relationship between miR-212-3p and placental growth factor (PGF). First, we used qRT-PCR to detect the expression of miR-212-3p and PGF in placental tissues of normal delivery (HC group) and FGR, as well as in human trophoblast cell HTR-8/Svneo. The results revealed that miR-212-3p expression was significantly upregulated and PGF was significantly downregulated in placental tissue in the FGR group compared with the HC group. In addition, interference with miR-212-3p expression increased the proliferation, invasion, and migration of HTR-8/SVneo cells and decreased apoptosis of cells. Meanwhile, Western blot results showed that miR-212-3p expression downregulation promoted the phosphorylated protein expression of Phosphoinositide 3-kinase (PI3K) and protein kinase B (AKT), which in turn activated the PI3K/AKT signaling pathway. And the results of dual luciferase reporter further showed that miR-212-3p could target PGF, and the expression of both was negatively correlated in FGR group tissues. In addition, downregulation of miR-212-3p expression reversed the inhibitory effect of PGF downregulation on HTR-8/SVneo cells. In conclusion, miR-212-3p can target and inhibit the PGF expression and regulate the PI3K/AKT signaling pathway to regulate trophoblast cell invasion, migration, proliferation and cell apoptosis. This provides a potential biomarker for the development of FGR.

Bismahanine exerts anticancer effects on human cervical cancer cells by inhibition of growth, migration and invasion via suppression of NF-kB signalling pathway.

The Editors of JBUON issue an Expression of Concern to 'Bismahanine exerts anticancer effects on human cervical cancer cells by inhibition of growth, migration and invasion via suppression of NF-kB signalling pathway', by Limin Yu, Yan Sun, Jing Su, Xiang Li, JBUON 2020;25(1):93-98; PMID: 32277619. Following the publication of the above article, readers drew to our attention that part of the data was possibly unreliable. We sent emails to the authors with a request to provide the raw data to prove the originality, but received no reply. Therefore, as we continue to work through the issues raised, we advise readers to interpret the information presented in the article with due caution. We thank the readers for bringing this matter to our attention. We apologize for any inconvenience it may cause.

Machine learning methods to predict mechanical ventilation and mortality in patients with COVID-19.

BACKGROUND: The Coronavirus disease 2019 (COVID-19) pandemic has affected millions of people across the globe. It is associated with a high mortality rate and has created a global crisis by straining medical resources worldwide. OBJECTIVES: To develop and validate machine-learning models for prediction of mechanical ventilation (MV) for patients presenting to emergency room and for prediction of in-hospital mortality once a patient is admitted. METHODS: Two cohorts were used for the two different aims. 1980 COVID-19 patients were enrolled for the aim of prediction ofMV. 1036 patients' data, including demographics, past smoking and drinking history, past medical history and vital signs at emergency room (ER), laboratory values, and treatments were collected for training and 674 patients were enrolled for validation using XGBoost algorithm. For the second aim to predict in-hospital mortality, 3491 hospitalized patients via ER were enrolled. CatBoost, a new gradient-boosting algorithm was applied for training and validation of the cohort. RESULTS: Older age, higher temperature, increased respiratory rate (RR) and a lower oxygen saturation (SpO2) from the first set of vital signs were associated with an increased risk of MV amongst the 1980 patients in the ER. The model had a high accuracy of 86.2% and a negative predictive value (NPV) of 87.8%. While, patients who required MV, had a higher RR, Body mass index (BMI) and longer length of stay in the hospital were the major features associated with in-hospital mortality. The second model had a high accuracy of 80% with NPV of 81.6%. CONCLUSION: Machine learning models using XGBoost and catBoost algorithms can predict need for mechanical ventilation and mortality with a very high accuracy in COVID-19 patients.

Analysis of the polarization effects in the gyrokinetic theory of magnetized plasmas.

By adopting the hybrid coordinates, in which the nonlinearity of polarization displacement is included in the configuration space variables compared to the conventional gyrocenter coordinates, the polarization effects are analyzed by using the modern gyrokinetic (GK) theory of magnetized plasmas. Based on the invariant property, the velocity transformation between the gyrocenter and hybrid coordinates is calculated, and the phase-space velocity in terms of the hybrid coordinates is obtained. The linear and nonlinear polarization distribution functions are defined, and the evolutions for the polarization distribution functions are derived. It is well known that the polarization density is important in the GK calculation of particle density. Analogously, it is shown that the polarization current should be considered in the GK calculation of current density. In the case with electrostatic fluctuations, the roles of the polarization current are illustrated in the derivations of the Hasegawa-Mima equation and the dispersion relation for geodesic acoustic mode. In the case with magnetic fluctuations, the procedure for the GK calculation of perpendicular current is clarified, the dispersion relation for compressional Alfvén wave is derived, in which the effect of polarization current is discussed.

Hypergraph Clustering Based on Game-Theory for Mining Microbial High-Order Interaction Module.

Microbial community is ubiquitous in nature, which has a great impact on the living environment and human health. All these effects of microbial communities on the environment and their hosts are often referred to as the functions of these communities, which depend largely on the composition of the communities. The study of microbial higher-order module can help us understand the dynamic development and evolution process of microbial community and explore community function. Considering that traditional clustering methods depend on the number of clusters or the influence of data that does not belong to any cluster, this paper proposes a hypergraph clustering algorithm based on game theory to mine the microbial high-order interaction module (HCGI), and the hypergraph clustering problem naturally turns into a clustering game problem, the partition of network modules is transformed into finding the critical point of evolutionary stability strategy (ESS). The experimental results show HCGI does not depend on the number of classes, and can get more conservative and better quality microbial clustering module, which provides reference for researchers and saves time and cost. The source code of HCGI in this paper can be downloaded from https://github.com/ylm0505/HCGI.

Fabrication of Silk-Hyaluronan Composite as a Potential Scaffold for Tissue Repair.

Interest is rapidly growing in the design and preparation of bioactive scaffolds, mimicking the biochemical composition and physical microstructure for tissue repair. In this study, a biomimetic biomaterial with nanofibrous architecture composed of silk fibroin and hyaluronic acid (HA) was prepared. Silk fibroin nanofiber was firstly assembled in water and then used as the nanostructural cue; after blending with hyaluronan (silk:HA = 10:1) and the process of freeze-drying, the resulting composite scaffolds exhibited a desirable 3D porous structure and specific nanofiber features. These scaffolds were very porous with the porosity up to 99%. The mean compressive modulus of silk-HA scaffolds with HA MW of 0.6, 1.6, and 2.6 × 106 Da was about 28.3, 30.2, and 29.8 kPa, respectively, all these values were much higher than that of pure silk scaffold (27.5 kPa). This scaffold showed good biocompatibility with bone marrow mesenchymal stem cells, and it enhanced the cellular proliferation significantly when compared with the plain silk fibroin. Collectively, the silk-hyaluronan composite scaffold with a nanofibrous structure and good biocompatibility was successfully prepared, which deserved further exploration as a biomimetic platform for mesenchymal stem cell-based therapy for tissue repair.

Boosting the biocatalytic precipitation with enzyme-loaded liposomes: Toward a general platform for amplified photoelectrochemical immunoassay.

Liposome-assisted photoelectrochemical (PEC) bioanalysis represents one of the latest frontiers in the arena of PEC bioanalysis. This work reports a general enzyme-amplified liposomal PEC bioanalysis protocol via the use of enzyme-loaded liposomes to boost the biocatalytic precipitation (BCP) effect. In the representative system, the horseradish peroxidase (HRP)-loaded liposome (HRPLL) and the Au nanoclusters (NCs)/Au nanoparticles (NPs)/TiO2 nanotubes (NTs) framework (AATF) were used as liposomal label and photoelectrode, respectively. In the detection, the sandwich immunocomplex reaction was accomplished in a 96-well plate to confine the HRPLL label, which was then lysed to release the HRP molecules to initiate the BCP process. Due to the amplified formation of HRP-induced BCP on the AATF scaffold, the photo-current response correlated closely with the immunorecognition process and the analyte could be detected very sensitively. This work features the first integration of enzyme-loaded liposomes and the BCP for sensitive PEC bioanalysis, which to our knowledge has not been reported. With the use of various other enzymes, this work could serve as a general basis for the PEC bioanalysis of numerous other target of interest.