Rhizophagus irregularis and biochar can synergistically improve the physiological characteristics of saline-alkali resistance of switchgrass.

Inoculation of arbuscular mycorrhizal fungi (AMF) or biochar (BC) application can improve photosynthesis and promote plant growth under saline-alkali stress. However, little is known about the effects of the two combined on growth and physiological characteristics of switchgrass under saline-alkali stress. This study examined the effects of four treatments: (1) no AMF inoculation and no biochar addition (control), (2) biochar (BC) alone, (3) AMF (Rhizophagus irregularis, Ri) alone, and (4) the combination of both (BC+Ri) on the plant biomass, antioxidant enzymes, chlorophyll, and photosynthetic parameters of switchgrass under saline-alkali stress. The results showed that the above-ground, belowground and total biomass of switchgrass in the BC+Ri treatment group was significantly higher (+136.7%, 120.2% and 132.4%, respectively) than in other treatments compared with Control. BC+Ri treatment significantly increased plant leaves' relative chlorophyll content, antioxidant enzyme activity, and photosynthesis parameters. It is worth noting that the transpiration rate, stomatal conductance, net photosynthetic rate, PSII efficiency and other photosynthetic-related indexes of the BC+Ri treatment group were the highest (38% to 54% higher than other treatments). The fitting results of light response and CO2 response curves showed that the light saturation point, light compensation point, maximum carboxylation rate and maximum electron transfer rate of switchgrass in the Ri+BC treatment group were the highest. In conclusion, biochar combined with Ri has potential beneficial effects on promoting switchgrass growth under saline-alkali stress and improving the activity of antioxidant enzymes and photosynthetic characteristics of plants.

The relationship between contrast-enhanced computed tomography radiomics features and mitosis karyorrhexis index in neuroblastoma.

OBJECTIVE: Mitosis karyorrhexis index (MKI) can reflect the proliferation status of neuroblastoma cells. This study aimed to investigate the contrast-enhanced computed tomography (CECT) radiomics features associated with the MKI status in neuroblastoma. MATERIALS AND METHODS: 246 neuroblastoma patients were retrospectively included and divided into three groups: low-MKI, intermediate-MKI, and high-MKI. They were randomly stratified into a training set and a testing set at a ratio of 8:2. Tumor regions of interest were delineated on arterial-phase CECT images, and radiomics features were extracted. After reducing the dimensionality of the radiomics features, a random forest algorithm was employed to establish a three-class classification model to predict MKI status. RESULTS: The classification model consisted of 5 radiomics features. The mean area under the curve (AUC) of the classification model was 0.916 (95% confidence interval (CI) 0.913-0.921) in the training set and 0.858 (95% CI 0.841-0.864) in the testing set. Specifically, the classification model achieved AUCs of 0.928 (95% CI 0.927-0.934), 0.915 (95% CI 0.912-0.919), and 0.901 (95% CI 0.900-0.909) for predicting low-MKI, intermediate-MKI, and high-MKI, respectively, in the training set. In the testing set, the classification model achieved AUCs of 0.873 (95% CI 0.859-0.882), 0.860 (95% CI 0.852-0.872), and 0.820 (95% CI 0.813-0.839) for predicting low-MKI, intermediate-MKI, and high-MKI, respectively. CONCLUSIONS: CECT radiomics features were found to be correlated with MKI status and are helpful for reflecting the proliferation status of neuroblastoma cells.

Identification of Prolactinoma in Pituitary Neuroendocrine Tumors Using Radiomics Analysis Based on Multiparameter MRI.

This study aims to investigate the feasibility of preoperatively predicting histological subtypes of pituitary neuroendocrine tumors (PitNETs) using machine learning and radiomics based on multiparameter MRI. Patients with PitNETs from January 2016 to May 2022 were retrospectively enrolled from four medical centers. A cfVB-Net network was used to automatically segment PitNET multiparameter MRI. Radiomics features were extracted from the MRI, and the radiomics score (Radscore) of each patient was calculated. To predict histological subtypes, the Gaussian process (GP) machine learning classifier based on radiomics features was performed. Multi-classification (six-class histological subtype) and binary classification (PRL vs. non-PRL) GP model was constructed. Then, a clinical-radiomics nomogram combining clinical factors and Radscores was constructed using the multivariate logistic regression analysis. The performance of the models was evaluated using receiver operating characteristic (ROC) curves. The PitNET auto-segmentation model eventually achieved the mean Dice similarity coefficient of 0.888 in 1206 patients (mean age 49.3 ± SD years, 52% female). In the multi-classification model, the GP of T2WI got the best area under the ROC curve (AUC), with 0.791, 0.801, and 0.711 in the training, validation, and external testing set, respectively. In the binary classification model, the GP of T2WI combined with CE T1WI demonstrated good performance, with AUC of 0.936, 0.882, and 0.791 in training, validation, and external testing sets, respectively. In the clinical-radiomics nomogram, Radscores and Hardy' grade were identified as predictors for PRL expression. Machine learning and radiomics analysis based on multiparameter MRI exhibited high efficiency and clinical application value in predicting the PitNET histological subtypes.

Evolving Tumor Characteristics and Smart Nanodrugs for Tumor Immunotherapy.

Typical physiological characteristics of tumors, such as weak acidity, low oxygen content, and upregulation of certain enzymes in the tumor microenvironment (TME), provide survival advantages when exposed to targeted attacks by drugs and responsive nanomedicines. Consequently, cancer treatment has significantly progressed in recent years. However, the evolution and adaptation of tumor characteristics still pose many challenges for current treatment methods. Therefore, efficient and precise cancer treatments require an understanding of the heterogeneity degree of various factors in cancer cells during tumor evolution to exploit the typical TME characteristics and manage the mutation process. The highly heterogeneous tumor and infiltrating stromal cells, immune cells, and extracellular components collectively form a unique TME, which plays a crucial role in tumor malignancy, including proliferation, invasion, metastasis, and immune escape. Therefore, the development of new treatment methods that can adapt to the evolutionary characteristics of tumors has become an intense focus in current cancer treatment research. This paper explores the latest understanding of cancer evolution, focusing on how tumors use new antigens to shape their "new faces"; how immune system cells, such as cytotoxic T cells, regulatory T cells, macrophages, and natural killer cells, help tumors become "invisible", that is, immune escape; whether the diverse cancer-associated fibroblasts provide support and coordination for tumors; and whether it is possible to attack tumors in reverse. This paper discusses the limitations of targeted therapy driven by tumor evolution factors and explores future strategies and the potential of intelligent nanomedicines, including the systematic coordination of tumor evolution factors and adaptive methods, to meet this therapeutic challenge.

ON-OFF Control of Marangoni Self-propulsion via A Supra-amphiphile Fuel and Switch.

Marangoni self-propulsion refers to motions of liquid or solids driven by a surface tension gradient, and has applications in soft robots/devices, cargo delivery, self-assembly etc. However, two problems remain to be addressed for motion control (e.g., ON-OFF) with conventional surfactants as Marangoni fuel: (1) limited motion lifetime due to saturated interfacial adsorption of surfactants; (2) in- situ motion stop is difficult once Marangoni flows are triggered. Instead of covalent surfactants, supra-amphiphiles with hydrophilic and hydrophobic parts linked noncovalently, hold promise to solve these problems owing to its dynamic and reversible surface activity responsively. Here, we propose a new concept of 'supra-amphiphile fuel and switch' based on the facile synthesis of disodium-4-azobenzene-amino-1,3-benzenedisulfonate (DABS) linked by a Schiff base, which has amphiphilicity for self-propulsion, hydrolyzes timely to avoid saturated adsorption and provides pH-responsive control over ON-OFF motion. The self-propulsion lifetime is extended by 50-fold with DABS and motion control is achieved. The mechanism is revealed with coupled interface chemistry involving two competitive processes of interfacial adsorption and hydrolysis of DABS based on both experiments and simulation. The concept of 'supra-amphiphile fuel and switch' provides an active solution to prolong and control Marangoni self-propulsive devices for the advance of intelligent material systems.

Constructing High-performance Cobalt-based Environmental Catalysts from Spent Lithium-Ion Batteries: Unveiling Overlooked Roles of Copper and Aluminum from Current Collectors.

Converting spent lithium-ion batteries (LIBs) cathode materials into environmental catalysts has drawn more and more attention. Herein, we fabricated a Co3O4-based catalyst from spent LiCoO2 LIBs (Co3O4-LIBs) and found that the role of Al and Cu from current collectors on its performance is nonnegligible. The density functional theory calculations confirmed that the doping of Al and/or Cu upshifts the d-band center of Co. A Fenton-like reaction based on peroxymonosulfate (PMS) activation was adopted to evaluate its activity. Interestingly, Al doping strengthened chemisorption for PMS (from -2.615 eV to -2.623 eV) and shortened Co-O bond length (from 2.540 Å to 2.344 Å) between them, whereas Cu doping reduced interfacial charge-transfer resistance (from 28.347 kΩ to 6.689 kΩ) excepting for the enhancement of the above characteristics. As expected, the degradation activity toward bisphenol A of Co3O4-LIBs (0.523 min-1) was superior to that of Co3O4 prepared from commercial CoC2O4 (0.287 min-1). Simultaneously, the reasons for improved activity were further verified by comparing activity with catalysts doped Al and/or Cu into Co3O4. This work reveals the role of elements from current collectors on the performance of functional materials from spent LIBs, which is beneficial to the sustainable utilization of spent LIBs.

Physiotherapy for patients with depression: Recent research progress.

Depression, a common mental illness, seriously affects the health of individuals and has deleterious effects on society. The prevention and treatment of depression has drawn the attention of many researchers and has become an important social issue. The treatment strategies for depression include drugs, psychotherapy, and physiotherapy. Drug therapy is ineffective in some patients and psychotherapy has treatment limitations. As a reliable adjuvant therapy, physiotherapy compensates for the shortcomings of drug and psychotherapy and effectively reduces the disease recurrence rate. Physiotherapy is more scientific and rigorous, its methods are diverse, and to a certain extent, provides more choices for the treatment of depression. Physiotherapy can relieve symptoms in many ways, such as by improving the levels of neurobiochemical molecules, inhibiting the inflammatory response, regulating the neuroendocrine system, and increasing neuroplasticity. Physiotherapy has biological effects similar to those of antidepressants and may produce a superimposed impact when combined with other treatments. This article summarizes the findings on the use of physiotherapy to treat patients with depression over the past five years. It also discusses several methods of physiotherapy for treating depression from the aspects of clinical effect, mechanism of action, and disadvantages, thereby serving as a reference for the in-depth development of physiotherapy research.

Challenges and support needs in psychological and physical health among pilots: a qualitative study.

Introduction: Physical and mental health problems among pilots affect their working state and impact flight safety. Although pilots' physical and mental health problems have become increasingly prominent, their health has not been taken seriously. This study aimed to clarify challenges and support needs related to psychological and physical health among pilots to inform development of a more scientific and comprehensive physical and mental health system for civil aviation pilots. Methods: This qualitative study recruited pilots from nine civil aviation companies. Focus group interviews via an online conference platform were conducted in August 2022. Colaizzi analysis was used to derive themes from the data and explore pilots' experiences, challenges, and support needs. Results: The main sub-themes capturing pilots' psychological and physical health challenges were: (1) imbalance between family life and work; (2) pressure from assessment and physical examination eligibility requirements; (3) pressure from worries about being infected with COVID-19; (4) nutrition deficiency during working hours; (5) changes in eating habits because of the COVID-19 pandemic; (6) sleep deprivation; (7) occupational diseases; (8) lack of support from the company in coping with stress; (9) pilots' yearly examination standards; (10) support with sports equipment; (11) respecting planned rest time; and (12) isolation periods. Discussion: The interviewed pilots experienced major psychological pressure from various sources, and their physical health condition was concerning. We offer several suggestions that could be addressed to improve pilots' physical and mental health. However, more research is needed to compare standard health measures for pilots around the world in order to improve their physical and mental health and contribute to overall aviation safety.

A Bayesian Change Point Model for Dynamic Alternative Transcription Start Site Usage During Cellular Differentiation.

ABSTRACT An alternative transcription start site (ATSS) is a major driving force for increasing the complexity of transcripts in human tissues. As a transcriptional regulatory mechanism, ATSS has biological significance. Many studies have confirmed that ATSS plays an important role in diseases and cell development and differentiation. However, exploration of its dynamic mechanisms remains insufficient. Identifying ATSS change points during cell differentiation is critical for elucidating potential dynamic mechanisms. For relative ATSS usage as percentage data, the existing methods lack sensitivity to detect the change point for ATSS longitudinal data. In addition, some methods have strict requirements for data distribution and cannot be applied to deal with this problem. In this study, the Bayesian change point detection model was first constructed using reparameterization techniques for two parameters of a beta distribution for the percentage data type, and the posterior distributions of parameters and change points were obtained using Markov Chain Monte Carlo (MCMC) sampling. With comprehensive simulation studies, the performance of the Bayesian change point detection model is found to be consistently powerful and robust across most scenarios with different sample sizes and beta distributions. Second, differential ATSS events in the real data, whose change points were identified using our method, were clustered according to their change points. Last, for each change point, pathway and transcription factor motif analyses were performed on its differential ATSS events. The results of our analyses demonstrated the effectiveness of the Bayesian change point detection model and provided biological insights into cell differentiation.

An Au bipyramids@CuZn MOF core-shell nanozyme enables universal SERS and a colorimetric dual-model bioassay.

In this study, a new type of gold nano-bipyramids@CuZn bimetallic organic framework (AuNBPs@CuZn MOF) nanozyme with high peroxidase (POD)-like activity and surface enhanced Raman scattering (SERS) activity was constructed with a special core-shell structure, which can catalyze the oxidation of TMB (colourless and Raman-inactive) into ox-TMB (blue and Raman-active). An AuNBPs@CuZn MOF-enabling universal SERS and colorimetric dual-model bioassay was thus developed for biomolecules with excellent performance, and has promising application prospects in the biosensing fields.

Settling velocity of atmospheric particles in seawater: Based on hydrostatic sedimentation method using video imaging techniques.

When atmospheric particles deposit to the ocean, their settling velocities and residence times associated are critical for their effects on oceanic ecosystems. We developed a hydrostatic sedimentation method using video imaging techniques to track particles of 5-20 µm in diameter falling into seawater and determine the particle settling velocities in relation to their diameter, shape, organic matter contained, and seawater salinity. The measured settling velocities varied from 0.025 to 0.41 mm/s. Irregular particle shape and organic matter contained in particles also, however, reduced the values. The settling velocities were decelerated by the dissolution process of particle in seawater. Combined with the experimental results, a formula for calculating the settling velocity formulae for atmospheric particles was estimated. Using this equation, the residence time of particles is estimated to be less than one month in continental shelf sea and more than 100 days in the oceans.

PheSeq, a Bayesian deep learning model to enhance and interpret the gene-disease association studies.

Despite the abundance of genotype-phenotype association studies, the resulting association outcomes often lack robustness and interpretations. To address these challenges, we introduce PheSeq, a Bayesian deep learning model that enhances and interprets association studies through the integration and perception of phenotype descriptions. By implementing the PheSeq model in three case studies on Alzheimer's disease, breast cancer, and lung cancer, we identify 1024 priority genes for Alzheimer's disease and 818 and 566 genes for breast cancer and lung cancer, respectively. Benefiting from data fusion, these findings represent moderate positive rates, high recall rates, and interpretation in gene-disease association studies.

Unified Model for Children's Brain Image Segmentation with Co-Registration Framework Guided by Longitudinal MRI.

Accurate segmentation of brain structures is crucial for analyzing longitudinal changes in children's brains. However, existing methods are mostly based on models established at a single time-point due to difficulty in obtaining annotated data and dynamic variation of tissue intensity. The main problem with such approaches is that, when conducting longitudinal analysis, images from different time points are segmented by different models, leading to significant variation in estimating development trends. In this paper, we propose a novel unified model with co-registration framework to segment children's brain images covering neonates to preschoolers, which is formulated as two stages. First, to overcome the shortage of annotated data, we propose building gold-standard segmentation with co-registration framework guided by longitudinal data. Second, we construct a unified segmentation model tailored to brain images at 0-6 years old through the introduction of a convolutional network (named SE-VB-Net), which combines our previously proposed VB-Net with Squeeze-and-Excitation (SE) block. Moreover, different from existing methods that only require both T1- and T2-weighted MR images as inputs, our designed model also allows a single T1-weighted MR image as input. The proposed method is evaluated on the main dataset (320 longitudinal subjects with average 2 time-points) and two external datasets (10 cases with 6-month-old and 40 cases with 20-45 weeks, respectively). Results demonstrate that our proposed method achieves a high performance (>92%), even over a single time-point. This means that it is suitable for brain image analysis with large appearance variation, and largely broadens the application scenarios.

Metabolic engineering Corynebacterium glutamicum for D-chiro-inositol production.

D-chiro-inositol (DCI) is a potential drug for the treatment of type II diabetes and polycystic ovary syndrome. In order to effectively synthesize DCI in Corynebacterium glutamicum, the genes related to inositol catabolism in clusters iol1 and iol2 were knocked out in C. glutamicum SN01 to generate the chassis strain DCI-1. DCI-1 did not grow in and catabolize myo-inositol (MI). Subsequently, different exogenous and endogenous inosose isomerases were expressed in DCI-1 and their conversion ability of DCI from MI were compared. After fermentation, the strain DCI-7 co-expressing inosose isomerase IolI2 and inositol dehydrogenase IolG was identified as the optimal strain. Its DCI titer reached 3.21 g/L in the presence of 20 g/L MI. On this basis, the pH, temperature and MI concentration during whole-cell conversion of DCI by strain DCI-7 were optimized. Finally, the optimal condition that achieved the highest DCI titer of 6.96 g/L were obtained at pH 8.0, 37 °C and addition of 40 g/L MI. To our knowledge, it is the highest DCI titer ever reported.

Intratumoral and Peritumoral Edema Radiomics Based on Fat-Suppressed T2- Weighted Imaging for Preoperative Prediction of Triple-Negative Breast Cancer.

AIM: Our aim was to explore the feasibility of using radiomics data derived from intratumoral and peritumoral edema on fat-suppressed T2-weighted imaging (T2 FS) to distinguish triple-negative breast cancer (TNBC) from non-triple-negative breast cancer (non-TNBC). METHODS: This retrospective study enrolled 174 breast cancer patients. According to the MRI examination time, patients before 2021 were divided into training (n = 119) or internal test (n = 30) cohorts at a ratio of 8:2. Patients from 2022 were included in the external test cohort (n = 25). Four regions of interest for each lesion were defined: intratumoral regions, peritumoral edema regions, regions with a combination of intratumoral and peritumoral edema, and regions with a combination of intratumoral and 5-mm peritumoral. Four radiomic signatures were built using the least absolute shrinkage and selection operator (LASSO) method after selecting features. Furthermore, a radio mic-radiological model was constructed using a combination of intratumoral and peritumoral edema regions along with clinical-radiologic features. Area under the receiver operating characteristic curve (AUC) calculations, decision curve analysis, and calibration curve analysis were performed to assess the performance of each model. RESULTS: The radiomic-radiological model showed the highest AUC values of 0.906 (0.788-1.000) and 82.5 (0.622-0.947) in both the internal and external test sets, respectively. The radiology-radiomic model exhibited excellent predictive performance, as evidenced by the calibration curves and decision curve analysis. CONCLUSION: The ensemble model based on T2 FS-based radiomic features of intratumoral and peritumoral edema, along with radiological factors, performed better in distinguishing TNBC from non-TNBC than a single model. We explored the possibility of developing explainable models to support the clinical decision-making process.

Epstein-Barr virus-driven metabolic alterations contribute to the viral lytic reactivation and tumor progression in nasopharyngeal carcinoma.

Metabolic reprogramming induced by Epstein-Barr virus (EBV) often mirrors metabolic changes observed in cancer cells. Accumulating evidence suggests that lytic reactivation is crucial in EBV-associated oncogenesis. The aim of this study was to explore the role of metabolite changes in EBV-associated malignancies and viral life cycle control. We first revealed that EBV (LMP1) accelerates the secretion of the oncometabolite D-2HG, and serum D-2HG level is a potential diagnostic biomarker for NPC. EBV (LMP1)-driven metabolite changes disrupts the homeostasis of global DNA methylation and demethylation, which have a significantly inhibitory effect on active DNA demethylation and 5hmC content. We found that loss of 5hmC indicates a poor prognosis for NPC patients, and that 5hmC modification is a restriction factor of EBV reactivation. We confirmed a novel EBV reactivation inhibitor, α-KG, which inhibits the expression of EBV lytic genes with CpG-containing ZREs and the latent-lytic switch by enhancing 5hmC modification. Our results demonstrate a novel mechanism of which metabolite abnormality driven by EBV controls the viral lytic reactivation through epigenetic modification. This study presents a potential strategy for blocking EBV reactivation, and provides potential targets for the diagnosis and therapy of NPC.