Myricetin mitigates motor disturbance and decreases neuronal ferroptosis in a rat model of Parkinson's disease.

Ferroptosis is an iron-dependent cell death form characterized by reactive oxygen species (ROS) overgeneration and lipid peroxidation. Myricetin, a flavonoid that exists in numerous plants, exhibits potent antioxidant capacity. Given that iron accumulation and ROS-provoked dopaminergic neuron death are the two main pathological hallmarks of Parkinson's disease (PD), we aimed to investigate whether myricetin decreases neuronal death through suppressing ferroptosis. The PD models were established by intraperitoneally injecting 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine (MPTP) into rats and by treating SH-SY5Y cells with 1-methyl-4-phenylpyridinium (MPP+), respectively. Ferroptosis was identified by assessing the levels of Fe2+, ROS, malondialdehyde (MDA), and glutathione (GSH). The results demonstrated that myricetin treatment effectively mitigated MPTP-triggered motor impairment, dopamine neuronal death, and α-synuclein (α-Syn) accumulation in PD models. Myricetin also alleviated MPTP-induced ferroptosis, as evidenced by decreased levels of Fe2+, ROS, and MDA and increased levels of GSH in the substantia nigra (SN) and serum in PD models. All these changes were reversed by erastin, a ferroptosis activator. In vitro, myricetin treatment restored SH-SY5Y cell viability and alleviated MPP+-induced SH-SY5Y cell ferroptosis. Mechanistically, myricetin accelerated nuclear translocation of nuclear factor E2-related factor 2 (Nrf2) and subsequent glutathione peroxidase 4 (Gpx4) expression in MPP+-treated SH-SY5Y cells, two critical inhibitors of ferroptosis. Collectively, these data demonstrate that myricetin may be a potential agent for decreasing dopaminergic neuron death by inhibiting ferroptosis in PD.

A CRISPR/Cas9 screen in embryonic stem cells reveals that Mdm2 regulates totipotency exit.

During early embryonic development, the transition from totipotency to pluripotency is a fundamental and critical process for proper development. However, the regulatory mechanisms governing this transition remain elusive. Here, we conducted a comprehensive genome-wide CRISPR/Cas9 screen to investigate the 2-cell-like cells (2CLCs) phenotype in mouse embryonic stem cells (mESCs). This effort led to the identification of ten regulators that play a pivotal role in determining cell fate during this transition. Notably, our study revealed Mdm2 as a significant negative regulator of 2CLCs, as perturbation of Mdm2 resulted in a higher proportion of 2CLCs. Mdm2 appears to influence cell fate through its impact on cell cycle progression and H3K27me3 epigenetic modifications. In summary, the results of our CRISPR/Cas9 screen have uncovered several genes with distinct functions in regulating totipotency and pluripotency at various levels, offering a valuable resource for potential targets in future molecular studies.

Value of endoscopic grading of gastroesophageal flap valve in gastroesophageal reflux disease.

OBJECTIVE: To investigate the significance of endoscopic grading (Hill's classification) of gastroesophageal flap valve (GEFV) in the examination of patients with gastroesophageal reflux disease (GERD). METHODS: One hundred and sixty-two patients undergoing gastroscopy in the Department of Gastroenterology, Xingyi People's Hospital between Apr. 2022 and Sept. 2022 were selected by convenient sampling, and data such as GEFV grade, and findings of esophageal high-resolution manometry (HRM) and esophageal 24-h pH/impedance reflux monitoring, and Los Angeles (LA) classification of reflux esophagitis (RE) were collected and compared. RESULTS: Statistically significant differences in age (F = 9.711, P < 0.001) and hiatal hernia (χ = 35.729, P < 0.001) were observed in patients with different GEFV grades. The resting LES pressures were 12.12 ± 2.79, 10.73 ± 2.68, 9.70 ± 2.29, and 8.20 ± 2.77 mmHg (F = 4.571, P < 0.001) and LES lengths were 3.30 ± 0.70, 3.16 ± 0.68, 2.35 ± 0.83, and 2.45 ± 0.62 (F = 3.789, P = 0.011), respectively, in patients with GEFV grades I-IV. DeMeester score (Z = 5.452, P < 0.001), AET4 (Z = 5.614, P < 0.001), acid reflux score (upright) (Z = 7.452, P < 0.001), weak acid reflux score (upright) (Z = 3.121, P = 0.038), liquid reflux score (upright) (Z = 3.321, P = 0.031), acid reflux score (supine) (Z = 6.462, P < 0.001), mixed reflux score (supine) (Z = 3.324, P = 0.031), gas reflux score (supine) (Z = 3.521, P = 0.024) were different in patients with different GEFV grades, with statistically significant differences. Pearson correlation analysis revealed a positive correlation between RE grade and LA classification of GERD (r = 0.662, P < 0.001), and the severity of RE increased gradually with the increase of the Hill grades of GEFV. CONCLUSION: The Hill grade of GEFV is related to age, hiatal hernia, LES pressure, and the consequent development and severity of acid reflux and RE. Evaluation of esophageal motility and reflux based on the Hill grade of GEFV is of significance for the diagnosis and treatment of GERD.

Glucagon Receptor Antagonist for Heart Failure With Preserved Ejection Fraction.

BACKGROUND: Heart failure with preserved ejection fraction (HFpEF) is an emerging major unmet need and one of the most significant clinic challenges in cardiology. The pathogenesis of HFpEF is associated with multiple risk factors. Hypertension and metabolic disorders associated with obesity are the 2 most prominent comorbidities observed in patients with HFpEF. Although hypertension-induced mechanical overload has long been recognized as a potent contributor to heart failure with reduced ejection fraction, the synergistic interaction between mechanical overload and metabolic disorders in the pathogenesis of HFpEF remains poorly characterized. METHOD: We investigated the functional outcome and the underlying mechanisms from concurrent mechanic and metabolic stresses in the heart by applying transverse aortic constriction in lean C57Bl/6J or obese/diabetic B6.Cg-Lepob/J (ob/ob) mice, followed by single-nuclei RNA-seq and targeted manipulation of a top-ranked signaling pathway differentially affected in the 2 experimental cohorts. RESULTS: In contrast to the post-trans-aortic constriction C57Bl/6J lean mice, which developed pathological features of heart failure with reduced ejection fraction over time, the post-trans-aortic constriction ob/ob mice showed no significant changes in ejection fraction but developed characteristic pathological features of HFpEF, including diastolic dysfunction, worsened cardiac hypertrophy, and pathological remodeling, along with further deterioration of exercise intolerance. Single-nuclei RNA-seq analysis revealed significant transcriptome reprogramming in the cardiomyocytes stressed by both pressure overload and obesity/diabetes, markedly distinct from the cardiomyocytes singularly stressed by pressure overload or obesity/diabetes. Furthermore, glucagon signaling was identified as the top-ranked signaling pathway affected in the cardiomyocytes associated with HFpEF. Treatment with a glucagon receptor antagonist significantly ameliorated the progression of HFpEF-related pathological features in 2 independent preclinical models. Importantly, cardiomyocyte-specific genetic deletion of the glucagon receptor also significantly improved cardiac function in response to pressure overload and metabolic stress. CONCLUSIONS: These findings identify glucagon receptor signaling in cardiomyocytes as a critical determinant of HFpEF progression and provide proof-of-concept support for glucagon receptor antagonism as a potential therapy for the disease.

Deep learning in pulmonary nodule detection and segmentation: a systematic review.

OBJECTIVES: The accurate detection and precise segmentation of lung nodules on computed tomography are key prerequisites for early diagnosis and appropriate treatment of lung cancer. This study was designed to compare detection and segmentation methods for pulmonary nodules using deep-learning techniques to fill methodological gaps and biases in the existing literature. METHODS: This study utilized a systematic review with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines, searching PubMed, Embase, Web of Science Core Collection, and the Cochrane Library databases up to May 10, 2023. The Quality Assessment of Diagnostic Accuracy Studies 2 criteria was used to assess the risk of bias and was adjusted with the Checklist for Artificial Intelligence in Medical Imaging. The study analyzed and extracted model performance, data sources, and task-focus information. RESULTS: After screening, we included nine studies meeting our inclusion criteria. These studies were published between 2019 and 2023 and predominantly used public datasets, with the Lung Image Database Consortium Image Collection and Image Database Resource Initiative and Lung Nodule Analysis 2016 being the most common. The studies focused on detection, segmentation, and other tasks, primarily utilizing Convolutional Neural Networks for model development. Performance evaluation covered multiple metrics, including sensitivity and the Dice coefficient. CONCLUSIONS: This study highlights the potential power of deep learning in lung nodule detection and segmentation. It underscores the importance of standardized data processing, code and data sharing, the value of external test datasets, and the need to balance model complexity and efficiency in future research. CLINICAL RELEVANCE STATEMENT: Deep learning demonstrates significant promise in autonomously detecting and segmenting pulmonary nodules. Future research should address methodological shortcomings and variability to enhance its clinical utility. KEY POINTS: Deep learning shows potential in the detection and segmentation of pulmonary nodules. There are methodological gaps and biases present in the existing literature. Factors such as external validation and transparency affect the clinical application.

3D embedded bioprinting of large-scale intestine with complex structural organization and blood capillaries.

Accurate reproduction of human intestinal structure and functionin vitrois of great significance for understanding the development and disease occurrence of the gut. However, mostin vitrostudies are often confined to 2D models, 2.5D organ chips or 3D organoids, which cannot fully recapitulate the tissue architecture, microenvironment and cell compartmentalization foundin vivo. Herein, a centimeter-scale intestine tissue that contains intestinal features, such as hollow tubular structure, capillaries and tightly connected epithelium with invivo-likering folds, crypt-villi, and microvilli is constructed by 3D embedding bioprinting. In our strategy, a novel photocurable bioink composed of methacrylated gelatin, methacrylated sodium alginate and poly (ethylene glycol) diacrylate is developed for the fabrication of intestinal model. The Caco-2 cells implanted in the lumen are induced by the topological structures of the model to derive microvilli, crypt-villi, and tight junctions, simulating the intestinal epithelial barrier. The human umbilical vein endothelial cells encapsulated within the model gradually form microvessels, mimicking the dense capillary network in the intestine. This intestine-like tissue, which closely resembles the structure and cell arrangement of the human gut, can act as a platform to predict the therapeutic and toxic side effects of new drugs on the intestine.

Characterization of neutralizing chimeric heavy-chain antibodies against tetanus toxin.

Tetanus toxin (TeNT) is one of the most toxic proteins. Neutralizing antibodies against TeNT are effective in prevention and treatment. In this study, 14 anti-tetanus nanobodies were obtained from a phage display nanobody library by immunizing a camel with the C-terminal receptor-binding domain of TeNT (TeNT-Hc) as the antigen. After fusion with the human Fc fragment, 11 chimeric heavy-chain antibodies demonstrated nanomolar binding toward TeNT-Hc. The results of toxin neutralization experiments showed that T83-7, T83-8, and T83-13 completely protected mice against 20 × the median lethal dose (LD50) at a low concentration. The neutralizing potency of T83-7, T83-8, and T83-13 against TeNT is 0.4 IU/mg, 0.4 IU/mg and 0.2 IU/mg, respectively. In the prophylactic setting, we found that 5 mg/kg of T83-13 provided the mice with full protection from tetanus, even when they were injected 14 days before exposure to 20 × LD50 TeNT. T83-7 and T83-8 were less effective, being fully protective only when challenged 7 or 10 days before exposure, respectively. In the therapeutic setting, 12 h after exposure to TeNT, 1 ~ 5 mg/kg of T83-7, and T83-8 could provide complete protection for mice against 5 × LD50 TeNT, while 1 mg/kg T83-13 could provide complete protection 24 h after exposure to 5 × LD50 TeNT. Our results suggested that these antibodies represent prophylactic and therapeutic activities against TeNT in a mouse model. The T83-7, T83-8, and T83-13 could form the basis for the subsequent development of drugs to treat TeNT toxicity.

Predicting lymphovascular invasion in non-small cell lung cancer using deep convolutional neural networks on preoperative chest CT.

RATIONALE AND OBJECTIVES: Lymphovascular invasion (LVI) plays a significant role in precise treatments of non-small cell lung cancer (NSCLC). This study aims to build a non-invasive LVI prediction diagnosis model by combining preoperative CT images with deep learning technology. MATERIALS AND METHODS: This retrospective observational study included a series of consecutive patients who underwent surgical resection for non-small cell lung cancer (NSCLC) and received pathologically confirmed diagnoses. The cohort was randomly divided into a training group comprising 70 % of the patients and a validation group comprising the remaining 30 %. Four distinct deep convolutional neural network (DCNN) prediction models were developed, incorporating different combination of two-dimensional (2D) and three-dimensional (3D) CT imaging features as well as clinical-radiological data. The predictive capabilities of the models were evaluated by receiver operating characteristic curves (AUC) values and confusion matrices. The Delong test was utilized to compare the predictive performance among the different models. RESULTS: A total of 3034 patients with NSCLC were recruited in this study including 106 LVI+ patients. In the validation cohort, the Dual-head Res2Net_3D23F model achieved the highest AUC of 0.869, closely followed by the models of Dual-head Res2Net_3D3F (AUC, 0.868), Dual-head Res2Net_3D (AUC, 0.867), and EfficientNet-B0_2D (AUC, 0.857). There was no significant difference observed in the performance of the EfficientNet-B0_2D model when compared to the Dual-head Res2Net_3D3F and Dual-head Res2Net_3D23F. CONCLUSION: Findings of this study suggest that utilizing deep convolutional neural network is a feasible approach for predicting pathological LVI in patients with NSCLC.

Lactobacillus rhamnosus GG triggers intestinal epithelium injury in zebrafish revealing host dependent beneficial effects.

Lactobacillus rhamnosus GG (LGG), the well-characterized human-derived probiotic strain, possesses excellent properties in the maintenance of intestinal homeostasis, immunoregulation and defense against gastrointestinal pathogens in mammals. Here, we demonstrate that the SpaC pilin of LGG causes intestinal epithelium injury by inducing cell pyroptosis and gut microbial dysbiosis in zebrafish. Dietary SpaC activates Caspase-3-GSDMEa pathways in the intestinal epithelium, promotes intestinal pyroptosis and increases lipopolysaccharide (LPS)-producing gut microbes in zebrafish. The increased LPS subsequently activates Gaspy2-GSDMEb pyroptosis pathway. Further analysis reveals the Caspase-3-GSDMEa pyroptosis is initiated by the species-specific recognition of SpaC by TLR4ba, which accounts for the species-specificity of the SpaC-inducing intestinal pyroptosis in zebrafish. The observed pyroptosis-driven gut injury and microbial dysbiosis by LGG in zebrafish suggest that host-specific beneficial/harmful mechanisms are critical safety issues when applying probiotics derived from other host species and need more attention.

Prediction of short-term progression of COVID-19 pneumonia based on chest CT artificial intelligence: during the Omicron epidemic.

BACKGROUND AND PURPOSE: The persistent progression of pneumonia is a critical determinant of adverse outcomes in patients afflicted with COVID-19. This study aimed to predict personalized COVID-19 pneumonia progression between the duration of two weeks and 1 month after admission by integrating radiological and clinical features. METHODS: A retrospective analysis, approved by the Institutional Review Board, encompassed patients diagnosed with COVID-19 pneumonia between December 2022 and February 2023. The cohort was divided into training and validation groups in a 7:3 ratio. A trained multi-task U-Net network was deployed to segment COVID-19 pneumonia and lung regions in CT images, from which quantitative features were extracted. The eXtreme Gradient Boosting (XGBoost) algorithm was employed to construct a radiological model. A clinical model was constructed by LASSO method and stepwise regression analysis, followed by the subsequent construction of the combined model. Model performance was assessed using ROC and decision curve analysis (DCA), while Shapley's Additive interpretation (SHAP) illustrated the importance of CT features. RESULTS: A total of 214 patients were recruited in our study. Four clinical characteristics and four CT features were identified as pivotal components for constructing the clinical and radiological models. The final four clinical characteristics were incorporated as well as the RS_radiological model to construct the combined prediction model. SHAP analysis revealed that CT score difference exerted the most significant influence on the predictive performance of the radiological model. The training group's radiological, clinical, and combined models exhibited AUC values of 0.89, 0.72, and 0.92, respectively. Correspondingly, in the validation group, these values were observed to be 0.75, 0.72, and 0.81. The DCA curve showed that the combined model exhibited greater clinical utility than the clinical or radiological models. CONCLUSION: Our novel combined model, fusing quantitative CT features with clinical characteristics, demonstrated effective prediction of COVID-19 pneumonia progression from 2 weeks to 1 month after admission. This comprehensive model can potentially serve as a valuable tool for clinicians to develop personalized treatment strategies and improve patient outcomes.

A review of the pollution signatures of polycyclic aromatic hydrocarbons in the sediments of the East China Sea.

Marine sediments serve as crucial reservoirs for polycyclic aromatic hydrocarbons (PAHs), and their PAH signatures offer valuable historical pollution records. This article provides a comprehensive review of the pollution status of 16 priority PAHs in more than 1000 sediments from the East China Sea (ECS). It focuses on the PAH sources, spatiotemporal distributions, driving factors, and ecological risks, with information derived from peer-reviewed papers published between 2003 and 2023. The results revealed that vehicular emissions, mixed combustion sources of coal, biomass, and coke, as well as petrogenic sources, were the primary contributors to PAH pollution in the ECS sediments, accounting for 50%, 34%, and 16%, respectively. Human activities, hydrodynamic mechanisms, and environmental variables such as particle size and organic matter, collectively influenced the distribution of PAHs. Additionally, the population size and economic development played a key role in the temporal distribution of PAHs in the ECS sediments. The ecotoxicity assessment of PAHs in sediments indicated a low risk level. These outcomes are expected to provide environmentalists with detailed and up-to-date insights into sedimentary PAHs in the ECS, helping to develop suitable monitoring plans and strategies for promoting better management of ECS environment.

Control of Fusarium Head Blight of Wheat with Bacillus velezensis E2 and Potential Mechanisms of Action.

Wheat plants are impacted by Fusarium head blight (FHB) infection, which poses a huge threat to wheat growth, development, storage and food safety. In this study, a fungal strain was isolated from diseased wheat plants and identified as Fusarium asiaticum F1, known to be a member of the Fusarium graminearum species complex, agents causally responsible for FHB. In order to control this disease, new alternatives need to be developed for the use of antagonistic bacteria. Bacillus velezensis E2 (B. velezensis E2), isolated from a previous investigation in our laboratory, showed a notable inhibitory effect on F. asiaticum F1 growth and deoxynivalenol (DON) synthesis in grains. The spore germination of F. asiaticum F1 was significantly reduced and the spores showed vesicular structures when treated with B. velezensis E2. Observations using scanning electron microscopy (SEM) showed that the hyphae of F. asiaticum F1 were shrunken and broken when treated with B. velezensis E2. The RNA-seq results of F1 hyphae treated with B. velezensis E2 showed that differentially expressed genes (DEGs), which were involved in multiple metabolic pathways such as toxin synthesis, autophagy process and glycan synthesis, especially the genes associated with DON synthesis, were significantly downregulated. In summary, those results showed that B. velezensis E2 could inhibit F. asiaticum F1 growth and reduce the gene expression of DON synthesis caused by F1. This study provides new insights and antagonistic mechanisms for the biological control of FHB during wheat growth, development and storage.

Association of body mass index with rapid eye movement sleep behavior disorder in Parkinson's disease.

Background: The association between body mass index (BMI) and rapid eye-movement (REM) sleep-related behavioral disorder (RBD) in Parkinson's disease (PD) remains unknown. Our study was to investigate the association of BMI with RBD in PD patients. Methods: In this cross-sectional study, a total of 1,115 PD participants were enrolled from Parkinson's Progression Markers Initiative (PPMI) database. BMI was calculated as weight divided by height squared. RBD was defined as the RBD questionnaire (RBDSQ) score with the cutoff of 5 or more assessed. Univariable and multivariable logistic regression models were performed to examine the associations between BMI and the prevalence of RBD. Non-linear correlations were explored with use of restricted cubic spline (RCS) analysis. And the inflection point was determined by the two-line piecewise linear models. Results: We identified 426 (38.2%) RBD. The proportion of underweight, normal, overweight and obese was 2.61, 36.59, 40.36, and 20.44%, respectively. In the multivariate logistic regression model with full adjustment for confounding variables, obese individuals had an odds ratio of 1.77 (95% confidence interval: 1.21 to 2.59) with RBD compared with those of normal weight. In the RCS models with three knots, BMI showed a non-linear association with RBD. The turning points of BMI estimated from piecewise linear models were of 28.16 kg/m2, 28.10 kg/m2, and 28.23 kg/m2 derived from univariable and multivariable adjusted logistic regression models. The effect modification by depression on the association between BMI and RBD in PD was also found in this study. Furthermore, the sensitivity analyses linked with cognition, education, and ethnic groups indicated the robustness of our results. Conclusion: The current study found a significant dose-response association between BMI and RBD with a depression-based difference in the impact of BMI on RBD in PD patients.

Diabetes mellitus as a risk factor for severe dengue fever and West Nile fever: A meta-analysis.

BACKGROUND: Dengue fever (DF) and West Nile fever (WNF) have become endemic worldwide in the last two decades. Studies suggest that individuals with diabetes mellitus (DM) are at a higher risk of developing severe complications from these diseases. Identifying the factors associated with a severe clinical presentation is crucial, as prompt treatment is essential to prevent complications and fatalities. This article aims to summarize and assess the published evidence regarding the link between DM and the risk of severe clinical manifestations in cases of DF and WNF. METHODOLOGY/PRINCIPAL FINDINGS: A systematic search was conducted using the PubMed and Web of Science databases. 27 studies (19 on DF, 8 on WNF) involving 342,873 laboratory-confirmed patients were included in the analysis. The analysis showed that a diagnosis of DM was associated with an increased risk for severe clinical presentations of both DF (OR 3.39; 95% CI: 2.46, 4.68) and WNF (OR 2.89; 95% CI: 1.89, 4.41). DM also significantly increased the risk of death from both diseases (DF: OR 1.95; 95% CI: 1.09, 3.52; WNF: OR 1.74; 95% CI: 1.40, 2.17). CONCLUSIONS/SIGNIFICANCE: This study provides strong evidence supporting the association between DM and an increased risk of severe clinical manifestations in cases of DF and WNF. Diabetic individuals in DF or WNF endemic areas should be closely monitored when presenting with febrile symptoms due to their higher susceptibility to severe disease. Early detection and appropriate management strategies are crucial in reducing the morbidity and mortality rates associated with DF and WNF in diabetic patients. Tailored care and targeted public health interventions are needed to address this at-risk population. Further research is required to understand the underlying mechanisms and develop effective preventive and therapeutic approaches.

Predictive value of radiomic features extracted from primary lung adenocarcinoma in forecasting thoracic lymph node metastasis: a systematic review and meta-analysis.

BACKGROUND: The application of radiomics in thoracic lymph node metastasis (LNM) of lung adenocarcinoma is increasing, but diagnostic performance of radiomics from primary tumor to predict LNM has not been systematically reviewed. Therefore, this study sought to provide a general overview regarding the methodological quality and diagnostic performance of using radiomic approaches to predict the likelihood of LNM in lung adenocarcinoma. METHODS: Studies were gathered from literature databases such as PubMed, Embase, the Web of Science Core Collection, and the Cochrane library. The Radiomic Quality Score (RQS) and the Quality Assessment of Diagnostic Accuracy Studies-2 (QUADAS-2) were both used to assess the quality of each study. The pooled sensitivity, specificity, and area under the curve (AUC) of the best radiomics models in the training and validation cohorts were calculated. Subgroup and meta-regression analyses were also conducted. RESULTS: Seventeen studies with 159 to 1202 patients each were enrolled between the years of 2018 to 2022, of which ten studies had sufficient data for the quantitative evaluation. The percentage of RQS was between 11.1% and 44.4% and most of the studies were considered to have a low risk of bias and few applicability concerns in QUADAS-2. Pyradiomics and logistic regression analysis were the most commonly used software and methods for radiomics feature extraction and selection, respectively. In addition, the best prediction models in seventeen studies were mainly based on radiomics features combined with non-radiomics features (semantic features and/or clinical features). The pooled sensitivity, specificity, and AUC of the training cohorts were 0.84 (95% confidence interval (CI) [0.73-0.91]), 0.88 (95% CI [0.81-0.93]), and 0.93(95% CI [0.90-0.95]), respectively. For the validation cohorts, the pooled sensitivity, specificity, and AUC were 0.89 (95% CI [0.82-0.94]), 0.86 (95% CI [0.74-0.93]) and 0.94 (95% CI [0.91-0.96]), respectively. CONCLUSIONS: Radiomic features based on the primary tumor have the potential to predict preoperative LNM of lung adenocarcinoma. However, radiomics workflow needs to be standardized to better promote the applicability of radiomics. TRIAL REGISTRATION: CRD42022375712.

Molecular engineering design of twisted-backbone pure Type-I organic photosensitizers for hypoxic photodynamic therapy.

Photodynamic therapy (PDT), an emerging tumor therapeutic strategy has received tremendous attention. Enslaved by the high dependence of oxygen, Type-II photosensitizers (PSs) mediated PDT is restricted by the hypoxic environment of tumors. By transferring electrons to water or other substrates instead of oxygen, Type-I PSs hold the promise of achieving an ideal therapeutic effect under hypoxic conditions. In this study, three twisted-backbone PSs (CBz-TQs-1, CBz-TQs-2 and CBz-TQs-3) are synthesized and studied. Owing to different substituent effects, the ROS generation mechanism transfers from pure Type-II of their prototype PSs (TQs-1, TQs-2 and TQs-3) to mixed Type-I/II of CBz-TQs-1 and CBz-TQs-2 to pure Type-I of CBz-TQs-3. Moreover, CBz-TQs-3 exhibits an ultra-high ROS quantum yield (∼1.0). The in vitro and in vivo PDT effects of water-dissolvable nanoparticles (NPs) of CBz-TQs-3 are investigated. The results show that the phototoxicity of CBz-TQs-3 is not affected by hypoxic environments. In addition, a remarkable tumor ablation can be found after CBz-TQs-3 NPs mediated PDT on Balb/c mice with xenograft tumors. It proves that a twisted backbone strategy is beneficial for designing pure Type-I PSs with high-efficient hypoxic PDT.

Spatiotemporal multi-omics: exploring molecular landscapes in aging and regenerative medicine.

Aging and regeneration represent complex biological phenomena that have long captivated the scientific community. To fully comprehend these processes, it is essential to investigate molecular dynamics through a lens that encompasses both spatial and temporal dimensions. Conventional omics methodologies, such as genomics and transcriptomics, have been instrumental in identifying critical molecular facets of aging and regeneration. However, these methods are somewhat limited, constrained by their spatial resolution and their lack of capacity to dynamically represent tissue alterations. The advent of emerging spatiotemporal multi-omics approaches, encompassing transcriptomics, proteomics, metabolomics, and epigenomics, furnishes comprehensive insights into these intricate molecular dynamics. These sophisticated techniques facilitate accurate delineation of molecular patterns across an array of cells, tissues, and organs, thereby offering an in-depth understanding of the fundamental mechanisms at play. This review meticulously examines the significance of spatiotemporal multi-omics in the realms of aging and regeneration research. It underscores how these methodologies augment our comprehension of molecular dynamics, cellular interactions, and signaling pathways. Initially, the review delineates the foundational principles underpinning these methods, followed by an evaluation of their recent applications within the field. The review ultimately concludes by addressing the prevailing challenges and projecting future advancements in the field. Indubitably, spatiotemporal multi-omics are instrumental in deciphering the complexities inherent in aging and regeneration, thus charting a course toward potential therapeutic innovations.

Treg-Derived Extracellular Vesicles: Roles in Diseases and Theranostics.

Regulatory T cells (Tregs), a subset of CD4+ T cells, are indispensable in maintaining immune self-tolerance and have been utilized in various diseases. Treg-derived extracellular vesicles (Treg-EVs) have been discovered to play an important role in the mechanism of Treg functions. As cell-derived membranous particles, EVs carry multiple bioactive substances that possess tremendous potential for theranostics. Treg-EVs are involved in numerous physiological and pathological processes, carrying proteins and miRNAs inherited from the parental cells. To comprehensively understand the function of Treg-EVs, here we reviewed the classification of Treg-EVs, the active molecules in Treg-EVs, their various applications in diseases, and the existing challenges for Treg-EVs based theranostics. This Review aims to clarify the feasibility and potential of Treg-EVs in diseases and theranostics, facilitating further research and application of Treg-EVs.

Deficit Irrigation Effects on Cotton Growth Cycle and Preliminary Optimization of Irrigation Strategies in Arid Environment.

With the changing global climate, drought stress will pose a considerable challenge to the sustainable development of agriculture in arid regions. The objective of this study was to explore the resistance and water demand of cotton plants to water stress during the flowering and boll setting stage. The experimental plot was in Huaxing Farm of Changji city. The plots were irrigated, respectively, at 100% (as the control), 90%, 85% and 80% of the general irrigation amount in the local area. The relationship between the various measured indexes and final yield under different deficit irrigation (DI) treatments was studied. The results showed that deficit irrigation impacted the growth and development processes of cotton during the flowering and boll setting stage. There was a high negative correlation (R2 > 0.95) between the maximum leaf area index and yield. Similarly, there was a high correlation between malondialdehyde content and yield. Meanwhile, 90% of the local cotton irrigation contributed to water saving and even increasing cotton yield. Furthermore, based on the results, the study made an initial optimization to the local irrigation scheme by utilizing the DSSAT model. It was found that changing the irrigation interval to 12 days during the stage could further enhance cotton yield and conserve resources.