Search

Abstract Background Compared with open radical prostatectomy (RP), laparoscopic or robot-assisted RP have shown a notable decrease in the incidence of organ dysfunction or potentially life-threating complications after surgery. However, despite advances, the postoperative length of hospital stay (LOS) remains longer than desired in many cases. The Postoperative Morbidity Survey (POMS) is a simple approach to detect complications capable of prolonging LOS. The primary objective of this study was to outline the incidence and type of early morbidity following laparoscopic or robot-assisted RP using modified POMS(m-POMS). The secondary objective was to investigate the correlation between m-POMS and postoperative LOS. Methods A retrospective study of the electronic health records was performed for all eligible patients undergoing laparoscopic or robot-assisted RP over a one-year period (August 1, 2022 to July 31, 2023). Morbidity as defined by m-POMS was collected on postoperative day 1 (POD1), POD3, POD5 and POD8. Poisson regression models were employed to assess the correlation between positive m-POMS and postoperative LOS. Results A total of 121 patients were included. Morbidity, as measured by m-POMS, occurred on POD1 (19.01%, 95% CI [13.01%, 26.91%]), POD3 (18.81%, 95% CI [12.39%, 27.52%]), POD5 (30.23%, 95% CI [18.60%, 45.10%]) and POD8 (35.29%, 95% CI [17.31%, 58.70%]). Two prevalent domains with positive m-POMS scores were infectious and pulmonary. The occurrence of morbidity as indicated by m-POMS was correlated with longer median (IQR) postoperative LOS on POD1 7 (5, 9) versus 4 (4, 6), POD3 7 (6, 11) versus 5(4, 6), and POD5 11 (6.5, 11) versus 7(6, 9) (p

Background Nowadays, cardiac rehabilitation is mostly transferred from the hospital to a home environment. Mobile health applications assist patients in bridging the support gap from hospital to home environment, which encourages home-based recovery processes. Unfortunately, numerous quality difficulties accompany its promise, and the service quality of cardiac rehabilitation applications must be ＂confirmed＂ as soon as possible. Objective To develop a quality assessment indicator system for home-based rehabilitation APP services for heart failure patients, and to provide a model for standardizing and increasing the quality of mobile health APP. Methods This study was conducted from July 2022 to January 2023, using semi-structured interviews, literature review, the expert consultation method and analytic hierarchy process (AHP) to determine the content of indicator system and the weight for each index. Results 2 rounds of expert consultations resulted in the effective response rates of 100%, with the authority coefficients of 0.867, and the coordination coefficients of 0.128-0.154 and 0.151-0.197 respectively (P

Abstract Government collaboration tasks are integral for grassroots governance and essential for government administration. Large-scale government collaboration tasks often involve multiple departments working together to solve complex tasks that require handling large amounts of data. However, existing offline processes make it difficult to manage complex collaboration scenarios and obtain timely execution results. We introduce the concept of a spatio-temporal task tree and propose a log-based incremental update method for updating statistical values in the tree structure to fulfill the need for real-time aggregation analysis of cross-temporal task execution situations. The spatio-temporal task tree fosters collaboration across various departments and organizational hierarchies to execute complex and large-scale tasks in government effectively. By flexible adaptations to different business models, this framework addresses the shortcomings of conventional systems in promoting inter-organizational collaboration. The log-based incremental update method for calculating statistical values within a tree structure allows for the updating of multi-dimensional data statistics in seconds. This approach effectively decreases computational expenses, enhances query efficiency, and improves real-time computation response. Results from experiments demonstrate the feasibility and high availability of the proposed approach, showcasing their potential to address the challenges associated with large-scale collaborative tasks in government affairs.

Abstract Active surveillance (AS) is the primary strategy for managing patients with low or favorable-intermediate risk prostate cancer (PCa). Identifying patients who may benefit from AS relies on unpleasant prostate biopsies, which entail the risk of bleeding and infection. In the current study, we aimed to develop a radiomics model based on prostate magnetic resonance images to identify AS candidates non-invasively. A total of 956 PCa patients with complete biopsy reports from six hospitals were included in the current multicenter retrospective study. The National Comprehensive Cancer Network (NCCN) guidelines were used as reference standards to determine the AS candidacy. To discriminate between AS and non-AS candidates, five radiomics models (i.e., eXtreme Gradient Boosting (XGBoost) AS classifier (XGB-AS), logistic regression (LR) AS classifier, random forest (RF) AS classifier, adaptive boosting (AdaBoost) AS classifier, and decision tree (DT) AS classifier) were developed and externally validated using a three-fold cross-center validation based on five classifiers: XGBoost, LR, RF, AdaBoost, and DT. Area under the receiver operating characteristic curve (AUC), accuracy (ACC), sensitivity (SEN), and specificity (SPE) were calculated to evaluate the performance of these models. XGB-AS exhibited an average of AUC of 0.803, ACC of 0.693, SEN of 0.668, and SPE of 0.841, showing a better comprehensive performance than those of the other included radiomic models. Additionally, the XGB-AS model also presented a promising performance for identifying AS candidates from the intermediate-risk cases and the ambiguous cases with diagnostic discordance between the NCCN guidelines and the Prostate Imaging-Reporting and Data System assessment. These results suggest that the XGB-AS model has the potential to help identify patients who are suitable for AS and allow non-invasive monitoring of patients on AS, thereby reducing the number of annual biopsies and the associated risks of bleeding and infection.

The purpose of this study is to develop a high-energy neutron shielding material applied in proton therapy environment. Composite shielding material consisting of 10.00 wt% boron carbide particles (B4C), 13.64 wt% surface-modified cross-linked polyethylene (PE), and 76.36 wt% tungsten particles were fabricated by hot-pressure sintering method, where the optimal ratio of the composite is determined by the shielding effect under the neutron field generated in typical proton therapy environment. The results of Differential Scanning Calorimetry measurements (DSC) and tensile experiment show that the composite has good thermal and mechanical properties. In addition, the high energy-neutron shielding performance of the developed material was evaluated using cyclotron proton accelerator with 100 MeV proton. The simulation shows a 99.99% decrease in fast neutron injection after 44 cm shielding, and the experiment result show a 99.70% decrease. Finally, the shielding effect of replacing part of the shielding material of the proton therapy hall with the developed material was simulated, and the results showed that the total neutron injection decreased to 0.99‰ and the neutron dose reduced to 1.10‰ before the enhanced shielding. In summary, the developed material is expected to serve as a shielding enhancement material in the proton therapy environment.

Background Telerehabilitation based on digital medical care can efficiently improve the health status of patients after radiofrequency ablation of atrial fibrillation. However, the current participation rate in telerehabilitation is low. Objective To analyse the reasons for refusal of exercise rehabilitation in patients after radiofrequency ablation of atrial fibrillation in the context digital medical care based on the theory of leisure constraints. Methods Patients after radiofrequency ablation of atrial fibrillation in the inpatient department or outpatient clinic of Department of Cardiology, at the First Affiliated Hospital of Nanjing Medical University from July to September 2022 were selected as the study subjects by using the purposive sampling method. The phenomenological approach was adopted to collect data from patients who refused exercise telerehabilitation after radiofrequency ablation through semi-structured interviews, and Colaizzi analysis was used to summarize the reasons. Results A total of 14 patients were finally included in this study. Three themes including self-limiting factors, interpersonal limiting factors, and structural limiting factors, and twelve sub-themes were extracted, namely, low level of digital literacy, negative illness perception, psychological distress caused by disease, digital medical trust crisis, deep-rooted personal exercise habits, alienation sense from rehabilitation team, insufficient social network establishment, economic burden related to equipment acquisition, harsh climate, low rehabilitation service capacity in primary care, constraints of available time by role pressure, and poor applicability of wearable devices. Conclusion The reasons for refusal to exercise telerehabilitation include lack of literacy and trust in digital medical care, high level of illness perception and psychological distress, poor exercise habits, rehabilitation team and peer alienation, economic burden, harsh climate, lack of capacity and personal time for rehabilitation service capacity in primary care, and inadequate applicability of existing wearable devices.

Abstract Biodetoxification fungus selectively degrades toxic inhibitors generated from pretreatment of lignocellulose without consuming fermentable sugars. However, one barrier for practical application is the sustained cell viability in the consequent fermentation step to compete the fermentable sugars with fermenting strains, resulting in sugar loss and reduced target product yield. This study investigated the competitive growth property between the biodetoxification fungus Paecilomyces variotii FN89 and the L-lactic acid bacterium Pediococcus acidilactici ZY271 under varying temperature and lactic acid osmatic stress. The results show that the L-lactic acid bacterium Ped. acidilactici ZY271 showed less thermotolerance to Pae. variotii FN89 at high temperature of 45 °C to 50 °C in both synthetic medium and wheat straw hydrolysate. In the higher temperature environment, the growth of the biodetoxification strian failed to compete with the lactic acid fermentation strain and was quickly eliminated from the fermentation system. The high temperature fermentation facilitated a fast transition from the detoxification stage to the fermentation stage for higher production of L-lactic acid.

Abstract Cancer is a heterogeneous disease. Although both tumor metabolism and tumor immune microenvironment are recognized as driving factors in tumorigenesis, the relationship between them is still not well-known, and potential combined targeting approaches remain to be identified. Here, we demonstrated a negative correlation between the expression of NAMPT, an NAD+ metabolism enzyme, and PD-L1 expression in various cancer cell lines. A clinical study showed that a NAMPTHigh PD-L1Low expression pattern predicts poor prognosis in patients with various cancers. In addition, pharmacological inhibition of NAMPT results in the transcription upregulation of PD-L1 by SIRT-mediated acetylation change of NF-κB p65, and blocking PD-L1 would induce NAMPT expression through a HIF-1-dependent glycolysis pathway. Based on these findings, we designed and synthesized a dual NAMPT/PD-L1 targeting compound, LZFPN-90, which inhibits cell growth in a NAMPT-dependent manner and blocks the cell cycle, subsequently inducing apoptosis. Under co-culture conditions, LZFPN-90 treatment contributes to the proliferation and activation of T cells and blocks the growth of cancer cells. Using mice bearing genetically manipulated tumors, we confirmed that LZFPN-90 exerted target-dependent antitumor activities, affecting metabolic processes and the immune system. In conclusion, our results demonstrate the relevance of NAD+-related metabolic processes in antitumor immunity and suggest that co-targeting NAD+ metabolism and PD-L1 represents a promising therapeutic approach.

Abstract Introduction Observational studies and clinical trials have supported the association between gut microbiota and psoriatic arthritis. However, the causal link between gut microbiota and psoriatic arthritis is still unclear. Methods A two-sample bi-directional Mendelian randomization analysis was performed using the summary statistics of gut microbiota from the largest available genome-wide association study meta-analysis (n = 13,266) conducted by the MiBioGen consortium. The summary statistics of psoriatic arthritis were extracted directly from the FinnGen consortium, which consists of 3186 psoriatic arthritis patients and 24,086 controls. Sensitivity analyses were conducted to assess the validity of our findings. Enrichment analyses were used to investigate the biofunction and pathways. Results Inverse variance weighted (IVW) estimates suggested that family Rikenellaceae (P = 0.032) and genus Ruminococcaceae UCG011 (P = 0.014) had a detrimental effect on psoriatic arthritis. We also noticed the negative association between the class Methanobacteria (P = 0.032), order Methanobacteriales (P = 0.032), family Methanobacteriaceae (P = 0.032), genus Eubacterium fissicatena group (P = 0.010), genus Methanobrevibacter (P = 0.031), and genus Butyricicoccus (P = 0.041) with psoriatic arthritis. Sensitivity analyses showed that genus Butyricicoccus had pleiotropy and heterogeneity. According to the results of reverse MR analysis, the causal effect of psoriatic arthritis was found on six taxa, respectivelyc family Clostridiaceae1, family Defluviitaleaceae, genus Butyrivibrio, genus Defluviitaleaceae UCG011, genus Clostridium sensu stricto1, and genus Ruminococcaceae UCG011. Conclusion This two-sample bidirectional Mendelian randomization analysis suggested that the gut microbiota had a causal effect on psoriatic arthritis and implied the potential role of probiotics in the management and prevention of psoriatic arthritis.

Background: Sepsis is a life-threatening condition marked by a severe systemic response to infection, leading to widespread inflammation, cellular signaling disruption, and metabolic dysregulation. The role of lipid and amino acid metabolism in sepsis is not fully understood, but aberrations in this pathway could contribute to the disease's pathophysiology. Methods: To explore the potential of lipid and amino acid compounds as biomarkers for the diagnosis and prognosis of sepsis, a two-sample Mendelian Randomization (MR) study was conducted, examining the relationship between sepsis and 249 serum lipid and amino acid-related markers. Key enzymes involved in synthesis of phosphatidylcholine, including choline/ethanolamine phosphotransferase 1 (CEPT1), choline phosphotransferase 1 (CPT1), and ethanolamine phosphotransferase 1 (EPT1), were also targeted for drug-target Mendelian randomization. Results: The study found that phosphatidylcholines (OR IVW: 0.88, 95%CI: 0.80–0.96, p = 0.005) and phospholipids in medium HDL (OR IVW: 0.86, 95%CI: 0.77–0.96, p = 0.007) potentially exhibit a protective effect against sepsis nominally. However, the potential drug target of CEPT1, CPT1, and EPT1 was found to be unrelated to septic outcomes. Conclusion: Our findings suggest that increasing levels of phosphatidylcholines and medium HDL phospholipids may reduce the incidence of sepsis. This highlights the potential of lipid-based biomarkers in the diagnosis and management of sepsis, opening avenues for new therapeutic strategies.

Abstract Purpose To develop and evaluate machine learning models based on MRI to predict clinically significant prostate cancer (csPCa) and International Society of Urological Pathology (ISUP) grade group as well as explore the potential value of radiomics models for improving the performance of radiologists for Prostate Imaging Reporting and Data System (PI-RADS) assessment. Material and methods A total of 1616 patients from 4 tertiary care medical centers were retrospectively enrolled. PI-RADS assessments were performed by junior, senior, and expert-level radiologists. The radiomics models for predicting csPCa were built using 4 machine-learning algorithms. The PI-RADS were adjusted by the radiomics model. The relationship between the Rad-score and ISUP was evaluated by Spearman analysis. Results The radiomics models made using the random forest algorithm yielded areas under the receiver operating characteristic curves (AUCs) of 0.874, 0.876, and 0.893 in an internal testing cohort and external testing cohorts, respectively. The AUC of the adjusted_PI-RADS was improved, and the specificity was improved at a slight sacrifice of sensitivity. The participant-level correlation showed that the Rad-score was positively correlated with ISUP in all testing cohorts (r > 0.600 and p

Abstract Exploring non-genetic evolution of cell states during cancer treatments has become attainable by recent advances in lineage-tracing methods. However, transcriptional changes that drive cells into resistant fates may be subtle, necessitating high resolution analysis. Here, we present ReSisTrace that uses shared transcriptomic features of sister cells to predict the states priming treatment resistance. Applying ReSisTrace in ovarian cancer cells perturbed with olaparib, carboplatin or natural killer (NK) cells reveals pre-resistant phenotypes defined by proteostatic and mRNA surveillance features, reflecting traits enriched in the upcoming subclonal selection. Furthermore, we show that DNA repair deficiency renders cells susceptible to both DNA damaging agents and NK killing in a context-dependent manner. Finally, we leverage the obtained pre-resistance profiles to predict and validate small molecules driving cells to sensitive states prior to treatment. In summary, ReSisTrace resolves pre-existing transcriptional features of treatment vulnerability, facilitating both molecular patient stratification and discovery of synergistic pre-sensitizing therapies.

Abstract Background Predictive biomarkers of immune checkpoint inhibitor (ICI) efficacy are currently lacking for non-small cell lung cancer (NSCLC). Here, we describe the results from the Anti–PD-1 Response Prediction DREAM Challenge, a crowdsourced initiative that enabled the assessment of predictive models by using data from two randomized controlled clinical trials (RCTs) of ICIs in first-line metastatic NSCLC. Methods Participants developed and trained models using public resources. These were evaluated with data from the CheckMate 026 trial (NCT02041533), according to the model-to-data paradigm to maintain patient confidentiality. The generalizability of the models with the best predictive performance was assessed using data from the CheckMate 227 trial (NCT02477826). Both trials were phase III RCTs with a chemotherapy control arm, which supported the differentiation between predictive and prognostic models. Isolated model containers were evaluated using a bespoke strategy that considered the challenges of handling transcriptome data from clinical trials. Results A total of 59 teams participated, with 417 models submitted. Multiple predictive models, as opposed to a prognostic model, were generated for predicting overall survival, progression-free survival, and progressive disease status with ICIs. Variables within the models submitted by participants included tumor mutational burden (TMB), programmed death ligand 1 (PD-L1) expression, and gene-expression–based signatures. The best-performing models showed improved predictive power over reference variables, including TMB or PD-L1. Conclusions This DREAM Challenge is the first successful attempt to use protected phase III clinical data for a crowdsourced effort towards generating predictive models for ICI clinical outcomes and could serve as a blueprint for similar efforts in other tumor types and disease states, setting a benchmark for future studies aiming to identify biomarkers predictive of ICI efficacy. Trial registration: CheckMate 026; NCT02041533, registered January 22, 2014. CheckMate 227; NCT02477826, registered June 23, 2015.

Abstract Background The detection of local recurrence for prostate cancer (PCa) patients following radical prostatectomy (RP) is challenging and can influence the treatment plan. Our aim was to construct and verify machine learning models with three different algorithms based on post-operative mpMRI for predicting local recurrence of PCa after RP and explore their potential clinical value compared with the Prostate Imaging for Recurrence Reporting (PI-RR) score of expert-level radiologists. Methods A total of 176 patients were retrospectively enrolled and randomly divided into training (n = 123) and testing (n = 53) sets. The PI-RR assessments were performed by two expert-level radiologists with access to the operative histopathological and pre-surgical clinical results. The radiomics models to predict local recurrence were built by utilizing three different algorithms (i.e., support vector machine [SVM], linear discriminant analysis [LDA], and logistic regression-least absolute shrinkage and selection operator [LR-LASSO]). The combined model integrating radiomics features and PI-RR score was developed using the most effective classifier. The classification performances of the proposed models were assessed by receiver operating characteristic (ROC) curve analysis. Results There were no significant differences between the training and testing sets concerning age, prostate-specific antigen (PSA), Gleason score, T-stage, seminal vesicle invasion (SVI), perineural invasion (PNI), and positive surgical margins (PSM). The radiomics model based on LR-LASSO exhibited superior performance than other radiomics models, with an AUC of 0.858 in the testing set; the PI-RR yielded an AUC of 0.833, and there was no significant difference between the best radiomics model and the PI-RR score. The combined model achieved the best predictive performance with an AUC of 0.924, and a significant difference was observed between the combined model and PI-RR score. Conclusions Our radiomics model is an effective tool to predict PCa local recurrence after RP. By integrating radiomics features with the PI-RR score, our combined model exhibited significantly better predictive performance of local recurrence than expert-level radiologists’ PI-RR assessment.

Since its initial discovery in sour milk by Swedish chemist C [...]

Leukemia caused by environmental chemical pollutants has attracted great attention, the malignant leukemic transformation model of TK6 cells induced by hydroquinone (HQ) has been previously found in our team. However, the type of leukemia corresponding to this malignant transformed cell line model needs further study and interpretation. Furthermore, the molecular mechanism of malignant proliferation of leukemic cells induced by HQ remains unclear. This study is the first to reveal the expression of aberrant genes in leukemic cells of HQ-induced malignant transformation, which may correspond to chronic lymphocytic leukemia (CLL). The expression of Linc01588, a long non-coding RNA (lncRNA), was significantly up-regulated in CLL patients and leukemic cell line model which previously described. After gain-of-function assays and loss-of-function assays, feeble cell viability, severe apoptotic phenotype and the increased secretion of TNF-α were easily observed in malignant leukemic TK6 cells with Linc01588 deletion after HQ intervention. The tumors derived from malignant TK6 cells with Linc01588 deletion inoculated subcutaneously in nude mice were smaller than controls. In CLL and its cell line model, the expression of Linc01588 and miR-9–5p, miR-9–5p and SIRT1 were negative correlation respectively in CLL and cell line model, while the expression of Linc01588 and SIRT1 were positive correlation. The dual-luciferase reporter assay showed that Linc01588 & miR-9–5p, miR-9–5p & SIRT1 could bind directly, respectively. Furthermore, knockdown of miR-9–5p successfully rescued the severe apoptotic phenotype and the increased secretion of TNF-α caused by the Linc01588 deletion, the deletion of Linc01588 in human CLL cell line MEC-2 could also inhibit malignant biological characteristics, and the phenotype caused by the deletion of Linc01588 could also be rescued after overexpression of SIRT1. Moreover, the regulation of SIRT1 expression in HQ19 cells by Linc01588 and miR-9–5 P may be related to the Akt/NF-κB pathway. In brief, Linc01588 deletion inhibits the malignant biological characteristics of HQ-induced leukemic cells via miR-9–5p/SIRT1, and it is a novel and hopeful clue for the clinical targeted therapy of CLL.

IntroductionSepsis represents a critical medical condition that arises due to an imbalanced host reaction to infection. Central to its pathophysiology are cytokines. However, observational investigations that explore the interrelationships between circulating cytokines and susceptibility to sepsis frequently encounter challenges pertaining to confounding variables and reverse causality.MethodsTo elucidate the potential causal impact of cytokines on the risk of sepsis, we conducted two-sample Mendelian randomization (MR) analyses. Genetic instruments tied to circulating cytokine concentrations were sourced from genome-wide association studies encompassing 8,293 Finnish participants. We then evaluated their links with sepsis and related outcomes using summary-level data acquired from the UK Biobank, a vast multicenter cohort study involving over 500,000 European participants. Specifically, our data spanned 11,643 sepsis cases and 474,841 controls, with subsets including specific age groups, 28-day mortality, and ICU-related outcomes.Results and DiscussionMR insights intimated that reduced genetically-predicted interleukin-10 (IL-10) levels causally correlated with a heightened sepsis risk (odds ratio [OR] 0.68, 95% confidence interval [CI] 0.52-0.90, P=0.006). An inverse relationship emerged between monocyte chemoattractant protein-1 (MCP-1) and sepsis-induced mortality. Conversely, elevated macrophage inflammatory protein 1 beta (MIP1B) concentrations were positively linked with both sepsis incidence and associated mortality. These revelations underscore the causal impact of certain circulating cytokines on sepsis susceptibility and its prognosis, hinting at the therapeutic potential of modulating these cytokine levels. Additional research is essential to corroborate these connections.

Autophagic flux refers to a series of dynamic process of autophagic bilayer membrane formation, autophagosome formation, autophagolysosomes formation and degradation. The etiology of cataract is complex, including congenital abnormalities in lens development due to genetic mutations, oxidative damage due to aging, abnormalities in glucose metabolism due to diabetes, and proliferation of lens epithelial cells(LECs)stimulated by postoperative inflammatory factor, all of which are associated with the development of cataracts. A growing number of research in recent years have discovered that altering the status of LECs can contribute to the pathophysiological process of cataract by regulating autophagic flux. This review summarized the impacts of autophagic flux regulation on cataract.

Data mining has achieved great success in air traffic management as a technology for learning knowledge from historical data that benefits people. However, data mining can rarely be embedded into the trajectory optimization process since regular optimization algorithms cannot utilize the functional and implicit knowledge extracted from historical data in a general paradigm. To tackle this issue, this research proposes a novel data mining-based trajectory generation method that is compatible with existing optimization algorithms. Firstly, the proposed method generates trajectories by combining various maneuvers learned from operation data instead of reconstructing trajectories with generative models. In such a manner, data mining-based trajectory optimization can be achieved by solving a combinatorial optimization problem. Secondly, the proposed method introduces a majorization–minimization-based adversarial training paradigm to train the generation model with more general loss functions, including non-differentiable flight performance constraints. A case study on Guangzhou Baiyun International Airport was conducted to validate the proposed method. The results illustrate that the trajectory generation model can generate trajectories with high fidelity, diversity, and flyability.

Metschnikowia bicuspidata, a globally distributed opportunistic pathogenic fungus, poses a significant threat to crustaceans in diverse aquatic ecosystems, causing severe diseases. Iron, recognized as a virulence factor, plays a crucial role in successful infection with M. bicuspidata. Therefore, this study aims to investigate the transcriptome response of M. bicuspidata to low- and high-iron conditions. Overall, 1082 differentially expressed genes (DEGs) (FDR < 0.05, |log2FC ≥ 1.5|) were identified, comprising 977 and 105 DEGs, in response to low- and high-iron conditions, respectively. These genes predominantly participate in altering metabolism, cell membranes, or cellular structure, allowing the organism to adapt to varying iron levels. Iron limitation-induced genes play crucial roles in energy metabolism, transport, and catabolism pathways. Moreover, 27 ortholog genes were associated with iron transport and homeostasis, with 7 of them participating in iron uptake and regulation under low-iron conditions. This study contributes to the comprehension of iron homeostasis in aquatic fungi. It may offer potential therapeutic strategies for managing M. bicuspidata diseases.

Highlights A virus-like nanoparticle (reBiosome) was developed via site-specific codon mutation for displaying unnatural amino acid (Azi) on virus envelope protein at a rational site, followed by conjugating weak acid-responsive polyethylene glycol polymer on Azi via bioorthogonal chemistry. The reBiosome exhibited reduced virus-like immunogenicity, prolonged blood circulation and enhanced delivery to weakly acidic disease foci. The reBiosome enabled efficient delivery of gene editing and gene silencing system, demonstrating remarkable therapeutic efficacy in breast cancer and arthritis, respectively.

Abstract Background Evidence shows that genetic factors play important roles in the severity of coronavirus disease 2019 (COVID-19). Sulfatase modifying factor 1 (SUMF1) gene is involved in alveolar damage and systemic inflammatory response. Therefore, we speculate that it may play a key role in COVID-19. Results We found that rs794185 was significantly associated with COVID-19 severity in Chinese population, under the additive model after adjusting for gender and age (for C allele = 0.62, 95% CI = 0.44–0.88, P = 0.0073, logistic regression). And this association was consistent with this in European population Genetics Of Mortality In Critical Care (GenOMICC: OR for C allele = 0.94, 95% CI = 0.90–0.98, P = 0.0037). Additionally, we also revealed a remarkable association between rs794185 and the prothrombin activity (PTA) in subjects (P = 0.015, Generalized Linear Model). Conclusions In conclusion, our study for the first time identified that rs794185 in SUMF1 gene was associated with the severity of COVID-19.

Abstract Interactions between oncogenic proteins contribute to the phenotype and drug resistance. Here, EZH2 (enhancer of zest homolog 2) is identified as a crucial factor that mediates HIF‐1 (hypoxia‐inducible factor) inhibitor resistance. Mechanistically, targeting HIF‐1 enhanced the activity of EZH2 through transcription activation of SUZ12 (suppressor of zest 12 protein homolog). Conversely, inhibiting EZH2 increased HIF‐1α transcription, but not the transcription of other HIF family members. Additionally, the negative feedback regulation between EZH2 and HIF‐1α is confirmed in lung cancer patient tissues and a database of cell lines. Moreover, molecular prediction showed that a newly screened dual‐target compound, DYB‐03, forms multiple hydrogen bonds with HIF‐1α and EZH2 to effectively inhibit the activity of both targets. Subsequent studies revealed that DYB‐03 could better inhibit migration, invasion, and angiogenesis of lung cancer cells and HUVECs in vitro and in vivo compared to single agent. DYB‐03 showed promising antitumor activity in a xenograft tumor model by promoting apoptosis and inhibiting angiogenesis, which could be almost abolished by the deletion of HIF‐1α and EZH2. Notably, DYB‐03 could reverse 2‐ME2 and GSK126‐resistance in lung cancer. These findings clarified the molecular mechanism of cross‐regulation of HIF‐1α and EZH2, and the potential of DYB‐03 for clinical combination target therapy.

Fast neutron radiography technology has unique application advantages in the field of non-destructive testing. However, due to the interaction between the gamma rays and the charge-coupled device camera, a large amount of gamma white spots noise is attached to the fast neutron images, which affects the subsequent analysis. In this paper, we propose a simple and effective method to remove gamma white spots noise in fast neutron images. First, the image is down-sampled by the dilated down-sampling method to reduce the influence of noise. Then, the existence of gamma white spots noise is accurately identified by noise detection. Finally, the image is denoised using an improved median filtering method. In order to verify the performance of the proposed method, several commonly used noise reduction methods and the proposed method are used to conduct test experiments on both simulated and fast neutron images, and several image quality evaluation indices are used for quantitative assessment. The results show that the proposed method not only performs well in visual effects but also achieves satisfactory results in evaluation indices.

Background & Aims: Association studies have greatly refined the important role of the major histocompatibility complex (MHC) region in autoimmune hepatitis (AIH). However, the effects of human leucocyte antigen (HLA) polymorphisms on AIH are not well established. The aim of this study is to systematically characterise the association of MHC variants with AIH in our well-defined cohort of patients. Methods: We performed an imputation-based analysis on the extensive association observed within the MHC region using the Han-MHC reference panel, and tested the comprehensive associations of HLA polymorphisms with AIH in 1622 Chinese AIH type 1 patients and 10,466 population controls. Results: A total of 588 HLA variants were significantly associated with AIH, with HLA-B∗35:01 (p = 8.17 × 10−304; odds ratio [OR] = 7.32) contributing the strongest signal. Stepwise conditional analysis revealed additional independent signals at HLA-B∗08:01 (p = 1.35 × 10−33; OR = 4.26) and rs7765379 (p = 5.08 × 10−18; OR = 1.66). A strong link between the lead HLA variant and clinical phenotypes of AIH was observed: patients with HLA-B∗35:01 were less frequently positive for ANA and tended to have higher serum AST and ALT levels at diagnosis, but lower serum IgG levels. Conclusions: Our study reveals three novel and independent variants at HLA-B∗35:01, HLA-B∗08:01, and rs7765379 associated with AIH across the whole MHC region in the Han Chinese population. The findings illustrate the value of the MHC region in AIH and provide a new perspective for the immunogenetics of AIH. Impact and implications: This study revealed three novel and independent variants associated with autoimmune hepatitis across the whole major histocompatibility complex region in the Han Chinese population. These findings are significant in identifying autoantigens, providing insights into the activation of the autoimmune processes, and further advancing our understanding of the immunogenetic basis underlying autoimmune hepatitis.

IntroductionPulmonary fibrosis is a terminal lung disease characterized by fibroblast proliferation, extracellular matrix accumulation, inflammatory damage, and tissue structure destruction. The pathogenesis of this disease, particularly idiopathic pulmonary fibrosis (IPF), remains unknown. Macrophages play major roles in organ fibrosis diseases, including pulmonary fibrosis. The phenotype and polarization of macrophages are closely associated with pulmonary fibrosis. A new direction in research on anti-pulmonary fibrosis is focused on developing drugs that maintain the stability of the pulmonary microenvironment.MethodsWe obtained gene sequencing data and clinical information for patients with IPF from the GEO datasets GSE110147, GSE15197, GSE24988, GSE31934, GSE32537, GSE35145, GSE53845, GSE49072, GSE70864, and GSE90010. We performed GO, KEGG enrichment analysis and GSEA analysis, and conducted weighted gene co-expression network analysis. In addition, we performed proteomic analysis of mouse lung tissue. To verify the results of bioinformatics analysis and proteomic analysis, mice were induced by intratracheal instillation of bleomycin (BLM), and gavaged for 14 days after modeling. Respiratory function of mice in different groups was measured. Lung tissues were retained for histopathological examination, Western Blot and real-time quantitative PCR, etc. In addition, lipopolysaccharide, interferon-γ and interleukin-4 were used to induce RAW264.7 cells for 12h in vitro to establish macrophage inflammation and polarization model. At the same time, HG2 intervention was given. The phenotype transformation and cytokine secretion of macrophages were investigated by Western Blot, RT-qPCR and flow cytometry, etc.ResultsThrough bioinformatics analysis and experiments involving bleomycin-induced pulmonary fibrosis in mice, we confirmed the importance of macrophage polarization in IPF. The analysis revealed that macrophage polarization in IPF involves a change in the phenotypic spectrum. Furthermore, experiments demonstrated high expression of M2-type macrophage-associated biomarkers and inducible nitric oxide synthase, thus indicating an imbalance in M1/M2 polarization of pulmonary macrophages in mice with pulmonary fibrosis.DiscussionOur investigation revealed that the ethyl acetate extract (HG2) obtained from the roots of Prismatomeris connata Y. Z. Ruan exhibits therapeutic efficacy against bleomycin-induced pulmonary fibrosis. HG2 modulates macrophage polarization, alterations in the TGF-β/Smad pathway, and downstream protein expression in the context of pulmonary fibrosis. On the basis of our findings, we believe that HG2 has potential as a novel traditional Chinese medicine component for treating pulmonary fibrosis.

Abstract Purpose To develop machine learning-based radiomics models derive from different MRI sequences for distinction between benign and malignant PI-RADS 3 lesions before intervention, and to cross-institution validate the generalization ability of the models. Methods The pre-biopsy MRI datas of 463 patients classified as PI-RADS 3 lesions were collected from 4 medical institutions retrospectively. 2347 radiomics features were extracted from the VOI of T2WI, DWI and ADC images. The ANOVA feature ranking method and support vector machine classifier were used to construct 3 single-sequence models and 1 integrated model combined with the features of three sequences. All the models were established in the training set and independently verified in the internal test and external validation set. The AUC was used to compared the predictive performance of PSAD with each model. Hosmer–lemeshow test was used to evaluate the degree of fitting between prediction probability and pathological results. Non-inferiority test was used to check generalization performance of the integrated model. Results The difference of PSAD between PCa and benign lesions was statistically significant (P = 0.006), with the mean AUC of 0.701 for predicting clinically significant prostate cancer (internal test AUC = 0.709 vs. external validation AUC = 0.692, P = 0.013) and 0.630 for predicting all cancer (internal test AUC = 0.637 vs. external validation AUC = 0.623, P = 0.036). T2WI-model with the mean AUC of 0.717 for predicting csPCa (internal test AUC = 0.738 vs. external validation AUC = 0.695, P = 0.264) and 0.634 for predicting all cancer (internal test AUC = 0.678 vs. external validation AUC = 0.589, P = 0.547). DWI-model with the mean AUC of 0.658 for predicting csPCa (internal test AUC = 0.635 vs. external validation AUC = 0.681, P = 0.086) and 0.655 for predicting all cancer (internal test AUC = 0.712 vs. external validation AUC = 0.598, P = 0.437). ADC-model with the mean AUC of 0.746 for predicting csPCa (internal test AUC = 0.767 vs. external validation AUC = 0.724, P = 0.269) and 0.645 for predicting all cancer (internal test AUC = 0.650 vs. external validation AUC = 0.640, P = 0.848). Integrated model with the mean AUC of 0.803 for predicting csPCa (internal test AUC = 0.804 vs. external validation AUC = 0.801, P = 0.019) and 0.778 for predicting all cancer (internal test AUC = 0.801 vs. external validation AUC = 0.754, P = 0.047). Conclusions The radiomics model based on machine learning has the potential to be a non-invasive tool to distinguish cancerous, noncancerous and csPCa in PI-RADS 3 lesions, and has relatively high generalization ability between different date set.

Background: The liver is the major metabolic organ of the human body, and abnormal metabolism is the main factor influencing hepatocellular carcinoma (HCC). This study was designed to determine the effect of glutamine metabolism on HCC heterogeneity and to develop a prognostic evaluator based on the heterogeneity study of glutamine metabolism within HCC tumors and between tissues.Methods: Single-cell transcriptome data were extracted from the GSE149614 dataset and processed using the Seurat package in R for quality control of these data. HCC subtypes in the Cancer Genome Atlas and the GSE14520 dataset were identified via consensus clustering based on glutamine family amino acid metabolism (GFAAM) process genes. The machine learning algorithms gradient boosting machine, support vector machine, random forest, eXtreme gradient boosting, decision trees, and least absolute shrinkage and selection operator were utilized to develop the prognosis model of differentially expressed genes among the molecular gene subtypes.Results: The samples in the GSE149614 dataset included 10 cell types, and there was no significant difference in the GFAAM pathway. HCC was classified into three molecular subtypes according to GFAAM process genes, showing molecular heterogeneity in prognosis, clinicopathological features, and immune cell infiltration. C1 showed the worst survival rate and the highest immune score and immune cell infiltration. A six-gene model for prognostic and immunotherapy responses was constructed among subtypes, and the calculated high-risk score was significantly correlated with poor prognosis, high immune abundance, and a low response rate of immunotherapy in HCC.Conclusion: Our discovery of GFAAM-associated marker genes may help to further decipher the role in HCC occurrence and progression. In particular, this six-gene prognostic model may serve as a predictor of treatment and prognosis in HCC patients.

Optically pure lactic acid (LA) is needed in PLA (poly-lactic acid) production to build a crystalline structure with a higher melting point of the biopolymer than that of the racemic mixture. Lignocellulosic biomass can be used as raw material for LA production, in a non-food biorefinery concept. In the present study, genetically engineered P. acidilactici ZP26 was cultivated in a simultaneous saccharification and fermentation (SSF) process using steam pretreated softwood solids as a carbon source to produce optically pure D-LA. Given the low concentrations of identifiable inhibitory compounds from sugar and lignin degradation, the fermentation rate was expected to follow the rate of enzymatic hydrolysis. However, added pretreated solids (7% on weight (w/w) of water-insoluble solids [WIS]) significantly and immediately affected the process performance, which resulted in a long lag phase (more than 40 h) before the onset of the exponential phase of the fermentation. This unexpected delay was also observed without the addition of enzymes in the SSF and in a model fermentation with glucose and pretreated solids without added enzymes. Experiments showed that it was possible to overcome the extended lag phase in the presence of pretreated softwood solids by allowing the microorganism to initiate its exponential phase in synthetic medium, and subsequently adding the softwood solids and enzymatic blend to proceed to an SSF with D-LA production.

As a non-destructive testing technique, neutron radiography has received increasing attention in scientific research. However, the inherent limitations of neutron images, such as insufficient resolution and contrast, have been attributed to the low conversion efficiency of the neutron conversion screen and the low-light quantum efficiency of the charge-coupled device camera. This paper presents a novel neutron image enhancement method that utilizes the sparrow search algorithm to automatically optimize the gain factor coefficient of the adaptive contrast enhancement algorithm and incorporates neutron image information to achieve image enhancement. Experimental results show that compared with other enhancement methods, the proposed method can effectively improve the quality of neutron images visually and numerically and has good operational stability.

BackgroundThe outcomes of patients with acute ischemic stroke (AIS) are related to cardiac function. Cardiac insufficiency can manifest as hydrostatic changes in the lungs. Computed tomography (CT) of the chest is commonly used for screening pulmonary abnormalities and provides an opportunity to assess cardiac function.PurposeTo evaluate the correlation between hydrostatic lung manifestations on chest CT and cardiac function with its potential to predict the short-term outcome of AIS patients.MethodsWe retrospectively analyzed AIS patients who had undergone chest CT at admission and echocardiogram within 48 h. Morphological and quantitative hydrostatic changes and left ventricular dimensions were assessed using chest CT. Improvement in the National Institutes of Health Stroke Scale (NIHSS) score on the seventh day determined short-term outcomes. Multivariate analysis examined the correspondence between hydrostatic lung manifestations, left ventricular dimension, and left ventricle ejection fraction (LVEF) on echocardiography, and the correlation between hydrostatic changes and short-term outcomes.ResultsWe included 204 patients from January to December 2021. With the progression of hydrostatic changes on chest CT, the LVEF on echocardiography gradually decreased (p

Objective: The aim of this systematic review was to evaluate the efficacy of laser therapy in temporomandibular disorders (TMD). Methods: Randomized controlled trials (RCTs) in regard to this issue were searched in electronic databases. Three investigators independently screened the eligible studies, and the quality of the included studies was assessed according to the risk of bias tool recommended by the Cochrane handbook. The primary outcome measure was the degree of pain, reported on a visual analog scale (VAS), and the secondary outcome measures were TMJ function, including maximum active vertical opening (MAVO), maximum passive vertical opening (MPVO), left and right lateral movement (LLE, RLE). Pooled effect sizes were calculated using random effects models and 95% confidence interval (95% CI). Results: A total of 28 randomized controlled trials were included. Laser therapy had a more significant effect in terms of VAS (SMD=﹣1.88; 95% CI=﹣2.46 to﹣1.30; P

The parasitism by Polyascus gregaria on Eriocheir sinensis induces feminization of the appearance of male crabs, misleading fishermen to bring them to the breeding ponds as female crabs to cultivate broodstock selection. However, there are few studies on whether P. gregaria feminizes the male germ cells, resulting in a decline in the fecundity of male crabs. Therefore, this study aims to clarify the changes in gene expression levels of male crab testes after being parasitized by P. gregaria through transcriptome sequencing to evaluate the change in fecundity. We selected parasitized and healthy male crabs from a pond culture for comparison of gene expression in germ cells. The results showed that, compared with healthy male crabs, there were 104 genes with significantly different expressions, of which 79 were up-regulated and 25 were down-regulated. These genes are mainly focused on the cytoskeleton pathway in cell components and cellular protein complex assembly in biological processes. Several spermatogenesis-related genes, such as Kazal-type protease inhibitor, which inhibits gelatinolytic activities of sperm proteases, and juvenile hormone esterase 6, which degrades methyl farnesoate, were up-regulated; while the down-regulated expression of certain heat shock proteins may lead to spermatogenic dysfunction. In addition, some immune-related genes, such as double whey acidic protein domain-containing protein and serine proteinase inhibitor 3, were significantly up-regulated. These results indicated that P. gregaria changed the development process and cell structure of male host germ cells to inhibit sperm proliferation and maturation, while multiple immune pathways in the hosts were activated to resist P. gregaria invasion.