Pan-cancer analysis of polo-like kinase family genes reveals polo-like kinase 1 as a novel oncogene in kidney renal papillary cell carcinoma.

Background: Polo-like kinases (PLKs) are a kinase class of serine/threonine with five members that play crucial roles in cell cycle regulation. However, their biological functions, regulation, and expression remain unclear. This study revealed the molecular properties, oncogenic role, and clinical significance of PLK genes in pan-cancers, particularly in kidney renal papillary cell carcinoma (KIRP). Methods: We evaluated the mutation landscape, expression level, and prognostic values of PLK genes using bioinformatics analyses and explored the association between the expression level of PLK genes and tumor microenvironment (TME), immune subtype, cancer immunotherapy, tumor stemness, and drug sensitivity. Finally, we verified the prognostic value in patients with KIRP through univariate and multivariate analyses and nomogram construction. Results: PLK genes are extensively altered in pan-cancer, which may contribute to tumorigenesis. These genes are aberrantly expressed in some types of cancer, with PLK1 being overexpressed in 31 cancers. PLK expression is closely associated with the prognosis of various cancers. The expression level of PLK genes is related with sensitivity to diverse drugs and cancer immunity as well as cancer immunotherapy. Importantly, we verified that PLK1 was overexpressed in KIRP tissues and could be an unfavorable prognostic biomarker in patients with KIRP. Hence, PLK1 may serve as an oncogenic gene in KIRP and should be explored in future studies. Conclusions: Our study comprehensively reports the molecular characteristics and biological functions of PLK family gens across human cancers and recommends further investigation of these genes as potential biomarkers and therapeutic targets, especially in KIRP.

Deleting SUV39H1 in CAR-T cells epigenetically enhances the antitumor function.

SUV39H1 ablation in CAR-T cells epigenetically enhances the antitumor function (by Figdraw). (A) Schematic illustration of SUV39H1 ablation-mediated enhanced antitumor function of CAR-T cells. Functional CAR-T cells eventually transformed into dysfunctional exhausted CAR-T cells under the exposure of chronic tumor antigens, accompanied by reduced proliferation level, effector function, and stemness/memory characteristics, thereby limiting the antitumor activity so as to cause the recurrence of solid tumors. Upon genetic engineering of SUV39H1 ablation, SUV KO CAR-T cells are endowed with increased proliferation level and stemness/memory properties, accompanied by reduced effector/exhausted phenotype. Augmented SUV KO CAR-T cells after in vitro expansion intravenously infusion to mice achieved stronger and more persistent tumor rejection. (B) SUV39H1 ablation-mediated epigenetic reprogramming mechanism of CAR-T cells. Epigenetically, under the stimulation of chronic tumor antigens, exhausted CAR-T cells were characterized by downregulation of proliferation, effector and stemness/memory-associated genes and upregulation of exhaustion-associated genes. SUV39H1 genetic ablation increased chromatin accessibility of stemness/memory-associated genes and reduced chromatin accessibility of inhibitory receptors and effector-associated genes in SUV KO CAR-T cells, epigenetically reprogramming human T cells to express higher levels of stemness/memory genes such as KLF2, LEF1 and TCF7 and lower levels of effector/exhaustion genes.

Comparing various Bayesian random-effects models for pooling randomized controlled trials with rare events.

The meta-analysis of rare events presents unique methodological challenges owing to the small number of events. Bayesian methods are often used to combine rare events data to inform decision-making, as they can incorporate prior information and handle studies with zero events without the need for continuity corrections. However, the comparative performances of different Bayesian models in pooling rare events data are not well understood. We conducted a simulation to compare the statistical properties of four parameterizations based on the binomial-normal hierarchical model, using two different priors for the treatment effect: weakly informative prior (WIP) and non-informative prior (NIP), pooling randomized controlled trials with rare events using the odds ratio metric. We also considered the beta-binomial model proposed by Kuss and the random intercept and slope generalized linear mixed models. The simulation scenarios varied based on the treatment effect, sample size ratio between the treatment and control arms, and level of heterogeneity. Performance was evaluated using median bias, root mean square error, median width of 95% credible or confidence intervals, coverage, Type I error, and empirical power. Two reviews are used to illustrate these methods. The results demonstrate that the WIP outperforms the NIP within the same model structure. Among the compared models, the model that included the treatment effect parameter in the risk model for the control arm did not perform well. Our findings confirm that rare events meta-analysis faces the challenge of being underpowered, highlighting the importance of reporting the power of results in empirical studies.

Tenecteplase versus alteplase for acute ischemic stroke: a systematic review and meta-analysis of randomized and non-randomized studies.

OBJECTIVE: Alteplase is the current standard of care for acute ischemic stroke. Tenecteplase is a newer fibrinolytic agent with preferable administration and lower costs; however, its comparative effectiveness to alteplase remains uncertain. We set out to perform a systematic review and meta-analysis to establish the benefits and harms of tenecteplase versus alteplase for acute ischemic stroke. METHODS: We searched PubMed, Embase, Cochrane Central Register of Controlled Trials (CENTRAL), and ClinicalTrials.gov from inception to April 2023 for randomized and non-randomized studies that compared tenecteplase versus alteplase for acute ischemic stroke. Paired reviewers independently assessed risk of bias and extracted data. We performed both conventional meta-analyses and Bayesian network meta-analyses (NMA) with random-effects models and used the GRADE approach to evaluate the certainty of evidence. Our primary efficacy outcome was excellent functional outcome at 3 months, defined as a score of 0-1 on the modified Rankin Scale. Our primary safety outcomes were symptomatic intracranial hemorrhage and all-cause mortality. RESULTS: Thirty-six studies were eligible for review, including 12 randomized (n = 5533) and 24 non-randomized studies (n = 44,956). Moderate certainty evidence showed that there was no difference between tenecteplase and alteplase in increasing the proportion of patients achieving excellent functional outcome at 3 months (odds ratio [OR], 1.10; 95% CI 0.98-1.23; risk difference [RD] 2.4%, 95% CI - 0.5 to 5.2), while moderate certainty evidence from NMA suggested that 0.25 mg/kg tenecteplase significantly improved excellent functional outcome at 3 months (OR, 1.16; 95% credible interval 1.02-1.32). Moderate certainty evidence showed that, compared to alteplase, tenecteplase may make little to no difference in the prevalence of symptomatic intracranial hemorrhage (OR, 1.12; 95% CI 0.79-1.59; RD 0.3%, 95% CI - 0.5 to 1.4), and probably reduces all-cause mortality (adjusted odds ratio [aOR], 0.44; 95% CI 0.30-0.64; RD - 4.6%; 95% CI - 5.8 to - 2.9). CONCLUSIONS: Moderate certainty evidence suggested that there was little to no difference between tenecteplase and alteplase in increasing the proportion of patients achieving excellent functional outcome at 3 months and the risk of symptomatic intracranial hemorrhage, while compared to alteplase, tenecteplase probably reduce all-cause mortality. Administration of 0.25 mg/kg tenecteplase after acute ischemic stroke is suggestive of increasing the proportion of patients that achieve excellent functional outcome at 3 months.

Improved beluga whale optimization algorithm based cluster routing in wireless sensor networks.

Cluster routing is a critical routing approach in wireless sensor networks (WSNs). However, the uneven distribution of selected cluster head nodes and impractical data transmission paths can result in uneven depletion of network energy. For this purpose, we introduce a new routing strategy for clustered wireless sensor networks that utilizes an improved beluga whale optimization algorithm, called tCBWO-DPR. In the selection process of cluster heads, we introduce a new excitation function to evaluate and select more suitable candidate cluster heads by establishing the correlation between the energy of node and the positional relationship of nodes. In addition, the beluga whale optimization (BWO) algorithm has been improved by incorporating the cosine factor and t-distribution to enhance its local and global search capabilities, as well as to improve its convergence speed and ability. For the data transmission path, we use Prim's algorithm to construct a spanning tree and introduce DPR for determining the optimal route between cluster heads based on the correlation distances of cluster heads. This effectively shortens the data transmission path and enhances network stability. Simulation results show that the improved beluga whale optimization based algorithm can effectively improve the survival cycle and reduce the average energy consumption of the network.

Design and statistical analysis reporting among interrupted time series studies in drug utilization research: a cross-sectional survey.

INTRODUCTION: Interrupted time series (ITS) design is a commonly used method for evaluating large-scale interventions in clinical practice or public health. However, improperly using this method can lead to biased results. OBJECTIVE: To investigate design and statistical analysis characteristics of drug utilization studies using ITS design, and give recommendations for improvements. METHODS: A literature search was conducted based on PubMed from January 2021 to December 2021. We included original articles that used ITS design to investigate drug utilization without restriction on study population or outcome types. A structured, pilot-tested questionnaire was developed to extract information regarding study characteristics and details about design and statistical analysis. RESULTS: We included 153 eligible studies. Among those, 28.1% (43/153) clearly explained the rationale for using the ITS design and 13.7% (21/153) clarified the rationale of using the specified ITS model structure. One hundred and forty-nine studies used aggregated data to do ITS analysis, and 20.8% (31/149) clarified the rationale for the number of time points. The consideration of autocorrelation, non-stationary and seasonality was often lacking among those studies, and only 14 studies mentioned all of three methodological issues. Missing data was mentioned in 31 studies. Only 39.22% (60/153) reported the regression models, while 15 studies gave the incorrect interpretation of level change due to time parameterization. Time-varying participant characteristics were considered in 24 studies. In 97 studies containing hierarchical data, 23 studies clarified the heterogeneity among clusters and used statistical methods to address this issue. CONCLUSION: The quality of design and statistical analyses in ITS studies for drug utilization remains unsatisfactory. Three emerging methodological issues warranted particular attention, including incorrect interpretation of level change due to time parameterization, time-varying participant characteristics and hierarchical data analysis. We offered specific recommendations about the design, analysis and reporting of the ITS study.

Identification and validation of a copper homeostasis-related gene signature for the predicting prognosis of breast cancer patients via integrated bioinformatics analysis.

The prognostic value of copper homeostasis-related genes in breast cancer (BC) remains largely unexplored. We analyzed copper homeostasis-related gene profiles within The Cancer Genome Atlas Program breast cancer cohorts and performed correlation analysis to explore the relationship between copper homeostasis-related mRNAs (chrmRNA) and lncRNAs. Based on these results, we developed a gene signature-based risk assessment model to predict BC patient outcomes using Cox regression analysis and a nomogram, which was further validated in a cohort of 72 BC patients. Using the gene set enrichment analysis, we identified 139 chrmRNAs and 16 core mRNAs via the Protein-Protein Interaction network. Additionally, our copper homeostasis-related lncRNAs (chrlncRNAs) (PINK1.AS, OIP5.AS1, HID.AS1, and MAPT.AS1) were evaluated as gene signatures of the predictive model. Kaplan-Meier survival analysis revealed that patients with a high-risk gene signature had significantly poorer clinical outcomes. Receiver operating characteristic curves showed that the prognostic value of the chrlncRNAs model reached 0.795 after ten years. Principal component analysis demonstrated the capability of the model to distinguish between low- and high-risk BC patients based on the gene signature. Using the pRRophetic package, we screened out 24 anticancer drugs that exhibited a significant relationship with the predictive model. Notably, we observed higher expression levels of the four chrlncRNAs in tumor tissues than in the adjacent normal tissues. The correlation between our model and the clinical characteristics of patients with BC highlights the potential of chrlncRNAs for predicting tumor progression. This novel gene signature not only predicts the prognosis of patients with BC but also suggests that targeting copper homeostasis may be a viable treatment strategy.

ß-Variational autoencoders and transformers for reduced-order modelling of fluid flows.

Variational autoencoder architectures have the potential to develop reduced-order models for chaotic fluid flows. We propose a method for learning compact and near-orthogonal reduced-order models using a combination of a ß-variational autoencoder and a transformer, tested on numerical data from a two-dimensional viscous flow in both periodic and chaotic regimes. The ß-variational autoencoder is trained to learn a compact latent representation of the flow velocity, and the transformer is trained to predict the temporal dynamics in latent-space. Using the ß-variational autoencoder to learn disentangled representations in latent-space, we obtain a more interpretable flow model with features that resemble those observed in the proper orthogonal decomposition, but with a more efficient representation. Using Poincaré maps, the results show that our method can capture the underlying dynamics of the flow outperforming other prediction models. The proposed method has potential applications in other fields such as weather forecasting, structural dynamics or biomedical engineering.

Heat Shock Protein Genes Affect the Rapid Cold Hardening Ability of Two Invasive Tephritids.

Bactrocera dorsalis and Bactrocera correcta are two invasive species that can cause major economic damage to orchards and the fruit import and export industries. Their distribution is advancing northward due to climate change, which is threatening greater impacts on fruit production. This study tested the rapid cold-hardening ability of the two species and identified the temperature associated with the highest survival rate. Transcriptome data and survival data from the two Bactrocera species' larvae were obtained after rapid cold-hardening experiments. Based on the sequencing of transcripts, four Hsp genes were found to be affected: Hsp68 and Hsp70, which play more important roles in the rapid cold hardening of B. dorsalis, and Hsp23 and Hsp70, which play more important roles in the rapid cold hardening of B. correcta. This study explored the adaptability of the two species to cold, demonstrated the expression and function of four Hsps in response to rapid cold hardening, and explained the occurrence and expansion of these two species of tephritids, offering information for further studies.

Characters of the MOCA family in wheat and TaMOCA1 function in salt stress tolerance.

MOCA1 encodes the last key glucuronosyltransferase for ionic stress sensor glycosyl inositol phosphoryl-ceramide (GIPCs) biosynthesis in Arabidopsis, which indicates that the MOCA gene family play important role in plant tolerance to salt stress. However, the isolation and function of MOCAs in staple crops have not been reported and the downstream targets of MOCAs in salt stress tolerance signalling pathway are not clear. In this study, we identified 110 MOCA genes in wheat which were classified into five clades and they differed in gene structure, protein length, conserved motifs and expression profiles in different tissues and under salt stress. TaMOCA1 was selected for further functional study in response to salt stress. TaMOCA1 was rapidly induced by NaCl treatment. The 35S::TaMOCA1-GFP construction showed the cell nucleus and cytoplasm location in wheat protoplast. TaMOCA1 over-expressing Arabidopsis seedlings formed longer primary roots and more lateral roots than the wild type ones under 50 mM NaCl treatment. The over-expressing Arabidopsis had higher expression levels of HKT1, but lower expression levels of NHX1 and SOS genes than the wild type. Also, the transgenic plants had higher SOD activity and lower MDA content than the wild Arabidopsis seedling under salt stress. These results may indicate that TaMOCA1 increases salt stress tolerance through decreasing Na+ loading from the xylem parenchyma cells to the xylem via SOS1 and HKT1, hence lowering root-to-shoot delivery of Na? and superior antioxidant ability. All these results lay a foundation for further functional study of MOCAs in wheat.

A systematic survey of adaptive trials shows substantial improvement in methods is needed.

OBJECTIVES: To investigate the design, conduct, and analysis of adaptive trials through a systematic survey and provide recommendations for future adaptive trials. STUDY DESIGN AND SETTING: We systematically searched MEDLINE, EMBASE, Cochrane Central Register of Controlled Trials, and ClinicalTrials.gov databases up to January 2020. We included trials that were self-described as adaptive trials or applied adaptive designs. We identified three frequently used adaptive designs and summarized their methodological details in terms of design, conduct, and analysis. Lastly, we provided recommendations for future adaptive trials. RESULTS: We included a total of 128 trials in this study. The primary motivations for using adaptive design were to speed up the trials and facilitate decision-making (n = 29, 31.5%). The three most frequently used methods were group sequential design (GSD) (n = 71, 55.5%), adaptive dose-finding design (ADFD) (n = 35, 27.3%), and adaptive randomization design (ARD) (n = 26, 20.3%). The timing and frequency of interim analysis were detailed in three-fourths of the GSD trials (n = 55, 77.5%) and in half of the ADFD trials (n = 19, 54.3%); however, more than half of the ARD trials (n = 15, 57.7%) did not provide this information. Some trials selected a different outcome than the primary outcome for interim analysis (GSD: n = 7, 12.7%; ADFD: n = 8, 27.6%; ARD: n = 7, 50.0%), but the majority of these trials did not provide explicit reasons for this choice (GSD: n = 7, 100.0%; ADFD: n = 7, 87.5%; ARD: n = 5, 71.4%). More than half (n = 76, 59.4%) of trials did not mention the accessibility of supporting documents, and two-thirds (n = 86, 67.2%) did not state the establishment of independent data monitoring committees (IDMCs). Moreover, unplanned adjustments were observed during the conduct of one-sixth adaptive trials (n = 22, 17.2%). Based on our findings, we provide 14 recommendations for improving adaptive trials in the future. CONCLUSION: Substantial improvements were needed in methods of adaptive trials, particularly in the areas of interim analysis, the establishment of independent data monitoring committees, and unplanned adjustments. In this study, we offer recommendations from both general and specific aspects for researchers to carefully design, conduct, and analyze adaptive trials.

Extracellular succinate derived from ectopic milieu drives adhesion and implantation growth of ectopic endometrial stromal cells via the SUCNR1 signal in endometriosis.

BACKGROUND: As a dual-function metabolite, succinate has emerged in cell function and plays a key signaling role in linking mitochondrial function to other cellular functions. Succinate accumulation in the cytoplasm is commonly associated with hypoxia in the microenvironment and immune cell activation. Extracellular succinate released into the microenvironment is considered an inflammatory alarm that can be sensed by its membrane receptor SUCNR1, which boosts proinflammatory responses and acts akin to classical hormones and cytokines. Succinate plays an important role in the development of inflammatory diseases. Whether succinate facilitates the progression of endometriosis (EMs), characterized by chronic inflammation and peritoneal adhesion, is worth exploring. OBJECTIVE: We mimicked the ectopic milieu in vitro and in vivo to evaluate the main source and potential role of succinate in endometriosis. We assessed the molecular and functional effects of succinate on macrophages and peritoneal mesothelial cells in peritoneal cavity. The effect of succinate/SUCNR1 signaling on ectopic endometrial stromal cells (ESCs) was further explored in this study. METHODS: In this study, we used targeted organic acid metabolomics analysis and in vitro assays to assess the potential accumulation of succinate in the peritoneal fluid of EMs patients. We examined its correlation with disease severity, Visual Analogue Scale, and the Endometriosis Fertility Index. Flow cytometry, enzyme linked immunosorbent assay, western blot assay, quantitative real-time PCR, and other molecular biology techniques were used to explore the potential mechanisms. RESULTS: By mimicking the ectopic milieu, we constructed an in vitro co-culture system and found that M1 polarized macrophages and that the peritoneal mesothelial cell line (HMrSV5) mainly released succinate into their microenvironment and activated the succinate receptor (SUCNR1) signal, which further polarized the macrophages and significantly enhanced the invasive survival of ESCs, and the adhesion to the peritoneum. We further investigated the pathological effects of extracellular succinate in vivo using a xenograft mouse models of endometriosis. CONCLUSIONS: Succinate-SUCNR1 signaling facilitates the creation of inflammatory cells and plays a vital role in EMs progression and peritoneal adhesion. Our work on the molecular mechanisms underlying succinate accumulation and function will help elucidate the phenotypic mysteries of pain and infertility in EMs. Video Abstract.

Early Diagnosis and Prognosis Prediction of Pancreatic Cancer Using Engineered Hybrid Core-Shells in Laser Desorption/Ionization Mass Spectrometry.

Effective detection of bio-molecules relies on the precise design and preparation of materials, particularly in laser desorption/ionization mass spectrometry (LDI-MS). Despite significant advancements in substrate materials, the performance of single-structured substrates remains suboptimal for LDI-MS analysis of complex systems. Herein, designer Au@SiO2@ZrO2 core-shell substrates are developed for LDI-MS-based early diagnosis and prognosis of pancreatic cancer (PC). Through controlling Au core size and ZrO2 shell crystallization, signal amplification of metabolites up to 3 orders is not only achieved, but also the synergistic mechanism of the LDI process is revealed. The optimized Au@SiO2@ZrO2 enables a direct record of serum metabolic fingerprints (SMFs) by LDI-MS. Subsequently, SMFs are employed to distinguish early PC (stage I/II) from controls, with an accuracy of 92%. Moreover, a prognostic prediction scoring system is established with enhanced efficacy in predicting PC survival compared to CA19-9 (p < 0.05). This work contributes to material-based cancer diagnosis and prognosis.

Self-Assembled Hyperbranched Gold Nanoarrays Decode Serum United Urine Metabolic Fingerprints for Kidney Tumor Diagnosis.

Serum united urine metabolic analysis comprehensively reveals the disease status for kidney diseases in particular. Thus, the precise and convenient acquisition of metabolic molecular information from united biofluids is vitally important for clinical disease diagnosis and biomarker discovery. Laser desorption/ionization mass spectrometry (LDI-MS) presents various advantages in metabolic analysis; however, there remain challenges in ionization efficiency and MS signal reproducibility. Herein, we constructed a self-assembled hyperbranched black gold nanoarray (HyBrAuNA) assisted LDI-MS platform to profile serum united urine metabolic fingerprints (S-UMFs) for diagnosis of early stage renal cell carcinoma (RCC). The closely packed HyBrAuNA afforded strong electromagnetic field enhancement and high photothermal conversion efficacy, enabling effective ionization of low abundant metabolites for S-UMF collection. With a uniform nanoarray, the platform presented excellent reproducibility to ensure the accuracy of S-UMFs obtained in seconds. When it was combined with automated machine learning analysis of S-UMFs, early stage RCC patients were discriminated from the healthy controls with an area under the curve (AUC) > 0.99. Furthermore, we screened out a panel of 9 metabolites (4 from serum and 5 from urine) and related pathways toward early stage kidney tumor. In view of its high-throughput, fast analytical speed, and low sample consumption, our platform possesses potential in metabolic profiling of united biofluids for disease diagnosis and pathogenic mechanism exploration.

Endovascular Strategies and Outcomes for Aberrant Splenic Artery Aneurysms.

OBJECTIVES: Aberrant splenic artery aneurysms (ASAAs) located at the splenomesenteric trunk (SMT) and the celiacomesenteric trunk have a close anatomical relationship with the superior mesenteric artery (SMA). The aim of this study was to review our institutional experience of endovascular treatment for ASAAs and evaluate the long-term outcomes. METHODS: A retrospective review of patients with ASAAs who underwent endovascular treatment between December 2006 and December 2022 was performed. The demographics of the patients, aneurysm characteristics, treatment strategies, perioperative and long-term outcomes, and complications were analyzed. RESULTS: A total of 29 patients with ASAAs were endovascularly treated at our institution. The SMT variant occurred in the majority of the patients. All ASAAs were characterized by eccentric growth and extremely short inflow arteries. Only 1 patient's inflow artery of the aneurysm exceeded 1 cm in length. Thirteen patients were treated by coil embolization alone. Four patients received bare stent-assisted coil embolization. A combination of coil embolization and covered stent placement across the orifice of the aberrant splenic artery was performed in the remaining 12 cases. Coil migration into the SMA occurred in 2 patients during the operation. Technical success was achieved in all patients. With a median duration of 63 (34-101) months of follow-up, no intestinal ischemia, aneurysm-related death, aneurysm rupture, or sac enlargement occurred. Three cases of aneurysm sac reperfusion were observed, and 1 patient underwent reintervention with secondary embolization. Asymptomatic occlusion of the covered stent was detected in 1 patient at 2 years. CONCLUSIONS: Endovascular treatment is a safe, effective, and durable option for ASAAs. Inflow embolization might be difficult to achieve in ASAAs and poses a high risk of coil migration into the SMA. Long-term observation indicates that reasonable use of the covered stent could achieve reliable inflow artery exclusion in ASAAs without intestinal complications. CLINICAL IMPACT: Aberrant splenic artery aneurysm (ASAA) is an extremely rare entity. This study reported a large sample size of ASAAs treated by endovascular techniques with long-term follow-up. The ASAA was characterized by an extremely short inflow artery and a close anatomical relationship with the superior mesenteric artery (SMA). Endovascular treatment is a safe, effective, and durable option for ASAAs. Inflow embolization might be difficult to achieve in ASAAs and pose a high risk of coil migration into the SMA. Long-term observation indicates that reasonable use of the covered stent could achieve reliable inflow artery exclusion in ASAAs without intestinal complications.

Identifying High Gleason Score Prostate Cancer by Prostate Fluid Metabolic Fingerprint-Based Multi-Modal Recognition.

Prostate cancer (PCa) is the second most common cancer in males worldwide. The Gleason scoring system, which classifies the pathological growth pattern of cancer, is considered one of the most important prognostic factors for PCa. Compared to indolent PCa, PCa with high Gleason score (h-GS PCa, GS ≥ 8) has greater clinical significance due to its high aggressiveness and poor prognosis. It is crucial to establish a rapid, non-invasive diagnostic modality to decipher patients with h-GS PCa as early as possible. In this study, ferric nanoparticle-assisted laser desorption/ionization mass spectrometry (FeNPALDI-MS) to extract prostate fluid metabolic fingerprint (PSF-MF) is employed and combined with the clinical features of patients, such as prostate-specific antigen (PSA), to establish a multi-modal diagnosis assisted by machine learning. This approach yields an impressive area under the curve (AUC) of 0.87 to diagnose patients with h-GS, surpassing the results of single-modal diagnosis using only PSF-MF or PSA, respectively. Additionally, using various screening methods, six key metabolites that exhibit greater diagnostic efficacy (AUC = 0.96) are identified. These findings also provide insights into related metabolic pathways, which may provide valuable information for further elucidation of the pathological mechanisms underlying h-GS PCa.

Developing Prussian blue/wood-derived biochar catalyst for persistent organic pollutant degradation: Preparation, characterization, and mechanism.

Biomass-derived biochar shows broad promise for persistent organic pollutants (POPs) degradation and thus establishes a more sustainable homestead. However, effective catalytic performance is still challenging. Herein, an efficient catalyst (Prussian blue decorated wood-derived biochar, PBB) was constructed by introducing Prussian blue (PB) into wood-based biochar to activate peroxymonosulfate (PMS) for removing POPs. After anchoring of PB, the degradation performance of biochar was enhanced (degradation efficiency of methylene blue (MB, 20 mg/L) increased from 52% of biochar to 95% of PBB within 60 min). The PBB presents effective MB degradation performance with a wide pH value (3.0 < pH < 11.0) or co-existing diverse anions (Cl-, NO3-, H2PO4-, and HCO3-). Electron paramagnetic resonance (EPR) analysis as well as electrochemical tests confirmed that the non-radical pathway (1O2) is the key to biochar activation of PMS, but by restricting PB into the biochar, the radical pathway (SO4•- and •OH), the non-radical pathway (1O2), and direct electron transfer can work together to activate PMS. In addition, the degradation efficiency could remain about 80% after five-time cyclic tests. This work elucidates the role of PB nanoparticles in enhancing biochar catalysts, which can inspire the development of a carbon-neutralized, cost-effective, and effective strategy for POPs removal.