In-Depth Proteomic Analysis Reveals Phenotypic Diversity of Macrophages in Liver Fibrosis.

Macrophages make up a heterogeneous population of immune cells that exhibit diverse phenotypes and functions in health and disease. Although macrophage epigenomic and transcriptomic profiles have been reported, the proteomes of distinct macrophage populations under various pathological conditions remain largely elusive. Here, we employed a label-free proteomic approach to characterize the diversity of the hepatic macrophage pool in an experimental model of CCl4-induced liver fibrosis. We found a decrease in the proportion of liver resident embryo-derived KCs (EmKCs), and a drastic increase in the proportion of monocyte-derived KCs (MoKCs) and CLEC2-Macs. Proteomic profiling revealed that MoKCs largely resembled EmKCs, whereas CLEC2-Macs exhibited greater proteomic alternations compared with EmKCs, suggesting two distinct destinations for monocyte differentiation during liver fibrosis. Furthermore, CLEC2-Macs were characterized by increased expression of proteins associated with inflammatory response, antigen processing and presentation processes, which may be involved in the pathogenesis of liver fibrosis. Collectively, our study provides insights into the considerable heterogeneity within the hepatic macrophage pool during liver fibrosis.

Seminal plasma microbiomes, sperm parameters, and cryopreservation in a healthy fertile population.

Background: Recent advances in microbiome research have revealed the presence of diverse microbial communities in human tissues previously thought to be sterile. The present study delves into the emerging field of seminal plasma microbiomics, examining the relationship between semen microbes and semen parameters and post-freezing tolerance. Methods: The study involved a cohort of healthy fertility males and microbial genome analysis using 16S rRNA to characterize the microbial diversity of seminal plasma. Microbial diversity analysis identified unique amplicon sequence variants (ASVs) and genera dominant in seminal plasma. Spearman's correlation coefficient was used to assess the relationship between flora and semen parameters. A paired t-test was used to compare the changes in microbiome expression in seminal plasma before and after cryo-resuscitation. Results: The relevant results show that the top five phyla in terms of abundance of seminal plasma microbiome were Firmicutes, Bacteroidota, Proteobacteria, Actinobacteriota, and Campylobacterota. Spearman correlation analysis highlighted the association between specific microbial species and semen parameters, between Porphyromonas_asaccharolytica and sperm concentration. Microbial changed significantly after cryo-resuscitation, affecting taxonomic units such as Campylobacter and Muribaculaceae, and KEGG enrichment analyses, suggesting that metabolic pathways are associated with sperm freezing. Eubacterium_coprostanoligenes and Eptoniphilus_duerdenii exhibited a potential impact, while Orynebacterium_tuberculostearicum demonstrated a positive correlation with the recovery rate of progressive motile sperm. Conclusion: The semen of normal fertile individuals contains a microflora component that is closely related to semen quality, including the sperm's ability to withstand freezing.

Integrated analysis of bulk and single-cell RNA-seq data reveals cell differentiation-related subtypes and a scoring system in bladder cancer.

Bladder cancer (BLCA) exhibits notable molecular heterogeneity, influencing diverse clinical outcomes. However, the molecular subtypes associated with cell differentiation-related genes (CDR) and their prognostic implications remain unexplored. Analysing two GEO single-cell datasets, we identified genes linked to cell differentiation. Utilizing these genes, we explored distinct molecular subtypes. WGCNA analysis further identified CDR-associated genes, and the CDR score system, constructed using Lasso and Cox regression, was developed. Clinical prognosis and variations in immune-related factors among patient groups were assessed. Core genes were selected and confirmed through in vitro experiments. Two BLCA subtypes related to cell differentiation were identified: Subtype B demonstrated a favourable prognosis, while Subtype A exhibited significant immune cell infiltration. The CDR score system of nine genes revealed a positive correlation between higher scores and a poorer prognosis. The comprehensive analysis uncovered a positive association between CDR genes and M2 macrophages and unresponsiveness to immune therapy. Functional experiments validated that ANXA5 downregulation influences tumour cell migration without affecting proliferation. Our study reveals distinct cell differentiation-related molecular subtypes and introduces the CDR scoring system in BLCA. ANXA5 emerges as a potential therapeutic target, offering promising avenues for personalized treatment strategies.

Risk analysis of enfortumab vedotin: A real-world approach based on the FAERS database.

Purpose: To analyze the risk of enfortumab vedotin (EV), a targeted therapy for advanced bladder cancer, using real-world data from the U.S. Food and Drug Administration's Federal Adverse Event Reporting System (FAERS). Methods: A retrospective pharmacovigilance analysis was conducted using FAERS data from Q1 2020 to Q1 2024. Adverse drug events (ADEs) related to EV were identified and categorized according to the System Organ Classes (SOCs) and specific events. Statistical methods, such as the proportional reporting ratio, reporting odds ratio (ROR), Bayesian confidence propagation neural network, and empirical Bayesian geometric mean were used to detect safety signals. Results: Of the 7,449,181 FAERS case reports, 1,617 EV-related ADEs were identified, including 101 preferred terms and 22 SOCs. The key SOCs included skin and subcutaneous tissue, metabolic, and nutritional disorders. Rare ADEs, such as lichenoid keratosis (n = 4; ROR 26.89), small intestinal perforation (n = 3; ROR 24.51), pigmentation disorder (n = 9; ROR 18.16), and cholangitis (n = 8; ROR 17.48), showed significant disproportionality. Conclusion: While most findings aligned with the existing data, new signs such as lichenoid keratosis and small intestinal perforation were identified. Further studies are necessary to validate these findings and emphasize the need for the clinical monitoring of EV-related ADEs.

Prognostic Significance and Immune Landscape of an Efferocytosis-Related Gene Signature in Bladder Cancer.

Bladder cancer poses a significant global health challenge, underscoring the imperative for precise prognostic instruments to advance patient care. Against the backdrop of efferocytosis's increasingly recognized role in cancer, this research endeavors to develop and authenticate a prognostic signature intricately linked to efferocytosis in bladder cancer. LASSO-COX regression analysis crafted an efferocytosis-related genes risk prognostic model, followed by the construction of a column chart. External validation sets confirmed the predictive accuracy of both the model and chart. Clinical, tumor microenvironment, drug sensitivity, and immunotherapy analyses were employed to comprehensively assess efferocytosis-related scores. The expression of TGFB3 key genes was validated via RT-PCR and western blotting. Further validation included Transwell, Wound healing, Colony formation, and EDU assays. We formulated and validated an efferocytosis-related genes risk model in bladder cancer, comprising 13 core genes. The risk model demonstrated autonomous prognostic significance in both univariate and multivariate Cox analyses. Following the multivariate analysis, we devised a nomogram. Moreover, by utilizing individual risk scores derived from this risk model, we successfully stratified patients into two discernible risk cohorts, unveiling noteworthy variances in immune infiltration profiles and responsiveness to immunotherapy. Notably, the model's key gene TGFB3 was validated through comprehensive experimental investigations, including Transwell assays for migration and invasion and Wound healing assays for motility on the T24 and BIU cell lines. This study has furnished innovative perspectives on an efferocytosis-related prognostic signature, elucidating the prognosis and immune milieu intricacies in patients with bladder cancer.

IDO1 inhibitors are synergistic with CXCL10 agonists in inhibiting colon cancer growth.

Indoleamine 2,3-dioxygenase 1 (IDO1) is an immune checkpoint that degrades L-tryptophan to kynurenine (Kyn) and enhance immunosuppression, which can be an attractive target for treating colon cancer. IDO1 inhibitors have limited efficacy when used as monotherapies, and their combination approach has been shown to provide synergistic benefits. Many studies have shown that targeting chemokines can promote the efficacy of immune checkpoint inhibitors. Therefore, this study explored the use of IDO1 inhibitors with multiple chemokines to develop a new combination regimen for IDO1 inhibitors. We found that IDO1 inhibitors reduce the secretion of C-X-C motif ligand 10(CXCL10) in cancer cells, and CXCL10 supplementation significantly improved the anticancer effect of IDO1 inhibitors. The combination of the IDO1 inhibitor with CXCL10 or its agonist axitinib had a synergistic inhibitory effect on the growth of colon cancer cells and transplanted CT26 tumors. This synergistic effect may be achieved by inhibiting cancer cell proliferation, promoting cancer cell apoptosis, promoting CD8+T cell differentiation and decreasing Tregs. Two downstream pathways of IDO1 affect CXCL10 secretion. One being the Kyn-aryl hydrocarbon receptor (AHR) pathway, the other is the general control nonderepressible 2(GCN2). Our study provides a new reference for combination regimens of IDO1 inhibitors.

Uniform Graft-versus-Host Disease Prophylaxis using Post-Transplantation Cyclophosphamide, Methotrexate, and Cyclosporine following Peripheral Blood Hematopoietic Stem Cell Transplantation from Matched and Haploidentical Donors for Transfusion-Dependent Thalassemia: A Retrospective Report from the Bone Marrow Failure Working Group of Hunan Province, China.

Although the survival of patients with transfusion-dependent thalassemia (TD-TM) is reportedly inferior after haploidentical hematopoietic stem cell transplantation (HSCT), the heterogeneity of transplantation approaches in studies suggests the need to assess the effect of conditioning regimen on matched and haploidentical transplantation outcomes. A novel post-transplantation cyclophosphamide (PTCy)-based approach for patients with TD-TM undergoing haploidentical HSCT was reported in our prior study. Here we aimed to retrospectively evaluate the real-world efficacy and safety of graft-versus-host disease (GVHD) prophylaxis in patients with TD-TM after HSCT from matched donors and haploidentical donors (HIDs). In this retrospective multicenter study, among 238 patients with TD-TM who underwent HSCT, 160 underwent peripheral blood HSCT, using uniform GVHD prophylaxis with PTCy, methotrexate, and cyclosporine, at member centers of the Bone Marrow Failure Working Group of Hunan Province between 2019 and 2023. The median age of the cohort at transplantation was 6 years (95% confidence interval [CI], 6 to 7 years). The 160 donors included 99 (61.9%) haploidentical family members, 13 matched sibling donors, and 48 matched or mismatched unrelated donors. The engraftment rate was 98.8% (95% CI, 96.1% to 97.7%). HSCT from HIDs had a lower risk of mixed chimerism (HR, .078; P = .022). Within 100 days after transplantation, 31 patients (19.6%; 95% CI, 14.0% to 26.3%) had grade II-IV acute GVHD (aGVHD), 9 of whom had grade III-IV aGVHD (5.7%; 95% CI, 2.9% to 10.1%). HIDs were significantly associated with a higher risk of grade II-IV aGVHD (HR, 3.973; P = .009). Nineteen patients (11.9%; 95% CI, 7.6% to 17.6%) developed late aGVHD after a median of 516 days (95% CI, 407 to 709 days). Twenty-six patients (16.5%; 95% CI, 11.3% to 22.8%) exhibited any 1 of the diagnostic, distinctive, or atypical features of chronic GVHD (cGVHD) according to the 2014 National Institutes of Health (NIH) criteria after a median of 690 days (95% CI, 496 to 902 days). Among these 26 patients, 7 had NIH-defined cGVHD, 14 had only 1 distinctive sign with no histologic evidence, and 5 had only atypical cGVHD signs. Of the 26 patients, 5 were classified with overlap syndrome. Of 21 patients classified with NIH-defined and potential cGVHD, 3 had moderate cGVHD and 1 had severe cGVHD. Logistic regression analyses identified that grade II-IV aGVHD independently predicted subsequent cGVHD (HR, 3.920; P = .006). The rates of cGVHD were similar in the matched donor and HID groups. Thalassemia-free survival (TFS) and event-free survival (EFS) were 97.5% (95% CI, 94.2% to 99.2%) and 90.6% (95% CI, 85.4% to 94.4%), respectively, after a median of 690 days (95% CI, 496 to 902 days). TFS rates were similar in the matched donor and HID groups (P = .549). The EFS rate was significantly higher in the matched donor group compared to the HID group (P = .033). Our study suggests that when PTCy-based uniform GVHD prophylaxis is administered, HSCT from matched donors and HIDs results in a low incidence of severe GVHD and treatment-related mortality with satisfactory survival.

Artificial intelligence-measured nodule mass for determining the invasiveness of neoplastic ground glass nodules.

Background: The nodule mass is an important indicator for evaluating the invasiveness of neoplastic ground-glass nodules (GGNs); however, the efficacy of nodule mass acquired by artificial intelligence (AI) has not been validated. This study thus aimed to determine the efficacy of nodule mass measured by AI in predicting the invasiveness of neoplastic GGNs. Methods: From May 2019 to September 2023, a retrospective study was conducted on 755 consecutive patients comprising 788 pathologically confirmed neoplastic GGNs, among which 259 were adenocarcinoma in situ (AIS), 282 minimally invasive adenocarcinoma (MIA), and 247 invasive adenocarcinoma (IAC). Nodule mass was quantified using AI software, and other computed tomography (CT) features were concurrently evaluated. Clinical data and CT features were compared using the Kruskal-Wallis test or Pearson chi-square test. The predictive efficacy of mass and CT features for evaluating invasive lesions (ILs) (MIAs and IACs) and IACs was analyzed and compared via receiver operating characteristic (ROC) analysis and the Delong test. Results: ROC curve analysis revealed that the optimal cutoff value of mass for distinguishing ILs and AISs was 225.25 mg [area under the curve (AUC) 0.821; 95% confidence interval 0.792-0.847; sensitivity 64.27%; specificity 89.19%; P<0.001], and for differentiating IACs from AISs and MIAs, it was 390.4 mg (AUC 0.883; 95% confidence interval 0.858-0.904; sensitivity 80.57%; specificity 86.32%; P<0.001). The efficacy of nodule mass in distinguishing ILs and AISs was comparable to that of size (P=0.2162) and significantly superior to other CT features (each P value <0.001). Additionally, the ability of nodule mass to differentiate IACs from AISs and MIAs was significantly better than that of CT features (each P value <0.001). Conclusions: AI-based nodule mass analysis is an effective indicator for determining the invasiveness of neoplastic GGNs.

Investigating drug-induced urinary retention: a pharmacovigilance analysis of FDA adverse event reports from 2004 to 2024.

BACKGROUND: Drug-induced urinary retention (DIUR) can severely impact patient quality of life and complicate treatment. This study investigates the incidence and characteristics of DIUR using data from the FDA Adverse Event Reporting System (FAERS) over 20 years. METHODS: FAERS reports related to urinary retention (UR) from Q1 2004 to Q1 2024 were analyzed. Potential causative drugs were identified, and the top 30 drugs with the most UR reports were ranked. Statistical disproportionality analyses, including Proportional Reporting Ratio (PRR) and Reporting Odds Ratio (ROR), were conducted to detect significant safety signals. RESULTS: Out of 17,703,515 reports in the FAERS database 28,423 cases of UR were identified. Anticholinergics, antidepressants, and opioids were the most frequently implicated drug classes. The highest ROR and PRR values were observed for drugs like ezogabine. Additionally, less commonly associated drugs, such as adalimumab and others, were implicated, suggesting potential under-recognition of this adverse effect. However, these associations should be interpreted with caution, as they do not confirm a direct causal relationship. CONCLUSION: This study underscores the importance of pharmacovigilance in identifying and understanding DIUR. Further research is needed to confirm these findings and develop strategies to manage and reduce the risk, improving patient outcomes.

A noncanonical E3 ubiquitin ligase RNF41-mediated MYO1C stability promotes prostate cancer metastasis by inducing actin remodeling.

Prostate cancer bone metastasis is a predominant cause of death for prostate cancer (PCa) patients. However, the underlying mechanisms are poorly understood. Here, we report that high levels of RNF41 are associated with metastatic human prostate cancer. RNF41 silencing inhibits prostate cancer cell growth, cell migration and invasion in vitro and in vivo. Mechanistically, we identify that RNF41 induces K27- and K63-linked noncanonical polyubiquitination of MYO1C to enhance its stability and induce actin remodeling, which promotes PCa bone metastasis. RNF41 was significantly upregulated in metastatic prostate cancer tissues and positively associated with MYO1C expression. Furthermore, we show in intraarterial injected-bone metastasis xenograft model that targeting MYO1C stability by inhibition of RNF41 markedly suppressed PCa bone metastasis. Collectively, our findings identify RNF41 is an important regulator of prostate cancer cell growth and metastasis and targeting RNF41/MYO1C could be a valuable strategy to ameliorate prostate cancer progression and metastasis.

Machine Learning-based Nomograms for Predicting Clinical Stages of Initial Prostate Cancer: A Multicenter Retrospective Study.

OBJECTIVE: To construct and externally validate machine learning-based nomograms for predicting progression stages of initial prostate cancer (PCa) using biomarkers and clinicopathologic features. METHODS: Three hundred sixty-two inpatients diagnosed with PCa at the First Affiliated Hospital were randomly assigned to training and testing sets in a 3:7 ratio, while 136 PCa patients from People's Hospital formed the external validation set. Imaging and clinicopathologic information were collected. Optimal features distinguishing advanced prostate cancer (APC) and metastatic PCa (mPCa) were identified through logistic regression (LR). ML algorithms were employed to build and compare ML models. The best-performing algorithm established models for PCa progression stage. Models performance was evaluated using metrics, ROC curves, calibration, and decision curve analysis (DCA) in training, testing, and external validation sets. RESULTS: Following LR analyses, PSA (P = .001), maximum tumor diameter (P = .026), Gleason score (P <.001), and RNF41 (P <.001) were optimal features for predicting APC, while ALP (P <.001), PSA (P <.001), and GS score (P = .024) were for mPCa. Among ML models, the LR models exhibited superior performance. Consequently, the LR algorithm was used for the APC-risk-nomogram and mPCa-risk-nomogram construction, with AUC values of 0.848, 0.814, 0.810, and 0.940, 0.913, 0.910, in the training, testing, and external validation sets, respectively. Calibration and DCA curves affirmed nomograms' consistency and net benefits for clinical decision-making. CONCLUSION: In summary, ML-based APC-risk-nomogram and mPCa-risk-nomogram exhibit outstanding predictive performance for PCa progression stages. These nomograms can assist clinicians in finely categorizing newly diagnosed PCa patients, facilitating personalized treatment plans and prognosis assessment.

Whole paternal uniparental disomy of chromosome 4 with a novel homozygous IDUA splicing variant, c.159-9T>A, in a Chinese patient with mucopolysaccharidosis type I.

BACKGROUND: Mucopolysaccharidosis type I (MPS-I) is a rare autosomal recessive genetic lysosomal storage disorder that is caused by pathogenic variants of the α-L-iduronidase (IDUA) gene. This study aimed to identify the genetic causes of MPS-I in a Chinese patient and construct a minigene of IDUA to analyze its variants upon splicing. METHODS: Whole-exome sequencing (WES) and Sanger sequencing were used to confirm the potential causative variants. Single-nucleotide polymorphism (SNP) array was subsequently performed to confirm uniparental disomy (UPD). Minigene assay was performed to analyze the effect on splicing of mRNA. We meanwhile explored the conservative analysis and protein homology simulation. RESULTS: A novel homozygous splicing mutation of IDUA, c.159-9T>A, was identified in an individual presenting with overlapping features of MPS-I. Interestingly, only the father and sisters, but not the mother, carried the variant in a heterozygous state. WES and SNP array analyses validated paternal UPD on chromosome 4. Minigene splicing revealed two aberrant splicing events: exon 2 skipping and intron 1 retention. Moreover, the specific structure of the mutant protein obviously changed according to the results of the homologous model. CONCLUSIONS: This study describes a rare autosomal recessive disorder with paternal UPD of chromosome 4 leading to the homozygosity of the IDUA splicing variant in patients with MPS-I for the first time. This study expands the variant spectrum of IDUA and provides insights into the splicing system, facilitating its enhanced diagnosis and treatment.

Shallow groundwater table fluctuations weaken nitrogen accumulation in the thin layer vadose zone of cropland around plateau lakes, Southwest China.

Excessive accumulation of nitrogen (N) in the soil profile in the intensive agricultural region will seriously threaten groundwater quality and safety. However, the impact of shallow groundwater table (SGWT) fluctuations driven by seasonal variations on the N accumulation characterizations in the soil profiles has not been well quantified, particularly in the regions with thin layer vadose zone. Through in-situ monitoring and simulation experiments, the changes in the SGWT and N accumulation of soil profile in intensive cropland around 7 plateau lakes in Yunnan were studied during the rainy season (RS) and dry season (DS), and the N loss in soil profile of cropland driven by SGWT fluctuations was estimated. The results showed that the SGWT and N accumulation in soil profile of cropland around the plateau lakes had obvious seasonal variation characteristics. The proportion of N storage in different forms in 60-100 cm soil layer in the RS was greater than that in the DS, particularly the proportion of NH4+-N storage was as high as 55 %, while N accumulation in surface soil was obvious in the DS. Compared with the DS, due to the rising SGWT in the RS, the maximum storages of TN and NO3--N in the 0-100 cm soil layer decreased by17% and 36 %, respectively. The TN loss intensities from the 0-100 cm soil profiles of cropland around Fuxian Lake, Yilong Lake, Qilu Lake, Dianchi Lake, Yangzong Lake, Erhai Lake, and Xingyun Lake were 74, 54, 127, 105, 93, 72 and 207 kg/ha, respectively. Moreover, if the SGWT was <30 cm, the average TN loss intensity and amount could reach 177 kg/ha and 1250 t, respectively. Therefore, the SGWT regulation was one of the key measures to reducing soil N loss from the thin layer vadose zone of cropland around plateau lakes and improving groundwater quality.

Enantioselective Copper-Catalyzed Sequential Hydrosilylation of Arylmethylenecyclopropanes.

Despite impressive advances in the construction of enantioenriched silacarbocycles featuring silicon-stereogenic centers via a selection of well-defined sila-synthons, the development of a more convenient and economic method with readily available starting materials is significantly less explored and remains a considerable challenge. Herein, we report the first example of copper-catalyzed sequential hydrosilylation of readily accessible methylenecyclopropanes (MCPs) and primary silanes, affording an efficient and convenient route to a wide range of chiral silacyclopentanes bearing consecutive silicon- and carbon-stereogenic centers with excellent enantio- and diastereoselectivities (generally ≥98 % ee, >25 : 1 dr). Mechanistic studies reveal that these reactions combine copper-catalyzed intermolecular ring-opening hydrosilylation of aryl MCPs and intramolecular asymmetric hydrosilylation of the resultant Z/E mixture of homoallylic silanes.

Deep learning for intracranial aneurysm segmentation using CT angiography.

Objective.This study aimed to employ a two-stage deep learning method to accurately detect small aneurysms (4-10 mm in size) in computed tomography angiography images.Approach.This study included 956 patients from 6 hospitals and a public dataset obtained with 6 CT scanners from different manufacturers. The proposed method consists of two components: a lightweight and fast head region selection (HRS) algorithm and an adaptive 3D nnU-Net network, which is used as the main architecture for segmenting aneurysms. Segments generated by the deep neural network were compared with expert-generated manual segmentation results and assessed using Dice scores.MainResults.The area under the curve (AUC) exceeded 79% across all datasets. In particular, the precision and AUC reached 85.2% and 87.6%, respectively, on certain datasets. The experimental results demonstrated the promising performance of this approach, which reduced the inference time by more than 50% compared to direct inference without HRS.Significance.Compared with a model without HRS, the deep learning approach we developed can accurately segment aneurysms by automatically localizing brain regions and can accelerate aneurysm inference by more than 50%.

Meta-analysis of the accuracy for RASSF1A methylation in bronchial aspirates for the diagnosis of lung cancer.

OBJECTIVE: To establish the diagnostic accuracy of RASSF1A (Ras association domain family 1 isoform) methylation using bronchial aspirates as an auxiliary method for diagnosing lung cancer through a systematic review and meta-analysis. METHODS: Studies published prior to October 30, 2022, were retrieved from the Embase, PubMed, Web of Science, and Wan Fang databases using the keywords "lung cancer", "RASSF1A", "methylation", and "bronchial aspirates". A fixed or random effect model was used to calculate the combined sensitivity, specificity, positive likelihood ratios (LR), negative LR, diagnostic odds ratio (DOR), along with the respective 95% confidence intervals (CIs) and the area under the curve (AUC) with Q index. The threshold effect was defined by using the Spearman correlation coefficient, and the Deeks funnel plot was generated to evaluate publication bias. RESULTS: Among the 12 trials that met the inclusion criteria, a total of 2388 participants were involved. The pooled results for the diagnosis of lung cancer were as follows, when compared to the pathological diagnosis: sensitivity of 0.47 (95% CI: 0.45-0.50), specificity of 0.96 (95% CI: 0.95-0.97), positive LR of 12.18 (95% CI: 8.96-16.55), negative LR of 0.56 (95% CI: 0.52-0.61), DOR of 24.05 (95% CI: 17.29-33.47), and AUC of 0.78 (Q index = 0.72), respectively. The sensitivity of the RASSF1A methylation assay was relatively low in a detailed subgroup analysis, fluctuating between 0.39 and 0.90, indicating a limitation in its diagnostic value for lung cancer. The RASSF1A methylation assay, on the other hand, demonstrated excellent specificity, suggesting a high exclusion value. Of note, the diagnostic sensitivity, specificity, DOR, and AUC for small cell lung cancer were 0.90 (0.84-0.94), 0.95 (0.94-0.97), 249.5 (103.94-598.8), and 0.98, respectively, showing that RASSF1A methylation was a promising biomarker for diagnosing small cell lung cancer with both high diagnostic and exclusion value. Furthermore, RASSF1A methylation using bronchial washings and bronchial aspirates showed a high AUC of 0.998 and 0.93, respectively, indicating excellent diagnostic performance. CONCLUSIONS: The methylation of RASSF1A in bronchial aspirates demonstrated a high level of diagnostic accuracy and has the potential to be a valuable supplementary diagnostic method, especially for identifying small cell lung cancer.

Development of a propionate metabolism-related gene-based molecular subtypes and scoring system for predicting prognosis in bladder cancer.

PURPOSE: Bladder cancer (BLCA) is a prevalent malignancy. Dysregulated propionate metabolism, a key cancer factor, suggests a potential target for treating metastatic cancer. However, a complete understanding of the link between propionate metabolism-related genes (PMRGs) and bladder cancer is lacking. METHODS: From the Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases, we gathered BLCA patient data, which was classified into distinct subgroups using non-negative matrix factorization (NMF). Survival and pathway analyses were conducted between these clusters. The PMRGs model, created through univariate Cox and least absolute shrinkage and selection operator (LASSO) analyses, was assessed for prognostic significance using Kaplan-Meier and receiver operating characteristic (ROC) curves. A comprehensive evaluation included clinical, tumor microenvironment (TME), drug sensitivity, and immunotherapy analyses. Finally, the expression of HSD17B1 essential genes was confirmed via quantitative real-time polymerase chain reaction (qRT-PCR), with further validation through Transwell, wound healing, colony-formation, and EDU assays. RESULTS: We discovered two distinct subcategories (CA and CB) within BLCA using NMF analysis, with CA demonstrating significantly better overall survival compared to CB. Additionally, six PMRGs emerged as critical factors associated with propionate metabolism and prognosis. Kaplan-Meier analysis revealed that high-risk PMRGs were correlated with a poorer prognosis in BLCA patients. Moreover, significant differences were observed between the two groups in terms of infiltrated immune cells, immune checkpoint expression, TME scores, and drug sensitivity. Notably, we found that suppressing HSD17B1 gene expression inhibited the invasion of bladder cancer cells. CONCLUSION: Our study proposes molecular subtypes and a PMRG-based score as promising prognostic indicators in BLCA. Additionally, cellular experiments underscore the pivotal role of HSD17B1 in bladder cancer metastasis and invasion, suggesting its potential as a novel therapeutic target.

Shallow groundwater table fluctuations promote the accumulation and loss of phosphorus from surface soil to deeper soil in croplands around plateau lakes in Southwest China.

The continuous excessive application of phosphorus (P) fertilizers in intensive agricultural production leads to a large accumulation of P in surface soils, increasing the risk of soil P loss by runoff and leaching. However, there are few studies on the accumulation and loss of P from surface soil to deep soil profiles driven by shallow groundwater table (SGT) fluctuations. This study used the intensive cropland around 7 plateau lakes in Yunnan Province as an example and conducted in situ monitoring of P storage in the soil profile and SGT during the rainy season (RS) and dry season (DS) as well as simulation experiments on soil P loss. The aim was to study the spatiotemporal variation in P accumulation in the soil profile of cropland driven by SGT fluctuations in the RS and DS and estimate the P loss in the soil profile driven by SGT fluctuations. The results showed that fluctuations in the SGT promoted P accumulation from the surface soil to deeper soil. The proportions of P stored in various forms in the 30-60 cm and 60-100 cm soil layers in the RS were greater than those in the DS, while the average proportion in the 0-30 cm soil layer in the DS was as high as 48%. Compared with those in the DS, the maximum decreases in the proportion of P stored as TP and Olsen-P in the 0-100 cm soil layer in the RS were 16% and 58%, respectively, due to the rise in the SGT (SGT <30 cm), while the soil TP storage decreased by only 1% when the SGT was maintained at 60-100 cm. The critical thresholds for soil Olsen-P and TP gradually decreased with increasing soil depth, and the risk of P loss in deeper soil increased. The loss of soil P was increased by fluctuations in the SGT. Based on the cropland area around the 7 plateau lakes, P storage, and SGT fluctuations, the average loss intensity and loss amount of TP in the 0-100 cm soil layer around the 7 plateau lakes were estimated to be 25 kg/ha and 56 t, respectively. Therefore, reducing exogenous P inputs, improving soil endogenous P utilization efficiency and maintaining deep soil P retention are the basic strategies for preventing and controlling P accumulation and loss in deep soil caused by SGT fluctuations.

Machine learning-based CT radiomics enhances bladder cancer staging predictions: A comparative study of clinical, radiomics, and combined models.

BACKGROUND: Predicting the accurate preoperative staging of bladder cancer (BLCA), which markedly affects treatment decisions and patient outcomes, using traditional clinical parameters is challenging. Nevertheless, emerging studies in radiomics, especially machine learning-based computed tomography (CT) image-based radiomics, hold promise in improving stage prediction accuracy in various tumors. However, the comparative performance and clinical utility of models for BLCA are under investigation. PURPOSE: We aimed to investigate the application value of machine learning-based CT radiomics in preoperative staging prediction by comparing the performance of clinical, radiomics, and clinical-radiomics combined models. METHODS: A retrospective cohort of 105 patients with initial BLCA was randomized into training (70%) and testing (30%) cohorts. Radiomics features were extracted from CT images using the optimal feature filter, followed by the application of the least absolute shrinkage and selection operator algorithm for optimum feature selection. Furthermore, machine learning algorithms were used to establish a radiomics model within the training cohort. Independent risk factors for muscle-invasive BLCA (MIBC) obtained by multivariate logistic regression (LR) analysis were separately used to construct a clinical model. For a clinical-radiomics fusion model, radiomics features were combined with clinical parameters. Performance was evaluated based on receiver operating characteristic curves, calibration curves, decision curve analysis (DCA), and standard performance metrics. RESULTS: Patients exhibited a significantly higher age (p = 0.029), larger tumor size (p = 0.01), and an increased neutrophil-to-lymphocyte ratio (NLR; p = 0.045) in the MIBC group than in the NMIBC group. LR analysis revealed age (p = 0.026), tumor size (p = 0.007), and NLR (p = 0.019) as significant predictors for constructing the clinical model. In the testing cohort, the radiomics model, which used an Support Vector Machine classifier, achieved the highest area under the curve (AUC) value of 0.857. The clinical-radiomics model outperformed the remaining two models, with AUC values of 0.958 and 0.893 in the training and testing cohorts, respectively. DeLong's test indicated significant differences between the three models. Calibration curves showed good agreement, and DCA confirmed the superior clinical utility of the clinical-radiomics model. CONCLUSIONS: Machine learning-based CT radiomics combined with clinical parameters was a promising approach in staging BLCA accurately, which outperformed the individual models. Integrating radiomics features with clinical information holds the potential to improve personalized treatment planning and patient outcomes in BLCA.