Facile engineered macrophages-derived exosomes-functionalized PLGA nanocarrier for targeted delivery of dual drug formulation against neuroinflammation by modulation of microglial polarization in a post-stroke depression rat model.

Post-stroke depression (POSD) is a common difficulty and most predominant emotional syndrome after stroke often consequences in poor outcomes. In the present investigation, we have designed and studied the neurologically active celastrol/minocycline encapsulated with macrophages-derived exosomes functionalized PLGA nanoformulations (CMC-EXPL) to achieve enhanced anti-inflammatory behaviour and anti-depressant like activity in a Rat model of POSD. The animal model of POSD was established through stimulating process with chronic unpredictable mild stress (CUM) stimulations after procedure of middle cerebral artery occlusion (MCAO). Neuronal functions and Anti-inflammation behaviours were observed by histopathological (H&E) examination and Elisa analyses, respectively. The anti-depressive activity of the nanoformulations treated Rat models were evaluated by open-field and sucrose preference test methods. Microglial polarization was evaluated via flow-cytometry and qRT-PCR observations. The observed results exhibited that prepared nanoformulations reduced the POSD-stimulated depressive-like activities in rat models as well alleviated the neuronal damages and inflammatory responses in the cerebral hippocampus. Importantly, prepared CMC-EXPL nanoformulation effectively prevented the M1 pro-inflammatory polarization and indorsed M2 anti-inflammatory polarization, which indicates iNOS and CD86 levels significantly decreased and upsurged Arg-1 and CD206 levels. CMC-EXPL nanoformulation suggestively augmented anti-depressive activities and functional capability and also alleviated brain inflammation in POSD rats, demonstrating its therapeutic potential for POSD therapy.

Biochemical transformation and bioremediation of thallium in the environment.

Thallium (Tl) is a toxic element associated with minerals, and its redistribution is facilitated by both geological and anthropogenic activities. In the natural environment, the transformation and migration of Tl mediated by (micro)organisms have attracted increasing attention. This review presents an overview of the biochemical transformation of Tl and the bioremediation strategies for Tl contamination. In the environment, Tl exists in various forms and originates from diverse sources. The global distribution characteristics of Tl in various media are summarized here, while its speciation and toxicity mechanism to organisms are elucidated. Interactions between (micro)organisms and Tl are commonly observed in the environment. Microbial response mechanisms to typical Tl exposure are analyzed at both species and gene levels, and the possibility of microorganisms as bio-indicators for monitoring Tl contamination is also highlighted. The processes and mechanisms involved in the microbial and benthic mediated transformation of Tl, as well as its enrichment by plants, are discussed. Additionally, in situ bioremediation strategies for Tl contamination and bio-treatment techniques for Tl-containing wastewater are summarized. Finally, the existing knowledge gaps and future research challenges are emphasized, including Tl distribution characteristics in the atmosphere and ocean, the key molecular mechanisms underlying Tl transformation by organisms, the screening of potential Tl oxidizing microorganisms and hyperaccumulators, as well as the revelation of global biogeochemical cycling pathways of Tl.

Prognostic prediction of gastric cancer based on H&E findings and machine learning pathomics.

AIM: In this research, we aimed to develop a model for the accurate prediction of gastric cancer based on H&E findings combined with machine learning pathomics. METHODS: Transcriptome data, pathological images, and clinical data from 443 cases were retrieved from TCGA (The Cancer Genome Atlas Program) for survival analysis. The images were segmented using the Otsu algorithm, and features were extracted using the PyRadiomics package. Subsequently, the cases were randomly divided into a training cohort of 165 cases and a validation cohort of 69 cases. Features selected via minimum Redundancy - Maximum Relevance (mRMR)- recursive feature elimination (RFE) screening were used to train a model using the Gradient Boosting Machine (GBM) algorithm. The model's performance was evaluated using the area under the receiver operating characteristic (ROC) curve (AUC), calibration curves, and decision curves. Additionally, the correlation between the Pathomics score (PS) and immune genes was examined. RESULTS: In the multivariate analysis, heightened infiltration of activated CD4 memory T cells was strongly associated with improved overall survival (HR = 0.505, 95 % CI = 0.342-0.745, P < 0.001). The pathomic model, exhibiting robust predictive capability, demonstrated impressive AUC values of 0.844 and 0.750 in both study cohorts. The Decision Curve Analysis (DCA) unequivocally underscored the model's exceptional clinical utility. In a subsequent multivariate analysis, heightened infiltration of the PS also emerged as a significant protective factor for overall survival (HR = 0.506, 95 % CI = 0.329-0.777, P = 0.002). CONCLUSION: The pathomic model based on H&E slides for predicting the infiltration degree of activated CD4 memory T cells, along with integrated bioinformatics analysis elucidating potential molecular mechanisms, offers novel prognostic indicators for the precise stratification and individualized prognosis of gastric cancer patients.

The value of multiparameter MRI of early cervical cancer combined with SCC-Ag in predicting its pelvic lymph node metastasis.

Purpose: To investigate the value of multiparameter MRI of early cervical cancer (ECC) combined with pre-treatment serum squamous cell carcinoma antigen (SCC-Ag) in predicting its pelvic lymph node metastasis (PLNM). Material and methods: 115 patients with pathologically confirmed FIGO IB1~IIA2 cervical cancer were retrospectively included and divided into the PLNM group and the non-PLNM group according to pathological results. Quantitative parameters of the primary tumor include Ktrans, Kep, Ve from dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI), ADCmean, ADCmin, ADCmax, D, D* and f from intravoxel incoherent motion diffusion-weighted imaging (IVIM-DWI) were measured. Pre-treatment serum SCC-Ag was obtained. The difference of the above parameters between the two groups were compared using the student t-test or Mann-Whitney U test. Multivariate Logistic regression analysis was performed to determine independent risk factors. Receiver operating characteristic (ROC) curve analysis was used to assess the diagnostic efficacy of individual parameters and their combination in predicting PLNM from ECC. Results: The PLNM group presented higher SCC-Ag [14.25 (6.74,36.75) ng/ml vs.2.13 (1.32,6.00) ng/ml, P<0.001] and lower Ktrans (0.51 ± 0.20 min-1 vs.0.80 ± 0.33 min-1, P < 0.001), ADCmean (0.85 ± 0.09 mm/s2 vs.1.06 ± 0.35 mm/s2, P<0.001), ADCmin [0.67 (0.61,0.75) mm/s2 vs. 0.75 (0.64,0.90) mm/s2, P = 0.012] and f (0.91 ± 0.09 vs. 0.27 ± 0.14, P = 0.001) than the non-LNM group. Multivariate analysis showed that SCC-Ag (OR = 1.154, P = 0.007), Ktrans (OR=0.003, P < 0.001) and f (OR = 0.001, P=0.036) were independent risk factors of PLNM. The combination of SCC-Ag, Ktrans and f possessed the best predicting efficacy for PLNM with an area under curve (AUC) of 0.896, which is higher than any individual parameter: SCC-Ag (0.824), Ktrans (0.797), and f (0.703). The sensitivity and specificity of the combination were 79.1% and 94.0%, respectively. Conclusions: Quantitative parameters Ktrans and f derived from DCE-MRI and IVIM-DWI of primary tumor and SCC-Ag have great value in predicting PLNM. The diagnostic efficacy of their combination has been further improved.

Achieving endogenous partial denitrification by cultivating denitrifying glycogen-accumulating organisms.

Although anaerobic ammonia oxidation (anammox) is considered a promising process due to its high efficiency and low energy in nitrogen removal, nitrite inadequacy was one of the bottlenecks for the application of anammox. However, endogenous partial denitrification (EPD) has been emerging as a stable pathway to provide nitrite for anammox. Furthermore, denitrifying glycogen-accumulating organisms (DGAOs) are believed to be associated with EPD. In this study, firstly, GAOs were gradually enriched in a sequencing batch reactor (SBR) with the dual strategy of influent phosphorus limitation and withdrawal after the anaerobic stage. DGAOs were successfully induced by adding sodium nitrate solution at the end of the anaerobic stage, resulting in NO3--N concentration increasing from 15 to 30 mg/L. During a typical SBR cycle, DGAOs contributed up to 96% of the conversion of intracellular carbon sources and up to around 95% of nitrate reduction during the anoxic stage. The maximum nitrate-to-nitrite transformation ratio (NTR) of the system reached 80%. Microbial community analysis demonstrated that the Ca. Compatibactors were the dominant functional bacteria for EPD, with a relative abundance of 31.12%. However, the relative abundance of phosphorous-accumulating organisms (PAOs) was only 1.02%. This study reveals the important role of DGAOs in the EPD process, which can provide a stable nitrite for anammox.

Predictive value of near-term prediction models for severe immune-related adverse events in malignant tumor PD-1 inhibitor therapy.

Immune-related adverse events (irAEs) impact outcomes, with most research focusing on early prediction (baseline data), rather than near-term prediction (one cycle before the occurrence of irAEs and the current cycle). We aimed to explore the near-term predictive value of neutrophil/lymphocyte ratio (NLR), platelet/lymphocyte ratio (PLR), absolute eosinophil count (AEC) for severe irAEs induced by PD-1 inhibitors. Data were collected from tumor patients treated with PD-1 inhibitors. NLR, PLR, and AEC data were obtained from both the previous and the current cycles of irAEs occurrence. A predictive model was developed using elastic net logistic regression Cutoff values were determined using Youden's Index. The predicted results were compared with actual data using Bayesian survival analysis. A total of 138 patients were included, of whom 47 experienced grade 1-2 irAEs and 18 experienced grade 3-5 irAEs. The predictive model identified optimal α and λ through 10-fold cross-validation. The Shapiro-Wilk test, Kruskal-Wallis test and logistic regression showed that only current cycle data were meaningful. The NLR was statistically significant in predicting irAEs in the previous cycle. Both NLR and AEC were significant predictors of irAEs in the current cycle. The model achieved an area under the ROC curve (AUC) of 0.783, with a sensitivity of 77.8% and a specificity of 80.8%. A probability ≥ 0.1345 predicted severe irAEs. The model comprising NLR, AEC, and sex may predict the irAEs classification in the current cycle, offering a near-term predictive advantage over baseline models and potentially extending the duration of immunotherapy for patients.

Hepatic arterial infusion chemotherapy enhances the efficacy of lenvatinib plus PD-1 inhibitors in hepatocellular carcinoma patients with tumor thrombosis in the inferior vena cava and/or right atrium.

RATIONALE AND OBJECTIVES: Hepatocellular carcinoma (HCC) with invasion into the inferior vena cava (IVC) or the right atrium (RA) presents significant therapeutic challenges due to its rapid progression and limited available treatments. MATERIALS AND METHODS: This retrospective study evaluated the effectiveness of hepatic arterial infusion chemotherapy alongside lenvatinib and PD-1 inhibitors (HAIC-Len-PD1) compared to treatment with only lenvatinib and PD-1 inhibitors (Len-PD1). A total of 115 patients with HCC and IVC or RA invasion were included. We analyzed groups for median overall survival (OS) and progression-free survival (PFS) through the Kaplan-Meier method, along with tumor response rates, disease control rates, and adverse event frequencies. RESULTS: The HAIC-Len-PD1 treatment showed a marked improvement in median OS (22.2 vs. 14.4 months; P = 0.007) and median PFS (13.8 vs. 5.1 months; P = 0.001) over the Len-PD1 regimen. There was also a higher overall response rate (68.7% vs. 37.5%; P < 0.05) and disease control rate (92.5% vs. 75%; P < 0.05) observed in the HAIC-Len-PD1 group. A subgroup analysis demonstrated consistent survival benefits across diverse patient demographics. Although the incidence of adverse events was higher in the HAIC-Len-PD1 group, these were generally manageable and well-tolerated. CONCLUSION: The combined regimen of HAIC, lenvatinib, and PD-1 inhibitors may improve survival and tumor management in HCC patients with IVC or RA invasion, suggesting a potential therapeutic option for this critically at-risk group. Further research in the form of randomized controlled trials are needed to verify these findings for advanced-stage HCC with vascular compromise.

Cognitive resilience to Alzheimer's disease characterized by cell-type abundance.

INTRODUCTION: The molecular basis of cognitive resilience (CR) among pathologically confirmed Alzheimer's disease (AD) cases is not well understood. METHODS: Abundance of 13 cell types and neuronal subtypes in brain bulk RNA-seq data from the anterior caudate, dorsolateral prefrontal cortex (DLPFC), and posterior cingulate cortex (PCC) obtained from 434 AD cases, 318 cognitively resilient AD cases, and 188 controls in the Religious Orders Study and Rush Memory and Aging Project was estimated by deconvolution. RESULTS: PVALB+ neuron abundance was negatively associated with cognitive status and tau pathology in the DLPFC and PCC (Padj < 0.001) and the most reduced neuronal subtype in AD cases compared to controls in DLPFC (Padj = 8.4 × 10-7) and PCC (Padj = 0.0015). We identified genome-wide significant association of neuron abundance with TMEM106B single nucleotide polymorphism rs13237518 in PCC (p = 6.08 × 10-12). rs13237518 was also associated with amyloid beta (p = 0.0085) and tangles (p = 0.0073). DISCUSSION: High abundance of PVALB+ neurons may be a marker of CR. TMEM106B variants may influence CR independent of AD pathology. HIGHLIGHTS: Neuron retention and a lack of astrocytosis are highly predictive of Alzheimer's disease (AD) resilience. PVALB+ GABAergic and RORB+ glutamatergic neurons are associated with cognitive status. A TMEM106B single nucleotide polymorphism is related to lower AD risk, higher neuron count, and increased AD pathology.

The value of abPG-SGA in the nutritional risk screening of patients with malignant tumors.

Nutritional risk screening 2002 (NRS2002) is a commonly used tool for screening the risk of malnutrition in hospitalized patients, while patient-generated subjective global assessment (PG-SGA) is a nutritional assessment tool for malignant tumor patients. However, there are still gaps in the rapid nutritional risk screening methods for cancer patients. We aimed to evaluate the value of abridged scored patient-generated subjective global assessment (abPG-SGA) for nutritional risk screening and prognosis in cancer patients. The NRS 2002 and abPG-SGA scores of 100 malignant tumor patients hospitalized in our department in December 2020 were collected. Take NRS2002 ≥ 3 as the positive standard (risk of malnutrition). Data were analyzed using Kappa test, ROC curves, cut-off values and Kaplan-Meier. In the screening of 100 patients, 25.0% of patients were at risk of malnutrition (NRS2002), abPG-SGA yielded a sensitivity and specificity of 92.0% and 72.0%, respectively (area under curve [AUC] = 0.884, cut-off value ≥ 4.5); In the screening of patients with digestive system malignancies, 22.6% of patients were at risk of malnutrition (NRS2002), and the sensitivity and specificity of abPG-SGA were 91.67% and 87.80%, respectively (AUC = 0.945, cut-off value ≥ 5.5). The results of survival analysis showed that the overall survival (OS) of patients with abPG-SGA ≥ 5 and < 5, NRS2002 ≥ 3 and abPG-SGA < 5, NRS2002 < 3 and abPG-SGA ≥ 5 were significantly different (P < .0001), the OS of patients with NRS2002 ≥ 3 and abPG-SGA ≥ 5, NRS2002 < 3 and abPG-SGA < 5 were not significantly different (P > .05). Like NRS2002, abPG-SGA can also be used for malnutrition screening and prognosis judgment in cancer patients. It can quickly screen out cancer patients who may be at risk of malnutrition and facilitate the development of nutritional assessments.

A natural language processing system for the efficient extraction of cell markers.

Single-cell RNA sequencing (scRNA-seq) has emerged as a pivotal tool for exploring cellular landscapes across diverse species and tissues. Precise annotation of cell types is essential for understanding these landscapes, relying heavily on empirical knowledge and curated cell marker databases. In this study, we introduce MarkerGeneBERT, a natural language processing (NLP) system designed to extract critical information from the literature regarding species, tissues, cell types, and cell marker genes in the context of single-cell sequencing studies. Leveraging MarkerGeneBERT, we systematically parsed full-text articles from 3702 single-cell sequencing-related studies, yielding a comprehensive collection of 7901 cell markers representing 1606 cell types across 425 human tissues/subtissues, and 8223 cell markers representing 1674 cell types across 482 mouse tissues/subtissues. Comparative analysis against manually curated databases demonstrated that our approach achieved 76% completeness and 75% accuracy, while also unveiling 89 cell types and 183 marker genes absent from existing databases. Furthermore, we successfully applied the compiled brain tissue marker gene list from MarkerGeneBERT to annotate scRNA-seq data, yielding results consistent with original studies. Conclusions: Our findings underscore the efficacy of NLP-based methods in expediting and augmenting the annotation and interpretation of scRNA-seq data, providing a systematic demonstration of the transformative potential of this approach. The 27323 manual reviewed sentences for training MarkerGeneBERT and the source code are hosted at https://github.com/chengpeng1116/MarkerGeneBERT .