A novel approach for estimating lung tumor motion based on dynamic features in 4D-CT.

Due to the high expenses involved, 4D-CT data for certain patients may only include five respiratory phases (0%, 20%, 40%, 60%, and 80%). This limitation can affect the subsequent planning of radiotherapy due to the absence of lung tumor information for the remaining five respiratory phases (10%, 30%, 50%, 70%, and 90%). This study aims to develop an interpolation method that can automatically derive tumor boundary contours for the five omitted phases using the available 5-phase 4D-CT data. The dynamic mode decomposition (DMD) method is a data-driven and model-free technique that can extract dynamic information from high-dimensional data. It enables the reconstruction of long-term dynamic patterns using only a limited number of time snapshots. The quasi-periodic motion of a deformable lung tumor caused by respiratory motion makes it suitable for treatment using DMD. The direct application of the DMD method to analyze the respiratory motion of the tumor is impractical because the tumor is three-dimensional and spans multiple CT slices. To predict the respiratory movement of lung tumors, a method called uniform angular interval (UAI) sampling was developed to generate snapshot vectors of equal length, which are suitable for DMD analysis. The effectiveness of this approach was confirmed by applying the UAI-DMD method to the 4D-CT data of ten patients with lung cancer. The results indicate that the UAI-DMD method effectively approximates the lung tumor's deformable boundary surface and nonlinear motion trajectories. The estimated tumor centroid is within 2 mm of the manually delineated centroid, a smaller margin of error compared to the traditional BSpline interpolation method, which has a margin of 3 mm. This methodology has the potential to be extended to reconstruct the 20-phase respiratory movement of a lung tumor based on dynamic features from 10-phase 4D-CT data, thereby enabling more accurate estimation of the planned target volume (PTV).

A Highly Sensitive and Rapid Colloidal Gold Immunoassay for Puerarin Detection.

Radix Puerariae is a traditional Chinese medicinal material with a rich history of use in East and Southeast Asia. Puerarin, a unique component of the Pueraria genus, serves as a quality control marker for herbal medicines like Pueraria lobata and Pueraria thomsonii in China, displaying diverse pharmacological properties. This study developed puerarin colloidal gold immunoassay dipsticks utilizing an anti-puerarin monoclonal antibody, resulting in a fast and sensitive detection method with a limit of 500-1000 ng·mL-1. Evaluation using tap water-extracted P. lobata and P. thomsonii samples showed consistent results compared to LC-MS analysis. Cross-reactivity assessments of puerarin analogs revealed minimal interference, affirming the dipstick's reliability for distinguishing between the two species.

The scheme, and regulative mechanism of pyroptosis, ferroptosis, and necroptosis in radiation injury.

Radiotherapy (RT) stands as the primary treatment for tumors, but it inevitably causes damage to normal cells. Consequently, radiation injury is a crucial consideration for radiation oncologists during therapy planning. Cell death including apoptosis, autophagy, pyroptosis, ferroptosis, and necroptosis play significant roles in tumor treatment. While previous studies elucidated the induction of apoptosis and autophagy by ionizing radiation (IR), recent attention has shifted to pyroptosis, ferroptosis, and necroptosis, revealing their effects induced by IR. This review aims to summarize the strategies employed by IR, either alone or in combination therapy, to induce pyroptosis, ferroptosis, and necroptosis in radiation injury. Furthermore, we explore their effects and molecular pathways, shedding light on their roles in radiation injury. Finally, we summarize the regulative agents for these three types of cell death and their mechanisms. In summary, optimizing radiation dose, dose rate, and combined treatment plans to minimize radiation damage and enhance the killing effect of RT is a key focus.

Optimal sequence of LT for symptomatic BM in EGFR-mutant NSCLC: a comparative study of first-line EGFR-TKIs with/without upfront LT.

BACKGROUND: The third-generation epidermal growth factor receptor tyrosine kinase inhibitors (EGFR-TKIs) can penetrate blood-brain barrier and are effective for brain metastases (BMs). There is no consensus on the optimal sequence of local therapy (LT) and EGFR-TKIs for symptomatic BM patients because patients suffering neurological symptoms were not enrolled in most clinical trials. METHODS: Non-small cell lung cancer (NSCLC) patients with EGFR mutation (EGFRm) and symptomatic BM receiving first-line osimertinib and aumolertinib from two medical centers were collected. All participants were allocated into the third-generation EGFR-TKIs (TKIs) group and the upfront LT (uLT) plus third-generation EGFR-TKIs (TKIs + uLT) group. Demographic data, survival outcomes, treatment failure patterns, and adverse events were evaluated between the two groups. We also conducted subgroup analyses to explore the impact of BM number on survival outcomes. RESULTS: 86 patients were enrolled, 44 in the TKIs group and 42 in the TKIs + uLT group. There were no significant differences in the short-term response between the groups. TKIs + uLT was associated with significantly longer overall survival (OS) (43 vs. 28 months; hazard ratio [HR], 0.36, 95% confidence interval [CI], 0.17-0.77; p = .011). No differences in progression-free survival (PFS), intracranial PFS (iPFS), failure patterns, or safety were observed. In subgroup analyses of oligo-BM patients, TKIs + uLT could prolong OS (43 vs. 31 months; HR 0.22; 95% CI 0.05-0.92; p = .015). CONCLUSIONS: EGFRm NSCLC patients with symptomatic BM might benefit from uLT, particularly oligo-BM patients. However, larger prospective cohort studies should be carried out to confirm the responses of the TKIs + uLT scheme.

Cost-effectiveness of tumor-treating fields plus standard therapy for advanced non-small cell lung cancer progressed after platinum-based therapy in the United States.

Background: Tumor treating fields (TTF) was first approved for treatment of glioblastoma. Recently, the LUNAR study demonstrated that TTF + standard therapy (ST) extended survival in patients with advanced non-small cell lung cancer (NSCLC). This primary objective of this study is to analyze the cost-effectiveness of this treatment from the United States healthcare payers' perspective. Methods: A 3-health-state Markov model was established to compare the cost-effectiveness of TTF + ST and that of ST alone. Clinical data were extracted from the LUNAR study, supplemented by additional cost and utility data obtained from publications or online sources. One-way sensitivity analysis, probabilistic sensitivity analysis, and scenario analysis were conducted. The willingness-to-pay (WTP) threshold per quality-adjusted life-years (QALYs) gained was set to $150,000. The main results include total costs, QALYs, incremental cost-effectiveness ratio (ICER) and incremental net monetary benefit (INMB). Subgroup analyses were conducted for two types of ST, including immune checkpoint inhibitor, and docetaxel. Results: During a 10-year time horizon, the costs of TTF + ST and ST alone were $431,207.0 and $128,125.9, and the QALYs were 1.809 and 1.124, respectively. The ICER of TTF + ST compared to ST was $442,732.7 per QALY, and the INMB was -$200,395.7 at the WTP threshold. The cost of TTF per month was the most influential factor in cost-effectiveness, and TTF + ST had a 0% probability of being cost-effective at the WTP threshold compared with ST alone. Conclusion: TTF + ST is not a cost-effective treatment for advanced NSCLC patients who progressed after platinum-based therapy from the perspective of the United States healthcare payers.

Radiotherapy in Preclinical Models of Brain Metastases: A Review and Recommendations for Future Studies.

Brain metastases (BMs) frequently occur in primary tumors such as lung cancer, breast cancer, and melanoma, and are associated with notably short natural survival. In addition to surgical interventions, chemotherapy, targeted therapy, and immunotherapy, radiotherapy (RT) is a crucial treatment for BM and encompasses whole-brain radiotherapy (WBRT) and stereotactic radiosurgery (SRS). Validating the efficacy and safety of treatment regimens through preclinical models is imperative for successful translation to clinical application. This not only advances fundamental research but also forms the theoretical foundation for clinical study. This review, grounded in animal models of brain metastases (AM-BM), explores the theoretical underpinnings and practical applications of radiotherapy in combination with chemotherapy, targeted therapy, immunotherapy, and emerging technologies such as nanomaterials and oxygen-containing microbubbles. Initially, we provided a concise overview of the establishment of AM-BMs. Subsequently, we summarize key RT parameters (RT mode, dose, fraction, dose rate) and their corresponding effects in AM-BMs. Finally, we present a comprehensive analysis of the current research status and future directions for combination therapy based on RT. In summary, there is presently no standardized regimen for AM-BM treatment involving RT. Further research is essential to deepen our understanding of the relationships between various parameters and their respective effects.

Lanthanide/B(C6F5)3-Promoted Hydroboration Reduction of Indoles and Quinolines with Pinacolborane.

We have developed a lanthanide/B(C6F5)3-promoted hydroboration reduction of indoles and quinolines with pinacolborane (HBpin). This reaction provides streamlined access to a range of nitrogen-containing compounds in moderate to excellent yields. Large-scale synthesis and further transformations to bioactive compounds indicate that the method has potential practical applications. Preliminary mechanistic studies suggest that amine additives promote the formation of indole-borane intermediates, and the lanthanide/B(C6F5)3-promoted hydroboration reduction proceeds via hydroboration of indole-borane intermediates with HBpin and in situ-formed BH3 species, followed by the protodeborylation process.

Identification and Validation of the Diagnostic Markers for Inflammatory Bowel Disease by Bioinformatics Analysis and Machine Learning.

Inflammatory bowel disease (IBD) is a chronic inflammatory disorder of the gastrointestinal tract which is mediated by the inappropriate immune responses. This study was aimed to identify novel diagnostic biomarkers for diagnosis of IBD and explore the relationship between the diagnostic biomarkers and infiltrated immune cells. GSE38713, GSE53306, and GSE75214 downloaded from the Gene Expression Omnibus (GEO) database were split into training and testing sets. Differentially expressed genes (DEGs) were screened using the "limma" package. Gene Ontology (GO) and KEGG pathway enrichment analysis of DEGs were performed by clusterProfiler package. The LASSO regression and support vector machine recursive feature elimination (SVM-RFE) algorithms were conducted to identify novel diagnostic biomarkers. The receiver operating characteristic (ROC) curve was applied to evaluate the diagnostic value of the candidate biomarkers. The relationship of the candidate biomarkers and infiltrating immune cells in IBD were evaluated by CIBERSOTR. Quantitative Real-Time PCR (qRT-PCR) was applied to measure the expression level of the biomarkers in IBD. A total of 289 dysregulated genes were identified as DEGs in IBD. These DEGs were significantly enriched in chemokine signaling pathway and cytokine-cytokine receptor interaction. RHOU was identified as a critical diagnostic gene in IBD, which was confirmed using ROC curve and qRT-PCR assays. Immune cell infiltration analysis showed that RHOU was correlated with macrophages M2, dendritic cells resting, mast cells resting, T cells CD4 memory resting, macrophages M0, and mast cells activated. Our results imply that RHOU may be a potential diagnostic biomarker for IBD.

Hinge-like paper-based dual-channel enzyme-free ratiometric fluorescent microfluidic platform for simultaneous visual detection of carbaryl and glyphosate.

On-site multi-pesticide residues detection is particularly urgent and challenging. Here, we fabricated an enzyme-free ratiometric fluorescent detection system in combination with a hinge-like dual-channel 3D microfluidic paper analytical device (3D µPAD) for simultaneous visual detection of carbaryl and glyphosate. Blue-emission 1-naphthol (Em. 470 nm) was hydrolyzed from carbaryl, while yellow-emission 2,3-diaminophenazine (Em. 570 nm) was produced with the aid of Cu2+ for glyphosate sensing. Inner-filter effect between 1-naphthol or 2,3-diaminophenazine and green-emission carbon dots (Em. 510 nm) realized two ratiometric fluorescent detection systems. Remarkable color variation of green-blue for carbaryl (50.00-1100 µΜ) and yellow-green for glyphosate (5.00-600 µΜ) were observed on a dual-channel 3D µPAD without crosstalk. Their detection limits were 1.11 and 0.63 µΜ, respectively. The strategy realized simultaneous visual detection of carbaryl and glyphosate in food/herbal with excellent accuracy (spiked recoveries, 91.00-107.2%), high precision (RSD ≤ 8.43%), and superior selectivity.

CT-based dosiomics and radiomics model predicts radiation-induced lymphopenia in nasopharyngeal carcinoma patients.

Purpose: This study aims to develop and validate a model predictive for the incidence of grade 4 radiation-induced lymphopenia (G4RIL), based on dosiomics features and radiomics features from the planning CT of nasopharyngeal carcinoma (NPC) treated by radiation therapy. Methods: The dataset of 125 NPC patients treated with radiotherapy from August 2018 to March 2019 was randomly divided into two sets-an 85-sample training set and a 40-sample test set. Dosiomics features and radiomics features of the CT image within the skull bone and cervical vertebrae were extracted. A feature selection process of multiple steps was employed to identify the features that most accurately forecast the data and eliminate superfluous or insignificant ones. A support vector machine learning classifier with correction for imbalanced data was trained on the patient dataset for prediction of RIL (positive classifier for G4RIL, negative otherwise). The model's predictive capability was gauged by gauging its sensitivity (the likelihood of a positive test being administered to patients with G4RIL) and specificity in the test set. The area beneath the ROC curve (AUC) was utilized to explore the association of characteristics with the occurrence of G4RIL. Results: Three clinical features, three dosiomics features, and three radiomics features exhibited significant correlations with G4RIL. Those features were then used for model construction. The combination model, based on nine robust features, yielded the most impressive results with an ACC value of 0.88 in the test set, while the dosiomics model, with three dosiomics features, had an ACC value of 0.82, the radiomics model, with three radiomics features, had an ACC value of 0.82, and the clinical model, with its initial features, had an ACC value of 0.6 for prediction performance. Conclusion: The findings show that radiomics and dosiomics features are correlated with the G4RIL of NPC patients. The model incorporating radiomics features and dosiomics features from planning CT can predict the incidence of G4RIL in NPC patients.

PD-1/PD-L1 inhibitor treatment and its impact on clinical imaging in non-small cell lung cancer: a systematic review and meta-analysis of immune-related adverse events.

Background: In the contemporary era of cancer treatment, lung cancer (LC) holds the unenviable position of being the primary contributor to cancer-induced mortality worldwide. Although immunotherapy has expanded the therapeutic landscape for metastatic non-small cell lung cancer (NSCLC), the advent of immune checkpoint inhibitors has been accompanied by a concomitant increase in immune-related adverse events (irAEs). Timely detection of irAEs is pivotal for efficacious management and enhanced patient outcomes. Diagnostic imaging, encompassing x-ray and CT scans, can facilitate the identification and supervision of irAEs, thereby ensuring the prompt recognition of associated patterns and alterations for expeditious treatment. Methods: The present inquiry undertook a systematic exploration of multiple databases, incorporating a diverse array of studies such as randomized controlled trials and observational analyses. Patient demographics, imaging outcomes, and risk of bias were extracted from the data. Meta-analysis was executed utilizing R Statistical Software, with the results of the risk of bias assessment summarized accordingly. Findings: The analysis unveiled a higher prevalence of irAEs in patients receiving first-line treatment for NSCLC compared to those receiving subsequent treatments, with a statistically significant distinction observed for both high- and low-grade irAEs (p < 0.001). Pneumonitis, thyroiditis, and colitis emerged as the most frequently reported irAEs, whereas hepatitis and pancolitis were less commonly documented. This investigation signifies a crucial advancement in elucidating the function of imaging in the treatment of NSCLC with PD-1/PD-L1 inhibitors and emphasizes the imperative for ongoing research in this domain.

Cost-effectiveness of neoadjuvant pembrolizumab plus chemotherapy with adjuvant pembrolizumab for early-stage non-small cell lung cancer in the United States.

Introduction: Perioperative (neoadjuvant and adjuvant) pembrolizumab has shown favorable efficacy in patients with early-stage non-small cell lung cancer (NSCLC). This study aims to evaluate the cost-effectiveness of this treatment from the perspective of the United States healthcare payers. Methods: We established a Markov model to compare the cost-effectiveness of perioperative pembrolizumab with that of neoadjuvant chemotherapy in 21-day cycles, utilizing data from the phase 3 KEYNOTE-671 trial. Additional data were extracted from other publications or online sources. Sensitivity analyses were conducted to evaluate the robustness of the findings. A willingness-to-pay threshold of $150,000 per quality-adjusted life-years (QALYs) gained was established. The main outcomes of this study were the measurement of QALYs, overall costs, incremental cost-effectiveness ratio (ICER), and net monetary benefit (NMB). Results: During a 10-year time horizon, the total costs of perioperative pembrolizumab and the control treatment were $224,779.1 and $110,026.3, respectively. The QALYs were 4.19 and 2.97 for the two treatments, respectively, which led to an ICER of $94,222.29 per QALY gained. The NMB at the WTP threshold at $150,000 per QALY gained was $67,931.3. One-way sensitivity analysis identified the cost of pembrolizumab as the primary factor influencing cost-effectiveness. Probabilistic sensitivity analysis indicated a 97.7% probability of perioperative pembrolizumab being cost-effective at the WTP threshold. Conclusions: From the perspective of the United States healthcare payers, perioperative pembrolizumab is a cost-effective treatment for patients with early-stage NSCLC.

Radiomics and deep learning models to differentiate lung adenosquamous carcinoma: A multicenter trial.

Adenosquamous carcinoma (ASC) is frequently misdiagnosed or overlooked in clinical practice due to its dual histological components and potential transformation from either adenocarcinoma (ADC) or squamous cell carcinoma (SCC). Our study aimed to differentiate ASC from ADC and SCC by incorporating features of enhanced CTs and clinical characteristics to build radiomics and deep learning models. The classification models were trained in Xiangya Hospital and validated in two other independent hospitals. The areas under the receiver operating characteristic curves (AUC), accuracy, sensitivity, specificity, positive predictive value, and negative predictive value were used to estimate the performance. The optimal three-class classification model achieved a maximum AUC of 0.89 and accuracy of 0.81 in external validation sets, AUC of 0.99 and accuracy of 0.99 in the internal test set. These findings highlight the efficacy of our models in differentiating ASC, providing a non-invasive, timely, and accurate diagnostic approach before and during the treatment.

CT-based radiomics for predicting radio-chemotherapy response and overall survival in nonsurgical esophageal carcinoma.

Background: To predict treatment response and 2 years overall survival (OS) of radio-chemotherapy in patients with esophageal cancer (EC) by radiomics based on the computed tomography (CT) images. Methods: This study retrospectively collected 171 nonsurgical EC patients treated with radio-chemotherapy from Jan 2010 to Jan 2019. 80 patients were randomly divided into training (n=64) and validation (n=16) cohorts to predict the radiochemotherapy response. The models predicting treatment response were established by Lasso and logistic regression. A total of 156 patients were allocated into the training cohort (n=110), validation cohort (n=23) and test set (n=23) to predict 2-year OS. The Lasso Cox model and Cox proportional hazards model established the models predicting 2-year OS. Results: To predict the radiochemotherapy response, WFK as a radiomics feature, and clinical stages and clinical M stages (cM) as clinical features were selected to construct the clinical-radiomics model, achieving 0.78 and 0.75 AUC (area under the curve) in the training and validation sets, respectively. Furthermore, radiomics features called WFI and WGI combined with clinical features (smoking index, pathological types, cM) were the optimal predictors to predict 2-year OS. The AUC values of the clinical-radiomics model were 0.71 and 0.70 in the training set and validation set, respectively. Conclusions: This study demonstrated that planning CT-based radiomics showed the predictability of the radiochemotherapy response and 2-year OS in nonsurgical esophageal carcinoma. The predictive results prior to treatment have the potential to assist physicians in choosing the optimal therapeutic strategy to prolong overall survival.

The protein arginine methyltransferase family (PRMTs) regulates metastases in various tumors: From experimental study to clinical application.

Tumor metastasis is the leading cause of mortality among advanced cancer patients. Understanding its mechanisms and treatment strategies is vital for clinical application. Arginine methylation, a post-translational modification catalyzed by protein arginine methyltransferases (PRMTs), is implicated in diverse physiological processes and disease progressions. Previous research has demonstrated PRMTs' involvement in tumor occurrence, progression, and metastasis. This review offers a comprehensive summary of the relationship between PRMTs, prognosis, and metastasis in various cancers. Our focus centers on elucidating the molecular mechanisms through which PRMTs regulate tumor metastasis. We also discuss relevant clinical trials and effective PRMT inhibitors, including chemical compounds, long non-coding RNA (lncRNA), micro-RNA (miRNA), and nanomaterials, for treating tumor metastasis. While a few studies present conflicting results, the overall trajectory suggests that inhibiting arginine methylation exhibits promise in curtailing tumor metastasis across various cancers. Nonetheless, the underlying mechanisms and molecular interactions are diverse. The development of inhibitors targeting arginine methylation, along with the progression of clinical trials, holds substantial potential in the field of tumor metastasis, meriting sustained attention.

CircRNAs and lung cancer: Insight into their roles in metastasis.

Lung cancer is the leading cause of cancer-related mortality worldwide. A major contributing factor to the poor survival rates in lung cancer is the high prevalence of metastasis at the time of diagnosis. To address this critical issue, it is imperative to investigate the mechanisms underlying lung cancer metastasis. Circular RNAs (circRNAs), a distinct type of ribonucleic acid characterized by their unique circular structure, have been implicated in the progression of various diseases. Recent studies have highlighted the close association between circRNAs and the occurrence and development of lung cancer, particularly in relation to metastasis. In this review, we provide a concise overview of the expression patterns and prognostic significance of circRNAs in lung cancer. Additionally, we summarized the current understanding of the clinical relevance of circRNAs in lung cancer metastasis. Furthermore, we systematically focused on the roles of circRNAs in each step of lung cancer metastasis, reflecting the sequential progression of this process. Notably, circRNAs exhibit dual functionality in lung cancer metastasis, acting both as facilitators and inhibitors of metastatic processes. Given their potential, circRNAs hold promise as novel biomarkers and therapeutic targets for lung cancer metastasis, warranting further investigation.

Adaptive restraint design for a diverse population through machine learning.

Objective: Using population-based simulations and machine-learning algorithms to develop an adaptive restraint system that accounts for occupant anthropometry variations to further enhance safety balance throughout the whole population. Methods: Two thousand MADYMO full frontal impact crash simulations at 35 mph using two validated vehicle/restraint models representing a sedan and an SUV along with a parametric occupant model were conducted based on the maximal projection design of experiments, which considers varying occupant covariates (sex, stature, and body mass index) and vehicle restraint design variables (three for airbag, three for safety belt, and one for knee bolster). A Gaussian-process-based surrogate model was trained to rapidly predict occupant injury risks and the associated uncertainties. An optimization framework was formulated to seek the optimal adaptive restraint design policy that minimizes the population injury risk across a wide range of occupant sizes and shapes while maintaining a low difference in injury risks among different occupant subgroups. The effectiveness of the proposed method was tested by comparing the population-wise injury risks under the adaptive design policy and the traditional state-of-the-art design. Results: Compared to the traditional state-of-the-art design for midsize males, the optimal design policy shows the potential to further reduce the joint injury risk (combining head, chest, and lower extremity injury risks) among the whole population in the sedan and SUV models. Specifically, the two subgroups of vulnerable occupants including tall obese males and short obese females had higher reductions in injury risks. Conclusions: This study lays out a method to adaptively adjust vehicle restraint systems to improve safety balance. This is the first study where population-based crash simulations and machine-learning methods are used to optimize adaptive restraint designs for a diverse population. Nevertheless, this study shows the high injury risks associated with obese and female occupants, which can be mitigated via restraint adaptability.

Integration of gut microbiota and metabolomics for the hematopoiesis of Siwu paste on anemia rats.

Background: To investigate the regulation mechanism of hematopoiesis of Siwu paste (SWP) in anemia rats, which is a classic Chinese prescription used for nourishing blood or blood deficiency over 1000 years. Methods: Blood cell and biochemical analysis were used to evaluate the hematopoietic function of SWP in anemia rats. The intestinal microbial composition was analyzed with 16S rRNA gene sequencing, and the metabolites were profiled using UPLC-TripleTOF system nontargeting metabolomics. Results: SWP can improve the levels of red blood cells, hemoglobin, platelet, hematocrit value, white blood cells, lymphocyte, EPO, TPO, and GM-CSF in anemia rats, and significantly change the microbial community and its metabolites. The correlation analysis of intestinal microbiota-hematopoietic efficacy shows that 13 kinds of different intestinal flora were related to hematopoietic efficacy, in which Prevotella_1, Prevotella_9, Lactobacillus, and norank_f__Muribaculaceae were significantly positively correlated with hematopoiesis, nine kinds of intestinal flora are negatively correlated with hematopoietic effect. Compared with anemia rats, 218 potential metabolic biomarkers and 36 metabolites with significant differences were identified in the SWP treatment group, and the key metabolites were mainly amino acids and lipids. An in-depth analysis of metabolic pathways showed that SWP mainly affected 7 metabolic pathways, including aminobenzoic acid degradation and tryptophan metabolism. Conclusion: The study provides novel insights into the regulation of hematopoiesis of SWP in anemia rats that were correlated with gut microbiota and the metabolites, which through the restoration of the firmicutes/bacteroidetes ratio.

MRI radiomics for brain metastasis sub-pathology classification from non-small cell lung cancer: a machine learning, multicenter study.

The objective of this study is to develop a machine-learning model that can accurately distinguish between different histologic types of brain lesions in patients with non-small cell lung cancer (NSCLC) when it is not safe or feasible to perform a biopsy. To achieve this goal, the study utilized data from two patient cohorts: 116 patients from Xiangya Hospital and 35 patients from Yueyang Central Hospital. A total of eight machine learning algorithms, including Xgboost, were compared. Additionally, a 3-dimensional convolutional neural network was trained using transfer learning to further evaluate the performance of these models. The SHapley Additive exPlanations (SHAP) method was developed to determine the most important features in the best-performing model after hyperparameter optimization. The results showed that the area under the curve (AUC) for the classification of brain lesions as either lung adenocarcinoma or squamous carcinoma ranged from 0.60 to 0.87. The model based on single radiomics features extracted from contrast-enhanced T1 MRI and utilizing the Xgboost algorithm demonstrated the highest performance (AUC: 0.85) in the internal validation set and adequate performance (AUC: 0.80) in the independent external validation set. The SHAP values also revealed the impact of individual features on the classification results. In conclusion, the use of a radiomics model incorporating contrast-enhanced T1 MRI, Xgboost, and SHAP algorithms shows promise in accurately and interpretably identifying brain lesions in patients with NSCLC.

Proteomic analysis reveals that Polygonatum cyrtonema Hua polysaccharide ameliorates mice muscle atrophy in chemotherapy-induced cachexia.

Polygonatum cyrtonema Hua polysaccharide (PCP) is the main bioactive compound derived from the herb Polygonati Rhizoma, known for its anti-fatigue, antioxidant, immunomodulatory, and anti-inflammatory properties. However, its effectiveness on alleviating chemotherapy-induced muscle atrophy has been unclear. In this study, we utilized proteomic analysis to investigate the effects and mechanisms of PCP on gemcitabine plus cisplatin (GC) induced muscle atrophy in mice. Quality control analysis revealed that the functional PCP, rich in glucose, is a heterogeneous polysaccharide comprised of nine monosaccharides. PCP (64 mg/kg) significantly alleviated body muscle, organ weight loss, and muscle fiber atrophy in chemotherapy-induced cachectic mice. Moreover, PCP suppressed the decrease in serum immunoglobulin levels and the increase in pro-inflammatory factor interleukin-6 (IL-6). Proteomic analysis demonstrated that PCP contributed to the homeostasis of protein metabolism in gastrocnemius muscle. Diacylglycerol kinase (DGKζ) and cathepsin L (CTSL) were identified as primary PCP targets. Furthermore, the IL-6/STAT3/CTSL and DGKζ/FoxO/Atrogin1 signaling pathways were validated. Our findings suggest that PCP exerts an anti-atrophy effect on chemotherapy-induced muscle atrophy by regulating the autophagy-lysosome and ubiquitin-proteasome systems.