Chlorella pyrenoidosa as a potential bioremediator: Its tolerance and molecular responses to cadmium and lead.

Heavy metal contamination negatively affects plants and animals in water as well as soils. Some microalgae can remove heavy metal contaminants from wastewater. The aim of this study was to screen green microalgae (GM) to identify those that tolerate high concentrations of toxic heavy metals in water as possible candidates for phytoremediation. Analyses of the tolerance, physiological parameters, ultrastructure, and transcriptomes of GM under Cd/Pb treatments were conducted. Compared with the other GM, Chlorella pyrenoidosa showed stronger tolerance to high concentrations of Cd/Pb. The reduced glutathione content and peroxidase activity were higher in C. pyrenoidosa than those in the other GM. Ultrastructural observations showed that, compared with other GM, C. pyrenoidosa had less damage to the cell surface and interior under Cd/Pb toxicity. Transcriptome analyses indicated that the "peroxisome" and "sulfur metabolism" pathways were enriched with differentially expressed genes under Cd/Pb treatments, and that CpSAT, CpSBP, CpKAT2, Cp2HPCL, CpACOX, CpACOX2, and CpACOX4, all of which encode antioxidant enzymes, were up-regulated under Cd/Pb treatments. These results show that C. pyrenoidosa has potential applications in the remediation of polluted water, and indicate that antioxidant enzymes contribute to Cd/Pb detoxification. These findings will be useful for producing algal strains for the purpose of bioremediation in water contamination.

KRAS status predicted by pretreatment MRI radiomics was associated with lung metastasis in locally advanced rectal cancer patients.

BACKGROUND: Mutated KRAS may indicate an invasive nature and predict prognosis in locally advanced rectal cancer (LARC). We aimed to establish a radiomic model using pretreatment T2W MRIs to predict KRAS status and explore the association between the KRAS status or model predictions and lung metastasis. METHODS: In this retrospective multicentre study, LARC patients from two institutions between January 2012 and January 2019 were randomly divided into training and testing cohorts. Least absolute shrinkage and selection operator (LASSO) regression and the support vector machine (SVM) classifier were utilized to select significant radiomic features and establish a prediction model, which was validated by radiomic score distribution and decision curve analysis. The association between the model stratification and lung metastasis was investigated by Cox regression and Kaplan‒Meier survival analysis; the results were compared by the log-rank test. RESULTS: Overall, 103 patients were enrolled (73 and 30 in the training and testing cohorts, respectively). The median follow-up was 38.1 months (interquartile range: 26.9, 49.4). The radiomic model had an area under the curve (AUC) of 0.983 in the training cohort and 0.814 in the testing cohort. Using a cut-off of 0.679 defined by the receiver operating characteristic (ROC) curve, patients with a high radiomic score (RS) had a higher risk for lung metastasis (HR 3.565, 95% CI 1.337, 9.505, p = 0.011), showing similar predictive performances for the mutant and wild-type KRAS groups (HR 3.225, 95% CI 1.249, 8.323, p = 0.016, IDI: 1.08%, p = 0.687; NRI 2.23%, p = 0.766). CONCLUSIONS: We established and validated a radiomic model for predicting KRAS status in LARC. Patients with high RS experienced more lung metastases. The model could noninvasively detect KRAS status and may help individualize clinical decision-making.

Colorectal cancer lymph node metastasis prediction with weakly supervised transformer-based multi-instance learning.

Lymph node metastasis examined by the resected lymph nodes is considered one of the most important prognostic factors for colorectal cancer (CRC). However, it requires careful and comprehensive inspection by expert pathologists. To relieve the pathologists' burden and speed up the diagnostic process, in this paper, we develop a deep learning system with the binary positive/negative labels of the lymph nodes to solve the CRC lymph node classification task. The multi-instance learning (MIL) framework is adopted in our method to handle the whole slide images (WSIs) of gigapixels in size at once and get rid of the labor-intensive and time-consuming detailed annotations. First, a transformer-based MIL model, DT-DSMIL, is proposed in this paper based on the deformable transformer backbone and the dual-stream MIL (DSMIL) framework. The local-level image features are extracted and aggregated with the deformable transformer, and the global-level image features are obtained with the DSMIL aggregator. The final classification decision is made based on both the local and the global-level features. After the effectiveness of our proposed DT-DSMIL model is demonstrated by comparing its performance with its predecessors, a diagnostic system is developed to detect, crop, and finally identify the single lymph nodes within the slides based on the DT-DSMIL and the Faster R-CNN model. The developed diagnostic model is trained and tested on a clinically collected CRC lymph node metastasis dataset composed of 843 slides (864 metastasis lymph nodes and 1415 non-metastatic lymph nodes), achieving the accuracy of 95.3% and the area under the receiver operating characteristic curve (AUC) of 0.9762 (95% confidence interval [CI]: 0.9607-0.9891) for the single lymph node classification. As for the lymph nodes with micro-metastasis and macro-metastasis, our diagnostic system achieves the AUC of 0.9816 (95% CI: 0.9659-0.9935) and 0.9902 (95% CI: 0.9787-0.9983), respectively. Moreover, the system shows reliable diagnostic region localizing performance: the model can always identify the most likely metastases, no matter the model's predictions or manual labels, showing great potential in avoiding false negatives and discovering incorrectly labeled slides in actual clinical use.

MRI-based radiomics to predict neoadjuvant chemoradiotherapy outcomes in locally advanced rectal cancer: A multicenter study.

Background and purpose: Predicting tumour response would be useful for selecting patients with locally advanced rectal cancer (LARC) for organ preservation strategies. We aimed to develop and validate a prediction model for T downstaging (ypT0-2) in LARC patients after neoadjuvant chemoradiotherapy and to identify those who may benefit from consolidation chemotherapy. Materials and methods: cT3-4 LARC patients at three tertiary medical centers from January 2012 to January 2019 were retrospectively included, while a prospective cohort was recruited from June 2021 to March 2022. Eight filter (principal component analysis, least absolute shrinkage and selection operator, partial least-squares discriminant analysis, random forest)-classifier (support vector machine, logistic regression) models were established to select radiomic features. A nomogram combining radiomics and significant clinical features was developed and validated by calibration curve and decision curve analysis. Interaction test was conducted to investigate the consolidation chemotherapy benefits. Results: A total of 634 patients were included (426 in training cohort, 174 in testing cohort and 34 in prospective cohort). A radiomic prediction model using partial least-squares discriminant analysis and a support vector machine showed the best performance (AUC: 0.832 [training]; 0.763 [testing]). A nomogram combining radiomics and clinical features showed significantly better prognostic performance (AUC: 0.842 [training]; 0.809 [testing]) than the radiomic model. The model was also tested in the prospective cohort with AUC 0.727. High-probability group (score > 81.82) may have potential benefits from ≥ 4 cycles consolidation chemotherapy (OR: 4.173, 95 % CI: 0.953-18.276, p = 0.058, pinteraction = 0.021). Conclusion: We identified and validated a model based on multicenter pre-treatment radiomics to predict ypT0-2 in cT3-4 LARC patients, which may facilitate individualised treatment decision-making for organ-preservation strategies and consolidation chemotherapy.

N-Acetylcysteine alleviates spinal cord injury in rats after early decompression surgery by regulating inflammation and apoptosis.

OBJECTIVE: Decompression surgery in patients with spinal cord injury (SCI) has a neuroprotective effect by alleviating secondary injury and improving neurological outcomes. N-Acetylcysteine (NAC), a drug approved by the United States Food and Drug Administration, has been shown to play neuroprotective roles via attenuation of apoptosis and inflammation. The purpose of the present study was to investigate the effects of early or late decompression surgery in combination with NAC administration on acute SCI, as well as investigate the underlying mechanisms of its actions. METHODS: In this study, an acute SCI model was established in rats. The rats were treated with decompression surgery 24/48 h post-SCI in combination with or without NAC. RESULTS: The results showed that decompression surgery in combination with NAC lead to a better outcome than decompression alone, as demonstrated by the higher Basso, Beattie, and Bresnahan scores. Histopathological examination demonstrated that early decompression surgery in combination with NAC exerted the best therapeutic effect on spinal cord recovery, which was further confirmed by the extent of inflammation and apoptosis. Additionally, we found that NAC might compensate for a lack of late surgery. CONCLUSIONS: Collectively, early decompression surgery and NAC could be a promising combination for the treatment of acute SCI, and its therapeutic effects may be associated with the regulation of inflammation and apoptosis.