CT-based radiomic consensus clustering association with tumor biological behavior in clinical stage IA adenocarcinoma: a retrospective study.

Background: Research has demonstrated that radiomics models are capable of forecasting the characteristics of lung cancer. Nevertheless, due to radiomics' poor interpretability, its applicability in clinical settings remains restricted. This investigation sought to verify the correlation between radiomics features (RFs) and the biological behavior of clinical stage IA adenocarcinomas. Methods: A retrospective analysis was conducted on patients diagnosed with clinical stage IA lung adenocarcinoma who underwent resection between May 2005 and December 2018. Detailed radiomics examination of the primary tumor was carried out utilizing preoperative computed tomography (CT) images. Subsequently, patients were grouped based on their RFs using consensus clustering, enabling comparison of tumor biological characteristics among the clusters. Survival disparities among the clusters were evaluated through Kaplan-Meier and Cox analyses. Results: A consensus cluster analysis was performed on 669 patients [median age, 58 years; interquartile range (IQR), 50-64 years, 257 males, 412 females], and three distinct clusters were identified. Cluster 2 was associated with radiological solid adenocarcinoma [119 of 324 (36.7%), P<0.001], larger tumors with median tumor size of 2.1 cm with IQR of 1.7 to 2.5 cm (P<0.001), central tumor [91 of 324 (28.1%), P=0.002], pleural invasion [87 of 324 (26.9%), P<0.001], occult lymph node metastasis (ONM) [106 of 324 (32.7%), P<0.001], and a higher frequency of metastasis or recurrence [62 of 324 (19.1%), P<0.001]. The frequency of histological grade 3 was the highest in Cluster 3 [8 of 34 (23.5%), P<0.001]. Cluster 1 was associated with pure ground glass nodules (pGGNs) [184 of 310 (59.4%), P<0.001], smaller tumors with median tumor size of 1.1 cm with IQR of 0.8 to 1.4 cm (P<0.001), no pleural invasion [276 of 310 (89.0%), P<0.001], histological grade 1 [114 of 248 (46.0%), P<0.001], ONM negative [292 of 310 (94.2%), P<0.001], and a lower rate of metastasis or recurrence [298 of 310 (96.1%), P<0.001]. Conclusions: Differences in tumor biological behavior were detected among consensus clusters based on the RFs of clinical stage IA adenocarcinoma.

Serum tumor markers: potential indicators for occult lymph node metastasis in clinical T1 - 2N0M0 small cell lung cancer patients.

In their letter-to-the-editor entitled "Letter to the Editor: Incidence rate of occult lymph node metastasis in clinical T1 - 2N0M0 small cell lung cancer patients and radiomic prediction based on contrast-enhanced CT imaging: a multicenter study", Prof. Chen et al. provided insightful comments and suggestions on our original study. We appreciate the authors' feedback and have conducted a preliminary exploration of the predictive value of serum tumor markers (TMs) for occult lymph node metastasis (OLM) in clinical T1 - 2N0M0 (cT1 - 2N0M0) small cell lung cancer (SCLC) patients. The results indicate that neuron-specific enolase (NSE), carbohydrate antigen 125 (CA125), and squamous cell carcinoma antigen (SCC) have potential predictive value for detecting OLM in cT1 - 2N0M0 SCLC patients. Additionally, further exploration and confirmation through prospective, large-scale studies with robust external validation are needed.

Deep learning model based on primary tumor to predict lymph node status in clinical stage IA lung adenocarcinoma: a multicenter study.

Objective: To develop a deep learning model to predict lymph node (LN) status in clinical stage IA lung adenocarcinoma patients. Methods: This diagnostic study included 1,009 patients with pathologically confirmed clinical stage T1N0M0 lung adenocarcinoma from two independent datasets (699 from Cancer Hospital of Chinese Academy of Medical Sciences and 310 from PLA General Hospital) between January 2005 and December 2019. The Cancer Hospital dataset was randomly split into a training cohort (559 patients) and a validation cohort (140 patients) to train and tune a deep learning model based on a deep residual network (ResNet). The PLA Hospital dataset was used as a testing cohort to evaluate the generalization ability of the model. Thoracic radiologists manually segmented tumors and interpreted high-resolution computed tomography (HRCT) features for the model. The predictive performance was assessed by area under the curves (AUCs), accuracy, precision, recall, and F1 score. Subgroup analysis was performed to evaluate the potential bias of the study population. Results: A total of 1,009 patients were included in this study; 409 (40.5%) were male and 600 (59.5%) were female. The median age was 57.0 years (inter-quartile range, IQR: 50.0-64.0). The deep learning model achieved AUCs of 0.906 (95% CI: 0.873-0.938) and 0.893 (95% CI: 0.857-0.930) for predicting pN0 disease in the testing cohort and a non-pure ground glass nodule (non-pGGN) testing cohort, respectively. No significant difference was detected between the testing cohort and the non-pGGN testing cohort (P = 0.622). The precisions of this model for predicting pN0 disease were 0.979 (95% CI: 0.963-0.995) and 0.983 (95% CI: 0.967-0.998) in the testing cohort and the non-pGGN testing cohort, respectively. The deep learning model achieved AUCs of 0.848 (95% CI: 0.798-0.898) and 0.831 (95% CI: 0.776-0.887) for predicting pN2 disease in the testing cohort and the non-pGGN testing cohort, respectively. No significant difference was detected between the testing cohort and the non-pGGN testing cohort (P = 0.657). The recalls of this model for predicting pN2 disease were 0.903 (95% CI: 0.870-0.936) and 0.931 (95% CI: 0.901-0.961) in the testing cohort and the non-pGGN testing cohort, respectively. Conclusions: The superior performance of the deep learning model will help to target the extension of lymph node dissection and reduce the ineffective lymph node dissection in early-stage lung adenocarcinoma patients.

Evaluating renal injury characteristics in different rat models of hyperuricemia and elucidating pathological molecular mechanisms via serum metabolomics.

Hyperuricemia has emerged as a significant global health concern, closely associated with various metabolic disorders. The adverse effects frequently observed with current pharmacological treatments for hyperuricemia highlight the urgent need for reliable animal models to elucidate the disease's pathophysiological mechanisms, thereby facilitating the development of safer and more effective therapies. In this study, we established three rat models of hyperuricemia using potassium oxonate, either alone or in combination with fructose and adenine. Each model exhibited distinct pathological changes, with the combination of potassium oxonate, fructose, and adenine causing significantly more severe damage to liver and kidney functions than potassium oxonate alone. Serum metabolomics analyses revealed profound dysregulation in the metabolic pathways of purine, pyrimidines, and glutathione, underscoring the pivotal role of oxidative stress in the progression of hyperuricemia. We identified key biomarkers such as orotidine, ureidosuccinic acid, uracil, and pseudouridine, which are associated with uric acid-induced damage to hepatic and renal systems. MetOrigin tracing analysis further revealed that differential metabolites related to hyperuricemia are primarily involved in host-microbiome co-metabolic pathways, particularly in purine metabolism, with bacterial phyla such as Pseudomonadota, Actinomycetota, and Ascomycota being closely linked to the critical metabolic processes of uric acid production. These findings not only enhance our understanding of the pathogenic mechanisms underlying hyperuricemia but also provide a robust experimental model foundation for the development of innovative treatment strategies.

Predicting MET exon 14 skipping mutation in pulmonary sarcomatoid carcinoma by whole-tumour texture analysis combined with clinical and conventional contrast-enhanced computed tomography features.

Background: Pulmonary sarcomatoid carcinoma (PSC) is a rare, highly malignant type of non-small cell lung cancer (NSCLC) with a poor prognosis. Targeted drugs for MET exon 14 (METex14) skipping mutation can have considerable clinical benefits. This study aimed to predict METex14 skipping mutation in PSC patients by whole-tumour texture analysis combined with clinical and conventional contrast-enhanced computed tomography (CECT) features. Methods: This retrospective study included 56 patients with PSC diagnosed by pathology. All patients underwent CECT before surgery or other treatment, and both targeted DNA- and RNA-based next-generation sequencing (NGS) were used to detect METex14 skipping mutation status. The patients were divided into two groups: METex14 skipping mutation and nonmutation groups. Overall, 1,316 texture features of the whole tumour were extracted. We also collected 12 clinical and 20 conventional CECT features. After dimensionality reduction and selection, predictive models were established by multivariate logistic regression analysis. Models were evaluated using the area under the curve (AUC), and the clinical utility of the model was assessed by decision curve analysis. Results: METex14 skipping mutation was detected in 17.9% of PSCs. Mutations were found more frequently in those (I) who had smaller long- or short-axis diameters (P=0.02, P=0.01); (II) who had lower T stages (I, II) (P=0.02); and (III) with pseudocapsular or annular enhancement (P=0.03). The combined model based on the conventional and texture models yielded the best performance in predicting METex14 skipping mutation with the highest AUC (0.89). The conventional and texture models also had good performance (AUC =0.83 conventional; =0.88 texture). Conclusions: Whole-tumour texture analysis combined with clinical and conventional CECT features may serve as a noninvasive tool to predict the METex14 skipping mutation status in PSC.

[Study on the total flavonoid content and antioxidant activity of Morus nigra Linn. from different origins in Xinjiang].

OBJECTIVE: Optimization of the extraction process of total flavonoids from Morus nigra Linn. based on response surface design, to compare the differences in total flavonoid content of Morus nigra Linn. from different origins in Xinjiang and its relationship with antioxidant activity. METHODS: A one-way test was used to investigate the effects of ethanol volume fraction, material-liquid ratio, ultrasonic power and ultrasonic time on total flavonoid content analysis of Morus nigra Linn. , Box-Behnken response surface design optimisation was used to derive the optimal extraction process parameters. Meanwhile, the in vitro antioxidant activity of Morus nigra Linn. was evaluated by antioxidant activity indexes such as hydroxyl radical scavenging capacity and ferric ion reducing capacity. RESULTS: The optimal extraction condition was 45% ethanol by volume, 1∶20 g/mL material-liquid ratio, 300W ultrasound power and 60 min ultrasound time. The total flavonoid content of Morus nigra Linn. s from six origins was detected and the result showed that Kuche >Kashgar >Kuche Wuqia Town>Hetian >Ying jisha >Yutian, in which the total flavonoid content of mulberry in Kucha city was(27.159±0.091)mg/g. The difference in the total flavonoid content of Morus nigra Linn. s in different origicns was statistically significant(P<0.05). In vitro antioxidant activity analysis showed that the antioxidant activity of mulberry from Kuche and Hetian was stronger, and the hydroxyl radical scavenging capacity, ferric ion reducing capacity, and total antioxidant capacity of mulberry from various origins were statistically significant(P<0.05). The correlation analysis showed that the total flavonoid content of Morus nigra Linn. was correlated with the antioxidant capacity to improve the scavenging of hydroxyl radicals. CONCLUSION: The ultrasonic-assisted method of extracting total flavonoids from Morus nigra Linn. is simple, and the model constructed has a high degree of fit, which can better compare the total flavonoids content of Morus nigra Linn. from different origins in Xinjiang.

Incidence rate of occult lymph node metastasis in clinical T1-2N0M0 small cell lung cancer patients and radiomic prediction based on contrast-enhanced CT imaging: a multicenter study : Original research.

BACKGROUND: This study aimed to explore the incidence of occult lymph node metastasis (OLM) in clinical T1 - 2N0M0 (cT1 - 2N0M0) small cell lung cancer (SCLC) patients and develop machine learning prediction models using preoperative intratumoral and peritumoral contrast-enhanced CT-based radiomic data. METHODS: By conducting a retrospective analysis involving 242 eligible patients from 4 centeres, we determined the incidence of OLM in cT1 - 2N0M0 SCLC patients. For each lesion, two ROIs were defined using the gross tumour volume (GTV) and peritumoral volume 15 mm around the tumour (PTV). By extracting a comprehensive set of 1595 enhanced CT-based radiomic features individually from the GTV and PTV, five models were constucted and we rigorously evaluated the model performance using various metrics, including the area under the curve (AUC), accuracy, sensitivity, specificity, calibration curve, and decision curve analysis (DCA). For enhanced clinical applicability, we formulated a nomogram that integrates clinical parameters and the rad_score (GTV and PTV). RESULTS: The initial investigation revealed a 33.9% OLM positivity rate in cT1 - 2N0M0 SCLC patients. Our combined model, which incorporates three radiomic features from the GTV and PTV, along with two clinical parameters (smoking status and shape), exhibited robust predictive capabilities. With a peak AUC value of 0.772 in the external validation cohort, the model outperformed the alternative models. The nomogram significantly enhanced diagnostic precision for radiologists and added substantial value to the clinical decision-making process for cT1 - 2N0M0 SCLC patients. CONCLUSIONS: The incidence of OLM in SCLC patients surpassed that in non-small cell lung cancer patients. The combined model demonstrated a notable generalization effect, effectively distinguishing between positive and negative OLMs in a noninvasive manner, thereby guiding individualized clinical decisions for patients with cT1 - 2N0M0 SCLC.

A comparison of machine learning methods for radiomics modeling in prediction of occult lymph node metastasis in clinical stage IA lung adenocarcinoma patients.

Background: Accurate prediction of occult lymph node metastasis (ONM) is an important basis for determining whether lymph node (LN) dissection is necessary in clinical stage IA lung adenocarcinoma patients. The aim of this study is to determine the best machine learning algorithm for radiomics modeling and to compare the performances of the radiomics model, the clinical-radilogical model and the combined model incorporate both radiomics features and clinical-radilogical features in preoperatively predicting ONM in clinical stage IA lung adenocarcinoma patients. Methods: Patients with clinical stage IA lung adenocarcinoma undergoing curative surgery from one institution were retrospectively recruited and assigned to training and test cohorts. Radiomics features were extracted from the preoperative computed tomography (CT) images of the primary tumor. Seven machine learning algorithms were used to construct radiomics models, and the model with the best performance, evaluated using the area under the curve (AUC), was selected. Univariate and multivariate logistic regression analyses were performed on the clinical-radiological features to identify statistically significant features and to develop a clinical model. The optimal radiomics and clinical models were integrated to build a combined model, and its predictive performance was assessed using receiver operating characteristic curves, Brier score, and decision curve analysis (DCA). Results: This study included 258 patients who underwent resection (training cohort, n=182; test cohort, n=76). Six radiomics features were identified. Among the seven machine learning algorithms, extreme gradient boosting (XGB) demonstrated the highest performance for radiomics modeling, with an AUC of 0.917. The combined model improved the AUC to 0.933 and achieved a Brier score of 0.092. DCA revealed that the combined model had optimal clinical efficacy. Conclusions: The superior performance of the combined model, based on XGB algorithm in predicting ONM in patients with clinical stage IA lung adenocarcinoma, might aid surgeons in deciding whether to conduct mediastinal LN dissection and contribute to improve patients' prognosis.

Evaluation of Prognosis in Patients with Lung Adenocarcinoma with Atypical Solid Nodules on Thin-Section CT Images.

Purpose To evaluate the clinicopathologic characteristics and prognosis of patients with clinical stage IA lung adenocarcinoma with atypical solid nodules (ASNs) on thin-section CT images. Materials and Methods Data from patients with clinical stage IA lung adenocarcinoma who underwent resection between January 2005 and December 2012 were retrospectively reviewed. According to their manifestations on thin-section CT images, nodules were classified as ASNs, subsolid nodules (SSNs), and typical solid nodules (TSNs). The clinicopathologic characteristics of the ASNs were investigated, and the differences across the three groups were analyzed. The Kaplan-Meier method and multivariable Cox analysis were used to evaluate survival differences among patients with ASNs, SSNs, and TSNs. Results Of the 254 patients (median age, 58 years [IQR, 53-66]; 152 women) evaluated, 49 had ASNs, 123 had SSNs, and 82 had TSNs. Compared with patients with SSNs, those with ASNs were more likely to have nonsmall adenocarcinoma (P < .001), advanced-stage adenocarcinoma (P = .004), nonlepidic growth adenocarcinoma (P < .001), and middle- or low-grade differentiation tumors (P < .001). Compared with patients with TSNs, those with ASNs were more likely to have no lymph node involvement (P = .009) and epidermal growth factor receptor mutation positivity (P = .018). Average disease-free survival in patients with ASNs was significantly longer than that in patients with TSNs (P < .001) but was not distinguishable from that in patients with SSNs (P = .051). Conclusion ASNs were associated with better clinical outcomes than TSNs in patients with clinical stage IA lung adenocarcinoma. Keywords: Adenocarcinoma, Atypical Solid Nodules, CT, Disease-free Survival, Lung, Prognosis, Pulmonary Supplemental material is available for this article. Published under a CC BY 4.0 license.

Enhanced removal strategy towards organic matter with low coagulability: Immediate entrapment and complexation of oxidized intermediates by the hybrid ozonation-coagulation process.

The existence of dissolved organic matter (DOM) with low coagulability poses great challenges for conventional coagulation (CC) in water treatment. As a kind of typical organochlorine pesticide, 2,4-dichlorophenoxyacetic acid (2,4-D) cannot be efficiently removed by CC. To enhance the 2,4-D removal, ozonation was applied with coagulation. The hybrid ozonation-coagulation (HOC) achieved 60.61% DOC removal efficiency, which was obviously higher than pre-ozonation coagulation (POC) (45.83%). Synchronous fluorescence spectroscopy revealed stronger complexation between modified 2,4-D and coagulants during the HOC than that in subsequent coagulation of the POC process. During the HOC process, ozone promoted the formation of polymeric Al species, such as Alb. To investigate the 2,4-D removal mechanism, γ-Al2O3/O3 process with the same oxidation ability as the HOC was established. 2,4-D was oxidized step-by-step to 2,4-dichlorophenol, 4,6-dichlororesorcin, 3,5-dichlorocatechol, 2-chlorohydroquinone, 4-chlorocatechol, 1,2,4,5-tetrahydroxybenzene, pentahydroxybenzene and oxalic acid in γ-Al2O3/O3 process. However, during the HOC process, these oxidized intermediates were readily complexed by coagulants and accumulated in flocs. Especially 1,2,4,5-tetrahydroxybenzene and pentahydroxybenzene, completely complexed by AlCl3•6H2O hydrolysates as soon as being formed. Immediate entrapment and complexation between coagulant hydrolysates and 2,4-D oxidized intermediates inhibited the generation of small-molecular-weight organics such as oxalic acid, which enhanced the removal of organics with low coagulability.

Breast cancer pre-clinical screening using infrared thermography and artificial intelligence: a prospective, multicentre, diagnostic accuracy cohort study.

BACKGROUND: Given the limited access to breast cancer (BC) screening, the authors developed and validated a mobile phone-artificial intelligence-based infrared thermography (AI-IRT) system for BC screening. MATERIALS AND METHODS: This large prospective clinical trial assessed the diagnostic performance of the AI-IRT system. The authors constructed two datasets and two models, performed internal and external validation, and compared the diagnostic accuracy of the AI models and clinicians. Dataset A included 2100 patients recruited from 19 medical centres in nine regions of China. Dataset B was used for independent external validation and included 102 patients recruited from Langfang People's Hospital. RESULTS: The area under the receiver operating characteristic curve of the binary model for identifying low-risk and intermediate/high-risk patients was 0.9487 (95% CI: 0.9231-0.9744) internally and 0.9120 (95% CI: 0.8460-0.9790) externally. The accuracy of the binary model was higher than that of human readers (0.8627 vs. 0.8088, respectively). In addition, the binary model was better than the multinomial model and used different diagnostic thresholds based on BC risk to achieve specific goals. CONCLUSIONS: The accuracy of AI-IRT was high across populations with different demographic characteristics and less reliant on manual interpretations, demonstrating that this model can improve pre-clinical screening and increase screening rates.

Observational Study of the Natural Growth History of Peripheral Small-Cell Lung Cancer on CT Imaging.

BACKGROUND: This study aimed to investigate the natural growth history of peripheral small-cell lung cancer (SCLC) using CT imaging. METHODS: A retrospective study was conducted on 27 patients with peripheral SCLC who underwent at least two CT scans. Two methods were used: Method 1 involved direct measurement of nodule dimensions using a calliper, while Method 2 involved tumour lesion segmentation and voxel volume calculation using the "py-radiomics" package in Python. Agreement between the two methods was assessed using the intraclass correlation coefficient (ICC). Volume doubling time (VDT) and growth rate (GR) were used as evaluation indices for SCLC growth, and growth distribution based on GR and volume measurements were depicted. We collected potential factors related to imaging VDT and performed a differential analysis. Patients were classified into slow-growing and fast-growing groups based on a VDT cut-off point of 60 days, and univariate analysis was used to identify factors influencing VDT. RESULTS: Median VDT calculated by the two methods were 61 days and 71 days, respectively, with strong agreement. All patients had continuously growing tumours, and none had tumours that decreased in size or remained unchanged. Eight patients showed possible growth patterns, with six possibly exhibiting exponential growth and two possibly showing Gompertzian growth. Tumours deeper in the lung grew faster than those adjacent to the pleura. CONCLUSIONS: Peripheral SCLC tumours grow rapidly and continuously without periods of nongrowth or regression. Tumours located deeper in the lung tend to grow faster, but further research is needed to confirm this finding.

Association of Single-Nucleotide Polymorphisms in the VDR Gene with Tuberculosis and Infection of Beijing Genotype Mycobacterium tuberculosis.

Background: The aim of the present study was to investigate the association between vitamin D receptor (VDR) gene polymorphism and tuberculosis susceptibility, as well as the potential interaction of host genetic factors with the heterogeneity of Mycobacterium tuberculosis in the population from Xinjiang, China. Methods: From January 2019 to January 2020, we enrolled 221 tuberculosis patients as the case group and 363 staff with no clinical symptoms as the control group from four designated tuberculosis hospitals in southern Xinjiang, China. The polymorphisms of Fok I, Taq I, Apa I, Bsm I, rs3847987 and rs739837 in the VDR were detected by sequencing. M. tuberculosis isolates were collected from the case group and identified as Beijing or non-Beijing lineage by multiplex PCR. Propensity score (PS), univariate analysis and multivariable logistic regression models were used to perform the analysis. Results: Our results showed that the allele and genotype frequencies of Fok I, Taq I, Apa I, Bsm I, rs3847987 and rs739837 in VDR were not correlated with tuberculosis susceptibility or lineages of M. tuberculosis. Two out of six loci of the VDR gene formed one haplotype block, and none of the haplotypes was found to correlate with tuberculosis susceptibility or lineages of M. tuberculosis infected. Conclusion: Polymorphisms in the VDR gene may not indicate susceptibility to tuberculosis. There was also no evidence on the interaction between the VDR gene of host and the lineages of M. tuberculosis in the population from Xinjiang, China. Further studies are nonetheless required to prove our conclusions.

Molecular Epidemiology of Clinical Mycobacterium tuberculosis Isolates from Southern Xinjiang, China Using Spoligotyping and 15-Locus MIRU-VNTR Typing.

Background: In the last decades, the molecular epidemiological investigation of Mycobacterium tuberculosis has significantly increased our understanding of tuberculosis epidemiology. However, few such studies have been done in southern Xinjiang, China. We aimed to clarify the molecular epidemic characteristics and their association with drug resistance in the M. tuberculosis isolates circulating in this area. Methods: A total of 347 isolates obtained from southern Xinjiang, China between Sep, 2017 and Sep, 2019 were included to characterize using a 15-locus MIRU-VNTR (VNTR-15China) typing and spoligotyping, and test for drug susceptibility profiles. Then the lineages and clustering of the isolates were analyzed, as well as their association with drug resistance. Results: Spoligotyping results showed that 60 spoligotype international types (SITs) containing 35 predefined SITs and 25 Orphan or New patterns, and 12 definite genotypes were found, and the top three prevalent genotypes were Beijing genotype (207, 59.7%), followed by CAS1-Delhi (46, 13.6%), and Ural-2 (30, 8.6%). The prevalence of Beijing genotype infection in the younger age group (≤30) was more frequent than the two older groups (30~59 and ≥60 years old, both P values <0.05). The Beijing genotype showed significantly higher prevalence of resistance to isoniazid, rifampicin, ethambutol, multi-drug or at least one drug than the non-Beijing genotype (All P values ≤0.05). The estimated proportion of tuberculosis cases due to transmission was 18.4% according to the cluster rate acquired by VNTR-15China typing, and the Beijing genotype was the risk factor for the clustering (OR 9.15, 95% CI: 4.18-20.05). Conclusion: Our data demonstrated that the Beijing genotype is the dominant lineage, associated with drug resistance, and was more likely to infect young people and contributed to tuberculosis transmission in southern Xinjiang, China. These findings will contribute to a better understanding of tuberculosis epidemiology in this area.

Polymer-flooding produced water treatment using an electro-hybrid ozonation-coagulation system with novel cathode membranes targeting alternating filtration and in situ self-cleaning.

Polymer-flooding produced water is more difficult to treat for reinjection compared with normal produced water because of the presence of residual hydrolyzed polyacrylamide (HPAM). A novel cathode membrane integrated electro-hybrid ozonation-coagulation (CM-E-HOC) process was proposed for the treatment of polymer-flooding produced water. This process achieved in situ self-cleaning by generated microbubbles in the cathode membrane. The CM-E-HOC process achieved a higher suspended solid (SS), turbidity and PAM removal efficiency than the CM-EC process. The SS in the CM-E-HOC effluent was ≤ 20 mg/L SS, which met the reinjection requirements of Longdong, Changqing Oilfield, China (Q/SYCQ 08,011-2019) at different current densities (3, 5 and 10 mA/cm2). The CM-E-HOC process greatly mitigated both reversible and irreversible membrane fouling. Therefore, excellent flux recovery was obtained at different in situ self-cleaning intervals during the CM-E-HOC process. Furthermore, alternating filtration achieved continuous water production during the CM-E-HOC process. On one hand, the effective removal of aromatic protein-like substances and an increase in oxygen-containing functional groups were achieved due to the enhanced oxidation ability of the CM-E-HOC process, which decreased membrane fouling. On the other hand, the CM-E-HOC process showed improved coagulation performance because of the increased oxygen-containing functional groups and polymeric Fe species. Therefore, larger flocs with higher fractal dimensions were generated, and a looser and more porous cake layer was formed on the membrane surface during the CM-E-HOC process. Consequently, the CM-E-HOC process exhibited better in situ self-cleaning performance and lower filtration resistance than the CM-EC process.

Unraveling the over-oxidation inhibition mechanism during the hybrid ozonation-coagulation process: Immediate entrapment and complexation between intermediate organic matter and coagulants.

Pre-ozonation coagulation process had a very low and narrow range of ozone dosages for enhancing the dissolved organic matter (DOC) removal efficiency, in which over-oxidation may occur if the ozone dosage was not strictly controlled. In contrast, the proposed hybrid ozonation-coagulation (HOC) process with higher oxidation ability notably inhibited over-oxidation in this study, and exhibited improved DOC removal efficiency compared with coagulation at a much wider range of ozone dosages at different initial pH for the treatment of WWTP effluent. The HOC process also had a higher DOC removal efficiency than pre-ozonation coagulation. According to zeta potential analysis, a rising trend indicated that complexation between organic matter and metal coagulants persisted throughout the HOC process. However, the zeta potential remained almost unchanged during subsequent coagulation after pre-ozonation at high ozone dosages. Synchronous fluorescence spectroscopy analysis revealed that immediate entrapment and complexation between hydrolysed coagulants and oxidized intermediate organic matter occurred in the HOC process. Furthermore, FT-IR analysis showed that more oxygen-containing functional groups were generated, which were effectively trapped by metal coagulants and readily flocculated. To further prove the immediate entrapment and complexation during the HOC process, UPLC-Q-TOF-MS was applied to analyze the intermediate organic matter in the supernatant and flocs. The results implied that C21- organic matter was oxidized and decomposed into C11-C20, and C11-C20 intermediate organic matter was trapped and complexed by metal coagulants once formed, which led to the increase of C11-C20 in the flocs. Nevertheless, the catalytic ozonation process (γ-Al2O3/O3) with the same oxidation ability as the HOC process decomposed the organic matter into C1-C10. XPS analysis further confirmed the immediate entrapment and removal of aliphatic/aromatic carbon and oxygen-containing functional groups during the HOC process. Therefore, over-oxidation can be effectively inhibited, and wide range of ozone dosages was obtained during the HOC process, which facilitate the application of the HOC process.

Seawater intrusion alters nitrogen cycling patterns through hydrodynamic behavior and biochemical reactions: Based on Bayesian isotope mixing model and microbial functional network.

Seawater intrusion is a global coastal environmental issue of great concern and significantly impacts the regional biogeochemical environment and material cycles, including nitrogen cycling. To reveal the mechanism of seawater intrusion altering nitrogen cycling patterns through hydrodynamic behavior and biochemical reactions, the Bayesian mixing model (δ15N-NO3- and δ18O-NO3-) and 16S rDNA gene amplicon sequencing are used to establish nitrogen cycling pathways and microbial functional network. The results show that the nitrate in the coastal groundwater is from manure and septic waste (M&S, over 44 %), soil organic nitrogen (SON, over 20 %), and nitrogen fertilizer (FN, over 16 %). The hydrological interaction has promoted the coupling between material cycling and microbial community in the coastal groundwater systems. Among them, precipitation infiltration has caused the gradual decrease of specific microbes along the flow direction, such as Lactobacillus, Acinetobacter, Bifidobacterium, etc. And seawater intrusion has caused the mutations of specific microbes (Planktomarina, Clade_Ia, Wenyingzhuangia, Glaciecola, etc.) and convergence of microbial community at the salt-freshwater interface in the aquifer. In the coastal intruded aquifer systems, the nitrogen cycling pattern can be divided into oxidation and reduction processes. The oxidation process involves the enhancement of nitrification while the weakening of denitrification and anammox with the increase of aquifer depth. The reduction process consists of the enhancement of denitrification and anammox while the erosion of nitrification and ammonification with increased seawater intrusion. In addition, seawater intrusion can mitigate nitrate contamination by promoting denitrification and anammox in coastal areas.

Number of involved nodal stations: a better lymph node classification for clinical stage IA lung adenocarcinoma.

Background: With the popularization of lung cancer screening, more early-stage lung cancers are being detected. This study aims to compare three types of N classifications, including location-based N classification (pathologic nodal classification [pN]), the number of lymph node stations (nS)-based N classification (nS classification), and the combined approach proposed by the International Association for the Study of Lung Cancer (IASLC) which incorporates both pN and nS classification to determine if the nS classification is more appropriate for early-stage lung cancer. Methods: We retrospectively reviewed the clinical data of lung cancer patients treated at the Cancer Hospital, Chinese Academy of Medical Sciences between 2005 and 2018. Inclusion criteria was clinical stage IA lung adenocarcinoma patients who underwent resection during this period. Sub-analyses were performed for the three types of N classifications. The optimal cutoff values for nS classification were determined with X-tile software. Kaplan‒Meier and multivariate Cox analyses were performed to assess the prognostic significance of the different N classifications. The prediction performance among the three types of N classifications was compared using the concordance index (C-index) and decision curve analysis (DCA). Results: Of the 669 patients evaluated, 534 had pathological stage N0 disease (79.8%), 82 had N1 disease (12.3%) and 53 had N2 disease (7.9%). Multivariate Cox analysis indicated that all three types of N classifications were independent prognostic factors for prognosis (all P < 0.001). However, the prognosis overlaps between pN (N1 and N2, P = 0.052) and IASLC-proposed N classification (N1b and N2a1 [P = 0.407], N2a1 and N2a2 [P = 0.364], and N2a2 and N2b [P = 0.779]), except for nS classification subgroups (nS0 and nS1 [P < 0.001] and nS1 and nS >1 [P = 0.006]). There was no significant difference in the C-index values between the three N classifications (P = 0.370). The DCA results demonstrated that the nS classification provided greater clinical utility. Conclusion: The nS classification might be a better choice for nodal classification in clinical stage IA lung adenocarcinoma.

Clinical and CT Features of Subsolid Pulmonary Nodules With Interval Growth: A Systematic Review and Meta-Analysis.

Background: Establishing risk-based follow-up management strategies is crucial to the surveillance of subsolid pulmonary nodules (SSNs). However, the risk factors for SSN growth are not currently clear. This study aimed to perform a systematic review and meta-analysis to identify clinical and CT features correlated with SSN growth. Methods: Relevant studies were retrieved from Web of Science, PubMed, Cochrane Library, and EMBASE. The correlations of clinical and CT features with SSN growth were pooled using a random-effects model or fixed-effects model depending on heterogeneity, which was examined by the Q test and I2 test. Pooled odds ratio (OR) or pooled standardized mean differences (SMD) based on univariate analyses were calculated to assess the correlation of clinical and CT features with SSN growth. Pooled ORs based on multivariate analyses were calculated to find out independent risk factors to SSN growth. Subgroup meta-analysis was performed based on nodule consistency (pure ground-glass nodule (pGGN) and part-solid nodule (PSN). Publication bias was examined using funnel plots. Results: Nineteen original studies were included, consisting of 2444 patients and 3012 SSNs. The median/mean follow-up duration of these studies ranged from 24.2 months to 112 months. Significant correlations were observed between SSN growth and eighteen features. Male sex, history of lung cancer, nodule size > 10 mm, nodule consistency, and age > 65 years were identified as independent risk factors for SSN growth based on multivariate analyses results. Eight features, including male sex, smoking history, nodule size > 10 mm, larger nodule size, air bronchogram, higher mean CT attenuation, well-defined border, and lobulated margin were detected to be significantly correlated with pGGNs growth. Smoking history showed no significant correlation with pGGN growth based on the multivariate analysis results. Conclusions: Eighteen clinical and CT features were identified to be correlated with SSN growth, among which male sex, history of lung cancer, nodule size > 10 mm, nodule consistency and age > 65 years were independent risk factors while history of lung cancer was not correlated with pGGN growth. These factors should be considered when making risk-based follow-up plans for SSN patients.

Association of Mannose-Binding Lectin 2 Gene Polymorphism with Tuberculosis Based on Mycobacterium tuberculosis Lineages.

Purpose: Polymorphisms in MBL2 may contribute to the susceptibility to tuberculosis. The aim of the present study was to determine the associations of the polymorphisms of five loci (rs1800450, rs1800451, rs7096206, rs7095891, and rs11003125) in the MBL2 gene with susceptibility to tuberculosis and specific lineages of Mycobacterium tuberculosis causing tuberculosis in the Uyghur population of Xinjiang, China. Methods: From January 2019 to January 2020, we enrolled 170 Uyghur tuberculosis patients as the case group and 147 Uyghur staff with no clinical symptoms as the control group from four designated tuberculosis hospitals in southern Xinjiang, China. The polymorphisms of five loci in MBL2 of human were detected by sequencing. Whole-genome sequencing was applied in 68 M. tuberculosis isolates from the case group and the data were used to perform genealogy analysis. Results: The distributions of allele and genotype frequencies of five loci in MBL2 varied little between the case and control groups and varied little among the groups, including those infected with different lineages of M. tuberculosis and the control (except those of rs11003125), the P values were all >0.05. The distribution of alleles of rs11003125 was statistically different between patients infected with lineages 3 and 4 M. tuberculosis (χ 2=7.037, P=0.008). The C allele and CC genotype of rs11003125 were found to be protective factors against lineage 4 infection when compared to lineage 3 (ORs were 0.190 and 0.158, respectively; 95% confidence intervals were 0.053~0.690 and 0.025~0.999, respectively). Conclusion: Our results suggested that human's susceptibility to tuberculosis is affected both by the host genetic polymorphisms and the lineage of the M. tuberculosis that people were exposed to. However, due to the limitation of the sample size in the present study, larger sample size and more rigorous design should be guaranteed in future studies.