Development of a clinical nomogram for prediction of response to neoadjuvant chemotherapy in patients with advanced gastric cancer.

BACKGROUND: The efficacy of neoadjuvant chemotherapy (NAC) in advanced gastric cancer (GC) is still a controversial issue. AIM: To find factors associated with chemosensitivity to NAC treatment and to provide the optimal therapeutic strategies for GC patients receiving NAC. METHODS: The clinical information was collected from 230 GC patients who received NAC treatment at the Central South University Xiangya School of Medicine Affiliated Haikou Hospital from January 2016 to December 2020. Least absolute shrinkage and selection operator logistic regression analysis was used to find the possible predictors. A nomogram model was employed to predict the response to NAC. RESULTS: In total 230 patients were finally included in this study, including 154 males (67.0%) and 76 females (33.0%). The mean age was (59.37 ± 10.60) years, ranging from 24 years to 80 years. According to the tumor regression grade standard, there were 95 cases in the obvious response group (grade 0 or grade 1) and 135 cases in the poor response group (grade 2 or grade 3). The obvious response rate was 41.3%. Least absolute shrinkage and selection operator analysis showed that four risk factors significantly related to the efficacy of NAC were tumor location (P < 0.001), histological differentiation (P = 0.001), clinical T stage (P = 0.008), and carbohydrate antigen 724 (P = 0.008). The C-index for the prediction nomogram was 0.806. The calibration curve revealed that the predicted value exhibited good agreement with the actual value. Decision curve analysis showed that the nomogram had a good value in clinical application. CONCLUSION: A nomogram combining tumor location, histological differentiation, clinical T stage, and carbohydrate antigen 724 showed satisfactory predictive power to the response of NAC and can be used by gastrointestinal surgeons to determine the optimal treatment strategies for advanced GC patients.

Prognostic significance of the systemic inflammation response index in gastrointestinal malignancy patients: a pooled analysis of 10,091 participants.

Background: We performed a meta-analysis to investigate the association of the systemic inflammation response index (SIRI) with long-term survival outcomes in patients with gastrointestinal malignancy. Methods: PubMed, Web of Science and Embase were searched for relevant studies evaluating the prognostic significance of the SIRI in gastrointestinal malignancies until May 2023. Results: 30 studies with 10,091 patients were included. The pooled results identified that patients in the high SIRI group had a worse overall survival and disease-free survival, which was observed across various tumor types, tumor stages and primary treatments. Conclusion: An elevated SIRI is negatively associated with worse survival outcomes of gastrointestinal malignancy patients and can be used as a risk stratification index for gastrointestinal malignancies.

Characteristics and molecular mechanism of drug-tolerant cells in cancer: a review.

Drug resistance in tumours has seriously hindered the therapeutic effect. Tumour drug resistance is divided into primary resistance and acquired resistance, and the recent study has found that a significant proportion of cancer cells can acquire stable drug resistance from scratch. This group of cells first enters the drug tolerance state (DT state) under drug pressure, and gradually acquires stable drug resistance through adaptive mutations in this state. Although the specific mechanisms underlying the formation of drug tolerant cells (DTCs) remain unclear, various proteins and signalling pathways have been identified as being involved in the formation of DTCs. In the current review, we summarize the characteristics, molecular mechanisms and therapeutic strategies of DTCs in detail.

A review of deubiquitinases and thier roles in tumorigenesis and development.

Ubiquitin is a small protein that can be added onto target protein for inducing target degradation, thereby modulating the activity and stability of protein. Relatively, deubiquitinases (DUBs), a class catalase that can remove ubiquitin from substrate protein, provide a positive regulation of the protein amount at transcription level, post-translational modification, protein interaction, etc. The reversible and dynamic ubiquitination-deubiquitination process plays an essential role in maintaining protein homeostasis, which is critical to almost all the biological processes. Therefore, the metabolic dysregulation of deubiquitinases often lead to serious consequences, including the growth and metastasis of tumors. Accordingly, deubiquitinases can be served as key drug targets for the treatment of tumors. The small molecule inhibitors targeting deubiquitinases has become one of the hot spots of anti-tumor drug research areas. This review concentrated on the function and mechanism of deubiquitinase system in the proliferation, apoptosis, metastasis and autophagy of tumor cells. The research status of small molecule inhibitors of specific deubiquitinases in tumor treatment is introduced, aiming to provide reference for the development of clinical targeted drugs.

Optimizing the radiomics-machine-learning model based on non-contrast enhanced CT for the simplified risk categorization of thymic epithelial tumors: A large cohort retrospective study.

PURPOSE: This study aimed to establish and compare the radiomics machine learning (ML) models based on non-contrast enhanced computed tomography (NECT) and clinical features for predicting the simplified risk categorization of thymic epithelial tumors (TETs). EXPERIMENTAL DESIGN: A total of 509 patients with pathologically confirmed TETs from January 2009 to May 2018 were retrospectively enrolled, consisting of 238 low-risk thymoma (LRT), 232 high-risk thymoma (HRT), and 39 thymic carcinoma (TC), and were divided into training (n = 433) and testing cohorts (n = 76) according to the admission time. Volumes of interest (VOIs) covering the whole tumor were manually segmented on preoperative NECT images. A total of 1218 radiomic features were extracted from the VOIs, and 4 clinical variables were collected from the hospital database. Fourteen ML models, along with varied feature selection strategies, were used to establish triple-classification models using the radiomic features (radiomic models), while clinical-radiomic models were built after combining with the clinical variables. The diagnostic accuracy, sensitivity, specificity, and the area under the receiver operating characteristic curve (AUC) of radiologist assessment, the radiomic and clinical-radiomic models were evaluated on the testing cohort. RESULTS: The Support Vector Machine (SVM) clinical-radiomic model demonstrated the highest AUC of 0.841 (95% CI 0.820 to 0.861) on the cross-validation result and reached an AUC of 0.844 (95% CI 0.793 to 0.894) in the testing cohort. For the one-vs-rest question of LRT vs HRT + TC, the sensitivity, specificity, and accuracy reached 80.00%, 63.41%, and 71.05%, respectively. For HRT vs LRT + TC, they reached 60.53%, 78.95%, and 69.74%. For TC vs LRT + HRT they reached 33.33%, 98.63%, and 96.05%, respectively. Compared with the radiomic models, superior diagnostic efficacy was demonstrated for most clinical-radiomics models, and the AUC of the Bernoulli Naive Bayes model was significantly improved. Radiologist2's assessment achieved a higher AUC of 0.813 (95% CI: 0.756-0.8761) than other radiologists, which was slightly lower than the SVM clinical-radiomic model. Combined with other evaluation indicators, SVM, as the best ML model, demonstrated the potential of predicting the simplified risk categorization of TETs with superior predictive performance to that of radiologists' assessment. CONCLUSION: Most of the ML models are promising in predicting the simplified TETs risk categorization with superior efficacy to that of radiologists' assessment, especially the SVM models, demonstrated the integration of ML with NECT may be valuable in aiding the diagnosis and treatment planning.

Differentiation Between Primary Central Nervous System Lymphoma and Atypical Glioblastoma Based on MRI Morphological Feature and Signal Intensity Ratio: A Retrospective Multicenter Study.

OBJECTIVES: To investigate the value of morphological feature and signal intensity ratio (SIR) derived from conventional magnetic resonance imaging (MRI) in distinguishing primary central nervous system lymphoma (PCNSL) from atypical glioblastoma (aGBM). METHODS: Pathology-confirmed PCNSLs (n = 93) or aGBMs (n = 48) from three institutions were retrospectively enrolled and divided into training cohort (n = 98) and test cohort (n = 43). Morphological features and SIRs were compared between PCNSL and aGBM. Using linear discriminant analysis, multiple models were constructed with SIRs and morphological features alone or jointly, and the diagnostic performances were evaluated via receiver operating characteristic (ROC) analysis. Areas under the curves (AUCs) and accuracies (ACCs) of the models were compared with the radiologists' assessment. RESULTS: Incision sign, T2 pseudonecrosis sign, reef sign and peritumoral leukomalacia sign were associated with PCNSL (training and overall cohorts, P < 0.05). Increased T1 ratio, decreased T2 ratio and T2/T1 ratio were predictive of PCNSL (all P < 0.05). ROC analysis showed that combination of morphological features and SIRs achieved the best diagnostic performance for differentiation of PCNSL and aGBM with AUC/ACC of 0.899/0.929 for the training cohort, AUC/ACC of 0.794/0.837 for the test cohort and AUC/ACC of 0.869/0.901 for the overall cohort, respectively. Based on the overall cohort, two radiologists could distinguish PCNSL from aGBM with AUC/ACC of 0.732/0.724 for radiologist A and AUC/ACC of 0.811/0.829 for radiologist B. CONCLUSION: MRI morphological features can help differentiate PCNSL from aGBM. When combined with SIRs, the diagnostic performance was better than that of radiologists' assessment.

Predicting Subtype of Growth Hormone Pituitary Adenoma based on Magnetic Resonance Imaging Characteristics.

PURPOSE: This study aimed to investigate the value of magnetic resonance (MR) characteristics in differentiating the subtypes of growth hormone pituitary adenomas. MATERIALS AND METHODS: The clinical and MR imaging data of 70 patients with growth hormone pituitary adenoma confirmed by surgery and pathology were retrospectively analyzed. The tumors were divided into dense granular (DG; 36 cases) and sparse granular subtypes (SG; 34 cases). The tumors' MR features were analyzed, including the mean and maximum diameters, T2 signal intensity, T2 relative signal intensity (rSI), homogeneity, enhancement degree, and invasiveness (Knosp grade). Mann-Whitney U test and χ2 test were used to analyze MR characteristics between the 2 groups. The independent predictors and predictive probabilities of tumor subtypes were obtained via a logistic regression model, and the efficacy was compared by receiver operating characteristic curve. RESULTS: The mean and maximum diameters of growth hormone adenoma in DG and SG were 1.77 versus 2.45 and 1.95 versus 3.00 cm (median, P < 0.05), respectively. There was a significant difference between the 2 groups in T2 signal intensity and rSI (P values were 0.02 and 0.001, respectively). Most DG adenomas (86.1%) appeared as hypointense on T2 images, and 38.2% of SG adenomas were hyperintense. There was no significant difference in tumor homogeneity (P = 0.622). A significant difference was found in the Knosp grade between the 2 subtypes (P = 0.004). In addition, the enhancement degree of SG adenomas was significantly higher than that of DG adenomas (P = 0.001). Logistic regression analysis showed that high T2 rSI value and marked contrast enhancement were independent predictors of the 2 subtypes, and the odds ratios were 4.811 and 4.649, respectively. The multivariate logistic model obtained relatively high predicting efficacy, and the area under the curve, sensitivity, and specificity were 0.765, 0.882, and 0.500, respectively. CONCLUSIONS: There are significant differences in tumor size, T2 signal intensity, T2 rSI, enhancement degree, and invasiveness between DG and SG adenomas. The logistic model based on the marked contrast enhancement and high T2 rSI value has an important value in predicting the subtype of growth hormone adenoma.

Differentiating thymoma, thymic carcinoma and lymphoma based on collagen fibre patterns with T2- and diffusion-weighted magnetic resonance imaging.

OBJECTIVES: The amount and distribution of intratumoural collagen fibre vary among different thymic tumours, which can be clearly detected with T2- and diffusion-weighted MR images. To explore the incidences of collagen fibre patterns (CFPs) among thymomas, thymic carcinomas and lymphomas on imaging, and to evaluate the efficacy and reproducibility of CFPs in differential diagnosis of thymic tumours. MATERIALS AND METHODS: Three hundred and ninety-eight patients with pathologically diagnosed thymoma, thymic carcinoma and lymphoma who underwent T2- and diffusion-weighted MR imaging were retrospectively enrolled. CFPs were classified into four categories: septum sign, patchy pattern, mixed pattern and no septum sign. The incidences of CFPs were compared among different thymic tumours, and the efficacy and reproducibility in differentiating the defined tumour types were analysed. RESULTS: There were significant differences in CFPs among thymomas, thymic squamous cell carcinomas (TSCCs), other thymic carcinomas and neuroendocrine tumours (OTC&NTs) and thymic lymphomas. Septum signs were found in 209 (86%) thymomas, which differed between thymomas and any other thymic neoplasms (all p < 0.005). The patchy, mixed patterns and no septum sign were mainly seen in TSCCs (80.3%), OTC&NTs (78.9%) and thymic lymphomas (56.9%), respectively. The consistency of different CFP evaluation between two readers was either good or excellent. CFPs achieved high efficacy in identifying the thymic tumours. CONCLUSION: The CFPs based on T2- and diffusion-weighted MR imaging were of great value in the differential diagnosis of thymic tumours. KEY POINTS: • Significant differences are found in intratumoural collagen fibre patterns among thymomas, thymic squamous cell carcinomas, other thymic carcinomas and neuroendocrine tumours and thymic lymphomas. • The septum sign, patchy pattern, mixed pattern and no septum sign are mainly seen in thymomas (86%), thymic squamous cell carcinomas (80.3%), other thymic carcinomas and neuroendocrine tumours (79%) and thymic lymphomas (57%), respectively. • The collagen fibre patterns have high efficacy and reproducibility in differentiating thymomas, thymic squamous cell carcinomas and thymic lymphomas.

[Plant species diversity of Carex peat mire in Changbai Mountains, China]. / é•¿ç™½å±±åŒºè‹”è‰æ³¥ç‚­æ²¼æ³½æ¤ç‰©ç‰©ç§å¤šæ ·æ€§.

We investigated the plant species richness both in cespitose Carex mires (C. schmidtii, C. meyeriana) and non-cespitose Carex mire (C. lasiocarpa) in Changbai Mountain. A total of 83 species (36 families, 59 genuses) was recorded in three sites. Among which, 71 species occurred in the C. meyeriana site, 61 species in the C. schmidtii site, and 26 species in the C. lasiocarpa site. The total species number and species richness in the two cespitose Carex mires were much higher than that in the non-cespitose Carex mire, while those on tussocks were much higher than between tussocks in the two cespitose Carex mires. Plant species richness on tussocks was positively related to the height, basal circumference and surface area of the tussocks, suggesting that tussocks were important for plant species diversity in Carex mires. Results of the canonical correspondence analysis indicated that the differences in soil water content, nutrient (soil organic carbon, total N, total P and C/N), and litter layer depth were the main factors influencing the differences of plant community composition on tussocks and between-tussocks. In the cespitose Carex mires, the hummock-hollow microtopography could foster high diversity by increasing surface area and creating multiple micro-habitats. Given its function in maintaining high species diversity, cespitose Carex could be a preferred species for vegetation restoration in degraded peat mires.

Usefulness of the Texture Signatures Based on Multiparametric MRI in Predicting Growth Hormone Pituitary Adenoma Subtypes.

OBJECTIVE: To explore the usefulness of texture signatures based on multiparametric magnetic resonance imaging (MRI) in predicting the subtypes of growth hormone (GH) pituitary adenoma (PA). METHODS: Forty-nine patients with GH-secreting PA confirmed by the pathological analysis were included in this retrospective study. Texture parameters based on T1-, T2-, and contrast-enhanced T1-weighted images (T1C) were extracted and compared for differences between densely granulated (DG) and sparsely granulated (SG) somatotroph adenoma by using two segmentation methods [region of interest 1 (ROI1), excluding the cystic/necrotic portion, and ROI2, containing the whole tumor]. Receiver operating characteristic (ROC) curve analysis was performed to determine the differentiating efficacy. RESULTS: Among 49 included patients, 24 were DG and 25 were SG adenomas. Nine optimal texture features with significant differences between two groups were obtained from ROI1. Based on the ROC analyses, T1WI signatures from ROI1 achieved the highest diagnostic efficacy with an AUC of 0.918, the accuracy, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) were 85.7, 72.0, 100.0, 100.0, and 77.4%, respectively, for differentiating DG from SG. Comparing with the T1WI signature, the T1C signature obtained relatively high efficacy with an AUC of 0.893. When combining the texture features of T1WI and T1C, the radiomics signature also had a good performance in differentiating the two groups with an AUC of 0.908. In addition, the performance got in all the signatures from ROI2 was lower than those in the corresponding signature from ROI1. CONCLUSION: Texture signatures based on MR images may be useful biomarkers to differentiate subtypes of GH-secreting PA patients.

The variations of surface wetness recorded by multi-proxies in Yuanchi peatland of the Changbai Mountains from 1962 to 2008.

Surface wetness of Yuanchi peatland in the Changbai Mountains were reconstructed by comprehensive analysis on plant residue, testate amoeba and humification, using the age-depth model established with AMS 14C and 137Cs dating methods. The response of surface wetness to climate change was addressed. Results showed that plant residues in the top 50 cm of peat sequence were dominated by mosses. Detrended correspondence analysis (DCA) of plant residue revealed that axis 1 of DCA biplot might represent mire surface wetness (MSW). MSW from 1962 to 2008 as indicated by axis 1 scores of DCA on plant residue were compared with depths to water table derived by testate amoebae-DWT transfer function and humification measurements, and the results showed that the trends of MSW reconstructed by three proxies were largely consistent. They were wetter MSW from 40-50 cm (1962 to 1975), 27-40 cm (fluctuating from wet to dry from 1975 to 1987), and 0-27 cm (drier from 1987 to 2008). Compared with instrumental data from nearby Erdao weather station, the reconstructed MSW was basically consistent with temperature change in this period. The decreases of MSW occurred when mean summer temperature and mean annual temperature were higher than long-term mean value. Our results suggest that the variability in MSW in the recent 46 years predominantly responded weakening of effective precipitation induced by increasing temperature.

[Spatial changes and sources of nitrate in Beijing urban ecosystem surface water].

The spatial variation in nitrate-nitrogen (NO3- -N) concentrations in surface water of ten sampling sites in the Beijing urban ecosystem from Kunminghu Lake to Tonghui River were assessed using monitoring data from 2009 to 2010. Nitrogen sources were examined using a hydro-chemical method. The results showed that the average nitrate-N concentrations of surface water in the Beijing urban ecosystem ranged from 0.7-7.6 mg x L(-1), with concentrations at all sites affected by human activities to a varying degree. The nitrate-N concentrations in the Dongbianmen and Tonghui River located in the southeastern of Beijing ranged from 7.0-7.6 mg x L(-1) and were significantly higher than those in the upper reaches (P < 0.01). For all sampling sites, the chloride concentrations fell between 14.8-86.0 mg x L(-1). The chloride concentrations at the furthest downstream sites, in the Dongbianmen and Tonghui River, ranged from 81.5-85.0 mg x L(-1) and were 2.3-5.8 times higher than those in the upper reaches. This indicates that the surface water in Dongbianmen and Tonghui River is clearly affected by human activities and that there are outfalls or pollutant sources near these two sampling sites. Further, the Cl-/Na+, SO4(2-)/Ca2+ ratios showed that the majority of nitrate-N came from a single source. The information given by the nitrate-N concentrations combined with the NO3- -N/Cl- ratio implied that sewage effluent, including industrial waste water, leakage from solid waste disposal and domestic wastewater mainly controlled nitrate distribution in the Beijing urban surface water. The results from this study suggest that surface water management should focus on downstream sites located in the southeastern region of Beijing such as the Dongbianmen and Tonghui River in the future.

[Assessment of shallow groundwater nitrate concentrations in typical terrestrial ecosystems of Chinese Ecosystem Research Network (CERN) during 2004-2009].

The nitrate-N (NO3(-) -N) concentrations of 38 shallow groundwater wells from 31 of the typical terrestrial ecosystems on Chinese Ecosystem Research Network (CERN) were assessed using the monitoring data from 2004 to 2009. The results showed that the average values of NO3(-) -N concentrations were significantly higher in the agricultural (4.85 mg x L(-1) +/- 0.42 mg x L(-1)), desert (oasis) (3.72 mg x L(-1) +/- 0.42 mg x L(-1)) and urban ecosystems (3.77 mg x L(-1) 0.51 mg x L(-1)) than in the grass (1.59 mg x L(-1) +/- 0.35 mg L(-1)) and forest ecosystems (0.39 mg x L(-1) +/- 0.03 mg x L(-1)). Nitrate was the major form of nitrogen, with between 56% to 88% of nitrogen in the nitrate-N form in the shallow groundwater of desert (oasis), urban and agricultural ecosystems. Nitrate-N concentrations for some agricultural ecosystems (Ansai, Yanting, Yucheng) and desert (oasis) ecosystems (Cele, Linze, Akesu) analysis exceeded the 10 mg x L(-1) World Health Organization drinking water standards between 14.3% and 84.6%. Significant seasonality was found in Ansai, Fengqiu, Yanting agricultural ecosystems and the Beijing urban ecosystem using the relatively high frequency monitoring data, with the higher nitrate concentrations usually found during summer and winter months. The monitoring results indicated that the shallow groundwater of agricultural ecosystems was contaminated by agricultural management practices, i.e. fertilization, while the shallow groundwater of forest ecosystems was under natural condition with no contamination from human activities.

[Niches of seven bryophyte species in Hani peat land of Changbai Mountains].

Based on field investigation and by using Levins and Pianka formula, the niches of seven bryophyte species in Hani Peatland of Changbai Mountains were studied. The results showed that the average niche breadth in nine environmental factors (total nitrogen, total phosphorous, K+ and Ca2+ in peat, pH,and electrical conductivity of surface water, tree coverage, shrub coverage, and depth to water table) of the seven species decreased in the order of Aulacomnium palustre > Sphagnum fallax > S. magellanicum > S. capillifolium = Polytrichum juniperinum > S. palustre > S. fuiscum. S. fuscum tended to be niche-specific species, while A. palustre tended to be niche-general species. Among the nine environmental factors, surface water electrical conductivity had the highest mean overlap value, while depth to water table, tree coverage, surface water pH, and shrub coverage had the lowest one, being the main environmental factors affecting the distribution of bryophytes. Most bryophyte species differentiated in their niches in the factors of depth to water table, tree coverage, surface water pH, and shrub coverage; while a few bryophyte species had higher overlap value in all of the test environmental factors due to their collaboration relationships in water use. This higher overlap value could offer indirect evidence of interspecific competition of bryophytes.