Effects of landscape changes on water quality: A global meta-analysis.

Landscape changes resulting from anthropogenic activities and climate changes severely impact surface water quality. A global perspective on understanding their relationship is a prerequisite for pursuing equity in water security and sustainable development. A sequent meta-analysis synthesizing 625 regional studies from 63 countries worldwide was conducted to analyze the impacts on water quality from changing landscape compositions in the catchment and explore the moderating factors and temporal evolution. Results exhibit that total nitrogen (TN), total phosphorus (TP), and chemical oxygen demand (COD) in water are mostly concerned and highly responsive to landscape changes. Expansion of urban lands fundamentally degraded worldwide water quality over the past 20 years, of which the arid areas tended to suffer more harsh deterioration. Increasing forest cover, particularly low-latitude forests, significantly decreased the risk of water pollution, especially biological and heavy metal contamination, suggesting the importance of forest restoration in global urbanization. The effect size of agricultural land changes on water quality was spatially scale-dependent, decreasing and then increasing with the buffer radius expanding. Wetland coverage positively correlated with organic matter in water typified by COD, and the correlation coefficient peaked in the boreal areas (r=0.82, p<0.01). Overall, the global impacts of landscape changes on water quality have been intensifying since the 1990s. Nevertheless, knowledge gaps still exist in developing areas, especially in Africa and South America, where the water quality is sensitive to landscape changes and is expected to experience dramatic shifts in foreseeable future development. Our study revealed the worldwide consistency and heterogeneity between regions, thus serving as a research roadmap to address the quality-induced global water scarcity under landscape changes and to direct the management of land and water.

Generation and applications of synthetic computed tomography images for neurosurgical planning.

OBJECTIVE: CT and MRI are synergistic in the information provided for neurosurgical planning. While obtaining both types of images lends unique data from each, doing so adds to cost and exposes patients to additional ionizing radiation after MRI has been performed. Cross-modal synthesis of high-resolution CT images from MRI sequences offers an appealing solution. The authors therefore sought to develop a deep learning conditional generative adversarial network (cGAN) which performs this synthesis. METHODS: Preoperative paired CT and contrast-enhanced MR images were collected for patients with meningioma, pituitary tumor, vestibular schwannoma, and cerebrovascular disease. CT and MR images were denoised, field corrected, and coregistered. MR images were fed to a cGAN that exported a "synthetic" CT scan. The accuracy of synthetic CT images was assessed objectively using the quantitative similarity metrics as well as by clinical features such as sella and internal auditory canal (IAC) dimensions and mastoid/clinoid/sphenoid aeration. RESULTS: A total of 92,981 paired CT/MR images obtained in 80 patients were used for training/testing, and 10,068 paired images from 10 patients were used for external validation. Synthetic CT images reconstructed the bony skull base and convexity with relatively high accuracy. Measurements of the sella and IAC showed a median relative error between synthetic CT scans and ground truth images of 6%, with greater variability in IAC reconstruction compared with the sella. Aerations in the mastoid, clinoid, and sphenoid regions were generally captured, although there was heterogeneity in finer air cell septations. Performance varied based on pathology studied, with the highest limitation observed in evaluating meningiomas with intratumoral calcifications or calvarial invasion. CONCLUSIONS: The generation of high-resolution CT scans from MR images through cGAN offers promise for a wide range of applications in cranial and spinal neurosurgery, especially as an adjunct for preoperative evaluation. Optimizing cGAN performance on specific anatomical regions may increase its clinical viability.

Clinical application of combination [11C]C-methionine and [13N]N-ammonia PET/CT in recurrent functional pituitary adenomas with negative MRI or [18F]F-FDG PET/CT.

BACKGROUND: We assessed the value of positron emission tomography/computed tomography (PET/CT) with [13N]N-ammonia ([13N]N-NH3) and [11C]C-methionine ([11C]C-MET) for the evaluation and management of recurrent secreting pituitary adenoma, which could not be detected by magnetic resonance imaging (MRI) or fluorine-18 fluorodeoxyglucose ([18F]F-FDG) PET. METHODS: Nine consecutive patients with biochemical and clinical evidence of active recurrent tumor not detected by MRI and [18F]F-FDG PET were enrolled in this study. All of the patients underwent [13N]N-NH3 and [11C]C-MET PET/CT, after which the pattern of tracer uptake was studied, the tumor position was located, and a clinical decision was made. RESULTS: In general, [11C]C-MET had a higher uptake in pituitary adenomas (PAs) than that in pituitary tissues, while [13N]N-NH3 had a higher uptake in pituitary tissue than in pituitary adenomas. Increased [11C]C-MET uptake was observed in all nine PAs and three pituitary tissues, while all pituitary tissues and only one pituitary adenoma showed increased [13N]N-NH3 uptake. Four patients had concordant imaging and surgical findings indicative of biochemical remission without hypopituitarism after treatment. Radiotherapy was adopted in two patients, medication in another two, and follow-up observation in one case. CONCLUSION: Combined [11C]C-MET and [13N]N-NH3 PET/CT is effective in the differentiation of PAs from pituitary tissue in recurrent functional PAs with negative MRI or [18F]F-FDG PET. These results provide a valuable reference for further disease management.

Heterogeneous responses of wetland vegetation to climate change in the Amur River basin characterized by normalized difference vegetation index from 1982 to 2020.

Climate change affects wetland vegetation dramatically in mid- and high- latitudes, especially in the Amur River basin (ARB), straddling three countries and distributing abundance wetlands. In this study, spatiotemporal changes in average normalized difference vegetation index (NDVI) of wetland during the annual growing season were examined in the ARB from 1982 to 2020, and the responses of wetland vegetation to climatic change (temperature and precipitation) in different countries, geographic gradients, and time periods were analyzed by correlation analysis. The NDVI of wetland in the ARB increased significantly (p < 0.01) at the rate of 0.023 per decade from 1982 to 2020, and the NDVI on the Russian side (0.03 per decade) increased faster than that on the Chinese side (0.02 per decade). The NDVI of wetland was significantly positively correlated with daily mean temperature (p < 0.05, r = 0.701) and negatively correlated with precipitation, although the correlation was not significant (p > 0.05, r = -0.12). However, the asymmetric effects of diurnal warming on wetland vegetation were weak in the ARB. Correlations between the NDVI of wetland and climatic factors were zonal in latitudinal and longitudinal directions, and 49°N and 130°E were the points for a shift between increasing and decreasing correlation coefficients, closely related to the climatic zone. Under climate warming scenarios, the NDVI of wetland is predicted to continue to increase until 2080. The findings of this study are expected to deepen the understanding on response of wetland ecosystem to global change and promote regional wetland ecological protection.

China's wetland soil organic carbon pool: New estimation on pool size, change, and trajectory.

Robust estimates of wetland soil organic carbon (SOC) pools are critical to understanding wetland carbon dynamics in the global carbon cycle. However, previous estimates were highly variable and uncertain, due likely to the data sources and method used. Here we used machine learning method to estimate SOC storage and their changes over time in China's wetlands based on wetland SOC density database, associated geospatial environmental data, and recently published wetland maps. We built a database of wetland SOC density in China that contains 809 samples from 181 published studies collected over the last 20 years as presented in the published literature. All samples were extended and standardized to a 1-m depth, on the basis of the relationship between SOC density data from soil profiles of different depths. We used three different machine learning methods to evaluate their robustness in estimating wetland SOC storage and changes in China. The results indicated that random forest model achieved accurate wetland SOC estimation with R2 being .65. The results showed that average SOC density of top 1 m in China's wetlands was 25.03 ± 3.11 kg C m-2 in 2000 and 26.57 ± 3.73 kg C m-2 in 2020, an increase of 6.15%. SOC storage change from 4.73 ± 0.58 Pg in 2000 to 4.35 ± 0.61 Pg in 2020, a decrease of 8.03%, due to 13.6% decreased in wetland area from 189.12 × 103 to 162.8 × 103 km2 in 2020, despite the increase in SOC density during the same time period. The carbon accumulation rate was 107.5 ± 12.4 g C m-2 year-1 since 2000 in wetlands with no area changes. Climate change caused variations in wetland SOC density, and a future warming and drying climate would lead to decreases in wetland SOC storage. Estimates under Shared Socioeconomic Pathway 1-2.6 (low-carbon emissions) suggested that wetland SOC storage in China would not change significantly by 2100, but under Shared Socioeconomic Pathway 5-8.5 (high-carbon emissions), it would decrease significantly by approximately 5.77%. In this study, estimates of wetland SOC storage were optimized from three aspects, including sample database, wetland extent, and estimation method. Our study indicates the importance of using consistent SOC density and extent data in estimating and projecting wetland SOC storage.

Abscisic Acid Regulates Carbohydrate Metabolism, Redox Homeostasis and Hormonal Regulation to Enhance Cold Tolerance in Spring Barley.

Abscisic acid (ABA) plays a vital role in the induction of low temperature tolerance in plants. To understand the molecular basis of this phenomenon, we performed a proteomic analysis on an ABA-deficit mutant barley (Az34) and its wild type (cv Steptoe) under control conditions (25/18 °C) and after exposure to 0 °C for 24 h. Most of the differentially abundant proteins were involved in the processes of photosynthesis and metabolisms of starch, sucrose, carbon, and glutathione. The chloroplasts in Az34 leaves were more severely damaged, and the decrease in Fv/Fm was larger in Az34 plants compared with WT under low temperature. Under low temperature, Az34 plants possessed significantly higher activities of ADP-glucose pyrophosphorylase, fructokinase, monodehydroascorbate reductase, and three invertases, but lower UDP-glucose pyrophosphorylase activity than WT. In addition, concentrations of proline and soluble protein were lower, while concentration of H2O2 was higher in Az34 plants compared to WT under low temperature. Collectively, the results indicated that ABA deficiency induced modifications in starch and sucrose biosynthesis and sucrolytic pathway and overaccumulation of reactive oxygen species were the main reason for depressed low temperature tolerance in barley, which provide novel insights to the response of barley to low temperature under future climate change.

Using 2-dimensional hand photographs to predict postoperative biochemical remission in acromegaly patients: a transfer learning approach.

Background: The primary treatment goals in acromegaly patients are complete surgical removal of underlying pituitary tumors and biochemical remission. One of the challenges in developing countries is the difficulty in monitoring postoperative biochemical levels in acromegaly patients, particularly those who live in remote areas or regions with limited medical resources. Methods: In an attempt to overcome the abovementioned challenges, we conducted a retrospective study and established a mobile and low-cost method to predict biochemical remission in acromegaly patients after surgery, the efficacy of which was assessed retrospectively using the China Acromegaly Patient Association (CAPA) database. A total of 368 surgical patients from the CAPA database were successfully followed up to obtain their hand photographs. Demographics, baseline clinical characteristics, pituitary tumor features, and treatment details were collated. Postoperative outcome, defined as biochemical remission at the last follow-up timepoint, was assessed. Transfer learning with a new mobile tailored neurocomputing architecture, MobileNetv2, was used to explore the identical features that could be used as predictors of long-term biochemical remission after surgery. Results: As expected, the MobileNetv2-based transfer learning algorithm was shown to predict biochemical remission with statistical accuracies of 0.96 and 0.76 in the training cohort (n=803) and validation cohort (n=200), respectively, and the loss function value was 0.82. Conclusions: Our findings demonstrate the potential of the MobileNetv2-based transfer learning algorithm in predicting biochemical remission for postoperative patients who are at home or live far away from a pituitary or neuroendocrinological treatment center.

Effects of cropland reclamation on soil organic carbon in China's black soil region over the past 35 years.

The long-term use of cropland and cropland reclamation from natural ecosystems led to soil degradation. This study investigated the effect of the long-term use of cropland and cropland reclamation from natural ecosystems on soil organic carbon (SOC) content and density over the past 35 years. Altogether, 2140 topsoil samples (0-20 cm) were collected across Northeast China. Landsat images were acquired from 1985 to 2020 through Google Earth Engine, and the reflectance of each soil sample was extracted from the Landsat image that its time was consistent with sampling. The hybrid model that included two individual SOC prediction models for two clustering regions was built for accurate estimation after k-means clustering. The probability hybrid model, a combination between the hybrid model and classification probabilities of pixels, was introduced to enhance the accuracy of SOC mapping. Cropland reclamation results were extracted from the land cover time-series dataset at a 5-year interval. Our study indicated that: (1) Long-term use of cropland led to a 3.07 g kg-1 and 6.71 Mg C ha-1 decrease in SOC content and density, respectively, and the decrease of SOC stock was 0.32 Pg over the past 35 years; (2) nearly 64% of cropland had a negative change in terms of SOC content from 1985 to 2020; (3) cropland reclamation track changed from high to low SOC content, and almost no cropland was reclaimed on the "Black soils" after 2005; (4) cropland reclamation from wetlands resulted in the highest decrease, and reclamation period of years 31-35 decreased when SOC density and SOC stock were 16.05 Mg C ha-1 and 0.005 Pg, respectively, while reclamation period of years 26-30 from forest witnessed SOC density and stock decreases of 8.33 Mg C ha-1 and 0.01 Pg, respectively. Our research results provide a reference for SOC change in the black soil region of Northeast China and can attract more attention to the area of the protection of "Black soils" and natural ecosystems.

Mapping global distribution of mangrove forests at 10-m resolution.

Mangrove forests deliver incredible ecosystem goods and services and are enormously relevant to sustainable living. An accurate assessment of the global status of mangrove forests warrants the necessity of datasets with sufficient information on spatial distributions and patch patterns. However, existing datasets were mostly derived from âˆ¼30 m resolution satellite imagery and used pixel-based image classification methods, which lacked spatial details and reasonable geo-information. Here, based on Sentinel-2 imagery, we created a global mangrove forest dataset at 10-m resolution, namely, High-resolution Global Mangrove Forests (HGMF_2020), using object-based image analysis and random forest classification. We then analyzed the status of global mangrove forests from the perspectives of conservation, threats, and resistance to ocean disasters. We concluded the following: (1) globally, there were 145,068 km2 mangrove forests in 2020, among which Asia contained the largest coverage (39.2%); at the country level, Indonesia had the largest amount of mangrove forests, followed by Brazil and Australia. (2) Mangrove forests in South Asia were estimated to be in the better status due to the higher proportion of conservation and larger individual patch size; in contrast, mangrove forests in East and Southeast Asia were facing intensive threats. (3) Nearly, 99% of mangrove forest areas had a patch width greater than 100 m, suggesting that nearly all mangrove forests were efficient in reducing coastal wave energy and impacts. This study reports an innovative and up-to-date dataset and comprehensive information on mangrove forests status to contribute to related research and policy implementation, especially for supporting sustainable development.

Comprehensive Analysis of N6-Methyladenosine (m6A) RNA Methylation Regulators and Tumour Microenvironment Cell Infiltration Involving Prognosis and Immunotherapy in Gastroesophageal Adenocarcinomas.

Objective: Gastroesophageal adenocarcinoma (GEA) is a high deadly and heterogeneous cancer. RNA N6-methyladenosine (m6A) modification plays a non-negligible role in shaping individual tumour microenvironment (TME) characterizations. However, the landscape and relationship of m6A modification patterns and TME cell infiltration features remain unknown in GEA. Methods: In this study, we examined the TME of GEA using assessments of the RNA-sequencing data focusing on the distinct m6A modification patterns from the public databases. Intrinsic patterns of m6A modification were evaluated for associations with clinicopathological characteristics, underlying biological pathways, tumour immune cell infiltration, oncological outcomes, and treatment responses. The expression of key m6A regulators and module genes was validated by qRT-PCR analysis. Results: We identified two distinct m6A modification patterns of GEA (cluster 1/2 subgroup), and correlated two subgroups with TME cell-infiltrating characteristics. Cluster 2 subgroup correlates with a poorer prognosis, downregulated PD-1 expression, higher risk scores, and distinct immune cell infiltration. In addition, PPI and WGCNA network analysis were integrated to identify key module genes closely related to immune infiltration of GEA to find immunotherapy markers. COL4A1 and COL5A2 in the brown module were significantly correlated to the prognosis, PD-1/L1 and CTLA-4 expression of GEA patients. Finally, a prognostic risk score was constructed using m6A regulator-associated signatures that represented an independent prognosis factor for GEA. Interestingly, COL5A2 expression was linked to the response to anti-PD-1 immunotherapy, m6A regulator expression, and risk score. Conclusion: Our work identified m6A RNA methylation regulators as an important class of players in the malignant progression of GEA and were associated with the complexity of the TME. COL5A2 may be the potential biomarker which contributes to predicting the response to anti-PD-1 immunotherapy and patients' prognosis.

Anti-Tumor Secondary Metabolites Originating from Fungi in the South China Sea's Mangrove Ecosystem.

A mangrove is a unique ecosystem with abundant resources, in which fungi are an indispensable microbial part. Numerous mangrove fungi-derived secondary metabolites are considerable sources of novel bioactive substances, such as polyketides, terpenoids, alkaloids, peptides, etc., which arouse people's interest in the search for potential natural anti-tumor drugs. This review includes a total of 44 research publications that described 110 secondary metabolites that were all shown to be anti-tumor from 39 mangrove fungal strains belonging to 18 genera that were acquired from the South China Sea between 2016 and 2022. To identify more potential medications for clinical tumor therapy, their sources, unique structures, and cytotoxicity qualities were compiled. This review could serve as a crucial resource for the research status of mangrove fungal-derived natural products deserving of further development.

Invasion of Spartina alterniflora in the coastal zone of mainland China: Control achievements from 2015 to 2020 towards the Sustainable Development Goals.

The Sustainable Development Goals (SDGs) and the Convention on Biological Diversity's 15th Conference of the Parties (CBD COP15) both emphasized the urgency of protecting biological diversity. Spartina alterniflora (S. alterniflora), as an invasive species in China, has posed severe biodiversity challenges, demanding nationwide control and management. This study aims to assess the effectiveness of S. alterniflora management during China's SDGs implementation from 2015 to 2020. Landsat images acquired in 2015 (the beginning year of SDGs), 2018, and 2020 (the end year of SDGs' targets 6.6, 14.2, 14.5, and 15.8 related to alien invasion) were applied to quantify the spatiotemporal dynamics of S. alterniflora extent. The results revealed a consistent shrinkage of S. alterniflora, with a net areal reduction of 2610 ha from 2015 to 2020, implying the effectiveness of control measures on S. alterniflora invasion. Provinces including Zhejiang, Jiangsu, and Shanghai have succeeded in controlling S. alterniflora, evidenced by the sharp reduction in S. alterniflora area by 4908 ha, 2176 ha, and 1034 ha, respectively, from 2015 to 2020. However, better management of S. alterniflora is needed in regions with more severe S. alterniflora invasion, e.g., Shandong, Fujian, and Guangdong provinces. Our results suggest that relevant policies, regulations, and ecological restoration projects implemented by national or local governments in China received satisfactory results in S. alterniflora control. Nevertheless, S. alterniflora potential utilities and its governance effectiveness should be objectively evaluated and weighed to obtain the greatest ecological benefits and promote sustainable coastal ecosystems. The results of this study are expected to provide important baseline information benefitting the formulation of coastal protection and restoration strategies in China.

Prevalence and clinical characteristics of Crooke's cell adenomas in 101 patients with T-PIT-positive pituitary adenomas: Case series and literature review.

Purpose: We aimed to perform a retrospective analysis of a rare subtype of corticotroph adenoma, Crooke's cell adenoma, to better understand its clinical features. Methods: We collected T-PIT-positive pituitary adenomas and screened Crooke's cell adenomas from January 2020 to December 2021 in our center. Case reports of such tumors were also collected through a literature search. Clinical data such as biochemical tests, imaging examinations, and pathological data of the above cases were analyzed. Results: A total of 101 T-PIT-positive patients were treated in our center in the last 2 years, and 4 were finally pathologically diagnosed with Crooke's cell adenomas. All of these patients were male with elevated adrenocorticotropic hormone levels, and 50.0% presented with hypercortisolemia, Cushing's syndrome, visual impairment, and headache. The tumor diameter was significantly larger in these 4 patients (37.0 mm) than in the other patients (26.0 mm), and their tumor invasive behavior was more pronounced. Cases reported in the literature were mainly female (72.8%), and the clinical presentation was also dominated by Cushing's syndrome (65.1%) and hormonal dysfunction. Tumors were more common as macroadenomas (33.2 mm) and suprasellar growths (63.8%). The tumor recurrence rate was as high as 55.6%, with 6 cases progressing to pituitary carcinomas and 7.7% of tumor-related deaths. Our further integrated analysis of our center and reported cases revealed that gender, Cushing's syndrome, visual dysfunction, hormonal disorders, and tumor growth characteristics were statistically different in different tumor categories. Conclusion: Crooke's cell adenoma is a tumor subtype with obvious clinical aggressive behavior, and an in-depth analysis of its clinical characteristics may assist in developing a comprehensive treatment plan.

Global divergent trends of algal blooms detected by satellite during 1982-2018.

Algal blooms (ABs) in inland lakes have caused adverse ecological effects, and health impairment of animals and humans. We used archived Landsat images to examine ABs in lakes (>1 km2 ) around the globe over a 37-year time span (1982-2018). Out of the 176032 lakes with area >1 km2 detected globally, 863 were impacted by ABs, 708 had sufficiently long records to define a trend, and 66% exhibited increasing trends in frequency ratio (FRQR, ratio of the number of ABs events observed in a year in a given lake to the number of available Landsat images for that lake) or area ratio (AR, ratio of annual maximum area covered by ABs observed in a lake to the surface area of that lake), while 34% showed a decreasing trend. Across North America, an intensification of ABs severity was observed for FRQR (p < .01) and AR (p < .01) before 1999, followed by a decrease in ABs FRQR (p < .01) and AR (p < .05) after the 2000s. The strongest intensification of ABs was observed in Asia, followed by South America, Africa, and Europe. No clear trend was detected for the Oceania. Across climatic zones, the contributions of anthropogenic factors to ABs intensification (16.5% for fertilizer, 19.4% for gross domestic product, and 18.7% for population) were slightly stronger than climatic drivers (10.1% for temperature, 11.7% for wind speed, 16.8% for pressure, and for 11.6% for rainfall). Collectively, these divergent trends indicate that consideration of anthropogenic factors as well as climate change should be at the forefront of management policies aimed at reducing the severity and frequency of ABs in inland waters.

A unified model for high resolution mapping of global lake (>1 ha) clarity using Landsat imagery data.

Lake clarity, usually measured by Secchi disc depth (SDD), is a reliable proxy of lakes trophic status due to its close link with total suspended matter, chlorophyll-a, and nutrients. Trained with in-situ measured SDD and match-up Landsat images, we established various regression models to estimate SDD for global lakes. We selected a unified model which demonstrated good spatiotemporal transferability, and has potential to map SDD in different years with good quality of Landsat top-of-atmosphere (TOA) images embedded in Google Earth Engine (GEE). The unified model was successfully calibrated (n = 3586 data points, R2 = 0.84, MAPE = 29.8%) against SDD measured in 2235 lakes across the world, and the validation (n = 1779, R2 = 0.76, MAPE = 38.8%) also exhibited stable performance. The unified model was tuned to historical SDD measurements coincident with different Landsat sensors (L5-TM, L7-ETM+, L8-OLI) launched over the past four decades (1984-2020), thus confirming its temporal stability. Global SDD was mapped using GEE with OLI TOA products mainly acquired in 2019 to examine the spatial variation of lake water clarity (lake surface area ≥ 1 ha) all over the world. Worldwide, lake water clarity averaged 3.13 ± 1.71 m in 2019, but exhibited remarkable spatial variability due to catchment hydrological and landscape settings, lake morphology, elevation and anthropogenic impact. Inland waters in Europe (4.18 ± 1.82 m) and North America (3.84 ± 1.77 m) had the highest clarity due to greater water depth combined with less human disturbance in the high latitude regions. Lakes in South America (2.50 ± 2.33 m), Asia (2.44 ± 1.63 m) and Africa (2.36 ± 0.72 m) displayed intermediate clarity. Lakes in Oceania (1.97 ± 1.48 m) exhibited the lowest clarity for all continents except Antarctica. Further, we used the mapped SDD to evaluate water trophic status using the Carlson trophic state index. Our results indicate that, in 2019, about 63.6% of the lake areas and 47.8% of total lake numbers (2,219,627/4,646,056) were oligotrophic for global lakes, while about 23.6% areal percent and 37.1% of lake numbers are eutrophic mostly as a result of their being located in agricultural and urban-dominated drainage basins. This study, for the first time, provides water clarity information for lakes with area ≥ 1 ha all over the world with 30-m resolution and facilitates the understanding of the water clarity relevant to TSM (r = 0.95), Chl-a (r = 0.73), total phosphorus (r = 0.75), total nitrogen (r = 0.60), which could further provide water clarity data and technical support for trophic level evaluations as well. This unified model could serve as a powerful research tool for long-term monitoring of aquatic ecosystems and assessing their resilience to anthropogenic disturbance and climate change-related stressors.

Prediction of the Recurrence of Non-Functioning Pituitary Adenomas Using Preoperative Supra-Intra Sellar Volume and Tumor-Carotid Distance.

Background: Currently, it is difficult to estimate the possibility of recurrence of nonfunctioning pituitary adenomas (NFPAs). Markers such as Ki-67 or transcription factors rely on postoperative pathology, while few indices can be used for preoperative prediction. Therefore, we aimed to investigate the predictive effectiveness of supra-intrasellar volume and tumor-carotid distance based on measurements derived from preoperative magnetic resonance imaging (MRI) data. Method: Ninety-eight cases of NFPAs were evaluated, along with their clinical characteristics and MRI features. Four radiologic indices were analyzed, including intrasellar tumor volume, suprasellar tumor volume, maximum horizontal tumor diameter, and intercarotid distance. The ratio of supra-intrasellar volume and ratio of tumor-carotid distance were measured using 3D Slicer software, and the sum of two ratios was defined as the V-D value. The correlation between recurrence and multiple factors was analyzed using univariate and multivariate logistic regression and Kaplan-Meier analysis, and ROC curves were used to estimate the prognostic performance of radiologic measurements in NFPAs. Result: The supra-intrasellar volume ratio, tumor-carotid distance ratio and V-D value were significantly correlated with the recurrence of NFPAs. The predictive importance of the V-D value reached 84.5%, with a sensitivity of 83.7% and specificity of 67.3%. The cutoff limit of the V-D value was 1.53, and patients with V-D values higher than 1.53 tended to relapse much earlier. Conclusion: The V-D value has predictive importance for the recurrence of NFPAs preoperatively. Patients with higher V-D values will undergo recurrence earlier and should be given greater consideration in terms of surgery and follow-up time.

Clinical Efficacy of Temozolomide and Its Predictors in Aggressive Pituitary Tumors and Pituitary Carcinomas: A Systematic Review and Meta-Analysis.

Background: A growing number of evidences suggest that TMZ applications can generate impressive benefits for APT and PC patients. However, the definite role of TMZ for individuals remains unclarified due to the variation between studies. And the predictive factors to alter its efficacy remain debatable. Objective: To evaluate the long-term effectiveness and safety profile of TMZ in the treatment of pituitary malignancies, and delineate the predictors during its clinical employment. Results: A literature retrieval was conducted from online databases for studies published up to December 31, 2020. Twenty one studies involving 429 patients were identified. TMZ exhibited 41% radiological overall response rate (rORR). The biochemical response rate was determinate in 53% of the functioning subset. Two-year and 4-year survival rate were 79 and 61%, respectively. TMZ prolonged the median PFS and OS as 20.18 and 40.24 months. TMZ-related adverse events occurred in 19% of patients. Regarding predictors of TMZ response, rORR was dramatically improved in patients with low/intermediate MGMT expression than those with high-MGMT (>50%) (p < 0.001). The benefit of TMZ varied according to functioning subtype of patients, with greater antitumor activities in functioning subgroups and fewer activities in non-functioning sets (p < 0.001). Notably, the concomitant therapy of radiotherapy and TMZ significantly increased the rORR (p = 0.007). Conclusion: TMZ elicits clinical benefits with moderate adverse events in APT and PC patients. MGMT expression and clinical subtype of secreting function might be vital predictors of TMZ efficacy. In the future, the combination of radiotherapy with TMZ may further improve the clinical outcomes than TMZ monotherapy.

Multiparametric MRI Features Predict the SYP Gene Expression in Low-Grade Glioma Patients: A Machine Learning-Based Radiomics Analysis.

PURPOSE: Synaptophysin (SYP) gene expression levels correlate with the survival rate of glioma patients. This study aimed to explore the feasibility of applying a multiparametric magnetic resonance imaging (MRI) radiomics model composed of a convolutional neural network to predict the SYP gene expression in patients with glioma. METHOD: Using the TCGA database, we examined 614 patients diagnosed with glioma. First, the relationship between the SYP gene expression level and outcome of survival rate was investigated using partial correlation analysis. Then, 7266 patches were extracted from each of the 108 low-grade glioma patients who had available multiparametric MRI scans, which included preoperative T1-weighted images (T1WI), T2-weighted images (T2WI), and contrast-enhanced T1WI images in the TCIA database. Finally, a radiomics features-based model was built using a convolutional neural network (ConvNet), which can perform autonomous learning classification using a ROC curve, accuracy, recall rate, sensitivity, and specificity as evaluation indicators. RESULTS: The expression level of SYP decreased with the increase in the tumor grade. With regard to grade II, grade III, and general patients, those with higher SYP expression levels had better survival rates. However, the SYP expression level did not show any significant association with the outcome in Level IV patients. CONCLUSION: Our multiparametric MRI radiomics model constructed using ConvNet showed good performance in predicting the SYP gene expression level and prognosis in low-grade glioma patients.