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The Wuwei area in the arid region of northwestern China is impacted by the harsh natural environment and human activities, and the problem of ecological degradation is severe there. In order to ensure the sustainable development of the regional social economy, it is necessary to monitor the changes in vegetation in Wuwei and its corresponding nonlinear relationships with climate change and human activities. In this study, the inter-annual and spatial–temporal evolution characteristics of vegetation in Wuwei from 1982 to 2015 have been analyzed based on non-parametric statistical methods. The analysis revealed that the areas of vegetation restoration and degradation accounted for 77 and 23% of the total area of the research area, respectively. From 1982 to 1999, vegetation degradation became extremely serious (14.4%) and was primarily concentrated in Gulang County and the high-altitude areas in the southwest. Since the ecological restoration project was implemented in 2000, there have been prominent results in vegetation restoration. The geographically and temporally weighted regression model shows that each climate factor has contributed to the vegetation restoration in the Wuwei area during the last 34 years, with their contributions ranked as precipitation (71.2%), PET (43.9%), solar radiation (34.8%), temperature (33.1%), and wind speed (31%). An analysis of the land-use data with 30 m resolution performed in this study revealed that the conversion area among land cover from 1985 to 2015 accounts for 14.9% of the total area. In it, the conversion area from non-ecological land to ecological land accounts for 5.7% of the total area. The farmland, grassland, and woodland areas have increased by 20.1, 20.6, and 8.5%, respectively, indicating that human activities such as agricultural intensification and ecological restoration projects have played a crucial role in vegetation restoration.

The arid region of northwest China provides a unique terrestrial ecosystem to identify the response of vegetation activities to natural and anthropogenic changes. To reveal the influences of climate and anthropogenic factors on vegetation, the Normalized Difference Vegetation Index (NDVI), climate data, and land use and land cover change (LUCC) maps were used for this study. We analyzed the spatiotemporal change of NDVI during 2000–2015. A partial correlation analysis suggested that the contribution of precipitation (PRE) and temperature (TEM) on 95.43% of observed greening trends was 47% and 20%, respectively. The response of NDVI in the eastern section of the Qilian Mountains (ESQM) and the western section of the Qilian Mountains (WSQM) to PRE and TEM showed opposite trends. The multiple linear regressions used to quantify the contribution of anthropogenic activity on the NDVI trend indicated that the ESQM and oasis areas were mainly affected by anthropogenic activities (26%). The observed browning trend in the ESQM was attributed to excessive consumption of natural resources. A buffer analysis and piecewise regression methods were further applied to explore the influence of urbanization on NDVI and its change rate. The study demonstrated that urbanization destroys the vegetation cover within the developed city areas and extends about 4 km beyond the perimeter of urban areas and the NDVI of buffer cities (counties) in the range of 0–4 km (0–3 km) increased significantly. In the range of 5–15 (4–10) km (except for Jiayuguan), climate factors were the major drivers of a slight downtrend in the NDVI. The relationship of land use change and NDVI trends showed that construction land, urban settlement, and farmland expanded sharply by 171.43%, 60%, and 10.41%, respectively. It indicated that the rapid process of urbanization and coordinated urban-rural development shrunk ecosystem services.

Background As the rupture of cerebral aneurysm may lead to fatal results, early detection of unruptured aneurysms may save lives. At present, the contrast-unenhanced time-of-flight magnetic resonance angiography is one of the most commonly used methods for screening aneurysms. The computer-assisted detection system for cerebral aneurysms can help clinicians improve the accuracy of aneurysm diagnosis. As fully convolutional network could classify the image pixel-wise, its three-dimensional implementation is highly suitable for the classification of the vascular structure. However, because the volume of blood vessels in the image is relatively small, 3D convolutional neural network does not work well for blood vessels. Results The presented study developed a computer-assisted detection system for cerebral aneurysms in the contrast-unenhanced time-of-flight magnetic resonance angiography image. The system first extracts the volume of interest with a fully automatic vessel segmentation algorithm, then uses 3D-UNet-based fully convolutional network to detect the aneurysm areas. A total of 131 magnetic resonance angiography image data are used in this study, among which 76 are training sets, 20 are internal test sets and 35 are external test sets. The presented system obtained 94.4% sensitivity in the fivefold cross-validation of the internal test sets and obtained 82.9% sensitivity with 0.86 false positive/case in the detection of the external test sets. Conclusions The proposed computer-assisted detection system can automatically detect the suspected aneurysm areas in contrast-unenhanced time-of-flight magnetic resonance angiography images. It can be used for aneurysm screening in the daily physical examination.

Parvoviruses are a group of single-stranded DNA viruses that lack an envelope and are widely distributed in both vertebrates and invertebrates. When they infect a host cell, parvoviruses take over the cell’s translational machinery to support the viral genome replication and proteins synthesis, following which viral proteins undergo various post-translational modifications (PTMs). Parvovirus non-structural (NS) and capsid proteins are modified by PTMs, including phosphorylation, ubiquitination, SUMOylation, and glycosylation. Phosphorylation of parvovirus mainly occurs on NS and capsid proteins, modulating the functions and activities of the NS protein and the assembly of the capsid protein. Ubiquitination and SUMOylation of parvoviral capsid proteins mainly affect intracellular trafficking during viral infection. Glycosylation of parvoviral capsid proteins is involved in the regulation of virion stability and infectivity. In this review, we summarize the PTMs of parvovirus proteins and discuss their impact on the viral life cycle, which will help in understanding viral replication and pathogenesis.

Women with polycystic ovary syndrome are prone to develop gestational diabetes mellitus, a disease which may have significant impact on the postpartum health of both mother and infant. We performed a retrospective cohort study to develop and test a model that could predict gestational diabetes mellitus in the first trimester in women with polycystic ovary syndrome. Our study included 434 pregnant women who were referred to the obstetrics department between December 2017 and March 2020 with a diagnosis of polycystic ovary syndrome. Of these women, 104 were diagnosed with gestational diabetes mellitus in the second trimester. Univariate analysis revealed that in the first trimester, Hemoglobin A1c (HbA1C), age, total cholesterol(TC), low-density lipoprotein cholesterol (LDL-C), SBP (systolic blood pressure), family history, body mass index (BMI), and testosterone were predictive factors of gestational diabetes mellitus ( P  < 0.05). Logistic regression revealed that TC, age, HbA1C, BMI and family history were independent risk factors for gestational diabetes mellitus. The area under the ROC curve of the gestational diabetes mellitus risk prediction model was 0.937 in this retrospective analysis, demonstrating a great discriminatory ability. The sensitivity and specificity of the prediction model were 0.833 and 0.923, respectively. The Hosmer–Lemeshow test also showed that the model was well calibrated.

Purpose To develop machine learning-based radiomics models derive from different MRI sequences for distinction between benign and malignant PI-RADS 3 lesions before intervention, and to cross-institution validate the generalization ability of the models. Methods The pre-biopsy MRI datas of 463 patients classified as PI-RADS 3 lesions were collected from 4 medical institutions retrospectively. 2347 radiomics features were extracted from the VOI of T2WI, DWI and ADC images. The ANOVA feature ranking method and support vector machine classifier were used to construct 3 single-sequence models and 1 integrated model combined with the features of three sequences. All the models were established in the training set and independently verified in the internal test and external validation set. The AUC was used to compared the predictive performance of PSAD with each model. Hosmer–lemeshow test was used to evaluate the degree of fitting between prediction probability and pathological results. Non-inferiority test was used to check generalization performance of the integrated model. Results The difference of PSAD between PCa and benign lesions was statistically significant ( P  = 0.006), with the mean AUC of 0.701 for predicting clinically significant prostate cancer (internal test AUC = 0.709 vs. external validation AUC = 0.692, P  = 0.013) and 0.630 for predicting all cancer (internal test AUC = 0.637 vs. external validation AUC = 0.623, P  = 0.036). T2WI-model with the mean AUC of 0.717 for predicting csPCa (internal test AUC = 0.738 vs. external validation AUC = 0.695, P  = 0.264) and 0.634 for predicting all cancer (internal test AUC = 0.678 vs. external validation AUC = 0.589, P  = 0.547). DWI-model with the mean AUC of 0.658 for predicting csPCa (internal test AUC = 0.635 vs. external validation AUC = 0.681, P  = 0.086) and 0.655 for predicting all cancer (internal test AUC = 0.712 vs. external validation AUC = 0.598, P  = 0.437). ADC-model with the mean AUC of 0.746 for predicting csPCa (internal test AUC = 0.767 vs. external validation AUC = 0.724, P  = 0.269) and 0.645 for predicting all cancer (internal test AUC = 0.650 vs. external validation AUC = 0.640, P  = 0.848). Integrated model with the mean AUC of 0.803 for predicting csPCa (internal test AUC = 0.804 vs. external validation AUC = 0.801, P  = 0.019) and 0.778 for predicting all cancer (internal test AUC = 0.801 vs. external validation AUC = 0.754, P  = 0.047). Conclusions The radiomics model based on machine learning has the potential to be a non-invasive tool to distinguish cancerous, noncancerous and csPCa in PI-RADS 3 lesions, and has relatively high generalization ability between different date set.

Purpose To investigate the value of CT urography (CTU) indicators in the quantitative differential diagnosis of bladder urothelial carcinoma (BUC) and inverted papilloma of the bladder (IPB). Material and methods The clinical and preoperative CTU imaging data of continuous 103 patients with histologically confirmed BUC or IPB were retrospectively analyzed. The imaging data included 6 qualitative indicators and 7 quantitative measures. The recorded clinical information and imaging features were subjected to univariate and multivariate logistic regression analysis to find independent risk factors for BUC, and a combined multi-indicator prediction model was constructed, and the prediction model was visualized using nomogram. ROC curve analysis was used to calculate and compare the predictive efficacy of independent risk factors and nomogram. Results Junction smoothness, maximum longitudinal diameter, tumor-wall interface and arterial reinforcement rate were independent risk factors for distinguishing BUC from IPB. The AUC of the combined model was 0.934 (sensitivity = 0.808, specificity = 0.920, accuracy = 0.835), and its diagnostic efficiency was higher than that of junction smoothness (AUC=0.667, sensitivity = 0.654, specificity = 0.680, accuracy = 0.660), maximum longitudinal diameter (AUC=0.757, sensitivity = 0.833, specificity = 0.604, accuracy = 0.786), tumor-wall interface (AUC=0.888, sensitivity = 0.755, specificity = 0.808, accuracy = 0.816) and Arterial reinforcement rate (AUC=0.786, sensitivity = 0.936, specificity = 0.640, accuracy = 0.864). Conclusion Above qualitative and quantitative indicators based on CTU and the combination of them may be helpful to the differential diagnosis of BUC and IPB, thus better assisting in clinical decision-making. Key points 1. Bladder urothelial carcinoma (BUC) and inverted papilloma of the bladder (IPB) exhibit similar clinical symptoms and imaging presentations. 2. The diagnostic value of CT urography (CTU) in distinguishing between BUC and IPB has not been documented. 3. BUC and IPB differ in lesion size, growth pattern and blood supply. 4. The diagnostic efficiency is optimized by integrating multiple independent risk factors into the prediction model.

Background Paraneoplastic cochleovestibulopathy (PCVP) is a rare, atypical neurological paraneoplastic syndrome of the nervous system that is often misdiagnosed and mistreated. Case presentation We report two cases of middle-aged women who presented with bilateral sensorineural hearing loss (SNHL) as the initial symptom. Pure-tone audiometry (PTA) demonstrated bilateral hearing impairment, while brainstem auditory evoked potentials (BAEP) and cochlear electrograms were within normal limits. Otoacoustic emissions indicated bilateral cochlear dysfunction. Cranial imaging excluded intracranial organic and vascular lesions. After corticosteroid therapy proved ineffective, positron emission tomography/computed tomography (PET/CT) combined with histopathological examination confirmed the presence of breast cancer, leading to a diagnosis of PCVP. Despite receiving endocrine therapy (Case 1) or surgical excision (Case 2), neither patient exhibited significant improvement in PTA during one year of follow-up. Conclusion PCVP should be considered in middle-aged patients who present with progressive bilateral hearing loss, often accompanied by vestibular dysfunction and clinical signs of cerebellar or brainstem involvement. Regular monitoring with PET/CT is recommended, and female patients in particular should undergo screening for breast cancer.

Objective To develop and validate a multimodal scoring system integrating clinical, radiological, and pathological variables to preoperatively predict extraprostatic extension (EPE) in prostate cancer (PCa). Methods This retrospective study included 667 PCa patients divided into a derivation cohort and two validation cohorts. Evaluated parameters comprised prostate-specific antigen density (PSAD), curvilinear contact length (CCL), lesion longest diameter (LD), National Cancer Institute EPE grade (NCI_EPE), International Society of Urological Pathology grade (ISUP), and other relevant variables. Independent predictors were identified through univariate and multivariate regression analysis to construct a logistic model. Coefficients from this model were then weighted to establish a scoring system. The predictive performance of the NCI_EPE, logistic model, and scoring system was systematically evaluated and compared. Finally, the scoring system was stratified into four distinct risk categories. Results Multivariate analysis identified NCI_EPE, PSAD, CCL/LD, and ISUP as independent predictors of EPE. In the derivation and validation cohorts, the scoring system demonstrated robust predictive accuracy for EPE, with AUCs of 0.849, 0.830, and 0.847, respectively. These values outperformed the NCI_EPE (Derivation cohort: 0.849 vs. 0.750, P  < 0.003, Validation cohort 1: 0.830 vs. 0.736, P  = 0.138, Validation cohort 2: 0.837 vs. 0.715, P  = 0.003) and were comparable to the logistic model (Derivation cohort: 0.849 vs. 0.860, P  = 0.228, Validation cohort 1: 0.830 vs. 0.849, P  = 0.711, Validation cohort 2: 0.837 vs. 0.843, P  = 0.738). Decision curve analysis revealed higher net clinical benefit for both the scoring system and logistic model compared to the NCI_EPE. Risk stratification using the scoring system categorized patients into four tiers: low (0–3), intermediate-low (4–6), intermediate-high (7–9), and high risk (10–12) with corresponding mean EPE probabilities of 9.9%, 26.0%, 52.0%, and 85.0%. These probabilities closely aligned with observed pT3 incidences in the derivation and validation cohorts. Conclusions The scoring system provides enhanced predictive accuracy for EPE, preoperatively stratifying patients into distinct risk categories to facilitate personalized therapeutic strategies.

In the field of green chemistry, the development of more sustainable and cost-efficient methods for synthesizing primary amines is of paramount importance, with catalyst research being central to this effort. This work presents a facile, aqueous-phase synthesis of highly active cobalt catalysts (Co-Ph@SiO2(x)) via pyrolysis of silica-supported cobalt–phenanthroline complexes. The optimized Co-Ph@SiO2(900) catalyst achieved exceptional performance (>99% conversion, >98% selectivity) in the reductive amination of acetophenone to 1-phenylethanamine using NH3/H2. Systematic studies revealed that its exceptional performance originates from the in situ pyrolysis of the cobalt–phyllosilicate complex. This process promotes the uniform distribution of metal cobalt nanoparticles, simultaneously enhancing porosity and imparting bifunctional (acidic and basic) properties to the catalyst, resulting in outstanding catalytic activity and selectivity. The catalyst demonstrated broad applicability, efficiently converting diverse ketones (aryl-alkyl, dialkyl, bioactive) and aldehydes (halogenated, heterocyclic, biomass-derived) into primary amines with high yields (up to 99%) and chemoselectivity (>40 examples). This sustainable, non-noble metal-based catalyst system offers significant potential for industrial primary amine synthesis and provides a versatile tool for developing highly selective and active heterogeneous catalysts.