Simulation and evaluation of ecosystem service value along the Yellow River in Henan Province, China

The unprecedented growth in population and swift industrial advancements exert considerable strains on the ecosystem, particularly within medium-sized and large urban landscapes. The critical investigation into the intricate links between current and prospective land utilization, as well as the ecosystem service value (ESV), holds considerable empirical relevance for the calibration of land usage frameworks, thereby contributing to the sustainable evolution of extensive urban zones. Utilizing GlobeLand 30 data, the present research probes into the pattern of land transformation and the spatial-temporal dispersal of ESV in Henan’s Yellow River vicinity over a span from 2000 to 2020. For the enhancement of land usage alignment, a Markov-PLUS fusion model was devised to gauge three disparate ESV transition scenarios slated for 2030, namely, natural development scenario (NDS), cropland protection scenario (CPS), and ecological protection scenario (EPS). The principal determinants of land transformation within the 2000–2020 period were recognized as elevation, populace concentration, and atmospheric temperature. Amid the rapid accretion of construction land engulfing substantial cropland and grassland areas, there was an ESV diminution to the tune of 1.432 billion RMB between 2000 and 2020. The ESV’s high-value regions were discerned within relatively undisturbed ecosystem zones, with the lower-value sections identified in cropland and constructed areas, where human interventions exerted pronounced effects on the ecosystem. In accordance with the 2030 land usage simulations and analyses, in contrast to alternative scenarios, the EPS exhibited the least fluctuation in land type alterations in 2030, demonstrated the most pronounced escalation in cold spot concentration, and reached a peak agglomeration level. This underscores that the EPS not only offers a refinement in land utilization configuration but also mediates the equilibrium between economic and ecological considerations. The insights derived from this investigation afford innovative evaluative methods for spatial planning, ecological recompense, and sustainable land exploitation within large- and medium-scale urban domains

Towards an Operative Predictive Model for the Songshan Area during the Yangshao Period

The literature in the field of archaeological predictive models has grown in the last years, looking for new factors the most effective methods to introduce. However, where predictive models are used for archaeological heritage management, they could benefit from using a more speedy and consequently useful methods including some well-consolidated factors studied in the literature. In this paper, an operative archaeological predictive model is developed, validated and discussed, in order to test its effectiveness. It is applied to Yangshao period (5000–3000 BC) in the Songshan area, where Chinese civilization emerged and developed, and uses 563 known settlement sites. The satisfactory results herein achieved clearly suggest that the model herein proposed can be reliably used to predict the geographical location of unknown settlements

The lncRNA landscape of breast cancer reveals a role for DSCAM-AS1 in breast cancer progression.

Molecular classification of cancers into subtypes has resulted in an advance in our understanding of tumour biology and treatment response across multiple tumour types. However, to date, cancer profiling has largely focused on protein-coding genes, which comprise <1% of the genome. Here we leverage a compendium of 58,648 long noncoding RNAs (lncRNAs) to subtype 947 breast cancer samples. We show that lncRNA-based profiling categorizes breast tumours by their known molecular subtypes in breast cancer. We identify a cohort of breast cancer-associated and oestrogen-regulated lncRNAs, and investigate the role of the top prioritized oestrogen receptor (ER)-regulated lncRNA, DSCAM-AS1. We demonstrate that DSCAM-AS1 mediates tumour progression and tamoxifen resistance and identify hnRNPL as an interacting protein involved in the mechanism of DSCAM-AS1 action. By highlighting the role of DSCAM-AS1 in breast cancer biology and treatment resistance, this study provides insight into the potential clinical implications of lncRNAs in breast cancer

Endometrial Cytology as a Method to Improve the Accuracy of Diagnosis of Endometrial Cancer: Case Report and Meta-Analysis

More and more researchers have reported that dilatation and curettage (D&C) or Pipelle had low accuracy, high misdiagnosis, and insufficient rate. Endometrial cytology is often compared with histology and seems to be an efficient method for the diagnosis of endometrial disorders, especially endometrial cancer. We report a case of misdiagnosed endometrial cancer by D&C, but with a positive cytopathological finding. Following that, a meta-analysis including 4,179 patients of endometrial diseases with cyto-histopathological results was performed to assess the value of the endometrial cytological method in endometrial cancer diagnosis. The pooled sensitivity and specificity of the cytological method in detecting endometrial atypical hyperplasia or cancer was 0.91[95% confidence interval (CI) 0.74–0.97] and 0.96 (95% CI 0.90–0.99), respectively. The pooled positive likelihood ratio and negative likelihood ratio was 25.4 (95% CI 8.1–80.1) and 0.10 (95% CI 0.00–0.30), respectively. The diagnostic odds ratio which was usually used to evaluate the diagnostic test performance reached 260 (95% CI 36–1905). So we recommend that D&C and Pipelle are still practical procedures to evaluate the endometrium, cytological examinations should be utilized as an additional endometrial assessment method

An Efficacious Endometrial Sampler for Screening Endometrial Cancer

Recently, the research on early detection of precancerous change and endometrial carcinoma has been focusing on minimally invasive procedures for screening. On this basis, we aim to verify the feasibility of endometrial samplers for screening endometrial cancer using Li Brush. We recruited patients undergoing hysterectomy for different diseases from the Inpatient Department of the Department of Obstetrics and Gynecology. Before surgery, endometrial cells were collected by Li Brush. The cytopathologic diagnosis from Li Brush and the histopathologic diagnosis from hysterectomy in the same patient were compared to calculate sensitivity (Se), specificity (Sp), false-negative rate (FNR), false-positive rate (FPR), positive predictive value (PV+) %, and negative predictive value (PV-). The research enrolled 293 women into this self-controlled trial. According to the hypothesis test of paired four lattices, we obtained the following indicators: Se 92.73, Sp 98.15, FNR 7.27, FPR 1.85, PV+92.73, and PV−98.15%. The endometrial sampler Li Brush is an efficacious instrument for screening endometrial cancer

Positive rate of malignant cells in endometrial cytology samples of ovarian cancer, fallopian tube cancer, and primary peritoneal cancer patients: A systematic review and meta-analysis

To estimate the feasibility of diagnosing ovarian cancer, fallopian tube cancer, and primary peritoneal cancer through endometrial cytology, we performed a systematic review and meta-analysis to calculate the pooled positive rate of malignant cells in endometrial cytology samples. We queried PubMed, EMBASE, Medline, and Cochrane Central Register of Controlled Trails from inception to November 12, 2020 for studies estimating positive rates of malignant cells in endometrial cytology samples from patients with ovarian cancer, fallopian tube cancer, and primary peritoneal cancer. The positive rates of the included studies were calculated as pooled positive rate through meta-analyses of proportion. Subgroup analysis based on different sampling methods was conducted. Seven retrospective studies involving 975 patients were included. Pooled positive rate of malignant cells in endometrial cytology specimens of ovarian cancer, fallopian tube cancer, and primary peritoneal cancer patients was 23% (95% CI: 16% - 34%). Statistical heterogeneity between the included studies was considerable (I2 = 89%, P < 0.01). The pooled positive rates of the group of brushes and the group of aspiration smears were 13% (95% CI: 10% - 17%, I2 = 0, P = 0.45) and 33% (95% CI: 25% - 42%, I2 = 80%, P < 0.01), respectively. Although endometrial cytology is not an ideal diagnostic tool for ovarian cancer, fallopian tube cancer, and primary peritoneal cancer, it is a convenient, painless, and easy-to-implement adjunct to other tools. Sampling method is one of the factors that affect the detection rate

Cold Plasma Preparation of Pd/Graphene Catalyst for Reduction of p-Nitrophenol

Supported metal nanoparticles with small size and high dispersion can improve the performance of heterogeneous catalysts. To prepare graphene-supported Pd catalysts, graphene and PdCl2 were used as support and Pd precursors, respectively. Pd/G-P and Pd/G-H catalysts were prepared by cold plasma and conventional thermal reduction, respectively, for the catalytic reduction of p-nitrophenol (4-NP). The reaction followed quasi-first-order kinetics, and the apparent rate constant of Pd/G-P and Pd/G-H was 0.0111 and 0.0042 s−1, respectively. The graphene support was exfoliated by thermal reduction and cold plasma, which benefits the 4-NP adsorption. Pd/G-P presented a higher performance because cold plasma promoted the migration of Pd species to the support outer surface. The Pd/C atomic ratio for Pd/G-P and Pd/G-H was 0.014 and 0.010, respectively. In addition, the Pd nanoparticles in Pd/G-P were smaller than those in Pd/G-H, which was beneficial for the catalytic reduction. The Pd/G-P sample presented abundant oxygen-containing functional groups, which anchored the metal nanoparticles and enhanced the metal-support interaction. This was further confirmed by the shift in the binding energy to a high value for Pd3d in Pd/G-P. The cold plasma method operated under atmospheric pressure is effective for the preparation of Pd/G catalysts with enhanced catalytic activity for 4-NP reduction

Gold-enhanced current-volt dielectrode junction for biosensing with an aptamer-insulin-like growth factor-1-antibody sandwich pattern

Insulin-like growth factor-1 (IGF-1) is a hormone comprising seventy amino acids that is a key regulator of muscle and bone health and is highly related to the development of cancer. An aptamer-antibody sandwich assay was performed to determine the presence of IGF1 on a gold nanoparticle-enhanced dielectrode junctional sensing surface by using this surface as a transducer. High-resolution microscopy observations showed the characteristics of the sensing surface and gold nanoparticles. The gold nanoparticle-enhanced surface induces high immobilization of the anti-IGF1 aptamer and enhances the interactions of IGF1. With this probe surface, the aptamer-IGF1-antibody sandwich assay resulted in an increased current for each different solution concentration of IGF1, and the detection limit was 0.5 pg/mL with an R 2 value of 0.9631 on a linear calibration curve with IGF1 solution concentrations of 1–16 pg/mL. In addition, IGF1-spiked serum reached a similar limit of detection of 0.5 pg/mL, confirming that selective IGF1 detection occurred for the biological sample

A Systematic Review and Meta-Analysis of the Prognostic Impact of Pretreatment Fluorodeoxyglucose Positron Emission Tomography/Computed Tomography Parameters in Patients with Locally Advanced Cervical Cancer Treated with Concomitant Chemoradiotherapy

Backgrounds: The purpose of this paper is to investigate the prognostic value of fluorodeoxyglucose positron emission tomography/computed tomography (FDG PET/CT) parameters in patients treated with concurrent chemoradiotherapy (CCRT) for locally advanced cervical cancer (LACC). Methods: Studies that met the following criteria were retrieved from PubMed and Embase: patients treated with CCRT for LACC; FDG PET/CT scans performed before CCRT treatment; and a detected relationship between the parameters of FDG PET/CT and the prognosis of patients. Pooled hazard ratios (HRs) with 95% confidence intervals (CIs) were used to estimate the overall survival (OS) or event-free survival (EFS). Results: In total, 14 eligible studies with 1313 patients were included in this meta-analysis. Patients with a high maximum standardized uptake value (SUVmax) have a shorter OS than those with a low SUVmax (HR = 2.582, 95% = CI 1.936–3.443, p &lt; 0.001). Primary tumor SUVmax values (HR = 1.938, 95% CI = 1.203–3.054, p = 0.004) were significantly correlated with EFS, with a relatively high heterogeneity (I2 = 84% and I2 = 69.4%, respectively). Based on the limited data, the combined HR for EFS with the highest primary tumor total lesion glycolysis (TLG) and metabolic tumor volume (MTV) was 1.843 (95% CI = 1.100–3.086, p = 0.02) and 2.06 (95% CI = 1.21–3.51, p = 0.007), respectively. Besides, the combined HR for OS with the highest nodal SUVmax was 2.095 (95% CI = 2.027–2.166, p &lt; 0.001). Conclusion: A high primary SUVmax has a significant correlation with the OS and EFS of patients treated with CCRT for LACC and may therefore serve as a prognostic predictor. Due to the limited data, to explore the correlation between survival and TLG, MTV, and nodal SUVmax, further large-scale prospective studies are needed

AC-ModNet: Molecular Reverse Design Network Based on Attribute Classification

Deep generative models are becoming a tool of choice for exploring the molecular space. One important application area of deep generative models is the reverse design of drug compounds for given attributes (solubility, ease of synthesis, etc.). Although there are many generative models, these models cannot generate specific intervals of attributes. This paper proposes a AC-ModNet model that effectively combines VAE with AC-GAN to generate molecular structures in specific attribute intervals. The AC-ModNet is trained and evaluated using the open 250K ZINC dataset. In comparison with related models, our method performs best in the FCD and Frag model evaluation indicators. Moreover, we prove the AC-ModNet created molecules have potential application value in drug design by comparing and analyzing them with medical records in the PubChem database. The results of this paper will provide a new method for machine learning drug reverse design