Resolution of occult anastomotic stricture with anal dilator: challenges with the conventional diagnostic criteria in low anterior rectal resection patient—a case report

BackgroundAnastomotic stricture (AS) is a common complication following rectal cancer surgery with anastomosis, but its diagnosis and management pose significant challenges due to the lack of standardized diagnostic criteria. We present a case highlighting the complexities encountered in diagnosing and managing occult AS post-rectal cancer surgery.Case presentationA 51-year-old male patient presented with symptoms suggestive of AS following robot-assisted laparoscopic low anterior resection for rectal adenocarcinoma. Despite conventional evaluations, including colonoscopy, digital rectal examination, and radiography, AS was not identified. Following prolonged and ineffective treatment for suspected conditions such as low anterior resection syndrome (LARS), the patient underwent anal dilatation, resulting in significant symptom improvement.ConclusionsThis case underscores the challenges associated with diagnosing and managing occult AS following rectal cancer surgery. The absence of standardized diagnostic criteria and reliance on conventional modalities may lead to underdiagnosis and inadequate treatment. A comprehensive diagnostic approach considering intestinal diameter, elasticity, and symptoms related to difficult defecation may enhance diagnostic accuracy. Further research is needed to refine the diagnostic and therapeutic strategies for occult AS

Mechanical, economic, and environmental assessment of recycling reclaimed asphalt rubber pavement using different rejuvenation schemes

Asphalt rubber (AR) is a prevailing sustainable paving material, but the recycling of reclaimed asphalt rubber pavement (RARP) faces the lack of proper rejuvenation strategies and the uncertainties in its economic and environmental values. To fill these gaps, this study assesses the comprehensive performance of RARP-included mixtures using different rejuvenation schemes through mechanical tests, life cycle cost analysis (LCCA), and life cycle assessment. The addition of rejuvenators increased the rutting depth of RARP mixture but their effects on the cracking resistance varied from different types of rejuvenators. The supplement of swollen rubber content was more effective in improving the cracking and aging resistances. Recycling 40 % of RARP into new AR mixture can save 24.5 % of greenhouse gas (GHG) emissions and 35 % of cost within the geographic scope of Hong Kong. The use of rejuvenator increased the GHG emission and cost, while incorporating extra rubber could further reduce the cost

Local flux coordination and global gene expression regulation in metabolic modeling

Abstract Genome-scale metabolic networks (GSMs) are fundamental systems biology representations of a cell’s entire set of stoichiometrically balanced reactions. However, such static GSMs do not incorporate the functional organization of metabolic genes and their dynamic regulation (e.g., operons and regulons). Specifically, there are numerous topologically coupled local reactions through which fluxes are coordinated; the global growth state often dynamically regulates many gene expression of metabolic reactions via global transcription factor regulators. Here, we develop a GSM reconstruction method, Decrem, by integrating locally coupled reactions and global transcriptional regulation of metabolism by cell state. Decrem produces predictions of flux and growth rates, which are highly correlated with those experimentally measured in both wild-type and mutants of three model microorganisms Escherichia coli, Saccharomyces cerevisiae, and Bacillus subtilis under various conditions. More importantly, Decrem can also explain the observed growth rates by capturing the experimentally measured flux changes between wild-types and mutants. Overall, by identifying and incorporating locally organized and regulated functional modules into GSMs, Decrem achieves accurate predictions of phenotypes and has broad applications in bioengineering, synthetic biology, and microbial pathology

Temporal-Spatial Pattern of Carbon Stocks in Forest Ecosystems in Shaanxi, Northwest China

<div><p>The precise and accurate quantitative evaluation of the temporal and spatial pattern of carbon (C) storage in forest ecosystems is critical for understanding the role of forests in the global terrestrial C cycle and is essential for formulating forest management policies to combat climate change. In this study, we examined the C dynamics of forest ecosystems in Shaanxi, northwest China, based on four forest inventories (1989–1993, 1994–1998, 1999–2003, and 2004–2008) and field-sampling measurements (2012). The results indicate that the total C storage of forest ecosystems in Shaanxi increased by approximately 29.3%, from 611.72 Tg in 1993 to 790.75 Tg in 2008, partially as a result of ecological restoration projects. The spatial pattern of C storage in forest ecosystems mainly exhibited a latitude-zonal distribution across the province, increasing from north (high latitude) to south (low latitude) generally, which signifies the effect of environmental conditions, chiefly water and heat related factors, on forest growth and C sequestration. In addition, different data sources and estimation methods had a significant effect on the results obtained, with the C stocks in 2008 being considerably overestimated (864.55 Tg) and slightly underestimated (778.07 Tg) when measured using the mean C density method and integrated method, respectively. Overall, our results demonstrated that the forest ecosystem in Shaanxi acted as a C sink over the last few decades. However, further studies should be carried out with a focus on adaption of plants to environmental factors along with forest management for vegetation restoration to maximize the C sequestration potential and to better cope with climate change.</p></div

Classifying Breast Cancer Subtypes Using Deep Neural Networks Based on Multi-Omics Data

With the high prevalence of breast cancer, it is urgent to find out the intrinsic difference between various subtypes, so as to infer the underlying mechanisms. Given the available multi-omics data, their proper integration can improve the accuracy of breast cancer subtype recognition. In this study, DeepMO, a model using deep neural networks based on multi-omics data, was employed for classifying breast cancer subtypes. Three types of omics data including mRNA data, DNA methylation data, and copy number variation (CNV) data were collected from The Cancer Genome Atlas (TCGA). After data preprocessing and feature selection, each type of omics data was input into the deep neural network, which consists of an encoding subnetwork and a classification subnetwork. The results of DeepMO based on multi-omics on binary classification are better than other methods in terms of accuracy and area under the curve (AUC). Moreover, compared with other methods using single omics data and multi-omics data, DeepMO also had a higher prediction accuracy on multi-classification. We also validated the effect of feature selection on DeepMO. Finally, we analyzed the enrichment gene ontology (GO) terms and biological pathways of these significant genes, which were discovered during the feature selection process. We believe that the proposed model is useful for multi-omics data analysis

Biomass-volume conversion formula for each forest type.

<p>B and V are the forest stand biomass (Mg ha^-1) and stand volume (m³ ha^-1). All the regression models are significant (P<0.05); Hardwood: wood density>0.7, which denotes that the hardness of the end of the wood is greater than 700 kg/ cm²; softwood: wood density<0.7.</p><p>Biomass-volume conversion formula for each forest type.</p

Methods used for estimation of forest ecosystem C stocks in our study.

<p>Methods used for estimation of forest ecosystem C stocks in our study.</p

Forest ecosystem C storage of each city in three regions of Shaanxi Province during 2004–2008.

<p>MAP: mean annual precipitation, MAT: mean annual temperature.</p><p>Forest ecosystem C storage of each city in three regions of Shaanxi Province during 2004–2008.</p

C density, storage, and area of forest ecosystems in Shaanxi Province during four periods: 1989–1993, 1994–1998, 1999–2003, and 2004–2008 (mean±95%CI).

<p>* These values represent the area-weighted mean C density of forest ecosystems during different periods.</p><p>C density, storage, and area of forest ecosystems in Shaanxi Province during four periods: 1989–1993, 1994–1998, 1999–2003, and 2004–2008 (mean±95%CI).</p