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BackgroundDiagnosis of kidney transplant rejection currently relies on manual histopathological assessment, which is subjective and susceptible to inter-observer variability, leading to limited reproducibility. We aim to develop a deep learning system for automated assessment of whole-slide images (WSIs) from kidney allograft biopsies to enable detection and subtyping of rejection and to predict the prognosis of rejection.MethodWe collected H&E-stained WSIs of kidney allograft biopsies at 400x magnification from January 2015 to September 2023 at two hospitals. These biopsy specimens were classified as T cell-mediated rejection, antibody-mediated rejection, and other lesions based on the consensus reached by two experienced transplant pathologists. To achieve feature extraction, feature aggregation, and global classification, we employed multi-instance learning and common convolution neural networks (CNNs). The performance of the developed models was evaluated using various metrics, including confusion matrix, receiver operating characteristic curves, the area under the curve (AUC), classification map, heat map, and pathologist-machine confrontations.ResultsIn total, 906 WSIs from 302 kidney allograft biopsies were included for analysis. The model based on multi-instance learning enables detection and subtyping of rejection, named renal rejection artificial intelligence model (RRAIM), with the overall 3-category AUC of 0.798 in the independent test set, which is superior to that of three transplant pathologists under nearly routine assessment conditions. Moreover, the prognosis models accurately predicted graft loss within 1 year following rejection and treatment response for rejection, achieving AUC of 0.936 and 0.756, respectively.ConclusionWe first developed deep-learning models utilizing multi-instance learning for the detection and subtyping of rejection and prediction of rejection prognosis in kidney allograft biopsies. These models performed well and may be useful in assisting the pathological diagnosis.

Water temperature is an important physical indicator of rivers because it impacts many other physical and biogeochemical processes and controls the metabolism of aquatic species in rivers. Having a good knowledge of river thermal dynamics is of great importance. In this study, an advanced machine learning based model that is fast, accurate and easy to use, namely the nonlinear autoregressive network with exogenous inputs (NARX) neural network, was coupled with Bayesian Optimization (BO) algorithm for optimizing the number of NARX hidden nodes and lagged input/target values and the Bayesian Regularization (BR) backpropagation algorithm for the NARX training, to forecast daily river water temperatures (RWT). Long-term observed data from 18 rivers of the Vistula River Basin, one of the largest rivers in Europe, were used for model testing, and model performance was compared with the air2stream model. The results showed that the NARX-based model performs significantly better than the air2stream model in the calibration and validation stages, and can better capture the seasonal pattern and peak values of RWT. Input combinations impact the performance of the NARX-based model in RWT modeling, and air temperature and the day of the year (DOY) are the major inputs, while streamflow and rainfall play a minor role on modeling RWT at the Vistula River Basin. Considering that future times series of air temperatures are easily accessible from climate models and DOY is easy to be considered in the model, the NARX-based model can serve as a promising tool to investigate the impact of climate change on river thermal dynamics.

A compound channel’s discharge capacity and boundary shear force can be predicted as a sum of the discharge capacity of different sub-regions once the apparent shear stress of the dividing line is reasonably quantified. The apparent shear stress was usually expressed as a coefficient multiplied by the difference between two squared velocities of two adjacent regions. This study investigated the range of the coefficient values and their influencing factors. Firstly, the optimal values of the coefficient were obtained based on experimental data. Then, comparisons between the optimal values and several parameters used in quantifying the apparent shear stress were conducted. The results show that the coefficient is mainly related to a morphological parameter of the floodplain and the ratio of resistance coefficients between the floodplain and the main channel. An empirical formula to calculate the coefficient was developed and introduced to calculate the flow discharge and boundary shear stress. Experimental data, including 142 sets of test data of symmetric-floodplain cases and 104 sets of one-floodplain cases, have been used to examine the prediction accuracy of discharges and boundary shear stress. For all these tests, the ranges of water depth of the main channel and the total width of the compound cross-section are about 0.05~0.30 m and 0.3~10 m, respectively; the Q range and the range of Froude numbers of the main channel flow are about 0.0033~1.11 m3/s and 0.3~2.3, respectively. Comparison with other methods and experimental data from both rigid and erodible compound channels indicated that the proposed method not only provided acceptable accuracy for the computation of discharge capacity and boundary shear stress of compound channels in labs but also gave insights for calculating discharge capacity in natural compound channels.

Transplant renal vein thrombosis is a rare complication after kidney transplantation, which can seriously threaten graft survival. Though the measures like thrombolytic therapy or operative intervention could be taken to deal with this complication, allograft loss is the most common outcome. Thus, early finding as well as decisive intervention is crucial to saving the graft. Here we present a 46-year-old male patient who underwent kidney transplantation from a cadaveric donor who developed a transplant renal venous thrombosis induced by acute diarrhea more than 1 year after renal transplantation with an initial symptom of sudden anuria and pain in the graft area. Subsequently, serum creatinine levels increased to 810.0 μmol/L. Pelvic CT showed increased vascular density of the transplanted kidney, and contrast-enhanced ultrasound confirmed venous thrombosis. The patient was treated with heparin sodium alone and diuresis gradually resumed. After more than 1 year of follow-up, serum creatinine returned to the baseline level prior to thrombosis. Our case indicates that quick ancillary examination and treatment without hesitation would be indispensable in rescuing allografts with renal vein thrombus. Unfractionated heparin can be recommended as an effective treatment for mid-long-term renal transplantation patients with renal vein thrombosis.

Sea surface temperature (SST) is an important indicator of marine system. In this study, the hybrid physically-statistically based air2water model was modified for the forecasting of SST. The hybrid model combines empiricism and theory, and balances the complexity and accuracy between the process-based physical models and statistical models. Daily observed SST data (2009–2019) from six stations in the Baltic Sea were used for the evaluation of model performance. Two metrics including the root mean squared error (RMSE) and the Nash-Sutcliffe efficiency coefficient (NSE) were used for model assessment. With the increase of air temperature, SST presents a clear warming trend (0.133°C/year–0.166°C/year), and air temperature warms faster than SST in the studied stations. The modelling results indicated that the model performs well for SST forecasting (in the validation period, mean value of RMSE is 1.245°C, and mean value of NSE is 0.961). Cross-validation results showed that the model is transferable in unknown stations. However, the model works a little bit worse in the warm period due to the impact of the upwelling phenomenon. Overall, the model is a promising tool for the prediction of SST.

Study region: The study region comprises 125 water gauges from 70 rivers covering the whole territory of Poland. Study focus: The air2stream model was used to reconstruct daily river water temperature (RWT) from 125 water gauges in 70 Polish rivers for the period 1966–2020 (55 years). Then, the annual and seasonal warming trends of RWT, and a biologically relevant metric (the annual number of days when water temperature exceeded 20 °C) were evaluated. New hydrological insights for the region: The modelling results showed that the air2stream model performed well for RWT modeling (the averaged Root Mean Square Error (RMSE) values for the calibration and validation periods are 1.21 and 1.32 °C, and Nash-Sutcliffe efficiency coefficient (NSE) for the two periods are 0.96 and 0.95 respectively). Annual averaged RWT of 121 gauges (96.8 % of the 125 gauges) showed clear warming trend in the past 55 years with the warming rate ranging between 0.08 and 0.44 °C/decade (average: 0.25 °C/decade). The warming trends of river waters on the main rivers and their tributaries are spatially differentiated. Seasonal RWT trends showed that summer is warming fastest, followed by spring and then by autumn and winter. Long-term increases in RWT were typically correlated with increases in air temperatures. For the three large rivers (Vistula, Odra, and Warta), the biologically relevant metric (the annual number of days when water temperature exceeded 20 °C) increased synchronously with the warming of RWT. The results reported in this study will be useful to stakeholders and the reconstructed dataset will be useful to the community for the further study of river thermal dynamics and aquatic habitats in Poland.

BackgroundPosttransplant renal function is critically important for kidney transplant recipients. Accurate prediction of graft function would greatly help in deciding acceptance or discard of allocated kidneys.Methods: Whole-slide images (WSIs) of H&E-stained donor kidney biopsies at × 200 magnification between January 2015 and December 2019 were collected. The clinical characteristics of each donor and corresponding recipient were retrieved. Graft function was indexed with a stable estimated glomerular filtration rate (eGFR) and reduced graft function (RGF). We used convolutional neural network (CNN)-based models, such as EfficientNet-B5, Inception-V3, and VGG19 for the prediction of these two outcomes.ResultsIn total, 219 recipients with H&E-stained slides of the donor kidneys were included for analysis [biopsies from standard criteria donor (SCD)/expanded criteria donor (ECD) was 191/28]. The results showed distinct improvements in the prediction performance of the deep learning algorithm plus the clinical characteristics model. The EfficientNet-B5 plus clinical data model showed the lowest mean absolute error (MAE) and root mean square error (RMSE). Compared with the clinical data model, the area under the receiver operating characteristic (ROC) curve (AUC) of the clinical data plus image model for eGFR classification increased from 0.69 to 0.83. In addition, the predictive performance for RGF increased from 0.66 to 0.80. Gradient-weighted class activation mappings (Grad-CAMs) showed that the models localized the areas of the tubules and interstitium near the glomeruli, which were discriminative features for RGF.ConclusionOur results preliminarily show that deep learning for formalin-fixed paraffin-embedded H&E-stained WSIs improves graft function prediction accuracy for deceased-donor kidney transplant recipients.

Fermentation results in the release and biotransformation of phenolic compounds in guava leaves. This study aimed to evaluate the bioaccessibility, safety and antidiabetic effect of phenolic-rich extract from fermented guava leaves (PE-fgl) using Monascus anka and Bacillus sp. BS2. The total phenolics and flavonoids were 51.18% and 16.60% bioaccessible after gastrointestinal digestion, respectively. High bioaccessibility was observed, particularly for ellagic acid, isoquercitrin, quercetin-3-O-β-D-xylopyranoside, quercetin-3-O-α-L-arabinofuranoside and avicularin, resulted in high recovery of α-glucosidase inhibition activity and antioxidant capacity. Moreover, PE-fgl was practically nontoxic with a median lethal dose (LD50) greater than 5000 mg/kg and was without evidence of toxicity following subchronic administration up to the dose limit of 2000 mg/kg. Furthermore, PE-fgl significantly improved serum glucose and lipid levels as well as antioxidant capacity in diabetic mice. Liver, kidney and pancreas damage was also significantly alleviated. These results suggested that PE-fgl could be a nontoxic candidate for diabetes treatment.

Objectives: We aimed to analyze the effect of cold ischemia time (CIT) on post-transplant graft function through mixed-effect model analysis to reduce the bias caused by paired mate kidneys.Methods: We reviewed all kidney transplantation records from 2015 to 2019 at our center. After applying the exclusion criteria, 561 cases were included for analysis. All donor characteristics, preservation and matching information, and recipient characteristics were collected. Transplant outcomes included delayed graft function (DGF) and estimated glomerular filtration rate (eGFR). Generalized linear mixed models were applied for analysis. We also explored potential effect modifiers, namely, donor death category, expanded criteria donors, and donor death causes.Results: Among the 561 cases, 79 DGF recipients developed DGF, and 15 recipients who died after surgery were excluded from the eGFR estimation. The median stable eGFR of the 546 recipients was 60.39 (47.63, 76.97) ml/min/1.73 m2. After adjusting for confounding covariates, CIT had a negative impact on DGF incidence [odds ratio = 1.149 (1.006, 1.313), P = 0.041]. In the evaluation of the impact on eGFR, the regression showed that CIT had no significant correlation with eGFR [β = −0.287 (−0.625, 0.051), P = 0.096]. When exploring potential effect modifiers, only the death category showed a significant interaction with CIT in the effect on eGFR (Pinteraction = 0.027). In the donation after brain death (DBD) group, CIT had no significant effect on eGFR [β = 0.135 (−0.433, 0.702), P = 0.642]. In the donation after circulatory death/donation after brain death followed by circulatory death (DCD/DBCD) group, CIT had a significantly negative effect on eGFR [β= −0.700 (−1.196, −0.204), P = 0.006]. Compared to a CIT of 0–6 h, a CIT of 6–8 or 8–12 h did not decrease the post-transplant eGFR. CIT over 12 h (12–16 h or over 16 h) significantly decreased eGFR. With the increase in CIT, the regenerated eGFR worsened (Ptrend = 0.011).Conclusion: Considering the effect of paired mate kidneys, the risk of DGF increased with prolonged CIT. The donor death category was an effect modifier between CIT and eGFR. Prolonged CIT did not reduce the eGFR level in recipients from DBDs but significantly decreased the eGFR in recipients from DCDs/DBCDs. This result indicates the potential biological interaction between CIT and donor death category.

The estimation of boundary shear stress distribution in a channel is a challenge in some hydraulic or environment investigations. A new partition model of a trapezoidal cross-section in a prismatic channel with composite roughness has been introduced based on a concept of a standardized cross-section using the “zero-shear stress” division lines. Based on this new model, an “equal local-region velocity” assumption, which can be regarded as an improvement of Einstein’s (1942) “equal velocity” assumption, has been proposed that is based on a discussion on the mechanism of energy transfer and velocity distribution at two sides of a dividing line. This assumption along with some empirical treatments have been employed to establish a new method to estimate the boundary shear stress of a side-wall or bed. Comparisons show that the proposed method can be applied to composite roughness cases and provides better prediction performance compared to other methods.

Renal ischemia-reperfusion injury (IRI) contributes to acute kidney injury (AKI), increases morbidity and mortality, and is a significant risk factor for chronic kidney disease (CKD). Macrophage infiltration is a common feature after renal IRI, and infiltrating macrophages can be polarized into the following two distinct types: M1 macrophages, i.e., classically activated macrophages, which can not only inhibit infection but also accelerate renal injury, and M2 macrophages, i.e., alternatively activated macrophages, which have a repair phenotype that can promote wound healing and subsequent fibrosis. The role of TSC1, which is a negative regulator of mTOR signaling that regulates macrophage polarization in inflammation-linked diseases, has been well documented, but whether TSC1 contributes to macrophage polarization in the process of IRI is still unknown. Here, by using a mouse model of renal ischemia-reperfusion, we found that myeloid cell-specific TSC1 knockout mice (termed Lyz-TSC1 cKO mice) had higher serum creatinine levels, more severe histological damage, and greater proinflammatory cytokine production than wild-type (WT) mice during the early phase after renal ischemia-reperfusion. Furthermore, the Lyz-TSC1 cKO mice showed attenuated renal fibrosis during the repair phase of IRI with decreased levels of M2 markers on macrophages in the operated kidneys, which was further confirmed in a cell model of hypoxia-reoxygenation (H/R) in vitro. Mechanistically, by using RNA sequencing of sorted renal macrophages, we found that the expression of most M1-related genes was upregulated in the Lyz-TSC1 cKO group (Supplemental Table 1) during the early phase. However, C/EBPβ and CD206 expression was decreased during the repair phase compared to in the WT group. Overall, our findings demonstrate that the expression of TSC1 in macrophages contributes to the whole process of IRI but serves as an inflammation suppressor during the early phase and a fibrosis promoter during the repair phase.

Abstract Background The contributions of various types of cell populations in dialysis‐related peritoneal fibrosis are poorly understood. Single‐cell RNA sequencing brings single‐cell level resolution to the analysis of cellular transcriptomics, which provides a new way to further characterize the distinct roles and functional states of each cell population during peritoneal fibrosis. Methods Single‐cell transcriptomics from normal peritoneal tissues of six patients, from effluent of patients with short‐term peritoneal dialysis (less than 2 weeks, n = 6), and from long‐term peritoneal dialysis patients (more than 6 years, n = 4) were analyzed. Results We identified a distinct cell component between samples among different groups. Functional analysis of the differentially expressed genes identified cell type specific biological processes relevant to different fibrosis stages. Well‐known key molecular mechanisms participating in the pathophysiology of peritoneal fibrosis were vitrified, and some of them were found to be restricted to specific cell types. Gradually growing enrichment of PI3K/AKT/mTOR pathway and impairment of oxidative phosphorylation in mesothelial cells and fibroblasts were found from healthy control, short‐term dialysis, to long‐term dialysis, respectively. The fibroblasts’ population obtained from the patients, who received peritoneal dialysis, showed a functional characteristic of immune‐chemotaxis and immune response, which was characterized by broadly significant increase in the expression of interleukins, chemokines, cytokines, and human leukocyte antigens. Furthermore, we described the intercellular crosstalk networks based on receptor‐ligand interactions, and highlighted a central role of fibroblasts in regulating the key mechanisms of peritoneal fibrosis through crosstalk with other cells. Conclusions In summary, despite describing information for fibrogenic molecular mechanisms in the resolution level of individual cell populations, this work identifies the significant functional evolution of fibroblasts during peritoneal fibrosis. This study also reveals the intercellular receptor‐ligand interactions in which the fibroblasts serve as a major node, eventually providing new insights into the role of fibroblasts during disease pathogenesis.

Guava leaves (GLs) were processed by solid-state co-fermentation with Monascus anka and Bacillus sp., and the impact of the fermentation degree on the phenolic composition and their bioactivities was investigated. Three representative HPLC fingerprints of phenolic compositions from GLs can be clearly distinguished for the different fermentation degrees. Four characteristic compounds, including gallic acid, quercetin-3-O-α-L-arabinopyranoside, quercetin, and kaempferol, that significantly responded to the changes of bioactivity in the fermented GLs were identified as the marker components by principal component analysis. High levels of the four marker components in the HPLC fingerprints are an indication that the fermented GLs are mature. In addition, the correlation analysis confirmed that the total phenolic, total flavonoid, and the quercetin and kaempferol contents strongly correlated with its bioactivities. These results can provide a theoretical basis to quickly evaluate and control the mature degree of fermented products, which will help to obtain high bioactive fermented products.

The aim of this work was to develop green and bioactive films with sodium alginate incorporating guava leaf extracts. Seven formulations were performed with a different sodium alginate: Guava leaf water extract (WE)/ethanolic extract (EE) proportions (100:0, 90:10, 85:15, 80:20), and glycerol were used as a plasticizer. The HPLC-PDA analysis showed the main phenolic compounds in WE were gallic acid, ellagic acid, quercetin-3-O-β-D-xylopyranoside, avicularin and quercetin. The main polyphenols in EE were rutin, isoquercitrin, quercetin-3-O-β-D-xylopyranoside, avicularin, quercitrin, quercetin and kaempferol. Guava leaf extracts could greatly enhance the antioxidant activity, antibacterial activity, tensile strength and water solubility of the sodium alginate film as well as the water barrier property, while inducing a decrease in the moisture content and elongation at the break. The FTIR and SEM analyses indicated that intermolecular hydrogen bonding between the guava leaf extract and sodium alginate resulted in a more compact structure in the composite films. These results indicated that sodium alginate-guava leaf extract films might be developed into antiradical and antimicrobial food packaging materials.

Background Laparoscopic renal surgery has been widely used in the treatment of renal diseases. However, there is still little research about its application in addressing renal tuberculosis. The purpose of this study is to retrospectively investigate the surgical results of laparoscopic and open surgery for nonfunctional tuberculous kidneys. Methods Between May 2011 and June 2016, 120 nephrectomies were performed in patients with a nonfunctional tuberculous kidney. Of these, 69 patients underwent retroperitoneal laparoscopic nephrectomy, and 51 patients underwent open nephrectomy. Data about the patients’ characteristics and surgical outcomes were collected from their electronic medical records. Outcomes were compared between these two groups. Results Our results showed that a number of renal tuberculosis patients presented no significant symptoms during their disease. Lower urinary tract symptoms (LUTS) were the most common at a rate of 73/120, followed by flank pain or accidently discovery (66/120), urine abnormality (30/120) and fever (27/120). Patients who underwent open surgery were similar to laparoscopic patients with regard to sex, BMI, location, previous tuberculous history, grade, anemia, adhesion, hypertension, diabetes and preoperative serum creatinine level, but were generally older than laparoscopic patients. There were no significant differences between open and laparoscopic surgery in estimated blood loss, transfusion, postoperative hospital days and perioperative complication rate. However, the median operation time of laparoscopic operation was much longer than open surgery (180 [150–225] vs 135 [120–165] minutes, P

Guava (Psidium guajava L., Myrtaceae) leaves have been used as a folk herbal tea to treat diabetes for a long time in Asia and North America. In this study, we isolated polysaccharides from guava leaves (GLP), and evaluated its antioxidant activity in vitro and anti-diabetic effects on diabetic mice induced by streptozotocin combined with high-fat diet. The results indicated that GLP exhibited good DPPH, OH, and ABTS free-radical scavenging abilities, and significantly lowered fasting blood sugar, total cholesterol, total triglycerides, glycated serum protein, creatinine, and malonaldehyde. Meanwhile, it significantly increased the total antioxidant activity and superoxide dismutase (SOD) enzyme activity in diabetic mice, as well as ameliorated the damage of liver, kidney, and pancreas. Thus, polysaccharides from guava leaves could be explored as a potential antioxidant or anti-diabetic agents for functional foods or complementary medicine.

Background. Several factors have been validated as predictors of disease recurrence in upper tract urothelial carcinoma. However, the oncological outcomes between different surgical approaches (open nephroureterectomy versus laparoscopic nephroureterectomy, ONU vs LNU) remain controversial. Therefore, we performed a meta-analysis to evaluate the oncological outcomes associated with different surgical approaches. Methods. We conducted an electronic search of the PubMed, Embase, ISI Web of Knowledge and Cochrane Library electronic databases through November 2015, screened the retrieved references, collected and evaluated the relevant information. We extracted and synthesized the corresponding hazard ratios (HRs) and 95% confidence intervals (95% CI) using Stata 13. Results. Twenty-one observational studies were eligible for inclusion in the meta-analysis. The results of the meta-analysis showed no differences in the intravesical recurrence-free survival (IRFS), unspecified recurrence-free survival (UnRFS) and overall survival (OS) between LNUandONU. However, improvements in the extravesical recurrence free survival (ExRFS) and cancer specific survival (CSS) were observed inLNU. The pooled hazard ratios were 1.05 (95% CI [0.92–1.18]) for IRFS, 0.80 (95% CI [0.64–0.96]) for ExRFS, 1.10 (95% CI [0.93–1.28]) for UnRFS, 0.91 (95% CI [0.66–1.17]) for OS and 0.79 (95% CI [0.68–0.91]) for CSS. Conclusion. Based on current evidence, LNU could provide equivalent prognostic effects for upper tract urothelial carcinoma, and had better oncological control of ExRFS and CSS compared to ONU. However, considering all eligible studies with the intrinsic bias of retrospective study design, the results should be interpreted with caution. Prospective randomized trials are needed to verify these results.

To evaluate the value of liquid-based cytology (LBC) in the diagnosis of urothelial carcinoma.Diagnostic studies were searched for the diagnostic value of LBC in urothelial carcinoma in PubMed, Embase, Cochrane Library, Web of Science, CBM and CNKI. The latest retrieval date was September 2014. The data were extracted and the quality of the included studies was independently assessed by 2 reviewers. Stata 13 software was used to perform the statistical analysis. The research was conducted in compliance with the PRISMA statement.Nineteen studies, which included 8293 patients, were evaluated. The results of the meta-analysis showed that the pooled sensitivity and specificity of LBC were 0.58 (0.51-0.65) and 0.96 (0.93-0.98), respectively. The diagnostic odds ratio (DOR) was 31 (18-56) and the area under the curve (AUC) of summary receiver operating characteristic (SROC) was 0.83 (0.80-0.86). The post-test probability was 80% when a positive diagnosis was made. Compared with high grade urothelial carcinoma (HGUC), the sensitivity of detecting low-grade urothelial carcinoma (LGUC) was significantly lower, risk ratio of sensitivity was 0.54 (0.43-0.66), P

Object: There ismounting clinical evidence that an increase in obesity is linked to an increase in cancer incidence and mortality. Although studies have shown a link between obesity and colon cancer, the particular mechanism of the interaction between obesity and colon cancer in females remains unknown. The goal of this work is to use bioinformatics to elucidate the genetic link between obesity and colon cancer in females and to investigate probable molecular mechanisms. Methods: GSE44076 and GSE199063microarray datasets were obtained fromthe Gene Expression Omnibus (GEO) database. In the two microarray datasets and healthy controls, the online tool GEO2R was utilized to investigate the differential genes between obesity and colon cancer. The differential genes (DEGs) identified in the two investigations were combined. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment studies were performed on the DEGs. The STRING database and Cytoscape software were then used to build protein-protein interaction (PPI) networks to discover hub genes. NetworkAnalyst was also used to build networks of target microRNAs (miRNAs) and hub genes, as well as networks of transcriptions. Results: Between the two datasets, 146 DEGs were shared. The DEGs are primarily enriched in inflammatory and immune-related pathways, according to GO analysis and KEGG. 14 hub genes were identified via PPI building using the Cytoscape software's MCODE and CytoNCA plug-ins: TYROBP, CD44, BGN, FCGR3A, CD53, CXCR4, FN1, SPP1, IGF1, CCND1, MMP9, IL2RG, IL6 and CTGF. Key transcription factors for these hub genes include WRNIP1, ATF1, CBFB, and NR2F6. Key miRNAs for these hub genes include hsa-mir-1-3p, hsa-mir-26b-5p, hsa-mir-164a-5p and hsa-mir-9-5p. Conclusion: Our research provides evidence that changed genes are shared by female patients with colon cancer and obesity. Through pathways connected to inflammation and the immune system, these genes play significant roles in the emergence of both diseases. We created a network between hub genes and miRNAs that target transcription factors, which may offer suggestions for future research in this area. [ABSTRACT FROM AUTHOR]