The connection between appendiceal disease and cancers of the digestive tract has attracted widespread attention, but conclusion remain controversial. It is still unclear whether appendiceal disease has a definite causal effect on cancer. Our research aims to explore the causal relationship between appendiceal diseases and digestive system tumors. A two-sample Mendelian randomization (MR) analysis was conducted using genome-wide association study datasets to explore the causal impact of appendiceal disease on the risk of cancers. Five different MR methods were used to investigate causality. The stability, heterogeneity, and pleiotropy of MR were also assessed. The presence of appendiceal disease could reduce the incidence of rectal cancer (odds ratio = 0.588, 95% confidence interval: 0.392-0.881, P = .010, by inverse variance weighted method). There was no significant causal effect of appendiceal disease on the risk of other cancers. No horizontal pleiotropy was observed in the MR analysis, and leave-one-out analysis confirmed the stability of the results. The potential causal relationship between appendiceal disease and cancer risk was only observed with rectal cancer in populations of European ancestry. Future work will provide more robust evidence for the connection between appendiceal disease and cancers.

This study aims to assess the association between the Dietary Index for Gut Microbiota (DI-GM) and sarcopenic obesity (SO) in middle-aged and elderly populations, and to evaluate whether the Hepatic Steatosis Index (HSI) acts as the mediation in this relationship. A cross-sectional analysis was conducted on 3746 participants from the National Health and Nutrition Examination Survey (NHANES) database from 2011 to 2018. Weighted multivariate linear and logistic regression models were used to explore the association between DI-GM and the prevalence of SO, DI-GM and HSI, and HSI and the prevalence of SO. Restricted cubic spline (RCS) analysis was used to assess the potential nonlinear relationship between DI-GM and SO. Subgroup analyses were performed to evaluate the consistency of this relationship across different demographic groups. Additionally, mediation analysis was conducted to explore whether there existed a potential association between DI-GM, HSI, and SO. Among the 3746 participants included in the study, 369 (9.8%) were diagnosed with SO. After adjusting for all covariates by weighted multivariate logistic regression, each unit increase in DI-GM was associated with a 15% decrease in the prevalence of SO [Model 3: OR = 0.85, 95% CI (0.76, 0.95), p = 0.006]. When DI-GM was categorized into quartiles, the results remained significant [Model 3: OR = 0.47, 95% CI (0.30, 0.75), p = 0.002]. Further analysis indicated that the protective effect of DI-GM was primarily attributed to the Beneficial gut microbiota score (BGMS). RCS analysis revealed a significant linear relationship between DI-GM and SO (p > 0.05). Subgroup analysis demonstrated the robustness of this association across various subgroups. Mediation analysis showed that 17.8% of the association between DI-GM and SO was mediated by HSI (p < 0.05). DI-GM is significantly inversely associated with the prevalence of SO in the aging population, and HSI partially mediates this association.

BackgroundAccurate preoperative assessment of lymph node metastasis (LNM) in pancreatic ductal adenocarcinoma (PDAC) remains challenging. We developed and compared ultrasound-based intratumoral, peritumoral, clinical, and combined models for LNM prediction and explored the complementary value of multi-regional imaging.MethodsNinety-nine patients with pathologically confirmed PDAC who underwent preoperative ultrasound were retrospectively enrolled. Intratumoral and 3-mm peritumoral ROIs were manually delineated. Radiomics features were extracted using PyRadiomics and selected via reproducibility filtering, correlation analysis, and LASSO. Nine machine-learning algorithms were evaluated to identify the optimal classifier for each region (intratumoral: logistic regression; peritumoral: random forest). A decision-level combined model was constructed by integrating regional model outputs with clinical information. Discrimination was assessed by AUC with sensitivity, specificity, accuracy, PPV, and NPV; calibration by calibration curves and the Hosmer–Lemeshow test; clinical utility by decision curve analysis (DCA). Model interpretability and inter-model relationships were explored using SHAP, correlation, and Bland–Altman analyses.ResultsThe intratumoral and peritumoral models achieved AUCs of 0.815 and 0.792, respectively. The combined model yielded the highest performance (AUC = 0.898, 95% CI: 0.770–1.000) with good calibration (Hosmer–Lemeshow p = 0.091) and the greatest net benefit on DCA. DeLong tests showed no statistically significant AUC differences among models. Intratumoral and peritumoral outputs were moderately correlated (Pearson’s r = 0.711, p < 0.001), and Bland–Altman analysis demonstrated overall agreement with several outliers, suggesting complementary region-specific information.ConclusionIntratumoral and peritumoral ultrasound radiomics provide complementary information for preoperative LNM prediction in PDAC. The decision-level combined model achieved numerically higher discrimination with favorable calibration, supporting further validation before clinical translation.

Objective:This study aimed to develop and compare predictive models for hepatocellular carcinoma (HCC) differentiation using ultrasound-based radiomics and deep learning, and to evaluate the clinical utility of a combined model. Methods:Radiomics and deep learning models were constructed from grayscale ultrasound images. A combined model integrating both approaches was developed. Model performance was assessed using receiver operating characteristic (ROC) curves, calibration curves, and decision curve analysis (DCA). Sensitivity, specificity, accuracy, and area under the curve (AUC) were compared, and statistical significance was evaluated with the DeLong test. Results:The radiomics model achieved an AUC of 0.736 (95% CI: 0.578-0.893), while the deep learning model achieved an AUC of 0.861 (95% CI: 0.75-0.972). The combined model outperformed both, with an AUC of 0.918 (95% CI: 0.836-1.0). The DeLong test indicated a significant improvement of the combined model over the radiomics model. Calibration analysis and the Hosmer-Lemeshow test showed good agreement between predictions and outcomes (p = 0.889). DCA demonstrated a higher net clinical benefit for the combined model across a range of thresholds. Conclusion:Integrating radiomics and deep learning enhances the predictive accuracy of ultrasound-based models for HCC differentiation, providing a promising non-invasive approach for preoperative evaluation.

BackgroundGallbladder neuroendocrine neoplasms (GB-NENs) are exceedingly rare in clinical practice. To date, no large-scale, multicenter prospective studies have been conducted on this disease, resulting in a lack of established diagnostic and therapeutic experience or consensus. This case series reports seven GB-NEN patients who underwent different treatment modalities with varying outcomes. By integrating our institutional experience with previous literature, we aim to provide some therapeutic recommendations for GB-NEN patients.MethodsThe clinicopathological data of seven GB-NEN patients treated at our institution between June 2013 and June 2024 were retrospectively analyzed, with a focus on their treatment courses.ResultsSeven GB-NEN patients did not exhibite specific clinical manifestations or distinctive imaging features. All patients underwent surgical intervention, including radical resection in four cases. The overall survival ranged from 3 to 55 months, with a median survival of 19 months.ConclusionGB-NENs are highly aggressive and associated with poor prognosis. We recommend: 1) Radical cholecystectomy as the primary treatment for resectable GB-NENs; 2) Platinum-based chemotherapy as the first-line regimen, with close monitoring for drug resistance; 3) Early assessment of chemosensitivity to guide further treatment decisions, postoperative chemotherapy combined with adjuvant therapies may improve surgical efficacy.

Gallbladder cancer is the most prevalent malignancy of the biliary tract and has a dismal overall survival even in the present day. The development of new drugs holds promise for improving the prognosis of this lethal disease. The possible anti-neoplastic role of morusin was investigated both in vitro and in vivo. Through cell viability and colony formation assays, we observed that morusin inhibited the proliferation of gallbladder cancer cells in vitro. Wound healing and transwell assays revealed that morusin impeded the migration and invasion of gallbladder cancer cells. Given the observed morphological changes, we examined epithelial-mesenchymal transition (EMT) markers. Subsequent investigations demonstrated that morusin treatment, both in vitro and in vivo, downregulated the expression of phospho-STAT3 (Signal transducer and activator of transcription 3) and HIF-1α (Hypoxia-inducible factor 1α) in gallbladder cancer cells. Furthermore, morusin effectively reversed EMT induced by phospho-STAT3 or HIF-1α. Morusin has a reversing effect on the EMT of gallbladder cancer cells by modulating STAT3/HIF-1α signaling.

BackgroundThis study aimed to develop and validate a predictive model integrating radiomics features and clinical variables to differentiate intrahepatic bile duct stones with cholangitis (IBDS-IL) from intrahepatic cholangiocarcinoma (ICC) preoperatively, as accurate distinction is crucial for determining appropriate treatment strategies.MethodsA total of 169 patients (97 IBDS-IL and 72 ICC) who underwent surgical resection were retrospectively analyzed. Radiomics features were extracted from ultrasound images, and clinical variables with significant differences between groups were identified. Feature selection was performed using LASSO regression and recursive feature elimination (RFE). The radiomics model, clinical model, and combined model were constructed and evaluated using the area under the curve (AUC), calibration curves, decision curve analysis (DCA), and SHAP analysis.ResultsThe radiomics model achieved an AUC of 0.962, and the clinical model achieved an AUC of 0.861. The combined model, integrating the Radiomics Score with clinical variables, demonstrated the highest predictive performance with an AUC of 0.988, significantly outperforming the clinical model (p < 0.05). Calibration curves showed excellent agreement between predicted and observed outcomes, and the Hosmer-Lemeshow test confirmed a good model fit (p = 0.998). DCA revealed that the combined model provided the greatest clinical benefit across a wide range of threshold probabilities. SHAP analysis identified the Radiomics Score as the most significant contributor, complemented by abdominal pain and liver atrophy.ConclusionThe combined model integrating radiomics features and clinical data offers a powerful and reliable tool for preoperative differentiation of IBDS-IL and ICC. Its superior performance and clinical interpretability highlight its potential for improving diagnostic accuracy and guiding clinical decision-making. Further validation in larger, multicenter datasets is warranted to confirm its generalizability.

Intrahepatic cholangiocarcinoma (ICC) is a highly malignant liver tumor associated with a dismal prognosis, largely due to chemotherapy resistance. However, the mechanisms underlying gemcitabine (GEM) resistance in ICC remain poorly understood. In this study, we established three GEM-resistant cell models and evaluated their resistance by assessing cell proliferation, cell cycle arrest, and DNA damage. GEM-resistant cells exhibited significant tolerance to GEM-induced growth inhibition, reduced cell cycle arrest, and decreased DNA damage compared to parental cells. We then explored potential resistance mechanisms and found that pathways and targets such as epithelial-mesenchymal transition, PI3K/Akt, p53R2, and IGF-1R did not show a significant correlation with ICC resistance. Interestingly, our findings suggested that reactive oxygen species might promote GEM resistance in ICC. In conclusion, we characterized a GEM-resistant ICC model, which can be employed to investigate alternative resistance mechanisms and explore new treatment approaches.

Purpose:The increasing prevalence of obesity among adolescents has resulted in an increase in the incidence of hepatic steatosis; however, the relationship between anthropometric measurements and this condition in youth remains underexplored. Aims:To evaluate the effectiveness of nine anthropometric indicators in predicting the risk of hepatic steatosis in adolescents. Methods:We assessed several anthropometric indicators, including the abdominal volume index (AVI), body mass index, body roundness index, body adiposity index, conicity index, waist-hip ratio, waist-to-height ratio, and weight-adjusted waist index. Statistical methods such as multivariate logistic regression, smooth curve fitting, and subgroup analysis were employed. Discriminative accuracy was determined using receiver operating characteristic curve analysis, and a tool based on the optimal Youden index was developed. Results:All nine indices were significantly correlated with hepatic steatosis in adolescents. AVI demonstrated the strongest predictive ability, with an area under the curve of 0.8454 (95% confidence interval: 0.8221-0.8687, best threshold: 14.9992). Variations in predictive accuracy were observed across racial and ethnic subgroups, highlighting the importance of demographic factors. Conclusion:All nine anthropometric indices are associated with hepatic steatosis, with AVI emerging as the most effective tool for assessing liver health in adolescents.

Background:Microvascular invasion (MVI) is a critical determinant of poor prognosis in hepatocellular carcinoma (HCC). Accurate preoperative prediction of MVI is essential for optimizing surgical and therapeutic strategies. This study aims to develop a combined model integrating intratumoral, peritumoral, and clinical features from ultrasound-based radiomics for MVI prediction. Methods:Ultrasound images of 119 patients with pathologically confirmed HCC were analyzed. A total of 1,414 radiomics features were extracted from intratumoral and peritumoral regions. Feature selection was performed using intraclass correlation coefficient (ICC) analysis, t-tests, and least absolute shrinkage and selection operator (LASSO) regression. Logistic regression, Random Forest, and other machine learning algorithms were applied to construct predictive models. The best-performing intratumoral, peritumoral, and clinical models were combined using logistic regression. SHapley Additive exPlanations (SHAP) analysis, logistic regression coefficients, and partial dependence analysis were employed to evaluate feature contributions and interactions. Results:Both intratumoral and peritumoral models achieved high AUCs (0.781 and 0.792, respectively), with no statistically significant difference between them. The combined model, incorporating tumor size, achieved the highest AUC (0.903, 95% CI: 0.780-1.000) and superior performance across all evaluation metrics. Tumor size exhibited the smallest logistic regression coefficient but the highest SHAP contribution, indicating strong interactions with intratumoral and peritumoral features. Interaction analyses revealed that the combined effects of tumor size and radiomics features significantly enhanced predictive performance. Conclusion:This study demonstrates that combining intratumoral, peritumoral, and clinical features enhances the predictive accuracy for MVI in HCC. The findings underscore the value of feature integration and interactions, providing insights for personalized treatment planning and advancing the clinical utility of ultrasound-based radiomics.

Desmoid-type fibromatosis (DF) is an uncommon, locally invasive, non-metastatic soft-tissue neoplasm with variable and unpredictable manifestations. The therapeutic arsenal of DF therapy is consistently expanding; however, there remains no standard treatment modality. Sporadic pancreatic DF is rarely described in current literature, reflecting a significant deficiency in clinical treatment experience, this case aims to share some clinical experiences that can serve as a reference for managing this rare disease. A 36-year-old male presented with occasional abdominal discomfort and weight loss over a year. Ultrasound revealed a large mass in the pancreatic tail, which was not observed a year ago. The diagnosis of DF was confirmed by immunohistochemistry nuclear staining of β-catenin. Distal pancreatectomy with splenectomy was performed and the patient received no further therapy. After 13 months of follow-up, no recurrence or distant metastasis was observed. DF is a distinct rare tumor entity, sporadic pancreatic DF is even rarer. It is imperative to select the individualized treatment strategy for each patient to optimize tumor control and enhance quality of life.

Pancreatic cancer (PC) is widely regarded as the deadliest form of malignancy with a notably bleak prognosis. Although survival rates have shown gradual improvements, the pace of advancement remains slower when compared to other forms of cancer. Mitophagy suppression has surfaced as a novel approach for cancer treatment. Hederagenin (HDG), a triterpenoid extracted from the Hedera helix, has been identified as a potent inhibitor of mitophagy in PC. HDG has demonstrated the capacity to suppress the growth of BXPC-3 and PANC-1 cells in vitro, while also showing efficacy in diminishing tumor expansion in vivo. Furthermore, HDG promoted the opening of mitochondrial permeability transition pores, and enhance the accumulation of ROS. In addition, HDG led to a disruption in autophagic flux and an increase in autophagosomes within PC cells. Western blot analysis suggested that HDG hindered the fusion of lysosomes and autophagosomes by downregulating the expression of SNAP29, LAMP1, and Rab7. HDG also altered mitochondrial morphology in PC cells by suppressing the expression of dynamin-related protein 1 (DRP1), a crucial element in mitochondrial division machinery. This inhibition subsequently triggered voltage-dependent anion-selective channel protein 1 (VDAC1) oligomerization, mitochondrial hexokinase 2 (HK2) dissociation, and downregulation of the PINK1/PARKIN pathway, ultimately inhibiting the proliferation of PC cells in vitro. Moreover, the anti-mitophagy impact of HDG was reversed by DRP1 overexpression, while DRP1 knockdown produced the opposite results. These findings collectively suggest that HDG exerts anti-tumor activity by inhibiting mitophagy in PC cells. The underlying mechanism may involve the suppression of the DRP1-VDAC1-HK2-PINK1/PARKIN signaling pathway.

BTB and CNC homology 1 (BACH1) regulates biological processes, including energy metabolism and oxidative stress. Insufficient liver regeneration after hepatectomy remains an issue for surgeons. The Pringle maneuver is widely used during hepatectomy and induces ischemia/reperfusion (I/R) injury in hepatocytes. A rat model of two-thirds partial hepatectomy with repeated I/R treatment was used to simulate clinical hepatectomy with Pringle maneuver. Delayed recovery of liver function after hepatectomy with the repeated Pringle maneuver in clinic and impaired liver regeneration in rat model were observed. Highly elevated lactate levels, along with reduced mitochondrial complex III and IV activities in liver tissues, indicated that the glycolytic phenotype was promoted after hepatectomy with repeated I/R. mRNA expression profile analysis of glycolysis-related genes in clinical samples and further verification experiments in rat models showed that high BACH1 expression levels correlated with the glycolytic phenotype after hepatectomy with repeated I/R. BACH1 overexpression restricted the proliferative potential of hepatocytes stimulated with HGF. High PDK1 expression and high lactate levels, together with low mitochondrial complex III and IV activities and reduced ATP concentrations, were detected in BACH1-overexpressing hepatocytes with HGF stimulation. Moreover, HO-1 expression was downregulated, and oxidative stress was exacerbated in the BACH1-overexpressing hepatocytes with HGF stimulation. Cell experiments involving repeated hypoxia/reoxygenation revealed that reactive oxygen species accumulation triggered the TGF-β1/BACH1 axis in hepatocytes. Finally, inhibiting BACH1 with the inhibitor hemin effectively restored the liver regenerative ability after hepatectomy with repeated I/R. These results provide a potential therapeutic strategy for impaired liver regeneration after repeated I/R injury.

RATIONALE:Neuroendocrine neoplasms (NENs) originating from neuroendocrine cells occur in the thyroid, respiratory, and digestive systems, with Gallbladder Neuroendocrine Carcinoma (GB-NEC) accounting for only 0.5% of all NENs and 2.1% of gallbladder cancers. Due to its rarity, little is known about GB-NEC's clinical presentation and treatment. PATIENT CONCERNS:We report a case of a 52-year-old male presenting with acute upper right abdominal pain, leading to further investigation. DIAGNOSES:Initial diagnostic workup, including abdominal ultrasound and contrast-enhanced CT, suggested gallbladder malignancy. Post-surgical pathology confirmed GB-NEC, with immunohistochemistry supporting the diagnosis. INTERVENTIONS:The patient underwent radical cholecystectomy, followed by etoposide plus cisplatin chemotherapy. After disease progression indicated by CT, the patient received additional cycles of chemotherapy with cisplatin and irinotecan, plus targeted therapy with anlotinib and immunotherapy with paimiplimab. OUTCOMES:The patient showed a partial response to initial treatment. Subsequent liver biopsy confirmed NEC, consistent with small cell carcinoma. With continued treatment, the patient maintains a good survival status. LESSONS:GB-NEC is associated with poor prognosis, emphasizing the importance of early detection and multimodal treatment strategies. Our case underlines the potential benefit of a comprehensive treatment plan, including aggressive surgery and chemotherapy, with further research needed to standardize treatment for this rare condition.

Background: Appropriate surgical techniques for controlling bleeding and preserving residual liver function are key to the success of laparoscopic liver resection. This study aims to evaluate the application effect of intraoperative ultrasound in the Pringle maneuver of laparoscopic liver resection.Materials and Methods: Between January 2022 and June 2023, 100 patients underwent laparoscopic liver resection and were randomly allocated to receive application of intraoperative ultrasound for Pringle maneuver (intraoperative ultrasound group, n = 50) or conventional Pringle maneuver (conventional group, n = 50). Intraoperative blood loss, blood transfusion, operation time, hepatic portal block time, complications (bile leakage, hemorrhage, ascites, and posthepatectomy liver failure), and hospital stay were compared between groups, along with the alanine aminotransferase (ALT), aspartate aminotransferase (AST), and total bilirubin (TB) levels at postoperative days 1, 3, and 7.Results: The operation time, postoperative ALT, AST, and TB levels on postoperative days 1, 3, and 7, complications (bile leakage, hemorrhage, ascites, and posthepatectomy liver failures), and hospital stay were comparable between groups. Compared with the conventional group, the intraoperative ultrasound group had significantly less intraoperative blood loss (P = .015), lower blood transfusion rate (P = .035), and less hepatic portal block time (P = .012).Conclusions: Applying intraoperative ultrasound in laparoscopic liver resection for hepatic pedicle occlusion is a safe, simple, and effective method.

Predicting the biological characteristics of hepatocellular carcinoma (HCC) is essential for personalized treatment. This study explored the role of ultrasound-based radiomics of peritumoral tissues for predicting HCC features, focusing on differentiation, cytokeratin 7 (CK7) and Ki67 expression, and p53 mutation status. A cohort of 153 patients with HCC underwent ultrasound examinations and radiomics features were extracted from peritumoral tissues. Subgroups were formed based on HCC characteristics. Predictive modeling was carried out using the XGBOOST algorithm in the differentiation subgroup, logistic regression in the CK7 and Ki67 expression subgroups, and support vector machine learning in the p53 mutation status subgroups. The predictive models demonstrated robust performance, with areas under the curves of 0.815 (0.683-0.948) in the differentiation subgroup, 0.922 (0.785-1) in the CK7 subgroup, 0.762 (0.618-0.906) in the Ki67 subgroup, and 0.849 (0.667-1) in the p53 mutation status subgroup. Confusion matrices and waterfall plots highlighted the good performance of the models. Comprehensive evaluation was carried out using SHapley Additive exPlanations plots, which revealed notable contributions from wavelet filter features. This study highlights the potential of ultrasound-based radiomics, specifically the importance of peritumoral tissue analysis, for predicting HCC characteristics. The results warrant further validation of peritumoral tissue radiomics in larger, multicenter studies.

Early and accurate diagnosis of focal liver lesions is crucial for effective treatment and prognosis. We developed and validated a fully automated diagnostic system named Liver Artificial Intelligence Diagnosis System (LiAIDS) based on a diverse sample of 12,610 patients from 18 hospitals, both retrospectively and prospectively. In this study, LiAIDS achieved an F1-score of 0.940 for benign and 0.692 for malignant lesions, outperforming junior radiologists (benign: 0.830-0.890, malignant: 0.230-0.360) and being on par with senior radiologists (benign: 0.920-0.950, malignant: 0.550-0.650). Furthermore, with the assistance of LiAIDS, the diagnostic accuracy of all radiologists improved. For benign and malignant lesions, junior radiologists’ F1-scores improved to 0.936-0.946 and 0.667-0.680 respectively, while seniors improved to 0.950-0.961 and 0.679-0.753. Additionally, in a triage study of 13,192 consecutive patients, LiAIDS automatically classified 76.46% of patients as low risk with a high NPV of 99.0%. The evidence suggests that LiAIDS can serve as a routine diagnostic tool and enhance the diagnostic capabilities of radiologists for liver lesions.

Gallbladder cancer (GBC) is characterized by a high degree of malignancy and a poor prognosis. This study revealed that circEZH2 was frequently upregulated in GBC tissues and correlated with advanced tumor-node-metastasis (TNM) stage in GBC patients. In vitro and in vivo experiments confirmed that circEZH2 promoted the proliferation and inhibited the ferroptosis of GBC. Besides, this study discovered that circEZH2 regulated lipid metabolism reprogramming in GBC cells. Mechanistically, circEZH2 promotes SCD1 expression by sponging miR-556-5p in GBC cells. In addition, IGF2BP2 enhances the stability of circEZH2 in an m6A-dependent manner, while circEZH2 suppresses the ubiquitination and degradation of IGF2BP2 by binding to IGF2BP2. Taken together, our findings indicated that circEZH2, upregulated via a positive feedback loop between circEZH2 and IGF2BP2, promotes GBC progression and lipid metabolism reprogramming through the miR-556-5p/SCD1 axis in GBC. circEZH2 may serve as a potential therapeutic target for GBC.

Rationale:Primary hepatic mucosa-associated lymphoid tissue (MALT) lymphoma is a rare malignant primary hepatic lymphoma. The sensible choice of treatment for patients with primary lymphoma combined with atrial fibrillation (AF) is controversial and challenging.Patient concerns:The patient presented with both primary hepatic MALT lymphoma and AF, which was difficult to manage.Diagnoses:Pathological and immunohistochemical examination are helpful for definitive diagnosis.Interventions:Surgical resection and subsequent anticoagulant therapy are main treatment methods, and adjuvant therapy depends on the situation.Outcomes:Primary hepatic MALT lymphoma is easy to misdiagnosis due to a lack of typical symptoms and imaging signs.Lessons:This case highlights for patients with primary hepatic MALT lymphoma combined with AF, toxicity caused by adjuvant chemotherapy should be fully considered, and careful selection should be made based on the general conditions and complications of patients.