tbuckley/cabot-search huggingface dataset:
Endoscopic functional luminal imaging probe for intraprocedural guidance of achalasia treatment: a systematic review and meta-analysis
Authors: Matyas Fehervari, Isla Middleton, Emma Rose McGlone, Michael G Fadel, James Brewer, Pranav Patel, Majid Hashemi, Naim Fakih-Gomez, Sacheen Kumar
Journal: International Journal of Surgery (2025)

Background: Functional luminal imaging probes (EndoFLIP) have shown potential as both diagnostic tools and intraoperative guides during achalasia interventions. This systematic review and meta-analysis aim to evaluate the intraoperative use of EndoFLIP during surgical procedures for achalasia, with a focus on its impact on the distensibility index (DI) and treatment outcomes. Materials and methods: A literature search of Medline, Embase, and the Cochrane Library was conducted for studies reporting the use of EndoFLIP and DI in achalasia treatment from January 2000 to January 2024. Meta-analysis was performed using random-effect models to assess treatment effects, with heterogeneity between studies evaluated. Results: A total of 32 studies with 2,681 patients were included in the qualitative synthesis, and 17 studies with 1,734 patients were included in the quantitative synthesis. A pooled random-effects analysis demonstrated a significant reduction in DI following myotomy, with a weighted mean difference (WMD) of -3.72 (95% CI -4.16 to -3.28; I 2 = 90%). The Eckardt score analysis, including data from 1,009 patients, showed a significant reduction by -5.21 (95% CI -5.8 to -4.6; p &lt; 0.001, I 2 = 89.4%). The random-effects analysis of DI pre- and post-myotomy without an endoscope demonstrated a significant increase with a WMD of -2.402 (95% CI -3.175 to -1.6; p &lt; 0.001, I 2 = 85.6%). A larger WMD was observed with endoscope placement above the FLIP device, measuring -4.14 (95% CI -5.4 to -2.8; p &lt; 0.001, I 2 = 91.2%). For the 30 mL balloon fill, the weighted mean difference (WMD) in the distensibility index (DI) post-myotomy was -2.89 (95% CI -3.2 to -2.4; p &lt; 0.001). For the 40 mL balloon fill, the WMD was -4.02 (95% CI -4.8 to -3.1; p &lt; 0.001). Conclusions: EndoFLIP enhances achalasia treatment by offering real-time feedback on the distensibility index, allowing for more tailored interventions and improved outcomes. A standardized protocol for EndoFLIP usage is necessary to further validate its role in clinical practice and ensure consistent, comparable measurements.
Transforming healthcare: the impact of artificial intelligence on diagnostics, pharmaceuticals and ethical considerations - a comprehensive review
Authors: V.C. Deivayanai, Pavithra Swaminaathan, A.S. Vickram, A. Saravanan, Shabana Bibi, Navidha Aggarwal, Virender Kumar, Albaraa H. Alhadrami, Zuhair M. Mohammedsaleh, Rawan Altalhi, May Nasser Bin-Jumah, Amany A. Sayed, Amirah Albaqami, Hitesh Chopra, Talha Bin Emran, Mohamed M. Abdel-Daim
Journal: International Journal of Surgery (2025)

Interpretability of results remains challenging in most health industries since patients may suffer life-threatening consequences from an inaccurate diagnosis. Artificial Intelligence (AI) integration has arisen as a prominent technology in the healthcare sector, transforming the field by advancing early diagnostics, surgeries, and ethical concerns. The present review analyses the multidimensional impact of AI on the health sector through enhancements in medical accuracy and diagnosis outcomes. Implementing AI techniques and machine learning algorithms in predictive analytics enables disease identification at a nascent stage, boosting decision-making accuracy. Advancements in genomics have demanded the employment of AI in decoding genetic information supporting personalized and targeted treatments. The review comprehensively examines the application of AI-based diagnostics addressing the impact on heart-associated diseases, cancer pathogenesis, and other general disease prediction. Different machine learning algorithms aid in identifying tumor behavior, risk factors, and tailored therapy in cancer treatment. In the context of cardiovascular disorders, AI-driven methodologies aid in assessing the patient data, risk factors, and forecasting the probable complications in preventative care. AI-based surgeries employing the da Vinci system highlight the use of AI in increasing the prediction of surgical success rate. Robotic automation in orthopedics advances spine and joint replacement surgeries, offering real-time guidance and enhancing patient recovery outcomes. Broader improvements in AI integration in healthcare have been discussed, focusing on refining algorithms for improved application.
Routine abdominal drainage after pancreatectomy: A Byesian meta-analysis
Authors: Shengxiang Hou, Zonghao Hou, Li Ren, Zhixin Wang, Haijiu Wang, Chengwei Tie, Manjun Deng, Haining Fan
Journal: International Journal of Surgery (2025)

Objective: This meta-analysis aims to evaluate the effect of prophylactic abdominal drainage on post-pancreatectomy complications, a topic that is still debated in the medical community. Materials and Methods: Following PRISMA guidelines, the authors conducted a systematic search across databases such as PubMed, EMBASE, Scopus, Cochrane Library, Ovid, clinicaltrials.gov, Web of Science, CNKI, and WanFang Data, focusing on studies comparing intraperitoneal drainage with no drainage after pancreatic surgery. Key outcomes included postoperative pancreatic fistula, clinically relevant postoperative pancreatic fistula, mortality, complications, delayed gastric emptying, bile leakage,intestinal fistula, abdominal abscess,postoperative bleeding, interventional radiology drainage, reoperation, and unplanned readmissions. Statistical analyses were conducted using either a Beta Normal Hierarchical Model or a random-effects model, providing combined odds ratios (ORs) with 95% confidence intervals (CIs). Subgroup analyses were also performed based on surgical procedures, specifically Distal Pancreatectomy (DP) and Pancreatoduodenectomy (PD). Results: This meta-analysis, incorporating 5 RCTs and 10 non-RCTs, identified a significant link between routine abdominal drainage and higher rates of postoperative pancreatic fistula, clinically relevant postoperative pancreatic fistula, and unplanned readmissions.The overall ORs were 2.46 (95% CI: 1.90-3.63), 1.92 (95% CI: 1.38-2.64), and 1.32 (95% CI: 1.04-1.65) . In the DP subgroup, the ORs were 2.48 (95% CI: 1.49-5.00), 2.75 (95% CI: 1.65-5.21), and 1.46 (95% CI: 1.06-2.18). In the PD subgroup, the ORs were 2.34 (95% CI: 1.70-3.36), 1.95 (95% CI: 1.17-3.19), and 1.25 (95% CI: 1.00-1.60). The use of drainage was associated with a decreased mortality following PD, with an OR of 0.49 (95% CI: 0.23-0.96),however, this association was not observed in relation to other surgical methods. No significant differences were found among the groups for the other outcomes. Conclusion: For surgeries other than PD, omitting drainage tubes may benefit patients postoperatively. However, unselected cessation of intraperitoneal drainage after PD correlates with reduced pancreatic fistulas but higher mortality. Future randomized trials should compare routine versus selective drainage.
The role of phenotypic age acceleration in aneurysmal subarachnoid hemorrhage: analysis of retrospective data from two hospital-based cohorts and prospective UK Biobank cohort
Authors: Jiarong He, Yuquan Chen, Sidi Xu, Pengwei Hou, Yan Cui, Kai Su, Xieli Guo, Ming Wang, Mingming Zhang
Journal: International Journal of Surgery (2025)

Background: Phenotypic age acceleration (PhenoAgeAccel) is considered a major risk factor for various age-related diseases, but its specific role in aneurysmal subarachnoid hemorrhage (aSAH) remains unclear. This study aims to examine the relationship between PhenoAgeAccel and the risk of aSAH, and further explores whether genetic susceptibility modifies this association. Methods: Using data from the UK Biobank, we performed cross-sectional and prospective analyses to investigate the association between PhenoAgeAccel and aSAH. Polygenic risk scores were calculated to evaluate genetic susceptibility, and interactions between genetic risk and PhenoAgeAccel were explored. Additionally, using hospital cohort data, we applied an XGBoost model interpreted via SHapley Additive exPlanations (SHAP) analysis to identify key clinical predictors, including PhenoAgeAccel, which were subsequently incorporated into a nomogram for clinical risk prediction. Results: Cross-sectional analyses revealed that each 1-year increment in PhenoAgeAccel was associated with a 1%–7% elevated risk of aSAH. In the UK Biobank, biologically older individuals had a higher risk of aSAH compared to biologically younger individuals (odds ratio [OR] = 1.48; 95% confidence interval [CI], 1.10–1.97; P = 0.009) for PhenoAgeAccel. Similarly, in the hospital datasets, biologically older individuals also showed increased odds of aSAH (Second Xiangya Hospital: OR = 16.45; 95% CI, 4.72–57.34; Fujian Hospital: OR = 12.41; 95% CI, 3.33–46.26). In the prospective analyses of the UK Biobank, PhenoAgeAccel was associated with an increased risk of incident aSAH (hazard ratio [HR] = 1.04; 95% CI, 1.02–1.07). Moreover, additive interactions between PhenoAgeAccel and genetic susceptibility were observed. Further validation using the XGBoost machine learning model and SHAP analysis confirmed PhenoAgeAccel as a key predictive factor for aSAH. Based on these findings, a nomogram integrating PhenoAgeAccel and relevant clinical parameters was developed to facilitate individualized risk prediction in clinical practice. Conclusion: PhenoAgeAccel is a significant predictor of aSAH risk, particularly among genetically susceptible populations. Identifying individuals with PhenoAgeAccel could serve as a novel clinical biomarker for assessing aSAH.
Treatment-related adverse events, immune-related adverse events and discontinuation in patients with solid tumors adding adjuvant immune checkpoint blockade: a meta-analysis of 38 randomized controlled trials
Authors: Wangcheng Zhao, Zhi Yang, Quanming Fei, Xinhang Hu, Yifan Ouyang, Xuyang Yi, Shouzhi Xie, Li Wang, Xingchun Huang, Yu He, Juan Luo, Ye Xiao, Zhe Zhang, Fenglei Yu
Journal: International Journal of Surgery (2025)

Background: The safety profile of immune checkpoint blockade (ICB) in the adjuvant setting has not been well characterized. This study aims to summarize the incidences of adverse events (AEs) in patients with solid tumors receiving ICB in adjuvant setting, and evaluate the effect of ICB addition on incidences of these adverse events. Methods: We searched public databases and relevant international conference proceedings up to 20 September 2024, to identify eligible randomized controlled trials evaluating the ICB-based treatments in adjuvant setting for patients with solid tumors. The primary outcomes included treatment-related death, treatment-related adverse events (TrAEs), immune-related adverse events (IrAEs), serious AEs, and discontinuation due to AEs. The GRADE approach was used to evaluate the certainty of evidence for primary outcomes. Results: Thirty-eight trials with 25 852 participants were included. Single-arm meta-analysis showed that combination therapies had higher grade 3–4 TrAEs than PD-1/PD-L1 monotherapy, while anti-CTLA-4-based therapies exhibited greater discontinuation rates (49.7% [39.4–60.0] for anti-CTLA-4 monotherapy) versus other ICB strategies. Treatment-related death was rare, occurring in 63 of 16 272 participants receiving adjuvant ICB-based treatments. The pairwise meta-analysis revealed that the addition of ICB was associated with increased treatment-related deaths (OR [95% CI]: 1.713 [1.117–2.628]), although this association was observed only in the CTLA-4 blockade and not in the PD-1 or PD-L1 blockade. ICB addition also increased incidences of TrAEs, IrAEs, serious AEs, and discontinuations, with consistent results across blockade types. Additionally, ICB addition was associated with higher incidences of 37 types of AEs, including 20 grade 3–4 events. Most results had moderate evidence quality. Conclusion: Adding ICB in adjuvant setting was associated with increased AEs, but the toxicity profile was largely similar to that in the advanced setting.
Optimizing lymph node evaluation in colorectal mucinous adenocarcinoma: a multicenter and population-based study
Authors: Qiying Yin, Yanlai Sun, Changqing Jing, Benjia Liang, Xu Guan, Peng Zhang, Kaixiong Tao, Yueming Sun, Yifei Zhang, Shanglei Ning, Minhao Yu, Yanfeng Lv, Lei Wang, Qi Sun, Jiangang Liu, Meng Jiao, Zhao Zhang, Congqing Jiang, Xianghai Ren, Guodong Yu, Bin Ma, Zhibin Ye, Jun Li, Dongning Liu, Xiangheng Kong, Jingbo Chen, Guangyong Zhang, Hui Yang
Journal: International Journal of Surgery (2025)

Background: While the currently recommended minimum number of examined lymph nodes (ELNs) for colorectal cancer is 12, the applicability of this standard to colorectal mucinous adenocarcinoma (MAC), a distinct tumor entity with high metastatic potential, remains controversial. This study aimed to establish and validate the optimal ELN threshold during surgery to achieve superior survival outcomes of MAC patients. Methods: Data from 21 Chinese medical institutions and the Surveillance, Epidemiology, and End Results (SEER) database were analyzed using Cox proportional hazards models to identify prognostic factors affecting MAC patient outcomes. Restricted cubic spline (RCS) analysis was subsequently applied to determine the optimal ELN threshold. Results: The ELN distribution pattern demonstrated significant concordance between the Chinese cohort ( n = 1086) and the SEER cohort ( n = 12 343), with identical median values (17) and overlapping interquartile ranges (SEER: 12–23 vs. China: 13–22). Multivariate analyses adjusted for potential confounders established ELN quantity as an independent prognostic factor (SEER cohort: hazard ratio [HR] = 0.987, 95% confidence interval [CI]: 0.985–0.990, P &lt; 0.001; Chinese cohort: HR = 0.975, 95% CI: 0.957–0.994, P = 0.011). The RCS models in both databases revealed a nonlinear L-shaped association between the ELN count and all-cause mortality risk, with 17 ELNs identified as the optimal threshold. Notably, patients with ≥17 ELNs exhibited significantly reduced mortality risks in both the SEER cohort ( P &lt; 0.001) and the Chinese cohort ( P = 0.45), particularly in node-negative patients and those without adjuvant chemotherapy. Conclusion: Elevated ELN counts are correlated with improved survival. Our findings strongly suggest that 17 ELNs is the optimal cutoff for evaluating surgical quality and prognostic stratification in MAC patients, challenging the conventional 12-ELN standard.
Evaluating topography of mutational signatures with SigProfilerTopography
Authors: Burçak Otlu, Ludmil B Alexandrov
Journal: Genome biology (2025)

Abstract The mutations found in a cancer genome are shaped by diverse processes, each displaying a characteristic mutational signature that may be influenced by the genome’s architecture. While prior analyses have evaluated the effect of topographical genomic features on mutational signatures, there has been no computational tool that can comprehensively examine this interplay. Here, we present SigProfilerTopography, a Python package that allows evaluating the effect of chromatin organization, histone modifications, transcription factor binding, DNA replication, and DNA transcription on the activities of different mutational processes. SigProfilerTopography elucidates the unique topographical characteristics of mutational signatures, unveiling their underlying biological and molecular mechanisms.
A multi-modal model integrating MRI habitat and clinicopathology to predict platinum sensitivity in patients with high-grade serous ovarian cancer: a diagnostic study
Authors: Qiu Bi, Conghui Ai, Qingyin Meng, Qinqing Wang, Haiyan Li, Ao Zhou, Wenwei Shi, Ying Lei, Yunzhu Wu, Yang Song, Zhibo Xiao, Haiming Li, Jinwei Qiang
Journal: International Journal of Surgery (2025)

Background: Platinum resistance of high-grade serous ovarian cancer (HGSOC) cannot currently be recognized by specific molecular biomarkers. We aimed to compare the predictive capacity of various models integrating MRI habitat, whole slide images (WSIs), and clinical parameters to predict platinum sensitivity in HGSOC patients. Methods: A retrospective study involving 998 eligible patients from four hospitals was conducted. MRI habitats were clustered using K-means algorithm on multi-parametric MRI. Following feature extraction and selection, a Habitat model was developed. Vision Transformer (ViT) and multi-instance learning were trained to derive the patch-level prediction and WSI-level prediction on hematoxylin and eosin (H&amp;E)-stained WSIs, respectively, forming a Pathology model. Logistic regression (LR) was used to create a Clinic model. A multi-modal model integrating Clinic, Habitat, and Pathology (CHP) was constructed using Multi-Head Attention (MHA) and compared with the unimodal models and Ensemble multi-modal models. The area under the curve (AUC) and integrated discrimination improvement (IDI) value were used to assess model performance and gains. Results: In the internal validation cohort and the external test cohort, the Habitat model showed the highest AUCs (0.722 and 0.685) compared to the Clinic model (0.683 and 0.681) and the Pathology model (0.533 and 0.565), respectively. The AUCs (0.789 and 0.807) of the multi-modal model interating CHP based on MHA were highest than those of any unimodal models and Ensemble multi-modal models, with positive IDI values. Conclusion: MRI-based habitat imaging showed potentials to predict platinum sensitivity in HGSOC patients. Multi-modal integration of CHP based on MHA was helpful to improve prediction performance.
Intrarenal pressure gradient in an ex-vivo porcine kidney model
Authors: Xiaohui Liu, Zhenyuan Han, Baosen Wang, Yajie Zhou, Xiaoqing Luo, Dong Wang
Journal: International Journal of Surgery (2025)

Introduction: Intrarenal pressure (IRP) is challenging to measure during retrograde intrarenal surgery (RIRS). Distal end-mounted pressure sensors in flexible ureteroscope (fURS) or ureteral access sheath (UAS) offer real-time pressure measurement, which may address this issue. Pyelorenal reflux occurs in the renal calyces, particularly at the fornices. No study has yet investigated whether there is a pressure differential between the renal pelvis and the calyces. This research aims to examine this topic using the porcine kidney model developed at our institution. Materials and methods: Twelve adult porcine kidneys were used. Three pressure measuring catheters were inserted into upper, middle, and lower calyces. IRPs were studied using either conventional UAS (cUAS) or vacuum-assisted UAS (vaUAS) with a fourth measure monitor catheter inside and level with the tip of UAS and a 7.5F fURS. Incremental rate of irrigation was delivered through the fURS and IRPs were measured and recorded. Result: In cUAS, there were statistically significant reductions in pressure, ranging from small to moderate, observed in both the upper and lower calyces compared to the renal pelvis. These pressure differences disappeared when irrigation reached &gt;90 c.c./minute. In vaUAS with vent closed: The IRP remained negligible even with irrigation up to 140 c.c./minute. Interestingly, there were very small but significantly less pressures in all three calyces as compared to the real pelvises when irrigation rate was ≧90 c.c./minute. There were no differences in pressure with irrigation below 90 c.c./minute. Conclusion: Real-time IRP measurement during RIRS with cUAS can provide valuable clinical insights. Porcine kidney studies show slightly less pressure in the calyces than the renal pelvis. vaUAS reduces high IRP and pressure gradients, potentially making real-time IRP measurement unnecessary.
Deep learning-based radiomics and machine learning for prognostic assessment in IDH-wildtype glioblastoma after maximal safe surgical resection: a multicenter study
Authors: Jianpeng Liu, Shufan Jiang, Yanfei Wu, Ruoyao Zou, Yifang Bao, Na Wang, Jiaqi Tu, Ji Xiong, Ying Liu, Yuxin Li
Journal: International Journal of Surgery (2025)

Background: Glioblastoma (GBM) is a highly aggressive brain tumor with poor prognosis. This study aimed to construct and validate a radiomics-based machine learning model for predicting overall survival (OS) in IDH-wildtype GBM after maximal safe surgical resection using magnetic resonance imaging. Methods: A total of 582 patients were retrospectively enrolled, comprising 301 in the training cohort, 128 in the internal validation cohort, and 153 in the external validation cohort. Volumes of interest (VOIs) from contrast-enhanced T1-weighted imaging (CE-T1WI) were segmented into three regions: contrast-enhancing tumor, necrotic non-enhancing core, and peritumoral edema using an ResNet-based segmentation network. A total of 4,227 radiomic features were extracted and filtered using LASSO-Cox regression to identify signatures. The prognostic model was constructed using the Mime prediction framework, categorizing patients into high- and low-risk groups based on the median OS. Model performance was assessed using the concordance index (CI) and Kaplan-Meier survival analysis. Independent prognostic factors were identified through multivariable Cox regression analysis, and a nomogram was developed for individualized risk assessment. Results: The Step Cox [backward] + RSF model achieved CIs of 0.89, 0.81, and 0.76 in the training, internal and external validation cohorts. Log-rank tests demonstrated significant survival differences between high- and low-risk groups across all cohorts ( P &lt; 0.05). Multivariate Cox analysis identified age (HR: 1.022; 95% CI: 0.979, 1.009, P &lt; 0.05), KPS score (HR: 0.970, 95% CI: 0.960, 0.978, P &lt; 0.05), rad-scores of the necrotic non-enhancing core (HR: 8.164; 95% CI: 2.439, 27.331, P &lt; 0.05), and peritumoral edema (HR: 3.748; 95% CI: 1.212, 11.594, P &lt; 0.05) as independent predictors of OS. A nomogram integrating these predictors provided individualized risk assessment. Conclusion: This deep learning segmentation–based radiomics model demonstrated robust performance in predicting OS in GBM after maximal safe surgical resection. By incorporating radiomic signatures and advanced machine learning algorithms, it offers a non-invasive tool for personalized prognostic assessment and supports clinical decision-making.
White matter fractional anisotropy decreases precede hyperintensities in Alzheimer’s disease
Authors: Pan Sun, Zhengbo He, Erwei Chu, Xiang Fan, Yue Cai, Guoyu Lan, Lin Liu, Dai Shi, Li Liang, Jie Yang, Anqi Li, Yalin Zhu, Xin Zhou, Lili Fang, Yiying Wang, Laihong Zhang, Zhen Liu, Ting Ma, Guanxun Cheng, Linsen Xu, Tengfei Guo
Journal: Cell Reports Medicine (2025)
Multimodal analyses reveal genes driving electrophysiological maturation of neurons in the primate prefrontal cortex
Authors: Yu Gao, Qiping Dong, Kalpana Hanthanan Arachchilage, Ryan D Risgaard, Moosa Syed, Jie Sheng, Danielle K Schmidt, Ting Jin, Shuang Liu, Soraya O Sandoval, Sara Knaack, Magnus T Eckholm, Rachel J Chen, Yu Guo, Dan Doherty, Ian Glass, Jon E Levine, Daifeng Wang, Qiang Chang, Xinyu Zhao, Andre M M Sousa
Journal: Neuron (2025)