Multiple Centers Research Articles

A multi-center study of symptoms in patients with esophageal cancer postoperatively: A networking analysis.

This study aimed to explore symptom clusters and the inter-relationship of symptoms in esophageal cancer (EC) patients during the first week after surgery. A cross-sectional survey across multiple centers was carried out using the EORTCQLQ-OES18. Patients with esophageal cancer within a week post-surgery were recruited from the "Be Resilient to Cancer" project in Guangdong, Hunan, and Sichuan provinces between January and September 2024. Exploratory factor analysis with a priori algorithm was used to identify symptom clusters and network analysis was employed to recognize the relationship among core and bridge symptoms. The sample consisted of 501 patients with esophageal cancer, who were predominantly male (83%), married (93%) and 57% were ≥60 years. Three symptom clusters were identified: "reflux-pain", "eating", and "dysphagia-dry mouth". Acid or bile coming up (support=40.1%, confidence=1, lift=2.53), eating difficulties (support=40.1%, confidence=0.990, lift=2.408) and dry mouth (support=42.9%, confidence=0.808, lift=1.298) were marked as sentinel symptoms for these clusters, respectively. Acid indigestion or heartburn was identified as the core symptom (EI=1.142 without covariates and EI=1.153 with covariates), and dry mouth served as the bridge symptoms (EI=0.63 and EI=0.656). Addressing acid or bile coming up, eating difficulties, dry mouth are imperative to help relief symptom burden at the cluster level. Furthermore, targeting acid indigestion and heartburn are crucial to break the chains among different symptom clusters.

Smart data-driven medical decisions through collective and individual anomaly detection in healthcare time series

BackgroundAnomalies in healthcare refer to deviation from the norm of unusual or unexpected patterns or activities related to patients, diseases or medical centres. Detecting these anomalies is crucial for timely interventions and efficient decision-making, helping to identify issues like operational inefficiencies, fraud and emerging health complications. ObjectivesThis study presents a novel method for detecting both collective and individual anomalies in healthcare data through time series analysis using unsupervised machine learning. The dual-strategy approach leverages two methodologies: a ’practice centre-based approach’ which monitors changes across different practice centres and a ’process-based approach’ which focuses on identifying anomalies within individual centres. The former allows for early detection of systemic issues, while the latter highlights specific irregularities within a centre’s operations. MethodsThe study utilised a dataset over 500,000 medical records from multiple GP practice centres in the UK collected between 2018–2023. Data are clustered using DBSCAN to identify collective anomalies from deviations from linear trends in consecutive two-month scatterplots. Individual anomalies are identified by examining the SOM-clustered time series of various medical processes within a specific practice centre, where graphs show deviation from the typical pattern. FindingsOur approach addresses some challenges posed by the complexity and sensitivity of healthcare data by not requiring personal information. The method offers accurate visual representations making the data accessible and interpretable for non-technical users. Unlike traditional methods focusing solely on subsequence anomalies, our technique analyses the collective behaviour across multiple time series providing a more comprehensive perspective. ConclusionThis study underscores the importance of integrating unsupervised anomaly detection with clinical expertise to ensure that statistically anomalous patterns align with clinical relevance. The dual-strategy clustering method holds significant potential for enabling timely interventions, proactively identifying potential crises, and ultimately contributing to better decision-making and operational efficiency within the healthcare sector.

Facile preparation, mechanochromic luminescence and excitation wavelength-dependent emission of tetra(1H-benzo[d]imidazol-2-yl)ethene Zn(II) complexes and their applications.

Nowadays, benzimidazole and its derivatives are widely assembled into multifunctional materials with various properties such as mechanochromism, photochromism, thermochromism and electrochromism. Herein, two novel zinc(II) coordination compounds, [Zn2(L2)Br4]·2H2O (1) and [Zn2(L2)Cl4]·2H2O (2) (L2 = tetra(1H-benzo[d]imidazol-2-yl)ethene), have been constructed via one-pot facile synthesis from bis(1H-benzo[d]imidazol-2-yl)methane (L1) and zinc(II) salts. The ligand L2 with a CC double bond was in situ formed by C-C coupling of two sp3-C atoms of L1 in solvothermal synthesis, which provides a new strategy to generate the conjugation system conveniently. Impressively, complex 1 shows distinct fluorescence and phosphorescence dual emission due to the heavy atom effect of bromide ions. Complexes 1 and 2 exhibit high-contrast mechanochromic luminescence properties due to the transformation from a crystalline state to an amorphous state. In addition, they also exhibited excitation wavelength-dependent luminescence in crystalline states and DMF systems. As the excitation wavelength increased, their luminescence color in DMF solution changed significantly from blue to yellow. Theoretical calculations show that the complexes have multiple emission centers due to the locally excited nature and intramolecular charge transfer. Due to their excellent excitation wavelength-dependent luminescence properties, PMMA films based on complexes 1 and 2 were prepared and could be applied in anti-counterfeiting and UV protection.

Abstract A007: Artificial intelligence in radiation therapy: Bringing in a new era of cancer therapy, accurate and fast

Abstract Introduction: The application of AI in RT has been adopted as innovative method that seeks to enhance accuracy, effectiveness and patient outcome of cancer treatment. This aims to understand current and potential uses of AI-based tools in RT; from planning, monitoring during therapy, adaptive approach, and after treatment assessment. This study defines the role of AI in facilitating decision-making, made to define treatment plans, and to organize the clinical practice in order to improve the therapeutic outcomes and provide individualized approach to the cancer patients. Methods and Experimental Procedures: Radiation therapy work flows which integrated AI algorithms for treatment planning and delivery were examined in a retrospective manner. Imaging data was used to predict tumor behavior and develop the best treatment plan since the models employed by practitioners involved machine learning. Another application of AI was for the shape matching of its patient anatomy during treatment and came up with better matching for therapy adjustments. This involved data from multiple clinical centers employing AI-assisted techniques for prostate, lung as well head and neck cancers. These QA procedures implemented with the help of AI were assessed with respect to their capability in identifying errors and maintaining dose accuracy. Results: Initial data showed that AI optimizes accuracy and time in treatment planning by streamlining the processes of outlining tumors and relevant tissues; planning time is cut in half in comparison with the conventional approach. Real-time positioning indicated by AI optimized adaptive therapy gave accuracy in tracking the anatomical variations thereby justifying the alterations in radiation delivery that enhanced the tumor control without affecting normal tissue. Automated quality assurance systems with AI capabilities successfully detected possible mistakes in the application of treatment processes and thus, decisively enhanced patients’ safety and minimized risks of radiation mishaps. The data also showed that models using AI enhanced accuracy in dose prediction and thus the overall treatment plans and results especially when the anatomy of a patient is unique and cannot easily be navigated. Conclusions: AI is becoming integrated into RT since it delivers, precision, personalization and efficiency that the industry desires. Advanced AI-based technologies help in treatment planning, facilitate monitoring and improve the technique of adaptive RT while also strengthening QA measures thus making patients safer and providing better therapeutic results. In the future, emerging technologies in AI offers prospects to address problems in radiation oncology for improved diagnosis and treatment of cancer. There is need for more clinical and research practice and development to actualize AI applications, remove the constraints and apply these systems in regular clinical services. The future of RT is going to be with AI incorporated in all the departments to ensure that cancer treatment is optimized for the benefit of the patient. Citation Format: Newtone Kisia. Artificial intelligence in radiation therapy: Bringing in a new era of cancer therapy, accurate and fast. [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Translating Targeted Therapies in Combination with Radiotherapy; 2025 Jan 26-29; San Diego, CA. Philadelphia (PA): AACR; Clin Cancer Res 2025;31(2_Suppl):Abstract nr A007.

Exploring the Clinical Diversity of Castleman Disease and TAFRO Syndrome: A Japanese Multicenter Study on Lymph Node Distribution Patterns.

Individuals diagnosed with Castleman disease (CD) and TAFRO syndrome (characterized by thrombocytopenia, anasarca, fever, bone marrow fibrosis, and organomegaly) displays a wide range of clinical symptoms, including varying patterns of lymph node enlargement, systemic inflammation, and impaired organ function. Some patients may present with both CD and TAFRO syndrome concurrently. A retrospective study conducted across multiple centers in Japan examined 321 cases to determine if the quantity and position of swollen lymph nodes could forecast the clinical progression and intensity of these conditions. Interestingly, the study revealed that patients with TAFRO syndrome exhibited lymphadenopathy across all ranges of lymph node region counts. Moreover, no specific clinical patterns were associated with the number of affected lymph node regions in CD patients, regardless of whether they also had TAFRO syndrome. These results enhance our understanding of the clinical variability in CD and TAFRO syndrome, suggesting that a comprehensive clinical evaluation, rather than relying solely on lymph node count, is crucial for effectively managing these conditions. Additional studies are required to establish reliable diagnostic markers and to predict disease severity at the time of diagnosis, ultimately improving patient outcomes.

A Multicenter Study on the Relationship of Tumor Lesion Location with Bilateral Parametrial Involvement and Pelvic Lymph Node Metastasis in Cervical Squamous Cell Carcinoma.

This study aimed to explore the relationship of cervical tumor lesion location (CTLL) with bilateral parametrial involvement (PI) and pelvic lymph node metastasis (LNM). The study retrospectively analyzed the clinicopathologic and imaging data of patients with cervical squamous cell carcinoma (SCC) retrieved from multiple centers. According to the CTLL, patients were allocated to three groups: a middle one third group, a unilaterally dominant group, and the entire-region group. Uni- and multivariate logistic regression analyses were performed to explore the preoperative risk factors related to PI and pelvic LNM. The rates of PI and pelvic LNM at the tumor-ipsilateral side and the tumor-contralateral side were compared using the Wilcoxon test. The study enrolled 776 cases. The CTLL was an important preoperative risk factor for both PI and pelvic LNM. Parametrial involvement occurred solely on the tumor-ipsilateral side (3.57 %) in the unilaterally dominant group, whereas the rate of pelvic LNM on the tumor-ipsilateral side was 11.22 %, significantly higher than on the contralateral side (5.1 %), with no pelvic LNM found on the tumor-contralateral side of patients with tumors smaller than 3.5 cm. Cervical SCC exhibits the characteristic of more accessible tumor-ipsilateral PI and pelvic LNM. When evaluation by magnetic resonance imaging (MRI) shows that the tumor lesion does not involve the contralateral one third of the cervix, a reduction in the resection scope of the contralateral parametrium can be considered, avoiding resection of the para-aortic lymph nodes, and if the tumor is smaller than 3.5 cm, a reduction in the resection scope of the tumor-contralateral pelvic lymph nodes also can be considered.

Installation of Multiple Chiral Centers in Aliphatic Polyesters Backbone Enables Unexpected Crystallinity via Asymmetric Catalytic Copolymerization

Installation of Multiple Chiral Centers in Aliphatic Polyesters Backbone Enables Unexpected Crystallinity via Asymmetric Catalytic Copolymerization

Effectiveness of telehealth in patients with spinal cord injury: A randomized controlled trial

Background: Spinal cord injury (SCI) impacts every aspect of an injured individual’s life and necessitates ongoing surveillance to prevent subsequent complications. The tele-assessment of different diseases has demonstrated encouraging outcomes, and there is limited literature available about SCI. The present study aimed to document the effectiveness of telehealth on clinical outcomes, quality of life improvement, accessibility, psychological health, patient satisfaction and engagement, and caregiver perspective for patients with SCI. Methods: This study was carried out in a tertiary care centre. Fifty-six patients with acute SCI were randomized into group “S” (study group), who were followed with telehealth telephonically and through video conferencing plus in-person visits to the hospital at second and sixth months, and group “C” (control group) with in-person visit to the hospital only. Patients were assessed for clinical effectiveness (spinal cord independence measure-III, SCI Secondary Conditions Scale, and pressure ulcer scale for healing), psychological health (World Health Organization Quality of Life, patient health questionnaire, and purdue posttraumatic stress disorder), and patient and caregiver’s perspectives (patient satisfaction questionnaire-18, Patient Health Engagement Scale, and Caregiver Strain Index). The outcome measures were compared between the groups at baseline and follow-up using an independent sample t test at a significance level of P <0.05. Results: The mean age of patients was 38.00 ± 14.15 years with predominantly males. The telehealth group showed statistically significant (P = 0.042) improvement in total spinal cord independence measure III (SCI-III) score at 6-month follow-up. The SCI Secondary Conditions Scale, which shows the presence of secondary conditions, had a significantly lower score in the S group at the 6-month follow-up (P = 0.01). The World Health Organization Quality of Life domain 1 was higher in the S group at 6-month follow-up (P < 0.05). Of patients, 50% in the telehealth group were satisfied according to the Modified Reintegration to Normal Living Index scale at monthly follow-up compared with 28.6% in the control group (P < 0.05). The Patient Health Engagement Scale showed that 42.9% of patients reached the final stage of the eudaimonic project in the S group at 6-month follow-up compared with only 14.3% in the control group. Conclusions: Telehealth is an effective tool in the continued care of SCI patients post-hospital discharge. Our findings indicate that patients in the telehealth group report improved physical quality of life, enhanced quality of daily living, and decreased occurrence of medical complications. We suggest that multiple centres at the national level should collaborate to further evaluate telehealth. In the future, we should promote telehealth in the rehabilitation of SCI patients.

P0377 Development of Crohn’s Aid mobile application based on AI algorithms to make an early diagnosis of Crohns Disease in TB endemic regions

Abstract Background Intestinal tuberculosis (ITB) and Crohn’s disease (CD) mimic each other clinically, endoscopically, and radiologically and are difficult to differentiate. This is a major barrier to make an early diagnosis of CD in TB endemic areas. We aimed to develop a high accuracy machine learning-based model which could be easily implemented in an innovative way. Methods We retrospectively analyzed data from 1066 patients. After data cleaning, 796 patients (514 CD & 282 ITB) and data from 28 variables were included. A super learner approach using random forest and support vector machine was used for differentiating ITB from CD. Data was divided into 80 percent training and 20 percent testing data sets followed by 10-fold cross validation. The optimal cut-off for the diagnosis was obtained using the Youden index-measure to optimize the balance between sensitivity and specificity and the model was evaluated at multiple thresholds for clinical utility. The best performing model was incorporated into a mobile phone-based application. Prospective validation on 37 patients was carried out with similar accuracy. Results The random forest model achieved a sensitivity, specificity and accuracy of 0.92, 0.83, 0.86 respectively and performed better than the support vector machine model trained with linear and radial basis functions. The random forest model was found to have the best AUROC with a cutoff of 0.4, predicting the diagnosis of CD with a sensitivity of 93%, specificity of 83%, and accuracy of 86%, positive predictive value of 76%, and negative predictive value of 95%. The random forest model was used for creation of the application. The app is being made available on smartphones free of cost for use by any physician. Conclusion Our model differentiated between ITB and CD with high accuracy and has the potential to makean early diagnosis of CD. The free to use mobile application would make implementation of thisalgorithm much easier, allowing for widespread use in clinical practice and helping make moreinformed decisions. More data from multiple centers and different geographical locationswould aid in further improving the model performance. Figure 1: Overall model training approach Figure 2: Model performance and selection of appropriate cut-off

Correlation Between Osteal Kneading and Carotid Artery Atherosclerotic Plaque.

Adjacent bony structures may directly rub the carotid artery during swallowing or head and/neck movement. Long-term repeated stimulation might be considered to be a potential risk factor for carotid atherosclerotic plaque formation, development, and hazard. we defined the process as "Osteal Kneading". A total of 192 consecutive (384 carotid arteries) neck CT angiography (CTAs) and cerebral MRIs performed within 1 month were analysed. The ipsilateral bones (thyroid cartilage and hyoid bone), carotid arteries, and plaque were characterised using the CTA images. Cerebral ischemic lesions were analysed using the MRI images. According to the relationship between the position of the plaque and the bony structures, the vessels were divided into the osteal kneading positive group (OK+) and the osteal kneading negative group (OK-). The effects of osteal kneading on plaque formation, development, and hazard were then explored. 95.7% of the OK+ plaques were located in the common carotid artery. The thyroid cartilage and hyoid bone were significantly closer to the carotid artery wall (p<0.001) and the vascular radius was significantly larger (p<0.001) in the OK+ group. The distance from the thyroid cartilage and hyoid bone to the ipsilateral carotid arteries was negatively correlated with plaque thickness, carotid artery stenosis rate, and calcified plaque occurrence (p<0.05). This pilot study provides preliminary evidence for the hypothesis of "osteal kneading" of the carotid artery, and also suggests that osteal kneading may play some role in carotid artery plaque formation and development. Further research is required to provide more concrete evidence for the hypothesis, such as the inclusion of samples from multiple centres, precise outcome observation, artificial intelligence assisted image analysis, and dynamic evaluation methods.

P0421 Clinical validity of a seroproteomic index of endoscopic activity in pediatric Crohn’s disease

Abstract Background A seroproteomic index (i.e. the serum index) corresponds to 13 protein markers, including CRP, combined as a multianalyte assay with a score range 0-100 where higher score is associated with higher probability of endoscopic activity in adult CD. We aimed to evaluate its performances in pediatric CD. Methods Children with CD were enrolled from two separate cohorts. Cohort 1 enrolled CD from multiple centers in the United States (n=118) and cohort 2, was multinational (n=160). A simple endoscopic score for CD (SESCD) was collected (&amp;lt;3 points indicate endoscopic healing) with a blood specimen. Clinical disease status was measured using the Harvey Bradshaw index (HBI, cohort 1) or pediatric CD activity index (PCDAI, cohort 2) (PCDAI&amp;lt;10 or HBI&amp;lt;5 indicate clinical remission). Fecal calprotectin (FC) was collected in 180 patients from both cohorts. Serum specimens were processed in Prometheus Laboratories, San Diego, CA. Statistical analysis consisted of linear regression analysis, calculation of receiver operating characteristics curves (ROC) with area under the curve (AUC), sensitivity, specificity, Odds ratio (OR). Results In the 278 enrolled children (median age 14 years [IQR: 12-16 years], 46% females), median SESCD was 5 points (IQR: 0-12 points, 63% with endoscopic active disease); median CRP was 2.5 mg/L (IQR: 0.3-8.4 mg/L), median FC was 592 µg/g (IQR: 135-1147 µg/g) and median serum index was 33 (IQR: 17-60 points). AUROC analysis yielded significantly better area under ROC curve (AUROC, 0.87±0.03) for FC than the serum index (0.78±0.04) and CRP (0.77±0.04) (p&amp;lt;0.05) in distinguishing endoscopic active disease from endoscopic healing. There was a 9-fold higher likelihood of endoscopic active disease with serum index above 32 points (OR= 8.95 [95%CI: 5.02,15.96] p&amp;lt;0.001) with 70% sensitivity and 79% specificity (PPV=85%; NPV=61% at 63% pretest) (Table). In multivariate analysis EHI above 32 points (aOR= 3.00 [95%CI: 1.25, 4.76] p=0.043), active clinical disease (aOR= 2.01 [95%CI: 0.85, 4.76] p=0.114), FC&amp;gt;250 µg/g (aOR= 5.61 [95%CI: 2.4,13.09] p&amp;lt;0.01) and CRP above 3 mg/L (aOR=2.09 [95%CI: 0.9-4.85])(p=0.08) associated with active endoscopic disease) (Figure). In the subset with clinical remission (n=158), or CRP below 3 mg/L (n=136), or FC levels below 250 µg/g (n=116), EHI above 32 points was still associated with higher likelihood of endoscopic disease (OR=5.4 [95%CI: 2.6,11.1], OR= 6.8 [95%CI: 2.9,15.8], and OR=4.0 [95%CI: 1.3,12.8], respectively) (p&amp;lt;0.05) (Figure). Conclusion These data support the association of the serum index with endoscopic outcome in pediatric CD patients.

DOP089 Shaping the Future of IBD Diagnostics: A Multicentric AI-Driven Capsule Endoscopy Study

Abstract Background Capsule endoscopy (CE), is a minimally invasive diagnostic tool critical for evaluating small bowel conditions, particularly Crohn's disease. Detecting ulcers and erosions is essential for assessing disease activity, guiding treatment decisions, and monitoring therapeutic responses. While CE revolutionized lesion detection, its manual interpretation is time-consuming and prone to variability. Artificial intelligence (AI) tools, including convolutional neural networks (CNNs), have emerged as transformative solutions, improving detection accuracy, reducing variability, and enabling faster, more reliable lesion identification. This study aimed to develop and validate an AI model capable of detecting and differentiating small bowel ulcers and erosions across multiple CE devices and clinical centers. Methods A multicenter, prospective study was conducted from January 2021 to April 2024, involving centers in Europe (Portugal and Spain) and the USA. The study utilized two CE devices (PillCamSB3 and Olympus EC-10) and analyzed 137 anonymized CE exams. The AI-assisted readings generated by the deep learning model were compared with standard-of-care (SoC) readings, using expert board consensus as the gold standard. Comparisons between SoC reading and AI-assisted reading were assessd by standard metrics (sensitivity, specificity, PPV, NPV, AUROC). Results Ulcers and erosions were detected in 56 patients (40.9%) during expert board review. SoC had 60.7% sensitivity, 98.8% specificity, 97.1% PPV, 78.4% NPV and 83.2% accuracy for detection of ulcers and erosions. AI-assisted reading detected ulcers and erosions with 94.6% sensitivity, 80.2% specificity, 76.8% PPV, 95-6% NPV and 86.1% accuracy. The AI-assisted model diagnosis was non-inferior (p&amp;lt;0.001) and superior (p&amp;lt;0.001) to conventional SoC diagnosis for detection of ulcers and erosions. The AI model identified 68 lesions compared to 35 detected by SoC demonstrating consistent performance across different CE devices and centers. These results were accompanied by a mean reading time of 239 seconds (SD 138 seconds) per exam with AI-assisted reading. Conclusion The AI-assisted model achieved superior diagnostic performance compared to SoC, with higher sensitivity (94.6% vs. 60.7%) and accuracy (86.1% vs. 83.2%), with a significant decrease in reading time.This landmark study represents the first interoperable, multicenter validation of an AI model capable of detecting and differentiating ulcers and erosions using data from multiple CE devices. By ensuring compatibility and high diagnostic performance across diverse settings, this technology is set to transform endoscopic practice and clinical management in IBD. References Piccirelli, S. et al. New Generation Express View: An Artificial Intelligence Software Effectively Reduces Capsule Endoscopy Reading Times. Diagnostics (Basel) 12 (2022). https://doi.org:10.3390/diagnostics12081783 Mascarenhas, M., Afonso, J., Andrade, P., Cardoso, H. &amp; Macedo, G. Artificial intelligence and capsule endoscopy: unravelling the future. Ann Gastroenterol 34, 300-309 (2021). https://doi.org:10.20524/aog.2021.0606 Cardoso P, Mascarenhas M, Afonso J, et al. Deep learning and minimally invasive inflammatory activity assessment: a proof-of-concept study for development and score correlation of a panendoscopy convolutional network. Therap Adv Gastroenterol. 2024;17:17562848241251569. Published 2024 May 27. doi:10.1177/17562848241251569 Afonso, J. et al. Automated detection of ulcers and erosions in capsule endoscopy images using a convolutional neural network. Med Biol Eng Comput 60, 719-725 (2022). https://doi.org:10.1007/s11517-021-02486-9

Dynamic Kinetic Resolution-Based Asymmetric Transfer Hydrogenation of Racemic 2-Substituted Quinolines.

The synthesis of chiral tetrahydroquinolines (THQs) has garnered significant interest from medicinal chemists due to their frequent presence as pharmacophores in bioactive compounds. While existing synthetic methods have primarily focused on THQs with single or multiple endocyclic chiral centers, the selective construction of THQs with both endo- and exo-cyclic chiral centers remains a significant challenge that requires further development. This study introduces a dynamic kinetic resolution (DKR)-based transfer hydrogenation of racemic 2-substituted quinolines, which yields structurally novel chiral THQs with consecutive endo- and exo-cyclic chiral centers in excellent yields and stereoselectivities (59 examples, with generally >20:1 dr and >90% ee, up to three consecutive stereocenters). Our approach offers a mechanistically novel method for the asymmetric transformation of electron-deficient aromatic N-heterocycles and presents an innovative way to expand the chiral N-heterocycle chemical space for medicinal chemistry.

P0579 Predictive Modeling for Colectomy Risk in Patients with Ulcerative Colitis and Primary Sclerosing Cholangitis: A Brazilian Multicentric Study Utilizing Machine Learning.

Abstract Background Ulcerative Colitis (UC) and Primary Sclerosing Cholangitis (PSC) are interrelated chronic inflammatory diseases. Patients with both conditions are at an elevated risk of colectomy, predominantly due to a higher incidence of dysplasia and colorectal cancer. Identify which patients are at higher risk of colectomy can be crucial to personalize their management. The primary objective was to develop a predictive model that can accurately forecast colectomy risk. Methods A cross-sectional study was conducted with patients from multiple tertiary centers across Brazil. We employed logistic regression and Random Forest (RF) models to analyze the data and identify significant predictors for colectomy. Data extraction was performed using Excel, and statistical analyses were conducted using R Studio. Results The study population comprised 83 individuals diagnosed with both UC and PSC. The mean age of the patients was 45 years (range 6 to 67), with 56 patients being male (67.5%). The mean age at diagnosis of UC and PSC was 32 years (range 18 to 77) and 36 years (range 8 to 72), respectively. Among patients with UC, the majority (74/83) had extensive colitis (89.2%), and 49.4% (41/83) had used at least one biological therapy. Only 13 patients (15.7%) required colectomy. Liver cirrhosis occurred in 15 cases (18.1%), and 11 patients (13.3%) required liver transplantation. Twelve patients (14.5%) had dysplasia or colorectal cancer. Our analysis identified several key predictors for colectomy. In the linear regression analysis, the duration of UC greater than 11 years significantly increased the risk of colectomy (OR=2.15, p=0.04). The presence of hepatic complications was associated with a tendency towards an increased risk of colectomy (OR=1.79, p=0.059). In the RF model, the presence of hepatic complications, and duration of UC greater than 11 years were significant predictors for colectomy. Protective factors identified by the RF model included use of biologic therapy, clinical and endoscopic remission. The RF model demonstrated an accuracy of 88.2%, outperforming the linear regression model and providing superior accuracy and robustness in predicting colectomy risk within the defined period. Conclusion Our study developed a predictive model using the RF algorithm, which demonstrated superior performance over linear regression in identifying high-risk patients for colectomy. This model can serve as a valuable tool in clinical practice, aiding in early intervention and tailored management strategies to mitigate severe outcomes in patients with UC and PSC. The use of RF provides a more reliable prediction, emphasizing the importance of machine learning in clinical decision-making. References Kim YS, Hurley EH, Park Y, Ko S. Treatment of primary sclerosing cholangitis combined with inflammatory bowel disease. Intest Res. 2023 Sep 1. doi:10.5217/ir.2023.00039. Epub ahead of print. PMID: 37519211. Kim YS, Hurley EH, Park Y, Ko S. Primary sclerosing cholangitis (PSC) and inflammatory bowel disease (IBD): a condition exemplifying the crosstalk of the gut-liver axis. Exp Mol Med. 2023 Jul;55(7):1380-7. doi:10.1038/s12276-023-01042-9. PMID: 37464092; PMCID: PMC10394020. Greener JG, Kandathil SM, Moffat L, Jones DT. A guide to machine learning for biologists. Nat Rev Mol Cell Biol. 2022 Jan;23(1):40-55.

Comparative analysis of deep learning and radiomic signatures for overall survival prediction in recurrent high-grade glioma treated with immunotherapy

BackgroundRadiomic analysis of quantitative features extracted from segmented medical images can be used for predictive modeling of prognosis in brain tumor patients. Manual segmentation of the tumor components is time-consuming and poses significant reproducibility issues. We compare the prediction of overall survival (OS) in recurrent high-grade glioma(HGG) patients undergoing immunotherapy, using deep learning (DL) classification networks along with radiomic signatures derived from manual and convolutional neural networks (CNN) automated segmentation.Materials and methodsWe retrospectively retrieved 154 cases of recurrent HGG from multiple centers. Tumor segmentation was performed by expert radiologists and a convolutional neural network (CNN). From the segmented tumors, 2553 radiomic features were extracted for each case. A robust feature subset was selected using intraclass correlation coefficient analysis between manual and automated segmentations. The data was divided into a 9:1 ratio and validated through ten-fold cross-validation and tested on a rotating test set. Features selection was done by the Kruskal–Wallis test. The Radiomics-based OS predictions, generated using Support Vector Machine (SVM), were compared between the two segmentation approaches and against OS prediction by the CNN model adapted for classification. Model efficacy was evaluated using the area under the receiver operating characteristic curve (AUC).ResultsThe clinical model AUC for OS prediction was 0.640 ± 0.013 (mean ± 95% confidence interval) in the training set and 0.610 ± 0.131 in the test set. The radiomics prediction of OS based on manual segmentation outperformed automatic segmentation (AUC of 0.662 ± 0.122 vs. 0.471 ± 0.086, respectively) in the test set. Robust features improved the performance of manual segmentation to AUC of 0.700 ± 0.102, of automated segmentation to 0.554 ± 0.085. The CNN prognosis model demonstrated promising results, with an average AUC of 0.755 ± 0.071 for training sets and 0.700 ± 0.101 for the test set.ConclusionManual segmentation-derived radiomic features outperformed automated segmentation-derived features for predicting OS in recurrent high-grade glioma patients undergoing immunotherapy. The end-to-end CNN prognosis model performed similarly to radiomics modeling using manual-segmentation-derived features without the need for segmentation. The potential time-saving must be weighed against the lower interpretability of end-to-end black box modeling.

The first multiple center prospective study of rhFSH CTP in patients undergoing assisted reproductive technology in China

We assessed the safety and efficacy of rhFSH-CTP, a novel long-acting FSH agent, in controlled ovarian hyperstimulation for patients undergoing ART. A multi-center, open-label, randomized, positive-control, non-inferiority clinical trial was conducted. The study consisted of a phase III randomized design, with a 1:1 ratio favoring the rhFSH-CTP group over the control group. Eligible patients in the rhFSH-CTP group received a single dose of rhFSH-CTP at 100–150 µg for the first 7d of stimulation following a gonadotropin-releasing hormone antagonist protocol. In total, 142 and 141 patients received rhFSH-CTP and rhFSH, respectively. At a confidence interval of 95%, the difference in the number of oocytes (1.13–4.22, 2.67) suggested that rhFSH-CTP was not inferior to rhFSH. Additionally, the top-quality embryos, implantation rates, and pregnancy outcomes were similar between the two groups (P > 0.05). In the rhFSH-CTP group, no cases of severe OHSS were observed, which was a significant improvement compared to the 1.4% incidence in the rhFSH group. With regard to another safety endpoint, no patients tested positive for adenosine deaminase (ADA) in the rhFSH-CTP group. The results demonstrated that the product had a comparable safety profile to pregnancy outcomes and newborn information in the control group, indicating its suitability for use.

Deep Learning-Based Glioma Segmentation of 2D Intraoperative Ultrasound Images: A Multicenter Study Using the Brain Tumor Intraoperative Ultrasound Database (BraTioUS).

Background: Intraoperative ultrasound (ioUS) provides real-time imaging during neurosurgical procedures, with advantages such as portability and cost-effectiveness. Accurate tumor segmentation has the potential to substantially enhance the interpretability of ioUS images; however, its implementation is limited by persistent challenges, including noise, artifacts, and anatomical variability. This study aims to develop a convolutional neural network (CNN) model for glioma segmentation in ioUS images via a multicenter dataset. Methods: We retrospectively collected data from the BraTioUS and ReMIND datasets, including histologically confirmed gliomas with high-quality B-mode images. For each patient, the tumor was manually segmented on the 2D slice with its largest diameter. A CNN was trained using the nnU-Net framework. The dataset was stratified by center and divided into training (70%) and testing (30%) subsets, with external validation performed on two independent cohorts: the RESECT-SEG database and the Imperial College NHS Trust London cohort. Performance was evaluated using metrics such as the Dice similarity coefficient (DSC), average symmetric surface distance (ASSD), and 95th percentile Hausdorff distance (HD95). Results: The training cohort consisted of 197 subjects, 56 of whom were in the hold-out testing set and 53 in the external validation cohort. In the hold-out testing set, the model achieved a median DSC of 0.90, ASSD of 8.51, and HD95 of 29.08. On external validation, the model achieved a DSC of 0.65, ASSD of 14.14, and HD95 of 44.02 on the RESECT-SEG database and a DSC of 0.93, ASSD of 8.58, and HD95 of 28.81 on the Imperial-NHS cohort. Conclusions: This study supports the feasibility of CNN-based glioma segmentation in ioUS across multiple centers. Future work should enhance segmentation detail and explore real-time clinical implementation, potentially expanding ioUS's role in neurosurgical resection.

Cu-Catalyzed Diastereo- and Enantioselective Synthesis of Borylated Cyclopropanes with Three Contiguous Stereocenters.

Direct synthesis of enantioenriched scaffolds with multiple adjacent stereocenters remains an important yet challenging task. Herein, we describe a highly diastereo- and enantioselective Cu-catalyzed alkylboration of cyclopropenes, with less reactive alkyl iodides as electrophiles, for the efficient synthesis of tetra-substituted borylated cyclopropanes bearing three consecutive stereocenters. This protocol features mild conditions, a broad substrate scope, and good functional group tolerance, affording an array of chiral borylated cyclopropanes in good to high yields with excellent diastereo- and enantioselectivities. Detailed mechanistic experiments and kinetic studies were conducted to elucidate the reaction pathway and the rate-determining step of the reaction. DFT calculations revealed that the π···π stacking interaction between the phenyl groups on the substrate and the phosphorus ligand, along with the smaller distortion in the CuL-Bpin part, contributed to the high diastereo- and enantioselectivities. The synthetic utility of the protocol was showcased by the facile synthesis of some valuable chiral cyclopropanes with multiple chiral centers.

Progress and Outcomes of Intraoperative Nerve Monitoring During Thyroidectomy

Intraoperative nerve monitoring (IONM) is not considered standard of care during thyroidectomy, and guidelines are vague about its use in the absence of strong evidence of superiority over visualization of the recurrent laryngeal nerve (RLN) alone. To characterize patterns of IONM use during thyroidectomy in the US and evaluate the association of IONM with postoperative outcomes. This cohort study used the National Surgical Quality Improvement Program (NSQIP) thyroidectomy data from January 1, 2016, to December 31, 2022. A nationally representative sample included adult patients without poorly differentiated thyroid cancer who underwent thyroidectomy at multiple centers. Data were collected from the time of surgery until 30 postoperative days through January 31, 2023. IONM during thyroidectomy. Prevalence of IONM during thyroidectomy and postoperative outcomes including RLN injury, hypocalcemia, and neck hematoma. A total of 44 265 patients undergoing thyroidectomy were included (77.2% female; mean [SD] age, 51.8 [15.2] years), with 30 633 (69.2%) using IONM. Common indications for surgery in the cohort were goiter (35.3%) and a single nodule or neoplasm (39.2%). The prevalence of IONM increased from 62.5% in 2016 to 75.9% in 2022. RLN injury occurred in 6.0% of cases. On propensity score-matched analyses, IONM was associated with decreased odds of RLN injury overall (adjusted odds ratio [AOR], 0.98; 95% CI, 0.97-0.99) and decreased odds among patients with differentiated thyroid cancer (AOR, 0.96; 95% CI, 0.94-0.99). IONM was not associated with postoperative hypocalcemia (AOR, 0.99; 95% CI, 0.99-1.00) or neck hematoma (AOR, 1.00; 95% CI, 0.99-1.00). This cohort study found that IONM during thyroidectomy has become routine, and use has increased over the last 7 years. IONM was associated with a slightly decreased odds of RLN injury, but no difference in hypocalcemia or neck hematoma. Although IONM use is widespread, further research is needed to identify patients who would benefit the most from this technology.

Peripheral neuropathy in neuronal intranuclear inclusion disease: a clinical and electrophysiological cross-sectional study.

Neuronal intranuclear inclusion disease (NIID) is a multifaceted disorder impacting both the central and peripheral nervous systems. This study aims to investigate the clinical and electrophysiological characteristics of peripheral neuropathy in patients with NIID. In this cross-sectional study, patients diagnosed with NIID were prospectively recruited from multiple centers across China between October 2017 and May 2024. Comprehensive neurological examinations, brain magnetic resonance imaging, and NOTCH2NLC gene analysis were performed. All participants underwent electrophysiological evaluations, which encompassed nerve conduction studies, F-wave studies, and needle electromyography. This analysis included a total of 78 patients diagnosed with NIID, with a mean age of 61.0 ± 9.9years, of whom 60.2% were female. Among these patients, 85.9% exhibited peripheral neuropathy. Specifically, 19 patients (24.4%) presented with symptomatic peripheral neuropathy, with 17 of these individuals demonstrating sensorimotor polyneuropathy. Additionally, 48 patients (61.5%) exhibited subclinical peripheral nerve damage. Segmental motor conduction studies indicated that both distal and proximal nerve segments were uniformly affected. F-wave latency prolongation was observed in 84.6% of tibial nerves, demonstrating high sensitivity (66/67) and specificity (11/11) for detecting peripheral neuropathy in NIID. The severity of peripheral neuropathy was stratified into four levels, with approximately two-thirds of cases presenting with pure demyelination and one-third displaying a combination of demyelination and axonal degeneration. Severe axonal damage was exclusively identified in the neuromuscular disease-dominant subtype of NIID. Importantly, male patients exhibited more pronounced peripheral nerve damage compared to female patients. This study identified a significant prevalence of peripheral neuropathy in NIID, exhibiting a spectrum of severity ranging from mild demyelination to severe axonal damage. Prolongation of F-wave latency emerged as a sensitive marker for the detection of peripheral nerve impairment. Furthermore, the findings underscore the potential influence of gender in the pathophysiology of NIID, warranting further investigation.