Utility of a large language model for extraction of clinical findings from healthcare data following lung ablation: A feasibility study.

To assess the feasibility of utilizing a large language model (LLM) in extracting clinically relevant information from healthcare data in patients who have undergone lung microwave tumor ablation (MWA). In this single-center retrospective study, radiological reports and clinic notes of 20 patients were extracted, up to 12 months post-treatment. Utilizing a LLM (GPT 3.

Extraction and classification of structured data from unstructured hepatobiliary pathology reports using large language models: a feasibility study compared with rules-based natural language processing.

Aims: Structured reporting in pathology is not universally adopted and extracting elements essential to research often requires expensive and time-intensive manual curation. The accuracy and feasibility of using large language models (LLMs) to extract essential pathology elements, for cancer research is examined here.

Methods: Retrospective study of patients who underwent pathology sampling for suspected hepatocellular carcinoma and underwent Ytrrium-90 embolisation.

A multimodal neural network with gradient blending improves predictions of survival and metastasis in sarcoma.

The objective of this study is to develop a multimodal neural network (MMNN) model that analyzes clinical variables and MRI images of a soft tissue sarcoma (STS) patient, to predict overall survival and risk of distant metastases. We compare the performance of this MMNN to models based on clinical variables alone, radiomics models, and an unimodal neural network. We include patients aged 18 or older with biopsy-proven STS who underwent primary resection between January 1st, 2005, and December 31st, 2020 with complete outcome data and a pre-treatment MRI with both a T1 post-contrast sequence and a T2 fat-sat sequence available.

Longitudinal clinical data improve survival prediction after hematopoietic cell transplantation using machine learning.

Serial prognostic evaluation after allogeneic hematopoietic cell transplantation (allo-HCT) might help identify patients at high risk of lethal organ dysfunction. Current prediction algorithms based on models that do not incorporate changes to patients' clinical condition after allo-HCT have limited predictive ability. We developed and validated a robust risk-prediction algorithm to predict short- and long-term survival after allo-HCT in pediatric patients that includes baseline biological variables and changes in the patients' clinical status after allo-HCT.

Application of Machine Learning to the Prediction of Cancer-Associated Venous Thromboembolism.

Venous thromboembolism (VTE) is a common and impactful complication of cancer. Several clinical prediction rules have been devised to estimate the risk of a thrombotic event in this patient population, however they are associated with limitations. We aimed to develop a predictive model of cancer-associated VTE using machine learning as a means to better integrate all available data, improve prediction accuracy and allow applicability regardless of timing for systemic therapy administration.

Fungal Systematics and Evolution: FUSE 5.

Thirteen new species are formally described: from Pakistan, from India, on from Iran, from China, on species of , , and (Coleoptera, Carabidae) from Nicaragua and Panama, on (Hemiptera, Veliidae) from Brazil, on (Blattodea, Termitidae) from the DR Congo, from Slovenia, from Peru, from China, on from Italy, from , on subsp. from Pakistan. The following new records are reported: on from India; on apple and quince fruits from Iran; from Turkey; and on from Italy; causing tip blight of '' from India; from Madeira, Portugal, new for Macaronesia and Africa; , , and from Russia; on from India; on from Italy; on from Austria; from Turkey; from Wisconsin, USA; and from Turkey.

Amorphous Carbon Dots and their Remarkable Ability to Detect 2,4,6-Trinitrophenol.

Apparently mundane, amorphous nanostructures of carbon have optical properties which are as exotic as their crystalline counterparts. In this work we demonstrate a simple and inexpensive mechano-chemical method to prepare bulk quantities of self-passivated, amorphous carbon dots. Like the graphene quantum dots, the water soluble, amorphous carbon dots too, exhibit excitation-dependent photoluminescence with very high quantum yield (~40%).