Pathology reports represent a primary source of information for cancer registries. Hospitals routinely process high volumes of free-text reports, a valuable source of information regarding cancer diagnosis for improving clinical care and supporting research. Information extraction and coding of textual unstructured data is typically a manual, labour-intensive process. There is a need to develop automated approaches to extract meaningful information from such texts in a reliable and accurate way. In this scenario, Natural Language Processing (NLP) algorithms offer a unique opportunity to automatically encode the unstructured reports into structured data, thus representing a potential powerful alternative to expensive manual processing. However, notwithstanding the increasing interest in this area, there is still limited availability of NLP approaches for pathology reports in languages other than English, including Italian, to date. The aim of our work was to develop an automated algorithm based on NLP techniques, able to identify and classify the morphological content of pathology reports in the Italian language with micro-averaged performance scores higher than 95%. Specifically, a novel, domain-specific classifier that uses linguistic rules was developed and tested on 27,239 pathology reports from a single Italian oncological centre, following the International Classification of Diseases for Oncology morphology classification standard (ICD-O-M). The proposed classification algorithm achieved successful results with a micro-F1 score of 98.14% on 9594 pathology reports in the test dataset. This algorithm relies on rules defined on data from a single hospital that is specifically dedicated to cancer, but it is based on general processing steps which can be applied to different datasets. Further research will be important to demonstrate the generalizability of the proposed approach on a larger corpus from different hospitals.

Recently, many studies used the Charlson comorbidity index (CCI) to predict the postoperative mortality rate of elderly patients with hip fractures. BAY 87-2243 chemical structure However, as a predictor, CCI did not include other preoperative risk factors, resulting in decreasing its predictive value. Therefore, we performed a study to focus on two questions as follows (1) What is the one-year mortality rate of elderly Chinese patients who underwent surgery for hip fracture? (2) Could risk-adjusted CCI act as a new predictor to predict the one-year mortality rate?

The risk-adjusted CCI could exhibit a good predictive value for one-year mortality of elderly Chinese patients who underwent surgery for hip fracture.

This retrospective observational cohort study is based on data collected from July 2012 to April 2016. Patients aged 65 years and older who underwent hip fracture surgery were included. The clinical patient data were obtained, including gender, age, BMI, types of fracture, smoking, injury side, time from injury to surgery, aue of the risk-adjusted CCI was highest among these risk predictors, whose AUC value was 0.816.

The risk-adjusted Charlson comorbidity index could be used as a guide to predict one-year mortality rate in elderly Chinese patients after the surgical treatment of hip fractures.

III; cohort comparative study.

III; cohort comparative study.

Fetal bilirubin is routinely measured at our center when taking a pretransfusion blood sample at intrauterine transfusions in hemolytic disease of the fetus and newborn. However, the clinical value of fetal bilirubin assessment is not well known, and the information is rarely used. We speculated that there could be a role for this measurement in predicting the need for neonatal exchange transfusion.

This study aimed to evaluate the predictive value of fetal bilirubin for exchange transfusions in severe hemolytic disease of the fetus and newborn.

A total of 186 infants with Rh alloantibody-mediated hemolytic disease of the fetus and newborn treated with one or more intrauterine transfusions at the Leiden University Medical Center between January 2006 and June 2020 were included in this observational study. Antenatal and postnatal factors were compared between infants with and without exchange transfusion treatments. The primary outcome was the fetal bilirubin levels before the last intrauterine transfusion in relation to the need for exchange transfusion.

In a multivariate logistic regression analysis, the fetal bilirubin level before the last intrauterine transfusions (odds ratio, 1.32; 95% confidence interval, 1.09-1.61 per 1 mg/dL) and the total number of intrauterine transfusions (odds ratio, 0.63; 95% confidence interval, 0.44-0.91 per intrauterine transfusion) were independently associated with the need for exchange transfusion. The area under the curve was determined at 0.71. A Youden index was calculated at 0.43. The corresponding fetal bilirubin level was 5 mg/dL and had a sensitivity of 79% and a specificity of 64%.

A high fetal bilirubin level before the last intrauterine transfusion was associated with a high likelihood of neonatal exchange transfusion.

A high fetal bilirubin level before the last intrauterine transfusion was associated with a high likelihood of neonatal exchange transfusion.The photodamage induced by PDT usually occurs at the site where the photosensitizers accumulate in the tumor cells, thus the modulation of intrinsic apoptosis by mitochondria-targeting PDT drugs might be a promising way to enhance the therapeutic efficacy of PDT drugs against tumor cells. Novel triphenylphosphonium-functionalized nanocomposites employed as carriers of a photoactive platinum diimine complex have been fabricated and characterized. Upon irradiation, the IC50 value of photosensitizer-loaded triphenylphosphonium-functionalized nanocomposites was found to be 17.4 or 14.4 times lower than that of the photosensitizer studied alone against HCT116 cells or A549 cells, respectively. The results suggested that the triphenylphosphonium- functionalized nanocomposites as drug delivery vehicles could significantly enhance the photodynamic efficacy of the photosensitizer.