Fecal pancreatic elastase-1 levels in older individuals without known gastrointestinal diseases or diabetes mellitus

Background - Structural changes occur in the pancreas as a part of the natural aging process. With aging, also the incidence of maldigestive symptoms and malnutrition increases, raising the possibility that these might be caused at least in part by inadequate pancreatic enzyme secretion due to degenerative processes and damage of the gland. Fecal elastase-1 is a good marker of pancreatic exocrine secretion. The aim of this study was to investigate the fecal elastase-1 levels among over 60 years old Finnish and Polish healthy individuals without any special diet, known gastrointestinal disease, surgery or diabetes mellitus. Methods - A total of 159 patients participated in this cross-sectional study. 106 older individuals (aged 60-92 years) were recruited from outpatient clinics and elderly homes. They were divided to three age groups: 60-69 years old (n = 31); 70-79 years old (n = 38) and over 80 years old (n = 37). 53 young subjects (20-28 years old) were investigated as controls. Inclusion criteria were age over 60 years, normal status and competence. Exclusion criteria were any special diet, diabetes mellitus, any known gastrointestinal disease or prior gastrointestinal surgery. Fecal elastase-1 concentration was measured from stool samples with an ELISA that uses two monoclonal antibodies against different epitopes of human elastase-1. Results - Fecal elastase-1 concentrations correlated negatively with age (Pearson r = -0,3531, P < 0.001) and were significantly lower among subjects over 70 years old compared to controls (controls vs. 70-79 years old and controls vs. over 80 years old, both P < 0.001). Among the over 60 years old subjects, the fecal elastase-1 concentrations were below the cut off level of 200 μg/g in 23 of 106 (21.7%) individuals [mean 112 (86-138) μg/g] indicating pancreatic exocrine insufficiency. Of those, 9 subjects had fecal elastase-1 level below 100 μg/g as a marker of severe pancreatic insufficiency. Conclusion - In our study one fifth of healthy older individuals without any gastrointestinal disorder, surgery or diabetes mellitus suffer from pancreatic exocrine insufficiency and might benefit from enzyme supplementation therapy.peerReviewe

Biflorin: an o-naphthoquinone of clinical significance

Biflorin is an o-naphthoquinone with proven cytotoxic effects on tumor cells showing antimicrobial, antitumor and antimutagenic activities. Biflorin is an isolated compound taken from the roots of the plant Capraria biflora L. (Schrophulariaceae), indigenous of the West Indies and South America, which is located in temperate or tropical areas. This compound has shown to be strongly active against grampositive and alcohol-acid-resistant bacteria. It has been efficient in inhibiting the proliferation tumor cell lines CEM, HL-60, B16, HCT-8 and MCF-7. Recently, SK-Br3 cell line was treated with biflorin showing important cytotoxic effects. In this article, information related to the first structural characterization studies are presented, as well as the latest reports concerning the biological activity of this molecule

JPN Guidelines for the management of acute pancreatitis: epidemiology, etiology, natural history, and outcome predictors in acute pancreatitis

Acute pancreatitis is a common disease with an annual incidence of between 5 and 80 people per 100 000 of the population. The two major etiological factors responsible for acute pancreatitis are alcohol and cholelithiasis (gallstones). The proportion of patients with pancreatitis caused by alcohol or gallstones varies markedly in different countries and regions. The incidence of acute alcoholic pancreatitis is considered to be associated with high alcohol consumption. Although the incidence of alcoholic pancreatitis is much higher in men than in women, there is no difference in sexes in the risk involved after adjusting for alcohol intake. Other risk factors include endoscopic retrograde cholangiopancreatography, surgery, therapeutic drugs, HIV infection, hyperlipidemia, and biliary tract anomalies. Idiopathic acute pancreatitis is defined as acute pancreatitis in which the etiological factor cannot be specified. However, several studies have suggested that this entity includes cases caused by other specific disorders such as microlithiasis. Acute pancreatitis is a potentially fatal disease with an overall mortality of 2.1%–7.8%. The outcome of acute pancreatitis is determined by two factors that reflect the severity of the illness: organ failure and pancreatic necrosis. About half of the deaths in patients with acute pancreatitis occur within the first 1–2 weeks and are mainly attributable to multiple organ dysfunction syndrome (MODS). Depending on patient selection, necrotizing pancreatitis develops in approximately 10%–20% of patients and the mortality is high, ranging from 14% to 25% of these patients. Infected pancreatic necrosis develops in 30%–40% of patients with necrotizing pancreatitis and the incidence of MODS in such patients is high. The recurrence rate of acute pancreatitis is relatively high: almost half the patients with acute alcoholic pancreatitis experience a recurrence. When the gallstones are not treated, the risk of recurrence in gallstone pancreatitis ranges from 32% to 61%. After recovering from acute pancreatitis, about one-third to one-half of acute pancreatitis patients develop functional disorders, such as diabetes mellitus and fatty stool; the incidence of chronic pancreatitis after acute pancreatitis ranges from 3% to 13%. Nevertheless, many reports have shown that most patients who recover from acute pancreatitis regain good general health and return to their usual daily routine. Some authors have emphasized that endocrine function disorders are a common complication after severe acute pancreatitis has been treated by pancreatic resection

Breast Lesion Classification with Multiparametric Breast MRI Using Radiomics and Machine Learning: A Comparison with Radiologists' Performance.

This multicenter retrospective study compared the performance of radiomics analysis coupled with machine learning (ML) with that of radiologists for the classification of breast tumors. A total of 93 consecutive women (mean age: 49 ± 12 years) with 104 histopathologically verified enhancing lesions (mean size: 22.8 ± 15.1 mm), classified as suspicious on multiparametric breast MRIs were included. Two experienced breast radiologists assessed all of the lesions, assigning a Breast Imaging Reporting and Database System (BI-RADS) suspicion category, providing a diffusion-weighted imaging (DWI) score based on lesion signal intensity, and determining the apparent diffusion coefficient (ADC). Ten predictive models for breast lesion discrimination were generated using radiomic features extracted from the multiparametric MRI. The area under the receiver operating curve (AUC) and the accuracy were compared using McNemar's test. Multiparametric radiomics with DWI score and BI-RADS (accuracy = 88.5%; AUC = 0.93) and multiparametric radiomics with ADC values and BI-RADS (accuracy= 88.5%; AUC = 0.96) models showed significant improvements in diagnostic accuracy compared to the multiparametric radiomics (DWI + DCE data) model (p = 0.01 and p = 0.02, respectively), but performed similarly compared to the multiparametric assessment by radiologists (accuracy = 85.6%; AUC = 0.03; p = 0.39). In conclusion, radiomics analysis coupled with the ML of multiparametric MRI could assist in breast lesion discrimination, especially for less experienced readers of breast MRIs

Radiomics and Machine Learning with Multiparametric Breast MRI for Improved Diagnostic Accuracy in Breast Cancer Diagnosis.

The purpose of this multicenter retrospective study was to evaluate radiomics analysis coupled with machine learning (ML) of dynamic contrast-enhanced (DCE) and diffusion-weighted imaging (DWI) radiomics models separately and combined as multiparametric MRI for improved breast cancer detection. Consecutive patients (Memorial Sloan Kettering Cancer Center, January 2018-March 2020; Medical University Vienna, from January 2011-August 2014) with a suspicious enhancing breast tumor on breast MRI categorized as BI-RADS 4 and who subsequently underwent image-guided biopsy were included. In 93 patients (mean age: 49 years ± 12 years; 100% women), there were 104 lesions (mean size: 22.8 mm; range: 7-99 mm), 46 malignant and 58 benign. Radiomics features were calculated. Subsequently, the five most significant features were fitted into multivariable modeling to produce a robust ML model for discriminating between benign and malignant lesions. A medium Gaussian support vector machine (SVM) model with five-fold cross validation was developed for each modality. A model based on DWI-extracted features achieved an AUC of 0.79 (95% CI: 0.70-0.88), whereas a model based on DCE-extracted features yielded an AUC of 0.83 (95% CI: 0.75-0.91). A multiparametric radiomics model combining DCE- and DWI-extracted features showed the best AUC (0.85; 95% CI: 0.77-0.92) and diagnostic accuracy (81.7%; 95% CI: 73.0-88.6). In conclusion, radiomics analysis coupled with ML of multiparametric MRI allows an improved evaluation of suspicious enhancing breast tumors recommended for biopsy on clinical breast MRI, facilitating accurate breast cancer diagnosis while reducing unnecessary benign breast biopsies