Automatic Generation of Interpretable Lung Cancer Scoring Models from Chest X-Ray Images

Lung cancer is the leading cause of cancer death worldwide with early detection being the key to a positive patient prognosis. Although a multitude of studies have demonstrated that machine learning, and particularly deep learning, techniques are effective at automatically diagnosing lung cancer, these techniques have yet to be clinically approved and adopted by the medical community. Most research in this field is focused on the narrow task of nodule detection to provide an artificial radiological second reading. We instead focus on extracting, from chest X-ray images, a wider range of pathologies associated with lung cancer using a computer vision model trained on a large dataset. We then find the set of best fit decision trees against an independent, smaller dataset for which lung cancer malignancy metadata is provided. For this small inferencing dataset, our best model achieves sensitivity and specificity of 85% and 75% respectively with a positive predictive value of 85% which is comparable to the performance of human radiologists. Furthermore, the decision trees created by this method may be considered as a starting point for refinement by medical experts into clinically usable multi-variate lung cancer scoring and diagnostic models

Antimicrobial, antioxidant, and cytotoxic activities of Bixa orellana Linn

Bixa orellana Linn., commonly known as "lipstick plant", is used in folk medicines to treat infections of microbial origin as well as coloring agents in food stuffs in the LDCs like Bangladesh. The minimum inhibitory concentration (MIC) and minimum bactericidal concentration (MBC) of the warm water extract of leaves of B. orellana were evaluated against 25 multidrug resistant (MDR) clinical isolates and 6 food-borne pathogens using the micro-dilution broth method modified to comply with the NCCLS standards. The total phenolic content and antioxidant capacity of warm water, ethanol, and methanol extracts of the seeds and leaves of B. orellana were also evaluated. The brine shrimp lethality assay was conducted to assess the toxicity of the extracts. Except Pseudomonas spp., all the MDR isolates and food-borne pathogens tested were susceptible to the warm water extract of the leaves. The MIC and MBC ranged between 8-256 μg/mL and 16 - 256 μg/mL, respectively. Among the test organisms, Streptococcus spp. and Shigella dysenteriae-1 MJ-84 showed highest susceptibility while Escherichia coli exhibited moderate susceptibility to warm water extract of the leaves. The highest total phenolic content (99.99 mg of GAE/g of extractives) and antioxidant capacity (IC50 value 13 μg/mL) were observed in ethanolic extract of seeds of B. orellana, whereas the IC50 of the reference standard BHT (tert-butyl-1-hydroxytoluene) was 59.2 μg/mL. On the other hand, in the brine shrimp lethality bioassay the methanolic extract of the seeds of B. orellana demonstrated strong cytotoxic activity with IC50 value of 19.3 μg/mL. These results suggest that the extracts of B. orellana possess bioactive compoundsColegio de Farmacéuticos de la Provincia de Buenos Aire

Deep Mining Generation of Lung Cancer Malignancy Models from Chest X-ray Images

Lung cancer is the leading cause of cancer death and morbidity worldwide. Many studies have shown machine learning models to be effective in detecting lung nodules from chest X-ray images. However, these techniques have yet to be embraced by the medical community due to several practical, ethical, and regulatory constraints stemming from the “black-box” nature of deep learning models. Additionally, most lung nodules visible on chest X-rays are benign; therefore, the narrow task of computer vision-based lung nodule detection cannot be equated to automated lung cancer detection. Addressing both concerns, this study introduces a novel hybrid deep learning and decision tree-based computer vision model, which presents lung cancer malignancy predictions as interpretable decision trees. The deep learning component of this process is trained using a large publicly available dataset on pathological biomarkers associated with lung cancer. These models are then used to inference biomarker scores for chest X-ray images from two independent data sets, for which malignancy metadata is available. Next, multi-variate predictive models were mined by fitting shallow decision trees to the malignancy stratified datasets and interrogating a range of metrics to determine the best model. The best decision tree model achieved sensitivity and specificity of 86.7% and 80.0%, respectively, with a positive predictive value of 92.9%. Decision trees mined using this method may be considered as a starting point for refinement into clinically useful multi-variate lung cancer malignancy models for implementation as a workflow augmentation tool to improve the efficiency of human radiologists

Morphological Characters of the Thickbody Skate Amblyraja frerichsi (Krefft 1968) (Rajiformes: Rajidae), with Notes on Its Biology

Detailed descriptions of morphological features, morphometrics, neurocranium anatomy, clasper structure and egg case descriptions are provided for the thickbody skate Amblyraja frerichsi; a rare, deep-water species from Chile, Argentina and Falkland Islands. The species diagnosis is complemented from new observations and aspects such as colour, size and distribution are described. Geographic and bathymetric distributional ranges are discussed as relevant features of this taxońs biology. Additionally, the conservation status is assessed including bycatch records from Chilean fisheries

Synthesis of Breast Cancer Targeting Conjugate of Temporin-SHa Analog and its Effect on Pro- and Anti-Apoptotic Protein Expression in MCF-7 Cells

The frog natural product temporin-SHa (FLSGIVGMLGKLFamide) is a potent antimicrobial peptide, as is the analog [S3K]SHa. By solid-phase synthesis, we prepared temporin-SHa and several temporin-SHa analogs with one or more D-alanine residues incorporated. The natural product and the analog [G10a]SHa were found to be cytotoxic in mammalian cell lines and induce cell death. To achieve selectivity, we conjugated the analog [G10a]SHa with a breast cancer targeting peptide (BCTP). The resulting peptide temporin [G10a]SHa-BCTP conjugate was selectively active against the MCF-7 breast cancer cell line with no cytotoxicity in NIH-3T3 fibroblasts. Unlike the natural product or [G10a]SHa, the conjugated peptide induced apoptosis, down regulating the expression of Bcl-2 and survivin and up regulating Bax and caspase-3

Population‐based cohort study of outcomes following cholecystectomy for benign gallbladder diseases

Background The aim was to describe the management of benign gallbladder disease and identify characteristics associated with all‐cause 30‐day readmissions and complications in a prospective population‐based cohort. Methods Data were collected on consecutive patients undergoing cholecystectomy in acute UK and Irish hospitals between 1 March and 1 May 2014. Potential explanatory variables influencing all‐cause 30‐day readmissions and complications were analysed by means of multilevel, multivariable logistic regression modelling using a two‐level hierarchical structure with patients (level 1) nested within hospitals (level 2). Results Data were collected on 8909 patients undergoing cholecystectomy from 167 hospitals. Some 1451 cholecystectomies (16·3 per cent) were performed as an emergency, 4165 (46·8 per cent) as elective operations, and 3293 patients (37·0 per cent) had had at least one previous emergency admission, but had surgery on a delayed basis. The readmission and complication rates at 30 days were 7·1 per cent (633 of 8909) and 10·8 per cent (962 of 8909) respectively. Both readmissions and complications were independently associated with increasing ASA fitness grade, duration of surgery, and increasing numbers of emergency admissions with gallbladder disease before cholecystectomy. No identifiable hospital characteristics were linked to readmissions and complications. Conclusion Readmissions and complications following cholecystectomy are common and associated with patient and disease characteristics

On the mechanisms governing gas penetration into a tokamak plasma during a massive gas injection

A new 1D radial fluid code, IMAGINE, is used to simulate the penetration of gas into a tokamak plasma during a massive gas injection (MGI). The main result is that the gas is in general strongly braked as it reaches the plasma, due to mechanisms related to charge exchange and (to a smaller extent) recombination. As a result, only a fraction of the gas penetrates into the plasma. Also, a shock wave is created in the gas which propagates away from the plasma, braking and compressing the incoming gas. Simulation results are quantitatively consistent, at least in terms of orders of magnitude, with experimental data for a D 2 MGI into a JET Ohmic plasma. Simulations of MGI into the background plasma surrounding a runaway electron beam show that if the background electron density is too high, the gas may not penetrate, suggesting a possible explanation for the recent results of Reux et al in JET (2015 Nucl. Fusion 55 093013)